')
- with gradio.Column():
- inputfiles = gradio.File(file_count="multiple")
- with gradio.Row():
- with gradio.Column():
- with gradio.Row():
- lr1 = gradio.Slider(label="Coarse LR", value=0.07, minimum=0.01, maximum=0.2, step=0.01)
- niter1 = gradio.Number(value=500, precision=0, minimum=0, maximum=10_000,
- label="num_iterations", info="For coarse alignment!")
- lr2 = gradio.Slider(label="Fine LR", value=0.014, minimum=0.005, maximum=0.05, step=0.001)
- niter2 = gradio.Number(value=200, precision=0, minimum=0, maximum=100_000,
- label="num_iterations", info="For refinement!")
- optim_level = gradio.Dropdown(["coarse", "refine", "refine+depth"],
- value='refine+depth', label="OptLevel",
- info="Optimization level")
- with gradio.Row():
- matching_conf_thr = gradio.Slider(label="Matching Confidence Thr", value=5.,
- minimum=0., maximum=30., step=0.1,
- info="Before Fallback to Regr3D!")
- shared_intrinsics = gradio.Checkbox(value=False, label="Shared intrinsics",
- info="Only optimize one set of intrinsics for all views")
- scenegraph_type = gradio.Dropdown([("complete: all possible image pairs", "complete"),
- ("swin: sliding window", "swin"),
- ("logwin: sliding window with long range", "logwin"),
- ("oneref: match one image with all", "oneref")],
- value='complete', label="Scenegraph",
- info="Define how to make pairs",
- interactive=True)
- with gradio.Column(visible=False) as win_col:
- winsize = gradio.Slider(label="Scene Graph: Window Size", value=1,
- minimum=1, maximum=1, step=1)
- win_cyclic = gradio.Checkbox(value=False, label="Cyclic sequence")
- refid = gradio.Slider(label="Scene Graph: Id", value=0,
- minimum=0, maximum=0, step=1, visible=False)
- run_btn = gradio.Button("Run")
-
- with gradio.Row():
- # adjust the confidence threshold
- min_conf_thr = gradio.Slider(label="min_conf_thr", value=1.5, minimum=0.0, maximum=10, step=0.1)
- # adjust the camera size in the output pointcloud
- cam_size = gradio.Slider(label="cam_size", value=0.2, minimum=0.001, maximum=1.0, step=0.001)
- TSDF_thresh = gradio.Slider(label="TSDF Threshold", value=0., minimum=0., maximum=1., step=0.01)
- with gradio.Row():
- as_pointcloud = gradio.Checkbox(value=True, label="As pointcloud")
- # two post process implemented
- mask_sky = gradio.Checkbox(value=False, label="Mask sky")
- clean_depth = gradio.Checkbox(value=True, label="Clean-up depthmaps")
- transparent_cams = gradio.Checkbox(value=False, label="Transparent cameras")
-
- outmodel = gradio.Model3D()
-
- # events
- scenegraph_type.change(set_scenegraph_options,
- inputs=[inputfiles, win_cyclic, refid, scenegraph_type],
- outputs=[win_col, winsize, win_cyclic, refid])
- inputfiles.change(set_scenegraph_options,
- inputs=[inputfiles, win_cyclic, refid, scenegraph_type],
- outputs=[win_col, winsize, win_cyclic, refid])
- win_cyclic.change(set_scenegraph_options,
- inputs=[inputfiles, win_cyclic, refid, scenegraph_type],
- outputs=[win_col, winsize, win_cyclic, refid])
- run_btn.click(fn=recon_fun,
- inputs=[scene, inputfiles, optim_level, lr1, niter1, lr2, niter2, min_conf_thr, matching_conf_thr,
- as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size,
- scenegraph_type, winsize, win_cyclic, refid, TSDF_thresh, shared_intrinsics],
- outputs=[scene, outmodel])
- min_conf_thr.release(fn=model_from_scene_fun,
- inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
- clean_depth, transparent_cams, cam_size, TSDF_thresh],
- outputs=outmodel)
- cam_size.change(fn=model_from_scene_fun,
- inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
- clean_depth, transparent_cams, cam_size, TSDF_thresh],
- outputs=outmodel)
- TSDF_thresh.change(fn=model_from_scene_fun,
- inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
- clean_depth, transparent_cams, cam_size, TSDF_thresh],
- outputs=outmodel)
- as_pointcloud.change(fn=model_from_scene_fun,
- inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
- clean_depth, transparent_cams, cam_size, TSDF_thresh],
- outputs=outmodel)
- mask_sky.change(fn=model_from_scene_fun,
- inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
- clean_depth, transparent_cams, cam_size, TSDF_thresh],
- outputs=outmodel)
- clean_depth.change(fn=model_from_scene_fun,
- inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
- clean_depth, transparent_cams, cam_size, TSDF_thresh],
- outputs=outmodel)
- transparent_cams.change(model_from_scene_fun,
- inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
- clean_depth, transparent_cams, cam_size, TSDF_thresh],
- outputs=outmodel)
- demo.launch(share=share, server_name=server_name, server_port=server_port)
diff --git a/imcui/third_party/mickey/benchmark/config.py b/imcui/third_party/mickey/benchmark/config.py
deleted file mode 100644
index a4f7845f24e6d41ffa0ccb494acc3234d38a3217..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/benchmark/config.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# translation and rotation thresholds [meters, degrees]
-# used to compute Precision and AUC considering Pose Error
-t_threshold = 0.25
-R_threshold = 5
-
-# reprojection (VCRE) threshold [pixels]
-# used to compute Precision and AUC considering VCRE
-vcre_threshold = 90
diff --git a/imcui/third_party/mickey/benchmark/mapfree.py b/imcui/third_party/mickey/benchmark/mapfree.py
deleted file mode 100644
index 6039e537b151723e7376a32868296b51083cf9dc..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/benchmark/mapfree.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import argparse
-from collections import defaultdict
-from pathlib import Path
-from zipfile import ZipFile
-from io import TextIOWrapper
-import json
-import logging
-import numpy as np
-
-from benchmark.utils import load_poses, subsample_poses, load_K, precision_recall
-from benchmark.metrics import MetricManager, Inputs
-import benchmark.config as config
-from config.default import cfg
-
-def plot_perfect_curve(P):
- total_bins = 1000
- prec_values = []
- ratio_values = []
- for i in range(total_bins):
- ratio_tmp = i/total_bins
- value = min(1, P / ratio_tmp)
- prec_values.append(value)
- ratio_values.append(ratio_tmp)
- return prec_values, ratio_values
-
-def compute_scene_metrics(dataset_path: Path, submission_zip: ZipFile, scene: str):
- metric_manager = MetricManager()
-
- # load intrinsics and poses
- try:
- K, W, H = load_K(dataset_path / scene / 'intrinsics.txt')
- with (dataset_path / scene / 'poses.txt').open('r', encoding='utf-8') as gt_poses_file:
- gt_poses = load_poses(gt_poses_file, load_confidence=False)
- except FileNotFoundError as e:
- logging.error(f'Could not find ground-truth dataset files: {e}')
- raise
- else:
- logging.info(
- f'Loaded ground-truth intrinsics and poses for scene {scene}')
-
- # try to load estimated poses from submission
- try:
- with submission_zip.open(f'pose_{scene}.txt') as estimated_poses_file:
- estimated_poses_file_wrapper = TextIOWrapper(
- estimated_poses_file, encoding='utf-8')
- estimated_poses = load_poses(
- estimated_poses_file_wrapper, load_confidence=True)
- except KeyError as e:
- logging.warning(
- f'Submission does not have estimates for scene {scene}.')
- return dict(), len(gt_poses)
- except UnicodeDecodeError as e:
- logging.error('Unsupported file encoding: please use UTF-8')
- raise
- else:
- logging.info(f'Loaded estimated poses for scene {scene}')
-
- # The val/test set is subsampled by a factor of 5
- gt_poses = subsample_poses(gt_poses, subsample=5)
-
- # failures encode how many frames did not have an estimate
- # e.g. user/method did not provide an estimate for that frame
- # it's different from when an estimate is provided with low confidence!
- failures = 0
-
- # Results encoded as dict
- # key: metric name; value: list of values (one per frame).
- # e.g. results['t_err'] = [1.2, 0.3, 0.5, ...]
- results = defaultdict(list)
-
- # compute metrics per frame
- for frame_num, (q_gt, t_gt, _) in gt_poses.items():
- if frame_num not in estimated_poses:
- failures += 1
- continue
-
- q_est, t_est, confidence = estimated_poses[frame_num]
- inputs = Inputs(q_gt=q_gt, t_gt=t_gt, q_est=q_est, t_est=t_est,
- confidence=confidence, K=K[frame_num], W=W, H=H)
- metric_manager(inputs, results)
-
- return results, failures
-
-
-def aggregate_results(all_results, all_failures):
- # aggregate metrics
- median_metrics = defaultdict(list)
- all_metrics = defaultdict(list)
- for scene_results in all_results.values():
- for metric, values in scene_results.items():
- median_metrics[metric].append(np.median(values))
- all_metrics[metric].extend(values)
- all_metrics = {k: np.array(v) for k, v in all_metrics.items()}
- assert all([v.ndim == 1 for v in all_metrics.values()]
- ), 'invalid metrics shape'
-
- # compute avg median metrics
- avg_median_metrics = {metric: np.mean(
- values) for metric, values in median_metrics.items()}
-
- # compute precision/AUC for pose error and reprojection errors
- accepted_poses = (all_metrics['trans_err'] < config.t_threshold) * \
- (all_metrics['rot_err'] < config.R_threshold)
- accepted_vcre = all_metrics['reproj_err'] < config.vcre_threshold
- total_samples = len(next(iter(all_metrics.values()))) + all_failures
-
- prec_pose = np.sum(accepted_poses) / total_samples
- prec_vcre = np.sum(accepted_vcre) / total_samples
-
- # compute AUC for pose and VCRE
- pose_prec_values, pose_recall_values, auc_pose = precision_recall(
- inliers=all_metrics['confidence'], tp=accepted_poses, failures=all_failures)
- vcre_prec_values, vcre_recall_values, auc_vcre = precision_recall(
- inliers=all_metrics['confidence'], tp=accepted_vcre, failures=all_failures)
-
- curves_data = {}
- curves_data['vcre_prec_values'], curves_data['vcre_recall_values'] = vcre_prec_values, vcre_recall_values
- curves_data['pose_prec_values'], curves_data['pose_recall_values'] = pose_prec_values, pose_recall_values
-
- # output metrics
- output_metrics = dict()
- output_metrics['Average Median Translation Error'] = avg_median_metrics['trans_err']
- output_metrics['Average Median Rotation Error'] = avg_median_metrics['rot_err']
- output_metrics['Average Median Reprojection Error'] = avg_median_metrics['reproj_err']
- output_metrics[f'Precision @ Pose Error < ({config.t_threshold*100}cm, {config.R_threshold}deg)'] = prec_pose
- output_metrics[f'AUC @ Pose Error < ({config.t_threshold*100}cm, {config.R_threshold}deg)'] = auc_pose
- output_metrics[f'Precision @ VCRE < {config.vcre_threshold}px'] = prec_vcre
- output_metrics[f'AUC @ VCRE < {config.vcre_threshold}px'] = auc_vcre
- output_metrics[f'Estimates for % of frames'] = len(all_metrics['trans_err']) / total_samples
- return output_metrics, curves_data
-
-
-def count_unexpected_scenes(scenes: tuple, submission_zip: ZipFile):
- submission_scenes = [fname[5:-4]
- for fname in submission_zip.namelist() if fname.startswith("pose_")]
- return len(set(submission_scenes) - set(scenes))
-
-def main(args):
- dataset_path = args.dataset_path / args.split
- scenes = tuple(f.name for f in dataset_path.iterdir() if f.is_dir())
-
- try:
- submission_zip = ZipFile(args.submission_path, 'r')
- except FileNotFoundError as e:
- logging.error(f'Could not find ZIP file in path {args.submission_path}')
- return
-
- all_results = dict()
- all_failures = 0
- for scene in scenes:
- metrics, failures = compute_scene_metrics(
- dataset_path, submission_zip, scene)
- all_results[scene] = metrics
- all_failures += failures
-
- if all_failures > 0:
- logging.warning(
- f'Submission is missing pose estimates for {all_failures} frames')
-
- unexpected_scene_count = count_unexpected_scenes(scenes, submission_zip)
- if unexpected_scene_count > 0:
- logging.warning(
- f'Submission contains estimates for {unexpected_scene_count} scenes outside the {args.split} set')
-
- if all((len(metrics) == 0 for metrics in all_results.values())):
- logging.error(
- f'Submission does not have any valid pose estimates')
- return
-
- output_metrics, curves_data = aggregate_results(all_results, all_failures)
- output_json = json.dumps(output_metrics, indent=2)
- print(output_json)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(
- 'eval', description='Evaluate submissions for the MapFree dataset benchmark')
- parser.add_argument('--submission_path', type=Path, default='',
- help='Path to the submission ZIP file')
- parser.add_argument('--split', choices=('val', 'test'), default='test',
- help='Dataset split to use for evaluation. Default: test')
- parser.add_argument('--log', choices=('warning', 'info', 'error'),
- default='warning', help='Logging level. Default: warning')
- parser.add_argument('--dataset_path', type=Path, default=None,
- help='Path to the dataset folder')
-
- args = parser.parse_args()
-
- if args.dataset_path is None:
- cfg.merge_from_file('config/datasets/mapfree.yaml')
- args.dataset_path = Path(cfg.DATASET.DATA_ROOT)
-
- logging.basicConfig(level=args.log.upper())
- try:
- main(args)
- except Exception:
- logging.error("Unexpected behaviour. Exiting.")
-
diff --git a/imcui/third_party/mickey/benchmark/metrics.py b/imcui/third_party/mickey/benchmark/metrics.py
deleted file mode 100644
index 99fb1cf271bc7f35809e50f9a28a3966340ce998..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/benchmark/metrics.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from dataclasses import dataclass
-from typing import Callable
-
-import numpy as np
-
-from benchmark.reprojection import reprojection_error
-from benchmark.utils import VARIANTS_ANGLE_SIN, quat_angle_error
-
-
-@dataclass
-class Inputs:
- q_gt: np.array
- t_gt: np.array
- q_est: np.array
- t_est: np.array
- confidence: float
- K: np.array
- W: int
- H: int
-
- def __post_init__(self):
- assert self.q_gt.shape == (4,), 'invalid gt quaternion shape'
- assert self.t_gt.shape == (3,), 'invalid gt translation shape'
- assert self.q_est.shape == (4,), 'invalid estimated quaternion shape'
- assert self.t_est.shape == (3,), 'invalid estimated translation shape'
- assert self.confidence >= 0, 'confidence must be non negative'
- assert self.K.shape == (3, 3), 'invalid K shape'
- assert self.W > 0, 'invalid image width'
- assert self.H > 0, 'invalid image height'
-
-
-class MyDict(dict):
- def register(self, fn) -> Callable:
- """Registers a function within dict(fn_name -> fn_ref).
- This is used to evaluate all registered metrics in MetricManager.__call__()"""
- self[fn.__name__] = fn
- return fn
-
-
-class MetricManager:
- _metrics = MyDict()
-
- def __call__(self, inputs: Inputs, results: dict) -> None:
- for metric, metric_fn in self._metrics.items():
- results[metric].append(metric_fn(inputs))
-
- @staticmethod
- @_metrics.register
- def trans_err(inputs: Inputs) -> np.float64:
- return np.linalg.norm(inputs.t_est - inputs.t_gt)
-
- @staticmethod
- @_metrics.register
- def rot_err(inputs: Inputs, variant: str = VARIANTS_ANGLE_SIN) -> np.float64:
- return quat_angle_error(label=inputs.q_est, pred=inputs.q_gt, variant=variant)[0, 0]
-
- @staticmethod
- @_metrics.register
- def reproj_err(inputs: Inputs) -> float:
- return reprojection_error(
- q_est=inputs.q_est, t_est=inputs.t_est, q_gt=inputs.q_gt, t_gt=inputs.t_gt, K=inputs.K,
- W=inputs.W, H=inputs.H)
-
- @staticmethod
- @_metrics.register
- def confidence(inputs: Inputs) -> float:
- return inputs.confidence
diff --git a/imcui/third_party/mickey/benchmark/reprojection.py b/imcui/third_party/mickey/benchmark/reprojection.py
deleted file mode 100644
index ebff993ed0d45379a838045a6fa916006751b5e2..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/benchmark/reprojection.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from typing import List, Tuple
-
-import numpy as np
-from transforms3d.quaternions import quat2mat
-
-
-def project(pts: np.ndarray, K: np.ndarray, img_size: List[int] or Tuple[int] = None) -> np.ndarray:
- """Projects 3D points to image plane.
-
- Args:
- - pts [N, 3/4]: points in camera coordinates (homogeneous or non-homogeneous)
- - K [3, 3]: intrinsic matrix
- - img_size (width, height): optional, clamp projection to image borders
- Outputs:
- - uv [N, 2]: coordinates of projected points
- """
-
- assert len(pts.shape) == 2, 'incorrect number of dimensions'
- assert pts.shape[1] in [3, 4], 'invalid dimension size'
- assert K.shape == (3, 3), 'incorrect intrinsic shape'
-
- uv_h = (K @ pts[:, :3].T).T
- uv = uv_h[:, :2] / uv_h[:, -1:]
-
- if img_size is not None:
- uv[:, 0] = np.clip(uv[:, 0], 0, img_size[0])
- uv[:, 1] = np.clip(uv[:, 1], 0, img_size[1])
-
- return uv
-
-
-def get_grid_multipleheight() -> np.ndarray:
- # create grid of points
- ar_grid_step = 0.3
- ar_grid_num_x = 7
- ar_grid_num_y = 4
- ar_grid_num_z = 7
- ar_grid_z_offset = 1.8
- ar_grid_y_offset = 0
-
- ar_grid_x_pos = np.arange(0, ar_grid_num_x)-(ar_grid_num_x-1)/2
- ar_grid_x_pos *= ar_grid_step
-
- ar_grid_y_pos = np.arange(0, ar_grid_num_y)-(ar_grid_num_y-1)/2
- ar_grid_y_pos *= ar_grid_step
- ar_grid_y_pos += ar_grid_y_offset
-
- ar_grid_z_pos = np.arange(0, ar_grid_num_z).astype(float)
- ar_grid_z_pos *= ar_grid_step
- ar_grid_z_pos += ar_grid_z_offset
-
- xx, yy, zz = np.meshgrid(ar_grid_x_pos, ar_grid_y_pos, ar_grid_z_pos)
- ones = np.ones(xx.shape[0]*xx.shape[1]*xx.shape[2])
- eye_coords = np.concatenate([c.reshape(-1, 1)
- for c in (xx, yy, zz, ones)], axis=-1)
- return eye_coords
-
-
-# global variable, avoids creating it again
-eye_coords_glob = get_grid_multipleheight()
-
-
-def reprojection_error(
- q_est: np.ndarray, t_est: np.ndarray, q_gt: np.ndarray, t_gt: np.ndarray, K: np.ndarray,
- W: int, H: int) -> float:
- eye_coords = eye_coords_glob
-
- # obtain ground-truth position of projected points
- uv_gt = project(eye_coords, K, (W, H))
-
- # residual transformation
- cam2w_est = np.eye(4)
- cam2w_est[:3, :3] = quat2mat(q_est)
- cam2w_est[:3, -1] = t_est
- cam2w_gt = np.eye(4)
- cam2w_gt[:3, :3] = quat2mat(q_gt)
- cam2w_gt[:3, -1] = t_gt
-
- # residual reprojection
- eyes_residual = (np.linalg.inv(cam2w_est) @ cam2w_gt @ eye_coords.T).T
- uv_pred = project(eyes_residual, K, (W, H))
-
- # get reprojection error
- repr_err = np.linalg.norm(uv_gt - uv_pred, ord=2, axis=1)
- mean_repr_err = float(repr_err.mean().item())
- return mean_repr_err
diff --git a/imcui/third_party/mickey/benchmark/test_metrics.py b/imcui/third_party/mickey/benchmark/test_metrics.py
deleted file mode 100644
index f8ad37da787ad1841679fbf152a4d5740c0233dc..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/benchmark/test_metrics.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import numpy as np
-import pytest
-from transforms3d.euler import euler2quat
-from transforms3d.quaternions import axangle2quat, qmult, quat2mat, rotate_vector
-
-from benchmark.metrics import Inputs, MetricManager
-from benchmark.reprojection import project
-from benchmark.utils import VARIANTS_ANGLE_COS, VARIANTS_ANGLE_SIN
-
-
-def createInput(q_gt=None, t_gt=None, q_est=None, t_est=None, confidence=None, K=None, W=None, H=None):
- q_gt = np.zeros(4) if q_gt is None else q_gt
- t_gt = np.zeros(3) if t_gt is None else t_gt
- q_est = np.zeros(4) if q_est is None else q_est
- t_est = np.zeros(3) if t_est is None else t_est
- confidence = 0. if confidence is None else confidence
- K = np.eye(3) if K is None else K
- H = 1 if H is None else H
- W = 1 if W is None else W
- return Inputs(q_gt=q_gt, t_gt=t_gt, q_est=q_est, t_est=t_est, confidence=confidence, K=K, W=W, H=H)
-
-
-def randomQuat():
- angles = np.random.uniform(0, 2*np.pi, 3)
- q = euler2quat(*angles)
- return q
-
-
-class TestMetrics:
- @pytest.mark.parametrize('run_number', range(50))
- def test_t_err_tinvariance(self, run_number: int) -> None:
- """Computes the translation error given an initial translation and displacement of this
- translation. The translation error must be equal to the norm of the displacement."""
- mean, var = 5, 10
- t0 = np.random.normal(mean, var, (3,))
- displacement = np.random.normal(mean, var, (3,))
-
- i = createInput(t_gt=t0, t_est=t0+displacement)
- trans_err = MetricManager.trans_err(i)
- assert np.isclose(trans_err, np.linalg.norm(displacement))
-
- @pytest.mark.parametrize('run_number', range(50))
- def test_trans_err_rinvariance(self, run_number: int) -> None:
- """Computes the translation error given estimated and gt vectors.
- The translation error must be the same for a rotated version of those vectors
- (same random rotation)"""
- mean, var = 5, 10
- t0 = np.random.normal(mean, var, (3,))
- t1 = np.random.normal(mean, var, (3,))
- q = randomQuat()
-
- i = createInput(t_gt=t0, t_est=t1)
- trans_err = MetricManager.trans_err(i)
-
- ir = createInput(t_gt=rotate_vector(t0, q), t_est=rotate_vector(t1, q))
- trans_err_r = MetricManager.trans_err(ir)
-
- assert np.isclose(trans_err, trans_err_r)
-
- @pytest.mark.parametrize('run_number', range(50))
- @pytest.mark.parametrize('dtype', (np.float64, np.float32))
- def test_rot_err_raxis(self, run_number: int, dtype: type) -> None:
- """Test rotation error for rotations around a random axis.
-
- Note: We create GT as high precision, and only downcast when calling rot_err.
- """
- q = randomQuat().astype(np.float64)
-
- axis = np.random.uniform(low=-1, high=1, size=3).astype(np.float64)
- angle = np.float64(np.random.uniform(low=-np.pi, high=np.pi))
- qres = axangle2quat(vector=axis, theta=angle, is_normalized=False).astype(np.float64)
-
- i = createInput(q_gt=q.astype(dtype), q_est=qmult(q, qres).astype(dtype))
- rot_err = MetricManager.rot_err(i)
- assert isinstance(rot_err, np.float64)
- rot_err_expected = np.abs(np.degrees(angle))
- # if we add up errors, we want them to be positive
- assert 0. <= rot_err
- rtol = 1.e-5 # numpy default
- atol = 1.e-8 # numpy default
- if isinstance(dtype, np.float32):
- atol = 1.e-7 # 1/50 test might fail at 1.e-8
- assert np.isclose(rot_err, rot_err_expected, rtol=rtol, atol=atol)
-
- @pytest.mark.parametrize('run_number', range(50))
- def test_r_err_mat(self, run_number: int) -> None:
- q0 = randomQuat()
- q1 = randomQuat()
-
- i = createInput(q_gt=q0, q_est=q1)
- rot_err = MetricManager.rot_err(i)
-
- R0 = quat2mat(q0)
- R1 = quat2mat(q1)
- Rres = R1 @ R0.T
- theta = (np.trace(Rres) - 1)/2
- theta = np.clip(theta, -1, 1)
- angle = np.degrees(np.arccos(theta))
-
- assert np.isclose(angle, rot_err)
-
- def test_reproj_error_identity(self):
- """Test that reprojection error is zero if poses match"""
- q = randomQuat()
- t = np.random.normal(0, 10, (3,))
- i = createInput(q_gt=q, t_gt=t, q_est=q, t_est=t)
-
- reproj_err = MetricManager.reproj_err(i)
- assert np.isclose(reproj_err, 0)
-
- @pytest.mark.parametrize('run_number', range(10))
- @pytest.mark.parametrize('variant', (VARIANTS_ANGLE_SIN,))
- @pytest.mark.parametrize('dtype', (np.float64,))
- def test_r_err_small(self, run_number: int, variant: str, dtype: type) -> None:
- """Test rotation error for small angle differences.
-
- Note: We create GT as high precision, and only downcast when calling rot_err.
- """
- scales_failed = []
- for scale in np.logspace(start=-1, stop=-9, num=9, base=10, dtype=dtype):
- q = randomQuat().astype(np.float64)
- angle = np.float64(np.random.uniform(low=-np.pi, high=np.pi)) * scale
- assert isinstance(angle, np.float64)
- axis = np.random.uniform(low=-1., high=1., size=3).astype(np.float64)
- assert axis.dtype == np.float64
- qres = axangle2quat(vector=axis, theta=angle, is_normalized=False).astype(np.float64)
- assert qres.dtype == np.float64
-
- i = createInput(q_gt=q.astype(dtype), q_est=qmult(q, qres).astype(dtype))
-
- # We expect the error to always be np.float64 for highest acc.
- rot_err = MetricManager.rot_err(i, variant=variant)
- assert isinstance(rot_err, np.float64)
- rot_err_expected = np.abs(np.degrees(angle))
- assert isinstance(rot_err_expected, type(rot_err))
-
- # if we add up errors, we want them to be positive
- assert 0. <= rot_err
-
- # check accuracy for one magnitude higher tolerance than the angle
- tol = 0.1 * scale
- # need to be more permissive for lower precision
- if dtype == np.float32:
- tol = 1.e3 * scale
-
- # cast to dtype for checking
- rot_err = rot_err.astype(dtype)
- rot_err_expected = rot_err_expected.astype(dtype)
-
- if variant == VARIANTS_ANGLE_SIN:
- assert np.isclose(rot_err, rot_err_expected, rtol=tol, atol=tol)
- elif variant == VARIANTS_ANGLE_COS:
- if not np.isclose(rot_err, rot_err_expected, rtol=tol, atol=tol):
- print(f"[variant '{variant}'] raises an error for\n"
- f"\trot_err: {rot_err}"
- f"\trot_err_expected: {rot_err_expected}"
- f"\trtol: {tol}"
- f"\tatol: {tol}")
- scales_failed.append(scale)
- if len(scales_failed):
- pytest.fail(f"Variant {variant} failed at scales {scales_failed}")
-
-
-def test_projection() -> None:
- xyz = np.array(((10, 20, 30), (10, 30, 50), (-20, -15, 5),
- (-20, -50, 10)), dtype=np.float32)
- K = np.eye(3)
-
- uv = np.array(((1/3, 2/3), (1/5, 3/5), (-4, -3),
- (-2, -5)), dtype=np.float32)
- assert np.allclose(uv, project(xyz, K))
-
- uv = np.array(((1/3, 2/3), (1/5, 3/5), (0, 0), (0, 0)), dtype=np.float32)
- assert np.allclose(uv, project(xyz, K, img_size=(5, 5)))
diff --git a/imcui/third_party/mickey/benchmark/utils.py b/imcui/third_party/mickey/benchmark/utils.py
deleted file mode 100644
index 5c6faad88942f588d64272166726afaa0bd398c5..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/benchmark/utils.py
+++ /dev/null
@@ -1,186 +0,0 @@
-from pathlib import Path
-import typing
-import logging
-
-import numpy as np
-from transforms3d.quaternions import qinverse, rotate_vector, qmult
-
-VARIANTS_ANGLE_SIN = 'sin'
-VARIANTS_ANGLE_COS = 'cos'
-
-
-def convert_world2cam_to_cam2world(q, t):
- qinv = qinverse(q)
- tinv = -rotate_vector(t, qinv)
- return qinv, tinv
-
-
-def load_poses(file: typing.IO, load_confidence: bool = False):
- """Load poses from text file and converts them to cam2world convention (t is the camera center in world coordinates)
-
- The text file encodes world2cam poses with the format:
- imgpath qw qx qy qz tx ty tz [confidence]
- where qw qx qy qz is the quaternion encoding rotation,
- and tx ty tz is the translation vector,
- and confidence is a float encoding confidence, for estimated poses
- """
-
- expected_parts = 9 if load_confidence else 8
-
- poses = dict()
- for line_number, line in enumerate(file.readlines()):
- parts = tuple(line.strip().split(' '))
-
- # if 'tensor' in parts[-1]:
- # print('ERROR: confidence is a tensor')
- # parts = list(parts)
- # parts[-1] = parts[-1].split('[')[-1].split(']')[0]
- if len(parts) != expected_parts:
- logging.warning(
- f'Invalid number of fields in file {file.name} line {line_number}.'
- f' Expected {expected_parts}, received {len(parts)}. Ignoring line.')
- continue
-
- try:
- name = parts[0]
- if '#' in name:
- logging.info(f'Ignoring comment line in {file.name} line {line_number}')
- continue
- frame_num = int(name[-9:-4])
- except ValueError:
- logging.warning(
- f'Invalid frame number in file {file.name} line {line_number}.'
- f' Expected formatting "seq1/frame_00000.jpg". Ignoring line.')
- continue
-
- try:
- parts_float = tuple(map(float, parts[1:]))
- if any(np.isnan(v) or np.isinf(v) for v in parts_float):
- raise ValueError()
- qw, qx, qy, qz, tx, ty, tz = parts_float[:7]
- confidence = parts_float[7] if load_confidence else None
- except ValueError:
- logging.warning(
- f'Error parsing pose in file {file.name} line {line_number}. Ignoring line.')
- continue
-
- q = np.array((qw, qx, qy, qz), dtype=np.float64)
- t = np.array((tx, ty, tz), dtype=np.float64)
-
- if np.isclose(np.linalg.norm(q), 0):
- logging.warning(
- f'Error parsing pose in file {file.name} line {line_number}. '
- 'Quaternion must have non-zero norm. Ignoring line.')
- continue
-
- q, t = convert_world2cam_to_cam2world(q, t)
- poses[frame_num] = (q, t, confidence)
- return poses
-
-
-def subsample_poses(poses: dict, subsample: int = 1):
- return {k: v for i, (k, v) in enumerate(poses.items()) if i % subsample == 0}
-
-
-def load_K(file_path: Path):
- K = dict()
- with file_path.open('r', encoding='utf-8') as f:
- for line in f.readlines():
- if '#' in line:
- continue
- line = line.strip().split(' ')
-
- frame_num = int(line[0][-9:-4])
- fx, fy, cx, cy, W, H = map(float, line[1:])
- K[frame_num] = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=np.float32)
- return K, W, H
-
-
-def quat_angle_error(label, pred, variant=VARIANTS_ANGLE_SIN) -> np.ndarray:
- assert label.shape == (4,)
- assert pred.shape == (4,)
- assert variant in (VARIANTS_ANGLE_SIN, VARIANTS_ANGLE_COS), \
- f"Need variant to be in ({VARIANTS_ANGLE_SIN}, {VARIANTS_ANGLE_COS})"
-
- if len(label.shape) == 1:
- label = np.expand_dims(label, axis=0)
- if len(label.shape) != 2 or label.shape[0] != 1 or label.shape[1] != 4:
- raise RuntimeError(f"Unexpected shape of label: {label.shape}, expected: (1, 4)")
-
- if len(pred.shape) == 1:
- pred = np.expand_dims(pred, axis=0)
- if len(pred.shape) != 2 or pred.shape[0] != 1 or pred.shape[1] != 4:
- raise RuntimeError(f"Unexpected shape of pred: {pred.shape}, expected: (1, 4)")
-
- label = label.astype(np.float64)
- pred = pred.astype(np.float64)
-
- q1 = pred / np.linalg.norm(pred, axis=1, keepdims=True)
- q2 = label / np.linalg.norm(label, axis=1, keepdims=True)
- if variant == VARIANTS_ANGLE_COS:
- d = np.abs(np.sum(np.multiply(q1, q2), axis=1, keepdims=True))
- d = np.clip(d, a_min=-1, a_max=1)
- angle = 2. * np.degrees(np.arccos(d))
- elif variant == VARIANTS_ANGLE_SIN:
- if q1.shape[0] != 1 or q2.shape[0] != 1:
- raise NotImplementedError(f"Multiple angles is todo")
- # https://www.researchgate.net/post/How_do_I_calculate_the_smallest_angle_between_two_quaternions/5d6ed4a84f3a3e1ed3656616/citation/download
- sine = qmult(q1[0], qinverse(q2[0])) # note: takes first element in 2D array
- # 114.59 = 2. * 180. / pi
- angle = np.arcsin(np.linalg.norm(sine[1:], keepdims=True)) * 114.59155902616465
- angle = np.expand_dims(angle, axis=0)
-
- return angle.astype(np.float64)
-
-
-def precision_recall(inliers, tp, failures):
- """
- Computes Precision/Recall plot for a set of poses given inliers (confidence) and wether the
- estimated pose error (whatever it may be) is within a threshold.
- Each point in the plot is obtained by choosing a threshold for inliers (i.e. inlier_thr).
- Recall measures how many images have inliers >= inlier_thr
- Precision measures how many images that have inliers >= inlier_thr have
- estimated pose error <= pose_threshold (measured by counting tps)
- Where pose_threshold is (trans_thr[m], rot_thr[deg])
-
- Inputs:
- - inliers [N]
- - terr [N]
- - rerr [N]
- - failures (int)
- - pose_threshold (tuple float)
- Output
- - precision [N]
- - recall [N]
- - average_precision (scalar)
- """
-
- assert len(inliers) == len(tp), 'unequal shapes'
-
- # sort by inliers (descending order)
- inliers = np.array(inliers)
- sort_idx = np.argsort(inliers)[::-1]
- inliers = inliers[sort_idx]
- tp = np.array(tp).reshape(-1)[sort_idx]
-
- # get idxs where inliers change (avoid tied up values)
- distinct_value_indices = np.where(np.diff(inliers))[0]
- threshold_idxs = np.r_[distinct_value_indices, inliers.size - 1]
-
- # compute prec/recall
- N = inliers.shape[0]
- rec = np.arange(N, dtype=np.float32) + 1
- cum_tp = np.cumsum(tp)
- prec = cum_tp[threshold_idxs] / rec[threshold_idxs]
- rec = rec[threshold_idxs] / (float(N) + float(failures))
-
- # invert order and ensures (prec=1, rec=0) point
- last_ind = rec.searchsorted(rec[-1])
- sl = slice(last_ind, None, -1)
- prec = np.r_[prec[sl], 1]
- rec = np.r_[rec[sl], 0]
-
- # compute average precision (AUC) as the weighted average of precisions
- average_precision = np.abs(np.sum(np.diff(rec) * np.array(prec)[:-1]))
-
- return prec, rec, average_precision
diff --git a/imcui/third_party/mickey/config/MicKey/curriculum_learning.yaml b/imcui/third_party/mickey/config/MicKey/curriculum_learning.yaml
deleted file mode 100644
index 9892b8b84a0c04c977032804e937eca0255129a9..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/config/MicKey/curriculum_learning.yaml
+++ /dev/null
@@ -1,97 +0,0 @@
-
-MODEL: 'MicKey'
-DEBUG: False
-MICKEY:
- DINOV2:
- DOWN_FACTOR: 14
- CHANNEL_DIM: 1024
- FLOAT16: True
-
- KP_HEADS:
- BLOCKS_DIM: [512, 256, 128, 64]
- BN: True
- USE_SOFTMAX: True
- USE_DEPTHSIGMOID: False
- MAX_DEPTH: 60
- POS_ENCODING: True
-
- DSC_HEAD:
- LAST_DIM: 128
- BLOCKS_DIM: [512, 256, 128]
- BN: True
- NORM_DSC: True
- POS_ENCODING: True
-
-FEATURE_MATCHER:
- TYPE: 'DualSoftmax'
- DUAL_SOFTMAX:
- TEMPERATURE: 0.1
- USE_DUSTBIN: True
- SINKHORN:
- NUM_IT: 10
- DUSTBIN_SCORE_INIT: 1.
- USE_TRANSFORMER: False
-
-TRAINING:
- NUM_GPUS: 4
- BATCH_SIZE: 12 # BS for each dataloader (in every GPU)
- NUM_WORKERS: 12
- SAMPLER: 'scene_balance'
- N_SAMPLES_SCENE: 100
- SAMPLE_WITH_REPLACEMENT: True
- LR: 1e-4
- LOG_INTERVAL: 50
- VAL_INTERVAL: 0.5
- VAL_BATCHES: 100
- EPOCHS: 100
-
-DATASET:
- HEIGHT: 720
- WIDTH: 540
-
- MIN_OVERLAP_SCORE: 0.0 # [train only] discard data with overlap_score < min_overlap_score
- MAX_OVERLAP_SCORE: 1.0 # [train only] discard data with overlap_score < min_overlap_score
-
-LOSS_CLASS:
-
- LOSS_FUNCTION: "VCRE" # VCRE or POSE_ERR
- SOFT_CLIPPING: True # It indicates if it soft-clips the loss values.
-
- POSE_ERR:
- MAX_LOSS_VALUE: 1.5
- MAX_LOSS_SOFTVALUE: 0.8
- VCRE:
- MAX_LOSS_VALUE: 90
- MAX_LOSS_SOFTVALUE: 0.8
-
- GENERATE_HYPOTHESES:
- SCORE_TEMPERATURE: 20
- IT_MATCHES: 20
- IT_RANSAC: 20
- INLIER_3D_TH: 0.3
- INLIER_REF_TH: 0.15
- NUM_REF_STEPS: 4
- NUM_CORR_3d3d: 8 # Bigger number of 3d-3d correspondences helps stability
-
- NULL_HYPOTHESIS:
- ADD_NULL_HYPOTHESIS: True
- TH_OUTLIERS: 0.35
-
- CURRICULUM_LEARNING:
- TRAIN_CURRICULUM: True # It indicates if MicKey should be trained with curriculum learning
- TRAIN_WITH_TOPK: True # It indicates if MicKey should be trained only with top image pairs
- TOPK_INIT: 30
- TOPK: 80
-
- SAMPLER:
- NUM_SAMPLES_MATCHES: 512
-
-PROCRUSTES:
- IT_MATCHES: 20
- IT_RANSAC: 100
- NUM_SAMPLED_MATCHES: 2048
- NUM_CORR_3D_3D: 3
- NUM_REFINEMENTS: 4
- TH_INLIER: 0.15
- TH_SOFT_INLIER: 0.3
-
diff --git a/imcui/third_party/mickey/config/MicKey/overlap_score.yaml b/imcui/third_party/mickey/config/MicKey/overlap_score.yaml
deleted file mode 100644
index e5dd5060dc7f4228c22b9662d2844739bb50c196..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/config/MicKey/overlap_score.yaml
+++ /dev/null
@@ -1,96 +0,0 @@
-
-MODEL: 'MicKey'
-DEBUG: False
-MICKEY:
- DINOV2:
- DOWN_FACTOR: 14
- CHANNEL_DIM: 1024
- FLOAT16: True
-
- KP_HEADS:
- BLOCKS_DIM: [512, 256, 128, 64]
- BN: True
- USE_SOFTMAX: True
- USE_DEPTHSIGMOID: False
- MAX_DEPTH: 60
- POS_ENCODING: True
-
- DSC_HEAD:
- LAST_DIM: 128
- BLOCKS_DIM: [512, 256, 128]
- BN: True
- NORM_DSC: True
- POS_ENCODING: True
-
-FEATURE_MATCHER:
- TYPE: 'DualSoftmax'
- DUAL_SOFTMAX:
- TEMPERATURE: 0.1
- USE_DUSTBIN: True
- SINKHORN:
- NUM_IT: 10
- DUSTBIN_SCORE_INIT: 1.
- USE_TRANSFORMER: False
-
-TRAINING:
- NUM_GPUS: 4
- BATCH_SIZE: 12 # BS for each dataloader (in every GPU)
- NUM_WORKERS: 12
- SAMPLER: 'scene_balance'
- N_SAMPLES_SCENE: 100
- SAMPLE_WITH_REPLACEMENT: True
- LR: 1e-4
- LOG_INTERVAL: 50
- VAL_INTERVAL: 0.5
- VAL_BATCHES: 100
- EPOCHS: 100
-
-DATASET:
- HEIGHT: 720
- WIDTH: 540
-
- MIN_OVERLAP_SCORE: 0.4 # [train only] discard data with overlap_score < min_overlap_score
- MAX_OVERLAP_SCORE: 0.8 # [train only] discard data with overlap_score < min_overlap_score
-
-LOSS_CLASS:
-
- LOSS_FUNCTION: "VCRE" # VCRE or POSE_ERR
- SOFT_CLIPPING: True # It indicates if it soft-clips the loss values.
-
- POSE_ERR:
- MAX_LOSS_VALUE: 1.5
- MAX_LOSS_SOFTVALUE: 0.8
- VCRE:
- MAX_LOSS_VALUE: 90
- MAX_LOSS_SOFTVALUE: 0.8
-
- GENERATE_HYPOTHESES:
- SCORE_TEMPERATURE: 20
- IT_MATCHES: 20
- IT_RANSAC: 20
- INLIER_3D_TH: 0.3
- INLIER_REF_TH: 0.15
- NUM_REF_STEPS: 4
- NUM_CORR_3d3d: 8 # Bigger number of 3d-3d correspondences helps stability
-
- NULL_HYPOTHESIS:
- ADD_NULL_HYPOTHESIS: True
- TH_OUTLIERS: 0.35
-
- CURRICULUM_LEARNING:
- TRAIN_CURRICULUM: False # It indicates if MicKey should be trained with curriculum learning
- TRAIN_WITH_TOPK: False # It indicates if MicKey should be trained only with top image pairs
- TOPK_INIT: 30
- TOPK: 80
-
- SAMPLER:
- NUM_SAMPLES_MATCHES: 512
-
-PROCRUSTES:
- IT_MATCHES: 20
- IT_RANSAC: 100
- NUM_SAMPLED_MATCHES: 2048
- NUM_CORR_3D_3D: 3
- NUM_REFINEMENTS: 4
- TH_INLIER: 0.15
- TH_SOFT_INLIER: 0.3
\ No newline at end of file
diff --git a/imcui/third_party/mickey/config/datasets/mapfree.yaml b/imcui/third_party/mickey/config/datasets/mapfree.yaml
deleted file mode 100644
index f44c5c88515fa92e1c769dbdf0af61ce85414d6b..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/config/datasets/mapfree.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-DATASET:
- DATA_SOURCE: 'MapFree'
- DATA_ROOT: 'data/'
- SCENES: None # should be a list [] or None. If none, use all scenes.
- AUGMENTATION_TYPE: None
- HEIGHT: 720
- WIDTH: 540
- MIN_OVERLAP_SCORE: 0.2 # [train only] discard data with overlap_score < min_overlap_score
- MAX_OVERLAP_SCORE: 0.7 # [train only] discard data with overlap_score < min_overlap_score
- SEED: 66
\ No newline at end of file
diff --git a/imcui/third_party/mickey/config/default.py b/imcui/third_party/mickey/config/default.py
deleted file mode 100644
index ce57235bf9cd851891c2780e4aa20784a19f7a6f..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/config/default.py
+++ /dev/null
@@ -1,141 +0,0 @@
-from yacs.config import CfgNode as CN
-
-_CN = CN()
-
-############## Model ##############
-_CN.MODEL = None # options: ['MicKey']
-_CN.DEBUG = False
-
-# MicKey configuration
-_CN.MICKEY = CN()
-
-_CN.MICKEY.DINOV2 = CN()
-_CN.MICKEY.DINOV2.DOWN_FACTOR = None
-_CN.MICKEY.DINOV2.CHANNEL_DIM = None
-_CN.MICKEY.DINOV2.FLOAT16 = None
-
-_CN.MICKEY.KP_HEADS = CN()
-_CN.MICKEY.KP_HEADS.BLOCKS_DIM = None
-_CN.MICKEY.KP_HEADS.BN = None
-_CN.MICKEY.KP_HEADS.USE_SOFTMAX = None
-_CN.MICKEY.KP_HEADS.USE_DEPTHSIGMOID = None
-_CN.MICKEY.KP_HEADS.MAX_DEPTH = None
-_CN.MICKEY.KP_HEADS.POS_ENCODING = None
-
-_CN.MICKEY.DSC_HEAD = CN()
-_CN.MICKEY.DSC_HEAD.LAST_DIM = None
-_CN.MICKEY.DSC_HEAD.BLOCKS_DIM = None
-_CN.MICKEY.DSC_HEAD.BN = None
-_CN.MICKEY.DSC_HEAD.NORM_DSC = None
-_CN.MICKEY.DSC_HEAD.POS_ENCODING = None
-
-
-_CN.FEATURE_MATCHER = CN()
-_CN.FEATURE_MATCHER.TYPE = None
-_CN.FEATURE_MATCHER.DUAL_SOFTMAX = CN()
-_CN.FEATURE_MATCHER.DUAL_SOFTMAX.TEMPERATURE = None
-_CN.FEATURE_MATCHER.DUAL_SOFTMAX.USE_DUSTBIN = None
-_CN.FEATURE_MATCHER.SINKHORN = CN()
-_CN.FEATURE_MATCHER.SINKHORN.NUM_IT = None
-_CN.FEATURE_MATCHER.SINKHORN.DUSTBIN_SCORE_INIT = None
-_CN.FEATURE_MATCHER.USE_TRANSFORMER = None
-_CN.FEATURE_MATCHER.TOP_KEYPOINTS = False
-
-# LOSS_CLASS
-_CN.LOSS_CLASS = CN()
-_CN.LOSS_CLASS.LOSS_FUNCTION = None
-_CN.LOSS_CLASS.SOFT_CLIPPING = None
-
-_CN.LOSS_CLASS.POSE_ERR = CN()
-_CN.LOSS_CLASS.POSE_ERR.MAX_LOSS_VALUE = None
-_CN.LOSS_CLASS.POSE_ERR.MAX_LOSS_SOFTVALUE = None
-
-_CN.LOSS_CLASS.VCRE = CN()
-_CN.LOSS_CLASS.VCRE.MAX_LOSS_VALUE = None
-_CN.LOSS_CLASS.VCRE.MAX_LOSS_SOFTVALUE = None
-
-_CN.LOSS_CLASS.GENERATE_HYPOTHESES = CN()
-_CN.LOSS_CLASS.GENERATE_HYPOTHESES.SCORE_TEMPERATURE = None
-_CN.LOSS_CLASS.GENERATE_HYPOTHESES.IT_MATCHES = None
-_CN.LOSS_CLASS.GENERATE_HYPOTHESES.IT_RANSAC = None
-_CN.LOSS_CLASS.GENERATE_HYPOTHESES.INLIER_3D_TH = None
-_CN.LOSS_CLASS.GENERATE_HYPOTHESES.INLIER_REF_TH = None
-_CN.LOSS_CLASS.GENERATE_HYPOTHESES.NUM_REF_STEPS = None
-_CN.LOSS_CLASS.GENERATE_HYPOTHESES.NUM_CORR_3d3d = None
-
-_CN.LOSS_CLASS.CURRICULUM_LEARNING = CN()
-_CN.LOSS_CLASS.CURRICULUM_LEARNING.TRAIN_CURRICULUM = None
-_CN.LOSS_CLASS.CURRICULUM_LEARNING.TRAIN_WITH_TOPK = None
-_CN.LOSS_CLASS.CURRICULUM_LEARNING.TOPK_INIT = None
-_CN.LOSS_CLASS.CURRICULUM_LEARNING.TOPK = None
-
-_CN.LOSS_CLASS.NULL_HYPOTHESIS = CN()
-_CN.LOSS_CLASS.NULL_HYPOTHESIS.ADD_NULL_HYPOTHESIS = None
-_CN.LOSS_CLASS.NULL_HYPOTHESIS.TH_OUTLIERS = None
-
-_CN.LOSS_CLASS.SAMPLER = CN()
-_CN.LOSS_CLASS.SAMPLER.NUM_SAMPLES_MATCHES = None
-
-
-# Procrustes RANSAC options
-_CN.PROCRUSTES = CN()
-_CN.PROCRUSTES.IT_MATCHES = None
-_CN.PROCRUSTES.IT_RANSAC = None
-_CN.PROCRUSTES.NUM_SAMPLED_MATCHES = None
-_CN.PROCRUSTES.NUM_CORR_3D_3D = None
-_CN.PROCRUSTES.NUM_REFINEMENTS = None
-_CN.PROCRUSTES.TH_INLIER = None
-_CN.PROCRUSTES.TH_SOFT_INLIER = None
-
-
-
-
-# Training Procrustes RANSAC options
-_CN.PROCRUSTES_TRAINING = CN()
-_CN.PROCRUSTES_TRAINING.MAX_CORR_DIST = None
-_CN.PROCRUSTES_TRAINING.REFINE = False #refine pose with ICP
-
-
-############## Dataset ##############
-_CN.DATASET = CN()
-# 1. data config
-_CN.DATASET.DATA_SOURCE = None # options: ['ScanNet', '7Scenes', 'MapFree']
-_CN.DATASET.SCENES = None # scenes to use (for 7Scenes/MapFree); should be a list []; If none, use all scenes.
-_CN.DATASET.DATA_ROOT = None # path to dataset folder
-_CN.DATASET.SEED = None # SEED for dataset generation
-_CN.DATASET.NPZ_ROOT = None # path to npz files containing pairs of frame indices per sample
-_CN.DATASET.MIN_OVERLAP_SCORE = None # discard data with overlap_score < min_overlap_score
-_CN.DATASET.MAX_OVERLAP_SCORE = None # discard data with overlap_score > max_overlap_score
-_CN.DATASET.CONSECUTIVE_PAIRS = None # options: [None, 'colorjitter']
-_CN.DATASET.FRAME_RATE = None # options: [None, 'colorjitter']
-_CN.DATASET.AUGMENTATION_TYPE = None # options: [None, 'colorjitter']
-_CN.DATASET.BLACK_WHITE = False # if true, transform images to black & white
-_CN.DATASET.PAIRS_TXT = CN() # Path to text file defining the train/val/test pairs (7Scenes)
-_CN.DATASET.PAIRS_TXT.TRAIN = None
-_CN.DATASET.PAIRS_TXT.VAL = None
-_CN.DATASET.PAIRS_TXT.TEST = None
-_CN.DATASET.PAIRS_TXT.ONE_NN = False # If true, keeps only reference image w/ highest similarity to each query
-_CN.DATASET.HEIGHT = None
-_CN.DATASET.WIDTH = None
-
-############# TRAINING #############
-_CN.TRAINING = CN()
-# Data Loader settings
-_CN.TRAINING.BATCH_SIZE = None
-_CN.TRAINING.NUM_WORKERS = None
-_CN.TRAINING.NUM_GPUS = None
-_CN.TRAINING.SAMPLER = None # options: ['random', 'scene_balance']
-_CN.TRAINING.N_SAMPLES_SCENE = None # if 'scene_balance' sampler, the number of samples to get per scene
-_CN.TRAINING.SAMPLE_WITH_REPLACEMENT = None # if 'scene_balance' sampler, whether to sample with replacement
-
-# Training settings
-_CN.TRAINING.LR = None
-_CN.TRAINING.LR_STEP_INTERVAL = None
-_CN.TRAINING.LR_STEP_GAMMA = None # multiplicative factor of LR every LR_STEP_ITERATIONS
-_CN.TRAINING.VAL_INTERVAL = None
-_CN.TRAINING.VAL_BATCHES = None
-_CN.TRAINING.LOG_INTERVAL = None
-_CN.TRAINING.EPOCHS = None
-_CN.TRAINING.GRAD_CLIP = 0. # Indicates the L2 norm at which to clip the gradient. Disabled if 0
-
-cfg = _CN
\ No newline at end of file
diff --git a/imcui/third_party/mickey/demo_inference.py b/imcui/third_party/mickey/demo_inference.py
deleted file mode 100644
index 760c734efee56b0a7378878f9472027d5667e9be..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/demo_inference.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import torch
-import argparse
-from lib.models.builder import build_model
-from lib.datasets.utils import correct_intrinsic_scale
-from lib.models.MicKey.modules.utils.training_utils import colorize, generate_heat_map
-from config.default import cfg
-import numpy as np
-from pathlib import Path
-import cv2
-
-def prepare_score_map(scs, img, temperature=0.5):
-
- score_map = generate_heat_map(scs, img, temperature)
-
- score_map = 255 * score_map.permute(1, 2, 0).numpy()
-
- return score_map
-
-def colorize_depth(value, vmin=None, vmax=None, cmap='magma_r', invalid_val=-99, invalid_mask=None, background_color=(0, 0, 0, 255), gamma_corrected=False, value_transform=None):
-
- img = colorize(value, vmin, vmax, cmap, invalid_val, invalid_mask, background_color, gamma_corrected, value_transform)
-
- shape_im = img.shape
- img = np.asarray(img, np.uint8)
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGBA)
- img = cv2.resize(img, (shape_im[1]*14, shape_im[0]*14), interpolation=cv2.INTER_LINEAR)
-
- return img
-
-def read_color_image(path, resize=(540, 720)):
- """
- Args:
- resize (tuple): align image to depthmap, in (w, h).
- Returns:
- image (torch.tensor): (3, h, w)
- """
- # read and resize image
- cv_type = cv2.IMREAD_COLOR
- image = cv2.imread(str(path), cv_type)
- image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
- if resize:
- image = cv2.resize(image, resize)
-
- # (h, w, 3) -> (3, h, w) and normalized
- image = torch.from_numpy(image).float().permute(2, 0, 1) / 255
-
- return image.unsqueeze(0)
-
-def read_intrinsics(path_intrinsics, resize=None):
- Ks = {}
- with Path(path_intrinsics).open('r') as f:
- for line in f.readlines():
- if '#' in line:
- continue
-
- line = line.strip().split(' ')
- img_name = line[0]
- fx, fy, cx, cy, W, H = map(float, line[1:])
-
- K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=np.float32)
- if resize is not None:
- K = correct_intrinsic_scale(K, resize[0] / W, resize[1] / H)
- Ks[img_name] = K
- return Ks
-
-def run_demo_inference(args):
-
- # Select device
- use_cuda = torch.cuda.is_available()
- device = torch.device('cuda:0' if use_cuda else 'cpu')
-
- print('Preparing data...')
-
- # Prepare config file
- cfg.merge_from_file(args.config)
-
- # Prepare the model
- model = build_model(cfg, checkpoint=args.checkpoint)
-
- # Load demo images
- im0 = read_color_image(args.im_path_ref).to(device)
- im1 = read_color_image(args.im_path_dst).to(device)
-
- # Load intrinsics
- K = read_intrinsics(args.intrinsics)
-
- # Prepare data for MicKey
- data = {}
- data['image0'] = im0
- data['image1'] = im1
- data['K_color0'] = torch.from_numpy(K['im0.jpg']).unsqueeze(0).to(device)
- data['K_color1'] = torch.from_numpy(K['im1.jpg']).unsqueeze(0).to(device)
-
- # Run inference
- print('Running MicKey relative pose estimation...')
- model(data)
-
- # Pose, inliers and score are stored in:
- # data['R'] = R
- # data['t'] = t
- # data['inliers'] = inliers
- # data['inliers_list'] = inliers_list
-
- print('Saving depth and score maps in image directory ...')
- depth0_map = colorize_depth(data['depth0_map'][0], invalid_mask=(data['depth0_map'][0] < 0.001).cpu()[0])
- depth1_map = colorize_depth(data['depth1_map'][0], invalid_mask=(data['depth1_map'][0] < 0.001).cpu()[0])
- score0_map = prepare_score_map(data['scr0'][0], data['image0'][0], temperature=0.5)
- score1_map = prepare_score_map(data['scr1'][0], data['image1'][0], temperature=0.5)
-
- ext_im0 = args.im_path_ref.split('.')[-1]
- ext_im1 = args.im_path_dst.split('.')[-1]
-
- cv2.imwrite(args.im_path_ref.replace(ext_im0, 'score.jpg'), score0_map)
- cv2.imwrite(args.im_path_dst.replace(ext_im1, 'score.jpg'), score1_map)
-
- cv2.imwrite(args.im_path_ref.replace(ext_im0, 'depth.jpg'), depth0_map)
- cv2.imwrite(args.im_path_dst.replace(ext_im1, 'depth.jpg'), depth1_map)
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--im_path_ref', help='path to reference image', default='data/toy_example/im0.jpg')
- parser.add_argument('--im_path_dst', help='path to destination image', default='data/toy_example/im1.jpg')
- parser.add_argument('--intrinsics', help='path to intrinsics file', default='data/toy_example/intrinsics.txt')
- parser.add_argument('--config', help='path to config file', default='weights/mickey_weights/config.yaml')
- parser.add_argument('--checkpoint', help='path to model checkpoint',
- default='weights/mickey_weights/mickey.ckpt')
- args = parser.parse_args()
-
- run_demo_inference(args)
-
diff --git a/imcui/third_party/mickey/resources/environment.yml b/imcui/third_party/mickey/resources/environment.yml
deleted file mode 100644
index 6b0039dfc4dae504750d54f8539ffedf8c046242..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/resources/environment.yml
+++ /dev/null
@@ -1,28 +0,0 @@
-name: mickey
-channels:
- - conda-forge
- - defaults
-dependencies:
- - python=3.8.17
- - pip=23.2.1
- - pip:
- - einops==0.6.1
- - lazy-loader==0.3
- - lightning-utilities==0.9.0
- - matplotlib==3.7.2
- - numpy==1.24.4
- - omegaconf==2.3.0
- - open3d==0.17.0
- - opencv-python==4.8.0.74
- - protobuf==4.23.4
- - pytorch-lightning==2.0.6
- - tensorboard==2.13.0
- - tensorboard-data-server==0.7.1
- - timm==0.6.7
- - torch==2.0.1
- - torchmetrics==1.0.2
- - torchvision==0.15.2
- - tqdm==4.65.1
- - transforms3d==0.4.1
- - xformers==0.0.20
- - yacs==0.1.8
diff --git a/imcui/third_party/mickey/submission.py b/imcui/third_party/mickey/submission.py
deleted file mode 100644
index 56f1170dc039a8eb68d5cd10b3293cc0079b75a5..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/submission.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import argparse
-from pathlib import Path
-from collections import defaultdict
-from dataclasses import dataclass
-from zipfile import ZipFile
-
-import torch
-import numpy as np
-from tqdm import tqdm
-
-from config.default import cfg
-from lib.datasets.datamodules import DataModule
-from lib.models.builder import build_model
-from lib.utils.data import data_to_model_device
-from transforms3d.quaternions import mat2quat
-
-@dataclass
-class Pose:
- image_name: str
- q: np.ndarray
- t: np.ndarray
- inliers: float
-
- def __str__(self) -> str:
- formatter = {'float': lambda v: f'{v:.6f}'}
- max_line_width = 1000
- q_str = np.array2string(self.q, formatter=formatter, max_line_width=max_line_width)[1:-1]
- t_str = np.array2string(self.t, formatter=formatter, max_line_width=max_line_width)[1:-1]
- return f'{self.image_name} {q_str} {t_str} {self.inliers}'
-
-
-def predict(loader, model):
- results_dict = defaultdict(list)
-
- for data in tqdm(loader):
-
- # run inference
- data = data_to_model_device(data, model)
- with torch.no_grad():
- R_batched, t_batched = model(data)
-
- for i_batch in range(len(data['scene_id'])):
- R = R_batched[i_batch].unsqueeze(0).detach().cpu().numpy()
- t = t_batched[i_batch].reshape(-1).detach().cpu().numpy()
- inliers = data['inliers'][i_batch].item()
-
- scene = data['scene_id'][i_batch]
- query_img = data['pair_names'][1][i_batch]
-
- # ignore frames without poses (e.g. not enough feature matches)
- if np.isnan(R).any() or np.isnan(t).any() or np.isinf(t).any():
- continue
-
- # populate results_dict
- estimated_pose = Pose(image_name=query_img,
- q=mat2quat(R).reshape(-1),
- t=t.reshape(-1),
- inliers=inliers)
- results_dict[scene].append(estimated_pose)
-
- return results_dict
-
-
-def save_submission(results_dict: dict, output_path: Path):
- with ZipFile(output_path, 'w') as zip:
- for scene, poses in results_dict.items():
- poses_str = '\n'.join((str(pose) for pose in poses))
- zip.writestr(f'pose_{scene}.txt', poses_str.encode('utf-8'))
-
-
-def eval(args):
- # Load configs
- cfg.merge_from_file('config/datasets/mapfree.yaml')
- cfg.merge_from_file(args.config)
-
- # Create dataloader
- if args.split == 'test':
- cfg.TRAINING.BATCH_SIZE = 8
- cfg.TRAINING.NUM_WORKERS = 8
- dataloader = DataModule(cfg, drop_last_val=False).test_dataloader()
- elif args.split == 'val':
- cfg.TRAINING.BATCH_SIZE = 16
- cfg.TRAINING.NUM_WORKERS = 8
- dataloader = DataModule(cfg, drop_last_val=False).val_dataloader()
- else:
- raise NotImplemented(f'Invalid split: {args.split}')
-
- # Create model
- model = build_model(cfg, args.checkpoint)
-
- # Get predictions from model
- results_dict = predict(dataloader, model)
-
- # Save predictions to txt per scene within zip
- args.output_root.mkdir(parents=True, exist_ok=True)
- save_submission(results_dict, args.output_root / 'submission.zip')
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--config', help='path to config file')
- parser.add_argument('--checkpoint',
- help='path to model checkpoint (models with learned parameters)', default='')
- parser.add_argument('--output_root', '-o', type=Path, default=Path('results/'))
- parser.add_argument('--split', choices=('val', 'test'), default='test',
- help='Dataset split to use for evaluation. Choose from test or val. Default: test')
- args = parser.parse_args()
- eval(args)
diff --git a/imcui/third_party/mickey/train.py b/imcui/third_party/mickey/train.py
deleted file mode 100644
index cf2452ef107d834b711da5ba90fc3782e9c36a88..0000000000000000000000000000000000000000
--- a/imcui/third_party/mickey/train.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import argparse
-import os
-# do this before importing numpy! (doing it right up here in case numpy is dependency of e.g. json)
-os.environ["MKL_NUM_THREADS"] = "1" # noqa: E402
-os.environ["NUMEXPR_NUM_THREADS"] = "1" # noqa: E402
-os.environ["OMP_NUM_THREADS"] = "1" # noqa: E402
-os.environ["OPENBLAS_NUM_THREADS"] = "1" # noqa: E402
-
-import pytorch_lightning as pl
-import torch
-from pytorch_lightning.loggers import TensorBoardLogger
-
-from config.default import cfg
-from lib.datasets.datamodules import DataModuleTraining
-from lib.models.MicKey.model import MicKeyTrainingModel
-from lib.models.MicKey.modules.utils.training_utils import create_exp_name, create_result_dir
-import random
-import shutil
-
-def train_model(args):
-
- cfg.merge_from_file(args.dataset_config)
- cfg.merge_from_file(args.config)
-
- exp_name = create_exp_name(args.experiment, cfg)
- print('Start training of ' + exp_name)
-
- cfg.DATASET.SEED = random.randint(0, 1000000)
-
- model = MicKeyTrainingModel(cfg)
-
- checkpoint_vcre_callback = pl.callbacks.ModelCheckpoint(
- filename='{epoch}-best_vcre',
- save_last=True,
- save_top_k=1,
- verbose=True,
- monitor='val_vcre/auc_vcre',
- mode='max'
- )
-
- checkpoint_pose_callback = pl.callbacks.ModelCheckpoint(
- filename='{epoch}-best_pose',
- save_last=True,
- save_top_k=1,
- verbose=True,
- monitor='val_AUC_pose/auc_pose',
- mode='max'
- )
-
- epochend_callback = pl.callbacks.ModelCheckpoint(
- filename='e{epoch}-last',
- save_top_k=1,
- every_n_epochs=1,
- save_on_train_epoch_end=True
- )
-
- lr_monitoring_callback = pl.callbacks.LearningRateMonitor(logging_interval='step')
- logger = TensorBoardLogger(save_dir=args.path_weights, name=exp_name)
-
- trainer = pl.Trainer(devices=cfg.TRAINING.NUM_GPUS,
- log_every_n_steps=cfg.TRAINING.LOG_INTERVAL,
- val_check_interval=cfg.TRAINING.VAL_INTERVAL,
- limit_val_batches=cfg.TRAINING.VAL_BATCHES,
- max_epochs=cfg.TRAINING.EPOCHS,
- logger=logger,
- callbacks=[checkpoint_pose_callback, lr_monitoring_callback, epochend_callback, checkpoint_vcre_callback],
- num_sanity_val_steps=0,
- gradient_clip_val=cfg.TRAINING.GRAD_CLIP)
-
- datamodule_end = DataModuleTraining(cfg)
- print('Training with {:.2f}/{:.2f} image overlap'.format(cfg.DATASET.MIN_OVERLAP_SCORE, cfg.DATASET.MAX_OVERLAP_SCORE))
-
- create_result_dir(logger.log_dir + '/config.yaml')
- shutil.copyfile(args.config, logger.log_dir + '/config.yaml')
-
- if args.resume:
- ckpt_path = args.resume
- else:
- ckpt_path = None
-
- trainer.fit(model, datamodule_end, ckpt_path=ckpt_path)
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--config', help='path to config file', default='config/MicKey/curriculum_learning.yaml')
- parser.add_argument('--dataset_config', help='path to dataset config file', default='config/datasets/mapfree.yaml')
- parser.add_argument('--experiment', help='experiment name', default='MicKey_default')
- parser.add_argument('--path_weights', help='path to the directory to save the weights', default='weights/')
- parser.add_argument('--resume', help='resume from checkpoint path', default=None)
- args = parser.parse_args()
- train_model(args)
\ No newline at end of file
diff --git a/imcui/third_party/pram/localization/matchers/__init__.py b/imcui/third_party/pram/localization/matchers/__init__.py
deleted file mode 100644
index 7edac76f912b1e5ebb0401b6cc7a5d3c64ce963a..0000000000000000000000000000000000000000
--- a/imcui/third_party/pram/localization/matchers/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-def get_matcher(matcher):
- mod = __import__(f'{__name__}.{matcher}', fromlist=[''])
- return getattr(mod, 'Model')
diff --git a/imcui/third_party/r2d2/extract_kapture.py b/imcui/third_party/r2d2/extract_kapture.py
deleted file mode 100644
index 51b2403b8a1730eaee32d099d0b6dd5d091ccdda..0000000000000000000000000000000000000000
--- a/imcui/third_party/r2d2/extract_kapture.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# Copyright 2019-present NAVER Corp.
-# CC BY-NC-SA 3.0
-# Available only for non-commercial use
-
-
-from PIL import Image
-
-from tools import common
-from tools.dataloader import norm_RGB
-from nets.patchnet import *
-from os import path
-
-from extract import load_network, NonMaxSuppression, extract_multiscale
-
-# Kapture is a pivot file format, based on text and binary files, used to describe SfM (Structure From Motion)
-# and more generally sensor-acquired data
-# it can be installed with
-# pip install kapture
-# for more information check out https://github.com/naver/kapture
-import kapture
-from kapture.io.records import get_image_fullpath
-from kapture.io.csv import kapture_from_dir
-from kapture.io.csv import get_feature_csv_fullpath, keypoints_to_file, descriptors_to_file
-from kapture.io.features import get_keypoints_fullpath, keypoints_check_dir, image_keypoints_to_file
-from kapture.io.features import get_descriptors_fullpath, descriptors_check_dir, image_descriptors_to_file
-from kapture.io.csv import get_all_tar_handlers
-
-
-def extract_kapture_keypoints(args):
- """
- Extract r2d2 keypoints and descritors to the kapture format directly
- """
- print('extract_kapture_keypoints...')
- with get_all_tar_handlers(args.kapture_root,
- mode={kapture.Keypoints: 'a',
- kapture.Descriptors: 'a',
- kapture.GlobalFeatures: 'r',
- kapture.Matches: 'r'}) as tar_handlers:
- kdata = kapture_from_dir(args.kapture_root, None,
- skip_list=[kapture.GlobalFeatures,
- kapture.Matches,
- kapture.Points3d,
- kapture.Observations],
- tar_handlers=tar_handlers)
-
- assert kdata.records_camera is not None
- image_list = [filename for _, _, filename in kapture.flatten(kdata.records_camera)]
- if args.keypoints_type is None:
- args.keypoints_type = path.splitext(path.basename(args.model))[0]
- print(f'keypoints_type set to {args.keypoints_type}')
- if args.descriptors_type is None:
- args.descriptors_type = path.splitext(path.basename(args.model))[0]
- print(f'descriptors_type set to {args.descriptors_type}')
-
- if kdata.keypoints is not None and args.keypoints_type in kdata.keypoints \
- and kdata.descriptors is not None and args.descriptors_type in kdata.descriptors:
- print('detected already computed features of same keypoints_type/descriptors_type, resuming extraction...')
- image_list = [name
- for name in image_list
- if name not in kdata.keypoints[args.keypoints_type] or
- name not in kdata.descriptors[args.descriptors_type]]
-
- if len(image_list) == 0:
- print('All features were already extracted')
- return
- else:
- print(f'Extracting r2d2 features for {len(image_list)} images')
-
- iscuda = common.torch_set_gpu(args.gpu)
-
- # load the network...
- net = load_network(args.model)
- if iscuda:
- net = net.cuda()
-
- # create the non-maxima detector
- detector = NonMaxSuppression(
- rel_thr=args.reliability_thr,
- rep_thr=args.repeatability_thr)
-
- if kdata.keypoints is None:
- kdata.keypoints = {}
- if kdata.descriptors is None:
- kdata.descriptors = {}
-
- if args.keypoints_type not in kdata.keypoints:
- keypoints_dtype = None
- keypoints_dsize = None
- else:
- keypoints_dtype = kdata.keypoints[args.keypoints_type].dtype
- keypoints_dsize = kdata.keypoints[args.keypoints_type].dsize
- if args.descriptors_type not in kdata.descriptors:
- descriptors_dtype = None
- descriptors_dsize = None
- else:
- descriptors_dtype = kdata.descriptors[args.descriptors_type].dtype
- descriptors_dsize = kdata.descriptors[args.descriptors_type].dsize
-
- for image_name in image_list:
- img_path = get_image_fullpath(args.kapture_root, image_name)
- print(f"\nExtracting features for {img_path}")
- img = Image.open(img_path).convert('RGB')
- W, H = img.size
- img = norm_RGB(img)[None]
- if iscuda:
- img = img.cuda()
-
- # extract keypoints/descriptors for a single image
- xys, desc, scores = extract_multiscale(net, img, detector,
- scale_f=args.scale_f,
- min_scale=args.min_scale,
- max_scale=args.max_scale,
- min_size=args.min_size,
- max_size=args.max_size,
- verbose=True)
-
- xys = xys.cpu().numpy()
- desc = desc.cpu().numpy()
- scores = scores.cpu().numpy()
- idxs = scores.argsort()[-args.top_k or None:]
-
- xys = xys[idxs]
- desc = desc[idxs]
- if keypoints_dtype is None or descriptors_dtype is None:
- keypoints_dtype = xys.dtype
- descriptors_dtype = desc.dtype
-
- keypoints_dsize = xys.shape[1]
- descriptors_dsize = desc.shape[1]
-
- kdata.keypoints[args.keypoints_type] = kapture.Keypoints('r2d2', keypoints_dtype, keypoints_dsize)
- kdata.descriptors[args.descriptors_type] = kapture.Descriptors('r2d2', descriptors_dtype,
- descriptors_dsize,
- args.keypoints_type, 'L2')
- keypoints_config_absolute_path = get_feature_csv_fullpath(kapture.Keypoints,
- args.keypoints_type,
- args.kapture_root)
- descriptors_config_absolute_path = get_feature_csv_fullpath(kapture.Descriptors,
- args.descriptors_type,
- args.kapture_root)
- keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints[args.keypoints_type])
- descriptors_to_file(descriptors_config_absolute_path, kdata.descriptors[args.descriptors_type])
- else:
- assert kdata.keypoints[args.keypoints_type].dtype == xys.dtype
- assert kdata.descriptors[args.descriptors_type].dtype == desc.dtype
- assert kdata.keypoints[args.keypoints_type].dsize == xys.shape[1]
- assert kdata.descriptors[args.descriptors_type].dsize == desc.shape[1]
- assert kdata.descriptors[args.descriptors_type].keypoints_type == args.keypoints_type
- assert kdata.descriptors[args.descriptors_type].metric_type == 'L2'
-
- keypoints_fullpath = get_keypoints_fullpath(args.keypoints_type, args.kapture_root,
- image_name, tar_handlers)
- print(f"Saving {xys.shape[0]} keypoints to {keypoints_fullpath}")
- image_keypoints_to_file(keypoints_fullpath, xys)
- kdata.keypoints[args.keypoints_type].add(image_name)
-
- descriptors_fullpath = get_descriptors_fullpath(args.descriptors_type, args.kapture_root,
- image_name, tar_handlers)
- print(f"Saving {desc.shape[0]} descriptors to {descriptors_fullpath}")
- image_descriptors_to_file(descriptors_fullpath, desc)
- kdata.descriptors[args.descriptors_type].add(image_name)
-
- if not keypoints_check_dir(kdata.keypoints[args.keypoints_type], args.keypoints_type,
- args.kapture_root, tar_handlers) or \
- not descriptors_check_dir(kdata.descriptors[args.descriptors_type], args.descriptors_type,
- args.kapture_root, tar_handlers):
- print('local feature extraction ended successfully but not all files were saved')
-
-
-if __name__ == '__main__':
- import argparse
- parser = argparse.ArgumentParser(
- "Extract r2d2 local features for all images in a dataset stored in the kapture format")
- parser.add_argument("--model", type=str, required=True, help='model path')
- parser.add_argument('--keypoints-type', default=None, help='keypoint type_name, default is filename of model')
- parser.add_argument('--descriptors-type', default=None, help='descriptors type_name, default is filename of model')
-
- parser.add_argument("--kapture-root", type=str, required=True, help='path to kapture root directory')
-
- parser.add_argument("--top-k", type=int, default=5000, help='number of keypoints')
-
- parser.add_argument("--scale-f", type=float, default=2**0.25)
- parser.add_argument("--min-size", type=int, default=256)
- parser.add_argument("--max-size", type=int, default=1024)
- parser.add_argument("--min-scale", type=float, default=0)
- parser.add_argument("--max-scale", type=float, default=1)
-
- parser.add_argument("--reliability-thr", type=float, default=0.7)
- parser.add_argument("--repeatability-thr", type=float, default=0.7)
-
- parser.add_argument("--gpu", type=int, nargs='+', default=[0], help='use -1 for CPU')
- args = parser.parse_args()
-
- extract_kapture_keypoints(args)
diff --git a/imcui/third_party/r2d2/nets/ap_loss.py b/imcui/third_party/r2d2/nets/ap_loss.py
deleted file mode 100644
index 251815cd97009a5feb6a815c20caca0c40daaccd..0000000000000000000000000000000000000000
--- a/imcui/third_party/r2d2/nets/ap_loss.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright 2019-present NAVER Corp.
-# CC BY-NC-SA 3.0
-# Available only for non-commercial use
-
-import pdb
-import numpy as np
-import torch
-import torch.nn as nn
-
-
-class APLoss (nn.Module):
- """ differentiable AP loss, through quantization.
-
- Input: (N, M) values in [min, max]
- label: (N, M) values in {0, 1}
-
- Returns: list of query AP (for each n in {1..N})
- Note: typically, you want to minimize 1 - mean(AP)
- """
- def __init__(self, nq=25, min=0, max=1, euc=False):
- nn.Module.__init__(self)
- assert isinstance(nq, int) and 2 <= nq <= 100
- self.nq = nq
- self.min = min
- self.max = max
- self.euc = euc
- gap = max - min
- assert gap > 0
-
- # init quantizer = non-learnable (fixed) convolution
- self.quantizer = q = nn.Conv1d(1, 2*nq, kernel_size=1, bias=True)
- a = (nq-1) / gap
- #1st half = lines passing to (min+x,1) and (min+x+1/a,0) with x = {nq-1..0}*gap/(nq-1)
- q.weight.data[:nq] = -a
- q.bias.data[:nq] = torch.from_numpy(a*min + np.arange(nq, 0, -1)) # b = 1 + a*(min+x)
- #2nd half = lines passing to (min+x,1) and (min+x-1/a,0) with x = {nq-1..0}*gap/(nq-1)
- q.weight.data[nq:] = a
- q.bias.data[nq:] = torch.from_numpy(np.arange(2-nq, 2, 1) - a*min) # b = 1 - a*(min+x)
- # first and last one are special: just horizontal straight line
- q.weight.data[0] = q.weight.data[-1] = 0
- q.bias.data[0] = q.bias.data[-1] = 1
-
- def compute_AP(self, x, label):
- N, M = x.shape
- if self.euc: # euclidean distance in same range than similarities
- x = 1 - torch.sqrt(2.001 - 2*x)
-
- # quantize all predictions
- q = self.quantizer(x.unsqueeze(1))
- q = torch.min(q[:,:self.nq], q[:,self.nq:]).clamp(min=0) # N x Q x M
-
- nbs = q.sum(dim=-1) # number of samples N x Q = c
- rec = (q * label.view(N,1,M).float()).sum(dim=-1) # nb of correct samples = c+ N x Q
- prec = rec.cumsum(dim=-1) / (1e-16 + nbs.cumsum(dim=-1)) # precision
- rec /= rec.sum(dim=-1).unsqueeze(1) # norm in [0,1]
-
- ap = (prec * rec).sum(dim=-1) # per-image AP
- return ap
-
- def forward(self, x, label):
- assert x.shape == label.shape # N x M
- return self.compute_AP(x, label)
-
-
-
-
-
diff --git a/imcui/ui/modelcache.py b/imcui/ui/modelcache.py
deleted file mode 100644
index 04fdac8ee7fd74b232571b16b2ce3d64e824a3a4..0000000000000000000000000000000000000000
--- a/imcui/ui/modelcache.py
+++ /dev/null
@@ -1,371 +0,0 @@
-import hashlib
-import json
-import time
-import threading
-from collections import OrderedDict
-import torch
-from ..hloc import logger
-
-
-class ARCSizeAwareModelCache:
- def __init__(
- self,
- max_gpu_mem: float = 12e9,
- max_cpu_mem: float = 12e9,
- device_priority: list = ["cuda", "cpu"],
- auto_empty_cache: bool = True,
- ):
- """
- Initialize the model cache.
-
- Args:
- max_gpu_mem: Maximum GPU memory allowed in bytes.
- max_cpu_mem: Maximum CPU memory allowed in bytes.
- device_priority: List of devices to prioritize when evicting models.
- auto_empty_cache: Whether to call torch.cuda.empty_cache() when out of memory.
- """
-
- self.t1 = OrderedDict()
- self.t2 = OrderedDict()
- self.b1 = OrderedDict()
- self.b2 = OrderedDict()
-
- self.max_gpu = max_gpu_mem
- self.max_cpu = max_cpu_mem
- self.current_gpu = 0
- self.current_cpu = 0
-
- self.p = 0
- self.adaptive_factor = 0.5
-
- self.device_priority = device_priority
- self.lock = threading.Lock()
- self.auto_empty_cache = auto_empty_cache
-
- logger.info("ARCSizeAwareModelCache initialized.")
-
- def _release_model(self, model_entry):
- """
- Release a model from memory.
-
- Args:
- model_entry: A dictionary containing the model, device and other information.
-
- Notes:
- If the device is CUDA and auto_empty_cache is True, torch.cuda.empty_cache() is called after releasing the model.
- """
- model = model_entry["model"]
- device = model_entry["device"]
-
- del model
- if device == "cuda":
- torch.cuda.synchronize()
- if self.auto_empty_cache:
- torch.cuda.empty_cache()
-
- def generate_key(self, model_key, model_conf: dict) -> str:
- loader_identifier = f"{model_key}"
- unique_str = f"{loader_identifier}-{json.dumps(model_conf, sort_keys=True)}"
- return hashlib.sha256(unique_str.encode()).hexdigest()
-
- def _get_device(self, model_size: int) -> str:
- for device in self.device_priority:
- if device == "cuda" and torch.cuda.is_available():
- if self.current_gpu + model_size <= self.max_gpu:
- return "cuda"
- elif device == "cpu":
- if self.current_cpu + model_size <= self.max_cpu:
- return "cpu"
- return "cpu"
-
- def _calculate_model_size(self, model):
- return sum(p.numel() * p.element_size() for p in model.parameters()) + sum(
- b.numel() * b.element_size() for b in model.buffers()
- )
-
- def _update_access(self, key: str, size: int, device: str):
- if key in self.b1:
- self.p = min(
- self.p + max(1, len(self.b2) // len(self.b1)),
- len(self.t1) + len(self.t2),
- )
- self.b1.pop(key)
- self._replace(False)
- elif key in self.b2:
- self.p = max(self.p - max(1, len(self.b1) // len(self.b2)), 0)
- self.b2.pop(key)
- self._replace(True)
-
- if key in self.t1:
- self.t1.pop(key)
- self.t2[key] = {
- "size": size,
- "device": device,
- "access_count": 1,
- "last_accessed": time.time(),
- }
-
- def _replace(self, in_t2: bool):
- if len(self.t1) > 0 and (
- (len(self.t1) > self.p) or (in_t2 and len(self.t1) == self.p)
- ):
- k, v = self.t1.popitem(last=False)
- self.b1[k] = v
- else:
- k, v = self.t2.popitem(last=False)
- self.b2[k] = v
-
- def _calculate_weight(self, entry) -> float:
- return entry["access_count"] / entry["size"]
-
- def _evict_models(self, required_size: int, target_device: str) -> bool:
- candidates = []
- for k, v in list(self.t1.items()) + list(self.t2.items()):
- if v["device"] == target_device:
- candidates.append((k, v))
-
- candidates.sort(key=lambda x: self._calculate_weight(x[1]))
-
- freed = 0
- for k, v in candidates:
- self._release_model(v)
- freed += v["size"]
- if v in self.t1:
- self.t1.pop(k)
- if v in self.t2:
- self.t2.pop(k)
-
- if v["device"] == "cuda":
- self.current_gpu -= v["size"]
- else:
- self.current_cpu -= v["size"]
-
- if freed >= required_size:
- return True
-
- if target_device == "cuda":
- return self._cross_device_evict(required_size, "cuda")
- return False
-
- def _cross_device_evict(self, required_size: int, target_device: str) -> bool:
- all_entries = []
- for k, v in list(self.t1.items()) + list(self.t2.items()):
- all_entries.append((k, v))
-
- all_entries.sort(
- key=lambda x: self._calculate_weight(x[1])
- + (0.5 if x[1]["device"] == target_device else 0)
- )
-
- freed = 0
- for k, v in all_entries:
- freed += v["size"]
- if v in self.t1:
- self.t1.pop(k)
- if v in self.t2:
- self.t2.pop(k)
-
- if v["device"] == "cuda":
- self.current_gpu -= v["size"]
- else:
- self.current_cpu -= v["size"]
-
- if freed >= required_size:
- return True
- return False
-
- def load_model(self, model_key, model_loader_func, model_conf: dict):
- key = self.generate_key(model_key, model_conf)
-
- with self.lock:
- if key in self.t1 or key in self.t2:
- entry = self.t1.pop(key, None) or self.t2.pop(key)
- entry["access_count"] += 1
- self.t2[key] = entry
- return entry["model"]
-
- raw_model = model_loader_func(model_conf)
- model_size = self._calculate_model_size(raw_model)
- device = self._get_device(model_size)
-
- if device == "cuda" and self.auto_empty_cache:
- torch.cuda.empty_cache()
- torch.cuda.synchronize()
-
- while True:
- current_mem = self.current_gpu if device == "cuda" else self.current_cpu
- max_mem = self.max_gpu if device == "cuda" else self.max_cpu
-
- if current_mem + model_size <= max_mem:
- break
-
- if not self._evict_models(model_size, device):
- if device == "cuda":
- device = "cpu"
- else:
- raise RuntimeError("Out of memory")
-
- try:
- model = raw_model.to(device)
- except RuntimeError as e:
- if "CUDA out of memory" in str(e):
- torch.cuda.empty_cache()
- model = raw_model.to(device)
-
- new_entry = {
- "model": model,
- "size": model_size,
- "device": device,
- "access_count": 1,
- "last_accessed": time.time(),
- }
-
- if key in self.b1 or key in self.b2:
- self.t2[key] = new_entry
- self._replace(True)
- else:
- self.t1[key] = new_entry
- self._replace(False)
-
- if device == "cuda":
- self.current_gpu += model_size
- else:
- self.current_cpu += model_size
-
- return model
-
- def clear_device_cache(self, device: str):
- with self.lock:
- for cache in [self.t1, self.t2, self.b1, self.b2]:
- for k in list(cache.keys()):
- if cache[k]["device"] == device:
- cache.pop(k)
-
-
-class LRUModelCache:
- def __init__(
- self,
- max_gpu_mem: float = 8e9,
- max_cpu_mem: float = 12e9,
- device_priority: list = ["cuda", "cpu"],
- ):
- self.cache = OrderedDict()
- self.max_gpu = max_gpu_mem
- self.max_cpu = max_cpu_mem
- self.current_gpu = 0
- self.current_cpu = 0
- self.lock = threading.Lock()
- self.device_priority = device_priority
-
- def generate_key(self, model_key, model_conf: dict) -> str:
- loader_identifier = f"{model_key}"
- unique_str = f"{loader_identifier}-{json.dumps(model_conf, sort_keys=True)}"
- return hashlib.sha256(unique_str.encode()).hexdigest()
-
- def get_device(self) -> str:
- for device in self.device_priority:
- if device == "cuda" and torch.cuda.is_available():
- if self.current_gpu < self.max_gpu:
- return device
- elif device == "cpu":
- if self.current_cpu < self.max_cpu:
- return device
- return "cpu"
-
- def _calculate_model_size(self, model):
- param_size = sum(p.numel() * p.element_size() for p in model.parameters())
- buffer_size = sum(b.numel() * b.element_size() for b in model.buffers())
- return param_size + buffer_size
-
- def load_model(self, model_key, model_loader_func, model_conf: dict):
- key = self.generate_key(model_key, model_conf)
-
- with self.lock:
- if key in self.cache:
- self.cache.move_to_end(key) # update LRU
- return self.cache[key]["model"]
-
- device = self.get_device()
- if device == "cuda":
- torch.cuda.empty_cache()
-
- try:
- raw_model = model_loader_func(model_conf)
- except Exception as e:
- raise RuntimeError(f"Model loading failed: {str(e)}")
-
- try:
- model = raw_model.to(device)
- except RuntimeError as e:
- if "CUDA out of memory" in str(e):
- return self._handle_oom(model_key, model_loader_func, model_conf)
- raise
-
- model_size = self._calculate_model_size(model)
-
- while (
- device == "cuda" and (self.current_gpu + model_size > self.max_gpu)
- ) or (device == "cpu" and (self.current_cpu + model_size > self.max_cpu)):
- if not self._free_space(model_size, device):
- raise RuntimeError("Insufficient memory even after cache cleanup")
-
- if device == "cuda":
- self.current_gpu += model_size
- else:
- self.current_cpu += model_size
-
- self.cache[key] = {
- "model": model,
- "size": model_size,
- "device": device,
- "timestamp": time.time(),
- }
-
- return model
-
- def _free_space(self, required_size: int, device: str) -> bool:
- for key in list(self.cache.keys()):
- if (device == "cuda" and self.cache[key]["device"] == "cuda") or (
- device == "cpu" and self.cache[key]["device"] == "cpu"
- ):
- self.current_gpu -= (
- self.cache[key]["size"]
- if self.cache[key]["device"] == "cuda"
- else 0
- )
- self.current_cpu -= (
- self.cache[key]["size"] if self.cache[key]["device"] == "cpu" else 0
- )
- del self.cache[key]
-
- if (
- device == "cuda"
- and self.current_gpu + required_size <= self.max_gpu
- ) or (
- device == "cpu" and self.current_cpu + required_size <= self.max_cpu
- ):
- return True
- return False
-
- def _handle_oom(self, model_key, model_loader_func, model_conf: dict):
- with self.lock:
- self.clear_device_cache("cuda")
- torch.cuda.empty_cache()
-
- try:
- return self.load_model(model_key, model_loader_func, model_conf)
- except RuntimeError:
- original_priority = self.device_priority
- self.device_priority = ["cpu"]
- try:
- return self.load_model(model_key, model_loader_func, model_conf)
- finally:
- self.device_priority = original_priority
-
- def clear_device_cache(self, device: str):
- with self.lock:
- keys_to_remove = [k for k, v in self.cache.items() if v["device"] == device]
- for k in keys_to_remove:
- self.current_gpu -= self.cache[k]["size"] if device == "cuda" else 0
- self.current_cpu -= self.cache[k]["size"] if device == "cpu" else 0
- del self.cache[k]
diff --git a/pyproject.toml b/pyproject.toml
index bf961d9ec38cb270edf75ac9cff7b580cc862ae9..2d4c90b4c4dd31de3ec69e2a0c7194a41de5485b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,16 +1,12 @@
-[build-system]
-requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
-
[project]
-name = "imcui"
+name = "ImageMatchingWebui"
description = "Image Matching Webui: A tool for matching images using sota algorithms with a Gradio UI"
-version = "0.0.2"
+version = "1.0"
authors = [
{name = "vincentqyw"},
]
readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.8"
license = {file = "LICENSE"}
classifiers = [
"Programming Language :: Python :: 3",
@@ -20,28 +16,21 @@ classifiers = [
urls = {Repository = "https://github.com/Vincentqyw/image-matching-webui"}
dynamic = ["dependencies"]
-
[project.optional-dependencies]
dev = ["black", "flake8", "isort"]
+[tool.setuptools.packages.find]
+include = ["hloc*", "ui",]
-[tool.setuptools]
-packages = { find = { include = ["imcui*"] } }
-include-package-data = true
-
+[tool.setuptools.package-data]
+ui = ["*.yaml"]
[tool.setuptools.dynamic]
dependencies = {file = ["requirements.txt"]}
+[tool.black]
+line-length = 80
-[tool.pytest.ini_options]
-minversion = "6.0"
-addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"]
-xfail_strict = true
-testpaths = ["tests"]
-filterwarnings = [
- "ignore::DeprecationWarning",
- "ignore::UserWarning",
- "ignore::FutureWarning",
- "ignore::RuntimeWarning",
-]
+[tool.isort]
+profile = "black"
+line_length = 80
\ No newline at end of file
diff --git a/railway.toml b/railway.toml
deleted file mode 100644
index 58accec161cc235ab3a2e1adcc8e2376e9470b56..0000000000000000000000000000000000000000
--- a/railway.toml
+++ /dev/null
@@ -1,11 +0,0 @@
-[build]
-builder = "DOCKERFILE"
-dockerfilePath = "Dockerfile"
-
-[deploy]
-runtime = "V2"
-numReplicas = 1
-startCommand = "python -m imcui.api.server"
-sleepApplication = false
-restartPolicyType = "ON_FAILURE"
-restartPolicyMaxRetries = 10
diff --git a/requirements.txt b/requirements.txt
index ac89a384cb11cf10942de68ebf716cb83235e2f4..3bee572d1d816d32f0483859c86e9b0f3cfeefd3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,34 +1,29 @@
-datasets
e2cnn
-easydict
einops
-fastapi
+easydict
gdown
-gradio<=5.4.0
+# gradio==5.4.0
h5py
huggingface_hub
imageio
Jinja2
kornia
loguru
-matplotlib<3.9
-numpy~=1.26
-omegaconf
+matplotlib
+numpy==1.23.5
onnxruntime
-opencv-contrib-python
+omegaconf
opencv-python
+opencv-contrib-python
pandas
+psutil
plotly
-poselib
protobuf
-psutil
+poselib
pycolmap==0.6.1
pytlsd
-pytorch-lightning==1.4.9
PyYAML
-ray
-ray[serve]
-roma #dust3r
+pytorch-lightning==1.4.9
scikit-image
scikit-learn
scipy
@@ -37,6 +32,7 @@ shapely
tensorboardX==2.6.1
torchmetrics==0.6.0
torchvision==0.19.0
+roma #dust3r
tqdm
-uvicorn
yacs
+fastapi
diff --git a/tests/test_basic.py b/test_app_cli.py
similarity index 80%
rename from tests/test_basic.py
rename to test_app_cli.py
index d35797f0ea475df8346db1986c7d758d5a24f032..f75ff6b1de6dc8227d3f18ee7bdcd84979a41434 100644
--- a/tests/test_basic.py
+++ b/test_app_cli.py
@@ -1,16 +1,18 @@
-import cv2
+import sys
from pathlib import Path
-from imcui.hloc import logger
-from imcui.ui.utils import DEVICE, get_matcher_zoo, load_config
-from imcui.api import ImageMatchingAPI
-ROOT = Path(__file__).parents[1]
+import cv2
+
+from hloc import logger
+from ui.utils import DEVICE, ROOT, get_matcher_zoo, load_config
+sys.path.append(str(Path(__file__).parents[1]))
+from api.server import ImageMatchingAPI
-def test_all():
- config = load_config(ROOT / "config/config.yaml")
- img_path1 = ROOT / "tests/data/02928139_3448003521.jpg"
- img_path2 = ROOT / "tests/data/17295357_9106075285.jpg"
+
+def test_all(config: dict = None):
+ img_path1 = ROOT / "datasets/sacre_coeur/mapping/02928139_3448003521.jpg"
+ img_path2 = ROOT / "datasets/sacre_coeur/mapping/17295357_9106075285.jpg"
image0 = cv2.imread(str(img_path1))[:, :, ::-1] # RGB
image1 = cv2.imread(str(img_path2))[:, :, ::-1] # RGB
@@ -23,19 +25,18 @@ def test_all():
if enable and not skip_ci:
logger.info(f"Testing {k} ...")
api = ImageMatchingAPI(conf=v, device=DEVICE)
- pred = api(image0, image1)
- assert pred is not None
+ api(image0, image1)
log_path = ROOT / "experiments" / "all"
log_path.mkdir(exist_ok=True, parents=True)
api.visualize(log_path=log_path)
else:
logger.info(f"Skipping {k} ...")
+ return 0
def test_one():
- img_path1 = ROOT / "tests/data/02928139_3448003521.jpg"
- img_path2 = ROOT / "tests/data/17295357_9106075285.jpg"
-
+ img_path1 = ROOT / "datasets/sacre_coeur/mapping/02928139_3448003521.jpg"
+ img_path2 = ROOT / "datasets/sacre_coeur/mapping/17295357_9106075285.jpg"
image0 = cv2.imread(str(img_path1))[:, :, ::-1] # RGB
image1 = cv2.imread(str(img_path2))[:, :, ::-1] # RGB
# sparse
@@ -68,8 +69,7 @@ def test_one():
"dense": False,
}
api = ImageMatchingAPI(conf=conf, device=DEVICE)
- pred = api(image0, image1)
- assert pred is not None
+ api(image0, image1)
log_path = ROOT / "experiments" / "one"
log_path.mkdir(exist_ok=True, parents=True)
api.visualize(log_path=log_path)
@@ -99,13 +99,14 @@ def test_one():
}
api = ImageMatchingAPI(conf=conf, device=DEVICE)
- pred = api(image0, image1)
- assert pred is not None
+ api(image0, image1)
log_path = ROOT / "experiments" / "one"
log_path.mkdir(exist_ok=True, parents=True)
api.visualize(log_path=log_path)
+ return 0
if __name__ == "__main__":
+ config = load_config(ROOT / "ui/config.yaml")
test_one()
- test_all()
+ test_all(config)
diff --git a/tests/data/02928139_3448003521.jpg b/tests/data/02928139_3448003521.jpg
deleted file mode 100644
index 102589fa1a501f365fef0051f5ae97c42eb560ff..0000000000000000000000000000000000000000
--- a/tests/data/02928139_3448003521.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4f52d9dcdb3ba9d8cf025025fb1be3f8f8d1ba0e0d84ab7eeb271215589ca608
-size 518060
diff --git a/tests/data/17295357_9106075285.jpg b/tests/data/17295357_9106075285.jpg
deleted file mode 100644
index 3d38e80b2a28c7d06b28cc9a36b97d656b60b912..0000000000000000000000000000000000000000
--- a/tests/data/17295357_9106075285.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:54dff1885bf44b5c0e0c0ce702220832e99e5b30f38462d1ef5b9d4a0d794f98
-size 535133
diff --git a/imcui/third_party/dad/licenses/aliked/LICENSE b/third_party/ALIKE/LICENSE
similarity index 99%
rename from imcui/third_party/dad/licenses/aliked/LICENSE
rename to third_party/ALIKE/LICENSE
index 3f1c35e2a29273aa6b19baef973196d387b371d3..4ee705bf59834a4b0195b1b0e499ee950469668e 100644
--- a/imcui/third_party/dad/licenses/aliked/LICENSE
+++ b/third_party/ALIKE/LICENSE
@@ -26,4 +26,4 @@ DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/third_party/ALIKE/README.md b/third_party/ALIKE/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..8f40f15c56f6c54b14bb438e47096737a440fe89
--- /dev/null
+++ b/third_party/ALIKE/README.md
@@ -0,0 +1,131 @@
+# News
+
+- The [ALIKED](https://github.com/Shiaoming/ALIKED) is released.
+- The [ALIKE training code](https://github.com/Shiaoming/ALIKE/raw/main/assets/ALIKE_code.zip) is released.
+
+# ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction
+
+ALIKE applies a differentiable keypoint detection module to detect accurate sub-pixel keypoints. The network can run at 95 frames per second for 640 x 480 images on NVIDIA Titan X (Pascal) GPU and achieve equivalent performance with the state-of-the-arts. ALIKE benefits real-time applications in resource-limited platforms/devices. Technical details are described in [this paper](https://arxiv.org/pdf/2112.02906.pdf).
+
+> ```
+> Xiaoming Zhao, Xingming Wu, Jinyu Miao, Weihai Chen, Peter C. Y. Chen, Zhengguo Li, "ALIKE: Accurate and Lightweight Keypoint
+> Detection and Descriptor Extraction," IEEE Transactions on Multimedia, 2022.
+> ```
+
+
+
+
+If you use ALIKE in an academic work, please cite:
+
+```
+@article{Zhao2023ALIKED,
+ title = {ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation},
+ url = {https://arxiv.org/pdf/2304.03608.pdf},
+ doi = {10.1109/TIM.2023.3271000},
+ journal = {IEEE Transactions on Instrumentation & Measurement},
+ author = {Zhao, Xiaoming and Wu, Xingming and Chen, Weihai and Chen, Peter C. Y. and Xu, Qingsong and Li, Zhengguo},
+ year = {2023},
+ volume = {72},
+ pages = {1-16},
+}
+
+@article{Zhao2022ALIKE,
+ title = {ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction},
+ url = {http://arxiv.org/abs/2112.02906},
+ doi = {10.1109/TMM.2022.3155927},
+ journal = {IEEE Transactions on Multimedia},
+ author = {Zhao, Xiaoming and Wu, Xingming and Miao, Jinyu and Chen, Weihai and Chen, Peter C. Y. and Li, Zhengguo},
+ month = march,
+ year = {2022},
+}
+```
+
+
+
+## 1. Prerequisites
+
+The required packages are listed in the `requirements.txt` :
+
+```shell
+pip install -r requirements.txt
+```
+
+
+
+## 2. Models
+
+The off-the-shelf weights of four variant ALIKE models are provided in `models/` .
+
+
+
+## 3. Run demo
+
+```shell
+$ python demo.py -h
+usage: demo.py [-h] [--model {alike-t,alike-s,alike-n,alike-l}]
+ [--device DEVICE] [--top_k TOP_K] [--scores_th SCORES_TH]
+ [--n_limit N_LIMIT] [--no_display] [--no_sub_pixel]
+ input
+
+ALike Demo.
+
+positional arguments:
+ input Image directory or movie file or "camera0" (for
+ webcam0).
+
+optional arguments:
+ -h, --help show this help message and exit
+ --model {alike-t,alike-s,alike-n,alike-l}
+ The model configuration
+ --device DEVICE Running device (default: cuda).
+ --top_k TOP_K Detect top K keypoints. -1 for threshold based mode,
+ >0 for top K mode. (default: -1)
+ --scores_th SCORES_TH
+ Detector score threshold (default: 0.2).
+ --n_limit N_LIMIT Maximum number of keypoints to be detected (default:
+ 5000).
+ --no_display Do not display images to screen. Useful if running
+ remotely (default: False).
+ --no_sub_pixel Do not detect sub-pixel keypoints (default: False).
+```
+
+
+
+## 4. Examples
+
+### KITTI example
+```shell
+python demo.py assets/kitti
+```
+
+
+### TUM example
+```shell
+python demo.py assets/tum
+```
+
+
+## 5. Efficiency and performance
+
+| Models | Parameters | GFLOPs(640x480) | MHA@3 on Hpatches | mAA(10°) on [IMW2020-test](https://www.cs.ubc.ca/research/image-matching-challenge/2021/leaderboard) (Stereo) |
+|:---:|:---:|:---:|:-----------------:|:-------------------------------------------------------------------------------------------------------------:|
+| D2-Net(MS) | 7653KB | 889.40 | 38.33% | 12.27% |
+| LF-Net(MS) | 2642KB | 24.37 | 57.78% | 23.44% |
+| SuperPoint | 1301KB | 26.11 | 70.19% | 28.97% |
+| R2D2(MS) | 484KB | 464.55 | 71.48% | 39.02% |
+| ASLFeat(MS) | 823KB | 77.58 | 73.52% | 33.65% |
+| DISK | 1092KB | 98.97 | 70.56% | 51.22% |
+| ALike-N | 318KB | 7.909 | 75.74% | 47.18% |
+| ALike-L | 653KB | 19.685 | 76.85% | 49.58% |
+
+### Evaluation on Hpatches
+
+- Download [hpatches-sequences-release](https://hpatches.github.io/) and put it into `hseq/hpatches-sequences-release`.
+- Remove the unreliable sequences as D2-Net.
+- Run the following command to evaluate the performance:
+ ```shell
+ python hseq/eval.py
+ ```
+
+
+For more details, please refer to the [paper](https://arxiv.org/abs/2112.02906).
diff --git a/imcui/third_party/ALIKE/alike.py b/third_party/ALIKE/alike.py
similarity index 57%
rename from imcui/third_party/ALIKE/alike.py
rename to third_party/ALIKE/alike.py
index 303616d52581efce0ae0eb86af70f5ea8984909d..b975f806f3e0f593a3564ae52d9d08187f514b34 100644
--- a/imcui/third_party/ALIKE/alike.py
+++ b/third_party/ALIKE/alike.py
@@ -12,46 +12,89 @@ from soft_detect import DKD
import time
configs = {
- 'alike-t': {'c1': 8, 'c2': 16, 'c3': 32, 'c4': 64, 'dim': 64, 'single_head': True, 'radius': 2,
- 'model_path': os.path.join(os.path.split(__file__)[0], 'models', 'alike-t.pth')},
- 'alike-s': {'c1': 8, 'c2': 16, 'c3': 48, 'c4': 96, 'dim': 96, 'single_head': True, 'radius': 2,
- 'model_path': os.path.join(os.path.split(__file__)[0], 'models', 'alike-s.pth')},
- 'alike-n': {'c1': 16, 'c2': 32, 'c3': 64, 'c4': 128, 'dim': 128, 'single_head': True, 'radius': 2,
- 'model_path': os.path.join(os.path.split(__file__)[0], 'models', 'alike-n.pth')},
- 'alike-l': {'c1': 32, 'c2': 64, 'c3': 128, 'c4': 128, 'dim': 128, 'single_head': False, 'radius': 2,
- 'model_path': os.path.join(os.path.split(__file__)[0], 'models', 'alike-l.pth')},
+ "alike-t": {
+ "c1": 8,
+ "c2": 16,
+ "c3": 32,
+ "c4": 64,
+ "dim": 64,
+ "single_head": True,
+ "radius": 2,
+ "model_path": os.path.join(os.path.split(__file__)[0], "models", "alike-t.pth"),
+ },
+ "alike-s": {
+ "c1": 8,
+ "c2": 16,
+ "c3": 48,
+ "c4": 96,
+ "dim": 96,
+ "single_head": True,
+ "radius": 2,
+ "model_path": os.path.join(os.path.split(__file__)[0], "models", "alike-s.pth"),
+ },
+ "alike-n": {
+ "c1": 16,
+ "c2": 32,
+ "c3": 64,
+ "c4": 128,
+ "dim": 128,
+ "single_head": True,
+ "radius": 2,
+ "model_path": os.path.join(os.path.split(__file__)[0], "models", "alike-n.pth"),
+ },
+ "alike-l": {
+ "c1": 32,
+ "c2": 64,
+ "c3": 128,
+ "c4": 128,
+ "dim": 128,
+ "single_head": False,
+ "radius": 2,
+ "model_path": os.path.join(os.path.split(__file__)[0], "models", "alike-l.pth"),
+ },
}
class ALike(ALNet):
- def __init__(self,
- # ================================== feature encoder
- c1: int = 32, c2: int = 64, c3: int = 128, c4: int = 128, dim: int = 128,
- single_head: bool = False,
- # ================================== detect parameters
- radius: int = 2,
- top_k: int = 500, scores_th: float = 0.5,
- n_limit: int = 5000,
- device: str = 'cpu',
- model_path: str = ''
- ):
+ def __init__(
+ self,
+ # ================================== feature encoder
+ c1: int = 32,
+ c2: int = 64,
+ c3: int = 128,
+ c4: int = 128,
+ dim: int = 128,
+ single_head: bool = False,
+ # ================================== detect parameters
+ radius: int = 2,
+ top_k: int = 500,
+ scores_th: float = 0.5,
+ n_limit: int = 5000,
+ device: str = "cpu",
+ model_path: str = "",
+ ):
super().__init__(c1, c2, c3, c4, dim, single_head)
self.radius = radius
self.top_k = top_k
self.n_limit = n_limit
self.scores_th = scores_th
- self.dkd = DKD(radius=self.radius, top_k=self.top_k,
- scores_th=self.scores_th, n_limit=self.n_limit)
+ self.dkd = DKD(
+ radius=self.radius,
+ top_k=self.top_k,
+ scores_th=self.scores_th,
+ n_limit=self.n_limit,
+ )
self.device = device
- if model_path != '':
+ if model_path != "":
state_dict = torch.load(model_path, self.device)
self.load_state_dict(state_dict)
self.to(self.device)
self.eval()
- logging.info(f'Loaded model parameters from {model_path}')
+ logging.info(f"Loaded model parameters from {model_path}")
logging.info(
- f"Number of model parameters: {sum(p.numel() for p in self.parameters() if p.requires_grad) / 1e3}KB")
+ f"Number of model parameters: {sum(p.numel() for p in self.parameters() if p.requires_grad) / 1e3}KB"
+ )
def extract_dense_map(self, image, ret_dict=False):
# ====================================================
@@ -81,7 +124,10 @@ class ALike(ALNet):
descriptor_map = torch.nn.functional.normalize(descriptor_map, p=2, dim=1)
if ret_dict:
- return {'descriptor_map': descriptor_map, 'scores_map': scores_map, }
+ return {
+ "descriptor_map": descriptor_map,
+ "scores_map": scores_map,
+ }
else:
return descriptor_map, scores_map
@@ -104,15 +150,22 @@ class ALike(ALNet):
image = cv2.resize(image, dsize=None, fx=ratio, fy=ratio)
# ==================== convert image to tensor
- image = torch.from_numpy(image).to(self.device).to(torch.float32).permute(2, 0, 1)[None] / 255.0
+ image = (
+ torch.from_numpy(image)
+ .to(self.device)
+ .to(torch.float32)
+ .permute(2, 0, 1)[None]
+ / 255.0
+ )
# ==================== extract keypoints
start = time.time()
with torch.no_grad():
descriptor_map, scores_map = self.extract_dense_map(image)
- keypoints, descriptors, scores, _ = self.dkd(scores_map, descriptor_map,
- sub_pixel=sub_pixel)
+ keypoints, descriptors, scores, _ = self.dkd(
+ scores_map, descriptor_map, sub_pixel=sub_pixel
+ )
keypoints, descriptors, scores = keypoints[0], descriptors[0], scores[0]
keypoints = (keypoints + 1) / 2 * keypoints.new_tensor([[W - 1, H - 1]])
@@ -124,14 +177,16 @@ class ALike(ALNet):
end = time.time()
- return {'keypoints': keypoints.cpu().numpy(),
- 'descriptors': descriptors.cpu().numpy(),
- 'scores': scores.cpu().numpy(),
- 'scores_map': scores_map.cpu().numpy(),
- 'time': end - start, }
+ return {
+ "keypoints": keypoints.cpu().numpy(),
+ "descriptors": descriptors.cpu().numpy(),
+ "scores": scores.cpu().numpy(),
+ "scores_map": scores_map.cpu().numpy(),
+ "time": end - start,
+ }
-if __name__ == '__main__':
+if __name__ == "__main__":
import numpy as np
from thop import profile
@@ -139,5 +194,5 @@ if __name__ == '__main__':
image = np.random.random((640, 480, 3)).astype(np.float32)
flops, params = profile(net, inputs=(image, 9999, False), verbose=False)
- print('{:<30} {:<8} GFLops'.format('Computational complexity: ', flops / 1e9))
- print('{:<30} {:<8} KB'.format('Number of parameters: ', params / 1e3))
+ print("{:<30} {:<8} GFLops".format("Computational complexity: ", flops / 1e9))
+ print("{:<30} {:<8} KB".format("Number of parameters: ", params / 1e3))
diff --git a/imcui/third_party/ALIKE/alnet.py b/third_party/ALIKE/alnet.py
similarity index 67%
rename from imcui/third_party/ALIKE/alnet.py
rename to third_party/ALIKE/alnet.py
index 53127063233660c7b96aa15e89aa4a8a1a340dd1..91cb7ee55e502895e7b0037f2add1a35a613cd40 100644
--- a/imcui/third_party/ALIKE/alnet.py
+++ b/third_party/ALIKE/alnet.py
@@ -5,9 +5,13 @@ from typing import Optional, Callable
class ConvBlock(nn.Module):
- def __init__(self, in_channels, out_channels,
- gate: Optional[Callable[..., nn.Module]] = None,
- norm_layer: Optional[Callable[..., nn.Module]] = None):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ gate: Optional[Callable[..., nn.Module]] = None,
+ norm_layer: Optional[Callable[..., nn.Module]] = None,
+ ):
super().__init__()
if gate is None:
self.gate = nn.ReLU(inplace=True)
@@ -31,16 +35,16 @@ class ResBlock(nn.Module):
expansion: int = 1
def __init__(
- self,
- inplanes: int,
- planes: int,
- stride: int = 1,
- downsample: Optional[nn.Module] = None,
- groups: int = 1,
- base_width: int = 64,
- dilation: int = 1,
- gate: Optional[Callable[..., nn.Module]] = None,
- norm_layer: Optional[Callable[..., nn.Module]] = None
+ self,
+ inplanes: int,
+ planes: int,
+ stride: int = 1,
+ downsample: Optional[nn.Module] = None,
+ groups: int = 1,
+ base_width: int = 64,
+ dilation: int = 1,
+ gate: Optional[Callable[..., nn.Module]] = None,
+ norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super(ResBlock, self).__init__()
if gate is None:
@@ -50,7 +54,7 @@ class ResBlock(nn.Module):
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
- raise ValueError('ResBlock only supports groups=1 and base_width=64')
+ raise ValueError("ResBlock only supports groups=1 and base_width=64")
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in ResBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
@@ -81,9 +85,15 @@ class ResBlock(nn.Module):
class ALNet(nn.Module):
- def __init__(self, c1: int = 32, c2: int = 64, c3: int = 128, c4: int = 128, dim: int = 128,
- single_head: bool = True,
- ):
+ def __init__(
+ self,
+ c1: int = 32,
+ c2: int = 64,
+ c3: int = 128,
+ c4: int = 128,
+ dim: int = 128,
+ single_head: bool = True,
+ ):
super().__init__()
self.gate = nn.ReLU(inplace=True)
@@ -93,28 +103,48 @@ class ALNet(nn.Module):
self.block1 = ConvBlock(3, c1, self.gate, nn.BatchNorm2d)
- self.block2 = ResBlock(inplanes=c1, planes=c2, stride=1,
- downsample=nn.Conv2d(c1, c2, 1),
- gate=self.gate,
- norm_layer=nn.BatchNorm2d)
- self.block3 = ResBlock(inplanes=c2, planes=c3, stride=1,
- downsample=nn.Conv2d(c2, c3, 1),
- gate=self.gate,
- norm_layer=nn.BatchNorm2d)
- self.block4 = ResBlock(inplanes=c3, planes=c4, stride=1,
- downsample=nn.Conv2d(c3, c4, 1),
- gate=self.gate,
- norm_layer=nn.BatchNorm2d)
+ self.block2 = ResBlock(
+ inplanes=c1,
+ planes=c2,
+ stride=1,
+ downsample=nn.Conv2d(c1, c2, 1),
+ gate=self.gate,
+ norm_layer=nn.BatchNorm2d,
+ )
+ self.block3 = ResBlock(
+ inplanes=c2,
+ planes=c3,
+ stride=1,
+ downsample=nn.Conv2d(c2, c3, 1),
+ gate=self.gate,
+ norm_layer=nn.BatchNorm2d,
+ )
+ self.block4 = ResBlock(
+ inplanes=c3,
+ planes=c4,
+ stride=1,
+ downsample=nn.Conv2d(c3, c4, 1),
+ gate=self.gate,
+ norm_layer=nn.BatchNorm2d,
+ )
# ================================== feature aggregation
self.conv1 = resnet.conv1x1(c1, dim // 4)
self.conv2 = resnet.conv1x1(c2, dim // 4)
self.conv3 = resnet.conv1x1(c3, dim // 4)
self.conv4 = resnet.conv1x1(dim, dim // 4)
- self.upsample2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
- self.upsample4 = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True)
- self.upsample8 = nn.Upsample(scale_factor=8, mode='bilinear', align_corners=True)
- self.upsample32 = nn.Upsample(scale_factor=32, mode='bilinear', align_corners=True)
+ self.upsample2 = nn.Upsample(
+ scale_factor=2, mode="bilinear", align_corners=True
+ )
+ self.upsample4 = nn.Upsample(
+ scale_factor=4, mode="bilinear", align_corners=True
+ )
+ self.upsample8 = nn.Upsample(
+ scale_factor=8, mode="bilinear", align_corners=True
+ )
+ self.upsample32 = nn.Upsample(
+ scale_factor=32, mode="bilinear", align_corners=True
+ )
# ================================== detector and descriptor head
self.single_head = single_head
@@ -153,12 +183,12 @@ class ALNet(nn.Module):
return scores_map, descriptor_map
-if __name__ == '__main__':
+if __name__ == "__main__":
from thop import profile
net = ALNet(c1=16, c2=32, c3=64, c4=128, dim=128, single_head=True)
image = torch.randn(1, 3, 640, 480)
flops, params = profile(net, inputs=(image,), verbose=False)
- print('{:<30} {:<8} GFLops'.format('Computational complexity: ', flops / 1e9))
- print('{:<30} {:<8} KB'.format('Number of parameters: ', params / 1e3))
+ print("{:<30} {:<8} GFLops".format("Computational complexity: ", flops / 1e9))
+ print("{:<30} {:<8} KB".format("Number of parameters: ", params / 1e3))
diff --git a/imcui/third_party/ALIKE/demo.py b/third_party/ALIKE/demo.py
similarity index 55%
rename from imcui/third_party/ALIKE/demo.py
rename to third_party/ALIKE/demo.py
index 9bfbefdd26cfeceefc75f90d1c44a7f922c624a5..a3f5130eea283404412b374c678ba3a1ae6d1c04 100644
--- a/imcui/third_party/ALIKE/demo.py
+++ b/third_party/ALIKE/demo.py
@@ -12,13 +12,13 @@ from alike import ALike, configs
class ImageLoader(object):
def __init__(self, filepath: str):
self.N = 3000
- if filepath.startswith('camera'):
+ if filepath.startswith("camera"):
camera = int(filepath[6:])
self.cap = cv2.VideoCapture(camera)
if not self.cap.isOpened():
raise IOError(f"Can't open camera {camera}!")
- logging.info(f'Opened camera {camera}')
- self.mode = 'camera'
+ logging.info(f"Opened camera {camera}")
+ self.mode = "camera"
elif os.path.exists(filepath):
if os.path.isfile(filepath):
self.cap = cv2.VideoCapture(filepath)
@@ -27,34 +27,38 @@ class ImageLoader(object):
rate = self.cap.get(cv2.CAP_PROP_FPS)
self.N = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1
duration = self.N / rate
- logging.info(f'Opened video {filepath}')
- logging.info(f'Frames: {self.N}, FPS: {rate}, Duration: {duration}s')
- self.mode = 'video'
+ logging.info(f"Opened video {filepath}")
+ logging.info(f"Frames: {self.N}, FPS: {rate}, Duration: {duration}s")
+ self.mode = "video"
else:
- self.images = glob.glob(os.path.join(filepath, '*.png')) + \
- glob.glob(os.path.join(filepath, '*.jpg')) + \
- glob.glob(os.path.join(filepath, '*.ppm'))
+ self.images = (
+ glob.glob(os.path.join(filepath, "*.png"))
+ + glob.glob(os.path.join(filepath, "*.jpg"))
+ + glob.glob(os.path.join(filepath, "*.ppm"))
+ )
self.images.sort()
self.N = len(self.images)
- logging.info(f'Loading {self.N} images')
- self.mode = 'images'
+ logging.info(f"Loading {self.N} images")
+ self.mode = "images"
else:
- raise IOError('Error filepath (camerax/path of images/path of videos): ', filepath)
+ raise IOError(
+ "Error filepath (camerax/path of images/path of videos): ", filepath
+ )
def __getitem__(self, item):
- if self.mode == 'camera' or self.mode == 'video':
+ if self.mode == "camera" or self.mode == "video":
if item > self.N:
return None
ret, img = self.cap.read()
if not ret:
raise "Can't read image from camera"
- if self.mode == 'video':
+ if self.mode == "video":
self.cap.set(cv2.CAP_PROP_POS_FRAMES, item)
- elif self.mode == 'images':
+ elif self.mode == "images":
filename = self.images[item]
img = cv2.imread(filename)
if img is None:
- raise Exception('Error reading image %s' % filename)
+ raise Exception("Error reading image %s" % filename)
return img
def __len__(self):
@@ -99,38 +103,68 @@ class SimpleTracker(object):
nn12 = np.argmax(sim, axis=1)
nn21 = np.argmax(sim, axis=0)
ids1 = np.arange(0, sim.shape[0])
- mask = (ids1 == nn21[nn12])
+ mask = ids1 == nn21[nn12]
matches = np.stack([ids1[mask], nn12[mask]])
return matches.transpose()
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(description='ALike Demo.')
- parser.add_argument('input', type=str, default='',
- help='Image directory or movie file or "camera0" (for webcam0).')
- parser.add_argument('--model', choices=['alike-t', 'alike-s', 'alike-n', 'alike-l'], default="alike-t",
- help="The model configuration")
- parser.add_argument('--device', type=str, default='cuda', help="Running device (default: cuda).")
- parser.add_argument('--top_k', type=int, default=-1,
- help='Detect top K keypoints. -1 for threshold based mode, >0 for top K mode. (default: -1)')
- parser.add_argument('--scores_th', type=float, default=0.2,
- help='Detector score threshold (default: 0.2).')
- parser.add_argument('--n_limit', type=int, default=5000,
- help='Maximum number of keypoints to be detected (default: 5000).')
- parser.add_argument('--no_display', action='store_true',
- help='Do not display images to screen. Useful if running remotely (default: False).')
- parser.add_argument('--no_sub_pixel', action='store_true',
- help='Do not detect sub-pixel keypoints (default: False).')
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description="ALike Demo.")
+ parser.add_argument(
+ "input",
+ type=str,
+ default="",
+ help='Image directory or movie file or "camera0" (for webcam0).',
+ )
+ parser.add_argument(
+ "--model",
+ choices=["alike-t", "alike-s", "alike-n", "alike-l"],
+ default="alike-t",
+ help="The model configuration",
+ )
+ parser.add_argument(
+ "--device", type=str, default="cuda", help="Running device (default: cuda)."
+ )
+ parser.add_argument(
+ "--top_k",
+ type=int,
+ default=-1,
+ help="Detect top K keypoints. -1 for threshold based mode, >0 for top K mode. (default: -1)",
+ )
+ parser.add_argument(
+ "--scores_th",
+ type=float,
+ default=0.2,
+ help="Detector score threshold (default: 0.2).",
+ )
+ parser.add_argument(
+ "--n_limit",
+ type=int,
+ default=5000,
+ help="Maximum number of keypoints to be detected (default: 5000).",
+ )
+ parser.add_argument(
+ "--no_display",
+ action="store_true",
+ help="Do not display images to screen. Useful if running remotely (default: False).",
+ )
+ parser.add_argument(
+ "--no_sub_pixel",
+ action="store_true",
+ help="Do not detect sub-pixel keypoints (default: False).",
+ )
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
image_loader = ImageLoader(args.input)
- model = ALike(**configs[args.model],
- device=args.device,
- top_k=args.top_k,
- scores_th=args.scores_th,
- n_limit=args.n_limit)
+ model = ALike(
+ **configs[args.model],
+ device=args.device,
+ top_k=args.top_k,
+ scores_th=args.scores_th,
+ n_limit=args.n_limit,
+ )
tracker = SimpleTracker()
if not args.no_display:
@@ -142,26 +176,26 @@ if __name__ == '__main__':
for img in progress_bar:
if img is None:
break
-
+
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
pred = model(img_rgb, sub_pixel=not args.no_sub_pixel)
- kpts = pred['keypoints']
- desc = pred['descriptors']
- runtime.append(pred['time'])
+ kpts = pred["keypoints"]
+ desc = pred["descriptors"]
+ runtime.append(pred["time"])
out, N_matches = tracker.update(img, kpts, desc)
- ave_fps = (1. / np.stack(runtime)).mean()
+ ave_fps = (1.0 / np.stack(runtime)).mean()
status = f"Fps:{ave_fps:.1f}, Keypoints/Matches: {len(kpts)}/{N_matches}"
progress_bar.set_description(status)
if not args.no_display:
- cv2.setWindowTitle(args.model, args.model + ': ' + status)
+ cv2.setWindowTitle(args.model, args.model + ": " + status)
cv2.imshow(args.model, out)
- if cv2.waitKey(1) == ord('q'):
+ if cv2.waitKey(1) == ord("q"):
break
- logging.info('Finished!')
+ logging.info("Finished!")
if not args.no_display:
- logging.info('Press any key to exit!')
+ logging.info("Press any key to exit!")
cv2.waitKey()
diff --git a/third_party/ALIKE/hseq/cache/alike-l-ms.npy b/third_party/ALIKE/hseq/cache/alike-l-ms.npy
new file mode 100644
index 0000000000000000000000000000000000000000..bd988fb065ecd4a900178a3cb974bbbf56de0dc0
--- /dev/null
+++ b/third_party/ALIKE/hseq/cache/alike-l-ms.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1350ab826afdd9b7542a556e2fda9ad9f94388a875c8edb7874e4bcdfebc63ca
+size 13124
diff --git a/third_party/ALIKE/hseq/cache/alike-l.npy b/third_party/ALIKE/hseq/cache/alike-l.npy
new file mode 100644
index 0000000000000000000000000000000000000000..7c63bbec1588af102721df60d0ab8043586036d1
--- /dev/null
+++ b/third_party/ALIKE/hseq/cache/alike-l.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:999daff1155f3d4736bb7374fb2058f520b0cb4c75b5d7d87fc1e7025a7d2a7d
+size 13124
diff --git a/third_party/ALIKE/hseq/cache/alike-n-ms.npy b/third_party/ALIKE/hseq/cache/alike-n-ms.npy
new file mode 100644
index 0000000000000000000000000000000000000000..02e2d32258dcaed882ca7a28e7dd47c97c4bb65a
--- /dev/null
+++ b/third_party/ALIKE/hseq/cache/alike-n-ms.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e5967048eddb61e423bf2ea05a2a626e18d8a716b6a0ad42471059aec0b934c
+size 13124
diff --git a/third_party/ALIKE/hseq/cache/alike-n.npy b/third_party/ALIKE/hseq/cache/alike-n.npy
new file mode 100644
index 0000000000000000000000000000000000000000..3ec339ab8cd7a629d752576e8b275cba215614da
--- /dev/null
+++ b/third_party/ALIKE/hseq/cache/alike-n.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e2eba5ff96b25d0a100b6c7273549de91586e6069dcb5320a20edbb24ea462e
+size 13124
diff --git a/third_party/ALIKE/hseq/cache/aslfeat.npy b/third_party/ALIKE/hseq/cache/aslfeat.npy
new file mode 100644
index 0000000000000000000000000000000000000000..24fb50ccae5d7fa86fb6d4224beb983e54160895
--- /dev/null
+++ b/third_party/ALIKE/hseq/cache/aslfeat.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce06fd1b6265e09ed3b26768b68f624e2d556358ab98addd8ebdb7a5a076abe8
+size 15352
diff --git a/third_party/ALIKE/hseq/cache/d2.npy b/third_party/ALIKE/hseq/cache/d2.npy
new file mode 100644
index 0000000000000000000000000000000000000000..741588a2e42c40fd8a3f7c097d56898ef66c5ceb
--- /dev/null
+++ b/third_party/ALIKE/hseq/cache/d2.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:976d81c6b51a98f89eac60c6d25990130c1df571ef6536280f4b00577eab56f0
+size 15352
diff --git a/third_party/ALIKE/hseq/cache/disk.npy b/third_party/ALIKE/hseq/cache/disk.npy
new file mode 100644
index 0000000000000000000000000000000000000000..27871bccf7a206df33b94f25db28259b2b7cd456
--- /dev/null
+++ b/third_party/ALIKE/hseq/cache/disk.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df2d9e0dfd0baa19f2af12f4604368ca65a1643159e7e3438e25efc41ab15357
+size 15352
diff --git a/third_party/ALIKE/hseq/cache/lfnet.npy b/third_party/ALIKE/hseq/cache/lfnet.npy
new file mode 100644
index 0000000000000000000000000000000000000000..2b3fc3514b2c85a856aae46f5f75bcf6cc6e2afd
--- /dev/null
+++ b/third_party/ALIKE/hseq/cache/lfnet.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:417327dee726cffccc6dfbc9b0e6b3c06b277ea8878ccf87b87475d1cd6e65ca
+size 15352
diff --git a/third_party/ALIKE/hseq/cache/r2d2.npy b/third_party/ALIKE/hseq/cache/r2d2.npy
new file mode 100644
index 0000000000000000000000000000000000000000..247b6e2952cf7a2a2e86479c4b888eb55f63cdd2
--- /dev/null
+++ b/third_party/ALIKE/hseq/cache/r2d2.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1375a21adcc932db2c9e210e52f633c1903cca6d37066391eb9d645ff87d0120
+size 15352
diff --git a/third_party/ALIKE/hseq/cache/superpoint.npy b/third_party/ALIKE/hseq/cache/superpoint.npy
new file mode 100644
index 0000000000000000000000000000000000000000..b2d1ec429e6ffd960bc8a35128d6926683ba5162
--- /dev/null
+++ b/third_party/ALIKE/hseq/cache/superpoint.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e4d4a4ca79518af47467e9ddd69fe159c9305a580dadc4fdab6ffde6f8b48c2
+size 15352
diff --git a/imcui/third_party/ALIKE/hseq/eval.py b/third_party/ALIKE/hseq/eval.py
similarity index 64%
rename from imcui/third_party/ALIKE/hseq/eval.py
rename to third_party/ALIKE/hseq/eval.py
index abca625044013a0cd34a518223c32d3ec8abb8a3..1d91398740e5dee9d2968fb418fcb45febd015ba 100644
--- a/imcui/third_party/ALIKE/hseq/eval.py
+++ b/third_party/ALIKE/hseq/eval.py
@@ -6,29 +6,53 @@ import numpy as np
from extract import extract_method
use_cuda = torch.cuda.is_available()
-device = torch.device('cuda' if use_cuda else 'cpu')
-
-methods = ['d2', 'lfnet', 'superpoint', 'r2d2', 'aslfeat', 'disk',
- 'alike-n', 'alike-l', 'alike-n-ms', 'alike-l-ms']
-names = ['D2-Net(MS)', 'LF-Net(MS)', 'SuperPoint', 'R2D2(MS)', 'ASLFeat(MS)', 'DISK',
- 'ALike-N', 'ALike-L', 'ALike-N(MS)', 'ALike-L(MS)']
+device = torch.device("cuda" if use_cuda else "cpu")
+
+methods = [
+ "d2",
+ "lfnet",
+ "superpoint",
+ "r2d2",
+ "aslfeat",
+ "disk",
+ "alike-n",
+ "alike-l",
+ "alike-n-ms",
+ "alike-l-ms",
+]
+names = [
+ "D2-Net(MS)",
+ "LF-Net(MS)",
+ "SuperPoint",
+ "R2D2(MS)",
+ "ASLFeat(MS)",
+ "DISK",
+ "ALike-N",
+ "ALike-L",
+ "ALike-N(MS)",
+ "ALike-L(MS)",
+]
top_k = None
n_i = 52
n_v = 56
-cache_dir = 'hseq/cache'
-dataset_path = 'hseq/hpatches-sequences-release'
+cache_dir = "hseq/cache"
+dataset_path = "hseq/hpatches-sequences-release"
-def generate_read_function(method, extension='ppm'):
+def generate_read_function(method, extension="ppm"):
def read_function(seq_name, im_idx):
- aux = np.load(os.path.join(dataset_path, seq_name, '%d.%s.%s' % (im_idx, extension, method)))
+ aux = np.load(
+ os.path.join(
+ dataset_path, seq_name, "%d.%s.%s" % (im_idx, extension, method)
+ )
+ )
if top_k is None:
- return aux['keypoints'], aux['descriptors']
+ return aux["keypoints"], aux["descriptors"]
else:
- assert ('scores' in aux)
- ids = np.argsort(aux['scores'])[-top_k:]
- return aux['keypoints'][ids, :], aux['descriptors'][ids, :]
+ assert "scores" in aux
+ ids = np.argsort(aux["scores"])[-top_k:]
+ return aux["keypoints"][ids, :], aux["descriptors"][ids, :]
return read_function
@@ -39,7 +63,7 @@ def mnn_matcher(descriptors_a, descriptors_b):
nn12 = torch.max(sim, dim=1)[1]
nn21 = torch.max(sim, dim=0)[1]
ids1 = torch.arange(0, sim.shape[0], device=device)
- mask = (ids1 == nn21[nn12])
+ mask = ids1 == nn21[nn12]
matches = torch.stack([ids1[mask], nn12[mask]])
return matches.t().data.cpu().numpy()
@@ -73,7 +97,7 @@ def benchmark_features(read_feats):
n_feats.append(keypoints_a.shape[0])
# =========== compute homography
- ref_img = cv2.imread(os.path.join(dataset_path, seq_name, '1.ppm'))
+ ref_img = cv2.imread(os.path.join(dataset_path, seq_name, "1.ppm"))
ref_img_shape = ref_img.shape
for im_idx in range(2, 7):
@@ -82,17 +106,19 @@ def benchmark_features(read_feats):
matches = mnn_matcher(
torch.from_numpy(descriptors_a).to(device=device),
- torch.from_numpy(descriptors_b).to(device=device)
+ torch.from_numpy(descriptors_b).to(device=device),
)
- homography = np.loadtxt(os.path.join(dataset_path, seq_name, "H_1_" + str(im_idx)))
+ homography = np.loadtxt(
+ os.path.join(dataset_path, seq_name, "H_1_" + str(im_idx))
+ )
- pos_a = keypoints_a[matches[:, 0], : 2]
+ pos_a = keypoints_a[matches[:, 0], :2]
pos_a_h = np.concatenate([pos_a, np.ones([matches.shape[0], 1])], axis=1)
pos_b_proj_h = np.transpose(np.dot(homography, np.transpose(pos_a_h)))
- pos_b_proj = pos_b_proj_h[:, : 2] / pos_b_proj_h[:, 2:]
+ pos_b_proj = pos_b_proj_h[:, :2] / pos_b_proj_h[:, 2:]
- pos_b = keypoints_b[matches[:, 1], : 2]
+ pos_b = keypoints_b[matches[:, 1], :2]
dist = np.sqrt(np.sum((pos_b - pos_b_proj) ** 2, axis=1))
@@ -103,28 +129,37 @@ def benchmark_features(read_feats):
dist = np.array([float("inf")])
for thr in rng:
- if seq_name[0] == 'i':
+ if seq_name[0] == "i":
i_err[thr] += np.mean(dist <= thr)
else:
v_err[thr] += np.mean(dist <= thr)
# =========== compute homography
gt_homo = homography
- pred_homo, _ = cv2.findHomography(keypoints_a[matches[:, 0], : 2], keypoints_b[matches[:, 1], : 2],
- cv2.RANSAC)
+ pred_homo, _ = cv2.findHomography(
+ keypoints_a[matches[:, 0], :2],
+ keypoints_b[matches[:, 1], :2],
+ cv2.RANSAC,
+ )
if pred_homo is None:
homo_dist = np.array([float("inf")])
else:
- corners = np.array([[0, 0],
- [ref_img_shape[1] - 1, 0],
- [0, ref_img_shape[0] - 1],
- [ref_img_shape[1] - 1, ref_img_shape[0] - 1]])
+ corners = np.array(
+ [
+ [0, 0],
+ [ref_img_shape[1] - 1, 0],
+ [0, ref_img_shape[0] - 1],
+ [ref_img_shape[1] - 1, ref_img_shape[0] - 1],
+ ]
+ )
real_warped_corners = homo_trans(corners, gt_homo)
warped_corners = homo_trans(corners, pred_homo)
- homo_dist = np.mean(np.linalg.norm(real_warped_corners - warped_corners, axis=1))
+ homo_dist = np.mean(
+ np.linalg.norm(real_warped_corners - warped_corners, axis=1)
+ )
for thr in rng:
- if seq_name[0] == 'i':
+ if seq_name[0] == "i":
i_err_homo[thr] += np.mean(homo_dist <= thr)
else:
v_err_homo[thr] += np.mean(homo_dist <= thr)
@@ -136,10 +171,10 @@ def benchmark_features(read_feats):
return i_err, v_err, i_err_homo, v_err_homo, [seq_type, n_feats, n_matches]
-if __name__ == '__main__':
+if __name__ == "__main__":
errors = {}
for method in methods:
- output_file = os.path.join(cache_dir, method + '.npy')
+ output_file = os.path.join(cache_dir, method + ".npy")
read_function = generate_read_function(method)
if os.path.exists(output_file):
errors[method] = np.load(output_file, allow_pickle=True)
@@ -152,11 +187,11 @@ if __name__ == '__main__':
i_err, v_err, i_err_hom, v_err_hom, _ = errors[method]
print(f"====={name}=====")
- print(f"MMA@1 MMA@2 MMA@3 MHA@1 MHA@2 MHA@3: ", end='')
+ print(f"MMA@1 MMA@2 MMA@3 MHA@1 MHA@2 MHA@3: ", end="")
for thr in range(1, 4):
err = (i_err[thr] + v_err[thr]) / ((n_i + n_v) * 5)
- print(f"{err * 100:.2f}%", end=' ')
+ print(f"{err * 100:.2f}%", end=" ")
for thr in range(1, 4):
err_hom = (i_err_hom[thr] + v_err_hom[thr]) / ((n_i + n_v) * 5)
- print(f"{err_hom * 100:.2f}%", end=' ')
- print('')
+ print(f"{err_hom * 100:.2f}%", end=" ")
+ print("")
diff --git a/imcui/third_party/ALIKE/hseq/extract.py b/third_party/ALIKE/hseq/extract.py
similarity index 66%
rename from imcui/third_party/ALIKE/hseq/extract.py
rename to third_party/ALIKE/hseq/extract.py
index 1342e40dd2d0e1d1986e90f995c95b17972ec4e1..df16ae246bf360b529f0640cab5ae79f495e4f61 100644
--- a/imcui/third_party/ALIKE/hseq/extract.py
+++ b/third_party/ALIKE/hseq/extract.py
@@ -9,23 +9,23 @@ from tqdm import tqdm
from copy import deepcopy
from torchvision.transforms import ToTensor
-sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
from alike import ALike, configs
-dataset_root = 'hseq/hpatches-sequences-release'
+dataset_root = "hseq/hpatches-sequences-release"
use_cuda = torch.cuda.is_available()
-device = 'cuda' if use_cuda else 'cpu'
-methods = ['alike-n', 'alike-l', 'alike-n-ms', 'alike-l-ms']
+device = "cuda" if use_cuda else "cpu"
+methods = ["alike-n", "alike-l", "alike-n-ms", "alike-l-ms"]
class HPatchesDataset(data.Dataset):
- def __init__(self, root: str = dataset_root, alteration: str = 'all'):
+ def __init__(self, root: str = dataset_root, alteration: str = "all"):
"""
Args:
root: dataset root path
alteration: # 'all', 'i' for illumination or 'v' for viewpoint
"""
- assert (Path(root).exists()), f"Dataset root path {root} dose not exist!"
+ assert Path(root).exists(), f"Dataset root path {root} dose not exist!"
self.root = root
# get all image file name
@@ -35,15 +35,15 @@ class HPatchesDataset(data.Dataset):
folders = [x for x in Path(self.root).iterdir() if x.is_dir()]
self.seqs = []
for folder in folders:
- if alteration == 'i' and folder.stem[0] != 'i':
+ if alteration == "i" and folder.stem[0] != "i":
continue
- if alteration == 'v' and folder.stem[0] != 'v':
+ if alteration == "v" and folder.stem[0] != "v":
continue
self.seqs.append(folder)
self.len = len(self.seqs)
- assert (self.len > 0), f'Can not find PatchDataset in path {self.root}'
+ assert self.len > 0, f"Can not find PatchDataset in path {self.root}"
def __getitem__(self, item):
folder = self.seqs[item]
@@ -51,12 +51,12 @@ class HPatchesDataset(data.Dataset):
imgs = []
homos = []
for i in range(1, 7):
- img = cv2.imread(str(folder / f'{i}.ppm'), cv2.IMREAD_COLOR)
+ img = cv2.imread(str(folder / f"{i}.ppm"), cv2.IMREAD_COLOR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # HxWxC
imgs.append(img)
if i != 1:
- homo = np.loadtxt(str(folder / f'H_1_{i}')).astype('float32')
+ homo = np.loadtxt(str(folder / f"H_1_{i}")).astype("float32")
homos.append(homo)
return imgs, homos, folder.stem
@@ -68,11 +68,18 @@ class HPatchesDataset(data.Dataset):
return self.__class__
-def extract_multiscale(model, img, scale_f=2 ** 0.5,
- min_scale=1., max_scale=1.,
- min_size=0., max_size=99999.,
- image_size_max=99999,
- n_k=0, sort=False):
+def extract_multiscale(
+ model,
+ img,
+ scale_f=2**0.5,
+ min_scale=1.0,
+ max_scale=1.0,
+ min_size=0.0,
+ max_size=99999.0,
+ image_size_max=99999,
+ n_k=0,
+ sort=False,
+):
H_, W_, three = img.shape
assert three == 3, "input image shape should be [HxWx3]"
@@ -100,7 +107,9 @@ def extract_multiscale(model, img, scale_f=2 ** 0.5,
# extract descriptors
with torch.no_grad():
descriptor_map, scores_map = model.extract_dense_map(image)
- keypoints_, descriptors_, scores_, _ = model.dkd(scores_map, descriptor_map)
+ keypoints_, descriptors_, scores_, _ = model.dkd(
+ scores_map, descriptor_map
+ )
keypoints.append(keypoints_[0])
descriptors.append(descriptors_[0])
@@ -110,7 +119,9 @@ def extract_multiscale(model, img, scale_f=2 ** 0.5,
# down-scale the image for next iteration
nh, nw = round(H * s), round(W * s)
- image = torch.nn.functional.interpolate(image, (nh, nw), mode='bilinear', align_corners=False)
+ image = torch.nn.functional.interpolate(
+ image, (nh, nw), mode="bilinear", align_corners=False
+ )
# restore value
torch.backends.cudnn.benchmark = old_bm
@@ -131,29 +142,34 @@ def extract_multiscale(model, img, scale_f=2 ** 0.5,
descriptors = descriptors[0:n_k]
scores = scores[0:n_k]
- return {'keypoints': keypoints, 'descriptors': descriptors, 'scores': scores}
+ return {"keypoints": keypoints, "descriptors": descriptors, "scores": scores}
def extract_method(m):
- hpatches = HPatchesDataset(root=dataset_root, alteration='all')
+ hpatches = HPatchesDataset(root=dataset_root, alteration="all")
model = m[:7]
- min_scale = 0.3 if m[8:] == 'ms' else 1.0
+ min_scale = 0.3 if m[8:] == "ms" else 1.0
model = ALike(**configs[model], device=device, top_k=0, scores_th=0.2, n_limit=5000)
- progbar = tqdm(hpatches, desc='Extracting for {}'.format(m))
+ progbar = tqdm(hpatches, desc="Extracting for {}".format(m))
for imgs, homos, seq_name in progbar:
for i in range(1, 7):
img = imgs[i - 1]
- pred = extract_multiscale(model, img, min_scale=min_scale, max_scale=1, sort=False, n_k=5000)
- kpts, descs, scores = pred['keypoints'], pred['descriptors'], pred['scores']
+ pred = extract_multiscale(
+ model, img, min_scale=min_scale, max_scale=1, sort=False, n_k=5000
+ )
+ kpts, descs, scores = pred["keypoints"], pred["descriptors"], pred["scores"]
- with open(os.path.join(dataset_root, seq_name, f'{i}.ppm.{m}'), 'wb') as f:
- np.savez(f, keypoints=kpts.cpu().numpy(),
- scores=scores.cpu().numpy(),
- descriptors=descs.cpu().numpy())
+ with open(os.path.join(dataset_root, seq_name, f"{i}.ppm.{m}"), "wb") as f:
+ np.savez(
+ f,
+ keypoints=kpts.cpu().numpy(),
+ scores=scores.cpu().numpy(),
+ descriptors=descs.cpu().numpy(),
+ )
-if __name__ == '__main__':
+if __name__ == "__main__":
for method in methods:
extract_method(method)
diff --git a/third_party/ALIKE/matlab/createfigure.m b/third_party/ALIKE/matlab/createfigure.m
new file mode 100644
index 0000000000000000000000000000000000000000..038090c7e570aeaed25bd4dfaffb71134d707082
--- /dev/null
+++ b/third_party/ALIKE/matlab/createfigure.m
@@ -0,0 +1,75 @@
+function createfigure(X1, YMatrix1, Y1, l1, l2, l3)
+%CREATEFIGURE(X1, YMatrix1, Y1)
+% X1: vector of x data
+% YMATRIX1: matrix of y data
+% Y1: vector of y data
+
+% Auto-generated by MATLAB on 29-Oct-2021 15:42:14
+
+% Create figure
+figure1 = figure;
+
+% Create axes
+axes1 = axes('Parent',figure1);
+hold(axes1,'on');
+
+% Create multiple lines using matrix input to plot
+plot1 = plot(X1,YMatrix1,'Parent',axes1,'LineWidth',1);
+set(plot1(1),'LineStyle','-.','Color',[1 0 0]);
+set(plot1(2),'Color',[0 1 0]);
+set(plot1(3),'LineStyle','--',...
+ 'Color',[0.87058824300766 0.490196079015732 0]);
+
+% Uncomment the following line to preserve the X-limits of the axes
+% xlim(axes1,[-1.1 1.1]);
+% Uncomment the following line to preserve the Y-limits of the axes
+ylim(axes1,[0 2.2]);
+box(axes1,'on');
+hold(axes1,'off');
+% Set the remaining axes properties
+set(axes1,'XColor',[0 0 0],'YColor',[0 0 0],'YTick',[0 0.5 1 1.5 2 2.5]);
+% Create axes
+axes2 = axes('Parent',figure1);
+hold(axes2,'on');
+colororder([0.494 0.184 0.556;0.466 0.674 0.188;0.301 0.745 0.933;0.635 0.078 0.184;0 0.447 0.741;0.85 0.325 0.098;0.929 0.694 0.125]);
+
+% Create plot
+plot(X1,Y1,'Parent',axes2,'LineWidth',1,'LineStyle',':','Color',[0 0 1]);
+
+% Uncomment the following line to preserve the X-limits of the axes
+% xlim(axes2,[-1.1 1.1]);
+% Uncomment the following line to preserve the Y-limits of the axes
+ylim(axes2,[0 1.6]);
+hold(axes2,'off');
+% Set the remaining axes properties
+set(axes2,'Color','none','HitTest','off','XColor',[0 0 0],'YAxisLocation',...
+ 'right','YColor',[0 0 0],'YTick',[0 0.5 1 1.5]);
+% Create textbox
+annotation(figure1,'textbox',...
+ [0.255427607968038,0.605539475745798,0.304947448327989,0.235148519909872],...
+ 'Color',[0.8 0 0],...
+ 'String',{sprintf('peak loss=%.4f',l1)},...
+ 'EdgeColor','none');
+
+% Create textbox
+annotation(figure1,'textbox',...
+ [0.631790371410027,0.083530640355914,0.178879315581032,0.235148519909871],...
+ 'Color',[0 0 1],...
+ 'String',{'keypoint'},...
+ 'EdgeColor','none');
+
+% Create textbox
+annotation(figure1,'textbox',...
+ [0.59663112557549,0.640686239621974,0.318247136419826,0.22093023731067],...
+ 'Color',[0 0.498039215803146 0],...
+ 'String',{sprintf('peak loss=%.4f',l2)},...
+ 'EdgeColor','none');
+
+% Create textbox
+annotation(figure1,'textbox',...
+ [0.595423071596731,0.415858983920567,0.318247136419826,0.235148519909871],...
+ 'Color',[0.87058824300766 0.490196079015732 0],...
+ 'String',{sprintf('peak loss=%.4f',l3)},...
+ 'FitBoxToText','off',...
+ 'EdgeColor','none');
+
diff --git a/third_party/ALIKE/matlab/peakloss_rect.m b/third_party/ALIKE/matlab/peakloss_rect.m
new file mode 100644
index 0000000000000000000000000000000000000000..fa0d811c126aec1d6f6868352d89be69ea351577
--- /dev/null
+++ b/third_party/ALIKE/matlab/peakloss_rect.m
@@ -0,0 +1,19 @@
+clear;
+close all;
+
+x = -1:0.01:1;
+
+p0 = 0.5;
+p1 = -0.5;
+
+d = abs(x - p0);
+
+c0 = 2 .* (x>=-0.75 & x <= -0.25);
+c1 = 2 .* (x>=0.25 & x <= 0.75);
+c2 = 1.25 .* (x>=0.1 & x <= 0.9);
+
+peak_loss0 = sum(d.*c0) / length(x)
+peak_loss1 = sum(d.*c1) / length(x)
+peak_loss2 = sum(d.*c2) / length(x)
+
+createfigure(x, [c0;c1;c2], d, peak_loss0,peak_loss1, peak_loss2);
\ No newline at end of file
diff --git a/third_party/ALIKE/requirements.txt b/third_party/ALIKE/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..14ca745ea1572bda6b2bd7c4eb88bb026b566781
--- /dev/null
+++ b/third_party/ALIKE/requirements.txt
@@ -0,0 +1,6 @@
+opencv-python~=4.5.1.48
+numpy~=1.19.5
+tqdm~=4.60.0
+torch~=1.8.0
+torchvision~=0.9.0
+thop~=0.0.31-2005241907
\ No newline at end of file
diff --git a/imcui/third_party/ALIKE/soft_detect.py b/third_party/ALIKE/soft_detect.py
similarity index 69%
rename from imcui/third_party/ALIKE/soft_detect.py
rename to third_party/ALIKE/soft_detect.py
index 2d23cd13b8a7db9b0398fdc1b235564222d30c90..636ba11d0584c513631fffce31ba2d71be3e6c74 100644
--- a/imcui/third_party/ALIKE/soft_detect.py
+++ b/third_party/ALIKE/soft_detect.py
@@ -17,13 +17,15 @@ import torch.nn.functional as F
# v
# [ y: range=-1.0~1.0; h: range=0~H ]
+
def simple_nms(scores, nms_radius: int):
- """ Fast Non-maximum suppression to remove nearby points """
- assert (nms_radius >= 0)
+ """Fast Non-maximum suppression to remove nearby points"""
+ assert nms_radius >= 0
def max_pool(x):
return torch.nn.functional.max_pool2d(
- x, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius)
+ x, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius
+ )
zeros = torch.zeros_like(scores)
max_mask = scores == max_pool(scores)
@@ -50,8 +52,14 @@ def sample_descriptor(descriptor_map, kpts, bilinear_interp=False):
kptsi = kpts[index] # Nx2,(x,y)
if bilinear_interp:
- descriptors_ = torch.nn.functional.grid_sample(descriptor_map[index].unsqueeze(0), kptsi.view(1, 1, -1, 2),
- mode='bilinear', align_corners=True)[0, :, 0, :] # CxN
+ descriptors_ = torch.nn.functional.grid_sample(
+ descriptor_map[index].unsqueeze(0),
+ kptsi.view(1, 1, -1, 2),
+ mode="bilinear",
+ align_corners=True,
+ )[
+ 0, :, 0, :
+ ] # CxN
else:
kptsi = (kptsi + 1) / 2 * kptsi.new_tensor([[width - 1, height - 1]])
kptsi = kptsi.long()
@@ -94,10 +102,10 @@ class DKD(nn.Module):
nms_scores = simple_nms(scores_nograd, 2)
# remove border
- nms_scores[:, :, :self.radius + 1, :] = 0
- nms_scores[:, :, :, :self.radius + 1] = 0
- nms_scores[:, :, h - self.radius:, :] = 0
- nms_scores[:, :, :, w - self.radius:] = 0
+ nms_scores[:, :, : self.radius + 1, :] = 0
+ nms_scores[:, :, :, : self.radius + 1] = 0
+ nms_scores[:, :, h - self.radius :, :] = 0
+ nms_scores[:, :, :, w - self.radius :] = 0
# detect keypoints without grad
if self.top_k > 0:
@@ -121,7 +129,7 @@ class DKD(nn.Module):
if len(indices) > self.n_limit:
kpts_sc = scores[indices]
sort_idx = kpts_sc.sort(descending=True)[1]
- sel_idx = sort_idx[:self.n_limit]
+ sel_idx = sort_idx[: self.n_limit]
indices = indices[sel_idx]
indices_keypoints.append(indices)
@@ -134,42 +142,73 @@ class DKD(nn.Module):
self.hw_grid = self.hw_grid.to(patches) # to device
for b_idx in range(b):
patch = patches[b_idx].t() # (H*W) x (kernel**2)
- indices_kpt = indices_keypoints[b_idx] # one dimension vector, say its size is M
+ indices_kpt = indices_keypoints[
+ b_idx
+ ] # one dimension vector, say its size is M
patch_scores = patch[indices_kpt] # M x (kernel**2)
# max is detached to prevent undesired backprop loops in the graph
max_v = patch_scores.max(dim=1).values.detach()[:, None]
- x_exp = ((patch_scores - max_v) / self.temperature).exp() # M * (kernel**2), in [0, 1]
+ x_exp = (
+ (patch_scores - max_v) / self.temperature
+ ).exp() # M * (kernel**2), in [0, 1]
# \frac{ \sum{(i,j) \times \exp(x/T)} }{ \sum{\exp(x/T)} }
- xy_residual = x_exp @ self.hw_grid / x_exp.sum(dim=1)[:, None] # Soft-argmax, Mx2
-
- hw_grid_dist2 = torch.norm((self.hw_grid[None, :, :] - xy_residual[:, None, :]) / self.radius,
- dim=-1) ** 2
+ xy_residual = (
+ x_exp @ self.hw_grid / x_exp.sum(dim=1)[:, None]
+ ) # Soft-argmax, Mx2
+
+ hw_grid_dist2 = (
+ torch.norm(
+ (self.hw_grid[None, :, :] - xy_residual[:, None, :])
+ / self.radius,
+ dim=-1,
+ )
+ ** 2
+ )
scoredispersity = (x_exp * hw_grid_dist2).sum(dim=1) / x_exp.sum(dim=1)
# compute result keypoints
- keypoints_xy_nms = torch.stack([indices_kpt % w, indices_kpt // w], dim=1) # Mx2
+ keypoints_xy_nms = torch.stack(
+ [indices_kpt % w, indices_kpt // w], dim=1
+ ) # Mx2
keypoints_xy = keypoints_xy_nms + xy_residual
- keypoints_xy = keypoints_xy / keypoints_xy.new_tensor(
- [w - 1, h - 1]) * 2 - 1 # (w,h) -> (-1~1,-1~1)
-
- kptscore = torch.nn.functional.grid_sample(scores_map[b_idx].unsqueeze(0),
- keypoints_xy.view(1, 1, -1, 2),
- mode='bilinear', align_corners=True)[0, 0, 0, :] # CxN
+ keypoints_xy = (
+ keypoints_xy / keypoints_xy.new_tensor([w - 1, h - 1]) * 2 - 1
+ ) # (w,h) -> (-1~1,-1~1)
+
+ kptscore = torch.nn.functional.grid_sample(
+ scores_map[b_idx].unsqueeze(0),
+ keypoints_xy.view(1, 1, -1, 2),
+ mode="bilinear",
+ align_corners=True,
+ )[
+ 0, 0, 0, :
+ ] # CxN
keypoints.append(keypoints_xy)
scoredispersitys.append(scoredispersity)
kptscores.append(kptscore)
else:
for b_idx in range(b):
- indices_kpt = indices_keypoints[b_idx] # one dimension vector, say its size is M
- keypoints_xy_nms = torch.stack([indices_kpt % w, indices_kpt // w], dim=1) # Mx2
- keypoints_xy = keypoints_xy_nms / keypoints_xy_nms.new_tensor(
- [w - 1, h - 1]) * 2 - 1 # (w,h) -> (-1~1,-1~1)
- kptscore = torch.nn.functional.grid_sample(scores_map[b_idx].unsqueeze(0),
- keypoints_xy.view(1, 1, -1, 2),
- mode='bilinear', align_corners=True)[0, 0, 0, :] # CxN
+ indices_kpt = indices_keypoints[
+ b_idx
+ ] # one dimension vector, say its size is M
+ keypoints_xy_nms = torch.stack(
+ [indices_kpt % w, indices_kpt // w], dim=1
+ ) # Mx2
+ keypoints_xy = (
+ keypoints_xy_nms / keypoints_xy_nms.new_tensor([w - 1, h - 1]) * 2
+ - 1
+ ) # (w,h) -> (-1~1,-1~1)
+ kptscore = torch.nn.functional.grid_sample(
+ scores_map[b_idx].unsqueeze(0),
+ keypoints_xy.view(1, 1, -1, 2),
+ mode="bilinear",
+ align_corners=True,
+ )[
+ 0, 0, 0, :
+ ] # CxN
keypoints.append(keypoints_xy)
scoredispersitys.append(None)
kptscores.append(kptscore)
@@ -183,8 +222,9 @@ class DKD(nn.Module):
:param sub_pixel: whether to use sub-pixel keypoint detection
:return: kpts: list[Nx2,...]; kptscores: list[N,....] normalised position: -1.0 ~ 1.0
"""
- keypoints, scoredispersitys, kptscores = self.detect_keypoints(scores_map,
- sub_pixel)
+ keypoints, scoredispersitys, kptscores = self.detect_keypoints(
+ scores_map, sub_pixel
+ )
descriptors = sample_descriptor(descriptor_map, keypoints, sub_pixel)
diff --git a/imcui/third_party/ASpanFormer/.github/workflows/sync.yml b/third_party/ASpanFormer/.github/workflows/sync.yml
similarity index 100%
rename from imcui/third_party/ASpanFormer/.github/workflows/sync.yml
rename to third_party/ASpanFormer/.github/workflows/sync.yml
diff --git a/third_party/ASpanFormer/.gitignore b/third_party/ASpanFormer/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a4b668777112a4fbc96b1763c8da4ad91c9bcac9
--- /dev/null
+++ b/third_party/ASpanFormer/.gitignore
@@ -0,0 +1,32 @@
+.vscode/
+__pycache__/
+*.pyc
+*.DS_Store
+*.swp
+*.pth
+tmp.*
+*/.ipynb_checkpoints/*
+
+logs/
+# weights/
+dump/
+demo/*.mp4
+demo/demo_images/
+src/loftr/utils/superglue.py
+demo/utils.py
+
+demo/*.jpg
+demo/*.png
+
+notebooks/QccDayNight.ipynb
+notebooks/westlake.ipynb
+assets/westlake
+assets/qcc_pairs.txt
+configs/.petrel*
+tools/draw_QccDayNights.py
+
+scripts/slurm/
+scripts/sbatch_submit.sh
+src/utils/client.py
+
+scannet_indices/
diff --git a/third_party/ASpanFormer/CODE_OF_CONDUCT.md b/third_party/ASpanFormer/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000000000000000000000000000000000..c991377a60951acbcd7f586ebcf0184840e30e55
--- /dev/null
+++ b/third_party/ASpanFormer/CODE_OF_CONDUCT.md
@@ -0,0 +1,71 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+ advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+ address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+ professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the open source team at [opensource-conduct@group.apple.com](mailto:opensource-conduct@group.apple.com). All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), version 1.4,
+available at [https://www.contributor-covenant.org/version/1/4/code-of-conduct.html](https://www.contributor-covenant.org/version/1/4/code-of-conduct.html)
\ No newline at end of file
diff --git a/third_party/ASpanFormer/CONTRIBUTING.md b/third_party/ASpanFormer/CONTRIBUTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..03d1703dce5cbd70896fcb8abc0fbdc664751320
--- /dev/null
+++ b/third_party/ASpanFormer/CONTRIBUTING.md
@@ -0,0 +1,7 @@
+# Contribution Guide
+
+Thanks for your interest in contributing. This project was released to accompany a research paper for purposes of reproducability, and beyond its publication there are limited plans for future development of the repository.
+
+## Before you get started
+
+We ask that all community members read and observe our [Code of Conduct](CODE_OF_CONDUCT.md).
\ No newline at end of file
diff --git a/third_party/ASpanFormer/LICENSE b/third_party/ASpanFormer/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..e20657c86559c67eb94e9b9269ba802de8cc9189
--- /dev/null
+++ b/third_party/ASpanFormer/LICENSE
@@ -0,0 +1,9 @@
+Copyright (C) 2021, 2022 Apple Inc. All Rights Reserved.
+
+IMPORTANT: This Apple software is supplied to you by Apple Inc. ("Apple") in consideration of your agreement to the following terms, and your use, installation, modification or redistribution of this Apple software constitutes acceptance of these terms. If you do not agree with these terms, please do not use, install, modify or redistribute this Apple software.
+
+In consideration of your agreement to abide by the following terms, and subject to these terms, Apple grants you a personal, non-commercial, non-exclusive license, under Apple's copyrights in this original Apple software (the "Apple Software"), to use, reproduce, modify and redistribute the Apple Software, with or without modifications, in source and/or binary forms for non-commercial purposes only; provided that if you redistribute the Apple Software in its entirety and without modifications, you must retain this notice and the following text and disclaimers in all such redistributions of the Apple Software. Neither the name, trademarks, service marks or logos of Apple Inc. may be used to endorse or promote products derived from the Apple Software without specific prior written permission from Apple. Except as expressly stated in this notice, no other rights or licenses, express or implied, are granted by Apple herein, including but not limited to any patent rights that may be infringed by your derivative works or by other works in which the Apple Software may be incorporated.
+
+The Apple Software is provided by Apple on an "AS IS" basis. APPLE MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, REGARDING THE APPLE SOFTWARE OR ITS USE AND OPERATION ALONE OR IN COMBINATION WITH YOUR PRODUCTS.
+
+IN NO EVENT SHALL APPLE BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) ARISING IN ANY WAY OUT OF THE USE, REPRODUCTION, MODIFICATION AND/OR DISTRIBUTION OF THE APPLE SOFTWARE, HOWEVER CAUSED AND WHETHER UNDER THEORY OF CONTRACT, TORT (INCLUDING NEGLIGENCE), STRICT LIABILITY OR OTHERWISE, EVEN IF APPLE HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/third_party/ASpanFormer/README.md b/third_party/ASpanFormer/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e1b788606b6acf4a1b5e0e40d07789ac8ea8ea5b
--- /dev/null
+++ b/third_party/ASpanFormer/README.md
@@ -0,0 +1,98 @@
+# Submodule used in [hloc](https://github.com/Vincentqyw/Hierarchical-Localization) toolbox
+
+# ASpanFormer Implementation
+
+
+
+This is a PyTorch implementation of ASpanFormer for ECCV'22 [paper](https://arxiv.org/abs/2208.14201), “ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer”, and can be used to reproduce the results in the paper.
+
+This work focuses on detector-free image matching. We propose a hierarchical attention framework for cross-view feature update, which adaptively adjusts attention span based on region-wise matchability.
+
+This repo contains training, evaluation and basic demo scripts used in our paper.
+
+A large part of the code base is borrowed from the [LoFTR Repository](https://github.com/zju3dv/LoFTR) under its own separate license, terms and conditions. The authors of this software are not responsible for the contents of third-party websites.
+
+## Installation
+```bash
+conda env create -f environment.yaml
+conda activate ASpanFormer
+```
+
+## Get started
+Download model weights from [here](https://drive.google.com/file/d/1eavM9dTkw9nbc-JqlVVfGPU5UvTTfc6k/view?usp=share_link)
+
+Extract weights by
+```bash
+tar -xvf weights_aspanformer.tar
+```
+
+A demo to match one image pair is provided. To get a quick start,
+
+```bash
+cd demo
+python demo.py
+```
+
+
+## Data Preparation
+Please follow the [training doc](docs/TRAINING.md) for data organization
+
+
+
+## Evaluation
+
+
+### 1. ScanNet Evaluation
+```bash
+cd scripts/reproduce_test
+bash indoor.sh
+```
+Similar results as below should be obtained,
+```bash
+'auc@10': 0.46640095171012563,
+'auc@20': 0.6407042320049785,
+'auc@5': 0.26241231577189295,
+'prec@5e-04': 0.8827665604024288,
+'prec_flow@2e-03': 0.810938751342228
+```
+
+### 2. MegaDepth Evaluation
+ ```bash
+cd scripts/reproduce_test
+bash outdoor.sh
+```
+Similar results as below should be obtained,
+```bash
+'auc@10': 0.7184113573584142,
+'auc@20': 0.8333835724453831,
+'auc@5': 0.5567622479156181,
+'prec@5e-04': 0.9901741341790503,
+'prec_flow@2e-03': 0.7188964321862907
+```
+
+
+## Training
+
+### 1. ScanNet Training
+```bash
+cd scripts/reproduce_train
+bash indoor.sh
+```
+
+### 2. MegaDepth Training
+```bash
+cd scripts/reproduce_train
+bash outdoor.sh
+```
+
+
+If you find this project useful, please cite:
+
+```
+@article{chen2022aspanformer,
+ title={ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer},
+ author={Chen, Hongkai and Luo, Zixin and Zhou, Lei and Tian, Yurun and Zhen, Mingmin and Fang, Tian and McKinnon, David and Tsin, Yanghai and Quan, Long},
+ journal={European Conference on Computer Vision (ECCV)},
+ year={2022}
+}
+```
diff --git a/third_party/ASpanFormer/configs/aspan/indoor/aspan_test.py b/third_party/ASpanFormer/configs/aspan/indoor/aspan_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..00ea16cd35dc4362d0d9a294ad8a1762427bc382
--- /dev/null
+++ b/third_party/ASpanFormer/configs/aspan/indoor/aspan_test.py
@@ -0,0 +1,11 @@
+import sys
+from pathlib import Path
+
+sys.path.append(str(Path(__file__).parent / "../../../"))
+from src.config.default import _CN as cfg
+
+cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = "dual_softmax"
+
+cfg.ASPAN.MATCH_COARSE.BORDER_RM = 0
+cfg.ASPAN.COARSE.COARSEST_LEVEL = [15, 20]
+cfg.ASPAN.COARSE.TRAIN_RES = [480, 640]
diff --git a/imcui/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py b/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py
similarity index 59%
rename from imcui/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py
rename to third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py
index 886d10d8f55533c8021bcca8395b5a2897fb8734..854132e8c8af3b3c9c85fa797a79a149aff545ef 100644
--- a/imcui/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py
+++ b/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py
@@ -1,10 +1,11 @@
import sys
from pathlib import Path
-sys.path.append(str(Path(__file__).parent / '../../../'))
+
+sys.path.append(str(Path(__file__).parent / "../../../"))
from src.config.default import _CN as cfg
-cfg.ASPAN.COARSE.COARSEST_LEVEL= [15,20]
-cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax'
+cfg.ASPAN.COARSE.COARSEST_LEVEL = [15, 20]
+cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = "dual_softmax"
cfg.ASPAN.MATCH_COARSE.SPARSE_SPVS = False
cfg.ASPAN.MATCH_COARSE.BORDER_RM = 0
diff --git a/imcui/third_party/ASpanFormer/configs/aspan/outdoor/aspan_test.py b/third_party/ASpanFormer/configs/aspan/outdoor/aspan_test.py
similarity index 63%
rename from imcui/third_party/ASpanFormer/configs/aspan/outdoor/aspan_test.py
rename to third_party/ASpanFormer/configs/aspan/outdoor/aspan_test.py
index f0b9c04cbf3f466e413b345272afe7d7fe4274ea..e2ff53d7a1943f4149c43cdb6f2547c2290651aa 100644
--- a/imcui/third_party/ASpanFormer/configs/aspan/outdoor/aspan_test.py
+++ b/third_party/ASpanFormer/configs/aspan/outdoor/aspan_test.py
@@ -1,12 +1,13 @@
import sys
from pathlib import Path
-sys.path.append(str(Path(__file__).parent / '../../../'))
+
+sys.path.append(str(Path(__file__).parent / "../../../"))
from src.config.default import _CN as cfg
-cfg.ASPAN.COARSE.COARSEST_LEVEL= [36,36]
-cfg.ASPAN.COARSE.TRAIN_RES = [832,832]
-cfg.ASPAN.COARSE.TEST_RES = [1152,1152]
-cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax'
+cfg.ASPAN.COARSE.COARSEST_LEVEL = [36, 36]
+cfg.ASPAN.COARSE.TRAIN_RES = [832, 832]
+cfg.ASPAN.COARSE.TEST_RES = [1152, 1152]
+cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = "dual_softmax"
cfg.TRAINER.CANONICAL_LR = 8e-3
cfg.TRAINER.WARMUP_STEP = 1875 # 3 epochs
diff --git a/imcui/third_party/ASpanFormer/configs/aspan/outdoor/aspan_train.py b/third_party/ASpanFormer/configs/aspan/outdoor/aspan_train.py
similarity index 74%
rename from imcui/third_party/ASpanFormer/configs/aspan/outdoor/aspan_train.py
rename to third_party/ASpanFormer/configs/aspan/outdoor/aspan_train.py
index 1202080b234562d8cc65d924d7cccf0336b9f7c0..b226243478579ba2f1d4f45d8c90c02fb347d7ff 100644
--- a/imcui/third_party/ASpanFormer/configs/aspan/outdoor/aspan_train.py
+++ b/third_party/ASpanFormer/configs/aspan/outdoor/aspan_train.py
@@ -1,10 +1,11 @@
import sys
from pathlib import Path
-sys.path.append(str(Path(__file__).parent / '../../../'))
+
+sys.path.append(str(Path(__file__).parent / "../../../"))
from src.config.default import _CN as cfg
-cfg.ASPAN.COARSE.COARSEST_LEVEL= [26,26]
-cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax'
+cfg.ASPAN.COARSE.COARSEST_LEVEL = [26, 26]
+cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = "dual_softmax"
cfg.ASPAN.MATCH_COARSE.SPARSE_SPVS = False
cfg.TRAINER.CANONICAL_LR = 8e-3
diff --git a/imcui/third_party/ASpanFormer/configs/data/__init__.py b/third_party/ASpanFormer/configs/data/__init__.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/configs/data/__init__.py
rename to third_party/ASpanFormer/configs/data/__init__.py
diff --git a/imcui/third_party/XoFTR/configs/data/base.py b/third_party/ASpanFormer/configs/data/base.py
similarity index 99%
rename from imcui/third_party/XoFTR/configs/data/base.py
rename to third_party/ASpanFormer/configs/data/base.py
index 03aab160fa4137ccc04380f94854a56fbb549074..2621621cd3caf2edb11b41a96b11aa6a63afba92 100644
--- a/imcui/third_party/XoFTR/configs/data/base.py
+++ b/third_party/ASpanFormer/configs/data/base.py
@@ -4,6 +4,7 @@ Setups in data configs will override all existed setups!
"""
from yacs.config import CfgNode as CN
+
_CN = CN()
_CN.DATASET = CN()
_CN.TRAINER = CN()
diff --git a/third_party/ASpanFormer/configs/data/debug/.gitignore b/third_party/ASpanFormer/configs/data/debug/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31
--- /dev/null
+++ b/third_party/ASpanFormer/configs/data/debug/.gitignore
@@ -0,0 +1,3 @@
+*
+*/
+!.gitignore
diff --git a/imcui/third_party/ASpanFormer/configs/data/megadepth_test_1500.py b/third_party/ASpanFormer/configs/data/megadepth_test_1500.py
similarity index 77%
rename from imcui/third_party/ASpanFormer/configs/data/megadepth_test_1500.py
rename to third_party/ASpanFormer/configs/data/megadepth_test_1500.py
index 9616432f52a693ed84f3f12b9b85470b23410eee..a8d07aafd1944188cec525043c775d268b01be1f 100644
--- a/imcui/third_party/ASpanFormer/configs/data/megadepth_test_1500.py
+++ b/third_party/ASpanFormer/configs/data/megadepth_test_1500.py
@@ -8,6 +8,6 @@ cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}"
cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/megadepth_test_1500.txt"
cfg.DATASET.MGDPT_IMG_RESIZE = 1152
-cfg.DATASET.MGDPT_IMG_PAD=True
-cfg.DATASET.MGDPT_DF =8
-cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0
\ No newline at end of file
+cfg.DATASET.MGDPT_IMG_PAD = True
+cfg.DATASET.MGDPT_DF = 8
+cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0
diff --git a/imcui/third_party/ASpanFormer/configs/data/megadepth_trainval_832.py b/third_party/ASpanFormer/configs/data/megadepth_trainval_832.py
similarity index 72%
rename from imcui/third_party/ASpanFormer/configs/data/megadepth_trainval_832.py
rename to third_party/ASpanFormer/configs/data/megadepth_trainval_832.py
index 8f9b01fdaed254e10b3d55980499b88a00060f04..48b9bd095d64c681d0e64ee9416fb63fbd1f27b5 100644
--- a/imcui/third_party/ASpanFormer/configs/data/megadepth_trainval_832.py
+++ b/third_party/ASpanFormer/configs/data/megadepth_trainval_832.py
@@ -11,9 +11,13 @@ cfg.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.0
TEST_BASE_PATH = "data/megadepth/index"
cfg.DATASET.TEST_DATA_SOURCE = "MegaDepth"
cfg.DATASET.VAL_DATA_ROOT = cfg.DATASET.TEST_DATA_ROOT = "data/megadepth/test"
-cfg.DATASET.VAL_NPZ_ROOT = cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}/scene_info_val_1500"
-cfg.DATASET.VAL_LIST_PATH = cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/trainvaltest_list/val_list.txt"
-cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val
+cfg.DATASET.VAL_NPZ_ROOT = (
+ cfg.DATASET.TEST_NPZ_ROOT
+) = f"{TEST_BASE_PATH}/scene_info_val_1500"
+cfg.DATASET.VAL_LIST_PATH = (
+ cfg.DATASET.TEST_LIST_PATH
+) = f"{TEST_BASE_PATH}/trainvaltest_list/val_list.txt"
+cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val
# 368 scenes in total for MegaDepth
# (with difficulty balanced (further split each scene to 3 sub-scenes))
diff --git a/imcui/third_party/ASpanFormer/configs/data/scannet_test_1500.py b/third_party/ASpanFormer/configs/data/scannet_test_1500.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/configs/data/scannet_test_1500.py
rename to third_party/ASpanFormer/configs/data/scannet_test_1500.py
diff --git a/imcui/third_party/ASpanFormer/configs/data/scannet_trainval.py b/third_party/ASpanFormer/configs/data/scannet_trainval.py
similarity index 69%
rename from imcui/third_party/ASpanFormer/configs/data/scannet_trainval.py
rename to third_party/ASpanFormer/configs/data/scannet_trainval.py
index c38d6440e2b4ec349e5f168909c7f8c367408813..a9a5b8a332e012a2891bbf7ec8842523b67e7599 100644
--- a/imcui/third_party/ASpanFormer/configs/data/scannet_trainval.py
+++ b/third_party/ASpanFormer/configs/data/scannet_trainval.py
@@ -12,6 +12,10 @@ TEST_BASE_PATH = "assets/scannet_test_1500"
cfg.DATASET.TEST_DATA_SOURCE = "ScanNet"
cfg.DATASET.VAL_DATA_ROOT = cfg.DATASET.TEST_DATA_ROOT = "data/scannet/test"
cfg.DATASET.VAL_NPZ_ROOT = cfg.DATASET.TEST_NPZ_ROOT = TEST_BASE_PATH
-cfg.DATASET.VAL_LIST_PATH = cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/scannet_test.txt"
-cfg.DATASET.VAL_INTRINSIC_PATH = cfg.DATASET.TEST_INTRINSIC_PATH = f"{TEST_BASE_PATH}/intrinsics.npz"
-cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val
+cfg.DATASET.VAL_LIST_PATH = (
+ cfg.DATASET.TEST_LIST_PATH
+) = f"{TEST_BASE_PATH}/scannet_test.txt"
+cfg.DATASET.VAL_INTRINSIC_PATH = (
+ cfg.DATASET.TEST_INTRINSIC_PATH
+) = f"{TEST_BASE_PATH}/intrinsics.npz"
+cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val
diff --git a/third_party/ASpanFormer/data/megadepth/index/.gitignore b/third_party/ASpanFormer/data/megadepth/index/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/ASpanFormer/data/megadepth/index/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/ASpanFormer/data/megadepth/test/.gitignore b/third_party/ASpanFormer/data/megadepth/test/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/ASpanFormer/data/megadepth/test/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/ASpanFormer/data/megadepth/train/.gitignore b/third_party/ASpanFormer/data/megadepth/train/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/ASpanFormer/data/megadepth/train/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/ASpanFormer/data/scannet/index/.gitignore b/third_party/ASpanFormer/data/scannet/index/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/ASpanFormer/data/scannet/index/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/ASpanFormer/data/scannet/test/.gitignore b/third_party/ASpanFormer/data/scannet/test/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31
--- /dev/null
+++ b/third_party/ASpanFormer/data/scannet/test/.gitignore
@@ -0,0 +1,3 @@
+*
+*/
+!.gitignore
diff --git a/third_party/ASpanFormer/data/scannet/train/.gitignore b/third_party/ASpanFormer/data/scannet/train/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/ASpanFormer/data/scannet/train/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/ASpanFormer/demo/demo.py b/third_party/ASpanFormer/demo/demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..dceb13523faec756063b40fd586bcd81f483e274
--- /dev/null
+++ b/third_party/ASpanFormer/demo/demo.py
@@ -0,0 +1,91 @@
+import os
+import sys
+
+ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+sys.path.insert(0, ROOT_DIR)
+
+from src.ASpanFormer.aspanformer import ASpanFormer
+from src.config.default import get_cfg_defaults
+from src.utils.misc import lower_config
+import demo_utils
+
+import cv2
+import torch
+import numpy as np
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "--config_path",
+ type=str,
+ default="../configs/aspan/outdoor/aspan_test.py",
+ help="path for config file.",
+)
+parser.add_argument(
+ "--img0_path",
+ type=str,
+ default="../assets/phototourism_sample_images/piazza_san_marco_06795901_3725050516.jpg",
+ help="path for image0.",
+)
+parser.add_argument(
+ "--img1_path",
+ type=str,
+ default="../assets/phototourism_sample_images/piazza_san_marco_15148634_5228701572.jpg",
+ help="path for image1.",
+)
+parser.add_argument(
+ "--weights_path",
+ type=str,
+ default="../weights/outdoor.ckpt",
+ help="path for model weights.",
+)
+parser.add_argument(
+ "--long_dim0", type=int, default=1024, help="resize for longest dim of image0."
+)
+parser.add_argument(
+ "--long_dim1", type=int, default=1024, help="resize for longest dim of image1."
+)
+
+args = parser.parse_args()
+
+
+if __name__ == "__main__":
+ config = get_cfg_defaults()
+ config.merge_from_file(args.config_path)
+ _config = lower_config(config)
+ matcher = ASpanFormer(config=_config["aspan"])
+ state_dict = torch.load(args.weights_path, map_location="cpu")["state_dict"]
+ matcher.load_state_dict(state_dict, strict=False)
+ matcher.cuda(), matcher.eval()
+
+ img0, img1 = cv2.imread(args.img0_path), cv2.imread(args.img1_path)
+ img0_g, img1_g = cv2.imread(args.img0_path, 0), cv2.imread(args.img1_path, 0)
+ img0, img1 = demo_utils.resize(img0, args.long_dim0), demo_utils.resize(
+ img1, args.long_dim1
+ )
+ img0_g, img1_g = demo_utils.resize(img0_g, args.long_dim0), demo_utils.resize(
+ img1_g, args.long_dim1
+ )
+ data = {
+ "image0": torch.from_numpy(img0_g / 255.0)[None, None].cuda().float(),
+ "image1": torch.from_numpy(img1_g / 255.0)[None, None].cuda().float(),
+ }
+ with torch.no_grad():
+ matcher(data, online_resize=True)
+ corr0, corr1 = data["mkpts0_f"].cpu().numpy(), data["mkpts1_f"].cpu().numpy()
+
+ F_hat, mask_F = cv2.findFundamentalMat(
+ corr0, corr1, method=cv2.FM_RANSAC, ransacReprojThreshold=1
+ )
+ if mask_F is not None:
+ mask_F = mask_F[:, 0].astype(bool)
+ else:
+ mask_F = np.zeros_like(corr0[:, 0]).astype(bool)
+
+ # visualize match
+ display = demo_utils.draw_match(img0, img1, corr0, corr1)
+ display_ransac = demo_utils.draw_match(img0, img1, corr0[mask_F], corr1[mask_F])
+ cv2.imwrite("match.png", display)
+ cv2.imwrite("match_ransac.png", display_ransac)
+ print(len(corr1), len(corr1[mask_F]))
diff --git a/third_party/ASpanFormer/demo/demo_utils.py b/third_party/ASpanFormer/demo/demo_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcc8f71e02406fef4ac97fef2d0fec7c9196ad57
--- /dev/null
+++ b/third_party/ASpanFormer/demo/demo_utils.py
@@ -0,0 +1,88 @@
+import cv2
+import numpy as np
+
+
+def resize(image, long_dim):
+ h, w = image.shape[0], image.shape[1]
+ image = cv2.resize(
+ image, (int(w * long_dim / max(h, w)), int(h * long_dim / max(h, w)))
+ )
+ return image
+
+
+def draw_points(img, points, color=(0, 255, 0), radius=3):
+ dp = [(int(points[i, 0]), int(points[i, 1])) for i in range(points.shape[0])]
+ for i in range(points.shape[0]):
+ cv2.circle(img, dp[i], radius=radius, color=color)
+ return img
+
+
+def draw_match(
+ img1,
+ img2,
+ corr1,
+ corr2,
+ inlier=[True],
+ color=None,
+ radius1=1,
+ radius2=1,
+ resize=None,
+):
+ if resize is not None:
+ scale1, scale2 = [img1.shape[1] / resize[0], img1.shape[0] / resize[1]], [
+ img2.shape[1] / resize[0],
+ img2.shape[0] / resize[1],
+ ]
+ img1, img2 = cv2.resize(img1, resize, interpolation=cv2.INTER_AREA), cv2.resize(
+ img2, resize, interpolation=cv2.INTER_AREA
+ )
+ corr1, corr2 = (
+ corr1 / np.asarray(scale1)[np.newaxis],
+ corr2 / np.asarray(scale2)[np.newaxis],
+ )
+ corr1_key = [
+ cv2.KeyPoint(corr1[i, 0], corr1[i, 1], radius1) for i in range(corr1.shape[0])
+ ]
+ corr2_key = [
+ cv2.KeyPoint(corr2[i, 0], corr2[i, 1], radius2) for i in range(corr2.shape[0])
+ ]
+
+ assert len(corr1) == len(corr2)
+
+ draw_matches = [cv2.DMatch(i, i, 0) for i in range(len(corr1))]
+ if color is None:
+ color = [(0, 255, 0) if cur_inlier else (0, 0, 255) for cur_inlier in inlier]
+ if len(color) == 1:
+ display = cv2.drawMatches(
+ img1,
+ corr1_key,
+ img2,
+ corr2_key,
+ draw_matches,
+ None,
+ matchColor=color[0],
+ singlePointColor=color[0],
+ flags=4,
+ )
+ else:
+ height, width = max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1]
+ display = np.zeros([height, width, 3], np.uint8)
+ display[: img1.shape[0], : img1.shape[1]] = img1
+ display[: img2.shape[0], img1.shape[1] :] = img2
+ for i in range(len(corr1)):
+ left_x, left_y, right_x, right_y = (
+ int(corr1[i][0]),
+ int(corr1[i][1]),
+ int(corr2[i][0] + img1.shape[1]),
+ int(corr2[i][1]),
+ )
+ cur_color = (int(color[i][0]), int(color[i][1]), int(color[i][2]))
+ cv2.line(
+ display,
+ (left_x, left_y),
+ (right_x, right_y),
+ cur_color,
+ 1,
+ lineType=cv2.LINE_AA,
+ )
+ return display
diff --git a/third_party/ASpanFormer/docs/TRAINING.md b/third_party/ASpanFormer/docs/TRAINING.md
new file mode 100644
index 0000000000000000000000000000000000000000..99238b612d961a5a6aa29885bad23808c7aa6e07
--- /dev/null
+++ b/third_party/ASpanFormer/docs/TRAINING.md
@@ -0,0 +1,72 @@
+
+# Traininig ASpanFormer
+
+## Dataset setup
+Generally, two parts of data are needed for training ASpanFormer, the original dataset, i.e., ScanNet and MegaDepth, and the offline generated dataset indices. The dataset indices store scenes, image pairs, and other metadata within each dataset used for training/validation/testing. For the MegaDepth dataset, the relative poses between images used for training are directly cached in the indexing files. However, the relative poses of ScanNet image pairs are not stored due to the enormous resulting file size.
+
+### Download datasets
+#### MegaDepth
+We use depth maps provided in the [original MegaDepth dataset](https://www.cs.cornell.edu/projects/megadepth/) as well as undistorted images, corresponding camera intrinsics and extrinsics preprocessed by [D2-Net](https://github.com/mihaidusmanu/d2-net#downloading-and-preprocessing-the-megadepth-dataset). You can download them separately from the following links.
+- [MegaDepth undistorted images and processed depths](https://www.cs.cornell.edu/projects/megadepth/dataset/Megadepth_v1/MegaDepth_v1.tar.gz)
+ - Note that we only use depth maps.
+ - Path of the download data will be referreed to as `/path/to/megadepth`
+- [D2-Net preprocessed images](https://drive.google.com/drive/folders/1hxpOsqOZefdrba_BqnW490XpNX_LgXPB)
+ - Images are undistorted manually in D2-Net since the undistorted images from MegaDepth do not come with corresponding intrinsics.
+ - Path of the download data will be referreed to as `/path/to/megadepth_d2net`
+
+#### ScanNet
+Please set up the ScanNet dataset following [the official guide](https://github.com/ScanNet/ScanNet#scannet-data)
+> NOTE: We use the [python exported data](https://github.com/ScanNet/ScanNet/tree/master/SensReader/python),
+instead of the [c++ exported one](https://github.com/ScanNet/ScanNet/tree/master/SensReader/c%2B%2B).
+
+### Download the dataset indices
+
+You can download the required dataset indices from the [following link](https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf).
+After downloading, unzip the required files.
+```shell
+unzip downloaded-file.zip
+
+# extract dataset indices
+tar xf train-data/megadepth_indices.tar
+tar xf train-data/scannet_indices.tar
+
+# extract testing data (optional)
+tar xf testdata/megadepth_test_1500.tar
+tar xf testdata/scannet_test_1500.tar
+```
+
+### Build the dataset symlinks
+
+We symlink the datasets to the `data` directory under the main ASpanFormer project directory.
+
+```shell
+# scannet
+# -- # train and test dataset
+ln -s /path/to/scannet_train/* /path/to/ASpanFormer/data/scannet/train
+ln -s /path/to/scannet_test/* /path/to/ASpanFormer/data/scannet/test
+# -- # dataset indices
+ln -s /path/to/scannet_indices/* /path/to/ASpanFormer/data/scannet/index
+
+# megadepth
+# -- # train and test dataset (train and test share the same dataset)
+ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/ASpanFormer/data/megadepth/train
+ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/ASpanFormer/data/megadepth/test
+# -- # dataset indices
+ln -s /path/to/megadepth_indices/* /path/to/ASpanFormer/data/megadepth/index
+```
+
+
+## Training
+We provide training scripts of ScanNet and MegaDepth. The results in the ASpanFormer paper can be reproduced with 8 v100 GPUs. For a different setup, we scale the learning rate and its warm-up linearly, but the final evaluation results might vary due to the different batch size & learning rate used. Thus the reproduction of results in our paper is not guaranteed.
+
+
+### Training on ScanNet
+``` shell
+scripts/reproduce_train/indoor.sh
+```
+
+
+### Training on MegaDepth
+``` shell
+scripts/reproduce_train/outdoor.sh
+```
\ No newline at end of file
diff --git a/imcui/third_party/ASpanFormer/environment.yaml b/third_party/ASpanFormer/environment.yaml
similarity index 100%
rename from imcui/third_party/ASpanFormer/environment.yaml
rename to third_party/ASpanFormer/environment.yaml
diff --git a/third_party/ASpanFormer/requirements.txt b/third_party/ASpanFormer/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..815830f7bd8115b858bf5e49e85aed4f62d3f3b0
--- /dev/null
+++ b/third_party/ASpanFormer/requirements.txt
@@ -0,0 +1,18 @@
+#opencv_python==4.4.0.46
+albumentations==0.5.1 --no-binary=imgaug,albumentations
+ray>=1.0.1
+einops==0.3.0
+kornia==0.4.1
+loguru==0.5.3
+yacs>=0.1.8
+tqdm
+autopep8
+pylint
+ipython
+jupyterlab
+matplotlib
+h5py
+pytorch-lightning==1.3.5
+loguru
+joblib>=1.0.1
+torchmetrics==0.4
\ No newline at end of file
diff --git a/third_party/ASpanFormer/scripts/reproduce_test/indoor.sh b/third_party/ASpanFormer/scripts/reproduce_test/indoor.sh
new file mode 100644
index 0000000000000000000000000000000000000000..41e5c76a146fb84a2296f7fc63e6da881c0c8e03
--- /dev/null
+++ b/third_party/ASpanFormer/scripts/reproduce_test/indoor.sh
@@ -0,0 +1,31 @@
+#!/bin/bash -l
+# a indoor_ds model with the pos_enc impl bug fixed.
+
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+# conda activate loftr
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+data_cfg_path="configs/data/scannet_test_1500.py"
+main_cfg_path="configs/aspan/indoor/aspan_test.py"
+ckpt_path='weights/indoor.ckpt'
+dump_dir="dump/indoor_dump"
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+python -u ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --mode integrated
+
\ No newline at end of file
diff --git a/third_party/ASpanFormer/scripts/reproduce_test/outdoor.sh b/third_party/ASpanFormer/scripts/reproduce_test/outdoor.sh
new file mode 100644
index 0000000000000000000000000000000000000000..817fe50b47f52dfa3f9b2d664f415527a7a9ea6d
--- /dev/null
+++ b/third_party/ASpanFormer/scripts/reproduce_test/outdoor.sh
@@ -0,0 +1,30 @@
+#!/bin/bash -l
+
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+# conda activate loftr
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+data_cfg_path="configs/data/megadepth_test_1500.py"
+main_cfg_path="configs/aspan/outdoor/aspan_test.py"
+ckpt_path="weights/outdoor.ckpt"
+dump_dir="dump/outdoor_dump"
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+python -u ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --mode integrated
+
\ No newline at end of file
diff --git a/third_party/ASpanFormer/scripts/reproduce_train/indoor.sh b/third_party/ASpanFormer/scripts/reproduce_train/indoor.sh
new file mode 100644
index 0000000000000000000000000000000000000000..705723bf14a6e6fbe949df64bbc3a68a9159e659
--- /dev/null
+++ b/third_party/ASpanFormer/scripts/reproduce_train/indoor.sh
@@ -0,0 +1,34 @@
+#!/bin/bash -l
+
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+# conda activate loftr
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+data_cfg_path="configs/data/scannet_trainval.py"
+main_cfg_path="configs/aspan/indoor/aspan_train.py"
+
+n_nodes=1
+n_gpus_per_node=8
+torch_num_workers=36
+batch_size=3
+pin_memory=true
+exp_name="indoor-ds-bs-aspan-bs=$(($n_gpus_per_node * $batch_size))"
+
+CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u ./train.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --exp_name=${exp_name} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \
+ --check_val_every_n_epoch=1 \
+ --log_every_n_steps=100 \
+ --flush_logs_every_n_steps=100 \
+ --limit_val_batches=1. \
+ --num_sanity_val_steps=10 \
+ --benchmark=True \
+ --max_epochs=30 \
+ --parallel_load_data \
+ --mode integrated
\ No newline at end of file
diff --git a/third_party/ASpanFormer/scripts/reproduce_train/outdoor.sh b/third_party/ASpanFormer/scripts/reproduce_train/outdoor.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c447e8feaa5c7ef7ff74da3b622151c7018447a6
--- /dev/null
+++ b/third_party/ASpanFormer/scripts/reproduce_train/outdoor.sh
@@ -0,0 +1,34 @@
+#!/bin/bash -l
+
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+# conda activate loftr
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+TRAIN_IMG_SIZE=832
+data_cfg_path="configs/data/megadepth_trainval_${TRAIN_IMG_SIZE}.py"
+main_cfg_path="configs/aspan/outdoor/aspan_train.py"
+
+n_nodes=1
+n_gpus_per_node=8
+torch_num_workers=8
+batch_size=1
+pin_memory=true
+exp_name="outdoor-ds-aspan-${TRAIN_IMG_SIZE}-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))"
+
+CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u ./train.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --exp_name=${exp_name} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \
+ --check_val_every_n_epoch=1 \
+ --log_every_n_steps=100 \
+ --flush_logs_every_n_steps=100 \
+ --limit_val_batches=1. \
+ --num_sanity_val_steps=10 \
+ --benchmark=True \
+ --max_epochs=30 \
+ --mode integrated
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/__init__.py b/third_party/ASpanFormer/src/ASpanFormer/__init__.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/__init__.py
rename to third_party/ASpanFormer/src/ASpanFormer/__init__.py
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/aspan_module/__init__.py b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/__init__.py
similarity index 69%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/aspan_module/__init__.py
rename to third_party/ASpanFormer/src/ASpanFormer/aspan_module/__init__.py
index dff6704976cbe9e916c6de6af9e3b755dfbd20bf..0603d4088cd41dc4669ff60368fd1547000c161f 100644
--- a/imcui/third_party/ASpanFormer/src/ASpanFormer/aspan_module/__init__.py
+++ b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/__init__.py
@@ -1,3 +1,3 @@
from .transformer import LocalFeatureTransformer_Flow
-from .loftr import LocalFeatureTransformer
+from .loftr import LocalFeatureTransformer
from .fine_preprocess import FinePreprocess
diff --git a/third_party/ASpanFormer/src/ASpanFormer/aspan_module/attention.py b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..10049e3b5a4e39147a17ce3683f760afd8de73ae
--- /dev/null
+++ b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/attention.py
@@ -0,0 +1,315 @@
+import torch
+from torch.nn import Module
+import torch.nn as nn
+from itertools import product
+from torch.nn import functional as F
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class layernorm2d(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.dim = dim
+ self.affine = nn.parameter.Parameter(torch.ones(dim), requires_grad=True)
+ self.bias = nn.parameter.Parameter(torch.zeros(dim), requires_grad=True)
+
+ def forward(self, x):
+ # x: B*C*H*W
+ mean, std = x.mean(dim=1, keepdim=True), x.std(dim=1, keepdim=True)
+ return (
+ self.affine[None, :, None, None] * (x - mean) / (std + 1e-6)
+ + self.bias[None, :, None, None]
+ )
+
+
+class HierachicalAttention(Module):
+ def __init__(self, d_model, nhead, nsample, radius_scale, nlevel=3):
+ super().__init__()
+ self.d_model = d_model
+ self.nhead = nhead
+ self.nsample = nsample
+ self.nlevel = nlevel
+ self.radius_scale = radius_scale
+ self.merge_head = nn.Sequential(
+ nn.Conv1d(d_model * 3, d_model, kernel_size=1, bias=False),
+ nn.ReLU(True),
+ nn.Conv1d(d_model, d_model, kernel_size=1, bias=False),
+ )
+ self.fullattention = FullAttention(d_model, nhead)
+ self.temp = nn.parameter.Parameter(torch.tensor(1.0), requires_grad=True)
+ sample_offset = torch.tensor(
+ [
+ [pos[0] - nsample[1] / 2 + 0.5, pos[1] - nsample[1] / 2 + 0.5]
+ for pos in product(range(nsample[1]), range(nsample[1]))
+ ]
+ ) # r^2*2
+ self.sample_offset = nn.parameter.Parameter(sample_offset, requires_grad=False)
+
+ def forward(
+ self,
+ query,
+ key,
+ value,
+ flow,
+ size_q,
+ size_kv,
+ mask0=None,
+ mask1=None,
+ ds0=[4, 4],
+ ds1=[4, 4],
+ ):
+ """
+ Args:
+ q,k,v (torch.Tensor): [B, C, L]
+ mask (torch.Tensor): [B, L]
+ flow (torch.Tensor): [B, H, W, 4]
+ Return:
+ all_message (torch.Tensor): [B, C, H, W]
+ """
+
+ variance = flow[:, :, :, 2:]
+ offset = flow[:, :, :, :2] # B*H*W*2
+ bs = query.shape[0]
+ h0, w0 = size_q[0], size_q[1]
+ h1, w1 = size_kv[0], size_kv[1]
+ variance = torch.exp(0.5 * variance) * self.radius_scale # b*h*w*2(pixel scale)
+ span_scale = torch.clamp((variance * 2 / self.nsample[1]), min=1) # b*h*w*2
+
+ sub_sample0, sub_sample1 = [ds0, 2, 1], [ds1, 2, 1]
+ q_list = [
+ F.avg_pool2d(
+ query.view(bs, -1, h0, w0), kernel_size=sub_size, stride=sub_size
+ )
+ for sub_size in sub_sample0
+ ]
+ k_list = [
+ F.avg_pool2d(
+ key.view(bs, -1, h1, w1), kernel_size=sub_size, stride=sub_size
+ )
+ for sub_size in sub_sample1
+ ]
+ v_list = [
+ F.avg_pool2d(
+ value.view(bs, -1, h1, w1), kernel_size=sub_size, stride=sub_size
+ )
+ for sub_size in sub_sample1
+ ] # n_level
+
+ offset_list = [
+ F.avg_pool2d(
+ offset.permute(0, 3, 1, 2),
+ kernel_size=sub_size * self.nsample[0],
+ stride=sub_size * self.nsample[0],
+ ).permute(0, 2, 3, 1)
+ / sub_size
+ for sub_size in sub_sample0[1:]
+ ] # n_level-1
+ span_list = [
+ F.avg_pool2d(
+ span_scale.permute(0, 3, 1, 2),
+ kernel_size=sub_size * self.nsample[0],
+ stride=sub_size * self.nsample[0],
+ ).permute(0, 2, 3, 1)
+ for sub_size in sub_sample0[1:]
+ ] # n_level-1
+
+ if mask0 is not None:
+ mask0, mask1 = mask0.view(bs, 1, h0, w0), mask1.view(bs, 1, h1, w1)
+ mask0_list = [
+ -F.max_pool2d(-mask0, kernel_size=sub_size, stride=sub_size)
+ for sub_size in sub_sample0
+ ]
+ mask1_list = [
+ -F.max_pool2d(-mask1, kernel_size=sub_size, stride=sub_size)
+ for sub_size in sub_sample1
+ ]
+ else:
+ mask0_list = mask1_list = [None, None, None]
+
+ message_list = []
+ # full attention at coarse scale
+ mask0_flatten = mask0_list[0].view(bs, -1) if mask0 is not None else None
+ mask1_flatten = mask1_list[0].view(bs, -1) if mask1 is not None else None
+ message_list.append(
+ self.fullattention(
+ q_list[0], k_list[0], v_list[0], mask0_flatten, mask1_flatten, self.temp
+ ).view(bs, self.d_model, h0 // ds0[0], w0 // ds0[1])
+ )
+
+ for index in range(1, self.nlevel):
+ q, k, v = q_list[index], k_list[index], v_list[index]
+ mask0, mask1 = mask0_list[index], mask1_list[index]
+ s, o = span_list[index - 1], offset_list[index - 1] # B*h*w(*2)
+ q, k, v, sample_pixel, mask_sample = self.partition_token(
+ q, k, v, o, s, mask0
+ ) # B*Head*D*G*N(G*N=H*W for q)
+ message_list.append(
+ self.group_attention(q, k, v, 1, mask_sample).view(
+ bs, self.d_model, h0 // sub_sample0[index], w0 // sub_sample0[index]
+ )
+ )
+ # fuse
+ all_message = torch.cat(
+ [
+ F.upsample(
+ message_list[idx], scale_factor=sub_sample0[idx], mode="nearest"
+ )
+ for idx in range(self.nlevel)
+ ],
+ dim=1,
+ ).view(
+ bs, -1, h0 * w0
+ ) # b*3d*H*W
+
+ all_message = self.merge_head(all_message).view(bs, -1, h0, w0) # b*d*H*W
+ return all_message
+
+ def partition_token(self, q, k, v, offset, span_scale, maskv):
+ # q,k,v: B*C*H*W
+ # o: B*H/2*W/2*2
+ # span_scale:B*H*W
+ bs = q.shape[0]
+ h, w = q.shape[2], q.shape[3]
+ hk, wk = k.shape[2], k.shape[3]
+ offset = offset.view(bs, -1, 2)
+ span_scale = span_scale.view(bs, -1, 1, 2)
+ # B*G*2
+ offset_sample = self.sample_offset[None, None] * span_scale
+ sample_pixel = offset[:, :, None] + offset_sample # B*G*r^2*2
+ sample_norm = (
+ sample_pixel / torch.tensor([wk / 2, hk / 2]).to(device)[None, None, None]
+ - 1
+ )
+
+ q = (
+ q.view(
+ bs,
+ -1,
+ h // self.nsample[0],
+ self.nsample[0],
+ w // self.nsample[0],
+ self.nsample[0],
+ )
+ .permute(0, 1, 2, 4, 3, 5)
+ .contiguous()
+ .view(bs, self.nhead, self.d_model // self.nhead, -1, self.nsample[0] ** 2)
+ ) # B*head*D*G*N(G*N=H*W for q)
+ # sample token
+ k = F.grid_sample(k, grid=sample_norm).view(
+ bs, self.nhead, self.d_model // self.nhead, -1, self.nsample[1] ** 2
+ ) # B*head*D*G*r^2
+ v = F.grid_sample(v, grid=sample_norm).view(
+ bs, self.nhead, self.d_model // self.nhead, -1, self.nsample[1] ** 2
+ ) # B*head*D*G*r^2
+ # import pdb;pdb.set_trace()
+ if maskv is not None:
+ mask_sample = (
+ F.grid_sample(
+ maskv.view(bs, -1, h, w).float(), grid=sample_norm, mode="nearest"
+ )
+ == 1
+ ) # B*1*G*r^2
+ else:
+ mask_sample = None
+ return q, k, v, sample_pixel, mask_sample
+
+ def group_attention(self, query, key, value, temp, mask_sample=None):
+ # q,k,v: B*Head*D*G*N(G*N=H*W for q)
+ bs = query.shape[0]
+ # import pdb;pdb.set_trace()
+ QK = torch.einsum("bhdgn,bhdgm->bhgnm", query, key)
+ if mask_sample is not None:
+ num_head, number_n = QK.shape[1], QK.shape[3]
+ QK.masked_fill_(
+ ~(mask_sample[:, :, :, None])
+ .expand(-1, num_head, -1, number_n, -1)
+ .bool(),
+ float(-1e8),
+ )
+ # Compute the attention and the weighted average
+ softmax_temp = temp / query.size(2) ** 0.5 # sqrt(D)
+ A = torch.softmax(softmax_temp * QK, dim=-1)
+ queried_values = (
+ torch.einsum("bhgnm,bhdgm->bhdgn", A, value)
+ .contiguous()
+ .view(bs, self.d_model, -1)
+ )
+ return queried_values
+
+
+class FullAttention(Module):
+ def __init__(self, d_model, nhead):
+ super().__init__()
+ self.d_model = d_model
+ self.nhead = nhead
+
+ def forward(self, q, k, v, mask0=None, mask1=None, temp=1):
+ """Multi-head scaled dot-product attention, a.k.a full attention.
+ Args:
+ q,k,v: [N, D, L]
+ mask: [N, L]
+ Returns:
+ msg: [N,L]
+ """
+ bs = q.shape[0]
+ q, k, v = (
+ q.view(bs, self.nhead, self.d_model // self.nhead, -1),
+ k.view(bs, self.nhead, self.d_model // self.nhead, -1),
+ v.view(bs, self.nhead, self.d_model // self.nhead, -1),
+ )
+ # Compute the unnormalized attention and apply the masks
+ QK = torch.einsum("nhdl,nhds->nhls", q, k)
+ if mask0 is not None:
+ QK.masked_fill_(
+ ~(mask0[:, None, :, None] * mask1[:, None, None]).bool(), float(-1e8)
+ )
+ # Compute the attention and the weighted average
+ softmax_temp = temp / q.size(2) ** 0.5 # sqrt(D)
+ A = torch.softmax(softmax_temp * QK, dim=-1)
+ queried_values = (
+ torch.einsum("nhls,nhds->nhdl", A, v)
+ .contiguous()
+ .view(bs, self.d_model, -1)
+ )
+ return queried_values
+
+
+def elu_feature_map(x):
+ return F.elu(x) + 1
+
+
+class LinearAttention(Module):
+ def __init__(self, eps=1e-6):
+ super().__init__()
+ self.feature_map = elu_feature_map
+ self.eps = eps
+
+ def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
+ """Multi-Head linear attention proposed in "Transformers are RNNs"
+ Args:
+ queries: [N, L, H, D]
+ keys: [N, S, H, D]
+ values: [N, S, H, D]
+ q_mask: [N, L]
+ kv_mask: [N, S]
+ Returns:
+ queried_values: (N, L, H, D)
+ """
+ Q = self.feature_map(queries)
+ K = self.feature_map(keys)
+
+ # set padded position to zero
+ if q_mask is not None:
+ Q = Q * q_mask[:, :, None, None]
+ if kv_mask is not None:
+ K = K * kv_mask[:, :, None, None]
+ values = values * kv_mask[:, :, None, None]
+
+ v_length = values.size(1)
+ values = values / v_length # prevent fp16 overflow
+ KV = torch.einsum("nshd,nshv->nhdv", K, values) # (S,D)' @ S,V
+ Z = 1 / (torch.einsum("nlhd,nhd->nlh", Q, K.sum(dim=1)) + self.eps)
+ queried_values = torch.einsum("nlhd,nhdv,nlh->nlhv", Q, KV, Z) * v_length
+
+ return queried_values.contiguous()
diff --git a/third_party/ASpanFormer/src/ASpanFormer/aspan_module/fine_preprocess.py b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/fine_preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c37f76c3d5735508f950bb1239f5e93039b27ff
--- /dev/null
+++ b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/fine_preprocess.py
@@ -0,0 +1,75 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops.einops import rearrange, repeat
+
+
+class FinePreprocess(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ self.config = config
+ self.cat_c_feat = config["fine_concat_coarse_feat"]
+ self.W = self.config["fine_window_size"]
+
+ d_model_c = self.config["coarse"]["d_model"]
+ d_model_f = self.config["fine"]["d_model"]
+ self.d_model_f = d_model_f
+ if self.cat_c_feat:
+ self.down_proj = nn.Linear(d_model_c, d_model_f, bias=True)
+ self.merge_feat = nn.Linear(2 * d_model_f, d_model_f, bias=True)
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.kaiming_normal_(p, mode="fan_out", nonlinearity="relu")
+
+ def forward(self, feat_f0, feat_f1, feat_c0, feat_c1, data):
+ W = self.W
+ stride = data["hw0_f"][0] // data["hw0_c"][0]
+
+ data.update({"W": W})
+ if data["b_ids"].shape[0] == 0:
+ feat0 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device)
+ feat1 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device)
+ return feat0, feat1
+
+ # 1. unfold(crop) all local windows
+ feat_f0_unfold = F.unfold(
+ feat_f0, kernel_size=(W, W), stride=stride, padding=W // 2
+ )
+ feat_f0_unfold = rearrange(feat_f0_unfold, "n (c ww) l -> n l ww c", ww=W**2)
+ feat_f1_unfold = F.unfold(
+ feat_f1, kernel_size=(W, W), stride=stride, padding=W // 2
+ )
+ feat_f1_unfold = rearrange(feat_f1_unfold, "n (c ww) l -> n l ww c", ww=W**2)
+
+ # 2. select only the predicted matches
+ feat_f0_unfold = feat_f0_unfold[data["b_ids"], data["i_ids"]] # [n, ww, cf]
+ feat_f1_unfold = feat_f1_unfold[data["b_ids"], data["j_ids"]]
+
+ # option: use coarse-level loftr feature as context: concat and linear
+ if self.cat_c_feat:
+ feat_c_win = self.down_proj(
+ torch.cat(
+ [
+ feat_c0[data["b_ids"], data["i_ids"]],
+ feat_c1[data["b_ids"], data["j_ids"]],
+ ],
+ 0,
+ )
+ ) # [2n, c]
+ feat_cf_win = self.merge_feat(
+ torch.cat(
+ [
+ torch.cat([feat_f0_unfold, feat_f1_unfold], 0), # [2n, ww, cf]
+ repeat(feat_c_win, "n c -> n ww c", ww=W**2), # [2n, ww, cf]
+ ],
+ -1,
+ )
+ )
+ feat_f0_unfold, feat_f1_unfold = torch.chunk(feat_cf_win, 2, dim=0)
+
+ return feat_f0_unfold, feat_f1_unfold
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/aspan_module/loftr.py b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/loftr.py
similarity index 70%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/aspan_module/loftr.py
rename to third_party/ASpanFormer/src/ASpanFormer/aspan_module/loftr.py
index 7dcebaa7beee978b9b8abcec8bb1bd2cc6b60870..eaad9fdac1fbfc7a77f2db7c98c67bc41e335945 100644
--- a/imcui/third_party/ASpanFormer/src/ASpanFormer/aspan_module/loftr.py
+++ b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/loftr.py
@@ -3,11 +3,9 @@ import torch
import torch.nn as nn
from .attention import LinearAttention
+
class LoFTREncoderLayer(nn.Module):
- def __init__(self,
- d_model,
- nhead,
- attention='linear'):
+ def __init__(self, d_model, nhead, attention="linear"):
super(LoFTREncoderLayer, self).__init__()
self.dim = d_model // nhead
@@ -22,9 +20,9 @@ class LoFTREncoderLayer(nn.Module):
# feed-forward network
self.mlp = nn.Sequential(
- nn.Linear(d_model*2, d_model*2, bias=False),
+ nn.Linear(d_model * 2, d_model * 2, bias=False),
nn.ReLU(True),
- nn.Linear(d_model*2, d_model, bias=False),
+ nn.Linear(d_model * 2, d_model, bias=False),
)
# norm and dropout
@@ -43,16 +41,14 @@ class LoFTREncoderLayer(nn.Module):
query, key, value = x, source, source
# multi-head attention
- query = self.q_proj(query).view(
- bs, -1, self.nhead, self.dim) # [N, L, (H, D)]
- key = self.k_proj(key).view(bs, -1, self.nhead,
- self.dim) # [N, S, (H, D)]
+ query = self.q_proj(query).view(bs, -1, self.nhead, self.dim) # [N, L, (H, D)]
+ key = self.k_proj(key).view(bs, -1, self.nhead, self.dim) # [N, S, (H, D)]
value = self.v_proj(value).view(bs, -1, self.nhead, self.dim)
message = self.attention(
- query, key, value, q_mask=x_mask, kv_mask=source_mask) # [N, L, (H, D)]
- message = self.merge(message.view(
- bs, -1, self.nhead*self.dim)) # [N, L, C]
+ query, key, value, q_mask=x_mask, kv_mask=source_mask
+ ) # [N, L, (H, D)]
+ message = self.merge(message.view(bs, -1, self.nhead * self.dim)) # [N, L, C]
message = self.norm1(message)
# feed-forward network
@@ -69,13 +65,15 @@ class LocalFeatureTransformer(nn.Module):
super(LocalFeatureTransformer, self).__init__()
self.config = config
- self.d_model = config['d_model']
- self.nhead = config['nhead']
- self.layer_names = config['layer_names']
+ self.d_model = config["d_model"]
+ self.nhead = config["nhead"]
+ self.layer_names = config["layer_names"]
encoder_layer = LoFTREncoderLayer(
- config['d_model'], config['nhead'], config['attention'])
+ config["d_model"], config["nhead"], config["attention"]
+ )
self.layers = nn.ModuleList(
- [copy.deepcopy(encoder_layer) for _ in range(len(self.layer_names))])
+ [copy.deepcopy(encoder_layer) for _ in range(len(self.layer_names))]
+ )
self._reset_parameters()
def _reset_parameters(self):
@@ -93,20 +91,18 @@ class LocalFeatureTransformer(nn.Module):
"""
assert self.d_model == feat0.size(
- 2), "the feature number of src and transformer must be equal"
+ 2
+ ), "the feature number of src and transformer must be equal"
index = 0
for layer, name in zip(self.layers, self.layer_names):
- if name == 'self':
- feat0 = layer(feat0, feat0, mask0, mask0,
- type='self', index=index)
+ if name == "self":
+ feat0 = layer(feat0, feat0, mask0, mask0, type="self", index=index)
feat1 = layer(feat1, feat1, mask1, mask1)
- elif name == 'cross':
+ elif name == "cross":
feat0 = layer(feat0, feat1, mask0, mask1)
- feat1 = layer(feat1, feat0, mask1, mask0,
- type='cross', index=index)
+ feat1 = layer(feat1, feat0, mask1, mask0, type="cross", index=index)
index += 1
else:
raise KeyError
return feat0, feat1
-
diff --git a/third_party/ASpanFormer/src/ASpanFormer/aspan_module/transformer.py b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba7dd05f86ce41ada9cdf568fd9a90a051b5febf
--- /dev/null
+++ b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/transformer.py
@@ -0,0 +1,353 @@
+import copy
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .attention import FullAttention, HierachicalAttention, layernorm2d
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class messageLayer_ini(nn.Module):
+ def __init__(self, d_model, d_flow, d_value, nhead):
+ super().__init__()
+ super(messageLayer_ini, self).__init__()
+
+ self.d_model = d_model
+ self.d_flow = d_flow
+ self.d_value = d_value
+ self.nhead = nhead
+ self.attention = FullAttention(d_model, nhead)
+
+ self.q_proj = nn.Conv1d(d_model, d_model, kernel_size=1, bias=False)
+ self.k_proj = nn.Conv1d(d_model, d_model, kernel_size=1, bias=False)
+ self.v_proj = nn.Conv1d(d_value, d_model, kernel_size=1, bias=False)
+ self.merge_head = nn.Conv1d(d_model, d_model, kernel_size=1, bias=False)
+
+ self.merge_f = self.merge_f = nn.Sequential(
+ nn.Conv2d(d_model * 2, d_model * 2, kernel_size=1, bias=False),
+ nn.ReLU(True),
+ nn.Conv2d(d_model * 2, d_model, kernel_size=1, bias=False),
+ )
+
+ self.norm1 = layernorm2d(d_model)
+ self.norm2 = layernorm2d(d_model)
+
+ def forward(self, x0, x1, pos0, pos1, mask0=None, mask1=None):
+ # x1,x2: b*d*L
+ x0, x1 = self.update(x0, x1, pos1, mask0, mask1), self.update(
+ x1, x0, pos0, mask1, mask0
+ )
+ return x0, x1
+
+ def update(self, f0, f1, pos1, mask0, mask1):
+ """
+ Args:
+ f0: [N, D, H, W]
+ f1: [N, D, H, W]
+ Returns:
+ f0_new: (N, d, h, w)
+ """
+ bs, h, w = f0.shape[0], f0.shape[2], f0.shape[3]
+
+ f0_flatten, f1_flatten = f0.view(bs, self.d_model, -1), f1.view(
+ bs, self.d_model, -1
+ )
+ pos1_flatten = pos1.view(bs, self.d_value - self.d_model, -1)
+ f1_flatten_v = torch.cat([f1_flatten, pos1_flatten], dim=1)
+
+ queries, keys = self.q_proj(f0_flatten), self.k_proj(f1_flatten)
+ values = self.v_proj(f1_flatten_v).view(
+ bs, self.nhead, self.d_model // self.nhead, -1
+ )
+
+ queried_values = self.attention(queries, keys, values, mask0, mask1)
+ msg = self.merge_head(queried_values).view(bs, -1, h, w)
+ msg = self.norm2(self.merge_f(torch.cat([f0, self.norm1(msg)], dim=1)))
+ return f0 + msg
+
+
+class messageLayer_gla(nn.Module):
+ def __init__(
+ self, d_model, d_flow, d_value, nhead, radius_scale, nsample, update_flow=True
+ ):
+ super().__init__()
+ self.d_model = d_model
+ self.d_flow = d_flow
+ self.d_value = d_value
+ self.nhead = nhead
+ self.radius_scale = radius_scale
+ self.update_flow = update_flow
+ self.flow_decoder = nn.Sequential(
+ nn.Conv1d(d_flow, d_flow // 2, kernel_size=1, bias=False),
+ nn.ReLU(True),
+ nn.Conv1d(d_flow // 2, 4, kernel_size=1, bias=False),
+ )
+ self.attention = HierachicalAttention(d_model, nhead, nsample, radius_scale)
+
+ self.q_proj = nn.Conv1d(d_model, d_model, kernel_size=1, bias=False)
+ self.k_proj = nn.Conv1d(d_model, d_model, kernel_size=1, bias=False)
+ self.v_proj = nn.Conv1d(d_value, d_model, kernel_size=1, bias=False)
+
+ d_extra = d_flow if update_flow else 0
+ self.merge_f = nn.Sequential(
+ nn.Conv2d(
+ d_model * 2 + d_extra, d_model + d_flow, kernel_size=1, bias=False
+ ),
+ nn.ReLU(True),
+ nn.Conv2d(
+ d_model + d_flow,
+ d_model + d_extra,
+ kernel_size=3,
+ padding=1,
+ bias=False,
+ ),
+ )
+ self.norm1 = layernorm2d(d_model)
+ self.norm2 = layernorm2d(d_model + d_extra)
+
+ def forward(
+ self,
+ x0,
+ x1,
+ flow_feature0,
+ flow_feature1,
+ pos0,
+ pos1,
+ mask0=None,
+ mask1=None,
+ ds0=[4, 4],
+ ds1=[4, 4],
+ ):
+ """
+ Args:
+ x0 (torch.Tensor): [B, C, H, W]
+ x1 (torch.Tensor): [B, C, H, W]
+ flow_feature0 (torch.Tensor): [B, C', H, W]
+ flow_feature1 (torch.Tensor): [B, C', H, W]
+ """
+ flow0, flow1 = self.decode_flow(
+ flow_feature0, flow_feature1.shape[2:]
+ ), self.decode_flow(flow_feature1, flow_feature0.shape[2:])
+ x0_new, flow_feature0_new = self.update(
+ x0, x1, flow0.detach(), flow_feature0, pos1, mask0, mask1, ds0, ds1
+ )
+ x1_new, flow_feature1_new = self.update(
+ x1, x0, flow1.detach(), flow_feature1, pos0, mask1, mask0, ds1, ds0
+ )
+ return x0_new, x1_new, flow_feature0_new, flow_feature1_new, flow0, flow1
+
+ def update(self, x0, x1, flow0, flow_feature0, pos1, mask0, mask1, ds0, ds1):
+ bs = x0.shape[0]
+ queries, keys = self.q_proj(x0.view(bs, self.d_model, -1)), self.k_proj(
+ x1.view(bs, self.d_model, -1)
+ )
+ x1_pos = torch.cat([x1, pos1], dim=1)
+ values = self.v_proj(x1_pos.view(bs, self.d_value, -1))
+ msg = self.attention(
+ queries,
+ keys,
+ values,
+ flow0,
+ x0.shape[2:],
+ x1.shape[2:],
+ mask0,
+ mask1,
+ ds0,
+ ds1,
+ )
+
+ if self.update_flow:
+ update_feature = torch.cat([x0, flow_feature0], dim=1)
+ else:
+ update_feature = x0
+ msg = self.norm2(
+ self.merge_f(torch.cat([update_feature, self.norm1(msg)], dim=1))
+ )
+ update_feature = update_feature + msg
+
+ x0_new, flow_feature0_new = (
+ update_feature[:, : self.d_model],
+ update_feature[:, self.d_model :],
+ )
+ return x0_new, flow_feature0_new
+
+ def decode_flow(self, flow_feature, kshape):
+ bs, h, w = flow_feature.shape[0], flow_feature.shape[2], flow_feature.shape[3]
+ scale_factor = torch.tensor([kshape[1], kshape[0]]).to(device)[None, None, None]
+ flow = (
+ self.flow_decoder(flow_feature.view(bs, -1, h * w))
+ .permute(0, 2, 1)
+ .view(bs, h, w, 4)
+ )
+ flow_coordinates = torch.sigmoid(flow[:, :, :, :2]) * scale_factor
+ flow_var = flow[:, :, :, 2:]
+ flow = torch.cat([flow_coordinates, flow_var], dim=-1) # B*H*W*4
+ return flow
+
+
+class flow_initializer(nn.Module):
+ def __init__(self, dim, dim_flow, nhead, layer_num):
+ super().__init__()
+ self.layer_num = layer_num
+ self.dim = dim
+ self.dim_flow = dim_flow
+
+ encoder_layer = messageLayer_ini(dim, dim_flow, dim + dim_flow, nhead)
+ self.layers_coarse = nn.ModuleList(
+ [copy.deepcopy(encoder_layer) for _ in range(layer_num)]
+ )
+ self.decoupler = nn.Conv2d(self.dim, self.dim + self.dim_flow, kernel_size=1)
+ self.up_merge = nn.Conv2d(2 * dim, dim, kernel_size=1)
+
+ def forward(
+ self, feat0, feat1, pos0, pos1, mask0=None, mask1=None, ds0=[4, 4], ds1=[4, 4]
+ ):
+ # feat0: [B, C, H0, W0]
+ # feat1: [B, C, H1, W1]
+ # use low-res MHA to initialize flow feature
+ bs = feat0.size(0)
+ h0, w0, h1, w1 = feat0.shape[2], feat0.shape[3], feat1.shape[2], feat1.shape[3]
+
+ # coarse level
+ sub_feat0, sub_feat1 = F.avg_pool2d(feat0, ds0, stride=ds0), F.avg_pool2d(
+ feat1, ds1, stride=ds1
+ )
+
+ sub_pos0, sub_pos1 = F.avg_pool2d(pos0, ds0, stride=ds0), F.avg_pool2d(
+ pos1, ds1, stride=ds1
+ )
+
+ if mask0 is not None:
+ mask0, mask1 = -F.max_pool2d(
+ -mask0.view(bs, 1, h0, w0), ds0, stride=ds0
+ ).view(bs, -1), -F.max_pool2d(
+ -mask1.view(bs, 1, h1, w1), ds1, stride=ds1
+ ).view(
+ bs, -1
+ )
+
+ for layer in self.layers_coarse:
+ sub_feat0, sub_feat1 = layer(
+ sub_feat0, sub_feat1, sub_pos0, sub_pos1, mask0, mask1
+ )
+ # decouple flow and visual features
+ decoupled_feature0, decoupled_feature1 = self.decoupler(
+ sub_feat0
+ ), self.decoupler(sub_feat1)
+
+ sub_feat0, sub_flow_feature0 = (
+ decoupled_feature0[:, : self.dim],
+ decoupled_feature0[:, self.dim :],
+ )
+ sub_feat1, sub_flow_feature1 = (
+ decoupled_feature1[:, : self.dim],
+ decoupled_feature1[:, self.dim :],
+ )
+ update_feat0, flow_feature0 = F.upsample(
+ sub_feat0, scale_factor=ds0, mode="bilinear"
+ ), F.upsample(sub_flow_feature0, scale_factor=ds0, mode="bilinear")
+ update_feat1, flow_feature1 = F.upsample(
+ sub_feat1, scale_factor=ds1, mode="bilinear"
+ ), F.upsample(sub_flow_feature1, scale_factor=ds1, mode="bilinear")
+
+ feat0 = feat0 + self.up_merge(torch.cat([feat0, update_feat0], dim=1))
+ feat1 = feat1 + self.up_merge(torch.cat([feat1, update_feat1], dim=1))
+
+ return feat0, feat1, flow_feature0, flow_feature1 # b*c*h*w
+
+
+class LocalFeatureTransformer_Flow(nn.Module):
+ """A Local Feature Transformer (LoFTR) module."""
+
+ def __init__(self, config):
+ super(LocalFeatureTransformer_Flow, self).__init__()
+
+ self.config = config
+ self.d_model = config["d_model"]
+ self.nhead = config["nhead"]
+
+ self.pos_transform = nn.Conv2d(
+ config["d_model"], config["d_flow"], kernel_size=1, bias=False
+ )
+ self.ini_layer = flow_initializer(
+ self.d_model, config["d_flow"], config["nhead"], config["ini_layer_num"]
+ )
+
+ encoder_layer = messageLayer_gla(
+ config["d_model"],
+ config["d_flow"],
+ config["d_flow"] + config["d_model"],
+ config["nhead"],
+ config["radius_scale"],
+ config["nsample"],
+ )
+ encoder_layer_last = messageLayer_gla(
+ config["d_model"],
+ config["d_flow"],
+ config["d_flow"] + config["d_model"],
+ config["nhead"],
+ config["radius_scale"],
+ config["nsample"],
+ update_flow=False,
+ )
+ self.layers = nn.ModuleList(
+ [copy.deepcopy(encoder_layer) for _ in range(config["layer_num"] - 1)]
+ + [encoder_layer_last]
+ )
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for name, p in self.named_parameters():
+ if "temp" in name or "sample_offset" in name:
+ continue
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def forward(
+ self, feat0, feat1, pos0, pos1, mask0=None, mask1=None, ds0=[4, 4], ds1=[4, 4]
+ ):
+ """
+ Args:
+ feat0 (torch.Tensor): [N, C, H, W]
+ feat1 (torch.Tensor): [N, C, H, W]
+ pos1,pos2: [N, C, H, W]
+ Outputs:
+ feat0: [N,-1,C]
+ feat1: [N,-1,C]
+ flow_list: [L,N,H,W,4]*1(2)
+ """
+ bs = feat0.size(0)
+
+ pos0, pos1 = self.pos_transform(pos0), self.pos_transform(pos1)
+ pos0, pos1 = pos0.expand(bs, -1, -1, -1), pos1.expand(bs, -1, -1, -1)
+ assert self.d_model == feat0.size(
+ 1
+ ), "the feature number of src and transformer must be equal"
+
+ flow_list = [[], []] # [px,py,sx,sy]
+ if mask0 is not None:
+ mask0, mask1 = mask0[:, None].float(), mask1[:, None].float()
+ feat0, feat1, flow_feature0, flow_feature1 = self.ini_layer(
+ feat0, feat1, pos0, pos1, mask0, mask1, ds0, ds1
+ )
+ for layer in self.layers:
+ feat0, feat1, flow_feature0, flow_feature1, flow0, flow1 = layer(
+ feat0,
+ feat1,
+ flow_feature0,
+ flow_feature1,
+ pos0,
+ pos1,
+ mask0,
+ mask1,
+ ds0,
+ ds1,
+ )
+ flow_list[0].append(flow0)
+ flow_list[1].append(flow1)
+ flow_list[0] = torch.stack(flow_list[0], dim=0)
+ flow_list[1] = torch.stack(flow_list[1], dim=0)
+ feat0, feat1 = feat0.permute(0, 2, 3, 1).view(
+ bs, -1, self.d_model
+ ), feat1.permute(0, 2, 3, 1).view(bs, -1, self.d_model)
+ return feat0, feat1, flow_list
diff --git a/third_party/ASpanFormer/src/ASpanFormer/aspanformer.py b/third_party/ASpanFormer/src/ASpanFormer/aspanformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b22d640b4f4e52405fb851c6283c7c0f22d0d918
--- /dev/null
+++ b/third_party/ASpanFormer/src/ASpanFormer/aspanformer.py
@@ -0,0 +1,180 @@
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from einops.einops import rearrange
+
+from .backbone import build_backbone
+from .utils.position_encoding import PositionEncodingSine
+from .aspan_module import (
+ LocalFeatureTransformer_Flow,
+ LocalFeatureTransformer,
+ FinePreprocess,
+)
+from .utils.coarse_matching import CoarseMatching
+from .utils.fine_matching import FineMatching
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class ASpanFormer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ # Misc
+ self.config = config
+
+ # Modules
+ self.backbone = build_backbone(config)
+ self.pos_encoding = PositionEncodingSine(
+ config["coarse"]["d_model"],
+ pre_scaling=[config["coarse"]["train_res"], config["coarse"]["test_res"]],
+ )
+ self.loftr_coarse = LocalFeatureTransformer_Flow(config["coarse"])
+ self.coarse_matching = CoarseMatching(config["match_coarse"])
+ self.fine_preprocess = FinePreprocess(config)
+ self.loftr_fine = LocalFeatureTransformer(config["fine"])
+ self.fine_matching = FineMatching()
+ self.coarsest_level = config["coarse"]["coarsest_level"]
+
+ def forward(self, data, online_resize=False):
+ """
+ Update:
+ data (dict): {
+ 'image0': (torch.Tensor): (N, 1, H, W)
+ 'image1': (torch.Tensor): (N, 1, H, W)
+ 'mask0'(optional) : (torch.Tensor): (N, H, W) '0' indicates a padded position
+ 'mask1'(optional) : (torch.Tensor): (N, H, W)
+ }
+ """
+ if online_resize:
+ assert data["image0"].shape[0] == 1 and data["image1"].shape[1] == 1
+ self.resize_input(data, self.config["coarse"]["train_res"])
+ else:
+ data["pos_scale0"], data["pos_scale1"] = None, None
+
+ # 1. Local Feature CNN
+ data.update(
+ {
+ "bs": data["image0"].size(0),
+ "hw0_i": data["image0"].shape[2:],
+ "hw1_i": data["image1"].shape[2:],
+ }
+ )
+
+ if data["hw0_i"] == data["hw1_i"]: # faster & better BN convergence
+ feats_c, feats_f = self.backbone(
+ torch.cat([data["image0"], data["image1"]], dim=0)
+ )
+ (feat_c0, feat_c1), (feat_f0, feat_f1) = feats_c.split(
+ data["bs"]
+ ), feats_f.split(data["bs"])
+ else: # handle different input shapes
+ (feat_c0, feat_f0), (feat_c1, feat_f1) = self.backbone(
+ data["image0"]
+ ), self.backbone(data["image1"])
+
+ data.update(
+ {
+ "hw0_c": feat_c0.shape[2:],
+ "hw1_c": feat_c1.shape[2:],
+ "hw0_f": feat_f0.shape[2:],
+ "hw1_f": feat_f1.shape[2:],
+ }
+ )
+
+ # 2. coarse-level loftr module
+ # add featmap with positional encoding, then flatten it to sequence [N, HW, C]
+ [feat_c0, pos_encoding0], [feat_c1, pos_encoding1] = self.pos_encoding(
+ feat_c0, data["pos_scale0"]
+ ), self.pos_encoding(feat_c1, data["pos_scale1"])
+ feat_c0 = rearrange(feat_c0, "n c h w -> n c h w ")
+ feat_c1 = rearrange(feat_c1, "n c h w -> n c h w ")
+
+ # TODO:adjust ds
+ ds0 = [
+ int(data["hw0_c"][0] / self.coarsest_level[0]),
+ int(data["hw0_c"][1] / self.coarsest_level[1]),
+ ]
+ ds1 = [
+ int(data["hw1_c"][0] / self.coarsest_level[0]),
+ int(data["hw1_c"][1] / self.coarsest_level[1]),
+ ]
+ if online_resize:
+ ds0, ds1 = [4, 4], [4, 4]
+
+ mask_c0 = mask_c1 = None # mask is useful in training
+ if "mask0" in data:
+ mask_c0, mask_c1 = data["mask0"].flatten(-2), data["mask1"].flatten(-2)
+ feat_c0, feat_c1, flow_list = self.loftr_coarse(
+ feat_c0, feat_c1, pos_encoding0, pos_encoding1, mask_c0, mask_c1, ds0, ds1
+ )
+
+ # 3. match coarse-level and register predicted offset
+ self.coarse_matching(
+ feat_c0, feat_c1, flow_list, data, mask_c0=mask_c0, mask_c1=mask_c1
+ )
+
+ # 4. fine-level refinement
+ feat_f0_unfold, feat_f1_unfold = self.fine_preprocess(
+ feat_f0, feat_f1, feat_c0, feat_c1, data
+ )
+ if feat_f0_unfold.size(0) != 0: # at least one coarse level predicted
+ feat_f0_unfold, feat_f1_unfold = self.loftr_fine(
+ feat_f0_unfold, feat_f1_unfold
+ )
+
+ # 5. match fine-level
+ self.fine_matching(feat_f0_unfold, feat_f1_unfold, data)
+
+ # 6. resize match coordinates back to input resolution
+ if online_resize:
+ data["mkpts0_f"] *= data["online_resize_scale0"]
+ data["mkpts1_f"] *= data["online_resize_scale1"]
+
+ def load_state_dict(self, state_dict, *args, **kwargs):
+ for k in list(state_dict.keys()):
+ if k.startswith("matcher."):
+ if "sample_offset" in k:
+ state_dict.pop(k)
+ else:
+ state_dict[k.replace("matcher.", "", 1)] = state_dict.pop(k)
+ return super().load_state_dict(state_dict, *args, **kwargs)
+
+ def resize_input(self, data, train_res, df=32):
+ h0, w0, h1, w1 = (
+ data["image0"].shape[2],
+ data["image0"].shape[3],
+ data["image1"].shape[2],
+ data["image1"].shape[3],
+ )
+ data["image0"], data["image1"] = self.resize_df(
+ data["image0"], df
+ ), self.resize_df(data["image1"], df)
+
+ if len(train_res) == 1:
+ train_res_h = train_res_w = train_res
+ else:
+ train_res_h, train_res_w = train_res[0], train_res[1]
+ data["pos_scale0"], data["pos_scale1"] = [
+ train_res_h / data["image0"].shape[2],
+ train_res_w / data["image0"].shape[3],
+ ], [
+ train_res_h / data["image1"].shape[2],
+ train_res_w / data["image1"].shape[3],
+ ]
+ data["online_resize_scale0"], data["online_resize_scale1"] = (
+ torch.tensor([w0 / data["image0"].shape[3], h0 / data["image0"].shape[2]])[
+ None
+ ].to(device),
+ torch.tensor([w1 / data["image1"].shape[3], h1 / data["image1"].shape[2]])[
+ None
+ ].to(device),
+ )
+
+ def resize_df(self, image, df=32):
+ h, w = image.shape[2], image.shape[3]
+ h_new, w_new = h // df * df, w // df * df
+ if h != h_new or w != w_new:
+ img_new = transforms.Resize([h_new, w_new]).forward(image)
+ else:
+ img_new = image
+ return img_new
diff --git a/third_party/ASpanFormer/src/ASpanFormer/backbone/__init__.py b/third_party/ASpanFormer/src/ASpanFormer/backbone/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae8593230b281e960ece68c04dcf214769e50f08
--- /dev/null
+++ b/third_party/ASpanFormer/src/ASpanFormer/backbone/__init__.py
@@ -0,0 +1,13 @@
+from .resnet_fpn import ResNetFPN_8_2, ResNetFPN_16_4
+
+
+def build_backbone(config):
+ if config["backbone_type"] == "ResNetFPN":
+ if config["resolution"] == (8, 2):
+ return ResNetFPN_8_2(config["resnetfpn"])
+ elif config["resolution"] == (16, 4):
+ return ResNetFPN_16_4(config["resnetfpn"])
+ else:
+ raise ValueError(
+ f"LOFTR.BACKBONE_TYPE {config['backbone_type']} not supported."
+ )
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/backbone/resnet_fpn.py b/third_party/ASpanFormer/src/ASpanFormer/backbone/resnet_fpn.py
similarity index 76%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/backbone/resnet_fpn.py
rename to third_party/ASpanFormer/src/ASpanFormer/backbone/resnet_fpn.py
index 985e5b3f273a51e51447a8025ca3aadbe46752eb..948c72940ab00e5741e2788eea841d124333c8ed 100644
--- a/imcui/third_party/ASpanFormer/src/ASpanFormer/backbone/resnet_fpn.py
+++ b/third_party/ASpanFormer/src/ASpanFormer/backbone/resnet_fpn.py
@@ -4,12 +4,16 @@ import torch.nn.functional as F
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution without padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False)
+ return nn.Conv2d(
+ in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False
+ )
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
+ return nn.Conv2d(
+ in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
+ )
class BasicBlock(nn.Module):
@@ -25,8 +29,7 @@ class BasicBlock(nn.Module):
self.downsample = None
else:
self.downsample = nn.Sequential(
- conv1x1(in_planes, planes, stride=stride),
- nn.BatchNorm2d(planes)
+ conv1x1(in_planes, planes, stride=stride), nn.BatchNorm2d(planes)
)
def forward(self, x):
@@ -37,7 +40,7 @@ class BasicBlock(nn.Module):
if self.downsample is not None:
x = self.downsample(x)
- return self.relu(x+y)
+ return self.relu(x + y)
class ResNetFPN_8_2(nn.Module):
@@ -50,14 +53,16 @@ class ResNetFPN_8_2(nn.Module):
super().__init__()
# Config
block = BasicBlock
- initial_dim = config['initial_dim']
- block_dims = config['block_dims']
+ initial_dim = config["initial_dim"]
+ block_dims = config["block_dims"]
# Class Variable
self.in_planes = initial_dim
# Networks
- self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False)
+ self.conv1 = nn.Conv2d(
+ 1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False
+ )
self.bn1 = nn.BatchNorm2d(initial_dim)
self.relu = nn.ReLU(inplace=True)
@@ -84,7 +89,7 @@ class ResNetFPN_8_2(nn.Module):
for m in self.modules():
if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+ nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
@@ -107,13 +112,17 @@ class ResNetFPN_8_2(nn.Module):
# FPN
x3_out = self.layer3_outconv(x3)
- x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True)
+ x3_out_2x = F.interpolate(
+ x3_out, scale_factor=2.0, mode="bilinear", align_corners=True
+ )
x2_out = self.layer2_outconv(x2)
- x2_out = self.layer2_outconv2(x2_out+x3_out_2x)
+ x2_out = self.layer2_outconv2(x2_out + x3_out_2x)
- x2_out_2x = F.interpolate(x2_out, scale_factor=2., mode='bilinear', align_corners=True)
+ x2_out_2x = F.interpolate(
+ x2_out, scale_factor=2.0, mode="bilinear", align_corners=True
+ )
x1_out = self.layer1_outconv(x1)
- x1_out = self.layer1_outconv2(x1_out+x2_out_2x)
+ x1_out = self.layer1_outconv2(x1_out + x2_out_2x)
return [x3_out, x1_out]
@@ -128,14 +137,16 @@ class ResNetFPN_16_4(nn.Module):
super().__init__()
# Config
block = BasicBlock
- initial_dim = config['initial_dim']
- block_dims = config['block_dims']
+ initial_dim = config["initial_dim"]
+ block_dims = config["block_dims"]
# Class Variable
self.in_planes = initial_dim
# Networks
- self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False)
+ self.conv1 = nn.Conv2d(
+ 1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False
+ )
self.bn1 = nn.BatchNorm2d(initial_dim)
self.relu = nn.ReLU(inplace=True)
@@ -164,7 +175,7 @@ class ResNetFPN_16_4(nn.Module):
for m in self.modules():
if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+ nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
@@ -188,12 +199,16 @@ class ResNetFPN_16_4(nn.Module):
# FPN
x4_out = self.layer4_outconv(x4)
- x4_out_2x = F.interpolate(x4_out, scale_factor=2., mode='bilinear', align_corners=True)
+ x4_out_2x = F.interpolate(
+ x4_out, scale_factor=2.0, mode="bilinear", align_corners=True
+ )
x3_out = self.layer3_outconv(x3)
- x3_out = self.layer3_outconv2(x3_out+x4_out_2x)
+ x3_out = self.layer3_outconv2(x3_out + x4_out_2x)
- x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True)
+ x3_out_2x = F.interpolate(
+ x3_out, scale_factor=2.0, mode="bilinear", align_corners=True
+ )
x2_out = self.layer2_outconv(x2)
- x2_out = self.layer2_outconv2(x2_out+x3_out_2x)
+ x2_out = self.layer2_outconv2(x2_out + x3_out_2x)
return [x4_out, x2_out]
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/coarse_matching.py b/third_party/ASpanFormer/src/ASpanFormer/utils/coarse_matching.py
similarity index 53%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/utils/coarse_matching.py
rename to third_party/ASpanFormer/src/ASpanFormer/utils/coarse_matching.py
index 281a410e02465dec1d68ab69f48673268d1d3002..c506479a978c3ebb20c6736ed30f0ef0a351d4b9 100644
--- a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/coarse_matching.py
+++ b/third_party/ASpanFormer/src/ASpanFormer/utils/coarse_matching.py
@@ -4,11 +4,12 @@ import torch.nn.functional as F
from einops.einops import rearrange
from time import time
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
INF = 1e9
+
def mask_border(m, b: int, v):
- """ Mask borders with value
+ """Mask borders with value
Args:
m (torch.Tensor): [N, H0, W0, H1, W1]
b (int)
@@ -39,22 +40,21 @@ def mask_border_with_padding(m, bd, v, p_m0, p_m1):
h0s, w0s = p_m0.sum(1).max(-1)[0].int(), p_m0.sum(-1).max(-1)[0].int()
h1s, w1s = p_m1.sum(1).max(-1)[0].int(), p_m1.sum(-1).max(-1)[0].int()
for b_idx, (h0, w0, h1, w1) in enumerate(zip(h0s, w0s, h1s, w1s)):
- m[b_idx, h0 - bd:] = v
- m[b_idx, :, w0 - bd:] = v
- m[b_idx, :, :, h1 - bd:] = v
- m[b_idx, :, :, :, w1 - bd:] = v
+ m[b_idx, h0 - bd :] = v
+ m[b_idx, :, w0 - bd :] = v
+ m[b_idx, :, :, h1 - bd :] = v
+ m[b_idx, :, :, :, w1 - bd :] = v
def compute_max_candidates(p_m0, p_m1):
"""Compute the max candidates of all pairs within a batch
-
+
Args:
p_m0, p_m1 (torch.Tensor): padded masks
"""
h0s, w0s = p_m0.sum(1).max(-1)[0], p_m0.sum(-1).max(-1)[0]
h1s, w1s = p_m1.sum(1).max(-1)[0], p_m1.sum(-1).max(-1)[0]
- max_cand = torch.sum(
- torch.min(torch.stack([h0s * w0s, h1s * w1s], -1), -1)[0])
+ max_cand = torch.sum(torch.min(torch.stack([h0s * w0s, h1s * w1s], -1), -1)[0])
return max_cand
@@ -63,29 +63,32 @@ class CoarseMatching(nn.Module):
super().__init__()
self.config = config
# general config
- self.thr = config['thr']
- self.border_rm = config['border_rm']
+ self.thr = config["thr"]
+ self.border_rm = config["border_rm"]
# -- # for trainig fine-level LoFTR
- self.train_coarse_percent = config['train_coarse_percent']
- self.train_pad_num_gt_min = config['train_pad_num_gt_min']
-
+ self.train_coarse_percent = config["train_coarse_percent"]
+ self.train_pad_num_gt_min = config["train_pad_num_gt_min"]
+
# we provide 2 options for differentiable matching
- self.match_type = config['match_type']
- if self.match_type == 'dual_softmax':
- self.temperature=nn.parameter.Parameter(torch.tensor(10.), requires_grad=True)
- elif self.match_type == 'sinkhorn':
+ self.match_type = config["match_type"]
+ if self.match_type == "dual_softmax":
+ self.temperature = nn.parameter.Parameter(
+ torch.tensor(10.0), requires_grad=True
+ )
+ elif self.match_type == "sinkhorn":
try:
from .superglue import log_optimal_transport
except ImportError:
raise ImportError("download superglue.py first!")
self.log_optimal_transport = log_optimal_transport
self.bin_score = nn.Parameter(
- torch.tensor(config['skh_init_bin_score'], requires_grad=True))
- self.skh_iters = config['skh_iters']
- self.skh_prefilter = config['skh_prefilter']
+ torch.tensor(config["skh_init_bin_score"], requires_grad=True)
+ )
+ self.skh_iters = config["skh_iters"]
+ self.skh_prefilter = config["skh_prefilter"]
else:
raise NotImplementedError()
-
+
def forward(self, feat_c0, feat_c1, flow_list, data, mask_c0=None, mask_c1=None):
"""
Args:
@@ -108,29 +111,32 @@ class CoarseMatching(nn.Module):
"""
N, L, S, C = feat_c0.size(0), feat_c0.size(1), feat_c1.size(1), feat_c0.size(2)
# normalize
- feat_c0, feat_c1 = map(lambda feat: feat / feat.shape[-1]**.5,
- [feat_c0, feat_c1])
-
- if self.match_type == 'dual_softmax':
- sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0,
- feat_c1) * self.temperature
+ feat_c0, feat_c1 = map(
+ lambda feat: feat / feat.shape[-1] ** 0.5, [feat_c0, feat_c1]
+ )
+
+ if self.match_type == "dual_softmax":
+ sim_matrix = (
+ torch.einsum("nlc,nsc->nls", feat_c0, feat_c1) * self.temperature
+ )
if mask_c0 is not None:
sim_matrix.masked_fill_(
- ~(mask_c0[..., None] * mask_c1[:, None]).bool(),
- -INF)
+ ~(mask_c0[..., None] * mask_c1[:, None]).bool(), -INF
+ )
conf_matrix = F.softmax(sim_matrix, 1) * F.softmax(sim_matrix, 2)
-
- elif self.match_type == 'sinkhorn':
+
+ elif self.match_type == "sinkhorn":
# sinkhorn, dustbin included
sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0, feat_c1)
if mask_c0 is not None:
sim_matrix[:, :L, :S].masked_fill_(
- ~(mask_c0[..., None] * mask_c1[:, None]).bool(),
- -INF)
+ ~(mask_c0[..., None] * mask_c1[:, None]).bool(), -INF
+ )
# build uniform prior & use sinkhorn
log_assign_matrix = self.log_optimal_transport(
- sim_matrix, self.bin_score, self.skh_iters)
+ sim_matrix, self.bin_score, self.skh_iters
+ )
assign_matrix = log_assign_matrix.exp()
conf_matrix = assign_matrix[:, :-1, :-1]
@@ -141,18 +147,21 @@ class CoarseMatching(nn.Module):
conf_matrix[filter0[..., None].repeat(1, 1, S)] = 0
conf_matrix[filter1[:, None].repeat(1, L, 1)] = 0
- if self.config['sparse_spvs']:
- data.update({'conf_matrix_with_bin': assign_matrix.clone()})
+ if self.config["sparse_spvs"]:
+ data.update({"conf_matrix_with_bin": assign_matrix.clone()})
- data.update({'conf_matrix': conf_matrix})
+ data.update({"conf_matrix": conf_matrix})
# predict coarse matches from conf_matrix
data.update(**self.get_coarse_match(conf_matrix, data))
- #update predicted offset
- if flow_list[0].shape[2]==flow_list[1].shape[2] and flow_list[0].shape[3]==flow_list[1].shape[3]:
- flow_list=torch.stack(flow_list,dim=0)
- data.update({'predict_flow':flow_list}) #[2*L*B*H*W*4]
- self.get_offset_match(flow_list,data,mask_c0,mask_c1)
+ # update predicted offset
+ if (
+ flow_list[0].shape[2] == flow_list[1].shape[2]
+ and flow_list[0].shape[3] == flow_list[1].shape[3]
+ ):
+ flow_list = torch.stack(flow_list, dim=0)
+ data.update({"predict_flow": flow_list}) # [2*L*B*H*W*4]
+ self.get_offset_match(flow_list, data, mask_c0, mask_c1)
@torch.no_grad()
def get_coarse_match(self, conf_matrix, data):
@@ -172,28 +181,33 @@ class CoarseMatching(nn.Module):
'mconf' (torch.Tensor): [M]}
"""
axes_lengths = {
- 'h0c': data['hw0_c'][0],
- 'w0c': data['hw0_c'][1],
- 'h1c': data['hw1_c'][0],
- 'w1c': data['hw1_c'][1]
+ "h0c": data["hw0_c"][0],
+ "w0c": data["hw0_c"][1],
+ "h1c": data["hw1_c"][0],
+ "w1c": data["hw1_c"][1],
}
_device = conf_matrix.device
# 1. confidence thresholding
mask = conf_matrix > self.thr
- mask = rearrange(mask, 'b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c',
- **axes_lengths)
- if 'mask0' not in data:
+ mask = rearrange(
+ mask, "b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c", **axes_lengths
+ )
+ if "mask0" not in data:
mask_border(mask, self.border_rm, False)
else:
- mask_border_with_padding(mask, self.border_rm, False,
- data['mask0'], data['mask1'])
- mask = rearrange(mask, 'b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)',
- **axes_lengths)
+ mask_border_with_padding(
+ mask, self.border_rm, False, data["mask0"], data["mask1"]
+ )
+ mask = rearrange(
+ mask, "b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)", **axes_lengths
+ )
# 2. mutual nearest
- mask = mask \
- * (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0]) \
+ mask = (
+ mask
+ * (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0])
* (conf_matrix == conf_matrix.max(dim=1, keepdim=True)[0])
+ )
# 3. find all valid coarse matches
# this only works when at most one `True` in each row
@@ -208,67 +222,79 @@ class CoarseMatching(nn.Module):
# NOTE:
# The sampling is performed across all pairs in a batch without manually balancing
# #samples for fine-level increases w.r.t. batch_size
- if 'mask0' not in data:
- num_candidates_max = mask.size(0) * max(
- mask.size(1), mask.size(2))
+ if "mask0" not in data:
+ num_candidates_max = mask.size(0) * max(mask.size(1), mask.size(2))
else:
num_candidates_max = compute_max_candidates(
- data['mask0'], data['mask1'])
- num_matches_train = int(num_candidates_max *
- self.train_coarse_percent)
+ data["mask0"], data["mask1"]
+ )
+ num_matches_train = int(num_candidates_max * self.train_coarse_percent)
num_matches_pred = len(b_ids)
- assert self.train_pad_num_gt_min < num_matches_train, "min-num-gt-pad should be less than num-train-matches"
-
+ assert (
+ self.train_pad_num_gt_min < num_matches_train
+ ), "min-num-gt-pad should be less than num-train-matches"
+
# pred_indices is to select from prediction
if num_matches_pred <= num_matches_train - self.train_pad_num_gt_min:
pred_indices = torch.arange(num_matches_pred, device=_device)
else:
pred_indices = torch.randint(
num_matches_pred,
- (num_matches_train - self.train_pad_num_gt_min, ),
- device=_device)
+ (num_matches_train - self.train_pad_num_gt_min,),
+ device=_device,
+ )
# gt_pad_indices is to select from gt padding. e.g. max(3787-4800, 200)
gt_pad_indices = torch.randint(
- len(data['spv_b_ids']),
- (max(num_matches_train - num_matches_pred,
- self.train_pad_num_gt_min), ),
- device=_device)
- mconf_gt = torch.zeros(len(data['spv_b_ids']), device=_device) # set conf of gt paddings to all zero
+ len(data["spv_b_ids"]),
+ (max(num_matches_train - num_matches_pred, self.train_pad_num_gt_min),),
+ device=_device,
+ )
+ mconf_gt = torch.zeros(
+ len(data["spv_b_ids"]), device=_device
+ ) # set conf of gt paddings to all zero
b_ids, i_ids, j_ids, mconf = map(
- lambda x, y: torch.cat([x[pred_indices], y[gt_pad_indices]],
- dim=0),
- *zip([b_ids, data['spv_b_ids']], [i_ids, data['spv_i_ids']],
- [j_ids, data['spv_j_ids']], [mconf, mconf_gt]))
+ lambda x, y: torch.cat([x[pred_indices], y[gt_pad_indices]], dim=0),
+ *zip(
+ [b_ids, data["spv_b_ids"]],
+ [i_ids, data["spv_i_ids"]],
+ [j_ids, data["spv_j_ids"]],
+ [mconf, mconf_gt],
+ )
+ )
# These matches select patches that feed into fine-level network
- coarse_matches = {'b_ids': b_ids, 'i_ids': i_ids, 'j_ids': j_ids}
+ coarse_matches = {"b_ids": b_ids, "i_ids": i_ids, "j_ids": j_ids}
# 4. Update with matches in original image resolution
- scale = data['hw0_i'][0] / data['hw0_c'][0]
- scale0 = scale * data['scale0'][b_ids] if 'scale0' in data else scale
- scale1 = scale * data['scale1'][b_ids] if 'scale1' in data else scale
- mkpts0_c = torch.stack(
- [i_ids % data['hw0_c'][1], i_ids // data['hw0_c'][1]],
- dim=1) * scale0
- mkpts1_c = torch.stack(
- [j_ids % data['hw1_c'][1], j_ids // data['hw1_c'][1]],
- dim=1) * scale1
+ scale = data["hw0_i"][0] / data["hw0_c"][0]
+ scale0 = scale * data["scale0"][b_ids] if "scale0" in data else scale
+ scale1 = scale * data["scale1"][b_ids] if "scale1" in data else scale
+ mkpts0_c = (
+ torch.stack([i_ids % data["hw0_c"][1], i_ids // data["hw0_c"][1]], dim=1)
+ * scale0
+ )
+ mkpts1_c = (
+ torch.stack([j_ids % data["hw1_c"][1], j_ids // data["hw1_c"][1]], dim=1)
+ * scale1
+ )
# These matches is the current prediction (for visualization)
- coarse_matches.update({
- 'gt_mask': mconf == 0,
- 'm_bids': b_ids[mconf != 0], # mconf == 0 => gt matches
- 'mkpts0_c': mkpts0_c[mconf != 0],
- 'mkpts1_c': mkpts1_c[mconf != 0],
- 'mconf': mconf[mconf != 0]
- })
+ coarse_matches.update(
+ {
+ "gt_mask": mconf == 0,
+ "m_bids": b_ids[mconf != 0], # mconf == 0 => gt matches
+ "mkpts0_c": mkpts0_c[mconf != 0],
+ "mkpts1_c": mkpts1_c[mconf != 0],
+ "mconf": mconf[mconf != 0],
+ }
+ )
return coarse_matches
@torch.no_grad()
- def get_offset_match(self, flow_list, data,mask1,mask2):
+ def get_offset_match(self, flow_list, data, mask1, mask2):
"""
Args:
offset (torch.Tensor): [L, B, H, W, 2]
@@ -280,52 +306,62 @@ class CoarseMatching(nn.Module):
'mkpts1_c' (torch.Tensor): [M, 2],
'mconf' (torch.Tensor): [M]}
"""
- offset1=flow_list[0]
- bs,layer_num=offset1.shape[1],offset1.shape[0]
-
- #left side
- offset1=offset1.view(layer_num,bs,-1,4)
- conf1=offset1[:,:,:,2:].mean(dim=-1)
+ offset1 = flow_list[0]
+ bs, layer_num = offset1.shape[1], offset1.shape[0]
+
+ # left side
+ offset1 = offset1.view(layer_num, bs, -1, 4)
+ conf1 = offset1[:, :, :, 2:].mean(dim=-1)
if mask1 is not None:
- conf1.masked_fill_(~mask1.bool()[None].expand(layer_num,-1,-1),100)
- offset1=offset1[:,:,:,:2]
- self.get_offset_match_work(offset1,conf1,data,'left')
-
- #rihgt side
- if len(flow_list)==2:
- offset2=flow_list[1].view(layer_num,bs,-1,4)
- conf2=offset2[:,:,:,2:].mean(dim=-1)
+ conf1.masked_fill_(~mask1.bool()[None].expand(layer_num, -1, -1), 100)
+ offset1 = offset1[:, :, :, :2]
+ self.get_offset_match_work(offset1, conf1, data, "left")
+
+ # rihgt side
+ if len(flow_list) == 2:
+ offset2 = flow_list[1].view(layer_num, bs, -1, 4)
+ conf2 = offset2[:, :, :, 2:].mean(dim=-1)
if mask2 is not None:
- conf2.masked_fill_(~mask2.bool()[None].expand(layer_num,-1,-1),100)
- offset2=offset2[:,:,:,:2]
- self.get_offset_match_work(offset2,conf2,data,'right')
-
+ conf2.masked_fill_(~mask2.bool()[None].expand(layer_num, -1, -1), 100)
+ offset2 = offset2[:, :, :, :2]
+ self.get_offset_match_work(offset2, conf2, data, "right")
@torch.no_grad()
- def get_offset_match_work(self, offset,conf, data,side):
- bs,layer_num=offset.shape[1],offset.shape[0]
+ def get_offset_match_work(self, offset, conf, data, side):
+ bs, layer_num = offset.shape[1], offset.shape[0]
# 1. confidence thresholding
- mask_conf= conf<2
+ mask_conf = conf < 2
for index in range(bs):
- mask_conf[:,index,0]=True #safe guard in case that no match survives
+ mask_conf[:, index, 0] = True # safe guard in case that no match survives
# 3. find offset matches
- scale = data['hw0_i'][0] / data['hw0_c'][0]
- l_ids,b_ids,i_ids = torch.where(mask_conf)
- j_coor=offset[l_ids,b_ids,i_ids,:2] *scale#[N,2]
- i_coor=torch.stack([i_ids%data['hw0_c'][1],i_ids//data['hw0_c'][1]],dim=1)*scale
- #i_coor=torch.as_tensor([[index%data['hw0_c'][1],index//data['hw0_c'][1]] for index in i_ids]).to(device).float()*scale #[N,2]
+ scale = data["hw0_i"][0] / data["hw0_c"][0]
+ l_ids, b_ids, i_ids = torch.where(mask_conf)
+ j_coor = offset[l_ids, b_ids, i_ids, :2] * scale # [N,2]
+ i_coor = (
+ torch.stack([i_ids % data["hw0_c"][1], i_ids // data["hw0_c"][1]], dim=1)
+ * scale
+ )
+ # i_coor=torch.as_tensor([[index%data['hw0_c'][1],index//data['hw0_c'][1]] for index in i_ids]).cuda().float()*scale #[N,2]
# These matches is the current prediction (for visualization)
- data.update({
- 'offset_bids_'+side: b_ids, # mconf == 0 => gt matches
- 'offset_lids_'+side: l_ids,
- 'conf'+side: conf[mask_conf]
- })
-
- if side=='right':
- data.update({'offset_kpts0_f_'+side: j_coor.detach(),
- 'offset_kpts1_f_'+side: i_coor})
+ data.update(
+ {
+ "offset_bids_" + side: b_ids, # mconf == 0 => gt matches
+ "offset_lids_" + side: l_ids,
+ "conf" + side: conf[mask_conf],
+ }
+ )
+
+ if side == "right":
+ data.update(
+ {
+ "offset_kpts0_f_" + side: j_coor.detach(),
+ "offset_kpts1_f_" + side: i_coor,
+ }
+ )
else:
- data.update({'offset_kpts0_f_'+side: i_coor,
- 'offset_kpts1_f_'+side: j_coor.detach()})
-
-
+ data.update(
+ {
+ "offset_kpts0_f_" + side: i_coor,
+ "offset_kpts1_f_" + side: j_coor.detach(),
+ }
+ )
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/cvpr_ds_config.py b/third_party/ASpanFormer/src/ASpanFormer/utils/cvpr_ds_config.py
similarity index 81%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/utils/cvpr_ds_config.py
rename to third_party/ASpanFormer/src/ASpanFormer/utils/cvpr_ds_config.py
index fdc57e84936c805cb387b6239ca4a5ff6154e22e..1ffe9c067b1fb95a75dd102c5947c82d03dbea89 100644
--- a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/cvpr_ds_config.py
+++ b/third_party/ASpanFormer/src/ASpanFormer/utils/cvpr_ds_config.py
@@ -8,7 +8,7 @@ def lower_config(yacs_cfg):
_CN = CN()
-_CN.BACKBONE_TYPE = 'ResNetFPN'
+_CN.BACKBONE_TYPE = "ResNetFPN"
_CN.RESOLUTION = (8, 2) # options: [(8, 2), (16, 4)]
_CN.FINE_WINDOW_SIZE = 5 # window_size in fine_level, must be odd
_CN.FINE_CONCAT_COARSE_FEAT = True
@@ -23,15 +23,15 @@ _CN.COARSE = CN()
_CN.COARSE.D_MODEL = 256
_CN.COARSE.D_FFN = 256
_CN.COARSE.NHEAD = 8
-_CN.COARSE.LAYER_NAMES = ['self', 'cross'] * 4
-_CN.COARSE.ATTENTION = 'linear' # options: ['linear', 'full']
+_CN.COARSE.LAYER_NAMES = ["self", "cross"] * 4
+_CN.COARSE.ATTENTION = "linear" # options: ['linear', 'full']
_CN.COARSE.TEMP_BUG_FIX = False
# 3. Coarse-Matching config
_CN.MATCH_COARSE = CN()
_CN.MATCH_COARSE.THR = 0.1
_CN.MATCH_COARSE.BORDER_RM = 2
-_CN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' # options: ['dual_softmax, 'sinkhorn']
+_CN.MATCH_COARSE.MATCH_TYPE = "dual_softmax" # options: ['dual_softmax, 'sinkhorn']
_CN.MATCH_COARSE.DSMAX_TEMPERATURE = 0.1
_CN.MATCH_COARSE.SKH_ITERS = 3
_CN.MATCH_COARSE.SKH_INIT_BIN_SCORE = 1.0
@@ -44,7 +44,7 @@ _CN.FINE = CN()
_CN.FINE.D_MODEL = 128
_CN.FINE.D_FFN = 128
_CN.FINE.NHEAD = 8
-_CN.FINE.LAYER_NAMES = ['self', 'cross'] * 1
-_CN.FINE.ATTENTION = 'linear'
+_CN.FINE.LAYER_NAMES = ["self", "cross"] * 1
+_CN.FINE.ATTENTION = "linear"
default_cfg = lower_config(_CN)
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py b/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py
similarity index 54%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py
rename to third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py
index 6e77aded52e1eb5c01e22c2738104f3b09d6922a..3f41b1db96016efb58888381284f86d448839ff0 100644
--- a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py
+++ b/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py
@@ -26,35 +26,46 @@ class FineMatching(nn.Module):
"""
M, WW, C = feat_f0.shape
W = int(math.sqrt(WW))
- scale = data['hw0_i'][0] / data['hw0_f'][0]
+ scale = data["hw0_i"][0] / data["hw0_f"][0]
self.M, self.W, self.WW, self.C, self.scale = M, W, WW, C, scale
# corner case: if no coarse matches found
if M == 0:
- assert self.training == False, "M is always >0, when training, see coarse_matching.py"
+ assert (
+ self.training == False
+ ), "M is always >0, when training, see coarse_matching.py"
# logger.warning('No matches found in coarse-level.')
- data.update({
- 'expec_f': torch.empty(0, 3, device=feat_f0.device),
- 'mkpts0_f': data['mkpts0_c'],
- 'mkpts1_f': data['mkpts1_c'],
- })
+ data.update(
+ {
+ "expec_f": torch.empty(0, 3, device=feat_f0.device),
+ "mkpts0_f": data["mkpts0_c"],
+ "mkpts1_f": data["mkpts1_c"],
+ }
+ )
return
- feat_f0_picked = feat_f0_picked = feat_f0[:, WW//2, :]
- sim_matrix = torch.einsum('mc,mrc->mr', feat_f0_picked, feat_f1)
- softmax_temp = 1. / C**.5
+ feat_f0_picked = feat_f0_picked = feat_f0[:, WW // 2, :]
+ sim_matrix = torch.einsum("mc,mrc->mr", feat_f0_picked, feat_f1)
+ softmax_temp = 1.0 / C**0.5
heatmap = torch.softmax(softmax_temp * sim_matrix, dim=1).view(-1, W, W)
# compute coordinates from heatmap
coords_normalized = dsnt.spatial_expectation2d(heatmap[None], True)[0] # [M, 2]
- grid_normalized = create_meshgrid(W, W, True, heatmap.device).reshape(1, -1, 2) # [1, WW, 2]
+ grid_normalized = create_meshgrid(W, W, True, heatmap.device).reshape(
+ 1, -1, 2
+ ) # [1, WW, 2]
# compute std over
- var = torch.sum(grid_normalized**2 * heatmap.view(-1, WW, 1), dim=1) - coords_normalized**2 # [M, 2]
- std = torch.sum(torch.sqrt(torch.clamp(var, min=1e-10)), -1) # [M] clamp needed for numerical stability
-
+ var = (
+ torch.sum(grid_normalized**2 * heatmap.view(-1, WW, 1), dim=1)
+ - coords_normalized**2
+ ) # [M, 2]
+ std = torch.sum(
+ torch.sqrt(torch.clamp(var, min=1e-10)), -1
+ ) # [M] clamp needed for numerical stability
+
# for fine-level supervision
- data.update({'expec_f': torch.cat([coords_normalized, std.unsqueeze(1)], -1)})
+ data.update({"expec_f": torch.cat([coords_normalized, std.unsqueeze(1)], -1)})
# compute absolute kpt coords
self.get_fine_match(coords_normalized, data)
@@ -64,11 +75,10 @@ class FineMatching(nn.Module):
W, WW, C, scale = self.W, self.WW, self.C, self.scale
# mkpts0_f and mkpts1_f
- mkpts0_f = data['mkpts0_c']
- scale1 = scale * data['scale1'][data['b_ids']] if 'scale0' in data else scale
- mkpts1_f = data['mkpts1_c'] + (coords_normed * (W // 2) * scale1)[:len(data['mconf'])]
+ mkpts0_f = data["mkpts0_c"]
+ scale1 = scale * data["scale1"][data["b_ids"]] if "scale0" in data else scale
+ mkpts1_f = (
+ data["mkpts1_c"] + (coords_normed * (W // 2) * scale1)[: len(data["mconf"])]
+ )
- data.update({
- "mkpts0_f": mkpts0_f,
- "mkpts1_f": mkpts1_f
- })
+ data.update({"mkpts0_f": mkpts0_f, "mkpts1_f": mkpts1_f})
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/utils/geometry.py b/third_party/ASpanFormer/src/ASpanFormer/utils/geometry.py
similarity index 59%
rename from imcui/third_party/EfficientLoFTR/src/loftr/utils/geometry.py
rename to third_party/ASpanFormer/src/ASpanFormer/utils/geometry.py
index f95cdb65b48324c4f4ceb20231b1bed992b41116..6101f738f2b2b7ee014fcb53a4032391939ed8cd 100644
--- a/imcui/third_party/EfficientLoFTR/src/loftr/utils/geometry.py
+++ b/third_party/ASpanFormer/src/ASpanFormer/utils/geometry.py
@@ -3,10 +3,10 @@ import torch
@torch.no_grad()
def warp_kpts(kpts0, depth0, depth1, T_0to1, K0, K1):
- """ Warp kpts0 from I0 to I1 with depth, K and Rt
+ """Warp kpts0 from I0 to I1 with depth, K and Rt
Also check covisibility and depth consistency.
Depth is consistent if relative error < 0.2 (hard-coded).
-
+
Args:
kpts0 (torch.Tensor): [N, L, 2] - ,
depth0 (torch.Tensor): [N, H, W],
@@ -22,33 +22,52 @@ def warp_kpts(kpts0, depth0, depth1, T_0to1, K0, K1):
# Sample depth, get calculable_mask on depth != 0
kpts0_depth = torch.stack(
- [depth0[i, kpts0_long[i, :, 1], kpts0_long[i, :, 0]] for i in range(kpts0.shape[0])], dim=0
+ [
+ depth0[i, kpts0_long[i, :, 1], kpts0_long[i, :, 0]]
+ for i in range(kpts0.shape[0])
+ ],
+ dim=0,
) # (N, L)
nonzero_mask = kpts0_depth != 0
# Unproject
- kpts0_h = torch.cat([kpts0, torch.ones_like(kpts0[:, :, [0]])], dim=-1) * kpts0_depth[..., None] # (N, L, 3)
+ kpts0_h = (
+ torch.cat([kpts0, torch.ones_like(kpts0[:, :, [0]])], dim=-1)
+ * kpts0_depth[..., None]
+ ) # (N, L, 3)
kpts0_cam = K0.inverse() @ kpts0_h.transpose(2, 1) # (N, 3, L)
# Rigid Transform
- w_kpts0_cam = T_0to1[:, :3, :3] @ kpts0_cam + T_0to1[:, :3, [3]] # (N, 3, L)
+ w_kpts0_cam = T_0to1[:, :3, :3] @ kpts0_cam + T_0to1[:, :3, [3]] # (N, 3, L)
w_kpts0_depth_computed = w_kpts0_cam[:, 2, :]
# Project
w_kpts0_h = (K1 @ w_kpts0_cam).transpose(2, 1) # (N, L, 3)
- w_kpts0 = w_kpts0_h[:, :, :2] / (w_kpts0_h[:, :, [2]] + 1e-4) # (N, L, 2), +1e-4 to avoid zero depth
+ w_kpts0 = w_kpts0_h[:, :, :2] / (
+ w_kpts0_h[:, :, [2]] + 1e-4
+ ) # (N, L, 2), +1e-4 to avoid zero depth
# Covisible Check
h, w = depth1.shape[1:3]
- covisible_mask = (w_kpts0[:, :, 0] > 0) * (w_kpts0[:, :, 0] < w-1) * \
- (w_kpts0[:, :, 1] > 0) * (w_kpts0[:, :, 1] < h-1)
+ covisible_mask = (
+ (w_kpts0[:, :, 0] > 0)
+ * (w_kpts0[:, :, 0] < w - 1)
+ * (w_kpts0[:, :, 1] > 0)
+ * (w_kpts0[:, :, 1] < h - 1)
+ )
w_kpts0_long = w_kpts0.long()
w_kpts0_long[~covisible_mask, :] = 0
w_kpts0_depth = torch.stack(
- [depth1[i, w_kpts0_long[i, :, 1], w_kpts0_long[i, :, 0]] for i in range(w_kpts0_long.shape[0])], dim=0
+ [
+ depth1[i, w_kpts0_long[i, :, 1], w_kpts0_long[i, :, 0]]
+ for i in range(w_kpts0_long.shape[0])
+ ],
+ dim=0,
) # (N, L)
- consistent_mask = ((w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth).abs() < 0.2
+ consistent_mask = (
+ (w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth
+ ).abs() < 0.2
valid_mask = nonzero_mask * covisible_mask * consistent_mask
return valid_mask, w_kpts0
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/position_encoding.py b/third_party/ASpanFormer/src/ASpanFormer/utils/position_encoding.py
similarity index 54%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/utils/position_encoding.py
rename to third_party/ASpanFormer/src/ASpanFormer/utils/position_encoding.py
index 07d384ae18370acb99ef00a788f628c967249ace..1da77ecef628e3e263b56fb501b6a6313f05c060 100644
--- a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/position_encoding.py
+++ b/third_party/ASpanFormer/src/ASpanFormer/utils/position_encoding.py
@@ -8,7 +8,7 @@ class PositionEncodingSine(nn.Module):
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
- def __init__(self, d_model, max_shape=(256, 256),pre_scaling=None):
+ def __init__(self, d_model, max_shape=(256, 256), pre_scaling=None):
"""
Args:
max_shape (tuple): for 1/8 featmap, the max length of 256 corresponds to 2048 pixels
@@ -18,44 +18,63 @@ class PositionEncodingSine(nn.Module):
We will remove the buggy impl after re-training all variants of our released models.
"""
super().__init__()
- self.d_model=d_model
- self.max_shape=max_shape
- self.pre_scaling=pre_scaling
+ self.d_model = d_model
+ self.max_shape = max_shape
+ self.pre_scaling = pre_scaling
pe = torch.zeros((d_model, *max_shape))
y_position = torch.ones(max_shape).cumsum(0).float().unsqueeze(0)
x_position = torch.ones(max_shape).cumsum(1).float().unsqueeze(0)
if pre_scaling[0] is not None and pre_scaling[1] is not None:
- train_res,test_res=pre_scaling[0],pre_scaling[1]
- x_position,y_position=x_position*train_res[1]/test_res[1],y_position*train_res[0]/test_res[0]
+ train_res, test_res = pre_scaling[0], pre_scaling[1]
+ x_position, y_position = (
+ x_position * train_res[1] / test_res[1],
+ y_position * train_res[0] / test_res[0],
+ )
- div_term = torch.exp(torch.arange(0, d_model//2, 2).float() * (-math.log(10000.0) / (d_model//2)))
+ div_term = torch.exp(
+ torch.arange(0, d_model // 2, 2).float()
+ * (-math.log(10000.0) / (d_model // 2))
+ )
div_term = div_term[:, None, None] # [C//4, 1, 1]
pe[0::4, :, :] = torch.sin(x_position * div_term)
pe[1::4, :, :] = torch.cos(x_position * div_term)
pe[2::4, :, :] = torch.sin(y_position * div_term)
pe[3::4, :, :] = torch.cos(y_position * div_term)
- self.register_buffer('pe', pe.unsqueeze(0), persistent=False) # [1, C, H, W]
+ self.register_buffer("pe", pe.unsqueeze(0), persistent=False) # [1, C, H, W]
- def forward(self, x,scaling=None):
+ def forward(self, x, scaling=None):
"""
Args:
x: [N, C, H, W]
"""
- if scaling is None: #onliner scaling overwrites pre_scaling
- return x + self.pe[:, :, :x.size(2), :x.size(3)],self.pe[:, :, :x.size(2), :x.size(3)]
+ if scaling is None: # onliner scaling overwrites pre_scaling
+ return (
+ x + self.pe[:, :, : x.size(2), : x.size(3)],
+ self.pe[:, :, : x.size(2), : x.size(3)],
+ )
else:
pe = torch.zeros((self.d_model, *self.max_shape))
- y_position = torch.ones(self.max_shape).cumsum(0).float().unsqueeze(0)*scaling[0]
- x_position = torch.ones(self.max_shape).cumsum(1).float().unsqueeze(0)*scaling[1]
-
- div_term = torch.exp(torch.arange(0, self.d_model//2, 2).float() * (-math.log(10000.0) / (self.d_model//2)))
+ y_position = (
+ torch.ones(self.max_shape).cumsum(0).float().unsqueeze(0) * scaling[0]
+ )
+ x_position = (
+ torch.ones(self.max_shape).cumsum(1).float().unsqueeze(0) * scaling[1]
+ )
+
+ div_term = torch.exp(
+ torch.arange(0, self.d_model // 2, 2).float()
+ * (-math.log(10000.0) / (self.d_model // 2))
+ )
div_term = div_term[:, None, None] # [C//4, 1, 1]
pe[0::4, :, :] = torch.sin(x_position * div_term)
pe[1::4, :, :] = torch.cos(x_position * div_term)
pe[2::4, :, :] = torch.sin(y_position * div_term)
pe[3::4, :, :] = torch.cos(y_position * div_term)
- pe=pe.unsqueeze(0).to(x.device)
- return x + pe[:, :, :x.size(2), :x.size(3)],pe[:, :, :x.size(2), :x.size(3)]
\ No newline at end of file
+ pe = pe.unsqueeze(0).to(x.device)
+ return (
+ x + pe[:, :, : x.size(2), : x.size(3)],
+ pe[:, :, : x.size(2), : x.size(3)],
+ )
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/supervision.py b/third_party/ASpanFormer/src/ASpanFormer/utils/supervision.py
similarity index 60%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/utils/supervision.py
rename to third_party/ASpanFormer/src/ASpanFormer/utils/supervision.py
index 5cef3a7968413136f6dc9f52b6a1ec87192b006b..16c468d8ee1425be0d4518477263f377bd09873a 100644
--- a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/supervision.py
+++ b/third_party/ASpanFormer/src/ASpanFormer/utils/supervision.py
@@ -13,7 +13,7 @@ from .geometry import warp_kpts
@torch.no_grad()
def mask_pts_at_padded_regions(grid_pt, mask):
"""For megadepth dataset, zero-padding exists in images"""
- mask = repeat(mask, 'n h w -> n (h w) c', c=2)
+ mask = repeat(mask, "n h w -> n (h w) c", c=2)
grid_pt[~mask.bool()] = 0
return grid_pt
@@ -30,37 +30,55 @@ def spvs_coarse(data, config):
'spv_w_pt0_i': [N, hw0, 2], in original image resolution
'spv_pt1_i': [N, hw1, 2], in original image resolution
}
-
+
NOTE:
- for scannet dataset, there're 3 kinds of resolution {i, c, f}
- for megadepth dataset, there're 4 kinds of resolution {i, i_resize, c, f}
"""
# 1. misc
- device = data['image0'].device
- N, _, H0, W0 = data['image0'].shape
- _, _, H1, W1 = data['image1'].shape
- scale = config['ASPAN']['RESOLUTION'][0]
- scale0 = scale * data['scale0'][:, None] if 'scale0' in data else scale
- scale1 = scale * data['scale1'][:, None] if 'scale0' in data else scale
+ device = data["image0"].device
+ N, _, H0, W0 = data["image0"].shape
+ _, _, H1, W1 = data["image1"].shape
+ scale = config["ASPAN"]["RESOLUTION"][0]
+ scale0 = scale * data["scale0"][:, None] if "scale0" in data else scale
+ scale1 = scale * data["scale1"][:, None] if "scale0" in data else scale
h0, w0, h1, w1 = map(lambda x: x // scale, [H0, W0, H1, W1])
# 2. warp grids
# create kpts in meshgrid and resize them to image resolution
- grid_pt0_c = create_meshgrid(h0, w0, False, device).reshape(1, h0*w0, 2).repeat(N, 1, 1) # [N, hw, 2]
+ grid_pt0_c = (
+ create_meshgrid(h0, w0, False, device).reshape(1, h0 * w0, 2).repeat(N, 1, 1)
+ ) # [N, hw, 2]
grid_pt0_i = scale0 * grid_pt0_c
- grid_pt1_c = create_meshgrid(h1, w1, False, device).reshape(1, h1*w1, 2).repeat(N, 1, 1)
+ grid_pt1_c = (
+ create_meshgrid(h1, w1, False, device).reshape(1, h1 * w1, 2).repeat(N, 1, 1)
+ )
grid_pt1_i = scale1 * grid_pt1_c
# mask padded region to (0, 0), so no need to manually mask conf_matrix_gt
- if 'mask0' in data:
- grid_pt0_i = mask_pts_at_padded_regions(grid_pt0_i, data['mask0'])
- grid_pt1_i = mask_pts_at_padded_regions(grid_pt1_i, data['mask1'])
+ if "mask0" in data:
+ grid_pt0_i = mask_pts_at_padded_regions(grid_pt0_i, data["mask0"])
+ grid_pt1_i = mask_pts_at_padded_regions(grid_pt1_i, data["mask1"])
# warp kpts bi-directionally and resize them to coarse-level resolution
# (no depth consistency check, since it leads to worse results experimentally)
# (unhandled edge case: points with 0-depth will be warped to the left-up corner)
- _, w_pt0_i = warp_kpts(grid_pt0_i, data['depth0'], data['depth1'], data['T_0to1'], data['K0'], data['K1'])
- _, w_pt1_i = warp_kpts(grid_pt1_i, data['depth1'], data['depth0'], data['T_1to0'], data['K1'], data['K0'])
+ _, w_pt0_i = warp_kpts(
+ grid_pt0_i,
+ data["depth0"],
+ data["depth1"],
+ data["T_0to1"],
+ data["K0"],
+ data["K1"],
+ )
+ _, w_pt1_i = warp_kpts(
+ grid_pt1_i,
+ data["depth1"],
+ data["depth0"],
+ data["T_1to0"],
+ data["K1"],
+ data["K0"],
+ )
w_pt0_c = w_pt0_i / scale1
w_pt1_c = w_pt1_i / scale0
@@ -72,21 +90,26 @@ def spvs_coarse(data, config):
# corner case: out of boundary
def out_bound_mask(pt, w, h):
- return (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h)
+ return (
+ (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h)
+ )
+
nearest_index1[out_bound_mask(w_pt0_c_round, w1, h1)] = 0
nearest_index0[out_bound_mask(w_pt1_c_round, w0, h0)] = 0
- loop_back = torch.stack([nearest_index0[_b][_i] for _b, _i in enumerate(nearest_index1)], dim=0)
- correct_0to1 = loop_back == torch.arange(h0*w0, device=device)[None].repeat(N, 1)
+ loop_back = torch.stack(
+ [nearest_index0[_b][_i] for _b, _i in enumerate(nearest_index1)], dim=0
+ )
+ correct_0to1 = loop_back == torch.arange(h0 * w0, device=device)[None].repeat(N, 1)
correct_0to1[:, 0] = False # ignore the top-left corner
# 4. construct a gt conf_matrix
- conf_matrix_gt = torch.zeros(N, h0*w0, h1*w1, device=device)
+ conf_matrix_gt = torch.zeros(N, h0 * w0, h1 * w1, device=device)
b_ids, i_ids = torch.where(correct_0to1 != 0)
j_ids = nearest_index1[b_ids, i_ids]
conf_matrix_gt[b_ids, i_ids, j_ids] = 1
- data.update({'conf_matrix_gt': conf_matrix_gt})
+ data.update({"conf_matrix_gt": conf_matrix_gt})
# 5. save coarse matches(gt) for training fine level
if len(b_ids) == 0:
@@ -96,30 +119,26 @@ def spvs_coarse(data, config):
i_ids = torch.tensor([0], device=device)
j_ids = torch.tensor([0], device=device)
- data.update({
- 'spv_b_ids': b_ids,
- 'spv_i_ids': i_ids,
- 'spv_j_ids': j_ids
- })
+ data.update({"spv_b_ids": b_ids, "spv_i_ids": i_ids, "spv_j_ids": j_ids})
# 6. save intermediate results (for fast fine-level computation)
- data.update({
- 'spv_w_pt0_i': w_pt0_i,
- 'spv_pt1_i': grid_pt1_i
- })
+ data.update({"spv_w_pt0_i": w_pt0_i, "spv_pt1_i": grid_pt1_i})
def compute_supervision_coarse(data, config):
- assert len(set(data['dataset_name'])) == 1, "Do not support mixed datasets training!"
- data_source = data['dataset_name'][0]
- if data_source.lower() in ['scannet', 'megadepth']:
+ assert (
+ len(set(data["dataset_name"])) == 1
+ ), "Do not support mixed datasets training!"
+ data_source = data["dataset_name"][0]
+ if data_source.lower() in ["scannet", "megadepth"]:
spvs_coarse(data, config)
else:
- raise ValueError(f'Unknown data source: {data_source}')
+ raise ValueError(f"Unknown data source: {data_source}")
############## ↓ Fine-Level supervision ↓ ##############
+
@torch.no_grad()
def spvs_fine(data, config):
"""
@@ -129,23 +148,25 @@ def spvs_fine(data, config):
"""
# 1. misc
# w_pt0_i, pt1_i = data.pop('spv_w_pt0_i'), data.pop('spv_pt1_i')
- w_pt0_i, pt1_i = data['spv_w_pt0_i'], data['spv_pt1_i']
- scale = config['ASPAN']['RESOLUTION'][1]
- radius = config['ASPAN']['FINE_WINDOW_SIZE'] // 2
+ w_pt0_i, pt1_i = data["spv_w_pt0_i"], data["spv_pt1_i"]
+ scale = config["ASPAN"]["RESOLUTION"][1]
+ radius = config["ASPAN"]["FINE_WINDOW_SIZE"] // 2
# 2. get coarse prediction
- b_ids, i_ids, j_ids = data['b_ids'], data['i_ids'], data['j_ids']
+ b_ids, i_ids, j_ids = data["b_ids"], data["i_ids"], data["j_ids"]
# 3. compute gt
- scale = scale * data['scale1'][b_ids] if 'scale0' in data else scale
+ scale = scale * data["scale1"][b_ids] if "scale0" in data else scale
# `expec_f_gt` might exceed the window, i.e. abs(*) > 1, which would be filtered later
- expec_f_gt = (w_pt0_i[b_ids, i_ids] - pt1_i[b_ids, j_ids]) / scale / radius # [M, 2]
+ expec_f_gt = (
+ (w_pt0_i[b_ids, i_ids] - pt1_i[b_ids, j_ids]) / scale / radius
+ ) # [M, 2]
data.update({"expec_f_gt": expec_f_gt})
def compute_supervision_fine(data, config):
- data_source = data['dataset_name'][0]
- if data_source.lower() in ['scannet', 'megadepth']:
+ data_source = data["dataset_name"][0]
+ if data_source.lower() in ["scannet", "megadepth"]:
spvs_fine(data, config)
else:
raise NotImplementedError
diff --git a/imcui/third_party/ASpanFormer/src/__init__.py b/third_party/ASpanFormer/src/__init__.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/src/__init__.py
rename to third_party/ASpanFormer/src/__init__.py
diff --git a/imcui/third_party/ASpanFormer/src/config/default.py b/third_party/ASpanFormer/src/config/default.py
similarity index 72%
rename from imcui/third_party/ASpanFormer/src/config/default.py
rename to third_party/ASpanFormer/src/config/default.py
index 40abd51c3f28ea6dee3c4e9fcee6efac5c080a2f..2850199cfb4d403fe4ec7aa5d61a7de524e4183c 100644
--- a/imcui/third_party/ASpanFormer/src/config/default.py
+++ b/third_party/ASpanFormer/src/config/default.py
@@ -1,9 +1,10 @@
from yacs.config import CfgNode as CN
+
_CN = CN()
############## ↓ ASPAN Pipeline ↓ ##############
_CN.ASPAN = CN()
-_CN.ASPAN.BACKBONE_TYPE = 'ResNetFPN'
+_CN.ASPAN.BACKBONE_TYPE = "ResNetFPN"
_CN.ASPAN.RESOLUTION = (8, 2) # options: [(8, 2), (16, 4)]
_CN.ASPAN.FINE_WINDOW_SIZE = 5 # window_size in fine_level, must be odd
_CN.ASPAN.FINE_CONCAT_COARSE_FEAT = True
@@ -17,14 +18,14 @@ _CN.ASPAN.RESNETFPN.BLOCK_DIMS = [128, 196, 256] # s1, s2, s3
_CN.ASPAN.COARSE = CN()
_CN.ASPAN.COARSE.D_MODEL = 256
_CN.ASPAN.COARSE.D_FFN = 256
-_CN.ASPAN.COARSE.D_FLOW= 128
+_CN.ASPAN.COARSE.D_FLOW = 128
_CN.ASPAN.COARSE.NHEAD = 8
-_CN.ASPAN.COARSE.NLEVEL= 3
-_CN.ASPAN.COARSE.INI_LAYER_NUM = 2
-_CN.ASPAN.COARSE.LAYER_NUM = 4
-_CN.ASPAN.COARSE.NSAMPLE = [2,8]
-_CN.ASPAN.COARSE.RADIUS_SCALE= 5
-_CN.ASPAN.COARSE.COARSEST_LEVEL= [26,26]
+_CN.ASPAN.COARSE.NLEVEL = 3
+_CN.ASPAN.COARSE.INI_LAYER_NUM = 2
+_CN.ASPAN.COARSE.LAYER_NUM = 4
+_CN.ASPAN.COARSE.NSAMPLE = [2, 8]
+_CN.ASPAN.COARSE.RADIUS_SCALE = 5
+_CN.ASPAN.COARSE.COARSEST_LEVEL = [26, 26]
_CN.ASPAN.COARSE.TRAIN_RES = None
_CN.ASPAN.COARSE.TEST_RES = None
@@ -32,7 +33,9 @@ _CN.ASPAN.COARSE.TEST_RES = None
_CN.ASPAN.MATCH_COARSE = CN()
_CN.ASPAN.MATCH_COARSE.THR = 0.2
_CN.ASPAN.MATCH_COARSE.BORDER_RM = 2
-_CN.ASPAN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' # options: ['dual_softmax, 'sinkhorn']
+_CN.ASPAN.MATCH_COARSE.MATCH_TYPE = (
+ "dual_softmax" # options: ['dual_softmax, 'sinkhorn']
+)
_CN.ASPAN.MATCH_COARSE.SKH_ITERS = 3
_CN.ASPAN.MATCH_COARSE.SKH_INIT_BIN_SCORE = 1.0
_CN.ASPAN.MATCH_COARSE.SKH_PREFILTER = False
@@ -46,13 +49,13 @@ _CN.ASPAN.FINE = CN()
_CN.ASPAN.FINE.D_MODEL = 128
_CN.ASPAN.FINE.D_FFN = 128
_CN.ASPAN.FINE.NHEAD = 8
-_CN.ASPAN.FINE.LAYER_NAMES = ['self', 'cross'] * 1
-_CN.ASPAN.FINE.ATTENTION = 'linear'
+_CN.ASPAN.FINE.LAYER_NAMES = ["self", "cross"] * 1
+_CN.ASPAN.FINE.ATTENTION = "linear"
# 5. ASPAN Losses
# -- # coarse-level
_CN.ASPAN.LOSS = CN()
-_CN.ASPAN.LOSS.COARSE_TYPE = 'focal' # ['focal', 'cross_entropy']
+_CN.ASPAN.LOSS.COARSE_TYPE = "focal" # ['focal', 'cross_entropy']
_CN.ASPAN.LOSS.COARSE_WEIGHT = 1.0
# _CN.ASPAN.LOSS.SPARSE_SPVS = False
# -- - -- # focal loss (coarse)
@@ -64,7 +67,7 @@ _CN.ASPAN.LOSS.NEG_WEIGHT = 1.0
# use `_CN.ASPAN.MATCH_COARSE.MATCH_TYPE`
# -- # fine-level
-_CN.ASPAN.LOSS.FINE_TYPE = 'l2_with_std' # ['l2_with_std', 'l2']
+_CN.ASPAN.LOSS.FINE_TYPE = "l2_with_std" # ['l2_with_std', 'l2']
_CN.ASPAN.LOSS.FINE_WEIGHT = 1.0
_CN.ASPAN.LOSS.FINE_CORRECT_THR = 1.0 # for filtering valid fine-level gts (some gt matches might fall out of the fine-level window)
@@ -85,24 +88,32 @@ _CN.DATASET.TRAIN_INTRINSIC_PATH = None
_CN.DATASET.VAL_DATA_ROOT = None
_CN.DATASET.VAL_POSE_ROOT = None # (optional directory for poses)
_CN.DATASET.VAL_NPZ_ROOT = None
-_CN.DATASET.VAL_LIST_PATH = None # None if val data from all scenes are bundled into a single npz file
+_CN.DATASET.VAL_LIST_PATH = (
+ None # None if val data from all scenes are bundled into a single npz file
+)
_CN.DATASET.VAL_INTRINSIC_PATH = None
# testing
_CN.DATASET.TEST_DATA_SOURCE = None
_CN.DATASET.TEST_DATA_ROOT = None
_CN.DATASET.TEST_POSE_ROOT = None # (optional directory for poses)
_CN.DATASET.TEST_NPZ_ROOT = None
-_CN.DATASET.TEST_LIST_PATH = None # None if test data from all scenes are bundled into a single npz file
+_CN.DATASET.TEST_LIST_PATH = (
+ None # None if test data from all scenes are bundled into a single npz file
+)
_CN.DATASET.TEST_INTRINSIC_PATH = None
# 2. dataset config
# general options
-_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.4 # discard data with overlap_score < min_overlap_score
+_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = (
+ 0.4 # discard data with overlap_score < min_overlap_score
+)
_CN.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0
_CN.DATASET.AUGMENTATION_TYPE = None # options: [None, 'dark', 'mobile']
# MegaDepth options
-_CN.DATASET.MGDPT_IMG_RESIZE = 640 # resize the longer side, zero-pad bottom-right to square.
+_CN.DATASET.MGDPT_IMG_RESIZE = (
+ 640 # resize the longer side, zero-pad bottom-right to square.
+)
_CN.DATASET.MGDPT_IMG_PAD = True # pad img to square with size = MGDPT_IMG_RESIZE
_CN.DATASET.MGDPT_DEPTH_PAD = True # pad depthmap to square with size = 2000
_CN.DATASET.MGDPT_DF = 8
@@ -118,17 +129,17 @@ _CN.TRAINER.FIND_LR = False # use learning rate finder from pytorch-lightning
# optimizer
_CN.TRAINER.OPTIMIZER = "adamw" # [adam, adamw]
_CN.TRAINER.TRUE_LR = None # this will be calculated automatically at runtime
-_CN.TRAINER.ADAM_DECAY = 0. # ADAM: for adam
+_CN.TRAINER.ADAM_DECAY = 0.0 # ADAM: for adam
_CN.TRAINER.ADAMW_DECAY = 0.1
# step-based warm-up
-_CN.TRAINER.WARMUP_TYPE = 'linear' # [linear, constant]
-_CN.TRAINER.WARMUP_RATIO = 0.
+_CN.TRAINER.WARMUP_TYPE = "linear" # [linear, constant]
+_CN.TRAINER.WARMUP_RATIO = 0.0
_CN.TRAINER.WARMUP_STEP = 4800
# learning rate scheduler
-_CN.TRAINER.SCHEDULER = 'MultiStepLR' # [MultiStepLR, CosineAnnealing, ExponentialLR]
-_CN.TRAINER.SCHEDULER_INTERVAL = 'epoch' # [epoch, step]
+_CN.TRAINER.SCHEDULER = "MultiStepLR" # [MultiStepLR, CosineAnnealing, ExponentialLR]
+_CN.TRAINER.SCHEDULER_INTERVAL = "epoch" # [epoch, step]
_CN.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12] # MSLR: MultiStepLR
_CN.TRAINER.MSLR_GAMMA = 0.5
_CN.TRAINER.COSA_TMAX = 30 # COSA: CosineAnnealing
@@ -136,25 +147,33 @@ _CN.TRAINER.ELR_GAMMA = 0.999992 # ELR: ExponentialLR, this value for 'step' in
# plotting related
_CN.TRAINER.ENABLE_PLOTTING = True
-_CN.TRAINER.N_VAL_PAIRS_TO_PLOT = 32 # number of val/test paris for plotting
-_CN.TRAINER.PLOT_MODE = 'evaluation' # ['evaluation', 'confidence']
-_CN.TRAINER.PLOT_MATCHES_ALPHA = 'dynamic'
+_CN.TRAINER.N_VAL_PAIRS_TO_PLOT = 32 # number of val/test paris for plotting
+_CN.TRAINER.PLOT_MODE = "evaluation" # ['evaluation', 'confidence']
+_CN.TRAINER.PLOT_MATCHES_ALPHA = "dynamic"
# geometric metrics and pose solver
-_CN.TRAINER.EPI_ERR_THR = 5e-4 # recommendation: 5e-4 for ScanNet, 1e-4 for MegaDepth (from SuperGlue)
-_CN.TRAINER.POSE_GEO_MODEL = 'E' # ['E', 'F', 'H']
-_CN.TRAINER.POSE_ESTIMATION_METHOD = 'RANSAC' # [RANSAC, DEGENSAC, MAGSAC]
+_CN.TRAINER.EPI_ERR_THR = (
+ 5e-4 # recommendation: 5e-4 for ScanNet, 1e-4 for MegaDepth (from SuperGlue)
+)
+_CN.TRAINER.POSE_GEO_MODEL = "E" # ['E', 'F', 'H']
+_CN.TRAINER.POSE_ESTIMATION_METHOD = "RANSAC" # [RANSAC, DEGENSAC, MAGSAC]
_CN.TRAINER.RANSAC_PIXEL_THR = 0.5
_CN.TRAINER.RANSAC_CONF = 0.99999
_CN.TRAINER.RANSAC_MAX_ITERS = 10000
_CN.TRAINER.USE_MAGSACPP = False
# data sampler for train_dataloader
-_CN.TRAINER.DATA_SAMPLER = 'scene_balance' # options: ['scene_balance', 'random', 'normal']
+_CN.TRAINER.DATA_SAMPLER = (
+ "scene_balance" # options: ['scene_balance', 'random', 'normal']
+)
# 'scene_balance' config
_CN.TRAINER.N_SAMPLES_PER_SUBSET = 200
-_CN.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT = True # whether sample each scene with replacement or not
-_CN.TRAINER.SB_SUBSET_SHUFFLE = True # after sampling from scenes, whether shuffle within the epoch or not
+_CN.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT = (
+ True # whether sample each scene with replacement or not
+)
+_CN.TRAINER.SB_SUBSET_SHUFFLE = (
+ True # after sampling from scenes, whether shuffle within the epoch or not
+)
_CN.TRAINER.SB_REPEAT = 1 # repeat N times for training the sampled data
# 'random' config
_CN.TRAINER.RDM_REPLACEMENT = True
diff --git a/imcui/third_party/ASpanFormer/src/datasets/__init__.py b/third_party/ASpanFormer/src/datasets/__init__.py
similarity index 98%
rename from imcui/third_party/ASpanFormer/src/datasets/__init__.py
rename to third_party/ASpanFormer/src/datasets/__init__.py
index 1860e3ae060a26e4625925861cecdc355f2b08b7..4feb648440e6c8db60de3aa475cd82ce460dcc1c 100644
--- a/imcui/third_party/ASpanFormer/src/datasets/__init__.py
+++ b/third_party/ASpanFormer/src/datasets/__init__.py
@@ -1,3 +1,2 @@
from .scannet import ScanNetDataset
from .megadepth import MegaDepthDataset
-
diff --git a/imcui/third_party/ASpanFormer/src/datasets/megadepth.py b/third_party/ASpanFormer/src/datasets/megadepth.py
similarity index 50%
rename from imcui/third_party/ASpanFormer/src/datasets/megadepth.py
rename to third_party/ASpanFormer/src/datasets/megadepth.py
index a70ac715a3f807e37bc5b87ae9446ddd2aa4fc86..7cbf95962df705c14d11483838f13bfd5e036166 100644
--- a/imcui/third_party/ASpanFormer/src/datasets/megadepth.py
+++ b/third_party/ASpanFormer/src/datasets/megadepth.py
@@ -9,20 +9,22 @@ from src.utils.dataset import read_megadepth_gray, read_megadepth_depth
class MegaDepthDataset(Dataset):
- def __init__(self,
- root_dir,
- npz_path,
- mode='train',
- min_overlap_score=0.4,
- img_resize=None,
- df=None,
- img_padding=False,
- depth_padding=False,
- augment_fn=None,
- **kwargs):
+ def __init__(
+ self,
+ root_dir,
+ npz_path,
+ mode="train",
+ min_overlap_score=0.4,
+ img_resize=None,
+ df=None,
+ img_padding=False,
+ depth_padding=False,
+ augment_fn=None,
+ **kwargs
+ ):
"""
Manage one scene(npz_path) of MegaDepth dataset.
-
+
Args:
root_dir (str): megadepth root directory that has `phoenix`.
npz_path (str): {scene_id}.npz path. This contains image pair information of a scene.
@@ -38,28 +40,36 @@ class MegaDepthDataset(Dataset):
super().__init__()
self.root_dir = root_dir
self.mode = mode
- self.scene_id = npz_path.split('.')[0]
+ self.scene_id = npz_path.split(".")[0]
# prepare scene_info and pair_info
- if mode == 'test' and min_overlap_score != 0:
- logger.warning("You are using `min_overlap_score`!=0 in test mode. Set to 0.")
+ if mode == "test" and min_overlap_score != 0:
+ logger.warning(
+ "You are using `min_overlap_score`!=0 in test mode. Set to 0."
+ )
min_overlap_score = 0
self.scene_info = np.load(npz_path, allow_pickle=True)
- self.pair_infos = self.scene_info['pair_infos'].copy()
- del self.scene_info['pair_infos']
- self.pair_infos = [pair_info for pair_info in self.pair_infos if pair_info[1] > min_overlap_score]
+ self.pair_infos = self.scene_info["pair_infos"].copy()
+ del self.scene_info["pair_infos"]
+ self.pair_infos = [
+ pair_info
+ for pair_info in self.pair_infos
+ if pair_info[1] > min_overlap_score
+ ]
# parameters for image resizing, padding and depthmap padding
- if mode == 'train':
+ if mode == "train":
assert img_resize is not None and img_padding and depth_padding
self.img_resize = img_resize
self.df = df
self.img_padding = img_padding
- self.depth_max_size = 2000 if depth_padding else None # the upperbound of depthmaps size in megadepth.
+ self.depth_max_size = (
+ 2000 if depth_padding else None
+ ) # the upperbound of depthmaps size in megadepth.
# for training LoFTR
- self.augment_fn = augment_fn if mode == 'train' else None
- self.coarse_scale = getattr(kwargs, 'coarse_scale', 0.125)
+ self.augment_fn = augment_fn if mode == "train" else None
+ self.coarse_scale = getattr(kwargs, "coarse_scale", 0.125)
def __len__(self):
return len(self.pair_infos)
@@ -68,60 +78,77 @@ class MegaDepthDataset(Dataset):
(idx0, idx1), overlap_score, central_matches = self.pair_infos[idx]
# read grayscale image and mask. (1, h, w) and (h, w)
- img_name0 = osp.join(self.root_dir, self.scene_info['image_paths'][idx0])
- img_name1 = osp.join(self.root_dir, self.scene_info['image_paths'][idx1])
-
+ img_name0 = osp.join(self.root_dir, self.scene_info["image_paths"][idx0])
+ img_name1 = osp.join(self.root_dir, self.scene_info["image_paths"][idx1])
+
# TODO: Support augmentation & handle seeds for each worker correctly.
image0, mask0, scale0 = read_megadepth_gray(
- img_name0, self.img_resize, self.df, self.img_padding, None)
- # np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
+ img_name0, self.img_resize, self.df, self.img_padding, None
+ )
+ # np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
image1, mask1, scale1 = read_megadepth_gray(
- img_name1, self.img_resize, self.df, self.img_padding, None)
- # np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
+ img_name1, self.img_resize, self.df, self.img_padding, None
+ )
+ # np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
# read depth. shape: (h, w)
- if self.mode in ['train', 'val']:
+ if self.mode in ["train", "val"]:
depth0 = read_megadepth_depth(
- osp.join(self.root_dir, self.scene_info['depth_paths'][idx0]), pad_to=self.depth_max_size)
+ osp.join(self.root_dir, self.scene_info["depth_paths"][idx0]),
+ pad_to=self.depth_max_size,
+ )
depth1 = read_megadepth_depth(
- osp.join(self.root_dir, self.scene_info['depth_paths'][idx1]), pad_to=self.depth_max_size)
+ osp.join(self.root_dir, self.scene_info["depth_paths"][idx1]),
+ pad_to=self.depth_max_size,
+ )
else:
depth0 = depth1 = torch.tensor([])
# read intrinsics of original size
- K_0 = torch.tensor(self.scene_info['intrinsics'][idx0].copy(), dtype=torch.float).reshape(3, 3)
- K_1 = torch.tensor(self.scene_info['intrinsics'][idx1].copy(), dtype=torch.float).reshape(3, 3)
+ K_0 = torch.tensor(
+ self.scene_info["intrinsics"][idx0].copy(), dtype=torch.float
+ ).reshape(3, 3)
+ K_1 = torch.tensor(
+ self.scene_info["intrinsics"][idx1].copy(), dtype=torch.float
+ ).reshape(3, 3)
# read and compute relative poses
- T0 = self.scene_info['poses'][idx0]
- T1 = self.scene_info['poses'][idx1]
- T_0to1 = torch.tensor(np.matmul(T1, np.linalg.inv(T0)), dtype=torch.float)[:4, :4] # (4, 4)
+ T0 = self.scene_info["poses"][idx0]
+ T1 = self.scene_info["poses"][idx1]
+ T_0to1 = torch.tensor(np.matmul(T1, np.linalg.inv(T0)), dtype=torch.float)[
+ :4, :4
+ ] # (4, 4)
T_1to0 = T_0to1.inverse()
data = {
- 'image0': image0, # (1, h, w)
- 'depth0': depth0, # (h, w)
- 'image1': image1,
- 'depth1': depth1,
- 'T_0to1': T_0to1, # (4, 4)
- 'T_1to0': T_1to0,
- 'K0': K_0, # (3, 3)
- 'K1': K_1,
- 'scale0': scale0, # [scale_w, scale_h]
- 'scale1': scale1,
- 'dataset_name': 'MegaDepth',
- 'scene_id': self.scene_id,
- 'pair_id': idx,
- 'pair_names': (self.scene_info['image_paths'][idx0], self.scene_info['image_paths'][idx1]),
+ "image0": image0, # (1, h, w)
+ "depth0": depth0, # (h, w)
+ "image1": image1,
+ "depth1": depth1,
+ "T_0to1": T_0to1, # (4, 4)
+ "T_1to0": T_1to0,
+ "K0": K_0, # (3, 3)
+ "K1": K_1,
+ "scale0": scale0, # [scale_w, scale_h]
+ "scale1": scale1,
+ "dataset_name": "MegaDepth",
+ "scene_id": self.scene_id,
+ "pair_id": idx,
+ "pair_names": (
+ self.scene_info["image_paths"][idx0],
+ self.scene_info["image_paths"][idx1],
+ ),
}
# for LoFTR training
if mask0 is not None: # img_padding is True
if self.coarse_scale:
- [ts_mask_0, ts_mask_1] = F.interpolate(torch.stack([mask0, mask1], dim=0)[None].float(),
- scale_factor=self.coarse_scale,
- mode='nearest',
- recompute_scale_factor=False)[0].bool()
- data.update({'mask0': ts_mask_0, 'mask1': ts_mask_1})
+ [ts_mask_0, ts_mask_1] = F.interpolate(
+ torch.stack([mask0, mask1], dim=0)[None].float(),
+ scale_factor=self.coarse_scale,
+ mode="nearest",
+ recompute_scale_factor=False,
+ )[0].bool()
+ data.update({"mask0": ts_mask_0, "mask1": ts_mask_1})
return data
diff --git a/imcui/third_party/TopicFM/src/datasets/sampler.py b/third_party/ASpanFormer/src/datasets/sampler.py
similarity index 74%
rename from imcui/third_party/TopicFM/src/datasets/sampler.py
rename to third_party/ASpanFormer/src/datasets/sampler.py
index 81b6f435645632a013476f9a665a0861ab7fcb61..131111c4cf69cd8770058dfac2be717aa183978e 100644
--- a/imcui/third_party/TopicFM/src/datasets/sampler.py
+++ b/third_party/ASpanFormer/src/datasets/sampler.py
@@ -3,10 +3,10 @@ from torch.utils.data import Sampler, ConcatDataset
class RandomConcatSampler(Sampler):
- """ Random sampler for ConcatDataset. At each epoch, `n_samples_per_subset` samples will be draw from each subset
+ """Random sampler for ConcatDataset. At each epoch, `n_samples_per_subset` samples will be draw from each subset
in the ConcatDataset. If `subset_replacement` is ``True``, sampling within each subset will be done with replacement.
However, it is impossible to sample data without replacement between epochs, unless bulding a stateful sampler lived along the entire training phase.
-
+
For current implementation, the randomness of sampling is ensured no matter the sampler is recreated across epochs or not and call `torch.manual_seed()` or not.
Args:
shuffle (bool): shuffle the random sampled indices across all sub-datsets.
@@ -18,16 +18,19 @@ class RandomConcatSampler(Sampler):
TODO: Add a `set_epoch()` method to fullfill sampling without replacement across epochs.
ref: https://github.com/PyTorchLightning/pytorch-lightning/blob/e9846dd758cfb1500eb9dba2d86f6912eb487587/pytorch_lightning/trainer/training_loop.py#L373
"""
- def __init__(self,
- data_source: ConcatDataset,
- n_samples_per_subset: int,
- subset_replacement: bool=True,
- shuffle: bool=True,
- repeat: int=1,
- seed: int=None):
+
+ def __init__(
+ self,
+ data_source: ConcatDataset,
+ n_samples_per_subset: int,
+ subset_replacement: bool = True,
+ shuffle: bool = True,
+ repeat: int = 1,
+ seed: int = None,
+ ):
if not isinstance(data_source, ConcatDataset):
raise TypeError("data_source should be torch.utils.data.ConcatDataset")
-
+
self.data_source = data_source
self.n_subset = len(self.data_source.datasets)
self.n_samples_per_subset = n_samples_per_subset
@@ -37,27 +40,37 @@ class RandomConcatSampler(Sampler):
self.shuffle = shuffle
self.generator = torch.manual_seed(seed)
assert self.repeat >= 1
-
+
def __len__(self):
return self.n_samples
-
+
def __iter__(self):
indices = []
# sample from each sub-dataset
for d_idx in range(self.n_subset):
- low = 0 if d_idx==0 else self.data_source.cumulative_sizes[d_idx-1]
+ low = 0 if d_idx == 0 else self.data_source.cumulative_sizes[d_idx - 1]
high = self.data_source.cumulative_sizes[d_idx]
if self.subset_replacement:
- rand_tensor = torch.randint(low, high, (self.n_samples_per_subset, ),
- generator=self.generator, dtype=torch.int64)
+ rand_tensor = torch.randint(
+ low,
+ high,
+ (self.n_samples_per_subset,),
+ generator=self.generator,
+ dtype=torch.int64,
+ )
else: # sample without replacement
len_subset = len(self.data_source.datasets[d_idx])
rand_tensor = torch.randperm(len_subset, generator=self.generator) + low
if len_subset >= self.n_samples_per_subset:
- rand_tensor = rand_tensor[:self.n_samples_per_subset]
- else: # padding with replacement
- rand_tensor_replacement = torch.randint(low, high, (self.n_samples_per_subset - len_subset, ),
- generator=self.generator, dtype=torch.int64)
+ rand_tensor = rand_tensor[: self.n_samples_per_subset]
+ else: # padding with replacement
+ rand_tensor_replacement = torch.randint(
+ low,
+ high,
+ (self.n_samples_per_subset - len_subset,),
+ generator=self.generator,
+ dtype=torch.int64,
+ )
rand_tensor = torch.cat([rand_tensor, rand_tensor_replacement])
indices.append(rand_tensor)
indices = torch.cat(indices)
@@ -72,6 +85,6 @@ class RandomConcatSampler(Sampler):
_choice = lambda x: x[torch.randperm(len(x), generator=self.generator)]
repeat_indices = map(_choice, repeat_indices)
indices = torch.cat([indices, *repeat_indices], 0)
-
+
assert indices.shape[0] == self.n_samples
return iter(indices.tolist())
diff --git a/imcui/third_party/ASpanFormer/src/datasets/scannet.py b/third_party/ASpanFormer/src/datasets/scannet.py
similarity index 53%
rename from imcui/third_party/ASpanFormer/src/datasets/scannet.py
rename to third_party/ASpanFormer/src/datasets/scannet.py
index 3520d34c0f08a784ddbf923846a7cb2a847b1787..615e98409b92713ab241aa8658c74cf7b2f8baae 100644
--- a/imcui/third_party/ASpanFormer/src/datasets/scannet.py
+++ b/third_party/ASpanFormer/src/datasets/scannet.py
@@ -10,20 +10,22 @@ from src.utils.dataset import (
read_scannet_gray,
read_scannet_depth,
read_scannet_pose,
- read_scannet_intrinsic
+ read_scannet_intrinsic,
)
class ScanNetDataset(utils.data.Dataset):
- def __init__(self,
- root_dir,
- npz_path,
- intrinsic_path,
- mode='train',
- min_overlap_score=0.4,
- augment_fn=None,
- pose_dir=None,
- **kwargs):
+ def __init__(
+ self,
+ root_dir,
+ npz_path,
+ intrinsic_path,
+ mode="train",
+ min_overlap_score=0.4,
+ augment_fn=None,
+ pose_dir=None,
+ **kwargs,
+ ):
"""Manage one scene of ScanNet Dataset.
Args:
root_dir (str): ScanNet root directory that contains scene folders.
@@ -41,73 +43,81 @@ class ScanNetDataset(utils.data.Dataset):
# prepare data_names, intrinsics and extrinsics(T)
with np.load(npz_path) as data:
- self.data_names = data['name']
- if 'score' in data.keys() and mode not in ['val' or 'test']:
- kept_mask = data['score'] > min_overlap_score
+ self.data_names = data["name"]
+ if "score" in data.keys() and mode not in ["val" or "test"]:
+ kept_mask = data["score"] > min_overlap_score
self.data_names = self.data_names[kept_mask]
self.intrinsics = dict(np.load(intrinsic_path))
# for training LoFTR
- self.augment_fn = augment_fn if mode == 'train' else None
+ self.augment_fn = augment_fn if mode == "train" else None
def __len__(self):
return len(self.data_names)
def _read_abs_pose(self, scene_name, name):
- pth = osp.join(self.pose_dir,
- scene_name,
- 'pose', f'{name}.txt')
+ pth = osp.join(self.pose_dir, scene_name, "pose", f"{name}.txt")
return read_scannet_pose(pth)
def _compute_rel_pose(self, scene_name, name0, name1):
pose0 = self._read_abs_pose(scene_name, name0)
pose1 = self._read_abs_pose(scene_name, name1)
-
+
return np.matmul(pose1, inv(pose0)) # (4, 4)
def __getitem__(self, idx):
data_name = self.data_names[idx]
scene_name, scene_sub_name, stem_name_0, stem_name_1 = data_name
- scene_name = f'scene{scene_name:04d}_{scene_sub_name:02d}'
+ scene_name = f"scene{scene_name:04d}_{scene_sub_name:02d}"
# read the grayscale image which will be resized to (1, 480, 640)
- img_name0 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_0}.jpg')
- img_name1 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_1}.jpg')
+ img_name0 = osp.join(self.root_dir, scene_name, "color", f"{stem_name_0}.jpg")
+ img_name1 = osp.join(self.root_dir, scene_name, "color", f"{stem_name_1}.jpg")
# TODO: Support augmentation & handle seeds for each worker correctly.
image0 = read_scannet_gray(img_name0, resize=(640, 480), augment_fn=None)
- # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
+ # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
image1 = read_scannet_gray(img_name1, resize=(640, 480), augment_fn=None)
- # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
+ # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
# read the depthmap which is stored as (480, 640)
- if self.mode in ['train', 'val']:
- depth0 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_0}.png'))
- depth1 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_1}.png'))
+ if self.mode in ["train", "val"]:
+ depth0 = read_scannet_depth(
+ osp.join(self.root_dir, scene_name, "depth", f"{stem_name_0}.png")
+ )
+ depth1 = read_scannet_depth(
+ osp.join(self.root_dir, scene_name, "depth", f"{stem_name_1}.png")
+ )
else:
depth0 = depth1 = torch.tensor([])
# read the intrinsic of depthmap
- K_0 = K_1 = torch.tensor(self.intrinsics[scene_name].copy(), dtype=torch.float).reshape(3, 3)
+ K_0 = K_1 = torch.tensor(
+ self.intrinsics[scene_name].copy(), dtype=torch.float
+ ).reshape(3, 3)
# read and compute relative poses
- T_0to1 = torch.tensor(self._compute_rel_pose(scene_name, stem_name_0, stem_name_1),
- dtype=torch.float32)
+ T_0to1 = torch.tensor(
+ self._compute_rel_pose(scene_name, stem_name_0, stem_name_1),
+ dtype=torch.float32,
+ )
T_1to0 = T_0to1.inverse()
data = {
- 'image0': image0, # (1, h, w)
- 'depth0': depth0, # (h, w)
- 'image1': image1,
- 'depth1': depth1,
- 'T_0to1': T_0to1, # (4, 4)
- 'T_1to0': T_1to0,
- 'K0': K_0, # (3, 3)
- 'K1': K_1,
- 'dataset_name': 'ScanNet',
- 'scene_id': scene_name,
- 'pair_id': idx,
- 'pair_names': (osp.join(scene_name, 'color', f'{stem_name_0}.jpg'),
- osp.join(scene_name, 'color', f'{stem_name_1}.jpg'))
+ "image0": image0, # (1, h, w)
+ "depth0": depth0, # (h, w)
+ "image1": image1,
+ "depth1": depth1,
+ "T_0to1": T_0to1, # (4, 4)
+ "T_1to0": T_1to0,
+ "K0": K_0, # (3, 3)
+ "K1": K_1,
+ "dataset_name": "ScanNet",
+ "scene_id": scene_name,
+ "pair_id": idx,
+ "pair_names": (
+ osp.join(scene_name, "color", f"{stem_name_0}.jpg"),
+ osp.join(scene_name, "color", f"{stem_name_1}.jpg"),
+ ),
}
return data
diff --git a/third_party/ASpanFormer/src/lightning/data.py b/third_party/ASpanFormer/src/lightning/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..9877df5980c73e9bfb5a1e6ec301e1a84a97ca56
--- /dev/null
+++ b/third_party/ASpanFormer/src/lightning/data.py
@@ -0,0 +1,405 @@
+import os
+import math
+from collections import abc
+from loguru import logger
+from torch.utils.data.dataset import Dataset
+from tqdm import tqdm
+from os import path as osp
+from pathlib import Path
+from joblib import Parallel, delayed
+
+import pytorch_lightning as pl
+from torch import distributed as dist
+from torch.utils.data import (
+ Dataset,
+ DataLoader,
+ ConcatDataset,
+ DistributedSampler,
+ RandomSampler,
+ dataloader,
+)
+
+from src.utils.augment import build_augmentor
+from src.utils.dataloader import get_local_split
+from src.utils.misc import tqdm_joblib
+from src.utils import comm
+from src.datasets.megadepth import MegaDepthDataset
+from src.datasets.scannet import ScanNetDataset
+from src.datasets.sampler import RandomConcatSampler
+
+
+class MultiSceneDataModule(pl.LightningDataModule):
+ """
+ For distributed training, each training process is assgined
+ only a part of the training scenes to reduce memory overhead.
+ """
+
+ def __init__(self, args, config):
+ super().__init__()
+
+ # 1. data config
+ # Train and Val should from the same data source
+ self.trainval_data_source = config.DATASET.TRAINVAL_DATA_SOURCE
+ self.test_data_source = config.DATASET.TEST_DATA_SOURCE
+ # training and validating
+ self.train_data_root = config.DATASET.TRAIN_DATA_ROOT
+ self.train_pose_root = config.DATASET.TRAIN_POSE_ROOT # (optional)
+ self.train_npz_root = config.DATASET.TRAIN_NPZ_ROOT
+ self.train_list_path = config.DATASET.TRAIN_LIST_PATH
+ self.train_intrinsic_path = config.DATASET.TRAIN_INTRINSIC_PATH
+ self.val_data_root = config.DATASET.VAL_DATA_ROOT
+ self.val_pose_root = config.DATASET.VAL_POSE_ROOT # (optional)
+ self.val_npz_root = config.DATASET.VAL_NPZ_ROOT
+ self.val_list_path = config.DATASET.VAL_LIST_PATH
+ self.val_intrinsic_path = config.DATASET.VAL_INTRINSIC_PATH
+ # testing
+ self.test_data_root = config.DATASET.TEST_DATA_ROOT
+ self.test_pose_root = config.DATASET.TEST_POSE_ROOT # (optional)
+ self.test_npz_root = config.DATASET.TEST_NPZ_ROOT
+ self.test_list_path = config.DATASET.TEST_LIST_PATH
+ self.test_intrinsic_path = config.DATASET.TEST_INTRINSIC_PATH
+
+ # 2. dataset config
+ # general options
+ self.min_overlap_score_test = (
+ config.DATASET.MIN_OVERLAP_SCORE_TEST
+ ) # 0.4, omit data with overlap_score < min_overlap_score
+ self.min_overlap_score_train = config.DATASET.MIN_OVERLAP_SCORE_TRAIN
+ self.augment_fn = build_augmentor(
+ config.DATASET.AUGMENTATION_TYPE
+ ) # None, options: [None, 'dark', 'mobile']
+
+ # MegaDepth options
+ self.mgdpt_img_resize = config.DATASET.MGDPT_IMG_RESIZE # 840
+ self.mgdpt_img_pad = config.DATASET.MGDPT_IMG_PAD # True
+ self.mgdpt_depth_pad = config.DATASET.MGDPT_DEPTH_PAD # True
+ self.mgdpt_df = config.DATASET.MGDPT_DF # 8
+ self.coarse_scale = 1 / config.ASPAN.RESOLUTION[0] # 0.125. for training loftr.
+
+ # 3.loader parameters
+ self.train_loader_params = {
+ "batch_size": args.batch_size,
+ "num_workers": args.num_workers,
+ "pin_memory": getattr(args, "pin_memory", True),
+ }
+ self.val_loader_params = {
+ "batch_size": 1,
+ "shuffle": False,
+ "num_workers": args.num_workers,
+ "pin_memory": getattr(args, "pin_memory", True),
+ }
+ self.test_loader_params = {
+ "batch_size": 1,
+ "shuffle": False,
+ "num_workers": args.num_workers,
+ "pin_memory": True,
+ }
+
+ # 4. sampler
+ self.data_sampler = config.TRAINER.DATA_SAMPLER
+ self.n_samples_per_subset = config.TRAINER.N_SAMPLES_PER_SUBSET
+ self.subset_replacement = config.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT
+ self.shuffle = config.TRAINER.SB_SUBSET_SHUFFLE
+ self.repeat = config.TRAINER.SB_REPEAT
+
+ # (optional) RandomSampler for debugging
+
+ # misc configurations
+ self.parallel_load_data = getattr(args, "parallel_load_data", False)
+ self.seed = config.TRAINER.SEED # 66
+
+ def setup(self, stage=None):
+ """
+ Setup train / val / test dataset. This method will be called by PL automatically.
+ Args:
+ stage (str): 'fit' in training phase, and 'test' in testing phase.
+ """
+
+ assert stage in ["fit", "test"], "stage must be either fit or test"
+
+ try:
+ self.world_size = dist.get_world_size()
+ self.rank = dist.get_rank()
+ logger.info(f"[rank:{self.rank}] world_size: {self.world_size}")
+ except AssertionError as ae:
+ self.world_size = 1
+ self.rank = 0
+ logger.warning(str(ae) + " (set wolrd_size=1 and rank=0)")
+
+ if stage == "fit":
+ self.train_dataset = self._setup_dataset(
+ self.train_data_root,
+ self.train_npz_root,
+ self.train_list_path,
+ self.train_intrinsic_path,
+ mode="train",
+ min_overlap_score=self.min_overlap_score_train,
+ pose_dir=self.train_pose_root,
+ )
+ # setup multiple (optional) validation subsets
+ if isinstance(self.val_list_path, (list, tuple)):
+ self.val_dataset = []
+ if not isinstance(self.val_npz_root, (list, tuple)):
+ self.val_npz_root = [
+ self.val_npz_root for _ in range(len(self.val_list_path))
+ ]
+ for npz_list, npz_root in zip(self.val_list_path, self.val_npz_root):
+ self.val_dataset.append(
+ self._setup_dataset(
+ self.val_data_root,
+ npz_root,
+ npz_list,
+ self.val_intrinsic_path,
+ mode="val",
+ min_overlap_score=self.min_overlap_score_test,
+ pose_dir=self.val_pose_root,
+ )
+ )
+ else:
+ self.val_dataset = self._setup_dataset(
+ self.val_data_root,
+ self.val_npz_root,
+ self.val_list_path,
+ self.val_intrinsic_path,
+ mode="val",
+ min_overlap_score=self.min_overlap_score_test,
+ pose_dir=self.val_pose_root,
+ )
+ logger.info(f"[rank:{self.rank}] Train & Val Dataset loaded!")
+ else: # stage == 'test
+ self.test_dataset = self._setup_dataset(
+ self.test_data_root,
+ self.test_npz_root,
+ self.test_list_path,
+ self.test_intrinsic_path,
+ mode="test",
+ min_overlap_score=self.min_overlap_score_test,
+ pose_dir=self.test_pose_root,
+ )
+ logger.info(f"[rank:{self.rank}]: Test Dataset loaded!")
+
+ def _setup_dataset(
+ self,
+ data_root,
+ split_npz_root,
+ scene_list_path,
+ intri_path,
+ mode="train",
+ min_overlap_score=0.0,
+ pose_dir=None,
+ ):
+ """Setup train / val / test set"""
+ with open(scene_list_path, "r") as f:
+ npz_names = [name.split()[0] for name in f.readlines()]
+
+ if mode == "train":
+ local_npz_names = get_local_split(
+ npz_names, self.world_size, self.rank, self.seed
+ )
+ else:
+ local_npz_names = npz_names
+ logger.info(f"[rank {self.rank}]: {len(local_npz_names)} scene(s) assigned.")
+
+ dataset_builder = (
+ self._build_concat_dataset_parallel
+ if self.parallel_load_data
+ else self._build_concat_dataset
+ )
+ return dataset_builder(
+ data_root,
+ local_npz_names,
+ split_npz_root,
+ intri_path,
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ pose_dir=pose_dir,
+ )
+
+ def _build_concat_dataset(
+ self,
+ data_root,
+ npz_names,
+ npz_dir,
+ intrinsic_path,
+ mode,
+ min_overlap_score=0.0,
+ pose_dir=None,
+ ):
+ datasets = []
+ augment_fn = self.augment_fn if mode == "train" else None
+ data_source = (
+ self.trainval_data_source
+ if mode in ["train", "val"]
+ else self.test_data_source
+ )
+ if data_source == "GL3D" and mode == "val":
+ data_source = "MegaDepth"
+ if str(data_source).lower() == "megadepth":
+ npz_names = [f"{n}.npz" for n in npz_names]
+ if str(data_source).lower() == "gl3d":
+ npz_names = [f"{n}.txt" for n in npz_names]
+ # npz_names=npz_names[:8]
+ for npz_name in tqdm(
+ npz_names,
+ desc=f"[rank:{self.rank}] loading {mode} datasets",
+ disable=int(self.rank) != 0,
+ ):
+ # `ScanNetDataset`/`MegaDepthDataset` load all data from npz_path when initialized, which might take time.
+ npz_path = osp.join(npz_dir, npz_name)
+ if data_source == "ScanNet":
+ datasets.append(
+ ScanNetDataset(
+ data_root,
+ npz_path,
+ intrinsic_path,
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ augment_fn=augment_fn,
+ pose_dir=pose_dir,
+ )
+ )
+ elif data_source == "MegaDepth":
+ datasets.append(
+ MegaDepthDataset(
+ data_root,
+ npz_path,
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ img_resize=self.mgdpt_img_resize,
+ df=self.mgdpt_df,
+ img_padding=self.mgdpt_img_pad,
+ depth_padding=self.mgdpt_depth_pad,
+ augment_fn=augment_fn,
+ coarse_scale=self.coarse_scale,
+ )
+ )
+ else:
+ raise NotImplementedError()
+ return ConcatDataset(datasets)
+
+ def _build_concat_dataset_parallel(
+ self,
+ data_root,
+ npz_names,
+ npz_dir,
+ intrinsic_path,
+ mode,
+ min_overlap_score=0.0,
+ pose_dir=None,
+ ):
+ augment_fn = self.augment_fn if mode == "train" else None
+ data_source = (
+ self.trainval_data_source
+ if mode in ["train", "val"]
+ else self.test_data_source
+ )
+ if str(data_source).lower() == "megadepth":
+ npz_names = [f"{n}.npz" for n in npz_names]
+ # npz_names=npz_names[:8]
+ with tqdm_joblib(
+ tqdm(
+ desc=f"[rank:{self.rank}] loading {mode} datasets",
+ total=len(npz_names),
+ disable=int(self.rank) != 0,
+ )
+ ):
+ if data_source == "ScanNet":
+ datasets = Parallel(
+ n_jobs=math.floor(
+ len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()
+ )
+ )(
+ delayed(
+ lambda x: _build_dataset(
+ ScanNetDataset,
+ data_root,
+ osp.join(npz_dir, x),
+ intrinsic_path,
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ augment_fn=augment_fn,
+ pose_dir=pose_dir,
+ )
+ )(name)
+ for name in npz_names
+ )
+ elif data_source == "MegaDepth":
+ # TODO: _pickle.PicklingError: Could not pickle the task to send it to the workers.
+ raise NotImplementedError()
+ datasets = Parallel(
+ n_jobs=math.floor(
+ len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()
+ )
+ )(
+ delayed(
+ lambda x: _build_dataset(
+ MegaDepthDataset,
+ data_root,
+ osp.join(npz_dir, x),
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ img_resize=self.mgdpt_img_resize,
+ df=self.mgdpt_df,
+ img_padding=self.mgdpt_img_pad,
+ depth_padding=self.mgdpt_depth_pad,
+ augment_fn=augment_fn,
+ coarse_scale=self.coarse_scale,
+ )
+ )(name)
+ for name in npz_names
+ )
+ else:
+ raise ValueError(f"Unknown dataset: {data_source}")
+ return ConcatDataset(datasets)
+
+ def train_dataloader(self):
+ """Build training dataloader for ScanNet / MegaDepth."""
+ assert self.data_sampler in ["scene_balance"]
+ logger.info(
+ f"[rank:{self.rank}/{self.world_size}]: Train Sampler and DataLoader re-init (should not re-init between epochs!)."
+ )
+ if self.data_sampler == "scene_balance":
+ sampler = RandomConcatSampler(
+ self.train_dataset,
+ self.n_samples_per_subset,
+ self.subset_replacement,
+ self.shuffle,
+ self.repeat,
+ self.seed,
+ )
+ else:
+ sampler = None
+ dataloader = DataLoader(
+ self.train_dataset, sampler=sampler, **self.train_loader_params
+ )
+ return dataloader
+
+ def val_dataloader(self):
+ """Build validation dataloader for ScanNet / MegaDepth."""
+ logger.info(
+ f"[rank:{self.rank}/{self.world_size}]: Val Sampler and DataLoader re-init."
+ )
+ if not isinstance(self.val_dataset, abc.Sequence):
+ sampler = DistributedSampler(self.val_dataset, shuffle=False)
+ return DataLoader(
+ self.val_dataset, sampler=sampler, **self.val_loader_params
+ )
+ else:
+ dataloaders = []
+ for dataset in self.val_dataset:
+ sampler = DistributedSampler(dataset, shuffle=False)
+ dataloaders.append(
+ DataLoader(dataset, sampler=sampler, **self.val_loader_params)
+ )
+ return dataloaders
+
+ def test_dataloader(self, *args, **kwargs):
+ logger.info(
+ f"[rank:{self.rank}/{self.world_size}]: Test Sampler and DataLoader re-init."
+ )
+ sampler = DistributedSampler(self.test_dataset, shuffle=False)
+ return DataLoader(self.test_dataset, sampler=sampler, **self.test_loader_params)
+
+
+def _build_dataset(dataset: Dataset, *args, **kwargs):
+ return dataset(*args, **kwargs)
diff --git a/third_party/ASpanFormer/src/lightning/lightning_aspanformer.py b/third_party/ASpanFormer/src/lightning/lightning_aspanformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b34b7b7485d4419390614e3fe0174ccc53ac7a9
--- /dev/null
+++ b/third_party/ASpanFormer/src/lightning/lightning_aspanformer.py
@@ -0,0 +1,374 @@
+from collections import defaultdict
+import pprint
+from loguru import logger
+from pathlib import Path
+
+import torch
+import numpy as np
+import pytorch_lightning as pl
+from matplotlib import pyplot as plt
+
+from src.ASpanFormer.aspanformer import ASpanFormer
+from src.ASpanFormer.utils.supervision import (
+ compute_supervision_coarse,
+ compute_supervision_fine,
+)
+from src.losses.aspan_loss import ASpanLoss
+from src.optimizers import build_optimizer, build_scheduler
+from src.utils.metrics import (
+ compute_symmetrical_epipolar_errors,
+ compute_symmetrical_epipolar_errors_offset_bidirectional,
+ compute_pose_errors,
+ aggregate_metrics,
+)
+from src.utils.plotting import make_matching_figures, make_matching_figures_offset
+from src.utils.comm import gather, all_gather
+from src.utils.misc import lower_config, flattenList
+from src.utils.profiler import PassThroughProfiler
+
+
+class PL_ASpanFormer(pl.LightningModule):
+ def __init__(self, config, pretrained_ckpt=None, profiler=None, dump_dir=None):
+ """
+ TODO:
+ - use the new version of PL logging API.
+ """
+ super().__init__()
+ # Misc
+ self.config = config # full config
+ _config = lower_config(self.config)
+ self.loftr_cfg = lower_config(_config["aspan"])
+ self.profiler = profiler or PassThroughProfiler()
+ self.n_vals_plot = max(
+ config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1
+ )
+
+ # Matcher: LoFTR
+ self.matcher = ASpanFormer(config=_config["aspan"])
+ self.loss = ASpanLoss(_config)
+
+ # Pretrained weights
+ print(pretrained_ckpt)
+ if pretrained_ckpt:
+ print("load")
+ state_dict = torch.load(pretrained_ckpt, map_location="cpu")["state_dict"]
+ msg = self.matcher.load_state_dict(state_dict, strict=False)
+ print(msg)
+ logger.info(f"Load '{pretrained_ckpt}' as pretrained checkpoint")
+
+ # Testing
+ self.dump_dir = dump_dir
+
+ def configure_optimizers(self):
+ # FIXME: The scheduler did not work properly when `--resume_from_checkpoint`
+ optimizer = build_optimizer(self, self.config)
+ scheduler = build_scheduler(self.config, optimizer)
+ return [optimizer], [scheduler]
+
+ def optimizer_step(
+ self,
+ epoch,
+ batch_idx,
+ optimizer,
+ optimizer_idx,
+ optimizer_closure,
+ on_tpu,
+ using_native_amp,
+ using_lbfgs,
+ ):
+ # learning rate warm up
+ warmup_step = self.config.TRAINER.WARMUP_STEP
+ if self.trainer.global_step < warmup_step:
+ if self.config.TRAINER.WARMUP_TYPE == "linear":
+ base_lr = self.config.TRAINER.WARMUP_RATIO * self.config.TRAINER.TRUE_LR
+ lr = base_lr + (
+ self.trainer.global_step / self.config.TRAINER.WARMUP_STEP
+ ) * abs(self.config.TRAINER.TRUE_LR - base_lr)
+ for pg in optimizer.param_groups:
+ pg["lr"] = lr
+ elif self.config.TRAINER.WARMUP_TYPE == "constant":
+ pass
+ else:
+ raise ValueError(
+ f"Unknown lr warm-up strategy: {self.config.TRAINER.WARMUP_TYPE}"
+ )
+
+ # update params
+ optimizer.step(closure=optimizer_closure)
+ optimizer.zero_grad()
+
+ def _trainval_inference(self, batch):
+ with self.profiler.profile("Compute coarse supervision"):
+ compute_supervision_coarse(batch, self.config)
+
+ with self.profiler.profile("LoFTR"):
+ self.matcher(batch)
+
+ with self.profiler.profile("Compute fine supervision"):
+ compute_supervision_fine(batch, self.config)
+
+ with self.profiler.profile("Compute losses"):
+ self.loss(batch)
+
+ def _compute_metrics(self, batch):
+ with self.profiler.profile("Copmute metrics"):
+ compute_symmetrical_epipolar_errors(
+ batch
+ ) # compute epi_errs for each match
+ compute_symmetrical_epipolar_errors_offset_bidirectional(
+ batch
+ ) # compute epi_errs for offset match
+ compute_pose_errors(
+ batch, self.config
+ ) # compute R_errs, t_errs, pose_errs for each pair
+
+ rel_pair_names = list(zip(*batch["pair_names"]))
+ bs = batch["image0"].size(0)
+ metrics = {
+ # to filter duplicate pairs caused by DistributedSampler
+ "identifiers": ["#".join(rel_pair_names[b]) for b in range(bs)],
+ "epi_errs": [
+ batch["epi_errs"][batch["m_bids"] == b].cpu().numpy()
+ for b in range(bs)
+ ],
+ "epi_errs_offset": [
+ batch["epi_errs_offset_left"][batch["offset_bids_left"] == b]
+ .cpu()
+ .numpy()
+ for b in range(bs)
+ ], # only consider left side
+ "R_errs": batch["R_errs"],
+ "t_errs": batch["t_errs"],
+ "inliers": batch["inliers"],
+ }
+ ret_dict = {"metrics": metrics}
+ return ret_dict, rel_pair_names
+
+ def training_step(self, batch, batch_idx):
+ self._trainval_inference(batch)
+
+ # logging
+ if (
+ self.trainer.global_rank == 0
+ and self.global_step % self.trainer.log_every_n_steps == 0
+ ):
+ # scalars
+ for k, v in batch["loss_scalars"].items():
+ if not k.startswith("loss_flow") and not k.startswith("conf_"):
+ self.logger.experiment.add_scalar(f"train/{k}", v, self.global_step)
+
+ # log offset_loss and conf for each layer and level
+ layer_num = self.loftr_cfg["coarse"]["layer_num"]
+ for layer_index in range(layer_num):
+ log_title = "layer_" + str(layer_index)
+ self.logger.experiment.add_scalar(
+ log_title + "/offset_loss",
+ batch["loss_scalars"]["loss_flow_" + str(layer_index)],
+ self.global_step,
+ )
+ self.logger.experiment.add_scalar(
+ log_title + "/conf_",
+ batch["loss_scalars"]["conf_" + str(layer_index)],
+ self.global_step,
+ )
+
+ # net-params
+ if self.config.ASPAN.MATCH_COARSE.MATCH_TYPE == "sinkhorn":
+ self.logger.experiment.add_scalar(
+ f"skh_bin_score",
+ self.matcher.coarse_matching.bin_score.clone().detach().cpu().data,
+ self.global_step,
+ )
+
+ # figures
+ if self.config.TRAINER.ENABLE_PLOTTING:
+ compute_symmetrical_epipolar_errors(
+ batch
+ ) # compute epi_errs for each match
+ figures = make_matching_figures(
+ batch, self.config, self.config.TRAINER.PLOT_MODE
+ )
+ for k, v in figures.items():
+ self.logger.experiment.add_figure(
+ f"train_match/{k}", v, self.global_step
+ )
+
+ # plot offset
+ if self.global_step % 200 == 0:
+ compute_symmetrical_epipolar_errors_offset_bidirectional(batch)
+ figures_left = make_matching_figures_offset(
+ batch, self.config, self.config.TRAINER.PLOT_MODE, side="_left"
+ )
+ figures_right = make_matching_figures_offset(
+ batch, self.config, self.config.TRAINER.PLOT_MODE, side="_right"
+ )
+ for k, v in figures_left.items():
+ self.logger.experiment.add_figure(
+ f"train_offset/{k}" + "_left", v, self.global_step
+ )
+ figures = make_matching_figures_offset(
+ batch, self.config, self.config.TRAINER.PLOT_MODE, side="_right"
+ )
+ for k, v in figures_right.items():
+ self.logger.experiment.add_figure(
+ f"train_offset/{k}" + "_right", v, self.global_step
+ )
+
+ return {"loss": batch["loss"]}
+
+ def training_epoch_end(self, outputs):
+ avg_loss = torch.stack([x["loss"] for x in outputs]).mean()
+ if self.trainer.global_rank == 0:
+ self.logger.experiment.add_scalar(
+ "train/avg_loss_on_epoch", avg_loss, global_step=self.current_epoch
+ )
+
+ def validation_step(self, batch, batch_idx):
+ self._trainval_inference(batch)
+
+ ret_dict, _ = self._compute_metrics(
+ batch
+ ) # this func also compute the epi_errors
+
+ val_plot_interval = max(self.trainer.num_val_batches[0] // self.n_vals_plot, 1)
+ figures = {self.config.TRAINER.PLOT_MODE: []}
+ figures_offset = {self.config.TRAINER.PLOT_MODE: []}
+ if batch_idx % val_plot_interval == 0:
+ figures = make_matching_figures(
+ batch, self.config, mode=self.config.TRAINER.PLOT_MODE
+ )
+ figures_offset = make_matching_figures_offset(
+ batch, self.config, self.config.TRAINER.PLOT_MODE, "_left"
+ )
+ return {
+ **ret_dict,
+ "loss_scalars": batch["loss_scalars"],
+ "figures": figures,
+ "figures_offset_left": figures_offset,
+ }
+
+ def validation_epoch_end(self, outputs):
+ # handle multiple validation sets
+ multi_outputs = (
+ [outputs] if not isinstance(outputs[0], (list, tuple)) else outputs
+ )
+ multi_val_metrics = defaultdict(list)
+
+ for valset_idx, outputs in enumerate(multi_outputs):
+ # since pl performs sanity_check at the very begining of the training
+ cur_epoch = self.trainer.current_epoch
+ if (
+ not self.trainer.resume_from_checkpoint
+ and self.trainer.running_sanity_check
+ ):
+ cur_epoch = -1
+
+ # 1. loss_scalars: dict of list, on cpu
+ _loss_scalars = [o["loss_scalars"] for o in outputs]
+ loss_scalars = {
+ k: flattenList(all_gather([_ls[k] for _ls in _loss_scalars]))
+ for k in _loss_scalars[0]
+ }
+
+ # 2. val metrics: dict of list, numpy
+ _metrics = [o["metrics"] for o in outputs]
+ metrics = {
+ k: flattenList(all_gather(flattenList([_me[k] for _me in _metrics])))
+ for k in _metrics[0]
+ }
+ # NOTE: all ranks need to `aggregate_merics`, but only log at rank-0
+ val_metrics_4tb = aggregate_metrics(
+ metrics, self.config.TRAINER.EPI_ERR_THR
+ )
+ for thr in [5, 10, 20]:
+ multi_val_metrics[f"auc@{thr}"].append(val_metrics_4tb[f"auc@{thr}"])
+
+ # 3. figures
+ _figures = [o["figures"] for o in outputs]
+ figures = {
+ k: flattenList(gather(flattenList([_me[k] for _me in _figures])))
+ for k in _figures[0]
+ }
+
+ # tensorboard records only on rank 0
+ if self.trainer.global_rank == 0:
+ for k, v in loss_scalars.items():
+ mean_v = torch.stack(v).mean()
+ self.logger.experiment.add_scalar(
+ f"val_{valset_idx}/avg_{k}", mean_v, global_step=cur_epoch
+ )
+
+ for k, v in val_metrics_4tb.items():
+ self.logger.experiment.add_scalar(
+ f"metrics_{valset_idx}/{k}", v, global_step=cur_epoch
+ )
+
+ for k, v in figures.items():
+ if self.trainer.global_rank == 0:
+ for plot_idx, fig in enumerate(v):
+ self.logger.experiment.add_figure(
+ f"val_match_{valset_idx}/{k}/pair-{plot_idx}",
+ fig,
+ cur_epoch,
+ close=True,
+ )
+ plt.close("all")
+
+ for thr in [5, 10, 20]:
+ # log on all ranks for ModelCheckpoint callback to work properly
+ self.log(
+ f"auc@{thr}", torch.tensor(np.mean(multi_val_metrics[f"auc@{thr}"]))
+ ) # ckpt monitors on this
+
+ def test_step(self, batch, batch_idx):
+ with self.profiler.profile("LoFTR"):
+ self.matcher(batch)
+
+ ret_dict, rel_pair_names = self._compute_metrics(batch)
+
+ with self.profiler.profile("dump_results"):
+ if self.dump_dir is not None:
+ # dump results for further analysis
+ keys_to_save = {"mkpts0_f", "mkpts1_f", "mconf", "epi_errs"}
+ pair_names = list(zip(*batch["pair_names"]))
+ bs = batch["image0"].shape[0]
+ dumps = []
+ for b_id in range(bs):
+ item = {}
+ mask = batch["m_bids"] == b_id
+ item["pair_names"] = pair_names[b_id]
+ item["identifier"] = "#".join(rel_pair_names[b_id])
+ for key in keys_to_save:
+ item[key] = batch[key][mask].cpu().numpy()
+ for key in ["R_errs", "t_errs", "inliers"]:
+ item[key] = batch[key][b_id]
+ dumps.append(item)
+ ret_dict["dumps"] = dumps
+
+ return ret_dict
+
+ def test_epoch_end(self, outputs):
+ # metrics: dict of list, numpy
+ _metrics = [o["metrics"] for o in outputs]
+ metrics = {
+ k: flattenList(gather(flattenList([_me[k] for _me in _metrics])))
+ for k in _metrics[0]
+ }
+
+ # [{key: [{...}, *#bs]}, *#batch]
+ if self.dump_dir is not None:
+ Path(self.dump_dir).mkdir(parents=True, exist_ok=True)
+ _dumps = flattenList([o["dumps"] for o in outputs]) # [{...}, #bs*#batch]
+ dumps = flattenList(gather(_dumps)) # [{...}, #proc*#bs*#batch]
+ logger.info(
+ f"Prediction and evaluation results will be saved to: {self.dump_dir}"
+ )
+
+ if self.trainer.global_rank == 0:
+ print(self.profiler.summary())
+ val_metrics_4tb = aggregate_metrics(
+ metrics, self.config.TRAINER.EPI_ERR_THR
+ )
+ logger.info("\n" + pprint.pformat(val_metrics_4tb))
+ if self.dump_dir is not None:
+ np.save(Path(self.dump_dir) / "LoFTR_pred_eval", dumps)
diff --git a/third_party/ASpanFormer/src/losses/aspan_loss.py b/third_party/ASpanFormer/src/losses/aspan_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc0f33391b95b6f4f39f673ebc07f6991a00491f
--- /dev/null
+++ b/third_party/ASpanFormer/src/losses/aspan_loss.py
@@ -0,0 +1,289 @@
+from loguru import logger
+
+import torch
+import torch.nn as nn
+
+
+class ASpanLoss(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config # config under the global namespace
+ self.loss_config = config["aspan"]["loss"]
+ self.match_type = self.config["aspan"]["match_coarse"]["match_type"]
+ self.sparse_spvs = self.config["aspan"]["match_coarse"]["sparse_spvs"]
+ self.flow_weight = self.config["aspan"]["loss"]["flow_weight"]
+
+ # coarse-level
+ self.correct_thr = self.loss_config["fine_correct_thr"]
+ self.c_pos_w = self.loss_config["pos_weight"]
+ self.c_neg_w = self.loss_config["neg_weight"]
+ # fine-level
+ self.fine_type = self.loss_config["fine_type"]
+
+ def compute_flow_loss(self, coarse_corr_gt, flow_list, h0, w0, h1, w1):
+ # coarse_corr_gt:[[batch_indices],[left_indices],[right_indices]]
+ # flow_list: [L,B,H,W,4]
+ loss1 = self.flow_loss_worker(
+ flow_list[0], coarse_corr_gt[0], coarse_corr_gt[1], coarse_corr_gt[2], w1
+ )
+ loss2 = self.flow_loss_worker(
+ flow_list[1], coarse_corr_gt[0], coarse_corr_gt[2], coarse_corr_gt[1], w0
+ )
+ total_loss = (loss1 + loss2) / 2
+ return total_loss
+
+ def flow_loss_worker(self, flow, batch_indicies, self_indicies, cross_indicies, w):
+ bs, layer_num = flow.shape[1], flow.shape[0]
+ flow = flow.view(layer_num, bs, -1, 4)
+ gt_flow = torch.stack([cross_indicies % w, cross_indicies // w], dim=1)
+
+ total_loss_list = []
+ for layer_index in range(layer_num):
+ cur_flow_list = flow[layer_index]
+ spv_flow = cur_flow_list[batch_indicies, self_indicies][:, :2]
+ spv_conf = cur_flow_list[batch_indicies, self_indicies][
+ :, 2:
+ ] # [#coarse,2]
+ l2_flow_dis = (gt_flow - spv_flow) ** 2 # [#coarse,2]
+ total_loss = spv_conf + torch.exp(-spv_conf) * l2_flow_dis # [#coarse,2]
+ total_loss_list.append(total_loss.mean())
+ total_loss = torch.stack(total_loss_list, dim=-1) * self.flow_weight
+ return total_loss
+
+ def compute_coarse_loss(self, conf, conf_gt, weight=None):
+ """Point-wise CE / Focal Loss with 0 / 1 confidence as gt.
+ Args:
+ conf (torch.Tensor): (N, HW0, HW1) / (N, HW0+1, HW1+1)
+ conf_gt (torch.Tensor): (N, HW0, HW1)
+ weight (torch.Tensor): (N, HW0, HW1)
+ """
+ pos_mask, neg_mask = conf_gt == 1, conf_gt == 0
+ c_pos_w, c_neg_w = self.c_pos_w, self.c_neg_w
+ # corner case: no gt coarse-level match at all
+ if not pos_mask.any(): # assign a wrong gt
+ pos_mask[0, 0, 0] = True
+ if weight is not None:
+ weight[0, 0, 0] = 0.0
+ c_pos_w = 0.0
+ if not neg_mask.any():
+ neg_mask[0, 0, 0] = True
+ if weight is not None:
+ weight[0, 0, 0] = 0.0
+ c_neg_w = 0.0
+
+ if self.loss_config["coarse_type"] == "cross_entropy":
+ assert (
+ not self.sparse_spvs
+ ), "Sparse Supervision for cross-entropy not implemented!"
+ conf = torch.clamp(conf, 1e-6, 1 - 1e-6)
+ loss_pos = -torch.log(conf[pos_mask])
+ loss_neg = -torch.log(1 - conf[neg_mask])
+ if weight is not None:
+ loss_pos = loss_pos * weight[pos_mask]
+ loss_neg = loss_neg * weight[neg_mask]
+ return c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean()
+ elif self.loss_config["coarse_type"] == "focal":
+ conf = torch.clamp(conf, 1e-6, 1 - 1e-6)
+ alpha = self.loss_config["focal_alpha"]
+ gamma = self.loss_config["focal_gamma"]
+
+ if self.sparse_spvs:
+ pos_conf = (
+ conf[:, :-1, :-1][pos_mask]
+ if self.match_type == "sinkhorn"
+ else conf[pos_mask]
+ )
+ loss_pos = -alpha * torch.pow(1 - pos_conf, gamma) * pos_conf.log()
+ # calculate losses for negative samples
+ if self.match_type == "sinkhorn":
+ neg0, neg1 = conf_gt.sum(-1) == 0, conf_gt.sum(1) == 0
+ neg_conf = torch.cat(
+ [conf[:, :-1, -1][neg0], conf[:, -1, :-1][neg1]], 0
+ )
+ loss_neg = -alpha * torch.pow(1 - neg_conf, gamma) * neg_conf.log()
+ else:
+ # These is no dustbin for dual_softmax, so we left unmatchable patches without supervision.
+ # we could also add 'pseudo negtive-samples'
+ pass
+ # handle loss weights
+ if weight is not None:
+ # Different from dense-spvs, the loss w.r.t. padded regions aren't directly zeroed out,
+ # but only through manually setting corresponding regions in sim_matrix to '-inf'.
+ loss_pos = loss_pos * weight[pos_mask]
+ if self.match_type == "sinkhorn":
+ neg_w0 = (weight.sum(-1) != 0)[neg0]
+ neg_w1 = (weight.sum(1) != 0)[neg1]
+ neg_mask = torch.cat([neg_w0, neg_w1], 0)
+ loss_neg = loss_neg[neg_mask]
+
+ loss = (
+ c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean()
+ if self.match_type == "sinkhorn"
+ else c_pos_w * loss_pos.mean()
+ )
+ return loss
+ # positive and negative elements occupy similar propotions. => more balanced loss weights needed
+ else: # dense supervision (in the case of match_type=='sinkhorn', the dustbin is not supervised.)
+ loss_pos = (
+ -alpha
+ * torch.pow(1 - conf[pos_mask], gamma)
+ * (conf[pos_mask]).log()
+ )
+ loss_neg = (
+ -alpha
+ * torch.pow(conf[neg_mask], gamma)
+ * (1 - conf[neg_mask]).log()
+ )
+ if weight is not None:
+ loss_pos = loss_pos * weight[pos_mask]
+ loss_neg = loss_neg * weight[neg_mask]
+ return c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean()
+ # each negative element occupy a smaller propotion than positive elements. => higher negative loss weight needed
+ else:
+ raise ValueError(
+ "Unknown coarse loss: {type}".format(
+ type=self.loss_config["coarse_type"]
+ )
+ )
+
+ def compute_fine_loss(self, expec_f, expec_f_gt):
+ if self.fine_type == "l2_with_std":
+ return self._compute_fine_loss_l2_std(expec_f, expec_f_gt)
+ elif self.fine_type == "l2":
+ return self._compute_fine_loss_l2(expec_f, expec_f_gt)
+ else:
+ raise NotImplementedError()
+
+ def _compute_fine_loss_l2(self, expec_f, expec_f_gt):
+ """
+ Args:
+ expec_f (torch.Tensor): [M, 2]
+ expec_f_gt (torch.Tensor): [M, 2]
+ """
+ correct_mask = (
+ torch.linalg.norm(expec_f_gt, ord=float("inf"), dim=1) < self.correct_thr
+ )
+ if correct_mask.sum() == 0:
+ if (
+ self.training
+ ): # this seldomly happen when training, since we pad prediction with gt
+ logger.warning("assign a false supervision to avoid ddp deadlock")
+ correct_mask[0] = True
+ else:
+ return None
+ flow_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask]) ** 2).sum(-1)
+ return flow_l2.mean()
+
+ def _compute_fine_loss_l2_std(self, expec_f, expec_f_gt):
+ """
+ Args:
+ expec_f (torch.Tensor): [M, 3]
+ expec_f_gt (torch.Tensor): [M, 2]
+ """
+ # correct_mask tells you which pair to compute fine-loss
+ correct_mask = (
+ torch.linalg.norm(expec_f_gt, ord=float("inf"), dim=1) < self.correct_thr
+ )
+
+ # use std as weight that measures uncertainty
+ std = expec_f[:, 2]
+ inverse_std = 1.0 / torch.clamp(std, min=1e-10)
+ weight = (
+ inverse_std / torch.mean(inverse_std)
+ ).detach() # avoid minizing loss through increase std
+
+ # corner case: no correct coarse match found
+ if not correct_mask.any():
+ if (
+ self.training
+ ): # this seldomly happen during training, since we pad prediction with gt
+ # sometimes there is not coarse-level gt at all.
+ logger.warning("assign a false supervision to avoid ddp deadlock")
+ correct_mask[0] = True
+ weight[0] = 0.0
+ else:
+ return None
+
+ # l2 loss with std
+ flow_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask, :2]) ** 2).sum(-1)
+ loss = (flow_l2 * weight[correct_mask]).mean()
+
+ return loss
+
+ @torch.no_grad()
+ def compute_c_weight(self, data):
+ """compute element-wise weights for computing coarse-level loss."""
+ if "mask0" in data:
+ c_weight = (
+ data["mask0"].flatten(-2)[..., None]
+ * data["mask1"].flatten(-2)[:, None]
+ ).float()
+ else:
+ c_weight = None
+ return c_weight
+
+ def forward(self, data):
+ """
+ Update:
+ data (dict): update{
+ 'loss': [1] the reduced loss across a batch,
+ 'loss_scalars' (dict): loss scalars for tensorboard_record
+ }
+ """
+ loss_scalars = {}
+ # 0. compute element-wise loss weight
+ c_weight = self.compute_c_weight(data)
+
+ # 1. coarse-level loss
+ loss_c = self.compute_coarse_loss(
+ data["conf_matrix_with_bin"]
+ if self.sparse_spvs and self.match_type == "sinkhorn"
+ else data["conf_matrix"],
+ data["conf_matrix_gt"],
+ weight=c_weight,
+ )
+ loss = loss_c * self.loss_config["coarse_weight"]
+ loss_scalars.update({"loss_c": loss_c.clone().detach().cpu()})
+
+ # 2. fine-level loss
+ loss_f = self.compute_fine_loss(data["expec_f"], data["expec_f_gt"])
+ if loss_f is not None:
+ loss += loss_f * self.loss_config["fine_weight"]
+ loss_scalars.update({"loss_f": loss_f.clone().detach().cpu()})
+ else:
+ assert self.training is False
+ loss_scalars.update({"loss_f": torch.tensor(1.0)}) # 1 is the upper bound
+
+ # 3. flow loss
+ coarse_corr = [data["spv_b_ids"], data["spv_i_ids"], data["spv_j_ids"]]
+ loss_flow = self.compute_flow_loss(
+ coarse_corr,
+ data["predict_flow"],
+ data["hw0_c"][0],
+ data["hw0_c"][1],
+ data["hw1_c"][0],
+ data["hw1_c"][1],
+ )
+ loss_flow = loss_flow * self.flow_weight
+ for index, loss_off in enumerate(loss_flow):
+ loss_scalars.update(
+ {"loss_flow_" + str(index): loss_off.clone().detach().cpu()}
+ ) # 1 is the upper bound
+ conf = data["predict_flow"][0][:, :, :, :, 2:]
+ layer_num = conf.shape[0]
+ for layer_index in range(layer_num):
+ loss_scalars.update(
+ {
+ "conf_"
+ + str(layer_index): conf[layer_index]
+ .mean()
+ .clone()
+ .detach()
+ .cpu()
+ }
+ ) # 1 is the upper bound
+
+ loss += loss_flow.sum()
+ # print((loss_c * self.loss_config['coarse_weight']).data,loss_flow.data)
+ loss_scalars.update({"loss": loss.clone().detach().cpu()})
+ data.update({"loss": loss, "loss_scalars": loss_scalars})
diff --git a/third_party/ASpanFormer/src/optimizers/__init__.py b/third_party/ASpanFormer/src/optimizers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4e36c22e00217deccacd589f8924b2f74589456
--- /dev/null
+++ b/third_party/ASpanFormer/src/optimizers/__init__.py
@@ -0,0 +1,55 @@
+import torch
+from torch.optim.lr_scheduler import MultiStepLR, CosineAnnealingLR, ExponentialLR
+
+
+def build_optimizer(model, config):
+ name = config.TRAINER.OPTIMIZER
+ lr = config.TRAINER.TRUE_LR
+
+ if name == "adam":
+ return torch.optim.Adam(
+ model.parameters(), lr=lr, weight_decay=config.TRAINER.ADAM_DECAY
+ )
+ elif name == "adamw":
+ return torch.optim.AdamW(
+ model.parameters(), lr=lr, weight_decay=config.TRAINER.ADAMW_DECAY
+ )
+ else:
+ raise ValueError(f"TRAINER.OPTIMIZER = {name} is not a valid optimizer!")
+
+
+def build_scheduler(config, optimizer):
+ """
+ Returns:
+ scheduler (dict):{
+ 'scheduler': lr_scheduler,
+ 'interval': 'step', # or 'epoch'
+ 'monitor': 'val_f1', (optional)
+ 'frequency': x, (optional)
+ }
+ """
+ scheduler = {"interval": config.TRAINER.SCHEDULER_INTERVAL}
+ name = config.TRAINER.SCHEDULER
+
+ if name == "MultiStepLR":
+ scheduler.update(
+ {
+ "scheduler": MultiStepLR(
+ optimizer,
+ config.TRAINER.MSLR_MILESTONES,
+ gamma=config.TRAINER.MSLR_GAMMA,
+ )
+ }
+ )
+ elif name == "CosineAnnealing":
+ scheduler.update(
+ {"scheduler": CosineAnnealingLR(optimizer, config.TRAINER.COSA_TMAX)}
+ )
+ elif name == "ExponentialLR":
+ scheduler.update(
+ {"scheduler": ExponentialLR(optimizer, config.TRAINER.ELR_GAMMA)}
+ )
+ else:
+ raise NotImplementedError()
+
+ return scheduler
diff --git a/third_party/ASpanFormer/src/utils/augment.py b/third_party/ASpanFormer/src/utils/augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..068751c6c07091bbaed76debd43a73155f61b9bd
--- /dev/null
+++ b/third_party/ASpanFormer/src/utils/augment.py
@@ -0,0 +1,65 @@
+import albumentations as A
+
+
+class DarkAug(object):
+ """
+ Extreme dark augmentation aiming at Aachen Day-Night
+ """
+
+ def __init__(self) -> None:
+ self.augmentor = A.Compose(
+ [
+ A.RandomBrightnessContrast(
+ p=0.75, brightness_limit=(-0.6, 0.0), contrast_limit=(-0.5, 0.3)
+ ),
+ A.Blur(p=0.1, blur_limit=(3, 9)),
+ A.MotionBlur(p=0.2, blur_limit=(3, 25)),
+ A.RandomGamma(p=0.1, gamma_limit=(15, 65)),
+ A.HueSaturationValue(p=0.1, val_shift_limit=(-100, -40)),
+ ],
+ p=0.75,
+ )
+
+ def __call__(self, x):
+ return self.augmentor(image=x)["image"]
+
+
+class MobileAug(object):
+ """
+ Random augmentations aiming at images of mobile/handhold devices.
+ """
+
+ def __init__(self):
+ self.augmentor = A.Compose(
+ [
+ A.MotionBlur(p=0.25),
+ A.ColorJitter(p=0.5),
+ A.RandomRain(p=0.1), # random occlusion
+ A.RandomSunFlare(p=0.1),
+ A.JpegCompression(p=0.25),
+ A.ISONoise(p=0.25),
+ ],
+ p=1.0,
+ )
+
+ def __call__(self, x):
+ return self.augmentor(image=x)["image"]
+
+
+def build_augmentor(method=None, **kwargs):
+ if method is not None:
+ raise NotImplementedError(
+ "Using of augmentation functions are not supported yet!"
+ )
+ if method == "dark":
+ return DarkAug()
+ elif method == "mobile":
+ return MobileAug()
+ elif method is None:
+ return None
+ else:
+ raise ValueError(f"Invalid augmentation method: {method}")
+
+
+if __name__ == "__main__":
+ augmentor = build_augmentor("FDA")
diff --git a/imcui/third_party/TopicFM/src/utils/comm.py b/third_party/ASpanFormer/src/utils/comm.py
similarity index 95%
rename from imcui/third_party/TopicFM/src/utils/comm.py
rename to third_party/ASpanFormer/src/utils/comm.py
index 26ec9517cc47e224430106d8ae9aa99a3fe49167..9f578cda8933cc358934c645fcf413c63ab4d79d 100644
--- a/imcui/third_party/TopicFM/src/utils/comm.py
+++ b/third_party/ASpanFormer/src/utils/comm.py
@@ -98,11 +98,11 @@ def _serialize_to_tensor(data, group):
device = torch.device("cpu" if backend == "gloo" else "cuda")
buffer = pickle.dumps(data)
- if len(buffer) > 1024 ** 3:
+ if len(buffer) > 1024**3:
logger = logging.getLogger(__name__)
logger.warning(
"Rank {} trying to all-gather {:.2f} GB of data on device {}".format(
- get_rank(), len(buffer) / (1024 ** 3), device
+ get_rank(), len(buffer) / (1024**3), device
)
)
storage = torch.ByteStorage.from_buffer(buffer)
@@ -122,7 +122,8 @@ def _pad_to_largest_tensor(tensor, group):
), "comm.gather/all_gather must be called from ranks within the given group!"
local_size = torch.tensor([tensor.numel()], dtype=torch.int64, device=tensor.device)
size_list = [
- torch.zeros([1], dtype=torch.int64, device=tensor.device) for _ in range(world_size)
+ torch.zeros([1], dtype=torch.int64, device=tensor.device)
+ for _ in range(world_size)
]
dist.all_gather(size_list, local_size, group=group)
@@ -133,7 +134,9 @@ def _pad_to_largest_tensor(tensor, group):
# we pad the tensor because torch all_gather does not support
# gathering tensors of different shapes
if local_size != max_size:
- padding = torch.zeros((max_size - local_size,), dtype=torch.uint8, device=tensor.device)
+ padding = torch.zeros(
+ (max_size - local_size,), dtype=torch.uint8, device=tensor.device
+ )
tensor = torch.cat((tensor, padding), dim=0)
return size_list, tensor
@@ -164,7 +167,8 @@ def all_gather(data, group=None):
# receiving Tensor from all ranks
tensor_list = [
- torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list
+ torch.empty((max_size,), dtype=torch.uint8, device=tensor.device)
+ for _ in size_list
]
dist.all_gather(tensor_list, tensor, group=group)
@@ -205,7 +209,8 @@ def gather(data, dst=0, group=None):
if rank == dst:
max_size = max(size_list)
tensor_list = [
- torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list
+ torch.empty((max_size,), dtype=torch.uint8, device=tensor.device)
+ for _ in size_list
]
dist.gather(tensor, tensor_list, dst=dst, group=group)
@@ -228,7 +233,7 @@ def shared_random_seed():
All workers must call this function, otherwise it will deadlock.
"""
- ints = np.random.randint(2 ** 31)
+ ints = np.random.randint(2**31)
all_ints = all_gather(ints)
return all_ints[0]
diff --git a/imcui/third_party/XoFTR/src/utils/dataloader.py b/third_party/ASpanFormer/src/utils/dataloader.py
similarity index 55%
rename from imcui/third_party/XoFTR/src/utils/dataloader.py
rename to third_party/ASpanFormer/src/utils/dataloader.py
index 6da37b880a290c2bb3ebb028d0c8dab592acc5c1..b980dfd344714870ecdacd9e7a9742f51c3ee14d 100644
--- a/imcui/third_party/XoFTR/src/utils/dataloader.py
+++ b/third_party/ASpanFormer/src/utils/dataloader.py
@@ -3,21 +3,22 @@ import numpy as np
# --- PL-DATAMODULE ---
+
def get_local_split(items: list, world_size: int, rank: int, seed: int):
- """ The local rank only loads a split of the dataset. """
+ """The local rank only loads a split of the dataset."""
n_items = len(items)
items_permute = np.random.RandomState(seed).permutation(items)
if n_items % world_size == 0:
padded_items = items_permute
else:
padding = np.random.RandomState(seed).choice(
- items,
- world_size - (n_items % world_size),
- replace=True)
+ items, world_size - (n_items % world_size), replace=True
+ )
padded_items = np.concatenate([items_permute, padding])
- assert len(padded_items) % world_size == 0, \
- f'len(padded_items): {len(padded_items)}; world_size: {world_size}; len(padding): {len(padding)}'
+ assert (
+ len(padded_items) % world_size == 0
+ ), f"len(padded_items): {len(padded_items)}; world_size: {world_size}; len(padding): {len(padding)}"
n_per_rank = len(padded_items) // world_size
- local_items = padded_items[n_per_rank * rank: n_per_rank * (rank+1)]
+ local_items = padded_items[n_per_rank * rank : n_per_rank * (rank + 1)]
return local_items
diff --git a/imcui/third_party/ASpanFormer/src/utils/dataset.py b/third_party/ASpanFormer/src/utils/dataset.py
similarity index 71%
rename from imcui/third_party/ASpanFormer/src/utils/dataset.py
rename to third_party/ASpanFormer/src/utils/dataset.py
index 209bf554acc20e33ea89eb9e7024ba68d0b3a30b..1881446fd69aedb520ae669100cd2a3c2d143a18 100644
--- a/imcui/third_party/ASpanFormer/src/utils/dataset.py
+++ b/third_party/ASpanFormer/src/utils/dataset.py
@@ -15,8 +15,11 @@ except Exception:
# --- DATA IO ---
+
def load_array_from_s3(
- path, client, cv_type,
+ path,
+ client,
+ cv_type,
use_h5py=False,
):
byte_str = client.Get(path)
@@ -26,7 +29,7 @@ def load_array_from_s3(
data = cv2.imdecode(raw_array, cv_type)
else:
f = io.BytesIO(byte_str)
- data = np.array(h5py.File(f, 'r')['/depth'])
+ data = np.array(h5py.File(f, "r")["/depth"])
except Exception as ex:
print(f"==> Data loading failure: {path}")
raise ex
@@ -36,9 +39,8 @@ def load_array_from_s3(
def imread_gray(path, augment_fn=None, client=SCANNET_CLIENT):
- cv_type = cv2.IMREAD_GRAYSCALE if augment_fn is None \
- else cv2.IMREAD_COLOR
- if str(path).startswith('s3://'):
+ cv_type = cv2.IMREAD_GRAYSCALE if augment_fn is None else cv2.IMREAD_COLOR
+ if str(path).startswith("s3://"):
image = load_array_from_s3(str(path), client, cv_type)
else:
image = cv2.imread(str(path), cv_type)
@@ -54,7 +56,7 @@ def imread_gray(path, augment_fn=None, client=SCANNET_CLIENT):
def get_resized_wh(w, h, resize=None):
if resize is not None: # resize the longer edge
scale = resize / max(h, w)
- w_new, h_new = int(round(w*scale)), int(round(h*scale))
+ w_new, h_new = int(round(w * scale)), int(round(h * scale))
else:
w_new, h_new = w, h
return w_new, h_new
@@ -69,20 +71,22 @@ def get_divisible_wh(w, h, df=None):
def pad_bottom_right(inp, pad_size, ret_mask=False):
- assert isinstance(pad_size, int) and pad_size >= max(inp.shape[-2:]), f"{pad_size} < {max(inp.shape[-2:])}"
+ assert isinstance(pad_size, int) and pad_size >= max(
+ inp.shape[-2:]
+ ), f"{pad_size} < {max(inp.shape[-2:])}"
mask = None
if inp.ndim == 2:
padded = np.zeros((pad_size, pad_size), dtype=inp.dtype)
- padded[:inp.shape[0], :inp.shape[1]] = inp
+ padded[: inp.shape[0], : inp.shape[1]] = inp
if ret_mask:
mask = np.zeros((pad_size, pad_size), dtype=bool)
- mask[:inp.shape[0], :inp.shape[1]] = True
+ mask[: inp.shape[0], : inp.shape[1]] = True
elif inp.ndim == 3:
padded = np.zeros((inp.shape[0], pad_size, pad_size), dtype=inp.dtype)
- padded[:, :inp.shape[1], :inp.shape[2]] = inp
+ padded[:, : inp.shape[1], : inp.shape[2]] = inp
if ret_mask:
mask = np.zeros((inp.shape[0], pad_size, pad_size), dtype=bool)
- mask[:, :inp.shape[1], :inp.shape[2]] = True
+ mask[:, : inp.shape[1], : inp.shape[2]] = True
else:
raise NotImplementedError()
return padded, mask
@@ -90,6 +94,7 @@ def pad_bottom_right(inp, pad_size, ret_mask=False):
# --- MEGADEPTH ---
+
def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=None):
"""
Args:
@@ -99,7 +104,7 @@ def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=No
Returns:
image (torch.tensor): (1, h, w)
mask (torch.tensor): (h, w)
- scale (torch.tensor): [w/w_new, h/h_new]
+ scale (torch.tensor): [w/w_new, h/h_new]
"""
# read image
image = imread_gray(path, augment_fn, client=MEGADEPTH_CLIENT)
@@ -110,7 +115,7 @@ def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=No
w_new, h_new = get_divisible_wh(w_new, h_new, df)
image = cv2.resize(image, (w_new, h_new))
- scale = torch.tensor([w/w_new, h/h_new], dtype=torch.float)
+ scale = torch.tensor([w / w_new, h / h_new], dtype=torch.float)
if padding: # padding
pad_to = max(h_new, w_new)
@@ -118,7 +123,9 @@ def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=No
else:
mask = None
- image = torch.from_numpy(image).float()[None] / 255 # (h, w) -> (1, h, w) and normalized
+ image = (
+ torch.from_numpy(image).float()[None] / 255
+ ) # (h, w) -> (1, h, w) and normalized
if mask is not None:
mask = torch.from_numpy(mask)
@@ -126,10 +133,10 @@ def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=No
def read_megadepth_depth(path, pad_to=None):
- if str(path).startswith('s3://'):
+ if str(path).startswith("s3://"):
depth = load_array_from_s3(path, MEGADEPTH_CLIENT, None, use_h5py=True)
else:
- depth = np.array(h5py.File(path, 'r')['depth'])
+ depth = np.array(h5py.File(path, "r")["depth"])
if pad_to is not None:
depth, _ = pad_bottom_right(depth, pad_to, ret_mask=False)
depth = torch.from_numpy(depth).float() # (h, w)
@@ -138,6 +145,7 @@ def read_megadepth_depth(path, pad_to=None):
# --- ScanNet ---
+
def read_scannet_gray(path, resize=(640, 480), augment_fn=None):
"""
Args:
@@ -146,7 +154,7 @@ def read_scannet_gray(path, resize=(640, 480), augment_fn=None):
Returns:
image (torch.tensor): (1, h, w)
mask (torch.tensor): (h, w)
- scale (torch.tensor): [w/w_new, h/h_new]
+ scale (torch.tensor): [w/w_new, h/h_new]
"""
# read and resize image
image = imread_gray(path, augment_fn)
@@ -158,7 +166,7 @@ def read_scannet_gray(path, resize=(640, 480), augment_fn=None):
def read_scannet_depth(path):
- if str(path).startswith('s3://'):
+ if str(path).startswith("s3://"):
depth = load_array_from_s3(str(path), SCANNET_CLIENT, cv2.IMREAD_UNCHANGED)
else:
depth = cv2.imread(str(path), cv2.IMREAD_UNCHANGED)
@@ -168,55 +176,57 @@ def read_scannet_depth(path):
def read_scannet_pose(path):
- """ Read ScanNet's Camera2World pose and transform it to World2Camera.
-
+ """Read ScanNet's Camera2World pose and transform it to World2Camera.
+
Returns:
pose_w2c (np.ndarray): (4, 4)
"""
- cam2world = np.loadtxt(path, delimiter=' ')
+ cam2world = np.loadtxt(path, delimiter=" ")
world2cam = inv(cam2world)
return world2cam
def read_scannet_intrinsic(path):
- """ Read ScanNet's intrinsic matrix and return the 3x3 matrix.
- """
- intrinsic = np.loadtxt(path, delimiter=' ')
+ """Read ScanNet's intrinsic matrix and return the 3x3 matrix."""
+ intrinsic = np.loadtxt(path, delimiter=" ")
return intrinsic[:-1, :-1]
-def read_gl3d_gray(path,resize):
- img=cv2.resize(cv2.imread(path,cv2.IMREAD_GRAYSCALE),(int(resize),int(resize)))
- img = torch.from_numpy(img).float()[None] / 255 # (h, w) -> (1, h, w) and normalized
+def read_gl3d_gray(path, resize):
+ img = cv2.resize(cv2.imread(path, cv2.IMREAD_GRAYSCALE), (int(resize), int(resize)))
+ img = (
+ torch.from_numpy(img).float()[None] / 255
+ ) # (h, w) -> (1, h, w) and normalized
return img
+
def read_gl3d_depth(file_path):
- with open(file_path, 'rb') as fin:
+ with open(file_path, "rb") as fin:
color = None
width = None
height = None
scale = None
data_type = None
- header = str(fin.readline().decode('UTF-8')).rstrip()
- if header == 'PF':
+ header = str(fin.readline().decode("UTF-8")).rstrip()
+ if header == "PF":
color = True
- elif header == 'Pf':
+ elif header == "Pf":
color = False
else:
- raise Exception('Not a PFM file.')
- dim_match = re.match(r'^(\d+)\s(\d+)\s$', fin.readline().decode('UTF-8'))
+ raise Exception("Not a PFM file.")
+ dim_match = re.match(r"^(\d+)\s(\d+)\s$", fin.readline().decode("UTF-8"))
if dim_match:
width, height = map(int, dim_match.groups())
else:
- raise Exception('Malformed PFM header.')
- scale = float((fin.readline().decode('UTF-8')).rstrip())
+ raise Exception("Malformed PFM header.")
+ scale = float((fin.readline().decode("UTF-8")).rstrip())
if scale < 0: # little-endian
- data_type = ' 0 else 0)
precs.append(np.mean(prec_) if len(prec_) > 0 else 0)
if ret_dict:
- return {f'prec@{t:.0e}': prec for t, prec in zip(thresholds, precs)} if not offset else {f'prec_flow@{t:.0e}': prec for t, prec in zip(thresholds, precs)}
+ return (
+ {f"prec@{t:.0e}": prec for t, prec in zip(thresholds, precs)}
+ if not offset
+ else {f"prec_flow@{t:.0e}": prec for t, prec in zip(thresholds, precs)}
+ )
else:
return precs
def aggregate_metrics(metrics, epi_err_thr=5e-4):
- """ Aggregate metrics for the whole dataset:
+ """Aggregate metrics for the whole dataset:
(This method should be called once per dataset)
1. AUC of the pose error (angular) at the threshold [5, 10, 20]
2. Mean matching precision at the threshold 5e-4(ScanNet), 1e-4(MegaDepth)
"""
# filter duplicates
- unq_ids = OrderedDict((iden, id) for id, iden in enumerate(metrics['identifiers']))
+ unq_ids = OrderedDict((iden, id) for id, iden in enumerate(metrics["identifiers"]))
unq_ids = list(unq_ids.values())
- logger.info(f'Aggregating metrics over {len(unq_ids)} unique items...')
+ logger.info(f"Aggregating metrics over {len(unq_ids)} unique items...")
# pose auc
angular_thresholds = [5, 10, 20]
- pose_errors = np.max(np.stack([metrics['R_errs'], metrics['t_errs']]), axis=0)[unq_ids]
+ pose_errors = np.max(np.stack([metrics["R_errs"], metrics["t_errs"]]), axis=0)[
+ unq_ids
+ ]
aucs = error_auc(pose_errors, angular_thresholds) # (auc@5, auc@10, auc@20)
# matching precision
dist_thresholds = [epi_err_thr]
- precs = epidist_prec(np.array(metrics['epi_errs'], dtype=object)[unq_ids], dist_thresholds, True) # (prec@err_thr)
-
- #offset precision
+ precs = epidist_prec(
+ np.array(metrics["epi_errs"], dtype=object)[unq_ids], dist_thresholds, True
+ ) # (prec@err_thr)
+
+ # offset precision
try:
- precs_offset = epidist_prec(np.array(metrics['epi_errs_offset'], dtype=object)[unq_ids], [2e-3], True,offset=True)
- return {**aucs, **precs,**precs_offset}
+ precs_offset = epidist_prec(
+ np.array(metrics["epi_errs_offset"], dtype=object)[unq_ids],
+ [2e-3],
+ True,
+ offset=True,
+ )
+ return {**aucs, **precs, **precs_offset}
except:
return {**aucs, **precs}
diff --git a/imcui/third_party/ASpanFormer/src/utils/misc.py b/third_party/ASpanFormer/src/utils/misc.py
similarity index 53%
rename from imcui/third_party/ASpanFormer/src/utils/misc.py
rename to third_party/ASpanFormer/src/utils/misc.py
index 25e4433f5ffa41adc4c0435cfe2b5696e43b58b3..d9b6a4a5f5920cde89bdecbf2a444aaea8ff51f3 100644
--- a/imcui/third_party/ASpanFormer/src/utils/misc.py
+++ b/third_party/ASpanFormer/src/utils/misc.py
@@ -11,6 +11,7 @@ from pytorch_lightning.utilities import rank_zero_only
import cv2
import numpy as np
+
def lower_config(yacs_cfg):
if not isinstance(yacs_cfg, CN):
return yacs_cfg
@@ -25,7 +26,7 @@ def upper_config(dict_cfg):
def log_on(condition, message, level):
if condition:
- assert level in ['INFO', 'DEBUG', 'WARNING', 'ERROR', 'CRITICAL']
+ assert level in ["INFO", "DEBUG", "WARNING", "ERROR", "CRITICAL"]
logger.log(level, message)
@@ -35,32 +36,35 @@ def get_rank_zero_only_logger(logger: _Logger):
else:
for _level in logger._core.levels.keys():
level = _level.lower()
- setattr(logger, level,
- lambda x: None)
+ setattr(logger, level, lambda x: None)
logger._log = lambda x: None
return logger
def setup_gpus(gpus: Union[str, int]) -> int:
- """ A temporary fix for pytorch-lighting 1.3.x """
+ """A temporary fix for pytorch-lighting 1.3.x"""
gpus = str(gpus)
gpu_ids = []
-
- if ',' not in gpus:
+
+ if "," not in gpus:
n_gpus = int(gpus)
return n_gpus if n_gpus != -1 else torch.cuda.device_count()
else:
- gpu_ids = [i.strip() for i in gpus.split(',') if i != '']
-
+ gpu_ids = [i.strip() for i in gpus.split(",") if i != ""]
+
# setup environment variables
- visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
+ visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
if visible_devices is None:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
- os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(i) for i in gpu_ids)
- visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
- logger.warning(f'[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}')
+ os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str(i) for i in gpu_ids)
+ visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
+ logger.warning(
+ f"[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}"
+ )
else:
- logger.warning('[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process.')
+ logger.warning(
+ "[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process."
+ )
return len(gpu_ids)
@@ -71,11 +75,11 @@ def flattenList(x):
@contextlib.contextmanager
def tqdm_joblib(tqdm_object):
"""Context manager to patch joblib to report into tqdm progress bar given as argument
-
+
Usage:
with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar:
Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10))
-
+
When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing)
ret_vals = Parallel(n_jobs=args.world_size)(
delayed(lambda x: _compute_cov_score(pid, *x))(param)
@@ -84,6 +88,7 @@ def tqdm_joblib(tqdm_object):
total=len(image_ids)*(len(image_ids)-1)/2))
Src: https://stackoverflow.com/a/58936697
"""
+
class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@@ -101,39 +106,79 @@ def tqdm_joblib(tqdm_object):
tqdm_object.close()
-def draw_points(img,points,color=(0,255,0),radius=3):
+def draw_points(img, points, color=(0, 255, 0), radius=3):
dp = [(int(points[i, 0]), int(points[i, 1])) for i in range(points.shape[0])]
for i in range(points.shape[0]):
- cv2.circle(img, dp[i],radius=radius,color=color)
+ cv2.circle(img, dp[i], radius=radius, color=color)
return img
-
-def draw_match(img1, img2, corr1, corr2,inlier=[True],color=None,radius1=1,radius2=1,resize=None):
+
+def draw_match(
+ img1,
+ img2,
+ corr1,
+ corr2,
+ inlier=[True],
+ color=None,
+ radius1=1,
+ radius2=1,
+ resize=None,
+):
if resize is not None:
- scale1,scale2=[img1.shape[1]/resize[0],img1.shape[0]/resize[1]],[img2.shape[1]/resize[0],img2.shape[0]/resize[1]]
- img1,img2=cv2.resize(img1, resize, interpolation=cv2.INTER_AREA),cv2.resize(img2, resize, interpolation=cv2.INTER_AREA)
- corr1,corr2=corr1/np.asarray(scale1)[np.newaxis],corr2/np.asarray(scale2)[np.newaxis]
- corr1_key = [cv2.KeyPoint(corr1[i, 0], corr1[i, 1], radius1) for i in range(corr1.shape[0])]
- corr2_key = [cv2.KeyPoint(corr2[i, 0], corr2[i, 1], radius2) for i in range(corr2.shape[0])]
+ scale1, scale2 = [img1.shape[1] / resize[0], img1.shape[0] / resize[1]], [
+ img2.shape[1] / resize[0],
+ img2.shape[0] / resize[1],
+ ]
+ img1, img2 = cv2.resize(img1, resize, interpolation=cv2.INTER_AREA), cv2.resize(
+ img2, resize, interpolation=cv2.INTER_AREA
+ )
+ corr1, corr2 = (
+ corr1 / np.asarray(scale1)[np.newaxis],
+ corr2 / np.asarray(scale2)[np.newaxis],
+ )
+ corr1_key = [
+ cv2.KeyPoint(corr1[i, 0], corr1[i, 1], radius1) for i in range(corr1.shape[0])
+ ]
+ corr2_key = [
+ cv2.KeyPoint(corr2[i, 0], corr2[i, 1], radius2) for i in range(corr2.shape[0])
+ ]
assert len(corr1) == len(corr2)
draw_matches = [cv2.DMatch(i, i, 0) for i in range(len(corr1))]
if color is None:
- color = [(0, 255, 0) if cur_inlier else (0,0,255) for cur_inlier in inlier]
- if len(color)==1:
- display = cv2.drawMatches(img1, corr1_key, img2, corr2_key, draw_matches, None,
- matchColor=color[0],
- singlePointColor=color[0],
- flags=4
- )
+ color = [(0, 255, 0) if cur_inlier else (0, 0, 255) for cur_inlier in inlier]
+ if len(color) == 1:
+ display = cv2.drawMatches(
+ img1,
+ corr1_key,
+ img2,
+ corr2_key,
+ draw_matches,
+ None,
+ matchColor=color[0],
+ singlePointColor=color[0],
+ flags=4,
+ )
else:
- height,width=max(img1.shape[0],img2.shape[0]),img1.shape[1]+img2.shape[1]
- display=np.zeros([height,width,3],np.uint8)
- display[:img1.shape[0],:img1.shape[1]]=img1
- display[:img2.shape[0],img1.shape[1]:]=img2
+ height, width = max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1]
+ display = np.zeros([height, width, 3], np.uint8)
+ display[: img1.shape[0], : img1.shape[1]] = img1
+ display[: img2.shape[0], img1.shape[1] :] = img2
for i in range(len(corr1)):
- left_x,left_y,right_x,right_y=int(corr1[i][0]),int(corr1[i][1]),int(corr2[i][0]+img1.shape[1]),int(corr2[i][1])
- cur_color=(int(color[i][0]),int(color[i][1]),int(color[i][2]))
- cv2.line(display, (left_x,left_y), (right_x,right_y),cur_color,1,lineType=cv2.LINE_AA)
+ left_x, left_y, right_x, right_y = (
+ int(corr1[i][0]),
+ int(corr1[i][1]),
+ int(corr2[i][0] + img1.shape[1]),
+ int(corr2[i][1]),
+ )
+ cur_color = (int(color[i][0]), int(color[i][1]), int(color[i][2]))
+ cv2.line(
+ display,
+ (left_x, left_y),
+ (right_x, right_y),
+ cur_color,
+ 1,
+ lineType=cv2.LINE_AA,
+ )
return display
diff --git a/third_party/ASpanFormer/src/utils/plotting.py b/third_party/ASpanFormer/src/utils/plotting.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ca3ef0a336a652e7ca910a5584227da043ac019
--- /dev/null
+++ b/third_party/ASpanFormer/src/utils/plotting.py
@@ -0,0 +1,253 @@
+import bisect
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+from copy import deepcopy
+
+
+def _compute_conf_thresh(data):
+ dataset_name = data["dataset_name"][0].lower()
+ if dataset_name == "scannet":
+ thr = 5e-4
+ elif dataset_name == "megadepth" or dataset_name == "gl3d":
+ thr = 1e-4
+ else:
+ raise ValueError(f"Unknown dataset: {dataset_name}")
+ return thr
+
+
+# --- VISUALIZATION --- #
+
+
+def make_matching_figure(
+ img0,
+ img1,
+ mkpts0,
+ mkpts1,
+ color,
+ kpts0=None,
+ kpts1=None,
+ text=[],
+ dpi=75,
+ path=None,
+):
+ # draw image pair
+ assert (
+ mkpts0.shape[0] == mkpts1.shape[0]
+ ), f"mkpts0: {mkpts0.shape[0]} v.s. mkpts1: {mkpts1.shape[0]}"
+ fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi)
+ axes[0].imshow(img0, cmap="gray")
+ axes[1].imshow(img1, cmap="gray")
+ for i in range(2): # clear all frames
+ axes[i].get_yaxis().set_ticks([])
+ axes[i].get_xaxis().set_ticks([])
+ for spine in axes[i].spines.values():
+ spine.set_visible(False)
+ plt.tight_layout(pad=1)
+
+ if kpts0 is not None:
+ assert kpts1 is not None
+ axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c="w", s=2)
+ axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c="w", s=2)
+
+ # draw matches
+ if mkpts0.shape[0] != 0 and mkpts1.shape[0] != 0:
+ fig.canvas.draw()
+ transFigure = fig.transFigure.inverted()
+ fkpts0 = transFigure.transform(axes[0].transData.transform(mkpts0))
+ fkpts1 = transFigure.transform(axes[1].transData.transform(mkpts1))
+ fig.lines = [
+ matplotlib.lines.Line2D(
+ (fkpts0[i, 0], fkpts1[i, 0]),
+ (fkpts0[i, 1], fkpts1[i, 1]),
+ transform=fig.transFigure,
+ c=color[i],
+ linewidth=1,
+ )
+ for i in range(len(mkpts0))
+ ]
+
+ axes[0].scatter(mkpts0[:, 0], mkpts0[:, 1], c=color, s=4)
+ axes[1].scatter(mkpts1[:, 0], mkpts1[:, 1], c=color, s=4)
+
+ # put txts
+ txt_color = "k" if img0[:100, :200].mean() > 200 else "w"
+ fig.text(
+ 0.01,
+ 0.99,
+ "\n".join(text),
+ transform=fig.axes[0].transAxes,
+ fontsize=15,
+ va="top",
+ ha="left",
+ color=txt_color,
+ )
+
+ # save or return figure
+ if path:
+ plt.savefig(str(path), bbox_inches="tight", pad_inches=0)
+ plt.close()
+ else:
+ return fig
+
+
+def _make_evaluation_figure(data, b_id, alpha="dynamic"):
+ b_mask = data["m_bids"] == b_id
+ conf_thr = _compute_conf_thresh(data)
+
+ img0 = (data["image0"][b_id][0].cpu().numpy() * 255).round().astype(np.int32)
+ img1 = (data["image1"][b_id][0].cpu().numpy() * 255).round().astype(np.int32)
+ kpts0 = data["mkpts0_f"][b_mask].cpu().numpy()
+ kpts1 = data["mkpts1_f"][b_mask].cpu().numpy()
+
+ # for megadepth, we visualize matches on the resized image
+ if "scale0" in data:
+ kpts0 = kpts0 / data["scale0"][b_id].cpu().numpy()[[1, 0]]
+ kpts1 = kpts1 / data["scale1"][b_id].cpu().numpy()[[1, 0]]
+ epi_errs = data["epi_errs"][b_mask].cpu().numpy()
+ correct_mask = epi_errs < conf_thr
+ precision = np.mean(correct_mask) if len(correct_mask) > 0 else 0
+ n_correct = np.sum(correct_mask)
+ n_gt_matches = int(data["conf_matrix_gt"][b_id].sum().cpu())
+ recall = 0 if n_gt_matches == 0 else n_correct / (n_gt_matches)
+ # recall might be larger than 1, since the calculation of conf_matrix_gt
+ # uses groundtruth depths and camera poses, but epipolar distance is used here.
+
+ # matching info
+ if alpha == "dynamic":
+ alpha = dynamic_alpha(len(correct_mask))
+ color = error_colormap(epi_errs, conf_thr, alpha=alpha)
+
+ text = [
+ f"#Matches {len(kpts0)}",
+ f"Precision({conf_thr:.2e}) ({100 * precision:.1f}%): {n_correct}/{len(kpts0)}",
+ f"Recall({conf_thr:.2e}) ({100 * recall:.1f}%): {n_correct}/{n_gt_matches}",
+ ]
+
+ # make the figure
+ figure = make_matching_figure(img0, img1, kpts0, kpts1, color, text=text)
+ return figure
+
+
+def _make_evaluation_figure_offset(data, b_id, alpha="dynamic", side=""):
+ layer_num = data["predict_flow"][0].shape[0]
+
+ b_mask = data["offset_bids" + side] == b_id
+ conf_thr = 2e-3 # hardcode for scannet(coarse level)
+ img0 = (data["image0"][b_id][0].cpu().numpy() * 255).round().astype(np.int32)
+ img1 = (data["image1"][b_id][0].cpu().numpy() * 255).round().astype(np.int32)
+
+ figure_list = []
+ # draw offset matches in different layers
+ for layer_index in range(layer_num):
+ l_mask = data["offset_lids" + side] == layer_index
+ mask = l_mask & b_mask
+ kpts0 = data["offset_kpts0_f" + side][mask].cpu().numpy()
+ kpts1 = data["offset_kpts1_f" + side][mask].cpu().numpy()
+
+ epi_errs = data["epi_errs_offset" + side][mask].cpu().numpy()
+ correct_mask = epi_errs < conf_thr
+
+ precision = np.mean(correct_mask) if len(correct_mask) > 0 else 0
+ n_correct = np.sum(correct_mask)
+ n_gt_matches = int(data["conf_matrix_gt"][b_id].sum().cpu())
+ recall = 0 if n_gt_matches == 0 else n_correct / (n_gt_matches)
+ # recall might be larger than 1, since the calculation of conf_matrix_gt
+ # uses groundtruth depths and camera poses, but epipolar distance is used here.
+
+ # matching info
+ if alpha == "dynamic":
+ alpha = dynamic_alpha(len(correct_mask))
+ color = error_colormap(epi_errs, conf_thr, alpha=alpha)
+
+ text = [
+ f"#Matches {len(kpts0)}",
+ f"Precision({conf_thr:.2e}) ({100 * precision:.1f}%): {n_correct}/{len(kpts0)}",
+ f"Recall({conf_thr:.2e}) ({100 * recall:.1f}%): {n_correct}/{n_gt_matches}",
+ ]
+
+ # make the figure
+ # import pdb;pdb.set_trace()
+ figure = make_matching_figure(
+ deepcopy(img0), deepcopy(img1), kpts0, kpts1, color, text=text
+ )
+ figure_list.append(figure)
+ return figure
+
+
+def _make_confidence_figure(data, b_id):
+ # TODO: Implement confidence figure
+ raise NotImplementedError()
+
+
+def make_matching_figures(data, config, mode="evaluation"):
+ """Make matching figures for a batch.
+
+ Args:
+ data (Dict): a batch updated by PL_LoFTR.
+ config (Dict): matcher config
+ Returns:
+ figures (Dict[str, List[plt.figure]]
+ """
+ assert mode in ["evaluation", "confidence"] # 'confidence'
+ figures = {mode: []}
+ for b_id in range(data["image0"].size(0)):
+ if mode == "evaluation":
+ fig = _make_evaluation_figure(
+ data, b_id, alpha=config.TRAINER.PLOT_MATCHES_ALPHA
+ )
+ elif mode == "confidence":
+ fig = _make_confidence_figure(data, b_id)
+ else:
+ raise ValueError(f"Unknown plot mode: {mode}")
+ figures[mode].append(fig)
+ return figures
+
+
+def make_matching_figures_offset(data, config, mode="evaluation", side=""):
+ """Make matching figures for a batch.
+
+ Args:
+ data (Dict): a batch updated by PL_LoFTR.
+ config (Dict): matcher config
+ Returns:
+ figures (Dict[str, List[plt.figure]]
+ """
+ assert mode in ["evaluation", "confidence"] # 'confidence'
+ figures = {mode: []}
+ for b_id in range(data["image0"].size(0)):
+ if mode == "evaluation":
+ fig = _make_evaluation_figure_offset(
+ data, b_id, alpha=config.TRAINER.PLOT_MATCHES_ALPHA, side=side
+ )
+ elif mode == "confidence":
+ fig = _make_evaluation_figure_offset(data, b_id)
+ else:
+ raise ValueError(f"Unknown plot mode: {mode}")
+ figures[mode].append(fig)
+ return figures
+
+
+def dynamic_alpha(
+ n_matches, milestones=[0, 300, 1000, 2000], alphas=[1.0, 0.8, 0.4, 0.2]
+):
+ if n_matches == 0:
+ return 1.0
+ ranges = list(zip(alphas, alphas[1:] + [None]))
+ loc = bisect.bisect_right(milestones, n_matches) - 1
+ _range = ranges[loc]
+ if _range[1] is None:
+ return _range[0]
+ return _range[1] + (milestones[loc + 1] - n_matches) / (
+ milestones[loc + 1] - milestones[loc]
+ ) * (_range[0] - _range[1])
+
+
+def error_colormap(err, thr, alpha=1.0):
+ assert alpha <= 1.0 and alpha > 0, f"Invaid alpha value: {alpha}"
+ x = 1 - np.clip(err / (thr * 2), 0, 1)
+ return np.clip(
+ np.stack([2 - x * 2, x * 2, np.zeros_like(x), np.ones_like(x) * alpha], -1),
+ 0,
+ 1,
+ )
diff --git a/imcui/third_party/TopicFM/src/utils/profiler.py b/third_party/ASpanFormer/src/utils/profiler.py
similarity index 88%
rename from imcui/third_party/TopicFM/src/utils/profiler.py
rename to third_party/ASpanFormer/src/utils/profiler.py
index 6d21ed79fb506ef09c75483355402c48a195aaa9..0275ea34e3eb9cceb4ed809bebeda209749f5bc5 100644
--- a/imcui/third_party/TopicFM/src/utils/profiler.py
+++ b/third_party/ASpanFormer/src/utils/profiler.py
@@ -7,7 +7,7 @@ from pytorch_lightning.utilities import rank_zero_only
class InferenceProfiler(SimpleProfiler):
"""
This profiler records duration of actions with cuda.synchronize()
- Use this in test time.
+ Use this in test time.
"""
def __init__(self):
@@ -28,12 +28,13 @@ class InferenceProfiler(SimpleProfiler):
def build_profiler(name):
- if name == 'inference':
+ if name == "inference":
return InferenceProfiler()
- elif name == 'pytorch':
+ elif name == "pytorch":
from pytorch_lightning.profiler import PyTorchProfiler
+
return PyTorchProfiler(use_cuda=True, profile_memory=True, row_limit=100)
elif name is None:
return PassThroughProfiler()
else:
- raise ValueError(f'Invalid profiler: {name}')
+ raise ValueError(f"Invalid profiler: {name}")
diff --git a/imcui/third_party/ASpanFormer/test.py b/third_party/ASpanFormer/test.py
similarity index 54%
rename from imcui/third_party/ASpanFormer/test.py
rename to third_party/ASpanFormer/test.py
index 541ce84662ab4888c6fece30403c5c9983118637..bed3060d931d2f9e5d60ef3b0eb6a9016322fa0f 100644
--- a/imcui/third_party/ASpanFormer/test.py
+++ b/third_party/ASpanFormer/test.py
@@ -10,33 +10,52 @@ from src.lightning.data import MultiSceneDataModule
from src.lightning.lightning_aspanformer import PL_ASpanFormer
import torch
+
def parse_args():
# init a costum parser which will be added into pl.Trainer parser
# check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags
- parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- parser.add_argument(
- 'data_cfg_path', type=str, help='data config path')
- parser.add_argument(
- 'main_cfg_path', type=str, help='main config path')
- parser.add_argument(
- '--ckpt_path', type=str, default="weights/indoor_ds.ckpt", help='path to the checkpoint')
- parser.add_argument(
- '--dump_dir', type=str, default=None, help="if set, the matching results will be dump to dump_dir")
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter
+ )
+ parser.add_argument("data_cfg_path", type=str, help="data config path")
+ parser.add_argument("main_cfg_path", type=str, help="main config path")
parser.add_argument(
- '--profiler_name', type=str, default=None, help='options: [inference, pytorch], or leave it unset')
+ "--ckpt_path",
+ type=str,
+ default="weights/indoor_ds.ckpt",
+ help="path to the checkpoint",
+ )
parser.add_argument(
- '--batch_size', type=int, default=1, help='batch_size per gpu')
+ "--dump_dir",
+ type=str,
+ default=None,
+ help="if set, the matching results will be dump to dump_dir",
+ )
parser.add_argument(
- '--num_workers', type=int, default=2)
+ "--profiler_name",
+ type=str,
+ default=None,
+ help="options: [inference, pytorch], or leave it unset",
+ )
+ parser.add_argument("--batch_size", type=int, default=1, help="batch_size per gpu")
+ parser.add_argument("--num_workers", type=int, default=2)
parser.add_argument(
- '--thr', type=float, default=None, help='modify the coarse-level matching threshold.')
+ "--thr",
+ type=float,
+ default=None,
+ help="modify the coarse-level matching threshold.",
+ )
parser.add_argument(
- '--mode', type=str, default='vanilla', help='modify the coarse-level matching threshold.')
+ "--mode",
+ type=str,
+ default="vanilla",
+ help="modify the coarse-level matching threshold.",
+ )
parser = pl.Trainer.add_argparse_args(parser)
return parser.parse_args()
-if __name__ == '__main__':
+if __name__ == "__main__":
# parse arguments
args = parse_args()
pprint.pprint(vars(args))
@@ -55,7 +74,12 @@ if __name__ == '__main__':
# lightning module
profiler = build_profiler(args.profiler_name)
- model = PL_ASpanFormer(config, pretrained_ckpt=args.ckpt_path, profiler=profiler, dump_dir=args.dump_dir)
+ model = PL_ASpanFormer(
+ config,
+ pretrained_ckpt=args.ckpt_path,
+ profiler=profiler,
+ dump_dir=args.dump_dir,
+ )
loguru_logger.info(f"ASpanFormer-lightning initialized!")
# lightning data
@@ -63,7 +87,9 @@ if __name__ == '__main__':
loguru_logger.info(f"DataModule initialized!")
# lightning trainer
- trainer = pl.Trainer.from_argparse_args(args, replace_sampler_ddp=False, logger=False)
+ trainer = pl.Trainer.from_argparse_args(
+ args, replace_sampler_ddp=False, logger=False
+ )
loguru_logger.info(f"Start testing!")
trainer.test(model, datamodule=data_module, verbose=False)
diff --git a/imcui/third_party/ASpanFormer/tools/SensorData.py b/third_party/ASpanFormer/tools/SensorData.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/tools/SensorData.py
rename to third_party/ASpanFormer/tools/SensorData.py
diff --git a/third_party/ASpanFormer/tools/extract.py b/third_party/ASpanFormer/tools/extract.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3dea56a14f6c100b2c53978678bab69a656cdeb
--- /dev/null
+++ b/third_party/ASpanFormer/tools/extract.py
@@ -0,0 +1,81 @@
+import os
+import glob
+from re import split
+from tqdm import tqdm
+from multiprocessing import Pool
+from functools import partial
+
+scannet_dir = "/root/data/ScanNet-v2-1.0.0/data/raw"
+dump_dir = "/root/data/scannet_dump"
+num_process = 32
+
+
+def extract(seq, scannet_dir, split, dump_dir):
+ assert split == "train" or split == "test"
+ if not os.path.exists(os.path.join(dump_dir, split, seq)):
+ os.mkdir(os.path.join(dump_dir, split, seq))
+ cmd = (
+ "python reader.py --filename "
+ + os.path.join(
+ scannet_dir,
+ "scans" if split == "train" else "scans_test",
+ seq,
+ seq + ".sens",
+ )
+ + " --output_path "
+ + os.path.join(dump_dir, split, seq)
+ + " --export_depth_images --export_color_images --export_poses --export_intrinsics"
+ )
+ os.system(cmd)
+
+
+if __name__ == "__main__":
+ if not os.path.exists(dump_dir):
+ os.mkdir(dump_dir)
+ os.mkdir(os.path.join(dump_dir, "train"))
+ os.mkdir(os.path.join(dump_dir, "test"))
+
+ train_seq_list = [
+ seq.split("/")[-1]
+ for seq in glob.glob(os.path.join(scannet_dir, "scans", "scene*"))
+ ]
+ test_seq_list = [
+ seq.split("/")[-1]
+ for seq in glob.glob(os.path.join(scannet_dir, "scans_test", "scene*"))
+ ]
+
+ extract_train = partial(
+ extract, scannet_dir=scannet_dir, split="train", dump_dir=dump_dir
+ )
+ extract_test = partial(
+ extract, scannet_dir=scannet_dir, split="test", dump_dir=dump_dir
+ )
+
+ num_train_iter = (
+ len(train_seq_list) // num_process
+ if len(train_seq_list) % num_process == 0
+ else len(train_seq_list) // num_process + 1
+ )
+ num_test_iter = (
+ len(test_seq_list) // num_process
+ if len(test_seq_list) % num_process == 0
+ else len(test_seq_list) // num_process + 1
+ )
+
+ pool = Pool(num_process)
+ for index in tqdm(range(num_train_iter)):
+ seq_list = train_seq_list[
+ index * num_process : min((index + 1) * num_process, len(train_seq_list))
+ ]
+ pool.map(extract_train, seq_list)
+ pool.close()
+ pool.join()
+
+ pool = Pool(num_process)
+ for index in tqdm(range(num_test_iter)):
+ seq_list = test_seq_list[
+ index * num_process : min((index + 1) * num_process, len(test_seq_list))
+ ]
+ pool.map(extract_test, seq_list)
+ pool.close()
+ pool.join()
diff --git a/imcui/third_party/ASpanFormer/tools/preprocess_scene.py b/third_party/ASpanFormer/tools/preprocess_scene.py
similarity index 59%
rename from imcui/third_party/ASpanFormer/tools/preprocess_scene.py
rename to third_party/ASpanFormer/tools/preprocess_scene.py
index d20c0d070243519d67bbd25668ff5eb1657474be..5364058829b7e45eabd61a32a591711645fc1ded 100644
--- a/imcui/third_party/ASpanFormer/tools/preprocess_scene.py
+++ b/third_party/ASpanFormer/tools/preprocess_scene.py
@@ -6,78 +6,63 @@ import numpy as np
import os
-parser = argparse.ArgumentParser(description='MegaDepth preprocessing script')
+parser = argparse.ArgumentParser(description="MegaDepth preprocessing script")
-parser.add_argument(
- '--base_path', type=str, required=True,
- help='path to MegaDepth'
-)
-parser.add_argument(
- '--scene_id', type=str, required=True,
- help='scene ID'
-)
+parser.add_argument("--base_path", type=str, required=True, help="path to MegaDepth")
+parser.add_argument("--scene_id", type=str, required=True, help="scene ID")
parser.add_argument(
- '--output_path', type=str, required=True,
- help='path to the output directory'
+ "--output_path", type=str, required=True, help="path to the output directory"
)
args = parser.parse_args()
base_path = args.base_path
# Remove the trailing / if need be.
-if base_path[-1] in ['/', '\\']:
- base_path = base_path[: - 1]
+if base_path[-1] in ["/", "\\"]:
+ base_path = base_path[:-1]
scene_id = args.scene_id
-base_depth_path = os.path.join(
- base_path, 'phoenix/S6/zl548/MegaDepth_v1'
-)
-base_undistorted_sfm_path = os.path.join(
- base_path, 'Undistorted_SfM'
-)
+base_depth_path = os.path.join(base_path, "phoenix/S6/zl548/MegaDepth_v1")
+base_undistorted_sfm_path = os.path.join(base_path, "Undistorted_SfM")
undistorted_sparse_path = os.path.join(
- base_undistorted_sfm_path, scene_id, 'sparse-txt'
+ base_undistorted_sfm_path, scene_id, "sparse-txt"
)
if not os.path.exists(undistorted_sparse_path):
exit()
-depths_path = os.path.join(
- base_depth_path, scene_id, 'dense0', 'depths'
-)
+depths_path = os.path.join(base_depth_path, scene_id, "dense0", "depths")
if not os.path.exists(depths_path):
exit()
-images_path = os.path.join(
- base_undistorted_sfm_path, scene_id, 'images'
-)
+images_path = os.path.join(base_undistorted_sfm_path, scene_id, "images")
if not os.path.exists(images_path):
exit()
# Process cameras.txt
-with open(os.path.join(undistorted_sparse_path, 'cameras.txt'), 'r') as f:
- raw = f.readlines()[3 :] # skip the header
+with open(os.path.join(undistorted_sparse_path, "cameras.txt"), "r") as f:
+ raw = f.readlines()[3:] # skip the header
camera_intrinsics = {}
for camera in raw:
- camera = camera.split(' ')
- camera_intrinsics[int(camera[0])] = [float(elem) for elem in camera[2 :]]
+ camera = camera.split(" ")
+ camera_intrinsics[int(camera[0])] = [float(elem) for elem in camera[2:]]
# Process points3D.txt
-with open(os.path.join(undistorted_sparse_path, 'points3D.txt'), 'r') as f:
- raw = f.readlines()[3 :] # skip the header
+with open(os.path.join(undistorted_sparse_path, "points3D.txt"), "r") as f:
+ raw = f.readlines()[3:] # skip the header
points3D = {}
for point3D in raw:
- point3D = point3D.split(' ')
- points3D[int(point3D[0])] = np.array([
- float(point3D[1]), float(point3D[2]), float(point3D[3])
- ])
-
+ point3D = point3D.split(" ")
+ points3D[int(point3D[0])] = np.array(
+ [float(point3D[1]), float(point3D[2]), float(point3D[3])]
+ )
+
# Process images.txt
-with open(os.path.join(undistorted_sparse_path, 'images.txt'), 'r') as f:
- raw = f.readlines()[4 :] # skip the header
+with open(os.path.join(undistorted_sparse_path, "images.txt"), "r") as f:
+ raw = f.readlines()[4:] # skip the header
image_id_to_idx = {}
image_names = []
@@ -85,19 +70,19 @@ raw_pose = []
camera = []
points3D_id_to_2D = []
n_points3D = []
-for idx, (image, points) in enumerate(zip(raw[:: 2], raw[1 :: 2])):
- image = image.split(' ')
- points = points.split(' ')
+for idx, (image, points) in enumerate(zip(raw[::2], raw[1::2])):
+ image = image.split(" ")
+ points = points.split(" ")
image_id_to_idx[int(image[0])] = idx
- image_name = image[-1].strip('\n')
+ image_name = image[-1].strip("\n")
image_names.append(image_name)
- raw_pose.append([float(elem) for elem in image[1 : -2]])
+ raw_pose.append([float(elem) for elem in image[1:-2]])
camera.append(int(image[-2]))
current_points3D_id_to_2D = {}
- for x, y, point3D_id in zip(points[:: 3], points[1 :: 3], points[2 :: 3]):
+ for x, y, point3D_id in zip(points[::3], points[1::3], points[2::3]):
if int(point3D_id) == -1:
continue
current_points3D_id_to_2D[int(point3D_id)] = [float(x), float(y)]
@@ -110,12 +95,10 @@ image_paths = []
depth_paths = []
for image_name in image_names:
image_path = os.path.join(images_path, image_name)
-
+
# Path to the depth file
- depth_path = os.path.join(
- depths_path, '%s.h5' % os.path.splitext(image_name)[0]
- )
-
+ depth_path = os.path.join(depths_path, "%s.h5" % os.path.splitext(image_name)[0])
+
if os.path.exists(depth_path):
# Check if depth map or background / foreground mask
file_size = os.stat(depth_path).st_size
@@ -152,32 +135,22 @@ for idx, image_name in enumerate(image_names):
intrinsics.append(K)
image_pose = raw_pose[idx]
- qvec = image_pose[: 4]
+ qvec = image_pose[:4]
qvec = qvec / np.linalg.norm(qvec)
w, x, y, z = qvec
- R = np.array([
- [
- 1 - 2 * y * y - 2 * z * z,
- 2 * x * y - 2 * z * w,
- 2 * x * z + 2 * y * w
- ],
+ R = np.array(
[
- 2 * x * y + 2 * z * w,
- 1 - 2 * x * x - 2 * z * z,
- 2 * y * z - 2 * x * w
- ],
- [
- 2 * x * z - 2 * y * w,
- 2 * y * z + 2 * x * w,
- 1 - 2 * x * x - 2 * y * y
+ [1 - 2 * y * y - 2 * z * z, 2 * x * y - 2 * z * w, 2 * x * z + 2 * y * w],
+ [2 * x * y + 2 * z * w, 1 - 2 * x * x - 2 * z * z, 2 * y * z - 2 * x * w],
+ [2 * x * z - 2 * y * w, 2 * y * z + 2 * x * w, 1 - 2 * x * x - 2 * y * y],
]
- ])
+ )
principal_axis.append(R[2, :])
- t = image_pose[4 : 7]
+ t = image_pose[4:7]
# World-to-Camera pose
current_pose = np.zeros([4, 4])
- current_pose[: 3, : 3] = R
- current_pose[: 3, 3] = t
+ current_pose[:3, :3] = R
+ current_pose[:3, 3] = t
current_pose[3, 3] = 1
# Camera-to-World pose
# pose = np.zeros([4, 4])
@@ -185,38 +158,38 @@ for idx, image_name in enumerate(image_names):
# pose[: 3, 3] = -np.matmul(np.transpose(R), t)
# pose[3, 3] = 1
poses.append(current_pose)
-
+
current_points3D_id_to_ndepth = {}
for point3D_id in points3D_id_to_2D[idx].keys():
p3d = points3D[point3D_id]
- current_points3D_id_to_ndepth[point3D_id] = (np.dot(R[2, :], p3d) + t[2]) / (.5 * (K[0, 0] + K[1, 1]))
+ current_points3D_id_to_ndepth[point3D_id] = (np.dot(R[2, :], p3d) + t[2]) / (
+ 0.5 * (K[0, 0] + K[1, 1])
+ )
points3D_id_to_ndepth.append(current_points3D_id_to_ndepth)
principal_axis = np.array(principal_axis)
-angles = np.rad2deg(np.arccos(
- np.clip(
- np.dot(principal_axis, np.transpose(principal_axis)),
- -1, 1
- )
-))
+angles = np.rad2deg(
+ np.arccos(np.clip(np.dot(principal_axis, np.transpose(principal_axis)), -1, 1))
+)
# Compute overlap score
-overlap_matrix = np.full([n_images, n_images], -1.)
-scale_ratio_matrix = np.full([n_images, n_images], -1.)
+overlap_matrix = np.full([n_images, n_images], -1.0)
+scale_ratio_matrix = np.full([n_images, n_images], -1.0)
for idx1 in range(n_images):
if image_paths[idx1] is None or depth_paths[idx1] is None:
continue
for idx2 in range(idx1 + 1, n_images):
if image_paths[idx2] is None or depth_paths[idx2] is None:
continue
- matches = (
- points3D_id_to_2D[idx1].keys() &
- points3D_id_to_2D[idx2].keys()
- )
+ matches = points3D_id_to_2D[idx1].keys() & points3D_id_to_2D[idx2].keys()
min_num_points3D = min(
len(points3D_id_to_2D[idx1]), len(points3D_id_to_2D[idx2])
)
- overlap_matrix[idx1, idx2] = len(matches) / len(points3D_id_to_2D[idx1]) # min_num_points3D
- overlap_matrix[idx2, idx1] = len(matches) / len(points3D_id_to_2D[idx2]) # min_num_points3D
+ overlap_matrix[idx1, idx2] = len(matches) / len(
+ points3D_id_to_2D[idx1]
+ ) # min_num_points3D
+ overlap_matrix[idx2, idx1] = len(matches) / len(
+ points3D_id_to_2D[idx2]
+ ) # min_num_points3D
if len(matches) == 0:
continue
points3D_id_to_ndepth1 = points3D_id_to_ndepth[idx1]
@@ -228,7 +201,7 @@ for idx1 in range(n_images):
scale_ratio_matrix[idx2, idx1] = min_scale_ratio
np.savez(
- os.path.join(args.output_path, '%s.npz' % scene_id),
+ os.path.join(args.output_path, "%s.npz" % scene_id),
image_paths=image_paths,
depth_paths=depth_paths,
intrinsics=intrinsics,
@@ -238,5 +211,5 @@ np.savez(
angles=angles,
n_points3D=n_points3D,
points3D_id_to_2D=points3D_id_to_2D,
- points3D_id_to_ndepth=points3D_id_to_ndepth
-)
\ No newline at end of file
+ points3D_id_to_ndepth=points3D_id_to_ndepth,
+)
diff --git a/third_party/ASpanFormer/tools/preprocess_undistorted_megadepth.sh b/third_party/ASpanFormer/tools/preprocess_undistorted_megadepth.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c983ee464bb36439d68f52d60f981414e2c6e84b
--- /dev/null
+++ b/third_party/ASpanFormer/tools/preprocess_undistorted_megadepth.sh
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+if [[ $# != 2 ]]; then
+ echo 'Usage: bash preprocess_megadepth.sh /path/to/megadepth /output/path'
+ exit
+fi
+
+export dataset_path=$1
+export output_path=$2
+
+mkdir $output_path
+echo 0
+ls $dataset_path/Undistorted_SfM | xargs -P 8 -I % sh -c 'echo %; python preprocess_scene.py --base_path $dataset_path --scene_id % --output_path $output_path'
\ No newline at end of file
diff --git a/third_party/ASpanFormer/tools/reader.py b/third_party/ASpanFormer/tools/reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..2734a7796ef8235bdbc1be317b6618f3d3185319
--- /dev/null
+++ b/third_party/ASpanFormer/tools/reader.py
@@ -0,0 +1,50 @@
+import argparse
+import os, sys
+
+from SensorData import SensorData
+
+# params
+parser = argparse.ArgumentParser()
+# data paths
+parser.add_argument("--filename", required=True, help="path to sens file to read")
+parser.add_argument("--output_path", required=True, help="path to output folder")
+parser.add_argument(
+ "--export_depth_images", dest="export_depth_images", action="store_true"
+)
+parser.add_argument(
+ "--export_color_images", dest="export_color_images", action="store_true"
+)
+parser.add_argument("--export_poses", dest="export_poses", action="store_true")
+parser.add_argument(
+ "--export_intrinsics", dest="export_intrinsics", action="store_true"
+)
+parser.set_defaults(
+ export_depth_images=False,
+ export_color_images=False,
+ export_poses=False,
+ export_intrinsics=False,
+)
+
+opt = parser.parse_args()
+print(opt)
+
+
+def main():
+ if not os.path.exists(opt.output_path):
+ os.makedirs(opt.output_path)
+ # load the data
+ sys.stdout.write("loading %s..." % opt.filename)
+ sd = SensorData(opt.filename)
+ sys.stdout.write("loaded!\n")
+ if opt.export_depth_images:
+ sd.export_depth_images(os.path.join(opt.output_path, "depth"))
+ if opt.export_color_images:
+ sd.export_color_images(os.path.join(opt.output_path, "color"))
+ if opt.export_poses:
+ sd.export_poses(os.path.join(opt.output_path, "pose"))
+ if opt.export_intrinsics:
+ sd.export_intrinsics(os.path.join(opt.output_path, "intrinsic"))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/third_party/ASpanFormer/tools/undistort_mega.py b/third_party/ASpanFormer/tools/undistort_mega.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcd5ff2d77cd45dc9e5cebc48d7a173e31e68caf
--- /dev/null
+++ b/third_party/ASpanFormer/tools/undistort_mega.py
@@ -0,0 +1,71 @@
+import argparse
+
+import imagesize
+
+import os
+
+import subprocess
+
+parser = argparse.ArgumentParser(description="MegaDepth Undistortion")
+
+parser.add_argument(
+ "--colmap_path", type=str, default="/usr/bin/", help="path to colmap executable"
+)
+parser.add_argument(
+ "--base_path", type=str, default="/root/MegaDepth", help="path to MegaDepth"
+)
+
+args = parser.parse_args()
+
+sfm_path = os.path.join(args.base_path, "MegaDepth_v1_SfM")
+base_depth_path = os.path.join(args.base_path, "phoenix/S6/zl548/MegaDepth_v1")
+output_path = os.path.join(args.base_path, "Undistorted_SfM")
+
+os.mkdir(output_path)
+
+for scene_name in os.listdir(base_depth_path):
+ current_output_path = os.path.join(output_path, scene_name)
+ os.mkdir(current_output_path)
+
+ image_path = os.path.join(base_depth_path, scene_name, "dense0", "imgs")
+ if not os.path.exists(image_path):
+ continue
+
+ # Find the maximum image size in scene.
+ max_image_size = 0
+ for image_name in os.listdir(image_path):
+ max_image_size = max(
+ max_image_size, max(imagesize.get(os.path.join(image_path, image_name)))
+ )
+
+ # Undistort the images and update the reconstruction.
+ subprocess.call(
+ [
+ os.path.join(args.colmap_path, "colmap"),
+ "image_undistorter",
+ "--image_path",
+ os.path.join(sfm_path, scene_name, "images"),
+ "--input_path",
+ os.path.join(sfm_path, scene_name, "sparse", "manhattan", "0"),
+ "--output_path",
+ current_output_path,
+ "--max_image_size",
+ str(max_image_size),
+ ]
+ )
+
+ # Transform the reconstruction to raw text format.
+ sparse_txt_path = os.path.join(current_output_path, "sparse-txt")
+ os.mkdir(sparse_txt_path)
+ subprocess.call(
+ [
+ os.path.join(args.colmap_path, "colmap"),
+ "model_converter",
+ "--input_path",
+ os.path.join(current_output_path, "sparse"),
+ "--output_path",
+ sparse_txt_path,
+ "--output_type",
+ "TXT",
+ ]
+ )
diff --git a/imcui/third_party/ASpanFormer/train.py b/third_party/ASpanFormer/train.py
similarity index 58%
rename from imcui/third_party/ASpanFormer/train.py
rename to third_party/ASpanFormer/train.py
index 21f644763711481e84863ed5d861ec57d95f2d5c..f1aeb79f630932b539500544d4249b1237d06605 100644
--- a/imcui/third_party/ASpanFormer/train.py
+++ b/third_party/ASpanFormer/train.py
@@ -23,41 +23,58 @@ loguru_logger = get_rank_zero_only_logger(loguru_logger)
def parse_args():
def str2bool(v):
return v.lower() in ("true", "1")
+
# init a costum parser which will be added into pl.Trainer parser
# check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags
parser = argparse.ArgumentParser(
- formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- parser.add_argument(
- 'data_cfg_path', type=str, help='data config path')
- parser.add_argument(
- 'main_cfg_path', type=str, help='main config path')
- parser.add_argument(
- '--exp_name', type=str, default='default_exp_name')
- parser.add_argument(
- '--batch_size', type=int, default=4, help='batch_size per gpu')
- parser.add_argument(
- '--num_workers', type=int, default=4)
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter
+ )
+ parser.add_argument("data_cfg_path", type=str, help="data config path")
+ parser.add_argument("main_cfg_path", type=str, help="main config path")
+ parser.add_argument("--exp_name", type=str, default="default_exp_name")
+ parser.add_argument("--batch_size", type=int, default=4, help="batch_size per gpu")
+ parser.add_argument("--num_workers", type=int, default=4)
parser.add_argument(
- '--pin_memory', type=lambda x: bool(strtobool(x)),
- nargs='?', default=True, help='whether loading data to pinned memory or not')
+ "--pin_memory",
+ type=lambda x: bool(strtobool(x)),
+ nargs="?",
+ default=True,
+ help="whether loading data to pinned memory or not",
+ )
parser.add_argument(
- '--ckpt_path', type=str, default=None,
- help='pretrained checkpoint path, helpful for using a pre-trained coarse-only ASpanFormer')
+ "--ckpt_path",
+ type=str,
+ default=None,
+ help="pretrained checkpoint path, helpful for using a pre-trained coarse-only ASpanFormer",
+ )
parser.add_argument(
- '--disable_ckpt', action='store_true',
- help='disable checkpoint saving (useful for debugging).')
+ "--disable_ckpt",
+ action="store_true",
+ help="disable checkpoint saving (useful for debugging).",
+ )
parser.add_argument(
- '--profiler_name', type=str, default=None,
- help='options: [inference, pytorch], or leave it unset')
+ "--profiler_name",
+ type=str,
+ default=None,
+ help="options: [inference, pytorch], or leave it unset",
+ )
parser.add_argument(
- '--parallel_load_data', action='store_true',
- help='load datasets in with multiple processes.')
+ "--parallel_load_data",
+ action="store_true",
+ help="load datasets in with multiple processes.",
+ )
parser.add_argument(
- '--mode', type=str, default='vanilla',
- help='pretrained checkpoint path, helpful for using a pre-trained coarse-only ASpanFormer')
+ "--mode",
+ type=str,
+ default="vanilla",
+ help="pretrained checkpoint path, helpful for using a pre-trained coarse-only ASpanFormer",
+ )
parser.add_argument(
- '--ini', type=str2bool, default=False,
- help='pretrained checkpoint path, helpful for using a pre-trained coarse-only ASpanFormer')
+ "--ini",
+ type=str2bool,
+ default=False,
+ help="pretrained checkpoint path, helpful for using a pre-trained coarse-only ASpanFormer",
+ )
parser = pl.Trainer.add_argparse_args(parser)
return parser.parse_args()
@@ -83,8 +100,7 @@ def main():
_scaling = config.TRAINER.TRUE_BATCH_SIZE / config.TRAINER.CANONICAL_BS
config.TRAINER.SCALING = _scaling
config.TRAINER.TRUE_LR = config.TRAINER.CANONICAL_LR * _scaling
- config.TRAINER.WARMUP_STEP = math.floor(
- config.TRAINER.WARMUP_STEP / _scaling)
+ config.TRAINER.WARMUP_STEP = math.floor(config.TRAINER.WARMUP_STEP / _scaling)
# lightning module
profiler = build_profiler(args.profiler_name)
@@ -97,16 +113,22 @@ def main():
# TensorBoard Logger
logger = TensorBoardLogger(
- save_dir='logs/tb_logs', name=args.exp_name, default_hp_metric=False)
- ckpt_dir = Path(logger.log_dir) / 'checkpoints'
+ save_dir="logs/tb_logs", name=args.exp_name, default_hp_metric=False
+ )
+ ckpt_dir = Path(logger.log_dir) / "checkpoints"
# Callbacks
# TODO: update ModelCheckpoint to monitor multiple metrics
- ckpt_callback = ModelCheckpoint(monitor='auc@10', verbose=True, save_top_k=5, mode='max',
- save_last=True,
- dirpath=str(ckpt_dir),
- filename='{epoch}-{auc@5:.3f}-{auc@10:.3f}-{auc@20:.3f}')
- lr_monitor = LearningRateMonitor(logging_interval='step')
+ ckpt_callback = ModelCheckpoint(
+ monitor="auc@10",
+ verbose=True,
+ save_top_k=5,
+ mode="max",
+ save_last=True,
+ dirpath=str(ckpt_dir),
+ filename="{epoch}-{auc@5:.3f}-{auc@10:.3f}-{auc@20:.3f}",
+ )
+ lr_monitor = LearningRateMonitor(logging_interval="step")
callbacks = [lr_monitor]
if not args.disable_ckpt:
callbacks.append(ckpt_callback)
@@ -114,21 +136,24 @@ def main():
# Lightning Trainer
trainer = pl.Trainer.from_argparse_args(
args,
- plugins=DDPPlugin(find_unused_parameters=False,
- num_nodes=args.num_nodes,
- sync_batchnorm=config.TRAINER.WORLD_SIZE > 0),
+ plugins=DDPPlugin(
+ find_unused_parameters=False,
+ num_nodes=args.num_nodes,
+ sync_batchnorm=config.TRAINER.WORLD_SIZE > 0,
+ ),
gradient_clip_val=config.TRAINER.GRADIENT_CLIPPING,
callbacks=callbacks,
logger=logger,
sync_batchnorm=config.TRAINER.WORLD_SIZE > 0,
replace_sampler_ddp=False, # use custom sampler
reload_dataloaders_every_epoch=False, # avoid repeated samples!
- weights_summary='full',
- profiler=profiler)
+ weights_summary="full",
+ profiler=profiler,
+ )
loguru_logger.info(f"Trainer initialized!")
loguru_logger.info(f"Start training!")
trainer.fit(model, datamodule=data_module)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/third_party/COTR/.gitignore b/third_party/COTR/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..0d20b6487c61e7d1bde93acf4a14b7a89083a16d
--- /dev/null
+++ b/third_party/COTR/.gitignore
@@ -0,0 +1 @@
+*.pyc
diff --git a/imcui/third_party/COTR/COTR/cameras/camera_pose.py b/third_party/COTR/COTR/cameras/camera_pose.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/cameras/camera_pose.py
rename to third_party/COTR/COTR/cameras/camera_pose.py
diff --git a/imcui/third_party/COTR/COTR/cameras/capture.py b/third_party/COTR/COTR/cameras/capture.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/cameras/capture.py
rename to third_party/COTR/COTR/cameras/capture.py
diff --git a/imcui/third_party/COTR/COTR/cameras/pinhole_camera.py b/third_party/COTR/COTR/cameras/pinhole_camera.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/cameras/pinhole_camera.py
rename to third_party/COTR/COTR/cameras/pinhole_camera.py
diff --git a/imcui/third_party/COTR/COTR/datasets/colmap_helper.py b/third_party/COTR/COTR/datasets/colmap_helper.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/datasets/colmap_helper.py
rename to third_party/COTR/COTR/datasets/colmap_helper.py
diff --git a/imcui/third_party/COTR/COTR/datasets/cotr_dataset.py b/third_party/COTR/COTR/datasets/cotr_dataset.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/datasets/cotr_dataset.py
rename to third_party/COTR/COTR/datasets/cotr_dataset.py
diff --git a/imcui/third_party/COTR/COTR/datasets/megadepth_dataset.py b/third_party/COTR/COTR/datasets/megadepth_dataset.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/datasets/megadepth_dataset.py
rename to third_party/COTR/COTR/datasets/megadepth_dataset.py
diff --git a/imcui/third_party/COTR/COTR/global_configs/__init__.py b/third_party/COTR/COTR/global_configs/__init__.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/global_configs/__init__.py
rename to third_party/COTR/COTR/global_configs/__init__.py
diff --git a/third_party/COTR/COTR/global_configs/commons.json b/third_party/COTR/COTR/global_configs/commons.json
new file mode 100644
index 0000000000000000000000000000000000000000..cb8ff5b015f4c64b09f33bd40bb09a5ca932698c
--- /dev/null
+++ b/third_party/COTR/COTR/global_configs/commons.json
@@ -0,0 +1 @@
+{"out": "../../out", "tb_out": "../../tb_out"}
\ No newline at end of file
diff --git a/third_party/COTR/COTR/global_configs/dataset_config.json b/third_party/COTR/COTR/global_configs/dataset_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..676d93743ae8b87b11b0049e138027fd9af8d46b
--- /dev/null
+++ b/third_party/COTR/COTR/global_configs/dataset_config.json
@@ -0,0 +1,41 @@
+{
+ "megadepth": {
+ "valid_list_json": "/media/jiangwei/data_ssd/MegaDepth_v1_SfM/megadepth_valid_list.json",
+ "train_json": "/media/jiangwei/data_ssd/MegaDepth_v1_SfM/megadepth_train.json",
+ "val_json": "/media/jiangwei/data_ssd/MegaDepth_v1_SfM/megadepth_val.json",
+ "test_json": "/media/jiangwei/data_ssd/MegaDepth_v1_SfM/megadepth_test.json",
+ "scene_dir": "/media/jiangwei/data_ssd/MegaDepth_v1_SfM/{0}/sparse/manhattan/{1}_rectified/sparse",
+ "image_dir": "/media/jiangwei/data_ssd/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/{0}/dense{1}/imgs",
+ "depth_dir": "/media/jiangwei/data_ssd/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/{0}/dense{1}/depths"
+ },
+
+ "megadepth_sushi": {
+ "valid_list_json": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/megadepth_valid_list.json",
+ "train_json": "/scratch/programs/COTR/sample_data/megadepth_train.json",
+ "val_json": "/scratch/programs/COTR/sample_data/megadepth_val.json",
+ "test_json": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/megadepth_test.json",
+ "scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/{0}/sparse/manhattan/{1}_rectified/sparse",
+ "image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/{0}/dense{1}/imgs",
+ "depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/{0}/dense{1}/depths"
+ },
+
+ "megadepth_sockeye": {
+ "valid_list_json": "/project/pr-kmyi-1/jiangwei/datasets/megadepth/MegaDepth_v1_SfM/megadepth_valid_list.json",
+ "train_json": "/project/pr-kmyi-1/jiangwei/datasets/megadepth/MegaDepth_v1_SfM/megadepth_train.json",
+ "val_json": "/project/pr-kmyi-1/jiangwei/datasets/megadepth/MegaDepth_v1_SfM/megadepth_val.json",
+ "test_json": "/project/pr-kmyi-1/jiangwei/datasets/megadepth/MegaDepth_v1_SfM/megadepth_test.json",
+ "scene_dir": "/project/pr-kmyi-1/jiangwei/datasets/megadepth/MegaDepth_v1_SfM/{0}/sparse/manhattan/{1}_rectified/sparse",
+ "image_dir": "/project/pr-kmyi-1/jiangwei/datasets/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/{0}/dense{1}/imgs",
+ "depth_dir": "/project/pr-kmyi-1/jiangwei/datasets/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/{0}/dense{1}/depths"
+ },
+
+ "megadepth_snubfin": {
+ "valid_list_json": "/ubc/cs/research/kmyi/datasets/megadepth/MegaDepth_v1_SfM/megadepth_valid_list.json",
+ "train_json": "/ubc/cs/research/kmyi/jw221/programs/COTR/sample_data/megadepth_train.json",
+ "val_json": "/ubc/cs/research/kmyi/jw221/programs/COTR/sample_data/megadepth_val.json",
+ "test_json": "/ubc/cs/research/kmyi/datasets/megadepth/MegaDepth_v1_SfM/megadepth_test.json",
+ "scene_dir": "/ubc/cs/research/kmyi/datasets/megadepth/MegaDepth_v1_SfM/{0}/sparse/manhattan/{1}_rectified/sparse",
+ "image_dir": "/ubc/cs/research/kmyi/datasets/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/{0}/dense{1}/imgs",
+ "depth_dir": "/ubc/cs/research/kmyi/datasets/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/{0}/dense{1}/depths"
+ }
+}
diff --git a/imcui/third_party/COTR/COTR/inference/inference_helper.py b/third_party/COTR/COTR/inference/inference_helper.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/inference/inference_helper.py
rename to third_party/COTR/COTR/inference/inference_helper.py
diff --git a/imcui/third_party/COTR/COTR/inference/refinement_task.py b/third_party/COTR/COTR/inference/refinement_task.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/inference/refinement_task.py
rename to third_party/COTR/COTR/inference/refinement_task.py
diff --git a/imcui/third_party/COTR/COTR/inference/sparse_engine.py b/third_party/COTR/COTR/inference/sparse_engine.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/inference/sparse_engine.py
rename to third_party/COTR/COTR/inference/sparse_engine.py
diff --git a/imcui/third_party/COTR/COTR/models/__init__.py b/third_party/COTR/COTR/models/__init__.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/models/__init__.py
rename to third_party/COTR/COTR/models/__init__.py
diff --git a/imcui/third_party/COTR/COTR/models/backbone.py b/third_party/COTR/COTR/models/backbone.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/models/backbone.py
rename to third_party/COTR/COTR/models/backbone.py
diff --git a/imcui/third_party/COTR/COTR/models/cotr_model.py b/third_party/COTR/COTR/models/cotr_model.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/models/cotr_model.py
rename to third_party/COTR/COTR/models/cotr_model.py
diff --git a/imcui/third_party/COTR/COTR/models/misc.py b/third_party/COTR/COTR/models/misc.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/models/misc.py
rename to third_party/COTR/COTR/models/misc.py
diff --git a/imcui/third_party/COTR/COTR/models/position_encoding.py b/third_party/COTR/COTR/models/position_encoding.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/models/position_encoding.py
rename to third_party/COTR/COTR/models/position_encoding.py
diff --git a/imcui/third_party/COTR/COTR/models/transformer.py b/third_party/COTR/COTR/models/transformer.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/models/transformer.py
rename to third_party/COTR/COTR/models/transformer.py
diff --git a/imcui/third_party/COTR/COTR/options/options.py b/third_party/COTR/COTR/options/options.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/options/options.py
rename to third_party/COTR/COTR/options/options.py
diff --git a/imcui/third_party/COTR/COTR/options/options_utils.py b/third_party/COTR/COTR/options/options_utils.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/options/options_utils.py
rename to third_party/COTR/COTR/options/options_utils.py
diff --git a/imcui/third_party/COTR/COTR/projector/pcd_projector.py b/third_party/COTR/COTR/projector/pcd_projector.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/projector/pcd_projector.py
rename to third_party/COTR/COTR/projector/pcd_projector.py
diff --git a/imcui/third_party/COTR/COTR/sfm_scenes/knn_search.py b/third_party/COTR/COTR/sfm_scenes/knn_search.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/sfm_scenes/knn_search.py
rename to third_party/COTR/COTR/sfm_scenes/knn_search.py
diff --git a/imcui/third_party/COTR/COTR/sfm_scenes/sfm_scenes.py b/third_party/COTR/COTR/sfm_scenes/sfm_scenes.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/sfm_scenes/sfm_scenes.py
rename to third_party/COTR/COTR/sfm_scenes/sfm_scenes.py
diff --git a/imcui/third_party/COTR/COTR/trainers/base_trainer.py b/third_party/COTR/COTR/trainers/base_trainer.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/trainers/base_trainer.py
rename to third_party/COTR/COTR/trainers/base_trainer.py
diff --git a/imcui/third_party/COTR/COTR/trainers/cotr_trainer.py b/third_party/COTR/COTR/trainers/cotr_trainer.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/trainers/cotr_trainer.py
rename to third_party/COTR/COTR/trainers/cotr_trainer.py
diff --git a/imcui/third_party/COTR/COTR/trainers/tensorboard_helper.py b/third_party/COTR/COTR/trainers/tensorboard_helper.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/trainers/tensorboard_helper.py
rename to third_party/COTR/COTR/trainers/tensorboard_helper.py
diff --git a/imcui/third_party/COTR/COTR/transformations/transform_basics.py b/third_party/COTR/COTR/transformations/transform_basics.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/transformations/transform_basics.py
rename to third_party/COTR/COTR/transformations/transform_basics.py
diff --git a/imcui/third_party/COTR/COTR/transformations/transformations.py b/third_party/COTR/COTR/transformations/transformations.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/transformations/transformations.py
rename to third_party/COTR/COTR/transformations/transformations.py
diff --git a/imcui/third_party/COTR/COTR/utils/constants.py b/third_party/COTR/COTR/utils/constants.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/utils/constants.py
rename to third_party/COTR/COTR/utils/constants.py
diff --git a/imcui/third_party/COTR/COTR/utils/debug_utils.py b/third_party/COTR/COTR/utils/debug_utils.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/utils/debug_utils.py
rename to third_party/COTR/COTR/utils/debug_utils.py
diff --git a/imcui/third_party/COTR/COTR/utils/utils.py b/third_party/COTR/COTR/utils/utils.py
similarity index 100%
rename from imcui/third_party/COTR/COTR/utils/utils.py
rename to third_party/COTR/COTR/utils/utils.py
diff --git a/third_party/COTR/LICENSE b/third_party/COTR/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/third_party/COTR/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
diff --git a/imcui/third_party/COTR/demo_face.py b/third_party/COTR/demo_face.py
similarity index 100%
rename from imcui/third_party/COTR/demo_face.py
rename to third_party/COTR/demo_face.py
diff --git a/imcui/third_party/COTR/demo_guided_matching.py b/third_party/COTR/demo_guided_matching.py
similarity index 100%
rename from imcui/third_party/COTR/demo_guided_matching.py
rename to third_party/COTR/demo_guided_matching.py
diff --git a/imcui/third_party/COTR/demo_homography.py b/third_party/COTR/demo_homography.py
similarity index 100%
rename from imcui/third_party/COTR/demo_homography.py
rename to third_party/COTR/demo_homography.py
diff --git a/imcui/third_party/COTR/demo_reconstruction.py b/third_party/COTR/demo_reconstruction.py
similarity index 100%
rename from imcui/third_party/COTR/demo_reconstruction.py
rename to third_party/COTR/demo_reconstruction.py
diff --git a/imcui/third_party/COTR/demo_single_pair.py b/third_party/COTR/demo_single_pair.py
similarity index 100%
rename from imcui/third_party/COTR/demo_single_pair.py
rename to third_party/COTR/demo_single_pair.py
diff --git a/imcui/third_party/COTR/demo_wbs.py b/third_party/COTR/demo_wbs.py
similarity index 100%
rename from imcui/third_party/COTR/demo_wbs.py
rename to third_party/COTR/demo_wbs.py
diff --git a/imcui/third_party/COTR/environment.yml b/third_party/COTR/environment.yml
similarity index 100%
rename from imcui/third_party/COTR/environment.yml
rename to third_party/COTR/environment.yml
diff --git a/third_party/COTR/out/.DS_Store b/third_party/COTR/out/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..72c426dbb181a002f267fe15b2907cdedf677961
Binary files /dev/null and b/third_party/COTR/out/.DS_Store differ
diff --git a/imcui/third_party/DarkFeat/datasets/InvISP/__init__.py b/third_party/COTR/out/.placeholder
similarity index 100%
rename from imcui/third_party/DarkFeat/datasets/InvISP/__init__.py
rename to third_party/COTR/out/.placeholder
diff --git a/third_party/COTR/out/default/params.json b/third_party/COTR/out/default/params.json
new file mode 100644
index 0000000000000000000000000000000000000000..74f0424530e6f2b634aebc7034b6fe2fd00565f7
--- /dev/null
+++ b/third_party/COTR/out/default/params.json
@@ -0,0 +1,638 @@
+{
+"backbone": "resnet50",
+"batch_size": 16,
+"bidirectional": true,
+"cc_resume": false,
+"command": "train_cotr.py --scene_file sample_data/200_megadepth.json --info_level=rgbd --use_ram=no --use_cc=no --batch_size=16 --learning_rate=1e-4 --lr_backbone=1e-5 --max_iter=300000 --workers=8 --cycle_consis=yes --bidirectional=yes --position_embedding=lin_sine --layer=layer3 --confirm=no --dataset_name=megadepth_sushi --suffix=stage_3 --valid_iter=2000 --enable_zoom=yes --crop_cam=no_crop --out_dir=./out/cotr --use_mlp=no --load_weights=model:cotr_resnet50_layer3_1024_dset:megadepth_sushi_bs:16_pe:lin_sine_lrbackbone:1e-05_suffix:stage_2",
+"confirm": false,
+"crop_cam": "no_crop",
+"cycle_consis": true,
+"dataset_name": "megadepth_sushi",
+"dec_layers": 6,
+"dilation": false,
+"dim_feedforward": 1024,
+"dropout": 0.1,
+"enable_zoom": true,
+"enc_layers": 6,
+"essential": false,
+"hidden_dim": 256,
+"info_level": "rgbd",
+"k_size": 1,
+"kp_pool": 100,
+"layer": "layer3",
+"learning_rate": 0.0001,
+"load_weights": "model:cotr_resnet50_layer3_1024_dset:megadepth_sushi_bs:16_pe:lin_sine_lrbackbone:1e-05_suffix:stage_2",
+"load_weights_path": "./out/cotr/model:cotr_resnet50_layer3_1024_dset:megadepth_sushi_bs:16_pe:lin_sine_lrbackbone:1e-05_suffix:stage_2/checkpoint.pth.tar",
+"lr_backbone": 1e-05,
+"max_iter": 300000,
+"max_rotation": 0,
+"name": "model:cotr_resnet50_layer3_1024_dset:megadepth_sushi_bs:16_pe:lin_sine_lrbackbone:1e-05_suffix:stage_3",
+"need_rotation": false,
+"nheads": 8,
+"nn_method": "overlapping",
+"num_kp": 100,
+"num_queries": 100,
+"out": "./out/cotr/model:cotr_resnet50_layer3_1024_dset:megadepth_sushi_bs:16_pe:lin_sine_lrbackbone:1e-05_suffix:stage_3",
+"out_dir": "./out/cotr",
+"pool_size": 20,
+"position_embedding": "lin_sine",
+"resume": false,
+"rotation_chance": 0,
+"scene_file": "sample_data/200_megadepth.json",
+"scenes_name_list": [
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0000/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0000/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0000/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0000/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0000/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0000/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0001/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0001/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0001/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0002/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0002/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0002/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0003/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0003/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0003/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0004/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0004/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0004/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0004/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0004/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0004/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0005/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0005/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0005/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0007/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0007/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0007/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0007/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0007/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0007/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0011/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0011/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0011/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0012/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0012/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0012/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0013/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0013/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0013/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0017/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0017/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0017/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0020/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0020/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0020/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0020/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0020/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0020/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0023/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0023/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0023/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0023/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0023/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0023/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0026/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0026/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0026/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0027/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0027/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0027/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0033/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0033/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0033/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0034/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0034/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0034/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0035/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0035/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0035/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0036/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0036/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0036/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0037/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0037/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0037/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0039/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0039/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0039/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0041/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0041/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0041/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0042/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0042/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0042/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0043/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0043/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0043/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0044/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0044/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0044/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0046/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0046/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0046/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0046/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0046/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0046/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0048/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0048/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0048/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0049/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0049/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0049/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0050/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0050/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0050/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0056/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0056/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0056/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0057/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0057/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0057/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0058/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0058/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0058/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0060/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0060/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0060/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0061/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0061/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0061/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0062/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0062/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0062/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0064/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0064/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0064/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0065/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0065/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0065/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0067/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0067/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0067/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0070/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0070/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0070/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0071/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0071/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0071/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0071/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0071/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0071/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0076/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0076/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0076/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0078/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0078/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0078/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0083/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0083/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0083/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0086/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0086/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0086/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0087/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0087/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0087/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0090/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0090/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0090/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0092/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0092/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0092/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0094/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0094/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0094/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0095/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0095/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0095/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0095/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0095/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0095/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0098/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0098/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0098/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0099/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0099/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0099/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0100/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0100/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0100/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0101/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0101/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0101/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0102/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0102/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0102/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0103/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0103/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0103/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0104/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0104/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0104/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0104/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0104/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0104/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0105/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0105/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0105/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0107/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0107/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0107/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0115/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0115/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0115/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0117/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0117/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0117/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0117/dense2/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0117/dense2/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0117/sparse/manhattan/2_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0121/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0121/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0121/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0122/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0122/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0122/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0130/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0130/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0130/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0130/dense2/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0130/dense2/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0130/sparse/manhattan/2_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0137/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0137/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0137/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0137/dense2/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0137/dense2/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0137/sparse/manhattan/2_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0141/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0141/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0141/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0143/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0143/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0143/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0147/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0147/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0147/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0147/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0147/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0147/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0148/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0148/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0148/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0148/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0148/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0148/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0149/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0149/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0149/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0150/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0150/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0150/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0151/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0151/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0151/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0156/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0156/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0156/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0160/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0160/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0160/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0160/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0160/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0160/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0160/dense2/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0160/dense2/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0160/sparse/manhattan/2_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0162/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0162/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0162/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0175/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0175/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0175/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0176/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0176/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0176/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0176/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0176/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0176/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0176/dense2/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0176/dense2/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0176/sparse/manhattan/2_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0177/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0177/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0177/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0178/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0178/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0178/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0181/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0181/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0181/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0183/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0183/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0183/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0189/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0189/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0189/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0190/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0190/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0190/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0197/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0197/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0197/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0200/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0200/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0200/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0200/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0200/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0200/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0204/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0204/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0204/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0205/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0205/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0205/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0209/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0209/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0209/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0212/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0212/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0212/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0214/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0214/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0214/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0214/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0214/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0214/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0224/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0224/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0224/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0224/dense1/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0224/dense1/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0224/sparse/manhattan/1_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0231/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0231/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0231/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0235/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0235/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0235/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0237/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0237/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0237/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0238/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0238/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0238/sparse/manhattan/0_rectified/sparse"
+},
+{
+"depth_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0240/dense0/depths",
+"image_dir": "/scratch/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0240/dense0/imgs",
+"scene_dir": "/scratch/dataset/megadepth/MegaDepth_v1_SfM/0240/sparse/manhattan/0_rectified/sparse"
+}
+],
+"shuffle_data": true,
+"suffix": "stage_3",
+"tb_dir": "./tb_out",
+"tb_out": "./tb_out/model:cotr_resnet50_layer3_1024_dset:megadepth_sushi_bs:16_pe:lin_sine_lrbackbone:1e-05_suffix:stage_3",
+"use_cc": false,
+"use_cuda": true,
+"use_mlp": false,
+"use_ram": false,
+"valid_iter": 2000,
+"workers": 8,
+"zoom_end": 0.1,
+"zoom_jitter": 0.5,
+"zoom_levels": 10,
+"zoom_start": 1.0
+}
\ No newline at end of file
diff --git a/third_party/COTR/prepare_data.md b/third_party/COTR/prepare_data.md
new file mode 100644
index 0000000000000000000000000000000000000000..2541921bfb168866ce2d85c15c9a041fa929a340
--- /dev/null
+++ b/third_party/COTR/prepare_data.md
@@ -0,0 +1,45 @@
+
+## Scripts to generate dataset
+
+### 1. Rectify the SfM models
+
+Megadepth has 2 raw folders:
+
+1. MegaDepth_v1: contains the resized images and depth maps.
+2. MegaDepth_v1_SfM: contains the raw images and SfM models.
+
+Notice that the raw SfM models inside MegaDepth_v1_SfM use SIMPLE_RADIAL(colmap) camera, we need to rectify the SfM model to PINHOLE. Because the resized images and depth maps inside MegaDepth_v1 were actually rectified.
+
+Use `rectify_megadepth.py` to generates rectified models. **Please specify the local path inside the script.** It will generate an rectified SfM model alongside with original model. For example, if the original model is at `/MegaDepth_v1_SfM/0000/sparse/manhattan/0`, then the rectified model will be at `/MegaDepth_v1_SfM/0000/sparse/manhattan/0_rectified/sparse`.
+
+### 2. Valid list
+
+Some depth maps provided by Megadepth are actually semantic depth, which is an ordering mask. We checked all .h5 files and filter out any depth map that the minimum depth value is less than 0.
+
+Use `prepare_megadepth_valid_list.py` to generate the valid list, or use the provided valid list(`megadepth_valid_list.json`).
+
+### 3. Train/val/test split
+
+We use scenes [0000, 0240] **EXCEPT** scene 0204 as training split, we use scene 0204 as the validation split, and the rest as the test split.
+
+Use `prepare_megadepth_split.py` to generate the split, or use the provided split files(`megadepth_train.json` and `megadepth_val.json`).
+
+### 4. Sequences control
+
+We use another json file to control the sequences we want to use during training. It allows us to use a smaller sequence to debug, and remove some unwanted sequences.
+
+Notice that in the final training(current version), we remove the overlapping scenes with IMW dataset as mentioned [here](https://www.cs.ubc.ca/research/image-matching-challenge/2020/submit/).
+
+Build you own sequence control json, or use the provided ones(`200_megadepth.json` for default training, and `debug_megadepth.json` for debugging).
+
+### 5. Distance matrix
+
+Under each `denseX` folder inside folder `MegaDepth_v1` , we add a `dist_mat` folder, and inside the folder is the `dist_mat.npy` which represents the distance matrix.
+
+For example: `/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0000/dense0/dist_mat/dist_mat.npy`
+
+The size of the distance matrix is N by N, where N is the number of images with **valid** depth.
+
+The index of the matrix aligns with the order in the "images.txt", thus we require at least python 3.7 which uses ordered dictionary as default.
+
+Use `prepare_nn_distance_mat.py` to generate the distance matrix, or use the provided distance matrix([link](https://www.cs.ubc.ca/research/kmyi_data/files/2021/cotr/MegaDepth_v1.zip)).
diff --git a/third_party/COTR/readme.md b/third_party/COTR/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..9a2bd729273f3422a10166ab4b6e9e9fc5b426c6
--- /dev/null
+++ b/third_party/COTR/readme.md
@@ -0,0 +1,157 @@
+# COTR: Correspondence Transformer for Matching Across Images (ICCV 2021)
+
+This repository is a reference implementation for COTR.
+COTR establishes correspondence in a functional and end-to-end fashion. It solves dense and sparse correspondence problem in the same framework.
+
+[[arXiv]](https://arxiv.org/abs/2103.14167), [[video]](https://jiangwei221.github.io/vids/cotr/README.html), [[presentation]](https://youtu.be/bOZ12kgfn3E), [[pretrained_weights]](https://www.cs.ubc.ca/research/kmyi_data/files/2021/cotr/default.zip), [[distance_matrix]](https://www.cs.ubc.ca/research/kmyi_data/files/2021/cotr/MegaDepth_v1.zip)
+
+## Training
+
+### 1. Prepare data
+
+See `prepare_data.md`.
+
+### 2. Setup configuration json
+
+Add an entry inside `COTR/global_configs/dataset_config.json`, make sure it is correct on your system. In the provided `dataset_config.json`, we have different configurations for different clusters.
+
+Explanations on some json parameters:
+
+`valid_list_json`: The valid list json file, see `2. Valid list` in `Scripts to generate dataset`.
+
+`train_json/val_json/test_json`: The splits json files, see `3. Train/val/test split` in `Scripts to generate dataset`.
+
+`scene_dir`: Path to Megadepth SfM folder(rectified ones!). `{0}{1}` are scene and sequence id used by f-string.
+
+`image_dir/depth_dir`: Path to images and depth maps of Megadepth.
+
+### 3. Example command
+
+```python train_cotr.py --scene_file sample_data/jsons/debug_megadepth.json --dataset_name=megadepth --info_level=rgbd --use_ram=no --batch_size=2 --lr_backbone=1e-4 --max_iter=200 --valid_iter=10 --workers=4 --confirm=no```
+
+**Important arguments:**
+
+`use_ram`: Set to "yes" to load data into main memory.
+
+`crop_cam`: How to crop the image, it will change the camera intrinsic accordingly.
+
+`scene_file`: The sequence control file.
+
+`suffix`: Give the model a unique suffix.
+
+`load_weights`: Load a pretrained weights, only need the model name, it will automatically find the folder with the same name under the output folder, and load the "checkpoint.pth.tar".
+
+### 4. Our training commands
+
+As stated in the paper, we have 3 training stages. The machine we used has 1 RTX 3090, i7-10700, and 128G RAM. We store the training data inside the main memory during the first two stages.
+
+Stage 1: `python train_cotr.py --scene_file sample_data/jsons/200_megadepth.json --info_level=rgbd --use_ram=yes --use_cc=no --batch_size=24 --learning_rate=1e-4 --lr_backbone=0 --max_iter=300000 --workers=8 --cycle_consis=yes --bidirectional=yes --position_embedding=lin_sine --layer=layer3 --confirm=no --dataset_name=megadepth_sushi --suffix=stage_1 --valid_iter=1000 --enable_zoom=no --crop_cam=crop_center_and_resize --out_dir=./out/cotr`
+
+Stage 2: `python train_cotr.py --scene_file sample_data/jsons/200_megadepth.json --info_level=rgbd --use_ram=yes --use_cc=no --batch_size=16 --learning_rate=1e-4 --lr_backbone=1e-5 --max_iter=2000000 --workers=8 --cycle_consis=yes --bidirectional=yes --position_embedding=lin_sine --layer=layer3 --confirm=no --dataset_name=megadepth_sushi --suffix=stage_2 --valid_iter=10000 --enable_zoom=no --crop_cam=crop_center_and_resize --out_dir=./out/cotr --load_weights=model:cotr_resnet50_layer3_1024_dset:megadepth_sushi_bs:24_pe:lin_sine_lrbackbone:0.0_suffix:stage_1`
+
+Stage 3: `python train_cotr.py --scene_file sample_data/jsons/200_megadepth.json --info_level=rgbd --use_ram=no --use_cc=no --batch_size=16 --learning_rate=1e-4 --lr_backbone=1e-5 --max_iter=300000 --workers=8 --cycle_consis=yes --bidirectional=yes --position_embedding=lin_sine --layer=layer3 --confirm=no --dataset_name=megadepth_sushi --suffix=stage_3 --valid_iter=2000 --enable_zoom=yes --crop_cam=no_crop --out_dir=./out/cotr --load_weights=model:cotr_resnet50_layer3_1024_dset:megadepth_sushi_bs:16_pe:lin_sine_lrbackbone:1e-05_suffix:stage_2`
+
+
+
+
+
+## Demos
+
+Check out our demo video at [here](https://jiangwei221.github.io/vids/cotr/README.html).
+
+### 1. Install environment
+
+Our implementation is based on PyTorch. Install the conda environment by: `conda env create -f environment.yml`.
+
+Activate the environment by: `conda activate cotr_env`.
+
+
+
+### 2. Download the pretrained weights
+
+Download the pretrained weights at [here](https://www.cs.ubc.ca/research/kmyi_data/files/2021/cotr/default.zip). Extract in to `./out`, such that the weights file is at `/out/default/checkpoint.pth.tar`.
+
+### 3. Single image pair demo
+
+```python demo_single_pair.py --load_weights="default"```
+
+Example sparse output:
+
+
+
+
+
+Example dense output with triangulation:
+
+
+
+
+
+**Note:** This example uses 10K valid sparse correspondences to densify.
+
+### 4. Facial landmarks demo
+
+`python demo_face.py --load_weights="default"`
+
+Example:
+
+
+
+### 7. Two view reconstruction demo
+
+Note: this demo uses both known camera intrinsic and extrinsic.
+`python demo_reconstruction.py --load_weights="default" --max_corrs=2048 --faster_infer=yes`
+
+
+
+
+
+### 8. Annotation suggestions
+
+If the annotator knows the scale difference of two buildings, then COTR can skip the scale estimation step.
+`python demo_wbs.py --load_weights="default"`
+
+
+
+
+ The DeDoDe detector learns to detect 3D consistent repeatable keypoints, which the DeDoDe descriptor learns to match. The result is a powerful decoupled local feature matcher.
+
+
+
+
+
+ We experimentally find that DeDoDe significantly closes the performance gap between detector + descriptor models and fully-fledged matchers. The potential of DeDoDe is not limited to local feature matching, in fact we find that we can improve state-of-the-art matchers by incorporating DeDoDe keypoints.
+
+
+
+## How to Use DeDoDe?
+Below we show how DeDoDe can be run, you can also check out the [demos](demo)
+```python
+from DeDoDe import dedode_detector_L, dedode_descriptor_B
+from DeDoDe.matchers.dual_softmax_matcher import DualSoftMaxMatcher
+
+detector = dedode_detector_L(weights = torch.load("dedode_detector_L.pth"))
+descriptor = dedode_descriptor_B(weights = torch.load("dedode_descriptor_B.pth"))
+matcher = DualSoftMaxMatcher()
+
+im_A_path = "assets/im_A.jpg"
+im_B_path = "assets/im_B.jpg"
+im_A = Image.open(im_A_path)
+im_B = Image.open(im_B_path)
+W_A, H_A = im_A.size
+W_B, H_B = im_B.size
+
+
+detections_A = detector.detect_from_path(im_A_path, num_keypoints = 10_000)
+keypoints_A, P_A = detections_A["keypoints"], detections_A["confidence"]
+
+detections_B = detector.detect_from_path(im_B_path, num_keypoints = 10_000)
+keypoints_B, P_B = detections_B["keypoints"], detections_B["confidence"]
+
+description_A = descriptor.describe_keypoints_from_path(im_A_path, keypoints_A)["descriptions"]
+description_B = descriptor.describe_keypoints_from_path(im_B_path, keypoints_B)["descriptions"]
+
+matches_A, matches_B, batch_ids = matcher.match(keypoints_A, description_A,
+ keypoints_B, description_B,
+ P_A = P_A, P_B = P_B,
+ normalize = True, inv_temp=20, threshold = 0.1)#Increasing threshold -> fewer matches, fewer outliers
+
+matches_A, matches_B = matcher.to_pixel_coords(matches_A, matches_B, H_A, W_A, H_B, W_B)
+
+```
+## Pretrained Models
+
+Right now you can find them here: https://github.com/Parskatt/DeDoDe/releases/tag/dedode_pretrained_models
+Probably we'll add some autoloading in the near future.
+
+## BibTeX
+
+Coming Soon ;)
diff --git a/third_party/DeDoDe/data_prep/prep_keypoints.py b/third_party/DeDoDe/data_prep/prep_keypoints.py
new file mode 100644
index 0000000000000000000000000000000000000000..616f91b875879f726218efdfe4bb6dc95297b33a
--- /dev/null
+++ b/third_party/DeDoDe/data_prep/prep_keypoints.py
@@ -0,0 +1,96 @@
+import argparse
+
+import imagesize
+
+import numpy as np
+
+import os
+
+
+base_path = "data/megadepth"
+# Remove the trailing / if need be.
+if base_path[-1] in ["/", "\\"]:
+ base_path = base_path[:-1]
+
+
+base_depth_path = os.path.join(base_path, "phoenix/S6/zl548/MegaDepth_v1")
+base_undistorted_sfm_path = os.path.join(base_path, "Undistorted_SfM")
+
+scene_ids = os.listdir(base_undistorted_sfm_path)
+for scene_id in scene_ids:
+ if os.path.exists(
+ f"{base_path}/prep_scene_info/detections/detections_{scene_id}.npy"
+ ):
+ print(f"skipping {scene_id} as it exists")
+ continue
+ undistorted_sparse_path = os.path.join(
+ base_undistorted_sfm_path, scene_id, "sparse-txt"
+ )
+ if not os.path.exists(undistorted_sparse_path):
+ print("sparse path doesnt exist")
+ continue
+
+ depths_path = os.path.join(base_depth_path, scene_id, "dense0", "depths")
+ if not os.path.exists(depths_path):
+ print("depths doesnt exist")
+
+ continue
+
+ images_path = os.path.join(base_undistorted_sfm_path, scene_id, "images")
+ if not os.path.exists(images_path):
+ print("images path doesnt exist")
+ continue
+
+ # Process cameras.txt
+ if not os.path.exists(os.path.join(undistorted_sparse_path, "cameras.txt")):
+ print("no cameras")
+ continue
+ with open(os.path.join(undistorted_sparse_path, "cameras.txt"), "r") as f:
+ raw = f.readlines()[3:] # skip the header
+
+ camera_intrinsics = {}
+ for camera in raw:
+ camera = camera.split(" ")
+ camera_intrinsics[int(camera[0])] = [float(elem) for elem in camera[2:]]
+
+ # Process points3D.txt
+ with open(os.path.join(undistorted_sparse_path, "points3D.txt"), "r") as f:
+ raw = f.readlines()[3:] # skip the header
+
+ points3D = {}
+ for point3D in raw:
+ point3D = point3D.split(" ")
+ points3D[int(point3D[0])] = np.array(
+ [float(point3D[1]), float(point3D[2]), float(point3D[3])]
+ )
+
+ # Process images.txt
+ with open(os.path.join(undistorted_sparse_path, "images.txt"), "r") as f:
+ raw = f.readlines()[4:] # skip the header
+
+ image_id_to_idx = {}
+ image_names = []
+ raw_pose = []
+ camera = []
+ points3D_id_to_2D = []
+ n_points3D = []
+ id_to_detections = {}
+ for idx, (image, points) in enumerate(zip(raw[::2], raw[1::2])):
+ image = image.split(" ")
+ points = points.split(" ")
+
+ image_id_to_idx[int(image[0])] = idx
+
+ image_name = image[-1].strip("\n")
+ image_names.append(image_name)
+
+ raw_pose.append([float(elem) for elem in image[1:-2]])
+ camera.append(int(image[-2]))
+ points_np = np.array(points).astype(np.float32).reshape(len(points) // 3, 3)
+ visible_points = points_np[points_np[:, 2] != -1]
+ id_to_detections[idx] = visible_points
+ np.save(
+ f"{base_path}/prep_scene_info/detections/detections_{scene_id}.npy",
+ id_to_detections,
+ )
+ print(f"{scene_id} done")
diff --git a/third_party/DeDoDe/demo/demo_kpts.py b/third_party/DeDoDe/demo/demo_kpts.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0ae36aa4bbe3439e96d7b45bfa809c48b6ebf45
--- /dev/null
+++ b/third_party/DeDoDe/demo/demo_kpts.py
@@ -0,0 +1,22 @@
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+from DeDoDe import dedode_detector_L
+
+
+def draw_kpts(im, kpts):
+ kpts = [cv2.KeyPoint(x, y, 1.0) for x, y in kpts.cpu().numpy()]
+ im = np.array(im)
+ ret = cv2.drawKeypoints(im, kpts, None)
+ return ret
+
+
+detector = dedode_detector_L(weights=torch.load("dedode_detector_l.pth"))
+im_path = "assets/im_A.jpg"
+im = Image.open(im_path)
+out = detector.detect_from_path(im_path, num_keypoints=10_000)
+W, H = im.size
+kps = out["keypoints"]
+kps = detector.to_pixel_coords(kps, H, W)
+Image.fromarray(draw_kpts(im, kps[0])).save("demo/keypoints.png")
diff --git a/third_party/DeDoDe/demo/demo_match.py b/third_party/DeDoDe/demo/demo_match.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ddecc453e1e3d0beb5e832819833209ad431048
--- /dev/null
+++ b/third_party/DeDoDe/demo/demo_match.py
@@ -0,0 +1,59 @@
+import torch
+from DeDoDe import dedode_detector_L, dedode_descriptor_B
+from DeDoDe.matchers.dual_softmax_matcher import DualSoftMaxMatcher
+from DeDoDe.utils import *
+from PIL import Image
+import cv2
+
+
+def draw_matches(im_A, kpts_A, im_B, kpts_B):
+ kpts_A = [cv2.KeyPoint(x, y, 1.0) for x, y in kpts_A.cpu().numpy()]
+ kpts_B = [cv2.KeyPoint(x, y, 1.0) for x, y in kpts_B.cpu().numpy()]
+ matches_A_to_B = [cv2.DMatch(idx, idx, 0.0) for idx in range(len(kpts_A))]
+ im_A, im_B = np.array(im_A), np.array(im_B)
+ ret = cv2.drawMatches(im_A, kpts_A, im_B, kpts_B, matches_A_to_B, None)
+ return ret
+
+
+detector = dedode_detector_L(weights=torch.load("dedode_detector_L.pth"))
+descriptor = dedode_descriptor_B(weights=torch.load("dedode_descriptor_B.pth"))
+matcher = DualSoftMaxMatcher()
+
+im_A_path = "assets/im_A.jpg"
+im_B_path = "assets/im_B.jpg"
+im_A = Image.open(im_A_path)
+im_B = Image.open(im_B_path)
+W_A, H_A = im_A.size
+W_B, H_B = im_B.size
+
+
+detections_A = detector.detect_from_path(im_A_path, num_keypoints=10_000)
+keypoints_A, P_A = detections_A["keypoints"], detections_A["confidence"]
+detections_B = detector.detect_from_path(im_B_path, num_keypoints=10_000)
+keypoints_B, P_B = detections_B["keypoints"], detections_B["confidence"]
+description_A = descriptor.describe_keypoints_from_path(im_A_path, keypoints_A)[
+ "descriptions"
+]
+description_B = descriptor.describe_keypoints_from_path(im_B_path, keypoints_B)[
+ "descriptions"
+]
+matches_A, matches_B, batch_ids = matcher.match(
+ keypoints_A,
+ description_A,
+ keypoints_B,
+ description_B,
+ P_A=P_A,
+ P_B=P_B,
+ normalize=True,
+ inv_temp=20,
+ threshold=0.1,
+) # Increasing threshold -> fewer matches, fewer outliers
+
+matches_A, matches_B = matcher.to_pixel_coords(matches_A, matches_B, H_A, W_A, H_B, W_B)
+
+import cv2
+import numpy as np
+
+Image.fromarray(draw_matches(im_A, matches_A[::5], im_B, matches_B[::5])).save(
+ "demo/matches.png"
+)
diff --git a/third_party/DeDoDe/demo/demo_scoremap.py b/third_party/DeDoDe/demo/demo_scoremap.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a0a2b2470783c69753960725aee1b689b0cb2cc
--- /dev/null
+++ b/third_party/DeDoDe/demo/demo_scoremap.py
@@ -0,0 +1,24 @@
+import torch
+from PIL import Image
+import numpy as np
+
+from DeDoDe import dedode_detector_L
+from DeDoDe.utils import tensor_to_pil
+
+detector = dedode_detector_L(weights=torch.load("dedode_detector_l.pth"))
+H, W = 768, 768
+im_path = "assets/im_A.jpg"
+
+out = detector.detect_from_path(im_path, dense=True, H=H, W=W)
+
+logit_map = out["dense_keypoint_logits"].clone()
+min = logit_map.max() - 3
+logit_map[logit_map < min] = min
+logit_map = (logit_map - min) / (logit_map.max() - min)
+logit_map = logit_map.cpu()[0].expand(3, H, W)
+im_A = torch.tensor(np.array(Image.open(im_path).resize((W, H))) / 255.0).permute(
+ 2, 0, 1
+)
+tensor_to_pil(logit_map * logit_map + 0.15 * (1 - logit_map) * im_A).save(
+ "demo/dense_logits.png"
+)
diff --git a/third_party/DeDoDe/requirements.txt b/third_party/DeDoDe/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0aba7e372c62658a3294419ccf3deccacb7e95c2
--- /dev/null
+++ b/third_party/DeDoDe/requirements.txt
@@ -0,0 +1,9 @@
+numpy
+matplotlib
+torch
+torchvision
+h5py
+tqdm
+pillow
+einops
+opencv-python
\ No newline at end of file
diff --git a/imcui/third_party/DeDoDe/setup.py b/third_party/DeDoDe/setup.py
similarity index 70%
rename from imcui/third_party/DeDoDe/setup.py
rename to third_party/DeDoDe/setup.py
index d175ab96e3493a2e53e2daaae99eb822a71b463e..94d1fd8ed2e5ac769222afce4f084ac19029a2a4 100644
--- a/imcui/third_party/DeDoDe/setup.py
+++ b/third_party/DeDoDe/setup.py
@@ -3,9 +3,8 @@ from setuptools import setup, find_packages
setup(
name="DeDoDe",
- packages=find_packages(include= ["DeDoDe*"]),
+ packages=find_packages(include=["DeDoDe*"]),
install_requires=open("requirements.txt", "r").read().split("\n"),
- python_requires='>=3.9.0',
version="0.0.1",
author="Johan Edstedt",
)
diff --git a/third_party/EfficientLoFTR/.gitignore b/third_party/EfficientLoFTR/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..e74031e54f3dac1e9e00c4f7caf9c04bcb794ff2
--- /dev/null
+++ b/third_party/EfficientLoFTR/.gitignore
@@ -0,0 +1,11 @@
+.vscode/
+__pycache__/
+*.pyc
+*.DS_Store
+*.swp
+*.pth
+tmp.*
+*/.ipynb_checkpoints/*
+
+logs/
+dump/
\ No newline at end of file
diff --git a/third_party/EfficientLoFTR/README.md b/third_party/EfficientLoFTR/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..5542a10866ba51492583faf7e90e50d75bb40a71
--- /dev/null
+++ b/third_party/EfficientLoFTR/README.md
@@ -0,0 +1,69 @@
+# Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
+
+### [Project Page](https://zju3dv.github.io/efficientloftr) | [Paper](https://zju3dv.github.io/efficientloftr/files/EfficientLoFTR.pdf)
+
+
+> Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
+> [Yifan Wang](https://github.com/wyf2020)\*, [Xingyi He](https://github.com/hxy-123)\*, [Sida Peng](https://pengsida.net), [Dongli Tan](https://github.com/Cuistiano), [Xiaowei Zhou](http://xzhou.me)
+> CVPR 2024
+
+https://github.com/zju3dv/EfficientLoFTR/assets/69951260/40890d21-180e-4e70-aeba-219178b0d824
+
+## TODO List
+- [x] Inference code and pretrained models
+- [x] Code for reproducing the test-set results
+- [ ] Add options of flash-attention and torch.compiler for better performance
+- [x] jupyter notebook demo for matching a pair of images
+- [ ] Training code
+
+## Installation
+```shell
+conda env create -f environment.yaml
+conda activate eloftr
+pip install torch==2.0.0+cu118 --index-url https://download.pytorch.org/whl/cu118
+pip install -r requirements.txt
+```
+The test and training can be downloaded by [download link](https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf?usp=sharing) provided by LoFTR
+
+We provide the our pretrained model in [download link](https://drive.google.com/drive/folders/1GOw6iVqsB-f1vmG6rNmdCcgwfB4VZ7_Q?usp=sharing)
+
+
+## Reproduce the testing results with pytorch-lightning
+You need to setup the testing subsets of ScanNet and MegaDepth first. We create symlinks from the previously downloaded datasets to `data/{{dataset}}/test`.
+
+```shell
+# set up symlinks
+ln -s /path/to/scannet-1500-testset/* /path/to/EfficientLoFTR/data/scannet/test
+ln -s /path/to/megadepth-1500-testset/* /path/to/EfficientLoFTR/data/megadepth/test
+```
+### Inference time
+```shell
+conda activate eloftr
+bash scripts/reproduce_test/indoor_full_time.sh
+bash scripts/reproduce_test/indoor_opt_time.sh
+```
+
+### Accuracy
+```shell
+conda activate eloftr
+bash scripts/reproduce_test/outdoor_full_auc.sh
+bash scripts/reproduce_test/outdoor_opt_auc.sh
+bash scripts/reproduce_test/indoor_full_auc.sh
+bash scripts/reproduce_test/indoor_opt_auc.sh
+```
+
+## Training
+The Training code is coming soon, please stay tuned!
+
+## Citation
+
+If you find this code useful for your research, please use the following BibTeX entry.
+
+```bibtex
+@inproceedings{wang2024eloftr,
+ title={{Efficient LoFTR}: Semi-Dense Local Feature Matching with Sparse-Like Speed},
+ author={Wang, Yifan and He, Xingyi and Peng, Sida and Tan, Dongli and Zhou, Xiaowei},
+ booktitle={CVPR},
+ year={2024}
+}
+```
diff --git a/imcui/third_party/GlueStick/gluestick/models/__init__.py b/third_party/EfficientLoFTR/configs/data/__init__.py
similarity index 100%
rename from imcui/third_party/GlueStick/gluestick/models/__init__.py
rename to third_party/EfficientLoFTR/configs/data/__init__.py
diff --git a/imcui/third_party/ASpanFormer/configs/data/base.py b/third_party/EfficientLoFTR/configs/data/base.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/configs/data/base.py
rename to third_party/EfficientLoFTR/configs/data/base.py
diff --git a/third_party/EfficientLoFTR/configs/data/debug/.gitignore b/third_party/EfficientLoFTR/configs/data/debug/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31
--- /dev/null
+++ b/third_party/EfficientLoFTR/configs/data/debug/.gitignore
@@ -0,0 +1,3 @@
+*
+*/
+!.gitignore
diff --git a/imcui/third_party/EfficientLoFTR/configs/data/megadepth_test_1500.py b/third_party/EfficientLoFTR/configs/data/megadepth_test_1500.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/configs/data/megadepth_test_1500.py
rename to third_party/EfficientLoFTR/configs/data/megadepth_test_1500.py
diff --git a/imcui/third_party/EfficientLoFTR/configs/data/megadepth_trainval_832.py b/third_party/EfficientLoFTR/configs/data/megadepth_trainval_832.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/configs/data/megadepth_trainval_832.py
rename to third_party/EfficientLoFTR/configs/data/megadepth_trainval_832.py
diff --git a/imcui/third_party/EfficientLoFTR/configs/data/scannet_test_1500.py b/third_party/EfficientLoFTR/configs/data/scannet_test_1500.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/configs/data/scannet_test_1500.py
rename to third_party/EfficientLoFTR/configs/data/scannet_test_1500.py
diff --git a/imcui/third_party/EfficientLoFTR/configs/loftr/eloftr_full.py b/third_party/EfficientLoFTR/configs/loftr/eloftr_full.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/configs/loftr/eloftr_full.py
rename to third_party/EfficientLoFTR/configs/loftr/eloftr_full.py
diff --git a/imcui/third_party/EfficientLoFTR/configs/loftr/eloftr_optimized.py b/third_party/EfficientLoFTR/configs/loftr/eloftr_optimized.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/configs/loftr/eloftr_optimized.py
rename to third_party/EfficientLoFTR/configs/loftr/eloftr_optimized.py
diff --git a/third_party/EfficientLoFTR/data/megadepth/index/.gitignore b/third_party/EfficientLoFTR/data/megadepth/index/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/EfficientLoFTR/data/megadepth/index/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/EfficientLoFTR/data/megadepth/test/.gitignore b/third_party/EfficientLoFTR/data/megadepth/test/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/EfficientLoFTR/data/megadepth/test/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/EfficientLoFTR/data/megadepth/train/.gitignore b/third_party/EfficientLoFTR/data/megadepth/train/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/EfficientLoFTR/data/megadepth/train/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/EfficientLoFTR/data/scannet/index/.gitignore b/third_party/EfficientLoFTR/data/scannet/index/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31
--- /dev/null
+++ b/third_party/EfficientLoFTR/data/scannet/index/.gitignore
@@ -0,0 +1,3 @@
+*
+*/
+!.gitignore
diff --git a/third_party/EfficientLoFTR/data/scannet/test/.gitignore b/third_party/EfficientLoFTR/data/scannet/test/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31
--- /dev/null
+++ b/third_party/EfficientLoFTR/data/scannet/test/.gitignore
@@ -0,0 +1,3 @@
+*
+*/
+!.gitignore
diff --git a/imcui/third_party/EfficientLoFTR/environment.yaml b/third_party/EfficientLoFTR/environment.yaml
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/environment.yaml
rename to third_party/EfficientLoFTR/environment.yaml
diff --git a/third_party/EfficientLoFTR/notebooks/demo_single_pair.ipynb b/third_party/EfficientLoFTR/notebooks/demo_single_pair.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..21036882f5243a1adab978bbe509b3ae6f5877f7
--- /dev/null
+++ b/third_party/EfficientLoFTR/notebooks/demo_single_pair.ipynb
@@ -0,0 +1,173 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Demo EfficientLoFTR on a single pair of images\n",
+ "\n",
+ "This notebook shows how to use the eloftr matcher with different model type and numerical precision on the pretrained weights."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.chdir(\"..\")\n",
+ "from copy import deepcopy\n",
+ "\n",
+ "import torch\n",
+ "import cv2\n",
+ "import numpy as np\n",
+ "import matplotlib.cm as cm\n",
+ "from src.utils.plotting import make_matching_figure"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Outdoor Example\n",
+ "\n",
+ "We recommend using our pre-trained model for input in outdoor environments because our model has only been trained on MegaDepth, and there exists a domain gap between indoor and outdoor data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'backbone_type': 'RepVGG', 'align_corner': False, 'resolution': (8, 1), 'fine_window_size': 8, 'mp': False, 'replace_nan': True, 'half': False, 'backbone': {'block_dims': [64, 128, 256]}, 'coarse': {'d_model': 256, 'd_ffn': 256, 'nhead': 8, 'layer_names': ['self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross'], 'agg_size0': 4, 'agg_size1': 4, 'no_flash': False, 'rope': True, 'npe': [832, 832, 832, 832]}, 'match_coarse': {'thr': 0.2, 'border_rm': 2, 'dsmax_temperature': 0.1, 'skip_softmax': False, 'fp16matmul': False, 'train_coarse_percent': 0.2, 'train_pad_num_gt_min': 200}, 'match_fine': {'local_regress_temperature': 10.0, 'local_regress_slicedim': 8}}\n"
+ ]
+ }
+ ],
+ "source": [
+ "from src.loftr import LoFTR, full_default_cfg, opt_default_cfg, reparameter\n",
+ "\n",
+ "# You can choose model type in ['full', 'opt']\n",
+ "model_type = 'full' # 'full' for best quality, 'opt' for best efficiency\n",
+ "\n",
+ "# You can choose numerical precision in ['fp32', 'mp', 'fp16']. 'fp16' for best efficiency\n",
+ "precision = 'fp32' # Enjoy near-lossless precision with Mixed Precision (MP) / FP16 computation if you have a modern GPU (recommended NVIDIA architecture >= SM_70).\n",
+ "\n",
+ "# You can also change the default values like thr. and npe (based on input image size)\n",
+ "\n",
+ "if model_type == 'full':\n",
+ " _default_cfg = deepcopy(full_default_cfg)\n",
+ "elif model_type == 'opt':\n",
+ " _default_cfg = deepcopy(opt_default_cfg)\n",
+ " \n",
+ "if precision == 'mp':\n",
+ " _default_cfg['mp'] = True\n",
+ "elif precision == 'fp16':\n",
+ " _default_cfg['half'] = True\n",
+ " \n",
+ "print(_default_cfg)\n",
+ "matcher = LoFTR(config=_default_cfg)\n",
+ "\n",
+ "matcher.load_state_dict(torch.load(\"weights/eloftr_outdoor.ckpt\")['state_dict'])\n",
+ "matcher = reparameter(matcher) # no reparameterization will lead to low performance\n",
+ "\n",
+ "if precision == 'fp16':\n",
+ " matcher = matcher.half()\n",
+ "\n",
+ "matcher = matcher.eval().cuda()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load example images\n",
+ "img0_pth = \"assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg\"\n",
+ "img1_pth = \"assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg\"\n",
+ "img0_raw = cv2.imread(img0_pth, cv2.IMREAD_GRAYSCALE)\n",
+ "img1_raw = cv2.imread(img1_pth, cv2.IMREAD_GRAYSCALE)\n",
+ "img0_raw = cv2.resize(img0_raw, (img0_raw.shape[1]//32*32, img0_raw.shape[0]//32*32)) # input size shuold be divisible by 32\n",
+ "img1_raw = cv2.resize(img1_raw, (img1_raw.shape[1]//32*32, img1_raw.shape[0]//32*32))\n",
+ "\n",
+ "if precision == 'fp16':\n",
+ " img0 = torch.from_numpy(img0_raw)[None][None].half().cuda() / 255.\n",
+ " img1 = torch.from_numpy(img1_raw)[None][None].half().cuda() / 255.\n",
+ "else:\n",
+ " img0 = torch.from_numpy(img0_raw)[None][None].cuda() / 255.\n",
+ " img1 = torch.from_numpy(img1_raw)[None][None].cuda() / 255.\n",
+ "batch = {'image0': img0, 'image1': img1}\n",
+ "\n",
+ "# Inference with EfficientLoFTR and get prediction\n",
+ "with torch.no_grad():\n",
+ " if precision == 'mp':\n",
+ " with torch.autocast(enabled=True, device_type='cuda'):\n",
+ " matcher(batch)\n",
+ " else:\n",
+ " matcher(batch)\n",
+ " mkpts0 = batch['mkpts0_f'].cpu().numpy()\n",
+ " mkpts1 = batch['mkpts1_f'].cpu().numpy()\n",
+ " mconf = batch['mconf'].cpu().numpy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Draw\n",
+ "if model_type == 'opt':\n",
+ " print(mconf.max())\n",
+ " mconf = (mconf - min(20.0, mconf.min())) / (max(30.0, mconf.max()) - min(20.0, mconf.min()))\n",
+ "\n",
+ "color = cm.jet(mconf)\n",
+ "text = [\n",
+ " 'LoFTR',\n",
+ " 'Matches: {}'.format(len(mkpts0)),\n",
+ "]\n",
+ "fig = make_matching_figure(img0_raw, img1_raw, mkpts0, mkpts1, color, text=text)"
+ ]
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "5b8911f875a754a9ad2a8804064d078bf6a1985972bb0389b9d67771213c8e20"
+ },
+ "kernelspec": {
+ "display_name": "Python 3.8.8 64-bit ('svcnn': conda)",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.19"
+ },
+ "orig_nbformat": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/EfficientLoFTR/requirements.txt b/third_party/EfficientLoFTR/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ab86d90f1cf68a8d50060cba9b4582d6b4883a1d
--- /dev/null
+++ b/third_party/EfficientLoFTR/requirements.txt
@@ -0,0 +1,17 @@
+opencv_python==4.4.0.46
+albumentations==0.5.1 --no-binary=imgaug,albumentations
+ray>=1.0.1
+einops==0.3.0
+kornia==0.4.1
+loguru==0.5.3
+yacs>=0.1.8
+tqdm
+autopep8
+pylint
+ipython
+jupyterlab
+matplotlib
+h5py==3.1.0
+pytorch-lightning==1.3.5
+torchmetrics==0.6.0 # version problem: https://github.com/NVIDIA/DeepLearningExamples/issues/1113#issuecomment-1102969461
+joblib>=1.0.1
\ No newline at end of file
diff --git a/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_full_auc.sh b/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_full_auc.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b2f2e9bf327b99f11bb7a85e0b9d0474edc3d532
--- /dev/null
+++ b/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_full_auc.sh
@@ -0,0 +1,35 @@
+#!/bin/bash -l
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+main_cfg_path="configs/loftr/eloftr_full.py"
+
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+ckpt_path="weights/eloftr_outdoor.ckpt"
+
+dump_dir="dump/eloftr_full_scannet"
+data_cfg_path="configs/data/scannet_test_1500.py"
+python ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --scannetX '640' \
+ --scannetY '480' \
+ --rmbd 0 \
+ --thr 0.1 \
+ --deter \
+ --ransac_times 5
+# Following the RoMa protocol, we repeat RANSAC 5 times to enhance robustness; however, this increases script runtime.
\ No newline at end of file
diff --git a/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_full_time.sh b/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_full_time.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d1dfe2cfb131c778b622946aadc1904da637124e
--- /dev/null
+++ b/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_full_time.sh
@@ -0,0 +1,33 @@
+#!/bin/bash -l
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+main_cfg_path="configs/loftr/eloftr_full.py"
+
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+ckpt_path="weights/eloftr_outdoor.ckpt"
+
+dump_dir="dump/eloftr_full_scannet"
+data_cfg_path="configs/data/scannet_test_1500.py"
+python ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --scannetX '640' \
+ --scannetY '480' \
+ --rmbd 0 \
+ --thr 0.1 \
+ --ransac_times 1
diff --git a/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_opt_auc.sh b/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_opt_auc.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f9b6ae8afb928a6e8f4eb7dfa585d9b7806353e0
--- /dev/null
+++ b/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_opt_auc.sh
@@ -0,0 +1,35 @@
+#!/bin/bash -l
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+main_cfg_path="configs/loftr/eloftr_optimized.py"
+
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+ckpt_path="weights/eloftr_outdoor.ckpt"
+
+dump_dir="dump/eloftr_full_scannet"
+data_cfg_path="configs/data/scannet_test_1500.py"
+python ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --scannetX '640' \
+ --scannetY '480' \
+ --rmbd 1 \
+ --thr 20 \
+ --deter \
+ --ransac_times 5
+# Following the RoMa protocol, we repeat RANSAC 5 times to enhance robustness; however, this increases script runtime.
\ No newline at end of file
diff --git a/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_opt_time.sh b/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_opt_time.sh
new file mode 100644
index 0000000000000000000000000000000000000000..dec08661bcee163dcb250c6896b2719cedcea940
--- /dev/null
+++ b/third_party/EfficientLoFTR/scripts/reproduce_test/indoor_opt_time.sh
@@ -0,0 +1,33 @@
+#!/bin/bash -l
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+main_cfg_path="configs/loftr/eloftr_optimized.py"
+
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+ckpt_path="weights/eloftr_outdoor.ckpt"
+
+dump_dir="dump/eloftr_full_scannet"
+data_cfg_path="configs/data/scannet_test_1500.py"
+python ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --scannetX '640' \
+ --scannetY '480' \
+ --rmbd 1 \
+ --thr 20 \
+ --ransac_times 1
\ No newline at end of file
diff --git a/third_party/EfficientLoFTR/scripts/reproduce_test/outdoor_full_auc.sh b/third_party/EfficientLoFTR/scripts/reproduce_test/outdoor_full_auc.sh
new file mode 100644
index 0000000000000000000000000000000000000000..08ed0fc7eccd33441d700d56f27bb573d4baca0e
--- /dev/null
+++ b/third_party/EfficientLoFTR/scripts/reproduce_test/outdoor_full_auc.sh
@@ -0,0 +1,35 @@
+#!/bin/bash -l
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+main_cfg_path="configs/loftr/eloftr_full.py"
+
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+ckpt_path="weights/eloftr_outdoor.ckpt"
+
+dump_dir="dump/eloftr_full_megadepth"
+data_cfg_path="configs/data/megadepth_test_1500.py"
+size="1152"
+python ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --megasize $size \
+ --npe \
+ --thr 0.1 \
+ --deter \
+ --ransac_times 5
+# Following the RoMa protocol, we repeat RANSAC 5 times to enhance robustness; however, this increases script runtime.
\ No newline at end of file
diff --git a/third_party/EfficientLoFTR/scripts/reproduce_test/outdoor_opt_auc.sh b/third_party/EfficientLoFTR/scripts/reproduce_test/outdoor_opt_auc.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4e1af5987b16785effc390424eb37f38a55f842f
--- /dev/null
+++ b/third_party/EfficientLoFTR/scripts/reproduce_test/outdoor_opt_auc.sh
@@ -0,0 +1,35 @@
+#!/bin/bash -l
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+main_cfg_path="configs/loftr/eloftr_optimized.py"
+
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+ckpt_path="weights/eloftr_outdoor.ckpt"
+
+dump_dir="dump/eloftr_full_megadepth"
+data_cfg_path="configs/data/megadepth_test_1500.py"
+size="1152"
+python ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --megasize $size \
+ --npe \
+ --thr 20 \
+ --deter \
+ --ransac_times 5
+# Following the RoMa protocol, we repeat RANSAC 5 times to enhance robustness; however, this increases script runtime.
\ No newline at end of file
diff --git a/third_party/EfficientLoFTR/scripts/varied_size/indoor_full_auc_varied_size.sh b/third_party/EfficientLoFTR/scripts/varied_size/indoor_full_auc_varied_size.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f6daa38224c5319549fe4ffe074f0c466d499eef
--- /dev/null
+++ b/third_party/EfficientLoFTR/scripts/varied_size/indoor_full_auc_varied_size.sh
@@ -0,0 +1,44 @@
+#!/bin/bash -l
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+main_cfg_path="configs/loftr/eloftr_full.py"
+
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+ckpt_path="weights/eloftr_outdoor.ckpt"
+
+dump_dir="dump/eloftr_full_scannet"
+data_cfg_path="configs/data/scannet_test_1500.py"
+
+declare -a scannetXY_arr=("640,480" "512,384" "384,288")
+
+for scannetXY in "${scannetXY_arr[@]}"; do
+ SCANNETX="${scannetXY%,*}"
+ SCANNETY="${scannetXY#*,}"
+
+ python ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --scannetX $SCANNETX \
+ --scannetY $SCANNETY \
+ --npe \
+ --rmbd 0 \
+ --deter \
+ --ransac_times 5 \
+ --fp32 # fp32 just for fair comparison
+ # Following the RoMa protocol, we repeat RANSAC 5 times to enhance robustness; however, this increases script runtime.
+done
\ No newline at end of file
diff --git a/third_party/EfficientLoFTR/scripts/varied_size/indoor_full_time_varied_size.sh b/third_party/EfficientLoFTR/scripts/varied_size/indoor_full_time_varied_size.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3a37e0cde504ac4643437870dfa58107e9454ed9
--- /dev/null
+++ b/third_party/EfficientLoFTR/scripts/varied_size/indoor_full_time_varied_size.sh
@@ -0,0 +1,42 @@
+#!/bin/bash -l
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+main_cfg_path="configs/loftr/eloftr_full.py"
+
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+ckpt_path="weights/eloftr_outdoor.ckpt"
+
+dump_dir="dump/eloftr_full_scannet"
+data_cfg_path="configs/data/scannet_test_1500.py"
+
+declare -a scannetXY_arr=("640,480" "512,384" "384,288")
+
+for scannetXY in "${scannetXY_arr[@]}"; do
+ SCANNETX="${scannetXY%,*}"
+ SCANNETY="${scannetXY#*,}"
+
+ python ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --scannetX $SCANNETX \
+ --scannetY $SCANNETY \
+ --npe \
+ --rmbd 0 \
+ --ransac_times 1 \
+ --fp32 # fp32 just for fair comparison
+done
\ No newline at end of file
diff --git a/third_party/EfficientLoFTR/scripts/varied_size/indoor_opt_auc_varied_size.sh b/third_party/EfficientLoFTR/scripts/varied_size/indoor_opt_auc_varied_size.sh
new file mode 100644
index 0000000000000000000000000000000000000000..fb1dfeb98a7bd22a6ec61baddc04833c77b76e22
--- /dev/null
+++ b/third_party/EfficientLoFTR/scripts/varied_size/indoor_opt_auc_varied_size.sh
@@ -0,0 +1,47 @@
+#!/bin/bash -l
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+main_cfg_path="configs/loftr/eloftr_optimized.py"
+
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+comment='reproduce_eloft_full_scannet'
+METHOD='loftr'
+
+ckpt_path="weights/eloftr_outdoor.ckpt"
+
+dump_dir="dump/eloftr_full_scannet"
+data_cfg_path="configs/data/scannet_test_1500.py"
+
+declare -a scannetXY_arr=("640,480" "512,384" "384,288")
+
+for scannetXY in "${scannetXY_arr[@]}"; do
+ SCANNETX="${scannetXY%,*}"
+ SCANNETY="${scannetXY#*,}"
+
+ python ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --scannetX $SCANNETX \
+ --scannetY $SCANNETY \
+ --npe \
+ --rmbd 1 \
+ --deter \
+ --ransac_times 5 \
+ --half \
+ --flash
+ # Following the RoMa protocol, we repeat RANSAC 5 times to enhance robustness; however, this increases script runtime.
+done
\ No newline at end of file
diff --git a/third_party/EfficientLoFTR/scripts/varied_size/indoor_opt_time_varied_size.sh b/third_party/EfficientLoFTR/scripts/varied_size/indoor_opt_time_varied_size.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8807e65f30e83cca7a46a6c960221fe82d8384f0
--- /dev/null
+++ b/third_party/EfficientLoFTR/scripts/varied_size/indoor_opt_time_varied_size.sh
@@ -0,0 +1,45 @@
+#!/bin/bash -l
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+main_cfg_path="configs/loftr/eloftr_optimized.py"
+
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+comment='reproduce_eloft_full_scannet'
+METHOD='loftr'
+
+ckpt_path="weights/eloftr_outdoor.ckpt"
+
+dump_dir="dump/eloftr_full_scannet"
+data_cfg_path="configs/data/scannet_test_1500.py"
+
+declare -a scannetXY_arr=("640,480" "512,384" "384,288")
+
+for scannetXY in "${scannetXY_arr[@]}"; do
+ SCANNETX="${scannetXY%,*}"
+ SCANNETY="${scannetXY#*,}"
+ python ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark \
+ --scannetX $SCANNETX \
+ --scannetY $SCANNETY \
+ --npe \
+ --rmbd 1 \
+ --ransac_times 1 \
+ --half \
+ --flash
+
+done
\ No newline at end of file
diff --git a/imcui/third_party/RoRD/demo/__init__.py b/third_party/EfficientLoFTR/src/__init__.py
similarity index 100%
rename from imcui/third_party/RoRD/demo/__init__.py
rename to third_party/EfficientLoFTR/src/__init__.py
diff --git a/imcui/third_party/EfficientLoFTR/src/config/default.py b/third_party/EfficientLoFTR/src/config/default.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/config/default.py
rename to third_party/EfficientLoFTR/src/config/default.py
diff --git a/imcui/third_party/EfficientLoFTR/src/datasets/megadepth.py b/third_party/EfficientLoFTR/src/datasets/megadepth.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/datasets/megadepth.py
rename to third_party/EfficientLoFTR/src/datasets/megadepth.py
diff --git a/imcui/third_party/ASpanFormer/src/datasets/sampler.py b/third_party/EfficientLoFTR/src/datasets/sampler.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/src/datasets/sampler.py
rename to third_party/EfficientLoFTR/src/datasets/sampler.py
diff --git a/imcui/third_party/EfficientLoFTR/src/datasets/scannet.py b/third_party/EfficientLoFTR/src/datasets/scannet.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/datasets/scannet.py
rename to third_party/EfficientLoFTR/src/datasets/scannet.py
diff --git a/imcui/third_party/EfficientLoFTR/src/lightning/data.py b/third_party/EfficientLoFTR/src/lightning/data.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/lightning/data.py
rename to third_party/EfficientLoFTR/src/lightning/data.py
diff --git a/imcui/third_party/EfficientLoFTR/src/lightning/lightning_loftr.py b/third_party/EfficientLoFTR/src/lightning/lightning_loftr.py
similarity index 97%
rename from imcui/third_party/EfficientLoFTR/src/lightning/lightning_loftr.py
rename to third_party/EfficientLoFTR/src/lightning/lightning_loftr.py
index a1a8e3ef2725e63b633c6022ae5bfd1a138e438b..38f6f4ae25cc99279c496a9ac7760296b1b03bb0 100644
--- a/imcui/third_party/EfficientLoFTR/src/lightning/lightning_loftr.py
+++ b/third_party/EfficientLoFTR/src/lightning/lightning_loftr.py
@@ -10,8 +10,8 @@ import pytorch_lightning as pl
from matplotlib import pyplot as plt
from src.loftr import LoFTR
-from src.loftr.utils.supervision import compute_supervision_coarse, compute_supervision_fine
-from src.losses.loftr_loss import LoFTRLoss
+# from src.loftr.utils.supervision import compute_supervision_coarse, compute_supervision_fine
+# from src.losses.loftr_loss import LoFTRLoss
from src.optimizers import build_optimizer, build_scheduler
from src.utils.metrics import (
compute_symmetrical_epipolar_errors,
@@ -56,7 +56,7 @@ class PL_LoFTR(pl.LightningModule):
# Matcher: LoFTR
self.matcher = LoFTR(config=_config['loftr'], profiler=self.profiler)
- self.loss = LoFTRLoss(_config)
+ # self.loss = LoFTRLoss(_config)
# Pretrained weights
if pretrained_ckpt:
@@ -157,10 +157,7 @@ class PL_LoFTR(pl.LightningModule):
self.logger.experiment.add_scalar(
'train/avg_loss_on_epoch', avg_loss,
global_step=self.current_epoch)
-
- def on_validation_epoch_start(self):
- self.matcher.fine_matching.validate = True
-
+
def validation_step(self, batch, batch_idx):
self._trainval_inference(batch)
@@ -178,7 +175,6 @@ class PL_LoFTR(pl.LightningModule):
}
def validation_epoch_end(self, outputs):
- self.matcher.fine_matching.validate = False
# handle multiple validation sets
multi_outputs = [outputs] if not isinstance(outputs[0], (list, tuple)) else outputs
multi_val_metrics = defaultdict(list)
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/__init__.py b/third_party/EfficientLoFTR/src/loftr/__init__.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/__init__.py
rename to third_party/EfficientLoFTR/src/loftr/__init__.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/backbone/__init__.py b/third_party/EfficientLoFTR/src/loftr/backbone/__init__.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/backbone/__init__.py
rename to third_party/EfficientLoFTR/src/loftr/backbone/__init__.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/backbone/backbone.py b/third_party/EfficientLoFTR/src/loftr/backbone/backbone.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/backbone/backbone.py
rename to third_party/EfficientLoFTR/src/loftr/backbone/backbone.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/backbone/repvgg.py b/third_party/EfficientLoFTR/src/loftr/backbone/repvgg.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/backbone/repvgg.py
rename to third_party/EfficientLoFTR/src/loftr/backbone/repvgg.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/loftr.py b/third_party/EfficientLoFTR/src/loftr/loftr.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/loftr.py
rename to third_party/EfficientLoFTR/src/loftr/loftr.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/loftr_module/__init__.py b/third_party/EfficientLoFTR/src/loftr/loftr_module/__init__.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/loftr_module/__init__.py
rename to third_party/EfficientLoFTR/src/loftr/loftr_module/__init__.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/loftr_module/fine_preprocess.py b/third_party/EfficientLoFTR/src/loftr/loftr_module/fine_preprocess.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/loftr_module/fine_preprocess.py
rename to third_party/EfficientLoFTR/src/loftr/loftr_module/fine_preprocess.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/loftr_module/linear_attention.py b/third_party/EfficientLoFTR/src/loftr/loftr_module/linear_attention.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/loftr_module/linear_attention.py
rename to third_party/EfficientLoFTR/src/loftr/loftr_module/linear_attention.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/loftr_module/transformer.py b/third_party/EfficientLoFTR/src/loftr/loftr_module/transformer.py
similarity index 97%
rename from imcui/third_party/EfficientLoFTR/src/loftr/loftr_module/transformer.py
rename to third_party/EfficientLoFTR/src/loftr/loftr_module/transformer.py
index e97a033a185049539a9f2fd29483333a839a3bcd..fd6eaeda7a4ac360c812d07c5c5757717bb39f3e 100644
--- a/imcui/third_party/EfficientLoFTR/src/loftr/loftr_module/transformer.py
+++ b/third_party/EfficientLoFTR/src/loftr/loftr_module/transformer.py
@@ -30,8 +30,7 @@ class AG_RoPE_EncoderLayer(nn.Module):
# aggregate and position encoding
self.aggregate = nn.Conv2d(d_model, d_model, kernel_size=agg_size0, padding=0, stride=agg_size0, bias=False, groups=d_model) if self.agg_size0 != 1 else nn.Identity()
self.max_pool = torch.nn.MaxPool2d(kernel_size=self.agg_size1, stride=self.agg_size1) if self.agg_size1 != 1 else nn.Identity()
- if self.rope:
- self.rope_pos_enc = RoPEPositionEncodingSine(d_model, max_shape=(256, 256), npe=npe, ropefp16=True)
+ self.rope_pos_enc = RoPEPositionEncodingSine(d_model, max_shape=(256, 256), npe=npe, ropefp16=True)
# multi-head attention
self.q_proj = nn.Linear(d_model, d_model, bias=False)
@@ -63,6 +62,7 @@ class AG_RoPE_EncoderLayer(nn.Module):
H1, W1 = source.size(-2), source.size(-1)
# Aggragate feature
+ assert x_mask is None and source_mask is None
query, source = self.norm1(self.aggregate(x).permute(0,2,3,1)), self.norm1(self.max_pool(source).permute(0,2,3,1)) # [N, H, W, C]
if x_mask is not None:
x_mask, source_mask = map(lambda x: self.max_pool(x.float()).bool(), [x_mask, source_mask])
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/utils/coarse_matching.py b/third_party/EfficientLoFTR/src/loftr/utils/coarse_matching.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/utils/coarse_matching.py
rename to third_party/EfficientLoFTR/src/loftr/utils/coarse_matching.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/utils/fine_matching.py b/third_party/EfficientLoFTR/src/loftr/utils/fine_matching.py
similarity index 97%
rename from imcui/third_party/EfficientLoFTR/src/loftr/utils/fine_matching.py
rename to third_party/EfficientLoFTR/src/loftr/utils/fine_matching.py
index 6adb6f8c8a1c3d25babda3d5cbd79b44285c2eb9..8d6da60c9fe8230e01a5ab47334d5ab506cbc7af 100644
--- a/imcui/third_party/EfficientLoFTR/src/loftr/utils/fine_matching.py
+++ b/third_party/EfficientLoFTR/src/loftr/utils/fine_matching.py
@@ -17,7 +17,6 @@ class FineMatching(nn.Module):
self.local_regress_temperature = config['match_fine']['local_regress_temperature']
self.local_regress_slicedim = config['match_fine']['local_regress_slicedim']
self.fp16 = config['half']
- self.validate = False
def forward(self, feat_0, feat_1, data):
"""
@@ -47,7 +46,7 @@ class FineMatching(nn.Module):
return
# compute pixel-level confidence matrix
- with torch.autocast(enabled=True if not (self.training or self.validate) else False, device_type='cuda'):
+ with torch.autocast(enabled=True, device_type='cuda'):
feat_f0, feat_f1 = feat_0[...,:-self.local_regress_slicedim], feat_1[...,:-self.local_regress_slicedim]
feat_ff0, feat_ff1 = feat_0[...,-self.local_regress_slicedim:], feat_1[...,-self.local_regress_slicedim:]
feat_f0, feat_f1 = feat_f0 / C**.5, feat_f1 / C**.5
@@ -59,7 +58,7 @@ class FineMatching(nn.Module):
softmax_matrix_f = softmax_matrix_f[...,1:-1,1:-1].reshape(M, self.WW, self.WW)
# for fine-level supervision
- if self.training or self.validate:
+ if self.training:
data.update({'sim_matrix_ff': conf_matrix_ff})
data.update({'conf_matrix_f': softmax_matrix_f})
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/utils/full_config.py b/third_party/EfficientLoFTR/src/loftr/utils/full_config.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/utils/full_config.py
rename to third_party/EfficientLoFTR/src/loftr/utils/full_config.py
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/utils/geometry.py b/third_party/EfficientLoFTR/src/loftr/utils/geometry.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/utils/geometry.py
rename to third_party/EfficientLoFTR/src/loftr/utils/geometry.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/utils/opt_config.py b/third_party/EfficientLoFTR/src/loftr/utils/opt_config.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/utils/opt_config.py
rename to third_party/EfficientLoFTR/src/loftr/utils/opt_config.py
diff --git a/imcui/third_party/EfficientLoFTR/src/loftr/utils/position_encoding.py b/third_party/EfficientLoFTR/src/loftr/utils/position_encoding.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/loftr/utils/position_encoding.py
rename to third_party/EfficientLoFTR/src/loftr/utils/position_encoding.py
diff --git a/imcui/third_party/ASpanFormer/src/optimizers/__init__.py b/third_party/EfficientLoFTR/src/optimizers/__init__.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/src/optimizers/__init__.py
rename to third_party/EfficientLoFTR/src/optimizers/__init__.py
diff --git a/imcui/third_party/ASpanFormer/src/utils/augment.py b/third_party/EfficientLoFTR/src/utils/augment.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/src/utils/augment.py
rename to third_party/EfficientLoFTR/src/utils/augment.py
diff --git a/imcui/third_party/ASpanFormer/src/utils/comm.py b/third_party/EfficientLoFTR/src/utils/comm.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/src/utils/comm.py
rename to third_party/EfficientLoFTR/src/utils/comm.py
diff --git a/imcui/third_party/ASpanFormer/src/utils/dataloader.py b/third_party/EfficientLoFTR/src/utils/dataloader.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/src/utils/dataloader.py
rename to third_party/EfficientLoFTR/src/utils/dataloader.py
diff --git a/imcui/third_party/EfficientLoFTR/src/utils/dataset.py b/third_party/EfficientLoFTR/src/utils/dataset.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/utils/dataset.py
rename to third_party/EfficientLoFTR/src/utils/dataset.py
diff --git a/imcui/third_party/EfficientLoFTR/src/utils/metrics.py b/third_party/EfficientLoFTR/src/utils/metrics.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/utils/metrics.py
rename to third_party/EfficientLoFTR/src/utils/metrics.py
diff --git a/imcui/third_party/EfficientLoFTR/src/utils/misc.py b/third_party/EfficientLoFTR/src/utils/misc.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/utils/misc.py
rename to third_party/EfficientLoFTR/src/utils/misc.py
diff --git a/imcui/third_party/EfficientLoFTR/src/utils/plotting.py b/third_party/EfficientLoFTR/src/utils/plotting.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/utils/plotting.py
rename to third_party/EfficientLoFTR/src/utils/plotting.py
diff --git a/imcui/third_party/ASpanFormer/src/utils/profiler.py b/third_party/EfficientLoFTR/src/utils/profiler.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/src/utils/profiler.py
rename to third_party/EfficientLoFTR/src/utils/profiler.py
diff --git a/imcui/third_party/EfficientLoFTR/src/utils/warppers.py b/third_party/EfficientLoFTR/src/utils/warppers.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/utils/warppers.py
rename to third_party/EfficientLoFTR/src/utils/warppers.py
diff --git a/imcui/third_party/EfficientLoFTR/src/utils/warppers_utils.py b/third_party/EfficientLoFTR/src/utils/warppers_utils.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/utils/warppers_utils.py
rename to third_party/EfficientLoFTR/src/utils/warppers_utils.py
diff --git a/imcui/third_party/EfficientLoFTR/test.py b/third_party/EfficientLoFTR/test.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/test.py
rename to third_party/EfficientLoFTR/test.py
diff --git a/third_party/GlueStick/.gitignore b/third_party/GlueStick/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..c246e14ed9611a54be01334d4c2e734dca731e4b
--- /dev/null
+++ b/third_party/GlueStick/.gitignore
@@ -0,0 +1,132 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+.idea/*
+*events.out.tfevents.*
+/outputs
\ No newline at end of file
diff --git a/third_party/GlueStick/LICENSE b/third_party/GlueStick/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..866f33543245c285b350696b00be76bc278ca4a7
--- /dev/null
+++ b/third_party/GlueStick/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Computer Vision and Geometry Lab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/third_party/GlueStick/README.md b/third_party/GlueStick/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3145f02d47f4c60dd7d9a7d04e10f87b8f55dad7
--- /dev/null
+++ b/third_party/GlueStick/README.md
@@ -0,0 +1,48 @@
+# GlueStick
+[](https://colab.research.google.com/github/cvg/GlueStick/blob/main/gluestick_matching_demo.ipynb) [](https://arxiv.org/abs/2304.02008) [](https://iago-suarez.com/gluestick)
+
+Joint deep matcher for points and lines 🖼️💥🖼️
+
+
+
+This repository contains the official implementation of
+[GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://arxiv.org/abs/2304.02008).
+
+## Install 🛠️
+
+To install the software in Ubuntu 22.04 follow these instructions:
+```bash
+sudo apt-get install build-essential cmake libopencv-dev libopencv-contrib-dev
+git clone --recursive https://github.com/cvg/GlueStick.git
+cd GlueStick
+# Create and activate a virtual environment
+python -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+pip install -e .
+```
+
+## Running GlueStick 🏃
+Download the weights of the model:
+```
+wget https://github.com/cvg/GlueStick/releases/download/v0.1_arxiv/checkpoint_GlueStick_MD.tar -P resources/weights
+```
+
+You can execute the inference with it with:
+```
+python -m gluestick.run -img1 resources/img1.jpg -img2 resources/img2.jpg
+```
+
+## Training 🏋️
+We want to provide you with high-quality and flexible code for training. Stay tuned, we will release it soon!
+
+## Citation 📝
+If you use this code in your project, please consider citing the following paper:
+```bibtex
+@article{pautrat_suarez_2023_gluestick,
+ title={{GlueStick}: Robust Image Matching by Sticking Points and Lines Together},
+ author={Pautrat, R{\'e}mi* and Su{\'a}rez, Iago* and Yu, Yifan and Pollefeys, Marc and Larsson, Viktor},
+ journal={ArXiv},
+ year={2023}
+}
+```
diff --git a/imcui/third_party/GlueStick/gluestick/__init__.py b/third_party/GlueStick/gluestick/__init__.py
similarity index 81%
rename from imcui/third_party/GlueStick/gluestick/__init__.py
rename to third_party/GlueStick/gluestick/__init__.py
index d3051821ecfb2e18f4b9b4dfb50f35064106eb57..4eaf01e90440afeb485a4743f181dac348ede63d 100644
--- a/imcui/third_party/GlueStick/gluestick/__init__.py
+++ b/third_party/GlueStick/gluestick/__init__.py
@@ -8,11 +8,12 @@ GLUESTICK_ROOT = Path(__file__).parent.parent
def get_class(mod_name, base_path, BaseClass):
"""Get the class object which inherits from BaseClass and is defined in
- the module named mod_name, child of base_path.
+ the module named mod_name, child of base_path.
"""
import inspect
- mod_path = '{}.{}'.format(base_path, mod_name)
- mod = __import__(mod_path, fromlist=[''])
+
+ mod_path = "{}.{}".format(base_path, mod_name)
+ mod = __import__(mod_path, fromlist=[""])
classes = inspect.getmembers(mod, inspect.isclass)
# Filter classes defined in the module
classes = [c for c in classes if c[1].__module__ == mod_path]
@@ -24,7 +25,8 @@ def get_class(mod_name, base_path, BaseClass):
def get_model(name):
from .models.base_model import BaseModel
- return get_class('models.' + name, __name__, BaseModel)
+
+ return get_class("models." + name, __name__, BaseModel)
def numpy_image_to_torch(image):
@@ -34,8 +36,8 @@ def numpy_image_to_torch(image):
elif image.ndim == 2:
image = image[None] # add channel axis
else:
- raise ValueError(f'Not an image: {image.shape}')
- return torch.from_numpy(image / 255.).float()
+ raise ValueError(f"Not an image: {image.shape}")
+ return torch.from_numpy(image / 255.0).float()
def map_tensor(input_, func):
diff --git a/imcui/third_party/GlueStick/gluestick/drawing.py b/third_party/GlueStick/gluestick/drawing.py
similarity index 74%
rename from imcui/third_party/GlueStick/gluestick/drawing.py
rename to third_party/GlueStick/gluestick/drawing.py
index 8e6d24b6bfedc93449142647410057d978d733ef..8365b7e1f91adedcd190c49b2a38cbcd817d84c2 100644
--- a/imcui/third_party/GlueStick/gluestick/drawing.py
+++ b/third_party/GlueStick/gluestick/drawing.py
@@ -4,8 +4,7 @@ import numpy as np
import seaborn as sns
-def plot_images(imgs, titles=None, cmaps='gray', dpi=100, pad=.5,
- adaptive=True):
+def plot_images(imgs, titles=None, cmaps="gray", dpi=100, pad=0.5, adaptive=True):
"""Plot a set of images horizontally.
Args:
imgs: a list of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W).
@@ -23,7 +22,8 @@ def plot_images(imgs, titles=None, cmaps='gray', dpi=100, pad=.5,
ratios = [4 / 3] * n
figsize = [sum(ratios) * 4.5, 4.5]
fig, ax = plt.subplots(
- 1, n, figsize=figsize, dpi=dpi, gridspec_kw={'width_ratios': ratios})
+ 1, n, figsize=figsize, dpi=dpi, gridspec_kw={"width_ratios": ratios}
+ )
if n == 1:
ax = [ax]
for i in range(n):
@@ -39,7 +39,7 @@ def plot_images(imgs, titles=None, cmaps='gray', dpi=100, pad=.5,
return ax
-def plot_keypoints(kpts, colors='lime', ps=4, alpha=1):
+def plot_keypoints(kpts, colors="lime", ps=4, alpha=1):
"""Plot keypoints for existing images.
Args:
kpts: list of ndarrays of size (N, 2).
@@ -53,7 +53,7 @@ def plot_keypoints(kpts, colors='lime', ps=4, alpha=1):
a.scatter(k[:, 0], k[:, 1], c=c, s=ps, alpha=alpha, linewidths=0)
-def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.):
+def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.0):
"""Plot matches for a pair of existing images.
Args:
kpts0, kpts1: corresponding keypoints of size (N, 2).
@@ -80,11 +80,18 @@ def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.):
transFigure = fig.transFigure.inverted()
fkpts0 = transFigure.transform(ax0.transData.transform(kpts0))
fkpts1 = transFigure.transform(ax1.transData.transform(kpts1))
- fig.lines += [matplotlib.lines.Line2D(
- (fkpts0[i, 0], fkpts1[i, 0]), (fkpts0[i, 1], fkpts1[i, 1]),
- zorder=1, transform=fig.transFigure, c=color[i], linewidth=lw,
- alpha=a)
- for i in range(len(kpts0))]
+ fig.lines += [
+ matplotlib.lines.Line2D(
+ (fkpts0[i, 0], fkpts1[i, 0]),
+ (fkpts0[i, 1], fkpts1[i, 1]),
+ zorder=1,
+ transform=fig.transFigure,
+ c=color[i],
+ linewidth=lw,
+ alpha=a,
+ )
+ for i in range(len(kpts0))
+ ]
# freeze the axes to prevent the transform to change
ax0.autoscale(enable=False)
@@ -95,9 +102,16 @@ def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.):
ax1.scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps)
-def plot_lines(lines, line_colors='orange', point_colors='cyan',
- ps=4, lw=2, alpha=1., indices=(0, 1)):
- """ Plot lines and endpoints for existing images.
+def plot_lines(
+ lines,
+ line_colors="orange",
+ point_colors="cyan",
+ ps=4,
+ lw=2,
+ alpha=1.0,
+ indices=(0, 1),
+):
+ """Plot lines and endpoints for existing images.
Args:
lines: list of ndarrays of size (N, 2, 2).
colors: string, or list of list of tuples (one for each keypoints).
@@ -120,18 +134,20 @@ def plot_lines(lines, line_colors='orange', point_colors='cyan',
# Plot the lines and junctions
for a, l, lc, pc in zip(axes, lines, line_colors, point_colors):
for i in range(len(l)):
- line = matplotlib.lines.Line2D((l[i, 0, 0], l[i, 1, 0]),
- (l[i, 0, 1], l[i, 1, 1]),
- zorder=1, c=lc, linewidth=lw,
- alpha=alpha)
+ line = matplotlib.lines.Line2D(
+ (l[i, 0, 0], l[i, 1, 0]),
+ (l[i, 0, 1], l[i, 1, 1]),
+ zorder=1,
+ c=lc,
+ linewidth=lw,
+ alpha=alpha,
+ )
a.add_line(line)
pts = l.reshape(-1, 2)
- a.scatter(pts[:, 0], pts[:, 1],
- c=pc, s=ps, linewidths=0, zorder=2, alpha=alpha)
+ a.scatter(pts[:, 0], pts[:, 1], c=pc, s=ps, linewidths=0, zorder=2, alpha=alpha)
-def plot_color_line_matches(lines, correct_matches=None,
- lw=2, indices=(0, 1)):
+def plot_color_line_matches(lines, correct_matches=None, lw=2, indices=(0, 1)):
"""Plot line matches for existing images with multiple colors.
Args:
lines: list of ndarrays of size (N, 2, 2).
@@ -140,7 +156,7 @@ def plot_color_line_matches(lines, correct_matches=None,
indices: indices of the images to draw the matches on.
"""
n_lines = len(lines[0])
- colors = sns.color_palette('husl', n_colors=n_lines)
+ colors = sns.color_palette("husl", n_colors=n_lines)
np.random.shuffle(colors)
alphas = np.ones(n_lines)
# If correct_matches is not None, display wrong matches with a low alpha
@@ -159,8 +175,15 @@ def plot_color_line_matches(lines, correct_matches=None,
transFigure = fig.transFigure.inverted()
endpoint0 = transFigure.transform(a.transData.transform(l[:, 0]))
endpoint1 = transFigure.transform(a.transData.transform(l[:, 1]))
- fig.lines += [matplotlib.lines.Line2D(
- (endpoint0[i, 0], endpoint1[i, 0]),
- (endpoint0[i, 1], endpoint1[i, 1]),
- zorder=1, transform=fig.transFigure, c=colors[i],
- alpha=alphas[i], linewidth=lw) for i in range(n_lines)]
+ fig.lines += [
+ matplotlib.lines.Line2D(
+ (endpoint0[i, 0], endpoint1[i, 0]),
+ (endpoint0[i, 1], endpoint1[i, 1]),
+ zorder=1,
+ transform=fig.transFigure,
+ c=colors[i],
+ alpha=alphas[i],
+ linewidth=lw,
+ )
+ for i in range(n_lines)
+ ]
diff --git a/imcui/third_party/GlueStick/gluestick/geometry.py b/third_party/GlueStick/gluestick/geometry.py
similarity index 79%
rename from imcui/third_party/GlueStick/gluestick/geometry.py
rename to third_party/GlueStick/gluestick/geometry.py
index 97853c4807d319eb9ea0377db7385e9a72fb400b..0cdd232e74aeda84e1683dcb8e51385cc2497c37 100644
--- a/imcui/third_party/GlueStick/gluestick/geometry.py
+++ b/third_party/GlueStick/gluestick/geometry.py
@@ -21,7 +21,7 @@ def to_homogeneous(points):
raise ValueError
-def from_homogeneous(points, eps=0.):
+def from_homogeneous(points, eps=0.0):
"""Remove the homogeneous dimension of N-dimensional points.
Args:
points: torch.Tensor or numpy.ndarray with size (..., N+1).
@@ -32,14 +32,22 @@ def from_homogeneous(points, eps=0.):
def skew_symmetric(v):
- """Create a skew-symmetric matrix from a (batched) vector of size (..., 3).
- """
+ """Create a skew-symmetric matrix from a (batched) vector of size (..., 3)."""
z = torch.zeros_like(v[..., 0])
- M = torch.stack([
- z, -v[..., 2], v[..., 1],
- v[..., 2], z, -v[..., 0],
- -v[..., 1], v[..., 0], z,
- ], dim=-1).reshape(v.shape[:-1] + (3, 3))
+ M = torch.stack(
+ [
+ z,
+ -v[..., 2],
+ v[..., 1],
+ v[..., 2],
+ z,
+ -v[..., 0],
+ -v[..., 1],
+ v[..., 0],
+ z,
+ ],
+ dim=-1,
+ ).reshape(v.shape[:-1] + (3, 3))
return M
@@ -67,7 +75,7 @@ def warp_points_torch(points, H, inverse=True):
H_mat = torch.cat([H, torch.ones_like(H[..., :1])], axis=-1).reshape(out_shape)
if inverse:
H_mat = torch.inverse(H_mat)
- warped_points = torch.einsum('...nj,...ji->...ni', points, H_mat.transpose(-2, -1))
+ warped_points = torch.einsum("...nj,...ji->...ni", points, H_mat.transpose(-2, -1))
warped_points = from_homogeneous(warped_points, eps=1e-5)
@@ -76,18 +84,27 @@ def warp_points_torch(points, H, inverse=True):
def seg_equation(segs):
# calculate list of start, end and midpoints points from both lists
- start_points, end_points = to_homogeneous(segs[..., 0, :]), to_homogeneous(segs[..., 1, :])
+ start_points, end_points = to_homogeneous(segs[..., 0, :]), to_homogeneous(
+ segs[..., 1, :]
+ )
# Compute the line equations as ax + by + c = 0 , where x^2 + y^2 = 1
lines = torch.cross(start_points, end_points, dim=-1)
- lines_norm = (torch.sqrt(lines[..., 0] ** 2 + lines[..., 1] ** 2)[..., None])
- assert torch.all(lines_norm > 0), 'Error: trying to compute the equation of a line with a single point'
+ lines_norm = torch.sqrt(lines[..., 0] ** 2 + lines[..., 1] ** 2)[..., None]
+ assert torch.all(
+ lines_norm > 0
+ ), "Error: trying to compute the equation of a line with a single point"
lines = lines / lines_norm
return lines
def is_inside_img(pts: torch.Tensor, img_shape: Tuple[int, int]):
h, w = img_shape
- return (pts >= 0).all(dim=-1) & (pts[..., 0] < w) & (pts[..., 1] < h) & (~torch.isinf(pts).any(dim=-1))
+ return (
+ (pts >= 0).all(dim=-1)
+ & (pts[..., 0] < w)
+ & (pts[..., 1] < h)
+ & (~torch.isinf(pts).any(dim=-1))
+ )
def shrink_segs_to_img(segs: torch.Tensor, img_shape: Tuple[int, int]) -> torch.Tensor:
@@ -102,7 +119,9 @@ def shrink_segs_to_img(segs: torch.Tensor, img_shape: Tuple[int, int]) -> torch.
# Project the segments to the reference image
segs = segs.clone()
eqs = seg_equation(segs)
- x0, y0 = torch.tensor([1., 0, 0.], device=device), torch.tensor([0., 1, 0], device=device)
+ x0, y0 = torch.tensor([1.0, 0, 0.0], device=device), torch.tensor(
+ [0.0, 1, 0], device=device
+ )
x0 = x0.repeat(eqs.shape[:-1] + (1,))
y0 = y0.repeat(eqs.shape[:-1] + (1,))
pt_x0s = torch.cross(eqs, x0, dim=-1)
@@ -112,7 +131,9 @@ def shrink_segs_to_img(segs: torch.Tensor, img_shape: Tuple[int, int]) -> torch.
pt_y0s = pt_y0s[..., :-1] / pt_y0s[..., None, -1]
pt_y0s_valid = is_inside_img(pt_y0s, img_shape)
- xW, yH = torch.tensor([1., 0, EPS - w], device=device), torch.tensor([0., 1, EPS - h], device=device)
+ xW, yH = torch.tensor([1.0, 0, EPS - w], device=device), torch.tensor(
+ [0.0, 1, EPS - h], device=device
+ )
xW = xW.repeat(eqs.shape[:-1] + (1,))
yH = yH.repeat(eqs.shape[:-1] + (1,))
pt_xWs = torch.cross(eqs, xW, dim=-1)
@@ -143,11 +164,17 @@ def shrink_segs_to_img(segs: torch.Tensor, img_shape: Tuple[int, int]) -> torch.
mask = (segs[..., 1, 1] > (h - 1)) & pt_yHs_valid
segs[mask, 1, :] = pt_yHs[mask]
- assert torch.all(segs >= 0) and torch.all(segs[..., 0] < w) and torch.all(segs[..., 1] < h)
+ assert (
+ torch.all(segs >= 0)
+ and torch.all(segs[..., 0] < w)
+ and torch.all(segs[..., 1] < h)
+ )
return segs
-def warp_lines_torch(lines, H, inverse=True, dst_shape: Tuple[int, int] = None) -> Tuple[torch.Tensor, torch.Tensor]:
+def warp_lines_torch(
+ lines, H, inverse=True, dst_shape: Tuple[int, int] = None
+) -> Tuple[torch.Tensor, torch.Tensor]:
"""
:param lines: A tensor of shape (B, N, 2, 2) where B is the batch size, N the number of lines.
:param H: The homography used to convert the lines. batched or not (shapes (B, 8) and (8,) respectively).
@@ -156,12 +183,16 @@ def warp_lines_torch(lines, H, inverse=True, dst_shape: Tuple[int, int] = None)
"""
device = lines.device
batch_size, n = lines.shape[:2]
- lines = warp_points_torch(lines.reshape(batch_size, -1, 2), H, inverse).reshape(lines.shape)
+ lines = warp_points_torch(lines.reshape(batch_size, -1, 2), H, inverse).reshape(
+ lines.shape
+ )
if dst_shape is None:
return lines, torch.ones(lines.shape[:-2], dtype=torch.bool, device=device)
- out_img = torch.any((lines < 0) | (lines >= torch.tensor(dst_shape[::-1], device=device)), -1)
+ out_img = torch.any(
+ (lines < 0) | (lines >= torch.tensor(dst_shape[::-1], device=device)), -1
+ )
valid = ~out_img.all(-1)
any_out_of_img = out_img.any(-1)
lines_to_trim = valid & any_out_of_img
diff --git a/imcui/third_party/RoRD/lib/__init__.py b/third_party/GlueStick/gluestick/models/__init__.py
similarity index 100%
rename from imcui/third_party/RoRD/lib/__init__.py
rename to third_party/GlueStick/gluestick/models/__init__.py
diff --git a/imcui/third_party/GlueStick/gluestick/models/base_model.py b/third_party/GlueStick/gluestick/models/base_model.py
similarity index 89%
rename from imcui/third_party/GlueStick/gluestick/models/base_model.py
rename to third_party/GlueStick/gluestick/models/base_model.py
index 30ca991655a28ca88074b42312c33b360f655fab..ef326bbb9e7deb78ee59d7cf9b2a76a5234106b4 100644
--- a/imcui/third_party/GlueStick/gluestick/models/base_model.py
+++ b/third_party/GlueStick/gluestick/models/base_model.py
@@ -13,7 +13,7 @@ class MetaModel(ABCMeta):
def __prepare__(name, bases, **kwds):
total_conf = OmegaConf.create()
for base in bases:
- for key in ('base_default_conf', 'default_conf'):
+ for key in ("base_default_conf", "default_conf"):
update = getattr(base, key, {})
if isinstance(update, dict):
update = OmegaConf.create(update)
@@ -49,10 +49,11 @@ class BaseModel(nn.Module, metaclass=MetaModel):
metrics(self, pred, data): method that returns a dictionary of metrics,
each as a batch of scalars.
"""
+
default_conf = {
- 'name': None,
- 'trainable': True, # if false: do not optimize this model parameters
- 'freeze_batch_normalization': False, # use test-time statistics
+ "name": None,
+ "trainable": True, # if false: do not optimize this model parameters
+ "freeze_batch_normalization": False, # use test-time statistics
}
required_data_keys = []
strict_conf = True
@@ -61,15 +62,16 @@ class BaseModel(nn.Module, metaclass=MetaModel):
"""Perform some logic and call the _init method of the child model."""
super().__init__()
default_conf = OmegaConf.merge(
- self.base_default_conf, OmegaConf.create(self.default_conf))
+ self.base_default_conf, OmegaConf.create(self.default_conf)
+ )
if self.strict_conf:
OmegaConf.set_struct(default_conf, True)
# fixme: backward compatibility
- if 'pad' in conf and 'pad' not in default_conf: # backward compat.
+ if "pad" in conf and "pad" not in default_conf: # backward compat.
with omegaconf.read_write(conf):
with omegaconf.open_dict(conf):
- conf['interpolation'] = {'pad': conf.pop('pad')}
+ conf["interpolation"] = {"pad": conf.pop("pad")}
if isinstance(conf, dict):
conf = OmegaConf.create(conf)
@@ -89,6 +91,7 @@ class BaseModel(nn.Module, metaclass=MetaModel):
def freeze_bn(module):
if isinstance(module, nn.modules.batchnorm._BatchNorm):
module.eval()
+
if self.conf.freeze_batch_normalization:
self.apply(freeze_bn)
@@ -96,9 +99,10 @@ class BaseModel(nn.Module, metaclass=MetaModel):
def forward(self, data):
"""Check the data and call the _forward method of the child model."""
+
def recursive_key_check(expected, given):
for key in expected:
- assert key in given, f'Missing key {key} in data'
+ assert key in given, f"Missing key {key} in data"
if isinstance(expected, dict):
recursive_key_check(expected[key], given[key])
diff --git a/imcui/third_party/GlueStick/gluestick/models/gluestick.py b/third_party/GlueStick/gluestick/models/gluestick.py
similarity index 54%
rename from imcui/third_party/GlueStick/gluestick/models/gluestick.py
rename to third_party/GlueStick/gluestick/models/gluestick.py
index 98550ff9d8918bcf49a13ae606d1d631448b8f96..8179f8ff779401f535260b930a3f5e4d957af614 100644
--- a/imcui/third_party/GlueStick/gluestick/models/gluestick.py
+++ b/third_party/GlueStick/gluestick/models/gluestick.py
@@ -1,4 +1,3 @@
-import os.path
import warnings
from copy import deepcopy
@@ -13,143 +12,178 @@ ETH_EPS = 1e-8
class GlueStick(BaseModel):
default_conf = {
- 'input_dim': 256,
- 'descriptor_dim': 256,
- 'bottleneck_dim': None,
- 'weights': None,
- 'keypoint_encoder': [32, 64, 128, 256],
- 'GNN_layers': ['self', 'cross'] * 9,
- 'num_line_iterations': 1,
- 'line_attention': False,
- 'filter_threshold': 0.2,
- 'checkpointed': False,
- 'skip_init': False,
- 'inter_supervision': None,
- 'loss': {
- 'nll_weight': 1.,
- 'nll_balancing': 0.5,
- 'reward_weight': 0.,
- 'bottleneck_l2_weight': 0.,
- 'dense_nll_weight': 0.,
- 'inter_supervision': [0.3, 0.6],
+ "input_dim": 256,
+ "descriptor_dim": 256,
+ "bottleneck_dim": None,
+ "weights": None,
+ "keypoint_encoder": [32, 64, 128, 256],
+ "GNN_layers": ["self", "cross"] * 9,
+ "num_line_iterations": 1,
+ "line_attention": False,
+ "filter_threshold": 0.2,
+ "checkpointed": False,
+ "skip_init": False,
+ "inter_supervision": None,
+ "loss": {
+ "nll_weight": 1.0,
+ "nll_balancing": 0.5,
+ "reward_weight": 0.0,
+ "bottleneck_l2_weight": 0.0,
+ "dense_nll_weight": 0.0,
+ "inter_supervision": [0.3, 0.6],
},
}
required_data_keys = [
- 'keypoints0', 'keypoints1',
- 'descriptors0', 'descriptors1',
- 'keypoint_scores0', 'keypoint_scores1']
-
- DEFAULT_LOSS_CONF = {'nll_weight': 1., 'nll_balancing': 0.5, 'reward_weight': 0., 'bottleneck_l2_weight': 0.}
+ "keypoints0",
+ "keypoints1",
+ "descriptors0",
+ "descriptors1",
+ "keypoint_scores0",
+ "keypoint_scores1",
+ ]
+
+ DEFAULT_LOSS_CONF = {
+ "nll_weight": 1.0,
+ "nll_balancing": 0.5,
+ "reward_weight": 0.0,
+ "bottleneck_l2_weight": 0.0,
+ }
def _init(self, conf):
if conf.bottleneck_dim is not None:
self.bottleneck_down = nn.Conv1d(
- conf.input_dim, conf.bottleneck_dim, kernel_size=1)
+ conf.input_dim, conf.bottleneck_dim, kernel_size=1
+ )
self.bottleneck_up = nn.Conv1d(
- conf.bottleneck_dim, conf.input_dim, kernel_size=1)
+ conf.bottleneck_dim, conf.input_dim, kernel_size=1
+ )
nn.init.constant_(self.bottleneck_down.bias, 0.0)
nn.init.constant_(self.bottleneck_up.bias, 0.0)
if conf.input_dim != conf.descriptor_dim:
self.input_proj = nn.Conv1d(
- conf.input_dim, conf.descriptor_dim, kernel_size=1)
+ conf.input_dim, conf.descriptor_dim, kernel_size=1
+ )
nn.init.constant_(self.input_proj.bias, 0.0)
- self.kenc = KeypointEncoder(conf.descriptor_dim,
- conf.keypoint_encoder)
+ self.kenc = KeypointEncoder(conf.descriptor_dim, conf.keypoint_encoder)
self.lenc = EndPtEncoder(conf.descriptor_dim, conf.keypoint_encoder)
- self.gnn = AttentionalGNN(conf.descriptor_dim, conf.GNN_layers,
- checkpointed=conf.checkpointed,
- inter_supervision=conf.inter_supervision,
- num_line_iterations=conf.num_line_iterations,
- line_attention=conf.line_attention)
- self.final_proj = nn.Conv1d(conf.descriptor_dim, conf.descriptor_dim,
- kernel_size=1)
+ self.gnn = AttentionalGNN(
+ conf.descriptor_dim,
+ conf.GNN_layers,
+ checkpointed=conf.checkpointed,
+ inter_supervision=conf.inter_supervision,
+ num_line_iterations=conf.num_line_iterations,
+ line_attention=conf.line_attention,
+ )
+ self.final_proj = nn.Conv1d(
+ conf.descriptor_dim, conf.descriptor_dim, kernel_size=1
+ )
nn.init.constant_(self.final_proj.bias, 0.0)
nn.init.orthogonal_(self.final_proj.weight, gain=1)
self.final_line_proj = nn.Conv1d(
- conf.descriptor_dim, conf.descriptor_dim, kernel_size=1)
+ conf.descriptor_dim, conf.descriptor_dim, kernel_size=1
+ )
nn.init.constant_(self.final_line_proj.bias, 0.0)
nn.init.orthogonal_(self.final_line_proj.weight, gain=1)
if conf.inter_supervision is not None:
self.inter_line_proj = nn.ModuleList(
- [nn.Conv1d(conf.descriptor_dim, conf.descriptor_dim, kernel_size=1)
- for _ in conf.inter_supervision])
+ [
+ nn.Conv1d(conf.descriptor_dim, conf.descriptor_dim, kernel_size=1)
+ for _ in conf.inter_supervision
+ ]
+ )
self.layer2idx = {}
for i, l in enumerate(conf.inter_supervision):
nn.init.constant_(self.inter_line_proj[i].bias, 0.0)
nn.init.orthogonal_(self.inter_line_proj[i].weight, gain=1)
self.layer2idx[l] = i
- bin_score = torch.nn.Parameter(torch.tensor(1.))
- self.register_parameter('bin_score', bin_score)
- line_bin_score = torch.nn.Parameter(torch.tensor(1.))
- self.register_parameter('line_bin_score', line_bin_score)
+ bin_score = torch.nn.Parameter(torch.tensor(1.0))
+ self.register_parameter("bin_score", bin_score)
+ line_bin_score = torch.nn.Parameter(torch.tensor(1.0))
+ self.register_parameter("line_bin_score", line_bin_score)
if conf.weights:
assert isinstance(conf.weights, str)
- if os.path.exists(conf.weights):
- state_dict = torch.load(conf.weights, map_location='cpu')
- else:
- weights_url = "https://github.com/cvg/GlueStick/releases/download/v0.1_arxiv/checkpoint_GlueStick_MD.tar"
- state_dict = torch.hub.load_state_dict_from_url(weights_url, map_location='cpu')
- if 'model' in state_dict:
- state_dict = {k.replace('matcher.', ''): v for k, v in state_dict['model'].items() if 'matcher.' in k}
- state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+ state_dict = torch.load(conf.weights, map_location="cpu")
+ if "model" in state_dict:
+ state_dict = {
+ k.replace("matcher.", ""): v
+ for k, v in state_dict["model"].items()
+ if "matcher." in k
+ }
+ state_dict = {
+ k.replace("module.", ""): v for k, v in state_dict.items()
+ }
self.load_state_dict(state_dict)
def _forward(self, data):
- device = data['keypoints0'].device
- b_size = len(data['keypoints0'])
- image_size0 = (data['image_size0'] if 'image_size0' in data
- else data['image0'].shape)
- image_size1 = (data['image_size1'] if 'image_size1' in data
- else data['image1'].shape)
+ device = data["keypoints0"].device
+ b_size = len(data["keypoints0"])
+ image_size0 = (
+ data["image_size0"] if "image_size0" in data else data["image0"].shape
+ )
+ image_size1 = (
+ data["image_size1"] if "image_size1" in data else data["image1"].shape
+ )
pred = {}
- desc0, desc1 = data['descriptors0'], data['descriptors1']
- kpts0, kpts1 = data['keypoints0'], data['keypoints1']
+ desc0, desc1 = data["descriptors0"], data["descriptors1"]
+ kpts0, kpts1 = data["keypoints0"], data["keypoints1"]
n_kpts0, n_kpts1 = kpts0.shape[1], kpts1.shape[1]
- n_lines0, n_lines1 = data['lines0'].shape[1], data['lines1'].shape[1]
+ n_lines0, n_lines1 = data["lines0"].shape[1], data["lines1"].shape[1]
if n_kpts0 == 0 or n_kpts1 == 0:
# No detected keypoints nor lines
- pred['log_assignment'] = torch.zeros(
- b_size, n_kpts0, n_kpts1, dtype=torch.float, device=device)
- pred['matches0'] = torch.full(
- (b_size, n_kpts0), -1, device=device, dtype=torch.int64)
- pred['matches1'] = torch.full(
- (b_size, n_kpts1), -1, device=device, dtype=torch.int64)
- pred['match_scores0'] = torch.zeros(
- (b_size, n_kpts0), device=device, dtype=torch.float32)
- pred['match_scores1'] = torch.zeros(
- (b_size, n_kpts1), device=device, dtype=torch.float32)
- pred['line_log_assignment'] = torch.zeros(b_size, n_lines0, n_lines1,
- dtype=torch.float, device=device)
- pred['line_matches0'] = torch.full((b_size, n_lines0), -1,
- device=device, dtype=torch.int64)
- pred['line_matches1'] = torch.full((b_size, n_lines1), -1,
- device=device, dtype=torch.int64)
- pred['line_match_scores0'] = torch.zeros(
- (b_size, n_lines0), device=device, dtype=torch.float32)
- pred['line_match_scores1'] = torch.zeros(
- (b_size, n_kpts1), device=device, dtype=torch.float32)
+ pred["log_assignment"] = torch.zeros(
+ b_size, n_kpts0, n_kpts1, dtype=torch.float, device=device
+ )
+ pred["matches0"] = torch.full(
+ (b_size, n_kpts0), -1, device=device, dtype=torch.int64
+ )
+ pred["matches1"] = torch.full(
+ (b_size, n_kpts1), -1, device=device, dtype=torch.int64
+ )
+ pred["match_scores0"] = torch.zeros(
+ (b_size, n_kpts0), device=device, dtype=torch.float32
+ )
+ pred["match_scores1"] = torch.zeros(
+ (b_size, n_kpts1), device=device, dtype=torch.float32
+ )
+ pred["line_log_assignment"] = torch.zeros(
+ b_size, n_lines0, n_lines1, dtype=torch.float, device=device
+ )
+ pred["line_matches0"] = torch.full(
+ (b_size, n_lines0), -1, device=device, dtype=torch.int64
+ )
+ pred["line_matches1"] = torch.full(
+ (b_size, n_lines1), -1, device=device, dtype=torch.int64
+ )
+ pred["line_match_scores0"] = torch.zeros(
+ (b_size, n_lines0), device=device, dtype=torch.float32
+ )
+ pred["line_match_scores1"] = torch.zeros(
+ (b_size, n_kpts1), device=device, dtype=torch.float32
+ )
return pred
- lines0 = data['lines0'].flatten(1, 2)
- lines1 = data['lines1'].flatten(1, 2)
- lines_junc_idx0 = data['lines_junc_idx0'].flatten(1, 2) # [b_size, num_lines * 2]
- lines_junc_idx1 = data['lines_junc_idx1'].flatten(1, 2)
+ lines0 = data["lines0"].flatten(1, 2)
+ lines1 = data["lines1"].flatten(1, 2)
+ lines_junc_idx0 = data["lines_junc_idx0"].flatten(
+ 1, 2
+ ) # [b_size, num_lines * 2]
+ lines_junc_idx1 = data["lines_junc_idx1"].flatten(1, 2)
if self.conf.bottleneck_dim is not None:
- pred['down_descriptors0'] = desc0 = self.bottleneck_down(desc0)
- pred['down_descriptors1'] = desc1 = self.bottleneck_down(desc1)
+ pred["down_descriptors0"] = desc0 = self.bottleneck_down(desc0)
+ pred["down_descriptors1"] = desc1 = self.bottleneck_down(desc1)
desc0 = self.bottleneck_up(desc0)
desc1 = self.bottleneck_up(desc1)
desc0 = nn.functional.normalize(desc0, p=2, dim=1)
desc1 = nn.functional.normalize(desc1, p=2, dim=1)
- pred['bottleneck_descriptors0'] = desc0
- pred['bottleneck_descriptors1'] = desc1
+ pred["bottleneck_descriptors0"] = desc0
+ pred["bottleneck_descriptors1"] = desc1
if self.conf.loss.nll_weight == 0:
desc0 = desc0.detach()
desc1 = desc1.detach()
@@ -163,79 +197,113 @@ class GlueStick(BaseModel):
assert torch.all(kpts0 >= -1) and torch.all(kpts0 <= 1)
assert torch.all(kpts1 >= -1) and torch.all(kpts1 <= 1)
- desc0 = desc0 + self.kenc(kpts0, data['keypoint_scores0'])
- desc1 = desc1 + self.kenc(kpts1, data['keypoint_scores1'])
+ desc0 = desc0 + self.kenc(kpts0, data["keypoint_scores0"])
+ desc1 = desc1 + self.kenc(kpts1, data["keypoint_scores1"])
if n_lines0 != 0 and n_lines1 != 0:
# Pre-compute the line encodings
lines0 = normalize_keypoints(lines0, image_size0).reshape(
- b_size, n_lines0, 2, 2)
+ b_size, n_lines0, 2, 2
+ )
lines1 = normalize_keypoints(lines1, image_size1).reshape(
- b_size, n_lines1, 2, 2)
- line_enc0 = self.lenc(lines0, data['line_scores0'])
- line_enc1 = self.lenc(lines1, data['line_scores1'])
+ b_size, n_lines1, 2, 2
+ )
+ line_enc0 = self.lenc(lines0, data["line_scores0"])
+ line_enc1 = self.lenc(lines1, data["line_scores1"])
else:
line_enc0 = torch.zeros(
- b_size, self.conf.descriptor_dim, n_lines0 * 2,
- dtype=torch.float, device=device)
+ b_size,
+ self.conf.descriptor_dim,
+ n_lines0 * 2,
+ dtype=torch.float,
+ device=device,
+ )
line_enc1 = torch.zeros(
- b_size, self.conf.descriptor_dim, n_lines1 * 2,
- dtype=torch.float, device=device)
+ b_size,
+ self.conf.descriptor_dim,
+ n_lines1 * 2,
+ dtype=torch.float,
+ device=device,
+ )
- desc0, desc1 = self.gnn(desc0, desc1, line_enc0, line_enc1,
- lines_junc_idx0, lines_junc_idx1)
+ desc0, desc1 = self.gnn(
+ desc0, desc1, line_enc0, line_enc1, lines_junc_idx0, lines_junc_idx1
+ )
# Match all points (KP and line junctions)
mdesc0, mdesc1 = self.final_proj(desc0), self.final_proj(desc1)
- kp_scores = torch.einsum('bdn,bdm->bnm', mdesc0, mdesc1)
- kp_scores = kp_scores / self.conf.descriptor_dim ** .5
+ kp_scores = torch.einsum("bdn,bdm->bnm", mdesc0, mdesc1)
+ kp_scores = kp_scores / self.conf.descriptor_dim**0.5
kp_scores = log_double_softmax(kp_scores, self.bin_score)
m0, m1, mscores0, mscores1 = self._get_matches(kp_scores)
- pred['log_assignment'] = kp_scores
- pred['matches0'] = m0
- pred['matches1'] = m1
- pred['match_scores0'] = mscores0
- pred['match_scores1'] = mscores1
+ pred["log_assignment"] = kp_scores
+ pred["matches0"] = m0
+ pred["matches1"] = m1
+ pred["match_scores0"] = mscores0
+ pred["match_scores1"] = mscores1
# Match the lines
if n_lines0 > 0 and n_lines1 > 0:
- (line_scores, m0_lines, m1_lines, mscores0_lines,
- mscores1_lines, raw_line_scores) = self._get_line_matches(
- desc0[:, :, :2 * n_lines0], desc1[:, :, :2 * n_lines1],
- lines_junc_idx0, lines_junc_idx1, self.final_line_proj)
+ (
+ line_scores,
+ m0_lines,
+ m1_lines,
+ mscores0_lines,
+ mscores1_lines,
+ raw_line_scores,
+ ) = self._get_line_matches(
+ desc0[:, :, : 2 * n_lines0],
+ desc1[:, :, : 2 * n_lines1],
+ lines_junc_idx0,
+ lines_junc_idx1,
+ self.final_line_proj,
+ )
if self.conf.inter_supervision:
for l in self.conf.inter_supervision:
- (line_scores_i, m0_lines_i, m1_lines_i, mscores0_lines_i,
- mscores1_lines_i) = self._get_line_matches(
- self.gnn.inter_layers[l][0][:, :, :2 * n_lines0],
- self.gnn.inter_layers[l][1][:, :, :2 * n_lines1],
- lines_junc_idx0, lines_junc_idx1,
- self.inter_line_proj[self.layer2idx[l]])
- pred[f'line_{l}_log_assignment'] = line_scores_i
- pred[f'line_{l}_matches0'] = m0_lines_i
- pred[f'line_{l}_matches1'] = m1_lines_i
- pred[f'line_{l}_match_scores0'] = mscores0_lines_i
- pred[f'line_{l}_match_scores1'] = mscores1_lines_i
+ (
+ line_scores_i,
+ m0_lines_i,
+ m1_lines_i,
+ mscores0_lines_i,
+ mscores1_lines_i,
+ ) = self._get_line_matches(
+ self.gnn.inter_layers[l][0][:, :, : 2 * n_lines0],
+ self.gnn.inter_layers[l][1][:, :, : 2 * n_lines1],
+ lines_junc_idx0,
+ lines_junc_idx1,
+ self.inter_line_proj[self.layer2idx[l]],
+ )
+ pred[f"line_{l}_log_assignment"] = line_scores_i
+ pred[f"line_{l}_matches0"] = m0_lines_i
+ pred[f"line_{l}_matches1"] = m1_lines_i
+ pred[f"line_{l}_match_scores0"] = mscores0_lines_i
+ pred[f"line_{l}_match_scores1"] = mscores1_lines_i
else:
- line_scores = torch.zeros(b_size, n_lines0, n_lines1,
- dtype=torch.float, device=device)
- m0_lines = torch.full((b_size, n_lines0), -1,
- device=device, dtype=torch.int64)
- m1_lines = torch.full((b_size, n_lines1), -1,
- device=device, dtype=torch.int64)
+ line_scores = torch.zeros(
+ b_size, n_lines0, n_lines1, dtype=torch.float, device=device
+ )
+ m0_lines = torch.full(
+ (b_size, n_lines0), -1, device=device, dtype=torch.int64
+ )
+ m1_lines = torch.full(
+ (b_size, n_lines1), -1, device=device, dtype=torch.int64
+ )
mscores0_lines = torch.zeros(
- (b_size, n_lines0), device=device, dtype=torch.float32)
+ (b_size, n_lines0), device=device, dtype=torch.float32
+ )
mscores1_lines = torch.zeros(
- (b_size, n_lines1), device=device, dtype=torch.float32)
- raw_line_scores = torch.zeros(b_size, n_lines0, n_lines1,
- dtype=torch.float, device=device)
- pred['line_log_assignment'] = line_scores
- pred['line_matches0'] = m0_lines
- pred['line_matches1'] = m1_lines
- pred['line_match_scores0'] = mscores0_lines
- pred['line_match_scores1'] = mscores1_lines
- pred['raw_line_scores'] = raw_line_scores
+ (b_size, n_lines1), device=device, dtype=torch.float32
+ )
+ raw_line_scores = torch.zeros(
+ b_size, n_lines0, n_lines1, dtype=torch.float, device=device
+ )
+ pred["line_log_assignment"] = line_scores
+ pred["line_matches0"] = m0_lines
+ pred["line_matches1"] = m1_lines
+ pred["line_match_scores0"] = mscores0_lines
+ pred["line_match_scores1"] = mscores1_lines
+ pred["raw_line_scores"] = raw_line_scores
return pred
@@ -254,35 +322,47 @@ class GlueStick(BaseModel):
m1 = torch.where(valid1, m1, m1.new_tensor(-1))
return m0, m1, mscores0, mscores1
- def _get_line_matches(self, ldesc0, ldesc1, lines_junc_idx0,
- lines_junc_idx1, final_proj):
+ def _get_line_matches(
+ self, ldesc0, ldesc1, lines_junc_idx0, lines_junc_idx1, final_proj
+ ):
mldesc0 = final_proj(ldesc0)
mldesc1 = final_proj(ldesc1)
- line_scores = torch.einsum('bdn,bdm->bnm', mldesc0, mldesc1)
- line_scores = line_scores / self.conf.descriptor_dim ** .5
+ line_scores = torch.einsum("bdn,bdm->bnm", mldesc0, mldesc1)
+ line_scores = line_scores / self.conf.descriptor_dim**0.5
# Get the line representation from the junction descriptors
n2_lines0 = lines_junc_idx0.shape[1]
n2_lines1 = lines_junc_idx1.shape[1]
line_scores = torch.gather(
- line_scores, dim=2,
- index=lines_junc_idx1[:, None, :].repeat(1, line_scores.shape[1], 1))
+ line_scores,
+ dim=2,
+ index=lines_junc_idx1[:, None, :].repeat(1, line_scores.shape[1], 1),
+ )
line_scores = torch.gather(
- line_scores, dim=1,
- index=lines_junc_idx0[:, :, None].repeat(1, 1, n2_lines1))
- line_scores = line_scores.reshape((-1, n2_lines0 // 2, 2,
- n2_lines1 // 2, 2))
+ line_scores,
+ dim=1,
+ index=lines_junc_idx0[:, :, None].repeat(1, 1, n2_lines1),
+ )
+ line_scores = line_scores.reshape((-1, n2_lines0 // 2, 2, n2_lines1 // 2, 2))
# Match either in one direction or the other
raw_line_scores = 0.5 * torch.maximum(
line_scores[:, :, 0, :, 0] + line_scores[:, :, 1, :, 1],
- line_scores[:, :, 0, :, 1] + line_scores[:, :, 1, :, 0])
+ line_scores[:, :, 0, :, 1] + line_scores[:, :, 1, :, 0],
+ )
line_scores = log_double_softmax(raw_line_scores, self.line_bin_score)
m0_lines, m1_lines, mscores0_lines, mscores1_lines = self._get_matches(
- line_scores)
- return (line_scores, m0_lines, m1_lines, mscores0_lines,
- mscores1_lines, raw_line_scores)
+ line_scores
+ )
+ return (
+ line_scores,
+ m0_lines,
+ m1_lines,
+ mscores0_lines,
+ mscores1_lines,
+ raw_line_scores,
+ )
def loss(self, pred, data):
raise NotImplementedError()
@@ -295,8 +375,7 @@ def MLP(channels, do_bn=True):
n = len(channels)
layers = []
for i in range(1, n):
- layers.append(
- nn.Conv1d(channels[i - 1], channels[i], kernel_size=1, bias=True))
+ layers.append(nn.Conv1d(channels[i - 1], channels[i], kernel_size=1, bias=True))
if i < (n - 1):
if do_bn:
layers.append(nn.BatchNorm1d(channels[i]))
@@ -343,17 +422,20 @@ class EndPtEncoder(nn.Module):
endpt_offset = (endpoints[:, :, 1] - endpoints[:, :, 0]).unsqueeze(2)
endpt_offset = torch.cat([endpt_offset, -endpt_offset], dim=2)
endpt_offset = endpt_offset.reshape(b_size, 2 * n_pts, 2).transpose(1, 2)
- inputs = [endpoints.flatten(1, 2).transpose(1, 2),
- endpt_offset, scores.repeat(1, 2).unsqueeze(1)]
+ inputs = [
+ endpoints.flatten(1, 2).transpose(1, 2),
+ endpt_offset,
+ scores.repeat(1, 2).unsqueeze(1),
+ ]
return self.encoder(torch.cat(inputs, dim=1))
@torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
def attention(query, key, value):
dim = query.shape[1]
- scores = torch.einsum('bdhn,bdhm->bhnm', query, key) / dim ** .5
+ scores = torch.einsum("bdhn,bdhm->bhnm", query, key) / dim**0.5
prob = torch.nn.functional.softmax(scores, dim=-1)
- return torch.einsum('bhnm,bdhm->bdhn', prob, value), prob
+ return torch.einsum("bhnm,bdhm->bdhn", prob, value), prob
class MultiHeadedAttention(nn.Module):
@@ -368,8 +450,10 @@ class MultiHeadedAttention(nn.Module):
def forward(self, query, key, value):
b = query.size(0)
- query, key, value = [l(x).view(b, self.dim, self.h, -1)
- for l, x in zip(self.proj, (query, key, value))]
+ query, key, value = [
+ l(x).view(b, self.dim, self.h, -1)
+ for l, x in zip(self.proj, (query, key, value))
+ ]
x, prob = attention(query, key, value)
# self.prob.append(prob.mean(dim=1))
return self.merge(x.contiguous().view(b, self.dim * self.h, -1))
@@ -382,9 +466,9 @@ class AttentionalPropagation(nn.Module):
self.mlp = MLP([num_dim * 2, num_dim * 2, num_dim], do_bn=True)
nn.init.constant_(self.mlp[-1].bias, 0.0)
if skip_init:
- self.register_parameter('scaling', nn.Parameter(torch.tensor(0.)))
+ self.register_parameter("scaling", nn.Parameter(torch.tensor(0.0)))
else:
- self.scaling = 1.
+ self.scaling = 1.0
def forward(self, x, source):
message = self.attn(x, source, source)
@@ -394,14 +478,14 @@ class AttentionalPropagation(nn.Module):
class GNNLayer(nn.Module):
def __init__(self, feature_dim, layer_type, skip_init):
super().__init__()
- assert layer_type in ['cross', 'self']
+ assert layer_type in ["cross", "self"]
self.type = layer_type
self.update = AttentionalPropagation(feature_dim, 4, skip_init)
def forward(self, desc0, desc1):
- if self.type == 'cross':
+ if self.type == "cross":
src0, src1 = desc1, desc0
- elif self.type == 'self':
+ elif self.type == "self":
src0, src1 = desc0, desc1
else:
raise ValueError("Unknown layer type: " + self.type)
@@ -427,11 +511,19 @@ class LineLayer(nn.Module):
# Create one message per line endpoint
b_size = lines_junc_idx.shape[0]
line_desc = torch.gather(
- ldesc, 2, lines_junc_idx[:, None].repeat(1, self.dim, 1))
- message = torch.cat([
- line_desc,
- line_desc.reshape(b_size, self.dim, -1, 2).flip([-1]).flatten(2, 3).clone(),
- line_enc], dim=1)
+ ldesc, 2, lines_junc_idx[:, None].repeat(1, self.dim, 1)
+ )
+ message = torch.cat(
+ [
+ line_desc,
+ line_desc.reshape(b_size, self.dim, -1, 2)
+ .flip([-1])
+ .flatten(2, 3)
+ .clone(),
+ line_enc,
+ ],
+ dim=1,
+ )
return self.mlp(message) # [b_size, D, n_lines * 2]
def get_endpoint_attention(self, ldesc, line_enc, lines_junc_idx):
@@ -447,22 +539,32 @@ class LineLayer(nn.Module):
# Key: combination of neighboring desc and line encodings
line_desc = torch.gather(ldesc, 2, expanded_lines_junc_idx)
- key = self.proj_neigh(torch.cat([
- line_desc.reshape(b_size, self.dim, -1, 2).flip([-1]).flatten(2, 3).clone(),
- line_enc], dim=1)) # [b_size, D, n_lines * 2]
+ key = self.proj_neigh(
+ torch.cat(
+ [
+ line_desc.reshape(b_size, self.dim, -1, 2)
+ .flip([-1])
+ .flatten(2, 3)
+ .clone(),
+ line_enc,
+ ],
+ dim=1,
+ )
+ ) # [b_size, D, n_lines * 2]
# Compute the attention weights with a custom softmax per junction
- prob = (query * key).sum(dim=1) / self.dim ** .5 # [b_size, n_lines * 2]
+ prob = (query * key).sum(dim=1) / self.dim**0.5 # [b_size, n_lines * 2]
prob = torch.exp(prob - prob.max())
denom = torch.zeros_like(ldesc[:, 0]).scatter_reduce_(
- dim=1, index=lines_junc_idx,
- src=prob, reduce='sum', include_self=False) # [b_size, n_junc]
+ dim=1, index=lines_junc_idx, src=prob, reduce="sum", include_self=False
+ ) # [b_size, n_junc]
denom = torch.gather(denom, 1, lines_junc_idx) # [b_size, n_lines * 2]
prob = prob / (denom + ETH_EPS)
return prob # [b_size, n_lines * 2]
- def forward(self, ldesc0, ldesc1, line_enc0, line_enc1, lines_junc_idx0,
- lines_junc_idx1):
+ def forward(
+ self, ldesc0, ldesc1, line_enc0, line_enc1, lines_junc_idx0, lines_junc_idx1
+ ):
# Gather the endpoint updates
lupdate0 = self.get_endpoint_update(ldesc0, line_enc0, lines_junc_idx0)
lupdate1 = self.get_endpoint_update(ldesc1, line_enc1, lines_junc_idx1)
@@ -471,26 +573,40 @@ class LineLayer(nn.Module):
dim = ldesc0.shape[1]
if self.line_attention:
# Compute an attention for each neighbor and do a weighted average
- prob0 = self.get_endpoint_attention(ldesc0, line_enc0,
- lines_junc_idx0)
+ prob0 = self.get_endpoint_attention(ldesc0, line_enc0, lines_junc_idx0)
lupdate0 = lupdate0 * prob0[:, None]
update0 = update0.scatter_reduce_(
- dim=2, index=lines_junc_idx0[:, None].repeat(1, dim, 1),
- src=lupdate0, reduce='sum', include_self=False)
- prob1 = self.get_endpoint_attention(ldesc1, line_enc1,
- lines_junc_idx1)
+ dim=2,
+ index=lines_junc_idx0[:, None].repeat(1, dim, 1),
+ src=lupdate0,
+ reduce="sum",
+ include_self=False,
+ )
+ prob1 = self.get_endpoint_attention(ldesc1, line_enc1, lines_junc_idx1)
lupdate1 = lupdate1 * prob1[:, None]
update1 = update1.scatter_reduce_(
- dim=2, index=lines_junc_idx1[:, None].repeat(1, dim, 1),
- src=lupdate1, reduce='sum', include_self=False)
+ dim=2,
+ index=lines_junc_idx1[:, None].repeat(1, dim, 1),
+ src=lupdate1,
+ reduce="sum",
+ include_self=False,
+ )
else:
# Average the updates for each junction (requires torch > 1.12)
update0 = update0.scatter_reduce_(
- dim=2, index=lines_junc_idx0[:, None].repeat(1, dim, 1),
- src=lupdate0, reduce='mean', include_self=False)
+ dim=2,
+ index=lines_junc_idx0[:, None].repeat(1, dim, 1),
+ src=lupdate0,
+ reduce="mean",
+ include_self=False,
+ )
update1 = update1.scatter_reduce_(
- dim=2, index=lines_junc_idx1[:, None].repeat(1, dim, 1),
- src=lupdate1, reduce='mean', include_self=False)
+ dim=2,
+ index=lines_junc_idx1[:, None].repeat(1, dim, 1),
+ src=lupdate1,
+ reduce="mean",
+ include_self=False,
+ )
# Update
ldesc0 = ldesc0 + update0
@@ -500,47 +616,75 @@ class LineLayer(nn.Module):
class AttentionalGNN(nn.Module):
- def __init__(self, feature_dim, layer_types, checkpointed=False,
- skip=False, inter_supervision=None, num_line_iterations=1,
- line_attention=False):
+ def __init__(
+ self,
+ feature_dim,
+ layer_types,
+ checkpointed=False,
+ skip=False,
+ inter_supervision=None,
+ num_line_iterations=1,
+ line_attention=False,
+ ):
super().__init__()
self.checkpointed = checkpointed
self.inter_supervision = inter_supervision
self.num_line_iterations = num_line_iterations
self.inter_layers = {}
- self.layers = nn.ModuleList([
- GNNLayer(feature_dim, layer_type, skip)
- for layer_type in layer_types])
+ self.layers = nn.ModuleList(
+ [GNNLayer(feature_dim, layer_type, skip) for layer_type in layer_types]
+ )
self.line_layers = nn.ModuleList(
- [LineLayer(feature_dim, line_attention)
- for _ in range(len(layer_types) // 2)])
-
- def forward(self, desc0, desc1, line_enc0, line_enc1,
- lines_junc_idx0, lines_junc_idx1):
+ [
+ LineLayer(feature_dim, line_attention)
+ for _ in range(len(layer_types) // 2)
+ ]
+ )
+
+ def forward(
+ self, desc0, desc1, line_enc0, line_enc1, lines_junc_idx0, lines_junc_idx1
+ ):
for i, layer in enumerate(self.layers):
if self.checkpointed:
desc0, desc1 = torch.utils.checkpoint.checkpoint(
- layer, desc0, desc1, preserve_rng_state=False)
+ layer, desc0, desc1, preserve_rng_state=False
+ )
else:
desc0, desc1 = layer(desc0, desc1)
- if (layer.type == 'self' and lines_junc_idx0.shape[1] > 0
- and lines_junc_idx1.shape[1] > 0):
+ if (
+ layer.type == "self"
+ and lines_junc_idx0.shape[1] > 0
+ and lines_junc_idx1.shape[1] > 0
+ ):
# Add line self attention layers after every self layer
for _ in range(self.num_line_iterations):
if self.checkpointed:
desc0, desc1 = torch.utils.checkpoint.checkpoint(
- self.line_layers[i // 2], desc0, desc1, line_enc0,
- line_enc1, lines_junc_idx0, lines_junc_idx1,
- preserve_rng_state=False)
+ self.line_layers[i // 2],
+ desc0,
+ desc1,
+ line_enc0,
+ line_enc1,
+ lines_junc_idx0,
+ lines_junc_idx1,
+ preserve_rng_state=False,
+ )
else:
desc0, desc1 = self.line_layers[i // 2](
- desc0, desc1, line_enc0, line_enc1,
- lines_junc_idx0, lines_junc_idx1)
+ desc0,
+ desc1,
+ line_enc0,
+ line_enc1,
+ lines_junc_idx0,
+ lines_junc_idx1,
+ )
# Optionally store the line descriptor at intermediate layers
- if (self.inter_supervision is not None
- and (i // 2) in self.inter_supervision
- and layer.type == 'cross'):
+ if (
+ self.inter_supervision is not None
+ and (i // 2) in self.inter_supervision
+ and layer.type == "cross"
+ ):
self.inter_layers[i // 2] = (desc0.clone(), desc1.clone())
return desc0, desc1
diff --git a/imcui/third_party/GlueStick/gluestick/models/superpoint.py b/third_party/GlueStick/gluestick/models/superpoint.py
similarity index 74%
rename from imcui/third_party/GlueStick/gluestick/models/superpoint.py
rename to third_party/GlueStick/gluestick/models/superpoint.py
index 872063275f4fde27f552bf2c2674dc60d5220ec9..19e66cdba41749a765829cce0ead608afb04964c 100644
--- a/imcui/third_party/GlueStick/gluestick/models/superpoint.py
+++ b/third_party/GlueStick/gluestick/models/superpoint.py
@@ -25,7 +25,8 @@ def simple_nms(scores, radius):
def max_pool(x):
return torch.nn.functional.max_pool2d(
- x, kernel_size=radius * 2 + 1, stride=1, padding=radius)
+ x, kernel_size=radius * 2 + 1, stride=1, padding=radius
+ )
zeros = torch.zeros_like(scores)
max_mask = scores == max_pool(scores)
@@ -54,33 +55,35 @@ def top_k_keypoints(keypoints, scores, k):
def sample_descriptors(keypoints, descriptors, s):
b, c, h, w = descriptors.shape
keypoints = keypoints - s / 2 + 0.5
- keypoints /= torch.tensor([(w * s - s / 2 - 0.5), (h * s - s / 2 - 0.5)],
- ).to(keypoints)[None]
+ keypoints /= torch.tensor([(w * s - s / 2 - 0.5), (h * s - s / 2 - 0.5)],).to(
+ keypoints
+ )[None]
keypoints = keypoints * 2 - 1 # normalize to (-1, 1)
- args = {'align_corners': True} if torch.__version__ >= '1.3' else {}
+ args = {"align_corners": True} if torch.__version__ >= "1.3" else {}
descriptors = torch.nn.functional.grid_sample(
- descriptors, keypoints.view(b, 1, -1, 2), mode='bilinear', **args)
+ descriptors, keypoints.view(b, 1, -1, 2), mode="bilinear", **args
+ )
descriptors = torch.nn.functional.normalize(
- descriptors.reshape(b, c, -1), p=2, dim=1)
+ descriptors.reshape(b, c, -1), p=2, dim=1
+ )
return descriptors
class SuperPoint(BaseModel):
default_conf = {
- 'has_detector': True,
- 'has_descriptor': True,
- 'descriptor_dim': 256,
-
+ "has_detector": True,
+ "has_descriptor": True,
+ "descriptor_dim": 256,
# Inference
- 'return_all': False,
- 'sparse_outputs': True,
- 'nms_radius': 4,
- 'detection_threshold': 0.005,
- 'max_num_keypoints': -1,
- 'force_num_keypoints': False,
- 'remove_borders': 4,
+ "return_all": False,
+ "sparse_outputs": True,
+ "nms_radius": 4,
+ "detection_threshold": 0.005,
+ "max_num_keypoints": -1,
+ "force_num_keypoints": False,
+ "remove_borders": 4,
}
- required_data_keys = ['image']
+ required_data_keys = ["image"]
def _init(self, conf):
self.relu = nn.ReLU(inplace=True)
@@ -103,18 +106,14 @@ class SuperPoint(BaseModel):
if conf.has_descriptor:
self.convDa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1)
self.convDb = nn.Conv2d(
- c5, conf.descriptor_dim, kernel_size=1, stride=1, padding=0)
+ c5, conf.descriptor_dim, kernel_size=1, stride=1, padding=0
+ )
- path = GLUESTICK_ROOT / 'resources' / 'weights' / 'superpoint_v1.pth'
- if path.exists():
- weights = torch.load(str(path), map_location='cpu')
- else:
- weights_url = "https://github.com/cvg/GlueStick/raw/main/resources/weights/superpoint_v1.pth"
- weights = torch.hub.load_state_dict_from_url(weights_url, map_location='cpu')
- self.load_state_dict(weights, strict=False)
+ path = GLUESTICK_ROOT / "resources" / "weights" / "superpoint_v1.pth"
+ self.load_state_dict(torch.load(str(path)), strict=False)
def _forward(self, data):
- image = data['image']
+ image = data["image"]
if image.shape[1] == 3: # RGB
scale = image.new_tensor([0.299, 0.587, 0.114]).view(1, 3, 1, 1)
image = (image * scale).sum(1, keepdim=True)
@@ -141,22 +140,24 @@ class SuperPoint(BaseModel):
b, c, h, w = scores.shape
scores = scores.permute(0, 2, 3, 1).reshape(b, h, w, 8, 8)
scores = scores.permute(0, 1, 3, 2, 4).reshape(b, h * 8, w * 8)
- pred['keypoint_scores'] = dense_scores = scores
+ pred["keypoint_scores"] = dense_scores = scores
if self.conf.has_descriptor:
# Compute the dense descriptors
cDa = self.relu(self.convDa(x))
all_desc = self.convDb(cDa)
all_desc = torch.nn.functional.normalize(all_desc, p=2, dim=1)
- pred['descriptors'] = all_desc
+ pred["descriptors"] = all_desc
if self.conf.max_num_keypoints == 0: # Predict dense descriptors only
b_size = len(image)
device = image.device
return {
- 'keypoints': torch.empty(b_size, 0, 2, device=device),
- 'keypoint_scores': torch.empty(b_size, 0, device=device),
- 'descriptors': torch.empty(b_size, self.conf.descriptor_dim, 0, device=device),
- 'all_descriptors': all_desc
+ "keypoints": torch.empty(b_size, 0, 2, device=device),
+ "keypoint_scores": torch.empty(b_size, 0, device=device),
+ "descriptors": torch.empty(
+ b_size, self.conf.descriptor_dim, 0, device=device
+ ),
+ "all_descriptors": all_desc,
}
if self.conf.sparse_outputs:
@@ -166,26 +167,36 @@ class SuperPoint(BaseModel):
# Extract keypoints
keypoints = [
- torch.nonzero(s > self.conf.detection_threshold)
- for s in scores]
+ torch.nonzero(s > self.conf.detection_threshold) for s in scores
+ ]
scores = [s[tuple(k.t())] for s, k in zip(scores, keypoints)]
# Discard keypoints near the image borders
- keypoints, scores = list(zip(*[
- remove_borders(k, s, self.conf.remove_borders, h * 8, w * 8)
- for k, s in zip(keypoints, scores)]))
+ keypoints, scores = list(
+ zip(
+ *[
+ remove_borders(k, s, self.conf.remove_borders, h * 8, w * 8)
+ for k, s in zip(keypoints, scores)
+ ]
+ )
+ )
# Keep the k keypoints with highest score
if self.conf.max_num_keypoints > 0:
- keypoints, scores = list(zip(*[
- top_k_keypoints(k, s, self.conf.max_num_keypoints)
- for k, s in zip(keypoints, scores)]))
+ keypoints, scores = list(
+ zip(
+ *[
+ top_k_keypoints(k, s, self.conf.max_num_keypoints)
+ for k, s in zip(keypoints, scores)
+ ]
+ )
+ )
# Convert (h, w) to (x, y)
keypoints = [torch.flip(k, [1]).float() for k in keypoints]
if self.conf.force_num_keypoints:
- _, _, h, w = data['image'].shape
+ _, _, h, w = data["image"].shape
assert self.conf.max_num_keypoints > 0
scores = list(scores)
for i in range(len(keypoints)):
@@ -199,8 +210,10 @@ class SuperPoint(BaseModel):
scores[i] = torch.cat([s, new_s], 0)
# Extract descriptors
- desc = [sample_descriptors(k[None], d[None], 8)[0]
- for k, d in zip(keypoints, all_desc)]
+ desc = [
+ sample_descriptors(k[None], d[None], 8)[0]
+ for k, d in zip(keypoints, all_desc)
+ ]
if (len(keypoints) == 1) or self.conf.force_num_keypoints:
keypoints = torch.stack(keypoints, 0)
@@ -208,14 +221,14 @@ class SuperPoint(BaseModel):
desc = torch.stack(desc, 0)
pred = {
- 'keypoints': keypoints,
- 'keypoint_scores': scores,
- 'descriptors': desc,
+ "keypoints": keypoints,
+ "keypoint_scores": scores,
+ "descriptors": desc,
}
if self.conf.return_all:
- pred['all_descriptors'] = all_desc
- pred['dense_score'] = dense_scores
+ pred["all_descriptors"] = all_desc
+ pred["dense_score"] = dense_scores
else:
del all_desc
torch.cuda.empty_cache()
diff --git a/imcui/third_party/GlueStick/gluestick/models/two_view_pipeline.py b/third_party/GlueStick/gluestick/models/two_view_pipeline.py
similarity index 64%
rename from imcui/third_party/GlueStick/gluestick/models/two_view_pipeline.py
rename to third_party/GlueStick/gluestick/models/two_view_pipeline.py
index e0e21c1f62e2bd4ad573ebb87ea5635742b5032e..07a7bf06ea8c7ad2abba5fac2568ebcaffd497b0 100644
--- a/imcui/third_party/GlueStick/gluestick/models/two_view_pipeline.py
+++ b/third_party/GlueStick/gluestick/models/two_view_pipeline.py
@@ -22,10 +22,12 @@ def keep_quadrant_kp_subset(keypoints, scores, descs, h, w):
h2, w2 = h // 2, w // 2
w_x = np.random.choice([0, w2])
w_y = np.random.choice([0, h2])
- valid_mask = ((keypoints[..., 0] >= w_x)
- & (keypoints[..., 0] < w_x + w2)
- & (keypoints[..., 1] >= w_y)
- & (keypoints[..., 1] < w_y + h2))
+ valid_mask = (
+ (keypoints[..., 0] >= w_x)
+ & (keypoints[..., 0] < w_x + w2)
+ & (keypoints[..., 1] >= w_y)
+ & (keypoints[..., 1] < w_y + h2)
+ )
keypoints = keypoints[valid_mask][None]
scores = scores[valid_mask][None]
descs = descs.permute(0, 2, 1)[valid_mask].t()[None]
@@ -46,47 +48,44 @@ def keep_best_kp_subset(keypoints, scores, descs, num_selected):
"""Keep the top num_selected best keypoints."""
sorted_indices = torch.sort(scores, dim=1)[1]
selected_kp = sorted_indices[:, -num_selected:]
- keypoints = torch.gather(keypoints, 1,
- selected_kp[:, :, None].repeat(1, 1, 2))
+ keypoints = torch.gather(keypoints, 1, selected_kp[:, :, None].repeat(1, 1, 2))
scores = torch.gather(scores, 1, selected_kp)
- descs = torch.gather(descs, 2,
- selected_kp[:, None].repeat(1, descs.shape[1], 1))
+ descs = torch.gather(descs, 2, selected_kp[:, None].repeat(1, descs.shape[1], 1))
return keypoints, scores, descs
class TwoViewPipeline(BaseModel):
default_conf = {
- 'extractor': {
- 'name': 'superpoint',
- 'trainable': False,
+ "extractor": {
+ "name": "superpoint",
+ "trainable": False,
},
- 'use_lines': False,
- 'use_points': True,
- 'randomize_num_kp': False,
- 'detector': {'name': None},
- 'descriptor': {'name': None},
- 'matcher': {'name': 'nearest_neighbor_matcher'},
- 'filter': {'name': None},
- 'solver': {'name': None},
- 'ground_truth': {
- 'from_pose_depth': False,
- 'from_homography': False,
- 'th_positive': 3,
- 'th_negative': 5,
- 'reward_positive': 1,
- 'reward_negative': -0.25,
- 'is_likelihood_soft': True,
- 'p_random_occluders': 0,
- 'n_line_sampled_pts': 50,
- 'line_perp_dist_th': 5,
- 'overlap_th': 0.2,
- 'min_visibility_th': 0.5
+ "use_lines": False,
+ "use_points": True,
+ "randomize_num_kp": False,
+ "detector": {"name": None},
+ "descriptor": {"name": None},
+ "matcher": {"name": "nearest_neighbor_matcher"},
+ "filter": {"name": None},
+ "solver": {"name": None},
+ "ground_truth": {
+ "from_pose_depth": False,
+ "from_homography": False,
+ "th_positive": 3,
+ "th_negative": 5,
+ "reward_positive": 1,
+ "reward_negative": -0.25,
+ "is_likelihood_soft": True,
+ "p_random_occluders": 0,
+ "n_line_sampled_pts": 50,
+ "line_perp_dist_th": 5,
+ "overlap_th": 0.2,
+ "min_visibility_th": 0.5,
},
}
- required_data_keys = ['image0', 'image1']
+ required_data_keys = ["image0", "image1"]
strict_conf = False # need to pass new confs to children models
- components = [
- 'extractor', 'detector', 'descriptor', 'matcher', 'filter', 'solver']
+ components = ["extractor", "detector", "descriptor", "matcher", "filter", "solver"]
def _init(self, conf):
if conf.extractor.name:
@@ -95,17 +94,16 @@ class TwoViewPipeline(BaseModel):
if self.conf.detector.name:
self.detector = get_model(conf.detector.name)(conf.detector)
else:
- self.required_data_keys += ['keypoints0', 'keypoints1']
+ self.required_data_keys += ["keypoints0", "keypoints1"]
if self.conf.descriptor.name:
- self.descriptor = get_model(conf.descriptor.name)(
- conf.descriptor)
+ self.descriptor = get_model(conf.descriptor.name)(conf.descriptor)
else:
- self.required_data_keys += ['descriptors0', 'descriptors1']
+ self.required_data_keys += ["descriptors0", "descriptors1"]
if conf.matcher.name:
self.matcher = get_model(conf.matcher.name)(conf.matcher)
else:
- self.required_data_keys += ['matches0']
+ self.required_data_keys += ["matches0"]
if conf.filter.name:
self.filter = get_model(conf.filter.name)(conf.filter)
@@ -114,7 +112,6 @@ class TwoViewPipeline(BaseModel):
self.solver = get_model(conf.solver.name)(conf.solver)
def _forward(self, data):
-
def process_siamese(data, i):
data_i = {k[:-1]: v for k, v in data.items() if k[-1] == i}
if self.conf.extractor.name:
@@ -124,21 +121,28 @@ class TwoViewPipeline(BaseModel):
if self.conf.detector.name:
pred_i = self.detector(data_i)
else:
- for k in ['keypoints', 'keypoint_scores', 'descriptors',
- 'lines', 'line_scores', 'line_descriptors',
- 'valid_lines']:
+ for k in [
+ "keypoints",
+ "keypoint_scores",
+ "descriptors",
+ "lines",
+ "line_scores",
+ "line_descriptors",
+ "valid_lines",
+ ]:
if k in data_i:
pred_i[k] = data_i[k]
if self.conf.descriptor.name:
- pred_i = {
- **pred_i, **self.descriptor({**data_i, **pred_i})}
+ pred_i = {**pred_i, **self.descriptor({**data_i, **pred_i})}
return pred_i
- pred0 = process_siamese(data, '0')
- pred1 = process_siamese(data, '1')
+ pred0 = process_siamese(data, "0")
+ pred1 = process_siamese(data, "1")
- pred = {**{k + '0': v for k, v in pred0.items()},
- **{k + '1': v for k, v in pred1.items()}}
+ pred = {
+ **{k + "0": v for k, v in pred0.items()},
+ **{k + "1": v for k, v in pred1.items()},
+ }
if self.conf.matcher.name:
pred = {**pred, **self.matcher({**data, **pred})}
@@ -161,8 +165,8 @@ class TwoViewPipeline(BaseModel):
except NotImplementedError:
continue
losses = {**losses, **losses_}
- total = losses_['total'] + total
- return {**losses, 'total': total}
+ total = losses_["total"] + total
+ return {**losses, "total": total}
def metrics(self, pred, data):
metrics = {}
diff --git a/imcui/third_party/GlueStick/gluestick/models/wireframe.py b/third_party/GlueStick/gluestick/models/wireframe.py
similarity index 52%
rename from imcui/third_party/GlueStick/gluestick/models/wireframe.py
rename to third_party/GlueStick/gluestick/models/wireframe.py
index 0e3dd9873c6fdb4edcb4c75a103673ee2cb3b3fa..9da539387c6da8a5a8df6c677af69803ccdb54b4 100644
--- a/imcui/third_party/GlueStick/gluestick/models/wireframe.py
+++ b/third_party/GlueStick/gluestick/models/wireframe.py
@@ -9,7 +9,7 @@ from ..geometry import warp_lines_torch
def lines_to_wireframe(lines, line_scores, all_descs, conf):
- """ Given a set of lines, their score and dense descriptors,
+ """Given a set of lines, their score and dense descriptors,
merge close-by endpoints and compute a wireframe defined by
its junctions and connectivity.
Returns:
@@ -26,29 +26,41 @@ def lines_to_wireframe(lines, line_scores, all_descs, conf):
device = lines.device
endpoints = lines.reshape(b_size, -1, 2)
- (junctions, junc_scores, junc_descs, connectivity, new_lines,
- lines_junc_idx, num_true_junctions) = [], [], [], [], [], [], []
+ (
+ junctions,
+ junc_scores,
+ junc_descs,
+ connectivity,
+ new_lines,
+ lines_junc_idx,
+ num_true_junctions,
+ ) = ([], [], [], [], [], [], [])
for bs in range(b_size):
# Cluster the junctions that are close-by
- db = DBSCAN(eps=conf.nms_radius, min_samples=1).fit(
- endpoints[bs].cpu().numpy())
+ db = DBSCAN(eps=conf.nms_radius, min_samples=1).fit(endpoints[bs].cpu().numpy())
clusters = db.labels_
n_clusters = len(set(clusters))
num_true_junctions.append(n_clusters)
# Compute the average junction and score for each cluster
- clusters = torch.tensor(clusters, dtype=torch.long,
- device=device)
- new_junc = torch.zeros(n_clusters, 2, dtype=torch.float,
- device=device)
- new_junc.scatter_reduce_(0, clusters[:, None].repeat(1, 2),
- endpoints[bs], reduce='mean',
- include_self=False)
+ clusters = torch.tensor(clusters, dtype=torch.long, device=device)
+ new_junc = torch.zeros(n_clusters, 2, dtype=torch.float, device=device)
+ new_junc.scatter_reduce_(
+ 0,
+ clusters[:, None].repeat(1, 2),
+ endpoints[bs],
+ reduce="mean",
+ include_self=False,
+ )
junctions.append(new_junc)
new_scores = torch.zeros(n_clusters, dtype=torch.float, device=device)
new_scores.scatter_reduce_(
- 0, clusters, torch.repeat_interleave(line_scores[bs], 2),
- reduce='mean', include_self=False)
+ 0,
+ clusters,
+ torch.repeat_interleave(line_scores[bs], 2),
+ reduce="mean",
+ include_self=False,
+ )
junc_scores.append(new_scores)
# Compute the new lines
@@ -56,50 +68,56 @@ def lines_to_wireframe(lines, line_scores, all_descs, conf):
lines_junc_idx.append(clusters.reshape(-1, 2))
# Compute the junction connectivity
- junc_connect = torch.eye(n_clusters, dtype=torch.bool,
- device=device)
+ junc_connect = torch.eye(n_clusters, dtype=torch.bool, device=device)
pairs = clusters.reshape(-1, 2) # these pairs are connected by a line
junc_connect[pairs[:, 0], pairs[:, 1]] = True
junc_connect[pairs[:, 1], pairs[:, 0]] = True
connectivity.append(junc_connect)
# Interpolate the new junction descriptors
- junc_descs.append(sample_descriptors(
- junctions[-1][None], all_descs[bs:(bs + 1)], 8)[0])
+ junc_descs.append(
+ sample_descriptors(junctions[-1][None], all_descs[bs : (bs + 1)], 8)[0]
+ )
new_lines = torch.stack(new_lines, dim=0)
lines_junc_idx = torch.stack(lines_junc_idx, dim=0)
- return (junctions, junc_scores, junc_descs, connectivity,
- new_lines, lines_junc_idx, num_true_junctions)
+ return (
+ junctions,
+ junc_scores,
+ junc_descs,
+ connectivity,
+ new_lines,
+ lines_junc_idx,
+ num_true_junctions,
+ )
class SPWireframeDescriptor(BaseModel):
default_conf = {
- 'sp_params': {
- 'has_detector': True,
- 'has_descriptor': True,
- 'descriptor_dim': 256,
- 'trainable': False,
-
+ "sp_params": {
+ "has_detector": True,
+ "has_descriptor": True,
+ "descriptor_dim": 256,
+ "trainable": False,
# Inference
- 'return_all': True,
- 'sparse_outputs': True,
- 'nms_radius': 4,
- 'detection_threshold': 0.005,
- 'max_num_keypoints': 1000,
- 'force_num_keypoints': True,
- 'remove_borders': 4,
+ "return_all": True,
+ "sparse_outputs": True,
+ "nms_radius": 4,
+ "detection_threshold": 0.005,
+ "max_num_keypoints": 1000,
+ "force_num_keypoints": True,
+ "remove_borders": 4,
},
- 'wireframe_params': {
- 'merge_points': True,
- 'merge_line_endpoints': True,
- 'nms_radius': 3,
- 'max_n_junctions': 500,
+ "wireframe_params": {
+ "merge_points": True,
+ "merge_line_endpoints": True,
+ "nms_radius": 3,
+ "max_n_junctions": 500,
},
- 'max_n_lines': 250,
- 'min_length': 15,
+ "max_n_lines": 250,
+ "min_length": 15,
}
- required_data_keys = ['image']
+ required_data_keys = ["image"]
def _init(self, conf):
self.conf = conf
@@ -139,78 +157,108 @@ class SPWireframeDescriptor(BaseModel):
return lines, scores, valid_lines
def _forward(self, data):
- b_size, _, h, w = data['image'].shape
- device = data['image'].device
+ b_size, _, h, w = data["image"].shape
+ device = data["image"].device
if not self.conf.sp_params.force_num_keypoints:
assert b_size == 1, "Only batch size of 1 accepted for non padded inputs"
# Line detection
- if 'lines' not in data or 'line_scores' not in data:
- if 'original_img' in data:
+ if "lines" not in data or "line_scores" not in data:
+ if "original_img" in data:
# Detect more lines, because when projecting them to the image most of them will be discarded
lines, line_scores, valid_lines = self.detect_lsd_lines(
- data['original_img'], self.conf.max_n_lines * 3)
+ data["original_img"], self.conf.max_n_lines * 3
+ )
# Apply the same transformation that is applied in homography_adaptation
- lines, valid_lines2 = warp_lines_torch(lines, data['H'], False, data['image'].shape[-2:])
+ lines, valid_lines2 = warp_lines_torch(
+ lines, data["H"], False, data["image"].shape[-2:]
+ )
valid_lines = valid_lines & valid_lines2
lines[~valid_lines] = -1
line_scores[~valid_lines] = 0
# Re-sort the line segments to pick the ones that are inside the image and have bigger score
- sorted_scores, sorting_indices = torch.sort(line_scores, dim=-1, descending=True)
- line_scores = sorted_scores[:, :self.conf.max_n_lines]
- sorting_indices = sorting_indices[:, :self.conf.max_n_lines]
+ sorted_scores, sorting_indices = torch.sort(
+ line_scores, dim=-1, descending=True
+ )
+ line_scores = sorted_scores[:, : self.conf.max_n_lines]
+ sorting_indices = sorting_indices[:, : self.conf.max_n_lines]
lines = torch.take_along_dim(lines, sorting_indices[..., None, None], 1)
valid_lines = torch.take_along_dim(valid_lines, sorting_indices, 1)
else:
- lines, line_scores, valid_lines = self.detect_lsd_lines(data['image'])
+ lines, line_scores, valid_lines = self.detect_lsd_lines(data["image"])
else:
- lines, line_scores, valid_lines = data['lines'], data['line_scores'], data['valid_lines']
+ lines, line_scores, valid_lines = (
+ data["lines"],
+ data["line_scores"],
+ data["valid_lines"],
+ )
if line_scores.shape[-1] != 0:
- line_scores /= (line_scores.new_tensor(1e-8) + line_scores.max(dim=1).values[:, None])
+ line_scores /= (
+ line_scores.new_tensor(1e-8) + line_scores.max(dim=1).values[:, None]
+ )
# SuperPoint prediction
pred = self.sp(data)
# Remove keypoints that are too close to line endpoints
if self.conf.wireframe_params.merge_points:
- kp = pred['keypoints']
+ kp = pred["keypoints"]
line_endpts = lines.reshape(b_size, -1, 2)
- dist_pt_lines = torch.norm(
- kp[:, :, None] - line_endpts[:, None], dim=-1)
+ dist_pt_lines = torch.norm(kp[:, :, None] - line_endpts[:, None], dim=-1)
# For each keypoint, mark it as valid or to remove
pts_to_remove = torch.any(
- dist_pt_lines < self.conf.sp_params.nms_radius, dim=2)
+ dist_pt_lines < self.conf.sp_params.nms_radius, dim=2
+ )
# Simply remove them (we assume batch_size = 1 here)
assert len(kp) == 1
- pred['keypoints'] = pred['keypoints'][0][~pts_to_remove[0]][None]
- pred['keypoint_scores'] = pred['keypoint_scores'][0][~pts_to_remove[0]][None]
- pred['descriptors'] = pred['descriptors'][0].T[~pts_to_remove[0]].T[None]
+ pred["keypoints"] = pred["keypoints"][0][~pts_to_remove[0]][None]
+ pred["keypoint_scores"] = pred["keypoint_scores"][0][~pts_to_remove[0]][
+ None
+ ]
+ pred["descriptors"] = pred["descriptors"][0].T[~pts_to_remove[0]].T[None]
# Connect the lines together to form a wireframe
orig_lines = lines.clone()
if self.conf.wireframe_params.merge_line_endpoints and len(lines[0]) > 0:
# Merge first close-by endpoints to connect lines
- (line_points, line_pts_scores, line_descs, line_association,
- lines, lines_junc_idx, num_true_junctions) = lines_to_wireframe(
- lines, line_scores, pred['all_descriptors'],
- conf=self.conf.wireframe_params)
+ (
+ line_points,
+ line_pts_scores,
+ line_descs,
+ line_association,
+ lines,
+ lines_junc_idx,
+ num_true_junctions,
+ ) = lines_to_wireframe(
+ lines,
+ line_scores,
+ pred["all_descriptors"],
+ conf=self.conf.wireframe_params,
+ )
# Add the keypoints to the junctions and fill the rest with random keypoints
- (all_points, all_scores, all_descs,
- pl_associativity) = [], [], [], []
+ (all_points, all_scores, all_descs, pl_associativity) = [], [], [], []
for bs in range(b_size):
- all_points.append(torch.cat(
- [line_points[bs], pred['keypoints'][bs]], dim=0))
- all_scores.append(torch.cat(
- [line_pts_scores[bs], pred['keypoint_scores'][bs]], dim=0))
- all_descs.append(torch.cat(
- [line_descs[bs], pred['descriptors'][bs]], dim=1))
-
- associativity = torch.eye(len(all_points[-1]), dtype=torch.bool, device=device)
- associativity[:num_true_junctions[bs], :num_true_junctions[bs]] = \
- line_association[bs][:num_true_junctions[bs], :num_true_junctions[bs]]
+ all_points.append(
+ torch.cat([line_points[bs], pred["keypoints"][bs]], dim=0)
+ )
+ all_scores.append(
+ torch.cat([line_pts_scores[bs], pred["keypoint_scores"][bs]], dim=0)
+ )
+ all_descs.append(
+ torch.cat([line_descs[bs], pred["descriptors"][bs]], dim=1)
+ )
+
+ associativity = torch.eye(
+ len(all_points[-1]), dtype=torch.bool, device=device
+ )
+ associativity[
+ : num_true_junctions[bs], : num_true_junctions[bs]
+ ] = line_association[bs][
+ : num_true_junctions[bs], : num_true_junctions[bs]
+ ]
pl_associativity.append(associativity)
all_points = torch.stack(all_points, dim=0)
@@ -219,38 +267,55 @@ class SPWireframeDescriptor(BaseModel):
pl_associativity = torch.stack(pl_associativity, dim=0)
else:
# Lines are independent
- all_points = torch.cat([lines.reshape(b_size, -1, 2),
- pred['keypoints']], dim=1)
+ all_points = torch.cat(
+ [lines.reshape(b_size, -1, 2), pred["keypoints"]], dim=1
+ )
n_pts = all_points.shape[1]
num_lines = lines.shape[1]
num_true_junctions = [num_lines * 2] * b_size
- all_scores = torch.cat([
- torch.repeat_interleave(line_scores, 2, dim=1),
- pred['keypoint_scores']], dim=1)
- pred['line_descriptors'] = self.endpoints_pooling(
- lines, pred['all_descriptors'], (h, w))
- all_descs = torch.cat([
- pred['line_descriptors'].reshape(b_size, self.conf.sp_params.descriptor_dim, -1),
- pred['descriptors']], dim=2)
- pl_associativity = torch.eye(
- n_pts, dtype=torch.bool,
- device=device)[None].repeat(b_size, 1, 1)
- lines_junc_idx = torch.arange(
- num_lines * 2, device=device).reshape(1, -1, 2).repeat(b_size, 1, 1)
-
- del pred['all_descriptors'] # Remove dense descriptors to save memory
+ all_scores = torch.cat(
+ [
+ torch.repeat_interleave(line_scores, 2, dim=1),
+ pred["keypoint_scores"],
+ ],
+ dim=1,
+ )
+ pred["line_descriptors"] = self.endpoints_pooling(
+ lines, pred["all_descriptors"], (h, w)
+ )
+ all_descs = torch.cat(
+ [
+ pred["line_descriptors"].reshape(
+ b_size, self.conf.sp_params.descriptor_dim, -1
+ ),
+ pred["descriptors"],
+ ],
+ dim=2,
+ )
+ pl_associativity = torch.eye(n_pts, dtype=torch.bool, device=device)[
+ None
+ ].repeat(b_size, 1, 1)
+ lines_junc_idx = (
+ torch.arange(num_lines * 2, device=device)
+ .reshape(1, -1, 2)
+ .repeat(b_size, 1, 1)
+ )
+
+ del pred["all_descriptors"] # Remove dense descriptors to save memory
torch.cuda.empty_cache()
- return {'keypoints': all_points,
- 'keypoint_scores': all_scores,
- 'descriptors': all_descs,
- 'pl_associativity': pl_associativity,
- 'num_junctions': torch.tensor(num_true_junctions),
- 'lines': lines,
- 'orig_lines': orig_lines,
- 'lines_junc_idx': lines_junc_idx,
- 'line_scores': line_scores,
- 'valid_lines': valid_lines}
+ return {
+ "keypoints": all_points,
+ "keypoint_scores": all_scores,
+ "descriptors": all_descs,
+ "pl_associativity": pl_associativity,
+ "num_junctions": torch.tensor(num_true_junctions),
+ "lines": lines,
+ "orig_lines": orig_lines,
+ "lines_junc_idx": lines_junc_idx,
+ "line_scores": line_scores,
+ "valid_lines": valid_lines,
+ }
@staticmethod
def endpoints_pooling(segs, all_descriptors, img_shape):
@@ -259,11 +324,21 @@ class SPWireframeDescriptor(BaseModel):
scale_x = filter_shape[1] / img_shape[1]
scale_y = filter_shape[0] / img_shape[0]
- scaled_segs = torch.round(segs * torch.tensor([scale_x, scale_y]).to(segs)).long()
+ scaled_segs = torch.round(
+ segs * torch.tensor([scale_x, scale_y]).to(segs)
+ ).long()
scaled_segs[..., 0] = torch.clip(scaled_segs[..., 0], 0, filter_shape[1] - 1)
scaled_segs[..., 1] = torch.clip(scaled_segs[..., 1], 0, filter_shape[0] - 1)
- line_descriptors = [all_descriptors[None, b, ..., torch.squeeze(b_segs[..., 1]), torch.squeeze(b_segs[..., 0])]
- for b, b_segs in enumerate(scaled_segs)]
+ line_descriptors = [
+ all_descriptors[
+ None,
+ b,
+ ...,
+ torch.squeeze(b_segs[..., 1]),
+ torch.squeeze(b_segs[..., 0]),
+ ]
+ for b, b_segs in enumerate(scaled_segs)
+ ]
line_descriptors = torch.cat(line_descriptors)
return line_descriptors # Shape (1, 256, 308, 2)
diff --git a/third_party/GlueStick/gluestick/run.py b/third_party/GlueStick/gluestick/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..89569b878cca84fc48ef0b772f71b07befeb45a6
--- /dev/null
+++ b/third_party/GlueStick/gluestick/run.py
@@ -0,0 +1,141 @@
+import argparse
+import os
+from os.path import join
+
+import cv2
+import torch
+from matplotlib import pyplot as plt
+
+from gluestick import batch_to_np, numpy_image_to_torch, GLUESTICK_ROOT
+from .drawing import (
+ plot_images,
+ plot_lines,
+ plot_color_line_matches,
+ plot_keypoints,
+ plot_matches,
+)
+from .models.two_view_pipeline import TwoViewPipeline
+
+
+def main():
+ # Parse input parameters
+ parser = argparse.ArgumentParser(
+ prog="GlueStick Demo",
+ description="Demo app to show the point and line matches obtained by GlueStick",
+ )
+ parser.add_argument("-img1", default=join("resources" + os.path.sep + "img1.jpg"))
+ parser.add_argument("-img2", default=join("resources" + os.path.sep + "img2.jpg"))
+ parser.add_argument("--max_pts", type=int, default=1000)
+ parser.add_argument("--max_lines", type=int, default=300)
+ parser.add_argument("--skip-imshow", default=False, action="store_true")
+ args = parser.parse_args()
+
+ # Evaluation config
+ conf = {
+ "name": "two_view_pipeline",
+ "use_lines": True,
+ "extractor": {
+ "name": "wireframe",
+ "sp_params": {
+ "force_num_keypoints": False,
+ "max_num_keypoints": args.max_pts,
+ },
+ "wireframe_params": {
+ "merge_points": True,
+ "merge_line_endpoints": True,
+ },
+ "max_n_lines": args.max_lines,
+ },
+ "matcher": {
+ "name": "gluestick",
+ "weights": str(
+ GLUESTICK_ROOT / "resources" / "weights" / "checkpoint_GlueStick_MD.tar"
+ ),
+ "trainable": False,
+ },
+ "ground_truth": {
+ "from_pose_depth": False,
+ },
+ }
+
+ device = "cuda" if torch.cuda.is_available() else "cpu"
+
+ pipeline_model = TwoViewPipeline(conf).to(device).eval()
+
+ gray0 = cv2.imread(args.img1, 0)
+ gray1 = cv2.imread(args.img2, 0)
+
+ torch_gray0, torch_gray1 = numpy_image_to_torch(gray0), numpy_image_to_torch(gray1)
+ torch_gray0, torch_gray1 = (
+ torch_gray0.to(device)[None],
+ torch_gray1.to(device)[None],
+ )
+ x = {"image0": torch_gray0, "image1": torch_gray1}
+ pred = pipeline_model(x)
+
+ pred = batch_to_np(pred)
+ kp0, kp1 = pred["keypoints0"], pred["keypoints1"]
+ m0 = pred["matches0"]
+
+ line_seg0, line_seg1 = pred["lines0"], pred["lines1"]
+ line_matches = pred["line_matches0"]
+
+ valid_matches = m0 != -1
+ match_indices = m0[valid_matches]
+ matched_kps0 = kp0[valid_matches]
+ matched_kps1 = kp1[match_indices]
+
+ valid_matches = line_matches != -1
+ match_indices = line_matches[valid_matches]
+ matched_lines0 = line_seg0[valid_matches]
+ matched_lines1 = line_seg1[match_indices]
+
+ # Plot the matches
+ img0, img1 = cv2.cvtColor(gray0, cv2.COLOR_GRAY2BGR), cv2.cvtColor(
+ gray1, cv2.COLOR_GRAY2BGR
+ )
+ plot_images(
+ [img0, img1],
+ ["Image 1 - detected lines", "Image 2 - detected lines"],
+ dpi=200,
+ pad=2.0,
+ )
+ plot_lines([line_seg0, line_seg1], ps=4, lw=2)
+ plt.gcf().canvas.manager.set_window_title("Detected Lines")
+ plt.savefig("detected_lines.png")
+
+ plot_images(
+ [img0, img1],
+ ["Image 1 - detected points", "Image 2 - detected points"],
+ dpi=200,
+ pad=2.0,
+ )
+ plot_keypoints([kp0, kp1], colors="c")
+ plt.gcf().canvas.manager.set_window_title("Detected Points")
+ plt.savefig("detected_points.png")
+
+ plot_images(
+ [img0, img1],
+ ["Image 1 - line matches", "Image 2 - line matches"],
+ dpi=200,
+ pad=2.0,
+ )
+ plot_color_line_matches([matched_lines0, matched_lines1], lw=2)
+ plt.gcf().canvas.manager.set_window_title("Line Matches")
+ plt.savefig("line_matches.png")
+
+ plot_images(
+ [img0, img1],
+ ["Image 1 - point matches", "Image 2 - point matches"],
+ dpi=200,
+ pad=2.0,
+ )
+ plot_matches(matched_kps0, matched_kps1, "green", lw=1, ps=0)
+ plt.gcf().canvas.manager.set_window_title("Point Matches")
+ plt.savefig("detected_points.png")
+ if not args.skip_imshow:
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/third_party/GlueStick/gluestick_matching_demo.ipynb b/third_party/GlueStick/gluestick_matching_demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6c02358f7e4d1b6a388c426eb19e3849e1c167b6
--- /dev/null
+++ b/third_party/GlueStick/gluestick_matching_demo.ipynb
@@ -0,0 +1,1132 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true,
+ "pycharm": {
+ "is_executing": true
+ },
+ "id": "_BY4CluidpCw"
+ },
+ "source": [
+ "# GlueStick Image Matching Demo 🖼️💥🖼️\n",
+ "\n",
+ "\n",
+ "In this python notebook we show how to obtain point and line matches using GlueStick. GlueStick is a unified pipeline that uses a single GNN to process both types of features and predicts coherent point and line matched that help each other in the matching process.\n",
+ "\n",
+ "\n",
+ "\n",
+ "If you use this python notebook please cite our work:\n",
+ "\n",
+ "> Pautrat, R.* and Suárez, I.* and Yu, Y. and Pollefeys, M. and Larsson, V. (2023). \"GlueStick: Robust Image Matching by Sticking Points and Lines Together\". ArXiv preprint."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Download the repository\n",
+ "!git clone https://github.com/cvg/GlueStick.git\n",
+ "%cd GlueStick"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "CVBUeKT4dqBu",
+ "outputId": "db7a0e29-d4b5-4609-d65b-4e0f50a3a1e9"
+ },
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Cloning into 'GlueStick'...\n",
+ "remote: Enumerating objects: 33, done.\u001b[K\n",
+ "remote: Counting objects: 100% (33/33), done.\u001b[K\n",
+ "remote: Compressing objects: 100% (31/31), done.\u001b[K\n",
+ "remote: Total 33 (delta 3), reused 24 (delta 0), pack-reused 0\u001b[K\n",
+ "Unpacking objects: 100% (33/33), 30.89 MiB | 8.17 MiB/s, done.\n",
+ "/content/GlueStick\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Install requirements\n",
+ "!pip install -r requirements.txt"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "v-5DsNXreiGn",
+ "outputId": "e0007926-eebc-4ab1-faf7-2fdce2bf08f0"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+ "Collecting git+https://github.com/iago-suarez/pytlsd.git@d518527 (from -r requirements.txt (line 12))\n",
+ " Cloning https://github.com/iago-suarez/pytlsd.git (to revision d518527) to /tmp/pip-req-build-u60qtkws\n",
+ " Running command git clone --filter=blob:none --quiet https://github.com/iago-suarez/pytlsd.git /tmp/pip-req-build-u60qtkws\n",
+ "\u001b[33m WARNING: Did not find branch or tag 'd518527', assuming revision or ref.\u001b[0m\u001b[33m\n",
+ "\u001b[0m Running command git checkout -q d518527\n",
+ " Resolved https://github.com/iago-suarez/pytlsd.git to commit d518527\n",
+ " Running command git submodule update --init --recursive -q\n",
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 1)) (1.22.4)\n",
+ "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 2)) (3.7.1)\n",
+ "Requirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 3)) (1.10.1)\n",
+ "Requirement already satisfied: scikit_learn in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 4)) (1.2.2)\n",
+ "Requirement already satisfied: seaborn in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 5)) (0.12.2)\n",
+ "Collecting omegaconf==2.2.*\n",
+ " Downloading omegaconf-2.2.3-py3-none-any.whl (79 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.3/79.3 KB\u001b[0m \u001b[31m404.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hRequirement already satisfied: opencv-python==4.7.0.* in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 7)) (4.7.0.72)\n",
+ "Requirement already satisfied: torch>=1.12 in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 8)) (2.0.0+cu118)\n",
+ "Requirement already satisfied: torchvision>=0.13 in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 9)) (0.15.1+cu118)\n",
+ "Requirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 10)) (67.6.1)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 11)) (4.65.0)\n",
+ "Requirement already satisfied: PyYAML>=5.1.0 in /usr/local/lib/python3.9/dist-packages (from omegaconf==2.2.*->-r requirements.txt (line 6)) (6.0)\n",
+ "Collecting antlr4-python3-runtime==4.9.*\n",
+ " Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 KB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->-r requirements.txt (line 2)) (8.4.0)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib->-r requirements.txt (line 2)) (2.8.2)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->-r requirements.txt (line 2)) (1.0.7)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->-r requirements.txt (line 2)) (1.4.4)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->-r requirements.txt (line 2)) (4.39.3)\n",
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->-r requirements.txt (line 2)) (23.0)\n",
+ "Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->-r requirements.txt (line 2)) (5.12.0)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->-r requirements.txt (line 2)) (3.0.9)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib->-r requirements.txt (line 2)) (0.11.0)\n",
+ "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from scikit_learn->-r requirements.txt (line 4)) (1.1.1)\n",
+ "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit_learn->-r requirements.txt (line 4)) (3.1.0)\n",
+ "Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.9/dist-packages (from seaborn->-r requirements.txt (line 5)) (1.4.4)\n",
+ "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch>=1.12->-r requirements.txt (line 8)) (4.5.0)\n",
+ "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.9/dist-packages (from torch>=1.12->-r requirements.txt (line 8)) (2.0.0)\n",
+ "Requirement already satisfied: sympy in /usr/local/lib/python3.9/dist-packages (from torch>=1.12->-r requirements.txt (line 8)) (1.11.1)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from torch>=1.12->-r requirements.txt (line 8)) (3.10.7)\n",
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from torch>=1.12->-r requirements.txt (line 8)) (3.1.2)\n",
+ "Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (from torch>=1.12->-r requirements.txt (line 8)) (3.0)\n",
+ "Requirement already satisfied: cmake in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch>=1.12->-r requirements.txt (line 8)) (3.25.2)\n",
+ "Requirement already satisfied: lit in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch>=1.12->-r requirements.txt (line 8)) (16.0.0)\n",
+ "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from torchvision>=0.13->-r requirements.txt (line 9)) (2.27.1)\n",
+ "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.9/dist-packages (from importlib-resources>=3.2.0->matplotlib->-r requirements.txt (line 2)) (3.15.0)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.25->seaborn->-r requirements.txt (line 5)) (2022.7.1)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7->matplotlib->-r requirements.txt (line 2)) (1.16.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->torch>=1.12->-r requirements.txt (line 8)) (2.1.2)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision>=0.13->-r requirements.txt (line 9)) (1.26.15)\n",
+ "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision>=0.13->-r requirements.txt (line 9)) (2.0.12)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision>=0.13->-r requirements.txt (line 9)) (3.4)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision>=0.13->-r requirements.txt (line 9)) (2022.12.7)\n",
+ "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch>=1.12->-r requirements.txt (line 8)) (1.3.0)\n",
+ "Building wheels for collected packages: antlr4-python3-runtime, pytlsd\n",
+ " Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144573 sha256=ac7a12e0ddab8ea2fd70b57eab16afa268aba7e1115fa14f726de7a6ee963d7a\n",
+ " Stored in directory: /root/.cache/pip/wheels/23/cf/80/f3efa822e6ab23277902ee9165fe772eeb1dfb8014f359020a\n",
+ " Building wheel for pytlsd (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for pytlsd: filename=pytlsd-0.0.3-cp39-cp39-linux_x86_64.whl size=66125 sha256=7cb1787ea41321dcaae4cdf9dfc9ef78db8ff1d8aa10b5da1caef0494b383c36\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-ycm_joyo/wheels/24/1d/6a/937976436d1167d79c0763e00e9cd181c385c79206149bfc3a\n",
+ "Successfully built antlr4-python3-runtime pytlsd\n",
+ "Installing collected packages: pytlsd, antlr4-python3-runtime, omegaconf\n",
+ "Successfully installed antlr4-python3-runtime-4.9.3 omegaconf-2.2.3 pytlsd-0.0.3\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.colab-display-data+json": {
+ "pip_warning": {
+ "packages": [
+ "pydevd_plugins"
+ ]
+ }
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Download the pre-trained model"
+ ],
+ "metadata": {
+ "id": "7McenwHtfGLE"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!wget https://github.com/cvg/GlueStick/releases/download/v0.1_arxiv/checkpoint_GlueStick_MD.tar -P resources/weights"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "jmdiMOTFfBNN",
+ "outputId": "5041123a-52a0-453a-bebc-54bda11d4e51"
+ },
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--2023-04-04 23:22:22-- https://github.com/cvg/GlueStick/releases/download/v0.1_arxiv/checkpoint_GlueStick_MD.tar\n",
+ "Resolving github.com (github.com)... 140.82.114.3\n",
+ "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n",
+ "HTTP request sent, awaiting response... 302 Found\n",
+ "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/622867606/b6e2035f-ead7-4d20-93f4-855c5396a8b2?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230404%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230404T232223Z&X-Amz-Expires=300&X-Amz-Signature=d7d6b2730dd0af6674207751cbb9655a3590b05d35fccf115fb9ae48905ff13a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=622867606&response-content-disposition=attachment%3B%20filename%3Dcheckpoint_GlueStick_MD.tar&response-content-type=application%2Foctet-stream [following]\n",
+ "--2023-04-04 23:22:23-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/622867606/b6e2035f-ead7-4d20-93f4-855c5396a8b2?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230404%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230404T232223Z&X-Amz-Expires=300&X-Amz-Signature=d7d6b2730dd0af6674207751cbb9655a3590b05d35fccf115fb9ae48905ff13a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=622867606&response-content-disposition=attachment%3B%20filename%3Dcheckpoint_GlueStick_MD.tar&response-content-type=application%2Foctet-stream\n",
+ "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.110.133, ...\n",
+ "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.109.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 112588421 (107M) [application/octet-stream]\n",
+ "Saving to: ‘resources/weights/checkpoint_GlueStick_MD.tar’\n",
+ "\n",
+ "checkpoint_GlueStic 100%[===================>] 107.37M 57.6MB/s in 1.9s \n",
+ "\n",
+ "2023-04-04 23:22:25 (57.6 MB/s) - ‘resources/weights/checkpoint_GlueStick_MD.tar’ saved [112588421/112588421]\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "udUG35j0dpC0"
+ },
+ "outputs": [],
+ "source": [
+ "from os.path import join\n",
+ "\n",
+ "import cv2\n",
+ "import torch\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "from gluestick import batch_to_np, numpy_image_to_torch, GLUESTICK_ROOT\n",
+ "from gluestick.drawing import plot_images, plot_lines, plot_color_line_matches, plot_keypoints, plot_matches\n",
+ "from gluestick.models.two_view_pipeline import TwoViewPipeline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0GkvjCpvdpC2"
+ },
+ "source": [
+ "Define the configuration and model that we are going to use in our demo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "lxWDkN5XdpC2",
+ "outputId": "3026899d-721c-4163-c1d0-81aea226b40a"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TwoViewPipeline(\n",
+ " (extractor): SPWireframeDescriptor(\n",
+ " (sp): SuperPoint(\n",
+ " (relu): ReLU(inplace=True)\n",
+ " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+ " (conv1a): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (conv1b): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (conv2a): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (conv2b): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (conv3a): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (conv3b): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (conv4a): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (conv4b): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (convPa): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (convPb): Conv2d(256, 65, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (convDa): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (convDb): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+ " )\n",
+ " )\n",
+ " (matcher): GlueStick(\n",
+ " (kenc): KeypointEncoder(\n",
+ " (encoder): Sequential(\n",
+ " (0): Conv1d(3, 32, kernel_size=(1,), stride=(1,))\n",
+ " (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (2): ReLU()\n",
+ " (3): Conv1d(32, 64, kernel_size=(1,), stride=(1,))\n",
+ " (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (5): ReLU()\n",
+ " (6): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n",
+ " (7): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (8): ReLU()\n",
+ " (9): Conv1d(128, 256, kernel_size=(1,), stride=(1,))\n",
+ " (10): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (11): ReLU()\n",
+ " (12): Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n",
+ " )\n",
+ " )\n",
+ " (lenc): EndPtEncoder(\n",
+ " (encoder): Sequential(\n",
+ " (0): Conv1d(5, 32, kernel_size=(1,), stride=(1,))\n",
+ " (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (2): ReLU()\n",
+ " (3): Conv1d(32, 64, kernel_size=(1,), stride=(1,))\n",
+ " (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (5): ReLU()\n",
+ " (6): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n",
+ " (7): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (8): ReLU()\n",
+ " (9): Conv1d(128, 256, kernel_size=(1,), stride=(1,))\n",
+ " (10): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (11): ReLU()\n",
+ " (12): Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n",
+ " )\n",
+ " )\n",
+ " (gnn): AttentionalGNN(\n",
+ " (layers): ModuleList(\n",
+ " (0-17): 18 x GNNLayer(\n",
+ " (update): AttentionalPropagation(\n",
+ " (attn): MultiHeadedAttention(\n",
+ " (merge): Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n",
+ " (proj): ModuleList(\n",
+ " (0-2): 3 x Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n",
+ " )\n",
+ " )\n",
+ " (mlp): Sequential(\n",
+ " (0): Conv1d(512, 512, kernel_size=(1,), stride=(1,))\n",
+ " (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (2): ReLU()\n",
+ " (3): Conv1d(512, 256, kernel_size=(1,), stride=(1,))\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (line_layers): ModuleList(\n",
+ " (0-8): 9 x LineLayer(\n",
+ " (mlp): Sequential(\n",
+ " (0): Conv1d(768, 512, kernel_size=(1,), stride=(1,))\n",
+ " (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (2): ReLU()\n",
+ " (3): Conv1d(512, 256, kernel_size=(1,), stride=(1,))\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (final_proj): Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n",
+ " (final_line_proj): Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 5
+ }
+ ],
+ "source": [
+ "MAX_N_POINTS, MAX_N_LINES = 1000, 300\n",
+ "\n",
+ "# Evaluation config\n",
+ "conf = {\n",
+ " 'name': 'two_view_pipeline',\n",
+ " 'use_lines': True,\n",
+ " 'extractor': {\n",
+ " 'name': 'wireframe',\n",
+ " 'sp_params': {\n",
+ " 'force_num_keypoints': False,\n",
+ " 'max_num_keypoints': MAX_N_POINTS,\n",
+ " },\n",
+ " 'wireframe_params': {\n",
+ " 'merge_points': True,\n",
+ " 'merge_line_endpoints': True,\n",
+ " },\n",
+ " 'max_n_lines': MAX_N_LINES,\n",
+ " },\n",
+ " 'matcher': {\n",
+ " 'name': 'gluestick',\n",
+ " 'weights': str(GLUESTICK_ROOT / 'resources' / 'weights' / 'checkpoint_GlueStick_MD.tar'),\n",
+ " 'trainable': False,\n",
+ " },\n",
+ " 'ground_truth': {\n",
+ " 'from_pose_depth': False,\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+ "\n",
+ "pipeline_model = TwoViewPipeline(conf).to(device).eval()\n",
+ "pipeline_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 163
+ },
+ "id": "SYTcXss9dpC5",
+ "outputId": "78b7b6ec-d760-4025-a35c-cec0a4d7dd0c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Choose the FIRST image from your computer (Recommended resolution: 640x640)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Saving img1.jpg to img1 (1).jpg\n",
+ "Choose the SECOND image from your computer\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Saving img2.jpg to img2 (1).jpg\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load input images \n",
+ "import sys\n",
+ "\n",
+ "IN_COLAB = 'google.colab' in sys.modules\n",
+ "if not IN_COLAB:\n",
+ " # We are running a notebook in Jupyter\n",
+ " img_path0 = join('resources', 'img1.jpg')\n",
+ " img_path1 = join('resources', 'img2.jpg')\n",
+ "else:\n",
+ " # We are running in Colab: Load from user's disk using Colab tools\n",
+ " from google.colab import files\n",
+ " print('Choose the FIRST image from your computer (Recommended resolution: 640x640)')\n",
+ " uploaded_files = files.upload()\n",
+ " img_path0 = list(uploaded_files.keys())[0]\n",
+ " print('Choose the SECOND image from your computer')\n",
+ " uploaded_files = files.upload()\n",
+ " img_path1 = list(uploaded_files.keys())[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "img = cv2.imread(img_path0, cv2.IMREAD_GRAYSCALE)\n",
+ "\n",
+ "gray0 = cv2.imread(img_path0, 0)\n",
+ "gray1 = cv2.imread(img_path1, 0)\n",
+ "\n",
+ "# Plot them using matplotlib\n",
+ "f, axarr = plt.subplots(1, 2)\n",
+ "axarr[0].imshow(gray0, cmap='gray')\n",
+ "axarr[1].imshow(gray1, cmap='gray')"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 386
+ },
+ "id": "h8cWFvtih1c-",
+ "outputId": "ea02228c-8227-4cdf-d1bd-b9ddbf3af11d"
+ },
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "execution_count": 8
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "image/png": "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\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "plot_images([img0, img1], ['Image 1 - point matches', 'Image 2 - point matches'], pad=0.5)\n",
+ "plot_matches(matched_kps0, matched_kps1, 'green', lw=1, ps=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "Kve9xdngdpC_"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.6"
+ },
+ "colab": {
+ "provenance": []
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/third_party/GlueStick/requirements.txt b/third_party/GlueStick/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6ccf01735a036ad91060ac884bbc94da275dd487
--- /dev/null
+++ b/third_party/GlueStick/requirements.txt
@@ -0,0 +1,12 @@
+numpy
+matplotlib
+scipy
+scikit_learn
+seaborn
+omegaconf==2.2.*
+opencv-python==4.7.0.*
+torch>=1.12
+torchvision>=0.13
+setuptools
+tqdm
+git+https://github.com/iago-suarez/pytlsd.git@37ac583
diff --git a/third_party/GlueStick/setup.py b/third_party/GlueStick/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1a9df947ac2b788597e3028226f8efbdcd21b94
--- /dev/null
+++ b/third_party/GlueStick/setup.py
@@ -0,0 +1,3 @@
+from setuptools import setup
+
+setup(name="gluestick", version="0.0", packages=["gluestick"])
diff --git a/third_party/LightGlue/.flake8 b/third_party/LightGlue/.flake8
new file mode 100644
index 0000000000000000000000000000000000000000..899119f2ffc38dfec543e2efab9abc3a006e305e
--- /dev/null
+++ b/third_party/LightGlue/.flake8
@@ -0,0 +1,4 @@
+[flake8]
+max-line-length = 88
+extend-ignore = E203
+exclude = .git,__pycache__,build,.venv/
diff --git a/third_party/LightGlue/.gitattributes b/third_party/LightGlue/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..60404dcd96640d5095b962678b8ede93465c5dfd
--- /dev/null
+++ b/third_party/LightGlue/.gitattributes
@@ -0,0 +1 @@
+*.ipynb linguist-documentation
\ No newline at end of file
diff --git a/imcui/third_party/LightGlue/.github/workflows/code-quality.yml b/third_party/LightGlue/.github/workflows/code-quality.yml
similarity index 100%
rename from imcui/third_party/LightGlue/.github/workflows/code-quality.yml
rename to third_party/LightGlue/.github/workflows/code-quality.yml
diff --git a/third_party/LightGlue/.gitignore b/third_party/LightGlue/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..4bce5dd4b29415d145c4a07c047f36d799607187
--- /dev/null
+++ b/third_party/LightGlue/.gitignore
@@ -0,0 +1,166 @@
+/data/
+/outputs/
+/lightglue/weights/
+*-checkpoint.ipynb
+*.pth
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+# in version control.
+# https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+# and can be added to the global gitignore or merged into this file. For a more nuclear
+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
diff --git a/third_party/LightGlue/LICENSE b/third_party/LightGlue/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..38a27f882c671ba9f15b35ec13ca7c0c296efe50
--- /dev/null
+++ b/third_party/LightGlue/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright 2023 ETH Zurich
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
diff --git a/third_party/LightGlue/README.md b/third_party/LightGlue/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f297cf29e022950649f7db6820b6f3f1e19a02d7
--- /dev/null
+++ b/third_party/LightGlue/README.md
@@ -0,0 +1,180 @@
+
+
LightGlue ⚡️ Local Feature Matching at Light Speed
+
+
+ LightGlue is a deep neural network that matches sparse local features across image pairs. An adaptive mechanism makes it fast for easy pairs (top) and reduces the computational complexity for difficult ones (bottom).
+
+
+##
+
+This repository hosts the inference code of LightGlue, a lightweight feature matcher with high accuracy and blazing fast inference. It takes as input a set of keypoints and descriptors for each image and returns the indices of corresponding points. The architecture is based on adaptive pruning techniques, in both network width and depth - [check out the paper for more details](https://arxiv.org/pdf/2306.13643.pdf).
+
+We release pretrained weights of LightGlue with [SuperPoint](https://arxiv.org/abs/1712.07629), [DISK](https://arxiv.org/abs/2006.13566), [ALIKED](https://arxiv.org/abs/2304.03608) and [SIFT](https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf) local features.
+The training and evaluation code can be found in our library [glue-factory](https://github.com/cvg/glue-factory/).
+
+## Installation and demo [](https://colab.research.google.com/github/cvg/LightGlue/blob/main/demo.ipynb)
+
+Install this repo using pip:
+
+```bash
+git clone https://github.com/cvg/LightGlue.git && cd LightGlue
+python -m pip install -e .
+```
+
+We provide a [demo notebook](demo.ipynb) which shows how to perform feature extraction and matching on an image pair.
+
+Here is a minimal script to match two images:
+
+```python
+from lightglue import LightGlue, SuperPoint, DISK, SIFT, ALIKED, DoGHardNet
+from lightglue.utils import load_image, rbd
+
+# SuperPoint+LightGlue
+extractor = SuperPoint(max_num_keypoints=2048).eval().cuda() # load the extractor
+matcher = LightGlue(features='superpoint').eval().cuda() # load the matcher
+
+# or DISK+LightGlue, ALIKED+LightGlue or SIFT+LightGlue
+extractor = DISK(max_num_keypoints=2048).eval().cuda() # load the extractor
+matcher = LightGlue(features='disk').eval().cuda() # load the matcher
+
+# load each image as a torch.Tensor on GPU with shape (3,H,W), normalized in [0,1]
+image0 = load_image('path/to/image_0.jpg').cuda()
+image1 = load_image('path/to/image_1.jpg').cuda()
+
+# extract local features
+feats0 = extractor.extract(image0) # auto-resize the image, disable with resize=None
+feats1 = extractor.extract(image1)
+
+# match the features
+matches01 = matcher({'image0': feats0, 'image1': feats1})
+feats0, feats1, matches01 = [rbd(x) for x in [feats0, feats1, matches01]] # remove batch dimension
+matches = matches01['matches'] # indices with shape (K,2)
+points0 = feats0['keypoints'][matches[..., 0]] # coordinates in image #0, shape (K,2)
+points1 = feats1['keypoints'][matches[..., 1]] # coordinates in image #1, shape (K,2)
+```
+
+We also provide a convenience method to match a pair of images:
+
+```python
+from lightglue import match_pair
+feats0, feats1, matches01 = match_pair(extractor, matcher, image0, image1)
+```
+
+##
+
+
+
+
+ LightGlue can adjust its depth (number of layers) and width (number of keypoints) per image pair, with a marginal impact on accuracy.
+
+
+## Advanced configuration
+
+
+[Detail of all parameters - click to expand]
+
+- ```n_layers```: Number of stacked self+cross attention layers. Reduce this value for faster inference at the cost of accuracy (continuous red line in the plot above). Default: 9 (all layers).
+- ```flash```: Enable FlashAttention. Significantly increases the speed and reduces the memory consumption without any impact on accuracy. Default: True (LightGlue automatically detects if FlashAttention is available).
+- ```mp```: Enable mixed precision inference. Default: False (off)
+- ```depth_confidence```: Controls the early stopping. A lower values stops more often at earlier layers. Default: 0.95, disable with -1.
+- ```width_confidence```: Controls the iterative point pruning. A lower value prunes more points earlier. Default: 0.99, disable with -1.
+- ```filter_threshold```: Match confidence. Increase this value to obtain less, but stronger matches. Default: 0.1
+
+
+
+The default values give a good trade-off between speed and accuracy. To maximize the accuracy, use all keypoints and disable the adaptive mechanisms:
+```python
+extractor = SuperPoint(max_num_keypoints=None)
+matcher = LightGlue(features='superpoint', depth_confidence=-1, width_confidence=-1)
+```
+
+To increase the speed with a small drop of accuracy, decrease the number of keypoints and lower the adaptive thresholds:
+```python
+extractor = SuperPoint(max_num_keypoints=1024)
+matcher = LightGlue(features='superpoint', depth_confidence=0.9, width_confidence=0.95)
+```
+
+The maximum speed is obtained with a combination of:
+- [FlashAttention](https://arxiv.org/abs/2205.14135): automatically used when ```torch >= 2.0``` or if [installed from source](https://github.com/HazyResearch/flash-attention#installation-and-features).
+- PyTorch compilation, available when ```torch >= 2.0```:
+```python
+matcher = matcher.eval().cuda()
+matcher.compile(mode='reduce-overhead')
+```
+For inputs with fewer than 1536 keypoints (determined experimentally), this compiles LightGlue but disables point pruning (large overhead). For larger input sizes, it automatically falls backs to eager mode with point pruning. Adaptive depths is supported for any input size.
+
+## Benchmark
+
+
+
+
+
+ Benchmark results on GPU (RTX 3080). With compilation and adaptivity, LightGlue runs at 150 FPS @ 1024 keypoints and 50 FPS @ 4096 keypoints per image. This is a 4-10x speedup over SuperGlue.
+
+
+
+
+
+ Benchmark results on CPU (Intel i7 10700K). LightGlue runs at 20 FPS @ 512 keypoints.
+
+
+Obtain the same plots for your setup using our [benchmark script](benchmark.py):
+```
+python benchmark.py [--device cuda] [--add_superglue] [--num_keypoints 512 1024 2048 4096] [--compile]
+```
+
+
+[Performance tip - click to expand]
+
+Note: **Point pruning** introduces an overhead that sometimes outweighs its benefits.
+Point pruning is thus enabled only when the there are more than N keypoints in an image, where N is hardware-dependent.
+We provide defaults optimized for current hardware (RTX 30xx GPUs).
+We suggest running the benchmark script and adjusting the thresholds for your hardware by updating `LightGlue.pruning_keypoint_thresholds['cuda']`.
+
+
+
+## Training and evaluation
+
+With [Glue Factory](https://github.com/cvg/glue-factory), you can train LightGlue with your own local features, on your own dataset!
+You can also evaluate it and other baselines on standard benchmarks like HPatches and MegaDepth.
+
+## Other links
+- [hloc - the visual localization toolbox](https://github.com/cvg/Hierarchical-Localization/): run LightGlue for Structure-from-Motion and visual localization.
+- [LightGlue-ONNX](https://github.com/fabio-sim/LightGlue-ONNX): export LightGlue to the Open Neural Network Exchange (ONNX) format with support for TensorRT and OpenVINO.
+- [Image Matching WebUI](https://github.com/Vincentqyw/image-matching-webui): a web GUI to easily compare different matchers, including LightGlue.
+- [kornia](https://kornia.readthedocs.io) now exposes LightGlue via the interfaces [`LightGlue`](https://kornia.readthedocs.io/en/latest/feature.html#kornia.feature.LightGlue) and [`LightGlueMatcher`](https://kornia.readthedocs.io/en/latest/feature.html#kornia.feature.LightGlueMatcher).
+
+## BibTeX citation
+If you use any ideas from the paper or code from this repo, please consider citing:
+
+```txt
+@inproceedings{lindenberger2023lightglue,
+ author = {Philipp Lindenberger and
+ Paul-Edouard Sarlin and
+ Marc Pollefeys},
+ title = {{LightGlue: Local Feature Matching at Light Speed}},
+ booktitle = {ICCV},
+ year = {2023}
+}
+```
+
+
+## License
+The pre-trained weights of LightGlue and the code provided in this repository are released under the [Apache-2.0 license](./LICENSE). [DISK](https://github.com/cvlab-epfl/disk) follows this license as well but SuperPoint follows [a different, restrictive license](https://github.com/magicleap/SuperPointPretrainedNetwork/blob/master/LICENSE) (this includes its pre-trained weights and its [inference file](./lightglue/superpoint.py)). [ALIKED](https://github.com/Shiaoming/ALIKED) was published under a BSD-3-Clause license.
diff --git a/imcui/third_party/LightGlue/benchmark.py b/third_party/LightGlue/benchmark.py
similarity index 100%
rename from imcui/third_party/LightGlue/benchmark.py
rename to third_party/LightGlue/benchmark.py
diff --git a/third_party/LightGlue/demo.ipynb b/third_party/LightGlue/demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1e8709167420bbbf059b40adbbdc188ed27781da
--- /dev/null
+++ b/third_party/LightGlue/demo.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# LightGlue Demo\n",
+ "In this notebook we match two pairs of images using LightGlue with early stopping and point pruning."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# If we are on colab: this clones the repo and installs the dependencies\n",
+ "from pathlib import Path\n",
+ "\n",
+ "if Path.cwd().name != \"LightGlue\":\n",
+ " !git clone --quiet https://github.com/cvg/LightGlue/\n",
+ " %cd LightGlue\n",
+ " !pip install --progress-bar off --quiet -e .\n",
+ "\n",
+ "from lightglue import LightGlue, SuperPoint, DISK\n",
+ "from lightglue.utils import load_image, rbd\n",
+ "from lightglue import viz2d\n",
+ "import torch\n",
+ "\n",
+ "torch.set_grad_enabled(False)\n",
+ "images = Path(\"assets\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load extractor and matcher module\n",
+ "In this example we use SuperPoint features combined with LightGlue."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded SuperPoint model\n",
+ "Loaded LightGlue model\n"
+ ]
+ }
+ ],
+ "source": [
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # 'mps', 'cpu'\n",
+ "\n",
+ "extractor = SuperPoint(max_num_keypoints=2048).eval().to(device) # load the extractor\n",
+ "matcher = LightGlue(features=\"superpoint\").eval().to(device)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Easy example\n",
+ "The top image shows the matches, while the bottom image shows the point pruning across layers. In this case, LightGlue prunes a few points with occlusions, but is able to stop the context aggregation after 4/9 layers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "
+
+Matching precision-recall curves on the [Wireframe](https://github.com/huangkuns/wireframe) and [ETH3D](https://www.eth3d.net/) datasets:
+
+
+## Bibtex
+
+If you use this code in your project, please consider citing the following paper:
+```bibtex
+@InProceedings{Pautrat_Lin_2021_CVPR,
+ author = {Pautrat*, Rémi and Lin*, Juan-Ting and Larsson, Viktor and Oswald, Martin R. and Pollefeys, Marc},
+ title = {SOLD2: Self-supervised Occlusion-aware Line Description and Detection},
+ booktitle = {Computer Vision and Pattern Recognition (CVPR)},
+ year = {2021},
+}
+```
diff --git a/imcui/third_party/SOLD2/sold2/dataset/transforms/__init__.py b/third_party/SOLD2/notebooks/__init__.py
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/dataset/transforms/__init__.py
rename to third_party/SOLD2/notebooks/__init__.py
diff --git a/third_party/SOLD2/notebooks/match_lines.ipynb b/third_party/SOLD2/notebooks/match_lines.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f10d98da893d69ea97ab41c53f36796c53ccda40
--- /dev/null
+++ b/third_party/SOLD2/notebooks/match_lines.ipynb
@@ -0,0 +1,237 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import cv2\n",
+ "import torch\n",
+ "\n",
+ "from sold2.model.line_matcher import LineMatcher\n",
+ "from sold2.misc.visualize_util import plot_images, plot_lines, plot_line_matches, plot_color_line_matches, plot_keypoints"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Matching from scratch given pairs of images"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "\t--------Initializing model----------\n",
+ "\t [Debug] Adding w_junc with value 0.000000 to model\n",
+ "\t [Debug] Adding w_heatmap with value 0.000000 to model\n",
+ "\t [Debug] Adding w_desc with value 0.000000 to model\n",
+ "\tModel architecture: simple\n",
+ "\tBackbone: lcnn\n",
+ "\tJunction decoder: superpoint_decoder\n",
+ "\tHeatmap decoder: pixel_shuffle\n",
+ "\t-------------------------------------\n",
+ "[Debug] detect_thresh: 0.25\n",
+ "[Debug] num_samples: 64\n",
+ "[Debug] sampling_method: local_max\n",
+ "[Debug] inlier_thresh: 0.9\n",
+ "[Debug] use_candidate_suppression: True\n",
+ "[Debug] nms_dist_tolerance: 3.0\n",
+ "[Debug] use_heatmap_refinement: True\n",
+ "[Debug] heatmap_refine_cfg: {'mode': 'local', 'ratio': 0.2, 'valid_thresh': 0.001, 'num_blocks': 20, 'overlap_ratio': 0.5}\n"
+ ]
+ }
+ ],
+ "source": [
+ "ckpt_path = '../pretrained_models/sold2_wireframe.tar'\n",
+ "device = 'cuda'\n",
+ "mode = 'dynamic' # 'dynamic' or 'static'\n",
+ "\n",
+ "# Initialize the line matcher\n",
+ "config = {\n",
+ " 'model_cfg': {\n",
+ " 'model_name': \"lcnn_simple\",\n",
+ " 'model_architecture': \"simple\",\n",
+ " # Backbone related config\n",
+ " 'backbone': \"lcnn\",\n",
+ " 'backbone_cfg': {\n",
+ " 'input_channel': 1, # Use RGB images or grayscale images.\n",
+ " 'depth': 4,\n",
+ " 'num_stacks': 2,\n",
+ " 'num_blocks': 1,\n",
+ " 'num_classes': 5\n",
+ " },\n",
+ " # Junction decoder related config\n",
+ " 'junction_decoder': \"superpoint_decoder\",\n",
+ " 'junc_decoder_cfg': {},\n",
+ " # Heatmap decoder related config\n",
+ " 'heatmap_decoder': \"pixel_shuffle\",\n",
+ " 'heatmap_decoder_cfg': {},\n",
+ " # Descriptor decoder related config\n",
+ " 'descriptor_decoder': \"superpoint_descriptor\",\n",
+ " 'descriptor_decoder_cfg': {},\n",
+ " # Shared configurations\n",
+ " 'grid_size': 8,\n",
+ " 'keep_border_valid': True,\n",
+ " # Threshold of junction detection\n",
+ " 'detection_thresh': 0.0153846, # 1/65\n",
+ " 'max_num_junctions': 300,\n",
+ " # Threshold of heatmap detection\n",
+ " 'prob_thresh': 0.5,\n",
+ " # Weighting related parameters\n",
+ " 'weighting_policy': mode,\n",
+ " # [Heatmap loss]\n",
+ " 'w_heatmap': 0.,\n",
+ " 'w_heatmap_class': 1,\n",
+ " 'heatmap_loss_func': \"cross_entropy\",\n",
+ " 'heatmap_loss_cfg': {\n",
+ " 'policy': mode\n",
+ " },\n",
+ " # [Heatmap consistency loss]\n",
+ " # [Junction loss]\n",
+ " 'w_junc': 0.,\n",
+ " 'junction_loss_func': \"superpoint\",\n",
+ " 'junction_loss_cfg': {\n",
+ " 'policy': mode\n",
+ " },\n",
+ " # [Descriptor loss]\n",
+ " 'w_desc': 0.,\n",
+ " 'descriptor_loss_func': \"regular_sampling\",\n",
+ " 'descriptor_loss_cfg': {\n",
+ " 'dist_threshold': 8,\n",
+ " 'grid_size': 4,\n",
+ " 'margin': 1,\n",
+ " 'policy': mode\n",
+ " },\n",
+ " },\n",
+ " 'line_detector_cfg': {\n",
+ " 'detect_thresh': 0.25, # depending on your images, you might need to tune this parameter\n",
+ " 'num_samples': 64,\n",
+ " 'sampling_method': \"local_max\",\n",
+ " 'inlier_thresh': 0.9,\n",
+ " \"use_candidate_suppression\": True,\n",
+ " \"nms_dist_tolerance\": 3.,\n",
+ " \"use_heatmap_refinement\": True,\n",
+ " \"heatmap_refine_cfg\": {\n",
+ " \"mode\": \"local\",\n",
+ " \"ratio\": 0.2,\n",
+ " \"valid_thresh\": 1e-3,\n",
+ " \"num_blocks\": 20,\n",
+ " \"overlap_ratio\": 0.5\n",
+ " }\n",
+ " },\n",
+ " 'multiscale': False,\n",
+ " 'line_matcher_cfg': {\n",
+ " 'cross_check': True,\n",
+ " 'num_samples': 5,\n",
+ " 'min_dist_pts': 8,\n",
+ " 'top_k_candidates': 10,\n",
+ " 'grid_size': 4\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "line_matcher = LineMatcher(\n",
+ " config[\"model_cfg\"], ckpt_path, device, config[\"line_detector_cfg\"],\n",
+ " config[\"line_matcher_cfg\"], config[\"multiscale\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Read and pre-process the images\n",
+ "scale_factor = 1 # we recommend resizing the images to a resolution in the range 400~800 pixels\n",
+ "img1 = '../assets/images/terrace0.JPG'\n",
+ "img1 = cv2.imread(img1, 0)\n",
+ "img1 = cv2.resize(img1, (img1.shape[1] // scale_factor, img1.shape[0] // scale_factor),\n",
+ " interpolation = cv2.INTER_AREA)\n",
+ "img1 = (img1 / 255.).astype(float)\n",
+ "torch_img1 = torch.tensor(img1, dtype=torch.float)[None, None]\n",
+ "img2 = '../assets/images/terrace1.JPG'\n",
+ "img2 = cv2.imread(img2, 0)\n",
+ "img2 = cv2.resize(img2, (img2.shape[1] // scale_factor, img2.shape[0] // scale_factor),\n",
+ " interpolation = cv2.INTER_AREA)\n",
+ "img2 = (img2 / 255.).astype(float)\n",
+ "torch_img2 = torch.tensor(img2, dtype=torch.float)[None, None]\n",
+ "\n",
+ "# Match the lines\n",
+ "outputs = line_matcher([torch_img1, torch_img2])\n",
+ "line_seg1 = outputs[\"line_segments\"][0]\n",
+ "line_seg2 = outputs[\"line_segments\"][1]\n",
+ "matches = outputs[\"matches\"]\n",
+ "\n",
+ "valid_matches = matches != -1\n",
+ "match_indices = matches[valid_matches]\n",
+ "matched_lines1 = line_seg1[valid_matches][:, :, ::-1]\n",
+ "matched_lines2 = line_seg2[match_indices][:, :, ::-1]\n",
+ "\n",
+ "# Plot the matches\n",
+ "plot_images([img1, img2], ['Image 1 - detected lines', 'Image 2 - detected lines'])\n",
+ "plot_lines([line_seg1[:, :, ::-1], line_seg2[:, :, ::-1]], ps=3, lw=2)\n",
+ "plot_images([img1, img2], ['Image 1 - matched lines', 'Image 2 - matched lines'])\n",
+ "plot_color_line_matches([matched_lines1, matched_lines2], lw=2)"
+ ]
+ }
+ ],
+ "metadata": {
+ "file_extension": ".py",
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ },
+ "mimetype": "text/x-python",
+ "name": "python",
+ "npconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/SOLD2/notebooks/visualize_exported_dataset.ipynb b/third_party/SOLD2/notebooks/visualize_exported_dataset.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5ca610dc697b5be20d321e2b21215601452029c5
--- /dev/null
+++ b/third_party/SOLD2/notebooks/visualize_exported_dataset.ipynb
@@ -0,0 +1,404 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import yaml\n",
+ "\n",
+ "from sold2.dataset.wireframe_dataset import WireframeDataset\n",
+ "from sold2.dataset.holicity_dataset import HolicityDataset\n",
+ "from sold2.dataset.merge_dataset import MergeDataset\n",
+ "from sold2.misc.visualize_util import plot_junctions, plot_line_segments\n",
+ "from sold2.misc.visualize_util import plot_images, plot_keypoints"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualize the exported ground truth on the Wireframe dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Info] Initializing wireframe dataset...\n",
+ "\t Found filename cache wireframe_test_cache.pkl at /home/remi/Documents/datasets/wireframe\n",
+ "\t Load filename cache...\n",
+ "[Info] Successfully initialized dataset\n",
+ "\t Name: wireframe\n",
+ "\t Mode: test\n",
+ "\t Gt: /home/remi/Documents/datasets/export_datasets/wireframe_test_adaptation_iter0_epoch043_ce1_detect_0.25_inlier_0.75_local_max_v1.5_refine-v2.h5\n",
+ "\t Counts: 462\n",
+ "----------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize the wireframe dataset\n",
+ "with open(\"../sold2/config/wireframe_dataset.yaml\", \"r\") as f:\n",
+ " config = yaml.safe_load(f)\n",
+ "config['return_type'] = 'paired_desc'\n",
+ "\n",
+ "wireframe_dataset = WireframeDataset(mode=\"test\", config=config)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Read in one datapoint\n",
+ "index = 4\n",
+ "data1 = wireframe_dataset[index]\n",
+ "\n",
+ "# Reference data\n",
+ "ref_img = data1['ref_image'].numpy().squeeze()\n",
+ "ref_junc = data1['ref_junctions'].numpy()\n",
+ "ref_line_map = data1['ref_line_map'].numpy()\n",
+ "ref_line_points = data1['ref_line_points'].numpy()\n",
+ "\n",
+ "# Target data\n",
+ "target_img = data1['target_image'].numpy().squeeze()\n",
+ "target_junc = data1['target_junctions'].numpy()\n",
+ "target_line_map = data1['target_line_map'].numpy()\n",
+ "target_line_points = data1['target_line_points'].numpy()\n",
+ "\n",
+ "# Draw the points and lines\n",
+ "ref_img_with_junc = plot_junctions(ref_img, ref_junc, junc_size=2)\n",
+ "ref_line_segments = plot_line_segments(ref_img, ref_junc, ref_line_map, junc_size=1)\n",
+ "target_img_with_junc = plot_junctions(target_img, target_junc, junc_size=2)\n",
+ "target_line_segments = plot_line_segments(target_img, target_junc, target_line_map, junc_size=1)\n",
+ "\n",
+ "# Plot the images\n",
+ "plot_images([ref_img_with_junc, ref_line_segments], ['Junctions', 'Line segments'])\n",
+ "plot_images([target_img_with_junc, target_line_segments], ['Warped junctions', 'Warped line segments'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9wAAAHICAYAAAC8iOK5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd5gdV3n/PzO337u9SNpd7aqserNcJFnuNpiOMRBC4iT0BAi/EEoSeq8hkIRAICQkdGx6BxuDbVxl2ZKbem8raaXd1bbby/z+uHrnvnP2ylhguZ7P8+yzu/dOOXNm5pzzfd/3vMfxPA+LxWKxWCwWi8VisVgsjy7u410Ai8VisVgsFovFYrFYnopYwW2xWCwWi8VisVgsFssZwApui8VisVgsFovFYrFYzgBWcFssFovFYrFYLBaLxXIGsILbYrFYLBaLxWKxWCyWM4AV3BaLxWKxWCwWi8VisZwBrOC2WCwWi8VisVgsFovlDGAFt8VisVgsFovFYrFYLGcAK7gtFovFYrFYLBaLxWI5A1jBbbE8AXAcZ77jOL92HGfMcRzPcZyrH+8yWSwWi8VisVgslj8OK7gtltPEcZxXnRTF8lNyHGfAcZyvOo7T8wce9mvAcuA9wF8B9z5qBbZYLBaLxfKkxBhvPNzPZY93WTWO41zgOM4HHcdpebzLYrE83oQf7wJYLE9i3g/sBeLA+cCrgIscx1nmeV7ukR7EcZwEsBb4mOd5nz8TBbVYLBaLxfKk5K+M/18BXFnn862PTXEeMRcAHwC+Cow+riWxWB5nrOC2WP5wfuV5nniiv+w4zhDwDuAq4LuncZzOk79HH8WyWSwWi8VieZLjed439f+O45wPXGl+/ofgOI4DxD3Py/6xx7JYLKfGhpRbLI8et5383S8fOI6zyHGc7zuOM+I4Ts5xnHsdx7lKff9BYP/Jf//lZFjYvsesxBaLxWKxWJ7UOI7zasdxbnIc55jjOHnHcbY4jvPGOtvtcxzn547jPNtxnHuBLPD6k9/Nchznp47jpE8e599ObjclXN1xnDWO41x/Mu9MxnGc3zmOc6H6/oPAv5z8d68Ke599ZmrAYnliYz3cFsujx+yTv08AOI6zFLgDGAA+CaSBPwV+7DjOSz3P+xHwQ6qe7X8DrgV+CUw+pqW2WCwWi8XyZOaNwGbgp0AJeCHwBcdxXM/z/tPYdiHV8caXgP8BtjuOkwJuArqAzwJHgWuAy80TOY5zBfArYAPwIaACvBq4yXGciz3PW091bLMA+HPgrcDQyd2PP1oXbLE8mXA8z3u8y2CxPKlwHOdVwFeAZwIPUJ3DvQb4ItAEzPM875DjOL8BpgGrPM/Ln9zXAW4HOj3PW3Dys9lU54L/o+d5n35sr8ZisVgsFsuTBcdxPg+8yfM8R32WMMPCHce5HpjveZ6OutsHzAKe43neDerztwGfAa72PO8nJz+LA/cBi4DLPc+75eQYZjuwB3iud1JEnMxFsxnY5Xnes05+9g9UvdxzPM/b9+jWgsXy5MKGlFssfzi/oWqtPQh8n6oH+6qTYrsNuILqXO5Gx3E6HMfpANqBG4D5f0RGc4vFYrFYLBYAtNh2HKf55Hjjd8Bcx3Gajc33arF9kudQjcb7qTpmjqoHXLMSmA98G2hXY5sU8FvgEsdxrLawWAxsSLnF8ofzJmAH0Ay8BrgEyJ/8bh7gAB85+VOPaVQ7OIvFYrFYLJY/iJPzpz9EdcWTpPF1MzCm/t9b5xCzgN3e1LDXXcb/80/+/trDFKeZk1PrLBZLFSu4LZY/nPWSpdxxnB9TDRX/tuM4C6lFj3yaqke7HmZHZrFYLBaLxfKIcRynn6p3eRvwNqpRdwXgeVTnT5se5z8mI7kc6x+B+0+xjc1DY7EYWMFtsTwKeJ5XdhznXcDNwP8D/u/kV0XP837z+JXMYrFYLBbLU5gXAjGqU9oOyIeO40xJePYw7AeWOI7jGF7uecZ2u0/+Hn8EYxubJMpiOYmdZ2GxPEp4nncLsB54CzAO3AK83nGcLnNbx3E6zc8sFovFYrFYTpPyyd86iVoz1czhj5QbgB5AL1saB/7a2G4DVdH9D47jNJgHMcY26ZO/W06jHBbLUxLr4bZYHl3+Bfge8Cqqc7xvBx5yHOd/qGb1nE51jtVM4KzHqYwWi8VisVieGvyaagj5zxzH+RLQQFUoH6O6zNcj4UtUo/OudRzns8AR4C+A3MnvPQDP8yqO47yO6rJgmx3H+QrVXDQ9VJcQG6fqcYeqOAf4mOM41wFF4Gee54kQt1ieNljBbbE8uvyQk9Zfqtk9zwM+QFWAt1PtAO8DPvw4lc9isVgsFstTBM/ztjuO8yfAR6nmjTlKdZnS49Smt/2+Y0yeXF/7c8DfU52H/XXgTuAH1IQ3J5cHWwu8j6pIbzh5zrupCnfZ7h7Hcd4HvIFqFnQXmEPN822xPG2w63BbLBaLxWKxWCyWAI7jvAX4N2Cm53l2VRWL5Q/ECm6LxWKxWCwWi+VpjOM4CWM97zjViLyQ53kLHr+SWSxPfmxIucVisVgsFovF8vTmh47jHKC63Fcz8JfAIqpzuS0Wyx+BFdwWi8VisVgsFsvTmxuA11EV2CFgC/Bnnud953EtlcXyFMCGlFssFovFYrFYLBaLxXIGsOtwWywWi8VisVgsFovFcgawgttisVgsFovFYrFYLJYzgBXcFovFYrFYLBaLxWKxnAEecdI0B6c62fsIcCWw+QyVyGKxWCwWwPM85/Eug+XJhR2rWCwWi+Wx5JGMVU7Pw72Lau5C24FZLBaLxWJ5ImLHKhaLxWJ5AvGIs5Q7RxyPvwBuPrMFslgsFosFrIfbcvrYsYrFYrFYHkseyVjlkQvuZY5nrcUWi8VieaywgttyutixisVisVgeSx5dwe04dsFui8VisTxmWMFtOV3sWMVisVgsjyWP/hxui8VisVgsFovFYrFYLI8IK7gtFovFYrFYLBaLxWI5A1jBbbFYLBaLxWKxWCwWyxnACm6LxWKxWCwWi8VisVjOAFZwWywWi8VisVgsFovFcgawgttisVgsFovFYrFYLJYzgBXcFovFYrFYLBaLxWKxnAGs4LZYLBaLxWKxWCwWi+UMYAW3xWKxWCwWi8VisVgsZwAruC0Wi8VisVgsFovFYjkDWMFtsVgsFovFYrFYLBbLGcAKbovFYrFYLBaLxWKxWM4AVnBbLBaLxWKxWCwWi8VyBrCC22KxWCwWi8VisVgsljOAFdwWi8VisVgsFovFYrGcAazgtlgsFovFYrFYLBaL5QxgBbfFYrFYLBaLxWKxWCxnACu4LRaLxWKxWCwWi8ViOQNYwW2xWCwWi8VisVgsFssZwApui8VisVgsFovFYrFYzgBWcFueEsRiMRKJBI7jPN5FsVgsFovFYrFYLBYAwo93ASyWP5aGhgae/exn09TUxPr16xkZGeH48eOUSqXHu2gWi8VisVgsFovlaYzjed4j29BxHtmGFstjSDweZ/Xq1SxevBjXdXFdl3w+z86dOxkdHWXTpk2Uy+XHu5gWi+UPwPM8G7JiOS3sWMVisVgsjyqzgfnAjfW/fiRjFSu4LU9aXNdl0aJFrFq1yg8n9zyPaDRKuVwmn89z/Phxjhw5woMPPkgul3u8i2yxWE4DK7gtp4sdq1gsFovlUaMR+C3QAfwFcNfUTazgtjyl6e7u5qqrrsJ1XTzPIxQKUalUCIfDVCoVPM/DcRzK5TK5XI7R0VF+8YtfUKlUbLi5xfIkwApuy+lixyoWi8ViOSVNwPgj3LYBuA+Yd/L/MQhdHGL68HTa29tpa2ujpaWFH//4x793rGLncFuelEyfPp2XvvSlFAoFAGbMmMHk5CSZTIZcLkc4XH20xesdiURob2/npS99KYODg2zfvp3JyUnGxsYez8uwWCwWi8VisVgsZ5qLgC8DVwE7pn7tOA6hUIhQKEQ4HCb0byEmZk/gcdKO2wzTvjeNqz57Fa5TzTteqVQe0amt4LY86ejs7OSKK66gUqn4WcmPHDniC+tQKASA53m+JzsWi5FOp6lUKnR2dtLe3k42m2X37t2Mj49z5MiRR/zSWCwWi8VisVgslicJVwH/B7QD3wDnrx2i26NEIhGi0SixWMz/Ox6Pk0gkSHwjwb7EPg5ccwAc6Lu3j8u+cRmu4+I4zmnpBiu4LU8qGhsbWblyJalUyhfcWlR7nofneZTLZRzH8UPKXdclm81SqVSIRCJUKhXa2tpobGwkk8kwPj7OoUOH2Llz5+N8hRaLxWKxWCwWi+VhmQa8EviX37PdC4H/pCq2AVYDX4GZ/ziThuEGX2iLiBZPt+u6zPnaHMLFMLnFOc75+jlEC1Eq4Qqu6/o645FgBbfliU8MuAEiL4iwZMkS+vr6iMVilEol8vk8lUqFZDLJ4cOHaWho8F+UcrlMOBwmGo1SqVQIhUKUSiUqleqLEg6HKZVKRCIRpk2bRnt7O4sXL2bPnj1s3brVnwdusVgsFovFYrFYniAkgDuBLiAD7pdcHK86/m9qaqKtrY2Ojg7a2tpIzUixbnAd+3v2gwOUoPfWXjoKHRCvRsQWi0VfQIfDYVzXpVwu45U8er7VQ7Q5ilNxyKVyvjfcdd1HXFwruC1PbHqAHwCroeXBFub/93wioxHK5TKhUIh4PE6lUmFs5hjtXjuVcjW8Q+ZfSAK1fD7vfy7iO5vNEolEfA+4CPfly5ezfPlydu3axa5du0in0/5ccYvFYrFYLBaLxfLY4jhOddw+s0z5Z2WYS1VAfw4Wzl3IOQ+cQ3NDsz+m9zzP1wCrP7Wa7FuzDJ01RPfXuplx3QzfqeY4ju+xliWGJcFyuVzGLbmESiGKDUVKpZLvyAM7h9vyVGAW8F/Amuq/x+cc55a/vIVLr72U2JGYP1f78OLD3Pzqm7ngRxcw/575lMtlX0gXCgVKpRLFYpFMJgPgh4mIiBYrlrxskt184cKFzJs3z1/T+9ixY4yPP9LUhhaLxWKxWCwWi+V0cRynOo86kfDnVCeTSRobG8m/NM+m3k3knJPL/TpQfFaRtmNtONnqVFI5hgjnQr7A0vcvZdfVu5j2f9OoOJXAuF+Es0TAAr4gl+/lR6avil54JFjBbXli0gp8Bbg8+HF+Ms/RQ0eZUZpBKBTi2FnHuOMv7yDTkuHWP7uVXDjH/JvnB6xV0WiUYrEI4L94Elaukx6EQiH/xZN54NFolP7+fsLhMCMjI0xMTLBnzx4OHz782NaHxWKxWCwWi8XyFCSZTNLc3ExjYyNNTU2kUini8TixWIxYLEY8HicSiVSniu6J0vvdXn7yyp9QCVdYdN8iLvvxZTjZ6hhelgvWU0M9z8MrenR/vRsv5PkaQNBTSPX8bMkTValUKBaLFItVL7dohkcaVm4Ft+WJRxi4GThLfeZB+/Z2zv3PcykdLzHsDDPRN8HGV28k25oFoJAosP4l64lORpl1zyxfbOv53OFwmHw+7ydYKxQKuK5LNBqlVCrheR6NjY0Ui0UqlQqFQsF/edvb2+no6KCnp4d8Ps+6desYHBy087wtFovFYrFYLJaHQQRuKpWira2N5uZmWltbaWpq8udERyKRwIpD4hSTMbx8PnvLbF7+tZdz98V386yfPYtoJko5VPa90JpyuVydj+15hMPhU4pr8VjL9FMt2MVhJ6JbkqxZD7flyctXgWXBjxp3NHLBuy8gH89z38fu49xfncuyoWUcvu4wB19xkFKqhFN06P55N+GfhjnsHfZf3kQi4b9ojuMQi8UCL5FYr0qlEuFwmEwm48/9luQJgswdb2ho4JJLLiEUCrFhwwYOHz5MNpv1w1gsFovFYrFYLJanE7KOtfzE43FaWlpoaWkJiGvxDodCoUBIN+CP0cV7LL9lLB+Px2lrbaPrRBfzvzOfQrZA2amNv3WYeLlc9o8tywXLSkYi3k8ltuU72bdcLvuCWxx2kUjkEdWL80i9c47jWDee5bHhucAPgXj1354tPSx57xIqyQqbX7+ZoxcfxS269H+/n5GFIzTvaebAcw7Q+8te5n15nj9Hu1Qq4TiOn3lQXnyZnyHPfjgc9l9ISagmL5DneUSj0cA8DRHV8p0kZNixYwejo6McPnzYJlmzWB4FPM97ZKZji+UkdqxisVgsZx6JIpVwb/k7mUzS0NBAY2MjDQ0NAXGtxSsQ+Ey8xabHWH8mAj0Wi/nebsnDJON4mRYqHul8Ps/ExATj4+O+Y0wEthxfyqQNAOVy2XfISRlc1yUWi9HY2EhlfoVUNEXbaBtf+MIXfu9YxXq4LU8s/gT4HL7YnvPQHC6+7mJKHSVueu1NHF1zFIBKpMLOP6+ume24DouuXUTXHV0cfsZhZt4800+Cpl+oSqVCJpPxsxxWKhXi8bj/4snLm8vlfAuYiG2ovoxisZPvc7lqwoZYLMayZcsolUocPHiQ8fFx9uzZQzqdfkyrz2KxWCwWi8VieTSJRqOkUilSqRSNjY0kk0ni8XhgnrVONmaKZxHCgohlU2DrZGS/T3zL2F57sU1EOEv+Jp3DqZ7YFgGvxbsg24w2j7LlNVuIEuXy/7287nlNrOC2PHF4NvB5YHrto+O9xyl2FmnPtLNo/yJfcGuGVg6Rb82z/9n7yTZnSTkpWm9qJZvLEg6FAxYrqIWUVCoVstmsb7VKJBLAyZfYdbjn7+5h9X+txilVX0gR47J+t4Sdy/IBItxnz55NoVBg9uzZFItFfvvb3/pJ2ywWi8VisVgslicq8XicpqYmmpqaAp5qmUcdjUZ9z7X2UOtxtplQzMzobYpZwfSG688Bf9ku+UwLbfOY4ukeuHyA/IE8qTtTvpA2EyXrc9YT234StWiJBz7yAJlZ1ZWPfvG2XzyiOrUh5ZYnDE7YIfbJGIW/L1AJVwgVQlx8/cWcfevZHO84zrVvv5Zy5PfPkXaLLud+7Fy2vnIr57/7fEInQoHkZzKHu1gsUi6XicfjeJ7nL2TfPqud2665jW3nbKPxcCOXvf8ywqM1cZ1MJv2XtFAoEIlE/Dkl8hKL5zsej5PNZjlx4gQPPvggIyMjZLPZM12VFstTAhtSbjld7FjFYrFYTo1MsRThGolEaG5uprm52Z9nLWJUz3PWEaOm99n8zPQeQ20etvZem+tgC6bglu8jkQikIBKNEC/FfQea9mCLl71cLlMoFti5eCe3vfk2qMCiv11E06amKecXx1w4HPaXDc7n8/4xdZ1t/eJWJs6eqK7/fRKP3z9WsYLb8oShra2NtWvXsv+N+9n2jG2s/NVKzr3hXI7MPcKv/u5XFBOn7yVu3d7Kgh8toPH+RirHKxRbi1RaK6QOpPwXTsJRurq6GC4Pc++L7+X4y477x+jc2smqL64iPhAnFAqRacrgNXm0DLYANY95NBoFCMz/Fk94sVgkFApRLBa56667yOVyDA0N2SRrFsvDYAW35XSxYxWLxfK0YBWwGcicehOZ7ywe6VAoRCqV8pffamxs9KdfiqjUAluHYGsRLNsI2lOsEw5rka29yFqYy+96IejyW8S21+hxz1X34EQdLrn+kmpmcpWDSZehWCyyfdl2bv27W31x7BQdFr1tES0bWgLnEsEtqxoVCgXfUTfF4x6D7Z/dztjZYwBMf2g6R5cftXO4LU8OYrEYZ511Fq2trXR8v4PGwUYW37GYSqhCuiWN5/5hY6gTC09w9zvvpufWHhJDCcY7xsm352nd3EqoGGLBtxb4lrx1F6xjxm9n4M4IrqmXi+YYc8YIl8KUkiXuf939FFoKnPeF80gMJPyGplgsBsLLdZI1PSf8oosuIpfLcejQIcbGxtixY8cp555YLBaLxWKxWCw+lwD/DdwIvBnwqgmAE4kEDQ0NJBIJP4lZIpEgkUgQi8UCa0drL68I6nprSsu2ZpIxqIlyjfYG6+zf9cLEBX0MLb71sTzX4/arb2frRVsBKIQKPPeHz8X13MBxpJw7LtrBnS+/M+iJdjyKXUU/ebI4yCT7ufyts5qb5QpVQiz72DJ2vm0nkXKE8756Hvzvw9yrk1jBbXnccRyHSy+9lK6urqp1quIx/3fz8UIeuVyOuffOJZwNc8ObbzB2fOTnGLhkIPD/2NIxnLJDoaHAws8vZN8r97H3T/aSmZWh7Z42Bi8e9LdN7k8S3xynGCty7z/ey/EVVe/3XW+/i8s+cBmRTIR8Pk82myWZTPoe7Wg06nu94/E4lUqFaDRKoVAgmUwyf/58stksPT09jI+Ps3HjRuvxtlgsFovFYrHUZwVVgTev+pNsTbLsP5b586sjkUhgjjUEvcWCFr+PxJOt5zTrxGM6QbF4iiV61JzLbf6vt/l9a1rf/Kqb2bV6l///5vM2U46Wef43nu9fjz5G+9523JILsdoxFnxkAV2/66K1s5XW1lYKhQIjIyMBoV4vlF3/7zgO4eEwCz67gGg4ipudaqSohw0ptzyuuK7LqlWrWLBggR+WLS+vpO53HIdQOMTu+bsZWzbGeOc4y69fzq/e/isKqT9y+a0yhDNhyvEyXsSDcnUOeCWurG1Fh1n/PYuJFRMMXzgM6t1q3dPKle+6EoBKY4Xfvvu3rH3/WuLZ6vJjk5OTpFIpP5THdV3y+Xxg6TGohqFns1n27dvHgw8+SD6ft4nWLE97bEi55XSxYxWLxfJkRkSe/ITDYVpaWmhvb6ehq4F7vnoPxRm18aFbcJnzvTnM/+Z8XwhHIpFAYjGNXuPaFNT+MZVANkW4DtuWbeUzGefKdjpkXHu864WY15vHLWUKhUIUm4t8/Z+/jheqfhZPx3n1v7ya+Hh8yhrepVKJm//8ZrZfsN3fHiCxP8F5rzyPkBMKiGwJJ5dQdIlK1UYGmeOtjQwSVfDLX/7SzuG2PHEJhULMnTuXZcuW+Wv4lctlP+FBsVj0M4FHIlUvsjzkkUiEie4Jfv2mXzPeNf6YlHfuZ+ey7/X7AmJ87SfW0nZHG/muPBvetoETi0/QeLiRC//lQhL7q1nPZc1veVEltFyus1Ao+Nclnw8MDPDAAw8wOTnJ+Ph43UyOFstTHSu4LaeLHatYLJYnCzLHWuZZh0IhGhsbaWtro62tjdbWVn8cDFURO9w9zK1/fyuTXZNQgb7r+1j6+aW+uJWxpqA9yOLJNudYCzpJmul91mixrcPSTcxQ9XpLfmlBL8etl9Ts2OJj/OwtP/OnmP7VZ/+K6YemBwS85GQqFovkcjlufMONHF5zGBxI7kqy5G1LiA5Hp9wDLbhLpZKfVNlM6iZ1K5+Hw2FisRg33HCDFdyWJy49PT2sWrWKWCzmz30Wy5wsNi+YS3vJy3i8/zh3vOoOpu+Yjud47Lh8x2N6DW7OZeY3ZpI+J83wucP+52072zjvS+cR3xn3LY2SjEEnpdDWxUqlQiwWw3Ec39hw7NgxDh48yODgIEeOHHlMr81iebyxgttyutixisVieSIiYlqW2ZI51vJ/Q0NDYExohjYDvpg8POcwG964gWkbprHkf5fgOjVBK9trj7SEecu0RTN5mexTL5O4YOrFUyU/M78/VYi56dU+1brXruty4OwD3PrKW8k35P3vVt6xkit+egVOuXadIrgLhQKTk5OM5ka593X3Mjlzkv5P9pPak5oiovWPXrNbC3kZt8t2+lri8Ti/+c1vbNI0yxOTxsZGzjnnHFKplO/BlkQFpVLJtzQBU6xXlUrFf4Fbt7dy6ZcvpfFoI6VKiWgpSt7Lc2TFESZnTJ7x63AqDh0HOyjNChoIYhMx3LFgWE+pVPJDymU9Q6g1UDLfRowNuVyO9vZ2Ojs7GRsbY3R0lF27dnHgwAHr8bZYLBaLxWJ5AhIKhWhpaaGtrY3Ozk4aGxt9b6j2ZkNNyGrBWS+8W4vZju0drPr3VdVISg8qXiWwjQ7ZlimN4tnW39ebP63ne+uyaIEsDjBzf+2ZluuR6aFyDDN5Wr155fq4ruvSPNpMuBQmT01wtw+34zBV9AfqMesx+7OzyTRlqnXlBj3q+rp0WcwM5qaHXmuSQuGRTW21Hm7LY04kEuEFL3hBwPMrD66IUAg+5MVi0V9mS6+VJ9YzeaEL4UJ1Dka8yE8/+VOKiSKhQohyrFw/03kZnLKDF/3DHu/wRJhL/uoSaIAH/uEBhlcO07SniQvfeyHueDX8JNQUws1WryWVSlEsFslms5RKJRKJhB9K7jiOXydipRMDQzweD8zr3rJlC1u2bAmEvFgsTzWsh9tyutixisViOdOYHtq2tja6urro7Oykubk5kLdHtpdxq4zv5HOoJS2Tv+slOYOah1vmGJsh2ILpXZZt6wneel5oc462zm6u52yboeFS5npZx/Xa3qZRQULczTBynbwt35HnG+/6BqVIict+dRln33k2TrEm7qXslUqFfD5POp1mbGyMiYkJf+xseuVNA0KxWPT1hr7Xuixy/+QehsNh1q1bZ0PKLU8sYrEYF154IZ2dnf4LosVzOBz2Gwbz5RMPcb0kDkDgeI7jMNE4wY5n7WDFdStY/6r17Lp8VyB5AmXo+kUXzfc3s/vvd1NsDiYpa9zdSLlUJrPwYRY5NN8KB1IHUpz7gXNpONZAenGae952D6s/sJrm481+yHi5XCbTmSGaiRLJREgkEpTLZVzXJZfLEY/H/TW8E4mE/4JLYy2N2bZt29i3bx9jY2Pkcrk/6t5YLE80rOC2nC52rGKxWB4tXNf1V5yRZbVSqRTTp09n2rRptLe3E4/HAXwRpkWljPfkby0OTfEKU8W2bCf/e55HoVAgl8v5x5Vj+MtnKcGq19iWY8h3DxcGrsW0lMP0BGvxbmpJPUavF4pubitiXHvmtWffdV1OzDpBspRk56qdXHDTBYE6lzqVn3w+z5GWIxS2FshPVJ1VIvr1tetyitjWqwXVMw7oegyFQkSjUW699VYruC1PHKLRKGeddRbz5s0LJEcAAp5rbbXTyxpA7SEX8Wl6uOVFkWW5QqEQpVKJXC7Hg699kN3P3+0fa9bPZrHki0uoVCrc/677Gby0thRY+8Z2ln96ORk3w/pvrj/ta215qIXpt01n75/vpdBaoGlbE0v/fSlth9oIh8PkenJsfMNGmgeaWfnNlbhF15/f7TgOsVjMv/ZCoUAsFqNYLPrzSPL5vJ9grVQqsXv3bgYHBxkYGGB0dPS0y2uxPBGxgttyutixisVi+UNwXZeGhgaamppobGz0l9Zqba0uIdXU1OSLaxGypVIpIEa1ptJzimV8ZwrseuHb9bzW8t3OtTvp/k03xWIxcF4trrUnW4t1KbdgCuZ687ZPNe9afybb1SuLOV9an0sbAsywdjOk+/Dyw9z8ipu5+JcXs3zj8kCkgPxo59xAzwC3veY2On7ewYxvzPA1g86hpMskoeGFQoFSqRTwyNczLmhRHgqFuOuuu+wcbssTA9d16e3tpa+vzxfBMqdEW7F04yJh1jKvWYe7yIMvL7W8JMlkMpDQQNa9LpVKLP7KYlq3tXJozSEajzey6NpFuFGXw2cfZmzxWKC8pWSJglNg/5v2/0HXO7p8lNHlo/7/44vG2fL2LZz9ybNx0y73v+l+RpaMcHzZcYqxIqu+sCoQVl4qlSgUCv66iul02v9eQoqy2SyJRALHcVi0aBH9/f0MDg6STqe57777GBkZ+cNulsVisVgsFstTFMdxaGlpobOzk46ODtra2nBdl0QiQTweJ5FIEIlEpnh6TVFteoFN8a0Fpyl6xblULzTbFLTlcplNL9jEpqs2Mdo6yoJrF/hTMPVccNObrdfLFkyvty6D3q9eJnL5vl5YtvwvxzTrSPaVz8ylxurhui6Hlh7itmtuI9Oc4earb6YQLnD2urOnnFtE94neE9z5qjsZnznOxOsmKEQL9H65N1Au89zm9erwd7OsEnKur/GRYAW35cwRohpyXYHW1lbOOussX0yKVVCSpQF+SIfMbYZguIckE3Ndl/7+fkZGRhgbG/NfDDl2uVz2j5vNZv0w82K+SNfvuujY0AF5KOfKVJwKrZtbad7TTK4zB061vHM3zqW90E7hlwUG19Y833jQdHcTPR/roeyW2fWNXVSaKoHvE1sS9P1TH0fefoTxK2pLlo3NH+Puj96NU3bIzDwZpu7AgUsOQAku+MoFzJkzh2PHjlEsFnFdl2w2i+M4NDU1Afhh5qlUym8EpM5c16Wrq4tyuUx3dze5XI67776bQ4cOnXbDYLFYLBaLxfJkxPSYtrS00NvbS09PD9OmTfMT14bDYd/rqT2XOmKynve5XtItLbZNr7EpSGUcLHO7zTWt5bzFYhHP8dj+nO08dPVDlGNldr50J5RgwQ8W4Hg1R5QuS73EZNpgoKNFJRGx6f02RadpMNAe9HrZz03vvDkd1BS6cj5dhpGeEW551S1km7MAFBIF7nj+HSQyCRY9uCjgeHMch2wyyy/+7hdkmqtjbC/scfiaw7hZl55v9UypG51IzkxiZ9aleNRFv+hw/keCFdyWM0MD8ElgEzR/r5lLL73Uf7kk1FsaE53BMBKJEIvFfEtVX18fmUyGEydOBF7WHTt2+I1kLpfzw61FxMt8jWw2G0iIUCqViGfj1fCSyMmQl7THyg+v5N4P3cvoWaPM/9F8uq/tJjcnx8b3bQxcVvxEnCv+5QomWya55533UGkMCtn4tjgLX7mQRFeCY7FjU6pl0YcWETke4YEvPEB+eh48aNzSSP+n+hl2h0mn00QiET+cPBqN4roumUwGx3H8+dy6gZBtxWDheR7JZJLm5mae85znAPCrX/2K8fFx0un0lCXXLBaLxWKxWJ6MSNZvGS8lEgl6e3vp6uqip6eHWCwWEFFQE1oisrVolShJUzCaIlTPoZYlpfRcbDNLtzhIdMi4bGuKefnf8zyGFgyx+fmbKceqorYSrbD3qr207Wyj4/4Ofx9BynYqoWx6qMXoYIZz6zozo0u1t9c0QmjRLPvXE/+mQcIMQ3ddl6bjTSy4awEPPOcBf/+uXV1M3zzdDxOXOi6XywxPG/bFtpDckaTrW12nNITodcdNwa/vTz2v9ul4uq3gtjz6xIGPAG+q/jtj6Qy8mzxfYEuotDSOumEwrUX791dDusUSKC+LNA7iCRbvtszDkAYToFAoBOY7A75F0c9G6MF5Hz6PQ39xiAXfXcDQ2BD7z9lPJRz0Xveu68XFpXBugXxXvuoRP0nLPS10vbULp+Jw6MJDnLjwRKBakvclyR/ME86G6VjXwcCLBmi/u52F71tIZjLjz0WXBGpSH9KZiAEhHA6Tz+eJx+O+oUGym2tLnzZkXHnlleTzeXbt2sXx48c5ePCgn7XRYrFYLBaL5YmM4zgkk0l/jnU8HiccDtPZ2ekvoSrT7E4lFoG6oliLq3rrQZvhx/W83jpplykyzWOaGb21mJVITRkDTt81nfO/cj53v/Jusm1ZYidirPjyCqY9OA1c/PGf9rya5ze9znJu7ek2w871Neu52rps5rzsetnQ5Xt9PPm+npjV240nxjk091DgOTgx/QTDM4Zp3N/ofyaiecf5O4LPTNGh7Y42Qs5U77kpts17ZBoYtJdeP0ePFCu4LY8+/wH8de3fna/fSaQ5Qv8P+gGouBW2vXQbC7+/0M8orhOgAeTzeT/MxVyvWhoVOPWcFdlOhHk+n/c94JlMxreMacLlMDO+PIPRyijhcJi5X59LpBRh9+uqidZ6v9nLgh8uoEKFQr4QyFA+/b7prPzySmiAY9OOMfKc4Pzppk1NzPvMPGKjMba/azsjLzj5vVcV/268mp1cMk/qdbq18UAskZ7nMTY2RnNzs9/ASeOnk8iJQcN1q0uSrVixgnK5zP79+5mYmGDTpk1kMg+Thd1isVgsFovlMaahoYHp06czffp0mpqaCIfDJJNJGhsbaWpq8qcemqLXdOBoAaX/1uJQe7hPhU7oW09UmyHI+nvtUQcCYtP08NabM92zoYfV+dWse8M6zv7C2Uy/ZzoA8XichoYGSqUSmUwmIAhNwW+GU9erGynHqZK76esyz2OWW3vMTa+y/s7cX5cndSzFuV88l/VvWM/owlHiI3FW/fcqph2YRjlWDuzveR5rr1tLZbLC9udsB6DvP/ro/kE3bqh2Tfp+Sd2Yof96G10HlUplSqK8R4oV3JZHn28CrwVOPrdu0aXrrq5qWA8et77jVoaWDlGmzLIfLfMTU0AwiYQIZGngRIjqRGgiSvP5fF0vuQhRSTwmL1UkEqFQKPgvkAh9LebL5TI93+mpzrVpLNH79V7G8+Nkz8vy0N88RL4171/y6JxR0tPSNB5vZPu/bSczS4lYDxIHEqQOpNj2oW2MXFET48PnD/PgJx9k5VtWkkgm2P5325n1tVl4J2rh9xJOLl5u160tUyHXIeWVOUlibJDfoVDIr7NIJMKCBQvI5/P09PQwOjrKPffcw+Tk5KP+KFgsFovFYrHUQwRLKpWip6eHWbNm0dfX54u+eDzuRyhK+LN4Wet5psUhIRGM2gurx3YSHahFohxLH1t7dvUcaD3fVy/zZYo202sr1yFjTe0t1fPHRZBrr+v0+6Zz6QcuJXUwRYXq5/l8fooo1nOLT+Vd1vVUz3EldWCKZG2cqCdedfnlWNrjXs/rbRo69P+e55E6kGL5R5dz78fvZeWHV9JwvIFMqmpcSCQSvg4AKEQKDJwzAMCcf5vDtB9P88+hM5Wb88hNL3s9o4f5/enM3wa7LJjlDOA4Do1XN5L7eo5QMcTVH7uayPEImWiGO99wJ4fPOewnJ1vxrRUs+vUiEqEE2Ww2EC6uM5mbwlt7fwuFQmAuh7woeqkA3UhLQyxCtFQq+ectlUp+I+2HzoRPWudKJxsxx2Pgzwc49NpDVOIV3LzL4u8tZt4P5xENRzm+4Di3fPSWamV40Lqhlf4391NuLLP1P7eSm1dbLzs6FOW8151HZaLCwdcdZODlA4TTYS5844WU99e81GJo0Ak+xJAg4URSN65b9ZYnk0k/7L6hoSFw/boDks8BfvGLX5DJZPx7YbE8nnh2WTDLaWLHKhbLEwsZp8g0wlgsRm9vL7NmzWLWrFnE4/Epok/GKfU8sFo8y7Z6X9NjbY5l9NhQe3VNUa2FqxxDO2XMkGsd1i0eUBHmkmwLpiYbM73cck166SmZLimOIrlGvY8+tjYEmF7pep5nXX/aSCHXp69Zi2XzfpwqFL3eNZrHNfcFyFQyPPicB8kfyzPtp9MI56vTUVOpFKlUikpPhdbJVhzHYbxxnO+953vkG6vOMKfksOi1i2jc1ujfi0gkMiU5m6y/bd5XXS65B6IXBPn73nvvtcuCWR57ent7uazxMvZ9dR8NRxuIj8QJRUMcWX6E0dmjtXnPLhy64BBz1s8hcqI6B3t0ziiNI40ksolAg6HX2RZBDLV5J+FwOLC97BOJRKhUKn62b1Nsep7nLzkm88QBP5lbKBTC9U5a5KI1gdp7bS9e2GPgLwfoubaHad+eRj6Wx0t6DC0Z8uvCKTtc/MmLGV84zgN/+0BAbAMs/shi4oU4B/7mAAMvr1rlSo0l7vnEPSz/2HIadjVQpszwOcPM2TWHyclJf756Mpn0X36ZtyTXIWtFSvI4uTbP8wJLXXhedem0YrFILBbjpS99KUNDQ2zdupWxsTEGBgbO7MNisVgsFovlKUE8HqexsdGfY+26Lq2trXR3d9Pb21sVSUZYtulFBnxni6BFkJmczBRpOgmZKcS1eK4n/MwEWloQ1wurluNpEa+vT4ch6/GXvnadXE1nRtdlrCf2dPi36YGWY8k5tEdbi169T70o0XqfST1pz7Ect56nv54HXY4t9V0vlLvslNn6rK1s/tPNAJQKJbp/2I1bqiafO7T0EPe+8V6e/b/PpmtvF1sv3koxUctN5IU9jv3lMVo/2Oo7q0wPu846bt5/c1vz+9MNK7eC2/KoMmPGDM477zxc16V/Yz/FYpFIMgJA98Zu1pbXcvtf306+IU/nrk5W//dq4sfj5Mt5RueOsvFvN9Iw0MDqL6zG8RzfY6sTnkWjUaBmMTMTVpjWTFNkQ62hyuVyfmMgwluOG41G8bxqojed+Vwa4Zlfm0n0WJTOX3aSc3JMTk5y/C+Os+/P9/nn9lyPHS/aQffd3TgNwZey5dYW3L0u6WyayWgwnLscLjMZmiTlpTj4qoMcfelRWr/Zivu9mvVSEqhJwyqWO2lYdEeRzWaJx+OB+d3i9Zf6kdD09vZ2LrjgAgqFAnv37mVwcJC9e/faJGsWi8VisViAqrieNm0aM2bMoKmpCcdxSKVStLS00NraSkNDAxAU0pLUVos8LZp1xCFM9cLqaEcdAq7z18h3WixJIjIdHam9rloc63Ge9pKbodSmOBbMMG7xrJohyYLeXydQkzJK2WVb+V4LSP2dnFuLTH2NZr3qujTFsa5Dc3yttzXFv1yLvlf1xub1wtTlsw1Xb+CB59eyk+97yz68lMfsa2dz6IJDbPvbbRRaCtz0ipu47NrLOPen51IeK3P/NfcDMO0n05j92dlTPPWm4Nb3Wf8t907Xq34GTaPI78MKbsujRkdHB+eee66/dJW8eJIlHGDaPdN4RvoZ3PHaO7jkfy4hfjhO2SlTmF5g/VvXM9k9yejsUcqxMms/uRaovajhSBi82vqF4oGWl0AEsk5okM/nfe+ubrAkiZqs1y1iU5bYkuOIyJds5jIHWhqItp+1QQg/TKp1fSv7/mKfXyeO59C1qYv2Y+2c97nzuPPddzI5c5KOzR2c9/XzcEoOI/ERxlaO+fu4OZdl71lG8/5m9r1mH/teto9KtMIdf3oHSweW0r2xG6g1DOL1dxyHw887TOeRTroOdgXWlgT8hBqJRMK/BrEi63qsVKrrls+ePZtkMsmcOXNYsmQJBw8eZMOGDWf2IbJYLBaLxfKEIhqN+mHgM2bM8FeakdBeLfxkrGV6sgFfAOqEZVrcaWcB4I/1ZOwViVQdOGYItCmItZiUcZI4b/QccBPzM729lFFvZ3qc9XXr7eVzc8Ud2UbKKMfRDqITC09wvPM4s347y6+vcDgcCHc3PfqnCtGWvyWEWoS73kaPDeUemWLexAwVr1dXUgfa6y7fm+LccRxm3D8jILidikPDXQ0MnjPI7jfupthSdQKNTRvjpr+8icv+/TKOzz3Ogi8tYHTmKLO+MItQLoTnTF0HXM4NtTn9+vq0oUKXT4ttKW+956gedg635VEhlUpx/vnn09vbO2Vuig4zgZPzriMFnEz1pStR4qef+CkTXRP+8Zyyw5zfzuHcL5+L4ziMdIyw6W82sfZf18IEvmCOx+MUCgV/fouEW+u5L/l83m/gpbGWpclyuZzfsEhjrOd3S7b0eg1oKBTyvb5+JnXXYWzpGNv+dRuO57DojYtoP9hOpCnCjjfu4OBzDoJbnVuy7AfLmP/j+fzmI79hfN54rTIr0H5TO63rWtn7tr2Uk7VrcfMuZ7/+bFqOtHDk2UdIJ9P0fKeHWCjG0DOG2PQPm3ArLlf80xW0DbYRi8UolUp+yHksFuPEiRO+J1zuj177WwwWkUiEXC7n1104HCaTybB//37uu+8+f/1zi+VM4dk53JbTxI5VLJbTQ8Y+OjFZT08P/f39zJo1i6amJj/HjWwDU8O5tbfaDMMWdAi26anVIqZUKjEyMuKHqCcS1Tw/OtJQexbFMy6fm6Hg+tgaLaC0V1x7ufU1meHsMHXOr4neXo+FdRlkX20oADjRc4JfvP8XeK7Hqs+toueuHqgEj2t6vc0wcO1B1vdcC15dd6fyfJ/qGuvNI9dz17UnWNetHFe8zObc9uHGYX7xuV+AA6GJEPNfN5/ErgQ0wYF3H2D4yuFAOUL5EOVImZZ7Wuj/QD+hEyH/udZ1o8smTjnz2TC99WL00WH9WpivW7fu945VrOC2/NGEw2FWrlzJ0qVLq5nIvWDqfMmurUOKKpUKuVyOeDxeFcsdBW56202cmH0CPJhz1xzWfG4NlUqF4XnD/O49v6OULNF9RzdLv7iUVDrle5+lodHzvAH/e6iK41wuRzQaJZfLTWlAdQciDawIcZkHrhsUHaYuljEJe49GowytGcLJOrQ/2I7ruoyeO8rOd+6k0FXwj9E40Mg5nziH2GiMX3/z1/7nPRt7WPAPC5jsnGTHe3YwtqLm/QaY/x/zKUwU2P+e6hrli/9vMeHhMA/940O1682GuOADF9CyrYWGhgY/wkAMAxI+L9euBbg5N0fPpdKd0t69e9m5cyejo6OMjo7+EU+QxVIfK7gtp4sdq1gspyYcDtPY2EgqlQrMse7p6WHmzJk0NDQExgCm11Z7OOsJMzNJlhYy8re5lGk9z6npuTU9wvLbDP3WHnQZA5peZj1+M8eNJlpwS7nkmFL2evvWE5U6nFzqRjzeMjbT4vPYvGPc8A83UI7Vjr/6X1fTc3tP3bLKsmkyzpWEwvo+ai+vaeTQHnGNbKMF6KmSn+n52+azo0O5H85bDnCs7xi/eftv/ARocz8wl47rO/A8j/FV4+x9314KMwp19wVov7Gduf8+l9h4LOCt1tEFxWLRF9xSPkGcbvqe6FWU5J7JMe+44w6bNM1y5lmxYgXLli0DaknMzNAdqGWOrFSqWcIl1Nx1XZomm7j4yxdz56vupPVgK2u+uQbCMLB4gPWvXU8pWX0hDl94GK/sseI/V+DknIAQ1pZRCYvWXm8dRqTD3XVDHo1GfSuWbhzkuNp4oBtQ2UY86G13tVWvO1Ktk8SdCeZ+ei573rWHQkeB+N44C/9tIS0DLWy9ZmugPs/78nlk5mTY+fqdU8Q2wM437wz8v/U1W6ds47kex9uO4467ZDIZYrEY0WjUX2JDOlS9hJgsrabDvGSOuFyjNkTMnTuXBQsWMDAwwKFDhzh06BBHjx49rWfHYrFYLBbLo084HKazs5Np06bR3NzsR7nNmDGD9vZ2UqnUFOGoRZe5iks9D632WGoxIuMGczUYUyxrQS0RdtozLeMQCM6Z1oKtXmi26dHVAliLbX0O7YAxvdn6HPK9duqYAtv0VOvy6WNqD6qMUaXOJnsm8dzgMSb7Jut6iqWuAbLZbKAeTc+1+SPI+O5UIeP6bzN6wbyXGnPuuH4GTO84wJF5R7j9Fbf7YhvgxOUnaLu7jfBYmNaNrbifcdn1jl0UO+pHWRbaC5RSJSKjkSnREPVC2Ot58PX43jS06ND0UxkNTKzgtvxRLF26lBUrVpBOp3Ech8bGRr/h1fN4otEolUrFF3XxeNz3hkujNuP4DC7/2uWEh8J4+eoLkTqeIpqNkibtnzOxL0ElUyHsVK1Le162h+6bu4kPxf0GQxp813XJ5/N+6LM0kiL0daOuM6DXC/FxHMcPZZcfKbss1yDnhlqyBagK+ekbphN/X5wtH9zC3PfMxdvhcd8/3cfBFx8M1Oldr7uLRR9aRPRgFFY/gpvgwZp/XsPgtEH2vWZf9dzFEEvvXcpEbMIP/Za59SLARYRLZyoNh2nRlXoLhUK+p1zubTweZ+7cuXR2dtLf38/w8DB79+5l9+7dp/kkWSwWi8Vi+UNwXZfu7m7mzJlDT0+PLwbEm51MJoFadBvUz9qthZN2UJhh3KZ3UkSr69aS0YpIN5e3Mud2m55iLXx1fhnZByAWq3ous9lsICRci1hzDKPRSdS0t1nqQX5rcWZ6aPX/pviV687nq6JRnDU6y7Uuo+M4RCIRYrGY/33/rf24aZdb3nQLAIu+sYj5P5tf9/xyjWbYuL63ch5TSOrIRjOSU98ffY26zmXfep5s/b8+56kMFK7rMjhvkLEZQWfTictOkP98HmekWp7m25uZf2I+W7+4FS8SvLepLSnmfmwu8SNxQuHaMrq6TDpEXDsKtRdc6q1eJIU2eJgGg1NhQ8otfzC9vb1ceOGF/vxgwPcsmw1XPp8nmUz6IjWTyVTnb5dKgYZTHnx5ccPhMOlomhs/dSPZtiz9P+5n8XWLKUwUIAT7nruPnX+zk/BkmItffzHeSK2Rn5iYoFgs0tLSEkiIINkipVxSbimP9uKGw2F/HrSEwkvHoq260iDpBrpeOE2pVMLtcAmPV+ePlxpLbP/Odj80JpQNMe8v5hHbHcNJOex/735Gnz1KKBPyG5X5b55PuBhm6xe2ggvLP7CcaQ9M4+hFR3noPSfDyj3ovq+biz57EaFiiCF3iA1v28C5Hz6XcKFqyZV1vSORCMlk0rd879+/368b8YZLtIJka5d76ziOb2jQHWa5XGb9+vV+dvN6c6cslt+HDSm3nC52rGJ5qiGiRg/+Z8yYQX9/P3PmzMFxqquqRKNREolELaeMEg3igNAhy2aCMjNcXG8jn+uIRd3n1/O6apGitYYWLToU+eGEuB5fAX7kYj1PrVlGsy5zueqqMqlUys9do7+XY2jjg+nZN6/HFJISySljTu1h1t5/vZ8WhfJTLBfZfdZuhrqHWPT9RYTKNWGsxfvvMwDU89Lq+2UaUH6fWDbDz00vbz2vbz2Dhek5d12XrJvlllffwoFzD1RzHhUcZv3LLDp+1gHlYNkmz5tk2xe3+ccIj4U567VnMfjMQRr2NdC5rhOXmgFFO5YKhYK/BLCZS0CXVydQNssq13D77bfbOdyWM0N7ezuXXHIJ06dP9xsWEaLyAIulTkRZpVIhlUoxMTHhC229TINOXKB/u65LpaHCfVffx4qvrKg++C7su3IfD7zhAX9d7/jROCvfuZLYvprnXESzHMvsgAqFgt+Q6/AiafylcZBOR0SnbCNJ1/QLqrfV64NLmH1jYyPHjh2rdSjNHlu/tJVKQ4XlH1tO4oEEk5OT/vIZBz96kEX/vIiRNSO4rkvP/T04jsORxUeotFaI/yxOtCvKQ597iMzcjH+PIpkIZ193NrMOzOLnb/s52cYs7Xe0s+LzKwgNh/zr06HmkUjEt1IDftK4UChELBYLWEmlzrTBQdb01nO/N2/ezL59+xgZGfGzwVssjwQruC2nix2rWJ7MuK7rZ/6WiLLm5mb6+/vp6+ujoaFhisetWCz6xnEZi5hh2FoEmxFs+ljag20KWQkNN5Nt6VDyet5QLbq12DIFqva0aiOAmfVcC3XZpp7n3RSZpnjWYzM91hPvtHaa6OPoz7RnVsaN2ouq52ubx9DGCr2vrpNSqeTPM9ZeWFPQmnWr67FewjRdR1Imffx6Zakn2B9OyOvj6/P/Ps9wsVhkcnKS2992O0Mrh+j5rx6mXTttyjFwYcd/72BiZS3hMkbrv+w9y+i4q8MX1No5JPPcdWSsfk/082J6t6GWL+CRzuG2gtty2iQSCS655BL6+vr8hlq/PJlMhkql4oeMixiXkGQRaWaDpUW2ni8knYMO+SmHymx840YOP/OwX67wWJgFn13A9Fum++HhgszNFuuuvChiIdXiWM5VqVR80atDxXUDpkPT9XVqK7JkPRdxK+WS+sjlchTnFMl352m7p80vi1yvNAgi2PUcbEkGFwqFmOydZMc7dpBekcYtuCz78jI6D3Wy4W83MDpz1K+L2etms+jzi3BGHMZ7xyk6RVr2tuC61aXNRHzLGo7FYtHPTip1If+Lp9/zPLafv50F6xfgem4gwkHamO3btzM8PMz+/fuZmFANpMVyCqzgtpwudqxiebIQCoVobW2lo6OD1tZWXNclGo3S2dnJjBkz/HnXegykxZpe2ghqXmKZxqfz1EBNsOp5s/WmkAmm+JCxnin4tYCW8RvUhLApQvXxTdEm5dPosaE+ny6bFr7mtlLXUqZTzd3V59PneDjxqYW7WX69nSlM5TM9dtZlEZEn0xXrHVsLZCmDKWbN+6zrXdeh+Zm+Hl1PppDW5dLlqbeN5lTn97zqsr0TExOMTI6w/9n7mfH9GQHxq89HG+x9716OX3i8/nkKDov+ZRHdN3f7Y1ap11wuNyW7vem9l3F4vfrT65zfdtttNmma5dHFcRwuv/xy+vv7fY+0eH0lxEceaN+D69Uy+pnhG+Id15kaRaTqDkCLb8/zqBQrrPjKCkKVEAefdRBKMP9D82m/t52yU/YtldJJyDlgqjXRdV3mz5/P4OAg6XQ6MMdJhKeIZrNsZhiP2bhLPWgxr7eR7xMHEyQPJfHw/DqROT9SZpkDrzs0x3H8tcSbDzez6NOL2Pq+rcz+/mym3zyd9Mw03mSw0UtMJGhNtZJtybL57zdTckos/9ByIgcivsCXMPJEIuGfW69ZrudlRaNRNl28ibtfdDeDcwZ55g+eCVQjFqTeABYuXIjjOPT39zM6OsqmTZsYHg4u62CxWCwWy1MN13Xp7Oxk7ty59Pb2+gbzVCpFQ0MDqVTKN7Q7jkMikSCdruWu0WJKIgO1+NDbmA4LQcY7njc14Zf29JpzfPWYw486VNvocshYRwtsPfbRYlrGQLp8cg4ply6Hvn6NHivKtub8cx2eLtegDRTakKH/N40Bur50mU3Bquva9IKb12iKcn2MesLXjCDQz4D2zOvzmMeVfcwIADPS0/TQa5Fsoq9Vzl3Ps67rw/xfylAoFChlSkz/3nQq3tRVcmRcnSgmOOt/zmJzdDNHVh2ZUian4pAYSQQcRFJPWkSbxzfrRpf1VHX/+7AebssjxnEcLrzwQlasWAHUXlqxvkmotRa30lCLANbLFAjSkOdyOf8lkrkvruv662jLZ/Lyl8tlyqkym9+ymRk/mEFyQxLXcf3EbLrxF0OANKSRSMRPpuZ5HqlUKnAt2uut1/bWVlxtWczlciQSiUBHJi+hdH6O43DWWWexc+dOJicn/YRlUk8Soh0Oh0mn0/7xJFpAh6lrI4XMD5K/i61FYhMxjh4+SmtrK840h/WfXU92RpYZ189g+f8sp6m5iV99+ldkOqoh6NGhKBe98SLc0epxiqEikXLEP2YikQCq2S/b29spukUiVAX4vgv3se4V6yjGijhlh6XrlnLJdy+hkCnguA5EoZKtheKLQSGfz5PNZrn11ls5ceLEKZflsDx9sR5uy+lixyqWxwMtphzHoa2tjSVLlrB48WI/YalMzdKRYTJm0vN5zcRaksz14RJuyW9T5JgCTUepSZlkTCZlMKf0aRFlei/reT7luHp6nSkwtYAxBT5M9Ujr77Twk2OaolAbFHSYux6/mf+fyrOtjR36nHpbMyRfGzz0WBRqCdvkXminjZ8Hxy1TKVYo5ounXHZMe7lNL7QYJup5kuuFg+t7qevNFNz6/uhzyvFPZVTQ9amPbT5D5XKZydwkY6NjTI5N+s+pNgKIZzkSiZBIJRi5cIR733yvv5qRX56Cw5q3rKF5T3MgsVuxWPSTCZtRBrp+TEdfvXsg5bn11lttSLnl0SEUCrFkyRLOO+884vF4oAGXOT1aZEvDIWHbpuVVN3I6g7g8wOIJL5fLRKNRVq1axa233uqHUenGLZfPUSwUp1gTJRREXp5sNusnTNOJwLToHh8fB2oZN8VbL4m/JExe6kSuX8SyhK7LtpJBU7zF0tDquVB6rpUcTydlM63D0imKccBxHGbPns3AwIB/7qamJv/vUqmEl/DY/rbtLPrEIsJumC0f2sKxi4/589/xYMZ9M7joExcxlhpj/bvX0//5flo2t+B5tdD5WCyGO8Nl3RvXsfh3i5ksTXL3W+6uHQeITkY5/5vnM++eeQyvHObu59zNM//nmSRGa95yCVWXNc7T6TQbNmxgaGiI4eHhuo2b5emHFdyW08WOVSxnmkQiQTKZ9I3IiUSCBQsW0N/fT2tra8C4bApZnexLG9q1aJTPAH98oQUo1Az5On+O6UnVQlci9KRMOpIPgl46z/N8kW8KWT0u0V5VU5Dr85qOCC3AdDSkaTDQYx9Bi1gzpF7vK9vI33IcmR8s0QVm+LuMXbX3U9dNPaEv+5jRBabHW67XNFLo367rkkvkuPPld9K5uZO+m/vwSlM94A/nKdb3SZ9fn8cU23IMfW/qGRj0s2QacvR1nur4Zt3p6/I8j3w0z70vvpfyrjId3+nAy9fOIUYceb5jsRijF4yy7r3rAmNQYdF/LWLmT2bCSR2un8VcLjfl2ZNnSt4TnclcP986o7v83HzzzVZwW/54HMdh1qxZXHLJJTQ1NfmfS4izdCA6YyTgz9cG/O/kwa4X5i2WXsdx/H21Z1k6CD0/Oz8/T/pEGmen43thfZHpBUN5YGqjo7NvxmIxMpmMXx4d7lSpVPwQbz03XDCtetrKqxsjbSgQy5i82NrKK8Idatk4pcHRmcL1EmiyjW4UQ6EQ8Xjcn68Sj8er1vRIiE3v2MTws6sh3dNuncbaz6+l0lXhntfew9EVRwllQiz+yGKmrZ/md0ql9hLb/nYbRy6eGroDEMlGWHXdKpbcuYR9Z+/j5tfdTDlaZvZDszn/G+fTOtkaaJDlGj3P86cT3H333UxMTDAwMOBnkbc8PbGC23K62LGK5dGkubmZ9vZ2mpqafAP5jBkz6O3tpb293e9/zbGF/Nbh2PKZfG5GzQFThIs5b1qLcX38euJQC1C9jTkG0mJCf6ZFqBxPn0MbErTIrDfO0p51+d4Up3oaoK4XU9CZXlVzfq3eRos6wfTOC6Zw1fdFylDP2y6/9TU83PnM8aAW0MV4kXUvW8f2S7aDByu/tJL+3/Rjoq/PNDyYgluXx6xHHa2p96kXDq6Pr8+j0fUj38lY1zTGmMevRCvc/eK72XLlFgD6PtdH17e7cJ2g91nyHyVTSR5864PsvWTvlPoR5n9hPrN/MpuQW8tOnsvlyOfzgXtm1oG8e+Js0/pFfssYOxQK8dvf/tbO4bb88TQ3N3P++eeTSqV8r6w8uOL5BPwXShKNSUNtrm2tRad4kEV4ynbinS0UCn4INdTmModCISanTbLlrVsoThaZ8445hMfDfsOjvd3aCy2fy3l1uLdYoiE4d0Y+l2uPxWJTLJVyTt1ZmdY7qQdtVdMdjlhVw+Gwf91QE/tyLAk/kzrSllUR4/l83r8+Lf4Bfy72nE/NIZQJQQL6/7Of0egoW/56C8eXVZNPlJNldv3jLhr+r4EZd8wg62V58K0PMrz6FPOuK3D+/57P/Pvms3vtbu56+V2Uo9WBwb7l+yi+usgV/3kFkWyEwysP0+l10rSjya+vbDZLPp9nzZo15PN5jh49yvDwMBs3bgzMs7FYLBaL5dGmpaWF3t5eurq6iMViOI5DY2Mjra2tNDU1TfECynhED8RNJ0I9byJMne+qxwzaO6s9aXrgr0W36ek0vdLac6sTwOpymJ5lM8RaCzw9rtLf63pJJpO4rsvk5KR/btlfttPCXv7X3zuOEwi/r1ceXUf6u3oGEHMcUc/ja4pQsw61gUHGwlALgdfh+NrZo58XqWNdH5VKBc/xuPUVt7J31UkB6cBDr3kIL+Wx8OcLT3lcfa36uvTx63mc63mxH+651Z/rc9e7D+a9NuvfNKK4rssd19zBtotqS3wdeOMByokyff/XFzieDisvRx5+KmLcixOLxgKOKdOIYhq0pEw60kFft/mumQaPU2EFt+VhiUQiPOc5z6G9vX1KhwEExLEO1xBvsDQE+kWThAg6kZjs09DQwOjoKLlczm8QxOst21UqFbLhLBs+uYFcdw6AXf+9i5WvXkkxXfQFajab9cPfZW1weUm0xz0cDpPJZBgfH6exsTHQsOss5LK/iFyZB97c3ExbWxuHDx/2LbkinMVLboaQAX5UgO5Y4/E4juOQyWT8F1m8vPplLxaL/rXppSckI2k8HvfrTkLPk8kk+XzeD5VvcVuI/k+UfClPOBeGKDTd2MTxJcc5uWwhDSMNZOZmGM+P88CLH2B4cX2x7VQcLv7sxXTf0005VuZI3xFyjTn1oMCsu2YRLUUZXzzOLa+4hagT5SWffgmxY9WBjcxtk3D8uXPn0tfXx6xZsxgaGmLDhg2k0+m6z6HFYrFYLA+HFgDNzc3Mnz+f+fPnE4/H/X4xFov5YlH3zyIkTGGnvZU6/FvGCUDdBGeyj/RneglR6dfF2K9Dw01Prjbem44CPV7R1y/HNucGy1hJxnEiOs0Ea2ZiMS0ahWw263+mw9u1U0ML9HoCX59D6sk8r/aca2eHWefmWNQ8vvmcaPGv61F7Rc195LcOp9dlNp044vjx68KDBb9bwL5z9+G51euNpCPMvGvmFGGvRa3+rY0N8lm9NaR1ubVBQteZPpdpwKgXNq/viRlCLt+ZZZNnqVQqMfemuWy7cJsfHu6WXbpu7AqsKy+OpVA0xLq3r+Pw+bWVii788oWwG+746B3gwNLPL6Xnxh4qXoVypYxXDhptzOs33z+5Pn1+U6Tr6Nbfhw0pt5ySWCzG85//fGbOnOk3CjI3CfAtl5FIBKh1Hqb1b3Jy0t9PhKuIaBHfMr9Zi2oIWsR02NA9n7iHkZUjgTnILbe20P/2fr+RkI5Kz7+Q8soLJUJcwkJ0EgVtvczlquJRwrvrzdHSnYl45PWcbj3fQ8SwCP5cLud3+JKhXBpJnfVdRwJI4yUe73g8PsXrLXOkdZ1Go1Hf4BGJRPylyorFImWvzJFrjnDodYeoxCvVNQ09cDyn2gGo9jM+EAcXys1lLv7WxczfOJ+iV+SBCx9gw19s8EV7OBdm9Y9Ws+imRRydcZRfffBXeCHP/+7FH3wxLSMtVCoVxtrGaBppIhaNEQqFyGQyvnGjUqkwNDTEHXfcQS6XC2RwtTw18WxIueU0sWMVSzQaJR6P+2OTRCLB7NmzWb58OS0tLb6AkGgvMwwbCPTXUBuMa8+gHqhLhJ8etJveX9NLanrMZKxiiiqoiXYt3kzPtg7NNr252jCvx2imSNUJa/X3MFWwR6PRgEgD/DFFNpsNOCDM8uqkW1r4a0+iHueYQt8UbPp+md5wM5Re6lafRwt6Hcauy6iFmmkYqGcoMEWdeU4tdP06cODgsoPc8tpbCGfDPOufnkU0Ew2UQws9fW/qPX/6/PpZ1sYObRjQz6T5zAqmwNROI9Prq58P00ts3vuh2UNc/+Hra4I753LOK84hcTThlzMcDhOPx9n157vY/pLtfhQlQMNgA2v/di3H+45z4pwTzP3+XCKFCIOXDTJy9giLvrSI8mg1pNz0cosGkOelXhZzfX1ezMNr9kiMVTOg33DDDTak3PKHEYvFWLNmDd3d3YHkW+Z6zHo+tbb4am+3CEHJHi7rSOfzeV9kZzKZKeE7hUIh4InW4eor37OSBz74gB/e3HxzMwvftxDCBBp58ZwWi8XAHG1pbPQSX7oDkkZCOjl5GbXY1UlLTtXgi8dZGyJ0wxGJRPzM7SKsy+Wy74kWA4TucIDA+uAi2PX8J7MDlpB07WmXxlUaF8maPuf7cxi/aJzRlaPVhs8Bj+AYNnVfisXvW0y0IcrkhZO0/q6V0cgoAy8dYMOfbAhsO337dBb8egG4sOOZO3zLLUA5Umb7hdu54JcXMLRgiBtfcyPnX3s+8zbN84W2XEs4HKazs5Orr76akZERtmzZwujoKMeOHbNeb4vFYnkakkgk6OjooKmpye+Xp02bxpw5c5g2bZq/nRbL8lsnNtUebJgqKrSA0gNzGa9oz7Cgvc6nEpdaxNYTUSJMdLZmXT6zLPpvLSK10BERUS6XSafTRKNR35kg5zVFqhkK7XleYHwj5RkbGyOfz9PY2BgYM+lwai3koGZMMEPKzWuS6zbLpP+u57U3j6k9/rp+TA+vKTLrnVePtep51/X2ppFGjh+Yy41D70O9rP7Gapp3NhPLxqoeWiP/jz6uNgzoZ8MUyrou9H3U913fH8H0YOv7obOsm/Usx9fU20Y+23nlzoBTpxKucPS5R+n7cp9viJIx7/IfL8eJO2x54Ra8kEfb/jbW/PsaShMlYvtjTL5okt3X7CZ1OMXWt2wFB5ycw6z/moWXDd5rPb+8HrpeACpuhYFrBphYOcHif1tM41DjKfcNHMd6uC0mkpH8oosuoqGhgXQ67TdQMv9HXlAtACWEWoQm1DzCsrSXzCFyHMf/zHWrS3npfVy3OvdHe3Z1YrByuYzX6bHt77bhpT36PtOHO15rELVYE1Esc69FZGvLnXR2sVgsUF4znEsMDjr8y/R2x+NxQqEQExMT/prWpqVWZyqVfUVgy3UC/rGy2ax/Tml0pMy6U3IcJzDfXSzveu6WaSkW40csFmNi7gRDLx5i6KIh8h31E5Y1bWhiwWcWENkX8Z+JUChELB5j20u3ceCvD/jbRrIRLv/y5bTd2cau5+xixt0z+Pl//txvVFf/eDULfriA4UXD3PWauxidMUo0HWXtd9eyeP3iwL2TiACoza+ZnJzk8OHDnDhxgi1btjwKT7/liYT1cFtOFztWeeoinuru7m6/v0ylUkyfPp3W1taAENF9oyD5UWQMI2MLbdiX3zrrt3jrtOjRxwX8KCztgdQeSaj1WxAMc5bv9DhAG8MhGMptJmEzhalpcNfjfNNjJ5+ZdaXHGrocuo5NwaTDws0ymB5eXQbTS6zLZm5vCmrTq6z3Nc9pbmt+r78zk5+dSiudyoMr+5jXrMdqpiFEC/BCoRAQraZY1s4pKYcplPX9OlX5zHLJ5+b/5rFkGy24zevQoddmXZjvD0C6lGb9y9az9wXVOeyz/msWPd/swXWqy9PGYjHi8TixWMw/75art3BgzQEu/srFxLfGOZ47zn1vv4+Ri0bq3q/pv5jOnI/NoVIOLtkmekHqX09R0DiOw8G/PcjhvzoMDrTe28pZnz6Lm6+zWcotfwBdXV284AUvIJlM+h5WHS5uZqaUhkMn8pIOTMK3S6VSwJMr+8hx9RJR8kyK9VQ869LJyAtcKpU4ETmB67kkMgk/66DuMKHWMJhWbG3V1A2VDuuSBkMMAo7j+EuDyRwnCcnWc7xlfrWUV+rEDB8HAnVXLpeJx+NTQsgdpzY/W+pfhwIJEkWg61JbanX0AdTC1fP5PJXpFbb81xZyM3NM4eTbn9yVZP7b59Mw0hCwcsq9rkQqHPmzI+x+7W4AXvjPL6R1aytbL93KfX92H8njScb7xsGBldeuZOmNS0lPT3Pj224k3VkLEY9NxFj1lVUs3rSYbHOWB698kLU/XOsnhatXp+Pj4+zdu5fNmzdPmbNkeXJiBbfldLFjlacGkUiExYsXs2TJElKplG88TqVS/ook2gBrClhTXOmoNy1EdTisKbC0sJbf+pyCPpYeg9TzMEp/L/2XFucQjLTTSWr1/tqAr8/nuA5eJRjG7nkeQ0NDNDQ0+GMLfR16HCfb6xVa5HMR+3rcoqP59P66fNoLa9abLjsEPb36c1MASt0BU4wPen9TSJueYC2uxVmhnRN6nGsaR/QzYdahvj75/lQi2RTcMh7WUyHlXDK+1NduGga0oNc/so1pENHohHpmQjstnuu9W/Xut1kX5n3Uz0W5XCaTyXCifIKtf7mV6ECUrh90Ea6Efa+2niriuA4hN4SbcMl35kkeSZLL5fj1e37N8NJTJPYFFr5zIS23tATqS44v91zG4vr+yHXu/3/7OfqnR/EitXvTsLeBiTkTVnBbTo+WlhZe/vKX09DQQKVSCYQq64dPI0IQai+ZzN/RL6T2aDuOQ1dXF9lslr17904RkeLtlsZfz/t2XRcPj0JzgcrRWlhLJpPxhbt8phOliUgHfOFmNrjiUdWWcP19Op0mFov51yoebh0KJceUzlTEt17iSq5DksVJhIAO05Hrl4EG1DzsIpIjkYgfWSCJ1HK5XGAtb22J7OnpIZ/PMzIy4h8HIN+a5/5v3k+pOShUnbxDaCRE3yf6OPaqY8z7f/MIFUIB4S6WwVAoxNq1a7ntrtvY96p9TNs4jdZtrYw/b5x7/vYevHDw2Tn3v89l4e0LiUai7L1sL7f/6e2UY2WcssP8m+ez6purcDocvvfB71FIFFj282WsuXEN4XI4cC91OL7U8759+7j//vspFAp1l3GzPDmwgttyutixyhMfWe1DJyOaP38+K1eupKOjwx9r6JwxMhbQfbbprdWeay2QZH+NbBOJRALJSet5pnXoqT6uaczW/b426tcbN8l16Bwtsr0OLxeHgxjRzTGCHGdg9gAbLtzAc77/HKL5qH9eGZ9odHl0eXUko65D3d+K+NZCSxvfdV3o8mqxqcWfeS0A4+PjTE5O0tbW5ifg1VPmAkYGZ6rn3xRK+rr1Pvo50vvL1D7zvmlPtnbo1BPk4nwyRboeA5v3QsafIrjN/cWZIv+fSjjrcWMymaSpqYlMJsPk5KQ/ttb3TR/LTGqn77M+vn4nTAOHWe8a04Alv2V99PHxcXKVHJSryXjlHkUiEd+7TRI2vGoD/Rv66dvWRzhUi0aZaJngR+/7EfnG+hGaiX0Jzv6rswm74cDqP3K9Mk1WRLfcM18jRCts+sImJhdNggOR8QgXvOcCbvncLVZwWx45zc3NvPCFLySVSgWsPjqcSHsOpYEWi5x0XPK/vNiyv4RXy5zudDodmBOuBXA8Hg90gOIdl8b6yOoj7H3jXha9fxENuxuIRqP+2nqCeJIlY7e2KOuGVnfcugGSsss5xVsvGdTlOqA2b1yHYMk5RPCZlj9JTqLnYUej0YDwljDqeh5+vZ9u8EzhrjtUqUvAT3jiui4H3nyAgZcNgBiLPWi8rZHE5gQdn+/w67FcLlOZU6GcKeMM1BpCObZ0jjJIKDWW2PypzaRXTE1w1rGng0s+dQlN+SZSy1J8/7nfZ3DxIAt/u5CLrruI0b5Rbnz9jYxPH/f3OftHZ7P65tWUMiW/4xlYPEDvjl5Kxdp0A5lSsGvXLvbt28fIyAijo6On/U5YHl+s4LacLnas8sQiFovR1tZGU1OT/9n06dNZuHAhM2bMAGqiUfpD7Y3Wwk/6eQgmopI+91TiVtCiSPf1+lhSHulvdX+q+2F9PC2KtFjWYyQtasxy6u8PzTlE94FunFJQxEJt4K+vNRQKsX/Bfq77q+uohCqsuHcFl//ychLZRMDrKL9FKEJt3FKvnkxvrOkZ16JZrl97W7WgK5VKTE5OEo/HSSaT/nnMOpcfM0xattPGECmL9PmyvVlG+W2G3ct2pnCV8Ys5z1uOoSMitJHFFMe6HvRnZnRCYD88Di04RPv97VMEvzY6mM+eHrNqQS9jRHl2ZR9d1/rZMiM6zCgIfU7zuddl1dGh5n3QaMNUNptlfHycdDoduFdyHRJO7ja7PPinD7L92dV1yp/9n89mzuY5/jWWy2WG+4a59fW3EklHGJ41TCVSu84Fn17AvFvm0dbaRigUYnx83E/OK8+W1JUIch1xEApVveqbPr2J/PQ8K/59BZ1bOvnZz35mk6ZZHhmNjY1cdNFFdHR0BEIp5CHTnZ/2nsrLK6JZ76sbQrHMiQdSXkTZTzdCOoO5bmzl58iVR9j+d9ur60S/axezPzab5PakL7QkXFs3FrqBNjNx6kbbbDREsMo1QHVetW74xfIsx9ZzwGOxWMC6LOFKuvGFmrDWDSJMndelQ5fi8bifDVTq3pwzo+dzQzUaQRoNfR9n/+dsvLTH4ddWl1jo/HEnfZ/uI1KJEJ8WJ5PJUCgUyE3PcfDtB4mVYsz/xHyckeCSGWKMkPNH01EWfGQBe9+1l9FzRv16aH2oldX/t5rGXCOTLZPccOUNDC4ZxCk7rPjKCsrRMhPJCUqRoMd9vHmcbD5L1KlGBey+cDfrXr6ONT9fw+KbF/vlkEFJf38//f39DA4OMjg4yL59+xgcHPxjXxeLxWKxnIJ4PM6FF14IQCqVoqenh7a2tilCSv+tB+jaMC7bSORbvRBwPVbQAtIUtfJbtqlXnlMJCS2WTC+t9nTr/l1HV8kxde4VQcqxe8FufnX1r1i+cTmX3HjJFCO7LLXV2NjojzcemP8AP3/uz6mEqsd78LwHKTtlnvuj5/rRYHK+U4V162s3DRja06/HKHq6m75G09spgqm5uXnKuWXMpecB6/urBaYWYNqAYAp8fd/0s6DHJeazdSrxaop97V3WRqJ6Ilg/L9qho8eyepwHsOXKLdz/gvs5+6tn03tb7xTBXU+4msYRfW3i+NCYxzSNRfo+6JwF2qN/quut996c6l0y74GZEVy2laX6otEobsRl4zUb2f2M3ScvAG5+zc2UvlOif12/f+/aD7Rz4Vcv5NCyQ5zoPUGF2nF3vHUHlXiF6ddNn/JOy3n18oClUomJiYmAPnCLLks+uoTsvCytD7Y+YiVtBbeFaDTKWWedxaxZswJhRSK49dwGbWWUTNrigZV9ZR89p1seaJlnbc490Q26hECZ1l2AwWcMsvMNOyknq2WcWDDBzg/sZNFbFhE+EvaPIQJaW5klO7rn1ZYyk+11pnVp6AEymcyUOVS6odeZxOVYcn1iYJCOTsLP9XJfLS0ttLW1sXXr1sByZWaDLgMOHSZfKBR8a79uxMVTLtcjZdXie968eezbt8+PTMhlcvR9o4+wFybfmWf2l2YTcSOUvVroWNbJsv9f95NbkiNNmq0f38qiNyyCSq3O8/l8YJBQbi4z/OJhFn9mMQ+97yHavtXG8Vccp++DfYwfG6fYWuS+99/H0Nyh6nW4Hve8/h4u+p+LaNvQxmUTl3HDe26gHC2z6PZFrPrJKkLFEIVygd1rd3Pvy+4ln8pz11V34TkeS29eGqg7qbe2tjbmzp3LvHnzGBkZ4f777+fIkSN//MtjsVgslgDPfOYzOeecc6aEYteblmaKBPlMfksbLsZsqJ8MSwsbgGPHjjF9+vTAGEN7rrVHUIsZUzDp/kTP7dUC29xfX4cWGfK3NsJL3zwwd4Drr76eiZYJ1l26Di/i8eybnk0ikSAejwO16Xt6GtcSbwk3VW4iQ+bkAWHR+CJSsRTFXHHKnF0tRE0PpIwt6tWFrj8ZX2nBrT3hetyi61HOpceKOprBrD9zfCjnNL3gur71PZPtzOdLECeNdnzI+XR4uunZ1h5ifVw9NdH0hJvPgRgtpKybnrWJjS/aSCle4r5X3Idbdpl518wpxgV9nfq50/VgGhbqefTN57lehIh5H+u9J3qMqZ8tM8zefC+02NYGCdP4IlrCdV3woGNHR01wA6FiiNYDrVMiMKbtnEY4G2br5Vshpm56CPa8Zg9lp0zvd3sD1yHPhETojo2NBepcv+uxkRjJDUkInzqhnskjW63b8pTFcapzp84++2x/WSjdkEjyDD3fxc9KfTJ5mLxY+Xze9y5mMpmAt1M3WpVKhfHxcf8F0Z5pebEkO7c0xtL4td/aTuvGVsRg5eQder7dw+iWUX8eNODPY3Ych+7ublpaWqaEdEP1ZZcwdMepJkSTzkTWwzYtoRIiJY1LPp/3604+12tmaoOEiHHxwo+OjnLw4MFAGL90wLrhcRwnEHKvIw8qlYovpvWcdZ0RXjwD0hgdOHDAL4vUSTlTZsbXZtD3mT7Kw9U54slkkvHxcUqlEof+7xC5xbWEauMrxtn6ya1+XU9OTgaMM06bw4avbODgnx7kwOUHWPj3C5l12ywW/fUiIgcijI6OctcH72JozpB6IOHABQe475r7CDWH2PDnGyhHyszYNIOV164kMlmdsz66eJR7/uIef55OMVHk7hfczb6l+yi0FvjFu39BubWMF6s2hJFIhEwmQ29vL3PmzOGZz3wm11xzDUuWLCEWi9UNq7NYLBbL6eE4DitWrPAFhYgK/b30RbrP1z+yHdQG0CKGtGdQxKdpQPc8j87OzrohyRD0dmvxa4oyx6nmbdHHkOVMs9msv42UKxQKcfTo0YDo1MfS4kPvN9E8wU+u+QnjLdXpU5VQhXvPv5cHL3uQRCLhjw1SqRTJZJJYLOZf8/SR6bz7h++mKdtEuBzm5etezhU7r2BG5ww6OjqIx+P+GEyHy4rX0DTqQ20coZ0NOqxeL9mq76cer8i91fdXX7P2yOp7AgSeCxnLaEeQGbWoy1nvOdNzs2VcphO06WdNxlrDw8N+NKaUW4SYTmimxau+v/UMOaaXV67h4KqD3P/C+ynFqwalfHOeDa/ZwNCsoSmOJ30/JITa9NDqc+j9zevWdWLeF33PpR71/lIW/UyZIl4w328ZK2vHmn6XdV1KOxGJRIiEIrQMtQTqI1QO0TTaVLcdaT3YyvPf83zCk8q37EHjrkZ6buzBi9XKrI0lkjNKR65KlnSZ9qmNArr9eTjsHO6nOXPnzuXqq6/2X5ZMJsPo6ChdXV3kcjkKhYIfQq1FHdSsz1B7yWWOkyT1EiGdyWQCYedQE9p6voyIUVkbUwtCCXMqFos89N6HGFk9Qt9/9dHxnQ6/YUwkEv75pXFoaWmhtbWVAwcOBOZr62vS1tlyuey/WJVKxQ/DzufzNDQ0UCgU/CXGRPyaluFoNBoIbdaWeSkf1ELltPVeytLT00OhUODQoUOBJde0ZV2H3mkjgF5CzMwYLwYA3UHp+eOSjE0MKlL3WbJs/uJm0our87EbNjYw+9WzCbth/5xyv0tzS2z92FYyczL+OXo+10P3d7oppos0NTVVrzcGD/zHA6SXnZzj7UH3jd2s+fEaNvz1Bg6cXVtibP6t81nxzRXE0jFisRi71uxi/TXryTfmiWQjnP/T8+ne2c3P3/Rz0q3V46347QpW/WwVTrpaP3Lf5J5IPTz44IMMDg4yNjbmRw1YHn88O4fbcprYscrjSyQS4R3veEdgrrBMizLXedbzoXW/pPsr6dtkG9nf9HBrb6OOntOiQ/pQU1DIeXRUnOmZNc+pB+g6Gk0G4TpM1hT7+kfCVwfmDvCdF3yHE00nCJVDXLbxMl607kW+kJDj6Dwx2oA/mhrltrm38bz7nxcob6FQIJ1O++tjT7RNECvHSEwm/Clxpqf0VAJMyq+p59nX9SRlN+tdH1uLQNNLrctherz1vdfb6PqSMpjnNu+rjsTU32tBaE47lHPVK488yzopm65L81o3XrqRe194L6VEifh4nPO+ch49d/VgYpZXP4daaJsGDn0tem63bG+GvZtGCG0YOZX3Whtc9Dtp1pH2asv1H+s5RnxXnGKmGJj6GI1G/ehUovDdr3wXL6SmfhZCnHPjOaz65aop77wYxo60HeH+V99Pem6a5P4kK963gv2v2k+IEP3f7MfNuL7I1qsyaUFtet4lYbGMzb///e/bOdyWUzNz5kye+cxnBrzXsViMadOm+YJQL+slYlfP3YlEIn4YsTSoet6SCFXzJdXWajPcplAoBLKI68RtnucxfMUw/f/aT/KCJJ0/7gQHPzGaWCHlPJVKhRMnTjAxMVG3MdKJ4cykErIOoraYSai4bC+WLnnx5Np1A6czi+tOQc//1vOv5f/9+/dPsRjqkHQ5nljXpJPRnaPcIzEkaAGuG3/t2ZfjSJK7SqVCKpWiMF5g3j/NY//79kMa5nxsDrFkzL/mXC5HpVIhMzvDwXceDIhtgMy8DPlKnng07s87H3n+CAvetYBDHzjE8fOO0/n9Tnr/vZfNz94cENsAOy/ZyYzfzqB7ezflcpn+df04jsPd19zNqp+son1/Oze++kZfbAM8+IwHKTklLvjeBYS8kJ99HmrZNSuVCqtXr6ZQKLB//36OHz/Orl27/O0sFovF8sg477zzpghjCM6d1uJYf24a2M2En3rMoAWGHrzXm7Mq/ahZJl0GXQ45lt7eFA+6DOZ11gsD1qHLruv6Bm7phxYMLuDlN72c655xHedvOZ/n3v1ccKbOF9Z1p8Vj43gjz7v/eYFIQs/zfLESCoU44Bzg18//NYl0gmf+8JnEvbh/PC2KtTNFhz2botn0TpuRYqZXXx9Xe47reYK1F9sUtGZ96+dAl0V7xc1IBi3q63nHtffVFOz6c/NZk/11Bn49fpQ6kFw/Ek0583szSY+l2flnO7n4+xfT+2AvGScTuE5dX2aWdPO6TNEo5TXfR9MYYL4b5rH1PZJj+FGNxj7mtub9kvo4vPAwt73yNmbfMpsF31ng33tz391X7MYz7KnJ8STn/vxcf562XE+lUmHP6j203t5KdGeUJW9ewuDlg7Td18aev97D4RdX8xWVQ2X6v9CPV5n6/sqzpp9DHXVhPje/Dyu4n6Z0dHSwdu1af4krLcgAfz1CyUwtL5YIPtNTrcN0RMBKAyOWqnqNlDQM4vU1X2TxgMvxBi8eZOff7qTh8gbmv20+Fa/ih5KHw2E6OjqIxWIcPHgwcD2e55FIJAIiSnc0OqS9XC6TTqf9dcihJs5EhGuPsxxHN+gyN1o3eLrTl320IJa6kfoS67eEg+tkcmJtk+3kHmhrsW6IPc+joaGB5uZmDh8+7J9Lr41eqVQCy7BMTEz4oe7Hjx+vLuNyJMzMj83EKTiEx8O4kWAmVmbA/g/vJ7MkKLa7bu5i3pfnUXbKlJ3qfT78isMMvGqA6TOnM/ufZzNt3jSa72uu1gHBRlWQZSHEGNP5604uOHYBM3fMJDMjQyQ9NbRny+VbKMfKXPh/F/rPdTaeZeuztrLyxyv9enAch7lz59LX10d3dze5XI7Nmzdz4sSJ3/M2WSwWiwVg5cqV/hxjqAlNLQJlLCFtLwSTdwF+f6fHAtpLrAf/ui/X4l0P/GHqvFI5jhk9pvtP/Z25BJLp2dbn0MJP/pbQVIn2M71+8/fM55WFV9I32Odfm74++UyLAblm7XmtN/4Kt4b58bN+zP6e/QDk4jle+pWXBryk2kOoBZkWFzL+MOtMC319frM+tXip50k1haIpegRTdOv6knOZz51gili5N/q+6fGajlSQ77QBaPNFm5m5YyZNg00B8aqn2InIlshRGXtp48zcX86l9VArnbs6yVQygbFePeOOWUf63mmhr+tMX795TaaBRNelri/9zOj7az735nOiRbs8X8P9w9zxyjuYnDbJ5pdsphwus+ibi3zHmT5+45FGHJza+NCDi667KFCH8vfe1Xu5+5q7aVnZwuJ3L6ZcLtN6Qyv737+f4y887tfJwasP4qZcLr/2ck6cOMHIyMiUOjHrTDvCRHs8EmxI+dOQVCrFFVdcwfz58/2HSYcYyxyVcrnsZ+nTHZ5ehkp7TD3P87eFaoepk2jl83nfSy6eXfGO6rnG0rGBWs/QqzB23hibPrSJcqoMHjSva2beP8zDLdY80NJ5a6+0zLXS3ulYLOZnEteCXhoIPWdIGgkptzSSet62hMsVCgUaGxsDRgaph1gsVneOlEQA6IzmpiU5Eon4XmHZTocsyfGkjBKJEAqF/HlHYumWgYysD67n4OhkLNLJym/tORALq1xjuVyGEGz+zmZys1VItgfNdzaz5MNLaIu0VdcdT0TZ87w9bH3FViqxCk7RYdp3pjH7v2YTC1XnUxcjRXa/aTdHX3i0mmmiDHO/PZeFP1xIe0O7/2xI/ch65sWGIje89wbGesbg5FgmnAvz/H96Ps1jzdX7Fy3zgw/8gHRbmrN/cjbLr1+OVwp2VIVCwV+Lfnx8nN/85jdMTk5O8Z5YziyeDSm3nCZ2rPL44TgOb3rTm+jq6vLFqu5HIOhZ054xLa4h2AdKf3oqsaYN93qQriO2ZIqY9H+ClFMLFr2Ukun504Z8EUJyPi2GZOBfKBRIJBIkEomAEd5cvlN71vR4S0e+mWJGi24xBpjXJGX6+Is+zr5p+2o3y4MFDy3gRd98UUBYmWJS6lEbCRzHCQgiGUu1tbURi8UYGRnxj6M9jrqepNza+aA94Pqe1PN0y3Wa/0sZ9X0wI/+0kNbHNUWj7K+NAIFs3Y7HjrN3cOtf3UokH+Gaj19DMpMMOHJkXe1CoTAlD4E+v/yYK+zUE6qm0UpvZ4roh/stxzM9uvo50oYHXS9mnZlC1Lwu/b/sl2/J86P3/CgQlRjKh1j+3eXM++m8wHMgHFt8jFvedQsAz/vi8+jd1uvXZaVSoeJVGFg2wC1vvIVioggVaL2rlf539FPJVij1lNh03SYqyZPPWSbE5f/vciJHI+RyOXK53BTvtTZeSFSsns/tui7f+c537DrcliDhcJgLLriAc889129URYxJiIsIU50YAmoWyGw2G3i5tUjW69fpkGw5jzSe8oJoMactjRMTEwHLXqg1xPp/X096du3FdDMuXf/VRc93qvNcpFN3XZd0Ou0LQsDvrEWU6rAw6cylM9TJ18y5Y7oedYcvol3KobcXL7V0iNlsNtCISAcu5xSPth5kSP3INUkHpxNmiPCWxlO81aVSKRCKLvPL9WBCz52TfcXrKx5lqQcxAGijgkwDKHeV2fwfm8nPyoMHretbOfe95/qGglKpxPjccTa+fyPZaVm/jiIjEeZ+ZC7t69v984Xmhdjw4Q1M9k7S/EAzSz66hMixqncgmUySSCQCnZMMAkYXjHLDB2/wU0Je9YGr6DjUQbFYZKxljFvffiujfaNVQe7BqmtXseTmJYQqId/YJGs+yj2WxHq33norIyMjjI+PT+nsLY8+VnBbThc7Vnn8WLRoES984Qtpamqa4onTXkft9dICwvTUmQN+qGXB1ttCUDhAbclR6ffEMC7tthZRIhKkH9TjFtPDaAofGft4nhdItCRli0ajvrFdrkeXTZ9LjMdmfYiRQAtzM0mXGNS1EUNvkwln+NSLP8WR1urqHD37e7jmi9dAJWgE0fcLCJRNX78+j45eNEU0TF0fu17fqQ3e9cohx9LlMdH3xvy+VCoxOjqK67o0NTVNGdNpkSzHqif4ZczkuA47Vu7g16/+tW/cT44neclnX0LyUNIX2TKmMM9lPrtyTn0P9TXo90LfW7P+9f/62Lrscg/0d+Z91efX9arLU+9dMA1U5rb6s2g0yuiyUX75il8y0TSBU3ZYdvsyzv3quf7a2DpSQDhy9hHCqTBzH5gbOK/neeQSOX76Tz9ltHvU395Nu3T/Zzcd13ZUx839Zbb92zZcx+Xsd51NYiDhG0Z0ngkttF3XpTitSLQcJZ6J+/O65ed73/ueFdyWIOeddx4XXHCBbx2DWsdTLpfJZrN+YikRxiKutPgQi6e5jIL8bwpFLdhEsIuIkUZaHnRtPQZ8b226J81Db3uI9FlpnJLDrP+aRftX2/350aY1TYe869AWeYlyuZzvEZYf13X95c7EGi6Nh+6sxdOtLfhQsyxLpyPnFWODiGBpSETga++7GCpEGMtxpAy68zWtpLJdsVj057XrwY5phZf95JolaZocWxsscrmcn0hNGnWpB7kmAM6GAx88QHl7mQUfWYBbcAPJL8LhMLtfspttr9kGQGwsxpIvLqHp+ibfIlyeX2bnP+5kbMWYX862O9qY/2/ziR6L+qF5IroTiQQAA8sHuPMNd5Jvyvv7LfvFMs767llEQ1HWv3w9m5+9OfBO9Nzbw5Vfv5JwNhyYWiGdlzzX8pyOj4+zfft2CoUCO3futML7DGIFt+V0sWOVx4+rrrqKNWvW+P2Nnv8LwTW3zXBl6ZfMhGeCDhHX2+v+rJ4XvZ4gkf5Yb6OFtYmU2fQ46qVGZcAu/WY0GvXFgllOKb+e/iVRhWZkHdTyo+iM5XKdgoSqa8O8nF/q/ljTMb5y6Vdwx1xe+L0XEs/Wct/I8UyBq++HxhQ6eh/526xLLWD1toI8F6bg08YX81hisND76e90ucz/TYOQFtv6OvU5fc+74/Hrl/+abedv878PZ8Os/NpK5tw0J1AW/byaHnN93HqitF496e/0vdFGmlPtp98ts3z6e/MzXW/1tjefbV2vAUOFUwtJj0ajNDU1cWjlIX76nJ+yeMti1ly3hhMnTpBOp6dEasjzLON1XXatWUa7Rrn11bdyfO5xnKJD7+d7af9Gu//Oh8NhMmdliIajtGxv8d8//X5L+aXM+Y48O9+6szpe/fISEqWEL7pd1+W6666zSdMsNZYuXcratWsDSbQg+BIlk8m6VmIRQvJw60ZcOh45nhbb8rCKiBGvqfnyyfm1x9bsSMtumfb723ETLtN/Op2OH3ZQpBh44XTGTfHaSyOrjQHy0moPrbzcOtxbX5PudCVbuTYq6EGF1Ik0AtqoEIvF6Onp4ciRI/7SY7qcIryl7iuVCi0tLVXv8Hh12RDTmqivUa7f7LikjBJ+LRnlxWAh90AEpvZCVCoVcrlc4HmQMgoysAhvCTPzAzNxDjp4WQ/PrS2lUigUyM/JM3DpgL9f/HicvnV9eM2enzl8cMVgQGxDNTS8kq8EPPfj4+N4XnV+fmNjI/su3RcQ2wCbnruJ+d+ez9GZRzmwLJiIrf++flZ8ZQX5E3lKoZJ/bLlu/VzL501NTaxYsYJYLEZvby9DQ0Ns27bNZje3WCxPa7RnSwzUegALtZBiUwSbA119PO01le/1IN/sC83vBR1erI8rfZ3pmdMiXLbTYwKZVib9riwxKSLVDM2WsYqehidjCLkO3VfL9UajUf+6dWSgrgvttNCCRMYzruvSNtLGy379MnLHcsSzcf+cgh5L1Su/FoSmF9Y0gMj4Qt97uaaGhgai0ShjY2NTRLnUtXk+PR7UAvNU0YgyTtWRFHpsaXp45XymONYeTimfRIOu+N8V5IZz7Hv+PvDg3C+cS++6Xipe0DCkBb9+zuV/nZPHTNZmGhl0Pchn5rOjMT3Xugz1BLVpIKj3uXkPBF0u/V7rZ12XW8afKw6soOGGBjp3djLqjQaMYGYkh9xzHdov74Q8A42HGln936u54/V30PndTlp+0ELZqZ27UqnQ8FBD9fmIVtj9+t3M/eJc/zjyLPj3PFlh27u3MXrOaPX/xgqrPr2K08UK7qcJvb29nHfeeSSTSV8wmUtwhEIhf56weHsdxwnMuc7lcv7LKqLV8zx/frZ0Ph0dHZTLZY4dO+a/hDpsXOYtQ81yLY0b1DzFUO1sMqkMD3zqASrxCn2f7aPxZ43g1pb2EtEo4rZcLvvXkEgkAi+7XId4RbWXWDonWQ5NryMq64rLfGqpk3g8HgirlxByEdOmeHacahIyHR2g60I6VW0JlVAXLe6lcc7n8/68cTmmziQPBNb1lvrWFkIpp+605JmQwUEikfAbIblG3Tj5idOAyr0nIyNcz/cOF4tFMuEM2z+5nUJPLXndRO8E2162jQXfWVAV+zOL7HjNjuADXIHo5iiRkQhOotboSlRGsVjkxIkT9P5rL0c/c5RsT7Ya5lWGsz90NkecI9z/D/eTa62J4s7tnZz9P2eTyqRw4y4FCpS8Ei61pDtSdj14CIWq2fwLhQK9vb10d3fT399POp3m17/+dd2BnsVisTyV6e7upq+vb4ro1YN3ncNE+k+ZIqWndWmxB1Pnnurj6nGD7GfmG9HCU4tE7UGVcmlvqXYaSD+uDfZybL1SiG77taCSMUkmk/H7cS2u9LXKZzq8XI5hCrZ6YdQ6VN1cC731aCujo6OUK+VAfQo6+kDGEXI/TSdIvQgCfRx9H7UHslKpkMlkAs+JHNPsP02xJdethbo5ftTiVY6n763/DIVORgucPJTUt9xjKXu5XCaXy/lRCP5a3BWPld9ZiZfy6Luvj+4N3XhuMLeAaSySz7XxwIz0MJ1Rui5krCX1pevo4QSydp6Yx9blMd8RU1BPqUNlkNKfaQOHvk797moj2Zx9c5jITfiRIjpaRYdu62dAX7OMWeV+tRxqYe3H1pI7kCPtpANGF9m/RInNH93M6HmjlJ0yff/RN+W+eJ7Hps9sYmLZhF8PRy88ykY2subf10y5Rw+HDSl/GtDZ2cmzn/1s2tra/A5POg5p9PTnIhZFlMl8bF9MVWpLRonI1VZE/Z1uwES86zkt2vqlBbsOI8nNyLHhSxsoNZ/sRCuw4E0LaLq3KWCBlRdSI6JXOjeoNaqSOE13RlBbTkJbi6WssgyaNDx63WrpFLVXWxp8WXZLRLsWxLr+ZT1suR49J0wamHA47Ceu040HTVB0i4RP1Kx/ZrRCOByms7OT48eP+x2H67q+gNQNb7lc9i3ruo7l+qRepJ6hFt0gDamEuYlwLZfLTC6eZOdndlLqKEEFOm/rZPW/roZydf9kMsnBsw+y/g3rKTYVcYoOM34yg9n/PptIKBJoxMVYIJETpVKJHDk2fWMT6d40Xf/bxaxrZ/HgFx4ksyCYOT2cCbPy2ytZctsShucO88v3/pJ5d83j/OvOZzQ2SudoZ6DudSci0RpCMpn0jR8DAwNs2bKF8fFx0uk0lj8cz4aUW04TO1Z5fDjnnHN4yUteEvDKyUBcouq0gJH+VLbTA2K9v+QfkX5Fz2OWbSAoFAF/2psWPUBAfGtvtS6bnEdvI+eIxWJ+f67PJ2UWMS1llvLKuEP6SrNMcjxtWBfHhhxPzqETpEn5tMdPyqoNxLLP5OSkv0zqw3lFZWxyqnrX16/rWsZNerlVbRzR16ojBmSMZho9pA/Wz4QWmOaUBH0/Be1UEYpOkQcveZB8U57zbzyfSDES2Fdfx+TkJGNjYwFDTkBQuoCHv7SU3EMZz4TDYTKZjD/G0h58vTKMfo7lHDJelOuT58aMBtUOD6kPMWLFYjEikWpSMG3o0PWoxag+t/ZUyz0zveV6H0HKVu97ue7GxkZaWlqIx+MUi0WGhoYYHBxkfHx8yjgyGo0Gpn+a6GezXC4zHh8nPZQmfTTtO2UCho4Wj4MfOMjYFWN+Pp+eb/TQ85Ue3Fww6VuxschDX3mIXFfVYdO8t5lnvOMZhKhpjmuvvdaGlD/dSSaTrFmzhs7OzkC4rFiRtMdTC2cRMtJwiGCSl08sSdFolEymtlagbtTkxRCRLeeVl0UaCRFv0hlJmeR4xy85TjmhGksXRl44QurulC+Y9dxoHSImidykw5LGW8/BlsZEN6baWmZ2IHrOuGyjOwTdCJph2dKQ6nrQVkLHcfyM5mbYmZRByqcbGC/hcfgNhyl2Fen7VB/eYC2kSwYNco6BgQG/rCLupaOTDlAbSBzHIZFI+HPetTdAPPx6brnuLORY2srbsLWBWR+YxYH3HKDpgSbmfnguI84IsViMRCJBOp0mtitG094mhs8axs24NN3cRNgN+14RqGVGFe+IDCpSTorum7vZ+YqdHHntEcKEWfK2Jez88E7GzqmGqbs5lzlfm0P39d0cuuAQt735NryQx86LdhIqhth/9n7O/8b5zLxnpv/sSAhhIpEgm836AwwdBhYKhZg5cyazZs1iZGSEzZs3k06nOXLkiPV6WyyWpyzS32lDvI5E0gZLQScw1cfRwlsbvrXo0IZ8QfpYfR4tKqU/1WG2MtYRZ4EcF2rjGdlOR6rpnDC63HrOs0yj0mMsbXiQcmpRqscTug+V42phqQWmvj5T3Or619GFD+cZleNoo4UWLFI/WuToY4oANJdM0mMEM4RXGxikXPpazP3lPpiGAPlO/jdFuuM6bLp8E3e+6E4AwqEwa361hkqu6nnP5/P+NDgtrOsJVM/z/FVOTGEv22vHiS6TKUjNcuu61Pe3nmDWx6r3TmknklyPaZTQxitd3/r6ZZxerwymYNdRnNprL2MpGTsCvoFK3iER2JVKhdHuUUYXjTJ7w2xiXmxKXgEpu9TNZMskd//F3YQOhuj+XDdOLji9ACC9IE1ufs5PeIcDA68YwPEcZv7vTCjWrj2eiXPWO89i67u3Es1GWf2vq3G9auK808F6uJ/COI7DM57xDJYvX+57GsXjZops8dhpoaVfIOkkdIZrsUSK8NKJSMTiKS+mIB5eLeZEOMmzKB5usWxXKhUGrhpg51t2ggPTvj+NmZ+biZMOJrqQzlBbnfVcKhH82msr34dCITo7Ozl06FDAWy5iThoDSfIwOTkJ1OYQ6bo0LZT6HdPfawOBbCv3Qjqeep5jc0k1x3HY/c7dDL94GICm25uY8645OJO1jsYsj5xTkr7UCwnSmdXlGHJvTAOCfCeGDDmP2XnqAdnYqjGatzYTyVcHUpKUze122fb+bQwtH/LrLXo4St8n+mi/p91vsCXsX84nDfTe1+1l95/urjWkHvRe18u0H01j+9u2M7lmkvn/Mp+eX/UwdskYW/9uK7mOqfOvYxMx+u7tw8Nj+Q+W0zDeQDweJx6Pk81m/eyzg4ODgeU5pM4l2/rk5CSHDx/m6NGj7Nmz55G9vBbAergtp48dqzz2pFIpXvKSl7B06dKAmNSCKSBQvGDY6amQPhJOvU4zBI27OrJMvtMOgXriXjiVYJHosHoh02ZknTgYJOJJn1MLV31uPf6RY9XzuJuRZdowoeu1Xp8r+6fT6cDyrXJ/tNdV9tXbSFl0328Ktnp6QtelvpeyjxzfDF3WEQz1DAL6udLf3X/R/Sy6dxHRTDRwLHk2XNflrivv4q4r78JzT5bXg2U3LeOsr5wVGAvpa9fGHtNIYJZDyl1PrJrPm75n+kejPfb63TpVvZjPhsmp7pV5v8xj19unXjnMz/UxxZmVSqVoamoilUr547mxsbHqdIeTY+1YLMZgYpDr/+p6hhcOM+vOWTzza88kHokHzqHHnIVYgd/+zW8ZWFHNE9T54076Pto3xbjkui65NTm2v387hc6CuiDo/m43cz43J2AAC4VC5BflSZVSNJ9o9u+JHPfb3/727x2ruL9vA8uTlwsvvJBFixYFRKi2Ioqwk5DreLz2EGvhNDk5yeDg4BRRLZZP+RGRLJ2iFtJiyZQf2UZEPtQaYzm3Dnef8bMZzP3UXDp/2knfF/uIl+KBUBvxpsdiMf8Y8p1OiDJ37ly6urqmvEiO4zA8POzXh+5AZTmRRCLhh+HL9UnD3NTURDKZ9K9L5lzrjkl+REjrAYJYHnX5gUBGRh2yJZbBSCTC3vfvZfiqYb+84xeNs/vzu2tzjiMh9nxmD547NQmK3G/TuqmfGdPoItck85wgaNWWzl0EsJ7rJZ1TuVym+Z5m3EzVQy116nkezqhD47pGkLbdg+jBKImtCf8chUKBZDJZFf+OF1jS7cjaI8EXwYFjlx4jeiRKak8KgGNXHqNUKRF9IErsSKx2LkW+Mc/Oy3ey6/Jd/Padv6XgFMjn84yPj5Mr5Ehn0mQyGf/+hEIhEomEL8qlvnp7e1m0aBFr167l6quvZuXKlY/o/bVYLJYnA6lUisWLFwcG5dLmay+kFinSx+rxg/Q7ekqZbGsKBz22EOOu9uJpkaMFgynutJDXQlD6OT1Wkr49n88H5vRKOXREoEydk/7OTOAqTg0t3kzvuHwv4y5Z5UOiubTHXde79szq/l6XSXumdd8vaJFdLBY5duwY6XQ6MO6S7QQ9ptD3UYeWS3l1fegxiNShXIs5XtHe8YAzw6vwwJoHuON5d/CDN/wAz/H8yDct6iYmJui8pRO3pOSPB923dAcMQKaINIVlvXLJs6LnG+vnRhxQOtrB9CrXE4amAJa6rTevWR9Tj7f082mKfD3mO5XYNu+TrntdHjmG1Id2hMk7X6lUqFDhO3/+HZxQLWy8oaGBzs5OZsyYwYwZM2jqauKXf/9LhhdWx7f71+7nN2/4DYVSbfqjGXlx/Vuu98U2wPEXHmff+/b57yrUxtVtD7Vx7rvOxS2oZ8GBIy89wp4375ly7Ka9TSQGE/yhWA/3UxDXdVm2bBlXXHGFL9I6OjoAOHr0qP/gS4Mv1k4Rebqxy2Qyvldbh0IBAS+4dK7a0qQ7AGlIJcGZ6fmWc+pQa2kcYrEY43PG2fF3O5j9D7OJjkb9bKDyElUqtSRgruv6c5x1aLkcU2fa1iJQl0V7yYvFIslkMpAsTToibcSoVKpLeaVSqepc5clJ3xutX1wdgiznl+10XUojJ52wng+mw7fy5NnyrS3k5lS9tJHhCEv+cgmhYyGKySL7P7qf8YvHiW+NM/sNs0mkqw2GbrAmJydpaGjwQ9kluiCXy/niXjz+Wozr8Ckx1MhzoJdWMztePa1AW6DlPITg4OsOcujPDtG4q5Fz33YuTtHx17UUo1GxrchD//EQK/9xJa2Z1mooWCTPvV+9l9x05bX2wCk4eGEPQtX/2+9rZ8VHV1CZrLDhcxuYXDBJKB3CC3lUEsbSMB40H23mef/8PLySx2/e+hvWfHENDYca/PnviUQiMPgTY4M8ezqDfKlUYvPmzezYsYN8Ph+YD26p4VkPt+U0sWOVx56ZM2fy1re+1R/M6xBuU7CYg30IJheDoJjW/Ywk9xRBJ+MSLd4hKBS0oNW5Y2QcJMc2xZY2AOjxjY5iEsOyFpF6DrZsr6dimSJHjyHkmrXBXcqlp5NJ2URAm1EApnFDjpvNZv17ZkYDmNubnkp97+SeSb8t4yxBjxPluLK9NuLr65Dt6j0zut4EGU9UKhXKXpltK7dx0ytvwgt51fm4B3t42ddfRnismvNmfHzc72cdx2Fi+gQ3fvBGPNfjss9cRvvOdryKESp+ClGnjTZm+bTRRP/o+yTby2899tHjUV0PptddO4W0jjMNIXIMbeDQ59COEx19oa9F3kHTWKWPL99po5uus8B9a61w/Zuv59isYyzev5hX/fpVJPKJKQazj7/64wy3DNeiFQG36LLiNytY+7O1AeOLvGsTTRP89P0/JddSHf+5GZfFf7qY2JGYf03iHBQnXa4vx+2fvJ1CcwE8aNzWyIo3rSBUrq1OJBpJjAhi/JJn9pvf/ObvHatYwf0Uw3EcZs+ezTOe8QxaWloCD6QWKo5TDeEtl8vE4/FAo6LnPetOzbQkyzrausHRczvkBZWGRn4E3RGJCNNWS2mkh5cOs+VTW6ikKrTc0sLcz8wlOZr0BaB0JlpI6/oQQavFom48dJi0zNXV6Izcruv6Yl/Pg5drk+QOZlibDkvX+0v9SePouq6/frZch5xXi3SdIMV1XfLJPNs+tY1iQ5HFH1lMcleSbEOWvW/ey4nnnfCvpfGuRuZ8Yg7xwbg/QJB60r+1kUI+04MG2cbs4HWHIfWonyNtRZVzy72QwZS2Ch94wwHmfWseoVItUYcM6jKzMux47w7Si9MkDiZY+IGFpHakqnkDuoo88P4HmFw0CR50resi25JldPGoX/7oSJR5n51Hxy0dHL30KDs+tIOe7/QQHYqy/7X7p4jucDbM/JvnM947zsDyAWKTMa747BW0b2v374kO+xekTsQQZA5a9u/fz549exgeHvanKliqWMFtOV3sWOWxxXEcXvCCF3DxxRcDtXm1WhhL/6ANxaaAMo3X0q+4rsvk5CTxeDwg5B4uPNkU9np8Y36vjet6/CBRS7Kt9lCaIbIypqrnXZT8NVqIyJxxU5QKej1tcz9dTvGy6230fdH9jE48p48n4zDdL2vxXa+fh+p4IJvN0tDQ4NeTPq82mOhymOXTz4l+HvT1FOIFRqaNMOPADP+zwBSCVJkfvOoHHJp7yP8+OhHl3OvOZd5t83zngbmc1PCCYQoNBbrv6w5426V8+tk0w8jN59fcT1+r/F/PmKBFYL0QeTl2PYNNPaOE/JaxltTVlIiASmXKdqYxTI+fI5FIYGUa8x6ZHmxzLC/bZzuzrHvVOg4tr92rc7afw9W3XE0qnQoc87tXfpf1y9cHBHdyOMlln7uMnsM9gYTI8lMsFjk6/Sh3vulOJmdVx1Px/XHmvWceqV0p38AgyQ/lnTq24Bgb/99GokejLHvXMtx80OuvBbeOapTr+ta3vmWTpj3daGlpYe3atTQ2NtbtuLS1TYsE6ZBMz688xJLNGoLza+T/eh2ZNB66w5FGRTzsutPS4eby2dCaIXa+ZSeVVPV8o5eNssfdw8KPL6Q0VvNES0ch3mfZH2pZ1XVDaFpSdZi0Lqtcn3SQ0mnpNb3lZRSPrtSV69bmvEtDJS+sPp/UkZRFL7EGNcGmrY56kFAqlYhMRlj6qaWU28vMSs9i0B2k6BahOfh8eCkPktVj5lvyTKyeoP2Gdr9zlA5SWzy1x12+0/dQDDYyGNEJXrQxRBphvZyKII2azt4eCoWY+YWZlCNlKk7FbxhDoRC53hx73rmH9OJqToJsb5Yd79jBvE/MI7EtQWwgxoKPL2DnO3bS8lALZ113FoWZBTa8YQPDy4YJZ8Kc/T9n03ZPG/teso9dr98FDoydPcaCf15AOB0mMzvDwMtPhiZVYM51czix8gRHlx8FIN+Q5/a/vp01X1nDtAen+denB4W6w9MGIS26+/r66OvrY2BggMnJSXbt2sXQUG3+usVisTxRcV2XCy+80O//zUG/bCPTx0xvqhnRJW1nNpv1p+YkEgm/T9SJM2Gq8NYeVUGLEtMYoPshOY7eRk8R02JJxi8yTtKCQ8pVLwpMxgXicZR60v2m1J92XJhjHS2g9XhLyqJzvZj9kRxff2dGF+i+Ss6jhWE0GvUFjz6mbK/vs1y7bKuFmek11hGBjuOAC7+7+nccnnWYK797JTP3z/TL4j8/Ew7P+Ooz+M2f/IaBFQO4RZfVX1/N7HWz8Qjmm9Fe57btbdV75QXvkfn8akwjjn5uTcOHKUj19+a42fRqm8Jdl+VUYlu/e/o7fV/l3Hqaht5OC3rZT5LFmuPTeuc2j6OPHwqFcFJOMAkykIlkyHpZ4uV4oIxX/eoqQpkQd51/l79tOVQmE88EnIJTypCHSKbm9MjNyrHnvXuY94l5NO1p8sWyzsvQ/FAzCz+zkPDuMF7Go+LU2g8x1OhIhLrn/T3YOdxPISKRCM9//vPp6uoKCGioWZ21tUYsNbqB01ZdybA5OjrqN2x63pFu9BzH8YW1iCZdLkFCRnSDrC1FUGss8vk8qb0poqPRwHze5s3NOLna8hhyLbFYbIo41cJazl+pVPxGQ8SxvOTiodahZjoUR9en2XiK91uH4Ui4sOM4/ndybm091NkadWOqralSdxKZoAWc67rED8dJPZRibGwMx3FIjiTp/3Q/jRur86ETBxIs/NhCUgdSOBGHXf+6iwNvPcDwJcMBa7+E2+iM8bpDh1oYkohguW8yGNCDDd1RS93J86WnL4hlXRBDh9S5XpqO4xDaFgo8F7FDMbyDnl9XiT0JVvzLCgozCgyNDpHflOesfz+L5v3NrPn4GmbfN5vhFw2z67W7qMSr93dy4STbPrCNjvUdzPq/Wcz4VtWivugTi+j9US/RB9SzCExMm+Cu19zF6NxR/33ad/E+ji04xvj4eOA9lHn7ut7kGsPhMD09PSxbtoxLL72U5z73uUybNu2U77rFYrE8UdD9rhk+LP2AbON5U5NpjoyMTEnQqcWc9EkyBtGGTe3tM4WAGL7le92v6z5VG9h1/yuZqrWwMj2F+prNSD4diirTzSSCTY9PtJfR7G9F4EofKcu2ajGsI9Vkez1fW/ohXTdaLOlpT/KdnFuPG8XwINuYXmEZ80j/X0+QmeNQN+QGhI/ez/M8rv+L69m8ejMnZpzghmtuYLx7HKiuUjI2NsbQ0BBDQ0N4+z0u+NoFdO7s5JLPXcKcu+f4x9DjTT2O0stF6fGPKZzN+tLb6+dAo/+XujJ/zOfX/JH3QJKymvh1qAxHD/fsSz2bRpJ6167HaePj4wwODjIxMeE7j8zpo/r518+a+Qx0Dnfygu+/gI7BDvBgzuE5/Olv/5T2dPsUYwYluPSmSznv9vOq0wJLDme//2ya1zX776bpcS+Xy7jHXRL7EoHxYeJggvhQLUpGPwsSvdK0sYnocDRgONJRGvXqTr+7vw8bUv4UIRwO84IXvID+/n7/4ZNGXwb49V4QeahSqRSxWIzh4eHAPGeohR2Nj4/7oUcy11o3ZvLQakuvDhmfPn06iUSCAwcO+CFi+mGX/WSt6M7OTjZu3AhJuOdL95DrytH99W5mfmUmIa/m5ZX9k8lkQOhr76L2vOqGQTd0IoL08lbSAEmnLNcj89ClrsS7ruefS+eu95dyyQusvbYS8q47Wm3l1McTQ4X8r5ce0SFTjuNQjBTZ8u9bOOsdZ+FmXCpNFe7/t/tJz0uDA07eYdE/LiJ+RxyvclKwtpSJZWIU8zUr/vj4OKlUKuChlg46HA6TzWYD4XVSD1JGsQzK8ymDgWQy6dejHFfuYywW858fwJ9HH41GcRMu29+xneOXH6ftvjYWvXMRTr42UHIbXA7+w0GOPPcIsb0xFrx6AfFcnFhbjNZYK17FI9ma5M433smhNYfAhVAuxOJPLWb6HdM5tvgYm/55E17M45y3nUNqQwov5HHgjQc4+OKDeGEPt+Cy4JsLWHL9EiLhCIdXHeaO/3cHTsXhuR95LtOOTPMz42sji9SDPBv6fusBQCaTYcOGDQwMDPgGracTng0pt5wmdqzy2HL++efz0pe+1M9loccfWjzotktHmcmPDrPWolh7W7WDAGoDbPG46oE/1OZAS/8i2+oxip7qBLX+X84jbbXpvNB9nmyvr6leJJeesmZ64OV6tWiW72SMocdwpoFaxg5msla9rrUeC2kvqD6njsCS66kXcVfPg6rvu+mk0IJQ+sJcMsd33/hd/uQ//4R4Ju5vJ+W95Xm3sOGiDVTCtbpqHGrkRe9+EWRr0QrpdBrP80ilUlRiFZy8A15wbq8YPhzH8Zf98jzPX6PaFHumx1nff/ncjP7T91HqQupADDy63kxDlT6OKeZMZ8Spnp1TfWYaAATzc9OYoMujtzXfb33v6p1f/o/H4zQ1NRFrj/H1a77OG374BsKF8JRrknc7l8sxeGKQ3179Wxq/0UjT3ibi8TipVIpkMuk7LKRM6XSa8fFxJgoTbH3HVoYuH6L17lYWv3cxkUotAbGeh12pVMhms+RyOX+cpetBws+1p1sbowC+9rWv2TncTwei0SirVq1izZo1gZdTGvd6ScoqlUogNOtUlmhpvOUz8QjrbV3X9a1Nso0510mEdb2wGW3tkwZMh2GdmH+C2KEYA38+QM/newKCTR56Eey689CNpxaukuFaJ/WSfUKhkJ9wTTprXT8iAPV8eLnGjo4OhoaGyOVy/jWKZV7C0nT96sZRBgDSQcp9k85Bd6aO4/hJzXRIuxbd0uHrdUCTySSFQoHD1xxm/6uDc5QbH2yk/2/6iVQipGem2fPPe+j5rx6ab2kOlFEPcMQAIOWWhHg6FE3fa72/XGcikfDLrg0J8hzL86cTiujBGcCOf9zBef99HuVSmdHR0ep+iRIDrx9g4GW1bJUN9zXQ974+5jCHUqlUjaBIVecMPfjeBxlcPshZ3zyLOTfNYeicIW5+x821uUNlWPEPK+h4sIOJ3gk2f2Qzuek5Zn17Fn1f7SO9NA1z4N633+vvEyqEeP6/PZ/OnZ2USiUOzjpI74FeIuGaoWrevHkcOnTINygUCgX/2vQgLpvNct9993HixAmGhoZ8Q8dTHSu4LaeLHas8tvzTP/0TnZ2dwFQvnxbfYlSUtk0LDy1U5ThanEr/BzWxLn/LsWQfHYKtBYwWPXIOXQ7d5op3XY8b5JiS88X0VmqDus5fIvVgOgDke31s0zOo99Gfm0JOjqPnGet7oMthjrukL5c+WfpbXef6nDB1uVX5DPD7ViBgfNfPg+M4nOg4wS9f8UuO9x6n40gHV33jKjpHOv17ImOmX734V+y4ZAc40DzQzBX/cQWNRxunCH4TbZgQg4uOkpAxh0Q0ms+ulFPfA31P5Rz6GawX/WCWSX9+qroxz63RY3w5v65/s060EadeHen3Qx9XPytyfXL+ekJej7friXYZLyeTSZqbm2loaAhEMOpnTZ7lYrFILpfj+PHjHD16lNHRUX+VnmQySTKZ9FcPkvqfnJxkcnKSbDZLuVxm9zt3M/9T8wNRvfJbBLc8s6JRvJMORBmXpc9N07Wzy39W5H3RxqRvfOMbdg73U51QKMSSJUtYunRp4MWHmiVWPGbywOks0fJg6zAh7VHU1k6zc5Pz6GQK4rHVYlWHbWmRrzswbVGUB7pUKnHinBNsf/t2pv96OrO/NJtcOeeLEnOesVyfvAjaU6g7+lAoFLBiiUCUc4tQ1clDZEAgHYlco66zwcFBvwxiWdUDAAkdl7ArOa9YznQnajbu0sDpc0qnL9uHQtUlMLLZbOA76fTE09pzbQ9ezmPfW/aBCy13tND/yX4iToRsf5YD7zxAbn6OPR/dQ+/Heum8vjPwzMmARK9prkPcdeNuDnbkeZFrKJVKgbXAzfnfEtouFs2JiYlAJ++6Lgv+ZQHDxeFgeFfSITsjq4tNsbFIviXPwOYB37qZy+WIRqMs+9Qy2i5pY9Ydsyh7ZYa6jDnUDpQWl5icnGTr27aS6c0QSodoH2mHZ8Cm120i1xVcy9tzPI60HaGl2MK+c/ex/lXrWfGTFSz/3XL/md23b59/LWNjYyQSiSlGBnkWVq5cSSQSYdeuXZw4cYL9+/eTTqexWCyWxwtp68WArccJeqxgikBzHAHB6C9zmpnuF/VngvS5OnOwKZhNYa2XNpV+WrxZOjRcrtP0YmvDvI6Y09drGgLkM10PWsBrg7bObG2KILkuqTct5KU80vfr7bSgkuvS4ztt2JbzaDGt75E2pOtz6+Pq+yXbjc8Y5zcv/w3He48DMNQ1xI0vv5ErrruCxkON/lQ8z/NY/X+rcXMuRxcf5YL/u4DGo43+cU2BK9eo76s2aEj5tPPJNLzo+6OfHb2v3scUyOac/Xri1PzOPI6mnuPIvF7TC24+X1JXZrSBeY5TCel6xzzVOc26lu/1+yJrbGsjgTYaaWOQLMGn547L2FreD3FQSX3oezzvn+fhhmoOLV1m/azG43E/ElHnZDj8zMPsfONOvK969N/WH5gSYR7n92EF95Oc7u5uLr300kDIuAgUeWBE2Ih11/M8vzGTF0C81LrRFW+v9kzq+dkiaicnJ/0XtVgsMm3aNEZGRvwlnKQcOqO3Fr9iBBCBK4IjvTzNtrdvI9+d58BfHqAYLtLzHz1AzXssjb/OEC0vozQK0nFpj7i21kmYiM6mrq3jsr9YQ835K9JZagu3JEURUSrby7XJ97qjFIEJQaOD9mLrxkxnjJR58Locsq14UXVW9Zk/mYmbdRl61hC9H+8lPBym0FJg54d2klmQAcBLeAz8wwC40P7zdiqxCoffdJiez/T416YbTDm3NJTasCPWcymrvm9SD4Kub7l/5XJ1SRPdUWqru5xTMspHT0SZ8+k5lENlxi4YIzIcYenHlxLfG6cYKfrRBtFotDrn7HiZ1p+0MhoeJR6Ps/BXCwkXw9z3uvsAqonRHpjGze+6mcm51cyX5VSZLa/fQrQQJdcaFNsAKz6zgpn3zOTIFUdYf816ck05Nr5sI5VIhcW/XOzfL6nHRKK6XJteCkaeDZku4XkeS5YsIZ/P09/fz+TkJLfffvvTxuNtsVieOCxdupRUKjVFyNXzqOo2XvcV5lQpLVi1SKg3yAf8iDLxWkn/LsfWv2GqJ07aWAlZT6VSU5ZQejjvpSlAtcNDthHhK2MMLdLlPFpoyP/1BGA9I4JeSkmP66SOtfARo8GpQr3lMx0yLGXTTg75rcsox04kEtx78b3MfXAujUONgSmKjuNAGZrHmxmgFoHmHnVJH0xTGg06OhzP4azvnsW8afNoO9wWqCPtTdXPnVyPdiZpISY/eilZ836Y9zhg0FfPhF9+tzYHX9eL+b2+F3Is/VPvXuj99LuknwHtGDKFrqCfU/N8Zrl0ObTDRJep3vOux4D6PNrDrOtbv48654B8JuH/Om+BRLRKuQbnD1LuLNNzV48/1pfjyLOnhbKuF61FdHRmOBxm4MoBdr5uJ8WmIptes4lIPML8W+dPqQ8ruJ8GNDU18aIXvYhUKgVUX+iJiYlAg25mlNYNqbm8l/yvPaZ6qS55ybXnGGpLXEhncuzYMf+BjMVi/nnFuy3H0i+YFvqlUolQT4gtH95CobO6bqIX9jj6J0dxj7vM+PaMKZ2gfkHlOnQ2wXA47BsG9FwuvYamzh4unaMZOpXL5QIhJcVikXw+73uvzQGGWMol3Fp3qvF43DdSOE41FD+fz/vWNhFRun50Fm/JEipJ1ETUyhJo0vDt2bPHfxb8SIaKR+9Nvcy4fQaV0QqhcIjKWIXe7/Sy4x078KIeVKB5WzPdd3VTbCyy5X+3kJuVw624dH2+i5gT9ASYDb/ppdcds35GtYUd8NcK1WH1YlzQ90d773VHKuGAoaMhFn5gIVv/dSvLPrwMBqBYKvpRBUAgMkPehVwuRzqdZvpPp7OMZcSIMed3c2huambpb5dyT9891TllFSiEC2RSmSnv5qJPLKLtpjaO9h9l419tJN9UfcdK8RL/n733jo/rrNLHn3vv9Bl1yZIsyXLvvSUuiROnOj0hBELvdalLXUogsPQlwC516QQSSCC9J6THcey4F7nbsmyrl9H0cu/vj/HzzrmvxiF8F3YJvzmfjz6SZm556znnOe3ddsU21MRqMGnjpMIcewF4AA+KOe4cF65v6eXgvmxqakIul0NTUxMGBwexefNmDA8Pl8F3mcpUpv8VWrRoEUKhkEsB1w3aOiCTYIRyVUbyyO8kSJJyWYIPVjCnzqIXCpNtkBFhlJcSZMlilvyebWebJBA2TVM5FaQBge+S90pAx6g+KTdlJKLUZ6SDQ/4Gip5nqfxLIEJdgXqc7qGXXnTOnW4skTob4M5L1g3ghmHANmzsWr4Lz6x7BhvWbsDbv/V2BFIB1S7btuEZ8mD5z5cj+s4ouuZ2Yfye8Vj9i9XwpDzIG3l1HZ9vpSw09jWiZlxNIT93dNTVX90zyzniM3SgJY0O0iHF+yUIl8+m/iXHTBqPOJdy/uS+kLqKNNDI9aT3i785h1LfknVxSoHsUv2Wa1N/r9xXOig/HVCXv/U1yPXCvSm/k3tS/riMEaaBrD875nO5X0ZaRrDxYxvhWA7O7D0T4Q1hV1qJ/KHuTiebXrxR4pqB+QPoeGcHshUFfSobyWLr67Yi0h9B8+5m17i8XCrncL9CKRwO46qrrkJ9fT1GR0cRiUQUoNUBsgTSVMYpKAh8uJkBuBYrUKwgLT3gFEz0OsriJEBxQ/BvPlfmccvQKXrYmbNRW1uL/un92P253Ug3pmHkDDTe3oiWb7WUDF2Xwlh6jSVTku/lNdKbSCas/06n0wqQsVgcn2uapgLhBIASIEugy/dmMhkX2OQPGRMNIAo4Wu4CcclZSYT3hxHwFUJgEomEywAhQ+u4JmgUILPMZDLw+/2qj7JQXO/VvTj07kOo2lmFGR+dgdz4HDpu6MDo3NFCfrIDtPysBeN/Mx5OwkEikUB2Yhb+QT+86WIIuGEYSCaTqq+0bJaq+sq22LaNcDismKkE8FJ4SCEjlSX29cwzz8Tzzz+vDB0kqQzqgjWRSKi54PMymQyCwSBqamoQCATg9XrRcVEHNl+1Gc17mjHvoXl4/F2PI1HnBt0TfjQBLb9vQVWwCj2rerDrX3YhU5WBlbIw+47ZmHP3HEQiEeQ8OWw+fzOydVks/9NymDHTFTmgC6ZwOKyiU1KplMtCT4PQ008/jUQigWg06rLAvxLJKedwl+mvpLKu8r9Hb3jDG7B48eIxwFiePgHABex0z6sEHiTyMwlA5XUSVOkgVf5IHUbqPzR0U2ZSxtbW1sIwDMTjcVc+p1TMddCf9+QxVDeEht4G1/uUoR42jtcfR9PJJpduJUGENBDIfFQp16XOIAGN1H90Y4CUdfI98j5JOhjUQ+75jFhtDL6MD4FEwD3mhoPdS3fjkesfUbVMKkYqcO1ProX/kN91DBnf88Q7n8Can6xxhfDrYF4C5FJ9kWBeriOuOUY+sKaNnuKYyWRUSDHHgb/lvAeDQfh8PsTjcVd6oQskivll6qIetq8DV9kHfc7YBuqI3FsyaoDXSH1In1uOmfz+dCBarinZL3196AYG3qc7QoBCKmEkEkFNTQ3C4fCYlFBpKOPnB1oO4M4L78TSG5citzendB/2Nz03jW0/2FZwEAGAAyz+1GIEngmoPiZmJ1B9sBpej3cM4Kb+JA0R1LPz+TyOnH8Ee962B7lIDt64FwtuWYApj04ZY4gwTbOcw/3PSuFwGKtXr0ZdXR08Hg+qqqpcjIECAsCYzcWNT4YTDAZdIVVchFz8BGJ8pvRaSm+yPJMSgAr5kFYruVFYkEBuLgoSr9eLRCKByPYIZtw0A3s/shf1j9Wj7T/bYFvu86vlOygcdIumrHAthbQE67rVXN4vQXlFRYXqK4ERGaVetV0KBpKsxE3m7/V6kUqlEAgE4Pf7lWIgq4uS0QwvH8bBzxzExN9MxNxn56rcFoJWvpeVv2WIPOeNnnddUeHaaL67GQ4cjH90PLLIIjY+hkxtplhAzADiM+PI+rLwJDxIT0/jxBdPoGJbBRpvalRCi6BeCiMZyUDPgDz+hUYbzrEM7ZOUy+VUVVwZwcFnvPjii+oZ7H8pqy/fVVlZiUwmo+4HoKIEcrkc+vv7VYGOyfdMht/rx/THpyMVTeHMX56JDW/ZgHhdMZ86MTcB7/1e5FN5ND/XDASAXe/chYl/mIip909FLBtD38V9GGgYwPZLtp8aHODM285E0BNUDJ9EI5eMzGCVVa5rj8eDcDiMdevWYWhoCAcPHsTQ0BCOHTtWcgzLVKYylen/lZqamjBu3DgF9Cj/gCLYk6HTjLzi51IW6N5CXZEH3GGv5NsSfANQEXXSs0gerkcLSXAHAPvm7cOSviXIR/PKGZF1stg5dycaexphOAbqTtbBNE301fcha2XReLIRz5/1PPYs2IN1t61DrDaGqR1TYaSKxtwXV7yIDWdvwLo/rEPrvlZX36TRQRr+2Xb+LasjA26wyf91gKZ7XOV4y3GVOgC/l8/V2xitieLRax9FxUgF1t69Fv6svwgyLRtdU7qKugKAtCeN/ZX7MTkxeQwIzOVyWP3D1cjaxagsORb8m7qJDgClM0KOh75mpP4j9TvDKERhMjpR3ieNI2wH0/KoA0rSjRl8n5wT/UefQ70Pcryoo0pnmAz71/VXOX/8TI9+0PVy3auu9+F0hgh9LE5n6LcsC/0N/RitGsWEgQmucdGNZrum7cKdV96JdDCNTR/YhKnfmIrAoYBq9/CyYRz+3OEi2D5F/Qv60fpMKxzHQe9ZvTj88cOY+aOZmPDshJJ8ReIJqW86joMJD0+AYRnY8+Y9mHfrPEz78zRYXncuuHzWX6Iy4H6Fkc/nw4IFCzBlyhQF3vx+vwLQsqgWAASDQQVsTLNQ7VhueC5eafmVG1ieoU0rFK+X9wFQlSDJIHktgbteDEtacOlF5sYLBoNwHAcNmxpgfNlAaEcIhlkE/gRZ+lEZkgFRAaClUeYh6TljulFBWlulQkGSQJ3ecn7OMaVHVwJe3dLJKt0AVOVMyRSlN2Bw0SA6P9WJ7LgsDr73IHK+HFr+0KIEMauRy37LyAV6+blW2F+uGRlO1nhHIzL5QhXPui11sL5kYc839iBfmUf1s9WY8LUJCMaDSLWkcOLfTyA5O4nkvCTsoI1J35jkyqGXTCmbzSorsYy84LxL8E0AzHElyGbYPdeb9K5w3mTVczJUerslY+WY8BxYWZ2VFmyulUQigUQiAb/fj8Y/NCIdKKzZ9l3t8P23D49+6FHkgjk0PtcIf58fhteAnSh4nRsfbYTdbaN+az3yoTyOX3YcO1+1E9lIcf/sOXcPsoEs1vx8DSzLwvbLt2PWY7PgSxbrBrCInOM4ao84jqPGjeNeXV2NxYsXI5PJYHR0FP39/diwYYMyPpWpTGUq0/+Epk6dira2ttN676Tso+JP+aRHklE2SIM9MBa48G9pWNc/o4wD3EdU8W9+R8MwAOyaswsPrXsIe0/sxbW3X6ue+cjlj2D3vN2oHqwGAFzzm2tg5A3cf939yJk5VPdX4+C8g3AsBw9e+yASkQT2zd6HS393KWzbxvqz12P9BeuR8+Xw0DUP4eI/XIyWQy0uGQzANUYSDOrGcF2x53UyXFx6v9lX6aCQYyn1Jfl8PkvqR7ZtIx1I44HXPYATU04U5imYxTW3XqMi8zKZDJb+dilysRz2n7cfsIGz/vsstG1rg2O4C+VJr6Ie0s62yvUhAZ4ERaWOR9N1OCnrpRFD6mhSX5H/y7mRhg3ZDr6XbWJ7ZKEuHezpuqe+nnX9VL9fGox0b7XUqfT38fl6+oD8Ldsggbn+Hb+X61YaOaTBwDRNJKoSuOfKe+D4HLztgbehbrBuzJgAwO4Zu3H/xfcjHSzoYNF5Uez7t32Y+5m5qIxXIhgMoveyXmTGFXVEUrq+oB/2runF4Q8fRrYmi473dyBYFcSExycoLCKNOFJv1/s56ZFJqByuxPjt4+GYbg+/PmZ/icoh5a8gMgwDc+bMwQUXXKAEF8+BJOPgkVZ+vx+pVMoVvstzrwlG/H6/EkDyyCouSJ2xyKqAMvyFXkYCRpmbTDAnD6knYKIgMU0T0WjUBUBtx0bf0j4kWhKov6Uedm5snhdDy/nOhQsXoqOjQ7VHMnQKJemRZqg3hbvX61VVvgmm0+m06wgBgjCC73Q6rcKh0+m08iw7jqMqksdiMVUQi/dyLDgHDBXSrY38LDM5g+0/2I58taiaGrcw6duT0PBAg9r0FFzSeCG9AbJ4HgtxyTUiPQ8yPN62bcRb4zj4mYOY8bEZqMpXIZlPYstvtiDVViwYZmQNNPyhAW03tRXWQFMWx997HO03tsPIFwU9w7+lksV5ITOkV1/2gcaYbDaLZDIJx3HUEW88jo7XA8VQxkAg4DK4yPxovp/V9tl3rjHOjTwqhhU2uWYCgQBS01J4+rqnUbejDvuv3o9AXwBrP7oW2WhWPccxHPSf34+OD3cgF3SDXzNjYs1H16BppAl7L9mLba/ahor+Clz2+cvgtb3Ke0PATcOKYRiquKHH41EGOMuy1LFnmUwGsVgMhw4dwqZNm14xZ3o75ZDyMv2VVNZV/nfo3HPPxate9SoXH6U8kfqDlL9AMZ2NvFgWQ5JADChGZOl54VLJl6SDER2ISx3AcRzYjo2j04/iT6/9E9KBNAzHwMyOmbjq9qvw4MUPYtvibbCtYnsi0QjgALGq2GnHxbANTN8+HW2H2vDkpU8i6y8aVSMjEbz+h69H9XC1C0yxvbLODcdOgjcJaOT4clwoy+VJINK5wDGhYVl6TQ3DgA0bexfsRcaTwfzN82FCHMfm2Lj5gzejp62n2FkbmLppKlb9YJUrdDgXyOHZ1z+L9qfb0bK7BQaKa4NtpcFfAkvdKSABJPtKPUDqTKXAD/Ud6scAlPzk2uMJKVwj+pjq41vKmy7XmQRspa6T0QucU31e5fN0cMd+yes4brItOvjl2te/l21h++TzJAiV81GqLTrAls83TRNWpYVbP3srorVRAEBlvBKf+PUn4I8X6wDxfQlvArdffTsOTD9QiJbIARO/OxGt97Yi5CvoeyOeEWz9+lbEZ8RdERXIA6GuEDJVGeSqizqWL+bD4m8vRuPmRni9XpVmIB021EmJdch7eK0cA90A9nJCysuA+xVE7e3teOMb34hcrnA8EXMRuBlkBW1uKBaB4rWWZalQWQIIeoClBVRWiQbgAqb0TPIegl4yQIJUClEKERbD4kakIMhmswokkKH1LOjB9q9vBwxgyremoO7OOph20frHtjAUiMdscQykUCbj5fl7bBdBGt8NFMEwUMxD57hJAwIZE58vC6nNmDEDBw4ccDFtaUGT7ydJsClD32zbhmEa6D+3H/u/vF9db2QMNP++GRN/PBGmUTR8SFBIJivTB6RA4/jJuZVWUc4RrcOGYcAxHNi5Yp2A3OQcdn93NzJNGcABqp6pwtSPToWdt5GdlMXuX++GHbLReEcjWv+rFcao2xDDsZMAUv+hUNZz0iV4DgaDGB0ddXkDqHyYZvGIOJmLJ9eHjKrg9RwH+VtXHOXcVdRUYOiaITx1/VMq173iSAUWfmYh/H2FVIF0axob/n0DUk1jq5qv+rdVaNjfgMMXHMa2d25Tz6jtrMWF/3khwsOnctsDDlKRFIL9xeiIMcJNpGfIiAuOSVdXF9avX49EIvEPfbRYGXCX6a+lsq7y96dIJIJrr70WixcvdoECAiHdkCpDTPVUmVQqpeSdfq3ka+TB8n+gyMdl7jOvZdskgJOKcyKQwM/f9XMM1g2qd3rTXtT112G4dhip4Fg+/XLIl/bh3EfOxVDtEDadsQm2ZSMYC+KKO67A5D2TXWG90oMrva2n081LGRn08ZBAXo4Dx0gCuuJgA/vn7Mddb7oLAHDxzRdj7s658FoFsJpKpTDqG8XvP/J7jDYWipbVH6zHJV++BJbhrqli2zYcOLDM4mkruhdU6ik66R5Y9ku2WQc8lHu8lkVofT6figyjnmpZlir2C2AM8JJyXwJOCS6lHsHPdcBfyhige0d1gwL/l15r6vX6s9hOaQiQ3m05xrIAoG6AKdVGfV3qgFr2Wzrg+BwZYeD1evHch5/DwUUHi+DYAWYcmYF3/PEdY9ZONptFNBbFrW++Fd1TutH6i1Y0/7rZNT+O48D0mtjzqz0F0H3qmTXba7D444txfN1x7H/XfuTDeVhJC1NvnorJd06Gx/KoCEtpyOMzqQ/LqGEJ0OW1kn7729/+RV1lbFZ9mf4hqampCevWrVOHxVdVVSEQKBQGYOI/C47Ri0xLM601snI5mU0qlVIWPwnSZJgxPWdkgDKHhUCP11mWpTy+ABQY5nulR5dMQoaG53I5nDjjBHb8+47C6jSAgx8/iJ6re1xg0e/3o7W11bX5JBCShgePx6O87hIYs92y0AjHjQyE9+nAi5uQBgcZJrdr1y6XBV4XpoFAQAF4mTeczWYRj8ddIVYA0H9JP/bfWATbcICWW1ow8UcTYZmWmk+e5y1DmySY5bogeb1e1NbWKiYiDSFcAxw7VcEdpjLe+Hw++I/6MfPGmQgcDqD2sVpM++Q0OLaD1OIUDnzvAOywDRhAzzU9OPauY7D9Bet7ZnYG2bZsyXZy7fE3DTtSYBJMkkHW1NSMCREyjGJagWmaYzz6fAfHi0w2n88jlUq5wtK5fqVXWyot+Xwew8lh7J6025XrPjppFLs+uQvJ1oI3PtwdxoKvLUCkMzJmj3cv74ZdaaNnSY/rGbGKGA41HirMnQVsvXQrnnjXExhpGlFtlzl+5A8yZF8v+jNp0iS85jWvwdq1a7FgwQKMHz/+5TGiMpWpTP+/p/r6eixdunSMt0ca2aUiLwGUNAjqnmgZ5SW90roRWtYnoQ4gi2FRXvC+YDCIUCikZCQ9m+F0GK+77XUYf6LA/8ycidWPrcZbv/9WnH3/2fClfK5+T944GbO3zVb/z9g9A/O3zUfVUBUaTzQCAKychTWPrcGyDctwwf0XYOmzSxGKhXDRPRdh0u5JLuAoAY702Eq9SwIZwF1hWnp8SdIQLOfBtm1EI1GcaDuhvpfz07GoA3e/8e6C7DGAB9/wIHYs3oGhoSH09vbi5MmTSHYlcd53z0PdoTq0bGvBBd+6AJbhPo6UlM8VK7JLQ7juFZWyiZ9JsCu/4zqS+qhcK1wvUv/SgaAEqbJIqgTX0pEigau8Xq49CU51kkaV05EcEz5PX+PyWo6FTDmUwF0+UwJxqRvK7+Uek4Yy2Xc5B6WeIdvL50hd8fLfXY752+er58w9MBdvveutY56l2pTNY9XXVqH91+2FAr3O2JB4r+nFgo8vQOu9rag4WIFx68dh6eeXwmt5MeHBCZj2y2nwxDyY9rtpmHxHAWwTPMu9UcpgIo0WkvQx0cf9paicw/0KoPr6epxzzjkIBoMYGBhQTIohzqZpoqmpSXn4aMmi8KmurgZQtEhJa5n0eknADRQ92dKiKBmPbdvKoywrDNJbKCuby3B0yaTkeXq817KtMWNgZIv5qbJtyWRSbR4KWaB4Frasvi2ZuLS0MlyYn8vxkaHEbKu0ospiGxxLeS2AMQXCCIL0sHpaXoGi5bL76m50vr/TbRqzgbZftrnysphKQG8B38t30mvLInk0hJw4cUIZPWSImcwDJ6OTFkuOn8fjQcX2Ckz92lR4Dnhg5k04hoNcKgc757Zc9762F1l/Fk2/bkLnpzthxk1M+dQU+OI+l4VXGjdk2LT0okjhkclk0N3d7fIMyHs4FtFoVI2HFCqMfpDeEBqzAKgICI4H76XFs6qqCsPDw3CyDmbdNAsd2Q70nd1X7HgOsLPFtV63vw7zvjcPWz+xFclxSXXZwSsPwgk6WPjDhdjyji3oObMHZtrEwv9ciMYXGxH3xLHnHXvQsa4DMIDn3/k8zvnhOQgOFus0sI0yHYDGFul1Ykhne3s72tvbMTo6iuPHj+PgwYM4cuTISyoHZSpTmcoEFL2KNO5LcCG9R9LILmW9lDmUnSRpCOczAPeZ1hLASXku3yV5uazRQj1mXN84XHnvlfjjlX/E4ucXY8H6BcjZOcxbPw9WwsJzlz2HiUcmwjIsnHX/WfCaXgSyAWS8GVz04EXw5/zoauuCP+nHHVffgWXPLcPSzUtheQrG/PMfOx+TOydj8p7JcAx31XXZv1JKuwQuEnTJNDEdiMj7gKIekLJSeODVDyBWGcNlt12GppNNrtBzKz9W74oPxdVJNHxW9clqrPzFSvhjfgTSRf2HhnH2hfOk91c6IPhuafjmGtL7zedIZ4TudZakA2/5Tqln6WHe0jgkdSCpO5LkuMg5kz98pgRv8vNS/0sdWQfRco/ofZfPKQXy9fEvZQCRbdDbIaMnJFiV7y0FUAmOL3vwMgScADK+DK58/EpYOfc8SANANpuFk3Yw4ZYJyJpZ1ztdz417Met7sxCdHoW32wsn4cCxCvxn8v2TER4Oo2l9k0uH1nU5uZdUZGkJ4w5Jd+y8XH2pHFL+D06hUAgrV67EnDlzil5GbeHbtq3ChROJhMu7K73VBHQEhN3d3QiFQqisrFTPkuHU0gvLc4vlc7nYeJyXBKtcuLJ4FkE5FzRBjn6IvW3bGFk0gk1f3wQAmPSVSWh6uAl2phgWQ0sj72XIkARP9LTzc+bdyjxwbjS98Iu+Lzj2vJeCIJ8vFlOTTFoWP5NjAhSFkWSm8m+2w7IsJBoS2P6D7a7iELM+PwuVj1bCa3nV+MtcKM4TSQL6QCCgUgqoBAFwFd7jGDPHHSjmlpEI6pLJJPx+P3w+n1ICGKLsTHKw5WdbkI+caksemP3p2TjwoQPItBT6E+gMILQ/hGmfmwbYpwSXBzDtwlrgOEoBKa26UljKPaGHhNPgQMWQn9MYJIWPjGCQwp9553wfDRlMqYhGo4W8/Tob2z61DYMLB1F1uApLPrcE6AZMrwkn58C0TBx+02EcevUh2D63UcLIG5j42ETMv30+nv/I85hx8wzU7q6FbdvY/bbdOHjJQTje4tqs6qrCVV+8Ch7H41o3LAhkGIV887q6OvT39yvDF41tbDvHK5lMIpPJYMeOHdi6dev/OfB2yiHlZforqayr/H3JNE1ceumluOiii1zKuQTBAFzRYxK0SCUXAOLxOGKxmOtkBr24Iw3V+pFirGshQQT5n8yP1fPMpa7Bdo7WjsI8UeDRwClwAgeJxgQi2UghdSlVMG6mA2k4loNwKqza6DgORmpGEB4Mw3RMwAQeuvQhrHxyJSpHK13Gax10SlkmHQLSScJxS6VSKiRW8mcHDg7NOoRUKIVZm2bBgKjBY9j43Xt/hxPtBe925XAl3v7ztyM8FEYikShEOuayODz7MB79wKMAgLU/WYsJGycANpRjh+MtnQbS0ML3yf7Jfp/uOxLnhjJaGm7k+OhpXZxH3kNnD9cCdQnWOgkGg2OOpeLz2K5SIdwS8EndQPei6x5vqftJgMrvdKCqe02lvqYbaXQvrGwHQSP/l861UhEH+jP0+/V0D11vKgXgeXpKdXV14cjXcBZ5J49gMui6nuOWy+WQSCTQ39+P3t5eDA0NuXRP2W/qaUCx6DB1G0azSD3XsiwVsSmNFJxv6kfEJ3w+dWg5PvyfxoFbbrmlnMP9Siav14szzzwTS5cuBTDWmsXPZIgD83glqJTMiec2A2MZBhknN69cgDIPm2ADgAphB6AWOo+pkkySHjWZE2yapmLk0huXzWaRm5BDcnwSfdV9qL+9fkyOkG7RDQQC6nkE2/Q4E6TxvRQYkolJJkQARu82STIUegulpVzmINPKzkgE3bPPewKBAGKxmGIafK9hGMhX5rHvM/swtGrIVRQi2BnE0rctRdAKoqGhAcePH0cikXBVUeeYctynTZsGx3HQ3d3tAuYysgFwV1Gn8JPMV/ZdChV5niHnwXEc5MflsfPnO5H35jH7c7Nx+J2HEZsfcxe5cIC6h+ow9dtTkanOYM/X9mDqe6bC2+9FKBQCUDyTXAJGzjvfSXAuBSX7IXP9ZS6TtOIyykE+m/2VRhlaPfk391gkElEpGqbXxNYvb8Wqb6yCk3Yw6B/E5m9sxqKvLsLAmQPoeHPHaRN66nbX4YyvnYFgJugyMjmmg+dvfB798/oBA/AP+bHmi2swbmCcK8qDwkYaz6RXSO47eT4px45rJ51OY9u2bdi3bx/i8biravz/FpUBd5n+WirrKn9f8vv9uOmmm9T/lB26MVr3jJGk7KA8TiaTyOVyiMfjymBNRZayXVfqKXcI+sj3qFsw9U03ZEsln+3jTywWU7Kb90jFPR6PKzAvI4p0L1/aSuPp857G86ufhy/tw7t/8G5UDVW5AGYpY7tU/jmWvEaOm4z84vVdM7vw+7f+HgBw5a1XYuaumTBOsc8Hrn4A25Zsg2MWt0bdsTpcdeNVMFDUYwzTwJElR5AxM5i8fjJMoygLOAZMS+NYk6QHmnOlgzqCGQmOpUGd4yfHQgJS6WSSBhw+j7KaYyLfz/XAtWIYxeKoEszLZ8r5kvMr28r7pS4tnyWfLe+Tepg0DOnjwLGV18tIEamzkOTaliHwsg96O+W9fIfUB3U9WRoJ5Lzze+4Rr9eLYDCIyspKRCIR15jLseNzWKOqr68PfX19yoko2yajN1nsNplMKr3T5/OpY3ZhAfHmOHxxHyKJCAKBgEtfpUNMFo/mHpP85XQRKOxDGXC/wmnx4sW48MILXaGgEkxwIyWTyUI+UjisvJcAlHdX5qFIYcK5l1YiArT+cf0IZoMID4VdijY9YFywEkDyuQTlFJy0IOubU7aT93g8HgwsHUDHJzow76vzMG7POIyOjioQTwDMTcD28rgm6en1eDwuYMENLsOLOD4E5roXkwyAY817mDMr+03QRwajwJdZPAJFCsnTGTl4/cDyARz+xGFkmovjH94ZxpwvzoG/x+8yUEimRYFWShhIwMg55JqQQkBWK5eAVOb76xZUyZAlM4tPjSPZnET149XI+/PY/639iJ05tsrruD+Nw9DqIWTHZRHZFsGMr8+A97AXo2tGUbe+Tq0detMl86dlVM6v9JJw7GVfZT9onOG6ojIon8n3mGYxH5z7xrZt1NTUKOWRffd6vUi1p7Dtg9swOGtwTJ9LUWAogEU/WwR/wo/qPdXwO36lfJp+Exs+vQEjjSNY/IPFqN1RqyyxPp8PlZWVrmgDAKpfcn6kF0FaqmnQ4Brg2O3YsQOdnZ3o7e3FyMjIy+rH34LKgLtMfy2VdZW/L+mAmzJR8l56mvi9lE26DJQpaczDJj8H3IWlGN4pjYVShslinBLQSQMzUNSjpCHV4/EgFouptktQInN3dYO/NLwbhoE00nhm7TN45uxn1BjVDNbgVbe9Cs3Hm8d4E6XMlO2WQFPK966JXWjuaYYnVfT2H5x1EHe88Y5iRXUHuOq2qzBn+xwFJO655h7sX7kfMICmfU04/wfnwzfqc8k3mTolxzabzbrq/hDsSFAqAZyuT+j945hK/amUx5mkA0X9HfqzZRV8yj6pQ+r6q4xG1EGs1GV0oCrnUPZLn1/dky/BqtShJNjVgb4+BtJRcrp28X9d95W6uCR9HPkZSRqndJIGAdlHn8+HSCSCqqoqhMNhl07JNnA+c7mcOsq0r68PAwMDCkTL/Sy926ZZSKmkU4C6i9/vh9/vR++Fvdj+se0Y9+I4LP7pYtREa8bMP0G3jLKhYYb8RkYSlDI2vJwq5eUc7n9QmjVrFtasWaNAHSuNS9BGSxELe1FIEQhwsXORcnPKPCYJVriI+mv78efX/xmhZAgX/OoClWchQ1FlGLgUQNwIkonq1kMJAPlchngMnDuAjvd1IFObwb5/24fIjyOwHi4CIEmpVMp1XBfbVl1djYGBAdi2rfpfiqnzmZLhyjxxMhXpBeSYkTlIIA5ACSipWJABSI+5ZDay0jyfAQC1G2rRv6sfA80DAIDw9jCmfHUKfN0+5O38GGElzzKXQlI/q9pxHHVmJg0HnCNpNZUChnNkGIXzqKUglYYKCeL4d/hAGBWHKpAzc7ByFqbcOAWdn+zE0Joh13z2XtOr/o4tiOHAJw8geCCI/gv7MfnHk9F4d6MSitLiyDngfLAtSgE6FfWgW8sBKOOJVMDktVIJ4VowDEMZweSeGBgozBPTL0zTRLopjc3v3YyRWaVBqjfqxdTfTEXf6j70L+iHJ+nB4h8vBvLAhvduwPjN47H818uLEQi2hcXfXYzh1mGM2zMOjuUogxUV1HA4rNYZ14BusNP3gzwOTZ71zTFevHgxFi5ciOPHj6Onpwfbtm3D4ODLMyCUqUxl+ueh5cuXu/g/gQ2NxdJYB5QGSzpopbyicZ/epWAwWBLUSt2Bz9XDbWX7dJKKu2wj4JblQDFnvFRf5LMUUDGBjN8dDWSbNjKeYhFOKW/kMyTJ8aGyf3jSYTx49YNoO9KGi267CKZzyvNZ4bijxgD0ZfowNDSkdL1VN6+CP+3HUNMQVv56JbxRLxyMLZqlj5H8X8oN6biQ8kXX1UrNv9R31BhpMlp6dfX8fim/qefKaDNpROCzJZjW88D15+rrQgf18p5SAFd/pgTQ+hzLdaAbEKReJsdO6jenA8+nexfHQ7ZRAmAdVMo1IKMySo2dDtipB1IP1dspdVL+5HI5pFIpVbhWRptwHKTTqFRxOWKa7qu7cfC9hcrovUt7sdW7Fat+uApWvHRONteQHHPp6S81R7qB4qWoXKX8H5Da2tqwevVq+P1+xSQIyiQodJximEw+n1fhULrVmABBB7wSUDrOqbN6rRgeeM8D6J3ciyNzjuDud9+NTDajwBTBJ3/Tqiy9aRSWtC4CcC1YWqcB94YbWjaEPe/bg0xtQViN1o1i/dvXY3DKoOs6ClH2M5lMoqKiQgFHWqm5WXm9tJzKnGyOFcdCt37LzwhQZR9lKE0wGFQhWLyfm5PvnDZtGhoaGmCaJhKJhAKKUgHI23kMXDCAkWVFoBY+GkbFsYrC96escY7jKCufPFddgicyMBbVM01TKTIcP8nQ+XypHNGKyHUgiYBPMlkJWP1+v3pWJpOB2W1iwtcmYOobpsLT5wEcoOHpBtQ/Xw8IvjWyYATdr+pGriKHQ+8+hK0/2or4rLhL+ZLKEavQcl1JCyvHVxZyMU0T4XDYlYMvhYssICfD9bnGGxoaxnhD5JhZlgXviBeNRxuL/XKAuvV1WPaBZQh3hrHwMwsx4Z4JmP+d+ag8XIlVX10FX9SHze/ZjGRDEofOP4SNb98IwxThk0MWGnY1qP0vDVaJRAI9PT0qj56CR18TPLscKFZn5/UVFRUuIS69GK2trZg7dy6uvPJKXHXVVWhtbX0pVlamMpXpn4wuuOCCkh5f6hFAkZdKRVbnk/r3UnEm6UqvBELksTICiyTfWQrM0HDKNvBaHWjJ98j2S+AmgREA+Gwf1v55LZa9sAxwAE/Wg+t+fR1aD7UqnUOCBr5HGerhri5NOdQ3vg/3v/p+DNcPY8eSHXjw+geVbJ30/CRc8NMLCnLGAc797bmYu36u0i2qq6sRQADL71mOs391NiK9ERc4lvqH1OckqGYkg148FoALsMtxkXOoA1z2jToQAZMEOHyennN7OsNHKBRCXV2d63QZ6neyfWwz+y91BOovch2WAvDsmz5+0lAgQaDUf2Wkhnym3i/5rJcCdXwX3yf1/1Lrv9QelN/J3GX5DjlPOukRdPyth2SXMirI9dLb3outK7eO4QPSWKDAuZ3Dpo9tQiqdcoWDO46D7ku6cegdh2AHim3tXtCNJz7+BGCOTQeQ+EA3NOjjLMPPOc4vh8oh5f9gxIrk7e3t8Hg8yGazauGz2vjg4KACXGREeiiMtDhKBm+apgq70MNkHMfBHz/5R/RM6Ck2yAambJmCc354jtoQLEhBZi8rkPP9DA2jQKFCL6+j942M3PSZOPzWwzh8zWE4XgdG2sD474/H+NvGK0su+yAZIvvL8HVpvWbbAPcRZwyNTiQSqKysdOVfyzGRQoLfmWax2ricBxk6T0HHIiP8Lp/PjzlSSo6TYRiwvBZOLj6Jji93uApkWaMWJn91Muoer1NzIXO/mcNMb7phFPLPJDNmW6QBJ5vNIp1Oq3Oo2Q6GJjM0mp5ltldGR8hQPXlElQRsFGS0XFqWBdQBh248hFmfngWv14stX9uC4dnD8A35kIvkkA8WmS4AmGkTKz6wAqGTITj1DjwjHjiJorLi8/mUYUBamjkGrDHgOI46F54gnICUhcQ4PjSghMNhl/IHFK3yjKSQRiY+07Zs7HznTnRd3IXa3bVY+aWVSEfTyHlzQPKUwavSwYYvb8Csn8zC1k9uRapBnP9qAzPumoE5N89RZ9azbcytYztt21Znq1dUVKgCITQM6GePlsqJt6zimfPcZzK6RgompnXccccd6OnpcQnJvwU55ZDyMv2VVNZV/n5kGAa+/vWvo7KyUslLyjbJg3gtUJRNvAYo5i1TdwGA4eFhJBIJpfP4/X6EQiEl46ThXN7H51FGyffrQEXqBVJX4s/o6Kh6dywWUzydz5byjMTnyHfZXhspXwoPX/gw1jy5BtUD1eodMrqQ93BcUv4UbnvDbVh35zrU9dcpPSIbyOInH/gJojVR9V4ra2HBIwuw7O5lhfsN4MDiA0gEEpj19CyYTkG2JxIJF8ALBoMAMKaWjQSRHL9MJqPS9UzTVKl/4XDYZYSQOoAErXKMpeeZ11EP1OdBtuN0ehifTXnMtcEivvSqMpycziipB8r1qHt9S30n2yGBoryPMlKCSrkf5LW8XuruEujpYyqfJfef1HPkc+VcSPAtgbZcw/ycQLLUvEiSbeL/8sfv9xci7hp9qPPXwQuvqy3cr3z/ieoT+PE7f4y8kcecm+ag9oFa5DNF/UPqaU61g41f2ojhqcOoebIG7Te0A6PC0+51cOCmAxhdMara5x314oIvXICanhrXXtTbQawhjQW6oYfEe2+99dZyDvcriYLBIM477zzMmjXLBVwlwwkGgyWLVukhM1zM9PgR4DD0mIwzEomoYzN8Ph+ygSxufsPN6JrQBQCYumsqXv27V8M0Ch5O3st35fN5Fe7O9phmoVI1GYG+0WWeBOAOb3EcB/vevg9dV3Sh+VfNaP1Vq7qGIACAyyspGYvcOPI6gkrbLlR0lwXV6HGnUiCfyefIEDkJLnXLoB7SIkNppNdXKgZ8FwVAJpDBs/c9OyZErO6OOkz40gR4PB6EQiGVny6FGcOpHcdxKSASaEmLr6x2KgWwNNLQAizvJ/FefscxlkKTXn96CgjoaPSor69Hb29vodBFKIB9792HeT+dh651Xdj1pl3IRrKucbASFibeNhEH33wQk343CTUdNajfUO+K5tCtrbTOSmONHH9WfqWCRaWOAoXgtFTBHAJZuUYYdZBOp5UxZ/d7dmPqf02FiWL+WzabRawqht3v343+Vf3wxDyYcOcEHHrDIVefW19sxbJfLINz3HGFwNt28QgwPQfOsixVqIRzJvPmqEgyGgQoWnYty1JREDx6j/sdKIagS6Xx9ttvx4kTJ/C3pDLgLtNfS2Vd5e9H06ZNw7vf/W5UVla6jHFSEdXBC3mRDoal8ZcpOYlEAolEAo5TSJWrqalxAVypZ/DdEvRIuS1TvnS9SAIbKSdorOV3BKwtLS0q5F3qOfIkDyUTPDaeXf0suhu7se6+dYjEIi49zTEcdE7qxMTDE11gJV4ZxwOXP4COmR3wp/x47a9ei9ZjrapfXZEu3P3GuzHUMgTYwNw/z8Wq21ep+9k/XS/kGMTjcfj9fiUPdMBN2SiBImUDT3xJJpPw+XxKNvBdUreRzgQd4Ml1IVPBpA4n5YwE9RLcSYeLdDrl83k1RzKtivJNptjJtSE9nLpXVxpF5JrWvcfyWrZdrjEdmPM+/T38XDdc6KC4lFFD3ss9oD+z1LqXfZB916mU173U3udaCIfDcCY4ePg1D2NJ5xKs3rEasN0eed7f2dqJn7/650j70+p9078yHY0PNcI0ikYIy7Jgt9rY9aFd6FtcPH617t46tHy7BdZwgR+l2lM4+pWjSM4uHr067c5pmPvHuQjki4UYqQ/rdYFklK6cNznWXIf5fP5lFU0r53D/g5BpmliyZAkmTJjgOlNXMk3HcZQFluCB35P5A0Ww5/F4MBIeQd+kPrRvaXcxMYaUVldXu4588ua9uPZP1+Luy+5GOBbGRXdfBMd2kHNyrpAjggUCWAkAuNnIpAgGJAMgMCxlJWz/YTs8Rz2o/1M9DNMdAqMzFv6mNY6AThZeIUOVR4hIAS6fI0PgyYikF1AyUylM9IJbtMJRIEtgIsGsBKHsR9eru8asD0+/B9WPV6s2cOylkJPvloBXMkHDKIYmc4zIcOg5JciS1mjpyaenmJ5Tzo0srCIVolgs5mofhR7XHYtwpVIp5HI5zPjBDHjDXrQ92IZ8Ko+dH9jp8vTnQ3kcfPNBAMDh1x3GkewRzPzRTDTf2azmj0YUKnkEpTJMnKBRhj/Jc9z1dSZz4YFiiBXnkQqkrnRyr87773lII62iOvL5PFADHPjgAfQv7y+stUhuDNgGgGQoiYSVQMgIufojvd3SOMA5YHqFDLcnj6BiIoWwjACJxWKuz1KplOo31y/HJpPJYGhoCGUqU5n+eWnt2rUIh8NKnsjILMCdG03+R3klo9Mod2S6EXmKx+NRDgHyJeob5G86b2UbpN4kj3UEoHiu5NG8RwJPCSK8Xi9qa2tVNJSU2/IaaYR+/KzH8eyaUwZzB7j8rsvhT/lVP5458xk8f9bzuOS+SzBrV8G5kvAncP8l92PvzL0AgHQgjbuvuxsX/vFCNO9qLsjZEybOvflcPPnGJ9GyrQXL71gOmO5QZGlMoKxmP0OhkEtvk4BJB3I6wOCz2QfOtbxffk6ScpT/S/1Lv0bqTrKNsh16u/R38plSVuteS/k+6p+yoKoOurkupE6gg2I5PlKmyjko1Q/5mdTb9Hfxb2l0kI4BUqm5lc/QQbV0EpW6RgeYeiSAfJZ8n2maiFfF8ew1z+LIlCM4Ovko0p40znnhHNdzOXZ9oT7Yhjs0O9mSHDN/Xq8XqeoUchXuowOztVnYARsWThlyanKwQ+7nxWviyBpZ+GzfmPXDsdCNPKX2g/65XM8vReUc7n8QWrFiBebOneuaQMnIaMllsSsJauS1ctF4Qh7c+/Z78edX/Rldc7uUZbK6uhqNjY2ora1VTEFWJG5INOCyuy/DOXefA4wWvJay8JTMtZGhFpKRSZAngbZuHSOzI4g/dtUx5Kbl0HhXoys8l+2TISAyXJxh2rTaEuxKZmSaRc8i20iBSpLMx3EKnk/OAcEYvZf83DAMVyiuBH0k9lGGWktLPa3J+XweJy8/Oca77RnwIPJUxCXU6HXUveoE9Bwj6QXgNaZpqsgGgmYZ2i4twVRsdK8w/6cFmcoSx5nRE8FgEKFQSB3vkkqlkEwm1XiGQiF1LaMjCPYmPD4BZ3z2DEz+5WSVn6aT43Ww7237cOyqY7BtG3s/sReGx61AMBKDRgF5vjd/OD+A2xDDfrKvHAs9tJ6KJdehfDc/I+hV69b2o/64O3e9al8Vzvj0GajaWwUAqD5WjRU/WYFwVxgv/suLyOWLaRkyWkGej8qjL4CCZ0OCYV7DeZUGOhmlIfP1ZCV8aQAk+f1+BcjLVKYy/XOSPCpHKtapVAq9vcWil9JzxZQdqTc4joNIJALbtlWFYZkiJoG7dDqQFyUSCXV2t9QppOJbCmxIwEVeJ50VEojLSCbpaOBzSnmEH7noEaw/a72S33vm7MEfrv+D4tVPrX4KT573JOLhOO5fdz8OzDpQiFgzQmjsaXTJAe+IF56jHpdhtP5QPc758TlYet9SeCyPq4+cH7/fr4y60lkhU9lokPb5fK4ot6qqKtTW1rp0NKkzEQyXAoPSuAGUrtVD3U0Hv/J9OjiW+pqcG3m/BEzURfUoNN1TKXUvjt1fOp9b9kPq3XKtymcz11euM7lW9XfofdK9rrqDQDqGqAPItE3OkXymjC6U7dD3UClQWSpqQRoX2O98Po+8lcdD73kIh2ceLnxnOHjozIfw6PJHXWHc3Pdzd8zFlb+9Uul5k342Ce2/aQccd2i9x+NB7fFaLP+v5YiciAAAIpsjaPtSG6wTRadT1Y4qTPvMNFjRwvoZ98Q4TPv+NDiD7lx0uW7kHEj9URpR5LhIw8bLoXJI+f8xGYaBWbNmYe3ata5QH4I4euJYLp/hnSx8xA0mGY3X60XKm8It778F/c2FM3u9aS/e+tu3YsrAFGQzRUux9FIyNxsoLnCCKbk5KAAonGiRJkNKJBKu47Vs23ZZiJlTROaTy+WQRx7dF3Sj44MdMLMmFr5hIby9XpcVTIbDykJjEvyTOci8ahlizDGXz6LlnHnMFFjSGsx2cKxl3q88Lou57T6fT/WZYyDzqDimjFYAikJ8pH0EW3+5VQltz4AHc6+bC2PIXdCCG50Kj86EpQFAt5xyrilwZb/kkRq2bav+Eahx7QDFCvMy9EYKVAJ/tltXnORa5xyk02nAD3S+vxMtm1pQt6MOyVwSe6/fiwmdE7Dl7VuQaEyM2Utm2oSZMpGL5OCJe2DAwKSfT0LTk03wjnrV2uba4Zn1BMM8Y5XjyHmjJ0b2I51Oq70mGTVQDLOXHmgqKtI7nslk0NDQgJnzZ+K3s36LA5cdQKgnhLM/ejZ8aR8y/gye+/JzWPG5FYh4I9j4no04euZR1O+rx5qvrYGVstDZ2YnGxkaEw2G1hoDisShAIS+bBpFIJILKykq1jynsAoGA6h+r/tu2rc5Bl8f78T18LnnPTTfd9LIFz8slpxxSXqa/ksq6yt+Hmpub8ba3vQ3t7e1jPGuUYTKii3KASr70PksHQjwed3nrKAOam5uVDNPllmEUorBCoZDKJ6cuJAGIBBqUOZS7EmTwWhbxIkCV8ks+FyhGj7FdlmUh7U/jh+/8IYZqiwbOd/3Xu9DQ3YAtS7fgocseQt5T9IZ7M1687vuvQ+XhSmSQwQuXv4Cta7eicqASr/nGa2AlCsp/KpVyOS5kUTD+Bgq6Dc/HTiQSLp2nlCe/FFHfojzWT7KgTCPpHk/qALoThu0HxhbV0x0wuhGFz9J1Pa4pOl+oh9Gbn8lkXOdvy4gK2S7dI8zP2VbOsQSbsr2yLfqY8+9S48U+6hGQuiFFgjp5ryS+Wx87HTRL0M7nyDmT18o26hEhxCXBYFAZeNgOrpPogijues9dyPqygAO09Lbgvbe+F9500RDDPqdSKYxER7CjbQcOjz+Mib+cCCPrNsQQ9xBfRK0o1t+wHlPePQU4larN9UH9zG6x0fHxDsz+zGxYKUsZVljfBijWM5DGNVk4TjoW5bxyzWWzWfzhD38o53D/I5NhGJg4cSLWrl2L6upqxfCBorCQOVKZTAbRaBShUMi1KAC3wAOA9Reux8bzNiLvLTL39uPtePtv3g7LtpRgpKACChY+5mmzDfwB3BY/MjgZTsvFR8Gmh/HqbVXMFA4OrzyMjn/rKILMIQ/mfGgOwgeL4WsEayR5BJZkRgxr5vnkfr9fAQgKWvaRm5NMhJufVl+GZRGcSa80+6fPqRRQ7DfbyfAz+RmtyKZpIjopii3f2oJcVTFcZvLnJqPmwRoYMFSxNhpgGLVg2zYikQji8bhLUEhLqy78CAjp2aRQ0j0FNEiQscqcX+nN57hR6aFCIg0A0ppKxsXrOb4juREcfO1BnHzzSQDAis+vQPWL1WpNGdUGnv3ys4hOKRaQOS2dspbO+eAc1HbXIm2k4e/xu471ku3mfHF9cF7IYPUCL1xT0vjBH2lg4bNoGJFh/RyX3e/ajZm/mQkjYbjSRuwKG7vftBuHLiyGmjdvbsYZPz0DZk/RO8O9JRUiKTz5WWVlpYomkMKW7aECQ4Mf/5feb6mMco6/+c1vlgF3mf7Pqayr/H1o3bp1uPzyy11glfSX9r2MEqIxN5VKIZFIuIz5kieRx5BPk9+SX0nAy+t0IC3vkW3RAQ0pk8mo40YBd/i07k01TRMnmk8gbaTRcqRFKeX7p+/HLa+7Reky19x6DebvnY+d03bi/svuR6KyaCyu6anBJT++BDV9NUpWP3vts1h611IFSmigN4zCkZwEHZL/SqK84fjI/hNwS6Ow7p2UxmbmbFNnIljhfEuPtfR2ynGSjheOKa/RPbES0EoPsg44abRhu6nHSqMAUHQYUU5LoC6BZCl6qevkeLL/AFw6RCmDAkka3kuBa33NlgLRelvkXuAcyvfJ/SDHkfdK/UW2V3dmca/K+dB1S8uyVDTt8YXHcddld6F2pBbvueU9sBz3PmKfMpkMBgYG0NfXh2g06irurEdkcG4TiQTi8fiYSuHkBZxzzpPjOGr/SP1GetoJ1Kn/yyhN27YRj8cVTpK53y8HcJdzuP8PqaamBmeeeSZqampcDIQAVm54Asna2lrXxiUIkSHhjuPgzIfOhNf24ul1TwMGMPPATFz30HXwGT44ZnHzkVnI0G6CAukFk8yN7wCK4cjSci2BqsyPoWLOBU7Al8llEJ8bd4VROz4H6RlpRA5F1IZnyCpBmrR8MxKAbWI78/m88jQbRrGSud/vVxWrpWAyTVN5W2ktlRuRZ3wTUBH4SkumPN9aFlSRBcZ0yyY944feccgFtgHg8GcPo/qhahiOuwAdmUU2m0UkEkFzczMOHjzoyrWWbdCBuG4A0RUYtov3SC8FjRoyvIqKAZ/P9SAZq2yLDCfjuocJdL2rCyevOan6v/FTGzHvu/PQ9ExT4d5hB8u+sQxHzzsKOMDRdUcx5dEpGJoyhJ4FosI+UFhTBrD3K3tRs7EG+Uge1Tur0fTbJtSEajA6OopUKuUyCHCvsVou+6IfAcY+5/N5pQDJ+aFBhj/0rEiLubTYz/3p3EIbPMUQstraWhz3HMdo46irW8naJBLVCUR6IsqSzzFkFXYWWNQNAtFoFPl8HpFIRM2DPhc0rHCe5fhw/5EvMCqiTGUq0/9/SNcF9AKa5HFAsSq5NMDKEHLpEafMoB4iea4EvdLTJBV33YMpDYXy+aX6I/UwXi/7Y8PGpkWbsGzLMhyYfAB3XHoH0v40LrvrMszrmIcd83bgnsvvcekyd736Ltj32Zj85GSsSa/Bn6/7M9LhNOqO12HtLWtR01fjes/K21YW+mwZirdz3BhZpEeOAUU5LseD/ednks/rxgYdyMm6NJT9MkqLz5ZgTAeSlIdS75FtlL85xhwL3WMqAZVuHJD3S48k+yXlMv+XjoCXAr6nI30N8lp9TLjOpf7F/ujg9nRzo+8B6fziu2VhOH2M5TN4v/xc6jNy3OXYybWj73X5HKlTeL1ezOucB8/jHkw4NgFG3kDeKUaIyHZSl9aLKksnhj43+rhJXYU/bKcMIdfXltSP5NhTn8rlcsqZJQ0O8v0vh8qA+/+IvF4vzjvvPDQ3N6vJYqEQGTJMi61c8LSqyDwdaeWj13LVc6sQtsLYP3k/LnvwMviiPhjeoiVOeq7z+bw6BkNuIoaN6pZKbtp8Pq+qioZCIRiGgcrKSlRVVeHgwYNqE8ozxdleWcBs4o8mwsyZOPqqAoiafuN0VD1VBdsoAmEKZTJ+6ZXVvd+O4yiABMDFgDmWBKT8nKG09ExKAOY4jiruxY1JxsCicVKpAIrAlvPHkGVZ6IrvtywL3Wu6EZ021mvb8rUWmCh64VOplCsvzjRNjIyMIBqNjgkf14WcZCgE+dJCK8EULeqVlZXw+/2KIerzz74S6BGsMl+az2W/CdIIYgnqcrkcbMdG4FjA1X8jZ8A8VjC4VFZWIhvMouu8Lsz8zUxks1nUbatD9eZqNLc0I/ORDIbmji3elavMoe+8QkXLweWDiLfHseSmJS6jB/+WioccCxYTo7Bk6BpQNHzxOrlO2H9ZXEeuVY6n3B809oyOjiKcD2PBDxfgxQ+9iKFZQwieDGLhdxaiprsGGSfjMhZ5vV4EAgGMjo6qfcs1Qg82rbQAEIlEXN+xsKA0KHFOWcFXpmrE43F4vV5XZEWZylSmfy6qrKxEa2urMkZSVwHcgJakAyx5koLkteR7VLSl3gMUgTrlPb8nn5Y8h5Fc+r06MOdvylNpIA6Hw2NOhgGAg1MPwnIs7J6xG9sWbMP+afvRM64H0aqCvH7w0gfh8/pQO1ILQw/KsQHrSAE4T9k8Bf6UH49d9xjO/dW5qO+qVwVppcIvAabeX7ZLgg8JdviZ7L+uB0gZJyPO9PGkXKATQYJFCQLlffLZ0mkkQaJcM7rRROqIcu7kZ7LvlHGUVdKBJL2a1JdlO+RzdF1ajiGvlXPDe6ShnWtP7oNS75LjLWW0rCWgy9NSwFwaVfQChvq6l9EO+lrgM3QDkzQIyNQK2XY+nw42tkVW+V54aGFBlzLcZ5vLtsn8c/lTylvPdssfjqt0+pXSgfku9kN3NujrSw+119vBsXs5VAbc/wdkmiYuvfRSTJ482bWYZbEGCgNOvsw78ng8YzxPBCy83+/3w+/xY9XWVViyYwlCmRBsp2gFk9W7mR/Ee0mSscpK2/IIIVloie8fHR11HVklwTaPEJNhHpZlIWgHMf3X0+EEHFQ/W42q56tg+Ap5WsFg0MX0pHWJeew6sCBI4HhLz5xhFEN2ufF0DzTDynRFgUYJ2X9pOZSbluPBsBbZDl6fzWZhOzZGzhjBvo/tQz7sPsfYSBuofrEa+VwetlE8hgUohmZJBi7ngv3XLazScMJQK3o0Od+WZanwdY49x51gmgDUcZwCmA476PhWBxb/22KMC43DwMCA6j+fm81mkUwm1ZqgBZGGCNjArGdnIVIRwYtvehFG1sCyf1mGyv5KeMIexPIxbPjGBiSaEnBSDtp+14aKjRWABdgHbcz5zBxs+c4WJJuTsAOnsU4bQM/aHmywN2DGV2fAZ/qQ9WXhiRcro3L8aLSRqRVSOZJCXipxEpRyr2YyGZV7x3UovRPyb44Pnx/uDmPFV1bgmRufwRk3nIHAcAAZT9Grzeu59oPBoCv6hPMvFQl698eNG6fmmcYcuW9oeJECXBr8TNNEV9fYyvplKlOZ/jmosbER8+fPh23bKlVL6i0yvUp60MhLyRulkspcYxLDf+V1MvqGoJy5lzJyiDILgEp/kYZUwJ3HLIF4Pp/H6Oho4egrx0bOm4MXp6KC4KCruQu3v+p2AEDGl4Ft2eiY0eEan3gkjrsuugtrf7IWRtYATh0IYWUtvO77r0P1oWrk8gWeP37XeLz6W69GIBoAjKIHr5RiLw3c0oEgU5uksi91Nh3ASl2SYyudLhJgUfZIh0QpT6AEn1IHkv1gW/lOqUNS79JPDCnVfpJu3NG9lrxXFg+Tz9XfIQGxPl5cw1IHkvqevE8+X+oL0vGiG0v4N++jzD1d2kMp4EfiXOn9I5UChTpolvtWf7f05Os6rwTccl7JC6gLy3UojQD5fF4d3ZtOp12pajLEm+/j+qT+yWfLfU3gLR1Ksn9ss1VlYeuXtmLFF1fAcor8iP3QjW9yXepGkb9EZcD9v0xerxcLFy5Ea2ura4FzIdHKw4VKgAoUPODJZBK5XA6VlZUKUMnQ1HA4rPKPbdtGNpGFx/DA8LtDqQzDUB4rCRj4nQzp4IKV1jQZQio9zLwuFouNYeYEBFy8PNsxl8vBCBrAeGDpL5YiNhpDzpNTOUO0SNFDSoEZCoXU3zLHS7fUyXwTXisrvXNeCNLlMxiOTgAqNyoZBYWFzNtxnOJ5yQyzlp5h2eZMMIO97907BmwDwJQvTUGoJ4S0nVb3er1epYDwffybxgE9hF9WWGX4EauW8n4yKq5B6S3QLcWZTEb9nUqlkGnJYNd/7EKyNYnNX9yM1k+1IpKMuM7lZvuZX6UXnuM4x0fiqLi5AtPy01C9vhpVA1Xw+X0YrR7Fxk9vRKw1BhjAvjfug5k20XJHC+ycjYqKCmTTWSx56xJkJ2Sx69O7AAOITYyhoqcCI+0jxYE1gYELBtB1rAs1j9Rgz017MPPzM+Ed8MI6XjRgSUHIceLa5T7guEhDDQ1kXEM0uiQSCSVAeD/XjVyH0sAFnBKAQwZWf2A1HNuBYxWNVxxfRlrQU8T9xWvS6TQikYjyngMFha6/vx/19fUu4wGVIGkcSyaTqrgaCyXyHeUjwcpUpn9eoncZcIMF3YgNuOtFSBBH4v2MejJNU0WWUe5QV9ABE2WTrCgtQYgOGkiUfZTb0nBvGAac8Q56Ij3Ih/K4++y7cf2t16NmtAadTZ34+Tt+Dsd8acXan/Rj3uPz8MLVLyAdLp4jfOZ9Z6Kys1L1h7w4OBpUR57KPGBpyKBuQ5kOQJ2GogOXUsBKfi51Lwl22H/KOGmQZb6sLBDK+eWYcixZcZ6F2qTMlylNesRCKWcT50XOGfsl9SupM+t6NN93ujB7OU6loiV0Q41cs6Zpqqi/0dFRV5/ls3Vvuuyv7mmWRg6+Uwd6OsnncY9IUMznkOSa4Tt10kF2KaCpg1CpW8t7OPfUqWWetf6Ov2QgkfOe8+YQq43BODz2HPJSxgsa8mSoOrFCsimJ3d/ZjVRLCi985gUs/8FyhEfDY8ZD7g32WXrXpeHwpagMuP8XybIszJw5E4sWLVKMoaqqSh2Jwc0mKycDxXL/pmm6jnAAivnK9Ioxv4ckLW46AyG4ksxLtoOLlmFFstAGwQSv57NkZWuCTQoTbg6GrSoroQl0XNuBkbNGsObXa2A+ZyqQKIudkbmw/QxrkxZOMmxpyZYWRhl6w/byueyzDJ2RRg2OmQz5p1GEY0EQKYWMLNYhi81RCHpzXkz57hTsummXa70EOgIwDhhqbuUzOCZSkLOdHAvJwKSlj/3P5/OoqKiAz+fD6GghR1gaTiRg5/qTHnEVtTAlhYOfOohkW8E4EV0exbF/O4bp350Of69fjeXA6gHUbKqBP+9XVURZdZae1Ugkohhr7UO1BcBXdaoYzTX7MTpD5DIbQMc7O2B7bEz/03REJ0XRM7MHLY+1wDxmYvkHl8MwDBy85iBmvDADm/9lM3oWF3O8fd0+OHCw/+v7kWnOYPt/b0flpkpM/vpkBLuDOLnqJGoernEZMQhAZYidVPK4P6VSID0tMkWEniKOO9ck104wGFTrnPc4jgNYcFWG53cM66ZBg2uSc24YhbNYU6mUOnaNRqSBgQH4fD5VSIR7mu+0LMuVOsE9wr3T39//1zHDMpWpTK8IsiwLU6ZMUbJGB9T8jPJVkpTXupeP/IM8k3qBLNwodQx6rGRKEtsnAYwO7qTxXf52HAdb5m7BpGOTcOfFd+LA1AOq3XdceQcuv+dy/PHVfywJtmt6a9C+tx37FuxD1p/FWfechcBwADtX7nTlb3fP6Eb+hTw8WY+rHfr48nOOrQSO1EkkCNNzaKWMkQCEfeVYS0BXam50EEzZzzbpZ5tTfjGtUK4Jfb5LedH1OZN909uuAyppLJDpBGyjzEGXVbR155EOxPX2yLZzTUsDsz7mpQwa+v9yfkoZqOTcyX7Kd7L91NX0dpYCzxKQl1onpX7zXvlu+R593VlWoc4AsYhudOD7qT/ato2j448im866jP56HxzHgQ0be67eg575PZjxjRmw9hSBLnV9/vDdcuxln+IT4+i8oROp1kJtqN5Fvdjyji1Y+oulCnTLaFp97OVzXy6VAff/Io0fPx5Lly5FOBx2CQKCKLkBKEToSWRIhR4SzUlntWoyQMmoSLo1kIteCjS52CVIkGHrkply49FIwPcQwBI8AMXK5vKYLsMw0PG2Dhy88iBgAg+/5mHM6piFQFfA5SXUPe4yvIREEMJ268XbCJZlm6WwZvVtAhYpzFjFWhYR4/PkPDI8WmeaBG2GYSivoAr9qnBw/E3Hx6yX0Ash+Hf5AV/RIkeQynFmW7iGpKVcr6rOtcE+AEA0GlUGAa4nPk8vuMYwYj1Pzm/7YWXcFr5gPggvvAiECky3b00fDr//MIY3D2P2V2crxYogDwCCwSAikYgyIvH5HLtJGybh+JLjGGkZcb0LQ0CqNYWtH9yK6NQohhcNY9nXlyGXLkQyTLp9EsyAiTnfm4P8+/LoX94Pb9yLafdOw6FzDyHZXjy+Lbo0ikOfOwRfvw9DS4ZgB23U3FGj1h33ovRKcG2y2J5upQaKxiv5nSzIpu8dPSyTQiwWiyGRSCAcDrvWvrTcyiPBdE/68PCwMuBwP3KuZWV2KrUE3gy5pOFN9z4MDg6OWb9lKlOZXvnk9/tx3nnnAYBKP5OGZ8DtVdXBNWUXAZ30BAJwySzppWMEG5+np1NJ3kWAJfUXwB3uyzZ0tXTh2aXPwnEc7J26F+O7x+Nw+2FXm45OPoq7r74bq59bjXsvudcFokPREC76/UUYf2Q8Ju+ejKw3iyk7piCTyWBtfC0efO+DyAaymLRjEtbesRaBZAC26QYokt9z/KShgJ/R0E3HA3UIXl/KqAC4U5Qk6SBMfiZlLlOhOGaybbpxQL5f7xs/0z3oHAf5LOWAgTtsWddHuWY4RhJIy3VIkqCU4yWdClI/lPfrxiOSDoJ1wCU9n3Ju5ZhIea2DZx2IlwLEUr+XuqQ+x3KOSo2N7JP0Pst9K+eYz+R18l7On346iwTk+vsdx0HXuC7ce9W98PR7sOCGBS4dSQJoj8eD7W/djgOXHABMYM/H9mDaZ6fB0+VxOQd9Pp/CDXIuxhik0gbMtHuOvWkvLHts0cRSnnkZqVFqr5Wil5fpXab/MUUiEVxyySWoqqpSii5DxPVFmslklNebEyrzaR3HcVXi9vv96sgmXcBIQJ1MJpU3MZFIuCyA9LiyCBnDiaQ1UhY5k/kaMt+CoFVaaglUpYDkBtn7hr04fOlhtRJHp49i+ze2wwm6c3JkOJvjOMrrx3wPEp8rN5xhGCrvS+bVECBL8E9rKQD1DGmlkyA9m80iGo0iEAggFAq5jiHg96UKeXDzMgcaCaDhwQbXegl2BNH480Y4TqFSeCwWU+uGbSZAchwH2VwWeaMI1GTFR2npY185H9KQAhRzxrgOyWAYwsfxYai9bdvwdfkw44szED4UBhyg9tlaTPneFATjBQ9t/4J+dHy4A5m6DHrO68Huz+6G4SmGLcuxl+d+ch6BgufX2mlh8b8thm/kVBVOG5jzvTkY99Q4PHfjc4hOLRSw6TmzB0/84Anse/s+GJ6ioaIqWoXF31uM6o5qnPW5s9D6YCtqt9YCWqp3dH4U/Wv7ka/Ko+ujXei4qwO7b9sNT6VHtZHjzvnl2uSYyH0nIwW4nziH/EzyAK7LeDyOWCxWCH1KJlUUAA1CbANJX9s0PsXjccUvmCtPAcr3Ok7xHFt6ymW6RjgchmUVqvvzfikYpRGnTGUq0z8PSVAr8yLJI3SFHCjyIH4mQ3t1RZggSxqQqSNIEK6HsupAkTJEApe8WdCh8sjjV1f9CgMNA/jNNb/BttnbsH3OdqT96TFgm9Q5oRMvLHsBr7v9dTBsA0bewKt+/iq89nuvxfgj42GaJuadnIcZe2YU+mTY2LtiL8778XkYf3A8zrvlPIQGC0U1KTcl0GJ7pbGcOpMEe9RDpJdWAgDpLZRjKEGfDuoAuEJsJYCU48j76PCRYEy2U3oS9XWjR31JfU2uFwnIS3lzJUhWzgqhj1JvlTqQbsQgSRDGvsj1lMlkMDQ0pDz3cvyCwSCamprQ2trqOoJTB3ey/XKN6gYEPXJDjoNudJD3Sd2B48dnyDQ1SaXAr942Cc7l5/qP/E6CYo/HA0/Ag5tfdzMcb3HcdKOSbdsYDA/ilutvQf+4fnTP7sZzX3kOtjnWwOM4Dna+ficOXXSoiBVmjWL3d3bD9o41FLBv3HPSIMCfcHcYs26chdDhEOAALRtbsOQ3SxBKhsakJsgfOc9lD/c/IAWDQVxxxRWorKx0MVRubGl5YxgGwY4E1xIwSg83QR4ZTTKZVMo1GRRDu+np4uek0wk+hhFls1ml/EuArVvuyDgCgYACCQBchdMIQgzDwMxbZmJ00ii6l3UDBuAb8GHhlxfCm/Mi42SUsSGVSqn7A4GAGi+CVsuyVDiuBI1kSmyzDE2XFirJMGQEAJ/Pezku/L6yslKBYPkcAh2gWP2cgJ2WY6DgFc0hB7iLW8J2bGTtLHyOT40vC7YFg0E1ngxnH54/jMEPDKLt423wjxQAWT6fLzxfgEIaRbjWpLJEocVxk3kpzIMBipEEslp6aDSEhW9fiI4vd2D+F+bDdEwYloFcdQ77P74f2cpCegRMoH9FP7qv68b0+6YjlUohHo/DarCQN/PwJXwqt5zrndXXg8Eg6pP1uPZz1+K+j9+HKX+egtmbZuORLz2CRH3xbFMYQGx8DLFXxRDIB1D5SCUCJwvFyrInslj8wcWorapFJpfB/F/Mh7fCi6PnH4VjjWWcdoWNdEUacICdt+zE7PfNhnWi6H2WSgqPFzOMQrVbCYA55lIpIEhl3jfnQjJxepWlR5rvZQgmc7mZ/sB2SKVEGlj4XPKAVCqFcLgQQpXJZFSdiKqqKsVTWIRNWrNlZESZylSmf06aNm2aS5aSj0nZL9NtKF9lmDP/lmHEOniiTJZOA/5NuSoNx6XAAo36o9WjGPWP4tbLbsX1d12PP6/8M3ZP2409U/f8xXxsUuVwJV73u9ehaqQKV9lXIZfKoW1nG+AApqeg9UejBSNvzIzh6euexr4z9uHovKN47U2vRTAWBEw3YJRgRgJejhv7QflKnS2bzSqjfiAQUA4ZFiHlaRMSGEqvpwQ7vEYWVZX6H1MZ+R3bzh/peNHBoJ7HL50y1CU5X9KDL3VPGvTz+bwy7sq26JFf/Ft6ZHUjuARbuqdWesC5rpieqb+PumBfX58LdPH5ci3yHj6XpIPV03mydeDL50jjk+615XXsp3Ra6Z5aHSjKeeT75d9yD8s5lHvWNE2kK9J44M0PoHNqJ256w01415/ehapY1ZixMQwDv3/N7zFaWUwTHJ08io4PdmDmt2e61q5lWZj3+3mIToyie9EprNDnw/RPTYeVs2CY7hx7riPqTFKn4v7yeDzwjfiw8v0rsfOGnTjzP86E1/IqPVAaE2S7OYbSgfZydSDj5aJzwzDKZ778P1A4HMZZZ52FmTNnupRpvWQ9FzEBn6zWyWtyuRxisZhShPP5PHw+H8LhsKvoBBkiwSoZO49tkkxAD6FhGwG3ZzmZTLoYsVT+2R+Gm/n9fjQ3N+PEiRMqV1S3RKdSKSWkfQEfXvjoC+hv78fM78xEzaYa1Q49fJogTA+hISOQ+T8k3UDAfrEdZByy6jb/dxxH5b2WCvXlmNOSls/nEQ6H1Tzwej1kSQqZk1ecxL6P7nO12Rww0fTVJlTeV+kC/hwDaVAZXjWMzm93wvE7qHy0Eu3faoe3zzum8qf0ZBKwyaJ8nDvdSi6t4oxYkB5bzhMFKsEhv09MTWD7x7ZjdPIojGzByDL5D5MV2MtV5XD4A4eBLDDzpzMRzoZVRILjOCq/nKH4oVDINfZpI41nP/IsepZqZ3CTbGDpT5fCPGiiakuVMnxwHKqqqrD1LVsRGx9Doj6BmgM16DqzC/ngWEt8ZGcE4z8zHpGuCMwKEwNrB9B0vAmhgyEkEgk1x9XV1RgdHVVMmXPOiBS5H+S65LqTio6sqcB5kkJet7CTL3D8KXBk8RJGY9BgJ1M/5DOqq6td9QrIdwj2gQJvuOWWW9Q8/S3JcfQzdspUppemsq7yt6VvfOMbqKmpQTqddlWclhE5uiwuBX500pV/HXRI/iiN9Lr3yzAM7J+wHxO7J8Kb8eJE1Qnctu42HGs59lf3dfaO2cj4MxipHsGVd16JpmNNiofKisiy3WlPGs+sewZbzt6ivqvtqcXFv70YjV2NLpAidQD2IZVKuWrzsE800lMfDIVCCiRzfBlpJoGp1Msk4KKslrJFEvVQOkkk6A+FQohEIrAsSxXElYYT+UwJQtkOCf5077SUY7yHwFbKNznmJDpTWOCT6V65XE71gyHG0iDBdrHN+rqS75Kf6+tT6sxSx+P3UleVoFQ+Q++bHC/9Wnm/BJGyDzLaQUZ7yLGXbeS75ZicjvR9LufXsizk6nJ44XUv4OCyg+qeKcem4NqHrkXtYK2rH47jIGpFccs1t6BzWicAYPz94zHzP2a6nFcuQ5UHWP/h9RhuH8a0r09D5eZKV18ZwUfDUCKRcDnreB3XCtcGHWHch6WiA+QYco1x7C3Lwq9+9au/qKuUPdx/R/L7/Vi6dCnmzp0LoLjYyRTkWXD8jgCHXmW5iblYONkbL9+I8586X4E7Cax0xiDzpuVZgZK5hEIheDwexONxtSGZC0RGDwATJkzAkSNHlFeXirzf71eetK6uLti2rayEjuOo6wGoRW3bNvLZPBb9YBG6W7pR8WIFYLhzeMjACWD5uW5tN00TTU1NiEajGB4edlUVBwrMWR5XJi3qZCAyH4weYvaRobaScUqPv8fjQSgUQktLC3p7e5WBIxgMupgTlYdcLoeeN/TgyDuPuNaNkTYw7jPjEHosBNu0XSBfFgUBgNF1ozj+seNw/IXnR8+P4mjgKFr+tQUhI+Qy8hCAK0UhnXatKcncZaiZHBc5NlKYy+rajKJgn717vJj5rZnY86k9mPzQZLTf0w7TU7Bu2x4b+z62Dz2rCmA5H8xjxX+tUEoGAFWsS3pPqAR5vV54Mh4s/cFS7HjHDnSu7By7EU1g07s2IXgyiDn/OQe1L9bC6/Wq8HWv14uFv1yIbH0WqcYUQttDqNteh80f3OzK3QMAwzIQqYrA1+fD/o/sx8CVA+jd1YvJX5yMcDyslIShoSHk83nU19fD5/NhaGjINQ8yHE9XTPSxJy/g/xQWMgdbGr3kuufnLqF4yjhCD7tuEWfudj6fx8jICOrq6hAMBl2KIQ1VlmWpmhRlKlOZ/vlIylvpZSb/+Uvhq6dT7vk35Yr+PglyTve9YRjY3b4bfzz/j5hxbAbO33A+/njxH/8qsF3XXYdFTy+Cbds4Y98ZSHgTiNZFMf74eNhwe9Ql0JDgRZ4vDAAOHJU2xToYwNh8XtM0lQG5FNiSPFyCRnmtBFBA0Wmjk17HhiR1LanbUG9k26PRqAvEybmU86iDSKCgt1RWViKdTiMajbr0Wdkfkg42ZR8oO2kEkUenyehE6tK6rJVt1QEtdTk1jxoAlfpyKfAtDU26B10H7vKHz5GgThqU5P2U+XIf6WNYqn2SpD7K39IJpBu0pJGEfdKf5zgOTMscgypt2MgjP+a5tm3Dk/Dg/FvOx8NXPwxrv4UpP5sypt2S9zi2gwX/uQD9Lf2IbI+M2XccOzoY9dpYEjPISAH+r89LqbUsx13e/3KorCX9ncg0TUyfPh2LFy9WIaRyM8n8Esl4GCLqOIXQaVouqWhXVlbCH/Dj8Tc9js3nbcajVz0Kj9edgymLJtHKQ8EYDAYBFMOC5UKkEk2FmuBUeukymQy6uroUGGUOC98nq1cT7BEQy6rOCjj4vNj08U1ACmja3aRAv7SYMm9VhnQTcEirYyaTweDgIKLRqGqvtCzTKCANE1VVVZgzZ46rwJhklPl8HsPDwy6wTcspifNLkDI8PKxCwEzTRCwWQzqdVp5NJWwsE/0r++H4tNCejIHqp6pdOT0EtVwL7L/xnAGzxwT4CBtoeL4BISNU8P6m00hn0jj4zYMwvabKwSWDCQQCmDVrFhobG0taU2ncYcSFrNBumiYcwy0kpFLGMbYsC+M6x2HhDQvRem+risZwHAc7v7wTPSuLnumTZ53Ehn/dAACor68HUCjyls0X1nYgEFDhzSMjI8poEI6Hsfzm5Wje2lzIyS5hqE02J7HjozsQnxFHIpLAgY8cQDgSVmvK6rVQsasChmFgwlMTsPZjazHz9pmFZzlA4GQA8788H9UnqnHoC4cwcHnhnPH4nDj2/cc+xP3xggKQLR45F4/HXYoKDTP6vpeWfo4frbRci1IY0sMg0z4AjKnlIPP+yB90hYs8h/MsIyMcx8Hw8DCi0ahSAhKJhCqkCBQUsVJ5eWUqU5le2TRlyhQ4TiHKiPKRukwul8PIyAi2b9+O/v5+peDK+iHS2J/P55HL53DL6lswZA0VjpMUBTJ1z10+n4dhFmufwAAOtB3Ac7Oeg+0UeFFncyduu+g2DFcN44XZL+An1/wER1uOujtxin/L/w3bwCW3X4KqwSpc+5trAQtoHGyE3/YjFA2hrattDCihHJahw6ZpIuVJ4ehM9ztjVTGcaD3h0iX4LF2Z1x0qvI48nlEFEviPjo4ilUopWU6gqBsvdABMGaADkFLeURoKKFtkkTwJ0Cj/JaiVMo3geHR0VB0xJvtKfVhGCsq2SdknwZf0fuqRFjQu6MWzCNL5PvZFfh+LxVTIvgS+cizlu+R6lf2QEWdSh5bf87n6NTI9QzcElALTsm3SCcX79Pfp/Sg1rqWcAmoLif7KtkdiEay7Zx0mH5gMOMC4/nG4/r7rMW5o3GnnLtwfxrIfLcOM382AlXWnCMh+0unmDDqIbIq4QtupK3OuZcSp3l+px+rFl/WxKmUU0fkBP385VA4p/ztRa2srXv/61yuGKUNtyCCBIqgjI2B4gwQtMpQ77Unj6Suexs5VOwGzIDhWPL8C5z5+LqyM5fJi0YvORci/aWVsaGhQXl/pNaRXWh5txe/4P88eJHgio+FC5QaRIdXS+pjL5ZAL5rD1PVvRtboL4eNhLP/YcgRjBW9wLBZTgI2bngCE4UayEIc0DEjvnwQ5EtxI7za9qQT17AM3Jg0PLPLFv0sxwmQyiYqKCrUBCbQlAwiFCoVUOq/rxKE3H4IdKApIq99C+6vb4e8pgHNpvHAcR7WTnsZcLgcrZGH3b3cjMz6D2m/Xou43dfBahSJn6UAah288jNiaGEJ7Q5j14Vlw+ouFOmicYT4354d/s5I4x1la69MtaRz6xiEs+PgCWEOWi6FKoW1Zlqu4CIV2JpNBOpjG+p+sR6q+YMQI9YVw/ofPR9gJq/cl5iWw9bKtWPKdJfAlfKisrFRtpPAMhUKFQnoeG56IB/fdeB8c00EqkkIu6M6vMbIGDMcoHCd273TM/PNMhAfCMB1T7RGudduwsfPNO3FiyQmc8d4z4E15ceQNR3DgugOw/W4vgrfXi3lXz8OJd5xARU8Fau+rRa46h+BwUO0ljgPDkTi2VCh4KgGFAI0mPEaN64FKid/vV/UZmFIhhUAul0MwGHTNaTKZdJ17Tm+3LFYHFNJhZDh7OBxGdXW1S5HKZrM4cuQIHnroIRXe+LckpxxSXqa/ksq6yt+OPvvZz2LatGlIp9MFfp1OIxgMKlDd2dmJAwcOIJfLoaGhAU1NTcrQLA27uVwOeX8ej5z1CJ5a/BRCqRA+fvPHUZ2sdkUuKbDm2Dg68SiemPMEXvvwa+FL+3C06Sh+eP0P4cDB6x98PWZ1zcLXX/t1jFQVT62wchb8WT8SwUJNDytvYcXmFbBsC88seQYVsQpM3DsR5959Ljx5DzJWBgfmH8D9194PwzHw1p+8FY3Hi8Zn8jqCVMB9RKht28jbeeyfsh8PvfkhZEIZGLaBGdtnYN3v1gG5sR5QoHhiDHUcHSgztNs0TRVOLvUxku60CYVCMAxDpfLpgFsaTXRvuwSj1KGy2aw6KlJ6b/WoBZI8FUY3LJTCGtSNZfoa14Lu5afOTMeDdAZwHKnfMQecKW6cK/ZVHzfdMCL1WN2zKR0SP2rB1AABAABJREFUvIe6mhzv04FG3iP7Kq+VoFRGFJKkMYLrRhow9D7q9+nXyLk5nedaf04p7zqj3WpqahCqDuE3r/0N3vKnt6gj8TiuHAMWch4YGMDg4KAycsjxkQYVrhcWfia+kNfIY3wTiYSrWLN8JiNFGVLONSXHXh8LaZTiPMsaUP/93//9F3WVMuD+O1BDQwOuuuoq1NbWKgYkCyoRQMvjtvRNRzAljyDK5/M40X4CD7/uYYw0FoVMfW89rr79arT2t7qsWAy3IQiVgFfmlhB06xWQ5aYiyOVzCdJlxWIyTP5PRZ79YniS4zjIRXLY/JrNOHxBsUJoza4aLPzuQgS6AqrtXPh8tmRsAFwgW3rP2UduVHr2lGdYeMXZV24g3kOwLJmwDO2VDF6OGdsrGZwMh5chRVu/sxUjC4tz2fKuFlQ/Vw2gwMhqa2tVlIIUAARf7HM6lMbo9aMY94txatyy1Vmc/MRJjFxWfH7Dlga0fakNRmdxbpMzk/Ae98KKFkPz2T6Or6x86ff70d3ejX3f2IdcfQ7Vm6sx/ZvTYR4ppgtIMMg1Ib3jck3ULKnBo+9+FHbWxprvrkFjthGxWAzZbBb9C/rx5CeehO210fpUK+b/Yj6q0lWunHaOB4Enj8ezLAu7Vu3C5tdsRjZcNFqVopU/WYkJT0yAY7vPLOVvaZDK5/M48LYDOPSqQ65Caw1PNsB3wIfjby8c8Tb5e5Nx4uoTmP656ag6VOUyIMnj/qThivMuQ56494Gi9VoXqLLNVODIVxjZEo/HXSHl9H4zV5ARD7ZtK35F4cQ9UVFRoXIJWSxw586dWL9+vdpLf0sqA+4y/bVU1lX+dnTDDTdg9uzZCsTE4/GCITedRjKZREdHB44fP674jc/nQ1VVFSKRCCoqKpQ8tX02njvvOTy56kn17HED4/D6e16Ppt4mAMVwXcuy0DGpA7+85pewTRtLty/FzCMzcesltyLnOWU8dYBr77oW406Ow23X3Ia+xj4YeQOr169G25E23LXuLrSMtKBxoBFXPHkFbNvG/Wffj7VPrIWdKnqvNs7eiPuvu1+1yZPx4LpfX4f2Q+0uGSiVdvJn8mTmiO5ZuAfPXfccJu6fiMtuvawkYJPgUdcVJFijY4HvZ9qO9LxRZki9qhQY57s5xhJoSmDMOQaK+duO46ioQ/0dpbyush/UJXgfv3up9lF3oN4hASGfJ3U6jpOM8qJhiDoInykdLnJuZJvl/5L0turXSWOMrvvpejfXj9QfdUCre7hLAXk5tqVI1w3kffp6PJ33Xu87nyeNKexvIBBAJBJBZWUlIpGIyyHG50jPfzabRTwex8DAAIaGhlTkqIxskBEY1BdTqZQC3BwrOsuoD2cyGRVBI+eIeiH5kjxyWXrepT4mx6QUduJc/fSnPy3ncP9vU3V1NVatWqWswECxKp6+uQG4wB8ZigwtoZea5yI2H2nG+X84H4+8/hFEa6Oo66vDpXdeivqueqSN4tE83AhUqHUrJ4mbUV/YZArSo0uwIQEjjzXjJmbbdW8+GTutjllvFtkKNwjK+XNI+9Lw5DyuXB1pBWab2Q+OH4t/kRnLHGLJvCR4MYxi8TTZZmkUkbnDOrOiVZht4qaVhWUMw1DM37Zt5eHM5/MYXDGI5Hi3V3Dk2hFEtkZgjBav5zvokeb9uVwO8fPiqDxcCU+XBw0/b0DeLlpCs1Z2DNBMmAmkPWkY6cJcpOemceKzJxA6GEL7je2wbMu1dmQxLYK39PI0jn3gGHL1hXYNLx7GgX89gKn/PhX+Ab9rbKRS4TjFPH4WQDMMAxV9FVj8X4uRT+fh7fEi5itUw+9Z2YONb9mojn3oOrsL3qAXK/97JTLRjDICcI/wfWSY1dXVOGPbGTAzJqLjozi56CQmPTMJHed1ID4u7hqX59/6PPKBPCY9MEnNm5x3n8+HZDKp0gKm/3I6jLSBA288AACYcP8EOBkHx95WzB089MFDAICDnz6I6d+cjqFFQ5j4p4mAXVQsuI4Z3cKx476R4ee6EYkAW1rGpXHKtm0F6IPBoLL6E2jLtTxz5kx0dHSo53KPEHx7PB5VHTeXy6G2tlgAhUf0lalMZfrnoba2NnUcIPc3PUGUazSyqaindBrd3d3w+/2IRCKIRCIIBALIB/LoNXpdz08jjd5sLyKxiKtg2J75e3DvxffCNgvv3DR/EzbN3zSmfUPBIcw6OQuX33k57rn6HszeMRtnPXEWkskkzk+ej3mj81BlVyld5rJnLiuchx0uFgA1WrRQWcNBLBxTskkCFWlc172xwy3DiMQjWPuntZi2d1pJ8Ks7DqS+oQNz8n7dISDlkjTC6uCUbZQgVfZD1/skkJBOBqmz6mBdeuxLAXs5VnKN6KBbgkapD+ugVT5PgmagKE/lUazSSSD1EL5XB8uUu5J0UK2/l8Q2SQNGqWfooe/6+zlO8rNShgzZj5cy7Mj5kWtPf16pa+W86vOty3uOL4uWSbyjt0f2nZGoenqoBPKlsJBuHJB7ifqUrE4udSQZRi7vk0BcXwdyfomD5Fzq3vDTURlw/w3J5/Nh4cKFmDp1qvosm80qzx4ZglwAspy8DCGRnmIuZKAg8CYenYhX3/xq/P4Nv8f5PzofzYlmBIIBWJaFqqoqDAwMKJDHhU4roHyuXGhcPDJ0hW1jyI4MBQHcG5uVueWRXPKeQCDg2izeIS+W/mopbL+NEwtPIDAQwOJvLkbgaADpbFp5APkeyVCB4pFdDIXmBqBgAdwWtUAgoI484uaQOdFklHpYraymTgBK0EtLmmTsbBeVEYbV8RnSSOA/4Id3xIvMuFOeQQeo2FkBI13MQzpy5IjLqssx9Xg8SKxIoPvz3RgcHcTUN01FPl6sdA8A3m4vmr/QjJORk0gsTcB72IuGf22AfeLU8WgTHHR9owuZiRmkZqRgh2xM++Q0xdgsq3Dcgp0vnkGeyWSQ3p4GugA0o1BUzAECHQH4kj41Rul0Ggc+egCzfzEbnrTHxUDz+bwrd6+npwcVg4XcaZhQDC18MAxv3ItkVVK9p66jDvlkXhlgOO+soMuCYUDxyJbpz0+H43cQ2xCD/7AfFc9X4OmvP418oLjWba+NzdduhmEYmPX4LJV3SLBNRYRj6/F4MOPOGfDaXkTro5j808mIzYrh2JVji/XEZ8TRcUMH0g1pJKclMe+b85SCyXUhBRPXZmVlJeLxuPpchppzfbDvAMaczy1/+vv71bohuKdF2XEcHDt2TIV48VnkG9xv/J7H3FVUVKjzSss53GUq0z8XLV26FOPHj3fVhZEFV5PJJBKJhNIhGF3DiKxEIoFoNIrKykqEw2G0vdCGPSv3AAC8aS8u/9nlaOxtRNpKu4o91h6phTflBYJQfP+C2y9AMB7E3W+5G3CAC++7EEs3LYXjOGg53oJrf38tagZrlG41ff90mEET2UDWpRNIA7tpmli3ax1CVgi3nXEbAODyX16OyR2T4XiKKXFA8SQJ3eAJAJnqDB542wPIerO48tdXwm/7YXiKwEGm9VGvkICBRmOCRFks13EKtXwk4JbAUwc11Bcpp2QkoIwWlF5N8m6OiWwD9RfZft5DQ4aUWxJYcax1b7Xef92QwPfIqE/pUZQgS6Yysr+yf3LMpSMLcEeLSYCpG7Cpj8jvJFgjcS75OdeP7Bvfy7bqhhKpJ/GZEqDqQJNUylDAd0gHEz+T4LkU6UYgvV2yzewfw/epJ0mPvjQmyHlizRg5LxKXSN1/96t2o+7eOqDTvfb1EHA5/tLYozsVZdQldXg9lFxvV6moDmmg+EtULpr2NyKv14szzjgD8+fPV8zG6/Wq8EtOjAxZMAzDtThpmWOoQygUUvmX3CSZTKaQL3WyAW/7j7ehrrtOCcR4PI4TJ06ocAsyI+aEA0UwQ4bMsvlAgSHFYjHs378fIyMjcBwH0WhUhUwxlEMKLeYTZ7NZFfZExk2mxCOeeIYkKTAcwNn/eTYadjdg9ftWw3/ErwwU9K6zL9KSThBA4SKZDzc3Nys3E40GHHsJngC4Nhs3nGmaCAaDChwxdySVSingTmswQSRBvdfrVdXJydgZDmMYBvLePI6/6jjik4ue1upbqhH6VaiQY2wUQ94pJDlPlmUhPieOzu93wm62kZqWwr7b9sGsc4cge71e+Pv9aH1XK0JbQ5j2ummI9EQKhUFSMRz44QFkJp4C+wYwfNYwjnz8iMptyU7JYv939sMOF9ZSKpUqzPNIGLM/ORvh3WEgB0x7eBrWPLoGuZFCiFfSSeLwhw/j5OUn8ewPn0XcG3cxbKnEyLwbKXxM00QkGcHaz69FqD8EM2tiye+XYMZjM+A1ioYSFueJRCIIBoPq3PGRkRGMjo4WQXjSQU1PDfx+P1oGWvCar7wG/kQRoMIGmvc3Y8GLCwpjbwAD8wew5d1b4ASKbeKacxwHSAMT/zgRC362AOFsGHVb6rDsc8tgJSx4Yp5C8bZTlGxLwg7Y6Dm/B7s/vhs7P7gTfXP7YFomslYWOX9OrWuuteHhYQwODqp0ApljR+XWsgpn3tOY46oOLwrcMFfJcRwsXbpUKXf8fHh4eIyFnEYGKl2JRCEvMp1OY2RkBD09PYjFYkgmk2Ms/mUqU5le2SQLlergirxIRoxRTkuP1ejoKDo7O7HH3IM/v/fPxYfbgNVjIR6Pq1zLY8eOYXR0FNYhC1d9/yqYtgkra+HsP5yNti1tePRVjwIAzlx/JhZvWQwvvAqkjxsYB7/hH+PpoqFAly0qHDuRxYL1C3DW+rNw1c+vQvuedhcYlR5Y/s3+OY6DEe8IfvvJ32KwcRCjtaO49X23orem12XspI4inRqnc7ronlcdIEhjK6/n97rnrpReJIuF6UCO1zOdj+/U51gCc+kJl/2U7ZagRu+PBEtsD/sgIxN1D6sE+XqVfII/6r1yPmVbS0V1SsAuI+f4XF7PZ+l91eUgQRp/qJfr4F8fDx0wy5RNeQ3/LzU++hzL7/XicdKDezqQqq8TSfIz9lF/tj62UW8Uf/j0H5AMJF2h5hxXgmB4gX1X7MOea/dg/Q/XI12THjNXbJs+1xwfqevI0HHqorLNutFFN8Do6533vxwq53D/jWj27Nm45JJLFCOXRy5JZiAZYS6XQ2VlJbxeL2KxmLIWy3BQoOipLWWp5UTLnFYZpgEUPeckWZyEllRZRIBt50ZnNUygWOWSRgH+zecSLFChZz/ZZ+bZEhx4PIVjyGThJ4bBypAQUiwWU4BZCiv2RfaBxgbJRGSlcwnk+T83p2SIfI4sxCatnBx/CisaDQyjEHZHCzUZpu230fn6Thx9k1ZNFUDz25tR+WylykU2DHdVdL/fj5yTQ8eLHWOqm1c8WYGJH5hYEBTIIjsnC9+2oteZDElZPCcYOPyfh5GangIcIHJPBBM+PaGQvzU/hQM/PgA7ZKP6gWpM+s4kmAOmAm+O48DxODj64aNo+3pbcd36bRx/23GcePMJ1a7KvZWY8+U58B0rFgO02214Mh74h/2ufUJBkqhOYMu7t6B5VzNan2tF1yVdWHLfErUvOAfxeFx54xlRwDOik8kkQqEQWltbceTIEbXe/H4/gsEgTrScwHPrnkNPaw+ajzZjxVdWqHV1cu5J3P+h+wEDmHHfDMy9bS58GZ/yDDNsiYqo4xQKCQYCAXSu7ES4L4wDlx3AwLIBBAYDiLXFxjING1j+1eUYmj6ERDiBGT+bgWA66PKgcJ/IAoByrTL0nPuVc0OvilQEpGGIhfz4fBktwH3F/Sgr+0teRiPSpk2bcPLkybH9+xuQU87hLtNfSWVd5X9OkUgEb37zm3H22WfDtm0MDw+rIzJzuULF6b1796K3t1dF4QDFNBfKbtah2f7V7RhaMaSeb+QMTLt/GlbesVLJJPLnwfGDePgDDyNWH0Pr/lYsfWwpnrj6CQw3Dqv7z3v4PKzcsBJGruiJSyQSSg+izCVwk+k65GO5XA4pO4Unlz+JnpoenHP3OfBH/YpXMvVG9whKIP3QuoewZdUWOGZxyU3dNRVX/fIql/evlEJOvszryO8p76mfsCaJbDt5PlA8gUUCD9lOCQ7lO6XnMZ/Pq3Qv0zRVmpA88lFGRurgQxoKSgF6+V79HmmE140j+jOoV0nvKIEUnUKyiJpM3eIz5LvZD2moke2Q48kxlKCOv6U+LcGenBs5ZrpeqzuKpGdaxw8SEEuPrJxT2Wf9Hvk+3eiirxf9O93gwN+BQABVVVUIjg8iV5tDw2DDaUGobds4UXUCv3/N7zHQMICqPVWY+9W5CJwIuIw2lmXBG/DiyKVHsOUtW9T9vm4fZn18FsKHwi7swrGjo451p3gNDT/SaCG92zrIprFH7hEaKoBi1f5yDvf/Mk2ZMgWrVq1S/+tgTebgys0uk/Vl+Al/pBUTKBbHoBdL5lOTEfJZ9D4TGBBY8xpuPJ7xS2JVP8BtdZMgncIoEomoI4/4TirqevvZRgI2AOhu6YbhGKg4UKE2JwGDZOzyf3qNJYiWm0UaETgX7AOt79yUOvNnO2UIDvslx156OnmdtM7Kezm/7LdhGEjUJkqC7dDGECLdEbWJpTeVAiWTycD0mKi9tRYDbxpQ95pxE7UP1qp29L+zH7HrYhj/+fEIrw+rdSLHpTHTiPDXw9j1kV0I7A6g9oZaZHIZJFcncfyG47BDhfEdXjeMk/6TaPhcA6yEpebRyTqY8I0JgFHsb96XR2J8wtWvbDiLZF0SvmOFuc2Mz6DjAx3wJXyYfdNsVKFKGVxM00SuOoft79yOnuU96FnWA9tvY+7tc5H1ZF25fsFgUEV9sJJrIpFQzLSiogKmaeLYsWNqD3Ad5PN51B6sxTU/uQZ7l+3FzI6Z8E0onLW+cfJGbHrHJvAM7r2X7oXjc7DwFwuRS+aUkYlAtbGxEaOjo2rft68vFNypuKkC3Rd2o/pYNTa/dzOi7VH3hJvAC595Qf3rWA5mfHcG7CYbqfoUxu0ep9Yc16VcF/xMGooYEcHwf6B4/ry0xpumifr6egwNDSlFVfIhoBhWLnkS3y/5ysu17papTGV6ZdDUqVOxfPlyAFC6BKOFKBNjsZgq0KnXOtHB4LSvTcMLdxV5nWEbCBwq1ISQBunu1m48+9ZnEasvGCi7pnWha1rXmPYNNA4AXsDIFxXkcDisIs9kpI8ELolEAsFgUHkvnzz/STy39rlTjQLO/t3ZyrAq62dIkjL0rD+eBSfhYMtFBUAw58U5uPCOC11ODh0IyXbpIF46HaTOIUkCPI6bHuZKotzXAbb0BrKtNNhSh5MeYvabzgiZQlfKKyq9jy/lIZXeWqkz6x566eBgu7nmpN7HuWbbdYDIZ/HduiFAjo18rwSYsg0SmLGvcuzZRl6j3yN1Td0YUgpI61EOssq7lNN6P07XNx1kSx2xlFwv9ZkCrAEDD1/0MAYaBnDtg9eioa/Bteb4vt7GXty97m4MNBT015FZI9j9kd2YfdNsRHojLs+6DRvDLcPuNvhtpFpSCB0MqTGV60WOlZxvkhxDOfZ6pIKeJldqnetj/peoDLj/h9TW1oZzzz0XFRUVClxL5ZVKsfTyknFks1kMDg4qi6VuCeWG5ebjbyrXMr9SblACwmQy6Vo8MpyDlh4ZziSVarmICfakJZLndAPuYxqAoqLOTcx3SK98rCmGTf+yCY7t4Mx/PxO+kz4XmKCQoUVJ9o9toCWTG4xAHhhbpETeRwale6xlBXO9SBvbLqs3y+91i6UUhDIk6aVyXcMvhuE76kPezruMKrS0K8XGMDHue+PgpBwMvmsQcIDmjzaj4rkKOIaDvg/1Yfgtw3B8Dk5+4STaPtOGyLaIa20AhXQCY4eBCTdMgHnChAMH3qAXzoADY9TNQLK7ssjEM/DkPGpdHP/ccbR9tQ2wUUx9GLIx4VsTYPgMDK0ZghW1MOWzUxA5GEEeeaQ9aez53B6MzCxUTs8EMlj5pZUKxGbtLDZ+bCN6Z58qsGMAO6/dCcNvYMHdC5ShJBAIYHR0VDFKHodi27byNNNAwyrdpmm6jpULBALw+XxoebwFqAB6W3vR2dyJKV1TsM3ehiyKhqiG7gaYhgl/qOABZ8VvuQ6l8cpxHIQ8IUx5fAoAYNn3lmH9J9dj1r2z8OLbXiw5/ycuPIFUfQq5ihxy4RzCJ8NY8u0l8KSKhjrHKVY4pfGA0Qvc4+Q3XPeMuJCGPuZeU2GmIsV1LgE855uhhlS2ZDvKVKYy/fMQU6KAIgCizOMpJfL0Bl151//f/8n9rufbPht73rEH/pAfs56dhUAggEAggLA/jOpsNYZQ9Ia339eO/gX9iLcW0q8WbV+EdY+tQ8AOIG8VAYsENzpYAYoAl/z/sYsfw6ZVxWJsHUs6kPalce3N18IyiyeeyIg36QElj112/zJ48h4kxyVx7v3nwpv1wrTcY0L9jW3Vgaf8jvyVwFYHLOTjOkCQuqMEibyulPdYjpUEhkBRX5JOHflsmZstw7/l57pjSLZFjqXuTZXhvrK98tnSSSVDtmW/dNAunydJn49S9+gAWc+Tl+2VOfnUh/Vc5ZdaC9Jzro+NNGbxt3xuqVz1UtEIMjpV9lH2QzeYyDHh31wb9732PhxYcAAwgFsvuxVvve2tCEVDat/xOcFEEOFY2DUOgd4ArJjl0qNN04SZMzH313Ph5B0cufgIkANmfX4WqrdVAx53RfdSxicZQi4Lokk+IT3epcZDN5Tous/LBdtAOYf7f0T19fU4//zz0dzcrPIoHcdRDB2AK+xULk55lpxkcPpRYRKg6wuD38mCbFxw9KZKwC6BuczBksCB+Zh8P0OzJVMjqOAmloXSJLhlGLpcuJZlIRFK4OEvPIyRthFE26N48htPIldZODePSr20FDKHlO+XDFgPP2coMcPKLMtyGQek0YOWOWkp5pwxLF7m8egRB5wvxylY+5nrroqWiSMH+NxsNgvnuIOJ35roWkvBzUFU/bLKVVyNY8yweI5hJpOBN+tF04+bUPPrGox/x3iEnwwjk8mg5zU96H9jvwo3z7RmcPSbRxGviiuB5PV64UQcDA8PIxqNwrPXA0/co54dOBrAxLdMhOekB8gBNT+uQcPPGuB3CuHa3rAXx288jv4r+3HgpwdgVVhqLXu9XgSGA5h0wyRUbqrErDfMQrgjrPq+87s7MTKjeEzZ4JJBbPzUxqIykXcw7U/TigPjFM7mnnTvJJdSwTUnlYF8vnB8Cq3bsl4A55d1BAiaR0dHkUwm0ZnvxG/f9Vs8fvXjGG4cxrVfv7ZQBC5nYsWvV2DmszMRChTOwubzybiTySRyuZyrYIgscujz+VB9tBrnffI8THt8Gpb/cjnMnAlP0gNPQtg8jcJ4RKdHkWhJoG9pH5769lPI+kvzEHpAuF+ocMq6AlyzXIO81+/3q/XKPcI5TKVSiEaj6ug/rneG2+uCqJQCU6YylemVSZZlob6+XtXHIFCQQHBwcFDxYvIIymH+Tf1l7w17MXTm0Jj3ZCNZbH/9diTPTqK+vh7V1dWoy9Yhkoioaxo3NsIwDSSailFT1SPV8CV9LicGMPbYTuo3yWRStZ3Ogw1rNmDzis2wrSJI9Wa8uPiJixEMBF1KOeBW7AG3l8xJOlj55EpcePeFCMQCrnfL8dDBMZ8l03dktCFQzKOXRg32WwfZMj8bKIa8cmyk11mCB+mJraqqQmVlpdJBydvlnEqvItute1+pG+meZt4nwbB0eEgAI98tx0y+R3oyJfCVfdUBqHwmP/d6vairq8PEiRMRiURc4y3HVBoW5Bhwnvls6pd+vx/hcFjJThmZpoeM622Suj/XBnVT6uU6HiDRaaXnzXMds8CZnv8u5Tv7oEcgyLbx57m3PoeD8w+qqMATjSfw39f/NwzL3Yd8Po/wSBiX3nIpxh8YD9hAwzMNmP696bCGi05KAlqv14tAOoA5P5uDlj+3YOG/LET19mp1jc/nUz+cFzrSJF6Quoo0cHDdRSKRwvnhp2punQ5U62PAdfdSjjRJ5Rzu/0cKh8NYu3Yt5syZo6oyS/Aq/6Y3VVqMuEm5gckspbdKbjx6llh2n98xV1MuKAKroaEhWJaFUCjk8ubKhSTnn6CW7QeKTIAAR1q9GZ4lLZsS4JNJ+Hw+BWyz2Sye/sDT6FzeqTYnHKD5qWYs+NoCVwg428G2kMHI/GzTLB57lsvlXAXqbLuYnyrD6nkNGR+jEmQYrgxhlyFEFFDc8Bw/CsFAIOBipNzczP3NZrNIjUth8582F/sPwBwx0fCNBlT8oUIxahZY0624uXk5hA+EYRmWyvdixINt2+j/dD+G3jAEeAHPSQ8mfnYiPM95lLc8MymDI98/grZPtSG0I6Tmi+NIBp6vzKP3Pb1o/FpjsYpshYPBjwyi//p+VUG26pkqTPjSBPgGfa5+61EC6XQa3lovdv5oJxJTCwpU5bZKrLlhDZx8YXzjc+J46qtPFc+3doC1X1qLxo5GtV8Y1igZI99B5p7JZBCJRNTfZMBS8eH1xyqP4YkbnygeU+cAF/3sIlR3VuPgnIOY++BctU/ZHwpbhnQBQDweV15ueovJzFntHgB8fh+2X7gdTc83wUyYePbGZ5GzcggMBDAwfcB1tjccoOJIBZZ8aQmqh6qRd/IYbh9G7dFal5GIv2XlffIHCgTue8lfOA5cs/Sa82+eLkCDnoxe4Zpev349BgcHxzLJvwE55RzuMv2VVNZV/mdUU1ODr3zlK8jlcuoUkGw2q/SIbDaLrVu3oq+vT0XS0aBHIz15CmXxru/twuBCN4/wpXw456FzsGzjsoL8DuTw5LlPYsOqDS7dAIBLVtaM1OC1t70WjccbC1+dUnx5UgWPMiO/P50H/t6L78XmZZvhWA6qhqvwqttehZZjLa5QbwmSpVJNnQgoRIpVVFS42iKj66Sjw+/3K3kk9QdeIwvY8iQI6WGn/NLlGO+Xz2SbKSNlH6Q3m3V6CM7j8ThM00QgEFCfcT51z7TunZXeRfmbbStloDUMQ6Uo0thLou6je+epC8loQxq+ZcVpHSCzLXxvKYArv5N6uL5+5LNI0tCtg1S5LqQDSm+HfJf8W14njT8cd7n2pCFCGgdkX+Ua0ME+f+vRIvr4SVD+6IcfxfEFxwEDqO2pxfvveD8qY5VKB2E70uk0otEo+vr78NQ7n8Ksf5/lMqSUArtSd4lGo+p4Ujr2pFOL0TdST+fz9QJ+LE5dVVWl0maYoshxlQYPzqvM3yb97Gc/K+dw/z3INE0sXboUc+fOdVkVJXPgRFMpB4qAUVo29XAULkx6Nel1IslnkuFwQUtm4fV6UV9frwqS8XN9U0lgS6OAtLJKiyQ3pxRgIyMjqK6uVp9LwC+973yG1+vFqh+sgpEycHRNIY+57bE2zP3uXJiWuxgI3y9BvC60pIVWFs9gf3gEEq2tMoSczyRw43sJWvQx19eAFIAyBF4aVigsCZ5N00T/Zf0uBcJIGqj/Vj0qb6tEKFwIwaFXQbYTAKJnRtH3tT40/KgBFbdUuPJ7yQCbvtkEM29i+NJhNHy+Ad71XjgozHV8ahzdX+5GdkIWnf/Vick3Tkbk6Yir0Jsa7xETjV9rVIYEwzCQrEgWKquz/QaQbkoj3ZSGd8DrEkS5XA520EZidQK1T9YWBGHSg1n/Ngv7/20/PHEPpn15GuKZuBqrF9//ohtwAuie0436XfUucE0ASMVKRpfwOoZ6A8XaADTWcO68Xi9G543C9on5NYA94/dg4X0L0bazDblwTnnGZdFAvpNAOhQKqUcwKoOAXypu6VQaM++ZqdbDOT8+B3FfHON2j8O2N2/Dzgt2utqSqcggNS0F7xYvDp97GHtfvRcLv7cQdZvrXEKEe4JrkWtICgbJA7iGOY7sCwUdlT6uCT5T8rBYLDbGK1OmMpXplUsEk9JbxIgxKqSUsdRBALjkJyPKKPfP/PqZ2PXxXYgGoxh3cBwOn3sYZz9yNpa8sAR5pxAVlg1l0dvc65KNkeMRjN86Hp2rO5GqTqFmsAZXP3A1WnpaCmdqC8O5bANlggQMMiTaMAycd+d5yKfyOLLwCK647wq0drUibxf7Q11GL7RKohFZ6kZ8Pvm+lFmUP7qzRDcIUHbJcHK+W+pSbIM0/uvATYJV6in6dfJ7qbfJmkPSQCvbxH7pYFHqcNIZJPsq25/JZJTRlvJLGoTZTpI0pkhAWgqs8V167SOOj9SHdYOA7vzhnPFeeRoNC7jqY6UDfDlmUncpBfzluOnrhO2TspztlMZ4/Z2kUgBcf6+8T86rbCN17RX/sQLPv/t5JFoSOP935yMdTSMeirui69iubDaLfC6PuV+bC8d0pxDItrAdMn2U+gbnWY+eYHs459SROJc6vmCkX6nIiVLrT34nx+HlUBlw/z/QokWLsHjxYhfDkpZGhhVLKxcnlEKMlhkyZqB4jq6shMe8Yhm2w8lmpWGGr8bjcZdXlwCfjET3YsmcZ5lzIr3C/J/Cg0XSWDlTKueyKro891sP7TEcA4t+uQhm2oRhG5jxyxlAHrBR9FryWhkepPcBGJsrTWOF3NixWAzhcNiVa65b7Xg/hbduWeW8cOPyPXLzcx2k02k1b2SGjHI48r4j6LrOXQTGTJqo/kM1srlCyFAwGFTecApJ27aRXp3G4A2DyDfn0fOJHjghB42/a3TNL4VQ/ffqEXk6gvCmMPL2KSvtFKD7q91IzSpY0XO1ORz55BG05drgf7xYnZV95XrgbwDwn/Sj5YstOPGVE0gsSsBz3IOGTzTAu98L22urnGJarQ/dcAijC0dhmzYqH6osrLsuH6Z+fSo8aQ88ox7kPYX2HbviGJL1yTH7bc69c8ZYZKWSxz3GtU/BwJQCni2tW7pp2Z/y5ynwJrx4+v1PAwYw876ZmPrrqTBRLMgWi8WUcJWnB9C6Ko0vUgGSSqkUDNzPXq8XFYcqUOOpgRkwsfzO5TBTJrZfvl2tjalfmIrQnhB2v2E3Oq7vQD6Qx7b3bcPC/1yI5o5mxW9kFIluhJD7imtK1kbQ889puNCFk7QmU1EqA+4ylemfhyZNmqT4B41uNFgCQDQaRSwWGwNI9Wg0yT8qUYllv1iGbSu2Ydb+WZh9fDamHpwKx3KUXPaEPEgGi/w/MBjA0v9civFd4zGvcx4efsPDuObOazChawJsw3bxIb5XN5aTdC+ebdswYCDvz+OK26/AlBNT4JjuM6nlsyljyN9lCht1IamQS55Pkh5IjhHfJSPneL9MC5TgVjpzTgfAJZjTvfylQATlhzxdR3/26QC9Dp4Z7UnnRynAKOWKNNLzfxmhIL8rBRBlIVupi+vgVD5bN5Dwt+5YkTqn1DvkmEjniNQxZHSf3ke+T7ZRv17+6OtIAmN9LiQ4lnOjO/XkMzgu8nsdYMs1KN9LI5xt25j7o7lwxjvwd/sxHBwuhJCfSvOTepoeHSExkhwvOhSTyaRyluj90Q0lch70OZd6o3RWyKOOdaOaxB9yP5QyPvwlKgPuv5JmzJiBJUuWKMuvzCOQlbWpzHJiQ6GQOrJAWuVKVXuUTEpa8WRFUOkxzuVyatPzvSSdCcgjveTikQw3lUqhr68P4XAY4XBYec4AoKKiQhVjY58TiYTrHdwIBPWO4yhGzn4F00HM/dXcQp9SFkxvcUzYPwJ3aczQARPbTeYuIwo4PrTUSiASCARcjJKMG3AzJBpIHMdRG5NjQVBCZs826mG9UgCPLB4BStSYovEil8spZcZlEZycRee/dyLXcOoYtLCDvvf3wRq2UH13tcs44TgOMrEMvM95AV8xZMvpcVD5cGXhGDALgA34t/rh3ehVoT7+oB+D7xlEaHMI/heK56Kr+TMc4CjQ9L4mdP2qC41va4S/x4+cVfQe2LYNw2dg71f2YuTcEcAEDn7iIKYOTkXV5kJFcs+hU4UFUXiv6TMxPHMY+bA7F2blN1fCSluwHbdRRa5bGdbDseT6I0BklAHXhCIP8NRbnsLcW+bizJvORHRJFEvuXQJP2IM4Cp53Fhjz+/0YHR0FAJXzwz7LtSBDAKXxiGuIxhr+L41iZtrEkgeXYMYzM/DMG5/B3JvnwjxgovOiTuy7bh/ygcL4JJuS2PzRzVj56ZWo6K1A5/JO5CI5ND7SCMsoWnGZQ8e6EVxjTKGgdTeVSrnWuMyRlx5yXXFi3YUylalM/xx05ZVXKsBEh0A4HFbAM5lMKl5KeSMjjSgzbNsGPMCeG/ag7U9t6G/qx+ErDuO4fRzv/PE7UVdXh1AoVFB2AwY+c9VnMFBdPHkjU5FB16outN7RiqmdUzHlV1NQHasGvEUgxDby/0gk4gJnEhyTbNtG1sniztffiQOzDwBBYMJdE2A5RR4nPYvSmEoZqxsWpKznu2TqH9tBOUC9kfqFBGi8Ry8CK0GQlCvSc8e26eBCBwV8h0wB4OfyeVLfku/lZ9JjyHHjuFCnksZf6X0tBVR0ECoNB0AhbSAWi6ncaHksk5RL0uGjh1/TOaQb8XUgq4+XDgbZPul9lf2Q7+T8Sp2O46dfr+vxHLtSoK7U+EkHmNRj5TOkd1jq3KWMEKXWjWwr9YdsNgtPunDUayKQUOvacRxUVFS41jvrKkkjiWyPHJNSAF03SlA3lONWaj51Q4Q0AHAtS92SRXdt21Z1bfS1UAbcfwcyDAPt7e1Ys2YNampqXOBPt2iRCevhyzIfxnEK+TrJZFJZf7hBWBRBes/YBmnBA6Byb/x+v4uhk7jAucFI3JTMzaVgpRGgoaHB5a2XxTfo8dZBITef9ATT6k3h4TgO0tk0kuOTCHeF4YcfWatoIZPWPgp1KTikV04yccmU2EYKY46HtFLx+BDDKIa9p1Iplbck87U4l7TkEVBLAUPGSQHA0GOG+zPcOHw8jNFpo8VyhQ4QOBhQa4R55NIIkMvl4D/qR/OXm3HiiyeQr87DyBio+0Mdqu+pVuF+juOoqAcCP4bq5HI5OFkH4342DrbfxsBbBhB+OozmjzSrcHNv2Iue63ow9MEhwAEmvnEifJuKxWksr4X+df1InZVC3Rfq0HZZG3weH/JmYawIyv1+P/pe04foiqjqZ74yjyOfOoIZb54Bf9yv5oZ5wcPnDGNgeVHZAoBgfxCjW0YxMjKiCpKx+jjnnQKc4Y66NT+RSIyZo0wmg5GREVS2VuL5q57HoZWHcGzxMVzz1WvQ8McGZOwM0nZaGWr8fj9CoRAGBgbUOjp58iSGh4dRWVmpCttw7xDY0qjDtQsUQx2lkKACppSXNFCRqsAF37qg8H8TUPliJdIPpXHk8iOwvTa8US/m/3g+QidD6F3Riw3/uqEwaO8CVv3rKlTurVR7l+tfViLPZDKqMjD3Kz0THCuuRwrjUCikzuIlX+EeL1OZyvTPQePGjYNhGC6jGw2PADA0NKT2PHmKdCIApwBmRQ6HP3QY/Wv68buzf1cIFTeAHHL48Yd+jC/c9wUEBwo1YU5ETuAdd78DP7zih4hWRwEHqNtRB2vEQsfrOzD+ufGoidfAQVHhJ/+UXi/AnSoneR/7kg1n8eC6B7Fv7j7AAHYs2IFAPoDzHj4PfvhdwEiCHvJBCd6ZqqZ7mSVI0D2KMoKQAElel8/n4ff7VaiyXg2e76G8kEZo/pYeSV6vh93zb+pKsi16GLAEwJRVcm3I+aeM5vhLGaH3QQdH0rghn8vreRqJTG2QRgsZdamPq+y3DsIlkJR6vASB0mnFZ5CkbkmSeoh8ttSbpe4sdVq2Sf6tr3H+1gGxfLYEp/I+6td8r3RcyHmQ8yGNPXwm9XHiB5neapqmSqUEoIxhMuWTz+R88G99jUlsoBsNJA7gWpDjX8oowT7IUHYZySCNV/xeRjLK9SPn/C9RGXC/TKqrq8OaNWvQ0NCgJkEHlkBRAEkhpAsGmUskNzOV4VAo5FoQuicacBeNkOHm3AS8T1by5Gajt4vEs4slYy7FxCQz4saQC5PAh1Y/yVwoNLxeL46efRS7X7Mbi25apCoO0iBBIC8Zg2VZroJeZBC8j4yX1ZlZPIMgSJ5nybGkAJVnc3Ms5BFSMvRXMkYKKqDISOW46ZY0noPc8HQDes7qgWMWPq94ogITPjkBpuX2FlCI8z2O7aD2yVqY/2Gi61+7UHtbLcZ/fzzyRt4Vvs61yfQEwJ0vlMvlUP0f1XDSDmp/VIt8rgDKbMdG/+v7MfSpU9VkDaDzJ51o+0Qbgn8uCP7RV4+i7/N9gAEYKQMNX2tAPl7MV5LjXfWzKmSdLAY+Ujgv1b/Dj8lfnAzvqBfZfGFceSxXNpuFtc9CsCuI0aqCBzl8IowF31+Aungd4CkyVzJwgr/h4WEV4cE1qQt7rh/uKdM04a/xY/2l69GxpgMAkA1m8dAHHsIlt16Cmr01yrNLK6xpmqitrVVrjf0dGiqMVyAQUAoSf0pZzw3DwFDDEBzTQU13jTLmSIHD9/kNv1Jyw6EwFv1uESzHwtELjmLez+aheUMzjq48iu2f2O7Kqd/wpQ1Y/I3FaNjcAI/Hg55ze1D9YLVa+9xT0rDD/cHvpaWce80wDLU/uO6l0lKmMpXplU2TJk2CaZoKNCUSCZX6ZhiFkx0Y7ROPF47pknoO+UE2mMXhdx9Gz0U9hQdrDqCMmcEjgUdwTvc52Ne+D3dcdAdWPbYKbUfbsKt6F1qea0GkM4K9b9wLoAC0Lnr2IvCkRvInyj1Zr0Ma6Pk/ZWvan8Yj5z+C7Qu3FxtjAF1NXRiuGEZDssEluyXQkp/JwmeAuzK21KX4LJnXS+OlLLQrdS0a9Um61081WwAPkgQllCdSP6POpgN1vf0SCOmgj8+UbZBjJcGh7lVku07n8dX7Id8hvZU0CshisYA4E1roXhJ0yc+lM4bv5BqWzimpT0jwL++X60QfG9lu+Z1cn9Igz+9lTR0d/OogUr5HNySUApzyB4ALJ8i1qwN7CcTZXt1RJj3+xCn8jjocPdzSQVKqfV2rulD7aO0Y77sE03J/60YgGeXAyF49n1sCftlvxyk4bGKxmCsqQi84LdfQX6Iy4H4Z5PP5sGrVKowbN25M+A9Q3CRy0ggcZcgNwTCZmrTC6QBNhqWSiQDuIyTkkRNsp9wIktHrQJFeWF1ASA+yaZrIWTnsumwXZv9ptiu/WzIdCgy2icxbWoXped173l5su34bcsEcNr93M+Z8ew6qdlTBNE1V9IwCh89l3wha2U4pGAh0+QwCJn1cCXDYRwmkOV8yLIXC1XEchEIhZeSgEOO98vgvjj/HaGRkBOFwGIOXDuLA+w7A8Qom73GUMkLBYxgGmpqakMvl0N/fr9phmiaq76lGfjCP6mcKOd+8niSZNIkMhevHtm3U/FcNPF4P4DslUBzA47jZgeM4yKUK1w+/eRh9H+lTbY1eF0U+lEfzvzbDMt3FU8iYan9aCyNhIPraKBr+rQE4CCSNpPLaU7ClGlPY94l9SM0p5Jb7B/1Y+P2FqNpaBcd0C2vDKFbsp2VepnewHdLwxLUkmfELb3oB+1e7z4aFDeTSObXmRmtGcXzacbT+uVXNLavW870Myx4dHUUmk8Hw8DD8fj98Pp8yCsj8pUxDBs++5Vk4hoMLf3UhKgYr1P5ju5mq4TgFD3NvVS8GJw5i3OPjMPfWuajfX4/xm8bD8BkI+ULQKRfJYfu/bMec789BenIaHa/uwMzITISPhOHr96HqcJXLcsvCglKg27aN6LQo9l+4H9P/NB2V3ZUqioa8heupTGUq0z8HXXnllaioqFAG7mw2i8rKSsV7yeekHsC/pXfJcAxYpXKnTtH5956PhesXYtu0bXjkskcQq4jhoaseUt/bXht7X7NX/f/UiqeQ9WZx2SOXKT1G5lbbdvEEFQDK+C+NAvl8HpZpwWO45dy4nnG44p4r0NjfCBvuPE1dCddBDfkm3ylBrgSjEsicTn+UDhF5BKN8biklXwewBDr8Tge+sk3Sm8uTNDiOOhgt9c6XAtkSnEnjrO6xlGOrO6F0x47U22SUA9cdPfWc71J9lm1leySQln3WAbI0Suuyr5TRQDfc6ABYri99XehGCf258v9ScyoNTy9lwJDzVmq+X+o7oFhzivqwjAYNhYqn4DiOg1xdDnvP2IvGuxrHhHCzn3z+vov2YedrdqK9th0tP29R7+OakMCX75RRvnx2KQ+/XGuSh5RagzqIL+XZLmUAKUVlwP0yaOXKlZg0aZLLGsJNSg8QULQSEbhJjzDzmQko9YUmF6u0QBLYMS8KKOYjkClnMhkVni7BoAQgBIAyzEYKBn4mc5Rsw8aTn34SA1MGYBomFj2wCAaMMeCGP9KyxfALMr5EIoHOczux7bUFsA0AybYkdnxmB5Z9fBlCJ0IKUEuLuew3+0EPMFA0LAQCASSTSZelm0dsEAxLoM2xlaE07HcqlVLvCIVCrjxVGg5kW3K5HOLxuCqeYlmWK/S5oqICMIGRuSPIVol8Vxuoe6QOZqaYq88+ddZ3Ir4qjpqf1MDyW0jHixEJlU9UImfn1DEtjCbQ86T4WwJwafWTwDeZTCL0ixBq07UY/PxgIaT87RPh2eIpnIO6MgEn6LbkJS5MwPJasBzLZWUGirnVDX9qQPCxIDzHPch53HnuqVQKVtjC3u/vRbqt2L9sRRYnF55E1dYqNVdUbKQyMjw8DABjCoKR0cu9ysJhXBtTnpriAty+hA9X/vBK1IzUwAybiDtx3P+v9yMdScOT9mDi5olqbROgejweFVURj8cVM2eOYywWQzAYVOGBodoQ7vzAnRhuLLT7rg/chTd9+00w40WvsyxwBgD5ijwe+uhDyAazWD6wHFXrq9D0QhM8voKS2/B0A1ZnVuPpTz7tSlMInwxjePYwjlxxBPlgHnuu2wNPygMza+Lcz54L83gxVI8RADJnMFoTxQufegHJhiSGFgzhnI+e4wpr1A1TZSpTmV75VFtbq2Q55SG9iPl8HqOjo0r+yeg7GqVJnpQHE380EbbPRvfF3WM83NuWbENgZwCPvfoxxKviY9pxctlJGI6hQsgNx8CynctgGAai0Sgsq3DcKdtAfUh6umQUH1CQfYFsAGsfWYs00ti+ZDvC8TCu/931qBqucoWrA2MjEKkvAEXnitTzeF08HodhGApsSBAheaf0ZsqCrTTgc/xJOoiX90pQK3O4pRGEAEfqbPKZMqKObaROJu+VOkWpMGQ5bno+uQSDsi8S3OrgUDqNuM6YxiUdYKWqkEtdUgdFMjKUz5cAje/mj/482UYJ8EpFFPA++a7T9Vnuo5cC2xJfSGOBbvSQz5ef0zkj3ynbw/fLd+vzK4/RkrWTZP42+5A1snj4Qw8jVhXDzL6ZaHiwYcw8OY4D27FxcO1B7Lp+F3LBHA6/9jDy2TzaftMGHhOoG3T01JLTGQkkeJaAu9Q4S2MO9TM6eeTY/zU6UBlwvwRZloUzzjgDy5cvV8BKhgsDbq+1nEQJQHUPmwR7DBmVIcScQGm1k0dXkamweJnMmyZxQciFJ4GytDICxUISBPfpcBobPrQBvbMKx3Rsv3Y7zJSJWX+eBa+nGPqdqE/A1+uD4RhqIVII0MOtNrNhF4puCXKsIlNgThMZpr4JCLpYPA6A2gAE5rqAAQrGDgIKetLZd5mXzWdxrmWleD6bRhMWm6KnlcJXRiYoIR3yoPOqTpy49ISr7+0/aUf70+0wqgqVPTmmyalJHP7FYcAEctEcBl49gInvmwjzZNGqns/nVTV06WFgW2hs4NxyTchUBypVXKO5XA6RX0VghA2ENodgvmjCsE6FG/d4gBxcHMNz3APTMGGZlmu+uIaYI+RP+mH5iuvfMAzlQcm0ZZBucStGDhzYhttaTAWFZyXKtSv7QK92Op12nSFtmqb6DHB788MjYbzpe2+Cd8gLmECsIoZbP3ArRmtHAQN48r1PIvSDEMbvGI9sJqvabxiGWg+hUEj1b2RkRK1hgu94PI7NH9yM4XHD6r0j9SO46y134bqfXucSxrFYDFVVVYhWRHHLR29BojIBGMAzn3wG5/z7ORi/b7wak0g4At8WH8781pnY9aZdiNfGUXOoBjPvmImNH92IfLAw9/lAXhVce/imh7HmY2vgGfXAHCmuW/Yp15LDk995Ut2bbEri8e8/jgs/cyFCQyFXCgP5V5nKVKZXNlVWViIQCCgZRq8xDWvJZBLDw8OwLAuJRMJVHVqmt1Hv8Ca9WPzDxdhZvRP9k/qRaEqod/W29GLX2l2Yf8d8bLp+E7J+d+HFmqEaXHfzdbj5rTcDJvCm378J406OA8xCO8nzmRYm04akjqTn8VqWBcM2sPSJpUiEErj6nqvhj/lheooRRlKBlkCNOgBlFwEViX/T2Esib9c9yrJd1C30cHLqUBLA6u2SAEMa2SVIkCDPNItHydq2rYq7UZ+h7ig9uuyH/C1DcvV8dNk2kmyXnCN9rHVvfimQC8C17lhbRLZJzoF8L9/FZ0snGfVz/q+DN6nbSVDK5+vFktluaZyRUaqcDz6H48u2yv7ooFHeI8EtP5e6t270oO6rRxPIvkqMoEeTEi8wNFwaL+R6yWQyiEajSFWlsOnfNyHWHAMMYOe/7sScxByMe3rcGOA7PH0YHa/vUI4522+j69VdCO8Po+75Olc/pJNTzoU+9/rn+vdyH8t1KA1NMrWW37OfujHndFROwDsNWZaFmTNnYtGiRWojAW7m6fP5XMdjcYFJZZwTyTBTVgEmYOZCoeIqqxYDUJ5ITrAMn2WuN72x3MinO7hdWoJ4H4GbBL25XA7dU7sx0jZStEybQOeyTox4RlT+Ut+kPjz2ucdwcuXJMRY1tkMu3vbH2jHnljnwpE6F5x4PYuFXF6Kip8IlAPTQbMk45EZg/6Twk9ZZzqO0xsmwdwJsekz5fsuyEAwGXcoHx9bn86G9vV15S5nTKzcix5me0GRLEgffc3DMbjMdE07eUYAtEokA5wKdN3cCXgAWcOKTJ5CenMahXxxCbE7MxWQ4X1xfDJ+WwFuuG649abHm30BBoQgHw6j7aR38m/yKaQNA0xeaUHl7pbIwRrZF0HRdE1LxlFrv8lmJWQk49Y7yAHMNkiFz/I//9PiYcQmeDGLqj6eOeWYgEIDf71ehb9yLFD7sI5+tH23Fueyc2YkHPv6Aet8Ft1yAwFBBYA80DOCut96FaF1UrX3HcrB7zW4YlqHGeGj5EHpX9qr9Iz3osr/xeFxdM+9z81D/fL16b/Vz1Vj9tdWIRqPq9AK/34+qqioYhoFDKw4hHUq72nFo3SF4fV6XALdMCy0vtOCyj16G2ffMxjlfPgfNu5qx6KeL4B8q5vKTcqEcHvvBY3jxwy8iVh+DYRk4uai4hw+dfwh5n7sQWiaSQdc5XaqIGvkXj1wrU5nK9Mqmiy++GM3NzcpYS4MuASCjdsjjpE4kFVkZ/Rf0BXHOj87BeR8/D+N2jVPvmrpxKi68+UIs27IMqx9cDV/Kh7nb52J813i0drXiLb98Cxr6GnDdH67Da+55Ddp62lxH9/Bd/FsWypJgR4IawzCQtJJ49NxH8fOP/Ryt/a0IZAKugq7yXn4GlA6rle+UwF4nKuuAO5ycehsjBCg3ZIQTdTXqVnyOBAMkCcIkINA9tgTHUneS4ySN57JP8rlsjzwqU3pxZXskSJSAmbot5ZjUT3R9ju01TRPhcBjV1dVqzH0+HxobG1VhWkk6MCo1dlJnlfMn50qCYD5XpmKy3/JUHH0NSDAv75dREnKMuH6pf1IH5djLlEbZV8633j7dUMBrZZSn/F6Og8zD5juloUA6q6j/8iedTuPw/MNI1iSLeMICei7vgRW0XIYN0zRRd6AOi36wCIGBU86REQ/av9eOqmeqXO2Wa1RGIpQCzHJs5LhL/VLfv/o4cI3Ivknjycuhsof7NNTS0oLly5e7chAkmAqFQmhoaAAA9PX1jbFscQNx0uRmIaOVFf7kQqcgY042AZ3cWNLqKRm4/JueWnmfbduuEGTJeLmB8vk8Wje3wvyhiU0f2YRcOIf6XfWY//35CI2EYHgN9E/px4vveRHxcXFseOcGLPQtxKTHJ7nAoN52y7Iw86GZ8Ka92H3tbsz7r3mo3VGrgAYZaj5fPO5DWmdlzoYEnLZtKyMGNxLzraW3WwoCuVHp8SajpMGC39GDnEql0Nvbi/7+fhdg53iS+BwKtshoBE2PNqH7/G7XGhtYPYCaJ2pg9Rb65Pf74avzwTDHWsuyzVkc/9JxTLhxAsLbC0e0xGfEYdaYiGyMuPoKFIUKx43t4Tjxhx56mfsiBQ0BlmEY8PR7UPOrGqRmpdB0YxMwDFX8jUzZ6/XCXmCj/4v9CHQFUPfhOljZYuihHCfTNFH1n1UY+MpAkRHbwOTfT1bXMqQtl8sVwt5DIWVcksyUCgv3BPeCtMZbloXDCw/j2eufdQHKbau2oelQE0L5EDJWBnnLzTynb5yOVb9fBY/hgek1cXDxQTz72meR8WawMLgQ0x6fpoQOAHWKAT383FP5bB7zvj0P+96/Dzknh4nfnogTsROFcPNQSIWosxq7J+mB4RTXwsznZmLZLctgGiZgFRVEy7KQSqXg8Xiw4I4FhbWAHNrWt8HKW3jxXS+i5dkWHLrkkKtf/Uv6seMDO1BxsgJdq7ow6yez0PpEK6b/ejrsmI2Dbz1YuNAB5t85H/60X80F57EMuMtUpn8O4nm50WhUyQGZysPUKvJYCeIoX/TcYcrHoB3Eip+swMa3bUTNSA1W/mmlkp/LnluGykwlFuxagP7GfngMD6pj1bAtG/8fe/8dZtdVnQHj7zm33ztzp480KqPeiyVZslXc5IqNDQQIJZBGSyGhB0IJJYFQQi8BQjUlhG4bcDfGHcmWLcnqvbfpM7eXc35/XL37vmfPNTjf88v34WT288wzM/eessvaa613tT337NxxOpV6/tQTq44KYHzuqg8ft19zO5668CkAwG8u/Q1KkRKuu+e6gJeS71JdxnGcwIkXBMZ8hwI7BbKqM6oext/UdxQ0aP42+6ON36lRQYGh9snWLzl3/Jx/Uxdt5CHmvHBdbT1UnTq6Ro3mQpsCQNU30uk0isUicrmcoS2+gzqO6imMIOMJM/b7VCfWz9WraRtT1Otq0xSvsedDf9tgTddSwTVQj0DUyFcbKKuOrv3SNdM5sWlf54H91v1jr4mOR//WfceoTC0ka2MZ/p506yQk/AT2/OMeIARMeWxK7czupI8TG06g985eM7+u62LaU9PgfMXBlr/dgtmfm432e9rHeaV1n9pj1blQMK8gW+lc8Zreq/Ope0f3tj23v6tNAO4GLZVKYePGjWhvbzdeMwABpun7PgYHB8dZ7aiQ0nIE1Auo8RkaHq4MVfN3FAxq7jYXn15mfseKfxRifJ5Wb6ZSrsIkHA4bL5yCUACY9MQkXPLRS/Dk65/Eqs+tQrIvCTfqIjspi81v3ozs5FruVSVZwfZXb4ef9zHz0ZkBBsqwrmq1Cs/34Doupt0zDamdKaQOpVD16jnwNmOht52GBwouVo9W4E3Go+NwHCdwPrfmTHGuWFnadV0Tdk7hzb6zfwy9YggNQ9hSqVSAGdDjSWNNKVnCyOKRIJH5QPpAGk25JrPhS6USWh5owcrRlXj8k4/Xr3Vq1ydOJhA9XpuL8uQyTn76JJyog0h/BLNePwso1Yu1qYFBrZNMQ9AKn9lsFplMBqlUCr7vm0rzGg3R964+DL1iCMl7kuh4RwdCfSGUvXo1fD6z0FnAqU+fQrm3jPLiMipfqWDW384ax/zIpHNX5QJzMvf9c9HxSAeqoWAVftI0j6SJx+NmznkNaUZz+bQg2NG5R/HInzyCXEvwnTMOzkCoHELVr2Ly6cm46bs34Ud/+yNk01nM2zEPV/7iSqSQgp/wsX/2fjzyikeQT9eA5tOvehoxxDD3kblmf7shFz6CR7QZYVT0seTrS5DJZeAOu3Cjrsl/isVipup63wv7sOe6PahE60aU03NOoxQpIRFOBJScUCiE5uZm8x4N7+p+tBtrT6xFy2ALnJCDg9cerNMUgP6V/ehfWSvMt/v1uxEuhnHu4nNY8vUlSMaSePqVT2Ptd9bi4LqDGJk2Aq/sYebdM0210QnAPdEm2nO/pVIpE6qtoFlT6QYHBwHAyD89KlQBBPkxAZCRk/0pbLh5A5rRjFg1hkg0YuTtip0r4DgOJvdPHqfQ2vnDQF1XYF9pmKeuAwSLQVarVfz8hT/HtuXb6oN2gN9e9Fv4ro/r775+nJGWhnby0mg0imw2GzD4cvzqBGH/9HnqbVZABMCExKt3ThvfbxeEUpCsgIHrYJ+iAtRBJiPwCO6BehSmGi4UdFPGK7jmszSqwTYQqAdR15HP1hQGz/NMPRQ1YKhDQb2bfK4NQtn4uRrobRDF/qgnv9H88287ZZBzoOH+OlalYX3e7wK39rwr/fAddrP3oPbZNhro2G2Q3GjMttHGdszY/+sahcNhRKNRtDzYgpSbwvHrjmP5fyxHfCyOBz78AMamjyFUDWH6fdMDz2t/rB0rj61EbE8MVac6bl14ndKqeve5LxoZerSPAAL0p9cprdugu9Gzf1+bANxWi0ajuOmmmzBlypTAZiIAAWoLpUdpkdH4fr1YGEOvKJQ0TFrDhzRcg0xXhQjfRwbJhdZzt3kNLYAKnNUiTAHAsFheSy+m7/uoeBVU2ipIZmset+ShJDb+40b4OR8ealat6Kkolv1sGR5/zeOoxqtwqg6mbJqCaY9NCwgBv9nH5rduxoJvLkDr2VbsfeFepEZT6L2/F81HmuGhNr+FQsEAMO2bAggybcdxjHFBjRe6UTkurg0FJL3mDDlXhl0ul016gDI8GksoVBhCxe+KxSJGR0dNEQ/OJ/Pz/ZCPR770CKqpoNe05aEWdH+8GyWnhNbWVgP4RyIj2PXGXbjgXy5AdmEWpZYSjl91HO072nHhJy+EH/YxMG0A+36wD17z+YJ8k0vY99N9WPCnC+AMBAt7Mbc9Foshl8sFrLccOwDjYS2Xy8jn65XEQ8kQzvz1GQz/6TAQArI3ZBEaDSH5b0k4FccYhRzHQdWv4uS3TqLUez6v1wHyG/I49YFTmPyByYhGo8hkMrXr0w5O/utJ5K+shxn1frkX3Y92144qC9WL7lEJIKBWxup5nhkrAb3SIPdUpi2DB1/3IPItdYAYKodw2a8vw8rHViJfzKMcL6MaraLlXAte85nX4JaX3YLLvnwZYtEYIokIBpoH8OtX/9qAbQAopUrY8sot6OzvRMeBDjhxBztu3IHEyQRmPzYbTmk8/boZF4WhAuDWjSE00F1wwQXYeXAnRqaOoJSs50dHChGs/8V6NI01wQk55gxtGt6UTyQSCbP3XddF7GwN+K/67iqs/K+VuO+f7sPQrKFxhYzKrWVsfddWeCEPw4uHcfn7L0fvA73Y/NbN6JtXq1C/+c82wxvzMPXBqaZY40SbaBPtud2WL1+O1atXG2BCGaCAQIuysqmHj/yHv5lOpPpM03ATYvEYoonoOK+1ggTKcOWbKsMVBLB/tpfK9hK+8K4X4kjvEYy0nTd++0BXfxeuuu+qQEioAhRVqFXfU8VbvZfqhWRjhJmCb3UgADBgnkYOvluvVY8y5YUNdNhP1f8U7NleVuoKlKFA3UnSKGSduiv/V31Cr9V5Yh8VRBod0QJ8qsMqONb32CHAjBil/qVgmvOha6X6j86VDdrt59hgTOeF9yg96v36Y+v9CuR07bWpUcAG4doHzr0917aBwQbYSu96r64nr9O+2A5Hvtv2JkejUYTcELoe7kLLQy2oJCt44MMPYGhRTQ/Z8fc7EClGMPnRybViieff03SwCSW/NG7OFQDboFtTERR42/8rbnsm4K7vU0dWI6PMs2kTOdzSEokELr/8ckydOhXNzc1GoQVqTJM5vZx4MkD+6KZJpVLGU5jNZpHP5xGJRExxB4I3eqQTiYT5m7nhtOqxQBc3KT1iujlsgtL+KYC0wySKxaI5a7hareLERSfw4PsfxNn2s8hkMrVjNfIIMAW/6mPS3ZOw5DtLEM6GMe3+aVj91dUI+/W5yTfn8fjrHseZlWfw8L89jIMvOIg9r9qDLX+zBYcuOWQInOOIRqPm7Gg9G5t5PSp4XNcNAF8KLQoTtfgSFGvujBpGeD8FCYUj545GD64/Q2lotW9qakJzc7NRSAqFghECsVgMqaYUvFjQGukUHKS2p+BWa97NgYEBFAoF5KbmsO/z+5CbncOBvziAnvt7sPyLyzH9julY+/61CFdrfet7XR+8lDzTAUpTSzj0xUOozK6N04QCzqyg1F6PhiB9qeBhNXbSBOc7EokgvyaP4TcOw5zwEgIKlxeQXZQ19ECPh1/1Mf3PpyP29Pm8YR9ovqMZXe/rMiGJQM3zkFmbQWlVKcCBBq8dBLqDFnk97sGEtp83DHBt+/r6AsxfoyvoyW8ebsaa768x7wqVQ1h972osu30ZqsUqIokInrriKdz36vtQ7CgikU/glTe/Eu1N7UilUnBdFz3FHtz0g5uQHkzXecZoApd8/xJ0HuxE2Sljz417sPulu/Hkm5/E0fVHxwk/0lIqlTKeI443n89j6+6t2P2C3Tj64qPmHeHRMC741gWY8tspCIfCAUWI91Ox5Z5ShcdE1JQdhPIhXPn+K9G1pwupcylM2TIFoYIUDIl4tcJxUzP4zZt+g0de/gj6FtaPg/PDPg7ecBB+uu5lmWgTbaI9txt5CPks5QX5CsPJbWCgBlCgDkopZ/hsGrmpDDPCDIAxCKvHnIBMU8rsYz41hUp1A/4oKHBdF+lQGq/92msx+cRkAEDviV684VtvQKQSCdxDvcCOEqM8oa6ggM4uTEZlXr2eqjfqnKvSr/nbNkjj/XaVZAUf6kSgrNRnqZdXi8FSX1EwrjnCtteQ77RrxZAuOCY2W1dVwK/NBqN8pr3eNLTbofq6/tofez75DK0FYxsXbIOOAjEbgOnffJc21d8bGRF4P+dT6UeBtJ4jbTtOlL50jfS5Sh86J3qvGnEa9ZXjYQqJ7TTQkHjSiClGXKqgOFrEoTWHMDpN6uREfBx+8WH4qbpeoQXZ7L6qrq77X9dL113XSe9XYK77qFHUjq6LRnX8d9qEh/t8i0ajWLFiBRYsWADP8zA6OmrAsQIzEqRNvLpIrDbJ5zqOYwoMMYSHgA4I5hrZFk1tuhk0hFari5N5AggcqcV38TpVxJkHfezaY9jxhh3wYh62v3k7Lvj8BWg63hSwRBJMuq6LOXfOgVtwMe3+afBD9SPAyrEydr5hJ05dUqvKXY1XsePVO8w4tr9xOwqhAtr2tsGreEjvr1cdDYVCgUJkGvrOebOrchNgMJSYG0I3vTJ4Cu50Oo2WlhYcP348wMAohDgeDdElw0ylUgBgjh6hgYSCge8/dtMxk+fMFhmKYMrNUwL500MdQ9j/5v3IzMsAALK9WWx/63Ys/9xyLPnyEhMNEA6HseLbK9AcbsauG3cFnltNVuF3+kicrFWWzU/O49SHTiE0EsLk905GOFMvUGKHxJGZB6y4SR8jL7ZC4QGERkIIZUMBZmyE4Wmg51096PvXPri7XEz610kB5Y3vio5G4WZcoFYGAc27m7H404uR6E+gjHJgTzD0jXSqDDQcDmPSpElm/zE8T3MPfd9HJV3B4bWHzRhi+RguvvNi845Nz9uEzddtrgkAD7jxlhsRyUQChdcKhQKmHZyGK394Je59xb0oJ8q45tZrMH3HdJTDZWy6YRN2vmCneccTf/sEyoky5t0zL7DfAYzzTkejtdz9A392AIdeGsy1bjrWhI5bO9AX60MqlUJTU5PZj6qgMRqDEQGOUz9uhkaWUqkEp+hg/efXY2T6CHp29ODAtQew40U70L2/G0fX1IF+9oLxx/V07+jGmq+tQaKQQCaUGff9RJtoE+251aLRKKZOnRrQbfg5ecjw8LDhrZQdTFOzFU+C6Xg8HuB5rL+h+ZOUqfReq1JLA7d6/VQuUP7ofWxqeFXDcFOxCTf84AYcvfAoVj69EpFypK70+/XwXxs8U08CanogebD2x46Os72F9jPVkKCykfKGgMYO8bYdKKonqsHA/swGlFxnzpPtibfvsUGygj19F9dU79P32t5W3qepk3yO/b194g9pTU/IUX1V+91oTXQOGgFsPsf2gNp0qMYV27jA5+uc8DrtB+lex29fy34pMLRBJmmITcdg04T+r2uj+EYNBVwj6rg2/fH9tmGGfeB9HT/tgFNycOgfD8GP+Ji0bRJWfXUVkpUk/HC9ZpPOm90vXRMds/ZH+YTSAj+3DSg6l7ZRrRHN6lo/mzYBuFGbuDlz5mDFihUmvJpeTADGy6pAWT2wtvWKi0JmybN4y+WyKbzBZ9GD18hqReCuIJfMhuCaoMK2nmkIOwBzjIcCLlaXdhwHx689jl1/uct4Y4cXDePJdz6Jde9bB2e0rrgDdYtuuVzGzPtnGuHAuUQJSOxNAJc8w4R7QCQTwdP/8DTgAyveswLxM7Wc3EQigWg0arz6at3kvPG3AmOgVsBJGYAyMQ1VZyg6C3FRsNsMhpuSlneCGd2gGr5OcEfLfqVSwdFrjo6PI3HqR5yRvqqjVSROJZBZVQcxib4EkplkwNPg+z6qXi1Eev6P5mPfy/bVaGUsirWfXQvnoIOcm4PT7ODYp48hv6gW/lxuKmPW62YFGCDBJOfJDvHyoh7Gbhwbt3yhkyFEB6NwQrXnJBIJM/ZQKAT/oI/uf+xGZCiCkBdCLBkLeEeKC4o4++GzKM+oe0eLnUXkWnKIloKhhkA9x0ut85ofx/QKx3HMWnK9AKDiVXD/392Ps4vOmvcVkgU8cuMjWHvrWjz2wsewdeNWo3gdWH4AP2v6Gf7kG39SXzJhtrP2z8KLvvMipNpTmHFiBvqifXj0JY9i92W7A2Haruei+2i9Mq8bc/HAOx9A9/ZuzL9lvuk3w/lcx0XT9ibgpTLXhRBWfGuFMdSxEBuNgYwKIV1yXoB6uJx6oQm8E9kEWva2oOJUMPeeueg40oFUfwolt4TTF54et+Zs7bvakTqTQtUPVnSdaBNtoj03W2trK6688koAMLKcOgTlK8+Wpo6hR1dRxtIASt1EC4rSoGuDbfIsAObYRt6redkKcPhOdXCoXOA4FGRUKhVUqhX856v/E1OOTcE1j15T67wzPlRVn2+PEcA4/Urfq/eoZ9BW1ClzyZtpuGABV86nGsEVIKjHW/vL96nRQN9p5uK80UTfpcBIvX06HjofdDzqJFKDuBoQFMDbQB+oe8TVI62gh/OvXs1nAra6ljb4YlOwpGNV4Kxrq0BNaUPBnL5fx8n7uXfs/qjxRVNT1QCiINEep9KTDdq1KS3o/Oh9dv2GRvNnz7u9TxXHECtwbukg9DwPrbe2Yk5uDs69+hzmfmIuvAEPpWjdqEeDkz5bvfXEQRrpoOuq96gR4JmMAbaRwqYFG9T/PwHcEyHlALq7u3HllVeaYwX0SChlLp5Xq4hIMMhFZ0gyJ17zbHzfN8Bdw60IIgkEgTrx8nkMwVKQr1UBGVarCjZDswhQ+F0kEkE+nzcgi97AilfBsYuP4ek3PI1Kk1S5LrqY96N5CI2EAgo7UA/n0f819DdUDWHRHYuw8IcL4VQchDNhc5wUUAtL3fPGPchOzyLbm8XmL21GMV3Ly87n8yjGiohEg0YOWtS16BuNF2RONCjQQKKAFggyDN7PiqwAAvPJnCACFD6bgtaMNVQ/woJ50BRKWtirPnggNBYywoOCMTIWwexPz0b7A+1AFWh/qh2LP7EYhaO1CpyFQqF2bdLHYx97DMfXHkemJ4NrXnMNurZ14ap3XIW2Y21oa2vD5MmTsff7e5FfWM81zl+cx/CPh7F48WJDZ67rohquohwqB6IjaDAop8aHC8e2x9D19i5UT1VRKBSM4cIWDKGDIVQHqgHDSaVSAdLAma+fCYBtVIGOhzrQsrUFrusinorjxNUncOolp+C7vjGMZDIZUxlTjyI7c+aMYeJcP1Uk0AL0ze0LrEHzQDNW3rkSALDmnjVoHmw2NBotRrHx1o3wq8HjQjhn4XAY3ce6kdqawpkzZ1AqlbDql6uQGkqZV4QLYbzgn1+ASccm1bwjsTLu/ed7cW75Oex6+S4cveEoWjpaAgYcpIF9b9wXmO+1712L1kOtASXLcRyMjIwgm80GKgpz/8diMSSTSaRSKfMZDVlKr8bwBwfp3Wk4Zx1c8qVL0L27Gxs/sXHc2nfv6MbSXy2F4zhGMZxoE22iPbcbU+BYiFQjumiYz+VyRm4qyAPqXiPl/0yRoyzVUFcNi7aBN5+p0W0KHjRSjwowlWAFy2oID4VC8BIebv7Lm3Fg7gE8cvkjeHjdw6g6VXO9ynby2Wg0GsgN5jjtkFn2UYuM2uBFFXg9slQNAhqqr41jsMEwAbvOj+ob2k97fsjDOV6d50gkgkKhgMHBQfN8PlvDpu3P1aBCPTeXy5lTfBQ02qBW55HzQD2TEZw6P9Q3OY+2p5fzzXttLzD/VkcV58w+PpXzze/Uo640QN2A96mhQEPX+Wwb4GkfVZ9WoK/P4lqrMYP/k9Y4PhuQ2v3WtW0EWPV4QOrJCoa5f3jMLo+pVfzE8ei72+5vw/w3zYdz7LzzsVLGyQtOYterdqHoF817yFuIi9SpYhuWSFOksUbfNfJS24YH9YorrTaKFtBrflf7P+/hbm9vx/Of/3wDtqnc07JIglLi0jwLDdkE6mDUZowECSRMWoU0H5WhtxQ2ypQ0X8JxHKRSKWQymYCgcxzHAH8FQhpyzTFSmJ5bdw5P/eNTAc9cZCyCxd9ejOmPTYcbdeGFPVMExSbWWCyG9vZ2nDlzxuRKO44DL+9hznfnoBKqYNpt0/DE+55AZn7Ne5vek8bowtH62NJl7PurfVj6iaUY6xnDzg/vxOJvL4Z70kXToaaABblUKpmiZJxbhueqRRVAoLCcWl+V0eg60TuqDF43FhUIIGh95bp5nodkMmny7od6hlCJ1Y0YtRuBxa9dPE75IC0tft9iHPyng1jw0QXwnXoVU8/zMNY8hj1v34OhxUMAgFOXnkJkLIKLPnERwtkwQpF6UbyWMy3ITQtW467uqWL37t2oVmsVZsvRMkb+agTVeBWTvjAJbrZO05FIBKfefyrY9yoQ3xqHk3PguLVoBDVGkamqxZV/03hUzVbR/u12nHv3OfPM7lu7sfCLC2vCxfFw7LJj2PPOPQCAkB9C9y3dcDzHCCrStrGUtraaari5XA6JRAJNTbWj0s61n8ND73qolpt8vnUe68SLP/dihAoh+I6PSD6CV3ziFbjlrbcgl8zhhT95Iab2T8VYbgxDQ0OYNGlSIOIBqCtLFCrxYhx//JE/xq1vvRXFVBFXfv1KdPV1IdwcxmByEI/9xWMYmlNbNy/qYctrtiCEELpu6zLHmhx+/WEUO4uBKd//qv3o+WhPgHZpOOPcjo6OGmGXTCaN4OTRg2qc0GgQ8hk9CcHxHFz7sWvxg6/9INAPt+Ki81AnopUo3Eg9hH2iTbSJ9txuPT09ho8DtX1NY7HjOBgeHjYebgXGanBTgEEjH+UunQCa/qK/+Tf5EfUVzYtu5LWj0VqBn+0Bd10Xo6lR/Oz6n+FI75HafeEq7rryLqAIXLzlYkTciNGrKD8pB/k8BfpMJ1P5xr4pCNOILPJZnTfKdeqa6iTQcQHBEHDNrbaj+BREKsgD6sfNcqz8jCBbdZ1kMhmIFmvkxbO96/Y11IdUT9C54vrrOiv44dxR9mrUA9/PCtgaDmz3TwGrHUWq+jPHop+zD/YcqA6soN32nOq1qidxnOrF1jE905zzcwXKOp9qBFPaUECu66Pv0XmxgbfdiEXstFjOm3qT2S81gPD9TtWBW3FRDtf2/ND6ITz5gSdrfSi6mPqDqfDz9doMasxQ8Kt9VAPAM82h7kMF7Gpk0ahdO1LDpvkJwP0sWmtrKzZu3Ii2tjazGemhtAsa2RuVwJIWGxKWMlX9YWEQEimfrUUHqtWaR5Cf03JHK4/mkLOYgAoF9h9AwPqnOc9sJLaBBQMBsO0WXcz7+jxMvXcqvJBnhBqfQeEB1ITC6OiomQf2RQXFwpsXolqtYuWHV2LX23fBH/Gx8DMLcegNh3Dm+tq51F23dGHOp+dgZNYIDrzrAPKz8tjyoS1IHkti6aeXIr2zdi6jAlN68ZPJJKZOnYpMJoO+vr7A3LLfbBo6FYvFjMCzwQjzvTiubDZrvNca+s/1SCaTAWDJ9xx71TEUu4MAqvNXnTWv/3nBQSBKJaVarWLZp5ahhJJhTiZvpruCUmcQ5OSn5xHtiiLuxwNh/6u/uBq/+PYvzHW9t/Zi9hdmIx/L144tq5Zx4nUnMPKaWo52uVJG98e64Xr1s9173t2Dg48eNM9o+04buj7WBT8UPA5DwaBWTiWTIjBzHAcjrxvBwDsG6gPwgNi2umHk7AvPYv9b9xua3Pm3O1FEEbNvm20ELt/Dv9XIxMIvlUoFY7PGsPkNm5GZFMw1XvtfaxEuhlGpVnB67ml0lbrQNtSGF3znBRiaPISe/T2oOBW0traiqanJeP91flWQGa9QwcF1X78Oo92jmHasVtcAAArdBRTarGreDjAwdwBTYlMQCtWiSNJ703Bvc3H8j44DAKY9Ng0rv7jS0KTtraBhj3SjoaChUMgc2ZVMJk1+N4UdDRc0wlWr9fPoD244CC8k3msfWPKrJVj+4+XwUfemj47WjWYTbaJNtOdec10Xr3zlKw0/r1aryOfzaG1tNXx9ZGTEAGneQx3AVjypYySTyYDHXNOC2FR5Vt5GI7SCCjon9B3VajVwrKeCIDX4jjWPYSxtpUY5wOlpp+Fv9XG+PIoZo+1t1N80ZPJvOy9XvWJ8TigUCuhv7B/1FM4hIwEoV6h7Kgi0DRYKdAhCbPmr/aAcY98bebBDoZDJ11cwpnOhP6oT81oFajp3/F7nS8dlgyf23wZa1DtUH7PXyvb88zpdJ13rRvOqfddnsQ969rztLdXPdL60b7bDQuezEeDVebJTAfQ5SjtcZ+4pDalW0KproX3VfqhDyp4vpW2lJ8UHatzguPi8/qv7cfjdh43ud/g1h+HEHMz69ix41WCBQJ1PnaNnMhKosZD6kfZXjRfqHbefp3//PqNEo/Z/NqQ8mUxizZo16O3tNco7mWJHRwfmzJmDRKJ23i3PVQZgKogzvEKFAxkavdm8j15vBWr8IZjmD89EZvgFNzRzFehF5v9A4zP3dOPwGhITz8/14aPSVMG8H82rTYoPXPCZC9B7d68B/8qgeS/fTevo4ODguLB5Em+5XMbQ0BDCJ8JY9JlFWPD5BXD7Xcz83ExM/ulkTPvZNCz8+kJUOirY8949GFlSL9KV681h59t2YnTWqNlYDFXhWhSLRZw6dQrDw8PmPt3YGo2g4Tye56GpqSlwxjpBCcEv6aStrW2coKVFmkdv0QDCSILRS0YxuHxwHN31/KwHMTdm+hUKhTB37lyEQiEMDw+bEGxlJASYHYc7sPxTyxE/EwcAtO1sw7IvLAOOwViAWYPgqdc/Zd45+0ezsei7ixBya2Hvra2tOPtPZzHy2vpcj/zpCM58+Ix5VygcwrkPngv0vfPmzoCCwDO9NZSMgkSLa6glOn4iHnim4ztoGWwxBpKms02B9AM4wKHXHcKRPzkSiDgYnTmKIxuOBELVtIBPtVrF0OwhDM0dCrxv3kPz0Ha6DdVqFX29fXjgTx/A7a+6HV7aQ+tQK2bummn4QKFQGKcUmSJssgc1IqJtsA29u3vNPiwUCmjf047Lvn4ZkkNJ04/Fv1mMNT9Yg3QijXQ6jTPXn8H+v9uPUlcJi360CFMemIJFX1qEUC5k6FkVGoY7+r6Pc+fOGbphpIzdT829TKVSaG5uDlSrZ4jcno17sOnlm4yxgG35PcvNmKgYZrPji6pNtIk20Z5bbcqUKUZma6Eupl1lMjWDpXpO1YhPvqR8kDVrqBfpaSONjOHkQxoqrgZCym/lxepJoyzVNECOqedUD278yY1oGWgx71uxdQVuuOsGRL2oeb7ebwN48l9NHbSBiO3QYJ91TApw9HsCShvAq4FCjRI2WLNBkgITG1BrTSKgFgmo4ecazWWDaTX8ah/UcWTPze8DQ41AG9dAjTycP8pD7Y+CJjukm/SoERYK/JQe1UhAfdEu5sfvbNDGvqo+r+HJCrJtLGCPkePknlL60vs19Fm/4w9PRkqlUmbNbXDO8fJ+XQudK3s83LMKPPmsRuuge1j7SwCc7EvC8YK0ke5LIxqJmmdxvemsUq+57ivbwKHrxT7aP2p4+F30rP/b4/l97f8k4A6Hw1i2bBkWLlwYsGpwQUdGRnDmzJmAIsqFKJVKyOfzgRwSVuRUwJFIJExVTgI1HiXFDW4TLPug+SPKzHVD814lDgXhFJhUwMnISaRO3MFT//IUTl15CmM9Y+i9rRcrPrICXQ90BTYB79WxqnfWDh+JRqMm/50MKtobxY4P70CiP4H4YA1wuWMuZn91NqZ8cQowBiQzSUy9fSqcSnDDZXuz2PbhbXA76kciVCNVPP6px4EkjAc1m80acNfc3Gw8nVOnTsXMmTNNhIEKtlwuF4hcUOuxhvYACBRW4/pwTuPxeODIEgCYtGcSOg50mPudioPeL/ai6XCToQdu2hMnTiAajaKlpcUIPgpBDckqFotI7Etg7dvWovlgM1Z9aBVCR0MmlDqbzSJXyeHxdzyOE+tPGEth8+FmVDP1ojaO46B4QRH2OcyFFQWT3nDqo6cwdlXQK3D0K0dRLBXNPDNHWBUK0iLzeEhLsVgM5XAZfW+s51I7RQcr37QSXTu7jNLR8ngLLnzHhXBKLB0LxAZimParaahWqxgbG8O56Dk88qFHsO2vtuHkqpOoelVjkaegyvRksP3l2wP9d6oO2o62IZFPoHlpM+548x0YnTyKMzPP4Oa/uRkFFIyQaiREuPdUUJEPaP4fj7SgchYKhdB1rAvP//DzEclHMO+383DRTy5CvBhHJBrBwBUDOPzWw6ikKzi7/ixGZoxg5bdWIpVNGVpwYy6iyaiJiOB8R6NRdHZ2mnBM9ocGP/ZVCyaqkmQUmpCLg+sOYtNLN6GcrOfXuxUXGz+/EZF8xBh0uK485m2iTbSJ9txsra2tBsCQb6TTaaOE8mhQx3FMUVJVUhUgUndJJBIGOFCWUjaSF9neW8oOGo4pq9RYrqGolDE2SLQ9c/y/51wPXvnFVyKRS2DJ7iW46e6bkCqmAiCUz27kHQNqMoCh8vzfdqbYIIzykLyXqT+qc5ZKpUAROvZjaGgIIyMj40JobcBD4Kb1fLSpzkl9jsYV9oHgTcPQ1bus0QWcI5UllPPUjThvuo6aFmADML1H86PpwKLxm433cl7suVPAx88U8Nqy3Z5PBdvqESWtaWSfPr8RKFaQZgM/O8rDNvpof/TdXEMNSVf9U41jdkj5M3mviVva29vR1tYWiFa095TtGVcsY//oHFNnUSMPdZzug924/F2XI1QKwa24WP2F1eh9qDdgJNJaSmpkaQR6dR+rcUXHq3xMDSM2kG/0o6BcjW2/q/2fBNzz5883RdI46coMHccxXmCGkwIwi03CoZVXz3rWomThcP3IIv5oVUrbWkRCIkPWqtu29cjOnVAQT2IgITIvPZfL1f5O5vH0O59G/5p++GEfZy45A9/10b6lHfAQ8OhrSAiFL/M/1WJG4iwWixgbGzPjK/QW8NjXHkPfuj7seNsOoB1IpVI1QFaJoTXSWgP2hQp6bu3B7K/Nhpuvk2X0XBQL37cQ4bHaXBfaCtj6sa0YXjqMhz/9MAqTCmYuyJBZWMv3fezduxcHDhww39MooYzPtpAD9UqkCjB0XTSkjutDxuC6LnJuDqVwPfy7694uzPzJTLQkWgIGGQJ5Cst0Oo22tjbzPq41GVUkEkF0IIpL33wpEvlEIP+pVCph/4b9OLvibGBn73/1fuTTeVNwDwDW/P0aNB9sNtckDyex+FWLjeez82udgXWIHIlg0qsnIRaNIR6PIxQKIZfLGVDHPUF6yGazBhwCQLY1i+M3H0dpXskA/enfnI6mQ00olUpGSQOAlu0tWPpPSxEZiiB5JInVr1mN4qkiRkZGkOvNYfM3NqPUUkIlWcET//QERtaOmDUql8soloooOSWs/tpqxEZqkQpu2cXyu5dj8a8XI5PJ4HvP+x4KTfUw76GOIfz6Bb8265pKpdDb2xtgqGp59n0/UJOBdKRFbFQZ8H0f6ZE0/uxNf4aN396IcLG2f4qTi3jkLY/AS55XGlzg1EWncOSGI+b+SqKC7X+6HTuft9PkoqvSpwqChkTm83lks1lks1mzH5RnkF6bm5uRm5TDphs2BWoORLNRbPjWBszZMQeOX1u04eFhDA0N1UL2x8ZXsJ9oE22iPXfam970JhPp4rouMpkMksmkkanDw8OGz5F/2GHXlFHkV7FYzES8kdfoKQqqnNqOBd5DnQNAQKlmvwhi+V4avBt56nzfx2j7KKLFKN7zmffgFT97BeKVeEB2UxdjnxSwaUqRfQSZeupV6acORvlge9LYCNxYR0OBO1OaFNDaoEZTH9XLbXt92QcNa2c/tEgYG6/VObVBqeqtfA/vVWCua833qJ5l91nBNOeeOgedP1x39Xo2MpJr//m3DbaU5hTo0pii71OAaxc107lTD7WCXfVI2zTRyJCi49N72Gcb1Oo8k6aIWzjftrdaPbkaEddoLjXCQI0EfB9/NKJAQblt1OH4WGyte6Abz//E87HmB2sw59E5cKqO2Wd2pIFGKXD/qS7P5+r4yStsQ4r+aH94ne5N21Cj8/f72v+5HO5Zs2bhiiuuQKFQCGwCMkfdHFoITYmElkLbWkvC5+LmcjlUq1UTduz7vvGG8dxmtdp5Xi1kmx7zUKh2ziVQL6JRqVRMftXQUC1clgsfj8cRi8WQz+cDYF7vr1arKE0tITc9V/dwOsDYnDEUW4sI5+pGBaCeq86/SfCVSq16KauRKgOmYMwuyWLH23egnK6B99NXnoZTdDD/K/OB86nNBL8EnjN+MqNmWZ+RweiqUcz75DwkdySR9bJwe10cevMhDF8wDADIzMhg29u3YcGnFiB6OGrWj+tRrVaN95kCXBmAerBp6SVwVQalDI6pBxRgBHoa5lLpqGDPa/agf02/eX6uN4fc7Bwix+qVHpVhATDegSlTphjjCJkt/1bGwc90nWbePRNlv4wDf30AftxH8/5mLPvMMjRnm1GJ1AUAysDK96zEjnftQMWtYOG/LkQynoQX9TCwYAAn33cSXlOdmSV/m0S0XA+/A2COcCmVSoYZcT44/+FwGKWeEk7/02nkL6hXTQeAsZVjyN+aR9gLB0IHAaB9UzvmfW4eEk8ngFz9GLWBiwfgRcWi7QDHVh5D2+Y2RCM1AXPkiiPY9Se7sPoLq7H8G8ux7bXbsOD+BVh560q4rotUKoXn//vzce+f34ujFxwFACx9aCku/8nl8ML1KJbh4WEzz5ovxX3LomTkHUoHute5F43SeP5IrUqlAm/EQ/eT3Th3YT18PzmQRNuBWl0JJ+5g58t2Yu/z9tboMexh6S1LUSwUA9EatpVbhTRQL9DDPaChXul0GlWnikkHJ2GsqwaiI/kILvyvCzH7kdmouvUjatrb2w29MUd8ok20ifbcbM3NzYYvkAezPkq5XEYmkwl4kCjbFajahctSqVTAqE3vLeWnRtsAQeOhgh2VyQrUqeAq0CUAYN/4PN/3MdA9gJ9e/1NM3j8ZL9z8QhO2St5sg0g1ICuf1Hv4LhtE67UKatl4Hw2gyq/1Gn6u19jP15NF+C7VfXRcmmJE/Y7zzWYXL2OEo32d3RcFeir/bOOCDeIV3GqzATIBOulMDTAKgjgumwb0WbZRR40COhZdv0Zg6pmep+/k/Cg20HnXedR1ZdO9wO9Uf1Wdwp53XqM6gQJUzpXd50qlEji5h3hHdWMF0DpO1VftcZG32B5mdTKSjjsPd6LjUAfGKmPGqGGDYw0nV2MNr1NDhR2KTh2o0ZrZc9vIqNHI4NHIw96o/Z8C3D09Pbj88suRTCbHhbaQENVyA9QtqRryoyErJAY2zfEAEKhYrsxCmTpQP4dRN5RuFhJXKBRCOl0rIsaiRexHuVxGR0cHfN/H4ODguP6SoTbvbcbSzy7F0+95GrmeHJr3N2PxZxYjdSplNpVaPBW0q5CilQ+o5wep1aw0WoJXsgROzkGpUIJTdkwIugGB598x4yczUAqXMDh3EM3bmuG45yuBlj14Wet5pVqVQ/VwEvgp4wWAOXPmYGRkBCMjI2aNubkYnqbeyUaMg2HejuOYcH2Cd8dxEEqG8OjfPIqzq84G+hmuhhGqhAJGmmq1ikOvPIQZP5+BJqdmyc7lcti3b19AoSANqiGE79O14D1z7pqDeCWOQy8/hOWfX46mQ02oenUvufHMDoex8LML4bkeYn0xOG6NLhNIIOyHUULdQz/yihFUEhX0vru3zpzCPvrf2I/Oz3YGrM8M3WefcrNyyF4WzPft+G0HFn5+IdycCz9UP49UBWzPw7Xq3GW/xnRTqRR6b+1FrBLD1r/fCgCY8l9TsOA/F8BBjb73X7sfO161A5VEBU/+9ZO48CsXYt1X12Hqjqmook4PbsbFVT+5Cg86D6KlvwXr7lgXUFxUeKtVmMqoWjVpENHf9FqQThoJt3AkjMevfhyzPzEbpTeXMLx+GJFcBBu+vgHdu7rhNDl49C8exaErD5l52/PyPUAcWP7D5cbwp3yDe5d0qjUBSDdz587F0aNHjbJ8uu807vub+3BoWf09vuPDT9Y9UxSayusm2kSbaM/d1tnZaXgVDYT0IlWrteJpY2Nj5hpt6ilVgx+9aiz+akeSqZODNXKAOoizvZz8TBV8ygi9j436Ce8baR7Bz2/8OU5MPYET008gnA7jpntuCjybz6Ec1eNI79p4F9b/cn0gWsv3fZzoPYHNqzfjsl9fhvS59DjPPcfIsSi4am5uNoXoNO2ITY3pnLN0Ol07MvV85IDqqfpenTfl+SrDKNu4XmpEUUChwNAOS2Y/uTbqqW4ExhrRjc6nPlPvZSNdNJLJunY2OLXXReeLY7PHrEBMDSx8turljeZfn91oHHZrdI++y36f/VwFtDqHOi7bkKT3a//sseiaKsDVOVIAzf+5XrYXnd81CqOnV9l1XZNG0Eh/afTeRmtpj6PRuFS3t+e8kVGm0Rz9d9r/Ge2pra0N69evR1tbW8CzzEWmUmx7Nu1Nxckn42C4Znt7uxEEBH4UYBRmQG1TMR9FQx3s0AatfqjFExzHwdmzZ41gVOu079cKKPE9GrpSLpfhRT3s+NcdWPnBleg60oXV71qNJ/71Caz6wCqEzoZQQSXgraYyr1ZmFYbNzc1mA2qYGYFfy9EWrPzgSmz+980oN5XRc0cP5nxrDlAAPMczgN2ODACASDmCrp1dQLjOLJJjSSz50hJsT2/H4IWDSO1LYe4H5yKRT8AJ1RmkhpOogD916pTpHzeohm3z/Rq1wOgEXstcKYbMUKhyLF7Sw9mVQbANH0gcTyBxPGGO+qp4FRx++WEcfuVhnFt3DuvfsR6xUMy8Q61xDIEplUqBPCyuMxnutGnTEIvFsG/fPvT8ugcd2zvQPNyMUKKes65hS+VyGbGTNUMFQsLw9wHhoTAwQ8ZQATr+q6Pu6Y04OP7V48itygEu0P7pdlTLVdM/MtB8Ko+zH7DmwwOSh5KInomi6gSLkHCtyLDj8bgJeeS4p9wzBa7n4tyMc5j/n/MRKUdQ8ko4tO4Q9r1yHyqJ2t7OTc5h81s244ZP34BYOFYPDUQFIT+EaH8UG3+wEcgDYScMzw8akPTYlnA4bM6ipbJAunAcxxgyuBaq9GjYOZ/puz4efOODOLHsBHoqPVj8ycXY8eEd2PC9Deg+1Q0/VKPTJQ8sweErDpsiZqFSCHPvn1sXZpEqHnnbI7j0k5cCFZhiRSqoisUiYrEYcrkcotEoDhw4YMYZS8Zw55vvxImFJwL0GhuLYfqm6WasHC8jc7RI4USbaBPtudeuu+46c0KL49RytPVEg2w2a+rOMHWI/IBGWyqr/M2oQDVO+/74vGzKCWC84twIpNoeKC2OxsbnGpAQ8vD1l38dfR3n64Y4wOZVmxHyQrjhvhsAwIBOO8rRg4dfXfsrbFm5BWfazuAF336B8YwPdQzhxy//MTLNGRyfeRyv/8LrES6ej2Ryq/j+33wf7X3tmHpsKtqH2jHrwCwAwPaV25GsJLFg34KajAi7KFVLiCQicFwHFacC13Ph4HwklFuF4zsIoXYihIKqqluFWw1WrFYAYc+ZGoPpoFHArWvBe4C6PmXrHGqAUeCs4KQRSCHYtz2MNsDR/pDOqLdTvtreTNt4o44KBen6Xhts8371SKtu22ien6nZ62Bfz+fZz7INDuq8UMDM1ihKgM/5XWBU95wd8cC+2UYGGzzbY9L3KZDlOHSP69/U16nXsAgx55/PqlaryEzP4MjGI+j9996Al1/XU40Y+r7ftVZ8t2I/nctGRjp7zX5fc571hY7z34Pyf0AtmUziqquuwpIlS8yE6mKSSQP1iadyroyd+a8avqFWTAVyuVzOPF+93QSYWnSNoFwBP4Uc30EGSYbBM7EJzmkAYJ9JPHxPoaOALZ/Ygty0HLo3deOCz16A0EgIvuvDK9fDxzkWErkedg/UwShD3wEEBAHnMBqNopguYsu7tmBw2SCaDzbjgvdegPC5cMC4wWrgFKJK9LqR1GpeRRU7PrwDi96zCKjUz+NMJBJm7njEEb/jxtDq8rQuJxIJE77PNVQBoWvL8XHMtIByk97+xduR65ZwfQCJIwms+rNVCDk1xp9sSeL0C09jx+t21HKtfaB1ZytW/MsKRIZqkQWpVArVahUtLS3G2sd3cq74TgV7XH+bOSoDKhaL5n49SspxHFSSFRz8m4M4/fzTZgzhoTB639mL5sdr4YfldBnH/+U4sldka9d4QOcXO9H17S74eT9gHHJcB4WrCjj5lZO1h1WBSfdOwvyPzkfYDR7pwXkPh8OBvcZ0A/5UKhX48BFPxAGvrhSUyiXs+stdOPqCo/AjdXaVOp7Cje+7EaFKCOWOMu5/6/245PuXYNrZaWYttVo9C8fF4/FABAvnTIuRcf+rEsAQezWacIzxeByFeAEPv/phHL34aG3+fGDGj2dg2S3LEC/HA8IhFAphbMEY7nnTPQj7YVz/r9ejebAZpVIJY81j+PX7fo2xnjFM3zYdF335IkTH6h4m0oEqMnwm1/zJlz6J3dfvDpxTnj6Xxos++CKkkDJzo4Y90tB3v/td/E833/ef3XkbE22inW/PZV3l/832hje8AVdccYWp2dLf34+WlhYDsA4dOoR9+/YhmUxieHg44C2kzgHAGCWr1SomT54ckLfRaNQU/rRDR8mD1JCv79BCXRrBUygUEAqFEI/HzffKf6n3RCIRnEqfwtdf8fXasWA+MOfIHLz2x6+FX2l8nJnneShHynjg8gfw0LqHDH+es2UObvrVTSi3l/HVv/4qKpHzqYA+kBpN4ZWffyUSTgI//9Of40TvCfOdAwcv+8bLUGgq4LaX3wYAcKsuXvbpl+H4suPIlrM4uuooNv56I3726p9hzcNrsPyp5Sg2FfG9v/weVmxagUt+dQkipQjG2sfQMtiCbFsW//X6/8KN374R0bEomseaMdo1iqaRJox0jpj5ahtow1hyDOnhtOHfnKPTkdPIh/NIpVJI5BNIDNd0JwUZqh+rzgzUQbYayTWS0vaIc71VR7ZBIME1ZSYdU6y0DaBWGDaXQyhUO3FF+2EDbvVi2uCW36uX3AZY/E5lOZ9DHaERkLX1LW0K5LWvjTz42k/bsKTvU0eVPZ9qJNNohEbzz3vUSKB9ZNSLXcDOBs9aE4DpeVpDRueE9JJMJpFKpZBMJg2+YK0knori+z6GJg/h3n+7F17Iw8zvzMTU70+Fn6unQGi+NfmNbZjhXKjuyblSL746QdlvO0ydcw4An//853+vrvK/3sMdCoWwZs0aLFq0CEAwHxtAQBnWMB4q0cogCJSVcOgBBYJ5uQSraq0jMTA8yd7UGjqhSrIKA70WCB6XoQBcx5qfnseut+2q5W0DOLf2HHa8bgcW/McCOP1BKxc3AP8m8FFBS0BNsELipCWxVCoh25LFgb87YI7GGps7hl1v34V5n5qHxLlEIAyWY7AZnAKWUqmE7u5utLe348iRI1j5/pXwXA+hRAidnZ0YHh4OnCnp+77ZvBQ0BOS6htw0zLdVI4mGxHA9OEZdL4K1M4vOoJKsBKt/e0DXY12IhOoF5jKhDE5edLJe2MwB8j15ZFZk0PNYD4rFIvL5vPHssogNw+89zwvkTdNSrVZhXU/13tNYQ8FBpcZxHJSjZRz6y0M4fePpwB5KbU8hcjhi5ja/OB8ofgYX6H9TP7yKh/SX0nB8sTjGfOTX1nN9w/kwlnxiCcpeGaVKyXgX6PFwnFoOYDgcrh1dJ3uEe854jH0Hni9ncobCWPC1BaiUKzjxshOACyS3JbH0I0uRK+RQnVbF1tdtRf+sftz+9ttx/Teux4z9M8YZlUKh2hFt3d3dgSgGphBo4x7x/XoFW90/mtbA2goHVx3E0bVH5SHA0ZcdxZydcxDfVT82jWvTcbgDl37zUjQVm9A82AzXdZGfnsdjf/4YxqbUcq6PrzgO75UeLvrBRYiNxgJFaTgGNu65SqWCnq09OHjZQRRaa7Uiug524cr/uLKWrx+pnwIAwNAjgEDKykSbaBPtudWam5vR0tJidATqJPRQ53I5k7KmzgEq0eol1arS5DWU7wosbI8beRSjZ/gsIFg7RfmV6h/q/dJ3qEe3s78TL/7Ri3HrC25Fz0gPXvmzV8Kv1sGXelt530jzCA7NPBSocTM4YxBD04ZwcP5BVEPiYHCAbEsWP/3TnyI5lMTJGScD3/nw8cPX/zAw91W3ih+86weBz3765z8FAGy+fDM2X77ZfP7U+qcQrobR2teKxy99HGvvW4ut67diqGsI3/2H72LawWlYsnkJHr3uUVzw2wvw8A0P1270gcvuuAy7V+zG8/7zeWg73oZ9S/Zh/s756JvUh3v/5F70Tat5/qfvn45rfnwNBnsGMWvnLDi+g9O9p9HX3ofFTy3GaOsoSrESWk+2NvRgqmfb1mN1LSlHCJ5JGzaA1PUkffi+bxxW6vwgfRD82p/bfbNBrHpGtfF7RkAqPTaiN/6vYFW/t0Gz9k/1aPUa838CyEbPst9hz6F6h7XQr15nGwF4jY1/GvVR57SR0cIeK9+j1/LvM8vPYPbx2Qj7YYMvABj+cmrOKTz41w8a58CRvzgC13Ex/TvTjdNQsRl1Re2rAmU7GkONHfb9diNd2fP9+9r/esC9bNkyLF++3Gz2RlYjVeoVQAEwHh5dMPVEK7HT68yFoAWXwkSBHhVzoA767fxIKskkBgWD7CctbQpQeS37NXDBAIaWDgWe7YU8eL6HeKR+nBmZJceom1EBPt/JqtmaS8ox5PwcKgiCE8+thdKqJ12fDdS9/Y0IemBgACMjI8Z6RS+eGic435VKxRz7RS+6enw5j8xTS6fTgXNCuS4aBqOWd51rrrUX9mAftTX1a1PR+5+9qPr1wlPxQhwL/20h9r51LwZXDyKUD2H5F5ej7dE2hJIhtLW1GW/79ou3Y/oT0xGp1AvPUDHSCAgNq2K/bCtetVpFV1cXPM/DwMCAYS7GE+o6tTFYzQ/5cMN1IZt4KIEpH5iCE/92AtX2+tq5CRduyEXYqZ/pefqfTmPk5SN1mo5VcfSPj6Ln+z3jaIDGIrvyOZUs7hMqcnbagO/Xwqd7PtcDb8zDyEUjmPEvM+Afr+WRb3/DdvQtrykZpUQJ9/7JvbjiR1dgzu45ht7JByZNmgTXdZFO17wDIyMjAYOaHfLGtA8VRno2ve7H9r3t6NjVgYHFA2Zeeh7tQfJUPfwyQFeeh+k7p9f4T+R8BEzEhRMOElssFUMkGjH1C46sOIL2c+1InkuaedR5PbfkHDa9ZpMB2+372rH2G2uR7EvCjdS99kA9Tz0cDmP7Zdvhfu//5AEXE22i/a9oixcvxrx588weJ2+jYl6pVJDNZg3ApkzR/F8AAblD+UnjtXq7qLjzOfweCDoSVJ9SZZ8y2wZKqryrJ4r8tlwuY8qhKbjp1pswIzcDoWqwMK7KDn4+aWQSXvTLF+FnN/0Mp3tOo2mwCdf86Br0nu1F7/FeuHkXD177YGA+S9VaXZr/ifb4pY+bv+98xZ2B707MOYETc2oedQO2AcABHryh1se7X3Y3uk90Y+/KvTj55En0T+s3YBsAjs87jnv++B70TenD8oeWY+rTU/HAyx/A4KRBnJl/Bpm2DIqxIq757jXYs2YP1t2xDr7vY+slWzHcNYzLf3a5mf/fXvdbLP7tYmy+djMWPr4QPQd7ACCgh9lAVfVcpQHVZfi3elLZqAcoSFQApOBVdWkbtNoeeDYbH/A9ug9swGVjC32W/Tf7ps4i2xjQyDtv6+eNALeO295bvM42FGif9XoaPKjrA0HQqo4q9abrOtpgnTzi7LqzNf3s6T5c8eMrArVj+HwvNF6/dmLnjW9OcE1/V7Pp7JnmptFc6jj0WjuS4Zna/2rAPX/+fFx22WWB8FsgWCyBYIMLTwVaJ1Q9hEB9gyrTp8BhOA2FEMEpBYyG/gLBIygIyCnYlHkQ7PN/Mi82DTvXsJzKnAqOvOJIcGJ8oG1HG9x+F9XQ+IqbSoQaHqbEr0ceNMrhaRpuQsvBFgysq4GKpgNNWPDxBYj3xRGKhgLh9Jpjw/XR6s9a9EONC5zf/v5+M4eu65oz0Wk5HxkZQSKRQCqVCgAOFnih1VTn3nEcE8qiUQpqpVNacl0XU56egss/fjnu+dA9gAPM+tYsTPnxFOPx1VykxJkEln56KbZ+cCvW/OcaNG1tQh41T3A4HEZXdxf2r96PnX+yE0eedwRXvOsKVIvBozCYf8X1J62yn5w3LeQ1MjISiGTgWjqOg2qqiuE1wwFSSe1IYeZHZsI9W6uuTW90+vE0el/fi8M/PAyEgM6vdKL7G90oekV4Tm2vVbwKRm8YDTzPLbloeaAFlUrFFFfz/doRVhSc2Ww2cOQc95xtXOFcUVlk6FLYCaPn5h60/LwFyb5kLZJgDOje1o2+C/pMmGCyL4n0/nTAas715p6ll4e0RsOaRkZwD2iOPecegPEI0bIfPxbHvA/NQ+7jOeRn5NG1rQurv7UazblmlJxSYA8zVNO2zjefaMbGr23E7W+7HaNdo5i+czou+dEliOQj8EIeTs49iSde+wSixShu+NANiGbr3mkAGJ4yjEff8ChynfWztDM9GQxNHkLziWazz5LJpFFmwpEwdqzbgU03bUJ4QRj4ZW0eJ9pEm2jPrcbwTRp2CZIpy3K5HAqFAqLRKEZHR8cVK1KnA2U3i9GqB1udFYzw4zNs2aqpfaq8a4qS3VRfsb1wdCA4joN5Z+bV+JhT11EULNFDSkV/8rnJeNmPXobv/Ol38IJvvACdZzvhNtWet+6RdSiXynjshscAB0iMJrDxCxsxdGoI2z+4HWOL5LjE87IG/vm//z9IkDnbexZne2t1VHZu2NnwmuPzjgMAtly1BbtW70KmMwMA2HHxDnPNra+/FZn2DAqpArpOduHRGx5FOVZGMVrExu9vxKYXbMLWK7Zi74q9GJo8hKMLjyIxlsD1370eO9fuxPpfrodXretfnu+ZfHXPP18bqVqB79WBM+WgnjGuRzbZIJF6vtbhUT2edMd7bADIz22ATtrwPM+EVCsItqMggcYeX90fGoWoWILvt3EIr1OjgZ3OaRsSGn1mGwlsXZZNdUTiC0b52WPj3tajSRntqpGnCrRd10UoHMLQ8iFs/autKLYVseuSXUAIuOwHlwXSNT3PQ+f2Tqz/5/W4/+P3ww/5mPH9GZjxwxlw/ZojQvujY1HDn+rLjYwNz7RutlFPP//vtP+VOdyO46CnpwcbN25EV1eX2QAkFHqGbU+rTqp6k4GgF5abppHnlO9IJpPm6JxsNmsWHagp6QyXZeN7NNRcmQfP+eb7TFitUy/ExGs8r14MIxQJ4eSVJ7Hjr3egmqjCqTiYdMskzPv3eXCq9WqYGhKuuVNaDZvjVk+j3W/HcVCqlnB642nsfsdu+OHzY/SAab+ahjlfmYNQoW680MY5nDRpEjo7O3Ho0CHDVFiwqqmpyVzLtWOfWKmc0QzM/WE+EJUC5ozY+ddk0spkKbTJNBg2zLkivYTDYZTby7jtLbdh1tdnYXjeMGZ9axa8crDSPJlnNBpFPB5HNVxFNVc/VqqtrQ2p5hROXHgC976+lqsCH2g50oINH9yA6GjUzFcqlUI0GjVHiJE2SY82I+U4otGoAYek66pXxeavbUZmVkYWBGi/sx0z3z/TeCWYP0fBU1xQRPbFWfR8rgfVUv24K7QDJ79yEoWVhYCScdEfX4T4ubih0fnz5+PYsWPIZDKGnhiWqELGtgaT7tQKWiwWzTE2HCf3UnNzMzq6O7DvZfuw48YdaD3Zihd+/IWI+JGA99aulcB9xr1FOiHv0JQDFhji/HCe2fdwJIzTS09jz8V7MPNjM4EKsPeTe3Hxhy5GIpQwRhQqniqkmIfEvU9FNu/kcc8b78Hzvvg8uJXaOp+dcRa/eOcvTLRCfCSOG95zA6KDUdO/glPAvuv2YeeLd6ISr8CpOJh/z3ys+eEaOF69EJwRRiHg2IZjuOdP7qkVcPMBPAbgRgDB4Jn/vzZ/Iod7ov0323NJV/n/ormui+uvvx6veMUrkMvl4Pu1U02SySTi8ThKpRIOHTqEAwcOIJlMor+/P1BclAZJAAG9oaOjIyAf4/E4EomEOaM3kUgYfqLGYhotmfKlxkq+k9fxFAzyXsp/BWfk3ZVKxeSn8zxrjl89pjouvp9G4Eq4guJI0RTvpGw5cvwITvadxM737sT8j89HePR8lFjcwVNfegrJE0m07GzBwdcfRGIggQ1v3IDTV57GrtfvQjVeReJcApV4TWZXkhXERmIodBbGrVU0F0U0F0W2PQvf/cMga8dzAB+mkCc8IFQJwQt74/von/8u5OGiOy/CqntXIVwJw4t7ePSGR9F1tAsdZzrwwEsewDXfvwYPvuRBrL5nNVp2tWBoaAitra2mUGcGGRRKBcSKMaO/KehRpwlQp02grhfxc36mDiSN6LI90apLMeVPK3bzOyAYWt7IM6+gnP3iM1SntI3sqpPo8zXdLjD1DTze9m/2QfVdG5TTIMf8bc6THvfHfhHYcp8UCgWUSqVxKYG8p9JZweYvbQ7QfrgYxppfrMEF910QmJd8Po+R0RGcajuF49cex8wvzkQimggcTcz+KKjnO+kQVdzA8TFdRcem86xrovOsIPwLX/jC/80c7o6ODlx22WWYMmWKmTR6wQCMY7Zq4VAGryG3ujEUwJN50yvLcFh6tXzfN5U/qZBzUdUr7nme8dCpVYv9tRP07ZAI9cxyHMzrnXzXZJSiJez/8/3oursLcz43B3CAaCyo3LOYFueLVivNW1XvvW5Keu4BoBAv4NSNp+pgG6jl+a7oR9fMLrTsagkwExaaIpgZGhpCNps1QtP3a2cF08KmG4VzomkArIzKSoehUAiJRMJsKIJCKgJaaIXj1/AXRh8MDw+jra3NnJetVtOh7iH85rW/wcjUEex7/T4s//ByREO1UBc7/FjD+PxS3WAQCoUwNjaGscoYHl/7eA1sA7Uc78l5ZK7PYNFvFmFsbMwwszOTziDtphEZq3lCY7GY8VpwXTWagFXQaZigAcp1Xcy4dQZ2vqVuAXfzLlp/3GrmkHPPOYxEInD2OUh8PIGSH6TbzMYMSrNKAbA97fA0xIvx+r2Og7179wbClpWWCDQ1FUO9JgT/pFcyVt2/XGegpiisvG0l3KiLC26/AG7FBUIwBdKUFjUMTA0Y8XjcXKNWeJ1H7ns14pRKJRxbegyPvOsRwAXKg2XM/+Z8XPSeixCJBsPhtAYDAT/XktXqgVq+ZMpJ4QVffEF9jzrA0zc8HUgNKCaL2HvFXlxwywW1OQ47OHT9IfQv6Me8X8/DgSsOYNaDs7Di5hXww7XUAs6BOeYnWsC2tdvqSpYDYD6AmwB8BxNtok2050ibNGkSrrnmmoARlXsdqMnT/v7+QMQU+SH1AjXGa8SY6kNamZw8kf838g7ZoIN9UbkABE90UV1O/wbG12rJ5XJGFmg/+ZtNxxyuhJGr5gLRYNVqFcVcEcgDi96zKPDucCmMla9bacbpNrmYee9MhCthzLl/DtyUi4EFA1jxyRXoX9SPilvB4IpBzL59Nna9fhcc10Hf4j70HO6BW3Uxe8tszH58Nu565V04tfIU0k+lkVmSQamzhPSWNIpTi2ja24Th1cNo2t2EkYvOp2/5QPq3aeTm5dC0tQmZlRlUOioID4XR/FQzMssyKHfV5Ez8ZBx+2EdxUhGhbAjJ40mMLRQvvdXGgWoXqEarjS92gGqk9t2mGzbB8Rwsv385nrjqCTx1xVOBS2/+wM0AgKMLj+KGL92Anp09hoaK0SKefP6TyCOPtT9bi5AXPNVH9WXqxjYQt4E2P7dlttKBDUzptCBN8fk2jlDdWPVIBbDabE/3M41Nx6TX2MCQ12pEp1kSS3fWvnGM9pwp4FcnpPZXr+P3+nxdExpDEtkE1n58Lba8dQvGpozBrbhYcc8KXHDfBQGdyDg1PR/NR5sx6/Ozak4fr2D0fz7T5jvUwWx8pLRhG1gUWzUy6DTiT8+m/a8D3NFoFOvWrcP06dMBIABgSXhUnmmFpfWEHibNKeFCE5ypVUstI6pgZzIZZDIZNDc3B8JL1Fus51zzOUDQw03iJtDkMwg4CNIABEAGBSnDP3zfR8/PeoAM0H13N8KxusCjwFGGQyKqVqsGvLGfnFPXrRUhY795fTgcRnO1Gcv+bRl2vXkXBlbVQspjp2OY/6n5aHq6CVXUQ/Q1d4w/GobP+SoWi4Eca+0vx6HrwnBYgi7HcYxlnEyCVkpWu9RK1ZxrjpsheBSsGuqS6c7gwVc/iP5Z/QCA3JQcdrxtBxZ/ejE6jnQELOhcT51zGmJoPXVKDpZ9Yhn2vGkPzq09B6fqYOV3VmLx1sUIR8Jobm5GuVzGuZ5zePI1TyJ1PIVV/74KTkWOnbIs/9pftUamUinjBRhrCQpZ3/VRbCuiGc1G0eCccBwMs1KvguM46PxlJzKvyCDXVgtZbn+8Hcu/vRzFXBEFv2CEmVaU5D5SgwcQNDjp+2gQ4rxy/+ZyOQPUNbyJz17x0xW1cTj1Ogt2qkc0GjWhY2r1rlZr58dGo1EjNDk3fB9TEdTId2zDMWx5zRZTKG/gpQM4mDqItd9Yi7AfNu9UwahpLIy24dpqf9RiGw6Fa56C4oPYffFuAMBFv7wIC3+1EH64xp+euPEJbH/R9lpRn6qPJbcsweI7FiMUDRlAz3exOZ6DKQemIO7FcXTBUSAP4E0AgrV/JtpEm2h/4C0Wi6GrqwuFQsHIJgJkhoFms1nDk1W2KnBWeUu9xD5eSo3jqm8oGAAQAPs2YACCNWRU7utvNhuUUEaQvzbyUPFardtCI7PqF67rmvOwFVwpT9afuT+dW+Pdkdr8zr9rPiq/qiAUCWHq/qkIhUKYtmsafN/HRf92ESKRCPqv6cfsrbMRqtT6UfEqWPTZRYitiKHznk4MbhhEYVoBHXd3IDMng7bNbei7oQ/tj7Wj/6aaoSQSjqD33l6cmn8KTXc3YfCqQRx71zHM+OQMtN/TjuErhlGYVlv/9KE0/IiPfe/ah96v9aJldwv2/8N+jC0Zw/zvzEdhRgGFVAGZaRnkenJoOtWE1LkUzq6ohamn+lKYf898HN5wGMMzhn8n7Y2mRvHoCx/Fjst2POM1XtjDfX9xHy77r8uweO9iVFHFIy9+BLs27KrRVNzF1T+8GtDadRbQJO2pTG8ESPV7OpL0mY10YgW5fK4+n89RA4AZmxcsUqg1i3QcjQChPlONPDY41PHZcpzNptdnmjfVd+3n8x4CXQW19n60+8n3hEIhdB7pxLpvrsNjr38MSx5bgpV3r4SPYDQKnZiFQiEQ7cvveASziewNhcb1k/NhGxw0KlT1T9tIoPPG59jP+33tfx3gXr9+PebOnRuw7KhFhgREItFQES6cetyAeg4miYqghj+6YX3fN14wDYVlU0+04ziGiID6hmX/KABJ8Hz+yMgIHMdBS0uLERJkCHw2+85CbqVSCVPumQI4dQtxsVg0xKTEBtSNDRoiq8KRc6WMg0aEcrmMyIkIlnx2Cba9ZxsyMzJY/v7liO+NjwvVSCQSBnDZIWLshwIRBWJasI3X2x5qrXbIuSJ4V2DEyISmpiYzZ75fP3KLYFOtegRjhe4Czi08F1jnzNwMirOL8A8H88k0n0VDjHRspVIJ4TNhLPrCIpSjZfTe2YuORzowkBpAKpWC67rItmXx0BsfwkjPCIZmDaGcKOPij1xsxqm5clwzzhOZPKMuwuEwKtUKRlbVi5vBB+b+w1wkH0ka+mMf6eXQ87Fd18WiRYvQ39+Pc+fOYeTPRlCYV6Pr9K40Fn92MYpna4ySx7Dp/lJljpEGpAX1QpBGlA7Yr0gkglmzZuH06dM4e/asobNoNIp0Oh14PudBI1coEFifgDTPtaeSlc/nA0YC7t1IJGLoiMYbGo5OX3ga5VTQqn3uunPAt4FqsWoMZPF4PCDYdR3b29vhOA5Onz4dECjcg4YmMy7W/WQdqqii63gXFv16EfxwbWxbXroFO67ZYSIPTq08hWJTEYvurHtqpk+fjqGhIXP2q+M6uPeN96J/Rj+W/HoJcuEc+t7cB/wKE22iTbTnWGN4tUZa6YkeY2Njhj8zlUt1E/XekW8xp5a6koZzAnVdSnUvPpORWbbhnLoYP9PcUUYbqe6kyjLld6lUMo4Pygzbg873UAap0Z4GeZUPjAhQA6jK8VAohLH5YygtL2HufXMDoEodL5wndQpEIhEs3rYY5UpdD61Wq6hkK+i4uwM+fLQ93GaMHNHHo/Dho+v2Lvi+j+6bu+sOl3AJ6WNpePDQ8esOJM4kkNyVBAC0PdBmdFTO79J3LUXT/iY4cLDgwwtQbC9i2vFpqDZXUXAKcDtdbPqnTbjoExchmoti8zs2Y2TGCDZ8cgPajrZh8hOT8eA/Poilty7FptdtGkd3i+5bhAtvuRBnF57FjkvrMmhc84FoPorO051IJBK45aW3YNeKXebrA+sOwEt5eP43nj9OByddKiDl2ihAbXSdeb1co9eR9u0IWaUD3Rv6LNVpGQ1C7yvBHq/Xfti0rftWr7F1yXFTahmC9B59jg2ogXqkCMemjgC9V3U66k28txEO4n4Mh8OYcnAKnvfV56G9r/0ZDQDUn+mQ0/VRfUnXWR107J/OJe/XfcB1UZzWyMBiz+2zaf9rALfrulizZg2WL18e8PySCLkoVOY1TJOLpTkJqngrI1dgoAJC80P4bGXszBtnH/RaeizVI8bz60jAfCePi3Jd1+S96vcE9KV4CY9/7HGsfPdKYKAeCkYrtG4+BTcMn9ZiUHw2PesqkCj0aGhQy1DkXAQr3rECvusjkoughOCxSmooYN90LoFgoQn2laBUhSWfQ0sXQQ8BGoWmAk81rjQ1NRlP5fDwsCmy5jiO8ZQTEOn6+46PSnOwGjsAuEUXfsEPMAcNdeE8eZ4XOFNdLXrRvihWfWgV/JwPP+KbswnhAr/5598g03M+39oBzl14Dk+/8Wms/OrKwBz7vo9qUxXIANFwNOD55XznQjls+vgmjCwWwO0AJ//2JBb+dqHxDpRKpUBIP5kU12vfvn3wHA+jfzSK0289DT963ghSDCM0FDKGKs65hlWppVAr2ypzpLEiHo8bYxHXvVqtIpPJGJCYTCYN+GU0BukhkUggm80aIJ3P540BBqiHkCsTJn1yT9OIpQKW/5Ne1Fi28t9XYrhjGCMLRmpGr1wIl37k0poXI+SY8WrxIp0bGtr0SB5VVG3FIllIYuMPNqKcL6NarkfUrLpjFY4vO47BqYOAA0TyEVz0rYvgVB2E4zXh39fXh0KhUNvb4QLuftPdOL2wdi77tuu24aJvX4TR+0dRRPEZuPFEm2gT7Q+xRSIRvPGNb0SlUsGxY8fQ2dmJTCaD7u5uw6v6+vowNjaGVCpleC6N1QACOgN1llgsZvQbVVbJd2ydSPUv6mN2+Koqz+SzLIjqOI4B26qTkW8zinBsbCzQdzuaT0E93w3Uo/hoILbBCsEqUDe8G/k928PWz20FQkCynMS0R6ch7NYjw1RPU30QMSDaGq0XqTwvq5kHq44JzTlXkMPnUofTsSV3JceBB9U70gfS8HwPPnxEj0cROxGD0+IgNBxCvBpHPBPH1W+5GpF8TU++5EOXwHd9xAox+K6PphNNuPYfrkXci2PO1jlwXRf3/t29uOjHF2H3xt24+KcXI1QOYdauWbjh6zfg7j+7G27VhVt1UUqUEMvHUIwXER+L48p/vBLVYhVDbUNY+59rcXjOYeTTtaKy8VwcV/74ynFAWNdeDRo6N6qzq9FeQaTKbtKX7cVU/V/XgM1+L5+jNKD7h9crqLO97fpb/9aIXcUB9nV2/2yAroYE/Y77ijqHzok6+BoZJuy50h/lEdFoFKlzNX3bD42v7M7IAO2jGj3YRwXMuqf5PO0Tx2FHDjQC2Oq81b3DuWwURdCo/a8A3KFQCPPmzcOqVasC5+Y2EhZcXIJrtVYSEKknnEBDrTy254meL7Wikunx2RoSznt5LftJazFQDy/l2b2qhHOzkjGTmHldZVoF296yDaPzR7Hp85uw7L3L0Hy82YyJwMEOSVHCLhQKSCQSaGlpQTKZxNmzZwNF33RO2TiOarWKXDKH0vIS0vvSCA+HEYlFArlbCpS4VjqHumm5KWmcaGlpwenTpwOh/NzAvI/MjPOoAIpzqbn59PTSUp/P5zEyMoKOjg6jNDBygbnD8XgcGSeDLddsCcxDNB/F8h8ux9QnpqIULQUYLddP88ZJl2T6gbMfM+fTDbx6WF2lUsGKt63A1o9sRWZuBvBrx0qt/856+HHfzEu5XEZpaglPvPkJ9P6qFz2/7glYzfmz+692Y2hJsPJValcKC9+4EJ5fzyNmIcBMJmP2BGmZc1pIFzDwZwMGbAPA6KxR9G3oQ/e93YHQI9ICx6wGKlbIpXHAVjbU+EMvMvOqNc0hkUigubkZxWIxEKGiCiANNDSyURBo9IQe7cc11Err+XxNGeCRXKRJXjt6ZhSz/2w29n91P8rTy1jy2SXoPtANhGHm0g4bz2az6OjoCBigtGo5owzUuMQ5oUEi4kTgxJx6MUUvij/62B/htrfchlxbDpd8/RJMOj0JFbcS6DP3+lM3PYXTS+rnslcjVezauAulRAmoFzifaBNtoj0HmuM4iMfjOHHiBM6cOYOWlhZj2Cf/HBgYCISOA8HTOajTqD7S1NRk+JDmTqpSrGdoK4BRuW0r9KqP0RNPPkdeSVkJ1FPb6A1ramoyz2dfbbmj0VOMJARgHAlaH6ZQKJiIOL3e6D1Lctj6ya3wErX+bH7rZrhhF3M2z4FXDR6lxJ9IJAI/7mPXi3YhPD2MS2+7FKFqzSA+0jmCTD4TMMhyflRn42eqW+rnquMpONL1VYMC9WENq/c8D5FcpJaK5PsIF87Dh/NY03VdJCoJxGIxJJ0kvKqHGz95I3zfR9e3usxawwN6n+zF5dHL0XaqDS1nW/DkjU9iw60bsPm6zZh//3zEwjHEUjXZ6uQc3PTPN+HuN90NRIAbv3YjEqMJVL26DqtONeoXSg8KthuBKXXqsJ9cU36mHlv+b9Or0hbfb4NxBXf6md6r/yvYVrDH71V/UR2A47L3q/ZNn6UGBb6Pc2Abd2ynC+dc51Dfr+Ccf5NH8Ef7w8Z9bx9JpnOiDjutRK40zvfZ82tjDV6nOrheo8+zC7U9m/acB9yO42DKlClYs2aNqZBpEwOJxs4voIeWzJmMRS1HBEe6ITUUCKiHWAMYt3C6IYF6CJNuWrXs8m+Gq7LPGopsM1NlqPnuPJ7+q6fRv6iWT1yYWsCed+/Bok8vQtOBpgCo0GrQfD+ZdmdnJxzHQSaTQTQaRSwWCwAsvpsgn5sdAJwmB0f//ijOXXcOk++djAWfWYBKqV68SkOodG6BuvDlOpLQ6dnr7+9HNpsNAHfOt/0/x6qbh+uuFlCGRtNIQqBGa7oW5yLIYfRDKpTCdd+8Dvf/yf04sfAE3IqLdT9bhwWPLUC5vWy8wrrmao3juFlNnYyF88t1Yr857mh/FIs/uhh73r4HicMJzPvcPAxFhswxTp7nYbRtFE/9zVMYXDSIoXlD8BIeZt490wgKx3GQmZ3B8Lzhcftq6uemIlKMoOTUz2CNRqOIRqPmfFYdk7HAV8KIHo2iuPB8gZt8CAu/shCd93aiWq2a4mSqNHFOdJ+oEYb7wW6kIaVhZcqk23g8Hsj/J/2RaZJeOO98nnou2DelVxqd+C41yqmwHpgzgEwyg/DuMBa8dwGqF1cx6clJKEQKaGlpGafwMay8ra3N9KdYLJqIC/K0SqWCpqYmowQq7yCN8VoqudVqFdVsFVd/9WoMTR/ClH1TzJEamjYBACNTRtC/oD8w5927uzHvE/Pw+NjjKKGEiTbRJtpzpzF3u1QqmSMYyTey2axJudIoGhuUUZbycxqpR0dHzbPUu0jZyVopqqSrXkXFXiN1VE/SPG/KTr1edSnP85DL5Yx3ms/T4prk6wrI9W/yTdW78vk8crmc0cdUbvm+j9yMXL2w5Pk2OmsU2FTXl+hcMbLC97DzZTux96a9tbF6VVz986sx2jGK+19yP6rHq5j7ybnmDHFdB3W4aFNgpF5f9lX7rMBO9WMWzKRsVz1VAZrqXLyGfVI5pGAMAOZvmm/eecltl8CHj9V3rK7p4W4wPzbRn8CGr25ApCmC9Nk0nHBdFrMP2j8dp01PtqHC1kFUz3IcB2fnnEU0G0XiSCIAPm2Aqs028Ojn9n220UDXUoGc3V+9T/ebglrVRxTQUz/ReVG64LvUq6zrqn21DQcKjhsZF6h38Xg3jU6hbsZ9rftEI2J1rHwe6VXBtdK/HjVsOzdsWla+Yhs8FGfoHno27TkPuFOpFC6//HL09PQEQqVpSQVgrEAKwqhck5EoIwTq3kSCMfUmEwhwsdRrSKuH5gUpwOH3VOaV+bNIkzaCCgX4xirq13NOjYI/6iNyJhJ4Rnw0jkQ2Ye5RYafzpV5vgkzf9zEwMGD6wkIrtGYDMDlcvl/Lmdr9nt3oX19T1M9cfQaVZAXL3rcsIFyVsXDTEFjzM86T5nfbkQM6h9xUHFs8HjfWaAJrFeqkDy1iRkCo+ekM283lcshmsyiXy0gkEnWr+7kkLvnuJbj/tfdj6YNLsfjJxUAM5l7P80wUBN+lAlsZGoAA6GFhMhVW9Lw2HWnCwn9biMRQAigBJb9Uz4duCeGJdzyBwfmDAAA/7GPXX+yCE3Uw+47Zph+xsRhiw8Ez3SffMRnNh5sDxqZqtYpz52p56mR+XDNj8HAdHP/kcWTXZ82z/JCP3LRcwMqoe0OFIVBT3mbOnImjR48aT3oqlQp4m8mIFZRrZXM2Fbi2wOffNJyogcxx6ikLyvTVaKdWWO4Vja5h33LTc9j65q3wih4WvG0B0mNpND/eDA91kM9n6v5ubm42+4lpEraCUS6XMTw8HAjnZJ9J56ysT37D/RQdjSKxLWHGokXgEokEhmPD+M3rfoOBGQNB2tg7GbFjsWctYCbaRJtofzjtBS94gdEpKpXasaUdHR3mOMW+vj4jzzVtK5BGJTyUIdcKftWgzWK06klupNzye/WCqWKrhkjVuZRvqnMFqBfE5N8AjJ6ioEvBDq+lbCBgp8wrFArmjHKVAbx/yr1T0BZuw6NvfxRwgMU/XYzlv1weeAfBAfXCba/ZhoPPO2ieteviXci2Z1FMFHFm+hlgDlCMFbHk3UsC4NBW+G1wpqBX36+AQ5/D5wIwa6YeUwUW6rWMRCJobW0FABPmn8lkzJooGGRTedrI86hgnZ91nqzldLtht+HYuP5Ko6rj8jsbkHL9lB75MzR1CA//5cMIFUO44uNXAOeCHli9X+mH0RwKevm/PZe6bjY96TPV8KPX2XqNzqO9pkofjYwtNvjW97HftrNEdTmdF3tu1IBir5v9Pupz6txSvGV71fXHnjulBT7H7qf9zEZrYl9n0+yzae7vv+QPt4XDYVx77bXo6uoKbFLb+0Qmp5PLyec52byOCjyBsYJa9T4CwWR+EgkBoyrpFChUsCkQGPap4Qk8n1mZcyNQpmE+rNrnui6ShSSWfHUJJj0wCfCAll0tWPqRpUgMJAIWYg3jIIBvbm423kCdP6AuvFKpVCB0g+CQ+bK7ProL/WuDXrH+tf3Y+d6dZo55P8ehVZ6r1arxCtPQ4bq1M881z4ubnuvD6zlGACbMl2CI72a/OTaGHetc8DMqHLyupaXFeHkzmYxhGu3D7XjJN16CJduXGCGhhon29na0traitbUV8XgcqVQKbW1t6OrqQldXl4m04HvoqabCoFZNpYXkkSQi2WDOcy6XQ+ZsBtO+Ow1O6Tzj8IHmY83ovqvbhFRHo1Ekh5NY9tllaDrSBHhA26/bMPkjk4FBIJfLBehZmQ2NEKOjo3XlJ+0gtzoYZxzOhtH7096AsqXV4jUNgfvy4MGDKBQKxjtNAQbUQwC5JxiZ4rquOZuT+4jpB5qmQPrSOg/K9GmZ1eeQT5DOuE6NhJoqMX63j4c++hCyM7LIz89j57d3wksHq/KWKiUU3IIxCpGmeSye8gdVfhhtQIMgaSMSiSCZTGLhwoWIx+Po7OwMGNS4722Byz1hUh3aK0Gw7QNTt07F0juWBs4Wn2gTbaI9d1pPT4+RtTQgNzU1GUVzbGzMGP01zYXAQfUjyqJkMmmMgpFIxOgv5MnKf4E62FJvM/WywcFBDAwMBGQoeSKNgurIYFMlmZ5o6jIK3LSRT9sKe6NUL/JgNaKrHKJuUywWEb0jiqXvX4p5t87DstuWIebHTH91TLxv2S3LEMvUjd6RcgSnZ5yuge3zrf/ifux6366AzCHP1lRIoF5oVnVQO9WK76cewDFSLtH5oDozn6NGXz5ndHQUuVxd9ivI0rDhaDRqaILrWqlUjEymUUOdSFpHiboGIzFs76saE2zjuw3IdO20XhN18Hx7Hre/83aM9IxgcOYg7vyXO1FJjq/Xo43vsQ3ziknsXGilrUaAWWlTPbmNgJ+CSTb2x+4nf9vv0gherrcaK4B63jj7zzVRcM7nqNdZDSyVRAV3vf8ulKPlQH+YvsgCwUDwJACOVcenRZBt4w6vicViaGpqQiqVGncijL1+jYxMjUC43vNs2nPWwx2NRnHxxRdjypQphrA58XYeAxeByjIXNRarMTmdVCrkBJ0Mr1Lmyu9IaLTGsPoyLXdUXllMie/lPbxWgTtQB+vMEwLqzJWh1XyOemCZb+5nfSz60CLgPcCyf1uGkBMyZ+sSuPBMS82dyOfzgQ3NsHbOI8dCxkxGRcYcjUbhtXrjzDjtT7dj6UeXwoMXCFPzPM9YnDmH6q2mNx2oVySvVus5t2rJ1vB9NjIkhmvzOq4jN77OKQUA6UGFGZkFN24ul0OulMNA2wCmDE5BdCgKPxyMhrCNJQzXz+VyKBaLKBaLSKfTcJxa4ZnR0VFDW6rwaGM/OUZVMKLRKFKpFEZHR5H+TRrzovNw6E2HkDqewob3boCL2tnT5XK55sVoLWH3G3YjMyODyFAEs745C6FSCHAR8PYz9FBD7KLRqCm2U6lUcPJdJ4Nnr/tA094mRLIRhOJ1ZkuvPfuveXC6z9RSaf9QwWtpacHg4CBGR0cDCgaFJgE714Q0R2XDDgM3536e3x9a6E3pQsGxHi2YSCRM+sUjb3gEpeZ62HW5o4zDbzuMlZ9eafbU6YtP4/DzD2Pdl9ahKdtk9lS5XMbAwAASiYQxApHWde+zb+qxGBkZMcWCSMMEycpjQqEQCoVCwIgBAAO9A/j5O38eoLnEWALP//LzUSqVMDo62lCwT7SJNtH+cFtzc7MBR5lMxtQ44ekKLDpJBZIAkfwDCJ7hy+KViUQCmUwmYEgnIKfcVu+QykVbWeVJDKpQ0wCqhlDKSPVo2ikx+txG3jvySjW2q7dQjdi+7xteyVQlNXSSD3d0dCCdTmP60ekoHywjGqlHp1Ges/AqjQfRTBQ3vvdG/Ob9v0E5VMYff/+P0VZswyff8UnT/1AuhIX/stAcZcn3Oo6D4vQiwufCcL3xocacdwDjAA+N3+3t7ejv7zfHg3L+NM1Sn8e/1SlFAKbgnXOsUYWkDep8tvdTa/mQ7tRQ0AgA8bcdHUq5qICRrdEc8X+GOt/2p7ehmKoXBs2157DtL7dh5RdWBuhKwZnSnRpYdD+xr+pQ498aXq2/Se9qVNJoOhsA2wYHBeO8p9E88B41PNkGD3VsqvONRiX+qIFN18pxHBSmFLDpvZswNn0Md77lTlz37euQGqoDbNXJisUiCoXCOP1eaUqfzfuVBvS52khPigcatUb8S/v4bNtz0sMdjUaxbNkyzJs3LzAB9ECTGEl8JFxaX23lXsE2rbcaykmhRNCr7yLQpaAhs9DwBzskmO+mMl8ulw241ER89olMQMfK7zQMor293Sj/vudj4YcXwoU7TugRTGueg+ZcUDjk83nDdPlO/q/GADLXswvPIt+eH7deSz+yFL5XB55879isMQz2DBpGqXm6aoXlnJGpUHAp47XzO5TpMAJAQ32UAZFR6HFXGsLruvWcbg1ddxwHB288iIff/jBOzz1taIbeUApXAleOh8eS0drG+Y/FYujo6EBXVxc6OzvR1tZmjmtRS7wyNdKK0jXpFgA67+zErC/NwtJ3L0UukwtEFAxiEE/+6ZM4tfYU4ADl9jJ2f3A3xhaNmefTEEEjA4UpaUTBbfPjzYDwnp4He3DBP18QMOpwbfmbtMPxKWNWIcK15lrSs0tjj+bnxGIxJBIJNDU1mWdoNVBeTxomM6cxShmoRoHofrMFkwo6gu7l/7Ic7Xe1m2dNu3MaVnxmhdnjp64+hcff/jj6lvZh82s2Y7RpFPs27DMCioYBNjuMn00NThSCOg5VQKlMcC8S0PPaU4tP4Y6/vQNeRBbSB2Y/MNvs00wmMwG4J9pEe461q666ypzGkclk0NTUhKamJpw7dw5DQ0PIZDLI5XJG3lAX0IgYVfiLxWIgB9MG0KrokvdoBJCtID+TYZVyWcNHbYMz+8lnU9/T52qUI1tLS4vh8TbQUSWd/B8ARkZGAryVz45EIhi6agjdvd1Ip9MIh8KmfzRWqz6p+kpnuRMv/dFLcdPPbkL36W5sWRYsxOpHfAxcPTDOG5+ZlcGef9qD4689Dt8df0QU+6ZAWOVXPp/HyZMnTYE49aJyHrimOv/q1aQeoOCZzQZavl+L3lIDjnp/qQfRCKz6pwJ5jlNpSOmGNKoh6VxLpW39m2MrFAoYHBzEVV+6CjMfn2neN/feubjoqxcFdAI1KPDH1gfU0BCLxcbVlaE+oDqdPSYdj+4ddTzZxqzfBQrVCGF7yXUf2HOn4F/3jA2ydf1tEDs2bQxPvulJjPWOAQ5wes5p/PoVv8ZY21jDdeQ+U6cl95t6+p/pvWooUENDIBVX+ITOD+dV9439Yxs3fld7znm4XdfF3LlzsXLlSqNY2kqoDbIBBDYBN7KGNCn44mfKvJUpk5hJZPoOoE6YeiYvwSSZUywWMyFSutjqhVJFn+Oj15nv4ffhcBiDg4PGW6sbmOHmBE8EeFokSgmKVm0AgcJV7COfS2BRrdaOZPIGPLjlIOFPvXkqKgMVeOVa/1OpFOLxOLLdWex55x74no8V/7wCkTMRw7zJhDgOO/RdraDsVyOwbls+qRwQuKggpzAOhULo7u7G0NCQCb1m0w3veR6euvEpPPG8J+CFPTz6F4/i6u9cjbYDbcaQEo1GkUgkAlESXCsFVUA9VJr5wKVSCW1tbYZWh4eHDZDXOgEEiqRLHn/FaI5SqYRJd0yC7/soOfUIDyPIBoIM0i25SJQSJmqCY1G6JO0R2BeLRQy9bAgn//5kgKMUW4uIxCJwy/VjWuipUBCtBcf4bF6vSo8dDUILeWdnp6HnarWKVCplDBUEonw+11+FB4VgLBbDwMCAUZJIN+QbNIyRlvQ79rdcLqO/vx+O4yA/nMf0T0yHU3KQrCax4LsL4FU8OGEHx553DNtftd1EBJxZcwaPJR/D4OxBlDpLWPGrFYEwcjs6Q4UJ55P0o2HmjPjR49zUKBgK1fIsh4eHUVpXwoOvehC5tmBawIW3XYglv1hiIn80rWaiTbSJ9txo0WgU/f39GB0dRWtrK1zXxdmzZzF16lQAMEAoHo8HImL0ZAYCE/IRRo9RVttAQ436fIbqWOptVY+V7VGirFcjM3meggU6MhSY8X22l5Pv4vvViKw6j+qBQ0NDpricNtd1cWbjGRx89UH0HenDpV+5dNx7+TM2Nmb0Cl4Tj8fRPtBek/GOj+RoMrh4PhAbiQWMH+WpZRx63yFkF2aRXZxFNVbFnM/MMXJCxwME08I4vzpv1AMdxwkUktU1svVUrg090rbXW9dO51ObAkc6qChT9fn6HJWD6tzStVSatWmP71WdRjEAUDvadd331sGtuAgPhLHkh0vge42L1Ck4VYBn06Z+p7Sj82L3Q+dc5460QF2P82a3RqBX+93of+2P3q9zZztIABh9VGme1/F3tBxFpGjVmSrEEa6EA3OoOEYNbDoXyieeCfja865/21GLakyy54af2UDeXpff1Z5zHu6Ojg5cfvnlaG1tRSKRMKCVE6eLQ2FBAjALfj4kiEozmad6vdUrrODUzgsnKFFrCRmXAnEulOZHKxEqQbMfNggmc6AHLxQK1RTl855VVs8kwWi4hw3C2X/f9wMefIJeerHtzUowDMDkWhcKBYTDYYytG0OhuxBYr77r+lCK1ARgKpVCpVLBqD+Kxz76GMbmjyGzMIMnPvMEKvGKeVc+n8fQ0BCqbjUwP2RaCjyAoOVNPd1cJ643vZG2h5LrRGF44sSJQBibCn2uw/aN2/HkNU/CC9f6MDx5GHe+/k5Uuipoa2szYWpjY2MGcKtl0zYmEPjH43FzHb2v8XgcPT09mDp1qgmR1qb5WgSKjQBsJpMxxV4cx0Gxs4j+q+r59tGBKBb/02JEDkUCxij2TQvJqfEGISCzOINqc114OVUHvb/sRXmsZgxg3hbnWelJGV1TU1MgrYH7Egh6TPjZ0NAQ+vr6TP42lZdUKmWuoyCicNbj5PhTKBQwPDwcAPY0XOixf+qpsT0u2WzWzFU2m62FII5FsPCrCzH/5vmIFmth9OVyGZMenYTUuRQgfPrcknOoJCrY8YId2LpxK/J+Hnf9w11wEg4QRsAropZrRuRoH7nP1QJuCzHORaVSgTPXwa9f92uMdY3VO+QBK361AktuXwK/5Af2/kSbaBPtudO49/v6+kwqycmTJ9HZ2YnW1lYUCgX09/cbxV2PMdW0G+os5CmJRMIYZZVfawSa8iqNUqOMUZBC/SuZTI7z3tlRPfxc+bh6u20PowK3RroiUD/yVJ0O1Itso4IB0a6D0fWj2PeWfSi1lbD7gt245y/ugR+q94vGYPabHmWtFWJkj+Ni6dal2PjtjXCqDpyyg+V/vxzpJ9J1z3q4im2f3IbMwsz5BQbOvfAcjv3dsYBMjcViCMVCOPDBA3jqG0+h4tYNCuqk0vnn2PS0FDb2UfVp1RHVmMKm/3N9dP10DflM1e30uaoHNgL16mRSZ5b2X3/bBh9+x76k8ims/d5aLPvJMvhZP2DYV7BH77UdOaqgU3UQ9QpzztWpp2NmnxTU6hrE43FTuJD3qRfYBpC2p9zWwVTHsulD11v7p/vbNlopDbmui9RACmu+sAbtB9oBH5ixewau+ulVaM43B6Ihiam0D0pnsVgM6XQaHR0dpraUjkXXUsds0wL79bsME/b7Gz372bTnFOBOp9O4/vrrTZEPIFgAzK5AxwWwPdJk+PQYqqdcQ8MpRDRknPfqRrGtsCocfL9WxCqfzwesddVqFblcLhCKoUAbgOkfn6+bmWG1zc11IuWckAFwQ7EPerQYry8WixgdHQ1US1brsxoY9Ieh0gRJqVQKc2+di+YDzWb+owNRLPzwQiRyiUAfdrx3B/Kd9dDzQncBe963x1g3U00pFC4p4OlPPY1KRyXg4VZhB9Q3MpmPClPOBw0S9FBS0CsIJwhRi6mCEjvsZNXDq7D4ycVwqrX3J8eSuOb71yA2FDMWRy1QNTQ0ZPKkCODYZyoXasRh0Taewzw6OopIJILOzk60t7cjnU6jqakpUNRFx0oaJEBkiDUNSZFEBE9/52lU2uqW+lJ7CfvfuN/sCc6HFsLhPBkw3hRG31/3YfCFg4G9uvjmxZj5xEw0p2qGB3pPqLypAqJFesrlMiZNmmTolN+rRVit/Mlk0jw3mUyayBEVBErDBKV6JAVz1zRnnvNIWqPnmIJNBTf7n0gkzBqMjY2ZeYzmo0j5KRMuF41GER+L46p3X4XWI63j+Fw1VsW2P9uG//zaf+L0otP45he+iUde8whKyZLJ06dBgnxMBTDTUxg6ykgOx6mlV+RyOWPwo2e+LdeGRQ8tMn1wyg7m3z4fC7+/EJVMxaQNPJMVeaJNtIn2h9vWr1+PdevWobe31/AHyphSqWRqcyhwIY+gMc9WoMPhsPGGq9eUvEYNrCqvyZsI0NVYzv9ZNZ3OC/I2m/faHm+NMlRDKHmeeuzZyKfVM0w5wWdTNmmorfGStVWw70376gZnFzi+4jgOXH0goDdQJukxpaFQCPnePMLRcEA38ys+Jt09CQu+sADL374c6X1p480sl8vwCz4WvWsRomfPF4n1gdbNrZj5lZkB547b6uLIu46g/9p+ZOdlsf1L21Hprp+QQ5lIPcFJOCj3lAP6tcpQlZ8aIq26NsdBOlAvu+rKCnaBYGEsW6/T3H3qVeqIsb2xlOPq/SY96DqyT41AGr8PZ8Nw88EK3KqHUgegXsd+K05QYKb0oE4ABZa2EYmeY/t5/LsRCGZkm0b0BdbaCZ5MpI4rNQzo9/pO5QUK9u11sec5FAohlU/hpn+9CbOfno1Xfu+V6Ax3jjv2T3Vw+/18B+v4tLe3o7m52WC1RukTNq0oH7ND1G0dx3b8qdFH6fX3tecM4E6n07jyyisNANGwBW7IpqYmY2ViOCYXyPM8EwKtG9IOlwHqm5wbVsN2qeySOdsFBZRJE/hQUSXjN6DH8iorSCAYUDDOTU0vpe/743Ko2HRTkah0TCQUFtmihVW9YwACYI73Mw8JqG0OKvzD84dRaqsXiWre24ymvibjaWd/ln9gObof7jbXTXlkCi786IVmrgcvGcT+z+zHyMoR7H3LXhQ7imZelGGxb/l83uTjGiAo+VpqLOGGVKHAzUQDAhmzPo+AjEKvUq7gqh9fhWWPLUNqNIUrf3Ileg/0IhFPGODFvsZiMSSTSUSjURSLRQM+1cLN9+pZgqTvZDKJ9vb2cUamdDqN1tZWtLW1mTGrlVQVCKAeulUqleBVPXQ/Wl8DAHDzLlq3tAZoRAUC9w9BYyKRQNfCLuTX5QHhN6mzKbQcbTHKEiNRlOGr15kV5EnvZ86cMXtI14vCm0V/RkdHTdVT1htobm5Gc3NzIH+PSpnSjed55oQCCjLSPveiemEYOq88h4qH7gka0vgMrRtBwwo9QiE/hCv+9QpM2jEJrXtbERuS49kc1Ljz+d/71+3HEy95ArvX7obne4be29raEA6Hkc/nTS66vkOFA+mdBgvSRNkr46mrn8KWm+p5g5F8BN1buhEOhU2BIIbx2wrrRJtoE+0Pu5Entra2Ynh4GAMDA8YA29fXZ4670pQtIFhsjPKHQJk8Xb1kqtzyfwXBQN0DrkZUGjt5rw3UbP7dSLFXvqfedkaM2d5U3kvdSsPobcBAnpfP58elJDl9DpZ+YCnSh9IAALfsYsUtK7Dk/iWBMalDgH+fmXsGt7/5duxetdsYIcLhMPYu34tCvoDun3ej+alm0wd1GsUPxzH/Q/MRPxZH26/bMP8f58Or1AFRMVTEgb84gJPXn6zJEQcYXTiKve/cC2+6Z3QNk+rlVXDsZcew6/27MDJlBEC9Pg+vob5iPO3iMNLogUahukofOre6rqr7GOOCZfzgM5WGbHCn+qHtada+2GDXBmSqB+nztN/287QPqkuqsUb7qDpbo/7bxiqgfmytynj9XudJ5/yZvP/ab9vIot54e8yN1lVp/ZnWIBwOI+yGccPXbkClVAngDTbiLtIBcRLxHHXBw4cP48SJExgdHTV7i3StAJzNpj0F9+oFVx7zTHTLfjxbneg5ER+YTCZx0UUXYebMmQHiAxAolkSCsRkAEDw3sFAoGGWY95Ho6ZnUBaA32XEc4/WiQk9BQQbPTUDQTg8SLcVaBZKCh2deKvHTaqwW53w+H7D2Uanne9TKBNTPayYD4/to+aKFmmO1Ny7nwhaaKpA4R2MLxrD77buRn1r3XA+sH8Cu8C4seN8CNPvNdSBX8rHsi8uwr7wP8IBFX18Ep1ozCpy8/CQO/v1B+NHaWvZf2o8diR1Y8sEliFSDxaBI8HY1US2+oWCO1j7bmMJn2gLFNqYoeOf/l/78UkzfNh2z9s6Chxrz1rBxPofhxixYpZubRiL1qnI9FRwRsNFLyXxi13XR2tpqgB4QrEJvjyEUCsHxHMz95Fz4oz7OveAc4AMzPzETk+6eBDdct5BynpVG+Fk4HEZqOIWeu3swtHQIQC3PbPWXV6NzZye8cG3P5fN55PN5E/atQJb7TxmaMkelWTscqFqt4uzZs4F8OCqBjDJQ5YZjIs2oR5sCQQUf9xNz1TVsn/SjIdosxEZvON+rwkT3led5SGQTWP755Rjzx1CYVMCut+3C3G/ORd/6PvSt7wvwwR2X7ICz3sFwyzBW/3I19l+6H8tPLUcsX/fUa6qK8j7lSa7rmtoLAPDUHz2FmBc8ix0A4tF44Agc13UD/GuiTbSJ9offKOdDoXoRT9ZjGBqq8W11SjA6h0ZH8kTKSMoAGuLIu8lDW1pajPGvkcebIIdFOPmZglwauRsBa76LfVRjrAIk/c13KKDhczhG6mqZTMakG/I+lat8r+pCrUdbcdl3LsNjf/MYFt+zGDPvngk/0hgIsb8DCwbw2GseQ7Yzi9uffzty4RzWbF6DhzY8hAc3PIjZmdno+l6X6a+CWbbUkynM/shsxA/GgTLgOaJ7hqqolBoAAQ9wECx8BQD7X7Mfx15+DHCBLX+7Bcs/thzpwbS5TfVT20llGxMoqxkpxz7ZwFf1SHVA0LhgwFk4HNALgMbnHyvoVeOMDeyVHlT3UDre9MJNWPWTVeMcWWqo0ffrGtu6tOpgdn/13XqvGvdtvUSPbFOPrz7D9n6TxnXMnAd9BmmMdKbYyu6jvZ8aGVLsPczrSSf5fB65XG6coUOjBHiP6mvqXLPD5nm9zrnOiUYfNDIENfJya99tOnu27Q8ecIfDYaxcuRJLly4FUD/OiMBZQw7obdIQJz1aKpfLjQtbIKHxGQq8dXOoB7VSqQSsJgpI1dLlOPUjBubNm4fjx4+jv7/fKOoEZKrEKnHRkKBAWgUMCZJ9IYhhH9VTbQsrIMhw1LDA+3UTO45jogYagZGWvha0HGxBZmamvnge0PFgB0LFECpuPQ8sGo0ilo1h0dcWwXVcRPNRVJwa+Gnb2oZYXwyV1orxmg6sHsC2j23DmreuqQPG83NRqBTghlyE3Ppc6oZgETLOpeZ2UUAzdJ5j4TrH4/FAlALnk3NSrVYR9sOYtWcW4NTmKJFImPkheKbwVoskPbOlUsmcd80oDa6XMlutUwDUPBapVMqcpcpjwUgH9MjyuCwafNSoFM6EMfvLs+HFPXQ92oW2e9sApx6Oxz2m+4peXI5nX2gfDv7ZQbPeL/rCi7AQC3EoecgodfzhetDAwP2pVdW1ngKvYzoGxx6Px5HP5wP7h3lM7KvtVWF/dQ+pdZi0CdRzxWOxmOExXHe+17bsArVQ8kwmY3L2GT2iNEEeQ2/xyMgIvHMeUkghdTiFVW9ZBeeAg9ZHWlH5UAVDy4YC0QO+6+Pp65/G0dVHkW3N4oncE2g924rrv3x94CQDGm8YkUODHmnddV1UvAqeetlT2Hn1TkzZPwUX3XYRNt+0GY7n4MbP3Ijmw81wIvXQMsdxTPrJRJtoE+250aZPn45rrrsGP7nkJ7jw/gsxNmMMp9edxvxN85HP5dHe3Y6D+w8GwrdV/6GsBOr8NJVKobm5OVBYkvoOdS71RNu8UoGV8mRt5OEK2G1PnAI+6gYcB1CPirO958rTVB+iLqTRgeR3rIGiAI7vDIfD6O3rxfTvTEfpQAnVUDUwJvViVqtVjHaO4uE3PoxsRxYAUIqV8Jtrf4OnLn4Kw23DKEfL2P+n+1HOlzH151ONfFI9k17Kpq1Ntb44QaeDW3Ex7T+mwYt6OPeicwCA9O40Fn5iIcKjYZT9uq60/8/34/hLjpuY15HFI9jykS244i1XwC254+iB8+/7fiAak3OsdNSIBny/dkJGIpFANpsN0BzXSouBPROwtAEs5avSKvtkA0FdH6ULDx4eftXD2LduH0aaRnDhFy+EVxl/DrTdH6VLfaYaNex+6XWNAKzSpV7XyHjEseg8q97UaL7subPfzc90L+tzVHdqZITQ/uv8cJ3V8aU6EvU6PVXH1on5bH6uur3Oh23sYf91XtVgpPreM/1wTnSMz6b9QQNux3Ewe/ZsrFxZO/eOwNIGmcw/Yq6iAh2dCB7HxGuomKsXTb3jXESCUMdxDGDXqr8qdNTrRSCdyWSwZcsW0x8q4+qFVMAP1Aldw+f5PvXS8RoyKwXnfKYWAiEBE/ClUikMDw8HKh6ywrOd60IApYChXC7Djbo4ftlxnLziZGD9Zn5/JmbePROheN3Tq2H0kbGaZ7rq1AuZJYYTuOCvLsBT33wK+Vk1b3msP4ZZ75iFYrFYH6MLnF1zFtvfsx0LvrkAc+6ag7BXrzROsAggcB6z/gAwIJbj19A2bjpNKWA/CRgZBsbP+ExdD8dxAoUtCoUC8vk8RkZGTP4TPb9DQ0MIhUJoaWkJrKuGyIdCIQwNDaG5uRktLS1obm5Gd3c3RkdHDXCPRqPm2BeGWxuBIkAzko1g3ofnwa/4BmxTcCoI5vg0OiA3NYed390JP1ZnsKP9o9h3bh+A+pFZDB9XjzIrk7NxjvleRi0QTDPCQ8OLqtWqmVf+zXv1bHV7vVUZA4KnHKhCo2vN/nGtWCiQvIZjZd2FRCJhzldvaWlBJBLB4OCgCdmkoYQGKAqZxIkEEK/lja1+92ps/tRmFDoKKHbVzwOtRqsYmlrzTJVSJYx0juCu192Fy759GZwxB4XuApoHm83eJQ2Rb5TLZfhRH0/f9DSevu5pwAVOLDpRO+v11wsx78F5aD3Riirq/JD0zyKAE22iTbTnSEsAd264E/csvAf3zb+vxvtdH1NapmD6tun4wOs+gPbN7VjwrQVAAhjFKGKnY+OAtqbq5XI5w9OBesix8nwFwdqoA6lh2i4Imc/nA9cCweMNY7FYoCAudRfKWnUO2DqVgi5bb7IjFgEYo/jY2JjRCcaBmxSANqB1qBVnyvWUKNtzSD7fNtyGi350ER7+84dRTpbhVl1EShH0TapHNXlJD4f+/hDix+No29QWAG6cV/1NOc1+eZ4HjAKzPzUbfsJHflYeK9+8En7JR7aaNfPoOA5m3TwLg3MHMbx6GHCA6HAUF3zsArilYJokx6HGCOp16oEE6oZrNRaoHqt59SzQy3XWddAIKzWyPBMA5rzzWvVa2tfZALwaq2LTCzZhz4Y9gAsc3nAYXtbDspuXIVqq07RGhKoxRQG8rWtqnxU4c71soG6PRa/Td5NWOb/aB6VRXTc+l00L+dqOJb5fPctcazvCz9af7J9GudJq/OCccG+rvkea0nGop1t/28Yd3RukRY3UI30pzdnroftZmxrtfl/7gwbc06dPxzXXXGMUfyDonWWoFD8nAbDAk4YZ8PtqtWqYNcODqFTbXja+hxZSZbTA+MqJ7IcKGXqT1PtqV0kG6sdCqVWQYcYKeIC6xZdjU0uOfsbf6rVWBlGpVNDS0gLHcQzQU/DB8RIwsW/sTyhUq8bsdXg4u/EsIHI1NhhD86FmOL6DqlffhOy/gkmCFq5FxItg5ZtWYtdHdqGcLGPJR5cglosFcjhGnzeKbe/eBjjAnr+qFVyb88s5qFbqR7VpvirpheNSRsUNxTBbDfkl4+fnjFiwN7WGojNFgEYfrjHXn2HuykQcxzEWX54DydQFVqVXwdPV1RUoQBYOh02e99jYGDKZjDniJZFIYHR01KQkVCoVOCEHAxcOoOXRFoTdMCqoA1EN+9aCgqRbvvfsS8+a0P/aRAL7r9uP5V9fHqgY2ciQRc825540yhw8mxHrZ8y3A2pCZvbs2aYAIVAzrNnzpesMwIB0es8J9rlneB/3M8fBPtJ4RoUhFAohk8kYYxpzBsPhMIaGhmrGjfNgO5PJYGRkxDyXPIL7ktE0YSeMtW9Zi8yUDLb94zZk5kv0iDYHOLzyMEK5EKbtmoZNf7wJG7+6ET37ewIGIc5DJBHBU9c9hW0v3hZ4xsCMAVxy8yVoP9qOUrVkBCl5RrFYNPxook20ifYcaBHg8MsP4/CywwCAaqiuZP58w8+BDbW/z15yFmcvOQsACI+EMeezcwAAbU+1IdQfGlekigZsKvkKtghItNaN6hUKgl3XNVFlGrrKyCbex2v5bFb55ufqnWZEEb8Dxoesq5fKcerHX5K3KzDL5XImWoygXsF6NVbFnlfswdH5R3HJjy8JGBzU8UEZ4jgOIuEIZj0xC36zj9+++LdYsm0Jljy5BHe8/A70ddZBd/JoEtHBaEBXDABEifBUIMA+ep4Hv+Jj/ofn1+YJ9chLoO448jwPC9+6EIc+eQhjM8ew9PNL0X60HY4bnDP1bAJoWKRUjQBKG7qG6sm2wRx1TJWLQF2XViCtY1X5rrSqn6u+TH1Xgerg5EGcWHCiXt3KBQaWDGB06ig6DnWY8dgeU32Pjll1cb1Pm40F9HOOxf7sd3mGtT9s2l/tE69VJxH1GpuutI+qr6ghSefBHifHUphUAFqA5FAywBN0fyogVlpRnPe7Cp3ZIFh1QO2vfvdMXvRGc2g77+y1e6b2Bwu4J02ahKuvvhrJZNIQgg5WLZO21c9mogpQuZG1qBoXmM0GUsD4g9B5j3rl2EjASkgEMbwfGG+R0kIGBHwsumRXyLSPHDMWTdStSwzJqVQqxvungAIADh06ZCIENL9ZN6cCJs1jdRwHqVQKZZQRzgVJySk7CBWCTJb3aESAjkmZQSwbw+J/W4xSUwnNR5vhhWsAKJFI4MTzTuDAGw8EQm13vXYX/ISP3u/1BsanHkS2bDaLlpYWA6qUoWuuru/748KZOE8MrVZmx3e0tLQgGo1iYGDA9IWWeI2oYHE13kfaZFXxSqViitkwOoNeBXrtKdy1ojZDqxkCV61WTQVH0uvOF+3E0T86ijlfnIPJ904OKANcG/aNc8l5IR3N+eocnHvZOWNoWXjbQqz8yUqEkvWQMPX+qxVUaZxGAwCmj+oFUZpVgwnn/siRI5g0aVJACdS9ROOaHr3CAnDMeyZt82/b4qrPVuMYrxsdHQ2skW2E41yy4Bvpkt+rsYN0xed3Dnbiqh9chd1du3H4FYcx8wczMbJ4BOc2ngvsuQMbDuDAhgMAgAdf8yAu+c4lGO4exrzH5iFcqBejccMuSslS4N74SBzrbl6Hnl09qPpVw8PIDzgHam2eaBNtov1ht1AkhNkXzcZ+7H/W91RaKtj7gb0AgM4HOhE9F0XyRBJTbpkS4IdAMFxTwRR5hu25Uw+RRs/ZyjIBAD2nQJ1PajVo8ni+i+k8jRRgGziovkF5U6lUjM5J/YTylzKK1yaTSVS9Kvb/3X6cftFpAECuksP6L69HxI8EPH6aBqcexBVbVqAj3oE5T81BLBTDi+54EX5y408w1DaExJEE5n58LlL7U/D88eeNK6C355fjL84tojiliI4nO8w1On59puu7WPG5FRiZN4K2p9rgRuoh5LYs428aWQh6FPDZa/tMgMgGXNpowLavbQRwbZBnA0Ttg4JufW/bsTZs+PYGPPiaBzHaM4qms0248KsXou1gG9xQ0IDS6F0KGFlgtlwum8LCOn57/u050mc3Ar3PBMR1b9j32HOi+pVGotgGAK4X8ZWu9TONR50dfF+5tYwtr9kCJ+ng6u9ejWRf/YQe7Tt1LQXBtqGDPwTfOh57nvS5CpDtcWnfGxly9HvbwPBs2h8k4E6n01i7dq0JySTT5eDoXaJ3SENVdDLVQ2wzYWUQXFgSHReRoBnAuDMJCRK04IYCSC6kWvxoTeMCUblmGKx6LG0CZOOzAAQEhRoNXNc1eTX0qlFJJhjh+JR5cRzMheB4OG56tpkXWq3Wjqvymjz0r+kP9LPQXcDQ/CETCkVLtm3J5JgIShV4RPuiKJ0swYNnwJDneWh7ug2hQgheUja856B9czui0agpihcOh9G3tA+VSRXMfnS2WUutWMo5UVCkBhMtpEZa5PnjXAOCRF4zMjJiUgaUwbuuG/Dk6j0AjBCncKeRhNZ+PQ+aHgHSF0O01VDQ0tKCdDodOBLO8zzsfcVeHH7hYXixWshazIuh4zcdJhwaCB6pZ4dM0XO/80M7A+cczHl8Dryyh3C0Tj9cN11rzqfv+wbw8j2kU+ak07DEI92oUJFGNEVBBYDuO80BJMgfGxszn/MZVBb1WaSLbDbbMFyOdMt0h0QiEUh7sZUJVrnVudV5YZ9VqSwUCog8EcHM0kykn0wjsT+BzrZOVONVDKwdOL8BAtsPma4MHvrLh1BKlHB09VFc94nrTD+qxSpW/2I1QqEQtl+9HU7VwZWfvBItB1vgx+o55mpwIw+dKJo20Sbac6e5ZReLv78Ynu/h4PMO1r94lnV++i+vyfVQJoSz153F3K/ORWJ3Ak+/6WlsuHkDHDdYV0b5LFsjANxIoVWjNnmPfdpEpVKrTEyjMvmTHuuoOlajv4EgCFTngupCdNpQfqlcYBTSkfcewdnnnzXjOrrqKIpvLeKGT91g5Il6xMjfqUelUims3LsSI+UROGEH049Mx7VfvBa3/dVtWPy+xUgcTZi1UkM155D9pS6g81dtr2L/x/YDSSD2wRjSe9JG11EAxr5FIhHEc3GEt4RRrpYDMs5OwwLqJ16oQ0tBj+11VJlKfbgROGK/2GyArQZxXdNGTcEWaYX/s3ZOMpkMhMRPOjwJV33hKtz9trtxyScuQfJI0uTHK90q/XB8du0jRhhS9tv385n2s2zgSJphH7nOGmGifVKgq0YCG0Sz6TPp+KEuRv2v0fpozZ1nMpqYdQj5ePg9D2N47jAA4Jd//Uu85OMvgVsMepKr1WqgQr09V+yn9of6vBq2SH+2cU4jeanL2Gug9zUyJthz+Wyb82xvchznv//0/wctmUzi8ssvx8KFCwOFK+jtI4i0idwOeSKz1vxKHuujz1UAQ+VbQ4gZukQlmYtNkEzQxIXTcGkNN9XQFfaPQsIG+Owv79FQagoX5gOrFVkNAPzNpsTDfihA57gZmqvGAL5HPcKpVAqTJk3CmTNnkClmcPT5R7Hnr/eY97U+0Ypl714GFOvnTjLEjLng7DPBCtdZix8Q2HJuCXDKLWU8+Z0nUWorITIWwYo3r0D8QNxEBHi+h8yiDB796KOAA1z86YsxdctUuE5dINDDzHkhI2eRM9vYQ8+2Wgg5dxSEFIZ6hnojowvXQMdWLpcDlbyZ6x0Oh02uFfNaCOqbm5tNioQt0IzgFY/p/tX7cd9L70MpUZ/X6FAUF77zQsQPxgPPIY3oWnAMR990FCdffBJ+uM4WUmdTeP47no+YEzNzwkKFfD/noVAomBx7NbaQ3iicNIeQuegKjpPJJFpaWoy3n8/gETcqCHS+uUcUhFPB0lQHZdLKcBWMnjlzxhgIeNROPB434XYc+/DwsMl9VIWBBiw1RDUqJsQ9yP4XnSLCqTAe+4/HUG4pIzoWRbGlCD9isWof6NnVgyu/dCXC8TB+8bZf4KYv3IRoJopHXvYI5tw1B22n2lApV8YJJ/JRhnfu2LEDO3fuHMe3/6ea7/vPvgToRJto+H9PV3kutEQigauuugoVt4Kdf78TC29ZiGKyiEff/yjgA6FiqHbs5rPcZW7RBTzAj/kIlUJY/YPVWPDIAsRCMSAFxKIxNLlNRk5oahd1MMonVloGENBlisWiMTjSgAnUjdYaIUceXqlUkMlkzPWNeLX+Tx5Pp4ymy6TTafP8kZERnD59GmNjYxgYGDD9MEAvCez4wQ4UptRkWXQ0iue9+3loy7YZfk35QX3H8PomF5N7JiNWiSGTySCZTKJcLuPw4cM4PnC8VmzWqg9kG5U1YoDX+b6PUksJO/5rByrtFbNuq16/CsmjyYCjgXNXTBaRdtJIJ2sGeqCel686NdeK60sPtBbsVOO87cigYVmN3DReMFWPxUe5/toPe8yKA9g3BefqMNK1U6Oxgk2OsVqtIoccyiP1I0P5uQ3EtE9qrOG4Vdbr+/i99v2Znqn95/hsw4uuv45Z559rovq2fS9T9pg+oWul+pAWPuT1CnwVAAPAlg9twbnV5+q8xgd6DvbgRZ9+UUCfKhQKyGazpl6M9tEOJ2fKpYaWN/pR/Y/4hQX7aHix58wG7jpHXB/dj1/5yld+Lxf9g/Jwh0IhLFmyBHPmzAkQJwleC1/Zm1onlAsN1I/YYq5mMpk0REWCZegrr6W3UC2HzO/VDaUAmt4pMisSHPtph0fo+Nhn3RRAvaAXmbUCeG5GvkMZuzJ4BTnqwWff7eMvmL/EeyuVSgCAsr9jY2MYGRmBG3Jx4qoTAbANAF6rB7/XR9OpJsPUCdjYuKa2VYneQYJfMi32IRqNAhXg4n+4GHtevwezfjYLnWc74aTrgHRgyQC2fmyrAR+PvfMxXPzxizF9y3QjaNViphuZa6FrqHSWTqdRKBRw6tQpdHZ2mrlWpsjraZDRcH6+Wy2RCpJZlIrnZBKQcn5isRi6u7uRy+WQyWTGnRGvfaHxgrS3ZPsS5FvzeOTaR1COlZEYSGDZF5fVjhUROhxdMorU7hTCTt26ybVIJBJY+b2VGLp0CNmeWpXV1mOtuOSTlyCKYJV7FTZqDaXRSqNDWEyMSpOCc2W4pJFEImGUOQWxXFPSLs9X5We2UNZzuDkHFGjMYyeNqHcAAAYGBuA4DpqamuA4TiAqwXEcYzChUqHKAg1btmBUyz8NOlTWSA/VahVRPwon42DV367C4PMGMfeWudj9ut04fNNh+KFgbv1A7wB2PG8HTi09hcFpg7jlbbdg7Y/XYsktSxDti6Ls1fP1Gc3D8afT6YD1fqJNtIn23GiLFy+u8Zyqg2WfWVaTfdUwVnxiBZySg44dHdj8z5sxuGrwWT3Pi9UVzEq8gt/+5W8RCUUw76l5ePKFTyIUDeGq+69CrBwLAAhNQyEvpUKvPJV6EdN++Df1Bo0Ko96l0VvqIACCYbV8F3m9HcXo+745epOymuk/NAAo4PB9H+FyGOvfsh7bP74dpVgJG76wAamhlDkSjAZVlV+u6yIfzuPpG59GS1cLNty+AbFobb7OdZ5D5mQG4VIYPuphsCrT2VfV5VRe+L6P039+GpUWqUUU83D0dUex5ANLzBpwngqTCtj37n3ofawXqftSAOoy0NZ9+J2urQJN2+tueyg1+oH6rB1hyOcpmG9kbFCd0Dauj6Nbb3wouq4H3x3QVcqNgaoNuFXvoG6i7+G6aESizkkjY5CuAemoEfi2aZs4SNMddb3tsGnFB3y3RiQqgLYdTbqf9f02nVKXWv+x9fjtB3+LM8vOAAB6DvXgpi/cNG6tFCvp/KiOrlG4Shd2n/g87Q/rMahBQNfLBtq6jkojNh08m/YHBbgXL16MNWvWGMJ0XdeE69r5zao0KzHoxqSHls+jZ1iP9FJGbxMz+6AKMFAXGPbmIIjXPqiFS+8joagFkx5zAoREIgHHccYdf8bxK/jwfT9QoZrj5X0KbtTqx3ExZJcVPtlf/iZI5BjphT120zHs+asg2AaASqKCSkcFzun6MRu0XmpVbs/zxuWQk+D1mDMA4xh44ngCK9+/sjanbp1Jh0IhOPOdcVb7kZ4R9Dq9Zu2r1Sry3Xnsu2IfZm+ejcl9k8cVUuN86SYPhUJIp9MB7zvnVK18pDNex/4z1Nw2yHC+VSnQMXEfsD+pVMr0MZ/PGzrR48xUeeBnS+9eCrfo4rfX/RYbf7IR7QfbUeishWuPjY1h4MIB7P+H/ei5uwezvj3LMGEaKhzHwfH1x1Fqqo2r43AH1n5zLVqHWg39cW0pCNVLztoKGoXBvU7jGBv3D5/HfRKLxYxHWQUTUK80y/lUI5YtwDQMnz+cXxox2EfuVS04w9CnpqYmkyfPvUlaqlarGBsbM8YWKnBajV37r8JY+0da4f4nT0yNpND0oyZ4YQ8Lv74QbtHF6XWnER2OYmj5ENyCi2k/nYbTa06jb3atIM/IpBHc9Xd3YcoTU7Dua+uQKqQM36MiyjkaGRmZCCefaBPtOdh42gV5DPlh1xNdNRkTdrH4nxfj2PXHTATavlfuGx8l8zvaw69+GH1z+mqVnQFUw1W88K4XwnWCYZ2UbeSf+p3yNyBY6FZ1L7vxcxojVUewdTb7bxoWqSOVy2WkUinzXMpVNRYrUKFMa6m04PL/uBxj8TG0Havl+qohWWW87/vwHA9PvOwJHLi6Vm8jW83iRXe9CP1d/bjz+XfCW+5h1sdmAfnxucbqzdQaG/ZYp39uOtyci9Ovr+WWT71rKuZ9fp75nv3Ltedw4B8OYPTCUexYuQOVWAXTfzw94NXUtCfKJgVwNviyAbqudaPQdNtZYevijZ5jgzGbPnS+9YfPtes6EXQqLqC+xP9t+axjVuDW6Dp7LNpf+3nEKzqWRiDdBuA2PlEAbuOjRvtJASX7Ye8bXXc+m89TkMpreJ3rugi7YVz+5cvx+Gsehxf3sPGHGxGpRky4vs6HepDtsSuNqAfdng97buyx67NtHVL1xUa40NYpdQ/8rvYHA7hnz56N9evXm2Ic4XDYFLbK5XLjLEv8n55TegMVwCpATKfTZhOqx4tKdyQSMZZIbj4lWCUq3gMEqy4yPEmFh/0dgMCYWDiJz1JLMMNb2VcKKjIMNSyEQsGK2JVKJZB3rtYaDa3Q83lTqZQBANp3EptayzgfqT0pwEOgQnl4LIwVH1uBtsNt8J3xFk6CBfXYAwgU/tIcUo5JwazjOAHLsRo6XNfFlDumoJqpYts7a5WYF3x1AabdOg1ZN1uvMt7s4qE3PYSB2QM4feFpXPOpa9BebDfAjvnCajHn2vF/TW8gQOOacG5ZlVwNGEoXaqDh/Vp0jwoB6YUMWZ+vc5rNZk1uEvPVFTCHQiFc8NgFmHx6MqYcm4JKR+27TCaD3Moc9v/1fhQnF3H0lUdRDpUx88szzRhDoRDObTiH3X+1G+XmGn21H29H14kueG4dUFKx4n5QIaHeUvVgq/Kgxg7OGfddqVRCIpFANBo11cYbnbGtAJl0omup9Rc0HUBTM2g84Dv5jHK5jLGxMRQKBSSTyYAw0qJ7lUrFhMdz/hzHMakU9NKThjW1RQv1qAKntEI647MjoQgW/HgBJj00CdFSFE/+45OY9/15aNrahAM9B4AlCLRTq0/hweSDuOHTN8Av15UXjldDNvn5RJtoE+0PvzVS+G1nQLlcRqwYw4zvz0AymazpAdtT8EM+HDjY94Z9yMx5htMRzjff9Q3YBoCtq7fCi3t4+S9eHgAPtifTBjMqG/id8n5bH+G9/F7TividAmPV2xoBGjvUl6HmPL6Kz7I9auFwGIlTCURKEVT9ek5xo3c6joOH//JhHNxQz6ffduE2jHaMIpfM4eyks8AUoBApYNF7Fo0zHqhnV59J+cBx+L6PqTdPRQQRFKYUMOOrM+Dka04Io9NGPOz58B5klpxfXxfY9+p98F0fC25bYJ6vc2x7/dSYoOuqujPpz36G53nmtBKumeqtdiEsXe9GnzcCaHqtDeQ07Fk/37duH9LH0mje1xx4vq6F6nO2QUVpVMGaNgV+St/sl+oQ9vN0rdnYB5uu9X1207XQ+23PMceme4MYRPtqv0Npk/ck8gls+PEGhGIhNI00BXLj1Umofddnk7aorymtKW2onvdM66CGCf6v86a8x+6LvXbPtj07WP4/3Hp6erB27VoD+DSEs6OjwxRPA8afQ8eBa06Ofg4gsJAKWviO5uZmA0pKpZJhAhreRKJjuK8eIUbF2maGvE498Ax/0qIivl8vHsUiC+wjf+gVZoiN5szYYbUUWDxOikckUXnW/G8WV+O9HLNGAXBsGhZLy2/TjiaseNeK2toUXERGI1j/xvVoP9JuPLJKkMzP4rwROHKOtegEN5Ad4kqApc9m6BYjGBzHwbRHpmHVp1Zh2feXYcUjK5CI1MDZ8PAwBsuDuOPDd2BgVi0va2j6EG770G0YwlAgf4xgWb28XEvmoLG/vIZjoCWO47Ir2mvEgn1UGIGY49SKBKZSKZPHzfsYcsX58H3f0AkLgUyaNAmxWMzkgZMGfc9Hx/4Osyc8z0Ops4RH3/IoipNrNO5HfJx+yWmc+5NzNTDoVTG0agg73rkDpbY68Dqy+giOrzuOdDodiCihh5tAlXSvwkPXUAEr+6wgG4AxLMXjcRP+ByAwN8qQub+oQCkYV16Rz+fND4WOLUTtM8C5n0KhWrE7zWVjGD+VCn2m53lIJBKIx+MYGxsLnPGu6QascsrnKS8jnXA+uAc8z8OFSy7E1L6piB2P4aJ3XoTuzd2IZWOY/4X56H6gu2Ygk9a3qA/3vuneoJGwrZ4WwP1A5XOiTbSJ9offFixYYAy4CoTskxQ0aqVYLKLlyRa0b2lH59ZOrHrrKlz62ksRzUQRzUSx/MPLseFVGxDKnC9cWQjjhn+5AbFsPTUtXohj4+0bzXsBBACZKro0LmoEHaPgVLZrhXCNKuT3Gp6uYEh5rhobNLJKDZiq3NMRQ10DgAHhAeDXUo9EorywC9+qgX7Nj9cE5itSiODwzMM1sA3UUoA2DGDPP+8JGIHtk07UCWR7Z33fR9SPovcHvZj72bmIjAZr0ZTLZYQqIcz+2my4+fM6qw80HWvC9F9ND9CRDcxsOaupVtRH+S5gfCVzNn5GXZfv0mezv/yO41MwaL/H7rfKT/3OxhC+4+PIyiN45FWP4K533oXR7tGADqcOHdVZ9Jmqt9qRtI10dQXa+jz2XZ+tY+e4+JlGwOpn/JxN50qL3qmBi/qRDXzVaMJ+aIE129Ot0bxKO01jTUgNpsy7FLwzh1sxgg1qlX8QGzHq0cZ2Oj7b6Kjg2f5b36NrpToq9T2brn9X+/+8aFp7ezuuuuoqTJs2zQyWCfEkFAI926OlVidOJje7TUAcp3oVFQBx4ZWZKUOh4s+cTmWEdn42gSt/CIzIiG3PpW4OKuxk2JVKxRR7U8+gHgvGdxBwKdhTIuNzuSnUS+r7vgF9zHcnmFPgo1YrABiaN4RNn9wEP+Jj2i+nYf435yOcrVeF5qYhUXLMOmdK0ECdWVA54H2MZOC4dX3UI64CVPvq+76pcL/jVTuw9/l74YUFfXjAgocXYO031xrA5/s+sk1ZVN0q0qPpQKiwbeUlffFvLRLHZtOPGmq4JhoqxjFyDfmdKhP8YSGyarVqiq+5rotsNotsNot0Oo1wuFbxXIEc6aVcLuPEzBO45y/uQaY9A7fqYul9SzH3q3MxMjKCjJPBprs3BUL1w4UwVv1iFZbeuTQg2AgySY8EqIVCwTDoeDxuGDvnQBUjjpcGMEaxMJw+nU4HDEwcA8Eq3xMOhwPh9rrfdA4JqrmmrC7KyAqlOxbRcRwHLS0t6OjoQDKZNMXFwuEwRkdHMTw8HMhnJ49SBq2CiQoeP9fQfL5f87gdxzFzSiME9zn3DVMY+J5t79mG7KQsxhaMAQBad7Ti6g9fjRBqhpKBOQP47dt+iw0f3YD2U+2mdsbIyAgeffRR9PcHTyT4n2z+RNG0ifbfbP9Tuspzra1atQrd3d0A6nxLdSXy13w+D9d1TTh1NBpFOp3G2NgYMpkM4vE4uru7jd7V39+P4Z5hHH77Yay5ew2m7ZsGb4aHn7/+54ADvOoHr0L7YLvpB6OsVPmm3kPdi98zpSgej48LDweCRdBUL2C9DuoCjcAK9QLKG03xKhQKJsqR7zl79ixyuRxOnTqF0dHRgCIOnI+qW+Xh6Q89jSs+eQWaDjWZfvF7Pos6FKOYMp0Z3Pf2+wAXuPZL1+LBVz2IMwvO1PtcdjDpB5PQ+6XecZEA7DvHpDqn6qB6Djo/1+fwWQOXDODwuw4jeTqJi955EaJu1FxrgyagrkPzxy6q1cgzTd1APye4UoMLDRqUvzTe87kcs9JBIyDId6r3uZGXV+fj8PLDuPtv7jb6TSgfwqXvvRQth1oCax8wTLtBT36jZyvNKC5Rw5E9Fr1W9W4FruMMBn492pP7Rw01Nl5SAwX3NnUJu88KonUdmVZnF1/Ta0OhkNFN6BCz54drlMvlDN9hdCS/Z59ZJI24Sp2QauDgXNjRE6pfNcIeapTQObKNk/Y6//u///sfdtG0aDSKCy+8EFOnTjVKor3YtjWC4JMFuOjdIXBTKyOfqYyITFk3pb2oVHpVgbdBDpVwMgESGC1yalHj5qAnNxKJBPJ7gHp+KAEClX/d1GQaJFj1kitz0UZlWwUB54gEqoA+FouZcyi5yXWjalGSsyvO4um3PG3yvU7ceALlchmLv7YYCacefquWNv1hfzVs2+4/gYNtCQeCOSYqyPW9BGm2ZXbFD1f8/9h77zDLjupafJ1zc+rbuSd1Tw4azUgzGmmUcwIhIRFskzEYMBiwjU0w2BiQjUkOmGCMMdkgkowQQYASAgmkkUaakSZoco7d0+nmcM75/XG16q5T3TLivWej93td39dfd997QoVdu/Zae9cuuJ6LrS/YahTsop8swrnfPDccrt/h4cHfeRCT/iSe+73nIlfIhcZO+0cBp+4f4tjpouc4jpn8CuBJMujCoYsqDQoqYZUVMnx8F8EjPeQ0ZkqlUiiMWkPzZ+2ehcu+cRl+9pKfYcXDK3D53ZejOb9F+tyz5p4pczg3nMPKH61ENB4NRVGwHrY3gGPG7Q+cizp3lXzgM9k/XEw6OjpMIkMF2wryacxpWLb2G/uY15MY4Xd8NuvG8a5UKpicnDQGGheSdDptwuPp2WbdGTbHvAycd2yXEm21Wi0ks5Qp13XNPWR2KVtqPHJM+TnnPa8944NnoNpbxRN/9gRiXgxLP7QUI+MjSCaTGD93HJv/eDMq3RVseNsGXPCFCxDf097mwPfPlJkyU569hcai5kWhjldwQB2XTqeRzWaRzWbNesCEYVyPSH4CQM/JHgzdPISuri7E4jHER+J47tefCzfqInMsAyfphPSoEvfqndM1n+uaFjXi1ShmoU7SSEIF26VSyUQ/qQ4H2vpUvc+0SRiJx6gmBa5crybOmcCev9yDem8d97/lfpz1qbPQs6snZIfwnZVKxWz9chwH+bE8Lv/C5UAUyB/P4+p/uRo/f/XPceCsA0AAzPnaHMz5lzkmcZquh3a/2J97nofx68cx5/45QLg7Q8CUP70P9CL52ST6t/Qj5rSjHxTUq83FQrBtA14F2eqZtLeH6Tja9p+ufxqJZjs4bDlRIsYmuu1ie8mPLTkWciYEkQCTKybRfaDb1Ne2i+01Vutl1xFo2/k2CcLP1H5VEt72vtrRDXo/f0i+q6da62RjK835ZBNkvNe2fdWene56toX2ih2FoH2hUTDaF0oOPF0IuF6vc1Vzf6mDUwkEG3Cz6GfTkTrT3fPrym8VcJ977rlYvXq1ERiCI04y9VRSyWnYtCpRdh6ZI3sCqtAytNQeVB2MWCwWCpPgNUA7wZqGqasQAjDv4H0UOC4+OoA26FbBYDspjJrBWeukxX4+BcZmRplkTb1+QHifqQ2OVbiTE0lEmuFQnfx4HlEnfC4jCQzWWbNOqrDy/QqCdHLZAIwywv5lPSknvu+bIz6KxaJhwTgGK7+zEk7ZwZZXbMGau9Zg5XdXolKuoIJW0q5cPoe7X303Dpx2AADw066f4rpPXoe4Hw8p2CBoh3Kzngr2FLxpqBzrTjlhCBsVBa/T0GWbfFElo2Ol/aMh+7lczmRZ53EIjuOE5tjCJxci+ZUkBg4MoOm0+jqZTGJhsBBPor1Xz/EcXHjrhVPkkbJjj60SCM1m0zDwNMY0HF8NNUYlsG3KkmpSMZV5gmolLyiH7FfWm/JOr0AQtFl69qPu99aj81KpFBKJBCqVivEGEWxTnunJ1718XKjt8Hvf900eCwAmaSTbk0wmjW6h3OtCrFEjSgSxvWxLejSNMz51BlzPRbQURT2o4+jyo9j5xp2odreM6onBCfzqdb/CBR+/ANgGs91mpsyUmfLsLgMDA8hkMiHdoGCEOj+TyZjfJGDr9bo5Lof6iJ5w5gaJx+NIJBKhEzIGTwy2yONoOwM111w75JohoBqFB7STNhKA2UatghQFL1z3lZyl/rUBHvUmw9dVP7KQDLdDyPm7eFYRe9+1F/WBFklbmFfAxjdvxLkfPxddB7vMmsJ1n+1U8rz3QG/rmQgQL8Wx5nNrUHlFBfHNccy9ZS6cyPRAh2uJHSbNsT510ykc+ZMjKF1YwvIPLg+1zbZnWb+598+dAprUPrbJEvU20zZUgti27dT2scdBo+zs+qkdyfroWq/gTe0Ltb1t+eLzbLC77rvrEKlFsOmGTQCANZ9cg6H7h6Yk9eJYcD3VOcZ32n9PVwe+V+tkg2f7WZwX+ix73Hy/lV2/UCiEQvVtp5za881YE9tfth0L/3Vh6HMdC90qoLb6dGBeAbkNlBWf2bJG20jJkOmAvI2fbAJA8YrKBYkYO9piuvrrM1X2WM/piIdfV34rgNtxHJxxxhk488wzQy5/oM10EXyPj4+bz9lwKnigfaSXesho0FOpU9lzwrPDGd5DZUHloR42G2TqfayX67aOtxoYGMDw8HAo2REnQKFQCHkT2UbdK64LEhWreqDtuujiopONyb40yZpNRqjwaCiGZtBmX/F7hm9RUBPbEzjvHefhZ5//Gfx4S6g7d3XCr/uoO/UQQaFgnqCKbbazQWpbuGhzcWD9NLuzAhv13LONyWTSLJoEXMzGvu4X67By80o4pxzE/BjyvXnDbP/0TT/F4RWHjWweWXwE33/r9/Gij7/IvIMeanonKSMEsNoeLixA+3gT9r967vmsZrNpwprZjyyaiZzjwcUzmUyiVCpNSQRIbwBDqzOZDBqNBkqlkslQT8U6e+/sVt+i7ZmftXdWeB4HDmbtn4VYvB32z2sjkYjZDx0ErXB3Lq4svJbzl0qQ4U3TKTf2lS0zqtBJqjDUSQEr9Uaj0TAGn3ptlcxT44+/i8UiarUa4vE4Lr/8cuzYscO0gR4MGqzsZxbKixIN6tHX7R1qEPD5BPnZbBaNRgPFYjHkEVJPP4kJ1oHjroRl4mjL8+NEWvOhd28vhjcN49g1xwC3RagseHgBOk91Ap3AgQMHjF6bKTNlpjx7C/O06LoAtAESdTWjoqhfaZByTaORTS9vOp02W7tIPtLeYEi6vY0ICB+Tys/r9XpIP9oG7HQgRW05eo55D+uogIv9wKKGPv+284horgpujbKBU2Z7Bp0Pd+LE7BNGV85/ZD56hnvgRMIAQvfD0u5TQ96smScjWPx3i9GYaCBoBAgQBj0KdhWU8Fme72H8qnEc/vPD8DM+Tl51EoEfYMmHl8Dxw9sQ2adOxEEk1XawkKS3AZMCG60LZYxrjkZ8KpDU97IPGVGmcqqyYhMNakvxOsqEPaZcv+32Klhlu4yjre7itNtOQwUV5HblMOf+OSFAq+1XsM/6qK1igzTtR42wtW16lTW91waI2hdKQmndpgO1Sm4YezIBPPB3D2BiaAKO52DRlxfBr4ftLnsOaf21P20SiMWP+vjRn/4I13/pesSr8SnRDqwP8ZCSC4qL9J18n02c6LPZj9qf092nRbHJdOSEPebPFGwDvwXA7bouVqxYgUsvvdSE+tj7ijlxe3t7kUgkMDExYQxJPaCc4Zz8PwiCkKeMyk4XG92PTFDPAdYOVY8ajWPez++DIDDGurKOXJiAqXH/KjjK+qpSpmHteZ7xKBFs6MJFQMDv2Yf0stlHVAVBYLKjE5Swn7Ru7EcFkvxMx8vzPFQSFRP2BAAbPrgBF7z7AuQ25cxYkIBw3dbe12w2a0L7VS7oyeR2gWQyiTlz5iAWi2Hv3r1m+4B6dXUR595kNRpKpRIKhYKpP9tuJl/DRebUU0eBOK3kWQMDA8hms8h8PoP/fNd/YniodZRS7nAON3z6hinnamroEseAgLlWq5mQbvaHGgmULTUySBqwLSrz7DOCaQJKm5jg+NrKnnt7xsbG4DiO8XjXajWMjY0hGo2acG2dPxP9E/jmW78ZCrnyIz5+9vKf4ZovXWPkXwFzvV433mCbNaXXw3XbXmWdG5x79ErHYjHkcjnjUValC7S3V+i2FM6v6RZvLnqqXElukABQjzTJMe4nj8ViePjhh5FOp012X53LDCfXRZ37jThvFVyrXlEjgCHilGff981+/GaziXK5bMaY1zBiRU9FoGzFYjEUCq292/F4PBSOGa1FcdrHTkMQCXDykpNY/KPFWP7N5XCiT3lh5ESFmTJTZsqzs9jeIDWWFZQoAKKuV+NdbQug7bUmsOYWpXg8jlwuZ+wjJb25vlUqFUSj7WSYJAFIMvJEC67RXEfYBtZX26Lb/dQho7qQul71IdtLPVytVs1aqs/m9iuti1lv6lGs/be1eDL/JA5eeBDL716OM75zBqKRKLxI2+DX9YlrSrlcNhECQRAADnBy8CRGvVEERwO4fvtYUK5d6v1V8K+2Q2OggSNvOQI/8xSojACjF43i1HNOYeCOAWOrGplwgZFrRlC8rIiVn1iJeLOdqFf73ZYprl3qSKB9bEcVKDDRyAXaLPazpovOtNdpfqeOG9sBNh3gV5tBn6/3pZoprPn6GtNX0Xj7aE+1pdgm1s0Gt4onVH7VFuP1rJsN5uz5Z9eD75iuz+05zzmgJE0kEkGlq4KH3/YwxheNAw6w7yX7kAySWHjrQvjlcLSBRliSZLFPUlKnpYng6/Vx/x/dj6Mrj+KWd9+CF376hegY7giBWcoOf1T2tN+nCwHXPlD9od53jpXaQvxcx1/fqU5OLdPJj33N05X/ccA9d+5cnHvuuSHPnRq/VDDNZhN79+41na2Guu3RohFLJUXh0HBTACGvs4asa1gLB07DRnTy8jcNZA3V0czZjuOEQtdtBaEMnO7F1klKTx4AA4JUENQA5vMjkUjIc6cef8dxQqCVCyRBLuvC/m82m+jp6UEsFsPY2FiISabwH7vwWOhIMADYv2Y/TnvkNNOfGp7b2dlp6svJxf4qFoumL5il8MCBA+adWj+CWhZGHthefC7wGuKsCoOLBZ8diURw/PhxI2vXfvxa3P2au4E0cP6/nA9v0kM9VQ+FxTuOY4ARZZN9TFKJybQUgNGAUQKEfcXvKIM2a6nfq1Lhdcry2VEk3Keve3Kj0Sg6OzuNLARBYEiRY3OP4c6X3YlKrn0+NgCsfHglLv7ixXAi7aPP2K+1Ws2EILLPdaHU+lHWOUf5fl5H7zFJGA2713mvClYVKskjvstO8EG54Rhy/rMfKauFQsF4c3juNj00HJtCoWCichiFMN08Y/vVGNRsrRxzjcKh3DebTYyNjRm9ogasGi36fNYhn8+jXC5PMRL42/d9nP7R05E7lsOK76xAPVo30QAzZabMlGd/yWQy6O7uDhGGnNtcH7j+kBTUk0uoY4CWPVIqlRAEQWjrmRrJ2Ww25A2nDlKdTHtB1wM7MpHruW28qiGvtqDqRduzbbdFo4l4D+0Y32/nxQmCAMVcEQeTB+Hvb+tr1oN1i0ajSCVTOPffzkXHqQ6c8b0zEDhByKZRIkBBn7672Wzi4PkH8Ys3/AKpvSks+ttFSO9Ih+wjvde2k9U2jZ+IY8FfLMCh9x1CZVkFbsXF/M/MR/+P+uEH4RBd3/dx4oYT2PPOPYADRGoRrPr3VXCr4VxFLLaXX0GnttMeP37OE19IiFPu1EmgdpjmtVF5sokklQ11ktmA1Lb91PZQr7fneQj8APQhqX3KsWMdtD/1f+0j+/n2NdP1lS1ver9dfyWUtG+03joX1DYMggBjc8dQ6im1HSkOML56HLGfxRBBxMwdbZNdb9bdJk8cx0Gto4atr9qKo2cdBQAUugu449V34PL/uBzdh8N74+msmI5g4vM1SZ+2z5YNBdWqq2gHab/ZOYKmCzd/usJ3KFj/r8r/KODOZDJYt24dMpmMyYSt4aT2UQqaRVxZNzbOcZxQVm4FIbyWnV+r1TA5OYlMJoN8Pm/ep0pXjXSdZApcdN8wryU4I8jXEHCd2FyU6AFT4eGzqPx5fzabDQm9gjlORF3Q6LHW7+lBJFiwgYmGm+tC6/ut4794rBPHolKpmFDzBd9cAJSBnW/eCQBY8LUFGPziIPzAD+33VNBIhW4zfhwvDbtXUkaVsk4qjr0qFR1/oA2IVFkw7F77jYSFmUCTwPlfOh9+3Mfs2mxEsm2WmjLBRFh8P5lbu81KuNDjSUODigAIgz2CUfaRei1d1zUkjBofbLvez9JoNFAul6dERqjCttlRt+HC8cJhM2f84gxc+IMLoed6Upb2D+xHIVJAx686jMxQhjWkiYaSfg60z4c3i00PcPR5R3Hmz84MJYnjs20Pucq4Murlchm5XM60VUkIrZMaHPyhN6darZo95Ky7Zs7XSBclTFh0ftvfs96q8BmlQh2WTCanbFfgvdw2wfvsxcnzPBw8eDBENgLAxMSEOUaOJMGC/1iARmTqcWUzZabMlGd3oZ2i28OUWObaoiCXaxHXKc0ToYmxqJ90PavX6xgZGUF3dzcymYzRNUA48lAjixiZpXk2qAdZN133+CwS7a7rohar4aELHsKFv7hwCuBgUVJewQyv0ySUQRCgEWvgBy/6AYZTw1h2dBkij0amtTlYX9dxsfq21XDctgOF7bDrTdKCYxQEAXZcsAMbX7IRQTRAeVkZe9+zF4v+ahHi++IhUAlMb5cqwRAEATp2dmDJh5dg1827MP+L89Hz4x6zB1kB6pHfOYJDf3jIAK2jVx2Fn/Gx/mPrEUEk1Fa+m21ToKPgyvae2uDTJh6m81SqTaa2/nRg234O+2U6AMw22OBX7RAloVhv21bWek7nFbXHReuuNi3vmy6awO437X/anHq9DS55Df9Xx5B9be+jvVj1T6vw2F89hmauiZ6NPVj9udVITibhJtp4RQGwkk5K4NBuVRmJIIJ4MxwZ5zZdOI2wo4i4ids5OQbsYxtgq26wQbfOO9VF/Ez7mHa6Anu91sYm9o9th/268j8GuCORCC6//HIMDg6aytp7bdk57FA7QRA7Qw1VKk072yQFjowJFbzjOOYzo2SfCm+hAHmeZzyjVPB8n05G1ofP1mzHntc6mkkXHJ3cTGhCYVaw4zgtTzSNbbKCysYosOIEJvgqFotmMVPPPQVEFy1OFO7l0hB2tm14eDjEErFuZLQHfzCIwA/gzHGw5LYlKHklNP3mtGdYK6ikEcBn69njNBY4Nkx+RlDhuq4BOkpC8DeVgJ7pqYnNdDLSiKBs+H4rUUwymWz1SyWOxmQDzUjTeEGZG4AgjGHFlF89ekAnN+tEBcb+qFarxqPMuaCsux7tYDPerD+AUFSFynQodFiUMX84RsoQ+37rvOiuY11IjaaAee35PGv/LCSbSbhpNzSWY11j+Plbfw7P8bBudB2y27Ih7z7npy5k3HKgpIoB2zHgsX96DLVZNeRTeZz5qzPhe+G98bFYzBwlQmOS7+Fz6BnnPOP4qVHKvtLIEe5DLxaLqFQqJgmQ7sOm3PEcb27p0EXR9pirrLMt3ErBcVAPkEYQ8Bk6h9XDxP+BdpZ/O9KEeoTnvat82ttmGH0xMTGBmTJTZsqzuyQSCXR2dprEjYwWow6wI8NyuZxJgMZkmkA7URb1NfUtrysUCsjlcsjlcohEIigWi8Y+YJQd0NIjtIXUCUAilLYB9SG3gtFWUpKcOrwRNPDl3/8yTvaeRMyJ4aINF8HF1Hwmqr/UZgLaIJW5SxpBA199zVdxdN5RwAG2/O0WrHzDSiSPPaXP3QA7P7YTQTTA8j9fjmK5iK58V4goNuvWU0UBKwkKri8H1xzExpduRD3b9qKXV5Sx6+O7sPIVK+EW2x5Sm/BQUMY28b2Z7Rmc/ienIzWSgo/wvmDel/9pHkdfchR+0m+R5p6D+T+ZDxdPv79Vw835bttxwrVM68O1zU6kqiCZ66jaHnbblUCn80gBkUZzKUDU93GM1G4GgCAa4Md/9GNc8tFLpjj3tC4sakvrtYoplKSwPcMKNhXQ2e+w+4r3qJzze62TkgH6Dj5XiYqex3twxlvPwPZ3bse898xrOUPTjkmOqGSc53vY+YKd6N7cjezW7LR5pPiOZrOJ2HgMa/9jLZAF9p+zH/nhPK793LVIT6QRoB3ZUq1WzRFjdvSj1luxIuXRlkudG7b+UNxpzwm7/+z2aGH/s3/t75+u/I+cwx2LxXDJJZdg7dq1U4w6AGYC6aRSEKVhzgwvtZk1ZUTU0wi0OouHqXd2diKRSJjje8jgKNtKw1eFmmCNe314D/tPlSlBFetDAaEhTRDJdlA52GFVypSRtVblxP4iqOR1fFe1Wg0BS3oN6d1XA1yZKqBNamiYD8eJQAYAqrOriB+LI/ADpDNpLF64GIcPH8bY2Fhokef1BISRSPsMPfV+ss22glJiRkGR7tlgmzhOJA90glGe7MVCJy4XZ3oHNfELlWkQBMazTSBGucnn82bvMYmYbDZr2DuVc443t1hwgqsi07kwHYHBEG56XhWE84d7fdXYUEOBQE/rVa1W4bouHrriITz0/PD529nRLG76h5vQX+03MlKcVcSX/+zLaMafqrvn4II/vQAdezpC0Qk2CUGPtZJBAFDrrOGJjz6B0tKnQp584Mr/uBJDvxgCvDBjbS/MjCCh3HIc2T41DFT2GDbJeeU4DsrlMsbGxkwfZzIZLFu2DABw/PhxI1fVatUYigqMudDr/FJlTr1AIK+LJ4kBBeo6HzhX2Z/UIQrg0+l0yOCjMUwwrmQaPVSVSsU8l6D8iSeewL59+/A/WYKZc7hnym9Y/ndslffSE1wAAQAASURBVP/bSyKRwAtf+EJ0d3eb9YY6iWCan0ciEQwNDaGnpwdA23HgeR4mJyeN/iFgZXJN6hLf99Hd3Y2uri6kUinzrmQyiZ6eHnR0dBgdwi1MatsBU0/SoJ2lzgBdL6LRKIrpIr5805dxcO7BFlj0Hdzw0xtwzuZzEPHDDhz1dilhTdtgYmICuVwOsVgMP3juD/DoukfhR9qgJ7kzidNeehq8rIeD7z2I8SvHW194wOLvLMbaH61FR6wDtXgNftRHI95AfDgON2gnnaXXVu2+SCSCwAnw6PMfxZbnboEXe8rOGo1i6Z8vRWZLxtSBa77avLSH9X/adkzeqWARCG+HbDabqCfq2HbLNjSzTaz5xBrMeXgOYtG2/UAbXb3MrLuG3trAiO/WzzRKlbaoJotlvhc6o9Qu5phN501mUXBpAyH2E21klY8gCFDKlvDjN/8Yw/OH0be1D+v/fj0iY5FQexTEqc08XR0o8yp7eo/eq3aQtpn3aXttIG2TGjZm0GdOVw/2AxPnlmoleLX2FsdEIoFUKmWcFYgDe67eg02v3gQncLD+reuR3JYMtUHBL5+Ty+WQzqVx1xvvwrVfvhZONQyAPa+V46FQKJhtezZZoXa9fe62yqGSgtzSyGupe1R27TFR+Z2ORLDBtY7jZz7zmV9rq/y3e7hjsRjWrFmDVatWhSai7qNkRypbo0kvlHUgI6qAGJgaWmInyIjFYgbUUPHr0V00iAmmpmNJaFQTzNvKRkMpCMDYJmV0baaN7+HA6r4dm+2zFyzWXYE4+4aGNg1rPaqKYWMUZPazCpSG5xPQEJA0m02MnzaOze/ajGVfXIaeO3swPjqOreWtxlPruq7ZR679wveQBFEPnJ1UhZ8rgFDSRrcduK5rkpyQUWfd+ZvtV6DOwrZrNALbrIpfw43L5bJRTFxUC4UCkslkyGiwj3Li53yXZkqlIikUCsa7b7Pa7CMFYuxj9fRSoaphQ1nSxZpjQ6MLaBlv46lxjMwfCYHtnpM9uOKrVyA9ksakP2me8ctlv0Qz0k48E7gBjl1/DN2f7g4RP5QtjgH7XMGi7/sonF9oHbvCd7vAzrN3Yu5DcxGvtxIeMomZPodGB2Vc9UwQBIbcU6JFZYHjxq0v9BClUinzc+LECTPGJDR4hAwNLD5fvcp8T7PZNDqBfa+LtOok7Sfd+kDDiR5+3kODWEkGJS1JxNkGDQ1rnTMcF4aozpSZMlOevSUajWJwcBCu6yKbzZo5T5KW+nJiYgJjY2MoFAro6uoC0HZ8qH7UY7Fc10VHRwd830e5XDaJ0KhnT506hWKxiCAIcOrUKSxbtswcKcYEtapvdM8m1wbqLOpOO+O57/vYuXgnRjtHzboQuAG2nLYFa55cg2g1GookUkcCdS2PN6Ruo7157e3Xot6s4/HzHwccIPtIFvPfMR/1TB3H//w4xq8al44G9rxkDxKxBJb/YDm2PX8bKj0VTMydwGk/OQ3L7lsWsluVNFXgc873z4Hnedj6/K2IDcew8CML0bG9A4EbTtrE/lCQpXYI8NTpJvMa8NIe8kfzyGQyKJVKoefQTvQ8D9FKFGe98ywUzihg4FcDCNx2ZIDtiNETb+xtkPb1tIvVUaD1pp2qP3YYN9dBJcfV1lasYAMnrTffrzmKlGif7J7EL17xCwwvaCXGHV41jEff+CjO/NyZSI4mQ/3G+jwdMFb7UJ1irINew2faNqjdHgXtQNgRY4NBlRPth+lsXP3e2BRBBIHbzjHAcaHtc/j6w9j8+5tb9yHAIx97BKvfvxqdj3SG+oZtiEQiJnI26kRx/eevb/VdJByeTltYbVL7R/tGgT3tPe0jOhXi8bgJT1cnlNrISrzo/bxmOpCt/aj22jMp/62A23VdLF68GGvWrDHhzqpggbCXSsN92Vh7EPlcBcwEG8D02f5YbM+fgnx2rM3SUKmpB51Kx4RZWM9QVg+AWZAoCARYfD4VIH8IJqnMmNGSbY3FYpiYmAgZ2dpu9i8XSn7GPtGFj1599g/7WUE/26JtG1sxhi1v3YLKQAVb3roFS6JLMOdHcww4UY8djX9VQKyLsoFko5igSQE/F18lHVRpabt1Hy3HUcePz9PQIU5elUvd+6F/az+SWKlUKkaJZ7NZeJ5nQvPoPWa9OcHZJ0B7r5kSH7oQsd/Zfl5LEkrvUePFjoawQ2k4t3SMTIh42sO9v3cv9p6+N9T2uVvnonNHJ6q1qjG8PM/D4L8NojHRwO7X7AYAnHbHaVh9y2o0Y20Dqh6t48BzD2DJ7UtCYVeqXJvNJtLpNBY9sAi5aA4b/ngD/JiPBZsX4OJvXIxkM2lC5di//HtiYsLIkE3SsM9SqVRoW4mCWc5Rjjm9Qjyrloqc38XjcZMJX2VFZYlySuMxCAKTJ0CNEZVVlQMyvJzTfIfOc17LOaR6U2VL+0rzUdAgYV/ZRCMZ45kyU2bKs7swTwOdDDRKE4mEIY/z+Ty6u7tx6tQpc2rGvvX7kHgsAXfMDdkWtDl0u48a/gBQLpdNSDmAUB4b1WW8x15b6d1mRA9/aKNQBwLAiidW4MG1D6KYLQIAlu5diuf/6PmIl+Lw4E1rw7DOzINBXcl1gjr43FvOxamDpzCxcAKDfzuI2HgM9Y46vGh4/WfZ9sJtmByYxOGL2seHbnjlBjTjTXQc7UBmMoND6w7hzNvPhONNPVoLAFZ8fQVqu2uIHo0i98scAoT3s+vab9toavf5PT72v2c/gmSAVR9ZhXwxP8XeYluBlk2XG8khd3cOQWTqcVM6PrR3lAixPblqa9rPmg6U2x5rXmfbBAruOabqgFCbSIEcAOMxp/2scuc4TusEjqgVEBODAZ42mNf+e7q2cGz1Om2Tfb+CcbWDprPdFUzbhDnXfB0DtZG0LdpHdHRodK0SG0xkO9mYDLXRcRwk862tl/uu24c5d82BW2nbNHbIt7ZpunlJcGyDX22XXRQD2LLuuq4h/Cir+l61i+12KbkzHehWIoo24TMp/62AO51OY+HChWZ/kLJDyvKw4cqWqZeG3h8No+VvBa7sKHYOJ6eGQLMO9AzpflZORh0UDQEG2uGfQDirnSo/VVh8FskBTTSg2ZZpzCrrQiJBPboE36OjoyG2jO8kE6yeRJsJI6hgqAXHQI//0joy9Kxer+PIkSOY6J3AY29/DOWBFqD0Mh72/OEeROoRzLp7lmF0h4eHzUKv4eush70vNRKJmJA1ggbWzff9kHedfa9jF4m0skez3brgUkFT8eh+aXvxp7xqXQma+R7KmeO0jtYisA2CdiIWLuYkXBhOxv3evD+RSKCrqwsnT54051VT1rPZbGjy8z7WXeWDY00CRSMYVGHoHOH/msfAJGKrepi/fX4bcAdA9+FuLPzPhTh27Jgxuti/Xt3DvG/OQ+AF8Lt9rPrBKiTdJJrJ1nW1eg2/eu+vML50HFEnigW3L0Dgtz3w9DjE43Ekk0nkcjl0Pd6Fvs/14dGrHsWlt1yKbDELP9ImbthG9k8ulwPQ3opC76+GzeuCoLqGIJhyw0zrnIuaA4DHq3HuUmZJtvA4OI4TPd+6NYQywT2EJE847rNnzzZZ+tWrTXKH84PGD3UijVfqUxJCPKceaG/F4GLK5/Ne9TTROCWBNFNmykx5dpaFCxfi1KlTANq5bJhokfYDAUs2m0U6nUatXsP+0/Zjwys2IHp9FKf//unwS+291iQteSQjSXXqMYYCK6mtx2AyUZjaR6q/uAarx5lrEPd1Um9GohFsPH8jjs09BgCYfXg2rv/u9eisdALO9KBFn896JBIJc4woAHPmdWGkgNmfnY2uZBcixyJwIy5iEzHM/vBseCkPhcsKoWgvADh84eHQ/37Mx6YXbUKsEkO0EUW5q4yJvgnM2jgLO5+/E1d99ipkR7KmX+v1Onq/29uybfz2yTrU3+qYAML7wY1tGgV2fXIXyitaOnrTBzdh7RvWAvWpSbUMCIuFT7BQj7T2oYJbTRaqXmaCKgU2rLsCqOkAIsdY36ngW+1tTZDK9uiz7UJ70baRWa/siSwu+fwl+OlbforRwVF0b+/Gmf9yJlJjqVCyOcq1kgw2CLTbqv2hxd4eYINkfa4CY/3Mfr+CftsJyHfaQFblajovLtvAkPJ535wHx3ew9w0te/DiD12Mvp192P2C3djz8j04ccUJnPv2c1v3uW1bR+1sxXt2u2lDaxsVUNvOIiVa2CbtI8qOtl37SOWFn+vzFcRrXRSoaz6mZ1L+2wB3JpPBddddFzKA6a0F2oCIxr6yYDQcdaKzQzTUgeyVTgR776uyQpwABEGFQsEsSKrMtL66j0W90KoQ7HAIZZUo+ARFBDZkdOj1Uk8w0GKINTETWSACgCAIDFOrgIUAgwpZBYl9wzqlUilEIq1kJ47jIJPJmHOTdb96qVTCnj17zL3Jo0ks/+pyPPHmJ9BMNeF4DgbuG0D/vf1G+OLxOPL5fIihUsZIw5IULLCeBDEKxAhw2A6CHFWi7GvtB4a06YTl+aEcV/aR4zihhZj143cEMmpwlEolAO290BwXkigEcppkTbNlB0GA4eFhY4BwrzALk+dVq1VMTk6iv78/pDC17lQCrJ+t3BgGSGKFxAWBmcpMBBF0BB2mHtFaFM95/3NQHa+iUq+Y91IGXdeFU3ew4NsL4PkehpvDmD17NtLpNIrRIu5/y/0YOb0Vnr71NVvhF30M3T1knm/qknDh9DmI+y0jccGOBViwbwGcugPfac0tzZlgR4bQ0KMssR+4qJD15Dt1IaDhUKvVUCwWzbn1lBXuaaLM0rvPec2FgXqO48Hwcd0qw3FVhc15UCqVcODAAWQyGTOeJMTYT5xL2j62nSSDLjbUmQBCID0Wi6GjowPpdBrHjx8PbcchAOczZ8pMmSnP3tLZ2Wk81iTTuC7xaCb+MJfD8Kph3PPWe+BHfdQ6atjyjS147geei+BEEEq4ph4o6hqC44mJCbMW+b5v9nTTY63kMHWeOjbs9ZvXK8lebVTxxOon8KOrfmT2WUeDKBJeqx3jmXHkG3k04g0kSomQ0cx3RaPtEzAajQbS6TS8uIefXfEzdB7oBLYCKAKJQgJNNM26Fh2PYujPh3DgXw6gNlRDkAoQuAGW/ulS7Hv/PtTmh3VjM9VEM9UmpPddsA/7z9uPIBrg1vffihvffSOSlSRKHSXUhlvRAM1EE0EyQKKWmAKEaD/QPlbCIhKJYM8/7EF5eZsQrcyvYMvHtmD1m1dPkRHf99HsbuKRzzyCs99zNlJjqVB0qe2NJNHL92mOI6BteyhBwJLNZpFIJExOAF1nFaCq3a92tQIg1p3v5G8bMNLWng5E6lYs4zAZz+G6m6/DT9/2U1z4NxciqAUIEPZO2ySAbf+rQ5FjZ0dR6hzQYttw2l51ioQIlqeK7ZRSO0KvtQE671EHIq+zvfrxeBxz585FIpFA/z39cDMuuh/rRnZvFgevOojHXvsYgmiA8ZXjuPO2OzHvrnk4/Uunww3C52arw5R1pT1vRzVMR0awXbTv9dm2bLAd2o+qX2yyxJZb1UlKCGhd+C619X9d+W8B3NlsFpdddhkGBgZMQ9hIepYV6DDUieBIJ4pOHIZIUWGSHVMlxGttdk5/2JEERsqE6ECpgateUJ1EauxTCao3TUE0FQ2Fhka6zbxoXfk5wS/fq2QChVaTdCnAthcbPg9AaD8zDXnNdE2PZ0iZ+sDQ/UPwcz62vnwrBn4+gDP+5Qx4rgcvEt4DynHUffeqwFlozNOTyHYpk8X+oiJjew1L/dSYkQWld13Dh9kfBCw6LuwjevTUG8CxBNoKUhcJ/q+MJGWa3k3u/yXhUq1WTSZ+gnkdU3rIKSO8n/XT9/E+yqMdcs8FgW3i2CuBwP6j7Dx51pP48e/82IxR4AY4ufAk0g+nQ4st+4jyQtms1+soFApwHAfHLjqGiaGJ9r67SIBj1x3DwK8GkGi2x88LPBy85iDGrxnHFV+9ArnJXGtultuLr2Ybt/fGkS3X5GJUijQSSQhRRpnbQRO5lEolQ26l0+lQNm+C5kKhgNHR0ZBsc2woT0yox7mjulDPJVUdwDZ6nmf24LGvOaaalNGOwmHkD+cL71W9pFnvSTKRjCFYZ7/Zi/JMmSkz5dlXuKbpmqdJFwuFQkhvRyIRxFNxbLhkA/zoU/PbAerddQw/bxhrfrrGgGzP81AsFo1uYy4b6hzNlQHAEN56UgltPt3GA4QT49reKLWPmqnmlKRmp7pPYffy3ege7cYtL7oF6x9aj0MLDuH5dzwfXcWuEOAAwkc3xmIx+HEfD1z8AH55wS+BC4AV+1Yg9s0YEIRzpTiOg4gXweI3Lm7p3/PqQA7Ibsli+WuWY/c/7Yaf89HsbKLZ3Qbaprit9RMAmskm7virOzDvkXnYee1ODH1iCP239ePIa46g0d3A/H+eD6fkoHpmFdnN2SleVa636uhZ+s6l2P2PuzF+4TgAIL8pj5VvX2lsJ7UrK/Mq2HvzXpQXlvHw3z+Msz50FtJH0+Z73UNvgy8FmupVBcLRmbSnJyYmzLpJOaAs8DpGeuk2LL6Tn6t9b7/H9l7ankjbtlZ5MNfVHVx282UtG8+Zar/q+mpjBduTr2S4ekP5PAXO9trOfmSxyQ+dY+wfvmM60EqZ17HjuNI+oK6w38tSKpWwd+9eE6k67/PzUKvVcCJ3Atsu3tYOyXcAP+Hj4PMOItFIYNWtq1BJVDAxawKzjs0ybUulUkin06jX6xgfH0e5XDZ1sQmS6dqjv7XOofF0wh5pdXzqOOg79Fn2+Csm4/W2x/2ZlP/jgDuVSmH9+vVYsmSJmZj0+BCwEFyxE+PxOPr7+1GtVnHs2LFQOC+AKd5QCrWCXBbtaO1QZX9s1kUHicDB9nTbrAgXGAX7fDbroIqdXjMa63wPj0ugYqfxrnungDa4m84ApgeK7wemgnL9f7pkcRRKDSclWcC+VKFqNpsY/MEgUAT67uqDH0wVar3XZuJYb0YY8J3KgNL4JxOm4e+6cOsEI1hV+SLjGQRByCPKa4xHNxJOTGaHTLHflaVjXyjY4bWUewXx7BeCafXY0xOv8s1+IYD0/XZGf91Txe9sGdA+9zzPhCN3d3cbpUvGMAgCc159MplEsacYmoeBE6DYWUQaaVMvvsc+Bo3yX6/XUSwWkf1BFuub6/HL1/8SzWQTfdv6cNa/noV0Iw030/by7nnRHuz4/R2AC/zspT/Dtd+8FsnhdgSKvR9HvdmO42BycnKKQcB99pxjCtCVSGLdk8mkWQToDWL0CBf/ZrNpokFsnaM/dqZ7OyJDt28kk0lUKhVDjtikBuWb46R5CvSIMI6DerT5HceJIJ3XqRyRiFR5ngknnykz5dld5s6dC6C9HumWKmAqIUzP3LKbl2HXH+/CyNUjQABc+dMrcdYDZ6ERbxj9yq0vamdMTEwgCFrZz5mIkaQr1zomE+UaoSAOaIf86rYaLQq88808LvnlJfj6734dABCtR3Ht969FupbGd278DgodBdx99d0AgNtxO2687UZkK9mQR1gdCbFYDPdedS8evOBB874db9+B2cFsdH+jO2TYq2HtOA7ym/MtIJkA3JKLRe9bBKfPQXNuE7v/dDf67u/D0euPPu1YlXvK2HntTgDAwbceRGVRBcPXtxJ3BdEA0ckoxi8cx4IPLUCtv4buu7oRrURD4xpaPwIHS96/BPvfsR9eysPSf1iKSD1iIsJYqnOr2P+e/SivaunzytwKtvz5Fpz16bPQvb8bQNujq2Nggxy1fRVAso/0O8qLEvvqHVbb2fYg69Go2l5d67T8V8BH5d8GT/b/Wg99Nq/RfCzT1UHtAT7LdgbafUtbzSYQ/iuyW9dtygZxDceADijFNArW7efZdoeCdL23MdzAgr9agAPvOYDCeYXQc3bdtAt+h48gHmB8wTgu+colGDg4YJwI5XLZeIZJ6vE336myRVtP5UZlS6/T/leSReeOOmK0T9h2m7yxSRsb/P9WAHc0GsWqVauwfPnykCHLY3QOHDhgjifSDN6VSgUjIyMhoEEwoVmV2TAKFBur3pogCEJJwAh4aXRzwhPAqIeIwFgnhbIjOsBciFQ4VHnw2DEmDiNAVG8rr6dBr0CagqPPtEE9WSGeGU0B5vNjsRi6uroMk+T7/hTvsbaLQJfKhHXR6AMFq9FoFHPungPP91rHczw1+Wmk6xmbQCvcbeHChdizZ4/xfNogkf9riDDbyvozc6qCEg3f4XjyXvY/ASaVH/uMY0P2nnWggcA2cVyUHABgFBrJE10Q+Zkm26KM8bqOjg4DoJjlNZPJGC+n4zgmqyoVD/tHyZ5EIoF8Po/JyUlMTk6ad3GMCfQJ/rlg0KvtOK3wQG7TOLT4UGh+X/vFa9HxQAeqTtX0m+79sj2qysJXKhVEb4ti9eHVOPCWA7j8q5cjOZFELV4zc3z37+zG7hftBp6yuY6cdgTf/4Pv48UffzHiXtzMebaXHlmSeUHQOn6GewYJiBWoUl40woGfMXrmyJEj5gzbdDptImt4n+M4ZkuIzbbrUSdKoFCWHKcVqdDV1YUjR46EFDmJN7bRnof8m/M0nU6becBICT4jlUqFjGDeRyOG46fySNDOPuHc1b36M2WmzJRnZ+E2JV3XFTRwPiugCYIAzqiDBf+8AH7cx+qjq7F261qzBUuJPOo06pCOjg40m01MTraTKXHdBWDsDs/zzJoCTM0rA7R1MJ0x1OFAGwAWU0Xcdfld5vNYM4aesR58+/e+jYnuiVBf7Fy6E1//3a/jxv+8ETtW78BFD1yE2593O675wTWh58/fNx8Pnv+gibxymg5yW3Ihp4kNMtkXuv4mjySRm8yha7QL6fekkTyUhB/4iB6O4uQLTmL2l2bjwLsOtN4TILwP3IEB2wAweu2o+Xv/e/bDS3sYuXEEbtNF151d6P1Wb8jApy0RL8ax8BMLgRiQGE2g6bVtZEPyjjpI7kuiuLZo6pI6kUJ8OD4FDHKc1AHCNclei9Qpw88UvClRrnY2ccB/Bd4VvGpYL8dDwZVNxlN+bADM9ikoU4xhRzHaQEvnmL6Tnym5oN5ilSXWQYsNoLX+dt/Y80e/1zbq9yyMcrOdd67rIp/Po6enB5FIBGNjYyFyXiPnfN9H5GAEgzcPYv9HniJxRK73XLHH/H3Pa+7Bc/75Oegt9Bobm4SferfZBrW7aeNRJ/HHJnV0bG35USer6j+bNNE62OBd+5d6jmNlj+PTlf9j53A7joOFCxfimmuuMYmrWNFoNIp0Om0SDNkMg3pWOZB24h67cLJrJyj7RIEHYDyoutdius7UUFUFXQRKKni8Tie2hlUp2GQ4Ks/AVG+iZhJn/RjGrd5n9frrBFFiww7HV+N+eHg4tM+gXC6HAAGZMBV81kkL+0CfQWAVibTPUKzVaqHwbvUMcyFTJcoxUS8w25BMJo2nm3JBYkHPd6SiIeHCPVrqHbaBMmVHgSnbQznVsCZOXH7P0GRV6nov28JFiPJJgBMErT3guieFfRsEgdl3l0wmTUI5vl/7iZ9ls1lEo1GMj4+bPiHI1kgA9UJQcZAQqlQquOvVd2HX2btMGBwAdB/pxnPf9Vwz7tPtW1FiQhdA9nk0FkU1UkUmaCXVyeVyZg7Xu+v40bt/hMJAiy2NVWO44bM3YPau2YaQ0j5mUQ8328e2sdissm674PiS4Dh48KDJTJ7NZpHNZo0cc46MjIwYb7rqAl24KYfsa8ohjUoeI8cxVdngM7iFQMePc0jHUz36lHOSjZyj1COu2zo+r6uryxCcmjFe5yL7eWxsDJs3b54y3v/dJZg5h3um/Ibl19kq/38sjuPgzDPPNPqXayN1g3oGuaaxFE8vYvjFw7jg1gswOzUbbtBKepZOp43dQ8JQSVXqpI6ODpw8edJERzEajXaM67aO6czlciHQpkenptPpkOGrugwAHNfBJ173CZzoO9Fus+9g9bbVmHVyFu6+5O6pmcQDIF6Lw4t6iDViqCVq6B3uxas/92qk4ilEmhH4DR9PLnkS3/2d78Kv+Vj+8uWIHYxNAdbU8TT+1WDXrUdq09ScGvymDzftIt6MY/jFwzjwqgNY/zfr8fB7H0a9M7xGPaNxrjsY+tsh9P20D0FTQGZngEwjg1i0nWCY9bYjA72YhwPvO4CRK0bQu6UX5/3deYjUImZt6evrM8eb2u3W48EUAKuX0V5r1RagU4PbALm1jgSy2uWUY10P+TwlqtX+VwcO66f38plAK8R/5zk7UUgVsPgHi1EvtZ17akOzTrTBdTtkyLaxbFvaIWr32kST2ov8XMkK2nXqeNM+tgGmXRTjAAjNf57AQltJCYlsNmuSAfPMetr19EqHcEjah5f38OQ3nkQQDxCpRtDINQDZrp45nsH177ge6VjaOI14/jYj+xTTsa7UI8RE/FEQrUXnrXqutV/V1p5OtrTfVLamGwO+/1Of+tSvtVX+j3m4BwYGcNlll5lJow3xfR/FYjs8VRlOKgQgnFhAjXUauwQcnPhqiGqHAW3BUmFTz7C991uZC9tYVw84Jx4HXYE966zMn4YiUyl3d3cjCALs378/FKasoJT9SGPYZq5U4bM93IOqodGVSgXj4+OmrVwwqfyYgVANehIdFDAFdwzzNiBJM4hGIqF2e55n9i+z/kqgsI4MryagJiBRcMt7Sc4wAzSfQ0CmzJgqPCVCSqUSOjs7QxNIgboaIwqGFNwoQCNpwgVruvtYh87OTgRBgLGxMXOdZjNnxAL3elcqFXO2uJ77bNeTMjI2Nhaqi+M4Jjyaho/rumYM+bmG4sfjcZx1/1nYd+Y+NBKtz/oP9+OGT96ARqRhSAmdWwR3LCSbNHTa8zx4TQ9xL46qVzVzpa+vD5P5STzwBw8YsA0Apz18GvqO9JnnZTIZE5KkZJyOCYueTMC+1WR0akgpG1ypVMx8zmaz6OjoMIQF5btQKEwhPyh3GrpOwodyNp3O4f1sH//XbQg0UJmsTcPYVc7s/Ay60NNgprGQy+XM93pOu85LXaRmEqbNlJny7C2Dg4Po7+8PRd6obUTdoODRdV1U11ex5RNbgAiwLboNfT/tQ6wcCxG+9pGu1IPU96VSCclkEolEwpzSUCgUDOkXibQSs/q+b5LoJhIJA7a59ivApl1lDOMAeN3XXod//91/x4k5LdC9fNdy3PTtm1p2SKSOB85/AAPDAzg896ms4Q5QT7baTTA+PDCMb7/y28iUMph3aB7WP7geS7YtweXfuhyH7zqM+KE4ArSBGzB99mP+rUBHbR3P8+DXnzL2fReRRASDtw1i6HtDcBwH5/3xedj4gY0oLSwh80QGpdNLgAskDiQQPxpH4ZzCtNZ5EA9w4OYDcOCg64ddrTV8WQN7/nEPlv/9cnQc6UDDbSDY397/bG83SgQJrP3YWmxtbMXaz6w16wLHgafLcK3iOqCOJq5fJB90LVXiWe0VRn3quwCEnCb6/OlAohbaIUB4n7KC2OnGj8/dec5O3Pv797bmR6WOhT9YCMeb6mHmM+hc4fOVZFDsonu4aVuz2N5q24Gnzi7iCQV10wFruw5KVtj3UCc00MDY4jHEH28fZ6XtqVQqRp4Vx2jOHK036gCGgdPfeDqq66oYvGMQ2/5iG45deQxwgNzBHM77m/PQKDdQTVTbTkenhrEFY4htiYXmFeWD7VFvN9uh9dbxnW6+qiyo7aR9aHvMpwPa9nOVWHom5f8I4O7t7cWVV16J7u7u0ETi3zbLwL85eOwMoJ1hWsEmMwVzgNU7qo3lc2gwK9uroFiNXYImBb7sTLJwvEe9d/oeHSiCUA2tpuHM6w8fPmwMch18TdKljCSNbfVIsl1kmuy94cpAcsFk//JahsvqmGlYqbKFbG8kEkFkdgRHh45i4KEB9PT0hMLMudgzjJ/hvvbnGgGgE8xxHLMYU3kpM0UlRhCuwIQyxX4mgOGzWf+Ojg6zoDApFYGRTQ4om6nfs71smxJHqjD5HGVJCZxVweiixonuum4oSRrbSNIBCB/3wPsJnHkdQTfDjJXds/Mp1Ot1HF5+GPe+7F4DtufsnYOrv3k1cvUcyskyqtWqidSgrKscs/0a/aDjxc/q9TrGxsaQSCRQnFtENVcN6ZWJ3glUYhXk3XwIVOupBlTGGh7NHz1LlrLHvmXdOG6UhZGRETSbTXNsnoJ4kn6Tk5MmaRDrQj1AeeR4cP7SM64kIXUdtxzQq1wul9HR0REyXijHOgc0oobfK+hWcpL7LVlfJopj2DnrrHOZ8ykajc6cwT1TZsqzuAwMDGDWrFkYGxvDxMTElLUMCB+VEwQBRi8axYH3HjBeqO1XbIcHDxd9+yLkEjk0m60TStLpNCqVCiYmJkIgmGu5eqOpA2OxmFkneI+S+FoXJfzVFlQvs+M4iE3EcMMtN+COF92BvkIfbvjBDQBaNsvl912OdDWNlU+uxHef913sWbJnSh+xHJh/AACw7bRtaLgNLNu0DM09TeSezKHiV8x71ThnUUCj4c26TUwJSups2h9cK2JHY1j+weU4sv4Iem/txYlXnEDhogKGbh5CdnMWR/7oCMavHUfsRAyls0tT2lBcVkT++3nUV9Zx4L0HUB+sY/vfbEfno51oxpsY/JtBuMdcswZon1Pfn/aPp8FNt0+HseVEbUbas/ze9iwqgLSBto6hDZrUc6sgyMYRNvhVG0v7mN/bzw+BQwDbLt6G+19yv/n/8Vc/jmakiSXfWRJqn9pouoWUddJ28zp9p9aHfamOgOmeo3PU7mOVQ6Ad3ccx0naz6JxiO3a8eAeOXnsUSz68BB2PdEzbv9OBULVfFPPwvtzBHHqO9wAusOKjK+CVPJRWlLDy4yuROJ6AF2l5zGkzb33JVhw85yCWfngpMpsy0EK7zQazrBf1kM5JrQ//V7D+dBEB2l57TNRhp3pruuf8uvK/Dbjz+TwuuOACdHZ2tvaRPAUaaahpyCIQ3ktA4MGQBQ2rVTCunameX3qjbLCiXk1V2twDUCqVQuELumAoyGQ9CcjpxbM9nsDU0BISAxR+9UrZe1kYcso6ElCqx5fXM0ybQB5oTbpUKmWMaYISXkvvb7VaDTFUfI6SDcrIcfKrgmwEDTz0pw+hMKsAp+Fg9ubZoT5WsMfxpvKhQrD3SvN6NRJIdrCvlFXjeCkY4hgoqOK+Z1X2BKQEvqlUKgRWfL+1t1v70FamlHEFftMpOSU92JbR0dEQGFP5plzF43HjQfV932SNZmbHUqkEz/PMEVWUEc3qTkBOYEgAp4QMyQe+v9Fo4OiCo7j3pfdisqe9L6+UK6GZaZNYvb29SKVSOHDgAAqFQmhhoOyr50IJKw2zptyfPHkSfUEfLv73i3HPn96DUk8Jc3fOxeXfvBy5Qs7IioI+lUv2xcDAAIaHh40+4feUKd1/Td1gZMWt4sEXPIi5n5iLSCRiPDZqqAAw0QKqlPl81knHXAkU3QvNecY5Rz1Gco16k2PPdjIawtY3ut2FfU6ihbLGezjX6J3goq2h+SpHvu/PJE2bKTPlWVxqtRoKhYLRXST0dS3n+kIdkTqZQqQegYf2upU8kEStXEPUbx8dOTk5ibGxMYyMjJh3ceuSOkjUC8ootFgsZrYgqRGroelq5ALtCEgFCgBQSpWwfdV2XHfrdej2uxGpR+BE2h7F9Q+uh+u6eN73n4fbbrwNQw8O4YGXPBDaFhUqDnDf5fdh2/JtmHQnEb86DpSABe9YAKAdpab2HqOXbKJTyQI9RUfbps6ler2O2JYYZm+cDQCY9clZ6PphFzI7MoADzPrsLOTvzSNSiODAhw6gcnp77ev/Qj8G/m0AtTk17P/AflSXtNa7ZkcTI5e1xqjxoQYWv3Ex3HoYbNltsoGdbc8qeOH6xX6wgYj2g9piPDqTmED7g2SMHoHLopGv6rSbzu62QbjiDBucO46DruNdcAIndPxXem86RDzr/QqGbVCndqzaCkouqQ2sQF6vs0GkPlN/s04KHolPtH+0b9Qe3fmandh/034E0QB73rUHy25eho5tHU9LINhg1O5XoO10VLlq1BqY98/zUOuvwTngoJgsmnF2XRfbX78d+67fhyASYOdf7ETySBIr3r8C0XLUPEtPLeL71ami2+u0b5QMU2eF7eiaro/5PHs75HRY7zct/1uAO5lM4pxzzsGCBQuMIDGkkoNhA1kFGTTw6vW6yWZpJ0DTWH0C92g0iqGhIXieh2PHjpmJq/sl+G6CPBq2tlBrYiy+l+wsAaDNiqgxrICLgk6wSHCrYS9cCAnYqtWqAXCqlJUMSKfTxhPFfVW2ICnQVWFgn9FDysXQVr4M1SUQ13aSaIh3xPGLv/wFRpeNAg6w6S82IfKeCPJP5gHAKE721XTMHidDJBIxbVE2le3nOOqizHM9FTgo88i/lWhR5Q60Fwu+TxOnqTeZ/aTEjXpm2Sa22Y66sOtCEkHlX5UbZZfGCGWEdajX65iYmEAkEjHkSrFYbGUAz2bNZyRQNBGaJrrh2DO8i3XxfR/RWBSl3hIme9tgGwAWbluI7kPdocWRHm72C70h3L+n4Vd6vJkmrTPgMh7F7it2ozPZiRf+/Qvx0z/6KW740g2ITEYQOIF5vu595gJG+fZ9H8eOHTPkkR3domPB9pJs8RM+vvPH38FY3xgapQZWfHOFCaWkLnMcB4VCwYRaqodZ66I5Djg3KXv21gvqPxovlMtIJGKOjOPc5uJCPae5A/RoP8oix9/WPwDMdgzWwV5I+Z3KJ8+anykzZaY8u0o+nzf7LTs6OpBKpVAoFELAVYlPoLWejj1nDI3u8Ikwu5+3Gwt+sgAxN2Zsulqthv3794fIZ+Z/od5SspFrNHUWdZXruvCjPm576W248qdXInk8aeqiRW0xft9MNPG5138OhUwBaSeNORvmhOwLrZe3x8Pam9fCm/BwzqZzcOiqQ1j3nXV48F0PYmTRSCixkx/1cWKoFaJemVcBfGD/J/dj8bsWw6/48JIenHJ4P60SknaUox0RRdtCw14BoJloollun/LhVB2ktqcQOE/ZGl4E6a2tPeGL3rQIzc4mdn19F7q+14X+f+1HpBGBc8JBz3d7cPRPjyKIhQFA6cwS9vzrHqx8w8oQ6RtEAhx88UHkRnKYs2FOqF4EM1wnXdeFl/QAf2rSLdpt+rlt89kOFSUn9HuNxFCwqODdHmeuT9rXWni/PkPvmbVrFm782I24/c9vR4AAZ3/4bPRv7A9hA1sGdauWYhS78DMbBCoOsm0E29mnwFDbrM+zwaACbK279u3em/Zi3w37zFFe1blVPPm3T+Ks15+F+Fh8CoGi754Od+jfJN8AtJPhVV3Ex+OooWZwWjwex8FXHsT+5+xHEHmqHkNVVIeq2Py5zTj7DWfDrU09ao3OQ7bJ/tE+INZSJ54NzFm0HfyffUCbSftzuv55puV/GXBHIhGsWrUKy5YtM4YkQYJ2gJ6drBOUHUHjVQdO91DTUPZ93xiKnufh0KFDxgC1QxyoFDUTMzuGBi8XAU1wwHpruK4CAwoMBYB10b1KNPA1CRw9uhxAGtbqveVZ5Dw3E0AoKoA/rKcdnsV20vAmoGD9KCSaZVInJr2dmunY3ge7+fmbMb5w3CxYzUwTW9+6Fef+yblwG622VpdUkT2Uhd8IhwOxr/g+2yPHflEWi7KjSUrYRrv+nJAkSnTMOYG4v41to9dUmS+2mWOjiTI47pQ3elyZLMV13SnAjv3HMVAA7Xle6AgmfQaVRLlcRqFQMIn3CKa4X45tZ12UIda+pyzqPnc1UnzfRzlXxkPXPhSa5/FyHPkjebgNF/FU3NSbYJPzIhaLGQ+87UVXz64uZJFIC1CfuPYEtvzRFgDANV+5Bq/4xCsQ+AH8WKtfGII0MTFh9gAq2cFx4jirwmQbKUO8l3piomMCP37tjzE6u0Ui7XvZPuRiOay9ey0iT8VaUha5/YDjkEwmzXcaHk7dxnGhUUNZ5/s5j6kParUaEomEkXueY0vZY+SLRm1QrpmXQPuZfc+6Ulbq9ToqlYp5NveJKztNedEInJkyU2bKs6/wdAoAKBQKiMfjSCQShkBX20bXpKHPDKE2UMOpq08BDpAdzuKij1yExmQD47Vx1Go1pNNpjI6OGmKORROp6Rrjui6KxaJ5H0F5LBZDtDeKDS/agB1n7MDeFXvx0k+8FL3He0OeT6Adtko9OZIbwddf+nWM58cBB7jz6jsR9+NY9/A6s+eWxOPIyAiGh4dRr7TWp9yjOSx9aCkmMYkr33sl7vvgfZgYmECkGUGxozilL+ECExdN4ODNB9H/zX7sfv9uLPizBYgOR+E6LhITiRDwUIBCXavrjIZQU6/X19Sx/a+2Y/FbFyO6L9xurv9aIhMRRCYiOP2C081nvuPDhYuBbwwgSAc4/trj8FNtMJHclcSCNyww6wUA+K6P0ReP4sBbWyH18b+JY/6m+aFxVQ/iqXmncP/b78c1/3QNkqfax7YpWOT16g1UEpl/02GkSXTpkOKzpgyF1IvrYSTSTrimdi+v57jwe5V32/vbvacbV/7TlRiLjmHWo7PguNMff2bXzwbUdui8Ami+j4BUCQd9htZNI830vXY4ur6TdbDxgP2chbctRLGviIPPPYggGiB2MoZl71+GyKmWLabP0D6ms8SOVOH42+3T8WUdaec3Gg3M+sIslPvKOPrco+ZUGgCoDFaw6Z834Yybz0BsOBaSAb7X9uxrseXo6UgZG6dqX2pRm1LbYffTMy3/y4B78eLFOOuss0LglHsbe3p6QmynAhFWnpXVicl9zjZLyAGnQqJwKVjmxFVAyfcxZLNUKpkQBRq2Clqp5HmPFltoWDdlzij49mSgAKqXnwsYkzhxYG0B5nPYdr6XRYEs+8n2avHoIPVc8zkES47jmJBkfs+JT3LitG+eBjSArb+3FXCB3k29WPmxlUAN8OFj/IxxbHvHNgzdNoQlP1wCACFPm90HSkAosxoEgdm3z2fwfv6vhAD7uFwuh/pPx5QyoAqZmRH5LNujPl1f6STjmOu52Opt548Cfob5c3FWb6zKtNab2V0plxxzvpfP8H3feK7ZBzz+hIBJtyvYcykxnsDVn7sa973qPgwvGEakHsG5t52L0x84HYi1gG8qlUImkzERHnoefTqdDhEoSjxp4hZNmnLweQex8092GhLnzlfeCf+bPs545AwDZjnuXV1dIYVLg7CnpwenTp2actSZziOVH8ofAJwaOIVyhxxn4QDVNVW4G1y4pbbXmd57m/G1WWC2T70dHE+NYOCCxDlpG106p/kMGi26CLDvu7u7jUwrMaShV1wASTZVq1UT9qmeAJVH1XczZabMlGdf4drGtUzXNKDtbeK6YMjJAFj014uAClBfV8f5Xzwfc8bnALOB0dFRswYXCoWQ7UFgzUI9aOez0LUxSAfYd9M+HDqvddRkI9HAd1/9XVzzjWuw/MTyEDBU8AEAJ+eeRCVdCenofUP7sOaxNYj57f3fk5OTGBkZMSeaaD6XIAgwcnIEz/30c3Fi/Qlk6hnc8fw7pgfdDjBy1QhGrmqFZ+/7xD5kNmeAABj88CDcsXb9bK8ti5L4qssnL5jE7r/ejWZXE/s/uB+DHxhEcmf7CDS1XfWZanPo967rYtYXZsH1XRx75TFkt2bRjDcx76/mwa26CKJtAHf8947j6NuOmn587D2PIfKpCOY/MD9E4Hqeh+OnH8eGP9qASk8F9735Plz+lcvRd7DPtEOBt9pMtv1Le4z/c22kTcf/FXjrs23wpqHsujbRxtR71E4HMAUQep6H7k3dyNQyIdBnAynta/1fQa5933T9oPNmum0JbIf2A++366PvVNlRZ5NNGFBuVn5mJfyKjxOXnsDCjy5E9rFs67x2N3y8rv5NXGGDbZusUafg0/0fiUQQj8Wx5jNrkIllsOuaXe3Od4BGdwO1pTXkx/OhUHJt+3QEoo371Hmndpptj9l4bbofHRtbP00H1J+u/C8B7kWLFuHSSy81+3g4YTo6OpDP543RPV0juK9Ww3XpdVPvoQIJ21Bl46fr6CBo7422mSYdPGVq1Cus7JLuwySQ0jqyzo1GI3QuND2YZIT0vZqZUxOOaMItjQbgtZ7nmb7TMC16dAGYUC5VQjrRdUwAhPaC64KsiyrD3llWfHcFos0oDp55ECv/eSUyYxl4EQ/jK8ax/W3bUZldwa4/2AUn42DJt5dg30v2Ye735iJVT5l+B1qh3DpxbaCqnmtOelXWCt51HBlyaydc0DEDYMLsdR+2KnR9P4vKJcEw60yFZBsmlHkFPQROunAqccN3EZDRO6sMKNDeB8w9yaw/k86VSiVUq1Wk02kAwPj4ODo7OxGLxfCrF/wKF//oYgRe+yg113XRd7wPl3/9ctz5mjux+vursfKxlYDb9oIXi0WzbzsejyOfz6PZbKJcLpv5yrmgMq0ySCAei8XQWe+col9i421SgPOAfUCGlH1UKBSMjNheGFWo3OdOOaGcD2wawLrhdbj/nfejmWli6PEhXPidC5GqpBCJtcaMx1Yo+Uc9QLnUsHElMXROU5dQNnTBpkwpY6uh5JQNGtfaPu7jnzNnDo4cORLay0+ArXkaqCuUnFEvkUbqUM5mykyZKc++Eo/HMXv2bOOoUOKdEUf8jA4N25Bf/KnFSK1LoftAN4JUa83L5/MYHh42OSvUqKSuoj7XLSjUkXotAHg1Dxi16o448sgbHa0RkuqQqcQr8CJh0q+Srpj9t1wTxsbGTJQb12NGyHGdLB4s4sz4ma31+zsxfO/F38Oqjavw0OXhyC4tjYEGxq8Zb70r7WHxxxZj5MIR9NzSY+quNq8CH6AdbTS2fgz737kfza6WXi2fXsbB9x3EgnctQPRgOHO3DTK4hqo9DbQTk8766iykdqUQ3xNHw20gdjQW8tg2m03ERsN63AkcxCfiZgx57fDKYWx8/UZUelpRc6NzR3HfK+/D1V+8Gp3HOkOyo/aRDcDUPgNaBLnaWEoG8928T4E3+5Z2HdAOu1YnigJuBYdql+nntvdUv9c12gaVeo/arDbgs/tESRS1/eggU2KCv20nm23P/leAUeuptr3jOFj074vQeX8nchtz05INtNc0hNsG2za+mA4Y6/jyJx6PI51Oo6OjAxd+70LM3TsXe87Zg0PrDiFSiWDNP65BzxM9cFwH2163DWu+usb0D5+nYeLTtVXlR/ta66t1mq5v1Ta3Qbzep8TGryu/MeCeM2cOrrjiChO+qAl9gLYHjsYtO8GeDAydpkBRcTBbNBOiqWLn5NH9IJzACoS5Z1TD2e2JGwSBOfZndHTUfMZrCbyVofF9P5ToTAdFFTwFmwpJJ5qd1ZMAgGHJNvjic5jlmH3IPlMFrQnk7ImjY2Xv4aahb4e+U8B1L3qj3kDvLb3ovL0T2UoWPnyUu8rY/FebUetr7bH14z52/t5OHLz6IKrdVRy97CjWv2k9nIYT2gdDzyPQCssmS0/gZgs5yRSOjxIXumdDvXl8jioP9QbYbaThQhCl7C3Dp3Vx1SPSWJQA4G9+rgp0ugWV48t6cO8yQZN6bSkr3HvOvd+VSgWxWAzJZBLNZhMTExMAWsdc+Y6P+190P7Zfvh2jC0Zx/aevB57axsd+7j3Sixs+cQOiI1H4rh8KlaYccV4kEgkTmsx9vpzDrB/D9kkQcPyi0Sjmb5mP/KfyuOuP7oIDB9d/5nrMemIW6pF6qK26NzqZTJoQet/3MTExEVL6lF+dd+l0GplMxswZzoHx8XHM2jkL6/9sPXb9+S5c8G8XIFKLIIi1AGelUsHY2NiUzOisC5PvkQTgO32/lfleiTvKLvuTcqueAQ1RJ5lnG7iUN8oX9e2JEyemkIlKcumxb3wf+4n6jDJjR1LMlJkyU559JRKJIJ/Pm7nMEG4SlJlMK/MvSV5GwHA99SIeUvUUOp/shJ/wQ3bY4OAgDh8+POUkCyBs3NskM+uiHsVIPYKhLw8hiAU4/OLDSFVS+J3P/g46y53wIGHPAkioa5c+uRQb1m1AZU7Ly+36LtY8vAaxZszoxsnJSZOcjZFrQdDeEsbnlcoleBEPju9gcOcgXvUvr0JHrQNnbDgDP3nBTzA8Zxiv+NQrsOXsLXjwigenJFwrXFTAtmXb0Mw14VQddH+vG0EsQKTZbquCNyUj8tvzyD2Zw6lZrRB+eED+7jyix6MhG0e9s7bTQPtZgUDgB8jen23pbj825WizIAjQ/ZNuJCIJ7Hpvy6N48fsuRs/OHlNvjmN+Tx49T/ag2F8EXMDxHcx/Yj5yI7kp3kK1SdjfNlAl6atH19J200gMXds0OoHrqcqcktm0fRQA8lra9bTnzNhEvGm3/6ldpbjFlkn2KcdIx13tdpUJ7Tf+ryCYn9v/qw1vg3r7e17DsWH92VaCerfpouPRjlA+Az5P26xtUtvHrpPeq1su2D+2Fz8SaW0HTPpJLNm4BH2b+lBL1bDisyuQ3Z2F53rY8pYtOHLlEVRmV3DxJy6eYmfb9eZvdcbZXmydMzbQ1rFSebPfo+Njg+9fV5xniswdxwk6OztxxRVXYMGCBVPCf+x4ffUAAjBhPspy8Ho1POn5rNfrJqEYjUENP+ag0nAnIFEFwMHXPdNU0Bzwvr4+VKtVjI2NmcmpHh0F7sr66OCQUVUB5P80mjlIlUoFvt8+qoFgm22iV5xKSr1Thi2WkGACZZ0kXHTVK6ceMn6mC6SCXR1Pjg3Hj+POzJOxWKwVgnXWCB5/x+NodIaTsLSkFMjtyuHMvzoTmclMSGkoIUHvtb3fmXVUjyfrQllxnHZSMlXaCsQUjBHQuG47ZwDHSSMMuE9XJ5kCThoyHCvf941HW0ENx0XZVxvYsz0cC8oP6+K6bshLq7JtzVUjF5xPvu+j6lTx+PWPY8eLd7QUbQAMbh7ExV+4GMlSMuRFJikWiURCHnnKB9tH2SOI5P5g9qdu++CYMXy/PruOe/7yHlz98asxNn8M2WgWi7cuhtdsE1cMwde99Lq4c+xIsilhpnVVIor9Wa1WMTExgUajgYGBAYxPjiMZT4aSwZ06dQoTExOGNOBYa7g7x0JlzPdb54tnMhns3bs3FOWiJw9w3isJxwR6CsLVg87xVq85o184dpQf5gigd0sXXT5Tk6hRP5KQ8rzWmboPPfTQbyW0PAiC3ywryUz5f744jvO/lkL2/8LS1dWFtZevRc2vIV1KG72cSqWMjdNsNjE6Omr0IXNRlHvK2PGJHVh982oMjAyYHDLUB81m0yRLA2BOPNAcEtRZLGqAci1W72lnVyd2/ekuXPPANYiOtXR5Op1GX18fSqWSsY/UuG82m6h7dXzpTV/CRN8ErrnzGqzduBYRt62zjh07hhMnThjSt6enBydOnEC5XA5FiBUvLGLyDyZx43duRGKytReYbfDhw3EdeHUPcIB7n3cvNl2wCR0THRjrHZva+T6w9G+X4uBrD2LBny9A5GQEsbHwKTYsBqC4wI6P7sDk+kn0faUPsz49C0yUrYQqMPXM9OnCkNlHtNM0hJ7EC9AGval0CievP4nO8U7M2T4nBCBDgNIBHvqTh3Do7EM4/een48JbL4TrTN2/bQMbPotrJOWGEZqNRgOdnZ1mTWK+AQVErK8S5uVyGbVaDZlMxkTsKchSm5Y2HqMzHccxOUw8z0O1t4qfvO0nOO+T5yG9K23GiM+jw4NyqP2s6zTQjlhTz7j2o46XHQWhdhHtQSOLfjhihPXis2z5UjnT5/LZWm/ahnakqA2iib08zwttS+H3Oq9pq9Impl3GdrNEIq1TFDo6OtDd3W3ktl6vw426cAIHRRTxyI2PYOdzdxo7deGDC3HB1y5AqpYyOk5JBrZDAT/QTlSrdpnKurZBn6XPmU6/2WPrui4+85nP/Fpb5RkD7mQyGVx00UVYsWKFUeRaWWWhFBgRRFBwOID8zE6YoRmNCSzVaA6CwBjgPPJIWSp2iBqVCmQ4CArAWU8qNbuw43WPs7KR7HwFFfxcAbEay3wOiQk9AkEXKwX8CvKV4FAv69jYGFzXRS6XM21WIaE3lHXlMzkBgbYHXFlW9RyT8VYW0nEcHL3oKHa+ZSc6N3Si1lHD5AXtbNfxsTgW//NiDNw3YJSfgk8u5Ow7+2g3BUya+IvypFnalTXlxFKyhfLJfiehoJOQ2wSi0ag5pokEB/vIlk0FggqSqeTUy8s5o0cz8Xr+zfrwWrbHVuoa0qykFu9hnWuzarjvD+/D8NJhMy65Ezmc/8XzMW/3PBw4+wDmPzw/RErZioXt1eQw9mJH2eGedbLPnOuZTAaFhQX84rW/wOiCUXSMdeB5X38eep7sCYFNBciqRLUvbOBL2VVDg/LFueQ4DoJUgCf7nkT+wTx6enpQLBaRTqfNYu66LjKZDPbv349Tp05Neaf2Mce8o6MDnZ2dGBkZMYCV/aHjq4SQPpNeAB1D1p/ecSXwdOFRIo/z0l5A+TnrTR2iR3Wwz6PR9lGFJ0+exBNPPBFaeP6nygzgnim/afl/CXDf+IobsfElG1FL1nD6505Hrtz2QpKoU5Kc6/zk3EnsetcuTK6aRGI0gTUfXYO5u+aatS4ajWJsbAwnTrQyeKse1jXUXht0XaINpiC8v78fvb29Rs/GYjFjfDebTZw6dSqks4H2+nXKP4UDFx3AeY+cZ9pFgvvw4cMYHx83W5vo9VSbcPTSUey8eSeCWICVW1biituuQKYYPvtXw3objQbuv+Z+nLnxTPzg934ABw5GZo2glp4+4id/dx4d93eg9ye9cGpTj2YyOt3xcOy1xzDrs7OmBdcstmHPYoM5DUdWopl2LZ8fi8XMUaLMrWQD7kwmg1Qq1dpuhABP/M4TOP9H55t1ZDoAqOOuTo1oNIq9Z+zFnI1zAK+d54kyxnVN66K2qTqDJicn0Wg0QoBb7QKtHz+rVqsol8smD47v+xjpGcGvXvsrnFx+EvGJOM79p3PR90TflLbRplQswT7XyA32P+vC8dGiNqyOtY0htC/1fx1rmxzRa+31WT3rxCzETbQz1D5Qm1NDt7kN0k6cajuzqG9oVzCxbyKRQLFYNI6YTCaDfD6PfD4fiv6ko2O4dxh3vPQOnBw6ad7VeaQTl3zlEszaPyv0bo3847vV/qZzR+eMziedl2rPKq7TsVcZsctnP/vZX2uruL/uApa1a9di5cqVSKfT5rgaZQ20oQQ3yowQuKiw0rOjgquGuioMerzVaNROZwepV1G9a+xc/tZ9nEA4bl9DiXVgOLhckHQQdMFhoYdP2Re73qy77anWCRyJREzoLn94ZJEa3gwF41nlQPhYNhUke2LH43FkMhmTZMtWnkEQmMVaGUT2c7PZRNedXVjyoSVY9MFFWPDBBej8eWfrWTUXp//j6Zhz/xzTVr6XQEI9cGyT9qmSENMxW/xbi048HTPXdY0HwHXdEKvPPqGXgMqdSkf3x6pRQxmt1WohkoP10v3e2r/8TiMcdMFU8klZS207+45KtFAoYHh42NSPBEfieALr/209une0Emwlx5M473PnoW9LHzZetxE/f9XP8cSlT7Q9IW4JT1zzRIjo0PfoOBJ0KilFJjOfzxujKpFIoDiniF+99lcYXdDa1DfZNYm7XnIXTi45aYCyGkycf47jhJIiaki1kmCaTA9AiF1nn97zgnvwwGsewKG1h8zeP92X7futJDzUUUp0cNHSMHKN1NAICpI6HGPd/kCgy6IKnTLO5/B7Egs0RKhjWT/uc9Nr+Fz7WMRUKmWiLlhXJbkoezMh5TNlpjwLiwvse9c+HH7eYQxfOYytb9uKqlMNJRmlziCR6XkeKn0V7HznTkyuahHite4anviTJzC8etjoCo1U0vWIBqz9Q/1D3cY1icS15vVg1AzXL+pdhoQD7SNJFQBl6hms37A+ROoCCL1LbQW+z3EcDD93GHv+Yo85Pmvbqm2448V3oOpUjb3JtgKt9TmdTuOa+69B6kQKV9xyBa7+xtW4+ttXw/Gmt6snrpzAofcdwqG3H4LruiheUkR5WXmK/Rk0Asz67KwpXjdew7bpWq92Jce1PlDH+I3joT5in/AztZcJPNQGne7ZxvaAg7O/d7a5hveqDTlFJJ+yter1Oh4941H89Hd/il/d8Ctjw0ajURMdaa852maVJZuMsEkKew3lGkxbjP0wmhrFL1/7S5xc3gJy9XwdG9+0EcNrhk1fqBNOI3Sn6zN1Aur3ai8oCNe+sx0ZNnhW8Kdtm67wudPZhrY9wecoCTCdrT0d+LQBttphurWYdkmtVkOlUjHZ5XU7Lv+2MVUsFsPA6ACu/PqV6DnUA6B1esKFX7wQfXv6Qn35dISF4qHpSCy73Xb7td32HFTnmmI4m2R5uvKM93CvWLEC0WjUeJc1gQEFX1krggkVUP6vTIhOFvXe0vBmByvzqKEz7BwKvjn/TYRCQyzppWWdVGhUSNW4pQeRYbMMU2JdWUegnbHTBt98nzJOygBrYhK7jxRosI7qxeLEdl3XsIeO45iwVQAhIddM1QqcAYQUoCEoXAf7LtgH75SH2Rtnh0JjXNc1iUoAoOuXXQiCAPGTcQx9cAjNXBOLvrAI+S15NIN2Bm4Kr+O0QldK/SUceP4BrPyPlQDCx2gxrF4XE+17m2nluCkZwJwBuvgEQWAy1ytgokzZY8A+0fAazgkdY9afkSAKfsgSOo4TOt6CY6J7pdUbbh83xfsAGE8kAVwymQx5zqlkms0mOo914vLPXo47334nzv+H89F9vBtP3PgEtl6/FV7cw6YXb0K8GceKh1bgJ3/+E0zMmwDqwGl3nwbXcUMGEceP2w3YDySadPsHx9nzPExkJnByQZu9RADk9+eRO5wzIDsWi6FcLmN4eBizZs2awsaz/WpkcEFn/TQUnkx6rVHDnS+7EzvP2YnADfD4Wx5H16e60LGvI6RvqtUqhoeHjdGZSCQM8LQNUNalXq8b8K4Lj8qDjqfqI5t0VD1AstEmOnUPmrLwrJcelUa9YZNVunhoxnQSQY7jmK0pM2WmzJRnUfk68PjKx82/w+uHsfEDG3H+X50fWsN5qoTnechmszhZOInOjZ0orCqYkM3EngRi22JoBA1jj2i0jTpGqJ/V9qGeU3JPAYTruqYe3OoEtPRisVgMJXGk/jHhuoGH23/3djzntueYZ+p2Mp6WofVUw9pxHGQeyiA6EkWzo9lqsw8kbk/g+IHj6Mp3Gf2pZC3fk8lkEJ9o2b2dI514MV6MDWdtQKKSwM5z2idssIzcOILy6nLrjPMqsOy1y+CedOH5Xqi/qOfZJhvEKpkNtD2pvu8jSAbY92/74HV7SNQS6Ly3MzQGSvJrRJQNvhS40FZmdno7sTH7R+vLtYf2XBAEcFwHR88/igdf/CDqmTqeuPIJRGIRXHT7RWZdZ+4TGyzbf09nS9N25fcKZjn2JGAI9D3PQ2Q8goEnBjC8bNjIffZIFvm9efMs9p2up1oPrZ/2Jd+rQNp+pg2qdW6w7xUDaNF13wbs2g9qw6rTjXXU/C7TlemcSSqrvNcmQGg/U9YoM0zcSzmh45DEHW0OnQe+76P7cDcu/8Tl+PHbf4wrPnYFuk91w425IcDLMbdJpsAN4ARtfDEdKOfnCs4V/+k2Zv3cJqymG/P/qjxjwN3Z2YkgCDA5ORlS5nb2YP4muKPwK7im91D3ArHwOzVYlamkEKghr0kY1LtI5ctn0cAF2sdVUUnokRasBwdUgRyP0SHI4Du0w9UTy8FgqKetoIaGhuD7Pg4ePGjAmO+3j3fiO9WItlkw9kEQBIZNVuDNcaLyJXgjaaJ/ExQaIO97OHrOUTzy5kdaC/MHEuh4pANes73/HmhHIGi4bXI8iRVvXgHXd1FBxQg428h+rs2p4ZFPPwIv7iERJLDk1iVwGu0+JwHDPlEFpuQBw1/YBgIyfq7bBng+Mo88YQg+66TAx1a+HHP2G/cZs78Z9quASEE05YOAkM/l9ZQVKjqVW/atgjS2nZ5cvlPHVMmZ3FgON/zlDfCqHvafsR9PXvckvHhLaTXSDfzy1b/Er175K/hRv7WX6/ceQqQewZJfLAmBXs4NDemnrCqBpVsURueN4mfv+VlIt/Qc6sFl/34Z0ARGJ0bR09NjDDSOBY00lX01Jjiu7JP+/n7UajWMjIyY/q9UKth8zWbsWbvHJMOpddbwyzf9Ei/+8IvhjYf7m/Xn+/lORkNMp2j1HsoMx1T1COcy5VINPJUH6gkmfOO+N+pXzg9V/GoYU6bVyONiwTmkCYbsLRMaWj5TZspMefaUJX+7BEcvPIryvDIAIHYyhrP+7iwzhxnFVSgUQvZMR6wDy7+xHH7Sx9EXHUX/9n6c+eEzUZ4oo95dN4ShJqq05/90uTUUNADh0114gsrExAQ8zzPkIcOFJyYmkMlkQqAhEomgHCnjx8//Mbav3Y4ji47gtZ97LXKFXEjn6Vqof3PNaTab6JjswI0334g7PnwHSp0lLPr0IvT/uB/DzjDgA7lc65k88YH6mzYAdW6z2cSCbQsw6/FZcB0XftLH7tW7w6A7ApSXls2/T976JNa+YS2OXnYUmAR6bukBvLBnWe0A9p3aG2yL4zgIBgLs+uIu1OfVAQfY/Xe7sfjPFiPz8wzgh4G62pvNZvuYTn2HkhRBEKAar8KNuMg0MyFAy/XDBsm2k+lU9ync+4J7Uc8+Zc9GfWw5fwt6j/Zi6L4hUxcWtZ+5ZikA0v3UWmib2V5o3/dDCUK59gdBgLU/WItIJoInrnoCXTu6cN4Hz0MUUQROMEVupmujjpVeNx0A03Ej+Lfnk23PsD8Ud9g2rw38WW/dmjudE4okhGIIBdBsg+57tvuez1eySPudji7b0273kd0O/s0tq/V6HelCGjf+xY2IBlG4kXAGdD5T5ScIAhQ7i/jh236I5/3D89BR7Ajhjek89RwffYYCeNqfSjZM13fPtDxjwE3vEStnh1RrZ2h4gRqSyphQoGzDUQdKJw1BDL/jswEYpkRZOnamDZz0t51si4KlbSTQV/aQSkw9+93d3Wg0GigUCqE2MrRC7+Ng1ut17N27F+l0OpSYzfdb4VW8zlbE7CcVAJsto8eRE4ULkyoCDSfl+3Ufteu6OHj2QTzy9kfMgvLg+x/EmX99JnoeamW3HF4zjPzGvBljnfBBECASRND02ok8VHH5vo/SyhK2fGALvGSrf3e+bCeCRoCl/7kUQaPNlOlkpkxw4jGzsud5oXOF2V/sk3K5bLy/rF9XV1eIEFJPdxC0w5MBGMBjM7qUXVVKqix5Dz2nGu6ixzBRlikHDDvmAqUTnMozCFr78gmc2FY+h6F8lHsThu1FUG1UsWjrIuAbwMaXbEQ9W0dqNIW+HX04cfoJ1DpaHt0gEmDHuTsw/5H5SJQTZuwOnX4I/Y/1I/DbizbBmS7g7Mtms4kdz92BICLMrO9gcNsgIkEETtRBLpcDAHR3d2NsbMzINME226FRGdQ3HG/f97F///4QGcj+XHXHKow1xrD9pu0IogHyh/O46otXIV1OA/G23OZyOfT39+PIkSMhw07nmdkPLsQO9aSdaCiVSoXAL8PqOjo6QvOTCwENEiUv8/m8SfLGd9J4tQ1Nk5hOMsOqgcVCY5g5DPgsGmeU35kyU2bKs6sM5Acw9M4hPPbex1Bza1jw7gWoDleRybSAEvM66HGhJGKDZoAl/7oEqUQK591+XivrdrOt52zvq+oWrvV2hJntUVMwp2S067rmaEl6unXNpp6sRCu476r78MS6JwAAk52T+MbLvoGbbr0JvcO9oS1tdoJOFrahu7sbCSRw5fuvxI6zd2DWHbPQ9Jpo+k2MjY0ZzxvXYxbd/wm093hHG1FEI1Fc94XrcNfL78JEzwSOLDoy7Th5WQ+PfP2Rdp38AMn9SXT8sh1VZQM11p39q4b92LVjaPY02yA/Cpz8nZOYf/98OP7U/dTsd/UO6rgpYPYyHja/ZDOCWIBzbz0XsXrb46/roA2adP3qPNmJa756De5+yd2Y7J1EpBLBWbedhRUPr0DFaUWMMXpC22//ng6ATgeutZ9oo9GZoVF3rusCPnDWt89Co9HA4i8vRtAI0EDY2aakjz0evI7rK2VP66ntsMeXf6sNrP2p8qt9q3YG66j1VTxgv99+nv7WcbQBsNoL7EvmmeERb3ZbbBCqDji+i31nExD8YXQNbXIez2oTLizEeKfmncJdf3AXJmZN4Cd/8hNc+cUr0Xu8d8p80DbaUQcqk9ORIXax9c2vK88YcFOhKZAgsCM4U+8Mv+Nkp+Frg3R6U3kPBV1BNNAOK9fB5AKi4bZUiDpJ6FVUIEDwYQurLh7qOaSCUIWs4UfqQY9EIiiXywY0k7nVeulzisViCBQQLOtA6tEc/JvX64KoSlA96rZys4kNBaZKSBB0hUpfq+1HLjyCJ9/8JGbfNhvz/n1ee1zSHoavHEbvrb1T2CCOG/u8kqzAj4T3Xtc76mh6TTh+2Dun7dWztunVpVKlB5KAiEqKwJR10gRb9oRTYoJ/k8BQb6cmgCPoIfiiYaIKnMBLFTOvV+DIOjP5Fp+hyS6UvSQoUtJAiRTer/txSawsvm8xgmqAx175GM790rmY++hc7D1rLza8eQO8hIc5W+dg3efWwT/lo4IKEokE9ly4Bw+95CGc/sPTMWvHLHi+h/49/aE5bm8FAIB1/74OTsnB7ufuBgCsv2M9zvrhWQicIFTnEydOmDYyGmE6w0c9tUroqLGhslGtVrHwSwvRLDRx/JrjOP/z56P7SDf8SFt/cIyZwJHgU4lCLgY6xhxL3YaQzWZDOo1zTTOt2gSFehKo31QvaBt5TUdHB6rVqkkuQ/lmvfiZ6mPKAhc4khnaf2p8zpSZMlOePcVxHMTH4lj2oWUoooj04TSqQdXoBG4fo07RnA9Ai7Q+4z/OgJNprfn9/f1GZ8ZiMcTj8VDiS/UUKnHOulCH8TfQ0m/Hf/c4UnemEHfaThE6J4IgMCQlbRGus82giXKiHGqzF/VQi7UcGL+87Jc4/97zQ7YbdTXrxMzWI1eMwN3lwj3mYvH3F6MWbTtBKpUKKpWKWX/trY50uvCZGpXUbDRx7S3XYrJ3EjtX7MQTFz2BVY+uQrGjiE0XbJp23I698xgi4xEMfmwQnT/qNHaa9qfac7ZB3/sfvXDKDo689wjgAvk78pjzoTlwmmGQpQDt5AtPYu49cxF121sZdY1xHAeIAI+88hHsu3Rf6xmJAFd89Qq4Qdhzq3aH7fnk2jG4axCXfO0S3POqezD/C/Mx8IsBVAeqxo6iXNEms23Dp3MU8TP2kdaLRHG5XEa9XjfEE9c52sHVahUrv7oSTb+JwJ3qMFSMo44bGxwyAq9QKBjCWvvI7hstSiDYNrK2U+12HVu1qViUsLfJEM4N3YKnY6+glHXS69V+pSNDI1rt0HMdJz6f79PoZ5UZtksxH/WY2mbTjdOpuafws5f9DOOzxwEAp+adws9e8TNc+R9XovtEd6gedr8oCcWxUZyqpI7dVwrWn0l5xoBbPafK/Ggj1H2vk0A9eHqdGpd8BtkPFT59ngIIgh2CEF0IFKRoCAWv4yKiqe95jS00CoSVYOBzHMcxiwfbS6/RxMQEDh06hMHBwVDYMicA36NsLQ1rHumk33GAlYBgPThONJRVWNWYJxgCEPIW0iOqjPSiny5CtBHFY29+DABwxkfOwMAvBnDynJPY8eYdaHQ1cOjlhxBEAyz8/EI06g08+dEnUV5WRhRRzPrhrJAxr1sBXNdF6hcpLP3Tpdj+hZbHcdY3ZmHwK4OIulGz55syoN5tghcFK1wY7W0FVBy2wmRfs066P1lBeBAEBtST5bPBET2NiUQCpVIpBGbodadi4jwiWGOd+X62SSNCVEFR0WnoC+XEjhKxFwF+r+Ct2Wxi8BeD6Drahe6DrWyxmTsyWH1iNQ6+/iDO/tez0THegZrXSkKyf/1+PPaSx1DP1vHEC57A7ondCIIAV//D1UgeSpp2Uolpu9AETvvaaXAjLvLFPFbeuRKNZlt2p/OUa2SDyjANJOoXXdj4fjLcnGuFQgGVSgXLvr8MS7ctRf+pfiAaJloikQgmJiZw4sQJs2WA0R9qkGrYG+WMBh+9ShoSr9dx/ilRpwnP6G1RnTQ+Pm50WyKRwMDAAI4fP94ar0wGXV1dIZ2kXio+115kSEToWNEI5faA32RBmSkzZab895ehoSETHePudpFqpACnpQuLxSKi0Sj2/vFeLP7aYmRqGVSr1ZAHLhJpRZ499OaHcNlXL4MTtPdGc7sLyWE1iOlBZFI1/vA7XWPdiItjLzqGg284iMKFBZz912cjEomEDPVisWh0fC6Xw8jICCYnJxGNRjErPguX3345GpEGdqzagXQpjRd+/YXoOtmFO55/Bx4/+3GU82Ws/+p6Y++pUU79N3LuCHa+eieS40lc8s5LTMgqMPUMZQV8GoqtW7q4ntZqNZP5OzYRwzkPnIMFOxag42QH3GzLNs6P59F/oB/f+sNvhcLOvU4Ph99+GKkghew9Wex63y4MvXsoZOgDUxPBsnTd2gWn7mDiqgnM+cgcRMfb9WN72I7hFw3j6JuOYuyqMVzw1xeEbGv1mD74lgdx8LyD5h271+9GM9nEdf92XWjN0Pppf9DZRJtt3pPzcOXfX4lgS4BSuYRTp06hs7PTPKNcLiMajRoQp3aPAiob/Nrjq39rRCntCMobiR3NwWNvHVBbSWVJARnrMz4+PsVhp1jCDkPXsVE7XtunDkGu4bb3mvNLn6tAlO+3MZvOf60f7+U8ZNJlJjHk+3k0G5/LOUKyTOVC+0nnF+0QzWFl11MjhHVbLJ+pfc72ZMYz6DrRhZGhEbM/v+d4D3Kl3BRSYzrZssfelu3pohcUCz/T8oyPBfvLv/zLQFlSRfie55lzYMmosjLpdBrd3d2oVCoolUqhSUWmRI1RsrI2mNSwYt7PCdRsNg0gYt04mDQodfIA7dBsmxDgoLBDKVCagZ3XELRzorEtQRCY0EyeQZhIJEJeLQoVvUtA2+NKL5gytex39VrxOWTtGo2GOdObbeT4aHgoBU0nsxIKBOEG0Do+Dj/vMLxTHnru7EF5Thmb/nkT6t3tUFO37GLoc0MYvXgUk2snAQdw6g5O+/Bp6PhJBxq1RkhZcywp4I3lDQy/ZBjLPrEMXrW96DHUSxUWAEO2kCBQgKfjSHBkKzmydVTQvJ77xe2s4vTWVqtVA0QoX/psBTUKHKlkVJlSxrgvl7LFNmpItCoflumYN22rvSAxSQm9HLrNg/NASQs/8OGkHUTrUdNHpaUl3POue1DvnBpmHK1E8bx3Pg+58Zz5TMkEJeYiqQhikRhSsVSIeNK5wfaReNI5qlEEnN/8mwsJ+499Nzw8jJGREQNQecSIEii+395ffeTIEdM3fDbnHnUUF3e9n7pGk9tRRrk/n3qMfaTgOB6Po6urC4lEAvv27UMymTTJ29g+Pb6HdaBXqlgsGmOCOok6iDKrckqyjrpMj3xsNBrYsGEDxsfHp4z3/0QJZo4Fmym/YXH+HzgWbNWqVRgcHAQAEw1EQrgZaeLw6w7j+CuOIzYewzmvPQeJYsIYq47joBap4ck/eRInrj6BzkOduPqDVyNZTYYipNSw5rpie6ei0SiKxaJJhFWtVjE+Po66V8fByw9i3zv3tTKDB0Dvhl6c/fdnI+WljC7lu/r6+jB79mw0m00cP34cExMTWLhwISKRCMbKY7j9Nbfjpu/ehA6nA/ddeR9+fvbPW8mRfAdn3nMmFn9pMSZPTk4hKUdOH8HOf94JP+EDAZA+lMaaP1oDd6LtzWs0Gpg1axY6OjpCSZxInHJN0G1h8XjcJFzVezTpqZNw4MJF0AxwbMExfOcPvoN60jpaqe4CDcBP+XArLvq+2Ifu73YjeioK+NOHmrdvbnn8ner0e8HdmIvxa8dx6AOHEMSfGoPHe3H+x85HopYw+t8QKh0Bfvj3P0S1q5UAN1lI4uUffDmSxWTotQpUAJhtAWpbURaYNKtUKqHZbKKzsxOZTMZsqers7DR2yXRgzXFanlTa98lk0hBB6lGl3VAqlTAxMWEcVo1GA5VKBbFYDNmuLPyUj2AsCOVgskkXyrbtbVX7k9fZAFr/14hbFsUQCuz0t11soMe5rg4VBZB6vak3fBQjRQSjbRlVUoHXxeNx5PN5zJo1C5OTkzh+/Lg5UUDlT7Ga5o/S5M+UCeIjPX87m80ae1YxQaVSQblcDm0JUHtEwbf2Q7PZRDWo4q4/vAuHVx/Gok2LcNWXr0IsaOcsUKKJ/2sfPx3wnm4sFGNyTJ/JOdzP+FgwGrFUQjTWafRVKhUMD7fO9+UxS2Qds9ksurq6jLFIoKkhmgqQi8UiisUiqtVq6Jglvo/gh0zu4OAgEomEMRzZmSqI6jnjvgsOKNA+WkJZC2U4dc+oXW96pIA2yKURnEwmkc1mkU6nDTvM/Q98Hw1l/mayE+17XqfMkIaVKIhmH+nn7A8mJbCTjiigZzsIOqNOFHO/Nxez752NiBNB8mASp33wNCROtAx4t+Zi0dcXIVlPtpKFPCV2QTzA/hfuRy1bQ2lNKdQ/GiURiUSQP5THGZ8+A9Gg1T4mL2HdarUaisWi8ZZ2dnZiyZIlJvmeEhGaGEQjCaLRqFH8juOYUCBNEkGQQTaPsspnkFxSryf7mQpWwZVGKnDhUBkMgsAsSJQ9Kie+j0qNbSkUCoZdpUxQ5lXZMhJA52ilUjEZ5W25oYJh/8SiMbhV19QhEokgtjWGtf+yFsnR8CIMAM1kE5tu2hTa21cqlUw/qQJLOAlEgkhoPzL7VKNcOG81XJ/XEUgq+2iTJ57noVavYXv/doyNjaFWqyGRSCCTyZg6aTgT602yx04cRCVNmbAXIfV0q67j8RjsZ+1vkkXUOQAwPDyMEydOGIOP9WVkD/uF+zRpsJCBBhDyHOi2GhJv7E8uiKlUKsQc04DRBDczZabMlN9uUXK1VCqFPkt2JnH01Udx7NXHEEQC1Hvq2PTPm1BbVDM6o5qoYsfrduDEtScAFxifP46fv+3nKPWVQrpU1xcFFfp3s9lEKpVCb28vBgcHsWTJEqxbtw4rLlqBymsq5hguOMD4snEcOetIaPsT653JZJBIJJBKpZDL5ZBIJIwOdOoOXvKVlyBTzmB0cBTblm8zSS8DN8CmqzZhy41bEMu110Lf9+G7Po69/lgLbD9Vh2p/FceefywEoDQ/CAl4rtUEhrT3qAtVb7Iwaoy2r9Nw4Nd9eE0Pc/bPwfW3XI/F2xcjVou174n78DM+4AJ+xseJt5zA9ru2o3hlccoWyineNB+I1MOAieuZ67rwcz6GXz7cAttPtX9y0SSOnnc0BC5p6yWqCVzz3mvQdaAL3Ue68cK/fyGSxWRojeOYM/KLthJtBo34A4B0Oh06frJSqSCTyaC3t9ckH+Zz2Z8anckxYb8rsFV7hWQHHVzEGsyQncllsO+qffjZm36GUk8pBEaV3Fd72HZGav+zj9VOV8eePkffpePDZ9K20e136nBU25LPVvCpW3ZpG6mt4kZcHL/iOB794KNwl7XwmB6/qnPe91vHyu7evRtHjx419sV0Dg/eR3lQb7jqJBZG5umcYb9xLmqEiXrCaeNTF6kjhH0aD+K49tPXYsUDK3DN569BLIhNAeq81wbUqttU1vX7EIHxFKnE8aIe/nXlGQNuGmjqBSU48TwP3d3dmDt3rjEK2VGVSgX79u3D0aNHDQhJpVIhMGULKjtTw6TZObZAnTp1Crt27UKxWDQLBCeBMjgq6Grg0xilx4jgRgfK9uirMJB9UXBGoQFgFhH1erJNeqY29y1xAmnWayUBVNnoJCARAoQZMc0c7/t+6F3qidMs5mqIc5FRsOO6Lno392LVv6xC8lQSy7+yHMtvW45FP1uEVZ9f1WJtAXQ80oEl71uCEy86gb0f3IvSxaXQcQCu62LssjE0u9vgn+2gcZDL5TBrVuuwe4Iseh/37t1r6mOTJfzRsOz+/n4sWrTIKHI9lovjYfcL68v+0OzvVKo6kTVBmrJ9fC7HhNnkGbqXSCRCx2SQsFGvOJ+RzWYNEKM8KOlDo0EVi8oMZYMAjR5TXXx0/rGdkUgr+de8R+dhzb+vQWIigUU/XmR0xLI7l2Hdv60zxgqVEcmt/ZfuRy3WWmCLxSImJycNA0750sVaw7b1c12AdF5xLig54vs+tl2+Dff80T04cN4B00/8IcFEwKkGG0kLgm/u6dY+VSaX8qZROhx7LhQ2q8r2cN6TFNRQOBoTqpPYPgX5CqK5lSGTyYQiaFhnDQPjOylHGn6ui8pMmSkz5bdf8vk8Ojs7jd3B4vs+oskoKrMroeu9pIdyV9noRT/hozGrEbqmnqmjnm/pCVunqZ7R72kLESioruhudOP5334+5h6YCwCIVqM44/NnIP+jvCFgdfscdavjOCaap7e3FwAwMTGB0dFROI6D0yZPw6sfeDV6C72h+u/4nR3Y+sqtOPy8w2a9alQaWPGBFRjaONS6KACWfGkJ5t8yP2RH0dHBtuoaaP/Y9hHtXa7NfKYCRzpghh4fwk2fvwkX/vBCpAtpXPiTCzFwcGDqADvAgb89gMJNhVB4tdrBHG9d+2xQ5I67GHr3ELIbswCASC2CM790Jhb8bIG5Tm1MAOiY6MBF/34RLv3Spegc6Zzi7WM91LtXr9dRLpdD4IzX67rqOK3EtaOjoyZClX2q8qXtIxbQKLPpgBLrwMg9OjhImG9/znb88mW/xLHVx7Dh9zegnC+bNdO2O3Q8FfgqrtG+1ogP3m9H6CpAte0AFns7hI6vbeOoDUJZ1Hqzn4IgwN6r9mLDGzdgbNkYNr95M7Co5bDq6OgwY6P4xpZ9m2RQJ4XaZrYnXu1exUt6ygqfs2/xPozkR0J2vP483Tv0e/bJpbdcGmq/Anlbt4WmnSVX+r/KvoJvlsnJySnjOV35jfZws4FUwOxIesw0TFINVwoyO9NmhwjgGeatip2dyHfyc/UkESzR4FVjmx2liwQFbLr6UGFQEDjBGDLFSaf9wUFVDxUVOd9LbxmvsYUymUyGhIoMiipbmwFTb5Wy0d3d3eju7sbx48cxOjo6ZXEBpoZCq9df2R62kyBRgefsR2ej4+87kN2ebSWgCAIM3TuEZCOJJ1/wJM78zJnYd9M+HHvJMQSxAHvesQfxv4sj82grmcXY+WPY//b9SB1N4fK/vRyl8VJovFmnZDKJzs5OTExMGAVPRRaLxTBnzhzkcjns3LlzipGgbRgZGTHg2fd9zJ07F319fdizZ4+JpiCYY+GzdB5wDDUqQMeJckQ5AGCMDBtwacK/6ZjBarUa8t6r99UGm5QRtoGA2d4PxO+4nwloh+gr4Ke8sM26HWHOQ3MQH40jfygPp+7AD3yc9u3T4HjtaBJGD0QiEey+YDc2v3IzDpxzABd95CKjD/ibfad9SX1CJp3tjsVioWPzNKqEx7xls1mkUilsvnwzHr7hYTSSDex68y6k42n0be4zusv2LCujrEBZFzaOna10KR9sh4JY13VDCfNsXUJ9yegTygHHWxcMhqSrXkskEujv70ehUEChUIDjOBgfHzcLUaVSMcSf47S3vjiOY2SJ81/3knPOz5SZMlOeHaWjowP5fN4Qc1pO7j2Jef84D07EwannnIJbcXHa+05DfmfenAMdHYli6T8tRTPaxMT6CSSHkzj742djeO0w4tU48sfzoWdybdIQWdW7thFKndRzsgfX/+f1uPVlt+KS71+Cvt19OJg7aJwjJDPphAmCVrRXuVw2OSlisZjZCtTV1YV4PI4Vx1bgD+/+Q/zDdf+AarwdrXXgpgOIFCOoe3X0fqsXyWQSc9JzMPTVIdSdOjrv78TcH85FM2gfkakGOICQV1LXdDsCi8S6elq5bqhjRCPnCO7W3L8GfQf6MHh4EEs3L8Vtr7kN433joT73Uz4O/MkBDAaDKM8ro+8rfXAmwom9+E4bKKjtkTiYwMIPLsTeD+3Fyu+sxOCjg2avP4sNQnoO97S2WcXD4xmLxZDP5020Fm0DOhoYvVmZVcH+Nfux6s5VRmZSqZTJbVMsFpHL5UyERq1WCzmcQqSBgFetI9c1JXto7+jWqXg8jn0v2YftL9puIi9PnH0CG965ARf85QWIeOF+UMxhyzTXZV3z+bfazPqdYgMlC9TW0/fa9gTnmpJeKpcqx3ovZWDfdfuw/RWt/EgAMLxqGA/92UO45iPXIF1KmzHTtrF/1YNs94viPyBMELB9tAPVQ81tftpHR4eO4o4X3IFEMYHn/ePzgFrbjtI2cX7RLrSx138Fpm3iSPtIiz2O9nhqfZT0KBaLU541XXnGe7jf8Y53BDSeyYAA7dDjzs5ONBoNTExMhMCoetc4iDQWFRAEQWBCJWns0+ijVxNoH9Nge/foydRrVPFp4iYqee10BVp8HxcZVSo0nLmfl0a4CqyCIrZTDWYOqipjVRK2V5p7yLkgUYi1HQxxZeZEhofS6NdkJ0oYKJvLOrEPtM/4XraP3j/ug+ZzGo0GYokYmqkmjlx5BDt+f4c58gsA4qfiWPdH61Dtr2LzRzbDT7f2VvXs78FlH7gMk0cnQ+QF3xOLxQzzrfJE72wymTRtpSzYk4fAhvel02nkcjnDjFIOqFybzaaRSa2PKntlJfVvm8wBEArlV1nk8+yQdAVpuodXx4ALOd+hzCz7igBQveb2XHRdN5TdmkSakmFKnrGfkskkak6rXW7dNR4Lgvpao4bj64/j8Xc9Di/hAQHQ/2g/1n90PSK1Nmmhc4b7tFgnjqHmh9A2sE2NRgOjo6OYnJxEPp/H5NWTeORNj6CRbntz4hNxXP6RyzH72Gzkcjmk02mzR0kNSN/3MT4+jsnJSXhe69x26gj2u+21VrDN+Vur1UxUBOep67rGYGOonYbNqdeduoVywjHjtYx04N+e5xn512gLJjZSfa/kmk2oMupnz5492L59+28trDyY2cM9U37D4vz/fA/3nDlzcNpppwGAIdWom4zNlfKx70P7sOQzS5A61I7cAdpOgmpnFVv/bisWvW0RGlc2sOttuxBpRHDTX96EXDFnAJXt2VNymL/Vo0d9wnWzlqohWooi8Ft2xL59+zA2NoZ0Oo1sNovBwUEMDg4akvDQoUOYO3cuuru7DSAYHR1Fb28vZs+eDaCla49njuPmF9yMWjx8kopTc7Dqk6uwYuMK9Az1oHKignFvHJXRChzPMcQi+yKbzSKXyxm9z3WdIbDNZtOABaDtPIlGWwmmlFRnP3AbnOp4Pts29OvZOr78ji+jnqijkXhqrQqAvo19yOzO4MCLDiA2HMPKl66EV/HgN334CR+RUgQIwh7FUD88ZVcmEgnEemPojHQi6oY92hw7tUPtqDoFWfybfaSfA8BYbAxf+8uvoZlo4qJvX4QzHzkTMTeGcrmM4eFhjI+PIwgCzJo1C+l0GhMTEwiCAP39/SGHBH/7vm9C+yORiLFvuU6RxKjVaiiXy3Bd1+wRLxaLiGajePxdj+PoeUdDfXPxX1+Mrse74HttG009ogq4WR+OoZICum5OV/R5ah+qI0fBN59pEwy2M0bBo00esbiui2hnFPf/xf04sfxEi2ipR3D+P52POZvmGFmgM8X3fXgxD4+9/jHMu20ektuSIWJe7VzbxlQvs4JbAu1UKoWOjg50dXWZLbNBEGCkbwRffuOXUUu25nDuZA7Xv/d6RCvhUHKVPe1Hzil1XOn2RZ3n2i/2PFE7U/ud3+vfrAvlplQqYdOmTXjkkUf+z+3h5qAwDJbGHwWdk8E2NqvVqjGq1atNsEhFTo8hQQE7Tr26Otk1kRrBJA12NU4ZVkJDmF5ABft8BhUmB4eAlV4eCgm9a0y2pF597r9lpj8NY+B+boZQMXxd28lr7XBRJqFQZaDANAgCjI2NmX2qBB56nBj7X/dcUHi4MOheXyoF9ezppPb91p4cCjvDwb2GB7fgYtEPF2Hhjxaa4yqSI0ms/ehadFQ6sOcte1pgGwAcYGzOGDZdtmmKwvO81j5S7nEmYOU1ZLu410oBGSc9Q7YJdNiGSqWCkZERc6+GYut4UwYoL6pQdI85s59S5nif7j+396+oIrMBJOtL0oHzhuNFmeEiqWeQsy6UQc4Xe0sGk/bRgADa20cow57X3reu72efJpFEzIsZ+aKsBkEAv8PH7lftboHtp8Z6dPkoDl520LyLSXeAFlFRLpcxOTlpwK4SIMoqBkErU+jo6OiUUPRqtYr49+MY+soQ3Eqrf+MTcaz70jr0HeozxJ4SZuwf3WPId3Nu6PuVLGB/VqtVFAoFo/ecDgeFgYJ5l03eMFdFEASGoKM+0bpw/DSKSBlkJTB1vvIZnE8E0jw6j+9jfdVg1H1ZM2WmzJTffqF9cfLkSQwPD6OrqwtAO2GX8TT1+Fj2t8sQ2xcz39Mo9jwPte4adr57JwZuHUDx/CKefOeT8OIe6pk6br/5dozNGQt5b3O5nHm2hm9qRB+Lkpa+7yNabIFtoJUIdPHixejr6zPro2aoVvJVwU0qlUJnZ6d5r+d56J3oxdt/8HYsPboUmWrGvD9IBDj6qqMYvnQYX//zr+P48uOox+uIu+3EpLRtaKtpaDWfz9wq1Kdqk6gu5zqgZD0Q3hJFu0XXEer0WCGG1/3163DT525CdiwLBMC87fOwcP9C7P+9/QhiAepz6tj5hZ0Y/othjLxhBLtv3Y3qqqppy3RgW0FHopqAE7RtIQ0jJqEysXQCjtveMsm2qteSdj3/1/eMzhrFt979LdSyNXgxD/e99D5sXbcVDa9h7DA6gkhykyS27VSOD2VI7WheF5Lnp5KgKY4AgNHzRzG6cjTUN5kjGaRGUnDQJkq0neq1NjJlAV72n+0JtrcYEBMAU/fgqy1lbxez36nh10poaQSC3s85Gq/H8YJPvACDewaRLCVxwZcuwMDDAygVSigWiybCwHEcBNkAW1+9FQevOIhf/dOvUFhZmLLFVvuGIeKUE7UV1NFEXJZOp0Mnn3i+hx/f8GMDtgGg2FPEYzc9ZvrPdiQpmFZHBTGnvZdcCUElVdiWeDyOXC6H/v5+dHd3hyJH1XnFcVTChO8vFAoYGxvDMynPGHCz0ymcCgh938eRI0dw8uRJwx4SSKrwEdTZ5xpOJ3TaoVRU/f39GBoaCoWP85kKtNkhBIIqmGQln441IQhlB3NiUxGrEp2OZVLgyu8YBcBBpgePnkCG09jAQicoCQNll3SvZSqVwvz589HT0xNKUmELZhAE5r1AO7QcgDGyNYTXcdpZ5Hm91lF/NDSYv1f/x2os++4yJEeSWP3p1ejb1gfUgVXvXYWuDS1jAT6w6KuLsOQbS0JtBGAIGfXU6vfariBo7UlmRnzKFwEn2876KUjT/lZAynfR28r3sa2250CZr0gkYsAslaQqJLZFJzMZvUajgcnJSZNAUA0N/s15QIXDevGZmt2bbdf66vs0KoALGOeCzk1VXARq3FLCMdKs8qlKCus+vA5d21pj7dZdLPvCMsz+3uxQtkzNZ8CoFiaHIyuuRiX1TFdXFzo6OgzRwbEkUTL7a7Ox8PMLEZuIYfm/Lkf/vf0G/Pq+j4mJCXieZ+rM/qGBoJ4d9inBuiZU07Br7mVzky52/P4ObH7bZpQXlE0fBUHrDM9UKhXKCUBdofLDulBvUm+pTlEDV+uh5JXqKp0v8XgcfX19pm4E7Tz2hyTfTJkpM+W3X6LRqPHGOo6DkZGRKd6Y+rw69r5nL/a/eT+QDnvnAKDZ28Tut+3G+Hnj2PvXe7HvA/tCR1ZVO6rY+IKNId3CPYq2TgmCwGxHUr1FfaXrC/VPPB7HwoUL0d/fb3QgSb7JyUmjd6mzqGfVbuTzBocH8fYfvB0veuBFSNRbUYLzTszD6i2r8dArH8Jk/yR+8u6fYPPrN6Oab+fEUINaI5XYT7ZhrSBOHUBq3CtZCoTP0OZ7aTdrYmHq7YEdA7jiW1fgrLvOwtVfvhqji0ZD41JeXMbxFx7HiTeeQGNOA/s/vh+li0soXlKE1x3e9sPx4Hqi5DGJXdbRcRwcWXsE977tXuy6bFdo3DUnEv8nsa996fs+Ts4+iUZMcgM4wMHBgyhXyyFc4LquyUTNvDUaZci+tYG3YgZ1Btn7u2m7RCIRLHpsEdbfsh7xUsth0bG/A+s+vg7pY+mQbQNMBXBKNKgdzmtte0gJCl2LFXyqvaxjZWMgXmOTAZRXO/KAcqnvNIA1AG768k249PZLsfzh5Wg2myYbuIlciwNPvOwJ7H7ubsABgmiAzR/YjLHzx0JYSOvMOvFz/cxuowJgQxY4Ll70Hy/C8i3LzXWn//h0rPvaOtMf1CGKCdVW17+1L1gvmwjkM9TBUiq1yAfOb8qdPldlT0sQBCiVSs/4FJdnvIfbZn80fFvBmHpNWKEQm/dUB7LRCtoJJpQRUuXOI5n0GB92jnrutGNYXypGG0gTVCmTps+hx1A/I/iisHJR4fN5hq6GOBPk2uyZAjsKGd+jikYVNuudTqcxODgYGnDHcULgkl5+9quyivTga1g5wUcqlQqFWeh4sg908ivjo2Egruti5XdWovvRbuQ35dEIWgypc9TBwo8sRPMvmpi9YTYW/GBBaMwJBuk5pxLVbMvKOus9Whcde/7PMdFwapIt7Ft6fIMgCJEsJCIAGJCqfcL7NeSXY80+4ZEH2l9UoKwv5YnXMNsm68DQKr5LF3q2iQuRypFtWKgi0SgUNdBUrijvnG9qSOlCqHojtjeG0z9+Ojb/xWYMfmMQ8+6bB8cNewpoZLHPNd9Bo9Ew+5Jd10U6nTZGGrdiFAqFUDSGGoZD3x1Cx8EOdD/ajSKKpo8JejkWSqTxWdMZmPSMsO85LuVy2RAHjuNgy1u34NAVhwAH2Py2zVj/d+uRHE2a9/T39+PEiRMhAiuZTJq5yPcyfJ46UJlsZW35v+oi1R9KwNEAqtVq6O3tRVdXF0ZGRhCJRMw2EdXzM2WmzJRnR+Gc1rUPeIpczvvY/zf7UTqzhAIK8LM+Vt28yugHJ+Fgx3t3YOysp/fIJItJnHXnWaE1iWuNGrRc/1gfBbHZbNZ4Q+01hmvL/PnzTdJQAIYwz+fz5ghVkv/ZbDaU9JHXsz/O23ke0l4a/3nuf2Ld/nW475z7UMm0k8cdPe8oaskaznnfOYj60dBaT92v+l3XENaDNhVtAF0/WQ99hp1nw75ObQ+u80NbhjBn0xy4rotLvn0JXLjYedbOacep2d/Ekb86AjhAfH8cC9+8EHgKP6l8KOnBbVdcW13XxbEzj2HDqzag0lnBQ7/zEJykgzPuO8O8Rz2rXPNosxCsBkGAFY+tQKKewH+++j8BFzjj/jOw/nvr4dU846BIp9NmPSe5kkgkQsd0aT+prKltrMCXdja3YpF4p52w4tEVSFQSeOClD+CsT56F/J68ITKmA/VmrsjfLDreGvnAuihO0r7he5RI0PYpIa7/04mo67heo3jHrjv7LRqNIl6JY+XDKzHWHDM2bSwWM7luEAXyE+HcDVE/ip5mDzKZjLFJ9J2KHyhnNk5knXUrsEbWJqoJXHXbVfADHx37O7Dq+6vgxNrtVCeWDexZl1q+hu3rt2PNnWtCBNN042LXjfOO9qbiMZVDHRuVUcoyIzR/XXnGgFs92lQ807npOSkImmlM6nX0bCnw0YGk4rW/Gx8fR7FYDO2n4fVAOxEEwY6ej6ieTHa0eikZiqKeSW2PMliaAToSaR1hFY/HzaBRMPWILXrGFLCpJ1u9fWooE0Rq2FKtVkMul0MkEsGxY8eMolJQZDPBuqjQi63P1ElNDyI93mQ1NTke99OqN1jHkOdKO46DGGIY3DuISqxiwP/kskmMXDGCZe9fhnQ1jSbaR0npIsWJRTngmCkDZoMi1lEXZ/Xus54aIqJyoqSQhrzRwCATGwSBCc2drm6UcT3Si/eRgQVg5IbywjoxkR4BLo98Yr2UfFCFzrHmu1RBcR5w7xnlhDJBwKanCNjhc1z8KB/cR6xKiXOX87jzWCcuuvkiYASoNWuhcaMxxRBDhj1rVAhDhcrlMkqlEnp6esw5m/Sycyxo6BjvQdND7qEcgmgQkv9CoWDOhqSnmXrNnsO65UPnlIZ5k1BJJBLY+qatOHzpYbOwjy8dxy//5pe4+m1Xw/Edk2k/GouiGWkiaAQhnUQZ1qgijqFGaDSbTWPc6n5wXktCQo0VJdyCIMDJkycBtDKXaptmwPZMmSnPrsLM3SwhQ90Bdv3rLlRWtIHmyKUj2P7u7Vj54ZWt6+sB5nxzTghwxw/F0f/1fhx5+xG4nos1b1qDlJPCIy9+BPMPz8ecHXNCoEMdJgr4gbb3iOsW1yp6M3V/sJL1nueZ7Wm25xdon/VsE8lqh6zavQrzj85H0kviUM8hPJp9NOQhPnXmKTx888NY/aerTX4LZrcmgat2nhr4bBt/0yaz11odE/sZukZzTVVbgTrXcZ7KnF6N49JbLkU5XsZ5952H777hu/BiYZ1cn9OyReqz6tj7+b1Y+ualOPaaY8j9PIfUjhSSTnJKOLDatKfmnMKDf/ggqp0t266RbOCh6x9CupTG0o1LQ+2mLcEIro6ODlNXruULty3E8//l+XjynCdx4e0XIuEnUIq2j2LNZrOIRCKho9dSqZSJBGQEIhA+ion/E4Dq9q9yuRzKd0LHn2bDHtwyiKvedxXcEy78YKqNab/P9mSqjPP/6QAv+8LeGmgTFiwK+mizqBxyvGwyR+8HwlEXvIf2g+aP0ShIRlnwyNGzHzgbQTTAYzc+Brfm4qJ3XYTcRA5u3A3ZQtO1RfuQskDswpOaOP8VzAJAYiyBi790MbySB9Rbx5hxXihuZAnhRLeG777juyjny4h7cZz5wJmhven2OOn9GiVgt0uLvs+WjWq1irGxsdCY/lflGQNuNQIVKKsxqF6RIAhM6LbNUhB000PG+zUER8NfOZH4bGVKWCd2Rjqdhu+39vTq9xpKazNTrIeCJhuw6OAomOL3muGb7ySjqCwKr9P9svyObeN7bSXJfvc8D+Pj4+a4LJ51yAQUeh2fqQuT7Z2cbgIrUOWiqVECJCbYPj3Ki2EamUwmFA1A7/nkskk88c9PAC4Qa8Yw/5b5cH3XTGhl8OmlJwOq7JyCWWVHlSDh+FJWK5VKCLynUilUKhXj0WY/USEpI1cul0NgVPtMvb4qJxxXjosSAZqpmopXDRj2GUEtmUb2MceXQFE9vJVKxRgpNuvI0D09mkGzg+u4U5ZU6fMztpMLHPuNpI/rusjn82aPNU629/OQONJwIbaBe8+pY7RfOW+4x5tEFsF2R0cHRkZGQkSfEh58bqFQMN6Kcrls9vGl02mk02lD1hHoqxJmPyjwJiHFPln5mZUozy5j+KxhwAHSx9M456/PQa1cM2Neb9RxbN0x7Hv+Pqz+6Gokx5NmrGhUaNi4gmBlyWkwUk9QJoKg7RFPp9PmmTaz3mw2USqVkM1mzVzLZDJTjI6ZMlNmym+vOI6Dzs5OAO01To3BwA+w6I2LsPsru1EbaoVHdmzpwPK/W46G1/LQulEXXodFpEWAgY0DyH8tj5X7VsL3fGy4ZAN237AbD+NhvPjTL8bQvqGQx4iAkzaFrmHxeNycrsE13I7c07Wo2WyaCKZZs2YZjzeAUFSV2gXqJAHa4c75Sh6u6+L1d70en3U/i00LNxnQ3XuiF7/31d/D8Z7jOHnyJCbnTCI4PjWrtIaua53ZZjts1TbS7bqyj/hMXW+5llNfs120pRLVBG74zA2Ix+L43X/8XXz/Dd9HxIugmC+iY6QDY7OfIk4coLSmhE33t9p74nUnEB2LYvWbVqOr2BUC9wStjuOg91gv1n5pLTb+wUbUc3VEGhGcec+ZWProUjOeStozAoptY/9HIhGTB2TWE7MwZ9scQ/Zq5B8AZLNZs05PTEwYW58yQ9BNmeL6r2BNj/NiH5OQB2Ai5nzfx0h+BPHhOOKn4qj7dSPDNgizw7ltR5QN0JSAIUZgezViUh1/atcrWNcoB7Vj2e/6XH0n7V59Hm0cAl0bxHNcmGzYyEYzguW3LsdEfQId3+pA4XgB9UTdRCHY4fOcv7RjNds528X+VEJCCQjzeTGGZqVp5oKds8h+juM4KHQX8L03fg+TvZOAA9z/u/cj6kWx+uHViGBqFn7W6emKfjfdPfputqFWq2FsbOwZOyeeMeAGwiHO9ETREGcF9EgZNQ5VmHSiKkAFwmBLG0FFSOZKEwTRQ8hn9PX1maRhdlgPJwKBtXou+T6+g+/VyamM7HRhVuoJ0/bxOjvknoqW71UWS8PWCXri8TgGBgZCdSHbWC6XTQjXdJ5V9dDZ9bOFmtdwYaCC43iXSiUDUNg2VQjqSebYN5tNjJ0zhk3v3ATOh4O/fxDRaBTLv7kcbuCGvMAqLxpWS8Wrya3YPg3Rstul2aI9r5WMjROcMkkwreNNuVeCQmXeHiMlY3i/yj2vUwCkyVRUXhR883N+x2c5TvvMSTKKjuOYfexKYvF5vM/eDqHGgbaZsqp19jzPEFysC+/h8/r6+kxiMPaJPlsjONifun+PR7/xGhIwSmBR3/B9PN9b+5jJMDjv+/r6kEwmTb1IHHHvckdHh5mvJCd0YaesaD+SqXccB/FYHOv/bj0ee/tjKM4pYs0n1yB3Kgffbc/HE5ecwKN/9iiCaIDtb9mO0z51GlITKUM8UR9Sjtkn8Xgc+XwejuOY8EDqQ5VZXbg10Q2JL8/zjJHEsVWGXhPkzJSZMlOeXUWBniEtJ+NY/PbF2P/+/UiMJrD85uXwmu110Yk7GD5/OPQcL+vBu9DDeT8/D4lEAvdecy92//5u8/33Xv89XPe167DsyWUhHagkq9alUCiY5Fhqx1G3KzBhvRmtxcRaQHvrSywWM+uMEg3q/ABgCFy+9/V3vh7fuuBbONZ9DHCBF/znCxBNRuHMdnBq2SnsePMODPzHAPpv7Ue9XjekqzpbNIqOYeWmL4UAZvu55rCNtOPUIaBbKXUs9beCmmSitX2p53APrv761UhWkth/+n4seWAJ7n7V3Ti67KkM3A5CHv1mdxO7PrwLHZ/qQPZw1qzJPJmF0WeLHlkEJ+3gkZc9glX3rsI5Pz4HgRPAD9o2O4lvrpfqxKI9wDWDEQpqy+pWsUQiYexVHscaj8dDuVU4ptz2ZUcHqsOB6zQTOjMrdjKZxPjQOO571X2Y8/AcLLpl0RTAzDqqvcn3aNv1M8UTQBu7cLyVWOF99rzR8aeNzGuUEKfNq2BQAbYtN2yXXkuAmEgk0NnZaTLv61YD2j6NegODnx1EoVBAxauY3DjcKqd5dpT4ojzo6UaIACcvOYklm5YY25eRJYqL1Mbhc7SPFKPomB2Zf6S1bYRd4wAHVx3EysdWItJsO4RsjKNzjv1mRzAo8WKDb8WnhUIBpVIpdM1/VZ4x4FYPmHrZqGTsTtT9v7Z3uFqt4uTJk8jlcshms+YZFDpbgPlMDa3mOzVsieHePBONg6cCrQwpFQCfTUCig63AWMOIFHjzebb3SME+601BpwJ7OjYNaCe10pAgThKCwmaziWKxiF27doWUhHpBFeRTORE0RKNRc6QWM2prHXRxA9qgkmG+9O5RMaRSbcDA/mCG7ng8DrfuwvGtxAOlAM1GE4EXXrSCIAgxXSprynZxLJUJVZbRJkV0jDgufIftDQba5IGG7SgpoUYF2Vmb5FBZIHhTjyjliO8lANK+UFaZhIrpQ6kH+5rvpwFBZcU9xhq5oB5stlEjR4D2fjoaDBx3lVtdvCYmJgzo137TKAvOX7KuGjWjJJuGIOr9Gl1TLpexdevWdtbNIAjpDdVFnueZqIxisQjP85DJZEz7yFqS0eeYEtjSa8N5pnOT9YkiinWfW4dSfwmd+zqBaDuxyYErDuDJ1z1pzsc8fsFxeEkPa25eg2jQJis1OoPjyG0Z2WzWLHKsi8oo56su+GogU7fYEQtKbj3TUKmZMlNmyn9vYTg556naIfw8CAIkdiew6IOLkJhMIFqJwnHbpHOkGcHijy2GW3Ex/LxhwAMW/M0CzN0+F8kFrSiZ5sTUOR9rtrf32IaoGpv0MlarVXR0dBggTd3IMl2YLEETo7NIYHL7nK5jCr7VRlHAE3gBXvrLl+J45ji8wMNsdzYq3RUcHzqOzTdsRnV2FQf/9CC8lIe5X58bImZ1vVb7gP1MO8vWrfo/r6W9pt5IBVssCgaoy7VdpVIJvZt7W4kuD/UhCAJcfsvluOdl92DulrlAHXjk9x4JjVtxYRGb37oZ537qXOQP5U10pMpQNBrFkgeWIF1NY+G2hQjc6c8rph1Ecp/rMwBD3tZqNUxOTiIej5v1ifYFyfJKpWL2D586dQrFYhFdXV1TbGDf99vRcU8Vrp+akZq2H9fhTCbTiqQcmMTPX/VzjCwYwcjQCMpOGcu+uCyEVezf2l7FMyw2wTXdmsu/7ftpM/Izffd03nWdX2qHTTf3pnseZVjliPva1ak0HW6jnUX7uNFomHHj0a2aeJr2IBP+ep6Hra/fiqNXHkXiGwmseWyNsTts/KS4idhCsYzOF3XeLnpoESKlCH76xp/Cj/pY8sgSXHzrxXCrLnzXD9VNsZ+2l98RC+q2UHVosn7a3/V6HadOnfqNnBLPGHATILJCNMpc1w15ztSgZWV1bygZQ+7lUECsSkaZSg1XohFIo1+ZFhYCPFXM7CTbA6nHmPG9AMy5xyoAQDvU02axCD4VMBqQ6bbPEybzy8FWbzvQPqdXQ4AATLuvie8B2uCW1zO0l4b50qVLcejQIRw/fjy0UBH00cOqZ0VzYtvPpadYQ+R1/HRhZb+zdG/pxto/W4sNn92AIBJg0RcWYfa3ZqNULZm98DbAY1HPsJI/XKx1kTQGRqSd7ZxhMcVi0Ux6gk+NJqDi0FAoBd1su7aLClO9vxxjeuN1ItsEAhcjXWA7OzuNd1iVAZWhto/zih5LACEPLOcNAaEaD5o/gAuwkgZqFPFdvu9jZGQE+Xx+iodD5xcXW6B1tMzSpUvx2GOPmfnLdxA0qpdB+5L1oAFDtp3tU7kA2uHpGvaoIeaO4+DUqVOh9vGsbXtuUQ5U36jBp+SdMuKu6yJWiCFZTMJDe7xisRgGtw7iwOgBNLKNFkPrA3PvnotEkDBGYzNo4pGbH8G5HzwXTtCOivF93xg3QdDep62yZSe4o5zVajVjVFar1VB0AMeNsqDjM1Nmykz57ZZ8vpXUSI1yNV4dx4Ef+CidXUJzeRPpW9Nm/yXvazQa8BZ4GD9vHACw+J2L0ferPqTntbfizbp9Fnzfx54/2QMEwHM++hwsKCzAxvM2onukGwv3LAxFRNmkseM4KBQKAFq6amxszHgdeQYvtxuqfUMSn2srDVk99cLWR7Y3T+0v1qm/0N+6zgVqvTXcetOtGO9otT9IBDj6uqOI1WLo+06fOfOZyaRsRwWJbq4p1KHUu+rw4LrENgJTHRist67p9hpv2hlB6PMgCNA53InnfPk5iE3G4DU9RCNRPPjCB0PnD40vGMcv3vULXP2eq5EstLzlgRvAdVw4aIOlZTuWIZoIH8VLO5TjzfGjvcQ92Ll8Dg+/7GEs/uFixA7GDLBn2+gdTaVSmJycRLPZSlpKckajCymrtJsmJyeRyWRM5B7Xct26RvubR14iA/zkT36Cyf7JpzoZ2Pv8vXBqDpbdsiwEtOh51QhIBWMcN7WbeI3KnYI5fq5gjbYHbQclEqYD67QVNCG02lk6NgrKeR8jA21PukYVshAD8fQV2+POZ+u71GGkdoQbdbH1DVtx8DkHEUQDbHz5RvTEe3D69tNDJJbaZYpfNHpCyQ8lIViXoS1DuO6T12HzNZtx8TcuRrLctrV0nikpok5DjgkjaCYmJkwUB69TokDxZLPZxPj4eCi7/q8rjoKG/6q8//3vD2q1Gvbv349IJILZs2cbEKLGrXqIOXH5o2wfG0oB0WRinNz8mwJte2cJ/hhKwg7lZFV2hAqCAqjhyBQ21o/7IQlYFfTbDJQyfSp8BKM0hvWsXQX99CwTTNHDzAWIz+Tz9WgqVc5koAhYyRTqkU/q0dT7qMR0nzYXPI0m4DsjkUgobEiJAH7H6zmBOf6sy6m5p1B4bgHLvroMXrOdiEzZLN3Tzd/03FJGOA4ajg2Es+OrAmNbmQxO38t90uPj4yZUmZONcqGRA0ou2cqWdVSlxrlCGbSPIWAdOT+Ghobg+z6OHz9u5EknP/9niJAaBlpHfsakd0re8H9li+n95buUjOAYKCBTkkQVtXpJOZaxWAyVSiW09UHHmPOWHmTNAaHKmUSZhjYCbaadY8bnKalGA8g+CoxtVG+M6i/qFz1aTuVQ504sFsPs2bPNWZecu5RV13VR9au4/5P3o9ZXw/J/W465P5kLv/kUM9vn4JF3PoJTq0+hc1cnzrn5HMTGWjqA5ATJLcq1RgBRFjUckv1L4M1+0bqr7I6Pj2P79u2GfPxtlCAIZjaSz5TfqDiO88yMmv+LiuM4WLZsmdFZtkEJtNaR6llV7PjcDsABFnxwAebcOQfw2gZ9cX4Rj3/pcQSxp7xbNQfrXrsOK9wVSCaTOHr0aGs/tevg0O8eQseWDnTt6kLkVRHc8/J7AAAv/9zLMXffXABtfQ20I6hKpZIJsxwfH0e1WjU6VENYNYlaOp1GX1+f0WVctwFg9uzZyGazxsZQ3QtM7+VTu9H+fOuCrfjKlV9BOVWG4ztYcv8SrPjHFZgYmzDrJgCTVIpRe+l02tgJfB/br0Y8x0t/890EFAqgtA0KkLgfORqN4kD3Adz/ivvxgi+8AKlCygBOBVAA4PkeNl67ETvX78R4/3gIePfs7MGV770StWwND/7xg1j9/dXo29cHP+ujq95lbEwbWDxdWxuNBorFIgrNArbcuAXbX7Ad0XoUL/3IS5E7kQsdLek47TPAaX8lk0kUCgWMjo6io6PD5Blhv7PvlHihXTo5OYlyuRyyMaLRKDo6OkzY9JHFR/D9t3//qQYAA48M4NwPnQu/MXWLqG4LtMEvr6MNovepE0L/V6eK4zjIZDJIJpPGgWLLJN+ldqQSWUq+TOeht4F9JpNBLpczCdHs61lX2lb1eh3j4+MYGRlBoVAwtoq2xZZzyqA6QuLxOE4+5yS2v2k7mpm27ZubyOFVX3gVuka7QoCVSe+IeyjvNllg11vJhwCt3BRRJ7y9w+5PnW+0X/U7jcy1Pd16P+3vY8eO4cEHH8SxY8f4/a+1VZ6xhzsIWp7SJUuWmDMZCQxsY5aDT0WgAIEGKwdSATIbpxNdjUHb62sbuRoWqQKiYEa96ipwmixLgS6frwCd4J/h2Cqc6iFTjxHBDhWKJhJhHXgvwRUzSZOcICgmq2oG8SlgSOKD/cEFin3MxYTfcyIq00fFwz4g2GIiKc/zDODQPVPqiVQmj0CAfcj3J3cmkdmTQT1ZN8ynRgkACCUT4X1A+3gtVWjKdiozx36zQ5VUlkqlkmnj4sWL4TgOJicnQxEdZP/UY2wTGmw/wTxlREkiBeAkmSiX7CP25549e0KZIalo6KHUfcXaF1oX1oOeeZIAKsNdXV3mrGuSUfZ+LG47YN+xX1X5K4hWo1DbTeOLhgx1hxo63DcUi8VCCwbfqZ4EDZ23QSP7TE9FYJ8VCgWTZVX3VbG4rotKpWLaynFkZlUaiwyfY3QG38P52dnZacLqaaTQ8Eo4CZz/7vNx7JJjmH/nfFTrLY99ubOMbW/YhtEzRgEA48vGsfnPNuP0T56O1MmUkZX/j733jrPrqs6Gn3N7nz6jkWbUu2RL7gXb2BTTMZAAoSWk8JJCeAOhJKQX0kMSUggJkAQIMQYMMSbGmAA2uMqWrd5HI2k0mn5n5vZ2zvfH9bPvc/Zc57MT0/LO1m9+M7r3lL3XXnut9ay19tr5i/PoHO+EU2htD1CHlRpPNEjYB2OgNVq1KGgIqqNUM0yW23Jbbt+flslkfJFSdfobeXBdHiN/NmLqo4z+xihCsRAGvzxoZOvkqybNNhYA8MIe5n9kHql7UkYWNhoNBLwAVt+2Go1GA+OvHMfxt7SOpvq3n/o3vORjL8HQ/iEjH2lsUz+xb47TzKJjCjCN+1AoZHSB53mYnp5GsVhEV1cXEomE0bsKvnTMQEu+sVEHKhBXO4x6auuprfiRwI/gizd+ETtO7MAL73khJvomEEDzvHEW3C0Wi8a2YxVzVr5WO0mzIO1AAK9ROcs+Ud/akWzOA7ORzm48iy+/9cuoxqv46hu+ipu/eDMycxnfSSIKGK+850pcec+VuOfH78GxK44BAIaPDeP6v7gepe4SHnvLY5jcNYnp7dO4+D8uxtTmKTzv1uehe77b6FXdpqc2Iu0o6tKegR7sv34/jrzkCACgHq3jS+/8El7+qZej90TvkkiiBmGIKcrlsqm5ArRsFqUHG3WS6nq1dZidmOhIYOriKXPf4AODuOLProDj+Y/UpB2oe851jdlAVm128qACYY0Cqx1EG4D1p5Suaitp03doUEF1vPaFzcZAdlRe+wzA2LZcp0BrX7q+UyPnxHOaaVCpVJpHFX9zGIkVCex97V7UojV0TnXiJV94CTpnO1GJVTC1YgpDo0Nm7mx7lTan2vI2WOa4OZ6AF4AT+K+PXCNNbLyiz1QHi77DdowBwMLCwjMORjyjc7gjkQi6uroQiUSQy+V8i4GTSqNTARCbGufKCP9VFNsG3xx0O0Yn2OAzlKjqCVIBR2+lClDdxwD4i0XZBq1G0DW9m8KZY1CAooucjYKXEVCNTtJg5v08o1JTkAhI1DtMsKaAQxcxmYlAngBanRT80bRUVu9W4UwvJheKMqjntY6Ry+VypnAG+YFAk84QBW36LApiOj90UbF/ClDVU2svGFUm7Fuj0cDIyIhvy4Lyoxo85FmCJ1v5qfOH1xB8EtyQF5jKzzkj/6gi173hmi5HI0fXF5/PdahzpTTQrAfOm9KUSigcDmN4eBj5fB4XLlzwpZxxntRzr3uO1bnBPms/WSWT86ag3XVdU72W2QcsrsL1zDVQKpVMXzXln+NiMRXSmw4ebvHQsZCf1ItNmmk0vNFomBoUnBugtb97enrajJ/zo5kiAJAqpbD6jtUIhoKGNrFkDKGEXzTXnTqcUMswmN4yjf2/uB89oz246M8vQgCtgjx8F3mD76di04g8x8e55/g4d8sp5cttuX3/GwG37WAFWvZQPVL3Fc0CgEbMX/Bz/d+sR7gaxrk3nQMADP3zEHbcuQP1nrqvhos6cN2Y36D34CHfyBvHrP4QkGr2WywWMyBaGx24lKWU46rHXbdZoLVdvRLqdQ1W2L/b2X+O4+Dyk5cj5sSw5eQWeB2eSXmfnJzE7Oys2cZVq9UwPz9v6nskk0kjxwEsybCiHce/2/WXARPP80xUV4M2tDUdx8Ho9lF883XfRDXetHlGt47ia6/5Gl76mZciUUwYnaZzQJvlps/ehEglgmJXETd94SYEE0Hc/5b7MXbJWJMWYRdP3PIEAOBbb/wWbv70zTh85WFc/rXLDc00KKP0pH1Vr9Xhxf1Ase7WkS1lkSqnALT0YSgUMvyRzWaRz+dNVHpmZsY42elooL2g/K4OdH7vus2M0EQiYSLo33zBN/H48x83fdr2yW2AC8Dx20ntgBz/1uCeBg80aGODX73XnvvFxUXfWNrhJ+2POo7+K2Cu76YtpZmUeo3tLNBgHI/lU9tb79e1ZYNeNjrWNty1AcFyEPtfsx/Pu/V5WHl2JeqROr760q9ibPUYXvLFl2Dl8ZW+dHJ1gOjfSg87q0VpzWvsOVUaAi28qEE82wmh77CdMOSD2dnZp33+NtvTBtwUPNx/AfhTfjVSpYuFxpx6lBSUKbjl/xkNVoZWo1gHDsBE/DTlVCN53OBPBtHUYlZI1IlRcKWTrEBUJ0snQ/dg09BX0MFomB60zu9skKk/9AaTLhSEmoLO7+iFWr9+PUqlEs6dO2eiX0w7pZJTLyEVAZUj91OFQiF48HDmbWew6q5VCM62shFIY9KMtOX+HgXqpEk2m0VnZ6cvFRmASRVWQU+60zOuypz9VgXIBcj+EJzSSaDAy04HIkhlUZF0Or0kI4CN71DnC/fLUsHo2qHCIR+qc0m9xro2FMRqNJzvVUcIr2dfyHP8jtsFuF2C2zD4fr5LI/+kb61Ww5kzZ3wZEwrYyEt0oqg3n/RlvzhXVKQcM+c+GAyiWCyiVCohl8uZwitKbz1BwHEcFItF8y5+p9sm+F6eH8q+1GrN4+tisdgS54AaVOpcUGeerjcF0sqTCuB1fjnnXBe1Ws04HwJTAez+293Y8949yG7LIn0qjW1/vA2B2QCqoSryw3nse+8+lPpLKA4XUQ1XcenvX4pwqKloNUuCa47rWXlODV07s0fnbbktt+X2/W22QdjO8O74zw6s/cW1OP3R0wCAdR9ah8E7Bn0GadgJY/hfhgEAjWIDA58cQPembkQiESwsLBjHt7bVd65GT6YHD//4wwCAnR/YicHzgwgkA6YWD7fNMDJcLBZ9+pxymzYIbRTVBxwXx0s9Nj4+jnq9joGBgbayzbbD1MHNH7XdeO+ukV2ou3U0Ak3HaSwWM+OZn583xTS5r5tBlaGhIczOzi4J4ug7aReo44K6k/IegMmi0vEq0FxxYQXS2TRynbmmM8UDhk8NI1LxF3IlTTXIFK6GccNdN8CNuUjmkqgFalh/fD3O7D6zxDEztmUMX/z5L2KxZxFuyMVVd10FOEAoHAKeZDOOUfkx6AZxzVevgeu5ePzGxxGsB/Hqj74asZMxFMoFY3/T7gBgipAyq4Hp1rRxqJtojyngoh7Toql8ZiqVQjgcxkOvewiHbzzsG+Nj730MN77/RpNOrk5oXR/Kgwr+bJCp92jgkN/p95z/dvyh17VzAviiuFaQ0m58byASwLff8W287FMv860JPkOfSZu1HZ5hUztGgyE6fl2Li4uLaDQa6PuPPrxg5AUYKgwhlAjh39/w7zi68yjgAF9+7Zfxmk+8BomTCR99dE3ZjirSkvOu99lOFLXzdB61z/ZYFJTb8tV2wOTzeeTz+We0fxt4hlXKFaCogCR41IFrxE+LTHBB6R5SZQKNxCqw9LzWnkxleqanUJhrtFmBmx1pI8jWBUfwqsCDfdOotE4EJ55eJQJjPoMRf42SccHpvllNR+EYFSCTBozuEdjF43FfcTE1+k+fPm2EW6PRQK1eQ627hshc64xhzm08HjdzyUqhQPOsPi/sYfyN4zj/lvOYeO0EnvO258AZd3zzo6CXDGo7SQiYBgcHzb18J+lrLyQCOnpHSUMWViAf2tXMFewpGCPo1jkmjYvFIgKBAHp7ew39tSI8+YrpZdxDxLmMx+MmAquAVccOwLc/TZUs54SfeZ5nQKICQnUwaXqMOrMUYPIe8j6j63qEAyPdSiedV73W9nTymY1Gw0SOtVCiplCpYCddSWsabTR+enp6ALTS0OlN5Fwrb2lqOSPYdraB7tHhPeqcoTxR54OmkjcaDeTzeVP0MRaLGeOAfMo55tnYwWDQFDcLBALG8aXCe926dYhEIjh58qSZs9h0DNf+xrV46HcfwtW/fTUa+QacsAM37GLv7+xFqf/JVCYHmLp8CiffdhI7P7nTrI2yU0YhWEC8FjfriU45ZjRotXrep7UubENkuS235fa9b4zcsan8pS1Evdf5WCc2/d9NqG2pYdUdq5optIHWGm94DSzuWsTY68YAD+g73IdYtSnHaA/wmbVkDU7FQSacQc9dPZivzCN2JobeJ3oxH543Mpt9DAaDSKVS6O7uNtvHaI9RzzN9dXZ21oBx7o+mjNJsQsrs2dlZ1Ot1dHV1+fS+Gsmkg25n02COnTWoIJlBi56eHoRCIUxOTmJ6ehoLCwtG99frzRNh9u3bZ7bY0SFM+1gNfA3wAGgLuID2QNbznqz3shjCSz70Enzp/V/C/OA8rvrWVbji3isQcAOA44/wqT7mc51FB7FSDK7jIhaO4bLHL4MX8fCdl30HkVIExUzRAFOe6b3n5j0I18I4dfEpvOLWVyA1lTJ6lTTX8YQqIVx1+1UoBUq45FuXYEVuBSrxZv0f7uXnFjJ1sqdSKczPzyMSiSCRSCCbzZoK9QwG6DxqdJup6dTFzDzwPA+X3345Lmy6gNk1s4ADhBfDuPRDlwINfyEztbts/lB+4g/nRcEh77PBMnlA58V2mKmNZfOygv52QUb7er6n0dnAPb97DxZWL+CeyD146RdeinCtVTeBfSP/u26rjhSLhSm4V/tUMy75LM3ktIGvW3fRc6EHgc4A7n3hvTix7YThtVxnDrf/xO143W+/bom8sx0edtRaA306fnUCKBaxAwmauWfPpToibMeCNgLuZ9qedtG0D3zgAx47TcYngdUbAbSO8FKgq4NUUMD3K9DS3Hp9Lg1YjcpQUehi0aimAjn1HvJ6O+2aQEGPPAoGg+b/7DsVDYUQx6sRe/abk8e0a/VEaiQe8J/RSGbQQmeklXpE9V475YfPqdfrOLP5DB59+6O44o+vQPdIt6/YHBuVrtlPHfQw9aYpnPmFM+aa6GQUF/3mRUifSJsFSFrS8cHxqjdKvdPca0OgAvi3GQDwKUN1SNgpsbzW9jASLCndGOFWgKQ8Y6eDaR94Hcerc8wxaH/pUGC/Kci0QCDfZS96deRo1gQFAJ1MjuOYOVPnlB05oGeYe2Tq9bov4qtV2O2MAR03+c0uWkbnkkZ6OUYaWuRh8rVuX+Bz6/W6Mb5UwbOCONcOf6gsOH7eQ74k2Od9Klf0/5pSzyNt1InDtauREp0zBeYqF/m3ZiKQd9kP7R8dH8yEIb9wrUajUdSH69jzq3uQ25ADPGDoK0PY8Tc7WtHsUB2n3ngKxb4itv/jdoTnw76MGDpUVC5ryiadDufOncOhQ4fw/WzectG05fYMm/O/rGja4OAgVqxYsWSbXruIjOphzciibM9el8XRPz5qDF/HdXDzX92MoUNDOHfunDnhpd5Xx+lfPo3MSAYX33UxcrM5Y2fFYjGk02lfZhAAX9Sa9pI6i2lfsa+UbxqIYCp3OBxGoVAwcjUYbG6RWbVqFQYGBnxOXnVu2w5s0kj1MGlBWqouoFwsFAqYnZ3FhQsXMDMz4wMkBMPJZBKJRMJU0Ka9qcBD3626j2O2AQ3HUSgUTJX0XC4HJID9r9yP5979XHONAgR1umhgSLM+FTw+8vxHsPmhzbjnx+/BYtci3KCLfNdSABHPx/Gqj70KA2cGlticCkypZ+l8oH3BCD7Pb2Z2Ied6bm4Onuehp6cH8/PzyGaziMfj6OjoMA5tzisLbHGPveu6iMfjyGQypnq6AeaxGj7zkc8gMZnArr/Yha6DXcaWUbrZPGA7TBSXaNPryGcKDJ/KCaKBRbV3lR9sQGzfr9Fb5bPyijKe+MUnMHPxjOnnrkd24Yav3IBYqZVhYJxvT2KnM4Nn0NjbQH4678MWOlbyNLGMVjNX/ib/U0Z0dXWZ4MTXX/l17L1mLxAA+i704eZ/vBmxszGf/cotB6QNx0c7ReWIOth0TSndOb/6Oe1MG8Tb86BzqM11XRw4cAB79uzxpZQ/HVvlaUe4aajRMNXCBvZgbQ+e7dWzQYUyqXqT1JPBgaqxSmJo5UsyhgpRTQtWUGB7nTg2FZoK8Nl/gnmNVKonk4zARuGuQtVecLqHp16vG4+2RgoVvJKO9oJs973neTh39Tk88tZHUElX8Pi7Hself3cpkvuSvggp+6Vpr3W3jnJ/2ccLXthDraeG4EjLM8do4OhLRrHmG2tQy7WimnQacMwKDDXiyf5qZWWOh8qM9OGi0VRYjUbr/HPRct5JUxVcpJldqVC9eOQRvVZBMh0eGo1X3lZAa/M8/9+u9gHXgQJ45T/lVwDGMKJwpJHEeeA2AT0/nSCL/K7Kk1H3dutB9wkxPZv8yP1YrMqpkQb2i2BVBbXuk+Y61agGv+O+I9KGmQpKT86Zve7sNUo+YHSc82AbTJ2dnb4IvqZmK4+qc45jo4KyU+IJtsnLnDdNq+Mz6vU6wmNh7PqLXTjwzgPoOdiDdR9dh4bbWkfH33YcZ25pOsicsINdH96Fer5ujB6Cf9v4U0+9Zjwst+W23L5/TZ2V6hxWsMbTWtyki5kXz6D/zn4AS/elnn73aV+qrRfw8PAbHkbfe/tMJo6bdjH63lFkn5tF9sYsnJSDlR9e6dMJ1EG0F9SpXqlUjL4BWhlTtq2hTkfVC+wz93Or4RyPxw3g5r0KWG2Qojab2kM2AGKj0z0ajWJgYMCA6ampZgEuntxCe4Zj4XuZUaj2oD6b46SOUv1GOa92JbNH424c1991va/quOol0kdtHwVkShvXdXHF16+A53l46b++FNMD06gH6vj6G7+OUtpfBKqUKuGuN96FF3z2BVh1apXPZm03Bxy7Oub11Bryg+M4JkOA+7mp+7kPmDZUqVQydKCu5RwlEgmTXaE21cEXHwQArHh0BfqO9qHhtGrY2E4WjsF2XrUD2hy32n1s6oBQ/mr3TDvqqjTkO3Rd2e+3AaLneagmqqgl/AG0YrKIRrgBlPz3sh/jW8fx7dd/G53f6cSav1yzZKz6Dg366FqywXEwGDTzrsWhX/DlFyBSi+D05tN40edehPRUGoVGwRcYVKcQ16LdF9d1MbNmBm7AxYozK3wywp4fnYOncsLZc6xz1W7ey+Uycrncf+v0lqcNuBXI0CBWwaYLSo1OjZjZBi6PDSCB6fHkswmi1XNJI1ajpRRsNLbZPwWhfEdXVxdWrVqFkZERk8LKMVFA8HmhUMgUi5qdnfUtegXzysS+lArXn1LNpnTjwtJMAQp1KldbqCqD0NvDhaCLlMWjTl90Go+/5XFU0k0DOjeUw6PveBS7f2s3QieaAm7yFZNIH08jeji6pBDAqr9bBQcOJn50AmgAW96/BV0nu9BwGmZxBYNBnH7xaRx981FMXjKJXb+5y/SH4Ij9UoWjgkkNCtJCiziRB8hT3BekUTpNeyH/Aa1FZhfU4jXsKwG3CgDOMeeIDgMFz1Qg7ZSpzp2msqiQovNAMyt0rzbHxi0FvF+PZVBa8X7SLBwOY9OmTTh06BCKxaL5THlMvaWkmTpK1MNJXlehxK0IHR0dxvlBY7HRaJgigurJJ2jm+Zl0YrB+gG7J4LrjfOj+du1To9EwhQXprGEfbBlB+cE5UAWiwJOVMyuVCpLJJBYWFkxfuPY4N7xfU7Z4tAxlAde47v9W2Un6qKebBdoqlQoypzPY/aHdSMwmgGDTcA4EAtj/s/sx9tIx84zz151HLV7DFb91xRLHHBsNZY6DSuWZFgRZbsttuT27LRwOo6Ojw8gTlbe2zgwEAxj58xGUtpcQRBDdX+4G0AKqnudh8wc348BfHTDAzWk4uOqjVyGXyzXlHzwc/+PjWLzsyfOLHeDMj5xB1ati+C+HjYxQZyidi7R7KE9ZeVpBsBrTGqWiPgKadlwul/OBKAYd0um0z8ZSO0IdrBrMYVNDnHpRn6//59hSqRTWrVtnCqrNzc0ZpzUdr9VqFel02uiPZDK5RCezKUBSsE86UGdSTmvqrO2QsAFPOxDB39TdClgDgQDi83Gsnm8eP/qyj70M97zlHmx6YhMeff6jxjETW4ghMh5BqVQyWQz6fHWmKBhnvR3NNKTtxS1u1PmVSsVEtnlCDG0bHi3GRn7jMWNsfMcjr3oEB28+CDjA+NXjGHhoAN1PdPt0vdJQ7XMF0rZdpDzUbg7Vpuc82NkMNt/ZGEF5j/9vBya1H/y760wXrv27a3Hfr96HQl8Ba06uwQu+9AIkF5M+/uPPzPAMvvHGb2CxZxGLtyw29/j/2fol76IMUWzGz9qBUuI98ooGPK77xnW46PGL0HmhE3k377OhlI/VqaCOrUAggIWuBXzzp74JL+DhpR95KXrnepc402z+tx1smrnAeWLTObXlLdCsTs5TfZ5pe9op5e95z3s8Lna7Ap4uOGVgLjb17NiCktEzLYalxrUShX2tVCo4e/Ysurq60NfXZyZUU9ZJZAJzLnDXdZFIJMxZwO2OXeJ5yxwbx0GBSCOazEFBQ2Wj3hkKCtKM46HCIrMBrZRwXsf/kz5MvdA0G71PaaUKLlfNYc+b9+D0807DC3oIVAPY9oltGL5zGK7r4vy153H4vYfhNBzseusuBEb8KWvBYBCBeAAj7xlBz7/1IHIoYlK8AoEAIvEI5l48h4PvPAg34gIu0PdwH3b8/g44JQf1Rh3Tl05j4coFDH9kGKi0FADHqmnWtgdQnTW6QJlurXOgWxnaRfs1TdheWGo0qDOETg2CT86lHRHWOdG5IXDU+bedVe08xIwo6LEoVMBaKV73DvNvNUoYKSUP8Rl2pFX38CrN1MNJw8hW+lQsBKca0VVHHdeOOjF0ewoBtq4jrWOgDpB6vW6ALz2qAEzqNemj53dqNJ1rVHlOr6ccURqrQ4c04ec0RAGYtE6VhaSHzrcaP6R5rVZDPB43spcyh3QNBoO+M0j1efVEHd/5+++g3NeUO8F8EDe88wbEJmLGoei6LqYvnsbMc2aw6R83wS366U0Z+8QTT2BmppWe9v1o3nJK+XJ7hs35X5RSnkwmsWXLFl8Gmm2EA4CX9DD6l6PIX5lv7u2tOFj/a+ux4pEVCAdbx/iEwiHM75jHoT87hAACeN4fPA9dJ7uQzWab5++ma9j773t9lclj52LY9ZO7ECw15QxTqZnyDLTkoA1qNUChtiZ1rdp2lJXFYtFXOJfPKW8sY/oXpvET//kTiHtxn95XfaS0UV2mEWA21be8Xu05Ntd1MTs7i3Pnzpkq5uVy2WQF8AhL7idmGi1tEjtQoraVOu1dt3kyRygUghN18NDzHsL+G/fjLR95C3pnen0A3LY9ORaOi02vJX2oh5X+nuehkWwgWAvi26/4NvZdtw89Ez14/V+/Hk6hZUszuszxleollL0ywpVWETy1a+y518xHx2nWwllYWEAq1axqPjs7Cy/oYfzV4whVQui7sw+1cs3HS+l0Gt3d3SZaTtoeuuEQ9rx2D+qxVhZsdC6KG953A6KTTftAgzd28Et5SLP5bKBLvKDAWO9V/tH+6dzYDpd2YJHfa0DOvo7Xci98ZCCCr/7CV/G6j78OoUoLf+l7i5EiPvW+TyHf2dpG4FQcrP7kagx/cti3pjXzDoDBTrrWFDDTMdbd3Y1MJmMy/9RerNfrvmKEBOg2tlSg3Gg0kE/lcetv3IpK8sl6Q6UI3vTBNyG5kDRzyvnQfulnKgNsOvJ7ez5UxoyMjOCBBx5ANpu173v2Usp1T44aerpvwmYEghA9o1GZTD2c9BYy6qbEU/DAdN0tW7b4oo0aneJ7NBLJNFrXbVWh1n4RgNDDqCnl6s1lI+iwAYl6b7lo2R/umaXhHAgEzP5sOhyUOTheglPP80zlbxt82B5dgoRUKoV4II4rP3ElGk4DY88Zw4ZbN2D1HasN2D706819mh487P+n/dj1S7sQPvDkecUX5ZA+kEaoEcL6D65vCqmwa4BBMBhEMVzEyVedbIJtAAgA2e1ZTF43ib6v9mH2ilkc/dPmvrG4G8fQp4bglFupJzQGSGPNiOB3CtCAlmJU5a5F4Ogc4fzawJzzzygxU+HogCHQU8GaSCR879Q9Jeo5JP3JD0yz1uvo2eW16qziewkauX+ZDgrXdc1eYXWAMcWFNOKeM02DZxRd91xRsWgVa92rQ56lY0GdUqSHbn1QpU7eVieUrgudLzqoCI6pQPQ+dUrQGcF79HtN1eN7qJy5xtTYogzRrBxNC9f7yFcAjDNAwbzyjWZB0KFB41llg8owOiE4T8CTSrJY9NVJ4PojDRuNBoKFIK59x7XY8wdNo2PH7+6Ad9pDNVg1789ensUjv/tI0zAvOth06yYEyq2sDAAmgrPcltty+/422jV2VgrQAqQzL5tBcXurAJYX9XDhbRcwcGQAtQU5S9n1kDyVxPA/DWNwahCb5jfhQu1C83SYNWUc/aOj/mPAPGDX23chXAnDc/z9oGxn1pKmRVPuBQIBX2aTAmsFodQV1MXUnZTPue05PPAHD8CNuPh8/fN49f2vRqqW8tFJZTRbINA8WzsQCBhdoo5WzSBUZyhprlsPe3t7kUgkcP78eVy4cMEUSGVVZlZmZ9ZWKpVCR0eHz25Um5I6Qm0S9ieUCGHvjXvx6EsfBQB88p2fxBs++gYMnBswNKG+tZs+k7yioEH7QcBjggCLDpyQgxu/dCO8oIfrvngdgvUgAqFWsVZmZ5XLZYSiIRy64RDOrT+HG2+7EYH5gCmExn3V7IOe0KLBJo5hcXERHR0dSGaSOHDtARx5a/Ns74tKF2HV11ahUW/yVDweN2nkagc3Gg2s++o65NI5HH7ZYbjhZvHRS/7iEsSmYggE/fpeeZCfMTvCdkionUleIz/bYFL5R0Gz7XSxQbgd2FDQZ6dWK9BnHxmwyDQyePPfv7nZF6/lTOJvAIiVY3jFP7wCd771TuT6c0ADGPzKoAHbiuM4FtKZY1TnAu+hTabAWWnGfpDGyou2k8h2TDiOg4de/RAqidZWt2qsiu/c8h288J9fuGSudLzKh0+Vpm8DcV7PPtDmWlxc/G8VTAOeAeDmy9XzQ8BMYaveHwWF7LAazU/lkdOiYypM1Nuiz1CvEwUk79d0W04AJ4Qgmqmi/F6VggIaZXj1bJFZdIEQdChI1zRpjpkTby9EFhtTsK+pyJwD9aTx/nZF4HjtJR+5BMmTSQx+fhD1QLNvC1sW/HMc8lDcVETfsT5M3zSNk+86iTV/twYXP3oxYrGYOYdZ99HHF+LY8JsbMPKrIyhcVECgFMD6v12Pnrt6MH3zNI6/57gxBEbeNIKqU8X6f1hv0os4Hl246o3Sha2Czign2SvLMev/SWMuOgWDKqx0YSvQVmeRLkwVkjof6gjRaC2dIPxNEMbILNcIn0c+UZDJgjLkH/6tR2MpLyl91UGgWRJ8NsdGOgUCTSVLZwSdFLrGFVByzWrVfM4HlS0BPN9hp89xbRYKBV9ldY2ekM/t1CvOO3lewTrHSnrQmcC1TzCqQt/mSZ4MwPFq1onKAwCm/319fSgUCsbhx0i3yhHbACSId13XRMk5XvKAepzV2eN5HkLzIez84E5UOiroHu1GINaKYFy4/gIO/1LryJRTrz0FL+Rh6ye2IuD50xKX93Avt+X2/WuO46Cnp8dnJ/FzwK8bez/fC8dzcP795+FFPHQ80YENf7gBzoJ/C4kX8nDm7WdQ3FnE2r9Zi/Pnz8NxmnvAJ350AqU1/n2JA18fQNSNwgm2L85GecoMKhY9s20uyioFlprNRxBGPcM+O46DmctncOCdB4xD/6HtD8HzPLz+269HpNE6UUMbn+k4Djo6OszzNKKp1/IzrZUCtPQw+51MJrFu3Tqk02lMTU2Z6wuFgrEpCoUCKpUKisUicrmcKRylupM6SMEMdSUAPPL8R/Dgix40faxFavjim7+IF932IgwfGzaf67Y1W0eqja1bn9Tm0RNP+EPnyE1fuKmp60Kez35VB/qeG/fgwVseBBwg4kTwvNueh45gB0qlEhYWFkx/yBOpVMo4jNXWCYfDWFxcRKlUwqlbTuHIG46YMXLuB784iHA4jEwmY4IYxCGsQRMOh3HVnVch4kVw+PrDuPgjF6NrXxfqnv+YUA2UqQ1kgzR1mitPqjNDt/61A46A/8gwDcwpyNNoLPuo4FCxDp9p28/EI/p+5QFtXWe6cO3HrsX9b70fXfd1Yd1H1gGBpUch63oiT9k1dZSPCfy1zpf2U21Q2oIMOKrcUL7meJ//r89HuBLGwRuae/S3P7gdN372Rh9NlIZ8tz5Pt5woDfUevVexSKlUwvz8vI9Pnkl7RoBbwagymYJmfqbGIH8rE5EoNGA1wqdGukaV7GcEg0F0dXWhu7sbk5OTJoJMIvK5fI7u2SVBaQgTPKnnQwGPCgc1kjlm3s9Irb2ICP41RVkzBBS8sDojFQBTcNkXLRLHPmpklmdpalpto9FA0Aliw39sQBGt46zW/OMaoAiM/WTzmJBL/vwS9N7XiwsvuoCRnx9BvbOOM+88g+Qnk9j0jU2GhpoNEAqFkDmbwdY/2Yojv3cEK/9mJbrv70bNqyE4GgRa2BwAEB+No1Fv+PqvClCP71LhpcA5EAgY7yzHr2m7BDeAP4KswJvCkgte51kXI+eO/dLCXOr14w+Ph+NYlJ/UMUTwrUdnKEjXMaug1VoC5px0z38sH3mN4I28yGeq55QCmWOyHRqxWGwJH7qui0aggdH/O4otf7vF8Fg7IcbnaQaD9sGO+tKw4ZxopoLKHY6Lz6UBqGOx59KmL+UD361Al1F/dXLx/zqv+h7yMtMtM5kMQqFQM03O82f42Pyua1WNIOVb0kr7TTqrHOqa7II34QFBf0ph/EIcTt1vnMbPxFEulREMtByn5LPlttyW2/enEXDzb65hG4ADTTnW+8VeODkHcz85hw1/tAGJ8QQCwVbBTQA4+r6jmH7xNOAAj7/rcVz9B1cjNhXD/CXzmHvOnO+ZK+5egc0f3YyoG0Uj2DKOqfcVrNXrzarUapTbMleDMkArkkc9ZO9XJriLz8YRLPsLkCUvJBFw/Sd3qLHO56tTU2moMpj38R7+toEO+xaPxzE4OIiOjg5MTk6a83gpv6n76SRltBdo2RyFQgGdnZ2+rYqkayQSQeeFziX8EK6GkVpI+fqietoGC3yfjrEdAOL3BJgKhmgPkB4amdzzsj147AWPGeftsd3HUIqV8Iq/e4Wpd0Knreu6JgWf20DVLqPuXlhYgHPUysz1mjqKx9fq6R0aNKEN+PgrHse6u9ch8VAC3Ue74QWXVqVnn7ie7Gi0rjXlDdXPer3NZ+oEsgG62ii8VgMeCsR1vdhYS/GYjVt0Xm1gr2Cy70gfdv/pbgQOB3zv41wD/gi02mpKG+DJDMKdDmZ3zmLo0SFjy2lTp45uheX82Vkw6qDwPA9ezcNVt1+FQC0AL+zh6i9fjaAXhBNcurde14DtYHkqWuua8bHgk9cUCgVks9nvPuBW8KMLkQPQ6K1GujQKpdE+oLWvmYYtQQIFlQJaGsIEqpyAubk5c0SXelPVMWAXbqDRrvu3+X4luHqH+H8VWmqgq9HO91K4al+0qceHNKaAptJhpE6N73ZeIPZFgbpG+ynwqPg45ka5gf6P9aMWrqHrcBeSDyaRvTiLI+84glrHk2d7p+o4/JbDcMYd9D7U63OaqBJNnElg1zt3IbgQhBtojil9JI2Lf+Zi7PuXffCCHjb86QasuGeF8ZgrGLQFky28FNyyCEyhUEB3d7fhGU1rsxcVHRFAq3K18iNpox5N9kW3CNjOGc6h7Yjh9Rpx52d61JvjOKYiq6afE0Rq4RXyip2qbDsTgNbRbtovrh/bm8g50PRuGg/KY1TK9XAd+/9iP/Jb8ggGgtj49xvRKDd89GJVbPI6+0+HnCorFd7sI40Uglvey33ibDpmyhSOiY4Cx3F864h90DWuKd2O42Dz5s2YmprC3NyczynH8eiz1KDUORkdHTVzyvvoZCAfMiWea13nWMdH/tY9T+RLKkjlS/ZZ5Vf3SDeuesdVeOAfHoAX9rDjozuw+purUQvUDO1J63aKZ7ktt+X2vWkqJ+0sJP1N/dOoN9DxtQ70Pt6LaCEKBP3HEJ18/0lM3zxtANLiukXc+8f34rLfvgyP/uqjqKVaFY47H+rEuj9fh7AbRjDsz7bSbUO2XKfMUMeoGryq12ivAH6npToUG40GMmczeMHvvgBf+9DXUI6Xse7f1iHxpQRmt89ixYoV8MIePNeD0/Cffa2gR50F+q52Rrr2E2iBcMp5ytx0Oo14PI5UKoWxsTFMTU0ZW5R1OAiqCTypeynn24FgAFi/dz1uLN+I+37mPrghF4nFBH7sIz+GWK4V2dW+qaODOlrtKeoc1Xd2JJYZB6SXZlfyudRrnudh97d34+gVR7HYswg4QLAWxPp/Xo/Z6VlzfSaTMRlykUgExWIRs7OzJnODz47FYojH45ifn0f6/jR2vX8X9v/+fjieg8s+cBm6DnYhkW4ewUbgp87vcDgML+Bh/837se+l+3Ds6mN44btfCM/x22j6t9JR515tUQXIvFeBL5tNS82YVP5WUK7Xq+NE58x2mNi6nnNCets2nc3b2phRkjySRKlcagvOdfwaBOC8qYM+n8pj7+/shRtxMZQewpaDW1Cr1ny0a4chNXuAdrkGIJSPA4EAgqUgLv/S5QiGgojWo4DjDyK1a/aYdN3rdxrcsoE6awYtLi7+t22jZ3QONzvEpoaqEp8Gt3rH2HEOVgtIkYF1vycJbt9DMKnEAVpgyQxMmJpGJPdOM+2JfVMhA8DHzLYXiiCaANteiGq081k0+icnJxGPx7FixQpTOVlTUtlPVQw66XZ6B9OvdAHyez1jm4qNqTe8Vr3SBG+O00wvm37JNE78wgk0Mg0Ei0Fs+JcNWPultQBgCoYwNYifqSOD3qpGo3mERnFjEYXLC1j1xVUIOM10VY0SE3gEg0EMDAwgFAqZMxpJR0295qJkQTJGCZVmFHykjypQdTqwD8oLHJcueCpOvp8GgX2ElKZLM4ODwlT3nmsxGXXiKB+xL9y7q1FtCj/SWp1G/DwQCJhCadqvZLJVuZL9VwHOcdmCvV6vo9RZwrEPHEP2smzTePOA4c8MY82n1sDLe2ZeNDpOmml2i65F0p88redQa+SfNOT6KxQKpqJtPB4385rL5ZBIJIxxaO/v59wpXShfKLt4tir30KuCVocB+bherxtHkCpOKkM9b5KfJ5NJU4VVM0fU4Ug+VtBuV3u10+s1ZZMOPC2IlxvOIXtlFpvu3GSuV5mYy+XwwAMP4PvdvOWiacvtGTbnf0nRtJUrV2LNmjW+fcSMgqrzn3/H43GUSiUjE9QB67ouzrz9DC684QK8SIs8yRNJwAUKWwrmM6fqYOVnV2LDxzaY59oGaDAYRDqdRiAQME5ZRinV5uN9sVjM53hmo1FNB7RGCvWa/v5+DO4axLcu+xYuufUSnDt3DvV6HZuv2IwDbzqAjrkO3LTvJkTQqskDtJy52hfbwNb38IeNNpHapLyWcrnRaCCfz2NsbMycZU5Z3tXVZXQQ97ZyfJTNtBGobyORSHMfeLmEkeeO4Pj1x3HLx25BrBgzc6rRWQVitj2rznTVU/yezmzS3XYoa4SanzN7MhAIYLGxiM/+0mdRSVfw/H9+Prr2dpmz3EOhEJLJJGKxmMEExWKxecpGJoNUKuUbR7VaxezsrDlFZfr6aaAIDDw+YM47V73teV4rTT/o4cQLT2DPm/YYmyRzNoOr/+BqxCfjvgxRHQ/n2AbSpJcGuXiv8rft5Fb+18/5nc1X2pd2vKjgj3yotohmIWYyGWQyGcNPfI9G5XVci4uLmJubQy6X8xWBtsEo7SXX9Z+/TV4IhUKorK9g74f3op55MqjoAa/69Kuw7ci2JcCV2y34Tj1diPytwNzOnFT6aVan9tkG3zof/9WcaLOdD6VSCYcOHcLBgwfbHgn2dGyVpx3hbuc9oWGoBqECCSWMCgRlAAUoJC7vU6a20xeA1qLQAhQklKat2gCC3+vzKaRogKtQU88eAQA9nUCrGJOCMWVWCqiBgQFDP927qsxDYaLMYi82Te21004pyDX1hwweCARMoQ8KPo5dBXe1WkXHv3dgDdbgzC+cwfC/DGPwC4OoB1vFUVKplI9+juNgcdciwmfDSOfSviOQYrEYgqeDiB2PoRZs7eflfmR7DqhguadHQaimJ5O2Ck45/yosVdkkk0lkMhksLCygXC4bBUWaa8SegJB8QHqxPxoNVOcMeROAD8Soc4bXqCLjGDRtT4/J0uOveJ0tUBn91MiyplqHQiETOda0MT7PREsaDSNU6FjhM/Ir882z2cmiDpDbkkM1UUU43wTC9FSSJpQJdvYJBa6mI6rDhj90yBAYqiGlY6UBaEej9UffYzv3AJhUSQBm24Jd+E0j1DQkeZ/NB5wjzUKhYZVOp33nlHMeKVPsvnmeh8HBQQAw1XLtPYe2wcn0Ty22lhnLoON8B2rwHynId3J8y225LbfvTxseHvY5G9X+siPKrusaGUo5qI7NQCCAtf+wFo7r4PxbzwMO0PFwB7b+wVak02k89s7HkL0yC3jA0K1DWPOPa+A5rZM7NGhCPUzdqhlGQCttWh3jlCeUb1pYktFK1cFsfHcymUSqmMLL7nsZqr1NQ/383HncftntGNvy5DGIQeDmx272ZQYpiGKzgQz1kK2fNMqtdq864zmWdDqNtWvXIhwOY3p62jiCZ2ZmjOO4UCggHo8jk8kYgE1ZrcV7zbs8YOcjO3HpE5c2++q0ov+qk0kn9lMd2PxMf/g57UTb1uRvAjnqK3UAG5u72MCL/+7FmF83j8HDgyg0CohEIkgmk2g0mtvlGO1nvRpGvOnIUSd2LBYzgK7v231Nx3K0tS1B7TGgVauoHqvj7PqzPpukkq5gbs0cVoyv8I2NtOH8Kp00wMZ+8TsFyAoAFeAp0NVr+Q51mutzFejyHr1G+U37y2fReaM8bzuWlG5aS8hOh9c1QtuLNrJiIT7X8zxkL8q2iiY/Sf873ngH6p+rY8cTO3zPVexnB7rscep86HiIWZRepIXep/xvOxNsOttzouOjbT0/P/8/so2eNuBmlEejNhpFUcBLI1rBpXpi1SOhBFNGUk+qen5s41k9UMps6sUjw6jwtR0AAHxKQgWvgmAytyoZPkuLq2k6t6auqwFteyt1b7AqBQXEqhRsrySfqYCG1ykYU9ry3e2e1fulXoSnw+h5uAdusAWK6Rjg2IPBIOqX1XH+fecRvBDE5vdu9i1iTatvt9jYRy662dlZU+kyFostASSkTb1eR0dHBzo6OjA9PW1SjJTWqkjJg0zn0v6Qx9QLz+JfVKq2l5P3xuNxH3+wgIcufKUtx6xg0nVdo2h0z506FdRZpPPN9/GZyn/K5/aPrmPyvkZT+GymRFOZpx5LYfMfbsbhDx5GrbuG9KNprP2jtYhMR1Cr13zKRtOuOe92Wh/QqvbtOI4xJpRurBVAY0MFosoSpkmx/wr0GeHl5zao5XOi0Sh27dqFxx57zDhMdB3SWFInFWmnmS+2EuN2GdKjVqthdHTUZ3RpZotW8OczXdfFwsKCiRgpD9t8z//raRDaJ869Kin2u50Hd7ktt+X2vWtqq1D2UReoPFU5r/qPulJruQx/fBiNXAOla0rY8KcbEJoNIdqI4tp/uRaPxR5D9KEoBj816JPVAHwF0tSWo7NTs6sA+OxCZveoTcVnMVuOdoUazrodkNuB+Fkmk8Hdb7wbY9eOmXfecfkdKDklvOyhlxka8FnaSEvKQW7N0QACG+/VKBt1gOonRnPXrVuHnp4eTExMYHR0FNls1hTn5Ekf+XzeB46YvRaJRMyZ65p1Zo9DM7P0O7WD1T5U54w9Hh2nzT/8sfUf9RD1Sud8Jzr2dqBUaWIEVicPBoMmg4vbwKhnSqUSYrGYL8qtfWUfotEoEokEAJhou+IK0j7eiOPqT16Nvc5enLvmHALVAC778GXo2duDhtvScbZtbdPDBnmA34mt9Na1126udC408GNfa4NB/UwBH9/ZLhtBt81SdtjyQd/NTEm1N7Qf+l5tOi4dx4p/X4FINYJDv3LIOD3coIt7XnkP6uE6LnnsEt8zbOyj2IX0AuBbk7yW19vbItW+5ndKj3YA23ZE6Jq2QXe5XEYul1uyNfiZtKcNuGdnZ7GwsIDVq1cjnU6bQbBxEsnMGuFSIEBhocTlD5kGaO3bJNBwXReFQsF3fJYyswJzNViViUlg25OqnkYlsL1o+L0aqQqsOWY+j32hMNf0HY1cqlOA33FBKNDkmBV4aToz+8NoKOlHQ79QKKBQKKBcLqNYLCKZbJ1dZwMc9it5bxJuxB9h47wbAbjOw4HfO4BqdxVYDRz7h2O47BcvQ6DhF3BUOKSves3ZVx2Xpq2rJ1b7UK/XkclkUKvVMDk56YuS63UENNVqFRcuXDDCTxWpDb74Pi4w9k3noFKp4MKFC+js7DSRYz3OSYWLZn4onzKCyqrXXC8EiUArSqHrRtO96Xwifev15jmHsVjMGFzKjxR2tlDRNcH+Uzmq4s4cyuCSX7wEB37rANb+ylpgFqhFaz5hZe/TJl+pQFQnCfun+9aVXpq1oQXV6PVmFJr00WPOGGHW7/R8c23VahWHDh0yConPtOtR0KgkrxFsk180K6ZaraJUKiEejy8xgNTQII/aDjM1EEqlks+g5fzxHtKC7yctySvkRfIV79EtG9yLuNyW23L73reenh4j36h3VMaVy2U0gg14FQ94Unw50ZYzWE9oUfDWqDQwdNsQ8FUgPB8GAs3P07NpbPqdTShNloAG4DqtdGXKTu7BpSys1+smgkldamdvUbdqkMDzPLOljPrKjiYCLRDJCt+0r2ZnZxGNRnHj4zdi5KoRuMEndUI5jKv3Xw0AZs8w+6JjUTuCukhtBdWNqhsoy9UW1C1J1J/d3d2IxWIIhUI4c+YMZmZmTHZlPB5HPB5HLpcz80OZzEyvzs5OwAEubL+A3IYcLr33Ujiu31HK/ihQotNawZltZ9lOYo2S24CUY9ItnLRR+X6gqZdZEC2ZTBodR5tLM9gIuufn55HP532OFi/oYf/P78fKT61EeCxs+CkWiyEYDJptkgT6jtMMnsRiMYTDYcysm8HkRZMAgKt+8yr0HO7x8aLuYVewxWvUvtC5V+eEBjR4nV6v9ygesSPfNsjWudLgoPJfOyDIgJ46cLTPNv7hd7SvaWuwD6SrOpU4Xv7YjgaOY+V9KxEPxvHYLz8GL9iUSx3ZDmw8tNHHc/zR4I9NX7vPdmBB361zzL7RbuN3amfxGWpb6vxxHnReXLd5nDQLIv5329MG3L29vejq6jLpMWQiBQc2GLAjLWQmAL5FQ4HXDvCSoWq1GmZmZhAIBLBy5UojFGlUMkLIdynAV0LzGDCmMSnw0jHocVoqtICWQCbjep5nUm8IILhvhZOl+3VVwJMZ+H5d6Ao0+bmmM5B2HLvSi+nt+kNvcjgcNp5UpbvSifPWaDSMcuT8aWo+HODwbx5ugm0AcIDipiJGfnEE2/52m+EDLmwCaBXu6l3mOAnOgVZkkHRU2hSLRZw8edIHhAl+2kVxCfLsVCr12itv0/NM2vA5THlOJpNmb7emXJHP2S/OC3mKwJpGFHlBU/EJ3LW2gZ3yRmeTeqHJT4FAwJzdHA6Hkc/nfY4p0py847quiWiTB9iH+fl5c/Yl13f4TBhb3rgFpXzJrGn2gTUK2Fe+t1qtGkdKu73nWmBM17KOW7dC6FpQBU3HldISaEX+NS1K30O5wxMPtMgcrzOOiTBQ66shOhH1Zftov9hn7mW31xl5hDxhj5X3qPygQqERzGi0gnLSXNPzKe/UoAJgMnO0MOBySvlyW27fv7ZixQokEgmz/lVfBQIBeCs9HP+r4xh67xDiJ+Oo99Ux+tejqA3UsPWtW5v7Vlc34MU8BE429W11sIp6rI7EaAJOtqm7Y7EY0uk0stksUAJCDX+RNr5btxipjlV7hXJHQZk6Km2wQhuF15mxiUHvui7i8biR357nobu7GwAQn43jJz7xE7jtx26DU3Gw4207MDU4hY5NHUbXpFIp038FO7QVCODYf83cU1nKpgCAoIQ04diCweb+9vXr16OjowNnzpzB+fPnDWAkQKQNTLuCgZJ8Po/qlVV8893fBBwgUo9g54M74db89qfdT5039pM6TYE4dQKPq9TM0HaRRx2/XT/A8zxTpZ3AmPYF9+VHo1HE43FDZ2YYLiwsIJ/PI5VKoegU8cQtT+DETScwcu0Irvn5axAcCZrMCm6xoy2aSCRMjZVgMIjJTZP46nu/CjyZDLvnN/fgue9+LtKTaTPnqj9JOxtoc25pU6lNyvupVzVbU3mY19MeIV+1A8M2YNf1oJF2Bd7qUOE7OAbyH+mmfbTXswYzFZQqfys+UZtE+0+eiQQj2HJgC9KfTuP+192PjvkOvPEv34gQQnADrfo7WodC32EDefZD20L/AjpnOn18r7yu6/Sp1q/OGdtTObHYGo0G5ubm/seZf08bcHOPCV/O/RZM37SZSydaJ0xBoUZZSBwFsyQCIz+rV682E0VhpZXObS8KJ5fG5uLiInp7ew2g0PeQwfVvToAe4cX/s3FPCoGTpl49lYcK8HvUCKTsBaFHVfH97JOm6hIYqWBltJSGfLFY9NGUY2UEXJWh3XdNYePnVDDBYBBb37kVJ3//JBavWGzuAfuPIWz5my0IhVtHXqi3WRvBO50d+j2VptKCQkIFitKH9GIKmzblS81IYOOecioNvg9oCTnymgpQCn3Sm3RmpJD73XVsKmw0Y0Gj7o1GwwA1fs+oq10pVtOlKQCp6PhM8ojtbeU8KE/weyqfUChksiL4vGq1imq5laHBsbOAnq5pHZ+uAwpfHp3CfV02MGbaIZ9Dbz75h2NSJUrjQuWByiDth4JYAnryoDq7OCfBUBCjrxzFhZsvYOsfbUXqVMpXl0GdYaQD08DVk6qpltpXoLVPSemgGTXkBcB/tBh5h+NMpVKIx+O4cOGC2X6g8pkOLqXF/9STu9yW23L77zfbGFdHeHlNGSPvHUF5QxmnP3Yagx8exPyL51Ha3jQGR/5yBIOfG8T468dRGaxg629tRWg8hJO/dhKlDSVs+o1N6L+/H47jIP/CPEL3Ns+Rpk6cuG4Cfff2+Yx6teWoI2j7USdS1lHGqDzSApx29AlYWsVZM/MSiYRPpqucWn1+NV51x6vQGGvAjbg4deoUKpUKNm3ahJMXn8TFpy5GwGnZdLZ9YwN9O8KpNplG/NpFhgF/XZVwOIz+/n7E43Ekk0lcuHABi4uLKBaLBhTRgayO3rHdYzj1gVMGPH7ztd+EE3Kw81s7DZ3VOQz4q2wrAFf7kn1kYMqOwJIeqjt0bjQIQ8c5bWMW0ON4OHcMbGk6uuc1t+FVKpVmEMgr4+CPHsSxlx5rPj/WwGN/+Bgu/uOLkTmZAQCfk5301Cy4E9ecMPQCgEakgbGrx7DpC5vM2PT9NshWvlJ68nqlaTv6KEDWzxQc8z3qzLLfp8+wnT/aFFCOXzOOyN4IsNiyRdXZrs/n35ptqbapjdM0i1iDXTZdTEAuGMLFj1+McCaMrQe3Iuy0at/wGbbzS2mjNLedEhe2XMA3fuobuPZz12Lzvs1LaGLThc/X34rrdPxsHBt/+Kx8Po/5+XmT1fvfbU8bcNsRIWVYFUwEMRoNpgDVVEs1voHWOdIaeVYQSMFOz5J6FCmMyWi2N4oMwcINapyrgAX8aTIqhLU/el0wGPQd8cDn2AW1FIgrkAkEAsZQ172t6rVROimwVC8RGZTRKT1HmILNcVpp3QryqBDZdKzqLOG4dCE6jgN32sWa31+Dsx84i9jRGIb/eRg1z7/H1QZbdFwQmCgfcWykH5WF7oMi/XRxkQf4Tk0x1nHpYtNFpfNWqVRMGpumEvM5HAfpo9/zeXRK8b26mFUI6TMUSOvz6GCy98GxP/y/ClICOr5bFZQ6xvgMpZ+mJfLzQCBgBI6mF5H27B+rrKp3lFkCuh9P5UY8Hsf27duRz+ebZ3E6rX3P2lfOpxp4Ov/qdNCmjhbOB8eufK7OJVVWKt8cx8GZN57ByTedhBfycOw9x7Djj3cgeS7pkzvkWc4F+0y+obOQ2zw6Ozt9BQXZTzr0GNGm84rRGTqjdK3yXmYG6Z5vfs/1x+gRnaCu66JYLGK5Lbfl9r1vmUzGbE9SsOB5Hiq9FRx/73Es7loEADS6Gxj77THf/cUNRZz6lVPm/yfeewKRyQiKW5tr+vSvnkb6n9JAGNj/4/sxuHIQqz6yCgAw9hNjGPuxMXi9HlbdsaopL5IeRn5xBJ33d6Ln2z0+8Go7BBXYqB7SYIDW49B93gp4eE84HDY6VOW/2hVbRrY0s7AG5gEA4+PjOHnjSex53h6c6z6Hl+95uXmuAmJ1zurz7Agk+6d6m02v4fPVhuR8bty4EZ2dnTh9+jSmp6fN+1VvA0D2+Vmce985eDF/BK5cKS8B2Go/2P1RGto2rud5Ro9rhFHtXbUn1ZmrIJMpyalUylcMjfcoiFenN4NTDNg98vpHcOpFLX4FAA8ePKe15Y42BFP1STPS/vJPXo5INYIjLz4CANj6ia1Yf8d6wMESPtR5V9ooELYBmeKKdkC13Wfsmw36OEe6XtrVN1CHW7t5DQQCGH3xKI78+BEU7ixgx+07lhRjDofDvkwA0s3e3sj3qP2sdi7tL103yvekEQNWlz5y6ZMLzh/xV7tRMUE7ZwX/dl0X5zadw31vug/FriK+/eZvAylg556dS/AJeVSfp7ay0tKeA7XpFbsQcD8b2+yeNuAmwZQYHIB+x2imnVZJQ5X32N4zwA/i7SiiDdbUU8X3qVC0gVYgEDCpPPSyaP8puDRdmEqFniLdh0TBoiCGCkH7rEBGBZntAVODmaCF+7H5Do0E815ND9EiX0DrDGJ6izlW2zNHg11Tq/g9o7aaeaDAgd7twJkAVv/WagTzQbgNFyW3ZEArKySzf6Sbgk4KY130BNg2/yjfKO2UDynEFFhoUx4kbfk5eYg8YTuQeL06dNQ5pM4MRphJO/ZVtwCoA0qBpC56rait3mpVjKSR8ndnZyeGhoZw9uxZQwfSmvfocVXqXCD/c350j7LuESR/8ntuaeC9Nu8bJ0+9hjN/eQZD7xqC53mYmZkx9FHwx3u4znTtc0ycf91jpO9Ug6GdU0MdTGooaF8Ibs/82BmMvH4EXqg5B7mtOez7vX14zi89B+FCq+o+jQXNPtH3kv85B8Visbl/Dy3wq/TlGCh7+DxWZed2Gc49gbo6p3Rfucpo2/D4n3pyl9tyW27/vdbZ2Wnqq2g2kuu6CC2G0PlYJxYvXjTHH8FD8++lAR8AQK23hlpvy6Fe66jh4E8fhOd4qKVqOPf6c2h4DQSKAZx/y3m4MRejbx9FyA1h1ddWYf9f7MfizkVkr8siXAwj/WgzTXfi+ROIl+MY2Dtg9Jnnec1zu2stZyid6zwvl9FfwH9sF4MpQKtIWzDY3CIE+NPN2Xh/OBxu1hZygPM3nsfDr3oY9Vgd37j8G0AAePEDLwbQ0nEK8FXX03ag/Lb3RetcqB1lO+TtYqs8pYY2zfnz530OUMrijkMdmJqeQr2nbub3Of/2HGy/fzvqXitrjD9qw6hNpDqI12gGlQIdYGmRTwUwfDbnTp+t+4dpL3K++C7Sglmymhb+0FsewsiNIz7eDZQDuOLXr0B6PG3O0U4kEuZ+DRIYEBmro5gpYuPdGxEbjWH4P4aX2NcacFHbVcGyOjHU0a8AUW0vjfq2A9o2eFZ+IZ9p1hrfD8DXV3t+4QDnnncOR37iCOrJOo6++igcONjxpR0IOi2nUq1WMyf9hCNhHH/xcQycHkDH0Q6z9Y41cOyx2ZmX7fiM39GesG1ZG8ya/sOP/ZRP1fHhOA5mBmbwzZ/4JgrdTcBbSVTwnVu+g2glik37N/nsZl2bGpCzA1RqF9pzbq8Bx3GwuLj4vQXcuVzOl6JAIpF4KkAp0MiwCsK5X9MeHPevKOB1Xdfs9dQ0V2VYElABF0EJIzeM7vKH3jhNbaIxymcp4Ob3BCoExHw3PXB8L410BZOMTOvzFIAQ0Os+dCogLSzFZ6mgUOOb+1wUHNveI85Nu2ic67q+6JrNzHof6WBA99yTjg60ouEqsLgoSV+N1HO8pKeCXc/zTKEo0o7zqDzG5+teaQWQTN3SYnpUqHyuOkmoHLRfTJdSYc/+qaeSi5m00wJd5DUFgpzbQKBZXI9HYKnHsV1WCdeJpupwThuNBnK5HBYWFpBOpzE7O2vWJu/TVHKm79XrrUJkAIyzRFO3Cfy4p18dOlQg6hWlk4rRilqyhtHfG0XuhhxGbxvFjnfvMOewk3/JV7rWOPd8J/mM71eHifIwnT1cT0xT1PHwHgp8AmzOUTweR7lcxsp/W4n5LfOYec4MEABCiyHs+NAOePMeKm7FGIt6P+UM+ZLzyz53d3ebyL+OWenAPimNuA4oA8ibGklQWUD+4LpggRv14NvRpuW23Jbb967ZugVoyadwPYzVn1wNL+Zh7EfH0HGkA7vetwujrx/FuTedgxt1EawEEagFUEs1QXagEkCgHEC9o1Vdt5JuOdS8iIfzbzrf/M+TSXyNVAPH3n0Mx//vcXjhJ/V5dw2H/+Iwtv3cNtR76jjxayfgwIHzPgcdj3U05XJXDfv/ZD+2/852pCdaacYIAePPG0d+XR7r/2m90VfUtZTp6vgEms7d6elphMNhZDIZI580K4fviMVimFs1h31v3od6+snaF6E67r3oXgxOD+KSk5f4zhAG/CmnKvtUl6nDXW1eO5tKgZtuPVMbo6+vD5lMBt3d3Th9+jRmZ2d99lR0NoptP7MNh//lMCrDFVz0uYuw4T83oPbkP4631FdCx0KH0f3KM1psV0/IUTuHY7ADEXbQww5E8BoWHY1Go8bBTietHvFpO71pn7F/V3zmCkysmcDChoXmfvW5CK545xVIzCTghFqZa6SnDfIcx0G5u4w7f+dOVJIVrL97PdZ8aw0ct3mMGm06td8JuDTDjZ/RNuJ37ewaxR5qlyu4JM1tZ4zyHOCvB2UDazvQoG1h0wIO/8Rh1JNPnsYUdXHsZceQPpPG8GPDhk7G4eXVcPLak9j7+r1wPAcv+PUXIHMqY9L+c105RCZap8PwvTwTXe11lUuO08zUY1BNt4Yqzyg2I135TBvQs5GmiTMJ7P7Sbjz8xqYTLVAPYNsj27B+/3rTFw2yakBGHUuaValzojhIbXRek8/nMTc396wAbufpGla/+Zu/aS60U2h1MRDkKFhTMAS0wCYNYAXMtqLhxFCIMDWVRjaJrN5GJbxGngOB5n7G/v5+5HI5LC4u+sCkTjwXmkaINb0TgEmd0argKkx0AXLSbYFP+mjVco5Do9e6ADR6y+/Zh1qthmKxaPZsKwOqMLWVju6VthlX+6rRWgW4ysgKzG2FxD6z6AXnUt9BcNZoNMxC5v+1P+okYZ8JZNXBohFfBVlcYMo3GqmmgqAg0RRrCiEVLqqcKpWKmXPlB/WsEuySNgROpK8aIVq5Xj21HKPWUrA9fFQ0/FvT25VWahxoxEL3jdMppDyla0fBtq5R0hhopkHO/tossi/LGhnSuacTm/9sMxJTCZ9jjmtKFSfHCjSdKKx2ymwC5WUaHIy4M6qvWSQAjFdet69wfmwh7bounICDI79zBPPb57H5rzaj7/4+IwsoK5SvmUJH2UJ6kIaq/Ckv2xlGWrRG+Zbrinytxokqa9spqVk72pdvf/vbS5T896N5nvcUcbvlttzaN8dxfmi9RdFoFJs3b0ZfX59xFHK92+tx9GdHseGfN8CrNoc78pMjGH/tONbdug6pyRQOvf0Q6uk6Vv/raqRPpHHil06g0tcERYkLCSAIFPtbW0cyJzMoDZRQSz/Dgol1YPuvbEf4VBijHxjFwhULCGfD2P5r29FxqAMAMPnSSRx7f3OP7trPrMW6T6+DU/FnGFH3q94CWnqenzO1OB6PIxaLoaOjwxxF1Wg0cHLTSdzxkjuw0LmAYD6ISz9/KV5y+iXo6+tb4rTm8xUsa9YUAJ/sV7Cl0U0222bie/T/dJjOzs7i/PnzmJubMzLYOEp7wyi+tYjdd+1eshVoZtMMvvlz38Q1t1+Drtku9JzuMbVi7G1QLFKr41C71P5b+UyBt+qGWq2GQqHgy5ArFosmcs1K5JwvBTuaRuy6bhPMVObw+O89DrffxbY/2oauE10+214LE9Nu5FgW1y7i3p+9F/Mr5w19t3xpCzZ/ZjNQbtHbBrPkAw3s2LpWsZHqSOUNBdhMl9ZsQ9rJ2mwb1n6PRmNteuk101dP4+DPH0Slr4LgYhBr/24thr46ZOw0zdQ9c/MZPPb2x8x7QqUQrvmDa9B7qBcTGyew9/17se2PtqH3id4ltnC1WvXVi1HbmgGiVCqF7u5udHR0+Cqms9+06UulkrEt2Uc7a1fHqMGkIy84gsde9Ri2PbgN13/x+iV0te0lpTOfZTs3dP507nWMMzMzeOCBBzA6Oor/qj0dW+UZ7eFWr5AyKwBfYTEFT/bROpoixQGp14ggltEcOy1BvRAkjIIHfb+m+zI66TgOstmsDyhwchX4kiHUQNVxsXGPrC2YNFVBC0awX6ShPku9d+wP79FjMbS6JxuFNcv90xvDRadGti1cCNDYdwoLzjE9pZquqt8r/TRKqwKThj+vJ6BSwMzGuaUXlbTUtHKlNbMgbI8ilbdmG2gEk/Ov6bv8jlFdzhWfpRFi5QWdYwWH5FXdm0swpHUROH4CavI8I5QqlDXSbB91we95v6Yl8xkqfGxjTrMj2s0LeVSdOfyOApnfMVJLEGw88KiiHPUX5SoHyyh4BUTqrcguHWy6lmKxmCkkx37q97bCYpSX2SL21hDep7IG8EcvtFiacTI0PGz5oy2Y2z2Hru90wQu2Ks6rIrIzfWwDx84AaZfqaDvHlHd0zaocUqcmZY7KI+UV5XsWW1xuy225fe9bIpFAb2+vz1i31z7lw7qPrjPHejmOg3WfWIfURAoDXx2A4zjYXNqMWlcNK760ovkMFyitLWHs1WPY+eGdcMIOnnjXE6h0VdBxogO7/noXsoNZHHzPQXhhD90PdKPzsU6Mvm0Ubqwp5we+PIDYWAxnfvZMKw3YAXLrcpj/0XnkrsgBAGpdNRz7lWNY8wdrUN5cxtlfPGvGOPrGUdRjdWz7+21GtqldAfgjhpRjlJ88bQOA0f2ZTAarV69GMpnE+mPr8bLGy3DHLXdg9627kbo9hYOJg9i5c6c5bs1+j+pBBYhqeKv9q/qYjXNlR4kV5PF3OBzGihUrkMlkkM1mUSqVfFsAI5EI0l9PI5pq2j/lchmlUgnnN53Hwz/1MApdBXz9p7+O7vFuPPdTz8Xg2UEfj6guse0HOxrIz7SvdvBEaUY7s1QqLdEjdCwzeMBWr9dRLBaRzWbhOI6x64rFIrySh21/sA2N4QaSh5KoB+o+e5mOhFwu5wtSBAIBnLrqlA9sA0AxXkTdqyME/zZE1aOkjUaqyYe0C9VO12t5v22D69YvDTraNqv9TJvH9FqdH9sJ1fdQH7bVtuHQuw9h1YdWIXVnCvOY94Ft/p0NZ3199RwP1UwVM7tmsP9n96PSU8GRDxzB7n/cjVUPrzL9U4c+bVd+R5uDdWV0a68GKWijcA3TBuE4tNkOK+3DRd+6CIlKAlse27IkCNKOxrZjQL/Te2za++jkeVhcXMTi4uKS7/477WkDbhLKTqug8cYBAa3Fq3s4NXpsL2DbQOTztKkwU8GoXhQbhANLz06s1+vIZrMGzGi0kFGySqWCQqHgAyr2ZASDrb1FeoQY0Fp47bw2XIiM7pKZCebI0PRUksm5eDh2RnwpjJnSw+vVIKcA0SgeAYUWkuOCULChz1FPFRea47SOuNJ55DNoNHD/DYsxUdiSBtFo1Oyx576gZDLp28tFBw5prICdkUUCVE1LV/7k/3UvP3lFU25V4aoDQkEM38fxKPAjb6rBoHxJ3qPHWAUxARTQjHhoupYqbfI6+YRrjJFWjZSWSiVftJn8qE4CAlwW3dP3qmOC/2c/ucbZf9KYaX98FzMWMAv0/kYvplJTqFxVQfxcHNv+cBti4zEEQv51z3XJdygvcL3qOlG6kj68NpFI+GSX8gOdcfyMBgR5TI81IY3cRRcd93bAC3hmHTLSQm+/67rNvYWAqepKfmdWijoKbacc+ZFOG65f5WstnkaaaVqcOs1oCKoi16wEW5Yut+W23L63TZ3CQCulljrU1jUKFga+OmBkZu99vc2UUacpY/oe7oPzqIO+vX3oOteFYDCIy377MjzxK09g95/uRmo8hcSJBJySg9HXjGLjn29EfDaO1NkU9v/JfvTf1Y/1H12PYD6IaCGK4798HACw5Xe2oOehHjgdDnJX5cze4+h0FMnzSUTzUTh1x6SmwwO6H+n2gRjKIY6Xn9mGNeA/vos6nIVh165di87OTmw9vRWpT6cQPx7HdPc0pqamsH//fmzbtg39/f1LAg5s6jBX0FMqlUwGFXUf0MruVOc+4Afk6kC3ARf3JrMoU7lSxn2vuw/XfOkauLVWECidTiO3NocHfvIBFPpbqa1zK+fwnz/5n3jZX78MXXNdvoit9kMdCppFoH1Rm1z7yvEykFMoFHyObGbFMcrdaDSMUySRSJgx0o6an5/H7OwsnICDkz9/EkOfGEJqPoVQPoQKKsY+4zFqrtvczlWv15HL5YzNNrt9FiPXjPgXjweseHgFAqWASSnXcSuAVDtBbTauL45daaF8Yl4pAFGvV15S4K1rl+8l36v+bRdAUZ5yXRe9D/di+y9vR/hgGI1A62gwzUIOBALo/5d+uNVmbQYAuPT3LkUoH8Kjv/YoSv3N0w0qXRXse9s+RKtR9O/rN2Mj9tCCy7pGx24ew6qFVVgxuwLBUNC3zYOYgr81wPhUGE8Dc4rvAGDzI5vhBPwZCLYcbDdP7YC54lD9XH9Xq1XMz88jl8vh2WhPO6X8/e9/v6eD0Ug0FzAHRyDGvRwK3NSboGBEQZ1GJVWZMNJEIJFMJs05wYD//G7HcUzKKA1SBQb8Xj1dBFeaok1wbDMBGVEBrXoTFSDzes/zzN5NvlvTktpFwRzHMd5E0oTGMdNky+UyisUiSqWSAd0K4mzwp4tGU10JnHVh0HCn00QjisqkGknlbwBmvARGtsNGPYoE2uoI4fjV88xjp1iQTQGSFrhSAKVpvJqOryBYo9UK2KhoeX+5XDZ9ZkEszp1Gn/lMni2tApbvT6fTGBgYwKlTp8wz220bIN3tbQH6PvWI8nPOOemi60NBpeu6Pg8meYOKleuCnnjygL1/TFPXOQcapddMgEakgXOfOIeL3nsRItWIL6uAAJHrw+ZjygOmlLuua7YfBAIBc8xNJpMxfKf8z35x7Jrxwv7amStAK2JOucUzxbV6vsournPeq043zqE6eWyvK5s6MNl3BcvkF85RMplEMNg6extoFc3Rgo+8lw4Zz/MwMTGB/fv3/0CAbm85pXy5PcPm/BCnlMfjcWzduhVdXV0A/DaNBhq4bu3aGpQpatyrDKPu4O9AIACkgECxZSC7ngs36sLNPwkMPBeNRANhNwyn+iQIQAPjrxxHaDGEwfsG0ag14EQcnHz7SYy/ahzJE0lc9IsXIVRt2gP5/jz2f3w/3JCL7b+7Hd0PdyPo+O08PTJRgYgazfybukRtiVqtho6ODmzYsAG9vb1GJxcKBUxPTyMajSKbzWLDhg0+0G073BVsUwYymst0abUf1HmvgNe2v2zgxPs5Z7laDnddfxceu/YxpOZTePNfvBmxYqyly8MBPHHdE/jWi7+FRrgFXAAgPZXG6377dQh7LXqqbav2JPtpR02VFuqMr1arKBQKyOfz5v5MJoNUKoVQqHnKBsEx080ZGIlGo0gmk6aSued5yOVymCvM4ciPHcG5159DZC6C637+OsSKMeP41qAB0Ar4kccjkQgaTgPHfuYYLrz+gun/hts3YPunt8Np+EGYHUSxwZ46vHXuFfja86nXsqkjXGlrP4N/q+Pb1rfkcc0OtcE40/m1zpM+l+8OhUIIRoMYe+sY0gfS6Hq0C67j4vxrz+Ps287Ci3pwag7Wfmkttn9mO0Jea21p1Jqt0WjAhYvz15zHo+98FAEvgFv+4BZUVlQwcdEEnnvXcxH1WlkOdCjR9tBU8nZRfsomzgu3LjADU8eo+Ir/twNL2uyAgtrE+gzHaRZL27t3L44cOeKjabv2dGyVpw24f/VXf9UAbp0EXcA0RAEs8WZQoAIw6Qc0Wu0oD+A//oqCiYuPxi0Ac9yNDR44kYA/4q4p6ep1UlBGbwzvV6D5JGF9fdTFaUf6OfmaIcB7VSmqMGYkVyOouneXeyEIPEulEvL5vKEln6nKmO8iICL9OU8axWW/SQc2pqkoaOc42SgcuNDq9boplkXlSpDApl4l/h0OhxGPx42CU4HD/mkVZgJEOoE4Tl1AupCBFsBWpc+m6fEaOVdQxmfYkXGCGU2fYbTXzkCo1+tYvXq1SbdS0G4rZj7Tdnoo3+jcc52pk0qjmeql9rxWYTpeT/CaTqexbt06nDlzBtls1nduKEE1x0PniNJRjQryMK8JhULIZDIGQHIN6l5o5T0CRs5DvV43tRhCoZBJ++baSCQSbdeormmCdeVfu2l/KLd0mwAzRdSgouNGHSd8NuchmUyadUtZR2VkK3TP84yDkePnXOk64niY+cF51zWuRhnXEWXm6dOnceLEiWXAvdx+KNsPM+AGmvbR2rVrMTw87AN2CoJUr9nGNZuCLdWdtAfUUap2HP9WB60dZW4XUWIfT73zFNb+7VoE6gFfPxc3L6I8XEbvPb2+AIca23yvPtu2MxQIUQ7q56lUChs3bkRPT4/RdQTd5883i8Pt3r0bK1asMLSxjXbKTrVn9DsF5TbN9Tfg30Ko86UZi7VQDd+49hv4+pVfN/cNjA3g5Z95OTqnOn3O2Eef/ygefOGDqEeacr/zbCdu+sub0LXQzFrIDeeQmE8gVo4ZGa9byozDu+HfX6x2DMfIiLUWVwVgbC3WDCqVSsbxTP3LgBud0plMBrFYDCWvhMdufgxHXnvEvDt1OoWLfu8ixM/EDaDWQBxP4tCMzEpXBfvfth8T102Y56z89krs/MediM5Hl4BZXUc2P6kjSsFcO9CtdpeNCcjryr9qN9rX8BntHOz6ne2053qm01+DjtofrnPOF9CsA1Uut7b0TbxtAlNvnULv7b1Y8+E1ZkuA4jnao7RlHMfB2HPG8NAvPYR27eKvXIzdd+5G3IsbO417wRkoo41ir2fu8w6Hw+jp6fEdu0zQTdlk20ekgdJB+cCeKzvzwH7m5OQk9uzZg/Pnz///2kNPx1Z5xudwt/PCcAFrurfNoAoKNIqlxjjgL4RFQOh5nmEWLhoCCQIaXkvhqM9VQ9cutEYhQ4bQ/qpRay9SGr3qndEoHsdO41wZVxeX3qtRVTWedSwUHJpKRQalAFUPmx71RSCn6T16/A9px4UVi8V8qcl8poIKbarAVbnH43Hf3lkVDpwz0k2FI8fFeaCzIRgMmuwGjcyqUKWS4feqJF3XNVW4CbZ1G4BGhu055jW6yIPB1pnVuo9HnS1qXNRqNbM/PRgM4sKFCz5+UJ5TgaAOIyojOgM0jYh0JJjiNgGb9loAjU4eO7LueR6y2awv6k7aqfdRszVs/uW643wpXbkm6EQjjzDlmmNUg0PBMnmBCkS9+pwLdQ6pI4gglHyiKdlq5AUCAV+VefaRIFwdDzq2SqViDBD+HQqFfFtQqAzpQFJZqIaA53lGAZVKJczPzxujhPKItFKPuPKN7eTjMzl+0pkZHMttuS23732rVqs4fvw4qtUqtm7d6ovMqUGoAEH1keoM2mYK1CnrFAiovFA9qrYS0NJrzB60AXswGMTmv93cfH/Yf7xr+lganSc74QaWOovVXqBc0rOi+Vv7pt/pWMrlMk6dap7r3NPTg2Cwmdo8MDAAAJiYmMChQ4fgui5Wr14NwF+XhzSizKbOoJ3GH46BekCBg2YaqCy1HQTG0RBqYKZjxs8H8SpKXSX0zvUa2ex5Hq6+92rE3Bhm+mZwbvAcrvjoFYheiKIcKCO/Lo8H3/wguse7ccOtNyDUaGWU8Yc6ywZlpCPp6jiOcWDzh9mDpJnyokYlWUjLdV0TTJidnUVHRweqvVUf2AaA/Lo8zr3gHNZ/dL3PIUBQWavVkEwmkU6nzbatbH8WjT5/pL/R10CkNwJnwb8twLa5dez8v/JVu7VkB320KZjmvTYIt4Mk7Jf9LNvhb88J/6YNo9iJ77b7qGuZ80SeH/zYICK5CPq/0EwjZ3YCbUo7DTwUCmH2VbM49HOH8FRt/8v2o9goYvsntiMcChs7Stewbp1Ueui2vImJCROsU/vFdpTpOO350jlp59iw7yX9qtUqFhYWsLi4+KzZQ08bcAOtqAwNeU3DsJmW+yVpFKshR+FsA6N2ngfbk0RGUS8G4PcW8loKF1287bymCtrt+wkoNHqo9xKMMhVaq5Ar4A8EAlhYWDDAQ4GRLmL+xOPxJX1WBwRTyXO5nE8xKSCjMQ/AeCKTyaQZBwGA7i3VKD/BHItUcX61gjTfqwBLBSYXFnnAHhsFtUbrSE86QZTuBD/xeNzwEZ9DGun1Wt2b4EcVRCqV8kWVeZ/jOD4wosYIAZP2RyMR5C87RViNFVVW9qIH/BkQfK+CYo1cqjFG+rNIGB1hTAXn+8h7zAhQ5xAj+LrOaIBwrSmA1UwI8jWVpEb6dX7U26hp13ZmgTq2bGOF/eXcKS0pzG3QqsopEGimntOo0j3uCtr5Tq1arvsp2TSrh2uLcjKdTuPiiy/GkSNHsLCwYGhQLBZNH8mf6hjiXNE5NjU1ZeQv6a/7BlUOM2uA12l/SVt1VDHbYfkM7uW23L7/bXR0FI1GA+vXr/dFKNXotI3hdo33qdOeTuBqtWq2ALYDAtRh7YxUyqmnCh5QbtmAmdfaslazoDyvWZWax21ynDZIUb3KRpB38uRJADBAOx6Po7u7G67rYnp6GkeOHEG1WsW6det8tGF/eBY60DqhRZ0E/M7Wh7QNFAzY9ygdHcdBspbEq7/1aniOh32b9yFWjuEtd74FmbMZVL3qEmB2yXcuQSVSQb4rj2QhiVw8h5nIDO77P/dhYfUCptdOoxqt4kUff5GxA6g/VQ9o0EHHp/3juGw9D7SyQTUoxTnkM3n05PT0NLLzWTzxvieW8GhyJImV/7ES0WgUnZ2dZk++zjdpy9Y93Y3rPnEdvvUL30J2dRaZUxns+utdWFlfCawApqenfdsUbP5jU/taecq2F5SH9Vp7zdg2nf0eBd42QFS9b68Ru6/2M+x1oeO2sZrd197behEIBYwdRD5hhqrNv9VHq0AdQGTJME1LHE2gXC6j6lRNhJ1Zk8qHOhfsO08iAFrH0Gnf2BfFnu14GFh6qlY7J1i7uXJdF4uLi8+qPfS0ATcHpCCEi4wMwsGygILtVdLFQmM5m83CdV309/f7vB28R4G3KhcFUDSudc+ngmcFzQQUGjll3/g+AhE+Q/dJKvNqBLVSqZjIMd+vSqdSqZhKd+vXr0cwGDRn3KnXiaBd+8wfLn4KOU0rJ+gjKFCnBos+JRIJn4AkUNW5c13Xt6ebgpbvtSOFHH88HkdfXx/y+byv0rUqctuzayts7QMBBRco9+sqjXTeXbdVWZz9Jo10PhWAsC98vqZ620pTowLKU57n+ap1UiCoQCGItAEV3893kuf5mfIR+ZZ7m+hVZ9SYAJf7d5kSrqCVNGZWBK/hdYwyc0+QRsqBZgXdQqFgFCHpxfXQzihS4cimQpKeTKC5T45zrlEFyhSOn6CQTrx6vW6OifE8z1dsJRaLmbkk/9C44pEyGqkm36XT6SV005oQFP7cc4YU0Ci0Cudw/kmPUqmEffv2mXQtlXGUWQTO9tpTJwJT68jfjUbDRxOVYbqNgPKJ8pLpf+R9NTg13Wy5Lbfl9v1rY2NjyOfz2LJlizmHmvKAIFA/o0ylzUCHqh0RUhBu2ypqBNu6G/AHHfR6O9Cg/dEMKPbdBq8KXCjHKf/pAOb3lMUqnxX0AUChUMCpU6cQCATQ0dGBQCBgClg6joOJiQkcPnwY1WrVODV0jPytIFrppk5zdcyr3WsDAVv3qx3VUe7Am/7zTSjFSnjdf74Ofbk+1Luae9C1SjvHG3WiSM4mgRQQiUdw+7tux8JA05kLBxi9ZBTfesu3kJpPYWB0AEMHhsz4lPYA0NHRYVKNdf7VpidN2tk31DntxkjnRa1Ww1x2DsP/PIyJv2ylgUenorjiPVfAm/NQDzR1WiqVMnaKBh+YSVar1RCMBZGcTeLG374R3/j1b+CqD1yFUD6EidCEj3/tpryrTWlrN3Ws27aMAtd2YFs/V6eUrknaURo80KYgUZ3q9ljIU5rFYEeWNdjJvmjQj99pgEYd9YFAAF0nu7DrZ3Zh/8f3wwt6uOpvrkLmVAZf/7Ovwwt52Pahbej/Zj/gAR78mR+K6ej40z6qfKv31HHPj9+DN932JuSn88a2awesld7qhGiXfaDyRm1v/Wk0Gma74rPVnvYe7l//9V/3aExqxJgdVdDNAWk6uHpoeS0LLFQqFaTTaVNcigahGqAKfIEW4FaARDCiQBrwR3fYRxYW032uCjIY9VZwrkyhk6IASSOCepaxgraenh6kUimcO3fOgDVNf9Ux00Dm71KphFKphGKxiIWFBd8xCaQLmUyVAftEIKICRD1OjuOgr68Pa9asweTkJC5cuGDmSitB03jnvt9wOIxt27ah0WgYz7yOQQWBXUGc2wU4JwTVpJnu63Ucx0QkKXwJyumpp+CgkiqXyyZ1mEJNo+J2NNlOqQ6Hw75FTqDP/9MhoXymIJNCFIAxIrQv6umj8KFXmDThfLFfWlxEvaC6/41OBFYP5TvovLDTpAneqUDpqCH/DQwMYOPGjXjwwQeRy+WW8BDfa0e1+TeBKNDakhGLxZDJZHx7zTXbQ5UH4C/8RZ7kfupYLOaL6NMJk0wm4bquAZK8jmCUz9X58jzP7Ovm+zXjhF5X13VR6C/g1J+cwsZPbMTA4wNmLJRJXOPq1NP5Xlxc9BULVKVIZwvnmfytGSnqYFPFyTWjRe+eSiFzzLVaDQ899NCzVpXzf9q85T3cy+0ZNueHfA93uxYKhbB9+3b09fWZzxTkcr1TbtifK1hSO4wGsIJhygg1TG39owCYn6njmM5ZbQoK+NvWuwoqKMs5Duol1a9qL1G3aDS6VqshnU5j69atyGQyBnSUSiXMzc1hfHzcODTWrVtn7Bq7PwoE7IxHm86kiaaVa+DBHjfHCjyp3zwX8LBEjlerVaPXJoYmsOrCKtO3UCiE2fgsbv3pWzHbNwt4wMr9KzE0MoRHXvUIAODlH3k5Vu1f5Uv/1qZ6QAE0dQr7QJvZdV0UCgUDirnvV8GQOoNc18VIxwju/f170Yi15m3XB3ah95Feo8+oX2kPKD3N1sSIh9OvOY3opiguve1SZE9nfbVIVJdzbMp7tB3ZTwXo9rW6Lmxeth3rOl77vfac63poh8Ns55jah4oFbGeA2gS8nv3kHD5VFJ22PXlcz+DmtbRdI5EIKrsqcLe6WH/fejQaDcysnMHM1hlsuHuD4R3aQjyJiM/XNaPrhOt+bnAO//Ge/0A5Xcamg5vw/M8/H5H5iC+oqmO0aavzofRWR5muTfaF9164cAEPPvggJicnl8xNu/Z0bJWnHeGmsah7ODQV3PawKANzEan3i+Cps7NzCSNy4Jwo9eRompJ6OGiE8hm254lMR2Jr+jAnkMBPPUFaUIuTqh48fqaGsHqvNF2ZgHB+fh7ZbNa36Ml0tqdZ04G8J72+8/PzPmBKcAnApH+kUimfAFOhznRsFh/QlFTP80wZfAXsCgo5ftKNwO3kyZPmuZr6Qf7hWGKxGIrFokk5ZoVkTRlxHP9xY1QSBFucK4JHpsbzfkY8OV6tcK8KRZUa55Q04ZgVnJPmbHQokb/YH+UzjVarMuCz1cCh0BscHMTU1JRxaJDmnEOOX8G1Ch06NrSugmYnEBSyP1qhnB5pglvO/8zMDCYmJswcEhxzL5GCSX0W+8nrdC2q8A8EAgYMM+pNEEhe4r5+lStA6xx6lSXqAFRnoQpjlRGqRLmWa7Wa2VPEtUDngeM4KK4s4uy7z6K4pYiDv3MQjT9tYOj+IWO8kMft56pzhtEadS5p5Ir0Z5Sf/9fn0PmijiEAxonpOM6SI+zYH4JyrRy63JbbcvvBafV6Hfv378eOHTuwatUqH4Bmo5wD/OndtkEK+A1Lyk11LvN5bCpX7aADP2N/PM9DOp02WY62Y5b3qu2o8tceG2U75ZWdFk1npo5F+8/08k2bNiGVSgFo6s/u7m4AwLlz53Dq1ClUq1Vs2rTJZPjRDrRpQB2mdqVGA+2IGsegkWEFafp8z/MQcALw4PmcDLFYzOiG/Wv34yuv+gqe97XnYdfeXUZXdOY78ZrPvQZ3vupOdE10AWeBR179iHn+XT9zFy6/83IMnh3E8MjwEoeK2gMKgJRnOIfU+3R6U3+ofaXzwOcVdhXghfzgcuGSBXQ/1O3bmkfbxbafSevjrz+OkTc1jwQrlorY8Q874FWWRix1POpEoM3Da54KZOuYle80m0+fqfrYdrDYn/FenXvls6e6z854VXuWtrNiFgabdJ3TfrJ5W9+lNpyNgzjWvtN9SE2nEAo37Yae8z3oHutGMOK3U7SvGhTQIKbagbMbZ/HNt3wT5UwzSHLiohPwKh5u+vxNiBaiZm50LSmAt50VttPC/kznF2jiioWFBZN1+Wy1Z5RSDvjPcNaq2PY+QhW0JIBGJRUwE4iqN0oXG5syNg1JRu7sftpCXKM/nKh2E88Fr0JTHQgUNGrM830KOHifekJV+XG8+n+OUfvI1mg0zzYkACPYte9Np9O++SEjATDFy9gfAhaCGb6blQ9dt7XPWwE2F6oyL68jcCCz69/qGU4kEr79xwqedJ7U+aApMPa9ekwSPXKJRMJ41fhuFeAUKhpBZJ8ZgVXHSyDQTMfWlGEFXwT88XjcV8ROPWrkUTqdlP9JXxoIjuPg5CtPYuDrAwjNhgwwI2+pE4AgX50IXV1dGBsbM8CM9ONc6P5hm795xBafRccI6aICjXOl64XP0RQi3qd9pEzQLRzqtLMBNHmRfddqqep95RpglFr3mfG5zKQhDW1lqTynkXeOu5ap4egHjmJhezONz425OP6O40AQWP2d1ebZlGX6HjU4VSGqbNDMFNJGZY+deq90Uhpq9EkjKWzkfXUYLrflttx+8NqxY8dQqVSwevVqn4Goadxql/A7tYUAvzPSjkjy/+3AgIIFNjv44TgOZmZmljgwbePathNtw9neoqQ/QKsYJOA3vinLFADNz8/j2LFj2LhxIzo7O40+7O7uRqVSwczMDMbGxuA4DrZv3+47blYBKPtj2zXMhNRMKe23RrtV96ljXt/DcVBO084ZvWgUd990N4qpIu55yT1ohBq4bM9l5t7+8X688kuvRGo+hVM7TuEwDpt5akQaePg1D6NjsgMv+JcXoH+03xftbgde1H7VbDxm2dFW0FM61C7g2KmTN9y9AY/96GNwQ80533rbVgx/ahgNp2F0oGZ0EiQyAy+TyWD/m/fj9MtPt9bEDcdQ8ArY/ee7fcBZ14PqW9K+3bGjtt2t68IG1/pduznn/e3WDX/rtku9TtcKn6Nj0P7pfbRZNHtT7T7tK8dsOyL0Pfp+tclIQw2skY9JA+UZG+TStm4nlzzPQ6QYQbjaslEAIJFLINhYer68Ntte0mtsPKqVnwk5AAEAAElEQVROSFt+BQIB5HI5s8Xw2WpPG3BrdEUXJwWCGtmO4xgQwXvJCJqmqxFQGns6gSr8bcayK/3y80QigUwmg0KhYNJeVfACrbRUetFouCpjsimo4kIA4PMy2WBChScXtgJF9STamQP6fBrVtVoN+Xwei4uLvj20BBMKDMLhsEmh1VRY0k+PzQoEAqaSpC580lMrWAMtoM132fNH5RMIBMwxZeQPLbahTKz0UiDKsVMYqOeaQJX8wSMGCAZd1zVnkieTSQNoNP2a/ECwSiBNPld66kLUyDF5nvtqNU2aEWCdawXvFE7KE6RHIBBAtV7F2EvHcPzNxzH68lHc8As3wKm0FHIikQDQ2s/PMalBs3r1asTjcRw7dszwKoE26cm1wPGR1pVKxUR27e/ZZ3t8yiucPyphdWKRVlxXiURiiVLk3HK9cn3Q2RYKtc5lJG9S7vA6dQzp3mvtY6lUMg4SLTRH/lb5pkqz0WggsBhA/7/3Y3HzYtNr7wGp0RR6Hu7xKTd1BOhJCCoLdewqJ0kDriVdC+rAUkOj3brXtDDSTiMSXLfFYvFZ3bO03Jbbcnv2Wr1ex8jICBYWFrB582ZTo4KyhnID8AdJgJZ+UMcs9ZgCTMog2giAv2iURp75txrOlLe2E0CNYwUsTwVgKHMjkciSwArHYzsX+U41/nlfPp/HyMgINm7ciHQ6jWCwecLFypUrEYlEMD09jdHRUVSrVWzfvt0EdShH1bmhoEKdsWzaLx0v5a1mbSrNlT5qD3ueh3N95/DZF34W+UQeAFCJV/CNF34DiXwC249uN7q/f6J5xviOvTvQqDXwlVd/BW6wRbuFgQV85ee+gtf86WuQmc4sAXUcG520DKxxTtUe18iqDRB5L4+FrTk13Peu+1CPNvlx/RfXY8vtWxAIB1CNV831AExwgTzImiNVVDF50SS8oD8Svfbf1i4JlOk8KA8TP6jTQx1ANgbQcdlA1KYbv38q+5jPUsCn9ygQt5+ttnAFFR++oO2lkW/NrOT3urZsftV5tJ0I7BP51MYAyhcK/u31TDvDdr7Z85O5kMHNH74Zd/zqHcj15LD1/q24/IuXAzWgjrpvrvS99lZWnUOlvcpIdXLwO2YhP9v1bJ424L7tttuwceNGrF27FslkErFYzLfYgFaqjYIaCnMOjoJcvQ1ahKnRaBiDWqPh6hEB/BFlBfYATDo1I3K8loYoPWm6p1Sr/rL4Evtpe2BUwCp4V8VHsKFKS88v5gQrY7CfjPYR8JbLZVM4Q6OVykC2IHQcxxfdU+HFxn4wyqgRsHg8boQej+LSPurC03RtAhSNjlLBc77VkUKaNRoNA5wJyrUSPoExwYt6ie0xKjAuFovmGYw+s4+BQHOvdGdnJ+r1ZnESRrw5z3qcmR7/oVFrNWDIy5o1QOHO/rmua5w8ykPG8RFyMPbcMRz6hUOAA5QGSvjOh7+Dq3/zakQutFLp+VtBG9dAqVTCnj17fHNH0KWGF/lP1wnPP2efOEdqXBC8a7EuHbO9v4+OEV07WtyM9AsGg+Z5HKPyDz3rfBfXCudBZQa/V3pRwVLwk78pL3i6AveqkbdZN4BriGB3+OvDqEfrOPOzZ5A6nsKuX97VTAkMtyLSlBuu6yKZTJrIgPKSyjh1Pqgy1vkgf1JmqhGgzjZV7CyKx/XAYoeUX9VqFYVCYTnCvdyW2w9wc90nKz5ns9i1axc6OjoA+EGvHSkFWqnZvJaONs/zfJV4A4GASbdtl1KtBjPfRbnE7zXSpjaU/bdty6htYEecFoYWkBxNLrFh1OahPUJ5yf7wmvn5eYyOjmLdunWmCF00GkVvb+ts8DNnzqBSqWDbtm3GllRns71djPpd+63ymDYA+6rBH6VFO2DPMQSDQQxND+GW+27B7TfejlKshHAtjOv3XI+dJ3ai1vBvT3QcBw4cXLzvYtRiNTxx6ROYXDEJL9B8djldxj0/eQ9u+cNbfDRSHaJj1W2k6rRWXaPjUSdIvV7HQmABj771UYxdOgY8aU7Xo3U0gg3Eo3ETpKHdxww7PpN22MlbTiK/Ku9bD4nxBBKzCdNn0lh5RLdFcm5Ul3OsGuCzQVs7nlQwrk2BJunLpn+3o5n9POWpQCCA+e3z2P8z+7Hl/VsQngz7TiNRftK54f/Vxte1w37Z2Q42aFdnAevO6DZQXssfxWhcL3okodo9ane4rovoQhSv/q1X48G3PIjnfua5cND+SGJ9hwJ4O1Cka9jmBfabNJmbmzNFrp/N9rQB96lTp3D27Fn09fVh5cqV2LRpE/r6+pBMJk3UTsERB8CFqgSxPRMqZBWQ0VjnszR9SH8r4xQKBZw5c8ZMLu/lsxU8qJCsVCqYnp4GAPT19SGRSCyZSE62Fgeh4a0AhEYwAQ4PelcBqnTg3xQyfX19aDQaOHPmDHK5HHK5HBYWFtoWb+Mi0UgpP1Ohw+iu7mNWAKELVJkxGAyafbW6IAhEFNhQcXMu7BQUGxTb0dVgMIjh4WFUq1WcOHHCp6AIkFgki5+R/gRRQMvzrgKYgr9QKCzZr0qlGwqFkM/nTZ8ajWYVbM3WUOXKfqtHUEGpOiWCwaDx1NuOHHvfbQ01jF8xbhQTHKCarmJ66zRWjq/0gVhdI8pbKvDV88m+aKSd1+neaDombAVsv4ONnlZN0dJsAYI63UbBPVvqbNO0ajqsdH6VZ7nW1IlAHnBd15cpoYqE14RCIVO5X4W18o2uDV0HHFOlUsHA5wbgBT0M3jmIgBPwFSlTZdNoNFAoFHxK0HEc81zSjOuX3lVd66rgVemTxyl3dI7se0gvOhZ0jdqKb7ktt+X2g9nq9ea+7u3bt5s0aduBz3oplBGUoyrXbYe/2lz8XGW92k5qS6ncoL5n04wyBSE2OFV7iM9wHAezN87i6DuOYtufbEPvo71LHAf2vZqxp45fOttPnDiB9evXo6urC0AzwNDV1WWun56ehud52LZtGzo7O824lU7UW5pKrc5TeyxKc32O7aS2ZTZ1T7VaxRXHrkAj0sC/X/vvuGnvTXjREy9CJVFBNdQqqGZnPl3xyBW4/OHLcffL78aeK/cADjC4bxA3/uONcBsuGliauq8gSPtDm4B6mZmDmumgcwA064ic3X0Wp69ppYEDQG5zDpXeCjrnOo1dR/3H+VNw5nkeeo73IJqLohgtmuf0P9IPVJcCSLXb1QljO080Sqx2mfKyAjk+W3EOP1PaA/DZaXy/Rq+12Q53fTb5YPLySTz+3sdRT9Rx8tdOYu0froU33spQU6cX+2djBZs2eo32UW1GfR7pRftb+VyDCOqE0fvbOQYpH9T+cBwH4WoYN/3zTXACS+tQKIbU+dH1pmCa71UnoF6n/JPL5VAsFvFst2d0DnetVsP4+DgmJiZw6tQp9Pf3Y/369RgaGkJfX5/vTF/7N9ACYMq4KpSBpRFU3qfEpvFuR7PUo6YRbBWGmkJOA50/jPIQfDBdi+mo6skhQzLCqPvQ7ZRQLnjbUaDRatKKwN/zmhXA8/m8KWBmFzcD/EeDqCLUpp5oFRTsg+1Z5RxQeKhXyQYt6jRRAKygD4AP3GoKjDpZqtUqzp4965tvdXrQ0cH32+m+9gJUAat8QDoT6Lqui3g8bsAZ3xeJRFAul81ca6RWFy3pRoCn9Ob7WEVbnSOcL51/13URqodw0V9fhKO1o7hw0wU4dQcX/cVF6H+o30cHOk5UUKnweaoj/NTJxXsBmCqjBMI6Nt1nzOeo0aPOHlUoNpC0PZRq/Om6UY+zXqM8rYZhrVYzEXR1sNiOMuVx3aOnW1R4T7lc9p2EwIwLrarPsay8bWXz/sjSNENmHyjdAoGmR59GHsdCr7HrukaeMmrDcfN68hr7wPWkBqDKOMo3paHt8W6Xurncltty+8FstVoNR48exdDQEFauXLkkIqVOVq592zZhU2eoGv5qIKu+0OeoIxbAkv8Hg0F0dXVhfn7efG9nLun1mokz/vxxHP0/R1HrqOHou49iy4e3oPv+bvNcjssGRionte/ZbBZAM4jEQmp0urMGjuM0o1yHDh3Czp07kU6nl9CL79SmDgQFik/lvFCd3A6o8xqedOF5Hq4+cDXSxTR2jOxAIBAwzvFgMIhSqeSbW33GTV+5CeFyGJP9k7j2tmsRyAdQ91r296OvehRXfuVKBL2ladmktQ3GOSbVm9Rd1MHFdBHHnn8Mdht4ZACZ8xl4Mb/jnn1WpwEz22qFGryGfw6CtSAc+PdQK71t8MXPiS84FuUfdSgRi6gjAPBnaygO0fvI53aAT3GR9kl5iI3rbOo5Uzj884dRTzTnbPHKRZz+jdPY+CsbEcj5j2TTQJPaZ/Z71S7RH81CtvmXNoYNxNV244+CWQW/tnOE8xwItLYbkmbtHBN8huIEG3PoO237tx3d+exSqYTFxcVnvWAa8AwBN5vrushmsyY9p7OzExs3bsT27duxcuVKnzHOfaAs6qQMSkbWPYfKEASZBCWMOOqi4Pc2oZXRtbgGDVeCHxVu8XgcsVjMV/iC4IzPrdfrmJiYQL1eb3t2OI1w3dtA0M5+5fN5pNNpk7Zre4s52QsLCyayTa8SjWIVpnrMFt9rL15lNlVm7LfSkAa7eoOAVrq9OhXUc8i/+TwCYtJPnSKhUMjwhPIDo3qkNZ/HPafspzpv7AUKtAAiFzD7rMaE67rmLPNisWjATiQSQTweNz+VSsWk99dqzbOuqeAU3NjOH1X0vF9T6GxwQ6HgOA5SpRR2fWwXGqkGVn1+FXr39QJOa5uGPWYVippZYappF4u+NHFuF1CBqWtSASn7zTnUcehWikKhAMdpbWXQdUQ6aAYF6aSZDnTYqLGn/Ms5JU/RSNKq5lQuKkc8zzNZGHSmKP/RQUFnA9/P36SHPoe0Is35HZs6o9RxwfGSlvoOrhd7zdKpwLnjuq/X62avocoAyj++i05IyiullTpgloumLbfl9sPVKpUKRkdHsbi4iC1bthgHbjAYRLFY9EXd1G6gPaZprFz7Ci5Ujmk0lk0LVqpNAPgBTzabhed5ZguP/lC/qx0HADNXzhiwDQCV/gqOvusoLpq9CB3HO3xyVI1pOsZVF9jydGFhAUePHsWmTZvQ0dEBz2vWX9GTc+bm5rB//35s3boV/f39Rmaqo5x0U3uANh9bOBz2bVtU+5J0UmBiO0TsAMRFpy+C6/mPTUskEsbWrrpV3Ppjt+L1//p6OO6Tp1S4YVx373UoB8uIN+IodTSPl224DTz4qgdx+IWHMXrJKNYdWIdr77zW8Ab1Rnd3N/L5/JKsU9pzAEyQyHEcJJPJpn7PhzG8dxgTGyeAAAAP6HqiCyu+tMJUstdoOevfFAoFBALNQsDkjfDpMCK5CEoDTTC0/kvrsem2TXDqTvM4NWlKY7Vj1SZp50giP+l3mq2m82TrSj7HjmLbINjO1uU1ts2jn3Ue7ETifAKV7koz+9EFeu7tQbjUqiDOd+p7yXMajFAesx0F7KcG1dhYvI62pWIN/a1BHrWL212r82SDcf5t096mv2b0KtBXGvPedqCb31MuZLPZ74od9N8C3GwUnpOTk5icnMTjjz+OLVu2YOvWrejr6zPHMdneEQVN/KGw5h4ORpjJwGqU6nnLfK6mU6lRHQwGzd5cm9HVILcjwzSwadTqpMdiMZTLZQM2NB2F0bBCoYBsNotQKISuri4f+OaG/NWrV/uYmiCACqhUKhmhrRFM0kWrRKqiUQcF6ccxEjTW63UDKDV6ppFTO1WXRr9WOCYtdYGTTloAg3ThwnfdZpqUOjMUvNvpKvaREfos5QGCOgAmK4EAyY6oE/x4noeVK1dixYoVOHnyJPL5vDl6jOBbC2rR21oqlUyFThZOYyRUwSb5WwUK54xjoXOJ76hWqwhUArji96+AV/fQQOtz8ogWAlMHBOfe9lQyYqrOB/K27jGzBZV6MhUkcl54D+dH0621Crg6Zfg7Ho8bZ4Duzad8aDQaPoCrBYB0HUejUbPvWnlYq89zrOoAJL9oKjrnzs6o0Ei37bjSwmdqHPE63kd6hMNhdHZ2mjXFueCWDKUt32OnbynP2wqaNNesHab8K2+ygBplBh1Dy225LbcfntZoNDA9PY2FhQVceuml5thHO81cgw+Ui9QnKk80OAIsraqscob3UOaqcQy0tvfYxjJ1FytcK0hxnGaWYceDHRj64hDOvOEM3KiLQDGAtZ9ai8SRBGpuzQdwqdc5NhZCVbCj2wEZ3BgZGcHmzZuRTqfheZ7ZTki6zc7O4oknnsDOnTsxODhobD7SyZaX6pBQh4XaQOyT3ku9QVmszgHOpdpzNjABmoGkQqqAj7/64xgfGMdnfvwzePVnX41YoVlzKVqPIlwNwwk39UEgEcD919+Pgy8+CASA7GAW2YEsnIqDS752CSJuxGSGLi4uIhqNmkABQXI4HEYmk0E0GsXCwgIKhYL5nUwmEYlEsOHLG5Cr5XD8dceRPpHGRb98EQJOAPVg3ZcKn0wmTdFf0oJBsHK0jH0/vg8LGxZafOl6cBsuQk7IN7ekJeewnY1KumnAwga5bEpnGzyzaaaFglflAwV/ijm0T9pvTQEPF8K46gNX4YE/eQD59XkM/dMQBr8wiKATRCDsd85oYEd5VNemHRzSday2qvaRTe1zfqdrhrKCPNIugKL/53s0Eq+t3bXap3bX8zob6/F6XZvauIX3u9H+R4DbbsViEY8//jiOHDmCoaEhbNq0CWvXrkVvb68x8ghONeKtAjkQCJh94cqkPA7L9k4xxZN7lWiQawRQwUgg0NrTqQXPAPgqp5NZFMzwGel0um2KkabNEhizaTryli1bUC6XfQzK/jYazX2ejHITcNB4BloeNzXClaE5bvZDx0lgB7Siclp0gYra9rLa2QmAP71cgQX/JrDiolPwotUnNf1Wz0bkXOh+caW39k+BpNKa/WQjoNDUdwCYmpoyBgP3EzGlmAYBBZJ6+Ah6ldeooNQDruBLBbY6RWKxmAGOpHG94i+6pcaS4zjm+LZMJtNUSk/+X3lWwTB5kfNOoEkHEnmG7+H1aqRoipQKXGDp1gFbaWj9AM6PFmJRg9BWTjp+AmuuI/Vc6polyLW9ouRvGn0EwnToaGSY9OaPjoG8yqPNABgD1q5rocKf60OBOmmiqeo2fVXZaESaBh15V+eRa9pO6yctVKZo5Gq5Lbfl9sPVqtUqDhw4YCpxM7KqAFlBH+B3XmpkG1ialq3yiHJHAw02uHGc5okaWu3XBjeUnQRyqlscz8HwPw2jgQbOv/Y81v7LWgx+abApvxy/E533tDP6NTKvIMjzmhWJz549i3Xr1hnnru7pdhwH2WwWR44cgeM4GBgY8MlJlaccv9b2AWAczJoVpvrTzvRj4z1PBc45Xn43m57F557/OYyvGAcAnN5wGne98i688M4XIp1P+9Kog8EgGgMNjF081ow8m4cDj73iMaAB7L5rN+qVlt7gHFFPa0ZgJpMxttLExARyuRxKpRJyyGFueA5DnxxC1a1i+N+GEYvETMBFTxppNBoolUo++4DvnRyexMy6mVZtGwCzu2ax6qurkDqX8oEs1W3aFHiRhnawjmuB9LVBtj5HbUsNvLUD7TbQ19/8XkGg3k9eCbgB7P7l3Rh99SiGPj3UvA9LC4DZDjTtkw0ySTPdf61ZDIrFlMeVlrZDgrzf7sd22JHe/LFrHOn79HO1Ne31YdNA50WBu9qmQFN+5vP578r+beBZBtxs5XIZJ0+exNmzZ9Hb24t169ZhzZo16OvrQ09PjxE2ytxq0KtiIGHsfZw0Drmflp43pmPyez5LgTQnnB4pNVh1Mvmden4A/2JRTw+VE9AEBIlEwpcmr6AnGo0aQaLGO//mvdy7Q4BH4Gx72JR5yPCa2sXx24pJ6a5eSx0TP1cPNmmk71eFpqBc6cYxMr1ZlblG5u3n0tnA9/Je9oegwla2fJ/uMabDhSm3QFMhnj9/3tfHer2OfD6PTCaDZDLp41k9u1gBLgBTeM326pHWClipnFXxquAgT+kRdhwr+aBer6NYLC7JtFCQTr6jkFKHgOu6JpqqXledK03NU15RIGivH50Hzi/frY4cjlcdP+Q1OjVoNKqjjLxK2pNHuUbIT5qxoeBdI8bkWRus6rxpVIN8pMaHKhBjMMrzldc1C4HrjxF4LYDCudUK/nqdevUdp5kxoIUJlfaa4sg+qaFoZ64st+W23H74WqFQwPHjxzE4OIiVK1f67AS1EdT5pgYt4LfDgFYwAfDXkVAbgXJT7Qt1EPK9fAYbHep6jdoAruti9cdXI34ujsFvDMKDX5/aoIa/9XM1ttUJzZ9CoYCpqSkMDg4a/UqdTzpls1kcOHAAlUoFQ0NDvgihyndN6W/3XttBwL6ozWQDKD5L77Ej3YFAAIgAjZDfaepFPSAEH035jo7ZDrz89pfjztfciQtDF3z3VaNVuF6ryBfHp1mfNnCKx+MYGBhAMBjE2NgYZuZmsO8n92Fu2xx2/vVObPnsFlTrVUSTUSSTSTiOY07GoL1FulLf0gauF+tw6q13pU+nsfMvdyI+GocXWFq9Wm0T5Vu1YW0Qps+wwbBer9/xe86PZpfqeuB1auvr3KtdZQNydcjEvTg23LoBZa8VbOB3+i5dB8pLyo9PFcTiWEk3bRxju8CIjoF9sgG+Xms7MGybsp1jgLwPwBfUUCeB/duWB/acc4zMTLZriz1b7bsCuNmq1SrGx8cxOTmJffv2YWBgADt27MCOHTuQSqWMt0yFlhKThHAcx+yvLRQKJu2SoKNQKJiFCrSYk8a5ekxsYUjCKnByXdcY/ypIOdl8Dn/T6OWxO6pA7D3eZBqt1G1Hb7mXnFF7nr2tzMsiXyr8OF6gdVwUx0awAcDsVeUZiQpIOZZQKGSOZ+OYy+WyiaDqYiDgUFqRxqyuqd4t24OlSpkRb/ZbG71wtnJWIdVOCdrPoLNDAbrntfYXa9STRyUx6ul5nuFFBVU6H/TcMqXd9jaqA0HTtdW76MGDg1bWAK/V+WR6MDMGSA8FohpZ5RYE0lr397luqwp3O76nEiGNyuWy7/36Hs5vT08P1q5di6NHj2JhYcHnDGnnleS7WbNA17I2121mxNBhQp5XQ4T/Z/o40/ypKAhmbeefCn51xOl17JvezzWn8608x/VIWioYV6NJDTMt6qb74xmxZ1+4lkk/RgYox7R+gG1883PderDcltty++FupVIJZ8+eRT6fx8aNG312i+pou45HO3mcTCZRKBTM5woCFRR7noe+vj4Ui0Wfgzyfzy9x8D0V2Ff5yr6ce8M59O7pbYJtATt2tJDGumY0stn36BiBZpDo7NmzKBaLWLdunTm+MRqNmj3doVAIMzMzOHjwIFzXxdDQEKanp9HV1WWOyVU7UwGCDSLUUQvAF/Vm9Fm3MvK56lxVRzrQ1Ed92T68+Z4346Mv/yjGe8ax4cIGvPHeNyLSiCAXzJm+8LSMeDyOFTMr8CO3/Qg+/ZOfxnznPABg1927sOvLu9CotDIEqBPVVlT9385p8vh7Hsf4deNAANj/y/txzQevQfSY3/ZUHaq043xWKhUU+grY9759KA22ClmV+koorC0gczKzxEGjc642h/KGfm9fo3zJxmw0ta2VHzWwZj+jHbhVmtoOFxtcso+001auXImFhQXMzs4auqu9otFfDZbZY7ZBOu1Ke31oEIB2OH8UaKs9TWzELaWa1WHTzAbWasu3u05tPR2DTSvbzmoHvNU5UiqVMD8/77vm2WzO032w4zjPSg8CgQD6+/uxefNmbNu2DT09PcboJ7MATQIzimgXz+Ki0EknUCGg44Sr8FbDloCJz9PItHo9FKyqwFMwYwNI/q3PITOw341Gw0TtlGE1egU0GaJYLCKXy6FQKJi+uG4zjZgCiYqTwlGZWkEbF5RG+Dgefk5Aw2gqn8VKx/w/nQu833WbBeF4hre9v1ifTzoRhBI8ab+5/5QLXD2B3OOue39JL438keb8jt9r5NNxHMMHmsbE+dfUe41uMtIaCoXM+c2qcLknl5VDldYq4FQJNxoNlIfLOPjug7j0ty6Fs+BP36cA0r3vGu1m38PhMBKJhI/3WfANgM85REBIYc5IKnmQ4JY1Dfj+XC6HRCKBVCpl3svrObbNmzdjcXERExMTS4Sq5zX3y2UyGcRiMaxevRqBQADHjx/3CULde65Kic40vUYdWtzDV68394kxs4HzowqSaY0Uurq9hO+09wuSplx33ApAEGw7WkhTNT7pLNHPOU4tfqdGF3mAz+dcMhVPawhodIK8z7WoEXfO6ZEjR8zxiD8IzfO8pdVNltty+y/as2Wr/G9pHR0d2L59u7E7NK1ZbSLAn0KuBqzeS9ms6ZiUZ4B/fyRlpR6nSZ2pzkX+1nsrjQqmbpnC6LtG4dQdXPP2axAc9RcjVbtMn8XvtS4I+6YAx95m47ou+vv7sXbtWrMlkLpxYWEBU1NTmJmZQSgUwvbt2zE0NGTsGNoDKqM1eKTfKRjju9XBrUC7nXPEHg/nwtgsoSo++qqP4hf+/RcQ8ZpbncrlsrEhgWY0j0WNAaARaWCuZw6P7n4UN95xIxbnFpHL5UxhTtrG5XLZ2IfpdBr9/f0mc5U24Pj4OL52y9dw/HnHfenqgUoAN7/lZniLrRM0eGwdi6xxHASKlUoF+VoeF153ASNvH2nyXMPB0DeHcPHfXgzU/FmDnHsF77bTg+9Q4GaDYZ0/oIkNBgYGEAqFMD4+buxCNtupooEUtXvVPre/4+f6w34w049rLxaLGceFOnZoG3G+9EQnNg1KakYrg4U2aFXwHIvFkEwmfQE5joH91O2r3IKp9LZppu/jOrCDF0ontc00GKJ4zZ5jdaDYwJvrfHR0FHv27EE+7z/r/em0p2OrfFcj3O2a67qYmJjAxMQEDhw4gHXr1mHjxo3o6+sz5x2SWe2JJ5MB/vQYElI9PSq4VCgr+NPqmoycZzIZ3/Fm7I9OII1r3X/JqJwuMvYT8B9tRueAeoX0Or6PQjwejyOdTmN+fr61LyaXw9zcnKlMyegrhYDuPXUcxyw+XsvjrvhOLlRVYlzMeryUfdSaAnW+a2FhAb29vSZiagsCoBWZVk+nghOCWo2s8nrOCYvHKWhVUKupx+rtJjgBYMbGauQE/gRtvMZOn6OTg/1SoKXGSSqV8kUZlT/pjNBq4PPb5vHEB59APV3HwfcdxLa/3oZkNrnEuaFgk7xPYcz3EGRyDJwv0k/nkbTUdUKDhZkWKgyj0ajZm6cCX50b4XAYIyMjvgiGzguvIbg8ceIEenp6DL01Oq1edNvTqoYKaURnjRaqUf6w1xodLeqM020K6sFX5x6N0nq9Di/sYXHbInqP9vrWB5+hzkSOgbxOxxKNOyo3Bdc6Vk3x53j5Ts41v+NWBBov8XjcfEfAb49xuS235fa/oy0sLODYsWNYs2aNWfsKeIEWaG4nIzVYoXaQbfwCLUc15VSlUvEZ5foubbTJTD+CDqZumcLp955uPjfk4ZEPP4Jdv7YLmSMZI/sUlLbrv/bbjhyqQ1I/m5ycBACsX7/e2AeNRgMdHR3mWbOzszh06BAqlQo2bNjgs/nUjuLY1L4C/BlU7K/qRV4DtAITNhhVp4bKesdxEKwE8Y7PvQMAUEPN2ImBQAClUgmVSgXJZBKlWAn5rjxWTK1AoB5A34U+vHj8xUAIiPY1bZnFxUXz7KpTxcS6CfQc6/FFuLVf1WoVM6kZLAws+PeGA+g/0w/U4dNTvD8ajZqCaZrlmEgmcO66cwZsA0A4H8bqu1bDqTsIBP3785WnlY8VGNrOf/K5gnPbQV6rNY9FVieFzcP6Ls240GBRu7VEXuVvtZlJZ+13o9EwFd61L+qMUfzE5/I7tT20b6S9PkfBKt9nBzIVy6jNT9tII+S8TwNEvLaGGqY3TGPFqRU+QKzOCqW3/l+faztPbODNz5U+jUYDi4uLvqNTn+32PQfc2rLZLLLZLI4fP46+vj5s3rwZa9asQX9/v8/YVeVATwyjrQTNgP+cb16vwF29tsoUZGSNqDKlGMASJuIPDVUFAXYqiRrFqmTYZ75PmV/7ynFEIhH09PQgnU6jXC5jenoaFy5cgOd5BtRR+DHNnEDWzgBgVIzP13HzGRwjATzv18VDoEbABACZTMZ3jzoPbM+vKmrSxlakFDZM0VYe0NQjFRLsIwU6hZRGQwn89T3kOfISx8CxMiqg9CHQ4TPq9brZb8t3sqgIAONg0CwMCrm5XXM49K5DqKebymbqqil4DQ/b/nQbwrlWOrt6SLXYnuu6JsoZDod9UW/lefKICh77aC41nJRvbEcY6c95Uf5Wfi6Xy4amlUrFB3ZV4OdyOaO0uQbVsQb404BsJ5EKVzZNe9M1qAqMfKiA2zZmlI8UbLOd/amzmLl5BvgzoP/x/iW04XriGFTpkcdtOcO+aR80MkVjk9skKFOU77km7OtUedZqtSWpoMttuS23/x1tbm4OtVoNK1aswMDAQFsjnP9Xu4MyRp3ONmAhILR1uMogBTT6DJXpCu7hAZWVFd8Y3KCLYncRKTfl08/AUgcyn8n+qTNRAYRm+Sk9pqenEYlEMDw87Mt2SqfThg7ZbBYnT56E53lYv369r/CmHQBQe0gDQtpHdQboXADw2bkcr01j/U7pDTQrL1MHJhIJhMNhlOol3PPiezC1YgqvvOOVWDW1yvSFz+no6EA8HkepVEKpVMITr3oCo1eO4sqPX4l1J9f50pdJ12w0i/tfcz8mL5r09Wv94fW48bM3wuv0sOAsGJuO2WW0pxkRNTZL0MOR/3PE96xqRxUnfuQELvndS8x47QiqgiulfTtdqnxo2xs2WNOCWu3Ash2cUV3Ne2y7V21Ke15tYKq8avdPgxF0EujWBOUR/mZ/7PVpOyBsLKN0VPoo//BaOzujXeaB67p44pYncOrqU7juU9dh7bG1vn7z+fZ82M4TmweUntonG9CXy2Vks9nvatDh+wq42fL5vElD6ejowOrVq7F161Zs2rTJTL6mi9PrE41GTaRWjUdlbmV49SDazJpKpUxqrC5S/uazqVxs7xEAX9VqnVzbqFcwOzo6ilgshv7+fgOa+ex6vY5yuYxIJGKKwzGdI5VKobu7G8Vi0RwVRa8gQSH3TzMaRmBKQKYAnYUpOP5Go2EqdCuoY3qughhmBOh54bq49W9djAr+OS/sr3rDNAVfAZXygn0cFPf5qyCyi5NRMBFs2CksWviLSlfP0mRjvyngSEvuwwfgA9iaJcC+5LbkcPjdh1EebFVzhQdEjkZQX6yjVmqBfd32QL5nGj+BvgI7AAaI24JT+ZQOCq2oqkYMDRSgVdWc59lTYCtIVkeNOpJ45nkymfQZP/p8WxGwtZs/jRzzO/K/GgI2mKXDgg4tvk95zTZeeK2mItbrdZx+x2mM/8g4vJCH4798HOEPhtF3os8oCFVaTPciX/AdPH+UvEK6kbe5lrgWbZ7nmrANO/Krris+h84pAvFlwL3cltv/zpbL5VAul1EqlbB27VqfbLUNVDoLKV/VzqEDj3JRZbsatu2AoP29DRyNrGx4WPNPaxBoBDD2ljEAwEW/fRE6H+v0yXEFCKq7VQbquLRRH3d2dprUaTXgJycn4bou1q5da3RuNBpFJpMB0LQP5ubmcOrUKdRqNWzevNmXKcYx23pHHeZqn2qmlvZZf3xOiSebOi/UccxnkK66ZzwSieDTr/w09m/eDzjAF17zBbzpU29CZj7jex71VSgUwv0/ej+OXH8EXtDDnrftQeYfM+ie6/aBx3q9jvnoPM7tPLeE/9aeWosV9RVwVjro7e01e5C5D7lWqyGXy6FWq5kgUqPRQL1RxzUfvwYP/J8HTIXyyHwEWz+ztS3/tHOqt5sHu4hou2fpFk+1IRTw2Q5+fYZGdBU8Kp/ZwNEG8Rr4sPGLrikFs8QoWptFeU95kO9X20Ob2lkMZJltCI2lW1hJJ7U12Gd7bvT3I699BAeedwBeyMP9P34/4p+IY+XoStMHpYnN4zovCrjt+SA9lX68hlt3v5uA+3u+h/vpNE7UunXrsHv3bmzcuNFnoCrIAlrMq5WJOUFasVfTUh3HMZEgjczZC0Oj7KyqqBWA7ZRoXYScWI3kKgCo1+vmKLNMJmP2Fv9XVSBpHFMJFotFU1itVCr5Kq8T8NXrzTMU+VxGahnl5k+j0TDHd3heMypJYavAgzRlpNiO8lLoaZq7ehNtkEPhp8qSTaOJFJDsjwIkAhQu9lqtZgpMaR/ouOAWAhZ0UAGsqbw8ZonAkvux1XlDwey6rboDjFYmk0nzTipsjpHgtFwuI+/mcfTHj+LUy08BAJy6g1WfX4XVH10Nt9xKv2ZqGPmClfk5D+QZOmsIqDh3Ghkm/9IZxX05AHz95JzRscPnsJAc15uuA/IQr9WMA/JgIpEwPwoo1XFEXlJFSWDPawgmKcvUUUMeoiOHfLiwsGDWB3lJ5QnpQo876cdnkufr9TrGXjuGkZ8agRtrKZPITARXvP0KhOeae5jIP/SO871qZHHN0bFCXua7+R37qHu7lQeVlpq5wPu0PgTH4rou5ubmsH//fp8h8v1u3vIe7uX2DNv30lb5YWyO42BwcBBr165dIg9Y+FHtGHUuU29RpqvjDmjpZjuDjTYXm0b26PRWUNloNOAFPNTTdYy9dQw93+5B3+E+eG4rWsg+sCkgVbstHo+bfcK0j2hTqJPANsKVVqtXrzbFKXlkaKVSwfz8PObm5lCpVLBq1Sps377dV0BNwZCOW4GIZtXR/iQN7HlTJ3Q7Jwj/VpCjkUXed9uNt+HBnQ/CDbR0Q/dsN37ur38OAbcF8Dgve27ag4de9BDqkZZuSGaT+Om/+2msia8x+qpcLuP02dPYe8lePP7Wx9EIN+C4DnY8vgM3334zYogZh3O1WjVbI+fn540dQprEYjFz/G61UcXhyw7j4bc+jGAliBt+7gbEF+JLIpw2XdQW0aaBH926yPupd5U3FYOojtX32O9TJwCxBu0VzpcCfXV82c/UbYO8xnbg0K7XIBWDQbYTjMEfzp3ad8prxFus78Oz1ds5N3QPOAAT2FNngMobOgX2PX8fHnvVY2hEWzyfmE/g9X/yeiQWE0to285hZzvVdLuITUuN4NMWPnr0KB5//HGzheKZtqdjq/xARLjtRmIcP34cp06dwsDAADZu3Ij169ejq6sL6XTaMK6CUiWwFstgJEijN7ZnV5mRwoZ/83PerwuFi5BVxPlMLXYE+NPdG42GYWwypB2R1PR0FQRcQHwXq2gmEgkDvgF/oSvP80yBA97HBc/v6b1ixDsWi/nSktkfjkMVBselAMLzPFPYi15SVSIcLxcuBQRpyihioVDwpfkrYGcfjCdUik8p7clP7COdNeQR8gznknNLo0E9kLzG9iqSpuoNLZfLWFxcNO8inzCya4RDGDj9wtMGbKMBrLpjFbZ8bAsaaKAWrBljguNXnqXRwrGTjpoarX1s5921z4zWPfPKI+QpzhF5UQWX0kIdLbYy4zPVUOKa0H3FLMBHJ4+9Lrmm6FBR/tbIO43JYDCInp6eJZXkyWfqLKIxaUek1eM89LkhVPuqOPfqc/BCHuKTcVz8RxcjvhhHw2llq7DQjNaPUAcJq93Oz8+b+eU8ULGqbFA6qOzTquWlUgmhUMjss2f2gTpfKNuKxeJ31bu73Jbbcvv+N8/zMD4+jnq9jlWrViGRSPjsETrkqVd1axjvVwc34I9AaURRI82adm7LIDWeHae5L3fihROYeskUtv3RNoQnw3CD/mg1ZbQ2BQCU33Sa6rYmG3zYIE2jcTwudGhoyNhrqnc9r7mn+/z58wCArVu3IpVKGXtHjX3NGFOnA3UswRidBUov0lxlvfab+llt23ZbtxqNBq4/dD32bdyHfKJVHOr6+66H47YAjDpwd929C4vxRRy46QC8oIfkdBIv/qcXo7PaCSfRsnsrlUrz/O5HdiM1mMIDL3wAW49txSu++Ao03AYQgLEhQqEQ0um0AdYa7OE1i4uLSCQScFIO+kb7sOtfd6HjoQ5Es1HU3ZaTXTPjyAdq29sRUtt21aaBHLUBaU/YPMLPbP7XNaIYglvqbKeM9tsOWmjmmgZ7dP2oc0ztrXbOHpsetn1mj4/9UQyhNNb7FcPQ3tJsSZUn7PeOu3egkC7g8M2H4YU8pGZSeP7Hn4/YfMzUAWi3blXW8MeWCbyXP/YzSN9sNmswy3er/UACbm2NRgPj4+O4cOECDh48iFWrVmHDhg1YvXo1+vv7fZFjG/xoNM8Ga3q9DcxshiIAYX8Av9Lh+9sBCzs9iAuOQIWeIxXKFIoKEG0ww/ExVZjCi0VR+J5SqYRyuYxAIGDeo94713UNqI3H4z7gy+vL5bJvrMqwqlT5XgXoCoZ5v6amULCyqaAPBpvnkcfjcdMHG9ySFhqJZ6PXThc2n68CWtOD1evFFHLOv/JKO8Wm4CUQaFbdpMeP1xPkERhGIhEc/7HjOPzaw6bfTsNB6njKF+nVegHc70ujg3vP7TQh0oJjocOA/Waf1PPI8RGgqZLWcXDOSTeOkzSlx53P5Vok8FVlV6/X0dfXh3Q6jQsXLpix6TxyXjTVm2Mmr3DcWsdAsyzIm+2KtpEH+FuLIDIThe9SGlNxrf+79XDKDiZeMIGtf7kV3ce64UT8+xX5ty1z2GfXddHd3Y1SqWRorY4OnUf2kwCa/VNHIzMoksmkyXDhetZoCkG68vByW27L7X93m5qaMlGtnp6ep7R1VN7x/7asUDmnznfVjaqTAH+2H7+njB2/ZRynfukUEASOv+s4Nv/5ZoSy/r3Cqh/VoFa9BcBnRKvtoiDUDsDoe5LJJObm5uB5nol0a0CEz5idncW5c+eQy+Vw+eWXI5PJtE0BZz9ID7Wf9O92tNM50LGz77Ydqe/T4qxjXWOoB/2ZTGcHzmJrfSvceivVVytxX/nZK1HP13HmmjO48mNXoudsD8LDrWM5OSee5yEej+O5jz4X6VAaV+y9Am7Y9Y1Fg0iO4yCTySCRSJjsAQBmi2elVsGDL38QY5vGcOlHLkV8PA4Xrm/saqu3ywJoN9dKc9ve0P8rX6r9z2fYgNsGnjbfA61UbJ0rXqPZGaFQCPVYHdOXTKPnnp4l42R/7bnWsWhQSdeNpobrWHQM7D9pTZtCwbPOqToGnoouOmZdb1d87goE60GMXDuC6z51HXpP9KIeqBubh80eu8os24Gi86CfUYbxuYuLi9/1gmnADwHgZvM8D3Nzc8hmsxgZGUFPTw82b96MDRs2YHBw0Ae8KeS0qrMCTZ0EjUqSaRg1VS+s7pO0GYDglMxspzmph1Ej7griyOgEL+y39pVMrP0liNCoezDY3A/a2dmJcrmM+fl5Y8STBlzMgB/QaESTYKVYLMLzPHR2dvoibto3jbKqALD3negiZAo7wT77onTgAk8kEr40e1bNJo1YDVXT2Bi1YzovBQEBC+dfhTFpTGCrXmYVXOrFs4U6/yZd1BvJ7AaCnHg8jviZuI/XHc9BZjpj+kGvpioI5R3SShWNGgKMdGvqmgJrHvWgPKhzSr4ivRRMc8zq4FLw3o4XWCVfvaeFQsH0S9eWepU11VH7pL/VuaIZHMqbtsLlfFHYsg/kP/ISx6DH12kq++p/WY2eh3uQ2JdANVpdsteewJnPIZjm/C0sLCCXyy3hQwX+nueZI8cCgVZVdjUi9fnBYBCZTAb9/f0YGRkxaXwAkE6nfWnpy225Lbf/txptg2KxiJUrVy75Xp3zAIzcp2yiTFMAAfgzwAD/sVj8XHWDGvoXXncBZ37mDPAkFpi7dg6Hk4dx6a9cikbZH9FT+adyXUGrfua6LlKpFEKhEBYWFpZcqzqB/aLMnpiYQCgUwpo1a4ydScd5b28vwuEwpqenMTc3hwMHDmDr1q3o6upaAoiURvoefZ8NvvmdXQBLv7OzB6jzgsGgCVhQH11y+BKECiH882v+GXCAK++/Etd+9VpUy1Uf3dQmcF0XWz67BT0P9GDF2AqEu1tZceoEqdfrpvDZ1XuvXuKcV11DO50BmHA4jFgshkKhYHT3d97wHRy+/jDgAHt+bg8u++BliI3FltDTBpq2Xcd323NBm8AGwDpu+x61xewosL5fn2c3fa7Ng4oxnnjPE5jbModtwW0Y/NagL+ChWR4MvjDYQPuCfAMszQQgr6id1G7cnFt7XGoL2zTg9wyEcWx25F3t2N1f3o01h9ZgcHQQCLYCehogpE1oB4MUk9iAvx1mUUfH4uKiwTnfzfZDA7jZPM9DoVBAoVDA+Pg4Hn74YWzduhXbt2/H6tWrDQNSIHKyAPgmiIojHo+bBafpto7TSk+1vUTtmFH3DgH+Ss80/OlR0kWgHiYApvCVpmZz/ykjdwqaOUb+aCo7jfzOzk5TXZPRbv2bz9WjzjSimslkDD3Yb/VqsnAdhSyP1yIIY1+4YDTCSYDPyplUoIzclctl5PN5I5DpsIhGoyiVSkbY8X1M16HgZiaBXXGefBGPx317chRYauSU9zJKqNfyei5we/Hr+MkH9B5XKhUk70rikvoleOKXn4DjOrjy/16J1KkUXGfpMRUalQTgKw7Hps4Ptmg0amhBoULArxFR8jgVJ2lFTzfPfrSjCnrcm2aWUClQ6ZP+WoeA92SzWbhu87xTVnYnb3JdscCeOonaKQa7qi7BNHlTHWQK9BnZ5trS7Si6Xx1onTDAzyuVChL7EuY78p3OoS3QdT61PgXpTlnANaFbShi1Zx84Pu5b5DqbmJjwGafpdNrIQRo7tpd/uS235fb/RqtUKjh79izC4TD6+/t99oztjKPso9yiPKZ+oG2kkV01bAnS6Qy0gXogEMBlhy7DwswCZodnAQdwqg5W/+tq1At1hENhnzxVAKG6mttoFBTwb+oAZo4paFCnAPWHOlvHxsYQCASwatUq8z5uv0un0wCaOnBychKNRgM7duxAV1fXEtBpO3ttAM3x8G8bFLKpXaDX8jqN2GlUEAA2Hd+EN/7rG3Fo0yHc8LUbEKgGUEMr29B2ZjQaDbglFz0neuBGWxFYX3OAcEfYFwVVJ0I7Z7dNf6B12s3Xb/46jl571BRLW1yziId+9yHc8LM3wKksLVRGcGcDZr7TdFPsBjvNX+0BG1Tr/Rrc03co9uA7OE8aNFA+sKOyjuMAUeDRX3kUk1dMAgHg8DsPI91IY3DfIDzXXw3ddq6fP38es7OzxhaKJWIYv2wcxcEi5rfPY+vfb0UgG4ATcRCotfqroNumIW1CDbbYNoOuQR2bgnHKFNo1pJ/neQg2glhxeoWZbwXotOu5NvXd2k87c1P7Y2ch896ZmRkUCgV8t9sPZNG0/06LRqNYu3YtduzYgS1btpijmThRZCICM0aYdYES/HGR6N5OwF/oQgtUkBl5j4JgTi4BI9DaI64OAD6LBr0KLO7/5L005DXKZadCqHBVwMl+l8tlEyWmEa+Vzunt0ag9BScrpXOhUIFxPBr1Y3/ZVDhxbyyLt2k0mAuHz9XiXqRTu0XGiG06nTbnUKvC4NnMpCuVrr03l/Olc87xsP/kLS38YntxbYVYq9WQz+cRj8cNCG40GkgkEph++TTCI2F0H+tGtdKqek6gq04a8hL7reCRvMqCfORJKiRmFYRCzUKAbNxCwHXCAnF0GOnzteaALfjVYcP1pYqJ/Y3H4+ZMSwp6FcYUlnq97bkm+OeaI+9zDhZ7F4FJIFRp7UnX/ijQpodYZQaAJWuUfEca6hmZBLxaY0Lpr+NXsMz1Tj7RrRZ8n2bq2MYwn08wzmq5Ojd8Lp/J+4Cm0X38+HGcO7e0uuz3s3nLRdOW2zNsP+i2yg9yGxoawooVK4wcBFryTx3HlD3UKepkVnmjgQpg6dE+vMa2EbyAh4P/eBDlwTK2/tVW9N3bZ+wP1TNq1CtY1ucquGNTOa7PYx/t6/V9oVAIQ0NDWLlypaETdVahUMDU1BSy2Szy+TxSqRQuvfRS9Pb2mudo9pG+W2mlAE9ppb/bOZuVhoB/b71mJjDgwmAJ7V79oW3C/uXzeSwsLCAUCiGTyWBwcBArV640eqtareJQ6hC+8rqv4C23vgX9uX6jqxWw2Q5zHRf7xr5me7K49advxULXAgAgPhHHpb9+KTLjGTMupZMCd7U97bo/tpNC6alA3AadNu/Z4N7mOx2bOn20j3q98urZW87i+E8cRyPRqquSHkvj5t+9GbFizNBSnw/A2BKVSsUEOSYvn8Qjv/GIeU6oGMLWT2/FwuYF7PzYTgQXWw4ytb05bwBMYd1MJoPi+iI6xjvg1T3f3Cnt+MNxKfhV0KuAm/aQ4jXbmaG1E/i54gp9rtqKvF9ljed5KJVKePjhh3Hs2LH/UdDh6dgqP3QR7qdqlUoFx44dw4kTJzA8PIw1a9Zg7dq1GBwcNMc42EKVkWMFb5reQOOU1wN+Q1iLYanXRVO3VVkpkzCypMyj79MK4QR17CsBOftCA1uNet0H4nmt6sfhcBjd3c2jHCYnJ80+b42SuW4z7ZdVz9UzxN+lUsmMm/0k0NbMAn6uzO66zWJOuVzOVD7UtBelBe9hxI4Fu/RvKhGN7K5duxYzMzOYmpry7em1lZR6ylR4kw7qlWNTZa1RfPaZAKrdPABAIpHwOW3C4TBqgzUkzyaRPtY655NjoVNCHRlUyOrZtT35dCaoIiYt7cwL3kvlyHklz2kkQlOLVJip4uC95GXXbUW1SatqtYpUKmUKiSkYVMcV0DQcBgYG4LouJiYmfNFrFvnzvFaNBs/zkBvO4eT7TyKzL4Phjw6jXm05yWg4KZjVdagebnuLBemraf7KF2qI0hDRc+9JN3UgkEd0zFqgj3OvXuF2joFEIoEVK1Zgfn4esVgMuVzORJ9sp4euR1Wsy225Lbf/N9vY2BhKpRJWrVpl9tBSVlIXKPAA4JNJNmilXFPbRkGL6lUFSI7rYMf7dmDh2gV0f6MbruPfF8t3AC15rpFUG1Db71WHOJ/BMbSzBxRI1Go1nD9/HqFQCIODg75013g8jt7eXvOOfD6PAwcOYPPmzRgYGPA9S52eNkBrB0RVJ6mzVfW72gA2nVR/HFl/BCuOrYCD1nU2eNSAj62faUvqPB5ffxyff+nnUUwUcftrbsdrvvIa9Ez2mH6p/tE+tQOkrutiom8Cd7/+bgO2AWD9F9cjM57x3adObNWFam/THmvnNOK7FdgpXW0njtoJ7IdmftjzqvQlPfm5OkHUnvI8D2vuWAMXLo7/zHF4YQ+ZIxlc8leXoD5VRzlU9tmQminLeWK/Jp47gUff8ShkqlFP1nHw7Qeb99aD2PnxnQiXw75xapCKlc6r1SrG1o5h7y/uxda7tmLtnWvheR6mrp5C7xO9CNdb2bU2Xyv92zkqVB4oFmtnw9rrVflGnUztHGc6p47jmIzp/wnYfrrtfw3gZnNdF2fOnMH58+dx4MABDA4OYuvWrRgaGjJ7FmnIkok0xVsNURq5mqbuuq7PEFZDWRevCnwFKnx+PB43XiRVAIA/oqb9UQBEJcZnEizz/byXjM/FAwCzs7MGhAIwZf6B5mJlpXMKKUa+OV4KX6ZjEXDT+UABol5Wvp+0CAaD6OjoMNH7RCKBQCCATCaD4eFhjI6OGrBMGsRiMXMMloItPf6LdBkZGTH0YF/osQ2Hw6bwCefUdV3Mz8+bY7coDFhwhXyjnn1V6OpUUcWkAE75R40LL+1h/7v3o5KpYPef7UbqZMoYOVoIrl0EVMG8gmDOD/eZUTgzkqsOEo5Lj8JKJBKIxWJYXFxEvd48e52gke/n36Q9i51xvAoG9RgtOo0I9ClQlQd1bXFdT01NmWPLFHDSCKKDIhgMothdxPFfPY789jwWdyyiFq1h8IODZl2o8KfzjSnc5CtmB3ANMctAU+q5BoLBIIaHh+E4Ds6cOePjW9dtHemlSojPUR6kgmPUnOua7yUdyZOkLWUAeZG/mVppGxOkL/tOGiy35bbc/t9us7OzKJVKGB4eRldXl09n28EBbnmhoWvbPnYKul7DppFrtrM/fxbrP74eA3cPwMNSkKwGs9ba4HVqhOtn+k7bPrMdp+wPgxTz8/NGt1UqFYyNjSEUCqG/v988NxQKIZVKmfunp6cxOzuLAwcOoNFoYOXKlb7thgoK1BalLiANaY+qHWHTRCO9pAW/U9vg4PqD+PzzPo9Vm1bh1be+2vRd7Rl1TtDpTLuLvMDsQQA4vOYwvnDTF1BMNG3HsyvP4vMv+zze8MU3oGO+w9gaOgc6P4D/GKdsZxZ3/9jdmBya9PHmuRecQ/dD3YhdiPnsC7VvbVCn79N55b0KnJUWCuCVFvoOm/5qz+h8Kn31fdS/tO3sOR76whDqC3WMv2Yca39/LWqjNcwGZpcUCKYdrsdwmYDPrAv8F1hy9IWjqMVruOSPL/Hxkc4P3zW3eQ5Hf+4oCgMFPP7Gx1EKlhA/H8fBnz6I3gO9uOxDl/kKGNtzYINrdcDodZxTdezoll/a+u1S+vX5Onc6B/r54uKiqWvz3W7/6wA3W73eLPM+Pz+PkZERdHV1YfPmzdi5cydWrFiBYDBoKnprlMeOXnOi+DkNfqBVxZF7TGnw63dcuIwk0ijmMwmeeR8Vh6aR66JlBEyjyVqUQA133kvwRhBhV/ymIgwEAgZUBQIBU/mY+2FZXV0rV6piYAq2Am2Ol++2ryX4zOfziEQi6O7uxvDwMObn55HNZn3gge9Sgalp4hr9p2AjAHccx+yB5b3q7bTpqPvZ+V5mJSi99HvSmZHjRqNhzipXhc+xhMNhhOIhPPChB5Db0Fzwj/7Oo7jm/16DRDbhO5NSBRWFMo9/Ih+kUimfYrQFngobFVpKTyryaDSKTZs24eDBg5iZmQEAk6KvWx4UsHEuqDhYA4HvoAAlXxAoc361IJumRPMdunVCjShVjvF4HHWnjr1/shfF4abyhwNM3TIFVIBVH1plnskK/9yDpylaGsFWPlcHFq/hOpqammquTzRw5B1HsPVzW5GYS5jsBI5fPew6D+VyGZFIBJlMxpfqp4aQZhvYCpxz+P+x995hdl3lufi7T2/Ti2YkzUgaaUayiiVbwjbYxgbTEmoghISEQAqp8EvCDZBy4aaHEm5yExICCYGQQCAkhBLAQAAb3HCRrN7LSDOaGU2f08ve+/fH8bvOu9ccBwi4AOd7nnlm5py9115tf9/3fm1NT08HohTUIKTvnxqN1LjUoha16AebCoUCzp49i9HRUXR2dprPbcWYvFrzHxVUUc9QcGQbpNUT6MLF+C+PY/rHppHflsfVb74aqAW9r+rFIp/T1BleR14Zj8eNMbgZCLO9liqvCDbz+XzA2A7UoyrHx8cRjUbR29tr+GsoVD+hRA26KysrOHLkCDzPw9DQ0CqdjWOhLqMeYZU77L8CDHXW6JxrVAFB/rm15/DRZ38U+WQey9uX4f64ix/56I+YaDh6TFXn0bWqVqtIJpPGAcH+bJjagHVX1mGpbamed+852Hl6JzqKHYF14prY4ErXxvM8pJZT2HxkM66svQI/9Mh+8oD+e/sRn2/UVgKCEQ52m7rOCmSpg9kGGeoyOgeqf9sAVJ+puhPboy6kYJ19aeaQYTscW61WQ/enupH4agLOjIOcn2s6ZuqDbW1tJmqQ6YvdD3Zj75v34sF3PgiEAN/xEXJD8CKP7Jsa0P/RfiyvLMNzG0fVsc/UzSq9FRx961GU++t6ghfzcPLlJ+u5/+1VTN48CYSBG959Q2CsGkGsYNeAbcfHkecdQf/5fqw7ty6wZpxToIGN2C/qiwqyVR/nO2TvNd07lUoFy8vL/+Ozt79d+r7J4f5Wqb29HVu3bsWOHTuwfv16E9JMhVNDzKmwqjeZLwYZUSgUQiqVguu6phK4bjBuGK0SqC8c0CjPr55wO2yXjAJohHyzDful53PZZ81jVqaqYci2IHWcesGyfD5vPPF8gRmuqso+gRT7qmBMPbBsi/2wQWA8HkdPTw/y+TyKxWIALOoLaEcpcB74MqqVj6CO866Fp9g3AnauaalUCoS4s3+ai6/PVA+m7hfbQEDwTXCz/537sbhnMRDu03mqEzf/5s0BwEcDDMfPMfI50WgU69evR3t7O06ePGmOH1PGrYYQji2TyZhoC4JOFgPj/LGKLeeMwpzGGXqHgcY5k+ol5z5RxcDz6uF3LODFvpJRcuycX+4V7leulXoENDJhac0SDrzzACr9FcAHuh7swrY3bEPIDxmPPZ+VTCZNNAefQY8732WtpMq1VxBt6jMkXJx81UlMvGwC4WIYN/zyDehZ6jHKFAWQAmFGd/BIFN/3A/tbBR/3MZ+nR3ypZ0aNf2og07Y4d45TD6u6++67n3Sg22/lcLfo26TvF13lyUKbNm1Cb29voNiZ7e37ZiBK+Q//t4GjG3Nx6ZWXMPEzE/Wzd32g594ebP3TrQgtNU5zobxQZZv9sPUEtq06jvYFaCjfzfrINhUwAnXHSzKZxMLCAqLRKDZv3ozu7u4AiPb9eirfzMwMrly5glKphEwmg7GxMaxfv75pv1UPU7DQTMfhD9fFBvC20b2SrOAdP/YOzHXOySIBVx28Cs/79PMQzoZN3RbqitQvWPPH9+unZKxbty5QpNhxHFTcCt7/0vfj3NA53HL/LXj2Xc+G5zY8mJonrtGOxtjiBnOuc4kcvvgTX8T5HecRqoYw8qkRjH5wFG416Mluts90btXwAARPO2k2v2qY5m81wui+s5+nbXBMmhZnP5fj17GwHTowqHs1G5tGzUWjUXR0dKCvr89Egy4vL6NQKMBxHCzvW0ZoNITZzbO4+jNX43Nv+BzKkTI2/+/NSN2XAvzmRgqS53tYfuoyxv9wHG6XC9SA2JUYKmsbkXHhUhh7PrEHO7+408yDPV/6f8kt4eiNR/HAqx4AALzoHS+qVyi39q/2Q9tSA5i9D7ifbD6hBo3l5WUcPHgQJ06cwHdK34qu8gMHuEmpVArDw8PYsmULNm7caDYpgQhBBIEYlXBdePWsaU41Q0l4jQ3iFZTaQoekjIAgSMG1FmJSocAXkKT3KfBRJqceYLsdbs5CoYB8Po9yuYzZ2Vlj4SQwVUuhfb6f/cKxGjkZOIV4JBJBb28vkskkLl26FKhCzjbIAO3wZqAhoBQIKWDjNZ5XP3ucoIr9UuBCpksvNYG7Xsf9oWvNPcC51KryvJ4Ad25kDkfedKThiX2Env2aZyO9nA4wJ41q4DzbYc0qxAAY0E0BQyVF953neUgkEkgkEoE+axv8oddec5+j0WjAgMF5UEbHsSvQ4zy3tbUFKnGrAYd7h89JJpPIZrMB5YL7hn1WQ8vUyBRO/9ZppC+kcc3br0ElX0E+nw/sBbZB0Mt55dywPfVG873k2ExeesTFqZ8+hQs/esHMX3I6iT3v2IPOE52mfXqFKMR7e3sRj8dx+fLlgHFIjWTcA6pEqhVao0d076tXRuee889ohaWlJdxxxx2rhPoTTS3A3aJvl77fdJUnmhzHQV9fHwYHB5FK1U9hUE8gr1H+osBRZRWwGhgDjzgZ1tZw6vdPIburEd6ZvJzE1ndtRcdDHaucAvbzKWf4nfJRVcpVPvHUDXVw2A4Rkh2yrDpfJpPBxo0bTdqi8d759VN15ufnTSG1cDiMsevGENsbw8jkiJkj22DBcdkGCht82EYBBeM24JjpnMGHn/dhXBpoFMfsudKD5338eRg4PxAonMux1Wo1ZLNZcwJMOp3G0NAQ1qxZE4gmdBwHNaeGL1//ZTz7nmcb2aUOC+ox7LvKUzV45BI5fPkFX8aJfXUglJnI4Bm/8gyjg1BuN9Ohm+0TjVyz94/9W2Wmep2bzTkdbNqmjonf6VpwbygW0P3G8VBHZrSbvacVR7AAbltbGzo6OhAKhUydpEqlYo7WZfpotVrFZPskzifOI/PFjNFt7XWw3yHHcbD47EVMvGkCnV/uRN+/9eHCH1xAcVsRTtXBzo/vxO7P717lENS5Z3sA8PAtD+PeV9xr5i9SjuDZ73s2ho8OB66z/9b55tzY/dY11f/12qmpKTz44IOYnp7Gd0rfiq7yfRtS/s2oUCjgxIkTOH/+PHp7e7Fx40aT683jrWxAxwJWDG3lwtJbS9CjIJyCgJuVL6DNvOk9bWZ5U8BOUsbFF06JfWPYMQtXsQCXVuqmkq8FzthfgiHmAjOXl56wdDptxk9mqBYkZYQaBqZVmdUrz/xxtdaqZ5ZMhsXwLly4YKyynDMydoJdDZ9lqkAqlWpYfh/x6CoIVfCiLy2FulouyfwJDFOplAGp9BzzfjJS13XhhJy6JV9o8+c2I15sFNjivlMGy7nmvBDga7iWGinIlNlnMkF6bFWoxGIxs8e5xhqloOuouf18BttRIa/95PrY66vh6NxrOt5YLIY1a9YgFAphaWnJtE0BpmHbZPQdRzow+o5RpMfTiCKKaLq+D7LZbCDywzZScZxMAeH6MjpC51KBtx/2zdmx+h6GIiEzV21tbYFCf6FQCHNzc8bDzXXh3PBdV6Oeero5rxq1ou8254j3qEFB0yZUCLWoRS1qEcn3fczOzqJYLGLLli3IZDJIp9NYWVkJyD4geJwm0Bww2kZV6heRKxFsfttmnHnzGeSuziG6EMXoO0fRfahRNE35O+UWeZfKahakpMPBVtwVXPE7TYdTXU7lHg0LAIx8pEyZmpoKpCKqDtbV1WXmM1vM4j+f/59wd7t42e0vw+bJzaYd+zm2J5if295vyiHKey3MpWPtX+zHSz//Unzshz6G6YFpdCx04Hn//jysu7gOVa8akDnsj+p3AIzeoHKD8xQOhw3YVh2J/dD5VaeGeqKrThVffNkXcXrXaXNvqbOEC8+8gLW3rw3Mhw2g+JxyuRyI8tLrdO4UbNugTPeMeqcJ8vgd944+o5luqHuf99pg2zYc8B41pthAWPtAQz7b4N/U6/jO1Wo1JM4n0D/fj7yfXwVmbaOFzm33f3UjXA2j/Z52hN0wtrx9C86+5SxG/msEW/9rK/xYY7+pgUPbN/jACaZDAPWQd3tddW/r/9TT+A6oYcTut64t5yCbzT5u+dvADzDgJpXLZUxOTmJ6ehqHDh3C0NAQdu7ciZGREXR01PNP1EpFZsdzfCkANKdarVY2YLOZKoAAWKW3TJkmK5DzWoJttRwTNKrXVXOQ7T5xHBQy7CP77/u+UeQ5NhYnYygzGRtze+m5zmazyOVygXlW8KdjpUCiIOFLwL4o4yHD53EHW7duDYAJzc9W4aQMi8CHL2ooFDKgisyJc8wxaiqBWnRtRkjQuXHjRniehzNnzgSqZnPNqtUqQuEQFrcvotwdDOGdvnYaez6zB/GV+KqwOeYxcy+RuMaaa+Q4DQs8n2sKij1yXBjDydVCrvnwnB96+QuFAorFYmD+VCnhHuJntkGIqRuqtOha6V4gIFYvwcTEhFkTNYjp3tUcNABoP1g/oaCSqBhliF4YCvpQKGRCs9kfNWDR0sz+0zigxjLf94E0cOW6K0H+0lHGwlUL2HB4g+knx6Xz1ky5I9kFErlH+bcN0Nl/7Zu2wagHNZA82ULJW9SiFj15iKDy+PHj2Lp1q+Ffdi41EKyYrIBMo/KUNGIqdSGFrb+3Fcf+7zFs//3tyJzPBJRyG8irU4SOCcrYVCpl6nQoUOH1vu8bPYUOD/aZMp+kAIvyXMfiOA6Wl5cxMzODkZERI1PVcdHT0wOEgP2v34/Z62cBB/jI8z+CX/jEL6B/tj9glACaF3ZTQE29hfOv+qZtrFd9oX+qHy/78Mvw4dd8GD/2jz+Gnrke1PxaQCbp+rFYp3pUaaxWPVIdTzovagzgXuDnNkD1PA+O72DLw1sagNsHYtkYuvd3B9q110r1RTqP7Kg/PsMuuKVrpfod69DwmZxPTZHT9VIdn/3je8L77L2o74k6ITQNkHtf5433AY2aU6rn02mhfac+V6vVjC5sO8XUIKXOMNVFu77eVV/3WBiZCxn0/HEPurJdCIeC1dp1nGqw4O8ND2zAN37sG48sDPD8v3o+Bs8OwkOwEFozAwPb5DvR7Dv9rfsGaADux7NY7A884Ca5rotsNotjx47h5MmTWLNmDXbu3ImdO3eira3NeET5MioD0pAG3awa6qzgXAECgYvmoqoCDiDATNg+z3CmoKDSrGBGKZ1OB/rJtvWlION0XdcUGVFASeBFr7Z6vhlaHg7Xq4+nUimT/80cIACmajLBDVBnFjQqcCxqvWQ0gRa2KpVKuHDhgjnHWb2mKqDYJoVjoVAwAJ3eZ1Ua2C8Ft9qWvty8RoWG4zg4ffp0YBxqIQUAP+xj8rmTOP3zp+te0Ucomoti7/v3IrYSM2Ow94xaaTWagnOrEQZcT7ZDoNXR0RHwMthHVmlkAMO1CXJ5VByAQDE4tT4S6OtZrgCMoYXXKIBlmPXGjRtRLpcxPT0dCD2zrfbFYtEUPVRBz0ritie4WCyaIoX0fuhaMnpDwTbfWxvQcuzsjwHnK8COv9iBh9/6MKptVcADeh/uxaZPbYIHL+DFtoswqgFCDQFcT663nRqhngVVsjgW7lEaxNTrzagXhlW2qEUtatF/R6VSCYcPH8bmzZvR2dlpgI2mY9mKLvWV9evXY3x8PADO1WngOA5qqRri+Tj2/vxehGsN/mt7uHivRnSRyK+z2eyqyD/qCup8UDChgFeBm0apKYCgrsC+TE5OmrFqXzgHZ37uDOb3zZuaLSvtK/ib5/4N3vyhNyMVTpmxqEFYZb6eJ63ArpnexHYUSHG8XUtdeO2fvxZ+2UfZKwf0VzX+Um9QWZpKpQLGfc4rf1NOsc9cK+0n/9eibBplGKvGTNuO6+Dm198M5AAnFASc9GJTFitgpMNAx81x6PP0Rw36lL/2XKs+rnvFdq4pAOd9/E5ltxogOIc67yQTCSKFbhVA0pnAZ9Ojre8adSU9g13b0n1gOzQ4BrbPeQiHw0heSSKWbhyTrO0qduF+dhwHK20r+OTvfdK0/dy/ei56T/bCCwXTOtWIYI9ZHTvN+q9jV32czojl5eVH9Yo/FtQC3E3IdV1cvnwZly9fxkMPPYQNGzZg27ZtGBgYQHd3dyAn1q5oyU1ohxHTg8QibUAwnInARsM9Gb6qISmO45gXiUCXn9MQQLDBglUAAtXC+VxuZgInjp1MVY8hIvDV88M9zzOWTjK9SqWCUqlkgB09ahr2wfnj8wuFAsrlsql+SWat41RPux7txtxizqEKWAUrBFUEGnwpmR7AeeD88xlaKExfWs4L50FzmEOhkJkrGkTYruM4KHYXcezXj63ad5s/vxm9+3uRq+YCFknOma0YELgpU49EIiaMTo0QZMBktnrMGa2hrFuggpTP4n5mioEyVGW8uk95v1pLuafVms9+1mo1zM7OmmNVlJFy/6jA5PvCd4LPcN16AUO+G5oKkkwmTc46vfW+76+q7K6hX+qdV+XJdV0jtPjOrz29Ft5fejjyS0fQdbwLe9+21/SLR9EFQPojRI8+hQzTQDT1gvOvigz7znec80/Dlq1gAAgobuRbnOsWtahFLfrvyPM8nDt3Dv39/Vi3bp0xfKpMJx9LJpNGFp47d87wrmY8qZwq4/wvnUcUUYz83QhQa9Sq4XMpV8jX7DQ8De1Vj14zYzzQAIAktgEgkKbDcSloYJ+AYKTWxYsXTRi58uparYZbP38rvISH/U/dDzhA5lQGo789ilPJU9iyZQtSqdQqg4T2XWWnjkPlqQIkEueAOp3neQi5IePZtj2F1J30BB414NtGeo5dj61UA4w6euw10nQn3/dx9uqz+PSrP90Ym+NjYecCuu/rDqyzzj/b1H2hwEz3nDoqbGCmhhhbD9I5Vv2H3+v7wc/Yhu2t1UgN20Ci66f3se8cjzqH+J6ovmX/zz1YqVQCdXn4fO2fGhBso4QCaM6Z/ZndX84DnzO3fg5f/sUvo5JueJfnRucwfGYYqAQjSnSOtR3bCWX2ixg41Dlm78VcLofl5eWmDsrHilqA+5vQ/Pw85ufncfr0afT392NsbAwbN25Ef3+/WUD1zNoeUnqK+UJRmaYnjhubIJU51wztBRrWGQJBfUY4HDYvENBQpkulEorFYgBQ20xIrUb8X72F+nw7/Fz7pAWn0um08SaSYbMPAIwhQfN1OR41GGj+LAs+lMtlU6iNwJ9kV4wm+GY/KpUKstmsERbJZBIAkMvlUC6XTVu6JmrF1KrQ6kWm51AtqTROUFDxM2MAWA5j7RfX4vJzLpv+t11uw+DBQSSTSfT19WFubg6FQsH0i3uDYdxqyVTvq+5HAn+1lHMMnFc945CglABX9y292m1tbWbP0fhBizYNFxTGzK1nX9gvNVYkEglks1mzd1id3igFoWCOE//mb1UGuIe4Fyh4Xbdxtjzniu8fUGfkl37qEtZ9aN0qAamCiEKdSgv3qCpj1WoVfXf14arqVeg92Buw8vI6pnuoIY7vHqMCCKRDoRB6e+vtLCwsmL2lBgb1NqjFV73eWs2c+0eVMM5Pi1rUohZ9M3JdF9PT0yiXyxgZGVmVKwsgwO9tRZ5kgHc8hAu/cQGzz50FAHgJD9vetQ1etSGH2T75moIj/Z/X2eCG1/FaBf3aN7ZBuaJkgygFInym67qYmppCOp02Tg961j3PwzM/9UyEi2Gc23QOO/9qJ/w5HxPOBHzfx8jICNrb2wPeaY5Bx8nn231nHxX4kIpOEffuuRe7794dkKcAzMkiOj8KmDhu6k4K5mxArf1WfVHXW3+r4wAAsmkrp9YBih3FwP322JvtL/W26jorCLMBosp7BXS6/+z1VuJ8UtdT/cQ2UNgecFvHUB3E1vW4P3Sfqn5vp0Rov0896xS6P929ysNuG0/sd9k29rC/1E0Z1q57S4G3OhjLqTLccBDoFtIF+I6PcCgceLaOwWwJAfu67+zrdTy6j2u1GlZWVkzk7eNFLcD9LRKT6ycmJtDW1obNmzdj27ZtGB4eBhB8KclogMbLTu+tLTj4YulLpwDCZqj8m0q/PpsvtRZZo4JOzyI9m/qCNmNGBGFAI69bra4ETL7vo6+vD+FwGNPT08azTuBQKpWMEOI96knmXBGc02upucqVSgXpdBqdnZ0GdOdyOQMM6c1jezRA0BtIwJjP5xGJ1KuE9/T0oLe3F8ePHw8wKT6fDE2ZMkESwSfniB5aAk32Sy1q3A+e58FZcdB7b68B3JFcBNf+ybXomupCOVQ253ROTEwYps3fysxYCIOgiaTWU/u85UgkgjVr1sDzPGSzWWMsUQMI2+beIYMvl8uIRqPIZDImhYDecs6fWoDZBz6bbXPulBFz7VnQT63IKliaGY08z0N2KIvZPbNY89E1gcrwNPbQ8KCF2wiYz/7yWUy/dBrugIuxvxwz49XoFY3M4P18P9VSzv3Qc1cPfPgoOaXA0Smcl0QigauuugoLCwu4ePGiuZfGiFAohM7OTlSrVRSLRaOEOY5jUjp0T7FdgmmuiQJ9XqN7kuv6eJ1D2aIWtej7g3zfx8LCAsrlMrZu3Woi98gjXddFLpczEUCUG3akkuM4OP7W45i/Zd60Pf3caXgxDzv+aMcqZR1YXftDPZxqKFdQDqz2+qkx1wbTauy1nR92BCMAE+UXiURQKBSwsLCAtWvXmjGaKLKqi6fc/hRsa98GZ8XBQvsClpaWcOHCBZTLZezatQvd3d3GeKq6peomHC/7rfKJ/TTj8T188MUfxPjAOKrlKvbct8fIAI124o+uoYJh6o+UISrzdV9oPxS824ZsBXD87uLWi8GN5gDze+Yx+PlBc73tRdZnqsHATr3SueP3lMv2HtH7bNBrj499eTQDj+rYthFCdXuS7YXnXuX1BMAaTUtqBqT59/Gbj+PASw+gfWc7tv7a1lWeZ3tebH1N+29jFAW29trqPDiOg8ETg3jmXz4Tn/vfn4MbdbH1a1ux79/3wSk5pmaDzpuOTcfIedcISAXmNkjXMaysrDzuqXQtwP1tEkMx5ufnTZG1HTt2YPPmzcZrZltPw+FwoIq0gg0qzgsLC8jlchgYGDD54grgFPxoHqZuIoZeK3h2XTcQ3sy/6c0EEPCQaZ/J7JVRKdDgfQSp9JopI2hvbzd5v5w7BSdkKhoaryFYaqQYGhpCPp/HxYsX0dXVFRAWyiDZd83BZog7x3Hp0iVcuXIFjuOYY7oABIA/gacKZb7YLJqnQEmFQDQaRSKRMOPiOErlEhY3LOLIm4+YPRXyQ2jLtpn5PXbs2CqGDDRyoMPhMHK5XMCCq5ZVjpkMiHtOjT8bN240x1ARlHIe1dtPxsZCX/yMhg4aRwgUdU3V6MN9yHx97i1ey3BrHYMqRToOAlj2vTZUw/1/dT+8iIfKQgWdn+yE4zkBgaDKiDEGJBxM/twkLr/8MhABpl48hZgTw8jfjcDNN6yvetQYx61GEO6DaDRqjDzchww9Z1QL39dyuYzTp0+bYzo4x4zcUMs4j+PTvG+dK76DGuamChHXQc/+ptffdV20t7c/roVDWtSiFn3/UD6fx9GjR7FhwwZ0d3cb3kSwQ/nJ84DJkynLqtUqRt4xguXdy6h11XWb2GIM29+9PZDupoo9ECzMxO9VwdYUJyBYaEt/69/qNLGBpHrQKYsoE6gXMW3L8zzMzs6ivb3dGJEpZwEg4SUQmYuglmmErS8sLGBqagqe52HTDZvQ096DWDX2qN48zgf5OXVMgmDjOEnU8IEf/gBObTgFOMCXnv8lhAthjO0fg+M3QsFJHDflOmUIn69GfD0dhH2kHkM9VQuZKlBSowbH8aUXfQnnd5w3Oe4AEF+MY9tfbTPXqe6g66JrpbqKAmjHccwaUYfQPdUM/KrTQAGg7h97D6lX205p0Gt1z/K5xAVa9K2Zs4J7n7qmnbOt1zgRB6f3nsbdP343vIiH+WvncfRdRzH626MI51dXUee86Xjsd8j1XVQyFaT9dKBvarjQ+VTDi+/76LnUgxe99UU49COHcONHb0TYDaYJ6Frqc/m3gmvOj+p5NvhXXXR5eRkLCwuPu97TAtz/Q/J9H8ViEadOncKpU6ewfv16bNmyBZs2bcLAwAC6urrM4itwscNLuFGo9KrnWvOH+aNHAOl3ypRtq5KGzzIcmOHmtMgyTIjtKulLzLGQoUWjURQKBSwtLQVeUtd1Tah2NBpFf3+9CifP1w6Hwya0WYGZClGCNABYWFjAgw8+aJ4Xj8cDhdaSyaQxKDAknSBFGYBaA+08VioABEmFQgGVSsX8r4xPBVA6nTbrQibOPiiD8X0f0VgUB//6ILxEg6lUE1WcfuFp7PzoTkQiETMv6k3X0G8FV9of2xusRheCPnpMl5eX0dnZafYUw9VVQeJ+5TxlMhmsW7cOV65cwezsrAHnvu8HqmLbCoIaWRgmzb2ne47eWfsoF91znBPutZXRFRz+s8Nwk3WAfPGtF+G5Hno/02vOoUyn02b9NbIiuzGL6adPN7hgGJi+YRpr7lqDrqNdAaWLXvfe3l5EIhFcunTJtMm+0vDEMHOujx3KzfEyNUI96OqRpgKXSqXQ39+P+fl5LC0tBbzZfDaVCAAB0K/rot4fAOjq6jIpL493aFWLWtSi7x8ql8s4d+4cCoUCBgcHDd+zQa7KF6ARKeQsOdjx2h049Y5TCCOM3W/ZjUg2glAkFJBrbEvrmyi/VK+e6kEADB/k3/xOQbZ6+LTPJAJa3ks5pjqTyr2lpSWsWbPGGGbn1s2hc64ToWJDZre3txt+vbS0hLO9Z/Gp138Ka5fX4pX/+Ur0LPQYOal6nPaV/bTHEwqF8PDmh3FxzUUDYt2Ii8/+xGdRq9Uw9uAY4AfTCdmGDUhpUFbPv0ZZkvT5CiI1Ess2aHD8t/77rSjGizi57yTgAG3n27Dv9/YhVogFnFi6zpS1Cm51/RSU8X8Fw6rrqK6upDoRn63j5Pzb4FCNS/xM51cBvM6ZeqkVANvAl31LJBJGh7fnNBQKoZwq49CzDsGLPLKXHSC3NYeFZyxgzefXBPrUbE5sfQwA5p85j6mfnsK+P9uH9EoQdFPfoCPFAH+nEU3q+z7ap9txy/tuqd/rBA0Q+t7az2bfdE30e3sv6lz6vo+lpSXkcrlV6/xYUwtwf5doYmICU1NTOHLkCAYGBrBr1y6sWbMGXV1dhknZhQD0JYzH4xgaGgowOgW2BFDqpWIb9PQBq6263PwKYu0XnuBYw1QI4tgGrUcETeVyOZDHqyFXaokEYPIlUqkU2tvbTV4TjxTT3FZa91jwiaFoPJKKR4ZxjghKKIDZNueUOdc0Zqi3WT3G7Kd6Z3neuBa04lySNLyGxgwyXYIZUjgcxsXnXYQfCb7k0UIUO/5lB3wEc0647pxrBdUKyDkHBHvKsJQhqdGA4M4OsdPCXcr4ub4zMzNmnvRMasepF9rTPHzuAxoN1EihfVahRMHCteXaaFi27vfKhsqq+axtq8H7tGcK0BDoquCIRCJoP9WOzW/bjHO/fQ7loTIS0wmMvWsM8QfiOPPSM1j7H2vhoGEoiMfjmJmZMUWCSGrwAoK5YzrnGn3A95L8gAY8DannM6rVKhYWFsy86rqyHT2mRQsj2sqt7zeiFbjerDXRoha1qEX/U6rVapiYmEC1WsWmTZtWKcH8nzJXU9Zcx0X2KVlse9s2RJ0oMvMZuHADEXv6oyBLjZiUI7bRV0GX8lZtW0Ecr7UVcgXytgdVv+MzFhcX0d3djUQigcn1k/jCy76ADec24Jn/+UzzzHA4jPb2doTDYcxdM4eTv3wSXtLDRHICH3/ux/Gjt/8oehd7zfW291R1Bs6teuOfcuwpgAv827P/DdVow5M9t3YOYxgL1Dxh+hVlvxJlvNZkoTfblkdqEGC7WnVbdVTVVX3fx20fvg2hYghTg1O4+i+vRvxKHJ7fuFZBrYJdBWZqLLENELYBx153XT+9v5kXV/ek7hG7bTUW6PcKKqmL69w38yzbYFtBcbM++L6PRC6BZ/3Ds/DVV34VM2MzCJVC2PiXG9Hz2R74oWAOOYCmoFvnceb5Mzj7v87Cj/k48mtH8LQPPg1tS23mmbVazRSuzWQygahJzj/ff3svqGHhm5Hue10TBdj22nieh8XFxSfEydAC3N9Fcl0Xc3NzmJubw4ULF9DT04OxsTFs3boVw8PDgUJH9ktOwUIQxDBTfeEJcBVYacVtzWMicNXqhXyZgIZHUV8ketQ1F0lfFIIx5mBrdWm2yXkAEAB/9DzzBQ6Hw6ZatBY2K5VKxjMej8eRSqWwYcMGXLx4EZOTk0agKIAkeGff1ArLatQs3qLXa2VxMmP2VQEJ16wZQ9S5ZxtcO2WCXMf2C+1wPAc+HmGIPrDvPfsMmFdrvR471czry/W2K7naDItrzXVh1IEKfF6v1nutCk8hlM/nA9XPaZDgevq+b46r4zM5NwTfXD9axzU8XeeU/eV4dXy8Z+COAYSWQzj8jsOAA/S9vw8D/zBQfz5cs2c5/wS+BOGdD3di9C2jOPWOU7jq965C56lOnH/1eVx85UXkN+Sx9f9tDQh1FiSkcUj3vRoDPM+DG3Nx+OcOY+ff7zTP53vFvcr5VaMI9wz3WiKRMHPK+WXBO66n7hVew/HqfOk+4Lxq2GaLWtSiFn0nNDMzg2q1iuHh4VVVt1UZViPrmbeewcJNCxj5wAiGPzkM12lEA1HuqtFSQY6CO9ujaId+KzhV/UuBMuWfgnoF7eyT/q3AXUOIy+UyFhcXEd8Vx+de/jksrFnA7MAsqrEqfugTP2TmJpFIYGZ0Bod+8hDKvQ3j5/mh8/jIiz6C137stUiW6oVeFUjYgFMBGvscDofxlJNPAVaAf3nFvwAOcN3nr8PeL+2F7zXGoaBF5YSOjcWxmgEaG7DaRgG2o/PDdpmS5nke/JqPff+6D1PhKYRPh+H5wXx1HZ+CKZ0LfT77qcYX3VPNvOE2gLWNPfbzbINQM8Cuz9N77bmh7mSDXRvcq1zX90rnh2PyPA9tl9tw3Xuvw9de/zWse986dNzRAT8ULMKn74LOk+qdMy+awYVfvgA/Vm//yvYr+PqvfB3PetuzkCwnAzo460fZ7yn/tw0XGgWhUR32OquepOulDkrdoyQ6xVZWVlAsFvF4UwtwP0aUy+WQy+UwOTmJb3zjG9i5cyf27NmDDRs2GEWYIKOrqwuhUAhTU1Nmc+uLxrxOerGAhjfWfiG0ArQyN76YVPj1pWIILEOw6Zkl6GXotm7gUKiRd8v7bEufhkHzHjJyMkTNA1bATM+37/u4cOGC8f6x37zH9loTADqOY/KFk8nkqsJqGuKkwISgiHNDbyDnpFKpmHBsDdfVI6rUM2sfH5YZzwBBozk6LnQYQM15JND3fd/Ms22w4DjI3BS02R5OVgRnGwrsFXTZQpPAlHOgFnYFitxfKuh4HjfnSL0LBJ0ULlwjeuxtKzPvV0swDQWZezPY8rotWLppCQPvHUCt2KgzwDlUQR2LxUyYezQaRdupNuz4yR2IFWO4+MqLGH/VOPyYj5kXziCGGEb+dgSoBEMFtS3dR0aJTHi47//eh/xQHiE3hK3/uBV+pfFeqsKghdxUCbH3EKM9WLyOfeH/AAL94H7l+nJOWMOB8/94Fw5pUYta9P1NzI/cuHEjMplMgOepd9uP+Tj1W6cw96w5IAycfe1ZxCtx9H6uF6gFi1NqTRZtR/UfO3xVgacN2oGGV1WjglSP0Qg4oBHyq8BBwYIaLsmHF8uLuONn70C2+5Hq2w5w+JrDSOQTuOXztxjZNjwzjK1HtmL/zfvhhx9pu+Kg/wP9yC5kke5PB57HZyogUXmpn3ueh83HN+MlH3gJJjdP4oY7bgBcoOo2gK6mqtEBQiMunRd6IgvnxzZSKGgnUe9To4DKeHUK1Go1hKthtK+0I+fnjL6gBnzVR3XubSMEZR/D5Skn1RFih5rbUQO61gpqFdDrOB7NA8910bpE9t5SnUU/55j1O17PdEDFAnoN579arSK+GMee39iD2nwNNa8Rgao6h21EUX3RDbnIbcqh1hY8RnTH7TsQzoexnF9GLBYzRaJVr9S9ZYNqPpP6FJ9rG2ZI9jxR79J1VaeYzkU+n0cul3tCnAwtwP0YE8Op77nnHuzfvx8jIyPYtm0bNm/ejL6+PiSTSSwtLQU8jpoLyk2oLzCvU/Csm4tCiEo1sDoHRY8mIoVCjaPAuNEJYAnWtfo2iUDLZvAUJOoh5YvBY890jGyfnm3mG7MSNgUvwal6eRW0sL+spE2gxyqqPI+Z4bQMbdYjvyhoeC/nI5VKIZ1Or7J4s2I3wWcikTBGEmUEp37hFLxo8EU/8AsHcMs7bglU/OT4WAyDVdbJFNmuhpqrl5PrrgKQc6+AnZ+xrwRhFLYUVtx3dkh9KBRCOp02YJlt8h56/Lkv2B73p+4VAOZYPI2k0CJ2NBARqHqeB7fqInVXCum70/V+RhtzoPneZPr8WVlZQTqdrgP3xRBWRlcw+ZJJY7n1Iz6mb55G5u4Muu7rMkKPe4vjViNOrVZDbbCGg791ELmNOcABzv3IOfgFH2OfGINfaSglGgkSjUYDIfB2hAXTLOjZ5h5RQUyhyneFSpe+00DDssy90Aonb1GLWvTdplwuh5MnT2J4eBj9/f0BpwAjcRaesYDlpy0Dj+AOL+7h7I+eReLeBGKXg/qLyjFVwu30J9awoKygLCJAVweAygYaJ4Eg+KJh1/bo2p5RNRArIHQLLnZ/djfuetVd9UH6wLaD23DzZ2+G7zT6WMvXcP3Hr0c1VMXhGw8jUoxg5G9GEP9UHIfbD8O52sHAwIB5ngJV20itQJE6oeM7GD0xis3HNtd1EqeRgw4Ej6VS0K1RUgSs1PXsUHEb/AII6FTatoYt6/9zG+eQPp42+iLXXeWaAjA1TpO4vkCjaJvquza4tXVjfq6/dZ51DhSc6l6114LzqB5YjRa0oxKBoPdcn20bF5oZmHRs7FO1WoWz7MCreav2SLO2FOQCQHZ3Fgu3LAQ+S11JIXMlg3g0jkRXw6ljO590TpTYD03p1EgNBeW23s11sdde97JiHM/zTP72E0EtwP04UqlUwrFjx3DmzBkMDg5iy5YtGB0dRV9fH1KpVNMKyEAwT0GtkQo8fb9eBXR2dhbhcBjpdNoo27rJCURsCxuFEytIc/MT9LJf9BrrS6TgnS8ZwRmZlFqcaCTgfQSOGn5Mqx2vYWgKgb/neSacHkAgdEX7xfOHCSwVrOjxSRqiT5BGEK/CQ4W+5sJSINFjyBx0FrKgda/nWA8u3XbJ5M0AQN/hPgOmVVFQARcOh03usIJl7ZOOndexj5ybUCiEZDKJdDptPOfNGCPXiUyL4TcMydHjq9hPAlo1HumYeC29slwjgkrd32roUMapQsl164XtWH9AhYNa23X9+S6pUYJjSZ1OYextYzj9xtMoDhURX4pj+99uR9eBLmMkoUJHwM+5o6HFdV0sDS6h3FVuVFp1gNxYDtVYFbFaI93A9kz8d4KbP9ybOqecH7anoYC24YW8gp9XKhUUCoVVQqxFLWpRi75TqlarOHv2LGq1GgYHBw2PI9Do+0of/KiPs79+Fm7GReZsBle96yq0LbQBiWCYqx2Fpcq5fqZyQL1qeo9eazsr1EPI62ncVbnGtpnOpvJKweTFF13EiZ86Yfrk+A7Wn11v2lePre/7uO2TtyFZTiI2FUPfN/qwnF7GysoKDh48iHK5jPXr1wfAvg3UHs3jTblgz5WG7lLv1ONAVSfR00T4PJXxNqDid7YuADQMv5p+dm7XOXz9VV/Hnn/eg77b+5rKRa6BAmbb4MDPm+ltlKMalaf7phmgJ2n7qtvaIFfnxm5D54NzqfNke3/tMWjb1GG55rY31wazGuHYrF/2Gmq6oeM46Hq4C5v/bDPOvvEsKn0VpK+ksffv9qL9eDtKTsnsZVsfs/eXvru6h3R+dKzshzpNdIzKJ9RAoXNH/X5lZQX5fB5PBLUA9xNAlUoF4+PjmJiYwMMPP4x169Zh69at2LZtW+BYDW4YBYgE2gy5BmAKLTHktFgsGi8lXzQ9/5qASK29AAIAlKT52mSOhULBePoAGJClL6t6R9lPrd7NdpUpKVBU7x+t4RQE9CZHo1Hk83kTVq0gS+dOi3sxeoAvqOZ7U5hEo1FzlFmpVDJAUY+ToHGABgACH2V8tVoNuVzOtD/5w5M4/prjq4p8JeYTZl9QsHH9VclQo4aCW51fIChQNe9fAShDuJXRcS21UJzuj2ZnxjOnSz2m7Bv3LHO+6AHWMarVVwWknsOuHlwFtpprrwDUBtdqCPI8z/RHvcME4JlDGYz98RiO/8Fx7PmrPRg+OYxKW8UYCNhHeuK5bhTaoVAI3Q93I3Mhg+LaRo7QujvXIZlLIpFKBOZBBQ4jMlRp49hsgaOGtr6+PsTjcVy6dMkY2RRoqzLgOPVq8+FwGEtLSwCAQqGAFrWoRS16rOjixYsol8tYt25d4FipcDiM3s/3wl/2MfEbE9jxrh3oONsBP9xIt1HApUZhG3Ta4EflPD/XVC1+Tv1CQ90pM+iESKfTmJ+fN98BQS8p/7cBi+u6cC8GPXDwgbZsvcCU1sjROiY3f/FmVKtVFHoKpt3l5WUcPXoUtVoNGzZsCAAh1QvUCMH+fPWmr+KqL16FhJ8wgEU9hpQ5lGN6Wg51Gzu6sZmhX+dB10yfyc9VLgHAhT0XcPcr7kapvYSHfvohbK1uxcB/DgTGZYNC27OtxhSV+7oXONeqI7BPqm83A8z6bOo39rMUMPI+NQ6YbSBzb/db+2r3kc/lflHA3cwAouuj0Qb6HJts44iub/c93Yj+URQn33oS1//19Vh3fh0QbUTY0QGitXlsYwCfzevYvj0fOucco+qDbM/WH+255ryUy2WsrKyY9/zxphbgfgLJdV3Mz89jYWEBJ0+exL333ovdu3djz549gdAhblDd0ATAQBAIdXV1oaury4Bp9VxyA1LZD4fDBjgQBCjx2alUyrRHIcfwaYKnWCxmftgntXSxL0AwFIuMGGhUyCbDJ5OlUIrFYubcx1QqhUKhgGQyGTj+i55uW+gQdAON40EUdHEsnueZXCW2x7nhcWbJZBLxeNx4egEYIKjjJ0MBANdxsbB5AdVMMF82VAth6OgQwslGlXO2yX41CxFXr62GOHGcKhSptFDRYdSChmbbOdWcDwJbx6mfV57P5wMKTTIZLOTCfcN5YwV5ZbAE+vS6k2EyIsFxGvnfWkleK41zHDSUqPWW3gc+v5kFW0E5BRhTDnrP9GLvr+5FciEJ9MAYJ/L5PCruI8fOOVED/NPptDniKxKJYPL5k1jYEwy7Ov6zx9F1vAuxpeB8sFKmVtrXIiMaLkWhw1QICp+lpSX09vYimUwaQ5HudRoIaKCqVCrYsWMHLl++jMnJydaRYC1qUYseU/J9HzMzMyiVStiwYYM5qhGoy7S2r7Zhz7k9SC2n4IeCoErljSrjajynjkTwTHmgupMaZVVJ14gg/mY7AEyqmToSbI+pAgESdYKB+wbQ/afd+MbvfAOhWgg/8YGfwPpz6+GH/AD40GMhmRrU1tYWcEzk83kcPnkYn3r9p/CrX/hVZEIZI880AtB498LAV2/4Ku684U4c2HIAL/6jF6Mz2RmYW5X1XCvNjedcMbLRBnG2R51tck4453ofdQ2O/fLQZdzx03egnKqnN1UzVZz42ROILETQf18jHUHX3wZXmsZmAzx+rutoG2z0et0PNvC0nUR2lAX3jkZ4qp5mA0POOx1auq94vfZLvdtMh+T9nOtASoProlKtoOSVAvVgNMLAbl+fY3ujAaDrYBee9utPQ3epG+FMOKBbqlOQBitGBfq+D4SBcCiMkNfQabVtjoVrZj8bCB7x2syQ08z4wPeHJx09EdQC3E8C8v16rvTExAQmJiZw7733YseOHdi1axfWrl1ryuqrV8wOFyX40Nxb5nuqF48eTIIYWsf4ovi+HziaiOBFhQxBJb2nBOLKLNgvAiJbyPHcar5EfGnYHw2xUmCv1mgWv2Lfy+UyCoWC8WByrrRfttWcIedkXolEwgg/AkNWNFQLr1rQKaw4n/zNcTqOg4lnTmDieROr1v7mP7kZWAIqbn0u+XwFvhR86hnlvtF1JWPkuLkX6KlWsM57+DefSfAJBAvz6RjVmMCCXVRS1IOtlkgFuzTiMGJDra78jGeD02BABltaX0LRKSJ2Imb2CL3srusin8/D8zxjmNEceu5hW1lSRYcMOrlQD99fWVlBV1cXMpkM4h1xHHrWIaz4K9j04U1IuImAMYlRFgOfGUBuOIfJF0/Cj/iILcew8//tRPRyFJVIHbCrZ4NrTIMVFSy+08lkEp7nmXPhbQWnXC7j4sWLZi1ZHE/nPp/Pm/nMZrN44IEHAnujRS1qUYseS/L9xvm3O3bsQCaTaRwh6YTgTDuoRqoB4KpAhjJIo8zUcE85ptfa3lGSFtCyI4ZsgEP9ijJYnwkEjxpTUMC/w5Ew3Ez92hd88gUYPj9sjrlSQ4AWzdQ2E4kEuru76zKhq4pDbzyEpW1L+Iv+v8Av/PsvoCffY+S2Gg/ckIt7rr4Hn7vxc4ADLKxdwGd/67N4/t89H23ZtoBXVQ307IcCMzV42wBTx61Ap1lb6sE1+p3jYXH9ogHbpIE7B9B3X1/gOc0AmK45T7ihQ0IdDuo11T2ma2fvAy0+rNfYHmwF+nZ05qOFcatOrA4BfscxNUv1Y/Qj9SzbaWQ7qy5vv4wDzzuArb+3FU6+8Xwt/Ms+cIy6ltpnYot0Po14Kh74jtiBaaDlchm5XM4cq+rHfBx73jFU01Vc+5/XIlqNBvaN/f7pe95szdUA1ixtQte9UqlgaWnJRPY9EdQC3E9CWlhYwNe//nUcOHAAGzZsMMeK9fb2BsJHFBiSqZBxAzB5uvRkMqSCnylTssN6tZojPa4sfqV54ArKueltxsE+quVRLbsscqZMgt+rZVvvVaClLxrP9qanj3Oh55rrS63MlpWr2Y9SqWQ8jwQ/7e3tZqwATM62MmmdD9/3Uc1UMb97vpHT+wj1nulFajEVmBeCy3Q6bQAYC5NoiBePNqDnOZFImOfqHGruNr2jepSEMjMCPzJp9UArI+f1Gk6ugkEFi1rE2UZnZyf6+/sxOzsbmCe2wevUixsOh7G0dQknfvsEyr1lbPrdTei7ry+QThGJRJBMJs26q2LGcWm+eDMrqipABPCu66KzuxNnX3EWR156pL5vfA8jHxhBxI8ErNHRaBTRSBTb37cdYT+MqdumsPN9OzF8ZBhuvGHlpnEkHo+bNaORQa3u7BPfUb5fFDI0gtgeBtvo5fu+iR5gnQaOseXhblGLWvR4Ua1Ww+HDhzE2Noa2trYAYLM9e8ov1Qivod820FHvJtsheKJTQa9nO/p8fbYaQJsBLvJY1Z/Umzv13Cmcev0pAMD5jecxcnQEqUrKAHkAAX3LBheuW68FU+4q49yPnMPSdUsAgNmeWfzTD/0TXn77yzG4MhgYeygUQrW7iv+49T8aE+8AVzZcwcFbDuKGT94Q0LHsuaJsZFs8wlWj7XQe7Tm1ATnHYetfvu+jGqri/M7zgT0SXYqi+3A3HDjw/Ea6lgJB2wMcDjeOJrU967q+7It6ym2Die0g4B6hvqDeYXV+qS6n1+t+USCp+4bRmfaaqIeZRH1H942Cdo45FArh/DXnccfP3QEv6uHkG05iwzs2ID4X/6Z9soG2/q0h7fY7Q/2EfeQ7V66WceIFJ3D45YfNc6/71HUI+8Fz7O19yPHwHjuCgZ8rhlFswXaLxSKWlpYCtZ4eb2oB7icx5XI5HD16FGfPnkVPTw82btyIkZERDA0NGS8uASE9j8DqYwSolKv1VBkPwQnQCGNVAKXePzJcz/NMODHzyW2Pu/6tOeRkWOpJNZZuAfIAzIsNBKtGEihQGLLatFo47fByvnz2MQh8PtvSULRKpYJ4PI7Ozk4ADUFCRheJREyVZzJNFfy1Wg3VchVuKZjH1X2+G0/9h6cicyWDit8IISbx2WRu9E6qt54hwqVSCd3d3WacBNNcL1swakE2m6Gp4NDnMDJCjznTgnw0eugcqiGETNh1XeRyOWMBtYWcHk2igmply0q9mNlwPS96/HfGEXpXCP139Zs54/7gsRSsZcB2VDhy3e00AyXOYa1Ww4GXHcDZl5w13028cgJ+0sfWv966quotBd3mv9uMzgOdWHd4HaLJqAHXJK2QT2VEzyPV91Zzu1U4cR7ZXzWQcR0I5DlHHCeF2hNxHmWLWtSiH1zyPA9nzpzBmjVr0NPTY4z5NnCzjbX6GakZ8LPDz9WYbEf/8Do1KrNdyrpMJoOlpaVVnkjyegX+JNd1MfVjU7jw2gvG2H5432GUo2W8+KMvRgiNXGK9X71zCjCS8STSsUYoPgBcHLqIf3vev+FVn3sVugvdAcAVK8Rwy4FbcOc1d5rru2a6MHZoLODVtI3PCmo5f9Qz1eOpxgHKLV0fvV+j3NQI7rouHM/BjR+6EfhxYPwp43CqDnb8+Q703dtnIgGaAVmS/bcCRTWcaJ/sdda9ZBs89DrbCcTxPpoRQEFqs/5on9Wbq3oRSXWXZtEGOje899R1p3Dvy+81RV8Xn74IL+ph7HfHECkF0y9INhBXDzxDxPv6+ozhXudGjQN0KFC/2f+y/Tj6wqPmOYefdxi1aA1P/ZenGlxgG7T4t76fmjKh77fq9NSbqP94Xj19b3l5eRX/eDypBbi/B6hUKmFychIzMzM4dOgQ1q9fj7GxMYyMjJjNrCX1yRi7u7vR3d2NK1eurLLqkDmod7tarSKfzwdCzekV1Txx3qOeaxJBFQEa0AjfIpOKRCImpFgZvfaNFkTmNKsXE2iAU75QmodML2YmkzFF3nK5XCC/RJkpw4/1eAsFjqxoqZZQ9STT2KEAk2sRjUaR78xj/rp5M77EYgLXv/N6xGfjqDm1ALPlOhSLRRNKrMJMjSDM8aLhQcNpVKiooGAePNvVNeXcaiQAQammJagBRJmXGmXU2si5jUQiJswon8+vCodiGwwF5z4od5Zx/I+OozzYCDmr9dVw4U0XkC6l0X2427RBgWT3NZVKmdoHfI4aH1TI2V5i3/ex5vY1OP+S8/Bijwgjz8HVD16NSDpiKl7aBo8oouh/oB+hVCiwp9lmOBw2Rfn4GaMl1MPAeeV3WkSQ/xP0qzJCAcQICd2/aqV/IgVQi1rUoh9MqtVqmJqaQi6Xw8jIyCqvqM2Dba+XDfxI+hn5ofLTZoBKea2G8JLvZrPZACh5NE9rAJzBw+K1i/CSQf667b5tcKsuwtFGYSkFvewHZRf7mcwm8YxPPAPleBkXrrpQB/EeUPl0BafvP41dY7vQ0dFh9Bh/xcezvvYsFP0i7r/mfiQKCbz4vS9G+3w7an4tMBdqNFe9UD2Vtty3ATb/1rXiXKqnUR0BfGZiKYF979+HUqyE4Y8Oo3N/J4AGUNW5VhmneqMddaCA1Qalule04C/vVU+9GoJsr28zYzhJo8t0HfVvfk95bO8n/Zx6suqduo81rJzPWH9yPdJLaZQyJbNf+r7Sh2gtaiIHuE7NwrE5r+phr7pVlMoltLW1IR6Pm3pOql80M7psvnszTvzQCbixR5wwnoOhLw2ZAshacV2dOYzy0/fVnqNm86+gnAWQs9nsE6rvOMqo/tsLHad1bsyTiBynfibj2NhYoLo5N9zMzAwSiQR27twJAAZsKtBQ5sKNyQrg6kGjtYjeW4IUBYdkokAjD4jPVIsUXxB6pPm/ni+sjJXV1j2vXoCER2PRi8exqeGAXj8C/VCoHh5eKBRMcSsCzlKpZAwDtPrauV92Lg77pwxCrWlqlUME+NK7voTcUOPcP6fmYOT2Eex6/64AQ+WzaRSIxWLo7OwMFCshYNQzxdVjr0W4NN/N8zysrKzA9+uF9dwhF23zbabvDDfmnlDQzf+1mJd6IQAE0hoYXaD5WzRcsB3mGqvxhnOux3xRMM/tm8PJt5xEreMRD38xhM3/uBkb/2MjatWGwFNGzc9UoeLvfD6PYrForLB6PcdLAw5D+rwRD1/7w6/BD/u45W23oP1Mu+mv6zYKtNm576FQyBS/4XvAfa0Cg8Jb14794fxohIEWieG6eJ5nojxouaYRTAWY5mzdc889T1ovt+/7zje/qkUtalBLV/neo1gsht27dwfS34wMRRAUk1ReKTDT75gmpvwfCIbIAg0jM4uh6jP0egV7zYAfZR0ATP3UFM7/4nlAHJqZlQxe9bevQupKyugW7J+mvamnWSO0arUaEAE++UufxOSmSWz6xCasfc9aVMtVDA8PY+vWrejo6DD9q1QqyJfy+PSLP42nf+bpiC3HArLA/juXyxmjOOVvPB7HmjVrsH79enPMKfUn9ZJzjjl/WhOGOpv9zHK5XNcVYjUUnALK02VUihV4brBitQJR9Xbyf9vLzH5Qd1PjBWWrgnXV4VQeaxQEr1OvvbbJsWoUmo5Vn8X+sA3qfKp/ci9oPSGmxSWTSbS1tZnjhG0AqsaQQrWAT/+fTyPXl8Pwu4fR/x/9CCEY+aH9o06meMJxHPgRH7UNNVz+8ctYe2ItNj2wCYlYwlzXLLWNf/M5K/0r+OL//iIQAm56203oONMB+I0TktTRp+8n29E+6nxSj1aPv95XLpdx/PhxHDhw4DE7Euxb0VVagPt7nEKhELq6urBx40Zs3boVfX196OnpMV5X5rhSiacFly88816TySSy2Syy2awBdQSZCm4ViPCFoOeNir0WJVMPNBDMCSegJ1MnU1FrJBmPgnYyIrVGq8HAtlDrS8jqiQyPZqE1giYyO2XKGoKv46LHmIyGIFjDgmq1GgpdBdz/pvsxt2UO8IGR20dw7T9ca9rSfGO2ReDc0dGBjo6OVVXTydw5pxoWzzVyHCdQZZ2gunRtCff+2r248WM3YuPDG83z7XFzfqgMlMtl+L5viuDZTFWFiJ6brsYIrcDOdnSdKPRDoRBSqVRgfHO3zWH8DeNw21wMvHcAQ/88hHg8jlQqZZQUtUKzPV2LWq2Grq4uMxauOfcY76PRhoCcZ5DOjs7CTboYOj5kFDqtpM55Y7sUwMlkcpVXhc8keOaeBRoGE8478/kpPBgB4HmeKR7IOeA+KBaLqFaraG9vN+8Mrflc02KxiAceeKAFuFv0fUMtXeV7k+LxOLZs2YJMJhMAPQACMt32cOuP7eGzFW9Ne3o0UpnEZ9rGWPJnAAHeb/SbNcDkGycxcVOwUOqL/vVFuGr/VQBgdBV1FlAm6Lg5DsoMz/PghT3cddtd2PpPW7G0tISVlRUAwNq1a7FlyxZ0dHTAcRwUCgVTSFSBtdadUQOx67pYXl4OHP2aSCSwdu1aDA4OmloxnBeCbk2r4hzpb66HhvhyrhZyCzj+o8exMLyAHX+9A6HJxvzzOuoR9l7geqmnWXUTXRsFcuxzM5lsP0O923we/9dxq86q1ygY1n1rPMaPzCGdQbru1F8I0qmHZDIZZDIZ44yxwajqlsViEbPeLE497RTW/PMa0wf2SQGx9p/gFwCckIPpl03j3G+ce+RlAp7yV0/Blvu2BOZX50lJ98Ps6Cz8tI/Bw4PG88x3iniAerfq3va7bGNX+/lqBKCec/z48ccsh/tb0VVaIeXf4+R5Hubn5zE/P4+TJ09i7dq1Jtx8cHAwYKkiM+TLoXk06mmjp45MR0OtgWDBKYJ5WuZ4tJdaeimc1PunXlOCCgJvggn2mUBDmYJagJXBJRIJA7xtZkiQRyHCPheLReRyORN2rn23GZN6djkGZQwqzOPxOHp6erAQW0CkXH/Vxj45hu0f2R4QABoez/95XJsd6t6sT2rsAIJhVirQ4/E4ZkZncOgXD6HQU8Cdr74TxWgRG+7cYMAar1WGTSao1ndViPi55mST2bE/anFlNXh9HvcGc4RUaFBId9zegQ3lDah11NDxbx3IVrOmQF4qlUI6nQ4IXFWUCOAp/OzCca7rmmr0driWRlb0nOypew1CeaRSKbOf1HvPZ6hB5PzTz2P46DBiSw2gzPlTQ4WGF9L4Q6Gq3hy7gA+VF17v+745zozHhFFpofGGUQlPZIhVi1rUohYB9YKnp0+fxsDAAAYGBgzvVpCtnlT9rVFAzbzEQDDFJh6PGyBqgzTlzUAwnJ1tqA4EBHPNgfoxoHk/6Elbd3Yd+sb7DEDSquoE35T/Gpmm8tY4GhDG07/4dJQ6SmZs2WwWExMTKBaLKPxyAc889sxVgFE9tlo4k3oZ5YIW46IOZoNH2/Chn/GZKrfUuK1A/Nirj+HYs48BDlD7lRp2vm0nwstBnVXXU9fMBlcKalVHUQDO+dT6NvbY7H2mf2vkhb239FnN+qp6kOrYOi+kR/MYU5bb52+TdC2oPzrLDtZ9dB2qfjUwJr23mR7AtiZ+cgIXf+GifAHs/8X98Nt8XPXVqwKGIW1H22b/B84N1Pd6NGRSHNVJYYN+Hae+Y/Yz9B3l5+xLLpfD0tJSwHn4RFALcH8fUS6Xw+nTp3Hx4kW0tbVhbGwMO3fuxObNmwNeSQUi1Wo1YNHkDxkwgRo9aFTeKRg014heQ9uSTCEHwIQbAwgAB1pLKQgYRqMhwuoBppBg4SmgYUUjQ02l6pVAmTOsYCsej6OtrQ2OU6/yTVBFAwCPMlDGSg85588Of2EoPucjFAohmori87/0eUyvnQYATF87jbGPjCGGmAFJZCxA8KgvDdnSkCi1SodCIXM+uD5XATKFR3FDEQfecADFNXVvZiVdwQM//gBqyzWsuWuN2UeaVx0KhTD+zHEkJhLoOtqFWCQWEAwqWJRRco5odWboWDgcNgVY9H5lqKlUKqBosS3HcdDz9TrgrXn1dslA6a1Np9PmHHU+zxZABN61Ws28FzT0aNoB54Jj5d7wfd9ERfT29qKzs9NEh6hXnWOavGkSx159DOfnz+O5b30u3EpDsFDgaooGP0skEoGoBUYOcK9zDvWeUqkUqKugxjDOAY0K3Ee2AGtRi1rUoieCKpUKJicnUSwWMTIyEvDMkqdSvthA2E6bIakRmTKDMkjDnml8tAGM7UAAGoBSi3JqXwodBeTHgoB7sW8RCx0L6JzpDIAI1TPUgK5GcLZLowJ1pHQ6HYjgy+VyOPysw5h4xgQujV3Ci9/3YqOXUBdSIEZ95tEAvuM4mNo2BfcaF2vOrFnlzWWf9Yefce4fDYABwN2vuBvHbzluisrN7pvF/j/Yj+vfcD1Czuqjx6grKqDXedPr7fWy107/13VvZkiwjT/qVNDr9TmcQ3XM2MYijS5QAw4QPHbOztduBkDtdeE9Grmp19iOJbv/pMs/fRkTr54AgsfMI1wJo/9w/6rna1Shzov9GYmgm3Oleq+mhuoc6D5QvmDPL/fa/Pw8stnsE67rtAD39xn5fj0ft1QqYW5uDg899BBGR0dx7bXXYvv27QacslgTmbiCBABGGBmL6iNAST2puvEJWGmpUoGiOSKhUONoLbVGl0olE16igJtggkIpmUwaYcnzhunNJhhSiyxBDcE3r6NntKOjA5lMxgA09iEWi6FYLAb6Y4+Fn3FcHIcCng+97EOYHpw267MyvIJ73nIPnvo7jcqMGt6vAo9jsa2kauzgT6VSMXnxXBv2i+13XunEtn/fhkM/dwhu3IXjOhj42gAyX8kgX60rB2pkQQi4eMNFHPmVI3B8B9f/r+uRmE0gsZIA0CiQR8ZHzzAFCa+xC6ZwblQJ4rN5Hw0admE3zj3DvZUJM7ybeWZ6jB0NOsydtg0cFJqJRALpdBrVahWFQsGsBfcB14d9m5mZge/XvckM3zKKHHxc2XsFh37zEPyoj4W2BXzhj7+A57z9OQhdaawv15DzpOfD0nPBUD5939QbwjnUdAOOl6SCmwaFYrHY8nC3qEUtetKQ67qYnZ1FPp/H9u3bjQxULy/llIJEBULqpdTirHQy5HL1mioa1kv5QhCl+pDySD5TvdPqTQ+FQsiczqDvU32Y/IXJ+nNqIex8cCc2nN4AoCG/KJ9V7qn3VQ3aHI/mfQN1A3UoFIIX8jDxgron0o/4OL35NP7px/4Ju9+2G/5KI6VKQamGVrOwqeM4JmJseWwZ9/zuPXDCDto+24adZ3cGPM8qE6kfqU6ga6EOHd5702duwtnrztaLegGIrkSx85074Xu+AXhqjGjmjVWgzev0f1vHsAGi3qPPsovIqjPn0SIHbAeRgnLKaergnH+m2jUDpFx7RuTpUbF22Lk9V5x3BbFqhLD3MvuqbQDAun9dh+W9y1jat2QMI/HlOJ73puchuZKEF2rsTfXYN5tz1f90Hfk5nR/UG9lnzj3ngDq2HTFrp2BwHCsrK0+K409bgPv7mAi+Dx8+jGPHjmHDhg3Yvn07tm3bhvb2dsNw4vF4wMLEF0RfIm58gjG+pHx59OXgi6PhtcpkFDiSeQGNomX8jF7EZDIZYOYMPSfzpneSZ2+rlU1BDKut88WtVquYn59HOp1GMpk0OdgMa2ahtWKxGGAYWu1cBa0WAuPno58dxdTOKfjhhgAY+Y+RABNX4wTnmoxVmRgQzFvSPH2uG/uoRhEtqjF65yjcpItjrziGTXdvwq5/2IWcm4PrN46g4piXf3gZh3+7fmaiDx/3/tW9SF1M4Zo/vQZdE12mTZ3rcrlsFAK1TnIPaLi2GhdUUDO8jvnn9I6rENOwaDWsMNKAfWPYdyqVMlXfAZjCbLwvFoshn88bxSuRSJj0CCpDCvrVu7y4uIhoNIr29nbTdigUQjlUxvgrxuFHH1l7B8j2ZXHmhjMYu33MtKehYxyrKol837ieAALA2fM8s1fYz2QyiXXr1mF8fNy8x3x31CvAeW1Ri1rUoicTFQoFHD9+HENDQ4E6HbYXVRVs23hIL5l69uhgABAAupQt+plt7LW9aAr8A9FQmQrKQ41TNfou9+Gmz95Ul3t+8PQQO7yZ/aZ8V3DHfvN6ystIJILkcBIzL5iBH2nIm5l9Mzj0S4dw1d9dhdKlRsFYfS7HQTlM2Zvbl8P+P91v5NcHX/hBvPqzr8bVp64OzIcN2DT6TdPcFARyzuaH5+GFG2u28y93om26DS4aIEs9s/xbgRjnXtdJwa4Nqvm/6qq61jbg1N/q/ND/bU8219DWcylzbWOR9plkG4+oJ1CXtR1mapTRva9gXvvMqEjuIb1fDU2hcgg7f3Mnjr/tOHJjOXSd6cLVH70aqWwK0VjU9FfXWz3MOgf80ffT9kirPk1jFJ1h5XLZ1LBhdKKunTof+Gyev83iyE8ktQD3Dwi5rotz587hwoULeOihhzA0NITNmzdj3bp16O/vDzAy26pLQGhbmrixKaDs4lG2wFJFX3OJgDqA0GOwNFeVx3ipQKzVaia3ldcSdFCoqCDjc8PhMBKJhOk3i6jxaAJl1O3t7WbsZFKxWCxwDBcZjIJ7FcYP/fRDAbANBzj6y0fxzAeeacZSLBZN8TXezyJWzGGh0kDLH4E6gAAoo8Cwjxoh2Eomk9j15V1IuSls/tpmVCNVMxa2RTCb7c+u2keF4QKO/MYR7P6r3ei82NnIEXIaZ2i7rhsI808mkxgcHEQ2mzUhbQSsBORsh2PmntWjvLiOtMRrRADQsO6S6TqOY4w2ZNisSK6ef7bBiAmNDgCA9vZ2pFIpTE1NBRQMrgc9Mul0OnDkWsJP4Jp3XoPDv3oYV66/AsdzsO9j+zB2xxjKXuPIPO5vrqOCbuZgq5ee3gHubX03uffm5uYCBRDVs0Bhxj3b8nC3qEUtejJSPp/H+Pg4+vr6sGbNmoBXUb16lIcKuBTUNANtzf5Wo6U6AzSUFQh6IG2Z4Sd8XPpflzD/3MZRoLnOHM7tOIdNRzaZtng/n6WgiPKajgG2DSAAvtR50J5vx3P/5bn4rx/7L0xtmjLPnn7WNNIdaWz4ww2oTdcC0V0KOFW21Wo1zLbNwkfQGDvbMRsYv+355xxxPJoOZdPl7ZfxhR/5AirJRhGryVsn0bm/E9F8NKC3qWyjfqbRDnwmP1OyHRA69zqGZmBQIxlUlyTpWuo9qkPr/iBphCcBse6tZj/6bDvaw9anqSNMXjsJHAD8ldVh9HY7zb433mkHaDvVhg1f3oChB4fqunc0vGrO7PvZFxq6Ojs7EY/Hkc1mjUNOI1bUkKHOEOqI1AfpXNMq9BoVouu2vLyMlZWVpnvw8aYW4P4BI8/zMDU1hZmZGRw/fhydnZ0m3Ly/vz9gBbYtxrairkyFG98u/mEr9QR0tqWYhdooTAja+IJp6BKfCTQKsrFfCuSAYC60nQetoSsacsP+qLDWYwu0gjStbuyPMhCbyQIAfGDvX+01gkhzrXVcFHqu65oIBA1B0zAaAjK7cJttHdWQ7813bA54lflMVTTW/vNaeAUPF1930YQSAcDy2DL2/+Z+7HnzHkRWIgFjin3eerlcRqlUMiHRCwsLAQbLOdK1ARoeXFaP59hpMOGYuA95nAsjGDh+zlOhUIDrusjn86YqPyMfdOxq8QUaVvCuri6kUiksLi6a+dFoDD67ra3NGHRc14WbcZFbXw9f9B0fs5tnseUrW8zeUiMW7wEawn/Tpk0olUqYmJgwRiXONfcH51n3mxqJdF/RyKNV7Fse7ha1qEVPVioWi7h8+TIqlQrWrl0biPhSIEJeqHzZBkQK3kgKCAnebUAMoCmA5/PVU+lGXMw/pwG2ASBeiqN3rjeQV8t2+AymJWlNDupCCropM2hEBRrpXX1X+vDcf3kuPv2zn8bCwMIjgwbCD4eBSuM4UXWK2MVl+dng7YNIVBM49YenAAAvuuNFuOngTYH54LXqqeScqqebY6XcLBaLcM47iGVjQC9MP9vOtcEprz5HXdfPNraovqXeWV7PfcLfzbyv3ANqgNCUNu4v2+hig3Xtsz7DNkaoM0odNzbIVi82ddBm3m3OOfsCAJfGLuHBn3sQkdkIdvzyDqCKVbqj3mfPn87x2TeexexzZxH7RAzhA8Gwc40K0PZ0fgmWl5eX0dHRYU5tYfQo50Cfb0crMOqT7wNPGOL+JzBXJ1mtVsPKyopJIXmiqQW4f0DJ8zxT6Glqagp33303RkdHsWPHDoyMjAQqHKtwoKJun69N0hwKAAa46jEUNjC2LaBaJVMZCkETmTzz0Am4mENOIEvwp33XZ7K/BPusVp7P580LTCscQW+tVkMmk4Hr1qubU3jQ81goFOpGhkgNB3/5IHKDwRc9Voih+0x3YI7UWk3rLS2U9HayqjcNGPQqGyH/iIVfLcoEqHqutQo+hueoQYICIxqNIoooBv91ELVIDVdeeQW1thoQAkLlEAY+NIDKxQrKtXKAUXPd2K4TdlCMFnH+/HmTBkBGqADX3jtllFH1GxUlFWTq2IFgPQIyf1bqVosz565YLBrwzXMs9X5VckKhkClGSKFH4bC4uIienh7j1S6Xy8hms4hEIkilUsgjj2/8/jdQ7HvkyC0HOHfDOUQXo9j14cYZ7Hymehr4jp48eRLpdNqsqR1tsrCwgK6urkDuFNtVpccemxrPWtSiFrXoyUy1Wg0zMzMoFotYv379qhoc/JvyU/leM0M9SQGXekJVV7H/VnChBtBQKIRyvIz9f78/YKCGD2QWM2ifaUc4Wr+WjgC2S5lGnqygX/m0ekbVqKA6Ve9iL17yzpfgo7/7URTbi9j+8e0Y/dIo3JiLmNM4zozj0cKaNEYw2m/g7gF0/WUXOnd14oaHbgAAFMt1PamtrS0A0tVby8/Yno6J8j85lcQz/+iZ+Nwffw6F3gI2/NsGbPiXDXCqDjwEQZsaQBSE0+hApwzlqOpFCrLtSAYFdepg0r3U7HPVefhs7gHb4aFro3u12d7Vcer9NCZxjyjgpd5APd3zPcxvnMdXf+2rqCVqQBdw8P0HsfPndyKUb4R12wYRzmfAWRN1Mf4b47jygitAGDj98tNIeAls/8L2QN/VuEUdw3jHZQ09z8PS0lIAS+hY2I6mHdpt0HFEfML6T9Vq1QBuOgCBejHpJ8uxpy3A3SLUajXMzc1hbm4O+/fvx+joKHbu3ImNGzeio6MjUP0aCIabNLPwqsUQgLE8aUVMvphs17aCKqMG6i9bOp0O5AuzOJxtveRLylxiPiMUChkDAsfteZ7JB9E8GIIox6kfs8Q8cQKrcDiMhYUFpNNpE+pWKpXM8WIXr7mI2afMBio7ZiYzuO3/3Ya4F0fRK64KoVHLO40GKshodVUQyc84Ll6v3moCNDWQ0GjB9eG1GhEQDocRRRQb/mkDtnx0C47+wVEs7V3Cpr/fhN7P9QZCtxXIaSj48jOXceVnr+Cq378KkZm6oGA1ePXWq4W8Eqng/CvOoxKrYM271wAFmPVUhcn3fVS2VRA6HTJh/gBMxEEzbzDbIJjOZrPGEJBKpYzBhkUAtdCI7i/HcbBmzZqA0GK4ei6Xg+d5yKQzuO0dt+HON9yJ7GAW8IDhrw9j5z/vhOfXPfzpdBqlUsk8g/3T/V8oFFZZ5unh6O/vN/tblQsKJtYboFWcgJzr/2QIs2pRi1rUom9Gvu9jeXkZADA4OBio0K0AW0Gg6hA2YG4Gqng95ZmmmfG3tg8Ej+Gc+JkJVAaC5/wOnR7CS97zElS9qpETtgNC+bGCRaABKvlsjQJs5vTwPA+RXAQvfMsLcfjZh3HVJ66q6wvRUOA+A9DEIAE0QoApPzcf3IyR7AiiiajRh3zfN8dxqrxSry/lJPUs2/MJANn+LG590604+iNHMfaBsfp9TvA620vNtgEEItoU1CqA1b+1FhH1D5W1qh+p3H80PS0cDpsoSR2frgmvZxvsn66xFlvT+xWE616zvejsp+M48BwPd//o3XWwDQAOUFpXwuRPTmL9e9cH5kf7Y5PjOMjtzWHx6YtGj/WjPs7ddg7rDq1D9+XuQDt2lIGOj+NlCqHuE3udeD2Bt71nbGeZ6rc0GjHcPJ/PY3l5OVA89okk51v1cDiO03KF/ABRPB7H4OAgtm7dim3btmHNmjVoa2sLhGDb1ict9KQgigxdw1j4OT3RGl4LBHNL2L5arumdtQWHWsB4/BeFrFpyyWj1fMlyuYyFhYV6PlR7u/Fw02pLIAY0jouKRqPGEFAsFk2/LvRcwJ2vuhNLG5cAAJ0XO7HvffvQd66vbgmXMHSGptsWdY45nU6bglwATF8BGIWARgBlQJxngnX7PHN66B2nUWiMHlSCQPXAFr0iLj/nMgY/PWj2gFaDp/BnsZcrz7uCS2+5BD/mo/OhTmz/8+1ITifNevJHw6Fd38WFX72ASy+/BADo+dcerHvnOsQQW7WHCtcUMP6WcXR8sQP97+5HKBTC8jOW0f5wO5zFRn6b4zgoJUrIPiWL/rv6V+1V7rXOzk6kUilEIhEDcnVfUdhx31EY0HjAdsncu7u7kU6ncXnDZdz9mrvRc7gHe/9xL+A18srUwELFStdIlUMVGmqgUGGln7MtHiWmCgkjHw4cOIDZ2dlvlz08buT7fpO8jBa16NGppat8/1Mmk8HatWuN0bMZ8OZv5bEk8mobcCgAVU+qDcbUwcD7KCcv/NwFXHxVPQ1r9OFR3Pax25AoJQJAVr3TlGu295Tt8jmsi0J+TlIdjLpAqVQyx7R6nhcwyAN1nePss85i/efW1yuCo5H3y3bi8TjS6TT6+vqwceNGoxuwTY1U4/2apkZdS6/ndZVKBZNbJnHPz96D9V9Zj43/tDGQy2zrfxyfHbpsR3HZxcjs+dF9oA4PG1zrfc3+VkOOAkPbSaSkQJTynBGMbEflPvUB9jMWi6GtrQ1tbW1mDzXrj+d5yMazuOs1d+HyUy4DPrD+veux9gNrVxkWdN24fzXVz3EcLN26hLO/eRbVnioyExnse+8+rDm9xujYtsFHQbielKJpj7rPdX9wDfUdsD3cuob8Ww0Y3Mee52F2dhZHjhzB/HwwxeOxoG9FV2l5uFvUlMrlMi5cuIDJyUns378fQ0ND2LlzJ7Zu3YpUKmXAFX/ITLRYFxBkOlrURK2KWhRLveS2dUsZNgGkhiizkAJfXAodDWVmNXbHqReo6uvrQywWw9TUlAlN1nxt5vHk83njwSboJqBXz3M0GkVtUw13v/huLA0umf5X0hXU2utMgCHoFPrmGKlHvLjKeNlXCluGzfi+j3w+byyGasBQxkNvLkOnaZlmyDeZLK3uWviNbZrnOlEMf34YNadxbJvdVz577sVzmHz9JPxYnVku7V3Csd89hr1v2YvQSsg8h+vKkPyLb7qImZfOmHmbf/k8kAJG/3g0oKCsbFrBhf99AeWNZZR+tgQkgY6HOzD55kksnl3E6K+PglFpvuNj/E/GURgrIIwwOu7oMOMmo+c6RiIRDA4OIhwO4+zZswFvAj0edtVwDddnZXYAJry8/2w/bnzvjQidCcHxHTihRng3gbsKDQpSKiYM1df8KyoWqkRxfvr7+5HNZo3RRK3nzY6baVGLWtSi7yXK5XLmBIahoSHDNxWAqW6iXksFdEDQmGmDduWvWrdGPdMKin3fR2F9wYSUr3StwHWCHk4FVAom9LnaJmWA67omjFsj+7Q/tueShlzWwyGdfcVZnHzpSRTWFTD2vrGA40LlT2dnJ3p6eowMUieKgmkbHHEc/M3wX94/u2EW977mXmTXZHHi5SdQckrY/P7Npu9qNLbnhetEuavyk/3XVEQFw9pX23Osc6d9aAbAeb/KXoJVdSqpjFU9WT3UtqdXjQr6XB6JpeHSzQwBnucBc8C2d29D+ZfLyDyYwcBHB4zeofnS7Lu+E/b70X9PP9JvS+Pom45i71/sRd/FPoSj4QAw1v2vfzfbm9q2zpWuker+thGjmUed7XHtWdeGaaJPFmoB7hb9t1StVjE7O4u5uTkcPXoU/f392LVrF0ZHR7F27VoTRkvgwKIcyvBtJqLVlTVvt1QqmYIhKiDpieYLC8CAHwUQ9tl9tVoNi4uLxjpMb3FHR4dhjFeuXEE0GjXW4FqthkKhgEQiYQAqveSRSMQUeSiXy8jlciiXy+jq6jLeUQBIlBO47uR1uL3/9vqRFx7Qf7wffcf7jKAi4KxUKua4LLWsA41wJRaKIKDTomSscg3UFYLl5WUzV+qlpmAi2WCL32tolIavk1GrhbK9vR3VahXZbNaMiR7v9i+2I/FDCeSvzQMO4FQdDHx0AJW5CqLhaIDpq6Dof38/Zp83Cy/ziBAqOeh5d4/xOPu+j3BPGOf+3zlUBx7J8Y4AV378CuZ+ZA5uxsVy3zJOvecUdvzmDsAHjr7jKFb2rQAOcP73zmPzGzaj81CnUXxCoRDcPhff+F/fQP+RfoznxrFx/8aA4sOiNJz/UDoEv1TP1XfLQQVKx8XIg85znfAd3xx3otZxOxRcvRFcY74DXA9dV/aJP4lEAu3t7ZiYmAgoUdw/3Cs8W7xFLWpRi77XqFwu4/z583BdF+vWrQtE+pAouzQCyTb4K3DW+xU8KIADgjneCviOvfUY5m9teNNmNszg31/373jl216JcCh4BjXlBfUTfbbjNI5IpRxxHAe5XG6VM0JltBN1cN8P34c1D6xBz4keI2epm5VrZZx63imcePkJeDEP4y8ah1fzsPkfNgPu6lofMzMzyGQy6OzsXAXMqC/xes6hzq3Kd85hsa2IL7/+yyh01Qte+REf4y8ZR3g5jE3/vsm0o4BKn6G6iK4T9Rbtp+u65lQTzivpm3m1+b/qnPa1OkbdH3Y7dgSm7lU1/Gv7+hwaTVjol+PVe9W77zgOYldiGP29Ubg5F467uqCq7huuj+rpjuPAj/hwky7Ov+I8nvZ7T0PXTNeq6uTNDPcKgvW3/bk+j+3oaUe6XjrnGgGhYFujFqrV6pNOx2kB7hZ9S0Smf+nSJVy6dAmdnZ3YuXMn9uzZg97e3lXFQ/SlUoGkXlH+ry8MQTfP2qNFj95bepPJdNTDTSZAIOu6LtLpNPL5vCmUVavVkM/nkclkjKdSBRiZ9MrKijmLmYAnlUohlUqhWCyavmazWcO4GHIf8kJ4+v1PR87P4Z4b78HgQ4N42nueVmcCftWMyT4OgiE3ZDqJRMLkSylT1HnVUCAeWcbjoNg+x9Xe3m6Yj1olCcpotFBGrOFu9OyzvY6ODvT39+Phhx9eBRpjxRi2/8p2nHjfCRQ3FzH6vlGsvWctym4ZpWrJMEQN+w6Hw0guJLHzp3bi5LtPwg/7GHn9COIzceMxcBwHWAY2v2kzzr7zLKprqoALxC/FURopPXIRkN2exeRPTMJxHeR25sz9bsbFpd+6hL5f7kPSq4e354fyuP8v7ke1rYrp66br173DxcD9A4EoAM7ZSvcK7n/L/djykS248IILuPpPr0ZyMRkIv1dPAdDIuaZxR0PxFNjb68Lvuf6aSqBrr9XWx8fHAwYtG6BzfQuFwqrwxBa1qEUt+l4hz/Nw4cIFlEolDAwMIJPJBAzWCgYUmKlhVL2bCnLVo6p8XXm1GsGra6oorysD1ilJL3rviwC/AcK6u7uxvLwcaIfGarsYLcGi9o0FoxKJhElhC4VCKDtlPHjrg3jgmQ8AzwR++E9/GN3Hu418dxwH+dE8zr7wLLzYI+OK+ph+zjTi03EMfn4Q0WrU6Emqm6l8B7BKtvx3tUAop4wesRjFM977DHzlF76CYncRjutg7X+txcZ/2wgnFARjatjQPmjUln6unmP2k30kadsKFHVv6FhtYE1wawNv6m3NwLsaKVhx3i62pnuLbTDfWaNJdV7tArL8bWrWlKJwPAc+gpEP9ti0fWPYSPi49FOXcOnV9fS+e0buwTN+/xnonO0MzIm22+w90/2iAFr7osYn9p/ON+qIGrGiHnHbYMbnVatVrKysPKmi+FqAu0X/I1paWsJdd92FQ4cOYePGjRgbG8Pw8DDWrFljlH+CNbsquL6cGvYTCoVMaK8t+NQqTIGjnm22ZVsQGS7MSoYUkCsrK8Y7T2BPIBwOh1EsFk21cvUqKjhua2sz/S8UCigWi4jH48bTve8/96EwX8DGf9sIF26A8auF07bakTn19vZiYGAAExMTWFxcDOTtME+aAIzgm0clkOF1dHRgeHjYMB4VkKVSKfBctsO1owDh+nFuuS7T09OYmZlZBZy5vq7r4qrfvArLP7yMdZ9ftwrAsy01ajiOg+R0EqNvqVdVTY+nEY43cps5f5ETEYz8yQguvPkC2u5pw8A/DmD898eRvTYLuMDaf1iL/g/0m+dN/NIEEAbaDrZhyx9ugVNwUPSLKF5dxIk3n0C1vVERHQDu/V/3Yvef70b/f/WbsGzHcVDZWMHBXzmI7IYsDvz2AQDA8V8/jp1/vRPObLAyqXqXfd+HH/Jx4foLGLxj0MwPi7alUilz9NiVK1dWAX3d+3pyAME2lTM+n0VDGAXCexjFwH30ZBJGLWpRi1r0P6Hp6WkUCgUMDQ2hs7PTfK4ATEEfiVFOttGThn2tVaMGZVX4Cf6ym7OodFsF0w4OwckGj4CanZ0N5I9rCDc/I9DmdRq9R55toq1CISAEPHjrg7j3OfeaZ3/p17+Em993MwYeGDDX9o734oa/uwHf+NlvIL8mj/hSHENfGMKVH7qCclcZmz64yYyRc0NHgIIlzc9W0MM5UUMGP9P82shMBJ0TnSh2FzH0mSFse882+E4wrNgGtmpAsfUlDdtW2aZzrOuuYdz8X/UgfmcDSY0Ws6+1vbH6v/aVUWm6lrqvdJ9WKhVTsNfOgaauZuuV/J+eYrtPtvfdJs/z4IQdTLx6Apdedcl8Xuou4b7fuA/X/9316L7QHYjks9tVzznH/2jzpJ9r2pyute4F6o3ck+q4Yj8YqbqysvJNx/t4Ugtwt+g7opWVFRw6dAinT59Gd3c3xsbGMDIygqGhIZNvRGupkoZW0ZLFH3rLyVg0J1w90mRavJ9CicwYgLEaK7AgWOXLyrBsepMZ+pTP5wE0wKe+7BSECvAdxzEhLPF4HAdvPIiNd25EPBI3/VJPsh5touchs3/z8/Orwrc4byzmprnQKqgcxzG5677vo6urC7lczoBj2wpLYcA1AYJFtiiw2D7vKRaLhhmqEOD6OAUHG27fgHC0kQfN+eXcaQ46n9t9rrsO+MPVVcdgcB66H+hG7E9iiDwcQdyN46o/vwrH33wcnV/sxOC/DgKheh/Xf3g9ouUo5p43h9F3jCI2GQMij6xrzgOaOXl9oDZXMykEoVAIbr+Lo796FEtXLQUuvXLdFTwcfRi7/89uJGqJgLLBVAUAOPjKgzh38zlsD2+vz8kjYX5afKSjowPLy8vGa6GGCVUOtUK8CjMKqlwuh2QyadaE96sg/GaeiRa1qEUt+l6hlZUVnDt3DkNDQ+jr6zNykPJCwYECbBtU8ZpkMmk8knqvXse/AcBNuPAjjbaGHx7GjR++EVgCatGauTcajZpTNWhUpcdac7Nt4KonerDoqvHCe0CikAjMh+M7iBajAV3L9330H+rH9e+5Hvf8f/dgy8e24NKzLmF5dBnLW5bhJTyM/PVIANSpfqPRbxqlBSAgXzQyQHUT13VRSpZwz2vvwez2erHOaqYaCKfWa9XIod5fDRO27+NnNml9IfWG670692xH9TIAAT1FdTq7TfXOq05q67NqPGDUgMp2tq1OB15vn/LCQmw2YG1mMLIBseoS5193HtOvmF41h6FaCDE3Fhg779d5V2MHSee4mTGDz9figBq5ynapQ1I/1UrzbL9arWJxcRGlUmnVGJ5IagHuFn1XqFgsYnJyEjMzM3jwwQexYcMG7N69G9u3bzcWOgJJWltpsdMX33Vd4+UmkAaCljFWdlRAXqvVkE6nA8U5TM5QsYhisWieScDMvNhKpYJcLodCoYBoNBo4V7tcLiMajZrq1cpkEolEwAJJL2KpXMLhPYfx9R/+OuJPi+MFv/sCoNYInWGbepwEgRcAA8RqtRpWVlaMl5LebN/3jceeTF6LdXAeE4kEIpEIVlZWDIPSPG3NSyMjpwFDmZiukwJ2rpsdcUABwtx3PTtalRagwUA1RI33qBee+8M2WHQd7kKpWkJndyc2Zjai/R3tyI3nEAo3mHS1WkXfJ/qw5u41iM3FUAs1wGr6XBrb37QdB//+IKpdVeARGbD7TbvRebATkXjjmDRnwUHPV3qwtHUpGDboAoNfGISX91BwC2bvmue7VZz4+RMYf/Y4/IiPo68+ilA+hKG76kYpKk9LS0tYWloyY9V8a36mkRgE8wxR41owPNEubMj3iHPcOhasRS1q0fcTFYtFnDt3zhiZWVMEQEAZ1yM3gYanW0N7V1ZWAjnKQAN0qSwLhUJY2rOE8284j1pXAwhe2XwF2cEsUoupVeHjCiBCoZCp9cFoPJWXQOPYKMpZyiRGvdVqNWz/2nbUvBruesldgAc854+fg85LnQiFQ6ZdGmp7j/filj+8BV9/09dRGKznUiMETL5kEqFKCCPvHwmAMTV4a3SVFrzVH85hM0/yl970JSxsWjDzNPWMKcAFdr5rZ0Cn4P10TOjcUT/gWmjdHwV1vE8jF0iqC9lAl3qGpkByLnQebEOEjtOOmFDPtZ3yR2JkhYLseDweKJhGndc2uHM+vHAjD1qBrRqYFMDbXuhQKIR1n16HmZfOmKK38IH0bBpP//OnI72SBkLB4/B4n47fRCNKhIRGCejY7bViTQM7ms+uwWB7+9lmrVbD0tKS0Y2eLNQ6FqxFjxmFQiGsXbsWu3btwp49e9DZ2WkKgAFBC6HmnvBFomDUkBIyG60GraCcgonguVqtIpfLBQpLkdHxRY5EIia8nO2yKnkoVD83uqOjw+QsR6NRLCwsBPpNAVkoFfDw6MO4/advr4MyH8hMZ3DzW29GcqGeM8y+MXxdjx2jN1IZfjgcNrntKuhVeKjFmd8lk8mA0kFjRD6fR6FQMAxZhaYKDQojGigAmGgBzvPCwgJisZgxSCjIo1FEK8OT0dKjzz5QqBJgJxKJQPE7DZ/zvMYZ2xw3AHOPnnGqglsL+umxXr7vo5Kq4OIbLqL7SDciMxH0PNizivGHw2FEohGc/rHTOPfj5xCfjaPSXcHoe0cxdPuQydPTn3A4jKlbp3Dy/zuJWltDGUvMJvD0P3o6uma6DHCmR5zPdRzHgHHur1gshkQiETBGUBnQ8HGOj8aOSCRijn7jOl68eBGnTp367rzsjxH5rWPBWvRtUktXaREArFu3DmvWrEEikQgAAXp8bRAFrD46SiOLgEcvqhWJRjD5o5MY//lxeCkPkXIE133mOuz68i74nh+I4lNwQeBP0MioJAINBUfsi+pBaiSoVCoolos48JwD6HuoDx3nOxBy6v2mAV0rlUciEcwMzOCet9yDcncZ8IHeb/Ri1//eBcevg8iOjg5zLKwdqqzGenrdOS5+z36p13oxvYhP/tUnTT/aT7Tj+t+4HrBsv6oHPRrQVQO+OgKox6h+qd+pgV/DsYFG9KV6uRUg2o4Dfq/ebW2Xa6fh5Dy6TesI6H7SvZJKpdDW1oaOjg6zl6kzarue52EhtYAvvOEL2Pn7OxE60TgNhn1XQKr91bGbCNJNFRx810E4IQexbAy3/s6tSHpJo7fqXLLPnHPqbKpf2/WB1BjAcWgb1WrVpE3q+2gfb8p71Yi2sLCAb3zjG5icnFz1zj5W9K3oKi0Pd4seM/I8DxMTE5icnMTDDz+MTZs2YcuWLRgcHERvb28gJxqov0wKtDRfWD2fCkD4YvOabDZrQKXv+2hvbzcvnB7vZXsFe3p60NPTYzziWt2QbdOq3N/fb/qtxTt834cX93D7q283RbrgALnBHI78xBHse/c+Y6XjGNUDqV5ltRKr9ZWecAIz+2guMuJ4PI5kMhkQ8Iwu4LiU2VNI2oKEvylA7TXo6ekxc6QhZbS8VyoVVCoVFAoFhMNhZDIZ46XVftDyzjZo7Ghra8PAwADGx8eRz+cNY9f8Jwo2ChebIWv6AUEoK37z2cWpIrb80RYTzhQKN87c5pq5rgvP9bDlo1vgOA4GPzuIK7dewfrPrYfn1wWLbZBwXReDdwzCz/g49fOnUEvXkJ5KY/e7dyN1MYWSXzL9pOdevSiMglDwzL3L6yjYGEqmhhzODavc8l47xaNFLWpRi76faHJyEsViEevXrzd6AGUM0ADTNtmhrhq1pfcCjZBjt+ai/yP9CMVDuPiqi7j29mux80s74SNYGJO8WVPMFHAxpLxZ9Bnlrx2GrGHSju9g52d21vUit4KaFzxyVcOxK5UKOsc7se8v9uHArxxA5lQG17z9Gnih4JGX6hVVzyiN9ex/NBo1skiJegB1nkvXNvKCux/sxp4/2mMqpCsY0zWiXNM51x/KM3WCqMdadS3Ou0YokFTnUZCqfaI+pvc3A9p6r/032+EY2baGi+v8s980uNhGI8dxsNi/iDtfdSeW1y3j/j+9H2NvGUNmfyYQyaF90c9sXdtxHCTGE9j5RzsRTUax5sSauk4XCQXWxjZ2sE8aMq8/ahzQPWwbNYwhSwxS3D/UydgOgbrug5WVFRQKhccNbH+r1NK6WvSYk+/7mJqawszMDI4cOYL+/n7s3r0bGzduRH9/vwGgAAKAA2gwTfVUqpVLwQkAE0LNsHDf9433lWCFQESBezabRTKZNEydXlu+1JVKBUtLSygUClhcXEQqlaofs/HIy08AV66WYVNiMYHRu0YDZ3Xr0WiPxhRUGKh3lx5ctSaSGfNveoA114UM31SwfCQCgDn2GpZOQ4T2r5lgIfP3/cYxZPQKq5BPpVKBXHoNg9LQMXpoOR5Wq+/s7GyEdj+yH2gUIRBVizYBv3oPKMx4D8OzgHrlWBUEXE81MHDfRCIRjH5sFLVaDUP/MQQfjTPQY7GYyWtnv3zfh+NJgRXPQcSJrJoDNbpw/SlEqNxwvjlnWlxQBbpaizkm7geGqtuKUYta1KIWfT/RwsICyuUyNm3ahM7OzoBcAoJ5uerdtj2ozUCG8mnP87B46yKGDg5hyBvC1oNb4fmrz4zWdC7b66geOjW2qsOB1ys4Yp/t8G79W1Oc+GzK2b7Dfbjm3dcgfjwOv9YIn3dCDk6+8iS2P7w94MElqWOA/xsDhIROs1+e5+HwDx/GoZcfMm1s+PgGhAuN0GZ7DSgTNRVR9Qpeq84b9YDbc6D1YNRQwXWy5eg3I+236kbcF+yfzruCddXN2AfdM4xo01OAOD7V17JdWdz5qjsxs2UGAFDtqeLMb5/Bpj/dhI79HYF1I+n+tsfBn+7j3XU9KRaslK5GF5K+NwBWhflz3DpejoN/q86m/aR+qsDbjlzQOV9aWnrS5W8DLcDdoseRPK+eF7WysoKJiQl0dnZibGwMV199NbZs2WKEhgIoEkE5i1hpQTAWO6M3mB5MHtkFIAC47CJSnlcPQS+VSkgmk/A8z+SDE0gS3DH8lx5Ex3HM86u1Kj75mk8GxuxUHdz8hzejb6YPNdTDnQks1cpHgUImRmbVjEkrs1FPLwEtc34YxsOogVwuZ0Ku1ZNORQBoWCpVgHO++CwVUsr8eD/vVYFC77wdmuU4jvF2s101stRqNZw5c8aMXxUVziFBqAp2Vaw0V8k23mhekIbmVatVk/5A0M0+675hX13XRaFQQDKZNGtPQTLzzBmc/PmTqKXr/cuty+GBX38AN7z1BqQvpc1+4By4roulpSWk02kTpcC5VSWHygjrE1BYaxiZWpQ5T8lk0uy3FrWoRS36fqZ8Po9Tp05h06ZN6OrqCoDaZgq7AiUF5CTb+woA2b1ZnH/TeUxWJvHiP36x+V4NxKFQPVUon88bI7d6zimr9HnUW4BgOLXydV5fdasBns/nqnxmm729vcjn85ibm4Pruug90lufk8gj1zrA4TcfxpUbr+CrQ1/FS+9+aQAwadg1P7ON6VpsznVdHPuhYzj6I0cDheVOvP4E9r1uH6LZhs6n/Wf7do0a27hsA3WVd/qd5s83IzXa655Qg4fuAe2LesfZLwXubFN1CH2mPk/3QzQaDdT4UW83KZ6NY/DUIKY3T9ejK30geTGJ9Ll00/5qX1XftD/TH3039DptV50+XDcNE7eNGWqo4BzZfeTnoVDI6LR8Ds/a5jxxjVuAu0UtEiqVSuZYqXvuuQe7du3C1Vdfbaqb08MINCyDvu8HvI3KJNTDmUgkDIPm9WxHPeO+76NQKAQAqzIfrSBNgF0ul01eOJlEpVLBirOCL73mSxgfG2+EkwO47bdvQ8elDrhhN5AXRGak1kQyehUsasWzQ4ntcDj2PZFImDlkv9kOGZI+xwagtKxrsQrbwq0hZJwD9W67rou2traAlZvMkjk5mgek88Cwbv6vlmgCYq4tc944f5yrcrlscrbVasx5Y2ESLS6i19Hww+dppXD2Wc/CZs41C9lp2GAZ5YCSAQBuzEU4VhdGPH6OezAWi6Grq8tcy37aQFv7oaFXaql2XTeQu05F087na1GLWtSi71eqVCo4deoUhoeHTVQd+bntZVNPKfmphlWrVxcActtzOPH/TsCP+qiiik/80Sfw0j94KdrybQaYkhfTCaDGYn7PaC4FH75fL/7Gwq8AAmCGfy93LOMzv/gZPOcdz0EqlwqAFTvf1vd9XLlyBW1tbYjH4yb9jjKsFCnh6C8exdTTp4AQcM++e5BBBs+49xkIVRoeSDUQqLGYpH1wXRebPrMJkxsnMX39NBACItkIrvrLqxAvxOH6wZxpG/ypYULbtR0zeo8aO5Rsry7vUVBN2d8sPFz1DE2Fs0Gj6qT2ntJCqHqN7eVlRJ1GRWo9ALM/SyHs+rddKKCAU88+hbbDbdjypi2IeBGEwqsruDfzeNtH1elPM3DdbKzUq6ijqbGpWQSBjl0dBtpXe16px9OpVKlUUCqVTNri8vLyk+78bVILcLfoCSUCsgMHDuDo0aNYt24dtmzZgh07dqCrqwvJZDLwwvEFJsAgEUjWajVTTKqtrQ3JZBKJRMIAGr6gdtgWvcsMEScDIROKx+PmJ5fLoVQqGU/68vIyLmy8gKmBqQDY7jnbg2Q+GQipUSZMpqMMRg0KQNDiaofbaFiVAlOCM86temQ5JgJDRhNw/vQe9Q5zbvWIEPaHoc7KoIG6UYWGEy0SR2WD/VZvA9th/yjUWICNYf/MdeN4E4mEOcZFK8tyfdkvzfe2Q544p9wrXA+gYYzRqrAEvhqZoJZ13jPytRGEOkM4/pPHUUvWkJxJYve7d6P9UjtcuAHvtKY+UDHTUHjbA0BjiV11l2NjTQIajzgHnNMWtahFLfpBIN/3cfHiRdRqNWzevDlgaLVDepmGRLnbLHeb8vTEX9fBNqmSruD4zcex97N7AxFjmqKlhm8FM9QrNKd2cXHRFB4lf6eMdhwHU+un8Nmf+Sxy3Tl89XVfxdP+/mlIX04HQJ7KUn7OEzEoIxlyXdpRwvLuZXMShx/ycXjkMK45eQ26p7uNLmEbdzlOPbtcDdRe2cOu/7ML1d+qIrs7i21/vQ29B3sBB3AiQXBuhwjzb51/rqnKQX5uh18r2NP+qsGf7elnaqyw+8Pn6Fh1jZtF2FE/scGvbWBgCLW2zz5p8THuCd/34VU9bHvfNhRzRQz9zRBcrxEZoXvWjn7TPW8DbH7Pa9T7r5/ZkQN2ZIEarOx+6D6x14ptcj3VO87PGHrPQnSLi4vI5XJ4MlILcLfoSUOVSgXnz5/H+Pg4jhw5gvXr12NsbAzDw8Po6+tbdV4z/yegpFAMh+tHKaRSKWQyGeOJpNdTj+PS845rtRqWl5cRj8cN8+TZ1ZqvrNa8aDSKfD6P3rt7cU34Gjz0uodQTVfRf7ofT/vg09BeaEcRdcs0zwQvlUoBgUrGRKudhmqRqapnXAG4Wng1BJoCmX3msWK0mrJNzhkZuHreqWjYxgL+aFEuFYSaD87cMU0BsAUkq26zfyQNAWPYPp/FudR9wEJrjuOYqvNasZykBg/NNycA1RAo9pX9c5x61VCCVq6JChuC+Xg8buZoy+e2IFqL4ujLj2LHu3eg66Eu1KK1gLXWPlOTx99pWCD3nxpVNCRfjTgKzvUIEo6tBbhb1KIW/SCR79frybiui7Vr1xr+b4crA8HwVjVKq9F85hUzAbANANFyFHs/u9cYqHlkqBpp2a7neaYP6uEDYGqMqBxV0AgAk+sn8ZVXfgW57jrAuDJ2Bfe+5l5c/7fXIzWbMvJYAbKOj99rf9acWIOnfehpuPvn70a+K4/eqV688HMvRN9yHzzHw1z3HK6kr2DjyY2r5laBPWUWjfWUZ5v/eDOyN2TRf38/nPBq77Q997bn29aHqNPYfWnmENDvOX6urQ3I7THZ+hH7qevBftpOIjWW6Jh4DQ3m+jn3iupK2h/+z/mm0WTT32wyup/qi7YOqW1pexxDuauMub1z2HDvhoDxoJmBwAbPzbz8Orc6Jnsu1Zig99hgXedJnTXVatVEgzzZqAW4W/SkI8/zTJG148ePo7+/Hzt27MDVV1+Nvr4+AKsLgwCN0KlUKhXwMDYDlrSKqRc7m82aUHF6ZUOhEIrFomHa7e3tpv1kMmlCfgCg7+4+3Fq5Fff/7P246e9uQsdcB5xIQwDRe6pWX/X2quWYgF6t4Mo4eZ+G1jNEOJPJIB6Pm2sJTrVSqLZRKBSwtLQUKM5hM3clZb4E4rYHnc/nUVQMi1LrN9dreHgYHR0dOHLkiPEWMxKBz9LzF6mQkAEzZ5159mrMYM48QX2tVjOGCQ0ZZL85P4lEwoyVYFXrCOhc2MKEQp65Vwx5Gv7iMLrOdiF2tJEuwX2r6879zD6rMqbCSYUPQTXHq0KZ81ypVAIh/5zHFrWoRS36QSHP8zA9PY3l5WVs3LgRnZ2dq0AXsDp02/bYOY6Djoc7cMm9BDAoygee8XfPMClF1DvWrVuHy5cvA0AAjFG+aCg5DaE2qFTeTWCRnk0jNZfC/MC8yd3tvNSJSDZidBY+k0ZYe3yUfQrIh08No/8D/fj0az6N53/o+UhOJ7Hcvoxwbxj//NJ/RilWwsuKL8Pa8bUG7FEH4N9azJXpeCd//STWvmst+u7qW2WoUIO6DT5VNnJNbM8054WRXbbHVA0WnH99nkYPUFdTwEq9ketv7ykbFGp0A6/h55pKQMeGOnMotzUNkp9p3+39pJ5z7ltNaVB9gXoV+6PPOfq7R1EcLqLcV0bCTWDo4aFVc6VgWddAdUH9ToGybQDRNdd11vtt4G3r/3x39BixJxu1zuFu0fcEhcP1I6V27tyJ3bt3Y9OmTQYwk2H5fuP8PwWqFDbc66x2rcy7Wq1ifn4euVzOeIXZHhlMOBxGW1tbIDe5VCpheXkZuVwOkUgEmbYM4l1xuCtuoPCW5vaqUNLQeGXsarlU5mIzdhZxo1ceqANuhuLHYjFTCG5lZcWcSa4VO6kY0EChIUZaHVKZpgoGoHE0VSwWMwCYgpgMnSHfasDgnKRSKSwvL5uIAw2rVoESyFsKBY/LcBzHgPRYLBYIDU+lUsYAoFZdjpMCiXPNAm/FYtEYPxjqTuHIz9TgQODMvqqSlk6nzdzTs0/DhFqkdT75GfeNhqyx7zQGuK6L4eFhRCIREzpJzwjnjcaDlZUV3H///fhW+f8TRX7rHO4WfZvU0lVa9K1SJBLB0NCQMeRr6KoCJRJ5vYKJ0lAJxz94HIgAt/7NrVh/dD1i0ZgxvPI5lLVaf4Nyh55w6hW+7xv5osVLKVcp/yqVCgp+Abf/+u24suUKNt+1Gdd98Dqg0tBzVFZrVJbWQtFUsVgshvb2drS3t6MSryBWrkdQFZNFfPhNH0a2IwsAiFaieNVfvwrdl7sDJ79wbIxYq9VqWCot4fBPHcbEiycQn4rj2l+8FplqZpWHUwGpelvVsK26gw38CMxUpio4V/1B9SrbG6z3qtOGsljBu0b5NQOG7Bf1QD2SVnUptsGfZDKJ9vZ2dHR0mP6FQvUTX1KplMnrd10XpVIJ2WwWi4uLRidU3ZJj0qhE1vtJJBJmX7txF8d+/RimnzFtUgpClRBuefstWHd6HSLh1ceHkWg80v2pe0vJjs5Uwwj/19QCtt+sDd1zk5OTeOihhzA7O9vkbX9s6VvRVVoe7hZ9T5DrulheXsbdd9+NQ4cOYXR0FNu3b8e6devQ09ODtrY24+UDGmHB9PrZjJMMSBlCZ2cnEomEYSQKlhXQLy8vB7y3juOgu7vbWETDpTBqfh1QE7ABDcupeh5JKhgV1LKf6iGmtRtAwCsLwITSE2STCQINSyjvs5mdhuxTcFBgav4vP7PHEovFjOLAeWHuGa+jt12t0eVy2RSjU+apyooKVs5DpVJZBVgZdaBeeAokIFiETo0IvN824NCowzGrBZn52zSc6F5VYU0gz9oC/GGIOo0LTDXgPeVyGel0OrAfgUbOlFrouacvXbqEnp4es7aMklClzQb3LWpRi1r0g0i1Wg0XL15EpVLBwMBAAPio3AEQkBcKCpMTSez57T2IjkbR+2AvapEa4DeOOFUjLbA69BpAQAbZ4evKpzUqjvw/WoviuW9/Lu79qXvx1H98al1Ge+6qqC3KIz2RhET5wmgw5gkna0mUqvXjPu+79T7kMo3c2Gqsijt+6A689P0vDegSStVqFUWniNM/cRoTL50AAJTXl3HsXcdw9duvRvJy41QPO/yY41XPJ9CICLO9ony+gjbOkQJh1WPUE6zeaM6TRglSP9I5Y38IxHmfzrd6vTlO2ztrGww0FVD3pOM4JsKOug+dOWb88LH4lEUkl5Nov9Bu9h31Lo6bzhQ+z+l0cOJVJzB923RgDb2Yh0MvP4T+P+lH2A+v6rPdd9v7bDsf1LDRzMBFXUbb11x6OxJFjUnZbBbZbBZPVmoB7hZ9z1E2m8X+/ftx7NgxDAwMYMuWLbjqqqswPDxsrHVkegSiygDU88liC9lsFuVyGYlEwpy/nc1mjSc4nU4bAM6KiJo3kkwmkUqljAWT3mUCdjIWgiqSbdEjcGX/7XMjyczUSkrA5jiO6Tst6WRUvJ6h10DQcs929H+1htN7zb5q+BHHoOFc/CFgpUVd77fDsjhfaiBgP9QgwGs5BjVe2MxdUwl0vgcGBhCNRnHx4sVAH9TIocDZZu7qIdAUABWyDCvks+m912v0GWp9BmCOBNOq8XZ4mVqbOadXrlwJeLY5zzQ0VCqVVv52i1rUohahLiMuX76McrmM4eFhA8gYzmtHftme1XA4jN7zvWifbwcQTA2LRqMIJ8I49pRjGPvamNFNNNRb76FsscPO1WFAsKngL+JEcP0HrkfVrTYFv5Sj9mkWCrqoGzG1TL3Jruvi+n+/Hk7ZwYEXHAAA7Dy4E8/71PNWgV0FRq7rIoccFnsXA3NezVRR7CkiMZkwn9lpUurF5lwp+OS4bE9ys0g8bV+j29SrroCce4DPsnUl+3k614/WbxpxbJ2P19GQwH5ybXSfcr8VCgVTGMx1XeTacpganULnXZ249OJLOPUrp5AeT2PHn+9A56lO01fuZZ1zOqVK6RJyPauLjQ0+OIin/P1TgApQizT2kM4vHVl2RIeth6mTxv6xw911zjl2nXPby16pVEwE55OVWoC7Rd+zVCqVcOHCBUxOTuLhhx/G8PAwdu3ahdHRUeNJVauZArxMJoNUKmXCb1jYZGVlBcViEe3t7Qb8EkyzKnm1WsXS0hJWVlYClb3ve/Z92HX3LlQvV03oLkPClZHb3msVpCpoNPSHRc9oiVam1Cycmc8gg1cvOOdBw+55v1pUaZhQoUIBwOerJZrMVgUDDRZsU9fEFlYUfAoqVUjqWqpSwjljWDXHwrw4VQj4/9LSEgYGBpBOp5HP5wNjCYVCBhzbVdnVCKCFyPhMevQrlYo5H5v30vPPPcTP0+k0BgcHcenSJaysrASUH7W0a0ibnuGqQsq2LHNNbVD+ZBZKLWpRi1r0eJLv+5ibm0OlUsH69etNhJh6oG1gRtmkp6CQB2uk3dde+zVc2HEBXsjDtju2BYzDtrdbeTT7pVFuCpZt4EiZ28wLyPY1Wk0BGI0L/E697Pze8zzs+c89cMoOFtYuYO8n9tZ1p3gjtxyAAV6UkYVwAaX1jTORIysR7PyTneg82wkn1IgAezSDhm3UV6N7MpnEyspKQL9rppuo7NZ1VOM8jdS6xrbRXdukwYL/sw/NDN2qo2lNAK3forog59zWYQiQdV/UwjXc8St3INedQ+Y5GSxsXwDCQH4kj2NvPIY9f7AHyYvJwHi5NgpY/Ys+NrxtA9y3uFjZuwIA6D/Uj73/sBep5ZQ5Xoz95PzoPtS9qHOp/bWNKapTcl45b7r+Gn2g7y0/KxaLWF5eflJH7rVyuFv0fUP0Mq5duxZXX301tm3bFggxplcUANrb2zE0NISpqSksLi6aXGwWu6K3kmc961ngnlfPh15cXESxWETVr+LE807g6E8cRTKbxAt/64XwlutMR0PAlXGQIWuYsc1Q+Lke3UUBoICNgpoGAeb4sLI3vfIM3S4Wi4H7mgE2FmAj6KMw5d+8nvnysVjMhOPTkp5Op1d5qCnsmOvTLP9Jw8oJDNWqqpZNtXjbhgRl5AraCcypxLCPNrinQGXoFtdEPfhUVHgfc+nZb86jhidG01Ec/ZmjGLxjEIMXBlGr1dDe3m6Ehm3pV0s4x0KvN3PmtQK9bb1nO7VazeTRnzt3DkePHv2uv4PfbfJbOdwt+jappau06DuhRCKBoaEhdHZ2BhR6NWb6vg8kgTPvPINr/uIa9Pv9q4yyfszHfb94H8avH4cf8hEuh/HUf3wqtnxjCyKhRvVptqlAXKPTKCf1LGbXdRGOhXHshmOolqrYeMdGeLVgOLSmLNlRW3wmZTnT0FKplEkN02dz7LVaDYVaAVVUgXwd9La1tSGdTpuaNjyeqVwuI1fM4Wvv+RqKw41Co47rYN3t67D9L7cHvMA6H/+dZ1/BZ2dnJ1KpFKanpwN6iXpHqZMoAFSgyefbnn/Opaa/2d5p1e10nrSfuo5aqFT1Ll2P3IYcpl81jeRSEmPjYxg8MQi35gbkuam7E6vizj+8E0ublgJH0irFp+O47jXXAYWgMUSdFuxzKBSC0+ng6F8cxXV/ch3SfhrJajJgiLEjB9hv/c31VMODHXWgkX66Vhrlp++U6jJ6H9fl0qVLeOCBB7C4GIymeLzoW9FVWoC7Rd+31NPTg82bN2N0dBRr1641XmuCFzKCUqmElZUVk5vd3t6Orq4uY2nWqs8AUCgUMDs7i2w2i5Jbwtlnn8X+V+83VULbL7fj6e98OjIzGSM8CQDVsmgLDyB4pqFt6QWCFnQyfLYVj8eRTCaRyWSQSCQChVpKpVLdOPBIrrDmo5FhMaQbQEAwaMgQhYfmKKm1lv3R0HzmHOnxJxwDjQdkpOqhVysqBYTmaXPOeByYbdW2rcXNLN/8m0oIDS4AGiGBoggwH49COJVKmbbovQ6Hw4F8bBZy830fXtLDpVddwpmXnwF84MbfvhHdx7uNYUUVG4a1qUdbFQLuqUgkUi9wU6lgbm7OzD/b0XAs0tGjRzE+Pv6dvF6PC7UAd4u+XWrpKi36TikSiWDdunXo7e01n2lObTFTxPk3nsfiMxcRW4nh1t+/FR0THQZoAMD4TeN44FUPoJKRaCIfuOk9N2H0oVHEojE0I/J5GtptI73neai5NZy+8TS++lNfBQDc+Pc3YuiOIcBrGOy1UCzlLT3WaqB2nHqtFabNMS2PMqtUKpk8YcokAnoWhk0kEqbeSLlcRqFQMDpVtiOLk+8+ifKmMuADvff14trfu9Y8g7Jcj8BSL6oawPmjIJVzzqgCW6eygbK2wb8VACroU72PxHY11Uz7o3qVHRGheo22Qz1idngWd73jLviRBgt79tufje6HuwOFfjlnJ37yBM699By8WPOq3LFLMWx5wxZkLmZMH+0oSPabDiauJyM37PBwjtceG50v1Lfs9Do+m/PKz9XYoaBajSaqA2t0B59dLpdx+vRpHDx40KRMPt70regqoW92QYta9L1K8/PzuP/++/GZz3wG//mf/4n9+/fj0qVL5oWkAGK1aIb3ptNpc143ryuXy4HjLkKhet52uiuN3DW5hnXRAcqpMubXzxvBREbE33yuFi+h0NDzqgmsFPRpTjAtiEBDCDQLR1OQrwXgbGupWnJpZLC98tpftbiSWPlSq5TzHhWEFFQDAwNYu3ZtwOOuQpHMm+vhefVjzOjRVa84sLqQhxIVJqABaDWUTq2xBLh6lJoaOBgFwTmi8OCYufY0hIRCIYRjYZz7qXM482Nn6vslBNz/v+/H1N4pEyrGeWI/KPCoBDAFQsdDxayjoyNwXJx6D1TBYjstalGLWtSi1cRialNTU0in0+ju7kZXVxc6OjqQXJvE5TdexuJti4ADVDoquPc37sXcyFwAUGy6ZxP2fnQvIkXJ3HSAu3/pbpy+6bThz+oZVc+uRjVpmpnneTh207E62HYeafPn7sbZZ5xFJBIx4fAqC1WmU47ZwMU+gsoGRdQVgEZROcepFz1dXFzE5OSkqZKtnlRn0sGm39mE1IkU+r7ch11/uMvIeLbD8TIyTMFws/ByNbjTmEBS3UHBtupZvE6/VxCqIFANFzo3vJ/32t5a3/fhRl1M3zQdAPkEttQ7+Xf56WU89H8eqh+X9si6wgG++v99FReuvWAiBwqFgqlQvuHvN2DDRzYAj3Qvc3cGff/cB7hA/Hwcw28dRuRUxEQcUMej7qCeaK616kSaZqDjs/VG7lkbnOt6cQ82K6CmIF31Fb1OwbjuARqBmFrwZKZWDneLvu9pZWUFKysruHDhArq6ujA8PIzR0VGMjIyYc7Fd10UymURHRwcymUzAsqxhMGQMtP51xbrwoi+8CF+OfBkHdhxAqBrCze+9GV37uwwDI7gh8FIGRIbn+/WjqLZs2YIrV67g8uXLAcYCNELFbCuvWlIBGIZuh4RpCLdWPNf8Z/soKdtaS9KCYGppJLPmtWoFpqArl8uGsZbLZbS1tQXOGKWnmEy9r68P3d3duHjxInK5nGHQDH3j2dMcsy20NTSdn1OBoMBXQcj5jUajgTMdCW7pfdCQfVVwKNQ456YomltFdK5RsA8AQl4I6WLaVDxnmHqzddUceY6BALxYLAYs9aVSKSA8OZfcv60c7ha1qEUtenTyfd/I4dHR0cBJKIlcInBtuBJGtNgAtJQrW762BfFiHHe87o6GUd4HUsupVaA4Ho8bPt4shFkNpqnl1Kr+ZlYyBpRQdtngM5FImIgrtsljPBUwqTdZPZR2GhjlL2Us66EAMLLTcRykT6ex5U+2oH2lHaFKCK4TjLqyvecKim3ATF3DNijb12v/eZ1Zr3DjHGy7KKqOWR0Btqdb27S96/x95A1HsHDtApyog7VfWxsA/eodLpfLKE2WgGZi2QVqkzVzBBhlOUHuug+ug5t1sbB3Aev+YB1i0zEkH0oiPhtH6kgKPhp6LB0nCp5tb7U9z5rzz7lTIK2OD71XvdS2LmRHH2hUp+pmagwh2Wvluq6JqNDIzCcjtULKW/QDR/Qkb9q0Cbt378bmzZtNiFQmk0FbW5sBlPSGM1RYw3H47kSjUVRTVfzzD/8zdvzHDqQPpQNHgfAIKwV6ZJahUChgnR0aGoLv+5icnAwwHwVbalFUJkXPciqVQiaTgeu6yOfzAOoMK5fLmXOotdiH7ekmc9MQcAogClt6URmC5HmeCaNmaBrnmjnkbI+AOx6PG4bJ8HKCR/aBecobNmzA9PQ0FhYWzPO7urpwww034MSJE5idnQ3MLY0G2n8Kc0YuqOW+2Rprrjxzpnk9P9MQfFajp3dfFYparQZEgbs+eBfKaxreZcd1sPELG7Hzb3eaNhg9wTnUfH3uR64394UqIbrftPCKyfuqVnH33XebUP8nM7VCylv07VJLV2nRd5Mcx0F7ezu2bt2KNWvW1GV2vIqjP38U47eNIzWfwm2/cxviS/GAEZy5vXCAS9dcwh2/egccOHjJ374EvUd64dbcAKjIZOrnUmez2QAgsgGj69aP/bq44yL+63X/BQB45v99JgYOD8BzvYBHNhRaXSDTlmFMRaO3VcGSyhTqHQTyCm548gVlj6a8EdiyvgyN+8280Ap4dbz2Z7xWZXUzQMz7OW7Np6bMVK+traPZxmpdLwWodgi0H/Zx5M1HMHPLDBACIrkI9r1rHwYPDcJBMLxci5cVegv4+t98HW7iESeL62DXq3chdSFl1kKfy7/dsItStATMB40OOrd2nxU405nAmjD8oeFDIwBtnUrP3gZg9A3b2KPP1pN6uA6aRqDe7GaRAzo+z/Nw8eJF7N+/H0tLS033wONB34qu0gLcLfqBpkgkgvXr12NsbAxjY2Po6OhoGprbrFIliYyiWC5idqae261eVYJqCjigISyABnjltQShGurDcCDex3t5DRml5lP5vo9cLmdCkVigTa2O6km2+8E8YPVwa6g0SStpkhnTas4CbWSoBKO0wnMMFILlctmEcQP1Aja2RZvAMRQKob29HZFIBMvLy6bf7DOfqWHdrJqu60+ArgYOGiVoLKAiwrULheq5/TyLPR6PB0LCeWRYoKBKxMe9f30vchsbR2+EqiGMfGYEOz60IzDHVGCYT6eCSIWdHf7GdVVhxXnX/2+//faAgvBkpRbgbtG3Sy1dpUWPBcXjcezduxdr166tF8f0XNz3i/fhmr+/BpFyoyimegJ5agUc4NxTzqHdacfI8RHDo9VTyd8K8EgKsAwIrlVx4doLgAusf2g94Dd0CY2g06JrKsOojzC0mWdNAw2dhuBZw4cViHPMWpWcKXj0pNfCNdT6a8jMZgIgjkBP5ZWCwFAohN7eXlQqFSwtLQXAMcdA2a0OCAXFdiQgSQGdtqUeb/XC8nteY4M+/SwcDmPiJRM49ZpTcFONEOfU5RRufNONiC3FTL/tVLVQKITqQBWXXnIJsYUY0g+kkTiWCHh6qZtohB+jKDUqUuW7bcBRbzSjGhi5ycJ5doi4Am0dM9ede0ZTE2wdheurbfJHdTw1gthRHvqOcR6PHTuGY8eOPaFOhBbgblGLvkVyHAe9vb0YGRnBtm3bsGbNGnR0dARypNXCRyaveUwrKytYWFhAPp83TE+PYWKYsu1xVSJI1NwtAIFnap4KhR0LZ/H4smQyCQAm54cFTvhcVjClYYHPswuCqcBmP/g9c6oBmP/1HHQKLi3Spl5u3qMFTwhwqXCoAKYw4xFpVE60oItWSqf1mM/lWGkgYJ80n41jVMuqHvGiofQMw6MhQ63B+j/HUavVkO/M49AbD2FxzyLgAyP/PoJd/7wrYEDRddDq5roX1evBPtreevvYMhXGX/ziF78Lb81jTy3A3aJvl1q6SoseS7ruuuuwYcOGwGkXjHaijFVPHkEs0CiQpYBEwRL1A61nAjQ/9kvzixWAqBfaBqhq8OfzEomEiaiiDFFAqkZwO8JPvZIKznkiiud7uPzTl7H4jEVsfftWdFzsCIQ0U+4ScKmco9Ohvb0dhULBGLHZfwV86tFvFl6un+t3zYCz3q+kegGpWWg72xh/2ThOvfoUvJiHzKkMrnrHVUifTQdOh1GgSa8zdRP7GjW4EHBTd6J+x3HYUZj83A4f132qDhvqkbxP94zOKedAIxm0Jk4zL7VGZur4/zsHAMekfSHl83k8+OCDuHDhwhOaw/2t6CqtHO4WtQh1Zjo7O4u5uTmcPHkSfX19GB0dxejoKPr7+wE0BBaZC0FdNBpFpVIxXmQKEFp9WfhKhWsikTDMUnNb1DoLwFxDLyqZmjJoOx9Gw86B4NmVNBwQLPN6DXknI61UKgFvMI0DCrrj8TiAhkWcYdDsA8PGbYCvIWDsm62skOzwK/ZVBSaNDvZxFwpY1SKuVb81r90+y1vD0DjXKrh4H8Gxrg2Vl1KpZOYpNBXC2J+N4eQbT6Lr4S5s+dgWVFAJKA/sG+eRe4P7jKH8nDP2g33lXlShpGFyT/Y8pxa1qEUterLSQw89hOXlZWzdujVwSgVljXqsm+UJl8tl420+ft1xrDmxBsmZZIBfsw2VZzbA5fWUgY9mcFX5R7lNeUJZptdpm3y+ym7by2t7iYEGGLv02kuYes0UEAbOvPkMdr19F1KXg/nnCrQ0lJuGBx7zxOfaXlAdt5KOReW5zjHv1zHob9tLrOCRc6e6lo594yc2Ajlg/EfGsfltmxE6FkLey68y8PMe28Fifwc0CqXq97omtkfY9syrEyOga8WB8Z8ax5aPbTEAmsYf3Q9sVwGyrWvqPuZ4lLStZlEGzZ6nXn1GKtKJlc1mkcvlnvQF04AW4G5RiwLk+z6WlpawtLSEixcv4v7778e2bduwdetWbNq0yRTKotCiQCoUCsjn84GiVxQEBFGak1SpVEy4sVqsCdooWBmiBTSKrtFCqhZutq/h3I8WWqYATYEjgb0WTtGzPIFgNXRlrCSGirMwF4UclQWGrbFfnHMWK9OQ9nA0XM93chuV4R2nccxYMpk0wJOGDA1Xp5LDKAD9W9dHlRWtAq8Cluum11cqlUCYHoVhoVAwc0GjSygUMv3NTGew++27Ec1GARdw4ZrIAIYEUuFSa79taGEfNHTd931TGIf7SqMYarUaVlZWvqN3pEUtalGLflDJdV2cPn0a2WwW+/btQzKZNAAhHo+vMgjbaUCVSgXlShkT+ybwtR/5GhL5BF72By9DuByMiCIAVgDE59f8Gny3AXgJNrROCQujAg25rUVBVY4o6LaBjpLWXbEBne2BnPqFKUy/chp4BHOtXLWCA398ADe+7kaEikGQr/oF9QA7PBxoGCIUtKoeYbelY1DZbXuvqcvpGtv3AQ29p1lItF5LmTvw2QEkv55E6GIIZbe8qs+qg6i813QDlfm6LvZ62euopOti//ghH0ffdRS57TlEwhGM/tuomX/qnLahg/uzWU0jnUO7QJu9n2iw0H7aRh/+zWjGTCaD7u76MWnz8/NYXl7+nqhHA7SOBWtRix6VyuUy5ubmcPfdd+MjH/kIPvaxj+H+++/H8vKyOX+SXkd6g/UIJ/VAM4/acRwD2plfbVsONayIHnC2CSBwfIJdNIShQcrUeSQE29ACJ/RKp1KpVVW3bYs3+8hrbOOA5hNRKDHHOZ1OG6+6ndND4ameZDft4uCvHcTiLYtIpOo5ZgSvrBCvRcb0GC+GpLO/Ggqn4Nm2TFPIce1oOKlWqygUCsazTMHIomVcl2KxaArT6bFcruuiWCwaz0YkEkFsLgY3X+9TMpkMeLFTqZSJZtA2CPCZ1869wLnmXmMBOu4nLcoXCoVaR4K1qEUtatF3QJ7n4fLly/jyl79sKotTvlImqicYgJGRoXAI50bP4bOv/izKqTKWe5fx8f/zcRQ6CkamUQ5S3mj0VqmzhE/+4Sex3LUcAIIEc9Qb2BcFg1r8i7KHnkz1Zmr/FXBpDRT2x85BZr83fngjOh/qNEdWRZei2PX2XfDzDYCoHlB9FvvLsWkYMj/nfNtedRtY28YFsw7iAFFHiUYkqGe4Wbh9M4832/Q8D3CBxHTCtMv5ZX/saDkF8fy/meeacl/D6flbc6+b7Vv1crvtLk686wRW9qzAi3s49apTmHjZBMKJRoSGrg+f4ziO0WV0rRRQc798K/nkarhQ77nqNly7XC6H2dlZeJ6H3t5e1Gq1FuBuUYu+X8j365WzDx8+jI985CP40Ic+hK9//esYHx834I55ysrkyfjYBr8jGFNBp0dT6b0Ediy+wmcw14a5V2R+/E2majNf3sPiJWpJpbeWHlYyOBVSfJZaYkOhUOBMSb2+GUDn/NCYQEFG8I924Oxrz2LqOVO47433YfJpk2YumoWB8X/HcZDfk0e0pxEart5/23KuuUdcI/6okYNF6ZhfzvGwLVU2aPxQLwDXt1qtolgsmnAo3sf8q0qlYv7WcH/ez/B0XsvcddvIo4KW6xQKhczeaIWUt6hFLWrRd075fB6f//znMT09HQAJBEkEuBo55Yd8HL/1OPzwI/LLAbK9WdzzE/cAaABG21vq+z7meufwxV/5IpbXLuO/fve/ML95PgCEGO2kegjliIYrUx5Eo1F0dHQE5Kl6E5sBOuonCppUbppCcH4Y239rO3q+1oP4TBw737ETnac6EXIashdAQD9R4KpAU0GiXvffOQg0hNz2dgMI6DfqFVaAaOsbagzQ5yoY5rVaTExBoz6LfdAxa+ShOidsb7rqYNpf7Q8/07HrCTq5a3IobCo0jqwLARNPnUCtoxYw4Ntt2IYMNbyQdLw6P/Y7Yht22JYeaWsbPlzXxcLCAs6fP4/FxcWAU+HJTC3A3aIWfRvkeR7OnTuHr3zlK/j4xz+Oz3zmMzh8+DDm5uYMGFMLablcRqFQwPz8vDk2SsNstCiYerSVgWm1UzJAPS+bedPKtHit3aYCUHpNKTi0Mqp6aG1gSkFNoczPbSu5Cht+RgZsGx/YXyfs4MgvHsH4D4/XJ9wBDvzqAZx5xhkTms+2dC7D4TBye3M4/qbjOP260whFVp+DrYKJ4H/uujnkh/OrhCnXzWb6GgKnQpwCQi3fvJfXawqBChkV2mqd51jL5bIBybzWDvEnCFcLvEYRqLW6dQZ3i1rUohZ9d6hareKBBx7AyZMnzbGNCizIdw0odB3c9P6bMHLPSKCd+fXzmN4xHZAtpHA4jGxfFne9+i7MjswCAPJ9edz3C/dhfnQ+4F1VgENgTRBOWcjvK5UKisVioKaKDb61gJsCRU3Zsr2pBkyHItj69q0Y+7MxdNzbsQr42gCR8ko93QTxKocVUCpI1XlXEG57w22grOBVAbHtvdbIOBtE6pyrQV+NLdS9FLCrp18j5tiuesOpK6oOodfYQF3XW+eJz+/8Wie2/NkWhHP15/ce7cV177kOiaWGV97WS1T30PS8Wq1mIgFt8K3zbRsZ9HuuhRpXVP+098Dy8rLJ8f9eoBbgblGL/gdUrVYxPT2Nhx56CF/4whdw55134tixY1hYWAhYkBl6w2O6CAAZFqzMVT3Deh6mXqM5va7rmpB0ZYR6RIfNwNWzrEKVDDkej5uQbc0LZ5tAMPeb5zWyHdtaS8bZ7Pgtu3prJBKBAwd99/eZMDQACBVDSN7fyJNT4cvQ+OyGLI6+6ShKQyVMP3cah950CJFoxOSsaUgTDQ1zY3M4+YaTOPIHR1BKl1bNr1b9BB4B6aFgiJ5dcE0FhVrt2WeNRlCruYYPcu0AmKgB+2gXFZ4acsd2+JmGJPKaVkh5i1rUohZ996hUKuH48eM4cOCAMWjaoIHGcsdxEM/Hcd1Hr8PwQ8OmjZX+Fdzx03dgceNiQDeg3Evmkui92NuQjT7QNtOGtittgcrQNiClHFBArQZoerIJTlW+Ue6o7KfuocZmNTQrQPJ9H4lyAn37+wAEC2w182RT37EBNe/V9DA1SmhbOkZtQ50Lthebclg95iTVHxQY22HS9rh0vrUtph7a8l9D9dV4r/OrBgn9TD361An4o1GPtpffcRx039uN3b+7G23jbdj37n1om2oLGG8495o3r9ESnlevE8O0RCU10Gifda10jjRST+dJDSi6b5eWlpDL5fC9Qi3A3aIWfQfkeR4KhQKmp6dx8OBB3Hnnndi/fz8uXbqEbDZrAHcymUQymQwcJdIs/CmVSgXyeRWE2dZgFYgKqNimetE1D0ytqgxXYzizWuNNns8jjFY93ryWzJPHfaklUpkl0ACeVDpsAR0KheDWXPTd0Yedf7oT4WIY0fkorvuZ65CaSAWiAVR4VKNV7P+D/SitqXsX4ADTT5vGiZ86EZg39fJnN2fx8J89jHJfGYXhAr7x/m8gn86bkLz29nbjxTeW2Ahw+YWXce7l5+BFvIBRRHPn1dKsXgFbEeP82+1QGeI9+jnHTMON4ziBKAHOjSpK/M3PWdStRS1qUYta9N2hWq2Gixcv4stf/rIpoGqDXwCmlksyl0TnlU6Eag01PNedw+2/eDuqyWoACAJAtBTFvo/uw+jXRuG4DgaPDuLW99yKZC4ZAKAKYIFGiDQBDCPmaPBWuU15o+CxGRBWmUIQruPUvrNfdiFTNUir/qJpWs2ivmxDhv08PkM9qUwLs0PBAQSMFKpD8TsFtByLfq7Xsk+PZljg33bRWe2nDd7tiAA1oOjc6BrxOs6XzpsCZv7dfbwbN73hJmTmMoF7NNVR9U3eq/pmJBIx6YqqB/GZiUQCPT09yGQygfloZjRRY4bOg/5fLpexvLxsokq+F6hVpbxFLfouke/7yOfzOHXqFC5cuID+/n6sX78e3d3dBpCRUXheI4ebXlsWN6P3l98ToGohLNvLCawuaqHM2/f9Vecq0tOtDE+ZtuY7k0EXCgUTnsacbjsHXK3Vvu+bM7M1jM0G8Zp/7Lke1nxlDWqpGtLfSCO02Kggztx1FXyoADvesAPH//A4ipuLgAes/dxajLx/ZFXlUfbp9CtPw4s3mHq1vYqJl09g6we3mvHG43EUi8V6obZUApeeewknX38SABD2w9j0qU1wfGdV4RCugfHai6BQYOz7PpACiluK6DzTiVKptEoZYltqqOEasQibHgGjBWx03PrMloe7RS1qUYseG8pms7jzzjtx7bXXore3NwCgwuF6kdBisYh4PI69H9+LWriG4886Dj/ko3u6G7f97W2IFqOAE/SOAoBTc/CU9z0FKAPXfvDaegVwJ+hBp+wBgkdp0ZisdWKAIKhjOLBG1ikpwKTuQJmlBWDLa+p/J2eTq/QToCHvFTgryGREHPvNPhG027JW27BT2HQcWu/ENoTYYeQK4tWYbdbCAsJq7NB+2/OnukGtVkMtVUN5QxltZ9tMv1QnI2kEI8E221Rvv+2QsD/TOTaOATgIV8JwkkFArs/XI1NtQ4iug66pGlbK5TIikQg6OjqQSCSQzWaNLqL91LnSuVdixEI2m1313ZOZWoC7RS16DKhSqWBiYgIzMzPo7OxEZ2cnent70dvbG6hAbTN5ZbhkQhROyshtrygZZywWM2HWCkrplWZFbPVmq1VXGSSBGi2qjuNgZWUFtVoNHR0d5jMCvlKpZI5IoRcdQAD0UiCRUaZSqUDoto55/WfX19tIwuQG0UBBRk+hnRhPYPRPR3Hmt86g54EebPn7LQGrOvO/Oe/b/ngbzvzaGcz80AwAYMM/bcCWj25BuVYO5Hzz99kXnMWp154y4zj58ydRi9Yw/KFhk0PP6uzqEdA+ci5NOFoIOPMzZ7CwbwHXvudapA+kAyH3GmalgkfXil4JCiCupSoWzea2RS1qUYta9NhQLpfD/v37MTY2hk2bNgUAJUEgj++6/qPXI1KNYOLaCTz7o89G71wvqk51lQxRr+2+D+4zRnGCaHqxKUMoaykPSJQLlHMkgjD1ctseZAVaGnnHe13XRaW7gtNvOA3P97D9z7YjvthIOeNzqM8oWNN2bbCm/eYc2l5QbUfHznv12fzMBqH6DHUSaPsKQvV/jVpU3U6fw2eY2jKxMM6//jxyO3MYe8cYkkeSq4wPnA+V/azXYhtXbOJ+0bXUuVSHijpLbE+zrafaYfnUs2xwzO/peGDRV9Vfdc0UeHP97M/5XuTz+e+5I05bgLtFLXoMqVqtYnZ2FvPz85icnERPTw+GhobQ0dFhhCDDsclktLCGCjQARniSuZPx8W+CLjIrHpsVDodRKBQMKKfABYKChQDetjwzvKizsxPhcBipVAoAAsdy2UeRqIKhzDwSiaC9vR1A0GrKcCUCcM3/5rMIMinQ2HYkEkH32W5c9UdXITGZAHyg5taMVZ/h4cZyXvOw6a83wYOHjskOrP/X9XC9hneYBgsK39ThFBzfgS/J5W1H24zyxArkzGvnXGruG4E55+XUr5/C5edfBkLA/tftx94/3ovIiQhKpZJRorg3dP25dmyfnn+gYZyxlRsC8VaF8ha1qEUteuwpl8vhyJEjWFpawjXXXBOIHnMcxxiBw+Ew9nx6D8YeGkP/XD+i8WhAzjXzTvKHKVp67jZlHQExgICRPhQKIZ/PG/mish4Inmyh0XJsy/b8qnfViTk49HuHsLx9GQBw+A8O4/o3Xg+/Ejyrmu3zf9UXFKyqwVpD15uBy2beVv2tHnVNz1JAq/nR2q4NtNXwzzXREG+NELAdBFrE9PRbT2PuuXOAA5z43RPY/sbtiF6MrvL4Um/R5/CH/Vaw+2hrqsel6rg0opLGIDXkPJoH2jZusH826Nacfzp/mkUa2PPI59jjrlQqWFxc/J45DozkfKvueMdxvnf89i1q0ZOUyODb2towODiI9evXI5PJmBBizaEGGqCNDIdMix5qhhSn02lz1jWZWjweR6lUwsLCgsnR1qOkgIbgIHBzHMccGUYA7Lqu+YyAkuCNIJNh3mTolf+/vTeNkus6r0P3rbmqq0d0NxoAATQmAsJEgOAgkjJlyZEiydKS35MdJ4qtxIqcOI7lYdmOYj0/+z0P8vBsS7ISyc56sZeHJT0PSqRQljVY1AQRAEnMQGOeGkMD6G501zzf+34U96l9TxdoMhIoNHj2Wr3QXXXvPeeeKtzv7O/b3/fV6yZnXZ0JlMxrgRdW4SbRpbQ6lUqFemOqXJpzJsEkGI3X9wGY/C0aklisTWxr0RoifgQJJMw1dE304V/eXMahjxwCPOCBX34Aw0eH0ah3WmYwMsAND4BQnhzXLx6P4+SPn8T5d5xvSwJfQKwUw+v+5euQqCQW5GyrwbF7qaojg2NpD3HeE9CWO37rW99aNJXKgyDw/vGjHBw6cHsVh7sJnudhZGQEr33taw2JUXVaNpsNtQpNp9NIJBOY2DCBm8tv4sG/fxCRVrjVlBbtop2icq4eqyPmxxA0OvVR6ERnQdRkMmlslRJD/qvEVsfUY2jTtYjp3t/Yi9kHZjttpgJg6OAQHvqlh0JOd3UgayT/dlFpnmvLyQklj7dThum4SiDVmUHoPFT+bisV9H6i0SgQAWqxGvxch1TaxJx7n4s/fRHX33UdQbzzuEpeS2Lnu3fCq4fno3sAqhj5HeCP5kyb+SCcxqZBBNaPSSQSpuCZvefQ4m622gGASWHQej265rrWnJM6RfR7pveon4OOxz1jPp/H4cOHcf78+bsmiPBS9iouwu3g8AqCBmxubg5zc3OYnJzE0qVLcd9996Gvr8889LQVBhD2+qpMuNlsmnPosWaLqHQ6jRUrVqBarWJ+ft48vOhtZPRUC4upcdIfkjz+kPzSI8p8Yj7ki8Ui6vW66fEJtA1UT0+P8VByLPX68x6azaZxKqizQT3fjLIzd0y9rUr+6QywPaaJRALRVhStoIVItB2l55hq7Ok0yJ7MYtsvb4Pf46N/fz/8aHuDpIXNeE40GjX3aEu1IpEImqlmiGwDQDPThBfxQmSbc9Fq6/q5sAKpLcvnHKgg4FqwdZ2Dg4ODw51HEAS4efMmdu/eja1bt2JkZMQQYN/3Ua1WTScKysEvbL+Az7/38wCAaDOKrV/aimgz3NKLijIlXP6Ij30/sg9LDi/B/U/fD/idKLFWqqYTnaSRNph2Xh27AAzR1dorHFsJ1rpPrsPs9jDhXvOXaxbYLY242oTaJr8aLWZaWbdIKu9Lr6+Rbbu2iUa87eg4j+e6dSsQZxN/3/dx83tu4tI7L+H+X78fkcnIAvLI6/u+jzUfW4PmYBPTb54GPCB9KY2NP78RqAEtvyP31jkFQafdqkaHGQChze8W6ddoeBAEaGQbaA41kZhNmPftoq105uhacd/ZjeTrmIrbFbrj9fRv3XvpZ8t7Z8CpWCwuuvQ4R7gdHL6LKBaLKBaLmJqaMnneg4OD6O3tDVX1VLk283Pt1k9AONdqZmYGpVIJjUbDXIcRcKDjnVSjTXKphksfwOl02kRwOR96y9XbOTIyYo4hIaYxoMSbJDCZTIYKqWkxNtsTzbnyfnnvfBBrDrRK0tV5ocXgAIQ2PYxI831bht3/fH/buETCEWZemxEDnqP3pJU+lzy3BNcfvY76YCfSvHr3apRmS4glO5sx3Syo1I3fAd3I0CjR+HEttDq9RikcHBwcHF4Z3Lp1C4cOHcLmzZsxPj4eiuZpmtCph05h94/uNqR13zv3wY/42PHUDgAdh65NporxIg798CFcfvgyLj90GX7Cx+a/3xyKVmqureZDcw5MQ9MIto7ZjXST4LVaLcxtmuuQ7Rcwt2kO/Yf7Q1FPjTarE50E3o5SE+p017nbEWwldSpn7ja25jnzfZ0P9y72vaujIBKJ4PpbrmPiZyYQxAOc+fkzGP+tccSvxUOEVW2v7/vY8KENiNajKGwuYN1vr0PicqIdAJA6LXqP6iDR6Lqdq63rZzsGgiAA0sDkT02idl8NWz+2FZlbmdD8bGeGvWdQAm+rNrqpDVTRoJ+XRq/ttbff5/UYsCoWi4tuL+MIt4PDXYByuYxyuYzp6Wmk02kMDw9j6dKlWLJkCYCw4VGPr/bMVg820PZOzs7OolqthiqK2g87LZ7C3+lBBdpybP6Q6KmcSXPOCT6A2bJBo84kvBrZJjGnceQ90bDQoGj+E6/BNQHCeWdqQEmElcDyPc6pXq+jt7fX5GCrE4PGhdFmldCrkVPjTKJNQ8m1azQa6P2HXmy+tRmHf/cwgniA+/7hPmz7i23w+r0FygbOkXJ09dprH3agY9i4aWJRHYJOEQcHBweHVxaUwlarVWzfvj1E3ID287v3Vi88S53ae7MXLb+FaKQji9LIdaPVwLM/+yxu7GwXAYUHHPvnx4A0sP1/bg85kLvtE5TUktSoMst25hJ2JHz8r8cRFAOc/emzAID7P3E/ln16GQIszINWImUTYpsk2hJl3QNpFPkfy7+2I+g6lh0tJ3QP0u19ALjytis4/2/PG3n4/KPzOPtrZ3H/T98Pv+iHAhe6jhE/gvWfWI/ysjIypzNoRppmT6Gkmefzs7Fl3rYTRu+Nx+g+78Qvn8DsG2cBAEc/cBSv+9DrkK6nQ0oDe6+oe7Zua9Btb2kfZ+9ju8F2aOj+iq+VSqVFkxancITbweEuQqPRQKPRQLFYNEXWhoaG0Nvbi3Q6DQChaKrmMWtRlCAIUK/XUa1WO62tXsiJVkm3yo8ZFWWeNQ0pSVo6nTYkWqPhSqAbjUZIWq0kVQt9kZhq/287R4gPWFbk1AJyKh/jWtjGIZFIYMmSJWg0GsjlciFjQoLP3pGUspOY81jem5Jv24Cw3RvvjdfS6LN9rUajgd79vdj2k9sw9aNT2PhfNqJV6xSy08+Exof57SqR5/u6ieL68z4oNw+Cdts6R7gdHBwcvjuoVCo4evQoSqUSdu3a1SFfL5DFJSeW4Pt/4/vxuf/zcwgiAd70yTdh/MA4GqkGWpWOwktJWdSPYvOnN2N66zT8uA8EQCaXwcavbgxFGzVP2o7SAuFeyLQTrKYOhAmTFjM15K7l477P3Ycg0nZy3/d37d95bdt+kjzaDnKtFG6TL0W3VDHuceyxNBptO6htGXO3qLDeu71vWvGtFbjx1hvIvSYHeIDX8DD2yTHEajF4MS/U8YXXI4GOVqPIns3CR3huSlrVaa9EWx0M9n6Or9mdSk5/8DRmXz9r7i23MYdv/to38ZYPvAVRREPRZc2xt1V7qiK014zfI0091Jxye6/XzaFifyf5Wq1WQz6fN/uaxQRXNM3BYRGgr68Po6OjWLJkienTnEwmze/9/f3o7e01+byVSsVEzRllVpnQ7TyVfLDqg5+ENJlMhq5PA2YbIkrHgXDhEl6PD0rmqedy7YqmGqVnxUweo5FpJZBq9Di+3teKFSswPDyM06dPo1gshry9JPNq2LLZLHzfR6VSCTkOdL6UvNNhYBsW9YarN57rwmg6o/h+4CMei4cqxqph1vwxnscNEDckmjLAivS810qlAt9vV2ufmJjA5OTkoiHdgSua5vAy4fYqDosFq1atwo4dOzA4OBgqnOr7PmY2zKCwpoAH9j6AqXVTePp/fxpv/tibkZpOGfUUbRLQtqvXH7iOZ3/yWSSKCbzzN9+JRCNhHL1UqWk6mC3Zpi2jI18d3RrhpKNdbbmd9wsPiHgdxZlKtLtJxfV3lW/TVncrpsU562u2A0PRzb5yLrpvUFJ4u4g4r2MKi3kB9vzeHpRXljH+0XGMfH4kRJh1DiTPNglVZaHuabg3orNea7YEQQA/5aM+UEfqesoEX24Hz/Nw8PcPYm7HHACg51oP3vgrb0S2nDWRbduxwM9cvxuRSMTsCfUYvV/dt/E7y79tgm6nCtxu7aampnDw4EHcvHnztvf43cBL2as4wu3gsEjgeR56enowMDCA5cuXG5KdTqcxODho2n0xelutVtuVuKXnNv+/p1IpAG0pu0qPaACAjiFLJBJIpVJIpVLwvLbkuVqtGvJrR2JpIPShrRJrjsWejCTvPI/F0Eg2gQ5Jt4uAkXBr2xLP80wOeL1eN3PRQh+8vpJVfeizKitJNz3LjGCrQ4C50jyOEWcdwy7motFurRaq0il+BlQr8DNkxXiNcKiXmCScmyzf983nfODAAUxNTX37X8ZXCI5wO7xcuL2Kw2LC6Ogodu7ciZUrV4acu3ymX9t1DU+/92k00g0sO7kMj//Z4+i51mOc4rTDtO2TD09i4NwA+uf6kclkjA058+gZbDm45bayZLWxWphL85Y1IkmlHO1Sq9XC1e+9ipEvj8BDWOKs0VclWxrdVrLMczTtjMfaDn4tvAaEW5kCCM1XiSDnp+RaVQC2PNveI3B8lWpXkhVMPTGFpU8tva1iT9PmtHuKHm+nGigR5r7GENi4h4vvu4j89jxe8wevQfZyNuTo7xZdbqVamPjgBJpjTTz08Yew5NoSs6dRxwbno58h58v8fVUv2k4P/dzUWaPkXO9TVQn6fdHv6blz53D06FEUCoWX8t/rFcNL2atE/rEDHBwc7g4EQYBisYgrV67g6NGjOHr0KC5duoRCoWDIKgBjgO22EfSG+76PoaEhbN++HT09PQA6xq1SqSAI2oUwkskkMpkMenp6Qi2t9GHJSC+Nh+bZqJEgSdbKqmoEeTzJYaFQQKVSCcmXSFwZ4aXXlwZIvfFcL96bRsJtoqobCZ6TTqeNMeR1NYdKz1MyrgbDzi3jOqihAmDah6jR0UI2Kt/ifdMwcr6UWnFeNIiU//MY2+Pv4ODg4PDdw82bN7Fnzx6cOnVqAbm8uPMivvHub6CRbpPhqU1T2P1ju9EYbBh7CoTzZ1fsXYH0jbSxufF4HMfefgzf+pffwv637A9FQLU2SjweN61D1Y7bEmBtIarjXn7nZZx8/0mcf995EwFXJR3tt8K+X77GHzqklZRzDlpA1SZwAEJtTW35MV/TQIT+3M4JwDlxXrakOlqIYuxzYwv2E7qedpFbnq/7Il6X+zoe32q14Kd8XHvvNXOdC++/gCvvvoL8tjxO/uJJ+Ms7BXWZPqftQT3PQ7QaxcaPbMTWj25F36W+Fy1mRuKuzpdQZD8IQk4Ugp+r1g5QJw/H4O88Tr8P/K7yflqtFvL5vFFtLja4HG4Hh0WISqWCSqWCmZkZnDlzBhs2bMD69esxMDBgDGKlUgGArkTx6tWrpkUUc8E1emddXgAANepJREFUP4ikMJ1OI5lMhrzf+jBXObUaJo02a3ETmxDSAPHhru2v+MClk4APaBpKNWJqHOyIejweDxFPfcgz/5reeq4HNxmNRsP8zWg+r6EefK4d0MmdTqfTqNfrJgJubxii0SjS6bT5vHic5tdzTK45nQu251jzu2xvs+/7JjfdEW4HBweHuwv5fB579+5FqVTCAw88YJ7hw8eH0Xe1D5WBSjs32Pcw/tw4Irk2EWFkm6ouJZCe56HRauC51z+HA285gGaiieff/DxiQQwPf/1hAB2Sp3aGdshWWSkZpEIrFouh1qjh0hsu4dS/OoVWpoXJd00CAbD2T9YCPkK2XKOcHMOOUCtx1iiz7gHUIa1RWVV+qTSb7/Ne6MC3I9kacU8kEqjVamZtNPLNv7vJ1nVMdYjoezyfZFXJpx3NB4BIMoJYEEPdr+PAhw+gtK6EeCSO+mAd195xzVSGz23O4dnffBZP/tSTiNQjC9adjnvf95HJZxCvxNGMNUOkXh0OXBOutb5v7790D8jPUo+3X9cIuN67fv76+Xqeh3w+j2KxuCjztwEX4XZwWNRoNpvI5/PYv38/PvOZz+BLX/oSzpw5Y/J2NdcHQMioTk1NoVAoGMLs+z7S6TTS6bSJHAOdwhws2kW5Msl3EASo1Wool8uGDPNY9VzqAzkIAhOVTiaTRq6uxpF540rCdYNQqVRM4TPNt1byGolEMDg4iM2bNyOVSnXyy9Ax7CrrJsnl/Jn3zUrfNFia605wHpFIxPSJVJUBkU6nkclkzBpzLfX+ucYqO08mk6bPOp0nWrCGx9ZqNbMWNK5MLdAWZw4ODg4OdwcajQYOHDiAvXv3msKl8UIcb/jtN2Dk9AiitSgee+oxPLzvYSQiCcwNzi2IHlL9RbXZzKYZHHnzETQT7ed+M9HE7u/fjbObzoYiqACME1jVWgTtuNqoRqPRrneyvoJz//ocWpkXCFkiwLW3X8Ps62YXyI1JomgTOb6qv1SRBSxUqPE9m/TxdTuqzPnzWjaxJeHTPQkVY1qt25aIk2QS6qCgLWZdFnUWaEsvXo+Res7F8zw0h5uIZCPIbc/h4O8cRHVdFUf+4AgKrynAT/o492PnkL2VxeCpQeCFjyo5k8SDv/YggnrYiaDyes0fj8fjpkaPrQjUOjSMMqtaQdePex9VVNrrZH+e3daMc1Q1Jv+NRCJG/bhY4XK4HRzuMcTjcQwNDZkK59ls1pA4Na5K9ugBXb58OZLJJHK5HMrlsiHD9PIyyqoR8VKpFJKOkUjS667eWj5UK5WKqZjNnGQSdM4HgCHirVYLpVIpVDFVc9E4F/UU87hYLIaVK1eiWCxiZmYGQDgni/PT6utAx3tuS9k1h1xJOtAx6FpohkbXjkbTOUBjy42SFknjmjG/yvM8lMtls1EhuVbJv46tJHxubg6HDx9eVAYrcDncDi8Tbq/isNixevVqPPjgg6aIp5/ycfKtJ7HrC7uQTqdxeudp7H77brzuE6/D2PmxBRFF/f3SQ5fw7I88i8pgxby+4xs78MTfPYFIJRIqjgYg1LqTNkpVbCrp5hg3dtzAsf9wDJWxCmKFGDb88Qas+OIKAGGSqzZaI5caJWUEmXsGlUbbEWle33aik9zR4aypa0rIeR6Pp+20CapGt9VRbxNHlanrtW0pvcrku6G+vI7TP3cafWf7MPmuSfjJ7sq0oaNDeOgPHsKRf38EheUFbPvoNgweHzTjqnNB14v7jWg0ilQqZfYotrNC709l+CqN19Q2fkbcd9ifi34PdD5KvHWdbYfJ2bNn8dxzz5l9z92El7JXcZJyB4d7DI1GAzdu3MD09DR6enowODiIoaEhjI6OGpLqeZ6pMElvNUl0X18fisWiaQNGI0iyCGBBdFVlWir5Uqg8nNdSA0ayCHQMUjeyq/IkNaxK6iORiCnoUq/Xcfr0adMWjTJvysZU0qYbAr2u5pLZ3nYep3IzbdlG8q8GjQbYSMZeIMsq/1c5mHqPueHhPFSyxc+GjgiVvvE9BwcHB4e7F5cuXTLy8uXLlyNSj2DrZ7eiFWvh8COHsfcH96KeqmPP+/bgiT95AqNnRwGE7SDtzfj+ccRbcXztJ76GZqptXw89eQiNVAP/5FP/ZAGRpS1TSbZGaTUqbFRwfgSe/4I9DACvGW7tZBNrm3BpxNQmfXYq2O1InM7Vnq8qzPQ9PScIgpDdpqpNSaTO/Xa2lHZaU8F4vC1f1zx07hHqfXWc/oXTuLXrFm49cuu235Hhg8PY/vHtSM4mseMPdyC3IoeB4wNmLXlPNmnV8RmoYBcVPfZ2+zj7murI0L2GLSHXcdUBotfSz5H/ct2LxSLm5uYWZf9twknKHRzuUfi+j0KhgMuXL2NiYgIHDhzAuXPnUCgUQoSMhjUWi2FmZgYXL15EtVpFMplENps17a+KxWKIxNbrdRPtViKsVUt5Xa3OrfJmjd5yPkoS+bCtVqvwvHZedCqVQjKZDL3P8dVA0GBrZJ7RaVZIJ/lmXnQmkzFReSAseaKTgX/z+vF4HKlUKuTRpvGh0dXXeT6AEDmvVCpmHlqwTSMNtVrNjKlF2myHgxpcu4+qg4ODg8PdjZmZGTzzzDM4e/asedafffQs9v5vbbINAPnleXzz330ThaUFY/9ow9T5vPrYasQa4fja8YeO40vv/lIoAs1xtNip2lZbEt1qtZBbncPhnz2M8vIyAKDZ18SZnziD6cenF0ROlVBpRFvJoe1M1xxfICxvJ1TKrBW/uxUio03WImBK+hiE4H3bHVd0r9Jtz6H53hoQiEajiMVjqG2s4eq7ryIS7bRq4zXjqTgOfuggbu3qQrQDYPt/3Y7HP/g4+s70YduHtyE9lQYAxPNxDJ8YNofqfXVTP/A1uyjdgiFFEs511c+In5PW7NE10r91DTX6reumY+rv3IMWCoVFHTRwhNvB4R4HieLMzAwmJiawf/9+nDp1Cjdu3AiRaIKyavUM0zAAMASZEWI+KO0HrHpwSc4jsQjqQ/VQxW2N6pJ0ct6a26RSJf4kEolQP1ISzHo9PIZKxOLxOHp7e839aNRZPbactxrsIAhMm7Vum4RUKhWKEHAu/F2rtXOuvH51oIpqvYr5+XnjYef6Us7FiubMbde8dt3AMBVAnQfdpG0ODg4ODncnKpUK9u7diyNHjqBWq2HlMyuxes9qeK02eYlX49j1P3YhPZUOEaNVq1aZ7iJ0tv6z3/hnyOQy5tqDs4N441+9cYFqi4XCgI7NsB3aSmQz5zNY+8m1iFRecFI3PIx9cQwD3xowDmJgYfVwJfG2sgxA6D11nCv51kipkmCer2RdFXpAe09j22DOk2um0nlVmPFvrp3dgsyWWnueB3/Ex75P7cP+T+zHufedw9QPTCGWjplOMLFYDFFE8ciHH0Ei196bIABSMylEahFs+m+bsOKpFRg6OoQn/uMTSM+mQ7JxnY9dpI331sg2UI909jR2lF9hf27djlXSrc5+Hm/ndOsYdlBA93q2k6DZbKJUKiGXy2Exw+VwOzi8SpFIJDA8PIyRkRFks1n09fWZAhpsC0a5OY2w/ZAmUaa8nA9Vrf7Nh2ij0cDlxy/jxA+dwI7f2YGByYEFnkz1DNPosYd0t7wgLcCiuUXqNGBxF56vY1KaDaCrMeV5zCXn9RqNBpLJZEhWx4i9Rvo5D8rhtZgb0PHsVu+v4vAHD2P9X67H6FdHzfqTQNOLzNw6ys94DZXJqZyNx7RaLUxOTuLYsWOLqnDaS8mLcnBQuL2Kw70Gz/MwPj6OrVu3ore3F8//m+dx8dGLeOypx7DtW9tQLpdDEW3aLNo3EtRby2/hyz/2ZaQaKbzrz9+FZCGJarUaIpbVajUU3S2XywsqaWt0uJatoTxUxtzOOZx/z3ks//xyrPv4ugXk2c6n1tfVJpOodiNvhF2sTO2ukk91EFA2zfQs/s1r25FbO6qu49mEk8fwHCWXkUgElfEKjn/gOPL350Of69ZPbMX434/DQ1gCfmvjLRz8mYNI3Urhgd99AFfedAXr/2r9Aom+7aTQtdL7CIIAlcEKTv7USfSe78WGv92ATCxj6ufo90bvl+NohBsI9wXX75dNmO18bf1cbSeLPSb3d0BbGXnmzBkcPHjwrq1Q/lL2Ko5wOzi8yhGPx9HT04OlS5di+fLl6OvrQ29vb6g6OKPPSnyVbAKddlgkverR9jwPF77vAo78myNoJVvoP9WPbR/dhr5LfebhmkgksHLlSnieh3PnzhmPdKVSQaPRMNFsLVpGcknomMyJpnxd5ep6HOXmvB6JMsdnlEClZGrwlfQD4VwzGjDt1c0587j6pjrOfOAM8hvziFaj2PxfN2PVl1aZyD2vxQg+15Zk3vO8UKswLWajEY5z587h2LFji0qS5Qi3w8uF26s43IuIRCIYGRnB9u3bMTw2jMknJrHx2Y1Ip9s9t1lVW4menUoUBAGmVk0hW8tiND9qWosd3XIUYxNjSOVT5lpBECCTyaDVapluJkq4gyBo55T/zGEUVxSx6fc2oXR/Ccu+tAxeEJajawSd81DnvUazNTKtfyvp18KgABZcu5sN1q4raie5f9C52uAeSKPXen11dBO0x+XlZRz5uSOY3zq/4Lrr/2Y97v/z+wF/YZR5dtsseq71IDGdCJ1jE1Pde/B9Oxe6nqnj+M8fx/Trptst2z69Fjv/v51GKaffMR2DrzFYYDtK9H7t6L5Nqm05+4uRcp1Lq9VCLpfDsWPHcP78+btWpfdS9iquaJqDw6scjUYD8/PzKBQKuHLlCpYsWYLVq1dj1apVIe8l5dTRaNS0tQI6D0zNwaHsGWg/MC+84QKOvucoWsn2wzK3MYeD/+kgdv3iLmSLWXieZ1qVZTKZUPQcgCHFmtND+ZtdGVMdASr3sgvDaFsvlcXzWiT26gHmJoLOCM1T97xOSzI1wJyTXck8EomgNdLCxK9MoDLeriDbSrVw8r0nEQ2i2LhnIzZs2IArV65gfn4+5JVntID3p5sTGm71SHczpA4ODg4OiwO+7+PGjRvYs2cPtm/fjtXN1WjEGqZlpDpzldTQcUvCuvTiUgBANdruynHpwUvY/QO70Tvdi7f/ztuNvWu1Wsjn8yF7pSq2AAEOfvAgbj5yE/CA4798HDt/YSfgA/A6NpXFWPU+tNgnYe81NGfcPpaOd7VrStTs6LiSZCAcIae91+4kOie1saoii0QiqI3VMPWmKaz75LqQQ14JZ7qURt+1PsxvmTf9shEAG/9qIzZ8ekN7XgjntgPA8LF2TnYQCUeB7Zx2O8rMvZMh4h5w6P86hPkH5l+4KeDCD1xANBPFI598xMzbJum6PqruU7m87WTgHHVeOleV9Gsk3v4MdY0Z4CgUCiHHwmKEy+F2cHAAANN66/Lly3jmmWfwla98BSdPnsT8/LzpR82Ha7lcRqVSCXkxa7UacrmcKe7FvtwAsPqZ1Vh2chlesCuI1qLY8MkNSOaSoSh6Pp/HjRs3TM63ttjg2FrBnDngttcUgJkHANPDmlFvrSBue+EZSc9kMshms0aiznlq9FvboTGiQOOlkW8eQ5LP/OpMMYPVf7YakeoLj2IfGDo0hNGnRzE/P4/JyUkzPtA2RFx73gMl9yThLASnhoyyfwcHBweHxYtisYhnn30Wp06dQr1eN9FoFu5kyhPJTTabNYXUtHCYH/g4u/osnvrhp1DpreDmmpv47C99FvVUPRT11LZgauuOvf8Yph+aNiSysrqCQ79/CC107F40GjXpZUrE7IisVkS3ZdwsmGXPQyXNdt6v/TuVYbTNNrlTUk9ngHZn4fqq877aV8Xej+/F+Xefx+UfuoxEJoFUKhXqNhKNRpEoJ7Dlj7Zg+TeXAy0gMZ/A+H8fx9pPrUW00Sm8xnuwO6Ao7EixytCVzNKBEI1GEfgBNn54I6LFDjHumenBrv++yxBnvW97/YOg05FGv0PqENE6N7wP2/Gh3wmVpNtE3ybd3Bdyn7OY4STlDg4Ot4XnechmsxgaGsKqVauQTqdDRcj0Ie37PiqViinWQmOQTCbR29uL3t5ePPWvnsLFtRex/S+3Y91X15kospJrjksjyzHo3WXeUbFYDM3Blo/RELA4CttrMXJOUsqcLpWiqRGnd5tj+b4fKpzG12nkNO8I6Bh0RqdJ/HmfrVYLV992FRd+4gKGDg5h+69vN9fV3txavMWWx2v0n9fl5oJreerUKVy6dOnOfVnuAJyk3OHlwu1VHF4NiMVi2LBhAzZt2oSBgQH09PQAaOdg5/N5pNNpY4u1/RNrmgSJAJ/92c/i5rqboeuu270OT/7Jk6YbiRb8JDmjQ/nABw7gxpM3AA/Instixy/tQPRmuM6KqsPs6LbaKZWsG7IokU4l6Uow1SnPMXmMFmezK6/bEWKV4gOdVDglpbTvuVU5HPnQEdSWdvpBb/9/t2PdF9chEnSiv0rYAeDI+49gy0e2hIio7SxQwquOCu6xtJJ8MplET0+PSb1jkVfeF8dvNpvIbc5h4oMTSJVSeMNvvgGZZiZUE0cJsM5Br6HpdRpsITQXXCXvWthV39eAhO0EMXL4eh0TExM4efIkqtXqS/q/8d2Ak5Q7ODh8WwiCAIVCAYVCAdPT0+jv78fQ0BCGh4fR29sLIGzoKCPn65S7cUPwtk++Dc+sfgYr/2ElEIMh7jRqAIyHHuhEs3l9la3H43FDyrVPNdAp6sGq3hqF13xs9SjzWJVBsTAaEM7V1vwtlZlzY8PrcnNjR+CVQCcSCaz+0mokY0mMPT0Wkmgpada1osxKJX6cBwu88Vga6Lu12IiDg4ODw8tDs9nEqVOnUCqVsGPHDsTjcdM2k/YAeCEa+wJR0ZZXrXIL3/eJ78Pu9+zG5e2XzXVvrbqF6dXTGL44bOyMypbVnm75nS2IVWIori5i84c3Iz2fRjMSrkLOOdjklbaaJJy2ylad8V401xrokHnO0T7HjvpqepjaeDuHWOeqBdBoQz3PQ21lDX4yLG/OrcrBh4+gFS4Eq8R0+8e2w4vdPnqtc1DSqUXQtFc426UuXboUyWQSuVzOOEmU9APAwIkBbPnoFgzODSJRTSCS6OzbNFKuxFuvoWtpOye6zd2Wi+ve6XbKBJ6vn3O9Xkc+n78n9i9OUu7g4PCSUC6XMTU1hdOnT+PQoUM4deoUisViSNKkxi4Wi2FwcBAjIyPo6elpP4grAdZ+fS2AsFFh5DeVSiGTyZhIsuZfaTS3VCoZA0uvPY05o9d8v9FoLMhn1kIgzPeOx+Mh762dj60GNJlMmiruBI2SvdnhPNLpNNLpdGizQfm653lY9Q+rkPATZi21aB3nodFyldfREKshpDc6lUqZazg4ODg43BvwfR+Tk5PYvXs3Ll++jHq9jmQyief++XOI9ITbdFJVRUe453lIziZx35H7QtecWzWH3e/bjcJYwTiR1f6G5OHRtr3Z/PubkTmfCTm8VZ4MhPOTlXwzagvAdPQAwr2kSX7tXGzdQ3Qjb7xP22Fg7z10PJ2vTShpg4e/OYwdH9ph2rOt+cIabP2zrQjqHWLZ7bNSB7ydZ27LuW2JtZJVdjGJxWKo1WqYmpoy+facu73GADB6dBTZ6ay5nh3d1z1ON1m77nd0nioTV8L+YkoCW8pvk3B+LrlczhTtW+xwEW4HB4eXhUajYR6Cly9fxvDwMMbGxjA0NGQelJFIBOl0Glu2bIHneSiVSpifnzdF12yDyYer77fbgfX395uos3qjFSrRppyK5JJEW0k2o8/0UqtBUakTJe6Uj9F5oN5mGh7tv+n7PsrlshmPJJkknq8rIdc+4gBCEXweD8BE2pU0a1ERzZdTOT/Xj84FBwcHB4d7C7du3cI3vvENVJoVzP/CPI68/ggmN07iB3/vB5FoJhYQ5UgkgkQigXK5jLXfWIvptdM4+9qzJgQ3v2Ie5YEyeq71hGyz2q56vI59H92H8soyYvMxrP3TtfCanVaaiURiAYlNJBJG9mzPifMCEFJ5KRil5nzseinqnAc6dl1zxJXo8Tgl5Hyd9l/zk2nno9Eoho4O4bEPPIbL77iMB/7iAURrUTS9ZmgsO6pvy+QJjQCrpF1tuc5fiTX3GqwPo9Fl7q3Uya+Sbr0uoVFxnb+ScX2Nc7adCDbh1oCE3ouer/Pk36VS6a6Wkr8cuBxuBweHbxue52FgYACjo6NYunQpBgcHMTw8jPvuuw/pdBrNZhPFYhEzMzOm+AUJYRAEJi+cUdtkMmkILIki/7WrntLosoKm5nSp8SHh1hZa3Ui0th3jeEB7s2DL5xmFpmy7Wq0ukF8xh05bfHF8LdKm79FQagV2zYmyvc+M8GtOFu+rVquhWCzi4MGDKJVK35kP/BXCS8mLcnBQuL2Kw6sSKQAfAPCraBcyC4BlF5bhrX/+VmTn2lFNjfLSoV2tVhGJRPCZ3/oMcstynesFwFt/660YmhgyDmwW5CwNlXDwPx3E/KZ5M9baP1uL1X+9GpF6h2Da49GOaYspwiaGQDiiqtJsJYsEHex0jitZVce+LW9WJ3e3CDnQSSdLJBLmGK1lo3ZbSbo69rUNmT13jaCzIKtNzHnvui63i/RzPN0LUcGXSCRM8TMtPGcHILpF6jXQoJ8HPx91ePA9BjBU/fBikXJd71arhQMHDuDUqVN3fcDgpexVXITbwcHh20YQBJibm8P8/DympqYwNjaGjRs3mkIuJMUq59Zz+TBmhU/1JPN85i6xarfmcpEUB0G7migAU0VdPcu2BJsEnGMQKluzjSrnxXM1Wp1MJhcYBjoD1AiRZHPzUavVkEqlzP1R0t5qtYzEvlwuG0NZr9dDEXlCjT7nzGPudoPl4ODg4PC/iCyAh9FpPeUBpb4SZodnkZnNmMNIrBgRZa2R8W+M4/APHQ4lmk4/MI2RUyPGLtGGlVaWUBushcbKbcyhkWggXo13JcTdIstK6mxHMu26LTXWY5X00aHeTaqt19W6KGrPbbLPa2u0m85+m6h2C1zqXOyotB21VsKsqjldRx1DU9x0PFsSTkUCnQ28F23tZcO+L43wa3Sbx2pEX+epgQUNpqjjQaPp6gDhuVRS3it7F0e4HRwcvmMIggD5fB7FYhHXr1/HsWPHsHXrVqxatQqZTMbIotXgaSEXOwKs+Tx8jw/vQqGAarVqvM4qcSJx5znxeNxcXwubAQhFl+mN7enpMcZENwJsj8F7VVk3ZV12FFpJvxoxNe6UTKVSKbPRaLVaSCQSGBwcxNjYGC5cuGAk67oJ0bW3c6Ion7eJuYODg4PDPYQZAP8ewB8BeBsQK8Tw8H9+GGtya9AIGiG7pQSTzuNtn9+GRCOB537kOQDA4089jp1f2YlytGwilHR6+1kfrUTHab5k7xLc/5H7EcvF0PLD1cyVdCp0Ht16bes5SvzsaKySwNup20heVa4MdKLN3WTk9rjdpNU6FyXyOp8XU6cp0eZ4hE1Iu0nLbUm2Xlevo/fMvYjeu65lN+hx/FfvTauQ63G6FrYzQR0KnC+dGtxTFYvFRafKezE4wu3g4PAdh+/7yOfzyOfzuHr1KgYGBjA+Po6lS5eahzrzjXm85mTZhkXJIvPDotEo5ubmQiRYPan68GeEmeRaoa9xvHq9btqJAeFWI5yDerBtj7ntxfb9dn9sepcZxeaGJ5vNhoxvvV43VeEZ3dZq42roKG3jpoj3TfkY59utkIuDg4ODwz2CywDeA+DvgOZ7mzg2ewxDTw5h2bJlIVJkE0PP84AWsPHpjUAUqEfr2PjV9u9M76oFNQQIMP/gPA7/h8No9LajjtkzWWz8fzYiOZcEvLBtBDqEWFtXquyZ5EqPVRvKY7pFXZXwarsxXpvXBDpRca4Ba8BoxFZrrOj+wR5Pf1KpFBKJBEql0gKngTrX7f0HbTjft89VIq1V3+01tKFrqPelkWO9lh6n89a142uco5Jl2/lgOwN4L7baQM+zI/msOTM/P2/av94LcDncDg4Orxji8ThGRkYwNjaGsbExpFIpQxxJEiORCGq1WqjvI729aoQajQZKpVKnr6gYF0If+NFoFKlUKkTIbZk4c54bjUaovZYaK0rQ1fCo3KybxIrGhR79ZDJpisrwWBoZtlbrVnSN0jluXnhNlaHruNzIXLt2Dfv27btzH+wdQuByuB1eJtxexeFVjxfyqgEgm83iySefxKpVqwDAOGlpO1qtlrF5rVYLkegL5CdoK66SySSKfUV8/t9+Hpv/ZjP2/8h+FJd1SFCkEsGaP12DVX/bvr7aaC0ISnupRNeWFPM4HsvfGfVUR7MdQSWUDGqrMPvadm45EM6NVmKrTgCmg2n19r6+PmSzWUxPT6NSqSyIkOtc9fq2A99WBNgE1VbH6X11Cw74vo/SihKyN7JIxVPGCc/9h6r4FL7vh1qX8rO056b7J95HN0m/3rP5inZR6On7hUIBx48fx4kTJ7p8we8+vJS9iiPcDg4Orzji8TgGBwcxOjqKoaEh9PX1mZ7d3ASQSJMMR6NRYwSYx12r1VAul+F57bZbAIwESY0VjWA2mw0RVkacKSVnXveLRbaZk81NC6/BvHIaKZJwFk0DYMhxN489DTodDIRG8EnWI5GIqfZKss1NiBJ2bk4uXryIo0eP3tHP9E7AEW6Hlwu3V3FwCCMajeKhhx7Cpk2bjOpJU6tot2hTlFA17mtg77/ei8lNk4hVYtjyt1tw+YnLuLX2FrymhzV/ugYr/mLFAlmzkmYltlrADAjXcOHfSiRVnaUEWP/WqDdf1+va0XC17zo3JYB2epY6EVgwldcGOoq9UqkUiubrj0r51ebzM+K49nEa0eY+Q0l8t0g0AMxvnsfR/3gU6z61Dmu/vhapVMrsL+g44Jgk+/xb69MwOm8Tbr0nwo58K8fsFk1X4q1zn5mZwaFDh3D58mUsBryUvYqTlDs4OLziaDQauHnzJmZnZ5HJZLBkyRKMjo5ixYoVyGQyIUKsVS7VM08JmVZPVRmbGixuKhid5jFKsOv1OprNpiGs8Xgc9XrdFCijYdBzSL7tvtz2BkIJM0l4IpEISfx088O50XixvRcj2JlMxhRZ43rwPlmtXT3mtVrtlfpoHRwcHBzuIrRaLTz33HOYn5/Hzp070d/fH1J60V7S2U2bVUqVsOdH9+DGphsAgGa6idPffxrrv7YefszH+JfHMfI3I6hH6iGyqrJudS5rxNSuc6IErZs6zJYq21JxPVfzn23nuV7XJtlAWBJPcqvrw9dpx20by7+DoFOLRfcjqpbTyDnHtiPGXEe7vo0WirXvKwgC5F6Tw8TPTaCyrIITP3kCkb4INn15kzlfybSebxN4nZc9nq1E0OO0JVw31aE9Zx3T930Ui0XkcjncS3CE28HB4bsGFj8rFou4du0aLl26hPHxcQwNDRlPfLFYRLlcRl9fH+LxOJLJpCHiaojUOAJYQL6TyeQC6ZVdcESvQU8zDaIWeFEjzwh2pVJZ4FVXWbpuOlReZ/fgVlk4jRjl5zTGdALwPvQ6zNfSzdO9UuXTwcHBweHlw/d9nDlzBsViEY8//jiGh4cBINSiS1OkPM9DrBzD2L4x3HjNjXb18gDonenFxq9sxNqvr0VyJolivIhqtWqiv7aDmSSTfyvZte0v7SnTs5R82sW/7CiqRl+1oJfaZPu4biRf09CAjqqN0Vx19muxL85RW5PqfOhE17nY98HxlBBzD6DXuV09Gl6vNFzC0f/jKKpL28VYW6kWJt49gUyQwcZvbQwRebtXuu2A0Pl1i153I8z2uvFzVrWCOhf0+tzfFItFVCqVLt/kxQtHuB0cHL7rYIT5xo0buHnzJjKZDJYvX4777rsPqVQq1JaDBFLJMw01/9ZibCSilF7TcPm+j1KpZPKxkslkSCLVrW+mnetFcsuCcPv370culzPGVUm5et85N/5wc6Fzb7Va6OnpQSKRQLFYNM4ARtjVuWBL9mq1mjmu0Wjcc4bLwcHBweHlwfd9XL16FV/4whfwxje+EWNjY4jH44Z0qw3yfR/xII71X1yPZqqJEz94AkPXhvCOD78D1VgV0UIUfsM3rTBLpZKxT0C4SjWhpI3Ei5FiJb7sKqJk3CZ7RLcItk2iKZ1W8m7nkCtJt2vC6L6BDn+9Rx5rE0tbLs1UNL2m3hed7XoOwdfoPNc14z3zuJ7ZHqz/+Hqc/MWTaGab8FoeVu1ZhTXfXAMvGs5ptwMQSuoV9jj23O2ofLe88G6fo6bjce2azSZyuVyofey9AEe4HRwc7ioEQYBSqYQzZ87gypUrGB0dxejoqDHk6kVnETTmntkGU40+H+wk7Swa0mg0jBFT8qubD0rWtS+4SsDPnTuHYrGIRCKBRCKBRqNhvP6ccywWQ71eh+d5psq65j+pkaYBi0ajeM1rXoPTp09jbm7O5F+pXJ7rAsBsVJj/Ti842445ODg4OLy6USgU8LnPfQ6PP/441qxZEyrgqUXIgiBAFFFs/sxmeDEPO7+6E/XeOvb88B6kplLY/NebEavGjB2mndH8bDsyqkSLBdyUzN1OXs65Eep0tsker8XjOKduxVc1d5lQQm5fl04F+/oazeXrGhXnMfxX5ez6up3bbUe/E4mESbNT0q1rGPEiWL53OfDHwKkfP4UVe1Zg13/bhWgyGiL7qtTjfeseiZ9dNweAKhVu95oer/fJMZXwq0qxXC4jn88vOH+xwxFuBweHuxaVSgWXLl3C1NQUent7sWzZMoyOjmJwcBCe55nCHpoj1Ww2TUSXhl2JrU3cKd+ikaUhU+8qC6Np/hbQMSD1eh1XrlwxrcT0WBLfRCJhrq/jAUA+n0cymVxQtT2Xy2FiYsIUfeM8aCxVrqWGnpI83ruTlDs4ODg4EK1WC/v27cP8/Dy2bNmCbDYbes92AG/+9GbUe+vY+y/24tLOSwCAeryOB//kQcTjcaRSKeP4tiXk3XKuuxFezXHWCKlNKjVv2I60dpOL29HqbiRdHQ1KQm2nPdeHewo7Smz/2Pej0WySWXXg2/NQRwjH0nZm/Fsj1Dx25VdWIh2ksfL5lSFlge6HXqwHuq6zzqvbMQRf057m9hoBCN2DXWunUCiYNmv3EhzhdnBwuOtRr9cxOzuL+fl5XLhwAaOjoxgbG0N/f7/x9qpBYpTaNsyUbDEKrXlnzWYTqVQKAAxhpdRdC6HYeVkAQkYT6BB9zwu36mKEmgYbaG8yUqmUuaY6EGKxmGl9xnVgP28AIWkb27pQHq9ec1c0zcHBwcFBUa/XceLECRQKBTz22GPo6ekxdrRarS4oSPa1n/sabm65ac4/+6azaEVbePCPHkQikUA2m0WhUECr1TLOZ+3WYdvJbkSZhNGWMAMInWe339TULyAsEecYarNt2DnJ9tjqvFcngp2DrnsNjejahUx5TLciapwrz6WTX6PJdo66pqvpvS/75jLEUjHE4rEF+yJeh+fY8nC9d3VAhKLpXYqskUDr58ZjuS66T9Pvgu/7KBQK92SQwBFuBweHRYNWq4VSqYQLFy5gcnIS/f39GBoaQjabRSaTCUWvKZNjNFq9raxITvKthlrbhilp5WuaS8XWGior479avKzRaKBWq3WV7rEdGXPXKHHXImraCo3R7r6+vpDXmPdRq9Xg+74pOscxHBwcHBwcFM1mExcvXsStW7fwpje9CX19fYhEIqH2kkDbfu786E589fe+inpfu9tH73QvHv3UowhiQYgkFwoF1Ot1o+pSskvypfnUsVgs1GVEI+LAwjZeJJSEHstxtG6KLYVWqJSZznB7fJvoqiNdneuci/2a3ivvQVty6T1o/jTnwdQwXQeNstuOC1vCr/Oyo81cd82l5tgaFNDz7N/t69opfUrw+dnrOut9tFot5HK5e5Jwuz7cDg4Oix6ZTAZDQ0MYGxtDOp0OSae6ycT4sFdZt0rEtH0YEK5oTqNMotxNSqc9sdVDzVxuEuhkMmnmwsrjKmsniWYv7lgsZtqFcRw1/tzg0FhFIhGUSiV8+ctffkU+h+80AteH2+Flwu1VHBz+15BKpfDa174WK1euDNk5LViaX5fHvp/dh3g5jn/6kX+KVDll3m82m6jX6yiXyyiVSm372htBeWUZ2dNZY4tVxQUgRD5pL0nStOBZNwKtknCea0fm7TGVmBKqLtNrATC2OB6Pm6JnGsXVnHVbym7Pn7+rc96+Hx3bltzb8nU7Ck/nOlPKuEfh3LX6u6al2RJzXk9l+DbRtiXxSrTt+ek87Qi4fhbXr1/H888/j+np6dt+T+9GvJS9iotwOzg4LHqUy2VUKhXMzs6it7cXg4OD6OnpMYYG6HhsNS/KNpR2Ow+7IIgaP83N0hZc9NhqzhZfo+SbPbRJtrX6uHqTdaOjxVfsoiRaSE4la41GA8Vi8U4vv4ODg4PDIke1WsWzzz6LfD6PDRs2GOUVi3XV63X0nevDAx97AKn5FIL5AEi3beGFRy5g5NAIYvMxpNNpAEClWsHZ953F/IPz2PQHm9B3tC9E7uxULyBcJEwjuYx+asEzjeCqbSY57DYWr2uTPnWmd4sUayRWU9X0fCWwPMaeP50IHMcu8sr9hB5jS+FVqs77saP43FNw36B7BpWf635BfyKRSChlzQ5c2HsVXeNuxyt0v2J/hrlc7p5NgXOE28HB4Z5AEASoVCqoVCq4desWUqkUBgYGMDIygr6+PmPEACwwlDTkvI59DA1UrVYz+Wk0vlo1nHlW6gXnmIy2qwFn1XIaXDoHVIbHOVFm3m3jwI2RysFI5B3hdnBwcHB4KSiXyzh27Bjm5ubwyCOPLJBBNxoNDE0MtauSx6uIRCKYemwKz//o8+h7Qx9e/+uvRxxtezTxMxO4+tarQAQ48QsnsO1XtyFzMbOAjNrRa9tWKiFXmbaSTCXCdv6zOrzt3GvaWXXI63xIxG01sDoGeC116HcjoTxPofJ3tfdaVE3P5VjqfNAIuK6LLW1XZZzOmefZ56rjQtfEVgF0i7TbAQc9lnPWteEYuVzunu2qEvnHD3FwcHBYXGg2mygWi7h69SqOHDmCkydPYn5+3uRH0wjwIa8Gj9I4jS5HIhHE43FDbHkMfwAgmUwik8mEvMKUqamXmu+rFI4ycZJ1rZAej8eRTqeN1FwNoeY98d5qtZoxfmxp5uDg4ODg8FJQr9dx8eJFfOUrXzEOW021IglttpqY3DCJ3T++G9X+Km5uvomn/++n4ad9HP/x47j2lmuGZZRXlrH/D/ejkezYX9YsUdky0CGE2mFESaFKxgm1t3aBMpVaM31LiaJWzNbItp1nznXQNbCjwN2k1XqeTb75u1ZB57jcfxDcMzC1TEksx4hEI2j1duTzdoSboENei7vpezov7dKiRJlro9FzPY/Xsp0jmsPNY5n+xvz/exGOcDs4ONyzoBG+ceMGjh49isOHD2NyctLkXzOficZOSTCNn5J05l3T0GkBtHq9jlqtZgyUbYB5nI5hy8Xo1Wa+GCPpqVQKyWTSeL7tCqgqCWMvcOZr3YvFRxwcHBwc7hyCIMDMzAy+9rWvYWpqKlS4k1HoSE8Ex99zHM3UC0U5PSC3OocLb7mAnls9iDTCFCN1NQUv6KjBmGKlTnDt7qGpX3aBMI1Wqw0Hwm27eF3aepJHlZsr0SaU8GvusxJ9JZ1Ap8Cpkm4gLAFXKbYd/eZYKo3X82z5PCXjOpf85jye++PnUFlTCa297ajXHGydO4/l/kbXj3Pj2tjOBzvqbZNxGxyD4xSLRZTL5Rf7Wi5qOMLt4ODwqkCr1cLc3BwuXLiAiYkJXLx4EYVCAcPDw1i1apXJoVajSyPD6uCMHieTSaTTaSSTyZCBbDabpj2XStdIlIFwa5N0Om0i2nqcSseazaaRWHEs9d7z2GQyiXg8jmazGcqBulfzoRwcHBwc7izy+TwOHjyICxcuGBJFUhWtRfHo7z6KkSMjAACv5eHB//Egtn1xGzb93SZs/aut8FptUjZ0cAhbfmULorWFEVE6qO1orcqs1S7bFcBVkkzYlcaVJJN087p2ay4lunQCUIVmF/ui84B/k0ASvEe7bozmP+v8NeJu3yOPU5Kq9zX98DQO/eohVMYqOPKBIyhsKCw4T/cW6qjwPM8EF+x9i7YMs9fUzuHW63EsLUjbDb7vo9FoIJfLoVQq3e6ruOjhcrgdHBxeVQiCAIVCAcViETdv3sT8/Dw2bNgQakuipJYGViVldq4Zq4prn0u7uieAUCVyACbKTiMHdKLcKrkCEPK0U1quxptknISb165Uwp5uBwcHBweHl4pCoYDjx4+jXC5jy5YtoRZSyetJ7PjEDux//36s/vpqbNq3CV6kTbzu//z9iNaiuPDEBWz92FbEZ+No+I0FecDAQqm1OpRpz+x8bzvSrOSPr6uTulv+t9poJf123jMjyo1GI0TmScLpKNdiZzYZ5T3xGJu86+92ITNVyQEd2T3vZ+ahGZz46ROoD7bl2Pm1eRx4/wF8zx9+DxLTia4ODL0+9xMk8xrR1wruOgeFXaBOc8/t43V9eGypVLqn87cBR7gdHBxepQiCdpG1c+fO4dKlS0gkEhgeHsbw8LApssZNBY1sMpk0r9Mw0IhSaq4FQgCYXDXNzeYmQiuck/CTMKdSqRBxVy8xSbf211bPejKZNHN1EW4HBwcHh28HtVoNp0+fRqFQwLZt25DJZExUNDOVwaO//SiihSgq8QoymYxRjK3/xnqs2LsCwXSARqxh8oZtZ7RNloFOkTAAIae2rQSzj1fpNc+zyS/VYxq1t2XeKommfU4kEqE0La0Bw9fpCNf7sqPBdgtRoEPebWeCOuR5j3YOev+xfgwcHsCN77sBRNpqg/E94+i91RuKMmskndfTwILuNTRf25bI2ySaZF7VC7ZjQv+1VYC1Wg35fL6r9PxegSPcDg4Or2rQoDUaDZRKJUxNTZme3tlsFgBCFU7VQNGgV6tV9Pb2mtxpm3SrAeOGgGOqjMzzPGQymdAGgO+zjzevx3FZ2ZyGTWXwlLg7ODg4ODh8O/B9H1evXkWtVsOWLVvQ399v7Flsvu1QrraqJl0qmUyi0WggW82inqwbpzWwMGdYyTDQ6epBQqudN0i8eQ07Us7r0E7yb41wa863RnntXG7OJRKJGOKrOeCMAOt4GiEGOiRTi7PqOLqv0Pnx+vF4POSsp9Ndo+TJehIPfuRBHO05iqldU9j4uY3Y8tSW9jwiXoica8RaI+vq3CdsJwShagT915byayE7O+rN+261Wsjn8/d8RxVHuB0cHBwE9Xod169fx8zMDHp7ezEwMIAlS5YgkUiEKnvSOLG4GdCRg9O40PhWq9VQ3hjPY/EzNf4AkM1mjfddDTQJNPPJuvUl5UaEcvJ72WPs4ODg4PDKYmZmBocPH8aGDRswOjoachjTEUwJttqlZDKJarVqCoDeLjcYCBcao42zC5VqNNWOCmthMZVDK+lWUq8RbiXIShKBTmRWW4VpjrV9HW2Fxfdpu9UJ362yucrJdX2UdKucPRlN4nv/+Htx6V9cwvhnx+F74Ui27dTQa9kFY+1iZ3ZU3ZbAcx3UyaDRcd0b6Xp6nodqtYp8Pn9PF0wDHOF2cHBw6Ipms4m5uTnk83lMT0+jt7cX/f39GBkZCUnb6vW6qWZKUH5GTzgLqSUSiZDh1bxtev8plwM6OWjcmKRSKTQaDdTrdZObVqvVQvnj3KBovpaDg4ODg8N3CvPz8zh27BjWrFmDlStXGvtER3Kj0TA2SokaK537vo9arRaSIWt1bjqsSSyBcNTbrpHSjViyGwnHJFSxZhNDex60p5ynFhOjzddouw11FNjRYt0HcE62RN5u2aXnazSaaWnVchW7vrgLWNLOvde0M9uxwd9V1s71UdWB7TRQKInX9+1IvEbj9Vh+D3K53D2/X3GE28HBweFF0Gq1UCwWUSqVMD09jZs3b2Lp0qUYGBgwhBqA8eYDCOWXsU1Xt3wzGjYapXq9boyRknUadL7PsSKRCFKpVKjKq+aFl0qlBQbbwcHBwcHh20WlUsGpU6dQLpexYcMGU8uEZLlUKiGbzYbaZNJu0SaylomSbpJElWiTbJNkNhqNkJOb59Le0d6SdNMm2pHublFfzoc/dnqYOsE5DqPNei92OzA7gs3xb5fDrUVRuynVdE21W8rs7KzpoKJE33b2a262rhv3ELo+hH6WfN9eV1s1YEvn7bWpVqsoFAr3vBrPu9dv0MHBwcHBwcHBwcHBwcHhuwHXh9vBwcHBwcHBwcHBwcHB4Q7AEW4HBwcHBwcHBwcHBwcHhzsAR7gdHBwcHBwcHBwcHBwcHO4AHOF2cHBwcHBwcHBwcHBwcLgDcITbwcHBwcHBwcHBwcHBweEOwBFuBwcHBwcHBwcHBwcHB4c7AEe4HRwcHBwcHBwcHBwcHBzuABzhdnBwcHBwcHBwcHBwcHC4A3CE28HBwcHBwcHBwcHBwcHhDuD/B9LNSYLW3ufDAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Draw the line points for training\n",
+ "ref_img_with_line_points = plot_junctions(ref_img, ref_line_points, junc_size=1)\n",
+ "target_img_with_line_points = plot_junctions(target_img, target_line_points, junc_size=1)\n",
+ "\n",
+ "# Plot the images\n",
+ "plot_images([ref_img_with_line_points, target_img_with_line_points], ['Ref', 'Target'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualize the exported ground truth on the Holicity dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Info] Initializing Holicity dataset...\n",
+ "\t Found filename cache holicity_test_cache.pkl at /home/remi/Documents/test_SOLD2_data/datasets/Holicity\n",
+ "\t Load filename cache...\n",
+ "[Info] Successfully initialized dataset\n",
+ "\t Name: Holicity\n",
+ "\t Mode: test\n",
+ "\t Gt: holicity_test_homograpy-export_512x512_v1.5_detect_0.25_inlier_0.9_local_max_refine-v2.h5\n",
+ "\t Counts: 520\n",
+ "----------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize the Holicity dataset\n",
+ "with open(\"../sold2/config/holicity_dataset.yaml\", \"r\") as f:\n",
+ " config = yaml.safe_load(f)\n",
+ "\n",
+ "holicity_dataset = HolicityDataset(mode=\"test\", config=config)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Read in one datapoint\n",
+ "index = 2\n",
+ "data1 = holicity_dataset[index]\n",
+ "\n",
+ "# Reference data\n",
+ "ref_img = data1['ref_image'].numpy().squeeze()\n",
+ "ref_junc = data1['ref_junctions'].numpy()\n",
+ "ref_line_map = data1['ref_line_map'].numpy()\n",
+ "ref_line_points = data1['ref_line_points'].numpy()\n",
+ "\n",
+ "# Target data\n",
+ "target_img = data1['target_image'].numpy().squeeze()\n",
+ "target_junc = data1['target_junctions'].numpy()\n",
+ "target_line_map = data1['target_line_map'].numpy()\n",
+ "target_line_points = data1['target_line_points'].numpy()\n",
+ "\n",
+ "# Draw the points and lines\n",
+ "ref_img_with_junc = plot_junctions(ref_img, ref_junc, junc_size=2)\n",
+ "ref_line_segments = plot_line_segments(ref_img, ref_junc, ref_line_map, junc_size=1)\n",
+ "target_img_with_junc = plot_junctions(target_img, target_junc, junc_size=2)\n",
+ "target_line_segments = plot_line_segments(target_img, target_junc, target_line_map, junc_size=1)\n",
+ "\n",
+ "# Plot the images\n",
+ "plot_images([ref_img_with_junc, ref_line_segments], ['Junctions', 'Line segments'])\n",
+ "plot_images([target_img_with_junc, target_line_segments], ['Warped junctions', 'Warped line segments'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9wAAAHICAYAAAC8iOK5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9d5xcV333/77T+8z2rpVW3So2lo3cC8aVjk0wxPSEDiEhjRASeJ7nF/IkJIQWDE9iCARCcdzANhhjg7uRq2zLsnrZ1fad3sv9/TH6Hp25GhHb2LJln/frpdfuztxy7p3RPefzrZZt2xgMBoPBYDAYDAaDwWB4bnG90AMwGAwGg8FgMBgMBoPhpYgR3AaDwWAwGAwGg8FgMDwPGMFtMBgMBoPBYDAYDAbD84AR3AaDwWAwGAwGg8FgMDwPGMFtMBgMBoPBYDAYDAbD84AR3AaDwWAwGAwGg8FgMDwPGMFtMBgMBoPBYDAYDAbD84AR3AaDwWAwGAwGg8FgMDwPGMFtMBgMBoPBYDAYDAbD84AR3AbDiwDLspZblnWLZVlpy7Jsy7Le+EKPyWAwGAwGg8FgMPxuGMFtMDxDLMt690FRLP9qlmVNWJb1bcuyhp7lYf8DWAd8GngH8MBzNmCDwWAwGAzHJI71xm/7d84LPVYdy7JOsyzrs5ZlJV7osRgMLzSeF3oABsMxzN8Au4EAcArwbuAMy7LW2rZderoHsSwrCJwK/H+2bX/1+RiowWAwGAyGY5J3OP5+J3B+m9efPDrDedqcBvwt8G0g9YKOxGB4gTGC22B49txs27Z4ov/Nsqw54C+A1wM/egbH6Tn4M/Ucjs1gMBgMBsMxjm3b/6n/bVnWKcD5ztefDZZlWUDAtu3i73osg8FwZExIucHw3HHnwZ9L5QXLslZZlnW1ZVkLlmWVLMt6wLKs12vvfxbYe/DPfzwYFrbnqI3YYDAYDAbDMY1lWe+xLOs2y7JmLMsqW5a1xbKsD7XZbo9lWT+1LOtCy7IeAIrABw6+N2pZ1g2WZeUPHueLB7c7LFzdsqyNlmX97GDdmYJlWb+2LOt07f3PAv948M/dWtj74ufnDhgML26Mh9tgeO5YfPBnEsCyrDXA3cAE8PdAHvg94DrLsi61bfta4Bqanu0vAv8F3ATkjuqoDQaDwWAwHMt8CHgCuAGoAa8D/tWyLJdt219zbLuS5nrjG8D/A56yLCsM3AYMAF8CpoC3A+c6T2RZ1quAm4EHgc8BDeA9wG2WZZ1p2/ZvaK5tVgBvA/4YmDu4++xzdcEGw7GEZdv2Cz0Gg+GYwrKsdwPfAl4NPEozh3sj8HUgBiyzbXvcsqxbgV7gZNu2ywf3tYC7gB7btlccfG0xzVzwP7Nt+wtH92oMBoPBYDAcK1iW9VXgI7ZtW9prQWdYuGVZPwOW27atR93tAUaBi2zb/rn2+p8A/wS80bbt6w++FgAeBlYB59q2/auDa5ingF3AxfZBEXGwFs0TwA7bti84+Nqf0vRyL7Fte89zexcMhmMLE1JuMDx7bqVprd0PXE3Tg/36g2K7E3gVzVzuqGVZ3ZZldQNdwM+B5b9DRXODwWAwGAwGAHSxbVlW/OB649fAmGVZccfmu3WxfZCLaEbj3aAds0TTA65zArAc+D7Qpa1twsAvgbMsyzLawmBwYELKDYZnz0eAbUAceC9wFlA++N4ywAL+98F/7eilOcEZDAaDwWAwPCsO5k9/jmbHk5Dj7TiQ1v7e3eYQo8BO+/Cw1x2Ov5cf/Pkfv2U4cQ6m1hkMhiZGcBsMz57fSJVyy7Kuoxkq/n3LslZyKHrkCzQ92u1wTmQGg8FgMBgMTxvLspbS9C5vBf6EZtRdBbiEZv600+P8u1Qkl2P9GfDIEbYxdWgMBgdGcBsMzwG2bdcty/oUcDvwUeCqg29Vbdu+9YUbmcFgMBgMhpcwrwP8NFPa9smLlmUdVvDst7AXOM6yLMvh5V7m2G7nwZ+Zp7G2MUWiDIaDmDwLg+E5wrbtXwG/AT4BZIBfAR+wLGvAua1lWT3O1wwGg8FgMBieIfWDP/UianGalcOfLj8HhgC9bWkA+EPHdg/SFN1/allWxHkQx9omf/Bn4hmMw2B4SWI83AbDc8s/Aj8G3k0zx/su4DHLsv4fzaqefTRzrIaB41+gMRoMBoPBYHhpcAvNEPKfWJb1DSBCUyjP0Gzz9XT4Bs3ovP+yLOtLwCTw+0Dp4Ps2gG3bDcuy/oBmW7AnLMv6Fs1aNEM0W4hlaHrcoSnOAf4/y7J+AFSBn9i2LULcYHjZYAS3wfDccg0Hrb80q3ueBPwtTQHeRXMCfBj4Xy/Q+AwGg8FgMLxEsG37KcuyLgP+D826MVM025TOcii97X86Ru5gf+2vAH9EMw/7O8A9wH9zSHhzsD3YqcBnaIr0yMFz3k9TuMt2myzL+gzwQZpV0F3AEg55vg2Glw2mD7fBYDAYDAaDwWBowbKsTwBfBIZt2zZdVQyGZ4kR3AaDwWAwGAwGw8sYy7KCjn7eAZoReW7btle8cCMzGI59TEi5wWAwGAwGg8Hw8uYay7L20Wz3FQeuAFbRzOU2GAy/A0ZwGwwGg8FgMBgML29+DvwBTYHtBrYAl9u2/cMXdFQGw0sAE1JuMBgMBoPBYDAYDAbD84Dpw20wGAwGg8FgMBgMBsPzgBHcBoPBYDAYDAaDwWAwPA8YwW0wGAwGg8FgMBgMBsPzwNMummZZlkn2NhgMBsNRw7Zt64Ueg+HYwqxVnlsikQg//OEPOe+88wCwbZt6vY5lNf9r1mo1rr/+er72ta/x5JNPks1mMbWBDAbDy4mns1Z52kXTzCRmMBgMhqOJEdyGZ4pZqzy3jIyMcO+999Ld3d0ipBuNhhLdlmWRTCa59tpr+clPfsI999xDJpN5oYZsMBgMRxUjuA0Gg8FwzGIEt+GZYtYqzy2XXnop3/ve9wAOE9yNRgOPx6OEN8DExAS3334711xzDb/61a8oFApHfcwGg8FwNHk6axWTw20wGAwGg8FgOIxzzjlH/W5ZFpZl4XK5cLvdLWJb3hscHOTtb387//qv/8pVV13FKaecgstllpoGg+HljfFwGwwGg+FFifFwG54pZq3y3OHxeHjkkUdYtmxZi3db92jryOuS491oNKhWq9x000184QtfYNu2beTzeZPjbTAYXlKYkHKDwWAwHLMYwW14ppi1ynPHK1/5Sn76058SjUZpNBrYtq281bpn27Zt9Z5t2y2CXHK9M5kMP/7xj7nuuuu4//77yWazL8g1GQwGw3PN01mrPO0q5QaDwWAwGAyGlwennXYafr9fiWgR141GA5fLpYR1o9EAUMIbDglyEeiJRIL3ve99XHLJJfzsZz/juuuu484776RYLL4AV2YwGAxHF+PhNhgMBsOLEuPhNjxTzFrlucHlcvH973+fN7zhDSo8HA4VThNvtiAebt3jrXu6dcFu2zbj4+Pce++9fO1rX+OBBx6gXq8f3Qs0GAyG5whTNM1gMBgMBoPB8IxYunQpS5YsUX/rxdJEWDtzuXWhLSJd/pb3RLiPjIxw6aWXcuONN/Kd73yH1atXEwwGj+o1GgwGw9HCCG6DwWAwGAwGg+K4446jt7e3RSTr4eK2bVOv16nValiW1SLEdfQwc31fCUsPhUK86U1v4o477uAf/uEfOOuss4jFYkfvQg0Gg+EoYHK4DQaDwWAwGAwAuN1u1qxZQ2dnZ8vruuAWjzc0q5K7XK7D2n/pnm7dI+70jjcaDUKhEO9973u56KKL+NnPfsZPfvITk+NtMBheMhgPt8FgMBgMBoMBgI6ODlasWIHX6z3MQw0o77Z4qUVo12q1FlHuFODOgmriORevN0B/fz/vfe97+epXv8qVV17JmWeeicdjfEMGg+HYxghug8FgMBgMBgMAAwMDrFy5siVvWxfQIsIlpFxCxPVwccGyLOr1ektRtEajQb1eV/ndeji6x+PB5XIxPDzMZZddxo9//GO++93vsnTpUnw+31G/FwaDwfBcYAT3ywSPx2MmK4PBYDAYDEfEsixGR0dZsWKF8kSL91mKoImo1sW1U4zrxdN8Ph9er7dFwIuIF0R06950l8tFIpHgjW98I5s2beILX/gCJ598MtFo9GjdDoPBYHhOMHE6LxPOP/98TjzxRDKZDBMTE/z6179mfn7+hR6WwWAwGAyGFwk+n4/jjz+ecDjc8rpecRxQnmldgOsCXRfcOvrrsr/uHXeKfPk9EAjwvve9j4svvpgbbriBW265hV/96leUy+Xn83YYDAbDc4Lpw/0ywLIsrrzySt7xjnfgdruZnJzkscceI5PJ8Oijj3LNNdewY8eOF3qYBoPB0ILpw214ppi1yu9GIpHgu9/9Luedd95hxdCcHu56vd7imZZe3CKgPR5PW/Gte7L1omrtRLuc2/n6nj17uPPOO/nud7/L/fffT61WO9q3ymAwGICnt1YxgvtlwFlnncVXv/pVVq9eTaPRoNFo4PF4sCyLQqFAMpkklUrx4IMP8rWvfY3HH3+cRqPRUgDFYDAYjjZGcBueKWat8rsxODjIAw88QEdHR8t6AVB51yK4q9UqLpcLv98PHBLPgMrJFmGu9+7Wxbds73a7W3K/RdwDqnib3s9bxjA3N8ftt9/O5z//eXbt2tWSK24wGAxHAyO4DXg8Hj72sY/x93//9+rvdhOiTG5ut5tKpcLDDz/Ml7/8ZZ588kmy2SzT09MUCoUX8lIMBsPLDCO4Dc8Us1b53Xjzm9/Mt7/9bZVvLT9t26ZWq9FoNFqKqElIt9vtVkXUvF6vEskigHXvNhzyVLvdbmq1Gi6X67Bq5E6xrq9ddK+5bdtUKhW+853vcNVVV7F9+3by+fzRumUGg+FljhHcBsbGxrj66qtZv349+XyecDjcMtE1Gg0qlQper7dlUgVUONjWrVu5/vrr2b17N5OTkzz00ENMT0+/wFdmMBhe6hjBbXimmLXK78aVV17JO9/5TvW3LpCdxnrd2yziWgS2VBvXe3DrIl4PTxfcbjdwSJwLetE2ed/lcikDgAh127aZnp7mhz/8Ibfccgt33XUX1Wr1+bhNBoPBoDCC+2WOZVm85S1v4T//8z9xu93Mzs6SSCSwLEtZoaVlh0xk8n2wbRu/398yMbrdbqampti0aRMHDhxg165dXH/99Sb/22AwPC8YwW14ppi1yrPH4/GwZcsWRkZGqNfrhxU9cwpevVq5CG5dWOuCu10uti7gdU+2vCbo6xL9PfGA6+MRz/e+ffu45ZZbuPrqq7nnnntMqLnBYHjeMIL7ZY7f7+eJJ56gWq3i8/no7+/H5/ORz+ep1WokEgkVEiaTop4rValUVG5WuVzG4/GoqqL1ep1SqcS+ffsol8ts3ryZr3zlKyr/W8/FMhgMhmeDEdyGZ4pZqzx7jj/+eG677Tb8fj+1Wq1lzodDgrtdsTQx3EvLL9le1hPOcHFdROvi2vm+LtCdv+s/ZVy6N71SqTA1NcVdd93F3//937Nz505Tl8ZgMDznGMH9Mufkk0/mnnvuoVqtqrYaYkWWMPJGo6Gs0PrkWSgUCAQCPPzwwyxbtoyuri4Vfi4TnNfrBZqTXLlcVuFku3bt4p//+Z956KGHKJVKzM3NUSqVXuC7YTAYjjWM4DY8U8xa5dnzx3/8x/zt3/6tWhvAofZfegE0p0Hd6cmWdDX9GCKs9XB0Pdxc//1IbcWkaJvuRYfW3t+yDdCSR16v1/nBD37Av/zLvzAxMWFyvA0Gw3OGEdwvc375y19y1llnHdaqQwqMSEi5WKF1K7Me/iWTpojtSqVCIBBQlu9arYbP51PH0C3OTz31FD/60Y/4zW9+wyOPPMLs7OyR23csAvzA9ufzrhgMhmMFI7gNzxSzVnl2eDwevv/973PhhRe29MeWtYBukBf0dDN9ez1P21nsTLzfuqiWY8k2euVy/VzOcHQR9rpIl/fFUOA819zcHN/73ve46aab2LRpk+njbTAYfmeM4H4Zs2HDBj7ykY/wzne+8zALshQaqdfrqrAJoCYwZ+/LUqnUMnnJpCcVzXVLs0y01WpVncvn87Fnzx7+9m//lk2bNhEOh9m/fz/1ep1UKtUccDfwHSAIvBvYe9RulcFgeJFiBLfhmWLWKs+OpUuX8qMf/YgVK1ao15w52/Ka3sJLBLdze92LDa1i29kZxZl/rffg1g34glP4y2t6BfV26F713bt387Of/YwbbriB++67z/TxNhgMz5qns1Y5PHHG8JLgD/7gDzjzzDNb2n7pOVb6hCY/Jf9JD+2S/G+pYi6TpBxPvOROq7e0B5FjAgQCAbxeL7//+7/PyMgIxx13HBdffDFLli+Bm4CLgXOAn9AU3gaDwWAwGJ531q5dSzweb5njASVg9ZxqqQyui3BobTUq20r+tt4FpVKptPTZdtZ9aVcFXcc5HhHzHo+nxQsu4eX6Ncl5lixZwvvf/36+8Y1v8JWvfIVly5Y9L/fVYDAYwAjulyTHH388J598MmNjYy15TdLvstFo4PV6CYVCajK0LAu/368EcrFYbMmFKhaLVKtV9Vq5XG4JMdcLlZRKJdxuNz6fT/XmlIJs9XqdX/3qVwwODmLbNul0muS3k3CSdgFrIXxXmGAw2LaYisILxJ7XW2kwGAwGw0say7JYt26dKqQqIrbRaFCtVtVrevVxQPXOlmg28RKLAd9ZXVze8/v9qriaHhIuTgHJuXbmbOueaxmf3opMz9cWYS2Gf72gmm5MGBkZ4W1vext33nknX/7ylxkcHCQQCDz/N91gMLysMIL7JYbb7eY1r3kNr3jFK4BDE121WlVW5WKxSKlUOswTXavVWqzFlmWpCuZ+vx+v14vP58Pj8RAMBvF6vVSrVQKBQIulWvegizjfsWMH27ZtY2RkhPXr15NMJuno6CCXy3Hq509lbOeYuobe7b380Y1/xLve9S42bNjAqlWrWLRoUesk6AY+CvwnMHSUbq7BYDAYDC8xenp6WLVqlRLK0BS0UnSsVqu1CGLn7+Jd1uu66GJYbx2mh3UDqjWpLuThkNdbRHc7T7d4zeUcep9vQT+n5Ji36wMeDAZ517vexb333stf/uVfcvLJJ6suLQaDwfC74vmfNzEcSwwPD6tCaXpYmFQUl7zrWq2mRHOpVFJVzGu1WkuFUfGK670xxYqth6LLefTQLZfLhc/nI5fLsWPHDubn5+nt7aVcLlOr1Thw4ACpVIpQKMS53zyXA2cdoGu4i1f98FW4vW4GBweJRCIEg0Gmp6cZHx+nVquRTCZ5+KKHqX2uBhZQB/4AmD/ad9tgMBgMhmObwcFBFi9e3JJ7LfO8RKbpOdv6dvraQLbTQ8x1r7MuiHWPtXN7ibzTz+n0luueeKcgd7Yca5dTLuPRHQQA0WiUj3/847z+9a/npptu4uabb+bee+81rU4NBsPvhBHcLzGGhoY488wzWyYtfTKybZtgMHhYf0yZFPWQq3q9fpiFVyYoaTXmcrkoFovK+6y3BBGh7vf7GRgYYHR0lGq1yv3330+hUKBUKjEyMoLH46E4UWT4H4bZcMoGOsodlPwl5WWfmJggHA6zYsUKuru7uemVN9E4u9EU2wBvBBLAq2mKb4PBYDAYDE+L/v5+hoaGWsS2rAXEe+3Mt9ZbcukCWuZ/8VrrRcycVcn10HTBWWTNWUhNH4fz/M48c92L7swZ18PY9fHJ9kuWLOHDH/4wF110EXfffTdXXnklTz755PNx+w0Gw8sAE1L+EiIYDPLpT38an8+nJphSqaRysDyepn3Ftm08Hg/lcplisQigJtVCoaBCyHXxrBcgkVAzv9+Px+MhFAq1TIQixqVVmMfjYePGjZx00kmqZ3e5XCYej5PJZJienqZcLTNz/AxPrnqSutXMN7/nnntIpVKEw2FSqRR+v59YLMYZvzmDxEICDkaX+So+Ln/ocn7v0t9j0aJF+P1+fD7fb8//NhgMBoPhZU4wGOSkk06is7OzJbdZL46qoxdBA1q82pLH7fV6VUVyZ6i4bOPMtdbDvtuFhMu5dcEuoe56hXPd467jzEN3escltN0p7BcvXszll1/OT3/6U7785S/T29uLz+d7bm6+wWB42WDagr2EWLlyJY888khLCy+pJK5bcfVq5FK4xLabvblFWMs+fr+/JS9bJshKpaIKowFks1mi0aiaTAOBQEtxkkwmw759+7jlllvYtWsX4+Pj7N27l0QiQTaXJfGhBHd98C4ALrj+Ak5+6GRoHJrcA4EAk5OTdHV14fF4mEpNcc0nryEbynLpTy6l6+EupqenqdfrRCIRisUiTz75JBMTE5TLZfL5PKVS6QX4VAwGw7PFtAUzPFPMWuWZ0dPTw7/+679y/vnnqyKncKjbiMz/YrCHQ5Fuzm4nIlxF7Iog1tuI6WHjzjBv+VsX6c68b2cvbv01fY0jldR1nD27ndXUBb1Ym+wn/4rFIt/+9re55ppr2LFjh+njbTAYntZaxQjulxA//OEPueyyy5RlVp8sZfLRq3kCVCoVfD6fmkyk8Imer1Qul5XHu1qtUiqV8Hg8Ki9cjicecQlH83g8VKtVarWaOkaj0WDHjh384z/+I9u3b6dWq7Hvgn0k/y55KMHBhrOvP5uN924kEAgQi8VIJBJMTEyoHPJMJkPPK3rY07mHgU0DHDhwgEAgQC6Xw+12E41GKZVKuFwuZmdnSafTTE1NMTU1RT6fJ5fLHZ0PxWAwPGuM4DY8U8xa5ZmxYsUKfvazn9Hb23uYV1vysuv1ekuEnBjqdfHqFK26+NUrljurnMsx9aKtsr8urvW1art1q1PES0i7vKcbBfRjyH66+Bb0wmp6i1WXy8XevXu5/vrr+eUvf8n999//7D8Ag8FwzPN01iomh/slwtjYGKeffjrlclmFSznzrmTSE++3Hr4lod8imGVy1bcRsS0CXQ/zKpfLhMNhNWHL/tLDW9qHeTweenp6mJ+fp7OzU4nyJMmW6+nKdqkCabZtUywWlUV9fn6e+fl5VhZW0lXs4gAHCAaDFAoFAoEApVKJSqWivOwul4uuri6WLVvGE088oXqKJ5NJ9uzZw8zMzNH6mAwGg8FgeNGwZs0a4vG48kbDIQEs6WNSt8XtdqvK5U4R7QwDl5+6p7hd/ra8LiHdcjznsZxIq1PZRi/Qprc1k+M7Rbv+/pHGe6Tx1+t1hoaG+MhHPqJyvL/zne+YHG+DwXBEjOB+ifDnf/7ndHV1tfS1hGaodzAYxO12k81mCYfD1Go1FRIuHmOxYBeLRbxer2ohBs18rnQ6DTQreGYyGWWNlvNFo1HlURdvNqD2i0QiattCocCpp57K9u3b8Xq9lB8rY33M4okvPYHL7eI1V72G1ftXQxBGR0dVSHggEFD9wTs7O9XEb1kWgUCASqVCKBRSE67H4yGdTuP3+0kkEszMzBCNRnG5XPT09NDT08N5553H/v37+eEPf0g8HieZbAp/3aBgMBgMBsNLDZfLxbnnnquM2SJCpdipXojM5/Op9qEidOGQKBWBrh+jXX61U+jq1c7FsC/n088hHuZ2DgXbtpVBQBf+QMs++vHgkBDXI/p0o8CRCrLp41myZAkjIyNceOGF/OIXv+Dzn/882Wz2sNx3g8Hw8saElL8EGBoa4vvf/z7r169X1mi9H6bf71feX/E069ZkKZLmzH/S/9YnQbGE+3w+8vm8EuzhcBi/368mZJk8pfWYnP8zn/kMu3fvJplMks/n2bdvH9FolOwZWU4890TeVH4Tmzdvpq+vj46ODqanp/H7/QQCAVKpFLZts7CwwIoVK6jX66TTaZYuXcrU1BTz883eYJlMhnA4jNfrZXp6mvPPP5+f//znuFwu5ubmKJVKDA8PMzo6Sjab5Qc/+AGnnXYaXV1d1Ot1Nm/ezMTEBIVCgUKhYPK0DIYXABNSbnimmLXK0ycQCHD33XezbNmylj7W+vyvi1E9H1sXqiJo9Qg6fR9oFarOv3VBC4fC0MUpcCT0fZxOgHaVx52h63qYuFyHM+Rcftc95HqoufNelUolrrrqKv77v/+b8fFxs3YwGF4GmJDylwmve93rWLduHaFQCMuyqNVqVKtV6vU6ExMTLFu2jGAw2JLbJOJX90broeS2bascaMuyVCsxvTcmNCuc5vN5YrEYLpeLJ554giVLluD3+1vC1MvlMtlslq6uLvx+P9PT06rKqNfrpVarMVQcwrvdy87qzhZPvExsBw4cIBKJUCqViMfjAKoa+fT0NLVaDZ/PRzKZxOVysWfPHhYtWkS1WuX6669XhoVoNEqhUCCZTBIKhSgWi1QqFQD6+vooFoucffbZ2LbN7OwsU1NTTE9PUyqVOHDgAJlM5ih/wgaDwWAwPLesXr2a0dHRw4S0XthM76+tC1RdLMt7Ymh3FiCDQ55mSW+TVLN2edXQWind6bWWbeXYutBu17tbP48zl1t3LOjb6sfQt9fr4OiVzgWfz8cf/uEfcvHFF3Pttddyzz338MADD/xOn5PBYDj2MYL7GKe/v59zzjlHhVJDa/EQfeKUkC8RwFJ5VBfiAKVSqcV6GwwG1Xvlcrmlb7fP51PF0PSQLhHKEvYtr6dSKTweD729vUxNTWFZFj6fD98iH7v+ehcH4geo/kOVZdYyQqGQCvHOZrN4vV6y2SzlcplkMsnY2JjynkuuWSaToVwuMz8/TyqVUvfJtm1SqRSWZREKhajVakQiEXp7e9m2dBuV8QqVSoVisUgymSQQCKh88+HhYXw+H9lslu3bt6vq7Pfeey/79u0zoecGg8FgOOY488wzVfFTiWLTvb/yt95bWw8Db9dnW/f+6t5pEduyv55rDYc86bq32Pm7XgRN39Z5Xvlb7+Oth487vedOz7gzP1yf448kyAX5e3BwkI9+9KNccskl3HXXXVx77bU88cQTz8GnZjAYjkVMo+JjnDVr1nDRRRe1tOHQe2gPDAyovGrxejcaDXK5nAotr1arKqdb+mtKf+1AINCSS+Xz+ahWq+p4N9xwA41GA6/XSygUYvXq1cpbLN5wyfHu6elRIdput5tCocD8/DyesIdd/76L1NoUUyNT/OrTvyLVSKn9SqWSsqJLj/Fisdic+LBp0AzjKhaL+Hw+ZUyQAnA+n49AIEAkEiGfz+P1ehkaGiIUDrG/fz83vPkGMt/K4FvZDJG3bZtQKITP51M57XLvVq5cybp161ixYgX9/f2mH6fBYDAYjknOPfdcgBbxK0Z4PZJN1hMyp0tamYR+i6B1im+9r7XufdYN/XqtGH0sTkeAvKeHs+v7OyuKO8PD9XHrrzkNCXIcyVd3jl03Auhecd2YoFdxHxsb461vfStf+cpX+OxnP0tnZ2dbj73BYHhpYwT3MUwoFOKSSy5RlmOxSustPLxeL41GgwMHDrB3715lwQ0EAsr7LCLW4/Go/GuZLDweD4VCQeViS6sNmbxe+9rXqnB1meDC4TDpdJpSqaTyv+Vnb28vXq+Xzs5ORkdH6ejoYOpfpqgsqajrKvYV+fUnfk29XleCVzzTbrebSCRCuVwmV8vx05N/yu2vuJ26u1lIrVwu09HRQWdnJ0NDQ7jdblKpFHNzcxQKBS666CIWL17M8PAw+WV5rvqDq8iFcjAE1/2f6ygPlVuqtkvOuhgUxLgQjUY599xzVW45QFdXF4ODg0QiEfx+P36//7B8NoPBYDAYXmiWLFnC8uXL1dyte5pFNMIhb7LeXtTZ31rP/XaGccvcrxv8nX2u2/W91kW8jENEsN4XWz+nHFvOI/vp2+kV0nWPt76P7t3XjQZybnnPWeemWq0e5vWWCL++vj7e/OY38/Of/5yPfexjDA4OGoO9wfAywoSUH8P09/fz/ve/X+VXy4NdemuXy2UCgQC2bTM0NKS2kYe87t0W0SwiWyaoSqWC1+tVr/l8PnK5nCrOVqvVVBESEcb6sdxud0tlU6/Xywc+8AGuuOIKNekt+pNFHPjnAyy8agGA0S2jvObfX0PZUyYajZLP54GmVdzv97N//356Bnr4xYm/4KY1N8GapgW+81udzM/N4/F4yGazzM7OUi43BXRHRwd9fX08+eSTNBoNkskkBz5zANulFWrx1Hn4hIc5YecJVKtVFhYW6OnpwbIscrkc8XicQqFAvV5nfn6eeDzesrgol8sEg0EGBgYIhUL4/X4VEl8ul5mdnVXh+gaDwWAwvFBIkVVnzrOISKnpoudoiwEawO/3KzEqXmlBDzkHVDcUOY8IV/Ga27ZNpVJpEcJ66LruPZb35aezvZju9Racc64zZ1w3DuhiWxfVeih8u/xwPQdez3nXrwOaUYLve9/7uOiii7j++uu59957eeyxx8y6wGB4iWME9zHMhz/8YQKBgMqfljxtefBL4TJd+Hq9XsrlMvV6XeVZSw62eLb1npuS5yQTj3idOzs7cblcKhRdzh0IBJQXXKze5XJZ5X57vV527dqlxuvz+Vi7Yi3u/+XGylt0jnRywlUn4LJdBKNBJe6ln7fb7SYUCnHdqdcxefKkuhc3n3MzfVv7sD93aNIqlUo0Gg26u7tVy7NkMkk8Hqerq4t3HHgH1+68luuXXQ9A91e62bhtI7lqDsuyiEQiFAoF4vF4iyW7XC6TSCTUAkIPx7NtW11bNBplxYoVxONxnnrqKYaHh5WBYnx83PT/NhgMBsMLwoknnkgsFlNCV5/n9Xxpj8fT0nNbBLjkOYt4FuGsFzrTq3/r+dTtwrqBlgri8r4ueNsVQXNWJNfFutPjLji90M5+3frY2+Vw69s6W4jqYl9/TzcEWJbF4OAgH/jABzj//PO58847+cUvfsGWLVue3YdpMBhe9BjBfQzz9re/XYltvTCZ5BW5XK6WUHBAFU6TsCo9z1nfVg8dFy9xo9EgEAgQjUbVecTKbVmWErh68TQJBRcPeK1WIxqNsnz5cvbt26eOG61HWfGNFSxetph+T39LK7LZ2VmGh4cJBAIsLCwQiURY8dgKJu1JkDm0BtPfmIaDGlYKmw0ODpJ8ZxL7EZvOPZ10dXVx1llnsWTJEgYGBrhixxVMTE7w4E0PctLWk/CN+lT4eKVSoVarUS6X1d9yXyV6QO4xHMpZtyyL0dFR8vk8xWJR9QgfGxsjEomQyWRYunQpXq+X+++/n1wux/T09NH74hgMBoPhZUtXVxfLli1Tc5gYj0X8Shi3GI/FkKyHncu8L6lpQMv6Q7bT87HFGaC3DhUvtbwvx9EFr/5TF9vyejuBrYt7p9jWf3eKdH08OjL/iwND3tevVz+XjEe/p3KvdQPBokWLeNvb3sY555zDfffdx1VXXcXs7Owz/kwNBsOLGyO4j1H+6q/+is7OzpZWGDLxiTc3FAqpCqSVSgWPx6MKigWDQUqlEh6Ph2AwSK1W45vf/CZXXHGFslTL5OlyufD7/cpLLYXVCoVCS+643++nWq2qnGYZl3i4q9UqPp+P1atX85a3vIV//ud/JpvN8tRTT5HNZol74pRTZTy9zVZipVKJaDTKsmXLqFQqLCwsqJZn54TPYcPVG/j6679OMVeEswHNOOxyuajaVQ5cfIDip4okG0mGPzrMu895N0NDQ+RyOcrlMgv7Flh+93L2PryX6lCVfD6vLPeBQIADBw7Q3d3dElofDAbxeDzMzc0RCoXIZrPqPkjkQDKZJBgMks1mKZVK5HI5CoWCigJwu93E43HOOOMMbNsmGGx685988kl27NihWqLJQshgMBgMhueCpUuXsnTpUpXupbfKknBoCSeX0HLxdMOhftVSVEwEqF4oTbYD1HayrpC1hS6I9fBt3Uuse4Z1Iez0bDtztHUB7txWR+ZYPQfdKcqBlu4sTo+/01uu30eJHpBtnAYOOcbAwACve93ruOCCC7jmmmv4wQ9+QDabVULfYDAc2xjBfQzS1dXFhRdeSKPRIJPJqOJcMqlVq1UluoPBIIDqhy2F0SQfGg4VLHnHO95BoVAgGo225ClJGzEJH69UKoRCITVBiaiWyt4y0YjYB4hEIsobXKlUOOGEE7jgggvYvn07Dz30EJ64h9mPz1LoL9BzQw8ddKhryOfzhMNhlX9uWRbJhSRjk2O88xfv5Cff/wmTT05iuQ5N1g27Ae+C4leKANjYPPyvD3PgNweIzB0supbLEQgEWH3Rah77ymNM/PsEI4+NYNs2mUxGWeLFUCCTtlQyl3x2mUzT6TRdXV3KEz86OorH46Gzs5NkMqny5fRQ9HK5zODgoAph7+vrY+PGjVQqFZ566il27NihQs+deWkGg8FgMDxTxsbGGB0dVUZyEb+611m8srrXWvfeyk/xdh8p/1oXvhINpgti4Uh51M6wbWdoeTsx3S6Mu12OtP6e7nl2zrV6oTU4vLe4fp3OEHPnNQEqxc85BokYuPzyyzn//PNVjve2bdvM/G8wHOMYwX0MctlllzE6OorL5WqpcqlPTmKZFk+p3g9bwqR1ESn51NKCS0SmHiIlAtg5cblcLsLhMHCoIqeEmHu9XvWeVE+vVCokEgne8Y53kEwm2fTgJj6d+DQzb2oKyzJlXnvda4n4IqTTaYLBIIVCgZGRESYmJgCUt3nj3Eb2pfcx455R7bsajQZul5vKcKtluGE1mPPPEZ9u5mT39/dT6C3w3RXfZXtsO9bHLSI/irD6Z6sBlLCWe1QsFlXvcSnMpluufT4f8Xgct9tNOBxWvbt7enrweDzKEFKv14lEIng8HsLhMLVajUKhgN/vV9Vfh4aG6Ovro6+vjxtvvJFQKMS6devI5XLs37/f5H8bDAaD4Rkj7TsjkQhwqEWXXuhL9zbrIeG6mBbBKHOWLm51j7R+nHa9reX4epst2c4pXkW46p5hZw0VXYA7j+F8T0c3DLQT8M4e5HLO/6kautPL7swfd4bhy3V0dHTwrne9i3POOYd77rmHO+64gyeffPJpfsoGg+HFhhHcxxixWIyNGzfS29uLbduUy2VcLheBQECJTQlhktxhmUjEUyziWoS1TIri+ZZ9RFyKeK7VaoRCIdX+wunF1idNr9eLy+Vi37599PX1EQqFVDgVNMVsIpGgu7ubz6/4PLOBQzlLO07ZwU9CP+GML55Bd3c31WqV/v5+Go2G6qUt/bQ9Hg/RaFRVT5WJq16v4/m/Hhq1Bo2/bU6IH7/j46zcs5J9B/YxODhIPVDn8yd9nh2xHQDYbpuH3vgQ1XqV0+49jXg8TiaTUR5rv99PPp+nu7ub+fl50ul0y4Rbq9UoFotEIhG1eMlms+zZs4dQKERXVxelUol4PI7P1+z5ncvlSCQShMNh1WIsn89TrVbVPbNtm87OTsbGxvB6vWzYsIH9+/dTKBTYvXs34+PjxvptMBgMhv+ReDzO8ccf3yL8pECqzFt6P21dRDvru0h4tISU6+LSmVutp6jporedd13fzunNdnqPnV52PaTcKW51IazjzL3WOVJRNqfXXX9fjAi61xwOha87w9CdY9CPPTo6ysjICKeffjqbNm3iRz/6kan5YjAcgxjBfYxx6qmncskllxxmFYVmuwmZAPVe3BLGbVnN1lVwqCWYhIjL75LTpRdLyWaz7N27l5/85Cd85CMfYXh4WIVSA0rsS36yeNO9Xq8S24CqZhoOh1URsoWFBT4f/Ty3+29nxmp6bX15Hyd9+yRSqRTd3d2qEvv4+Dh+vx+Xy0UsFmN+fp7+/n5KpZK6TvHg1+t1/FU/0SujBMeCvDb4WlbmVhKJREgkEszPz7NodBFvffCt/MNp/0DVXwUbErsSjPx0hFq8RiqVUuH1kUhE3ZPp6Wni8TixWEyF8QMqXF9+tyyLrq4uli9fzubNm1s+o6mpKeX9lyrsMu5wOEw2m2ViYoJcLgedkEqnWFhYYGxsjHw+T39/P9PT05x22mnq89qyZQsTExPqOM7vh8FgMBhe3nR0dHDccce1DbsWY73uNZY0KF046/nacgw9dFoEuDMUXU85EwO8RH1JfRmgZRu9kJouvqE1B1oKturjd4pawRniLdegv6dHDOrndwp459ic98EZNu+8J+0837pwl22Hh4fp7+/n1a9+NTfddBP/9V//perCGAyGFz9GcB9D+P1+1q9fTyQSoVKpqP7Ztm1TKpUolUpKyIqwLZVKLb0wZRIVUSZ52fJ7OBxWbUJEBHq9XlasWMGnPvUpyuUy2WyWYDCoPNm2bSvRXq1WiUajapIREaqHjWUyGQCCwSDBYJC4K87PF37OBfULKNaKnP3Fs/HN+wh3hVmyZAkLCwsUi81cbDEQ5HI5enp6qNVqdHd3q8lNxmxZzerosUCMjds2snJsJdFolHg8zuzsLJZlUSlXGN03ymsmXsPPX/9zArsCvP6rryfXaBY4C4VCzM/P09XVpfLP5B6WSiX8fn9LT3Op+q5Xbhcv9oknnqiKoOVyOXp7eykUCuTzebLZbMv9iUajuFwuBgYG2BneSf2qOq6PuggFQ6TTaWXAKBQKxGIxRkdHVeh6vV4nl8sxMzPDjh07qFQqqlp6u4WHwWAwGF4eWJbF2rVr6evrOyzUWYSv7tHV24PqXmo41JXD6XWW8zjFpV5ETDzSumdcT8+S84vXW6/2LUYBwSlg9bBvZ/E1PSwcaDmnoIfVyznldRmT7uH+beHrzrE4vdj69s7zOY0Ksm8oFOKyyy7jwgsv5Oqrr+bee+9l//79RngbDC9yjOA+hujq6uI973mPmnTC4XCLZToUCqlK4hLOJIXSZBKRauEiXL1eL7OzsxQKBQYGBpTHViZIOYbeQ1MP25Jz69513VIsIen5fF69L+HmQqlUwjfr49QfnMq+2X1EkhECsQCWZbFp0yYCgQAdHR2EQiFyuZzKhRajQzabBVAh9vJ7KpWitqzGzW+5mdnILH+y+0+w7EO56tBcNHT/upvORzpZunMppWBJhd7LdZfLZfx+P6FQqCWU3VnYRYwVYpAQ40M2m1Ve6UQioQqyQdOIIl77QqHQvBcH7+Wevj3s/OROaj01Mt/OsP/r+1myZQmpVIpQKMTIyAihUIhwOMz09DRut5vu7m58Ph8jIyOsXLmSUqnEwsICyWSSVCrFzMwM8/PzJgTdYDAYXma43W7OOuuswzzU+nwgIrTde+Jx1oWhnjuti3Wnh7mdoG/XplTOowtZ2UbWJc5caqdQdeZV6+OR1+X87cLFZXsdOa4IW6eRoV2oub6dM8TemZvuPGa73/VrCIVCXHHFFZxzzjnce++93H333Wzbtg2DwfDixAjuY4jXve51DA4OKrHndrspFostbSakj7a0/ZJwcqlKLgXILMtSRboCgYDyRFcqlRahLMeTyU4824IUA9PDy2VSlPBur9eLZVkq9NyyLFUkTAR8JBIhtD0EWyHVlyISieDz+QgEAsRiMYrFohLAqVQKr9erxhoOh9VkLXlctm3TubqThSsXsI+3ud2+nXq4zp898GeUSiXVriuVSjXDstbUqO5qVlAVD3Y4HOaUU05h586dlMvlFk9xV1cXhUKBYDCIz+dT7/t8PtX/W+5bo9FQrcRSqVRL2FsulyMWi+H1evF6vfT29pLJZJjqmOJnv/cz0j1pAErxEg+89wGsb1oMPDaA3+/Htm38fj+zs7MMDg6qquuNRoNSqaQWL0NDQ6xevZq5uTkAUqkU9XqdzZs3s2fPnqP19TUYDAbDC4jb7eb0008HDkW8OQWm7t3VBej/VMhM9nUKV6AlTU3fVi+UJmlQ0n5LzgXtq4/Lz3b51E6PtRP9NVnb6KJd1hL6/XEaHtpdvzNMX79ep8fd6bl23lfdoNDunspxh4aGuOyyy9i4cSObN2/m+uuvV8VlDQbDiwcjuI8RvF4vf/3Xf628n263m0KhoFp/2bbdkkMMUCwWVRVsr9erBKFMnMVikS1btrBhwwYVRl4oFJRQLpVKJBIJ4FAfTX1ykjFYlqUKl+mFUcSTro/N5XKp0HfdymzbNoFAAJfLRTAYJJFIKM+y3uZM2qD19fVRLBaxLKvpyT4YIq/6fjZqpK5NYa84OEFZcFfvXfjW+fi9yd8jGo2Sz+eJJqLseu0uZt88S/rtaZZ+dimVuWZIvsfjYdu2bcqiLyI3EAioiVDupXiX5+bmyOVyzM3N0dvbSy6Xw7Is4vE4ExMTqghcOBymXq8T6g7xg4/9gNd96XV4a14ef/xx8vk8XQNddN7USfo9afCA1bDofrSbgZ0DLCwsYFkWY2NjVCoVFdIfiUSYmpoiGAyq8dZqNbLZLNPT0/j9foaHh2k0GnR0dJBIJFi9ejX1ep1du3axf/9+tQAyHnCDwWB4abF27VpGR0cPE9p68U+nZ1rvfe2sug2HhLgIQDGEtwv9lpxtPS9cF/4y/+jjE5y500f659zXKXSdnm7ntu3ErYxfji9rIGdIuvOn/K737dadAu3GJ685Q8r1bZ3GEdu2WbRoEUNDQ5x11lncdtttfP/731edVQwGwwuPEdzHCJdffrmqci150+l0mng8roSsz+dTIkuKmlmWpUR6IBBQ4lu82MPDwyq3GJoPdJkkY7GYCuMWj7nkiOuiW0S09MqWXG7xkMsxy+UypVKJQCBALpfD4/G0tNcS0RyNRtU1lctl5VUPhUIUi0UCgQB79uxhZGQEgGg0qrz1hUKB888/n0cffZTSm0tkrstQX1YHG1ZNrOL3b/99ntr1FNVqleWrlnPz0M3cd/x9YEGpp8T1n7mec/7hHFxTzWsS4RoMBpW4rVarlMtlOjo6WL9+PfV6nUWLFvHggw+2GBzS6TTZbJZYLEaj0VDtwQCy2SyFngK//MgvmRuZ49o/upZX/sMrsVNN8VzcVWTNxBrcUTe737ybsYfGuPCGC2mEGgyuHSSdTjMzM0N3dzelUolisajumRR5KxQKDA0Nkc1mufHGG1m0aJHyyBeLRaLRKB0dHTQaDUZHR+ns7CSXy/HEE0+wfft2yuUyxWKRQqFw2OLDYDAYDMcWZ5999mE5xXBIyMncJSK6XVsrMUBLpJbMOxJ5p4tJMeTr+dpSV0aEt23bKnVND08/kujXQ9d1D7fTM6+HvOvbitCXbZytyvRQ8t8mcCW1rFgsHnaP9OvQOZI3++kYDdoZA/Trkjz8WCzG61//ei644AKuueYa7rrrLiYnJ1UPdIPB8MJgBPcxQCAQ4EMf+hDQnGxEdHZ2dqoJo1qtKhFbq9WYm5tTYgoO9cBuNBpUKhUVzi2C3babuVnieRZhL+3AoPnQz2QyKpxbRKfsKyHWfr9fbW9ZVkvBLumVXSgUiEQiqkhYsVikr6+PLVu2ADA5OcmiRYsolUrU63U6OjpIp9MqhB0gk8nQ29vbMkafz8ett95KrVYjXogTfHeQ8r+W2di7kStuv4JGo6FajBVrRR7qeAhkfrWgFq2RXp6mM9mJ2+0mk8ng8/nUvdWrlUto+eTkJOPj4ypsvFqtqoJyXr+XyVdN0vlIJ9VqlVKpRDAYZC4+x6/f/mvmFjXDvOfG5njwQw9ywtdPoLC5wNjYGB0dHazYtIJ7gvew4roV+DqbIfbi+ZdQdlnUuFwuvF6vMkxIWzPJH9+zZw8+n49oNIrb7aazsxNALYDm5+dxuVysXbuWxYsXq/z1nTt3kkqlmJubY3b2UPs2g8FgMBwbuFwuzjjjDDV/Cc76LLpHVq8irudRC7qBXhd0zmg4Oab+vl4NXAzVzjBqQcbTzissx5K1kNOg0E646mODVq+3Mxdcr1ejvydz75HEsIxL7xijj1n+OVuU6e+38/Tr90j/bPV9obluvOKKKzj33HO54447uO+++9i+ffth5zIYDEcHI7iPAc477zxGRkZUQTTxskrxr2AwqEK4xdo8MjKixJ9urZYJoFAo4PP5lHiWSVjEmlTfLhQKh7Uak3xsOWe9Xleh3xJWDihxb1kW6XRahTq73W7lZZcQdhHlUuxNrrPRaBAIBMhkMti2TSgUYnp6mmAwqLz35XJZ3RN9IrYsC/sRm3X/so73vOk9dEQ7KJfLZDKZ5mRaaPCu37yL5FSS7Ru3Y9UsTvn6KYw9NQYelHddDBR6mJwUndMrpEqVdBHqjUaDA396gIU3LGD9l8Xx9xxPJpMhkUiQq+Woplstzt6KF7tss3r1auLxOKVSiXK5zIprVhAMBlWOdjQapVwuE41GmZubY2Zmhr6+PhXyLp+nRAhs2rSJ448/ns2bN7Nu3Trl5ZYFTm9vr7KO53I5yuWyqoS+bNkyhoaGKBQKZLNZZmdnqdVqbN++nT179pjQc4PBYDgGGBkZYdWqVUpUi4B25jCLyAaUJ9spJPX6LvKa7nXV1xqyJpF/7Yqd6QKynVhvV9hM/yfbiQD2er3qup0iVz+H/rtT4OpGAef+clxdMOtea30//T7rnnj9POJ5l+1/W7i53GPnWseJjGt4eJi3ve1tnHLKKWzevJmbbrqJ/fv3/9bvisFgeO4xgvtFjmVZvPrVr6a3t1dNXiJKy+UylmWRzWbx+/2qqJgUJAOU6BLBHAgEKBQKAEp027atxJpU2M7n89i23cwnPhjK7nK5VCsyaLb18nq9LcXKpL2YFErL5/NKNMOhSUyEvYTCS765FP3q7Oxs6ckpYe2NRoPu7m5mZ2fp6upSrczEQq6TzWaJxCIcOPEAWypbOLl2ssr5fvTRR7niiisIZUOc8O8nkKqniF8VZ5VvFaVGs1K5x+OhVCqpSALx/APk83lqtVqz9VgsRiqVolQq0dXVRT6fJxgJMvlnk8y+ZRbba/PYFY9RnC+y5K4lhKthagdqnPbN07gncQ/zq+bpfryb0791Ogl/ojnuSITJyUk8AQ/RUFQZKSKRCPV6nbm5OTWB9/b2Ak1PRS6XY9++fbzhDW9gfHycu+66i2w2y/3330+1WiWVStHb26uq2vf19ZHNZqnVmn3HM5mMEuRSdV0MG11dXXg8HuLxuBLh5XKZvXv3snXrVsrl8mGhcgaDwWB44dmwYQOxWKwllFvmWKAlpFuM3no/bT1cWs/Tlue93uHEWRytWq2q9DKn19vpgZZ9nOd0btMutFp+OvPAj/QT2hdVEzGrV1F3FkHT57p2gredh13Plde92Po1tPOWO4X1bxPuYtRwXs/IyAiLFi3izDPP5Ne//jU/+MEPyOVyZr42GI4SRnC/yDnttNNUJepQKKQ8kR6PR1mMxdMtIeASFi6WaQmzktBzEW1SHE2fqETIiwc5kUgwPz9PJBJRedLpdJpYLKY83iLCRexLQTCZtGVsIrBdLhe5XA6v10uj0SCbzartRMiOj48rT3IgEFB537FYTHnLM5kMsViM+fl5yuVyS39MgGA8SOUDFdIfTvPP/DOf+8XnWJ1dTSKR4NRTT1U9uzP7MtTfWCcyFCG/Pq9acxUKBfr7+1VOvOSX5/N5Ojo6AIhEIriiLmy/TW2qpjzx8+fMM//aeWzvwcVIsMaOd+/Av9lPN9309fXhslyc8ekzuP+v7ud1//Y6aIDX71UT9eC6QW5+282s+9U6YvfE1LVJJIHP51OLgmQySU9PD5VKhUQiwdDQEKVSSeWNz8/Ps3jxYoaHhymVSszPz7NixQoltmVBIfdR7yUuxhPxmsv9aTQaDAwMsH79ei699FIWFha45ZZbmJmZoVAoqAgGg8FgMLxwWJbFiSeeqDy/eo6xLlbFaC3Gc6e31dmSS2+rJfO5zL96DrWIdGfbL11sSg65c9y6B1poN3bZXsauo3vv9VByXdjqhoIj5X+LoNU96/qY9J9yXuc91O+Nfh36ceT1drnpsq2s4eQz0cPp5Rzyt0QMWpZFd3c3l156KW984xv53ve+x+23387c3JzJ8TYYnmeM4H4R4/P5eOUrX8nb3vY2FYpdr9cpl8uq6JmeV1Uul1WosIRX6cXNREx5PB4lKPVCKHp18VKphN/vV0XDstms8lSL19yyLOX5npmZodFoEA6HyWazSpDJJK1binO5XEuBFpkgOzs7VU/q3t5e5W2XcPNoNEqhUCCdTlOtVlm7di1+v59YLKa85LKgqDfq2B+zKX62CICNzedf9Xn+6J4/Iry1OcapqSmmpqaaE9E5FvmdeSU+a7UanZ2dytDhcrlYWFhQxoxAIECxWKRMGf4WGAA+BDFixONxujd30/3tbp5835NUw1VCEyFG/vcIw5lhXN0uFY4XDUfZ+H824hn0UK6W8Xq91Go1puwpHrv0MXav3s2eFXu40Hsha59aS6PRUIJfJlipJF+pVFQ19GQySbVapb+/n1gsRiaTYenSpUQiEZLJJPF4nGKxSC6XIxwOk8/nVYRELpdTvchnZ2dJJBJ4vV6SySSRSER9p6rVKrOzsyrqwLIsTjrpJLq6ulSu9+OPP04ul+PAgQMq2sFgMBgMR4+uri7Wr1+vItmcIcu6MJM1hhj3Bae4leJpQjvPrAhUWZc4t3cWOHN6cnVDgB7xpu+vj08/hv67LkLbhag7x6Of+0gh3rpxod21O8V7uwJqgh6Crh9PF9Dtwsj1lDZ9zE7jgv63bOP1ennve9/LRRddxC9+8Qs2bdrE1q1bMRgMzw9GcL+IGRwc5IorrmhpdyUTmFTklvBsvSiaVPysVCpKbEuva6k8LfvIA1jEuXilvV6vysuuVquqv7a06JLCXel0Gr/frwqXWVazv7eEpENTCJZKJTUBSfi75AtLWLt4y+v1OlNTU3R0dKjiY8lkUvUSlxDuZDJJR0dHy6JAn9TdHnfL/bSxqdVrKlRezp3emCbzhxkajzWwv3+o+mi9XieTyRAIBKjX64yMjKhrlHDqOy+/k/lz55uF1/4f9Hyhhw67A5fLxfDPh3EVXGx7/zZO+/Zp9GR6iA5FVdE4KRgXDAapVqv4/X4KhQJdQ13c+M4bGT9+vDlut82v3v4ruAHWP7xehW5LOzjbtuns7CSVSpHL5fD7/UxOTlKv14nFYlSrVTo7OxkdHSWZTFIul+nr66NUKimhLG3YotGoMpIAhMNhFhYWgKa1f3p6WrWAk1xy+SwTiQSJRALbtlX0QCKRYN++fQwODuJ2u8lms6oIm97CzmAwGAzPD2NjY/T39x+x8JnuQXV6cfW1h+Rli5Fe1g96IVGnWNT/6SJVF8i6GJX1ji4odXHvzP2W13Qhrl+bfu52AlbfTr8v+n2S/Z1rMaeg1u+dc3/9HuvXL+jRAs5ztLunzj7qulGhXcqA4DRMDA4O8s53vpMzzzyThx9+mJ/97Gfs3r37sP0MBsPvhhHcL1Isy2L9+vWsXLmSWq3WIsj279/P4OCgEjXT09NYlqUEKqBEs3iXJfcYUF5U6dEtIl1ClWWbarWqcqSTySTBYLBZ/Tsep16vMzExQU9PD7VaTbXygkO5XHrlUHldrk0vEDI7O0tPTw/VapV0Ok2xWKRSqajWZoFAQFUAl8mmXq+ryuwyucViMUZGRnjssceax/6Ki1gtRuZvMgB84L8/gHezF1fYRUdHR/P+HF9n88bNVDuqJIeT3Jm4k1P+5RR8Ph+VSkW1QYvH43j9XnK5HKVSiWq1ysPvf5htZ207VOX8fNg6spUTP3gi0UgUj8fD0geX0vWZLgayA4RiIbUw6e/vVxXfZTERCASYnJzEX/Oz7NFljL9ivHlsG0L5EKM7R6lWqy1i2+/3Ew6HSafT6vWBgQEymQyRSIRAIMC+ffuIRCIqaiEYDBIIBFSOdqFQUOK5s7NTRS0kk0lVlM7lctHX10e5XGZ6epr+/n4syyIWi6kJXIrtpVIpyuWyMtb4fD4GBgYYHBxkZmaGWCxGZ2ezavumTZsYHz9oWLBNLpnBYDA81yxfvpyBgYEWAS1eT0EEocyzTgEr3l6Zu6Ugq+551sPHobWtmJ6TrQtHp0daz+vWQ9flePqY9P3beYXb/e0UynJeZ764cxt9W31s8lq7nGqnQNeNC+1el9d08a3fJz0/u931ylhEbDuNCPrn7rzWsbExFi9ezGmnnca9997L97//fVKp1GHXbzAYnh1GcL9I8Xg8fO5zn1MPYhHXoVCIsbExNaFJ0S7JfZZ9RSRJsRKZDOQ4kocLzQJgUmm8WCzi9/sJhUIsLCxg27YK4ZZiaclkkmg0SjweVxO0nter53JLOLhMIDIBS+6wy+VSHvJQKEQkEiEUCtFoNIhEIqoPtBgbJiYmCAQCKsdYBJ3L5SKTybB161Y1wdQKNRr/1CARSXBJ8BJGSiN0rehiamqK8fFxBlcO8i9n/AvFSDPsHAsOnHSAJy57gnX/vU6F03s7vOxs7OQ37/8Nr/ruq4jsi+D1etnwHxuYGp0iPZYGwJ/0s+iTi4jH4gwMDLCwsNA0BEzGWKg072WxWKSjo4O5uTkleiX8ul6v093dzdTUFIvvWsx5wfP49Rt/TSgd4jWfeQ2+gI9SraRafqVSKSKRiMqvn5+fp6+vj507dyov98jICEuWLFETsrRxk8iBUqmkcvnL5TJ79uxRkRISpi9CXKrDZ7NZ9Rk1Gg3S6TRTU1PEYjEikQi2bavw+0ajQV9fX0uv0tWrV5PJZAiHw1xw0QVko1kG6gPs2LGDhx56SBkBisXi0fnPZjAYDC9RQqEQxx13HLFYrEUYQmsutXiJZc0gaWbOMGwR5Holcl0o6u9J5JwcW8S0HpWm98WWujP6mHSvfDvvdTvBqYtwPRTbKfad/9qJdV38Os8LHHZM2UZ+OtufOe+bHKudMcH5GTkNB/o1y/7ONmPtQtJt224pYqenHkpxtcsuu4xvfetb/PznP1cpagaD4dljBPeLlBNPPJGRkRFKpZJ6OAKq2rh4mfP5vAodl/dFMHm9XtWmq1arKa+x3+/H6/XicrlaBLHP51Mid25uTk0MhUJBecKlDRlAIpEgk8lQKBRUTrOEf8vDXXLNJSxbvMOAspbLpAzNyumFQoF4PE4+n1d9pXO5HNAUjDIGyUWXVmVi0dWtzm63m5FHR1j6yqVKbHo8HgYGBojYET7x80/wDxv+gfTiNDRg6U1LWfafy6h6mmO0wzYPvf4htl20DYCbPnUTG/9uI0smlmDXbM75m3O476/uIx/L84ovvoKEO4HP52P37t243W6Vaz04OEij0WDz5s0q97xWq1EoFPB6vZTL5ZbcfI/Lw9htY6RraVY+vJJKpkKqlMLr9TI0NKTucSQSYf/+/aqX+a5duwiFQixatEhVqk+lUrhcLhYtWqQ+a/lc/X6/Ml7Mzs6Sz+fVdysUCqmq5iMjI2zbtk0VkstkmlEDExMTBINBYrEY3d3dTExMqB7r8j2Viueyj4SjAzy5+kkeettDvOXHb+HE2Imcd955BINBbr31Vq699lrl/XcWwTEYDAbD/0xHRwfLli1TqWZ6YTRorbbtcrlU8TOgRfDKmkKM/SKsda+0zOli8NdFuxxfvKt6wVbn8USg6+HjumB1ikfZv91P/dzO7Z3i3hl67swB19GPr4fgO4/tFN36WHThrVcwl3vv9KDr43eGrOvjkrXQkZD7q38eevi5OGc++MEP8qY3vYlrr72W+++/n+3bt5tINIPhWWIE94uUv/7rv1ZVxeXhrQtVPfdZHpSScy0ebvkdUMXHZB/damtZVkv+t1RAlwd6IpFQE6ku4MU7LaHdQIt4lvFKrrmIfPGoStVSZ46SWNnD4bAKa5eJoVar4fV6yefzyiAghdacIXLFYhHPiR6e+rOnuCFwA39w5x8QcUVaFgKJ6QRr/3ktD374QXru6eGkX57E3hV7CQfC+Db7eOKKJ9jz6j3qmNVolUc/+Si1L9cI3hUkHo/zyq++kungNKEnQtTddRKJBKFQiEwmo8LRZSI9+eST1bUWCgVisRjZbFZ5FAKBAMlkUuXMr7ttXdNAsHKAfD6vog1CoRAej4dIJKL27+vrU9EMuVyOrq4u0uk00eihvPFyuczCwgI9PT0qIkHy1Wu1GoFAANu26erqUvnlgUCAcrmsWrtVKhVVdE8MGD6fj2QyqVIXpBd5d3e3+qzK5TLd3d1K0G9as4k7XnsHlUCFq19zNed9/zxOrJxIb2+v6g3e1dVFMBikv78fn8/H/v372b17t5n0DQaD4WnQ1dXFmjVrDos0E0T0yVysezz1quS6t9sZVi3RdSIuZY7TxbYIfV3w6wJSz93WX4fDi53JWJ253Pr2Tq90u+1kW327dj91nNcvYz+Sx/1IQly2k89CL4Dm3N7pURdnwpGuyxkKL2s2cU7oY3eO1cnAwAAf+chHuOCCC3jggQe48cYb2bFjx2HbGQyG344R3C9CTjnlFNatW9fycCyXy+qhKwXTRCwBSjCJJVVEZalUUm2+JCw7FoupImcinD0ej9pfQtH9fr86n2VZLW28ZOKW9lTValVN2jIu8eBKZfR0Oq0mSj0EDWgpYibFu9LpZqi2VDmVUPVSqaRyh23bJpPJtFQxFTHtX+yn9J8lWAWP2o9yZfBKPvHzT5DNZpmZmWHRokUA9B/op/PDnfhmfCTPS/LIJx/B7XLjnneTWpVq/XBs8M35iI5HKdVKTQ+wr5eR+giNUEP1Cpe+1uFwWN1fMU6I0SOdTqvc60qlQrlcVi3PAoEApVKJUrlEzBdjenpaefOlNVmhUGB+fp6Ojg58Ph979uxh6dKlJBIJksmk8lZ3dXWp1muzs7Nks1nC4XCL5ziXy+HxeAgEAnR2dqrPS1qiidcil8tRLBYZHBxUPbklt9/n8zEyMsLMzAwDAwMqVF5C6GOxGIlEgpmZGbZt2MYdFzfFNsD84Dy3vOsWTrz9RKqFZr9wWeStWbOGgYEB/H4/w8PDnHzyyTQaDZ588kl27NjRUp/AYDAYDE1cLherV69mcHCwpZiXFFkFWjybupCUf+IZh8PDm/UwY3lPQpWhtaK4GMxlDM4e37p32Zlbrnt/neJbjq//lN/b/a3/c3qmnR5pp9dZ95bLds52pE7BrZ9bNwQ4Q7/bbeu8t0cyBOifie5J1/eV8eufbbt7rW+r53qvWrWKlStXcvbZZ3P33XfzH//xH8zNzbW9BoPBcDhGcL/IcLlcvPe97yUWi+F2uwmFQirsWLzd+XxeVRl3Fi2Rtl35fF5Vvw6FQiqfGg6FGLvdbhKJBPl8XuV25/N5Fe4s5ysUCi0CORAIkMlkVKi3FF8TfD4fuVxO5YIBZLNZNSGUy2WVByZj8vl86loDgQDVapV8Pk8gEGgpxibHLRaLLF68mGw2S0dHh2phVqlUmpORBeUby7Dq4KAs2DK0ha9v+DoX7b6IwcFB5ubmVJuxyv4KdrfNLz7/C+qhg+HLfY4Px4boniinffY0ivNF6vW6qvYdDocJBoMq/N7j8dDf3w+g7lOxWKRarRKLNXtqDw4OkkqlsG2bSCTCwMCAqvpeq9Uoe8rc+7F7OeWeU6jcXVGh2NK2ze12093dTTqdxuv1smzZMkqlEgsLC6rwmRhm5L7WajUV6SCTaS6Xo7e3V1WwTyaTuN1uduzYgW3bLF++XBVpE693NptV3y0Zb39/P9PT0yxbtoxKpcKSJUvw+XyMj4/TaDSYmppSho61T61l66at7D1zL7jBV/PxyeIn6S31Mj09rcIWJV9fDDvDw8OkUikWFha44IILOPPMMzlw4AD79+9nz549qpaBCUE3GAwvd7xeL6effvphLaF08ecMU3YWIXP2d9YFpkSriYFdF4vyU9YReti0HiauF1OVc0hbsiOJamdUnD5eXezrgtJZmEy2aYfTe94uv1rG5wwX/21ecWeovO7ldnr05f46Pw/9eE4xrV+fM1ReF/q6wUEfo4zP4/GodaVuZHC73SxZsoTR0VF+7/d+j29/+9v88Ic/VOsBg8FwZIzgfpGxdu1aVq1apcSjy+UiFAopESHCRzzPyWSSUCikPOASJgy05Fyn02k8Hg/z8/MMDQ21CN58Pk+1WlViWryusVhMTaKAEnA+n6/F+10ul5UAlIeunnsrRdokrF0MAiKSpV2Zy+UiHA6rAmzi2Y5EIqRSKeVJF097pVIhkUgwOTmpKqbqE6jvIh+VayvYJ9pgw/LNyznnP8+h4Wt66ZevXs5keJJgNkjjXxpMrpls/TBs6H6wm2K8iCfkwb3Xzdr/tZa6u9luSxYx8XgcQIV2S4hepVKhr6+PZDJJo3GosnipVFKF5iS0fnZ2ljVr1tDZ2Um9XmeuPsfDlz7MnlP2sO+kfVxYvRBrk6XEcjAYZHZ2lpmZGUKhEMVikQMHDhAMBolGo6roWTAYpFwuMz8/TyQSobOzk66urhYPRblcZnx8XBlXPB4PwWCQlStXcuDAAZW/v337dsbGxvB6vXR1deH1elWOtxhIEomE6uM6OTlJLBYjFApRKpXYvXs3fr+f++67j5mZGbzf9OL+khvvZV4u33w54bvC5L15FUYukRHyHZdrAxgaGsLtdhOLxQgEAixevJhXvepVTE9Ps23bNqampshms+q77QyxMxgMhpc6fr+fM844o2UOdwpTEaW6N1oXtLog18OR9fdkH/1v+V0X47Im0AWtnNsZBq4fy3l8aXXq9PjqIlS2199v5/X+bZ5jpyddrt05nzi92+3209P0nO+1m5+OFJrebszOz1RPsdONDrrQd16vvCeRD7K/bmyQz1Ai9j760Y/y9re/nf/4j//gzjvvZMeOHcbYbTAcASO4X0S4XC5e+cpXMjY2RrlcVjnOgLISCxK2HIlEVGi3ZVlKOOuWS3mA+nw+pqam6O3tVR5EEdlSEEUepnoFS6FSqRAMBltyqSuViirCJu3BJM/YmS+UzWYpl8uqWmo+n1e54/l8Xk3G8jMSiRCNRqlUKsrgkEgkaDQayksuRb+kx7MeAuaecxP4wwCNbzRY61nLa25+DeG+sOoffvMJN3PrkltJrE+wcNzCYZ/H8luWs+7f15EZyuCOuXFtctHgUBV28c6LIUJysCV/W0LDJQ9e8s0B1cdc8p+j0ajKcy42ivz6Tb9m56k7m5+1p8FtH7iNs8JnMbR9iFKppM4n+fEiPi3LIplMMjg4yPz8PJZlqWgEGWsul8PtdlMul1W7sGAwiM/nIxaLNXPfPR5mZmZaoiZOOOEElWteKpWo1+v09/dTLBab4e+lEnv27FHpEOFwmH379pFMJtm1a5cyzCxbtkz17fZ92Mea3Bo2VDeQzCXp7e3Ftm127NihFgASOSAt4vx+vyoUKGNrNBoMDAzgdrtVxIXkvI+PjxONRpmYmGB8fNz0/zYYDC8LlixZwtKlS1X4M7QWS9MLZ4lxXMSsLl5t+1CxTWfdFafw1r3kuhDVf9erkbfzcLcT8/pPPWdaR/ck655f/ThH6tctOEW6/rvztXa54s7j6O+1834faV8xLBxpW90IonuvnSHjRzIA6MfR0xDbva8f10lXVxef/OQnee1rX8tdd93FDTfcwLZt2454nQbDyxUjuF9EDA4OcvHFF6uiZSKqhFKppCauUCikQrukKJUzREgKlYkgr1arrF+/nmq1qsKLvV6vEo6BQIBCoaCqkIvXWS/qIYXPxJMr3mp5X3KBfT6fqoQtD2oJixdjAqAs4DJOsX6LKJc+kLFYjEKhwMLCgvpbzzmHwwul1Go1eBw2fGUDG8c20tPVo8Z/3UnXccf6O2h4G+Q784d9FqNXj3LSLScRiAQIzAQo7ClQsg9VRxfPvAhpmZiSySS2bavrk/xnyY+WEO94PK6iAbxer6oon81myeaydE90s5Odajy+mo/u9KECZFIoTnLn5T5KxfFSqUQikWjue/Czr9VqZDIZAoEA3d3dFAoFAMLhsKoKb1kW4XAYaOZeS0RBR0eHEuqFQoEDBw7g9/tZsWJFS/G38fFxyuUys7OzFItFMpkMxeKh8PtAIKDG2Wg0qNt1skuzlB9thhAWCgVCoRC7du1SeeNLlixhcnKSSqXCwsICo6OjHDhwgCVLlijDkFRYl7Z2Uuk+FAoRjUbp6+tj2bJlJJNJFhYW2L9/P+Pj48YabzAYXrK86lWvAloLo8o/efbpQlgPI3bm8uo54IL+t+4J15+ruoCV+d3tdqsIPqc3Xfe+Oj3pzpZaRxKCek0XoZ2gd3p6nWHb7Tzr+uvtcqX1UHHnvXSewxnu7Tx3u/xxfTzOUPd2nnM5ljhenPfXeT5dqDsrl+viW16Tz3Pt2rWsW7eO8847j7vuuourrrqKyUlH1KDB8DLGCO4XEUNDQ2zcuFGJwkQioSzL+Xyenp4elb8rIdzJZJKpqSlWrFihJgOpNJ3NZlVlab3aqORVywM4Ho+r3GfpvSwCsFqtUq1WcblcSvTK31K8TcYi5wVaRLhtN8OoA4GA6tOsTyxS0Mu2m7nMuVxOiT/xiIuIlXO4XC4WFhaUyLIsS4Xel8tloCm4w4kwWzZuYXXHajyZ5jluP+527nnlPTS8jjAuGzqe6uDE/3UinoIHK2wxl52jt7dXeXH1MDmfz0c+n1fjK5fL6jOQ+yxeWCluZlmW8nLLBCo574lEgnA4TDqdZuUvV5Kr5Njy+1vwl/xc/sXLiZaiZF3NgnXbtm1jdHRUtfZKJBLKCNHX18e+ffuwvBaWz4KD7aw7OzuZn58nGo1yoHiA2//4dl771dfi9XqVkUVy/6PRaEuu/4EDB6hWq6xYsYKtW7cyMjKCbdvs3LmTnTt3sn//fnK5nDKapNNpLMsiEAgwMDCgWs0VCgUqlQrVapWBZQMUflBg+8rt3Nh3IxfeeqH67OS8Ho+HhYUFOjo6aDQaqh1aR0cH8/PzKqVBvrcSURAKhZT3ZGxsjGQySXd3N4lEgs7OTgYGBjjvvPMolUrs2LGDxx9/XBlwjAg3GAzHOpZlce6556o52FnzRRdtTm+1iFkRxhKxJoJXF5zt9pUwchmH7vnWQ53biVRdqAIt49X/PpIw1oV/u7BzfbxO4dtuG11gOqMHnTnh7c7lvEdyvt8mymWbdseV3G59nO3yveXa9fsinmz9nrfzWst1yXGdgt7pqZfzejweVq9ezfLly7n88su56qqruOqqq8jlcmZeNbzsMYL7RYLf7+cTn/iECtMulUoqXFpEqfQ0Fq+2z+ejp6dHFVDz+XxUKhXS6TShUEh5mQEl4jOZjKqWLeFj8vCWPtDy4Ha5XFQqFVXRWh7WUt0cmg/gaDSqvKrFYhG3262KsOkFRfQwdHlA12o1lWMeDodVH2/xwEuotd/vV8aDXC5HqVSip6dH3Tun1dXr9eKL+Sh9rET1Q1Wu4ioC1wdYc2ANJ91/Ek8Vn+Lhsx/G9tp4Z7zYXpvIgQhn/vWZ1CrNgnFWpCmOo9EoiUSCrVu3sm/fPvx+P729vUrsibVdjyYQY4R4+SVn27Ztent72b17tzI0ZDIZvF4v09PTVKtV4vE42WyWsf8eo+avcfLDJ9OYb5CyUxQKBbLZLEuWLCGfz6ue2PV6nR07dhAOh5uGErvKvgv2MblokrEvjTHgHWBhYQHbttkf2s+dn76TfHeea664hhO+dALF8aZnWDzAHR0dPP7448TjcXp6eujt7SWZTPLwww+ze/duHnvsMaanp1WOeL1eZ+3atUxNTQGowitS6VzC4OWzzHXk2PXVXbAasOD2tbdTnC2y8daNLO5drIq2yHfL4/GQSqXo6uoilUoRCATIZrN0dnaysLBAPB7HspoF4CRFIhKJqIJry5YtY+vWrViWpYoOWlaz7Vh3dzennHIKmUyG2dlZtmzZQrlcJpvNksvlfmsYoMFgMLwYGRwcZP369S352brHVI8oc3q3dc8oHDLOO0OTdW+1rBH0fUWcS5i6E6dHVa/+LfvqYly2/W1h3/o2zhZk+ra6WNavVcblFKbtROaR3nfmkrfzHutjlvPrBntn9IAulp3i/Uj54XJ9uqfamRagf67Oc8t49Pvk9Irr6y553ev1EggE+NM//VM+8IEP8MUvfpHbbruNPXv2mOJqhpctRnC/SBgbG+O8885TlmUJvZWe1N3d3SrHSgpiuVwu1WpLLxomhdPuvvtuzjrrrJYcaEDlTotAF4Gq58bqYlzyrSQHFw5NwHqYGjS95VIgRTy8gDp/o9EsdiYiSoS4GBbk/JOTkwwMDBAIBFRhNwmXluJrImTlvkBrrnv1z6tU/+zQw/3fX/PvvPH6N1L9ryrRG6IsmlrE9EXTrPzySohD9wPdFHIF5dmfnJwkHo8zPT1NT08Pxx9/PJlMhvHxcQYHB1WYdKPRLMKWyWTo6uoimUzi9/tVOzaXy6XCqmOxGLOzsyoSwefzMTMzo4wrEsYtho61V6+l6qlie2xV7Vwq2Nu2zYOJB4nmohSfKCojh2VZ7L5sN49d/hhYUC1WGfjvAayCRXFVkU3v30S+pxmBMH/SPHe9/S46P91JOBtWk2xXV5cKIxcRGg6H2bZtG8lkUkUkeDwe9V3YvXu3+lsMM41Gg4WFBQKBALFYjP7+fjo7O+l8bydbz9p66D+ABfe96j5Oeuok7JKt2qDJAqNSqeDxeKhUKkQiEeWJFy/4zMyMSnPw+/3E43GSySSxWIx0Oq3aoUn6goT4ywJvfHycSCTC+vXrWbZsGQsLC8zPz7Nnzx4VpbB58+a2xW0MBoPhxcapp57aEkasiyy9SJn+GhzeHxtQUW06urDWveT667Im0fd3erGdRgD9+PLT6ZHV08hkv3YC0nk++Vv2abe/Ljr17fQQe/13/ZjOcznPp4/NuW07j7fT0ODEGRYvwvpIIezOa5Nt9eJqci79n/M4RypYp28vx4tEInzuc5/jrW99KzfffDM33XQTTzzxxGHXYjC81DGC+0XCX/3VX6kHlExO8nDU+1qK8JD2UtB84OniWURHf3+/Erji9RPvs3hnJZdWjuF2u8nn8y3nrFaramySh53P55XYkXHq4WYy0Xq9XlXgTHK2JORMPMONRrNgWyaTwbIsent7lUCX9lZ6WJSMX+5RIBBQ7UnUREiD2kdbi2OVvWVuHryZlftXEgwGWfqfS4n9JkbfU33K2OD2uZVolPNJrvPU1BSZTEa1/pJoA6m8Hg6H1f2t1+vE43FyuRzQDOeWa5dCc4VCgUgkosKlxajx2GOPEYlE1OQpudgzMzM0Gg3V+qu8ocxjH3qMSCrChs9sIGA3Re0DlzzA1ku3wsE598CrD3BHxx2c/oXTidkxgvVg632ZKDO9d5rB0KCKlqhWq6rKtxhjJHRbrw0AKMPJwsICIyMjqhic9BJfs2YNixcv5oEHHsDtdjM+Po7/Gj/BDUGKxxfVOM7efDbxbJz5zHxLuKPcX7fbrT6nXC5HT08P6XRa/T9YWFhQofH1ep1MJqO+m1u2bGFsbIxQKIRtH2p1J/8HgsGg+r+TSqXIZDKqv3k4HKajo4Pe3l5KpRKTk5PGUm8wGF7UvPKVr1SGbF1EO/+JYVS2c4ZMi2gXr7NT2Ip4l7/19/X6JmIU1z23TsEv59YLW+qiUo7nFO96qzJdpAq6KGyHvq8zTNxpfJDX5Fi611mO1S43Xa5XP2c7YdxOzDsNCs6xO3O55afzvuiRgO1wevb1cenXqn8megV0p7dbH+OaNWtYs2YNl1xyCXfddRf/9m//xt69e484FoPhpYYR3C8CFi1axAknnKCqgItnWMSOZVlKoIbDYfUwk4Jkfr+fUqmkhHqtViMWi7F06VLcbjfxeFwJaslByufzSggHg0HVoqrRaPbNDIfDSqhLlWsRtOIhlPxuyQmXfeWhXK/XVUE2mVD1fuLyezKZZGJigk2bNqlxjYyMqIlc7sGuXbuIxWIEg0EikQi1Ro2HNjxEPVnH5W61yvu9fnqu6GHqB1PY3mZbMM+0h5G/PnTcYqbI0PYh/EG/6ikt3tRIJKKuPxqNkslkeOKJJ8jn82rh4fV6VesrEcIi/uS6JAdZxHitViOVShGPx/F6veTzeRVqHgwGqdVqTE1N0d3dTd9wHw+/92ECXw+wylrVsjBJD6R5+C8fphwtUxoqcd8X7uP0Pz2d+fl5lvx8CXsu2EOx82AV9ZqLE24+ga7OLrwFL5f8+yVc/eGrWRhYgB8BfwIdvg6KxaJq8SWTfH9/P5ZlUSqV2L9/P6lUSo1DQvklRO24445jYWGBRYsWMTw8zOLFi9m7dy/79u3j8ccfZ2FhgdnZWQqFAuG5MLwOrNst7CU2p247lfNvOx8vXnbN7VLfa/muhcNhFU4v9Qwk9cGymu3S0um0iuaYmZnBsizVLq+7u1sVipM6AfF4nEajoXLVJQdfFn5iXMjn8/T29rJmzRrq9TqveMUrmJmZIZVKkc1mefTRR8lms0f5qWEwGAzt6enpYe3atcqrDa2CSOYRXXjL63CoC4ozn9sZaq7vo4dyS+Savr3Tw+vs2+30KutCU7bVBWM7MdtuXM7tdPGp4wyp1rfVaeexFnSRKhEDTjGuh3fL+Zyecuc+YrCQz1Pe1wvZ6eN3RhyI8Nar0Lfz6Ovh5LKGk+gIZwE1/Xr1Yx7JKy9/r1u3jtWrV3PppZfy7W9/myuvvJJcLmeixwwveYzgfhHw/ve/n0Qiofoni0dVhLR4SsUrKg++UChENpvFsg612XC73Xi9XkqlEl6vVwkL6UUsYdjS3kmvbi4PykQioQqRlctllcMtvaVFiEg7rlqtprzggBLSMtnm83ls+1A/Tsm/Bdi5cye33norU1NTLF269FD16oOitlxuVq8eGhpicHBQ9ZzGBfMXzHPbG28DwPqphXVDs8K2bdssWbKExbXF7P/Ufh7/1ON4ch5WvmMl3b5uSsGSyvGtVqvNomW9OXITOULuEIlEQuVVyyQQjUZZsmSJykHu6upSHle5NpfLRTabVceF5kQ0OjpKuVxWHuJarcb09DS2bTM0NEQ4HFbFwvL5PGeccQZzxTmeesdTTL5uEutCi+h7o6yx1xAIBJicmWTH53ZQjjb3wYLMaIY9H9rDqT86FZfLxev/6vXc+Dc3Ug/XOf/b59O7rxfb05zAyxNlNrx/A7e/73bsd9i4XW48IY8SspOTk0rESqpCPp8nm82qnGiPx0N3d7cyiECzBsBZZ53Fnj172L9/P4888gi5XI6+vj7Vsi0Wi6kCPAlXgsrFFeq31jnv++cR6gjhC/uUN0aMGrZtqwr9/f397Nixg2q1SjqdJh6P09vby/79+1VUQqlUIhqNqsVEPB5naGhItTgrFot0d3ermgN61dvZ2Vn1f0/qCYhxBFAGrYGBAZYuXUqhUKBcLrNp06bn7wFhMBgMz4AVK1aoNolOEeoUyCKs5DWZz0U0iRFSxBegjNbOY+vvy762bas0Mqfo12vB6N025DVddOqpbjJHtAs318Wy7q3Wc9nlfTm283ztvOP6vk6xrgtMmUt+m6Btt2+7a5ZzOUPH23m6dRGse7z18G9nsTXnd0EfkxhZ9LEc6dz6e3qUhPwt91qvc9Pf389f/MVf8PGPf5zPfe5z3HrrrYyPj5vIMcNLFiO4X2CGh4dZv379Yd5sCdWu1+st1cYlfFcmH+lTLeJXQrhFsIin2imWZUKVfGU9z1Y82VL5WUSM/IRD/Zyl4napVFITpVijxQMu1aPlAV4ul9W4k8kkY2Nj9PX1tYSLyeQsec7pdFrlA+XzeXadtYv733W/Cpu2f2Tjf7+fVY+vUgaFhfkFwvNhRv/vKPG9ccKEVf663Gu3201hWYFHP/QoA/cM8MrbXonf5yebzSoPdz6fJxKJEAwGyeVyjIyMEAgEVOsv3eARjUaBZqstKfw1OTmpelxLVXPJVZfPUTyx5XKZVCHFjnfvYPcbdzevzW+z7Uvb6L2yl7H9YwS8Ad70zTfxk7f+hKl1U2DD4p8u5rQbT6NSragw6fO/dD6p4RT9W/qxXJaaCPv7+8nlcoTfG6boKqrvlNfrJZvNquJkYgCSvtz9/f10dHQoo4/X66Wvr490Ok0+n2ffvn385je/IZ1O4/F4CIVCqphZV1cXtVqNZDKJz+dThfJSb07RkehgYc0CPdM9qq2XfMckP76vr49EIsH09DTRaFR930qlEtu2bcO2bTo7O/H5fAAq+qBWq5FOp1sWZJOTkwwODqrX+vr6mJ+fZ2ZmhoGBATo6Oti9e7eKwhCPOKDqCMzOzmJZlqonICH3BoPB8EKzfPlyuru7W8SRzKsy5wCHiThdNOleUxG8+u+64JY1iS60xcPdLjzc6fmEQ2JQr8mii1HdQaBfkyDrJafw1gWz08uuj8Hp6XeKY31fpzdWrr9dTreO8+92Xv12Y9JFq9O73O5+tsun1j3X+jW2e08fix5G7gxRd94X/T1Zb+i/69vLuSORCF/4whfYsmULV199NTfffDNbtmzBYHipcXjZSMNR5aKLLuL4449vmQDFs6oXNBPRLBOm3+8nl8uxZ8+elglQrwwu/a71nNtqtapCgfVe2iL0peK0LgRF6IZCIRXyLpO3eIj1B7FeBVNylgOBgNperOgej4dYLEY4HKZUKjE9PU2j0VBCPhwOk0wmyWazLblkkUiEWq41P9uyLF6x+hUqpFuEWqPRoOvXXcRmYmpcUlTO5XJRHCny4IcfJL0szVNXPMWDb3iQp556CjiUR1YqlZiamiKVShEOh1Vushgg9Dz4XC6Hz+fDsixSqRSNRkPlYFtW0wMvRfFcrmav9Xw+r4p6NRoNatUavbHelutz4cLT8KjvhGfBw+n/cTqDjw2y9rq1nH7D6QSDQZWrX61WqW+t03NXj+rVXSwWse1m/vKePXuU6JdQdsmPlmJkUgm/o6ODoaEhQqEQc3Nz6rOYn5/nySefVFZpKZgnk6gUXZNc6vn5edLpNNAUrtbHLfgyJGNJfnzBj9nRvwOfz8fu3btVm7mOjg56enpUtXCPx8Pg4CDhcFiNV4RutVqlr6+PSCQCQLFYVAYlSVUQw5JUkJdFmt/vZ+XKldTrdeXhFyOYeM5dLhepVIq5uTnl5Rfve7sKvAaDwXC0CYVCrF69mnA43PK6LqD0CDdo9XYLIrrkOSmvtRPbsp3M8RLhJsZ3EeG6s0CvQq7/rgs3Ed+6yHWOSX9PF/5H8lbr/+R87fLbna85j9HumNBaTE4Xs/rfzmvU72k7L7IucNsZTPT74Lx23bvuPKbcSzGOOP/p+fntvPPt0hL08ehpA0cyVMi/tWvX8tnPfpavf/3r/P3f/z1jY2MYDC8lzCrxBaSvr4+zzjqLYDCowqVrtRrFYlEJiUajWSRMqpbDIYtjKBSir6+PQCDQkhstglqqPOvWznA4rFpHiTfQtm16enoIBAIqZFiKsIlQk9BeyeXRRaP02BbjgIQiy0O7s7MTaK1oLnnL4sXO5/NkMplm7+yDRaoAOjo6Wvbbv38/fr+fzls6OeOrZ2A1LKyGxal/dypDm4YIBoMEg0HC4bDK2ZW2VFLVPZVKUS6X8XZ4eeh/P0RyabJ5H1w2Wy7ewr5371Mh+eLJBZSolF7khUJBhYjL+KSY3MzMDDMzM0qgSis2aeEl4heaE18oFMLr9eL1eumMdrL+xvWcdddZYINVtFj8B4vp2taFZVnE43FKpRLh2TBnX3U2HVd2QPVQ3ni1WlU5UXIdIhDFWy+V0EVIirj2+/3EYjG6urpUFIMYSNLpNJOTk8o4UCwWKRaL5HI5VfROvovFYlHlOWcyGSYnJ+ns7OSMM85g3fp1lK4okf6zNBxcE852zPLdi77L1spWduzY0bwPBwvNSX2CvXv3kslkiMfjqiCgz+dTNQBs22bv3r2qeFs8Hqezs1MVRZN7Lt9zWYCIQUKuz7ZtlZcfj8dVekUq1WzLJv8/bdsmmUwqMW8wGAwvNL29vaxateowgQXtC1tJNJqsE/QwYtle91ZK5JaeT+3cRt9PGYC1sHU45J3WC7GJKHMeW8SfHEcXqTJWXcw5vdXt8oOd++vjc3rJ9XO2Qz+fXvC2Hc7jtxOqzjE7DSG6kHcaP5xeaj0Uvt0x9OPrn7nzPrUzQjgjDuQzl2PpRfD0iAn9O+D8rDds2MAHP/hBbrzxRj7zmc+QSCSOeC8NhmMJI7hfQF7xildw6aWXYts26XRaeUclH1i8eB6Ph0gkorzR8r7kN0tos3hSbbuZNy0PbLFCSl5qPt9sCSUToQhkQAltyZWSSs2SKw6HQr9kH/kp+a1+v1/tL97wcrmsvNuRSASfz0epVCKVSrF//34AJYwl33lmZkYdV8KY6/U68/PzdHd24/pvFxdffTGXfOsSBjYP0NXZ1dJqrFgskkgk8A/78cabfSHD4TD9/f3Na8jDys+txJtsCmps6H6wm9H/HFXRASKipbCWGBkSiQSxWAyXy0UoFKJaraqw8GQySSQSoaurKZDls11YWFA91ovFYktRGSkEJ9ENrrKLDddtYNmNy7DWWXj3eJXwF9GYyWQo7C4QdoVJp9NKzMtnKt8b+dvtdlMqldizZw979+5V+e5SUV0iIObm5ti3bx+VSoVEIkE+n1dVuaVon3jCA4EAkUhEiVKpFC593js7O5VgDofDbN26lf09+5n9+CyNyKEFhb/o55JbL2G4Mkwmk1EGjZGREYLBIIVCQVWjn56eplwuE4lEVI2BUChEKpXC6/USj8d57LHHlMFDvtOdnZ0qJUGqqEejURYWFrAsi927dzM4OKjSB8QI4na7CQaDqo7A0NAQgUBAfW4SVWAwGAwvNH19fSxfvvywAmO6cHKKY1lnAIcJI6eoknlLjitCVES4XrBLF6Gyr95jW+qgOD3nTo+w7rnVPel6u1P9+E7x7Qx7ljHpodpy7fo/OZ8uQp2ecP08uvjVr0H3TuuGgCNFFujnce5zpLB8p3HE6WV3jtP5vdDP7cS2D+XhO++t7nF3jlfGI+vAI90j+ae3phsZGeHP//zP2bNnDx/4wAdYtGiRchIZDMciRnC/QAQCAS6++GLlJZUK2bq3VMRjoVAgl8up0NdSqUQymSQUCqmc6Eqloqy/1WpVCSHLstRDSoSQPLBFLEgxMwlDl5BovbKlHKtUKrWEoafT6ZaQXRGpeosy8bzLA1faf8lxRcCGQiHlfRfhZNs2kUiE3t5e1YNb7omFxbqH1rFm2xrshk02m8XtdreEH+c6c2z/q+1MvnuSmqumBGggEAAbBsYHOOFfTiA8F6bnjh5O/vzJeCyPmrir1SpdXV3KQ1qv1+nq6iKbzarPampqShlKwuGwCg0Ph8NKJAeDwRbDRygUAlD3TkR5oVBQ56qUKlh/YuGb8CmvsXwXKpUK8XicQCBAZ2en6vedSCRIJBJ4vV5Vfb5arRIMBimXyypPX96TaAYRj41Gg46ODjo6OtQ5xavu8/nUvZXvh/45u1wulZvt9XoZGhoiHo+zdu1aRkdH6ezspLe3l/iOOMGPBbH2Nyf3QCHAq296NQP3DGBZljLu1Go19u3bp0S1RGLE43EWFhaIRCIMDg6qXvFimJGIgtHRUaLRKF6vVxmjAGUIkRZvkjOeSCRIpVLk83lVzV/C5KWKfS6XY35+XoWrZ7PZFmOUIP+f2+F83elxCgaDKqrFWPYNBsPTxe12s2rVKgYHB9Vr7cRWO++pM4xYnut6uLfMeU5vtRjv9eOKUNXFn6AXPdNFnH5sOFQPRs8jln/OCCUR8Po59ZBmfbztXpPX9YJwTmGrb6eLRhmP7t1tJ2Z1A4KOjFP/TJzRAu1Cs3UBq/90inTn90DGrZ/POWbneZz58rqXWyrYy/dFPn89MkD/HPWwd92Yohebk++J3+/nn/7pn/jpT3/KRz7yEY477jgzLxqOSUzRtBeIRCLBW97yFpVXHAqFlIdULLdSoEy2qVQqqpCVCG3xvskDTYSC5E7rxdfK5bKyRIqnNRQKEYvFyGazLZZqGYucX6oxA8pIIKJSKkhLYbZkMklnZ6eqTl4sFlUlbl3cSwE3224WG5Pw8mw2Sy6Xa1ayTiRwu90Ui0UlRJzWbblG27aZm5sDmhWzGx0N9vzlHuZfOc/cK+couUqs/OZKSqWSOqbb7Wb4sWHC/xbG+6CXeq2u8n6j0Si5XE5N4mJ0kLZeeqicjE+MDen1aayyxcD4gIoIkJ8SySBGlUAgQDweb4a5H/So+v1+ZcQIhUJ0dHQoT4D0pZbPqlgsEgqFlLFD8tSlGriEkouBY+/evcpDrbfegmaEg4hWiUoQY4GE50tkBaCOMTg4yOTkJMuXL6dYLJLP59Wix+v1kkqluOuuuw599jt9BGoBqt+o8uZ73syiBxYR6Yio+yCCW4Rro9FQ4epi7JDK5/Idkcl5YmKCaDRKMBhs+b6JZ7tSqdDR0aGMP5JqcMYZZ/Cb3/yGXC5HtVolEomQTqfVtc7MzKh7IGH4UgfBuZh5JgsCZzifGBDC4TDVapVEIqGeBxLKbjAYDE78fj8nnXTSYa/rXlpnKLHuwW4nMJ1eSNlO/6lvq4tFCQ/XPZdOL+mRwqV176z+ty5aJcXNeV7ntcu5hXYi1BnuLO/rY3Cii0bndQu6CNfvkz5WZxi/8/xO0a4fW783so/cG/01HTm/85ztjBDyejuPfLuQcuf5ZI0m91EcObqgd35uen0BOdby5cv5/Oc/zxve8Abuvvtu/u3f/o3du3djMBwrGMH9AvGBD3xAFavK5/P4/X7V+1kmCAkdn5+fV4tw6b0tnkTxgonYlQepiALxQIoYgaY1VSqbi1gUS6SEeotXXESb5FaLaBdxL+JXrN9SRA1a+22KuHS73So8XoSh2+1mfn6e3t5ebLsZXl8ul0kkEio/fG5ujq6uLlXhGlCCRI4jbassy6JSrfDY/32M3HHN6tJYMPHmCepWncVfXIzf76e/v5+ZmZmm6H5imEwlQ5lyS5iZCGLxiIpwTafTxBNxqqEqT773SVZ9aZWKDigtK/HQHz+E1bA48c9PpK/Rp7ylck8l73l4eFhFL+zdu5dQKMTMzAyrV69WnmfJbx8dHVVefkAZLWq1Grlcjlqths/nU23cyuWyyjmXgnjynQPUd0AmUMnXHhkZUaH9EjY/MTFBd3c38/PzeDwe+vr6GB4eZmxsjEgkwhNPPEEqlVI58noOuR5GKIuAYDCI904vF151ISd4TmCiPAE0C+KJccnn83HOOeeQz+d5+OGHVZrAgQMHCIVCpNNparUaiUSCsbExFSUQiURUJXrJtx8dHVX/98T4VCqViEQiqsL4rl278Hq9xGIx9X9QPlOpM6D/37Btm5UrV+LxePjhD3/Y8v9b0jbaoS9GZLEoiNGpUCioVJGOjg6SySSDg4PMz8+r/xvT09P/02PGYDC8jPD7/WzYsAFARf04xbHuHdaRdYSO/K17HHWcnlDZRw8f1kWoLvBlOzmuXuFcF2LicRXDqdNwIL/rXlR9zPL7/ySenfdC39eJLmr1EG+n+NTFsn7vnOLYeV/bGRnkfjv319+X84uAb3ds5/76/WoXrt7uc9MrxuufsX6P23no241ZjwzQvyci1OV7LPufcsopnHTSSVxyySVce+21fPWrX2VhYaHNp2gwvLgwgvsFIBgM8ta3vpVKpaIeSrJ4FjEgubMul0sVcJIK141GQxUyE5ElRbAknziRSCgvtXhMG40GyWSSRCKhclBlv46ODizLUiHNMg5AebCz2awKq5WcZTm/iHI9VFoemlKh3LIsJcBkf+mLvHLlSu6++26VTy6iVLyz0WhUhSxLfvqJJ55IoVBQorRWqzE/P6+MC6v/bjUPf+1havEa2BDaG2LJ15fQsJuT1uzsrLoWKWoWDofVQqBQKNBwNRgvjuPLNlukZfIZAgMBfBEf9/7NvaTWpGh4G0Q9UZZ/aznFjiL3fvFeGv7mOe77xn287i9eR2W8afgIh8PE43Hq9TpTU1MqX14qbgcCAXX9MtmEQiElMKXtmITyi5Dt6+tTHnEJv85kMgCqAF00GqVer7OwsKAqh8v3K5/PMzw8zKJFi9izZw9+v5/h4WEVBbB48WIKhQLLli1T/V2r1So7duxQ45G+8NLGTqINoGm4kJ7l8rfVbXHdW65j3Z3r6Cx0EgqFOHDggPLi+v1+7rjjDpV2IIUE6/U6PT09pFIpFR0ihpdSqcTg4KDKA5fUi9nZWbZt24ZlWRx33HHKACRjB5ienlb/h4rFItFoVLVIE4OWhM9J9MkjjzzCU089dVgu4G9DX+zIglb3cEidBq/Xq1I4oBkVk0wmVUG7oaEhvF4v4+PjyhtvvN8Gw8uXpUuXsnLlShVZpHss24Xs6qHI8jwSY708z/RwYaeXWxdievsvXVTpzzhn/m87ga97jXUBJ2sMOZa+j6ALPBGG+nXqHmG5Rl10thOa7UStCEN9G30757F1MatfE7T3gDs9zUcS2vq16MdzesrbfS66F9x5v9qNQRfzzu+BHsLezuvtNE7o59DH0O66nfvJd+G4445jxYoVfOITn+Av//Ivue6661hYWGj5HhoMLyZMDvcLwAc/+EFisZiaEG3bZmRkBEAttMVzLJXKRbDo+THSy1oqY0vBLfFmygNKhE65XOaaa65R7bgECUsuFosqnLtWqymhr082ImxcLhfRaJREIqEe+FLUTB54jUZDtTQTcS/iMhwOKyHT2dnJjh07mJmZUaJGwrfFU6i3ZpJFw969e1WYem9vL9FolGg0Sk9PT1Ok7XBx3J8fR3A8SPTRKK/4yCsIuUOqaFsul8PlcqkQbTE4yLncHjf7zt/HA59+gMnQJPlCnspbKmz94lZ2/OUOFjYs0Ag0wA1bLtjC1rdsZdMbNtHwadb+QJ37z72fZDLJwsKC8hZPT08r4TgwMEAwGCQej6vrE2+4FOTSFz0i0sPhsAqzT6VS6jMqFouqbzk0Q9jFsKB/nlKYz7ab/dgjkQgLCwst4nh4eJh169axePFiBgcH2bdvHw8++CD33Xef6rktHmIpjudyuVTYvtfrZfny5axdu1YtmFwuF5XRCnPfniO3JMffXfJ3bA9vVwaWer2uvh/RaJSpqSmGhoaAZjqDIN97yUPPZrMqh35mZoZ0Os3AwABdXV1kMhmGhoaUAcfj8TA7O0smk1HF1vTFiBSHk/uZyWRIJpMEAgHK5TL5fF61LWu3sPltOBcr8v9ZFptSA0Dek9B+qdNw6qmnks1miUQiNBrNtm4nnHCCimAxGAwvT84++2wl9pwCWV7TRbJTKEpEl0SeiXFfF1vyrJT5RMSkM7dajquLXjjk0dS9sDJWEWcS3eNE1h7i8ZbrkePrOcPtwq114aobHZz/4FCYs1MgynXqIdsyb7cbq35+3auvH1PG5DyX7Os0lOjv68doh3Mb3RCg52vr0RD656mnzgEt6Xx6RKZzHnTmdOvjcW6r31fdOOG8t07DhThXvvjFL3LHHXfwwQ9+kOOOO66tV99geKExHu6jTHd3NxdeeKEKUxWBIl5Nv9+vQmql0JUeciPh4qFQSLVhkirh4XCYSqWiQoolRFweUl6vl3e9611qovR4PCr0V6pISriy5KjqXj09X1p/WEsOsQguj8ejcq2l0JtMThICXalUVO5wsViks7OToaEhJUwbjUZL9VJoeiK7urqUEUJfGIiFVkLO5V76nvSx/J+WE54I4y658Uf8LVVW68N1UoMpFqIL+G73MRwaVoJz4rIJdr57J7bbZu+n9uJ7zMfsFbPYXlu1EtN5KvcUHX/Rge/PfVT+sLlgiP6/KMPfGiZXyymP6sDAAD6fj9nZWXV/pSCaCOBkMqlEnoR3W5alIgrEkCHGjHw+r4wwcMhwI5EOElpdLpdbPM+ShiDttvSiZ6FQiMnJSbZu3UqpVKK7u5uJiQnl4Zb7LdES0i7L6/WqiIFarcbMzIwKcQ+Hw1SHqhS+WKDxyoOfa3eaWy6/hbff9nbCT4XV90SuN5PJ4PV6lYFGziXv5fN5wuGwqmQfCoUYGBhQ92xhYUHloEtxPllEyoLBsixisZgKZZfcdklTkO+f5KJJmoG+KHk26F4bMXwsW7aMnTt3qoWJVJyXYnlPPPGEGr8Y4ebm5kxrMoPhZc65557bIqgFmcOdxj7nXC4iRy9WJoZ8eHpiSY6tez6d4la2AdpuA4eHs+vHl2vSPdqy5hF0D7ATp5jV92n3LG93jCOFazu9uLqBW96XY+oi21lwTTdA6Os/J85ztbvH7e6ts7Wb3D9n/rTcd+e9lRRBvT6AGDtkXaYbCpzGgnbpVO1C2uW69ePo0Q/C6OgoX/jCF9i0aRO/+tWv+Na3vsWuXbsOu18GwwuFEdxHmYsuuojFixfj9XpVVW8RsiJaA4EAyWRSLfqlFZUssvWHrxROkwnVsqyWUHJ5IEoYDqBEUbVaVWHp0hdbvJV63q0UdNMFvIRcA6oat+5BlfGKoBOhLftLKHkwGKS3t5dMJkMoFFJh0eVyWVUdF1HR09NDOp2mUCgQjUaVgJT+1lLBfWFhgWXLlpHNZpuRBJubLbXS5XTLIsMT9/DQ/3qISrxCw9vA+1ov8T+O4/F42Hf5Pna+vSm2AVInpeDwWjQK94/d+P7aR3oiTd8X+pivzTPUMUT/j/sJR8MqF10qpIt4qtfrqhhWJBIhFAoxNjZGX1+fEnZ6lU+ZFKUgmIjKYDDIqlWruPfee1V+uHzWEj7udrtJJpPMzMwow4kesq73CJ+amlIeZOnLLe3OgsEgqVRKiepisai+yxL+DajrTCaTNBoNRkdHGRgYYNH6RdxTvIfdHCp40l3sJlFMsGX7FuXhsG2bUqnEokWLsCyLrq4u1f7Mtm3VjiyXyzE4OMj09DRdXV0q3Nvv96tq4tKHXSqvS8syKewnHmTxbku1e/Fgl8tlZcyQ4zUaDVKplMot/G1ehnaI8cz52vz8PJZlqbSCYrGo/g9YlkU0GlX/j6TPezabfcbnNxgMLx0GBwc57rjj1N/OUGddrLQLnXamxegirVartbSldIZqy/nkPHJMXVTJvCtrAl1Yypyhe7qdIdPtwp2dXnNdnOti0+nJd3q42wnTdpFL+nn0Ymz6s9d5LOc91j8PHf0+6Z+ffg1yPuc1Os/pvH9Oz7n+WevviTNCjDP65ynI+WXu0iM1dQGvf9/06vH6tepRDiLi230++rXr7euc1+NyudiwYQMbNmzgggsu4MYbb+RrX/sa8/Pzh32WBsPRxsRdHEVisRgbN26ko6NDCS0RtHCoQIiE3Xq9XizLUiHE8XicaDRKKBRSBZ+kynij0SCXy6mCYyLSxHMtDzEpQCGTnuR2Ay39t91uN4FAgGAwSDKZJJ1O43a7lUCRMerF1SzLUj24JSwdDuXcAEoMiSfRsiyVWyuh3m63m1Qqpaqyi8dfBGoikcDlcjE7O0s0GiUcDpPP59m0aZMS6Hv27KFWq7GQX+DJ9zzJvjX78Af9Kje9Eqzwm2/8huyyLOXeMtWOKoUNBZ648gnGLxhn99t20wi2Wrq9eS8cnCM9ZQ+umgt/zo/vFz5CHw1RnWxWNncX3CT+PkHflX10Bjrp7OxUixWv10smkyEYDDI4OEgikaC3t5euri7y+bzyxk5PT5PNZlV1eJmsRIBLKoEYSHK5HFu3bm1JVZDiWxIuLu3IxDDR3d2tFkQzMzPkcjl1Tpkkg8EgmUyGubk59u7dq84n0QUS+iffTen9HQwGGRsb45xzzuG0006js7OTcrnMAw88wI3fu5GZt8/g+aEH6jCyc4TTv3o63jkv+/btU4YGMfosX75chdb7fD7S6TQ+n4/h4WESiQQdHR0Ui0WVliHfabG4S8E9SRWQHttyrdIaT4w18/PzeL1eNm/ezMzMjPo/kUgklMc5FAoRj8dZsWKFikp4puiLznPPPZfh4WFs2+bAgQMqjE+MGmI8kP9jEgVRLBbp6ek5YgimwWB4eXDKKaeoVCwJw5W5X37qIkUXNGLMk2eS7p2U/WVNoYeSA2puk2PBIREnz3A5n56jLcfWPe/6WPVj6kZn2VcXdM5z6Oj3wyn09XM4n+HtBJ9+Ll346VFO7eYCZ5628z45x6rfdxGlToNGu/G1O/eRIhKc90H/LPTQfP0zk9ByWds5+6DrRgU5jzPc3vkZ6tu0u6Z2nwlwmAHGuf/69ev5kz/5Ex555BHe//7309HR0bKPwXC0MYL7KHLCCSdw0UUXEQ6HVbsmqcoMMD8/r7xsIqIBZR3UvYkimqXNkTz8e3t7lTdZ/ukCRiyY0vtbxJNlWYRCIRWKLP2wZdtIJKIeauKRlWJt+gNYiqTJ9enjFyEPhyyV4XCY1atX43a7GRwcZPHixeo6JY9cQq1jsRjhcFi1EIvFYiwsLLDVvZV0Nc3y5cupVqukUqlmcTZPjV1v3cXkFZM8+YUn2bViFy6Xi1AoxJOfeZLicBH0uciC3FiOerTOqqtX4S4eejjHpmJc8n8uYfjJYfxZP2d+90zW3rqWt/312+j/g37KqXJL3lgtUaPgL6jFiVRnh+ZEk8vlmJubI5fLqZBxPXQsHo+r+ybV6iX8WLybIqx9Pp+qIC5VtaHVaiwh/tL+ze12k8lklEVZT3GQUPBCoaDCtAOBABs3blRV6EOhED09PUSjUYaGhli6dCnxeJwTTjiBvr4+Ojs7mZmZ4a677uLRRx/Ftm3Gx8dVaHej0KDz452Erg7x6V9+moQvobzMkpueSCSIx+MUi0XGx8fZtWsXuVyOzs5Okskk27ZtI5VKUSgUyGQyZDIZVek/mUyya9culZ8/Pj6uFnRSLK+np4eOjg58Pp+qSdDb26uMOfPz8wQCgZYCgxLOn8lkyOVyLCwsqJSJZ4Mshm677TbGx8cJh8PAoTw58WxLNEcikWDXrl1q8SXXIAXyDAbDy5Ozzz67xQvt9G4DKo3LWWRL/sn+lmXh9XrV9npbJ2cIsoh1Xfzq3lNdkOrpabKe0MWwnp/rFHuyzmgnvPVx/LZw6Hbh807Bqo9XQuv18+j3QBfGuqDXOZJ41EW60yPt3NcptPVxOj9v/f12XnonzmPJ56PnX8s24gQSw8tvCxt3RhPI+tPpcXdGSzg/X+fx9G3ldadhSfb3+Xx0dnbyxS9+kfvvv593vetdLFu2zAhvwwuCCSk/Svj9fk477TS6u7vVxBMIBFQFcMmZlYedPMykLzOgRJkIagnblv7EgKoqHQ6HmZuba7Zf0gpCSUVnmewkNFlyiSVfSxe94l2U30WEFYtFJSZlktWFve7x1vNLJTxNrMSSwy3eRRF4uVyO2dlZPB4P8XicfD6vxi+FtaYXTfObd/4G94ib0647DRcu5dWc/eQsE7/fbDeFBRNfnCD8j2FG7hppX1SjDqPfGGXpr5c2Ba3t5ZF3PELHTAdnfudMBlIDnHfleTy16imWPryUkTtHwIWKIpB75B5xk/5MGm/My9A3h3CVm5XA5b7XajV13bKPRDrofdXle+D1eltek3tbr9dVJetQKKQqiZdKJTo6OpiZmVEeYIkOkONJXrDk7Mv3QcS6WLrlPcuymJ+fZ2hoiJ6eHmUMyWQy7Nu3j61bt6oICb3NnPQcX1hYUPdJDAalUonGfQ0yQ5kWY4S0I5PzykQqrfM8Ho+qRp9IJAgGg9i2zcLCgioWp4voRqPB3r17VR50V1cXMzMzBINBZmZmGBgYUJEAco7Z2VmWLl3a7Kl+sIp5KBSiVquRSqWUgUJC1Z8tzgWxFO3TvU3d3d0sW7aMhx9+mO7ubqanp9X/00KhQE9Pz7POITcYDMc+iUSCtWvXHhbSreMMQdbFkFNIitByhpvrxR114a2LH10YtvOo68Zj2UfWOE5hrIc+S0FNGbcu9HUvvS7gnN5pOFQtXY7vDO/WvaVOI4PzWd8uNFw/ny4SneJaP5bz89LvlfOYgvM9+d0p3nUjhn69zvxp51ykH1Pek6Jv4kyRdEinocTpvda/R+0MHUdqY6ZXuG/nqdejAfRjyva6wO/v7+fLX/4ymzZt4tZbb+W//uu/TI634ahiPNxHiY6ODj74wQ/idruZmGiKQP0hIl5kQLXGgkMhO05rZjweJxwOEwqFsCxL5WXrFkKpei2LdxEiEsL+3e9+V4lqeV1EkUxeEr6ay+VUqK7uoZcQW7GE6w9cOZaMS0SfeNTFOytFtqRSuogx8fI2Gg0mJydxuZrF5bLZbDPXtWueO99zJ3NDc0y/fZpH//BRVUjL4/XAU62fgVWzcO1t5p53fa0LV6b16x/64xDrfr2OkZERQqEQa29fy6u+/CpOu/I04lviTXGVc9F1UzP8W1qoSR6tZVngh9mvzFK/pM70GdPc/4n7qTaqKt9dtw43Gg11HyuVCqVSSU1eEq4t90smEsmr1tuHiWjOZDIqTF+J/4OGESlmJ15wMZTI7/Id1EV5d3c3q1atoru7m0AgoML0i8UiCwsL7Nq1i+3btxMKhejs7GxZUEiUg7QPE2ODTIKWZVH5ZIXy/ynzw40/VIajyclJGo1m+zrxsIuXPpFI0NnZyfj4OF1dXarImxhugBYDlRR3k1oA/f39xONxFVUgiwVpMweoez0xMcGKFStIJBIqFF/qFsj3vFKptNQpeLboC5xisQigrqejo0MZZ/SwST3qRdIvDAbDy5PVq1fT3d3d4jGGVu+1M8dZF6S6ENLrwejrCV1wQ2sVbl3cyXPKKeh0T6fTGynzAxzKGZdziEde5iwZv2wr84Ne7Evek231sTiNBc7tnWOX13Svqy7wdHQh3W47pzh2nk8Xt/r9aieW9c/FeQz9fd3IIq/pn5tzHzmO8zvi/B6IANfnpXZja2eMcUYv6N9Dp1FBxirH1A01zs9INwbpxhl9/40bN/IXf/EXXHXVVXz2s5+lr68Pg+FoYDzcR4nXv/71Suh2dnZiWZbKlRWPtd5v0Ov1srCwQG9vr8rh9fl8yrsskxagipzZtq1yuGRy0ntxyrbi1X7ta19LMBhUorhcLqs8URFoUmhLhLWcR3pp27ZNMBhUnnpoFlETj6ZMprpIlCJtUoTN6/Wye/duXC6XKtxFCJU3Ds2HqxSqikajuCNurv/U9RR6D7aJcsHuV+1mYfcC/s/6adgNku9orSTeCDWovKPCydefTG9vL/5f+/nUxZ+iRg3roxb179apn1dndnZWjW3RY4uo1+sqxD8QCChxI72mRaR2dHSQuyFHZcMh8bPwigXu+/R9rPmzNcpwEgqFWLt2LbfffjuNRoN8Pk8sFlOLnHA4rArl6d5OPVxQBJhUspfQ43w+rwrYSQExEaESsi0edvnMxNDj8/lUXnggECCbzfLkk0+q71p/fz/lcplkMqkWXeFwmNnZWRWJoFu/Ja9f0gFSqVRz4rMa5N6Xo/HnDQjAw6c+TNAX5LIHL1OTZDweZ9GiRQSDQWZnZwmHw6rXdzqdpq+vj1AopKIvCoUCvb29AOo7tLCwoCIiJIKiWq2qauMej4c1a9YwNzfH/Pw8PT09+Hw+MpmM+j+qe1EqlQp+v19VLT9w4AAdHR0MDw+zd+9estnss3o2yPGdVnkxzulREFK9Xu93PzMzc1jeosFgePlw/PHH09PTo/4+UnizHlIt87J4m2XtoUc3ybwBh1pzwuH9oPUcbz28WveYy++yztH30d+Tc0uknJxHjiPrCr1Ypax35Jgi3vRjOj3IuiB+Oq/rHnFduOronmVdoMvPdsd1XrdzO3lPL7Cpe6JlnmpnONEjGPTP1il09TWj/ppEUtq2rdYVegSBOGFkO/l8nJ+/83vijCzQPeAyXt1woc+P+j3Xj68bHPTvlaxHZIyyzQknnMC6deu4/PLL+dKXvsT3vvc91bXHYHg+MB7uo0AoFOIv/uIvlFiTolXBYFC18rJtWxVwkt7CTzzxhHqwS9/gm2++WeVay0PU5/O1TIYiYuWf/rDy+/3Kcyo52CKMdC+7hOTatq3OJ/2OxeMXDAaJRCLqQWZZlgp9tm27pRe4eGzlYShebbknvb29yvjgeYWHHdfvgFXNXsv5fB6Xy8Xk5CQHDhygUChQy9Y44dMnEJxuendpANdA7mM55ufnmZ2ZxT7Phm0HPwQb1kyt4dN7P83atWspl8v0Znr5m5v/hrN/ejZL9i3B8lpKTIm3Vx7QtVpNtUmTya1cLjM+Pt6ycOl/Tz/uLYfyg3yP+TjzH89UXgMRiHfeeSf5fF7135bJToqipVIplWvt9/vVd0VyfLu6upR3N5fLqRBxQHnPxVOdy+VUNXufz0csFmN4eFhN4LK/FAuTKAER8DLJ7d27l2AwqCZumQhLpRK5XE6Fb8v3odFotgJbvHgx2Wy26Q1uVCi/q0zjn5piG6DhafDo2ke5w3+H+r6Fw2EWL17MokWLWr576XSanp4eJicnSSaTHDhwgMnJSfW5SYV+QLWHk4k3EonQ0dGhipy5XC7S6Wbl+q6uLrLZLKlUirm5OQKBALFYTP2fkG4BXq+Xzs5O4vE4g4ODqoihhOI/G/TFpBhDvF4vg4OD6v+nhLyL0SCbzare6vr/M4PB8PIiGAyydu1aNXfrbS+BFgEm85CkKelG/na5z/Js0nO85fkv/0QgynNLBKDMBTpOD7DMSbph2RmK7RSKUkNGT+M6ksjVx6cbH/QoMKdnWBfduie+nXdZ9hOc90wXgrK/UxDr19kO/Ri6V1h/v110gvN4+r1y3jc99Nvp7ZbXpOCuLtDL5XKzXo5W3Vzek/Ho45TrFGOJnkut3w89SkMPM9e92M5jOqM79DpC+vWLAUkiPoeGhvi7v/s7Nm/ezFvf+lYWL17csp/B8FxhBPdR4M1vfjO9vb1qQV0qlVr6F0r4rRQpg+aD9YwzzmgJLU8kErzpTW9SPZvL5TKpVEoVhpI84UKhoAQioMSAPilJyyH9waIv3OU9Eeu5XI5UKoXL1SyaJqHf8k8etKVSSVUil7AhvRCbPvnovcflPhRPLpL6TorKYIXH/uYximuKalKWNmky/u6Zbk77xml0zXYx9MshfO/xtUyskUwE7+97sR62WLdtHR+59SMUC81xBwIBctkcneOdLJlYQvqbaWqfrFEql1QROhGfLpdLVYkXES5h8XIt0BS6pWSJjnd2EN4UJrEpwbJPLIMaSpTJQ97r9apCdNPT07hcLtVaS7zZ8n2QcH4J/87n86qXtliZp6enD1toScV3mdQklDqTyTA5OamKkskE5ff71YTq9/vVOAOBgLpOvb2W1ADo7e1VuftSIR+aC8ElS5aoNm6Dg4OEE2GsV1stxeqC2SAX/+xiVu9erb6Xfr+fvXv3smfPHvx+P/l8nvn5efL5PLOzs6rGQKVSoVAokM1mW/pkS8SAx+NRhcXkOnt6elqMPYVCgcWLFzM1NdWykDtw4IDKC8/n88r6vW/fPvL5PNlsVkUUyD16tuieCfl/s2rVKuURkPuqG7f0/9NGcBsML08GBgZYvHixErm6J1CfD3UxCLSIIv35oYs1p5dUN6bq4cS611NEn/PYemqcLl71tDn9Nd1DKqJLX7PIc0/mVafXV4Sf07Ot76u/rgttfR51hmbL8XUR6BTdznuprxV0Qavv7zQa6DiNJs5t9M8bDuVE6wXs9BQAZ0i+eND18HBdnOvCXP+c9Urt+vdBR793ulh3picIzigCp7FE9/br99I5J+rec93Drxs+9M+9o6ODr3zlK3zzm9/kox/9KEuWLDnsczAYfheMGed5xuVy8da3vrXlP7aImVqtpry+8nCTBb9Manq/bHm46FWoRVTJJCf5vBJuXKlUlFCTB79YF8vlcktBNWgKbQln1tsSiXip1+tEo1Hy+bx6QEt4soSnS1E0EQmWZR12nXIPoBk6Ho1GmV0xy4637KDc3SywVlpWYssnt7DoU4voqHeoquq6kF8zv4ZF1y5i639vZbY+q96DpgD2POFh9RdXs2FwAyxGiSwJw5+OT3P9OdczPzwPn4at127luO8cpz4HqXo9OztLIpFQbbW8Xi/xeJzHH39cWfTlOrvSXfT/XT8d0Q4GegbweDwqLD+dTqsK8FIxXMKeq9WqqlqfyWTIZrNUKhUVsh8Oh3G5XKpKe6VSoVqtks/nW0LQi8UiXV1dyvsZCoVUGLx4d2Vi9Hq9RKNRlf+dyWTUhBcKhajX6yoqIx6P4/f7VbE1j8dDX1+fyuFPJBIARKNR0uk02WyWV7ziFWzevFmNtVat4f+EH7toU7+8jrvm5g3XvIHjJ4+nFjjUDk++d5lMhlgsxooVK9i7dy/T09P09/fTaDRrAoRCIdWmS+6JeNpFvIdCIZW7Ho1GicfjeL1elY/tcjWrmEu1cTF0iKdbjCR+v1+ldMj3J5/P09nZqc7fbrH0/7P352GWnWW5MH6vPc9D7V3z3NVz0t1JpzMPTUKCyHQIICIcJA44oSB8Inyg4kEUPAdBzk+PggzKOaIgKLMGhRCSAIHMU3en5655V+15Htfvj537qWe/vTuCwnc07Oe66qqqvdd617ve+X7uZ+gn/a7VB4ROp4N//ud/FsWO1vJTWcf+Jos/kIEM5EdPJicnsW3bth5ABZyfx9kEjhoI6/81Q8vPmD2FJuYmADLBO4G/CZo04NKizY1Ns3X9HO6zJAP6gTEN0lknE6T2+04DbrOumlTQoJzAU78Hf19IKWoy+LreJjDV/5v+8hr09ytH97kJNC8EUs13Zxn6ftM8XUew556rWXMNflmuycLzcyp0zCjiJpPN99LnGL6PHntaAaDv02OQ44n3XXXVVbjiiivw4z/+47jnnnvwoQ99CGtraxjIQP69MgDcP2T5sR/7MczMzAig4oSvVCrCtGrfXi5EBD96U/T7/SiVSsJ4MWURTcMJaAmMuRgzujJZZYJyAmL6perI5J1OB9VqVVjdWCwmDDiBOIEHF1QGfNJmvFxENTNMn2OClrm5ORQKBUTPRhF6KITMTZmu7YUNzByZwWhpFK6wS0x6qYSgKXb9njpGAiOwLEtM9MPhcDf4XLuJ03tO45qJa+BsbPkxVyoVlFDCx179MaSj6W5nOYBTLzwFV8uFy75wGQCITzv9oQnEtVKBbVAsFreCXD1aRcfbgXvMLf3n8/nE95bKDvYBQSxNzmkRwDbmdc1mN9f32toafD4fgsEgYrGYAEH6ZafTaWEDdPA0/k2zfu33DXQBOX2ttSaaSpLx8XEkk0mJ3p3P57G2tibByarVqjDChUIBjz/+OGKxGNbW1rb8u7IODL1jCMXhIm78zo2Yyc3APeLG2bNnJeK6ZVkoFApIJpNwu91YXl6W963X6ygWi+h0OmLeHQwGZY6Uy2Vks1mMjY1JnATGFWA/JZNJCTBHUMtnOhwOyXWfzWbleyowGFiNwQOLxeL3DLQpJguiRR8ieTAii0O/RprQU4HHoHwDGchAfnTE6XRix44dmJiYANDL+GmQRNH+sxqE6nXeBIMEpWQVqeTT1+tYKya72E8BoMvWCmuTNTUVBmYwN1NpoIFaP0Cu241yIQZbl9GPqTbBLq83o6Sb7WK2L6/V5tr8jPea9THBP9vHfI4OMqfBumlGb9aF+x0ttwhuaTFHhTbrrX22tb++qQzRZ1TdLhr8mgohfQ7RbaSVNBro8z5dJ3PM8D7twqXb0ul04sorr8ShQ4fw8pe/HH/2Z3+Gv/qrv0K5XP6+9/qBDIQyMCn/IYrH48ELXvACDA8PCzhmRGr6LXHSE/hQS10ul4W9tixL0j8RJFELyCji3HD0Qub3+yWYlvbdAiDMdiQSkbzFjApNUOp2u8VvudPpiH+5Xqzp46vzB5MNJCMLbPkWEaiS8WY5LpcLpdUSZt81i6GvDMFqWth31z4852vPQTKYRCgUQrlcFrPfSCTSNV8P1vDdt34XxxaOod1pC6gsl8uodWqwf9VG8S1FfPDVH8STsSeRTqexsrKCWCyGqfgUXvOl1yBU7Pq+wQZGvjuCbf9nmwBrWh1ks1kEg0Hxp2ZqLZrtO51OxGIxUZ64R91wDG0BJPo58b2Zx5l++zTV1+CKWttIJCJtyA1wYmJC+qJQKCCbzUq/0K+ePr6dTge1Wk18jjle+B3HEAB5XjgchsPhwPbt27Fr1y5Eo1EAwCOPPILbb78dd955Jx588EHce++9ePzxx1EoFCTQHscrGWpaJvDH6XTCzthI/mwSe9N7pR6rq6uo1+vY2NhAKpWSDZJm7KlUCuVyGWNjY8JMc3NdXV1FKpWS91haWsLGxoZEID979qxYaQSDQRQKBWQyGQSDQfGVbrVaWFpaQqFQwMzMDHw+n0QwZd/Ytt1jbcB5cskll4gi6/uVhYUFHDhwQP7XG7pWkPBA4Pf7kUwmMTU1hWQyibGxsfMOcAMZyECe+RIMBnHllVf2sJfaj1ULLXVMUKx9u03AagYg41mFz9GgT0cJJ/Ciz6wGVCZoBHpzP2vwSNcl/Te/06CTgUG1EkDnGzcVACZQ77d+mkDbBKz6/6cTEzj3e1Y/hQV/9NqvFQhsL22Kr99Fvyf7Q58VzSjz+n4dLFQrPliuNtPWZ1jzHpNlZr88HYOt20dbEeizJ7/je2hFzAMPPIBsNnteP2lWnMoCMts6UCmZe8aNmZubw7vf/W4cO3YMP/ETP4HJycnz6j+QgXwvMmC4f4hy2WWX4Yorrugx++Sm4HQ6BXzpQBeNRgNOZzePNtNENRoNFAoF8SlmZGouZFw8WJ42yXU4HJILm9fQDJcgnwsXzXmo7avVagLY+TyCQAJ7Mt0aeLIs7aPO1FQEZVpzSvYVACzbwsSbJ2C9zsLC7QvY8G2gUqmIRjIej4upcy1cw7de9C2c2XMGeA/gWnHB90Wf+Li73+pG491dU2obNj7wwg/geR97HubW53D//ffj2c9+NqZT03jBZ16AL73gS2je1cTeP90Lb2TLtJj1jcViYkkQj8cFgGmFSDAY7Ppxh2tIvTGFZDCJoT8fQqgV6vH3ImCjdUE2m0U4HJZ2Brqm71RmEHgyYNuOHTtw5MgRyXFtWRaSyaSAs0KhIOMiHA6LX302m0WxWBQLBioDOB5pWREOh9FoNLC6uooTJ06I6X86nRZfdG60TqdTwDgPQnRBsKyuCf1DDz3UAxzpItFqtLBjxw5JBcbx6/f74ff7MTo6iqGhIZk7Ohr+8PCwWI0UCgU5PDDY3OzsrBzCarUaRkdHhaXxeDySA31paQmTk5MS3I1R/u+9916pB+cBfdapKGM+bs4rv9//b2KaT5w4IYfJYDCIcDjcE3yI4y6ZTIpCi32ezWZx8cUX4/Tp09/3cwcykIH855ZAIIDLLrusB7ABvWBEiwl8+wE1/R0BGfdDDWq1j68GhQSBVL4S4JlsKkG7dgPTSgMN1nTUaQ0o+UOgpBX8fF+tfDABuBm1W7cDRSscTECpfcdNltwEh/3A9oUYeLM/tKJCK0p0HfX76nFgKlf4uTarJ/jUlgR0J9Pvrutm5kUHtqLI85oLxRHgM3Q/aAbatMTQrLVuN7PtHA4HDh06JOcobUbOd+L/jB2kFUZ6/On+bbVaiEQi+NCHPoQHH3wQf/M3f4OvfOUrOHPmTN++G8hA+skAcP+QxOPx4Morr8TMzIyAG+2bQn9rAhDtk0kGVC9oXBAJeMl2EkBwQSHYIZDis7jI6HRQeiHzeDzIZrM99QO6JtVkBvk/FzqamJvvZNu2pEiiuTrfi77rOjK60+lEKBRCIpHA4uJiF0z8cRL27u5iSkCn8zq7Ai589+e/i6XLl7oN7gJaf9pCPVyH9bFuUK5WpzdCqsPhwOjEKBZaCzh58qRsOMMPDGPyvkmsf3EdrdkuM0yftVKphEgkIqbZZEJppWD6z3pCHqT/exr1Z9exjGW0fW3s/YO90jaMtE3TZaYAs21bfp87dw5TU1NiWs6IshwHJ0+elE2Nlgc0dWZ/EZjR/FhHmOcY8nq9KJVKsqEwMjlBNfuaz6fPOYCeCOixWAw+nw+FQgEve9nL8MUvflEUMHos8BDGZ9duq2G1viqbI9lvHRyHqeGoJOJYiMViaDQaSKfTAoipiHE4HIjFYtJP+XweiURC+pDgmMqUpaUlBINBWFbXb51zKZFIAICMUwZpY9sCED/7RqOBZDKJdDp9wfWg3W6LGXo8HkepVJJgiU6nE5VKBfPz8xgbG+sG9XvKAgKAKBU4DqicaTabOHr06MDMbSAD+RGU+fl5zM3NAehlB7nf8nMNeDTAJoDRTC9BCq/XoJpBXDXo1EBMu7KZZr+6Lv3Wq34AUrPhJng0QWQ/JtoE6PqdTACpxTRxpuJbAzItZn31u5qm4qbvtP5tlqfBn/mOuu66fM2K62u0tYKpGNCAVitAzD7g9zqgLp+vFfe6zZ6uXgDOy8tu/m22ie4vvo8+YwPnA3heY1ph6POrBvm67jw3s/0OHTqESy65BC94wQtw77334sMf/jDW19cxkIH8azIwKf8hydjYGF7zmtfA4/GIDzXBMQ/ftVqtxw+GzBUXhUKhAADinwtAABHNdGlqTtBOQMhFjD7ewFbkcYIuAn6ag0ciEWHZLatrJk4mnODP4XD01R7SvJYR0ul/TDaVGkVqvfk5/WuZ+mt+fh6W08L6n6+j1WmJltXj8Uj6qVQqhWa1iW0PbAO4BtsA0kDnX57SrMMCPgD43u4DOoDVsfDaT78WC8cWxP+XOSRdLhdat7fQKXbBq9fr7ebpfkpZEI1GYVmW+NRvbGwIa0yWmSb6ax9cQ+GmgoyDtWet4ZHffgS1Wg25XA7lclkWdubEZluxr7iZsX2Zp5zicDhQrVYRiUQkANvY2Bjy+bzcx37VY4sbHqVWq2F4eBiWZSEcDve4OLRarZ7gealUSurDsca+ZiC4iYkJ3HXXXbJhsU1o/u7xeBCLxeAP+OH+BTdyb83hz1/658iX82KCblmWtH2pVEIwGMT09LQoFhj8TaciOXr0qIDPsbExicROIMx24NjnuKboGAiclx6PBxsbG8jlcjhz5gw2Njbg9/slZRn7nyDY5XJhenoagUAAoVAIl112GcbGxhCPx7Fr1y5cddVVeO5zn4s9e/Zg79692L17N/bu3Yvt27djcnISY2Nj2LNnD8LhMHbu3Il2u418Pn9efzWbTQQCAUSjUczOzmL79u3npd4ZyEAG8swXy7Jw/fXXi0uQBiQEDtxjNWNtAi4NMDTrqVljDRzpYsZrua5qE3ENUE0Ap5lrnnf4o12FtC80zw2sm34Hk1nX4NR8nr5e10dfr0HphUzHNZg3yzMDv/W7z6yHLl/XWyvz9f6t287sR/O9eb0Gm+wXDW5NpQsA2Vt027Nd+K76DEvLLPOdTCWHtlrQTLeunzlO9ZjR9eS1fD/NSpvuFbrePOvosW32A9tFtwPPbzfeeCN+/dd/HZ/73Ofwute9TtLyDWQgF5IB4P4hyf79+7Ft27Yekyz6HVM42XngByAgLBAIIJFIoN1uY2hoCKFQSBaner2OSqUioAOA+BTrSMvciAme6/X6eTm7yejZto1jx44JOMnn80in08KSEogQ4DHQV7vdRiaT6QGHtt3NDe12u4VV7HS6QdgIdHgvGcNQKNSNfu4rY+lTS8gdzuHut96NE5snBBwPDQ1JWxZyBUzfPY3LP3Q53HU3AqkAgtcGYS1ubQZ23Yb9fhue3/fg5X/6ckwuTgpjzHrHYjFYlgX/rB/lb5bRGO0GXCPL7fV6sba2hlqtJn2QTCZlk4hGo6jX6yiVStjc3ETkFyNwrm9tgt41L3a9a5dEAWd6KbalNmmjWTWZfJpA12o17Nq1S/ozl8uJqX+pVBJWnMxro9GQIHfU/LIfk8kkkskkgK7ZOoGjvtfj8UigMJr7k+nWmypjAvj9fuRyOZRKJWxsdCPF1+t1jIyMYG5uDl6vF1NTU93UNXYbledVcO63zsGO2FifWMffv+XvUQ/XxUScUdeHh4d7AvO1220J2JbJZCQH6MLCAjY3N+HxeLC2toZUKoV8Po9cLiebbq1Ww+LiIjqdjuSytu2uWWQ6nUapVAKw5ecYi8UQiUQkHR/T8AEQM/ahoSHZ3KmEuPbaa7F3714kEgns27cPs7OzGBsbQyAQQCQSQTQaFeWR0+lEJpNBp9NBOByW2APf/e53RRFERQ2fXywWsbq6ilqthkceeQSPP/44UqnUD3MpG8hABvIfUCzLwg033NBj1tuPOSYw0Sa6+n8CGA1wNdjgPgJsKe0JcDSg1i5KPMdooKtBlwZBmtXs5xeu2UZ9r7YY1O+h20GXYzKbFJOt7qeM4HUalPUD4+Z76PJ4DYG0Bua6DL6rGfTOZLt12/YDldpVkeXwudonWoNyEjW6vhqUaoUGLS21okV/zzqYQdT6gWYK93m+p1aWaGUMv2Nb8RzMsdfPAkCTQzyn8od11KCd9dGKJ1MJ4HK5sHv3brzzne/EI488gltvvRUjIyMDH++B9JUB4P4hiNPpxNve9raeSXvs2DExmaWPMaNSc0NhuipqkQEIOKRvDH1hCRwdjm5EZQ2ENjc3xRSVCxfBeKfTzS/MNFV8tsvlQiKRkOvIRnOx5QJFVt3n8wlI8/l8wnYCW6bDBIyss8fjwebmJkqlEpxOJ1KpFDY2NjAyMoL19XU8UXoCj73lMWR2ZAALyF+Tx+k3n0Yz3pRc1MlkUnxcy6Uy9nxzDw5/5TBe/N4Xw1F1nOc/5HK4MPKhEeAewO5s5fhkNPDFxUUUxgpdRn1XC9/+79/G0tiSfE+/cZryhkIh5PN5BAIB0bqTzQ2Hwwi1Qph59QyCx4IIPRHCJa+7BL6OT8A62eR4PA7LssSPqFqtYmVlRRbzZDIp4E+zqmzzYrGIcDgs44d9RlaYQJjm1Rw/9OXmOKWZvE6L4XA4RCkAdDeV8fFxFItFjI2N9VhtMAgZ0A3WFgwGEQgExJ95dXUVJ0+exOLiIlKpFIqtIvAr2HJmsYBCooBvjX0Lm5ubsG1bFEnavI0pvDKZjCgJqHRiILtisYjNzU1hyDOZDDY3N5HL5QBAAgRWq1VpA86ZSqUijPjq6ipyuZwAdmquS6WSmN3TtJ1m5gTSPITQlJ1xAJi7nn7hQ0NDYorPQwmDATLtGSOROxwO6WvGPtjc3JQAgZlM5ge7gA1kIAP5Dy8jIyO49NJLe84ZBE0ETgQbBBxAb25tigazJuDVaxQ/08DJBIVkqk0GV4M5E9SZdTJBMT/XAFK7HrEMDYx0HTWwejr2WV9jmpXz+aZioJ+iwGSrTeCu2133i75Hm7CbIFa7+bFvzTrp77RywGwDgkPNDOtMMyaYN+urRT+P12l3AN6jTc01i6/7xWxPDZp5nSa0TKWFNuXXbc+66WBrWrmhy9GB/Pr1Ef92u90YGRnBxz72MfzDP/wDXvOa12Bubq7nuoEMZAC4fwhyxRVXYNu2bQAgLG8+n+8Br1ozaNu2gOZwOCyLh/bBJujQWmX64nJx1OCLzwB6N9NisYhYLCa5irkJlstlJJNJAU0agHHzpL+x1+sVP3Ay13xParYff/xxSftFEFyv1zExMYHR0VG0222MjIxIvuhCoYClzBLS9V4fWOeQEw1HQxZtbgoejwehUAgulwtXf/dqJOoJATra9MjhcKB2Sw3WgoVUKoVMJiORpVutFjLRDG5/2e1Ibe+yhLWxGh7+9YdR3FOUQGQ6nRsDktFXmub37C/btuE660Lk1yPY/q7tiJajPdezXvl8Xhh0Aiumw/J6vaI0qVaraDabwmpWq1WJ2k0WPpvN9hw6ACCbzaJarcLhcKBSqSAQCGBoaAijo6PSb0xNZ463Tmcr13s0GoXL5RLmmsCP/epwOCQYG9NjFYtFySNer9eRSCS2fKNrTsz/zjy2Pd6dH46WAzd99iZcet+lwq5HIpGeSPlkqDc3N1EulyV2AOeS2+1GPB5Hp9NBIpGQscJo5IVCAcvLy6jVanLwI+Pv9/sxNjaGUCgkrgKhUEiUFcvLy1haWpJ2Zf5uHlKoxAIgViqWtcV4U1HFQ3C5XJb0e9zMfT4fisUistmsKGZs25Y0csPDw3C73T1/LywsYHZ2FjMzMz+4hWsgAxnIfxq57rrrejIjcE0kAOznY62vBXrZUBPkagBuBiQzWT/+D0CAOfckYAsomyC2H8DWzLV+Hw0WTYbZBPAaCGsQZQLjfuy2eQ3LNcEYxWxnzVpT9H0mCOz3TBM8mnU0r7Gs/sHpTFZWm4+bIJy/+WMCfh3Jm2Vo0NuvD0zgrOuk2063kTZb15YNvEa7J/xrY0m7Lup6mHNFM/gUPcZ1/AJdd83u87tLLrkE73//+/GBD3wAb3nLWzA8PIyBDAQYAO4firz97W+XQzeZ32c/+9k9ab7IYBOwad9YstVkEOv1+nkAneCBC4IG8bFYTPI9OxwOCXBFtlAHz+J3BCAEfdwwuXgzEFexWESlUhGmj+XQN50L8LZt2+RvRo6mJpDvatu2mEYHg0GMZEbw8n98Ocaz44ANBB8KYv4984jmo2LaxraiQoDt9E+v/Cc0O82ehb/dbqN6RRXpd6Vx+y/dDkfUgUgkIkx9vV5HrBVDfCne4wvuXfGieaL7rmbwDQYho6KCZu5sC6Brqu096oX/TDdIWiKRwNzcnNSXyguywKlUSkypGaWe7WRZXVadoD8cDksQN9u2RfFA6wLmCqdCRedJ73Q6kloO6ILyXC4n4HZ1dVXGXygUwtzcHBqNBorFIjKZDFqtFs6dO4fNzU2Mj49LH3LsuVwuFAoFyVXZ6XQwMzMjTGw2m+3WLR/Gc/7hOdi5uhPP+ehzsPCtBQmMRj9uBpADgHw+j06ng3K5LNH36TefSCSknWq1GlZXVyUHOccjTSJrtRqKxWJPG9A9olgsIhQKoVar9bhscJxRycFr6/U60uk08vk8FhYWMDw8jEgk0nPYokk4LTyKxSJOnz6NTCaDdDotaQA7nW4E9muvvRbxeFzmuU4TNzU1hVgshuHhYfGXn5ubw65du/oyDQMZyECe2XL99deLi5gGlgRLGkxoMVlCLWYZJjjUzCWF5xbuW1wnNUOq2WjWl8/vx8oCOO8e00dcm5OzTiQRTIbaZKFN4NwPHF8I1JttqdtRg0ddlv77QswtRde3nyJEs8Ua/PEZeiywD3T7UUmtzbVZPs8npgJA//Qzz9eAF+hNWcb6EigzBoAmRUxTbV0H3S9mv9u2LSTLhRQl2ldbKwF07m3T9F37bZvAnG1q5u7mnOMZ5sYbb8Qb3vAGfPKTn8Sv/MqvDHy8BzKIUv6DlsOHD2Nubk78YXW+Xpr7WlY3ly4XSZqdmuYqZNmOHj0Kl8uFHTt2yEE+FArB4/GgUqnIRHe73RJ5mWDc9Iex7a5/NhcMra3U/rKsB6NfZ7NZAfT0E6dfqV5I6vW6BJeqVCoIhUI9Gw7rR1M1Lr6xWAzbtm1D/ck6rvt/rsM//fo/4dK3XgpUgWasKSb3lmWJz3utVkNkLILPvvSzOH7pcVjfsOD9cS9am93FFZcBrc+3gACQtbP42Bs/hjf99ZvgbDglR3mgHcBNX7wJ+WYeZ284i8QTCVz9x1cjYAVQqBckRRb7icApHA6jXq9LLud2u5smKh6Po1qtIjgSRHQsilqxhnK5LHm8Cbq18iQYDKLRaGBkZAQ+nw+JRALFYrEnZ2o4HBazdubY9vv9cDqdknNydHQUfr8f+XwexWJR/JK9Xi/y+bz4JQeDQTGzpstCqVTC+Pg4ACAcDkvfOxwO5PN5eS+6FCwuLqJYLGJ4eBjFYlEUH2YKOPqdz83NIZPJdJUg1SqCuSB+7tM/hzPHziAY6VpOVL1VdBxdiwnmC69Wq3KoZJszjzuwdSizbRtDQ0Nixk2LgqmpKcnfHo/HBeiOj49L3u9KpYLTp0+LkoKWGvV6Hbt27cLm5iY2NzfhdDoRDoclYjpN08+dOyeRz3VgRKAb8HB8fBxerxeJRAKXXHIJAMh7LS8vI5FIiFn6+Pg4qtUqzp49i1gshk6ng2KxCMuy5PfExARWVlawsLAgoH8gAxnIj47EYjFce+21PX64BEImO9yPceT//dhYrWwnYKF7EwEFsGXRQ6V5rVY7LxiaBj46Dao+j2jLKg2YNGgmGDKVB5qdBLbSfPE99Hvzc+2iZ4JZ/WyeobT0K1uLqZzoB5Z1vU1wyN/6c7NM01zeZPFNgK/bp1/bsXytJNH3a5Btgm+SMSbY5hmR92hlihlYjWBYxxwyx6xuf9ZRA2dNiuh2JqlkKpnMs7F2wWDbkIzitaaSRrehtr7Ucy4YDOLAgQPYt28f3vCGN+A3f/M38Y1vfENIhIH8aMmAGvkBisvlwq233opYLAaXyyVmtGSpl5aWeqKUk2Wjr6wOlMXyKpUKtm3bhm3btsHlcgkIJ3tOFkwHYNMbBjdKoLsx+nw+AetceBjxmn65XMy8Xi8mJiZEI0rTcubkJvhjqiVu/LVaDZlMBqdPn4bP5xMNtd74LKubcotmPM1mE0eOHEE6nUbcHcfQi4YQsAJiKux0OrGysiJtVK/X0fA3cPstt+PIZUcAB2BfYqPx1w0455xwOB3AHwAIPNU5FlAIFnD73tth27ZoITudDqr5KmJvisHzvz048KYDcDa6uZuZ6oog2eFwSGR39h/N65mOrdPpwI7YOPPLZ/DQSx5Cup6WhZtsqd7ICcQajYaAumw2i1KpJJsIWfVAICDpogj8Nzc30Ww2xVRcB9IbGRmRVGQM7sa6cGPZsWMHrrnmGuzfvx+RSERAeigUEnaboJuKo1qtJlHl3W43RkdH5fpms4l8Pi/tu7q6inA4DI/Hg+HhYcTj8S6gnWx03QVqXbPxs52zyHwwg85rOihXylheXsbGxoakxtJMAg+XmUwGqVRKgP7u3buRz+fFXJsHQkb7TqVSEiV+eXkZm5ubEoiMvvSXXXaZjEsA2NjYgMvlwuTkpCg4/H4/3G43otEotm/fLmN87969mJ6exs6dOzE7O4u5uTnMzs5KX3HjLpVKCAQCmJyc7CofgkFUKhVks1npI86Z4eFhtFothMNh7NixA6Ojo9i2bZv4zfdjXQYykIE8s2X//v2IRqM9DKYGWiYzq4GF/oygWJuGA/2DX9G9TLt3aSDH72hdZJoNazZQs5omGNTnGdNn21RW9/O3vhAzzLapVqtYWlqSva4f6Nb+vVo0oNNtbJq5AzhP4cB27Kc4YJm6nvq3+Q5mX2tG2GxvzcA+XVtpc3MzerdpsWD2J+uigamp8Oi3V+lxp+tgWiPwTGP6WmsTeL67Zqq12TffQc8Vnj3NOaLPtzxv6H7i2ULnotdBA3k/Qb3f78f4+Dg++tGP4vOf/zxe+tKXYmZmZmCd9iMmA4b7Byh79uzBZZddJqBMm6t6PB7s3btX/J3JcBOE0XwcgGj6zLzEtVoNgUCgR5sWjUbP82vSG5a5OLI8puIiSCdoJtCmya7D4ZAAaQQxZO9pQk2QztRTzWYTw8PDElEa6OYrZvqQfD4vaah0CqyJiQkJalXz1HDyipOY+9acPH9+fr4LkJ/yY25H28hFcz190BnqoDXUAk4B1issOD7kQPtlbcAGrr/zehy+6zAs59YGQCsEr9cLx6840Llqi2GlppZKAQImguxoNIrR0VFks1npv7azjc23bKL88jLKKMPhd2D7n26H1bB6zJyZr5ybTSgUEvaZ/UxwzP5PJpNYX1+Xtg4EAigWiyiXy6hUKnA4HAIeGRCNqd6o6WW0bAbEO3LkCFqtFrZv345isShm0doMkGM5FApJkLD2rW247+r6M9Nqg0x4IpFA9XAV49lxOJa2NMWxWAzNZhPenV586tmfwmx0Fjev3Iyqu4r7XnofitNF4HKgM9mB/ZWtw1ahUBDFko4hEAqFxHw8n8/j7NmzACAb3ObmpmzCDocD5XJZ5off78fk5CRqtRr279+PkydPIpfL4YEHHkAoFOqxCgiFQpiamhK/9Ww2i0QigY2NDaytrcHj8UhcAprCV6tVORgw5zfzgWezWfh8Pun3arWKiy++WJQsDN7mcHR9yDmHq9UqEomEpI/rdDriOjKQgQzkR0cuvvhicY8C+oNGk/XUgFArwE0gpANQaVeqXC7XE6wVOD8fN/cLHWiK19FsF0Bf8PZ07LEJXIEtsNbvHS7EhgNdCy5a3plssFmG+Xc/1rrf9ea7mwC1X3lUZLCuGhib5ZkAsB8rrO8x66tN8FnehRhtrYThOUdnKjEVHBoU6+ebhIvuF329Pn8QtOqxQlBsKn/MfuJ5WgccpjJBK4N0n5h145mPSnDOHR05Xc5+CmjzuRS6OViWhX379uEDH/gA7rnnHnzjG9/AJz/5yUGmkR8RGQDuH5BYloVLLrkEu3btQigUEpNaLhw0tWWwMa/XK2avZKG1ho4LC82YmSqJJrU0Y9agXZvI0CxWm5fpQGjNZhNer1euobaXJslkkbnoMZWR3++X1FVcuKrVKkqlEoaGhmSRI3gn66o3FDKEBOOWZSEej2NjYwMOhwNerxfFjxSR3ZdFJBTB1D1TWF1dxezsrNwXiURQSVdw2YcvwyPhR5C6OAWcBZy3OWE9bqHdacPKWfD+phcNVwOHWodw8L6DcAS2olqm02n4/X4UCoVuu33QRvsjbWG3NSiqVCqyIDPIVbPZRDQa7TFZyr4/i/qtdRkXK7euAAFgxx/uQLlcFmVLoVAQEHr69GnJYb5t2zYJlKbTr2SzWQwPD0uEdLLN1Kqurq5idHRUxhNBqfbhJnAeGxvDysoKUqkU6vW6MOcOh0PM0x/76ceQ/F9JWJYl6cuY2sx6iYXyu8twPunEjt/cAXTQE4Xd+xwvjrz1CNZL67j2t65FKVUSs/BMI4MH3/YgcjtyWMc60q9Mo4EGFicXuw3mAPK/mUd6Oo2RL4zIuOF7VCoVjI6OCsu+traGcrksmy8Drq2ursLlcqHZbGLfvn04deoUpqenUS6X0Wq1UK1WMTk5iVwuh5GREdTrdaRSKUxNTaFeryMejyOTyWBkZAQAxDyefoqtVguVSgW5XA7JZBKxWAypVEoiynOux2IxeL1ehMNhpNNpBINBRCIR+Hw+pFIpmfOcfww8x9zvVKS4XC4MDQ2JtcXo6Cg6nQ5Onz7dlwUZyEAG8syUQCCA/fv3S7wVgjMCDw2iTPNjHT3cBDEaYJvsONfSjY0NTE1N9QRB0z+aNdXR0oH+JtPmd1zvLwSEdb35P7Bl2st7tIlwP5bblAt9Zt6rgZp+rqksMMt9urr0UzboewCc56Nslt2vrQgEWVey9ibLrfua8UZ47uDvfqm92M/9LBX6KTO0Oby28NTtrOvB7/U78rdWGPFa/S5U7rAOHMOmwkSPV+1eod+Hc4njnW1JIkG/o1aGsM+01QPP+T6fD4cPH8aVV16JW265Bbfffjv+6q/+Ss4BA3lmygBw/4BkfHwcL3nJS4SJJitL0ETGimbdTNtFRrher8shm8GaqN2juXa5XEYgEBAw22w2ZYEEuqCAKaEYYIzAGADK5bI8kwGoyIQy2nKn05G/uUgxMjejU3PBajQaYnZNn1v6BNOvhr6pLIPvxPzRZC1zuVyXsayk8fe/8PeoXFwBLOChX30IVtGCM9Vd8Ah+CdbjxThe8JEX4Atv+AIy12bg3HSigy1fHitlIfnmJEYOjKAQL6Be6vqkb25uIhKJYHh4GE1HEyu/v4L6DXU8sv8RHPh/D4jG0uv1IpfLyULd6XSwubmJ4eFhiarNhdbtdiP4ziDqt9SBcHdcuMtu7PvkPmSrWUSjUQSDQVFoAF0/uHQ6jfHxcQFxk5OTSKfTOH36tPRHMplEPp+XIHW0SGBatkKhIGOjXq/DcljYbG4i5ooh5oqJNQL7sRPtAH8HdF605UfncDhgeSys/PIKVl++isx1Gey7bR9isRiGhobQaDbQuraFu958F+yQjc5wB8f/4jhufM+NaOQbCAQDyIxn8OjvPYpWsIXaSA13/OEdOPS6Q2hUuvV69E8eRXF7UebNyYmTsIoWrJoF22cDNhBYCmD2H2eRTnfN8anUsW1b8lVbVje92vbt2zE6Oop8Pg+n0ymmjLRQIDu+bds2JJNJrK2todPpYHZ2FktLS4hGozJG4/G4WAjMzs6iUCgIi0RzecYeOH36NAqFAtbW1sRyg6zz0NAQms0mzp0718Mmud1uMbfP5/PiYkIlGtlyMuz02Z6fn0ehUJB4CUtLSwC6G3o63RvVfyADGcgzWyYnJ7GwsCBACOhlCQkW+L8GGZrhNkGRBuBAL6h0OBwYHR2VIKEUgmOa5ZoAksCHZxENQFgnmuxqgKLZcA06dR01cNSiQbv+MRnmfiy1bgtdnmZRdZ20655uMxPUsVxTeaAVB0/HSJtg2wTAunzu52wjHWTMbCfdzrxOuxb0nA3U8zR4NsGyro/JPpvlapcBs836jWUNqPW5y1TiaGWEVkaZFp96XuixScU2mel2uy1kkna75DmbZ2UdwI0An+WakeGJB6688kpceuml+Jmf+Rm8853vxB133CHn44E8s2TgQPADkj179uDmm2+WhZ65tunfy0lPpphRj8l+l0olWSR5uGdKqmaziWAwKCnDOIG9Xi+CwaCYf/Ngr/2LCM7JANKfOxKJiCKATHqr1ZIFhL7dlUoFkUhEFkqaytIcnD6ooVAITqdTmFrLskQDyGdwoWPe5Hg8LubIo6OjcLlcOHLdEaQWUsBTe0Pb38bxnzuOirObwiqXy8G2bSwuLorvrL/px8/82c9guDPcEw2d2s/yehnNHU24/W5MTk6KCbDH40HZVcb9r7wf5248B7iA/IE8jr3zGHK+HKrVqrDAFK/Xi5GREVl0x8bGpN1zuRxcqy7EfywOz1kPfGd8uOIXr0BrqSUmzVROMPhcs9nE7OysmEqTvW2324jFYsKGLy8vS5osy+qmOMMw0Bx5KnhcJCIRym3bxsaBDXzzS9/Eg7/6ICqBivgd1Wo1ZEYyKH6jCFwPBG4PYHj/MHw+H5KTSUTeFcHKz6zAdtuozdRw9M+PwrngRDQahX/Ij4d++iHYoac2AgvIL+Rx9JajCAaDqNfr+M7PfAetYEu+L42XcPJVJyXI3hVvuwLh02Fpz8QTCey5Zg/2vnMvAoUAJpcncehXDiHmiCEWi2FhYQFDQ0PYsWMHtm/fjvHxcczPz+Piiy/GwYMHsXPnTvj9flx66aWYnp6W9FmRSASJRALz8/OIRCKIx+MAgKGhIYkHQNBOs3TONx4qA4EA8vk8IpGIWJqUSiWxNtmxYweArtaaCicqhIrFIqLRqPjUN5tNiTafyWR65h4Z90wmg7Nnz4ofOjMBOJ1ObG5uyjPIwDM3+mBjHshAfnRkbm4OMzMzPSy2Fp4BNBjXAIeKdhOQ6z2aljymDzX3OgpBhPbvZhk8k2jGzwS62u+Y1/J6/Tew5SNu1oXvYJqxmyymZsMvxDibQMyM4q1FtxX/vxBY1yyvqeToVwcTnOvyNcDvB+z5jrrdeV8/33j2GYFiv3GkfbhZL21BwTOh7i+T9TbfWbexbhfzPv25qaQwAbp2Z9Dm7pqZNttcu2FyXGlXR7aldu/kM7VSyCxX/88ytKLI5XKJ9WAwGMTCwgI+/vGP46677sKLXvQiTE1Nndd+A/nPLQOG+wcgHo8Hr3vd62RhaLfb4qNLgEvml1G/vV6vMFs0bx4ZGRFWtd1uywGd+Zjpm2r6SVHDLEG7jIXL4dhKVeByuSSXMc1hmN6JgJlAnf6oZEXJvAMQxtHcPJiujOy+DnahNe00M9eb7cjICG546AY4HA586yXfQsfVQeKJBC7/88vhiDgkuBzBLv3hAeDRg4/C+3de7NmzB0888YQssO12G61rW/jKL30Fhx87jPEHxuWeVquFdqSN1cSqAHxYQC6aw6Z/E5PZSQCQyOq8R5uc0we8UCiIf3vrWAtDbxpCxBuBc9kJ22WLGbzOkU5FiW13c1dTi0rtamFHN5f1lVNXyjigmbtr2IXvvOI7qLqruPRDl6KZ6iplWq0WFq9cxPG3HAcsYPXGVTxoPYhdf7oLzrITlR0VPPxrD6M+1lUi5K/Mw/pdC7H3xWB3bCxPLPe0RSPaQHW2ivrROvwuP3a/bTee+I0nULq8BLSBnZ/YiZlPzyBVT8HhcGDP/7sHJ3/rJDau2gBsYOfnd2LX3+yCFejGG/D5fLjmvdfgwV97EP6GH9d/5HqEbwijudHEti9uw9zpOeTGu0qVoaEhhMNhOBwObGxsiL94IBBAJpORvOScU1rJs7S01PUlr1YBAOfOncPIyIiYbF1zzTX47ne/C4fDgaWlJVEcUUFEJppRzBlhXluGZLNZXHnllWg0Gjh37lzXcuCpzTQajYrfYzablcB17XYbExMTspkPDw+LFYvT6UQ8HkckEpFc4FS+VatVnDlzBhMTE3IgYG7ygQxkID8a4nK5sHPnTllD+jGNQC+rqoGVZvt0MClgy2/YvEcDWa5bGuBoAMhzhL6H4EWDV13XfvXQZwvNevMZ+nvWhWcdiukv3o+NpZgm6RcC03xePybVLFMztGa/mNfoMvRnOl6P/k4z+/0Y7n510f1kBs+j6POjmb5Nl8uxx7bTbgBauaGDmem+5zjhc7RPuanU0XXTz9T9QEtPncpWv7O2UOTfZv34P1l/bSqvAbluT51rXrt3crzrfmK5xAQkQfhcum3Mzs7iox/9KL7+9a/jjjvuwN/93d9hfX39vPYYyH8+GQDuH4Bs374dhw8f7tnMaEJNcGdZW7m36dsEQJjPAwcO9KQh0DkACYTJBtPMVpuq0JyF9+jUWfSZJqvGDZELFKVWq4lvSiaTEZ/YUqkkiwHNbAiYfT6fKBIACCvIFF6M0K79h3w+n7QD28DhcKBUKqHZbOKyey7Dkw8+idptNex53x5ES1EsF5YxMTEhvsTJZBKRSAR+vx8P3/Aw7n3hvYi2o8DbDD+oqyzUPlCDPWvjzsk74Qg6cN2d1wHoLnjxfBy3fOoWfO7WzyGzOwPfqg8737MT0VNR1FxdBUM0GoXT2c0TvbGxIYsmmXy+MwBRMrjvccM74oVv3Cfm3E5nlymemZlBJpPBqWtPIXdDDof+5BBa2S2W07ZtFCeKWHz9ItobbRT+sIDmYpd5DQQCKFVKeOA3HkDqim6gjUfjj+Ly370cjVoD6zeu4+QvnUTHv7XQn33WWZRdZUz/z2mcesMp5Lf1ppHyFD2o5qsIOALY9kfbYMHC5nWbcJad2PWuXYg9HkPBUeheXAIO/vlBHIkewczdM7D/l43k3qQoYJLuJLZ9dhu+Hf02Ek8mcPFXLoZ3oqtkYHq5WCaGKz5yBeyijag7Cn/Qj/X1dex6cBfq9TparRZisRiArQjj7XYbo6OjaLVaSKfTopDixkYT+0AgIIHNOG84D9bX16XvcrkcIpGIpHjjYYBzqN1uo1wuIxKJSCA/Mjm1Wg3hcBipVEp817mxMuhdPp+XNF9UwDFSutfrlSB5nJvFYlGUCTQzp0n7+vo64vE4ms0mcrmcpBqkQmAgAxnIj4aEw2EcOHCgBwjyIK+jKZssqAYf/Zg4/b0Gl5qVNsEc920NMjRwNkGzBrU8A2m/b9OHls/X5tP6twk6NZjWzzdBUj+LAPMeimlKborJYmoxGWETAJvlXKh8U0lh/m8CWC3m+5jvqttGM8j8XFssaACqz426Xixbuy7o8nmNHqd8jtl/ZrnafF+PU5ZnKkpse4vRByBnXz0O+U68XperTd5JcrFPdfn6vM7rtMKK78J7SRhppZT+nO1200034fDhw7jllltw55134iMf+cggBeh/chnYK/wA5Nd+7dcAQECmbXcjEw8NDckkJZjkQZ5sKCMnA1smKjQ9L5fLwjSHw2E59NNHmuwoFxGaWDPIVKvVkoN+vV6XZ/l8PsTjcdi2Lf7khUJBfLRrtRri8Tji8TgCgYBEiLZtWwAJIyU3Gg2kUilhG8vlco8mnSCFwIdpmPieqVQKq6urcs3ExARarRaGPj+E0Z8dhe/slt94rVbD2tqaRNz2+Dx47OrH8O3/8m00/U1svmwTZ994FnA95Y80CTT/tgl751OHB1cbd159J7516FsIh8PSft5zXuz77X3wnfBh9GWjmNmYEeUGF1PWOxqNSlt3Oh20O21MLUwBgDCgbq8bFbuC079+GpUdFdz1W3eh3qmLGf/Zc2exevkqnviVJ7B4xSLu+L074Al3fY9rtRrK/jLu+J07UJgvwL7Cxnf/+LvwxDzIZrM4s3wGX3/L15G6fCuq5ebFm/jWf/sWSqUSciM5tGKtnvFpNS0M/cUQvOte+D7vAxg8swNE/ymKiT+aQBzx7kFpGZj6nSnEHo7hqjddhfhjXbP/YDAoLgSBlQCuf9/12HPXHuzauUvag6bcoWwIN37sRhz82kHMT81L1H2Ox0QigR2dHZiyprqR/O02Nl+4iXPXnsPy6jJKpZJECbcsC2tra/D7/ZIejD/FYtcX3Ol0YnFxUVhpp9OJWCwGy7IQiUSEJU8kEhgbG4PP58OxY8dk3sXjcczOziKRSEiQs5mZGdkgqTQLhUIIBAKisIrFYhKEzuv1ymaby+XENz8YDEr0fZ/PB7fbLdHIw+GwtA3TCEYiEQngZttdywf6bJdKJYyMjKBWq6FYLEr6s4EMZCA/GhKJRHDZZZcB2AJ7POBrllGbzpp+xSaQYfRmDWo0YOBvxgvhmsXn6OBatL7RwIbgmq41ZAq1ua5mWvk5gY7+4flKAylNTvCcwXMRWVr9o0UztaZywLQKMO/TQFB/RjHBrfYPNtnift+bz9WuguZn/FuzyxT2halYMQONaUCpLRhMFhzAeUoVrTTRLDTbQUcU78eqaxac95jvRzEBszl+OCbIOPN6BizTfafL1Ey2Hg+6z/W7sP30ONZKLa0MY320woDjmfUnKca+t6xusOJrr70Wb3nLW/DVr34VL3nJSyRY4kD+88kAcP87ZXZ2FjfddBP8fj8ACIijySfNQd1ut0RI5iLAjS4UCp23WKXTaZm4XNzcbjfuuecefPGLX0S1Wu1hiPUGS59xMmcEjdosmibmlUpFFhIdFZILChk1LjRktOmD/pd/+ZcS5drr9Qqby8WNSgYuJPq9CewXFhbgdrsxMTGBVCqFZDKJQDiAzfomNjY2sL6+Do/Hg+XlZcnLHQwGsRZfw7GfOIaW9ymA6QHwKqD9/K5WHCuA6+ddwFPWOFbbwvw35jH8yWEsLS3B4XAgnU5302qdrcDeZ8O17pII5HznjY0NUVg00cSqfxUbGxsol8tYmV/Bd9/3XQR3BrtjwAF0XtFBcbWI0s+UcPf/vBu5y3M48Z4TSHVSKBaLOLPrDB5/1+NoB9tdE/bZHO783TtRiVaw7FvGV97/FdTiNemL5mQTx36zCxDPvuIsshdlt8y+AXjOerDrjbvQ6XSw/W+2Y+xzY7Ba3Qs8WQ8u/s2LMfTkEDzwYNdnd2H6r6fhqDuQuCeBA+85gPnEPMbHxxEMdt8h6Uzi5j+4GaGzIcmhzUBes7OzmJ6eRrASRLPa7PFrIhCt1+uI23G0K91811ToUGPcbDYlOFmtUcO98/fiH1/2j/jUf/kU6j9ex/ETxyX/99LSklgXtFotBAIByQHOZ9H0nJYTVABsbnbHTzQaRSKRgNvdTWM2NjaGfD4v5uJUvpAdr9VqSKfTCIfDiEajEqzQ7XZjfn4ePp8Pfr+/q3wIBCRo2tDQEKrVqgQ/BLoHhnw+L+bl8Xgc27dvRzabFZY6n88jFAphYmICgUAAR48eFWUW15NoNIpoNIrNzU2USiVks1l514EMZCA/GjI1NYU9e/b0AL1+zKcGjRpsakAHQICXZhw7nY4EatXrmC6fMS00SOZ1GqBrtyl9HwGIBmW6jjzTaGCiwZj+0YCLz9PKBgIe3VZ8V36mwa/+TtfRBH4UDRa1m58uRz+nX5/pttXvpZlf3acmu63rofvDBJYUrXww2V7d19oc23x2PyCs/ex5HfsagPS/2Qa6TqaSg+NHW1NoJYdWwpsKJw1cNROuwS/rotl7vitJIlqVEpBTqaPHNs3D2TamMobP4P1a0aCVHabShGeTPXv24OMf/zjuvfde3HzzzRgfHx/s///JZNBb/0559atfLaxTu90W1olAmybdtm2LDy/Ns4vFouRwJnPc6XTER5hCYFCv13HVVVfhhS98IRwOh0Tr5oLGCRsIBGQxoO+pw+EQ31OygAQGDG5C81SHwyFpzZjnlyCZ/ieMyPya17wGwFbwlE6nI+CKGyXBERe8U2dP4Z6xe6Qs+sVmMhkkEgnU6jVkX51F/W/raB9oI5lMwuPxYHZ2FqVSCRsbGwCAufIcrv7zqxFZiwAArKwF11tcwN8/tZjbgP3PNhy/6gBWgUvuuQTP/odnY2J8AgcPHhRLBCoMYG+ZsFEjX7SKWHnRCtZHuqj9/mvvx0O/+xCy81kUDxdxx1vuQOvSFlofamH+pnm4X+tG9gPZ7syyIL/Xr1zHgz/7II4+6yi+9dZv9c48C2iONVHYWcD6wXW0fb2sZfizYSz8zgIcDgem/nIKs38zKyx1+NEw9r9lP6KObgT0cCiM/X++H/O3z8Oz6cFFf3IRhh8ZFn/8ZqOJ7X+5HTv+bgcOvecQwqEwMpkM1tfXEYvFtmIINNvi/8xNgf7GmUwGrVZLAqXRmoLpuSKRiIwZmmNzvI6Pj4t1hNvtxpnDZ/DNX/lmt60s4Eu3fQnB13bNskulkpiNM6f2vn374HA4hLUGgI2NDcRiMfFhZ/24UdZqNVEmMHVdIBCA3+8Xs25uqEzpR5NwZhfQqeCCwWA3yFwyiUQiIfc0Go2uwiKZFIa8Wq1iaWlJrESCwSCWlpYkjR7nDtsrl8tJfIBsNotwuBtgLp1OC4MTj8eFwe93mBrIQAbyzBOHw4HDhw+fBxQ1oNZAU4MQDSi0cE/mj2aTgS1QZZr8akCmTcoJqLQywOl0SuwRlqUDsunrNDPeb23Tn+no52ZZvPZC720y/frzfqbtJoNrti3rq8GkVgaYjKQG5Pr5F3pvfmcCev1uJsDTrK1+jwsBXd1WZhtqX33dLrodNDNrvr9uJ17PM2U/U3F9n2aNzX7VbaZZeVPxoKWfFYDuDx14l0ojYMuSQrsK6PtoWdqP4dd/U/Qc0UodDcR5jY7ZND8/j09/+tP44Ac/iNtuuw1jY2N9x8tA/uPJwIf73yELCwu46aabeiYwNxf6kOr0WOFwWBhf+rTSPJpm016vF4uLi/j7v/97vO51r5N7A4FAj6YW2DKt4eZI7SBNtni9uRn5/X4BJFzk2u02KpVKD4PIA74OpqY101QocEHi39pkh3UjkGu1WvjiDV/EkcuPoH5/HTvu2SGRvhn9+cHnPYhz151Dx9HBybeexPCfDsN9vGtu4/f7xSc8n8/D+y0vDvsO4+u/8HVMvW8Kxz9+XNqCpvnWly3su3gfhh8Zxuj+UWmTYrGI8fFxFK4p4NiLjqG11oLvj7qKjmKxiEAogKNvOIrUjSlEj0axvryOpcNLsF02Hvm1R1CL1YRdL19bxso7V1DYU+hhn7V4HV6sn1s/b0N1VpzY/4H9SBxNoPFIAyP+Edz/q/cDFrDrjl0Y+cgIEqMJlEolOBwO7PzUTgSsAM5cfgb7/uc+jLXGgBB68rTv//h+jNw3gtEHR1F2lGUsptNpeL1eHPzyQdR93WBj+XxeFDNkim3bRjAYRLPZ7PErnpycxMbGBoLB4HnB/MiK8BDG/i4WixgbG0O1WsXm5qa4L2SzWWwsbfS0hQUL8UBcLEaY6s62bYyMjOAzn/kMxsbGRJHldHZz1BeLRfHJpikZ3SH8fr9sXvl8voedicViaDabKJfLiMfjYja/sbGBkZGRnoBpnU4HGxsbknqMijCfz4fh4WGUSiVhpRllfGFhQTTmlUoFm5ubcLvdSCaTmJiYAACcPHkS6+vrSCQS8Pv9Mj/5DLqT0K+d68b6+nqPVn0gAxnIM1ccDgduvPHG88CTBiAaBGrwQ9Gsnsnq9mNgNbDQwEaDJe6nGnjxrEBArOvHdyHQ0Pfo//spC/T7muAS6GXi9Tvybw0+9ftoAGmCMRN0689MsKfbWdfR7IN+DLW+jvuobhMTKD4ds90PYOtxpIFdv/fs987aekH3x4WUCryfgcx0nc021GPVNBnXbWmy1BpQ9+tDM7Vcv7GulUdaoaHHpGa3zXGon6MDALJt9HNNdwLdl+ZYZaBl1kFHkaeP9zXXXIPnP//5uPvuu/GRj3wEhULhvD4YyH8cGQDuf4c85znPwcGDB8UMlSwZ2T9gazGhuS2wld5CL5Isw+PxoN1u46abbhKW2Aw4ZprTcMNjmikG89L5f7UZOCOI85mczFwcmIeb33HzJADn4qODgel0YsxnzFzkNPVyeVz4yMUfwQM7HkDb2cYnDn0CL1x5IbY/sh3xWBznzp3D4isXcc9V96Dj6L5jdU8V9779Xlzz+mtQr9fRbrel/Fwuh3A4jJGjI7jtY7fh7i/fLW3MtvN4PGi0Gjh66VEce8kx7Pj4DkRD3WjRsXgMS2NL+OJLv4hisAh0gOXpZfje6oM/4Mfj73wc6eu6eY7zu/PI794KWJGdz/YOhg4w/+A8js4eRdVXFdBttS3YDhuBrwQw9/45uIouuH/fjbvfcXf3e9vCLX94C1yPulBpVTA6OgrvP3pxsHEQqUtSmP3zWfjdXQCWSCQk93bkqxGMfX0M7bNt+CZ8stDTOqJWq2H+2Dza3m6/h0IhbG5uYmRkpCfASKlUEqUQASzZapbTaDTQaDQwNDSESqWCUCjUDQz3FHBstVrw+XyIRCLI5/NIpVIYGhqCx+NBqVSCZVkol8u46KKLcPz4cQGs2WwWk3dNYu3cGpb/aBk2bDznz56DyDcicMa742h9fR2NRqOrAAkEMDIyIjm2E4mEgGial09OTmJtbU3eMRKJIBqNYmlpCWNjY8LCmAoCzqtarSYsfK1WE/aecRRSqVSP+whTeiWTSbhcLuTzeaTTaQmC1m63Rdng8XgwNDTUA+bZ5kwNGA6HxUogEAjg1KlTMie5Dng8HuTzeUklOJCBDOSZL4lEApdddlkPIDGZNg1A9HW8lsDFNCnW5Zkgm//znMCzBc8Bmt3l3yZw4flFgzczyJc2rdUgSIMV7aeuzyYXAo+6jUxQqMvuBwT7KSDMupvAtB9Y16bX5vdmvXktAbH5/k/3jnovMJlmfZ0Gl/p7DSSptDbfUZevXRTMnNNm2+oxxM9N4K7FBLWsK5+nmV9ezzpqgomix6fZr2ZdNUjWYJ9m5DqbjLYgIMFERRPL0P1PKxJzfrCsfooC3U/aNRQA/H4/fvzHfxzXX389Xvayl+H9738/vvCFLwyCqf4HlQHg/jfK2NgYbrihm8Iql8shFAoJE8YAW16vVyYi2UICVS4+p06dgmVZmJubE7/nmZkZlMtlAFuMualxI3tbqVR6fKRrtRoCgUCP2ROZbq/Xi3K5LJOX7JzeYDVgZf0LhYJERq5Wq8IIau0jwT7NkLUPKiOaf3noy7hz251oO7vPqHgr+NyNn8NrzrwGj9/9eDci+F9EMZWcwpnLzgAOwJV14cAfH0DCmUCukRPGf2RkBNlsFm63u2uy37YxOjIKoFfTaYdstH6vheoLuiD407/0abzi069ApBnBt6vfxkM//xA6zqcWRAdQvLmIM79/Bq6MC5krMhdkqz15D9reNtq+Npx1J+Y/Po+5f5pD9HNR3PsX96IdaiNQDOCaP7oGD73iIVz7l9ei5q+h5W7B/7gf1//h9ciFc1i4awHNchOBYAAOhwPBYBDFYhHb7t2GoX8eAmwA/u7CzfbtdDrwwYd4LQ470V3kC4UCYrGYpCrTKT04rmKxGNLptLgV+P1+cTUgOwxA6sHP2aeNRqPHKoJ/A92AXh6PB0tLS6LksayuXxOZ7pWVFRljlUpFxteNmRux+TebKG2WELo7hEQyISDd5/MhGAxKSjHLsrC5uSlm3Ol0Wsy46/U6bNvGzMyMpKdjBHnNsjidTkmv5/F4cO7cOQwNDaFYLEqKsVarhXA4jFwuJ5sezdWnp6dRq9WwsLAAn8+HkydPymY5MjICAEilUuLfDUCUGrlcToB5oVBAIBCQvOxOp1PcJRqNBoaHh5HNZuHz+bCxsSG+7DRtP3fu3GBjHchAfkTkhhtukLODVtabjDVFM78UE1TpNZFrs8kSA+eDVhILGgTyvEHlP63xWKYGNnSr08CHTKgJzPSZR5sw85m8X//WbKjJaveTfsxxP+Zas5b9gHS/8k3QqwORmSbaZr/pcs2y9bnNZJJ1G5jvp99DP6vT6fRk1dH10yCZv3UdeY1O6dVvXLJPtAk7P9cgn+URYJKw0uOVZA7Br2nNabanHvetVkusMfsx4xxnOjCaPmezvYGtiPscoyS42P5aEcJ3Yr0JvDUJR9JDKxm0yybbn+cSh6PrCnfJJZfgYx/7GFZWVvDzP//zePLJJ7GxsdEzTgfyf1cGgPvfKLt378bhw4fhcrmELdMTm6mGdJ4/TkQAwnjt2LEDnU5HmEOyyoxWrH03GLDs3LlziEQiiMfj3bzPyjyck4tMKJlppg3TGjbLsgRMaXMaXkONGoG6zndMnyyn0ylRl7lYceGg6S4XyRsXb0TFW8GnL/k06u46wuthHPzTg1h9eFWAlMPhwBV/dAU6r+9gdfsqFt63AOc3nVixVoQ9dzqdCAaD2Nzc7LbH1Dl87We/huFzw3B/zb0VETJgo/DmAvAzW/22uLCIL734S3jBP70AxZ8uCpMuYgG1fTXM/LcZhN1hnH7pafG3Hn5gGJ6WB5ntGRz40AEUp4p48qVP4uIvX4z5f5rvKiMKTVz3u9chty+HfffvQ7FYxHX//Trxf7csCy6nC0P3DiFux1FrdIOjUYkRDAYRDoe7qdgsF8KRcI9mlb67BG4AJJgdAV0sFhPFBKNZJxIJeDweJBIJWaQZvIObCn2GCcS5oDN3tbasKBQKwrrW63XEYjE4nU4MDw+LkqjZbCISiUj9aP5MYMz0WOVyGRN3dPNLd+Jds22CVQYU5ObtdDrFr7tUKokfNpURjJpP/+toNCrP29jYkDkYj8eFuWcedY5X+kk3m91UbHT5oOKIgc6YA5zBAsmQ5/N5qTMPnZlMRjZGfWCu1WpIJBLI5/NyDfOH27YtkdkdDgdSqZQoVfL5vKSoG8hABvLMl5tvvrnHjFcDKqC/aa552DZZbQ1ueY8pJoOufWQB9AAP/RnBBcvoxxJrRlcDNr3X8LcGfxp8XchEXptNc88zAaxZrlYs9AO5WvS1+rMLAfx+rL35nf6+3726fJ0urR9za5oz62sdjq2o2uaZz3wXiu4vs1zud2Z/8Fn6edqasp/ovjDbQjPApkLJND9ne/F/rSBgsDOzz9iGZtA1WqHq4HumEgLoDRBnjlPdT3oO6bzcnDc8o9NFkG3LPtK4QreRw+HA1NQUvvSlL+Gee+7Bxz/+cdxxxx1YW1u7YHsP5P87GQDuf4N4vV688pWvhNfrFdNpPfipJSTLRm0WD/7aLJsm3uFwGJZlSV5egixqupjeS2sxCaC9Xq+kw9CsJQApnz68NEPl5OWmRDZaa7cJxgFIYDWdbsE0M+dzCcb4bC62lmXhRadfhEA7gP+z8H9w+V9cjoW1BdRjdYyOjmJxcbGr0XS6cPmHLse3vd/GzJkZNN1NAWwMpFWr1bo5inev4/6fuh/VWBXld5ThzDvR+cunNoVhAG84v//ObZzDvQ/ci8kvTyK/mEfqv26l2PKkPNj9R7sxdHIIgbUA/G0/nnjFE5j5zgwu/6vLUUlVUDhYQOjOECZDk4gWopj8+iSKlW40+FgsBt+GD+3PtJGJZEQxwcVXbz6dTkcYS8uyEAwG0Wg0hMXQ/uocYzStpoaXAIybBKPhU4tMZtjv92N1dRWjo6OSYqtYLGJoaAi1Wg2FQkHANgABln6/X6LUFwoF6UeOHTLhjLrNcc+xTfPtXC4nrg608nC5XMhX8zj+wuPY/0/7JbYB2We+AxU9zEEZi8WQzWaRTCYBQOphWRZKpRLm5uawubmJeDwuuawZzHB4eBjj4+NIpVJwOBwyb2j+nkwmJSp5oVBAsVjsCXrIKOSFQgGbm5sYHh4WE/x8Pi/j1LIsmQt8BwZKLBaLOHTokMxH7XfudrvF1J0ae7qZ5HI5CdDYbDYH/tsDGciPiDAdmAk+NEig6D1cM78aTPVjL3mPBvMa2GjwQbNZsnG6DNYB2GLi+ByTkdaudlp4remDawJFzVpq8KHfA9hSAOgfs03M5z+d6HLM9tUg0ATBJqA1+838TPeLVnro3+Z3JqNrSj+rB/0cE9hr5Q3/1tdpMcebCQo1m94PiOoy9Ltp5pl11r7cvJb1NBUhuixz3OsxpoE5n8NxqM9trIvuC91PHPeaiNNWGrpuGpibyixiCVMhwM9M1wZtPXHDDTfgyiuvxL/8y7/gnnvuwUc+8hEhTwbyf0cGgPvfID6fD7feems3mrPSNNFEhew1mW+HwyEghCw1JxCZaYJi+nHze4/Hg1qthlqtJr6r09PTaLfb8Pl8YuZNEESAS9aSJjlchLQWjamFeC3rR7Nzgjey4wQC1WpVIjtzAdI5v/md3uyoFLAsC89efDaGTgxh8cgilrJL8Hq9PVYCzWYTzVITlbsqKO0pSURTPoMpmbLjWTzw+gdQHekqBuy4jdZ/byHuiGP47mEMTw1j/cPrOPFzJ7qm4Tbge8iH5FuTKNVK8Pl8WPjfC3D73Fh+6TIcLQcOveMQvMe8CIVDcDqd2Pm5nRh7eAxDxSE40054mh5E747C5e0qHOJfjqPt7WpsL730Ujz55JPyHj6fD7lcDpFIBIVCQXKn53I52HY3KEY0GpUFnf73jPKtg+sxGIZOs0L/6lgshkql0pPf2eFwSMorssiBQEAC8NF3m+PZtm0BtoVCQZhjml5xbFAJFIlEUKlUephfKnsKhYLkkGcsA1FGPJWuq9VqodFs4K4334XURSm02i1c/KWLJUgZ22dzc1PGRzQaFZeJQqGATCYDl8uFvXv34syZM8JI1+t1CTxWLBYlrzfb+OTJkygUCqItLhQKmJiYwLFjxzAyMiKxAcjmuN1unDhxAolEAvV6vZsr/an2XFxcxMjIiMxzKqoKhQLm5uaQTCZl/gYCAWSz2Z4NlX7e7ItKpSJrRyaTQTgclnlDQM6AdcxsMJCBDOSZLQcOHMDo6Oh5DC3FZBs1COV+oQ/qpsmyLoOfaxCtfU51HUzQo1l3DdK0GXGn0xHrPRPgA1uMuWZw9TsTwLBOT8fy9gN0JlA3r9XgBdhiMPkuev3Wbd2vTfs9718Dqvoes7/NfjHfjXXS95iiFf+6rXTK2n4KBQK8C0UevxAbreus9zpdtwspCDRA1+NX+25r0KnbQgNpDZy1D/SF6szPecbVrDIVTrrvKVQ+8Uez+Rqsa0WTVk7oMWey9LrvtFLFtAbR9/j9fjz3uc/F4cOH8fznPx8f+9jH8JnPfGZwdvi/JIO0YP8Geetb3yosK4EpwS4PxozgTfaYTBWZawJQHe2Ti53WhJEFjUajqFQqAmSZl7hQKPQsJE6nU/x8ae4rIPYpMM56kk2PRqNSP60AIDAk2ObiR1NiLpwE+KVSSVhMsnIE6TSNbTab8Hl8GF4fxtDQEHbu3InZ2VlUq1XMz89jenoarVZLokEz1RLN7J1Op4Ay/1k/tn9mO5z1pxbcloWZb83g4jMXY35+Hs6WE8MfH8bIn4/AaljwHvdixy/ugDvVNdHJ5XKwShYm/3gS+67dh+e+6rnwH/f3KC5ivhjGF8fhzrilHwg8ga3DSKfTwX333Yf19W76sNHRUcmfSAsG+uqybQhOb7jhBmHC6d/LQ1LaTuML7/wCOmMdCVRG02P6azOXOvuGOdoZcZ55xIEuoFteXobP5xNFSrlcFhcGAtFSqSSpsEqlEtbX14VRdrvdCIVCPcHDtO/X0NCQRNJmyrdOp4NSqYR0Oo2xsTF4E17c99b7sHLpClreFh54yQM4evNROLxbJpNML8ex7vP5EIvFEA6HMTQ0hEKhANu28cQTTyCfz6PRaKBUKqFUKqFWq6FarQpAXVhYkIBr9DOv1WqixEkkEgJkM5mMsPtzc3MAuhkJ0uk03G436vU6JiYmEI/HEQgEpL0nJyfF19vv92NjYwPj4+NyCMjn8wgEAt3I+IWCpBjjwTEYDIrSjkqSUKibC93r9YqpeS6XE8Z8IAMZyDNfDh06JCk6NRjj3s99CNgCGEzvZR7ENYBkUEgKy9LKcq7v/GG5BNH8mzExeA8/0/EzWBemWyKAM83FqSjwer3iimUyqwTdBDW00OMZqtlsyvlDl2uWYTK+GnRpM3S2PT/XJtRmoC1T8aDLNcG9CUxN8KefqwG4/q5fH+rydZ1ZjvYRpoLbfAdgCxxqywLdnrrdTNBsKg5MRpb11u+mwbE+H+t3Ma0BzHI5LrV1JT/T78j34Nxhv+n5ZCo7qGji8zjOaPWhFR6cGwDk3M36a7N13sO+I3DnmZltod+Hn2nrjn7KHoejm0b1uuuuw5/92Z/hvvvuw1VXXYV4PH7e9QP54coAcH+fMjIygttuuw21Wk3MYpkaSW86NBHnhscNKJ1OS/ogbjQ0Edagl6ww/UY4oTXDCQCBQEA2JgZaY4A2midzw+MzmDNYBzXh5gdA6hqNRmWD5SJB0E9/WS5EDI7FgFM0Xem3KFYqFUSjUfFVPn36tPjDZjIZAF0TOi6E9IulEoNKBZfDhbl/msNl/3AZ3DU3dn9jN7Z/YDs69Y4oIprVJib/YhJjnxjDtldtQ6fSEeAeiUS6C1XbgYgjgspGF8jT77hSqYivtNPpxOrqKqrVKrxeL8LhsIwB3lMulyWvN/1+aarNA0Cr1UIoFMLU1JT45nzxi18EADlc+Hy+bqTrkSIe+b1HUNhWwJ1/cCfa+9rCgKZSKWGrG42GsMoEe/TpLhaLMv6oAKHJOn2rmXbK7/dLXWkVwYOMzhnd6XSQTqd7NN60tNjc3BTzbb7rqVOnkM1mRXHj9XqxevEqVmdXJSid7bRx9OqjyPvzAoT1/PJ4PCgWi1L+2NgY6vU6lpeXxXSdgXro4+/xeIQVX1paEvPx0dFRSdFH3+319XXMzc2hWCxKpHBu8NVqVUzaA4GAKJS4wVELznZjzu+JiQkcPXq0O1ZdLkSjUYn54PF4EAgEcPr0adi2jZWVFZRKJVSrVYRCIVk7tJVMMBiUPqN1wUAGMpBntvh8Plx66aWS3lADHZPZ1mCyH2trAkIdZwXoHzlaAzV+r0Eyy9dsm75WAzETkLBcnmsouk58Hn/rHOH6GpPZpbudBmoaYPRjmk3AawJjk/llPcx302WZTK4GdboPzd+6HPM5JojVQNIcFxeqt2kGzfu0a6RWrLBNTYbVfF+zzS7ExLIMYMuaQY8T3qOVF3rsMwYNz4k6iB+BOt9HW3jq8vgsntO14oRnH5NBJwlFEov5uoEtE3DNpOu5oNuo33tRqcHsJzpYmmb7G42GvLe2vOAzNGGnlVkulwvbtm3DV7/6VXzqU5/Ci170okEe7/8PZQC4v0/5pV/6JWEjCXR9Pp98rxcVasnI9DHwGDcXp9Mp/qFkFYEtf2lgSwPIa/g9gQ7Za4JyPpOTlgs8D+2MYE2/Vda53W6L4kBrFflcAgzWnewtsBVhVJuNkakjQGYQCIfD0cN4hsNhAb5kCmu1GkqlkiwcLpdLgCMBqWVZsple9JWLcP0/XI9nf+7ZArxoPsyFc/aDswjYASmT19BnmItVsVgUwMeNg5YMBJ2Mjk2FBFOUEfBxIaRShWnYaJpNxrhcLqPT6SAUCiGfz6NcLgs47kx38OjrHkVmb1cBUUqW8K1f/BaKu4rSnqFQSMYIF1wGX6vX6z0++NyY2PeMKk4mmD787DeXyyWm0/x+ZWVFQDPNnKkgyufz0q7sl5GREXi9XmHXvV4vcrkcVlZWMHHvBK7+xNXwVLpjcOT0CA79ySF0zm5tJrlcDj6fTywKOI9SqZTMo9HRUYRCIWzbtk1YdZfLhWw2K4Cd46VQKMCyLNlgMpkMvF4vksmk1JPAmOOPPuxutxsjIyM9CjC2K33ws9ksUqkUfD6fgGa6iXAsBoNBUYbQWsHr9WJ6ehq7d+/Gnj174HQ6JdXZyZMnpQ85pqkMGTDcAxnIM1/m5uYwOzvbw6jyYH0h0GOyjPpwD2yBaG2WrcEO0AtidVn6eVyHuH/quDAUE7gAW4E+dTYNTQr0Y51N5T3PIWbwL5MFNkGw2VYsv9/nmsk1294E3fq9TeWHvt58t37tapbfj9lmffp9pgF5PxCuRTP0Zv200kW/i7YM0AyxyXqzbfqx37rftQk3zxdaoayBsu5rHR9HC1l5cyxqQKrnju4r8/21gom/taUA60VSxVQw6EBqnG+cM1oxoME4FR4AzlOm8Byt25Z90K8sip4flmXhmmuuwUc/+lG8733vw6/+6q+KxeJAfngyANzfhySTSTzrWc8SoMFJTrCpTbU6nY6YV/l8PgQCAfh8PoyPj8vnvIa+M0y9xUmjtWIEM5y4jGZOky2Xy4VAICAm4TTH5iTlvfSF5oTudDrCjAJbQdbIrtOci4sg35HgHYAsesxbrDXSkUhEACs3VzL0Y2NjAl6Wl5fF3JZtxJRO9CkmuKGVgE4zdeC7B9BqtKQ/2G585plfPwNXzCWmbCZD7/F44I/6ceQNR1AoFMSkx7IsicDN+3jv5uamACYN9NnnBKPsv0wm03PwoWk+/fFjsRhisRja7TbC7TDCq+Ge8RfMBeFY7Y6PYrGITCaDTCYjYJ8KACoQbNsWsyEG86KihaaEVAQ5HA4Bzo1GQ66nMoaxBXQAPipoGLCMY4VKKG4YuVxO3CvoWtButzHx7QkcfNdBDG0M4dbP3oq54hxqtRqmp6dFqbW5uSn+TJ1OR3zCT5w4IXnA9+zZg6WlpR7TrWQyKVYmDocDExMTcLvdyGaz4mc/NDQEy+oGWmN6ruHhYcRiMYlrUCqVRFlB824qasLhMGKxGFqtFiqVijAqrDuVW0zdNz09LX1Olnp1dVXGbC6XQz6fF4XK6dOnRUFTKpVQKBSQSqVQKpV6DkgDGchAnrkyPz+PycnJ81hLrm0aIJh+zby+H7jU+7Q24dVB0CjmvQQVOmUTzxXcB1h+v2fps4N2pdMmuMAWoOAZwwQQGgyZAMoEkdqUmucos5102aYiQu/d5n0mwO33fz/lRT8wbAJmExRqMqefAkGTPvxbs+MakGmwqPuoX3vqPjGVBwSPuv793qefIkDXkeXwb9PcWz9bg2bWV7+zVuCY86VfX+i66H7RViWahOLzOPbNdjRZdbaXnhMmWGYcH84lbU2g20b3n/lss494nVYCcCz4/X7ceuuteMc73oHPfOYzuO2224SIG8gPXgZB074PufXWW3HgwAFhzjhoudCQhSVw1v6rOk1Gp9ORQznBtm3bAmhpOq6ZS048msxqs3IuvF6vV0y+gS1QTCaTm5aeyAwuxWfoyOMABBATnLvdbgEFvKdYLAro18oCDbTJWrKcSDyCh44/hNHEKGZmZjAxMYHV1VXEYjHx2yZgIWtZq9VQLBYl/3GxWOwB406nE+VyGe12W1JGwQOkfi2FtZeuIXdNDnt/ei9czW57+3w+xONxZDIZ2CEb33jvN1CaKyEYCGLXn+2CF91gXQwwVqlUelJVNZtNTExMIJfLSfAymvNT8UHzd5o4h8NhCQRGbW4ikYBt2zh79izC4XBX2dAM4Kq/uwruITeOX34cQ8eHcOX7rgTKgD/gx9TUlIDGcrmMQqEggbloUl6r1bC4uIhEIoFcLodAICCpwdbW1lAsFmWcMdidZXWjpZP5Jogku5xMJkVZxDgGzFmdSqVkbA8NDYn5+NTUFHw+H+r1utzPfOPjx8ax8LsLqJVrYkVRKBQk8NmpU6dw/fXX49SpU6hWq+Jr7nA4EIvF4PF4cPToUWQyGQG/NIsvl8toNpuSG5vfh0Ih8ddmmrVarSaB4tbW1lCv18UHnoqk4eFhCWhH5QPnmMPhEN/6RqOB9fV1AdiJRALlchnFYlGAOVOpMQgbGfVGo4FarYZ2uy3WAgCwsbGBoaEhMa0vlUoDk/KBDOQZLi6XC3v27MH4+Ph5gFD/1sGWTAbTZBdNMMnDPNd13qPFBLs8Q2ggoQOlmiwbTYZ1AFQTAGqAxXI0AOR92uwXwHkggvVi2Wa9NfAwwd+FmFgT6PFz1l0DI7ONTRaa+4Y26e7Hevd7B7NMs5/Md9DX9xsXur58pnan0m0GbJlKm/WhpYMJNHVfsU70B9fP13EIWA6ZXV2mqWQA0JP3Wo8VrXBh3+hga+xX/dtkpnWANX0OZxk6Wj/rrQOq6bbT1qM8i5tnf17H57OdWDbfXyun2C4aE+io/FrBoPub72pZ3TTGN954I6688kq8/vWvxy//8i/jiSeeQLFYPG98DeTfLgOG+3uUaDSKyy67rAds0fyZmwcP7DTb5bU0v7VtWyIQl0qlHjCttYBMDcRJRdZMa23NYCHcLLXfCAGWXiz5PDKUZNx4X71eR6VSQb1eF39ZrdXTSgEusgygxQBejA7NuuuNvVKpoIMOvjb/NXzl576CSqyC48ePAwDGx8cRjUbRarUwPDwsrLHX68XIyAh8Ph+mp6eFjXU4HOKLSxaRJsLJZBK218aZnzqDcz95DnABtekanvxfT6IyVhEWu1AoIBfO4dHffxSlhRLgBE7efBJHXnUEFUdFzL6pTCHrSQUD00Ylk0lUKt3rGT2efsi2bQuzzCjkZBLog+9yuSRgXKVS6b5PuY0Df3QAk1+dxKE3HkIz2xQFR6VSgdvtFuaZ7aRNu1qtFoLBIEKhkJhgdzpd/+vh4WGJQE/XgEqlgnQ6LYw5FQQOh0NyS7Odg8GgmNY3Gg1xDaD//uLiIjY3N4WNZeCzYDAo7gOxWAzVfVVYhS47vrGxgW3btqFarWJ0dFRANdueTLPX60UsFhPTedvuRtx3Op04d+4cKpUKstlsT/DA5eVlsfqgpcTIyEhP3IJqtYp8Po9ms4lTp07JM5lZgHOM5bKdR0ZGxIzd4/EgGAxieHgYgUAAnU4HQ0NDkuKNzDmVY8PDwxJJnszP+vo66vW6KDVWV1fhcDjEUobxDgYykIE8syUej2P//v095rdAb2oo7ueaOQPOz+2sTa81GOM9mi02gSg/M1lUXmcGdOKPtv4j8AbOz9Otzzo0Fac1Gu+jIl8zhprl1s/VzKfJrurP9buwPO1PbDK1/ZhfDZZNRYgGh3wO2VFTgWL2y4XeQYvJ1pv9diFLKP0sLSaQN9lgglM+l+QOiZF+ZelxqEkqWk1SUcNyuUdq4EngyLMS91DLsiQwnrZ00GOjXzvxs35Mth5rWhGjy9VjhGOP72/GOSDI5lmFY4Htx3min8V5o91CTYWCNh9nO2lrAD1G+H4XmvcsIxgMYvfu3bjjjjvw5S9/GTfddJMQFgP598sAcH+PsnfvXjz/+c8XcwsCHKYs4mca+HJh0QwzJyIXAJr3cvLxM22qQ1NcbRJVKpUEzNN8lYAeAKrVKorFojB0rIu5aZCd1SZHjIocCAQkAjYP+Zyw9LHhu9RqNQAQH1e9YGuNqNvtxhfmvoAPH/owTu05ha+++KsIbwsjkUggn89jeXkZLpcLq6urslDk83msr69LcDCyhPF4XCI36yAV9IV3hV2wL7YlMBcAtKItVMeqknO63W7Dvd2NZlz5w1pAebqMot0F0/rdQqFQj+aVDDajs+v0aQRJXHR139BsnyC40+lGjGW9Go0GVldXkU6nsesPd8Hv9YsZMkE8lTLcvLTJtdPpRDgcRiAQwMbGhlgE6MBzNBsnaCfLTeuEer0uftvNZlNM+FutFtLptLDAAMT0e3p6GuVyGYuLi+IPX6vVkE6nxSSf8+DI3iP4+v/zdZx81kkBo6dOnUIkEpENwuPxoFwuIx6PSxRwpsQKhULyP+uxf/9+AdLDw8PSVvQJT6fTMv4jkQiCwaBYJVCx4nK5cPDgQSQSCViWhUQigWQyKanI2OY8ALDNx8bGxOzc5/OJZYS2QOD4oZn4wsJCj+LK4XBg7969CAQC4iKSSCQkowDzgOsNdSADGcgzU+LxOPbu3dsXnF2ITdViAmaTvdX3m5/zO5NVNIGfw+EQxT3/p0uVjhRNIG0qD3j24H6qSQXN0PVjnjUrqUGEWU8NZsz36AegTcDc7z7ddrqNzev0Nfr3v9Yf/Fuz6/qzpxNTWWDWy1QSmMBYt6dZZ+3PrN0eSUJp02v+aNNwEjzaN9kE8Vo5o5XtvI5glOXxXKKBO9/Dtu2e1Hj9gg5qZZVWFJj9ot9Bm8JrpYBuLw2wWR/dPvo9tAKKMYD0b37HtmFddD/x3fXztdsA68KYMqyTOXYA4LLLLsOnP/1pvP/978drX/vagY/3D0AGgPt7EK/Xix/7sR9DMpkUZpWTgKYkHMA0E+aEZGAQzTwT2HBCMCKhXvwZZAvYCpwGoCfHMxcAml4TdLE+2ueDi6PX6xWNWSAQEDNwAi+yeZzg2icc6E5iAjQCQ82AM6ADF2b6ChOgf2735/C3+/9WQPADcw/gk//lk7CdtoAf7Z+uNzwqD5giyrZtyYccCoUE1PJ9PWUPJt8zidA/d4OLOYtOzP/WPEL3dk2hM5lMty/u92Pb726Da7PbR4kHEtj7J3vhzXqlLWnmSx91RvRmm5L5rNfrAtoY8IvjgYs6FRIMpsUNSweVi0aj8Hq9iEQiorDhWNQB7/SCSdCey+WQzWZF49lsNrGxsYFQKATbtpHL5WQDYN1zuZxErOXmRN9mXkczcSp2gsEgGo2GKDwKhYIA4WAwCK/Xi6WlJXQ6nR4rjWAwiPVr1nHXT9yFWriG+3/qfhx51hF5rmVZWF1dxeLiogQwS6fT4jIwPj4uTC/Lpmk/x//GxgYKhQIcDodESc/lcnj44Ydx8uRJnDlzBkCviRzZbCoYGMHc4/FgYmJCIp7TzNuyLKysrKDT6eY+j0ajYuWRzWaRz+eFeefBk8oip9OJbDaL4eFh8W8/ceIE6vU6SqUSQqGQHE7Z37ZtIxKJCOAfyEAG8swVy7IwMzOD7du3A9g62APng2R9iNffaxPtftf3M63Wey7XLr3PmCwrsMV86r2IZxszoJVZNoEAz04mA62Bgqlc0CywycT2e99+bLcJTPWzTUBsAlMTlGoxlRT92stsE/NeEyjr32wbE9T3s1DoJ0/XDrxXv7/J6PJvbULO84bJyHIs8DyqxyTPsBps6n7X5JVpEaqBq+4/3U96bmhFgjln9LlVA2dzLPQD7eZYN824eS/3frNuJgDXmYR4LtRWIvqddT36jV1znOp31mNQtyfbORAI4MUvfjF+//d/H3/913+N2267rcfMfiDfnwxa7nuQoaEhvOlNb+oBnRQe2HmQBrZAUb8Ni+YyNMOlWTEnhj5I8yBOzRLNiDnhstmsmCRzASBTSPYPAMrlMizLEv9rHQG93W6jXC5LKiRey/rH4/Een1ICHKZDoFn9yZMnJdVVrVYTEzdzEb/xxI34xrZvYCW8AliAq+3CL6R+AdMT0wh4Ajh27BhGRkZw+PBhnDp3CnBAgnL5fD4xm6Zps2VZsL024ADKhS6zyjRrzWYTnqoHO/5gB47Hj2P7/9iOoc0htAJb5vCVSgU+nw+R4xEsvHIBy+9fxsLbFmDXusx4LBbD0tKSmFYHAgFks9mePiXQ1qbH6XQawWAQiURCgCJNjsLhbjA0BidzuVzY3NzE0NCQBBXb3NxELBbD8vIywuEwisWipB4Dunm+AUhdnE4nQqGQRJGn5QIAYRui0ajkEtdKDY5JAJLGjOZ9MzMz4rM/OjoqObyz2axYEtCyg2B/cnISfr8f1WoVIyMj8u623Q3iafehtgABAABJREFUdnb4LP75Zf+MaqgbRb0RaOCBlz0Aa81C+KthYeK1K0ar1UI+n8fGxgb2798v/uLc7FKpFGzbxvr6ulgJEHBzXhSLRcRiMZw+fVr8z8PhMLZt24Z0Oo1ms4l8Po96vS5zudFo4P7778fY2BjW1tYQiURw7NgxUbb5/X489thjAojJeNNnfXl5WUzpyQJpd4zHHntMwD0j3q+trQEAhoeHsbm5Kebo2rKBbT+QgQzkmSlOpxNXXnmlZEExgaVm2zTwMQ/bmlHWZwyCCtlH1YGd6xvQe0jn8wmqCE6472k/Xh1QzQT8rDcVz+12W9ZQ7eOq663NaIHewFD6el1n3s/rNfjXDGA/kKLb0ixXm2pzj6HVE6/TZVFMVtt8hn4uQU8/ppnP7VdPE2SZ7WC+j66zrmO/67XfsG5jDdw08KZyXteZzzSZWFqHkrgywSPPWxdSDOgI+RdSGlC0EtsEo7pt9b0koXRdSPKQhOMZn4y8fr6pkNLP1OcczgEqozh/aMmqA7ZpJQXHiz7PmQoTYGuumAoRzcLr8W1ZXavXW265BTfccANcLhc+/OEPYyDfv1hPpwHrudCyvrcLn4Hy+te/Hu9973sBoGcT0qC60WgIG8w2pZ9lNBoVlpRmzCYLzAM6sBUEolAoIBaLAdgK2FCr1WQRIyOtP+dmp81PdD1pOqwjUXMh4X2c9HqD1AsoFxKCNqaOInjj83QgCADI5/PdP7zAO378Hcj5cnjtva/FweWDYjY+OjqKsbEx5Kt5vGPjHahP1TH+P8fhqXmkTekf2/K2kJvK4cSrTmDfd/ZhxyM7cOzYMWQyGYRCITG7t20bzR1NeM544IKrh3nXua9brRbsoI1yoAzfqk8AMwO9lUolAdVcYKkEYI5m9l29Xkc0GhV3AW5C+XwewWBQImizn1KpFDwejwBUnce80+lgZGREgra5XN38zNlsFrZti7mxzsvt9/uRy+WQSCS2fMLbbbEEINBn8C+tfbYsS8A9lSsE53Qj0C4ObrcbsVgM6XRa/qeJdy6XE1PoSCSCVCqFWDyGe+buwSM/9wg6sU43ENx7/bjxOzdKGiwqf8bGxnpyxFOhxE0lGo0inU7LnKRihgHbOJ/4jrZti8KDmw0DqTkcXV/1cDiMRqMhacm4sWll0/r6uswpKoRo9QJ03QbIwpPZB7obPZ9v27aYx/PZp0+f7ste00wTAEKhEDKZzI8Ey23b9vl02kAG8jTyTDmr+Hw+fPazn8UNN9wAAOcBIy0aFJkMGNB7wO9ndq4P/QTgJuBmHbhv6fOGNvnVuYhZnq6j6VNqnj/6+ZxqJrDf+cRsE5ar/Vz19f2AGetmAmJ9jf7pZ3quFRZ8ZxPw9QPQpnsA29Z8Jw3eTDDO79lX2txbiwa9Gphx36/X6z1nVA3IeIbUZWnFC+8hCNXWkRxT9L3WQFKDxX7sP8vTZ2yCfwJtbWWhU+tq5lYrEkgacTz06yt+p9+VY0Sfl3RsAQ1weaY3xwnL1L7gLN9UxmhXDf6vx4z5rlSI8d10v+hxpN9X96/+Xo85h6MbGPaNb3wjPvnJT2IgvfK9nFUGDPe/IoFAAL/xG78hzJRebIEtDRIjGmq2mdGHo9EoAPQEJKEGmJtCtVqVsgkmyHwD6GHUdN14WOcCZOZTBtATZdwM9KYBNBcPPsuyLHz729/GFVdcIRpB3q8Drdm2LaCCwE5r3AlqVlZWEAqFkEgk8Bt3/AaOjxzHweWD4pe8Y8cOab+vX/Z1nNt1rlt+x8a+v9oHV8slYL5UK+Hkfz2Jsz9xFgDwtUu/BuuvLbSf6IJKtlUsFkN6TxpH3nQEo58cxcKXFqQvGBwM6LL9tWYNmZ/LIHtxFgf+5ABcJ7dyVjMwnI5OH4/HhbkdHh6WYHjcOIrFIhqNBuLxuJgnx2IxUa6wDev1ukQbZ7Rq7RdXb9XxxIEnsPP+nfB4PDhx4gRGR0cF0APAw/sfxsydM7Ih5XI5LC8vi3LloYsfwtw9cxKAjPeFw2GUSiUMDw8LMKav9enTpzE9PS2R2dfX12URZ/A39vXJkycxPDzck5OVPvcjIyNYW1tDLBbb0lz/tYXkZhIbb9mA+71u+P6XD991fRcOh0P8nWkZMDExIQoMjmeadmcyGak3x3ssFkMqlUI0GkU2m0UkEkGhUBALDSoZCKzJTDCPOrAVA4FR38nSOxwOZLNZcaughQo3YLLyZGx4EGXQOFpnBINBVKtVhEIh2Rw59vW6Qul0unEcAIjyYCADGcgzV5LJJC699NLzTGT1gd78DujP9urrzMO/NqHVP/o8oJ9FNlH7kZrmv9o0VZ9rCIw0iKdoZo1ghp+zbA2ICX50e5hA2QSMpuKgXzv1q5duN5Z5IRZc94HZ3hqkU0wmvF955nVmOfp9zf7X9/f7W5fBIKcXul/7b+sI3fosqUGsZq05Nnhm5R6p363VavX4+fMZDM5Gl0FeC2xZmZIMoCUEgJ7I+yZzqyN562eZ53vt9qfv1WVR6cQxRpDLuuhyOKfMMajHs2aftTKM92g3xX6gne+mlWv9lFN6TdGxllgfUxH00EMP4Wtf+9p5Y2Mg35sMAPe/Ii9/+csRj8cB9Kbc4OSoVCoSCImaQQLv4eHhnvzHPHD3S6FFkKPNc/WA54FeRyO1bVuACZk3vQiYB3amtiKjS1Nb3mvbtjB8zWazx+/UsiwJgEWgoTcwTnxtYkZNG9BdFBcWFuS6eDmOaxavQQcdAZbcRD6x+xP43I7PSd1TP5HCE7EnsPvdu+Wzo687ipUXrmy9q8PG11/ydUwuTyLyiQgCgQAymQxKe0pYevMS6uN1LP3aEhxBB8b+akzMtJvNJoaGhgAAJ197Esu3LgMW8PAbHsa+d+6DK9dduAqFgphiMzBarVaTPOMMgsbFi764ug0YdM7n82F5eRnFYhGJREIY6XQ6jfn5eQF/QNcd4L6fvg/pH0/DEXFg7p/n4HA4sLi4KCz9k696EmdefAatRAtzn56TKO90Hzj1X05h6cVLsD5nYe9fdwNylUolYVqXl5dx8mdOYv7/zCPo6gYQq1arwgy7XC5Uq1VROBSLRWHXebhyOBxIp9OoVCoYGhpCqVRCMBjE5OQkOp2OsLOBQEBM8z3/xwPcBjje74Az4BTmneOIiqjFxUVRytBCQ5vwUdEEAJFIBG63G8vLy1hcXJRNmBuUuaFwXpA558ZCxQA3IpqOEXzr1HtUKJmiN1Oy//yfFgyFQkGuXVhYEEuFfgceCp89kIEM5JkrV199NcLh8HkHXxMsmSDcZKg0YDYP+RpwUPqBXvNZJgOmAXg/EKrPBLquev/Q4EIzkZqRBdBz5tDvwGf2AyAU/a4m02yaXveTC7HfGnxfqC80O8r30mK+Bz8zAZcJnEy2Uredydya9e0nGoTp600GlgCWba6JFu5RnU7nvGBbegxqM/x+ygjdxwB6iCLgfIsECkkrTfpok3MtpnWHZqtNKw+tfNF9quuo54C+h3NAnzGooNBzXM8TXTdTiaNdL0xljVZssUzzWj0OtJJFz0M9JzqdDrLZLDY2NvqOm4H86zIA3E8jlmXhla98Jfx+f48JCE2pLcsSE13LssTXkpoi27Z7/LMJJgheCaq4GBDI0heE/rgE6gxSpVMV8Zp8Pi9MWqfTkfzb2nRKpxOiKTR9cQnAvV6vsHmlUgl79+6VtiCLyPro4HBMO0WfaO1XZllbKRO4cPO9CDa138nzVp6H23fdjoqjy+g5O05c8o1L8OJXvBiFQgF//6y/x+olqz3Rx2EDWAUqn6ggdy7XBb3JOprvbKIz/pSG09PBuVefA0pA82+b8Pv9qFQqXTb4jctdAP9UmentaXz7976NS19zKWKBGJxOJ5aWlhCLxZDJZIQdPXv2LKanpzEyMoJTp05JdahUodaTi6jf78fi4iJcLhfGx8dRr9dx9dVX47HHHoPD4ZAI5sViEYFQAEd+4QjWX7wO22Xj/lfcj+JKEQecB/D4Y48jX8xj8dWLOPPSM+h4O3jsZY+hVW5h25e3IZVKYXl1GblX5PDIrY+g5WvhzEvOwG7ZuPgzF6NSqXSjq9cqOPuzZ7H5sk1kLspg9+t2o5jtpswqlUqYmJiQ+kciEZw+fRqRSETGNefCiRMnkEwmZfwxKjoj3lOTzc34xKkTyL0zB3vORvtTbbRua8Hu2MJI0xSdyg26UHBs0qxda91brRaazSZyuZwEt9Mbsj4omIdUuhnw8KgPLQTTjDhubkLmIc58HsvXddFzg0oYug7QXFyb2mmm3OVybblnDGQgA3lGyuHDhwGcD/J4eNdgit/xt2bQNGjS5fUDdkAvW63vA7aUlCzbZMUo2ixd+17re/X63K9eGqBooKTBhgYFer01n6efS4Ut38dsV5MB1G2i20m/twmCNRvJ//XfJtjW728qFsz+0woKs2zzGlN5YdbZBOuaWdV1JCDTMUj67X+mWbK5z+m6E2iy7bXSxtxH9R6ozciBLXCt92vt+qD7n/Un8cV2oIWqZVk9ftJaka4BMetNQoJlaEKA768BsgbnPAsx9Z12p9DzgLiAZeh5z/fQZxvTUoT9qcekbhPeZwJsc/40Gg1J4TuQf5sMfLifRl74whfive99L+bn53sYaTLJerLx/3a7Lf7AWttHEEJfaKfTKdeRpaaQbfN6vQI26vW65FPmxCW452LFycFJSeaPwJzMmdPpFNOhbDYrWkgdkI0pPUqlkgSwol8wgB7TIUap5rO4SBCokWEMBoNiFssFVG98BKS2bSPrz+Jtz3obmo4m3v7A2zG/Or+1wHrd+J2rfwePJh8VgBxZjCB4fRClTLe+rVYL7qAbvjf7UHxHUdrW94APF73jInjS3TbN5/NIJpNw+9y47xP3oTZRk2v3/fQ+eJ7ovkex2C1jbGxMFA18R2ArUF6tVsPw8LBoAVutloDXTCYjihkqN9i/R48exdDQkPhODw0NIf3cNI6/4TjsqAo0s+LE6KtG4T7tRvhlYRx52xG0h7YCwTg3nLjoty9C5ksZhA6HcOoDp9BINLa+zzkx9445WF+20HK24Px/nDj5cye7+QpswHmHE8FfCGI2MIuVlRWMj4+LP3QwGMTZs2fFz1xvhuVyGTMzMzhz5oyMa7fbDb/f38NuNBoNOMIOPPnKJ4E3QZ6LTwL+N/kRtaOYm5uTtpyZmUE6nUY4HEY+n8fExAQ2Nzelbb1eL9LpNFwulzDrnBvhcFgAPBl71oPsf7PZxPDwMNLptFgZkNmnJYjOnb2xsYGhoSFsbGxI3ASWF41GBTzraORUOJkWIZFIBGtra2KhEovFxGw/l8thaGgI+XxeNnFao+jx+EwXe+DDPZDvU54JZxWv14uHHnoIMzMzPZ9roK0BF0WDun5sJ695OhbXDKrWTzS7Z4LEC/lO96u3eb1Oq2kCZ81C6rWU+4sGQiY40e+v60OhBSKVt/o7E/jqMjUbrIGvqbzo1yf9lB792Md/rS94r3mdWZ4GgSaY1Swz66cVz3rMaEWMCeZ1W3FfM/Nma0UM79FxhmzbljOSdhlgn7FcrQTQ7DW/55jQ5t8EvKYSQLc1r+H5TL+fnhsakGsmu9PpyPmH5BrvJXDl/aw/y9D/83yszdP5fvybxI4ZzM4MnKb7SVuT6nFm9qVuEwBYXV3Fddddh5WVLcvSgWzJ93JWGTDcF5BgMIjnPe95mJ6elonASUMtmN5sOEDJxPEwb9s2vF4vHn30Uezdu1c0bjxEcyGi2SoP7JoRJeAhENeaOwDClFMT5nA4UCqVUKlU4Pf7ZeLati3BzQiEmZqIixZNo9vtNorFYs/EpVkxzXf5W2+kVDyQLeQCwIjgbEd9D83udVkTmMBbv/1WpP1pTJyZALxb/jjNehNvu/tteG3stehs6yB5MokDf3sAjzsfR7qe3tpYwzacL+oNPGLNWQjdHMLcg3Oi6HC73SheVoQj0qt19r7Eiz3OPdhY28DMzEwP+AIgDLnD0fXrnZycRDqdRjKZRCQSEc1+Pp/H5OQkkskkQqEQlpeXMTMzI7mrT58+jWQyKcHg4vE4isUiJr8+iZqnhuXXL6MdaCO8GsbQW4cw3ZrGpn8TgbsCmH3fLM688Qw6iQ58Gz5Mv2ca1a9VMTs7i+V7lrH3fXtx7A3HUB2pwpFxYPqPp+H4Rwe8Pi/KkTLOXnR2KzmgBdg7bLivcWP99nXEYjEBhDSHB4Ann3yyx8KC0cCZlktrSw8cOICHH35YFvlmswnffh9cz3ah5WjJc3EJUFuooXp3FWtrazI2jhw5Ihso83T7fN2AdtzA4vE4Op2OpI2jOTjN0KvVKqanp1EqldBqtVAul5FIJMRqhD7UjMrOg5pWXllWNwBKMBhEoVCQPNoMWKJzs1tWN/VZu92W1G7ValU+46bqcDgwMTEhY/D48eOyOTcaDaRSKViWJRYjDM62vr7+A1jhBjKQgfxHlUOHDiESifQFZqbbmAmsKOZn2lRWs14EDQRGGiTq5/N/s06axeRZiHsfzzQaVOo1kOulZW356ZpBoUgUaDBggkYN1jSwBHrBrwba/F+vxxpom+3IPU2DSu4RZnv3A9EmQ3qhe/S9Zn2e7hlmP/E7Ppftoc9fus3092wb/Ty2kwnotauWLtNk2PmZtnjQqWR1edVqtccMnHs0SQ6tJNBkE3+02xXHnOlDbQJR1on/m77bHM+sP8eCBtq6bFPho8vWJvgsk/eYbhkarJvKK61AoELDtJDQUc1ZD1NJp8f2hSwsGo3GAGz/O2UAuC8gBw8exHOe8xwZiASyeqEgkCT44ETQvqAc2MyfSxN0asdMv2yCQL2JcQLTHFybi5FdZv7qTqdris4UQjTLZbqucDgs5t+tVgvz8/My8cvlco/Wjgw2gJ53J1OpFweHo+uXS99zHQCK/3Oh0iYy2iSNdXW5XCgUCpivz2MuNwdnsDfYXLvdRqfRwew7Z5EbzmF/dT883m7aqzNnzmxtUpuA6xddcL3HhdZNLThKDky9cwqt+1o4bh+XhSwUCmEjsYFmq9c3tlgrolAooN3u+uqfOnUK27Ztg8fjQSAQwObmJqLRqJg202c7m82i2WwiGAxiY2MDo6OjkjN6ZWUFTmc3YBvzLe7Zswff/OY3kU6nRbFCgDX+2XFUUhXkfiOHqXdOIXQ0BMvXDdjm9Xox/+15ON7twLm3n8Mlf3oJho8PYzG4iGAwiG3btsF1nwv4I+CJ33wCl3z4Eri+6kJ0exQrKytYCCwg+sdRPOl6EpUrKnAVXJh99yx8D/gQ3hZGuVzG/Py8mNJzvNN1IBaLYWNjA3v27EE2m5UAYNwAPR4PpqamUCwWJSgcA8g1/kcDT7z5CeBSwL/hx4GPHoDbciP8vLBsVDoAIedYp9M1u9am58lkUjTHnU6nJ0Wbx+ORiPWxWAxerxe1Wg3RaBTr6+sYHx+XDb1cLmN6ehrpdFpiAJCtpgsEzcoBiNUGxzLHQbVaxZEjRySAIIO65fN5Ua5pMzKa3vv9foyPj4vioFwuy/ygH30gEJD5M5CBDOSZKVdddVWPUr4fmNJATh/qTeaKB3VtVgr0Mp4mKOb9+ln6sK9Zap5HtOjvWR7PNPyc5Whwoa2hdFkafOoy9Xfmu/GZJlOnn0kxFQl8jtnW/dpLg0Wzj8y/zbr3u8esaz/pB7JNuZASRreT7uN+ANusF8cjQZxlWbI3moocXY5W9mjLUM3iamWM7kedi1qDeA1++4FTniE0+OZ5msSZPqub48LcY1lPnt+1f7cGyZqV5z080+so63rc63R6OruPrg8VAOa7MJOKbkfOBxOL6HYyxwT7TgNtPQbuv//+vuNsIN+7DAB3H3G73bj00ksxPT19nnZJM3Uej0dMr3UwBw5cbcYyNjYmDDIBLg/dnGwul0uCS3FicnEjy603nkgkItHIGcyMeZxt25Yo5kCXnQbQkxbs9ttvx3Of+9yeRZCgBdhKfQZA/K0JZKhtZE5gmszqjYsmL1qbyPpqTSl9X2gir0EbAAHhVDqQaQ5VQ6jcV4G9t/usSqWC3bu7gdUIfLwlL5K/n8S58XOYfM8kxk6PdXN2l8twOp2IRqNwOBwYemAIV/zWFfjmB74J22Fj99/uxuwnZwXkO51OTE5OSmCvUqnUY1nA9lhZWRFAXi6XEQgEUCwWhaVlHm+/349gMIijR4+i2WxiZmYGzWazJw4AAIyMjMBzvwfxP46jdLwEK2JheHhY+rNYLCJxdwKjbx7FXG0OrUAL8XgcLpcLmUwGk5OTmHhsApPvmYTjmAMlbwlTU1MAulYcvqoP3v/uxaN/+Ch2/+Fu7CrtQn42j2q1iqGhIbTbbczMzCASich4sCxL8pI7nU6Ew2EEg0FJd8Y+83q9cDgcmJycxNzcHMrlMnK5XJfx3ahiNbeKQrOAG99zI+wjNobnh4Upz+fz2LlzJyyry+iurq5ibGwMV1xxBWq1muRGv/LKK1EoFHrywnLebWxsIBqNwrIsJJNJbGxsoF6vY3R0FKdOnUI4HMb8/DzK5TK2bduGRx55RIDv2NiY+NIXi0XJrb22toapqSn4fD7k83mZoz6fD6lUSubH8PAwJicnsb6+LnNuY2MD2WwWV199NdbX1yV93fDwsERbHx4exsmTJ2XeHD9+HBMTE5ienhZXg6985Ss4e/bsD37hG8hABvJ/XTweD6666ipJrcg9SJuhatCrxQRZJmA2TZ31AVzv35pZNYGgBhGmIh6AACRdFwImve+bZuEECDy/aD9wKu913XWdNFOuP+f7sUz9Xb/3uxBI5Tvw+bqN9JlHt6NuKw2ONWjUYtavnzm6vl+/04X6SD+D5yrNZOv7tWWaBre6fH0tz3GmFYP2r+eY4xlPK21YT54TtdKEeznPyyyLf2sLT/aLBqn6DMl7eU7vx+JqVw0CWRJL2nWQ9eP5jOJwbKXm1e2k60jzdr4Ty+f32kpO9y3flfNDW+DxTEzLU2IKcz7o57P9dP30taZCic9617vehYH8+2QAuPtIMpnEa17zGgHWtVoNwWBQBme1WoVlWTJZyEiSUSPTaVmWBDoDuuw1NxSmDKKJsm3bwkQTLNNP1uFwSOCpWq3Wky6Meb1rtZosRmRTbduW+7g5ZjIZmejPec5zegCubXdzFPP9tEbS6XSK6a5t21hbW0OlUsHo6KikPGo0GmLGy/p0Oh3J7ciga+FwWIJfmRrAfhsFNagul0uAn23bCMaDsDpdk/xcLof5+XkA3QWCfr3tdhvRehSet3rgWfegaTWlTQiU6AvsPenFzW+7GaeuPIXtn9yORrWBmqMm/cp3YYopMpwulwupVEryUcdiMZTLZWxsbMDv92N+fh5DQ0M4ceIEfD6fBBSbmJjAwsICNjY2sGPHDqTTaWkfMpmVSgUHLz2IlTMraPm2gvOtr6/D5XIhEAggl8thdGMU7iG3AFpqoGkC7TzuRCQWQTweR6PRkPYJBAKI5WN4ybtfgkaxgehQFF6vF4VCAdVqFalUCmNjY/D7/fB4PMjn89JPTIlFs2mOTwaVK5fLqNfrGBsbE2WL3+9HMBHE6ktXkb0hC2fHiZgrhpyVk34Ph8MYGRmRDdeyLBw6dAhHjx7F2toaCoUCOp0OTp8+LS4RpVIJHo8HxWIRHo9HWOhkMolqtYpsNotsNiuKlvX1dfh8PmQyGWxubmJkZAStVgt333039u3bh/vuuw+tVguJRALNZlPYfNu2kUqlJL95OBxGLpdDsVjE9u3bsb6+Loqc+fl5GVu2baNYLGJmZgZjY2PI5XIYHh5GPB5HJBJBNBpFPB7H5uYmQqEQfD4fZmZm4PF4kEgkAADT09Not9uibBnIQAbyzJOFhQVMT0/3mI73Y4zNQzEPzSYY470EUdoM1gTt/UArPzfBsgbbuj4EYTzQ0+VGs6D8Tpu16wO/Lp8gUbvGmaIVBxp4mUBb199kvtlGJiPIH+0j24/J1W1l9hs/N5nffn+bZVDYJqZJu/lu+n4NqE2/Xt0OwJZFo/6O//Mz07KB7aJBPEkktg/PWnq8EkSyPLpfmt/xHp37m2dnbbrO8zfHPi0qdf9rJlyDen7XT3HEsanrrkkyWqmyDN1WptJKj0NtkUe3NH7GtmV/aYUCz0M6DZoOrMx+1hYBHBta4cF+02uF7mMTgLfbbZw+ffq8MTmQ70/626v8iMuhQ4dw4MABGYiMIEw/EDKaDodDQIRewDkhCPjcbrccoinaP4V/EwiTVdYLKlkyh8Mhwb4ajQbK5bIEdtA+4KwTF6Vms4l8Pi8AMRgMIhaLweVyIRwOy+TS7Cqfx4VDB4IIBoNIJpPweDyyMfr9fiQSCTGb9Xq94p9Nf1av1yumt51OR96D3zscDgm0pc3N+Q7MmwwXUPj5ArL/vyxSvhTK5TJSqZQs3LZti89rdUcVp/7kFMovKaN4SVH6p16vo16vI51O49y5cygVS3AuOjHz7Rl0mh2Jxh0OhxEOhyWH9sjIiGwAfDe2K/2E3W43EokELMvC0tISHn/8cVk4aaKcy3Wjqe/cuVP84+PxOIaGhsQnB4CAeZqZp1IpJJNJbNu2DfF4HNPT05LuLRaLIZ/PIx6PY2JiQszUp6amBKg1m03Mzc0JqLdtG6nFlKTvIHsfCAQwMzMDt9stc4DjTEeXZ755jhe2Ty6XkzarVCoolUpoooknXvAE7n/e/YATaLvb+OybPovq3qpstLFYrGcO+Xw+UW7QooJm2LZti+l2OByG2+0W/3nOAdY7Go1Kvmu6PnAuc7xPT09LjmzLsjA6Oop4PI5AIIBgMIjNzU0JesIxHAqFxLzd5XIJmF9eXhY//Uql0mOOb1kWstmsKARoGpbL5bBjxw4ZQ3wXjjdaLgxkIAN5Zsru3bsxPj7e9zsN+C4EsDTQNAEWASl/no7FNJlZzSbzvMJ9yTQN5+cmMKWilgd+1lmz3fxMxw3R70oAbpqL6/OSfqYG42Zbmu1q/m0qIPq1icky6+/N8gmmTIa3n6JDt4upHNEssfl+/VwMdJmmD7kGZjrwHEX3C/d4xjzR7WGOJ97Hc3O9Xpfzm75GW0NoEA5sjTWagdM6U3/P99IKHT0HNODmO/O5lUpFUr3qNmY7MPWqLpMkFOOtAFupOgnAWb75v3m+b7e7wVB1Nht9zmKQZPYNz8psC9ZFg349x/Q6oK1ENNA25585tjudDk6cONEzDgbyb5MBw22Iw+HAO97xjvMmH4V+xgQn2iSFm5mpSeSiQOZZa0oJyHmtDvFPk3BGWNaaRC6QBPEsl2XRPFmbkjDCMXMJEySxTC6IDO7E51BD2W63US6XhdnkAkENIwGd3mQJbhYXF7F9+3ZRDlArR/C5vLyM4eFhRCIRYfr5XqaJu9PpxCd3fhL37boPsIAn3/gkdv7BToSL4R4z/Hq9DuwHjr35GMrTZRz5zSPwrHiAPwIS9yUEIMfjcQSDQdQ7dTz6s48iv5DHwrsXMLM2I6bgwJbmlZpFKhbi8TjOnj0r44UWD2wHv9+PVquFiYkJaT+WSZDFugSDQaTTaWHV/X4/fD6fKCDa7TZmZ2d7AoS53W4BhZZlYWpqSuoSi8Uk8juBKzdKalaHh4dFI03z6VarJX7XDBLWbDaxsbEh5uPtdhuhUEjGCjdC1ml0dBSJRALFYlGUL/laHpVIpWfO2S4bdsyGa3PLdYKKDG4yLCMWi6FQKGBkZEQUVsViUVKCcc6wr2h6TpcFj8eDsbExbNu2DZ1OR9qWEdCp9CoUClhYWEC5XEYmk5F2pP81ALHuWF5eFnMyHbWUJuwMmKZN88vlsiiu2H7VahUbGxvIZDJwOp0oFoty8A6FQuh0OjJ+BjKQgTzzxOVyYdeuXRgaGjqPFdNigu5+jCYPy2agK5N509fyvGI+W9+nQY4ug4d4DWxN4M+69PPT5hmD5wZdR37G8vheGrhpgMEyNbgz68rnafbebFe+D4kHsx9MZYLuH7P+pvLhQn1rglZ9Xz9FgPl+pqKF9/Vj2zXY12ctbVKulRisk2ZB9RjSbW+2iQbTpoUBf7O/eEbku2jFRD+Azj30QooClm3b9nlm9fq3rg+V3gS1fDaVTSSA9LjnmGN9NHOtg7BpJYl+Jz1/TEWJOceo5OB5gEw055Y5H7SJvclim/1J4b3vec97enzhB/JvkwHgNuTmm2/Gnj17ZLDysMzBbVmWmBMTNBFgabDXz4xLR4TkZOJnZJapoaK/NJ/HiUVzGoISAD2Lkz7we71elEoleZbWglHjGIlEetJzkUHXwSa42PE9GQRKB8LQZkBer1dMwAkc6dvLz4Auaz86OgrbthGNRgXAckNmPSqVCiKRiADd/733f+PzC5+XlGCpQynUfr+GQ284hGK+uBVMY6SNE+86gerclgluY6KBE285Ac9/88D6toVQOITUjSk40g6cfd5ZpJ+VBizg2G8fQ+x3YxguDEuKKbYB25bKjkajgUgkgkqlgmAwKEHrMpkMbNsWEEszeyo++J6M3k5fHJfLhWKxKKbYtm1jeXkZ4XBYNKs08afi5/jx42i1uv7bTmc3DVsoFOpJOefxdAPLtVot5PN5bG5uIh6PyzjgmM5ms2i1WohEImi32xgaGupxG+BzNzY2kEwmxa+dOaptu2tdwCB8tVoNyWQStVoNPsuHPX+1B7VqDeeefw7OlhPP+ZPnwHrIQtldRigUEoVLs9nE0NCQuELQ/SEWi6Hd7kbRj8fjiMViyGazyOVyYm0Qj8dFScFxT9BaqVSQyWSkH5iqjsqF8fFxhEIh2HY3qn8kEoHP58O5c+eQy+UQDofRarWwvt6N5E4Xj0AggNXVVTQaDdRqNWSzWdGC83+HoxuV3+VyYWpqCtlsFo8//jjq9Tq2b98uCob5+XmsrKwgl8thfHxc2mFtbe0Hut4NZCAD+Y8jQ0NDuOiii3piogC9oEqbvvJcoRXx+j59LfcS7bcJ9II64HzQog/5WsmumVcNArivmddrAEzQYwIIDQC5drMsKvbNtEq63jyzaABvglbzb7N9NcDWosHyhUCurr8JRAlUeY8GUBeqiwmM+J4moNff6ef2q6O+To8LLeb/Jsuuz4uarddAj4oMbRFhMusmq6qfx3Oc2S/6enN8mgoTff4myaPfX7czsGVarn3FdQBjXqNZZF0Pnm15Ltbl8B79XF7LOcIy9DzR88Y0o+f9ehzp+cl7tZLE7EtT2WCOF8uycPTo0fPGxEC+fxmYlCtxOp145zvf2ROkgSbQwJb5MBcQAKJ51aH3NQDlgCWQJVgjqO50OvjSl76EWq0mAIHm6uZiqHP52rYtZjBkrwl0aQ6byWR6NqdgMCipRvx+v4AbTuZIJCLvyUBf3Bhp2ttqtcQXnemKmB9Ys9JkHGkCw+jUZBm5mbJ9COJoRk4TfB1IjkqFV595NWYqM938zQBcZRcOfvAgSvkSotGo+FG75lw9YJtSH6nj8Xc8jk6yg/TlaTzypkfw0O89hPThtID42nQN9/zPe9DxdrD54k08/vzHEUlEelKYRSIRuDwunLzoJO5/xf2AD4hGo5K3nKA8FAvBinUXXQZMsyxLUkLRD9rn86FeryMajWJ4eFjaq1QqIRQKAYD4gNMsuVgsiv+6x+PBxsYGHnroIQnO1mg0kMvlBAyXy2WxEmC0a61l5SJbKBSwubkp44xjbmhoCK1WS9LOFYtF5PN5lEolAazcZKPRKM6ePQuv1ytMsdfrRdKTxEvveSn2P7wfB3/uIOZz80gmkz1+SYFAAGNjY9jc3MTKykq3HUMhUcoQ0OuxWyqVJAI5xw59oDmPQqEQKpUKJicnMTo6CofDIab5Z86cQbVaRaVSweWXX47x8XE0m03cf//9OHbsGCqVCkZGRhCNRtHpdLpWEU8FN6T5t2VZCAQCqFQqSCQSmJ6e7o6nWk2Y8MnJSVx66aXYuXMnyuWyBFCjtcXU1JSkLbMsC+fOnUM+n5c5OZCBDOSZKSMjI9i7d6/8z8O4Xpt51uC6A2ydTUwAoIkADdxMkEbwoJ9rMrc8a/Awr81m+T0P9tpNR7N9PN9oM2ENXHgG0iy2zrms/WQJHoAtYMqyKZrNZPuZ5vQm8KAQ1PRj/nTfaBZSn9t0W/dTaJhMZ786aEWEBvtmUDLdfiZD2q8MPUb0c3W5ejyYdTctCfRzdT8Q8GnFia6/OT7JJut25zjR78nnsA/0+/JZ2qyaddBKGJ7RdcYQfk5T8mazKedYnt/ZbhxbWglBi1WOQU1I6XM7f5hZSJ+/9P/axF+TZuwjHexY9x3bjtfrOaEVLeYYBnr92W3bxsrKyiBuzA9IBgy3khtuuAHRaFS0qJxUejGiSQkHuAbfnPhM7URQTQDNCaq1iw6HA7fccguAXvMrgksCfE5k5rfWOQW5wHChYt05abkZ+/1+Ydw4oZhrmybYXEA0E12tVnvAGcEffV9ous76M50RNwD6mHCzZQAysvRURrAO9AsGIAsTFw6fzwerauF933wf3nzZm3GmeQb73rcPwyvDyIS7vq0ulwuhcAiP/PIjffs5sBLAwm8voHGggSf+4AkB2aa0/C0cufwIjvzskW6/wouZz8+g2djKM756aBVfetWXAAuwKhb2fXkfhoaGJIVTs9XE6ZtO49Tlp3D444cx7hmXBSydTndBlN+Jtck1jC2NYXR0FMvLywgEAhIkIxQKiaVFrVaTPjt79qwE6AuHwxgbG0O9XkcikcDq6qoATTKx6+vrcLvdEv2WKeJo1k8QTrBKk3GtVXU4HCgUCrBtW5h8+nPl83kxTy8WiwAghy+C9UQigfX1dbQrbTzrY89Cxs5I/3LsRaPdtGVUFnDTrtfryGazPdYX7XYb6XRarD8412htcPr0aUxOTiIajeLJJ5/EyMgIVlZW0Ol0sGvXLuTz+W5ft7rp4Rgr4dy5c5LyjZHHnU4nSqUSxsfHsbm5Kebg3HCXl5dh292MAdPT08jn86jVati/fz/S6TTC4TDi8bjM8UqlgjNnzsjBhCnKxsbGxMKjUCiIgiaXyw1ycA9kIM9QsSwLk5OTmJ+f72GqgPNTRfGwzD1XH/xNgKnL1/dp8KPNjTVb2+l0ZC/XAE77bfMsocEtyzfd6zRI1mchTXLo+032UiseTKaff7MM7bOrGW0NFk0/cs28moywZmfZD/1AaT/R1+r21N/p99dist+6rvoeEzzrNtPlmGbtT6ecYf+bjL+22NQAT9eJrDKv52fatFm3vwmkue+zTjxLc4/n2VeXo+tovm+73RZrNM2e83yslUj6GZpR1u5cbFetMLJtG6VSqWec6/FmjiWWQxad12vWmqBfRyBn/XlOY5uyD/tZWujI7hRiHD7XVJzYto1PfOITWFxcPG9cDuT7lwHD/ZS4XC684AUvwNjYWI//LUGIuahpk3FtgsLBTsDEFFZut1sYXB1wBIAwvgBEm8bgXwxKxucx4jgAMWnnhqv9qWu1Gvx+v0xKatbIAvJeBrjSG7dOyWXbtixSBFZaI05mXTP+5uKt/Z11oAf6/HLxoMkycxDTL5Y+s2zrer0Ou2rjp/7+p5B4ewLOO7fyF7L81HoKB/7gABL/0o3wDBvAnwCecx7sef8ezKZmYV16AaRNcQBH3nBE/v3OT30Hp152SgDkAxc/gM++/LMC2B980YN44EUPoNXeyhv52E2P4Vu3fQvr+9Zx96vuRtaflbHBBfLsT5/F/W++HxsXd31+k8kkksmksLHnzp1DIVDA0Z1HUSgUAEAAss/nw+7du9FqtZDL5WScsf1rtZqYXEejUWFfaTZOc3AGr2MEcOa2djq7wcc2Nzel7TnG6BMOdBfrSCQiTLXT2c01PjIyglKp1LO5ERj7fD7EYjExvQ8EAmIiDwDZbFZ8t227a6bOWAGlUkmsH2q1GiKRbgT2ZrMpFil+vx9+v19S5XFs0SWCCiNG/m82m5IOjIHJVldXEQqFEAqFcPbsWVE4hUIhmcsMHkcfbb/fj0KhgFgshoWFBfEx54amGRWm1KMShOk9GMWfB9lqtYpcLofHH3/86cfsQAYykP+U4na7ccUVV8gaaJrTAr2mtPrMoYGXebjXIFaDUNOc1wQ9fKY+vJvCc4ze/03Qo//WgFczyLyPynkNljT413UjMcByTDChGUjdPhdioTXY0H/rdzVBjPmj31m3Zz8gbT5ff6bf1zSjNvuvX7/oZ5omxBwHWsGgy9dWCea40L81yaIVFHoM6D2PZ2MSRHSfIlOrz2+6fnrs8BxrlqPnhG47DVh5fjFBM5lslqffmWNMjzP93nTN1KSXBsza9FtbkWhFCOvI8li2Zui1WbvJSJvtw3u0BYppKaOZbT1fzbHscDhw+vRpOVMN5N8nA8D9lFx00UW47LLLztNM6QANZLR5CKbGCehdMAlweX273cb73/9+AL2pKrjA8D6CXcuyevJlE2iyXnrya1NuHQyLIIXPJ8jhxkiGXvtvA11/dAByLSex1gzqSODaxJ4abf5NgM/FgIoHBo/jO5gboxk4g+/k8/kk97nP54Nr3QXnV5zi2xsIBMScO5/PA1lg34f3IXlnEq63uYC3Au2fbGPk8RFcfvnleOXiK/Gib7xI3v25n32umKlfSOLn4rCsbjqxJw4+gZanN4DV0ZuPotPp+gU/+mOP4olXPSGzbOWSFXz1F7+KtmvLWuHR//ooHrjlAdSn6njglx/AudFzaDQayGazEqGy4+zgm7/+TTzwcw8gc3lGomjrQGfNcBOPvewxAcSBQKDH4sL22Ljv1fedl2ouFotheHgYwWCw52BFE6J2u5tyjvEEarWaWCMwgBmvYSRuh8MhEes5jwhQs9ksgsEggG409HA4LLmyW62WmE2TWW80GlJOrVZDoVBAsVgUqwuHw4FEIiHzhYodpsZIJBIC0icnJxGLxRAMBmVcxeNxjI+Pi9KLZuoXX3wxgsGgfEffSsuyJL1YLBZDp9MN2kYFCRUf2polEonA7XajXC6j3W6jVCqJooLKsbm5OSSTSYTDYXlvzqW1tTXUajVRBgxkIAN55onX68W1117bF+xpJpGigSf/1wdrfnYh5tVkVE22UgPZC5Wjzw7832T8+Dn/1plNtLmsVvhrcMjnc13X5uUAet6f9/LdWG8N7vW79msHE3RfCFjrPtBtZwL6fgDcBPz8Xp/zTLaR1/TrV/O99Fghw6v7inU028BUtPRjRE3yiecJDfT0e2kQy+u1Wb92LdDWDLzXBLIkbvoBdB29W5tks77mmNN5vvlDcMt6aeUDr+VnwJalhn5/npPNutGaz/xcm7FfaN7wffQ1bFsN+KnE0MoQ3Q7mGOC79RtD6XQam5ubGMgPRgYm5ehOxMsvvxxXXHEF8vl8T95qYAt4e71emWyc9ATfXCjoU6E1fdVqFT/90z+NSqUiZt1kxZj6Qg9202RGT3LLsiTXsF4Y6f9KsEPfEAYrK5VKALbSi2nT20gkIoxyLBYTk2UCX7/fj1Kp1M2h/FReZ9u2xXe7WCxKCib6LrOugUAA0WhUojLz/Wq1moB/vqdt22JmHggEBNiZCwXfuWN1UL6tjHwoj+S3k5Lvm6nJ4vE4SqUS9vzhHjz52JNI19PwPOJB5IURDA8Pw+v14seO/xg8IQ9GOiO4LH8Z2u9u41/e9C+wLAtXvuNK+ODDN/7bN2BbNq74gyswvzqPsV1d3+Kb/+JmfPrtn0Yp0W1bV92FF77nhbA73Q1lM76JlrsXkG+Ob6Jlt+CyXXjkxx7ByR8/CdvVXVDLY2Xc9aa7cPXrrsa4s2u2XHfWcf8f34/ijiJgAd/5je/A+m0L44vjMi7T9TS++affRH2ojna1jdlPzqK02PVnB4DVwioe+pOHUJwpwmk5sedje+C3/HI4cblc8Pl9yAxn8PDLH8bNf3MzrI1uGq2m3US1WZXYAD6fTzaAjY0NJBIJ6VOXyyXzhynXOG6ZJ5t+3fSf5gaUyWQkBVaz2RQgPzIygkajIWPF6XQimUwin88Lk8+AdmS80+l0D9j1er3w+/146KGH4HA4kEwmJdjaiRMnJKL65uamMPSPP/44ZmdnJap4q9US323btpHL5cS1IpPJoNVqYWlpCU6nU1gqj8eDYDCIo0ePIpPJiPVMp9MRZQo35pMnT0qwt0KhgEKhIOb0BPkDk/KBDOSZK4FAAFdccYX8T1ClD9f92GGTVeT/tBzToK0fmCNI0Gwky+M9F1IC8DeBg64Xz0hm/TRDx3v0tU6nUxSUJtPHOpFI0EykBpomKOc791MEaIKF7XShezTYNFlmk3XUIFZ/9nTCe7RZu+5n3f8sS5NC7A9tgcky2JYcVxrc6vL1mNHvyv4yXRZ47uU1PLNpM2yTtNJgmOVps2p+TwUMy+CZhxZhPG/r87iuH8cX+4ZxkGg9xncwQTXPJlpZY85BzdDrtqBrpv6c7p56PPKMbQJj08Wg1WpJHAPdjzzzaDNynunYfvxe5y3ne2oLGj0f9Dj81re+hXvuuedpx+xAvncZAG4A4+Pj+Mmf/ElYliUpkbhgMcWR9mHmhCCg0Jo6vRBy0rBMDnA9obhwcAKTreb3NP3O5XKSiotm6mQTuegwaBoX0nA4jHw+3+PDQjNg3k+TXL/fD6/XK39rjWKn05Fo1gzIxrIIhGiqa27Q9Hstl8sIBoOyMHOT1QcALpwAxKS2XC6LcoEB1xwOB2rNGtafv47cJTnkkIP/j/yIfyUuSgSga5LcbDbRqrZQzVfROdiB9biFxcXFrTRTgRhe9ESX5S6WiwjfH8bo60cxMTqBqVNTaLfbuO5/XIdmqImd53ai2qji2LFjiEajcOQd+Inf+wl8/s2fR9VXxeEPHkZiKYH11DqcTieu+atr0Pa1sXj9Ytfs/ASAFwAP1B7A2NgYpv92GilvCvlX5gEX4FhyYObtM9h8YhPO0e7ieOQnj6A0WxKz9VaghaO/ehTjbx/vsrhzbdz71ntRHakCFnD8NccxNzaH9nu7m08lUcFjb3wMhfkCYAFP3PIE7KKNhU8twNnYMgtPz6Rx++/cjo67g2AniIv+90VweB24e8/dKC4UceAvD2DMM4ZqtQqXy4VkMol6s47itiKa32lK7AOyvLRI2JjdQLgWlojpDMYWi8WQn8xjtNQF3hMTE5KPMpfL9bgbTExMyLyo1+sIBoMSFZ1jYn19XeZTLBYTk/NisYhUKiW5zicmJjAxMQHb7qZJKxaLSCQSmJubE+UTI8Rz3lSrVXi9Xhw9ehSjo6MIhUKwLEv8u5nKLZVKYW5uDpFIBKdOnUIul0On08HU1BRmZmYQCoXEvH9zcxPRaBRjY2NIJBIYHR1FLpdDOp0WpVkwGITX68XExARisZjkGx/IQAbyzJNbbrlFzgP9QB/Qm07JBFb9gCPPBhR9yAZ6XcC0L6cJuPSeri2h9JmH+7lm17VpsAZ6GtiZZry81+/3yxkLQE/eY15Ha0INrPhOmqFlm2kfcNbXZGVNNlgDad0XGhRpppBtwv/7sci6TK0QMZUGWjTwNa81yzLL16DbVBb0A++mQkY/nwoQAkieQ1m+Vt5ohQvL0s/l2ZllsA7aRYFWXRrE8nqSW9ryiwC1XzA/06VC9x9BKfuYVqI68r1WfmlFkJ5rBNLmnOXY06bk/K3dKPSY4TuyXXQ/sL302O83B4gZCOx12lqOMT1u9JzJZDJIp9MYyA9GBoAbwM6dO3HllVfKIKUWDNiKjG2a9zAImPZN0doyrZnUQRkIlqlBA7YiCOo8k7yeg59sGrVY2iyd/icejwflclmYeKab0trFYrEoIMvhcIh/LZl2TkhtRkYtnsfjETMe+pVWq1U4HI4eZQTTWzGqOTdPrZnX2j3to02NHBlx7T/D8p1OJ74w+wV88KIPChB94I0P4GDgIKb/eVoYUtbBtm24XuVC57910HpDC/WhOjqzHdTSNTE9dji6qafcbjcc5xxolbbSQUwd7QJvy28hn88jmUzC4XAglUph2BrGCz72AiyHlxF6qBuNPBqNSvTwS99/KXyWD8eHjgO/AOAYYM1ZEhtg4j0TQBOoPq+KxO8k0LmnA9tno1KpoFwuY+rDUxgbGcOD/+VBwAKGHxzG9R+9HtFAFA1XA2tTa6gFaluB3yzgQf+DuMh7EQCgOddEI9Ho+f7IS4/A5XNh+p+m0Wl2sLhjEXfedic6nu6Yf/jqh1EqlhCvx3H0hUe7488XgvNPnGgXtg4TG7du4J6b78Gh+iH4jvkQDofFXLrdbiP17BTufMmduPKjVyL6ZBSRSATZbBZerxeP+x/HQ696CDse3YE9f7cHXo+3JzhfLBZDo9HAqatOYfvKdrRKLZln1WpVNiQdk6BarYpWXFuQhMNhFAoFBINBzM7OYn19Hc1mU4ITjY+PIxqNIpfL9WjCOZ9arZb4tns8HuTzeeRyOTkMWpaF0dFRTExM4Pjx4zKnLatrjcJYBBMTEwC6QQhZ/tzcnCgR1tbW5B1CoZCw5EA3IBvXooEMZCDPPHnWs551QUANnB88ywRX/F+zl2b2FF2WPpibpsP6WRqk6WebbK0J3MyyTZCogQUV/LyWbB7dxbQZsAngNHDTbGq/9jHrrckPlqvNeE2wyc90fbTockwQo/tUv4PZHv1Muc1+6VdnE2zxev0+BGFmkDHdhv3aj/drsGi2p6lkMAEn+4d7o2aV9bsQfOryyJQznhIDA+tzobYKtSxLzhIAegA8AMkKxM/IVmtSTLPwZv9oBY7uS00UmEoc/tY+5P2UPLpN9TzSlg5a8WNaMWgTeZ6H2O66v/uNFV1uvV7HuXPn+o7Dgfzb5EcecDscDhw+fLjH91hr5mgmTZCozcy15ooTnmbQ1WpVmDDN3OrJRrCnNY5kwunHSV9xTkhtlqL9yWu1Gr7+9a/j2muvRbvdFjDACMc0ywH+/+y9d7hdV3UtPvY+vbfbi+q9ara6ZMkVy7g7NtUlmMRgagjF4GBIAk4gIYZHh/DoPKptYmLADVuucpdlNUuW1XV1ez29l71/f2yNdeY5ln+PJOQF7Lu+T9/VPXefXdZae605xhxzTigvcz6fb8huyPhtli2TXnlmDedCxXNRPs4+IyHAxBaSNSfryMWBWcmdTqcCGuVyGU6nU8XwctEj8CcIjhaiLxvLymA9YRZl98lkEmMXjCF9QxoIA+WvlbEzvhPpSBofeegjKBfrGd27u7vxQuYFJG5OIIkk+r7fBwxCgUg5NtlsFi0tLdA0DS3JFjgHnaj6q+qe6Zn3eXxY/ZPVGD82jszTGdVnBO1erxdtX2tD9fEqbE/bYJhGQxIOu92O1Q+sRmokhcl1k1j67aUI28LQdGtMe3f0wvhfBp7/zPMwXAbmbZ2HZT9eBl3X4ff7ETgYgOcHHjx6w6MoBoqqr4K5IDTzRKx2AtCqjcbTkQuPNPx+4MwDSNQSWPW5VVZ5q/cXcfzy46i6q9jxvh2wf88O/1Y/YrGYFd9+2j4895bnUAqUsO2d2+D8pRM9z/TAMAxk52TxwkdeQG5+Drvn7UYOOay6dRXsdqvc1/HjxxEOhzFy8Qi2vmkrki8mcdZPzlIe63w+D13XMTo6quKraTDYbDYcOnSoIdM6vS4ejwe5XE69A/TW+3w+bN26FaZpqpAJyt9HRkYQi8XUe+jz+TA1NQVNs/ouHA4romx8fBymaWJychJOp1OVp2NitpmZGWSzWTXPJSvOZHIkqwConAUjIyOKIDtZeMVsm22z7U+7+Xw+nH322QAaQY30+jUDHPlTehWbPZTNBjs/A+rgvNkD2SyJbQbXvK400CVokteVnjQCgOZYWgnCuF7zu7JiS7M3WgIigpNmhV2zZL4ZWDeDZJnIjc8h/9/cB/zZ3F+vRFQ0H9N8TumtlNdpVh7I63PsJCCUnzePV/M+0kwGyO+fjOCQ/Sv/JueVHM9mJQbHW461jEeWfSMVGnL+yPuR98vna3aCycRj0it8Mg82z8t9Wh4v5yjtZx7He2weB2nP8Z6k517OT4JjPguJEP7j88uxku8hvyfl6RIzyLnIPjzZ2jAyMoKf/OQnmG1/uPaaT5rW19eHv/7rv1Zgmh4lLj5ygssMmgTDBLuc0PyO3+9Xiwy90AAaXkC5KPFvTArCe5EvlUw0QUBOgz0QCGD9+vXqhSaBIIG+3Bi9Xq96VjKB/F3TNAXY6fkmyCQY1DRLKu/1euHz+RrkZlLCImN2ZNkxHidj1DVNU2XA5MJuGIZK3MWF7tzkufjU85+CVtWgVTSc+rFT0Xe4T8nig8EgqtUqZtbP4PiNx1ENn5AXtZkwlhg41H4It1x2CzR33QvgiDlw/9/ej8KyAvLL8njg7x+ALWRTEmbGMJfLZYTDYXW/lNJXKhXkcjlMTU0BgKofnp/Iw3yuvnFw/FjS6/ILL4dvh08BcNM0FWCvVCooFUqI3hpF64dbEYqHFFHhdrvh9XrRvq8dr7v5dejY1YGV31kJf94Pn88Hp9OJQCCA9qPtOOuTZ8FWsUGraVj141Vo+VULpqemMTY2huq2Ks7+1NmwZ+1ADdA/rwMLAUyKF6UCZG/OYteuXRg4awBHrzuKqtvq01xrDk+9/yk8m3wW27dvxzOhZ7D1qq0oBSwCpxAq4Imrn8BW21bk9Bye/8zzyMy3yAfTZuLIpUfwwhtfQKlUQjQahcvjwpHVR/Dslc+iEqjgxfUv4ql3PAWby6aAcKlUQkdPB176m5dQ6bQ8IFNTUypJH2O8vV4vDHu9XB1Jm3A4jLGxMTUv6QE3DCsJ2vHjxzE5OalCD8bGxhRTTDmZ2+1Wdd9JEOm6rvIaMEka54eu6xgfH8fk5CS6u7tV9tRisYhkMolarYZAIIDOzk717jG+vK+vD8DLkxTNttk22/7028qVKxVxCKABKEgvLvfYZi9iM2gDGsuMyt+Bk2fIBtCQtOxkQKbZqJet+W/NYOBkIFfei81WTwLLuFOZBIvrqHQ+SAcJlYQSZNCOa45lb/4pvbvyn+yHVwK0QCPwl02SH9K7KPurGdzymnLM5LHNtpbcE04G7jmmBHY8l5RP835eyXPP1uxZlcSJVHJK0Evlonyu5mRl0tamLcXxow3KvbI5jhyASjxGW7pWq6kEr/Je5BxpTlpM21KCdYYz8jMZ0kVygPYqiXSJB2S+AjlPeH5+X5IgtHl5LAB1r3I9kH3ZPBckHuHz8R2RYy3HSq4phmEl/h0eHj7pPJht/7n2mvdwf+Yzn1EeaXqCCWbkZtVcT9Lr9apJLBlcxlxLVtHlcqmXiufiSyIXUMlK8V74UtZqNfXyyxhzfq9Sqagav5LNovyW8lq/349araYk15VKRXndbDabKpFEYMmFkvcaCATUS8+ET7xnuQlyYczlcg2MLRNrkcXjC+5wOJQXT2asZP9Sis/xyWay6DrShUX3LoKZNtFzqAdlm+WFjkQiSKVS8Hq9SMfSMAInBykjgRHc0nsLLv33S2G32/Hrd/wamVBG/T0Xy2HLe7Zg5T+sVPJ0GgWU+NPD7/P5kM1mUSgUEAwGEY/HlWLA6XSqpHUAVNw858Tdd9+tiAuOBeOQCOSmRqYsMN5m1XnmP5I03n1erPq7VTDsBkp6CXPnzgUARQj0Fnpx7Vevxd6FezH37rmolCswPaZSI+jDOs5631nYds42mP9sIp/LA0sBPAtoIQ09N/bAM+ZB2VtGZEsEE7dOIPXOFEynCXvKjsDfBRCbimEiPgH9pzoC0QASH0rA8BrQMzpc/+zCxK8nEHfE0af14ciXjyAfyQMGEHksgnk/m4eyvYypqSno83S8eN2LKHsseZZpM3Fg+QG0HGjB3EfnIp1OI6fnsOOKHTi47iD2r9yP6791PTACHD9+HIsXL1Zz77h5HPffdD/e8uu3IBC3MoAnk0mV2bylpQWDzkFk5mXQMWklwxsYGEBHRwcmJiaQdWeBKFAYsuTu9LDLxG61Wg2tra0olUqIx+PI5XJYvHixypofj8cxNTWF1tZW9d6mUilomhWiEI1GVRiIx+NRJcl4XHt7O6rVqpKmz7bZNtteXe2ss85SShkpjZbe1pMBHen5YjuZNPVkAL0Z+DaDr2ZPsPSONXtgJWCQ9lAzAJXhcEA9R470cDZ7qbnGymeSx/PYZjKSBCnvVcbWymdujiGXYFX2JxVT8tkksDkZgJa/Nycbk3///wO4zcdJObckGORYSGAo+0eCR/YLf5eVNeTfeM7m+2r+TM41CeAkcUQnEBWS/KzZ3qOdy7kkPdRAPXlbM7Dn9eS7wfxCsj+lnJ3/mt8pqa6Uc0k6i6T9LftYSs1lBvHm94bPKoGy9MTz2tLLT/KJ9y/7gf0m5eV852SiOD6nvF7z+z02NnbSOTnb/vPtNe3hXrhwITZs2KAShjHrMWXYEiQCdXZRslZ8IWV5Lb5kbARFEkRzcjeX2ToZYytfBqAej8INQDLB9JqlUimVCbxUKikpbS6XU5scACWFocHPz3g/ZAll8hIuLnyhKQfn88uYcq/X28DIMhkaE7ZJmbl8HpfLpTKs22w25WHmYuJwOOB0OOH5rQfOB53K42+aVqwsa55Hk1F4D3lPOv6nPHsKln55Kfbv34/x8XHM2Tan8QAT6N3ai2g0CofDgUgkouT2sgRWuWwBRYL9UCikSm253W4kEgnYltqAZdZpmZiOz2yz2dDa2oqFCxcqMkdK9XXdyqzNmo/sd7fbrRJ/2W12RMIRVU96dHRU9QlLiPmP+7HinhXwuK361KlUColEAgAQCoUQyAQQ/VwUpaJFFCAO6G/S4fuYD9rjlqIhGAwil81h7rfnovWnrbAlbOj9Si+WH1yOzs5O9PX1Yc2aNTjjqTPQd2sfHDkH+n7ch3mbrWRiAOB7yYfzfn4eQjMhuH7pQvCvgkin0qqGtm/KhzO+eQZ8A1b5MEfJgY2/3Yh5j81DPB6HzWfD9jdsx8ELDgIaUHFW8Itrf4Hy+jLWr1+PUChkJWmbW8ST738Sya4kbn3HrahtqteAd7vd8Pv9yC3N4bZrbsPtN9yOmTNnoGmayk5eDVex5917cPemu2GEDBTdRRybf0x5nzs7O5FvzSO9MI2BgQFMTk6iXC7D7XYrkD0yMoKxsTG4XC5MTExgdHRUJSAMhUIq+RvrgpMo8vv9qjIA39vh4eGGdWW2zbbZ9qff3G431q1bp1R2zO/RTLhLQClJeuDlib2a/w6cPEO2BH/NYFwa/VJ5J0GC9KpJgCCJgmbwJT14BFvN8bJSzcd/zYCW35EAkX+XIEZ+Ju+r2bsqAVvz58191Xw+SVycrI/ZTuZBbj6v/PxkoFaOa7Ok+2TPzHNJ+1TajPLZJanT/Az0xjaPVfNzcmwl6JRgn3uYdNxI5UPzvfJ33i8rfEgbWYJb3r+cu7yv5n5q7q9mkkc6luRz0WaVz0kQy/PK0ED5bOx79qWM95b9wb6VatjmecTjZT/xnkgq0KvNc8owz1dSOBiGgVtuuQWz7Q/bXtMe7ne84x1oaWlpiM2WLwxQXwy5+fEYmbSBUmO+GBIgN7PKfBG4UDOWBWisVygnv5Smc4OSZIBcDAimXS6XknDzWVgrmbGhTPDAbOBA3UMvMzby+3Jxl3EskumVCdfYZ36/H6lUqiFmhSWWGLPqdDqVB8/tdjc8C+VlUuZjt9vh8XjQ0dGBI0eOKBBeqVSwbds2uFwuBINBePZ7sPCWhTj4jwdRmlOC7SUbbMds2DR/Ey584UJUz6xiYmIChmHg3jfcW08uBgA6cPCag1i3f10Ds8lMlLlcTgFn3m80GsX+/ftht9vh9Xotj/g8F8x/Mq1zXwv1OWPX7XY7ZmZmkEgkVBiBzCqpafUEeJSZy7nocDgQi8VQq9WQSqWs5z4BqDOZjApPKJVKDfkIZKI9TTtRBkyU9wAAbb8G94Qb7qhbkS+pVAo+nw99P+lD+/F2BB4JoGarqfj58fFx6LqO6A+jmNk2A89WDwrFgprrLpcLSwaXIP+dPLZ9axsqtooiDwKBgJUsbqQHq/51FXbcuANLf7kUawfXImu3Mo/bbXYE3IGGd1nTNOimrtQquUgOj1/xOIa7LElUwVfAXZffhXXj6+B6yYUFCxZgJDqC+954HxKdFulw67m3YuPYRqx4aQWSuSSefv/TOHbKMQBAzpaDaZiY6p7CBeULEHguAHurHQ9f/TBKnhI25TYhNhVDJpNBMBiE3W5HMplEpaeC4RXDWLJjCcLhsBrvWq2Go0ePWv19moYZcwbOR51ob2+HplmhCoFAQOVOkAkLZ9tsm22vnjZ//nwsXLiwwWZoBrHNnikJqJvB0cmAn1TQAfWkjhLESvBBG0YCUJ6XhvsreWUlKDyZh50OCLmHSbDWbP+wyXuRXttmwCr36mYP7cmALZuUNDc/nwTd0tt+smds7vNmEN/8TPJ4eZ5mL3jz3yRBIL20PF7GATffC78rbdVm8Nk8t+Sc5HklcJVx+CQj+Dc5X082ttKGbH5+Gf8vfzbfBz3OUgF6MkJEzvVmEC4dSXwO/p33KPtJzmtZNUjeq3x/TdNskPafjChqfuebSZDm9UHa5XLu8pkYKiqfQXrFT0au1Go1bN++HbPtD9tes4B70aJFOOecc5Q03DRNVU+aIJAvD4EOJysXD8nk8gVl3K5cLOmxlgkT2Pji8JxMniSvIxds+QKmUiklOQagvKWshU0ZKgEWX0BKygnE6DXm4lssFtVza5ol585ms0qWzuN4Ppnpm4CR4IvggfdPKTlf+EAg0BDHQgk75dhcyAjKSQLk83lougbzWhPYBdQOWGCTMbWmaSIWi6FYLKJ3ohfxa+MY+fEIzMtMrF2xFm946xsQDodhBA10d3ejVCrhH3f+I27cdCMM24nFrKrhvG+ch0wmo+owc9wzmczLjBS73Y7x8XEAFiuZzWYR7gjj6X94GkbXieMeBPJvy6ts8VNTU6psHGOUwuGwIiB0XUcmm0HFrCiwTJIlm80iEAgoqTK92SQeWIaKC63P51P3RrkzF3duLhxLNgJk/r9YLCIQCFiyx3IVlV9VkHfnUSwWMTU1hZ6eHlQqFbS2tlrhBL/WkfKlGsIOuAFEd0URtofh9XqV1Ixj7Xa70XqkFRd/+WK05lqRrWVVzLu9Zsf8n85HvpLH/ov2w1Fy4NKvXQrvmBdG1FIf2E07OnZ24GjnUYvoMIHwWBido53Iu/NIO9P49XW/RiqWUs9aDBXxzNuegfeHXuw8byfGltUlVUdW1JPIPfAXDyC6OYr8dXnM9FolM+794L1Y8941cBVc6OjoAABE5kTw8xt/jqKniPB3w+ge6UZfXx/8fj+SySQSiQRKHSU8+u5HUdEqOGP6DIzsHEEoFEKxWFTv2cjoCOJXxHHk6BFg3++zus222Tbb/lTakiVLEI1GlfEtAZKULDcb5vw/gJcZ72wyvEwqzXhsMxDjz5MlUZIgSUp/pfEvjX2u+bL8Fp+JNkmzF06CGt4DgVQzKGO/NN8/vYYE0FI2DTTK2k8GaprBdjMxIfv7ZOCYf5cASnosT2bTydZs550M/DffH68hge/JMrbLczRfS/Zls5xaKhukPFxeVzqSpKdYOm1eiQCRc4geXl63WQHKa8ryVpzjtLGlvc2xps0qHVucy/LdkPOg2fvcXKJM3ht/53PIfpH3KPubRIFMDEgVJZ9VJkmT85N9JN9FuTbw2aSHvDnhmjyO42Kz2TAyMvKKhNps+8+31yTg1nUdF154IdavX98wWZlVnIsRGavbb78dF154IVpbW9XfKVMBgMnJSYyOjmLp0qXKa9wM2mWmcd6D/MmXmZOc4FPWF+RxgPVCh8NhAFYW7YceeggXXHBBQzkkXtM0TWXAE1xzQSUQp/SFXnTeBwG2pmnKe+1wOPDCCy9YZa26uhpe3AMHDqgET8yCToBFME3ZO/vS6XQ2LE70AkrVAGPC/X6/FXNs17Gjcwc2b9gM8xoT3k960b63HfF4HLquIxAIIB6PK8LAtd8F93o3ip4ijHEDtWQNtrZ6rIzL5ULbZBve89334AdX/QC6ruPUG05FxB5B0bCUAU6nU8nk0+k0vF4vZmZmEIlEGoA4y1s5nU5sfs9mpDvT9QFcCBz9ylFUr68iGAwqcoFJuDRNU6XdlBx8XgUDfz+A7uu7kc1mlYw8k8kgHo+ju7tbkRrt7e1wuVxIJpPw+/0NEiXGwZfLZUvmfiIuiYqCTCbTMAc5ZjyPpmlqLClvZyw5k8ANDQ2p/Ae6btXXHhgYgK7rCIVC8Hg8iEajKPvKGE4OI5lMKsUFCQBN0xAMBuH3++HP+RXZo+s60um05cW3BbH+l+tR0At43eOvA4aBGqzs8Axj2PDYBpQcJezatAvh/WGc/uXT1TlcWRfWfW8dnvrwUyj6LC+yrWzD8keWo21vGy4dvBT/9ul/QyZaj+lnywVzyH01B3TXP8vEMnjipicQuCiAHTt2IO1Po/ZUDbWWGqABT9zwBEY/OAr/o374/X7E43HMPWcuHvzig6h6LBncllu24OyPnq1CFFpaWuD2upFYk8Djb38c5ttM4AIAW15hYZtts222/Uk1XdexfPlyRKNW1Y2TeTxP9vkreUb5s9nzy/212WMH4GUgVoITaQ/xb5qmNVSA4PmagW0zSJTXlaExEpDIGF8ZU8tjeA0J4pqBPq8tY2ybY1W590lA+krgU34uf2/2ODZ76pvJjJOB65OFDDaDpldqUlEpCQTp1JASYTlOchzlMbzH5pAA2jemab4seZ0Eb3KMOO68lkx2JokfVvaRfcX51Uy0yO/yXHJ+0tZudiRwvOnYYAil7Gfapuxb9lezd1oSEhJ0S5tWglo57vJ77BPm0ZHOFJkgUTq4mj3ZEnRzLsqxku8NyS9+LkkTec+maeJf/uVfGpxJs+0P016TMdxtbW04++yzXyarki8WPayapuGaa65BNBpFMpnExMREQ8yEaZro6OjAqlWr4HQ6lZybycUIYJsXDb50coPiQmYYVgktLury5eELVSqVFHB1Op249NJLFUjjgkIwReDNBYfZj3kMZcfSYz8yMqLAq3wRuSAsWrQIXV1d6rxcEGKxmALajKMGoGTYXPj27dunkqBxoSM4TqfT6vkAS07rcrkaFtInOp7AlzZ+CaZuAnZg9xd2Y2jVEDwejyr/1MyEVq6sAL8DkpclUYqV1KLDfna5XChtK6HzY52Y/8n5yD6bRSFvgf9gMIhgMIiRkRFMTk6qOHmOLftlZmZGAelKpYLT/uU0dDzXUZ98vwMC1wQUIPf5fGpzoXc6GAxacdl2O3Krcrjr43ehekoVhR8V4F3uhcPhUPHU9IzIhDucFzMzMw1ziwm8CGA5n5hMj7HHkv3mQk0POgkll8ulksg5nU71/3w+j2AwiKGhIRw6dAjRaBQdHR1ob29Xi33ancbDb34Y+6/dD/igVBl+v18BeofDgUAgAIfDoWpgu1wuhEIhjI+PI5fLwe/x44wfn4HCwQJaWloQDAYVEZBMJlHIFnD2787G6kdWY+XHV8Ln8qlnCvgD6HqpC2f/7Gx4Eh7oVR1rH1iLFXevQKlQQmGygDd9/U2IHI6ge6gbC59bCHD/2QbgQgD/Vh/W3gO9mPdeK049EonA/24/jIihQhRMm4nDFx7GC3tfwM6dO3Hw4EH8rvd3qDrqhqdpM7F1w1YcPnwYBw8exJYtW/DCqS/gsfc/BtNmAg4AmwFc+l9Z/WbbbJttfyyttbUVS5YsaQAc0qgmcJLgrtlekaQqvyfl0TyWf5dJmP7/gDv3Ztn4uzTkpfFPo745Dl3aMrQDeB15n7RRpDdPAiOCCQJzec8S2DocDnX9QqGAgYGBukJKVAuRTfYF77fZPuQznkxqLb/bDOTk+eXPk4Hr5uP43PJZ2Q+yyb6SccbNRIIcaxmLLftcNhlnTKeI/Ezaw83PJe+zWeYtPby0K2jn8h6lfdz8rLQ9+Xd5j6ZZr9XNMWJYIkkG+fwE4vxMxnDL78oxISiXNmCz46z53ptLC0uvOD+Tzy3tdokfmvtbfia/K9cG2vJyrvDYZmJmVk7+39Nekx7u+fPnY9OmTWoyEpDyJWaWcsmQceJLqbVkiYGXZwKlTIRgVDKgchEEoOpP09vLlz2fz8PlcjXIY5o3OS6w9H5KSTa9pwTTPC9ZND4PSxtIRpgefoI4Po9h1Et0yFh2m82GlpYWvPDCC+jq6lLSY13XGxYMv9+P5cuXNywQkpCgpFpKYSizJ6NX9pZfNq5m0FTH0VggCCu8r4DaB2uACziw4gB+MfQLfHT7R+GpetSiVygUEI1GYft3G3SPjtaOVhiGoWpwM4N7qVRSsbbpdLqhbzs7OxsWZbfDjTXfXIP7j9xvSdU/DLgcrpexzF1dXUqFQHCZXpPG8U8eRyViLciTp0zimXc+gzO/eyYCCMDlss7DGHkC77GxMXg8HiXXZ99ns1m4XC54vV4rOZnP1yBvjptxVN5Rgfm9xoQsfA9oSDHBIOcg48pJ4vD5Ge5w6NAheDweRCIR2Pw2PPeO5zCzYgZYAdgMG6qfr6pYeJbyope8VquphHE2mw2BQED1O5OjUYrPuUzgz41tw90bsLe4VxFB4XAY4+PjKJVKiDwawYr4CpSjZSzbugzQoUB/7WgNG7+3Eb2dvchsz+DY4mMwLjGAdwE4Aujv0y1J/EIvrnjgCtg32RUZMnp8FBOPTeCui+8CNGDNnjXo39wP7a2aisUe+c0ItFM1bLtiGwBg7m/mIvPxDHZVdqFcLiObzWL4xaayHBqA4H9i0Ztts222/dG1jo4OLFiwQP0ugR5bs6fxZN7PZjB2MkdCM/Br/q48t/SQn4wIkES1PMcr/b/ZQ1m2lfEDxw/wnvx7ANTD4ZpBhfRCN98X955m4CIBDWAlBGXJx2YniwSZ0pvJa3Kflp7z5n6UHl3Z+L3/GwCX5AM/bx5POZZSui2vIb8vv0tbSHr8T9Z4vCQUaI9Ie4VjyD6XZS+lTSrtyWY7meeWc06ONZ08vJYsKcv+bvZ+S89u85yhHS/HST4vzyOJAnqgeV9S0SGvIUFrMzEmCQQ+o3yP5Bjys+akZnxe+Xlzf8o1g88rw2BlLiep3pBqVt7P4OCgIkBm2x+2veYAt9PpxDvf+U5Eo9GG+BJ6CLmIc1LKxA+hUEjVyuSxuVxOTWZKgjWtXkeQQEGen4sCwSr/Lhd3eq51XVcyWSa6kiSAXPR37tyJ9evXNyx48j4AKM8x78FmsynAxuyPLpcL0Wi04Rr8vvSeN0vK2Lq7u1XMtYyBkbXL5ebGcmBc/Px+vwLb7Af2L8trvf7o66FXdXxj5TcAAOu/vB7+h/0wHSay2SwMw0AqlUJvby8OXngQ4+8YB1z1ebCzdyc+7/k8bn7kZhi1+uJdKpXgutoFu80O53PW/cdiMRQKBUxOTiIUCiGZTKq+leXiGHtjt9tVXHY+n0c6mYb9JjvKlTIwCUSXR+H3+9VYlEolTE1NqVJjpmnC5/MhPBG26iD2QMUhe3Z7kBnMIBAOIBgMqnibcrmMaDSKRCKhMpXbbDakUikFhN1utwpzAKwFNhaLIZ1Oo2pUsetfdsHsNoECgJ9BLdosq8G5yDwFzH7u8/msRGW5HPx+PxKJBDTNCtHYv3+/UlkAwOT3JlE4q6DGoXZdDamOFEK3h1SVAG50TqdTZYNXNc1PJI/jnKcnJZfLwTRNhEIhVcuVZFWtVsPSpUuh6zomJydVngI+f/BYENk9WUxnpxWYb29vh67riI3FUJ2pYmxkDOavTeAnAE6Ec9uLdpz681PRMqcF7a3tMCKGIg5OPfVULHxxIXwOHw7MO4A/3/7n8JzqUUoKRZ6ldfRu70XGl8GbC29G9sasugeHw4HhsWGkHkvh9nNvty56JYDf/qeWvtk222bbH1nr7e1Ff38/gEZD+GTguhmYyeOlwd0MHJo9ufLc0uiXYFPaFs33QpuFCqlmAMRjCDZ4vPI8Vsq42n81nrM9Bw0a3lt8bwMYkLaBBOF8fnoWJejhZ1K5xfMwrI12GdV0JyMnmvuV12WTwPyVSAa25vM3HyNBdfM5mv8mP5dy42YiQwJOjo0cVynFloBOXltK+SV4l8/Pa53MicTjm695MvJGglT+3gxCOb7sy2ZFqHRqSaBOdSPfgea/Nc95Prucu9LhII+VHnA+iyQ0JDHC3zlHZXJESdY0Hy8VHFKOL98R2dfNc4j9IlUpchzYD825pW6//XZMTU1htv3h22sOcIfDYVx99dVqQsvSG5TNEhw888wzmDNnDrq7uxviZYB6fWO32418Pq88fpVKRTFRzUnUGKtCAKxp9WydplmXsXODIEClTNwwDJVIi/cQCATUQrNixYqGF1vGhtBTS082E1VNT08rAA1ASa7oyeUL6fV6kc1mG2KDeA161Ckbj8ViDeSFBFCMA+fz04vu9XrVIkVPLxPasU81TVMy9Wq1inMHz8XR4aPY9+w++J72WV5Yj11J6xl7bIvaYDhezkDvi+7DJ3o+gUt+dom1gBo1FM4s4NC1h6BBQ/FDRZwyeYoC+ZFIBIVCAV6vFwcOHEBHRwfsdjueffZZ9PX1weVyNdTR1nVdeZkxBeAEaZjNZuH1ehWLSLCYTqcVeDVNE8gCq29ejZf+90uYmTuD1jtasegXi9AaaYXNZrO80vG42gzYxx0dHZiamkIul0M0GkU+n1f9nk6nG2qsDwwMQI/q2PrxrcguzFrA/vsAcoD5axP5fB5DQ0PKoy/nYyaTUXLwSqWCtrY2OBwOZDIZxGIx+P1+HD16tAEI+2/24/hdx1EIWKDbMeZA1+e6kGnLwDRN9T74/X7kcjn4fD5Vs9rhcMDv92NqakptJMViEW63GyMjI5g/fz5yuZzqx0qlogixRCKBxYsXq1h8VhYYHR1VmdHD4TBiMSvTeDgcRjKZVKqA4eFhmHlTgW2+K22ONgRzQYyUR9Db26uY+EKhAJ/Ph7V712LTyCY44IDpNRV5RAIq4ArgrUffamVxN+3wtnlVKTCXy4UVK1agGC/CttWGX3zxF8Bd/6Xlb7bNttn2R9LcbjfWrl0Lr9cqW3kyz6MkF2WT+zXtkmZAIaXa3COoxmo20pvB0sl+8lj+lOeR/5fGuwROtVoNM5UZvCf4HjxqexTQgE+5PwWP4cE1pWvg0OqJXaVMuRlE0gEgwR77QII0qayTf5PexGYvKb8jAcnJWrOXWj4vgVQzcG8eV9nnryQ7b74HnrsZgMp+ph0lwwYIJBnmSFsyn88jFAohFAqp+2ieD3y2Zs+3fE75TBIUSzAsE9jJkAT2vSzf1Uy4pNNptLa2NgBhAMpZRXtc2vS0O6UqlPNQkkaSDJL9ymOb3xXeQ/M8lXNQEgd8pua4ao4Zx0omu5Phpa/k9ZakCs8vf8rxkP3Kc5TLZRw9ehTj4+NYu3atSra7b98+VR1ltv1h22sOcH/qU5+C3+9XLwkn38mYn1WrVikAwJ98weilYuIxAmZO7kqlgng8DqfTqUCxlGTXajV4vd4GlpL3QmaO8mheR8Z60Psta+xxQZMyYsMwlLyWLzZBLMEXQTwlyvzc7XYrIF0sFtX9sA9k/cR4PI729nYlay8UCiommf1HOTKvz2zUpVJJASB6VQlaSFgQyLBVq1XYbXZ03tWJPZv3QFtQX0CZMI2J6zqf7cTgmYNI94rkZQBanmpB5MMRPF5+3Ir3+jM7nr/ueUAHTJg4/O3DCH0qhAWHFyhgSXn5woULYbfbkc/nsWLFChiGgWQyqTKAcwy5YMoFkp5b0zSRSCQakuq5XC5FONjtdkTKEVx8y8W4Y/0dcN/iRm1BDR6PRy3qbW1tqNVqSCQSaoMaHBxUNcBrtRrC4TASiYQqGUbADQA+nw97T9uLVEeqXhLNBeBGQH9Ih5az7j2VsrJ50zvMUAVmMO/q6lKebW7g2WwWnZ2dGB8fV4aePW7HG7/xRjzwrgeQGkuh74Y+hOwhJYmPxWJq/vJ83ByBegxUoVBQHuxq1ZKkj4+PIxAIwOfzwefzqWz9o6OjmJmZQVtbG9xuN1paWjA4OAhN05Tnn7kD+P4Wi0Ukk0nEYjG4XC7MzMy8bC3hfXGzn56ehtvtVmDf4XDANEzoVR3JTBLz5s3D+Pi4IqGownC5XKgWqjBtpopZl6SWzWaD54ce2H5rQ82cLQs222bbq6F5PB6sW7euAVwRpJ3MEwc0yoRpO0gvYalUgmmaKiRHEuPS0yY9pfLavKbcr5q95M1glCABsAAKlVu1mlUqMpfLoVAooFarYXNsM3aFd6m9pqpV8WP3j3FJ7RJEK9GXScpP5uXn/Ujwzdb8PM1hUc1AVx5L26b5PLwujzmZpFd6+CURwHM1j5/8frOXV/axPD/PS7KBc0OCQtpABNc8nmNSKFhEN/dG7kFerxcul+tlqj35DFJp0Ezs8B4l+OT/peeWdotMuicBIseMthP7gU4UOsMk0G8eA46hlKgzOZnsVzpF5H3Juc1+lM8v57wkS2T8uATCPBd/SmKGx0lPdrNyQPYjx1eOCe9LkgzNc0euGfw9k8lg27Zt2LlzJ/bt24fHH38c3d3dOH78OF544QXMtv+e9poC3F1dXbjiiisaJmHzZCbLVqvVgY30bEtZl1zI+SJIuQnlS5zox48fRz6fxymnnKIYJslUM6EYz0MvMGWqzfFMksHj8ZVKRX1GT7CMIZeEwYEDB3DKKac0LM5SRswFhF5BAIrVkxtfIpFAMplEZ2enWmRkXxJ8ymQVXq+3YQHns3Bc+H2CUfZR89hEIhG1QAeDQRVrHY/H0dXVZTG5oSJq7kag0vVgF+Z9cR5cEZeKzd/5/p2NaQR1YP+H9mPuB+fCZrOpODDWi+Z9SSImHA4rSXt/fz8OHToEAHBscKBYLML/ol9lX6chwu8Hg0FVHk2C4lKmBP+n/PC1+VSWdhIRnCPRaFSNVywWQyKRaPB4ML6+WCwil8upRT6TyWDe/fPgMBzY+s6tMOwGcD+A91mS6WAkqFQSLpcLPp9PeeQJvvP5PJLJJLxeL3K53MukU4zJJ+j2H/fjjB+cgWfvfxaIA+H+MILBoCIj2B9erxd+v19tfDMzM7Db7SiVSgiFQla28RPx5OFwWJEy6XQaHo8HlYpVSo1KiXK5jGQyiSVLlmBgYEAZFzxvLBZDIBDAzMwMKpWKkuZPT0+j9L4S8EUAYhqZN5go7rOk6fl8Xs1pqjQki22z2XD48GGV1I+GDoG3LIvGdYWkWzabVeEes222zbZXR4vFYli7dq1at6V3kU16DKWxT8DRDJKr1SoGBwcRiUTQ1tYGoO795bnoLQNOHk8rgTavJ414ekhpa3A/yOfzCnBTrQfUlX4OhwNvyL8B3kEv/nneP6NgK2BVZhW+Xvw62l3tMO2NcdXS9pJ7mQwFlFJbadfxd2njMaxPPrMEidIObPZaSlB2Mu+2tG14j7Lv5M/m/mwG6M19LwkPCbJORhwUCgVkMhk1PvI5AKh9mCFvvH6zV7U5IVvzs0ivt5w3zf3zSnOoeRzYZ7JP5fVoe3G85fw42djw+pwrMoxRzi95bdkf0kMMQPWlnDMSYPOe5bsm74/P2awckP0nSS7Z5/Jdl9dik/Ofn/OzfD4PTbOqvvAYwzAwMzODhx9+GIVCAW1tbYjH4xgYGFAVl2bbf097TQHud77znQiHw8rQBuqJzSTbJtkyNrmI67qOTCajYm55LoJewHpBI5GIik8ulUqIRqPKu84NSV6HXmRN09TixwVDgnwCIKDu+eWmxNJKZEHj8TjS6TTa2trUC8tjFyxYoKTq0vsqPecE4EBdbi5jQQqFAiKRiGLUKTGnLL5YLDZsZlxcyFjyWFkeQS5WL730Ejo6OqxkZie8iTJ+TNM05D+Sh2+XD/Ypu9o4Fi5cqKT+wYNBmJ8z8cwtz6DmqSF2XwzLfroMTocTZbOsPKJ7HXtRQt1Q4Jiyb5g0jbXaOW5yLjkcDlXjOp/PW/Nrjon8B6265OW3ljE9Pa2AFLOSk21lTL3cLCKRCPx+PzKZjCqjNTMzo+TqlM5ns1kEg0FFVLS3t6NYLKp5AaCB0KHXwev1ouN3HdiQ24Bn1j0DvB/AIFDVq8qLTQJJvhs2m01Jr8my8p5oEFDWTYOHORDCB8IIT4SRK+dQLBaVpI0qAl3XVc4CPgPfq6mpKZx55plIJBKK9WYZNCor+B57vV44nU4VkxQMBpFOpzEzM6O8/7lcDg6HA6lUCvPnz1fqlWAwiFwuh/1X7EfhygKwFMB1JybGvwLl68t46e6XsObXa9SaEAgEkEqlFOlVLBbh8XgQCoVgGMbL6s2TjGKmVpID7EPOu+bygLNtts22P+22Zs0ahEKhBhAgSbpmYNPsBeU+KUGY1+tFb2/vy+JrgUaAeDIQwPuQ4AmAshGy2SwSiYQi/5rVW9yvdF1XVSt4Pel1vCB+AaJ6FF/p+ApuHrgZITOEcpdFIkvPIp0ZMiabfSETaDXLhpuTW0nP4MnIC/4kKONn0saQx8r/N9uK8h75d9nfzaQAv9PsJZZj3+wYYt9wz8jlckgkEup8NptNEeqUWTdfn33CPSqbzaqkvc2SZD6LBLfyc3m/sp8lccG50Xxu/pReWjlfXylUgf0mnUi0W2lrmKaJLbEtCJkhrE6sftn35b1J4kUCZv7eHNdOe4hztPm8csxor/JeJZkhlafNn0l1RvN7yfuQYJ33zSotHo8He/fuxcjIiFIOnnPOOQCAgwcPKlXk3LlzMT4+rhyAkiybbX/Y9poB3K2trTj33HMbamMD1kvrcrlgs9UzdssFTS6gcoLL2GlKXHlOSrN13YrhLRaLSpbt8/nUAkdQzRdLglUmipISbsMwlAyboJ3eXcbkSpaPXuVAIKDktwS7BBtMSsVsz9LLzGemTI3gwOv1qnhhv9+vym3Qm5/NZtXzyI1Tet4LhYJaRHRdV3FsZMdJPPT19al4Z5Yzoxe0UClg6m1TmFgygUQhgYtuuAj6qK5qeNP7HolEsCizCOFPhfHE257Ayh+vREegA6l8Sm0ApVIJF3/2Ytz1hbtQ9lmeZWfWiTNuOgO6rqsSXplMBsPDw1iwYIGVEC2dbjAACOQYM52wJbD1c1tRC1qAsbyljOwZWbSb7WqsZZw/CQd6aNmYrI7ALRQKqXGQDCgBKb2znI8kATRNUxsrpdDpdNoa8zs1+L/oR3bUilk3TSuWmuzo+Pg42tvb4fV6kUwmAUCNBzcgJjAzDAN+vx8tLS0NCW3oOZbl67jJMhs/k+0NDw9D13VFbMXjcVSrVfT29mJ4eFgRH+FwGOl0GsViUSkGZmZmkM/nEYlEsHPnTiUbn5iYQKlUQktLi0rsFwqF1JhlMhkFih1uBw6edxAHrj0A02EC18LycKcAvBcwHAYOvekQ/Lofy+9fruYoPfmlUglmwITNtDUoXbxeL6anp1EoFJTEP5Wy5uLU1BRcLhfC4XBDvobZmKrZNtteXe31r399g7Euq4k0ezTZaMSzSQDJ7/h8vpft4c0AsVneys8LhQKq1SoKhQLK5bIKL2N4nIw35fWkvJ17EI+jV5KkPUnYdYl1+D+5/wN32Y1MMYPR0VF0dnaq/ZgAhHaZ9NZKkkESC7Qv2KdS/dfsbSShS/Akf0owLvu+2cMo+1iCUN6LPF6O18kIFem5lWPM+wKgwHEul1NSfYJL2pw8B+eRnBtSeSZJCO67Mr5fzrdmgMf7Zf83e48l+cD7kaSLvKfmuUlgy/klx4P9Uq1WMRwaxu19t+Om/TfBWavPLTqkakYN20Pb8dm+z0KHjq/u+CqWFpcq25F2liSWCIybCRvpjaedw77i8RKgy4pCfBY5B6XDQhJq/I7ss+a/c55IgkL2Wy6Xw/PPP4+jR4/C4XAgm7WSsO7btw9+vx8jIyPKxqczbGBgALVaTTluZtt/X3vNAO4LLrgAS5cubQDQzYyaXOwkoyaPaY4FkUkTAOsFzWQyABqTTVBiyg2JpZlCoZCKr2GyJAmeeL+U0jIemwueTPBBOS8BtWlaZZZcLheeeuopnHLKKQrEM3kGF3PKxunFlfIt1rWWSR0ANIBmfld6rMkAlstlhEIh5UnkQkSARlaOcd68BqXbABS44vc0u4aHFjyEHy37EaABJWcJD3/xYbz+S6+H7aBNgTpmPE+n04gvjgMB4OB7DyKdSmPFQyuQnk6rmGFjyMCl/3gpHvzQgwCATV/ahLAZht1nRy6XQ7lcVom1JPNIZp5ATWYy3/mNnSgHRYmFKJDenEZlqUWodHR0qDEPhUIKDDNpHb3+AJT302azKUk1S5lxnhYKBYRCIYyOjiqZdSaTURnAgbqsTM4RXddx/PhxQIS5m6aV8Z1e61gshvHxcSVjl4n9SBLQ2GN/MUEciZ6uri4U3UUMuYbUGIdCIeWpB6A8KIFAQM0hKisYa53NZtHe3q4IGr/fj0AgoLzeIyMjCqy3tLSgVqs1xNin02krAzygwHF7e7syDP1+P9APHLn0SD3hng3AJQCqsGpiAzAdJg6cdQBdu7vgOeBRz2oYBgb6B/Dsnz+La267BvMq8xSwjsfjSurFuU+Wn5teMplUm3ehUMDExMT/bYmbbbNttv2JNLfbjdNPP/1l4IbENA16oNFr2uyhlZ5enqPZkye9bQRtvA49WjK8iWs61VqGYYVuyTA5EvWapqlqGbwHSYoThEgAbrPZsNu9GysLKwFAJRudnJxEe3t7g+NAetAlICRoIVHKvuBzy0SrEozLcDhJdsj8NCeL9eZxMgu6HDcJuHic/F0e2/y5tLV4HtpPDE9Kp9NK7eV0OhvId+m04Z4i7dFmbzP/xrEg0RIOh5V9K//OeybRQu8rx0mCU15f9jv7r3nu0oaSBAfnsrSv5TwHgIPhg7hpw02o2Crw1rx439H3IVgLNoD4Z33P4salN6pcAR9Y+wF8Y/c3sCK3Ql1TOoUkqSHHR75n0gHH4yR5QHJI2uTNz8xzyfdUkkfNhAeJIQm+a7V63ig51olEAlu3bsX+/fuVLarrOvL5vLJRp6enlUOC+ZNSqRRCoRCmpqYwOTmJ2fbf114TgDscDuOss85CLBZrkHPIl0UyvgQJMpM4vYVckMikSg+pZCN5Lnqfa7UagsEgstlsA7gmE1qtVhUbRTkWF11mYQSgXmg+h5TocJOTLxjvYd26dSpJBq/LF5LXIoiXDDgBM39KmYymaSpxWHNSEYJuHsuNwjAM9Tyytrb04LO/yLBTxi0lZrqmY39wfz3RF4CKs4Lp9mn0HOtRhkUwGEQwGMRLa17C1vdsheE0kFqUwjCGkS1nsfSOpQ0bNg4DG7+9EaZhIhQPAVo9w3wgEFDgp729XcnBaajwH8fRMAwseHQBXrj2hXpsuAlEfhVBzWHNk1QqBV3XVWkwn88Hr9eLSCSixrS9ux3la8vQflf3gNMT63K5UCqVMDo6qgArE7JJQiUUCiGRSMDhcKhkdiR1mG0/Go3i2LFjqj8Zty0TpHGe0/Cg8cYxZNw0PdjcSAjEiyjiqcufwkH7QXh3e+GPWzHpTB7n9XpVLHRLSwuy2Syq1apKAqdpGrq6urB9+3YsWbIE0WhUeYdlIrjW1laMjY2hVquhra0N6bTFJEQikYbkaATItVoNra2tmJmZgcvlQi6Xg3OfE6d96zRsefsWlBaVgBkA3wNwAaxSbSeaf9wPZ9bZECZwdM1RPH3906h4K7jnrffgjfe+EcHhYMN1S+USnlzyJNbvXq+MXM57mQSFddJn22ybba+OtmrVKpV1uZnUJ1CR+6z0lkn742SeVBLWJCMlwOa6DKDBFmB+DtM0VSJMkpTSo8pzkhBtb28/KYCSgI/gk+Dpd4Hf4SsLv4JPjH0CF05dqI7J5/PI5/MIBoPQ7Tp+Zv8Z3lZ4myJ3JdCW998sXQbqoYISkPL/0vaRwLv52GbnC59HjlEzwJVjKL8nP+cYyd8LhYLqW96frE5DJRyr4XDe0M7ivUuyRXpqqbKUEuhmzz9JCullZYge+1sqIJttQfmZdF7JaxBQS5uPTYLdV1IN7GzdiW+s+gYqNmsO39t1L0zTxNufezv0sq6qBt1vu7+x3zUT+8P7saqwSj0/bWhJoMj+433wfaH9y3ttVpA0y8XlnKKty/lJ8kAqU5rnpFS58KdUL7C/2Vejo6M4cOCAUpiSICORXyqV0NHRgVqtpoA3q8HI3Eez7b+vvSYA99KlS/GWt7ylQerBTaY5PoXyYQJtHmsYRkN2Yb6kTCDCBUECZQI1yaqWSiWVNCufzyvQRA8gF/tyuYx83or7lXHclHt5PB5FBORyObX4yrrVNptNeSElA0rSgF5zeX4ZDy5ZOL68LIslvXNSakYpPCXV8pxyAWEmcwAqOyb7mYw3vcfcbLghVKtVODQH/urAX8GtuXFfz33Qaho2fG0DOvZ2oGbUlJy/Wq1i32n7sPPanTCcjXKZA1cdgOkxsfK2lUqBYLPZkNJSGL96HJ4fedCWb4Pdbkd7e7vawOhppxEj45R9Pp/6u67raP1pKxbNLMLBGw4CAPq/04+ue7tQWlr3ajJ+GLBk0/F4XMm9Q6EQHr7yYcRXxhHpicC8x1JQcFzL5bLyXuu6rtQVra2tSKVSiMVicDgcyOVyaG1tVUYT5zPvU9d1RKPRhnwElNHL8iKMCZObOYknJixzOByqH1iii8859tExJDYmAA2o/GsF7R9tRzqdVu8Wn4XvFj0OklUHgP7+fuV959wgAcJNz+12IxKJQNM0dHR0YGZmRvU553NbW5siIKanpxXJQJVIwV2A8X8M4L2wYttfBLC2aYGZBCqpCvSSNV+HNgzh2aufRcVrbdTHu4/j3y7/N7z9l2+HO+5W8vD7LrgPO1bvQM6VwznPnqNkkyQPPB4PJrIT+NW6X2H629P/pTVwts222fbH08455xy1h0ugIYGL9MKxNYM/ksUMpyG4lnYJAQPXT6/Xq9ZIrrEyJIfqJP6jAo+A2ePxoKurC9FoFOFwWMmR2WQIGVC3X2w2Gx6IPICvdX8NKUcKX+j6Aoq1Iq6IX6Fsm6mpKWiahs+2fRa3O29H0kziPZn3AGiUeEvvNAEHAbC05YA6OJJAS4L2Zvku12H2sQSzBDJ8plcC5tLxIgGnJE/S6XRDRnfaXBxv2oOSRADwMqAvbTs+E8lwVhRhCGEoFGogc3kOVq2hTSz7g/0gP5cqTylv5rNJgqMZjMu5LceKNoX0rPN5lY1SisJpOBveJfuwHYnpBLIJy2ZMpVI48+iZKMaLeOLPngAA3HDgBlwxeQUMvdEObbZj5DsmVaQ2m005GTRNU+Q+7dFaraZy4tDmr1QqSKVS8Pv9Krxx/vz5CIfDqn8kcJYkhJzbsv/5HkvlR0Wr4CvtX8HCXy9EOp1WuaOG5g+h6q4i/HQYk5OTqFarSCaTGHzvINoG2hDzxdDW1ga/34/7L78fvZ/vxWz7722vesDtcrnwZ3/2Z0p2yxdXMnwyJoNNyrfY6OmlzJWMpJQT0aNNjy5Bb61WQyaTURssWSe58HHhZXM4HMo4Z2woWU8CLilhIZBmDCxlX9z46LmTknRuLpTWcCGR8hkCXbnJMKFVuVzG/fffj8suu0xtGKzXzc1AxvfS60qSoVk+JEEowTwl+XwGLs7D/mFsad8CADB1E/vevA+xfTGEfWFVEsxut2N0wSjKXiHrPtFM3cT03Ol6cjOYGOkcwc5P7kQtWMPUvCmc/d6z0eHpUJmxHQ4HOjs7X7b51WpWCZR4PI5YLIZYLIZkMomAP4Dwr8KYb86HI+BA3wN9MF0m7JpFeIRCIWQyGUQiEVSrVZX93DRN5Ct5HPjYAUyeMQnTZuLY+47BUXSg7YE2tRFwDF0ulyoxRRZcSpTo9c7n8yo+mIQN504ymVTzmXOZZd5oMBUKhYYyFpy/JJqkmoH5BrixzPyvGeTPzytVQm1NDcd+dAyR6yJq7mUyGaRSKVXujBJG1kInseHxeFQuBLvdjkgkoqRXTqcTBw8ehM/nw/T0tCKKWOKrtbUViUSiITN4oVBo2ACr1SpGukaw9SNbUSlVgD8HsPnExHk7gEcArARwDzD9jmk8WHgQDocDbW1tsL1kg6PfgdK5JUvZUAHmPzQftbEa8rU8bC4bNr9uM55f9zwMm4GHznoIHnhw+s7TUcrXExJN56bx8w/+HFOxKWg3aMA/oCFL+mybbbPtT69pmoZzzjnnZXWHJRBoBtU05AmkaGfIXBhA3UDneg2gQSLMNZY5Z7LZLPL5PDKZTEMMdLMnFrAMfbfbje7ubnR1dTXkquFeTvBBG4HNMAzs8O3A53s+j5TdIpdT9hS+1vs1tNXacFriNBiGgUwhg8/bP4/fOH+DqlbFLd5b4Kl58Lb822BD3T7jM8n4az53sxeRUl/pEZTPRztHflfaYXKMJNhtjt2VoXq0vaLRqAoLK5VKSslF24b3Q0AnnSLNIJf/aFM126a0QUicNCvMZFUV2nIcN84f6cFl/zTv9fyOBIYSMJ7M489zsV9luIQEvjI8Qnqg2eZl5uELT30BHzz3g8jZc7hk5yW4bP9lGBocavDgV7NVnPb0aSgbZfRH+7H44GKYC0yYWmMmejmH5DjL+Syz8hNEF4tFpFKpBkeU3+/HzMyMcmSkUilEo1FMTk4iGo3i0KFDsNlsaG1tbbA1+LzyPWt+75q92WpeOg18cPkHccB7AF2nd6F7Rze0pIbiwiJe/OcXAR1Ye/NaxPbHYOomBq4ZwPBVw4i/Lo7+f+zHyOQI9vz5HgyfPYwjtSPAowBebirPtj9Q0072Ypz0QE37/Q78I2s9PT3YvXs3wuFwg7eWLzPZPf5Oabff72/w7ElPNV88Gb+qaZqKgyYwIajxeDyqtI9MDkYpb61WQy6XU57A5gWACycAda9utxuZTAaapqFYLCpgz4W6VmvMLs5GT64EjGTA+Zw+n095svP5PHbu3ImlS5fC7XYrwEPJPa9HAMSYEV6XnksCPT4rmXbpZSfrRxkTNzP2Oz2sqVQKptvEJ8/7JIbDw+rZbGUblv52KRb/cjHK5TICgYDFxjrseOa6Z3Do7EMwbfVpHNoTwvmfPR/VimUklFeUsfmfNjfIv71xLy74lwvgn7Dmw9TUFNrb21GtVlUpq/HxcTXGg4ODcLlcCnC7XC61AHu8HricVhmyTCajnofeTM4nboAvLn8Rhz96GNVgVd2zd8KL0z59GsJTYbWJM9M2vfq1Wg3RaFT1I4mioaEhFafMucO4eZvNhqNHj+K5555T46dpGlpaWuD1elU5MMm4e71eVRIuEAgoOZrL5VK1pKXc0eV2oXhXEVMrpizQfRhY/sHlWORfpOTkdrsdPp8PyWQSuq5jdHRU9afNZsOhQ4fg8XiwYMECaJqmDAsmB+SzM17bNE1MT0+jtbUV8XhceezHxsZU8kHW8ub7aLPZMNg+iEdvebQ+FyoANgLYAWAugHsAhAE0kcLcQB1OB0q/LsE804Tjcw7gy7DAuM2G3Lk5xL8Uh9lan4v2UTve8MM3oGuqy0pEGMzi12/7NYZ7h62+MgH8LYCvAXgNJRE1TVP7vx8122Zbvf2x2yrLli3D7bffjoULFzYo5QieZVkt2hFc4wArZEaqfSRBSgky1zIpF56amsLIyAjS6XSDB1yCRvlPxk7rug6fz4eenh60traqtZP7B8G6DEdTa+EJJ0OhUMAvfL/A9/u/j6KrCFfZhXcNvAvvSL1D7R0PBR7C5+d/HklHUvVXb60Xt2dvx8LiQgXK6EFv9ho3e1EpA5ZAWibHlfJxAl2OCZ9DOi0kGKbakJ5kgmw6ZKSHWpZ8lMlkgZfHSgN1sCm97UBdys5QASZRo1KhGZwBltKrq6tLJT9lP2g2DVOxKXQmOhXgbW1tVTl8pCpRAn/aCzL2nECZtqoEv2yS3JBeXc5ROXfkM8v5xOMy/gx+HPkxLn/iclXus1KpKJubx3p9XnR3d2P+vPlqrkqgLR1MtIWZRJZSa46X3W5Xajz2t6bVc7DQFuG7bLPZGsIEbTYb8vk8LrvsMmU/SwWonLOcNzLUk0rWyclJa+zDRdx99d0YWDCgbITuH3Uj+lwUe/91r5XoFQAMYPH7FyPZn8TE30yoY0OHQggfDOP4ZcfrNsYvAfw1gFll+X+4/T62yqvew/3mN7+5IZ5ZMmmcyPxcsoGMlZVsKTczl8ulMvwRAEu5LUFt83d9Ph+AeiIILv5k0SR7RQDGF7FZ6s2XVXriuXhwA6RUVjK4QD2+STKd/D7LdVE6bbfbsXr1anWP9FzzWqZZrxnNhdHj8ajFgn3NhcztdiuAw0WL8dxyceezUuLLhY3ltrzw4qbHb8K3T/82Xmp9CTCA5Xcvx6JfLUKumAMAJTM2DRPdL3bjyBlHULPVN4DYkzHksjkl3X5+5fMNMeHQgJq3huTyJHzjFkNMuTRBKeOt4/E4pqamGsAcE2NxE3Q6nCphV6VSUTXEZdwOx07TNCzeuRi+n/nwwjtfQMVbQXAoiHXfXYfgVBC6rquENqlUCsFgEMlkUkmo4/G4IlUIYKPRqMqsTrDNTcftdlvlyeZUAS+AHdamyE06n8+rsSdJwnwEzJLKucQ5zXeN91StVHHBNy/Ajo/twKHKITje50AtU4O5pF6OjknjDMOKW+vt7cXIyIiKHe/q6mrIhi4JAI/Ho7LrJhIJFXPu9XpVOTACavW8J8gPEnI0/EbWjTTOBRuAywFkAXwfwKkAEgAuA3Bv/TBlWJVM4FeA/rAO7esaakZNyboctztghx2VL1WAVkA7osH9MTcefOxBBAIBK878rDSGzeH6PWgAXgfgB3hNAe7ZNttebW3u3LnI5XIYGBhQtgCBNvc9p9MJu92uYqvZ+HeZ6IwAnWuk3W5HMBhU5TpJcqfTaSVjll5KGS9qmvVcMFQoMXcGlVtcq5vztkh7x2az4bjjOPK2PBbnFyOXy2F6ehorZlbgiukrcPfGu3HJc5dg3ZF1SHQnEAqF4HK5cGHuQpSHyvjSnC8hbU+jr9aHr2a/ir5SHwyzURpOG452jPRMysSeMjeNXOMJvGhbybhcKS+mbSTl31IVJTNec9yAOvlKp4NU9En5uVSMSXuU4FqSMFRPytJs7HOCTCk35jllmVkes3X5Vjy84WH8+f1/joXDC9V1JeCVgJj2iRxzqTQgcSD7jY3nlOERDZ5aozH5G+9TqiR43nw+D1fZhStevALFUlGFQ7AMLJP2FYtFZNIZ1NpryvNMBwSVHvF4XPUTnUWs6OPz+ZQU3OfzIV/O49jqY1i0a5Gy/+mAy2QySCQSDRVbGN5BYiQQCCAej2P79u0q3JGVY1KplLLlOK84xiSNpKpR0zRkT8tiyjvVYCPYz7FDW6RBiEEADfC+y4tqqIoJbUJ9Vo6VkVuca7QxlgKYj1nA/d/UXtWA2+Vy4frrr1cblK7rKt6omb3j71NTUzhy5AhWrVqlNkBuWmS2WPaI8coEvlwI6ZWV55YAhCAym80qmTi94nxhKQHmAiYZPrkoM5soz+3xeNRi6fV6lfe7uXY3GTsADcSD9IhKD77MuikXSnrkJTPZnBRNetW56EuZOxdVySqT2JBsNe+TgMnhdqBsq+tfis6iIhkke22z2WDL2ywGT7RTe0+F0+9UJbP6ftEHj+bBrit31edQ2oW5j85FxbTIFi6UfDbTNBEIBDA8PKxk2QS0gUBAMZvcrMrlskpMQVDOPiQDK70S3Zu7kRpOYegjQ1j+teUID4RRqVVUjDvnMzPGs0wZiQ3KBmXmTG4s8rr5fB6VYAWV71UAP4BrAXN/PZsl62SbfSYKKwoI3RVqmBMME6BRQHUF5XSK1EmVcd4d56H4QhETL0wg25ptYHonJiawZMkSFT9eLBbR3t6ujA8AaiOkeoSl2DgmmqapBDOcf5TP81m5Kba3tyMUCinCKBwOI5PJYMN9G9AZ6sSWC62QBXwSwC9gMcBnnZgcEQDfhsUI342GOYePAsanDejf1NX7Xq1WVby5/isdZspE9VtV4Hqg+HRRrSnxeBylfSXYDttQ+1UNCAG4D8BfwUrcNttm22z7k2y6rivPdiqVUgBXhgIBUPaCjMMlwUnZcLFYbEim6nK50N3djba2NuUFJ0DjXh4KhTAzM9NQvpDeSZb5DIfDaG1tRSgUUmsovWs8njaAzLFBO0jXdSQcCXxm7mdQ1Iv42z1/C+24lfyxVqvhzBfORCgbwpKDS5BAQu3nBIqXJi6Ft+bFF+d+EZ+b+hxOd58O3VFPmHoyqTFtBAI27rkEefxus3xXglAeI2Xh3CsInqTtQmJBEiK8LylblnYNbSTpmZfSdILBbDarQD6Bl+xjXt9ut+PQuYfQ9UIXXJMudR7+jcSJLCFlmiaeWPsE7j/7flQcFdxx/h142yNvw9yhuUilUg3Hnwy0N8vGSXwAdW8956S0Z2Tfsy+kh/hkYyNtXZIIJJoYLmaaJqLRKFwul9pfbTYbZmZmUC6XVSUY5pUxTRPFchG/2fAbnP/g+Uo9wgo9exfvhWfQg47pDlX1xGaz4fnrnsexjcewZmYNOn7XoRxXANQ7CkCpYQmmNc1Kcjs4OAjDMDA2NtYQQlAsFtX8Z4k2KkB1XYfH42mQ79N26RzqRMcPO/D4Rx5HKVBC9/5ubLpjE6KpKOyDduy4dgcA4PQHTsfZT5yNfDCPh/SHsH/RfjhKDlx+5+UI5oO496p7MdY5BhwB8G4A2/+vy9hs+0+2VzXgvvbaa9HV1aUmMBc+xh9TRirlPOFwGG1tbSqrI2At5pQOSa8tgeLU1BR6e3uVJIwMIBdkgmJekzWvJRNL4JnL5dSmws2YgIreTybp8vl8Sl7EzZCJ2CRjbRiG8oZy8eAmxPsicONCTSmVYRiKtSXTCkAtUOxbAA2sH+9HgnYujpTxcrEigCVYpweTfcjFiGAml8shW8vis2d+FmO+MWuwdeDAxQegmRpW3LoCTqdTGRV2ux1z9s9B/3v7sf+HVmbzjT/eiPOK56GywZLTx+NxxLNxPHHOEw1zKBvNYvvV23HKT05BJpNRsdI0ULgoEpCm02lVI5vsp8vtwpFVRzATnEHLAy2KBHE4HCpZ19y5czE0NKSMDyYKcbvdCD0UwpyJOfBN+2DYDBXrzcySJFkoMyNYnZqaAoCG8iHSo03jqFwuwxP0YMvnt8DsPcFKPACYq02k4inkcjmLCQ5rML5vwPAZ0DIaok9GsWDBggZWPJfPATZAq2kqK3oikUCtZmW1dblcKA2V4Nxbz2/AfpI1RjOZDNrb2zExMaFYa4ZcMHM9jQD+v1gsIpPJqKz2NCgp9eJ4hUIhFItFJJNJBbYp8efmnclk0HNHD7qHuzFy/QjwPlgZyn+DOuAG4BhwwLPHAwRPxJdrJvQP6qj8g0Vc1G6soYYa9C/USRTTNKE7dBjvMoAWQLtag2uXC2bVbFij9Kd0lC8qo/a1GvAOAFN/mHVxts222fY/06LRKPr6+lQlBgLCarWKmZkZldQyk8mo9Vyqx7jWUrpK4tztdqOzsxO9vb3w+/2KtJcybyqRuGdRjWa32+H3+xEMBtUaHQgEGs4D4GWAT4a+SclxzV7D9Quvx4B7AABw44ob8aFtH4Kn6FG2wKlHT0VNq1fqGB4exoIFC+B0OlGpVHBW/Cz05/vRU+tBKpxqSIYp1Yi8NgE2VXMyuSf7WKr6gHqlFpIY+Xwek5OTcDqdqvII8+jwfLSjJKAGoGwlqVKTTp2aUYOhGdDMuuKNXvJisYhJ3yS2vW4b1v90PWqVunfbNE3ADjzy8Udw4TcuBCr156/Wqpg6dwrbrt4G16UuXPbJy+A23OpeI5GIUtHRq69pGrYt34bNZ25GxWHZPlPRKfzk4p/gQ3d8CP4pf4M0XXrO+WxSti69+0CjZ5tjTU+4lITL+dPs8Zdx3LJPeX5WXKEdRdsrm81ienpa5UYyTANjF4+hjDJel34d4vG4ldhMq+G2K2/Dkb4jqDlreNOWN6GYL8Ln92GofwgPXP4AbCUbzv34ubAlbMgVcjj0oUMYfN0gTLuJ5655Dj0He2D/jR1ulxtAXTXqdtd/j0Qi8Hg8Dc4OadMycTNJLSoHvV6vAuzM6UPnHm1lm82GXC4H7wEv3viVN+KRdz2Cy269DO6UG2WjjA3bNsDn9aHiqWDdI+tgwkSsEMMVv70C+avzeNOWN6Ftqg3VahXvuPMd+OabvonseVlg8L++xs22V26vWsDt8Xhw7bXXNsh5gMaskVxMKP0lU9fW1qYWGMYM0+MqZR2AxWa1tbWppE4EolwkyEYTULMsEz3hXIgYs8rFTG1eJ9hVlhHjgseSWQTIUmpCz59c8CkPJ7BlHDUAVS6MiyHBCaX1si/ISErpm2EYKjaWpIKUq3PDo5EBWItj8zjQMJDsKjdSLkhkmANmAJ948RO44bQb1JibDhPZniwQBWqp+sbH7JG1XTW4b3KjuLiIrrEuuHpcsMO6Vm9vLzqrnfjQIx/C1y/7OrLBLGACLXtasPrfVmMqNdUA5JrLnAWDQUWcTE5OWpKfE3NieuM0dv/dbgDA6txq+H7ngwZrzN1utyJOKH8juaLrOnK5HELBEHzTPkV40MjKZrNoaWl5mXQrGAyiWCwqdcbhw4cxf/58RY7QsHC73WhpabFUHR8+gkx3pv4C9QKue11wXuBEe3s7ZnwzSPwuAXQA0IDBrw1i5A0j0DfrqvSazWbD2PIx+P7Oh2WfX4aWVAsAqxTXoUOHYJom/CE/Jt2TagOV2ed13aqbnc1m4fP5MDAwgHK5jPb2dkxNTanScJqmYXp6WmX8P3bsGHRdx/z585XhOD4+rlh3v9+PqakpFItFTExMKMKkp6dHEV79/f0YGxtDNBq1VBIuB4rnFzHyxhGrPxbAYn6/17jOtLa14nVXvA72hDW/x+eM48G/fhAVv2XMwA3oH9HhO+yDfbNl9CS1JEr/WoLxZwagAcb7DeRzeeAzgAceReZUu6swbjXgusKF0tSsjny2zbY/9UZPHMk+Ep75fB4TExMqfIb2BfdEoO7xo1eZQLutrQ3z5s1DS0sLqtWqiunlXks7AgCCwSDsdjuSyaTat7l367qOQqGgCMpcLqdCzLjnSCk6bQACXBKfH+36KI67jqtnnnJP4d+v/Xdc9+PrGrx03PNJNmiahnnz5ik7aY45BzXTUgK43W6lmtK0enJXoB5/KyXOzbYE94JyuayekX0gVXHcO0nya5oGv9/fIDWnx5nAlC3pTEIv6nBVXA32jKmZ2L14N/bM2YNL7rsEtbiV0ZqKhUQsgXtvvhc1Zw1GxsDae9bCUbYAXMaTwVMffgoTSyZwz9/dg0u+dQna0Q6ny4mBNQN46E0PATpQcVfwwJcewF/d/leYa5urgB8TqPF+NE3D+hfXY6RtBM+ufBaGzYCn4MGVW65EW74NhrtR2i3nniRaOF+krco+pu0mbe5mwC6JD9qIctzkPDNNE0WziElzEj3ogaZZZD4rorASCud5pVKBAQNj547h0WsfhQYN+k90RF+MouwtY8ufb8GRRUcADXh6zdOYOT6Dnh/3ID4/jl1/uwumbgIe4Hdf/h36ru5D+YwyRjaNwLSfcBD5q5j4+ARWja6CZ8yj8hvQMUDyQL4vnCu0/W02G/x+vwqDIyESCoWUHUhyJ5fLob29XTmcSAZR6WceMvG2r74NTpsTNq9N2dlnPX8WdJsOOOskhjPtxHX/5zrkMjkUPNa8Lx0twVxqAun/0tI2236P9qoF3BdffDEWLVqk4jYodSGY4YsgY5a4uLAONwC1mbDWNEEMQSUBKoEXZeLynHzZqtWqksnyvFJGQwB3MpaQL6XMPMkNTp4XgPISyyRvBG8EXQTHBLSSfWR/kYkj4GUjAyeTXVA2To82iQ7JclLWLxdsetC5gfG+KK8j+OemypJNxWIRrqQLC2cW4kjsiLq3RFcC093TmFuaqwBZNpu1pN5XplH8jNVHd+JOeJ7yYNP+TQ2KhY6ZDpz/o/Nx75vuRct4CzZ+ZSMcPgf8fr+6PhlvEis+n68hnrmtrQ2GYcDv9+PwysPY9/f7VPKtnZ/cib5CHxY+t1AxtIwj9ng8DUlXGLPOEmpMCmaaJkZGRhCNRlEqlTA9Pa3qK5IgcrvdykPR39+vDAXG3y1cuFDNjQULFsBziwcD3gEMnTMEAOja04V5n54H81RL8lT5ywpS0RQM7QS4twH6u3XYHrUhk8lYcemXVoBfAjlHDum3pdF2SxtcKUtmzhjv3St2Y89le9ByvAXmoKm8LfQmeL1eZSDJOUkQLpObpFIplUW9q6tLySd1XW/IRcAEgx6PB36/X30uybF4PK5UAuVyGYlsArv6dtWTpmkAWmDFbIuWak0hvTSN9m3t0DQNnUOduPjOi/Hgmx5EJpyBL+/DpQ9fipV9K5Fpz2BychIDPQN48fUvYlKbtE6iA53XdsL7qBfTz0xbY7RGx8xXZmAuMFG6swRcC+D5//x6ONtm22z7n22apmHOnDlYsGCBKg2UzWaVHJWELfdSGuEybEfXdVUK0eVyIRKJoL29XSVhoodcAnZem4Cyvb0d4+PjKJVK8Pv9iEQiaq+nbcQQJe6LdAbwXBLQMlyGQOJfjv0L4oE4nu+zFqwLjQvxrfy3sHf+XgwNDTWEn/F8pmlaJYsGB9HT06PABEHU+Pg4Wlpa1P5FO4J2HPuKwBuoJ8WSsbRUAFJabLPZGhLqAlD1yAEoO4rkQnP8O8mPGe8Mfvm6XyI2EcOFT14Ie82u+mTPij2486I7AQ0ozBSw/o71cBYtL+X4/HE89t7HUHNZ9tWei/fALJvY9PgmIADsecseTCy14m5n5s/g2fc/i8vvuxzunBs7T93ZsD8V/UWMrhnFssPLADQCX9pwfMaL7rkImXQGh9YdwsUPXoxTh05FzbRCA6nkk5Jp2d+0GaW0nT/luNKe4zyU4yn7m9+leoL7v/rMruPevnvxdOvTePsjb0fLTIuy6zgmPAfJk+ybsnj0ukcBDTBh4t/+4t+wYO8C2FI2jLSONMQtT62cQm9PL1JXpiywfaKZXhPud7ix7u51OHTnIey4cgeq7ir8I36c9r3TEMgGUPPWFBkUDAYVTqANAkDZ7iz1GgwGG7z7oVAIqVRKgfBMJmPlKfJ61btHgol9Ta+3x+NBMBi0bDuvsx6ypuuw207E15s1dR7a7nbNruyg7du3o5gr/r7L2Gz7L7RXJeD2+/0499xz0dra2sAIA3X5ET230jjn5iIXXL78XOgla0e2jkCLmw9BExsXIb449AzLBQeox0NxoSLw5fdlco/meBgCEQmgCbzlIiezS/KZeA2Z0IL9xWtJtpybz8mYSYJG9qn0jhcKBVVKhAtqc2yTktOekNvz+5R+lUoljI+PW5mzx3z4wPMfwP9e979xJHYEnoQHG36wAW372lDUioqg8Pv9ePGSFzF53WRDf9++8Xbk9Twu3H2hup9arYbQrhCii6JY+dhK+F1+JQvK5/NIJBKKvQyFQkoazsWRsn2Oid1nb0y+BcDht5J6MP7f7XZjYmJCGRky9osbU0tLS4PBxXslE8r4YKoedF3H2NgYVq1aBbvdrmpMM1EIyRrOCafDiTXfWwM9p6MSrWDF91YgEA7ACJ4oX/Gshq5gF5555zOABnTc04Heb/WissgySIbPHUbyH5PAiRwnxTcXMeIcgf42HfbqiWRrH7Nj5LoRmG4ThX8ooCXXAkfGoWT0zPROg8/tdqtM+fSg0zvNGts0qoLBICYnJ1WyQRIg0mvhdDoRjUYV60+yjSVxmGnfMAx0t3Wj8uUKZt46g8zlGasc1wcAPAfLy70JcOad2HTrJiw4tAAlRwnBYBC5XA69u3txmXYZ7n7r3XjDA2/AisMrYNpMtLe3o6OjAyu1lTj1wVPx0wt/ilQwhd6pXlz10FUIbgxioGMAia4EHrz2QVSWnlhDFsFKlvZOADt/vzVwts222fbH1dxuN5YvX47Ozk4YhqHAnww/41qey1mJPxmLzf3C6/XC5XIhGAw21NPO5/PK88okTvRg+/1+eL1eFVLjdrvR2tqqDHC5/7IOtyxfKu0HoDHXipR18xzTk9N44xNvRGVDBXqLjptxMzraO+BeZyWvHRwcbLAnaGPVajVMT1uEY29vrwIstB3i8Tg6OjpgGAa+5/sers1dC2fFqe5LJhbjvfN5CI6ZBI5qRaBR+sw9l04USVgAdaeIvPe8K4/bNt2GF3tfBOYCOS2HM289E+VyGS+e8yKeuuwpZQPsO3sfCvYC3vTrN8Fpc6LUUYLNITNcAcEWK+Fd2pOG5mg0HkybCdgAu2nHm+97M+6r3Iddp+6CZmr4y61/iTOOnoEa6lnWmeRNOkxoh552+2noeKkD8w/PR62n1hBWyO/TVqR9KEG79GJLQM1/7HOgXtpKhjhI208CdRIotHFuX3w77lh8B0zNxLfXfhvzPjUP2qClIOR3+E7wmYv5RgCpaRoi7RFE90TR8f0ObL9hO/KxPKJHo9j4/Y1oL7aj42cd0DM6Dl9yGABw+p2nY+lDS6F5Nax4fAWcFSd2XL4Da7+zFj2DPbBFbcrepx3CfqAjhcQO5xkxA8ve0v6TWextNhtisZh6fr/fr1SoDAd0u91IJpPw+Xxwu90NCf1UHpkT744c00KhoPqjWCzC7/djYmKiYX7Mtv++9qoE3KeccgouuugiAFAeVwIqvsj0WtNAJ4gCoOREBDSyNADjewhsGXdBqYfMQE4vOL3PEuzKhYiLBF8OLvz08BKk8j5ZNxqAuid6zPmiUfYsJS5kv5oBvJSF8Zy8Jy4oPFbK1/k7fzJ+SioH6N1sjpcnQ0fvNjduysmAOjmSSqVUzcNAIIBYLAbA2vzm5ebhzG+eiZH3jGDt59YiPBmGzWNTIIv9OOeFOThSO4KaKGRcs9Vw79p7oVU1tP6iFYsXL7Y815cWET8rjmeWP4NLPn0JCoWCqmNJ+RJjvlKpFHp7e9ViZ5pWJu2xsTGLHDhj/GWAe+p1U+h/ql95/MfHx9W4Ma5L1cp2OvDSZ1/CnB/PAQpQngd6eT0ejyrJxdrjnCstLS0qgVsikUAikcDk5CTsdrvKU9DV1aW85/u378ey2jJU7VU4006YTlPN61AohIWPL0Q+ncfg4kF0fLUDIT2ESH8EhUIBbdk2PFd8Dlkzq0pMLNizAO1r2pFJZnD84uNIfiIJWEo3GMsNJL6TQOXDFRWLnkgkGrLYc1NhX+i6jnA43ED46LqOeDwOp9OJQCCgxsHv96tN3uv1IpVKKSLM7/crYsfhcGBmZkYpLWRCNXvODvPDJmAA+BmAO63nmvcP8zA9bxqX//ZyePZ4YA/bG2LMTNPE4oHFcH/bjb5yHwqVgsqIXiwWEQwGsXhiMT66+aP41oXfwl8/+tfwprxABGhtbUXNV0NST+Ix87F6uY4XABz7LyyKs222zbb/0ebxeLBw4ULlheY+D9Szg1NRBlh7OBNvMtaTslX+pBydYJvrD8H7ggUL0NbW1gCcWN2hXC5jZmZGeQV5H1w3gXr2aQANgFR5Ts0a/nb+3+KW47dAN3QMDg5iYGAApXgJm+7fBM2lYYdvB7r+rAutra1YvXo1arUahoeHlaqJ+xlBy8TEBBwOB7q7uxUpr+s6stksxsfHce/8e/GlwJfwO9fv8N0D30W1Ugc2VPnJWPNAIKDsNfYrbRRpp0m7C6hL1QEo2477M/tbt+n4wbt+gOMddQn982ueR7VWxRs3vxHuGTd2VHYg48yotfzssbMRDoZht9kRGA/gL2/7S3z7Xd9G1V7F6dtOx6bHN0Gv6ohkI3jj796IX3p/ieM9x9Ex0YE3/fZNCKQDqGk1+Ao+XLr5UhTsBZx+7HSsH1vfYN+Q0OH4sT9YQnNqago923pQcBeUDcD9nh5baZtx/nA8pLNFAmo519ik15q/08ZutkdpF+m6jl8u+yV+3fdrmCcq/U30TyDxzwmsfPdK5Vmm7U5Zt2masD1lg27q2P7XVgawdbesw5zdc2AGTEQPRRH8X0E8fuPjOO8758E36QN0wFayYeO9G2H32RE4GsD8x+fD1E0FTkO/CeGs3WfBN+RDzVvHBrRj2Q90fPGemAMhFAo1qFeZxygejyuHA98v6XijF5+qRYapMtkiCSk6/YgJGKrHcsWs1lSpVFTYI8NHZtv/m/aqq8PtcDjwgQ98AF/+8pcb6uFJ9o7yIgAq1lnKuLjgyE2HBr+UGAHWwsGYLE3TVPkkTmQuNNxQmfCA8l7pyZTZvMkOMgaJbG+tVq9jzXrZ/FwyjgTG3NT4NwISGY9D7963v/1tXHXVVYhEIg2bLO9JLgZSzsX7BuoEAr28QN1zz8VDZlanzJ+eSHqS6aElE0omHqh75nmuf3/833Hv396Li750EdyTlsESCoVUKaxqtYrHrn8Mx844VpdgAYABzN01F2+9462IBWNwe9w4vvQ4vnn+N2HYDKtW4UQI53/mfHiyHjVGum4lSYvH4/D7/fD7/YpdD4VCyGazylNbdVXxwFceQKHNGifHhAPnfeQ86Ol6SRPWdpSbvWma0CM6Xvr4S5g+axrhoTBO//TpmDkwo7wejB2ipIpglXIrGl0HDhxAKBRCT08PnnzySQX8Vq9ejXnz5iGVSmH//v1YsmQJUqmU8uIHg0HlUfd4PEgmkxifHIepm3Bqlrc4FothbGzMSrwW82DzlzajFClh7a1r0XVvF1wOF1atWoUnnn0Cu2/YjeGzhwEd0NIaWm9sxdLDS5HNWLLKtrY2tLe3q36NxWIYHh5Gf38/hoaGlGeoUqlgYmIClUoF4XAYpVIJgUAAiUQCkUhEKQ+Gh4dRq9XQ09OD8fFxlWCF5bk0zSp1Q3KG7xprxh8/fhz33XcfymYZqEJluV+zZg1WnrYSbq1evYAGysyMlUbc5/MhEokgk8mov/MdDIVC6p2r6BWUM2VFRHFO2N12/PTcn2Lbwm0wf2sC18C6h9dYM2frcM+2/2D7Y7VV5syZg2984xtKQUePID1hlJuWy2UMDAwgk8nA7/cjGo2qtZ3EL20SrhfValXJp10uF2KxGKLRqLoGs14TbNOmkGFdJDqldJj3J4l6wLKzkkYSn5n7GTwWeQxzMnNw0/03ITuQVflZJAnZ1taG888/Hz09PZiamsLTTz+N0dFRRapLryidA319fSo8q1KpIF/O4zfB3+Bfl/+rlXDNBFanV+Mzez4Df8Wvnp32hSQR+FOqAGgPsf/ZPzwuk8kooE9VmSQJeK/JcBJfvearyHqtnC0dUx348M8+DFvV6ueip4ivvPMrKDlLeOsDb8WpL52KWlWE8ZkG4pE4njz9SVzxwBXQjbqRYhgGTIeJH1/zY1x3+3XQK3VVIPsuW8rCaXNiTs8c9ay1Wk3VBZfkA205r9eLl156CTMzM3A4HOjs7EQ0GlVzIxgMwufzKWeKDMEkGJYeaqBOZDSPPX8nUU47h3OXv0uZPgF4GWV87szPYW/bXkADHGkH1n1qHYIHg0rdRycYE/7R7i9Xyxi8cBARI4K+F/qga7pyQpVKJaSKKRgFK1Ey+9LtdqOqVeFyuGBU6uEcVKLwvWB4IZ+fqrpYLIZaraZqsDMpnt/vV44QKhsJ1DkHGSpCG12RB7Z65n1iFMavs2+ptGWCY/6N7z1D9Zh4lveye/dubN68WSlqZtt/vv0+tsqrDnC3trbiwQcfxJIlSwDU6+JyMgKNMUjN9Qml0Svl0HypuJDJeCHWBJayK5ZtkpOboJULPmscE3wTOMkXkeCSnjfKjqXUm95jvsxk2LggsskkYpTSU2ZysmRtQD2ropSpyLJPZPnI8Ml7Ijtus9mUJIafcfHmtbg4GYaBdDoNwzBUluv29na18Mn7TKfTGPWO4hfn/QIDcwbgmfHgnK+dg87hTtXPBIqJlQk89jePoeqqoxZH3oE3v+vNMA3L+ChUC3jq757C2Klj9WNKDvT/pB/99/UrhlDGxbN0CiV8xWJRZZBluZAZzwye+NgTKFfL6PhQB+ZU56jFj7HalBIS+JX8JQx8cACTl9Zl8LFdMSz64iL4Jn1oaWmpA12PBy0tLWouFYtFxGIxNTenp6dRrVYxMDCgSIipqSksW7YMCxYsQDKZRKFQQCQSweTkJGq1GlpbW1WJEBoY2WxWbW5udz0bKpUEuVwOti4bRs8dxcpHVipm2Ol0Khn73pv24kj/EYQ+G0L75nYsXLhQGZaAlR+AcdnRaBRDQ0OYN28eBgYGLO/vibkyPj6Ozs5OlTQlEAhgbGwMnZ2daqPKZDKYnp7G4sWLcfjwYXR0dMDlcqkyYlRM8P+VSgUdHR3KeJ2ZmcH999+vvBxsq1atwmmnnaaen5n+aQjb7Xa0traqOc01hBmBafxw8+N6Q/k815VqtYofrPgB9p639z+5Gv7pt1nAPdv+o+2P1Va56KKLcNNNNzXYGAQpuq6r2tmapiEej6v1iXsenQS0Qeglpz0gPWf06DIDsryOlATzM6CeERqoe0MlQS/BUcaRwde6v4a7W+9Wzzf36Fxc/pvLEUlG1H7N6xmGgblz52LTpk0qhvypp57CzMxMQ5JUXofe/a6uLpWQNGVP4aMLP4q9wfp6GKlEcOPwjbgsdVlDyB1tIAI+ru/M50KAxH4leKKNRjUe12JpD3L8pIz6eOtx/OzCn8Gf9+P6O6+Hu+JWa7uu60jEEjjScwTrX1ivxolj0OxdJxiV9hjHh/chiYpqtYrp6WksWrRIJcxiWALPSbuU/cqkpAMDA8pB0dXV1eAxbmlpUc4qfp9jSeDH1uzV5j3K67LfpCxdNv5dKkqr1SpgA245/Rbst+/H8n9djti2mJrfvF/aJLK0HnMUaJqmwu2o1gSg8jTJecJ3hzYc1Rc8hnOGORjcbrdSEfCZOS9kqGkgEFCVB3hPDJEMh8MYGxtTHnrOY0ny0GFIxSJtDebGod0hCRHa1sytwPXA5/MhW85iuGcYR/7PETz00EO//yI2216x/T62yqtOUn7eeedhyZIlDYsvWSnp2eVnMm5CTlbJ5MqF1jRNlVlQypIo0QKg2FPptWz2KvN+uAlIKTA3HsrdgXrskIzNIHjlhkLPOZ+HLyRQX6z5XaAuayfwlguhPBevTa+cjIEvl8tqsyLYJnPO78t4bPYPF5BCoaDKQFGevnPnTqxfv14REgS3jEEmqBy1jeLuC+/G4ByrlkEhVsCz73sW67+zHrHDMUVamKaJhDsBA/WNDgDedOhNWL1qNaamppDP5+GHH5t+uAlbrt6CkY0jgAGsvnU15j8yH5pHa/DSk7CQieyAurIAsDah6elpeNwe+D/oRzwTx/iucRRCBSxYsEDVx67VrFJXTOhVq9Xgm+ODGW20G2dWzSCxOgHbb23KaGB2Tm4kzHru8/kUeCYYZrI81nClKoOEFLPS0ihhnzNUgPIkyfpSjUDQXR4qo/3WdiTdSbWhUWXicDiw6dZNiPXFkHk4A7vLup/e3l4lJSMrTxa6tbVVvWvsZ4/Ho+ZWPB5HPB5X5b2YJ4AZfDVNU6w3QyRcLpclg29rg8vlwtTUlCIbWBGAZff8fj+SyaQaAyYE5PjSI861gUwyPVZ8N6hsYaMMTCZYkQYck/NsvHUj9uK1C7hn22x7NTRN03DKKaeoBGkk02OxGFpaWuDz+ZSKiyWdSPJnMhmlmKN6jAQfARlVNEAduAB1MCE9cVxvmoE6DXvaKyQFJDnONaxqr1oyadHKzjLKrrLa5wkAaIONjo7i2Wefxemnn462tjasX78ee/bsUSFV0klA4r1arWLOnDlWHHrFi7/d/7f4Uv+XsDOyE+6aG39z/G9wQeICmPZ6yUV5n3yOQqGARCKhwBOVYHxmAA3J0ZptK+l9l+dnX3eNduHKB69EOB2Gq+xCzaifQ9d1RONRRONR9X15LvYRUI+FluQG/8njZL/a7XYEg0FFkPNZ5Pdon0jPP5VglJFLbzafj/fK4xiWKWPw5U8JXuXn8ru0Nfl32ScyF4A837u3vBu/PP5LhHaGUKqVGp69UqkoYorOMfYRHRL5fL4h8zxzNdH2MQxDZXanUoSOLKoFaSfzHvnO0YZj3wNQjhfpkJOl6ujVLpfLSCaTyt7m/dlsNmVrAVCAnXaCHGPOM/bjU6c9hXXPrINu6IqU83q9KhlbtVrFY5c8hh39OxB8LPh7rV+z7Q/TXlWAW9M0/N3f/V2Dp7W5lIXMBsrYBXkM0MjGyUWXpbX4ssgFUyY443UZV0vjWwIxm82mpFdcXGXsFEEmGTY+H5+NLyvZO0rFeD98CSn3PnbsGGKxGEKhUEOsN+t8ylrOvB8uJgRzJB/4ogNQMSokL5hgTMb+ECjx3nRdRyaTQSqVUkDOMAxougYNVibXarWqJE3MKjo5Oak2YZvNhkR/AoMLGwsHpnpSiM+Jo/VoqyJGHA4Hlu1ehtpna9j1z7sAAF1f78LpztMBlyX1o0fUm/Ri7Y/XooAC4hviGF00ipZftaj+obHC+2Z9RM43LrwkSqLRKJLJJDLPZqDXdBiwFj9KAwm6crkcEomEShBW21+Db6sPUxumGmLAB64eQMveFuSPWVks6Ykul8tKtkTQTGBKlpreWM531qvk4s7kOoZhoKWlxSpJFgqpzLI+n08xqtzAstmsktQ7HA4EAgGEw2Fks1n1rpEAME0TuXgOc56ag6m2KcUmM/t4Op2G3W5XpbkKhQL8fj9yuZzyANFjLj0TgFVHPZVKIRQKqbnG99HtdiMYDMI0TXR2duLYsWMKuLPv4vF4w7hxk21OJuJ2uzF85TA6ZzrRMtWCQCCAYDCoPO3sZ0nE0QgIhUIvk4KxfyQRIomy0dHR/8qSONtm22z7I2i6rmP16tUKFMZiMbS3t6vkZYZhlf/J5XJq7eI6QcOdhj7jubkO0m4hUJOxn7WaVVYrk8mo5JD0ZhJwSkBGgCodFSTb5Zoazodxw8EbkO5LY0d4BzqLnbjphZuQTWRRMAoN4B2oA9PDhw9D13WsW7cOXq8XHR0dSCaTyhaS1UtIoAJAT08Pst4s7m2/FzcduAk3n3Iz3j/4fpyZPRM2R90LS8KaNgVD8EggExRJ+67ZGcImwVWzApL9zf4wTRPzhubBNE1Uqo3ye9paUpLNzyVgfSUwLj/nsfw+74GJszjm8plovxGgsq+CwSCi0SgmJyfVc/E73HOj0WiD3dZsq8pnJKEjyQjZj7SRXul5pH0rz1+tVuHIOLBg/wIk9AQAKNKcnmiZLZ/XZMjlzMyMAuVer1fZk9JzDUDtv/Qem6ap6tbTwSTVI6lUSpFcJMHC4bB6V5xOp7IpDMN42dzTdV0pKWgHsI+dTieSyaTCGfF4XB1P+8br9aq8PbzWIxsewWNnPIaR7hG8++F3q75lRQO73Y5fnfUrPLXyKRg2A+lPp4F9AJ76j6xms+0/215VgPviiy9W9Ya5eNPop+HLZEyMe5BJJfg7Fw6+8FI6QyDhcDhUkiW+PMxwzBhwbpqs8w3UswNSDsONkUyajM+SGxw9ZdITzQWMgBmoM58AXib1piSaQECCZqBxs2ViKSZx4bUJ4MiU0xvJczEOKhKJNCRtoByOYPzIkSOYN2+e6hOX14Un+55E1V/Fpj2bYJQsA0TWTibYIxDrHu/GxY9djPs33d8wD2reGipmBVNTUwqEFgoFRJ+LwnudF/lT85j8h0mU/tHyqHZ2dsJmsxKt+UI+7OrZhcTaBKABw6cPw/wbE2f//Gzkp/PKg0pvBDcmkiCynidDAbZv3w7TZcKoGUDBkjLZ7XbE43FEIhG1kDPsoFKpYGZmBpGfRNC+qB2TF03C1E3oJR3z/m0eHCMORFujCIVCKJVKDdLkQCCgPL4ca25wc+bMgc1mU0nCurq6GhQTo6OjmDdvnpLIU3lA2RTnOBPH0APsdDpVzLzb7VZECue3TCpCYD9//nwkEgk1L5LJJOx2O9rb21XMXDweV0n3uKEahqE8zpynixcvxszMDMLhMNrb25XRwI3t6NGjmJqawooVKzA4OKjewdHRUcRiMVVyLBaLwTRNFUt1pPcIMhdmgI8CKAKwAdmrs8h9OId7a/fisk9chog9ot55Sdzpuq4ULjRA6L0nw16tVnHvvffi+uuvRzptFcGkIUGiYXCwkVCabbNttv3ptUWLFqmwGSkbHRwcVEY1lTzMQkwgIEPVZHwmPWUAVDbufN7ao1KpFNLpNDKZDJLJpLrm/Pnz0d/fD5/Pp1Q/lOFKJR9Qz8fC34H6mlqr1RAxI/jSoS/hhqU34Iv7vwi3341DnYdw8OBBAFDJryRRb5om9u61yoMtX74cgUAAnZ2dGBoaQjZrxUBTxs5rT05OIqEl8M9//s/IODNwm258dc9XETSC0Jx1JSG92LlcDtFoFIFAQKmmCGzkfkjbS6oKCa5lPxBkSqBJR4xUCPDYnCcHs2rCV/Y1KJdom1WrVQz2DOL5Vc/jigeugLNWVyZIzy/tu+YmbUkex/5NpVINOQJkviFZIpc2rcwHQ5UdVVwcAxlSQLuZ98A5yXuXIQvsM7kfm6aJnCsHb9Hb6GBCBXf23YlYJoazhs9qcNJkPBncvP5mnP7U6TAMA8FgEE6nUyVQBeqJ23gvkrAgCc9G+5rvI50m/Ec1IKKArWhr2NfT+TQGLh6ArWLD8ueWq32fHnCWK1WKTh04vuI44v1xnLH5DPidfmSzWVVCjGFmkUhEjQ9tg46ODjUWHCP5DCQmnE4nKqjg0aWP4pE1j6Bmq+GlU1/CHd47cN0z10Ev1pMHPrT8ITy7wqq/DgDoBfBLAGsBTLxsqs22P3B71QBul8uFD33oQ8pryM9kPWwCW6Auc+UCKBlhxqxqmqbORW+xTJggy09ISTfjpiizlcyerusqQdXJZF9kKSlHlcCWi52MFyEbBkBtLNwE+HdmIuV5pCyIMd88ns/OhUQumlwAuRAz9pjPT1aP3mKy1JVKBYODgzBNU8WQLFy4UP0ODXhu6XO49exbAQC1Ug3LHlmG0aFR1ccEb7quIxKJwOv1IhKJYAmWYGtxKxLuhJoLR885iv4X+xEYtbJWEyTO75+Px1oeA7oBf49fAS+gzsYW2grYc+YemI4TRoYOTK+cxvCOYXQ916VqHvKeaFTk83klG4pGo8p4KJfLWHvOWmw5bwuqqAJ/b52WSgCWtOJcK5fLCAQClic/V8VZPzwLeyJ7cOiUQ5jzozlY+OhCZMoZZSiQuc3n8xgYGMCCBQtU6S/GYIXDYUXokOiZN29egzchHA6rhG8kd1imi15ZmQHTZrOhpaUF8XhcxZ1zXjqdTszMzMDr9apzsI8jkQgAKHa5ra0NuVwOlUoFyWQSc+bMUfNGMsR2ux2JRELdVywWQ1dXF7LZrEqWlsvlVKwWySLW5/Z4PEgkEggEAsjlcgiHw4hGo4hEIpiYmFClMVwuF9LpNAYXD2LzjZstdUEOwGcAvBXAD6y6nlVU8cC/PAD9qzp8By0Dh0mKisWiypju9XpRLBYRDofVxky1BABce+21qp44DSSn06nWmunp6T/cIjnbZtts+x9pZ5xxBmKxWINklMQ9bRKGB0lFHtd4kvkAkEqlUCqVMDExoQA2z1Or1VSMaHd3t7Jt2traEI1GVciLBJOy6ook8+mpa5ZPS5vAZbrwwwM/BDSg4qhg0aJFcLvd2L9/v9q7pUeWa2AikcCxY8dw+umnY86cOfD5fNi1a1dDEljaHMmuJH76xp8i6U4CAH4070ew1+w4d+e5qBaqSqGUSqWQz+cxd+5c5fFlUjCCKylZB6BsKgJb6d2XRAOf3zAMFPUiJkIT6JruagDcmqZhuHMY37vqe4gmo3jbb9+GlniLcsjwuoMLB/H9q74PUzfhKDlwwZYL4Cq5GoA9zydLcUklgvQsE2wyV0ozwSEBugyZ1DQN0WgU09PTKrkXHUymaSKVSqln5p7EknIy7r7ZU110FDHpmcSc9JyG+W4YBg5EDuCfVvwTPr3901iYXGjZvEYJdy24Cz9f8nN1nhV7Vljj2hPHVzZ9BdPeaTzx0Sdw2ndOgzvrVs9BW4xzVQJ8JlWjIySn51BsKyI0HFL3XS6Xke5KQy/qcFadypGWWJjA5g9sxnnfOg9zxuZY3y/mcPC8g9h21TZrDAwT/Y/3q/5hGFx7e7uyu15Y8AIeusqKkY54I7h0z6WYZ5sHAIqUb1Yk8Hx8X+g9J0EhCSFmQB/xjODOljtRs1nz2NRN7G3fi90tu7F6eLVyUmx4egOm9ClsWbvFAt1HALwds2D7/1F71QDujRs3YvHixQCggKOUbQD15GQEjATPv/71r3H11Vc3MMoyDptAQjJZzERumqZ60biwMBabDBSBLmAtlPPnz2+I4abXmJ4vAgzKy2QsMhcwoFFSJF9YspfcNKPRqEqqJZlTAny+4GwnS8BAD2qzTJ99zAW5VCrh+PHjWLBgAY4ft0plcLFgEgh+h5vLM2ufwW0bblPX//ez/h3Hxo5hzfCaBkbV6/UiGAyitbUVwWDQMhQcNgRLQQW4w8fDWPftddCP6UgUEqpOYTgSxp437IFxkbU5JJFEeiiN/v5+NW7VahX6MR1X5a/C7RfcjumeaTgyDqz64Sr07OhBpWaNVTKZVCCZDDbjp+X9apqGGmrYf91+lN5gJcSDA3B/x414PK6+w7nFeWqzWdm0CZzP+ck5GPGOwHG3A77VFmtOEoEZKw3DSrIxOTmppOCcb5RJsVRWpVJpKH8VCoVQqVaw95y9WP748oaajpxnlOuRiCGQ5Hgy+ydjyaimYGkSSt5ZTo0yahnf5HQ6GzzpJIkIhF0ul0oEQ0KHRBfLsFGBwg2eEsPJyUkEg0FMTEyoerSZTEZliOdx2WwWo2eO4tl3PluX8t8Iq5xZUyqmolHEvUfuRfShqNrcu7u7EYlE0NraqjxHHR0dqv6tLB/HZ2xra2soKSjDT2azh8622fan3ex2O84991y0tLQoAMO9lWsoQRJg7b/j4+OqykK1WlXEpsyZQoARDoexaNEitLa2oqWlRYEPJmz0eDyKWJdJ0CRJyusAdU+kBJ7yvgE0xIsDdZtL0zTMmzcPtVoNR48eVUQB7QgpjR8YGIBpmuju7ka5XFaeSO6njGvfv2A/ap5GT++W6S1wPOeAmTNV31HFRTswmUwiEAio9VQ+A/cOCaxN04ThMPDknCdx9uGzXybv1jQNjy17DFPRKRzoOoCrHrwKPSM9aj0/NOcQfnvZb1FylTDWPoY7Lr4D5/z0HHiHvIpcHz9jHE9e/iRM3brm1tO2wrAZuPjei2EzbQ0e4ubEbNIeYx9JRwgAlROIxArtTgn4ZQ4h2jB09Mh8QqzzTrWex+NBDjk81vMYNo1swpbuLdg4tRFuw61sXUM38MP+H2J/eD9u2HsDFuUWKfvg+djz+M6K72DGM4Mvrv4irtx8JVr2tWDPG/bgl8t+qcb2G6u/gY37NkJ7ScOLF7+IpDcJwHJ87HjPDpz+w9Phirsa4s1pcxuGgYnzJtC7uxfIQ5EIpm5i5zU7MbN4Bmf86AzEjsfgcDgw2TKJ597xHMKjYVzxwBUwiyaGFg7h0bc/inwkjy0f2IJL7rwEi44vwotnvIhtF29T9/nEtU/AHXFj055NAKBsK6ppty3bhjvPv1Md//DZD8Pus+Oixy9CuVRWoSLSeUK1RSqVUnOS4awSs/Ddpj2UTCYRvT+K8X8aR215Dd6CF294+A1YeGQhsnpWhaxlMhlsemATjLyBLXO3AO8D8Ox/bl2bbf/x9qoA3Jqm4YILLkBra6vyOPKnjAWhR1LTNJVAwel0YsmSJWrRopycQIIMI2W/0itNCQkX7OZsxgShANSEZzInerAIsrlI0jPHxYMSYz6DjJHRNK2BGABeHsfFkgDcULnRSvkYWTN+l15y3ruMaeffJBvH7xJU9fT0NMSGG4ahFm/Kwhl/Wy6XERoKQTtNgykQTWS4njQmFAphzpw5KlyAKoYjtiP4wYofYCg4pL5XCpRQi9VgG6wnwbPb7dh+1XbsOX9PfXCuBW7bexvO/+b56O7uRltbGzweD1wuFzrHOuE/5Md01zTO/MaZWDS4CCVXCalUqiHuhpkxCZg5Z7LZrPIc7HrfLoxdWs96jvcC473j6Ptsn9o8mY2d84dZuqempizP7FQC+mYdtm4rWZrT6cT4+DiKxSLmzZunZO1MrMGYZSYAk3M7HA6r+Hk+QyQSwa4/34VDZx2CrcWGhbctVF5tjheNtnw+j0AgoN4Ryb7m83nlzaYSQMbo0xDM5/OqfIaU+7W2tqrn59yQyUL8fj8ymYyS7HM+8jterxeJRAKFQkHJFEOhEIrFovKi8x3m2JF0Ym4At9uNaDwKu2FHGeX6uO0B8AsAeQA3wQLf1wDl35UxjnF12ODgIJxOZ0O8e0tLi5LR0yvf19eHrq4upT7he8i1S9bGnW2zbbb96bbe3l50dnYqkjMej6NQKCCZTMLhcKC1tRUdHR3q3U+n05ienlY2h2ma6O3tRSQSUUokJg6t1WoquzmBeKVSUVnOmbuCiR25HzaDaynDlUC02U5gIxkrk2RJB0NfXx8cDgcOHTqkSqUyHl3mdzl27BiGh4fR3d2NlpYWlbuC6iaHw4Fz8+ei5YUWfGLtJ1CxVbB8x3Kcc9850Is6aqiXeJ07dy76+vqU7eH3+zHpncTT7U/jiiNXoFwp43urv4f37Xyf2g9pvz09/2l0pjtxz7J7sLdjLwzNwBn7zlB9V6lU8OS6J7H5zM0oOSzy/PYLb8cZXzoD7uNuJBYl8OzFzyIZTqo+GpwziLuvuRvLb1oOd8ZtOUKOAFpJA3z1+dE+0w67zQ7UGj3XkgiQfS0/Z5+zSTuQhIkkZ5ibhECbdkM+n8cjb3kEVzx0hbIBa0YNt55zKzYNbkLKm8KagTX47jnfxaG2Q3ii5wkcCh3ClsQW3PTETQos/uy8n+GR3kcADfjiqV/ERd+6CMZRA+OLxvH0+55G3BMHAIz4RvCDDT/Agt8uQOJ7CeDr9f4wayYquyqwj9uhxTVgbv1v42vGkb8zD3/GrxLtMpmvy+XC8FnDeP7q53F4w2Fc8q1LlER+89WbcfDMg4AGPPGuJ3DlL66Eo+LAU+94CjO9M5jpn8Hmls3Y+ORGPHnlk8jGLDsmE81g81s2I/jbILpmuhreaw0a/AN+RYozfI4A2oABbAIgtvGtP9yK7G4rWS5zQkk1LG10Am9pp0gHiMQk/B3bgNCHQ0j9OIXzf3s+1hprodnroSIkcEqlEpbftRxbntoCPHeSBWu2/be1VwXgXrlyJTZt2qTkq5qm4Z577sGqVauwYMECAHUpkYxp5kRcvny5WpAIihkPQlYYgAJIXq9XeaYp++HmJ7Nj5nK5BrmXrD2taZoy8OlhJnvF/5O9AqBIAr50Un56/PhxpNNpLFmyBDMzMzh27BhWrFjRUL6rOTEcgIYYGvYFPdgElgTeMlmD3FxlSTHGq4fDYZXkgRswn4kbXDQaVYvAmvQa3PibG/H1N3wdBgxs/PxGLBxeCLfHjTVr1qClpUXVJyVr63a7ER4J43XHXofblt+Gml4DDKD7pW607WtTSSoo3Vv4u4V46ZyXUPFU1POb/2Li2RefVbFtS5Yswfy++di2chsGzxiEBg3dk92q3wKBQIMnl55bt9vaTIeGhhSzzD5cd/c63H/u/aj4T1w3A9T+tgbDZag5I5PrBYNBhMNhTExMqPkQ7AqqUACv1wuv16v6Nx6PI5FIKDkzE3zIecS5wiycNL7K5TIcXge2Xr0VL216CabNxL4370N8NI45/z4HqEAlWSkUCrCFbSozuWEYGBsbg6ZpmJiYQGtrK9ra2lTGTYJ+Zk71eDxqU04kEli6dCkGBweVt5wsezqdVjL7rq4uNSfT6bSqlUq2ngaIx+PBKaecoqRtExMTsNvt8Hg8imzq7u5WhkVXVxeOHz8OwzDQ2tqqYrAZR9WWbsOSty/B7tt2w3SaWPDNBfBu8yIejqP2nRpyXTl4H/Si8EwBWVu2wUtCrzqTrwHA1NQUNE3Drl27lPqEZdU6OjqwbNkyGIaBUCiEU045RZUClLGBs222zbY/zeZyufDwww8r1RJzT7S2tmLu3LkqmVEmk0EikUCpVEJbW5sqkRiNRpUcnMmU0uk0arUa5syZg0gkopRABOuUltOAlzW2pSyc+z9tG6mek3JhNv6d9gTBtvTE0hM3b9482Gw2HDp0qCG8DqhLuatVqwwUk3D6fD6U7WV85rTP4OZdN8OvWZU7vDu9+Ot9f417192Lc+84F3pBRzKUxJa3bEH70XYsNBbi+2d9H988+E3Ys9ban9ST+NiZH0PBXoCr4sLj3Y9jX2wfKnoF1z93PfSKjmKpiO3t2/Gj034Em2FDwVmAqZm4bcNtGHhhAB1PdqBUKuHIpiN46eyXUHXUnSoTsQlsvnEz1l+3Hv69frQ83oL0G9Iw9BNy9aoNpz15GnqiPWjtb1W5ctb/Yj2++p6vouwo4+KHL8a659fBqBgvW/Ol84RksbTZJDnCfq3pNaRqKdirdthhh6Eb0OwadIeOYksRt55+Kz74xAdhwMAXLvwCPv7Yx3Fg+QHcsfQOFHwFpOenccN9N8DUTXz9sq/jQOcBbJ23FYZmwLXahbQ7DVMz8Xzb8wCA59ufx0cXfxR9N/Vh4H0DGOoeUsqw48Hj+MX1v8DyK5fDPmCHZ4EH2hs1mDYTWk1D55ZOdCW70DnRiblfnIunb3gamqnhjJvOQMdYh2VbzEwgYSSAutmKJz/yJK78hysR0kNwuVwIBoOwO+w43HcYT1z7BCquCsbXjOOxmx7DNf92DR5+/cM4vOGwuq9kbxK3vfs2wACybdkTExvYt3Qfiqkiep7uQeKShIWOqoDjHgf2/mQvpkem0f1gN0a+OwKbbsPrP/96eA54EA/FlWOJoNbtdqN3tBd/+Y2/xE8+8hPUUEPkxgjcd7sxYhtpUClwjDmmklSR0nJZQUCNt/h/uVxG+fkyohdHEVwXRHKhpfCQXnLar2bZnAXb/wPtT74Ot81mw3ve8x587WtfU8CCni+CCnohCUDlpJVJFySYpewcADKZjLoWPcpkilgWiedgmQG+SIzHzuVyDbE2shwB/9GzzWvJF48eP5n4ghuYLEvFBVgmMpNsKV9smQiOXnN5Hnrn5P3w/imDI2kgZef8rFAoYHJyUm2sJB7ooWb8GpNiAcD+OfuRs+XQv7sfDocDvb29CjDS20fGmTG9ereOb6z5Bna07IB73I0Nn94A36hPxe/ze9uv34795+yHaatPYz2vY/E7F8Ox31IRhCIh5N+axws3vKAWZ1/ch4u/dDG8w144HA4kEpZ0nUnTmJmbUi4Zd6dpVrzxsH0Yj3/qcUAHtD/TEJ4Io7W1tSGxGfu1ra1NyQltNhv6LuvDi19+Ed6/8KJ1qBU9PT0K0Hu91j1NTEwgGAyqElcsV0GPAUkKlqCQNd5n1s1g2we2oRAuqH7RjmtY9ull6BjtUDHN5YVl7P7Cbqz4+xXonunG9PQ0pqenVUbyzs5O+Hw+TE1NoVQqIRKJqKzcgOXxzuVyiMfjyOfzOOWUUxRBQGl7Op1WScMAS1odiUSUcckEbceOHYPD4VDZ5avVKoaGhhCNRtXGx5jtaDSKiYkJGIaBWCym6nezRExnZ6fKTkrPf39/vxW73pFG9rws1jyyRknGHQ4HZmZmcPjwYVU6jGVEAoEABgYGVL1PmfWVMYV2u12RgpL0A6AIEo/Hg2XLlsHlcuHxxx9XhN9rsZmzdbhn23+w/THZKrqu413vehcuu+wyVZmCP6k+YpjS+Pi4SoIZDofh8XhUrgkSksw2rmlWlQmZLJXlwxie01xDW0qVScpKJ4GMa+a6JHPH8DoAGjI5k/yUHnAS68zfsn//fqXkoS0hbYhKxYr/ji6L4mvLv4bnW59HW6kNn9/+ediOWCFDrFyRz+cxEhnBPZ+8p4FAB4DebC9ufu5mVLUqPn36pzHtOZEDgzNCs/6/8qmVWHH7CozMHcEjf/NIQyUQtuhUFEs+sAS2kA0v/q8XEY/GG/4eSUTwttvfho6ZDkWc33PBPXhu7XNwVBzY9MAmLHt8GTRNU3J5jkUqksLuU3fj3KfObQgzkk0qDzgGdAaNto+ia6ILOb8VdhgsBFHWy9iycQs2n74ZV9x3BTYc3ICn1zyNZEsSQy1DGGyzEnCecewMzPhmcKDtQP1ifH4T6H2xF7aKDQOrBk7aL83NXrCj78d96LmzB3v/cS/GzxgHNMA/7sfrPv86+MZ8ak49d/1zOP6641j84GKs+MUKaKiHOB45/Qhcky74t9cTt9kddjz67kcx9XqrWktwOohLv3UpWmdalQNI13XkXXn84v2/QHZutn5jGWDprUtx6sOn4sH3PIjk+UlAA2wDNvR/uB/lZBnHf3Qctb4aYAKeRz2I/IWlrEz/YxrZt2fhuc2D0N+HGhxf5UvLOKXvFCw+uljZV7LKCseKCXsH2waxw9wB83tmQzI42Qi0+S41h37wHaHNTwxDHCK947FYDOeccw4ikYhyftATTnn//fffjwMHDmC2/eHa72Or/Ml7uFtbW3HVVVcpwEImkJOUpY/4YkqpNF8OTmxKYzm5uRAQvNIzyBeCtfoI4oHGWo48J2OyuQGRYeamRe8jzw2gIZEHX7paraY2LXquCdopq5EvIs/D56ZUiISBTGoir02voixNBlibLJM7cdOUiwefnWwc+4ZAojl+igCe35l/yJKMw2clFZuYmFCeZZZeoizdMAwk3Un88tRfYkfLDgBAsaOIPZ/cg/XfXQ/joKFIF5vNhvU/Xg+9ouPFC1+sTx4H4LjAgf5av1U+Ij2D1JpUwyZTcVcwPX8a/VP9sNvt8Pv9SKfTiMfjiEajSg7OBZJJvgiKAcA15oL+FzpMuwn9kI6ORR2oVquYmpqCYRiIRCJKEs2Yt97eXhSXFbHzEztRa6+h/LMyYv8cQ2VPRSkskkmr1nUoFFIGEhUTAJRxwji2SqWivCGAJcPufKETG362Ac/8xTMohUuwD9jhv9EP/ZAOvcNKBje1YArHbz6OUnsJL/7LizC+aMA54VReWpZFO3bmMTh+5VBMPjPLB4NBpeIguZNOpxEIBNTcNU0rOzhlVJz/VHkwfpvhF7FYDOVyuSE5XaVSQUtLC9LpNAqFAsbHx1VCHZJBcl6zDI9pWmV6mDMhmUxaZEIxjLaftmHSOQlN0xS4pgx0/vz5ipQaGhqC3+9Hb2+vSn6XyVjJ7UKhEEKhECYnJxGPx5VHP5fLoVQqqbnGTT0ej+PJJ5/8L6yKs222zbY/hhaLxfD6178e69atQzqdVjJp5sMg2KYn2+/3qzhtysftdjtmZmYwNjYGp9OJaDSqABxDl5LJJMrlssqNIauvkNCXNo0si8T1l00q2QCofZTgnOCJ4FE2KX9mSNz8+fNhGAaOHTumSGmZoA2wbLHB4iB+svAn2N62HQAw6Z7EZ5d+Fm8deCt8BZ8q7zTeN45H/uIRVLwvJyKHAkP46qqv4tSZU1Gw1UnkBuCoAYWFBTg7nSicUzgpqOyd7MVb7n0LWlZZpPWK+1bgVxf/ChMtVnaplqkWvOHeN6BjpqNBnn7hvRfCWXDCn/Zj6RNLUSwVVfiUtJWC8SDOfvxsmLr5MvBlmiaqzioO9x/Goj2LlO1K++ylhS/hzkvvxDlbz8FQ1xBsmg0X3nMhnt/4PB454xEAwF2X3oVMawaPnvboy57t6QVPv/yBRd8kQ0noNf33Att6VceqO1ZhwWMLUA1WcdpXTsPuym7E58Wx7rvr4B31QrfVvbmrv7savkkf1j60FmWtXu9a13V0PtxpSa0r1p44PDyMQqEAz3s9iHwpguppVWz44QboB3Wk7CnlbOI83vCFDdj54Z2IL4kDZcB3iw8T353AlD4F51NOeL/qRXlFGeGPh5HanoJhGAi/L4zUV1OwH7Uj9vEYaqZllwb/MQhtSkPwfweh2ev2rWmaCD4SRGA8gEprRcW+04HB8rwkSiqVCtyTbqwurMZu227lgKDTjKGvxAd8FtoVUoFCW18N1Yn5ZHfZUXhjAb5f+1QIH/PwMJ+N3+9XeCIWi81WP/kfan/ygHv16tXYuHGjSioANGbTlLGjnMhyk5FJQzg5m2NkCIq5wXGzIMCkB4oAVHrI+Tul6nyxKEMny9ws5ZKJIMgCs0k5GGXyXJD5TAAaPO/8PxlrJsEgOya9/fRuy7jk5mtS2ktAy4VHMrIOh0NJWHhuZmjmJs54dnrvm/tD13VVb5HfJavvhBNaUgNEaE18YRzbPrAN676wDq54owTr1FtPRS1Tw/637AcAbPzxRvQ+0gtDt2pMp9NpbLh1A8qpMsausOKuXRkXFjyzAOVaXSbM/mb5NxILBLiMk6ZXQtM0mM+diIdDTSUNYwxVsVhENBoFAJUttLqgitwXc6jNsxbJWnsNg383CM8XPQgdCak5kclkVP1sZulmDDkXc3pRaHiRtOA8n/P8HNjyNjz6l48C1wHl58s4bj9uxRiudWD05lEUF1gkVb4tj70f2ov+TD/sWy0SKBAIYOCiARx971G0LGzBaT85TRl9jMemjLJaraos7H6/H5OTkw3hCwybkACYMdiMHaeigOQE3zFmJCXg1jQNw8PDSKfT0DQNQ0NDKBaLKhaS9b+ZfI7XoGddxu+RFOH8ZtI7AOr/zHiuaZq6xtTUlDK4KpWKSvhH8q5YLKqYTq/Xq+bAbJtts+1Pv82bN08RswAaElLm83lMTk6iUCggEAgoGTm9Ug6HQ5W6YrZtkpLZbFbFaheLRYRCIQQCASXPlmS4jPmUWZ35GfdI2jWSLJZkvJSLSxmsrIwCvNyGsdls6O/vtxKg7d+PVCqlAKqKRQ248Ju3/AaH5x1u6D972Y6gHkR7V7uqutG/oB+7fLuQQeakfe6quHDBwAVYlFiEz532OZiaiY0vbMTBuQcRD8WxYGIB3v7E2xEKhND+QDu0goanz7VA6Ju3vBlPLn8S1zx4DXqzvdBC1t7ZPdSNTT/dhHuuuweXP305QjMhdA93K7tKxkqf99h5lvqxmlcxxIloAsfmHMP6PetV/xEsNsdr12o13PWGu3B8/nEkM0nM3TpX2ZuJMxN48qInkfVlcd9596lnjiOOwX4BojScFGz/35on7sHG72yEbup46iNPIdte9xivuHMFvHEvDlx4AMvvWo49f7YHC+5bgJ4He1DRLLvSY/NgzU/WINGSQOBgALqnbgvzGRf/ZjFyWk7ZzcxfQ7svGAyqPs1kMjBNE+HPhBE9MwrbMRumalPW3BD2tNvthmfIg6VfXooXPv4CCl8pwP0rN0ycSP5X0RC8OQhjoQH7Ljs0/cR47dMQ/ZsoMAxUMpUGYt7zdQ/ylXwDriB+YH1vOjwMw1D2HN8/JpilsyEWi6lz0aYxDAO2eTaULinB/1O/Cj2tVCooXlWEf58fviGfciTquo6hvxmC55NWObFgMIipz07BvNyEo9MB1w9dyg5lnhubzcqYzmeToaqz7f9t+5MG3DabDR/+8IcbgIVMIkCgyJhfgk2gDkZrtRq++c1v4vrrr1dxTnLzYEIDgnUyWUx+BkB51gErfjSbzWLOnDkqZpteYk5ynkeeSybAIGjm/cryXDIxipTHS/KAfUGwTGZMxq/zHpgdm98nyKe3VEl2TtQS13VdZTyVSRzofSa4Y//yfPybVAnQS08pPTcsZvsmwGcSqWw2q+p622w2BLQA/nLPX6IaqeL5zufVvIg8GYFz2omqUVWJw2ZmZuDz+bD010uhe3V4j3qxYNsCZItZVUvc6XTCmXfirLvOws45O3F41WFkY1lseesWrPnZGrjtljSHIQJOp1MRBvQeEAQTEHO+8TjTNNV3AcvLnM/n4XK5EAqF0NLSYsm0K1mMPzyO5DuSgA2AAQT2BODe64Y74laLOqXzCqhXq2oes8a03+9HpVLBvn37EAqFlKSfXvVcLgc8CAR+HkBqbwpVVJVc3r/fD9sDNiub5Yn76NjfgfBAGGkjDbfHjczlGRx+92HUfDWMXz6OPZ49OP2205FKpVRsIkM7wu1htIXaYJpWibDR0VG43W7EYjGVqTsQCKh7pkJlampKETuZTAZtbW3Ku04gTAM1Go3C6XRi69at6m/FYlFJuX0+Hw4dOoSenh4V1sCsn9PT0/B4POjo6FCkiWFYGeBbW1sxPT0Np9OJYDCIsbExRKNRJRF1OBxIJpNoaWlR5+3v71fjEggEEIlElApCqiVisRgCgQAAoKOjA5VKBUNDQw2x4LNtts22P63W29uLefPmqbWJirdkMqlCUqLRKMrlslovXS4XEomEAsAMu6KjoFwuI5fLqVA3htqwSYm3zFdDO0DaCFJO3pwI9WQx11Kl1gzoaYvwHmTuG7vdjt7eXmiaht27d6s9ErD2xjvffSeOzT9W7zgTmFOYgy8e/SLCnWHo3XqDzfalF76E96x9D1LOFDRTgwYNNa2G/mQ/PrbrYwhVQmibbMPfP/X3uCt6F8767Vk4O3w2fv72n+MDD34AjrgDU1NTcDvdePO+N8MZcmJOYg5W71+NlYdXIpQNwdTq8dPZbBbz0vPw/p+/H7FiDEbNgKm9vE43+4Z7jcfjQdFdxA//4ocoOUtwl904Zf8pMGrGy/rOMAyYdhO/ufo32L9sP0zdxJY/34LwY2E4H3ci159D4ZoCSsG6KoptcNEgtKqmsp/DtLzPhqMpD4gBLNy8ELm2HMZXj0Ov6DBtJkybCVvJhos/dzG8Exbxe/7nzsejn3gUyx5chlQohaV3L4VW1tC1qwuepAfth9qBSStunIqKUqkER8WBYDzY4PWlfci5RNUjbSfpxWefj4+PI5FIwDRN9Hh7EDoQgubS1Dk4x0jsa5oG11EX2q5vw8juEZS1sgoprFarqE5WgUkgX8kruxQA8CygGzqqlWrDe1ONVFH7Tg326+xwaNb96zYd8cvjePrTT+O0fz4N+qiuciQUCgUkQ0kcfMdBnP7z04EqGuzmOX1zcOBbB9Dzvh44shaotoftmLx/EmbEREgLwXmHEy6XC9nzsih/oYxkIYnYm2LQx3Q4PA5M3jKJyqUV2HvsaPlkC+KfjiP31hxgBxJ/n0Cv2Qv8Eirsk2FwMuzzmWeeeU2Hqf1Ptj9pwL106VKsW7dOyaRlkjCyR8ViEb/97W/x9re/vSFDJpknwzDwkY98RMVHyNrXtVpNydMJkLnoM2M1Y03oIWYWYrlZcTOTpcO42ciNjJsgr09vH5+LoI1AXkq+eB0pgZfJTLgRSLkLSQigXhOQrJwkH1hOjQsmF0QppeLz5vN5jI2Nob+/X9Vjbmtrg8vlUrHXx44dQy73/7H332FyVVfaN/w7lXN1dXVOkrrVygEFJAQIkXM0wRjbGHscMDg9nnEOYw/OxjbOaRwJNibYBJNERggQCOUcutWtzqlyrnO+P47WrlPC8z7zvGEM39X7urhEd1edsM8+e4X7XvdKM3fuXAKBgDLi0sPbbjdbUYyNjamEh1DrrCUBmqbxesvr7KndU7UuBs4ZYPmB5TgOOAiHw0o8S2juS25fQiFfoOwuKwqe1P3n83nGsmMksib1x3AYHL7gMI6Cg0X3LcKpm4G5ZDWnpqZUvVwymVTXaa1/E8q1zJHNZlOGRALiVCqlVMfb2tqYmJjA8wcPrrCL3kt7cTzqoOGLDXiiHnp7e6mvr8flcpHJZJicnKSmpoaJiQmi0agKAIU2KIFjW1ubEnUbGBhA13Wlap5IJKAi9o5hGGbv0ixEvxNFd+lk35XF+6AX2+dtjNhGzPXRqbP3qr2U/WU1X0fXHqWvp4/wI+EqHYTx8Divff812t/fjrvPTTKZJBQKKVG1QqHA2NiYolR6vV61HqPRKLFYjMbGRmpqalTtdyaTUYi2rFlRbY/FYgopFyp4oVBg586dNDc3MzAwQKFQUKJ85XKZpqYmABKJhAripUa7v79fBcW5XI6mpibVx1TajMn77XQ6GRoaUtl62S9E2FEQolKpRFdXF8FgkLGxMdUPPBwOq/1jekyP6fHWGz6fj0WLFuHz+RQDLJlMMjExwdTUFC0tLXR0dGC325WCua7rKiErdt6q2SI1mMPDwxiGQWdnp9rToSJqBhWkWRSRRW/GGtxYqapSYieJY6ioXcs+fLy+jZXqbPV5VABpVLRjbDYbdXV1zJ49m3379jGRn0Cv0ZkVmsUt227h802fp89vorSzsrO4Y/cdJhjgMNQ1SSIgkAjwg8d/wB8X/JFZY7OYWZrJw10P85lXPwNlyJZMltCMkRlcM3YNLo+L5kgz33joG2TTWaZSU/h8PiKRCE6nk2tfvVaBNKFCCDTUuTKZDBMTE2Y9faaWUrlSOmcNmMXHEsZebW0tsdoYv7nhNyQDSdDgz2/7M9f95Tpm75mNTauAMAIuvLLyFQ52H1SBczFQJPWdFMtvWo4j4WD0F6Psv3k/5WB1m7Tg0SAnfOUEXv3Gq+QacwSHgpz2rdN47ovPUXKWKHlKBMYDRA9FWf675WiaxsZ/28ipt53K0ZOOsuviXaz5/hpCiRA2l/nMa5I1XP6ly7HbTLHUcsn0aQLJACWjhGPMARo4PZWALh6PV/nYMgTEkTJK8U10XSfeEqd2pFYJssp68Xg86O062qhGqi1VYQsaBobToNhQVN1o5LiaphFIBSh3luFAtRiwzWejHC3jGnYpMMQIGxS+V8C9wY33Li8e1zE1+S4Y+usQRsTAY/dQd0sdJCF1YYrEbQmwwQs/eoHzP38+oeFjNr4pzmNfe4yyu4yj6GDpPUvRsse6C9UW2PjRjcTnxcndmaPjpg4CzQGO3n4UvU0HDYa/PcwMbQZGwWDo+0Ngg3KozOG/H2bhJxcyctkI8UvNksfsxVkOX3TYpP0fe+WNoMHwJ4ZZdnQZxqChfJ5SqUQ8Hlcs0b6+PpUYmx7/s+MtHXD/27/9m6JJCQVagkyv16sC5uuuu04ZA1FLFiMkxspa+3y8sZA6SwmkrNRXoV9Jtk7qxoW2JeiZ3+9XxlD6IssmIS2y5DqslCzZvKziJtb/t9ZaiVGzUuplWNuLyXXISyebklB7ZS6sQmlS8y1BjWT1crmc2gRFvVsMswSSPp+PbDarrlPaN/X29hKNRjEMA5/PV0V7n5iYUAZQaNti9IVG73A46Eh00Jho5HD9YXWvNTtqcMVdON1OJbQidByhuUuywZoBlfvLd+XJzMhUFpoGkwsnKT5TREtpCrWV74sohd/vVxlX2fxdLhdjY2PY5togC/oeXaltC5IvjpAkI0ZHR2ltbSUUDNHx1w5cuovRm0fR6yvq8ILyiqiZw+Ggvr6eQCCgrslq4DweD7FYTK016Wkuz99K+4eKaq2IfBkfNbCN2nB9x0XWm1X17KmtKWo/VsvkdyYpdZXQkhotP22h/tV6BscHmTFjBqVSiaGWIXZ8bgfZpixHfnqE1s+1kuxJ0t7ebmZ0j1HPvV6TKjU0NERNTY2ao0gkolTMpQZaRmNjo0LqrQZdnq3P5yOTyRCPx1XiZWJigqamJqampqirq1N9L0UtVyjeQl2vq6tjcHCQyclJdY0+n494PM7w8DDBYJB0Ok1dXZ2qqZcygWQySSaTIZfL0d7erpxoqfWXJFo+n6e5uVmVCYyMjPwf74nTY3pMjzfHCIVCzJo1S9VXT05OKpbWnDlzqKurUyUzVt0T2ROOr9cUu+pwOKitra3yEaAStAHK/xBxNGvgKwl/a1At/oScS9hwVsTaKgJpreu2fk78LGtXFGvC3+Fw0NHRQd7I82jro6Rnp5m/dT5RT5Tv7vguX5v/NezY+eqBr+LQHRhUxKDEt9vh2YF9yk5dsY7rN1yPpmn4fD4Wji2s8nny+bzJjNLsqotHMVtUSvAyh7IPW+vS5Z5yuRyJRILa2lplU+WedF1n9+zddB/sxq5XtHKEVed0Orn/3PtJBi3Udw12LtpJ194udT9WdHzliyspUGDD+RvQHTq2bTZmfmMmjripvN72dBuJYoLBjw7ifMiJbZ4Nu2bnpJ+ehGPEwfJvLWfgwgEWPriQ0ESItd9fSzwYJ9OeYc5zczCShgICTrn1FGx2GzNensHMV2aa922rCPsWCgU0NDDAYXegl3X1bCXxYvUdrYxMa3ecUChU1dpWgkBN05hcbZYAzr9rPp59Hnw7KyKqxdlF9G/ouJ5xMfmeSZJfT+La4UKzaRy59AhHTz/K7FtmEz4Srlp76VPS6F/RCX0mRPCZoJnoSicpfrYIp0PoMyHYC7awjdgXYpSuLVF6e4lafy3+e/3kFuYYvXUUvfYYWPC2JEbawLHPQeyWWCXAdRg8/bmnWfOjNbg1Nxtu3EDZY74He87dAyVYeNdCcoEcm9+zmZH5pj3Pz8sz/r1x5qfmM9I0Qkk7Vi5qg/qP1Jv185Zcuz1kp+0bbaT96UpdvQYzkzOZE5vDCy0vkHVkCR4NMvtbs6mdqsUesFd1R0qlUlWg1fT454y3bMC9YMECli5dWoW8ygYp/ayPFx4QQQGp77Sq/UFFhVyEpoR2IhuwFe21ZjeFMi3GTzZcoXHLJiXU4ePrnawZ7OMpW3LdVoQ6FosxOTlJV1eXMmIS4MmmL5sbUGVArO25AIW8WwXGBBEUZNZa7yu1JLLRWoXS5Fwyb36/KXRSV1enFJodDgfBYFDVskm/0ebmZubMmaOCTxF8iMViNDc3A9XKqKp+uljCRjUSGPAFiE3FsE3ZVDAsoloyv06nk0QiQV1dHVNTU9TU1KjMdMO+Btb85xo2fnQj6Uia+kP1nHX3Wdgn7GgerarsQJ6tZF+lfkZoPLlcjrgvTvq7xzbL66AwWqCxsbFqPqVuXhDa/fv3Y7PZmDdvHrNvn81gYVCdMxAIKLq/qF9LkJ/NZkmn08ycOZNMJqOSPZJ8ElQbUOwNySwLPRtQQa0IgJVKJQLfDlAbra0SwdN1Hd/rPox/M5j88SShb4TwP+8n3hQnk8nQ39+P7QQbBz95kFSHeez8jDwDXx2g4YcNpHvSCpUX6rasC1mTQheLx+M0NjYyPDxMNpulvt5stSLtLiKRCGNjY/j9fgYHB1W5hBx3cHBQCRhayyaam5uJRqP09PSoZJ1cj9VZra+vV+tTRI2kJV9tba3aV1KpFE1NTfT391NXV6fuSZI9EmDLO2K329X6NAyDmpqaKi2J6TE9psdbbwQCAWbNmkU6nWZ8fJxYLEYgEKCxsbFKh0LsvNhQqy6FNZiVQFnQb2HSiD2Sz0hyXPZtCaxlT5EACarbEB2fuD9+iK8kiUxrSdnxIq+S3D5eQFSO/cAZD7ClfQto8PvA7/nq3q/SVGjiswc/i8vhoq5QR4mK6KrY7V2uXdy28DbCs8Jce9+1hGwhFUxbg/5CocDExARg7tvSLlNK2g6vPIwj7cAx4VD7rNX3KpVKbDxhIy0vtuC2uVVNvdy3zWZj+6Lt/P2cv7N011LOe/Q8NddStrS3ay9DTUNVc3jilhM576nzcNldFPWimnsr0LLi6RUkh5LsWreL6OejZHdnOVA4QF1dHa2trTT+rZHx3eM4HnbgXerF7/GTzZsJ4tDeENFDUdPHcxQJ9gQJGAFsO8znYbgqukRWATxZV+Ibil8qdkkSKKJLc3yJggA1Ys+sfnXan2bnu3aCATPumkFkIgLA8EnDbH3/VvI1ebbetJVwX5i535qLb4+PUluJ8c+OU1pWorTSXL8HP3sQx/ccxJfGOXzdYQy7Qc8Xe1j4zYV4h0x/e2TVCP2f6KdcX2bqO1PYbrEReChA4T8KZD6cARtMfHuCWV+cRf9N/WQuOwasaDD1lSlq2mpYHVvNC+EXGGdcPbfFixbTNauLO7ij6nnmI3n6z+tn7vq5b3hf3A6z/NPpcGLTqt+nrlldfGTfR9ixewffWvItDM3g4omL+UDfByjnyvxa+zWPznwUDPj0kU9zycQl7LDv4JbuW+gN9tKR6+Dzhz9PfW89npc8bLpsE+946R30DfdheM1yPaGzi5hyPB6nv7+fycnJN1zr9PifGW9ZzuL73/9+1WNbEGtpOyUZMqhGgyUAko1ANgxrrdPxAbAYQ/mMVQVaMshyHnGkJXiSrLAg3bJJQSVDLPUnQhWTzd5Kn7ImB+x2u6oxFYq7ldItaLxkraX+3FpDbhWosFLLJOADqhx+qyqpGE7JZMr3pUeoZDZl3kKhkEqCyCYsm7fMfy6XU0JiLS0tRKNRRfWqqanB5TLrWoLBoOqtvX37dkZHR6kfq+c9f38PkYS5iTe/2syS3y3BmXAqoy/JjlwuRzqTZvfpu3nw6w8S98SVgRTxr1wuR3NzM229bZz0xZPwHfWx6D8W4T3sVUi90KEcDpOyvmzZMkUdF5qa1N0ZLoOe/+xBP0WHk4En4fNf/zwtLS20tbXR3NxMU1MT4UiY5uZmIpHIGxIaVjEdwzAIh8Pk83mmpqaIxWLqmQkiDCYlWlgGQoMOBAJ0dHSo81jr/uIpE+GV5yx/E2fK6/USDAYVnVveB3mWzk1OotdEaXq+Sb1vDQ0NlEolxl8dx3jRqLRmMcCz3YPriFkLLUbAqrgvyZlCoaCQ5pqaGtxutxIeEzE9SQ55vV4CgUBVq45gMIjP51NBsVDHJRkk1PSpqSmam5sVxd3tdhMIBJQDIu+VOLSS1HO73QopCYfDRCIRFdzLc4jFYuTzeZqampQTbJ1ja6cDodcLg2J6TI/p8dYbmqbR1dWlSo1sNhtNTU00NTXhdDqJx+PE43ElYibBzz/ScbEGgjIkWWoFA8R/kWBb/CKodGSx9vKVYF5soNhKK/ptLZmTa5PrkkDfmtw/XoBN/BYAm93GA20PcMOKG3i0/VGF1m2v286nTvgUDpeDmfmZtKRblJ9hpWv3lfr48uIvczhwmC2NW/j5FT8nEq22l2W9zHMdz/FCzQvEYjG8Xq/SnBG2275Z+/jr2X/lRxf+iKQ9qfwt5QsaOq8teo1HT3uUuz94N76wT7HscrkchWKBLW1bePjch8n4M7yy4hUeufARs+WU3Y7f72ekY4S/X/53EuFEZVEYMPPITJyZ6pJAuU/xXWOxGM0PNzP/0/Nx7Kjo+0xNTZFMJk379IibfC5PcWdRtd0SFt/xz9Pr9Vbp5fj9fkKhkHqWxwM71lJI+bsE2y6XC0/Ag9PtVH6A2+2m97Re0uvSvPTZlyhpFZHiglHguX9/jt4zeuk9s5dN/7GJrCvL4OxBtnxoC7loTs1BvCPOtn/fBrNhx3d3kFtW+RtApjnDzk/vpOfqHtXeNTUvxfZvbKfsLxNfEGf/p/aTbzBttV6nM/GVCQYfGST7gayKdgonFuj/VT9rj65F0yslGJ6ih1NGT2Fedh7vu/99BDIBMGDtgbX8y8F/4eKhi/m3V/8NTbo/GWbrs0V3LqLhcAPn3noujpwDDFjy1BKW/G0JIXeI2lwtZ959Jm3728CA2cnZfPngl+nSurgofREfffajnD55Ov868K+0+dpor2nnXw7+CxeOXMgtR27hitQVGIbB3Oxcvr3n27TmWvnJwZ+wJL8Eu93OVaWr+PmBn3Nx9GK6u7uVKnkkEnlDjDA0NKRAlenxPz/ekgh3d3c38+fPB+Dll19m7dq1CtmTzVWcWHG8ZQMXdMpqDATRFUEpa10yoDJ8UKFfCxosm6bQaeTvEiwIkiYBhATaEpAJiig1K4K6pVKpKuMlm6IEF7KpipG1JhXEkEoduPxNqO5WCpkos1sFUKwtygThFMTWSkeXY0mLI1GVlmDEbrczNTWljL3X661SW4cKfU2o9y6XS9F+JRiX+xcU2e12s2jRImpra0mn04R9YZz2Y3XsRZ1yoYzD7lAJCJWt1QscXnuYXR/YBXbY/MfNuD7swpkyv9vc3IzH4+HIkSN4PB466CD4r0FKmRK5oIl+e71epfIoNTFbt25VgZmoxzY3N5NOp9n+5e1kZ1hUp7vglpW3EPx6UAV49tV2DvzLAU7/8el0FjrVXMTjcWpqaojH41U9TEWLQOrIRVxM1F9FtE5QbREMc7vNummZ73g8TlNTExl/hqnHpshfk4cxc41LCxupEy+VSiqgl0SGtQ6rWCziHfSS0lPKOXE6TaOsT+gs+MUCRmaM0HdKH84HnYT+LYR3bkWx3ufzKY0CoWLLPEjJiJQd6LpOXV0dhw8fVorfkuQQtkk2m6Wrq4uBgQGcTifNzc1K1VdqCQcGBpRDmc1mVSY4l8sRi8Xo7Owkm83S1tam5lQSArFYDMMwqnpxyzsrKLwk0GTNyPll35iamlI0+Pb29qpayWkl0ekxPd66w263c8opp6gSH2G8yb4szCsBAWQPtSLLggzLfisMJStN3BqMyzHEZlpZdBI4WbVFJLgWlBVQ5xM7LwGY2+2uSgBa/QWr9oz1mqw07Xw5z2N1j/GDmT9A195Iaf3aga+BXmlJag2Qs9ksuqHzoas+RNqVVt/pa+rj16t+zYdf+7B5TgxebnyZX6z6BQDvmnoXp+ZPJZVKmW0aU0kOtx7mzrffiW7XSXvSfPPd3+RTd3+KYNJsUZkv5tk+Zzv3nncvuk0n35zntzf+lvf++b0EDLMbyJhvjCeueIKsz7Trul1n65KttE+2s2rbKgACEwHWPreWJ899kpK7hKPk4LSXTmPx7sXY7LYqn07TNHS7TtKXpDRVYmBgwPR14pqiv7vdbjo7O4lEIqrt5pYtW5SfIInf+vr6qpIqee5ix63K2cK4kgRyoVhgIjKBPWPHKBt4i6Z9drvdlGpKkIXxhnEe+cIjzH12LivuXUHAHuDA4gNs+tAmc73YDB7/xuOc8KkTMHSDrbduJdVeCfCyzVme+tFTNF7aiMPnIP+ZPPgq6yDfmGfzFzbT/ZVudn9vN6VIxV+0Z+x0/raT8bXjjK8ZBxs44g7mf2c+JKFYLqLbqteWy+ti3bPr2M52huYOgQb+tJ8bHr+BwNEA9ffVc+8l9+IuuvnkHz6Jw2tS4B2ag6/e+1X+eOIfuebZawhGg7jdbk4ePJmJZye4ffXteNNeLv7CxRQTRQhAZCLCFV+8gq0XbmXpHUtx2p0K8HPGnJz5nTNZ/4n1XPHnK3Bc5CBGDJ/u49SJUznzpTMJNgUpFE3xuaAe5H/t/l943B7SelqBZO5xN79K/AqtqDFhTJigXa6AZ8CDN+xlzpw5TE5OqtLWRCLxhtZj0+OfN95yAbfNZuPUU09l/vz5FItFGhsblRoiVOg+1joloe1aDYwErACHDh1SNFKoUIvEaAklV3pnWkXPxNjId6yqn7LRWVUYrecX1FmoSrJx6rpOKBRS9FeotDqT88jvrP9Z1S5lDqw0J0C9dPKztVZL5kkMtFDQ5VyAotRbae8ilCHzJAyCUqlEd3c3o6Ojyojm83m8Xi+NjY309/dXUbjGx8dpbGxUCovhcFhRwaTuWoI8qQs66jvKXSffxah/FICRk0fYkt7C8j8vJ95nClU1NDTg8XjYs2YPuz5Y6cNdDBXZcusWVt66EluPTT2nhoYGRT/Tc3qVqrQopEuyxIr6Dw0NKZQykUiQyWS45q5ruKtwF0MnmtQy7/NeuAyS2SSpVIrI1RFGvz6K7tV5/abXWXPHGgLxgEKUPR4P+2bvo/xEWbWNkjpsccSsAiVC+ZM1IehEe3s7Bw8exOFwEIvFVAZ8IjTBK+97hVhXDB4H3gb6C7pCDQTplWctbAHrWpfEg6i3FwoFhQiLA+h1eTnnjnN4euppsh/J4g/71b1IP25xJsVBKhaLKqkg60aSP0pV/phiuLzrkuiSAHvu3LlMTEwwPDxs1sUfWzdjY2MsWbJElSnYbDbljCSTSSYnJ/H7/Wpth0IhxsfHVSKpWCwyNDREXV0dhUKBqakpdQ3SXkyC8ZGREWpqatR8CutE6sDtdjtDQ0OKkSDO0fSYHtPjrTk8Hg8rV66s8ikkYSdBtTVolgBIStrE/thsptKw1+tV2i9WmjlUUGVRfJZA2+pnyLlk37cew8oMlADZ6uNYg3c5hkIwj4EKgs6LnyPfke89WP8g35393X84V7OHZ1MYLdA70UssFlMdOPL5vNr3x1eMk7dXq3Ov7VvLza/fjI55jpdnvMyPTvqRQs7veOcdRJ+NsuDwArMu2e3iD9f9Ad1+LCjTIOFPcNfZd3Hj3240bZ7N4NUTXq0Ebsf6Uh+Yf4ATt52IYRg0ZBt410Pv4i/n/4Xx2nGcRSdnbzyblVtWYmjm/CQ8CcI7w6wqruK1C1/j5M0nc9YLZ5lzSIWtIIj61lVb2bJkCyt/vFKthVAoxIwZMxgZGanSucnlcoRCIaWFk0qlFJpZLBaJdcZwDbkw4qYPYNUtKkVLjC0eo+VQC2MzxmjZ2gKGeR0Dywd45qPPUL+/HlvJRv3Oemb+ZSZG0GD7e7YT6Y2w67xd6E6dPefswVawERoK8dK/vFTVszs5M8mm/7UJ+2Y7mUjmDT3QS6ESE+dO4LrNhX2dnfKZ1QJeXeu7qD1Sy8KvL+TApw/gOugitSRF9++6aXy8kcbHG9n95d0kFiaY/YPZeDd7KTvKBLYF6P52Nwc/cZBiQxFfxsd5T5zHkuEldP6sk0c++AiJlgSXP3I5TaNNZG1ZFh1eROnpEjNHZlJj1JB35tVaDhfDXP/o9RS1CvXf5XSxYPMClvUso2tPF7a8qQsl73RNooYz7j4D3aErgVQpY7Xb7Zzz3XMYqBng1vZbqbHV8P7D78ftcjN0dIh0PK0AMNGDEh9O/H4JvKWri8QOUppSV1eHP+BnY3gjqydWq1p8j8fsbHP06NF/+A5Oj/+Z8ZYLuKPRKGeddZaiGre2tlYZCqlrtWZsBWEV1E3quuUlmTdvnqJdSMBqrX2SIYGWYRiqxRNUqOnWgNpK2xIjZ81MS92vtbYJqHLorcc5nlZuNaKSSLAqkMu9WOngEvRaEW2ZI/mM/Ct0Yfm8te5b0FYJHmRurEY9k8ko1DKZTKrMq8yz0JHkZ6Fryfet9WwipiUbTrFY5OjRo5TLZV5b+Rq9bb1Vz6nvnD7mPjUXvdesoa+pqSGVTlEKVT9PMFtapLU0ES1CPB5X4nYOh4NCqcCRdxzhhMdOUCir1ETL84pEIlUJAdngbDabSU92eTn51yezKbuJsr1M9JYopZYSqVSKsZPHGP7mMJhtxTm05BDZa7LM/dpcGr2NRCIR9qzaw7Yrt+GyuyjdV1LBtIiZiRCGsDJkLgVlzufz6npFsVvKG9KBNDveuYPhecPmBdQBvwM+BMXniup4opJvzY7LEKfKWssvvavBpJVns1k1N6vuXsUT+SdUHaLUjQmlvFgsKv2EmpoawOxzLXR+QfxFmVze6XA4zOTkpEL43W63ajfndDo54YQTGB4eVrV4jY2NBAIBxcjYtm2bqrcWByabzZJMJpVInSSXrHR1QYREVXxqakqpC8u9iGCgJEM0TVMJNaCqNVkmk6lqFTg9psf0eOuNOXPm0NHR8Qa6ruxxhmEwMTGBruskEgnFhBMmjCTumpqamDFjhvJpjg/SxWaLNoSw6cSOiq2Xz1mZcVZfR4Yk7qESyItPIMcQhpX4VfKzlYp8PAX+p50//S/navVLqzm07RCASjYI6ykSiTB08hC/X/57SnaL/Tbghu03qPt8uvNpfr/099XBHZB0mnolTqeTgD3ARdsu4oEVD6i/uwtuTtt9mvI3vIaXd61/F39Z9xd2dO3Aptu4+vmrOWHXCZQoqX28o7+Dtz32Nu656B7OePUMVr66Et04xnZ0Fnn4gocZ9Yxyyf2X0PxEM8t2LquqlRefFeClU1/imXOfwbAZbHj3BuYPzcc35FP+kzyLcDisfErDMGhpaVHsg2g0isfjYbxjnJdueInwcJiVP16JR/OoZ4IPXnvva/Sd2EfH9g6Gu4ZZ+NeF1P6+lskLJ9n1/l0YNoPReSZ4MbxwmKP6UYqtRVJLUvQtsfT5BnZdtIv/ahQdRZx3OgnuCZL8UbISaRhQ+81a3L9x46xxUvNADUdPOoruq6yVvC+P2+Omfls94f8MY7xmMHHiBO3PtVOymwFn97e7SS5LEn05iuaqgF/hZ8LM1eZy4NMHuGj9RSzesxjsEPKFOPMPZ1JaXKKrvws0c05SqRTzX5tPuVxmqDCEYRiMj49jt9tV8CtzLwwUt9tNQA+w45wdrL53NXYq+k3iLwu7UsRd/X6/iUZrBZ6/7nnls07lp/h05tM0NDQohp0IPhcKBSKRiCmm6+lhyDXE8tHlpFIplcB/MPogpydOx5/zK+Zf/zv72TF/B7Ofn82cV+YoYKCnp4exsbH/8plNj//vx1su4G5paeGiiy5SFCehKUvQISiTIFZioKSPpRUdFmddghj5VwyRZILFoEivQaGEyuZnpWcJDVqMnlyLGCN5aa2qpEI7kSBcjKFcn9yXNWgWejpUgh4r7VaQO6i0WJAg/R8ZxuONqzXTLRR4CUStKLpcH6CoTA6Hg6mpKaXcLaijbD6Cjvr9fhVgiJKz3LOV6i5Z7kAgwNjYGIcPH8bv9xMIBFg3uo6jfUfZ3b7bNLYGzPrLLJyHnZQx51V6J896YBZ6XmfL9aZYCymwX26nmC+yLbGNGTNmEAwGCQaDTE5OsvHDGxlcPYjm11h631JFT5eMpq7rTE5OVhQ9jzk4EuzKuWup5YQ/nEAmZ2bu7a3mBt0z0cORxBHyRl5de+GJAod3HabP6KN0VYnRa0Yp+ooU/7VIsiVJx4MdYEAwGKS/v1/VuYuTJiiwtIGR2vJYLKbWnASamkejsaeR4SXD6vz0A7vNtZFMJhWaLoZH1pPQB2UdidMmawPMHuPJZLJqDUs9eTKZJJFIKIVySdJYa8YkM2s9hwh+CPXcOv+ikC9oj1KmPfb+Sns4odXLe+JyudRaFOV3oWWJ0KHdbicajarrrK2tVQrs0hpOhNbAbEsoImvWZJrH42F0dFQlrayK67quVyUipsf0mB5vzTFv3jwGBwcpFouqVltaGEpgnU6nVbvMUChEfX09mqZRW1urBLKKxaKy3arGWK8IpDkcDhVsS5Jdgl8rtdsaqAkAID6FtTQOKjRya222lXYuAYX8Xv4V+yB+kqZpOJwOvj3r2+TslnpcySMeC44fWfsINw7cSDAZVIndQCBANBqlpqaG7KEsrtkuMyF8zE7duPNG/CWTPaBpGosmF+Euu8k78+ocH3r2Q6zsXQm2Y73ADQcX7boIBw7uW34fADc/djPdg92qvrdYLOLP+bn6mavJuXKcuuVUFh1aVKXoLvPYdbSLG+69gfqJ+sq9afDHq/7I4Vlm15QHrn+AD975QXXP4sPJcV489UU2nLFBtQGLL4mz42s7WPvptYRtYQYHB1Wpo4jkiU3u6OigsbERu91so5pryLHhoxtINiaJzYhR8BQ4+/tnm89fL/P8Z55neIGZXJfgeevbtuJZ7KHQUVCtPa33MnXd1H+9yI97jlV/mmXQfV43R1YfeaNS1EVwQ+kGJY66/+v72fi1jQDM/cNcZj0wi2zWpOu37WpjMjuJ91kvdkdlnTqyDuo31aNj+qHSQtTlcuF72ccJ/3kCsxKzKOhmUjuTyTDWM4a7x81mfbNat9JCVTSIAoGAYprlcjmi0SjFUlEl9QG2dW3j5YUvk/VnydZmOf3npytQQViedoed565+jjPWnwHxio/+5IefpG9hJXHx7MxncQQcfH7X503BOaedL876IrccuoWpySmTKenL8KmFnyJjy/DJwU/SPNJMKpXi+fbn+d2M3/G33N/49N8+TSqR4oV1L/DQgofI2/L87bS/8Q7ewcrelfh8PgWOTY9/3tD+uyiKpmn/dLjF6XTyq1/9ine84x3KsIgitBgNQVqF9iz1ShIQiZPb39/P2NiYQretNVTj4+OKogMVuoYIowmaaK2xFuRVAk5rFtrarkyCFa/Xq2i3QhFKJpMqYADUd6FaYVRoJvLz8bRvCeBLpZIKFuTvViq79dlb68GtKpZCf5XvSTAs12az2RStzDrkWHfeeSfd3d20tbWprKHT6SSZTHLgwAGy2aw6X3NnM/s/tJ8Tjp7A8tHlGOVKltwwDPbu3UsqlaK9vV3RdAKBAHktz3fP+y797f00PNnA3G/OxWVzKURUrsfj8WBz2dh+znb2nr0XTgWOmPcejUa55JJLTDGywhS7bt5F32l9YAOtrDH/T/OZ89AcsvEs3d3djI2NkUqlqK+vVwrUIyMjNDQ0KFTUZrMRDAYZHx9XaCtU0/KzZHnhRy+Qa8zR8ccO2u5sY3RwlPKSMr2/6KUcrRhBraTR8eMOZj4yk7mdc1WrmSOZI8yJzsFpdzI+Pk4ul8Pv9yvjPHPmTBXAChItAn844dXLX+XQxYdgO7AWgs4gdXV1TExMKBqjx+NRtCSZU4fDUYWqW2vFBF3v7u5m5cqVSrl7cHCQ9evX09TURGtrK8FgUNWjS91yXV0dhw4doqmpiYaGBnRdZ9++fRiGQXt7O319fTQ0NGAYhupp3dDQQDQaZXJyUql/OxwOjh49quh5w8PDqk2bMC4kMI/H46pvd6lUorGxkWuuuYZbb72Vmpoa1WKmqamJfD5PY2MjY2NjSq/A4/GoBNr4+DhHjx6lra1NvaOizO/xeBgcHFSBus1mo76+XinqJpNJlixZwo4dO6bbggGGYfwDd256TI//evyzfRWbzcaVV16puojIniYU6YaGBjo7O5k9e7ay/U6nUwl7itCVNdkvCXVr0lP8DWsALr6IFcG2tmQSu20VMbV+V3woOY/8a02yi+8gx5QyMqs4q81mI0uWn3b8lL82/7Wqbvvzr36eny75KXF3XP3Ol/Nxy/23EC1GlQjrkbYj3FlzJ2f++Uzsmp3ffOI3xHwx3rnnnVx86GJseoXlZxgGMWeMT1/4afL2PO/Z8B5WH1mtfAirf1OixMPLH6a7r5vZg7Ox2yrsPLGPdrudkqOErWRDL1U0ceRc4rPI85bn/Mer/sje2XurgtDG0UY+/tuPo5d1xW6QZ1HQCtxz/T30zu4FDRxpByd85QQa9zTicrk4evQofr8fd4cbX9KngBjpqCIlgE6Pk/V3racYsNTZlzW6nu1ixa9XsOnGTfSe1qsC+6qRBlvChl6vvwGC03QNT95D1put+r0j62Ddz9dRd6SOv/7HXymEK/6fXbdz5t4zueqVqzBcBp+55DPEQ+azDmQDfOXer+BLmaWCO4Z2MLhrkOG5w8SXxJl992z0QsVvrq+vZ3h4WKHFAiIYhqF8BytgVSqVyAfybP7ZZkKpEJf8+hICpYDy1V0uFwVfAa/upZwzfWcRSU35U7imzOBbEGSb18Zf5/yV5lIzZw2exfa67Xx1xVcp2o7Nsw5dr3Vx5p/OxEiZ8UagPsCG8zew/YztBJIBbvjhDQTzZleCSfskd37uTnKhY8zQsp0v3PkFnH1OUo4Ud95wJwONAyw9tJTz7z6fgr3Arz/2a5K+Y0yNkpMP//HDFFuL/PrsX5usDwMa+xvpfLaTV972Cnqg8ny6HGMAAQAASURBVK45J53M+cgcAocDHDx4UPkZ0+P//fHf8VXeUgh3a2srV111lcrUOBwO1U/ZuiFaaVMSFFjp1VL7HY1GgUogKi/s7bffzs0331wljmbtIy09egWdFmMoSLZ1g5e2HRK8izGTDVeCATm//F1oyVbDJwZSNher4bNu+kJft/YqhkoLMqvRsNaXy5zK9QoybhWEs7YvkrkTQTUJssQIGIbBNddcw5EjR1TgLiieIMHJZNJUfvfqPH7K4+zu2M3f2//OZzZ8hoW9C8lkMgwMDKBpGoFAgLq6OnUtSnkTD1f8+Ap+/K0fU4gWcM5wkjuQI5lMqkDJ6XTS1NREXV0dzu874b3mnAh1edmyZZx++ulMTk7ySs0rJE9MqsysYTfY/a7dzDg0g+j+qEKv6+rqVF9yl8ulVLmlH/Po6CipVErRizRNIxaL4fP5VPDlKXtY/bHVDFw7wNwH52IL23DZXEwcmGDe9+dx4JMHKERNY2Y4DCYvm6RuYx2Tk5Nma7P2BAc/fZC6v9URfCao6OKC5NpsNtLpNHa72YtU6IvCAIn6okS+GcE97Cb/yTzkIZVPKQqj1JIHAgGFasu7ILoI8vxlvUo9oCTEtmzZQltbGw6Hg7GxMbX+pF1bIpFQAaus45aWFsLhsKplKpVKSkytubkZp9PJ1NSUon6nUqZYm1Dm4/G4UrSXHqpCaxchIysDRu4nVhsjM5GheLTIj3/8Y/VOChqeSCSq3mW/308qlVJ0zlAoRCwWo6amhkgkwuHDh/F4PArlGh0dVfMmCLyIxTkcDtrb20mn028oZ5ke02N6vDXGzJkzee9730tdXR0+n4+GhgbF9pFuC9KFwzBM4UVhEwWDQWX/pVRNAjsr+8cabEvgKzYGUHuUsNIUMmhRIRebb63Jlr3OyqazlslZ2XrWGm0BBgB21e5icWIxL9W8xH0t91XNTWO8kXJPmXl183hl5isqMM26s2xct5GPHPgITqeT10Kv8alFn6KslSmfW+ZDvR/iRy//iIfbH+aS/ZeYiQS9ElwWCgW8eS9ffOKL7K7fzZoja1RZjxWNNwwDO3Yuf/1yk7VYLlAqVu7RWrtODspGuYqaL7bCGnQLvX60dpR4OF4VbLcOtvKeu9+DXq7ueiNlgvHGOJfdeRl/uvRPxGbEWPCzBTTsbiCbyzIyMoLdbic+N86uW3bR+blOCs8UVOlRNputqLifblB2VyPUnoSH1b9bjWE3OPk/T6bsK9N3Yl/V9bmzbmb/fjbhu8LEvh1j9ym7K383YMWeFazeupo/XfQn9EEdZ95JoivBstuX0bylGcMwWPuFtbz8qZexF+1kWjOs613HdZuuM3uoZ0pc/7PruePtd+AJe/jg+g8SzAYpU2Y4NMwjb3+Elj+00Px8M817mpXQq7Wc7XjRYbHF4ksKkFUsFsm0Ztj5uZ2km9KkSfPC+1/g4kcuxjVmMj4z0QwPXfoQi3oXseC5BQrw6Wvv448X/ZEbHryB5n7zOvJ6ns3rNvOXhX8B4OjwUba2bq0E2wA2OLTqEA0HGpizfg41zTVsPn8z29ZtAyAZTnL3++/mkjsvITIawZ12s+SxJWy6xhSZK9vL3HrBray+fTUHzz/I0Sazxnrr7K3sm7UPvayTd1W0C4qOIr+d/1u0gFYpsdBgIjqBdkSj5ZctDN88TMlTIhAP0PGtDgKHAySTyelg+00w3lIB90c+8hEV3AjqKsJi1tpfybrKZ2TTPT5LLBunBOwSKH74wx9Wwbo1Y2ylkstmLn+TgNIq2mbtg2mlsEvgLAGjtUe1XI+V0i1GTwyvHEtQUsk+y7+SiBDDKoG2HEOCbDEkVgEWOb5kyGWzk+9Zs7oyz/J3MNuVSB2K3Htzc7Pqi6xpmupXLPdsc9jYfPVm9p+737wADW5bdRsXTVzEvL3zVGsPa42MVXm+VCrRc0kP2CC2PMa2D2/DdZMLZ9xkPmQyGVKpFLpu9mO20p+7urq48MILmTNnDsVikWg0ygXGBQwMDfDC7Beq5uXwuYdZvH+x6m8YCATYcfYOuh7sUhQooQeWSiVF4xEny+FwKEX3aDSqanrrtDp8t/soG+YcKtXqrTo1v6vhtQ+/Rt6fJ7A3wJxb5+AacDHmGsO+zM6WG7eQ7kjz7AeeZXFpMbNfmg2g6uRlHQrqLoGdruuqzt7v9+P/kl8xLsTxkPsQVoOUcFiRAKtSucyp3+/H7XYTiUTUu1EqldixcgeTL00qBMfrNYvXRQ3d5/OpFnlCwZSguaGhgUAgoAwxoGrKs9ks4XBYUdQnJiaIRCL4fD5lZNLptApupd5aviPrsdBSYO8n9mJL2vDd6oMMqi5b6qtEPE/uU9bT1NSUorQFAgH1Ts+dO1fVgsdiMfUMxJEQNEXmzufzMTw8PC2aNj2mx1t0rFq1iqVLlyo7NTk5qUrFGhsbqzqhSOeCXC5HJBJR+7I1IJYh+7bsjcez7MSmW4NuoY/LsayggOzz1gBagkoBE6yJVKv9tTLPrL7Ji/Uv8t3O73LeyHkc9VQLNLWmW/nY7o8x3zuftYfX8tvQb7k/ej8A1w9fz4f6P4Tm0Xgy9CS3zr6VsmYec+tJW7mv7T4+uvOjXHvwWsr2ctX1y7/lcpn6yXpOmzgNgwp1XoYVkbeCFdZkgfhMVhXx45F9KzNQjjFaO8o959/DUEOl7/asvlnMPDITu17Z361MgcGmQR6+7GHq+uuY+7W5jC0Yo/G1RhxuR4WWf5bG3o/tpVBfYO9X9qINaWiPaVV+n81mg9lUU7sN6HygU+mv6LrOyb84GWfGyaHTD1H/eD3xk+Jc9tJlLE8tR7tMI/tMlnvS97DrPLM2++StJ3P5c5ejlTROu+M00lvTuFNuRk8aZdamWZQMc56CA0EWf28xPsNH6MIQFw9fTFmvlDOW+kqc8otTmLdiHm3JNnR0JuonuOeUexhqGmLkf42w0LuQGetnqGBaABl5BpIkF1/abreTbE+iR3RqdtRgs9lINaXY88k9JOZWWrHt6d6DcZ7Bmt+soaSVeOGcF+jt7OXArAMMxgbpuq+L0RNHefncl0kFUvzunN9x4q9PxPOYh4mPTfDykpfVsf56zl9Z9fQqDr16CC6rfuf7lvYxd+9cbHYb8dp41d9yzhwJX4IIETx+D6mm6rZcJUeJkcYR0u501e8blzXS+a1Ock05Np5jUu7nvzKfzts6cTY62X7zdg7PPYytbOOsR89i5sBM3G43h548xLNnPMvFj15Ml6sL5wVOenp62L17N9PjnzveMgF3U1MT5557rgoGhRJrNQgighAIBN4Q4ErwGwgE1O+FWivGQpA/qdM8XhDEaoSkptuaGZZrswaggAp6rErSEnBbUWG5DwlwZKOU71hRemvW1kopsxoJa227DGuNuBgO63GtwbbNZmNsbIzR0VG6urqUcbJmdSXAt26EVuq9JDgOHz7M3LlzqzLkUmdeLBSZE5/Dfvar63ToDrrSXYo6LAGMpmkKzRQE4JnVz/D4iscxjjEJJ1ZNwE8gdEmIfDKvqNypVKpKrMRms7F48WKamprUdcrfLtpzEXub9jIWqIhMdIx3YKeC4D5/0fPsOWsPB7oP0PJCC51PdypUWFgQuq4rsRN5zk6nE7/fr+iE0vpE1lg4HFbCMQ27GnB9z8XG926k+2vdNEw2kLAloAVe/+TrxNvNzb3sLbPn/Xso5oss2LIAQNHKt1y6hc4nOnGUHUo505qll7ph65D5kKB5ampKOSvy/sj6lTZ7clxBlz0ej5qH/jP7OXDJAezNdmrfXatU3oU9Mjk5qZwHUTaXv1sz2oZhKFRZfieZ78nJSUUVl/7dk5OTSoStvr6eUqmkWghan7fu1dnx5R0k55rUrZ2BnSz9t6UYRYPeq3qZ+dpM6hx1is4naLu1l3cmk1HK5IZhEIvFGB8fV73BpZ+4oOn5fJ62tjYAGhsbmZiYqKrBnB7TY3q89cby5ctV+c3Y2Bi6rhONRgkGg0oQsVgsMjExofQfRIfjeKacsH4EOMhms6qVk+wV1iBQPi8+iZXtBxXFcSs1XfZYa3247MXW7i/WxL7s01Yk/LXoa3xv1veYdE3yp/Y/vWFe2ovtnG4/HXuLeV0fG/gYGFCTq+G9w+9FQ+O50HPc1nUbcVd10NIV68IoVez28f3CxSeRa7fq0hz/d2s9tpURaf2dHENsDlQE5aygjc1mI+vPcucld1YF2wCxUIxNKzbR19bHDXfdQLlUSRTEa+I8eNWDjDWMMdIyQv1N9Sz77jJsdvM5eTwesouy7P7EbrJNx+jcbWD8zMD9QTeRHREaGhqw2Wz09vYS/3XcrGH/lZk0X/rbpXQ93qWeV6lUwpaxsegPi/A95CNyJMKMwzNYUVxB2WneUz6b54T7T8ChOQjWBbnwpQuxlW1kshnq9tXhiJtz3vZCGyV7xd/TdZ3w/jDRaJTlu5ejhyr+aDabJZFI0Fxqpm2oDS2gkXKn+NUZv6I/2m8+L5fOnvfuQUOj5dEW5bMPrRii1ldLz9IeFv5sYRWDI+lNsvUzW9E9Oiu+uQLvfi/2hB3PkAeWVK+7ww8fpvxsmdhdMca6j/lzGrx20Wv0zO4h1ZIy1dSBRH2CDe/ZQNPmJsr3leGcynGMosHArwboGu1irHGMxEmVwL5usg533o1RNDj1vlPJFXIcWHEAV87FpXdcSqQngs1rQy/orHlgDU67k22nmCh42VMm4Axw5YNXcu8N9zIVmaKjt4MLHryAmtYabC/b8Nq9jNaOcvKDJ+NpNpl69ffWk70my4IXFrDs4DKMGpM1uPTlpTQNN1F/qB6X19xvdu36rwXupsf/3HjLeHXvfve7aW1trerlLAGf0FLT6bQyGBJkyd81TVNOrmGYqp7SdkPaYgAKfZL+1RKsW5FuCdShIiAFlQ1ZlDYF4ZW6ZclKSg9OyWJa25pZa7ZjsZi6V7kWocYKTVm+YzUeYiyt2W75vQR8VvRcDIx8Rwy/1J52dHRUXZcgtfL/YtwkgBbk2hqYd3d3q6AuFAopA26z2bBpNhrWN3DOXefgKDnw5D189s+fZVZiVtUasCYspN7XZrOxdvtaQplQ1Wc9t3hITiTV8/Z4PKr22JpoGBsb48UXX+TJJ5/kqaeeYmpqimKxSFuija8/8XV8aR9aQWPV3atofbCVTDJDwSiw/ert7DxnJyVXiaGFQ2x57xZ6V/Vic9iUiI0kcKRFmMytYRhMTU0Ri8UYHBxUzyORSKDruhL1stvtxONxeA7W/ds6miab8Hg8BAIBQtkQ8/8+H1vh2CusYxrhFxuUQxaMBNl1yS52XLKDx775GAnNfEe8Xi/hcJi6ujrK5TIjIyOkUpWsqyRWpIWWtRWXOC1CkU4mk+r9CgQCOBwOJUQSi8VIZ9MMrBpg1827yPlzpNemGfvjGAVHgVgspmjqso6sybFcLsf4+LhyGIrFIj09PYDZys/n86k1Jf/v9/tpbGxU609Q7XQ6zejoKMPDwzgcDurq6pQq/uTkJBtv2UhyTlLNQeyEGNtv2c7w24bp/1A/L/3oJYZLw0p/IRaLqXdYkgCNjY04nU7i8TjpdJpcLsfY2BiHDh1S72GhUCAcDuPxeIhGo6qvqiRj/H6/ootOj+kxPd5ao6mpiQULFlAsFhkcHGR8fNxkEB1LsKbTaUZGRhgeHqZYLFJXV0dNTY3yL6xJb7EB4nNIYlZsp9XWA1UMNKgk0sVHEWaQ2HkR1BRbLvbYGqCrhKRFcM16Xvl7v7efb837FuPu8X84L7XFWm7tv1V9x+12EyqH+Nf+f+UDox/Ag4dX3a/ylblfYcJdob46dAcf2PUBLjhyAU6bU51fwA/xecQXsSYNjteokd9ZqckyhLko96ZpGnlnHl3TKXqLlB1lSnbT/yt4TX+v7Cnzm2t/Q8FZ4KStJ73hnqdqpkj70hyaeYjfXPcb8nbTFylpJW5/z+2MNVSCv/HTx9n94d0UCgWGh4cpl8t0JjqZ8dwMOMZgtuk2Fu9bzLu638XatWuZN28eZ555JqtXr8bv81P7aC0LvruAFXetYM7Tc9D06lJBu92Ov+An+nqURlsj82PzlfCZlAu6ii4ufOlCLtlwCe68u6q2X+bX7XbjcrlU0lx0dGR+BDTSNI2kLYkW1bCHK76iL+/jvG3nVc2Vf8hPdENU+RvjC8bZ+pmtPPOxZ+g7u4+N391I2Wuu+7w3z6ZfbiLVmSLTkuGlb71EsaVIIB8gMhBBK1WX0urv0Uk6kri/6MaetR+7YfAe9dL5hU7qn6tHKx9LuJRszH1lLqeGTuW09Gmce9u5OAoOnHkn7//V+7mi+QouXX4p7/7bu2nqa0LTNbpf7GbZn5cRImT6Amk/lz54KbP2zOKqr19FfX89Xq9XPYMwYU56+CS69naZ68hbZvMVm+md2cs1P7iG5v5mrr3jWlqKLXg8HkLuEKe8dArn/+18vJlKC7JIIsL7//Z+Vvesxukw5zufz5PNZGntb1XPNJlMsmPHjn/4Xk6P/9nxlkC4GxsbOfPMM1VPS3H8pV+zbAQS4IiIkWEYxONx5ZRLva1VRVw2BxFfk88KEmatmTIMQwVOUi8KKBVuawAqG7ogbdKHTzYvuQ+73a7EIATZdjgcuFwudu3aRXd3N/X19SoottLH5PqnpqZUgCTXIpl0awY7m80qMRYr0mw1sFaRN9kgPB6PcgasNHS5Xzmutf5GkgMy35OTk6oftqCeogjpcrkI+oKs2r0K/3N+uvu78cf8ZPSMmn+5F6g4DIK0xgKx6pYhQK4tBxq4nC6i0Shr165leHhYnVMy82NjY5xwwglqHg4cOEAkEiESiQBw/Q+u5281f6P277VkQiaNr6e2hwMnHcBwVAy67tZ59V9fRfuehvNpJ442ByWjRHAyqOjdkUhEta4SIa5YLMbk5CTlsqkw73A4SKfTVUkNj9tDSAuRcZhUQ7/fz9TUFL57fcwtzeXAew/QuLuRld9dSTASpFAokM6n2bxmM69e8SpokK3J8tCXHmL5V5bTmmqtauniXuyGMcDCaBLUOpfLqTUjQaAgw+Pj44RCIcU4AVSCRTQEnDVOUp9PoXuOCXnYoLCiQP6aPOVHzIBYkHgJOmX9SmJi5cqVvPrqq3g8Htra2pSjKsY5nU6rtS/15bJmpObf7/eTSCTU+zU+Pk5DQwOJRIK6ujpO/OyJvPr9V0l1H0s8vAjaoxr7v74fNCi6imz93VaWf245tqM21e4sGo1iGAaJzgTJl5PUhesIBAJqPTU2NioxJKfTSUtLC7putgISkT2p1QsEzForeQ+nx/SYHm+tIQKI/f39uN1uurq6VKtJeb+lxEkQykwmo2yrtc4XKp0/pMbb2jLQmtSGSjmX2GYJPq1BuYAC8jlJlgsjDiq0c2sZHlSQY2sts/zXbG/mwvELuaPljjdOigFLMkvwl/3YXDaFKGuahlf3otk0ekZ6+Pj5H1csNRlrh9dy+ZHL0Q2dUrlU5T/JNcl9yTVZS3Tk8+LjyP3IPMt/48FxXFkXY3VjuMou/Fk/d55/J8u2LuO5Nc8x9+Bc7Iad2T2z+esFf+XKR65k44qNHJx5kG996Fv/14tCg8GWQTafsJnlLy5Hs2mc8uwpPHzlw2p+Gl9sZN5P52Gz2xS7MTWVIvD9AJFYhPh74py852Su3ns1xVlFBt2DDA4OEgqFWLJkCUeOHMEwDLpe6DJ9T6cNj89TBU5J+1K3201TU5MqkwITaJKSKL/Dj71ox8BQ4EY+nyc9J43vsA87dgVW2d12xuaP0bu8l3g5zsojKxUgNdkwyU+v+iljgTFmHZlF7WO1ROIRyvYyUzXV6ufxOXH2fWQfC36xgImZE7z89ZerKPKxOTE2f2Ezi7+/mCPvOEKhtiLSVvKV2HPTHlb+x0pm3jeTfCBPzxU96E4d95ibpd9bSjgYxpF00PjNRnZ+Yif+CT/n3nIutpCNuQ/NZVPNJg6sPcCSZ5ew5uk14Dffg7reOjz3evBMegj0BMB+LHlRtnPGA2fw9+v+zurnVxNyhlSXFbvdTm48x7k/PJdwOIzT51T+k2gWjS4bZbB1sPKeO8u8vvZ1OvZ28N5fvNd8t22VbgbiT0lCQ4BHW9qGy212XBGhZ/H5JQYSVt30+OePt0TAfdZZZ7Fo0SKFWEkwK4JdEhhLAJvJZAgGK4GObMbWDLAEa2C+WGKspC5UKNHyOfmetO6yBvWywJPJpHL+hW4lgkk9PT3MmzcPqCCIEqRba8Ot1NmTTjpJIeNQUQyFSmCsaRojIyMEAgFVXyvDWgcOKAE0Qd2FamsNtsUgy88SlFnFVI4XWpN5lwy6dV7k+uvq6pThF1q7IPM2m41YLIbNZuOk104yA6d8Vp3DioBaqXZCMXp02aPEArHqRfNzCD0Qoi3apujwknQQYRqpnwsGg7S1tTE0NMSRI0cYGRkhmUzS2tpqKnT/xkN+Vl4lFZz7ncy/bT67P7abfLMFidRgbOEYhdkFUnNS6E6dxp2NdP6qE13X6enuYUZ8BskDSeLxuEpKyPoSpFhqu6214rquK/q5rDen00nbg2047A7mvjiXIkVFl/ZFfcS6Y1VGSw/osAD8u/yqDjAzK8PhDx0mvToNnwRK5vqU9Q+QSCTUepLfSd21sEasZQ2pVEpluL1ZL12f62Lo60NMzpuEPNR8s4b6jfX4an0qKSMaAZlMRrFLxFnau3evCsLdbjeHDh3C6/WSy+WYmJhQa0mCbUl0jY6OKgr9kSNHVMs8ofkXi0XVrzvsDHPK909h5yd2MrBnAD4Ek9+arJq/nC3HdrYze3g2ra2tlEpm27Dcqhybrt1E+xPtuH7pUnR6h8NBMpmkpaVFzamg7ZqmUV9fTzqdVvfe2NhIMpmsqomfHtNjerx1xqxZs5g5cyaNjY1Kt0MSl7FYTAlZSgswYSNZA0cJEIVCLsl+sVkSJFpttwTtIsh4PP1byuQGBgYAs72qJEutDDcr+8sarFtBAivCXC6XMWwGf+z4I3e33G1OggGXjV/GA/Vmv+sLpi7g3/v/vQq9B5Qv0N/fz/7D+1nRtoLXFr+m5jJQCLB6bLX6XEpLsa1xGycNnqTQaKhQxEtaiccWP4amaSztW0rTZBOGYTBQO8CO9h2s3rua7R3bWbNrDYZh8Gr3qyw9uJSp0BT3nHUP4USYbXO24c/6aZpo4nDbYQ63me29xqMmcv/8Sc8D8J/X/Wflof9vtIltZRtnrD+DE185kaJRZMuJW3ji0ieqPlOzs0aBM+FwuEqlvuEHDcyrn8eVfVdiaIZiT9XU1CiE+dRTT2VkZER155AyBGvCpFgsMhWcwrbMRpToG1lleome03vMPtWYyZ6cK8eeeXso1hZ58X0v0rm+kxOeOEEluLeeuZXXrn0NNOill+CLQZb+ZSkjDSNsuGwDY0ETxe+Z2cPtJ91O53c7mbxikh3L34i4Dp42iFNz4ol5/uE85qN5svVZFv9sMfaSnd5LewFof7GdE39xIg6f6bcvvHMhRt5g4IIB5v94PtHtUdx+k9np3eXF9VsXrX2tuG1u3F5TWHjtPWtpyDaw8tmVFO1FVbJot9uZ+cpMU+um1q+eyZ7OPTxy1SNkg1nWv2c9Z//5bCI9EVVeJmxPYdIKgCDsgAV7F4AbHr34UXJeE6xY/eJqOoodFG1F5T+JHxEKhapK7qyJM/ErJNEmn5HYYsOGDW/QaZoe/5zxpg+4a2trOeOMM2hsbKyq5XU6nW8QOxMU1NoSTIySOOTSBiCbzVYpgVvVLI+v0xFjZ3WErcGoFQn3+/1K/MntdqvAUwRR5HjyEkq22CpGIoGqUN2tAiFQ6eUo5+zs7FTXbFUVFfqrbKoi2CSiLNIaBCrZYGtmW/49PlsuCKeVSm6t3wZU4CQblM/nI51O43Q6Vf2siLtJPS5UVNKt9WdWVF/X9TeIxZ22+TQOtB0gEazU1Jxwxwm0rm7FKJnXlUgkVAJFqMT4oHRLicIBM9CdM2cO4XCYTCbD+Pg42WxW9RMXA+bzmUFi2642Qj8O8eIXX6TkMw3+zD/NpNBWYOD8AWWEk11JCo4C9c/Xs/u9uzmUOsSa/1iDo+wgHo8rurv0cJYkzMTEBA6Hg2AwSC6XU+dNpVJV6HQ2myXy5whao8bU1BS1tbXU1taSSqZYfedqyvkyh089jFbUWPzNxYR3hcmGzHrxYmORV256hVhXDLqBAPC+SuLF6XSqhJUE+pJUsWoMWPUF5P2R769Zs4aDBw+y+O7FPPPBZ0j/e5rmjc34OipovqzBTCajKOmC4tTX16t3V+ajpqbGTCoce8+am5vRNE21CYnFYgAKgRb6t9SjJxIJZs6cyYEDB2hsbCQUMrPTrYVWMt/OMPr0KMXRIvwvIA/caLZHOe2XpzH43CCHRw4zMjJiBuuneRh+xzCphhR7r91LY66R7tu7aW1tVe+IlJkMDw+rDgLCsnE6ncyePZt9+/apfeX47gLTY3pMjzf/8Pl8LFq0iObmZvL5PCMjI4oKPjk5qcQfvV6v2rtlf7AyysTPSaVSSttDgmErjdpKi5Yktjj64tfId5LJJAcPHmTnzp2USiXmzZtHd3e3Kr+x2vjjlb2tKLhcq6BmDoeDHy/8MQ+3PFw1FzlbjuuHryfmjPGJo5/AUXJgaBWRVanvHRgY4Pbm21k8uJiP936ce+vu5YHmB9AMjc/t/BwnDJ+Ajo7dYedHy3/EwehBdENnzeAalczY0rmF2nQtT3Q/wYudL4IGm2Zs4ubHb6ZIkd+d/jv66/rZ0baD/vp+Jo1J3Ak3609bz2tzXyPjztDb3KuuveAqMBX+v+g//b8Zlzx2CQ+d95DyAS5+4GKWbFmi7GTNVM0bvuMacJHLmZ1V6uvrVdkSmAzPdVvXYa+vCKAahqEE+ABVjiS6PR6Ph0QiUVUGUPaW2fm5nej1OkufXEpgMMBIaIQ9zXuY3TebDW/fwKE1h7A/bWf++vloNo1HbnyEvro+HAUH6YY0u96+C1vAxprH1/D6Ra+z5bwtVQmH59c8zx7bHvINeRKtiap77D+hn4lPTtD2TNt/OXfOESfzHpiHe9LN9vdsV7/3Tng58Scn4t1vovKL7liEq+QiG8my+PeLsRfsaDZNsVizzVnKrjLF2mJVuYHT6WTm6zNN39rpUGUbmqax6vlVuDwu9e5IT22JOcQPOzT7EOsvXE82aNbWj7SM8Pg7Hue8X53HzOJM8vm8+r6Uk8o5UqmUAnzW9K6haX0Tv7j4Fxg2g9dOfI3ZPbOJJqKqfNXr9SrBV2tZpyDlArIJSAOV8gG55t27d08n8N8k400fcM+ZM4dLL720SqTLGlwKxVYUlMXAWetI5GcJxiVwEGp4Pp9XRkrOIcibDAlCrMrkQhMRtFoo72L4JKvsdDoVRVkMplyfvCzWTUFqtCWTJQGz3LtkMOV4NpuNo0eP0t7ergL14+lTMl+SYZPrlPZU1vuUeZPvWVXgrTR0a82ZNeMu/1l/p+s6R48eJRAIEA6HFZU+GAwSi8VwOBzEUjG2n7qduQfm0n60XT0rr9eramWhIm4n89Y22sZNd97Ed67/DrpLZ+2ja8n9MkdtVy3Dw8Pq2TudTrxeLz6fD1fQRempElMLp9i7cy8LdplCYzU1Nfh8PmpraxkaGmJsbEwJ1ViDdZvNRltfG2d//GxGO0cZnzdOsbbI4JrB6oy3Dfov6Gdo3RDFUJF+o5+J2ybAgDkPzGHeU/PI5/KKYWFV2ZcEhzg31vZskUhElUuUSqWqpEU+nycUCpEfzLPu3nXoQZ15989D26HhC/hwuVzE03Ge/c6zpFqOUag14F2YtPKPVmrdDJvB1Hum8PR48D/tJxgIAqh+31bE/XjhvIaGBl5++WVqamoIDgRZ8bkVbHp0E45mB+FwWBkQoarL+ymK/5JYGR4eZsGCBezbt0+JkMk5m5ubVa/uUqmkkhfitObzedVTMxaL0d7erjL60WiUaDTK1NQU6XSampoa9H06+tFjxikOfAq0kMY1qWtom2gj9C5TK2BoaIhXjrzCru/uotBwTMPBaTD6rlH8BT8tL7So5IhhGNg8NkbvGqX7O92UR8uqdVwsFmPfvn0qiSD7y/SYHtPjrTXC4TCtra1MTk6qvVDaN4VCIZqbm6v8DfEbrAGu+CbS3UICbSsKLXtjoVBQYqw+nw/gDeKXYOpt7N27V9UGp9Nptm/fTiaToauri87Oziq2n+zfx5/bmsg3NIP1netpcjZxc/JmntKfIms3AxCP4eGjIx+ltlhLmTJ+3U/ZqCDS0vbwUO8hHmh5gMdPfJwXF7/Indvu5Kaem0jb0px++HSWTC5BN3QKFPjmqm/yWoOJpP7yxF8S2Bigs6+THdEd/Hr1r7HrdtKutLK9R+qO8PXLv46BQcJvBn4HWw8C8OSpT6LpGkVPkd0z/+8pN2u6hmZo6HYdDLOfsqEZXP3g1SzcuxBv2suR1iOseWkNgXigai5n7pvJpT++lAdvehCApV9eSvS1KPYaO5OTk0ozSOzH7NmzVbJYWHnSIcNut5PTcxRKBRVoFwoFxsfH8fq8HFl4hGRLkoUvLuTJLz9JvMMUo/vPy/+TS39wKQ+94yHyzjzPnvws6XAaw27w5JlPcmj7IabOnGK8a7yaJefU2XXBLo6cdIR0JE3ZWY2cOjIO5v5sLmNrx0jOTGLYK4xLW8nGgkcXUL+pnhpvDa9+6FU0XUN3mnM46++zmPuXudhKNtofaEe36WhZjSNnHWHZV5cRToXJ6ab/5cq7mPuXubj9bux5OzrHWvO6bWy+aTNDa4fADns/uJeQEaL15VY1/yKYLMlv8futTFIJZCXoleRSKpUiHoqTDCWr7nu8eZwHb36Qm356Ex7No7rRiM8p/om8WwL8hXaF4GLzGKMto/zufb/jpp/dRESLKB8sEAhUAYuASh5IMJ9Op80yhGPtB0WrScCq6fHmGG/qgNvlcnHRRRcRDJpOfiaTUeiPBFtC4ZB/ARVAWoMUWXxgGhVZxFbEVAIcySjJ9yUgcLlcyjAKQmUVJ5MX1BoYQXXvbQkiJdgVSogcWzYDKx07m80qqphkmq0Zb13XGRsbo6WlRc2PtY5cDJ2gldY6J0Hh5ToBlViQebPSksCkJlnr3SXxYK2NttLPwWQqzJo1C6fTWVV7L+eOZWPsOnMXPat6eGrVU3z0zx+l5UiLooFbld+FQmO321XCoDZZS8M5DRjvMrDdbSObyHLffffh8XhobW1VCrG5XI5SYwnjpwacAIZm8PCyh/Hg4Zwd5+DQzA3upZdeYubMmXR1ddHT02OKV1j6pUubrHApjGOjg7ZX2nA5XTwXeY6xE8cqRkoHbb9GccEx5RMNMrWmIubW925Fz+l0Pdul0A5ZH7KOw+GwqtEXUUBpJyMZTKkHF2q6z+dTiRVb2sbZPz2bfC5P1p9VAn+U4MQvncgrX3+FTFPGFE95AviY+efR0VHShTS2G20kP2kal5k3zaR+Vz26rtPR0aGeTTKZZGpqStUp5/OmMvzIyIjKvHq9Xtq0NjZrm6v0EaLRKIVCQXUFEMp1IpEgHA7j8/mYmpoikUjQ1tZGKpWivr5esTfkuW7dupVFixap9VlbW6tomdFoFE3TFDU+Ho8rx1LE4hwOBwMDA2zfvr3aQKVg9ldn03h+I4bDUDXnLpeLltYW1r24jt+e81sSgQQ23caCVxfQ9nAbU/oUIyMj5PN5/J1++r/bT3pZmmdve5bOGzsJhUKEQiFFKY/FYrS2tTLVNoUz9kaneXpMj+nx5h6RSITW1lYVRCcSCUqlEqFQiIaGBpWAFxssbCafz6fAAwm2rewu8S3Ensv+L34FVIRTrUG5JEHlM1JCY1XvtnaCsLYjlWQoVBh/ErCUKfNU21PcNvc2AL5z+Dv8ae+fuGn2TeiGzo/3/5iaZA3FcpF0Ok2sHGNiYoJEIqFq0fOlPC8vfJnnz33eFA1zjPP+Ze/na5u/xr9u/VeTMeW2E7PH+NH8H6lgGyDjyvDD7h+yYvsKnnn3M/8lpTvuj//D35dc//uEZiAVoGwvE0wEmYpM4Uv50Ms6/pyf8aZxlry+hKwvy57Fe/Bn/Jz33HnYDBuL9yxGL+ss3LWQxXvN/y/pJXSjumTA8YKDBUML0Fwavud9TOXM1pOBQIBQKMRY0xilTSVqampoaWlRifV4PE4ikSASieD1eom74tx15l3UP1lPywstKuHt9XoZXTbKxk+Y7aR23FBN4U570/zpc39Sc1d0V3pLl9wlBt83SC6Ye8PcamWNuRvnsuZPa9jwtg3sO3uf0rLxD/tZ87k1OCec1D5SiyPoYO/b9lJ2l3GlXJx0x0l0Huok58/h3eJl7IUxAgcDDJ00RHAoyKq7VqHZNXRNx67bWfDwAsrlMvV/qcfv8+PyuZQfr3zVrEHZqAj17rp8F33rKr3Gy74yL33iJS78xIWERkPq+9KZRfxKeQ+hooJvZXlav7Ps9WUkQ0meX/M8Jae5lgITAS7+5cXY8jbKlCsaNk4n0WhU6RyJ9o2U9N1/xf1VugXJYJJfffBXfPh7H1bHECBPknii/5DL5VRSRjSlXC6XKlM0DIMtW7YwNfV/n60xPf7fHW/qgLu2tpZ3vvOdyhgd3+Tebrer2k4xSm63m2w2q5BnMRIi/iTCTFb0V6jp1sBT0FnpXyzBjCBQsqAleJYXVBB2yU6LkVS0nmPOvJXCJS2WrH01rcIfElzKiy+BhLWWa9GiReoa5bNW9FnOZUXPJAkgmTRrXbs1CSGOgRhvCdKt2UGpIZFEgNyPoOq5XI5EIqHmQu7N4XBgYND79l56ru5R1/brK37NNQ9ew8LehYrWI/MiInRyrRLEuwfc2L5ro9RSUrWw8XicWCxGQ0MDDQ0NRKNR7MvsGC1GxZho8EDsAdxb3Myvn09zczOnnHJKFTOgUCioTVPmQ4IlwzDQ8zqGx2De5+dR+myJcncZ3aFTs7+GzMczOO90MrJ4pHqBa7D9w9vR3Brtf2tX6y0SiTA+Pk6iM4Hu0nHGnSpxoZDnY/9ms1n6+/tVIqC21my5lUqlVB9rqfOanJxUSuJut5twOYz3l15e+eArxDbGcL7Pic1rU0F5+oY0fLNyuX0/6iN6axTvI16lGZDP57EtsuEr+fAcMoPlVCpFoVAglUqp80sZh8yfGAqpcxKBMWtpgdvtVs9O3j2A4eFhtZZyuRx+v5+FCxdSLBaJx+NMTU0RDodVYknq2nVdZ3JyUjmsorUQj8epqakhlUopIUT1iDSNYCCo3g3ZbySJEOgNcOX6K7nv7PtYcmAJ79n/HgLvMftwj46OsuHABh4+/2H05ce6J3QUOPqdozR8voFYf4xgMEhtbS2BQIDYGTEO3HyA0CdD0MP0mB7T4y0ybDYbnZ2d1NbWKnQymUzS3NxMc3MzDodDBbTW5L7YNGHkSHeL40vaxK4Wi0VFNXe5XIRCoaoOKNY6bECV2ixcuBC/309fXx8AXV1ddHR0qISl2OTjWXVyTCvL6uHWh/n5gp8r+/nZzs/yxQNf5P1Pv590Lk1/bz+7EruqtF90XVe6Hz6fj/Vz1vP8Bc9X2eC4I87u0G6ack1qPo4EjtDr7q0K/Lonujnv/vPYc+ae/8tnctLBkyhR4rXZr1X9vma8BmfeyVjrGM6ik5XbV9Lf3E8gHeDQzEO4c24ueuQiMt4M3fu6eX3R69Ttq6NUKDEnNofnL3ietD/NnsXm+dP+NC+ufJErHrwCvVxBIA29Mo/yHA3DUEJY9a+Y5VLOsFP5jIFAgMGTBtl24zba/6Odzr5OwuEwNpuNdDqt9ImCwSAJV4L71t7Hzs6d8H5YVV5F1wtm/fXAmgE23Ljhv0xGNG9pZmzeGCXvG5MPwYEgJ//+ZEa7Rtl69VYMu0HToSaS0SRtG9tYde8qsMOqP63CKBrsu3gfkb4IK3++kpp0Da5ak6a97JFl2LCRi+ao7amlfUM7RYoUnAW2XLOFnjN64AxY/LvFzH9svtk73VbxU8XmOx2V0kp5LwQplrJFQaDrjtQRnAySjFYQ6KYdTXgLXhVgC5sUUP6sgB3i50KlRZysXyuifMGmC3A5XDy+xmxHu/DRhYSPhin4C4plIHpSdXV1jI+PMzw8zOLFiwkEAvT39+PxeLjsz5fxvVu+Vwm6Nch78wwsHqBtZ5vS77G+/8LaLRaLeDwexQiUe7IyBQ8cOPAGn2Z6/PPGmzrgvvbaa5W4iCx2CfQEwZPgwKqcCaggNZvNqgDQGhBaqbpScyEBshgsQbatwhKAekHlJTxeaEQo5fI7q5iBBAHiwFuDTwmS5Wer2riVTi4ZZ3nBrBRuQT6hglTLJiHDmoiwGnwJuq31W/L34wXbjq8rs9ZZyznkWFILHI1Gyefzil1QLpcrvUlz1UtRMzScRWfVfcucWvufG4ZBMplUSKrcfzabpaamhkQiQaFgtqAKBoOmAvgTDmYMz+DAdw6gB3Xs6+04Pubg2eSzHO44zOLFi+nu7qahoYF8TZ7+i/uZ1CcJZANEY1H1zOV6pB6vXC7jwoXz40788/zMmjML514nu8d2s/xXy3n9xtcZXjj8hnVuS9tUfY9Qy/MdeXZ+dCc+zcfJ3z+ZxJEEHo8HTdMUVUkCR1nz0uc5kUiogFWeT6FQIBgMouu6qq8H6J7qhl/B6MZRmGOuu1gsZiYUImn66a8895LOlg1bCO0M0dTUZNb/zXKw65O7MAyD5d9ejm23jebmZgzDYGhoSL1b5XKZ0dFRZYxEAC2bzapWd/IOBwIBAoGAUveWtSbHyWZN6qIkXERATdacvAfW916MrWGYfdIlkSbMkkKhUCX+JkNq7CWRJnuOqKmXSiXmHZjHlbkrmTsyF92nK8bKwoULCXeFmVw2yQY2VI5ZMEhPpckMZ5SewsS5Ewx9YohSsMTkNyZNOvvf3rBUpsf0mB5vwuFwOFi+fLmyCWC2v5Q9WYJPQaKtNlMABKGIWwNxK9XcmvCXRLig1uLPiK8k55BAoq7O7J4g9eWS5LMieBJcWDVa5FyyL3q9Xu6Zd081zVjT+U7Hdzhr91ksfHkhhzOHcblchMNhIpHIGxiET694mvUnrq86hkN38Kk9n2LF0AqKuplY1XWdlsEWLum9hDsvupN4XZxZU7P4wKYP0FLbQveL3WQmMry07iUA3v7627l72d2gwbrd67jilSvIF/PYM3ZeWfIKZz9/NhuXbOT020+n0d/IfVfdx7oN61i2YxnDjcP4s36OtBzBlXcxa/8sZUuWrV/G5OSkKRQWdLD2ubWsP3t91fPXShpaoRJYW/+FCntQavvFhoDpv4nQ7NhZY+z8wE6KNUWOfuEoCx5aQGmkxNTUFOPj48TjcVwuFxPxCZ55zzPsnLnTPIENXr/hdQyPQdcTXRhJQ7X8krH2T2vJ1ecYqhti1R9WMThrkJc/ZqqBdzzSQUt/C9uv2s7aP6ylpbeFpgNNOEtODp58kJN/fzKpmhTRnVGcLqfy7Vbct4JIX4T6iXpqBmsoOUpqbRcKBebeP1clbJLlJJqmseWTW+g7rU9d165378IRdDDv/nnqd7KWZf1JkluSDtLRRd4T8Xk793fiu9PHo+9/lLzP9N87n+vEn/Hj9rirVP6t7FQBqaxMEQnABQQTYEx8jYKropR+cM1Bmrc005hqrIoVisUizz77bNU7Lr52OBzG6XZy1tNn8eTZT6pjGWWDcrICBkrnH0kWyH4gyTBpsyvvLpjxj1z/9HjzjDdtwK1pGh/84AfV4hSESqik1kyPUGt1XVdKxLLZibMsVF0JWCWLJQ45oAyUINdWxBdQwaOmaYpSLAiuleZrRcjl5ZMNVs4rNRnWANf6wsj1C5IrvcFlbqAS+Fp/lmNb0XJrAG3NDlrrsR9//HG6urqUoreVFi4ortyLbG7WwF1+tiY+hJEAKMq/bJaCxvv9fuw2OzMemkGpWGLnDTuxGTY+dt/HCPWF0NEJBoOqHleuQ2g2wmSYik2RnZGF94L2W00F9IVCgVAoxLJlyxgfH6evrw+fz8dMz0wavtDA9pu2031bNzlHDnudncOHD9PT00N9fT3t3e1s+tUmEq4EiVKCeDHOpV++lPxoXm2guq4TCASqjKxt0ob2qkakFGHTpk1kMhk4DKt/tJpSqMTj//G4mVkuw5LvLaF7Szc5Lac2V3ejm61f3kq+MU+aNE//+9Os+JcVpCfSqvZH0zRVj97f319Vl19fX68EU5xOp1LstwrVSSu6ZDJJYChAiRLBGUEMw6Cpqck0QNt03F93c/CzZu2bdq6G9pJGwjD7efeM9KDdqZFrNVU2X/nWK5z+sdMJ2U0DMDg4qJIdhVKB4ZFhhoeHmTlzZlVdlLzTU1NTai4nJyfJ5XJmJj+RULXZpVKJ1tZWlUCJxWJKYKixsZF0Ok0gEFDvljAjxMCKcyOtu0RRvVAoKMr58UOSOsFgUKHzLpdLJQ0Mw6C7txuv36v2DckyR4wI171yHUmSbJu5jaapJj68+cPYzrORPDnJyMgI25u2s+1ft6EHjwX7LcAvMFu1vfi/2Sinx/SYHv/04XK5OO200wgEAsrWi308PpEulF9BtCXpJvbZGoiLTZXvOp1OQqGQOp4kaK3Bufgx8rPs/W63m8bGRvUZYawdf3wrtVxsvoALt829jSnXcRRVA4LJIAt6FhAMBgkEAsrGSM24nOOFpS/w95V/p+goVh3i65u/zpKJJar1F5iJ9YmJCQL9AW4u3Mzvrv0dX3r1S3hiHnqO9DA8PMwV5SuoqauhMdXImj1rWHR4ERgQSodwZB3YdTtXvXwVZ7x+BkavQevTrczSZuEtevngPR/EN2UKxzaNNKHrOvOn5pv2wlYBINLptLIZDocDe8HOOU+eg+7S2b5sO6FkiOvvvZ5gIggaVckFxRawaexYtgM9pqPt11QdL8DU1BROp5P46ji7b9xNMWjOTaGuwNNvf5rBLw4SfL1i63K5HPG740zNmKp6Bt6kl84dnTgcDlp3tHLmN87kqS89BRqs+tUq5m+fT8leYrZtNq64i+7Xu3H+2En/sn6W3bMMV85F875mfKM+yi5zDS3asIiZr8/EM+EhdDRkJpltJZUod2tuZr8820zOGKWq9SN6BeKfC/Cz6IVF9J/Sr+q7nTknXRu7lK9oTay7XC7VrUX8GeksAhWFequGTHR3FHvBDiZDnNff9ToNfQ00pZr+YV2zMOWOB+SAqoSZ/JzNZnns1MfYeMJGhUxPdE3w1Kee4p3feafSlbKyUI4cOcKBAwdoaWmhtrZWgRLlYpkTnj2BfD7PCxe8gF23c90vr2NmdibeWq96J8PhsHonxRe2itQKaGHtdNPb20t/fwUwmR7//PGmDbhvvPFGIpGIQpKsdcJWdWShqoqxkvonMUgSnFtpUkJRFqffarzkBbbWbefzeV5++WUGBga49NJLqxBWycBZRduEKi1B3/E13rLxSCBrpWHLtUuWWuq3JciyUsesAbogdXIuq4Kp3J81uLfOhcvl4pxzzlFGESq9rq2IuXxWPmc17NY+5FYqmvU8Pp9PPTdrr0AJxpc+uxRn2Mnp2dOpHazF7rZXOR66rqv1YM1IAuRm5Bj6xhC6Q8db9uL4sgMtrak6aKfTSVNTEw0NDUxMTDA2NkbrwVbOu+U8UqTI1GXUmgCYcE+w51t7KNWWKrVjRoa/feNvnP7Z03GNuNS9SjZTUORIJKJE9ObPn8+BAwdMlH7YAQNw4acvZP2/rif6hyj6fTq93l503Wz7lW/Mc98t95GPVNqNJVuS7P/3/Zx060lq3U1NTVFfX68CQZkfl8ulkOR0Ok1tba1Sw7YiI4J8S+9IazJJssnFYpGuF7uw/cSGt89LUAtSXlVmaGiI8fFx9Pt1Mi0ZdZ3ZpizP3PQMK760gmg0itfrZWRkhJwjx9OXP824bxxHj0OVIUgyKZFIEIvFlFMniTPDMAiHw4rOn06n8Xq99Pf34/V61bW7XC5qa2uVo2sVQJQ1m0gkTLG8Y7VOsVgMr9dLXV2dah0Wj7+x5k/2lEwmo1Bw0Q4Q0RprMqi+vl7tS5K4i9qj3PzMzfz4jB/zrgffhZEzCNeFaWpqYsaMGZxkP4knDz3Jg8EHKTqLkAK+Bmz8P942p8f0mB7/hCGK31Z7bk26WxPtVm0YoZBbS8CEeSMIswR9gmaJ7bcy36w2F97Yl1r2ewnMxf+ASscV+Z61/MzaAjUXyHHUf5SyVi3C1BRr4osPfJFMKUO6lK7yV2QI40rEv6yjLldHS6qlau6y2ayi4XZ0dDCzZSZrXlxDIVNgImV28Fi2bBl2u51rNl9j3mtZpznerO67rJnz4il48I346BvoI+gJ4moxQZbApJl0lX7T1k4xcu2iNRIOh6tYi96ilysevgLdrXPZ3y/DU6joBlmfPwA22DV/Fw9d/hAApwyeQsPWBlNXJZslm80SiATIz8qrYFtGxpdhR3EHsydn43P5VKu50I9CrP/eelKNpgZJcCTIZV++DEfRQVkzA7DO4U70b+ukW9PMfG4mBU9BtT/NFk3NmKYXm2h6qYlywRQcC4wHsDlsVUGmJ+tRtlQS1dJbXkACYXvpuq7+JsBIPp8nHA6rft41e2o4/wfn8+QHnsRVcnH51y/HHXdT9lQYHD6fD6/Xq5iN8v6ILynvgzBea2pqlO+/4eYNZMIVvyQXyfHQ1x7iyvdeiStlsj3EzxFwyuv1VoFXUElmiT9kLYs8b+N57J2/l9G6UfX5C/58AV68hGvCpi93LNmv6zoNDQ10dnaqGEHXdVWLnxxPsuTxJeSdeVYcXGEmMnS3SlTIPUppq/hp0u1EmLyJREL9rVQqMTQ0NB1wv8nGmzLgDofDXHPNNUqNWDZroErMwFpLLcamVCqpns5S+ykvkTjH8n3JoklgKItaarrl3MVikWXLlrFs2bIq8RB5maybrJxHhpWyIp8XVM2KnlsNo5U2YqWNSyAhhlPOJ/coG4b1GMIEABQ6bb0eqATkcq3WzPfxFHUrNV6ex86dO1m0aNEbDLwERHJOqxMh1yq13oVCAQqwbsM6ZnfPJmPLVAm+CC1Y5l9EttxuNz1tPdx+8e3oLvOaDl5/kJqJGpY+vJT4ZJxQKKSuv6WlBYfDwbZt28jn83R2dqrNPBQKEQwGzXYl5w+gN+jVNVAa5GpzvPreV1n6xaWKzWCz2ch2ZSmMFXDGnUxNTSkjEQwGaWpqIplMVkT2+jUWXbOIRCKBbjfXTiqVolwuM3X5FEVfseq8LRtaOPVnp+IL+dSzSyQSTIQmyIay6JNmgCp9PAUhlvUuRkJo0ZqmKbEcec6iki2bupRsuJ1u5j8z32zBVZNSz7etrQ3H1x3ssO9gYu0EAIGnAxQuK/BK/pVKP1ifxuCNg4yvG4d1UG+rx/O0R7EPBO3xeDxMTU0pAygJGqlVsrIqpDZKelQKgiOMA7kvqRcXI63rulLwlCx5Op0mmUxSoMDgkkHYZ3neteBZ5sGT8ah+45KUyWQy6l0XZ0Tm1jAM/H6/osTLs33v395rOpseVJ2mvA9nbDqDbCHL40sfp/yVMvzkf7tNTo/pMT3eJOOUU05R9l3sqtV+AVX2Wlg58nkr20z+E18nn8+rMiJBWeV41u9aE9ziJ8gxodJpReyxtexLaL+yX1oRWtywvXE7ulvnqPdo1X13D3Vz49M3Yi/ZVfAlrEK5VzCDl+bmZt4VexfJzUn+uuyvlJwlZqRm8Ok9n6ap3ESRogIpkskkw8PDBAIBZbPFZojwHFRK1wQZlzmxMs5SqRSDg4PqWMezDmTuxD7L73O5nNL3kDI4mRux+2//69vN72jVZYXyDHVdZ9eSXTxw1QPKpr/4uRdZ+d2VRJ6JkE6nzfagUYOxU8f+4dqqu7GO1fbVONIOdY/5fJ4zv3YmL338JTRDY/UPVkMWikZRoaCGYdC8uxn7Pju6w7zfgYEBlUAWOwWgOaoDOJlbAX+sPrfMk+gUidCfPG+rUGqxWOlpLboqhUIB38s+lhSWUD9VjzamkSll1NqUtSoBsXWN/leMNQn0C4UCDbsbGF48TNldSQzN2DGD+rp6bNgUUCblllLOZg24rXpIx+vn2Gw2jrQdIefOVT2nw92H6ZzoVPdtLZ3TNBP8yWaz+Hw+hdqnUinlh6x+eDVHTjjCM1c/w3UPX8ecoTmqPZzsAVNTU9hsNmKxmPLzbDYb4+PjVc+nXC7/QwBhevxzx5sy4D7jjDOYOXOmQuYkOyuLXQyZBKzSJ9nqWFszuaKaLAiaLEorPdpK/5ENAypG07qJCY1cEHerarbUVsjGeDzSfXx7K2tGTa5dNjepzbIaY3HQ5XvWIFvOA7whmAdUpk5+Zz22GAn5nWym1sDcauiholYej8er7kXmy/p5eZZyHXI+adtkrXOTZy1q4HLPolYvbd5sNhuZTIZMOaNUQGXMWTIH/7N+9GKlh6jNZqOvrw+v18vs2bOJRqOMjIwoNe3u7m5V49uxvoM6bx2vfuzVqtYWjpSDtofaiMfjKmGQn5Hn4I0HsSfsLPj6AjWnYtRqamqUMqWIxgijQTZUmY/mPzWTG88x/JVh0CD6tygLbl9AMptEL+lq07Y12thy4xYyZFjwrQWkh9NEImYrifHxcRwOh8qGW9ejBLPWZyrGIZlMUltbq2qQJZi01vaJOJ68K7WfrkX7iobdYafzh51kZmeUIm25XCb3tRyJd1eo2uNfGifcEqbt2TYaGxuVguaRjiMYTQYt8RZlVK3GPBAIoGkVpfFyuayo9PX19Wars3hcqXpKH3MrYiQMi1QqRV1dXVUCaOe1O0kuSkIZuB9wAj+HwgkF4t+J07enz6zhs+wXsrdIGz95z3Td7HRgRcZlrQeDQRWAS8/3YDCI0+nkom0XYfQYPPq9R//Brjg9psf0eLOOFStWVCmLH48YS9AiQY10pRCbCxU7bfUJJGEubD9x3uWz1gARKtRaQCGy4ttYy8Os/oFco/wnQY5855dzf8mLDS/y2SOf5fO9n+eLnV8k5Ugxf3g+73vlfYSyIUYnRxUDTa5bjltbW0traytut5uRkRFO2nISEWeEh+c+zMd3fZxZk7MoaSWFkEqwbbPZmDVrlmrrKPuqCFEJWig+ity7HEcC04mJCUXLlUS4lS1oBRjEfxLBVZlza4Au37PaRitDQT6jmIXOamEyA4O8La+SrsFgkDrqCP8gzNYbtxJfVQmUWre1ctIfTqI8WaZQLqhj5/N5XMMuVv1yFW6Xm2AmSCqbUkw1sfvC2BP/UFqcylqR0kYrO1LmxBrAWYEt8WOEqSm+tJVZJkrZMqdWUV7R9Jm326zZdrqcVWCK+FVW1p48X0nky/qUtW5lli56chF7L9lL1p1V87jqgVXoJR27s8IsFZ9CxH+FdaKQ/WOBvFUDSVr8lmwldK3a53ztwtc4e8vZSgQWKj6++FgCRllL/dxuNz6fj11Ld7H+kvVkfVnuOf8e1vx+De7n3ExMTKigW/yq46n0Ho+HVMp8/oFAgHg8rp7z9HjzjDddwO33+1m3bh0NDQ1vMB6CbMkmIAtXglp5+Ts6Oqpo1LIRWPtTy++F2ip0cglIxKBZ+1PKRicviWxSPp9PvUiySUktqQgsCO1D6pat2TQrknx8Dbe1rkWuQ+7dmukbHh4mn8/T0dFRRUWTDG9dXV1VUCABjWzEVtTfmjG3qprLdcr1yIa3bNkytTnKJm5F/uQ4gqpaFec1TaOtrY1EIqGovnX1dWBUEgRC2Zb+pVARyioUCnQe7uRdd7yLX3/g1+g2nXUb13HySycz1TLFlpEtAOQKOQ5+8yDdn+tGT+uKxu7xeIhGowohjsdNRNzj8dDyQgtn2M/g6Y88bWanDai/vp7iviJFTEeo4Cuw75v7yM8wKeDbv7Odmgtr0AxzXQwNDdHb28ucOXOUwRPEWZ6jXIcYKM+dHuiHyNUROn7SQTaXpegwVbULhQKG3eDFL79IvNM0zFu+toV1n1oHQCKRUEZW1qCmaSozKvVFVqMsiuHyzMTw+P1+dU5BYxwOB5FIhGg0Sm9vL522Tmx3muslMZmgpqaGUChELpcjl8sx9twYw/8yrLL7WkEjsjGi7t/lcnE0eJTNH92MDRudv+rEfrBSqhGJRPD5fKpHtbxPUvKhaZpS4ZT3UtrxiJMgrdOcTqe6D+lk4Ha7eeVdr7Bn3R6wAz/FFCz7BHARFLUiB//jIHNumoMzVpkzv99PKpVSmWu32004HFaMGYfDQSaTUYJ6cs1SflEulxkfHyccDqs9xWaz0fxM8//hjjk9psf0+GeOlpYWZsyYoX62Bl1AVYIym80qH8bKMLLaWWtZmQQ71tpS8QWEmi4BuOzhYletassyZE+U/VOOJUGC7F2apmGz2/hB9w94uPlhdE3nGzO+wZd2fYlPrv8kv139Wz6w4QPUpmsZnTJptcFgsIoBuK9jH96ZXh5rfIzPDH2Gw3sOMzAwQH19PZePXc6a3BoaY41VieBUKsXIyAiTk5PMnj2b2tpaZSPFlra2thIKhZT/YxUOterwSMlULBYjEokQCATUZ60BNxqUSxXNGgNDJUvr6uqUb/P86c+zYt8KolPRqgD7eEq/FbzRNI1lW5dRSBVY/05TaG3Vd1bR8moLRX+RWCxGOBw2fcWhHDO+OoMDtx6g++5uBq4dYMUvVuCMOSkYhaogDs2sJ/aPmfRtu8NexbiTQFGGlGXZ7XbC4bD6nfggsu6sJZzHswUkoWKlyxuGoUq15PdWhiiglLTFh3c6nfTN7OP5y5/nrL+eRctQi2qTdXyyXQAw6xoW4VJJpktCS96b19/9OvlgpSQP4MkPPMnV371aHXdiYoL9+/erbgLiq1rnQnxZYQTKu+j1eqmpqcF2qg38VLER77n6Ht72m7cprYVUKqWS8takh/jaPp/JWjy88DBPXvQkWZ+ZJJiMTLL+HeuJPhFFO6CRTCbV+pZrsv4rbFKhn0uZ7fR4cw3tv/tQNE37H3l6K1eu5I9//CNdXV3KuFjpy/IyWzNmsilI8CnUT8m2ivGRDUZUjiVzKVkuEfeQc8p/snnk83n1UlsDVSsaLo62IJiFQqGq5sJa0yUvYV9fH01NTWrTslLFrEi5BNKySUrgWi6XFY1ZlKjl+8PDwxiGoQJuMQyyYQlyqYzNsePKRmbNtss9ijGR48m8ys9WSpUEF7I57Ny5k0AgoJwIaZ906NAhs8arK8L977uf6x6/jsZEo6I4yTxbaXGCEsj6GK4b5pXFr3DO4+fgwKGov4PZQR543wNMzp8k/FqYzs92EigFVH1uuVwmFApx+PBhJiYm8Pv91NXV0dHRARp4ajxsPm8zgecCaJs0EvGEmXhohJHHRig3WGraDNByGs3PNnPiHSfy0pMvkc1mWbNmjdIDcDqdjI+PV4mCiCCY9Po+0n+EUCSE1+FVySBBevd/Zz8Tp0xUNnoDfBM+zv/y+ZQGTKMZDAax++2k9TTGpKEcKjGQhmHWyYm4x9TUlEJRZG5TqRS1tbXqufv9fpVwmZiYUDQ7wzAIhUIcPXpUPRtBwh1OB7nTcjxz4zMUk0UWXr+QLk8Xra2tJkrt7ufRbz+K7j72jucdXPipC2kttQKmUyBiaocPH6a5uZlkMqnqxKTlnPQC7+jowG43W7YFg0FF1w8GgyprL0qjNpuNA+cdYOu1Wym5LSjEUaABqAgOox3UcCx10N7UTnNzMw0NDYp6X1NTo2rOWltb1VwdOXLEpOIfQ7QlcSDXLrXesvcYhsELL7zAs88++/9kC/3/q2EYxn/R2GZ6TI9/PP6nfBUZl112GZ/73OdU3bKVwi02sVgsMjU1pXwP+b2VmQcVJFI+Y02MSiBkDayP3a/6V/wHq1K5BCZWDZzj6dfWgF/Q6bvm3cWdM++s6hMcSUX4zL2foYYaCskCY2Nj2Gw2M/iz1O7uqd3Dz676GWimink4Feb6H19Pi72FefPm4ff7VbAs85DL5ejv72dwcJD29nZmzZql5mZqaorh4WGi0ahCvOVaBT0U31BGMpmkp6eHaDRKbW0tyUASf9KPoRvqngcaBlh/1nqu/eu1uDIuJhon+MtZf+G8n55Hnb2OhoYGMqUMLyx/gafOfgpHycEnfvMJIrEIgAIXrKi5+DpW9LvvaB9bl2/FU/DQurGVYsFMxErby1KpxNjYGIZh4Al7CHvD2L12bMWKTo/4Pxl/hg3/sYHzf3g+3gmvum8JOgXM0PWKiLAkmIWlJmtLgCfxe6V7h6wXaeVm7cxj1UoR9Ff8PPFxBc2VZLTVJ5/qnOLeT96L7tCxlWy868fvInQ4pMoFrEKq0gIvHA5XsfGEaWYFq2QdbfyXjRw69ZCZQD82nEednPYvpzE5NsnExIR6bvJOyLsqoJ747tbnKMi8CLYVnAV6Hu6h1HqsF3cywA233kC4HFbK4RIPOJ1OkslkFTXeMAwF3KVyKZ455xm2n7Ed3aGjZTS0T2s4f+vErlXKKqXsUfYa8WETiQT19fWKGRqLxdi/f///s41tevwfjf+Or/KmQrgdDgerVq2is7OzKmizCiZYBdOsddHyd8mGSeZIEDFAqfhZkUUrrVoCXnkZ5D8RcjAMQx1DajCECiPHFYqOKJNbM4RyzcfTwPv7+4lGo1WbiDVTChVamNU4ysZkt9sV3dqKVGuaRlNTk9oE5T6tSRarCJrMubWG3DrH1oDaipYLzc16bHlGggDKHEkAZzVKzc3NHD16lBHXCA+d/RATLRPc9vbbuP6e65kxOEPNh5XaL5u+tW52Zmom0SeiZpDs8VAsFsnUZHj5+peZnDMJQHxVnL3/ay9t323DH/PjbfNSjBTJ95iIbltbGz6fj+HhYfbu3asCqqafNlFbW4vWrpGKpBgbG2PiAxPoNdW0IjQwvAaDFwyyU99JS18Lfbv7SNWmCNYEqZmoIRKJEAqFSCQSqsZJUNnm5mbK5TLtre1KMEMy0yKONv8L89n1zV1MnTylzpkL5dh7yl667+nG5XJx8MhBRm4YId4VZ9EvFpktzaJRFaROTEyoQFnXderr6xUrQmqarGrfmUxGJQMA6urqsNvtjI2NoWkaY2NjKrgUapeu63S0d2Dvs8PdsP3u7QTiATw1ldrr3ZfuVrX3ACVXid6repl1/yympqbQdbN/ayqUItueRStXtwER4xgMBvF4PMrhkHuQa7Y6AtJaZcaMGQQ3BiECWy7YQtlZhgPA9cD7gPejkhrGjwxK+RKHDx+mr68Pu91OY2Mj8+fPV8Y0FApRV1en3utoNEoymVSZeqnXEqaLXK+u6+qzY2P/uI5vekyP6fHmHIsWLQJQvocExRJoCA1abJW1rttKJ7cm2q3lXGLfrD/LZ44voYNKm9Djjy17pgQZMo5n07lcLmK+GH2hvqpgu3GqkeufvJ56vd4MsihQV1dHJBKhpqYGMH2SbfXb+Pnyn1O2VYLfWDDGgzc+yKe2fQpPwVOlLSM+l5R31dTUqN7l5bLZTrKnp4e8lie7NEttprYqQTAYGMSZc+JP+JW/2FPXQ3nATGj6/X6Gmob43YW/47JnL2NRzyIzqT3jCL+75ncUnUX+dv7fWLRrEfdfcj95T55n3vMMb3/u7dT4a9iydAtPrjDbNhVdRX7x7l9w/b3X0zbcpkAKKxpqTXyUSmZLr0QsQccTHWZC2+lgdMRkBfh8ppR2yVUiMT9BYFcAR9mBTbPh1J1gr06opJvTbLppE4mOBI9+7lHO+eU51PbWKn/U6/VWJWzEPorPZi0VEwDHuhYlESIdUGS9CuvNCgQJi02OLwloAcKsPqPP58PtdtPf2c+DH34Q3XFsbTp07rrpLi78yYU07W9SCehSqaRQWhEPEwE2secSkMvfxedb8sMllIwSR9YdAQ0CewKE3h1iR/8OFQTL+yp+vQANgNKv8fv9VfR7QPn+Uk4ZuTjC5G8miTZHedtdb8OX81G2lYnFYmoPEIDD7/czPj5OIBBQ6ucy7z6Xj7OfOBscsHP1Tlr+swX7I3ZoQonHZTIZJTxoZcLW1tYqPRtJxEmp3vR4c403VcAdDof50Ic+pNDh47O1svFYhbSgEqBa+2bLyyN/lw1RAmQxSlaatPXz1jpXMURimDRNY2BgQKFcYhyPf5HlHiT7KZSd46k6J510krqm4+locm+Aqp2xfl+uUZIIVoRc6CXWIF/XdZWEkGBbMpfyssq9y3kl4y3nsFLFxcBY51TOL2iuHEOErGSTtmb1qYOt79jKxHJTgCvry3LPpfdw5eNXMndgrno2ErxYqXXybIXyZM34Gn7jDeIWkc4IkdYI6QNpBj8+SLY9y9IfLWW8cxz/fX5SoylyuRyNjY2KkhaPxxkbG6O9vR2fz8fs2bNpvq+ZAc8Ae2/YWy2udmwcuugQjlEHzq852fuJvbgDbtb9fh16TFdCbtls1mzNdWwTDgQC7N27l4MHD6r2WbKZyn0Xi0VmfnEmzs86GT13FAxo/1k7HY93kDNMxH7wxkEOXnnQRBgMndDRECfcewIup0sJd0iwJ9lboT+Pjo4q6pRkwK2iMdYkl1AQJSklbdjESEigW/9UPR1THYTCIVVbaLfbWfXbVfh1PzvPNvuJnvjciax5YI1Zm3/MWSgFSmx6zyZSrhTNv2smvzNPKBRSRl0C7HQ6Tblcrnqfp6am1DWHw+Gqte/1ekkkEsy/dz7Dh4Y5etlR+ADwMnD2cQ/zNtBCGsbXK8KIfX19TExM0NHRoVDt3bt309HRwYwZMxTy4XQ6FV3OZrORSCTU+9HU1KSy/5K4mB7TY3q8NUY0GqWrq6vKFln9ClEit/o0UAmiBGm2JsPlM8f7OfI7AQGsdFr5vth92Z+hus5bfBWxI1JmJEG50+kk781zW/dtvBB9QR2jJd3CR3Z9hAWuBfjazSCxo6ND7b1CVQfYFNlUFajLKLvLFINF7DF7FXX5qZanmL13NuMj4zidTlpaWnA6nYwao2yKbGLmnpm4XC5evvJltrVt40ObP8TiscUADPoG+c2q3xDIBXjfC+/DlXWxv2E/d627i0hzhPe++l5inTH+tO5PTNRMcNcFd3H181fjSru496x7za4QwI4FO9ixYIe61t4lvfzN/TfOvedctrdvr74PW5mkL/kGBqbMs5VZIAkDEfm0ovmaptHY2IjNbmPP9XsYO2GMzm91UnuwtqqEUEYymuS1D77GxDzTR0rWJXnm+mc47Q+nETlklmlJuzlBnWU9WRFgWVPWRM7xLEfr+onFYlUostyzVUdJkG9hZMgxpOxPunaMe8bfsDYMzWDKO0UgZSqgC1oux7f6exL8i78tYI74feL7n/jrE/GUPIy3jtP1rS4O9hxUPrnYfmu3IGlVCzC2bIzAeAD/gMnoy2QyeL1e7B47A5cOUPu7WvXOFGNF3J9zE7kxQjlTEdWTJIS1pNThcCjGgWgTCQsXzHLaMx4+g3BfmPZX25mYN8Ho6Kiai2g0qhis1u/puk5tba1iUJZKJSYmJt7w/k2Pf/54UwXcZ599NvPmmUIK1uBSevkdTyOxGhLJ1ln7UlqDQAkcZByfDRajmEgkFDVKXnTZlOQ85XKZxsbGqn7gQhuHitGVz4uRlJdEehIKEihUV2udC1BliOW+oaLMac2iWmum5XyycUmAIxuVbDRWOpkYBzHeErRYa9WtwbR187ZuzjKP1kDMyiiQWlarcmOhUCBoCxLtizK0bEjVS4eyIerj9WqTlXPJf1YkVeY9l8uRTqdV5jgyFuGyBy/jz2//M8P1wzT1N3HRAxfhrHXylx//hfGTxkGDrZ/ZSrYhizHXoOM/Omhra1OUI+k5aRgGg4ODRCIRFVB2PtCJvWxnfNY4kXsiHPz6QUrRY1ljA9yb3WTuypBdbVIJn/roU5z9lbOxZW0qkB4YGFDXr+s6+MD2Mxven3jxuD1qPUiSwuFwoKd0mr7bRF7L07Snibq/1lFymWUPPTf1MHD5gEoCjJ48yqg+itagceafzzT7Yh8zjrJ2JicniUQiio4k1CWhQ4vBlDUofe+FIikofU1NjRIBk/doampKHV+cOvmeXtBZ+OeF6GWdsDvM0oeWYpRNVD8ajVIsFfn7J/7OyPwRAF74+Aus+rdVaq7q6+tJpVKKKj81NaWCfKFwa5qmriWZTKr1c/DgQbxeL16vl8BvAvAHYNuxhfyc+fystH37Bjs2h01lrIXm2dvbW6U1sWPHDrW+Z82aRXd3d5UwoMy9OEfyznq9XlWrNT2mx/R484+uri4aGxurytDEJqXTadUKyYoqSjAmAYIERNaaW2tgJAlRseuS4LYy545nqcmwIupW30CGFWl2OBw4nA4+teBTbAlvqdykAS2lFs5ynYXWUmGbHQ9qyL1dOH4h32j7RnX7MAOask0sjC2sSti+0PIC/zn/P2loaOAde9/BrBmmSFqZMresuIVB7yDXjF/DrtZdvDznZQybwS9W/oKPv/Bx6uJ1/ODUH9BX0wdA6owUVz5zJXecdQdj4TFGThvhl12/JO1PMxIx7UfOneP+U+5nxfMrcKacEKxcX+VmzJ/bR9vRUhoL/rwAGza2n70dzdC4/v7rmXF0BmWj8gysIIfc266lu3APuyntrfhnIqIpc1cqldh5804Gzh4AG/R8qYfILRHCY+Eqn8swDNLhNGOLqhOyoViIUCKkxOCkBM+qnWMFl8RHFjab+KryHfEFhR0qpZS6rhMIBEin0zgcjqo2WtY1JGCHMCzk+IpVtmEG9qydZz/+rJrndT9ZR+vWVrCjgmJ5NwTsEV0aQdZF6VsCcrG/Qn0v6AXGusfIBDKUGkpEIhGVDCgUChiawdA3h5jx7zNUa1pN04jPj3PkC0dwpB2s+vQqtPFKPfnBfz9IbHWMGa4ZtN3Tpt630dmj7D11L6MnjnLdz67DyBqqL7bUrwvoJu+5lVkrvrjNZiMcCnPSnpPI15nxTyabwWGvAARW4MXKdJEONZqmKX9oerz5xpumhtvhcLB582aVLbZmzoR2axiGypiFw2G1cQs6JEJPsulJoCyGQDYUq9ER+sXGjRuZNWsW9fX1KlsGKPqyBJHiZFuNnSQErCi8nN9K/7YGtxIEA1Wbi6idH58BlxdW5kU2YRFksM6RFYWUINpKWZNNWObBujEeT6WXJIF1PuU7YjQkmAYUoicJjlKppAIUMUTbt29X1HL57oEDB+gb7mPbtds4cP4BGqca+fjdH8en+9Tc5HI5is4i9156L+e+dC5dmS6FaloZAkJvkkC9VCqRdWS544Y7uOH2G/CUPDxw0QNsXroZw3bcsi5Dw0MNzPzBTMppU0m6VCoxPj6uaE65XI76+npaWlooFosEI0HsHjvOkpN9sX3svX8vJWeJ6JeiJC5JUFhbqArcWg628I5fvkOt2d7e3kqbqKDOxh9uJNeco/GeRub9fh72UiWzKwGb9GMtOopQBKdmGpPR0VFipRhHHzhKviNfhbxrukbXk12s/NNKMpMZampqKGgFdENnrH+M1tZWJiYmyOfzNDQ04PF4cLlcTE5OqnIKqftOJpOkUilCoRCxWEzRr2Rd1dXVKRGxQqHA0NAQgUCARCJBW1ubWvNOp5PR0VFqGmuojdSST5p1Y5OTk7jdbp65+RmOLD8CltatkUMRrvnuNUxMTBCJRCjrZeIr42xu2UzXL7uo8dYQi8VoampicHCQqakpamtrcbvd1NXVKWr+yMiIUqLdtm0bvb29VUvBttaG7XEbGhqX/PASgnuDxONxBgYGOHToUJVgEZgZ6kQiUaUOKrQygFmzZtHR0YHH4yEQCNDe3q6+L8mXr371q9Pqopbx36mLmh7Twzr+v/ZVrOPaa6/lE5/4hErwAoo+bmVwiT20IoNiv0W13Ol0qtIya5mcNcFtDait9F752RrYWwVOreir2HZJykvJkiPi4HOLPscNAzfwhTlfIO0wxSjb8+38ae+f8Ok+FSTI9VtrsOVahoeHefjIw/zy3b+k4CmAAR2ZDn6y6Sd4SmbiWjd0Xq17la+d8DUKdvMz3ePdfPWVr1Iul/nq2q9yMGIytJxFJ2V7Gd1WSezXpmtxlB2Mhip9kDVDoyZVw1SwQqe16Ta8OS95V56So4Smayzfu5wrH78Sw2Fw6/W3kggkOOulszjptZP4+bt/zkTNBGtfXcuZz5xJ74FeRkdHaZrRxOvXv86aPWtoH25/wzqwzqlm09g3Zx9/edtf0HSN0z9/OqEjIRW4jY2NKdSz56M99FzSU1VvbM/YueCDF+DMVBIwJU+JJ3/4JNn6ivK2VtaY/+R8Tr73ZLWuRONHnoWwzsSvlDUIlaS52CerTyy+rN1uZ3BwUHXTkHpuQZWtCDpQlXiWoF0+EwgEzPVjg96lvbx4/Yuc+ttTad/RTqlQUuK4drudVCqlrslaO51KpZQ+i/ia8jkBcsqBMo985RGSTUnQwJa10frOVhzbHHg9XvxNfvbctofUghR1T9Wx+EeLcRQcZGdmeemHL1H2mu+TZ9TDqTefStgT5pUbX+HomqNgM4Vf62+pp/6heiZnTTJy1wi6VwcDouNR3vfL95EbzSlFc9Hq8Xq9SqvF2rfcit4LaDaZnmTT5ZvwbvASfjmMy+kyhdpstqpjyn5gLRPZsmULr7322nTQ/T88/ju+ypsG4b7wwguZPXv2G6jGshBlo4jH42zatIkLL7xQUVwkcwSVDJsEp1aVcmvwKRtDuWyqhp966qmquX06nVablIgnSBAuG5og7FDdi/f4LKf8XgJmqPTLtCLJYhQl+2nNSkugfHwGXOpC5IW19iGW41sTDMcro0rwfXyQbg2orUH78TVj8oys/wrKLOcSUTDZ1GUurFlRqRt2Gk5O/euphCIh3rHtHbg1Nw63Q6k8l8Nl/n7a39k9fzf7u/fz/rvez5ziHGXohLorGVyZJ5/Ph0f38OHff9i8doedSx++lIwnw+55u6vp4HaInRwj9nSMpm1N6j5F5EvQhlwup+i/iUSCpqYmilqR2cHZBD4aYGTJCI4HHRi3GyTuT1A82XzOtldsnPiDEyk2Favqp202G1O1Uzz7wWfJtZsB18g7RnCVXHT+qROtUBGg8/v9lYxvxlSoTJfTjI+P4/P5aI400/HRDl6/9XXicyotRgybweDyQaa2TdG8u5m8I8/eq/eSIUPHtg6CfUFisZii+4kugGSNhTYttCWZY5/PRzgcJho11VsjkQhjY2OMj4+rNiByr16vV6l3yloIh8Pk4jnKHpONMTExoQLQeV+aR+prKSZWmBSpaE+UFZ9dQWR2RK3LbdFtbPrQJtBAy2gseXCJYibMmDFDPbtEIkEikVCqtW1tbQSDQbXPWOseASK7Iyz6ySL8Lj8NRxoINgRpaGhgzpw5rFmzhmQyydGjRzl69CipVIprrrmGn//854o6JvMkPbv37NnDjh07sNvthEIhGhsbqaurw+12M2PGDDXP02N6TI83//D7/cyePbsqYJGynFQqVYUCWzukAMruCwOstra2SiBNbLg1SLb+DFQ569ZE+T8q/bEm7q19hcHcv1PBFN+a8y22hrfyFd9X+PDRD3NX812ES2F+0/MbvIbX1CexUN6tjEMwu2L09/ezZ88enJNO3vnnd/L3a/5OtBzl+1u/j71UoUmnjTS3d91uBtsAGhyNHOXRxkcpaAWOho4qu1x0Fpk9MZvDkcPoNp3GRCMfee4jRBNRvnPed+it6wVgwdEFXL3xan5+7s9NRNuA1btXc9UTV/H8sud57OTHWHhoIW//+9vNeSnAx3//cZ5b9RxnbDgDm83Gx/74MR4/5XHOeeqcqjZLIXeIyx+/3KQwG5X5tfp8MvbP3c9dV9+lrv+pbz7FWV87i+CuoEKhhQnmH/Njz9kp+ytsgOBAEJtRyTDruo5Ld9H1YBc7/2XnsV9C59OdLPn9EiYLk1WMQrvdroJba1JG1mc6nVZ1ylb/zxp8C3tNdEVk7TQ0NFShtHa7vUoIUKjsiUQCl8ulrktKxCQp0Lmtk1mfnGX+3e2gZDeRd/GtUqmU8h3lvRFfVFiSVs2kZDKpPr/nsj2k69Jq/nWvTv/d/TRe10h5sMyhLxwiv9gsPx0/d5ztie3M+MUMBm4cUME2QD6a58DbD+Db7mNy6aRK+hsuA/1mndmp2Wz+yGYz2D62huOROK+e8iqnPHWK0m4IBoOqE0kqlcLv96vyAqGdi1hcJpMhb8+z6bxN/z/2/jtMsqs6+4Z/p3IOnXPP9ISeHDQzykI5oIQkQEgPSEICmWQwNtiAhbGMARuTZTIYkQVIQigL5SyNRjPSSJOnp6dzrK7qyrnq++No7drVwq/t57Wx+N7e16VrNFNVJ+yzz17rvte91uKVs16BM2HrjVvpeqWrrg2xBKBkPxDCz+FwcOTIkUWw/QYdbwjA7XA4eM973qNArb5Y9AIQ5XKZpqYmLrjggrpIsmwsugRdwKw40wKWBCjrm5BsnhK5luiogFiRuOgbkxhKQF23bAJybWIkdQZarkcHv3oOkGx4emRcrwy+kH0UIKT3+JNz6XlExWKRiYkJli5dquZDfiP3od+XbKhiUOQadYMugF4YVXEEhFyQfxP5vJ4npBfnqlQqdW0+Ln3iUkq2Eg6Pg1QqZUqQyXLfGfexc+1O8xy2ErdcdgtXPnwlvQd663pCCqGQy+UUGyvnkdQEq9XK2+98O3cX72amYYa8M89s8yzuuJv1P1xP6ZkSZXeZiYkJent7mT92Hu+QF9u0Ke8RaXKpVMLn8zE3N4fH4zGVDAer9M/0k+vMEQwGiX4sytwX50gX0lj+zMLDMw/T399PR0cHTU1NqnXXXMccRX99PYF4e5yitUgulVO9KaVIiDClovDo7e2lubmZWCxGMVFk7WfXMnzGMDOnzlBcVsQ2a2P5N5YTGggxX5xn73V7OXzOYQAOnX2I1O0pls4tVe+dRF0kB0sqk0uRMin0Jc9NjGwsFlNAs1AoEA6H68C2btydTifz8/N1RUm8Xi9gMvAN4QZO+vpJ7P7z3aRcKY79/rFYc1aVfnH4mMO89M6XlHE9esVRLC4LK36wQjHA0WiUQCBAV1cXwyuHyb+Sx5v3Eo/HiUajdaoNfdhsNpbuW2qC52JesdPC3nd1dbFkyRKVizg8PMzJJ59MLpdjenqamZkZVUlfJ9LKZbPqrhQ28Xg87NmzR+XRL47FsTje+KOpqYm+vj5lzyRSLZE6qIFisZG60kwAtthQvd/vH5KNC3iBmm+h52TL/qVHx3WwIn8Xv0auIefLcVPfTTzT8AwA8/Z5bm+7nYtiF3Hh3IX4qj6w1gN8uTar1UrFqHCL+xbWvrKWQ4cOkc1mCYVCbHVupf9wP8sKy/Bb/FTdtSiwr+zjht038I113+ClxpewVCxcc+AaLpy50Ax2WFz8dPNPqRpVNk9v5r0vvJf7l9/Pix0vct3z19E114VhGHzoyQ/xbyf8G+6Um6ufvBpnwcm7HnkXvzz9lywfW85bn3grVUuVU3adgrVkZdsr2+rmw5138+an30zFeC2HvWDn/EfOp1wts2/NPpp3N7OkaYkCj1DLrdfJDz3daqJ9om6dVC1Vot1R7DvtdSlYlUoFx4gDa7YGuJtebmL1V1Zjzdai1fl8nsHLB9lz5R51zNUPrWb9zetVgVgJxgjglVQl8Sn1IFEwGFTpcgsVm7JW5fdiw0WVJUXZxDeUyLmsa12FKgq2XC6nZOkWi1m4LZVKqXRFSXWQ44ofLXMqOc8SZBB1hRDugKqmbrfb2XrXVvwOPy++5cVaMMUCTWc1EdoXYr53njy1tmGOfgertq1i2TeXsf292xk50UxR2PDABrY8sIVgIMjR+45y/8X3k3flaRtv44I7LqDR0UjnTzu588I7Gd82DlV40+Nv4rhHj6NcLatuPOl0GovFogrPSlBR5kGqnjudThLJBC+85QVeOe21ugEGvPTJl3D+yMmql1bhdDpJpVJ0dXUxPT2tfFCZf2kPtjjemOMNAbhPPPFE1q9fr148HVjqAFLPNdIXlQAsAabyO73AiGyMxWKRoaEhuru7TaY1na6L+uqF1WTIMQWQyvEXRrT1CLaexyLfke+JU61L0OU7AkYkai/GfCGIlw1KyAW9WIWA5oUR7Ugkolpt6P8tlIPrgF7PA9Krxev5P/rzkucjxIPO6MsxwuGwim4K8Pf5fLhcLpLJpOp/KXOSy+XAZuZj68NesOOL+eqem0T8dQMkc+90OtWGZBgGDhycfvfpZINZcuS47V23ccltl9Aw28B0/zRTU1NYrVZm1syw7wP7cEVdHPM3x2Cv2mlqaqK9vZ18Ps/hw4dJJBKquJjP51NrccmSJXhnvYS/GmZweJDCbIF8Ic+hQ4c4cuQIPp+PNWvW0NraStvBNoLTQRItCXWP4YfCVCI1+b48F3kmgOrrrhNKlUoF64iVZT9ZBr+Dye9M0vTRJuxH7cwEZjj08UOMnzdem0wDdly8g0w1Q/+t/SpHSmRnetV9MYI2m02xt/Ic9WIhYujL5TJ+v1+x3IFAoM5I+/1+ZmdnSafT5HI5Ojo6FAjv6upiZmaGrn/pwhq00lhsJGVLMTY2ht/vxz/tx1LS9OZAeU+ZyGwEp91kvnO5HE1NTUwum+TJdz6J7TwbJ33mJNLTabVPSMFFfejrWV87IqVbunQpQ0ND6n6am5uVdOzAgQPs3buXqakpIpEI09PTar8QYkjWv8yLvL+LY3Esjjf+aGxspLe3V0W1Rdkl/oLYXl1lJzZebKcUcJQ9W7f1OqjVe3EvBHqyj8pv5N9lyDH0NDOxl06nE5vVRnepXiYdKAe4MHYhnYVODHvNR1lYfMswDD7T+hnu9dzLmU1ncvLQyXR1dREMBmlubmZp9rXWXq/lc+tgvzXbyof3fJgvb/gy5w6fy5kTZwIm6Dtv4DzcFTdP9z7Nn+36M8KZMJftvoxjjx5Lb6SXUsX0RcKxMFc9dRXWjBVX0UWVKkunl3LtI9fSEG2gUq75Xye+Ykqvy9VaO1e5Dz0IU6lU2LNiD4+c/wiN2xq5+pdX16kK9HmU3+rHOP2Z06mkKzxx3hMAnPxvJ9P9VDdz5bm6quETaybY9+F9FJpMgsa23Yb3U15mJmaYLE/icJgFThMfSxB9W7ROiTffaSrRSsVaa1xApSrq/rOAXyHHnU5nXRBKfFLx63Q1RFtbm6o/IjnEss7FhumtxUTNJj6upDsK2VwsFsm4Muz4ix2s3L6Svu19qmioHENfm7JmZL71QsQCNIUwkHtwOBzEOhZU6TYg05thzSNr8HzLw7OfeJZ0a5qm/U0cd/NxBLIBMuUMx99yPDaLjZa5FtbcuwarxfQfN+zdgDvn5vcX/J5L77yU9lg7JVcJa9HKmb89k8ccj7Fuah2bnt6kSAy5X11yL5F7AdnyWSgUMtWB1jBN0011l27HTr+1n9bWViXNl/dQyD3xzQcGBhSpszjeeON/PYfbMAw+//nP85GPfERFjnRjIjJuj8dTx+IuLEAC1Bmtu+66i40bN9Lb26uMXLFYZHh4mEqlwqFDh/D7/WzatAlA5cbqIF3+DrWG8xJZ1JlqnSSQDVleBv0apZCSXKNsHroMHGoMqlyXyEgkwimyGgH9wkrqIEY/hsyzFKLTwZoUmBIZsWwSIm/Vc3T0qLvck0TCdfCuGy6ZH5nTSsUsJjMwMKAAqzy/3bt3k8/nCXeFWdKyhHwur/os22w2bB4b9264l99v+z2urItP/fxTOBNOFRl0uVzMp02g5rTUwJ6+YYucSlcGKMPRUGLXA7s45phj1LUP+Ya446/uoOQrQRX8Q35O+etTcFVdBINBpqamVAuNmZkZfD4fQ0NDZLNZlSIRiURwu92Ew2GKxSJHjhwhEokQi8UUYdLS0oLlQxbGPjJWJ2uyzdpYe91anDNORaoIWSHKCilCJlU7Rbolm/PExAQHowfp9fTS2NiI1+tlx/gOUttTVIP1r7Uta2PDjzew9JGl5tyXcgxdNoR/3I/3BS+esimV9ng8KtUinTZz/aQXtRgBcUi6uswCI7Ozs1itVtUSQycphoaGaGlpUeBcpPNSsO3IkSMqj2tsbIyGhgaam5vJ5XIMGUM89e2nKFvKnH7b6dh+bsNtd6u+4dlslvn2eXZ8fQd5T94syDcS4Iy/PYPCvJkP//zzz9flcBs+g9U9qznuuOOw2+2KPRej7nQ6aWxsVHnrgUBASUO9Xq/KDy+VSiQSCYaGhpienmZoaIhkMqlyN/XolpAbi6M2/jN5UYtjcejjj5HDbbVaeetb38pf/uVfkk6nlW3TSXSxsXr9GLG9bre7rmCVnhMte5bs57oNXlgnRuytXJP+mR6s0AGiSqHxWPjY6o/xpYEv4bK4+Fr31/ht42/pKnTx88M/J0xYHVPOL/dSrVbJV/N8vuPz3NVwF2WjjK1o40MHP8Rbom/BaXMqGyQ9suU65fqEwE3akrgKLoyKUQemykaZnC2Hv+yvy3fVfShA+Yg6KJb5lvvV0wF1ab8e6ACoUuVwx2F+9rafkXeatqJvqI+rf3M1Llx18nFdFbVwro9OHOW5U58jNBdi6fNLsWJlfHyceDxOIBCguqzKs//8LIXGmhrCSBv4/85P4Fazh7PH4zFBaLfByJ0jlMOvAfy8hRM+dQJdE13qOwJWJaVS/C8pwqsD1UqlQtVdxVFxYDVqKslSqUS+mFctuyhCKV9S0Vmr1WxNatgNivYitoINj8OjyCaPx4PdYSfWF+PJjz3J2l+tZd3OdVSKFfV+FOwFHvzSg6Tb0tiyNk755ikEnwuSzZhKsGAwSLlcJh6P4/F4lO/ndDpVlxF9zeuklRQntFgsJBoT3PX5uyi7zDmzJ+1c+ulLscy8VsugzcJ9H7mPUz97Kq6si3A4TCJhBjosQQvlbJmwJ8zU1BThcJhwOMzwyDCVcAVjzlDBIZvNxtzcHPYmO2uXrsVpcSrfrLm5WaWVie8fjUZxu90qGCG+kviJNpsNi93CzrU7ue/i+zDKBtf/6/UEI8E60kzmRdIexP9+9NFH2bt37//NlrY4/l+O/4yv8r8e4V6/fr3KnxaQulCGvHCTE4dfNg952YT5NQyD8847ry4XWj7v7u6mWq3S1tYGULdxC6MmIEwWt0SfxIjp+TIyhB2U69Ej8tKkXqTdC3OhdSZyYTE1AVR6hFvuUY9gSyRYWEBdega16o8yxEA98sgjnHLKKWpj1r8PNUm7gPqF8rSFEnN5PhLllrkRZ0TydBwOh6ogLa0dWlpaeDHyIr9+96+56rmrWDG1Qs2xYRhYShaOvf9YRiIjLL9zOb41PooUFSgrWAs8ceoTlK1lzn7ubMr5GlMrUUW5BzG6stEBNNobOfnkk9WzqRpVnnvPcybYBjAg1ZVi4KoBNv1yE/F4nKamJhKJBFNTU6TTaQ4fPqycp3379qlIfTAYZG5uDrfbTXt7uzKC+XyeRCJhGrSvWbG4LZTfXwYHWKYstH6ileJgkULVlFrLWpCq4c5+J6XpEraiTcnnxbloamrCZjNz4CvTFQqdBeVsdRqdzJ09x9wtc1SWvWZg89BxRwfLHltmRgEsFUYvGmXX5bsAsBasnP6p0/EOetWzFPApbU4kUixrtFKpkEgkVI5ROp1WagbJSY9GozQ1NdUV3ItGo2p99Pb2Aqhe5c3NzdjtpspgZmYG+1E7F33uIg72HKThoQZyFrNSq9frJZ1OE2oI8dRHnjLBtjzHzhT7rthHz009VKtVmpuba4B7JfB7sH7GqtJJ7HY7wWCQeDxeJ+cUUkAcDomWC0svke/29nYcDgeTk5PMzMwwNzfH8PAwo6OjpFKpOgd5cSyOxfHGHg6Hg/7+fiWflSie7Hs6uBG7k0wmSafTNDU1vU69JyA2l8sRjUYZGxvDarXS3d1NQ0NDXUVwsdNiQ3RiXPwO/d+gvrCp0+kk7UvzheVfYFdgF+9a9y6+PfBtPj3+aSqWCp8Y/wRu3BiWWtBAB9tyf78K/4o7Gu5QkdeSvcRty2/j9MHT6cp21dWukevUU9nENwuUA5SqJcqV+vZnVqzYy/a6e9FBrgBMoM6nkqFL+OV8uoT5DwHnnC3HXWfdZYJtAANGO0d5/tjnOfnZk+v8nIUKQTlWNBolF8+x7vZ1pt/hsBGNRsnn8/j9flpbW8lFciz/5+Uc+sQhSk0lLAULy+5fxrqhdRjHGrWq4005nvvL5yiHaudY9utlNI43YlgM5ufnqVQqhEIh5bNG/BH8eT+2rLlmZP3JvOeb8jz1wafov6efjh0dtWi31cLkCZOMnDqCtWCl4dkGlj29jEqponzYTDHD4JmDDHxogA0/28Dy+5erdVgsFplfNc8jn30ELPDi+1/E9QMXrQ+3UswXqfRWePqvnybdahL0JXeJxz7+GKf/y+k07GhQRLxE4J1Op2pNKmtanqHcl0jUZW2K3Q3MBTjtU6fx7CeexVKxcOwnj+XQq4fU8WwjNrZcv4W0kSZvyyuS32KxUIqVlNy7ra1Nrf8Vy1eYvl1rbd3o/rIEcmRNShV18W8lkg2oIInkoEvgQXyejbs2kqwmCb8axp1w4/A5FAHh8XiYnJwEqCPk9KDd4nhjjv9VwG2z2TjxxBPZtm2bWiz6CyWLR49cSxELHfgKmJQNWIC2gGx5AcRoCAsIqM1CZ0R1ZlScaZfLpV58YUr1HGohB4Qg0POjFx5bNgpheEV2prcL0H+vH1dnd3V5ltVqVSBmoRGAWg6Y3JNcwxlnnKFYUvntwuj2H5LHCwEgUWJ9Y9QZfrlv+a0Ue5BnKfk51WqVREeCHVfuIL00zY/bf8zl913OuiPr1HxKNdWOb3RgeOujCKlsit+f/HueONGUcVVtVdrm2jhuz3EqiqxvzgL0S6USra2taqPXQaPNZuNdv3oXt198O4dXHIYqbHhwA23faSNOXN2Dw2Fuhg0NDXR0dNDS0sLs7Cyzs7PMzMyQSqVUazGpet/S0kJ7eztut5tDhw4xOTlpyqhucFJKl6i+u4r9L+3E742Tc+bqohfV6mttJza6GPzkIOln0iz/3nIlJRKDJQVSpOiJ9KCUKudHdx+l8c8bcV7nZOasGZpub8L9ZTej4VF8Ph/jF46z/137a2vIWeaZTzzDCd8/gcDhALFYjHA4rJ6PgHtpHWKxWAiFQqTT6bqq9dlsVjkuVqtVVX2fn59X70E4HFbFQWZnZ9U7K1XFnU4n27dvx+UylQYchGW7l1Gy1iRukss2MzPDhhs2MPDpASa3TJpVc2/t4bj7jyPfkFfEBwBbgH+D6pIqh796mIbvNNBzuEdF76XtSi6XI5FIqCiUSOv1VmSFQoFMJqN6b0q+v9/vZ9WqVaxZs4ZkMsnU1BTRaJRnnnlmsS3Y4lgcfwLDbrfT2dmp3nWd4NZl3zrxKMUuhYgTeyw2PJfLMTo6yoEDBzh69CgWi4Xu7m5WrFhBT08PwWCwjjTWU7x0IKifW49Iy3dSnhRf6/saz4aeBWDSOcnfLv1bbhy7kRvHb6xTGOrRd9m/i8Uih5OHear5qfqCo8DVk1fTlmyjYqm87hrFp1gYDNAj1HKNenuphcBY9lL594XKQv17OpiGWn0Z+Z0MpZzM2bjsJ5fxu0t+x9TKKYyKwVlPncWbdryJqqW+R/ofAjpCIGez2ToVg9SiEdXV3Nwc+XKewK8DzF8+T/NtzTT9rIl8uKYei0QiRLoipP3punlOrEmQeyCHLW9TAFOeT7orza537yJ4JMi6n67DVjG/k0gkzEKijVkOX3uY2VWzRJZH2PyNzfjv9uNyuYhcHGHn+3eq4mATx01g99npvrdbgfmD5x/kyPuOAPDK1a9Qtpbx3OQhnUoTPznO5Mcmax1FDHj6vU/jP+jH+VMnlcYKcVu8fs0YMLpmlMYXGxWxnc/nVZoV1PwZWT86wJYe2fJcheiqVqsER4IsuXMJ3pSXpfalrDh2hQpKyXFkLUirV7HbItuW4q2Su77weevnE9/R5/OpNSJpn/I9CWxJgEICPoJp9PW75dkt5m/tVVUjQhRzEvBwuVxqDxIif3G8ccf/KuBuaGjg7W9/++s2yYUvjh7t1plaMSgydJZXjxbpQAVq+S167rew0VDLGZG/Sx6KGFZ52fU8poUbvnxHzifgWTcAYhT1/3TWWzZSYYb14mZyHh0Q60ZI/l0MnJ4zKueSfFQZAvhlyDzIxqQzy7qkR58vMTDT09NEo1FWrlxZxwRLvk17ezvxeFwV5So1lbjr7LuYaTbbfORcOX539u+w2qysObRGzZ1EShduYveefi/PHPOMuvYntzyJo+Ag58xx+kunK+mOLvOT579v6T4cKQfLZ5crZ0Ykvt6sl4sfuJi7jLtYOrGUY54/huI6Uw49ODhINBpVa3nw0kHaHmhT1bmDwSC9vb0MDQ0pgzc3N6eAr8xnR0eHarlx+PBh+BJkn8hif8Ze18d8dnZWOV1RR5SZ982Q7k7DO8DwGnT9Q5ciP3SyJhAIKEAp5FM8Hjerrx4O0PK1FtyPu3E968LqrhFGxuzrFTLZ1iwvvOcFNn59I8ndSdVbe/jcYbr2dtHqaFUEkrwvhUKBdDqNy+Wivb1dqUakQrk4oLIWhBWGWs96qY46Pz9PS0uLigrL98S5E5ZcKqFbLGa/c0/Fw5Z/28IuYxfOV52suWMNWXuWWCyGz+czC5itAn4IbDTvNdeUY9f7dmH7ng1jwGBsbKyO4FtYp0BPGZGCMLJ35PN59T77/X71fvf397Ny5UpyuRwHDhxYBNyLY3H8CYzOzk6ampoUCNCrO8ufsv/JdyQlCOprsRiGQTKZZHh4mJGREfL5PIFAgFwux+HDhxkaGmLVqlVs3bqV5ubmOnAhvgxQB7jlHPL/QgzjgE+v/DTbQ9vr7sdVdeHF+7rUsEKhwPz8POl02izGWSwSj8c5Mn+ERCABHbVjbI5vZnNsc10QQI9sQ80Xk2sSYL1Q4i02T/wYXVUn97aQcNCBj0TPFz4D+TfdX5NhGAbpdBr7tJ1zbz+Xh9/1MNsObOPYncfW+XbyfOUYMioVs7K3EOJCelcqFUUUS67urHOW0X8YBSv4/sVH+WdlDpUO0dPTQ1tbG/Pz88zMzOCMOHG+zUmu3exc0rKjhTXfXIMr7cLqrPXOzufz5EI5nr72aeZWzDG9Ypo0aUJ/HQJes/s+GL9xnPRmM8JctVV5+T0vE5wMYg/Ymbtmrq79JoYJqvO2PP339LP/7fsZfNtg3brZe/leXJMuLH9vId+Qp1J4fYQ1NZgiF89h/MKAMeBOwP3amrl/M/2/7cflc9UpKETlmkql1HrUaxTJOpBIsa5gEMXJfMs842eOYyvb6J/sx5V1KXwh3VNkLcn7IYBaQL60QZOIt77GJKfd6XQSi8WUvRcfN5vN4vf7lfRc/G/xZZLJJOVyWUXAZQ1bLBay2axZRC2RYLY4y/S505zwxAnKb3e5XIrEkzai0Wh0EXC/wcf/KuBeu3Ytxx13nNrM9Lxj2cj0wlz65qk7sALEdZAK1G32ApTlGLLgdaZZj4zp+eRS1EQArLwgIvkWgCEGVN/Y9Ui8vinoUjM5htyLfr/yn2wyIvGSSKV+PmHO5IUU5lg3VLrhkPnWJV66dAdquWDCzOnPZyGTLIDdZrPR3NxMMBhU/y4VMd1ut8qPF2Abj8dxzjvZvH8zg42DZm/sKrRH2lk+tlw5KT6fT0UYJTdGNunjtx/PCxteoGivyfwLjgIPnPgAtqKN43cfT7lSxnAYlNO1iq+DrYPcfvHtGGWDq79/NY2xRtxuN5VKRV1rKB7ibXe+DVvehsVqoaG1gZaWFkKhEAcOHCCTy7Dv9H0cvOwgh049xOkfPZ2gNagY04aGBjKZDKOjo5TLZQKBADMzMyQSCfx+Px0dHQSDQYrFIl1Lu5j5/Ayui1yKCJI5EgbV7XUzd+ccpZU1qfv0m6cppAt0faFLqQbk9zabjbLNlN3rhENLSwvpdJpEIoHzSSe5fE5FaN1uN8G7g6zMreTQDYdqfUIr4H/JT2lXiY6ODtKZNBOnTrD33Xs5lD7E2//h7VQSZn5ea2srqVSK2dlZmpqalNRaDFQoFFLqA2HqJXdbjFuxWCSZTCqJvMjq/X6/WldSRbVcLjM/P08sFiMQCNDY2EgsFlPye8uchc3f2Iwla6FcLNPR26H6rHd2djLx6gTlu8uwHvN+K1B8vMjYXWMEe4LKuOqRKdlj9HmVyuRCDsk+Ic9eIvdi4HXQvjgWx+J4448tW7aotDaRgktgoFKpkEqlyOVyqiK0OPC6QkzspxCcqVSKRCKhiGWPx6M6NnR1ddHY2FgnZ9aj2QuVZ3oes6jWypYyf7HmL3jV/2rtRqrQm+3lnw79E7aojYnyBMPDw6RSKZLJpCrypBeDK5VK+Kw+rtx+JXe23cmunl30Zfv4/OHPEyqGqFpqZL8e1a5UKpQoYTEsGFVD7e9yXPEv9HPpcnbxS3TwroNnPSdWD1ToyjsdJOvgXcBONps17UGqk2t+dw3enJdK2Uwv04MiOklQpcrhvsNM+afovadXpfXJsScmJjAMA5/PR6FQIGfNceTHRyh2mr5K6aMlTtx3Io6cQ62lYDBIc2szgxcOkjimVkTVO+klnA1TtdZS5RwOB1jhsU8/RqLrte8aMH7WOFMfmcL64deiw5Yqla9X4CeA3Xz2lcMV5n86j2E1qF5ehX5qEegquOIu+nf043A46Hmwh8HzBykEa3nnJCH39RxG1qD6dBXLmyxUdlfABZTAfbmbwj0FMF6rR/SYBcebHeQfzLPmkTWsv3M9ifkEecP0D/1+P6lUSpHUxWKRUChUl4KgB7Mk0ivkt/ibJX+JZ//hWXKNJhi9+xN3885/fieWXH1xQvlzamqK2dlZ/H4/iURC+cyigiwUCjQ2NhIKhVQdp3A4zNDQkKoc3tjYSE9Pj6qVJASMrgbN5/Mqoq0XuJW1KKS9gHab18YDH3+ATEsGJ06Ofe5YrNVadyPBPOIbL1S2Lo431vhfK5pmsVh46KGHOOmkkzAMg/3797N8+XL1mbBPAn71yGy1apa/l/wF2YT1DVXyQxdKkmSDF4AtL50wkfqf4sTL3+X7Ig+WzVG/NomcCtDR87x1YkA3uFADtrJZL4w0y/cLhYICYVK0SmfY9b7eUJOwAwrULIzGy4YgwF7Ij4WR9D8EuPX/12Vh+m/l3PI8ZSMaHh5WObAej4fYfIw7Vt7BjvN20Dfdx7W3XItRrZEFEnXeuXMnB6oHWO9YT2NjI83NzVitVmJNMX545Q+JB2q9pwE8MQ/X3HYN+OG+k+7jqruuIpgNMtYxxk1X3GQCfMBWtPG+772PrlTX6+ZYL26hA+EyZR7reYxHr3zUZIdfK6x24udOpLnQrJ6txWKhtbWVkZERjh49isPh4ODBg+RyOfr7+01AGsyz+9O7mV8/T/BokDV/s4aBJwcU+JQK2Ha7nUJHgehvo1R7THLC+byTzqs78bq9dSRRqVRi1DLK7K9maXl3C+1JM5c4kUiowh/ZbLauT7bO+FqsFqJvjXLkA0cou8qE7gux7B+W4ff5cbqcTJ4wySuffkXdu3fOy1mfPQvvnFf1xbRaraqlhxRZE8BttVqZn59X8i1pI5ZMJnE6nUxNTam2J+3t7apaqtVqJR6Pq96WPp+P0dFRpQTYsmUL09PTZlu2aJRIJKIAu55aIPn7kUiEl19+2XQ2vgx8ELgLuMK8L8MwUxCWLl3K6tWr6ejooLu7W51b5nr58uXKYOttguT+fT6fchxFQl+tVpmfn+fmm29mamrqP9w7/780qotF0xbHf3H8d/sqf+D4fOlLX2L9+vV1smmJblostSKn4oc0NJg5qn9IuSd2d25ujsHBQebm5lSQoLm5mZ6eHlpaWtQeoqfD6Yo6Oa5eUBZQRaW+vfTb/KbtN5QsJXUvwfEgH/jBB6hka6ojSdMRdZQUdUx702SiGVITKTo6Oli9ejX+oJ9PLvsknzvyOYxizVcQRZX4DNVqlYw1w/eXfZ9l6WW8efzNlPP1cnfxGyQAIPckvp0OtMQGQy3lUHy3hSoD3bfSfR6d6CyXy0y6JikdLWHDRlNTkyJEZYhvIz6jXMuRJUf4yTt/QpUqx/7bsSx7chlG2dy24vE4kUgEi8VCY2MjVquVJ//qSWKnxOqAbfvL7Rz/j8crW9/Y2Ei0P8qzH3uWXDinrsEZd3LaD08jtCOknq203Eo1pXjsxscothehCjwOlnMsWKj5T6VSiep1VUr/UsIYNHC9yYWlYvqtbb1tzD04Rzacxaga2BN2TvzzEwnagirPmEa489N3kmvP4Rh34DrdhTvpxmJYFGFe7a8S/U6U3p/04r7f/EwUd+l0GrvDztJlS7Hb7JSLZWWL9ci1RHjlnZI0s0KhQCwWU/6CHv2WwJXdbud3N/2OTEOmbo79R/2c8uFTSCaTyoeQCLG8I7oCxe/3q24r8i7owTgpECuycZGd6xXZ/5D83O+v9WOX38n8CMDPZrMkvAluu+Y2pjunzfuowqbvb+KckXOIzkRV0EHe2zvuuIPp6en/yla2OP4bx3/GV/lfi3CvWbOGbdu2KQMRiUTo7+9XL46wd8I06fIoWex6XrcOriVyqkudZcPVZT664ZKXQSoRSx4z1PKUhUXTc2bk+Lq8XVjZbDbL7OwsoVBIXa9uIHWpu56/JPco+Sm6NFwiswKy9QiZEBWyCchLrF+bbAJiOEZHR2lublbMmhhI/XtynzqTrj8PuYeFki/5u27oZF4TiYRyOCRqaGCw6YFN2B12Lj16KUWjSLFUVNKzfD6Py+UiuiXKoT87xPK7llPaXmJycpKGhgYao4286553sX3tdvat2EfGk8E76+WEn5xApCXC7976O4r2Ine8+Q4ufehSdq7ZSVXzzcrWMoc2H2LJc0vq8uxlvQgwFGekUqlQcpSYOGGiLm8pG8oyvXqa0IshlR9fqVQYGBigWq2q3s379+/H7/czPj5OtiHL9J9PM79hHoB4X5y9f7OXJeUl8JqKa3R0VBXaqB6p4rnGQ+7bOexjdkLvC1EsFUlX0oqsMgwD17Eu8p/IU+2rEr0lSvgzYRqGGhQpJYqKTCaD1+tVjo6sSarQ80APFXuFSGOEpV9ZSgVzHbd3t3PgzQfq7j3dlOaJjzzB6T85ncpARRkteZ/T6TTlcplwOIzD4aChoUGpVRwOB6FQSPXozOVyuN1uWlpaiMfjqhq6FE9TKQxOC4dXHyY0G1IkQrVaJZlMKnm5VEifmpoiEAjg9/upVqu43W4l/WpubjYjTJ+qYpQNbJ+1YXgNNefZbJb9+/czMDBAd3c3a9eupb29Xa1ryacS0ksnuPR+45KrJrlhhmEwPDz8B1uTLY7FsTjeWENAsEg5AaUs0+21kLWSQgTmniz/L1Fdse2tra34/X4ikQhDQ0PYbDZ6enpobW1VdlOXQYs9lr1Qj3zreapiXz8y+hGwwC9bfwkGdA92c9aPzqJcMFVH0gUiEAjU2W2bzcasY5ZfHf8r7FN2rvVdy4qOFQQCAQwMPn/g84qoh9f7AuVymaK9yI+W/oi7Ou8CoFgtcuHQhXW+nfhzOmiWsTB6D6iI9sJouvgsuvLvD82N7sNMtE3wmwt+w6rHV/GmV9+kfL2FwFyPphuGwd7le7nlkluUH/HCe1+gWCnSc1+PIlzy+TzNzc3YbGZLreb3N5P/lzyZC80c5Z5dPZx000nkMNeTECueOQ8NAw1MbDP7erviLo758TH4n/eTLWSVvyo+c/FwkfaPtDP22TE8Ex54B1g8FjweDzabDa/XSyaToXpvlUpvBdutNrxdXkXUNIYaWf0Pq5ncNImlZMG7y0slWSHtSCubVRovsfYTazl80WE6bu3AH/Jja7KpgFOpVKIQL9B1VRfNzc14V3jVM7Xb7czMzJgR4lIVw2oo+7gwGCT+uKg2xU6KwlEk2uKLS4Vy8cu6X+zm4DkHawvIgEq4gu10Gyv2r1D+nawN8dllTUhXEnkXBJzrfruezma1WhUhICoVIdpDoRClUon5+Xnls+vtdwuFgvK9pF5UtVpluGeYZDBZIw0MSJ+dxnWHi6Xeperdd7vdzMzMMDs7+1/axxbHH3/8rwHuz3zmM3Vy7lNPPVVtnvJy6lHV6ms5EcISC4BcWBFcwJkcV/6uy9GBOjCpb8KgMYHVWjsxAV+ysSxkSvXz6lFtYf3EiMiLLdegA1QBdlLxEWqRbz2qqkusJBq+UKKqGwmRGAsQ041ZJpOpy+mWTVM/psybHhVfWOREhi6PgVq+t1yP3GMkElGbjzxbp9Psm9z7y14qm8xrVFUlXyMwDvQdYOeZOykECzx4+YOc5T6L5S8sV1HMJSzh0qFLaW9r5/HLH+cdj7yD8aZx7r74biU339u3l8I5BYK54OvWZaFo5qxJ70mZM4vFgt/vB1Bsr8ViwVax8fbfv537zruPnUt3Yi1bufyhy8k/n8fpNVtHTU5O1j2LlpYW5ubmWLNmDU6nk2g0yqHhQ+Tj9YDL6/XS3d1NV2sXk5OTGIbB5OSkip7wMtj/0g5HITGdUPJDFTXoNzjyl0dI9pl5wcXOIkN/N4T9n+zkHzcl56FQSIHB2dlZAoGASh8QyXY+n8f/Yz/5SJ5yuKzyrNPzadbetJbIxRHSb0+r665WTANoFAxaWlpIJpOMHjOK7aiN0ERIGRe9RoFEhaSyuMjE3G63kjWKEdPlg7Ozsxz96FFGThzhOM9xtD7aitvtVtVbPR4PPp+PtrY2AgGz0JvL5VLSwcbGRtLpNB0dHXi9XiKRCIlEAuvNVkLrzYJvqVSKTCajcvNzuRxHjhxhdHSUlpYWAoEAra2tbN26lWAwqOZHAL3sY3raiDDlct+ZTKZu31gci2NxvDHHunXrVFRKoq7ZbJZUKkVLSwsul4uGhoY65ZjYel31pUdMZa/weDwEg0FaW1splUpqr1yoFJP/F7CiK/30Iq4VZ4VfdP6Ca6avwTAMPjz2YSqJCs/Zn+PS+y+lI9hRRw6I7Fmix1arlWQ1yU/P+ikHlx6EZdDU18Tnxj5XB5DF5sv/iz8h1/WVlV/h/rb71bV/d8V3SVfTvG3wbXUKRJkPvUaO3L/s/fqcyXlkPuU4+jF1CbpOUsj5Jhsn+e35v2WmZYbIZREcjQ7OeOEMda26vyTHVaTAH8gEEkVAPB5nfn6+7tzpdJp0Ik3HP3RQ9pSxuq1su2UblrJFRaorlQoFZ4EXP/AiUxtMxZMlb2Hrd7fS80oPVXtVqSj14FQ+n6fyfIWGv2mgNd1KLmRKn30+H16vV63HcDiM60EXzpZaelQ2mzX7Q8dcLHtimVpfRbdpkyQNqlKpUB4u0/xsMw6vg1w1pyKzYtOlSGi1WjVro1Crt2QYBmV/mcNvPszqh1bX9QSXVEP9+YniU3CAXnBQxwqiSBX/e8stW3CX3bx8/st1z6ZaqdalsMl1Kmn+a0MCa/J+6+kJ+vVJEWBRvgqpIv6y1K6Zm5tTvr+oUV0ul9nKdn5eHVtPUdtyZAu2X9u4+5q7KdvL9B/s5y0PvgVbxkaZmupDT0NdHG/s8b8CuDdu3MimTZuUUZCNT5xqAZUCEmWDFSAqL5V8JvnBUCuSoTOaC1lPnVUTIK1vwAKywZS26BFDXQIlEWX9GvXzGoZBY2NjXURbpMnyG/3FFSAuhkV6j+uMnpxX8lZ1IyP3JC+tDrKF5da/m8/nWbbM3FxlDvVcdQEEiUSCaDTK0qVL1X3p7bXk+gWsy+/kMyW/1vKeRDIcCARUbos4G6lUilQmhcflUa0Q0uk0U71T/Obc35D0mgAy48nw0IUP4Sl66Hm1h1gsxt69e+no6OBEy4ms+tUq+kp99Ln62JPfw2h1VLGFh5ceft26tJatbDm0Ba/XW7f5yYaukwMigzcMg0A6wKUPX0ri3ARnvXgWy8eWM9U3pSqVZ7NZM1+5mOf5P3+eLd/ZgrPspLW1FZvNxubNmzGyBi9ve7nueqLdUaKNUdpGzRZ2kiNks9mYmppicHCQ5FNJZZDEWIvEqWAtkFxZX4Qr15tjNjCLI2cWAenp6aFSqajCYZWK2Sfd4XAoObdhGHXFOBwOB/F4nP379+NwOFgyvISx0Bjxs+IYowaWd1iIuqJK6j+ydISXrnsJI2tw0T9ehKtkFvyYmZmpaxHX2NioelRKLrfdbicajRIMBlVutwDxqlHl0IcPMXruKFVblZ3X7GTJ4BJCkyEFhGX9WixmdwNdMSMGVp51KBRS77NUrhd5WGtrKz09PczNzTE1NaVkXBMTE4yPj3P48GFeeeUVvF4vbW1tnHHGGbS1tanCbRJpF8mkvLu6nHQxwr04Fscbf2zatEnZOHl/i8Ui4XBY7VliC6HWrUPkpgtJa7HNoiqzWq1KdrqQPIcaMNV9GEABLxXZtVv5i7V/wUHvQWzYeOfMO0nGkhy36zj6Lf2029opuAqqkKf4J7rSDeC7b/kuRzuPqr8/1PYQFquFTx34lNofBShBfVFXMEHTqUOn8kDrAyoS7Cg7OGnqpDpVon5/4vfodk2/d5k/GXLNMs+63yf+1x8qnpbz5PjpZT8lGjaLn1ZsFZ485UmcOHnTzjfVqfhkvvUAz5oja7jqV1fx4//zYwC2/XAbPY/0qK4VVqvZPUbSqwSALwktof3OdgyLgSvvImvJqvTETDbD/q/tZ37VfO3c9grjJ46zZM8SFbgwjFpL1Xw+T65gRsgbDjXgb/BjuE3AJ/MnYFjv6CNzKsBNorK6wkzmVfweqbYukeVMJqP8at23FjJA1nG5XCZbyDL4i0HyS/I4DSfLHl6GAzOaHAqFmJ+fV2144/G4yqOWeRefWN4dqZ0gPoOQEPaqnb5f9ZFIJxh82yCWooWzvngW4ekwZaMGtnWgKspWnQhzOp2q/opgkkwmo+Y0m80qnzkSiSi/I5vNqtRTAL/fr5QIEhgB6joqyXXIvFerVXr39HLWl89i91t386afvwmv3YvdYVfvu9PpJJ/P88orr7xun1ocb7zxRwfcVquVK6+8UlXblBdUFp28bD6fr46B0p1kiQ5JFEmAurwEskHo4FBAKtSiyVLQQJgn2ZxlM5NNW5euiPGU/5eXTTY9kY/q1yDAvFQqEQgE6gCvHpWW38g161XJoRbNl3vWC4gIoNXnS4CiGBk5lw6KdaWAbKA6oyibm0ji5f51Gb5ct04AyHXohlcUArrUTa5Fimd1d3fz3NhzfON93+ADt34Aa0YzuAaULfVFISqWCljNjau7u5t4PE4ikTClW8Vm8oE8vqKPj/z6I3ztyq8x0Tzx767N997zXjqSHVhcFpVrLEZanqm0+xBWVDZ+e8zOe297Lw6rg3wxT0dHB83NzVSrVQYGBsg5cuz9s71MbJtgfvU8p99wOqVRc17Hx8dZaazE8ZiDnW/dSclhOhKeJz1M3D5B05Im4vE4S5YsobGxUeW+NzY2KsY3FouRzWYVMVCpVHA946Lxxkain4xS9VQhD60/a6Xl4RbKfrN9ibCshmEWd4vFYqRSKfWnw+GgqakJt9utDCvUGHuJEM9fP0/mOxnCfx7GVXIxY5uhWCySWJ1g++e2U7VVIQR3/ctdvOOf3oErYq4VAfL2JjtTqSnlEOiyK3n3YrGYIkNmZ2eZvmSayXMmzWMDWW+WwU8McvzHj1fycSliMjMzQ2dnJy6XC6/XW6dWaWpqMquazs/j9/tZvnw5c3NzSo1QKBTo7OwkmUzS0NDAqlWrsNlsHDx4kOnpacX+z83NMTc3x8jICDt37sTj8dDV1cWqVatUnr5E7mUORW6/GOFeHIvjjT+CwSBLliwB6ttxCcBKJpOqpoNOvAOKbJM9bWHXkYWqO11ZBrV2pQJCdeWYXIeS4XpyfHr1p9nt3w0G/Gv3v5KL5eg/2E85V2ZJ0xJKlJS/JSBAB77io1x7z7Xc9O6biDjNehc9mR4+uvejFMqFOtCmR9oWRpW3pLfwTzv/ic9v/Dz2sp2vPf01GnINqn2YPk969wcBl/o16VFuOb4cQ/5fP6b4f7oUXSn40g7Ov/N8bn37reS9eYyKQf+Rfk7adVKdrVsYaNGfS2hXiDMmzmDSO0nbQ2ZLtKmpKZX6JMRuIpFQNT/a2toIVoKkEilSBbNAndjXSqVCwaoVJnttFAyzzaSeQyxrYj48z6tffZXWK1vx41dBJ/FLpdWYBGD0aO5CHxJQvmwsFlPEh4BHQKkTxfctFArKv5bnJAEWRXqEywx+eZD0arPN2YvXvIi9aKf36V4cVociezKZDOl0Wr1L4usL0NWVsV6vV/nvAvLFZy2kCyy/eTkFa4GeO3uYG5wjakSVdLuxsVH5b9lsltbWViKRCOFwmGg0qj6XiP3g4KA6j9frZXx8nK6uLqLRqIpwi08rudqixHM4HCqNTZ6DPBPJKZf9QIqyCQHXdaSL0D+GMDwG6VBazb28d/l8nmeeqXXoWRxv3PFHB9zLly/n+OOPV0yS3jfaZrMxOTnJ0NAQZ599NlB7sQX0QK36uM706TkkMnTplh79k4UtG7HIZqWwkkTdSqWSKnIkYFYAsb7ZyEurR3vFoIpREAZYfqtviDorLUMYPV0Crxsl2XD0Qin6vSrJ82vAfWEetm585HxAXasEMEFyKBSqi04vlKvr1yrGTAyeyI10Jr5aNQtSSDsHna08GjrK49c+Trw5zk/e+hP+z/3/h7ZYGw6Hg76ZPi679zLuPPdOkv4k7oybs35/Fv0H+ylSY3wBJSeWayjlSnz41g/zqzN+xXTTNFNNry9O9YOLf8D77nsfyw4vU+x0LpdT1Wj1qLbcs1w3gN2wY1gNpUYQyX/fpj5u33Y7B7eZOUWZlgxP/tWTbP76ZjpLnUod0Huol6OzR5npNFujxS6METsSI/+vebx4FTCTXOAlS5YoIufAgQNKviUbdblYxv1DN27cZD6ewfY9G9UvV7F0WMiXTIJD7xUKJhMrPaflvdCZfSkuIoVGpIq4z+Wj+l6zcmc6nSaXy5FMJtl7wl4FiAEKrgIvb3mZbfdtM9n8TAZXu4s91+whG8/S8mCLWuPRaFTlRFqtVlKplClv9/uZn5+n855OkkaS/Vfsp+KsEJgOsPqLq/HMeig4CqpliDgzlUqFxsZG1cdbnI5QKKTAr2EYqiq6vKMej4e+vj4mJiYUiWW321mzZg1LlixRsnMhPUSCnkwm2b9/PwcOHMDhcNDa2qp+EwqFlNRPrx6/OBbH4njjjiVLlqjosx5JdTqd6j0WQKTbPD2apgNnPZIsdlDsh4BY8X/ENkM9KBVbI8e02Wy81PASw55hpeiqGBXub7iflkoLXYEuVXxKAIXuW0j0UwjHtrY2vrb/a9y48kY8BQ+ffeWz2Co2DEst0ivXKdeq+2RCJmxLbOMv9v0Fbck2WsutdeS5+Cbyez1aLvOngzedxLBYLMqPWAiq5XtyXzoINAyDYqFI48uNnGM5h8cve5xlI8t4x13vMM9hrc/51pUEOhivlCt07+umMdNIxWaef3x8nKmpKcLhMMFgkHw+r1qDtre3K/AFqFRK8V3y+Tzr/3w9h750iNgG06b3PNfDcTcdR5mykv/LOoksjbDzYzspNheJ/yhO91e7sQ7VgjF6wMnr9Sq7pkumJRotEXG9zZ0EGwQc6sAdampN+X8BjLoSslQqET0+Sr4rr9Zk1Vpl4JQBmp9pppguqjkRvzYYDJLNZolGo1SrVRoaGtTzkPvRFYgSoRa7brfbmZ+fp+vpLlpppXFVo+qDLtFnWRPyTgYCAQBVpFACfPKO64E9n8+nah/I7/1+P+l0WlU7P7ziMCsHVjI0NERnZydtbW0mzvFP4ig76Kh0YLVamZ6eNqP5bhcja0bo2tOF1WqlsbHRTIEbypJaksJj9ZBJZ5Tf5nA4VPrc4njjjz8q4DYMg61bt7Jp0yYlAdEjyTabjdWrV7N8+fLXATfZJHXQqBdW00Gw/j29gBGgNqmFBcHkJdcjtgsZO8kxkQIm8p9uBKQokjjsYij0XHQ9yiu/l+vQDbC+qQv40/OwxTjL34VNk8igfm2yucomKMfVW5Lpclv5nVyPfv26XEs3rMLK6dF5fS504xwIBIjH47hcLsXUDjcNc+859xJvMquMj7WOcdt5t/G2e99Ga7yVcrnM2oG1zEfnefiKhzn/ofPZtH8TVqe1Th3g9/sVS+rxeBRzHHaGufb31zLZM8nPzvgZE8H6aHfZWuYnZ/6Ey+2Xs27fOhXtl/xxqFXNl/sQgyRzLP8u0qtyuQw2KPsWtGtwgbfFS2WsogqBxCtxCpZ6ZrulvwWby0YhUeDgwYNEo1F8Pp/678j6IwQGA3R1deH1elVqQCQSIZ/PmyD1C3mMEYPKzyqk3WlKpRLLli0jlUrhcrkUsNaZfyEcAoEAhmEQi8Xw+/3MzMxQLpcJhUJ1nQAikQjJZJKOjg5V2bNUKtF6QyuxfIzEO8x2JcF/CeK9x0ukJWIWbXNZee6y55g61iRADI/Btu9tI+ANEI1GcTgcimGWwinVapW2tjbS6TRLbl2CrWDj0MWHOP7fjqdhsoGkJala/pXLZquwvr4+5ubmFEE3NzdHZ2enUle0tLQwPz9PtVpV1cVXr15tAvvXotsit3O73SQSCaUCsFqtdHd309fXRy6XY25uThnB2dlZEokEhUKBkZERRkdHcTrNdILe3l5Wr15Ne3s7iUSCxbE4Fscbe/T19REKhersGdRyPnWALLZRJ8kXpnXJb8Uf0X2YhVJr3Z4vBJNQX8/m7PmzsQ/buXHpjWStWZaNLOPyhy+nw9FhKo9eK+zk8/ledz3io4TDYbq6uszCT6kQHz/wcbxZL968Fyz1LVt1ckGfm4Ug/NTZU007bTHq7kn3AxfOix65lu/oIFv3XRbOtR6Akc91okRI1mMGjyH4+yCrR1fXkQc66aGTAXK9yWSSaDSqyHW5H7vdTlNTE36/XxHUUgy0ubmZSsVM3Zqfn1d+pajTAOwZO8fcdAz7PrwP54yTLT/fonxm8RMLhQKRFRF2XL+DbLPpnyQ3JDn08UP0f6Ef57xTybAFlOq+8ULCSHKw9cCUFNOV78o1SkcRWQP6fwsVAQLqg3cGqeaqDP/DMFVnlc5XOjnp5ydBHgxrrTWwz+dT99na2qr+LsEFkY3rqlBJCxXfU4JMiU0JXvngK8wcnuG0W04DUEETuVYhCWTNydqS4IGQGwJwxacVn1t8JwHBDQ0NOJ1OXjz2RR45/RHyv8/TX+hX8vRYIMYd592BJW/hnQ++E7vNrtbEMyc+wxMnPMG53nPZunereoeMNQYPXv4g4XyYd9z7DgrZAoFAALfbzVNPPfV/s5Utjv+F8UcF3E1NTVxyySXqpdYjolBb7NLUXTcoAoQBtbnrAFF/AXSWTI8cyfH0TctqtSomq7u7m0KhoCKvsmHrMhapIChGQl54+RNq1Qv1axIgrhtcqBkBYduUfJp6wyX/LsycGGg5lsyJzJH+mc4Yy6YkwFhn4PW5lWNI1XOR9erXo+eU68XF5BokCqAXizIMM69Iov5yjzabjbZCGy3zLYw1jqk2CG3TbfjSPnU9TqeTZfuWMfiBQdob2qk01wgLObcYEM8yDw+sfIA3P/lmRS6Uy2W6xru45s5r+O4l3+XsF8/mN6f/Rp3PW/DSG+nl4eMeZt3edXRkOtRz1tMSxGAJ6aGnKyzsa27P2rn40YspWArsXbkXf9rPxT+7mMlnJyl1lFSuffBAkJO/ejIPfuZBCr4CS7YvYcOtG3CucdLV1cWjjz5KOp2mWq2SSCRIHp/kwEcOYJ23cspfn0KYsKpIKi2zxJA4f+2kaJiFRYaHhxkfH8dutyvZm0jLy+WykpLLe2KzmW1SZL2INE4i/j6fT71rMzMzdcoOV9lF85easbvsuA65cPzEQcwSY2Zmhmq1SvynceZOrOWHHz7xMCVbiTf/9M3qfTMMg0rVbFujKolXq7S0tJjv1H0WrE9b8af9zKfNgnc6KSTSeyF34vG4cigSCbPYXCKRULlr8hyFFPL7/eodEhJF0l6kArHI4MXwyvx1dHSQTqeZnJxkbm4Om81GJpNhbGyMiYkJdu/eTTAYJJmsz7VfHItjcbyxhtvtpq+vT0nGxabpBaxE/bIQcOvAQ/YJXRGXyWQUsaj7NwtBpg4E9airnEuvvnzG/BkYuwy+3vN1rnzwSror3VSoKIAYCASUX6JLw91uN52dnap7CZh+QX+83/SF7PUEvdzTHyIE5O+6LyaBEN3HW6jc0+9ViG2oRdJ1Gb5EaOX6F/pYOkCWcxmGQSabIRqNKkC69vBaEzRaa4rIhX6UukfMvycSCUWQiI8gCqmWlhbVdmp6eppKpUJ7e7sqqCdqKPEbJRgk9+mf87P525upJCpU8hVy1pwiwiUd0jHqwDvtJdmVVD5Mw1ADtoTp2judTlUwTdRimUxGRYdlfgXU6m0rdf9XAlbpdBqLxaLSsuS+VUsw7dnpqZjyvfCDYTwlD5E/i3DKz07BE/WQsWSULF38D5fLRTweVz6ivCey5gR464E0id5XKmYQI9YV46WPvkS2Nctg1yAWr4Uzbz5T+YHSu1r816amJmKxWF2aZyAQUDnVEk2X+QgEAqrukMfjUWvT4/HwxJoneHTTo+QdeZ5681OU8iW27tzKWHSMuz5wF7OtZkXxH/l/xLu/926MisFTJz3F0296mqK9yIPnPYjTcNK/q59KuML919xPtCXKLLP8wvcL3nTjm6BqkgfPPvvsf8v+tjj+58cfFXAvXbqUt7zlLUBt0wPqgJfOikm+iuT76kZGCgzJhrEwoqwXupJNUge8QB07CjWw+Yc2er2omchs9ciubPy6dF2OrUfQFxZr0MGoXJM49HJ+Oa4wqAuVAXqkGWpgWComynHEGZA8Mv2e5JnolaNlPnTZuP6MhHFdyMRLawOLxVKnBpDosESIhV2vVCpmm6i0g3c99C4y1gyHeg+xYf8Gzn/wfAKuANhrPcYNw8D6qhXrGWZekcib5Vl4PB6mq9Pc+NYbyTqyOEtOTnrmJAKOgHKMlpeW84mffoJcJscZ/jN4dNujWMtWrvzllexYsYPfH/97Ht36KH/7878lnAkrYyXzImylzIFEXwV4y/OT9RosBLn691fzI9ePuPbha3EFXRTfXCQej3PkyBFlvBrLjVirVhoHGznphydhKVpIl03JcmtrK8PDw0xOTjLTN0PlxgoVZwUa4MlvPsnWD2zFY/cQi8VYv369KuoViUSoVqsqT7hSqahiXvv27WPJkiVUq7W+k0KI6K0r5P0MBAJK/iZSbeknDaj8f8nJz2azWAtWmv++menxafLksYasijSwv9+O5TELlabXSKaIjeNuPo5ipaiiAMllSV6+6GU2f32zqgSazWaZnJwkGAySTqTJHsxS7i+rNJB4PK5adUmrm3LZ7IcrrVp8Ph+JREIViJH7bmhowO12Mzo6qtaxVBuVd6KpqQmPx8Pc3ByJRIJsNquiQcJ6z87OsmLFCgYGBli5ciWxWExVmZf3Ip1OL+ZvL47F8ScwGhoaaGhoMHsJaznYQJ39FtUYUAdedEJYAIbkp+rRO12WrkcbBbSILRcAL0Wj5Jpkz87lcnQMdXD9Y9cTcoQo2s06FblcjlAopI4lIFvuo7mtGVerCwe1axBQrivdFkrHZSwMlug+StqSxopVRSDlnBKl1CP92CBjyeDIONS8GIZBxprBUrJQzdUCLdVqlXKlTM6bw5fzmb4fVdKuNO60W12HHD9ejfPTq3/KCTefQIurhYq7Qslawlv0YrXUghmVaoWMO4Mn4yHnymEv2BlqGuKFY1/g7FvPVgR41Vklb80zl5pj9PAoBXeBdle7KtRpt9tp7Gkk3BzGbreTdCWVzZcIt6RyWSwW8oE8uVQOy5gFu9VOidLrfC2n0wkxWPcP68j+Y5bE+gTLn1rOpl9tYi47p9ILxPcClO0U9Z48X4nSik+oio+9ptoQfxpQPpxh1AoZiz+cz+dVIEZXn8r6LJfLtB9oZ+WNK3HYHWQKGXV94rcKAV52lsFj9jIPBoOq7aa8P66Qi92X76b5QDOtz7fWRbrtYTsP/+PDlF2vKQsNGNg2QHYyy6YfblLvjfiq8XhcEV56BFveaUlpE/m5zWZTLX8LhQI+n49MJkMgEGBk5QjRy6KqFk/elef5i56nI9XBk2c9yWxLrX3XaOsov/k/v2HTS5t49k3Pqk46WXeWe8++F8eQgwff8SDRlqj6zWDXINMXTWN9t1X5EIvjT2P80QC3zWbjox/9qHopBXQJ2BaDouck+3y+OkZTlxHJZqJvQAIKdbCrM5SyOejRO9nkZfPTK3vrEhz9OiTqJyBDj+TKiyrGVD//5OSkqgpdZ1ioSa/0qJ7chwB1XWK1UPKktwfTKy2KY6/ni4mxlmi9zszrjLz8TjeMC5UJcg1Qy+3WSQud0ZcNXY4pTofNZiOVSjE1NUU2m+UdP30H9154L+986p2UjFobBjm+3Nfs7CwtLS1qLQhbedR/lF9c9gvSLnMjuu+E+yjnypy5+0wsWJSUzG118/jWx3l026Pm+rGV+cYHv6HWbN6a58uXf5mrf3M1yxLL1DOVe9FTECqVimqxJgypGCIxWjbDxvvveL+5qZfMjTUUCrFq1SpGRkaY8E7wyEceIRvI4kg7SDYnsR60Mj4+rljUU045hVBDiF+86xdMOifNCzUg15Dj0DmH8NzlUc/X7/ezdOlS1fZL5j+fzytiIp/Ps3//fhoaGpSETAyb5PdlMhmamprUGpDIiLSQSSQSDA8Pq+itxWL2/tR7TxsFg4ZggyLBrFYrwWAQI2dge7uNme/OUC6Waby2kSPGEVpaWiiXy6Q3p3nkU49QsVcwrjfY+sut2OK1+ZdWgV1dXeq6/H4/09PThMNhAFpbW1WRE+mLLmSQ3+9XoDqfz9PU1KSeq4B7mU+Hw0FjYyNHjx5lbm5O9eV0OByqfVk+nycYDDI7O6sKQ3o8HpLJJMGg2YbO6/XicDiYnp5W+54Y88WxOBbHG3NIEaTZ2VmCwWBdpFtsnJ6/LfYaUHmgYod18C12Vmy8nmcstlXAyEJS3W63k/QnwQkd+Q4FdhKJBIODg0xOThIOmvvg3NwcB9oOsGZ2jYoK65XVKxWzj/jzK57njvY7uOHADbSn25UPo0fldWJ+d2g3G+MbMaiBb7H7Q54hQrkQ7qKbefs831r5LXqzvbxj6B3YDFutqKtR5UDTAVbPrTaPW63w+9bf80zvM3zghQ/QmjUBVcwZ46dbfkrnbCfnvHpOXQre3r693H787Vz/wPU0zzRzqPsQvz7z11x1x1W0T7crhVo6kObOc+5kaMkQU5+a4uQdJxNpjDDbMMtpO05j65Gt6t4O9R3izrPu5MJHLuSZrc+w6tAq7jvzPirWCvm5POt+tg6rYWX/xfvJtmSJdkfx/MTD/JXzdH6xE2PcTO+KVqKkPpwi78+TP5xn+zu2c9wXj8N/xF9XrMxisRBfG+flv32Z4790PP59fuWPScBInoHY8Wq2ysr3r2T6hmlOvP1EssWsqp7d2tqK1WplcnJSrZ+Ghgblf8kzF6AvPqisU1mz8ryl/omezii+tQQfdN9AiHfdTwz4A9isttepJ/Uq545mB8+/5XmsBSvH3Xcc1kJNQZrP58ENB99+kEMXHuLQBYc4/svHs2TXEkUA5bN5GkYamF1ZA7dGxiCxI8ELL7xQl34guEN8aN0/1a/L6XSqQIX4+dJmVAqnxmIxHE86aPx8I9G/jVIMFnEmnZxxzxmsmlxF/639/OSdP2GwYxCAvsE+rv7t1TisDhKPJnjkjEcouAp4E14uvO9CViZX0vTDJm699lbGu8YBWPLKEnq/0QubYXZ2lkOHDtWlTiyON+4w5CX4D79oGP+5L/47Y/ny5ezYsUMVLZCXTI/mVqu1Bva6NBlqxTN02fVC+ZK8CAJ29Ki5Xr1QB4bC2glYE/AsnxcKBXbu3MnatWvViynGVIa8mPL/whhKNWthzI4ePcrKlSvrpFVy31CTQOn3XC7XqkzqUnz5/R+SWumRVd1g68ZaVwYIYNQj6kJm6CSCbNxOp1MBMN1Q6HOgqw6kb7QAF8MwK1FKf+pQKKQAqxAT4XC4rud4JGJWSA0EzNze+cw8e7bt4YKxC6hWazJuu93OwWUHuf2820n4anmxJ+w9gfPuPQ+34VYVIpPFJHeffTdPrHzi31237qybt97/VjYd3aTWqYxMJqOAoygy9DUQj8dpampSpImsGZE3iwEtFApMeaf41Vm/YqRvRB2/8Ugjm765CfeAG7/fr57PgQMH6FzfyUvve4mxE8agCpYbLLTc3KLy1/1+P52dnXWqhWKxSCQSUVFWyTeamppSbbk6OjoIh8OKEZf3KBgM0tnZydGjR5WR9nq9TE9PMzMzw/DwMHa7nZaWFmVs5bx6dXqZI/lMlCrGVoNitkjlpYqK+lTeXGHqs1OUWmrGZOnTSzn2R8dCBlWURoqoiXRO2GghPkSKPj8/Xycja2ho4OjRo3R2dqp9RyI9AriFCReSJRwOq9YumUxGgXK9sEowGCQWi+Hz+VQxRnFWJycnmZmZMSPzr1XBz+fzjI+P16lEFoc5qtWq8R9/a3Esjtr4f+ur/KFhtVo588wzueaaa1Rqit5FRWwm1CK88s7rUbOFRPv/09BBtg5i9d8XfAW+suor5O15bhi6gbZCG5lMhqNHjyqb6XQ6yWazPNv7LL+/4PdcvPNizj1yLoZh0NTUhMvlUmqz25pu4xt936BkKbEpuom/2vdXdBY669R+eo2YRxsf5WvLvsa7ht/FJSOXKNtvsVg44j7CV1d/le50N+858B6+t/Z7PNpqktuXH7mcK1+9UtnTO1bcwe+W/I7rtl/HuqPreHLTk/x444+pGlU2TGzgwjsvxF10c89b7mF773aowvm7zmf9raaSa+TkEe46/y7yjjw9Uz0sH1zOc5ufI+vO0jzRzEk3n4T1JSuEYe9H93Jg7YE/OOe2ko1LHrmEbbu38XL/y9xx7h3kXLl/9xn1Pd4HSRi8aPB1nzUeaKTnsz0kXk4wfsM4uXfWHycwFGDp55di3WVVvkT5rDLDNwyTbc7infKy+TubadzdWKdQlGcApsRbirG1tbXR1tbG3NwcY2Nj+P1+WltbicViHD7pMC2PtxAZieD1eut85ebmZlLnpQi+HMSdMu2fPEfxA6c3TcNBsI5aFUEzOztbt9ZdLhfDFw/T+bvOuv7ksv7lmPFr4qx8YKXKjZaghChH45k4+/98PyPnmr7Q6kdXs/nmzRTzZuqF1WZl/3X7OXxxrbWrNWflmB8dQ9+TfTUFSUuBZ695lrnj5qAMK76xgtY7WlXKqrxL4mOKTF6CKoINJDovuEGKywEqhU+v4yN1AaIXRHn84sc54TcnsOGVDYRCITMA1l7hF6f+AlJwzh3nEK6GCYVCxONxXjnhFe4/8X4uvv9iluxcourGxJvj3HnBnTimHZx313k4imbQ8NFHH2XPnj3/qf1kcfzPjv+Mr/JHi3Bff/31r+vPLPlDAhR1yc9CVkwYPvmdGCAZOqMsTrIeEdVBrS6lgnowKucXiYthGHR1damNB2oVMAXU6nJreRn9fr+6bjnnihUrKJVKyqkXlk2kMBJx1iPKY2NjFItF1S9bosISBV8IroW5FvZQZ/F0ObgYOh2o63I0PQ9Hj44Hg0H1LHTmVaKEeqReZwp15YKoCESSK9JjAXLSksvv96sN0Gq1KkNhtVrZ8ec72NO9h4YnGtiyfYsClpVKhY3jGwneF+Rf3/qvlK1lTthzApc+fymGpbY+SqUSlUyFsx48i3KpzNNrnsZSsnDpzZcytnaM7Sdux6gYXHnblXQNdFF0m4SN5PQIQNOLkejtzfSIvjDAslaE/AFUjpyn4KEl28IINcDtmHXgSrjqpF+lUsl0kjIu1n53LVihY6CD4sNFos4oo6OjFAoF5ufncTgcNDQ0mAy6rczRDx+l45861MY/NzfH5OSkKgSYyWQ4fPiwqp7t8XhU241yuUw6nVbrxeFwEAgEOHr0qLk2Pgqux11Yo1aVNy7tLqxWq2KCBfDqNQRKpRKNRxpNaZ2voFIEGAUjZUBLbR/pmOuglC1hlM1q4n6/n+fe+xzbvruNbDZLtVolFArhcrmw2+3E43E19xIVl3Ubj8cV+M5ms7S3t6v3QCqP+nw+QqEQ0agp6ZIKp8FgUK1NMciSdiAF0MLhMOPj43WOizwTIWlEqr4IthfH4njjDrfbzaZNm1T+ra4EA+pspfxd/2xhRFAn7MUu64S4HF9suvybTrZjgRvX3siu0C4APrHsE3ztpa9RTps+QjgcxuFw4HK5eKHnBR5a8xBZb5a7TriL1s5W3jn3TqWAMwyDXzX9iu+2fZeSxSQ4X254mX/c8I98/ZWv46v41PWJTX+m+Rm+sewbzDvm+eHSHzI7P8vmRzeTTqcpN5e55d23cNR/lP2B/Yy6RtnfsF/d821Lb2Nsdoz1P17P4SsO89SKpyhai/zwmB/SuqSVofYh1bP7lY5XmDpvCnvFznjv+GuTBvdvvJ+B0QH8R/y8etqrFBwmSTzSNsJIW82OznbM8vv/83u2HtlKYD6A66gL1v7h51yylbjn1HvYvXI3ky2T/49gG2DwtNcDbRlzq+ZI/12aarRK/sT86z5PLEmw96/3EromRPlomdLxJdJ/labSbD7jdFuane/fyaZ/3kR4IKyimLoiwefzceSSIzQ/1UzYCCtiXWxNpVJh7OwxRq8fZf6MeZqua1IKU7fbTWlZiVc+9grpvjSecQ/bPrENS9VS508mNyTZ/YHdWKIW1n9kvaoj1NjYCNTW68DlAwy9Y4jCygLH33x8naJD1vezlz/LyLkj5Hw51v56LYFAQAFWUZ8+86FnGD9lXM3T/tP3U7QX2fadbYBJNnj2eeDi2lwaZQPvoFfNkWEYeGNeNn53I7ucu1j25DI6n+jE3elWgTBReQoZJnnY8rn4AhL9l7QPeV+EKBD/X0+/s1gsGCMGS+9cSvBwkIK1oObBmDC49MFLKWfLuCounC7T93K73Zx68FTCY2GWTCzB8JvncLlcOOIOLrjjAiwpCwFrgIpRqcNPi+NPY/xRAHdbWxtvectbFHAQwCwvmBgRkcjIorXb7aTTaXw+n2KPJMIrwEvPc5KonQBmXc4lDr6e0yyOuC6R1kG8RPlEGipRXr1YhLBy8jKCKT3TJdnC3ok0Rc+5LpfLZLNZJXPXc6IKhQLd3d1q85MX3ePxqHnQ2UGJDOpDDLsezRdArhMRuoxNQLRElg8ePMjpp5+OYRgqV1k/vkQrgbo50qVCpVJJVa7W892lXZPkvurSJJHHS3VPm81mRqXfeTevLnuVqqXKfWfchy1vY/3u9QSDQQWMeid6uehzFzF8xTCXPXEZHsNDwVbrvZzP56lUK4w0j/DCihfMa7dW2HXeLt5957spOAsct/842ofayRVr1eFzuRyZYoaHT36YZbPLWDO0RgFWvQaARA90skGPVsicq9xtGrno8YuY889xtOcoXbNdvPuFdzPvmWc6OW2SSD47u6/cTe9TvXiHvYzsH+H0H5+OtWTFsdmUOw8NDXHvvfcSCARIJpOm/LEzyMgtI2SXZsmkMqz98VqsmC0nRHmgr4NoNMrc3BxOp5Oenh7a2sy2bJlMhng8rt4fl8uF1WGleEWRwkcKRLNRbKfacCVc6pnLetUVLRaLRUXBW1paiEajTE1NqZYe8XicarWK74CP9svamXhoglKoROjrISI/jeBuc+Pz+Qi0Btjx8R3MnDRDPBhnzafW4Lea/XBjsRihUEitt7m5OVX8LJfLKcDc2dmp2PlcLqcKpcm7KPK5QCDA7OysIoOsVivRaJS+vj5cLhcjIyNmpCCVwjAMRkZG8Hg8RCIRJT0/evSoOqYcr7Ozc1EOtjgWxxt8uFwuVqxYUUeeiU0WZxzqi45KutTCNDCob2cl/o9uf3U5q5DUegqb2+3mMxs/w67gLnWN+zz7+NjGj/GjAz+isbFR2Zq97r38YPkPSNvNqFzWnuVHS37EMscyzkycqa7r0vilPBx6mJ3enap10+HAYT626WP8dP9PAVQXjEPuQ3xx2RdJOExyMWfLcefGO0nsS7B6z2puvvxmYn6zpRUGHAgfwF12k7Wa1bQbC4382fSfcf9l9/P0CU9TtJq+SdwfJ+fOYa/YKVQLYICj5OC9R99LIBfg73v+nrzdBK/hdJirx66mWC6Sm8yxL7iPqlHFWrRiyVsoeopmNfWSlVNePYVN7ZuwtFpY8/wanEEnO07agaPoIOfUQHUVuqa6uPLeK7nzjDt5ZdUrVC3Vus8dOQclW4mNv97I8ueWc/fn7iYXfj0wN/IGvm/5cO5yMvHABNVAPTAyigZL7l9Cq7OV6uoq5XSZ0adHmXzHJFWr+d1se5aXPvMSZ/zVGXjjXhXYMAwDm9PG/pP2M3jNIMNXDtP9t91UYqYv29HRgd1pZ3jrMAf+/AAVV4XYiTHsv7Bz2rdPw5q3Umws8rsv/I6S/zWpdjjP9q9sZ+snt+IsmXYv2h5l+99up+QpQQvs+ckeLv38pdhzduWjGnaDfWfu48jbjlCxVxg/d5yX7S9z7C3HYiubPnewOchzFz7H8NnDVK1VBt8+iMtwsfbetVgK5vvidDrJ5/Ns/sVmpo+ZpuR7rS5MykHHTR1YnK/VBCpX6Nneg+9bPp67/jksZQtn/c1ZeKe9KughaXyhRIjNX9pMs9GMM+hU76qklol9TyQSSjYuKZpSLE0wgWAA+Q2g8ITsEeKPS0Cuc6SThDeBrWJjfn5eSfKrw1W8bi+GzfT3k8mkSnXsjHSSyqdUi1bBL/aInVQqRWNXI4ZhMD4+rtR2i+NPY/xRJOWf/vSn+au/+iu1UGVRStsmvWiDnjOigxdxgAUU62Bbz7vQnVdh1eQ3klst4Nowar135fhSoVyMqS4xl6imMGJ6jz4BmTqZIEZTLwonjJ+ALzGmgIrMQ01CL2yZHEuOr0vj5d90uZmeR66rAXQ5vdyDzrgL2NYLgkkEW5wJXb6tR7oXFkHR21jIPclGZLfbGRkZUdJbqeZptVpJJpNMT0+rnslyDpvNxo6VO/jtqb9V+dkAzXPNXH/r9TTnTMADKOZSqm/Ktcl8GIZBmjQ3vf0mxprH1LHseTsnP3oyxz91vPqeFEWzWq3YfXaeOOYJ7j3pXgDee8d72TC+oa7olYA8kRe53W4FrMWoyLWolAN/iduOv40d63aoaqPrDq7jskcuI3kkyXh8nP1v38+r574KVXjLV95CeF+t57VI13O5HPF4nHw+z+DgIDOuGbLfz1I9uaqOu+w3y1hx6wqqqarKWZb8o0KhwNGjR5mfn1cRY4fDQVtbG42NjVitVjweDw0NDaQyKfZv28/4P44rB80yZaHxikbCk2EqFbPvtbQ+EdnV9PS0ighLHmQkElHGTleyuFwuaIHxy8YJfSWkjKG3y8vYX4wxc+GMuq/GJxs56ecnkR3OKlAteYqxWEzJQOfn5+no6FCpC4FAgGw2y9DQEP39/SoFQldoyPUkk0llbD0eD7lcjsbGRiYnJxWRUKlUGBgYoL+/X8nSi8Uig4ODWK1W2tvbKRaLRKNROjs7GRwcZM+ePf9Xe+v/v49FSfni+K+O/wlJ+YYNG/jqV7+qbK2e1qadF6gBZ/lTl5Hrij39WAuJb538l31I7K7L5TLbNVkNPrjmg+wO7AagP9vPzYduxlVxqesQ/+GB0AN8of0LzNvm8ZQ9fGj2Q1wzd03ddQKUyiWuW3IduwImkF+WWsZ39nwHb8Vbd32zs7PcabuT2864jVwgh6vs4orDV/DWgbeSzWZ5aM9D3PnhO5lpmoEqnBw7mavHrubvVv4d3rKXb+75Jv6i2c/833r+jV/3/pqitUg4F+aDL36QdZPr+PvT/54p3xTv2fEeto6aedV7Q3v53qnfw51z8+FbP4wlVUtl+8lbfsLh3sNs+902On/bya6P7WJk0wgnPX4SJzxxgrp+yTG+7833cdoLp/Gjy35Ezpkj78jTMdnBe379HuVP/eLiX3Cw7yAdUx1EGiMEY0FO+/5p7F63m7V3rMVmszFvn+eJv3+CrCVLJVghMB0g1Zai+SvN2H5kxrRKK0pEfxzFWrVin7eTXplmyS+X0PvLXrUuxD849OFDHD33KFVrFfeMm+O+ehwNhxvqAjJOt5Pxc8Z58qonle31xDyc95Xz8Ayb6qxCoMB9H7+P3IoaGWBJWFh18ypWR1dz38fvI+vP1q1za95K/2/66fxZJxUq7PryLtULHIAihH8Wpu3Lbeqas8uyjH1zjFJvzfe2zdpo/0w7nsc8ABROKjD6hVFKrbXveGY8nHzTyYQHwnVBt2w2S7Ynywt/9wIOq4NzvnoOrblWUqkUmUymLlB05KwjNA400jjaWOdXi18ccUbY+Rc76X+ynxU7Vyifzm63EwqFVFqfBKH0Divy3opfIqSXSM4l6CDvs1RZl3fP6XTyYsuLPHX2U1z6i0vpKHQo5awUcbXb7SrokcvlKJVKqli09E0XjKHjD4vFwq5du3j88cf/H3atxfHHHG8ISXl7ezunnnoqwWCwToYrcg4Bflarta7qtRQEESMjL5BId4G65vULo9O6kdMNn0TUBYQKgIQa2Fwo4wIUMyabnvSOFsAsbLJchy7J1gupgAnIJCKtv6ALpfJyD1CTsQN1G7Qub9dlqfq9y3H0glVyT/KnLunXZUBQY+7knmT+hdCQY4s8TZfGLSRCRNYrzzmXyylQrDsvwqSLCkKUBE3xJvwZfx3gbp9ppxQvUbQUlUQ/n8+TTqfxer3qOYnTIlWpbRYb1919HT8/8+cM9prSMGfeSdtsm5p/+VOey/MnPc+9W+5V5/7JRT/hioevYOO+jWrDl+cuSgchDPTCdAJyZa2lXCliTTFlPDFgPjTPnrk9bGnbwktXv8Srm15Vnz3wgQc49rvH0vB0g1pbUqSko6MDv99Pb28vrza+yoFlB0gZKfXb4eAwbZU2Gp2NJBIJ9R42NTUxOTlJc3Mzfr+faDSqinlNT08TiURoamqioaGBfD7PVGSKxHWJ2jUDVXeV0roS5THzeUoLGiGALBYLzc3NKuorRkoqpOvvOrxWH2GiSuBLAYrlopJITpemSXem6+ar0F7A2mXFOWVGoSWaLcoSIffkOYm0PZ1O43a76egwiw5JD2+pGiuF4ACltIhEIqpViMxTIBBQ87Vs2TLy+TwzMzP09fVhGGYhIyGvkskk5XJZScsXx+JYHG/csXWr2RNXz/2EWqeThUT4QiCuE9piv3W7KHZUvqO3QtKVbOL8A9itdr46+FU+u+SzZK1Zbhy7Ea/hVRFZ/XjnJ86nUCnwpc4v8b7p9/Gu6LsoluvbLIFp07948Iv849J/JOlI8snDn8RZcFI2ail70tawfaKdKzxX8NvTfsuVQ1dy+cTlpKtp9u7di2POwYee+hC/POOXdGW7+NSRT2EtWvnYgY/RXmjHk/NQqpo28Ir9V1DJVbiv7z7e/+r72Ta3jbK1zCef+yQvN73McRPHUTFM32bV3Cre/dy7CUfCeMteckZO+TlX3nklT69+msa7Gylbylzy20t4JfIKxz1/HFhr9WakoOelj15KtVrlqruvIh6IE/VGWbd/Xd0zfNsdb+OFLS9w/I7j2bNqD22H20hOJNk8vJkCpt9on7ez5G+WkPQm8W7wsnLfSg6vO0zhlgJ5i6na9I36CN4YxIMH/5Cf6cun6b2jl6q1quy2SJe3/XQbroqLo1uPsuk7mwgeCFIxanWKisUi+07fx4GrDtTZ3pwlx7OZZwnvNIvklctl2kbbmPzcJPlj8hg5g8Z/aST/qzxPXfoUeWOBzL0CG27fwJrfryHtTVMoFDj2C8fy6l++ysRxE1CFnl/3EPpGCMNhKELHN+3D+Y9ORj8zSqYngzPuZNV3V+F71Ye11fQlK0cqhL4VYv9f7CfXmMM35eOY7x1D6HAIw1JfZ8ZisWA7YmPZPyyjwduAZcpC3BFXPphUMi+VSvQ91KcCdXpQK5FIUGot8cp1rxDbHGP7hu2UflhizbNr6jqPAOrvUodBL1Asz0Z8NalBBaiAiviu0q5XfNx9G/dx55l3UrQVufet93LBnRfgS9R8Xd3nz+fzyleRKDigwD2YaS0SUJHWbovjT2v8jwPus846i61bt9ZFnnW58MLIqix8eQF10ChOL6AMkD50KYfOgsn/y0YqIFvPrZLWDHJNOuAWsCbHlRdEl36JAX3ppZdYvny5arshn+lF3AS867nkC9uAQA0Qyxzoxcz0F1a+owNLOYfMqUSV5TMhNGQudSmcbKTCFsq16/nZC69RIsfigMhnQnIIEJZ5lwi4xWJRrRn0Vm4yvzLUPSTt2HL1yzY/nOe5J57j4jMuViCoXC4r5hCom3+J4BuGQdFWJO2pgfdUIMX9F9/PsZPHsjm2WTk40m7CPe2uO7dRMbBOWhUrKefXCRZdZSBrR8/3drvdOKIO1oyvYaB9QB17xfQK+pJ9OP1OgvPBuvNaShYcUYc6ZjKZVGA4m80Si8WIx+N0xbpov7mdhz7yEDl/Dvsjdjx/42EoOoSx1CAUClEul9n/7v0svXkpLS0tdHSYTOzk5CRDQ0Mq17hYLDIyMsLU1BSBQADDblDqqJdDV21Vyi3luhQMMR6iVhCWWp61x+NRTqthGFx00UU88sgjdeoQyakWws0z4SH8hTBD/zhEanUKDoHlvRbGcmN4vV4CgYCStUvrrlAopMgyp9NJR0eHAsJjY2Ns3LhR9eNemB4Qj8dZtmwZ2WxWkSn5fF6pFWRtSYsv6ekpv5XaBz09PebcGQbNzc24XC6V8704FsfieOMNwzDYuHGj+rsuD9f9F6DOR9GjZGL3F0ayZd/7944n/y7pVjoYMAyDcDXMp8Y+Rc7I0VnurDuHyNmF4L4ofhEt1RaOSx6nAL6Q9GKvyuUy/oqfvx7+a9KWNJ25TiV7BbNI1/DwMENDQ3R3d3Nm9UzW7VvHhsgGiuUiBw4cYHp6mlWrVrHStZLOg500FhqpZCtUqLBxxiSmK5YagAS49OClrIqtYkNkg5qTYDHIiSMnqnkVe7p2dK1JRFgrdSpDe9XO6kdXM1GaMOt42F0c+9yxGJba3Ms8yj1Xq1Wao820J9qV31C2aUGQqpXjXjgOw2Kw8pWVRCIRBXQEYGWzWewDdtqd7TTPNeMNeOl+oJuDpYPKf3S5XPj2+0y/z2al764+DJuhfDbxqzweDy6Xi7W/XkvoiRDtR9uxuWyqIKi0xfLOeqFKDXBXYPUXVhPcHqQcqJEjuZkcDV9v4PCNh+m7tY/wM2HsfXaMVwxi34ix4293qGP0f6ufroe7KDgLyjdzGA6O/emxvGB5AeceJytuX4G9214HUG02G/mpPK3/2sqOT+zg5JtPpnFfI0ZPLW3SbrdTOFSg9QetPP5nj7PlG1toPNRIxVJrBVapVAgGg8rf9e/142/wk7GYhVYld13WoaSBye91/9UVdvHoXzzK7BqzSnnVWmX3Vbtx+Bxsfm6zIrUkjVUCV+KPZjIZGhoaVFBI/A55XhJgk/dU5qJSMdt07Vy9k4dPeZiizfQ/R5ePcufb7uTKn1+JJ+tREXCJdDscDlW4UFfCiG/g9XqpVmtV4iX1b3H8aY3/UcDd0NDAaaedRiAQIJPJqGg01BfxksWlF0zT5d16rz9x4nVJufxW5Bjyubw8OmiC1/e6jsfjPPfcc3R2drJ06VLFUom8RRxkidbKJrpQFm4YBqtWrcLj8dQm+DUArMvF5RgCzGSz1SPQQh7obKsuydYJjIUAWOZQj3jLnOu5tfp/evRczqFHYsVISZGpcrnMY489xqZNm2hqaqqTmoszsVB2r88XmH24JZdW5sFut6s+yLKxiDICoDXeypW/uJLvXfc9EuEE217ZxnGPH8eEe4IdO3bQ3d2t+immM2m8YS+2ik3Jo2Ve5TpaM61sHdvKvc21qHXbUBtdE10cmThCb28vfr9fPZflzy/nHbyD2867jWq5yps/92ZyAznmWucIBAJYrBbGO8fZs3wPl+y8RLWEksJa+rMTMGq329kf3s+jax+te17b+7ez6k2rcI+6OfHlE6laqtxz8j1Yy1Y+fMuHsVQtDPuHmZqaorunm0PLD5HuSbPmwTVk0hlmZmZob28ncCjA277yNh685kFO+fUp7Cvt48jsEebm5nD5XJRvLJM+L83EyglO+8xp2LARi8Voa2tTFbil8qnH4yEUCuH3+5mYnCD/uTzcDTgwHYBxKPywQCZngmqRdevrfn5+XpE6EukGk5hIpVK8+uqrprOQy9WpT2RdSxV216SLzvd1MvD9Aezn26lEKhx1HiUcDitAm0qlcLlczM/Pqz1IFBVSybxsK2N32JUywuPxEI1GlWJDcqmk7oMoPiQiL884EAhgsViIxWKqb6jX61XRbLfbTTgcJpfLEYvFlNFd7KO5OBbHG3e0traq/G0hkRd2KxGHXGynbkd1nwFQbQfFVuvqLwHf4jOIKkoiXWKH9AJsbeW2OoCuE/e6dL1arXJ88niTcK7WilbqakDZZ1uLrZQKJaz2mtIon88zNjbG8PAwvb29rFmzBp/Px7HJY0kYCSZGJpiYmKCpqYm+PjPy2BPvMeeD+qJwujLParXidrjZFN2EYTVUFFOCEqKEEp9E97+k9kuhUCCbzTIzM4PL5VK2S5foWyyWuoJjOuAXH1SX98ucVqjw0vqXKKQLNDzQUFefxmo1O2JIAKhQKDA9Pc3s7KzyWaUomE5uCHCT+dZbuRaLRcq5MuH9YUq2kvLXvF4zh7tQKNDxcgf2z9rZfsN2AE78/Ik07G+gHCrXBVA8Hg/2OTuhG0LY43bsPtMHdTgc+F/24/wXJ4MbBln6s6U45hxU7BU1n7LGHbMOtn17G+VkmVK5hNvnVsEbicjm83mcg07O+cw55nGo1S/Se827Drg499Pn4og4sDqsSqVqsZiFS6VGTCKVIFPK4Eg58Hg8ihASgltSt6TQcLVaJZvNqqBZ3sgzt3yu7j0ueorMLJlh7oE5bNhUgEfep3K5TDKZVMeWIIqQNBIwEp9lfn5etQGVd1WOtW5oHbund5NeYqrwLGULW3ZswZV3EYlEFJgvl8vMzc1RLpfNujSvYSUJCMm9STuy2dlZGhsbiUQiHD58mMXxpzX+RwF3f38/F198sVo0kqetS5xFTq4XO9AjpPKiCSOlO6diIPTorDBBcgyJromzrEusJQrncrk488wzAVQEyzAMJT3SAaoYUr3StERMxRBInoXkC+n9BSVip0vQZYPL5XIK3Mk9y8YmG7oAc4lgy/WI4dQl9KVSif3797NhwwY1XwJuxbDohesEAOg57tVqta4vuIANp9PJWWedpQy5RJDl+HpxODFcej6K3LseIZTnIZ9JvovMlygRmsvNnP7h03n+6uc5+aGTaQg30LG1g927dzM9PU0gEKClpYXJYya555R7uOaha/BavGrDFiNgGAb5ap4Uqbp1W3FUaOpqwl12s3fvXlauXKmY14AvwAVzFxDeG2bynkms+6zMZ+bNaEM4zEDDAD+44gdUqeK1eDlz55l4PB6VyysgU+/xnE6n6cv3cdFzF/G7k39H1pHFUXBw9gtn0z/YTypvAsPz9p5HxVlh7d61BGIBvG1egsEgFouFgSUDPPvXz5rPOVvF+3MvXq9XFQrL7sly0sdPwu1xc8EFF/DYY48xPjdO7PoY+Q/mwYD4yjhP3/A0G764QVXtlmrly5cvx+v1ks/nVRX0XDZH4IUA2auy5L+ZhykwjjXXTM6RU8X8stmsWh8ul0u1ONPrHujv8NjYmCpwKEoYeYelqqmQFYFEAMtGC+VqmcaWRuUAVKtV5ubmiEQiJJNJDMOsai4twgRIZxuyPP/nz7P+N+spj5fVPpROp9X6F9l9PB6npaVFrdtSqaQqogv7nEwm6ezsVHub3J8Y0FgsplQxQjBks/V5dItjcSyON87YuHFjXUtIIRDFboqtFjut72Xyma4M00GdENk6SS3nEMJaolpiU6E+oi7Hl31FgKoO9uW65LxQA5fy7wLydSJB0oAkrWhycpJQKERfX5/qtnLYf5hPdH+CC5+5kE5vJ1u3blURS93PkusV0lk/p4BX8Q/EX9QDCHrnGLEfMhfVapWBgQHK5TKdnZ11nWDk+OJzyX86MSLzoafzVSoVSpUSr6x9hTsuugOA0+Ons+LlFZRLZhvQkZERRkZGFJHrcDiYnZ0lmUwCtRagLpdL1QXR/bRqtYrFaiGzNEP7XLsKFkigQ64VUMU8AawWKy0vt7DlS1uwGTZaXm2haq2lC0ItOOJwOHDmnZTtZeW7VSoVcpkcoadDrH98PQaGWWBOT+XS1oZRMKCMIjgMwyCVShEKhRSR7XA4yMayJFIJlY6VzWbriowVi0WYgGw5q5RrXq9XSaRtNhsWm4X4BXFib4rR+d1OPFkPHo+HfD6vIsLig8v9CRaQII2tbOPMG87kqb95ilxzLYd9dP0oHcd00LOrR0W2Za6cTic+nw/DMNSfIhuXWjyiflvYmcdisah5zefz5BN5rv35tfzbu/6NqfYpTnvgNFZtX4U76Ka9vV2R8WAWZRayQAqyVioVUznx2rtcKBTw+/0q4CDvzeL40xr/Y4Dbbrdz/vnnqwrj0WiUQqFAT09PHbsozrQsWp1l1cGrbMgej0cZPAFjAiB1cKeDWz0/STYRMMH13r172bBhQ93mLxUKdXZYN7JSVVyMRrFYVAWU9Ei0Thjo7Td0CZEe1ZeNVo/eL8ypFhCvG++FkXJdxt7a2lpn7IXJE5nzwoi2DP14Qo4IaQGojU5nqWW+dCMmxlXmS+6vUqm1ACsUCnWF2MTxkGent3pQ0rx0lcBHAhgX1Vqcbd68mYmJCWKxGDOnzfDby35LxVrhFyf+gksevoRAIaBYSJmjkcoIO4wddWs31hIj2ZakLduG3W5ndHRUsZJut5tAIMAJL53AkdwREj0Jpqen8Xg8vNr5Kvdeea9qZ3LXlrsoFAucs/0cqNScKkBJxPTidCftP4mipcg9x9/DuS+ey2m7TgMDBcwNw2DVbavwBX1UjapimO2X23nh/BeUNGzntTs5znUcrh+bcmX5ntftVUTD6tWrWduxlqevfJpB47XWJgbM+ec45DhER9Xsxy2VuSc3TbLauRpn2cnevXuJx+Pq/crfncfqslJ+tEw1V6VgFNQ7KJIsaa8hRgxqDoGwwnrREvm+GPhSqaRUCnpdgEwmQ6Vc2wfcbrfKh5Ooh5Ah8Xgct9ut5PHpxjQD7xsgtirG0594mtxXc7Q936acXHlWUtgtnU4rYycRbmnRJ2QYmI6Rvm7D4XBdFVIh8Px+vyKhFsfiWBxvzLF58+Y6cK1HQ8Xf+Pdk4ToAEj9GB396mpvsabo9FrJZbKrYbTm+nkInxP4fsukqX9wGj4Yf5ZzkOcqR1x13AecLlYTz8/MMDQ0Rj8fZtGkTra2tlMtlnvc/z+dWfY55xzyPXPcIH331o3hsHnUs/U/xhcSW6WSBAG39fmSO5frlWDppIPP5cvvLzD0/x9LOpXR0dKi5LRQKRJujWOwWOuY76p6N3KecR/ef5HyvbHqF317wW2VbH/uzx8h/P8+Sp5bgcrmUb9Pa2lrnz1QqFaqbqlgyFtxJt5JeL/TdqtUq06dOc+hDhzj228fS8WqHWkt6mp4QMLr/l8/maX/eBOmGo5ayJ+tOCt1CrdaAREutVqtSgsViMWWHC4UCmYwp4RYwqvtd4rtZLBZCoVBdYETvYCPPUq6hXC4zvXyambYZWu9pxWaxqWi0gGcJ/gyfPsyBD5j56XsLe9n44411PrCQ9/JbIaIkOCXDGDbYctMWdr1vF9muLI6kg2N+dgydOzrJFrKKmBfyXp6/APhKpUIqlaJYLCoCR9SW5XJZgeRw2MyZF8WczI3VauVdv3oX29dtZ+uOrcwlzc4vfr+fTCbD/Pw8oVBIdYSRtFbx94S0kvsVvwZgaGjoP964FscbbvyPAW6/388VV1xRx0bpzKvOpOkMsbzQYtjk5dYlXLIRCBDWN2dhQmVT1plUfbOVz2WTkWuTHFxdpq0ztQIK9fxmOa4M2Th0AC4gQ4DBwpxpuRa5zkwmo6Tp+jyIkZA5EIAqc6nnWns8nrrNQwycTkzI7/W8poUst04IyD3Idcl3xHEQ8CQbo2xiuoROji+yYanQKJtJHbOqOTSyscsGKQbLZrMpJrWlpYX9x+/niTc/QcVqzsuelXsoOou867fvqmsFZ7FYKJaLVB31RW3LljK5slkMQ3JvLRYLU1NTisTIZDK8eOaLnPLqKXR0dJh5woUELGinbM2ZDovNXouQyHoSQyHV5AFOeeUU/HE/G4c2YrGaxl+YYt3o6akKzorzde+fkTFUz2iXy0UgEFBF+kTuFoqH2PaDbaSuSDGzYQZLzIL3I14iD0WI2+NMT0/T0dHBzAkzHHz3QZr2NnHMV48hHA7jdptMbTabZXx8nPitr7XSctbSE8R5lGeeTqeVQyIgVpeFqfnXIi0iNbNYzFz3SCRCOBxWx5GcNnnvJbVDAK+QYdLWLJfLmcA9UOXghw6SWG/mSFWcFXZ/YDdFW5G+7Warr0wmo9agzWZTTLQ4GYlEAr/fTzKZVKSC1WpVqoOZmRnC4TDFYpFEIkE8HmfVqlVEIpE6pYB+74tjcSyON84IBoOsWLECQO0nQB0A0AGaDiQXBgLkPRfbqttCsdE68JPIoIBuIaLFR9JrwuiRY7kO3S8Ru/HFzi9yX/A+ctM5LoldAtT8C51k14nvTCbD4OAgc3NzrFy5ktbWViqVCi8EX+DLy7/MvGMegNGuUX7o/yF/t+fvaM42v25e5N71OdP9EV0NoAMnuS+ZG32eqtUqu3t2c9sptxFuC3PicyfWV3ZuznP7ebeDBa65/xpC8dDr0u90EmVhxNuaqxEmai5z5lwOnjRIbC5GsBhUgSPZ14srikS+GMGWttH8mWZsRVudHyY+5NDpQwy8f4BSoMSLf/YiW364hdadrerZin8jtlwFTlw2ht8zzPpb1yv/QFRW4l/L8xOVgoBw+VyUjOKHiXRbfBMBweJr6GS4btulw4z4Mw0NDWodik2OrYyx89qdJNoT9Hb2svWHW+sIFzAxw76z9rH/qv2K4Bg7c4yyr8w53z/HJBleK2AmPp/kces+hPjzDoeDpleb2PSvm3jpb15i8w8207enD6fbbD8q96sHqHTCQkAwoNS1wWBQrT9dGSrpDUK8K1VrtsKxzx0Lhnl/UEudk/ehublZtUKVgIjX6+Xo0aPKdxOyXvDUYleTP81h+Y+/8n83rrvuOnp7e9XG3dzcTGdnJ2BGlkVmKsBL8iR06bCwqzo4XQhwdSMhv5MFLzIXySFZGH11uVxKLiZ5QrIJCLDWQZJsdrocXG+tJSBADK1cs0Tp9WvXZfV6azHZBPX2VboBlxdaNj45hm7IdKk21EvvdXZeN2rCPJfLZfbs2cP4+HhdETr5jc5gyiYlc6n3WhYJtT7vch55btlsFr/fj8/nU+tBeg4KSSPXrisCKpUKeUeexA8SFCu1DVGM0+bpzYTTYTOnGLOw2fqX1mMv1yKokgu1xL6E45PHq+9Sha7pLtpn2tVmKptid3c30WiUX9/6a34U+hEPHf8Q337rt6kYFcLhMNvS2/jgbz6IpWIx23r9bB199/aRTqZrMn2HwQ/e8gOyhayqaC0kT7FS5Ddn/oaO8Q51rzKHohDo6OhQ9ysStf6j/Vz7y2sxKgZGxeDy31/OORPnKPmzqEImWyZ5+ZKXcbqdqg9lw3wDb/7lm2kaa+LSmy5l+fhyRfhEIhH2hPZw4GMHKLQUmDh1gu0f3U6hXCAYDOL1euno6GDlypV4vd66Hpa64yRrK5fLkU6nKZVKpFIpZRglX0zk1uVyWfWzlvdQ1q7T6VTFaqxWqwLFsj7FgZB9Rl/bqVSKfD5v/hnN473bC+Xac3dPu+k62KWcVynmJmBfSA/5rLGxkVAopORo8XicdDqt0lJyuZxyjCuVClNTU8zNzak5mp+fZ/fu3f+VbXVxLI7F8UccfX19quaDRL8kwiiqnIUSYR3ALoz46cBW7O1CACj+i/xdxkLiW5dY64BWB3UqOk6Ff+n6F25tuJW4Lc6X2r7Eg74HqVQrdWAWar5BuVwmV8rx8bUfJxKLsGLFCrq7u5VdWjK3hO54d83OVg22TG/Bl/bVqfxkr9YBtuyr4kMt9HPk+3pkV5eeyxwcbjnMz077GfO+eYZOGOKWt9xCsWL6DSV7iW+99VsMdw4z3D7Mty77Fnlbvg64yz3rUVz9/9cdXMflv74cKqYfcda/nkXf7j6GNw7z3Due49C3DmENm+AslUoxNDTEvHWeqZunyK/Jk96WZs8391CiVr9G1sjMCTMMfMAE2wDZ5iw7PriD7JqsAoMScRZi2mq1ghWe/btnOfSWQ+x5xx7SmbSqJ1KtmrnM8hsZ8v86gBTVmKjJJB2rXC7XyflFoSkkgfxd79iRTCaZnJysA+miApsLzvHcx54j0WF2NBk5e4Qd1+0gXzSjxdJLulKpsOLVFTjTzpo/VoHeB3tJzieVElKXVQuwl2uW4mPikzscDloOtnDCDSfQ/VJ3nR8tPmomkyGTyZBMJqlUzKJnUkhVCqNmMhmmpqaIxWLMzs6Sz+dVbR5514T0yGQyxONx4vE4LpeL2NoYz5/wPHZHrQVYuWx2ZrHZbKomjkSwJVLe09NDW1ubUqRKa1op6rY4/vTG/0gfbq/Xy759+2hrM9sr6RFLARd6sQSJfuoGRjZ9XX4jFcRlw5J+uXr0VzZqHaSLXFkYQnlJ8/m8MnbC6MkxBBzrbZ50IymgWi9eBqh8Ud1gVqu1/HWdfZTP9cqHYmDkM10ipt+fbngFlOoEhmw2+vMV46zL0xcyyjrJoX9PzqNLyuW+5Dxy/XKdAlAELOrPUSKF8XicyclJ7HY77e3t6nwjIyMEAgFVnVovdDfvnecrb/8KCX+CY/Ycw0UPXUTQCBKPx/H7zd6eFVuFr131NRKhBBc/cTHbdm/DZq1VcBeW0TAMisEiPz/v5xxccpCmSBPX/fI6/Am/yiOWPOhisUjZKPNY32M89vbHTLqqCkunlvK++9+HN+PFZrMxGBxkR/sONt61kXTSBGArV66ENvjepd9jommC1SOrufr3VxMomTlJJWeJ3x7zW57e8jSugouP//zjeGbMe29oaABqkRHJ7ZHnIQ7TwJIBpsPTbNuxDZ/HzEFKpVJMTEzwUvElnr7paaqWKsffcTxrH1lLZj6jGNRsMUshW8BhdxAIBJifn+fJPU8y8KsBKr1au7msQe9Pelnx2xUKDAcCAQUwh4aGGBgYUFF7AZfJZLLu3wS0yrspxUn0aqHyXso7KyqIxsZGBcIPHDig8rIBZZQCgQCjo6OkUilaWlrUOeqIrmKB0UtGKfx9AcuAhb4r+uju6Mbn85HL5ejp6VGAOpfLMT8/r56B5N4vX76cRCLB7OwsDQ1mr1SpcyAGVNIH4vE4lUqFiYkJxsfHVfG3wcHB/+zW+v+5UV3sw704/ovjv+Kr/Efjyiuv5JprrqlTRQnZLYohnYiGemJaCDfZw8Qe636NfE/2cafTWZfvK7Zej3rqyhs5hk7wi50XUHlH8A6+0PIFUtZavZKWQgvf3vdtOnOddb8FM5o/VZnihtU3sDe4l5WzK/nMy5+hy9mlbPvg4CA7du3g0Y8/yljfGG8bexvXDlxLuViuA9l6FFAH07qKTeZMV7HpQQRdEi3zlLAk+MLbvkAsVOsTbS/aOWf7OZy982y+f9H32bd0X62SdxX6h/u5/vbr1fPRpfwLSRMwfZ6x8TF2r96Nw+Jgyc4lTKyZ4NGPPUrVWoUquOZcnPrJU8kN5BgdHWX8N+Pkt2gttyrQ9kQbG/55gyLQAaZPmObVv3qVkq/mQzrmHZzwuRPwH/SrIJAo/zKZDNVwlT1/s4fZbbPmfVXgmNuPYfndy7GWrEp9pc+lpGXF43HzHK+BN0mvymQyqlaR+HJ6zSHdLxfALn6Ww+EgkUioCL+khYmqM5VN8cA/P0Cip74Thy1jY+tvtrLhuQ3qu6I+myvM8fQ3n6bgK7D121tpfa6VcCisyIRq1ayrIsBa1I3yPCWAJiq1KcsUL/7zi5z8xZNxT7gVZrDZbASDwbo1L0V+PR4zb1xyqqGWhiDrUgIgEs2W1l7iq3g8HmaWzPDDa39IlSpn33k2vQ/10tPRU1fhXvxaaZHb3t5OqVRSPb6PHDlCLBbD5/NRKpXYt28fd9xxxx/arhbH/+L4z/gq/yMR7quvvlr1yZMN1mKx1Mk+hBnS86tlE9YNiB7lWgjGRfKhy7DkWHokW48aS+EmvfCSGCj9HPrmq0fNBIzr1ygvn+Rf6CBWz1uWzUonAPRItER0dbCuGyZdkiSfyTWKIkDuR+5FN3j69QrwF4O3kOzQJf8yP7IZ61FqYUp1Vl+eq2yCsmlJBF+ev2EYNDY2qhwYObbkxuqVMuXZTjRO8IOLf0AiYLKlu9bv4r7T7iNjy9TVA6jkKlz30+u48LkLOWHPCTjsNaJAwKrX66UQKnDbm27j4JKDAESaIvz2rb8l1ZoilUoRDJrtuEROWPaUGT5uuPbmGBAJRhjoHlAOWOdMJxe8eAENoQaCwSDhcJhBY5Cbz7yZieYJMGB/735uO/024u44eSPPA8c9wNNbnwYDcs4c333rdxluGVZGT8+1E2MgzoKsmVVjqzhx14mUi+W6mgOZYzO8+OUXqdqqYIHn3/o8O07agdVuVaRHtVg1C6eAIhk2925m099tonWy9bUFCvav2Cl9vsTMzIwipeYa5pgNzVIul+nu7mbdunW0traqyt5i3OTdENmdEGDCLstaFQZXpNiyrvTqs9lsVlUJrVardbI5cTZ0Es8wDFpaWtQ6TaVSxKIxil8pwj+AcYZBIpZgZGSEyclJRYTJeyVOgRSRa2hoqCt20t7ejtPpJBgMqnoO8lxmZ2cZHh5mdnZW5UGGw2FVLGZxLI7F8cYbbreb5cuXKxChR7dlX5b9V7fFCyPbOqDTo9syxEew2+2Ew2H8fn9d+y+xmXo0Wyfc9c90dZFeR+KiuYt439j7cJVNJ74n18MXjn6B3lKv8oV0Nd+EdYJ/XvHP7A3tBQMOtRziO1u+w6zLjO5NTU0xMDBAOBDm71/4e94+/HauO3wdRrU+11fmRQfMerrawuCI7oPo86pHw+VPS8LCxd+/mNDREACWioUzXjyDM144g2KxyFW3X8W6I+vUHK0ZXMN773pvXVBhYfBC5kG+k8vlyGVzdD/dTcsTpmJs4PQBE2wDGFD0FZk4cUJdf/s17XieqXWqaXu8jdWfW62ev/g3TU830f/dfuwJE4C7Z91svmkz3v1e5U8Kqe5wmER4ZWuFTF+mRiJYYHTTKFl/VvnAsnakFonYvFAopNplZrNZVVdECB4BorryUsCjFMQVtWg6nVbrWq/NIrnGQkhbqhZOvvFkmvc3q/mw5C3039JP5z2dRKNRcrkckUiEubk5CoUCbd421nxkDcu/uZyGJxqwWW2vS+cQn1FAqsy9rGHxmSLNEV769Euke9I8ceMTzC6fxWKxKLAeiUSIRCKkUqm6dyaZTBKLxSiVSnXRfvHx5V4jkYgqJigFnSU1dWz1GD++6sdULBWqlioPXvog4xeM1/nv8m6L8s5isRCJRBgbG2P79u1MTEzg8XhoampSnz/11FP/wc61ON6o4789hzscDnPBBRcoUCtVocUwyQIT8KBX7RZ2V5eH6IyoHsnVwboAx4UMry4Z0iO2UAOVsuGIAdULL+iMlhxToqN6ZFuuXY/ii8MPqA1Lfi9GW/9TvxcBKDqQ1eVZYrAl2iz3ohstMbryuZ47rueeCQCX88rv9XvTWXz5vhhNyavRq4KKI6LL5eQZyLUKgCmXyyp/RZwO6U+oS8/gNRmUUaJi1CdKl+1lCqUCQWdQPcdKpULIGuLEnSditdXmNJ/P181XtDHK7jX1st6BzgEOtx/G86SHzs7O+mqRyQIX/e4i7rnwHo4sP4Kj6ODyRy9n49BGikaxTkUQCoUIBoPkcjmO2I6Qr+brzpMnT6FcwG13k6W+UnXVqFKxV+okSyK3FwZXnqGekiCGz2o125U4nU4cQQeGpZ58s/qtlMolLNYaSeV2u+uY1MbGRsKVMOnfpLn/8vsp/LBA+h/SjFfGicViZnXSPhej147icDrouaGH9nI74XBYyc+mp6eJRCLqvAtz+fX3p1AoqGuW9S9GfWHFVjE+sl5lPYpTkM1mFcssUW+9HUxjY6PK/+bLUDEq2NrNfSiXyzE2NqaKmgi5Ie94KpUiEAgQDofV+gyHw0QiEQWy0+m0ajEildpbWlqURHB+fl49x8WxOBbHG2+0trbS3t6u7NUfso9A3Wd6Fxaxj7ofArWAgNgisevi7+jKON1m69FuscF6zqnu9+jXmslkSCQSXDJ/CQF7gO80fYcbhm9gQ2KDqhOikwWFQoG53Bzzmfm6+yxZTNubSqU4ePAguVyO9evX09LQwtWHr6ZUreWPy16py9XlnuTaFkrNdZWdfE8+08kAGXNzcxSHipxvPZ9Hr3qULfu3cOZLZ5ptmCwWjKLBFQ9fwZ2lOylT5tLHL8VSsmBYjbrr0e2Q/PuR3iMYMYPiQJF4PF6r/VKqsOFfNxB9S5TkW5NQhQ03byB8W5i5+TkzaFKo0nFDBzM3zuBNeFn5/ZVYjFrwSECdxWKh48EOnBUn+/5sH8fffDwNrzRQspfUd4SAljlpe7mN4390PE9/4Gny/jxNB5vY8r0tMApp0uo+xE/O5/N1QRh5PnqARtR7uiJBT+GSzysVs1WWKMyE+JZ7knl0u90kk0kVaPKUPGz9zlZ2vHcHkfUR1v3bOpY/spwqVeVzi6rObrcTiUSoTFXojHYSaA4omyq+jtyPqGVFLZfL5dT9ut1u4o1xdr9/N4lVZnQ915xjz0f34P6Bm46jtdS8hcE3fc3J5xI8lPdYKozrak19/djtdgq2AlXqxTZ5W14RGTKHMn+Vipl2VqlUGB4eJhaLEYvFWL58eZ0SZrGryZ/u+G8H3Keeeirr16+vW8RifORl1sHoQlYWTGD2ve99j2uvvVaV55fv68ZHKgbKZyLN1uXoYtwE0Imx1CNhkisk4FIAji65lg1S2EndAAijJyBDgLIcTyQnOkiSiL/MwcIoukTu9E1ASAa9ersO5KHWT3Ih2NaZd11OJc9lodRdCAAx8HoVSTmXMM16ZFlXKMizkTwYIVsEEOukS3d3twLbAhgl+l+pmDUAZmZm6J7t5voHruerl32VpMdsvbFmcg22lA3DZ9TNhVST150UuTapDt461cqpL5/KE5ueUGt49aHVrD26FpZCJBJRBb5kbTWlmuiJ9HBk+RFsZRtrDq+hZJSULEjP/QdTftWf7afp4SZ+FPoR043TdBzs4Iw7zsDn8JFypTjYc7DuPUq6k0z3TrNuft3rohmS/iCyKbvdrgqC6ZVSfT4fhUKB3n29XB+/npuuvImqUeWCFy5g/UvrGSwOUigX8Pl9PHHtE5x6y6mKUJqdnaWpqcnMsz/k58xvnYl1zErl7RX27dvH/v37GRwfxHKXheIqk3lOfCmB54MeAs4APp+PdDpNf38/Pp8Pq9XKxMQEkUhEFQOU90OMvRTQkzWrFwSUtSprolgsYlgMSl8qUf58GaNQe/bCuM/OzhKLxcjn8/T19ZFMJlVetuRZy5C1Ku8KmM9+dHQUq9VKQ0MDjY2NKhIg+VnCjE9NTZFOp2lsbKyLyouKxe1243K56OnpIRAIUCgUGBkZUe1jFsfiWBxvrNHa2kpzc7PyOaAWkYUaYNTtquxTOgDX0+bkGPJ9sft62yg9gq2TznqkW0D6wvQ2UZ0JGI9EIoyPj+PxeFi6dCmXxi5l1dwqlmSXUKWq9l+o5Yhns1ky+zJc8MoFpK5IMdY2xprUGj458Elu6byF1TtXMz4+zurVq2lvb6+Tuet+h1yD/JvuX4ivIHOj+yQyR7qMXgerhUJBpaK5XC5WllbSe08vgViAYrl2L06nE2/Jy2VPXUahVMCdcWNY6luK6QpEOfdo0yi3XnArRtHgTXvfhK/gqwNgM8MzhD9n9sheenApHc90kCgmzIjua8/MMeug9wu9eC1ejLyB1VFTKoiqS47X9UwXjZONNEw2gA3VclI6YMjzl+tr2tXEsZ85lt0f283Wr23FM+vB7rGrOjgy1w0NDcoWiQ+nA0eZB2mFKSoO6T8tPqMEdgR4JhIJ5YNKz+qFKQB6G7tqtYpnwsPqL64m6U7SPdeNw+VQKlVdWi1pVtPT08QTcbVmpGCZkAVSkFgPYsncyrPMhXJE+mtkP0C8I858+zztg+2vq5ck600wB9T8aFGdSjDCMAwVZJK0R1nXMpcr9q3geuN6vv22bwNwyh2nsOqpVaSsKTweD1artU7dl0wmmZ2dxeVyKf9UgPjIyAhNTU2Mjo6+Lo11cfzpjP/WHG6v18tnP/tZPvKRj7xO3q0DV2Ha9J7NwlTpFa5FxqLndUqJfh3U6gyqHFeYLt34yCYjQ4yU3htaB4HyEkUiESWlEeMmpEE8Hlfsm1yDLqeCWkEvuRc9aqtHfOWFBZQRkO/KfMh92+12ksmkyi/WjZ5cg24E5T+ZU91ZkN/IOXXiQpexyWf6ueQ+ZJMRB0COoeeoLCRVxKiKlMjlcqliXrlcjoGBAWXQ5dkahsGcZY6vXvNVkj4TrFjLVq76yVWsm19HPpdXc6TnBevKA/0epjum+c5bvkPKU8ttc+VcXHL3JfTv7VfnlKrzLq+LF/tf5HcX/s6sgl6FvtE+rr3rWtx5t5pTcbQAReZUq1XS1TTff8v3ufCbF5JPmH0lly1fxsHug3z3zO+Sc+agCmsG1nDt/dfitXtfJ7sTAyk5R2Dmd8k5ddJI5FDlSpnJrkn29O/h4u0XU86bz+TI1BHuP/d+9p+wH9+sj4s+fxGBQq0qpjhgDoeD6elpmpqaiEajjI6OMnHzBLGtsbocOdczLtb95TrVnzqRSKgo8b59+4hGo2qeE4mEOof0+NbXoazVcrmMz+fD6/WqCrkjMyPYP2tn4qoJmIem05rwJMy1I7K5eDxONBolm80SCATo6ekhHA6TyWSYm5sjFosxV5yDAmxZu4XZ2Vm1P4TDYRoaGpS0LpVKqfkVkqqrq0s5KX6/n2g0qtapSOqWLFnCkSNHcDqdXHnllfzud79jamoKv99PtVrl0UcfrYvaLI76UV3M4V4c/8Xxn/FV/qNhtVq57LLLuP766+uiVno0SgeJOqEtNk4H3/KOL0wR06XqC4G27m/oAEA/rq40kz2zVCqRTqeZnp5meHgYv9/P2rVr8Xq9ddFsSWGT40qqzfbt25mbm2Pt2rUsWbWEj6//OJ979XP8uvfX/LLrlzizTj5288fY2rVVBVTkHvXjSbtDPZINvO57OoEg0VSodZiR38hnmUyGgYEBotEoPT09qsaJ/F7m1uv1KmWYfgyZSz2FUfzI2cZZbrr2Jko2E9TY03Yu+dtLcMVcSm328ssvk81mCbWECHqD5DI5hoeHlZpT1J3ybHUJsqwDiQy7XC7cbrfy7wKBAEDdNctzlfzicrlMsVQ0lX3xgiKh5XuyRkROLr+RoI+e/tjc3Kwqfct39d/KGtFT2NLptAKFUtBV3g3xSeV6RGEh/oq8K3IOaX9Vl0JWrbCvZx+Wt1vY9vNtGBmz8rmkkMnakDxn8Q8cDoeSujudTrL5LINnDrLnuj1UXBUsJQvLH1jOttu28f9j76/DLD2rtG/4d293KdfuLml3Tcc67oqEkDBAcJfg8I4AIww2MINLJkASJMSIG/F0p5O0pF2rurpct7vc7x+717WvXczzPYy9w/DVdRx1JF219y2XLDnXudaqD9ars6zvm9ltisWmLRQK6r304sBS40XeTerSSAHnYrHI8QXHGWge4KytZ0EJZVPkcjmCwaBaH5mvgYEBDh8+TDQaJRAI0NjYyLx586ivr+e+++7j2LFj/ynZNjf+e8YfY6v8l0a4Fy9ezBVXXKGMZkBRh+VwQrU1hF5sS8/V1aOqIqRkM+pCWY9GnXphtdn1apCznVmdJi2f0asH6hSmUqnSGkoOne78iaMuCk+Emk6jF0EO1Vxy+azu8M6OUOu57Tq6rSsu6QMpSkQEAaCihvIdERAiQMUJFPBACtgBNYaE7pDL+wvFWuZUnGthDAA1ik7yaUTxyLVFqOv0MXl+QYv1dZFo+o51O8i6smrdS9YSW8/ZysK7Fqp7yF6QHB+pSi7IbCKRwGKx0DnVyZuefBN3nXcXcX8cV9bFpc9dyurjq8mSVUJUgKK8O88r619RLccwYLxhnAM9B1izd01NJFzfm7LHHUUHH7vvY0y7p4lkIySTSY4dPYYv52NZzzJ2Lt6JrWjj7FfOxigYFCio+ZR1lgjwbMNNgCVZQx0Vz+fytA+30znaSclS2QfRYpRdb9jFwdUHAUg2J3ny409y1o/OojnerKLkYlRJPntXVxetra14f+XlHu89jCyr5K/xe8hdkeOQ4xC9vb2qZ7kwHNxuN/Pnz6/Zi6lUSp03ORd67rTse9lnAIlcgsQHEmRvOrUH6iFyXwTjfQa+Pp+SHRMTE9TV1akiaIcOHQKgqampEv0P5+HvgMMw8OMBSumKom5sbCQYDNLY2EhdXR0Wi4XJyUlVvVRSXsbHx1XkulAoMDY2RmNjI4ZhqLYfUigtFovxyiuvKOPFZrOpHLq5MTfmxp/WkIiwDhbrDpuAb6JDRTbpDjdU06jk+6LP5TN6gTTRu1Bba0Vk+Wz2mh7IELkpTKepqSlGR0dxu910dXWpwo3iHMiz6Toik8kwODhILBajubmZBQsW4Lf7+fb+b3PbvNu4vfP2yuc8GW696VY69nTQlej6A1BeZLXYH7qjKZ8V0Fvur9PK9WvJ/wtQXi6XicfjinkmLRjFppFrypqJvaNH2WXudPaU2FRPnfEURWs1glhwF9h/+X42/HID8XiciYkJJiYmKh0qPCF8Xh+lQqlm7cSe1Avf6etvt9txNjqJNETwj/pxu90qD1mcNLFhBHSRtVOOoaPCYnP4HApIEadcbG6pVC7rLHtA3zeSeiaRbvms2NlCKRcmp9VqpaGhoWb+ZF3L5TI+n0+tfzKZVCld2fos2VIW+8nq+6RSKRXIkOt7PB4mN09y8hMnwQBnwcnKO1cqRxoq9qRetE0CIrq9WigUKOQKtD3QRtaW5fhbjtP7Qi/nPHAOZXu1ALFeTV1sRmGPut1uFbCQuRObS9isot/1PaXbylarleVjy5l/bH4NuOPxeNRcSfqkrH93dzcdHR1MTExQLpcJBAIEg0FlO8+N/73jv8zhttvtnH322XR3d6uND9QIO1FUoiB0ISUOnAx9c8rndLqzHv0V4S1OpwgM+dHRZ6Gs6rRnvfe27izNjurqlGhxmKXPrnxW0DW5hwg/yVXRFagu6HVFLQJbDrfcS881EudTR41l3mTotDU98qlTm0R46NfVn0XPa9fRZpkXqRSv31MHUPQ8IVH0sges1kpv7VAoxIkTJ2oMC6FF61FzQUkv3XkppViJRy55BAxoe66Na7dei81SzYcxTZOiv8gzpz3D8hPLWTS2CKhW2dQruC8/tpx0Ks19V9+HFSu+tK8GdZd5dTgc2Ao2bnj4Bu659B6OzTuGvWDn2qeuZd2RdZSMKr1P2BUCHOhzK8/ocrnwer1MZaa494x76VtcqVZdtBf59dW/5s3PvpkV/SvUnhCKkcyj0LaEfiRKQ5B7QZ1lf+osCcMwcAVdpPypmnOctqSZYYZmmhV7wuv1KuUkdC6AyESEC2+5kK1v3VpRTF+wMOOsFEHp6+vD4/HQ2lrJ6XY4HLS2tjIwMIDH48HlclFfX09raytTU1M1eVB6D1iRHxLBT6fT5Mt5PIs8ZKmCLobbwNJsoXC4oGSGVGcVuRAKhYhGo8TjccLzwmQ/lYVrKt+f8k5h/3xlLtva2vB4PIpa5/V6FXVf3iWRSBCNRtUZiMViNRH6QqFAa2srMzMzOJ1OOjs7KRaL+P1+VcVcqsbOjbkxN/60hs/nY8mSJUru6cY2UPP/YoeIk6LbK1BbU0UH33Vqs+g++b7IMHEy9SixblvoRn6xWGR6epqBgQGmpqYIhULMnz9f9UXW9bY8hx7hm5yc5MiRIwSDQdatW6fos0WKDJlDNfNTsBaIO+NY01Z1zdnz8m+9m25n6fR8eX6djad/R+n0U3VBPB4PHR0dCrSXa0ja2+x8XH3+9SCMrJOs2ZseexOuvIuX1r4EwMoHV7Lu3nU4nBXHcGxsTMn5QqHA1NQUMzMzCgSX95Z9IzpYD/CUjBJH3neE6JIovu/5sB6p1HuRd9XZbGInWCwWYqEYRy86Su/2XkInK59Pp9NYLJX0RP0dpQOLBJ8kwPJvsTLkGQ3DqK75qSi4AEJ2u51gMKgqceuBDbmvRJ9F94rdGLVFee6G5yg4Cqz69ipc4y4VmfZ6vXi9XuLxOC6Xi8ObDrPnvXsUa+7wpYcpO8us+fGaCkPwlD0lkXi9npOso+wJsY977uvBmXKycOtC8r68ChoJy8Dr9aqK7eK8iw0eiVQq4EsPbJvNprqRCLNN0sLkeSTVz2qt5s0PLhykPFzGcbxiT0gdKOnkI8y9ZDKpzmlTU5M6I4VCgcnJybkUtP/l47/M4fb5fHzwgx+scbT1PGoRynI4xAkVZ02PXotDIIa3HIDZiO/27dspl8ts3LixxpERZ2N2pFbaH+hRYBH0sx1XURZ6JFiPCOvI42yauMViUcJH3k+uI++oo97yd1WYo1yt7Ky/s3xXhhxMqFbRlnfWnWb5nc400FFo3aHXkXWJOupGwL9FDxfGgSgLub44ezoVGipFNaQnM6AoTVI9U4S8CEFRXsFgUCn2s187mxNHTlC+oMxpD57G9Mw0we6gUjg2u43b3nAbfR19HFh6gHfc/w46Ih01DihUqPAul4uZ+TMUHAWy9iy/u/B32PN2lpxYouZV3tFqtRKaDvHGx97I7VffzlWvXkXXwS7sLrtCVnV6kjjb4qRKxD4QCCiF4wv7WJVbRZ/ZpxSNJ++hLdZWU7NA5hRQOUySiyxOsayB7AmdISHPJmvsSrm48skryV6YZU/3HrwpLzc9ehM+w0fJV2JqakpR4V684kUW3LMAd7ZaENBqteKIO9h428YKJayxQNQe5cSJE0xOThKPx5mZmSEUChEKhViwYIFKG8jlctTV1REKhejo6GBsbIwTJ06QSCQUiBWLxRTdTt47lUqRz+dp/EYj+VKe5HVJyILnLzxYD1spUzk7jY2Nqu04+fvcAAEAAElEQVSfXnfA5/NxzrnncMfb76CwvlopmI9BwVageHORY8eOUSgUqKurVJkXUEoUnxT1a2hoIB6PMz09rYwS6dHpdrsJh8NMT09TLBbxeDzEYjGy2SyhUIh8Pj+nPOfG3PgTHa2trbS1VQor6dHZfyuYIHpa9KTuhMjQI+C6I6RAXaOWPiwOvnxOrvF/omfn83kmJycrqT4jI9TV1bFs2TIaGhpU1FSeWX8WcbSi0aiin69Zs4ZgMFjV+1kLZ/zuDI6uOEr/ef04yg6+tPdLLEosUsU4dTtId2x1e0K3kfSghszVbEaYzgC0Wq1E7VHu6rmL3ld6aW1tpb6+vsZJF5tRl/dAjcOrO9dyHz1Cb5QMznjgDGZmZnDFXPTe3UuBAnZbxd4T4NXr9VIuVwpYzczMKDaeMPdEbwqALBHRcrnM3k/tZfS8UTBg1827OOcb52DMVNPw9D2l9p3fwvM3P0+0O8rY+jEu+9ZluMfcan6kLorYeGIfiL0nf5f9JPZWKpVSz+9yuairq1MOuDjfEvwRu10H8MXBFaBF0kRl/xbLRR7/wONMLp4EYPtnt3PJ312CJWtRRWz1fVJ3og5r3krRc4plYILrVReFfAGHzVFTKFjsKnGO9c484szK2s6smMHyUjXtQs6B2FDRaFSBJ6ZpKmaknEm91pO0IJW19ng8LFiwgLq6OqanpwmHw+oMZ7NZRttGuefye7BmrFz/o+txFyu533q0XPav7KtsNqvmtVSq9Ozet29fTQHaufG/b/yX5XC/5S1v4ZZbblF5lwCPPfYYqVSKq6++Wh3yQCBQo5B0GghUq5aLIwxVRaMLJKvVSiQSUXQMcVB1JxIqhyKRSPxB/pJsdol4SqQ2lUrVtFLQkWA5cPIsuVyOaDRKfX29OiRCkRGwQApC6MJJBKo4cXq0WwAJESKAuq8IPnGE9UJtOmKpv7vupOnGAFSVnvxOhJPkY+trI4JXR4Z14Qao+dQVqQxxBMvlssqlln8XCgVOnjyJz+cjFArhdrtJp9O8/PLLLFu2rKbv9MjICOVymaamJm777W0s2rCIDfM20N/fXymC1txMxpbhthtu42TnSeXAOvNOPvvLz9KSaVHUO3FEX1nxCveccw85R7WKuC/p432/eh+B0YBCPSXfSAyBnRM7WV63HErV/SE0MqFj65F8idrLXpU5KhaLlCwl7l9yP8+d+xy+pI/3f/f9NHua1VwIYiq1CWSPlUqVQiOCuupFx2T/iuAWZNo0TVpbW5menubYsWNY/VYeeM8DvOPJd+CMOpUynZiYoGgp8vyZz7P7yt04404u/sTFeHIehTQHg0Hi8XjNHgqHw5RKJZ599lmGh4fVGaqvr6exsVEZSoJuC73M4/Fw8uRJcrkcAwMDNftX9pzdbsftdmOaJlPJKTI/yGD5sgXfkE8ZHhJZFvaEoMmyjhlnhsNPHsb0aiKtDJaHLbiud6noi8zjwoULWbx4sapKLmdfn4NYLKaKsklhmZaWFg4cOIDdbmfZsmWq3oOg4jt37qS/v/+PEb//fzvMuRzuufHvHP83W+X/NiwWC29/+9t561vfqn4n9onIIT0FRvScVJMWxw8gS5Zv9n6TK4auYP7ofBwOBx6PRzlkpt3k+63fZ11qHeckz8FiWMhasnyu43N8dOKj9OR6lI4W20O3b4rFIlNTUxwdPcp3LvgO5/ziHBY5F7F69Wrq6+tr9LMOHOh1PhKJBMePHyeZTNLV1UVbW5uyfzKZDNu2bWNgYIBFqxZx/xvu590n3828zLwaOro4k/o8iX0nukcH9vXcc91uERtED0yUSiUS9gSfveKzxB1xznrqLK46eBUBV0C1mDQMg6g/yq8v/zUffuzD+PApO0+eS89Zl+vq0XeR7f39/Uwlp/A4PRjFii4sOUr09fWRmkrR1tamWHszMzNMTU1VmHx1Tga/OcjqW1fjGnQpRpmABxaLhT0f3MPQZUM1DXk9Ex6u/PSV2Io2VTjWMCppScViESNk8NCXHiLRlKjaM3Enl9x8Ce6Um2AwWFObR2xM2Y+6HSuOuPw9Go3idrsJhUKqJ7ff71dsOtM0VXEwWVfJo3c6nTidThKJhLK9A4FADfv0/k/fz1jPWE2dF/+In4s+epHS5YCyZWZmZpi2TbP1J1sx3SZrfryG9qfasWJVNWHEuRbbp1wu43K5FECQSCRUf3Cb38aBmw8wfPYw9f31XPCPFyiH9/jx40xOTqpr6CzEbDarUsVcLpeyu6CSbiJ2u1Da9XRRv99PKBSirq6OwPoAP37/j8k7Kj6RL+Hjwz/4MLaETdWtkVQAu92uGHHiS+g+x3PPPcezz1aL+86NP63xx9gq/yURbqfTySc+8YmayKxEqjdv3qyEgSBGYkSLgS4HVOjXIhR1ISLGsjgv5XJZ5VfqQlQimHq0T8/f1Z372XnmgmDKJhcnWRA/HZwQASYAghwaeSc5kOIU686unn+rR8/FwRWFo0fzxVGSQ6jPszyPDnaIgBclInMuikenEUFt5VRRVCdOnKCtrU0JAwEnZlO9dZBAp8LpNB9x+i0WC8lkUlXTlhwZoWCLU+J0OmlqalKUf3nHUChUQfyt4PqQi0NnHmLxI4tpaWlhYmKC/v5++q7qY6xJE/JAzp7j3rPv5V0Pvks5xvL8px04jQPzDvDa4mp7sLMfOZuG6QYypYxSri6Xi3Q6TTAYJNOe4cnrn6T8fJllx5epvBwRllJdVBxjQO0T2RfigFutVmyGjeX3LadvrI/LDl6GUTKI5CKqtVUymcThcHDkyBG6ursYWzBG93g3hmEo9FoUvJwRYZFIjpSg27lcjkgkgtPppLe3l0KhwIfu/VAFEPI61b4PNgbZuWUnO0/fCUAmnOHpv32ac//lXNqT7eTzeWZmZlRVT9lrAgL19vbS0NCg8t6mp6eZnp6u5L+FQgqFFuWeSCQol8vMmzePSCSijMtUKqXoX3Ku/PP9FGIFeHOlhVrRXfyD9l1i6OXzecLhsIooT948iemstck9z3ngzah51NMoDh8+TCaTYf78Ss/a1tZWVZgtHo8rwCMcDiuqWkNDA5s3b8bv9xOLxYhEImQyGRYtWqSoiMLwmBtzY2786QyLxcLmzZuVc6iD1LMDA1DVnbqeL5VKpKwpfrrgpzza8iiPNz/OV176CutS66rsKiPHrXW38rOmn/Ez82d87+T3WJFcwbdav8XvA7/nBd8L3HL8FtqH21WU2mKxKHkIldZYw8VhHrjsAY50HmHk0yN8befXCFlDNXaM2Fez84n3+fZR3FFkeHCYnp4eWlpa1Dtls1mOHz/OwMAAwWCQFb0r2Hx8c8VxtlWLz4pNo9PC9ei02CEil/VotNxnNlAvcwgwFhzjm2d/k5inkoLz/MXP0xpu5YJ9F+AwKzbXQHiAH7zhB6TdaX52/s94y4tvIZgJKodf7BCdwq4HHqaD0zhzTpiqsKjIQq5QaU1b8pTYcc0OxofH6fhpR029mmQyWanZ02Zn5sszJM5OsG3dNlZ8ZAWW3RaVl2yaJrmmHInmRI2zDbD565sxcgYWu6XGORe9vv2K7aTqUrX2jC/H4bce5uw7zlb7Tu4jYIrYIhIFFn0qa59Op/H5fEoXif0o+dGyhvJ9sXElGi56Uk/jFMdU1tp30gfd1Dy7/6RfXUcYhvF4XIEFrWYrPX/Rg3G5QfsT7ZTLZRqaGlT0We4nto6shfxbbC5fu489b97D8LnDlbPSO82T732S5v+nmfjBuKoto7MH5d9yDz3oFwgEVN2ZcrlcbSsKNalipmkSj8cZHh7m6DuOKmcbIOVN8cRZT3DuPecqBovValWOtg5AeDweBYgMDw8zPDz8xwuxufEnOf5LHO4LLriA7u7uGmqVw+HgmmuuUUJUinXIBoNqOyw9B1uUlx4xlOJk4gzr7cB0VFWGjgLrNCzdKRB01zRNdT1RHLqzq1Ok5NpQSweRv8lndcdbf2f9HXWHWwS4jnaJgpAD/+qrr7Jp0yY1b3IdHU2U59MpXHJNPZov8yXXEIBCHCXJzxkaGqKpqUk9h3xGFJWe4yxDHG5ZRz0aPzQ0hMvlUiCFACcul4twOEw0GsXr9SqqkJ6jJO8mf//9mt/zypmvgAHGhQY3Pn8jrdZWTpw4Qdf9XRglg6eveRrTWnnX9QfWc+NzN9a0e5O9daL5BBP1EzXv8Vrna/Qe7qXB2qDeUQysEc8Iv930W4aahvjV1b/iDU+8gdOOnKZABlnrdDqtWlQJRUj2ip5DLmuVzWZp/pdmXGe4KDvKysiSKp8NDQ0sXLiQ7au38+gZj/Kmp9/E0kNLVSsPMSzEwZ9dyEdf93K5rArH6ZEAvbih3WPnZPhkzbxkHVmGXEP4hn2KOpdMJlVagBhS5XKZlpYW/H4/XV1d9Pf3MzIyoiqHp1Ip1ed6/fr1jIyM4PV6lcMqxcei0SgNDQ0MDQ1VAR9bnrFPjVFsLMJ7gWOoFA7JZROqmGEYhMNhFQUBCH85TCaaIf2RNADOe5y0frkVWirfj0QqQIc4+Q6Hg+HhYQYHBwFobGykt7eXzs5OmpqalAEsRonT6SQUCqkou2EYqopqOp3GNE1FVZ8bc2Nu/GmNuro6urq6amSjnG3dCReDWU8lE1shb+b5Uc+PuL/jfgDKRpmvbPgKnzv+ObZEtmCxWvhpx0+5pfmWyk0N+GTHJzl38lweDT8KQM6S40NNH+Ky5y9j3uF5GIZBJBJRkbV0Os1UaYqXb3qZkaWVwpVJR5J/XPOPfP7451kZXVkT4YXaWi/bGrbxraXfYt2JdVxrXsvChQtrinNGIhH279+P1+vl7LPPVoW99Mg2UGM3yb91EF4AXJmz2cVp9Vxr3e6SMeIdIW1L16zRROsE3iEvRsZgf3A/d5xxB2l35TO7u3ZjNa285YW34Eq5aurS6MEduddUcIq7L7mbQDzAGbefoWwwALvbzq4bd9F/foWJFA1HWfLbJZTyJaLRKKlUilg5RubmDOalp/L7vSVe+8vXcH7YiXe7t8okrDfJ+Gt7KLfuasUZqdbiEbBcz4lf+POFZONZjtx4RDmuix9fzOl3no7dZVf1TcQ2lgiszLWA4GKDCOvSNE2i0SixWIxUKqUi3QJS6OCIFOiV30uqnVD4xXbI5/Oq4FihUGDtj9ZiZk2OX34cgK7nulj5vZXkijnVLlRYj7JPIpEIjhEHjXc14qx3KkaZ2F+AiqrrbYdjsVhNYMcMmKRaa0HtmU0zJD6ewPFRB450tbp6IBCoASwkdUDsqVgspnK9xc4Thq2AC4VCgWg0qjqalEol/O/2U/jHArk3VuyTjS9s5LxHzgNbNQAjDDwBK+T+NptNOeDxeJyTJ2ttsbnxv2/8px1uoV8JZVt3gvXoqdCwhZahf1/Pq0mn0+oQz6ZxyXVEaIjwFAEgTpQ4FHreiVxDryyuJmEWOivfffzxx1m0aBFdXV01AlC/pvxeqE1Cs9GdF4nMyZB76NQpHW0Vpw2qTlNjY6OKhM/Oj9Lp6PLuuvKX/+rOtChCPQpfLpdVhBzg7LPPVo6anlMlgi+Xy6nCUjqFWV9bXUh7vV4ANT+imEulEh5PhaZcKBRU7q3H41FrIWCAaZo8uvFRntj4hFI++5bu4+e+n/OO37yDBQsWkEwmqd9ej7Po5JE3PcKm/Zu44ukrcFlcSrHLOzudTgK5AO5clTYF0FZqY3JwkqnEFKtWrVL55VlPltsvv53BlorzVbAXuP/8+zFsBmtfW1v5nUb/F6fNYqkW+piN6MreyWQyNbk7Orgj9PTtm7Zz/2n3k3PkuPOcO3l94fVsHtusrim52mKQ6edNzotE36WGgigHiZALQuwquXjj82/EipVXlryCrWDj7Y+8nVA2RMQTUWCIrK2ss6ybVD+12+10dXWp4j3bt29XjnUikWDp0qXMzMwounkikcBqtTI9PU06naajowNAIdzxH8bJX3lqn94Brte7OHPJmWzdulW9ZzqdVsh3JBJRLb5kPzv/wUl6Kg2LwPu3Xsr5CkgYDofp6emp0BgTCfbt26cMCVk7AQ4OHjxId3c3nZ2d+P1+UqkUiUSi0pIkk2FgYABA9SRvaGhQskbvmTo35sbc+NMZa9euVXpRhuhV0W+6vhOdLDYIQDFfpCnSBB3V69rLdpqzzRXbxWJlfmZ+zX3Ngkn+QB5aqr9zFp00FZoU0N3S0qJsBq/XizVrJRgJMsKI+o674Maf8tfYOvpzW61Wttdv5zuLvkPUFeWFq16g61gXG6Ib1OcOBA7w0ImH6LH3sG7dOsLhcI2togcmhDkl766ni+mBBfkRe0hnO4o9Jd+Bah2g7gPdnLfjPH73kd9RdpQ5u+9s3r7/7ThLTky7SbPRjLfkrZnLukQdlrxFPd9sKrk8R9Ke5I4r72CotVIUbur6Kc7/l/NVe6cXb3qRY2dW2zBNvHmC3Q27WfS1Rap+itPixBw1yVB1pt1FN92eblxd1ahnabRE+W/LHP3WUfKNeZr2NrHmR2sIZ8Jky1kFGosTKfnNAEvuXYKr7GLPW/ew6N5FrPrdKgrFAmbZrJlrndkpRUOla47YpDogIkXQpN6MaZqqJomsp8/nUwEqmUOJoEtwRfa+3++viaRn4hk23L0Bv9VP0pFk0S2L8Nl9lK3VYsL6+ZHUxWRrkvwVeYLPBBXAJPo7m80qR9Q0TVXzR4rqyl70TntZ/p3lvHbza0RXRdXaWEes+Kw+bB6bslckXUxYAbNbm+XzeZVOqDMeGxoalB0j7VNlPtPpdCVC/Xk3lGGlbyVbtm0Bs7ZWlD63Qs0XH0BvJazb5nPjf+f4Tzvc5557LuvWrQNqc4Z1AWexWJRDLhtHBJ4IPxHK4iDoVRX1XGa5vggMnSauF1TQo7w6cqo76vKj99UTtKxcLnPmmWfi8/mUEyIFF4AaRSOKTRwl3bmFqkOvKxhBx3WnU3JTZS4l/7ZcLtPd3V2DtuvFxfR5FqcJUE6HONc6aiyCTY+EC6qn08Pl4Isw0XPGhckg19bp7EInl3ez2+3U19fX0PQlAqmDIfK+Xq+3BnktlUoqN+nCwxfy6tJXmQxOggHWvJULn78QC1VadywWo/XJVj524mM0OBpw5B2kLemqQXTqvSwWC30dfQw31tJ1Dq06xNqFa5l4YYJXX32V7u5uGhsb8Zf9nPfaedzReAclawlMaJ9oZ9mhZcpwKBQKivL3f9qbkh6h72l3yM3Q14aI7I7gn/TXGAYul4vdvbt5YNMDKtc84Utw98V303BvAwsiC2oYJkJr11MBRMEJy8Plcqk5FZaHILjynN6Ml8sfupy4Jc6V26+keaIZ9zw3jY2NjI6OcujQIZqamojn4jz3l89x2rdOU4j10NAQ4XAYwzBUEbFsNktDQwPHjx9ndHSUiYkJHnvsMUqlkvq8aZq0t7crY0BPIRn+h2Hyl2mKZyPkHs2x54I96qz19/fXAGFStCwYDKrK4vmGPHwUsIP1cSvGixXDQSh1pmnS2dnJvHnzOHToEAcPHlTovZzlqakpJicn2bVrFw6Hg9NPP53W1lZlFMg5sNvttLa2qpoPLpdL7YW5MTfmxp/WWL9+vdITun6SoQPmOjtIDzgYZYPLBy7HtJn8bPHPcJVd/Ozgz6jP1VM2K5/d3LeZ9wy/h1tOuwWraeXDv/gwnkkPGTPDS+e+hCfj4aZ/vQlX0gVWVOqNOKSlUgkzZbLlmS14mj3sOm0XTdkmvrLzK/jyPgpm1bmSdyiXy/S5+vjq4q8SdUYByNvz/LL3l4T2hrgmdQ3DnmE+vezTZBZm+FD4QywILcBhr3ZNEVtKQACx33TGlg60y+/E/tK/o9tLej0bnVo+MTFB6GSIm397M8+c8wzv2PkOPGUPxXLFTqmbqeMTT36CL13+JSZ9k5xz6Byu2XkN1qIVi63qzOm2lTzvj970I0aaq2DFydUnef4Dz3Phv15IPp+n9xe99K3ro+w55SClrfTc1qMYZXa7HUvJQvjWMImmBJNvmcQ542Tdp9dhi9mwBKr1eUqlEu5JNwu/tJDnP/k8F//4YhwFB85gJUgjFarFbpIIbi6Xw5q3sujRRSzesRhPrhKJLRtVB1p0k+44SztWmU/R86KbBBQul8v4/X6CwSCtra0qGq72mFlNQ5T1keuLYy12ijDyBMS3Wq2U4iXW3r8W0zCxuiq2dzqdJhaLqXWWFMNyuUzGm2Hw1kFMn4m/7Kf9uXZlCwnQJc8ngQgBw+U9ZT5CkRAr/2olr37rVTILMrjvdOP8ohOjbOAL+lRKwMzMjLIZCoWCSj8TR17mWG8b5vf7FchgmpX2XtPT06RSKaampmhoaGDBggWV730jz8IVC2lZ3kIulyOTySjnWlhvYpOLPyJ1DwzDYHR09L9Qws2N/6nxnyqa5nA4+MpXvsKHP/xh5TTognZ27qqeO6sfVl0pzHZItfvXfG92dBdqUWfdwdUNWxG0cm+J0ErUUxAleQ85XOKEQKVfpU4jk+INenEmPW9Iz9EW4S+5y+LE68CDQkRL1V7dujAQASrzIN8tlyutPVpaWmoEhJ4Lr0eqDcPg5MmTdHR0KAUqayfPJQJbz30aGRnB6XTWFBCTewkFR9ZBqEACLsgayRzJc5fLZYaHhzEMg0AgoCqUT05OqmfTqVKm1+QT7/4EGHDOT85h8auLWbhwIV6vl0QioYpaSXsmPUpstVqJxWKKDpTwJ7j1/Fs52nlU7ZPzt5/PBdsuoBArcOjQIUyzUmisq6uLfD7Pzo07ue/0+2gbaeOmO27CLJuqJ6jsFXGAhV6uV4mXCqGSqxUxIzx0zkO8vOpl7AU7b/vB2wgPhWvylTweD0+f9jS/P+P3FOwFvFkv1z1zHWsOr1H7Wc6XFA7RlZREzGX/6nnsEomXcyRUcVlzoTgJ8KKf4/5UP3ddfBcjG0ZwzbjY/IXN2PoqRUHEgZY0AbvdrvLHo9Eohw4dUi21BLDxeDx0dnaq+0jKQ6FQYHpmmvjdcVKbK1Qx61ErjosclEYr+1Po48lkkkAgoIrJJRIJBd7lFuWYun+qWjTNhMa3NRLYFlDtgKSwm44+j4yM8NprrzE2NlZTBFEQ6ba2NnXPxsZGBU7pkQJ5v+HhYR5//PG5SuX/l2HOFU2bG//O8W/ZKn/s8Hq9/OAHP6ClpUUxoERH6aC1Tk2WITpad+hsNhu3dd3GRWMX0VHoUMZ0KpXi+PHjnBg4we7Nu+md7qV5uJlIJEIul2P7G7dz9razCeQCQLV1lW4HSe5zd3c3y5Yt43srv8f7j70fW7aqd+SZhMlULBaZnJzkEdcj3HXhXWQDWexFO+dtP48tz2whuzjLV6/9KgXbqei+CT849gM2pzYrWabXfxG9Iqw0cYZE5svQdb4U79QZcJlMpiYvXnTo2NgY+/fvx+l0snbtWnx+HzZrNYABqJzasqvMHRvv4O0vvl1FXUWG6wwFHSBJu9J8783fY7x+HIDWw61c88/XYJYr65zJZHhh8gUGfziIw+5g9WdW4x51MzMzQyQSUXaYALoH33+QJb9agrfgVfcW57bQUMDv9tNsaVbRS9lLEizRWYhiSwljQAIk8llhCegOmtfrVXMoRdfEIZSIuayX3W4nGo2SyWTweDx4vV4VtdaDKnqetji6elBGnlf2pNjTsqbOoBNHr4PAWED9Xi9KFgqFSCaTZLNZJusmeekfXyJfl1f7b8O3NrD64GqSiWRN4Vd5F6i2EdYj/BKpzmazJLIJjn32GIv/cTGFfEHkBNFoVNmlcrYFJJczLnOhgwjpdFqBE6FQiKmpKZxOp+qqI3tHbGKLxcLChQvp7u6uKQLscrmIRqPk83laWlpwOBxEo1FVoFfAg29/+9tzEe4/8fHH2Cr/qQj30qVLOeecc+RmNZtTj0TPPuhiwIuDoH9XbymlKxlxQIWaq9OSxeHTn0MEqkTMi8Wicjr1z8h3dEfz34p2S8RSj/LqNHT9uiIQxdDWgQFxZPV3z+fzKsIscyDCQ88nFwUl15KoqSiUQqHA/v37qa+vV0JSp2RLFF2cVrvdTl9fH+3t7TXCxTCqVRr1d5K5kGJPHo9H0XskCqsLah1oEeUgCqFYLKpoqgg8n8+naMMSBQ6Hw4yNjdW0hzIMgxcWvaD24ciqEZp3NLN3716WLVumHCs9j0zmQaeTC8o4Fhgj4ovU7O3+hn5iRoxmfzOrVq3ixIkTHD9+HKfTicvl4vyj5/Na72u86aE3YbPasNhr8/1kz42MjNDU1KSimqKAZc8EAgESxQQPn/UwL69+GYCCo8Cdb7mTy++8nEVji2ryuy7acRFWq5Wn1j/FFU9fwarDqzCshjoTukEjSLWsg8yFKMRisaicYAGLZL/quccy5OzJebPZbGS8GX5/we8ZWVmJEmTrs+z63C42/XATnkEPY2Nj1NXVqX1QKpUU0OF0OtmwYQO5XI7h4WH6+/tJJBKk02mOHj2K2+1WrcNEcXncHpo/1czRvzpKsaVI4+casVvsxHwxZdDJf2OxmIqMNzc3q4q88TPimHbNJjcge34W93OVivAnTpzA6XRSV1en9l0+n2fBggUEAgH6+vqYmZkhmUwyNjamQJO2tjb279/PkSNHCAQChMNhlixZQiAQqDESxsbGGB0dVfM9N+bG3PjTGMuXL1eFOSWyJ3aAOJB6ZFZsCDHI9Rxcl8uFw+HgPaPvqdgwtsr5TyaTnDhxgr6+PlwuF+cdOo98Pq8M7XA4zFXPXVW5rq1qN+jRO3FQurq66O3txel08tEDH61EiksFZXvpdUPEsR0ZGSHQH+Di4sX8/qrfs2XnFi559RKytiwvul+kZJRq5uQJ+xNsKG5Q1xOnTKd/67RXPZVQbBfdyRUAU2wCAS51sFp0zPj4OBaLhQULFlRoxCY1Ml7sJMMwcJQdvGPbOyiZ1Ui8OD+ik+XZxEYNFAO89Xdv5ZcX/ZLySJkLfn4B5VJ1vsbGxrD0WVjw1wtobmimOdNMzIgxNTWlGIR6he/wF8PkQjmyZpZ0Oq2eLRfKMfCOARq8DQR+GcCesitHTABwyeeVXGyxl4+vOE7r/lZsZZtyrMV+lPfp29xH18tdOGwOtT91MKNcLjNy4Qhdz3ep1E6r1apsRQmKpFIpFQzS2VwylzLvYp/3ndVH79bemqCUblcbVoMDVx9g+sxpzrzlTOoH69U1s9ksFotFtcssFAoMzhuk4NBSrQyYWj8Fh6vrrq8j1NKz9cBCOBxWf3NanKz6p1WUrCXShbR6XrF1pR2XRMrFJpIhfoxQyJubmwGUQ6/XjvF4PMrOkdS+ZDKp7DA9VbJcLhMIBGqKDFssFvVdncEwN/73j/+ww22z2Vi/fj1LlixRzpsY8zrtSEfBRAjohQ5kSMRNkGSJcgttST6rO4YSXRKBDdUcbXEQgZpnECdr9vV0pSoHQ6K7FoulJmdEBJIIF0G1xBkHVIuCY8eO4XQ66ezsrFFAghiK8JLr69F9cVB0upouZGUe9Hc8/fTT1btI1P3fUj7yrmeeeWYNSgjV/uQ6WqlXOu3p6fkDyrgIBF0Yy7pKdFJaQOnvKc62tEuZnJxUz2G32/H5fNTV1TEzM0MwGMRut/PE6id4aNNDKof76GlHMYIGW769hUOHDtHR0YHf71dCU/aQDrjI+ubzeTqOdXB99np+fvXPSXqT9Az0cM0j11Cfq8fiqDz/vHnzqK+v5+jRowQCAQ6vOcxQeIgHz3+QGx65Qe0bfU+JQSF7SLWKOUUVEgMmnovjy1cdWwBLyYKr4FLz43a7cblcZDIZLtxxIU2jTSw6vohopkJR1q+vz62shTArpFCcGITyHHpVbqHGp9NplaIgZ1n2j3zWaXESIFArG4o2rNnaiqWydwSwGBsbq6lQ39XVRVNTE6Ojo8RiMUZHR1UxsnQ6rbocNDc3Y81a6fiHDmbsM/hH/ASbg0oeTExMqHMi510Am7a2tsoz/MiJ8RkD03HK6f5H8P7Iq541Eonw2muv0dbWRnNzs1LGdrudQCDApk2bME1TFRU6fvw4kUiEl19+GbfbjcVSqcSfSqWIRCLU19dTX19Pa2uragkmwNLcmBtz409nLF26FI/HUxMRFV0pMlDkKlAj2+Q8i0wT3Sm63jRNEokEQ0NDDA0NqahiqVRSUVq/31+TfqS3kbRaKxWYxSnq6Ohg0aJFBAKBmmfRHSPRx+Iw9PX1cejQIRobG7l48mIWvriQhX0LiWQixONx2n/dzqbJTbx000sAnP3c2Zy26zT62/uZN2+e6s6gR7AlAmuz2ZSO0utl6M6FfEf0mq6nZOjR7YmJCerr6xVgKvae/k6zbSK5tqyXzhjU50ecnMBwgPN+eR7ZoSz2rJ2iUVQdOA4cOIDH46FrrAvblI2ckWN6evoPdGUkEsE0Terr69W7SGpbqpiicEuB7IYsQwyx1beVC39wITajYn673W4ViNJtV4fDwdGVR9n11l0MHRjitH8+Tek1neV5cMtBdl+/m+jiKJt/tVnNswQ0rFYr+6/dz8GrD5JtzrLuoXXKyZU9rneNMU2T4+cep3l/M6FISO3/crmsotKZTIbD1x7m4OsPkm5Ns+w3y4BK1x4B/J1OJy+/8WWOXHUELPD8Tc9z1vfOIjgeVM8nayr2x7wn5+Er+9j7V3vBgK5HuljzmzWULdW0Az3YZLPZFIVd/l/yrIXZVigUsLvtHHnbEbp+1KXmVt93uj0vY2xsrCbtVWoW5XI5BebPnz9f+TVip2UyGYLBik2SyWRUEEQYMzLHOvNTbCMBrXSWwIEDB/5LZdzc+J8b/2GHOxQK8da3vlVtGBHwUKWg6rQf+b041XphMF14i5IRZ0miajqFW4/W6QdGj6oLQifXF+dHEEQ1AZpglgMt+bVyyMRZk3vL7z0eT41y0xWeIL3t7e01rbz0iLHMiV5EZPaziyBQDo6W464rEaHA6LQpccDkkOvzriPJch/J7ZF10JFqfY3lnnItQWN1+r3kzeq9IWXdpKiUvL+OXIqhIo5eqVRShbSEinPa0dN4duWzRGwRMCrO6Xl7z6OtvY0jh48wODjIwoULMSwGJ1eeZNwxzul7Tsdhq66DrKu05qo/VE/X/V2M/csYb3n0Lfizfiy26hoJhd7j9bCjbgePXPwIOU+OHYt3kC/nufGRG3E73GoOJOIvfSllTvW0gVQqpYTreVvPI2fmeGHTC9hSNtrf1k6+Pk+sKaZo0fL9YqFI79FevP6KIxiLxXC73cRiMUWFtFqtZPNZ9pyxh1AsRO+RXvUu0ivabrerQi2yXlJpXPa77FXZUwKWyXrno3mufPZKcrYcO5fspC5ax/seeR+uBhfDuWFOnjypjJsCBV74/Atc+6NrCYVCNT1UvV4vgUBAsSqcTieDg4MVGvn0NFChDhpGJeUgsShBblmO/C/yipYmaQcej4eBgYEKZfBUrl2xWGR8fJx8OU/ih4naHtwvQmQsAiHUO5bLZY4dO0Y6nWb+/Pk1DAWoGEnBYJC6ujo2bdrE6Ogou3btYmpqqiadRFqHTE1NsWvXLpqbm+nt7VU0v7kxN+bGn8Zwu90sWbJE6QSR0zo1WqcJ67pcdHY+n1e1MURuiiwtFAoMDAwwMjKidIOkgQnDzePxKBtJQGzd2Rdjvauri0WLFtHQ0KDkteh+PY1IZEwmk6m0zOzrw+124/f7cbvdrB5dTd5aAZ5HRkYYHx9n9aurmT9/PtP+aS7acxF2087IyAiJRIK2tjaamppU1wWr1UqePIZZtaF0naGDEGXKlChhoZYBpz+/YRhYnVbyyTyDg4O43W4WLVpU02pK2SYO1FzNdpZkbXSHW6fBm6ZJ3sxjliqVup1RJ2bWJEdOASETkQkwKrausPgkEutyuXC5XAqEkL0htGqxr9xuNyM/GSG7PquebXj9ME995Cku+OcLVFEzibLKGgOMLR9j+7u3k/PmGDxjkJJRYu3X15JJVOygXCHH8JZh+m/op+gucvDcg5XK4LetxWJW9pvFZuHw1Yc5cO0BSs4Se6/ci6VkYfHvFpOP52si6dlslkAwwJHVR9h7014OpQ9x0Scuwp6xq/NhmiaxRIwTV53gwBsr1zx09SFy6RxL715aY4Puu24fxy47ptqgRboiPPWZp7jsM5cp9gdU0jgEtGlubsb2vI3WD7RSfl2ZNb9agw8fhVJBpeXpZ8EwKvnfcl71AJ2ylyiy6+92EV0eJV/Is+hni7CY1dx6AcV0xzcYDNawSsRZlqg8VGypcDhMKpVSgS+n06lsV2FxClivM3VTqZSKhPv9fjKZjArcCVhRKpUIhUI888wz/6Vybm78z43/sMO9Zs0azj777BpqdalUUlEzHR3WW0mJItAj0Dq6CVX0E6hBNMVx1nOQoUpdEsUnlZr1qCOgUCOhOOsHU4xyEeByLYmiu91ustmsihrLc+rOh1xH2gJInozQhnSjXZSMINByTTmQcqj1ohS6UhEnXtBmmWuZf3k2PTotClKGngck15fnk0JlAi7I9SVCKvMj8ycRYxEqIjAKhYJyvCWfWwdmxEGtceJORTMFdCgWi3R0dBCPx4lEIviLfj71s0/xrbd+i7QrzfWPXs/yseVY663Yl9s5duwYW7duxX2Nm3uuuAcTE1fJxdoDazHK1TUTGrVpmpTaSwx+YpBoIMp9597HtY9cS8gM1ayx0+kk5U3x7BufJeepKEXTYnJo4SG2T25ny+4tlEol+vv7cblctLS0qMrcsndlTXO5nGrL5fF4sGFj0QOL2O3ezdWvXo27082+ffs4efIkS5YsUZFWAaFKpZKiKglIkc/nicfjlfNmM9izeg+/PO2XALwn8R5Wja9Shp3sT0HL5RmlVUcul1MGgCgTna4l/y0Wi3isHq5/6HpK1hKX/+Zy/HY/nqBHGZWpVIqhwhAv3vwi00umufNjd7Lp7zfBNKoOQCKRUDlkhUJBdQcYGRlhbGyMSCRSocWNjDDRO0HxtmLFGHKGMO4xiE1WerTqxV8mJyfV2VBRl5tNipcUa3uhfhtyp1UKmsn5a2pqUq3xZmZmWLFiBYZRodjLvInytVqtLFy4kJaWFgYGBjhw4ACRSEQVwUmlUqpC/+DgIMlkUp3LuTE35safxmhtbSUUCikgWRw2qKWz6oWoDMNQhr8YyiKPRZ+KzXHixAnFeKuvr1dAnNgUeoQLqrm9pVKJMccYmf4MlqKFxsZGWlpaCIVCNdRhPWosQQvR3xMTExw/fpxUKsWSJUtUActkMsmga5DESIL+/n46OjtwLHGwec/mCnBrt1KylBR4+Nprr1FXV8eKFSvo6OhgzDHGjR038rUjX6Mz1QnUMqDglL3jMHik6RGOuY7xnuPvwVlw1uhEcbzjtjjfWv4tOp7qwBq1smD+AlW4Vrdnxr3jfO2ir/G5Jz9HU6pJyWG5r75uYkOJjjMMg5gnxo+v/DFXPHwFZp+p0t28Xm8l59eZYN9X9lH6Rom6yTplK0iHCofDQSAQwO/3qyCI2C4SObZaK21Ml/1sGTs371T7LDAUYMu/bFHPKuslaWEWi4W0K80Lb32BnLdiZ2CBkTUjxFfHCfy0AtTkFuSIvDMCpxqsmDaTo5uPMvXQFOFHwhW7c2OGkQtGMJ2n5sNRYv9F+3HtdhF+LawqkDscDgyLwcCqAV795KtggYK7wKPffJQV719Bbk9O6bLUqhRTl03BqQYoZUeZ45cdZ/qhaVzPVmxcu90OL4HT5SRzSQYs4Il5uPCHFxK0BSlbyoo6r+/VfD5PPBbH97SP0K4Q6XAaV50Lv9+vAgWSeiA2tP59v9+vzqNhGNhb7Rz50BEiqyvBmeEbh3GVXPTc04MlZ1EOeyqVUsEEcZjD4bDaQ1KjJRKJqHQ6m81GKBRSARyRCxJc0u17n8+nOslIvnsul1PAlQR1hIGby+VUezUBYObG//7xH3K4DcPgc5/73B9QdXSKkCgicXp1B1F37vQcHEEhPR5PDfqkFyPR0WWdziX30VEkXSmKU6G3NpBnFudWDoooPb1yplxXKD1CYwGU8Q2o3GndudaBCP39RXDI+4nCmD0EWROwIp1OUywW8fl8ioqjO3N69fPZObwSwdfbp+k5MHLIBTSR38uzynxI5FqccQE8ZlO+9BQBeU8RmjI/Mlcyb4L2QrUirNDu0+lKr00fPt7923dzsu0kawbXYFgMhSgvWbKEgXUDPPLORzBP1c/59aW/JlvOct7x85TikL6LE80T/PLCXxINRgHYs3gPjoKDq565inqjXhk0xWIRT9bD2+57G7+57DeMtYxhK9q4dPulbH55MyVrZX3b29uVkSUCVBxXYW4IvV5ypKecU7x81cske5L8ZtlvuNpyNcvN5QwMDLBt2zaWLVuG1+utpCqUiuxduJdVx1Yp51koS9Ju68g5R7jrgrsU7f6W193Cux5/Fwv3L1T7Wehhpmni8XjUuYCqgalHKQR0MYxqjr+kLLgcLt775Hvpj/cTtUbVOevo6GDMPsYT5z/B9OJKpHqmZ4bXPv4ai/5pEeF8WCka2RM+n4/p6WnsdjuNjY1s2LCBF154gUQiQeT0CMU7inAKNzr56ZMUKZL/Wp7W1lasVitjY2OEQiGVBzU+Ps7ExESlcNo2A2PMwOw5BcTtAus7rJRnymTNrDKqZQ9KhH3v3r2EQiGVriDnRwBFoVT29vYyb948RkdHOXLkCKlUipGREZLJpELJpaifHpGZG3NjbvzPjvb2dlpbW4GqjtJpyTJETum0aUnL0SNlYnQnEgnGx8cZHh7GarXi9/spFArE43ESiYTSe3I/nQVlsViYaJzg3ivvZf6r8zn9+dNZuHAhnZ0V51YMc92WqQkkWEu8EHoB324f+XyehoYGVbwzl8uxb+k+fnPab9gS3cLi8mIiWyL87HU/451Pv5MlQ0tUBFSAfXF6Tpw4wWHbYf5pxT/R7+jng4s+yF8e+EsWjy9W9pce9Xts3mN8u/vblfkrWbhhzw2YmWphOavVSsQS4fb1t7O1eStcD5fmL2VTZBOxWEzZPoVCgZG2EX52wc+Y9E/yzQu+yQe3fZDumW5l8wntWGxFqC1MNh2Y5tfn/JrB5kFuefMtXJy6mNbXWpX9kw6kee7a5xhZOYJxi0HsWzFCO0OqurSwDES/yPoLo0oKshqGQXRjlCNfrvbPBpj//HwK6QIWoxrQEUqy2IjenJdVf72KnR/bSWFlAfJg/J1B9htZslRS0hgH4y8MzJ+YsAiIg/ULVrI/zzLtmK7o7YfAZbjIfiOL2WriirhYfctqGvY1YHPalI2YTqfxBX2cPOtkFYw2IOfOsattF85HnNVCx8NleC/wfaCTynPcbBC/K07SklRnwm6343yLk8xoBm/My9m3n03raCuRZETli6fTaYLBoAJfZI9NTEzgcXsUU1YYEHoNpNm2rg60COgx2TZJrCVWnX8DptZO0fRQE664qyaAJP6HzibUa0Op4IwGlAi9XWxysX3lDEoAIZvNEg6H8fv9qpCdAEFCQ5+ensZmsxEMBtV1Tp48OWcn/BmN/5DDvXHjRjZv3lwj5IXuoUdJdWEi0VGdiqzTmeVQiWDUD5HejkDQQJ0aLiiuFH+QgyttEqAajRbnSRxYObSAUkJ6ZF6eVQ6H5AzJu4jzIY6s0EeEFq070AIUyEHVq4Lr0Va9ArJE03QlLHRlQfZ0Z242C2B2RFnABJ36oj+bHHT5vKCFMrfSwkAEnSgp+a7cU1BTEVh6br2gyYACLnRgJZ1O09LSolDpQqHAkSNHWLRoEXV1dYot0J5rJ5aJMdI5QmNfo5p7m83G4esOY9pqC2M9ee6TnHnwTLVn5F3kXWvGqX/qRdeEct8508kbH3sjd155J0lvkgt3X0iGTA2gIsCM7Fk9L1/eCypUP9Nn8tuLf8uB+ZVcnZK9xGPXPcYlnktY++pauru7OXr0KC+88ALLli1j97W72bphK4UXC6x6aZVqgaEbXJlchtnDxFToqs/nIx6Pq/dOJpNKuQkdTt93OrLscrlUVVoBUmTtm5ubFVNErmO1VRkcMorFIl6fF1e6ArY4HA6mpqZobGwknU7j8VSUrdRCWLx4MblcjpGFIxyxHqFMVQmNjIwQKoXUmSwUCkxOTuLxePD7/TQ1NREMBjlRf4Khvx2qOtuVScFus1O2l2sK/iSTSaUopcYAVAzc+fPnK0q8yAsBUeTd582bR0NDAxaLhf3793Pw4EGllHXZNzfmxtz4nx82m4358+crR0mnkQtzTX6v2y0CdEutEAEa5TO5XI6RkRGGh4exWCw0NDRQKpWIRCo505L3PDslTHTumHeM+664j5H2EUZbRwm3hbk0eqnSKwKIyrPpTLtyucyPl/yYZxuf5fKxy+lKdTE5OcmJEydoa2uj79w+Hj/ncbLuLC+96yV6B3s5sOgAKU+K28+9nRueu4HFfYuJRCIkEgkaGxsJBALk83l2xXbxu+7fccR9BIBJxyR/v+Dved3+19F4qKqLAXZfvJsHFzyo5vPe+fcynZ3mgt9coBh2ZWuZh976EHvb91Y+ZIFn3vwMloctLHhygSoCOtQwxNObn2YiOAHASGCEn2z6CR/Y/gHaJtv+gK0nzpnMR9wR545z7+BIZ+W5C+4Cz7zlGc61ncv8vfPJ2/I89xfPMbhyEKhEjXe8bwelX5QI/C6g7Kzm5mYaGxuVnSf7RE+HLBQKFEtFTGpTh2z2ChPCLJsqx1fsMX3tHUccuD/ipvDdAvwYLD+oBpwUU/RlC3wEcrfksP2VDcdvHRhWQ/UIt1gsuO53UW+rJ/LNCN1f66blSAsWq0XZDWKXpeIp1v5oLUbGYOD8ATCBD0H5F2XVY1w+63jcQfmDZYo/KWL5gAXrQ1bKRpXRIXo499Ec2KD+eD2t+1opWAoKjJHzIN1nxOYQG1uKukqhMafTyfT0NB6PRwWFAGWfqxZqWgCvaW8Ta763hh2f3kE+lMe9w03D3zQwdWRKgWRer1edJ3k/nSkhdq5+1oCaYsc6I1fsZ2GQ6gBEKpVSdHVArb+8rwT60uk0brebZ599dq646p/RsPzfP/KH49Of/nRN4TPJe9BpzLqC0tFb3ZEV4SiHTTZ5NptV0XGobNxYLFaT1ywHQ8+x0CNx8jc9UidOgsViwefz1bRQEGdCotjiZInTrh9q3cEUJ8vj8ahrieMtTrPkZkkEXW8vIgpXAAWo5pHqdHxBmUXxyNzKnOjFLPT3F8UnB1ycB7keVMEIeXfJRYNaZFiADp2+JQJC6NFC1RZnWxxQmTsdXJG/66wD+YwUkSmVSng8HmKxGC+88IJyDMfHxyuR6ct+yU8u+gkjrpGatXvjvW/EVtDaypkG77z/nTWAjkQph+qHmA5N1+zx453HyXgyCtyQ/8radEe7CaaCZB1Zbj/3dkyqbfF0xFveFSpIv7yvrLvT6cRWsLH0yFKUXjbBF/PR3t9OOBxmzZo1rF69mlw+x90r7+bZ054l48pw/5n3s3vF7pp9I87fup3ruPSuS8GsvPt77n8Pa0+sVVGXVCqlnsU0TAxLNT9fj+7oaRhCkRIgKRKJKHBIL2aiFxoB8M34ePPv38yCkQVgQsdYB29/9u10G91q74jB6vP5iMViKiqdTCZVD8pQKMSy/mWc99XzoEzl56PA9yAei3Pw4EH6+/trch+lBUkoFKKr3EXzSHPNPFtesZA/XkHPJS9Pis0kEgkFGObzedXOa/fu3WzdupVMJoPf71eFj6Slj5wpccJPO+00rrrqKq677jo2btyoirDNjbkxN/40hs/nY8GCBTVGtshDHUAVOSjyTvSoyD3RWVAxyEdHRxkcHMQwDNW3NxKJMDo6qkBYsS0kwiYOeNaS5RfX/4LBjlPOn8XkyXVPckf3HYrCKp8XG0WlZtmtfH/V93l43sMkvAkeuPQBptdNs3DRQpYtW8bIGSM8fmHF2QaI1Ed4Zc0rpDyVDiTTvmluO+c2JuZP0NLSwrx581RBSI/HQ4etgwUDC2pkafNUM12ZLlpbW1Uk3ePxsLpvNfaiXc21YRos3blUFZ7yer1QhNDjoZrrOZIO2va34fP5SKfTDAwM4Bhy0DrcWvO5rukuwtFwTWRSbDGdBVkul3EX3Cw9Uatrg5EgrcOtlZQAnMx7ZR4KzzXBGXPie6XCupK0I2HiyTokEgn1N6Fp2+12Gnc1suqTq6BUudbq36xm/t3ziUaiTE9Pq3xwPfAj6WKJRILSqyVsr7dh/6kdg2phOEDZVPZtduyX27H+xqrsKhkS+Jq3bx7rP7We1p2tyl7W66ZIYMqZcbLwRwsJPBLAeIsBt1frJun0Z8MwCGwNUPeGOpyPOdUZkVx3u8OO8SUDPgtYYXTVKIc3HlY6XlIoJJ95cnKSWKySFiZ7OBwOEwqFlEMr8y7+g9gjoueloJkEAMRWbtrfxIbPb8A/6Oe0b5+G64RLzZOe1jU7FVOqicsekuCQ2LPC8Mzn84yNjXHy5EmGhobo6+tT9YakmJ7YCCIzstmsshcmJiaYmpoCoKGhQUXc5TN6Wuzc+N89/t0R7jVr1rB8+XKF8ohzoTvNQm2VHNDZP1DN2REBCahWYSKsJBor1FX9MOiRIj1fSI8om6apkD5xUiVSJYUt9CiuDiDo0W9xIPQWY9ICSy+6Fo/HayLNeoEzQDlkEhXWfy/Oik5JkeuLcNArVIrTJs8jcyCCRhxtnY6vv5sIKkGDRSmlUqmaiulWq5VoNKqin3JPYTSIAs7n8zWF0MTRFMWko44idHRQxDRN/H6/mkPZE+KY+v1+Tpw4QSaTYc2aNfQ5+vjF639ByVpZ7++85zt89vbP4kpWlFdrrJUP//DD/PQdP6VsLfOeB95D22Abps1UYIDM6eb+zRwaOcSu7l1q71z0ykX4x/0U3UXFKpD3LNgLPHT+QxzrPIZpmLy84mVsJRsXPXERdc46Naeyl2WuZT1lvn0+H6lUimwqy6bXNpEqpXjsnMdoSDTwvlvfh61so0iRVCpFV1cXkfMjvHjRixTtlTXNurI8fP7DNI430j7ZrhRQsViEMpx24DScTzhpzbeydGgp8XRc7UkVaXcXufvCu1nTt4ZFhxeponY6wiuKS/aiKOyTJ08ClbxpyWkSpSXrKwi1O+3mI/d8hB9e/UPeedc7sZVtZJoyikrV2NiIaZqMj4+Ta8pRPF7Z08FgkHw+j9frJZPJEAgEaDzSyIbPbGCgeQDX/S6m7dNkSxVaXDqdJpPJ0NHRgcPhIBaL0dzcXDFCMtDxNx3krXlmtszQ+HgjDd9r4GTxJDkjp86tnF29tsP4+DhjY2OqtZgAK01NTTQ1NSknXQxyodwLm6SlpYVsNsuZZ55JKBRi69atzMzM/HvF79yYG3Pjv2EEg0F6e3trItg600tktuhUkZOis0Uviw6WVocDAwMYRqW4UrFYJBKJqCrG4kiIHhYdLLLZ7/Lznhfew/cv/z4xewxMWBNdww0DN1CmmoonP6IzTdPkgY4HeLrtacqWiueY9qW589I7edt330aD0cDaE2sZ2TfCtg3bKFlLOPNOAtkAEW+EorWIo+DgmgPX0BvrJW9UmYfibIQsId68783k7XmeX/Y8y8aWcfO2myupPm5U4bFyuUwoHeKLd36Rr7zuKxSsBd7zxHtYEltCuaes7EaPx8MV6SvofLyTu8+7G0/Gw2d/9Vlsbhumy1TzZ7FYOH3X6dzRdAcvdb7E6YOn895d78VqsVJyllQgR7c39Tmym3bOeuUs4oU4z571LPUj9VzxzSswigZFW5FYLMbY18fwH/ST+lIKT8zDNV+6hpGhEaampsjnK6lLkt6WSCRUtFUYUmI3yZrUnajj4r++mJH1I6x4aAXYIO/KK6dM5lUcVmFWSWAmFAkRbAvW2F8SuEkkEmSzWYKRII1LGpVN63K5VCVyj8dDKBTCMmzBxFSMULFrZI6kOn9+NI/nvR6K0SJ1bXXq73o0X+yB6LGocqLFRs5msyQuSmB+xFT55QVvgR037KB5spnwQFgVwhUGp8fjUXVtdHtTghR6jrTYxmLbSmtaKdir08PlmQMnAlz+hctJx9KMl8dJp9OK5i22mnRmkTzwtrY2rFYrQ0NDNDY2ql7bwWCQ8fFx6uvrmZmZIRwOE41GicVitLe3E4lE1HNLQURJJdEp616vl1gspoA68Q9CoRDpdJqZmZm5/O0/s/HvcrhtNhvXXnstHR0dKoKr062kmIDu9On5T3IY9Ii4fEav7KcXWZPPCcIlvQ8lEiq5GmLwyz10Z1QizhKd0x11+RFnSp7NNE1FU5corh5JBNTBF8qtLjB1tFuEsSjlfD5Pf38/LS0tBINBoOqUi6CQa8523MRZ1em8IuDlWQQE0amr+lzrBe7ESdLTA+Sech+Jnsu764i/zJU42wK+iHEiilo3LPR8ep2+I/Mmcw5VkEL6H5bLZbZv386jP35UOdsARXuR59c+z7XPXgtUFEJjopF3/O4dpLwpWvtbsdlt6t31YmFTwSki7to+3KPNo6zwriCbzqqoeTwex+Px0BfsY2/zXpUfjgHH5h1jffN6QjMhNceyf/1+P1DNjxZgQz5jt9sxyyahn4doO9bGdTPX4bQ6MS3VdXQ4HKw+tpq4I86j5z5K1pXFNmJjw20b8Ex6yLgyNWcKwGJYOHvv2RUE3W7FFXapfZjL5Si6izy46UG2LdrGtoXbeDfvZumBpX9Q/0AUrpx5UXw9PT3qfcTZB9TaicMp+6WcLfO+376vcm1bxSjo7e2lo6ODiYkJstksExsn2P+h/Zz+rdPx7fepVI1MJqNy7gE6+zoJ7g5CL5S6SvT19RGNRlV/2mPHjtHf34/FUimkIuBGoVBgxZdX0PeRPhZ+fyHl+jIBb0C1IdMjGLJP5B0FACwUCtTV1TE5OcnRo0fp7u6mp6dHFVqRfG3DMFQLHzknTqeTQCCgQK25MTfmxv/8aGpqorW1tYZ5ooPpOvgm9o4enRadLXbDyMgIx48fx2q10tjYqIA6ierODhSIjNApw6FQiFX+VYSPhPlG7zdYmFzIX+/6ayhXmWe6fhdHI5vNsvnVzQxEB3hs42MUbUUaZxq54dEb6LR3kivnMMoGb9j2BhwuB1uXbOXql6/m/APnc++me3l2+bNcuetKzt57NgWjUGNPCWAuKVYXP3gx5OEv9vxFjX7T6e3lcpm6TB0ffuTDTAYnWTqylFK5pGqciIxPJBKs2bOGjCXD0mNLsWQslMySKq4l+a0hf4ibX72ZWwu3ctPumyib1Y4qQI3dowd4FAU5b2XFfSuIpWOsfXwtbpsbbJXvJRIJojNRWn7XQrmrzMLtC8mkMqrGiFR31x1isbsSiYSyc4T1EF8Zp2m4iWB/kKaTTRU2WKli6wYCAfVuep68zWbD4raQXJfEPlRpRRkMBpXzpRfKFWc1GAzW1F+Roqfl5jL2VjvOuLOmXpHYX2KDyT6XGjAumwvTWwE6ZP119qo4+6ZpEgqFqKurU/ZePp+nsKfA1LenGP/EOGV/GfeUm/U/WY/rkIu0mVZgg1QoF8apFE8VWrm8k7A9hfno8XhquvBIbSEdwCgUCkSj0Uowy+VgetU01t9bFUAgLdyEzi1AhR7QymazKr3R6/VSV1fH+Ph4ZW1PFUDLZrNkMhkMw2B8fFzZd1I/aWRkBMMwCAaDLFmyRDETotGoqpnkcrlUF5ZYLIbdbmfPnj1zoPyf2fh3Odw9PT2cffbZyniEagVOnWohKJ8eLYrH40pJ/Ft0Sl1QwR+2UNJpuvJ3cU51B12EvBj5Qk8WBE8cPJU7dIqCq9N0dAq1tEkShaZThsUxEUddDi7A9PS0omDJ+8l8uFwuwuEwdXV1ypGRFgF6AQ2dui1RxnQ6re4pgkbeWeZdFKJ8T3KG9cg2oHLIRUjqOcYShZeUAEH7xbEShSv3EEqbXF8Uh9De/k9UZfkR5oHT6cTj8ZBKpVQV1nK5jM/nUy0W9m3eR85di/wZJYPNd2/GrK+mAhiGQc9UD+akCc5qLp70nFTF3JxFCrbaitEpe4qiUcRhOJSyk9zizolObnz6Rn55wS+ZCE/QEG3gzb9/M+1j7eSpAiuyT8VRlH0oe0X2rNQjcLlc2BN2rEZ1HuUcCaBx2u7T8ODh/rPuZ9OvNpG6N8WrrldZtGgRnZ2dam3ljM7Ojxdgw+F2cNd5d/HS0pdOTSDcdv5tvM76Ojbu3Viz12VvyvflXEkkR2o1CD1OZIKwIPT8J4fDoRSRnG2/38/09DSTF05y8IMHKQQKvPzBl1n+7eXUH6xXtEm5hzjf5XKlZ3Y4HKa3txfTNBkbG1NMCAGojh07pnLBJbLU9vU2Sq4S4+PjhEIhent7SSQSxONxBgYG1L6VM6IDcaZpMjQ0VLPXRQYsWrSIYDCogDd5XwEl+vv7icVixOPxP1ruzo25MTf++4bVamXlypVK5ul2yuyos3x+NttMT1sbHBzkwIEDFXbY8ihRa5SWoRYikQqoK0WRVI0LzUGUSNira17lOvM6fKaPc1LnYO2zsnhmMYZpgFGtuSLfkWtI/Yrp6WnOmzgPa8bKi+te5Ponrmf+2HzKVFsUWSwWXvfi6+iY6GDDoQ3kyXPli1fSNN3EWX1nUaZaa0ScGH1eLBYLgUCAN738JnBUC8RCtcCm2B/lcpm2iTY6pjrAoErxdrsViFtXV4mmrn95fcWWcVlrqPvicEt7qnftfRempVrESnc4oZpHrztt4oy63W7Of/Z8MuUMZaMaXIhGo0AFLO58qLNC/y1nVTRWKpPrdUtKpZKyhYQubbPZiK6PcvDmg0y/Ns2W32zBLJs1qXWiN6AaaCmXyyQSCXbesJORTZXaJN69XhXRlvcQ+0yCTFLIrQbICcLElyawNFnwfs1LcDxYs+8FyJZ0B4BiQ5HhRcOUb69tLyfzKvtc9KOkSoqel31hsVhovq8ZS87C5CcnWfX9VXQf71Zrqp8zAeWlmJ/YB5IfPbvDj54+qgen5L6yv3VA6+AbDjJ49SDLysuw/saq7BRx0KUujZxti8Wi2HUyr1arVaWh6mBXIBCgpaVFBQPF5pPAhtitVqu10tI0kVD7WQeEpHe37CvZU3Pjz2f8u3K4V69ezZo1a2qcNN0ZnV0lUw6DjkgJIifCuVwu8+qrrzI1NVUTMdfzM/TomtvtVnQeqVg926EoFosKOZUcbBH+qtjEqdwWncIt6K38VxxQ/T2Eii7/lWvqaK5hGDz66KM1SkfPT7HZbLS1tdUcJh2hlfnUFbkeTZd/Q7XF1OziDCLMdSqOOHmSUw7Vwg4yL+Io6vnd+vwIUKHTjCSNQBwjuRdQo5zdbrcSquKs6UXZAOUMP/vssyofSN5TgJHW461Y87Wt5K5/6HoooqKLFouFYqm2Z6ogwuKQi7BrH2/nrQ+9lWAyCCY0vtTI2p+vxZf11SCPAgSYpsn8oflc9+vrCMVDfOjJD9E71quAB9n/krOfzWZrFKs419LP1G63ky/kGb9snMEPDXL32+6mUCwooSwOutCeVr62khtvv5Fz0+dy1llnEQ6HOXr0KHv37lU5PzabDbfbXdMDXj8DlGHBkQXo9VzsBTvdY901URZZe6fTycjISE3KhIBDgv7ra6jXGhBjRmdtyHoKy2LyjEkOvPcAhUDFqEi0J9h9825S81Iqx0xo+FarlWQySTgcJhAIqAJ8dXV1zJs3j82bN9PT06N+PzExQV9fHxMTE5RKJQKBAMe+fIxMJkMikWBkZETltPf09LB+/Xra29sV+q4bHGLcyh4TA6m/v5/+/n4ee+wxHnvsMXbt2oXVaqWhoUExWTwej8r1Etk1N+bG3PifHVarleXLl6vggN5DG6o2xc7wTl5oe6GmJoro4Yw9w9+3/j0jo5XINkB2QZY7Lr6DW86/hSP5SscCMc4bGxtVuyuRnS6Xi9bWVg5uOsgdq+7gM0s+Q6lcAao3TG7Amam1Y+Q5JB8WYGZmhkgkooz1M3adwU333UTPaI9y0kSGl8tlSoUSGw5tqEnJ23hwY409ogdSxMYQW0T0hAC9uv0nbACxlySIIc6h3E/WQGw/mQ/dnrLb7dQ31CtmkOhzfY1mp9rpDC1A1SlJJpPqOcQOyOfzPHPlMxybOUYoFFI56ADDw8NMTU1hmqZiJOrOnby75PE7HA5SS1Ic+OQBMq0ZTlx8gm3v2IaJqfpdz7ZvRb9ls1l2v3c3I68bwZxnEv96HHOzqWwvoKYQsaTcFYtFFVWV9qBHv3mU2LkxIssivPY3r5F3VxmoYlfKPi4UChh2g+c//TyjnxklfUm6JmCkU/TFXpSzIiloQtMWW8fhcOC+y83STy6lY0+Hsh2dTqeye6R7iBQsFptB7NZCoaB+J/teHHz5jvzE4/FKlfl0mlQqpdId+97WR9/1feSCOfa9Zx9Tp0/VsEHEXtKDhDolXfa+zJ2km4ldC9R09pE5Ep0vbIdoNKr2s1xP3kPsokgkQjqdplAokEwm/zNibW78CY4/2uFuaGjgjW98Iz6fr4bCAdWqhCI89L/J76RY12yBrBdjEEdMnHnd0BVHRXpH6sW/ZLOK86g78xKt1inVOi1bUDT9gEk1YT3nVt4HUI6lfE+PSIswuPrqq5VDAShFoedO6xWtxZnS0XXJifF4PEohifMhTo8AHeIIyBzJ/OjUKlFuOoVbRwblHhKtlvUTQSetzwRsEBRS5k7mRp5dcq/ld1BB8qQ3tQ6syHUBEs4E276/jcnipEJvxeH1+Xw4o06YVbixNFCivqFeFTZJuBJ8/13fZyI/UUPhE6dfz9/P5/M0RZp4z3ffQ8uBFt5875spDBfYt2+fek95N6vVqqKkXeku3ver9+E+7q5hIUgKglCK5YwIOGIYhspdjsfj5It5Di07xOOvf5xSoMSJeSe47Z23kbQkVaEOUTw2m41SsURPvEfN57JlywgGgxw7doxDhw4xPDxMLp9jZ8dO7j3zXnCijEk5C7FojLVH1/LmR96MLW/DlXDx6V98mrZ4mzoXej6ytPCQaLcoY/1cC/It8yDOtKydniMtZ1YQ5NUjq9l4ciOW8ilGSMHGGdvOoC3ZpqrkSqRfcgOLxaKia8o+C4VCtLS0sGjRIlauXMm8efMUCJTNZjk0fIh7P3Iv0xdOc+jrhyi6Kr1VT548ST6fx+12EwgE6OnpYcOGDfT29iqFKRR5ibQL+Fcul4lGo6rNjzjy99xzD7feeitbt25V1VUXLlxYQ2+cG3NjbvzPjlAoxKpVq1TESc631PkwTZO+xj6+tOFLfG3513gx8CK5fE7p0Zg1xhsWvoH7Wu7jW23fImNmKHeU+faN32YqOMVEaIKffvinNK1uwu/3k0ql8Pl8dHZ20tzcTHt7O8uWLWPVmlUMnzbMd1Z8h6QjyU7fTj607ENkHdkap1aGzjLKZDKkUinGx8cVGwrAho3W6VblkAmVWXek9EiwyGtxDHWbQa+WLPJXfi/OqzjBYhPI32bbaGIPxEtxfnHlL5jorFQdF9tQ/1zSkuQHN/wA5qOCNfI5oAYMmJ1KJzoq78rz1au+yqRrUjly4jzlyLH96u3sPX8v40+OE+gKqMJnkqcNKIBX7q8HWYSmbLPZKAfLvPLNV8g2V+wZ02Jy9Kyj7HjTDjXn8n4yh7FYjEwmw8GrDnLi3BOY1lOARFuJI39/hKwvq95N1kPsEZvNhtfrpb6+XrEsp78/TXpdFdRNd6V59auvKvtHIrxiFyasCR78mweZ7prGbDSJ/zCOcbGBxVqloMtecLvdBINBFcEVG0r2hLDQTNPE7XKTPppW6ySOcSqVIpFIEA6H1bNYrVa1j8VWkHkStprYbsJOlP3s9/sVQ0UCHXa7ncHzBul/XT9lR2W/FeoKjHxphGRHUu13/RzIO8heF5kgDrDYeXrgQKLTck/xZWR99FbCYsP7fD4ikQjJZFKx3QKBAF1dXTQ3NzM9Pc3Ro0f/aBk2N/53jD/a4V6yZAlXXnmlOqSCBsnB1/NAdCcYqjRtcX51J61YLLJ06VLq6urUhoVK1dDZkfJCoUAkEuHhhx9WjqteMEz/nI58inDNZDLEYrEaVFV6AMuhFgq5IF9SyE3eVXdO5TMSkQaUMJe2RjJkDuR74qhI1Fz+Xz/EYszraLPuPApKN5v6o0eYhVEgCluEhcyR/jtxlmSd5PuCAkuuiQg/mStZU3FGhDosQ2hHgiiK4JH1kX1gsVgYrRvlm6/7JmMtY3zzom8y6h0lk8kQj8eVUzd4zSDFQK3HvecNezCsBl6vl23JbXzrum8x1jrGr97yKyJ1kSryfMoBBhSFSIRudjhL29vaaA40093djcvl4sCBA2pvzM5/Gukc4Qc3/oCReSM1iLtESPQ2UwImiJLWEeqiu8izpz1LyXbKCTNgomWCQ6sPKaRfKNSmWSkuJ6wFifiuXbuWTZs2EY1G2b9/P9tbtnP7G2/niVVP8LtVvyNnyakCHgKGeN1eerb2sPaOtVzwVxcwc3hGCX8xPJ1OJ5H6CGlfWlHW9eiFsE508CoUCqn9JMpLlKUerZf5KBaLkIfXP/l6Nu/bjDPv5JIXL+Gc/efQNa+LpUuX0tDQQDweZ3KyYjCZdSbT9dPk83mi0aiibcsZEfrXihUr2LJlCwsWLMDV5aJ8a5ni2UWwQHxLnPEvjVMMV5zugYEBTp48yeTkJMlkUq3d8uXLWbJkiQIZvF6vKuQiayBV1gEikQiHDh1icnKSSCTCzp072b59O08++STHjh1Te2FuzI258T8/1q5dW+M86ZGrQqHAy56X+fj6j5O35ilYC/z1+r9mZ9tO7HY7/fZ+3tfzPkYdo5SNMtvO2MaOy3bw4utfJGevpj3lnXleueIVli1bRjgcJhgMsnDhQlasWEF3dzft7e0YDQaf6voUBcup9CYDjnmO8Vj4MQAVydaj62IzJZNJBRrqubnyTqKzxCYR+0wPRMgQe0bsAClEKcCr0JDlOeQ74lzPtgVEx4o9Jz9JR5KHL3mYA8sP8N0bv8uxtmN/wGqM++Lcd919nJx/kk+e80kO+w8r4Fl3rmc7wPL7crnMjGeGH5z7A443H+cb13+DsbYxFSzJmln2XLqHnZfuxLSalJpLHPzRQRIdCfXuQp8OhUKqA4uwuMrlsmJbJpNJMpkMkTURys7atElrxopz2FkTsZfn1W2QRfctouPeDoxiRfe4h9ys+MsVWCNWBQLJWom+kwJ1Uh+kvr6e1ptb8T/vV/evO1rHxs9uVAB6MplUTqVhGAxcMECyJVntV+2C8feOky1nlW6VNZYIuuhICQLJ3pJ9USwWia2NcfKek0x2TNYAOnoOvIDxeuRbZ2pKqoYEmgTotlgsChiS9ARJf5S5WvrKUpbfuxxrtvJMnikPXV/swj/sr2GqyPwDivEgdpLofJ/Pp1h2klcPKNtGt43FXhanW4Jh0m60UCjQ0NBQscNOFU+bmZlRNqOkvc6NP6/xRzvcH/rQh/7NXrq60BVBIBFmKXQhG1CoG3rOtUSiRSmUy2WmpqbYvn27ijSLQpCqghdeeOG/SVnW80H0olgSmQKUoBVlpLfSkkrbsunL5bKiEIsTJc8reSV6/pREBOW74tTOpksZhlGTHy6UHEEJxdkRRSIKRiL6AhDIoYZqBFXyUUQZybzIZ/SItXxW1kzeQdZKz9fSc9clcik/En2VoYMkMmfyHOLcy3v5/X5VqXG4fpjvrP0OQ94hAPob+7ntrNsYcgxhukz6tlTaLXTf0U3XrV2KDr22by3vffK9lPIlYl0xXrv5NVKtFcf6eMtxfn3ur4kGo+q+ArgIQivv7Al4OHHTCQ4uOkhTUxNLliyhUChw4MABxsbGKJVK+P1+TNNkf+t+fnHZL4j749x+xe0cmX9EratEsWczLPSzIWtitVohBjc9fhPzj8+v/K5k5Zrnr+G0PafV1EcAlEEjTq4oBcMwaG9v58ILL8R8q8nDNz6slOfvN/+eR854BKerWmVfzs7w8DANv20g8kqEVCrF4OCgouUHAgHiDXFu33I7d519FyVPSRUlkyKB+nkSI296erqG3SFnV86G7AOdBijpAtc8fg0XPn4hZ247E8Oo5JZJFGjBggU0NjZSsBfY8849HPjEAdKdaaxWqzKAoFJB3+VyEQwGVVSiu7ub9Reux99bNUIAaANruHJ+p6en6evrU47yzMwMLpeLuro66urqWLhwIeFwWMk8OdPJZFIZCwKSCb1OlKsg5ENDQ3OVR+fG3PgTGhs2bFC6Vgxdnfk1Fh6rFsg8NU76T2K1Wom4I6SttekhRq/Blye/zJsib1K/u2HqBv5m7G9ob29n3bp1tLa2UipVirUKffmO+jtqrmMv2/lw/4e5euxq5ZDMTmmxWq2kUinGxsZUmyHdkdZzqiVoMDsvVY92y7vrARNxesS+E4dELxgrnxcQVqfmyv11Vk+6lOa+LffxyqpXAChZS9xx9R0c6D6g9HHaneb+i+/nUO8hABKOBN9Y+w32hfcBKFtSz6eV38vv0r40d5xxB/s6Kt9J+9M8fMPDnOw8WYm8UyTaGK2Z97KnTKG54mxL6y5x9MRh1HN9JZorczHZNFm7H4oGy/51GQseXVCTFih6UA9alctlWr/div0bdjwjHlb8ywp8h3w10Vhx8HQbTxw+0UF2m52ev+qh47kOQjtCrP7qanxlX00QQJ7FMAy67+tm4a0LVTs079Neev+fXsysWcMY0MF2AZnlvroj2tTURPaSLKPfGiXXnGPvZ/aSXp1W4ILYi+l0WoEYOutR7Hev16vy/AWoFl9BUiOFii65/brNCrDswWWs+NUK3FE3G362gbpX6pQdL8Vrpa2YOPP5fJ7p6WlisRjRaFQVde3v72diosKaHBsbU8xPWRMBPWbniMven5ycVEw9nSnhcDgIh8NqPwwODjI3/vzGH1007fLLL/8DCoaeS6L/AOpACj1cfidOtxx02ZhSYAgqBZ9aW1trcj9FkEOl6IgoBhHoQuMQ411/TomoysGOx+M1LcukTZQ41lDNYZbfS1RY/5s4WBKxE8GnonbU9oHU84dFSIgC3bFjB263m0WLFiknSyKyIph1pSLzKJ+V50qlUmquRBjLZzKZjHLudZRcBwdE6MvvRfjp0UN5X7mGCF6JYEItgi4CRYwZnSWgI9GhfIhwJgyh6r6rS9RRZ63jtitu40DdAZqHmul+pptFDy7CVrBhXGTwuudfR72jnmJjkUwxQzAdZJRRdY3DnYe55dJbeNft78JZrNCqQ6EQqVRK7VGLxcJTb32KmcUzPJB8gMBTAdaMr2HDhg2Kql0oFFi+fDnHG45z21m3MeWv9E6cCczwmwt+w9sffzsLRhaoORIloef/ADWKU/Kv/BN+Vv7LSkbfMcrrj7+eVUdWgRUF6gilWyLoutKVlAaotOja6NnIa8ZrZKlGUudNzcNiWCia1ch0NBpVfa8nJyfV846Pj1fAnFY3Pzz/hww0DEBbpQ3Z++9/P7ZytSicUOglnULfQxIB1iMQQkHUwSSXy6WK1ZSLZTbt3ETRqLbEkyh6d3c36XSan73+Z0xtqMz9jk/vYM2n1+CwVGlbwkqRfSqKfJ1rHcWvF9nzN3vI9GSw7LBgvN/AO+YlZamcGzFaGxoaGB8fx+v1YrVWqtc6nU7WrFnD5OSkopEPDw9jGEZNKoq8s8ikXC7HwMAAq1atYsuWLTzxxBP/PyTt3Jgbc+P/q+H1elm+fHlNVFUYTVABSK8YvAKP6eFry78GwM2DN3P95PVYLBY2pTfx1RNf5X0L30fSmuTS+KV8fvzz1Jv1fGryUzgMB86Sk/dPvx+rYWVqaoojR44wMTFBe3s7PT09OJ1O/rnjn/lV069qnu2Lx7/IlvEtFIpVnSlyVGyQVCrF9PQ0MzMzNc61AN4Za4Znzn2Gq569CqAmOi2Ap66n9ciazpjTnS75vP5v0SnibAt4IT8ydCe+ZaIFllff156zE5wK1tB1myJNHOKQ+owv56MuXadsErmerJ3+fBaLBUvOQmOkETqq97FFbZQHK7aNpWhhyY+XMDI6QvLaJLasjXO/ey7NJ5sZNAbJZDLkcjnq6upq6OPyXsLW9Hq96t077+jEYTrof38/AKd//3Sanm7C4XTUFMqSd5R0J6kQHolEcP+Tm86DnTRMNpAoJ1RwS95NQI/ZNqDMvdPpxIybzP/WfIruItZJKylSeL1eFZiqyeUvlej8XSfWrJXDaw8T+kIIFy6ytmr6oNiSorfle9L2VbcTM+dk6PtEH4W6yl6KtEV46q1Pcd53zsM/6CcQCNSw5CTYpbM2JL9ZrxkjYIr8Wy/WBtVisdKmEyr26bzfzaOpv4nG440MuYaUv5FOp9WcSFRd6OCxWEwVyxO7JZFIKDs/Ho8r/0DmR2xKi8VCf3+/YrTmcjlCoRD9/f0YhkFvb686U5JiIimkx48f5/nnn/+/ia658b9w/NEOt54vLRtfj66KgBUB4HQ6SaVS6iAKLQfgJz/5CZdddhnNzc3KmQRIJBKqxYH0XRSnTygrUsVQz3fW85M9Ho+KUsvGF2cvnU6rwyjUWb2wlDjk0rtb6Ngi5KR1mAg0QamgCkCIs687nyKQhaYiFYwl1wQqBenkevIugq4B1NfXVxbs1LzPFlCiMPXiD7rDI1FAcXhFeEuekq4sAcVOEGEiz7R3717sdjuLFy9WQEogEKhRfBJh16PaOuo9O1ddqFqBVICPvvpR/m7T33Go8RAr+1dy7YvXctsFt7F3fqUN1/DnhgkSpH5/PU33NtG5rxN3rxuzsQKc1Kfquf6+6/nZ9T9joH1ARXkHGgf4yZt/wvtufZ+aP3mngrXA3RfezY5FO8CAuD/Ov176r3zswY+xNLmU5cuXq5YVExMTdOQ62HB8A4+vfpyypYylbGFj/0Zah1splqq92WXPCAIuZ0OoR3pKhmmahCNh1nx5DWvOXENdQx1Wq5V4PK5QZclfEqNI9o5ujAF0jHfwwX/9IN9993cpWUp0frGT4ZeHsXXaWLhwYU2FUdmn7e3tSvELmPW7d/+OiYYJJQMOzz/MrRffylseeIsyAAzDqGlXsmPHDs466yxV8EPl0J0yDnRUXeYhEomoc6xTyeRz8XhcVfZ95M2PMLZiTD1TdEGUl//xZc6/+fyaNJNMJoPb7cbj8ag+nYlEgu5iNw1fbGDb323jslsvY2thK+4ON0ePHlVGlHQCGB0dpbu7u4Y6J2taX1/PvHnzqKurY2hoSMlFl8ulHG9hgAigeN5557F48WJeeeUVhoaG/ljROzfmxtz4bxqLFy8mGAz+QdRRd1ysVitXRK/A2eckao9y3cR12IwqpXllYSV39d/Fl1u+zN+O/C3uUiXS5i15+djIx5iZnmFoZohYLKYKiNntdpqbm/F6vdzWfBu/bfotJaPqmLpKLlbFVykZIsChHtXMZrNMTk4yPj6uqOSiK0zTJGvL8v2bvs9MaAZLwcL5z52P3ahGRqGaAqdHukUv6zntMh+6vSG2gu5QixM/m1WnO96maZKIJ1jz/Bpy5Pj9lt/jyrp450/eiTfjpcgpVmPC5OpdV+Np8vB4z+O0plr50stfwlPwULZUAf/ZEXV9Lb0lL9fuuJakJcnWpVsJj4e56idX4Sl4KHkr+iIyFCH42SBFT5HT7jkN67CVSXNS1Qryer00NDRU8r1P6bpSqaSKlElEU5wvO3YW3LcAi8tCQ7SBnt095J15leMtrSL1faanBuRyOTw2D42DjZQdlSis3u1lNrtTd+Blf4kjWx4v47A5yJQzNTR8n8+nWJLST940TfIP5Zn+0TR+i5+Sv9oGD1BdSMQOkZQ8iRTLmtvtdjyveAhuDTJx5QRYK1H+nmd6CE4EwYJiAtrtdlX3xOv1KuDBNE1mZmYUjVyqlct6CyU+FAopnS/dYMT5lX1YLBYxyya2wzZypVxNzSOhfc+ubB4Oh1VkXebJbrfT0NAAoNi2cq1AIFDjbMuZqqurIxKJKDvP6XSycOFCoOJTJZNJ9bzii4TD4bmiqn+m4492uHWhKZEb2YT63+SgSzRVBLaex3DNNdfg9/tV/rKgO36/X6Fdck9xVORg6wZ5Pp/H4/HUUNtFCIjDKbkcgkxK5Et3UtLptHpW6dsXj8dpamqqUXJyYKamplRUVw600GeFIiMoogiAZDKpnPFyudLmSnfAJZI5m0I/NjbGvn37OPfcc2lpaVFCT3JWRPEWi8WavCIBGux2u6LL6M6P3prC4/Eox8lut1cQ1lPVwuXdhCmwYsUKtQckiidCW+Za6EEul0sJf+kLKeukU39isRj19fW4XC6mhqa48ac38sB1D3D9g9fz4rIXOdp6tErp88Dwe4bp+VIPjpiD7GQWs6eK9vr9fkrREjf+8EZ+/YFf099eQZnrhur46MMfxeGvFvgSxbF73m72LdiHaanSBnPOHL/d8ls++8BnsdvtdHZ2MjMzwwsvvMDSpUu5JHUJqVKKbau3cfq+07ni+SswDVMVOxHwRI9GCNghBdMaGhpq6glkmjLs/eRe1o2sw7q/tu6Bx+NRayhrLesgwIqsu8vlojHeyMfu+hhH647iG/BxOHqYHRM7yGaztLa2qjMnxXSkJYtEzwuFAuf/w/k88f88wXT7NAC9w73c9NhNGI6qUacbUXa7ndbWVqamphSLAFBRfkmjkD6besqFnGHZT4Ikezwe1Qu0UChw9d1XE3fH6VvYB0DjRCPzPjyPhpZKjncgEFDvGIlEFHgm0Xer1Yon5+H8T52Pw+9g/vz5qoBJLBZT9G+pNDw4OEgsFqO1tRWXy8X09LQ60319fbhcLrZs2cL09DS7du2qKfwohdWgYoSMjIywbNmyuZZgc2Nu/ImMZcuW1aSziTwT3W6z2RQwfsXkFeps63rMMAzaMm1879j3KJVKJAoJlVsq0b9gMMi8efOU7aTr0LdNvI1R2yh3NtxJySjRlG3ii4e+SGOxEbujyqbTad2ZTIbx8XGVoqI7y06nk1goxs+u+BlTdVNgwDOnP4Oz7OS8V8/DKGiVu41qFxJ5/9lOsw66z6aN65FuneUo3xF5KO+sg8yWkoUtL26hYC2w4dUNuFIuTKNaE8fpdNIcbOZjxz6G1Wnl3QfejaPsAGs1wCHX1G0xeWax3Zymk/N+fR6RSyNceN+FmHkTi7Wim4eGhjhw4ACkYMPfbiDcEKZgFlTFa8nTlVQi0Vl6WprYUTpjy2axseSuJfh8Psq2sgrqACoP3u/3KydR6tTEG+Ik+5LU+eoIBoM17Exdt0hwSxiZYuPqLNLy+jKh0ZBiw0n9HAm8iM0krStLpRK5dA5ryoq7ya3o3XqlcL04mgTWxHbVnX9n2UnPV3vIlXMkL0uy9MGlLL5/MTiqre+kmJ7YoNlspf2arH+5XFZAuwTLJNosvgKgggcCZIntLXOUzWZJrEiw86M7Oefr52CNV4Ea8Qn0tAB9v+rFicVm9Pl8jI+Pq8/k83n8fr9yksUPktz6kZERdV05owKMSFBPmA3hcJh9+/b9V4q4ufEnNP5dfbhFEOv5EXLAxDkwTZPp6WkCgYBy7iSSJUheY2Oj2sASOZc2VXI9qFZilOuIkpJ761ExMdhFcYrhrjuT8oxyIHRadDpdzQWdmJjg4MGDdHR01LStEpqs5C3rcyHRS71NhCgeee6BgQEWLFgAVGnYcghFSErkW4TNqlWrWLZsmaK66NFqub68l46CyjPoVHmhPwlYId8VZE/Q8b6+PlVNWUARfc6kSrTMt7y7jnQLSCLvp7dmk88J5Vecv5mZGbLZLHXhOj7y8kco1Ze4oO8CAO6/4H6K9iLzR+ez8acbcWQcWP0VQ0Z3TO12O3V1dUxNTXHdbdfx6PWPMpwcZuMPNpJrz+Gr93Gk4Qj+lJ/QTAiADX0bsDxn4a5z7yLrrNCwwy+Fefuet2Oz2rBYK+BFXV0dy5YtY2JiouIwtzTgWOaga6wLq9VaA2oI7UwEvsyzFB0ZGRmhoaFB7edoOMrua3aT6c7w0+U/5U3mm1h/aL3an7KHxLgQBapTtNPptCrsYRgGzaPN1A/WU1hcYNGiRWzfvp3Dhw8zOjrKokWLCIfDlMtlkqcn8U/5sRk25SSWy2U8OQ8X/eginv6LpymPlbnsicsYL47T2NhYY3TK2mazWRobG2vSGsrlsgKI9HMpqR+yFwR4EwNVp2brFG2LxcL1d17Pg697kFgwxrUPXktgQ0BF/IV6v3TpUiYmJlTkJxwOq/6XQtEPBAK0trZSLpc599xzOXLkiGrxJbTKRCKhDAEBqorFIjMzM7jdboWwS8Tb7/dz9OhR5VQ7nU4V8d6+fTt+v1/1450bc2Nu/M8Np9PJggULlAMB1TZf4syInSK6d7aDKc6SXtsCUFHElpaWGntAT93SActPDX4Ka9HKk3VP8tn+z7I6vRrDVnWydcpusVhkenqakydPqvon8iwSmR4NjBJ3xqtFsAwYah4iQwaP6VEBEz2FDKo0Z12n647CbEq0Tt+W99adeUC9szyfzs7KZrOc9cRZ6nryX5vNRkdHB01NTVitVj5++ONghaJZrSCtgwN6pFjsH3kmYW1d9NuLKrTfXEp9fmhoiJmZGVpaWggEAup5o9FopVq2y07hTQXy2ytOlbAsC4WCKsCqV9kOBoMMXDTAoucWKT2tAxZiP1qtlVo8Yq+Vy2USrQmOfPwIyVeSdP+8W72LpCXJulgsFvVvmS+x4WR/TF06xeTnJ/H8xEPo9yHVCk2qlIsNqFIPMhkMq8HYZWNYfloBI/Rn1aO/Mv/CdJiYmFBpXzrwYLVaaf5CM/Uj9azethq3z632u+TG6/WEPB4PgUBAOatOp1M9s/gCYjPIWRJ9HovF1BkQ+1iYbuObxnnpfS+R9+fZ9sFtdP1DF6mhlLKHLRaL6q2uMzNlz8vayb4S5ovMo3Qs8nq9TE1NKdkhn9eZqIlEgmg0SjgcVmxbscWTySSNjY28/PLL/0GJNjf+1Mcf7XCLkpidk6k7BBaLhZmZGXbv3s1ZZ52lch3k77pC0L8jv4eq4y5Ilh4tlgiu7jjrQlYcS8m7EYdWjHw5kGNjYzidTrxeL/F4XCkyERrhcJhNmzbVOO/iPEQiESVYoRJR1517QQD1+4szNDo6yoIFC9RhBtSziwCV9xXkT8AKEQZCXXI4HCqvVKex69edTXeXtZJiYTrlXkf0urq6FP1XHHtRzDoVW6c4y/OKANMBDonOynrLfST6a7fbGRwcVJXIZV7FiW57qI3T+0/nyBuO8PqHX0/YFmbIV6HxSq9lnboGlVzmXbt2cYXvCmZyM4wOj7JjbAdtV7Rx9wV34864efd978ZbqOTybzy8EXvazq3X3krbwTYW/XARu2O7cW120dLSovZmW1sbHo+HrfO38vTlT5P1ZLnn3HsoUmT13tXqmZLJJFartQbYkP/a7XZWr16tnMGit8htF92movFFW5H7zrsP025y2oHTaowpceRlHcRwknWQyIqeMiBI9KZNmxgeHmbnzp08+eSTLF26FLbA/pv2c2LiBG++7c0qIgyVPHzPsIfN/7qZSH+EkfIIoVBIMQnEqDzQeoBCsUDX8a5KasCpFAO9SJoYC5LPJGdK9qQg/fKOTU1NCrEXGr7ki1uLVq58/EqK4SJNiSaS1gr1T9Ypm83S19dHNBpV51/2v8gvOS9er1f1Wff5fLS3t5NKpRRyL7n7UjwvHA7T2Nio2Bpy1gEaGxsVABSPx4nFYsTjcSVHIpEIBw8erKmUPzfmxtz4nxktLS00NzfXOEWik8SZEL0HVYdTGHzJZJLp6WkMwyAQCOBwOFTxKPmsbrCLXBUZLqCe2BbvHXwvZ86cydrkWqwOaw01Wq5hGIaqHZHJZP4gh1WYaG372nhj6o3cdv1t5Fw5lhxewlVPXIUz4yRfqupw0bez87J1oFOvx6LbFeJw6zagfFd3TvTrQ7W9pq7X5LOiz+rr6xUgrTuseoRXBzJ0h16/n6Qs6c6oOJGFQoHjHzuO57OVwnWSyyvsq2w2y/iXx8lelSW6I8q8B+bRsLdBfVfygMUGy+VyHP+L4xy95ihGh8Ga360BqAlKSMtIsYvkufOhPAc+fYDo4iisgsH5g3T8oEOBBTJfemBL9L7Mo6SCzVw7w/inxikFS+x61y6W55bTuq1VgfHi6AorU2zVPe/ew8BZA9Sb9dgerdjoEvmFqoOt1yLSwShpYSpMzEKhgMflofupblxhV02tIwGu5bv5fF5R7U3TJJVKMX/+fHU2xdEWx1uAA92+kmCXsAzy+TwDKwZ49W2vkvdXWLPTPdNkP5PF93Yf9mG7Sn8UyrrsJbF//X6/YqfK/Ao7V561VCqpvSP7W5xrAQ8kml0sFpmamqKhoUHdR5xysfsnJ2uL7s2NP5/x745wQ5WKIY5zf39/hf7T3EwgEGDz5s01vbpFIAgFXASFHBR1OD0etamtVqs6DCKwJKonxroUa9JzN6VFgQjYaDSq/iaHae/evZxxxhmKTqPnWdrtdoWu6bkjQhc5dOgQTU1NtLe3k8lkVF9i+X9RCOJkyPMFAgHWrVunaGaNjY2qOrogYhJBE2Eq0XWJIEIl4iaCU6oi6kiyrI8INp3mLUXVhBqvC05AzUV9fb0COCRyK2sg9xbhFAgESCaTKpIpSlfWUqKVUBHY6VKaL2z4Al/d/1XcZgW5lEik0G9lXoRuHovFaHq6icXDiwk7K1WnFy5cSP9AP/FMpT1FfX29WmOhHMViMXL9ORa1L6J+RT0P7n6Qx65/jLQvDWH45+v/mY//4uP4bJUqoEuOLeHzP/o89rydUkeJY9ljPPHEE5x//vl0dHSouZ1YNsGzlz1L1l2JhifdSe479z4a8g007m1UPaNlf0nlWDEIdBqcaZrYsjZO33U6Ay0DlK1lMKFppollR5bVGHxQ7Qwg50HalujRCplHXaGZpkldXR1er5e6ujpmZmZ4fPhxRt8/Ss6fI9GQ4Ffv/xUX/93FGAVDne1isYhvyIfDdFAoViptHz16FK/Xy8qVK+nz93HLxbdgmiYfvO+DzJ+ZTzKRVO+mt7YT+SHGg1T6lz0je1be9+DBg7S0tBAKhRRLRd7fl/ZRTpbJW/JqLsWQ8/v9FWrnKcPG7XYzNjamznbRWsTn95FIJAgEAupZhDa4evVqnE4nbW1tjI+PE4lElMyampoiEongdDoVONfW1qYqlUvUOxwOUygUFP1sz5499Pb2snHjRp599tl/j9idG3Njbvw3jPnz56ucTNHbehFN0SXiUEp+6cREpa6Fz+ejrq5C/RWnS480il7WC65KTnjOmuMDnR/gn4/8My4qKVu2ko01iTVg1HZfEWBPcmVHRkaYmZlRDpgecdado96xXj50x4e456J7eMODb8CZdlKk+Aefn00Jl3fW053EsZDPyxzp0T/RbzrQK9cFiCfjvHj6iyyILaDraBcG1cJqAFa7lX2b9uG1etlY3Kh0h9hDs4FrnaIu91bReJeFcr6sWkkKACyBkbK1zOPvepzJ5ZM01DUQ+mkIr8eranEUKRL7eozUm1Jgg5nzZoivi7Pq46sInKgAyhK0KZfLpHNpTt54kpPXnsR0muy7ch9mwWTp75ZiFk1lg0ndFAEP8vk8Zco889fPkJhX6feNAePnj/Ny8WXav9iu9oDMpehHWRc9EBY5I8LYp8YoByufydfl2f+R/fiTfsKHwsq51gGRolHkwLsOcOLiE5g2k5lPzzDjmsH5K2dN+oAeAJN8atM0VZs7qLTEFBZkIpfg8NcO03NLT801ZI7lbIijqheAk6i27HGbzcbk5CTxeJz58+dTKpUUu0OeQ+6hB+sWjy9mYHCAY3XHwACjZNC1rYtkJEk6n1YBJ/EBpNWvBDCkqK34JeLkS8BDgLPJyUlVrV3muK6uTvk5Qt+Xlmpi10vqrTAeXnrppRp7b278eY0/ui2YIJ3iPEBVYO/Zs4dQKKQMXsn5BWpQH/1wyJBop/Td1p1D+bsYzXJP2fxyGHSFpNOfi8Wi6lMcCoWUUX7JJZcoQ1+QM3kmiT4JpUZoYuKQb9iwgWXLlqmolhxSHYGU4mlSdEMUueR0hEIhRd0VASN9FEW46s60IGlSUMFur/QAlsMtP4JeisKU9RLkT1A2QdVkvsUY0KOIAh7o/ch1ar8oPL2tlghIUQpivIgBEnfF+eL6L/Ja+DXev+H9jLvGmZiYUFWx5RrRaBS73c7Q0BCjo5Vq46VCCXe82n8xEA4QuTzCvk/vI70kTaKQUOi4AEKNjY2cPHmS8fFx5s2bR+yHsYqzfWpM1E3w3Ru/y7h9vCJMbXYaM400GA3U19ezZMkSli9fzrZt2zhy5Ih6n5UzK3n9a6/Hla8gxI68g0tfvpTewV6VHyX7UOoRiJLR0VhZI7NosuHYBi65/xIsCQvzx+bzsbs+RpiwEsbpdJrh4eEaB1uo6i6XSxX806naeoTBMGoLjXQv7ubkfSfJ+U+1qDJgfP44z37gWXUtuZ4oC2FsWK1WZmZmeHT8Ub5+/ddJuVKk3Wn+6fp/YqR5RDmxgmjr6LUUjpmNcgsQIawAwzBYsmSJapkj4JfO2hCjT/aEnLV8Pk9zczOrVq1i4cKFCmk2TZNMfYbH/uYxJron8Hq9FAoFYrEYqVSKcDhMNpvF7/fT0dHB8PAwa9eu5fTTT6erq6umJkUqlWJqaorh4WHGxsbI5XIq3y6bzRKPx3G5XExOTuJ0Ount7aW5uZnGxsY/VuTOjbkxN/6bhsViobOzU6WEQG23DdGl+XyeSCTC6OgoY2NjRKNR2tvbWb58OT09PbS1tSkbQx+SOiT6WI+4DlmGuKHnBnb4d/CJJZ8g6oqqZxL9r+tRsT/0tkR6pFNnmEl0Vmyi0EiIm35+E66Mqwb01K8vDjJUKeVSb0PsPbFJ5LrC5JPfi76Q55HrKluMIgfPO8jWq7fyy7f+koHuAfWuVqsV0zDZv2I/D7/+YX57zW/Z3bObUrmkbE2d1q4zpuSdBOC2WCykPCm+ceY32NG+g2QySSQSIZvNEgqF8Hq9pJ1pnrj+CfpW9WHaTCYvnGTve/eSd+WJRqMMDg4ydcEUqctSNSGpYrDIzh/vZFtmGzt37qSvr48jR46w5+AeXtr4EgPvHsB0ngIzHGWOXXKM6PKoembRc6JTVZqCabD8k8txj1ZsXExoONjAyu+sVPaVOHliR8g15feyz/zP+Wn4SQNG+lRqRMpG7y29GC8ZSre53W68Xi9+vx+bzcbY5jGGtwxj2k49u6fMibedINocVftKZ9Cl0+mad5menmZgYIChoSG1F2YsMwx+fZDEGQke/dqjxOsrTqbQ7w2j0vJTQG5hl4p9I3ax2K5SA6itrU2dNUlVk0CZvJvYQ6Zpkp/Os+FrG2h9tRVL1sKS3y6h87edBN1Benp6qKurU7aB2CbCVPH7/YqtokejZU/KvIuN5/f7OXHihGolPDw8zPDwMMFgUKWmSQs/PRAmvkWhUGDr1q1zDvef8fijHW7ZBLJBoNrP95prrlFttsQglw0qAlHPpxFnVaKsIpTF4Nbp4noOkUR8xfmXhvci4MWZTCaTigqj5w+J06gb63q+ufxNlJIUahDakM/nU9QSua5OZ9L7eUtxs1wupxwkmReoUrZ00ECnxsv/S8RSnl3e2zAMRW8XR1nmWRxOETxyX4fDofJs5PldLpei5wj1NhwOq3WW4mlyDbmuODkCLkg0XNBCQZx9Pl+l+qYvyde7vs4roVfAgBPuE/zlgr+k39FPIBCoyX3ft28f/f39TE5Oqtx6gPHxcaVwnpr3FL+69FfsXLyTr13/NZ5a9xQWW5UubBgGXq8Xj8dDNBplZmaGd9/zbpYcX1Ld1AaMNI9w5+V3MhmYVHMj+7Curo6Ojg6am5vZs2cPu3fvJp/Pk06l2bJ3C2987Y24c24u23YZZ+86m3KpamAIFV8MD721hVSY1xWngUH4rjDunW7e++h7KRVLNSkNFouFYDBIIBBQwluPTuiVRhOJhDLS9MJ9AmIZhoHNYmPV4VU1Z9yet9N1pKuGOibnRu4jFVttNhsn1p+oKTRXNsrs6tmFx+OpKSooRqycOT16DlUwThxmQeD1yJBeIEc3rmw2G9PT08pJ19HzUqlEMBhk5cqVrF69GrPL5NUPv0piYYKnP/M0xxYfU+knNptN5cBLpwKfz0djYyMdHR0sX76cZcuW0dbWVmOkp9Npjhw5wrFjxygUCkxMTChqXSwWUwaT0+kkFotx+PDhP1bkzo25MTf+m0YwGKS9vV05PbqzKN0oRkdHGRkZIRqNEgqF6OrqYvHixdTX1yudKPaIAMtir4isnQ2wDjgH+ELHFzjuOg4GvOx7mX/o+AdivlgNBR2okZ25XI6xsTFmZmb+oMCb6Bkx9PV7G4aBWaq2cNWjdaKbxJETe0MPWAhoL3IYqIlu6yllOuAtNpFEAJ9f/TwPXvJgJafcgF//xa/Z37NfUab3rt3L/dffr/7+lTVf4fctv1fyXo++iy6Sd9fBg6gjyo9X/ZidLTv59lnf5qUFLxEMBlUF7FKpRCQUYbhhuCa/PdIVYcpdAVBTqRT2O+04/8YJs+tbWsFxgwOv16vWxRKwYL/CXr0e4Il42PyLzbQda1M2mAxhZoptbBgGgXiAJX+5BPsBO62vtHLmV84kn6s65VCldOs0bJ1abpqVbjjz75xP7696safsrPzVSrqf7lY2rOxT3Q6f//J81tyxBnuqsm88Qx56v9yLs8/5B/pU7AqxLYXZKi227HY71iYrI58eYfyMcTAg1ZDiqQ88xWjjqALeBUCS/PREIqEo+jabjVAohNvtVgXmhOkoDrbMgeROCz0eKkxPCS5YrVYaGxo561tn0fPrHub/fD6ZdCU639fXx9TUFKVSibGxMUZHR4lGo8TjcdWDXdrtSYtemQOhmTc1NQHVOjY+n0+xeIVmL+mi0i0llUpx/PhxFSSDCngQDAaV8z43/jzHH+1w69QfqFYt12k84mTplCU5TEJBFgUkglqEPVR7NwsVXZxoEfh6YROJnum5I/IcVqtVAQDiVIrwFwdYBMlDDz2klIk4uXJIxCmW9xWnVJxvqFC89RxvodrrNFlxlCWqJ78T50KQaXGuIpEIzz//PF6vF6/XqxwUmVdxCMU5EAdfQAP98yKM5V7iSAmaJ/kyOoVNV/qSLyPOjfyIsBOKv57nJo630K3sdjtOuxOPzVO7qYpQzpfVtYSqJBXBXS6Xqg4tlKWZmRkeX/Y495xzT42C+9263/HA6Q+ovSX31VHFsC3MhQcv/IO9fXTeUe689E4mjUkFsAjq7/F4mDdvHuFwmF27drFz506lMM/fdz5n/OQMNm/bTKFQUJFUoerrNDiZd92Z1Wlx5XKZ6JYo2cVZ7j/z/poIuOxPj8dDLpdTSDCglI9uQOkOq+wVmV+JfjisDq594lo27d5UmQQTtty+hYVPLVTRDTmTw+uHcZ7mVJGPsbExyuUyS3+9lNV3rlbzuPaetZzxyBnMzMzUREH0Aj3yX32PejwelVahO/pyzsXI0N9HUj5mO+uyfnrk2+Vy4Z7n5rWPv8bY8kpLsYKnwLabtjGyaUQVbJQ6A1LEL5vN0tDQgGlWcgoXL17Mhg0bWL16tarALu+QzWY5ceIEfX19nDx5knQ6TTQaVUaNgHFTU1N/sP/mxtyYG//fjlAoxIIFC5SujkQijI+PMzw8TDweVy2Aurq6WLhwIcFgUMki0anijOosMT3iqkeP7XY7M64Zvtj5RXb4dtQ8i1E0oMQf5GOLTSFFNgcGBojH44p1J+CtRMF16rXuWOsMNP1HZKzoId1mEdtKZCtUWVr69fRI/uworID65XIZt+GuXQATjHwVrLDk/tAUdZmuGmakbmfqtp7o0qK1yHfXf5dtndsqn7OYPHL1I7y26TUikYhyUF0HXHR8sQNbX+W9gieDnHHrGTSMN6jnL5fLuH7hIvzZMGgBx44fdDD/X+crtlJDQwNN9ibav9SO7+mKTWjL2jj9J6fTur1V6RR5TnlWt9utQHmZv9xLOZo+38Sa76+hnCzXAB2yprrtIClWMu8SIAkGg6x4eAWbf7iZRU9WirdJ4CSdTivmpehQh8NB97PdLPrqIuxjdhZ+ZSF1++qqS6WttXxe1jWdTpNKpWhoaKC7uxur1crUxBSpSG2dknK+TDaRVYxLm82mbCZhRwgzTM6LrLMEhPT0s3Q6TTwep1wuK4BfzoDugwjobzWt9PymR+WuS/VzYQbIOZB9n0wmlV2czWaVTSMOudVq5eTJkxw6dIhMJsPRo0dJpVLU19dTKpUYHh4mHA7XMGaj0ahix0mamqRoAkxOTip7YW78eY4/OofbNE1FeZbNKQivDNmwOoVGnDzdkROnQwSzIJS686nnUokTBVVEU+4vjqxcT/JHxBGUgl3iaIjyEDrKFVdcoQSJUF5FSOp0Lol2S86yKEbJF5eCUKIc5N7Sb1BHM30+Xw09X+ZE0GWv18uqVavU7wS5lmvKPIuhL063OIJCHxeHUT43m54mzyQRO7kHoISivNeePXsolUqqjYooyrq6uhq0XQqK6GtutVppKbfw2aHPkrQl2RrYSkesg4+89BGazCZSxZRSICMjIypnHFAUJnmfYrHIkv1L2Hr6Vgr2gnK6TcPkieVPUC6UuWLrFcpYicfjeDwelZe/eGoxFx28iCeWPKG+a5gG60+uJ0SIgq1QE0UtlUqEw2HVPmpkZISDBw+yfv167HY7G8c3YvFXK+6LchW6ncyNzJe0gNBZB4ZhcKz1GM9e8iwlb4mXm1+mSJEbHroByhVDR5xRcaj1wjGydvp5dDqdqhhYqVSpMSDrKZ/zlXyc+/C5RAtRFu5fiP8pPwV7Ze3kftNLp9n6jq3sMnZx5r4z8SV86t5WrHT+tpNMLkOZMp0PdnLAeoD169fjdrvJZrO43C72N+3nhOcEl/Vfps6AKHBJ3RBgTWSDDuTJ2ZT3lc/InDY0NGAYldQLvQaC7tS7c25OHz2de3ruqbSYM8E36qPuYKXf+eTkJEuXLq2pHyG5+A6Hg1CoUuk1GAzi9/uVsZDP5xkYGFDATiqVIhqNkslklEGWz+cJhUI0NjZy9OjRf7+UnhtzY278lw4pctbf308ymaS7u1sB2GLA67RlPd9Zt4F0cFo39kXPKoq1UeZ9C97HMdex6kOYcFr8ND4z8BmCxSB5S77GvhHKsIAB4qzoAQ15NqjaRjrbR3dO5RnlsxJM0N9v9nvK/WanjQkoqgdhZOjPUyhUWmzNf3I+lxYv5dFrHsXA4LofX0fHyQ6lCxbtXkTAE+A3b/gNBgZf3vtlNk9tVn/X09Xk2vrvrVYrzrKTC4YuYHfLbiXjPTEPnXs7VeuqXC5XcZ6Ol2n8i0Yiv42w5WtbCEQDDA4NKgfJYrFU9sMTDpre2ETikgQUoOOuDkrOSqBIWrtaLBYKMwU6v9TJYN0gZ957Js39zSpfXkBmsRl1AFxsWInkNg024Xf5SZfTZDKZms40UqRWbGq5tx5MUfsTg/oX6kkVUzV7WMD4crmsgmDyvo4HHdQ9WYcHD0Wz+uzyXb3WkBR8zeVypFIpEokEwWCwovNdLtbdsY7+zn6GzxjGM+Fh8zc244/5KZiFmnUEVGRbzpWwIvSuOlITRewACUbJ+RP7UNLfZO+JHeZwOLA5bbjsLsUSzWQylEolAqEAJiYet0cF22R/50t5Nf+6ne3xeFRUXe4v4JfYzNI6TIrMig/i9/sJ14fxBXyquFs0GuWVV16Zaxn6Zz7+aIdbkCLZwPI7oSfLBtcjo3qRBajma8vByOVyqs2CCHu73V7jpMRiMeUcizMoSFM+nycYDKrv6/2iRekJDUVyvqVioETfhT5dLBZrClpI1UBFGzr17jrFWSJhIhjq6+spl8uqmb2OEEukTT4vPQNlPkXIAArRE0HkdrvV80hEUA52MpmsLOQph0UUpgjhRCJBfX09VquVRKJSlEMcKqHJiPDLZrOqCJwIWTEcenp6lKMmVHlZZ4k4irAUhoGAIULvN5IGb33lrSQuTnDz8zdjKVooOSqfn56e5vjx48zMzCjF4vV6a2hTIuwDUwG+cMcX+Ifr/4F4Xa2Asjls6r0lb1/afYy4RmhMNPKGF95Aopjg5eUvYylZuGr3VZz56plMt07zk/N+wrvvfjfOjFO9u9vtpr6+Hr/fj9PpZP/+/WQyGRacvYCffuKnvPfO99KQrlYvlf0k3xfDyDAMtY56m7YIEW678DZS3oqyNw2TPV17aF7bzEW7L1LnRtZoamqK5uZmtc5Wq1XRnUzTVGfL7/dXrnfKMNIVvMPhoLGxkejRKAv/diEN9Q34wpW2XFLde6Jrgoc//TCm1SRLlse+9RgXffoiApGAekdn2UnPnT2Vc5dPkzEyPPvss9TX17N06VL6Wvv48et/DEDL8y2s2LkCu8WulNXsfD8dGJNzIcUE9dwmicqk02kCgQCpVEoBVJLzLsaHoP5Nh5tImSl+f/rv6Zjq4AOPf4BYQ4zRwqi6r9vtJhKJUCgUGB0dJRaLsXTpUtU7HKCuro50Ok1nZ6cyBk6cOKEo5OVymenpaaanp3G5XHR2dtLY2Mjk5CT9/f1/rMidG3Njbvw3DLvdzsqVK/F4PLS3tyv9ALW1IUTGADWpLvCHLUkF4NNbFektRS2GhX8a+Cfe2f1OpuxTYMLaxFq+feTb2AwbFluV7SR61jRNotEoR44cUdExsbkymQzT09PU1dUpO0N0jgCNch1hQcn7CX1e5KnoJ7HlZqcN6k6jvLfI7P+Tky5D2YCGhVXbV1G0FWkYaaD9ZHsNm9HhcHBB7AIW7F+Ax+rhrMhZFM0iZbMK7svciF2iRzJN06RUKHHaydN4f/n93Lr2VpwzTq7/x+uxlWzKdjRNk0gkQjKZpNnVzOmfOB27YSeajKqoowRQnE4nHrcH50kn/n+t6FIstQW6ZI48Hg/t3nbW/tNa7IadQrFQE1zSU/jEzpyZmanaNzaTQlOB2GSMPXv2qHf2+/3U19eTzWZJlpKUXWWIoFiKshelgJsRNLB6rbhxq0CN2CViewhzTJiXomfL5TLuUTfFxmJNmqOsqe4ES0BGgPxkMonL5SKVSlWc7pMumm9uJv21NK+7+3V4nV7KDdXWoMKiE4dT9qGkysn8y5kQ3SvOrc6CExvLarWqyLVEzXO5HJlMhsz8DM+/+3nO/PKZ2KcqdndPz//L3nvH21WV6ePPPr332/vNTe5N7wkhBEKvQURAQMSG7TuM4hTbqKOjDjqjOAVRxxEEwTKCIiA1hAQIKSSkkXprbi/n3NP72Xv//jh51lknfL/zxe/4c4p3fT75JLlln73XXut93+d5n/dd8zCeHEfkryNw73YjsCcgnlnTNGAl0PvNXnTc1QF1SBXrWdd1FIIF2NI25JN5sYaTySQMXQbYJ+yClFJVFXlTHgVPAYXJsnpiOjmNkTUjmOycxMUnLy4fXTY1JXrAzI3/uUOh0fi/Db08qmpouODJtKXTacTjcQBAa2vrv2u02aFYNtQMVrl5ZPaNYFzOdgIQ4JPf57WZDaVhlhldZpjZaZFf5+YnsOOzJpNJ0eiCgT/rz/l9GjbOi6wAkDOabBRFgECjR8ctgzNmeenUZCNJyZbcmIpGkTIbGi9KZmkgSSDwDEDeG7/Gz6OBZXM3StQobZalzjLQ5HXkzP7IyAgGBwfhcrnE9aLRqKj9npiYwOnTp0XAQiNPJpLnNNfX14tu0IdSh7Dtw9sw0z4DRVdw6eFLcePrN4psZzQaxcGDB+F2u5E4J4Hnb30eH3rpQ1gWXoZSqYTvLf4e8sN5XH/0ehTXF/G9i7+HhD2BeSPzcONzNyKyN4Kuri5BKBiNRkSjUYTDYRwxHsGu/7UL6Y40aiO1eM9v34PmcLOYZ74fMs8EmHRkXO98X9GmKB689EFM1E7AoBpw6Z5Lcdnuy8Qa41rM5XLo7e1FU1MTvF5vVa2dnD0m8cHgh0GW3F8hn89jenoag4ODqK2tFUQTSbCX3v8SRi8arRgLVcHSx5di4S8XCoac70sO4IT06zLg2Y89i5K5JK7xjqfegVVvrBINW+hYyVzLgJsqGQaJ3JMMXMhQkz1mwKjrOmZmZtDe3l7VnIR74tm1z+LiAxfDqlkFaJ+ZmRHkHBueJZNJzJ8/HzabDalUChMTE6IzscFgwODgoNh/iUQC2WwWfX19oikKBxuwlErlLudv1+bODUDXdeX//lNzY25UhqIo/+4Gczqd+Na3voXly5dXBetyNlcu/5Gzq2f7eaASn8hlRAQ5snpN0zQc8RzBV9q+gpZ8C+4+eTcMxUoSgyQjgX4sHcNzxefg3+uvUq/J/W2ixShmW2fRMdZRVdYlK6zohxmPyD1W5Ax4KpVCIpGA1+sVSroz8yniEvlIMzl2I7ig5BwoA36S7bx3kgEyyFcUBa2trejp6RHJFRlkESTKYIT/Ziwjx0vxeBy/qvsV3C+6YZ21wu/3w2AwIJPJIBqNYteuXSiVSli8eDECgYDIRk5NTYlTZAhoGefJhC99Oslg1arCuMGIhVMLBdkiKwkAVPUKCQQqcm2TyYRoLIodS3dg6vIp1H+mHu4Rt/hcrh/dpGP0/aNIdCfQ/s12OMNOMQ/C17iBsTvH4GhwYMmPlkCJKEICfnZSjE15M5lyp+7prmmMPz+O0mzl+FjGPVqTBs2pwTXsEskyJm2sVitisRgymYyYSyZHstks6uvrhTqQJBQJEypCU6mU8PWspV6wYAFqamqq1CRybDg7OwsAQpXCrDcAEadz/kbbR/HCR19A1pNF6M0Q1v/rethGbciZcthx+Q6kPpACdGDRNxYBD5d/P7shi4nvTkBza3Bud6L2r2vhnC0ngJQFCia+NgHLcxbUPVwHXS0nOfQrdIx+eRRL714KdVt5DxQNRWQ/k8WYZwx1f1OHQDGAgZsHEP+zMk66dvu1WPLiEkyOT2L79u0YGBj4d23b3PivO95OrPK2M9w0ojLoJjNGR+FyucQB9DSmBJE0/vzDoPzsrwOoau5EQ5lOp+Hz+cpg58gRXHLJJVW1S/w53gv/pFIpAep4zzT+NptNGBzeqwxcmCVTFEWAVpkdLhaLCIfD6O3txapVq8TvElSTwaOTO7uBGpkxfvbZygGyeXJzOaBSS0VHzt+Vm07QWbrdbhQKBZw6dUqcIUzjVyyWj2KjRJ6sJWU5DALk+jRZ3svGWGQ5CcSpBOAzzM7OCtKCigc6bda6JhKJqmwnwTuf12KxoLOzE8lkEplMBkePHoWqqlj53ZXY97F9WDG2AlcevBLH6o7B6XOidbhVZHhPLTqFAzcdQMaVwYObH8T7974f68Prcfve27F//37s7N6JwxsPI2EvZ8v7W/rx2OWP4fzh80Vmk4oEs9kMdaGKo1cdRbqpnJGeDk7jF5f9Au/Z+h60J9pFAxACV1nGxW6b8vvWdR2tiVZc/LOL8curf4lLxi7B5YcvR0kpCYJDfs9dXV1Ip9PCsXA/cr65BuVu/kajsYqoIvnC8gk5u8Ls+Oafb8YbeAPHLjoGAFj3+DrMf3w+Cnqhak9zXzFLzTKA8Ez4LYytwWfA6Ogo2traBFEj16DTLshrMJvN4siRI+jp6RFrkEQFnS2fiYFXNBoV53LLNgUArn7j6ip1ABu/RCIRjIyMIB6PI5lMwuFwIJFICCBPFp+kn3ych9FoRG1tLYxGI5qbmzE1NSWa/qVSKaFEmRtzY2785w6eQMGsJ22JnNUjoJLJc6CSZDhbhk1fxZgIgLBNBJn5fB6LoovwmdJnMC8/D3aDHbqlki2mDQTKNao/6PgBngk9g6ujV2PpyaXiHkQ2ulTEs1c9i5meGVz71LVoG2gTdo2AVY6HAFTdK++dz8PGrHL2VgaOBGx8zrMJBzlmIdHPuIVZTLmfDtWCfr8foVBIxFgEV/xcOT6UEzEAqkrxCAaTySQWHl+ITD6DrCGLRCIBq9WKZDKJI0eOIJVKoampSfjVXC4nVIkkWmw2mzhilvchzz/flcFowPCfDyN/QR61P6lF45uNVfXxvH85DpRPfjGbzei7vg/j7xiHbtQR/bsoer7fg7pknfDhqqritetfw/j144ACjJpHsfH7G2FLVSTNJa2EnR/ciciWCAAgWUhi7T+vhdVgFWuRsmj6TQLY2NIYDn34ENR1KhZ/azHymUp8pwQU9H6hF6pbReffdMI96a6KRTNaBuHbwwg9FKpSUVCuPnbbGEJbQ0JazfiP5Y75fB4nbjyBZb9ZJnxs4pMJTD4wCY/HA5vNhqPXHsWK51fAY/cIMoTKDqpEZZk617GmaRieN4wdt+5A1lNOroWXhLH/o/vxoZc/hGc3PIvUsjN+WQFO3HUCrcZWWCYtmP70NDR3eS+mN6cR/tsw7J+xw+Aw4PQXTiO7PAssBZwhJ87dei4OzD+Avk/0oRQs4dAnDyEwHUDNgRoM/8UwEjeW48qMP4P8ZB7xLXGxfp88/0nES3H4v+vH+Pj4/8VyzY3/7uNtA25mOoEK43h2YzCDwYDGxkYAEGCSAb4AK1KmjvJXOhmCQI/HI7JXuq7jjTfegMPhEOcIr1q1CkAlc05DTXaVxoSbm86B4IX3LhtXdkyks5GbNMisMuuLQ6GQkIDU1tYKUCgTEzKAZlb3oYcewpo1a7BgwQJh7A0GA3w+H5LJpLgngiDKhgCIs6ppWJj1oyGTO7KbzWaMjIxgcnISl112GTo6OgTBQRaZrK3T6RSyZRpGZrz5ToBKs7pUKiWcBx20eOfGssKAwczExARUtdyluaGhQTQIsVqt4tgudtUm6GfDLq6LxsZGLF26FOPj4xgaGqqqV9cP6VjwpQVY07UGp2tO48HLH4TBZMCnMp/CLxf/Em16G/bdvg95X5ktj7qjeGDDA/Ds9GCRUq5Hjx6LAoMAfBB13b0tvbhm3jUoxUriXvjcdcU6tMZaMdE4Uf55HWiONiOUDSGZTIou7gw++G/uHb4fuUulqqrAPmDTyU24eNHFYv3wWTn4dZfLVRUoUoLPoIjrlpkK/p7c4I73QQDpcrkEweB2u2HOmbHuN+uQL+Xhn/Gje3s3VF0VoNpkMiHbnMXoxlHM+9k88V75+XU762D8vBFHvnUEAHDZk5fhwvELMR4cx/T0NNra2iqSNk0tN9I5s5ZIaJGlbm5uFgGVnFnhfpWzMTyGSy5poTM3Gst19JxbHgGiaRqCwSBsNhs6OjrEUSe0F8ViETU1NSgWy2du5nI5tLa2inXY1NSESCRSbqJTWwuPx4O6ujpMTExgdHRUBLlzY27Mjf/csXTpUqE4kmMUWRItg04Ogj05MywTnTLw49cINtmAyel0Ynlsefl75kqDTX6+pmkolor4545/xhOtT0A1qHjqiqcwdHgIbSfbEAgEkEqlMDI6gsGvDWL43GFAAX593a9x08M3wTfkqwLJJODP7iAuZzFlUuHs55DVjLzm2UCbsYrcrFTTNBxvPg69RsfCNxaKeaYdNJlMsFgtsNls6O7uRl1dnYj3eH+MTxh/sUSL70uWdNMORyIRDA4OCh9EP8meOZOTkzAYDKipqRHgl/6PvpTxlHysKZ9XPhYXCjD0xSGErwoDBmDH7TtwwT9dAH+vX9wbM8lApXyKn+dwOPDGZW/g4DUHoRvPHEu7PI19X9qH9/zTe2Avlc8h37ZlG4Y3DYvYJHZODHvr9+Kme26CUSm/x6dvexqRNRGxVmcvmsXBwEGc85VzxP3Lx2vynStLFey5cw/SoTTwLmCiaQKb79sM6OVz0R//7ONIdZdBaf8/9OPGb90IY6ocE6qaiuf/7HnEFsXg9/uxeOtikVjK5XM4et1RDN08hGh3FGvvXotgIChiZMbgu2/bjb7NfSjUFDD/nvmY+csZxN4dg9qpwv8dPyY+NoGBdwwg3hXHVQ9cBYfhTAdwg4KphVOIe+No29EGs8lcRWYYjOUyBd+YD65pF5J1SRGrLYotgj1jx6JTi7B/6X4xrzbNhuu918Nit+AnxZ9gTK90sk9uTGLgXwcAM5CbV45PYQDGbh/D9su2I+qJouQpr+1iSxHhb4aRiCSQ7cmKdzKzbgY4S3tj0A3YMLMBp+2nRdw7N/7njrctKdc0TWedqNvtrgqI5SCZBpkAjoaSoFZu/qSq5TOX5bN5AYjmIMwI0vCLDssS6BO1J3Z7layLdUgEPGxyJANWZncpfyHQldlddk8koOXxPqwPTqfTyGQyVceiMRvPaxEMRSIRcUQAa8+BasaUAJfzQyNpNptFlozgib9H8EawUwXiUGHaWfMus4GUtrMRBJ9BZqb5HMycDgwMIBAIiC6XlEbrXh13ttyJzw58FgsNCzE1NYVwOIx0Og23212Wjhcm4Ff8KOaK6O/vx/T0NAqFggiA+N5JxLChzbp163DkyBFs375dZBbZpVNRFDgXOvHUN59C0VqeU3PJjKKxCOsLVpiHzMh8IAPNrMFUMmHLG1uw5cQWWIxl8Nnb24v9b+7Hq199FYlFCWFkLQUL/vLBv4R71i2CCmaLVaOKn135MxzqOoSlA0tx61O3lmvSz5ALdNSKosDsMCOJJEwJk3gfNpsNsVgMqqqKoyK27t2KhfMWor2+XRAj7HNQKBSEakBWInA9cC1QtkcpH0G4XFahaeVGfEajEcORYQwMDqDZ1yzAuVzrVSqVADOQSWUwPTENr9cr9kupsYTn7nkOmllDz4970PZkG4xqJUAhkB5bNoZUWwqdj3di07mb4Pf7kc/n4fF4UCgWcLzlON6Y9wau3349HHCIMhWuOc4Zn481/mS45ZIVeS+k02nxfc4nn4nH8ZlMJlEuwuCOBOLo6Kg44358fByKoqChoUEAbrfbLWyVz+cTdXL8eVVV0dTUhGg0iqNHj2JiYqJKaj43/u9jTlI+N37X8e9KymuAL//pl3HxhRdXkXbMQlPNRrtBEj9mi6FWq61SEcnxB4Ekyb1SqXx00Pj4OMLhMFwuFzo7O+H1egWolJuSAZX60F8Gf4n7Ou5D3lzpWGybtWHz5zbDGy93S9//jv04vuU4dEvlUd2zbiy9ZSnqnHUwmUyYnZ3FxMQEcrkcamtrUVtbi2AwCJPJJEporFYrGhsb4fF4hG1kFlGWlZ9dlkf7THICQJUKYLxtHI/c8QigANc+fC2a32iG0WAUBCqUsvIsuiKKjw99HC2hFkE+yDEp4yA5080555ypqoq4OQ5X0YWp8SmcOHGiqo7dYDAglUrh2LFjeP3119HS0oLu7m6R8ODxT3x+qhxInAs/LvXVUVUVY+8bw/iHxsXZ2wDgCDtw49dvhD1nFyCfc8n/y+tjYnYCvd/qRXJDGRAaEga0fbINvsM+uJyucpJHy+DNH76JXHeu/DNRA9betRZNqXL/AU3TMJOdwdZ/2IpiY9m/WGNWnP8n50MJV96poiiiD43T6YRqUfHEN55Atq4CCpWigiXPL8G5T56Lp97/FMbWjVXOMtKB0EAIt917G/LWPJ65/RkMLRwClHKn/UsevwRLX18KVVNxYOMBvLLllTKRoAHt+9txyc8ugVMrx8mqRcWuK3fhwOYD5aNFNcBYMEI1q4ARgAYY8gboFr18DR2Y9/o8bP7pZmhJDRNNE3jhiy9AV3Rc9K8XoetIF6CV49mENYGXv/wybvzBjbDGrNDMGh77k8cw2TKJrle78M5d74QlY0FJK6FvaR+efNeTMBfM+OA/fBB1SvmIs7ySx33vuw8ToXJSxZ6x465f3AVrvRXf2PwN5Ow5QAcWTC3ApX2X4ofrfoiCpZIYseVtuPnfbsbTVz6N6dA0AMCZdeLLz3wZcXsc377o21A0BR995KNoiDZg27ZtePbZZ/+PZmtu/NcfbydWeduAO5vN6rt370ZtbS16enpE8E/ATSPFrLUso6JhprGmzJbfByp1r3RY/FvOojILLdelABBNkgh2GDTzegCqAm+5NpRMH4FrNpsVAN1sNsPpdAppOT9PDvjZUV0+XoCAkQ3PWPOiqqqQqTqdTgHydV1HKpWq6kBOuZXdbq+q7ea8qqoqWFJFUYTclc1TOOfMoJPlls/U5j0T5POzKEEj+6soinA8hUIBDzzwAD7wgQ+IOXa5XBjVR3F3893Y5t4Gt+rGd059B/7jfjG3NpsNE84JfHv5t3Hx8MUI/iqIiYkJoZrgfMsZbqfTic7OTtjtdrjdbrz66qsYHBwUgJLArlgs4uQnT2L0mtGqo8I4Ol7pgE23oW99Hy554xLcdPImEWg5HA4kk0ns2bMHe0J7cOArB6oOy/OkPbjjiTvQNNFU1USN//7Fpl/ghu03VNXBc54p+9uzeg8OdB7ArS/eimAiWEUgkRGOmqP47aW/RVOuCZe9fhmsekXKlkwmq6TbDO74/tjFnGQUz/jmvbJjuNyQcO/evehZ24On1j2FpJrEhsc3wJyuSP4pCztbUslGL6meFF777GvIBypB4cIfLETn050o5StH4AHA4OAgrFarIA7q6+tRU1ODpqYmjK0eww+v/iGgABceuRBX77oa1oJVBFdkrXnvcrM1GWyTtJEllawR43X4h+oOBm4MGOVaTFkymc/n8eabbwqZutPpRDgchsPhwOjoKAKBAHw+nzjWBIA4y7e7uxvxeBzFYhHHjh2bO4f7dxxzgHtu/K7j/wi4FwP4OfCp5KewJbdF2BTaA8YKtM/8s8u5C1/t+CruOX0PFmcWVyUVaItpV3laQTQRxWuG1+B43YFQKITu7m7U1tYKdZlc3y379Ewmg+HhYXw/+H28fvnr0K06PDMebPzXjag9WSv8xenTp3HwfQeRfH8SMAKBsQCuuP8K+Kf8VSU2zA7zuCtKx0lYp9NpYbN4GkOxWMT09DSi0Sjy+Ty8Xq+QP7O8j01XWa7HYw+tVivy5+Xx4p+/CM10JhOsA1se3IKaV2pEbBi+IIyn3lc+l/vm4Ztxx9gdsJQq2U+SrhwyKSHPv6IoGHWM4lsrvoXL+i5D0wtN5eZVZ2w5UCYHhoaGsHv3buRyOSxatAiNjY3Q9fKRqKOjo4jH44JokI/LktWTshqB72/irglM3TYFGAHPqAfn/vO5aIm2iHujok9uRBeLxUTyIpPJIJVJ4c2vv4nk/CRavtEC27M2rmPxd8FUwOgPR1EIFVD76VpYXreI7wkFZqOC8e+PQ7EpWPPNNbCdtsHtdguFHe+DyZpkMonT5tM4+pWjZTCvAZ5HPGj+RnN5PWsqRr87iuwlZUBufd2K1V9YDVvJhpFzRjDwqQGooUqXeuthK676/lXQLBqe/cizyC+qxAbOqBObf7EZ847Pg8FgwFBoCC9+4EUkG5Nve187Z51Y/YPVUAoKXvn8K9DMlfV1yQ8vQdeBLoy7xrHjIzsQnx+Hf8qPax66Bg3hBqTyKfzyol8iH89jydIluG7PdShGyjHnvmX70DraiuBMUCjgrFYrEmoC9225DylXCtc/fj06pzthMplwuOEwHr/qcTTEG/CR33wEal7FnmV78MR5TyBny8Gb8eLmV2/G6tOrEdfjuPeqe5G2p/G+re9Dy2QLzGYzTsw7AUvWgtbhVqRSKdxzzz2IRCL//gTMjf/S4+3EKr9Tl/KlS5eKjBkdh9wQBIBoFkUgJ0uuCBwJQIFqo0IjSdAgZ8wp+ZFBJYCqM4npYNgITA6gZadK4EzAQ/Avn/1HIMwmcKzHJfAgiOTvEKgSoMgAwO12Q9d1JJNJUd+byWRErbeu64L5phPm75IN5T3x8+VGcsxk8t80wAaDQRxzBkCADz6L3Mnx7EYZ/Dc/UwYy733ve8X82O12RLQI7m4qg20ASBqT+Hzr5/HJ5CfRPdINo9GIKfsU7l16L04GTuKU9xQ29W/CoplFIjjgeuEc+Hw+LFy4EKFQCCdOnMDx48fx5ptvClJEJnhUVUXPvT2w63b0XvvWY5cGNw2ie183Vv9qNS6ZuAR5Z14QQKy1bmxshN6ovwWw57QcBnIDaDG0vEVmqOs6tjy3BbpFF0oMriOu522rtuGJjU9AV3T8YvMvcPMLN8MVd4m1UygUkDfl8cTFT+DIwiM4iIPIG/K4bud1UFA5f5qkjhzoyf/m+0un01WdXwn8Sfgwa9PW2Yatl23FG8veKO8/i4LNP9+MYq4SCHKPyA0ISTZMm6ZRUqpl0vFAXMi45ffKejUC2UQigVgshmMrj+H1S14Xc/7S0pdQNBbxrhffBYupcuYt51JupMZsBPehXNfH4Ia/Q7khv5fL5cT+4OB+4p4sFouisVsul8OqVasQiUSQSCRQKBSEXeC1GbS63W4ROHMueH/cV3NjbsyNP/BYDOBHAJYA96n3Ifl6EvO2z4PL5RL+nwoyh8MBt9sNl8uFw12H8Q+d/4CoOYq/av4rfOrYp7Aqu0qQmIlEQqhcstksJicnEYvFsG3DNuxevRtX61djDdaIBlDyOc8su5LrjycnJ3Hw4EF4j3uxOLIYg9cO4ryHz0PHWAcUd7kBazweRzQaReO3GqEEFEyvncbmhzfDO+FFSasQ5kClTpsxBWMAAOJoJR6rKN9LKBSqIvrpe+SyJIJUmWA2Go0IB8PlY7mkMWwahj5dJkaHLx7GqXefEnb/560/R8lcwp29d4qEBVAtWZcHbbuu6xi1juK7y7+LXl8v+lf249KJS7Fi5woRMxH0jo+PI5lMoqmpCYFAACaTSTRRY6zJ2Imya9k/AJV6cTbbVRQFnT/oRMgVwtjGMZz3k/NQM1MDk80kEi1USfL/qqqKrLTVai13IS8F4fu+D9NLplHXX4dCW0HEu3KfnNDfhZBuTsM94UaxrlhFThgMhjJgvtsD1aoifzQPu69S1ma1WuFyuUSvI74rz7QH874+DwOfH4Bvvw+uv3Uhm8+KazZ/rhmzxVkU3UU0fKUByakk4loc9qfsaNPaMPSFIWhODbZ9NoQ+H8KbQ28CAEK9IYS/EUZ+aR6WpAXrHlqH1pOtMNrKfZVqM7W48GcXYvt7tyNRm4Bz2on259txetNppNpSME+b4X/Ej+ilURSXFGFL2rDxkY1oPNWIvk19b43TGnPIzeSw/+b9iM8r++ZoXRTP3/w8Lv3FpfCP+WEymDDz3hm8htdgdBjh/5wfJpiA3cCp0im43W6R9DKbzfD7/bj0x5ci4o+gabYJuqUcby0eXQzXay7Mm5kHi8ECs8eMiwcuhtPsxKPrHsV7dr0H6ybWARYgqAXx8Vc/jqg7iq5kF1x15YbN50TOKSesPCUkk8k5sP1HMt424DYYDPD7/aKOmQG0XFfDDByZO7K3craMQb8MbvnvdDotGFPKVuXmSazVYTYPAKLRqGiSwKwsP4NOhplH1uDIknegkiGXHSJBKYFgJpPBwYMH4XA4sHjxYgCVeiZmZrPZrKjXZlM23rPcbI3XByrM6ezsrDhmjM3WSqUSDh48CKvVihUrVohMHgEd2W6gunMmHSSNOlAGTmzWRScm13fJBAd/z263C3UBpVByvTeJBo/Zg+VYjm36NmEIfSUfGjINAICsMYu719yNfm8/AEA36th19S6oioqVL62syjQDgM/nw4oVK1BfX4/x8XEcPnwYsVhMvEsZLDNAcLvdWHN4DfY17cPW1Vvfsn7bptrg/o0bxZUVGT8AsV6sVitqH63Fxa6L8eJNL5ZlUpqCW395K1LPpBDuDsPr9QpHLzbQmTXOo9voVO12O7afsx3PrHlGBB8n20/i/mvuxyce/URVk5mfvOMnONF+QlzzxaUvIqfncMOOG6reCQMREiQkpoAKYUG5OWXVcl2ifLTGi7e+iDeWvCE+89S5p6DaVVz6w0ursr4yqSGXh8w7Og+uv3Vhzzf3AArQ8esOzP/5fNjMtqoSDPl+GexxH2T3ZKG/WwdsZ25CB1pHW2GAQfRYYFkK55z2gEEuf47kFAE27QqDNmbXuRdJyjHolQknTdPKcndJZs4GLo2NjZiamhK1+iStLBYLxsfH4ff7hd1pb28XARqPTJkbc2Nu/IFHLYCfAVha/m/emMePF/8YrVtbEXwoKPY8STICLu0KDb2X9SJuLgfwQ/Yh/O2Cv8X8v5wPW79NqHhISBKAxv88joHzB6CaVWy9bivWnlqLtnSbkKfLSjUAAhROTk7i+PHjOHXqFMxmM5bvWI55I/PQMNEAo6Uc/yQSCYyOjiKXy6GlpQX1T9Ujuz8L14gLaZTtC+2m2WwWJLacFeZnyyV4cn03fRxjJvoQZkfpR3w+XxW5SqLfu9OL3oO9iPxzGURsemwTFu1eBEtHGah7Ch4MaAMo4EwGWwcWRRfBbKoolmRym6BSTuqoqoq0KY27V9+NQW/5qEXNqOGlS1+CqqtY/vJycT+zs7OYmpqCruuora0VpYCFQkEQJrIiC6j0x6G/YInY2ckcj8eDJTuWID+Uh2vUBdVS/jxVLXfNJknMmItxGckPqrTUlArzXjNggpB8ywpSxl04DViaLVXZc/5csViEnjgzZ3VK1VqmKtDhcAiySFVVBAIBuGIu1N5bC/uUHYYug3huKizzD+eRUlPwmX0wd5iFckx9Q0Xg6wEc+/Ax9HyrB7a0Dcb6cqxsHbai7u46HP/749j4o41oHmxGLB8TcbzVakVTfxMu/t7FePbOZ7H2G2vhH/Kj9HgJvf/QC9N7TPCMe+B/2Y+he4ew4XsbUHu6FkarEcv2L4NbceOZ9z8DAFj+0HLM3zkfuluHfcwOzKts/VA6hHrU47e3/xYTKybE11+Z/wrMt5jh/xO/iJs9Ho8g200mE3w+H0ql8mlGb9rfFO/KYCifwrNT3SkSXYxLW1taMRYZw87anYJk8fl8qAnVYCo3VT6Tvq1NdGZ3Op2YnJz8f7dtc+O/1XjbgJtgj1kjWVpD8MiOw319fSLYBCAWKLO+mUxGZM5oWNgQjFk/HiTP2k1m1eXMGeuO6RBoKMxms+iQSak25Ukmk0kEvjKQJ8DgZuNxQjabTTTe2LBhg+g0zpocdmM0mUwiSOcxHMzE0ZAzK0+ZEmXRPF4hn88Lxp2AYtWqVeKoCn4u69WdTicSiYQAzrwmnRMNhAy05XppAmkZvMiyeM4zSRDWe59NYlgVKz4e/ThSagoP+B5ATbIGf7Pzb+BQHciUMrDAgpsP3Ixvn/dt5Ezl2pfasVqs2rsKRlN5/iORCFRVRTAYxIYNGxAIBAQp4PV6BZmRSqWE4+Iz2+12LF68GCFfCDOzM+X6bVOlVtZStGCDcQPCC8LIZDKoqakR75xZV5/PB7/XD+/LXhRKBezZsgefeOETaIw3YqhxCH19fZg3bx7MtWaY1IoTBSDkyVyfPP9y85HNONx5GEO1Q4ACWItWXPfqdcgn8zDay047EAjgnVvfiXtuuwd5WzmQ8Wf82LJ7i2DYrVaraPhFMEmAKHehpUyNTc2YeWBXeaCcgU2lUrhl3y041XUKKXs5q2PNWLH+p+vFPmO2h/cgE1QE997DXqz/s/UYv2QcXQ90QSkqSJaSQqptNBqRbEpi7K4xuL7qgtvgFn0CVFWFcdiINXeswd4H9kK36bj62auxemg1oAC5fE4QbzFDDM68s6rGjj0hmD2Wg1i51o9kCGXxnDsSWvw/S1bkXhLcH/xZr9eLTCaDxsZGNDQ0IBKJYGhoCMPDw+JYtVQqJWyYoiiIRqMCrMdisbdrbufG3Jgbv68RBvA1AA8AcADQgZpjNejc2wndUznGy+v1Crl3qVSCul2FY6UDiS0J6EYdhqIBtvttSO1PIaNlhL2hPcvn85h91yymrp0ScteYM4ZvLvom7jtyHxoyDW8Bufx/NptFOBzGxMQErFYrfD4f7HY7fNM+mKwmAQ4nJiYQi8VQX1+PYDAINaXCMeiAZqicqS2X8BGU8V5lhR8/n/bQ4XAIubOcSJFLoOSzxfl5BIRMxsSiMdTtq0PDNxoQXBLE0p3lut5MrtwsrEarwVce/wq+dtPXUDQU8ZmTn8EF0xcgr1cakjG+kIkCoAK+DQYD7EU73nvivfjm6m8ib8qXm5dONGPjiY1Q7aooiTt16hQmJibg8Xjg8XhEFnNqakp8DgGmfJIKiQuCW86FXNfucrmg5BUoRxRkDVm4XC7RXJM/y5I9+jMmWUrGEhK5BKx6+eiyWCwmyAwCcgBC0ce4kIQzs+ckfCiF5xqWS6UYGzBJYbVa4XA4RPmZ87SzTJIrlXJP+k5j2gin4oRiK793ZuhLpRJ8Az40/3Uz9JwOQ51BEDKKogBpoPXzrfAoHpjMJuFbWUY4MzMDb9aLW75+C/SYDsWvwP6GHZ7zPHCWyjXmjgEHFr5nIUK1ISi2SiKp7tU6nJ8+H7mGHJbtXgaL2QJDwYALH70QmlPD4MpBNJ1owvn3nw+D0YBNj25C/7x+FDxncEDChK57u2BpsAiyRVEUoVRjfM855Z5g8pBHog4NDQk8VCqVYD5oRtQcrUruKYoiCBw20WOfHJPJhKmpqd+vvZsb/2XH267hLhaLuixtljNVZH0IYB555BHccMMN4jxrGib+O5vNihogXoNSVVVVEY/HhSzU6XSKo8Z4frbcXI3sJEEkDRIzXayxpoSINdkAxLl9dLiqqlZleglkCDAIzkulEtxutwjICdBlY8xnYXaOYJGNsCgDpwOxWCyIx+OCbWctudPpRCqVEkwmDTFBPLOJAES2k9eg9Ots586vy6oBAFUZcRIdciM5uaaWNd+8JxqkL7q/iDWPr8H8hvni92iEdzfvxn2L7kNwKojrv3s9CvlCVVM8r9eLjRs3or29vXxe8rPP4ujRo8LhZjIZQZ5w3gm26+vrhSN5YfEL+M2q3yBnycGZceKGl26AP+bH/Vvux+2P3o4V+goAFbmzyWRCMpnEq6++KljNXC6H9vZ2NDWVa8IOHz6MRE8COz+1E3f85g40zjSKdcC5k/8WANUE3LPlHkR8Edz26m3oPtmNw4cPo729XewJk8mEXk8vfnbdz+BQHfj8i58HZiuBHBue0NjzncjrjqCRDpeGniQAf5ZOOxQKYdw4jn+84h9RQgnv+NE74Bp3icCAa1A0uTmz1kiMqaqKo0ePoq2tDRaLBdFoFIVCAR6PRwDOzKIMXv3aq1BtKmqfqcX878+Hp+iBrutVvQ/yrXlMrJxAy69a0NDQgEWLFonmLr3+Xjxw9QO44/E70BJtEXtOdvqy7eEe5DndbJjDc7BZoycHdPl8vooIoL3iXufaJ4nHzzQYDBgfH8fk5CTi8TjS6TTS6TSSyaTY062trSgWi/B6vfj+97+PaDT6OxvpP+YxV8M9N37X8X+q4XZ+yonC3xTQM9ODd/74neLr9NMElCSW6Sdf+8BrOL7iONa8sAZdv+iqig9IApLoVhQFJ285iTevfhOqWUUoF8JfHP0LrAivECCYv8sMbCaTwcjICA4cOIBoNCrsDD+fdi4SiaC/v7+cSWstH3tJMMXPBirHI9IPnJmTqp419Mv8Wf4u/YecjZcl3DKgZ1wlZ6Snp6dx6NAh+P1+9PT0VPX/oG1taWnBypUrMRYYw1HvUVwzdo24R6BCGhBky3ZZfgbe40sNL+H+ZfejfrIeN/3oJihQRAZ3dnYWr7zyCuLxONrb29HW1iaeaWZmBuFwWJDT7HfCGElWPwmQfCZxkfVkoRpVdJg64PP5xL2z+RyVbowxOIeMhXLGHI7dcAy6Wcfyx5ZDjanindNvkcQneGNMXCwWkcvlUCqVBHlAUphNgJ1Op0hcUekAQCSSDAaDAJVMYjCLTqDJJBkJKDZ/4zuhik7uhyIr7pgUIl6QFaXpdFp8jrwG9+7dK/YAr19TU4OurvK+8/l8sFqtgixhDMqEA5/1+Zufx8b7N0JTNUGaj1nGsOsLu6BpGtZ8ZQ2CsaAg7ZnM0DStKqnG2JZ9hYgFSqWSaJjMeIDqQrl0j+QI9wefl+sjn8/j1KlT/0GLNzf+K4zfaw03Fw03B1DtqLh5TSYT3v/+9wOAYNnkeiIaceDMAfNn5JalUknINB0OhwDjilLurEgjQ2cng0YGz5SUM8hmIC0eVgIqZJcIYgjeWU9NMMBr8V74LMwayjXg/H2Cc4JkSuiDwWBVfSdQAWixWExcnww0DRWfF6hIx2WAR6dKBpM/Q/aSGT5ZTkamUJ4XGlk6IFk6zq+zOzMl+HxmgvYvx76MpzNPi/XBWmWLxYLN4c0oHS+h6c0mTJunoakV0G+xWERjGVUtN/U6fvy4UAXIjfh4vxaLBc3NzaipqREgz2Aw4LJjl6GUKeGZc57BdS9dB0fOgYe2PISEJ4FfXP8LOHY6sCi8qIo0ojOVzy5NJBKw2+0IhUIwXGHAji07kPFm8PCWh3HL1lvQOtJaJe3n+yNAVhQFmWQGH3zqgxhoHsDCwYUoaSUsXry4KgNrNBrhLrnhnfUiVAihkCrApJmE8SeJw0yFzWYTpBTXPd8z3xnXN7t1E3ATlGcyGXg0Dy744QXIIw/3lBtmi1nMhyzVloM2dmoFgIULFyKVSpXLCjweBAIBnD59GgaDAdE1URy88yBUW3mdT185DQUKlv5gKZSMIj7HZrNhZXAlXnnkFaS1NEZGRlAsFlFXV4fM+Rk8fs3jiDlj+MlVP8FNL9yEtrE2MceyLZIDT6Ci9ADK0kcSRcxmcC3xZ+RminyPJBgpo+c+oB2yWq2ora1FXV0d0uk0Dh06VJVtp22UJZtzY27Mjf+csejVRWh8qRGXRy6Hrd0m/CjLyJjppU0mOFnw2gJsT27Hhb0XQluhVR0hqmmayKSyZvfKPVeip70HL/S8gE+e/CTWxtdCNVQaNNIO6LqOQcsgjs4eRXxfHOFwGPX19VVyZvryeDyOkZER6LqOmpqaqmZYcqZTjgsI8ghMCd759bMH70u2U7T9crKFg/6JAKNQKGBqagpmsxn19fVVxC0AAUp40kV3oRsLphegpFROmSCReXamW1Zayc9XLBbRvb8bl41fhpYDLchlKyqlUqmEaDSKVCqFUCiEhoYGcQQka+15TcZ1tP1M2vC5CSoLhQJyjhwmPzcJxaGg7UdtQBYixuH1ZCKccQbjgpJewsF3H0TfNX0AgEwpg/U/Ww+TwSTeJf0FY2pK9wn85BNlSApz/WqahkQiUdVXiHEUSXODwSDAJmNDOalFkM2yKa4Jh8MhvieXdnKv8BQgfh7fG+eTz8ZSQN4jSQoe66Uo5UbA6XQaoVAIJpNJKDVjsZj4t0wGsYxC0zRc9PBFMDsragUAKE2UcOH9F6JQKMCf9cMdcotSCe4Plhtks2W1gtfrBQBhI5jZdjqdIjnB8gTuVVkJwmdn8kImT4Dysblz449n/E6ScqByViQ3MWWr6XRaZGzJ7sjHURF0kj0k08PgngBKBs4Oh0MwlbxuoVAQzR/kTsI8+5lgl0ylzK7yd7jBZTk174VAlOCCBo/Gl8AFqMh54/G4ADlyYE2ygMdX5fN5kV0HINjHbDYrfu7s4DyXy4nNSzkSu6jTgQGocuacE96TwWAQqgK58ZQMiOUjpsji8lk5/2QPadj4TvmsPKOSRpv3xSYdALBhaAMMHgPUoIqxsTEhMVqxYoWojT948CD27NlTqSHK55FIJETNLX+nsbERHR0dwviSHbbZbDj/zfPRGG8E8sBPr/gpZj2zAIAZ3wwe2vQQGuIN+OCOD8KYrHR1D4VCogMr1Q8jIyM43XYaL7zrBWTc5Xc37Z/Gzy7+GW5/+nbMS82rWlO8D64tRVHgyXuwanCVeO90AHR8JUcJ//aOf8NwwzCGMYyCo4AP/uaDbwF+MsAk4cQ1CVSTKGxiQ8UGgTsZdwYF1uNWmDUzEIJgePluyXBz/3B90akwMOLnO51OOByOcmA3qkHJVANM17QLXocXyWxSgHmuFe69jo4OzM7Ooq+5D70X9iLjLM/5VHAKv7j0F3jvb9+LxnCjCDhoP4BKJoJOjaUlcuMZKlY4l3IGX97rclaA75Sfx2Cb6gjKEdetW4dSqYShoSHs379fZOGp2uF9zo25MTf+sMNoNKK5uRkXnb4ImqIhrVey2CQq6XtlmwcAVosVFx65UABC2jjGLWxIxUaLtbW1OHf8XGzQNmBZfBkMxsp51rIsOWKI4G8X/i1m581i/Z71aDO1wefziXiJ8cTAkgH07+9HKVzC/Pnz0djY+JajJ+l/aMv4u7Stch2xbLf5OyTqZUUbUOnGLSv+aGfZc4V+ZahrCKPOUTQXmkX8Jccy0y3TMC0y4fzS+eJ3gGqgfzbBe/bXCbDo+xKJBMLhMBaOLyy/N2NFds7jGKm8CgQC8Pv9AoTzBBY+E+vx6QOASg8TEguKScHI340gdW655GqvZy9ueeAWIFcpEZRl6iQcCCw1TcOxTx5D/0X9Ym2evuo0DE4D1ty3pioLSukySXTGm/KxqLxHWb7O8gPGbDKB4XQ6q472ZD8mj8cj9gljccYQBN78npxtZwwrg3aDwYCxxWMoFUuoO1wn/K98Ao0c6/MzKO1m9jiXy8HlckHXdYyMjKCurk6UXDLWYczPNcM5AconhTgcDng8HpFU8/X7ymVonnJsLiew+LmcD8q/GRM6HA4Eg8Fy2UQshlwuh/r6eqFaZSPkUCgk5ohxMIkUAnS32w2LxYLXX3/9P2DV5sZ/t/FWmvP/MAh2CoUC0ul0lVEBIDYsABGAMrClhJMBqmzAz+6kzN8n0JPrMlmvSQdBBwiUN5fcrMlkMsHr9cJut4u6DAINua7VbDaLjqSyHFZ2NHSyMgCXj9YAKswoQRbZO2ZMaUzIQpJNpAEjO84u0zxGidfm58pOFKg4O4I3/i1LsoxGY5VMbdu2bXj++ecBAJlMRrBvNFxsMCXXuM/MzAjQxg7NvC5VCZQXJRIJQVCwrp+MHuelvb29fDa3w4hHP/YoulaXJUN9fX147bXXcPLkSSHZAcq1xzxDtFQqYeXKlVi8eLEwyAThdAIOhwOr4quwIL0AS08vhaJVwN+kZxIHWg7ga1u+BqOr/AzsBZBMJkX2gDW5hsMG1J2qq9oP04Fp3Peu+xCxRwRIJRClI5dLG/g9AlWg4ijvvf5enG44La59tOMoHrziQZEZ6OzsrCI2yPYyKytL/7kWCBDpoEmsUNJItpzvkOTY2acPcH3RmXIfkJH2eDwiSJmcnBTBi+u0Cxs/vxHmWTOgAsrdCiY+NYGjB44K51YoFDA4NYjjx4+LfdHd3Q0A8Pf64d/rB86c/KGoCpadXIZgOCjsUDabRVpNo1AsiDIDzpFMRAFAJBKpanIm1+sxOCSRxvkiMcVglsw+f4+BzfT0tKjds9ls6Onpwe23347169fD6/WiVCrh+PHjItiYG3Njbvxhh91uF30fSKjJvU8I0hiT0HbT3rKPCf0qfSvBTD6fh9PpRHt7O2pra2G32LEkukTYOgBVoDihJvCRFR/BSc9JzIRm8MLnXoC51Sx6gDCOGOscw7b3bUP/t/pRd2EdGhoaBHiRiUHaYNo42mjGMIw9SGLLzZ6Y4VVVFclkUqgDCRgZt5HIZAaQvkXTNMw0z+DlO19G79/0wrqxIjPms6dCKTz1p0/hN1f+BgPdA9ChC3UAYx7GOQT8lN3KRDKvSYAZjUYRjZZrZl0ulwCcmUwGp0+fxtTUFGw2G3w+HzRNw9TUFKanp5FOp0VsI2fR+ZxyGR0zr8ViEf339CO1ISXW1eyiWTz20cdE2aNMylIxGAgEEAqFEAwGy6TPzotgyVvENUwFE8557hwhaSfgdjqdcLvdAhjy+wDEOwHKJ5PEYjGhYJD7COVyOaRSKSSTSaGsNBrLx7jlcjnRUC4WiyEajQq/zPIoquPo7xnv0/9yf9hsNoRCoXIfgHkxvHLnK9h11y7kl+Th9/tFTMlYi3GE3W6Hy+US8Sn3CU/3cLlcYj2nUimEw2FxykmxWEQymRTZaKfTWZVMIghXVVXMHfcOEyrEJ4FAAIFAAHV1dWhsbITX6xV9jUjW8BqhUAh1dXVobW0VqgWv1ysI9mAwiNraWvHevF4vfD4fACAej8Pv94s5ZK+cufHHMd424JY7NeZyOSSTSSGxZLZN7vJJYEFjyf9nMhlhxCldliUddAoOh0MY17M7RtLZEUAYjUb4/X5h/Fm/WSgUEA6H8cwzzwjjoOvlo754XTpLfl+umaHBkeXyul4+5svv98Pj8cBiscDtdldJwMhMMutLAMCv8XzHZDJZlWEnWJbZQ847jz+jrFuWxTKIkLPKgp0/0wyCWdxisYjOzk5ceOGFsNlscDqdApyxOZvVakUwGBRBhslkEoaPn3s228z3ZrPZ0NbWJkgPAhqyhUDZyHk8HtQurcWT73sSA+0DuLb9WuyJ7MGOHTswPj4uDB4BIgOJUqmE1tZWdHR0CJaUa0uumWIpQClZwjtffic2Ht8Ig1a93Kc8U/jq1V/FhGUCuq5j3rx5Qj3AGim73Y7RplH0z+vH2SNjz+C+dfdV1bfRSTDg4N/s0s/giM9ltVrxyac+idpIrbhu8EQQNz9xswCCAwMDVRJGZrwJGuW6e85vNpsVDkhWpfD3OGfxeByRSERkVQjCuZfovLnXqaSgk+W6Z5Aj13Z58h703NCD4MNBzLt/Hiy6BQMDAxgYGEA6nUaqKYXDPzuMmcYZQe5wLRViBXT+XSfqnqmDIW/AsheWYd1v18Fr81aa0/nTuO+6+3Cs/RiSqaQIvGw2m7h/stFyAMigjmSFXG7Bjv2sa2OZBuvlGHwUi0XE43F4PB40NjaKOSDxw9KU9vZ20YxpLsM9N+bGf84wGAwYHh7GK6+8glOnTiEQCGDRokXo7u5GW1sbGhoaEAqF4PF4hL8iACNByTiGhHwUUUxbpoU0l0dO0ecClVIVWUau6zq+3f5tTFkrzZLyrjyeuP4JYf8sFgumu6fx+KceR8FRgObTsPcHe5FdmBUZdqrf6O9IGvDegQqpS3/M5Ibcv0Qu+SNJzudl3S5Lapgp5LUVRUG0K4onvvgECs4CVI+K337ptxhpHAEABAIBxJpj+PkXfo68K4+cJYdPL/40djp3VvkSOQvLe2BsJ0v3KdelXdf1ci8cTdMwOzuLZDIJTdMQjUYxMjICk8mEzs5OBAIBMbc8NYPXJ3HB+EhWk3F+CZ4XfnYhbG/axHsLTYaw5d4tAvD5/X6huJuenhbdqElgl0ol6CM6bvvWbfCGvXDPuHHzt25GMBUU80yyhzEVfz+bzYpjKeU4h0CRPs5kMiEUCiEQCJSTGmfiBJmQUBRFqARJRlAJCUAckcWmxlzLMzMzVRJ8vjsSJCe8J/DYZx5D0VlE0VXEE194Aic8J0Sj5MHBQfT19WF4eFisJcrfOc8kg+x2uwCrjY2NqK+vR3t7O4LBoPDxbEjKWnSuTfriVColCIdCoXycJzvT5/N58f9YLIZ4PC5iPyogSMKnUinE43HRFJWlCTU1NYJQ4PuLRCLI5XKir00ymUQ4HIaul4+7NRqN5e7wLtfv28zNjf/i421LyglAuFkJCgkMuSnT6bQASGcfq8DNzMCWIIB1EQzqGXjTSbCrOCUkZJ5k6TeNBWXFbMJmsVhw9dVXiwZuNBAE9HLtLQGxXAfNwJ2ZLjJjBBckDGiAOGiUGMDTyfFzzm4oR3BqtVqFjF5m/OR6FblGHECVM5cHCQbKW8hgj4yMwGgsd2VNpVIiM8zGIXQ6DDTINspnQ7KjOp025wyAKAUgoSEzocwSxMwx/Pycn+N403EAwKRlEl/s+iLO9Z0Lj8cjridnHTOZDAKBAHp6esQRcgS6XEtyDZGQOCkGvHfne2GBBdsWb5MmCEhYExjwDsAVKbOs4XD5+C86PV3X0XqyFRse3oDd792NrDcrft211YUV/7ICE8snEAwGheORJeO6rou6JzmQ4bvM5XIoJou4/fHb8W9X/hv0aR0bvrsB+6L7sGzZMsHI8v3IzWP43HK9m6yMYF0T5zyZTIp74xpyOp0iCOPvMDAge0tlB98/f06u0yIo5fsWcvQw0PjtRtS21aKtrQ1Hjx5FMplEoiuB1F+nkG/I4+g3jqLrq10IHg7ixIkTVWTYvLvnwT5hR/tT7Rh0DCKfz6Ompga5mhwePf9R9Df2Y+iaIVybvRatu1vR0NDwltouOXhiAMn9wPmRSwEslvJZ3Vxf/Drrx9gXIhQKVdWwcb1x3jWt3DSmra0N09PTYp/OjbkxN/5wQ1EUdHR04IILLoDdbseiRYvQ0dEh/BUbXQEQCQWS5gyYCZ5o4zJKBi9e8iKS3iTetfVd6HJ1IRQKiWvQT/PftDNUQN364q0YbhvGqQvLDZN6jvfgnY+9EyW9cmLKoeZDVc+hG3RMLJtAy4stVXEPwSr/LSudKGWW7T5ttSzHlUEVwafsX5hxZUZaVjgeajoEHVLsoQCDiwbRPNYMi8WCqaVT0Ixa1ff31u7FOfFzhC8EUPWZ4pn1ylGyvM9ToVPwxr1IjicxNTUl7D0bmBYKBczMzCASicDpdKK5uRmBQACFQgGRSATT09NCrUf/IMuy+Q5p+xkHlUolaAUNrR9vReQfIjC5TLjiZ1fADjtgqSgYTCYTampqUFtbKwheWd1ptVqBSeDyBy9HXsvDMexAVq3IzlVVhcPhQCqVqioFpF+WEwyMBWZnZ6tKAxm30kfz/yzN4/MCEOCWfpBxixyzMwZpamoScQbvhT+v6zqiG6LVZ2QrQOr8FDxPeYRfpAKRJLRcUkYfzViZSQPOG8nzxsbGqoQeCXGWozJmzWazmJ6ehsfjEfdMzFIoFERyCYDozxKPx0V38lQqJRQvVMZGo1FomiaSjrJyFaguA6UEXq4RJ+nz0ksvibKMufHHMX6npmlAZePJ0m4yunL9CkGbLGHlhuP15PpNSnjlLJsse6VROFtOTqBNFlCuKydbKDN/AESmndISGny5HpagXAaxcpMruXEKM88AhNT3bPaYhpGbj8aV3+fGY4M2PrPRaEQymcQrr7yC9vZ2dHZ2iqO9SEww82yz2YTkiPdLB0ajYbFYsGzZMtFILRAIAICo5eG98vdlUJLNZrF9+3ZcffXVosO2XLvOd+t0OkUDGBIuxWIR6XS6QhjoRthLlXsFAOQAQ74CWunwmIX0er3o6uoSjLYsNyPIlmV/VAUQcF3++uVVgNtSsmDL4S0oWivZeKo3uC64pjoOdMBatOLVO17F2pNrMWoYRf0P6hGfiOOE9QQWLlyI5uZmZDIZ4dTb29sF08p3xXnieuH+cI26sPYHa+GHH16vFy/uexETExNYv349mpubq+r7WWtGMoLrhdcHKhJJkkrctyRQSACQ6Zbr/Fj3zfUrkwT8W64V5Ocyw07FRDKZxMzMDPx+P3S9XK/d3d2NWH0Mb/7Vm1C7yxnfQl0BfZ/ug+GbBuiHdfEegDI5t+CXC5DRy8qYsbExjCXG8Potr6O/taw6UI0qnr32WdxYeyPMp80iu+/1esVe43vksXp8fn6d987PZKDMOSaAphRVbhjDuWBwxHniXuKRPwx+5sbcmBt/uGGxWHD++edjy5YtwhcSSNGm8g/3LQPuWCyGTCaDdDpd6SWjlvDUDU/hxIoTAIDHHI9hxeEVACq2QAZf/BzGNtPT0+g91Ys1O9fApbig1qq44tkrYFEt0AxlO5xMJlHzTzWYHZ1F5BPl86w3P7EZ6/asQ1GvZH8ZZ8iAW+7FcTbJR5UT74v+h7/Lkhm5vI8ZQbm3h8lkEoq4xh81IhvOYvSTowCA1Y+uRveT3YgUIpidnUXHSAcC5gAeu/IxAMCtw7fifUPvA1Dxf5wfvgPZj/Hfmqahz9uH763+HtwpN646eBVUVRU9fRgDzMzMYGBgQBD0VDoVCgXMzs6K+JHxHDPcfH65FpnAlsDZZDKhVqnFml+tgdVjhT/lB0yVuI1kLFWSzKwyI8usKwC4j7vhNXih6ZXmtQSvciIIqPQToGKQKggZhBPg8d4tFgsymUxV3TeTI1SCsaaYcTj9HFUEpVIJLper6mgyACKeY2xAgnrZ48tgzpux5117AABrfrkGq15dJd4NANFZXe4OL+87rlO+F6/XW44bYjGRvCJo5pzOzs6KPcx9IXee57GcXCdy8ieTyQg5ONWnrHHP5/OYnJwUkvl4PI5oNAqv1yv2aalUbhrrdrvh8XgQjUaRSCRErEcVIpOWjFmnp6fnYoI/svG2ATezjrJUmQzw2UE5WSS5iyYNFwBxPJbMHjKbRuaNC1+W0tIYMaBnsEzwB0DIS2hQCVRowHhNbkS5ppYyE/m+5ToQWQLF7BUz3NxENNoEPJQxyaCIn8uujnJ9Lo0XGy25XC7Y7XasWrVKMJ0Ez3a7XTR0IJtNI0VGk+CD90fWkGxrIpGA0+kUTjSZTApnSrKCcjKn04m1a9eKuaVDovEGyqQFP5sSH9aqy41ZLGkLPnTiQyjYCtheux2+sA8X3nchnEknVFOFeOG7MJlMWL58OUKhkHgPDGpk1leuu5WzzQBw35X3Va3porGIp5c9jZwxB6fqRE9vD3w+n3D+NTU1MBgMIjs5f3A+Nj2xCe58ufZ8vHsch3KHRFCSK+VgMBpEqUEkEkEikUBDQwM8Hk9VYx6SLXxf+XweiV0J1C+oR317PS6//HJs3boVe/fuhcFogGOdAy83v4xLXrpEkA006Lyew+FAIpEQjT8YuHBO+DUy1CPOEey7ZR82Pr4RJoOpqhsp1yfnUV5bVLnwHE9mW7j/LBZLVc0YwTvvwZ/wo/VoKwYXD5aLWjTAedgJxylHVcM+zg/fP2vqS8kSap6qwcBHB6Ab9fK57qlaLB9bLphmPjftClABzVTUMLBk8MlnlDuL83OZQfF4PKJ+rb6+HtFoVEjEGEhxLdJOARVFzNyYG3PjDztsNhuWLVsmAAZBnSz1JnCRy90SiQSmpqYwOztbVdP7xI1P4OTSk+L6fc19+Gv3X+OHh34IAML2ELikUilomiYUZQMDAxgcHERNTQ2u3nM1VKMKQ9yARLocpOdyOYyOjiI5m8SyZ5ch0hqBX/Nj/cH1ZXuoVcAim2oReDBTSMJSruOWVXj0A3KWlD6Jz0rAKINONpTi52SzWeSzeSx5aQnqG+sBE7DkxSVQTApypbLtDQaDuDR9KTqPd6LP0Yeb+26G2WAW98R7kf25/DefI+aO4e82/R1i9hjgAaIfjuLm79ws/AoAAaqj0SiCwSBaWlpQLBYF4UlSVC71kwlSg6HSa0eUpZUqncJhLsutXREXTHETcsiJPkB813z/VEhSSUCfYHPboOZUIU+W+wGRqObRWABEXT+BHfvNkASQO8UTbPMd8m9+FuMvAmr6OSZguG4YRzOOJWjnujAajXC73SIp4/P5BAm0dtdaaIoGu82OFftWwGgyiqahVElwbmw2mzhK0+12i7XI5shmsxnxeBy9vb1VsdLMzAwcDodQWbLsk3FnqVRCbW0tnE6nUGeS+GAcRJUb1xjjeO5BNuClhJxrhskYZtF1XRf7kMkC4gOn0ylOICKZVVtbK4ifufHHNd424AYqASs3GaUvzApbrVZEo1Hs3LkT69evFxuSRpzd/FgbkkqlRLArN2QjgOZmYM0UUDljGqgYIjbMUBRFBMA0IAxymXnitZhpZgaVDB5Z29raWiFDZSaXG1SWvPOadFZshMAaDwJigmKCZYJG3g+NMY27LJHmuckkLPgO2FyFToIbmhlhMpJyFpp/8w8NJd8DCQkAggwhmGeTLMqFqSAAKkfAmUwm1NfXY9u2beLaZE6paOC7dmtufHLPJ1FYV8AXjn8Bo0tGsWPHDmEQ+SxutxvLli2D1+sVc897lptjUCVBWRJQqTNTVRUfeeIj+Pa7vy06luuKjqizfC7y9y//Pu5I3yEyBDS6csO3QqGAUDpUdjy5cs33+Pg4IpEIZjCDX13yK1yy7xIEE0G0O9vh9/tFzwISDnIWmI6eX9drdPRN9aGzsxN+vx9btmzBa6+9hqeHn8bJu09CM2qwqlZcsPcClFKlqiBJXgv8jGw2K9QJmlbpbaAoCmINMdx33X0oGUpwKS6seWENzEqFvSbY5fyRBOKzcG+cnUHnvy2W8pFtExMTokkg2Wtkgfk/ng/NqmH46mE4tjqgfEBBPBhHKBQSNZGFQgHFliJsEzaYUOkbYDPZEHg6gKXKUhx931H4wj7c9Zu74Pf4UVJLVWtCztbIRBqJPTkgJdAnq879QhBO+0PbwZovElpc/9w74XAYPp9PBENzY27MjT/88Hg8WLBggfCLJCm57wEIoELyNpPJIB6PC6AjE7fX/fo6zNbOYrxhHABQl63D1w9+HflCXtjgbDaLXbt2Yf/+/VAUBRdffDF6enrQ29uLU6dOwe12IxQKwaJbUMgUUFLKvjuTyWB4eBgjIyPw+/1oDDRi/rPzyxJcswbVWOlYLav2+DdLYqhIYxwhPyfBH8ETe27IDcMIIKiYIxChJJ2ydJ7Q0hBoQMuLLXDYHViyZgkaGhpw5MgRjI2NoaWlBSFfCFdOXVmeRxNEfMd5JXg8WxFJXwwF+Mamb5TB9pkxXT+NZ655Bhf94iIBdpPJJI4dO4ZMJoOOjg7RMbpYLGJ6elrEefJgvCVANSrxDOMhg8GAfE0eR+85irV/txa5dE5kUdmzBYCYO/l69DvJZBIJTwI7PrcDF/7jhQhlQ6LMj9liEsyBQED4YfaFodpQbkTLe6e/JqHE+mQ5k825ZtKKQBSo9pMARKM4AFVAm3EgP0teR+z3YjKZ0P14N0xmE5J6UsTjjBt8Pl9VaSgbjqVSKYyOjkLXdYTDYeFzqWDgvXBufT6fuC6l74qioKamBoqiiCw8M/9MUDGu5v2waTDLUWOxmFiPbrdbxCK8ltPpRDKZFElFxlhMHLpcLhHrJpNJ0TCNdodnb4+Ojv6upmxu/Dcfbxtw89gvWVLC4JSGmQxcW1sbvF6vMMqUZ9MZyMwSDQRZRxonSsgpFyf7CUBku8lEEjDLmT5mSPlzDMDpqJgFJ0iNx+OC9SLTBkBI2vn53DByrZTcEZQbl4H68PAwHA4H5s2bBwCCNSPoJIiWG62xURMZbLJj3Pi8n0wmUyWJ57yS0aS8nTXwZrNZyORZ58JnACAcER06P5Pfl2vRyL7KUiHOkaIomDdvnnD8PLdbrn/n/BtgwFePfRUWiwV+vx81NTWIRCLis4rFIubNm4empiaxltigi2QEgSGDC1kSTOdiMBhQr9bjo099FA9e9CDceTcGGgdQNJ+RZSs6Di4/iPn++ZiNzIqMZS6XE4oAi8UianroDJcuXYo3Jt7AU1c9henWafS29iIQD+COl+7AgsgCOByOKik93y/XMO8x5oxh9IujsE3ZUDxUxLFDx7Bs2TIEbwji5ZtfhmYqr8fnz3semq7hvB3nQS/p4j7OZtEZPMqEB9f30dBR3H/J/SgZy+/tjavfgMVswdpn1wKlytmnLJcAKlJJBiRyFkLuh3A2SNU0TUi0KCMjQbb0/qXQkhoa7mvAjLtcczc7Oys68WaWZ3DqS6fQ9uM2tLzUUgX+S6USAv8WwAIswML9C9Fv70dra6voeCo3a+ScyyUhcrkFbQjXvVxXyH1gMplEGQrJCwZilIuxTIA2YXh4GDabTagO5sbcmBt/+LFmzRrR/EluOAlABPUy2GbTJAInWVqsaRpMMOETT3wC/3bDvyFpSeIzb34G7pwbMEBk4Pr6+rBjxw6kUilceOGFaG5uxuTkJEZHR2G1WsVxQiyDoi1l1+1gMIj6+vqyHNZqg9NRaV4ly66ZpaUyjt+nrBxAlX/kZzFLySaxBLkyIKcdpM1mZ+lCoYCELYFx7ziU0wpqa2vLtlUzIZPO4JVXXgFQBkdtbW1obW0VQFLXquvF5fpzxlC0y1Vyf03H5176HP7h3H/A0YajAIDFuxfjnEfOgc1VaZI5NDSEyclJABAglYommeCm3aY/IEks+wy5XCrdnEbfZ/uQac9g7zf2IvhgEO1D7QJonp18YjaXz1koFJBoTuC1D72GZGMSWz+3FRf/8GLUnqwVGWegUnoIQJQuETzL2XBK1dn/h4kik8kkCG6qUvP5PNxut1hjjIkY07BHCWNlJn4YZzIBwudkzM9YlKSIwWAQ9dBy4kguqeMzAuXYOpFICDJf7oBO/0tw3NTUBFVVkclkRIxDgorxJ5uQsWSU656JNJnQoLJT0zSRSefveDweMR8yCcV5Y1zldrvF+mRtOksz2UQtEokIUp5zQEUkSYK58ccz3jbgJuvLzUJ2B6h03uT3VqxYIaTJ3LhyAzUZaMoSbG4YbgxK2GU5qny0Dg0M2Vw6DzljxWwV2TeCJRpbuTaLBAINsCxPpvMhS0VQZ7fbEY1GhSTbYDCIeVFVFX6/H3V1dcKAkxSgQ5Rlv5TtAKhiETlXzMzTUcnM9Nn1wXweBhRUGNAZ8x4IvJlxpQyWxlmW3DBAkBukMcvJ3+Ec0UiT5eNzyI6VGWwyxQaDAYsWLcLr+17H3qv2Yv3T67Fo0SLU1dWJYAeAyObzOWXJnKwCACrnaHLegxNB3Pj8jbCn7Oif349fXvJLQAHOO3Yertt5HfbW78XI0hGE3ix3rE2n00gkEvD7/VUScEqHFJeCfR/Zh+muabEuZ72zeHjzw7j1xVvRNNIk5ktem5RVaZqGrCmLJ655Aic6TwA9gL3GjgunLywTUE77Wxh5g6nscIpasapGkAGDnPmQpU/smGq2mKsbmwCIJWNl+b/NVdWgRi4h4f8JTvkMVIDwvTD44xpjgxQGMsViUTS0WfDQAmSNWTQ2NiIWi2FmZgaDg4NwXuRE/NNx5Bpz6P1UL0rWEtqfaReSNBJ1Xc90wWA1YCI2gWg0ira2NtTW1gonyj0vkzF8Njrcs7NcskqEzyg7ayo8WB5CUkZWhlDJQ5WLfD7o3Jgbc+MPNzZs2CDsBVABNTLAk+t0z25OSeBFn+JyuTCvbh4+1/85JMwJNMQaMBOZEVm4sbEx7Nq1C5qmYfXq1Vi2bBmeWPEEav6lBql4SqjnZDur6zqi0SiGhoaQy+XQ0tJS1f1Y/jmSiQRkQCUjSx9IJZ48ZLk4bRqzhXJNNwE2UOn2zJp2ACgZS9j9gd2YDc5ipbYSvrivqtZYURSUfCUMnDuANbE1QukIVGq2ZZk63wH9IQGc/H2DwQBb1oZbt92KH6//MZzDTmx6ZhNMbpMof+KJG5pW7h4tn5HM52bsxEwtfTmfV1EUIU/nPOZr8xj47ACSy8t9drKeLF68+UVc+uilaDjeIGLKcDgsgCxQTiwwE5quSWP/h/ZjdkFZXZfxZbD9tu1Y98N1qD9VL+JJAAKoAxBJIyZ1CIJJfhAgMx6nzyLg5dFiBKu8PmNmxsIABMikJFrXy83YmNShf6Ovk8sNrFarSFSV6ksYnjeM7r3dVT2FiAfk48mYNJmdnRUnHp2tNmPHb8azdrtdEGIWi0U0RGOpHBN4xCs8K5xA1+PxiHVQLBaRSqUEscCklDz3JE9YcsnGvG63W4DmmZkZIWdnzMXfo+pP7mMUi8V+Rys2N/4njLcNuBnoErRRIstFS5aVQTY3OYNegmqCSLJJ27dvRzKZxJVXXimuUSqVhHxDbjLBQJ8bX64rZdaJ9VL8XTKEzLYT1BIocIMymJZZ3mw2KwLvdDot5KFAJbMr18UAFXkSv9/Y2AgA4rB7SrUoV5JlLTRCDA5kA0sDRGKhWCzC4/GIYF/uggpUjnGTj1Lju2NGj+SALKUiAAcgwIJ8xrpciy+DLzo9ghVKzjs6OjA0NIRUKoX+/n709PSItcE5oFG02Wzo7OrEv17wrzjScQSuZhc2924G9IqxZl2cw+FANBqtcpjMSMjPRxIgFouJeeyIdiCfz2PVwVVwmV043ngcN+2/CSaTCc/9+XNIO9IIPhDE+on1aDI1IRaLiSNIZMdoNpvhVJxYP7EeJ+adqAKx475x3H/p/fjgTz6IJjRVZSdkWZuu67j/xvsx1DQkfnd3z24Uby7i3c++G4smFuEDP/0A7vvAfdAMGjzf8WD28VmULi1VlQcwYOI1CQi5puU9sXhmMT79wqfxpWu+BNWgYs3za7B8+3JYjVaxr7mOudZkWb1cVqLrOjwej5h77g3+npwlMZlMGBkZQWNjoziKT24i6PV64Xa7MWwcxuQ/TUJrPcOI2zUMfWQIdsUO+0t2QU4xq85rF4tF9PX14cSJE1i6dCna2tqq9hB7Tsg9FGgHuEcYsDAIkVU4cj0315jL5RIBgaz2cDqd6O7uFvVpcxnuuTE3/vDDbDbj/PPPf4vqibZRBtL0Wzxn+uwsq9FYPn6UZ+xa8hbUZGuQN5blzKdOnYKqqoLYvPzyy9GzsAe/WfsbPNn1JBo/3IjrHrpOZCBlVVAqlUJfXx/S6TRqa2vh9XoFeU3VXz6f/99KxGln+TOyjeKQYxs5m0/1jyw35r+ZsCCoA4BCqYCtf7YVEwsnAAU48PkDaPlOC9zhSvmgYlbw1J8/hXRNGstPLkdXpEvML9UCZ/slfrZM9MqJE/FnWMOm45ugxTXk8uVYhbEiFQQWiwWNjY1iXklS0AfLGW5ZFs13zHsFznQrn9Hh2udCfFm87ON1wD/lR2AsIGIup9Mp+rSI39N1kZFOaSnUnqhFeH5YXMM77oV9wC5iLMbSVGvKZYH0p3JSgWtaVpLKGV8mvei7qFZk1p8xI1Am1FkGyd49lFTzPth4zul0inhVJmtsNhtK5hJ+/fFfI+POwFQwofvNbkxPTyMYDIp4UlZnMv5j/Mb7p7rSZDKhtrYW9fX1VU1eS6WSUELSBxPQygkjEv2Mc2VFB0l3vivODfsUMF4kviCQ57nxNpsNXq8XiUQCiUSiSuZOFaacpPN4PEgkEhgdHcXRo0d/z5Zubvx3GG8bcHPTnV1LLXdb5nFQ+XxegDugUlsr/z6BY3d3t8gcykxzNBpFTU2N+D+NsiwFZfMI/pHP4COjRXm2DCLpcEql8tl9gUBAGHZmcWWDbDQaUVNTA6DSTIrNwejsKIchWJGlpuwUKTdz4/9lhyODFGba+LysfSbBQCPq8/mEpIhGkllquT5KZj+ZYaUx5bzIcwxUgw2Xy1XVaEqu2QUgWHjOS319PVKpFE6fPi1k5StXrqzK9NMAkiVMK2ncu+BevN74OnRFx2urXkOjrxFX7bsKSrYSGPAscs4X66DoXPg8/AwyjMyq890kk0l07OzAct9y5N15fO36ryHuKTvVV+58BbZ/smHZ5DIhByQbyoCJCoONpzaiYCzgsXWPoWAqrwNz1ox37nonFtgWIKtmxdqg9Ayo1N3d/NjN+O6Hvouks8yg24ZtOP+n58NYU14jbbE23PXIXdi7YC86jnRg18QuPPXUU1i5ciVaWlrK3XRdMSjxypF2fH+cC8rO2bm7JdGC2759G17ueRlLnlgiJJdkwfmO5cwu55jlIgxY6US57ijLpOyKEu9CoQCfzydsADPVMsA3Go2oz9XD8X0HBv9qEKpTBUqAf6sfgd8GULKURLkIUD6ai2w771dRFBw/flycfVlbW4sFCxZUBQ6cFzp19pMgQUfpJdcP95PL5RLP63a7BWPNd8nsjNVqFceUMNMxN+bG3PjDjuXLl1dJW+V4gf1T5MwxCUUCsawzC2PaCKVQDvTr6upQW1srfDKDdTbzJCix2WwINATw3LLn8Hj349AUDaeXn8bWO7bi+qevh7VoFfczXZpG32gfpqenUV9fj4aGBuH/mQHkPVNOy3uUiU+WWzHRIfcvoV08m0A4O8ssK7Fo02nHstksXrvlNUx2TwpyOVubxXOfeA7v/tq7YVSNyLvzePITTyLSHAEU4B+X/iPsh+y4YPaCKuk6gaJM0jLu4LzKZIimaQKI+rI+6GYdikURyYKZmRkMDQ0hkUjA7XYLEEoChYQoYzPGUbTbjMd4D/xMg8EAq2ZFx086YPKZMPKOETQMNuCS710CE0zIqtmqtcBrKYoiiBuPxwO7bsfyXy5H0VxE3yV9CB0LYf231sOgGlBSSkgkEsJ3M3ZmjEX5eG1tregFxHfNz2MjO/oj+tlotNyj5uyyTJIz8jthbMVYjzEW40DGUMyA+3w+EZNls1mczp7Gji/uQLo+DSjACx98AZYfWNBj64HdVgbJgUBAqPGAclbd5/PB7/cjEolUJVCYYU4kEiIuZ+d1kgryugUq/pfv3mKxIBgMitiYcYnX661KHFApqqrlOmwCZ84vzz+nskA+0cZsNosMOMkcVVURjUbh9/vF+dzymd+JROL/B2s3N/6rj7cNuOUaafmsPBoxbgDKnFhXTSCby+Xg9XrFRiBTFgqFREBOdspsNoszbmVpNR0Ea074tXg8LkABnZLcPZ2biCBMNq5msxknT57EggULxLMwCynLdWWA5XA4MDs7K0AJUAapbrdb3C+fk3MlZ7/k65MNY/MNAl86IzKQDBD4WQTQckOLWCwm5p+ORO6SSYaQLJ4s9+X9MvtP4/bGG29g7dq1ACAMK3+HgJkMIA0NZUGZTAbBYBCzs7Oibp01vwT9+XweBw8exLJlyzDkHsLhwGHoyplssKJjd3A3VrtXoyPVIZQAdAB0tgROnCeZWeb8n91UjqSKqqoYtYziV5f/CtPeiixcN+l4/q7noXxPwbqpdXA6nZiamkIkEhEN7PiZJoMJV/RdgaKpiDfdb6K/qR8rfr4CLRMtMDQbhAPgnMlrsFQqwa/58fHHP46Hr3wYxqQR9R+tx3HjcSiLyg1AjEYjmiPNaHi1AdpCDfU19dixYwcOHjwIAChuKuKx6x7DB5/9IJqGmkSQAkAwsVxXrIEaGhqCN+zFit0rYAqahBM8O0vCNcO9Icv9uH9IiDFo5c9OTU3B6XQKggqAmDMAQm6XSqVEVicej8NitqBxZyOUf1Yw+PFBWH5rgfdLXmT8GVj9lfVZ7CpiKjOFWlNtVfmFw+GocoSKooiO/wxYud4nJiZE51dZVslA3G63i2w3S1C4jngWvBy0M4NTLBZFd9K5Gu65MTf+c8b69euFXQIqx2LJ5TIsT2Lmj4H+jGsGD573IBb0L8DFRy5GXU0dgsGgkJjLZLhMytPnDigD2ObfBk05Y48VYLx5HKMto+g41QEASLgTePayZxEJRjB/ej4aGhrgdruFxBWoNEk9uxZW/p5criXbbblfjizV5RzIDeTY2JQ+g5/H0yg0TcPKf12JRD6BiSvLGe7agVpc8S9XwKAbYDAZ0LeuD6lQSgBy1aDiydYnsT6yHhat0ttGru09O6PN5+Cz8fvT09MYHx8XMQavQ0Uks4yhUEhIyRkfyNlcllgxhiIIJfHA2AyoZJF1Xcd5vz4Px/3Hsfm5zbB4LIKMJTFAYoQJHfok+jmb2YZzfnYOHBYHFv94MTRogLGS+ed58KpabojLuJh1wdPT08K/UF3Fkj2CVF5DLo9keZV8kk0gEBDSc7l/CWMnmYjg9Rh3yQ30gIr0PnplFIVAQbx7zajh4AUH0fwvzchlc+J9yf2XwuGwiOO4xtg9XW74yux9LBYTknbGkXyncu03UCEZmJBiPxXiCjmZJDel0zQNsVhMZMYJ/lkvz+y7xWIRx39RkcJnSyQSqK+vF2WyPLNblqHPjT++8bYBNzcdwQ2DUdZHyFJoWbIFQGS8w+GwCF5pMLlB6KSYMSWzS1ApNwGhg1NVVbBYlHbncjnx+ZRs0xnx9wCIjRiNRnH8+HG0t7eLDCSZLYIJ1r/QuDscjirWl8/A7DGN+NlsORt90YDT2NHgEtAnEgnBABLUk5kjYKATpVNSFAV+v1/MkSyh5xwAlawq2VO5IYRMODALL8u/5CymfG8yGKORo1Qnk8lgZGQEbW1t4j7IBHI98RnOKZ0DW78NX+/6OqZsU2iON+PDez6MjlSHWEcyK8970nVdqCs4mB2QVQSyQ5ePnMuZc8gZKr8rhgHY/sHtMD1hQs/rPYKdZRaBNUN01Jcfvhxr4mvwa9OvYf2VFYMdg3A6nXC5XDjlPYW8lkfLSItoviUHO23xNtzy4i2wxW3IL8vj2LFjOHjwIBYsWICFCxeKd0n2evPmzdi9ezd2+Hfg9NWnkXVm8eDFD+LGF29E12BZwic3M5FLDuLxOCYmJmA0GhEKhURNUjqdFhIxKhVkgClLF2W5oryGOMdUH5Bkk7ML3J9simg0GoX0j+uvVCqh/ul6KDkF5t+YkSqVJZdsJpRpzmDgLwZgjpnh+CeHuBcGCMxsxGIxTE9Po7W1VdR3c+5pV15d9CquTV8LU9YkOsCSpJHljMPDw4jFYmhpaRHf4/NzPpgh4jqjXZAzJ3NjbsyN//+HxWLBypUrhT2TJeW0QXKGWAbQEWsEPznvJzjZfBInm07C4rPgYzMfE2BbVmqxTE3uBl0oFJA7kcPK7Svx2odeQ7wrDnfWjdtevQ09kR7oAR0xxPDUpU+hd0EvsBCItcbgesElbCUAoa4BIGyTTF4DlTI2xj6y35Zl3PSF8lGOssLI5XIJAp2+U1YClEolzM7OouXuFrjgQnJ+Ehc8fAHsMTt0pexnVu9bjaA9iEevfBSaUcOG6Q248/idMBVNUIyV87blOeRzyll4grt+bz+yShbNQ82YnZ0VfoPvgb/be34vUq+l4PP50NbWBpvNhkw+g2NXHUPg/oC4NoEea44ZX1JiLxPUXBecD7fbjQueugCqpiKjZ0S9M5NHjOGY7SSoY4KCfnbjrzbCFKhkUwnamRhhbA1Uyiip2nS73QLo89pyiQB/lvEqYxX6RWZgNU0TcSbBrpwU4Kk/uVxOxNHcM1wXLIlgkmfF9hUw5o149bZXAQXo2t+FjT/fCKNmhNFU6aIuJz3q6uoEyJUTI4ypeE+qqorkg1zqQV/LjDHfndznZXZ2VsytqqqYnS03xWUzV6pS5GbHdrsdTqdTJAGZIJHl6el0WmAWfh7XtBwfyafe5HI5HDhw4A9jAOfGf7nxtgG30WjE5OQkTp8+jWXLlsFkMgkmisCK2SQCLwJzOgJZeilLMAlc5E0sNwPjBpUNCjcn5aEyuOWGldlcfjY3BoG/0WjEpk2bBNDgRuG/CeLk48FkcoGMIZlBXpNdJRmY09mbTCYcOXIEXq8XTU1N4hkBiKyzyWSqqu8mC8e6GzJuZG9lqQ3nkfU6srOVmW8AVYCPGU5Zig0A5557rnifvEeCBznzT8dAiREJFKfTidHRURiNRsybN084JY/HI5zC0qVLxTrqGunCZ8Ofxd3n3I2vHP8K3Ak3XG6XOK+R50nKBMrZpAGfg6wiUMl8U+LL92exWNA81YwPb/0w/vHaf0TMGata93l7Htuu3gY9o2P+oflVRpVrjOSRqqoI+ULYdGITDpgPYHR0FDabDa3nt+LhKx6GBg3v+fl74B5zC0MtBxh1Q3VlB9hZvt7AwACGhoZgMpnQ0dEh3iWfp+m9Tdj1jl0o+srZm1nfLH5x6S/wnl+/Bx3hDqG+4NomccOaemY0OI/cN5xLAG9pymOxWDA1NQW32y2AK38PqDQ8pHNmOQcJIzY65PoxGAyor69HLBYT0n8etZHL5dD0chNUjwq3rRxohMNhzKqzyD+UR6azvPf3evbi3M+dK67JZioMVq1WK6anpzE7O4umpiYsWbJEkC6vL30dP1v2M+xP7cff7PwbsfeYFQsEApiamhIKlJGREUxOTiIQCMBsNiObzYrgSHa2nHseMTI35sbc+MOOlpYWUf4il0cBqCJt5QamJpMJVrcV9266F6drTpcvpABPLnkSzhEn3j/4/rcQbXJZFlCOFcbHxzEwMID6TD3e99z78NPQT/HeZ9+L9ul2lFBCoVjA/Tffj4GmAfEZvdf0or61Hh84/AHE43HEYjFBSNKG0Hez+7RcHifHJ/TpMulKv06fI2fECTaoogNQddqF2WxGNBpFKpVCjb8GS55fgsKeAjxhD3RDddPXS6cuRdehLjzV9hT+19H/BX/OX0XuU20AoIq8luXjuq4j4ozg3nPvRUkp4baR20ScA1Sywrquo29DH07dcArF1UX4P+IXPuvkF09iat0UVKOKxh83ijOboyuiCLvCCG0NiZjA6/UK6S/nL5vNIl1K48QnT2DV91ZhcHAQLpcLHo+nKlnEHjoEqoyNGQOyBw5BOOeAySV+nkzuykmccDgMRVHEWdDMBKtq+Ugt/hznzmq1inOvuc7ZIJQ+0eVyiXidPpvSaTkZxDkm4SKrBJmplZNbXdu7YNbM6F3Wi7UPr4UlbUFBKVRlx2V1HIl57kuqTbg2qfRgR3BK74PBoFDKMplF7EEZP9euTICfLRXnezEaKw3ceAY6k1+M4RlXMBGhqioCgYBQGUSjUfE9xsUkLIhjfD4fZmZm/h+s2dz4nzDeNuAmaPb5fGIBycfkEIRxI5EVJauYz+cRCASErEXuHMmNQRaKkg+g0ryCsia507a8+eUMLetsabjkLLrMSCcSCXHmNxu9cdAIUJrELp1kB2XpDQEuZS1kPDkn/D3eEwN5mdkjE8faEl5frqd2OBzC4cjsHKXTcodxoLpenSCUAJ8gWXbGJEcoJ6Mcm45XDlbIkNN5kmmlM+fvqKqK5uZm9Pf3w+12i+MdEomEICsMBgPi8bj43QWZBbhvx32oc9YhYolUOSky0My2k+zhfWYyGWEYOb88EorsKAGfrKJwj7tx1wN34Z733YOCpYCctezADJoBXf1daDzWiFwuh7q6OvHZXKfymlBVFfX19eL9H5o+hB9f/2PkbWXn+v0Pfh8fu/djCCgBwVRz3dDZ5nI51NTUiNq0Q4cOwW63o7m5Wewnk8mEZbFlmByYxMuBl6EaVSAPrHhtBVpmWqBqqnDwAMRaT6fTQkrG5nUkbag8YKND+Sg3WcXAczS5v7mnGBhS9eJ2u4WcWpb1yXbB6/Wivr6+HAid6UQaj8eF7I8EnMPhwIIFC+D3+/Hmj99ErrOiSIgujuKNz7yB1X+3WoBgAl4GCqlUCg6HA319fTh+/DgWLFwAvAv44cofomQs4aD/IL5wzhfwudc/Bz/8Yp1HIhEA5aDKbrdjxYoVCIfDiMfjIlsiE1QMGiORCFwuV5VqZ27Mjbnxhxvd3d2itpW+C0AVoGDswv8Xi0VoqoYP7PsAvnnxN5G1ZAEd6Ex34pbTtwCodNqWr8uvpVIpTE1N4cSJE4hGo2htbUVNvgZ/8chfwFK0QFfKwCo6G0XLV1ow9PdD0Lxlm+GP+HHOk+egv9QvytEodyUwImBhAy2gYpvlZqSyKo02kM8JVPdcMZlMIkMrK+ZIIlssFgEoLBYL6urq4IYbWlSDbqiomiwWCwKBAELBEJrDzVgXXgeLahH3Isve5QwyUOkhw//PWmbxuSs/h4y1TKree/u9uKH/Brhn3SLLWNJK6O3pxY7bdkAza8B5wMgjI3B+yYnxT41jctMkYAAmPjKB9HQawV8EYVtnw4lvnoAOHQFzAN6dXljMlqpYjPcDH7Dnq3uQak7BbDJj+Y+Ww1Asx1MEbYVCAbnuHJ698Fls+sEmOLOVDuOsO06lU5haNoXhlcPY+OuNsJvsIn6Sj4l1Op0iNqZPtdvtouyQ74++kb6W11LVSi+bWCwm4nCSLvKZ8tPT09D1ctMwdvoGIOIoEvRUMDI+8/l8ImYmic46b67Jnn096NjbgVKqhHQpLQCz/L4Zs87OzopYhPEBY3yWLPJoVX6eyWTCxMSEUKMyiUaSXv594gSfzyeAMedKVhoytua9MJYJBoMitpmYmBCKRf5+NBoVOIDPyBJKxkAej0fU0s/1cvnjHm8bcLNJQigUEuCLzQMYqNN4U9ICQICDVColamwYbMuSKJOp3IF73759WLx4MZLJpABKckDPWkgaGYIFBtj8nizR4e/ILCwJBFVVRU0IAafBYBBNpCjhJjiiPE1mjGWwJH8u50A+8xooH1NC58IMnNzUi+QCry0/p6qqohb1bBkSASUdMQMCOmv+DVTqwGmg5XOK+VzZbFacvy5LwAjoeW3Kszmfcj2+wWAQwPbEiRNwuVxwu90iuJFleDLD7rV4hbEkQyuXBNBJ8T0yg8/3zLkBILIEvD+SAXKWwGg0wpww4zP//BkMdg/itTWvYaBhAItHFiOoBbH7ut1Y/JPF8CfKDDo7ksqqBKvVilgsJox6XV0d+m/vR95aAVwFSwE7r9qJlt+2VMmweb63XE/GeiyPx4O9e/dieHgYa9aswXj7OFrHW2E323H1q1ejZCxh19JdqHmwBtn7s5g5b0Y0AqTige+kv7+/6lzOsbExNDc3V4JNKXvDfXJ2QxKuRb4vrhuuS5IQPG6D74HPysw+mePJyUmkUilRu8Y1xoY9NptNMNh+vx/rPrsOh759CPEFcQCAcasRK/5xhQhm+XmUSgIQJQdcp3uO78FBz0FxFjkUYMA3gNc7XseWqS3iZAQGOSwF4fPRIXM+AAinn8/nMTAwgGAwCJfLhampqbdrZufG3Jgbv4dhNBrR3d0Nr9criGgZWJ/9bxnIFgoFtEy04E9f/VPcf+79aCo04Z6D98CgG6BqlfIsZrZpr3he8smTJ4VdDQQCZTWN5oBiUkSDx97eXkSOR7DoLxdh5O4RuLIuvOeB98CYNyJXzAn7yhpe+i/2kKFNl+MO+jPaZ/pPuf6b5UW0U5qmCVJCrvVmzFEsFpEuptHn74N6UkVjYyOsVqvIbtLmqQ4V2c4s6t318Hq95XhENQKmCsEhy/blxACvQ0Jd13X8es2vy2THmVE0F7Fryy5svG9jJbFhLWDPhXvKYBsAFCA/P4/+v+pHZn4GKL8e6GYd8RvjSI+lUfpuCSgnwPH6l16H+zY3bC+Uz012uVyil4ferqP3071ItZb9wMBFA/AoHpzz9DnQk7ooSYwsiODR//UoVLOKPbftwYW/vRDWlLWq1HGwexDb/nQboABqQsW5L5wLQ6YSfzAhc/ZJH4y55COvuNaYFKH/5TsjuRsMBqsy4ZSHy6WcjKeYLGGSRU7yMKFCCTpJccrm6RflE0CsRiucRieiWhlksrEpnwGAaBzL+2ffFRL9jBWYtWasRaxAv8s4QU56UbqtqqqIrTnXVAIYDAaRiZZLMXltJhSoyLPb7aJzORM6JKKYlKOClv2nuIdZTpBKpfDLX/7yP2zb5sZ/3/G2AffZ9bDclDzu6uyaIXljc0OxLpT1IrKhYVAdi8WqapJoiOkweD05o8igmg5TzgJy0zAjTENDgCbLP8ke8hl4TV6Hxof3R0dG0Mv5odHj53LDy3Vf3Ijc6KwnBSAaW8mZaQJXOavIDS5epsTOUmnAQTKE88dsP1lOyn1lWRozjLLxAyDIA9kw82sExPJRI6FQCBaLBbFYDMPDw5g3b14VuAQqAJpzRwMry6hkQ0vDzGcjoGRQQakS35XVaoXdbhfHezHwIBAj2aGqKpqONOFj4x/DzmU7MR2cxvMrny+/z2IOpodMaGhoEGuCzoyAkIQOFSDnPngutLSGU5efAgBsPLoR79r+LhjM1cfT0DlzvctN8RKJBFpbWzExMYEnlSex//L9uPzQ5di4dyN0Xce126+FN+JF84FmvGl+E9u3b8fmzZvR0tIigiaz2YxUKoWZmRlx3qVcjmCxWATo5Xrj81B2KZdnyOQKAOGsyVzz83w+nwgcuf9kUK4o5dpxNj6kCoT15ADg8/kEo280GmHRLFj996tx6M5DyA/noXxcwWnvafFeaCdoB4BKyQnvRYtoWP8P67H/4/sRXRaFWTXjoyc/issnLkdOzQkmnu+GhJ/dbkd7e7tQ1nD9896SySQ0TUN3dzfMZjNGRkYwNjb2ds3s3Jgbc+P3MEKhEFpbWwXBxyFne/lvAIJsp2TV7XZjc34zfH0+LEotglE3QtMrQFEmnelv0+k0RkdHEYlEUFNTg7q6OmHDgLLUPJFIYGBgQCi+lmAJlj+xHMFYEJaCBape6bUiNyRl3JXJZJDJZKqaU7J8ixk1kgZyjS1QUQsClZMr2BRSLr0i2GM8s/fSvTi45CCWK8vhmHEInwCU7b5u0PHKVa8gviSOrmNdqM3XVpWdyXJh+mfOvVyLzbnVdR23774dxoIRW5dsBQD0vNKDdT9ZB6ujYs/zyTzaPtOGo3cdhX6RDkPJgOUPL0fw10EMrxlG36f7oPpUKIcUGD9qBNZCgHAOdYmK/FN5kbFknKe4FORN1cqkeDCOgqmAbKLcWC+8Nox9790H1Vx+1v5N/fD4PFj/4/VAoRzDjJw3gldveVU0Ejt29TGYnWac82/nwKBWmt4ydmLiI5vNihiF4J7KTsZkZ/s2xrpUlBKEUv7t8XgEaCXhROBL303/SR/N+5Ozuh6PR5Dh9N8GgwHBYFCAfkVRxBnU9Luy4pIlZIxjGcPxHkgAyOpUxuaMVeTyCvpgoBw/5/N5+P3+qubDMmagT+f+kpuoMTaS55X3w5OJqCDgUcEOh0PEgHw3vFc2nHY4HAiHw/++4Zob/6PH79Q0Tc6SMujmGZH8nmzcZZaHG+l/17mbP2Oz2XDZZZcJMEwZKoNbWSLM35Ol3GSbyPYR8JIQ4AblvdJxEOiRPQMgjh7gZxGI8rlpyEgypNNpYQATiQT27t2L888/XzwLM9i8PhlBt9tdJaeSO34T8HLz0xgQPJ9dOysf4UCmWAbhdGiyPJzGmcZXBhqUF/H9ksHk9eSaKjnwKBaL8Pv9VcCKBMjIyAgaGhrQ1NQkukpSZSBLuviZQCVjzfnn8RGcV7lBCX+XMnPW6vK9yIEKm2XQAckscalUwkhwBLsW7xJ7oO+CPuSteTiPO2HoNGD1qdXCgfAaZEP5Hs1GM9b+ai2MJSNMThOuOXYNUASKarHKYQCVJn28b37f5XKhp6cHM6tnsO2mbSi5Snhi3RMoGAu49PVLYTKZsPnNzVDml9/9vn378MQTT2D16tU477zzAAAPb34YGx7YUN70Z2TbwWAQjY2NgpRgNpqORu6zQMKN80XSguQC2XA5C55KpRAMBsXzcb9Q1UDWmXZDLhHh+mL2v7a2VnQZzeVy8IQ9WHbvMiTHk0i5U5icnEQ8HkdLS4uQgbFJDde5xWIRQF7TNHhnvNjwow3Y/cnd6P51N1pMLVAWV0oi2OyR9i+ZTFatMbmXAJ9Pto2c67kjQObG3PjDjrq6OjQ3N4tAW/ZNJIvl7GA2m8Xw8DBGRkYAlI8Ts9vt2BTbVC6P04rCHtH20U4za93f34/e3l5YrVY0NTVVqfhUtdwga3x8HH19fVBVFZ2dnQgGg6gbqCvbDKUizea15VI5gmHZ9tDOyM9CAAtAAAsAomcHyQWquzg3jBH4TLlcDjuv3Yn95++HZtRw7FPH0PzjZlhPW6t6mbx4y4s4tvYYoAD3rLgHXz/8dTTmGsU8y7J7+f+M/xjvAJU4QtM0bNm9BenZNMK5MFb+eiXUrIqiuSieMZfLIXk4CdPHTSg9WEL3c91o2t+EorEI1/Mu1A/XY/Lrk/B+wgulT4FxyAij3YiJv5sAADR+uxG+n/tQqq+8VwHcBoDinxcx8cAE1BoVrUdbsfmXm+HOuuGpK9dw17vqcVw7jiwqmXjDCQPikTjMejk+sZ+2QykqgL2yNl19vUxeagAA4ydJREFULmTSGWhq5bllEpv9gqiyJAHPjCxjKZLc9N30qxwEegTRpVJJyJ0pJSfw5mfzPpjkkMsueB/MrpM0kYH5Cze+gEsevQTQymuP8myZeGIsbLPZYLVaRXImkUiIzzmb0Ge9OZWjfF+MUQls/X6/kLzLGW2n0ylKAAuFglDfyaQTn0Vu/gtAxN9ysoBlatyHVKLwuUh8sKZcJuPnxh/v+J0k5Qy0mTHjIHCUGTZm+Mi8ymfbstGDLNkmqyR/nRIOGmlKYuQst3wmH8E3r0NwRXDGeyWwlpsq8GfYbZSfRRmqz+dDPp8XBoCbDYBoFkaHZzAY0N7eXpEqn6mlkZ0njycgyCVLRlZSdmhyvTpZOL4DAgkaBQI9AJicnEQwGBTGlc9OEJ1OpwVglY9rItCX1QpynTKAKvaVX2OGmO+zu7tbdMPm/RUKBbz00ku45pprRNDCd0oZuiyD57PxuVhvRDDOr59NJPDaXLc0vvwajTrldJSAMUuey+Ww8ZmNODDvALL2skO1Fq24fvZ6fP+W7wMmwJgyYunoUhgNRkE8sREc58VutyMfy2P5o8uRSqcw2TiJlpYWYeyNRiMSyQTgAV5d+ypa4i1Y2LdQBFV8xpnADF6+7mWUXOW1XjQX8fyq5+FOuLHh1Abxjrq6uqAoCl555RUcPHgQVo8Vh+48hEMLD2H4Q8O47J8vE3shnU1DtZT3EY8u4fzI70vO5sv7EUAVISMrTbiOyajzeegIGfDKZSksYaATlu0OFQrpdBqBQACJRALesBc+qw/qgnJ5xMTEBI4cOYJgMIilS5eK/c5aSAYO/FqpVEJzrhnnfPEc6HEdrzlfw+TkJBYvXixqJ0kAqKqK4eFhqKqKBQsWQNd10VCF3UoptQPKzdLYKZ114HNjbsyNP8xoaGhAfX29AGdyrTYBHmWjcjbX5XIhEAigqalJ+FlZvkoilH+XSiWk9BQ+c/5ncO6L5wIFoLGxsUrxxj/hcBi9vb1IJpOYP3++uD/GMgTTzDDKiQzaQfoVgg9ZCivHZLKvZl1wNpsVBOfZcmXadH5GLpfD/gv2442Nb0Azlj8jVZvCcx95Djf//c2wxct1sq+88xWcWHVCZHBPO0/jL1b8BR7Y/QAshkrplpwxZLZQVgnSJ/O+dV2HMW/Ephc3YTY2C0VVoForYCyTyWB4eBjT09Mw6kY0fbwJdrMdMVtMvDfLaxbUbamDMqvA6DxzpvhWB+x/Y0fWn0XNv9XAZrdBcVTegawCUMYUzLtrHo5+6SiWfX0Z4vk4dE852xsOh2GNWnFZ72X41d/8CnlXHuueX4fVu1Yja8iK92GZsODWe27FQ59+CJpZwzkPn4PGbY0ooSR8BYndXC4n+p0Q0HGwKRlQjj0LhUJV3TUbyhYKBfj9fgHWI5FIlQ/m2dkEk+l0uqoclHEMATxJHpInTqdTnCgil6zBCjz37udwavUpzARmcPU/Xw2rZq1KGjGWLRaLIrYHKqf7iPKEM6QDEwDZbBYTExNobGwUOEPOoDOeKJVKQvLO5mfMvHO/AxDA3O12C3zCeFYujyV2YIweiUREzMxSPxJp8n4k0U8Azwz54cOHqxSpc+OPb7xtwM0MlJwllhkgykPkroNc+IVCQTT+IHtGZ8efk5lNuT6Un0XHA6BKSkwwxp/TdV3UrsiSXcpV+LlyxprPRekN74msMbtQalrlSA0AVQ3eOD+qqsLj8YguiXzGdDqNmZkZdHR0CGNBBkyulyL4JXvJzc97l48foEHgXMhZZqPRiGeeeQa33nprlaxYZpLJfsvGUL62nGmlI8rn87Db7eLn+NlcC3IQMzY2JrLIlNWTzczlckLyw3p9u90uaoVYY0MShdlNGlgAQrnA/1PmFIlEqqT4fF6n0yneH2X1cm2bLN1XFAWBQgB3PXAXfvTuH0Ezabh2/7W47/L7UDSVSZfvXvld3PnMnVh0elEVoPT7/fD5fJidnQVQzq5qWQ1ukxuxWAx1dXXifEZFUTC+fBz/cu2/iL324d9+GKvHVot3bTQa0ZptxW3bb8MjFz6CpDMJQ96A+U/Nx/wj84EQqt5tfX09br75Zjy3+zk8su4RlJaUymfA9ozjhQ+8gM0/3wyn6kT/+n6cXH8SlzxyCZRC+Z2zDpuOn3+MRiPcbrdovCLvezor7kv2TWDTMKByXAedGp8tk8m85dgUSrXYFMZsNiMWi4lzrdkplMFjqVRCR0cHrFYrIpEIMpkMTp06hdbWVrF3WabBtcB9lc/noc1qInDeuXMnxsfHsW7dOtTW1lZlAlq7WzFuHgcTGnV1deJkAAaRcg36qVOn4PF45pzs3Jgbf8DhcDiwbNkyUS7E+IGARc5YAUCfrw+LLIvEsUP0e/x5/g7tB1DpLD1pncTXln8Ng95BxD4fw/UPXw+fxSeISdrPoeAQJveUVTitra2YN2+ekOnKZTlMBsh9WQBUgRb5b94LP4/BPe+ZvlzuW8K4iuCGSjl+LgHNomcXYdQwisGrBgET4Jp14bIfXwZP1gPdXE6KXPnClVAaFRxYcABQgMZ0I75+4OswFA1QlUo5GtVQct28nFHmZ/K+8/k8IpEIiskiLCULdJMu5kZRyvXyk5OTUFUVbW1t6KzrFH5KVk0paQWKTRFKSSOMqHm6/J5hriR3SHBQDccYtNvVjRXfXwGlrnxvvEeC2EKsgKs/dzV6t/Si/aF2zGgzIh5iEqN4oogNd21AbF0MgccDKJqLMDvMIlnEteZ2uwUIl4kFqvQ4T06nU8jD8/k80uk0CoUCZmZmkE6nEQ6HkUgkxBGYfDYmfRKJhFj/BM1sOEvfRb/OsgZ+vtlsRjAYRDKZRC6XKx+f1eDEjst24OS6k+U4Y944XvjwC7jkF5fAlDIJTMB583q9Yj+azWYkk0n4/X7x3CxJ41oh+Z1KpUSWn3NBn6vrujjilH2YuDc8Hk+ZgDkTN/B9cy651+Q9Lyt3x8bGUCqVRA8r2g+elS7vN5IDJOtJaADA2NhYlU2ZG39843eSlJO1JUAhsKOjkKVWcldsgjrKX+SMLmtVgIojlBc+Qbp85JbcFZEBMaWidJhyYC83TpCfhRIZNg4DIGqF5doUoAJouVnlpkn8TF6XWT8aL6PRiFgshv7+fnR1dVXJXeksCa6ZYeRnyrJdzik/kzJ4Zpplx6HrOj7ykY8AgMgUy2UBMvvGueIcvfzyy7jgggtEFpD3KrPwfD6gIvk2GsvnKTNgkWtoHA4HZmdnxb2HQiEEAgFMTExUGWSej0mHwMZqMqnB9ST3FKCBTiQS4jkohabcx+VyVWUFqLzgmmXQwTkzGAywR+y48fEbYfab8ZuNvxFgu7yQgAcuegDf+cl3xP0fPHgQmzdvFhLzaDQq1gQDnNOnT6Orqws2mw0Huw/ip5f8VGQIAOCBKx5AYVsBq0+urpLdLx5ajOu3X4/HLnwMa3etheMBBw6aDqKzsxNdXZWztwky11+1HsOXD2NAqRw9k65NI+6KY2T5CF657RXoBh07btyBjQ9vhDahiX1JoEviBag+T5p1SnR6sjye51eyVpzrhA6fAZGiKOKzuD7ZkZUlAXT4tA+KorxFHcIAjg43HA4jlUrhxIkT8Hq9oocA9xvXLN8Hg05+fWJiAq+88gqCwSDOO++8suzebMLD7Q9jb2AvPn3y01iQXiBsCfeI/H+/34/u7m709/e/XRM7N+bG3Pg9DLvdjo6ODnEaw9nKLDleea3+Ndy39D78Sd+f4NKZS0XsQZtAO0W/B1SI/UnHJL7T8x30+noBANHWKLbfth2B5wIIRUMiE368/TgeveJR+Cf86Ix2orOzU2QseS2WZ9Hm0qbJ9peAhzEC/bD8b96jnLgAKnEUfTOBmJxd53Upg52enkbLt1tgN9oxfM4wLvjZBWgaaoKqVO7LZrPhrn134RHXIzjpOYm7jt6F+lQ9NFTPOZ+TIFuWsfMdcW5ZKzw9PV2lEpCTHmNjY+I0C8qNSeSHw2Hhy9l0k7Eh54J/5JiGg76ftcKJRAJ2ux1er1dkXlkyZ7Va4Sl50PhsI7LubFVszNhK0zQ0JZqwYNcClIKVUkg55pTPW2eGl4Qzs6iUXdvt9qqMN+MuAnGgHEexhwr7F8nJIpa90S8yxpZVmLqui94qTqcTyWRSqFnZNM9isWB4ahj9tv5KDKMAMW8MEWcEtphN3DNJ8lQqhUAgIIhsqkM5Xy6Xq6rT9/DwMDweDzo7OyvKgTO/y0xzsVhENptFNpsV4FrXyyefxONxsXdYGpbNZsXpKIwfiVcYz+ZyOVGrzWPJGOt4PB5RZgdUTnGiQpZN0kighMPhOeJ9brx9wE1wzaCVToIbgMZUbojGP3IATYBJQ8qAnAu3UCgIyTkNFw2U0WgUgIrsGEEZ75HXpOGWGzDIknUaxkgkIgwfjSw3v1wXIwMLOgkyZgSvzOIBlXpOSlQaGhpQV1f3lvuVQQ2ZRn4fgMgKywwju5YKOY/0eceOHYPT6cSCBQsEUCNTKDsYOmm+Pzn7J9ems1O1/E5nZmYQDAarHLyiKEJ+z/cjO+WamhoMDw8LozQ9PY2WlpYqeTsBNd+3qqpCrWC1WsW52gRpsgSMAIzzIGetqSSIxWKiCykbXHA++PM8Ai4QCGB2dhaqqqJ+oh7ulBtbdm/BfVffh5JJOldZAYwmo3BcnZ2dbyF2zm6aF4lE4Ha7MX35NB479zEUzRKIB6BAgTfjrZIzU32wemA1vJoXLb0tmOiewBtvvIFdu3ahVCqhp6dHMNaqqqIx14j3v/Z+/PjCH2OgZgC2MRsW3L0AI2tHcOzGY9AN5Xc3uGIQeUseF91zEQyaoeqMdLO5fHQJSSsGTjLRwYAoGo2KbvIE/TKZxN+jmoIkBx0d1wv3bC6XE+eaMtChPTEYDCKAICFFdUJLSwsKhQKGh4cxMTGBmZkZNDQ0wOfziRMJWI7B0hCZ+OOeHh8fx44dO9DS0oLdN+/Gb1t/C13R8fcL/x6fP/h5dJY6q5ozMkufTqcF6z4+Pv52TezcmBtz4/cw/H4/urq6qjLbBHhAJU7YWb8TP1zyQ8Stcdy74F6oJhVXTV0lfpZA5GwZNLOFyVQSpVAJCFU+25K1QEtpIh7o7ejF45c8jpQnheyfZ9HQ2QD3UbfwDfQJcrMqEvpyPwz+zbIoDlkZKJPmcsKCP8dr048zzpHlsLTV0WgUkUgEXq8Xi15YhNnBWTT2N0JTKo1eFaVcHhjyh/AnfX+CEdsIetI9UA3VtduMQc6eU35fzuLza/KxsbLqzmazYWpqCoODg8jn86irqxOAmzJqyuflE1wYz9CHyMQG54j3wPngv4PBYJWvS6VSmJ2dFX6HiZtUKiX6Bnm9XpGI4fphAogkuly+xTmV54NHghEMy6WPJCUYc+dyOZGo4Bzw3uLxOAwGg1ASykfcUubP+eFzcp5437JvNpvNog7carXCEDXgil9fgR3eHRhrH4M35sWWX2+Bpc+Coqko5NjySTMEyMyqE+wDEL4UKMfks7OzIkkFlMFtJpMR2e76+nqhiCsUCuJ56+vr0dDQIBJLzDDziE9FKTchZBzOPUeVg6qqYv4ZK/N4MllxwqQfCS5iIJI9qVS5xwwVj3Pjj3f8ToCbAJLOgRs+Ho8LiSwBEiU9Miukqqpo2sGFzAYGP/rRj/DhD39YbEZZ1s3aZnZRZgBOuSadllwjxPuTm3r975wMryHXL8nybn6WLDEny0wZDg2rXOPOf/MZCQz4uWzaIHcdJ+iUZWPMWKdSKZH9IwnAQJ/gEwCamprE10XjrjPAjXXccuMwAlw+t8ViwcaNGwUbJzs6g8GAWCyGH/3oR/jsZz9bJdEDIKRHdAw2mw3pdFrUyxgM5fO2Dx06hJaWFqxfv16wnmx0QeBDZ8js9tkMPGXmpVJJHPvGTKlM3rAWmM9HRpNNsciOnk1IsHO+DBBbelvwp7/8U/zTTf9UPvcaQMaSwQ8u/AHu2H4HDCUDQqGQIE4MhvL/ZZKI1w2Hw6jfX4/6hfXot0vscB749DOfRv1YPWCsHH9HObyqqlg4thAz6RkoioKLLroIY2NjeO211+B2u1FbWyucp81mQ0OiAVd9/yo89P6HEHpPCH1H+9A10gX7pXYUW4qAAhhKBqzcsRJuqxvFQhGRSEQEIXSwsvSN2XvOGTu78tg/rjMGhzILzGwwz70Mh8Ow2+1if7GejXuHe57ZBa4NBpbZbBZOp1M4XFmVsXDhQjQ0NOD06dMYGhpCMBhEe3u7qGFTdRV5LY9iodLUjc9CqRoAvHTpS+hr64OulAOCXmcvPrvys3jk8CM4VnsMJ0oncMPkDTBqRqHW4L309fW9fWs8N+bG3PgPj8WLF8PtdgOogGs5o1ooFHDYdRj3Lb0PKVu5TjZhTuC+zvvgzrixanJVVeaT/k1uZJZOpzF1dArNjzZj4i8nEFkRQcdoB2569iYYU0bo0DHuH8dPL/8pUq4zRww6VRy64RDqzfVYfGhxVSDPeIi2RybCeQ8EYgQWjA84mJmVr0P/fXb5HFCR79LH8fdpy202WzkTqVvQ0NcgfBQ/0+12o7W1tXzvqo6eVA8ACOAkA0qCZv5flvDy34xNstmsaFBJEpz3nEqlMD4+LjKgwWBQvNt0Oi3K0eg7CMr47uUTdRiPkHy12+2iDIrHQBFgy0mempoaAKjqnUPCn4Q9pcSM5Zh1ZTKDPpQAmTEDY0z2BEmn01VlmWw0xp8h8GaWnH1X6B+9Xq8AkFQLOp1O8XOMs5g0I7nO2JZqLcrQOR88SmtmZqac1R2xYcPfbcALn3sBl95zKUJKCNaaMrhnQ1wCZ2aA5cQbEyrMGsukSENDA7q7u8X6JREi13szocL4OhwOi1iPc+9wlDvsj42NCdUbyXVZnUsFLhUnqVQK8XgcgUBAgGn2EmKJI2XvLG9k3JPP5+F0OpHJZOYy3HPjd5OUA9VnGvP8ZQCimQBQOZ6KC85iscDn8wmQQ0PPjeN2u3HnnXcK6TEdCrOjZCyBSjMQZqcI7mQmij9LYEy2kuCcDoHglcY4k8mIYF/OvjGrRiKAkhICVM5HLpcTjaLkeikSBtzQrMlhhpYASa4rASqduuUadTpiAlPeHx2Xw+EQTKKmaZiamkJDQ0NVB3P5bGVKk2iUeJ8kKPg+6fT9fr8A2zRKNJx0MPx9GlZZtuZ0OtHd3Y3JyUmMj49jZmZGnDftcrmqMoWcGwYLBM18PrkhBd8x69M0rXxuPNlek8kkOsLL5QSBQACRSEQEKQToQEVdkEwmxX11RbvwsRc/hu9e9t3yO1J07G3bC9MGE96z7z0wpU1V8v9kMlkV5Mjy/+xYFh/9+UfxvVu+h4nQBGoTtXB83gFnhxMGr6HKEXAOzWYz4vE4jEYjAoEA3G43TCYTpqen8fTTT2PVqlVYvHgxLBYL4mocWUMW8d44Nn9iM3Sfjje9b2Lw9UGs/dhabHtiG3STjnMfPRcdxzpQKBUE8OU+4bukgoEEFI/BoAPVdR0NDQ1iLzLYYTDDWql0Ol1Va2i324VEjQ5ODtBIhsViMbFHeIa4fJ57PB4XTLNMrrhcLnR2dsLlcmFsbAx9fX1wOBwINYTQf1k/5r9zPixftGD02CgACIcpn1Xe9sM2pBvTGDtnDFAAd8yNLx35El53vI7Pd3weOnRYNSsuHLxQ2BSgHJjOHQMyN+bGH3asWLFC9GKRZbn0t6lUCpk9GXT2deLoB45CdaqwlWx498C7sXpqNXRUju0kOUvQVigUkEgkMDw8jN7eXjgKDnzwFx/Eb52/xW2/vg0GGKBYz5x00GvBoh8swr4P7YPm02AsGrFm9xosOrSoKtsO4C2ZVgJluVeKrMaSlWsysGZ2m76bdjyVSgm/JBPtAIRKh8Btenoas7OzCAaDormVnImVVWs+n68KOJHMl7PyMtEty8o5ZEUYEziM4fg9AsiJiQlMTk6iWCzC5XKhsbFRxFPhcBjRaFQkEBijMaMrZ/aZdSSRz8wks5B+vx/t7e0iaw5U1ATpdBrRaFTcIzt3EwjLx2tm6jOwzljhtDhFXEz/RiUe40tZgUl/KPtCEg/JZFIQFLJyM5PJCH8jJ6k8Hg+8Xq94lyScGUMy7uR7pP+S42g5JmSpIDP6fDeurAs3fPUGWEwWZPWseA5m1nkUGMkJro1kMolQKIRsNotEIlFFnOu6jpqaGng8HtFUlRl9r9cr+ruQtKKalvEdM+NMdiiKgvb2drhcLkHiM7ajdJ73RBKC88s9pOu6SCZy7zIhxmuw4R2TjHNge24AvwPgttls6OvrQzweR0dHB06cOIEVK1YIYEUDw3pVGl1me+n45AwVATgXsszWyudXE2iw3lqWS8n1vLIchpshm82Ks/IAiOyozFozSGdWmNdOpVJCNk8WlawbNxDlNrJ0jU6Q9yJLn+X6IDaH4BzJxxhxLmXJNg2vDEJ5b7wH3r/NZkM8HsfOnTtx0003CbAuHxMCQBhuGnOZJef/+XM0zDQ2BI7y/fLzCf55bw6HQzTOY/fISCSCbDaLUKiiyaNDloET68IJtNmci/X5dPIsW6BsSJZx87ryudulUgljY2Pi6yQyZPmdXOvLz3CH3WiaasJY3ZnzlRVgrHYMUW8UTcUm8V6DwaAoWWD2n++SzGw6mcZHfv0RHFhxAJefvBzKhspa4udS3k2Hzpos7i+n04lVq1bBZDJhdHQUiqJgwdIFeGbTM+i19GLt8bVwqS5YrVZ0d3djZGQEw2uHRcZ2rGMMPYd7YIqaBNnE4I2gGqgQaPIalGsk6aDYUIR7moQZz3tnsCjLJLnP+LnJZBJut1vMgSzVk+0LAwsGkHKG3Ov1IplMwuVywel0QlVVRCIRJBIJJO9IYvxD4ziAA2i8qBHdp7uBDEQ3c/aXsFgscDlcOPcfz8Vrudcw3TGNVT9ehTfWvYEHz31QzOF3ur+DrJrFVQNXVUnc5yTlc2Nu/GHHsmXL3lLuxExkJpNBf38/Thw/gZbjLWib34ZtF23DrUO34h1970BRK1ZJnQloSGhTstrX14dSqYSWlha4HC687/H3QTEowt/wc/LP5rGgtACDfzKI9fvWY9PWTVD1SgNYZtnkWOFs6bucyabvoI+ikorPS1AJQNhUGeTy+CaS1QBEsoE1sLFYDH6/H3V1dVVSY/pyTdPg9XpRW1srPhuo7otxdomaHA/y3mUgTvtN2y/HhPR7zCYy40wJtcvlQqlUquoiTXBJXyyXKvGZ3W43li5dKshcuWaf9dBer1co6QjKZFk6741KPZ5cYbFYEOuIYccHdqD79W6s2b4GZlO5HNBut8NgMAhQxuvLiQM+OwloJhLkOeY75hwXi8Wqo11NJpPIilO1R2KFiRe55IA9bThPstKUEmmDwYDJyUmhYKytra1at6PnjmLZyWUoForia7ye1+sV6ktZuSHL6c8uH7NYLPD7/WIeiCP4bplYIVBmLM13zPO6Gd9ZrVZEo1GYzZWGZtlsFoFAAED5dBHODW2AoihVPZe453gSCZUGTLSx9JEx+vDwME6cOPF7sW1z47/3+J2OBXO5XIJFYn0rUGb4uFFpRDlkSQ1QqZ+mkeBmpJFOJBJwOp3imAR5MxIQ03jw+rIEmxuCIJKbj7UrmlZuukFWkc5IBpgkBYaHh1FfXw+gUtvD32N9h/zzcj2yfEQZwah81AGNmWxgyUDLoIa/y3mgc+K1+AzsYE7ZOu/1/PPPr3JuNLIES3R+zP4SNMnAmUGE7HA591wDNDry87JpidyYjc5Nzurn83k0NjZicnJS3A+dA1UDuVxOdMaU1QK8J5PJJCQ+MmMu1+dT6ivL9gjSyX7KQRaBMhuFACizsRMh3LLtFjxy+SOYCEygId6AO3bfgaZwEx5d9SjesecdokkIs728HoAqFlnXdbjhxqYDm1CyVJrc0DFRhsb3wr0oBzmKUq6lW7JkCYaGhjA8PIwDHz2AN1a+ASjA7jt245J/uQTmnBkNDQ2Y3DKJk7efBM7wLoPrB7HVvhXn//P5sMAi1hiVJJwzOfhjtttoNAolwezsrJjjRCIBh8MBh8OBVColSDnuEa5zEivcK+z6zbkKh8OoqamBzWYTgVhdXV3VkR6apsHj8VSdvcnstt/vF2fO8ozuE9efwPidFSA8/q5x6E4dzV9qrsoScT2QqV/yvSWYqJtA8Y0iXk29CvUSVcwhAOhZXThr7uuJiYm3a2LnxtyYG7+HEQgEBGlJe8Qs1MDAAA4ePIhsNotzzjkHG3IbsPLoSqyfWI+SWqryXRwM9FW13ATzyJEjiEQicDgcogETSVD6TappFEXBmjfXYNGvFmHJ4BLoBl2Q7/RjtLdy7xHGBnINshyjyKCVPpDXlZVpjCOY2WY/E1kZRmBUKpUwMzODXC6H+vr6KqWOfL8WiwW1tbUiZmCcwmvIvyPfO+MGubZeJmwJkBjTyGo5ZnjHxsaQy+XQ0tKCzs5OkV2mcopgl/Ju1tHyWrwOEzFOp1PU/BKYWiwWQfZqmoZYLCbmihldNiejXDiZTAKA6LcTa4xh5/t2ItYaw96mvSiZS1j1VLlUgTEKwS5JBar2qGzku6PSjjELABELyfHa9PS0mCuqC3lajjz/jAPl9SQr2oDKCUCMlQnGWWrFUj+uQZvNhhMXnsCua3dhZu8MNj67URw1yu9TMcZ7MBgMcLlcYt+Ew2EBmHlPXMO8Hzask+up4/E4EolEVebZ6/Uil8uJcj4C8pmZGaRSKeTzedHFnco4RSk3ZM1kMlVnocsKl2QyKcoYAIjmaYzHSHiQ+AeAkZGRufrtuQHgdwDcQNmR0XiuWrVKMHNAhaUjSCB7x+6IBFxyXYss+yQAAyDkLmQlZSm3bPwBCOkIwbQsGafDopHLZrOiUzWNh5wtp0FWVRWJRAKDg4OC5ZTl1mS9yMaZzWbRhMvtdsPtdgt5DoCq3wEgsnoyW8p7ZZDA//Me6Xh5LdazyPIygkoaJs6L7OCYraRj4XuQJcucQ9khcE7NZnNV45az62lkYsBoNCIej6OxsRGapolzE+nww+GwcBh+vx/JZFLUDRGgcW0RNHEeGRTJQQZVCvI6AyCOinC5XIKBltlzEjhytoBOms9NqTDfR9NUEz619VP4+yv+Hp989pOoy9fh4fUPY8eCHZh2TeOWJ29Bb2+vCFzIzrN+iqCMPQ+41vmHc8Radb5vkkh858z8ms1m0aV734f24ciqI6LmbmrxFJ7982dx+z/eDk3VsHRkKQbSA0g4E+Wf0YCWHS2wGCyAVgmSJiYm0NbWJhyr1WoV65z7rlgsYmZmRhArfGe6rotnLZVKgrAwmUzCEXN+S6WSaFJHkoXrSG6gyPc2NTUl9g6vSakcgw0GblwngUAAPp8PmqYhMBrA0+rTKJkrze+MDxurAj2uZTZ/M5vNQBIIhoPQzBpc211Y/pnlOPjtgwCAOw/eiS3xLYDpjIrFbMBX1n4F6Xz6dzGxc2NuzI3/4ODelYEg65JHRkaQTCaxcOFC9PT0wGw2Y/34+qpaZhnwMFbhcUvHjh1Df38/6urqhJyZIJSgZHJyEidPnoSmaViyZAmam5thOGmAbtRFjwjWgNKH8Z5l0C2DaVmmTeDF7KOcFZel2nwW1vQCqKrz5c+ws3MqlUImk0EwGITP56sqJeN8WCwW1NXVCbBCYhao2GGSHDJJwJgEqD7CVAaBciKHwJhlebquI5lMiv4iPHqTqrRwOCyyqSxfIuHP+eMfxoJ1dXUYGxsT88Y5t1qt8Hq9QiLMum6CX16b9dXxeFwoLxVFgcFnwDMffwaJmkT52Yw6Dl5+EFpOw5qta8S7ph+Xyx/keeM7leNCkggej0cAZ/ai4bGdmUwGmUwGXq9XvGc+C0G9x+OB2WwW2V4qwORkgzj260zcxnXKWEtVy0fgptNp9G3sw65rd6FgL2DXxl0oFotY++RalIolEX9zvVBpKZMrzGazP5G8zqlu4DNks1lxJJysxKCCljGK2WyG1+sV60yeP8ZMLMmTGy3rui7IJJ/PJ1SgJD6IaRgDxeNxMfdUWHAO5b05N+bG2wbc0WhUSGJzuZzIKBEwsSEDQWswGBTZJtbPkiEmE0nmKhwOC3ZRlm4zU332WXcEU7LcmbIbOdNOholHGzBbSRZUzuTKcica6nPPPVdksQlcS6WSuHduYIvFIhhkGnUAVWd9y+whJSrcmATznA8eGUFALbOP8jPzGTj/BNClUgnxeFwYL1kBAFS6o8vZZMptZAUCUDlvlFlqOkDeLw0zr03Dyt9vaWnB1NSUkCABEOxwJBJBXV0dYrGYuG8aWbmuhsezMVtJp5fL5aq6lXNO5HfF+ysWi6ipqalSBsj13lwzfAZZyibXvJGQUFUVOA188adfRF7L48E1D2JHzw7oio7XO15HZlMGoW0hlErl8xuTySQSiYR432azGSMjI+Va63gcbW1twuFxfVMaLSsKdF0XLCqPneD98L3c9MpNiHXGMFI/AiiANWzFNf96DXSt/L4dUQcu+NMLsP2ftiPryWLjzzZi7chapI1paIpWJTdjgxAAVeUEXFMcstKC81kqlRAMBsW/zWYzEomE2GcyGTQxMSHWjaweoT1gmQDXK8mKfD6P+vp6lEol0QBR7jPBOjWv14tsNotCoYCj1x+Faql2ghN/NgHlOgW1tbVVDfVkmR3tGefGvteOy79zOfR5OmrfrMVU21T5XrwlfHnRl3HEewTYDeAqANNv19LOjbkxN/4jQwYx9CeRSAS7du36/9h77zA7z+pafH2n9zpnetGMZtRlaTSSJVmWXAQumGJMMNUGQy4kEJJAIJCE8rtAEgIJHUKPbcAEQjHuRZabbMuSLFm9jDSj6eXM6b1+vz+O1p73yOReyE0g5ezn8WPNzClfeb9377322mtjamoKq1evxmWXXVZHmaYxqVUpvuVyGWOGMUwPT+P8+fPwer1oa2sTESb66Eqlgmg0inPnziEajaK/vx+dnZ110zNICdY0TcBIAAK8q0kqfRLjAO69TL6YPKrAOM9BTdgZAzB+4HEwaapUan3HY2NjKBQKWLJkSR3gze/lHt7b2ysJGs9bpfLyfOi7CGbwOFXNGQAIm8NwZp2i/g2gLnHO5/OYt81j4rEJ5HI50XcpFArQnBrmM/OYm5uT3mAmgfx8XlMm/Cy8sOrJ19BvsceZQAvvh+rfAIgAGVlcvB75Qh6XfesyPP6+x1HwFGqA9rEuDO0eksow2Xj8XPYuU1NHbQtgzKW2KrK6zdGbY2Njwk6w2Wzo7OxEc3OzxAcqYMDYggKf6ppj9Z7Xl2CDyoakfzWbzUikEhgZGsEzb3lGpp6UTWUcuuwQeuO92DK9Re494/NKpTbCs7OzUz4zn89jdHRUzhVYBEHURJhgh9frhabVRvfOzMyIT/Z4PHA4HEgmk4hEIkIRL5fLCIVCaG1txblz5+qYo4VCQQCTdDotyTjBKa5bTavpDLHFz+l0SvzJmJ75RaFQwNzcHAwGA/bs2fPvtLM17L+6/doJ90c+8hFs2rQJ/f39CIVCWLJkiQT+VCsnsmSz2RCLxSSxIG1UpTyThs6kk4kNH4KLe5rUapZa8VYrt+pmQEebyWSkLzOZTNYlD2oVmcdmsVgwPz8Pm80mlXdW9kTd+ALiBkCOn4ABAHGEajWWmx1R44v7tSjYpiZZpEar1CpuQGpCro4HIz2H368mRTx2NXFXHb3aw672kV08uk2tujMw4DE//vjj2LlzZ53DBSB0IlVwKxqNShVe12vK9fF4XJw51xUpOyqVnIknk2dWhZlcU1SFYzhIuWIiz/9rWq1X3+VySVLHNcbNU6VF8ZisVmstecyWkGpPYWTpiPTzQgOObzgO3xt9yH0vh3W966RSOzIygp6enjq6PdFVrn9WrrkmmWhOWCaQM+YQjAfhcDhE7RsA5ufn5Zk5deoUbvv+bbjr5rswq82i71N9yCaz8LR65Lx9Nh9+76u/h1PLT6FtVxsy/owERrwunZ2d4jz47GUyGRGSY9AGoG4UBgVE2F+vUgLL5bIk0/zv4jXFkSps3SCAxOCB953PEdX9ubaJyPOZYcWFFLbr//l6PGx+GMOX1ubneg96Yb7ZLLNfeb05hoQBPAMyr9crtLF18+tgWDBgOj6N6alpeAY8eOr3nsI+377aWhgCcCeAPwBw/tfdbRvWsIb9W212dha5XE6op3Nzczh8+DDC4TC6u7tx+eWXo729XfYFJrU0Jt3c9/e37MfX1nwNaw6sQdAYRGdnp+wR9LtGY23yyPj4OMbGxuD3+9HZ2SkgMz+XgT5jIyayjBPUxFRNcrjPqvslAPGNTOj5XoKVTLbUaqoq0MbzZyGkqalJqMRMtAimulwutLS0iBiVCuKr7EK1OqvGDCqbjucz7ZnGd7Z/BxtPbkTPmZ46vRQeY3hJGE+9+ylUC1WYpkzo6emB3+9HtpzFsWuPYS40B99f+ZBZnYH2lIbp6Wnkh/LwjHnED+QuyaG8pAykAeMvarRktRrPgozb7UYwGJQJJaRe0/dUq1UkEgkAkEoyGZOMH2xWG/qm+1D8XhHPv+15tJxtwRXfuAK+gE9iVFLEGVeSJckYlFo1jC+ZDDOOZiWeFf729na0tbVJ7MNiiwrcUHmeAmYEggBIAm80GqU1jP6Z11B9HSvKFUsF5644J8k2ANjyNux8dCfanmvDSGVEri2nfmiaJjoBHA0WCoXqilKcwU3QhL3WjA15TIwVyFwlyF6tVtHS0iLCeUajEfPz8zJJhQCAmmuwYs2YEIAIM7PwQMaDOtOc7AwCUnw2qC0wOzv7G+9hDfvvab92wn3HHXfgRz/6EVpaWtDd3Y2+vj4MDQ1h1apVWL58Obxe70toRHyAmIyrPZxc6FykrDiqmzn/RnEDJpcMfClYoCoeq4m4OvaAya7BYJAEgk5J3Qi4sTGxo6NQk3pV3ZxBOYA6eg4/gxsIUI/68m9qoqGqfDOZVtXDVRq+mkwTeVWRQQIL/DudAn/P68LNR+2NBhZHSPAaqfQ03mN+Ns+tXC5j5cqVwnSoVmvCJPPz8yiXy4IoMwmm043FYpLklkqlunnZXDM8BhWhV/t8SDXiNVbVWMlQACCoJHumkskkTp06Ba/Xi76+PnFuPAcm6xSh4+errIeWaAvevuftuOPKOzDmHZPrHn9THKccp9B2ZxvaW9rR1NSEaDQq99Dj8WBycrKGevcVEQvGsDWyVc6PYIPFYsGMNoM7L78T1WoV737m3TCka/eJWgaqU7Pb7dCyGt666604Xz2PTDKD6elpuQccyRU+GsbqudVII43JyUmp+BBIU6vcdFpchwwQGWSoxnNzOBzIZDJ1VEJed36O2WzG2NiYVILIMCG6rCrMst/earUK+EGFc7UlgnuRGqgyIGKyfNntl8FetCPsDKP373tR9BeRt+cRDocFVCGKbjabZWQJ1xHnhxKgYoA6MjGCucRc/eZpBWBBwxrWsN+CaZqGpqYmCbKPHj0qYwE3b96MYDAoYJ5KPwcWK52MIZ7qfAq3b7gdWWsWR99/FFf+6Eq44i7xyfSFiVICD614COX7apW0tWvXoqmpqS755J6uaRoq1Qr2XL4HVz59ZR3wz+O4OJFlcq+2hLGyzJ8vBrkB1H2uuo8DEGr1/Pw8ZmdnBQRm7MRjoY8NBAJ1yt0qMEEQXKUl02czvqPxb9OOafzT5f+E883nMRYcw6XRS7HynpXiZ8rlMmJLY3j21meRbk0DXwYCrQG0vtAKt9uN/W/dj/HXjAMaUPnrCgpDBRS/WoThoAGZv84gfSwN/5/5URmqYPYjs8j2ZmFIG9Dj6IHpEZP4Dfqj2ZtmMXRiSGIZVlMZbwKQnnCVUUiQl5VPrqueIz3QbtfQeqYVRoNRGFaatjj/eXhoGF3jXbDOW4WuTGCE913VBlITbcaKBAMoxsbxWEyuWVmmT1f7khkP0pdzPfO8eZ6kc/NnrtFsNosrb78S+27Zh3Prz8FQMeCaX16DwZODqDoXq8hms1mKGoy3eU2p98I4XW2LILOQxS/26qtAF+8h17nJZJI+a94fq9UKv9+P6elpuQd8xtT2CsYUZnNN9JYaPBT4ZQxIhivZraoQ3txczf9bLBaEw+F/+0bWsP929hv1cBeLRUxMTGBiYgLPPvss7rnnHni9XjQ1NWHz5s3YuHEjtm3bhs7OTtnY2dvNPgk+QNzsmGBT5ELtN2LAzIeKaB4TeTUJ5OsZIBO55ObHpMlischmRgqYSvuuVCqCTKliFXQyavVc7a+qVCrYu3cv/H4/Nm/eXJf48zXqcfNYVaqTClbwNaQdEdnkdwOLSS5pyED9TGwm8gwqWKFXAQh+lzoejNdQ3ZC5wQCLMwbVmeBArbre19cnyTBpN5FIBE6nE263G06nU2ZlUrRM0zS5D+xJ5kbJXiXeRzof9mJzBITf739JmwDp+aQCp9NpBINBuba5XA7nz58XilEgEKhzsCrtjoEDk0Y6MofDgXg8jqbxJrzzoXfic6/8HFLOlDwzyVcncSB4ADd8/wZYLBb09/djbm4OMzMzMBqNCIVC6FjbgS++7IsoWorwP+9Hx/mOusCrbCzjH676B0wGa6OrvnTNl/CJ+z+BcrYsz5bNZkM8HofdbseyZcsQj8fhzXrh031IrElA0zQcOnQIK1eulOdj//79uOqqqwSN1TQNiURCZmwyKOUsU9LwKAjCNUHwgQ6OYBmfH657/o60N659Jrbq2iQ4xPXp8XhEFIZCOQRYyEKgI1fbNFRHzmtqMpnQZGrCW068BWMLYzgXPQdLm0UqChz7Mjw8jObmZgQCAQGfiGSTqcF1ajKZEI1G4df92PGjHchZc5gdmAWOA3g7gEUcpmENa9h/oDH5i0QiOHPmDE6ePAm3242tW7eiv79fwHr6KDVBBRaB8X2t+3DH4B1IW2vVrlxnDs/8/jPo+VEP7FG7+JFSqYQfvelHGGsdQ2+kF+tfXI/m5ua6hFz9bJPZhF+85hc4sfoEitYiXvnUKwVsVpPvH7z6B7jpxzdB0zQ8vP1hbDu2DYFMoG4/k4q4dkHgFLV/Q1+k5JLlc7F+DoXkIpEILBYLgsGg9DzzPWpM5Ha7hVGlxj4qoErfz/NW6dy8zkajEXlzHl952Vcw46+JSupGHQeuP4BioYiV966sgQOdVex65y6kWi74UxeQ/IskZr89i7Mrz2LimgnRKcldXQOe438SBxYA9ACFrgIS7QlUA1VUemqgR9VVxcT7JpCYT8D+s1o102q1In9LHrO3zSI+E8ebv/lmlIoloRozic7n89I3TjYgk2MmfSwa0Qf1n+6v+Ui9IrEU1+fYmjE8+4Zn4Uw7cePf3AitoEkllwkl9VPKtjIOv/0whr41JMAzgQKHwyG9xmR78d+MjRknMM6jIj2TdVbS1dgUqB9bR/+sJumZTAaejAdX/ewqlB1lrHpiFZaeXopz8+dkbBzjSvWzqctjs9kwMDAAo9EoFGwWEbi+GbeyNZSAO4sijPsIqlcqFYTDYQEZ2NoWiUQwPj4u948j09TvIPOWzwaF2XRdRzAYrGPkqlpAfF74HJEFsW/fvn/rNtaw/4b2GyXcqul6bV5iIpHA+Pg4Dh8+jO9+97vwer1Yu3YtNm/ejO3bt6O3txc+nw8ABFkDFnumAcimR2owUHvQGVQzeWdCplbZmFiTCsJEkYkygLrebT78pKxQvIrHwe/lZsoqZyaTEWdDyrZawdc0DS9/+csFmc1ms3C5XELtJg1ZfTj5nXSArNbzoeZ7gMUeWSYurOqyCk7Ks0rL4cbARPFiuj5/VlUh+RlMpC6msjGB5XVQkUI6VLVioFKEuTGqaHAymYTL5RKGAyvVrNir56wmYbymZDao302HrwIEPG8imJVKBRMTExgbG0MikYDL5cLZs2fR3Nwsn5fL5eB2u0VZne0RKtOAlc5CoQDfvA9/9aO/wt+85W+QtNcEU3wZH/7w0B/iWPIYKpUK2tra5HrHYjHk3Xl8+o2fRsZTSxY/ueOT+Hjm4+iOdss5fubyz2AyMCnP3ph3DF/Y+QV84KEPyD0olUoyNq1cLqO5uVmqyz6fD+3t7YjH45iYmJBnprO3Ezl7DubUYm8+HTATTHk2PUWUMov9XlarVcZ5UdBEFXdLp9MyboPOkPeDYmqkr9Eph8NhoYFy3XGd8bnnGmKSnc/n61pX6CAp0shqBKvWXBc+nw/pyTQy4xkZV8Igg8KHk5OT4rg7OzuFtq4CZPPz82hpaYHP50NzczOAWuXoxn+8Ed+/5fvIXJ4Bcr/BxtqwhjXs/8moHn7q1Cns378f1WoVK1euRGdnJwCIABJ9Bv2+CoYXi0V0HevCMsMyvLDlBcAMmIombHlqC0LhEHTDhV5qewl33XwXzveeBzRg9H2jWHv/WliPWQFFJoK+qmgq4t7r7sXRS45CN+jYs2kPbLoN1xy4BuZqbS8v28v43su/h2Pdx5B6ZwqrxlbhqS1PYe/QXnzwnz4IX9YnibOu68hUM3ho50M4vP4w3vyDN2PP5XvwyntfiSa9CRaTpW6msclkQgkl5JFHyVxCejaNcDgsVF2VJcZro2kaAoEAOjs76/7G5EkFLlRGnOqLVZ9pMBhgyptw6/O34stXfxk5Sw7Qga6JLuw4tAPV4GJMsv2R7Xjo9x5C1V4FKkBobwh9e/tgPmxGeE0Yqa5U3b2H68J/AGAASoOll6yPsqeM6Eej0J7RgBcBwxsNqPxlBTADs/2z+NG7f4Trvn4drCWr9OrTr9GHcCZ0qVQSjRmVGUE/kU6nEYvFUK1WMTMzIzPGDVcZcPgdh1ExV5B35/GTv/wJrvyrK+HNe+vEU202G+KmOB78+IPIBDIwVUwY+PYAKoUK3G633AMC5IyhUqmUaJGwlYpMBFVENpfLSQuYy+WqE78li4wtYPTBKitECl/zOq6+82o4sg4BCRg/8NpQS8Hn84m6OMGFVatWIZvNyntpKuBOZgrjl0QiIeuLMQXBDH4GCyU8H/WaqLR1s9kMn88nsbuaP5CZSYE0tqiqzwKLSGTB8d40ppQ0TLV/c8J9sanI0u7du7F79278wz/8A1asWIE1a9Zgw4YNGBoaQigUQldXl/TI8D/OV+Q4AyZcFGgj8scHgdVOVtEopkEaEFFi0jwYLDNpoxASaTJ8+Bicq7QZPrRMUIn0qbOQVVRQ0zRROSRqnUwm60YI0KGwd5doMntXWWkm+sbjZjDAjYVJ6cUbDRNm/szKPjcPNflQkxvVmdJx8rwIhlCYjJ+hUqCB+vmlTHQvBhgo0uZ2u0VUTEXXi8UiFhYW0NPTU9dCUKnUlDHZR8zkm1QjBhd0LnQSpFd5PB7E43Ekk0lMTU3h/PnzkixyRjNnMrKnXF1TAIQaxuOfnp6W7wrag/jI/R/B1676Gsww4w8e+gO0elqhr9Zx+vRppNNpNDU1yfp9bvtzyNqzcv1LxhJ+ue6X+JMn/0TQ2w898SF87qrP4VjLMQBA3/k+fOj5D8Fir63pZDIpKuDswyYgxHvT3NwMh8OB48ePo7OzE+MT4yi/vozntj+Hy793OXwxn6wVUvzplEq9JTz9rqex7OFl6N3TK8kphW34/PNZ5P3mWufzyvtPVgRbQujUKdCSyWTq9BzoXNkW4fV6BeU/c+YMli9fXvccejweAUK4fj0eT11vt8lkkj4tVsadTqcg3ADQ0dGBZDKJfD6P06dPo7e3V4J0rn+er8lUG3l32WWX4fnnn4ee02F5mQWZXEOlvGEN+21aNpvFmTNncOLECZTLZQwODmL9+vXCouPeyH2d+xMrtOVyGbFYDKeOnoLrX1wY+PAAxq4aw7bd27Bh7wboJh1VU803Huo4hIngYqVVN+k4sPEABk4PwFV21VV4jUYjxrrGMNI3Ij2vVUMVh5YewoqjK9AWaUPWlcUDlz+AYz21vX60YxSjHaMAgLwtj6+/9et476PvRXe0u5b4GXJ4ZPAR7N+wHwBw+ztuBwB88c++iOv2XoeXv/BymComKQoUK0W8sPkFnO45jYqxAtMvTQiOBCWJSKfTdVV2o9GIhXUL2OLZIq1yF1ewVfYQfaQKlksVHotVS+jAsulluPWxW3HX5XchMBbAzT+4uRYnGGr+JB6Po/T9EoKngwh/IAzTgyYE/jaAKccUHA4HNr1vE5792LMo+8uouqqoNlehxTUYZ4worywDVcD7tBfFliJyKxZRT21Gg/lPzagerkL36dDfpwNm/hGItccwsn4E/U/3Cw1b3qvQ+zkHnElsOBzGkSNHZHylquytsg0KlQL0v6v/zpQ3hQfaH4Dvmz4BgFtaWhC6PIQ9f7gHmaaaHxl5xQisVStW/3Q1XEaXAOzq8RmNiz3qTGhtNhumVk7BccYhfpLUarZFLiwsyHi7YrGImWUzaBlukc9ndZxMVVbMASDXnsPuW3dj5b6V2HJ8C7xer4iO8voZDAYEg0Fhi/p8vjpFccY7Kl2c143Vbz6f/Dtjgng8jlQqJerynKNONsjMzAycTidaW1sRDoeRSqXqtHp4j5xOpyibqwU1jv4jgM8Yk8esat0wOWf7YMMaRvt3S7h/lRWLRRw5cgRHjhzBXXfdhd7eXixZsgSrV69GX18fli1bht7eXgSDQdmY6ADppEjvVkcpMYkgtZX9q6xK8jP4e1ZXSQ0hGqjret1MbJUyzgSB/aN8ONVRCaxmA/WiIColnD2rHC2mUsyY8HPDIV1VdXhMgog087xIleGGdvFGyJ9JSVc3fIIGKnhwcQ8Wjc6VyZEqOMHPUKuMwKJT5XXkxs/ryYTKarUiGAxi06ZNsnnHYjFJuuPxOOLxOPr6+iTZZ/KtVsLVe80KPUdOqJVPn88nIhi8Hy6XC0uXLkU2m0UgEJA1wveQWcFAQmU0MBkkOJTJZESY521PvA12ox1tlTaYLWZ0dnbieP9xzB+chylukgRz2Z3LYNfsOPaGWoC1Y3QH3n7g7XK+hUIB1VIVf7z3j/GtDd+CyWjCTU/dhEgsgmAwWAfsMAlm9VkFPoi8rl69GnNzcxi5cQTH33YcMADPve05XHnnlbBH7ULZjkaj8Hq9SLWk8PRbnsZC7wIi74ygYCpg9dOrZU25XC6USrWRO3w2iVST5n0xcKMKFfGYGdyxQk0qONeCKtDIZ99sNmPFihWybtXzZdBQLBaFYcHn2+l0omwv4+jyo+h6rAsOh0Oq9VzHBF3IHGDrgdvtFmGhgq2Ayasm0XKiRZ6hI0eOSLuG2rfYsIY17LdjExMTOHbsGCKRCC655BJs2rRJAmXuRUB9hZb7EFlXZ8+exeTkJLxeL9Y8uAYj+RGs27dOvoN7m+GfDWg60ITpT05Dd+joGu3Cq+59FZxZJyrVSl0sAADdI9244d4bcM+N9yDlSSEUDuHGe29EcCaIgl5A0pZEzBz7V88t6o7iybVP4h3PvgPlahn3bb0Pu1fs/pWvfWjLQ4AdeO3zr5W98OGhh/H41Y8LQIA/Bux+Oy555pK6/llWc0euHsGhNx7CwIkBrEytrItfmJSoAD73cYKu9JUqK42/A4D+4/3YMb0DwSNBJItJSbwYD5ZKJZi+Z4J5zozKDyqYd89jyZIlMBqNiE/EEXhfAPm2PHLOHHKfz8HzKQ88Ex6E/zYMz/MedH21C7mlOZz/6HlkV2eBGOD4cwc8T3ugNV841z8tI/X5FHJbczCWjLjiF1dg7bG10AKaxFdqSyTZWvw/k7axsTFMTEwgWUqi+sYq8N3Fe6G2Feq6DrwFwNcBvB6ADuAvgcqXK4gggmg0CoPBgLGxMWgWDfm3LSb8ADBnnkNgIQCLd7FwxCSb/doGg0F8msvlwtkdZ7H/9ftx6S8uxYqnV0ihhffCaDTi3A3ncNnBy6DpGiY2TOCJ1z+BVbtX4dJdl6JareLk5SfR+0wvbAZb3bSaXCiHJ9/0JKZ6pzDdPY20nkb//f1wOp3SnsaqO8XSbDYbfD6fVJXZTkpWhApsUKiUE0gAyLUnC8DlciEQCMg5MR5h/N7R0QGgBsbNzs4K25YiaAaDQRiC6v1ibFIul6X4wthb7eE2GAwyio1x8QsvvCCFpoY1DPgPTrgvttHRUYyOjuLJJ58UNciWlhasW7cOmzdvxsqVK2VWLnskmOiymqxWq1Xqkko9VuksAOoeENLK2RvMh0oVbGB1W539DCyO4lL7lgDIa/idqlNisg4sVndV+hoTWm7m6vcw4WAFXqWfs9eFVHomG/weUm553fh7VfmUx8sEn+eivlZNzLkBqo7VaDRieHgY0WgU27dvB7CYpPM1VEZ1Op3w+XxyvqTcnzlzBr29vQgEAlKZJ91pdnYW/f39darrvH7s/c9kMlK9VIEKUr1isVoAk0qlUC7XxkdpmoYNGzYIQspxJ01NTQJ+cJ2oCS1pVgBEi4CosMoE6I3VKsFGsxHZbBbDfcN4ePPDMFxqwJUfvxIt3hYYjUb09vZi3Yl1GDw6iPPu87j18K3wGr2AEVI1r1QqsKfsuO3AbTCZTfA7/Dg9cVrmyfKYCMBwnZNazd8ZDLXRas/seAanrjgFXMBVplZP4eE/eBjX/831MOu19erz+VBylrDr3bsQ7a6NatHNOo68+QisDiuW3L+kTv2b94Prk9VvgmBqkKW2cPT09GB+fl4cFgBpo1CdHgMwl8tV13cFQKoyuVxOqPB+vx+JREICA7JA2H9/5+/diYnQBNYtrEPLwy3CYqGxUu7z+RCLxUSXIpPJYHZ2Fi63C+PfG8fx1cdhNpix8tDKumeYgF3DGtaw364dOXIEkUgEK1euxJYtW2REqVoJZGVWbc9inLCwsIDh4WHouo5QKASHzYGhg0OoGuop6HNzczh//jz85/xY07UGL77hRbzml69BMB6U6jmwuN9xgkbvcC9+74e/h5+96We46cc3oTPeCV2rgYbemBevvu/V+NnrfoaJzolaMgZIghyIB7BlzxZE41FomobOiU5g+eLfL7ZH1j6CsqmM1z/zejy4+UE8Pfh0/WsNwMk3nYQ76MbQI0N1yfKhjYdw4MYDKNqLuPOSO+E468Ar518p+zKvo9qfrSbUTJouvu4AhHk4MzOD0PFQ7fogL9e3XC5jfn4eZ8+erTHZ7tRgNVpFUyObzdZmcpc1aGc0IAt45jxoOd9S8xEf88A2a4PJYILznBPBPw3C4ragkqnAdsgGf8i/qORd1WD/lh3Hmo/hknsvwdLhpdCci3O71Qk0VJ1nKx99RiQSESG06o+rwGUX7sl38JJzhxnA3wK4H7WE+w8AfBcviR3L5TK0JzWY32pG6eESEADan2nH6u+vhsfmER+lJqcsJjFuBoDTl5/GsdcfQ9FVxPOvex65fA4d93cgn6+JhJpMJkTfHcX5m88jsTQB7wNenLr5FHLeHF581YswOAxwxp149tXPYnTNKG66/SY4HDXqeNlSxv2/fz/CXTVxMN2o48BrD8Dpc+LKA1fKNBqK1TIeI+OMDDe2hzGWuDjO4+ek02mpynNSC1tPCbwDqJsdzqQZWByJy0SZcYRazOKa5f7AOJwFQRaYGB9zIhMBDk66mZqaalS4G1Znv9WEm1atVqX/e2RkBPv378edd94Jm82GLVu2YNu2bdi8eTNWr14tcwvVCi2p45zDS+qqruvw+/1CD2PViyM8+BBzQyKtXd2gWMFVA3QeMxNxtYJLYS4ikolEAitXrpQkgccHLNKsKLal0rLo6Oj4gcVqKh9aOi5u8nzo6RDUpEZFVNXES9M0Sc4IaLDaSBSXCTPfw0ROdQZM2M1mM0KhkPRFM+Fl4myxWODz+bCwsAAA6Ovrk34X0rKz2axQyl0ul6hsqor0gUAAs7OztdmPF0ZEsQrtdDqxsLBQV+E3mWqz3zOZDIaHh9HR0VHXQgAAnZ2dqFQqeP7552XcCc+T95uBGQW1KNJVKpXgdrvlPHm92traZP0BQLlSxmjTKL54+RdRMBeAXmDX3+zC9Z++HkFLLRCs5Cq44tAVuM5zHQx5Awp6QSjLTqdTlMcDhQCKqSIKhgI6OjrwxBNPIBwOY3BwUFBWTdPqWA08Dq5tALj+7PUYXTeK8ZaawqupYMLWn2+F3WiHZtQWK/tlM9b/fD2e/MMnUbFWAB0InAuge3e3BB4qgEMtBbvdjmw2WxvdojhT9o1Rpd5isWDz5s245557xHmx94nUT3Ut6bouwj5cC6yW01nyOWO7AZ0r53EXTAXc9ba7MNY9BmjAvnfsw/bCdiw/vRzJeLJOU4Dggd/vF/bE2NgY4qU4zn7zLAobC4AG3POqe5CdzmL97HqYDCapikiA1bCGNey3ZmNjYwiFQlizZg0CgUBdH7EKBjO4JmOrUChgdnYWBw8eRCqVQk9PDwKBgOxfbIOqVquYnZ2VpHxgYAB9U30Y/KdB2Io26Fic7c39i8kZfZT/nB+v//TrYcwZMYMZCfw9Hg+CqSBu+/Ft+PYbvo1Xfv2VOLHxBPa+ei+seSt+77O/h3KhjJHiSM33n7XhirErsOfWPaiYK7DmrSjYC3It3Ek3Ln/0coQzYazbtQ7Pdz6PSGtkMenWAc+EBx0/70DWsNiGM716Gntv3Iuis8bSSZvT+Gb/N9FV7sJQckjiK2AxUQQWNVRojEkIZnC/BmrVxmSypnWiMgspQhaJRGSUp8PhQFdXF7q6umCxWBCPx+v6xo1GIwJnAnAFam1J9ulaTGix1663ecQMR742jtTld0lsw/9C2RC6v9ANU94E3bIowMUKss/nqxPJymQySKVSMBoXhYF1u47Sz0vA1aiB2V8GtKQGwy8M0PTaNag6qsDjANYBeGPtGmlnNWjQYLaYJbEDaj7b4DRA/0sd7e9vR/kDZQx9ZQjGghG6oSYKyJjG6XRKxZWCffl8HsmhJI69pZZsA0DRUcTBmw/i6N1HoT+uo6JVgD8Eqm+tQrfoGN48DMN6Q+04AVTMFbzwsheg6Roq5grG1o7h52/7Oa7+1tWwGWzwWr24/NnL8Ys3/ELWU9NkE1Y9tQqas8aAC4VCLymO8bpRuIyjzvjM8P/lclniEb/fj0AggFQqhVQqJbOuyXYkVT0YDErbIenmzB8ikYgk0vTpjJMINLH/ndeTxRa+n3PgqeXEZ11t7SRbtWENU+13knBfbNyIs9ksHnjgATzwwAMwGo1Yv3491q9fjw0bNmDFihXw+/0IhUKSTLDyys9Q0SQmkUwyWbklrYxUaiaNdApMZukA6CgupkOpzgZYnMXn8XgE8TUYDDIzmEkDnbZ6jCoizIqh6qjYt65SuNhLpYqcscrO7+f71WTMYFgUGePfyRZgsqoCDvwufi+Pk+hjtVpFU1OTXAN+Fu+J2WyGx+PBzMyMjIqqVCrSQ8PNKxKJIJFIiHIlha1mZ2cRiUTQ0tIiwh9sMzCZTAiFQiiVSlhYWHjJubLPasOGDXUVWAZcVLq87LLLAEBQStLN0+l03X0sl8t189pZ4SeAwfXBa2m325Ev5fHjzT+uJdsAoAGZ9gxm3jKD5p83i9hHOVeG0+tEXsvXjZoymUwCIvH8KpXaKK4lS5bg3LlzOHnyJFavXi30cpPJJMekAk2CGOeM+ON//mN87eavYc41h23f34bA0QAqpsURXzzXZaeXoXxHGfvevA/eES+u+sxVMJvMyFfyUn3mWmUvExU6N27ciCeeeELaBUht57rVNA0PPPCAJM4AxNmxnaRUqs1Tn5mZkWRepcjzPpONQtRbreoT3LHb7Tjw6gMYW74oGV61VnHsdcfQ9MkmGCoGARJ4XkTB2bLidrsR+WAEhcsWg9qSuYQH3/sgej7Wg3ZbO3Rdx8TERKPK3bCG/Q7MZrNhcHAQLS0tAnKTyaUC0yp9ulKpTSw5ffo0otEo2tvbZbQY93W2iKRSKYyMjCCRSKCnpwcDAwO1sUUlHZph0aeqoqaMU4SKXCzBAQfMNrPEMgCkv1zP63jTF94EXdex9bmtgB0YPDyIgDmAslZejBuqGlYcWIGKs4LxjeN49c9fjftvvh8xXwymjAmv+9rrMJ+cF3bOlR+8Eo9+7FHkAjnAAHgjXlz9v69GMVdEypKSa+Td58W2pm3Ye9NeZK1ZuMtu/NHYH9Ul27x+KmgBoO4aM4nmXsjrkMlkRI+D+j0ESAm6z83NiZBnZ2cnurq6pFKaTqfremwv1sTxeDzix0+fPi2Ac2dnJ1paWgQE0TRNetPNObOAvQDqgFzqolB8Uy24UAsk+5psDYRlR54dCH4hiNBsCJaEBSlvChOfmkBp6ALN+IK4m/6oDuO1RpieM8nxVKtVZO1ZlP62BP21Ouaun8O2v9wGY2FxVJlaoWURQBUsy2Qy8B3y4VLrpXj+dc+j5C4BMUD/iI7igxfGvi1HrcLOsZVGoJqoAlEAnQDygD6so7r2QmytAWNdY7g9dzvMPzQj9OYQpm6ekmfPUDFg4z9vhLlgRgYZSVIdDgfS6bRo2CSTSYltHQ6HxDxs21TvK1mcajzPyj4ZCCrAxXiZCvMEv0OhEFpaWiRX4N/UqUXt7e2IRqPQdR1ut1sKLQCktY3XGoAUzhhbsSXtxIkTUmRqWMNo/ykS7l9llUoFL7zwAl544QXceeed6OjoQF9fH1atWoUNGzagr68PnZ2daG1tlc2dDzUA+ZkPlSqKRqeiUrYvTsyj0Sg6Ojqkj+hiug97ZFS6Sn9/f22jvCACxc/mA8r3q/3IqvNX1R8vrqgDizMHgVoypm4E6nVT6S98H89VTR74MxN2GpNTJu8qVV6toAOQz1URarW6Thozr3GxWEQwGBQgg5sjk0Oz2Sx91nTgk5OT6OzshN1urxtzQTObzXC73UgkEnXHwO+js1SpYZqmyTgU9ftJH1d74gGImBbRd1LR6Sz4Xqq8UhRMr+h43+734Y4dd+BQ5yFAB159+NV4zdhrcMJ+AufPn0dPT4/cV4PBIFVgNZHlGuR1TSQSiMfjaG5uxujoKBwOB1auXFkXvKkVABUs0nUdZt2MW39xK063nkbgRACpakpAL64/sjRWHVqFKqoIPRtCLpuD0WWUNUZmBtdRKpUSBPvRRx+VY2EQxPVHpgDPs1AoCBDF+8sglwADK+AErggOsc2DLQR01rzP5XIZbrcb1nVWTHUuBge0S39+Kex5O7KV2nM7Pz8vc0fJViDo5PF44N/nR2RHBKXQ4jPT9ngbSskS8u689LE3KtwNa9hv33bs2IH+/n7ZkwHUValU2ir3ung8jlOnTmFsbAw+nw9NTU3CACPwBtSSbfZ3Nzc3o7e3F263W/wkwXwmRfT7KqWVPhNY9N08FsYpBJTZqzr00BAAIGlIyjHTh2qahpWPr8SWg1vg8Xhw6x234kzXGZinzTCnzdC1xfa2crKMZR9ehtjSGMwOM/qm+lAulOv2UwBwuVy4KXoTNoxuwLf6v4X3jL4HN8zfIMesFiSY6BKc4Pmovd0XX49IJIJYLFYnjJXL5ZDP52stWMPDQnd2Op11CtrhcFhGVfKaWSwWOJ1OYQl0d3fD6XTWJoFcSOAqlQqampoQDAblc/j5vL+ZTKauDY8FklQqJT8z3mD1lIK+5jvNMJ0zofT5EuAA2s+345qfXAPvoBcAMNs7i8RAAhFE6hesBvS/tR/2iF3A+oKxgOSHkqi+9UKl2VbB/o/sx8Z/3IiuM13iG1nQUFsdGfuw+NPxUAe8J7xY+P8WgMOA9p3FMXI4DeBtAL4NYO2Fw3lUg+FuAypfrcDwjwYY7jGg/M0ysAVABsAHAe0ODcVqEVNd9f5UN+gori/Cf8j/kmITwQSVtUaV92AwWKdDQ8vlckgmk7JuyICjbyZzjgzJi4WMySYlq5J6UBQ75TXkfjA5OVkHJKnPPosrZF+yhUAF9Ugpn5ubk37zhjWM9p824VatVCrh/PnzOH/+PJ544gn4/X40NzejubkZW7duxfr16zE4OIjOzs46GjQfODXRoFACHaQq0kGnc+7cOZw4cQJdXV2yiQGLNG0mcKoACx0LEWrOqCbdnc6IYmMXU5fVCjsTZJ673W6XDYHHQIEpVvPo0NSKvapUTiRUTUZ4TkyYVMo9q4wqXUytnNP4HrWXnNeF14qbG3tf3G63ODAmg7OzsyiVSggEAnViZxaLBR0dHUgkEkJNslqtdckVe62JvBOVJAqubuJMotlKoI5SU4Ve2CagJtU8Lr5OVVXn59MRcE0AgCVhwa3P3wrdqOOS+UvwstMvg81uw6pVq1CtVhGJROB2u9HS0gJgUTuAjiWZTNYdJ4OB7u5ueL1eRCIRPPHEE9B1HX19fbhnwz247uR1wBxEpV0FRLi+/AY/Lh2/VOacplIpuf8AhFptMBiw9NmltXYEa1aupwo2MTln9Z/PAp0qK9aqCKJaPeZap8gKj5uBmPp6sgzUZ5LPNJ9vslJ4DU0mEwoLBbjynBlTs969vQhNhuD1e6UFgVQzovC8nwwg2l9oBz4GnPrSKehWHUueXgLnJ504kj2CwcFBeDwexGKxRoW7YQ37HVh3d7e0DjEJ5l6i+mzu2ZlMBqOjoxgbG4PL5UJbW5v4MybQpI/Ozc2J4vGyZcvq2Eeqn2HipPpsCr8CqPOhfB33MIK5TBroR9RKseqHGddQoDWfz6PtRFuNzacvAuusHpbnymgON9emQjgtMJqNdcekaRpCoRB6enowGB1E9+luXLJwCaqGxeuoHrcak6j0fb6OvpavochmPB6XFiGj0SjgazKZxMLCgiTIXV1dAmrkcjnR5qBvsdlsIphlt9vR3t4uGj3pdFoKI263G21tbfB6vSgWi0gkErBarejs7ITVahVRLMYCTOBZNSWgz/5ztiGwWl4ul1H9ThUdtg4UP1zEdT+9DqFoCKiRptAz3YNrfngN7vn9e5DxLU6vWP/99Vi9ezUcGx2LrQ52A15sfxFP42l5nbViRVOlSa4p4y6uZfprtm4Bi0Uawx0G4M8BrKnRx3Fn7TMNBgOqz1eBtwP4JWpV7asB7V80GN9qBJ4Bqpuq0O7XoLt0aJ/UgJ8CVf1CxftvAa2oQf9MbY1e+aMrMbB3AFFzVLRVVDYgCxFUE2cbI4EWVTSVxQ273Y7W1tY6dgSLRGRC+v1+WT+M52OxGHK5HOLxuBRLTCaTzNfm7G3GC2xV5fPHwlMkEpHvO3v2rMTrsVhMxv8yDuQabVS3G/ar7L9Ewq0aE5RIJIKTJ0/iueeeg81mg9frxdDQEHbs2IEtW7ago6NDZksyGSOtlA6IjkLTNBk5ZjQa0dHRgdbWVkkU1L5qlbrOJJKK5ewLV/vEmGTH43F4PB7ZzOncWFlm4s+fWfEjugagLtliksLElpVV0rFVJ6gm8io6zveqVGWieKrQmyoQp/67UqngM5/5DD7wgQ9IYqYmXjQeH8ddMFml2BcVVPfs2YOVK1cKAulyucQZVyoVTE9Po6+vD0BtVjITUyZZBB14fbjB8jXs8eH3AZD7Qsq4StWnajo3awBCj2Yyz+vZ2tqKQqGAVCol4jjqCDZ33I3bHr0NPrMPZsNi20J/fz/GxsaEWsdklTQpfi8DGFa7W1pa0NTUhGKxiL6+PqTTaRw+dhh7rtiDp9Y/hQP9B/DBH34QtpJN1hWTW95TBpFWqxUDAwOIxWKIRqPCRuD3qRUam80mPVVEnPnZRK95/9hDRdCCa0OtpLjdblmr/N5yuQyHwyFoMo9bHTOi0saLxaKM8uOa4prhd1arVYz4RjDWv0gnB4DJdZM4e+9ZrIytlOettbVVnl1d10VpnawSXdfhP+rHG778Bhx61SFc9tPLcDRzFNMz08hms2hubsbMzExdi0vDGtaw346xbYV7M/0aE1pgMXlNlVP422v/FsvuXQaDwYCenh4B/fj8apqGfCGPyYVJjI6OQtd1LF++HG1tbXUtaGT9cG9jss+9hH5BBSzpf9WWJB47wWsCmwQbAdT5cYKZQE1AUj0GvrZSqY2PpIAoq8KqqCtb+3S9NrKJ7UVDiSFUjYtsAJVppZ6rSjVX4x+18spEl0KlFDrl92azWczPzwvYqYIRnDxBMFUtmtDfNDc3w+fzCVA9Pj4uvt7r9SIYDIqmR7lclpgEwEvEtAqFgrRHeTweGTdJVexIJIJUKoVsNotqtaZLZDKZ0H+kH31f6IO37IXBtsimqFar8I/60fOGHpz6zCkY7jCge6Abnbs7odt0mF1mGYlrMBhw2ZOXoaAXsO/KfbClbbjmr6+BKWYS7SL6QvpktcJqMpnEhxvtRhS+XwD8AIwAvgo4y07gXqCYLwJmoPrOKtByYb106ih/vwztFRrQC+i/1GuZgg3QP6HD+ogVek7RRPiyDniBXlcveo71wGQ3yfnyNWwhZNzhdDphs9mEpl8oFKQtUG0PsFgsCAaDwjCkMBmAuphPpZhzXRDcX1hYqD3DFya3sIWA60IdEcz1Wq3WdBoojGgymWTtqvoFfL64BzAuIlO2YQ1T7b9cwn2xMXlKJpOYmJjA3XffDavViuXLl+PSSy/F5s2bsXHjRthsNplVrDpfOhD285J+ygebDz83fSavdI7s+aRzVikoKsr9rW99C3/wB38An88njoebBVE/9t4ycaEz5Wfxd/w/qerpdFpEtqRPV0EDiSSq48O4OfGYgcWZ40xML6bmApCkiA709a9/vQQ4DAzUnlyeY2trKzRNw+TkJKrVKoLBoDg9JrHso+NcdbPZLD0yHR0dGB0dlV5eAgRqn7nX60Umk0EkEkE6ncaxY8ewfft2EREjYqpWSzlWjr11bBXgtadyNs+ZipR8Lc83mUxKcul2u2G32wVF5fe6tNqs05KxJA7R7/fDYrFgobSAcmsZzpRTAjNdr81/ZM8Z1x3RdN7XtrY2xNIxPL7mcRy56gigAbOeWXzlDV/Bex99LxwRhzgiVhIYUBD4YBDBdcLng681m80CVrlcLpkxWS6XkUql5DpEo1GkzCnAD1RLVbmvRN1ZaSGzgyg41686B5aoPUVPmEzzOqu0RYoaMdkGIN8rTjxdgbVkRcm6SAU3ZU3IxXKYmppCIBCQ59dgqCnAcrYn70NHRweeffZZVCoVWI9bcXPqZhw+f1gAgWg0img0ing83qCUN6xhvyPj/vmr2F9AzZ8uWBfw5W1fxvnQeUS/GMW1X7sWHrOnjk1mMBigGTQcHziOB695EB1HO7B6yWr09PTIfgzUt1bRd1Jvgnst21/on7mX87voP1k1Vavy9OscXUSAmy1rTFrVmANYnNBCgbJqtQqv1ytMLVZwVRBg7dq1WLt2rVw3tU1HpcMDqEty+Fq1XQ1YZMYRQGZixUox3x+Px7GwsICJiQnk83l4PB4sWbJExkhls1kkEom6a0pwolAowOfzob29XSbRzM/PIxKJCBjc19cnMSDPSW0H0DRN/A8TMrbEqdT4WCwm/ntgYADz8/M4efKk0JpDwRCCehAmq0mOhde3UCigOl5F6BUhaNCwZPUSBDuDsFqtokvCWMOm2dD6lVaEZkPY8dQOIA7kyzXml8fjkSSU64mFDAL9k5OTaGpqwvjOcaS2pmrJNgC4AdPnTbih5QaYi2a8cMMLOHbdsXr1eh+gP3PBf6m/XwUUHi7A+hYrjJNGVFCB9hYN+kd0jGIUm/55E5oO1qrwPp9P2JOcSEMjG4ExIBltjM/YRpDL5TA6OgqTyYREIiHFILfbDZ/Ph5aWFjgci7PF+WwQHLLb7di2bZuswWg0KgUDsh2ZPKfTaeRyOYyMjCAajSISidTRwrkGVMq5WiQjk4OvbVjDLrb/8gn3r7JCoSDzv7/3ve+ht7cXAwMDWLduHQYHB6X/m4nRr0o8VWEEFWVm0s1kTBVHuRglAxYT1Q9+8IPidPj5lUpFKn90Okx6uGEw+WFSqD7kfM3x48fR3d0ttCqDwYDZ2Vn4/X4RnqIzYMLDhJOJMx2k6ixVIQoV4WtqapJNvq+vT5Bh9f1M/vlvoogejwcnTpwQURN+Ph1dNpsVZ1Iul9HU1ISpqSl0dnaivb0dExMTkpAx0eemSYZBJpNBsVhET0+PJIherxcLCwvCAmBgA0CSftLQVbBC7fuuVCro6OhAKpUS58Z7yUSNdCc6a4Iv6txLXiMGf7ADP1n9E5S6S3j/qffDOGmUPm4KeNBhq9UPJp2tra0oO8q4b8N9i85RAxK2BCaCE1iTXCMtCLx2vAYXt1u0tbVB13XMz88LykwaP506hczK5TISiYTQ2jRNg+bTMP2Oadhb7PB/24/EaEKCTwIxKq2dfV1U82f/E1ADOZYvX44TJ04IwEQnTYoiK/bqPeDz0dHRUZeg9432wXafDfffcD/S7jTs83YMfWcI7ZF2ZIwZaUUgNY+fy6A4HA5jbm5O1svExASee+455HI5+P1+CRhYrWlYwxr2uzHu2WrlmS011WoVs+ZZ3L7pdgw3DwMAkkuS2P/e/Wi7rw1Nsaa6Ku6h1Ydw7033QjfqSP1dCi2PtMBustclkqovVZNkoOZfVDVj1beqxwgsJsisYDI2YCyi9oITUFB9ryrCygSEPa7ZbFaYYxeLt/Jz3W43mpub64AJNXHmOdM3qLGD6i8sFguKxWKd/g2TZIPBINVNFiy4/7Nn12q1oq2tDc3NzTIneWxsDJFIRI4VgPh2Cupy/8/n83V7dWtrKwKBgMRU6XRaYr9EIiHnyxiCCRmvu8o24H1hsSeXy4nuTCgUQigUkmurtpfpur5YIS1XEAgE0BRskoo0Yyr6ykwmg2wmi/V3rIelyQLdoEvcwr8znmQBIplMIhwOy3pKJBLQvqXBesyK3CdzgB1onmjGy374MgTLQWRCGSSWJV4yVs74tBE4ClT+V6U2xkweLMC9zI1Vt64CHgAObj1Y61m/8P5/uflf8Aq8ApvObILBYJB4iEUVXgeuQQDChOPzSfCe19lqtcLpdAqApbJD2MLJ9wGQ4pXP55O4nK9jgYqxEFCLzaampjAzM4NIJCIFD943tY3iYuYIYxi1MMXnrmENu9j+WybcqlWrVZw7dw7nzp3Dww8/jKamJnR0dKCnpwfr1q3D0NAQhoaGhJ5L0QU6CzoVoqB0KOzz5Hfouo7Dhw9D0zSsWLFCHkw6WlatmSgBNUdss9mEFkTnxd4kIsx0iKrwGZM2VquZFJAivGvXLrziFa+A1WrF5OQkxsfHsXnz5jrAQK1gq2JyasLNz+fPbrdb3sPNk8aNTt2ceJydnZ0y4iOdTiOZTErfGXun2F/D3ikqbfr9fgSDQXE20WgUHo8HiURCjo+Bhslkwpo1a5BMJpHJZHD8+HEMDAxIpZUjyAYGBsRpVauL8925oVcqFaGwEQnldzAp07TaiDUKeVSr1ToRtUqlIig8rweBByZmAPCdS7+D/b37AQCft34eH85+GA5jTQCE64KJJY+DAQADA5/BhxsfvhE/yf4EExsnYMgZMPjFQaxtXgubvbZO4/E4yuUyfD4fyuUyJicnRclVpbL39fWhUqmIqByDJlb7uS41TROxOpPJBLPVjBfe+wISWxJIIAHdpaP/z/pRSdecYTKZhMPhEOo8K05qMMlKOpFiBlgMaFjpJ3LOiryqg8BntaenB+Pj4/Js6LqOVWdWodXdiu9d8T3s/NFOmA6ZoGu6fB5BjlKphFAoVPc9ZDiwz3Dr1q248sorcd999yGRSEhlpOFsG9aw350x+FaTX2Cx/apcLiM2H0NiJlHrWb1gxqIRemGRxqxpGg4OHcSua3ZBN9b83Py2eTzW+hhu+ektMJTrKdMM6NXkjS0vTJDVdjPVmOhxv1H7n7l3AYsBPeMLJocE0mn011TR5hgt0mnZV8vPBGr6JsuXL0dzc7P49ouPQxW0Uqve/By+jwUBvpd7N5mDrOSShURfGY1GpU2PxQKqSGcyGRGjVBNpu92OpqYmtLe314nVUkCXyRd9DfuEeQ0IAqtxEbVDeHx8LeMbu90uc6zT6bRUXnt6ekSsDEDdNSQTE4AwohhDqowDgtvsPe7r64PH4xHhL/Y9E+BnUYHgcDKZlDFZ9GflL5Vhjpnh+WsPrv/Z9QhFQtAtOiwpC3b+eCceeeMjmO6brt3XPQaY3mNC9XQV5nNm5P8hL/fcnDPjytuvRE+4B8YtRrRsbsE9uEf+rkGDo+iQ+Fhdl6oGAYs3HPHKWJPsjkKhIK1iKnuTYARj3WQyKQUaxkl8fnkPyYDjfVV1EEqlEsbHx3H69Ok6UEz2BOU+quubsS3XzMWgXsMa9qvsv33CrZqu6wiHwwiHwzh8+DAefvhhuFwu+P1+DA0NYfPmzXjZy14mauPcBNWqFVE3Prw0JmhqhY3VO9Up87XsXeLGoFbE+VkqXYrIKh26impv2LABmqZJf5bZbMYrXvEKmWEeCATEwakUMVoul5MECFhE0XkMrEiyV5rXkhVFvv/iZBtYTM64kep6TaH0yJEjWL58uQicMDCi8/L5fELxHx4exrZt21Aul9Ha2orJyUlks1k0NTXJvaF4CgMlUokHBgZgt9sRj8cFVWfySIfK+d/c/NWWAbVvTJ35zsBNFQIBasJaFHRTAy/21gH1VYNvbP4Gnl3yrNyLw77D+Pi2j+PLz35ZNvSLKX0Mprh+OIu8XW/H1u9tRc6Wg/9v/cAMcHjVYaxatQputxvr1q3D6Ogo/H4/ZmdnZS2QkkXk3Ol0oqenB1arFbOzs6hUKnKPWfUlhZsgQ6VSweN/+DimN03LucytnUPuczmsfs9queZcU3R2ausCFUcdDod8ZiwWkzXPIMftdgv4pArgkbKeTqdhNtdGc3DdM4gxGo0YOD+AW07cgviLcZS0Reom1y9pbrFYDHa7Xar6ZC3kcjnkcjm0trZi/fr1OHjwIMLhMHw+H7q7uzE+Po5MZlEUp2ENa9hvz5jwqb2gKvskl8shN5pD/5P9SH44iej6KNom23DDT26AM+tECSVJxIz3G2FcawR6AWiAVtWw8cWNMJQMqFQXBdmA+vnBNPomNQbg/q32cPM/UmIZ0NOvEMRlVZXvpz8mSE8wn8cfj8dlPBITE7U1TVhOJg1uX626zQSH14zVafV9jG94viq4zv1dZXOVy2XMz89jZGRE/maz2UQhPJfLYXZ2FrOzs9B1XcTS6C+YiLNoobbOeTwetLe3y1jMfD6PsbExSVCtVit8Pp/ETDwHxlO8DzxvNe4jSECNH94L3ie2ARJMbm1trWu5430g4EztF6vVCr/fL+fPY+A1NZvNqKIKm7MW07Bvm21kKrOODEYWHOx2u2jQUOhN13W4fuHCTu9OtFnaoJk1uY9NsSbc9KOb8KN3/gjr/2Y9jj55FPnxPCqWCnw/96FtdRsqHRXMDMzgyp9eiaaJJhEEHjwziNx3c3j07Y9C0zTc/N2b0X66Ha4el2gEcM1Xq1URqmO85/F44Ha7Bcgm4EFmBuMAFr84R9vr9cozwOtls9mQy+WEqcbKebValdiPcRN1eag2TtDnYlNbM7jOL65yq4A813/DGvar7H9Uwq2aGuAvLCxgeHgYP/7xj2EwGLBhwwbs2LED27Ztw8DAAPx+P/x+vyRi3CxY/WTlknMimfDRoamVZDpVIpRMZlmZphMhqivCFxcQdLWiy/+YaBJZZRIFQP7t9XqlIqpW/Pi5TKBU2jOTZTohAC9xtKpQmYo8qxuTpmkIBAKYnZ2VqujCwgJmZmZqtKqmJqH9korGYMLv9+P8+fMoFApoamqCrutYs2YNjh8/jvXr10v/8MWBj6ZpQmFikkyKelNTk4hsJZNJUStntYDXg0qZZBLY7Xap5rL3W0Vh6RRCoRCy2awkZ3SCPC4qiZrNZrz34Hsx5Z3C2cBZAEBzrhkf2/sxQIckwLzOvNYq4EMWgMvlQnNzMzqnOrHuA+uQSWVQtVZx5MgRVKtVLFu2TFDg6elp5PN5dHZ21gEoXJc8ts7OTvj9fkkgLxayWbduHU6fPi3vX/WpVQh/IYx8/wVEfBRY9eFV8pmBQECES/gsqYwLVr8BSKtFPB4XZDsQCMhMbgZcFF3hTG6v1ytVFAJVuVyuNif3wlqoVCroqnRhNj8ra4dMk1QqJWuU9Ean0ykBj9pGYnaYEfPE0N3dLQr2pA02VEob1rDfjTGZoi9UtS+y2SxGRkZw8OBB6LqOm75xEx7/08dx8103Q6tqMJlNkvzOz89j+vA0mq9rRmx3DPmmPK69/1osO7xMFLuZMDO5Uitd9M8MxknnJo05Go2K/+axcm/k3s69hp+hVrPZrkSdDbWVjSBpNput63slSKomDZpNw4lrTyC+PI6ro1fXXUt+H1AvGKsmIGpCpAIFjF+q1dqo1EgkItdGrYCXy2Ukk0mcP38e6XQaXq9XwHQAiMVi0tfN76cfpPCZSldOJBKYmJiQ/T8QCIgIJ1Dr1VVbvLg2mHQRXKZvYAJL0NhisSAWi9Xo2hfupaZp6O7uRltbG1wuVx31nveO7LpMJiMxGUfP8T9pGdRLODR0CNPXTaP7gW44daeAxgCEhcXqdiwWw7lz59Da2gq/349EIiGgOkEFp92JUCEEs9Ms38N4EHHg7V94O8Znx2FP2FE01FgZDrMDq59eDafrAqW7XIXVZZVYQtd1VH9chXW/FVevvRrto+2wWCyIRCJ161YFq9mHD0DuC+NgFewxmUxoamqC1+sVUIHtCIy5uUbYDsi53pOTk/B4PFJAMRgMws4ko4F7BAtSjPXUKjZjd/U/FThTKeQXs1Ya1rCL7X9swv2rjLSV/fv3Y//+/fjCF76Anp4ebNiwARs2bMAll1yC9vZ2dHV1wefz1dGpScnlQ0j6keoIWa2+GF3lZkRjgqVS1Xhs6obAZEKlGXEDUunufA8dpIpYA8D58+eRSCSwefPml1BuuMGrYil0mirdjL9TEXe12s3zUAVbwuEwYrEYmpqaEAgEMDk5KUIZZBj4fD6k02mcOnUKq1atQnNzM0KhENxuN06ePInm5mb5Dva4q0GKSjWn4jkFx6iGres64vG49CIz8SeyTQfKe0PklDMeVXVzAjgEXhiAMLGkyA0VzZEC/mL3X+ALl30BWU8WHzj0AbSiFaVqSb6XVQ6uF1IVmUAyMHA6nbXzqehob29HPp9HJpPBgQMHYLfb0d3dLdR0rgH2Z3N9kMnAzwOAlpYWzM7OikPjNSVNMZlMwufzocvbhaXvWoqpz08hUUrA9HYTxmJj6O/vF+q6WjGm0J1aPeD9Y2DD/8xmcy0Anp6WY+ZrqXNAOhjF3lQVdoI4AOSZYw8+Reh4PABE5MVut8u6CYfDSKVStfNw2PHcuudwaPUhXHvkWrRl2mSE39zcXN3e0LCGNey3axeDkqyYzc/PY2xsTOjIHpcHN3//5pp/0hYD6mg0ipGREaTTafT39GPJN5ZgZssMho4PQTMvAsmqL1VBbAB1lWi1xYhJKIFs+k+CsWp7Fyt1KqWcfa1MAFXxMACSAORyuTrKMfd+9bh1Tcexq4/h8OsOAwCCk0HcOn4rzDDXxTMq6K/6cx4Tz41xh1q9J73bYrHA5/MJG437cjqdRiQSkbacYDAoNONMJiPnxz2ee7XT6URTU5P0brPyHY/HBWRwOBxoa2sTQINMO4qEsjDBsVRqqx0TNYLALFRUKjXVbcYA6XQaNptNAGreU7X1qVgsSvsak7+Wlha4XC6Jt6hRYjKZ8NTKp/DklU8CALw2L4Z+PgSUIfGeOoqKiXdXV5ckkxRUI7vBaDTC7XYLuKMCRWRtApD1xFiura0NHnetkGM2mgHjIv1d4tuqjiVPLoE77kbSUwOomcgbDAa0t7djampKmGP02bz/wWCwbuQaCxz0+4zXNE1DKpWSvnmO2C2VSpibmxNGKQDRQiIbjefK+8V1wESd8YHa/seYTY2bVWM8rLZmNKrbDfs/WSPh/j9YtVrF6OgoRkdHcffddwvNqaenBxs3bsSqVauwYcMGBIPBOqRL7QOmYyUlnBU2OmgmcAz6+WCrlWuV9kRkEIBQk/h7lT7Hv3MjVRN/NREuFApob2+XUUj8vZro8/3qe4lIEjxQE2wGOCrNjEGE2WyWCnc8Hsf09DR6enrke4kyhsPhuvFmTKZICe/s7MTBgwfhdDoRCASEtk06EfvhuKFys2cQoSKS3FTT6TSamprqqGdMaPlZTHTp4HVdl00eqI0PofIq7zmwSHkidcrv9yMcDuPcuXPwJXz4I/sfoeAvoDPaiUQxIaJmTAh5fRmokAXBY2OwwNFYFARk8DA8PIxsNosVK1bA6/WK8+K1JQtArVqkUinY7Xa0tbUJ4h8MBnHu3DkYjUaMj4/DYDCgra1NKNfWhBUr/34l0tU0EukEZhZmUC6X0dbWhmAwCJ/PJ8ku6X9qXzvphHNzc7Ku2M9HSiUdvdFolNF/DCSoAcDxfCoN0mw24+jyozjTcwbbfrJNUG6uLVL7+PxxvTIxpzo+AMy/Yx4vXPkCdJOO+15zH66MXom2RJsEpo0+7oY17Hdj9HlqUlGt1saJnjhxAvF4HK2trfD5fHUq3fSb6XQaZ8+exczMDJqamtDf3w9/1Y/2/e0iXAVAkgf6cu4XBEkZB/CzWeGLRqOS1AC1BFlNGHncqi+nv+XexOSPYCMBf/prJoMEAVndVvelarWKY793DMdec0x+972O7yGrZfEnE38ivbfq8atVPl5XNa5gwqGCDkw22YpE3RNqYkQiEYyNjSGVSsFms8Hv94vatsFgQCwWq2vrK5fLCAaDCAQCCIVCwobid1LUFICA+UzSGT/w80lZ9/l8AriTxszrzkIGCwH8PSnS0tLV3i5xh9obbjDURNzIpgMgdHIVPKHv2D20G3dvvFvuyYHtBxAvx7HxWxtRLBZljCnBBbZZqfRyMsbU72TFV2UtqC0AVa2Ko689CuOLRkmop989jaEDQxJzMF5VRd4Yh4ZCIaFucyoJ2Zt8La+F0+mUdstyuYyZmRlZt/TX9MWJRELG3rIKzuvKa80YyWg0SqsCgDpAiuJpTMKluo/FFoyLWyNUII3Pqhpv8/nkGmn4/Yb9n6yRcP+aVqlUMDc3h7m5ORw4cAAPPfQQ3G43vF4vBgcHsX37dmzfvh29vb2CcrOCyCqoqtjJJIEbHympAOpoZTQmX/xMbtCky0nP2YVNCVgUK1GFttTP5GZB2g1/Vr+DVXkijgDqKseqA1JpdUzk6LDU82GF12g0YmZmBidPnsTKlSvR09ODSCSCZDIpG5fNZkM4HEZ3dzcmJydRLpfR2dkpFOqRkREkEglRiSZiqybERHpZ7VTnTHNsWrlcxtTUFLxer1x/FRABao4cWJzzynvM++l2u5FMJuvOlRs0Ay8KgjFQ6urqgq7rCBVCMMwbkMsvzvsmBZJBk3qtdV2XCgr7tRi4qdV0BhWapmFqagrVahWDg4O1hBdV5LN5+Tw6MgYvpMObzWYEg0G43W5MTEwI4FCpVPDMM89g+/btUgUOBALQZ3X4LD6E+kPSd1UoFBCPx7Fp06aaw9Kr0nNNIRRWyjs7OxGPx2EymUSXgJUAViZ0XZfj4/HSeZrNZkG/+WxZrVacW3oOd191N9L2NMbeOobLPn6ZBKFM+tVAkr2FDHCsVitaW1sx/NphTL5tErqp5oRnu2fx4B88iJv//mY0mZvqevQb1rCG/fZNpU2XSiVEIhGcPHkSs7Oz8Pl80p6i+jCCtufPn8fU1BSMRiNaW1vFJ6j+UWWZMflQf2Y1jXsBwXBVJ0X1mdyrgMV+bP6NSaKu18YmErTlvq6eM/1CKpVCOp2WtiybzVZHOQdqQPOSfUtw4lUnUDVcEJaDAS+PvLwuwaAP5TW6mDGnaZrEMvT3wOL0DwpTkWbOqnE+n0cikcDk5CSmp2vaH4ypgEWqPMXPzGZzXatda2srWlpa6kCAaDSK+fl58d2c3ELWFCu+pLkTwCWTgAkzW8LY+82kNZ/Py1gwTdOQTCaRz+fR1dUlM76ZrKl95rlcTvqPPR4PQqGQxAiMV/j/9efX4/7196NivFAtrQLtT7ZL5dftdksRg614BoNBRlEy7mFimUgkUKlUZGSa2oJHOrfZYsY/3/zPmOibgDvjhvVvrUh+PYmFVy1gT9ce7HxwZx14xcQ3k8lgtnUW+dfnoR+rfXdTU5MA6bwOgUBAWhwACOOB7YS83lyf7JGnoLDX65WfuXZ5bdXnj1NdPB4PqtUqUqkUKpWKMAIIms/Pz8s6djgccs8YS6gaBBcXlfjsqu2XKtjVsIb9a9ZIuP+NlslkapvN7CzOnDmDn/70p7DZbFi1ahW2bt2Kq6++GqtWrZJkQqWXq0qfai+L6pSZCDBRBhbFzVSHxuo3NwI6XJU2dLGjVRNqJmdqVZzon9PplGRPFUTjJqRStFSny0CB8xABSM9ztVpFd3c39u3bJ4kse3Kq1arMQKeYCOdEv/jiixgcHMT4+Djm5uawatUq6V0Kh8PYunWrJPHcQEl3ZgLNmYoEQNTRIEQ3Wd0GFmeO8/wYMBCFpZGd0NbWJkg4KwxqlVkVwOFnOZ1OxONxxGIxoUGpFKZisQiv1yvXu1gsCtW5XC5jbGwMXV1d0kdMh38x4GCz2RCPxzE3N4fx8XF0XNaBz1z2Gbz/vvfDWlyce06HbDAYBB1nFd/lcqG/v18qFpVKBVdccUWdKBAAAUCcTif6+/thMpmQSqUwPz+P0dFR9CzvwbEbj8Gb8uLSY5eimC3KuqeyPNXRWeknDVwVIVJFdKh8SxVaVsTZlzfdN4273nwXqsbavZ0amMLeT+zFzm/thKFgkCoJry2wGFwWCgVB5I1GIzae2IjUQgqxrpisgaFHhmApWmCymxAKhf4fd5eGNaxh/1ajT1QTnVOnTmF8fBxutxudnZ1SiVN7OQuFAsLhMM6fP49KpYIVK1Zg6dKl0naksn8A1CUvam+xCqqrgDr3uIvbvBgfMDGkf2ElkXT4dDqNhYUFFAoFoUKrmi7FYhFllJHwJJBO1Giy6ixkmkpn9+k+fPqBT+Oz138WVVTx+VOfx9rCWlFl5/mqvd9qMYGfRYCcky3YUpXNZhGPx8WP83OoFA6gLvElsEr/zXFmKvjPnuqmpqa6vmb250ciEUk8g8FgXRsWE176T/qZVCoFj8cDAHV6JdFoFHa7XdhTpKjz2NkO2NPTg0AgIHEOYwKgFqdRzZxU8/7+fgwMDEhBhHFLpVKBo+TAH3/zj/HFW74I3aJj+1e3o2usC0XU/PvU1JQkjuxZ5hrO5XIiEOtyuWQOOWeaq8klq/6aR8MvXvkLnFlxBtCA5HuTwP8CYAVgAA5sOwBr2YornrkCJr12j/jds/5ZnPrTU4ARGH1gFCv3rJSCBGeuu91uiXlZaGBsMz8/L8AU14ZK0eb5AYuFIT5DrGpXKpW6qjf9drlcFp+dz+eFbZHNZuXcHQ4HlixZguHhYWE9spikFk3U51ul4quTjdQ54w1r2K+yRsL972B0OOl0Gvv27cO+ffvw1a9+Vfq/OXqsublZBEG4KQGLFG7+m5sJH3qVrk3HzQREdTjcyNQebJXidTEthpse6bfqsdAR8T1MPnl8pGWrPdEqIk9ggK9n3xJpUAx47Ha7jLHg5xuNRhEW4WzmSqWC5uZmlEolPPjgg+ju7obT6cTmzZsFraRKPB06r6PH45GNmtRwKok7HA5xAn6/H8lkUoIfIt+xWEx6jehI6biYjJvNZsTj8ToAhFVf9g3xPXT0PKaWlhbpVfN4PBJksWJN8IW9SKxee71erF27to7KpzISYrGYUPiJ+ubzeTxdeBoHthxAPpDHN6/5Jt7xzDvQlmmr678jJUsFcgwGA7xeL5YvX46RkREkk0lxeASB2IvF+26z2bBy5Uqk02mcOXMGR04dwcRtEwjfHK4d73d0DDwxAKvFKhRCg8EgAilut1uCMc7OtNlsMiecQSp7w0lBZ0XHYDCgVCnh4csflmS7ttCBhfYFnF1yFt3z3fJslctlGSfGPvJKpYJisQin01lrM1k7irQvXbcHnNpyCl37uqBHFoPPhjWsYb99Y3DPCuDExATOnDkDu90utF81eaT/XFhYwNmzZ5HJZLBkyRJJUNTeZbXKrbZY0Q+Q1cU9g79nkiA0VA2YWD2BpcNLEeuMwVA2wJKr7aEETNVWqoWlCygcLmBq5RRCh0PC5mGiDdT2/tErRnHgvQfgf9SPga8MwFAx1I04BRY1YtIr03jqfU9hxcQK/PWpv0bekMfyxHIUUJDzVLVL1BYsXoOLW86CwaCAzdVqFdPT0xgZGZEiAPdv+rFEIiHtYy0tLejp6RFWQTwex7lz50Rok34gGAyir69P9FvkfC70gvO4Ozo66kZ2qiOnmLRz8gV9MYF3xiqk6/O8yShk5Zjg8iWXXILW1lYZ68XYgJV1JnKapsHj8cDn89VdP8ZjpJhbz1ix83M7ke5Io+VwC7LlbB2DkCAG1xmryC6XCx6PB5qmYWZmRtYGheV4/DSr1YqxnjFMtE8szuI2ArAvPk+6QcfwqmFsOLMB1nmrXIvTodP4+nVfh26pfd6Dr3oQuVwOQ/uH0BSsxbic6c440efziVK5rteU6qPRKNxut8RWpVIJfr9fGAeRSESeK5WZQKFbVXuFFedIJFLHGCVTkdcslUrV6R7x+rPvnFo1XNtc//xMxtlshVPj64Y17F+zRsL9H2SVSgUjIyMYGRnBT3/6U7S3t6O7uxtdXV3YuHEj1q5di+XLl6Orq0sovarzUh0dE0cqKXPjZmKnCoOpPV/cJLgZqD3a3CRIy6WYF1BDEtXEHoBQdumYGGxQwEpV/VRHlvE1RILL5TKam5thsVik53phYQHd3d0wGAwypi0ajaJarQl3ud1uzM7Owmq1wuPx4LLLLpOqLxNWHocqjMXfEdxg37C6UQYCASwsLMBut4saprp50mERRdW0RQV6brpEy+lUOGaL99RorI0u4WsrldqYE27qvPasZPCYOf+TKty8dyaTqW7MBX9PB8bkU+0nI2tgtm8Wx//0OPJNNYT+TOsZ3Ln1Trxn/3tQjdWQcAZMpKcTXCEA4fF4ZN1OT09LRcNoNGKuPAf3G9zwPrZIzadC+dKlSxG7KYbwe8PynBx4+wHktBzWP7FeRFTUa0fghUEPj42AhqpAHAwGBb3mmiOT4BU/eQXcN7vxwqoXausja8LgdwfRub8TRrNRngcAoorPKhPPvVKpwOPxYOVzK2E32rHntj2omqroP9OPrXduha1kQ7aQlaCvYQ1r2G/fmNAlk0nMzs7i5MmTyOfz6OjokL5SJi70BzPWGRzrPgbTMROy78li4OyATP1Q/bOadKs9uo9f8ThcGRe2vri1zt+p/bJqZfvIlUdw4PoDmNk3g6mlUwgmg7jp3ptkT6NpmobRzlE8+ppH4Rn0YGZwBuZvmdG1t0v8EMUoj15xFAfffBAAEHt5DOet5xH4SgCF7CJDh7YwsIBnb3sW6WAan/V8Fu8feT92zu9EVV/02cBiAnPxuauUaTUWABZ9JgAkk0lJMgns02eVSiXMzMwgHo/DaDQKfZuK4VQTZy88RdBaWlpkYokKbiYSCWn78nq96Ovrk+tPijN9NFuI6EOZaKn3ilVXlbYMQCjcsVgMuVxORpuSMaHGSVRgn5qakpiqubkZbW1tAuZcDGSUSiXMz8+jG91AFEgb0nU91DSC8AQiWJUnMEyA3mg01ol/AottgQCwamIVTI+bcNc1dyHtSMN82Az7PXZk3plBpb2C5vlm3PTATWiNtsJgMUgiXPAWpBVB7rcriWgsinKpLHoypPIzYa1Wq/D5fDAajaKizziUMSOfUYL/jAXL5TKi0Wjd2mOhhnEdgXvGxLlcDqVSCbFYTMSOCYSpompq2wiAOnYj1zgLIQaDQQoUjcp2w35dayTcvyWbnp7G9PQ09u7diwcffBB+vx/Nzc1Yt24d1q1bh6uvvhpLly59iaNjksPfq0mzpmnIZDKSjNC4eagOnwgzq+CqgjM3GiawTOhVRWtVxEJFevlvbkZ0UkSl1cSbm6DL5arrxV1YWEC1WkUsFpMkLxAI1KqTF/qQWbHu7OxELBbD+Pi4UHcpvsFkmVXhXC6HfD4vCa3f70c8HpcggLRpi8UizodU5Xw+L8lxKBSS6+DxeOr68dmPzgrs6Ogoli9fDoPBIBu5KmxH4RUCA+pcb6BGZ2MFnNeLVWtVgI1Vb95H9hyTusdecYIpBB9ac60Yj48j0Z6oIdo60Jfug6vsQrFUrGNI0NGpPWH8HI4/aW5uxunTp2vrxWrG3LfnMNM/Az2vo+f5HmiaJqM/QqEQdpR24DE8trhYK4DhaYPQ7Xj8bNlgmwHvLSs6rAQwUTYYDPB4PLBYLEilUohGo/IVdrsd0VIUE60T8jtTwYTO5zuhGTWpbDPY47+5Jvj8pVIpud/LDyzHNVuuwXf838H1v7wepqwJqUqtP7AxEqxhDfvdGYG2e5fdi8yLGeRyOXR3d0u1jb6TCU5Wy+KXt/0SSWsSjlc7kF6WxlMnnsLrH3i9gKDsdaUgE9usyuUy7rv6Pjw39BzMZTM0aBh8fvAl4k+kjZdKJTyz+Rk8f83zKNvKeOGqGgA4q8+i7Cjjg7s/KH7b6/ViJjiDr639GmK+GGIttRaWY+8+hu2Xbsf22HaMj49j3759OH3NaRy58Qiq5sUEaH7HPA4FDuFdP3lXXavSjHsGv3zTL5EMJQEASXMSX+r9Eiy6BVdErpDkRU001P5dlUKvXkv6eIfDgWKxiGSy9vnUF2EyxAplPp+XUWEtLS1obm6WhHB+fh7Dw8MC/Ko+kOPV1D7efD4vGi+km1MjhKy7XC4Hh8MhyR4TUSZMbBuz2+0SOwCoa/GrVCoSH/Aet7S01LUAcs0AtXhiamoK09PTAsK0tLRItZ1MQFZbjUajtF+xqMBe9lKpJOPSuPYcDoew4hhnMBFnFd9kMglTkPEj4w8mrqvGVuG6r12He954D1r+rAXVU1X0jfZh5B9GcMu9t6Al2gKTzSTguslkwqaJTcj+JIvvvvm7AIDtj23Hhqc3wNZkE3FXMgvYR64CNEajEfPz89Kjr4IJNM5SZ6zLuJMgitVqldZHtoUR9FBbKxn/UbiPsQTvrdVqlXFljJMZWzC+UHWWCEIxVlLZoA1r2L9mjYT7d2DpdBrpdBoTExN48cUXRYBi9erV2LFjB7Zv346hoSFxVGpFlsktEzH2kalou5poP/TQQ1izZg08Ho9UonO5nPTGEqUDFunswOJoJVZr1V41JtSqQAywKE5FI9WYG9HCwgKy2SyamprqKqfcRBOJBCwWi1TAWeE1m82IRCKibt3e3o4TJ07U9Slzk2WSHI/HpfLPCmmlUoHf769zSBej0UQ9z5w5g9WrV8v1VnvKuAmr46zMZjPm5uawZ88ezM3NYefOnVJNYcKr9vLzGuZyOblOZAbE43GhSanIKpF/Coiw6s5gyGAwIKWnsOcv9uCVj78S5aOLPWRutxvVahVdpi74v+HHvX9yL8K9YVw1ehXeduptMBqMMLYYUTKUkC1l4TF66tgVRqMRLpdLKHkWi6VOeGgsNobv3fI9FDoLgAac/MhJWD5pQfMLzUKFczqdyD6exabUJhz62CFUihW4r3GjkCgg35uXYKOlpQVut1vWEJVGWdkn5YvoOB3kxMSErCVSywHAYDLgnj+5B9HAYhJe8BRw6A8PYes/bZVAi1UbfgcDTz5vvBbVahUupwubw5sx8q0RdHR2IGKqUdimpqYaFe6GNex3aCW9hEeWPoKfDf0MWANc+4FrEbKG6nREuF/GEced77kT8WAc0IBEKAEAOLruKLLJLLb+aCtcBhcAIBgMYsmSJSLQWTVX8cOBH+K5gedQNVRRMBZw/8vvx4blG/CG5BuQS+fq9i673Y5nmp7BvoF9KFsvajvRgOElw/jxdT/Gn5/885ofMuTwiY2fQNQerXtp3pvHdy79Dvz3+zFxfAKjo6NYtWcV5gbncG7ZOaEG2/N23Pr0rdKbzCS6u9SN645eh7uvuBtFYxEG3YDt0e3YFt8mfpJVfQB1MQE/hz5JVXZmAmyxWJDJZDAzMyOiYkx82C9NuvjCwgKsVis6OzsRCAREUZ3fT+CZgKymafD5fBJ3MKkbGxvDmTNn5HgoMsrEjAkwhdoIrtLPkobNWIZJHOOqXC5XV3FPpVJCW+7t7a2b8UxQmJ81NjaGRKK2rjwej1Tn6XPoT8mSOzV/ColUQtqpFhYWkEgk4PP5pJ+Z1xKogfS5XE7E1EjbJ+hfLpfR0dEh56gmtmoC6z3jRderumDMGZHW0+gId+DV33k1bGUbDBZDXYsi39t6vBW+m3wYeN8Atjy+BaV8CbplsVjEfm9Vh4Yq9blcDsFgUIogvOf8HsYMoVAIXq+3rl2DtHTeJ6fTiUKhgLGxMYlNWahhaxpjJYq7OhwOJBIJYTnQ9zMZZ4Wcwm3cPxgXcb0wFmwk3A37v1kj4f4dm9rvtXfvXuzduxef/exn0dfXh8suuwxbtmzBwMAAWltbZeMBIMkuUD+2g5sNUWdN0yTBZTVaTc4BiJMgfYobhyr6olJ9mDCqiShHVPHz1Mo8Udfm5mZEo1FBdrPZrKCws7Ozdcgt0WMmo6xG+/1+GRsyMTEhaDGdL0eA8bUqhUzXa2O+XC6XKLmy95w0bgBCI6YQl0oFZBWfmy9Bj1QqhbGxMbz44ouIRCLo7u5GX18f7HY7MpmM3DNeL/U6E93mebCyrKrak8VAWjwDC4qXaZqGvDuPe264BxPNE/j2wLdxw+dvQPBcEDabTY7baDTCUrHgxs/fiOfe8hyufeZalLpKMNlNyFQzuHvZ3Zgzz+FNB98EPamLoJ3a302QgOfQ19eHgx0HEWuLSbCnW3Wce8s5NB1rkiAulUrB6/HCe8YL67esmDwwieTZJHKunCDZdrsd09PTElwYjUaEQiEcP34cPp9PUHo6OVYp1DXNNcPnQtM13PpPt+Int/0E083TgA4MHBvAax96LQqBAjKZjDjii4M0Bjh03FwXiUQCmVQG1+24DmNjYwAgNEPe74Y1rGG/fdu9ZDdu33h77QcH8NTnn8Kluy7FJZVLZP/lc/yLDb9AJpBZ7F+9YLpBR2RjBCs9K3FF7grxRdxf8vk8jjmPYU/bnjpabcVUwe3+29F/rh++ok/2EiY/KyIr8ObSm3HXmruQM+eULwQGJwbxrn3vQrQalZjgfbH34atXfhXzwXl5qT1ix2XfvwxHnjmCRKKWmOViOWz99FZk/jSD2aFZBKIBvOWetyCYDKKCSt2e5vf7cVP4JgRGA7iz905cvXA1Pnzuw7XjR0V8harYrFa0AQg9mEmSymBjhZf0biY7apyQSqUwNTWFQqEAt9tdN4ozkUhgbGxMmFv0hV6vtzY7/QLLjJMj2DpA/+bxeBAIBOQc6A/o33n/VcGtdDotfoUiaWrrmMlkgsfjkVYFavaEQiG0t7fXxVME7wmqh8NhWXeBQAAABFSnn+I1nXBM4I533IG2TBuMo7XYpb29HR0dHRLPcS2y6MLrxqSPgEcsFpNe776+PmEC8h4xfgNqQMTkxCSQrq0Bg8EAg2aArWwT4Ve2s6ntjOH5MLyHvFj3o3WwtdlQMVSEvs11RACEhQ8Wm0KhkMRnqn4PP1+tQicSCbnnRqMRfr9fwA9S0im0G4lE4HK5JDYql8syc50xFnV51HWtgvVkCDImAVBHJ6fx+VCp6A1r2L9mjYT7P6mx//uuu+5CS0sLent7sWzZMgwODmLZsmUYGBhAT0+PJKnqRshEsFqt4uqrr67rg2J1UEUqibaS9kUnxyqiqrR9sWCKKvahUsu40dExUBwjmUxieHgYXq9Xeq6p6k2n63K5hGZUKBTQ2tqKRCKBo0ePwufzwePx4JJLLsGLL74oVV+3241AIID5+XnkcjmcPn0aq1evlvEQTJINBoOgoWp/Dvu5CBIEg0FJpomGEzhQkz6eczgcxpEjR6QHfe/evTCZTOjt7ZWNnorfPE9W7wmWuFwuoarF43EYDLWxJERb1f55BhaJRKKGwpry+McV/4hdwV0AgLKtjF3v2oWdP9qJnuEeeR+djMFgwLV3X4uUrUZf6+ruws/W/Qw/6/tZbU0YdHTEO/C6kddB02pqs6TUU3SIjAaLxYI3h98MFIHvrf8eqoYqjLuMaP+79hrVyqHL9We1vHVPKzqMHZhdO4uRkRGcPn0aHR0daGlpAVBLcElHJxpdLpeRyWTg9XrrVNVtNpuMwKFDpLPn97rSLrz90bfjh1f9EOazZmz/8XZoHk2cN3vHuba53qmZwPvEapXFYsHs7Cw8Ho88N5qmIZ1ON0TTGtaw36F9e++3gY2LP6eLaXz36e/iqvmrhBJLdtTqM6txdtVZvPj6F+uSbk/Og7ftext6ZnowYZyoS3JIk3Xrbrw99Xbcvu12hD01XYolk0vwtmfehmwyi1QlJVVMu90u/u7yw5cjNh/DA9sewJon1mBy5SSCsSBufvZmhE1hYTMBgGHKgFdFX4V7Xn0P+ib7cGzZMVz50yvhfM6J2flZYcfNzMwgn8/j8q9ejvCtYSwdX4qWqRaUUduLstksYrEYbDYb3G43/H4/3jr3VjjhxGvDr5V+Vfoo+kWC7qrvV5Nw/p4xBFBjurG/mfswq8WsXs7Pz0uVsqurS2jeul5TBmfCRp8L1GZq9/T0wOv1CmOAyTtHYvH81EolAOkjJyWYPk1Eyi60ChGwV30HR1nRT1PFnLohVLHn9eF56rqOeDyOhYUFpFIp6LouIm5kK6rFi0nPJL67+buY9E1i5kMzqPxjBZ1PdNbRmjkxA1hkHrhcLqlus7rONggAcj1UQTBqE/C/E8tPYO6hOQHSTSZT3Zx6Fnd4XdX2At4HMtAASOxIgbqLe+6z2SzGx8dhsVgQjUYFAGNroGrRaBTRaBTZbBaBQEAmoJTLZWSzWZn5bbPZRBSRzMKFhQWYTCbMz88L84z3jr5dbX8EUDevndVstcDEWIzrGsBLjrlhDftV1ki4/5NbtVrFzMwMZmZm8Nxzz+HnP/85mpqa0NTUhE2bNmFoaAibNm3CsmXLZFMmmq1S6ACI4Berefwbnbbas83NmRVz9gcRyWVCTufBz+QGy+SHvVcGg0FUvleuXImFhQWhI6t07nQ6LfRnUr+Iivb392N8fByrV6+GxWJBZ2en9AZxrmcwGAQAGQGzbt06aFpN9TWRSODAgQN4+ctfjkAggFKpJH1mTNKIOKu98qxYElEn+l0qleB2uxGJRDA2NoZwOAyv1wufz4dUKoXh4WEEAgF4vV4BIuiM6XiYvNKZqrS2QqGAZDIpDpbBTz6fh8fjESdut9vhsDnQnmoHgotrx1qwwp/2C2Agv7+A6JNyZbVa8Y2V38BjvYu91U/1PgVT1YS8PY83H3uz9MHz/hNNV4VmXjH+CriNbvwi8AsMPjCIwyOHMW+dRyAQkGAnHA5LYAQA/f39MJvNOHbsGM6fPw+j0Yjm5mYBhCiCps55TaVSohpOtX6CCQxqSdFjAGI0GuE478C1P74WmdEMjGWjOFwGR+zzUulqPFd1LjsTfZ/Ph2w2Kz3z5XIZExOLfeINa1jDfgf2EQAFAH8GoArgDcDEIxO4E3eKxgeTGLPZDIPFAOwFsB3AtwB8Ayj+ryJ2z+3GbuyWqqfT6ZTkTK3wrn58NfZ8Yg8C6QBu+MUN0KIaJquT0jfMxIcAXqFQgMVkwY79O7ByYSWMU0a0mlrhMrtEw4O+NpvNIjAcwKt+/Cq0Zlux6sVVaBtvQ6atBkjOzs6Kf2ptbUVvqBcb9m6oXQcD6qjhNpsNu9+wG0MjQ3LsN87eCGhAWS8LkEs/o/ZMM5lW93u11UZNzhcWFhAO1wAIFahmxTqRSGBkZET8dTAYFECCImMqVV3V7yCdXGUGcuQW/cqSJUuEJcbXEdRXEyeg5tMzmYzEOATDSY3P5/MC/lLktVKpiPZLT0+PsOTUajPjiGg0Kr7SbrfD7/fL39RWsJglhn/c9o8YC9TYUhVbBUffcRRGoxF9z/YJpZ7JHynznMPNaSQsHBiNRoTDYZjNZpnawe9lTziT5P0t+/Gzy36GUqiElltaUC7Wps7sfeNerBleAxMW2ZG8foxl2JvONkm2ggG1RD+dTtcpexMoCYVCSCaTyGazIoIGoC7pVcUNzWazgAbqRBsyF6j3w3VOAMnlcsFqtQqzQgUbWPRgbKNqLjAhZ2GFzzvXPY2frRawGtawf80aCfd/ISOdKZlMYmRkBAcPHhQq9KpVq7Bz507s3LkTK1asqENRVaVV9gKxH4ufq/ZCc5MhFUfttQZQ15/En+kIWCmkyIRKMyPqqGkaenp6EIlEMDc3B4vFIvOl6Uy4uZdKJbS3twuKzTmUfX19CAQCSCaTkrhzDNbCwoIIXDidTunXtlgs0pdNxJsOPZ1Ow2g0yrmptCmitclkEl1dXUgkErDZbFINKBaLmJqaEqdNB82kkD1L6XQahUIBgUAAqVRKvpsjLngdKNwifdmplKiAk6rPQIKAgDFvxG3F22CwG3B76HZYY1Zc+ueXwuqyIqknpX+fIm08LqPRiLm5OWx6YBOeX/I8krak3NeyoYx7B+6FTbPhplM3SXWXx0BnJSi3ZsBVU1dhcGwQ8d44bJttePLJJ3H+/Hn09/cjFApJ9cDhcEhP3NKlS+H3+3Ho0CGMjIxgcnISg4ODSJaS2PeX+9C+q12CRlK96GxZdaYTpHMkUs8ee13XkUql4FhwwKJb4PA5MDc3Jz37BoNBQIF0Oi3UfqL2ansG71thSQHfDH0Tr73ntdB1HW1tbXUaBg1rWMN+B5YB8FEADgA/BbB78U9Mvl5iXwXwbQBpAENAPpLHERyRP18Mwqr+T9M0lG8vY0FfwO252+X3VNVmUsCkUsZMTek4pB2C9uLi6EyKernd7roEvzfZC5vNhkA8gJnCDMLhMBKJhPheq9WK1tZWaZfisTIJNDqMeOzmx3B07VF8buBz+PuTfw9jZbGtST0/7p9MpNURperf1ISb72OPNPdU6tCoVOhsNit7LJXg6Qvo25hoMdFnX7LX65X9ntRlzk1nzy+BUh67GpMAqFMrZ3vbxYUGFcgls8vtdiMej2N6ehrxeBxutxvt7e11hQ6CFuz9DYfDwg5zuVwSq6hitbquw1f24doT1+K7W76LiqkC6EBwOIiegz3SBx2JRGA0GpFIJLCwsCCiYZxYEo/HAUCuqZqUulwuSZLZQgcAR31H8a0rv4W8OQ9cCsz88wxa3tGC6T+eRuqaFL686cv48D0fhqFokJhDpV5nPBlkf5qF9wdeOR7GkyyW8P4wwTUajWhvb4fP50NLSwtsNhsOHTokYAXjPhZ7PB4PQqEQ0um0iNxx1BoA6Z+n6G6xWMTk5CQ8Hg8cDgdcLhcCgQCy2Symp6eRTCYxOTkpIopcv5wdD0Cuq0oZZ5FFZXHymBvWsF/HGgn3f2FjBS+TyeCpp57CU089hU996lNYsmQJtmzZgm3btmHTpk3weDzweDwIBoPw+XzyXpVmDiz2kwOoqwQQ1aXghcFgkCQEgCD+3Cy5GVHVmQEGN8bm5mahbjGRGR0dRSAQQFdXlyR0p06dEhG0jo4ORKNRtLa2Ch28UqnA7XaLYBnRaTrdRCIBl8sFs9ksAlvNzc1S9QRqzom0I2AxQCGCzH4oVjnUyj8dbWtrK/r7+xGPx6XS2dzcjFWrVtUJcTCJphNR1cX5GiakDJx4j+LxOHw+nwQDrOCS2u10OqHpGq598lrs2bwHO761A3lDHvF4HC6XS4TyWAXmOkin0zVqe9KMr+/5Oj627WMYtY8urjFjGbu6d2Freiv65/ulMs2kk+qgmUxGesX9Bj+cHU686lWvwqpVq3D//ffj3LlzNRp8wINYWwzu+UVqHVWEzWYzDh8+jJGRERwPH0f6z9NIXJLAV3q/ghv+/gYE54ICErDaXi6XBS0nsMNZ6FyfvKdUz6UIj6pGr+uLavmslEQiEQFcCDwREEn0JfClDV9C2VRGbksOm3+2Gas8q6QVomENa9jv0PIA3vMbvL504T8A+BWah0ww/lULA1VUkUG9fgPFsn4TI+hM6rfL5YLT6RRhUIKcTF5ZNcxkMpifn5dqM5PosrOM5655DkcuOQJowGH/YXxyxSfxoXMfgie3KBbGFjAmHfwcAALI0ydx71X7genzs9msiKUSlPZ6vXJ8ExMTwtKiHgvFr6iBwWouW81aW1vR0dEhiTLbsaLRKCKRiPhJgqbq/apWq2htbRURspmZGYkrmIBms1lh+zFRL5VKQotmnBKPxzE7OyuK4a2trS9hkJFeHo/HpU+9UqmNJVXHvjFWMplMMMKIDSc24Nz8OTz+8sfhP+HHmo+uQc6aQ9W6KEBGZprJZILf75cqsaq/o1b1DQYDQqEQQqGQJPmMfTKVDL6/8fu1ZBsANCA/mMfYoTH5eappCl95xVfwriffhWAuKNfTaDRiJjCDF25/AWlLGo+bHscND9wAW9kmFWP6WbYWsH3QZDKhtbVVfo7FYtIy5nQ6ZUZ2tVpFJpOB2+2WiToEzjmrmxR/stg43pWxKkXReP+ohs8CRCqVgsfjgcvlEgCea79YLMLr9UocrGoNcHJKOp3GzMxMo8LdsF/LGgn3fzMrl8s4e/Yszp49ix/84Afo6OjAihUrsHLlSgwODmLp0qXo6elBS0tLXVVQpZ2TssMNXK2oss+YwjHqbEc6LyY4TEyJClIp1Gw2S8WaFLtAIFDXw0vaE2lnQ0NDolQ+MjJSN1eUs5rpCAgWjIyMYGJiAn19fUJt4lgItd+ciSiZAEy2WXVnZdhgMCASiQi9nqZpGpYvX45oNIqJiQlUq1VRI2UyDUAQfKqKUjCO15+gBsdbEWFnf3AsFpPeJQrCkb7GCv1J/0lE3VGc2noKg6lBZDIZQYYpJMLrr872TKfTSJ1N4X8H/zfuCd6DJ5ufRNgWRnOuGe86+C50znfC7rdLSwIAuf7q6DGVAmaxWLB8+XLouo7HH38c58+fR/lVZZy55gyu+dE16DjXIeJ1lUpt/NratWuhBTWceu8plHfWaPd5Tx6Pv/txXH775QicDUggmMlkpFrB4JLicwCEKcBgaNIxibg1jvaZdgl0KGyjVnfU6gipkKQv2mw2hNeGsfuW3Siba8d3ZMsRRMNRGB4zNBxvwxrWsP8nY7LH+c2/rlksFqnMqSykcncZ568/v9ijrgFPTz4N88/MWJlZKWPOWFHnSCfSeB0OhwhHAS+llatCdOxXZmWVgqLZbBalUglzc3OYmZmBrutobW1FS0uLiGoWCgXRYGEhwGazobm5GU1NTfD5fLLPc+zn9PR03bGwagksAq2slpLSTH0WMh3Y98330C+y6s6xVul0Wij/ZrMZoVCoblYzrwF9CfuLCTpQO4D3RrVKpYJcNgfDNwzoHunGwNMDKFfK0lfOPnG2KJRKJcTjcVSrVWk7U2nxbLHjdb5YYVvXddirdnzgyQ/gm1u+iRNtJ4Aq4Lndg1xPDqWdJVkrCUcCc545BLIBqVqf8Z/B1zZ9DWlrDWA+tfYULLoFL7v7ZTDnzeKLycxrb2+XWI8FIMZqRqMRyWRS1o3L5UKlUps0w/GxXCMulwttbW3CFlTnqwvApIxVpWAeALnuVEcn06JSqUiSzs9gUUItRpHlSJ0BxmyMaxvWsP+bNRLu/+Y2NTWFqakpPPbYY/B6vWhra0NnZyfWrFmD9evXY3BwEKtXr5ZEgaqdNDoRJtCkFJEizvcwgSXqrf6OyWkwGESlUkE6nYbVapXxVpqmIRgMoqOjA93d3fI+JqW5XA6BQACZTAYnTpzA+vXr62aCM2HkxlytVmV0GOldFEUhCkrxETofji9Rhb1Ii2dVlO9PJpMiUqIioRs2bJBRYRTvUAXGKMISi8WENq/2i6kVbybfTAadTifi8bggvUxuAUhCf9RyFN9d+V0suBbw7HXPomQu4YrdNYVdAgVMjtVEHqjR7FKpFFYkVuB9qffh8ujl+NTKT+Gjpz8Kz1kPFooLcsxqr5Sq3Mm/EUCgwFpnZyd27NiBnyz/CU7ddAq6Vcdjb3gML/v+y9A82iw0OwBoaWlB2V5GyVvCaZyWdWgum+Es1xJ8l8sFTdMwNjYmYAPpiQRTGISxVz5pS+Ke192DsqWM626/Dq4Jl6xRVo/YSsFEm8wIjm8T4Zm4AYZy/czQpkoTIuHGOLCGNaxhvxujr6I6uNhZAJMA7gCwBcAIgNuA3Yd243HtcUnS1P+YrKs/q5R6JuFOpxMOh0P8YSQSEQCdvbQUtyL9O5vNiv8nUE5Qu6urC4FAAL29vVi6dCkikQiOHz8uY544isvj8cg4KcYnLS0t8Hq9UhCwWq1SVaYvZbLK6mksVpttzsSfPdBk7BF44Oxwtou5XC6pbhMcp5EtEIlEBEy22WzCVuBrCEyzoJFKpZDP57Hs0WW16xnU6qbCEARmRZdAQrFYFNEwFglisZjEWRznSf/GvuNqtYpgNohbnr4F39n2HaT+KQXP9z1I+VJIfi6J3I4cXDkXbnviNvSH+6EZNTluZ9kJa+Wi9qkwYNRr40N5DNVqFStXrkRTU5OABbFYDAsLCzIuzOVyIZlMYmZmBj6fr07hvqurSyaFsCJN0IBFErYQsP2CivMc+aYCEbxXFDdtbm6WQgqLFwBkfbAyz2Sdwrt8Fqg50LCG/TrWSLj/B1kikUAikcCpU6fw1FNPSdVz7dq1MoLskksukd5mYFF0hRQhJo6kXbOHmJuV2tPF5E4VlmhtbRUkkUl7W1uboIejo6NSQWZSTHR2yZIl4pQ0TUMikZC+K1WFk/TfbDaL7u5uvPjii2hra4PJZJJKMBMqHoPL5aobFVIsFuX4KdLC983OzgoFnSNEisUigsEgrrrqKsTjcQSDwbrxXkajUYTa+FkAJMlnQMMknMfFnjUqq/O6ktpPBfTHjjyGb//BtxG21cRqqqYqDl51ENa8FUNPDqFSqeDs2bNYunSpfD/BBvZmk9LX2tqKofQQ/vHgP6K13IpcW04S03K1jEeXPIpquIqrs1fDAotQtoDF+aBMZlk9GL1hFCPrR6Cba6+LNcfwwG0P4B3ffAccMYcAOel0Gm64cdWuq2DwGHByy0mYp83oeV8PXK0ulFCSVoSmpia59vwdryt77tLpNPJaHne99y7Em+IAgLv/6G5c/5HrYdSNAgDxPpKNEY/HZb2QdVAqlWoBX9wM8y1mILT4bDmPOrEwvdBQKG9Ywxr2n8/OAHgNav3s16GWgGNRyO03rdKxEkhgWq10s4+ZezFjAjLWCG7OzMzA5XJJPy8Tw+npaXR0dCAUCgngXC6XMT4+DqCmVp5KpTA7OyuUfbfbja6urjolbgB101fIZuJxsFppMpkwOzsLn88n40oJ0PP4Y7GY9A6XSiU0Nzejs7MTNputTkgWgIjHTk1NCeW7tbUVwWBQjoXXjNeQwH25XK4DTNReaF4HthpEIhGk02nxgxR/I5WaDC/SqxmLEUDh5BRv0ot1n1qHk3tPooIKzNNmXPqlS3Gi5wTes+c9aM+0w2BavNe6rqMn14M/evCP8InrP4GUN4VlTy7Dlge2wOGuJcfJZBK5XA4Oh0OAedLiqVBfqVSkH54gjdVqlRa/eDyOYrEo518ulzE/XxuP53K55DoSgMlkMnUiv2q8R2BF0zQsLCxIxVr9bLZmkMnm9XqlrYHgBlvR2Aff8PcN+02skXD/DzXpRU0kMDU1hYceegiapmFgYABbt27Ftm3bMDQ0JHOvAYg4CwDpa+GoC3XWNQCZYc3eLVaaZ2Zm0NnZCafTiWXLlomSJgDp3SoUCvD5fAgGgzJWpVgswu12Y/ny5TJ/+/Dhw+jr64PX661TdtV1HV1dXTh79qxUnDOZDFpaWuB0OqX/mk6V/WPlcrmuDxuA9KobDAZROvX7/XUVf6K+7NMiUp5IJASUMJvNCAaDmJubq0Oc2b+m9psRLWcVu1AoYHZ2Fn19fSgUanOjM5kMQqGQXOsOSwc+duZj+MTyTyBmjUGraOjd24vBJwZhtVlFTXVmZgahUKhO5IVOlIqvLS0tMJlMaCu3wWa3Sa8VjMCull34u76/A/qAL1a/iK889xWsqKyoGylDx0xBGq/Xi3fm34nYXAz3t9+PqqEKU9SE1f+wGqacCSW9JNeMyu92ox1v3/N2/MD+Azj+zIEXnnoBUy1TGBoaEnoaK+6sSqj95KSbm0wm7L5lN+LBuKz9jCeDPe/dg5d/9uV1ird2u10UhUlrCwaDEiwajUY0NTXh1KtP1WaOK3bg1gPYvH9zg1LesIY17D+nzQNYC+DfQeNJ7RX+t1ixWMTY2BjGxsbkd2Rbqaw4/p7ilRaLRSrFkUhEWqTYw84ecCZS7Hu32Wwyh5oJIJNvqlmzKk+aMkFc+habzSaznZubmxEIBOoKBwAkCYzH44jH4wK2u91ueDweoauz51xiFrOOKeuUVNTpu0iBZmWbrXGTk5MSezCp5nVg8YOtAtSx4bESHCFTLpPJoDRdQiVXu5dWqxVdji7cePeNMJvM0E26ABO8F0ajEYWxAlp3tqL7G914w543AD5IUYYUbQIpapGFa4fxI2OrZDIpmkFkrJlMJoyPj4sqfFtbm8RuVHwHIPEH1yPvCeMa3jtee95Tqp4TpOH7c7kcEomEtEZQgJexEYtAe/bs+Tet/4b9z7RGwt0wMV3XcebMGZw5cwZ33HEH2tvbsXr1aqxfvx6rVq3CkiVL0NXVJZue2qvNfituqKRPMwEmuszk2OFwoLm5GcDiuDKTyYRYLIZwOIxQKCToLlDbAEllM5vNOHjwIK644grMzMxI1Z3iGqlUCslkEtFoFJqmiUhbsViUmcukqZOOzGpyIpGoE4Ohk8jn8zh9+jT6+vqg6zrS6TT8fj+y2Sx8Pl/dtVDnQBKgIJVKFThhRZ30JjqRY8eOwel0orm5WXrqZ2dnsWTJEnEYFFizWCxwOBxYsWIFsuNZvH7i9fjpVT/FpXOXov9f+jEbnkVHRwfMZjMCgYDQqUjlczgcMsfcYDCgs7MT+XweU1NTaGtrk7VhMpnwQMsD+Ouev5ZewJKxhI9u+ig+fvrjWFVeVSegQzCDx5pJZ/Duw++GVtbwROgJbP6XzSg+U8SIbwR+vx+dnZ3IZrNCH+O/X/3TV2OkZQTzXfOYmZnB4cOHYTab0dTUVMe4YI+b2j9H8OMVP3gFDCUDTmw6AQBYenwptnxpiwQbZGbwGQAgbAyj0YiFhQVZB7lcDsvuXgaTxYSjrz8K3aSja6YLwT8PImhSZrI1rGENa9h/NvtPLKisqj1f/G8WCABgYWHhJe+dnZ3FiRMn6n6nVrappcJCAYFaMulIh2efOF8DLLK22NPNcWBksP2q400mk8hkMjAYDDKH2uv1yvcy6eT77++/Hw9seABX5K5Ay2wLwuFwnUBqOp2WCjGBAgITaqGDx0hf6PV6EQgE6nR62BLGuEwdXUdRVYfDAQ2aAABqXMfPyeVyMOVMWP+l9ZjzzYkGQKVSgclkEoZZNBqFy+USID4YDEq7mdFoxLPPPguz2SwiaNVqVdoJrVYrgsEg/H6/VMK9Xq/cL2BxMgkr1Zx2Q+YgALnH/Bsp9QRIyMjgeapTY1RNF/aNk2HYUChv2G9ijYS7Yf+qTU9PY3p6Grt27YLH45Fk+5JLLsGGDRuwZs0aLF++XBIfOhMilOosQzoZq9WKZDIpY1JYCaZQmMFgkPnSJpNJhDROnTqFSy65BM3NzViyZAn2798vFexsNove3t66CmtraytKpRISiQRWrFiBWCyGo0ePorW1VcTT2JNNx6PrOoLBoDhKAKJ6Xi6X0dvbC7fbXadQzv5xAHXvoTPkRp7JZAQxVWdNMpFjYKBpmhw7nYjf75exFgBkRjSdC99bLBaxLb0Nfaf7sCa6BpntGTz99NOi1m6329HT04OZmRmMj4+LIwYgjp3o+MGDB+FyubBx40ahVtmMtpeskQXrAj438Dl8sPpBrI2trav8J5NJeDweQcUB4N1n340tiS0IGUM4tv4Y7r//frS3t8Pj8QjqzYoDRebWrl0Ll8uFPXv2YHZ2Fnv37sXq1avR29srx0x6GFALOmw2m1zrUqmEHf+yA6aiCWlLGlfefSUcdgcSxUQdtZ7Xtlqt4vLLL0cikahDzqPRqKDblzx4Ca65/Br82PFjvO2Zt+HRsUdRCBb+ox7FhjWsYQ1r2G9g6lQJzir/dYwJGIFxJlaqONuePXtw/PhxAItjVRnT2Gw2TE1NYX5+Hvl8XsTjLta8AWoxwM/X/hx3r7kbVUMVz//+89jy1S2wxC3CQCNlWqWf53K5uoouxWBZuGCPt9raxsSR1HVWw6eWT2Hy5CQwUTv/zPoMUhtT0HKa6OSo02yYzLNKnE6nRY+HCTm1a1hoiEQict4ulwvd3d0iCEcQgxVnfr6MTLvQ1814jerynEhCH55MJqXSTy0fMgzZa53L5STZZqWbsQnBERVQYJGHrY1sd+RknYY17DexRsLdsP+r6bou/d/Hjh3Dk08+CbfbDZfLhUsuuUT6v5cvXy6oInt01BFNRFw5g9pgMKC9vV02W6KGHPXBzVPTNHi9Xuzbtw87duyQxDEcDqOrqwvt7e2LtGcsCpJkMhlMTU1hYGAAJpMJK1aswLFjx9DZ2QmTyYRoNFrXy2Q2m0UMZmZmRmjmpH5TfIVoqa7r0kNNUIG0aLvdLgrtTNrz+Tx8Pp8ocLL3i8kuf+aMavYvkdJPh8G+bwYU5XJZZloGAgEsSS1B1ViFq8WF7du3495778XZs2exYsUKEaijmAuPjaqhw8PDWLNmjYzzWrFiBbxeL3RdxzVz16CcK+OTA5+Erl1AdnWgN9mLvkyf9KHzXjudTrmuvA5WqxWXxS9DNBjFunXr0NHRgXvuuQcnT54UER2T2QRzeZEex1nsW7duxZ49exAOh3HkyBGYzWb0Lu3Fve+6F9d/83qYUBs3wuCK4nAAYMwasf3+7ShUC7CkLKigIr1eBHXC4bBUIY4ePYqWlhap0pOFwLVqNptx3eh1aA+3I5QP4QnzE9In1rCGNaxhDfuvaUzG1FFfF9uxY8de8jsVhFcr2AaDAU8++ST2798viSTN8HED5lfNo2qoJXkLvQvY/We7seLNK2DOm4X6TfaY1WoVCrmqws3qstFoRDweRyqVkr5oVnlZeeZ7i8UipkJTuPPKO5HenEbP23uQz+YR/mIY97nuQ8+9PViaWSrHy5YtFlMWYgtYuGMBvb/ohcvmkmQagCikMwbimC8AkpgzJshms6KhQj/LpDmXy2F4eBj5fF6YcBRZm5+fRzKZhM1mE/o5BVgJLAAQhkA+nxeleNL06d/dbjdsNpvEkCaTCX19fXA4HDIRZWFhQVrVSINvWMN+E2sk3A37ja1YLCISiSASiWB8fBz3338/TCYTenp6sGXLFlx99dVYvnw5AoEAAoGA9E4R/Z2fn0cwGBRk0+PxiNIjRy7E43ERTfH5fJiamsLc3JzQy7q6ujA/P4+zZ89iy5YtUllnP3Y2m0UoFMLy5cslUfL5fDJ+guPM6AS4CauVToqjqKM9iLBenKBRMZPOjNVo0svY0zQ/P79YMb5AuSdS6/f7pRpMVFpVIXW73fB6vTJWhWg31UjL5bJUdika0tnZibe+9a34+c9/jrNnz6Knpwderxc9PT04f/48RkZGsGbNGqGWEyVuaWnBVVddJX30JpMJ5WIZNyRvgDap4buh72LGMoNNiU349MinUcgVYHAsCsewOgxAUGPStThLlAyAnTt34tlnn8XU1BQ6OjuQ35jHszufxU333QRLziIOvrW1Fddccw327t2LiYkJ7Dq4C9U/q6K0uoRf/Pkv8OpvvBrxeBxerxfhcFjuMcVkzGkz9KyOkqEkiTwdLcVVeG8TiYRQ1KiUT0YGf1fKluBL+pAr5+oobg1rWMMa1rD/Wfav9bSzbYxMuDr7IwBtAF4FwAAgDGRfl8Wh5w/9H79LZcQxiVbnT7PX22KxYGFhoa5qTCp9uiuNj1/7cVSMFcABnLv3XO3DzUAKKXzu5s/hoz/9KHqyPXVaL7quI2vL4vlPPo94cxw/bPshbvzOjei0dkp8Fo/Hkc1m0dfXh/b29rpqPEd9UbyMxQkqkJNtyELAkiVLkM1mkcvlMDMzA7fbjbm5OYTDYWmpczgc8Hg8UtnnyFnSz30+nwiskpVIIV+bzSaJtxrjLSwsCLPAbrdjyZIlooRuMBjw+OOP/+p72rCG/SvWSLgb9v9k3ICLxSKGh4cxPDyMH/zgB2hpacGaNWuwceNGbNiwAV1dXejs7JQKLulJTHqoksqZiM888wy2bt0q1On29nY89NBDyGazokba3d2NEydOIBaLSWJosVikb7tUKmHXrl246aabYLfbZWai0WiUHnHOemTFmO8Haigtq+BqpZbnzUo0q/qkMLtcLlEaZVVX7QsnpYl0Jva5J5NJobHxdwQLCCjoui4ostrXRYG2EfcIgpUgmvPN8lnBYBCDg4M4fvy4JPw+nw9dXV3wer2Yn5+viYdoObS+slVmUA4PD8Pv90vfUrVahc1qwyvnX4lXzr8SX+74Mn7/3O/DYrKgaq5ibGwMVqsVbW1tMsasXC4jFosJrS6fz0tPGK/V4OAgTCYTnnvuOZxbdQ6H3nMIMAIPVx/G9buuhzlprmMI7Ny5E/sn9uPx1z4O/aoaQDLXNYcH3/wgdty1A560R9RYSScj4m00GgW1pvI6BfnC4bAALVarVZw3EW0yIEgTJJ1tfHwcDoejgXg3rGENa1jDfn2rAHgtgB8D2AzgPQD2Avr/pdme9GvVLmZY5XI53HnnnXW/o4ia0+nE5F9MomJQAIL60eCoGCr4h/w/4K373iq+z+12w9hhxH1b78OxllqVP9wbxqO3PIobH7oRvoRPesDj8TiGh4dlfFylUkFbW5vMPG9tbYXb7capU6fk80njpv+uVquiWq6K6bW0tKC9vV1iLsYnZAZSS4dAP8XlOFpsYaE2UcTpdEpcR1V6dfwtE2rGMgBEgK3h7xv2m1oj4W7Yv7vpuo7Z2VnMzs5i165d8Pl8WLJkCbq7u7Fy5UrkcjlcccUVQpVqbm5GoVBAIpEQoatsNovh4WE0NzeLGJrT6UQmk4Hb7cbk5CT8fj+ampowOTkJr9cLp9MJo9EoVeHm5mYRL2Hlu6OjA9FoFGNjY4K8svput9uRSCSkt5kJNalh7C8muqxSklmRZzKtqnCSNsWkk4kwABHtIrrK97GHjIk6HQodEYA6SrqmaThpPIk7V9+JpmITPjH8CVg1q/x9xYoVMBqN2LdvH0ZGRtDd3Y2WlhbY7XZYLBaEF8I48ScnMLdmDsGRIDbGNwrtzO12C7ASj8cFXPhfZ/+X0NX8fj/OnDmD6elpBINBUWZlnxbPmUkvR4ZNTU2hWCxi6dKlGNs+ht2bdgO1S4MX1ryAiqWCG39+I/RK7R6lUqlaP/2yXoyuGsUoFvuoSsYSSijJvVLBCjpRgiMcWUJnS0VV6g5wligTdavVWjfnFFhURo3FYjCbzY15nA1rWMMa1rDfzHQA7wRwOYAH/2O/ij3M4XAYeC+ABIAPXfjjp1ETRP2rCz9/Dkj/VRrfrHxTROccDgf0Th2zoVnghsXPPT9zHvc9ch+8817xqWyt8/l8Qnnn79xut+ilsPWQRQbOUGerHavKTLbpk1UwncKp7F8nlT6Xy0mfeCwWg9FoFBFdFlsYTzmdTqTTaSm8sHWMCvdkzOm6jhMnTjQmkjTsN7ZGwt2w/3CLx+N48cUX8eKLL+Lhhx+GzWbDfffdh1AohKVLl+Laa6/FsmXLpKdH13UR0SoUCjJjmwqjrERaLBY0NzdjdHQUBw8exMqVK+uUI00mE+bn5zExMQGLxYJQKCQV8nPnziEajQrqSXE30ryZvDPJpKKlOt+TxwksjjTxeDyickkK18TEBDwej6huMsFOpVLweDzSm6WqvdMBse+KySKdAQEIUte1Zg1fXftVzPnmMIxh/IX1L/C1E18DAOlp7u/vRzKZxMTEhIwV8/l88Hq9eORNj+DUxlOAAfjMwGfw2bOfRSAQwOzsLJYuXQqLxYJMJiMJuMPhEHE8oJb8u1wuPPPMMzAajVi1ahW+tOpLuPXwrXK+AETJ3mw2S5L6k5/8BNdffz3W+9fjXv1e5PV8zfHrQM/JHpRyJdgcNiQSCWEFNBea8fKfvhz3vOkezPbPAscBvBPINGUQ6ArItedxmUwmSYiJdpO5wDVH5XYq6nNciNqTR1EafgYpej09PXjsscf+4x+mhjWsYQ1r2H8vS+E/PNl+iZUB/O8L/84A+LsL/64AsAH4ZO01Omqj0VgUwQyAd6NWlb8MwFEAtwFnx87WfTxbxjhGjFNnVL0bTdNkfBqTb4rF0bdy/BrHdrJSTqaZ1+uF3++X8bHpdBput1vG1RIs4CQWNf4AIDEVdXQA1CX7+XxeKuIUuP1NRPga1jBaI+Fu2G/V1I17dHQUBw4cwM9+9jN0dXVhYGAAq1atwtDQkIiDFAoFxGIxGRnBDdXn8yEejwuV/MSJE3C73ejt7YXH46kTQDMYDIhEImhqakIwGBQRtgMHDmDnzp2Ym5sTYQw1OWTyGwwGJdGj+Acp8KlUCjabTQS2stmsJNtU1by4ksoeJaPRKMk1+8krlYrQ0VnhVpFdAHX0p3w+j1w+h7/Y9heY883JdX7R8yI+2v1RfOjoh6TKa7VasXr1aphMJpw6dUqqvbu37cbZjWdrPWQA5qxz+PP+P8fXpr+G4kJRxnwBEKfFviYqvJfLZQQCAbhcLvzLff+C+Lo4TnacxIHAAXz5uS9Dj9co8y6XS5RjOQLk2LFjOHnyJG655Rb8Tepv8KFXfghJYxLBTwdRPFSE9dKaojwBFzrF5lIz3vHP78Dtb7odxtcZsTCxgOPNx2G322VeJu+V2+0WNdLm5mZMT09LP3axWEQ2m0V7eztyuZyg2pqmyb3g91LhnjNO6biprtqwhjWsYQ1r2H8JywD4ywv/Ll/4/6cu+vlX2RSAlwF4BMC1AH4Fu5otcaRi/yaWSCSkOECjUC0AAcGZmBsMBtG4IUuSlHGXy4WWlhY4HA4RWaMortlslgp7KpUS6jor6A6HA52dnQgEAvL7mZkZnDx58jc+p4Y1rJFwN+x3ZqwaVioVnD17FmfPnsWDDz6IQCCA/v5+DAwMoLe3FzMzM3jd616HQCAgNGoqc2cyGTQ1NaGpqUkQT1YtmdCuXr0ad9xxh/Qus0d5ZGRERDnUnmnSk6jgzX5fle7NvyeTSelDp7jX2NgYfD6fjLOg8AbpTolEom6+JcU+eNykn/N7qLapKr7b7Xbp+a5Wq/jIIx/B51/+eQz7hwEAl05fivcfej+MVqPMxORYsmw2i4GBARw/fhw+nw8ve+5lKLlK2L1mN3SDjr58H/5m5G/QZGnCoblDGBgYgMvlkhniVIDncTCpNZvNWLp2KXZfuxsnr6o5pHnHPP5i/V/g/Qfej/5qv5xPLpfD6dOn0dbWho6ODpw9exZPP/00li5div/v4f8Pu/y70DrXiucnnscT2SewZcsWoZURhMhms3BqTtx2+22IvSyGJ554AufPn8f999+PHTt2YOnSpUJjU+n3hw8fhs/nQzabletJNJwKtXa7HYFAQJBvm82GVCol78nn8zgcOAzveS8qlQqmpqYEqGlYwxrWsIY17L+EXZwP/7r5cR7Ajn/nY1GMbWy/yn6Vry0UCr9yRvv/yVSBWYLmLFBEIhGMjo5KOxwnxlDArWEN+02tkXA37D+dRaNR7Nu3D/v27RPBjbGxMZTLZcTjcdxyyy3QNK1udiPHQVHcjGMncrkcbDYblixZgsOHD2P9+vUIBoOwWq1YunQpzp8/D5vNJtTqTCYjSCorpKQXRaNR6eFlPzVpxQCkr9tkMiGdTsNiscBsNiMWi0nVnXPI+dmiAH6h0srKMX+nzgnP5/Oi6lkul4VurmkagoUgPnjog/j6+q+jKd2Et+1/G3LVnDgRosBmsxnNzc0IhUKwWq14/vnnYbFYcM1D1yA5m8Ts1ln81eRfYXV1NVKWFNra2hCJRGCz2WTWJpNXtWovPfJ9HbB32+vuZ9FaRMlfgjFuRCqVgt1e+/v69ethNpvxmte8Bo8//jhGR0fxxBNP4GrD1Xh9+vUoby0jGAzi0KFDOH78OHp7exEMBoXqRbaE2VwbIUaRvePHj+PFF1/EwsICBgcH4XQ6AUAq3Bz3RsfK0S2ZTEZ665nUBwIBuUdkWVSrVZy86iRuX3Y73hh9I1qHWxsV7oY1rGENa1jD/gsZ9XFUY4GDxhiTU1wa1rB/qzUS7ob9pzaKfOzatQtGoxHHjh3DI488guXLl2PFihUYHBxEIBCQ/h4qd1utVjQ1NSEcDiORSGBwcBAHDx7Eo48+ivXr18Pr9cLj8cDhcCAWi6FYLKKnp0fGdDHx8vl8SKfTQl0i7VvXdRFII7W6UCggl8vV0a5Zkc1kMpKksgpPYS/OJVeTcI6e4msAyPuYnDudTjz77LPo6empzajMd+IDJz4Ae8GOarkKzahJ1Z10fLPZjEsu+f/bu/fgvO7ywOPf817OOe9577q9sm6WdbElXyTHtzhOQtIEQwPuLEnpMFxCdml3YAgt02nLdGbbsjO7lD8KNLPM0tIFGgp0uiyBtGFJHMehmDiJiW+SE8d2EssyjiRbfqX3et73vNf9Q/n9IhcoTre2cfJ8ZjyjiV5L55z3jec857mN6eElruvy4osvsri4yNbzWxmxRhgLj1EPLpVtq3VXHR0dlxyzeuCghpGpByCtvlbu2n0XF7IXOLftHMFckE1/sYl4f5zaipouBVPTQwG2bdtGV1cXDz/8MPl8nsXFRTo7O3Ech02bNuHz+di3bx/pdJpNmzZh27a+Hp7ncfvtt7N//346OjoYHR0llUpx+PBhTpw4QblcZvyGcY7cf4Q7v3UnoVBIB+3qoYrK3qvp5aocbfmxql3tbW1tTLxtgv037adiV/j29m9z5/k7KTxbkAy3EEIIIYT4GRJwi+uG2o148eJFJicndf9sV1cXY2NjbN269ZKhZaovxzAMLly4wC233MITTzzBiy++yM6dO+ns7MQwDA4ePMji4iJtbW16EJkqdc/lcjoAV5Orq9Uqtm1TKpX0QDPVu+26LsVikWq1yooVK/QKMhVYqzJ3NfFalamrdWUqu+15HpFIhHK5fEnw7XmeDtAjkQgbNmzQ/dWu65KsJ5cyz05VB8Nq6qca+AVLDzLC4TDj4+PUajWOHTtGJVXh5Q0vs31mO43qUjl9Mpnk7NmzlwwyUcNF1BRv1YPebDaXHnQ02tj+pe3s9e8l/Hth5hvzHN50mDvuuAPDMC4ZXqLOf9WqVdx+++2cOHECx3F0BrrZbDI4OEgikeBb3/oWBw8epHdlL9wFcyvn2PSDTezbt09fo0AgQF9fHz6fj+eee46XZ1/mlQdfobK5QsPf4B3/5x06UFcDXQBdrq72lapAW/Xaq7L0Iz1HeHzH41SspafgBbvA47sep/U7rZLhFkIIIYQQP0MCbnFdUllHz/M4deoUp06d4qGHHqKtrY2xsTHWrFnDypUrSafTzMzMMDo6it/vZ+vWrTo4VsMyUqmU3mmp+otV8KWCv+UlxqVSSff6qOndan3XihUrdB+RCkJVCbb6nWow1/LeIfWgQGVYVRm4GvyhJmarPnCVVVcTQFXPuiqTVsesfo46plqtpgfAua5LOBxmbGyMw4HDfO+T36NhNog0I3x8+uNYuaXhYLFYTPfHq0DZNE1d4q4efLiui+d5tLa2EvPHaNzZwGqzCPWFePXVVzl58iTd3d06gFX93CpzHg6H9QqR5dNKQ6EQPT09fPjDH2bPnj0cSh3i5Q8tTUR1/A7D/ziMZVgUi0U9ZXxwcJC0mWb2vlnYuvSZObXtFFbV4uZHbsbxOXied8maM8MwdCZblZmrz4F6X3pne+k73cfJ0ZP6szhwbIDcqZxkuIUQQgghxM+QgFu8aTSbTebn59m7dy979+4lHA4Tj8cpl8ts3ryZdevWkUgkmJqa0sEroMuMLWspaFOZ0lKpRLlc1oGtCpxVcFwoFHRQC6/vxXYcRwdpaoVEMBgkFouRy+UuyTyrEnHV/728p0iVaqsecVXmrnrHq9Wq3mftuu4lqzUA/bUKkNXPUvusn3/+edatW8fLAy/zo7f/iIa5FDB+t/W7VKtV/uinf0StVNMBst/v/5nebXUd1bn+yx2b8XicSCRCLBZjcXGRbDbL9u3bl8rEW/exObuZuBVncXER13VpaWkhHo/rBwbqd9brdVKpFMEPB5m6e2ppbRjw6M2PknEz3HXgLgr5gi63j0QirLplFaduOMU88/ozMhOeoRgsEmvGdN+1elihrq86P7U/XH3fMAwyoQwXnUsHs5yPnsdtulfiIy2EEEIIIa5zEnCLN61isUixWOShhx7iBz/4AR0dHbS2thIOh7nppptYvXo1q1ev1tPCVUZ7+RC0iYkJBgYG6Ojo0NnhcDis92ir8nDXdfH5lvZqVSoVHMehVCrhOEuZVFU6vTybrUqWbdv+mbVjauibCpRVQK4Geqg94Oq/qwy9ygyrjPjyyd7qOGFprUZvby8+n49UM4WFdcm166p04cdPoi3BxYsXWVxcpKOjA8dxOHbsGH6/n76+Pj2xW+1NN02TWCyms/uALiMPBoPMz8/z8ssvM71pmr8f/3sGs4P8+bE/X/qdXV04jnNJqbcaZGbbNpZlcWvbrezx76G0bA9J62KrflChHjIYhkHylSTv+va7ePi+h8m2ZPE/66fxuw1e5EVCm5ZK9OPxOIFAgEwmQ71eJxqN6iFp6pjVoDTDMGg718Ztf3sbT3zsCXKdOXpmetjwNxvYP73/Sn2MhRBCCCHEdUwCbvGWUCqVmJ6eZnp6Wvdtm6bJihUr2LJlC93d3dx0002sXLlSD0Pz+/24rqt3fathWrCUZfb7/bqUWvX+ZrNZPbQtmUwyPT2tg0hVCq4CYMuyiMfjzM/P64yu6rdWAbfKhKtAXe34Vmu4YCn4VuuvYCnTH4/HKZVKuudcTTP3PE9PQlfBZtfFLr6Y+yL3bb6PQqDAfefu456pe6g0KzRCDZ3lDYVCFAoFXf69fIWayqqrYWPL943H43Fs29YPNJ4LP8cjtzxC2Spz0DrIH2/8Y/70uT/FcRx9bqosHsC2bcLhMLZtszazlk986xM88KEHaNCg7Y/bKBwpMDs8Sz6fp1Qq6d8XDoexZize+5fv5aHfeYi1f76WU2dOcSx/jHw+z7p16/Q1VOcSi8V0RYBpmgB6TZs6nv58P5/6/qf4q3v+io/+40e50LzAs/5nr96HWQghhBBCXDck4BZvOWrCeKlUIpvNcuLECQBaWlrYunUrGzduZHh4mI6ODjZt2kSz2aRarerAs1QqYVkWtm2TzWbJ5/NEIhF8Pp8eqKbKy+Px+CXl1irAU1nYSqWig2MV5Le0tHDhwgVd4qxKttVqL5UJV6XP6px8Ph+e5+lyc7UyKx6P68x2MBjUve9qbVmlUiFUCvH1Q1/nO33f4f75+6mH6uTzeZ15V6sxfD4fIyMjwFKWPJPJ6AnoavJ5s9kkFovpQW8q412r1SgHy+z+D7spW6+t4jDghcQL/NOqf+Le2Xv1tHZVGm7bNtFoVFcEBAIBbkneQurpFEf9RymdKXHolUMYDYNwOExHR4feUa6qCsy0yW/+99/kpexLrFu3jjNnznDu3DkAnZEPh8Pkcjm9Fk5VCKg+bvXAQT3oaK218uCRB3k2/SzlcllXNwghhBBCCLGcBNxCvGZhYYHdu3eze/du4vE4/f39DA8PMzQ0RFdXF93d3fT29upy50QiodeBqQFo58+fp6urSw8+a2lp0YO4VJY8nU4TiUTI5/O6ZHl5D3Eul9M/L5/P68wwoHu+1ZRzQAfdKkBUU8/V5G5APyxYPoFb9Uc7jrM0xKzq8PEzH8ewlyaRh0Ihvcd8amqKtrY23WeuppWrTLf6WYZh6LJ2FdAnEgk9dC6YD/LuL7+bH973Q+YG5/A1fLzr0Lt4z/n3YMQMfQ38fj+O4+hMvzpPy7IIBoMMLw7Tkm4he3OWsBPmwIEDOI5DLBbTgbl6IKEGya1cuZKWlhZqtRqlUonZ2VnOnz/Pli1bGB4evmRSuRoGt/yaBYNBvV+92WxSLpWxbZtCofAzuzuFEEIIIYQACbiF+Lmy2SwTExNMTExg2zatra2kUilGRkYYHBxkcHCQVatW6X5u13UJBAKcPXuWvr4+6vU64XBYB28qg62yxGowms/nIxqN6sy5WlmlyrFVttWyLL3/W/WcA7r33DRNPaW8UqlgGIbOjKufC+he5GazqcvYLcvSa8L8fr8eHKf6sFUZvCqhVz3mapicqgBQmW7Lsujv7yeRSOh1ZKo3OhKJEPbC3Pm/72TvvXvp3tvNmlNrSA+n9bA527ZpNBq6f7tcLuueeFU6r752HIeRkRHdb3/y5MmlvvRUikAgoPuv1Wtd12VkZIRwOMzp06c5f/48zz//PJZlMTAwwPS6aZrzTXpne7Ftm3K5jGEYWJaly+VbWlpIJBIYhqEfOKj3QwghhBBCiOUk4BbilyiXy7z66qu8+uqrTExMEAqFsG2brq4uNm7cyIYNG3RJ8ubNm3W/cj6fxzRNCoUCgO51Vruda7WaLg9X2VuVGVbl5irQCwaDlEolvQJMlZerwLzRaOihYUo4HNbBved5uhxaBY3pdFoHiz6fj9P103yn/zvc/+L9GOWlQW2qhzwSiXDmzBnGxsZYXFwkEonooB3Q5+N5Ho7jUK6WqVSXsr6e55FMJimXy1iWhed5pNIpdv2vXRhpA8/yOH36tN6prsrjA4EAtm3rhwrqQcPyiemq77utrY1kMsnu3buZmJhYagno7ODI3UdYsW8FXekuveJNPQjo7Ozk6NGjnD17ln379jHVOcXUB6agCh/75scov1LW1QSNRoNKpaJ79vP5/NLU9Ney6BJwCyGEEEKIn0cCbiHegHq9TqFQoFAocPHiRY4dO6YznYODg2zfvp3Vq1fr6eWO4xCNRvXk81wuh23buke4WCziui7JZFJP2lZBdjgc1hOzVdZblWiXSktTun0+H//8z//Mxo0bKZVKhEIhQqHQUtD7WoCtyp1VwOn3+/Xws0qlQqFQwO1y+Z0tv0PZVyZYC3LvqXtp9bfq7HQsFuPChQvMz8/rSeoqWFeT0tXgtJniDN/b9T2mJ6aJTEb0+S8fBtdoNAjMB3RmPpPJMDU1pdenOY6jg1nbtvXQt4WFBT20zXVdHMchHo9z/vx5+vv72bJlC4899hiZUgbrNyxevvNlfLf7eP/n3k9gNqDL7k3TpLW1lRtuuIG2tjaeKj3F8S8fh6WKdr740S/y4c9/mNbFVmq1GoVCQffGT05OUiwWGR4eJhQK6cF0QgghhBBC/EsScAvx/6HZbOps9tGjRzl69CiO49Df309/fz+Dg4N0dHTQ09ODaZpEIhGy2Sy5XI6uri58Pp8OXFWPtyrfVplxNTFbDUJT/dkqM93R0aGD2lAoRL1eJ5vN6oy6CthVNl0F/ypLfiJxggfWP0DZv1Tm/u2eb9P0mnzwpQ/S29Grh8A9/fTTbNy4UZeUq+npyzPzaS/N1/q/xg97fgh/DaXPl0g2k3qA2fJVaMAlu72z2SxHjhxhbGyMQCDAi7EXSS4kWRFcAXBJzzugg31Vcm7bNps3b8YX8PH1zq9T+49LWedGoMFD9z/Ezr/ZSdsLbXoavKpUGBgYYOaDM5zwndDvaz1Q5+jYUXY8tuOSqgHVa++6rn4vpH9bCCGEEEL8IhJwC/HvzHVdjh8/zvHjx7Esi/b2dtrb2+nu7mZgYEBP3lYZadW7rCZ8L18RpkrJ1aRsWMqS+3w+vfe6r69P92+rABbQ/dWA7mVevgZL7QmvG3UaNC45BzNi6iy5KqceGhrCdV1CodAlfeJ+v38piPfBA8MPsDu1e+mHGHDikyd45sfPsP3gdj18bPnObLVeTZ1ftVpd6pu/zebvNvwdndlOPn3i0zTKDV1Wrs5v+eo0lQmPRqOMrh3lzu13spvdr5+QAYFQAMdxdICshtkZhsFt37sNo2jw4rtfBMD+bzZ9E32UW8s6w65K+QOBAPF4HMMwcF1XAm4hhBBCCPELScAtxBXkeR7nzp3j3LlzTE5O4jgOpmmSTCZZs2YN27ZtY+PGjXradaPR0MPW/H4/0WiUQqGgg2MVaNfrddLpNPl8np6eHr3jW60GUwPPlk/dXp5hBnRWeN3iOv7s2T/jD2/7Qyr+CrumdvGh2Q8R8oV05t3n82HbNul0mra2NhzHoVgsAujd1W7B5e3+t7O7YzcsJdEJeAG6J7vx+/16EFs4HCafz1MoFPTxmaapz33WnuUrN3+FhfACZ5wz/In5J3z62U/rye2RSISFhQU9LV7tMg8EAhSLRWLhGO848g5qtRp7b94LDVj9u6vpifbgC/h0wK8qCQASoQQ3PnYjdeqce+4czQea7Df3s3HjRkZHR/VgvGQyqVeENZoN8vm8XrkmhBBCCCHEv2Som+9f+kLDuLwXCiEuiwr4WlpaWLduHUNDQwwNDdHb20skEtF93qqnW00zr9frl6y26urq0t9TmWL1teotrlQqrwfGrqsD/EgkoiemnzPP8egNj/KJ5z9BNBTVGWmVxa5Wqzz77LPs3LlTB/Gu65JIJPQqrvRCmn3JfXx25LM08g1u+93buHP8Tmzb1v3k/f39ZLNZpqenyWazegVZOBymYTT43H/6HBdbL+rr5G/4eeeZd/LR5z+KZVm69L5YLF6yV1z1U/t8PvL5PKVKie9u/S7FrxSZ/cEsLS0tjI6O6h541dPebDaJx+PUajXypTwDqwZ46NsPcfz4cRzH4YYbbmBoaIhoNIppmpw6dYq1O9fy1fd9lbv/x908+vVHmZqauiafoTe7ZrNpXOtjENcXuVcRQghxNV3OvYoE3EL8CgmHw3r399q1a2lpacFxHEKhEMFgUAenajK567pEIhHq9TqVSkX3VMNS73e9XtcBaDAY1APbVN+1+v9fDVBTe6djsZheyRWNRqnVakQiEZ566im2bdumV4WpcvflwfmRI0f4kvsl3Cdd1gfW87a3vU3//HK5zPj4OLVajXQ6zezsrP67qiw8F83xrd/8FtOpaWjCjS/cyO+/8Pv6Oniep0vubdtmcXERwzD0wwY1md0wDDKZDOVymT179vDCCy/Q0dHB8PAwfX19FAoFAoGAPu9oNIrneXR2dnL8+HFOnDjB9PQ0juOwevVqenp6GBgY4IR9gu/f9328Xo/oiSjWf7a4+NTFf+1tFf9GEnCLN0ruVYQQQlxNl3OvIiXlQvwKKRaLeviabdukUina2tro6elh5cqVDA0N0dXVhWEYegWXYRiX7Mdevi9brdZSe8ANw8C2bd3TvXw/tyo79zyPxcVFLMvCtm0dzBaLRer1OgsLC7S3t+vgVvUwq/LsarWK92UPo2HgDXm6lF1lp1Xpu3p9uVxmamqKer2O4zikainuffJevvG2b9Az18M797yTC+ELtLS0EA6HdcY9n89TqVSIRqP62FTmXT2QUA8pdu3ahWmaHD9+nGPHjmGaJrZt63Vmqkw/EAiQTqdJpVJEIhFCoRCvvPIKTz/9NGvXriU/lGfiYxN4bR4A+ZE8+c/k4SPAK1f/8yKEEEIIIX61ScAtxK+ocrnM9PQ009PTHD16lEgkQiwWI5VKMTg4yNq1axkaGtK9zT6fTwfH1WoVx3F0ULt8KrjruvrvqEFsao+3yp4Xi0VqtZruG1eTzVX/tCqvVllytbIsl8vRbDZ1wK76tqvVqi6hVxlt27ZZsWIF09PTrFixQk8OB2ifb+cDj32AqBslGAiSyWR49dVXSSQSetJ6IBAgHA5TLpf1g4VCoaD3eMPru88Btm/fTnt7O48//jjPPPMMW7Zs0Q8gTNPEsiyCwSDFYhHP82htbWVsbAyA559/ntOnTxOdjGLOmND22pvUBE4AkuAWQgghhBA/hwTcQlwH1KqvbDbLuXPnOHLkCIFAgGQyyfr169m8eTMbN27Ua8Esy6Jer+ss9PIMdjAY1K9RWXC17zudThOPx/H5fEQiET0YTQ0ri8fjXLhwgVWrVmFZll5dls/n9WA113VJp9MMDAywZs0aPWm9Xq/T09Ojd45nMhn9O5ZPDLdtm/n5ebpKXdTqNQgsrV87fPgwjUaDu+++m/n5ebyAR7PSpJxfWpOmMvyqnDwSiRAIBPQk887OTiKRCD09PXzzm9/k6NGjrFy5kpUrVxKOhikECzQXmrqfXO35Hh8fx3Ecjh8/zqsnXiX7chbGXntjHgV+D/CuycdCCCGEEEL8ipOAW4jrjCqbrtfrzM3NMTc3xxNPPKF7jdesWcPg4CDd3d0kk0kSiYSeDK6C7nq9TqlUolqt4vP5CIfDBINBTp06xU033UQoFKJcLuv+72QyST6fJxKJ6FJutd+7vb0dwzB0SXahUNC7u9VqMZXRjsViGIZBpVLRw+C6u7spFAqUy2VyuRywlIlXJe3z8/PUajXa29tJJpOk02lKTom/Xfe3tBfa+bUDv0bEWhr+ptaGGYahe7RV/7nP59MPH+644w7279/P5OQkmUyG1G+n+Ml7fsJdX7sLfgotLS2USiVdLj88PIzdarPv9n2U7im9/mYkgW7g9NX/HAghhBBCiF99EnAL8Sbhuq7u/zZNk+7ubnp6ehgcHKSvr4/Ozk5aWloIBoM0m01d8r08EN+2bZsuyY5GozpzrYLver1OJpNhbm6O7u5uarWaXtEVi8WYn59nenpa79b2+XyUSksBarFY1Ku8LMvCNE3S6bQu6a5Wq5imqbPr1WoVz/OIRCK6H7ter5PxMjx4w4PsXbEXgKyX5b2H3nvJjnFVWq9+1/L95sFgkB07dpBIJNi/fz/Tt0xz5L4jNMNNnvjAE2z7m22wgC6XN02TZrPJilUrSN2c4gxnXr/oLUAXEnALIYQQQoifSwJuId6EKpUKU1NTTE1N8cwzzxCLxUgkEnR0dLB+/XpGRkbo6+sDXs+Y+3w+HfCqLHEwGKRQKFCtVnX/tOM4NBoNqtWqzigHAgGdyVa939FolHQ6TSKR0APKEomELnVXe8YzmQzhcJhwOKy/F4vF9Fo01U996623YhgGX9z+RX7S/RN9rj9Y+wOqvir3TdynA23V3+267iXBvG3beiL68PAwZ24+w9GdR2mGlwYbzw3M8eOP/ZhbP3srXYEunbk3DINgLsht37kNwzSYWj9FMBuk9097Of2URNtCCCGEEOLnk4BbiDc5lYVeWFhgamqKw4cPEwwGicVirF+/nvXr1zM2NkYymdTl5uVyGdu29XRxn89HLpcjGAxiWZYOhk3T1JPNa7UalmXh9/t1b7da1VWr1QB0sKt6ypvNps6Eq2C40WhQLBaxLEtnqdXfr9frfODYB5hITeAFlhqnzbzJb730W7oMXpWq+/1+WltbqVQqev+4yt6rP++qvosz+TNMxCfAACow9uQYbc023Re+PFseuBDgjq/dwe5P7Ob+A/fzlcNfucrvphBCCCGEuJ5IwC3EW0iz2aRSqVCpVCgWi8zOzrJnzx4cx2F0dJTBwUHWrFlDZ2cn0WhU7+Q2DAPXdVlYWNBBscpGw9LO77Nnz7J69WqSyaSeDK4Gs/l8PpLJJIZh6DJtn89HtVrVU8dVybmarK4CcpU5V1nqXreXz+z/DJ/Z9BmyM1na3t9G/sN5kn1JnZU3TVP3hS+fjL58Mnu9XsdX9PHbD/82f/Frf8G5leeIfDbC3HfniK+Jk0ql9HC5Wq1GR0cHhmGQzWb51P/9FADpdPoavItCCCGEEOJ6IQG3EALXdTl06BCHDh3Csiy6urro6+ujq6uL3t5eBgYGdGl4vV7n/PnzlEolarWaLjOfnJykt7eXbCpL5G0RYqUYjuPoKeWRSEQH/GqYmQpoPc/D8zydofb7/bqPWw1YU+u/6vU6PXM93Pfj+3jsq48xf3aehx9+mF27djE0NKQDc/U7KpWKXlum+rvr9TqFQkGvN7vjf97BmTvOcP5758nmsrzyytJS7d7eXiqVCs1mE9d12bRpE/v379drx/L5/LV824QQQgghxK84CbiFEJfwPE/3f/v9fuLxOG1tbXR0dDA8PEwqldK91YlEgkqlQrlc5rbbbmPeN8/jH3yc3D056p+rE41GKRQKZDIZ6vU6pmlSLpepVCo6821ZFgDRaBTP8/SOcLVPXE01t22bYDCI53nU63U2zG3Av8rPRG6CyclJcrkcq1ev5p577tG95Kp0XPVyq8FvXV1dlMtlstkspVKJ4Z5hRk+P8tTQU5RKJWZnZzl58iSNRoP29nY9kf2ll17Ctm0SiQQzMzM6ky+EEEIIIcTPIwG3EOIXqtfruv/7pZde4uDBgwSDQaLRKOPj49x4442sW7duqew8ZPAHN/4BM84MAD/57E/Y+e2d1EpLQ9T2799PPB4nk8nQ1tZGNBrVATWA4zh6Ini1WsW2bQzDIBQKMT09jWmaOiteqVQwTZO1a9eSSCQol8tMTEwwMzPDunXr2LJlC47jUCqVKBaLuudclbDn83lisZj+eS0tLQQCAXbs2EGtViOdTvPVr35Vl803m01SqRSZTAbP82hra9NZcCGEEEIIIX4Ro9lsXt4LDePyXiiEeMswDINwOMzIyAgn/+tJ8nflwffaN5sweGaQ+755H5VKhX379tHV1cWGDRtYu3YtsVgM0zT1Oq9wOEyhUNA91sFgENd1KRaLep93vV4nm83iui7hcFgPRJufn+fJJ5/k6NGjNJtNPvnJT9LX16d7zYvFoh6AFgqFKJVKBINBGo0GgUAAy7KwLEuXmudyOXK5HHv37mV2dpbOzk42bNigX3vLLbdw4MABPv/5z1+jK//W0Gw2jWt9DOL6IvcqQgghrqbLuVfx/bIXCCHEL9JsNikUChw8eJD8b+Th71//nv9RPzs+u0MHzSpzPD4+TmtrK47jYNu2LhV3XVd/XS6XKRaLehJ6rVbT68vUhPVKpQJAtVqltbWVnTt3sn79egzD4Mtf/jL79u3TZeV+vx/TNDFNE4BQKESz2dR/isUi2WxWD3QLh8NEo1G2bdtGa2srCwsLnD59Gs/zMAyDTCbDT3/602txyYUQQgghxHVESsqFEP8+msDHgTyQAP4AHik9QjKZJBQKUSwW6e7upl6v6wnk6g8sla97nnfJHnAVKBeLRd33HQqFdLCterobjQbJZJKdO3dSLBY5fvw4e/bsIRQKsWPHDoLBIIFAQGe6q9UqPp+PcDhMqVTC7/dTrVb1BHafz0c0GmXVqlWEw2EOHDjA9PQ0hUKBoaEhXrj7BbJ/l71GF1oIIYQQQlwvpKRcCPHvKwYEgWUbs9Qua9M06e3tZcOGDYyPj7Nx40Y9wCwQCFAqlfSgtUAgoPdmu66LYRi6FFytNvP7/ZTLZd2jHQgEOH36NPv27WNycpJkMsmmTZvYtWuX3hfeaDRYWFggGo1iGAae5xGLxfRgNeCSr2u1GouLizzyyCO8cvoVjP9isPCxBQKHAni3etC4+pf4rUJKysUbJfcqQgghrqbLuVeRgFsIcdUZhqEzzOPj44yMjDA+Pk5fXx+JRIJms4lhGDQaDSzL0pPQi8UipmnSaDT0MDO/308+n8fv97O4uIhlWZw/f56nnnqKQ4cO0Wg0eO9738uOHTuIxWLk83kuXrxINBqlUqng8/l0GXuz2dQBfa1WIx6P62OdW5jjH1r+gUPvPwR+ljL6u4EPAIvX9nq+WUnALd4ouVcRQghxNUnALYS4boRCIfr7+xkdHWVkZITe3l6SySTRaJRgMIjf79d7tAOBAK7rUq/X8fv9LCws4DgOmUxGl6RfuHCBZ555hsOHD5PJZNi5cydvf/vbSSaT5HI5TNPUpem2bZPP52k2m8TjcVzXBdDl7pZlcbZ6lm+85xu81PbS6wd9Drgf+KerfLHeIiTgFm+U3KsIIYS4miTgFkJcl4LBIB0dHXR2dtLd3c3AwAD9/f2sWrWKSCSie61VwF0sFqnX60xPTxMKhQgEAgSDQS5evMj+/fs5cOAAhmFw88038+53v1tnyYvFIs1mUwfwlUpFl5oHAgE9MT0UClGv10m3pfnSDV/ipc6XIAPcC3z/Wl6pNzcJuMUbJfcqQgghriYJuIUQ1z21eiwcDtPe3s74+Dijo6OMjY3R1tbGwsIChmFQrVa5cOECtVqNhYUFUqkUAJVKhSNHjvDkk0/ieR5DQ0N86EMfwrZtKpUKnucRDAb17m/P8/A8D8dxdN95NBql0WhQq9U46zvLA3c+wMxvzMDT1/jivMlJwC3eKLlXEUIIcTVJwC2EeNNRa76i0Sijo6MMDQ2xfft2UqkUjUYDz/M4deoUgUCAzs5Oenp6mJmZ4emnn+aJJ54gk8lwxx13sHXrVtrb26lUKliWhW3bFAoFvX7MNE2dKV8+Db3RaHDkxBH++i//mlqtdq0vx5uaBNzijZJ7FSGEEFeTBNxCiLcE0zQZGhqiv7+fkZERkskkjUaD7u5ufD4ftm2zuLjIc889xw9/+ENc1+WGG27g13/910kmk1SrVer1OgCNRgPbtmk2m9RqNUKhEIZhUKlUqFarzMzMcPDgQXbv3q1LzsWVIQG3eKPkXkUIIcTVJAG3EOItx+fzkUqlaGtro6+vj76+PlKpFJ2dnfh8Pn784x+zf/9+XNdl48aN3HTTTbS0tOA4Ds1mU+/jNk2TWq2GbdvA0uA0wzBwHIcvfOEL/OhHP+Jy//0U/zYScIs3Su5VhBBCXE2Xc68SuBoHIoQQV0uj0WB2dpbZ2VleeOEFwuEwkUiERCLB4OAg3d3dDA8Pc/LkSSYnJ5mbm+MjH/kIwWCQWq2G53lEIhH8fj+NRoNKpYJpmno9mGVZpNNpCbaFEEIIIcQvJQG3EOJNq9FokM/nyefzzM7O6t5ulcFuNBq4rsuDDz7I+973PlauXInf78eyLL0LvFqtApDL5QgEAoTDYb1OTAghhBBCiH+NBNxCiLeMer2ue7VV0Oy6LseOHcO2bbZt20Z7ezuO42CaJo7j6AFqtm0TCoW4cOEC5XL5Wp6GEEIIIYS4TkjALYR4y6tWqzz99NM899xztLa2kkql6O7upre3l0QiQSqVor29HcuyOHPmjGS4hRBCCCHEZZGAWwghXlOtVpmbm2Nubo7JyUkikQjRaJRYLMbo6CgjIyPkcjk8z7vWhyqEEEIIIa4DMqVcCCEuQzAYxDRNfD4fxWJRVoJdBTKlXLxRcq8ihBDiapK1YEIIIa5bEnCLN0ruVYQQQlxNl3Ov4rsaByKEEEIIIYQQQrzVSMAthBBCCCGEEEJcARJwCyGEEEIIIYQQV8Bl93ALIYQQQgghhBDi8kmGWwghhBBCCCGEuAIk4BZCCCGEEEIIIa4ACbiFEEIIIYQQQogrQAJuIYQQQgghhBDiCpCAWwghhBBCCCGEuAIk4BZCCCGEEEIIIa4ACbiFEEIIIYQQQogrQAJuIYQQQgghhBDiCpCAWwghhBBCCCGEuAL+H8ZZQgkzysDAAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Draw the line points for training\n",
+ "ref_img_with_line_points = plot_junctions(ref_img, ref_line_points, junc_size=1)\n",
+ "target_img_with_line_points = plot_junctions(target_img, target_line_points, junc_size=1)\n",
+ "\n",
+ "# Plot the images\n",
+ "plot_images([ref_img_with_line_points, target_img_with_line_points], ['Ref', 'Target'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualize the exported ground truth on the merged dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Info] Initializing wireframe dataset...\n",
+ "\t Found filename cache wireframe_test_cache.pkl at /home/remi/Documents/test_SOLD2_data/datasets/wireframe\n",
+ "\t Load filename cache...\n",
+ "[Info] Successfully initialized dataset\n",
+ "\t Name: wireframe\n",
+ "\t Mode: test\n",
+ "\t Gt: wireframe_test_adaptation_iter0_epoch043_ce1_detect_0.25_inlier_0.75_local_max_v1.5_refine-v2.h5\n",
+ "\t Counts: 462\n",
+ "----------------------------------------\n",
+ "[Info] Initializing Holicity dataset...\n",
+ "\t Found filename cache holicity_test_cache.pkl at /home/remi/Documents/test_SOLD2_data/datasets/Holicity\n",
+ "\t Load filename cache...\n",
+ "[Info] Successfully initialized dataset\n",
+ "\t Name: Holicity\n",
+ "\t Mode: test\n",
+ "\t Gt: holicity_test_homograpy-export_512x512_v1.5_detect_0.25_inlier_0.9_local_max_refine-v2.h5\n",
+ "\t Counts: 520\n",
+ "----------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize the merge dataset\n",
+ "with open(\"../sold2/config/merge_dataset.yaml\", \"r\") as f:\n",
+ " config = yaml.safe_load(f)\n",
+ "\n",
+ "merge_dataset = MergeDataset(mode=\"test\", config=config)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Read in one datapoint\n",
+ "index = 0\n",
+ "data1 = merge_dataset[index]\n",
+ "\n",
+ "# Reference data\n",
+ "ref_img = data1['ref_image'].numpy().squeeze()\n",
+ "ref_junc = data1['ref_junctions'].numpy()\n",
+ "ref_line_map = data1['ref_line_map'].numpy()\n",
+ "ref_line_points = data1['ref_line_points'].numpy()\n",
+ "\n",
+ "# Target data\n",
+ "target_img = data1['target_image'].numpy().squeeze()\n",
+ "target_junc = data1['target_junctions'].numpy()\n",
+ "target_line_map = data1['target_line_map'].numpy()\n",
+ "target_line_points = data1['target_line_points'].numpy()\n",
+ "\n",
+ "# Draw the points and lines\n",
+ "ref_img_with_junc = plot_junctions(ref_img, ref_junc, junc_size=2)\n",
+ "ref_line_segments = plot_line_segments(ref_img, ref_junc, ref_line_map, junc_size=1)\n",
+ "target_img_with_junc = plot_junctions(target_img, target_junc, junc_size=2)\n",
+ "target_line_segments = plot_line_segments(target_img, target_junc, target_line_map, junc_size=1)\n",
+ "\n",
+ "# Plot the images\n",
+ "plot_images([ref_img_with_junc, ref_line_segments], ['Junctions', 'Line segments'])\n",
+ "plot_images([target_img_with_junc, target_line_segments], ['Warped junctions', 'Warped line segments'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Draw the line points for training\n",
+ "ref_img_with_line_points = plot_junctions(ref_img, ref_line_points, junc_size=1)\n",
+ "target_img_with_line_points = plot_junctions(target_img, target_line_points, junc_size=1)\n",
+ "\n",
+ "# Plot the images\n",
+ "plot_images([ref_img_with_line_points, target_img_with_line_points], ['Ref', 'Target'])"
+ ]
+ }
+ ],
+ "metadata": {
+ "file_extension": ".py",
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ },
+ "mimetype": "text/x-python",
+ "name": "python",
+ "npconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/SOLD2/requirements.txt b/third_party/SOLD2/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..421b52557bb98a7663f6bbf8ddca84b5000a0a0f
--- /dev/null
+++ b/third_party/SOLD2/requirements.txt
@@ -0,0 +1,20 @@
+pyyaml
+tqdm
+attrdict
+h5py
+numpy
+scipy
+matplotlib
+seaborn
+brewer2mpl
+torch
+torchvision
+tensorboard
+tensorboardX
+opencv-python==4.0.1.23
+opencv-contrib-python==4.0.1.23
+scikit-learn
+scikit-image
+kornia==0.3.0
+shapely
+jupyter
diff --git a/third_party/SOLD2/setup.py b/third_party/SOLD2/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6c9cdcb47bdd73758cbd2d5b125dcb91306705f
--- /dev/null
+++ b/third_party/SOLD2/setup.py
@@ -0,0 +1,4 @@
+from setuptools import setup
+
+
+setup(name="sold2", version="0.0", packages=["sold2"])
diff --git a/imcui/third_party/SOLD2/sold2/misc/__init__.py b/third_party/SOLD2/sold2/config/__init__.py
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/misc/__init__.py
rename to third_party/SOLD2/sold2/config/__init__.py
diff --git a/imcui/third_party/SOLD2/sold2/config/export_line_features.yaml b/third_party/SOLD2/sold2/config/export_line_features.yaml
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/config/export_line_features.yaml
rename to third_party/SOLD2/sold2/config/export_line_features.yaml
diff --git a/imcui/third_party/SOLD2/sold2/config/holicity_dataset.yaml b/third_party/SOLD2/sold2/config/holicity_dataset.yaml
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/config/holicity_dataset.yaml
rename to third_party/SOLD2/sold2/config/holicity_dataset.yaml
diff --git a/imcui/third_party/SOLD2/sold2/config/merge_dataset.yaml b/third_party/SOLD2/sold2/config/merge_dataset.yaml
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/config/merge_dataset.yaml
rename to third_party/SOLD2/sold2/config/merge_dataset.yaml
diff --git a/imcui/third_party/SOLD2/sold2/config/project_config.py b/third_party/SOLD2/sold2/config/project_config.py
similarity index 80%
rename from imcui/third_party/SOLD2/sold2/config/project_config.py
rename to third_party/SOLD2/sold2/config/project_config.py
index 42ed00d1c1900e71568d1b06ff4f9d19a295232d..6846b4451e038b1c517043ea6db08f3029b79852 100644
--- a/imcui/third_party/SOLD2/sold2/config/project_config.py
+++ b/third_party/SOLD2/sold2/config/project_config.py
@@ -5,26 +5,29 @@ import os
class Config(object):
- """ Datasets and experiments folders for the whole project. """
+ """Datasets and experiments folders for the whole project."""
+
#####################
## Dataset setting ##
#####################
- DATASET_ROOT = os.getenv("DATASET_ROOT", "./datasets/") # TODO: path to your datasets folder
+ DATASET_ROOT = os.getenv(
+ "DATASET_ROOT", "./datasets/"
+ ) # TODO: path to your datasets folder
if not os.path.exists(DATASET_ROOT):
os.makedirs(DATASET_ROOT)
-
+
# Synthetic shape dataset
synthetic_dataroot = os.path.join(DATASET_ROOT, "synthetic_shapes")
synthetic_cache_path = os.path.join(DATASET_ROOT, "synthetic_shapes")
if not os.path.exists(synthetic_dataroot):
os.makedirs(synthetic_dataroot)
-
+
# Exported predictions dataset
export_dataroot = os.path.join(DATASET_ROOT, "export_datasets")
export_cache_path = os.path.join(DATASET_ROOT, "export_datasets")
if not os.path.exists(export_dataroot):
os.makedirs(export_dataroot)
-
+
# Wireframe dataset
wireframe_dataroot = os.path.join(DATASET_ROOT, "wireframe")
wireframe_cache_path = os.path.join(DATASET_ROOT, "wireframe")
@@ -32,10 +35,12 @@ class Config(object):
# Holicity dataset
holicity_dataroot = os.path.join(DATASET_ROOT, "Holicity")
holicity_cache_path = os.path.join(DATASET_ROOT, "Holicity")
-
+
########################
## Experiment Setting ##
########################
- EXP_PATH = os.getenv("EXP_PATH", "./experiments/") # TODO: path to your experiments folder
+ EXP_PATH = os.getenv(
+ "EXP_PATH", "./experiments/"
+ ) # TODO: path to your experiments folder
if not os.path.exists(EXP_PATH):
os.makedirs(EXP_PATH)
diff --git a/imcui/third_party/SOLD2/sold2/config/synthetic_dataset.yaml b/third_party/SOLD2/sold2/config/synthetic_dataset.yaml
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/config/synthetic_dataset.yaml
rename to third_party/SOLD2/sold2/config/synthetic_dataset.yaml
diff --git a/imcui/third_party/SOLD2/sold2/config/train_detector.yaml b/third_party/SOLD2/sold2/config/train_detector.yaml
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/config/train_detector.yaml
rename to third_party/SOLD2/sold2/config/train_detector.yaml
diff --git a/imcui/third_party/SOLD2/sold2/config/train_full_pipeline.yaml b/third_party/SOLD2/sold2/config/train_full_pipeline.yaml
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/config/train_full_pipeline.yaml
rename to third_party/SOLD2/sold2/config/train_full_pipeline.yaml
diff --git a/imcui/third_party/SOLD2/sold2/config/wireframe_dataset.yaml b/third_party/SOLD2/sold2/config/wireframe_dataset.yaml
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/config/wireframe_dataset.yaml
rename to third_party/SOLD2/sold2/config/wireframe_dataset.yaml
diff --git a/imcui/third_party/SOLD2/sold2/model/__init__.py b/third_party/SOLD2/sold2/dataset/__init__.py
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/model/__init__.py
rename to third_party/SOLD2/sold2/dataset/__init__.py
diff --git a/imcui/third_party/SOLD2/sold2/dataset/dataset_util.py b/third_party/SOLD2/sold2/dataset/dataset_util.py
similarity index 75%
rename from imcui/third_party/SOLD2/sold2/dataset/dataset_util.py
rename to third_party/SOLD2/sold2/dataset/dataset_util.py
index 50439ef3e2958d82719da0f6d10f4a7d98322f9a..67271bc915e6975cad005e9001d2bb430a8baa14 100644
--- a/imcui/third_party/SOLD2/sold2/dataset/dataset_util.py
+++ b/third_party/SOLD2/sold2/dataset/dataset_util.py
@@ -8,53 +8,50 @@ from .merge_dataset import MergeDataset
def get_dataset(mode="train", dataset_cfg=None):
- """ Initialize different dataset based on a configuration. """
+ """Initialize different dataset based on a configuration."""
# Check dataset config is given
if dataset_cfg is None:
raise ValueError("[Error] The dataset config is required!")
# Synthetic dataset
if dataset_cfg["dataset_name"] == "synthetic_shape":
- dataset = SyntheticShapes(
- mode, dataset_cfg
- )
+ dataset = SyntheticShapes(mode, dataset_cfg)
# Get the collate_fn
from .synthetic_dataset import synthetic_collate_fn
+
collate_fn = synthetic_collate_fn
# Wireframe dataset
elif dataset_cfg["dataset_name"] == "wireframe":
- dataset = WireframeDataset(
- mode, dataset_cfg
- )
+ dataset = WireframeDataset(mode, dataset_cfg)
# Get the collate_fn
from .wireframe_dataset import wireframe_collate_fn
+
collate_fn = wireframe_collate_fn
-
+
# Holicity dataset
elif dataset_cfg["dataset_name"] == "holicity":
- dataset = HolicityDataset(
- mode, dataset_cfg
- )
+ dataset = HolicityDataset(mode, dataset_cfg)
# Get the collate_fn
from .holicity_dataset import holicity_collate_fn
+
collate_fn = holicity_collate_fn
-
+
# Dataset merging several datasets in one
elif dataset_cfg["dataset_name"] == "merge":
- dataset = MergeDataset(
- mode, dataset_cfg
- )
+ dataset = MergeDataset(mode, dataset_cfg)
# Get the collate_fn
from .holicity_dataset import holicity_collate_fn
+
collate_fn = holicity_collate_fn
else:
raise ValueError(
- "[Error] The dataset '%s' is not supported" % dataset_cfg["dataset_name"])
+ "[Error] The dataset '%s' is not supported" % dataset_cfg["dataset_name"]
+ )
return dataset, collate_fn
diff --git a/imcui/third_party/SOLD2/sold2/dataset/holicity_dataset.py b/third_party/SOLD2/sold2/dataset/holicity_dataset.py
similarity index 68%
rename from imcui/third_party/SOLD2/sold2/dataset/holicity_dataset.py
rename to third_party/SOLD2/sold2/dataset/holicity_dataset.py
index e4437f37bda366983052de902a41467ca01412bd..af182c5ef46d68d595da4c3dda76c1f631d56fcc 100644
--- a/imcui/third_party/SOLD2/sold2/dataset/holicity_dataset.py
+++ b/third_party/SOLD2/sold2/dataset/holicity_dataset.py
@@ -26,12 +26,19 @@ from ..misc.train_utils import parse_h5_data
def holicity_collate_fn(batch):
- """ Customized collate_fn. """
- batch_keys = ["image", "junction_map", "valid_mask", "heatmap",
- "heatmap_pos", "heatmap_neg", "homography",
- "line_points", "line_indices"]
- list_keys = ["junctions", "line_map", "line_map_pos",
- "line_map_neg", "file_key"]
+ """Customized collate_fn."""
+ batch_keys = [
+ "image",
+ "junction_map",
+ "valid_mask",
+ "heatmap",
+ "heatmap_pos",
+ "heatmap_neg",
+ "homography",
+ "line_points",
+ "line_indices",
+ ]
+ list_keys = ["junctions", "line_map", "line_map_pos", "line_map_neg", "file_key"]
outputs = {}
for data_key in batch[0].keys():
@@ -40,14 +47,16 @@ def holicity_collate_fn(batch):
# print(batch_match, list_match)
if batch_match > 0 and list_match == 0:
outputs[data_key] = torch_loader.default_collate(
- [b[data_key] for b in batch])
+ [b[data_key] for b in batch]
+ )
elif batch_match == 0 and list_match > 0:
outputs[data_key] = [b[data_key] for b in batch]
elif batch_match == 0 and list_match == 0:
continue
else:
raise ValueError(
- "[Error] A key matches batch keys and list keys simultaneously.")
+ "[Error] A key matches batch keys and list keys simultaneously."
+ )
return outputs
@@ -57,7 +66,8 @@ class HolicityDataset(Dataset):
super(HolicityDataset, self).__init__()
if not mode in ["train", "test"]:
raise ValueError(
- "[Error] Unknown mode for Holicity dataset. Only 'train' and 'test'.")
+ "[Error] Unknown mode for Holicity dataset. Only 'train' and 'test'."
+ )
self.mode = mode
if config is None:
@@ -71,17 +81,18 @@ class HolicityDataset(Dataset):
self.dataset_name = self.get_dataset_name()
self.cache_name = self.get_cache_name()
self.cache_path = cfg.holicity_cache_path
-
+
# Get the ground truth source if it exists
self.gt_source = None
- if "gt_source_%s"%(self.mode) in self.config:
- self.gt_source = self.config.get("gt_source_%s"%(self.mode))
+ if "gt_source_%s" % (self.mode) in self.config:
+ self.gt_source = self.config.get("gt_source_%s" % (self.mode))
self.gt_source = os.path.join(cfg.export_dataroot, self.gt_source)
# Check the full path exists
if not os.path.exists(self.gt_source):
raise ValueError(
- "[Error] The specified ground truth source does not exist.")
-
+ "[Error] The specified ground truth source does not exist."
+ )
+
# Get the filename dataset
print("[Info] Initializing Holicity dataset...")
self.filename_dataset, self.datapoints = self.construct_dataset()
@@ -92,22 +103,22 @@ class HolicityDataset(Dataset):
# Print some info
print("[Info] Successfully initialized dataset")
print("\t Name: Holicity")
- print("\t Mode: %s" %(self.mode))
- print("\t Gt: %s" %(self.config.get("gt_source_%s"%(self.mode),
- "None")))
- print("\t Counts: %d" %(self.dataset_length))
+ print("\t Mode: %s" % (self.mode))
+ print("\t Gt: %s" % (self.config.get("gt_source_%s" % (self.mode), "None")))
+ print("\t Counts: %d" % (self.dataset_length))
print("----------------------------------------")
#######################################
## Dataset construction related APIs ##
#######################################
def construct_dataset(self):
- """ Construct the dataset (from scratch or from cache). """
+ """Construct the dataset (from scratch or from cache)."""
# Check if the filename cache exists
# If cache exists, load from cache
if self.check_dataset_cache():
- print("\t Found filename cache %s at %s"%(self.cache_name,
- self.cache_path))
+ print(
+ "\t Found filename cache %s at %s" % (self.cache_name, self.cache_path)
+ )
print("\t Load filename cache...")
filename_dataset, datapoints = self.get_filename_dataset_from_cache()
# If not, initialize dataset from scratch
@@ -117,56 +128,56 @@ class HolicityDataset(Dataset):
filename_dataset, datapoints = self.get_filename_dataset()
print("\t Create filename dataset cache...")
self.create_filename_dataset_cache(filename_dataset, datapoints)
-
+
return filename_dataset, datapoints
-
+
def create_filename_dataset_cache(self, filename_dataset, datapoints):
- """ Create filename dataset cache for faster initialization. """
+ """Create filename dataset cache for faster initialization."""
# Check cache path exists
if not os.path.exists(self.cache_path):
os.makedirs(self.cache_path)
cache_file_path = os.path.join(self.cache_path, self.cache_name)
- data = {
- "filename_dataset": filename_dataset,
- "datapoints": datapoints
- }
+ data = {"filename_dataset": filename_dataset, "datapoints": datapoints}
with open(cache_file_path, "wb") as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
-
+
def get_filename_dataset_from_cache(self):
- """ Get filename dataset from cache. """
+ """Get filename dataset from cache."""
# Load from pkl cache
cache_file_path = os.path.join(self.cache_path, self.cache_name)
with open(cache_file_path, "rb") as f:
data = pickle.load(f)
-
+
return data["filename_dataset"], data["datapoints"]
def get_filename_dataset(self):
- """ Get the path to the dataset. """
+ """Get the path to the dataset."""
if self.mode == "train":
# Contains 5720 or 11872 images
- dataset_path = [os.path.join(cfg.holicity_dataroot, p)
- for p in self.config["train_splits"]]
+ dataset_path = [
+ os.path.join(cfg.holicity_dataroot, p)
+ for p in self.config["train_splits"]
+ ]
else:
# Test mode - Contains 520 images
dataset_path = [os.path.join(cfg.holicity_dataroot, "2018-03")]
-
+
# Get paths to all image files
image_paths = []
for folder in dataset_path:
- image_paths += [os.path.join(folder, img)
- for img in os.listdir(folder)
- if os.path.splitext(img)[-1] == ".jpg"]
+ image_paths += [
+ os.path.join(folder, img)
+ for img in os.listdir(folder)
+ if os.path.splitext(img)[-1] == ".jpg"
+ ]
image_paths = sorted(image_paths)
# Verify all the images exist
for idx in range(len(image_paths)):
image_path = image_paths[idx]
if not (os.path.exists(image_path)):
- raise ValueError(
- "[Error] The image does not exist. %s"%(image_path))
+ raise ValueError("[Error] The image does not exist. %s" % (image_path))
# Construct the filename dataset
num_pad = int(math.ceil(math.log10(len(image_paths))) + 1)
@@ -176,82 +187,77 @@ class HolicityDataset(Dataset):
key = self.get_padded_filename(num_pad, idx)
filename_dataset[key] = {"image": image_paths[idx]}
-
+
# Get the datapoints
datapoints = list(sorted(filename_dataset.keys()))
return filename_dataset, datapoints
-
+
def get_dataset_name(self):
- """ Get dataset name from dataset config / default config. """
- dataset_name = self.config.get("dataset_name",
- self.default_config["dataset_name"])
+ """Get dataset name from dataset config / default config."""
+ dataset_name = self.config.get(
+ "dataset_name", self.default_config["dataset_name"]
+ )
dataset_name = dataset_name + "_%s" % self.mode
return dataset_name
-
+
def get_cache_name(self):
- """ Get cache name from dataset config / default config. """
- dataset_name = self.config.get("dataset_name",
- self.default_config["dataset_name"])
+ """Get cache name from dataset config / default config."""
+ dataset_name = self.config.get(
+ "dataset_name", self.default_config["dataset_name"]
+ )
dataset_name = dataset_name + "_%s" % self.mode
# Compose cache name
cache_name = dataset_name + "_cache.pkl"
return cache_name
def check_dataset_cache(self):
- """ Check if dataset cache exists. """
+ """Check if dataset cache exists."""
cache_file_path = os.path.join(self.cache_path, self.cache_name)
if os.path.exists(cache_file_path):
return True
else:
return False
-
+
@staticmethod
def get_padded_filename(num_pad, idx):
- """ Get the padded filename using adaptive padding. """
+ """Get the padded filename using adaptive padding."""
file_len = len("%d" % (idx))
filename = "0" * (num_pad - file_len) + "%d" % (idx)
return filename
def get_default_config(self):
- """ Get the default configuration. """
+ """Get the default configuration."""
return {
"dataset_name": "holicity",
"train_split": "2018-01",
"add_augmentation_to_all_splits": False,
- "preprocessing": {
- "resize": [512, 512],
- "blur_size": 11
- },
- "augmentation":{
- "photometric":{
- "enable": False
- },
- "homographic":{
- "enable": False
- },
+ "preprocessing": {"resize": [512, 512], "blur_size": 11},
+ "augmentation": {
+ "photometric": {"enable": False},
+ "homographic": {"enable": False},
},
}
-
+
############################################
## Pytorch and preprocessing related APIs ##
############################################
@staticmethod
def get_data_from_path(data_path):
- """ Get data from the information from filename dataset. """
+ """Get data from the information from filename dataset."""
output = {}
# Get image data
image_path = data_path["image"]
image = imread(image_path)
output["image"] = image
-
+
return output
-
+
@staticmethod
def convert_line_map(lcnn_line_map, num_junctions):
- """ Convert the line_pos or line_neg
- (represented by two junction indexes) to our line map. """
+ """Convert the line_pos or line_neg
+ (represented by two junction indexes) to our line map."""
# Initialize empty line map
line_map = np.zeros([num_junctions, num_junctions])
@@ -262,59 +268,60 @@ class HolicityDataset(Dataset):
line_map[index1, index2] = 1
line_map[index2, index1] = 1
-
+
return line_map
@staticmethod
def junc_to_junc_map(junctions, image_size):
- """ Convert junction points to junction maps. """
+ """Convert junction points to junction maps."""
junctions = np.round(junctions).astype(np.int)
# Clip the boundary by image size
- junctions[:, 0] = np.clip(junctions[:, 0], 0., image_size[0]-1)
- junctions[:, 1] = np.clip(junctions[:, 1], 0., image_size[1]-1)
+ junctions[:, 0] = np.clip(junctions[:, 0], 0.0, image_size[0] - 1)
+ junctions[:, 1] = np.clip(junctions[:, 1], 0.0, image_size[1] - 1)
# Create junction map
junc_map = np.zeros([image_size[0], image_size[1]])
junc_map[junctions[:, 0], junctions[:, 1]] = 1
return junc_map[..., None].astype(np.int)
-
+
def parse_transforms(self, names, all_transforms):
- """ Parse the transform. """
- trans = all_transforms if (names == 'all') \
+ """Parse the transform."""
+ trans = (
+ all_transforms
+ if (names == "all")
else (names if isinstance(names, list) else [names])
+ )
assert set(trans) <= set(all_transforms)
return trans
def get_photo_transform(self):
- """ Get list of photometric transforms (according to the config). """
+ """Get list of photometric transforms (according to the config)."""
# Get the photometric transform config
photo_config = self.config["augmentation"]["photometric"]
if not photo_config["enable"]:
- raise ValueError(
- "[Error] Photometric augmentation is not enabled.")
-
+ raise ValueError("[Error] Photometric augmentation is not enabled.")
+
# Parse photometric transforms
- trans_lst = self.parse_transforms(photo_config["primitives"],
- photoaug.available_augmentations)
- trans_config_lst = [photo_config["params"].get(p, {})
- for p in trans_lst]
+ trans_lst = self.parse_transforms(
+ photo_config["primitives"], photoaug.available_augmentations
+ )
+ trans_config_lst = [photo_config["params"].get(p, {}) for p in trans_lst]
# List of photometric augmentation
photometric_trans_lst = [
- getattr(photoaug, trans)(**conf) \
+ getattr(photoaug, trans)(**conf)
for (trans, conf) in zip(trans_lst, trans_config_lst)
]
return photometric_trans_lst
def get_homo_transform(self):
- """ Get homographic transforms (according to the config). """
+ """Get homographic transforms (according to the config)."""
# Get homographic transforms for image
homo_config = self.config["augmentation"]["homographic"]["params"]
if not self.config["augmentation"]["homographic"]["enable"]:
- raise ValueError(
- "[Error] Homographic augmentation is not enabled")
+ raise ValueError("[Error] Homographic augmentation is not enabled")
# Parse the homographic transforms
image_shape = self.config["preprocessing"]["resize"]
@@ -324,30 +331,33 @@ class HolicityDataset(Dataset):
min_label_tmp = self.config["generation"]["min_label_len"]
except:
min_label_tmp = None
-
+
# float label len => fraction
- if isinstance(min_label_tmp, float): # Skip if not provided
+ if isinstance(min_label_tmp, float): # Skip if not provided
min_label_len = min_label_tmp * min(image_shape)
# int label len => length in pixel
elif isinstance(min_label_tmp, int):
- scale_ratio = (self.config["preprocessing"]["resize"]
- / self.config["generation"]["image_size"][0])
- min_label_len = (self.config["generation"]["min_label_len"]
- * scale_ratio)
+ scale_ratio = (
+ self.config["preprocessing"]["resize"]
+ / self.config["generation"]["image_size"][0]
+ )
+ min_label_len = self.config["generation"]["min_label_len"] * scale_ratio
# if none => no restriction
else:
min_label_len = 0
-
+
# Initialize the transform
homographic_trans = homoaug.homography_transform(
- image_shape, homo_config, 0, min_label_len)
+ image_shape, homo_config, 0, min_label_len
+ )
return homographic_trans
- def get_line_points(self, junctions, line_map, H1=None, H2=None,
- img_size=None, warp=False):
- """ Sample evenly points along each line segments
- and keep track of line idx. """
+ def get_line_points(
+ self, junctions, line_map, H1=None, H2=None, img_size=None, warp=False
+ ):
+ """Sample evenly points along each line segments
+ and keep track of line idx."""
if np.sum(line_map) == 0:
# No segment detected in the image
line_indices = np.zeros(self.config["max_pts"], dtype=int)
@@ -356,35 +366,38 @@ class HolicityDataset(Dataset):
# Extract all pairs of connected junctions
junc_indices = np.array(
- [[i, j] for (i, j) in zip(*np.where(line_map)) if j > i])
- line_segments = np.stack([junctions[junc_indices[:, 0]],
- junctions[junc_indices[:, 1]]], axis=1)
+ [[i, j] for (i, j) in zip(*np.where(line_map)) if j > i]
+ )
+ line_segments = np.stack(
+ [junctions[junc_indices[:, 0]], junctions[junc_indices[:, 1]]], axis=1
+ )
# line_segments is (num_lines, 2, 2)
- line_lengths = np.linalg.norm(
- line_segments[:, 0] - line_segments[:, 1], axis=1)
+ line_lengths = np.linalg.norm(line_segments[:, 0] - line_segments[:, 1], axis=1)
# Sample the points separated by at least min_dist_pts along each line
# The number of samples depends on the length of the line
- num_samples = np.minimum(line_lengths // self.config["min_dist_pts"],
- self.config["max_num_samples"])
+ num_samples = np.minimum(
+ line_lengths // self.config["min_dist_pts"], self.config["max_num_samples"]
+ )
line_points = []
line_indices = []
cur_line_idx = 1
for n in np.arange(2, self.config["max_num_samples"] + 1):
# Consider all lines where we can fit up to n points
cur_line_seg = line_segments[num_samples == n]
- line_points_x = np.linspace(cur_line_seg[:, 0, 0],
- cur_line_seg[:, 1, 0],
- n, axis=-1).flatten()
- line_points_y = np.linspace(cur_line_seg[:, 0, 1],
- cur_line_seg[:, 1, 1],
- n, axis=-1).flatten()
+ line_points_x = np.linspace(
+ cur_line_seg[:, 0, 0], cur_line_seg[:, 1, 0], n, axis=-1
+ ).flatten()
+ line_points_y = np.linspace(
+ cur_line_seg[:, 0, 1], cur_line_seg[:, 1, 1], n, axis=-1
+ ).flatten()
jitter = self.config.get("jittering", 0)
if jitter:
# Add a small random jittering of all points along the line
angles = np.arctan2(
cur_line_seg[:, 1, 0] - cur_line_seg[:, 0, 0],
- cur_line_seg[:, 1, 1] - cur_line_seg[:, 0, 1]).repeat(n)
+ cur_line_seg[:, 1, 1] - cur_line_seg[:, 0, 1],
+ ).repeat(n)
jitter_hyp = (np.random.rand(len(angles)) * 2 - 1) * jitter
line_points_x += jitter_hyp * np.sin(angles)
line_points_y += jitter_hyp * np.cos(angles)
@@ -394,10 +407,8 @@ class HolicityDataset(Dataset):
line_idx = np.arange(cur_line_idx, cur_line_idx + num_cur_lines)
line_indices.append(line_idx.repeat(n))
cur_line_idx += num_cur_lines
- line_points = np.concatenate(line_points,
- axis=0)[:self.config["max_pts"]]
- line_indices = np.concatenate(line_indices,
- axis=0)[:self.config["max_pts"]]
+ line_points = np.concatenate(line_points, axis=0)[: self.config["max_pts"]]
+ line_indices = np.concatenate(line_indices, axis=0)[: self.config["max_pts"]]
# Warp the points if need be, and filter unvalid ones
# If the other view is also warped
@@ -419,37 +430,43 @@ class HolicityDataset(Dataset):
mask = mask_points(warped_points, img_size)
line_points = line_points[mask]
line_indices = line_indices[mask]
-
+
# Pad the line points to a fixed length
# Index of 0 means padded line
- line_indices = np.concatenate([line_indices, np.zeros(
- self.config["max_pts"] - len(line_indices))], axis=0)
+ line_indices = np.concatenate(
+ [line_indices, np.zeros(self.config["max_pts"] - len(line_indices))], axis=0
+ )
line_points = np.concatenate(
- [line_points,
- np.zeros((self.config["max_pts"] - len(line_points), 2),
- dtype=float)], axis=0)
-
+ [
+ line_points,
+ np.zeros((self.config["max_pts"] - len(line_points), 2), dtype=float),
+ ],
+ axis=0,
+ )
+
return line_points, line_indices
def export_preprocessing(self, data, numpy=False):
- """ Preprocess the exported data. """
+ """Preprocess the exported data."""
# Fetch the corresponding entries
image = data["image"]
image_size = image.shape[:2]
# Resize the image before photometric and homographical augmentations
- if not(list(image_size) == self.config["preprocessing"]["resize"]):
+ if not (list(image_size) == self.config["preprocessing"]["resize"]):
# Resize the image and the point location.
- size_old = list(image.shape)[:2] # Only H and W dimensions
+ size_old = list(image.shape)[:2] # Only H and W dimensions
image = cv2.resize(
- image, tuple(self.config['preprocessing']['resize'][::-1]),
- interpolation=cv2.INTER_LINEAR)
+ image,
+ tuple(self.config["preprocessing"]["resize"][::-1]),
+ interpolation=cv2.INTER_LINEAR,
+ )
image = np.array(image, dtype=np.uint8)
-
+
# Optionally convert the image to grayscale
if self.config["gray_scale"]:
- image = (color.rgb2gray(image) * 255.).astype(np.uint8)
+ image = (color.rgb2gray(image) * 255.0).astype(np.uint8)
image = photoaug.normalize_image()(image)
@@ -459,11 +476,21 @@ class HolicityDataset(Dataset):
return {"image": to_tensor(image)}
else:
return {"image": image}
-
+
def train_preprocessing_exported(
- self, data, numpy=False, disable_homoaug=False, desc_training=False,
- H1=None, H1_scale=None, H2=None, scale=1., h_crop=None, w_crop=None):
- """ Train preprocessing for the exported labels. """
+ self,
+ data,
+ numpy=False,
+ disable_homoaug=False,
+ desc_training=False,
+ H1=None,
+ H1_scale=None,
+ H2=None,
+ scale=1.0,
+ h_crop=None,
+ w_crop=None,
+ ):
+ """Train preprocessing for the exported labels."""
data = copy.deepcopy(data)
# Fetch the corresponding entries
image = data["image"]
@@ -483,13 +510,15 @@ class HolicityDataset(Dataset):
w_crop = np.random.randint(W_scale - W)
# Resize the image before photometric and homographical augmentations
- if not(list(image_size) == self.config["preprocessing"]["resize"]):
+ if not (list(image_size) == self.config["preprocessing"]["resize"]):
# Resize the image and the point location.
- size_old = list(image.shape)[:2] # Only H and W dimensions
+ size_old = list(image.shape)[:2] # Only H and W dimensions
image = cv2.resize(
- image, tuple(self.config['preprocessing']['resize'][::-1]),
- interpolation=cv2.INTER_LINEAR)
+ image,
+ tuple(self.config["preprocessing"]["resize"][::-1]),
+ interpolation=cv2.INTER_LINEAR,
+ )
image = np.array(image, dtype=np.uint8)
# # In HW format
@@ -504,7 +533,7 @@ class HolicityDataset(Dataset):
# Optionally convert the image to grayscale
if self.config["gray_scale"]:
- image = (color.rgb2gray(image) * 255.).astype(np.uint8)
+ image = (color.rgb2gray(image) * 255.0).astype(np.uint8)
# Check if we need to apply augmentations
# In training mode => yes.
@@ -514,16 +543,17 @@ class HolicityDataset(Dataset):
### Image transform ###
np.random.shuffle(photo_trans_lst)
image_transform = transforms.Compose(
- photo_trans_lst + [photoaug.normalize_image()])
+ photo_trans_lst + [photoaug.normalize_image()]
+ )
else:
image_transform = photoaug.normalize_image()
image = image_transform(image)
# Perform the random scaling
- if scale != 1.:
+ if scale != 1.0:
image, junctions, line_map, valid_mask = random_scaling(
- image, junctions, line_map, scale,
- h_crop=h_crop, w_crop=w_crop)
+ image, junctions, line_map, scale, h_crop=h_crop, w_crop=w_crop
+ )
else:
# Declare default valid mask (all ones)
valid_mask = np.ones(image_size)
@@ -534,20 +564,28 @@ class HolicityDataset(Dataset):
to_tensor = transforms.ToTensor()
# Check homographic augmentation
- warp = (self.config["augmentation"]["homographic"]["enable"]
- and disable_homoaug == False)
+ warp = (
+ self.config["augmentation"]["homographic"]["enable"]
+ and disable_homoaug == False
+ )
if warp:
homo_trans = self.get_homo_transform()
# Perform homographic transform
if H1 is None:
- homo_outputs = homo_trans(image, junctions, line_map,
- valid_mask=valid_mask)
+ homo_outputs = homo_trans(
+ image, junctions, line_map, valid_mask=valid_mask
+ )
else:
homo_outputs = homo_trans(
- image, junctions, line_map, homo=H1, scale=H1_scale,
- valid_mask=valid_mask)
+ image,
+ junctions,
+ line_map,
+ homo=H1,
+ scale=H1_scale,
+ valid_mask=valid_mask,
+ )
homography_mat = homo_outputs["homo"]
-
+
# Give the warp of the other view
if H1 is None:
H1 = homo_outputs["homo"]
@@ -555,8 +593,8 @@ class HolicityDataset(Dataset):
# Sample points along each line segments for the descriptor
if desc_training:
line_points, line_indices = self.get_line_points(
- junctions, line_map, H1=H1, H2=H2,
- img_size=image_size, warp=warp)
+ junctions, line_map, H1=H1, H2=H2, img_size=image_size, warp=warp
+ )
# Record the warped results
if warp:
@@ -565,52 +603,59 @@ class HolicityDataset(Dataset):
line_map = homo_outputs["line_map"]
valid_mask = homo_outputs["valid_mask"] # Same for pos and neg
heatmap = homo_outputs["warped_heatmap"]
-
+
# Optionally put warping information first.
if not numpy:
- outputs["homography_mat"] = to_tensor(
- homography_mat).to(torch.float32)[0, ...]
+ outputs["homography_mat"] = to_tensor(homography_mat).to(torch.float32)[
+ 0, ...
+ ]
else:
outputs["homography_mat"] = homography_mat.astype(np.float32)
junction_map = self.junc_to_junc_map(junctions, image_size)
-
+
if not numpy:
- outputs.update({
- "image": to_tensor(image),
- "junctions": to_tensor(junctions).to(torch.float32)[0, ...],
- "junction_map": to_tensor(junction_map).to(torch.int),
- "line_map": to_tensor(line_map).to(torch.int32)[0, ...],
- "heatmap": to_tensor(heatmap).to(torch.int32),
- "valid_mask": to_tensor(valid_mask).to(torch.int32)
- })
+ outputs.update(
+ {
+ "image": to_tensor(image),
+ "junctions": to_tensor(junctions).to(torch.float32)[0, ...],
+ "junction_map": to_tensor(junction_map).to(torch.int),
+ "line_map": to_tensor(line_map).to(torch.int32)[0, ...],
+ "heatmap": to_tensor(heatmap).to(torch.int32),
+ "valid_mask": to_tensor(valid_mask).to(torch.int32),
+ }
+ )
if desc_training:
- outputs.update({
- "line_points": to_tensor(
- line_points).to(torch.float32)[0],
- "line_indices": torch.tensor(line_indices,
- dtype=torch.int)
- })
+ outputs.update(
+ {
+ "line_points": to_tensor(line_points).to(torch.float32)[0],
+ "line_indices": torch.tensor(line_indices, dtype=torch.int),
+ }
+ )
else:
- outputs.update({
- "image": image,
- "junctions": junctions.astype(np.float32),
- "junction_map": junction_map.astype(np.int32),
- "line_map": line_map.astype(np.int32),
- "heatmap": heatmap.astype(np.int32),
- "valid_mask": valid_mask.astype(np.int32)
- })
+ outputs.update(
+ {
+ "image": image,
+ "junctions": junctions.astype(np.float32),
+ "junction_map": junction_map.astype(np.int32),
+ "line_map": line_map.astype(np.int32),
+ "heatmap": heatmap.astype(np.int32),
+ "valid_mask": valid_mask.astype(np.int32),
+ }
+ )
if desc_training:
- outputs.update({
- "line_points": line_points.astype(np.float32),
- "line_indices": line_indices.astype(int)
- })
-
+ outputs.update(
+ {
+ "line_points": line_points.astype(np.float32),
+ "line_indices": line_indices.astype(int),
+ }
+ )
+
return outputs
-
- def preprocessing_exported_paired_desc(self, data, numpy=False, scale=1.):
- """ Train preprocessing for paired data for the exported labels
- for descriptor training. """
+
+ def preprocessing_exported_paired_desc(self, data, numpy=False, scale=1.0):
+ """Train preprocessing for paired data for the exported labels
+ for descriptor training."""
outputs = {}
# Define the random crop for scaling if necessary
@@ -622,51 +667,66 @@ class HolicityDataset(Dataset):
h_crop = np.random.randint(H_scale - H)
if W_scale > W:
w_crop = np.random.randint(W_scale - W)
-
+
# Sample ref homography first
homo_config = self.config["augmentation"]["homographic"]["params"]
image_shape = self.config["preprocessing"]["resize"]
- ref_H, ref_scale = homoaug.sample_homography(image_shape,
- **homo_config)
+ ref_H, ref_scale = homoaug.sample_homography(image_shape, **homo_config)
# Data for target view (All augmentation)
target_data = self.train_preprocessing_exported(
- data, numpy=numpy, desc_training=True, H1=None, H2=ref_H,
- scale=scale, h_crop=h_crop, w_crop=w_crop)
+ data,
+ numpy=numpy,
+ desc_training=True,
+ H1=None,
+ H2=ref_H,
+ scale=scale,
+ h_crop=h_crop,
+ w_crop=w_crop,
+ )
# Data for reference view (No homographical augmentation)
ref_data = self.train_preprocessing_exported(
- data, numpy=numpy, desc_training=True, H1=ref_H,
- H1_scale=ref_scale, H2=target_data['homography_mat'].numpy(),
- scale=scale, h_crop=h_crop, w_crop=w_crop)
+ data,
+ numpy=numpy,
+ desc_training=True,
+ H1=ref_H,
+ H1_scale=ref_scale,
+ H2=target_data["homography_mat"].numpy(),
+ scale=scale,
+ h_crop=h_crop,
+ w_crop=w_crop,
+ )
# Spread ref data
for key, val in ref_data.items():
outputs["ref_" + key] = val
-
+
# Spread target data
for key, val in target_data.items():
outputs["target_" + key] = val
-
+
return outputs
def test_preprocessing_exported(self, data, numpy=False):
- """ Test preprocessing for the exported labels. """
+ """Test preprocessing for the exported labels."""
data = copy.deepcopy(data)
# Fetch the corresponding entries
image = data["image"]
junctions = data["junctions"]
- line_map = data["line_map"]
+ line_map = data["line_map"]
image_size = image.shape[:2]
# Resize the image before photometric and homographical augmentations
- if not(list(image_size) == self.config["preprocessing"]["resize"]):
+ if not (list(image_size) == self.config["preprocessing"]["resize"]):
# Resize the image and the point location.
- size_old = list(image.shape)[:2] # Only H and W dimensions
+ size_old = list(image.shape)[:2] # Only H and W dimensions
image = cv2.resize(
- image, tuple(self.config['preprocessing']['resize'][::-1]),
- interpolation=cv2.INTER_LINEAR)
+ image,
+ tuple(self.config["preprocessing"]["resize"][::-1]),
+ interpolation=cv2.INTER_LINEAR,
+ )
image = np.array(image, dtype=np.uint8)
# # In HW format
@@ -676,7 +736,7 @@ class HolicityDataset(Dataset):
# Optionally convert the image to grayscale
if self.config["gray_scale"]:
- image = (color.rgb2gray(image) * 255.).astype(np.uint8)
+ image = (color.rgb2gray(image) * 255.0).astype(np.uint8)
# Still need to normalize image
image_transform = photoaug.normalize_image()
@@ -686,7 +746,7 @@ class HolicityDataset(Dataset):
junctions_xy = np.flip(np.round(junctions).astype(np.int32), axis=1)
image_size = image.shape[:2]
heatmap = get_line_heatmap(junctions_xy, line_map, image_size)
-
+
# Declare default valid mask (all ones)
valid_mask = np.ones(image_size)
@@ -701,7 +761,7 @@ class HolicityDataset(Dataset):
"junction_map": to_tensor(junction_map).to(torch.int),
"line_map": to_tensor(line_map).to(torch.int32)[0, ...],
"heatmap": to_tensor(heatmap).to(torch.int32),
- "valid_mask": to_tensor(valid_mask).to(torch.int32)
+ "valid_mask": to_tensor(valid_mask).to(torch.int32),
}
else:
outputs = {
@@ -710,38 +770,36 @@ class HolicityDataset(Dataset):
"junction_map": junction_map.astype(np.int32),
"line_map": line_map.astype(np.int32),
"heatmap": heatmap.astype(np.int32),
- "valid_mask": valid_mask.astype(np.int32)
+ "valid_mask": valid_mask.astype(np.int32),
}
-
+
return outputs
def __len__(self):
return self.dataset_length
-
+
def get_data_from_key(self, file_key):
- """ Get data from file_key. """
+ """Get data from file_key."""
# Check key exists
if not file_key in self.filename_dataset.keys():
- raise ValueError(
- "[Error] the specified key is not in the dataset.")
-
+ raise ValueError("[Error] the specified key is not in the dataset.")
+
# Get the data paths
data_path = self.filename_dataset[file_key]
# Read in the image and npz labels
data = self.get_data_from_path(data_path)
# Perform transform and augmentation
- if (self.mode == "train"
- or self.config["add_augmentation_to_all_splits"]):
+ if self.mode == "train" or self.config["add_augmentation_to_all_splits"]:
data = self.train_preprocessing(data, numpy=True)
else:
data = self.test_preprocessing(data, numpy=True)
-
+
# Add file key to the output
data["file_key"] = file_key
-
+
return data
-
+
def __getitem__(self, idx):
"""Return data
file_key: str, keys used to retrieve data from the filename dataset.
@@ -761,27 +819,25 @@ class HolicityDataset(Dataset):
if self.gt_source:
with h5py.File(self.gt_source, "r") as f:
exported_label = parse_h5_data(f[file_key])
-
+
data["junctions"] = exported_label["junctions"]
data["line_map"] = exported_label["line_map"]
-
+
# Perform transform and augmentation
return_type = self.config.get("return_type", "single")
if self.gt_source is None:
# For export only
data = self.export_preprocessing(data)
- elif (self.mode == "train"
- or self.config["add_augmentation_to_all_splits"]):
+ elif self.mode == "train" or self.config["add_augmentation_to_all_splits"]:
# Perform random scaling first
if self.config["augmentation"]["random_scaling"]["enable"]:
scale_range = self.config["augmentation"]["random_scaling"]["range"]
# Decide the scaling
scale = np.random.uniform(min(scale_range), max(scale_range))
else:
- scale = 1.
+ scale = 1.0
if self.mode == "train" and return_type == "paired_desc":
- data = self.preprocessing_exported_paired_desc(data,
- scale=scale)
+ data = self.preprocessing_exported_paired_desc(data, scale=scale)
else:
data = self.train_preprocessing_exported(data, scale=scale)
else:
@@ -789,9 +845,8 @@ class HolicityDataset(Dataset):
data = self.preprocessing_exported_paired_desc(data)
else:
data = self.test_preprocessing_exported(data)
-
+
# Add file key to the output
data["file_key"] = file_key
-
- return data
+ return data
diff --git a/imcui/third_party/SOLD2/sold2/dataset/merge_dataset.py b/third_party/SOLD2/sold2/dataset/merge_dataset.py
similarity index 61%
rename from imcui/third_party/SOLD2/sold2/dataset/merge_dataset.py
rename to third_party/SOLD2/sold2/dataset/merge_dataset.py
index 178d3822d56639a49a99f68e392330e388fa8fc3..1f6395873dcfdea0c35898eefbf4c74a8cfac7a1 100644
--- a/imcui/third_party/SOLD2/sold2/dataset/merge_dataset.py
+++ b/third_party/SOLD2/sold2/dataset/merge_dataset.py
@@ -14,23 +14,24 @@ class MergeDataset(Dataset):
# Initialize the datasets
self._datasets = []
spec_config = deepcopy(config)
- for i, d in enumerate(config['datasets']):
- spec_config['dataset_name'] = d
- spec_config['gt_source_train'] = config['gt_source_train'][i]
- spec_config['gt_source_test'] = config['gt_source_test'][i]
+ for i, d in enumerate(config["datasets"]):
+ spec_config["dataset_name"] = d
+ spec_config["gt_source_train"] = config["gt_source_train"][i]
+ spec_config["gt_source_test"] = config["gt_source_test"][i]
if d == "wireframe":
self._datasets.append(WireframeDataset(mode, spec_config))
elif d == "holicity":
- spec_config['train_split'] = config['train_splits'][i]
+ spec_config["train_split"] = config["train_splits"][i]
self._datasets.append(HolicityDataset(mode, spec_config))
else:
- raise ValueError("Unknown dataset: " + d)
+ raise ValueError("Unknown dataset: " + d)
+
+ self._weights = config["weights"]
- self._weights = config['weights']
-
def __getitem__(self, item):
- dataset = self._datasets[np.random.choice(
- range(len(self._datasets)), p=self._weights)]
+ dataset = self._datasets[
+ np.random.choice(range(len(self._datasets)), p=self._weights)
+ ]
return dataset[np.random.randint(len(dataset))]
def __len__(self):
diff --git a/imcui/third_party/SOLD2/sold2/dataset/synthetic_dataset.py b/third_party/SOLD2/sold2/dataset/synthetic_dataset.py
similarity index 66%
rename from imcui/third_party/SOLD2/sold2/dataset/synthetic_dataset.py
rename to third_party/SOLD2/sold2/dataset/synthetic_dataset.py
index cf5f11e5407e65887f4995291156f7cc361843d1..4a1dab47bd81ec831554ba42a635a350ef7a73dc 100644
--- a/imcui/third_party/SOLD2/sold2/dataset/synthetic_dataset.py
+++ b/third_party/SOLD2/sold2/dataset/synthetic_dataset.py
@@ -25,9 +25,8 @@ from ..misc.train_utils import parse_h5_data
def synthetic_collate_fn(batch):
- """ Customized collate_fn. """
- batch_keys = ["image", "junction_map", "heatmap",
- "valid_mask", "homography"]
+ """Customized collate_fn."""
+ batch_keys = ["image", "junction_map", "heatmap", "valid_mask", "homography"]
list_keys = ["junctions", "line_map", "file_key"]
outputs = {}
@@ -36,27 +35,31 @@ def synthetic_collate_fn(batch):
list_match = sum([_ in data_key for _ in list_keys])
# print(batch_match, list_match)
if batch_match > 0 and list_match == 0:
- outputs[data_key] = torch_loader.default_collate([b[data_key]
- for b in batch])
+ outputs[data_key] = torch_loader.default_collate(
+ [b[data_key] for b in batch]
+ )
elif batch_match == 0 and list_match > 0:
outputs[data_key] = [b[data_key] for b in batch]
elif batch_match == 0 and list_match == 0:
continue
else:
raise ValueError(
- "[Error] A key matches batch keys and list keys simultaneously.")
+ "[Error] A key matches batch keys and list keys simultaneously."
+ )
return outputs
class SyntheticShapes(Dataset):
- """ Dataset of synthetic shapes. """
+ """Dataset of synthetic shapes."""
+
# Initialize the dataset
def __init__(self, mode="train", config=None):
super(SyntheticShapes, self).__init__()
if not mode in ["train", "val", "test"]:
raise ValueError(
- "[Error] Supported dataset modes are 'train', 'val', and 'test'.")
+ "[Error] Supported dataset modes are 'train', 'val', and 'test'."
+ )
self.mode = mode
# Get configuration
@@ -67,14 +70,14 @@ class SyntheticShapes(Dataset):
# Set all available primitives
self.available_primitives = [
- 'draw_lines',
- 'draw_polygon',
- 'draw_multiple_polygons',
- 'draw_star',
- 'draw_checkerboard_multiseg',
- 'draw_stripes_multiseg',
- 'draw_cube',
- 'gaussian_noise'
+ "draw_lines",
+ "draw_polygon",
+ "draw_multiple_polygons",
+ "draw_star",
+ "draw_checkerboard_multiseg",
+ "draw_stripes_multiseg",
+ "draw_cube",
+ "gaussian_noise",
]
# Some cache setting
@@ -88,11 +91,14 @@ class SyntheticShapes(Dataset):
self.print_dataset_info()
# Initialize h5 file handle
- self.dataset_path = os.path.join(cfg.synthetic_dataroot, self.dataset_name + ".h5")
-
+ self.dataset_path = os.path.join(
+ cfg.synthetic_dataroot, self.dataset_name + ".h5"
+ )
+
# Fix the random seed for torch and numpy in testing mode
- if ((self.mode == "val" or self.mode == "test")
- and self.config["add_augmentation_to_all_splits"]):
+ if (self.mode == "val" or self.mode == "test") and self.config[
+ "add_augmentation_to_all_splits"
+ ]:
seed = self.config.get("test_augmentation_seed", 200)
np.random.seed(seed)
torch.manual_seed(seed)
@@ -104,7 +110,7 @@ class SyntheticShapes(Dataset):
## Dataset construction related methods ##
##########################################
def construct_dataset(self):
- """ Dataset constructor. """
+ """Dataset constructor."""
# Check if the filename cache exists
# If cache exists, load from cache
if self._check_dataset_cache():
@@ -117,13 +123,14 @@ class SyntheticShapes(Dataset):
print("\t All files exist!")
# If not, need to re-export the synthetic dataset
else:
- print("\t Some files are missing. Re-export the synthetic shape dataset.")
+ print(
+ "\t Some files are missing. Re-export the synthetic shape dataset."
+ )
self.export_synthetic_shapes()
print("\t Initialize filename dataset")
filename_dataset, datapoints = self.get_filename_dataset()
print("\t Create filename dataset cache...")
- self.create_filename_dataset_cache(filename_dataset,
- datapoints)
+ self.create_filename_dataset_cache(filename_dataset, datapoints)
# If not, initialize dataset from scratch
else:
@@ -135,7 +142,9 @@ class SyntheticShapes(Dataset):
# If export dataset does not exist, export from scratch
else:
- print("\t Synthetic dataset does not exist. Export the synthetic dataset.")
+ print(
+ "\t Synthetic dataset does not exist. Export the synthetic dataset."
+ )
self.export_synthetic_shapes()
print("\t Initialize filename dataset")
@@ -146,7 +155,7 @@ class SyntheticShapes(Dataset):
return filename_dataset, datapoints
def get_cache_name(self):
- """ Get cache name from dataset config / default config. """
+ """Get cache name from dataset config / default config."""
if self.config["dataset_name"] is None:
dataset_name = self.default_config["dataset_name"] + "_%s" % self.mode
else:
@@ -157,7 +166,7 @@ class SyntheticShapes(Dataset):
return cache_name
def get_dataset_name(self):
- """Get dataset name from dataset config / default config. """
+ """Get dataset name from dataset config / default config."""
if self.config["dataset_name"] is None:
dataset_name = self.default_config["dataset_name"] + "_%s" % self.mode
else:
@@ -166,7 +175,7 @@ class SyntheticShapes(Dataset):
return dataset_name
def get_filename_dataset_from_cache(self):
- """ Get filename dataset from cache. """
+ """Get filename dataset from cache."""
# Load from the pkl cache
cache_file_path = os.path.join(self.cache_path, self.cache_name)
with open(cache_file_path, "rb") as f:
@@ -175,10 +184,9 @@ class SyntheticShapes(Dataset):
return data["filename_dataset"], data["datapoints"]
def get_filename_dataset(self):
- """ Get filename dataset from scratch. """
+ """Get filename dataset from scratch."""
# Path to the exported dataset
- dataset_path = os.path.join(cfg.synthetic_dataroot,
- self.dataset_name + ".h5")
+ dataset_path = os.path.join(cfg.synthetic_dataroot, self.dataset_name + ".h5")
filename_dataset = {}
datapoints = []
@@ -187,8 +195,7 @@ class SyntheticShapes(Dataset):
# Iterate through all the primitives
for prim_name in f.keys():
filenames = sorted(f[prim_name].keys())
- filenames_full = [os.path.join(prim_name, _)
- for _ in filenames]
+ filenames_full = [os.path.join(prim_name, _) for _ in filenames]
filename_dataset[prim_name] = filenames_full
datapoints += filenames_full
@@ -196,34 +203,30 @@ class SyntheticShapes(Dataset):
return filename_dataset, datapoints
def create_filename_dataset_cache(self, filename_dataset, datapoints):
- """ Create filename dataset cache for faster initialization. """
+ """Create filename dataset cache for faster initialization."""
# Check cache path exists
if not os.path.exists(self.cache_path):
os.makedirs(self.cache_path)
cache_file_path = os.path.join(self.cache_path, self.cache_name)
- data = {
- "filename_dataset": filename_dataset,
- "datapoints": datapoints
- }
+ data = {"filename_dataset": filename_dataset, "datapoints": datapoints}
with open(cache_file_path, "wb") as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
def export_synthetic_shapes(self):
- """ Export synthetic shapes to disk. """
+ """Export synthetic shapes to disk."""
# Set the global random state for data generation
- synthetic_util.set_random_state(np.random.RandomState(
- self.config["generation"]["random_seed"]))
+ synthetic_util.set_random_state(
+ np.random.RandomState(self.config["generation"]["random_seed"])
+ )
# Define the export path
- dataset_path = os.path.join(cfg.synthetic_dataroot,
- self.dataset_name + ".h5")
+ dataset_path = os.path.join(cfg.synthetic_dataroot, self.dataset_name + ".h5")
# Open h5py file
with h5py.File(dataset_path, "w", libver="latest") as f:
# Iterate through all types of shape
- primitives = self.parse_drawing_primitives(
- self.config["primitives"])
+ primitives = self.parse_drawing_primitives(self.config["primitives"])
split_size = self.config["generation"]["split_sizes"][self.mode]
for prim in primitives:
# Create h5 group
@@ -234,22 +237,23 @@ class SyntheticShapes(Dataset):
f.swmr_mode = True
def export_single_primitive(self, primitive, split_size, group):
- """ Export single primitive. """
+ """Export single primitive."""
# Check if the primitive is valid or not
if primitive not in self.available_primitives:
- raise ValueError(
- "[Error]: %s is not a supported primitive" % primitive)
+ raise ValueError("[Error]: %s is not a supported primitive" % primitive)
# Set the random seed
- synthetic_util.set_random_state(np.random.RandomState(
- self.config["generation"]["random_seed"]))
+ synthetic_util.set_random_state(
+ np.random.RandomState(self.config["generation"]["random_seed"])
+ )
# Generate shapes
print("\t Generating %s ..." % primitive)
for idx in tqdm(range(split_size), ascii=True):
# Generate background image
image = synthetic_util.generate_background(
- self.config['generation']['image_size'],
- **self.config['generation']['params']['generate_background'])
+ self.config["generation"]["image_size"],
+ **self.config["generation"]["params"]["generate_background"]
+ )
# Generate points
drawing_func = getattr(synthetic_util, primitive)
@@ -260,14 +264,21 @@ class SyntheticShapes(Dataset):
min_label_len = self.config["generation"]["min_label_len"]
# Some only take min_label_len, and gaussian noises take nothing
- if primitive in ["draw_lines", "draw_polygon",
- "draw_multiple_polygons", "draw_star"]:
- data = drawing_func(image, min_len=min_len,
- min_label_len=min_label_len, **kwarg)
- elif primitive in ["draw_checkerboard_multiseg",
- "draw_stripes_multiseg", "draw_cube"]:
- data = drawing_func(image, min_label_len=min_label_len,
- **kwarg)
+ if primitive in [
+ "draw_lines",
+ "draw_polygon",
+ "draw_multiple_polygons",
+ "draw_star",
+ ]:
+ data = drawing_func(
+ image, min_len=min_len, min_label_len=min_label_len, **kwarg
+ )
+ elif primitive in [
+ "draw_checkerboard_multiseg",
+ "draw_stripes_multiseg",
+ "draw_cube",
+ ]:
+ data = drawing_func(image, min_label_len=min_label_len, **kwarg)
else:
data = drawing_func(image, **kwarg)
@@ -284,21 +295,24 @@ class SyntheticShapes(Dataset):
image = cv2.GaussianBlur(image, (blur_size, blur_size), 0)
# Resize the image and the point location.
- points = (points
- * np.array(self.config['preprocessing']['resize'],
- np.float)
- / np.array(self.config['generation']['image_size'],
- np.float))
+ points = (
+ points
+ * np.array(self.config["preprocessing"]["resize"], np.float)
+ / np.array(self.config["generation"]["image_size"], np.float)
+ )
image = cv2.resize(
- image, tuple(self.config['preprocessing']['resize'][::-1]),
- interpolation=cv2.INTER_LINEAR)
+ image,
+ tuple(self.config["preprocessing"]["resize"][::-1]),
+ interpolation=cv2.INTER_LINEAR,
+ )
image = np.array(image, dtype=np.uint8)
# Generate the line heatmap after post-processing
junctions = np.flip(np.round(points).astype(np.int32), axis=1)
- heatmap = (synthetic_util.get_line_heatmap(
- junctions, line_map,
- size=image.shape) * 255.).astype(np.uint8)
+ heatmap = (
+ synthetic_util.get_line_heatmap(junctions, line_map, size=image.shape)
+ * 255.0
+ ).astype(np.uint8)
# Record the data in group
num_pad = math.ceil(math.log10(split_size)) + 1
@@ -306,17 +320,13 @@ class SyntheticShapes(Dataset):
file_group = group.create_group(file_key_name)
# Store data
- file_group.create_dataset("points", data=points,
- compression="gzip")
- file_group.create_dataset("image", data=image,
- compression="gzip")
- file_group.create_dataset("line_map", data=line_map,
- compression="gzip")
- file_group.create_dataset("heatmap", data=heatmap,
- compression="gzip")
+ file_group.create_dataset("points", data=points, compression="gzip")
+ file_group.create_dataset("image", data=image, compression="gzip")
+ file_group.create_dataset("line_map", data=line_map, compression="gzip")
+ file_group.create_dataset("heatmap", data=heatmap, compression="gzip")
def get_default_config(self):
- """ Get default configuration of the dataset. """
+ """Get default configuration of the dataset."""
# Initialize the default configuration
self.default_config = {
"dataset_name": "synthetic_shape",
@@ -324,43 +334,43 @@ class SyntheticShapes(Dataset):
"add_augmentation_to_all_splits": False,
# Shape generation configuration
"generation": {
- "split_sizes": {'train': 10000, 'val': 400, 'test': 500},
+ "split_sizes": {"train": 10000, "val": 400, "test": 500},
"random_seed": 10,
"image_size": [960, 1280],
"min_len": 0.09,
"min_label_len": 0.1,
- 'params': {
- 'generate_background': {
- 'min_kernel_size': 150, 'max_kernel_size': 500,
- 'min_rad_ratio': 0.02, 'max_rad_ratio': 0.031},
- 'draw_stripes': {'transform_params': (0.1, 0.1)},
- 'draw_multiple_polygons': {'kernel_boundaries': (50, 100)}
+ "params": {
+ "generate_background": {
+ "min_kernel_size": 150,
+ "max_kernel_size": 500,
+ "min_rad_ratio": 0.02,
+ "max_rad_ratio": 0.031,
+ },
+ "draw_stripes": {"transform_params": (0.1, 0.1)},
+ "draw_multiple_polygons": {"kernel_boundaries": (50, 100)},
},
},
# Date preprocessing configuration.
- "preprocessing": {
- "resize": [240, 320],
- "blur_size": 11
- },
- 'augmentation': {
- 'photometric': {
- 'enable': False,
- 'primitives': 'all',
- 'params': {},
- 'random_order': True,
+ "preprocessing": {"resize": [240, 320], "blur_size": 11},
+ "augmentation": {
+ "photometric": {
+ "enable": False,
+ "primitives": "all",
+ "params": {},
+ "random_order": True,
},
- 'homographic': {
- 'enable': False,
- 'params': {},
- 'valid_border_margin': 0,
+ "homographic": {
+ "enable": False,
+ "params": {},
+ "valid_border_margin": 0,
},
- }
+ },
}
return self.default_config
def parse_drawing_primitives(self, names):
- """ Parse the primitives in config to list of primitive names. """
+ """Parse the primitives in config to list of primitive names."""
if names == "all":
p = self.available_primitives
else:
@@ -375,42 +385,42 @@ class SyntheticShapes(Dataset):
@staticmethod
def get_padded_filename(num_pad, idx):
- """ Get the padded filename using adaptive padding. """
+ """Get the padded filename using adaptive padding."""
file_len = len("%d" % (idx))
filename = "0" * (num_pad - file_len) + "%d" % (idx)
return filename
def print_dataset_info(self):
- """ Print dataset info. """
+ """Print dataset info."""
print("\t ---------Summary------------------")
print("\t Dataset mode: \t\t %s" % self.mode)
print("\t Number of primitive: \t %d" % len(self.filename_dataset.keys()))
print("\t Number of data: \t %d" % len(self.datapoints))
print("\t ----------------------------------")
-
+
#########################
## Pytorch related API ##
#########################
def get_data_from_datapoint(self, datapoint, reader=None):
- """ Get data given the datapoint
- (keyname of the h5 dataset e.g. "draw_lines/0000.h5"). """
+ """Get data given the datapoint
+ (keyname of the h5 dataset e.g. "draw_lines/0000.h5")."""
# Check if the datapoint is valid
if not datapoint in self.datapoints:
raise ValueError(
- "[Error] The specified datapoint is not in available datapoints.")
+ "[Error] The specified datapoint is not in available datapoints."
+ )
# Get data from h5 dataset
if reader is None:
- raise ValueError(
- "[Error] The reader must be provided in __getitem__.")
+ raise ValueError("[Error] The reader must be provided in __getitem__.")
else:
data = reader[datapoint]
return parse_h5_data(data)
def get_data_from_signature(self, primitive_name, index):
- """ Get data given the primitive name and index ("draw_lines", 10) """
+ """Get data given the primitive name and index ("draw_lines", 10)"""
# Check the primitive name and index
self._check_primitive_and_index(primitive_name, index)
@@ -420,40 +430,41 @@ class SyntheticShapes(Dataset):
return self.get_data_from_datapoint(datapoint)
def parse_transforms(self, names, all_transforms):
- trans = all_transforms if (names == 'all') \
+ trans = (
+ all_transforms
+ if (names == "all")
else (names if isinstance(names, list) else [names])
+ )
assert set(trans) <= set(all_transforms)
return trans
def get_photo_transform(self):
- """ Get list of photometric transforms (according to the config). """
+ """Get list of photometric transforms (according to the config)."""
# Get the photometric transform config
photo_config = self.config["augmentation"]["photometric"]
if not photo_config["enable"]:
- raise ValueError(
- "[Error] Photometric augmentation is not enabled.")
-
+ raise ValueError("[Error] Photometric augmentation is not enabled.")
+
# Parse photometric transforms
- trans_lst = self.parse_transforms(photo_config["primitives"],
- photoaug.available_augmentations)
- trans_config_lst = [photo_config["params"].get(p, {})
- for p in trans_lst]
+ trans_lst = self.parse_transforms(
+ photo_config["primitives"], photoaug.available_augmentations
+ )
+ trans_config_lst = [photo_config["params"].get(p, {}) for p in trans_lst]
# List of photometric augmentation
photometric_trans_lst = [
- getattr(photoaug, trans)(**conf) \
+ getattr(photoaug, trans)(**conf)
for (trans, conf) in zip(trans_lst, trans_config_lst)
]
return photometric_trans_lst
-
+
def get_homo_transform(self):
- """ Get homographic transforms (according to the config). """
+ """Get homographic transforms (according to the config)."""
# Get homographic transforms for image
homo_config = self.config["augmentation"]["homographic"]["params"]
if not self.config["augmentation"]["homographic"]["enable"]:
- raise ValueError(
- "[Error] Homographic augmentation is not enabled")
+ raise ValueError("[Error] Homographic augmentation is not enabled")
# Parse the homographic transforms
# ToDo: use the shape from the config
@@ -464,33 +475,35 @@ class SyntheticShapes(Dataset):
min_label_tmp = self.config["generation"]["min_label_len"]
except:
min_label_tmp = None
-
+
# float label len => fraction
- if isinstance(min_label_tmp, float): # Skip if not provided
+ if isinstance(min_label_tmp, float): # Skip if not provided
min_label_len = min_label_tmp * min(image_shape)
# int label len => length in pixel
elif isinstance(min_label_tmp, int):
- scale_ratio = (self.config["preprocessing"]["resize"]
- / self.config["generation"]["image_size"][0])
- min_label_len = (self.config["generation"]["min_label_len"]
- * scale_ratio)
+ scale_ratio = (
+ self.config["preprocessing"]["resize"]
+ / self.config["generation"]["image_size"][0]
+ )
+ min_label_len = self.config["generation"]["min_label_len"] * scale_ratio
# if none => no restriction
else:
min_label_len = 0
-
+
# Initialize the transform
homographic_trans = homoaug.homography_transform(
- image_shape, homo_config, 0, min_label_len)
+ image_shape, homo_config, 0, min_label_len
+ )
return homographic_trans
@staticmethod
def junc_to_junc_map(junctions, image_size):
- """ Convert junction points to junction maps. """
+ """Convert junction points to junction maps."""
junctions = np.round(junctions).astype(np.int)
# Clip the boundary by image size
- junctions[:, 0] = np.clip(junctions[:, 0], 0., image_size[0]-1)
- junctions[:, 1] = np.clip(junctions[:, 1], 0., image_size[1]-1)
+ junctions[:, 0] = np.clip(junctions[:, 0], 0.0, image_size[0] - 1)
+ junctions[:, 1] = np.clip(junctions[:, 1], 0.0, image_size[1] - 1)
# Create junction map
junc_map = np.zeros([image_size[0], image_size[1]])
@@ -499,7 +512,7 @@ class SyntheticShapes(Dataset):
return junc_map[..., None].astype(np.int)
def train_preprocessing(self, data, disable_homoaug=False):
- """ Training preprocessing. """
+ """Training preprocessing."""
# Fetch corresponding entries
image = data["image"]
junctions = data["points"]
@@ -509,29 +522,32 @@ class SyntheticShapes(Dataset):
# Resize the image before the photometric and homographic transforms
# Check if we need to do the resizing
- if not(list(image.shape) == self.config["preprocessing"]["resize"]):
+ if not (list(image.shape) == self.config["preprocessing"]["resize"]):
# Resize the image and the point location.
size_old = list(image.shape)
image = cv2.resize(
- image, tuple(self.config['preprocessing']['resize'][::-1]),
- interpolation=cv2.INTER_LINEAR)
+ image,
+ tuple(self.config["preprocessing"]["resize"][::-1]),
+ interpolation=cv2.INTER_LINEAR,
+ )
image = np.array(image, dtype=np.uint8)
junctions = (
junctions
- * np.array(self.config['preprocessing']['resize'], np.float)
- / np.array(size_old, np.float))
+ * np.array(self.config["preprocessing"]["resize"], np.float)
+ / np.array(size_old, np.float)
+ )
# Generate the line heatmap after post-processing
- junctions_xy = np.flip(np.round(junctions).astype(np.int32),
- axis=1)
- heatmap = synthetic_util.get_line_heatmap(junctions_xy, line_map,
- size=image.shape)
- heatmap = (heatmap * 255.).astype(np.uint8)
+ junctions_xy = np.flip(np.round(junctions).astype(np.int32), axis=1)
+ heatmap = synthetic_util.get_line_heatmap(
+ junctions_xy, line_map, size=image.shape
+ )
+ heatmap = (heatmap * 255.0).astype(np.uint8)
# Update image size
image_size = image.shape[:2]
-
+
# Declare default valid mask (all ones)
valid_mask = np.ones(image_size)
@@ -544,7 +560,8 @@ class SyntheticShapes(Dataset):
### Image transform ###
np.random.shuffle(photo_trans_lst)
image_transform = transforms.Compose(
- photo_trans_lst + [photoaug.normalize_image()])
+ photo_trans_lst + [photoaug.normalize_image()]
+ )
else:
image_transform = photoaug.normalize_image()
image = image_transform(image)
@@ -554,40 +571,46 @@ class SyntheticShapes(Dataset):
# Convert to tensor and return the results
to_tensor = transforms.ToTensor()
# Check homographic augmentation
- if (self.config["augmentation"]["homographic"]["enable"]
- and disable_homoaug == False):
+ if (
+ self.config["augmentation"]["homographic"]["enable"]
+ and disable_homoaug == False
+ ):
homo_trans = self.get_homo_transform()
# Perform homographic transform
homo_outputs = homo_trans(image, junctions, line_map)
# Record the warped results
- junctions = homo_outputs["junctions"] # Should be HW format
+ junctions = homo_outputs["junctions"] # Should be HW format
image = homo_outputs["warped_image"]
line_map = homo_outputs["line_map"]
heatmap = homo_outputs["warped_heatmap"]
valid_mask = homo_outputs["valid_mask"] # Same for pos and neg
homography_mat = homo_outputs["homo"]
-
+
# Optionally put warpping information first.
- outputs["homography_mat"] = to_tensor(
- homography_mat).to(torch.float32)[0, ...]
+ outputs["homography_mat"] = to_tensor(homography_mat).to(torch.float32)[
+ 0, ...
+ ]
junction_map = self.junc_to_junc_map(junctions, image_size)
- outputs.update({
- "image": to_tensor(image),
- "junctions": to_tensor(np.ascontiguousarray(
- junctions).copy()).to(torch.float32)[0, ...],
- "junction_map": to_tensor(junction_map).to(torch.int),
- "line_map": to_tensor(line_map).to(torch.int32)[0, ...],
- "heatmap": to_tensor(heatmap).to(torch.int32),
- "valid_mask": to_tensor(valid_mask).to(torch.int32),
- })
+ outputs.update(
+ {
+ "image": to_tensor(image),
+ "junctions": to_tensor(np.ascontiguousarray(junctions).copy()).to(
+ torch.float32
+ )[0, ...],
+ "junction_map": to_tensor(junction_map).to(torch.int),
+ "line_map": to_tensor(line_map).to(torch.int32)[0, ...],
+ "heatmap": to_tensor(heatmap).to(torch.int32),
+ "valid_mask": to_tensor(valid_mask).to(torch.int32),
+ }
+ )
return outputs
def test_preprocessing(self, data):
- """ Test preprocessing. """
+ """Test preprocessing."""
# Fetch corresponding entries
image = data["image"]
points = data["points"]
@@ -600,20 +623,24 @@ class SyntheticShapes(Dataset):
# Resize the image and the point location.
size_old = list(image.shape)
image = cv2.resize(
- image, tuple(self.config['preprocessing']['resize'][::-1]),
- interpolation=cv2.INTER_LINEAR)
+ image,
+ tuple(self.config["preprocessing"]["resize"][::-1]),
+ interpolation=cv2.INTER_LINEAR,
+ )
image = np.array(image, dtype=np.uint8)
- points = (points
- * np.array(self.config['preprocessing']['resize'],
- np.float)
- / np.array(size_old, np.float))
+ points = (
+ points
+ * np.array(self.config["preprocessing"]["resize"], np.float)
+ / np.array(size_old, np.float)
+ )
# Generate the line heatmap after post-processing
junctions = np.flip(np.round(points).astype(np.int32), axis=1)
- heatmap = synthetic_util.get_line_heatmap(junctions, line_map,
- size=image.shape)
- heatmap = (heatmap * 255.).astype(np.uint8)
+ heatmap = synthetic_util.get_line_heatmap(
+ junctions, line_map, size=image.shape
+ )
+ heatmap = (heatmap * 255.0).astype(np.uint8)
# Update image size
image_size = image.shape[:2]
@@ -638,7 +665,7 @@ class SyntheticShapes(Dataset):
"junction_map": junction_map,
"line_map": line_map,
"heatmap": heatmap,
- "valid_mask": valid_mask
+ "valid_mask": valid_mask,
}
def __getitem__(self, index):
@@ -649,8 +676,7 @@ class SyntheticShapes(Dataset):
data = self.get_data_from_datapoint(datapoint, reader)
# Apply different transforms in different mod.
- if (self.mode == "train"
- or self.config["add_augmentation_to_all_splits"]):
+ if self.mode == "train" or self.config["add_augmentation_to_all_splits"]:
return_type = self.config.get("return_type", "single")
data = self.train_preprocessing(data)
else:
@@ -665,7 +691,7 @@ class SyntheticShapes(Dataset):
## Some other methods ##
########################
def _check_dataset_cache(self):
- """ Check if dataset cache exists. """
+ """Check if dataset cache exists."""
cache_file_path = os.path.join(self.cache_path, self.cache_name)
if os.path.exists(cache_file_path):
return True
@@ -673,7 +699,7 @@ class SyntheticShapes(Dataset):
return False
def _check_export_dataset(self):
- """ Check if exported dataset exists. """
+ """Check if exported dataset exists."""
dataset_path = os.path.join(cfg.synthetic_dataroot, self.dataset_name)
if os.path.exists(dataset_path) and len(os.listdir(dataset_path)) > 0:
return True
@@ -681,32 +707,30 @@ class SyntheticShapes(Dataset):
return False
def _check_file_existence(self, filename_dataset):
- """ Check if all exported file exists. """
+ """Check if all exported file exists."""
# Path to the exported dataset
- dataset_path = os.path.join(cfg.synthetic_dataroot,
- self.dataset_name + ".h5")
+ dataset_path = os.path.join(cfg.synthetic_dataroot, self.dataset_name + ".h5")
flag = True
# Open the h5 dataset
with h5py.File(dataset_path, "r") as f:
# Iterate through all the primitives
for prim_name in f.keys():
- if (len(filename_dataset[prim_name])
- != len(f[prim_name].keys())):
+ if len(filename_dataset[prim_name]) != len(f[prim_name].keys()):
flag = False
return flag
def _check_primitive_and_index(self, primitive, index):
- """ Check if the primitve and index are valid. """
+ """Check if the primitve and index are valid."""
# Check primitives
if not primitive in self.available_primitives:
- raise ValueError(
- "[Error] The primitive is not in available primitives.")
+ raise ValueError("[Error] The primitive is not in available primitives.")
prim_len = len(self.filename_dataset[primitive])
# Check the index
if not index < prim_len:
raise ValueError(
"[Error] The index exceeds the total file counts %d for %s"
- % (prim_len, primitive))
+ % (prim_len, primitive)
+ )
diff --git a/imcui/third_party/SOLD2/sold2/dataset/synthetic_util.py b/third_party/SOLD2/sold2/dataset/synthetic_util.py
similarity index 60%
rename from imcui/third_party/SOLD2/sold2/dataset/synthetic_util.py
rename to third_party/SOLD2/sold2/dataset/synthetic_util.py
index af009e0ce7e91391e31d7069064ae6121aa84cc0..63e41c5bbcadd4a1a633a2b33392dc6d4fd088ff 100644
--- a/imcui/third_party/SOLD2/sold2/dataset/synthetic_util.py
+++ b/third_party/SOLD2/sold2/dataset/synthetic_util.py
@@ -17,8 +17,8 @@ def set_random_state(state):
def get_random_color(background_color):
- """ Output a random scalar in grayscale with a least a small contrast
- with the background color. """
+ """Output a random scalar in grayscale with a least a small contrast
+ with the background color."""
color = random_state.randint(256)
if abs(color - background_color) < 30: # not enough contrast
color = (color + 128) % 256
@@ -26,7 +26,7 @@ def get_random_color(background_color):
def get_different_color(previous_colors, min_dist=50, max_count=20):
- """ Output a color that contrasts with the previous colors.
+ """Output a color that contrasts with the previous colors.
Parameters:
previous_colors: np.array of the previous colors
min_dist: the difference between the new color and
@@ -42,7 +42,7 @@ def get_different_color(previous_colors, min_dist=50, max_count=20):
def add_salt_and_pepper(img):
- """ Add salt and pepper noise to an image. """
+ """Add salt and pepper noise to an image."""
noise = np.zeros((img.shape[0], img.shape[1]), dtype=np.uint8)
cv.randu(noise, 0, 255)
black = noise < 30
@@ -53,10 +53,15 @@ def add_salt_and_pepper(img):
return np.empty((0, 2), dtype=np.int)
-def generate_background(size=(960, 1280), nb_blobs=100, min_rad_ratio=0.01,
- max_rad_ratio=0.05, min_kernel_size=50,
- max_kernel_size=300):
- """ Generate a customized background image.
+def generate_background(
+ size=(960, 1280),
+ nb_blobs=100,
+ min_rad_ratio=0.01,
+ max_rad_ratio=0.05,
+ min_kernel_size=50,
+ max_kernel_size=300,
+):
+ """Generate a customized background image.
Parameters:
size: size of the image
nb_blobs: number of circles to draw
@@ -71,22 +76,30 @@ def generate_background(size=(960, 1280), nb_blobs=100, min_rad_ratio=0.01,
cv.threshold(img, random_state.randint(256), 255, cv.THRESH_BINARY, img)
background_color = int(np.mean(img))
blobs = np.concatenate(
- [random_state.randint(0, size[1], size=(nb_blobs, 1)),
- random_state.randint(0, size[0], size=(nb_blobs, 1))], axis=1)
+ [
+ random_state.randint(0, size[1], size=(nb_blobs, 1)),
+ random_state.randint(0, size[0], size=(nb_blobs, 1)),
+ ],
+ axis=1,
+ )
for i in range(nb_blobs):
col = get_random_color(background_color)
- cv.circle(img, (blobs[i][0], blobs[i][1]),
- np.random.randint(int(dim * min_rad_ratio),
- int(dim * max_rad_ratio)),
- col, -1)
+ cv.circle(
+ img,
+ (blobs[i][0], blobs[i][1]),
+ np.random.randint(int(dim * min_rad_ratio), int(dim * max_rad_ratio)),
+ col,
+ -1,
+ )
kernel_size = random_state.randint(min_kernel_size, max_kernel_size)
cv.blur(img, (kernel_size, kernel_size), img)
return img
-def generate_custom_background(size, background_color, nb_blobs=3000,
- kernel_boundaries=(50, 100)):
- """ Generate a customized background to fill the shapes.
+def generate_custom_background(
+ size, background_color, nb_blobs=3000, kernel_boundaries=(50, 100)
+):
+ """Generate a customized background to fill the shapes.
Parameters:
background_color: average color of the background image
nb_blobs: number of circles to draw
@@ -95,20 +108,22 @@ def generate_custom_background(size, background_color, nb_blobs=3000,
img = np.zeros(size, dtype=np.uint8)
img = img + get_random_color(background_color)
blobs = np.concatenate(
- [np.random.randint(0, size[1], size=(nb_blobs, 1)),
- np.random.randint(0, size[0], size=(nb_blobs, 1))], axis=1)
+ [
+ np.random.randint(0, size[1], size=(nb_blobs, 1)),
+ np.random.randint(0, size[0], size=(nb_blobs, 1)),
+ ],
+ axis=1,
+ )
for i in range(nb_blobs):
col = get_random_color(background_color)
- cv.circle(img, (blobs[i][0], blobs[i][1]),
- np.random.randint(20), col, -1)
- kernel_size = np.random.randint(kernel_boundaries[0],
- kernel_boundaries[1])
+ cv.circle(img, (blobs[i][0], blobs[i][1]), np.random.randint(20), col, -1)
+ kernel_size = np.random.randint(kernel_boundaries[0], kernel_boundaries[1])
cv.blur(img, (kernel_size, kernel_size), img)
return img
def final_blur(img, kernel_size=(5, 5)):
- """ Gaussian blur applied to an image.
+ """Gaussian blur applied to an image.
Parameters:
kernel_size: size of the kernel
"""
@@ -116,33 +131,39 @@ def final_blur(img, kernel_size=(5, 5)):
def ccw(A, B, C, dim):
- """ Check if the points are listed in counter-clockwise order. """
+ """Check if the points are listed in counter-clockwise order."""
if dim == 2: # only 2 dimensions
- return((C[:, 1] - A[:, 1]) * (B[:, 0] - A[:, 0])
- > (B[:, 1] - A[:, 1]) * (C[:, 0] - A[:, 0]))
+ return (C[:, 1] - A[:, 1]) * (B[:, 0] - A[:, 0]) > (B[:, 1] - A[:, 1]) * (
+ C[:, 0] - A[:, 0]
+ )
else: # dim should be equal to 3
- return((C[:, 1, :] - A[:, 1, :])
- * (B[:, 0, :] - A[:, 0, :])
- > (B[:, 1, :] - A[:, 1, :])
- * (C[:, 0, :] - A[:, 0, :]))
+ return (C[:, 1, :] - A[:, 1, :]) * (B[:, 0, :] - A[:, 0, :]) > (
+ B[:, 1, :] - A[:, 1, :]
+ ) * (C[:, 0, :] - A[:, 0, :])
def intersect(A, B, C, D, dim):
- """ Return true if line segments AB and CD intersect """
- return np.any((ccw(A, C, D, dim) != ccw(B, C, D, dim)) &
- (ccw(A, B, C, dim) != ccw(A, B, D, dim)))
+ """Return true if line segments AB and CD intersect"""
+ return np.any(
+ (ccw(A, C, D, dim) != ccw(B, C, D, dim))
+ & (ccw(A, B, C, dim) != ccw(A, B, D, dim))
+ )
def keep_points_inside(points, size):
- """ Keep only the points whose coordinates are inside the dimensions of
- the image of size 'size' """
- mask = (points[:, 0] >= 0) & (points[:, 0] < size[1]) &\
- (points[:, 1] >= 0) & (points[:, 1] < size[0])
+ """Keep only the points whose coordinates are inside the dimensions of
+ the image of size 'size'"""
+ mask = (
+ (points[:, 0] >= 0)
+ & (points[:, 0] < size[1])
+ & (points[:, 1] >= 0)
+ & (points[:, 1] < size[0])
+ )
return points[mask, :]
def get_unique_junctions(segments, min_label_len):
- """ Get unique junction points from line segments. """
+ """Get unique junction points from line segments."""
# Get all junctions from segments
junctions_all = np.concatenate((segments[:, :2], segments[:, 2:]), axis=0)
if junctions_all.shape[0] == 0:
@@ -159,7 +180,7 @@ def get_unique_junctions(segments, min_label_len):
def get_line_map(points: np.ndarray, segments: np.ndarray) -> np.ndarray:
- """ Get line map given the points and segment sets. """
+ """Get line map given the points and segment sets."""
# create empty line map
num_point = points.shape[0]
line_map = np.zeros([num_point, num_point])
@@ -183,7 +204,7 @@ def get_line_map(points: np.ndarray, segments: np.ndarray) -> np.ndarray:
def get_line_heatmap(junctions, line_map, size=[480, 640], thickness=1):
- """ Get line heat map from junctions and line map. """
+ """Get line heat map from junctions and line map."""
# Make sure that the thickness is 1
if not isinstance(thickness, int):
thickness = int(thickness)
@@ -195,7 +216,7 @@ def get_line_heatmap(junctions, line_map, size=[480, 640], thickness=1):
# Initialize empty map
heat_map = np.zeros(size)
- if junctions.shape[0] > 0: # If empty, just return zero map
+ if junctions.shape[0] > 0: # If empty, just return zero map
# Iterate through all the junctions
for idx in range(junctions.shape[0]):
# if no connectivity, just skip it
@@ -209,13 +230,13 @@ def get_line_heatmap(junctions, line_map, size=[480, 640], thickness=1):
point2 = junctions[idx2, :]
# Draw line
- cv.line(heat_map, tuple(point1), tuple(point2), 1., thickness)
+ cv.line(heat_map, tuple(point1), tuple(point2), 1.0, thickness)
return heat_map
def draw_lines(img, nb_lines=10, min_len=32, min_label_len=32):
- """ Draw random lines and output the positions of the pair of junctions
+ """Draw random lines and output the positions of the pair of junctions
and line associativities.
Parameters:
nb_lines: maximal number of lines
@@ -228,9 +249,9 @@ def draw_lines(img, nb_lines=10, min_len=32, min_label_len=32):
min_dim = min(img.shape)
# Convert length constrain to pixel if given float number
- if isinstance(min_len, float) and min_len <= 1.:
+ if isinstance(min_len, float) and min_len <= 1.0:
min_len = int(min_dim * min_len)
- if isinstance(min_label_len, float) and min_label_len <= 1.:
+ if isinstance(min_label_len, float) and min_label_len <= 1.0:
min_label_len = int(min_dim * min_label_len)
# Generate lines one by one
@@ -258,10 +279,8 @@ def draw_lines(img, nb_lines=10, min_len=32, min_label_len=32):
# Only record the segments longer than min_label_len
seg_len = math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)
if seg_len >= min_label_len:
- segments = np.concatenate([segments,
- np.array([[x1, y1, x2, y2]])], axis=0)
- points = np.concatenate([points,
- np.array([[x1, y1], [x2, y2]])], axis=0)
+ segments = np.concatenate([segments, np.array([[x1, y1, x2, y2]])], axis=0)
+ points = np.concatenate([points, np.array([[x1, y1], [x2, y2]])], axis=0)
# If no line is drawn, recursively call the function
if points.shape[0] == 0:
@@ -270,19 +289,16 @@ def draw_lines(img, nb_lines=10, min_len=32, min_label_len=32):
# Get the line associativity map
line_map = get_line_map(points, segments)
- return {
- "points": points,
- "line_map": line_map
- }
+ return {"points": points, "line_map": line_map}
def check_segment_len(segments, min_len=32):
- """ Check if one of the segments is too short (True means too short). """
+ """Check if one of the segments is too short (True means too short)."""
point1_vec = segments[:, :2]
point2_vec = segments[:, 2:]
diff = point1_vec - point2_vec
- dist = np.sqrt(np.sum(diff ** 2, axis=1))
+ dist = np.sqrt(np.sum(diff**2, axis=1))
if np.any(dist < min_len):
return True
else:
@@ -290,7 +306,7 @@ def check_segment_len(segments, min_len=32):
def draw_polygon(img, max_sides=8, min_len=32, min_label_len=64):
- """ Draw a polygon with a random number of corners and return the position
+ """Draw a polygon with a random number of corners and return the position
of the junctions + line map.
Parameters:
max_sides: maximal number of sides + 1
@@ -303,31 +319,42 @@ def draw_polygon(img, max_sides=8, min_len=32, min_label_len=64):
y = random_state.randint(rad, img.shape[0] - rad)
# Convert length constrain to pixel if given float number
- if isinstance(min_len, float) and min_len <= 1.:
+ if isinstance(min_len, float) and min_len <= 1.0:
min_len = int(min_dim * min_len)
- if isinstance(min_label_len, float) and min_label_len <= 1.:
+ if isinstance(min_label_len, float) and min_label_len <= 1.0:
min_label_len = int(min_dim * min_label_len)
# Sample num_corners points inside the circle
slices = np.linspace(0, 2 * math.pi, num_corners + 1)
- angles = [slices[i] + random_state.rand() * (slices[i+1] - slices[i])
- for i in range(num_corners)]
+ angles = [
+ slices[i] + random_state.rand() * (slices[i + 1] - slices[i])
+ for i in range(num_corners)
+ ]
points = np.array(
- [[int(x + max(random_state.rand(), 0.4) * rad * math.cos(a)),
- int(y + max(random_state.rand(), 0.4) * rad * math.sin(a))]
- for a in angles])
+ [
+ [
+ int(x + max(random_state.rand(), 0.4) * rad * math.cos(a)),
+ int(y + max(random_state.rand(), 0.4) * rad * math.sin(a)),
+ ]
+ for a in angles
+ ]
+ )
# Filter the points that are too close or that have an angle too flat
- norms = [np.linalg.norm(points[(i-1) % num_corners, :]
- - points[i, :]) for i in range(num_corners)]
+ norms = [
+ np.linalg.norm(points[(i - 1) % num_corners, :] - points[i, :])
+ for i in range(num_corners)
+ ]
mask = np.array(norms) > 0.01
points = points[mask, :]
num_corners = points.shape[0]
- corner_angles = [angle_between_vectors(points[(i-1) % num_corners, :] -
- points[i, :],
- points[(i+1) % num_corners, :] -
- points[i, :])
- for i in range(num_corners)]
+ corner_angles = [
+ angle_between_vectors(
+ points[(i - 1) % num_corners, :] - points[i, :],
+ points[(i + 1) % num_corners, :] - points[i, :],
+ )
+ for i in range(num_corners)
+ ]
mask = np.array(corner_angles) < (2 * math.pi / 3)
points = points[mask, :]
num_corners = points.shape[0]
@@ -349,8 +376,7 @@ def draw_polygon(img, max_sides=8, min_len=32, min_label_len=64):
seg_len = np.sqrt(np.sum((p1 - p2) ** 2))
if seg_len >= min_label_len:
segments = np.concatenate((segments, segment[None, ...]), axis=0)
- segments_raw = np.concatenate((segments_raw, segment[None, ...]),
- axis=0)
+ segments_raw = np.concatenate((segments_raw, segment[None, ...]), axis=0)
# If not enough corner, just regenerate one
if (num_corners < 3) or check_segment_len(segments_raw, min_len):
@@ -372,15 +398,12 @@ def draw_polygon(img, max_sides=8, min_len=32, min_label_len=64):
col = get_random_color(int(np.mean(img)))
cv.fillPoly(img, [corners], col)
- return {
- "points": junc_points,
- "line_map": line_map
- }
+ return {"points": junc_points, "line_map": line_map}
def overlap(center, rad, centers, rads):
- """ Check that the circle with (center, rad)
- doesn't overlap with the other circles. """
+ """Check that the circle with (center, rad)
+ doesn't overlap with the other circles."""
flag = False
for i in range(len(rads)):
if np.linalg.norm(center - centers[i]) < rad + rads[i]:
@@ -390,15 +413,22 @@ def overlap(center, rad, centers, rads):
def angle_between_vectors(v1, v2):
- """ Compute the angle (in rad) between the two vectors v1 and v2. """
+ """Compute the angle (in rad) between the two vectors v1 and v2."""
v1_u = v1 / np.linalg.norm(v1)
v2_u = v2 / np.linalg.norm(v2)
return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
-def draw_multiple_polygons(img, max_sides=8, nb_polygons=30, min_len=32,
- min_label_len=64, safe_margin=5, **extra):
- """ Draw multiple polygons with a random number of corners
+def draw_multiple_polygons(
+ img,
+ max_sides=8,
+ nb_polygons=30,
+ min_len=32,
+ min_label_len=64,
+ safe_margin=5,
+ **extra
+):
+ """Draw multiple polygons with a random number of corners
and return the junction points + line map.
Parameters:
max_sides: maximal number of sides + 1
@@ -413,11 +443,11 @@ def draw_multiple_polygons(img, max_sides=8, nb_polygons=30, min_len=32,
min_dim = min(img.shape[0], img.shape[1])
# Convert length constrain to pixel if given float number
- if isinstance(min_len, float) and min_len <= 1.:
+ if isinstance(min_len, float) and min_len <= 1.0:
min_len = int(min_dim * min_len)
- if isinstance(min_label_len, float) and min_label_len <= 1.:
+ if isinstance(min_label_len, float) and min_label_len <= 1.0:
min_label_len = int(min_dim * min_label_len)
- if isinstance(safe_margin, float) and safe_margin <= 1.:
+ if isinstance(safe_margin, float) and safe_margin <= 1.0:
safe_margin = int(min_dim * safe_margin)
# Sequentially generate polygons
@@ -435,8 +465,10 @@ def draw_multiple_polygons(img, max_sides=8, nb_polygons=30, min_len=32,
# Sample num_corners points inside the circle
slices = np.linspace(0, 2 * math.pi, num_corners + 1)
- angles = [slices[i] + random_state.rand() * (slices[i+1] - slices[i])
- for i in range(num_corners)]
+ angles = [
+ slices[i] + random_state.rand() * (slices[i + 1] - slices[i])
+ for i in range(num_corners)
+ ]
# Sample outer points and inner points
new_points = []
@@ -444,29 +476,38 @@ def draw_multiple_polygons(img, max_sides=8, nb_polygons=30, min_len=32,
for a in angles:
x_offset = max(random_state.rand(), 0.4)
y_offset = max(random_state.rand(), 0.4)
- new_points.append([int(x + x_offset * rad * math.cos(a)),
- int(y + y_offset * rad * math.sin(a))])
+ new_points.append(
+ [
+ int(x + x_offset * rad * math.cos(a)),
+ int(y + y_offset * rad * math.sin(a)),
+ ]
+ )
new_points_real.append(
- [int(x + x_offset * rad_real * math.cos(a)),
- int(y + y_offset * rad_real * math.sin(a))])
+ [
+ int(x + x_offset * rad_real * math.cos(a)),
+ int(y + y_offset * rad_real * math.sin(a)),
+ ]
+ )
new_points = np.array(new_points)
new_points_real = np.array(new_points_real)
# Filter the points that are too close or that have an angle too flat
- norms = [np.linalg.norm(new_points[(i-1) % num_corners, :]
- - new_points[i, :])
- for i in range(num_corners)]
+ norms = [
+ np.linalg.norm(new_points[(i - 1) % num_corners, :] - new_points[i, :])
+ for i in range(num_corners)
+ ]
mask = np.array(norms) > 0.01
new_points = new_points[mask, :]
new_points_real = new_points_real[mask, :]
num_corners = new_points.shape[0]
corner_angles = [
- angle_between_vectors(new_points[(i-1) % num_corners, :] -
- new_points[i, :],
- new_points[(i+1) % num_corners, :] -
- new_points[i, :])
- for i in range(num_corners)]
+ angle_between_vectors(
+ new_points[(i - 1) % num_corners, :] - new_points[i, :],
+ new_points[(i + 1) % num_corners, :] - new_points[i, :],
+ )
+ for i in range(num_corners)
+ ]
mask = np.array(corner_angles) < (2 * math.pi / 3)
new_points = new_points[mask, :]
new_points_real = new_points_real[mask, :]
@@ -480,28 +521,32 @@ def draw_multiple_polygons(img, max_sides=8, nb_polygons=30, min_len=32,
new_segments = np.zeros((1, 4, num_corners))
new_segments[:, 0, :] = [new_points[i][0] for i in range(num_corners)]
new_segments[:, 1, :] = [new_points[i][1] for i in range(num_corners)]
- new_segments[:, 2, :] = [new_points[(i+1) % num_corners][0]
- for i in range(num_corners)]
- new_segments[:, 3, :] = [new_points[(i+1) % num_corners][1]
- for i in range(num_corners)]
+ new_segments[:, 2, :] = [
+ new_points[(i + 1) % num_corners][0] for i in range(num_corners)
+ ]
+ new_segments[:, 3, :] = [
+ new_points[(i + 1) % num_corners][1] for i in range(num_corners)
+ ]
# Segments to record (inner circle)
new_segments_real = np.zeros((1, 4, num_corners))
- new_segments_real[:, 0, :] = [new_points_real[i][0]
- for i in range(num_corners)]
- new_segments_real[:, 1, :] = [new_points_real[i][1]
- for i in range(num_corners)]
+ new_segments_real[:, 0, :] = [new_points_real[i][0] for i in range(num_corners)]
+ new_segments_real[:, 1, :] = [new_points_real[i][1] for i in range(num_corners)]
new_segments_real[:, 2, :] = [
- new_points_real[(i + 1) % num_corners][0]
- for i in range(num_corners)]
+ new_points_real[(i + 1) % num_corners][0] for i in range(num_corners)
+ ]
new_segments_real[:, 3, :] = [
- new_points_real[(i + 1) % num_corners][1]
- for i in range(num_corners)]
+ new_points_real[(i + 1) % num_corners][1] for i in range(num_corners)
+ ]
# Check that the polygon will not overlap with pre-existing shapes
- if intersect(segments[:, 0:2, None], segments[:, 2:4, None],
- new_segments[:, 0:2, :], new_segments[:, 2:4, :],
- 3) or overlap(np.array([x, y]), rad, centers, rads):
+ if intersect(
+ segments[:, 0:2, None],
+ segments[:, 2:4, None],
+ new_segments[:, 0:2, :],
+ new_segments[:, 2:4, :],
+ 3,
+ ) or overlap(np.array([x, y]), rad, centers, rads):
continue
# Check that the the edges of the polygon is not too short
@@ -515,20 +560,19 @@ def draw_multiple_polygons(img, max_sides=8, nb_polygons=30, min_len=32,
segments = np.concatenate([segments, new_segments], axis=0)
# Only record the segments longer than min_label_len
- new_segments_real = np.reshape(np.swapaxes(new_segments_real, 0, 2),
- (-1, 4))
+ new_segments_real = np.reshape(np.swapaxes(new_segments_real, 0, 2), (-1, 4))
points1 = new_segments_real[:, :2]
points2 = new_segments_real[:, 2:]
seg_len = np.sqrt(np.sum((points1 - points2) ** 2, axis=1))
new_label_segment = new_segments_real[seg_len >= min_label_len, :]
- label_segments = np.concatenate([label_segments, new_label_segment],
- axis=0)
+ label_segments = np.concatenate([label_segments, new_label_segment], axis=0)
# Color the polygon with a custom background
corners = new_points_real.reshape((-1, 1, 2))
mask = np.zeros(img.shape, np.uint8)
custom_background = generate_custom_background(
- img.shape, background_color, **extra)
+ img.shape, background_color, **extra
+ )
cv.fillPoly(mask, [corners], 255)
locs = np.where(mask != 0)
@@ -537,7 +581,8 @@ def draw_multiple_polygons(img, max_sides=8, nb_polygons=30, min_len=32,
# Get all junctions from label segments
junctions_all = np.concatenate(
- (label_segments[:, :2], label_segments[:, 2:]), axis=0)
+ (label_segments[:, :2], label_segments[:, 2:]), axis=0
+ )
if junctions_all.shape[0] == 0:
junc_points = None
line_map = None
@@ -548,14 +593,11 @@ def draw_multiple_polygons(img, max_sides=8, nb_polygons=30, min_len=32,
# Generate line map from points and segments
line_map = get_line_map(junc_points, label_segments)
- return {
- "points": junc_points,
- "line_map": line_map
- }
+ return {"points": junc_points, "line_map": line_map}
def draw_ellipses(img, nb_ellipses=20):
- """ Draw several ellipses.
+ """Draw several ellipses.
Parameters:
nb_ellipses: maximal number of ellipses
"""
@@ -585,16 +627,16 @@ def draw_ellipses(img, nb_ellipses=20):
def draw_star(img, nb_branches=6, min_len=32, min_label_len=64):
- """ Draw a star and return the junction points + line map.
+ """Draw a star and return the junction points + line map.
Parameters:
nb_branches: number of branches of the star
"""
num_branches = random_state.randint(3, nb_branches)
min_dim = min(img.shape[0], img.shape[1])
# Convert length constrain to pixel if given float number
- if isinstance(min_len, float) and min_len <= 1.:
+ if isinstance(min_len, float) and min_len <= 1.0:
min_len = int(min_dim * min_len)
- if isinstance(min_label_len, float) and min_label_len <= 1.:
+ if isinstance(min_label_len, float) and min_label_len <= 1.0:
min_label_len = int(min_dim * min_label_len)
thickness = random_state.randint(min_dim * 0.01, min_dim * 0.025)
@@ -603,12 +645,19 @@ def draw_star(img, nb_branches=6, min_len=32, min_label_len=64):
y = random_state.randint(rad, img.shape[0] - rad)
# Sample num_branches points inside the circle
slices = np.linspace(0, 2 * math.pi, num_branches + 1)
- angles = [slices[i] + random_state.rand() * (slices[i+1] - slices[i])
- for i in range(num_branches)]
+ angles = [
+ slices[i] + random_state.rand() * (slices[i + 1] - slices[i])
+ for i in range(num_branches)
+ ]
points = np.array(
- [[int(x + max(random_state.rand(), 0.3) * rad * math.cos(a)),
- int(y + max(random_state.rand(), 0.3) * rad * math.sin(a))]
- for a in angles])
+ [
+ [
+ int(x + max(random_state.rand(), 0.3) * rad * math.cos(a)),
+ int(y + max(random_state.rand(), 0.3) * rad * math.sin(a)),
+ ]
+ for a in angles
+ ]
+ )
points = np.concatenate(([[x, y]], points), axis=0)
# Generate segments and check the length
@@ -624,7 +673,8 @@ def draw_star(img, nb_branches=6, min_len=32, min_label_len=64):
# Get all junctions from label segments
junctions_all = np.concatenate(
- (label_segments[:, :2], label_segments[:, 2:]), axis=0)
+ (label_segments[:, :2], label_segments[:, 2:]), axis=0
+ )
if junctions_all.shape[0] == 0:
junc_points = None
line_map = None
@@ -638,19 +688,25 @@ def draw_star(img, nb_branches=6, min_len=32, min_label_len=64):
background_color = int(np.mean(img))
for i in range(1, num_branches + 1):
col = get_random_color(background_color)
- cv.line(img, (points[0][0], points[0][1]),
- (points[i][0], points[i][1]),
- col, thickness)
- return {
- "points": junc_points,
- "line_map": line_map
- }
-
-
-def draw_checkerboard_multiseg(img, max_rows=7, max_cols=7,
- transform_params=(0.05, 0.15),
- min_label_len=64, seed=None):
- """ Draw a checkerboard and output the junctions + line segments
+ cv.line(
+ img,
+ (points[0][0], points[0][1]),
+ (points[i][0], points[i][1]),
+ col,
+ thickness,
+ )
+ return {"points": junc_points, "line_map": line_map}
+
+
+def draw_checkerboard_multiseg(
+ img,
+ max_rows=7,
+ max_cols=7,
+ transform_params=(0.05, 0.15),
+ min_label_len=64,
+ seed=None,
+):
+ """Draw a checkerboard and output the junctions + line segments
Parameters:
max_rows: maximal number of rows + 1
max_cols: maximal number of cols + 1
@@ -664,57 +720,63 @@ def draw_checkerboard_multiseg(img, max_rows=7, max_cols=7,
background_color = int(np.mean(img))
min_dim = min(img.shape)
- if isinstance(min_label_len, float) and min_label_len <= 1.:
+ if isinstance(min_label_len, float) and min_label_len <= 1.0:
min_label_len = int(min_dim * min_label_len)
# Create the grid
rows = random_state.randint(3, max_rows) # number of rows
cols = random_state.randint(3, max_cols) # number of cols
s = min((img.shape[1] - 1) // cols, (img.shape[0] - 1) // rows)
- x_coord = np.tile(range(cols + 1),
- rows + 1).reshape(((rows + 1) * (cols + 1), 1))
- y_coord = np.repeat(range(rows + 1),
- cols + 1).reshape(((rows + 1) * (cols + 1), 1))
+ x_coord = np.tile(range(cols + 1), rows + 1).reshape(((rows + 1) * (cols + 1), 1))
+ y_coord = np.repeat(range(rows + 1), cols + 1).reshape(((rows + 1) * (cols + 1), 1))
# points are the grid coordinates
points = s * np.concatenate([x_coord, y_coord], axis=1)
# Warp the grid using an affine transformation and an homography
alpha_affine = np.max(img.shape) * (
- transform_params[0] + random_state.rand() * transform_params[1])
+ transform_params[0] + random_state.rand() * transform_params[1]
+ )
center_square = np.float32(img.shape) // 2
min_dim = min(img.shape)
square_size = min_dim // 3
- pts1 = np.float32([center_square + square_size,
- [center_square[0] + square_size,
- center_square[1] - square_size],
- center_square - square_size,
- [center_square[0] - square_size,
- center_square[1] + square_size]])
- pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine,
- size=pts1.shape).astype(np.float32)
+ pts1 = np.float32(
+ [
+ center_square + square_size,
+ [center_square[0] + square_size, center_square[1] - square_size],
+ center_square - square_size,
+ [center_square[0] - square_size, center_square[1] + square_size],
+ ]
+ )
+ pts2 = pts1 + random_state.uniform(
+ -alpha_affine, alpha_affine, size=pts1.shape
+ ).astype(np.float32)
affine_transform = cv.getAffineTransform(pts1[:3], pts2[:3])
- pts2 = pts1 + random_state.uniform(-alpha_affine / 2, alpha_affine / 2,
- size=pts1.shape).astype(np.float32)
+ pts2 = pts1 + random_state.uniform(
+ -alpha_affine / 2, alpha_affine / 2, size=pts1.shape
+ ).astype(np.float32)
perspective_transform = cv.getPerspectiveTransform(pts1, pts2)
# Apply the affine transformation
- points = np.transpose(np.concatenate(
- (points, np.ones(((rows + 1) * (cols + 1), 1))), axis=1))
+ points = np.transpose(
+ np.concatenate((points, np.ones(((rows + 1) * (cols + 1), 1))), axis=1)
+ )
warped_points = np.transpose(np.dot(affine_transform, points))
# Apply the homography
- warped_col0 = np.add(np.sum(np.multiply(
- warped_points, perspective_transform[0, :2]), axis=1),
- perspective_transform[0, 2])
- warped_col1 = np.add(np.sum(np.multiply(
- warped_points, perspective_transform[1, :2]), axis=1),
- perspective_transform[1, 2])
- warped_col2 = np.add(np.sum(np.multiply(
- warped_points, perspective_transform[2, :2]), axis=1),
- perspective_transform[2, 2])
+ warped_col0 = np.add(
+ np.sum(np.multiply(warped_points, perspective_transform[0, :2]), axis=1),
+ perspective_transform[0, 2],
+ )
+ warped_col1 = np.add(
+ np.sum(np.multiply(warped_points, perspective_transform[1, :2]), axis=1),
+ perspective_transform[1, 2],
+ )
+ warped_col2 = np.add(
+ np.sum(np.multiply(warped_points, perspective_transform[2, :2]), axis=1),
+ perspective_transform[2, 2],
+ )
warped_col0 = np.divide(warped_col0, warped_col2)
warped_col1 = np.divide(warped_col1, warped_col2)
- warped_points = np.concatenate(
- [warped_col0[:, None], warped_col1[:, None]], axis=1)
+ warped_points = np.concatenate([warped_col0[:, None], warped_col1[:, None]], axis=1)
warped_points_float = warped_points.copy()
warped_points = warped_points.astype(int)
@@ -735,15 +797,30 @@ def draw_checkerboard_multiseg(img, max_rows=7, max_cols=7,
colors[i * cols + j] = col
# Fill the cell
- cv.fillConvexPoly(img, np.array(
- [(warped_points[i * (cols + 1) + j, 0],
- warped_points[i * (cols + 1) + j, 1]),
- (warped_points[i * (cols + 1) + j + 1, 0],
- warped_points[i * (cols + 1) + j + 1, 1]),
- (warped_points[(i + 1) * (cols + 1) + j + 1, 0],
- warped_points[(i + 1) * (cols + 1) + j + 1, 1]),
- (warped_points[(i + 1) * (cols + 1) + j, 0],
- warped_points[(i + 1) * (cols + 1) + j, 1])]), col)
+ cv.fillConvexPoly(
+ img,
+ np.array(
+ [
+ (
+ warped_points[i * (cols + 1) + j, 0],
+ warped_points[i * (cols + 1) + j, 1],
+ ),
+ (
+ warped_points[i * (cols + 1) + j + 1, 0],
+ warped_points[i * (cols + 1) + j + 1, 1],
+ ),
+ (
+ warped_points[(i + 1) * (cols + 1) + j + 1, 0],
+ warped_points[(i + 1) * (cols + 1) + j + 1, 1],
+ ),
+ (
+ warped_points[(i + 1) * (cols + 1) + j, 0],
+ warped_points[(i + 1) * (cols + 1) + j, 1],
+ ),
+ ]
+ ),
+ col,
+ )
label_segments = np.empty([0, 4], dtype=np.int)
# Iterate through rows
@@ -751,12 +828,18 @@ def draw_checkerboard_multiseg(img, max_rows=7, max_cols=7,
# Include all the combination of the junctions
# Iterate through all the combination of junction index in that row
multi_seg_lst = [
- np.array([warped_points_float[id1, 0],
- warped_points_float[id1, 1],
- warped_points_float[id2, 0],
- warped_points_float[id2, 1]])[None, ...]
- for (id1, id2) in combinations(range(
- row_idx * (cols + 1), (row_idx + 1) * (cols + 1), 1), 2)]
+ np.array(
+ [
+ warped_points_float[id1, 0],
+ warped_points_float[id1, 1],
+ warped_points_float[id2, 0],
+ warped_points_float[id2, 1],
+ ]
+ )[None, ...]
+ for (id1, id2) in combinations(
+ range(row_idx * (cols + 1), (row_idx + 1) * (cols + 1), 1), 2
+ )
+ ]
multi_seg = np.concatenate(multi_seg_lst, axis=0)
label_segments = np.concatenate((label_segments, multi_seg), axis=0)
@@ -765,20 +848,31 @@ def draw_checkerboard_multiseg(img, max_rows=7, max_cols=7,
# Include all the combination of the junctions
# Iterate throuhg all the combination of junction index in that column
multi_seg_lst = [
- np.array([warped_points_float[id1, 0],
- warped_points_float[id1, 1],
- warped_points_float[id2, 0],
- warped_points_float[id2, 1]])[None, ...]
- for (id1, id2) in combinations(range(
- col_idx, col_idx + ((rows + 1) * (cols + 1)), cols + 1), 2)]
+ np.array(
+ [
+ warped_points_float[id1, 0],
+ warped_points_float[id1, 1],
+ warped_points_float[id2, 0],
+ warped_points_float[id2, 1],
+ ]
+ )[None, ...]
+ for (id1, id2) in combinations(
+ range(col_idx, col_idx + ((rows + 1) * (cols + 1)), cols + 1), 2
+ )
+ ]
multi_seg = np.concatenate(multi_seg_lst, axis=0)
label_segments = np.concatenate((label_segments, multi_seg), axis=0)
label_segments_filtered = np.zeros([0, 4])
# Define image boundary polygon (in x y manner)
image_poly = shapely.geometry.Polygon(
- [[0, 0], [img.shape[1] - 1, 0], [img.shape[1] - 1, img.shape[0] - 1],
- [0, img.shape[0] - 1]])
+ [
+ [0, 0],
+ [img.shape[1] - 1, 0],
+ [img.shape[1] - 1, img.shape[0] - 1],
+ [0, img.shape[0] - 1],
+ ]
+ )
for idx in range(label_segments.shape[0]):
# Get the line segment
seg_raw = label_segments[idx, :]
@@ -787,20 +881,21 @@ def draw_checkerboard_multiseg(img, max_rows=7, max_cols=7,
# The line segment is just inside the image.
if seg.intersection(image_poly) == seg:
label_segments_filtered = np.concatenate(
- (label_segments_filtered, seg_raw[None, ...]), axis=0)
+ (label_segments_filtered, seg_raw[None, ...]), axis=0
+ )
# Intersect with the image.
elif seg.intersects(image_poly):
# Check intersection
try:
- p = np.array(seg.intersection(
- image_poly).coords).reshape([-1, 4])
+ p = np.array(seg.intersection(image_poly).coords).reshape([-1, 4])
# If intersect with eact one point
except:
continue
segment = p
label_segments_filtered = np.concatenate(
- (label_segments_filtered, segment), axis=0)
+ (label_segments_filtered, segment), axis=0
+ )
else:
continue
@@ -814,8 +909,7 @@ def draw_checkerboard_multiseg(img, max_rows=7, max_cols=7,
label_segments = label_segments[seg_len >= min_label_len, :]
# Get all junctions from label segments
- junc_points, line_map = get_unique_junctions(label_segments,
- min_label_len)
+ junc_points, line_map = get_unique_junctions(label_segments, min_label_len)
# Draw lines on the boundaries of the board at random
nb_rows = random_state.randint(2, rows + 2)
@@ -826,33 +920,52 @@ def draw_checkerboard_multiseg(img, max_rows=7, max_cols=7,
col_idx1 = random_state.randint(cols + 1)
col_idx2 = random_state.randint(cols + 1)
col = get_random_color(background_color)
- cv.line(img, (warped_points[row_idx * (cols + 1) + col_idx1, 0],
- warped_points[row_idx * (cols + 1) + col_idx1, 1]),
- (warped_points[row_idx * (cols + 1) + col_idx2, 0],
- warped_points[row_idx * (cols + 1) + col_idx2, 1]),
- col, thickness)
+ cv.line(
+ img,
+ (
+ warped_points[row_idx * (cols + 1) + col_idx1, 0],
+ warped_points[row_idx * (cols + 1) + col_idx1, 1],
+ ),
+ (
+ warped_points[row_idx * (cols + 1) + col_idx2, 0],
+ warped_points[row_idx * (cols + 1) + col_idx2, 1],
+ ),
+ col,
+ thickness,
+ )
for _ in range(nb_cols):
col_idx = random_state.randint(cols + 1)
row_idx1 = random_state.randint(rows + 1)
row_idx2 = random_state.randint(rows + 1)
col = get_random_color(background_color)
- cv.line(img, (warped_points[row_idx1 * (cols + 1) + col_idx, 0],
- warped_points[row_idx1 * (cols + 1) + col_idx, 1]),
- (warped_points[row_idx2 * (cols + 1) + col_idx, 0],
- warped_points[row_idx2 * (cols + 1) + col_idx, 1]),
- col, thickness)
+ cv.line(
+ img,
+ (
+ warped_points[row_idx1 * (cols + 1) + col_idx, 0],
+ warped_points[row_idx1 * (cols + 1) + col_idx, 1],
+ ),
+ (
+ warped_points[row_idx2 * (cols + 1) + col_idx, 0],
+ warped_points[row_idx2 * (cols + 1) + col_idx, 1],
+ ),
+ col,
+ thickness,
+ )
# Keep only the points inside the image
points = keep_points_inside(warped_points, img.shape[:2])
- return {
- "points": junc_points,
- "line_map": line_map
- }
-
-
-def draw_stripes_multiseg(img, max_nb_cols=13, min_len=0.04, min_label_len=64,
- transform_params=(0.05, 0.15), seed=None):
- """ Draw stripes in a distorted rectangle
+ return {"points": junc_points, "line_map": line_map}
+
+
+def draw_stripes_multiseg(
+ img,
+ max_nb_cols=13,
+ min_len=0.04,
+ min_label_len=64,
+ transform_params=(0.05, 0.15),
+ seed=None,
+):
+ """Draw stripes in a distorted rectangle
and output the junctions points + line map.
Parameters:
max_nb_cols: maximal number of stripes to be drawn
@@ -868,73 +981,84 @@ def draw_stripes_multiseg(img, max_nb_cols=13, min_len=0.04, min_label_len=64,
background_color = int(np.mean(img))
# Create the grid
- board_size = (int(img.shape[0] * (1 + random_state.rand())),
- int(img.shape[1] * (1 + random_state.rand())))
+ board_size = (
+ int(img.shape[0] * (1 + random_state.rand())),
+ int(img.shape[1] * (1 + random_state.rand())),
+ )
# Number of cols
col = random_state.randint(5, max_nb_cols)
- cols = np.concatenate([board_size[1] * random_state.rand(col - 1),
- np.array([0, board_size[1] - 1])], axis=0)
+ cols = np.concatenate(
+ [board_size[1] * random_state.rand(col - 1), np.array([0, board_size[1] - 1])],
+ axis=0,
+ )
cols = np.unique(cols.astype(int))
# Remove the indices that are too close
min_dim = min(img.shape)
# Convert length constrain to pixel if given float number
- if isinstance(min_len, float) and min_len <= 1.:
+ if isinstance(min_len, float) and min_len <= 1.0:
min_len = int(min_dim * min_len)
- if isinstance(min_label_len, float) and min_label_len <= 1.:
+ if isinstance(min_label_len, float) and min_label_len <= 1.0:
min_label_len = int(min_dim * min_label_len)
- cols = cols[(np.concatenate([cols[1:],
- np.array([board_size[1] + min_len])],
- axis=0) - cols) >= min_len]
+ cols = cols[
+ (np.concatenate([cols[1:], np.array([board_size[1] + min_len])], axis=0) - cols)
+ >= min_len
+ ]
# Update the number of cols
col = cols.shape[0] - 1
cols = np.reshape(cols, (col + 1, 1))
cols1 = np.concatenate([cols, np.zeros((col + 1, 1), np.int32)], axis=1)
cols2 = np.concatenate(
- [cols, (board_size[0] - 1) * np.ones((col + 1, 1), np.int32)], axis=1)
+ [cols, (board_size[0] - 1) * np.ones((col + 1, 1), np.int32)], axis=1
+ )
points = np.concatenate([cols1, cols2], axis=0)
# Warp the grid using an affine transformation and a homography
alpha_affine = np.max(img.shape) * (
- transform_params[0] + random_state.rand() * transform_params[1])
+ transform_params[0] + random_state.rand() * transform_params[1]
+ )
center_square = np.float32(img.shape) // 2
square_size = min(img.shape) // 3
- pts1 = np.float32([center_square + square_size,
- [center_square[0]+square_size,
- center_square[1]-square_size],
- center_square - square_size,
- [center_square[0]-square_size,
- center_square[1]+square_size]])
- pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine,
- size=pts1.shape).astype(np.float32)
+ pts1 = np.float32(
+ [
+ center_square + square_size,
+ [center_square[0] + square_size, center_square[1] - square_size],
+ center_square - square_size,
+ [center_square[0] - square_size, center_square[1] + square_size],
+ ]
+ )
+ pts2 = pts1 + random_state.uniform(
+ -alpha_affine, alpha_affine, size=pts1.shape
+ ).astype(np.float32)
affine_transform = cv.getAffineTransform(pts1[:3], pts2[:3])
- pts2 = pts1 + random_state.uniform(-alpha_affine / 2, alpha_affine / 2,
- size=pts1.shape).astype(np.float32)
+ pts2 = pts1 + random_state.uniform(
+ -alpha_affine / 2, alpha_affine / 2, size=pts1.shape
+ ).astype(np.float32)
perspective_transform = cv.getPerspectiveTransform(pts1, pts2)
# Apply the affine transformation
- points = np.transpose(np.concatenate((points,
- np.ones((2 * (col + 1), 1))),
- axis=1))
+ points = np.transpose(np.concatenate((points, np.ones((2 * (col + 1), 1))), axis=1))
warped_points = np.transpose(np.dot(affine_transform, points))
# Apply the homography
- warped_col0 = np.add(np.sum(np.multiply(
- warped_points, perspective_transform[0, :2]), axis=1),
- perspective_transform[0, 2])
- warped_col1 = np.add(np.sum(np.multiply(
- warped_points, perspective_transform[1, :2]), axis=1),
- perspective_transform[1, 2])
- warped_col2 = np.add(np.sum(np.multiply(
- warped_points, perspective_transform[2, :2]), axis=1),
- perspective_transform[2, 2])
+ warped_col0 = np.add(
+ np.sum(np.multiply(warped_points, perspective_transform[0, :2]), axis=1),
+ perspective_transform[0, 2],
+ )
+ warped_col1 = np.add(
+ np.sum(np.multiply(warped_points, perspective_transform[1, :2]), axis=1),
+ perspective_transform[1, 2],
+ )
+ warped_col2 = np.add(
+ np.sum(np.multiply(warped_points, perspective_transform[2, :2]), axis=1),
+ perspective_transform[2, 2],
+ )
warped_col0 = np.divide(warped_col0, warped_col2)
warped_col1 = np.divide(warped_col1, warped_col2)
- warped_points = np.concatenate(
- [warped_col0[:, None], warped_col1[:, None]], axis=1)
+ warped_points = np.concatenate([warped_col0[:, None], warped_col1[:, None]], axis=1)
warped_points_float = warped_points.copy()
warped_points = warped_points.astype(int)
@@ -944,15 +1068,18 @@ def draw_stripes_multiseg(img, max_nb_cols=13, min_len=0.04, min_label_len=64,
for i in range(col):
# Fill the color
color = (color + 128 + random_state.randint(-30, 30)) % 256
- cv.fillConvexPoly(img, np.array([(warped_points[i, 0],
- warped_points[i, 1]),
- (warped_points[i+1, 0],
- warped_points[i+1, 1]),
- (warped_points[i+col+2, 0],
- warped_points[i+col+2, 1]),
- (warped_points[i+col+1, 0],
- warped_points[i+col+1, 1])]),
- color)
+ cv.fillConvexPoly(
+ img,
+ np.array(
+ [
+ (warped_points[i, 0], warped_points[i, 1]),
+ (warped_points[i + 1, 0], warped_points[i + 1, 1]),
+ (warped_points[i + col + 2, 0], warped_points[i + col + 2, 1]),
+ (warped_points[i + col + 1, 0], warped_points[i + col + 1, 1]),
+ ]
+ ),
+ color,
+ )
segments = np.zeros([0, 4])
row = 1 # in stripes case
@@ -960,27 +1087,39 @@ def draw_stripes_multiseg(img, max_nb_cols=13, min_len=0.04, min_label_len=64,
for row_idx in range(row + 1):
# Include all the combination of the junctions
# Iterate through all the combination of junction index in that row
- multi_seg_lst = [np.array(
- [warped_points_float[id1, 0],
- warped_points_float[id1, 1],
- warped_points_float[id2, 0],
- warped_points_float[id2, 1]])[None, ...]
- for (id1, id2) in combinations(range(
- row_idx * (col + 1), (row_idx + 1) * (col + 1), 1), 2)]
+ multi_seg_lst = [
+ np.array(
+ [
+ warped_points_float[id1, 0],
+ warped_points_float[id1, 1],
+ warped_points_float[id2, 0],
+ warped_points_float[id2, 1],
+ ]
+ )[None, ...]
+ for (id1, id2) in combinations(
+ range(row_idx * (col + 1), (row_idx + 1) * (col + 1), 1), 2
+ )
+ ]
multi_seg = np.concatenate(multi_seg_lst, axis=0)
segments = np.concatenate((segments, multi_seg), axis=0)
# Iterate through columns
- for col_idx in range(col + 1): # for 5 columns, we will have 5 + 1 edges.
+ for col_idx in range(col + 1): # for 5 columns, we will have 5 + 1 edges.
# Include all the combination of the junctions
# Iterate throuhg all the combination of junction index in that column
- multi_seg_lst = [np.array(
- [warped_points_float[id1, 0],
- warped_points_float[id1, 1],
- warped_points_float[id2, 0],
- warped_points_float[id2, 1]])[None, ...]
- for (id1, id2) in combinations(range(
- col_idx, col_idx + (row * col) + 2, col + 1), 2)]
+ multi_seg_lst = [
+ np.array(
+ [
+ warped_points_float[id1, 0],
+ warped_points_float[id1, 1],
+ warped_points_float[id2, 0],
+ warped_points_float[id2, 1],
+ ]
+ )[None, ...]
+ for (id1, id2) in combinations(
+ range(col_idx, col_idx + (row * col) + 2, col + 1), 2
+ )
+ ]
multi_seg = np.concatenate(multi_seg_lst, axis=0)
segments = np.concatenate((segments, multi_seg), axis=0)
@@ -988,8 +1127,13 @@ def draw_stripes_multiseg(img, max_nb_cols=13, min_len=0.04, min_label_len=64,
segments_new = np.zeros([0, 4])
# Define image boundary polygon (in x y manner)
image_poly = shapely.geometry.Polygon(
- [[0, 0], [img.shape[1]-1, 0], [img.shape[1]-1, img.shape[0]-1],
- [0, img.shape[0]-1]])
+ [
+ [0, 0],
+ [img.shape[1] - 1, 0],
+ [img.shape[1] - 1, img.shape[0] - 1],
+ [0, img.shape[0] - 1],
+ ]
+ )
for idx in range(segments.shape[0]):
# Get the line segment
seg_raw = segments[idx, :]
@@ -997,15 +1141,13 @@ def draw_stripes_multiseg(img, max_nb_cols=13, min_len=0.04, min_label_len=64,
# The line segment is just inside the image.
if seg.intersection(image_poly) == seg:
- segments_new = np.concatenate(
- (segments_new, seg_raw[None, ...]), axis=0)
+ segments_new = np.concatenate((segments_new, seg_raw[None, ...]), axis=0)
# Intersect with the image.
elif seg.intersects(image_poly):
# Check intersection
try:
- p = np.array(
- seg.intersection(image_poly).coords).reshape([-1, 4])
+ p = np.array(seg.intersection(image_poly).coords).reshape([-1, 4])
# If intersect at exact one point, just continue.
except:
continue
@@ -1025,7 +1167,8 @@ def draw_stripes_multiseg(img, max_nb_cols=13, min_len=0.04, min_label_len=64,
# Get all junctions from label segments
junctions_all = np.concatenate(
- (label_segments[:, :2], label_segments[:, 2:]), axis=0)
+ (label_segments[:, :2], label_segments[:, 2:]), axis=0
+ )
if junctions_all.shape[0] == 0:
junc_points = None
line_map = None
@@ -1045,32 +1188,44 @@ def draw_stripes_multiseg(img, max_nb_cols=13, min_len=0.04, min_label_len=64,
col_idx1 = random_state.randint(col + 1)
col_idx2 = random_state.randint(col + 1)
color = get_random_color(background_color)
- cv.line(img, (warped_points[row_idx + col_idx1, 0],
- warped_points[row_idx + col_idx1, 1]),
- (warped_points[row_idx + col_idx2, 0],
- warped_points[row_idx + col_idx2, 1]),
- color, thickness)
+ cv.line(
+ img,
+ (
+ warped_points[row_idx + col_idx1, 0],
+ warped_points[row_idx + col_idx1, 1],
+ ),
+ (
+ warped_points[row_idx + col_idx2, 0],
+ warped_points[row_idx + col_idx2, 1],
+ ),
+ color,
+ thickness,
+ )
for _ in range(nb_cols):
col_idx = random_state.randint(col + 1)
color = get_random_color(background_color)
- cv.line(img, (warped_points[col_idx, 0],
- warped_points[col_idx, 1]),
- (warped_points[col_idx + col + 1, 0],
- warped_points[col_idx + col + 1, 1]),
- color, thickness)
+ cv.line(
+ img,
+ (warped_points[col_idx, 0], warped_points[col_idx, 1]),
+ (warped_points[col_idx + col + 1, 0], warped_points[col_idx + col + 1, 1]),
+ color,
+ thickness,
+ )
# Keep only the points inside the image
# points = keep_points_inside(warped_points, img.shape[:2])
- return {
- "points": junc_points,
- "line_map": line_map
- }
+ return {"points": junc_points, "line_map": line_map}
-def draw_cube(img, min_size_ratio=0.2, min_label_len=64,
- scale_interval=(0.4, 0.6), trans_interval=(0.5, 0.2)):
- """ Draw a 2D projection of a cube and output the visible juntions.
+def draw_cube(
+ img,
+ min_size_ratio=0.2,
+ min_label_len=64,
+ scale_interval=(0.4, 0.6),
+ trans_interval=(0.5, 0.2),
+):
+ """Draw a 2D projection of a cube and output the visible juntions.
Parameters:
min_size_ratio: min(img.shape) * min_size_ratio is the smallest
achievable cube side size
@@ -1088,46 +1243,68 @@ def draw_cube(img, min_size_ratio=0.2, min_label_len=64,
lx = min_side + random_state.rand() * 2 * min_dim / 3 # dims of the cube
ly = min_side + random_state.rand() * 2 * min_dim / 3
lz = min_side + random_state.rand() * 2 * min_dim / 3
- cube = np.array([[0, 0, 0],
- [lx, 0, 0],
- [0, ly, 0],
- [lx, ly, 0],
- [0, 0, lz],
- [lx, 0, lz],
- [0, ly, lz],
- [lx, ly, lz]])
- rot_angles = random_state.rand(3) * 3 * math.pi / 10. + math.pi / 10.
- rotation_1 = np.array([[math.cos(rot_angles[0]),
- -math.sin(rot_angles[0]), 0],
- [math.sin(rot_angles[0]),
- math.cos(rot_angles[0]), 0],
- [0, 0, 1]])
- rotation_2 = np.array([[1, 0, 0],
- [0, math.cos(rot_angles[1]),
- -math.sin(rot_angles[1])],
- [0, math.sin(rot_angles[1]),
- math.cos(rot_angles[1])]])
- rotation_3 = np.array([[math.cos(rot_angles[2]), 0,
- -math.sin(rot_angles[2])],
- [0, 1, 0],
- [math.sin(rot_angles[2]), 0,
- math.cos(rot_angles[2])]])
- scaling = np.array([[scale_interval[0] +
- random_state.rand() * scale_interval[1], 0, 0],
- [0, scale_interval[0] +
- random_state.rand() * scale_interval[1], 0],
- [0, 0, scale_interval[0] +
- random_state.rand() * scale_interval[1]]])
- trans = np.array([img.shape[1] * trans_interval[0] +
- random_state.randint(-img.shape[1] * trans_interval[1],
- img.shape[1] * trans_interval[1]),
- img.shape[0] * trans_interval[0] +
- random_state.randint(-img.shape[0] * trans_interval[1],
- img.shape[0] * trans_interval[1]),
- 0])
+ cube = np.array(
+ [
+ [0, 0, 0],
+ [lx, 0, 0],
+ [0, ly, 0],
+ [lx, ly, 0],
+ [0, 0, lz],
+ [lx, 0, lz],
+ [0, ly, lz],
+ [lx, ly, lz],
+ ]
+ )
+ rot_angles = random_state.rand(3) * 3 * math.pi / 10.0 + math.pi / 10.0
+ rotation_1 = np.array(
+ [
+ [math.cos(rot_angles[0]), -math.sin(rot_angles[0]), 0],
+ [math.sin(rot_angles[0]), math.cos(rot_angles[0]), 0],
+ [0, 0, 1],
+ ]
+ )
+ rotation_2 = np.array(
+ [
+ [1, 0, 0],
+ [0, math.cos(rot_angles[1]), -math.sin(rot_angles[1])],
+ [0, math.sin(rot_angles[1]), math.cos(rot_angles[1])],
+ ]
+ )
+ rotation_3 = np.array(
+ [
+ [math.cos(rot_angles[2]), 0, -math.sin(rot_angles[2])],
+ [0, 1, 0],
+ [math.sin(rot_angles[2]), 0, math.cos(rot_angles[2])],
+ ]
+ )
+ scaling = np.array(
+ [
+ [scale_interval[0] + random_state.rand() * scale_interval[1], 0, 0],
+ [0, scale_interval[0] + random_state.rand() * scale_interval[1], 0],
+ [0, 0, scale_interval[0] + random_state.rand() * scale_interval[1]],
+ ]
+ )
+ trans = np.array(
+ [
+ img.shape[1] * trans_interval[0]
+ + random_state.randint(
+ -img.shape[1] * trans_interval[1], img.shape[1] * trans_interval[1]
+ ),
+ img.shape[0] * trans_interval[0]
+ + random_state.randint(
+ -img.shape[0] * trans_interval[1], img.shape[0] * trans_interval[1]
+ ),
+ 0,
+ ]
+ )
cube = trans + np.transpose(
- np.dot(scaling, np.dot(rotation_1,
- np.dot(rotation_2, np.dot(rotation_3, np.transpose(cube))))))
+ np.dot(
+ scaling,
+ np.dot(
+ rotation_1, np.dot(rotation_2, np.dot(rotation_3, np.transpose(cube)))
+ ),
+ )
+ )
# The hidden corner is 0 by construction
# The front one is 7
@@ -1145,18 +1322,26 @@ def draw_cube(img, min_size_ratio=0.2, min_label_len=64,
face = faces[face_idx, :]
# Brute-forcely expand all the segments
segment = np.array(
- [np.concatenate((cube[face[0]], cube[face[1]]), axis=0),
- np.concatenate((cube[face[1]], cube[face[2]]), axis=0),
- np.concatenate((cube[face[2]], cube[face[3]]), axis=0),
- np.concatenate((cube[face[3]], cube[face[0]]), axis=0)])
+ [
+ np.concatenate((cube[face[0]], cube[face[1]]), axis=0),
+ np.concatenate((cube[face[1]], cube[face[2]]), axis=0),
+ np.concatenate((cube[face[2]], cube[face[3]]), axis=0),
+ np.concatenate((cube[face[3]], cube[face[0]]), axis=0),
+ ]
+ )
segments = np.concatenate((segments, segment), axis=0)
# Select and refine the segments
segments_new = np.zeros([0, 4])
# Define image boundary polygon (in x y manner)
image_poly = shapely.geometry.Polygon(
- [[0, 0], [img.shape[1] - 1, 0], [img.shape[1] - 1, img.shape[0] - 1],
- [0, img.shape[0] - 1]])
+ [
+ [0, 0],
+ [img.shape[1] - 1, 0],
+ [img.shape[1] - 1, img.shape[0] - 1],
+ [0, img.shape[0] - 1],
+ ]
+ )
for idx in range(segments.shape[0]):
# Get the line segment
seg_raw = segments[idx, :]
@@ -1164,14 +1349,12 @@ def draw_cube(img, min_size_ratio=0.2, min_label_len=64,
# The line segment is just inside the image.
if seg.intersection(image_poly) == seg:
- segments_new = np.concatenate(
- (segments_new, seg_raw[None, ...]), axis=0)
+ segments_new = np.concatenate((segments_new, seg_raw[None, ...]), axis=0)
# Intersect with the image.
elif seg.intersects(image_poly):
try:
- p = np.array(
- seg.intersection(image_poly).coords).reshape([-1, 4])
+ p = np.array(seg.intersection(image_poly).coords).reshape([-1, 4])
except:
continue
segment = p
@@ -1190,7 +1373,8 @@ def draw_cube(img, min_size_ratio=0.2, min_label_len=64,
# Get all junctions from label segments
junctions_all = np.concatenate(
- (label_segments[:, :2], label_segments[:, 2:]), axis=0)
+ (label_segments[:, :2], label_segments[:, 2:]), axis=0
+ )
if junctions_all.shape[0] == 0:
junc_points = None
line_map = None
@@ -1204,29 +1388,25 @@ def draw_cube(img, min_size_ratio=0.2, min_label_len=64,
# Fill the faces and draw the contours
col_face = get_random_color(background_color)
for i in [0, 1, 2]:
- cv.fillPoly(img, [cube[faces[i]].reshape((-1, 1, 2))],
- col_face)
+ cv.fillPoly(img, [cube[faces[i]].reshape((-1, 1, 2))], col_face)
thickness = random_state.randint(min_dim * 0.003, min_dim * 0.015)
for i in [0, 1, 2]:
for j in [0, 1, 2, 3]:
- col_edge = (col_face + 128
- + random_state.randint(-64, 64))\
- % 256 # color that constrats with the face color
- cv.line(img, (cube[faces[i][j], 0], cube[faces[i][j], 1]),
- (cube[faces[i][(j + 1) % 4], 0],
- cube[faces[i][(j + 1) % 4], 1]),
- col_edge, thickness)
+ col_edge = (
+ col_face + 128 + random_state.randint(-64, 64)
+ ) % 256 # color that constrats with the face color
+ cv.line(
+ img,
+ (cube[faces[i][j], 0], cube[faces[i][j], 1]),
+ (cube[faces[i][(j + 1) % 4], 0], cube[faces[i][(j + 1) % 4], 1]),
+ col_edge,
+ thickness,
+ )
- return {
- "points": junc_points,
- "line_map": line_map
- }
+ return {"points": junc_points, "line_map": line_map}
def gaussian_noise(img):
- """ Apply random noise to the image. """
+ """Apply random noise to the image."""
cv.randu(img, 0, 255)
- return {
- "points": None,
- "line_map": None
- }
+ return {"points": None, "line_map": None}
diff --git a/imcui/third_party/SOLD2/sold2/model/nets/__init__.py b/third_party/SOLD2/sold2/dataset/transforms/__init__.py
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/model/nets/__init__.py
rename to third_party/SOLD2/sold2/dataset/transforms/__init__.py
diff --git a/imcui/third_party/SOLD2/sold2/dataset/transforms/homographic_transforms.py b/third_party/SOLD2/sold2/dataset/transforms/homographic_transforms.py
similarity index 58%
rename from imcui/third_party/SOLD2/sold2/dataset/transforms/homographic_transforms.py
rename to third_party/SOLD2/sold2/dataset/transforms/homographic_transforms.py
index d9338abb169f7a86f3c6e702a031e1c0de86c339..b9c63613b57f9064333bf80bd59fa6553f3ccb8e 100644
--- a/imcui/third_party/SOLD2/sold2/dataset/transforms/homographic_transforms.py
+++ b/third_party/SOLD2/sold2/dataset/transforms/homographic_transforms.py
@@ -12,11 +12,21 @@ import shapely.geometry
def sample_homography(
- shape, perspective=True, scaling=True, rotation=True,
- translation=True, n_scales=5, n_angles=25, scaling_amplitude=0.1,
- perspective_amplitude_x=0.1, perspective_amplitude_y=0.1,
- patch_ratio=0.5, max_angle=pi/2, allow_artifacts=False,
- translation_overflow=0.):
+ shape,
+ perspective=True,
+ scaling=True,
+ rotation=True,
+ translation=True,
+ n_scales=5,
+ n_angles=25,
+ scaling_amplitude=0.1,
+ perspective_amplitude_x=0.1,
+ perspective_amplitude_y=0.1,
+ patch_ratio=0.5,
+ max_angle=pi / 2,
+ allow_artifacts=False,
+ translation_overflow=0.0,
+):
"""
Computes the homography transformation between a random patch in the
original image and a warped projection with the same image size.
@@ -51,11 +61,12 @@ def sample_homography(
shape = np.array(shape)
# Corners of the output image
- pts1 = np.array([[0., 0.], [0., 1.], [1., 1.], [1., 0.]])
+ pts1 = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 1.0], [1.0, 0.0]])
# Corners of the input patch
margin = (1 - patch_ratio) / 2
- pts2 = margin + np.array([[0, 0], [0, patch_ratio],
- [patch_ratio, patch_ratio], [patch_ratio, 0]])
+ pts2 = margin + np.array(
+ [[0, 0], [0, patch_ratio], [patch_ratio, patch_ratio], [patch_ratio, 0]]
+ )
# Random perspective and affine perturbations
if perspective:
@@ -65,25 +76,25 @@ def sample_homography(
# normal distribution with mean=0, std=perspective_amplitude_y/2
perspective_displacement = np.random.normal(
- 0., perspective_amplitude_y/2, [1])
- h_displacement_left = np.random.normal(
- 0., perspective_amplitude_x/2, [1])
- h_displacement_right = np.random.normal(
- 0., perspective_amplitude_x/2, [1])
- pts2 += np.stack([np.concatenate([h_displacement_left,
- perspective_displacement], 0),
- np.concatenate([h_displacement_left,
- -perspective_displacement], 0),
- np.concatenate([h_displacement_right,
- perspective_displacement], 0),
- np.concatenate([h_displacement_right,
- -perspective_displacement], 0)])
+ 0.0, perspective_amplitude_y / 2, [1]
+ )
+ h_displacement_left = np.random.normal(0.0, perspective_amplitude_x / 2, [1])
+ h_displacement_right = np.random.normal(0.0, perspective_amplitude_x / 2, [1])
+ pts2 += np.stack(
+ [
+ np.concatenate([h_displacement_left, perspective_displacement], 0),
+ np.concatenate([h_displacement_left, -perspective_displacement], 0),
+ np.concatenate([h_displacement_right, perspective_displacement], 0),
+ np.concatenate([h_displacement_right, -perspective_displacement], 0),
+ ]
+ )
# Random scaling: sample several scales, check collision with borders,
# randomly pick a valid one
if scaling:
scales = np.concatenate(
- [[1.], np.random.normal(1, scaling_amplitude/2, [n_scales])], 0)
+ [[1.0], np.random.normal(1, scaling_amplitude / 2, [n_scales])], 0
+ )
center = np.mean(pts2, axis=0, keepdims=True)
scaled = (pts2 - center)[None, ...] * scales[..., None, None] + center
# all scales are valid except scale=1
@@ -91,17 +102,27 @@ def sample_homography(
valid = np.array(range(n_scales))
# Chech the valid scale
else:
- valid = np.where(np.all((scaled >= 0.)
- & (scaled < 1.), (1, 2)))[0]
+ valid = np.where(np.all((scaled >= 0.0) & (scaled < 1.0), (1, 2)))[0]
# No valid scale found => recursively call
if valid.shape[0] == 0:
return sample_homography(
- shape, perspective, scaling, rotation, translation,
- n_scales, n_angles, scaling_amplitude,
- perspective_amplitude_x, perspective_amplitude_y,
- patch_ratio, max_angle, allow_artifacts, translation_overflow)
-
- idx = valid[np.random.uniform(0., valid.shape[0], ()).astype(np.int32)]
+ shape,
+ perspective,
+ scaling,
+ rotation,
+ translation,
+ n_scales,
+ n_angles,
+ scaling_amplitude,
+ perspective_amplitude_x,
+ perspective_amplitude_y,
+ patch_ratio,
+ max_angle,
+ allow_artifacts,
+ translation_overflow,
+ )
+
+ idx = valid[np.random.uniform(0.0, valid.shape[0], ()).astype(np.int32)]
pts2 = scaled[idx]
# Additionally save and return the selected scale.
@@ -113,39 +134,60 @@ def sample_homography(
if allow_artifacts:
t_min += translation_overflow
t_max += translation_overflow
- pts2 += (np.stack([np.random.uniform(-t_min[0], t_max[0], ()),
- np.random.uniform(-t_min[1],
- t_max[1], ())]))[None, ...]
+ pts2 += (
+ np.stack(
+ [
+ np.random.uniform(-t_min[0], t_max[0], ()),
+ np.random.uniform(-t_min[1], t_max[1], ()),
+ ]
+ )
+ )[None, ...]
# Random rotation: sample several rotations, check collision with borders,
# randomly pick a valid one
if rotation:
angles = np.linspace(-max_angle, max_angle, n_angles)
# in case no rotation is valid
- angles = np.concatenate([[0.], angles], axis=0)
+ angles = np.concatenate([[0.0], angles], axis=0)
center = np.mean(pts2, axis=0, keepdims=True)
- rot_mat = np.reshape(np.stack(
- [np.cos(angles), -np.sin(angles),
- np.sin(angles), np.cos(angles)], axis=1), [-1, 2, 2])
- rotated = np.matmul(
- np.tile((pts2 - center)[None, ...], [n_angles+1, 1, 1]),
- rot_mat) + center
+ rot_mat = np.reshape(
+ np.stack(
+ [np.cos(angles), -np.sin(angles), np.sin(angles), np.cos(angles)],
+ axis=1,
+ ),
+ [-1, 2, 2],
+ )
+ rotated = (
+ np.matmul(
+ np.tile((pts2 - center)[None, ...], [n_angles + 1, 1, 1]), rot_mat
+ )
+ + center
+ )
if allow_artifacts:
# All angles are valid, except angle=0
valid = np.array(range(n_angles))
else:
- valid = np.where(np.all((rotated >= 0.)
- & (rotated < 1.), axis=(1, 2)))[0]
-
+ valid = np.where(np.all((rotated >= 0.0) & (rotated < 1.0), axis=(1, 2)))[0]
+
if valid.shape[0] == 0:
return sample_homography(
- shape, perspective, scaling, rotation, translation,
- n_scales, n_angles, scaling_amplitude,
- perspective_amplitude_x, perspective_amplitude_y,
- patch_ratio, max_angle, allow_artifacts, translation_overflow)
-
- idx = valid[np.random.uniform(0., valid.shape[0],
- ()).astype(np.int32)]
+ shape,
+ perspective,
+ scaling,
+ rotation,
+ translation,
+ n_scales,
+ n_angles,
+ scaling_amplitude,
+ perspective_amplitude_x,
+ perspective_amplitude_y,
+ patch_ratio,
+ max_angle,
+ allow_artifacts,
+ translation_overflow,
+ )
+
+ idx = valid[np.random.uniform(0.0, valid.shape[0], ()).astype(np.int32)]
pts2 = rotated[idx]
# Rescale to actual size
@@ -153,27 +195,33 @@ def sample_homography(
pts1 *= shape[None, ...]
pts2 *= shape[None, ...]
- def ax(p, q): return [p[0], p[1], 1, 0, 0, 0, -p[0] * q[0], -p[1] * q[0]]
+ def ax(p, q):
+ return [p[0], p[1], 1, 0, 0, 0, -p[0] * q[0], -p[1] * q[0]]
- def ay(p, q): return [0, 0, 0, p[0], p[1], 1, -p[0] * q[1], -p[1] * q[1]]
+ def ay(p, q):
+ return [0, 0, 0, p[0], p[1], 1, -p[0] * q[1], -p[1] * q[1]]
- a_mat = np.stack([f(pts1[i], pts2[i]) for i in range(4)
- for f in (ax, ay)], axis=0)
- p_mat = np.transpose(np.stack([[pts2[i][j] for i in range(4)
- for j in range(2)]], axis=0))
+ a_mat = np.stack([f(pts1[i], pts2[i]) for i in range(4) for f in (ax, ay)], axis=0)
+ p_mat = np.transpose(
+ np.stack([[pts2[i][j] for i in range(4) for j in range(2)]], axis=0)
+ )
homo_vec, _, _, _ = np.linalg.lstsq(a_mat, p_mat, rcond=None)
# Compose the homography vector back to matrix
- homo_mat = np.concatenate([
- homo_vec[0:3, 0][None, ...], homo_vec[3:6, 0][None, ...],
- np.concatenate((homo_vec[6], homo_vec[7], [1]),
- axis=0)[None, ...]], axis=0)
+ homo_mat = np.concatenate(
+ [
+ homo_vec[0:3, 0][None, ...],
+ homo_vec[3:6, 0][None, ...],
+ np.concatenate((homo_vec[6], homo_vec[7], [1]), axis=0)[None, ...],
+ ],
+ axis=0,
+ )
return homo_mat, selected_scale
def convert_to_line_segments(junctions, line_map):
- """ Convert junctions and line map to line segments. """
+ """Convert junctions and line map to line segments."""
# Copy the line map
line_map_tmp = copy.copy(line_map)
@@ -188,9 +236,9 @@ def convert_to_line_segments(junctions, line_map):
p1 = junctions[idx, :]
p2 = junctions[idx2, :]
line_segments = np.concatenate(
- (line_segments,
- np.array([p1[0], p1[1], p2[0], p2[1]])[None, ...]),
- axis=0)
+ (line_segments, np.array([p1[0], p1[1], p2[0], p2[1]])[None, ...]),
+ axis=0,
+ )
# Update line_map
line_map_tmp[idx, idx2] = 0
line_map_tmp[idx2, idx] = 0
@@ -198,46 +246,50 @@ def convert_to_line_segments(junctions, line_map):
return line_segments
-def compute_valid_mask(image_size, homography,
- border_margin, valid_mask=None):
+def compute_valid_mask(image_size, homography, border_margin, valid_mask=None):
# Warp the mask
if valid_mask is None:
initial_mask = np.ones(image_size)
else:
initial_mask = valid_mask
mask = cv2.warpPerspective(
- initial_mask, homography, (image_size[1], image_size[0]),
- flags=cv2.INTER_NEAREST)
+ initial_mask,
+ homography,
+ (image_size[1], image_size[0]),
+ flags=cv2.INTER_NEAREST,
+ )
# Optionally perform erosion
if border_margin > 0:
- kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
- (border_margin*2, )*2)
+ kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (border_margin * 2,) * 2)
mask = cv2.erode(mask, kernel)
-
+
# Perform dilation if border_margin is negative
if border_margin < 0:
- kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
- (abs(int(border_margin))*2, )*2)
+ kernel = cv2.getStructuringElement(
+ cv2.MORPH_ELLIPSE, (abs(int(border_margin)) * 2,) * 2
+ )
mask = cv2.dilate(mask, kernel)
return mask
def warp_line_segment(line_segments, homography, image_size):
- """ Warp the line segments using a homography. """
+ """Warp the line segments using a homography."""
# Separate the line segements into 2N points to apply matrix operation
num_segments = line_segments.shape[0]
junctions = np.concatenate(
- (line_segments[:, :2], # The first junction of each segment.
- line_segments[:, 2:]), # The second junction of each segment.
- axis=0)
+ (
+ line_segments[:, :2], # The first junction of each segment.
+ line_segments[:, 2:],
+ ), # The second junction of each segment.
+ axis=0,
+ )
# Convert to homogeneous coordinates
# Flip the junctions before converting to homogeneous (xy format)
junctions = np.flip(junctions, axis=1)
- junctions = np.concatenate((junctions, np.ones([2*num_segments, 1])),
- axis=1)
+ junctions = np.concatenate((junctions, np.ones([2 * num_segments, 1])), axis=1)
warped_junctions = np.matmul(homography, junctions.T).T
# Convert back to segments
@@ -245,41 +297,43 @@ def warp_line_segment(line_segments, homography, image_size):
# (Convert back to hw format)
warped_junctions = np.flip(warped_junctions, axis=1)
warped_segments = np.concatenate(
- (warped_junctions[:num_segments, :],
- warped_junctions[num_segments:, :]),
- axis=1
+ (warped_junctions[:num_segments, :], warped_junctions[num_segments:, :]), axis=1
)
# Check the intersections with the boundary
warped_segments_new = np.zeros([0, 4])
image_poly = shapely.geometry.Polygon(
- [[0, 0], [image_size[1]-1, 0], [image_size[1]-1, image_size[0]-1],
- [0, image_size[0]-1]])
+ [
+ [0, 0],
+ [image_size[1] - 1, 0],
+ [image_size[1] - 1, image_size[0] - 1],
+ [0, image_size[0] - 1],
+ ]
+ )
for idx in range(warped_segments.shape[0]):
# Get the line segment
- seg_raw = warped_segments[idx, :] # in HW format.
+ seg_raw = warped_segments[idx, :] # in HW format.
# Convert to shapely line (flip to xy format)
- seg = shapely.geometry.LineString([np.flip(seg_raw[:2]),
- np.flip(seg_raw[2:])])
+ seg = shapely.geometry.LineString([np.flip(seg_raw[:2]), np.flip(seg_raw[2:])])
# The line segment is just inside the image.
if seg.intersection(image_poly) == seg:
- warped_segments_new = np.concatenate((warped_segments_new,
- seg_raw[None, ...]), axis=0)
-
+ warped_segments_new = np.concatenate(
+ (warped_segments_new, seg_raw[None, ...]), axis=0
+ )
+
# Intersect with the image.
elif seg.intersects(image_poly):
# Check intersection
try:
- p = np.array(
- seg.intersection(image_poly).coords).reshape([-1, 4])
+ p = np.array(seg.intersection(image_poly).coords).reshape([-1, 4])
# If intersect at exact one point, just continue.
except:
continue
- segment = np.concatenate([np.flip(p[0, :2]), np.flip(p[0, 2:],
- axis=0)])[None, ...]
- warped_segments_new = np.concatenate(
- (warped_segments_new, segment), axis=0)
+ segment = np.concatenate([np.flip(p[0, :2]), np.flip(p[0, 2:], axis=0)])[
+ None, ...
+ ]
+ warped_segments_new = np.concatenate((warped_segments_new, segment), axis=0)
else:
continue
@@ -289,9 +343,9 @@ def warp_line_segment(line_segments, homography, image_size):
class homography_transform(object):
- """ # Homography transformations. """
- def __init__(self, image_size, homograpy_config,
- border_margin=0, min_label_len=20):
+ """# Homography transformations."""
+
+ def __init__(self, image_size, homograpy_config, border_margin=0, min_label_len=20):
self.homo_config = homograpy_config
self.image_size = image_size
self.target_size = (self.image_size[1], self.image_size[0])
@@ -300,31 +354,33 @@ class homography_transform(object):
raise ValueError("[Error] min_label_len should be in pixels.")
self.min_label_len = min_label_len
- def __call__(self, input_image, junctions, line_map,
- valid_mask=None, homo=None, scale=None):
+ def __call__(
+ self, input_image, junctions, line_map, valid_mask=None, homo=None, scale=None
+ ):
# Sample one random homography or use the given one
if homo is None or scale is None:
- homo, scale = sample_homography(self.image_size,
- **self.homo_config)
+ homo, scale = sample_homography(self.image_size, **self.homo_config)
# Warp the image
warped_image = cv2.warpPerspective(
- input_image, homo, self.target_size, flags=cv2.INTER_LINEAR)
-
- valid_mask = compute_valid_mask(self.image_size, homo,
- self.border_margin, valid_mask)
+ input_image, homo, self.target_size, flags=cv2.INTER_LINEAR
+ )
+
+ valid_mask = compute_valid_mask(
+ self.image_size, homo, self.border_margin, valid_mask
+ )
# Convert junctions and line_map back to line segments
line_segments = convert_to_line_segments(junctions, line_map)
# Warp the segments and check the length.
# Adjust the min_label_length
- warped_segments = warp_line_segment(line_segments, homo,
- self.image_size)
+ warped_segments = warp_line_segment(line_segments, homo, self.image_size)
# Convert back to junctions and line_map
- junctions_new = np.concatenate((warped_segments[:, :2],
- warped_segments[:, 2:]), axis=0)
+ junctions_new = np.concatenate(
+ (warped_segments[:, :2], warped_segments[:, 2:]), axis=0
+ )
if junctions_new.shape[0] == 0:
junctions_new = np.zeros([0, 2])
line_map = np.zeros([0, 0])
@@ -333,11 +389,11 @@ class homography_transform(object):
junctions_new = np.unique(junctions_new, axis=0)
# Generate line map from points and segments
- line_map = get_line_map(junctions_new,
- warped_segments).astype(np.int)
+ line_map = get_line_map(junctions_new, warped_segments).astype(np.int)
# Compute the heatmap
- warped_heatmap = get_line_heatmap(np.flip(junctions_new, axis=1),
- line_map, self.image_size)
+ warped_heatmap = get_line_heatmap(
+ np.flip(junctions_new, axis=1), line_map, self.image_size
+ )
return {
"junctions": junctions_new,
@@ -346,5 +402,5 @@ class homography_transform(object):
"line_map": line_map,
"warped_heatmap": warped_heatmap,
"homo": homo,
- "scale": scale
+ "scale": scale,
}
diff --git a/imcui/third_party/SOLD2/sold2/dataset/transforms/photometric_transforms.py b/third_party/SOLD2/sold2/dataset/transforms/photometric_transforms.py
similarity index 76%
rename from imcui/third_party/SOLD2/sold2/dataset/transforms/photometric_transforms.py
rename to third_party/SOLD2/sold2/dataset/transforms/photometric_transforms.py
index 8fa44bf0efa93a47e5f8012988058f1cbd49324f..5f41192cd2cba7b47939f031027e8dce6e1a406f 100644
--- a/imcui/third_party/SOLD2/sold2/dataset/transforms/photometric_transforms.py
+++ b/third_party/SOLD2/sold2/dataset/transforms/photometric_transforms.py
@@ -9,17 +9,18 @@ import cv2
# List all the available augmentations
available_augmentations = [
- 'additive_gaussian_noise',
- 'additive_speckle_noise',
- 'random_brightness',
- 'random_contrast',
- 'additive_shade',
- 'motion_blur'
+ "additive_gaussian_noise",
+ "additive_speckle_noise",
+ "random_brightness",
+ "random_contrast",
+ "additive_shade",
+ "motion_blur",
]
class additive_gaussian_noise(object):
- """ Additive gaussian noise. """
+ """Additive gaussian noise."""
+
def __init__(self, stddev_range=None):
# If std is not given, use the default setting
if stddev_range is None:
@@ -30,14 +31,15 @@ class additive_gaussian_noise(object):
def __call__(self, input_image):
# Get the noise stddev
stddev = np.random.uniform(self.stddev_range[0], self.stddev_range[1])
- noise = np.random.normal(0., stddev, size=input_image.shape)
- noisy_image = (input_image + noise).clip(0., 255.)
+ noise = np.random.normal(0.0, stddev, size=input_image.shape)
+ noisy_image = (input_image + noise).clip(0.0, 255.0)
return noisy_image
class additive_speckle_noise(object):
- """ Additive speckle noise. """
+ """Additive speckle noise."""
+
def __init__(self, prob_range=None):
# If prob range is not given, use the default setting
if prob_range is None:
@@ -48,7 +50,7 @@ class additive_speckle_noise(object):
def __call__(self, input_image):
# Sample
prob = np.random.uniform(self.prob_range[0], self.prob_range[1])
- sample = np.random.uniform(0., 1., size=input_image.shape)
+ sample = np.random.uniform(0.0, 1.0, size=input_image.shape)
# Get the mask
mask0 = sample <= prob
@@ -56,14 +58,15 @@ class additive_speckle_noise(object):
# Mask the image (here we assume the image ranges from 0~255
noisy = input_image.copy()
- noisy[mask0] = 0.
- noisy[mask1] = 255.
+ noisy[mask0] = 0.0
+ noisy[mask1] = 255.0
return noisy
class random_brightness(object):
- """ Brightness change. """
+ """Brightness change."""
+
def __init__(self, brightness=None):
# If the brightness is not given, use the default setting
if brightness is None:
@@ -83,7 +86,8 @@ class random_brightness(object):
class random_contrast(object):
- """ Additive contrast. """
+ """Additive contrast."""
+
def __init__(self, contrast=None):
# If the brightness is not given, use the default setting
if contrast is None:
@@ -103,9 +107,9 @@ class random_contrast(object):
class additive_shade(object):
- """ Additive shade. """
- def __init__(self, nb_ellipses=20, transparency_range=None,
- kernel_size_range=None):
+ """Additive shade."""
+
+ def __init__(self, nb_ellipses=20, transparency_range=None, kernel_size_range=None):
self.nb_ellipses = nb_ellipses
if transparency_range is None:
self.transparency_range = [-0.5, 0.8]
@@ -136,39 +140,40 @@ class additive_shade(object):
# kernel_size has to be odd
if (kernel_size % 2) == 0:
kernel_size += 1
- mask = cv2.GaussianBlur(mask.astype(np.float32),
- (kernel_size, kernel_size), 0)
- shaded = (input_image[..., None]
- * (1 - transparency * mask[..., np.newaxis]/255.))
+ mask = cv2.GaussianBlur(mask.astype(np.float32), (kernel_size, kernel_size), 0)
+ shaded = input_image[..., None] * (
+ 1 - transparency * mask[..., np.newaxis] / 255.0
+ )
shaded = np.clip(shaded, 0, 255)
return np.reshape(shaded, input_image.shape)
class motion_blur(object):
- """ Motion blur. """
+ """Motion blur."""
+
def __init__(self, max_kernel_size=10):
self.max_kernel_size = max_kernel_size
def __call__(self, input_image):
# Either vertical, horizontal or diagonal blur
- mode = np.random.choice(['h', 'v', 'diag_down', 'diag_up'])
- ksize = np.random.randint(
- 0, int(round((self.max_kernel_size + 1) / 2))) * 2 + 1
+ mode = np.random.choice(["h", "v", "diag_down", "diag_up"])
+ ksize = np.random.randint(0, int(round((self.max_kernel_size + 1) / 2))) * 2 + 1
center = int((ksize - 1) / 2)
kernel = np.zeros((ksize, ksize))
- if mode == 'h':
- kernel[center, :] = 1.
- elif mode == 'v':
- kernel[:, center] = 1.
- elif mode == 'diag_down':
+ if mode == "h":
+ kernel[center, :] = 1.0
+ elif mode == "v":
+ kernel[:, center] = 1.0
+ elif mode == "diag_down":
kernel = np.eye(ksize)
- elif mode == 'diag_up':
+ elif mode == "diag_up":
kernel = np.flip(np.eye(ksize), 0)
- var = ksize * ksize / 16.
+ var = ksize * ksize / 16.0
grid = np.repeat(np.arange(ksize)[:, np.newaxis], ksize, axis=-1)
- gaussian = np.exp(-(np.square(grid - center)
- + np.square(grid.T - center)) / (2. * var))
+ gaussian = np.exp(
+ -(np.square(grid - center) + np.square(grid.T - center)) / (2.0 * var)
+ )
kernel *= gaussian
kernel /= np.sum(kernel)
blurred = cv2.filter2D(input_image, -1, kernel)
@@ -177,7 +182,8 @@ class motion_blur(object):
class normalize_image(object):
- """ Image normalization to the range [0, 1]. """
+ """Image normalization to the range [0, 1]."""
+
def __init__(self):
self.normalize_value = 255
diff --git a/imcui/third_party/SOLD2/sold2/dataset/transforms/utils.py b/third_party/SOLD2/sold2/dataset/transforms/utils.py
similarity index 65%
rename from imcui/third_party/SOLD2/sold2/dataset/transforms/utils.py
rename to third_party/SOLD2/sold2/dataset/transforms/utils.py
index 5f1ed09e5b32e2ae2f3577e0e8e5491495e7b05b..4e2d9b4234400b16c59773ebcf15ecc557df6cac 100644
--- a/imcui/third_party/SOLD2/sold2/dataset/transforms/utils.py
+++ b/third_party/SOLD2/sold2/dataset/transforms/utils.py
@@ -9,7 +9,7 @@ from ..synthetic_util import get_line_map
from . import homographic_transforms as homoaug
-def random_scaling(image, junctions, line_map, scale=1., h_crop=0, w_crop=0):
+def random_scaling(image, junctions, line_map, scale=1.0, h_crop=0, w_crop=0):
H, W = image.shape[:2]
H_scale, W_scale = round(H * scale), round(W * scale)
@@ -18,42 +18,46 @@ def random_scaling(image, junctions, line_map, scale=1., h_crop=0, w_crop=0):
return (image, junctions, line_map, np.ones([H, W], dtype=np.int))
# Zoom-in => resize and random crop
- if scale >= 1.:
- image_big = cv2.resize(image, (W_scale, H_scale),
- interpolation=cv2.INTER_LINEAR)
+ if scale >= 1.0:
+ image_big = cv2.resize(
+ image, (W_scale, H_scale), interpolation=cv2.INTER_LINEAR
+ )
# Crop the image
- image = image_big[h_crop:h_crop+H, w_crop:w_crop+W, ...]
+ image = image_big[h_crop : h_crop + H, w_crop : w_crop + W, ...]
valid_mask = np.ones([H, W], dtype=np.int)
# Process junctions
junctions, line_map = process_junctions_and_line_map(
- h_crop, w_crop, H, W, H_scale, W_scale,
- junctions, line_map, "zoom-in")
+ h_crop, w_crop, H, W, H_scale, W_scale, junctions, line_map, "zoom-in"
+ )
# Zoom-out => resize and pad
else:
image_shape_raw = image.shape
- image_small = cv2.resize(image, (W_scale, H_scale),
- interpolation=cv2.INTER_AREA)
+ image_small = cv2.resize(
+ image, (W_scale, H_scale), interpolation=cv2.INTER_AREA
+ )
# Decide the pasting location
h_start = round((H - H_scale) / 2)
w_start = round((W - W_scale) / 2)
# Paste the image to the middle
image = np.zeros(image_shape_raw, dtype=np.float)
- image[h_start:h_start+H_scale,
- w_start:w_start+W_scale, ...] = image_small
+ image[
+ h_start : h_start + H_scale, w_start : w_start + W_scale, ...
+ ] = image_small
valid_mask = np.zeros([H, W], dtype=np.int)
- valid_mask[h_start:h_start+H_scale, w_start:w_start+W_scale] = 1
+ valid_mask[h_start : h_start + H_scale, w_start : w_start + W_scale] = 1
# Process the junctions
junctions, line_map = process_junctions_and_line_map(
- h_start, w_start, H, W, H_scale, W_scale,
- junctions, line_map, "zoom-out")
+ h_start, w_start, H, W, H_scale, W_scale, junctions, line_map, "zoom-out"
+ )
return image, junctions, line_map, valid_mask
-def process_junctions_and_line_map(h_start, w_start, H, W, H_scale, W_scale,
- junctions, line_map, mode="zoom-in"):
+def process_junctions_and_line_map(
+ h_start, w_start, H, W, H_scale, W_scale, junctions, line_map, mode="zoom-in"
+):
if mode == "zoom-in":
junctions[:, 0] = junctions[:, 0] * H_scale / H
junctions[:, 1] = junctions[:, 1] * W_scale / W
@@ -61,53 +65,55 @@ def process_junctions_and_line_map(h_start, w_start, H, W, H_scale, W_scale,
# Crop segments to the new boundaries
line_segments_new = np.zeros([0, 4])
image_poly = sg.Polygon(
- [[w_start, h_start],
- [w_start+W, h_start],
- [w_start+W, h_start+H],
- [w_start, h_start+H]
- ])
+ [
+ [w_start, h_start],
+ [w_start + W, h_start],
+ [w_start + W, h_start + H],
+ [w_start, h_start + H],
+ ]
+ )
for idx in range(line_segments.shape[0]):
# Get the line segment
- seg_raw = line_segments[idx, :] # in HW format.
+ seg_raw = line_segments[idx, :] # in HW format.
# Convert to shapely line (flip to xy format)
- seg = sg.LineString([np.flip(seg_raw[:2]),
- np.flip(seg_raw[2:])])
+ seg = sg.LineString([np.flip(seg_raw[:2]), np.flip(seg_raw[2:])])
# The line segment is just inside the image.
if seg.intersection(image_poly) == seg:
line_segments_new = np.concatenate(
- (line_segments_new, seg_raw[None, ...]), axis=0)
+ (line_segments_new, seg_raw[None, ...]), axis=0
+ )
# Intersect with the image.
elif seg.intersects(image_poly):
# Check intersection
try:
- p = np.array(
- seg.intersection(image_poly).coords).reshape([-1, 4])
+ p = np.array(seg.intersection(image_poly).coords).reshape([-1, 4])
# If intersect at exact one point, just continue.
except:
continue
- segment = np.concatenate([np.flip(p[0, :2]), np.flip(p[0, 2:],
- axis=0)])[None, ...]
- line_segments_new = np.concatenate(
- (line_segments_new, segment), axis=0)
+ segment = np.concatenate(
+ [np.flip(p[0, :2]), np.flip(p[0, 2:], axis=0)]
+ )[None, ...]
+ line_segments_new = np.concatenate((line_segments_new, segment), axis=0)
else:
continue
line_segments_new = (np.round(line_segments_new)).astype(np.int)
# Filter segments with 0 length
segment_lens = np.linalg.norm(
- line_segments_new[:, :2] - line_segments_new[:, 2:], axis=-1)
+ line_segments_new[:, :2] - line_segments_new[:, 2:], axis=-1
+ )
seg_mask = segment_lens != 0
line_segments_new = line_segments_new[seg_mask, :]
# Convert back to junctions and line_map
junctions_new = np.concatenate(
- (line_segments_new[:, :2], line_segments_new[:, 2:]), axis=0)
+ (line_segments_new[:, :2], line_segments_new[:, 2:]), axis=0
+ )
if junctions_new.shape[0] == 0:
junctions_new = np.zeros([0, 2])
line_map = np.zeros([0, 0])
else:
junctions_new = np.unique(junctions_new, axis=0)
# Generate line map from points and segments
- line_map = get_line_map(junctions_new,
- line_segments_new).astype(np.int)
+ line_map = get_line_map(junctions_new, line_segments_new).astype(np.int)
junctions_new[:, 0] -= h_start
junctions_new[:, 1] -= w_start
junctions = junctions_new
diff --git a/imcui/third_party/SOLD2/sold2/dataset/wireframe_dataset.py b/third_party/SOLD2/sold2/dataset/wireframe_dataset.py
similarity index 73%
rename from imcui/third_party/SOLD2/sold2/dataset/wireframe_dataset.py
rename to third_party/SOLD2/sold2/dataset/wireframe_dataset.py
index ed5bb910bed1b89934ddaaec3bcddf111ea0faef..44341d7394303188db3ba69123bb4b4212700466 100644
--- a/imcui/third_party/SOLD2/sold2/dataset/wireframe_dataset.py
+++ b/third_party/SOLD2/sold2/dataset/wireframe_dataset.py
@@ -27,12 +27,19 @@ from ..misc.geometry_utils import warp_points, mask_points
def wireframe_collate_fn(batch):
- """ Customized collate_fn for wireframe dataset. """
- batch_keys = ["image", "junction_map", "valid_mask", "heatmap",
- "heatmap_pos", "heatmap_neg", "homography",
- "line_points", "line_indices"]
- list_keys = ["junctions", "line_map", "line_map_pos",
- "line_map_neg", "file_key"]
+ """Customized collate_fn for wireframe dataset."""
+ batch_keys = [
+ "image",
+ "junction_map",
+ "valid_mask",
+ "heatmap",
+ "heatmap_pos",
+ "heatmap_neg",
+ "homography",
+ "line_points",
+ "line_indices",
+ ]
+ list_keys = ["junctions", "line_map", "line_map_pos", "line_map_neg", "file_key"]
outputs = {}
for data_key in batch[0].keys():
@@ -41,14 +48,16 @@ def wireframe_collate_fn(batch):
# print(batch_match, list_match)
if batch_match > 0 and list_match == 0:
outputs[data_key] = torch_loader.default_collate(
- [b[data_key] for b in batch])
+ [b[data_key] for b in batch]
+ )
elif batch_match == 0 and list_match > 0:
outputs[data_key] = [b[data_key] for b in batch]
elif batch_match == 0 and list_match == 0:
continue
else:
raise ValueError(
- "[Error] A key matches batch keys and list keys simultaneously.")
+ "[Error] A key matches batch keys and list keys simultaneously."
+ )
return outputs
@@ -58,7 +67,8 @@ class WireframeDataset(Dataset):
super(WireframeDataset, self).__init__()
if not mode in ["train", "test"]:
raise ValueError(
- "[Error] Unknown mode for Wireframe dataset. Only 'train' and 'test'.")
+ "[Error] Unknown mode for Wireframe dataset. Only 'train' and 'test'."
+ )
self.mode = mode
if config is None:
@@ -72,18 +82,17 @@ class WireframeDataset(Dataset):
self.dataset_name = self.get_dataset_name()
self.cache_name = self.get_cache_name()
self.cache_path = cfg.wireframe_cache_path
-
+
# Get the ground truth source
- self.gt_source = self.config.get("gt_source_%s"%(self.mode),
- "official")
+ self.gt_source = self.config.get("gt_source_%s" % (self.mode), "official")
if not self.gt_source == "official":
# Convert gt_source to full path
self.gt_source = os.path.join(cfg.export_dataroot, self.gt_source)
# Check the full path exists
if not os.path.exists(self.gt_source):
raise ValueError(
- "[Error] The specified ground truth source does not exist.")
-
+ "[Error] The specified ground truth source does not exist."
+ )
# Get the filename dataset
print("[Info] Initializing wireframe dataset...")
@@ -95,22 +104,22 @@ class WireframeDataset(Dataset):
# Print some info
print("[Info] Successfully initialized dataset")
print("\t Name: wireframe")
- print("\t Mode: %s" %(self.mode))
- print("\t Gt: %s" %(self.config.get("gt_source_%s"%(self.mode),
- "official")))
- print("\t Counts: %d" %(self.dataset_length))
+ print("\t Mode: %s" % (self.mode))
+ print("\t Gt: %s" % (self.config.get("gt_source_%s" % (self.mode), "official")))
+ print("\t Counts: %d" % (self.dataset_length))
print("----------------------------------------")
#######################################
## Dataset construction related APIs ##
#######################################
def construct_dataset(self):
- """ Construct the dataset (from scratch or from cache). """
+ """Construct the dataset (from scratch or from cache)."""
# Check if the filename cache exists
# If cache exists, load from cache
if self._check_dataset_cache():
- print("\t Found filename cache %s at %s"%(self.cache_name,
- self.cache_path))
+ print(
+ "\t Found filename cache %s at %s" % (self.cache_name, self.cache_path)
+ )
print("\t Load filename cache...")
filename_dataset, datapoints = self.get_filename_dataset_from_cache()
# If not, initialize dataset from scratch
@@ -120,30 +129,27 @@ class WireframeDataset(Dataset):
filename_dataset, datapoints = self.get_filename_dataset()
print("\t Create filename dataset cache...")
self.create_filename_dataset_cache(filename_dataset, datapoints)
-
+
return filename_dataset, datapoints
-
+
def create_filename_dataset_cache(self, filename_dataset, datapoints):
- """ Create filename dataset cache for faster initialization. """
+ """Create filename dataset cache for faster initialization."""
# Check cache path exists
if not os.path.exists(self.cache_path):
os.makedirs(self.cache_path)
cache_file_path = os.path.join(self.cache_path, self.cache_name)
- data = {
- "filename_dataset": filename_dataset,
- "datapoints": datapoints
- }
+ data = {"filename_dataset": filename_dataset, "datapoints": datapoints}
with open(cache_file_path, "wb") as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
-
+
def get_filename_dataset_from_cache(self):
- """ Get filename dataset from cache. """
+ """Get filename dataset from cache."""
# Load from pkl cache
cache_file_path = os.path.join(self.cache_path, self.cache_name)
with open(cache_file_path, "rb") as f:
data = pickle.load(f)
-
+
return data["filename_dataset"], data["datapoints"]
def get_filename_dataset(self):
@@ -152,14 +158,18 @@ class WireframeDataset(Dataset):
dataset_path = os.path.join(cfg.wireframe_dataroot, "train")
elif self.mode == "test":
dataset_path = os.path.join(cfg.wireframe_dataroot, "valid")
-
+
# Get paths to all image files
- image_paths = sorted([os.path.join(dataset_path, _)
- for _ in os.listdir(dataset_path)\
- if os.path.splitext(_)[-1] == ".png"])
+ image_paths = sorted(
+ [
+ os.path.join(dataset_path, _)
+ for _ in os.listdir(dataset_path)
+ if os.path.splitext(_)[-1] == ".png"
+ ]
+ )
# Get the shared prefix
prefix_paths = [_.split(".png")[0] for _ in image_paths]
-
+
# Get the label paths (different procedure for different split)
if self.mode == "train":
label_paths = [_ + "_label.npz" for _ in prefix_paths]
@@ -171,17 +181,18 @@ class WireframeDataset(Dataset):
for idx in range(len(image_paths)):
image_path = image_paths[idx]
label_path = label_paths[idx]
- if (not (os.path.exists(image_path)
- and os.path.exists(label_path))):
+ if not (os.path.exists(image_path) and os.path.exists(label_path)):
raise ValueError(
- "[Error] The image and label do not exist. %s"%(image_path))
+ "[Error] The image and label do not exist. %s" % (image_path)
+ )
# Further verify mat paths for test split
if self.mode == "test":
mat_path = mat_paths[idx]
if not os.path.exists(mat_path):
raise ValueError(
- "[Error] The mat file does not exist. %s"%(mat_path))
-
+ "[Error] The mat file does not exist. %s" % (mat_path)
+ )
+
# Construct the filename dataset
num_pad = int(math.ceil(math.log10(len(image_paths))) + 1)
filename_dataset = {}
@@ -191,25 +202,25 @@ class WireframeDataset(Dataset):
filename_dataset[key] = {
"image": image_paths[idx],
- "label": label_paths[idx]
+ "label": label_paths[idx],
}
# Get the datapoints
datapoints = list(sorted(filename_dataset.keys()))
return filename_dataset, datapoints
-
+
def get_dataset_name(self):
- """ Get dataset name from dataset config / default config. """
+ """Get dataset name from dataset config / default config."""
if self.config["dataset_name"] is None:
dataset_name = self.default_config["dataset_name"] + "_%s" % self.mode
else:
dataset_name = self.config["dataset_name"] + "_%s" % self.mode
return dataset_name
-
+
def get_cache_name(self):
- """ Get cache name from dataset config / default config. """
+ """Get cache name from dataset config / default config."""
if self.config["dataset_name"] is None:
dataset_name = self.default_config["dataset_name"] + "_%s" % self.mode
else:
@@ -218,35 +229,27 @@ class WireframeDataset(Dataset):
cache_name = dataset_name + "_cache.pkl"
return cache_name
-
+
@staticmethod
def get_padded_filename(num_pad, idx):
- """ Get the padded filename using adaptive padding. """
+ """Get the padded filename using adaptive padding."""
file_len = len("%d" % (idx))
filename = "0" * (num_pad - file_len) + "%d" % (idx)
return filename
def get_default_config(self):
- """ Get the default configuration. """
+ """Get the default configuration."""
return {
"dataset_name": "wireframe",
"add_augmentation_to_all_splits": False,
- "preprocessing": {
- "resize": [240, 320],
- "blur_size": 11
- },
- "augmentation":{
- "photometric":{
- "enable": False
- },
- "homographic":{
- "enable": False
- },
+ "preprocessing": {"resize": [240, 320], "blur_size": 11},
+ "augmentation": {
+ "photometric": {"enable": False},
+ "homographic": {"enable": False},
},
}
-
############################################
## Pytorch and preprocessing related APIs ##
############################################
@@ -280,13 +283,13 @@ class WireframeDataset(Dataset):
# TODO: How to process mat data
if data_path.get("line_mat") is not None:
raise NotImplementedError
-
+
return output
-
+
@staticmethod
def convert_line_map(lcnn_line_map, num_junctions):
- """ Convert the line_pos or line_neg
- (represented by two junction indexes) to our line map. """
+ """Convert the line_pos or line_neg
+ (represented by two junction indexes) to our line map."""
# Initialize empty line map
line_map = np.zeros([num_junctions, num_junctions])
@@ -297,59 +300,60 @@ class WireframeDataset(Dataset):
line_map[index1, index2] = 1
line_map[index2, index1] = 1
-
+
return line_map
-
+
@staticmethod
def junc_to_junc_map(junctions, image_size):
- """ Convert junction points to junction maps. """
+ """Convert junction points to junction maps."""
junctions = np.round(junctions).astype(np.int)
# Clip the boundary by image size
- junctions[:, 0] = np.clip(junctions[:, 0], 0., image_size[0]-1)
- junctions[:, 1] = np.clip(junctions[:, 1], 0., image_size[1]-1)
+ junctions[:, 0] = np.clip(junctions[:, 0], 0.0, image_size[0] - 1)
+ junctions[:, 1] = np.clip(junctions[:, 1], 0.0, image_size[1] - 1)
# Create junction map
junc_map = np.zeros([image_size[0], image_size[1]])
junc_map[junctions[:, 0], junctions[:, 1]] = 1
return junc_map[..., None].astype(np.int)
-
+
def parse_transforms(self, names, all_transforms):
- """ Parse the transform. """
- trans = all_transforms if (names == 'all') \
+ """Parse the transform."""
+ trans = (
+ all_transforms
+ if (names == "all")
else (names if isinstance(names, list) else [names])
+ )
assert set(trans) <= set(all_transforms)
return trans
def get_photo_transform(self):
- """ Get list of photometric transforms (according to the config). """
+ """Get list of photometric transforms (according to the config)."""
# Get the photometric transform config
photo_config = self.config["augmentation"]["photometric"]
if not photo_config["enable"]:
- raise ValueError(
- "[Error] Photometric augmentation is not enabled.")
-
+ raise ValueError("[Error] Photometric augmentation is not enabled.")
+
# Parse photometric transforms
- trans_lst = self.parse_transforms(photo_config["primitives"],
- photoaug.available_augmentations)
- trans_config_lst = [photo_config["params"].get(p, {})
- for p in trans_lst]
+ trans_lst = self.parse_transforms(
+ photo_config["primitives"], photoaug.available_augmentations
+ )
+ trans_config_lst = [photo_config["params"].get(p, {}) for p in trans_lst]
# List of photometric augmentation
photometric_trans_lst = [
- getattr(photoaug, trans)(**conf) \
+ getattr(photoaug, trans)(**conf)
for (trans, conf) in zip(trans_lst, trans_config_lst)
]
return photometric_trans_lst
def get_homo_transform(self):
- """ Get homographic transforms (according to the config). """
+ """Get homographic transforms (according to the config)."""
# Get homographic transforms for image
homo_config = self.config["augmentation"]["homographic"]["params"]
if not self.config["augmentation"]["homographic"]["enable"]:
- raise ValueError(
- "[Error] Homographic augmentation is not enabled.")
+ raise ValueError("[Error] Homographic augmentation is not enabled.")
# Parse the homographic transforms
image_shape = self.config["preprocessing"]["resize"]
@@ -359,67 +363,73 @@ class WireframeDataset(Dataset):
min_label_tmp = self.config["generation"]["min_label_len"]
except:
min_label_tmp = None
-
+
# float label len => fraction
- if isinstance(min_label_tmp, float): # Skip if not provided
+ if isinstance(min_label_tmp, float): # Skip if not provided
min_label_len = min_label_tmp * min(image_shape)
# int label len => length in pixel
elif isinstance(min_label_tmp, int):
- scale_ratio = (self.config["preprocessing"]["resize"]
- / self.config["generation"]["image_size"][0])
- min_label_len = (self.config["generation"]["min_label_len"]
- * scale_ratio)
+ scale_ratio = (
+ self.config["preprocessing"]["resize"]
+ / self.config["generation"]["image_size"][0]
+ )
+ min_label_len = self.config["generation"]["min_label_len"] * scale_ratio
# if none => no restriction
else:
min_label_len = 0
-
+
# Initialize the transform
homographic_trans = homoaug.homography_transform(
- image_shape, homo_config, 0, min_label_len)
+ image_shape, homo_config, 0, min_label_len
+ )
return homographic_trans
- def get_line_points(self, junctions, line_map, H1=None, H2=None,
- img_size=None, warp=False):
- """ Sample evenly points along each line segments
- and keep track of line idx. """
+ def get_line_points(
+ self, junctions, line_map, H1=None, H2=None, img_size=None, warp=False
+ ):
+ """Sample evenly points along each line segments
+ and keep track of line idx."""
if np.sum(line_map) == 0:
# No segment detected in the image
line_indices = np.zeros(self.config["max_pts"], dtype=int)
line_points = np.zeros((self.config["max_pts"], 2), dtype=float)
return line_points, line_indices
-
+
# Extract all pairs of connected junctions
junc_indices = np.array(
- [[i, j] for (i, j) in zip(*np.where(line_map)) if j > i])
- line_segments = np.stack([junctions[junc_indices[:, 0]],
- junctions[junc_indices[:, 1]]], axis=1)
+ [[i, j] for (i, j) in zip(*np.where(line_map)) if j > i]
+ )
+ line_segments = np.stack(
+ [junctions[junc_indices[:, 0]], junctions[junc_indices[:, 1]]], axis=1
+ )
# line_segments is (num_lines, 2, 2)
- line_lengths = np.linalg.norm(
- line_segments[:, 0] - line_segments[:, 1], axis=1)
+ line_lengths = np.linalg.norm(line_segments[:, 0] - line_segments[:, 1], axis=1)
# Sample the points separated by at least min_dist_pts along each line
# The number of samples depends on the length of the line
- num_samples = np.minimum(line_lengths // self.config["min_dist_pts"],
- self.config["max_num_samples"])
+ num_samples = np.minimum(
+ line_lengths // self.config["min_dist_pts"], self.config["max_num_samples"]
+ )
line_points = []
line_indices = []
cur_line_idx = 1
for n in np.arange(2, self.config["max_num_samples"] + 1):
# Consider all lines where we can fit up to n points
cur_line_seg = line_segments[num_samples == n]
- line_points_x = np.linspace(cur_line_seg[:, 0, 0],
- cur_line_seg[:, 1, 0],
- n, axis=-1).flatten()
- line_points_y = np.linspace(cur_line_seg[:, 0, 1],
- cur_line_seg[:, 1, 1],
- n, axis=-1).flatten()
+ line_points_x = np.linspace(
+ cur_line_seg[:, 0, 0], cur_line_seg[:, 1, 0], n, axis=-1
+ ).flatten()
+ line_points_y = np.linspace(
+ cur_line_seg[:, 0, 1], cur_line_seg[:, 1, 1], n, axis=-1
+ ).flatten()
jitter = self.config.get("jittering", 0)
if jitter:
# Add a small random jittering of all points along the line
angles = np.arctan2(
cur_line_seg[:, 1, 0] - cur_line_seg[:, 0, 0],
- cur_line_seg[:, 1, 1] - cur_line_seg[:, 0, 1]).repeat(n)
+ cur_line_seg[:, 1, 1] - cur_line_seg[:, 0, 1],
+ ).repeat(n)
jitter_hyp = (np.random.rand(len(angles)) * 2 - 1) * jitter
line_points_x += jitter_hyp * np.sin(angles)
line_points_y += jitter_hyp * np.cos(angles)
@@ -429,10 +439,8 @@ class WireframeDataset(Dataset):
line_idx = np.arange(cur_line_idx, cur_line_idx + num_cur_lines)
line_indices.append(line_idx.repeat(n))
cur_line_idx += num_cur_lines
- line_points = np.concatenate(line_points,
- axis=0)[:self.config["max_pts"]]
- line_indices = np.concatenate(line_indices,
- axis=0)[:self.config["max_pts"]]
+ line_points = np.concatenate(line_points, axis=0)[: self.config["max_pts"]]
+ line_indices = np.concatenate(line_indices, axis=0)[: self.config["max_pts"]]
# Warp the points if need be, and filter unvalid ones
# If the other view is also warped
@@ -454,20 +462,24 @@ class WireframeDataset(Dataset):
mask = mask_points(warped_points, img_size)
line_points = line_points[mask]
line_indices = line_indices[mask]
-
+
# Pad the line points to a fixed length
# Index of 0 means padded line
- line_indices = np.concatenate([line_indices, np.zeros(
- self.config["max_pts"] - len(line_indices))], axis=0)
+ line_indices = np.concatenate(
+ [line_indices, np.zeros(self.config["max_pts"] - len(line_indices))], axis=0
+ )
line_points = np.concatenate(
- [line_points,
- np.zeros((self.config["max_pts"] - len(line_points), 2),
- dtype=float)], axis=0)
-
+ [
+ line_points,
+ np.zeros((self.config["max_pts"] - len(line_points), 2), dtype=float),
+ ],
+ axis=0,
+ )
+
return line_points, line_indices
def train_preprocessing(self, data, numpy=False):
- """ Train preprocessing for GT data. """
+ """Train preprocessing for GT data."""
# Fetch the corresponding entries
image = data["image"]
junctions = data["junc"][:, :2]
@@ -476,23 +488,27 @@ class WireframeDataset(Dataset):
image_size = image.shape[:2]
# Convert junctions to pixel coordinates (from 128x128)
junctions[:, 0] *= image_size[0] / 128
- junctions[:, 1] *= image_size[1] / 128
+ junctions[:, 1] *= image_size[1] / 128
# Resize the image before photometric and homographical augmentations
- if not(list(image_size) == self.config["preprocessing"]["resize"]):
+ if not (list(image_size) == self.config["preprocessing"]["resize"]):
# Resize the image and the point location.
- size_old = list(image.shape)[:2] # Only H and W dimensions
+ size_old = list(image.shape)[:2] # Only H and W dimensions
image = cv2.resize(
- image, tuple(self.config['preprocessing']['resize'][::-1]),
- interpolation=cv2.INTER_LINEAR)
+ image,
+ tuple(self.config["preprocessing"]["resize"][::-1]),
+ interpolation=cv2.INTER_LINEAR,
+ )
image = np.array(image, dtype=np.uint8)
# In HW format
- junctions = (junctions * np.array(
- self.config['preprocessing']['resize'], np.float)
- / np.array(size_old, np.float))
-
+ junctions = (
+ junctions
+ * np.array(self.config["preprocessing"]["resize"], np.float)
+ / np.array(size_old, np.float)
+ )
+
# Convert to positive line map and negative line map (our format)
num_junctions = junctions.shape[0]
line_map_pos = self.convert_line_map(line_pos, num_junctions)
@@ -509,7 +525,7 @@ class WireframeDataset(Dataset):
# Optionally convert the image to grayscale
if self.config["gray_scale"]:
- image = (color.rgb2gray(image) * 255.).astype(np.uint8)
+ image = (color.rgb2gray(image) * 255.0).astype(np.uint8)
# Check if we need to apply augmentations
# In training mode => yes.
@@ -519,7 +535,8 @@ class WireframeDataset(Dataset):
### Image transform ###
np.random.shuffle(photo_trans_lst)
image_transform = transforms.Compose(
- photo_trans_lst + [photoaug.normalize_image()])
+ photo_trans_lst + [photoaug.normalize_image()]
+ )
else:
image_transform = photoaug.normalize_image()
image = image_transform(image)
@@ -549,13 +566,11 @@ class WireframeDataset(Dataset):
"image": to_tensor(image),
"junctions": to_tensor(junctions).to(torch.float32)[0, ...],
"junction_map": to_tensor(junction_map).to(torch.int),
- "line_map_pos": to_tensor(
- line_map_pos).to(torch.int32)[0, ...],
- "line_map_neg": to_tensor(
- line_map_neg).to(torch.int32)[0, ...],
+ "line_map_pos": to_tensor(line_map_pos).to(torch.int32)[0, ...],
+ "line_map_neg": to_tensor(line_map_neg).to(torch.int32)[0, ...],
"heatmap_pos": to_tensor(heatmap_pos).to(torch.int32),
"heatmap_neg": to_tensor(heatmap_neg).to(torch.int32),
- "valid_mask": to_tensor(valid_mask).to(torch.int32)
+ "valid_mask": to_tensor(valid_mask).to(torch.int32),
}
else:
return {
@@ -566,14 +581,23 @@ class WireframeDataset(Dataset):
"line_map_neg": line_map_neg.astype(np.int32),
"heatmap_pos": heatmap_pos.astype(np.int32),
"heatmap_neg": heatmap_neg.astype(np.int32),
- "valid_mask": valid_mask.astype(np.int32)
+ "valid_mask": valid_mask.astype(np.int32),
}
-
+
def train_preprocessing_exported(
- self, data, numpy=False, disable_homoaug=False,
- desc_training=False, H1=None, H1_scale=None, H2=None, scale=1.,
- h_crop=None, w_crop=None):
- """ Train preprocessing for the exported labels. """
+ self,
+ data,
+ numpy=False,
+ disable_homoaug=False,
+ desc_training=False,
+ H1=None,
+ H1_scale=None,
+ H2=None,
+ scale=1.0,
+ h_crop=None,
+ w_crop=None,
+ ):
+ """Train preprocessing for the exported labels."""
data = copy.deepcopy(data)
# Fetch the corresponding entries
image = data["image"]
@@ -593,13 +617,15 @@ class WireframeDataset(Dataset):
w_crop = np.random.randint(W_scale - W)
# Resize the image before photometric and homographical augmentations
- if not(list(image_size) == self.config["preprocessing"]["resize"]):
+ if not (list(image_size) == self.config["preprocessing"]["resize"]):
# Resize the image and the point location.
- size_old = list(image.shape)[:2] # Only H and W dimensions
+ size_old = list(image.shape)[:2] # Only H and W dimensions
image = cv2.resize(
- image, tuple(self.config['preprocessing']['resize'][::-1]),
- interpolation=cv2.INTER_LINEAR)
+ image,
+ tuple(self.config["preprocessing"]["resize"][::-1]),
+ interpolation=cv2.INTER_LINEAR,
+ )
image = np.array(image, dtype=np.uint8)
# # In HW format
@@ -614,7 +640,7 @@ class WireframeDataset(Dataset):
# Optionally convert the image to grayscale
if self.config["gray_scale"]:
- image = (color.rgb2gray(image) * 255.).astype(np.uint8)
+ image = (color.rgb2gray(image) * 255.0).astype(np.uint8)
# Check if we need to apply augmentations
# In training mode => yes.
@@ -624,40 +650,49 @@ class WireframeDataset(Dataset):
### Image transform ###
np.random.shuffle(photo_trans_lst)
image_transform = transforms.Compose(
- photo_trans_lst + [photoaug.normalize_image()])
+ photo_trans_lst + [photoaug.normalize_image()]
+ )
else:
image_transform = photoaug.normalize_image()
image = image_transform(image)
-
+
# Perform the random scaling
- if scale != 1.:
+ if scale != 1.0:
image, junctions, line_map, valid_mask = random_scaling(
- image, junctions, line_map, scale,
- h_crop=h_crop, w_crop=w_crop)
+ image, junctions, line_map, scale, h_crop=h_crop, w_crop=w_crop
+ )
else:
# Declare default valid mask (all ones)
valid_mask = np.ones(image_size)
-
+
# Initialize the empty output dict
outputs = {}
# Convert to tensor and return the results
to_tensor = transforms.ToTensor()
# Check homographic augmentation
- warp = (self.config["augmentation"]["homographic"]["enable"]
- and disable_homoaug == False)
+ warp = (
+ self.config["augmentation"]["homographic"]["enable"]
+ and disable_homoaug == False
+ )
if warp:
homo_trans = self.get_homo_transform()
# Perform homographic transform
if H1 is None:
homo_outputs = homo_trans(
- image, junctions, line_map, valid_mask=valid_mask)
+ image, junctions, line_map, valid_mask=valid_mask
+ )
else:
homo_outputs = homo_trans(
- image, junctions, line_map, homo=H1, scale=H1_scale,
- valid_mask=valid_mask)
+ image,
+ junctions,
+ line_map,
+ homo=H1,
+ scale=H1_scale,
+ valid_mask=valid_mask,
+ )
homography_mat = homo_outputs["homo"]
-
+
# Give the warp of the other view
if H1 is None:
H1 = homo_outputs["homo"]
@@ -665,8 +700,8 @@ class WireframeDataset(Dataset):
# Sample points along each line segments for the descriptor
if desc_training:
line_points, line_indices = self.get_line_points(
- junctions, line_map, H1=H1, H2=H2,
- img_size=image_size, warp=warp)
+ junctions, line_map, H1=H1, H2=H2, img_size=image_size, warp=warp
+ )
# Record the warped results
if warp:
@@ -675,52 +710,59 @@ class WireframeDataset(Dataset):
line_map = homo_outputs["line_map"]
valid_mask = homo_outputs["valid_mask"] # Same for pos and neg
heatmap = homo_outputs["warped_heatmap"]
-
+
# Optionally put warping information first.
if not numpy:
- outputs["homography_mat"] = to_tensor(
- homography_mat).to(torch.float32)[0, ...]
+ outputs["homography_mat"] = to_tensor(homography_mat).to(torch.float32)[
+ 0, ...
+ ]
else:
outputs["homography_mat"] = homography_mat.astype(np.float32)
junction_map = self.junc_to_junc_map(junctions, image_size)
-
+
if not numpy:
- outputs.update({
- "image": to_tensor(image).to(torch.float32),
- "junctions": to_tensor(junctions).to(torch.float32)[0, ...],
- "junction_map": to_tensor(junction_map).to(torch.int),
- "line_map": to_tensor(line_map).to(torch.int32)[0, ...],
- "heatmap": to_tensor(heatmap).to(torch.int32),
- "valid_mask": to_tensor(valid_mask).to(torch.int32)
- })
+ outputs.update(
+ {
+ "image": to_tensor(image).to(torch.float32),
+ "junctions": to_tensor(junctions).to(torch.float32)[0, ...],
+ "junction_map": to_tensor(junction_map).to(torch.int),
+ "line_map": to_tensor(line_map).to(torch.int32)[0, ...],
+ "heatmap": to_tensor(heatmap).to(torch.int32),
+ "valid_mask": to_tensor(valid_mask).to(torch.int32),
+ }
+ )
if desc_training:
- outputs.update({
- "line_points": to_tensor(
- line_points).to(torch.float32)[0],
- "line_indices": torch.tensor(line_indices,
- dtype=torch.int)
- })
+ outputs.update(
+ {
+ "line_points": to_tensor(line_points).to(torch.float32)[0],
+ "line_indices": torch.tensor(line_indices, dtype=torch.int),
+ }
+ )
else:
- outputs.update({
- "image": image,
- "junctions": junctions.astype(np.float32),
- "junction_map": junction_map.astype(np.int32),
- "line_map": line_map.astype(np.int32),
- "heatmap": heatmap.astype(np.int32),
- "valid_mask": valid_mask.astype(np.int32)
- })
+ outputs.update(
+ {
+ "image": image,
+ "junctions": junctions.astype(np.float32),
+ "junction_map": junction_map.astype(np.int32),
+ "line_map": line_map.astype(np.int32),
+ "heatmap": heatmap.astype(np.int32),
+ "valid_mask": valid_mask.astype(np.int32),
+ }
+ )
if desc_training:
- outputs.update({
- "line_points": line_points.astype(np.float32),
- "line_indices": line_indices.astype(int)
- })
-
+ outputs.update(
+ {
+ "line_points": line_points.astype(np.float32),
+ "line_indices": line_indices.astype(int),
+ }
+ )
+
return outputs
-
- def preprocessing_exported_paired_desc(self, data, numpy=False, scale=1.):
- """ Train preprocessing for paired data for the exported labels
- for descriptor training. """
+
+ def preprocessing_exported_paired_desc(self, data, numpy=False, scale=1.0):
+ """Train preprocessing for paired data for the exported labels
+ for descriptor training."""
outputs = {}
# Define the random crop for scaling if necessary
@@ -732,36 +774,49 @@ class WireframeDataset(Dataset):
h_crop = np.random.randint(H_scale - H)
if W_scale > W:
w_crop = np.random.randint(W_scale - W)
-
+
# Sample ref homography first
homo_config = self.config["augmentation"]["homographic"]["params"]
image_shape = self.config["preprocessing"]["resize"]
- ref_H, ref_scale = homoaug.sample_homography(image_shape,
- **homo_config)
+ ref_H, ref_scale = homoaug.sample_homography(image_shape, **homo_config)
# Data for target view (All augmentation)
target_data = self.train_preprocessing_exported(
- data, numpy=numpy, desc_training=True, H1=None, H2=ref_H,
- scale=scale, h_crop=h_crop, w_crop=w_crop)
+ data,
+ numpy=numpy,
+ desc_training=True,
+ H1=None,
+ H2=ref_H,
+ scale=scale,
+ h_crop=h_crop,
+ w_crop=w_crop,
+ )
# Data for reference view (No homographical augmentation)
ref_data = self.train_preprocessing_exported(
- data, numpy=numpy, desc_training=True, H1=ref_H,
- H1_scale=ref_scale, H2=target_data["homography_mat"].numpy(),
- scale=scale, h_crop=h_crop, w_crop=w_crop)
+ data,
+ numpy=numpy,
+ desc_training=True,
+ H1=ref_H,
+ H1_scale=ref_scale,
+ H2=target_data["homography_mat"].numpy(),
+ scale=scale,
+ h_crop=h_crop,
+ w_crop=w_crop,
+ )
# Spread ref data
for key, val in ref_data.items():
outputs["ref_" + key] = val
-
+
# Spread target data
for key, val in target_data.items():
outputs["target_" + key] = val
-
+
return outputs
def test_preprocessing(self, data, numpy=False):
- """ Test preprocessing for GT data. """
+ """Test preprocessing for GT data."""
data = copy.deepcopy(data)
# Fetch the corresponding entries
image = data["image"]
@@ -771,31 +826,35 @@ class WireframeDataset(Dataset):
image_size = image.shape[:2]
# Convert junctions to pixel coordinates (from 128x128)
junctions[:, 0] *= image_size[0] / 128
- junctions[:, 1] *= image_size[1] / 128
+ junctions[:, 1] *= image_size[1] / 128
# Resize the image before photometric and homographical augmentations
- if not(list(image_size) == self.config["preprocessing"]["resize"]):
+ if not (list(image_size) == self.config["preprocessing"]["resize"]):
# Resize the image and the point location.
- size_old = list(image.shape)[:2] # Only H and W dimensions
+ size_old = list(image.shape)[:2] # Only H and W dimensions
image = cv2.resize(
- image, tuple(self.config['preprocessing']['resize'][::-1]),
- interpolation=cv2.INTER_LINEAR)
+ image,
+ tuple(self.config["preprocessing"]["resize"][::-1]),
+ interpolation=cv2.INTER_LINEAR,
+ )
image = np.array(image, dtype=np.uint8)
# In HW format
- junctions = (junctions * np.array(
- self.config['preprocessing']['resize'], np.float)
- / np.array(size_old, np.float))
-
+ junctions = (
+ junctions
+ * np.array(self.config["preprocessing"]["resize"], np.float)
+ / np.array(size_old, np.float)
+ )
+
# Optionally convert the image to grayscale
if self.config["gray_scale"]:
- image = (color.rgb2gray(image) * 255.).astype(np.uint8)
+ image = (color.rgb2gray(image) * 255.0).astype(np.uint8)
# Still need to normalize image
image_transform = photoaug.normalize_image()
image = image_transform(image)
-
+
# Convert to positive line map and negative line map (our format)
num_junctions = junctions.shape[0]
line_map_pos = self.convert_line_map(line_pos, num_junctions)
@@ -819,13 +878,11 @@ class WireframeDataset(Dataset):
"image": to_tensor(image),
"junctions": to_tensor(junctions).to(torch.float32)[0, ...],
"junction_map": to_tensor(junction_map).to(torch.int),
- "line_map_pos": to_tensor(
- line_map_pos).to(torch.int32)[0, ...],
- "line_map_neg": to_tensor(
- line_map_neg).to(torch.int32)[0, ...],
+ "line_map_pos": to_tensor(line_map_pos).to(torch.int32)[0, ...],
+ "line_map_neg": to_tensor(line_map_neg).to(torch.int32)[0, ...],
"heatmap_pos": to_tensor(heatmap_pos).to(torch.int32),
"heatmap_neg": to_tensor(heatmap_neg).to(torch.int32),
- "valid_mask": to_tensor(valid_mask).to(torch.int32)
+ "valid_mask": to_tensor(valid_mask).to(torch.int32),
}
else:
return {
@@ -836,26 +893,28 @@ class WireframeDataset(Dataset):
"line_map_neg": line_map_neg.astype(np.int32),
"heatmap_pos": heatmap_pos.astype(np.int32),
"heatmap_neg": heatmap_neg.astype(np.int32),
- "valid_mask": valid_mask.astype(np.int32)
+ "valid_mask": valid_mask.astype(np.int32),
}
-
- def test_preprocessing_exported(self, data, numpy=False, scale=1.):
- """ Test preprocessing for the exported labels. """
+
+ def test_preprocessing_exported(self, data, numpy=False, scale=1.0):
+ """Test preprocessing for the exported labels."""
data = copy.deepcopy(data)
# Fetch the corresponding entries
image = data["image"]
junctions = data["junctions"]
- line_map = data["line_map"]
+ line_map = data["line_map"]
image_size = image.shape[:2]
# Resize the image before photometric and homographical augmentations
- if not(list(image_size) == self.config["preprocessing"]["resize"]):
+ if not (list(image_size) == self.config["preprocessing"]["resize"]):
# Resize the image and the point location.
- size_old = list(image.shape)[:2] # Only H and W dimensions
+ size_old = list(image.shape)[:2] # Only H and W dimensions
image = cv2.resize(
- image, tuple(self.config['preprocessing']['resize'][::-1]),
- interpolation=cv2.INTER_LINEAR)
+ image,
+ tuple(self.config["preprocessing"]["resize"][::-1]),
+ interpolation=cv2.INTER_LINEAR,
+ )
image = np.array(image, dtype=np.uint8)
# # In HW format
@@ -865,7 +924,7 @@ class WireframeDataset(Dataset):
# Optionally convert the image to grayscale
if self.config["gray_scale"]:
- image = (color.rgb2gray(image) * 255.).astype(np.uint8)
+ image = (color.rgb2gray(image) * 255.0).astype(np.uint8)
# Still need to normalize image
image_transform = photoaug.normalize_image()
@@ -875,7 +934,7 @@ class WireframeDataset(Dataset):
junctions_xy = np.flip(np.round(junctions).astype(np.int32), axis=1)
image_size = image.shape[:2]
heatmap = get_line_heatmap(junctions_xy, line_map, image_size)
-
+
# Declare default valid mask (all ones)
valid_mask = np.ones(image_size)
@@ -890,7 +949,7 @@ class WireframeDataset(Dataset):
"junction_map": to_tensor(junction_map).to(torch.int),
"line_map": to_tensor(line_map).to(torch.int32)[0, ...],
"heatmap": to_tensor(heatmap).to(torch.int32),
- "valid_mask": to_tensor(valid_mask).to(torch.int32)
+ "valid_mask": to_tensor(valid_mask).to(torch.int32),
}
else:
outputs = {
@@ -899,20 +958,20 @@ class WireframeDataset(Dataset):
"junction_map": junction_map.astype(np.int32),
"line_map": line_map.astype(np.int32),
"heatmap": heatmap.astype(np.int32),
- "valid_mask": valid_mask.astype(np.int32)
+ "valid_mask": valid_mask.astype(np.int32),
}
-
+
return outputs
def __len__(self):
return self.dataset_length
def get_data_from_key(self, file_key):
- """ Get data from file_key. """
+ """Get data from file_key."""
# Check key exists
if not file_key in self.filename_dataset.keys():
raise ValueError("[Error] the specified key is not in the dataset.")
-
+
# Get the data paths
data_path = self.filename_dataset[file_key]
# Read in the image and npz labels (but haven't applied any transform)
@@ -923,12 +982,12 @@ class WireframeDataset(Dataset):
data = self.train_preprocessing(data, numpy=True)
else:
data = self.test_preprocessing(data, numpy=True)
-
+
# Add file key to the output
data["file_key"] = file_key
-
+
return data
-
+
def __getitem__(self, idx):
"""Return data
file_key: str, keys used to retrieve data from the filename dataset.
@@ -951,30 +1010,27 @@ class WireframeDataset(Dataset):
if not self.gt_source == "official":
with h5py.File(self.gt_source, "r") as f:
exported_label = parse_h5_data(f[file_key])
-
+
data["junctions"] = exported_label["junctions"]
data["line_map"] = exported_label["line_map"]
-
+
# Perform transform and augmentation
return_type = self.config.get("return_type", "single")
- if (self.mode == "train"
- or self.config["add_augmentation_to_all_splits"]):
+ if self.mode == "train" or self.config["add_augmentation_to_all_splits"]:
# Perform random scaling first
if self.config["augmentation"]["random_scaling"]["enable"]:
scale_range = self.config["augmentation"]["random_scaling"]["range"]
# Decide the scaling
scale = np.random.uniform(min(scale_range), max(scale_range))
else:
- scale = 1.
+ scale = 1.0
if self.gt_source == "official":
data = self.train_preprocessing(data)
else:
if return_type == "paired_desc":
- data = self.preprocessing_exported_paired_desc(
- data, scale=scale)
+ data = self.preprocessing_exported_paired_desc(data, scale=scale)
else:
- data = self.train_preprocessing_exported(data,
- scale=scale)
+ data = self.train_preprocessing_exported(data, scale=scale)
else:
if self.gt_source == "official":
data = self.test_preprocessing(data)
@@ -982,17 +1038,17 @@ class WireframeDataset(Dataset):
data = self.preprocessing_exported_paired_desc(data)
else:
data = self.test_preprocessing_exported(data)
-
+
# Add file key to the output
data["file_key"] = file_key
-
+
return data
-
+
########################
## Some other methods ##
########################
def _check_dataset_cache(self):
- """ Check if dataset cache exists. """
+ """Check if dataset cache exists."""
cache_file_path = os.path.join(self.cache_path, self.cache_name)
if os.path.exists(cache_file_path):
return True
diff --git a/imcui/third_party/SOLD2/sold2/experiment.py b/third_party/SOLD2/sold2/experiment.py
similarity index 64%
rename from imcui/third_party/SOLD2/sold2/experiment.py
rename to third_party/SOLD2/sold2/experiment.py
index 3bf4db1c9f148b9e33c6d7d0ba973375cd770a14..0a2d5c0dc359cec13304813ac7732c5968d70a80 100644
--- a/imcui/third_party/SOLD2/sold2/experiment.py
+++ b/third_party/SOLD2/sold2/experiment.py
@@ -19,7 +19,7 @@ torch.backends.cudnn.benchmark = True
def load_config(config_path):
- """ Load configurations from a given yaml file. """
+ """Load configurations from a given yaml file."""
# Check file exists
if not os.path.exists(config_path):
raise ValueError("[Error] The provided config path is not valid.")
@@ -32,7 +32,7 @@ def load_config(config_path):
def update_config(path, model_cfg=None, dataset_cfg=None):
- """ Update configuration file from the resume path. """
+ """Update configuration file from the resume path."""
# Check we need to update or completely override.
model_cfg = {} if model_cfg is None else model_cfg
dataset_cfg = {} if dataset_cfg is None else dataset_cfg
@@ -57,23 +57,23 @@ def update_config(path, model_cfg=None, dataset_cfg=None):
def record_config(model_cfg, dataset_cfg, output_path):
- """ Record dataset config to the log path. """
+ """Record dataset config to the log path."""
# Record model config
with open(os.path.join(output_path, "model_cfg.yaml"), "w") as f:
- yaml.safe_dump(model_cfg, f)
-
+ yaml.safe_dump(model_cfg, f)
+
# Record dataset config
with open(os.path.join(output_path, "dataset_cfg.yaml"), "w") as f:
- yaml.safe_dump(dataset_cfg, f)
-
+ yaml.safe_dump(dataset_cfg, f)
+
def train(args, dataset_cfg, model_cfg, output_path):
- """ Training function. """
+ """Training function."""
# Update model config from the resume path (only in resume mode)
if args.resume:
if os.path.realpath(output_path) != os.path.realpath(args.resume_path):
record_config(model_cfg, dataset_cfg, output_path)
-
+
# First time, then write the config file to the output path
else:
record_config(model_cfg, dataset_cfg, output_path)
@@ -82,23 +82,32 @@ def train(args, dataset_cfg, model_cfg, output_path):
train_net(args, dataset_cfg, model_cfg, output_path)
-def export(args, dataset_cfg, model_cfg, output_path,
- export_dataset_mode=None, device=torch.device("cuda")):
- """ Export function. """
+def export(
+ args,
+ dataset_cfg,
+ model_cfg,
+ output_path,
+ export_dataset_mode=None,
+ device=torch.device("cuda"),
+):
+ """Export function."""
# Choose between normal predictions export or homography adaptation
if dataset_cfg.get("homography_adaptation") is not None:
print("[Info] Export predictions with homography adaptation.")
- export_homograpy_adaptation(args, dataset_cfg, model_cfg, output_path,
- export_dataset_mode, device)
+ export_homograpy_adaptation(
+ args, dataset_cfg, model_cfg, output_path, export_dataset_mode, device
+ )
else:
print("[Info] Export predictions normally.")
- export_predictions(args, dataset_cfg, model_cfg, output_path,
- export_dataset_mode)
+ export_predictions(
+ args, dataset_cfg, model_cfg, output_path, export_dataset_mode
+ )
-def main(args, dataset_cfg, model_cfg, export_dataset_mode=None,
- device=torch.device("cuda")):
- """ Main function. """
+def main(
+ args, dataset_cfg, model_cfg, export_dataset_mode=None, device=torch.device("cuda")
+):
+ """Main function."""
# Make the output path
output_path = os.path.join(cfg.EXP_PATH, args.exp_name)
@@ -113,7 +122,14 @@ def main(args, dataset_cfg, model_cfg, export_dataset_mode=None,
output_path = os.path.join(cfg.export_dataroot, args.exp_name)
print("[Info] Export mode")
print("\t Output path: %s" % output_path)
- export(args, dataset_cfg, model_cfg, output_path, export_dataset_mode, device=device)
+ export(
+ args,
+ dataset_cfg,
+ model_cfg,
+ output_path,
+ export_dataset_mode,
+ device=device,
+ )
else:
raise ValueError("[Error]: Unknown mode: " + args.mode)
@@ -126,28 +142,43 @@ def set_random_seed(seed):
if __name__ == "__main__":
# Parse input arguments
parser = argparse.ArgumentParser()
- parser.add_argument("--mode", type=str, default="train",
- help="'train' or 'export'.")
- parser.add_argument("--dataset_config", type=str, default=None,
- help="Path to the dataset config.")
- parser.add_argument("--model_config", type=str, default=None,
- help="Path to the model config.")
- parser.add_argument("--exp_name", type=str, default="exp",
- help="Experiment name.")
- parser.add_argument("--resume", action="store_true", default=False,
- help="Load a previously trained model.")
- parser.add_argument("--pretrained", action="store_true", default=False,
- help="Start training from a pre-trained model.")
- parser.add_argument("--resume_path", default=None,
- help="Path from which to resume training.")
- parser.add_argument("--pretrained_path", default=None,
- help="Path to the pre-trained model.")
- parser.add_argument("--checkpoint_name", default=None,
- help="Name of the checkpoint to use.")
- parser.add_argument("--export_dataset_mode", default=None,
- help="'train' or 'test'.")
- parser.add_argument("--export_batch_size", default=4, type=int,
- help="Export batch size.")
+ parser.add_argument(
+ "--mode", type=str, default="train", help="'train' or 'export'."
+ )
+ parser.add_argument(
+ "--dataset_config", type=str, default=None, help="Path to the dataset config."
+ )
+ parser.add_argument(
+ "--model_config", type=str, default=None, help="Path to the model config."
+ )
+ parser.add_argument("--exp_name", type=str, default="exp", help="Experiment name.")
+ parser.add_argument(
+ "--resume",
+ action="store_true",
+ default=False,
+ help="Load a previously trained model.",
+ )
+ parser.add_argument(
+ "--pretrained",
+ action="store_true",
+ default=False,
+ help="Start training from a pre-trained model.",
+ )
+ parser.add_argument(
+ "--resume_path", default=None, help="Path from which to resume training."
+ )
+ parser.add_argument(
+ "--pretrained_path", default=None, help="Path to the pre-trained model."
+ )
+ parser.add_argument(
+ "--checkpoint_name", default=None, help="Name of the checkpoint to use."
+ )
+ parser.add_argument(
+ "--export_dataset_mode", default=None, help="'train' or 'test'."
+ )
+ parser.add_argument(
+ "--export_batch_size", default=4, type=int, help="Export batch size."
+ )
args = parser.parse_args()
@@ -159,28 +190,29 @@ if __name__ == "__main__":
device = torch.device("cpu")
# Check if dataset config and model config is given.
- if (((args.dataset_config is None) or (args.model_config is None))
- and (not args.resume) and (args.mode == "train")):
+ if (
+ ((args.dataset_config is None) or (args.model_config is None))
+ and (not args.resume)
+ and (args.mode == "train")
+ ):
raise ValueError(
- "[Error] The dataset config and model config should be given in non-resume mode")
+ "[Error] The dataset config and model config should be given in non-resume mode"
+ )
# If resume, check if the resume path has been given
if args.resume and (args.resume_path is None):
- raise ValueError(
- "[Error] Missing resume path.")
+ raise ValueError("[Error] Missing resume path.")
# [Training] Load the config file.
if args.mode == "train" and (not args.resume):
# Check the pretrained checkpoint_path exists
if args.pretrained:
checkpoint_folder = args.resume_path
- checkpoint_path = os.path.join(args.pretrained_path,
- args.checkpoint_name)
+ checkpoint_path = os.path.join(args.pretrained_path, args.checkpoint_name)
if not os.path.exists(checkpoint_path):
- raise ValueError("[Error] Missing checkpoint: "
- + checkpoint_path)
+ raise ValueError("[Error] Missing checkpoint: " + checkpoint_path)
dataset_cfg = load_config(args.dataset_config)
- model_cfg = load_config(args.model_config)
+ model_cfg = load_config(args.model_config)
# [resume Training, Test, Export] Load the config file.
elif (args.mode == "train" and args.resume) or (args.mode == "export"):
@@ -195,33 +227,35 @@ if __name__ == "__main__":
print("[Info] No model config provided. Loading from checkpoint folder.")
model_cfg_path = os.path.join(checkpoint_folder, "model_cfg.yaml")
if not os.path.exists(model_cfg_path):
- raise ValueError(
- "[Error] Missing model config in checkpoint path.")
+ raise ValueError("[Error] Missing model config in checkpoint path.")
model_cfg = load_config(model_cfg_path)
else:
model_cfg = load_config(args.model_config)
-
+
# Load dataset_cfg from checkpoint folder if not provided
if args.dataset_config is None:
print("[Info] No dataset config provided. Loading from checkpoint folder.")
- dataset_cfg_path = os.path.join(checkpoint_folder,
- "dataset_cfg.yaml")
+ dataset_cfg_path = os.path.join(checkpoint_folder, "dataset_cfg.yaml")
if not os.path.exists(dataset_cfg_path):
- raise ValueError(
- "[Error] Missing dataset config in checkpoint path.")
+ raise ValueError("[Error] Missing dataset config in checkpoint path.")
dataset_cfg = load_config(dataset_cfg_path)
else:
dataset_cfg = load_config(args.dataset_config)
-
+
# Check the --export_dataset_mode flag
if (args.mode == "export") and (args.export_dataset_mode is None):
raise ValueError("[Error] Empty --export_dataset_mode flag.")
else:
raise ValueError("[Error] Unknown mode: " + args.mode)
-
+
# Set the random seed
seed = dataset_cfg.get("random_seed", 0)
set_random_seed(seed)
- main(args, dataset_cfg, model_cfg,
- export_dataset_mode=args.export_dataset_mode, device=device)
+ main(
+ args,
+ dataset_cfg,
+ model_cfg,
+ export_dataset_mode=args.export_dataset_mode,
+ device=device,
+ )
diff --git a/imcui/third_party/SOLD2/sold2/export.py b/third_party/SOLD2/sold2/export.py
similarity index 65%
rename from imcui/third_party/SOLD2/sold2/export.py
rename to third_party/SOLD2/sold2/export.py
index 19683d982c6d7fd429b27868b620fd20562d1aa7..ec5bf2dcb1c51999c80b6d1ff170c238883e34a0 100644
--- a/imcui/third_party/SOLD2/sold2/export.py
+++ b/third_party/SOLD2/sold2/export.py
@@ -17,7 +17,7 @@ from .dataset.transforms.homographic_transforms import sample_homography
def restore_weights(model, state_dict):
- """ Restore weights in compatible mode. """
+ """Restore weights in compatible mode."""
# Try to directly load state dict
try:
model.load_state_dict(state_dict)
@@ -38,15 +38,14 @@ def restore_weights(model, state_dict):
def get_padded_filename(num_pad, idx):
- """ Get the filename padded with 0. """
+ """Get the filename padded with 0."""
file_len = len("%d" % (idx))
filename = "0" * (num_pad - file_len) + "%d" % (idx)
return filename
-def export_predictions(args, dataset_cfg, model_cfg, output_path,
- export_dataset_mode):
- """ Export predictions. """
+def export_predictions(args, dataset_cfg, model_cfg, output_path, export_dataset_mode):
+ """Export predictions."""
# Get the test configuration
test_cfg = model_cfg["test"]
@@ -54,10 +53,14 @@ def export_predictions(args, dataset_cfg, model_cfg, output_path,
print("\t Initializing dataset and dataloader")
batch_size = 4
export_dataset, collate_fn = get_dataset(export_dataset_mode, dataset_cfg)
- export_loader = DataLoader(export_dataset, batch_size=batch_size,
- num_workers=test_cfg.get("num_workers", 4),
- shuffle=False, pin_memory=False,
- collate_fn=collate_fn)
+ export_loader = DataLoader(
+ export_dataset,
+ batch_size=batch_size,
+ num_workers=test_cfg.get("num_workers", 4),
+ shuffle=False,
+ pin_memory=False,
+ collate_fn=collate_fn,
+ )
print("\t Successfully intialized dataset and dataloader.")
# Initialize model and load the checkpoint
@@ -87,11 +90,18 @@ def export_predictions(args, dataset_cfg, model_cfg, output_path,
# Convert predictions
junc_np = convert_junc_predictions(
- outputs["junctions"], model_cfg["grid_size"],
- model_cfg["detection_thresh"], 300)
+ outputs["junctions"],
+ model_cfg["grid_size"],
+ model_cfg["detection_thresh"],
+ 300,
+ )
junc_map_np = junc_map.numpy().transpose(0, 2, 3, 1)
- heatmap_np = softmax(outputs["heatmap"].detach(),
- dim=1).cpu().numpy().transpose(0, 2, 3, 1)
+ heatmap_np = (
+ softmax(outputs["heatmap"].detach(), dim=1)
+ .cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)
+ )
heatmap_gt_np = heatmap.numpy().transpose(0, 2, 3, 1)
valid_mask_np = valid_mask.numpy().transpose(0, 2, 3, 1)
@@ -99,15 +109,22 @@ def export_predictions(args, dataset_cfg, model_cfg, output_path,
current_batch_size = input_images.shape[0]
for batch_idx in range(current_batch_size):
output_data = {
- "image": input_images.cpu().numpy().transpose(0, 2, 3, 1)[batch_idx],
+ "image": input_images.cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)[batch_idx],
"junc_gt": junc_map_np[batch_idx],
"junc_pred": junc_np["junc_pred"][batch_idx],
- "junc_pred_nms": junc_np["junc_pred_nms"][batch_idx].astype(np.float32),
+ "junc_pred_nms": junc_np["junc_pred_nms"][batch_idx].astype(
+ np.float32
+ ),
"heatmap_gt": heatmap_gt_np[batch_idx],
"heatmap_pred": heatmap_np[batch_idx],
"valid_mask": valid_mask_np[batch_idx],
- "junc_points": data["junctions"][batch_idx].numpy()[0].round().astype(np.int32),
- "line_map": data["line_map"][batch_idx].numpy()[0].astype(np.int32)
+ "junc_points": data["junctions"][batch_idx]
+ .numpy()[0]
+ .round()
+ .astype(np.int32),
+ "line_map": data["line_map"][batch_idx].numpy()[0].astype(np.int32),
}
# Save data to h5 dataset
@@ -117,19 +134,18 @@ def export_predictions(args, dataset_cfg, model_cfg, output_path,
# Store data
for key, output_data in output_data.items():
- f_group.create_dataset(key, data=output_data,
- compression="gzip")
+ f_group.create_dataset(key, data=output_data, compression="gzip")
filename_idx += 1
-def export_homograpy_adaptation(args, dataset_cfg, model_cfg, output_path,
- export_dataset_mode, device):
- """ Export homography adaptation results. """
+def export_homograpy_adaptation(
+ args, dataset_cfg, model_cfg, output_path, export_dataset_mode, device
+):
+ """Export homography adaptation results."""
# Check if the export_dataset_mode is supported
supported_modes = ["train", "test"]
if not export_dataset_mode in supported_modes:
- raise ValueError(
- "[Error] The specified export_dataset_mode is not supported.")
+ raise ValueError("[Error] The specified export_dataset_mode is not supported.")
# Get the test configuration
test_cfg = model_cfg["test"]
@@ -137,66 +153,87 @@ def export_homograpy_adaptation(args, dataset_cfg, model_cfg, output_path,
# Get the homography adaptation configurations
homography_cfg = dataset_cfg.get("homography_adaptation", None)
if homography_cfg is None:
- raise ValueError(
- "[Error] Empty homography_adaptation entry in config.")
+ raise ValueError("[Error] Empty homography_adaptation entry in config.")
# Create the dataset and dataloader based on the export_dataset_mode
print("\t Initializing dataset and dataloader")
batch_size = args.export_batch_size
export_dataset, collate_fn = get_dataset(export_dataset_mode, dataset_cfg)
- export_loader = DataLoader(export_dataset, batch_size=batch_size,
- num_workers=test_cfg.get("num_workers", 4),
- shuffle=False, pin_memory=False,
- collate_fn=collate_fn)
+ export_loader = DataLoader(
+ export_dataset,
+ batch_size=batch_size,
+ num_workers=test_cfg.get("num_workers", 4),
+ shuffle=False,
+ pin_memory=False,
+ collate_fn=collate_fn,
+ )
print("\t Successfully intialized dataset and dataloader.")
# Initialize model and load the checkpoint
model = get_model(model_cfg, mode="test")
- checkpoint = get_latest_checkpoint(args.resume_path, args.checkpoint_name,
- device)
+ checkpoint = get_latest_checkpoint(args.resume_path, args.checkpoint_name, device)
model = restore_weights(model, checkpoint["model_state_dict"])
model = model.to(device).eval()
print("\t Successfully initialized model")
# Start the export process
- print("[Info] Start exporting predictions")
+ print("[Info] Start exporting predictions")
output_dataset_path = output_path + ".h5"
with h5py.File(output_dataset_path, "w", libver="latest") as f:
- f.swmr_mode=True
+ f.swmr_mode = True
for _, data in enumerate(tqdm(export_loader, ascii=True)):
input_images = data["image"].to(device)
file_keys = data["file_key"]
batch_size = input_images.shape[0]
-
+
# Run the homograpy adaptation
- outputs = homography_adaptation(input_images, model,
- model_cfg["grid_size"],
- homography_cfg)
+ outputs = homography_adaptation(
+ input_images, model, model_cfg["grid_size"], homography_cfg
+ )
# Save the entries
for batch_idx in range(batch_size):
# Get the save key
save_key = file_keys[batch_idx]
output_data = {
- "image": input_images.cpu().numpy().transpose(0, 2, 3, 1)[batch_idx],
- "junc_prob_mean": outputs["junc_probs_mean"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx],
- "junc_prob_max": outputs["junc_probs_max"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx],
- "junc_count": outputs["junc_counts"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx],
- "heatmap_prob_mean": outputs["heatmap_probs_mean"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx],
- "heatmap_prob_max": outputs["heatmap_probs_max"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx],
- "heatmap_cout": outputs["heatmap_counts"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx]
+ "image": input_images.cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)[batch_idx],
+ "junc_prob_mean": outputs["junc_probs_mean"]
+ .cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)[batch_idx],
+ "junc_prob_max": outputs["junc_probs_max"]
+ .cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)[batch_idx],
+ "junc_count": outputs["junc_counts"]
+ .cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)[batch_idx],
+ "heatmap_prob_mean": outputs["heatmap_probs_mean"]
+ .cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)[batch_idx],
+ "heatmap_prob_max": outputs["heatmap_probs_max"]
+ .cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)[batch_idx],
+ "heatmap_cout": outputs["heatmap_counts"]
+ .cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)[batch_idx],
}
# Create group and write data
f_group = f.create_group(save_key)
for key, output_data in output_data.items():
- f_group.create_dataset(key, data=output_data,
- compression="gzip")
+ f_group.create_dataset(key, data=output_data, compression="gzip")
def homography_adaptation(input_images, model, grid_size, homography_cfg):
- """ The homography adaptation process.
+ """The homography adaptation process.
Arguments:
input_images: The images to be evaluated.
model: The pytorch model in evaluation mode.
@@ -222,121 +259,140 @@ def homography_adaptation(input_images, model, grid_size, homography_cfg):
for idx in range(num_iter):
if idx <= num_iter // 5:
# Ensure that 20% of the homographies have no artifact
- H_mat_lst = [sample_homography(
- [H,W], **homography_cfg_no_artifacts)[0][None]
- for _ in range(batch_size)]
+ H_mat_lst = [
+ sample_homography([H, W], **homography_cfg_no_artifacts)[0][None]
+ for _ in range(batch_size)
+ ]
else:
- H_mat_lst = [sample_homography(
- [H,W], **homography_cfg["homographies"])[0][None]
- for _ in range(batch_size)]
+ H_mat_lst = [
+ sample_homography([H, W], **homography_cfg["homographies"])[0][None]
+ for _ in range(batch_size)
+ ]
H_mats = np.concatenate(H_mat_lst, axis=0)
H_tensor = torch.tensor(H_mats, dtype=torch.float, device=device)
H_inv_tensor = torch.inverse(H_tensor)
# Perform the homography warp
- images_warped = warp_perspective(input_images, H_tensor, (H, W),
- flags="bilinear")
-
+ images_warped = warp_perspective(
+ input_images, H_tensor, (H, W), flags="bilinear"
+ )
+
# Warp the mask
masks_junc_warped = warp_perspective(
torch.ones([batch_size, 1, H, W], device=device),
- H_tensor, (H, W), flags="nearest")
+ H_tensor,
+ (H, W),
+ flags="nearest",
+ )
masks_heatmap_warped = warp_perspective(
torch.ones([batch_size, 1, H, W], device=device),
- H_tensor, (H, W), flags="nearest")
+ H_tensor,
+ (H, W),
+ flags="nearest",
+ )
# Run the network forward pass
with torch.no_grad():
outputs = model(images_warped)
-
+
# Unwarp and mask the junction prediction
- junc_prob_warped = pixel_shuffle(softmax(
- outputs["junctions"], dim=1)[:, :-1, :, :], grid_size)
- junc_prob = warp_perspective(junc_prob_warped, H_inv_tensor,
- (H, W), flags="bilinear")
+ junc_prob_warped = pixel_shuffle(
+ softmax(outputs["junctions"], dim=1)[:, :-1, :, :], grid_size
+ )
+ junc_prob = warp_perspective(
+ junc_prob_warped, H_inv_tensor, (H, W), flags="bilinear"
+ )
# Create the out of boundary mask
out_boundary_mask = warp_perspective(
torch.ones([batch_size, 1, H, W], device=device),
- H_inv_tensor, (H, W), flags="nearest")
+ H_inv_tensor,
+ (H, W),
+ flags="nearest",
+ )
out_boundary_mask = adjust_border(out_boundary_mask, device, margin)
junc_prob = junc_prob * out_boundary_mask
- junc_count = warp_perspective(masks_junc_warped * out_boundary_mask,
- H_inv_tensor, (H, W), flags="nearest")
+ junc_count = warp_perspective(
+ masks_junc_warped * out_boundary_mask, H_inv_tensor, (H, W), flags="nearest"
+ )
# Unwarp the mask and heatmap prediction
# Always fetch only one channel
if outputs["heatmap"].shape[1] == 2:
# Convert to single channel directly from here
- heatmap_prob_warped = softmax(outputs["heatmap"],
- dim=1)[:, 1:, :, :]
+ heatmap_prob_warped = softmax(outputs["heatmap"], dim=1)[:, 1:, :, :]
else:
heatmap_prob_warped = torch.sigmoid(outputs["heatmap"])
-
+
heatmap_prob_warped = heatmap_prob_warped * masks_heatmap_warped
- heatmap_prob = warp_perspective(heatmap_prob_warped, H_inv_tensor,
- (H, W), flags="bilinear")
- heatmap_count = warp_perspective(masks_heatmap_warped, H_inv_tensor,
- (H, W), flags="nearest")
+ heatmap_prob = warp_perspective(
+ heatmap_prob_warped, H_inv_tensor, (H, W), flags="bilinear"
+ )
+ heatmap_count = warp_perspective(
+ masks_heatmap_warped, H_inv_tensor, (H, W), flags="nearest"
+ )
# Record the results
- junc_probs[:, idx:idx+1, :, :] = junc_prob
- heatmap_probs[:, idx:idx+1, :, :] = heatmap_prob
+ junc_probs[:, idx : idx + 1, :, :] = junc_prob
+ heatmap_probs[:, idx : idx + 1, :, :] = heatmap_prob
junc_counts += junc_count
heatmap_counts += heatmap_count
# Perform the accumulation operation
if homography_cfg["min_counts"] > 0:
min_counts = homography_cfg["min_counts"]
- junc_count_mask = (junc_counts < min_counts)
- heatmap_count_mask = (heatmap_counts < min_counts)
+ junc_count_mask = junc_counts < min_counts
+ heatmap_count_mask = heatmap_counts < min_counts
junc_counts[junc_count_mask] = 0
heatmap_counts[heatmap_count_mask] = 0
else:
junc_count_mask = np.zeros_like(junc_counts, dtype=bool)
heatmap_count_mask = np.zeros_like(heatmap_counts, dtype=bool)
-
+
# Compute the mean accumulation
junc_probs_mean = torch.sum(junc_probs, dim=1, keepdim=True) / junc_counts
- junc_probs_mean[junc_count_mask] = 0.
- heatmap_probs_mean = (torch.sum(heatmap_probs, dim=1, keepdim=True)
- / heatmap_counts)
- heatmap_probs_mean[heatmap_count_mask] = 0.
+ junc_probs_mean[junc_count_mask] = 0.0
+ heatmap_probs_mean = torch.sum(heatmap_probs, dim=1, keepdim=True) / heatmap_counts
+ heatmap_probs_mean[heatmap_count_mask] = 0.0
# Compute the max accumulation
junc_probs_max = torch.max(junc_probs, dim=1, keepdim=True)[0]
- junc_probs_max[junc_count_mask] = 0.
+ junc_probs_max[junc_count_mask] = 0.0
heatmap_probs_max = torch.max(heatmap_probs, dim=1, keepdim=True)[0]
- heatmap_probs_max[heatmap_count_mask] = 0.
+ heatmap_probs_max[heatmap_count_mask] = 0.0
- return {"junc_probs_mean": junc_probs_mean,
- "junc_probs_max": junc_probs_max,
- "junc_counts": junc_counts,
- "heatmap_probs_mean": heatmap_probs_mean,
- "heatmap_probs_max": heatmap_probs_max,
- "heatmap_counts": heatmap_counts}
+ return {
+ "junc_probs_mean": junc_probs_mean,
+ "junc_probs_max": junc_probs_max,
+ "junc_counts": junc_counts,
+ "heatmap_probs_mean": heatmap_probs_mean,
+ "heatmap_probs_max": heatmap_probs_max,
+ "heatmap_counts": heatmap_counts,
+ }
def adjust_border(input_masks, device, margin=3):
- """ Adjust the border of the counts and valid_mask. """
+ """Adjust the border of the counts and valid_mask."""
# Convert the mask to numpy array
dtype = input_masks.dtype
input_masks = np.squeeze(input_masks.cpu().numpy(), axis=1)
- erosion_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
- (margin*2, margin*2))
+ erosion_kernel = cv2.getStructuringElement(
+ cv2.MORPH_ELLIPSE, (margin * 2, margin * 2)
+ )
batch_size = input_masks.shape[0]
-
+
output_mask_lst = []
# Erode all the masks
for i in range(batch_size):
output_mask = cv2.erode(input_masks[i, ...], erosion_kernel)
output_mask_lst.append(
- torch.tensor(output_mask, dtype=dtype, device=device)[None])
-
+ torch.tensor(output_mask, dtype=dtype, device=device)[None]
+ )
+
# Concat back along the batch dimension.
output_masks = torch.cat(output_mask_lst, dim=0)
return output_masks.unsqueeze(dim=1)
diff --git a/imcui/third_party/SOLD2/sold2/export_line_features.py b/third_party/SOLD2/sold2/export_line_features.py
similarity index 54%
rename from imcui/third_party/SOLD2/sold2/export_line_features.py
rename to third_party/SOLD2/sold2/export_line_features.py
index 4cbde860a446d758dff254ea5320ca13bb79e6b7..6df203c6ad62a559a1617744b200df283b9bb9a7 100644
--- a/imcui/third_party/SOLD2/sold2/export_line_features.py
+++ b/third_party/SOLD2/sold2/export_line_features.py
@@ -12,24 +12,29 @@ from .experiment import load_config
from .model.line_matcher import LineMatcher
-def export_descriptors(images_list, ckpt_path, config, device, extension,
- output_folder, multiscale=False):
+def export_descriptors(
+ images_list, ckpt_path, config, device, extension, output_folder, multiscale=False
+):
# Extract the image paths
- with open(images_list, 'r') as f:
+ with open(images_list, "r") as f:
image_files = f.readlines()
- image_files = [path.strip('\n') for path in image_files]
+ image_files = [path.strip("\n") for path in image_files]
# Initialize the line matcher
line_matcher = LineMatcher(
- config["model_cfg"], ckpt_path, device, config["line_detector_cfg"],
- config["line_matcher_cfg"], multiscale)
+ config["model_cfg"],
+ ckpt_path,
+ device,
+ config["line_detector_cfg"],
+ config["line_matcher_cfg"],
+ multiscale,
+ )
print("\t Successfully initialized model")
# Run the inference on each image and write the output on disk
for img_path in tqdm(image_files):
img = cv2.imread(img_path, 0)
- img = torch.tensor(img[None, None] / 255., dtype=torch.float,
- device=device)
+ img = torch.tensor(img[None, None] / 255.0, dtype=torch.float, device=device)
# Run the line detection and description
ref_detection = line_matcher.line_detection(img)
@@ -39,21 +44,29 @@ def export_descriptors(images_list, ckpt_path, config, device, extension,
# Write the output on disk
img_name = os.path.splitext(os.path.basename(img_path))[0]
output_file = os.path.join(output_folder, img_name + extension)
- np.savez_compressed(output_file, line_seg=ref_line_seg,
- descriptors=ref_descriptors)
+ np.savez_compressed(
+ output_file, line_seg=ref_line_seg, descriptors=ref_descriptors
+ )
if __name__ == "__main__":
# Parse input arguments
parser = argparse.ArgumentParser()
- parser.add_argument("--img_list", type=str, required=True,
- help="List of input images in a text file.")
- parser.add_argument("--output_folder", type=str, required=True,
- help="Path to the output folder.")
- parser.add_argument("--config", type=str,
- default="config/export_line_features.yaml")
- parser.add_argument("--checkpoint_path", type=str,
- default="pretrained_models/sold2_wireframe.tar")
+ parser.add_argument(
+ "--img_list",
+ type=str,
+ required=True,
+ help="List of input images in a text file.",
+ )
+ parser.add_argument(
+ "--output_folder", type=str, required=True, help="Path to the output folder."
+ )
+ parser.add_argument(
+ "--config", type=str, default="config/export_line_features.yaml"
+ )
+ parser.add_argument(
+ "--checkpoint_path", type=str, default="pretrained_models/sold2_wireframe.tar"
+ )
parser.add_argument("--multiscale", action="store_true", default=False)
parser.add_argument("--extension", type=str, default=None)
args = parser.parse_args()
@@ -67,8 +80,15 @@ if __name__ == "__main__":
# Get the model config, extension and checkpoint path
config = load_config(args.config)
ckpt_path = os.path.abspath(args.checkpoint_path)
- extension = 'sold2' if args.extension is None else args.extension
+ extension = "sold2" if args.extension is None else args.extension
extension = "." + extension
- export_descriptors(args.img_list, ckpt_path, config, device, extension,
- args.output_folder, args.multiscale)
+ export_descriptors(
+ args.img_list,
+ ckpt_path,
+ config,
+ device,
+ extension,
+ args.output_folder,
+ args.multiscale,
+ )
diff --git a/imcui/third_party/SOLD2/sold2/postprocess/__init__.py b/third_party/SOLD2/sold2/misc/__init__.py
similarity index 100%
rename from imcui/third_party/SOLD2/sold2/postprocess/__init__.py
rename to third_party/SOLD2/sold2/misc/__init__.py
diff --git a/imcui/third_party/SOLD2/sold2/misc/geometry_utils.py b/third_party/SOLD2/sold2/misc/geometry_utils.py
similarity index 77%
rename from imcui/third_party/SOLD2/sold2/misc/geometry_utils.py
rename to third_party/SOLD2/sold2/misc/geometry_utils.py
index 50f0478062cd19ebac812bff62b6c3a3d5f124c2..024430a07b9b094d2eca6e4e9e14edd5105ad1c5 100644
--- a/imcui/third_party/SOLD2/sold2/misc/geometry_utils.py
+++ b/third_party/SOLD2/sold2/misc/geometry_utils.py
@@ -7,8 +7,9 @@ import torch
# Warp a list of points using a homography
def warp_points(points, homography):
# Convert to homogeneous and in xy format
- new_points = np.concatenate([points[..., [1, 0]],
- np.ones_like(points[..., :1])], axis=-1)
+ new_points = np.concatenate(
+ [points[..., [1, 0]], np.ones_like(points[..., :1])], axis=-1
+ )
# Warp
new_points = (homography @ new_points.T).T
# Convert back to inhomogeneous and hw format
@@ -18,10 +19,12 @@ def warp_points(points, homography):
# Mask out the points that are outside of img_size
def mask_points(points, img_size):
- mask = ((points[..., 0] >= 0)
- & (points[..., 0] < img_size[0])
- & (points[..., 1] >= 0)
- & (points[..., 1] < img_size[1]))
+ mask = (
+ (points[..., 0] >= 0)
+ & (points[..., 0] < img_size[0])
+ & (points[..., 1] >= 0)
+ & (points[..., 1] < img_size[1])
+ )
return mask
@@ -30,8 +33,12 @@ def mask_points(points, img_size):
def keypoints_to_grid(keypoints, img_size):
n_points = keypoints.size()[-2]
device = keypoints.device
- grid_points = keypoints.float() * 2. / torch.tensor(
- img_size, dtype=torch.float, device=device) - 1.
+ grid_points = (
+ keypoints.float()
+ * 2.0
+ / torch.tensor(img_size, dtype=torch.float, device=device)
+ - 1.0
+ )
grid_points = grid_points[..., [1, 0]].view(-1, n_points, 1, 2)
return grid_points
@@ -44,8 +51,9 @@ def get_dist_mask(kp0, kp1, valid_mask, dist_thresh):
dist_mask1 = torch.norm(kp1.unsqueeze(2) - kp1.unsqueeze(1), dim=-1)
dist_mask = torch.min(dist_mask0, dist_mask1)
dist_mask = dist_mask <= dist_thresh
- dist_mask = dist_mask.repeat(1, 1, b_size).reshape(b_size * n_points,
- b_size * n_points)
+ dist_mask = dist_mask.repeat(1, 1, b_size).reshape(
+ b_size * n_points, b_size * n_points
+ )
dist_mask = dist_mask[valid_mask, :][:, valid_mask]
return dist_mask
@@ -75,7 +83,8 @@ def mask_lines(lines, valid_mask):
def get_common_line_mask(line_indices, valid_mask):
b_size, n_points = line_indices.shape
common_mask = line_indices[:, :, None] == line_indices[:, None, :]
- common_mask = common_mask.repeat(1, 1, b_size).reshape(b_size * n_points,
- b_size * n_points)
+ common_mask = common_mask.repeat(1, 1, b_size).reshape(
+ b_size * n_points, b_size * n_points
+ )
common_mask = common_mask[valid_mask, :][:, valid_mask]
return common_mask
diff --git a/imcui/third_party/SOLD2/sold2/misc/train_utils.py b/third_party/SOLD2/sold2/misc/train_utils.py
similarity index 65%
rename from imcui/third_party/SOLD2/sold2/misc/train_utils.py
rename to third_party/SOLD2/sold2/misc/train_utils.py
index d5ada35eea660df1f78b9f20d9bf7ed726eaee2c..99113247351ceef152f308e793234a952df78166 100644
--- a/imcui/third_party/SOLD2/sold2/misc/train_utils.py
+++ b/third_party/SOLD2/sold2/misc/train_utils.py
@@ -10,7 +10,7 @@ import torch
## image utils ##
#################
def convert_image(input_tensor, axis):
- """ Convert single channel images to 3-channel images. """
+ """Convert single channel images to 3-channel images."""
image_lst = [input_tensor for _ in range(3)]
outputs = np.concatenate(image_lst, axis)
return outputs
@@ -19,29 +19,32 @@ def convert_image(input_tensor, axis):
######################
## checkpoint utils ##
######################
-def get_latest_checkpoint(checkpoint_root, checkpoint_name,
- device=torch.device("cuda")):
- """ Get the latest checkpoint or by filename. """
+def get_latest_checkpoint(
+ checkpoint_root, checkpoint_name, device=torch.device("cuda")
+):
+ """Get the latest checkpoint or by filename."""
# Load specific checkpoint
if checkpoint_name is not None:
checkpoint = torch.load(
- os.path.join(checkpoint_root, checkpoint_name),
- map_location=device)
+ os.path.join(checkpoint_root, checkpoint_name), map_location=device
+ )
# Load the latest checkpoint
else:
- lastest_checkpoint = sorted(os.listdir(os.path.join(
- checkpoint_root, "*.tar")))[-1]
- checkpoint = torch.load(os.path.join(
- checkpoint_root, lastest_checkpoint), map_location=device)
+ lastest_checkpoint = sorted(os.listdir(os.path.join(checkpoint_root, "*.tar")))[
+ -1
+ ]
+ checkpoint = torch.load(
+ os.path.join(checkpoint_root, lastest_checkpoint), map_location=device
+ )
return checkpoint
def remove_old_checkpoints(checkpoint_root, max_ckpt=15):
- """ Remove the outdated checkpoints. """
+ """Remove the outdated checkpoints."""
# Get sorted list of checkpoints
checkpoint_list = sorted(
- [_ for _ in os.listdir(os.path.join(checkpoint_root))
- if _.endswith(".tar")])
+ [_ for _ in os.listdir(os.path.join(checkpoint_root)) if _.endswith(".tar")]
+ )
# Get the checkpoints to be removed
if len(checkpoint_list) > max_ckpt:
@@ -55,7 +58,7 @@ def remove_old_checkpoints(checkpoint_root, max_ckpt=15):
def adapt_checkpoint(state_dict):
new_state_dict = {}
for k, v in state_dict.items():
- if k.startswith('module.'):
+ if k.startswith("module."):
new_state_dict[k[7:]] = v
else:
new_state_dict[k] = v
@@ -66,9 +69,9 @@ def adapt_checkpoint(state_dict):
## HDF5 utils ##
################
def parse_h5_data(h5_data):
- """ Parse h5 dataset. """
+ """Parse h5 dataset."""
output_data = {}
for key in h5_data.keys():
output_data[key] = np.array(h5_data[key])
-
+
return output_data
diff --git a/imcui/third_party/SOLD2/sold2/misc/visualize_util.py b/third_party/SOLD2/sold2/misc/visualize_util.py
similarity index 67%
rename from imcui/third_party/SOLD2/sold2/misc/visualize_util.py
rename to third_party/SOLD2/sold2/misc/visualize_util.py
index 4aa46877f79724221b7caa423de6916acdc021f8..2d1aa38bb992302fe504bc166a3fa113e5365337 100644
--- a/imcui/third_party/SOLD2/sold2/misc/visualize_util.py
+++ b/third_party/SOLD2/sold2/misc/visualize_util.py
@@ -20,15 +20,17 @@ def plot_junctions(input_image, junctions, junc_size=3, color=None):
if image.dtype == np.uint8:
pass
# A float type image ranging from 0~1
- elif image.dtype in [np.float32, np.float64, np.float] and image.max() <= 2.:
- image = (image * 255.).astype(np.uint8)
+ elif image.dtype in [np.float32, np.float64, np.float] and image.max() <= 2.0:
+ image = (image * 255.0).astype(np.uint8)
# A float type image ranging from 0.~255.
- elif image.dtype in [np.float32, np.float64, np.float] and image.mean() > 10.:
+ elif image.dtype in [np.float32, np.float64, np.float] and image.mean() > 10.0:
image = image.astype(np.uint8)
else:
- raise ValueError("[Error] Unknown image data type. Expect 0~1 float or 0~255 uint8.")
+ raise ValueError(
+ "[Error] Unknown image data type. Expect 0~1 float or 0~255 uint8."
+ )
- # Check whether the image is single channel
+ # Check whether the image is single channel
if len(image.shape) == 2 or ((len(image.shape) == 3) and (image.shape[-1] == 1)):
# Squeeze to H*W first
image = image.squeeze()
@@ -46,30 +48,38 @@ def plot_junctions(input_image, junctions, junc_size=3, color=None):
junctions = junctions.T
else:
raise ValueError("[Error] At least one of the two dims should be 2.")
-
+
# Round and convert junctions to int (and check the boundary)
H, W = image.shape[:2]
junctions = (np.round(junctions)).astype(np.int)
- junctions[junctions < 0] = 0
- junctions[junctions[:, 0] >= H, 0] = H-1 # (first dim) max bounded by H-1
- junctions[junctions[:, 1] >= W, 1] = W-1 # (second dim) max bounded by W-1
+ junctions[junctions < 0] = 0
+ junctions[junctions[:, 0] >= H, 0] = H - 1 # (first dim) max bounded by H-1
+ junctions[junctions[:, 1] >= W, 1] = W - 1 # (second dim) max bounded by W-1
# Iterate through all the junctions
num_junc = junctions.shape[0]
if color is None:
- color = (0, 255., 0)
+ color = (0, 255.0, 0)
for idx in range(num_junc):
# Fetch one junction
junc = junctions[idx, :]
- cv2.circle(image, tuple(np.flip(junc)), radius=junc_size,
- color=color, thickness=3)
-
+ cv2.circle(
+ image, tuple(np.flip(junc)), radius=junc_size, color=color, thickness=3
+ )
+
return image
# Plot line segements given junctions and line adjecent map
-def plot_line_segments(input_image, junctions, line_map, junc_size=3,
- color=(0, 255., 0), line_width=1, plot_survived_junc=True):
+def plot_line_segments(
+ input_image,
+ junctions,
+ line_map,
+ junc_size=3,
+ color=(0, 255.0, 0),
+ line_width=1,
+ plot_survived_junc=True,
+):
"""
input_image: can be 0~1 float or 0~255 uint8.
junctions: Nx2 or 2xN np array.
@@ -85,15 +95,17 @@ def plot_line_segments(input_image, junctions, line_map, junc_size=3,
if image.dtype == np.uint8:
pass
# A float type image ranging from 0~1
- elif image.dtype in [np.float32, np.float64, np.float] and image.max() <= 2.:
- image = (image * 255.).astype(np.uint8)
+ elif image.dtype in [np.float32, np.float64, np.float] and image.max() <= 2.0:
+ image = (image * 255.0).astype(np.uint8)
# A float type image ranging from 0.~255.
- elif image.dtype in [np.float32, np.float64, np.float] and image.mean() > 10.:
+ elif image.dtype in [np.float32, np.float64, np.float] and image.mean() > 10.0:
image = image.astype(np.uint8)
else:
- raise ValueError("[Error] Unknown image data type. Expect 0~1 float or 0~255 uint8.")
+ raise ValueError(
+ "[Error] Unknown image data type. Expect 0~1 float or 0~255 uint8."
+ )
- # Check whether the image is single channel
+ # Check whether the image is single channel
if len(image.shape) == 2 or ((len(image.shape) == 3) and (image.shape[-1] == 1)):
# Squeeze to H*W first
image = image.squeeze()
@@ -111,7 +123,7 @@ def plot_line_segments(input_image, junctions, line_map, junc_size=3,
junctions = junctions.T
else:
raise ValueError("[Error] At least one of the two dims should be 2.")
-
+
# line_map dimension should be 2
if not len(line_map.shape) == 2:
raise ValueError("[Error] line_map should be 2-dim array.")
@@ -122,8 +134,10 @@ def plot_line_segments(input_image, junctions, line_map, junc_size=3,
raise ValueError("[Error] color should have type list or tuple.")
else:
if len(color) != 3:
- raise ValueError("[Error] color should be a list or tuple with length 3.")
-
+ raise ValueError(
+ "[Error] color should be a list or tuple with length 3."
+ )
+
# Make a copy of the line_map
line_map_tmp = copy.copy(line_map)
@@ -136,14 +150,17 @@ def plot_line_segments(input_image, junctions, line_map, junc_size=3,
# record the line segment
else:
for idx2 in np.where(line_map_tmp[idx, :] == 1)[0]:
- p1 = np.flip(junctions[idx, :]) # Convert to xy format
- p2 = np.flip(junctions[idx2, :]) # Convert to xy format
- segments = np.concatenate((segments, np.array([p1[0], p1[1], p2[0], p2[1]])[None, ...]), axis=0)
-
+ p1 = np.flip(junctions[idx, :]) # Convert to xy format
+ p2 = np.flip(junctions[idx2, :]) # Convert to xy format
+ segments = np.concatenate(
+ (segments, np.array([p1[0], p1[1], p2[0], p2[1]])[None, ...]),
+ axis=0,
+ )
+
# Update line_map
line_map_tmp[idx, idx2] = 0
line_map_tmp[idx2, idx] = 0
-
+
# Draw segment pairs
for idx in range(segments.shape[0]):
seg = np.round(segments[idx, :]).astype(np.int)
@@ -151,8 +168,14 @@ def plot_line_segments(input_image, junctions, line_map, junc_size=3,
if color != "random":
color = tuple(color)
else:
- color = tuple(np.random.rand(3,))
- cv2.line(image, tuple(seg[:2]), tuple(seg[2:]), color=color, thickness=line_width)
+ color = tuple(
+ np.random.rand(
+ 3,
+ )
+ )
+ cv2.line(
+ image, tuple(seg[:2]), tuple(seg[2:]), color=color, thickness=line_width
+ )
# Also draw the junctions
if not plot_survived_junc:
@@ -160,45 +183,63 @@ def plot_line_segments(input_image, junctions, line_map, junc_size=3,
for idx in range(num_junc):
# Fetch one junction
junc = junctions[idx, :]
- cv2.circle(image, tuple(np.flip(junc)), radius=junc_size,
- color=(0, 255., 0), thickness=3)
+ cv2.circle(
+ image,
+ tuple(np.flip(junc)),
+ radius=junc_size,
+ color=(0, 255.0, 0),
+ thickness=3,
+ )
# Only plot the junctions which are part of a line segment
else:
for idx in range(segments.shape[0]):
- seg = np.round(segments[idx, :]).astype(np.int) # Already in HW format.
- cv2.circle(image, tuple(seg[:2]), radius=junc_size,
- color=(0, 255., 0), thickness=3)
- cv2.circle(image, tuple(seg[2:]), radius=junc_size,
- color=(0, 255., 0), thickness=3)
-
+ seg = np.round(segments[idx, :]).astype(np.int) # Already in HW format.
+ cv2.circle(
+ image,
+ tuple(seg[:2]),
+ radius=junc_size,
+ color=(0, 255.0, 0),
+ thickness=3,
+ )
+ cv2.circle(
+ image,
+ tuple(seg[2:]),
+ radius=junc_size,
+ color=(0, 255.0, 0),
+ thickness=3,
+ )
+
return image
# Plot line segments given Nx4 or Nx2x2 line segments
-def plot_line_segments_from_segments(input_image, line_segments, junc_size=3,
- color=(0, 255., 0), line_width=1):
+def plot_line_segments_from_segments(
+ input_image, line_segments, junc_size=3, color=(0, 255.0, 0), line_width=1
+):
# Create image copy
image = copy.copy(input_image)
# Make sure the image is converted to 255 uint8
if image.dtype == np.uint8:
pass
# A float type image ranging from 0~1
- elif image.dtype in [np.float32, np.float64, np.float] and image.max() <= 2.:
- image = (image * 255.).astype(np.uint8)
+ elif image.dtype in [np.float32, np.float64, np.float] and image.max() <= 2.0:
+ image = (image * 255.0).astype(np.uint8)
# A float type image ranging from 0.~255.
- elif image.dtype in [np.float32, np.float64, np.float] and image.mean() > 10.:
+ elif image.dtype in [np.float32, np.float64, np.float] and image.mean() > 10.0:
image = image.astype(np.uint8)
else:
- raise ValueError("[Error] Unknown image data type. Expect 0~1 float or 0~255 uint8.")
+ raise ValueError(
+ "[Error] Unknown image data type. Expect 0~1 float or 0~255 uint8."
+ )
- # Check whether the image is single channel
+ # Check whether the image is single channel
if len(image.shape) == 2 or ((len(image.shape) == 3) and (image.shape[-1] == 1)):
# Squeeze to H*W first
image = image.squeeze()
# Stack to channle 3
image = np.concatenate([image[..., None] for _ in range(3)], axis=-1)
-
+
# Check the if line_segments are in (1) Nx4, or (2) Nx2x2.
H, W, _ = image.shape
# (1) Nx4 format
@@ -207,18 +248,20 @@ def plot_line_segments_from_segments(input_image, line_segments, junc_size=3,
line_segments = line_segments.astype(np.int32)
# Clip H dimension
- line_segments[:, 0] = np.clip(line_segments[:, 0], a_min=0, a_max=H-1)
- line_segments[:, 2] = np.clip(line_segments[:, 2], a_min=0, a_max=H-1)
+ line_segments[:, 0] = np.clip(line_segments[:, 0], a_min=0, a_max=H - 1)
+ line_segments[:, 2] = np.clip(line_segments[:, 2], a_min=0, a_max=H - 1)
# Clip W dimension
- line_segments[:, 1] = np.clip(line_segments[:, 1], a_min=0, a_max=W-1)
- line_segments[:, 3] = np.clip(line_segments[:, 3], a_min=0, a_max=W-1)
+ line_segments[:, 1] = np.clip(line_segments[:, 1], a_min=0, a_max=W - 1)
+ line_segments[:, 3] = np.clip(line_segments[:, 3], a_min=0, a_max=W - 1)
# Convert to Nx2x2 format
line_segments = np.concatenate(
- [np.expand_dims(line_segments[:, :2], axis=1),
- np.expand_dims(line_segments[:, 2:], axis=1)],
- axis=1
+ [
+ np.expand_dims(line_segments[:, :2], axis=1),
+ np.expand_dims(line_segments[:, 2:], axis=1),
+ ],
+ axis=1,
)
# (2) Nx2x2 format
@@ -227,11 +270,13 @@ def plot_line_segments_from_segments(input_image, line_segments, junc_size=3,
line_segments = line_segments.astype(np.int32)
# Clip H dimension
- line_segments[:, :, 0] = np.clip(line_segments[:, :, 0], a_min=0, a_max=H-1)
- line_segments[:, :, 1] = np.clip(line_segments[:, :, 1], a_min=0, a_max=W-1)
+ line_segments[:, :, 0] = np.clip(line_segments[:, :, 0], a_min=0, a_max=H - 1)
+ line_segments[:, :, 1] = np.clip(line_segments[:, :, 1], a_min=0, a_max=W - 1)
else:
- raise ValueError("[Error] line_segments should be either Nx4 or Nx2x2 in HW format.")
+ raise ValueError(
+ "[Error] line_segments should be either Nx4 or Nx2x2 in HW format."
+ )
# Draw segment pairs (all segments should be in HW format)
image = image.copy()
@@ -241,21 +286,41 @@ def plot_line_segments_from_segments(input_image, line_segments, junc_size=3,
if color != "random":
color = tuple(color)
else:
- color = tuple(np.random.rand(3,))
- cv2.line(image, tuple(np.flip(seg[0, :])),
- tuple(np.flip(seg[1, :])),
- color=color, thickness=line_width)
+ color = tuple(
+ np.random.rand(
+ 3,
+ )
+ )
+ cv2.line(
+ image,
+ tuple(np.flip(seg[0, :])),
+ tuple(np.flip(seg[1, :])),
+ color=color,
+ thickness=line_width,
+ )
# Also draw the junctions
- cv2.circle(image, tuple(np.flip(seg[0, :])), radius=junc_size, color=(0, 255., 0), thickness=3)
- cv2.circle(image, tuple(np.flip(seg[1, :])), radius=junc_size, color=(0, 255., 0), thickness=3)
-
+ cv2.circle(
+ image,
+ tuple(np.flip(seg[0, :])),
+ radius=junc_size,
+ color=(0, 255.0, 0),
+ thickness=3,
+ )
+ cv2.circle(
+ image,
+ tuple(np.flip(seg[1, :])),
+ radius=junc_size,
+ color=(0, 255.0, 0),
+ thickness=3,
+ )
+
return image
# Additional functions to visualize multiple images at the same time,
# e.g. for line matching
-def plot_images(imgs, titles=None, cmaps='gray', dpi=100, size=6, pad=.5):
+def plot_images(imgs, titles=None, cmaps="gray", dpi=100, size=6, pad=0.5):
"""Plot a set of images horizontally.
Args:
imgs: a list of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W).
@@ -265,7 +330,7 @@ def plot_images(imgs, titles=None, cmaps='gray', dpi=100, size=6, pad=.5):
n = len(imgs)
if not isinstance(cmaps, (list, tuple)):
cmaps = [cmaps] * n
- figsize = (size*n, size*3/4) if size is not None else None
+ figsize = (size * n, size * 3 / 4) if size is not None else None
fig, ax = plt.subplots(1, n, figsize=figsize, dpi=dpi)
if n == 1:
ax = [ax]
@@ -281,7 +346,7 @@ def plot_images(imgs, titles=None, cmaps='gray', dpi=100, size=6, pad=.5):
fig.tight_layout(pad=pad)
-def plot_keypoints(kpts, colors='lime', ps=4):
+def plot_keypoints(kpts, colors="lime", ps=4):
"""Plot keypoints for existing images.
Args:
kpts: list of ndarrays of size (N, 2).
@@ -295,7 +360,7 @@ def plot_keypoints(kpts, colors='lime', ps=4):
a.scatter(k[:, 0], k[:, 1], c=c, s=ps, linewidths=0)
-def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.):
+def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.0):
"""Plot matches for a pair of existing images.
Args:
kpts0, kpts1: corresponding keypoints of size (N, 2).
@@ -322,11 +387,18 @@ def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.):
transFigure = fig.transFigure.inverted()
fkpts0 = transFigure.transform(ax0.transData.transform(kpts0))
fkpts1 = transFigure.transform(ax1.transData.transform(kpts1))
- fig.lines += [matplotlib.lines.Line2D(
- (fkpts0[i, 0], fkpts1[i, 0]), (fkpts0[i, 1], fkpts1[i, 1]),
- zorder=1, transform=fig.transFigure, c=color[i], linewidth=lw,
- alpha=a)
- for i in range(len(kpts0))]
+ fig.lines += [
+ matplotlib.lines.Line2D(
+ (fkpts0[i, 0], fkpts1[i, 0]),
+ (fkpts0[i, 1], fkpts1[i, 1]),
+ zorder=1,
+ transform=fig.transFigure,
+ c=color[i],
+ linewidth=lw,
+ alpha=a,
+ )
+ for i in range(len(kpts0))
+ ]
# freeze the axes to prevent the transform to change
ax0.autoscale(enable=False)
@@ -337,8 +409,9 @@ def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.):
ax1.scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps, zorder=2)
-def plot_lines(lines, line_colors='orange', point_colors='cyan',
- ps=4, lw=2, indices=(0, 1)):
+def plot_lines(
+ lines, line_colors="orange", point_colors="cyan", ps=4, lw=2, indices=(0, 1)
+):
"""Plot lines and endpoints for existing images.
Args:
lines: list of ndarrays of size (N, 2, 2).
@@ -361,16 +434,19 @@ def plot_lines(lines, line_colors='orange', point_colors='cyan',
# Plot the lines and junctions
for a, l, lc, pc in zip(axes, lines, line_colors, point_colors):
for i in range(len(l)):
- line = matplotlib.lines.Line2D((l[i, 0, 0], l[i, 1, 0]),
- (l[i, 0, 1], l[i, 1, 1]),
- zorder=1, c=lc, linewidth=lw)
+ line = matplotlib.lines.Line2D(
+ (l[i, 0, 0], l[i, 1, 0]),
+ (l[i, 0, 1], l[i, 1, 1]),
+ zorder=1,
+ c=lc,
+ linewidth=lw,
+ )
a.add_line(line)
pts = l.reshape(-1, 2)
- a.scatter(pts[:, 0], pts[:, 1],
- c=pc, s=ps, linewidths=0, zorder=2)
+ a.scatter(pts[:, 0], pts[:, 1], c=pc, s=ps, linewidths=0, zorder=2)
-def plot_line_matches(kpts0, kpts1, color=None, lw=1.5, indices=(0, 1), a=1.):
+def plot_line_matches(kpts0, kpts1, color=None, lw=1.5, indices=(0, 1), a=1.0):
"""Plot matches for a pair of existing images, parametrized by their middle point.
Args:
kpts0, kpts1: corresponding middle points of the lines of size (N, 2).
@@ -396,19 +472,25 @@ def plot_line_matches(kpts0, kpts1, color=None, lw=1.5, indices=(0, 1), a=1.):
transFigure = fig.transFigure.inverted()
fkpts0 = transFigure.transform(ax0.transData.transform(kpts0))
fkpts1 = transFigure.transform(ax1.transData.transform(kpts1))
- fig.lines += [matplotlib.lines.Line2D(
- (fkpts0[i, 0], fkpts1[i, 0]), (fkpts0[i, 1], fkpts1[i, 1]),
- zorder=1, transform=fig.transFigure, c=color[i], linewidth=lw,
- alpha=a)
- for i in range(len(kpts0))]
+ fig.lines += [
+ matplotlib.lines.Line2D(
+ (fkpts0[i, 0], fkpts1[i, 0]),
+ (fkpts0[i, 1], fkpts1[i, 1]),
+ zorder=1,
+ transform=fig.transFigure,
+ c=color[i],
+ linewidth=lw,
+ alpha=a,
+ )
+ for i in range(len(kpts0))
+ ]
# freeze the axes to prevent the transform to change
ax0.autoscale(enable=False)
ax1.autoscale(enable=False)
-def plot_color_line_matches(lines, correct_matches=None,
- lw=2, indices=(0, 1)):
+def plot_color_line_matches(lines, correct_matches=None, lw=2, indices=(0, 1)):
"""Plot line matches for existing images with multiple colors.
Args:
lines: list of ndarrays of size (N, 2, 2).
@@ -417,7 +499,7 @@ def plot_color_line_matches(lines, correct_matches=None,
indices: indices of the images to draw the matches on.
"""
n_lines = len(lines[0])
- colors = sns.color_palette('husl', n_colors=n_lines)
+ colors = sns.color_palette("husl", n_colors=n_lines)
np.random.shuffle(colors)
alphas = np.ones(n_lines)
# If correct_matches is not None, display wrong matches with a low alpha
@@ -436,15 +518,21 @@ def plot_color_line_matches(lines, correct_matches=None,
transFigure = fig.transFigure.inverted()
endpoint0 = transFigure.transform(a.transData.transform(l[:, 0]))
endpoint1 = transFigure.transform(a.transData.transform(l[:, 1]))
- fig.lines += [matplotlib.lines.Line2D(
- (endpoint0[i, 0], endpoint1[i, 0]),
- (endpoint0[i, 1], endpoint1[i, 1]),
- zorder=1, transform=fig.transFigure, c=colors[i],
- alpha=alphas[i], linewidth=lw) for i in range(n_lines)]
-
-
-def plot_color_lines(lines, correct_matches, wrong_matches,
- lw=2, indices=(0, 1)):
+ fig.lines += [
+ matplotlib.lines.Line2D(
+ (endpoint0[i, 0], endpoint1[i, 0]),
+ (endpoint0[i, 1], endpoint1[i, 1]),
+ zorder=1,
+ transform=fig.transFigure,
+ c=colors[i],
+ alpha=alphas[i],
+ linewidth=lw,
+ )
+ for i in range(n_lines)
+ ]
+
+
+def plot_color_lines(lines, correct_matches, wrong_matches, lw=2, indices=(0, 1)):
"""Plot line matches for existing images with multiple colors:
green for correct matches, red for wrong ones, and blue for the rest.
Args:
@@ -476,15 +564,21 @@ def plot_color_lines(lines, correct_matches, wrong_matches,
transFigure = fig.transFigure.inverted()
endpoint0 = transFigure.transform(a.transData.transform(l[:, 0]))
endpoint1 = transFigure.transform(a.transData.transform(l[:, 1]))
- fig.lines += [matplotlib.lines.Line2D(
- (endpoint0[i, 0], endpoint1[i, 0]),
- (endpoint0[i, 1], endpoint1[i, 1]),
- zorder=1, transform=fig.transFigure, c=c[i],
- linewidth=lw) for i in range(len(l))]
+ fig.lines += [
+ matplotlib.lines.Line2D(
+ (endpoint0[i, 0], endpoint1[i, 0]),
+ (endpoint0[i, 1], endpoint1[i, 1]),
+ zorder=1,
+ transform=fig.transFigure,
+ c=c[i],
+ linewidth=lw,
+ )
+ for i in range(len(l))
+ ]
def plot_subsegment_matches(lines, subsegments, lw=2, indices=(0, 1)):
- """ Plot line matches for existing images with multiple colors and
+ """Plot line matches for existing images with multiple colors and
highlight the actually matched subsegments.
Args:
lines: list of ndarrays of size (N, 2, 2).
@@ -493,8 +587,9 @@ def plot_subsegment_matches(lines, subsegments, lw=2, indices=(0, 1)):
indices: indices of the images to draw the matches on.
"""
n_lines = len(lines[0])
- colors = sns.cubehelix_palette(start=2, rot=-0.2, dark=0.3, light=.7,
- gamma=1.3, hue=1, n_colors=n_lines)
+ colors = sns.cubehelix_palette(
+ start=2, rot=-0.2, dark=0.3, light=0.7, gamma=1.3, hue=1, n_colors=n_lines
+ )
fig = plt.gcf()
ax = fig.axes
@@ -510,17 +605,31 @@ def plot_subsegment_matches(lines, subsegments, lw=2, indices=(0, 1)):
# Draw full line
endpoint0 = transFigure.transform(a.transData.transform(l[:, 0]))
endpoint1 = transFigure.transform(a.transData.transform(l[:, 1]))
- fig.lines += [matplotlib.lines.Line2D(
- (endpoint0[i, 0], endpoint1[i, 0]),
- (endpoint0[i, 1], endpoint1[i, 1]),
- zorder=1, transform=fig.transFigure, c='red',
- alpha=0.7, linewidth=lw) for i in range(n_lines)]
+ fig.lines += [
+ matplotlib.lines.Line2D(
+ (endpoint0[i, 0], endpoint1[i, 0]),
+ (endpoint0[i, 1], endpoint1[i, 1]),
+ zorder=1,
+ transform=fig.transFigure,
+ c="red",
+ alpha=0.7,
+ linewidth=lw,
+ )
+ for i in range(n_lines)
+ ]
# Draw matched subsegment
endpoint0 = transFigure.transform(a.transData.transform(ss[:, 0]))
endpoint1 = transFigure.transform(a.transData.transform(ss[:, 1]))
- fig.lines += [matplotlib.lines.Line2D(
- (endpoint0[i, 0], endpoint1[i, 0]),
- (endpoint0[i, 1], endpoint1[i, 1]),
- zorder=1, transform=fig.transFigure, c=colors[i],
- alpha=1, linewidth=lw) for i in range(n_lines)]
\ No newline at end of file
+ fig.lines += [
+ matplotlib.lines.Line2D(
+ (endpoint0[i, 0], endpoint1[i, 0]),
+ (endpoint0[i, 1], endpoint1[i, 1]),
+ zorder=1,
+ transform=fig.transFigure,
+ c=colors[i],
+ alpha=1,
+ linewidth=lw,
+ )
+ for i in range(n_lines)
+ ]
diff --git a/imcui/third_party/SuperGluePretrainedNetwork/models/__init__.py b/third_party/SOLD2/sold2/model/__init__.py
similarity index 100%
rename from imcui/third_party/SuperGluePretrainedNetwork/models/__init__.py
rename to third_party/SOLD2/sold2/model/__init__.py
diff --git a/imcui/third_party/SOLD2/sold2/model/line_detection.py b/third_party/SOLD2/sold2/model/line_detection.py
similarity index 61%
rename from imcui/third_party/SOLD2/sold2/model/line_detection.py
rename to third_party/SOLD2/sold2/model/line_detection.py
index d0d1928515a8494833a8ef6509008f4299cd74c4..8ff379a8de3ff5d54dc807b397f947ea8f361ef9 100644
--- a/imcui/third_party/SOLD2/sold2/model/line_detection.py
+++ b/third_party/SOLD2/sold2/model/line_detection.py
@@ -7,14 +7,25 @@ import torch
class LineSegmentDetectionModule(object):
- """ Module extracting line segments from junctions and line heatmaps. """
+ """Module extracting line segments from junctions and line heatmaps."""
+
def __init__(
- self, detect_thresh, num_samples=64, sampling_method="local_max",
- inlier_thresh=0., heatmap_low_thresh=0.15, heatmap_high_thresh=0.2,
- max_local_patch_radius=3, lambda_radius=2.,
- use_candidate_suppression=False, nms_dist_tolerance=3.,
- use_heatmap_refinement=False, heatmap_refine_cfg=None,
- use_junction_refinement=False, junction_refine_cfg=None):
+ self,
+ detect_thresh,
+ num_samples=64,
+ sampling_method="local_max",
+ inlier_thresh=0.0,
+ heatmap_low_thresh=0.15,
+ heatmap_high_thresh=0.2,
+ max_local_patch_radius=3,
+ lambda_radius=2.0,
+ use_candidate_suppression=False,
+ nms_dist_tolerance=3.0,
+ use_heatmap_refinement=False,
+ heatmap_refine_cfg=None,
+ use_junction_refinement=False,
+ junction_refine_cfg=None,
+ ):
"""
Parameters:
detect_thresh: The probability threshold for mean activation (0. ~ 1.)
@@ -41,7 +52,7 @@ class LineSegmentDetectionModule(object):
self.inlier_thresh = inlier_thresh
self.local_patch_radius = max_local_patch_radius
self.lambda_radius = lambda_radius
-
+
# Detecting junctions on the boundary parameters
self.low_thresh = heatmap_low_thresh
self.high_thresh = heatmap_high_thresh
@@ -65,56 +76,61 @@ class LineSegmentDetectionModule(object):
self.junction_refine_cfg = junction_refine_cfg
if self.use_junction_refinement and self.junction_refine_cfg is None:
raise ValueError("[Error] Missing junction refinement config.")
-
+
def convert_inputs(self, inputs, device):
- """ Convert inputs to desired torch tensor. """
+ """Convert inputs to desired torch tensor."""
if isinstance(inputs, np.ndarray):
outputs = torch.tensor(inputs, dtype=torch.float32, device=device)
elif isinstance(inputs, torch.Tensor):
outputs = inputs.to(torch.float32).to(device)
else:
raise ValueError(
- "[Error] Inputs must either be torch tensor or numpy ndarray.")
-
+ "[Error] Inputs must either be torch tensor or numpy ndarray."
+ )
+
return outputs
-
+
def detect(self, junctions, heatmap, device=torch.device("cpu")):
- """ Main function performing line segment detection. """
+ """Main function performing line segment detection."""
# Convert inputs to torch tensor
junctions = self.convert_inputs(junctions, device=device)
heatmap = self.convert_inputs(heatmap, device=device)
-
+
# Perform the heatmap refinement
if self.use_heatmap_refinement:
if self.heatmap_refine_cfg["mode"] == "global":
heatmap = self.refine_heatmap(
- heatmap,
+ heatmap,
self.heatmap_refine_cfg["ratio"],
- self.heatmap_refine_cfg["valid_thresh"]
+ self.heatmap_refine_cfg["valid_thresh"],
)
elif self.heatmap_refine_cfg["mode"] == "local":
heatmap = self.refine_heatmap_local(
- heatmap,
+ heatmap,
self.heatmap_refine_cfg["num_blocks"],
self.heatmap_refine_cfg["overlap_ratio"],
self.heatmap_refine_cfg["ratio"],
- self.heatmap_refine_cfg["valid_thresh"]
+ self.heatmap_refine_cfg["valid_thresh"],
)
-
+
# Initialize empty line map
num_junctions = junctions.shape[0]
- line_map_pred = torch.zeros([num_junctions, num_junctions],
- device=device, dtype=torch.int32)
-
+ line_map_pred = torch.zeros(
+ [num_junctions, num_junctions], device=device, dtype=torch.int32
+ )
+
# Stop if there are not enough junctions
if num_junctions < 2:
return line_map_pred, junctions, heatmap
# Generate the candidate map
- candidate_map = torch.triu(torch.ones(
- [num_junctions, num_junctions], device=device, dtype=torch.int32),
- diagonal=1)
-
+ candidate_map = torch.triu(
+ torch.ones(
+ [num_junctions, num_junctions], device=device, dtype=torch.int32
+ ),
+ diagonal=1,
+ )
+
# Fetch the image boundary
if len(heatmap.shape) > 2:
H, W, _ = heatmap.shape
@@ -123,39 +139,47 @@ class LineSegmentDetectionModule(object):
# Optionally perform candidate filtering
if self.use_candidate_suppression:
- candidate_map = self.candidate_suppression(junctions,
- candidate_map)
+ candidate_map = self.candidate_suppression(junctions, candidate_map)
# Fetch the candidates
candidate_index_map = torch.where(candidate_map)
- candidate_index_map = torch.cat([candidate_index_map[0][..., None],
- candidate_index_map[1][..., None]],
- dim=-1)
-
+ candidate_index_map = torch.cat(
+ [candidate_index_map[0][..., None], candidate_index_map[1][..., None]],
+ dim=-1,
+ )
+
# Get the corresponding start and end junctions
candidate_junc_start = junctions[candidate_index_map[:, 0], :]
candidate_junc_end = junctions[candidate_index_map[:, 1], :]
# Get the sampling locations (N x 64)
sampler = self.torch_sampler.to(device)[None, ...]
- cand_samples_h = candidate_junc_start[:, 0:1] * sampler + \
- candidate_junc_end[:, 0:1] * (1 - sampler)
- cand_samples_w = candidate_junc_start[:, 1:2] * sampler + \
- candidate_junc_end[:, 1:2] * (1 - sampler)
-
+ cand_samples_h = candidate_junc_start[:, 0:1] * sampler + candidate_junc_end[
+ :, 0:1
+ ] * (1 - sampler)
+ cand_samples_w = candidate_junc_start[:, 1:2] * sampler + candidate_junc_end[
+ :, 1:2
+ ] * (1 - sampler)
+
# Clip to image boundary
- cand_h = torch.clamp(cand_samples_h, min=0, max=H-1)
- cand_w = torch.clamp(cand_samples_w, min=0, max=W-1)
-
+ cand_h = torch.clamp(cand_samples_h, min=0, max=H - 1)
+ cand_w = torch.clamp(cand_samples_w, min=0, max=W - 1)
+
# Local maximum search
if self.sampling_method == "local_max":
# Compute normalized segment lengths
- segments_length = torch.sqrt(torch.sum(
- (candidate_junc_start.to(torch.float32) -
- candidate_junc_end.to(torch.float32)) ** 2, dim=-1))
- normalized_seg_length = (segments_length
- / (((H ** 2) + (W ** 2)) ** 0.5))
-
+ segments_length = torch.sqrt(
+ torch.sum(
+ (
+ candidate_junc_start.to(torch.float32)
+ - candidate_junc_end.to(torch.float32)
+ )
+ ** 2,
+ dim=-1,
+ )
+ )
+ normalized_seg_length = segments_length / (((H**2) + (W**2)) ** 0.5)
+
# Perform local max search
num_cand = cand_h.shape[0]
group_size = 10000
@@ -163,85 +187,88 @@ class LineSegmentDetectionModule(object):
num_iter = math.ceil(num_cand / group_size)
sampled_feat_lst = []
for iter_idx in range(num_iter):
- if not iter_idx == num_iter-1:
- cand_h_ = cand_h[iter_idx * group_size:
- (iter_idx+1) * group_size, :]
- cand_w_ = cand_w[iter_idx * group_size:
- (iter_idx+1) * group_size, :]
+ if not iter_idx == num_iter - 1:
+ cand_h_ = cand_h[
+ iter_idx * group_size : (iter_idx + 1) * group_size, :
+ ]
+ cand_w_ = cand_w[
+ iter_idx * group_size : (iter_idx + 1) * group_size, :
+ ]
normalized_seg_length_ = normalized_seg_length[
- iter_idx * group_size: (iter_idx+1) * group_size]
+ iter_idx * group_size : (iter_idx + 1) * group_size
+ ]
else:
- cand_h_ = cand_h[iter_idx * group_size:, :]
- cand_w_ = cand_w[iter_idx * group_size:, :]
+ cand_h_ = cand_h[iter_idx * group_size :, :]
+ cand_w_ = cand_w[iter_idx * group_size :, :]
normalized_seg_length_ = normalized_seg_length[
- iter_idx * group_size:]
+ iter_idx * group_size :
+ ]
sampled_feat_ = self.detect_local_max(
- heatmap, cand_h_, cand_w_, H, W,
- normalized_seg_length_, device)
+ heatmap, cand_h_, cand_w_, H, W, normalized_seg_length_, device
+ )
sampled_feat_lst.append(sampled_feat_)
sampled_feat = torch.cat(sampled_feat_lst, dim=0)
else:
sampled_feat = self.detect_local_max(
- heatmap, cand_h, cand_w, H, W,
- normalized_seg_length, device)
+ heatmap, cand_h, cand_w, H, W, normalized_seg_length, device
+ )
# Bilinear sampling
elif self.sampling_method == "bilinear":
# Perform bilinear sampling
- sampled_feat = self.detect_bilinear(
- heatmap, cand_h, cand_w, H, W, device)
+ sampled_feat = self.detect_bilinear(heatmap, cand_h, cand_w, H, W, device)
else:
raise ValueError("[Error] Unknown sampling method.")
-
+
# [Simple threshold detection]
# detection_results is a mask over all candidates
- detection_results = (torch.mean(sampled_feat, dim=-1)
- > self.detect_thresh)
-
+ detection_results = torch.mean(sampled_feat, dim=-1) > self.detect_thresh
+
# [Inlier threshold detection]
- if self.inlier_thresh > 0.:
- inlier_ratio = torch.sum(
- sampled_feat > self.detect_thresh,
- dim=-1).to(torch.float32) / self.num_samples
+ if self.inlier_thresh > 0.0:
+ inlier_ratio = (
+ torch.sum(sampled_feat > self.detect_thresh, dim=-1).to(torch.float32)
+ / self.num_samples
+ )
detection_results_inlier = inlier_ratio >= self.inlier_thresh
detection_results = detection_results * detection_results_inlier
# Convert detection results back to line_map_pred
detected_junc_indexes = candidate_index_map[detection_results, :]
- line_map_pred[detected_junc_indexes[:, 0],
- detected_junc_indexes[:, 1]] = 1
- line_map_pred[detected_junc_indexes[:, 1],
- detected_junc_indexes[:, 0]] = 1
-
+ line_map_pred[detected_junc_indexes[:, 0], detected_junc_indexes[:, 1]] = 1
+ line_map_pred[detected_junc_indexes[:, 1], detected_junc_indexes[:, 0]] = 1
+
# Perform junction refinement
if self.use_junction_refinement and len(detected_junc_indexes) > 0:
junctions, line_map_pred = self.refine_junction_perturb(
- junctions, line_map_pred, heatmap, H, W, device)
+ junctions, line_map_pred, heatmap, H, W, device
+ )
return line_map_pred, junctions, heatmap
-
+
def refine_heatmap(self, heatmap, ratio=0.2, valid_thresh=1e-2):
- """ Global heatmap refinement method. """
+ """Global heatmap refinement method."""
# Grab the top 10% values
heatmap_values = heatmap[heatmap > valid_thresh]
sorted_values = torch.sort(heatmap_values, descending=True)[0]
top10_len = math.ceil(sorted_values.shape[0] * ratio)
max20 = torch.mean(sorted_values[:top10_len])
- heatmap = torch.clamp(heatmap / max20, min=0., max=1.)
+ heatmap = torch.clamp(heatmap / max20, min=0.0, max=1.0)
return heatmap
-
- def refine_heatmap_local(self, heatmap, num_blocks=5, overlap_ratio=0.5,
- ratio=0.2, valid_thresh=2e-3):
- """ Local heatmap refinement method. """
+
+ def refine_heatmap_local(
+ self, heatmap, num_blocks=5, overlap_ratio=0.5, ratio=0.2, valid_thresh=2e-3
+ ):
+ """Local heatmap refinement method."""
# Get the shape of the heatmap
H, W = heatmap.shape
increase_ratio = 1 - overlap_ratio
h_block = round(H / (1 + (num_blocks - 1) * increase_ratio))
w_block = round(W / (1 + (num_blocks - 1) * increase_ratio))
- count_map = torch.zeros(heatmap.shape, dtype=torch.float,
- device=heatmap.device)
- heatmap_output = torch.zeros(heatmap.shape, dtype=torch.float,
- device=heatmap.device)
+ count_map = torch.zeros(heatmap.shape, dtype=torch.int, device=heatmap.device)
+ heatmap_output = torch.zeros(
+ heatmap.shape, dtype=torch.float, device=heatmap.device
+ )
# Iterate through each block
for h_idx in range(num_blocks):
for w_idx in range(num_blocks):
@@ -254,25 +281,29 @@ class LineSegmentDetectionModule(object):
subheatmap = heatmap[h_start:h_end, w_start:w_end]
if subheatmap.max() > valid_thresh:
subheatmap = self.refine_heatmap(
- subheatmap, ratio, valid_thresh=valid_thresh)
-
+ subheatmap, ratio, valid_thresh=valid_thresh
+ )
+
# Aggregate it to the final heatmap
heatmap_output[h_start:h_end, w_start:w_end] += subheatmap
count_map[h_start:h_end, w_start:w_end] += 1
- heatmap_output = torch.clamp(heatmap_output / count_map,
- max=1., min=0.)
+ heatmap_output = torch.clamp(heatmap_output / count_map, max=1.0, min=0.0)
return heatmap_output
def candidate_suppression(self, junctions, candidate_map):
- """ Suppress overlapping long lines in the candidate segments. """
+ """Suppress overlapping long lines in the candidate segments."""
# Define the distance tolerance
dist_tolerance = self.nms_dist_tolerance
# Compute distance between junction pairs
# (num_junc x 1 x 2) - (1 x num_junc x 2) => num_junc x num_junc map
- line_dist_map = torch.sum((torch.unsqueeze(junctions, dim=1)
- - junctions[None, ...]) ** 2, dim=-1) ** 0.5
+ line_dist_map = (
+ torch.sum(
+ (torch.unsqueeze(junctions, dim=1) - junctions[None, ...]) ** 2, dim=-1
+ )
+ ** 0.5
+ )
# Fetch all the "detected lines"
seg_indexes = torch.where(torch.triu(candidate_map, diagonal=1))
@@ -285,20 +316,23 @@ class LineSegmentDetectionModule(object):
line_dists = line_dist_map[start_point_idxs, end_point_idxs]
# Check whether they are on the line
- dir_vecs = ((end_points - start_points)
- / torch.norm(end_points - start_points,
- dim=-1)[..., None])
+ dir_vecs = (end_points - start_points) / torch.norm(
+ end_points - start_points, dim=-1
+ )[..., None]
# Get the orthogonal distance
cand_vecs = junctions[None, ...] - start_points.unsqueeze(dim=1)
cand_vecs_norm = torch.norm(cand_vecs, dim=-1)
# Check whether they are projected directly onto the segment
- proj = (torch.einsum('bij,bjk->bik', cand_vecs, dir_vecs[..., None])
- / line_dists[..., None, None])
+ proj = (
+ torch.einsum("bij,bjk->bik", cand_vecs, dir_vecs[..., None])
+ / line_dists[..., None, None]
+ )
# proj is num_segs x num_junction x 1
- proj_mask = (proj >=0) * (proj <= 1)
+ proj_mask = (proj >= 0) * (proj <= 1)
cand_angles = torch.acos(
- torch.einsum('bij,bjk->bik', cand_vecs, dir_vecs[..., None])
- / cand_vecs_norm[..., None])
+ torch.einsum("bij,bjk->bik", cand_vecs, dir_vecs[..., None])
+ / cand_vecs_norm[..., None]
+ )
cand_dists = cand_vecs_norm[..., None] * torch.sin(cand_angles)
junc_dist_mask = cand_dists <= dist_tolerance
junc_mask = junc_dist_mask * proj_mask
@@ -306,21 +340,21 @@ class LineSegmentDetectionModule(object):
# Minus starting points
num_segs = start_point_idxs.shape[0]
junc_counts = torch.sum(junc_mask, dim=[1, 2])
- junc_counts -= junc_mask[..., 0][torch.arange(0, num_segs),
- start_point_idxs].to(torch.int)
- junc_counts -= junc_mask[..., 0][torch.arange(0, num_segs),
- end_point_idxs].to(torch.int)
-
+ junc_counts -= junc_mask[..., 0][
+ torch.arange(0, num_segs), start_point_idxs
+ ].to(torch.int)
+ junc_counts -= junc_mask[..., 0][torch.arange(0, num_segs), end_point_idxs].to(
+ torch.int
+ )
+
# Get the invalid candidate mask
final_mask = junc_counts > 0
- candidate_map[start_point_idxs[final_mask],
- end_point_idxs[final_mask]] = 0
-
+ candidate_map[start_point_idxs[final_mask], end_point_idxs[final_mask]] = 0
+
return candidate_map
-
- def refine_junction_perturb(self, junctions, line_map_pred,
- heatmap, H, W, device):
- """ Refine the line endpoints in a similar way as in LSD. """
+
+ def refine_junction_perturb(self, junctions, line_map_pred, heatmap, H, W, device):
+ """Refine the line endpoints in a similar way as in LSD."""
# Get the config
junction_refine_cfg = self.junction_refine_cfg
@@ -330,14 +364,23 @@ class LineSegmentDetectionModule(object):
side_perturbs = (num_perturbs - 1) // 2
# Fetch the 2D perturb mat
perturb_vec = torch.arange(
- start=-perturb_interval*side_perturbs,
- end=perturb_interval*(side_perturbs+1),
- step=perturb_interval, device=device)
+ start=-perturb_interval * side_perturbs,
+ end=perturb_interval * (side_perturbs + 1),
+ step=perturb_interval,
+ device=device,
+ )
w1_grid, h1_grid, w2_grid, h2_grid = torch.meshgrid(
- perturb_vec, perturb_vec, perturb_vec, perturb_vec)
- perturb_tensor = torch.cat([
- w1_grid[..., None], h1_grid[..., None],
- w2_grid[..., None], h2_grid[..., None]], dim=-1)
+ perturb_vec, perturb_vec, perturb_vec, perturb_vec
+ )
+ perturb_tensor = torch.cat(
+ [
+ w1_grid[..., None],
+ h1_grid[..., None],
+ w2_grid[..., None],
+ h2_grid[..., None],
+ ],
+ dim=-1,
+ )
perturb_tensor_flat = perturb_tensor.view(-1, 2, 2)
# Fetch the junctions and line_map
@@ -351,16 +394,20 @@ class LineSegmentDetectionModule(object):
start_points = junctions[start_point_idxs, :]
end_points = junctions[end_point_idxs, :]
- line_segments = torch.cat([start_points.unsqueeze(dim=1),
- end_points.unsqueeze(dim=1)], dim=1)
+ line_segments = torch.cat(
+ [start_points.unsqueeze(dim=1), end_points.unsqueeze(dim=1)], dim=1
+ )
- line_segment_candidates = (line_segments.unsqueeze(dim=1)
- + perturb_tensor_flat[None, ...])
+ line_segment_candidates = (
+ line_segments.unsqueeze(dim=1) + perturb_tensor_flat[None, ...]
+ )
# Clip the boundaries
line_segment_candidates[..., 0] = torch.clamp(
- line_segment_candidates[..., 0], min=0, max=H - 1)
+ line_segment_candidates[..., 0], min=0, max=H - 1
+ )
line_segment_candidates[..., 1] = torch.clamp(
- line_segment_candidates[..., 1], min=0, max=W - 1)
+ line_segment_candidates[..., 1], min=0, max=W - 1
+ )
# Iterate through all the segments
refined_segment_lst = []
@@ -373,36 +420,37 @@ class LineSegmentDetectionModule(object):
# Get the sampling locations (N x 64)
sampler = self.torch_sampler.to(device)[None, ...]
- cand_samples_h = (candidate_junc_start[:, 0:1] * sampler +
- candidate_junc_end[:, 0:1] * (1 - sampler))
- cand_samples_w = (candidate_junc_start[:, 1:2] * sampler +
- candidate_junc_end[:, 1:2] * (1 - sampler))
-
+ cand_samples_h = candidate_junc_start[
+ :, 0:1
+ ] * sampler + candidate_junc_end[:, 0:1] * (1 - sampler)
+ cand_samples_w = candidate_junc_start[
+ :, 1:2
+ ] * sampler + candidate_junc_end[:, 1:2] * (1 - sampler)
+
# Clip to image boundary
cand_h = torch.clamp(cand_samples_h, min=0, max=H - 1)
cand_w = torch.clamp(cand_samples_w, min=0, max=W - 1)
# Perform bilinear sampling
- segment_feat = self.detect_bilinear(
- heatmap, cand_h, cand_w, H, W, device)
+ segment_feat = self.detect_bilinear(heatmap, cand_h, cand_w, H, W, device)
segment_results = torch.mean(segment_feat, dim=-1)
max_idx = torch.argmax(segment_results)
refined_segment_lst.append(segment[max_idx, ...][None, ...])
-
+
# Concatenate back to segments
refined_segments = torch.cat(refined_segment_lst, dim=0)
# Convert back to junctions and line_map
junctions_new = torch.cat(
- [refined_segments[:, 0, :], refined_segments[:, 1, :]], dim=0)
+ [refined_segments[:, 0, :], refined_segments[:, 1, :]], dim=0
+ )
junctions_new = torch.unique(junctions_new, dim=0)
- line_map_new = self.segments_to_line_map(junctions_new,
- refined_segments)
+ line_map_new = self.segments_to_line_map(junctions_new, refined_segments)
return junctions_new, line_map_new
-
+
def segments_to_line_map(self, junctions, segments):
- """ Convert the list of segments to line map. """
+ """Convert the list of segments to line map."""
# Create empty line map
device = junctions.device
num_junctions = junctions.shape[0]
@@ -416,10 +464,8 @@ class LineSegmentDetectionModule(object):
junction2 = seg[1, :]
# Get index
- idx_junction1 = torch.where(
- (junctions == junction1).sum(axis=1) == 2)[0]
- idx_junction2 = torch.where(
- (junctions == junction2).sum(axis=1) == 2)[0]
+ idx_junction1 = torch.where((junctions == junction1).sum(axis=1) == 2)[0]
+ idx_junction2 = torch.where((junctions == junction2).sum(axis=1) == 2)[0]
# label the corresponding entries
line_map[idx_junction1, idx_junction2] = 1
@@ -428,7 +474,7 @@ class LineSegmentDetectionModule(object):
return line_map
def detect_bilinear(self, heatmap, cand_h, cand_w, H, W, device):
- """ Detection by bilinear sampling. """
+ """Detection by bilinear sampling."""
# Get the floor and ceiling locations
cand_h_floor = torch.floor(cand_h).to(torch.long)
cand_h_ceil = torch.ceil(cand_h).to(torch.long)
@@ -437,63 +483,83 @@ class LineSegmentDetectionModule(object):
# Perform the bilinear sampling
cand_samples_feat = (
- heatmap[cand_h_floor, cand_w_floor] * (cand_h_ceil - cand_h)
- * (cand_w_ceil - cand_w) + heatmap[cand_h_floor, cand_w_ceil]
- * (cand_h_ceil - cand_h) * (cand_w - cand_w_floor) +
- heatmap[cand_h_ceil, cand_w_floor] * (cand_h - cand_h_floor)
- * (cand_w_ceil - cand_w) + heatmap[cand_h_ceil, cand_w_ceil]
- * (cand_h - cand_h_floor) * (cand_w - cand_w_floor))
-
+ heatmap[cand_h_floor, cand_w_floor]
+ * (cand_h_ceil - cand_h)
+ * (cand_w_ceil - cand_w)
+ + heatmap[cand_h_floor, cand_w_ceil]
+ * (cand_h_ceil - cand_h)
+ * (cand_w - cand_w_floor)
+ + heatmap[cand_h_ceil, cand_w_floor]
+ * (cand_h - cand_h_floor)
+ * (cand_w_ceil - cand_w)
+ + heatmap[cand_h_ceil, cand_w_ceil]
+ * (cand_h - cand_h_floor)
+ * (cand_w - cand_w_floor)
+ )
+
return cand_samples_feat
-
- def detect_local_max(self, heatmap, cand_h, cand_w, H, W,
- normalized_seg_length, device):
- """ Detection by local maximum search. """
+
+ def detect_local_max(
+ self, heatmap, cand_h, cand_w, H, W, normalized_seg_length, device
+ ):
+ """Detection by local maximum search."""
# Compute the distance threshold
- dist_thresh = (0.5 * (2 ** 0.5)
- + self.lambda_radius * normalized_seg_length)
+ dist_thresh = 0.5 * (2**0.5) + self.lambda_radius * normalized_seg_length
# Make it N x 64
- dist_thresh = torch.repeat_interleave(dist_thresh[..., None],
- self.num_samples, dim=-1)
-
+ dist_thresh = torch.repeat_interleave(
+ dist_thresh[..., None], self.num_samples, dim=-1
+ )
+
# Compute the candidate points
- cand_points = torch.cat([cand_h[..., None], cand_w[..., None]],
- dim=-1)
- cand_points_round = torch.round(cand_points) # N x 64 x 2
-
+ cand_points = torch.cat([cand_h[..., None], cand_w[..., None]], dim=-1)
+ cand_points_round = torch.round(cand_points) # N x 64 x 2
+
# Construct local patches 9x9 = 81
- patch_mask = torch.zeros([int(2 * self.local_patch_radius + 1),
- int(2 * self.local_patch_radius + 1)],
- device=device)
+ patch_mask = torch.zeros(
+ [
+ int(2 * self.local_patch_radius + 1),
+ int(2 * self.local_patch_radius + 1),
+ ],
+ device=device,
+ )
patch_center = torch.tensor(
- [[self.local_patch_radius, self.local_patch_radius]],
- device=device, dtype=torch.float32)
+ [[self.local_patch_radius, self.local_patch_radius]],
+ device=device,
+ dtype=torch.float32,
+ )
H_patch_points, W_patch_points = torch.where(patch_mask >= 0)
- patch_points = torch.cat([H_patch_points[..., None],
- W_patch_points[..., None]], dim=-1)
+ patch_points = torch.cat(
+ [H_patch_points[..., None], W_patch_points[..., None]], dim=-1
+ )
# Fetch the circle region
- patch_center_dist = torch.sqrt(torch.sum(
- (patch_points - patch_center) ** 2, dim=-1))
- patch_points = (patch_points[patch_center_dist
- <= self.local_patch_radius, :])
+ patch_center_dist = torch.sqrt(
+ torch.sum((patch_points - patch_center) ** 2, dim=-1)
+ )
+ patch_points = patch_points[patch_center_dist <= self.local_patch_radius, :]
# Shift [0, 0] to the center
patch_points = patch_points - self.local_patch_radius
-
+
# Construct local patch mask
- patch_points_shifted = (torch.unsqueeze(cand_points_round, dim=2)
- + patch_points[None, None, ...])
- patch_dist = torch.sqrt(torch.sum((torch.unsqueeze(cand_points, dim=2)
- - patch_points_shifted) ** 2,
- dim=-1))
+ patch_points_shifted = (
+ torch.unsqueeze(cand_points_round, dim=2) + patch_points[None, None, ...]
+ )
+ patch_dist = torch.sqrt(
+ torch.sum(
+ (torch.unsqueeze(cand_points, dim=2) - patch_points_shifted) ** 2,
+ dim=-1,
+ )
+ )
patch_dist_mask = patch_dist < dist_thresh[..., None]
-
+
# Get all points => num_points_center x num_patch_points x 2
- points_H = torch.clamp(patch_points_shifted[:, :, :, 0], min=0,
- max=H - 1).to(torch.long)
- points_W = torch.clamp(patch_points_shifted[:, :, :, 1], min=0,
- max=W - 1).to(torch.long)
+ points_H = torch.clamp(patch_points_shifted[:, :, :, 0], min=0, max=H - 1).to(
+ torch.long
+ )
+ points_W = torch.clamp(patch_points_shifted[:, :, :, 1], min=0, max=W - 1).to(
+ torch.long
+ )
points = torch.cat([points_H[..., None], points_W[..., None]], dim=-1)
-
+
# Sample the feature (N x 64 x 81)
sampled_feat = heatmap[points[:, :, :, 0], points[:, :, :, 1]]
# Filtering using the valid mask
@@ -502,5 +568,5 @@ class LineSegmentDetectionModule(object):
sampled_feat_lmax = torch.empty(0, 64)
else:
sampled_feat_lmax, _ = torch.max(sampled_feat, dim=-1)
-
+
return sampled_feat_lmax
diff --git a/imcui/third_party/SOLD2/sold2/model/line_detector.py b/third_party/SOLD2/sold2/model/line_detector.py
similarity index 81%
rename from imcui/third_party/SOLD2/sold2/model/line_detector.py
rename to third_party/SOLD2/sold2/model/line_detector.py
index 2f3d059e130178c482e8e569171ef9e0370424c7..33429f8bc48d21d223efaf83ab6a8f1375b359ec 100644
--- a/imcui/third_party/SOLD2/sold2/model/line_detector.py
+++ b/third_party/SOLD2/sold2/model/line_detector.py
@@ -14,7 +14,7 @@ from ..misc.train_utils import adapt_checkpoint
def line_map_to_segments(junctions, line_map):
- """ Convert a line map to a Nx2x2 list of segments. """
+ """Convert a line map to a Nx2x2 list of segments."""
line_map_tmp = line_map.copy()
output_segments = np.zeros([0, 2, 2])
@@ -27,22 +27,23 @@ def line_map_to_segments(junctions, line_map):
for idx2 in np.where(line_map_tmp[idx, :] == 1)[0]:
p1 = junctions[idx, :] # HW format
p2 = junctions[idx2, :]
- single_seg = np.concatenate([p1[None, ...], p2[None, ...]],
- axis=0)
+ single_seg = np.concatenate([p1[None, ...], p2[None, ...]], axis=0)
output_segments = np.concatenate(
- (output_segments, single_seg[None, ...]), axis=0)
-
+ (output_segments, single_seg[None, ...]), axis=0
+ )
+
# Update line_map
line_map_tmp[idx, idx2] = 0
line_map_tmp[idx2, idx] = 0
-
+
return output_segments
class LineDetector(object):
- def __init__(self, model_cfg, ckpt_path, device, line_detector_cfg,
- junc_detect_thresh=None):
- """ SOLD² line detector taking raw images as input.
+ def __init__(
+ self, model_cfg, ckpt_path, device, line_detector_cfg, junc_detect_thresh=None
+ ):
+ """SOLD² line detector taking raw images as input.
Parameters:
model_cfg: config for CNN model
ckpt_path: path to the weights
@@ -51,7 +52,7 @@ class LineDetector(object):
# Get loss weights if dynamic weighting
_, loss_weights = get_loss_and_weights(model_cfg, device)
self.device = device
-
+
# Initialize the cnn backbone
self.model = get_model(model_cfg, loss_weights)
checkpoint = torch.load(ckpt_path, map_location=self.device)
@@ -65,20 +66,21 @@ class LineDetector(object):
if junc_detect_thresh is not None:
self.junc_detect_thresh = junc_detect_thresh
else:
- self.junc_detect_thresh = model_cfg.get("detection_thresh", 1/65)
+ self.junc_detect_thresh = model_cfg.get("detection_thresh", 1 / 65)
self.max_num_junctions = model_cfg.get("max_num_junctions", 300)
# Initialize the line detector
self.line_detector_cfg = line_detector_cfg
self.line_detector = LineSegmentDetectionModule(**line_detector_cfg)
-
- def __call__(self, input_image, valid_mask=None,
- return_heatmap=False, profile=False):
+
+ def __call__(
+ self, input_image, valid_mask=None, return_heatmap=False, profile=False
+ ):
# Now we restrict input_image to 4D torch tensor
- if ((not len(input_image.shape) == 4)
- or (not isinstance(input_image, torch.Tensor))):
- raise ValueError(
- "[Error] the input image should be a 4D torch tensor.")
+ if (not len(input_image.shape) == 4) or (
+ not isinstance(input_image, torch.Tensor)
+ ):
+ raise ValueError("[Error] the input image should be a 4D torch tensor.")
# Move the input to corresponding device
input_image = input_image.to(self.device)
@@ -89,15 +91,18 @@ class LineDetector(object):
net_outputs = self.model(input_image)
junc_np = convert_junc_predictions(
- net_outputs["junctions"], self.grid_size,
- self.junc_detect_thresh, self.max_num_junctions)
+ net_outputs["junctions"],
+ self.grid_size,
+ self.junc_detect_thresh,
+ self.max_num_junctions,
+ )
if valid_mask is None:
junctions = np.where(junc_np["junc_pred_nms"].squeeze())
else:
- junctions = np.where(junc_np["junc_pred_nms"].squeeze()
- * valid_mask)
+ junctions = np.where(junc_np["junc_pred_nms"].squeeze() * valid_mask)
junctions = np.concatenate(
- [junctions[0][..., None], junctions[1][..., None]], axis=-1)
+ [junctions[0][..., None], junctions[1][..., None]], axis=-1
+ )
if net_outputs["heatmap"].shape[1] == 2:
# Convert to single channel directly from here
@@ -108,7 +113,8 @@ class LineDetector(object):
# Run the line detector.
line_map, junctions, heatmap = self.line_detector.detect(
- junctions, heatmap, device=self.device)
+ junctions, heatmap, device=self.device
+ )
heatmap = heatmap.cpu().numpy()
if isinstance(line_map, torch.Tensor):
line_map = line_map.cpu().numpy()
@@ -123,5 +129,5 @@ class LineDetector(object):
outputs["heatmap"] = heatmap
if profile:
outputs["time"] = end_time - start_time
-
+
return outputs
diff --git a/imcui/third_party/SOLD2/sold2/model/line_matcher.py b/third_party/SOLD2/sold2/model/line_matcher.py
similarity index 69%
rename from imcui/third_party/SOLD2/sold2/model/line_matcher.py
rename to third_party/SOLD2/sold2/model/line_matcher.py
index bc5a003573c91313e2295c75871edcb1c113662a..458a5e3141c0ad27c0ba665dbd72d5ce0c1c9a86 100644
--- a/imcui/third_party/SOLD2/sold2/model/line_matcher.py
+++ b/third_party/SOLD2/sold2/model/line_matcher.py
@@ -19,14 +19,23 @@ from .line_detector import line_map_to_segments
class LineMatcher(object):
- """ Full line matcher including line detection and matching
- with the Needleman-Wunsch algorithm. """
- def __init__(self, model_cfg, ckpt_path, device, line_detector_cfg,
- line_matcher_cfg, multiscale=False, scales=[1., 2.]):
+ """Full line matcher including line detection and matching
+ with the Needleman-Wunsch algorithm."""
+
+ def __init__(
+ self,
+ model_cfg,
+ ckpt_path,
+ device,
+ line_detector_cfg,
+ line_matcher_cfg,
+ multiscale=False,
+ scales=[1.0, 2.0],
+ ):
# Get loss weights if dynamic weighting
_, loss_weights = get_loss_and_weights(model_cfg, device)
self.device = device
-
+
# Initialize the cnn backbone
self.model = get_model(model_cfg, loss_weights)
checkpoint = torch.load(ckpt_path, map_location=self.device)
@@ -46,23 +55,22 @@ class LineMatcher(object):
# Initialize the line matcher
self.line_matcher = WunschLineMatcher(**line_matcher_cfg)
-
+
# Print some debug messages
for key, val in line_detector_cfg.items():
print(f"[Debug] {key}: {val}")
# print("[Debug] detect_thresh: %f" % (line_detector_cfg["detect_thresh"]))
# print("[Debug] num_samples: %d" % (line_detector_cfg["num_samples"]))
-
-
# Perform line detection and descriptor inference on a single image
- def line_detection(self, input_image, valid_mask=None,
- desc_only=False, profile=False):
+ def line_detection(
+ self, input_image, valid_mask=None, desc_only=False, profile=False
+ ):
# Restrict input_image to 4D torch tensor
- if ((not len(input_image.shape) == 4)
- or (not isinstance(input_image, torch.Tensor))):
- raise ValueError(
- "[Error] the input image should be a 4D torch tensor")
+ if (not len(input_image.shape) == 4) or (
+ not isinstance(input_image, torch.Tensor)
+ ):
+ raise ValueError("[Error] the input image should be a 4D torch tensor")
# Move the input to corresponding device
input_image = input_image.to(self.device)
@@ -76,29 +84,40 @@ class LineMatcher(object):
if not desc_only:
junc_np = convert_junc_predictions(
- net_outputs["junctions"], self.grid_size,
- self.junc_detect_thresh, self.max_num_junctions)
+ net_outputs["junctions"],
+ self.grid_size,
+ self.junc_detect_thresh,
+ self.max_num_junctions,
+ )
if valid_mask is None:
junctions = np.where(junc_np["junc_pred_nms"].squeeze())
else:
- junctions = np.where(
- junc_np["junc_pred_nms"].squeeze() * valid_mask)
- junctions = np.concatenate([junctions[0][..., None],
- junctions[1][..., None]], axis=-1)
+ junctions = np.where(junc_np["junc_pred_nms"].squeeze() * valid_mask)
+ junctions = np.concatenate(
+ [junctions[0][..., None], junctions[1][..., None]], axis=-1
+ )
if net_outputs["heatmap"].shape[1] == 2:
# Convert to single channel directly from here
- heatmap = softmax(
- net_outputs["heatmap"],
- dim=1)[:, 1:, :, :].cpu().numpy().transpose(0, 2, 3, 1)
+ heatmap = (
+ softmax(net_outputs["heatmap"], dim=1)[:, 1:, :, :]
+ .cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)
+ )
else:
- heatmap = torch.sigmoid(
- net_outputs["heatmap"]).cpu().numpy().transpose(0, 2, 3, 1)
+ heatmap = (
+ torch.sigmoid(net_outputs["heatmap"])
+ .cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)
+ )
heatmap = heatmap[0, :, :, 0]
# Run the line detector.
line_map, junctions, heatmap = self.line_detector.detect(
- junctions, heatmap, device=self.device)
+ junctions, heatmap, device=self.device
+ )
if isinstance(line_map, torch.Tensor):
line_map = line_map.cpu().numpy()
if isinstance(junctions, torch.Tensor):
@@ -115,7 +134,9 @@ class LineMatcher(object):
line_segments_inlier = []
for inlier_idx in range(num_inlier_thresh):
line_map_tmp = line_map[detect_idx, inlier_idx, :, :]
- line_segments_tmp = line_map_to_segments(junctions, line_map_tmp)
+ line_segments_tmp = line_map_to_segments(
+ junctions, line_map_tmp
+ )
line_segments_inlier.append(line_segments_tmp)
line_segments.append(line_segments_inlier)
else:
@@ -127,18 +148,24 @@ class LineMatcher(object):
if profile:
outputs["time"] = end_time - start_time
-
+
return outputs
# Perform line detection and descriptor inference at multiple scales
- def multiscale_line_detection(self, input_image, valid_mask=None,
- desc_only=False, profile=False,
- scales=[1., 2.], aggregation='mean'):
+ def multiscale_line_detection(
+ self,
+ input_image,
+ valid_mask=None,
+ desc_only=False,
+ profile=False,
+ scales=[1.0, 2.0],
+ aggregation="mean",
+ ):
# Restrict input_image to 4D torch tensor
- if ((not len(input_image.shape) == 4)
- or (not isinstance(input_image, torch.Tensor))):
- raise ValueError(
- "[Error] the input image should be a 4D torch tensor")
+ if (not len(input_image.shape) == 4) or (
+ not isinstance(input_image, torch.Tensor)
+ ):
+ raise ValueError("[Error] the input image should be a 4D torch tensor")
# Move the input to corresponding device
input_image = input_image.to(self.device)
@@ -150,34 +177,39 @@ class LineMatcher(object):
junctions, heatmaps, descriptors = [], [], []
for s in scales:
# Resize the image
- resized_img = F.interpolate(input_image, scale_factor=s,
- mode='bilinear')
+ resized_img = F.interpolate(input_image, scale_factor=s, mode="bilinear")
# Forward of the CNN backbone
with torch.no_grad():
net_outputs = self.model(resized_img)
- descriptors.append(F.interpolate(
- net_outputs["descriptors"], size=desc_size, mode="bilinear"))
+ descriptors.append(
+ F.interpolate(
+ net_outputs["descriptors"], size=desc_size, mode="bilinear"
+ )
+ )
if not desc_only:
junc_prob = convert_junc_predictions(
- net_outputs["junctions"], self.grid_size)["junc_pred"]
- junctions.append(cv2.resize(junc_prob.squeeze(),
- (img_size[1], img_size[0]),
- interpolation=cv2.INTER_LINEAR))
+ net_outputs["junctions"], self.grid_size
+ )["junc_pred"]
+ junctions.append(
+ cv2.resize(
+ junc_prob.squeeze(),
+ (img_size[1], img_size[0]),
+ interpolation=cv2.INTER_LINEAR,
+ )
+ )
if net_outputs["heatmap"].shape[1] == 2:
# Convert to single channel directly from here
- heatmap = softmax(net_outputs["heatmap"],
- dim=1)[:, 1:, :, :]
+ heatmap = softmax(net_outputs["heatmap"], dim=1)[:, 1:, :, :]
else:
heatmap = torch.sigmoid(net_outputs["heatmap"])
- heatmaps.append(F.interpolate(heatmap, size=img_size,
- mode="bilinear"))
+ heatmaps.append(F.interpolate(heatmap, size=img_size, mode="bilinear"))
# Aggregate the results
- if aggregation == 'mean':
+ if aggregation == "mean":
# Aggregation through the mean activation
descriptors = torch.stack(descriptors, dim=0).mean(0)
else:
@@ -186,7 +218,7 @@ class LineMatcher(object):
outputs = {"descriptor": descriptors}
if not desc_only:
- if aggregation == 'mean':
+ if aggregation == "mean":
junctions = np.stack(junctions, axis=0).mean(0)[None]
heatmap = torch.stack(heatmaps, dim=0).mean(0)[0, 0, :, :]
heatmap = heatmap.cpu().numpy()
@@ -197,18 +229,23 @@ class LineMatcher(object):
# Extract junctions
junc_pred_nms = super_nms(
- junctions[..., None], self.grid_size,
- self.junc_detect_thresh, self.max_num_junctions)
+ junctions[..., None],
+ self.grid_size,
+ self.junc_detect_thresh,
+ self.max_num_junctions,
+ )
if valid_mask is None:
junctions = np.where(junc_pred_nms.squeeze())
else:
junctions = np.where(junc_pred_nms.squeeze() * valid_mask)
- junctions = np.concatenate([junctions[0][..., None],
- junctions[1][..., None]], axis=-1)
+ junctions = np.concatenate(
+ [junctions[0][..., None], junctions[1][..., None]], axis=-1
+ )
# Run the line detector.
line_map, junctions, heatmap = self.line_detector.detect(
- junctions, heatmap, device=self.device)
+ junctions, heatmap, device=self.device
+ )
if isinstance(line_map, torch.Tensor):
line_map = line_map.cpu().numpy()
if isinstance(junctions, torch.Tensor):
@@ -226,7 +263,8 @@ class LineMatcher(object):
for inlier_idx in range(num_inlier_thresh):
line_map_tmp = line_map[detect_idx, inlier_idx, :, :]
line_segments_tmp = line_map_to_segments(
- junctions, line_map_tmp)
+ junctions, line_map_tmp
+ )
line_segments_inlier.append(line_segments_tmp)
line_segments.append(line_segments_inlier)
else:
@@ -238,25 +276,25 @@ class LineMatcher(object):
if profile:
outputs["time"] = end_time - start_time
-
+
return outputs
-
+
def __call__(self, images, valid_masks=[None, None], profile=False):
# Line detection and descriptor inference on both images
if self.multiscale:
forward_outputs = [
self.multiscale_line_detection(
- images[0], valid_masks[0], profile=profile,
- scales=self.scales),
+ images[0], valid_masks[0], profile=profile, scales=self.scales
+ ),
self.multiscale_line_detection(
- images[1], valid_masks[1], profile=profile,
- scales=self.scales)]
+ images[1], valid_masks[1], profile=profile, scales=self.scales
+ ),
+ ]
else:
forward_outputs = [
- self.line_detection(images[0], valid_masks[0],
- profile=profile),
- self.line_detection(images[1], valid_masks[1],
- profile=profile)]
+ self.line_detection(images[0], valid_masks[0], profile=profile),
+ self.line_detection(images[1], valid_masks[1], profile=profile),
+ ]
line_seg1 = forward_outputs[0]["line_segments"]
line_seg2 = forward_outputs[1]["line_segments"]
desc1 = forward_outputs[0]["descriptor"]
@@ -264,16 +302,15 @@ class LineMatcher(object):
# Match the lines in both images
start_time = time.time()
- matches = self.line_matcher.forward(line_seg1, line_seg2,
- desc1, desc2)
+ matches = self.line_matcher.forward(line_seg1, line_seg2, desc1, desc2)
end_time = time.time()
- outputs = {"line_segments": [line_seg1, line_seg2],
- "matches": matches}
+ outputs = {"line_segments": [line_seg1, line_seg2], "matches": matches}
if profile:
- outputs["line_detection_time"] = (forward_outputs[0]["time"]
- + forward_outputs[1]["time"])
+ outputs["line_detection_time"] = (
+ forward_outputs[0]["time"] + forward_outputs[1]["time"]
+ )
outputs["line_matching_time"] = end_time - start_time
-
+
return outputs
diff --git a/imcui/third_party/SOLD2/sold2/model/line_matching.py b/third_party/SOLD2/sold2/model/line_matching.py
similarity index 71%
rename from imcui/third_party/SOLD2/sold2/model/line_matching.py
rename to third_party/SOLD2/sold2/model/line_matching.py
index 89b71879e3104f9a8b52c1cf5e534cd124fe83b2..bfceb5a161732c3f7f4cf97e988d5e369a4c25fa 100644
--- a/imcui/third_party/SOLD2/sold2/model/line_matching.py
+++ b/third_party/SOLD2/sold2/model/line_matching.py
@@ -10,11 +10,19 @@ from ..misc.geometry_utils import keypoints_to_grid
class WunschLineMatcher(object):
- """ Class matching two sets of line segments
- with the Needleman-Wunsch algorithm. """
- def __init__(self, cross_check=True, num_samples=10, min_dist_pts=8,
- top_k_candidates=10, grid_size=8, sampling="regular",
- line_score=False):
+ """Class matching two sets of line segments
+ with the Needleman-Wunsch algorithm."""
+
+ def __init__(
+ self,
+ cross_check=True,
+ num_samples=10,
+ min_dist_pts=8,
+ top_k_candidates=10,
+ grid_size=8,
+ sampling="regular",
+ line_score=False,
+ ):
self.cross_check = cross_check
self.num_samples = num_samples
self.min_dist_pts = min_dist_pts
@@ -27,13 +35,11 @@ class WunschLineMatcher(object):
def forward(self, line_seg1, line_seg2, desc1, desc2):
"""
- Find the best matches between two sets of line segments
- and their corresponding descriptors.
+ Find the best matches between two sets of line segments
+ and their corresponding descriptors.
"""
- img_size1 = (desc1.shape[2] * self.grid_size,
- desc1.shape[3] * self.grid_size)
- img_size2 = (desc2.shape[2] * self.grid_size,
- desc2.shape[3] * self.grid_size)
+ img_size1 = (desc1.shape[2] * self.grid_size, desc1.shape[3] * self.grid_size)
+ img_size2 = (desc2.shape[2] * self.grid_size, desc2.shape[3] * self.grid_size)
device = desc1.device
# Default case when an image has no lines
@@ -48,13 +54,17 @@ class WunschLineMatcher(object):
line_points2, valid_points2 = self.sample_line_points(line_seg2)
else:
line_points1, valid_points1 = self.sample_salient_points(
- line_seg1, desc1, img_size1, self.sampling_mode)
+ line_seg1, desc1, img_size1, self.sampling_mode
+ )
line_points2, valid_points2 = self.sample_salient_points(
- line_seg2, desc2, img_size2, self.sampling_mode)
- line_points1 = torch.tensor(line_points1.reshape(-1, 2),
- dtype=torch.float, device=device)
- line_points2 = torch.tensor(line_points2.reshape(-1, 2),
- dtype=torch.float, device=device)
+ line_seg2, desc2, img_size2, self.sampling_mode
+ )
+ line_points1 = torch.tensor(
+ line_points1.reshape(-1, 2), dtype=torch.float, device=device
+ )
+ line_points2 = torch.tensor(
+ line_points2.reshape(-1, 2), dtype=torch.float, device=device
+ )
# Extract the descriptors for each point
grid1 = keypoints_to_grid(line_points1, img_size1)
@@ -67,8 +77,9 @@ class WunschLineMatcher(object):
scores = desc1.t() @ desc2
scores[~valid_points1.flatten()] = -1
scores[:, ~valid_points2.flatten()] = -1
- scores = scores.reshape(len(line_seg1), self.num_samples,
- len(line_seg2), self.num_samples)
+ scores = scores.reshape(
+ len(line_seg1), self.num_samples, len(line_seg2), self.num_samples
+ )
scores = scores.permute(0, 2, 1, 3)
# scores.shape = (n_lines1, n_lines2, num_samples, num_samples)
@@ -77,16 +88,15 @@ class WunschLineMatcher(object):
# [Optionally] filter matches with mutual nearest neighbor filtering
if self.cross_check:
- matches2 = self.filter_and_match_lines(
- scores.permute(1, 0, 3, 2))
+ matches2 = self.filter_and_match_lines(scores.permute(1, 0, 3, 2))
mutual = matches2[matches] == np.arange(len(line_seg1))
matches[~mutual] = -1
return matches
def d2_net_saliency_score(self, desc):
- """ Compute the D2-Net saliency score
- on a 3D or 4D descriptor. """
+ """Compute the D2-Net saliency score
+ on a 3D or 4D descriptor."""
is_3d = len(desc.shape) == 3
b_size = len(desc)
feat = F.relu(desc)
@@ -94,11 +104,9 @@ class WunschLineMatcher(object):
# Compute the soft local max
exp = torch.exp(feat)
if is_3d:
- sum_exp = 3 * F.avg_pool1d(exp, kernel_size=3, stride=1,
- padding=1)
+ sum_exp = 3 * F.avg_pool1d(exp, kernel_size=3, stride=1, padding=1)
else:
- sum_exp = 9 * F.avg_pool2d(exp, kernel_size=3, stride=1,
- padding=1)
+ sum_exp = 9 * F.avg_pool2d(exp, kernel_size=3, stride=1, padding=1)
soft_local_max = exp / sum_exp
# Compute the depth-wise maximum
@@ -116,7 +124,7 @@ class WunschLineMatcher(object):
return score
def asl_feat_saliency_score(self, desc):
- """ Compute the ASLFeat saliency score on a 3D or 4D descriptor. """
+ """Compute the ASLFeat saliency score on a 3D or 4D descriptor."""
is_3d = len(desc.shape) == 3
b_size = len(desc)
@@ -141,8 +149,7 @@ class WunschLineMatcher(object):
score = score / normalization
return score
- def sample_salient_points(self, line_seg, desc, img_size,
- saliency_type='d2_net'):
+ def sample_salient_points(self, line_seg, desc, img_size, saliency_type="d2_net"):
"""
Sample the most salient points along each line segments, with a
minimal distance between each point. Pad the remaining points.
@@ -167,8 +174,9 @@ class WunschLineMatcher(object):
line_lengths = np.linalg.norm(line_seg[:, 0] - line_seg[:, 1], axis=1)
# The number of samples depends on the length of the line
- num_samples_lst = np.clip(line_lengths // self.min_dist_pts,
- 2, self.num_samples)
+ num_samples_lst = np.clip(
+ line_lengths // self.min_dist_pts, 2, self.num_samples
+ )
line_points = np.empty((num_lines, self.num_samples, 2), dtype=float)
valid_points = np.empty((num_lines, self.num_samples), dtype=bool)
@@ -182,17 +190,19 @@ class WunschLineMatcher(object):
cur_num_lines = len(cur_line_seg)
if cur_num_lines == 0:
continue
- line_points_x = np.linspace(cur_line_seg[:, 0, 0],
- cur_line_seg[:, 1, 0],
- sample_rate, axis=-1)
- line_points_y = np.linspace(cur_line_seg[:, 0, 1],
- cur_line_seg[:, 1, 1],
- sample_rate, axis=-1)
- cur_line_points = np.stack([line_points_x, line_points_y],
- axis=-1).reshape(-1, 2)
+ line_points_x = np.linspace(
+ cur_line_seg[:, 0, 0], cur_line_seg[:, 1, 0], sample_rate, axis=-1
+ )
+ line_points_y = np.linspace(
+ cur_line_seg[:, 0, 1], cur_line_seg[:, 1, 1], sample_rate, axis=-1
+ )
+ cur_line_points = np.stack([line_points_x, line_points_y], axis=-1).reshape(
+ -1, 2
+ )
# cur_line_points is of shape (n_cur_lines * sample_rate, 2)
- cur_line_points = torch.tensor(cur_line_points, dtype=torch.float,
- device=device)
+ cur_line_points = torch.tensor(
+ cur_line_points, dtype=torch.float, device=device
+ )
grid_points = keypoints_to_grid(cur_line_points, img_size)
if self.line_score:
@@ -206,25 +216,26 @@ class WunschLineMatcher(object):
else:
scores = self.asl_feat_saliency_score(line_desc)
else:
- scores = F.grid_sample(score.unsqueeze(1),
- grid_points).squeeze()
+ scores = F.grid_sample(score.unsqueeze(1), grid_points).squeeze()
# Take the most salient point in n distinct regions
scores = scores.reshape(-1, n, n_samples_per_region)
best = torch.max(scores, dim=2, keepdim=True)[1].cpu().numpy()
- cur_line_points = cur_line_points.reshape(-1, n,
- n_samples_per_region, 2)
+ cur_line_points = cur_line_points.reshape(-1, n, n_samples_per_region, 2)
cur_line_points = np.take_along_axis(
- cur_line_points, best[..., None], axis=2)[:, :, 0]
+ cur_line_points, best[..., None], axis=2
+ )[:, :, 0]
# Pad
- cur_valid_points = np.ones((cur_num_lines, self.num_samples),
- dtype=bool)
+ cur_valid_points = np.ones((cur_num_lines, self.num_samples), dtype=bool)
cur_valid_points[:, n:] = False
- cur_line_points = np.concatenate([
- cur_line_points,
- np.zeros((cur_num_lines, self.num_samples - n, 2), dtype=float)],
- axis=1)
+ cur_line_points = np.concatenate(
+ [
+ cur_line_points,
+ np.zeros((cur_num_lines, self.num_samples - n, 2), dtype=float),
+ ],
+ axis=1,
+ )
line_points[cur_mask] = cur_line_points
valid_points[cur_mask] = cur_valid_points
@@ -246,31 +257,34 @@ class WunschLineMatcher(object):
# Sample the points separated by at least min_dist_pts along each line
# The number of samples depends on the length of the line
- num_samples_lst = np.clip(line_lengths // self.min_dist_pts,
- 2, self.num_samples)
+ num_samples_lst = np.clip(
+ line_lengths // self.min_dist_pts, 2, self.num_samples
+ )
line_points = np.empty((num_lines, self.num_samples, 2), dtype=float)
valid_points = np.empty((num_lines, self.num_samples), dtype=bool)
for n in np.arange(2, self.num_samples + 1):
# Consider all lines where we can fit up to n points
cur_mask = num_samples_lst == n
cur_line_seg = line_seg[cur_mask]
- line_points_x = np.linspace(cur_line_seg[:, 0, 0],
- cur_line_seg[:, 1, 0],
- n, axis=-1)
- line_points_y = np.linspace(cur_line_seg[:, 0, 1],
- cur_line_seg[:, 1, 1],
- n, axis=-1)
+ line_points_x = np.linspace(
+ cur_line_seg[:, 0, 0], cur_line_seg[:, 1, 0], n, axis=-1
+ )
+ line_points_y = np.linspace(
+ cur_line_seg[:, 0, 1], cur_line_seg[:, 1, 1], n, axis=-1
+ )
cur_line_points = np.stack([line_points_x, line_points_y], axis=-1)
# Pad
cur_num_lines = len(cur_line_seg)
- cur_valid_points = np.ones((cur_num_lines, self.num_samples),
- dtype=bool)
+ cur_valid_points = np.ones((cur_num_lines, self.num_samples), dtype=bool)
cur_valid_points[:, n:] = False
- cur_line_points = np.concatenate([
- cur_line_points,
- np.zeros((cur_num_lines, self.num_samples - n, 2), dtype=float)],
- axis=1)
+ cur_line_points = np.concatenate(
+ [
+ cur_line_points,
+ np.zeros((cur_num_lines, self.num_samples - n, 2), dtype=float),
+ ],
+ axis=1,
+ )
line_points[cur_mask] = cur_line_points
valid_points[cur_mask] = cur_valid_points
@@ -290,23 +304,18 @@ class WunschLineMatcher(object):
# Pre-filter the pairs and keep the top k best candidate lines
line_scores1 = scores.max(3)[0]
valid_scores1 = line_scores1 != -1
- line_scores1 = ((line_scores1 * valid_scores1).sum(2)
- / valid_scores1.sum(2))
+ line_scores1 = (line_scores1 * valid_scores1).sum(2) / valid_scores1.sum(2)
line_scores2 = scores.max(2)[0]
valid_scores2 = line_scores2 != -1
- line_scores2 = ((line_scores2 * valid_scores2).sum(2)
- / valid_scores2.sum(2))
+ line_scores2 = (line_scores2 * valid_scores2).sum(2) / valid_scores2.sum(2)
line_scores = (line_scores1 + line_scores2) / 2
- topk_lines = torch.argsort(line_scores,
- dim=1)[:, -self.top_k_candidates:]
+ topk_lines = torch.argsort(line_scores, dim=1)[:, -self.top_k_candidates :]
scores, topk_lines = scores.cpu().numpy(), topk_lines.cpu().numpy()
# topk_lines.shape = (n_lines1, top_k_candidates)
- top_scores = np.take_along_axis(scores, topk_lines[:, :, None, None],
- axis=1)
+ top_scores = np.take_along_axis(scores, topk_lines[:, :, None, None], axis=1)
# Consider the reversed line segments as well
- top_scores = np.concatenate([top_scores, top_scores[..., ::-1]],
- axis=1)
+ top_scores = np.concatenate([top_scores, top_scores[..., ::-1]], axis=1)
# Compute the line distance matrix with Needleman-Wunsch algo and
# retrieve the closest line neighbor
@@ -339,30 +348,33 @@ class WunschLineMatcher(object):
for j in range(m):
nw_grid[:, i + 1, j + 1] = np.maximum(
np.maximum(nw_grid[:, i + 1, j], nw_grid[:, i, j + 1]),
- nw_grid[:, i, j] + nw_scores[:, i, j])
+ nw_grid[:, i, j] + nw_scores[:, i, j],
+ )
return nw_grid[:, -1, -1]
def get_pairwise_distance(self, line_seg1, line_seg2, desc1, desc2):
"""
- Compute the OPPOSITE of the NW score for pairs of line segments
- and their corresponding descriptors.
+ Compute the OPPOSITE of the NW score for pairs of line segments
+ and their corresponding descriptors.
"""
num_lines = len(line_seg1)
- assert num_lines == len(line_seg2), "The same number of lines is required in pairwise score."
- img_size1 = (desc1.shape[2] * self.grid_size,
- desc1.shape[3] * self.grid_size)
- img_size2 = (desc2.shape[2] * self.grid_size,
- desc2.shape[3] * self.grid_size)
+ assert num_lines == len(
+ line_seg2
+ ), "The same number of lines is required in pairwise score."
+ img_size1 = (desc1.shape[2] * self.grid_size, desc1.shape[3] * self.grid_size)
+ img_size2 = (desc2.shape[2] * self.grid_size, desc2.shape[3] * self.grid_size)
device = desc1.device
# Sample points regularly along each line
line_points1, valid_points1 = self.sample_line_points(line_seg1)
line_points2, valid_points2 = self.sample_line_points(line_seg2)
- line_points1 = torch.tensor(line_points1.reshape(-1, 2),
- dtype=torch.float, device=device)
- line_points2 = torch.tensor(line_points2.reshape(-1, 2),
- dtype=torch.float, device=device)
+ line_points1 = torch.tensor(
+ line_points1.reshape(-1, 2), dtype=torch.float, device=device
+ )
+ line_points2 = torch.tensor(
+ line_points2.reshape(-1, 2), dtype=torch.float, device=device
+ )
# Extract the descriptors for each point
grid1 = keypoints_to_grid(line_points1, img_size1)
@@ -374,9 +386,8 @@ class WunschLineMatcher(object):
# Compute the distance between line points for every pair of lines
# Assign a score of -1 for unvalid points
- scores = torch.einsum('dns,dnt->nst', desc1, desc2).cpu().numpy()
- scores = scores.reshape(num_lines * self.num_samples,
- self.num_samples)
+ scores = torch.einsum("dns,dnt->nst", desc1, desc2).cpu().numpy()
+ scores = scores.reshape(num_lines * self.num_samples, self.num_samples)
scores[~valid_points1.flatten()] = -1
scores = scores.reshape(num_lines, self.num_samples, self.num_samples)
scores = scores.transpose(1, 0, 2).reshape(self.num_samples, -1)
diff --git a/imcui/third_party/SOLD2/sold2/model/loss.py b/third_party/SOLD2/sold2/model/loss.py
similarity index 60%
rename from imcui/third_party/SOLD2/sold2/model/loss.py
rename to third_party/SOLD2/sold2/model/loss.py
index aaad3c67f3fd59db308869901f8a56623901e318..c1d2bfd232958fc19a4a775fe561dd5089079bff 100644
--- a/imcui/third_party/SOLD2/sold2/model/loss.py
+++ b/third_party/SOLD2/sold2/model/loss.py
@@ -7,17 +7,16 @@ import torch.nn as nn
import torch.nn.functional as F
from kornia.geometry import warp_perspective
-from ..misc.geometry_utils import (keypoints_to_grid, get_dist_mask,
- get_common_line_mask)
+from ..misc.geometry_utils import keypoints_to_grid, get_dist_mask, get_common_line_mask
def get_loss_and_weights(model_cfg, device=torch.device("cuda")):
- """ Get loss functions and either static or dynamic weighting. """
+ """Get loss functions and either static or dynamic weighting."""
# Get the global weighting policy
w_policy = model_cfg.get("weighting_policy", "static")
if not w_policy in ["static", "dynamic"]:
raise ValueError("[Error] Not supported weighting policy.")
-
+
loss_func = {}
loss_weight = {}
# Get junction loss function and weight
@@ -27,14 +26,16 @@ def get_loss_and_weights(model_cfg, device=torch.device("cuda")):
# Get heatmap loss function and weight
w_heatmap, heatmap_loss_func = get_heatmap_loss_and_weight(
- model_cfg, w_policy, device)
+ model_cfg, w_policy, device
+ )
loss_func["heatmap_loss"] = heatmap_loss_func.to(device)
loss_weight["w_heatmap"] = w_heatmap
# [Optionally] get descriptor loss function and weight
if model_cfg.get("descriptor_loss_func", None) is not None:
w_descriptor, descriptor_loss_func = get_descriptor_loss_and_weight(
- model_cfg, w_policy)
+ model_cfg, w_policy
+ )
loss_func["descriptor_loss"] = descriptor_loss_func.to(device)
loss_weight["w_desc"] = w_descriptor
@@ -42,26 +43,26 @@ def get_loss_and_weights(model_cfg, device=torch.device("cuda")):
def get_junction_loss_and_weight(model_cfg, global_w_policy):
- """ Get the junction loss function and weight. """
+ """Get the junction loss function and weight."""
junction_loss_cfg = model_cfg.get("junction_loss_cfg", {})
-
+
# Get the junction loss weight
w_policy = junction_loss_cfg.get("policy", global_w_policy)
if w_policy == "static":
w_junc = torch.tensor(model_cfg["w_junc"], dtype=torch.float32)
elif w_policy == "dynamic":
w_junc = nn.Parameter(
- torch.tensor(model_cfg["w_junc"], dtype=torch.float32),
- requires_grad=True)
+ torch.tensor(model_cfg["w_junc"], dtype=torch.float32), requires_grad=True
+ )
else:
- raise ValueError(
- "[Error] Unknown weighting policy for junction loss weight.")
+ raise ValueError("[Error] Unknown weighting policy for junction loss weight.")
# Get the junction loss function
junc_loss_name = model_cfg.get("junction_loss_func", "superpoint")
if junc_loss_name == "superpoint":
- junc_loss_func = JunctionDetectionLoss(model_cfg["grid_size"],
- model_cfg["keep_border_valid"])
+ junc_loss_func = JunctionDetectionLoss(
+ model_cfg["grid_size"], model_cfg["keep_border_valid"]
+ )
else:
raise ValueError("[Error] Not supported junction loss function.")
@@ -69,7 +70,7 @@ def get_junction_loss_and_weight(model_cfg, global_w_policy):
def get_heatmap_loss_and_weight(model_cfg, global_w_policy, device):
- """ Get the heatmap loss function and weight. """
+ """Get the heatmap loss function and weight."""
heatmap_loss_cfg = model_cfg.get("heatmap_loss_cfg", {})
# Get the heatmap loss weight
@@ -78,19 +79,20 @@ def get_heatmap_loss_and_weight(model_cfg, global_w_policy, device):
w_heatmap = torch.tensor(model_cfg["w_heatmap"], dtype=torch.float32)
elif w_policy == "dynamic":
w_heatmap = nn.Parameter(
- torch.tensor(model_cfg["w_heatmap"], dtype=torch.float32),
- requires_grad=True)
+ torch.tensor(model_cfg["w_heatmap"], dtype=torch.float32),
+ requires_grad=True,
+ )
else:
- raise ValueError(
- "[Error] Unknown weighting policy for junction loss weight.")
+ raise ValueError("[Error] Unknown weighting policy for junction loss weight.")
# Get the corresponding heatmap loss based on the config
heatmap_loss_name = model_cfg.get("heatmap_loss_func", "cross_entropy")
if heatmap_loss_name == "cross_entropy":
# Get the heatmap class weight (always static)
- heatmap_class_w = model_cfg.get("w_heatmap_class", 1.)
- class_weight = torch.tensor(
- np.array([1., heatmap_class_w])).to(torch.float).to(device)
+ heatmap_class_w = model_cfg.get("w_heatmap_class", 1.0)
+ class_weight = (
+ torch.tensor(np.array([1.0, heatmap_class_w])).to(torch.float).to(device)
+ )
heatmap_loss_func = HeatmapLoss(class_weight=class_weight)
else:
raise ValueError("[Error] Not supported heatmap loss function.")
@@ -99,28 +101,28 @@ def get_heatmap_loss_and_weight(model_cfg, global_w_policy, device):
def get_descriptor_loss_and_weight(model_cfg, global_w_policy):
- """ Get the descriptor loss function and weight. """
+ """Get the descriptor loss function and weight."""
descriptor_loss_cfg = model_cfg.get("descriptor_loss_cfg", {})
-
+
# Get the descriptor loss weight
w_policy = descriptor_loss_cfg.get("policy", global_w_policy)
if w_policy == "static":
w_descriptor = torch.tensor(model_cfg["w_desc"], dtype=torch.float32)
elif w_policy == "dynamic":
- w_descriptor = nn.Parameter(torch.tensor(model_cfg["w_desc"],
- dtype=torch.float32), requires_grad=True)
+ w_descriptor = nn.Parameter(
+ torch.tensor(model_cfg["w_desc"], dtype=torch.float32), requires_grad=True
+ )
else:
- raise ValueError(
- "[Error] Unknown weighting policy for descriptor loss weight.")
+ raise ValueError("[Error] Unknown weighting policy for descriptor loss weight.")
# Get the descriptor loss function
- descriptor_loss_name = model_cfg.get("descriptor_loss_func",
- "regular_sampling")
+ descriptor_loss_name = model_cfg.get("descriptor_loss_func", "regular_sampling")
if descriptor_loss_name == "regular_sampling":
descriptor_loss_func = TripletDescriptorLoss(
descriptor_loss_cfg["grid_size"],
descriptor_loss_cfg["dist_threshold"],
- descriptor_loss_cfg["margin"])
+ descriptor_loss_cfg["margin"],
+ )
else:
raise ValueError("[Error] Not supported descriptor loss function.")
@@ -128,79 +130,88 @@ def get_descriptor_loss_and_weight(model_cfg, global_w_policy):
def space_to_depth(input_tensor, grid_size):
- """ PixelUnshuffle for pytorch. """
+ """PixelUnshuffle for pytorch."""
N, C, H, W = input_tensor.size()
# (N, C, H//bs, bs, W//bs, bs)
x = input_tensor.view(N, C, H // grid_size, grid_size, W // grid_size, grid_size)
# (N, bs, bs, C, H//bs, W//bs)
x = x.permute(0, 3, 5, 1, 2, 4).contiguous()
# (N, C*bs^2, H//bs, W//bs)
- x = x.view(N, C * (grid_size ** 2), H // grid_size, W // grid_size)
+ x = x.view(N, C * (grid_size**2), H // grid_size, W // grid_size)
return x
-def junction_detection_loss(junction_map, junc_predictions, valid_mask=None,
- grid_size=8, keep_border=True):
- """ Junction detection loss. """
+def junction_detection_loss(
+ junction_map, junc_predictions, valid_mask=None, grid_size=8, keep_border=True
+):
+ """Junction detection loss."""
# Convert junc_map to channel tensor
junc_map = space_to_depth(junction_map, grid_size)
map_shape = junc_map.shape[-2:]
batch_size = junc_map.shape[0]
- dust_bin_label = torch.ones(
- [batch_size, 1, map_shape[0],
- map_shape[1]]).to(junc_map.device).to(torch.int)
- junc_map = torch.cat([junc_map*2, dust_bin_label], dim=1)
+ dust_bin_label = (
+ torch.ones([batch_size, 1, map_shape[0], map_shape[1]])
+ .to(junc_map.device)
+ .to(torch.int)
+ )
+ junc_map = torch.cat([junc_map * 2, dust_bin_label], dim=1)
labels = torch.argmax(
- junc_map.to(torch.float) +
- torch.distributions.Uniform(0, 0.1).sample(junc_map.shape).to(junc_map.device),
- dim=1)
+ junc_map.to(torch.float)
+ + torch.distributions.Uniform(0, 0.1)
+ .sample(junc_map.shape)
+ .to(junc_map.device),
+ dim=1,
+ )
# Also convert the valid mask to channel tensor
- valid_mask = (torch.ones(junction_map.shape) if valid_mask is None
- else valid_mask)
+ valid_mask = torch.ones(junction_map.shape) if valid_mask is None else valid_mask
valid_mask = space_to_depth(valid_mask, grid_size)
-
+
# Compute junction loss on the border patch or not
if keep_border:
- valid_mask = torch.sum(valid_mask.to(torch.bool).to(torch.int),
- dim=1, keepdim=True) > 0
+ valid_mask = (
+ torch.sum(valid_mask.to(torch.bool).to(torch.int), dim=1, keepdim=True) > 0
+ )
else:
- valid_mask = torch.sum(valid_mask.to(torch.bool).to(torch.int),
- dim=1, keepdim=True) >= grid_size * grid_size
+ valid_mask = (
+ torch.sum(valid_mask.to(torch.bool).to(torch.int), dim=1, keepdim=True)
+ >= grid_size * grid_size
+ )
# Compute the classification loss
loss_func = nn.CrossEntropyLoss(reduction="none")
# The loss still need NCHW format
- loss = loss_func(input=junc_predictions,
- target=labels.to(torch.long))
-
+ loss = loss_func(input=junc_predictions, target=labels.to(torch.long))
+
# Weighted sum by the valid mask
- loss_ = torch.sum(loss * torch.squeeze(valid_mask.to(torch.float),
- dim=1), dim=[0, 1, 2])
- loss_final = loss_ / torch.sum(torch.squeeze(valid_mask.to(torch.float),
- dim=1))
+ loss_ = torch.sum(
+ loss * torch.squeeze(valid_mask.to(torch.float), dim=1), dim=[0, 1, 2]
+ )
+ loss_final = loss_ / torch.sum(torch.squeeze(valid_mask.to(torch.float), dim=1))
return loss_final
-def heatmap_loss(heatmap_gt, heatmap_pred, valid_mask=None,
- class_weight=None):
- """ Heatmap prediction loss. """
+def heatmap_loss(heatmap_gt, heatmap_pred, valid_mask=None, class_weight=None):
+ """Heatmap prediction loss."""
# Compute the classification loss on each pixel
if class_weight is None:
loss_func = nn.CrossEntropyLoss(reduction="none")
else:
loss_func = nn.CrossEntropyLoss(class_weight, reduction="none")
- loss = loss_func(input=heatmap_pred,
- target=torch.squeeze(heatmap_gt.to(torch.long), dim=1))
+ loss = loss_func(
+ input=heatmap_pred, target=torch.squeeze(heatmap_gt.to(torch.long), dim=1)
+ )
# Weighted sum by the valid mask
# Sum over H and W
- loss_spatial_sum = torch.sum(loss * torch.squeeze(
- valid_mask.to(torch.float), dim=1), dim=[1, 2])
- valid_spatial_sum = torch.sum(torch.squeeze(valid_mask.to(torch.float32),
- dim=1), dim=[1, 2])
+ loss_spatial_sum = torch.sum(
+ loss * torch.squeeze(valid_mask.to(torch.float), dim=1), dim=[1, 2]
+ )
+ valid_spatial_sum = torch.sum(
+ torch.squeeze(valid_mask.to(torch.float32), dim=1), dim=[1, 2]
+ )
# Mean to single scalar over batch dimension
loss = torch.sum(loss_spatial_sum) / torch.sum(valid_spatial_sum)
@@ -208,19 +219,22 @@ def heatmap_loss(heatmap_gt, heatmap_pred, valid_mask=None,
class JunctionDetectionLoss(nn.Module):
- """ Junction detection loss. """
+ """Junction detection loss."""
+
def __init__(self, grid_size, keep_border):
super(JunctionDetectionLoss, self).__init__()
self.grid_size = grid_size
self.keep_border = keep_border
def forward(self, prediction, target, valid_mask=None):
- return junction_detection_loss(target, prediction, valid_mask,
- self.grid_size, self.keep_border)
+ return junction_detection_loss(
+ target, prediction, valid_mask, self.grid_size, self.keep_border
+ )
class HeatmapLoss(nn.Module):
- """ Heatmap prediction loss. """
+ """Heatmap prediction loss."""
+
def __init__(self, class_weight):
super(HeatmapLoss, self).__init__()
self.class_weight = class_weight
@@ -230,7 +244,8 @@ class HeatmapLoss(nn.Module):
class RegularizationLoss(nn.Module):
- """ Module for regularization loss. """
+ """Module for regularization loss."""
+
def __init__(self):
super(RegularizationLoss, self).__init__()
self.name = "regularization_loss"
@@ -242,14 +257,23 @@ class RegularizationLoss(nn.Module):
for _, val in loss_weights.items():
if isinstance(val, nn.Parameter):
loss += val
-
+
return loss
-def triplet_loss(desc_pred1, desc_pred2, points1, points2, line_indices,
- epoch, grid_size=8, dist_threshold=8,
- init_dist_threshold=64, margin=1):
- """ Regular triplet loss for descriptor learning. """
+def triplet_loss(
+ desc_pred1,
+ desc_pred2,
+ points1,
+ points2,
+ line_indices,
+ epoch,
+ grid_size=8,
+ dist_threshold=8,
+ init_dist_threshold=64,
+ margin=1,
+):
+ """Regular triplet loss for descriptor learning."""
b_size, _, Hc, Wc = desc_pred1.size()
img_size = (Hc * grid_size, Wc * grid_size)
device = desc_pred1.device
@@ -259,12 +283,11 @@ def triplet_loss(desc_pred1, desc_pred2, points1, points2, line_indices,
valid_points = line_indices.bool().flatten()
n_correct_points = torch.sum(valid_points).item()
if n_correct_points == 0:
- return torch.tensor(0., dtype=torch.float, device=device)
+ return torch.tensor(0.0, dtype=torch.float, device=device)
# Check which keypoints are too close to be matched
# dist_threshold is decreased at each epoch for easier training
- dist_threshold = max(dist_threshold,
- 2 * init_dist_threshold // (epoch + 1))
+ dist_threshold = max(dist_threshold, 2 * init_dist_threshold // (epoch + 1))
dist_mask = get_dist_mask(points1, points2, valid_points, dist_threshold)
# Additionally ban negative mining along the same line
@@ -276,11 +299,17 @@ def triplet_loss(desc_pred1, desc_pred2, points1, points2, line_indices,
grid2 = keypoints_to_grid(points2, img_size)
# Extract the descriptors
- desc1 = F.grid_sample(desc_pred1, grid1).permute(
- 0, 2, 3, 1).reshape(b_size * n_points, -1)[valid_points]
+ desc1 = (
+ F.grid_sample(desc_pred1, grid1)
+ .permute(0, 2, 3, 1)
+ .reshape(b_size * n_points, -1)[valid_points]
+ )
desc1 = F.normalize(desc1, dim=1)
- desc2 = F.grid_sample(desc_pred2, grid2).permute(
- 0, 2, 3, 1).reshape(b_size * n_points, -1)[valid_points]
+ desc2 = (
+ F.grid_sample(desc_pred2, grid2)
+ .permute(0, 2, 3, 1)
+ .reshape(b_size * n_points, -1)[valid_points]
+ )
desc2 = F.normalize(desc2, dim=1)
desc_dists = 2 - 2 * (desc1 @ desc2.t())
@@ -288,20 +317,23 @@ def triplet_loss(desc_pred1, desc_pred2, points1, points2, line_indices,
pos_dist = torch.diag(desc_dists)
# Negative distance loss
- max_dist = torch.tensor(4., dtype=torch.float, device=device)
+ max_dist = torch.tensor(4.0, dtype=torch.float, device=device)
desc_dists[
torch.arange(n_correct_points, dtype=torch.long),
- torch.arange(n_correct_points, dtype=torch.long)] = max_dist
+ torch.arange(n_correct_points, dtype=torch.long),
+ ] = max_dist
desc_dists[dist_mask] = max_dist
- neg_dist = torch.min(torch.min(desc_dists, dim=1)[0],
- torch.min(desc_dists, dim=0)[0])
+ neg_dist = torch.min(
+ torch.min(desc_dists, dim=1)[0], torch.min(desc_dists, dim=0)[0]
+ )
triplet_loss = F.relu(margin + pos_dist - neg_dist)
return triplet_loss, grid1, grid2, valid_points
class TripletDescriptorLoss(nn.Module):
- """ Triplet descriptor loss. """
+ """Triplet descriptor loss."""
+
def __init__(self, grid_size, dist_threshold, margin):
super(TripletDescriptorLoss, self).__init__()
self.grid_size = grid_size
@@ -309,23 +341,35 @@ class TripletDescriptorLoss(nn.Module):
self.dist_threshold = dist_threshold
self.margin = margin
- def forward(self, desc_pred1, desc_pred2, points1,
- points2, line_indices, epoch):
- return self.descriptor_loss(desc_pred1, desc_pred2, points1,
- points2, line_indices, epoch)
+ def forward(self, desc_pred1, desc_pred2, points1, points2, line_indices, epoch):
+ return self.descriptor_loss(
+ desc_pred1, desc_pred2, points1, points2, line_indices, epoch
+ )
# The descriptor loss based on regularly sampled points along the lines
- def descriptor_loss(self, desc_pred1, desc_pred2, points1,
- points2, line_indices, epoch):
- return torch.mean(triplet_loss(
- desc_pred1, desc_pred2, points1, points2, line_indices, epoch,
- self.grid_size, self.dist_threshold, self.init_dist_threshold,
- self.margin)[0])
+ def descriptor_loss(
+ self, desc_pred1, desc_pred2, points1, points2, line_indices, epoch
+ ):
+ return torch.mean(
+ triplet_loss(
+ desc_pred1,
+ desc_pred2,
+ points1,
+ points2,
+ line_indices,
+ epoch,
+ self.grid_size,
+ self.dist_threshold,
+ self.init_dist_threshold,
+ self.margin,
+ )[0]
+ )
class TotalLoss(nn.Module):
- """ Total loss summing junction, heatma, descriptor
- and regularization losses. """
+ """Total loss summing junction, heatma, descriptor
+ and regularization losses."""
+
def __init__(self, loss_funcs, loss_weights, weighting_policy):
super(TotalLoss, self).__init__()
# Whether we need to compute the descriptor loss
@@ -338,23 +382,26 @@ class TotalLoss(nn.Module):
# Always add regularization loss (it will return zero if not used)
self.loss_funcs["reg_loss"] = RegularizationLoss().cuda()
- def forward(self, junc_pred, junc_target, heatmap_pred,
- heatmap_target, valid_mask=None):
- """ Detection only loss. """
+ def forward(
+ self, junc_pred, junc_target, heatmap_pred, heatmap_target, valid_mask=None
+ ):
+ """Detection only loss."""
# Compute the junction loss
- junc_loss = self.loss_funcs["junc_loss"](junc_pred, junc_target,
- valid_mask)
+ junc_loss = self.loss_funcs["junc_loss"](junc_pred, junc_target, valid_mask)
# Compute the heatmap loss
heatmap_loss = self.loss_funcs["heatmap_loss"](
- heatmap_pred, heatmap_target, valid_mask)
+ heatmap_pred, heatmap_target, valid_mask
+ )
# Compute the total loss.
if self.weighting_policy == "dynamic":
reg_loss = self.loss_funcs["reg_loss"](self.loss_weights)
- total_loss = junc_loss * torch.exp(-self.loss_weights["w_junc"]) + \
- heatmap_loss * torch.exp(-self.loss_weights["w_heatmap"]) + \
- reg_loss
-
+ total_loss = (
+ junc_loss * torch.exp(-self.loss_weights["w_junc"])
+ + heatmap_loss * torch.exp(-self.loss_weights["w_heatmap"])
+ + reg_loss
+ )
+
return {
"total_loss": total_loss,
"junc_loss": junc_loss,
@@ -363,32 +410,47 @@ class TotalLoss(nn.Module):
"w_junc": torch.exp(-self.loss_weights["w_junc"]).item(),
"w_heatmap": torch.exp(-self.loss_weights["w_heatmap"]).item(),
}
-
+
elif self.weighting_policy == "static":
- total_loss = junc_loss * self.loss_weights["w_junc"] + \
- heatmap_loss * self.loss_weights["w_heatmap"]
-
+ total_loss = (
+ junc_loss * self.loss_weights["w_junc"]
+ + heatmap_loss * self.loss_weights["w_heatmap"]
+ )
+
return {
"total_loss": total_loss,
"junc_loss": junc_loss,
- "heatmap_loss": heatmap_loss
+ "heatmap_loss": heatmap_loss,
}
else:
raise ValueError("[Error] Unknown weighting policy.")
-
- def forward_descriptors(self,
- junc_map_pred1, junc_map_pred2, junc_map_target1,
- junc_map_target2, heatmap_pred1, heatmap_pred2, heatmap_target1,
- heatmap_target2, line_points1, line_points2, line_indices,
- desc_pred1, desc_pred2, epoch, valid_mask1=None,
- valid_mask2=None):
- """ Loss for detection + description. """
+
+ def forward_descriptors(
+ self,
+ junc_map_pred1,
+ junc_map_pred2,
+ junc_map_target1,
+ junc_map_target2,
+ heatmap_pred1,
+ heatmap_pred2,
+ heatmap_target1,
+ heatmap_target2,
+ line_points1,
+ line_points2,
+ line_indices,
+ desc_pred1,
+ desc_pred2,
+ epoch,
+ valid_mask1=None,
+ valid_mask2=None,
+ ):
+ """Loss for detection + description."""
# Compute junction loss
junc_loss = self.loss_funcs["junc_loss"](
- torch.cat([junc_map_pred1, junc_map_pred2], dim=0),
+ torch.cat([junc_map_pred1, junc_map_pred2], dim=0),
torch.cat([junc_map_target1, junc_map_target2], dim=0),
- torch.cat([valid_mask1, valid_mask2], dim=0)
+ torch.cat([valid_mask1, valid_mask2], dim=0),
)
# Get junction loss weight (dynamic or not)
if isinstance(self.loss_weights["w_junc"], nn.Parameter):
@@ -398,9 +460,9 @@ class TotalLoss(nn.Module):
# Compute heatmap loss
heatmap_loss = self.loss_funcs["heatmap_loss"](
- torch.cat([heatmap_pred1, heatmap_pred2], dim=0),
+ torch.cat([heatmap_pred1, heatmap_pred2], dim=0),
torch.cat([heatmap_target1, heatmap_target2], dim=0),
- torch.cat([valid_mask1, valid_mask2], dim=0)
+ torch.cat([valid_mask1, valid_mask2], dim=0),
)
# Get heatmap loss weight (dynamic or not)
if isinstance(self.loss_weights["w_heatmap"], nn.Parameter):
@@ -410,8 +472,8 @@ class TotalLoss(nn.Module):
# Compute the descriptor loss
descriptor_loss = self.loss_funcs["descriptor_loss"](
- desc_pred1, desc_pred2, line_points1,
- line_points2, line_indices, epoch)
+ desc_pred1, desc_pred2, line_points1, line_points2, line_indices, epoch
+ )
# Get descriptor loss weight (dynamic or not)
if isinstance(self.loss_weights["w_desc"], nn.Parameter):
w_descriptor = torch.exp(-self.loss_weights["w_desc"])
@@ -419,27 +481,27 @@ class TotalLoss(nn.Module):
w_descriptor = self.loss_weights["w_desc"]
# Update the total loss
- total_loss = (junc_loss * w_junc
- + heatmap_loss * w_heatmap
- + descriptor_loss * w_descriptor)
+ total_loss = (
+ junc_loss * w_junc
+ + heatmap_loss * w_heatmap
+ + descriptor_loss * w_descriptor
+ )
outputs = {
"junc_loss": junc_loss,
"heatmap_loss": heatmap_loss,
- "w_junc": w_junc.item() \
- if isinstance(w_junc, nn.Parameter) else w_junc,
- "w_heatmap": w_heatmap.item() \
- if isinstance(w_heatmap, nn.Parameter) else w_heatmap,
+ "w_junc": w_junc.item() if isinstance(w_junc, nn.Parameter) else w_junc,
+ "w_heatmap": w_heatmap.item()
+ if isinstance(w_heatmap, nn.Parameter)
+ else w_heatmap,
"descriptor_loss": descriptor_loss,
- "w_desc": w_descriptor.item() \
- if isinstance(w_descriptor, nn.Parameter) else w_descriptor
+ "w_desc": w_descriptor.item()
+ if isinstance(w_descriptor, nn.Parameter)
+ else w_descriptor,
}
-
+
# Compute the regularization loss
reg_loss = self.loss_funcs["reg_loss"](self.loss_weights)
total_loss += reg_loss
- outputs.update({
- "reg_loss": reg_loss,
- "total_loss": total_loss
- })
+ outputs.update({"reg_loss": reg_loss, "total_loss": total_loss})
return outputs
diff --git a/imcui/third_party/SOLD2/sold2/model/lr_scheduler.py b/third_party/SOLD2/sold2/model/lr_scheduler.py
similarity index 77%
rename from imcui/third_party/SOLD2/sold2/model/lr_scheduler.py
rename to third_party/SOLD2/sold2/model/lr_scheduler.py
index 3faa4f68a67564719008a932b40c16c5e908949f..fa3f5903c92a61f01eaa8aed95fb2261212f3762 100644
--- a/imcui/third_party/SOLD2/sold2/model/lr_scheduler.py
+++ b/third_party/SOLD2/sold2/model/lr_scheduler.py
@@ -5,18 +5,17 @@ import torch
def get_lr_scheduler(lr_decay, lr_decay_cfg, optimizer):
- """ Get the learning rate scheduler according to the config. """
+ """Get the learning rate scheduler according to the config."""
# If no lr_decay is specified => return None
if (lr_decay == False) or (lr_decay_cfg is None):
schduler = None
# Exponential decay
elif (lr_decay == True) and (lr_decay_cfg["policy"] == "exp"):
schduler = torch.optim.lr_scheduler.ExponentialLR(
- optimizer,
- gamma=lr_decay_cfg["gamma"]
+ optimizer, gamma=lr_decay_cfg["gamma"]
)
# Unknown policy
else:
raise ValueError("[Error] Unknow learning rate decay policy!")
- return schduler
\ No newline at end of file
+ return schduler
diff --git a/imcui/third_party/SOLD2/sold2/model/metrics.py b/third_party/SOLD2/sold2/model/metrics.py
similarity index 74%
rename from imcui/third_party/SOLD2/sold2/model/metrics.py
rename to third_party/SOLD2/sold2/model/metrics.py
index 0894a7207ee4afa344cb332c605c715b14db73a4..668daaf99acb9bbb80d7ca2746926f9d79d55cf0 100644
--- a/imcui/third_party/SOLD2/sold2/model/metrics.py
+++ b/third_party/SOLD2/sold2/model/metrics.py
@@ -10,15 +10,26 @@ from ..misc.geometry_utils import keypoints_to_grid
class Metrics(object):
- """ Metric evaluation calculator. """
- def __init__(self, detection_thresh, prob_thresh, grid_size,
- junc_metric_lst=None, heatmap_metric_lst=None,
- pr_metric_lst=None, desc_metric_lst=None):
+ """Metric evaluation calculator."""
+
+ def __init__(
+ self,
+ detection_thresh,
+ prob_thresh,
+ grid_size,
+ junc_metric_lst=None,
+ heatmap_metric_lst=None,
+ pr_metric_lst=None,
+ desc_metric_lst=None,
+ ):
# List supported metrics
- self.supported_junc_metrics = ["junc_precision", "junc_precision_nms",
- "junc_recall", "junc_recall_nms"]
- self.supported_heatmap_metrics = ["heatmap_precision",
- "heatmap_recall"]
+ self.supported_junc_metrics = [
+ "junc_precision",
+ "junc_precision_nms",
+ "junc_recall",
+ "junc_recall_nms",
+ ]
+ self.supported_heatmap_metrics = ["heatmap_precision", "heatmap_recall"]
self.supported_pr_metrics = ["junc_pr", "junc_nms_pr"]
self.supported_desc_metrics = ["matching_score"]
@@ -38,14 +49,13 @@ class Metrics(object):
# For the descriptors, the default None assumes no desc metric at all
if desc_metric_lst is None:
self.desc_metric_lst = []
- elif desc_metric_lst == 'all':
+ elif desc_metric_lst == "all":
self.desc_metric_lst = self.supported_desc_metrics
else:
self.desc_metric_lst = desc_metric_lst
if not self._check_metrics():
- raise ValueError(
- "[Error] Some elements in the metric_lst are invalid.")
+ raise ValueError("[Error] Some elements in the metric_lst are invalid.")
# Metric mapping table
self.metric_table = {
@@ -57,18 +67,29 @@ class Metrics(object):
"heatmap_recall": heatmap_recall(prob_thresh),
"junc_pr": junction_pr(),
"junc_nms_pr": junction_pr(),
- "matching_score": matching_score(grid_size)
+ "matching_score": matching_score(grid_size),
}
# Initialize the results
self.metric_results = {}
for key in self.metric_table.keys():
- self.metric_results[key] = 0.
-
- def evaluate(self, junc_pred, junc_pred_nms, junc_gt, heatmap_pred,
- heatmap_gt, valid_mask, line_points1=None, line_points2=None,
- desc_pred1=None, desc_pred2=None, valid_points=None):
- """ Perform evaluation. """
+ self.metric_results[key] = 0.0
+
+ def evaluate(
+ self,
+ junc_pred,
+ junc_pred_nms,
+ junc_gt,
+ heatmap_pred,
+ heatmap_gt,
+ valid_mask,
+ line_points1=None,
+ line_points2=None,
+ desc_pred1=None,
+ desc_pred2=None,
+ valid_points=None,
+ ):
+ """Perform evaluation."""
for metric in self.junc_metric_lst:
# If nms metrics then use nms to compute it.
if "nms" in metric:
@@ -77,27 +98,31 @@ class Metrics(object):
else:
junc_pred_input = junc_pred
self.metric_results[metric] = self.metric_table[metric](
- junc_pred_input, junc_gt, valid_mask)
+ junc_pred_input, junc_gt, valid_mask
+ )
for metric in self.heatmap_metric_lst:
self.metric_results[metric] = self.metric_table[metric](
- heatmap_pred, heatmap_gt, valid_mask)
+ heatmap_pred, heatmap_gt, valid_mask
+ )
for metric in self.pr_metric_lst:
if "nms" in metric:
self.metric_results[metric] = self.metric_table[metric](
- junc_pred_nms, junc_gt, valid_mask)
+ junc_pred_nms, junc_gt, valid_mask
+ )
else:
self.metric_results[metric] = self.metric_table[metric](
- junc_pred, junc_gt, valid_mask)
+ junc_pred, junc_gt, valid_mask
+ )
for metric in self.desc_metric_lst:
self.metric_results[metric] = self.metric_table[metric](
- line_points1, line_points2, desc_pred1,
- desc_pred2, valid_points)
+ line_points1, line_points2, desc_pred1, desc_pred2, valid_points
+ )
def _check_metrics(self):
- """ Check if all input metrics are valid. """
+ """Check if all input metrics are valid."""
flag = True
for metric in self.junc_metric_lst:
if not metric in self.supported_junc_metrics:
@@ -116,19 +141,31 @@ class Metrics(object):
class AverageMeter(object):
- def __init__(self, junc_metric_lst=None, heatmap_metric_lst=None,
- is_training=True, desc_metric_lst=None):
+ def __init__(
+ self,
+ junc_metric_lst=None,
+ heatmap_metric_lst=None,
+ is_training=True,
+ desc_metric_lst=None,
+ ):
# List supported metrics
- self.supported_junc_metrics = ["junc_precision", "junc_precision_nms",
- "junc_recall", "junc_recall_nms"]
- self.supported_heatmap_metrics = ["heatmap_precision",
- "heatmap_recall"]
+ self.supported_junc_metrics = [
+ "junc_precision",
+ "junc_precision_nms",
+ "junc_recall",
+ "junc_recall_nms",
+ ]
+ self.supported_heatmap_metrics = ["heatmap_precision", "heatmap_recall"]
self.supported_pr_metrics = ["junc_pr", "junc_nms_pr"]
self.supported_desc_metrics = ["matching_score"]
# Record loss in training mode
# if is_training:
self.supported_loss = [
- "junc_loss", "heatmap_loss", "descriptor_loss", "total_loss"]
+ "junc_loss",
+ "heatmap_loss",
+ "descriptor_loss",
+ "total_loss",
+ ]
self.is_training = is_training
@@ -144,21 +181,23 @@ class AverageMeter(object):
# For the descriptors, the default None assumes no desc metric at all
if desc_metric_lst is None:
self.desc_metric_lst = []
- elif desc_metric_lst == 'all':
+ elif desc_metric_lst == "all":
self.desc_metric_lst = self.supported_desc_metrics
else:
self.desc_metric_lst = desc_metric_lst
if not self._check_metrics():
- raise ValueError(
- "[Error] Some elements in the metric_lst are invalid.")
+ raise ValueError("[Error] Some elements in the metric_lst are invalid.")
# Initialize the results
self.metric_results = {}
- for key in (self.supported_junc_metrics
- + self.supported_heatmap_metrics
- + self.supported_loss + self.supported_desc_metrics):
- self.metric_results[key] = 0.
+ for key in (
+ self.supported_junc_metrics
+ + self.supported_heatmap_metrics
+ + self.supported_loss
+ + self.supported_desc_metrics
+ ):
+ self.metric_results[key] = 0.0
for key in self.supported_pr_metrics:
zero_lst = [0 for _ in range(50)]
self.metric_results[key] = {
@@ -167,7 +206,7 @@ class AverageMeter(object):
"fp": zero_lst,
"fn": zero_lst,
"precision": zero_lst,
- "recall": zero_lst
+ "recall": zero_lst,
}
# Initialize total count
@@ -176,18 +215,18 @@ class AverageMeter(object):
def update(self, metrics, loss_dict=None, num_samples=1):
# loss should be given in the training mode
if self.is_training and (loss_dict is None):
- raise ValueError(
- "[Error] loss info should be given in the training mode.")
+ raise ValueError("[Error] loss info should be given in the training mode.")
# update total counts
self.count += num_samples
# update all the metrics
- for met in (self.supported_junc_metrics
- + self.supported_heatmap_metrics
- + self.supported_desc_metrics):
- self.metric_results[met] += (num_samples
- * metrics.metric_results[met])
+ for met in (
+ self.supported_junc_metrics
+ + self.supported_heatmap_metrics
+ + self.supported_desc_metrics
+ ):
+ self.metric_results[met] += num_samples * metrics.metric_results[met]
# Update all the losses
for loss in loss_dict.keys():
@@ -200,8 +239,8 @@ class AverageMeter(object):
# Update each interval
for idx in range(len(self.metric_results[pr_met][key])):
self.metric_results[pr_met][key][idx] += (
- num_samples
- * metrics.metric_results[pr_met][key][idx])
+ num_samples * metrics.metric_results[pr_met][key][idx]
+ )
def average(self):
results = {}
@@ -217,21 +256,22 @@ class AverageMeter(object):
"fp": self.metric_results[met]["fp"],
"fn": self.metric_results[met]["fn"],
"precision": [],
- "recall": []
+ "recall": [],
}
for idx in range(len(self.metric_results[met]["precision"])):
met_results["precision"].append(
- self.metric_results[met]["precision"][idx]
- / self.count)
+ self.metric_results[met]["precision"][idx] / self.count
+ )
met_results["recall"].append(
- self.metric_results[met]["recall"][idx] / self.count)
+ self.metric_results[met]["recall"][idx] / self.count
+ )
results[met] = met_results
return results
def _check_metrics(self):
- """ Check if all input metrics are valid. """
+ """Check if all input metrics are valid."""
flag = True
for metric in self.junc_metric_lst:
if not metric in self.supported_junc_metrics:
@@ -250,7 +290,8 @@ class AverageMeter(object):
class junction_precision(object):
- """ Junction precision. """
+ """Junction precision."""
+
def __init__(self, detection_thresh):
self.detection_thresh = detection_thresh
@@ -262,8 +303,7 @@ class junction_precision(object):
# Deal with the corner case of the prediction
if np.sum(junc_pred) > 0:
- precision = (np.sum(junc_pred * junc_gt.squeeze())
- / np.sum(junc_pred))
+ precision = np.sum(junc_pred * junc_gt.squeeze()) / np.sum(junc_pred)
else:
precision = 0
@@ -271,7 +311,8 @@ class junction_precision(object):
class junction_recall(object):
- """ Junction recall. """
+ """Junction recall."""
+
def __init__(self, detection_thresh):
self.detection_thresh = detection_thresh
@@ -291,7 +332,8 @@ class junction_recall(object):
class junction_pr(object):
- """ Junction precision-recall info. """
+ """Junction precision-recall info."""
+
def __init__(self, num_threshold=50):
self.max = 0.4
step = self.max / num_threshold
@@ -316,12 +358,21 @@ class junction_pr(object):
# Compute tp, fp, tn, fn
junc_gt = junc_gt.squeeze()
tp = np.sum(junc_pred * junc_gt)
- tn = np.sum((junc_pred == 0).astype(np.float)
- * (junc_gt == 0).astype(np.float) * valid_mask)
- fp = np.sum((junc_pred == 1).astype(np.float)
- * (junc_gt == 0).astype(np.float) * valid_mask)
- fn = np.sum((junc_pred == 0).astype(np.float)
- * (junc_gt == 1).astype(np.float) * valid_mask)
+ tn = np.sum(
+ (junc_pred == 0).astype(np.float)
+ * (junc_gt == 0).astype(np.float)
+ * valid_mask
+ )
+ fp = np.sum(
+ (junc_pred == 1).astype(np.float)
+ * (junc_gt == 0).astype(np.float)
+ * valid_mask
+ )
+ fn = np.sum(
+ (junc_pred == 0).astype(np.float)
+ * (junc_gt == 1).astype(np.float)
+ * valid_mask
+ )
tp_lst.append(tp)
tn_lst.append(tn)
@@ -336,12 +387,13 @@ class junction_pr(object):
"fp": np.array(fp_lst),
"fn": np.array(fn_lst),
"precision": np.array(precision_lst),
- "recall": np.array(recall_lst)
+ "recall": np.array(recall_lst),
}
class heatmap_precision(object):
- """ Heatmap precision. """
+ """Heatmap precision."""
+
def __init__(self, prob_thresh):
self.prob_thresh = prob_thresh
@@ -352,16 +404,18 @@ class heatmap_precision(object):
# Deal with the corner case of the prediction
if np.sum(heatmap_pred) > 0:
- precision = (np.sum(heatmap_pred * heatmap_gt.squeeze())
- / np.sum(heatmap_pred))
+ precision = np.sum(heatmap_pred * heatmap_gt.squeeze()) / np.sum(
+ heatmap_pred
+ )
else:
- precision = 0.
+ precision = 0.0
return precision
class heatmap_recall(object):
- """ Heatmap recall. """
+ """Heatmap recall."""
+
def __init__(self, prob_thresh):
self.prob_thresh = prob_thresh
@@ -372,21 +426,20 @@ class heatmap_recall(object):
# Deal with the corner case of the ground truth
if np.sum(heatmap_gt) > 0:
- recall = (np.sum(heatmap_pred * heatmap_gt.squeeze())
- / np.sum(heatmap_gt))
+ recall = np.sum(heatmap_pred * heatmap_gt.squeeze()) / np.sum(heatmap_gt)
else:
- recall = 0.
+ recall = 0.0
return recall
class matching_score(object):
- """ Descriptors matching score. """
+ """Descriptors matching score."""
+
def __init__(self, grid_size):
self.grid_size = grid_size
- def __call__(self, points1, points2, desc_pred1,
- desc_pred2, line_indices):
+ def __call__(self, points1, points2, desc_pred1, desc_pred2, line_indices):
b_size, _, Hc, Wc = desc_pred1.size()
img_size = (Hc * self.grid_size, Wc * self.grid_size)
device = desc_pred1.device
@@ -396,32 +449,37 @@ class matching_score(object):
valid_points = line_indices.bool().flatten()
n_correct_points = torch.sum(valid_points).item()
if n_correct_points == 0:
- return torch.tensor(0., dtype=torch.float, device=device)
+ return torch.tensor(0.0, dtype=torch.float, device=device)
# Convert the keypoints to a grid suitable for interpolation
grid1 = keypoints_to_grid(points1, img_size)
grid2 = keypoints_to_grid(points2, img_size)
# Extract the descriptors
- desc1 = F.grid_sample(desc_pred1, grid1).permute(
- 0, 2, 3, 1).reshape(b_size * n_points, -1)[valid_points]
+ desc1 = (
+ F.grid_sample(desc_pred1, grid1)
+ .permute(0, 2, 3, 1)
+ .reshape(b_size * n_points, -1)[valid_points]
+ )
desc1 = F.normalize(desc1, dim=1)
- desc2 = F.grid_sample(desc_pred2, grid2).permute(
- 0, 2, 3, 1).reshape(b_size * n_points, -1)[valid_points]
+ desc2 = (
+ F.grid_sample(desc_pred2, grid2)
+ .permute(0, 2, 3, 1)
+ .reshape(b_size * n_points, -1)[valid_points]
+ )
desc2 = F.normalize(desc2, dim=1)
desc_dists = 2 - 2 * (desc1 @ desc2.t())
# Compute percentage of correct matches
matches0 = torch.min(desc_dists, dim=1)[1]
matches1 = torch.min(desc_dists, dim=0)[1]
- matching_score = (matches1[matches0]
- == torch.arange(len(matches0)).to(device))
+ matching_score = matches1[matches0] == torch.arange(len(matches0)).to(device)
matching_score = matching_score.float().mean()
return matching_score
def super_nms(prob_predictions, dist_thresh, prob_thresh=0.01, top_k=0):
- """ Non-maximum suppression adapted from SuperPoint. """
+ """Non-maximum suppression adapted from SuperPoint."""
# Iterate through batch dimension
im_h = prob_predictions.shape[1]
im_w = prob_predictions.shape[2]
@@ -430,17 +488,19 @@ def super_nms(prob_predictions, dist_thresh, prob_thresh=0.01, top_k=0):
# print(i)
prob_pred = prob_predictions[i, ...]
# Filter the points using prob_thresh
- coord = np.where(prob_pred >= prob_thresh) # HW format
- points = np.concatenate((coord[0][..., None], coord[1][..., None]),
- axis=1) # HW format
+ coord = np.where(prob_pred >= prob_thresh) # HW format
+ points = np.concatenate(
+ (coord[0][..., None], coord[1][..., None]), axis=1
+ ) # HW format
# Get the probability score
prob_score = prob_pred[points[:, 0], points[:, 1]]
# Perform super nms
# Modify the in_points to xy format (instead of HW format)
- in_points = np.concatenate((coord[1][..., None], coord[0][..., None],
- prob_score), axis=1).T
+ in_points = np.concatenate(
+ (coord[1][..., None], coord[0][..., None], prob_score), axis=1
+ ).T
keep_points_, keep_inds = nms_fast(in_points, im_h, im_w, dist_thresh)
# Remember to flip outputs back to HW format
keep_points = np.round(np.flip(keep_points_[:2, :], axis=0).T)
@@ -454,8 +514,9 @@ def super_nms(prob_predictions, dist_thresh, prob_thresh=0.01, top_k=0):
# Re-compose the probability map
output_map = np.zeros([im_h, im_w])
- output_map[keep_points[:, 0].astype(np.int),
- keep_points[:, 1].astype(np.int)] = keep_score.squeeze()
+ output_map[
+ keep_points[:, 0].astype(np.int), keep_points[:, 1].astype(np.int)
+ ] = keep_score.squeeze()
output_lst.append(output_map[None, ...])
@@ -506,14 +567,14 @@ def nms_fast(in_corners, H, W, dist_thresh):
inds[rcorners[1, i], rcorners[0, i]] = i
# Pad the border of the grid, so that we can NMS points near the border.
pad = dist_thresh
- grid = np.pad(grid, ((pad, pad), (pad, pad)), mode='constant')
+ grid = np.pad(grid, ((pad, pad), (pad, pad)), mode="constant")
# Iterate through points, highest to lowest conf, suppress neighborhood.
count = 0
for i, rc in enumerate(rcorners.T):
# Account for top and left padding.
pt = (rc[0] + pad, rc[1] + pad)
if grid[pt[1], pt[0]] == 1: # If not yet suppressed.
- grid[pt[1] - pad:pt[1] + pad + 1, pt[0] - pad:pt[0] + pad + 1] = 0
+ grid[pt[1] - pad : pt[1] + pad + 1, pt[0] - pad : pt[0] + pad + 1] = 0
grid[pt[1], pt[0]] = -1
count += 1
# Get all surviving -1's and return sorted array of remaining corners.
diff --git a/imcui/third_party/SOLD2/sold2/model/model_util.py b/third_party/SOLD2/sold2/model/model_util.py
similarity index 74%
rename from imcui/third_party/SOLD2/sold2/model/model_util.py
rename to third_party/SOLD2/sold2/model/model_util.py
index f70d80da40a72c207edfcfc1509e820846f0b731..037239e45d50123c7d679e36df5c6b0de314fa8b 100644
--- a/imcui/third_party/SOLD2/sold2/model/model_util.py
+++ b/third_party/SOLD2/sold2/model/model_util.py
@@ -9,7 +9,7 @@ from .nets.descriptor_decoder import SuperpointDescriptor
def get_model(model_cfg=None, loss_weights=None, mode="train"):
- """ Get model based on the model configuration. """
+ """Get model based on the model configuration."""
# Check dataset config is given
if model_cfg is None:
raise ValueError("[Error] The model config is required!")
@@ -18,26 +18,27 @@ def get_model(model_cfg=None, loss_weights=None, mode="train"):
print("\n\n\t--------Initializing model----------")
supported_arch = ["simple"]
if not model_cfg["model_architecture"] in supported_arch:
- raise ValueError(
- "[Error] The model architecture is not in supported arch!")
+ raise ValueError("[Error] The model architecture is not in supported arch!")
if model_cfg["model_architecture"] == "simple":
model = SOLD2Net(model_cfg)
else:
- raise ValueError(
- "[Error] The model architecture is not in supported arch!")
+ raise ValueError("[Error] The model architecture is not in supported arch!")
# Optionally register loss weights to the model
if mode == "train":
if loss_weights is not None:
for param_name, param in loss_weights.items():
if isinstance(param, nn.Parameter):
- print("\t [Debug] Adding %s with value %f to model"
- % (param_name, param.item()))
+ print(
+ "\t [Debug] Adding %s with value %f to model"
+ % (param_name, param.item())
+ )
model.register_parameter(param_name, param)
else:
raise ValueError(
- "[Error] the loss weights can not be None in dynamic weighting mode during training.")
+ "[Error] the loss weights can not be None in dynamic weighting mode during training."
+ )
# Display some summary info.
print("\tModel architecture: %s" % model_cfg["model_architecture"])
@@ -50,7 +51,8 @@ def get_model(model_cfg=None, loss_weights=None, mode="train"):
class SOLD2Net(nn.Module):
- """ Full network for SOLD². """
+ """Full network for SOLD²."""
+
def __init__(self, model_cfg):
super(SOLD2Net, self).__init__()
self.name = model_cfg["model_name"]
@@ -65,8 +67,7 @@ class SOLD2Net(nn.Module):
self.junction_decoder = self.get_junction_decoder()
# List supported heatmap decoder options
- self.supported_heatmap_decoder = ["pixel_shuffle",
- "pixel_shuffle_single"]
+ self.supported_heatmap_decoder = ["pixel_shuffle", "pixel_shuffle_single"]
self.heatmap_decoder = self.get_heatmap_decoder()
# List supported descriptor decoder options
@@ -96,10 +97,9 @@ class SOLD2Net(nn.Module):
return outputs
def get_backbone(self):
- """ Retrieve the backbone encoder network. """
+ """Retrieve the backbone encoder network."""
if not self.cfg["backbone"] in self.supported_backbone:
- raise ValueError(
- "[Error] The backbone selection is not supported.")
+ raise ValueError("[Error] The backbone selection is not supported.")
# lcnn backbone (stacked hourglass)
if self.cfg["backbone"] == "lcnn":
@@ -113,79 +113,73 @@ class SOLD2Net(nn.Module):
feat_channel = 128
else:
- raise ValueError(
- "[Error] The backbone selection is not supported.")
+ raise ValueError("[Error] The backbone selection is not supported.")
return backbone, feat_channel
def get_junction_decoder(self):
- """ Get the junction decoder. """
- if (not self.cfg["junction_decoder"]
- in self.supported_junction_decoder):
- raise ValueError(
- "[Error] The junction decoder selection is not supported.")
+ """Get the junction decoder."""
+ if not self.cfg["junction_decoder"] in self.supported_junction_decoder:
+ raise ValueError("[Error] The junction decoder selection is not supported.")
# superpoint decoder
if self.cfg["junction_decoder"] == "superpoint_decoder":
- decoder = SuperpointDecoder(self.feat_channel,
- self.cfg["backbone"])
+ decoder = SuperpointDecoder(self.feat_channel, self.cfg["backbone"])
else:
- raise ValueError(
- "[Error] The junction decoder selection is not supported.")
+ raise ValueError("[Error] The junction decoder selection is not supported.")
return decoder
def get_heatmap_decoder(self):
- """ Get the heatmap decoder. """
+ """Get the heatmap decoder."""
if not self.cfg["heatmap_decoder"] in self.supported_heatmap_decoder:
- raise ValueError(
- "[Error] The heatmap decoder selection is not supported.")
+ raise ValueError("[Error] The heatmap decoder selection is not supported.")
# Pixel_shuffle decoder
if self.cfg["heatmap_decoder"] == "pixel_shuffle":
if self.cfg["backbone"] == "lcnn":
- decoder = PixelShuffleDecoder(self.feat_channel,
- num_upsample=2)
+ decoder = PixelShuffleDecoder(self.feat_channel, num_upsample=2)
elif self.cfg["backbone"] == "superpoint":
- decoder = PixelShuffleDecoder(self.feat_channel,
- num_upsample=3)
+ decoder = PixelShuffleDecoder(self.feat_channel, num_upsample=3)
else:
raise ValueError("[Error] Unknown backbone option.")
# Pixel_shuffle decoder with single channel output
elif self.cfg["heatmap_decoder"] == "pixel_shuffle_single":
if self.cfg["backbone"] == "lcnn":
decoder = PixelShuffleDecoder(
- self.feat_channel, num_upsample=2, output_channel=1)
+ self.feat_channel, num_upsample=2, output_channel=1
+ )
elif self.cfg["backbone"] == "superpoint":
decoder = PixelShuffleDecoder(
- self.feat_channel, num_upsample=3, output_channel=1)
+ self.feat_channel, num_upsample=3, output_channel=1
+ )
else:
raise ValueError("[Error] Unknown backbone option.")
else:
- raise ValueError(
- "[Error] The heatmap decoder selection is not supported.")
+ raise ValueError("[Error] The heatmap decoder selection is not supported.")
return decoder
def get_descriptor_decoder(self):
- """ Get the descriptor decoder. """
- if (not self.cfg["descriptor_decoder"]
- in self.supported_descriptor_decoder):
+ """Get the descriptor decoder."""
+ if not self.cfg["descriptor_decoder"] in self.supported_descriptor_decoder:
raise ValueError(
- "[Error] The descriptor decoder selection is not supported.")
+ "[Error] The descriptor decoder selection is not supported."
+ )
# SuperPoint descriptor
if self.cfg["descriptor_decoder"] == "superpoint_descriptor":
decoder = SuperpointDescriptor(self.feat_channel)
else:
raise ValueError(
- "[Error] The descriptor decoder selection is not supported.")
+ "[Error] The descriptor decoder selection is not supported."
+ )
return decoder
def weight_init(m):
- """ Weight initialization function. """
+ """Weight initialization function."""
# Conv2D
if isinstance(m, nn.Conv2d):
init.xavier_normal_(m.weight.data)
diff --git a/imcui/third_party/TopicFM/configs/data/__init__.py b/third_party/SOLD2/sold2/model/nets/__init__.py
similarity index 100%
rename from imcui/third_party/TopicFM/configs/data/__init__.py
rename to third_party/SOLD2/sold2/model/nets/__init__.py
diff --git a/third_party/SOLD2/sold2/model/nets/backbone.py b/third_party/SOLD2/sold2/model/nets/backbone.py
new file mode 100644
index 0000000000000000000000000000000000000000..26b5a1366223b9148bc110ec28917cc1f81b5cbf
--- /dev/null
+++ b/third_party/SOLD2/sold2/model/nets/backbone.py
@@ -0,0 +1,62 @@
+import torch
+import torch.nn as nn
+
+from .lcnn_hourglass import MultitaskHead, hg
+
+
+class HourglassBackbone(nn.Module):
+ """Hourglass backbone."""
+
+ def __init__(
+ self, input_channel=1, depth=4, num_stacks=2, num_blocks=1, num_classes=5
+ ):
+ super(HourglassBackbone, self).__init__()
+ self.head = MultitaskHead
+ self.net = hg(
+ **{
+ "head": self.head,
+ "depth": depth,
+ "num_stacks": num_stacks,
+ "num_blocks": num_blocks,
+ "num_classes": num_classes,
+ "input_channels": input_channel,
+ }
+ )
+
+ def forward(self, input_images):
+ return self.net(input_images)[1]
+
+
+class SuperpointBackbone(nn.Module):
+ """SuperPoint backbone."""
+
+ def __init__(self):
+ super(SuperpointBackbone, self).__init__()
+ self.relu = torch.nn.ReLU(inplace=True)
+ self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
+ c1, c2, c3, c4 = 64, 64, 128, 128
+ # Shared Encoder.
+ self.conv1a = torch.nn.Conv2d(1, c1, kernel_size=3, stride=1, padding=1)
+ self.conv1b = torch.nn.Conv2d(c1, c1, kernel_size=3, stride=1, padding=1)
+ self.conv2a = torch.nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1)
+ self.conv2b = torch.nn.Conv2d(c2, c2, kernel_size=3, stride=1, padding=1)
+ self.conv3a = torch.nn.Conv2d(c2, c3, kernel_size=3, stride=1, padding=1)
+ self.conv3b = torch.nn.Conv2d(c3, c3, kernel_size=3, stride=1, padding=1)
+ self.conv4a = torch.nn.Conv2d(c3, c4, kernel_size=3, stride=1, padding=1)
+ self.conv4b = torch.nn.Conv2d(c4, c4, kernel_size=3, stride=1, padding=1)
+
+ def forward(self, input_images):
+ # Shared Encoder.
+ x = self.relu(self.conv1a(input_images))
+ x = self.relu(self.conv1b(x))
+ x = self.pool(x)
+ x = self.relu(self.conv2a(x))
+ x = self.relu(self.conv2b(x))
+ x = self.pool(x)
+ x = self.relu(self.conv3a(x))
+ x = self.relu(self.conv3b(x))
+ x = self.pool(x)
+ x = self.relu(self.conv4a(x))
+ x = self.relu(self.conv4b(x))
+
+ return x
diff --git a/third_party/SOLD2/sold2/model/nets/descriptor_decoder.py b/third_party/SOLD2/sold2/model/nets/descriptor_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..449bac37e6b0e6ff7802c0dbcea92f4829786578
--- /dev/null
+++ b/third_party/SOLD2/sold2/model/nets/descriptor_decoder.py
@@ -0,0 +1,20 @@
+import torch
+import torch.nn as nn
+
+
+class SuperpointDescriptor(nn.Module):
+ """Descriptor decoder based on the SuperPoint arcihtecture."""
+
+ def __init__(self, input_feat_dim=128):
+ super(SuperpointDescriptor, self).__init__()
+ self.relu = torch.nn.ReLU(inplace=True)
+ self.convPa = torch.nn.Conv2d(
+ input_feat_dim, 256, kernel_size=3, stride=1, padding=1
+ )
+ self.convPb = torch.nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0)
+
+ def forward(self, input_features):
+ feat = self.relu(self.convPa(input_features))
+ semi = self.convPb(feat)
+
+ return semi
diff --git a/imcui/third_party/SOLD2/sold2/model/nets/heatmap_decoder.py b/third_party/SOLD2/sold2/model/nets/heatmap_decoder.py
similarity index 68%
rename from imcui/third_party/SOLD2/sold2/model/nets/heatmap_decoder.py
rename to third_party/SOLD2/sold2/model/nets/heatmap_decoder.py
index bd5157ca740c8c7e25f2183b2a3c1fefa813deca..11828426a2852fb3e9ee3e6a3310ca89cbcd4d78 100644
--- a/imcui/third_party/SOLD2/sold2/model/nets/heatmap_decoder.py
+++ b/third_party/SOLD2/sold2/model/nets/heatmap_decoder.py
@@ -2,7 +2,8 @@ import torch.nn as nn
class PixelShuffleDecoder(nn.Module):
- """ Pixel shuffle decoder. """
+ """Pixel shuffle decoder."""
+
def __init__(self, input_feat_dim=128, num_upsample=2, output_channel=2):
super(PixelShuffleDecoder, self).__init__()
# Get channel parameters
@@ -10,35 +11,46 @@ class PixelShuffleDecoder(nn.Module):
# Define the pixel shuffle
self.pixshuffle = nn.PixelShuffle(2)
-
+
# Process the feature
self.conv_block_lst = []
# The input block
self.conv_block_lst.append(
nn.Sequential(
- nn.Conv2d(input_feat_dim, self.channel_conf[0],
- kernel_size=3, stride=1, padding=1),
+ nn.Conv2d(
+ input_feat_dim,
+ self.channel_conf[0],
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ ),
nn.BatchNorm2d(self.channel_conf[0]),
- nn.ReLU(inplace=True)
- ))
+ nn.ReLU(inplace=True),
+ )
+ )
# Intermediate block
for channel in self.channel_conf[1:-1]:
self.conv_block_lst.append(
nn.Sequential(
- nn.Conv2d(channel, channel, kernel_size=3,
- stride=1, padding=1),
+ nn.Conv2d(channel, channel, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(channel),
- nn.ReLU(inplace=True)
- ))
-
+ nn.ReLU(inplace=True),
+ )
+ )
+
# Output block
self.conv_block_lst.append(
- nn.Conv2d(self.channel_conf[-1], output_channel,
- kernel_size=1, stride=1, padding=0)
+ nn.Conv2d(
+ self.channel_conf[-1],
+ output_channel,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ )
)
self.conv_block_lst = nn.ModuleList(self.conv_block_lst)
-
+
# Get num of channels based on number of upsampling.
def get_channel_conf(self, num_upsample):
if num_upsample == 2:
@@ -52,7 +64,7 @@ class PixelShuffleDecoder(nn.Module):
for block in self.conv_block_lst[:-1]:
out = block(out)
out = self.pixshuffle(out)
-
+
# Output layer
out = self.conv_block_lst[-1](out)
diff --git a/imcui/third_party/SOLD2/sold2/model/nets/junction_decoder.py b/third_party/SOLD2/sold2/model/nets/junction_decoder.py
similarity index 54%
rename from imcui/third_party/SOLD2/sold2/model/nets/junction_decoder.py
rename to third_party/SOLD2/sold2/model/nets/junction_decoder.py
index d2bb649518896501c784940028a772d688c2b3a7..ea90a6b6821d994461dee83f85a6d2851d78e055 100644
--- a/imcui/third_party/SOLD2/sold2/model/nets/junction_decoder.py
+++ b/third_party/SOLD2/sold2/model/nets/junction_decoder.py
@@ -3,25 +3,27 @@ import torch.nn as nn
class SuperpointDecoder(nn.Module):
- """ Junction decoder based on the SuperPoint architecture. """
+ """Junction decoder based on the SuperPoint architecture."""
+
def __init__(self, input_feat_dim=128, backbone_name="lcnn"):
super(SuperpointDecoder, self).__init__()
self.relu = torch.nn.ReLU(inplace=True)
# Perform strided convolution when using lcnn backbone.
if backbone_name == "lcnn":
- self.convPa = torch.nn.Conv2d(input_feat_dim, 256, kernel_size=3,
- stride=2, padding=1)
+ self.convPa = torch.nn.Conv2d(
+ input_feat_dim, 256, kernel_size=3, stride=2, padding=1
+ )
elif backbone_name == "superpoint":
- self.convPa = torch.nn.Conv2d(input_feat_dim, 256, kernel_size=3,
- stride=1, padding=1)
+ self.convPa = torch.nn.Conv2d(
+ input_feat_dim, 256, kernel_size=3, stride=1, padding=1
+ )
else:
raise ValueError("[Error] Unknown backbone option.")
-
- self.convPb = torch.nn.Conv2d(256, 65, kernel_size=1,
- stride=1, padding=0)
+
+ self.convPb = torch.nn.Conv2d(256, 65, kernel_size=1, stride=1, padding=0)
def forward(self, input_features):
feat = self.relu(self.convPa(input_features))
semi = self.convPb(feat)
- return semi
\ No newline at end of file
+ return semi
diff --git a/imcui/third_party/SOLD2/sold2/model/nets/lcnn_hourglass.py b/third_party/SOLD2/sold2/model/nets/lcnn_hourglass.py
similarity index 93%
rename from imcui/third_party/SOLD2/sold2/model/nets/lcnn_hourglass.py
rename to third_party/SOLD2/sold2/model/nets/lcnn_hourglass.py
index a9dc78eef34e7ee146166b1b66c10070799d63f3..c25594d9dda28624337546fd8fec27e1c59b452f 100644
--- a/imcui/third_party/SOLD2/sold2/model/nets/lcnn_hourglass.py
+++ b/third_party/SOLD2/sold2/model/nets/lcnn_hourglass.py
@@ -39,8 +39,7 @@ class Bottleneck2D(nn.Module):
self.bn1 = nn.BatchNorm2d(inplanes)
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1)
self.bn2 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
- stride=stride, padding=1)
+ self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1)
self.bn3 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1)
self.relu = nn.ReLU(inplace=True)
@@ -116,15 +115,17 @@ class Hourglass(nn.Module):
class HourglassNet(nn.Module):
"""Hourglass model from Newell et al ECCV 2016"""
- def __init__(self, block, head, depth, num_stacks, num_blocks,
- num_classes, input_channels):
+ def __init__(
+ self, block, head, depth, num_stacks, num_blocks, num_classes, input_channels
+ ):
super(HourglassNet, self).__init__()
self.inplanes = 64
self.num_feats = 128
self.num_stacks = num_stacks
- self.conv1 = nn.Conv2d(input_channels, self.inplanes, kernel_size=7,
- stride=2, padding=3)
+ self.conv1 = nn.Conv2d(
+ input_channels, self.inplanes, kernel_size=7, stride=2, padding=3
+ )
self.bn1 = nn.BatchNorm2d(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_residual(block, self.inplanes, 1)
@@ -215,12 +216,11 @@ class HourglassNet(nn.Module):
def hg(**kwargs):
model = HourglassNet(
Bottleneck2D,
- head=kwargs.get("head",
- lambda c_in, c_out: nn.Conv2D(c_in, c_out, 1)),
+ head=kwargs.get("head", lambda c_in, c_out: nn.Conv2D(c_in, c_out, 1)),
depth=kwargs["depth"],
num_stacks=kwargs["num_stacks"],
num_blocks=kwargs["num_blocks"],
num_classes=kwargs["num_classes"],
- input_channels=kwargs["input_channels"]
+ input_channels=kwargs["input_channels"],
)
return model
diff --git a/imcui/third_party/TopicFM/viz/configs/__init__.py b/third_party/SOLD2/sold2/postprocess/__init__.py
similarity index 100%
rename from imcui/third_party/TopicFM/viz/configs/__init__.py
rename to third_party/SOLD2/sold2/postprocess/__init__.py
diff --git a/imcui/third_party/SOLD2/sold2/postprocess/convert_homography_results.py b/third_party/SOLD2/sold2/postprocess/convert_homography_results.py
similarity index 66%
rename from imcui/third_party/SOLD2/sold2/postprocess/convert_homography_results.py
rename to third_party/SOLD2/sold2/postprocess/convert_homography_results.py
index 352eebbde00f6d8a9c20517dccd7024fd0758ffd..61045777bde0190e872c1c3983f1172ef36d8f1c 100644
--- a/imcui/third_party/SOLD2/sold2/postprocess/convert_homography_results.py
+++ b/third_party/SOLD2/sold2/postprocess/convert_homography_results.py
@@ -2,6 +2,7 @@
Convert the aggregation results from the homography adaptation to GT labels.
"""
import sys
+
sys.path.append("../")
import os
import yaml
@@ -17,9 +18,10 @@ from model.metrics import super_nms
from misc.train_utils import parse_h5_data
-def convert_raw_exported_predictions(input_data, grid_size=8,
- detect_thresh=1/65, topk=300):
- """ Convert the exported junctions and heatmaps predictions
+def convert_raw_exported_predictions(
+ input_data, grid_size=8, detect_thresh=1 / 65, topk=300
+):
+ """Convert the exported junctions and heatmaps predictions
to a standard format.
Arguments:
input_data: the raw data (dict) decoded from the hdf5 dataset
@@ -31,28 +33,29 @@ def convert_raw_exported_predictions(input_data, grid_size=8,
# Check the input_data is from (1) single prediction,
# or (2) homography adaptation.
# Homography adaptation raw predictions
- if (("junc_prob_mean" in input_data.keys())
- and ("heatmap_prob_mean" in input_data.keys())):
+ if ("junc_prob_mean" in input_data.keys()) and (
+ "heatmap_prob_mean" in input_data.keys()
+ ):
# Get the junction predictions and convert if to Nx2 format
junc_prob = input_data["junc_prob_mean"]
junc_pred_np = junc_prob[None, ...]
- junc_pred_np_nms = super_nms(junc_pred_np, grid_size,
- detect_thresh, topk)
+ junc_pred_np_nms = super_nms(junc_pred_np, grid_size, detect_thresh, topk)
junctions = np.where(junc_pred_np_nms.squeeze())
- junc_points_pred = np.concatenate([junctions[0][..., None],
- junctions[1][..., None]], axis=-1)
+ junc_points_pred = np.concatenate(
+ [junctions[0][..., None], junctions[1][..., None]], axis=-1
+ )
# Get the heatmap predictions
heatmap_pred = input_data["heatmap_prob_mean"].squeeze()
valid_mask = np.ones(heatmap_pred.shape, dtype=np.int32)
-
+
# Single predictions
else:
# Get the junction point predictions and convert to Nx2 format
junc_points_pred = np.where(input_data["junc_pred_nms"])
junc_points_pred = np.concatenate(
- [junc_points_pred[0][..., None],
- junc_points_pred[1][..., None]], axis=-1)
+ [junc_points_pred[0][..., None], junc_points_pred[1][..., None]], axis=-1
+ )
# Get the heatmap predictions
heatmap_pred = input_data["heatmap_pred"]
@@ -61,34 +64,29 @@ def convert_raw_exported_predictions(input_data, grid_size=8,
return {
"junctions_pred": junc_points_pred,
"heatmap_pred": heatmap_pred,
- "valid_mask": valid_mask
+ "valid_mask": valid_mask,
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
- parser.add_argument("input_dataset", type=str,
- help="Name of the exported dataset.")
- parser.add_argument("output_dataset", type=str,
- help="Name of the output dataset.")
- parser.add_argument("config", type=str,
- help="Path to the model config.")
- args = parser.parse_args()
-
+ parser.add_argument("input_dataset", type=str, help="Name of the exported dataset.")
+ parser.add_argument("output_dataset", type=str, help="Name of the output dataset.")
+ parser.add_argument("config", type=str, help="Path to the model config.")
+ args = parser.parse_args()
+
# Define the path to the input exported dataset
- exported_dataset_path = os.path.join(cfg.export_dataroot,
- args.input_dataset)
+ exported_dataset_path = os.path.join(cfg.export_dataroot, args.input_dataset)
if not os.path.exists(exported_dataset_path):
raise ValueError("Missing input dataset: " + exported_dataset_path)
exported_dataset = h5py.File(exported_dataset_path, "r")
# Define the output path for the results
- output_dataset_path = os.path.join(cfg.export_dataroot,
- args.output_dataset)
+ output_dataset_path = os.path.join(cfg.export_dataroot, args.output_dataset)
device = torch.device("cuda")
nms_device = torch.device("cuda")
-
+
# Read the config file
if not os.path.exists(args.config):
raise ValueError("Missing config file: " + args.config)
@@ -96,41 +94,43 @@ if __name__ == "__main__":
config = yaml.safe_load(f)
model_cfg = config["model_cfg"]
line_detector_cfg = config["line_detector_cfg"]
-
+
# Initialize the line detection module
line_detector = LineSegmentDetectionModule(**line_detector_cfg)
# Iterate through all the dataset keys
with h5py.File(output_dataset_path, "w") as output_dataset:
- for idx, output_key in enumerate(tqdm(list(exported_dataset.keys()),
- ascii=True)):
+ for idx, output_key in enumerate(
+ tqdm(list(exported_dataset.keys()), ascii=True)
+ ):
# Get the data
data = parse_h5_data(exported_dataset[output_key])
# Preprocess the data
converted_data = convert_raw_exported_predictions(
- data, grid_size=model_cfg["grid_size"],
- detect_thresh=model_cfg["detection_thresh"])
+ data,
+ grid_size=model_cfg["grid_size"],
+ detect_thresh=model_cfg["detection_thresh"],
+ )
junctions_pred_raw = converted_data["junctions_pred"]
heatmap_pred = converted_data["heatmap_pred"]
valid_mask = converted_data["valid_mask"]
line_map_pred, junctions_pred, heatmap_pred = line_detector.detect(
- junctions_pred_raw, heatmap_pred, device=device)
+ junctions_pred_raw, heatmap_pred, device=device
+ )
if isinstance(line_map_pred, torch.Tensor):
line_map_pred = line_map_pred.cpu().numpy()
if isinstance(junctions_pred, torch.Tensor):
junctions_pred = junctions_pred.cpu().numpy()
if isinstance(heatmap_pred, torch.Tensor):
heatmap_pred = heatmap_pred.cpu().numpy()
-
- output_data = {"junctions": junctions_pred,
- "line_map": line_map_pred}
+
+ output_data = {"junctions": junctions_pred, "line_map": line_map_pred}
# Record it to the h5 dataset
f_group = output_dataset.create_group(output_key)
# Store data
for key, output_data in output_data.items():
- f_group.create_dataset(key, data=output_data,
- compression="gzip")
+ f_group.create_dataset(key, data=output_data, compression="gzip")
diff --git a/imcui/third_party/SOLD2/sold2/train.py b/third_party/SOLD2/sold2/train.py
similarity index 51%
rename from imcui/third_party/SOLD2/sold2/train.py
rename to third_party/SOLD2/sold2/train.py
index 2064e00e6d192f9202f011c3626d6f53c4fe6270..148c9b23464d975f1efc03ea459c82d4a0759b05 100644
--- a/imcui/third_party/SOLD2/sold2/train.py
+++ b/third_party/SOLD2/sold2/train.py
@@ -15,12 +15,15 @@ from .model.model_util import get_model
from .model.loss import TotalLoss, get_loss_and_weights
from .model.metrics import AverageMeter, Metrics, super_nms
from .model.lr_scheduler import get_lr_scheduler
-from .misc.train_utils import (convert_image, get_latest_checkpoint,
- remove_old_checkpoints)
+from .misc.train_utils import (
+ convert_image,
+ get_latest_checkpoint,
+ remove_old_checkpoints,
+)
def customized_collate_fn(batch):
- """ Customized collate_fn. """
+ """Customized collate_fn."""
batch_keys = ["image", "junction_map", "heatmap", "valid_mask"]
list_keys = ["junctions", "line_map"]
@@ -34,14 +37,14 @@ def customized_collate_fn(batch):
def restore_weights(model, state_dict, strict=True):
- """ Restore weights in compatible mode. """
+ """Restore weights in compatible mode."""
# Try to directly load state dict
try:
model.load_state_dict(state_dict, strict=strict)
# Deal with some version compatibility issue (catch version incompatible)
except:
err = model.load_state_dict(state_dict, strict=False)
-
+
# missing keys are those in model but not in state_dict
missing_keys = err.missing_keys
# Unexpected keys are those in state_dict but not in model
@@ -53,12 +56,12 @@ def restore_weights(model, state_dict, strict=True):
dict_keys = [_ for _ in unexpected_keys if not "tracked" in _]
model_dict[key] = state_dict[dict_keys[idx]]
model.load_state_dict(model_dict)
-
+
return model
def train_net(args, dataset_cfg, model_cfg, output_path):
- """ Main training function. """
+ """Main training function."""
# Add some version compatibility check
if model_cfg.get("weighting_policy") is None:
# Default to static
@@ -74,44 +77,50 @@ def train_net(args, dataset_cfg, model_cfg, output_path):
test_dataset, test_collate_fn = get_dataset("test", dataset_cfg)
# Create the dataloader
- train_loader = DataLoader(train_dataset,
- batch_size=train_cfg["batch_size"],
- num_workers=8,
- shuffle=True, pin_memory=True,
- collate_fn=train_collate_fn)
- test_loader = DataLoader(test_dataset,
- batch_size=test_cfg.get("batch_size", 1),
- num_workers=test_cfg.get("num_workers", 1),
- shuffle=False, pin_memory=False,
- collate_fn=test_collate_fn)
+ train_loader = DataLoader(
+ train_dataset,
+ batch_size=train_cfg["batch_size"],
+ num_workers=8,
+ shuffle=True,
+ pin_memory=True,
+ collate_fn=train_collate_fn,
+ )
+ test_loader = DataLoader(
+ test_dataset,
+ batch_size=test_cfg.get("batch_size", 1),
+ num_workers=test_cfg.get("num_workers", 1),
+ shuffle=False,
+ pin_memory=False,
+ collate_fn=test_collate_fn,
+ )
print("\t Successfully intialized dataloaders.")
-
# Get the loss function and weight first
loss_funcs, loss_weights = get_loss_and_weights(model_cfg)
# If resume.
if args.resume:
# Create model and load the state dict
- checkpoint = get_latest_checkpoint(args.resume_path,
- args.checkpoint_name)
+ checkpoint = get_latest_checkpoint(args.resume_path, args.checkpoint_name)
model = get_model(model_cfg, loss_weights)
model = restore_weights(model, checkpoint["model_state_dict"])
model = model.cuda()
optimizer = torch.optim.Adam(
- [{"params": model.parameters(),
- "initial_lr": model_cfg["learning_rate"]}],
- model_cfg["learning_rate"],
- amsgrad=True)
+ [{"params": model.parameters(), "initial_lr": model_cfg["learning_rate"]}],
+ model_cfg["learning_rate"],
+ amsgrad=True,
+ )
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
# Optionally get the learning rate scheduler
scheduler = get_lr_scheduler(
lr_decay=model_cfg.get("lr_decay", False),
lr_decay_cfg=model_cfg.get("lr_decay_cfg", None),
- optimizer=optimizer)
+ optimizer=optimizer,
+ )
# If we start to use learning rate scheduler from the middle
- if ((scheduler is not None)
- and (checkpoint.get("scheduler_state_dict", None) is not None)):
+ if (scheduler is not None) and (
+ checkpoint.get("scheduler_state_dict", None) is not None
+ ):
scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
start_epoch = checkpoint["epoch"] + 1
# Initialize all the components.
@@ -121,40 +130,45 @@ def train_net(args, dataset_cfg, model_cfg, output_path):
# Optionally get the pretrained wieghts
if args.pretrained:
print("\t [Debug] Loading pretrained weights...")
- checkpoint = get_latest_checkpoint(args.pretrained_path,
- args.checkpoint_name)
+ checkpoint = get_latest_checkpoint(
+ args.pretrained_path, args.checkpoint_name
+ )
# If auto weighting restore from non-auto weighting
- model = restore_weights(model, checkpoint["model_state_dict"],
- strict=False)
+ model = restore_weights(model, checkpoint["model_state_dict"], strict=False)
print("\t [Debug] Finished loading pretrained weights!")
-
+
model = model.cuda()
optimizer = torch.optim.Adam(
- [{"params": model.parameters(),
- "initial_lr": model_cfg["learning_rate"]}],
- model_cfg["learning_rate"],
- amsgrad=True)
+ [{"params": model.parameters(), "initial_lr": model_cfg["learning_rate"]}],
+ model_cfg["learning_rate"],
+ amsgrad=True,
+ )
# Optionally get the learning rate scheduler
scheduler = get_lr_scheduler(
lr_decay=model_cfg.get("lr_decay", False),
lr_decay_cfg=model_cfg.get("lr_decay_cfg", None),
- optimizer=optimizer)
+ optimizer=optimizer,
+ )
start_epoch = 0
-
+
print("\t Successfully initialized model")
# Define the total loss
policy = model_cfg.get("weighting_policy", "static")
loss_func = TotalLoss(loss_funcs, loss_weights, policy).cuda()
if "descriptor_decoder" in model_cfg:
- metric_func = Metrics(model_cfg["detection_thresh"],
- model_cfg["prob_thresh"],
- model_cfg["descriptor_loss_cfg"]["grid_size"],
- desc_metric_lst='all')
+ metric_func = Metrics(
+ model_cfg["detection_thresh"],
+ model_cfg["prob_thresh"],
+ model_cfg["descriptor_loss_cfg"]["grid_size"],
+ desc_metric_lst="all",
+ )
else:
- metric_func = Metrics(model_cfg["detection_thresh"],
- model_cfg["prob_thresh"],
- model_cfg["grid_size"])
+ metric_func = Metrics(
+ model_cfg["detection_thresh"],
+ model_cfg["prob_thresh"],
+ model_cfg["grid_size"],
+ )
# Define the summary writer
logdir = os.path.join(output_path, "log")
@@ -176,7 +190,8 @@ def train_net(args, dataset_cfg, model_cfg, output_path):
metric_func=metric_func,
train_loader=train_loader,
writer=writer,
- epoch=epoch)
+ epoch=epoch,
+ )
# Do the validation
print("\n\n================== Validation ==================")
@@ -187,21 +202,22 @@ def train_net(args, dataset_cfg, model_cfg, output_path):
metric_func=metric_func,
val_loader=test_loader,
writer=writer,
- epoch=epoch)
+ epoch=epoch,
+ )
# Update the scheduler
if scheduler is not None:
scheduler.step()
# Save checkpoints
- file_name = os.path.join(output_path,
- "checkpoint-epoch%03d-end.tar"%(epoch))
+ file_name = os.path.join(output_path, "checkpoint-epoch%03d-end.tar" % (epoch))
print("[Info] Saving checkpoint %s ..." % file_name)
save_dict = {
"epoch": epoch,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
- "model_cfg": model_cfg}
+ "model_cfg": model_cfg,
+ }
if scheduler is not None:
save_dict.update({"scheduler_state_dict": scheduler.state_dict()})
torch.save(save_dict, file_name)
@@ -210,16 +226,17 @@ def train_net(args, dataset_cfg, model_cfg, output_path):
remove_old_checkpoints(output_path, model_cfg.get("max_ckpt", 15))
-def train_single_epoch(model, model_cfg, optimizer, loss_func, metric_func,
- train_loader, writer, epoch):
- """ Train for one epoch. """
+def train_single_epoch(
+ model, model_cfg, optimizer, loss_func, metric_func, train_loader, writer, epoch
+):
+ """Train for one epoch."""
# Switch the model to training mode
model.train()
# Initialize the average meter
compute_descriptors = loss_func.compute_descriptors
if compute_descriptors:
- average_meter = AverageMeter(is_training=True, desc_metric_lst='all')
+ average_meter = AverageMeter(is_training=True, desc_metric_lst="all")
else:
average_meter = AverageMeter(is_training=True)
@@ -244,11 +261,23 @@ def train_single_epoch(model, model_cfg, optimizer, loss_func, metric_func,
# Compute losses
losses = loss_func.forward_descriptors(
- outputs["junctions"], outputs2["junctions"],
- junc_map, junc_map2, outputs["heatmap"], outputs2["heatmap"],
- heatmap, heatmap2, line_points, line_points2,
- line_indices, outputs['descriptors'], outputs2['descriptors'],
- epoch, valid_mask, valid_mask2)
+ outputs["junctions"],
+ outputs2["junctions"],
+ junc_map,
+ junc_map2,
+ outputs["heatmap"],
+ outputs2["heatmap"],
+ heatmap,
+ heatmap2,
+ line_points,
+ line_points2,
+ line_indices,
+ outputs["descriptors"],
+ outputs2["descriptors"],
+ epoch,
+ valid_mask,
+ valid_mask2,
+ )
else:
junc_map = data["junction_map"].cuda()
heatmap = data["heatmap"].cuda()
@@ -260,58 +289,74 @@ def train_single_epoch(model, model_cfg, optimizer, loss_func, metric_func,
# Compute losses
losses = loss_func(
- outputs["junctions"], junc_map,
- outputs["heatmap"], heatmap,
- valid_mask)
-
+ outputs["junctions"], junc_map, outputs["heatmap"], heatmap, valid_mask
+ )
+
total_loss = losses["total_loss"]
# Update the model
optimizer.zero_grad()
- total_loss.backward()
+ total_loss.backward()
optimizer.step()
# Compute the global step
global_step = epoch * len(train_loader) + idx
############## Measure the metric error #########################
# Only do this when needed
- if (((idx % model_cfg["disp_freq"]) == 0)
- or ((idx % model_cfg["summary_freq"]) == 0)):
+ if ((idx % model_cfg["disp_freq"]) == 0) or (
+ (idx % model_cfg["summary_freq"]) == 0
+ ):
junc_np = convert_junc_predictions(
- outputs["junctions"], model_cfg["grid_size"],
- model_cfg["detection_thresh"], 300)
+ outputs["junctions"],
+ model_cfg["grid_size"],
+ model_cfg["detection_thresh"],
+ 300,
+ )
junc_map_np = junc_map.cpu().numpy().transpose(0, 2, 3, 1)
# Always fetch only one channel (compatible with L1, L2, and CE)
if outputs["heatmap"].shape[1] == 2:
- heatmap_np = softmax(outputs["heatmap"].detach(),
- dim=1).cpu().numpy()
+ heatmap_np = softmax(outputs["heatmap"].detach(), dim=1).cpu().numpy()
heatmap_np = heatmap_np.transpose(0, 2, 3, 1)[:, :, :, 1:]
else:
heatmap_np = torch.sigmoid(outputs["heatmap"].detach())
heatmap_np = heatmap_np.cpu().numpy().transpose(0, 2, 3, 1)
-
+
heatmap_gt_np = heatmap.cpu().numpy().transpose(0, 2, 3, 1)
valid_mask_np = valid_mask.cpu().numpy().transpose(0, 2, 3, 1)
# Evaluate metric results
if compute_descriptors:
metric_func.evaluate(
- junc_np["junc_pred"], junc_np["junc_pred_nms"],
- junc_map_np, heatmap_np, heatmap_gt_np, valid_mask_np,
- line_points, line_points2, outputs["descriptors"],
- outputs2["descriptors"], line_indices)
+ junc_np["junc_pred"],
+ junc_np["junc_pred_nms"],
+ junc_map_np,
+ heatmap_np,
+ heatmap_gt_np,
+ valid_mask_np,
+ line_points,
+ line_points2,
+ outputs["descriptors"],
+ outputs2["descriptors"],
+ line_indices,
+ )
else:
metric_func.evaluate(
- junc_np["junc_pred"], junc_np["junc_pred_nms"],
- junc_map_np, heatmap_np, heatmap_gt_np, valid_mask_np)
+ junc_np["junc_pred"],
+ junc_np["junc_pred_nms"],
+ junc_map_np,
+ heatmap_np,
+ heatmap_gt_np,
+ valid_mask_np,
+ )
# Update average meter
junc_loss = losses["junc_loss"].item()
heatmap_loss = losses["heatmap_loss"].item()
loss_dict = {
"junc_loss": junc_loss,
"heatmap_loss": heatmap_loss,
- "total_loss": total_loss.item()}
+ "total_loss": total_loss.item(),
+ }
if compute_descriptors:
descriptor_loss = losses["descriptor_loss"].item()
loss_dict["descriptor_loss"] = losses["descriptor_loss"].item()
@@ -323,34 +368,75 @@ def train_single_epoch(model, model_cfg, optimizer, loss_func, metric_func,
results = metric_func.metric_results
average = average_meter.average()
# Get gpu memory usage in GB
- gpu_mem_usage = torch.cuda.max_memory_allocated() / (1024 ** 3)
+ gpu_mem_usage = torch.cuda.max_memory_allocated() / (1024**3)
if compute_descriptors:
- print("Epoch [%d / %d] Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f), descriptor_loss=%.4f (%.4f), gpu_mem=%.4fGB"
- % (epoch, model_cfg["epochs"], idx, len(train_loader),
- total_loss.item(), average["total_loss"], junc_loss,
- average["junc_loss"], heatmap_loss,
- average["heatmap_loss"], descriptor_loss,
- average["descriptor_loss"], gpu_mem_usage))
+ print(
+ "Epoch [%d / %d] Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f), descriptor_loss=%.4f (%.4f), gpu_mem=%.4fGB"
+ % (
+ epoch,
+ model_cfg["epochs"],
+ idx,
+ len(train_loader),
+ total_loss.item(),
+ average["total_loss"],
+ junc_loss,
+ average["junc_loss"],
+ heatmap_loss,
+ average["heatmap_loss"],
+ descriptor_loss,
+ average["descriptor_loss"],
+ gpu_mem_usage,
+ )
+ )
else:
- print("Epoch [%d / %d] Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f), gpu_mem=%.4fGB"
- % (epoch, model_cfg["epochs"], idx, len(train_loader),
- total_loss.item(), average["total_loss"],
- junc_loss, average["junc_loss"], heatmap_loss,
- average["heatmap_loss"], gpu_mem_usage))
- print("\t Junction precision=%.4f (%.4f) / recall=%.4f (%.4f)"
- % (results["junc_precision"], average["junc_precision"],
- results["junc_recall"], average["junc_recall"]))
- print("\t Junction nms precision=%.4f (%.4f) / recall=%.4f (%.4f)"
- % (results["junc_precision_nms"],
- average["junc_precision_nms"],
- results["junc_recall_nms"], average["junc_recall_nms"]))
- print("\t Heatmap precision=%.4f (%.4f) / recall=%.4f (%.4f)"
- %(results["heatmap_precision"],
+ print(
+ "Epoch [%d / %d] Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f), gpu_mem=%.4fGB"
+ % (
+ epoch,
+ model_cfg["epochs"],
+ idx,
+ len(train_loader),
+ total_loss.item(),
+ average["total_loss"],
+ junc_loss,
+ average["junc_loss"],
+ heatmap_loss,
+ average["heatmap_loss"],
+ gpu_mem_usage,
+ )
+ )
+ print(
+ "\t Junction precision=%.4f (%.4f) / recall=%.4f (%.4f)"
+ % (
+ results["junc_precision"],
+ average["junc_precision"],
+ results["junc_recall"],
+ average["junc_recall"],
+ )
+ )
+ print(
+ "\t Junction nms precision=%.4f (%.4f) / recall=%.4f (%.4f)"
+ % (
+ results["junc_precision_nms"],
+ average["junc_precision_nms"],
+ results["junc_recall_nms"],
+ average["junc_recall_nms"],
+ )
+ )
+ print(
+ "\t Heatmap precision=%.4f (%.4f) / recall=%.4f (%.4f)"
+ % (
+ results["heatmap_precision"],
average["heatmap_precision"],
- results["heatmap_recall"], average["heatmap_recall"]))
+ results["heatmap_recall"],
+ average["heatmap_recall"],
+ )
+ )
if compute_descriptors:
- print("\t Descriptors matching score=%.4f (%.4f)"
- %(results["matching_score"], average["matching_score"]))
+ print(
+ "\t Descriptors matching score=%.4f (%.4f)"
+ % (results["matching_score"], average["matching_score"])
+ )
# Record summaries
if (idx % model_cfg["summary_freq"]) == 0:
@@ -362,7 +448,8 @@ def train_single_epoch(model, model_cfg, optimizer, loss_func, metric_func,
"heatmap_loss": heatmap_loss,
"total_loss": total_loss.detach().cpu().numpy(),
"metrics": results,
- "average": average}
+ "average": average,
+ }
# Add descriptor terms
if compute_descriptors:
scalar_summaries["descriptor_loss"] = descriptor_loss
@@ -374,10 +461,13 @@ def train_single_epoch(model, model_cfg, optimizer, loss_func, metric_func,
scalar_summaries["reg_loss"] = losses["reg_loss"].item()
num_images = 3
- junc_pred_binary = (junc_np["junc_pred"][:num_images, ...]
- > model_cfg["detection_thresh"])
- junc_pred_nms_binary = (junc_np["junc_pred_nms"][:num_images, ...]
- > model_cfg["detection_thresh"])
+ junc_pred_binary = (
+ junc_np["junc_pred"][:num_images, ...] > model_cfg["detection_thresh"]
+ )
+ junc_pred_nms_binary = (
+ junc_np["junc_pred_nms"][:num_images, ...]
+ > model_cfg["detection_thresh"]
+ )
image_summaries = {
"image": input_images.cpu().numpy()[:num_images, ...],
"valid_mask": valid_mask_np[:num_images, ...],
@@ -386,22 +476,23 @@ def train_single_epoch(model, model_cfg, optimizer, loss_func, metric_func,
"junc_map_gt": junc_map_np[:num_images, ...],
"junc_prob_map": junc_np["junc_prob"][:num_images, ...],
"heatmap_pred": heatmap_np[:num_images, ...],
- "heatmap_gt": heatmap_gt_np[:num_images, ...]}
+ "heatmap_gt": heatmap_gt_np[:num_images, ...],
+ }
# Record the training summary
record_train_summaries(
- writer, global_step, scalars=scalar_summaries,
- images=image_summaries)
+ writer, global_step, scalars=scalar_summaries, images=image_summaries
+ )
def validate(model, model_cfg, loss_func, metric_func, val_loader, writer, epoch):
- """ Validation. """
+ """Validation."""
# Switch the model to eval mode
model.eval()
# Initialize the average meter
compute_descriptors = loss_func.compute_descriptors
if compute_descriptors:
- average_meter = AverageMeter(is_training=True, desc_metric_lst='all')
+ average_meter = AverageMeter(is_training=True, desc_metric_lst="all")
else:
average_meter = AverageMeter(is_training=True)
@@ -427,11 +518,23 @@ def validate(model, model_cfg, loss_func, metric_func, val_loader, writer, epoch
# Compute losses
losses = loss_func.forward_descriptors(
- outputs["junctions"], outputs2["junctions"],
- junc_map, junc_map2, outputs["heatmap"],
- outputs2["heatmap"], heatmap, heatmap2, line_points,
- line_points2, line_indices, outputs['descriptors'],
- outputs2['descriptors'], epoch, valid_mask, valid_mask2)
+ outputs["junctions"],
+ outputs2["junctions"],
+ junc_map,
+ junc_map2,
+ outputs["heatmap"],
+ outputs2["heatmap"],
+ heatmap,
+ heatmap2,
+ line_points,
+ line_points2,
+ line_indices,
+ outputs["descriptors"],
+ outputs2["descriptors"],
+ epoch,
+ valid_mask,
+ valid_mask2,
+ )
else:
junc_map = data["junction_map"].cuda()
heatmap = data["heatmap"].cuda()
@@ -444,47 +547,70 @@ def validate(model, model_cfg, loss_func, metric_func, val_loader, writer, epoch
# Compute losses
losses = loss_func(
- outputs["junctions"], junc_map,
- outputs["heatmap"], heatmap,
- valid_mask)
+ outputs["junctions"],
+ junc_map,
+ outputs["heatmap"],
+ heatmap,
+ valid_mask,
+ )
total_loss = losses["total_loss"]
############## Measure the metric error #########################
junc_np = convert_junc_predictions(
- outputs["junctions"], model_cfg["grid_size"],
- model_cfg["detection_thresh"], 300)
+ outputs["junctions"],
+ model_cfg["grid_size"],
+ model_cfg["detection_thresh"],
+ 300,
+ )
junc_map_np = junc_map.cpu().numpy().transpose(0, 2, 3, 1)
# Always fetch only one channel (compatible with L1, L2, and CE)
if outputs["heatmap"].shape[1] == 2:
- heatmap_np = softmax(outputs["heatmap"].detach(),
- dim=1).cpu().numpy().transpose(0, 2, 3, 1)
+ heatmap_np = (
+ softmax(outputs["heatmap"].detach(), dim=1)
+ .cpu()
+ .numpy()
+ .transpose(0, 2, 3, 1)
+ )
heatmap_np = heatmap_np[:, :, :, 1:]
else:
heatmap_np = torch.sigmoid(outputs["heatmap"].detach())
heatmap_np = heatmap_np.cpu().numpy().transpose(0, 2, 3, 1)
-
heatmap_gt_np = heatmap.cpu().numpy().transpose(0, 2, 3, 1)
valid_mask_np = valid_mask.cpu().numpy().transpose(0, 2, 3, 1)
# Evaluate metric results
if compute_descriptors:
metric_func.evaluate(
- junc_np["junc_pred"], junc_np["junc_pred_nms"],
- junc_map_np, heatmap_np, heatmap_gt_np, valid_mask_np,
- line_points, line_points2, outputs["descriptors"],
- outputs2["descriptors"], line_indices)
+ junc_np["junc_pred"],
+ junc_np["junc_pred_nms"],
+ junc_map_np,
+ heatmap_np,
+ heatmap_gt_np,
+ valid_mask_np,
+ line_points,
+ line_points2,
+ outputs["descriptors"],
+ outputs2["descriptors"],
+ line_indices,
+ )
else:
metric_func.evaluate(
- junc_np["junc_pred"], junc_np["junc_pred_nms"], junc_map_np,
- heatmap_np, heatmap_gt_np, valid_mask_np)
+ junc_np["junc_pred"],
+ junc_np["junc_pred_nms"],
+ junc_map_np,
+ heatmap_np,
+ heatmap_gt_np,
+ valid_mask_np,
+ )
# Update average meter
junc_loss = losses["junc_loss"].item()
heatmap_loss = losses["heatmap_loss"].item()
loss_dict = {
"junc_loss": junc_loss,
"heatmap_loss": heatmap_loss,
- "total_loss": total_loss.item()}
+ "total_loss": total_loss.item(),
+ }
if compute_descriptors:
descriptor_loss = losses["descriptor_loss"].item()
loss_dict["descriptor_loss"] = losses["descriptor_loss"].item()
@@ -495,32 +621,67 @@ def validate(model, model_cfg, loss_func, metric_func, val_loader, writer, epoch
results = metric_func.metric_results
average = average_meter.average()
if compute_descriptors:
- print("Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f), descriptor_loss=%.4f (%.4f)"
- % (idx, len(val_loader),
- total_loss.item(), average["total_loss"],
- junc_loss, average["junc_loss"],
- heatmap_loss, average["heatmap_loss"],
- descriptor_loss, average["descriptor_loss"]))
+ print(
+ "Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f), descriptor_loss=%.4f (%.4f)"
+ % (
+ idx,
+ len(val_loader),
+ total_loss.item(),
+ average["total_loss"],
+ junc_loss,
+ average["junc_loss"],
+ heatmap_loss,
+ average["heatmap_loss"],
+ descriptor_loss,
+ average["descriptor_loss"],
+ )
+ )
else:
- print("Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f)"
- % (idx, len(val_loader),
- total_loss.item(), average["total_loss"],
- junc_loss, average["junc_loss"],
- heatmap_loss, average["heatmap_loss"]))
- print("\t Junction precision=%.4f (%.4f) / recall=%.4f (%.4f)"
- % (results["junc_precision"], average["junc_precision"],
- results["junc_recall"], average["junc_recall"]))
- print("\t Junction nms precision=%.4f (%.4f) / recall=%.4f (%.4f)"
- % (results["junc_precision_nms"],
- average["junc_precision_nms"],
- results["junc_recall_nms"], average["junc_recall_nms"]))
- print("\t Heatmap precision=%.4f (%.4f) / recall=%.4f (%.4f)"
- % (results["heatmap_precision"],
- average["heatmap_precision"],
- results["heatmap_recall"], average["heatmap_recall"]))
+ print(
+ "Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f)"
+ % (
+ idx,
+ len(val_loader),
+ total_loss.item(),
+ average["total_loss"],
+ junc_loss,
+ average["junc_loss"],
+ heatmap_loss,
+ average["heatmap_loss"],
+ )
+ )
+ print(
+ "\t Junction precision=%.4f (%.4f) / recall=%.4f (%.4f)"
+ % (
+ results["junc_precision"],
+ average["junc_precision"],
+ results["junc_recall"],
+ average["junc_recall"],
+ )
+ )
+ print(
+ "\t Junction nms precision=%.4f (%.4f) / recall=%.4f (%.4f)"
+ % (
+ results["junc_precision_nms"],
+ average["junc_precision_nms"],
+ results["junc_recall_nms"],
+ average["junc_recall_nms"],
+ )
+ )
+ print(
+ "\t Heatmap precision=%.4f (%.4f) / recall=%.4f (%.4f)"
+ % (
+ results["heatmap_precision"],
+ average["heatmap_precision"],
+ results["heatmap_recall"],
+ average["heatmap_recall"],
+ )
+ )
if compute_descriptors:
- print("\t Descriptors matching score=%.4f (%.4f)"
- %(results["matching_score"], average["matching_score"]))
+ print(
+ "\t Descriptors matching score=%.4f (%.4f)"
+ % (results["matching_score"], average["matching_score"])
+ )
# Record summaries
average = average_meter.average()
@@ -529,143 +690,182 @@ def validate(model, model_cfg, loss_func, metric_func, val_loader, writer, epoch
record_test_summaries(writer, epoch, scalar_summaries)
-def convert_junc_predictions(predictions, grid_size,
- detect_thresh=1/65, topk=300):
- """ Convert torch predictions to numpy arrays for evaluation. """
+def convert_junc_predictions(predictions, grid_size, detect_thresh=1 / 65, topk=300):
+ """Convert torch predictions to numpy arrays for evaluation."""
# Convert to probability outputs first
junc_prob = softmax(predictions.detach(), dim=1).cpu()
junc_pred = junc_prob[:, :-1, :, :]
junc_prob_np = junc_prob.numpy().transpose(0, 2, 3, 1)[:, :, :, :-1]
junc_prob_np = np.sum(junc_prob_np, axis=-1)
- junc_pred_np = pixel_shuffle(
- junc_pred, grid_size).cpu().numpy().transpose(0, 2, 3, 1)
+ junc_pred_np = (
+ pixel_shuffle(junc_pred, grid_size).cpu().numpy().transpose(0, 2, 3, 1)
+ )
junc_pred_np_nms = super_nms(junc_pred_np, grid_size, detect_thresh, topk)
junc_pred_np = junc_pred_np.squeeze(-1)
- return {"junc_pred": junc_pred_np, "junc_pred_nms": junc_pred_np_nms,
- "junc_prob": junc_prob_np}
+ return {
+ "junc_pred": junc_pred_np,
+ "junc_pred_nms": junc_pred_np_nms,
+ "junc_prob": junc_prob_np,
+ }
def record_train_summaries(writer, global_step, scalars, images):
- """ Record training summaries. """
+ """Record training summaries."""
# Record the scalar summaries
results = scalars["metrics"]
average = scalars["average"]
# GPU memory part
# Get gpu memory usage in GB
- gpu_mem_usage = torch.cuda.max_memory_allocated() / (1024 ** 3)
+ gpu_mem_usage = torch.cuda.max_memory_allocated() / (1024**3)
writer.add_scalar("GPU/GPU_memory_usage", gpu_mem_usage, global_step)
# Loss part
- writer.add_scalar("Train_loss/junc_loss", scalars["junc_loss"],
- global_step)
- writer.add_scalar("Train_loss/heatmap_loss", scalars["heatmap_loss"],
- global_step)
- writer.add_scalar("Train_loss/total_loss", scalars["total_loss"],
- global_step)
+ writer.add_scalar("Train_loss/junc_loss", scalars["junc_loss"], global_step)
+ writer.add_scalar("Train_loss/heatmap_loss", scalars["heatmap_loss"], global_step)
+ writer.add_scalar("Train_loss/total_loss", scalars["total_loss"], global_step)
# Add regularization loss
if "reg_loss" in scalars.keys():
- writer.add_scalar("Train_loss/reg_loss", scalars["reg_loss"],
- global_step)
+ writer.add_scalar("Train_loss/reg_loss", scalars["reg_loss"], global_step)
# Add descriptor loss
if "descriptor_loss" in scalars.keys():
key = "descriptor_loss"
- writer.add_scalar("Train_loss/%s"%(key), scalars[key], global_step)
- writer.add_scalar("Train_loss_average/%s"%(key), average[key],
- global_step)
-
+ writer.add_scalar("Train_loss/%s" % (key), scalars[key], global_step)
+ writer.add_scalar("Train_loss_average/%s" % (key), average[key], global_step)
+
# Record weighting
for key in scalars.keys():
if "w_" in key:
- writer.add_scalar("Train_weight/%s"%(key), scalars[key],
- global_step)
-
+ writer.add_scalar("Train_weight/%s" % (key), scalars[key], global_step)
+
# Smoothed loss
- writer.add_scalar("Train_loss_average/junc_loss", average["junc_loss"],
- global_step)
- writer.add_scalar("Train_loss_average/heatmap_loss",
- average["heatmap_loss"], global_step)
- writer.add_scalar("Train_loss_average/total_loss", average["total_loss"],
- global_step)
+ writer.add_scalar("Train_loss_average/junc_loss", average["junc_loss"], global_step)
+ writer.add_scalar(
+ "Train_loss_average/heatmap_loss", average["heatmap_loss"], global_step
+ )
+ writer.add_scalar(
+ "Train_loss_average/total_loss", average["total_loss"], global_step
+ )
# Add smoothed descriptor loss
if "descriptor_loss" in average.keys():
- writer.add_scalar("Train_loss_average/descriptor_loss",
- average["descriptor_loss"], global_step)
+ writer.add_scalar(
+ "Train_loss_average/descriptor_loss",
+ average["descriptor_loss"],
+ global_step,
+ )
# Metrics part
- writer.add_scalar("Train_metrics/junc_precision",
- results["junc_precision"], global_step)
- writer.add_scalar("Train_metrics/junc_precision_nms",
- results["junc_precision_nms"], global_step)
- writer.add_scalar("Train_metrics/junc_recall",
- results["junc_recall"], global_step)
- writer.add_scalar("Train_metrics/junc_recall_nms",
- results["junc_recall_nms"], global_step)
- writer.add_scalar("Train_metrics/heatmap_precision",
- results["heatmap_precision"], global_step)
- writer.add_scalar("Train_metrics/heatmap_recall",
- results["heatmap_recall"], global_step)
+ writer.add_scalar(
+ "Train_metrics/junc_precision", results["junc_precision"], global_step
+ )
+ writer.add_scalar(
+ "Train_metrics/junc_precision_nms", results["junc_precision_nms"], global_step
+ )
+ writer.add_scalar("Train_metrics/junc_recall", results["junc_recall"], global_step)
+ writer.add_scalar(
+ "Train_metrics/junc_recall_nms", results["junc_recall_nms"], global_step
+ )
+ writer.add_scalar(
+ "Train_metrics/heatmap_precision", results["heatmap_precision"], global_step
+ )
+ writer.add_scalar(
+ "Train_metrics/heatmap_recall", results["heatmap_recall"], global_step
+ )
# Add descriptor metric
if "matching_score" in results.keys():
- writer.add_scalar("Train_metrics/matching_score",
- results["matching_score"], global_step)
+ writer.add_scalar(
+ "Train_metrics/matching_score", results["matching_score"], global_step
+ )
# Average part
- writer.add_scalar("Train_metrics_average/junc_precision",
- average["junc_precision"], global_step)
- writer.add_scalar("Train_metrics_average/junc_precision_nms",
- average["junc_precision_nms"], global_step)
- writer.add_scalar("Train_metrics_average/junc_recall",
- average["junc_recall"], global_step)
- writer.add_scalar("Train_metrics_average/junc_recall_nms",
- average["junc_recall_nms"], global_step)
- writer.add_scalar("Train_metrics_average/heatmap_precision",
- average["heatmap_precision"], global_step)
- writer.add_scalar("Train_metrics_average/heatmap_recall",
- average["heatmap_recall"], global_step)
+ writer.add_scalar(
+ "Train_metrics_average/junc_precision", average["junc_precision"], global_step
+ )
+ writer.add_scalar(
+ "Train_metrics_average/junc_precision_nms",
+ average["junc_precision_nms"],
+ global_step,
+ )
+ writer.add_scalar(
+ "Train_metrics_average/junc_recall", average["junc_recall"], global_step
+ )
+ writer.add_scalar(
+ "Train_metrics_average/junc_recall_nms", average["junc_recall_nms"], global_step
+ )
+ writer.add_scalar(
+ "Train_metrics_average/heatmap_precision",
+ average["heatmap_precision"],
+ global_step,
+ )
+ writer.add_scalar(
+ "Train_metrics_average/heatmap_recall", average["heatmap_recall"], global_step
+ )
# Add smoothed descriptor metric
if "matching_score" in average.keys():
- writer.add_scalar("Train_metrics_average/matching_score",
- average["matching_score"], global_step)
+ writer.add_scalar(
+ "Train_metrics_average/matching_score",
+ average["matching_score"],
+ global_step,
+ )
# Record the image summary
# Image part
image_tensor = convert_image(images["image"], 1)
valid_masks = convert_image(images["valid_mask"], -1)
- writer.add_images("Train/images", image_tensor, global_step,
- dataformats="NCHW")
- writer.add_images("Train/valid_map", valid_masks, global_step,
- dataformats="NHWC")
+ writer.add_images("Train/images", image_tensor, global_step, dataformats="NCHW")
+ writer.add_images("Train/valid_map", valid_masks, global_step, dataformats="NHWC")
# Heatmap part
- writer.add_images("Train/heatmap_gt",
- convert_image(images["heatmap_gt"], -1), global_step,
- dataformats="NHWC")
- writer.add_images("Train/heatmap_pred",
- convert_image(images["heatmap_pred"], -1), global_step,
- dataformats="NHWC")
+ writer.add_images(
+ "Train/heatmap_gt",
+ convert_image(images["heatmap_gt"], -1),
+ global_step,
+ dataformats="NHWC",
+ )
+ writer.add_images(
+ "Train/heatmap_pred",
+ convert_image(images["heatmap_pred"], -1),
+ global_step,
+ dataformats="NHWC",
+ )
# Junction prediction part
junc_plots = plot_junction_detection(
- image_tensor, images["junc_map_pred"],
- images["junc_map_pred_nms"], images["junc_map_gt"])
- writer.add_images("Train/junc_gt", junc_plots["junc_gt_plot"] / 255.,
- global_step, dataformats="NHWC")
- writer.add_images("Train/junc_pred", junc_plots["junc_pred_plot"] / 255.,
- global_step, dataformats="NHWC")
- writer.add_images("Train/junc_pred_nms",
- junc_plots["junc_pred_nms_plot"] / 255., global_step,
- dataformats="NHWC")
+ image_tensor,
+ images["junc_map_pred"],
+ images["junc_map_pred_nms"],
+ images["junc_map_gt"],
+ )
+ writer.add_images(
+ "Train/junc_gt",
+ junc_plots["junc_gt_plot"] / 255.0,
+ global_step,
+ dataformats="NHWC",
+ )
+ writer.add_images(
+ "Train/junc_pred",
+ junc_plots["junc_pred_plot"] / 255.0,
+ global_step,
+ dataformats="NHWC",
+ )
+ writer.add_images(
+ "Train/junc_pred_nms",
+ junc_plots["junc_pred_nms_plot"] / 255.0,
+ global_step,
+ dataformats="NHWC",
+ )
writer.add_images(
"Train/junc_prob_map",
convert_image(images["junc_prob_map"][..., None], axis=-1),
- global_step, dataformats="NHWC")
+ global_step,
+ dataformats="NHWC",
+ )
def record_test_summaries(writer, epoch, scalars):
- """ Record testing summaries. """
+ """Record testing summaries."""
average = scalars["average"]
# Average loss
@@ -675,30 +875,30 @@ def record_test_summaries(writer, epoch, scalars):
# Add descriptor loss
if "descriptor_loss" in average.keys():
key = "descriptor_loss"
- writer.add_scalar("Val_loss/%s"%(key), average[key], epoch)
+ writer.add_scalar("Val_loss/%s" % (key), average[key], epoch)
# Average metrics
- writer.add_scalar("Val_metrics/junc_precision", average["junc_precision"],
- epoch)
- writer.add_scalar("Val_metrics/junc_precision_nms",
- average["junc_precision_nms"], epoch)
- writer.add_scalar("Val_metrics/junc_recall",
- average["junc_recall"], epoch)
- writer.add_scalar("Val_metrics/junc_recall_nms",
- average["junc_recall_nms"], epoch)
- writer.add_scalar("Val_metrics/heatmap_precision",
- average["heatmap_precision"], epoch)
- writer.add_scalar("Val_metrics/heatmap_recall",
- average["heatmap_recall"], epoch)
+ writer.add_scalar("Val_metrics/junc_precision", average["junc_precision"], epoch)
+ writer.add_scalar(
+ "Val_metrics/junc_precision_nms", average["junc_precision_nms"], epoch
+ )
+ writer.add_scalar("Val_metrics/junc_recall", average["junc_recall"], epoch)
+ writer.add_scalar("Val_metrics/junc_recall_nms", average["junc_recall_nms"], epoch)
+ writer.add_scalar(
+ "Val_metrics/heatmap_precision", average["heatmap_precision"], epoch
+ )
+ writer.add_scalar("Val_metrics/heatmap_recall", average["heatmap_recall"], epoch)
# Add descriptor metric
if "matching_score" in average.keys():
- writer.add_scalar("Val_metrics/matching_score",
- average["matching_score"], epoch)
+ writer.add_scalar(
+ "Val_metrics/matching_score", average["matching_score"], epoch
+ )
-def plot_junction_detection(image_tensor, junc_pred_tensor,
- junc_pred_nms_tensor, junc_gt_tensor):
- """ Plot the junction points on images. """
+def plot_junction_detection(
+ image_tensor, junc_pred_tensor, junc_pred_nms_tensor, junc_gt_tensor
+):
+ """Plot the junction points on images."""
# Get the batch_size
batch_size = image_tensor.shape[0]
@@ -708,45 +908,61 @@ def plot_junction_detection(image_tensor, junc_pred_tensor,
junc_gt_lst = []
for i in range(batch_size):
# Convert image to 255 uint8
- image = (image_tensor[i, :, :, :]
- * 255.).astype(np.uint8).transpose(1,2,0)
+ image = (image_tensor[i, :, :, :] * 255.0).astype(np.uint8).transpose(1, 2, 0)
# Plot groundtruth onto image
junc_gt = junc_gt_tensor[i, ...]
coord_gt = np.where(junc_gt.squeeze() > 0)
- points_gt = np.concatenate((coord_gt[0][..., None],
- coord_gt[1][..., None]),
- axis=1)
+ points_gt = np.concatenate(
+ (coord_gt[0][..., None], coord_gt[1][..., None]), axis=1
+ )
plot_gt = image.copy()
for id in range(points_gt.shape[0]):
- cv2.circle(plot_gt, tuple(np.flip(points_gt[id, :])), 3,
- color=(255, 0, 0), thickness=2)
+ cv2.circle(
+ plot_gt,
+ tuple(np.flip(points_gt[id, :])),
+ 3,
+ color=(255, 0, 0),
+ thickness=2,
+ )
junc_gt_lst.append(plot_gt[None, ...])
# Plot junc_pred
junc_pred = junc_pred_tensor[i, ...]
coord_pred = np.where(junc_pred > 0)
- points_pred = np.concatenate((coord_pred[0][..., None],
- coord_pred[1][..., None]),
- axis=1)
+ points_pred = np.concatenate(
+ (coord_pred[0][..., None], coord_pred[1][..., None]), axis=1
+ )
plot_pred = image.copy()
for id in range(points_pred.shape[0]):
- cv2.circle(plot_pred, tuple(np.flip(points_pred[id, :])), 3,
- color=(0, 255, 0), thickness=2)
+ cv2.circle(
+ plot_pred,
+ tuple(np.flip(points_pred[id, :])),
+ 3,
+ color=(0, 255, 0),
+ thickness=2,
+ )
junc_pred_lst.append(plot_pred[None, ...])
# Plot junc_pred_nms
junc_pred_nms = junc_pred_nms_tensor[i, ...]
coord_pred_nms = np.where(junc_pred_nms > 0)
- points_pred_nms = np.concatenate((coord_pred_nms[0][..., None],
- coord_pred_nms[1][..., None]),
- axis=1)
+ points_pred_nms = np.concatenate(
+ (coord_pred_nms[0][..., None], coord_pred_nms[1][..., None]), axis=1
+ )
plot_pred_nms = image.copy()
for id in range(points_pred_nms.shape[0]):
- cv2.circle(plot_pred_nms, tuple(np.flip(points_pred_nms[id, :])),
- 3, color=(0, 255, 0), thickness=2)
+ cv2.circle(
+ plot_pred_nms,
+ tuple(np.flip(points_pred_nms[id, :])),
+ 3,
+ color=(0, 255, 0),
+ thickness=2,
+ )
junc_pred_nms_lst.append(plot_pred_nms[None, ...])
- return {"junc_gt_plot": np.concatenate(junc_gt_lst, axis=0),
- "junc_pred_plot": np.concatenate(junc_pred_lst, axis=0),
- "junc_pred_nms_plot": np.concatenate(junc_pred_nms_lst, axis=0)}
+ return {
+ "junc_gt_plot": np.concatenate(junc_gt_lst, axis=0),
+ "junc_pred_plot": np.concatenate(junc_pred_lst, axis=0),
+ "junc_pred_nms_plot": np.concatenate(junc_pred_nms_lst, axis=0),
+ }
diff --git a/third_party/SuperGluePretrainedNetwork/.gitignore b/third_party/SuperGluePretrainedNetwork/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..75cda0e0c3af34c06f50dc0ebba6e338d5b78d02
--- /dev/null
+++ b/third_party/SuperGluePretrainedNetwork/.gitignore
@@ -0,0 +1,3 @@
+*.pyc
+*.DS_Store
+*.swp
diff --git a/third_party/SuperGluePretrainedNetwork/LICENSE b/third_party/SuperGluePretrainedNetwork/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..afc1ed1db5d14d18e546de546762dd45fc78ef1d
--- /dev/null
+++ b/third_party/SuperGluePretrainedNetwork/LICENSE
@@ -0,0 +1,48 @@
+SUPERGLUE: LEARNING FEATURE MATCHING WITH GRAPH NEURAL NETWORKS
+SOFTWARE LICENSE AGREEMENT
+ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
+
+BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT. IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
+
+This is a license agreement ("Agreement") between your academic institution or non-profit organization or self (called "Licensee" or "You" in this Agreement) and Magic Leap, Inc. (called "Licensor" in this Agreement). All rights not specifically granted to you in this Agreement are reserved for Licensor.
+
+RESERVATION OF OWNERSHIP AND GRANT OF LICENSE:
+Licensor retains exclusive ownership of any copy of the Software (as defined below) licensed under this Agreement and hereby grants to Licensee a personal, non-exclusive, non-transferable license to use the Software for noncommercial research purposes, without the right to sublicense, pursuant to the terms and conditions of this Agreement. As used in this Agreement, the term "Software" means (i) the actual copy of all or any portion of code for program routines made accessible to Licensee by Licensor pursuant to this Agreement, inclusive of backups, updates, and/or merged copies permitted hereunder or subsequently supplied by Licensor, including all or any file structures, programming instructions, user interfaces and screen formats and sequences as well as any and all documentation and instructions related to it, and (ii) all or any derivatives and/or modifications created or made by You to any of the items specified in (i).
+
+CONFIDENTIALITY: Licensee acknowledges that the Software is proprietary to Licensor, and as such, Licensee agrees to receive all such materials in confidence and use the Software only in accordance with the terms of this Agreement. Licensee agrees to use reasonable effort to protect the Software from unauthorized use, reproduction, distribution, or publication.
+
+COPYRIGHT: The Software is owned by Licensor and is protected by United States copyright laws and applicable international treaties and/or conventions.
+
+PERMITTED USES: The Software may be used for your own noncommercial internal research purposes. You understand and agree that Licensor is not obligated to implement any suggestions and/or feedback you might provide regarding the Software, but to the extent Licensor does so, you are not entitled to any compensation related thereto.
+
+DERIVATIVES: You may create derivatives of or make modifications to the Software, however, You agree that all and any such derivatives and modifications will be owned by Licensor and become a part of the Software licensed to You under this Agreement. You may only use such derivatives and modifications for your own noncommercial internal research purposes, and you may not otherwise use, distribute or copy such derivatives and modifications in violation of this Agreement.
+
+BACKUPS: If Licensee is an organization, it may make that number of copies of the Software necessary for internal noncommercial use at a single site within its organization provided that all information appearing in or on the original labels, including the copyright and trademark notices are copied onto the labels of the copies.
+
+USES NOT PERMITTED: You may not distribute, copy or use the Software except as explicitly permitted herein. Licensee has not been granted any trademark license as part of this Agreement and may not use the name or mark "Magic Leap" or any renditions thereof without the prior written permission of Licensor.
+
+You may not sell, rent, lease, sublicense, lend, time-share or transfer, in whole or in part, or provide third parties access to prior or present versions (or any parts thereof) of the Software.
+
+ASSIGNMENT: You may not assign this Agreement or your rights hereunder without the prior written consent of Licensor. Any attempted assignment without such consent shall be null and void.
+
+TERM: The term of the license granted by this Agreement is from Licensee's acceptance of this Agreement by downloading the Software or by using the Software until terminated as provided below.
+
+The Agreement automatically terminates without notice if you fail to comply with any provision of this Agreement. Licensee may terminate this Agreement by ceasing using the Software. Upon any termination of this Agreement, Licensee will delete any and all copies of the Software. You agree that all provisions which operate to protect the proprietary rights of Licensor shall remain in force should breach occur and that the obligation of confidentiality described in this Agreement is binding in perpetuity and, as such, survives the term of the Agreement.
+
+FEE: Provided Licensee abides completely by the terms and conditions of this Agreement, there is no fee due to Licensor for Licensee's use of the Software in accordance with this Agreement.
+
+DISCLAIMER OF WARRANTIES: THE SOFTWARE IS PROVIDED "AS-IS" WITHOUT WARRANTY OF ANY KIND INCLUDING ANY WARRANTIES OF PERFORMANCE OR MERCHANTABILITY OR FITNESS FOR A PARTICULAR USE OR PURPOSE OR OF NON-INFRINGEMENT. LICENSEE BEARS ALL RISK RELATING TO QUALITY AND PERFORMANCE OF THE SOFTWARE AND RELATED MATERIALS.
+
+SUPPORT AND MAINTENANCE: No Software support or training by the Licensor is provided as part of this Agreement.
+
+EXCLUSIVE REMEDY AND LIMITATION OF LIABILITY: To the maximum extent permitted under applicable law, Licensor shall not be liable for direct, indirect, special, incidental, or consequential damages or lost profits related to Licensee's use of and/or inability to use the Software, even if Licensor is advised of the possibility of such damage.
+
+EXPORT REGULATION: Licensee agrees to comply with any and all applicable U.S. export control laws, regulations, and/or other laws related to embargoes and sanction programs administered by the Office of Foreign Assets Control.
+
+SEVERABILITY: If any provision(s) of this Agreement shall be held to be invalid, illegal, or unenforceable by a court or other tribunal of competent jurisdiction, the validity, legality and enforceability of the remaining provisions shall not in any way be affected or impaired thereby.
+
+NO IMPLIED WAIVERS: No failure or delay by Licensor in enforcing any right or remedy under this Agreement shall be construed as a waiver of any future or other exercise of such right or remedy by Licensor.
+
+GOVERNING LAW: This Agreement shall be construed and enforced in accordance with the laws of the State of Florida without reference to conflict of laws principles. You consent to the personal jurisdiction of the courts of this County and waive their rights to venue outside of Broward County, Florida.
+
+ENTIRE AGREEMENT AND AMENDMENTS: This Agreement constitutes the sole and entire agreement between Licensee and Licensor as to the matter set forth herein and supersedes any previous agreements, understandings, and arrangements between the parties relating hereto.
diff --git a/third_party/SuperGluePretrainedNetwork/README.md b/third_party/SuperGluePretrainedNetwork/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ab08335ce7bb237fd8108470d53b0aac11acc01f
--- /dev/null
+++ b/third_party/SuperGluePretrainedNetwork/README.md
@@ -0,0 +1,388 @@
+
+
+### Research @ Magic Leap (CVPR 2020, Oral)
+
+# SuperGlue Inference and Evaluation Demo Script
+
+## Introduction
+SuperGlue is a CVPR 2020 research project done at Magic Leap. The SuperGlue network is a Graph Neural Network combined with an Optimal Matching layer that is trained to perform matching on two sets of sparse image features. This repo includes PyTorch code and pretrained weights for running the SuperGlue matching network on top of [SuperPoint](https://arxiv.org/abs/1712.07629) keypoints and descriptors. Given a pair of images, you can use this repo to extract matching features across the image pair.
+
+
+
+
+
+SuperGlue operates as a "middle-end," performing context aggregation, matching, and filtering in a single end-to-end architecture. For more details, please see:
+
+* Full paper PDF: [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://arxiv.org/abs/1911.11763).
+
+* Authors: *Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich*
+
+* Website: [psarlin.com/superglue](https://psarlin.com/superglue) for videos, slides, recent updates, and more visualizations.
+
+* `hloc`: a new toolbox for visual localization and SfM with SuperGlue, available at [cvg/Hierarchical-Localization](https://github.com/cvg/Hierarchical-Localization/). Winner of 3 CVPR 2020 competitions on localization and image matching!
+
+We provide two pre-trained weights files: an indoor model trained on ScanNet data, and an outdoor model trained on MegaDepth data. Both models are inside the [weights directory](./models/weights). By default, the demo will run the **indoor** model.
+
+## Dependencies
+* Python 3 >= 3.5
+* PyTorch >= 1.1
+* OpenCV >= 3.4 (4.1.2.30 recommended for best GUI keyboard interaction, see this [note](#additional-notes))
+* Matplotlib >= 3.1
+* NumPy >= 1.18
+
+Simply run the following command: `pip3 install numpy opencv-python torch matplotlib`
+
+## Contents
+There are two main top-level scripts in this repo:
+
+1. `demo_superglue.py` : runs a live demo on a webcam, IP camera, image directory or movie file
+2. `match_pairs.py`: reads image pairs from files and dumps matches to disk (also runs evaluation if ground truth relative poses are provided)
+
+## Live Matching Demo Script (`demo_superglue.py`)
+This demo runs SuperPoint + SuperGlue feature matching on an anchor image and live image. You can update the anchor image by pressing the `n` key. The demo can read image streams from a USB or IP camera, a directory containing images, or a video file. You can pass all of these inputs using the `--input` flag.
+
+### Run the demo on a live webcam
+
+Run the demo on the default USB webcam (ID #0), running on a CUDA GPU if one is found:
+
+```sh
+./demo_superglue.py
+```
+
+Keyboard control:
+
+* `n`: select the current frame as the anchor
+* `e`/`r`: increase/decrease the keypoint confidence threshold
+* `d`/`f`: increase/decrease the match filtering threshold
+* `k`: toggle the visualization of keypoints
+* `q`: quit
+
+Run the demo on 320x240 images running on the CPU:
+
+```sh
+./demo_superglue.py --resize 320 240 --force_cpu
+```
+
+The `--resize` flag can be used to resize the input image in three ways:
+
+1. `--resize` `width` `height` : will resize to exact `width` x `height` dimensions
+2. `--resize` `max_dimension` : will resize largest input image dimension to `max_dimension`
+3. `--resize` `-1` : will not resize (i.e. use original image dimensions)
+
+The default will resize images to `640x480`.
+
+### Run the demo on a directory of images
+
+The `--input` flag also accepts a path to a directory. We provide a directory of sample images from a sequence. To run the demo on the directory of images in `freiburg_sequence/` on a headless server (will not display to the screen) and write the output visualization images to `dump_demo_sequence/`:
+
+```sh
+./demo_superglue.py --input assets/freiburg_sequence/ --output_dir dump_demo_sequence --resize 320 240 --no_display
+```
+
+You should see this output on the sample Freiburg-TUM RGBD sequence:
+
+
+
+The matches are colored by their predicted confidence in a jet colormap (Red: more confident, Blue: less confident).
+
+### Additional useful command line parameters
+* Use `--image_glob` to change the image file extension (default: `*.png`, `*.jpg`, `*.jpeg`).
+* Use `--skip` to skip intermediate frames (default: `1`).
+* Use `--max_length` to cap the total number of frames processed (default: `1000000`).
+* Use `--show_keypoints` to visualize the detected keypoints (default: `False`).
+
+## Run Matching+Evaluation (`match_pairs.py`)
+
+This repo also contains a script `match_pairs.py` that runs the matching from a list of image pairs. With this script, you can:
+
+* Run the matcher on a set of image pairs (no ground truth needed)
+* Visualize the keypoints and matches, based on their confidence
+* Evaluate and visualize the match correctness, if the ground truth relative poses and intrinsics are provided
+* Save the keypoints, matches, and evaluation results for further processing
+* Collate evaluation results over many pairs and generate result tables
+
+### Matches only mode
+
+The simplest usage of this script will process the image pairs listed in a given text file and dump the keypoints and matches to compressed numpy `npz` files. We provide the challenging ScanNet pairs from the main paper in `assets/example_indoor_pairs/`. Running the following will run SuperPoint + SuperGlue on each image pair, and dump the results to `dump_match_pairs/`:
+
+```sh
+./match_pairs.py
+```
+
+The resulting `.npz` files can be read from Python as follows:
+
+```python
+>>> import numpy as np
+>>> path = 'dump_match_pairs/scene0711_00_frame-001680_scene0711_00_frame-001995_matches.npz'
+>>> npz = np.load(path)
+>>> npz.files
+['keypoints0', 'keypoints1', 'matches', 'match_confidence']
+>>> npz['keypoints0'].shape
+(382, 2)
+>>> npz['keypoints1'].shape
+(391, 2)
+>>> npz['matches'].shape
+(382,)
+>>> np.sum(npz['matches']>-1)
+115
+>>> npz['match_confidence'].shape
+(382,)
+```
+
+For each keypoint in `keypoints0`, the `matches` array indicates the index of the matching keypoint in `keypoints1`, or `-1` if the keypoint is unmatched.
+
+### Visualization mode
+
+You can add the flag `--viz` to dump image outputs which visualize the matches:
+
+```sh
+./match_pairs.py --viz
+```
+
+You should see images like this inside of `dump_match_pairs/` (or something very close to it, see this [note](#a-note-on-reproducibility)):
+
+
+
+The matches are colored by their predicted confidence in a jet colormap (Red: more confident, Blue: less confident).
+
+### Evaluation mode
+
+You can also estimate the pose using RANSAC + Essential Matrix decomposition and evaluate it if the ground truth relative poses and intrinsics are provided in the input `.txt` files. Each `.txt` file contains three key ground truth matrices: a 3x3 intrinsics matrix of image0: `K0`, a 3x3 intrinsics matrix of image1: `K1` , and a 4x4 matrix of the relative pose extrinsics `T_0to1`.
+
+To run the evaluation on the sample set of images (by default reading `assets/scannet_sample_pairs_with_gt.txt`), you can run:
+
+```sh
+./match_pairs.py --eval
+```
+
+
+Since you enabled `--eval`, you should see collated results printed to the terminal. For the example images provided, you should get the following numbers (or something very close to it, see this [note](#a-note-on-reproducibility)):
+
+```txt
+Evaluation Results (mean over 15 pairs):
+AUC@5 AUC@10 AUC@20 Prec MScore
+26.99 48.40 64.47 73.52 19.60
+```
+
+The resulting `.npz` files in `dump_match_pairs/` will now contain scalar values related to the evaluation, computed on the sample images provided. Here is what you should find in one of the generated evaluation files:
+
+```python
+>>> import numpy as np
+>>> path = 'dump_match_pairs/scene0711_00_frame-001680_scene0711_00_frame-001995_evaluation.npz'
+>>> npz = np.load(path)
+>>> print(npz.files)
+['error_t', 'error_R', 'precision', 'matching_score', 'num_correct', 'epipolar_errors']
+```
+
+You can also visualize the evaluation metrics by running the following command:
+
+```sh
+./match_pairs.py --eval --viz
+```
+
+You should also now see additional images in `dump_match_pairs/` which visualize the evaluation numbers (or something very close to it, see this [note](#a-note-on-reproducibility)):
+
+
+
+The top left corner of the image shows the pose error and number of inliers, while the lines are colored by their epipolar error computed with the ground truth relative pose (red: higher error, green: lower error).
+
+### Running on sample outdoor pairs
+
+
+ [Click to expand]
+
+In this repo, we also provide a few challenging Phototourism pairs, so that you can re-create some of the figures from the paper. Run this script to run matching and visualization (no ground truth is provided, see this [note](#reproducing-outdoor-evaluation-final-table)) on the provided pairs:
+
+```sh
+./match_pairs.py --resize 1600 --superglue outdoor --max_keypoints 2048 --nms_radius 3 --resize_float --input_dir assets/phototourism_sample_images/ --input_pairs assets/phototourism_sample_pairs.txt --output_dir dump_match_pairs_outdoor --viz
+```
+
+You should now image pairs such as these in `dump_match_pairs_outdoor/` (or something very close to it, see this [note](#a-note-on-reproducibility)):
+
+
+
+
+
+### Recommended settings for indoor / outdoor
+
+
+ [Click to expand]
+
+For **indoor** images, we recommend the following settings (these are the defaults):
+
+```sh
+./match_pairs.py --resize 640 --superglue indoor --max_keypoints 1024 --nms_radius 4
+```
+
+For **outdoor** images, we recommend the following settings:
+
+```sh
+./match_pairs.py --resize 1600 --superglue outdoor --max_keypoints 2048 --nms_radius 3 --resize_float
+```
+
+You can provide your own list of pairs `--input_pairs` for images contained in `--input_dir`. Images can be resized before network inference with `--resize`. If you are re-running the same evaluation many times, you can use the `--cache` flag to reuse old computation.
+
+
+### Test set pair file format explained
+
+
+ [Click to expand]
+
+We provide the list of ScanNet test pairs in `assets/scannet_test_pairs_with_gt.txt` (with ground truth) and Phototourism test pairs `assets/phototourism_test_pairs.txt` (without ground truth) used to evaluate the matching from the paper. Each line corresponds to one pair and is structured as follows:
+
+```
+path_image_A path_image_B exif_rotationA exif_rotationB [KA_0 ... KA_8] [KB_0 ... KB_8] [T_AB_0 ... T_AB_15]
+```
+
+The `path_image_A` and `path_image_B` entries are paths to image A and B, respectively. The `exif_rotation` is an integer in the range [0, 3] that comes from the original EXIF metadata associated with the image, where, 0: no rotation, 1: 90 degree clockwise, 2: 180 degree clockwise, 3: 270 degree clockwise. If the EXIF data is not known, you can just provide a zero here and no rotation will be performed. `KA` and `KB` are the flattened `3x3` matrices of image A and image B intrinsics. `T_AB` is a flattened `4x4` matrix of the extrinsics between the pair.
+
+
+### Reproducing the indoor evaluation on ScanNet
+
+
+ [Click to expand]
+
+We provide the groundtruth for ScanNet in our format in the file `assets/scannet_test_pairs_with_gt.txt` for convenience. In order to reproduce similar tables to what was in the paper, you will need to download the dataset (we do not provide the raw test images). To download the ScanNet dataset, do the following:
+
+1. Head to the [ScanNet](https://github.com/ScanNet/ScanNet) github repo to download the ScanNet test set (100 scenes).
+2. You will need to extract the raw sensor data from the 100 `.sens` files in each scene in the test set using the [SensReader](https://github.com/ScanNet/ScanNet/tree/master/SensReader) tool.
+
+Once the ScanNet dataset is downloaded in `~/data/scannet`, you can run the following:
+
+```sh
+./match_pairs.py --input_dir ~/data/scannet --input_pairs assets/scannet_test_pairs_with_gt.txt --output_dir dump_scannet_test_results --eval
+```
+
+You should get the following table for ScanNet (or something very close to it, see this [note](#a-note-on-reproducibility)):
+
+```txt
+Evaluation Results (mean over 1500 pairs):
+AUC@5 AUC@10 AUC@20 Prec MScore
+16.12 33.76 51.79 84.37 31.14
+```
+
+
+
+### Reproducing the outdoor evaluation on YFCC
+
+
+ [Click to expand]
+
+We provide the groundtruth for YFCC in our format in the file `assets/yfcc_test_pairs_with_gt.txt` for convenience. In order to reproduce similar tables to what was in the paper, you will need to download the dataset (we do not provide the raw test images). To download the YFCC dataset, you can use the [OANet](https://github.com/zjhthu/OANet) repo:
+
+```sh
+git clone https://github.com/zjhthu/OANet
+cd OANet
+bash download_data.sh raw_data raw_data_yfcc.tar.gz 0 8
+tar -xvf raw_data_yfcc.tar.gz
+mv raw_data/yfcc100m ~/data
+```
+
+Once the YFCC dataset is downloaded in `~/data/yfcc100m`, you can run the following:
+
+```sh
+./match_pairs.py --input_dir ~/data/yfcc100m --input_pairs assets/yfcc_test_pairs_with_gt.txt --output_dir dump_yfcc_test_results --eval --resize 1600 --superglue outdoor --max_keypoints 2048 --nms_radius 3 --resize_float
+```
+
+You should get the following table for YFCC (or something very close to it, see this [note](#a-note-on-reproducibility)):
+
+```txt
+Evaluation Results (mean over 4000 pairs):
+AUC@5 AUC@10 AUC@20 Prec MScore
+39.02 59.51 75.72 98.72 23.61
+```
+
+
+
+### Reproducing outdoor evaluation on Phototourism
+
+
+ [Click to expand]
+
+The Phototourism results shown in the paper were produced using similar data as the test set from the [Image Matching Challenge 2020](https://vision.uvic.ca/image-matching-challenge/), which holds the ground truth data private for the test set. We list the pairs we used in `assets/phototourism_test_pairs.txt`. To reproduce similar numbers on this test set, please submit to the challenge benchmark. While the challenge is still live, we cannot share the test set publically since we want to help maintain the integrity of the challenge.
+
+
+
+### Correcting EXIF rotation data in YFCC and Phototourism
+
+
+ [Click to expand]
+
+In this repo, we provide manually corrected the EXIF rotation data for the outdoor evaluations on YFCC and Phototourism. For the YFCC dataset we found 7 images with incorrect EXIF rotation flags, resulting in 148 pairs out of 4000 being corrected. For Phototourism, we found 36 images with incorrect EXIF rotation flags, resulting in 212 out of 2200 pairs being corrected.
+
+The SuperGlue paper reports the results of SuperGlue **without** the corrected rotations, while the numbers in this README are reported **with** the corrected rotations. We found that our final conclusions from the evaluation still hold with or without the corrected rotations. For backwards compatability, we included the original, uncorrected EXIF rotation data in `assets/phototourism_test_pairs_original.txt` and `assets/yfcc_test_pairs_with_gt_original.txt` respectively.
+
+
+
+### Outdoor training / validation scene splits of MegaDepth
+
+
+ [Click to expand]
+
+For training and validation of the outdoor model, we used scenes from the [MegaDepth dataset](http://www.cs.cornell.edu/projects/megadepth/). We provide the list of scenes used to train the outdoor model in the `assets/` directory:
+
+* Training set: `assets/megadepth_train_scenes.txt`
+* Validation set: `assets/megadepth_validation_scenes.txt`
+
+
+
+### A note on reproducibility
+
+
+ [Click to expand]
+
+After simplifying the model code and evaluation code and preparing it for release, we made some improvements and tweaks that result in slightly different numbers than what was reported in the paper. The numbers and figures reported in the README were done using Ubuntu 16.04, OpenCV 3.4.5, and PyTorch 1.1.0. Even with matching the library versions, we observed some slight differences across Mac and Ubuntu, which we believe are due to differences in OpenCV's image resize function implementation and randomization of RANSAC.
+
+
+### Creating high-quality PDF visualizations and faster visualization with --fast_viz
+
+
+ [Click to expand]
+
+When generating output images with `match_pairs.py`, the default `--viz` flag uses a Matplotlib renderer which allows for the generation of camera-ready PDF visualizations if you additionally use `--viz_extension pdf` instead of the default png extension.
+
+```
+./match_pairs.py --viz --viz_extension pdf
+```
+
+Alternatively, you might want to save visualization images but have the generation be much faster. You can use the `--fast_viz` flag to use an OpenCV-based image renderer as follows:
+
+```
+./match_pairs.py --viz --fast_viz
+```
+
+If you would also like an OpenCV display window to preview the results (you must use non-pdf output and use fast_fiz), simply run:
+
+```
+./match_pairs.py --viz --fast_viz --opencv_display
+```
+
+
+
+
+## BibTeX Citation
+If you use any ideas from the paper or code from this repo, please consider citing:
+
+```txt
+@inproceedings{sarlin20superglue,
+ author = {Paul-Edouard Sarlin and
+ Daniel DeTone and
+ Tomasz Malisiewicz and
+ Andrew Rabinovich},
+ title = {{SuperGlue}: Learning Feature Matching with Graph Neural Networks},
+ booktitle = {CVPR},
+ year = {2020},
+ url = {https://arxiv.org/abs/1911.11763}
+}
+```
+
+## Additional Notes
+* For the demo, we found that the keyboard interaction works well with OpenCV 4.1.2.30, older versions were less responsive and the newest version had a [OpenCV bug on Mac](https://stackoverflow.com/questions/60032540/opencv-cv2-imshow-is-not-working-because-of-the-qt)
+* We generally do not recommend to run SuperPoint+SuperGlue below 160x120 resolution (QQVGA) and above 2000x1500
+* We do not intend to release the SuperGlue training code.
+* We do not intend to release the SIFT-based or homography SuperGlue models.
+
+## Legal Disclaimer
+Magic Leap is proud to provide its latest samples, toolkits, and research projects on Github to foster development and gather feedback from the spatial computing community. Use of the resources within this repo is subject to (a) the license(s) included herein, or (b) if no license is included, Magic Leap's [Developer Agreement](https://id.magicleap.com/terms/developer), which is available on our [Developer Portal](https://developer.magicleap.com/).
+If you need more, just ask on the [forums](https://forum.magicleap.com/hc/en-us/community/topics)!
+We're thrilled to be part of a well-meaning, friendly and welcoming community of millions.
diff --git a/third_party/SuperGluePretrainedNetwork/demo_superglue.py b/third_party/SuperGluePretrainedNetwork/demo_superglue.py
new file mode 100644
index 0000000000000000000000000000000000000000..c639efd7481052b842c640d4aa23aaf18e0eb449
--- /dev/null
+++ b/third_party/SuperGluePretrainedNetwork/demo_superglue.py
@@ -0,0 +1,322 @@
+#! /usr/bin/env python3
+#
+# %BANNER_BEGIN%
+# ---------------------------------------------------------------------
+# %COPYRIGHT_BEGIN%
+#
+# Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
+#
+# Unpublished Copyright (c) 2020
+# Magic Leap, Inc., All Rights Reserved.
+#
+# NOTICE: All information contained herein is, and remains the property
+# of COMPANY. The intellectual and technical concepts contained herein
+# are proprietary to COMPANY and may be covered by U.S. and Foreign
+# Patents, patents in process, and are protected by trade secret or
+# copyright law. Dissemination of this information or reproduction of
+# this material is strictly forbidden unless prior written permission is
+# obtained from COMPANY. Access to the source code contained herein is
+# hereby forbidden to anyone except current COMPANY employees, managers
+# or contractors who have executed Confidentiality and Non-disclosure
+# agreements explicitly covering such access.
+#
+# The copyright notice above does not evidence any actual or intended
+# publication or disclosure of this source code, which includes
+# information that is confidential and/or proprietary, and is a trade
+# secret, of COMPANY. ANY REPRODUCTION, MODIFICATION, DISTRIBUTION,
+# PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS
+# SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS
+# STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND
+# INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF THIS SOURCE
+# CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS
+# TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE,
+# USE, OR SELL ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART.
+#
+# %COPYRIGHT_END%
+# ----------------------------------------------------------------------
+# %AUTHORS_BEGIN%
+#
+# Originating Authors: Paul-Edouard Sarlin
+# Daniel DeTone
+# Tomasz Malisiewicz
+#
+# %AUTHORS_END%
+# --------------------------------------------------------------------*/
+# %BANNER_END%
+
+from pathlib import Path
+import argparse
+import cv2
+import matplotlib.cm as cm
+import torch
+
+from models.matching import Matching
+from models.utils import (
+ AverageTimer,
+ VideoStreamer,
+ make_matching_plot_fast,
+ frame2tensor,
+)
+
+torch.set_grad_enabled(False)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(
+ description="SuperGlue demo",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+ parser.add_argument(
+ "--input",
+ type=str,
+ default="0",
+ help="ID of a USB webcam, URL of an IP camera, "
+ "or path to an image directory or movie file",
+ )
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ default=None,
+ help="Directory where to write output frames (If None, no output)",
+ )
+
+ parser.add_argument(
+ "--image_glob",
+ type=str,
+ nargs="+",
+ default=["*.png", "*.jpg", "*.jpeg"],
+ help="Glob if a directory of images is specified",
+ )
+ parser.add_argument(
+ "--skip",
+ type=int,
+ default=1,
+ help="Images to skip if input is a movie or directory",
+ )
+ parser.add_argument(
+ "--max_length",
+ type=int,
+ default=1000000,
+ help="Maximum length if input is a movie or directory",
+ )
+ parser.add_argument(
+ "--resize",
+ type=int,
+ nargs="+",
+ default=[640, 480],
+ help="Resize the input image before running inference. If two numbers, "
+ "resize to the exact dimensions, if one number, resize the max "
+ "dimension, if -1, do not resize",
+ )
+
+ parser.add_argument(
+ "--superglue",
+ choices={"indoor", "outdoor"},
+ default="indoor",
+ help="SuperGlue weights",
+ )
+ parser.add_argument(
+ "--max_keypoints",
+ type=int,
+ default=-1,
+ help="Maximum number of keypoints detected by Superpoint"
+ " ('-1' keeps all keypoints)",
+ )
+ parser.add_argument(
+ "--keypoint_threshold",
+ type=float,
+ default=0.005,
+ help="SuperPoint keypoint detector confidence threshold",
+ )
+ parser.add_argument(
+ "--nms_radius",
+ type=int,
+ default=4,
+ help="SuperPoint Non Maximum Suppression (NMS) radius" " (Must be positive)",
+ )
+ parser.add_argument(
+ "--sinkhorn_iterations",
+ type=int,
+ default=20,
+ help="Number of Sinkhorn iterations performed by SuperGlue",
+ )
+ parser.add_argument(
+ "--match_threshold", type=float, default=0.2, help="SuperGlue match threshold"
+ )
+
+ parser.add_argument(
+ "--show_keypoints", action="store_true", help="Show the detected keypoints"
+ )
+ parser.add_argument(
+ "--no_display",
+ action="store_true",
+ help="Do not display images to screen. Useful if running remotely",
+ )
+ parser.add_argument(
+ "--force_cpu", action="store_true", help="Force pytorch to run in CPU mode."
+ )
+
+ opt = parser.parse_args()
+ print(opt)
+
+ if len(opt.resize) == 2 and opt.resize[1] == -1:
+ opt.resize = opt.resize[0:1]
+ if len(opt.resize) == 2:
+ print("Will resize to {}x{} (WxH)".format(opt.resize[0], opt.resize[1]))
+ elif len(opt.resize) == 1 and opt.resize[0] > 0:
+ print("Will resize max dimension to {}".format(opt.resize[0]))
+ elif len(opt.resize) == 1:
+ print("Will not resize images")
+ else:
+ raise ValueError("Cannot specify more than two integers for --resize")
+
+ device = "cuda" if torch.cuda.is_available() and not opt.force_cpu else "cpu"
+ print('Running inference on device "{}"'.format(device))
+ config = {
+ "superpoint": {
+ "nms_radius": opt.nms_radius,
+ "keypoint_threshold": opt.keypoint_threshold,
+ "max_keypoints": opt.max_keypoints,
+ },
+ "superglue": {
+ "weights": opt.superglue,
+ "sinkhorn_iterations": opt.sinkhorn_iterations,
+ "match_threshold": opt.match_threshold,
+ },
+ }
+ matching = Matching(config).eval().to(device)
+ keys = ["keypoints", "scores", "descriptors"]
+
+ vs = VideoStreamer(opt.input, opt.resize, opt.skip, opt.image_glob, opt.max_length)
+ frame, ret = vs.next_frame()
+ assert ret, "Error when reading the first frame (try different --input?)"
+
+ frame_tensor = frame2tensor(frame, device)
+ last_data = matching.superpoint({"image": frame_tensor})
+ last_data = {k + "0": last_data[k] for k in keys}
+ last_data["image0"] = frame_tensor
+ last_frame = frame
+ last_image_id = 0
+
+ if opt.output_dir is not None:
+ print("==> Will write outputs to {}".format(opt.output_dir))
+ Path(opt.output_dir).mkdir(exist_ok=True)
+
+ # Create a window to display the demo.
+ if not opt.no_display:
+ cv2.namedWindow("SuperGlue matches", cv2.WINDOW_NORMAL)
+ cv2.resizeWindow("SuperGlue matches", 640 * 2, 480)
+ else:
+ print("Skipping visualization, will not show a GUI.")
+
+ # Print the keyboard help menu.
+ print(
+ "==> Keyboard control:\n"
+ "\tn: select the current frame as the anchor\n"
+ "\te/r: increase/decrease the keypoint confidence threshold\n"
+ "\td/f: increase/decrease the match filtering threshold\n"
+ "\tk: toggle the visualization of keypoints\n"
+ "\tq: quit"
+ )
+
+ timer = AverageTimer()
+
+ while True:
+ frame, ret = vs.next_frame()
+ if not ret:
+ print("Finished demo_superglue.py")
+ break
+ timer.update("data")
+ stem0, stem1 = last_image_id, vs.i - 1
+
+ frame_tensor = frame2tensor(frame, device)
+ pred = matching({**last_data, "image1": frame_tensor})
+ kpts0 = last_data["keypoints0"][0].cpu().numpy()
+ kpts1 = pred["keypoints1"][0].cpu().numpy()
+ matches = pred["matches0"][0].cpu().numpy()
+ confidence = pred["matching_scores0"][0].cpu().numpy()
+ timer.update("forward")
+
+ valid = matches > -1
+ mkpts0 = kpts0[valid]
+ mkpts1 = kpts1[matches[valid]]
+ color = cm.jet(confidence[valid])
+ text = [
+ "SuperGlue",
+ "Keypoints: {}:{}".format(len(kpts0), len(kpts1)),
+ "Matches: {}".format(len(mkpts0)),
+ ]
+ k_thresh = matching.superpoint.config["keypoint_threshold"]
+ m_thresh = matching.superglue.config["match_threshold"]
+ small_text = [
+ "Keypoint Threshold: {:.4f}".format(k_thresh),
+ "Match Threshold: {:.2f}".format(m_thresh),
+ "Image Pair: {:06}:{:06}".format(stem0, stem1),
+ ]
+ out = make_matching_plot_fast(
+ last_frame,
+ frame,
+ kpts0,
+ kpts1,
+ mkpts0,
+ mkpts1,
+ color,
+ text,
+ path=None,
+ show_keypoints=opt.show_keypoints,
+ small_text=small_text,
+ )
+
+ if not opt.no_display:
+ cv2.imshow("SuperGlue matches", out)
+ key = chr(cv2.waitKey(1) & 0xFF)
+ if key == "q":
+ vs.cleanup()
+ print("Exiting (via q) demo_superglue.py")
+ break
+ elif key == "n": # set the current frame as anchor
+ last_data = {k + "0": pred[k + "1"] for k in keys}
+ last_data["image0"] = frame_tensor
+ last_frame = frame
+ last_image_id = vs.i - 1
+ elif key in ["e", "r"]:
+ # Increase/decrease keypoint threshold by 10% each keypress.
+ d = 0.1 * (-1 if key == "e" else 1)
+ matching.superpoint.config["keypoint_threshold"] = min(
+ max(
+ 0.0001,
+ matching.superpoint.config["keypoint_threshold"] * (1 + d),
+ ),
+ 1,
+ )
+ print(
+ "\nChanged the keypoint threshold to {:.4f}".format(
+ matching.superpoint.config["keypoint_threshold"]
+ )
+ )
+ elif key in ["d", "f"]:
+ # Increase/decrease match threshold by 0.05 each keypress.
+ d = 0.05 * (-1 if key == "d" else 1)
+ matching.superglue.config["match_threshold"] = min(
+ max(0.05, matching.superglue.config["match_threshold"] + d), 0.95
+ )
+ print(
+ "\nChanged the match threshold to {:.2f}".format(
+ matching.superglue.config["match_threshold"]
+ )
+ )
+ elif key == "k":
+ opt.show_keypoints = not opt.show_keypoints
+
+ timer.update("viz")
+ timer.print()
+
+ if opt.output_dir is not None:
+ # stem = 'matches_{:06}_{:06}'.format(last_image_id, vs.i-1)
+ stem = "matches_{:06}_{:06}".format(stem0, stem1)
+ out_file = str(Path(opt.output_dir, stem + ".png"))
+ print("\nWriting image to {}".format(out_file))
+ cv2.imwrite(out_file, out)
+
+ cv2.destroyAllWindows()
+ vs.cleanup()
diff --git a/third_party/SuperGluePretrainedNetwork/match_pairs.py b/third_party/SuperGluePretrainedNetwork/match_pairs.py
new file mode 100644
index 0000000000000000000000000000000000000000..9dcbcadd3ca8efc053cf4ea33c825ff75728bef1
--- /dev/null
+++ b/third_party/SuperGluePretrainedNetwork/match_pairs.py
@@ -0,0 +1,521 @@
+#! /usr/bin/env python3
+#
+# %BANNER_BEGIN%
+# ---------------------------------------------------------------------
+# %COPYRIGHT_BEGIN%
+#
+# Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
+#
+# Unpublished Copyright (c) 2020
+# Magic Leap, Inc., All Rights Reserved.
+#
+# NOTICE: All information contained herein is, and remains the property
+# of COMPANY. The intellectual and technical concepts contained herein
+# are proprietary to COMPANY and may be covered by U.S. and Foreign
+# Patents, patents in process, and are protected by trade secret or
+# copyright law. Dissemination of this information or reproduction of
+# this material is strictly forbidden unless prior written permission is
+# obtained from COMPANY. Access to the source code contained herein is
+# hereby forbidden to anyone except current COMPANY employees, managers
+# or contractors who have executed Confidentiality and Non-disclosure
+# agreements explicitly covering such access.
+#
+# The copyright notice above does not evidence any actual or intended
+# publication or disclosure of this source code, which includes
+# information that is confidential and/or proprietary, and is a trade
+# secret, of COMPANY. ANY REPRODUCTION, MODIFICATION, DISTRIBUTION,
+# PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS
+# SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS
+# STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND
+# INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF THIS SOURCE
+# CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS
+# TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE,
+# USE, OR SELL ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART.
+#
+# %COPYRIGHT_END%
+# ----------------------------------------------------------------------
+# %AUTHORS_BEGIN%
+#
+# Originating Authors: Paul-Edouard Sarlin
+# Daniel DeTone
+# Tomasz Malisiewicz
+#
+# %AUTHORS_END%
+# --------------------------------------------------------------------*/
+# %BANNER_END%
+
+from pathlib import Path
+import argparse
+import random
+import numpy as np
+import matplotlib.cm as cm
+import torch
+
+
+from models.matching import Matching
+from models.utils import (
+ compute_pose_error,
+ compute_epipolar_error,
+ estimate_pose,
+ make_matching_plot,
+ error_colormap,
+ AverageTimer,
+ pose_auc,
+ read_image,
+ rotate_intrinsics,
+ rotate_pose_inplane,
+ scale_intrinsics,
+)
+
+torch.set_grad_enabled(False)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(
+ description="Image pair matching and pose evaluation with SuperGlue",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+
+ parser.add_argument(
+ "--input_pairs",
+ type=str,
+ default="assets/scannet_sample_pairs_with_gt.txt",
+ help="Path to the list of image pairs",
+ )
+ parser.add_argument(
+ "--input_dir",
+ type=str,
+ default="assets/scannet_sample_images/",
+ help="Path to the directory that contains the images",
+ )
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ default="dump_match_pairs/",
+ help="Path to the directory in which the .npz results and optionally,"
+ "the visualization images are written",
+ )
+
+ parser.add_argument(
+ "--max_length", type=int, default=-1, help="Maximum number of pairs to evaluate"
+ )
+ parser.add_argument(
+ "--resize",
+ type=int,
+ nargs="+",
+ default=[640, 480],
+ help="Resize the input image before running inference. If two numbers, "
+ "resize to the exact dimensions, if one number, resize the max "
+ "dimension, if -1, do not resize",
+ )
+ parser.add_argument(
+ "--resize_float",
+ action="store_true",
+ help="Resize the image after casting uint8 to float",
+ )
+
+ parser.add_argument(
+ "--superglue",
+ choices={"indoor", "outdoor"},
+ default="indoor",
+ help="SuperGlue weights",
+ )
+ parser.add_argument(
+ "--max_keypoints",
+ type=int,
+ default=1024,
+ help="Maximum number of keypoints detected by Superpoint"
+ " ('-1' keeps all keypoints)",
+ )
+ parser.add_argument(
+ "--keypoint_threshold",
+ type=float,
+ default=0.005,
+ help="SuperPoint keypoint detector confidence threshold",
+ )
+ parser.add_argument(
+ "--nms_radius",
+ type=int,
+ default=4,
+ help="SuperPoint Non Maximum Suppression (NMS) radius" " (Must be positive)",
+ )
+ parser.add_argument(
+ "--sinkhorn_iterations",
+ type=int,
+ default=20,
+ help="Number of Sinkhorn iterations performed by SuperGlue",
+ )
+ parser.add_argument(
+ "--match_threshold", type=float, default=0.2, help="SuperGlue match threshold"
+ )
+
+ parser.add_argument(
+ "--viz", action="store_true", help="Visualize the matches and dump the plots"
+ )
+ parser.add_argument(
+ "--eval",
+ action="store_true",
+ help="Perform the evaluation" " (requires ground truth pose and intrinsics)",
+ )
+ parser.add_argument(
+ "--fast_viz",
+ action="store_true",
+ help="Use faster image visualization with OpenCV instead of Matplotlib",
+ )
+ parser.add_argument(
+ "--cache",
+ action="store_true",
+ help="Skip the pair if output .npz files are already found",
+ )
+ parser.add_argument(
+ "--show_keypoints",
+ action="store_true",
+ help="Plot the keypoints in addition to the matches",
+ )
+ parser.add_argument(
+ "--viz_extension",
+ type=str,
+ default="png",
+ choices=["png", "pdf"],
+ help="Visualization file extension. Use pdf for highest-quality.",
+ )
+ parser.add_argument(
+ "--opencv_display",
+ action="store_true",
+ help="Visualize via OpenCV before saving output images",
+ )
+ parser.add_argument(
+ "--shuffle",
+ action="store_true",
+ help="Shuffle ordering of pairs before processing",
+ )
+ parser.add_argument(
+ "--force_cpu", action="store_true", help="Force pytorch to run in CPU mode."
+ )
+
+ opt = parser.parse_args()
+ print(opt)
+
+ assert not (
+ opt.opencv_display and not opt.viz
+ ), "Must use --viz with --opencv_display"
+ assert not (
+ opt.opencv_display and not opt.fast_viz
+ ), "Cannot use --opencv_display without --fast_viz"
+ assert not (opt.fast_viz and not opt.viz), "Must use --viz with --fast_viz"
+ assert not (
+ opt.fast_viz and opt.viz_extension == "pdf"
+ ), "Cannot use pdf extension with --fast_viz"
+
+ if len(opt.resize) == 2 and opt.resize[1] == -1:
+ opt.resize = opt.resize[0:1]
+ if len(opt.resize) == 2:
+ print("Will resize to {}x{} (WxH)".format(opt.resize[0], opt.resize[1]))
+ elif len(opt.resize) == 1 and opt.resize[0] > 0:
+ print("Will resize max dimension to {}".format(opt.resize[0]))
+ elif len(opt.resize) == 1:
+ print("Will not resize images")
+ else:
+ raise ValueError("Cannot specify more than two integers for --resize")
+
+ with open(opt.input_pairs, "r") as f:
+ pairs = [l.split() for l in f.readlines()]
+
+ if opt.max_length > -1:
+ pairs = pairs[0 : np.min([len(pairs), opt.max_length])]
+
+ if opt.shuffle:
+ random.Random(0).shuffle(pairs)
+
+ if opt.eval:
+ if not all([len(p) == 38 for p in pairs]):
+ raise ValueError(
+ "All pairs should have ground truth info for evaluation."
+ 'File "{}" needs 38 valid entries per row'.format(opt.input_pairs)
+ )
+
+ # Load the SuperPoint and SuperGlue models.
+ device = "cuda" if torch.cuda.is_available() and not opt.force_cpu else "cpu"
+ print('Running inference on device "{}"'.format(device))
+ config = {
+ "superpoint": {
+ "nms_radius": opt.nms_radius,
+ "keypoint_threshold": opt.keypoint_threshold,
+ "max_keypoints": opt.max_keypoints,
+ },
+ "superglue": {
+ "weights": opt.superglue,
+ "sinkhorn_iterations": opt.sinkhorn_iterations,
+ "match_threshold": opt.match_threshold,
+ },
+ }
+ matching = Matching(config).eval().to(device)
+
+ # Create the output directories if they do not exist already.
+ input_dir = Path(opt.input_dir)
+ print('Looking for data in directory "{}"'.format(input_dir))
+ output_dir = Path(opt.output_dir)
+ output_dir.mkdir(exist_ok=True, parents=True)
+ print('Will write matches to directory "{}"'.format(output_dir))
+ if opt.eval:
+ print("Will write evaluation results", 'to directory "{}"'.format(output_dir))
+ if opt.viz:
+ print("Will write visualization images to", 'directory "{}"'.format(output_dir))
+
+ timer = AverageTimer(newline=True)
+ for i, pair in enumerate(pairs):
+ name0, name1 = pair[:2]
+ stem0, stem1 = Path(name0).stem, Path(name1).stem
+ matches_path = output_dir / "{}_{}_matches.npz".format(stem0, stem1)
+ eval_path = output_dir / "{}_{}_evaluation.npz".format(stem0, stem1)
+ viz_path = output_dir / "{}_{}_matches.{}".format(
+ stem0, stem1, opt.viz_extension
+ )
+ viz_eval_path = output_dir / "{}_{}_evaluation.{}".format(
+ stem0, stem1, opt.viz_extension
+ )
+
+ # Handle --cache logic.
+ do_match = True
+ do_eval = opt.eval
+ do_viz = opt.viz
+ do_viz_eval = opt.eval and opt.viz
+ if opt.cache:
+ if matches_path.exists():
+ try:
+ results = np.load(matches_path)
+ except:
+ raise IOError("Cannot load matches .npz file: %s" % matches_path)
+
+ kpts0, kpts1 = results["keypoints0"], results["keypoints1"]
+ matches, conf = results["matches"], results["match_confidence"]
+ do_match = False
+ if opt.eval and eval_path.exists():
+ try:
+ results = np.load(eval_path)
+ except:
+ raise IOError("Cannot load eval .npz file: %s" % eval_path)
+ err_R, err_t = results["error_R"], results["error_t"]
+ precision = results["precision"]
+ matching_score = results["matching_score"]
+ num_correct = results["num_correct"]
+ epi_errs = results["epipolar_errors"]
+ do_eval = False
+ if opt.viz and viz_path.exists():
+ do_viz = False
+ if opt.viz and opt.eval and viz_eval_path.exists():
+ do_viz_eval = False
+ timer.update("load_cache")
+
+ if not (do_match or do_eval or do_viz or do_viz_eval):
+ timer.print("Finished pair {:5} of {:5}".format(i, len(pairs)))
+ continue
+
+ # If a rotation integer is provided (e.g. from EXIF data), use it:
+ if len(pair) >= 5:
+ rot0, rot1 = int(pair[2]), int(pair[3])
+ else:
+ rot0, rot1 = 0, 0
+
+ # Load the image pair.
+ image0, inp0, scales0 = read_image(
+ input_dir / name0, device, opt.resize, rot0, opt.resize_float
+ )
+ image1, inp1, scales1 = read_image(
+ input_dir / name1, device, opt.resize, rot1, opt.resize_float
+ )
+ if image0 is None or image1 is None:
+ print(
+ "Problem reading image pair: {} {}".format(
+ input_dir / name0, input_dir / name1
+ )
+ )
+ exit(1)
+ timer.update("load_image")
+
+ if do_match:
+ # Perform the matching.
+ pred = matching({"image0": inp0, "image1": inp1})
+ pred = {k: v[0].cpu().numpy() for k, v in pred.items()}
+ kpts0, kpts1 = pred["keypoints0"], pred["keypoints1"]
+ matches, conf = pred["matches0"], pred["matching_scores0"]
+ timer.update("matcher")
+
+ # Write the matches to disk.
+ out_matches = {
+ "keypoints0": kpts0,
+ "keypoints1": kpts1,
+ "matches": matches,
+ "match_confidence": conf,
+ }
+ np.savez(str(matches_path), **out_matches)
+
+ # Keep the matching keypoints.
+ valid = matches > -1
+ mkpts0 = kpts0[valid]
+ mkpts1 = kpts1[matches[valid]]
+ mconf = conf[valid]
+
+ if do_eval:
+ # Estimate the pose and compute the pose error.
+ assert len(pair) == 38, "Pair does not have ground truth info"
+ K0 = np.array(pair[4:13]).astype(float).reshape(3, 3)
+ K1 = np.array(pair[13:22]).astype(float).reshape(3, 3)
+ T_0to1 = np.array(pair[22:]).astype(float).reshape(4, 4)
+
+ # Scale the intrinsics to resized image.
+ K0 = scale_intrinsics(K0, scales0)
+ K1 = scale_intrinsics(K1, scales1)
+
+ # Update the intrinsics + extrinsics if EXIF rotation was found.
+ if rot0 != 0 or rot1 != 0:
+ cam0_T_w = np.eye(4)
+ cam1_T_w = T_0to1
+ if rot0 != 0:
+ K0 = rotate_intrinsics(K0, image0.shape, rot0)
+ cam0_T_w = rotate_pose_inplane(cam0_T_w, rot0)
+ if rot1 != 0:
+ K1 = rotate_intrinsics(K1, image1.shape, rot1)
+ cam1_T_w = rotate_pose_inplane(cam1_T_w, rot1)
+ cam1_T_cam0 = cam1_T_w @ np.linalg.inv(cam0_T_w)
+ T_0to1 = cam1_T_cam0
+
+ epi_errs = compute_epipolar_error(mkpts0, mkpts1, T_0to1, K0, K1)
+ correct = epi_errs < 5e-4
+ num_correct = np.sum(correct)
+ precision = np.mean(correct) if len(correct) > 0 else 0
+ matching_score = num_correct / len(kpts0) if len(kpts0) > 0 else 0
+
+ thresh = 1.0 # In pixels relative to resized image size.
+ ret = estimate_pose(mkpts0, mkpts1, K0, K1, thresh)
+ if ret is None:
+ err_t, err_R = np.inf, np.inf
+ else:
+ R, t, inliers = ret
+ err_t, err_R = compute_pose_error(T_0to1, R, t)
+
+ # Write the evaluation results to disk.
+ out_eval = {
+ "error_t": err_t,
+ "error_R": err_R,
+ "precision": precision,
+ "matching_score": matching_score,
+ "num_correct": num_correct,
+ "epipolar_errors": epi_errs,
+ }
+ np.savez(str(eval_path), **out_eval)
+ timer.update("eval")
+
+ if do_viz:
+ # Visualize the matches.
+ color = cm.jet(mconf)
+ text = [
+ "SuperGlue",
+ "Keypoints: {}:{}".format(len(kpts0), len(kpts1)),
+ "Matches: {}".format(len(mkpts0)),
+ ]
+ if rot0 != 0 or rot1 != 0:
+ text.append("Rotation: {}:{}".format(rot0, rot1))
+
+ # Display extra parameter info.
+ k_thresh = matching.superpoint.config["keypoint_threshold"]
+ m_thresh = matching.superglue.config["match_threshold"]
+ small_text = [
+ "Keypoint Threshold: {:.4f}".format(k_thresh),
+ "Match Threshold: {:.2f}".format(m_thresh),
+ "Image Pair: {}:{}".format(stem0, stem1),
+ ]
+
+ make_matching_plot(
+ image0,
+ image1,
+ kpts0,
+ kpts1,
+ mkpts0,
+ mkpts1,
+ color,
+ text,
+ viz_path,
+ opt.show_keypoints,
+ opt.fast_viz,
+ opt.opencv_display,
+ "Matches",
+ small_text,
+ )
+
+ timer.update("viz_match")
+
+ if do_viz_eval:
+ # Visualize the evaluation results for the image pair.
+ color = np.clip((epi_errs - 0) / (1e-3 - 0), 0, 1)
+ color = error_colormap(1 - color)
+ deg, delta = " deg", "Delta "
+ if not opt.fast_viz:
+ deg, delta = "°", "$\\Delta$"
+ e_t = "FAIL" if np.isinf(err_t) else "{:.1f}{}".format(err_t, deg)
+ e_R = "FAIL" if np.isinf(err_R) else "{:.1f}{}".format(err_R, deg)
+ text = [
+ "SuperGlue",
+ "{}R: {}".format(delta, e_R),
+ "{}t: {}".format(delta, e_t),
+ "inliers: {}/{}".format(num_correct, (matches > -1).sum()),
+ ]
+ if rot0 != 0 or rot1 != 0:
+ text.append("Rotation: {}:{}".format(rot0, rot1))
+
+ # Display extra parameter info (only works with --fast_viz).
+ k_thresh = matching.superpoint.config["keypoint_threshold"]
+ m_thresh = matching.superglue.config["match_threshold"]
+ small_text = [
+ "Keypoint Threshold: {:.4f}".format(k_thresh),
+ "Match Threshold: {:.2f}".format(m_thresh),
+ "Image Pair: {}:{}".format(stem0, stem1),
+ ]
+
+ make_matching_plot(
+ image0,
+ image1,
+ kpts0,
+ kpts1,
+ mkpts0,
+ mkpts1,
+ color,
+ text,
+ viz_eval_path,
+ opt.show_keypoints,
+ opt.fast_viz,
+ opt.opencv_display,
+ "Relative Pose",
+ small_text,
+ )
+
+ timer.update("viz_eval")
+
+ timer.print("Finished pair {:5} of {:5}".format(i, len(pairs)))
+
+ if opt.eval:
+ # Collate the results into a final table and print to terminal.
+ pose_errors = []
+ precisions = []
+ matching_scores = []
+ for pair in pairs:
+ name0, name1 = pair[:2]
+ stem0, stem1 = Path(name0).stem, Path(name1).stem
+ eval_path = output_dir / "{}_{}_evaluation.npz".format(stem0, stem1)
+ results = np.load(eval_path)
+ pose_error = np.maximum(results["error_t"], results["error_R"])
+ pose_errors.append(pose_error)
+ precisions.append(results["precision"])
+ matching_scores.append(results["matching_score"])
+ thresholds = [5, 10, 20]
+ aucs = pose_auc(pose_errors, thresholds)
+ aucs = [100.0 * yy for yy in aucs]
+ prec = 100.0 * np.mean(precisions)
+ ms = 100.0 * np.mean(matching_scores)
+ print("Evaluation Results (mean over {} pairs):".format(len(pairs)))
+ print("AUC@5\t AUC@10\t AUC@20\t Prec\t MScore\t")
+ print(
+ "{:.2f}\t {:.2f}\t {:.2f}\t {:.2f}\t {:.2f}\t".format(
+ aucs[0], aucs[1], aucs[2], prec, ms
+ )
+ )
diff --git a/imcui/third_party/TopicFM/viz/methods/__init__.py b/third_party/SuperGluePretrainedNetwork/models/__init__.py
similarity index 100%
rename from imcui/third_party/TopicFM/viz/methods/__init__.py
rename to third_party/SuperGluePretrainedNetwork/models/__init__.py
diff --git a/imcui/third_party/SuperGluePretrainedNetwork/models/matching.py b/third_party/SuperGluePretrainedNetwork/models/matching.py
similarity index 83%
rename from imcui/third_party/SuperGluePretrainedNetwork/models/matching.py
rename to third_party/SuperGluePretrainedNetwork/models/matching.py
index 5d174208d146373230a8a68dd1420fc59c180633..c5c0eda3337d021464eb6283e57b7412c08afb03 100644
--- a/imcui/third_party/SuperGluePretrainedNetwork/models/matching.py
+++ b/third_party/SuperGluePretrainedNetwork/models/matching.py
@@ -47,14 +47,15 @@ from .superglue import SuperGlue
class Matching(torch.nn.Module):
- """ Image Matching Frontend (SuperPoint + SuperGlue) """
+ """Image Matching Frontend (SuperPoint + SuperGlue)"""
+
def __init__(self, config={}):
super().__init__()
- self.superpoint = SuperPoint(config.get('superpoint', {}))
- self.superglue = SuperGlue(config.get('superglue', {}))
+ self.superpoint = SuperPoint(config.get("superpoint", {}))
+ self.superglue = SuperGlue(config.get("superglue", {}))
def forward(self, data):
- """ Run SuperPoint (optionally) and SuperGlue
+ """Run SuperPoint (optionally) and SuperGlue
SuperPoint is skipped if ['keypoints0', 'keypoints1'] exist in input
Args:
data: dictionary with minimal keys: ['image0', 'image1']
@@ -62,12 +63,12 @@ class Matching(torch.nn.Module):
pred = {}
# Extract SuperPoint (keypoints, scores, descriptors) if not provided
- if 'keypoints0' not in data:
- pred0 = self.superpoint({'image': data['image0']})
- pred = {**pred, **{k+'0': v for k, v in pred0.items()}}
- if 'keypoints1' not in data:
- pred1 = self.superpoint({'image': data['image1']})
- pred = {**pred, **{k+'1': v for k, v in pred1.items()}}
+ if "keypoints0" not in data:
+ pred0 = self.superpoint({"image": data["image0"]})
+ pred = {**pred, **{k + "0": v for k, v in pred0.items()}}
+ if "keypoints1" not in data:
+ pred1 = self.superpoint({"image": data["image1"]})
+ pred = {**pred, **{k + "1": v for k, v in pred1.items()}}
# Batch all features
# We should either have i) one image per batch, or
diff --git a/imcui/third_party/SuperGluePretrainedNetwork/models/superglue.py b/third_party/SuperGluePretrainedNetwork/models/superglue.py
similarity index 67%
rename from imcui/third_party/SuperGluePretrainedNetwork/models/superglue.py
rename to third_party/SuperGluePretrainedNetwork/models/superglue.py
index 5a89b0348075bcb918eab123bc988c7102137a3d..70156e07b83614b1dfb36207ea96b4b79a6ddbb9 100644
--- a/imcui/third_party/SuperGluePretrainedNetwork/models/superglue.py
+++ b/third_party/SuperGluePretrainedNetwork/models/superglue.py
@@ -49,13 +49,12 @@ from torch import nn
def MLP(channels: List[int], do_bn: bool = True) -> nn.Module:
- """ Multi-layer perceptron """
+ """Multi-layer perceptron"""
n = len(channels)
layers = []
for i in range(1, n):
- layers.append(
- nn.Conv1d(channels[i - 1], channels[i], kernel_size=1, bias=True))
- if i < (n-1):
+ layers.append(nn.Conv1d(channels[i - 1], channels[i], kernel_size=1, bias=True))
+ if i < (n - 1):
if do_bn:
layers.append(nn.BatchNorm1d(channels[i]))
layers.append(nn.ReLU())
@@ -63,17 +62,18 @@ def MLP(channels: List[int], do_bn: bool = True) -> nn.Module:
def normalize_keypoints(kpts, image_shape):
- """ Normalize keypoints locations based on image image_shape"""
+ """Normalize keypoints locations based on image image_shape"""
_, _, height, width = image_shape
one = kpts.new_tensor(1)
- size = torch.stack([one*width, one*height])[None]
+ size = torch.stack([one * width, one * height])[None]
center = size / 2
scaling = size.max(1, keepdim=True).values * 0.7
return (kpts - center[:, None, :]) / scaling[:, None, :]
class KeypointEncoder(nn.Module):
- """ Joint encoding of visual appearance and location using MLPs"""
+ """Joint encoding of visual appearance and location using MLPs"""
+
def __init__(self, feature_dim: int, layers: List[int]) -> None:
super().__init__()
self.encoder = MLP([3] + layers + [feature_dim])
@@ -84,15 +84,18 @@ class KeypointEncoder(nn.Module):
return self.encoder(torch.cat(inputs, dim=1))
-def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> Tuple[torch.Tensor,torch.Tensor]:
+def attention(
+ query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
+) -> Tuple[torch.Tensor, torch.Tensor]:
dim = query.shape[1]
- scores = torch.einsum('bdhn,bdhm->bhnm', query, key) / dim**.5
+ scores = torch.einsum("bdhn,bdhm->bhnm", query, key) / dim**0.5
prob = torch.nn.functional.softmax(scores, dim=-1)
- return torch.einsum('bhnm,bdhm->bdhn', prob, value), prob
+ return torch.einsum("bhnm,bdhm->bdhn", prob, value), prob
class MultiHeadedAttention(nn.Module):
- """ Multi-head attention to increase model expressivitiy """
+ """Multi-head attention to increase model expressivitiy"""
+
def __init__(self, num_heads: int, d_model: int):
super().__init__()
assert d_model % num_heads == 0
@@ -101,19 +104,23 @@ class MultiHeadedAttention(nn.Module):
self.merge = nn.Conv1d(d_model, d_model, kernel_size=1)
self.proj = nn.ModuleList([deepcopy(self.merge) for _ in range(3)])
- def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
+ def forward(
+ self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
+ ) -> torch.Tensor:
batch_dim = query.size(0)
- query, key, value = [l(x).view(batch_dim, self.dim, self.num_heads, -1)
- for l, x in zip(self.proj, (query, key, value))]
+ query, key, value = [
+ l(x).view(batch_dim, self.dim, self.num_heads, -1)
+ for l, x in zip(self.proj, (query, key, value))
+ ]
x, _ = attention(query, key, value)
- return self.merge(x.contiguous().view(batch_dim, self.dim*self.num_heads, -1))
+ return self.merge(x.contiguous().view(batch_dim, self.dim * self.num_heads, -1))
class AttentionalPropagation(nn.Module):
def __init__(self, feature_dim: int, num_heads: int):
super().__init__()
self.attn = MultiHeadedAttention(num_heads, feature_dim)
- self.mlp = MLP([feature_dim*2, feature_dim*2, feature_dim])
+ self.mlp = MLP([feature_dim * 2, feature_dim * 2, feature_dim])
nn.init.constant_(self.mlp[-1].bias, 0.0)
def forward(self, x: torch.Tensor, source: torch.Tensor) -> torch.Tensor:
@@ -124,14 +131,16 @@ class AttentionalPropagation(nn.Module):
class AttentionalGNN(nn.Module):
def __init__(self, feature_dim: int, layer_names: List[str]) -> None:
super().__init__()
- self.layers = nn.ModuleList([
- AttentionalPropagation(feature_dim, 4)
- for _ in range(len(layer_names))])
+ self.layers = nn.ModuleList(
+ [AttentionalPropagation(feature_dim, 4) for _ in range(len(layer_names))]
+ )
self.names = layer_names
- def forward(self, desc0: torch.Tensor, desc1: torch.Tensor) -> Tuple[torch.Tensor,torch.Tensor]:
+ def forward(
+ self, desc0: torch.Tensor, desc1: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
for layer, name in zip(self.layers, self.names):
- if name == 'cross':
+ if name == "cross":
src0, src1 = desc1, desc0
else: # if name == 'self':
src0, src1 = desc0, desc1
@@ -140,8 +149,10 @@ class AttentionalGNN(nn.Module):
return desc0, desc1
-def log_sinkhorn_iterations(Z: torch.Tensor, log_mu: torch.Tensor, log_nu: torch.Tensor, iters: int) -> torch.Tensor:
- """ Perform Sinkhorn Normalization in Log-space for stability"""
+def log_sinkhorn_iterations(
+ Z: torch.Tensor, log_mu: torch.Tensor, log_nu: torch.Tensor, iters: int
+) -> torch.Tensor:
+ """Perform Sinkhorn Normalization in Log-space for stability"""
u, v = torch.zeros_like(log_mu), torch.zeros_like(log_nu)
for _ in range(iters):
u = log_mu - torch.logsumexp(Z + v.unsqueeze(1), dim=2)
@@ -149,20 +160,23 @@ def log_sinkhorn_iterations(Z: torch.Tensor, log_mu: torch.Tensor, log_nu: torch
return Z + u.unsqueeze(2) + v.unsqueeze(1)
-def log_optimal_transport(scores: torch.Tensor, alpha: torch.Tensor, iters: int) -> torch.Tensor:
- """ Perform Differentiable Optimal Transport in Log-space for stability"""
+def log_optimal_transport(
+ scores: torch.Tensor, alpha: torch.Tensor, iters: int
+) -> torch.Tensor:
+ """Perform Differentiable Optimal Transport in Log-space for stability"""
b, m, n = scores.shape
one = scores.new_tensor(1)
- ms, ns = (m*one).to(scores), (n*one).to(scores)
+ ms, ns = (m * one).to(scores), (n * one).to(scores)
bins0 = alpha.expand(b, m, 1)
bins1 = alpha.expand(b, 1, n)
alpha = alpha.expand(b, 1, 1)
- couplings = torch.cat([torch.cat([scores, bins0], -1),
- torch.cat([bins1, alpha], -1)], 1)
+ couplings = torch.cat(
+ [torch.cat([scores, bins0], -1), torch.cat([bins1, alpha], -1)], 1
+ )
- norm = - (ms + ns).log()
+ norm = -(ms + ns).log()
log_mu = torch.cat([norm.expand(m), ns.log()[None] + norm])
log_nu = torch.cat([norm.expand(n), ms.log()[None] + norm])
log_mu, log_nu = log_mu[None].expand(b, -1), log_nu[None].expand(b, -1)
@@ -194,13 +208,14 @@ class SuperGlue(nn.Module):
Networks. In CVPR, 2020. https://arxiv.org/abs/1911.11763
"""
+
default_config = {
- 'descriptor_dim': 256,
- 'weights': 'indoor',
- 'keypoint_encoder': [32, 64, 128, 256],
- 'GNN_layers': ['self', 'cross'] * 9,
- 'sinkhorn_iterations': 100,
- 'match_threshold': 0.2,
+ "descriptor_dim": 256,
+ "weights": "indoor",
+ "keypoint_encoder": [32, 64, 128, 256],
+ "GNN_layers": ["self", "cross"] * 9,
+ "sinkhorn_iterations": 100,
+ "match_threshold": 0.2,
}
def __init__(self, config):
@@ -208,46 +223,51 @@ class SuperGlue(nn.Module):
self.config = {**self.default_config, **config}
self.kenc = KeypointEncoder(
- self.config['descriptor_dim'], self.config['keypoint_encoder'])
+ self.config["descriptor_dim"], self.config["keypoint_encoder"]
+ )
self.gnn = AttentionalGNN(
- feature_dim=self.config['descriptor_dim'], layer_names=self.config['GNN_layers'])
+ feature_dim=self.config["descriptor_dim"],
+ layer_names=self.config["GNN_layers"],
+ )
self.final_proj = nn.Conv1d(
- self.config['descriptor_dim'], self.config['descriptor_dim'],
- kernel_size=1, bias=True)
+ self.config["descriptor_dim"],
+ self.config["descriptor_dim"],
+ kernel_size=1,
+ bias=True,
+ )
- bin_score = torch.nn.Parameter(torch.tensor(1.))
- self.register_parameter('bin_score', bin_score)
+ bin_score = torch.nn.Parameter(torch.tensor(1.0))
+ self.register_parameter("bin_score", bin_score)
- assert self.config['weights'] in ['indoor', 'outdoor']
+ assert self.config["weights"] in ["indoor", "outdoor"]
path = Path(__file__).parent
- path = path / 'weights/superglue_{}.pth'.format(self.config['weights'])
+ path = path / "weights/superglue_{}.pth".format(self.config["weights"])
self.load_state_dict(torch.load(str(path)))
- print('Loaded SuperGlue model (\"{}\" weights)'.format(
- self.config['weights']))
+ print('Loaded SuperGlue model ("{}" weights)'.format(self.config["weights"]))
def forward(self, data):
"""Run SuperGlue on a pair of keypoints and descriptors"""
- desc0, desc1 = data['descriptors0'], data['descriptors1']
- kpts0, kpts1 = data['keypoints0'], data['keypoints1']
+ desc0, desc1 = data["descriptors0"], data["descriptors1"]
+ kpts0, kpts1 = data["keypoints0"], data["keypoints1"]
if kpts0.shape[1] == 0 or kpts1.shape[1] == 0: # no keypoints
shape0, shape1 = kpts0.shape[:-1], kpts1.shape[:-1]
return {
- 'matches0': kpts0.new_full(shape0, -1, dtype=torch.int),
- 'matches1': kpts1.new_full(shape1, -1, dtype=torch.int),
- 'matching_scores0': kpts0.new_zeros(shape0),
- 'matching_scores1': kpts1.new_zeros(shape1),
+ "matches0": kpts0.new_full(shape0, -1, dtype=torch.int),
+ "matches1": kpts1.new_full(shape1, -1, dtype=torch.int),
+ "matching_scores0": kpts0.new_zeros(shape0),
+ "matching_scores1": kpts1.new_zeros(shape1),
}
# Keypoint normalization.
- kpts0 = normalize_keypoints(kpts0, data['image0'].shape)
- kpts1 = normalize_keypoints(kpts1, data['image1'].shape)
+ kpts0 = normalize_keypoints(kpts0, data["image0"].shape)
+ kpts1 = normalize_keypoints(kpts1, data["image1"].shape)
# Keypoint MLP encoder.
- desc0 = desc0 + self.kenc(kpts0, data['scores0'])
- desc1 = desc1 + self.kenc(kpts1, data['scores1'])
+ desc0 = desc0 + self.kenc(kpts0, data["scores0"])
+ desc1 = desc1 + self.kenc(kpts1, data["scores1"])
# Multi-layer Transformer network.
desc0, desc1 = self.gnn(desc0, desc1)
@@ -256,13 +276,13 @@ class SuperGlue(nn.Module):
mdesc0, mdesc1 = self.final_proj(desc0), self.final_proj(desc1)
# Compute matching descriptor distance.
- scores = torch.einsum('bdn,bdm->bnm', mdesc0, mdesc1)
- scores = scores / self.config['descriptor_dim']**.5
+ scores = torch.einsum("bdn,bdm->bnm", mdesc0, mdesc1)
+ scores = scores / self.config["descriptor_dim"] ** 0.5
# Run the optimal transport.
scores = log_optimal_transport(
- scores, self.bin_score,
- iters=self.config['sinkhorn_iterations'])
+ scores, self.bin_score, iters=self.config["sinkhorn_iterations"]
+ )
# Get the matches with score above "match_threshold".
max0, max1 = scores[:, :-1, :-1].max(2), scores[:, :-1, :-1].max(1)
@@ -272,14 +292,13 @@ class SuperGlue(nn.Module):
zero = scores.new_tensor(0)
mscores0 = torch.where(mutual0, max0.values.exp(), zero)
mscores1 = torch.where(mutual1, mscores0.gather(1, indices1), zero)
- valid0 = mutual0 & (mscores0 > self.config['match_threshold'])
+ valid0 = mutual0 & (mscores0 > self.config["match_threshold"])
valid1 = mutual1 & valid0.gather(1, indices1)
indices0 = torch.where(valid0, indices0, indices0.new_tensor(-1))
indices1 = torch.where(valid1, indices1, indices1.new_tensor(-1))
-
return {
- 'matches0': indices0, # use -1 for invalid match
- 'matches1': indices1, # use -1 for invalid match
- 'matching_scores0': mscores0,
- 'matching_scores1': mscores1,
+ "matches0": indices0, # use -1 for invalid match
+ "matches1": indices1, # use -1 for invalid match
+ "matching_scores0": mscores0,
+ "matching_scores1": mscores1,
}
diff --git a/imcui/third_party/SuperGluePretrainedNetwork/models/superpoint.py b/third_party/SuperGluePretrainedNetwork/models/superpoint.py
similarity index 72%
rename from imcui/third_party/SuperGluePretrainedNetwork/models/superpoint.py
rename to third_party/SuperGluePretrainedNetwork/models/superpoint.py
index 0577e1ec47c3397e45bc9a3cf2e47f211c32877e..9f0d205a9d85ea02bb1b16f1ad40d550d8a6f789 100644
--- a/imcui/third_party/SuperGluePretrainedNetwork/models/superpoint.py
+++ b/third_party/SuperGluePretrainedNetwork/models/superpoint.py
@@ -44,13 +44,15 @@ from pathlib import Path
import torch
from torch import nn
+
def simple_nms(scores, nms_radius: int):
- """ Fast Non-maximum suppression to remove nearby points """
- assert(nms_radius >= 0)
+ """Fast Non-maximum suppression to remove nearby points"""
+ assert nms_radius >= 0
def max_pool(x):
return torch.nn.functional.max_pool2d(
- x, kernel_size=nms_radius*2+1, stride=1, padding=nms_radius)
+ x, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius
+ )
zeros = torch.zeros_like(scores)
max_mask = scores == max_pool(scores)
@@ -63,7 +65,7 @@ def simple_nms(scores, nms_radius: int):
def remove_borders(keypoints, scores, border: int, height: int, width: int):
- """ Removes keypoints too close to the border """
+ """Removes keypoints too close to the border"""
mask_h = (keypoints[:, 0] >= border) & (keypoints[:, 0] < (height - border))
mask_w = (keypoints[:, 1] >= border) & (keypoints[:, 1] < (width - border))
mask = mask_h & mask_w
@@ -78,17 +80,20 @@ def top_k_keypoints(keypoints, scores, k: int):
def sample_descriptors(keypoints, descriptors, s: int = 8):
- """ Interpolate descriptors at keypoint locations """
+ """Interpolate descriptors at keypoint locations"""
b, c, h, w = descriptors.shape
keypoints = keypoints - s / 2 + 0.5
- keypoints /= torch.tensor([(w*s - s/2 - 0.5), (h*s - s/2 - 0.5)],
- ).to(keypoints)[None]
- keypoints = keypoints*2 - 1 # normalize to (-1, 1)
- args = {'align_corners': True} if torch.__version__ >= '1.3' else {}
+ keypoints /= torch.tensor(
+ [(w * s - s / 2 - 0.5), (h * s - s / 2 - 0.5)],
+ ).to(keypoints)[None]
+ keypoints = keypoints * 2 - 1 # normalize to (-1, 1)
+ args = {"align_corners": True} if torch.__version__ >= "1.3" else {}
descriptors = torch.nn.functional.grid_sample(
- descriptors, keypoints.view(b, 1, -1, 2), mode='bilinear', **args)
+ descriptors, keypoints.view(b, 1, -1, 2), mode="bilinear", **args
+ )
descriptors = torch.nn.functional.normalize(
- descriptors.reshape(b, c, -1), p=2, dim=1)
+ descriptors.reshape(b, c, -1), p=2, dim=1
+ )
return descriptors
@@ -100,12 +105,13 @@ class SuperPoint(nn.Module):
Rabinovich. In CVPRW, 2019. https://arxiv.org/abs/1712.07629
"""
+
default_config = {
- 'descriptor_dim': 256,
- 'nms_radius': 4,
- 'keypoint_threshold': 0.005,
- 'max_keypoints': -1,
- 'remove_borders': 4,
+ "descriptor_dim": 256,
+ "nms_radius": 4,
+ "keypoint_threshold": 0.005,
+ "max_keypoints": -1,
+ "remove_borders": 4,
}
def __init__(self, config):
@@ -130,17 +136,21 @@ class SuperPoint(nn.Module):
self.convDa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1)
self.convDb = nn.Conv2d(
- c5, self.config['descriptor_dim'],
- kernel_size=1, stride=1, padding=0)
-
- path = Path(__file__).parent / 'weights/superpoint_v1.pth'
+ c5,
+ self.config["descriptor_dim"],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ )
+
+ path = Path(__file__).parent / "weights/superpoint_v1.pth"
self.load_state_dict(torch.load(str(path)))
- mk = self.config['max_keypoints']
+ mk = self.config["max_keypoints"]
if mk == 0 or mk < -1:
- raise ValueError('\"max_keypoints\" must be positive or \"-1\"')
+ raise ValueError('"max_keypoints" must be positive or "-1"')
- print('Loaded SuperPoint model')
+ print("Loaded SuperPoint model")
def forward(self, data, cfg={}):
"""Compute keypoints, scores, descriptors for image"""
@@ -149,7 +159,7 @@ class SuperPoint(nn.Module):
**cfg,
}
# Shared Encoder
- x = self.relu(self.conv1a(data['image']))
+ x = self.relu(self.conv1a(data["image"]))
x = self.relu(self.conv1b(x))
x = self.pool(x)
x = self.relu(self.conv2a(x))
@@ -167,25 +177,37 @@ class SuperPoint(nn.Module):
scores = torch.nn.functional.softmax(scores, 1)[:, :-1]
b, _, h, w = scores.shape
scores = scores.permute(0, 2, 3, 1).reshape(b, h, w, 8, 8)
- scores = scores.permute(0, 1, 3, 2, 4).reshape(b, h*8, w*8)
- scores = simple_nms(scores, self.config['nms_radius'])
+ scores = scores.permute(0, 1, 3, 2, 4).reshape(b, h * 8, w * 8)
+ scores = simple_nms(scores, self.config["nms_radius"])
# Extract keypoints
keypoints = [
- torch.nonzero(s > self.config['keypoint_threshold'])
- for s in scores]
+ torch.nonzero(s > self.config["keypoint_threshold"]) for s in scores
+ ]
scores = [s[tuple(k.t())] for s, k in zip(scores, keypoints)]
# Discard keypoints near the image borders
- keypoints, scores = list(zip(*[
- remove_borders(k, s, self.config['remove_borders'], h*8, w*8)
- for k, s in zip(keypoints, scores)]))
+ keypoints, scores = list(
+ zip(
+ *[
+ remove_borders(
+ k, s, self.config["remove_borders"], h * 8, w * 8
+ )
+ for k, s in zip(keypoints, scores)
+ ]
+ )
+ )
# Keep the k keypoints with highest score
- if self.config['max_keypoints'] >= 0:
- keypoints, scores = list(zip(*[
- top_k_keypoints(k, s, self.config['max_keypoints'])
- for k, s in zip(keypoints, scores)]))
+ if self.config["max_keypoints"] >= 0:
+ keypoints, scores = list(
+ zip(
+ *[
+ top_k_keypoints(k, s, self.config["max_keypoints"])
+ for k, s in zip(keypoints, scores)
+ ]
+ )
+ )
# Convert (h, w) to (x, y)
keypoints = [torch.flip(k, [1]).float() for k in keypoints]
@@ -196,11 +218,13 @@ class SuperPoint(nn.Module):
descriptors = torch.nn.functional.normalize(descriptors, p=2, dim=1)
# Extract descriptors
- descriptors = [sample_descriptors(k[None], d[None], 8)[0]
- for k, d in zip(keypoints, descriptors)]
+ descriptors = [
+ sample_descriptors(k[None], d[None], 8)[0]
+ for k, d in zip(keypoints, descriptors)
+ ]
return {
- 'keypoints': keypoints,
- 'scores': scores,
- 'descriptors': descriptors,
+ "keypoints": keypoints,
+ "scores": scores,
+ "descriptors": descriptors,
}
diff --git a/imcui/third_party/SuperGluePretrainedNetwork/models/utils.py b/third_party/SuperGluePretrainedNetwork/models/utils.py
similarity index 63%
rename from imcui/third_party/SuperGluePretrainedNetwork/models/utils.py
rename to third_party/SuperGluePretrainedNetwork/models/utils.py
index 1206244aa2a004d9f653782de798bfef9e5e726b..d302ff84cf316f3dad016f1f23bbb54518566d2e 100644
--- a/imcui/third_party/SuperGluePretrainedNetwork/models/utils.py
+++ b/third_party/SuperGluePretrainedNetwork/models/utils.py
@@ -51,11 +51,12 @@ import cv2
import torch
import matplotlib.pyplot as plt
import matplotlib
-matplotlib.use('Agg')
+
+matplotlib.use("Agg")
class AverageTimer:
- """ Class to help manage printing simple timing of code execution. """
+ """Class to help manage printing simple timing of code execution."""
def __init__(self, smoothing=0.3, newline=False):
self.smoothing = smoothing
@@ -71,7 +72,7 @@ class AverageTimer:
for name in self.will_print:
self.will_print[name] = False
- def update(self, name='default'):
+ def update(self, name="default"):
now = time.time()
dt = now - self.last_time
if name in self.times:
@@ -80,29 +81,30 @@ class AverageTimer:
self.will_print[name] = True
self.last_time = now
- def print(self, text='Timer'):
- total = 0.
- print('[{}]'.format(text), end=' ')
+ def print(self, text="Timer"):
+ total = 0.0
+ print("[{}]".format(text), end=" ")
for key in self.times:
val = self.times[key]
if self.will_print[key]:
- print('%s=%.3f' % (key, val), end=' ')
+ print("%s=%.3f" % (key, val), end=" ")
total += val
- print('total=%.3f sec {%.1f FPS}' % (total, 1./total), end=' ')
+ print("total=%.3f sec {%.1f FPS}" % (total, 1.0 / total), end=" ")
if self.newline:
print(flush=True)
else:
- print(end='\r', flush=True)
+ print(end="\r", flush=True)
self.reset()
class VideoStreamer:
- """ Class to help process image streams. Four types of possible inputs:"
- 1.) USB Webcam.
- 2.) An IP camera
- 3.) A directory of images (files in directory matching 'image_glob').
- 4.) A video file, such as an .mp4 or .avi file.
+ """Class to help process image streams. Four types of possible inputs:"
+ 1.) USB Webcam.
+ 2.) An IP camera
+ 3.) A directory of images (files in directory matching 'image_glob').
+ 4.) A video file, such as an .mp4 or .avi file.
"""
+
def __init__(self, basedir, resize, skip, image_glob, max_length=1000000):
self._ip_grabbed = False
self._ip_running = False
@@ -119,45 +121,45 @@ class VideoStreamer:
self.skip = skip
self.max_length = max_length
if isinstance(basedir, int) or basedir.isdigit():
- print('==> Processing USB webcam input: {}'.format(basedir))
+ print("==> Processing USB webcam input: {}".format(basedir))
self.cap = cv2.VideoCapture(int(basedir))
self.listing = range(0, self.max_length)
- elif basedir.startswith(('http', 'rtsp')):
- print('==> Processing IP camera input: {}'.format(basedir))
+ elif basedir.startswith(("http", "rtsp")):
+ print("==> Processing IP camera input: {}".format(basedir))
self.cap = cv2.VideoCapture(basedir)
self.start_ip_camera_thread()
self._ip_camera = True
self.listing = range(0, self.max_length)
elif Path(basedir).is_dir():
- print('==> Processing image directory input: {}'.format(basedir))
+ print("==> Processing image directory input: {}".format(basedir))
self.listing = list(Path(basedir).glob(image_glob[0]))
for j in range(1, len(image_glob)):
image_path = list(Path(basedir).glob(image_glob[j]))
self.listing = self.listing + image_path
self.listing.sort()
- self.listing = self.listing[::self.skip]
+ self.listing = self.listing[:: self.skip]
self.max_length = np.min([self.max_length, len(self.listing)])
if self.max_length == 0:
- raise IOError('No images found (maybe bad \'image_glob\' ?)')
- self.listing = self.listing[:self.max_length]
+ raise IOError("No images found (maybe bad 'image_glob' ?)")
+ self.listing = self.listing[: self.max_length]
self.camera = False
elif Path(basedir).exists():
- print('==> Processing video input: {}'.format(basedir))
+ print("==> Processing video input: {}".format(basedir))
self.cap = cv2.VideoCapture(basedir)
self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
num_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
self.listing = range(0, num_frames)
- self.listing = self.listing[::self.skip]
+ self.listing = self.listing[:: self.skip]
self.video_file = True
self.max_length = np.min([self.max_length, len(self.listing)])
- self.listing = self.listing[:self.max_length]
+ self.listing = self.listing[: self.max_length]
else:
- raise ValueError('VideoStreamer input \"{}\" not recognized.'.format(basedir))
+ raise ValueError('VideoStreamer input "{}" not recognized.'.format(basedir))
if self.camera and not self.cap.isOpened():
- raise IOError('Could not read camera')
+ raise IOError("Could not read camera")
def load_image(self, impath):
- """ Read image as grayscale and resize to img_size.
+ """Read image as grayscale and resize to img_size.
Inputs
impath: Path to input image.
Returns
@@ -165,15 +167,14 @@ class VideoStreamer:
"""
grayim = cv2.imread(impath, 0)
if grayim is None:
- raise Exception('Error reading image %s' % impath)
+ raise Exception("Error reading image %s" % impath)
w, h = grayim.shape[1], grayim.shape[0]
w_new, h_new = process_resize(w, h, self.resize)
- grayim = cv2.resize(
- grayim, (w_new, h_new), interpolation=self.interp)
+ grayim = cv2.resize(grayim, (w_new, h_new), interpolation=self.interp)
return grayim
def next_frame(self):
- """ Return the next frame, and increment internal counter.
+ """Return the next frame, and increment internal counter.
Returns
image: Next H x W image.
status: True or False depending whether image was loaded.
@@ -184,9 +185,9 @@ class VideoStreamer:
if self.camera:
if self._ip_camera:
- #Wait for first image, making sure we haven't exited
+ # Wait for first image, making sure we haven't exited
while self._ip_grabbed is False and self._ip_exited is False:
- time.sleep(.001)
+ time.sleep(0.001)
ret, image = self._ip_grabbed, self._ip_image.copy()
if ret is False:
@@ -194,15 +195,14 @@ class VideoStreamer:
else:
ret, image = self.cap.read()
if ret is False:
- print('VideoStreamer: Cannot get image from camera')
+ print("VideoStreamer: Cannot get image from camera")
return (None, False)
w, h = image.shape[1], image.shape[0]
if self.video_file:
self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.listing[self.i])
w_new, h_new = process_resize(w, h, self.resize)
- image = cv2.resize(image, (w_new, h_new),
- interpolation=self.interp)
+ image = cv2.resize(image, (w_new, h_new), interpolation=self.interp)
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
else:
image_file = str(self.listing[self.i])
@@ -229,19 +229,20 @@ class VideoStreamer:
self._ip_image = img
self._ip_grabbed = ret
self._ip_index += 1
- #print('IPCAMERA THREAD got frame {}'.format(self._ip_index))
-
+ # print('IPCAMERA THREAD got frame {}'.format(self._ip_index))
def cleanup(self):
self._ip_running = False
+
# --- PREPROCESSING ---
+
def process_resize(w, h, resize):
- assert(len(resize) > 0 and len(resize) <= 2)
+ assert len(resize) > 0 and len(resize) <= 2
if len(resize) == 1 and resize[0] > -1:
scale = resize[0] / max(h, w)
- w_new, h_new = int(round(w*scale)), int(round(h*scale))
+ w_new, h_new = int(round(w * scale)), int(round(h * scale))
elif len(resize) == 1 and resize[0] == -1:
w_new, h_new = w, h
else: # len(resize) == 2:
@@ -249,15 +250,15 @@ def process_resize(w, h, resize):
# Issue warning if resolution is too small or too large.
if max(w_new, h_new) < 160:
- print('Warning: input resolution is very small, results may vary')
+ print("Warning: input resolution is very small, results may vary")
elif max(w_new, h_new) > 2000:
- print('Warning: input resolution is very large, results may vary')
+ print("Warning: input resolution is very large, results may vary")
return w_new, h_new
def frame2tensor(frame, device):
- return torch.from_numpy(frame/255.).float()[None, None].to(device)
+ return torch.from_numpy(frame / 255.0).float()[None, None].to(device)
def read_image(path, device, resize, rotation, resize_float):
@@ -269,9 +270,9 @@ def read_image(path, device, resize, rotation, resize_float):
scales = (float(w) / float(w_new), float(h) / float(h_new))
if resize_float:
- image = cv2.resize(image.astype('float32'), (w_new, h_new))
+ image = cv2.resize(image.astype("float32"), (w_new, h_new))
else:
- image = cv2.resize(image, (w_new, h_new)).astype('float32')
+ image = cv2.resize(image, (w_new, h_new)).astype("float32")
if rotation != 0:
image = np.rot90(image, k=rotation)
@@ -296,16 +297,15 @@ def estimate_pose(kpts0, kpts1, K0, K1, thresh, conf=0.99999):
kpts1 = (kpts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None]
E, mask = cv2.findEssentialMat(
- kpts0, kpts1, np.eye(3), threshold=norm_thresh, prob=conf,
- method=cv2.RANSAC)
+ kpts0, kpts1, np.eye(3), threshold=norm_thresh, prob=conf, method=cv2.RANSAC
+ )
assert E is not None
best_num_inliers = 0
ret = None
for _E in np.split(E, len(E) / 3):
- n, R, t, _ = cv2.recoverPose(
- _E, kpts0, kpts1, np.eye(3), 1e9, mask=mask)
+ n, R, t, _ = cv2.recoverPose(_E, kpts0, kpts1, np.eye(3), 1e9, mask=mask)
if n > best_num_inliers:
best_num_inliers = n
ret = (R, t[:, 0], mask.ravel() > 0)
@@ -315,36 +315,42 @@ def estimate_pose(kpts0, kpts1, K0, K1, thresh, conf=0.99999):
def rotate_intrinsics(K, image_shape, rot):
"""image_shape is the shape of the image after rotation"""
assert rot <= 3
- h, w = image_shape[:2][::-1 if (rot % 2) else 1]
+ h, w = image_shape[:2][:: -1 if (rot % 2) else 1]
fx, fy, cx, cy = K[0, 0], K[1, 1], K[0, 2], K[1, 2]
rot = rot % 4
if rot == 1:
- return np.array([[fy, 0., cy],
- [0., fx, w-1-cx],
- [0., 0., 1.]], dtype=K.dtype)
+ return np.array(
+ [[fy, 0.0, cy], [0.0, fx, w - 1 - cx], [0.0, 0.0, 1.0]], dtype=K.dtype
+ )
elif rot == 2:
- return np.array([[fx, 0., w-1-cx],
- [0., fy, h-1-cy],
- [0., 0., 1.]], dtype=K.dtype)
+ return np.array(
+ [[fx, 0.0, w - 1 - cx], [0.0, fy, h - 1 - cy], [0.0, 0.0, 1.0]],
+ dtype=K.dtype,
+ )
else: # if rot == 3:
- return np.array([[fy, 0., h-1-cy],
- [0., fx, cx],
- [0., 0., 1.]], dtype=K.dtype)
+ return np.array(
+ [[fy, 0.0, h - 1 - cy], [0.0, fx, cx], [0.0, 0.0, 1.0]], dtype=K.dtype
+ )
def rotate_pose_inplane(i_T_w, rot):
rotation_matrices = [
- np.array([[np.cos(r), -np.sin(r), 0., 0.],
- [np.sin(r), np.cos(r), 0., 0.],
- [0., 0., 1., 0.],
- [0., 0., 0., 1.]], dtype=np.float32)
+ np.array(
+ [
+ [np.cos(r), -np.sin(r), 0.0, 0.0],
+ [np.sin(r), np.cos(r), 0.0, 0.0],
+ [0.0, 0.0, 1.0, 0.0],
+ [0.0, 0.0, 0.0, 1.0],
+ ],
+ dtype=np.float32,
+ )
for r in [np.deg2rad(d) for d in (0, 270, 180, 90)]
]
return np.dot(rotation_matrices[rot], i_T_w)
def scale_intrinsics(K, scales):
- scales = np.diag([1./scales[0], 1./scales[1], 1.])
+ scales = np.diag([1.0 / scales[0], 1.0 / scales[1], 1.0])
return np.dot(scales, K)
@@ -359,24 +365,22 @@ def compute_epipolar_error(kpts0, kpts1, T_0to1, K0, K1):
kpts1 = to_homogeneous(kpts1)
t0, t1, t2 = T_0to1[:3, 3]
- t_skew = np.array([
- [0, -t2, t1],
- [t2, 0, -t0],
- [-t1, t0, 0]
- ])
+ t_skew = np.array([[0, -t2, t1], [t2, 0, -t0], [-t1, t0, 0]])
E = t_skew @ T_0to1[:3, :3]
Ep0 = kpts0 @ E.T # N x 3
p1Ep0 = np.sum(kpts1 * Ep0, -1) # N
Etp1 = kpts1 @ E # N x 3
- d = p1Ep0**2 * (1.0 / (Ep0[:, 0]**2 + Ep0[:, 1]**2)
- + 1.0 / (Etp1[:, 0]**2 + Etp1[:, 1]**2))
+ d = p1Ep0**2 * (
+ 1.0 / (Ep0[:, 0] ** 2 + Ep0[:, 1] ** 2)
+ + 1.0 / (Etp1[:, 0] ** 2 + Etp1[:, 1] ** 2)
+ )
return d
def angle_error_mat(R1, R2):
cos = (np.trace(np.dot(R1.T, R2)) - 1) / 2
- cos = np.clip(cos, -1., 1.) # numercial errors can make it out of bounds
+ cos = np.clip(cos, -1.0, 1.0) # numercial errors can make it out of bounds
return np.rad2deg(np.abs(np.arccos(cos)))
@@ -398,27 +402,27 @@ def pose_auc(errors, thresholds):
sort_idx = np.argsort(errors)
errors = np.array(errors.copy())[sort_idx]
recall = (np.arange(len(errors)) + 1) / len(errors)
- errors = np.r_[0., errors]
- recall = np.r_[0., recall]
+ errors = np.r_[0.0, errors]
+ recall = np.r_[0.0, recall]
aucs = []
for t in thresholds:
last_index = np.searchsorted(errors, t)
- r = np.r_[recall[:last_index], recall[last_index-1]]
+ r = np.r_[recall[:last_index], recall[last_index - 1]]
e = np.r_[errors[:last_index], t]
- aucs.append(np.trapz(r, x=e)/t)
+ aucs.append(np.trapz(r, x=e) / t)
return aucs
# --- VISUALIZATION ---
-def plot_image_pair(imgs, dpi=100, size=6, pad=.5):
+def plot_image_pair(imgs, dpi=100, size=6, pad=0.5):
n = len(imgs)
- assert n == 2, 'number of images must be two'
- figsize = (size*n, size*3/4) if size is not None else None
+ assert n == 2, "number of images must be two"
+ figsize = (size * n, size * 3 / 4) if size is not None else None
_, ax = plt.subplots(1, n, figsize=figsize, dpi=dpi)
for i in range(n):
- ax[i].imshow(imgs[i], cmap=plt.get_cmap('gray'), vmin=0, vmax=255)
+ ax[i].imshow(imgs[i], cmap=plt.get_cmap("gray"), vmin=0, vmax=255)
ax[i].get_yaxis().set_ticks([])
ax[i].get_xaxis().set_ticks([])
for spine in ax[i].spines.values(): # remove frame
@@ -426,7 +430,7 @@ def plot_image_pair(imgs, dpi=100, size=6, pad=.5):
plt.tight_layout(pad=pad)
-def plot_keypoints(kpts0, kpts1, color='w', ps=2):
+def plot_keypoints(kpts0, kpts1, color="w", ps=2):
ax = plt.gcf().axes
ax[0].scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps)
ax[1].scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps)
@@ -441,59 +445,116 @@ def plot_matches(kpts0, kpts1, color, lw=1.5, ps=4):
fkpts0 = transFigure.transform(ax[0].transData.transform(kpts0))
fkpts1 = transFigure.transform(ax[1].transData.transform(kpts1))
- fig.lines = [matplotlib.lines.Line2D(
- (fkpts0[i, 0], fkpts1[i, 0]), (fkpts0[i, 1], fkpts1[i, 1]), zorder=1,
- transform=fig.transFigure, c=color[i], linewidth=lw)
- for i in range(len(kpts0))]
+ fig.lines = [
+ matplotlib.lines.Line2D(
+ (fkpts0[i, 0], fkpts1[i, 0]),
+ (fkpts0[i, 1], fkpts1[i, 1]),
+ zorder=1,
+ transform=fig.transFigure,
+ c=color[i],
+ linewidth=lw,
+ )
+ for i in range(len(kpts0))
+ ]
ax[0].scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps)
ax[1].scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps)
-def make_matching_plot(image0, image1, kpts0, kpts1, mkpts0, mkpts1,
- color, text, path, show_keypoints=False,
- fast_viz=False, opencv_display=False,
- opencv_title='matches', small_text=[]):
+def make_matching_plot(
+ image0,
+ image1,
+ kpts0,
+ kpts1,
+ mkpts0,
+ mkpts1,
+ color,
+ text,
+ path,
+ show_keypoints=False,
+ fast_viz=False,
+ opencv_display=False,
+ opencv_title="matches",
+ small_text=[],
+):
if fast_viz:
- make_matching_plot_fast(image0, image1, kpts0, kpts1, mkpts0, mkpts1,
- color, text, path, show_keypoints, 10,
- opencv_display, opencv_title, small_text)
+ make_matching_plot_fast(
+ image0,
+ image1,
+ kpts0,
+ kpts1,
+ mkpts0,
+ mkpts1,
+ color,
+ text,
+ path,
+ show_keypoints,
+ 10,
+ opencv_display,
+ opencv_title,
+ small_text,
+ )
return
plot_image_pair([image0, image1])
if show_keypoints:
- plot_keypoints(kpts0, kpts1, color='k', ps=4)
- plot_keypoints(kpts0, kpts1, color='w', ps=2)
+ plot_keypoints(kpts0, kpts1, color="k", ps=4)
+ plot_keypoints(kpts0, kpts1, color="w", ps=2)
plot_matches(mkpts0, mkpts1, color)
fig = plt.gcf()
- txt_color = 'k' if image0[:100, :150].mean() > 200 else 'w'
+ txt_color = "k" if image0[:100, :150].mean() > 200 else "w"
fig.text(
- 0.01, 0.99, '\n'.join(text), transform=fig.axes[0].transAxes,
- fontsize=15, va='top', ha='left', color=txt_color)
-
- txt_color = 'k' if image0[-100:, :150].mean() > 200 else 'w'
+ 0.01,
+ 0.99,
+ "\n".join(text),
+ transform=fig.axes[0].transAxes,
+ fontsize=15,
+ va="top",
+ ha="left",
+ color=txt_color,
+ )
+
+ txt_color = "k" if image0[-100:, :150].mean() > 200 else "w"
fig.text(
- 0.01, 0.01, '\n'.join(small_text), transform=fig.axes[0].transAxes,
- fontsize=5, va='bottom', ha='left', color=txt_color)
-
- plt.savefig(str(path), bbox_inches='tight', pad_inches=0)
+ 0.01,
+ 0.01,
+ "\n".join(small_text),
+ transform=fig.axes[0].transAxes,
+ fontsize=5,
+ va="bottom",
+ ha="left",
+ color=txt_color,
+ )
+
+ plt.savefig(str(path), bbox_inches="tight", pad_inches=0)
plt.close()
-def make_matching_plot_fast(image0, image1, kpts0, kpts1, mkpts0,
- mkpts1, color, text, path=None,
- show_keypoints=False, margin=10,
- opencv_display=False, opencv_title='',
- small_text=[]):
+def make_matching_plot_fast(
+ image0,
+ image1,
+ kpts0,
+ kpts1,
+ mkpts0,
+ mkpts1,
+ color,
+ text,
+ path=None,
+ show_keypoints=False,
+ margin=10,
+ opencv_display=False,
+ opencv_title="",
+ small_text=[],
+):
H0, W0 = image0.shape
H1, W1 = image1.shape
H, W = max(H0, H1), W0 + W1 + margin
- out = 255*np.ones((H, W), np.uint8)
+ out = 255 * np.ones((H, W), np.uint8)
out[:H0, :W0] = image0
- out[:H1, W0+margin:] = image1
- out = np.stack([out]*3, -1)
+ out[:H1, W0 + margin :] = image1
+ out = np.stack([out] * 3, -1)
if show_keypoints:
kpts0, kpts1 = np.round(kpts0).astype(int), np.round(kpts1).astype(int)
@@ -503,42 +564,77 @@ def make_matching_plot_fast(image0, image1, kpts0, kpts1, mkpts0,
cv2.circle(out, (x, y), 2, black, -1, lineType=cv2.LINE_AA)
cv2.circle(out, (x, y), 1, white, -1, lineType=cv2.LINE_AA)
for x, y in kpts1:
- cv2.circle(out, (x + margin + W0, y), 2, black, -1,
- lineType=cv2.LINE_AA)
- cv2.circle(out, (x + margin + W0, y), 1, white, -1,
- lineType=cv2.LINE_AA)
+ cv2.circle(out, (x + margin + W0, y), 2, black, -1, lineType=cv2.LINE_AA)
+ cv2.circle(out, (x + margin + W0, y), 1, white, -1, lineType=cv2.LINE_AA)
mkpts0, mkpts1 = np.round(mkpts0).astype(int), np.round(mkpts1).astype(int)
- color = (np.array(color[:, :3])*255).astype(int)[:, ::-1]
+ color = (np.array(color[:, :3]) * 255).astype(int)[:, ::-1]
for (x0, y0), (x1, y1), c in zip(mkpts0, mkpts1, color):
c = c.tolist()
- cv2.line(out, (x0, y0), (x1 + margin + W0, y1),
- color=c, thickness=1, lineType=cv2.LINE_AA)
+ cv2.line(
+ out,
+ (x0, y0),
+ (x1 + margin + W0, y1),
+ color=c,
+ thickness=1,
+ lineType=cv2.LINE_AA,
+ )
# display line end-points as circles
cv2.circle(out, (x0, y0), 2, c, -1, lineType=cv2.LINE_AA)
- cv2.circle(out, (x1 + margin + W0, y1), 2, c, -1,
- lineType=cv2.LINE_AA)
+ cv2.circle(out, (x1 + margin + W0, y1), 2, c, -1, lineType=cv2.LINE_AA)
# Scale factor for consistent visualization across scales.
- sc = min(H / 640., 2.0)
+ sc = min(H / 640.0, 2.0)
# Big text.
Ht = int(30 * sc) # text height
txt_color_fg = (255, 255, 255)
txt_color_bg = (0, 0, 0)
for i, t in enumerate(text):
- cv2.putText(out, t, (int(8*sc), Ht*(i+1)), cv2.FONT_HERSHEY_DUPLEX,
- 1.0*sc, txt_color_bg, 2, cv2.LINE_AA)
- cv2.putText(out, t, (int(8*sc), Ht*(i+1)), cv2.FONT_HERSHEY_DUPLEX,
- 1.0*sc, txt_color_fg, 1, cv2.LINE_AA)
+ cv2.putText(
+ out,
+ t,
+ (int(8 * sc), Ht * (i + 1)),
+ cv2.FONT_HERSHEY_DUPLEX,
+ 1.0 * sc,
+ txt_color_bg,
+ 2,
+ cv2.LINE_AA,
+ )
+ cv2.putText(
+ out,
+ t,
+ (int(8 * sc), Ht * (i + 1)),
+ cv2.FONT_HERSHEY_DUPLEX,
+ 1.0 * sc,
+ txt_color_fg,
+ 1,
+ cv2.LINE_AA,
+ )
# Small text.
Ht = int(18 * sc) # text height
for i, t in enumerate(reversed(small_text)):
- cv2.putText(out, t, (int(8*sc), int(H-Ht*(i+.6))), cv2.FONT_HERSHEY_DUPLEX,
- 0.5*sc, txt_color_bg, 2, cv2.LINE_AA)
- cv2.putText(out, t, (int(8*sc), int(H-Ht*(i+.6))), cv2.FONT_HERSHEY_DUPLEX,
- 0.5*sc, txt_color_fg, 1, cv2.LINE_AA)
+ cv2.putText(
+ out,
+ t,
+ (int(8 * sc), int(H - Ht * (i + 0.6))),
+ cv2.FONT_HERSHEY_DUPLEX,
+ 0.5 * sc,
+ txt_color_bg,
+ 2,
+ cv2.LINE_AA,
+ )
+ cv2.putText(
+ out,
+ t,
+ (int(8 * sc), int(H - Ht * (i + 0.6))),
+ cv2.FONT_HERSHEY_DUPLEX,
+ 0.5 * sc,
+ txt_color_fg,
+ 1,
+ cv2.LINE_AA,
+ )
if path is not None:
cv2.imwrite(str(path), out)
@@ -552,4 +648,5 @@ def make_matching_plot_fast(image0, image1, kpts0, kpts1, mkpts0,
def error_colormap(x):
return np.clip(
- np.stack([2-x*2, x*2, np.zeros_like(x), np.ones_like(x)], -1), 0, 1)
+ np.stack([2 - x * 2, x * 2, np.zeros_like(x), np.ones_like(x)], -1), 0, 1
+ )
diff --git a/imcui/third_party/SuperGluePretrainedNetwork/models/weights/superpoint_v1.pth b/third_party/SuperGluePretrainedNetwork/models/weights/superpoint_v1.pth
similarity index 100%
rename from imcui/third_party/SuperGluePretrainedNetwork/models/weights/superpoint_v1.pth
rename to third_party/SuperGluePretrainedNetwork/models/weights/superpoint_v1.pth
diff --git a/third_party/SuperGluePretrainedNetwork/requirements.txt b/third_party/SuperGluePretrainedNetwork/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bf29a527d2077048833b9c1f25d12b7e4573b590
--- /dev/null
+++ b/third_party/SuperGluePretrainedNetwork/requirements.txt
@@ -0,0 +1,4 @@
+matplotlib>=3.1.3
+torch>=1.1.0
+opencv-python==4.1.2.30
+numpy>=1.18.1
diff --git a/imcui/third_party/TopicFM/.github/workflows/sync.yml b/third_party/TopicFM/.github/workflows/sync.yml
similarity index 100%
rename from imcui/third_party/TopicFM/.github/workflows/sync.yml
rename to third_party/TopicFM/.github/workflows/sync.yml
diff --git a/third_party/TopicFM/.gitignore b/third_party/TopicFM/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..7ed07d081a940b02ce92ceb6aa8fb66925e32224
--- /dev/null
+++ b/third_party/TopicFM/.gitignore
@@ -0,0 +1,130 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+.idea/
diff --git a/third_party/TopicFM/.gitmodules b/third_party/TopicFM/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..313403ddfa5b06a038a75467352c3821a19a78c4
--- /dev/null
+++ b/third_party/TopicFM/.gitmodules
@@ -0,0 +1,3 @@
+# [submodule "third_party/loftr"]
+# path = third_party/loftr
+# url = https://github.com/zju3dv/git
diff --git a/third_party/TopicFM/LICENSE b/third_party/TopicFM/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/third_party/TopicFM/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
diff --git a/third_party/TopicFM/README.md b/third_party/TopicFM/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..be60b38c8c265deeef5d7827d9fae4f65e842868
--- /dev/null
+++ b/third_party/TopicFM/README.md
@@ -0,0 +1,130 @@
+# Submodule used in [hloc](https://github.com/Vincentqyw/Hierarchical-Localization) toolbox
+
+# [AAAI-23] TopicFM: Robust and Interpretable Topic-Assisted Feature Matching
+
+Our method first inferred the latent topics (high-level context information) for each image and then use them to explicitly learn robust feature representation for the matching task. Please check out the details in [our paper](https://arxiv.org/abs/2207.00328)
+
+
+
+**Overall Architecture:**
+
+
+
+## TODO List
+
+- [x] Release training and evaluation code on MegaDepth and ScanNet
+- [x] Evaluation on HPatches, Aachen Day&Night, and InLoc
+- [x] Evaluation for Image Matching Challenge
+
+## Requirements
+
+All experiments in this paper are implemented on the Ubuntu environment
+with a NVIDIA driver of at least 430.64 and CUDA 10.1.
+
+First, create a virtual environment by anaconda as follows,
+
+ conda create -n topicfm python=3.8
+ conda activate topicfm
+ conda install pytorch==1.8.1 torchvision==0.9.1 cudatoolkit=10.1 -c pytorch
+ pip install -r requirements.txt
+ # using pip to install any missing packages
+
+## Data Preparation
+
+The proposed method is trained on the MegaDepth dataset and evaluated on the MegaDepth test, ScanNet, HPatches, Aachen Day and Night (v1.1), and InLoc dataset.
+All these datasets are large, so we cannot include them in this code.
+The following descriptions help download these datasets.
+
+### MegaDepth
+
+This dataset is used for both training and evaluation (Li and Snavely 2018).
+To use this dataset with our code, please follow the [instruction of LoFTR](https://github.com/zju3dv/LoFTR/blob/master/docs/TRAINING.md) (Sun et al. 2021)
+
+### ScanNet
+We only use 1500 image pairs of ScanNet (Dai et al. 2017) for evaluation.
+Please download and prepare [test data](https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf) of ScanNet
+provided by [LoFTR](https://github.com/zju3dv/LoFTR/blob/master/docs/TRAINING.md).
+
+## Training
+
+To train our model, we recommend to use GPUs card as much as possible, and each GPU should be at least 12GB.
+In our settings, we train on 4 GPUs, each of which is 12GB.
+Please setup your hardware environment in `scripts/reproduce_train/outdoor.sh`.
+And then run this command to start training.
+
+ bash scripts/reproduce_train/outdoor.sh
+
+ We then provide the trained model in `pretrained/model_best.ckpt`
+## Evaluation
+
+### MegaDepth (relative pose estimation)
+
+ bash scripts/reproduce_test/outdoor.sh
+
+### ScanNet (relative pose estimation)
+
+ bash scripts/reproduce_test/indoor.sh
+
+### HPatches, Aachen v1.1, InLoc
+
+To evaluate on these datasets, we integrate our code to the image-matching-toolbox provided by Zhou et al. (2021).
+The updated code is available [here](https://github.com/TruongKhang/image-matching-toolbox).
+After cloning this code, please follow instructions of image-matching-toolbox to install all required packages and prepare data for evaluation.
+
+Then, run these commands to perform evaluation: (note that all hyperparameter settings are in `configs/topicfm.yml`)
+
+**HPatches (homography estimation)**
+
+ python -m immatch.eval_hpatches --gpu 0 --config 'topicfm' --task 'both' --h_solver 'cv' --ransac_thres 3 --root_dir . --odir 'outputs/hpatches'
+
+**Aachen Day-Night v1.1 (visual localization)**
+
+ python -m immatch.eval_aachen --gpu 0 --config 'topicfm' --colmap --benchmark_name 'aachen_v1.1'
+
+**InLoc (visual localization)**
+
+ python -m immatch.eval_inloc --gpu 0 --config 'topicfm'
+
+### Image Matching Challenge 2022 (IMC-2022)
+IMC-2022 was held on [Kaggle](https://www.kaggle.com/competitions/image-matching-challenge-2022/overview).
+Most high ranking methods were achieved by using an ensemble method which combines the matching results of
+various state-of-the-art methods including LoFTR, SuperPoint+SuperGlue, MatchFormer, or QuadTree Attention.
+
+In this evaluation, we only submit the results produced by our method (TopicFM) alone. Please refer to [this notebook](https://www.kaggle.com/code/khangtg09121995/topicfm-eval).
+This table compares our results with the other methods such as LoFTR (ref. [here](https://www.kaggle.com/code/mcwema/imc-2022-kornia-loftr-score-plateau-0-726)),
+SP+SuperGlue (ref. [here](https://www.kaggle.com/code/yufei12/superglue-baseline)).
+
+| | Public Score | Private Score |
+|----------------|--------------|---------------|
+| SP + SuperGlue | 0.678 | 0.677 |
+| LoFTR | 0.726 | 0.736 |
+| TopicFM (ours) | **0.804** | **0.811** |
+
+
+### Runtime comparison
+
+The runtime reported in the paper is measured by averaging runtime of 1500 image pairs of the ScanNet evaluation dataset.
+The image size can be changed at `configs/data/scannet_test_1500.py`
+
+ python visualization.py --method --dataset_name "scannet" --measure_time --no_viz
+ # note that method_name is in ["topicfm", "loftr"]
+
+To measure time for LoFTR, please download the LoFTR's code as follows:
+
+ git submodule update --init
+ # download pretrained models
+ mkdir third_party/loftr/pretrained
+ gdown --id 1M-VD35-qdB5Iw-AtbDBCKC7hPolFW9UY -O third_party/loftr/pretrained/outdoor_ds.ckpt
+
+## Citations
+If you find this work useful, please cite this:
+
+ @article{giang2022topicfm,
+ title={TopicFM: Robust and Interpretable Topic-assisted Feature Matching},
+ author={Giang, Khang Truong and Song, Soohwan and Jo, Sungho},
+ journal={arXiv preprint arXiv:2207.00328},
+ year={2022}
+ }
+
+## Acknowledgement
+This code is built based on [LoFTR](https://github.com/zju3dv/LoFTR). We thank the authors for their useful source code.
diff --git a/imcui/third_party/XoFTR/configs/data/__init__.py b/third_party/TopicFM/configs/data/__init__.py
similarity index 100%
rename from imcui/third_party/XoFTR/configs/data/__init__.py
rename to third_party/TopicFM/configs/data/__init__.py
diff --git a/imcui/third_party/TopicFM/configs/data/base.py b/third_party/TopicFM/configs/data/base.py
similarity index 99%
rename from imcui/third_party/TopicFM/configs/data/base.py
rename to third_party/TopicFM/configs/data/base.py
index 6cab7e67019a6fee2657c1a28609c8aca5b2a1d8..1897a84393e186cc46f34fe856243756e8393a2a 100644
--- a/imcui/third_party/TopicFM/configs/data/base.py
+++ b/third_party/TopicFM/configs/data/base.py
@@ -4,6 +4,7 @@ Setups in data configs will override all existed setups!
"""
from yacs.config import CfgNode as CN
+
_CN = CN()
_CN.DATASET = CN()
_CN.TRAINER = CN()
diff --git a/imcui/third_party/TopicFM/configs/data/megadepth_test_1500.py b/third_party/TopicFM/configs/data/megadepth_test_1500.py
similarity index 100%
rename from imcui/third_party/TopicFM/configs/data/megadepth_test_1500.py
rename to third_party/TopicFM/configs/data/megadepth_test_1500.py
diff --git a/imcui/third_party/TopicFM/configs/data/megadepth_trainval.py b/third_party/TopicFM/configs/data/megadepth_trainval.py
similarity index 72%
rename from imcui/third_party/TopicFM/configs/data/megadepth_trainval.py
rename to third_party/TopicFM/configs/data/megadepth_trainval.py
index 215b5c34cc41d36aa4444a58ca0cb69afbc11952..7b7b0a77e26bbf6e7b7ceb2cd54f8c2e3b709db4 100644
--- a/imcui/third_party/TopicFM/configs/data/megadepth_trainval.py
+++ b/third_party/TopicFM/configs/data/megadepth_trainval.py
@@ -11,9 +11,13 @@ cfg.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.0
TEST_BASE_PATH = "data/megadepth/index"
cfg.DATASET.TEST_DATA_SOURCE = "MegaDepth"
cfg.DATASET.VAL_DATA_ROOT = cfg.DATASET.TEST_DATA_ROOT = "data/megadepth/test"
-cfg.DATASET.VAL_NPZ_ROOT = cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}/scene_info_val_1500"
-cfg.DATASET.VAL_LIST_PATH = cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/trainvaltest_list/val_list.txt"
-cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val
+cfg.DATASET.VAL_NPZ_ROOT = (
+ cfg.DATASET.TEST_NPZ_ROOT
+) = f"{TEST_BASE_PATH}/scene_info_val_1500"
+cfg.DATASET.VAL_LIST_PATH = (
+ cfg.DATASET.TEST_LIST_PATH
+) = f"{TEST_BASE_PATH}/trainvaltest_list/val_list.txt"
+cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val
# 368 scenes in total for MegaDepth
# (with difficulty balanced (further split each scene to 3 sub-scenes))
diff --git a/imcui/third_party/TopicFM/configs/data/scannet_test_1500.py b/third_party/TopicFM/configs/data/scannet_test_1500.py
similarity index 100%
rename from imcui/third_party/TopicFM/configs/data/scannet_test_1500.py
rename to third_party/TopicFM/configs/data/scannet_test_1500.py
diff --git a/third_party/TopicFM/configs/model/indoor/debug/.gitignore b/third_party/TopicFM/configs/model/indoor/debug/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31
--- /dev/null
+++ b/third_party/TopicFM/configs/model/indoor/debug/.gitignore
@@ -0,0 +1,3 @@
+*
+*/
+!.gitignore
diff --git a/imcui/third_party/TopicFM/configs/model/indoor/model_cfg_test.py b/third_party/TopicFM/configs/model/indoor/model_cfg_test.py
similarity index 100%
rename from imcui/third_party/TopicFM/configs/model/indoor/model_cfg_test.py
rename to third_party/TopicFM/configs/model/indoor/model_cfg_test.py
diff --git a/third_party/TopicFM/configs/model/outdoor/debug/.gitignore b/third_party/TopicFM/configs/model/outdoor/debug/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31
--- /dev/null
+++ b/third_party/TopicFM/configs/model/outdoor/debug/.gitignore
@@ -0,0 +1,3 @@
+*
+*/
+!.gitignore
diff --git a/imcui/third_party/TopicFM/configs/model/outdoor/model_cfg_test.py b/third_party/TopicFM/configs/model/outdoor/model_cfg_test.py
similarity index 100%
rename from imcui/third_party/TopicFM/configs/model/outdoor/model_cfg_test.py
rename to third_party/TopicFM/configs/model/outdoor/model_cfg_test.py
diff --git a/imcui/third_party/TopicFM/configs/model/outdoor/model_ds.py b/third_party/TopicFM/configs/model/outdoor/model_ds.py
similarity index 88%
rename from imcui/third_party/TopicFM/configs/model/outdoor/model_ds.py
rename to third_party/TopicFM/configs/model/outdoor/model_ds.py
index 2c090edbfbdcd66cea225c39af6f62da8feb50b9..e0c234e8b3c932656052aa58836ed2b158344fb5 100644
--- a/imcui/third_party/TopicFM/configs/model/outdoor/model_ds.py
+++ b/third_party/TopicFM/configs/model/outdoor/model_ds.py
@@ -1,6 +1,6 @@
from src.config.default import _CN as cfg
-cfg.MODEL.MATCH_COARSE.MATCH_TYPE = 'dual_softmax'
+cfg.MODEL.MATCH_COARSE.MATCH_TYPE = "dual_softmax"
cfg.MODEL.COARSE.N_SAMPLES = 8
cfg.TRAINER.CANONICAL_LR = 1e-2
diff --git a/third_party/TopicFM/data/megadepth/index/.gitignore b/third_party/TopicFM/data/megadepth/index/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/TopicFM/data/megadepth/index/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/TopicFM/data/megadepth/test/.gitignore b/third_party/TopicFM/data/megadepth/test/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/TopicFM/data/megadepth/test/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/TopicFM/data/megadepth/train/.gitignore b/third_party/TopicFM/data/megadepth/train/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/TopicFM/data/megadepth/train/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/TopicFM/data/scannet/index/.gitignore b/third_party/TopicFM/data/scannet/index/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31
--- /dev/null
+++ b/third_party/TopicFM/data/scannet/index/.gitignore
@@ -0,0 +1,3 @@
+*
+*/
+!.gitignore
diff --git a/third_party/TopicFM/data/scannet/intrinsics.npz b/third_party/TopicFM/data/scannet/intrinsics.npz
new file mode 100644
index 0000000000000000000000000000000000000000..4d1fe65c8834ebc44b12870d36edbf57db216f08
--- /dev/null
+++ b/third_party/TopicFM/data/scannet/intrinsics.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46db15f5ed21f34998613d07110e577205736a57eb5dfd04db96c189958d79f6
+size 343135
diff --git a/third_party/TopicFM/flop_counter.py b/third_party/TopicFM/flop_counter.py
new file mode 100644
index 0000000000000000000000000000000000000000..915f703bd76146e54a3f2f9e819a7b1b85f2d700
--- /dev/null
+++ b/third_party/TopicFM/flop_counter.py
@@ -0,0 +1,82 @@
+import torch
+from fvcore.nn import FlopCountAnalysis
+from einops.einops import rearrange
+
+from src import get_model_cfg
+from src.models.backbone import FPN as topicfm_featnet
+from src.models.modules import TopicFormer
+from src.utils.dataset import read_scannet_gray
+
+from third_party.loftr.src.loftr.utils.cvpr_ds_config import default_cfg
+from third_party.loftr.src.loftr.backbone import ResNetFPN_8_2 as loftr_featnet
+from third_party.loftr.src.loftr.loftr_module import LocalFeatureTransformer
+
+
+def feat_net_flops(feat_net, config, input):
+ model = feat_net(config)
+ model.eval()
+ flops = FlopCountAnalysis(model, input)
+ feat_c, _ = model(input)
+ return feat_c, flops.total() / 1e9
+
+
+def coarse_model_flops(coarse_model, config, inputs):
+ model = coarse_model(config)
+ model.eval()
+ flops = FlopCountAnalysis(model, inputs)
+ return flops.total() / 1e9
+
+
+if __name__ == "__main__":
+ path_img0 = "assets/scannet_sample_images/scene0711_00_frame-001680.jpg"
+ path_img1 = "assets/scannet_sample_images/scene0711_00_frame-001995.jpg"
+ img0, img1 = read_scannet_gray(path_img0), read_scannet_gray(path_img1)
+ img0, img1 = img0.unsqueeze(0), img1.unsqueeze(0)
+
+ # LoFTR
+ loftr_conf = dict(default_cfg)
+ feat_c0, loftr_featnet_flops0 = feat_net_flops(
+ loftr_featnet, loftr_conf["resnetfpn"], img0
+ )
+ feat_c1, loftr_featnet_flops1 = feat_net_flops(
+ loftr_featnet, loftr_conf["resnetfpn"], img1
+ )
+ print(
+ "FLOPs of feature extraction in LoFTR: {} GFLOPs".format(
+ (loftr_featnet_flops0 + loftr_featnet_flops1) / 2
+ )
+ )
+ feat_c0 = rearrange(feat_c0, "n c h w -> n (h w) c")
+ feat_c1 = rearrange(feat_c1, "n c h w -> n (h w) c")
+ loftr_coarse_model_flops = coarse_model_flops(
+ LocalFeatureTransformer, loftr_conf["coarse"], (feat_c0, feat_c1)
+ )
+ print(
+ "FLOPs of coarse matching model in LoFTR: {} GFLOPs".format(
+ loftr_coarse_model_flops
+ )
+ )
+
+ # TopicFM
+ topicfm_conf = get_model_cfg()
+ feat_c0, topicfm_featnet_flops0 = feat_net_flops(
+ topicfm_featnet, topicfm_conf["fpn"], img0
+ )
+ feat_c1, topicfm_featnet_flops1 = feat_net_flops(
+ topicfm_featnet, topicfm_conf["fpn"], img1
+ )
+ print(
+ "FLOPs of feature extraction in TopicFM: {} GFLOPs".format(
+ (topicfm_featnet_flops0 + topicfm_featnet_flops1) / 2
+ )
+ )
+ feat_c0 = rearrange(feat_c0, "n c h w -> n (h w) c")
+ feat_c1 = rearrange(feat_c1, "n c h w -> n (h w) c")
+ topicfm_coarse_model_flops = coarse_model_flops(
+ TopicFormer, topicfm_conf["coarse"], (feat_c0, feat_c1)
+ )
+ print(
+ "FLOPs of coarse matching model in TopicFM: {} GFLOPs".format(
+ topicfm_coarse_model_flops
+ )
+ )
diff --git a/third_party/TopicFM/requirements.txt b/third_party/TopicFM/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9edb3640108d86b645f234894469a915a364f527
--- /dev/null
+++ b/third_party/TopicFM/requirements.txt
@@ -0,0 +1,18 @@
+albumentations==0.5.1
+einops==0.3.0
+future==0.18.2
+fvcore==0.1.5.post20220512
+h5py==3.1.0
+joblib==1.1.0
+kornia==0.4.1
+loguru==0.5.3
+matplotlib==3.5.1
+opencv-python==4.4.0.46
+Pillow==9.0.1
+pytorch-lightning==1.3.5
+scikit-image==0.19.1
+scikit-learn==1.1.2
+tqdm==4.62.3
+yacs==0.1.8
+torchmetrics==0.7.0
+gdown
\ No newline at end of file
diff --git a/third_party/TopicFM/scripts/reproduce_test/indoor.sh b/third_party/TopicFM/scripts/reproduce_test/indoor.sh
new file mode 100644
index 0000000000000000000000000000000000000000..76494f2e1734bfd3a2653ef3c96a557793b54f05
--- /dev/null
+++ b/third_party/TopicFM/scripts/reproduce_test/indoor.sh
@@ -0,0 +1,29 @@
+#!/bin/bash -l
+
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+# conda activate loftr
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+data_cfg_path="configs/data/scannet_test_1500.py"
+main_cfg_path="configs/model/indoor/model_cfg_test.py"
+ckpt_path="pretrained/model_best.ckpt"
+dump_dir="dump/loftr_ds_indoor"
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+python -u ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark
+
diff --git a/third_party/TopicFM/scripts/reproduce_test/outdoor.sh b/third_party/TopicFM/scripts/reproduce_test/outdoor.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e6217883a1ea9c17edf2ce0ff0ee97d26868b5d9
--- /dev/null
+++ b/third_party/TopicFM/scripts/reproduce_test/outdoor.sh
@@ -0,0 +1,29 @@
+#!/bin/bash -l
+
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+# conda activate loftr
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+data_cfg_path="configs/data/megadepth_test_1500.py"
+main_cfg_path="configs/model/outdoor/model_cfg_test.py"
+ckpt_path="pretrained/model_best.ckpt"
+dump_dir="dump/loftr_ds_outdoor"
+profiler_name="inference"
+n_nodes=1 # mannually keep this the same with --nodes
+n_gpus_per_node=-1
+torch_num_workers=4
+batch_size=1 # per gpu
+
+python -u ./test.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --ckpt_path=${ckpt_path} \
+ --dump_dir=${dump_dir} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers}\
+ --profiler_name=${profiler_name} \
+ --benchmark
+
diff --git a/third_party/TopicFM/scripts/reproduce_train/debug/.gitignore b/third_party/TopicFM/scripts/reproduce_train/debug/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31
--- /dev/null
+++ b/third_party/TopicFM/scripts/reproduce_train/debug/.gitignore
@@ -0,0 +1,3 @@
+*
+*/
+!.gitignore
diff --git a/third_party/TopicFM/scripts/reproduce_train/outdoor.sh b/third_party/TopicFM/scripts/reproduce_train/outdoor.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d30320f04e0b560f4b4de9ee68305a4e698b538b
--- /dev/null
+++ b/third_party/TopicFM/scripts/reproduce_train/outdoor.sh
@@ -0,0 +1,32 @@
+#!/bin/bash -l
+
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+# conda activate loftr
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+data_cfg_path="configs/data/megadepth_trainval.py"
+main_cfg_path="configs/model/outdoor/model_ds.py"
+
+n_nodes=1
+n_gpus_per_node=4
+torch_num_workers=4
+batch_size=1
+pin_memory=true
+exp_name="outdoor-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))"
+
+python -u ./train.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --exp_name=${exp_name} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \
+ --check_val_every_n_epoch=1 \
+ --log_every_n_steps=30000 \
+ --flush_logs_every_n_steps=30000 \
+ --limit_val_batches=1. \
+ --num_sanity_val_steps=10 \
+ --benchmark=True \
+ --max_epochs=40 # --ckpt_path="pretrained_epoch22.ckpt"
diff --git a/imcui/third_party/TopicFM/src/__init__.py b/third_party/TopicFM/src/__init__.py
similarity index 91%
rename from imcui/third_party/TopicFM/src/__init__.py
rename to third_party/TopicFM/src/__init__.py
index 30caef94f911f99e0c12510d8181b3c1537daf1a..aa7ba68e1b8fa7c7854ca49680c07d54d468d83e 100644
--- a/imcui/third_party/TopicFM/src/__init__.py
+++ b/third_party/TopicFM/src/__init__.py
@@ -1,11 +1,13 @@
from yacs.config import CfgNode
from .config.default import _CN
+
def lower_config(yacs_cfg):
if not isinstance(yacs_cfg, CfgNode):
return yacs_cfg
return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()}
+
def get_model_cfg():
cfg = lower_config(lower_config(_CN))
- return cfg["model"]
\ No newline at end of file
+ return cfg["model"]
diff --git a/imcui/third_party/TopicFM/src/config/default.py b/third_party/TopicFM/src/config/default.py
similarity index 74%
rename from imcui/third_party/TopicFM/src/config/default.py
rename to third_party/TopicFM/src/config/default.py
index 591558b3f358cdce0e9e72e94acba702b2a4e896..a252b1a13952480b5c22e50d6b90432f5a328112 100644
--- a/imcui/third_party/TopicFM/src/config/default.py
+++ b/third_party/TopicFM/src/config/default.py
@@ -1,9 +1,10 @@
from yacs.config import CfgNode as CN
+
_CN = CN()
############## ↓ MODEL Pipeline ↓ ##############
_CN.MODEL = CN()
-_CN.MODEL.BACKBONE_TYPE = 'FPN'
+_CN.MODEL.BACKBONE_TYPE = "FPN"
_CN.MODEL.RESOLUTION = (8, 2) # options: [(8, 2), (16, 4)]
_CN.MODEL.FINE_WINDOW_SIZE = 5 # window_size in fine_level, must be odd
_CN.MODEL.FINE_CONCAT_COARSE_FEAT = False
@@ -18,8 +19,8 @@ _CN.MODEL.COARSE = CN()
_CN.MODEL.COARSE.D_MODEL = 256
_CN.MODEL.COARSE.D_FFN = 256
_CN.MODEL.COARSE.NHEAD = 8
-_CN.MODEL.COARSE.LAYER_NAMES = ['seed', 'seed', 'seed', 'seed', 'seed']
-_CN.MODEL.COARSE.ATTENTION = 'linear' # options: ['linear', 'full']
+_CN.MODEL.COARSE.LAYER_NAMES = ["seed", "seed", "seed", "seed", "seed"]
+_CN.MODEL.COARSE.ATTENTION = "linear" # options: ['linear', 'full']
_CN.MODEL.COARSE.TEMP_BUG_FIX = True
_CN.MODEL.COARSE.N_TOPICS = 100
_CN.MODEL.COARSE.N_SAMPLES = 6
@@ -29,7 +30,7 @@ _CN.MODEL.COARSE.N_TOPIC_TRANSFORMERS = 1
_CN.MODEL.MATCH_COARSE = CN()
_CN.MODEL.MATCH_COARSE.THR = 0.2
_CN.MODEL.MATCH_COARSE.BORDER_RM = 2
-_CN.MODEL.MATCH_COARSE.MATCH_TYPE = 'dual_softmax'
+_CN.MODEL.MATCH_COARSE.MATCH_TYPE = "dual_softmax"
_CN.MODEL.MATCH_COARSE.DSMAX_TEMPERATURE = 0.1
_CN.MODEL.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.2 # training tricks: save GPU memory
_CN.MODEL.MATCH_COARSE.TRAIN_PAD_NUM_GT_MIN = 200 # training tricks: avoid DDP deadlock
@@ -40,8 +41,8 @@ _CN.MODEL.FINE = CN()
_CN.MODEL.FINE.D_MODEL = 128
_CN.MODEL.FINE.D_FFN = 128
_CN.MODEL.FINE.NHEAD = 4
-_CN.MODEL.FINE.LAYER_NAMES = ['cross'] * 1
-_CN.MODEL.FINE.ATTENTION = 'linear'
+_CN.MODEL.FINE.LAYER_NAMES = ["cross"] * 1
+_CN.MODEL.FINE.ATTENTION = "linear"
_CN.MODEL.FINE.N_TOPICS = 1
# 5. MODEL Losses
@@ -57,7 +58,7 @@ _CN.MODEL.LOSS.NEG_WEIGHT = 1.0
# use `_CN.MODEL.MATCH_COARSE.MATCH_TYPE`
# -- # fine-level
-_CN.MODEL.LOSS.FINE_TYPE = 'l2_with_std' # ['l2_with_std', 'l2']
+_CN.MODEL.LOSS.FINE_TYPE = "l2_with_std" # ['l2_with_std', 'l2']
_CN.MODEL.LOSS.FINE_WEIGHT = 1.0
_CN.MODEL.LOSS.FINE_CORRECT_THR = 1.0 # for filtering valid fine-level gts (some gt matches might fall out of the fine-level window)
@@ -75,25 +76,33 @@ _CN.DATASET.TRAIN_INTRINSIC_PATH = None
_CN.DATASET.VAL_DATA_ROOT = None
_CN.DATASET.VAL_POSE_ROOT = None # (optional directory for poses)
_CN.DATASET.VAL_NPZ_ROOT = None
-_CN.DATASET.VAL_LIST_PATH = None # None if val data from all scenes are bundled into a single npz file
+_CN.DATASET.VAL_LIST_PATH = (
+ None # None if val data from all scenes are bundled into a single npz file
+)
_CN.DATASET.VAL_INTRINSIC_PATH = None
# testing
_CN.DATASET.TEST_DATA_SOURCE = None
_CN.DATASET.TEST_DATA_ROOT = None
_CN.DATASET.TEST_POSE_ROOT = None # (optional directory for poses)
_CN.DATASET.TEST_NPZ_ROOT = None
-_CN.DATASET.TEST_LIST_PATH = None # None if test data from all scenes are bundled into a single npz file
+_CN.DATASET.TEST_LIST_PATH = (
+ None # None if test data from all scenes are bundled into a single npz file
+)
_CN.DATASET.TEST_INTRINSIC_PATH = None
_CN.DATASET.TEST_IMGSIZE = None
# 2. dataset config
# general options
-_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.4 # discard data with overlap_score < min_overlap_score
+_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = (
+ 0.4 # discard data with overlap_score < min_overlap_score
+)
_CN.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0
_CN.DATASET.AUGMENTATION_TYPE = None # options: [None, 'dark', 'mobile']
# MegaDepth options
-_CN.DATASET.MGDPT_IMG_RESIZE = 640 # resize the longer side, zero-pad bottom-right to square.
+_CN.DATASET.MGDPT_IMG_RESIZE = (
+ 640 # resize the longer side, zero-pad bottom-right to square.
+)
_CN.DATASET.MGDPT_IMG_PAD = True # pad img to square with size = MGDPT_IMG_RESIZE
_CN.DATASET.MGDPT_DEPTH_PAD = True # pad depthmap to square with size = 2000
_CN.DATASET.MGDPT_DF = 8
@@ -109,17 +118,17 @@ _CN.TRAINER.FIND_LR = False # use learning rate finder from pytorch-lightning
# optimizer
_CN.TRAINER.OPTIMIZER = "adamw" # [adam, adamw]
_CN.TRAINER.TRUE_LR = None # this will be calculated automatically at runtime
-_CN.TRAINER.ADAM_DECAY = 0. # ADAM: for adam
+_CN.TRAINER.ADAM_DECAY = 0.0 # ADAM: for adam
_CN.TRAINER.ADAMW_DECAY = 0.01
# step-based warm-up
-_CN.TRAINER.WARMUP_TYPE = 'linear' # [linear, constant]
-_CN.TRAINER.WARMUP_RATIO = 0.
+_CN.TRAINER.WARMUP_TYPE = "linear" # [linear, constant]
+_CN.TRAINER.WARMUP_RATIO = 0.0
_CN.TRAINER.WARMUP_STEP = 4800
# learning rate scheduler
-_CN.TRAINER.SCHEDULER = 'MultiStepLR' # [MultiStepLR, CosineAnnealing, ExponentialLR]
-_CN.TRAINER.SCHEDULER_INTERVAL = 'epoch' # [epoch, step]
+_CN.TRAINER.SCHEDULER = "MultiStepLR" # [MultiStepLR, CosineAnnealing, ExponentialLR]
+_CN.TRAINER.SCHEDULER_INTERVAL = "epoch" # [epoch, step]
_CN.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12] # MSLR: MultiStepLR
_CN.TRAINER.MSLR_GAMMA = 0.5
_CN.TRAINER.COSA_TMAX = 30 # COSA: CosineAnnealing
@@ -127,25 +136,33 @@ _CN.TRAINER.ELR_GAMMA = 0.999992 # ELR: ExponentialLR, this value for 'step' in
# plotting related
_CN.TRAINER.ENABLE_PLOTTING = True
-_CN.TRAINER.N_VAL_PAIRS_TO_PLOT = 32 # number of val/test paris for plotting
-_CN.TRAINER.PLOT_MODE = 'evaluation' # ['evaluation', 'confidence']
-_CN.TRAINER.PLOT_MATCHES_ALPHA = 'dynamic'
+_CN.TRAINER.N_VAL_PAIRS_TO_PLOT = 32 # number of val/test paris for plotting
+_CN.TRAINER.PLOT_MODE = "evaluation" # ['evaluation', 'confidence']
+_CN.TRAINER.PLOT_MATCHES_ALPHA = "dynamic"
# geometric metrics and pose solver
-_CN.TRAINER.EPI_ERR_THR = 5e-4 # recommendation: 5e-4 for ScanNet, 1e-4 for MegaDepth (from SuperGlue)
-_CN.TRAINER.POSE_GEO_MODEL = 'E' # ['E', 'F', 'H']
-_CN.TRAINER.POSE_ESTIMATION_METHOD = 'RANSAC' # [RANSAC, DEGENSAC, MAGSAC]
+_CN.TRAINER.EPI_ERR_THR = (
+ 5e-4 # recommendation: 5e-4 for ScanNet, 1e-4 for MegaDepth (from SuperGlue)
+)
+_CN.TRAINER.POSE_GEO_MODEL = "E" # ['E', 'F', 'H']
+_CN.TRAINER.POSE_ESTIMATION_METHOD = "RANSAC" # [RANSAC, DEGENSAC, MAGSAC]
_CN.TRAINER.RANSAC_PIXEL_THR = 0.5
_CN.TRAINER.RANSAC_CONF = 0.99999
_CN.TRAINER.RANSAC_MAX_ITERS = 10000
_CN.TRAINER.USE_MAGSACPP = False
# data sampler for train_dataloader
-_CN.TRAINER.DATA_SAMPLER = 'scene_balance' # options: ['scene_balance', 'random', 'normal']
+_CN.TRAINER.DATA_SAMPLER = (
+ "scene_balance" # options: ['scene_balance', 'random', 'normal']
+)
# 'scene_balance' config
_CN.TRAINER.N_SAMPLES_PER_SUBSET = 200
-_CN.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT = True # whether sample each scene with replacement or not
-_CN.TRAINER.SB_SUBSET_SHUFFLE = True # after sampling from scenes, whether shuffle within the epoch or not
+_CN.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT = (
+ True # whether sample each scene with replacement or not
+)
+_CN.TRAINER.SB_SUBSET_SHUFFLE = (
+ True # after sampling from scenes, whether shuffle within the epoch or not
+)
_CN.TRAINER.SB_REPEAT = 1 # repeat N times for training the sampled data
# 'random' config
_CN.TRAINER.RDM_REPLACEMENT = True
diff --git a/imcui/third_party/TopicFM/src/datasets/aachen.py b/third_party/TopicFM/src/datasets/aachen.py
similarity index 52%
rename from imcui/third_party/TopicFM/src/datasets/aachen.py
rename to third_party/TopicFM/src/datasets/aachen.py
index ebfeee4dbfbd78770976ec027ceee8ef333a4574..71f2dd18855f3536a5159e7f420044d6536d960b 100644
--- a/imcui/third_party/TopicFM/src/datasets/aachen.py
+++ b/third_party/TopicFM/src/datasets/aachen.py
@@ -9,7 +9,7 @@ class AachenDataset(Dataset):
self.img_path = img_path
self.img_resize = img_resize
self.down_factor = down_factor
- with open(match_list_path, 'r') as f:
+ with open(match_list_path, "r") as f:
self.raw_pairs = f.readlines()
print("number of matching pairs: ", len(self.raw_pairs))
@@ -18,12 +18,20 @@ class AachenDataset(Dataset):
def __getitem__(self, idx):
raw_pair = self.raw_pairs[idx]
- image_name0, image_name1 = raw_pair.strip('\n').split(' ')
+ image_name0, image_name1 = raw_pair.strip("\n").split(" ")
path_img0 = os.path.join(self.img_path, image_name0)
path_img1 = os.path.join(self.img_path, image_name1)
- img0, scale0 = read_img_gray(path_img0, resize=self.img_resize, down_factor=self.down_factor)
- img1, scale1 = read_img_gray(path_img1, resize=self.img_resize, down_factor=self.down_factor)
- return {"image0": img0, "image1": img1,
- "scale0": scale0, "scale1": scale1,
- "pair_names": (image_name0, image_name1),
- "dataset_name": "AachenDayNight"}
\ No newline at end of file
+ img0, scale0 = read_img_gray(
+ path_img0, resize=self.img_resize, down_factor=self.down_factor
+ )
+ img1, scale1 = read_img_gray(
+ path_img1, resize=self.img_resize, down_factor=self.down_factor
+ )
+ return {
+ "image0": img0,
+ "image1": img1,
+ "scale0": scale0,
+ "scale1": scale1,
+ "pair_names": (image_name0, image_name1),
+ "dataset_name": "AachenDayNight",
+ }
diff --git a/third_party/TopicFM/src/datasets/custom_dataloader.py b/third_party/TopicFM/src/datasets/custom_dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb3bd7a083baf5d0a1e8a9a21b97a08dcc22f163
--- /dev/null
+++ b/third_party/TopicFM/src/datasets/custom_dataloader.py
@@ -0,0 +1,151 @@
+from tqdm import tqdm
+from os import path as osp
+from torch.utils.data import Dataset, DataLoader, ConcatDataset
+
+from src.datasets.megadepth import MegaDepthDataset
+from src.datasets.scannet import ScanNetDataset
+from src.datasets.aachen import AachenDataset
+from src.datasets.inloc import InLocDataset
+
+
+class TestDataLoader(DataLoader):
+ """
+ For distributed training, each training process is assgined
+ only a part of the training scenes to reduce memory overhead.
+ """
+
+ def __init__(self, config):
+
+ # 1. data config
+ self.test_data_source = config.DATASET.TEST_DATA_SOURCE
+ dataset_name = str(self.test_data_source).lower()
+ # testing
+ self.test_data_root = config.DATASET.TEST_DATA_ROOT
+ self.test_pose_root = config.DATASET.TEST_POSE_ROOT # (optional)
+ self.test_npz_root = config.DATASET.TEST_NPZ_ROOT
+ self.test_list_path = config.DATASET.TEST_LIST_PATH
+ self.test_intrinsic_path = config.DATASET.TEST_INTRINSIC_PATH
+
+ # 2. dataset config
+ # general options
+ self.min_overlap_score_test = (
+ config.DATASET.MIN_OVERLAP_SCORE_TEST
+ ) # 0.4, omit data with overlap_score < min_overlap_score
+
+ # MegaDepth options
+ if dataset_name == "megadepth":
+ self.mgdpt_img_resize = config.DATASET.MGDPT_IMG_RESIZE # 800
+ self.mgdpt_img_pad = True
+ self.mgdpt_depth_pad = True
+ self.mgdpt_df = 8
+ self.coarse_scale = 0.125
+ if dataset_name == "scannet":
+ self.img_resize = config.DATASET.TEST_IMGSIZE
+
+ if (dataset_name == "megadepth") or (dataset_name == "scannet"):
+ test_dataset = self._setup_dataset(
+ self.test_data_root,
+ self.test_npz_root,
+ self.test_list_path,
+ self.test_intrinsic_path,
+ mode="test",
+ min_overlap_score=self.min_overlap_score_test,
+ pose_dir=self.test_pose_root,
+ )
+ elif dataset_name == "aachen_v1.1":
+ test_dataset = AachenDataset(
+ self.test_data_root,
+ self.test_list_path,
+ img_resize=config.DATASET.TEST_IMGSIZE,
+ )
+ elif dataset_name == "inloc":
+ test_dataset = InLocDataset(
+ self.test_data_root,
+ self.test_list_path,
+ img_resize=config.DATASET.TEST_IMGSIZE,
+ )
+ else:
+ raise "unknown dataset"
+
+ self.test_loader_params = {
+ "batch_size": 1,
+ "shuffle": False,
+ "num_workers": 4,
+ "pin_memory": True,
+ }
+
+ # sampler = Seq(self.test_dataset, shuffle=False)
+ super(TestDataLoader, self).__init__(test_dataset, **self.test_loader_params)
+
+ def _setup_dataset(
+ self,
+ data_root,
+ split_npz_root,
+ scene_list_path,
+ intri_path,
+ mode="train",
+ min_overlap_score=0.0,
+ pose_dir=None,
+ ):
+ """Setup train / val / test set"""
+ with open(scene_list_path, "r") as f:
+ npz_names = [name.split()[0] for name in f.readlines()]
+ local_npz_names = npz_names
+
+ return self._build_concat_dataset(
+ data_root,
+ local_npz_names,
+ split_npz_root,
+ intri_path,
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ pose_dir=pose_dir,
+ )
+
+ def _build_concat_dataset(
+ self,
+ data_root,
+ npz_names,
+ npz_dir,
+ intrinsic_path,
+ mode,
+ min_overlap_score=0.0,
+ pose_dir=None,
+ ):
+ datasets = []
+ # augment_fn = self.augment_fn if mode == 'train' else None
+ data_source = self.test_data_source
+ if str(data_source).lower() == "megadepth":
+ npz_names = [f"{n}.npz" for n in npz_names]
+ for npz_name in tqdm(npz_names):
+ # `ScanNetDataset`/`MegaDepthDataset` load all data from npz_path when initialized, which might take time.
+ npz_path = osp.join(npz_dir, npz_name)
+ if data_source == "ScanNet":
+ datasets.append(
+ ScanNetDataset(
+ data_root,
+ npz_path,
+ intrinsic_path,
+ mode=mode,
+ img_resize=self.img_resize,
+ min_overlap_score=min_overlap_score,
+ pose_dir=pose_dir,
+ )
+ )
+ elif data_source == "MegaDepth":
+ datasets.append(
+ MegaDepthDataset(
+ data_root,
+ npz_path,
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ img_resize=self.mgdpt_img_resize,
+ df=self.mgdpt_df,
+ img_padding=self.mgdpt_img_pad,
+ depth_padding=self.mgdpt_depth_pad,
+ coarse_scale=self.coarse_scale,
+ )
+ )
+ else:
+ raise NotImplementedError()
+ return ConcatDataset(datasets)
diff --git a/imcui/third_party/TopicFM/src/datasets/inloc.py b/third_party/TopicFM/src/datasets/inloc.py
similarity index 53%
rename from imcui/third_party/TopicFM/src/datasets/inloc.py
rename to third_party/TopicFM/src/datasets/inloc.py
index 5421099d11b4dbbea8c09568c493d844d5c6a1b0..dc176761b7626aafd90e9674c5d85ff6e95f537c 100644
--- a/imcui/third_party/TopicFM/src/datasets/inloc.py
+++ b/third_party/TopicFM/src/datasets/inloc.py
@@ -9,7 +9,7 @@ class InLocDataset(Dataset):
self.img_path = img_path
self.img_resize = img_resize
self.down_factor = down_factor
- with open(match_list_path, 'r') as f:
+ with open(match_list_path, "r") as f:
self.raw_pairs = f.readlines()
print("number of matching pairs: ", len(self.raw_pairs))
@@ -18,12 +18,20 @@ class InLocDataset(Dataset):
def __getitem__(self, idx):
raw_pair = self.raw_pairs[idx]
- image_name0, image_name1 = raw_pair.strip('\n').split(' ')
+ image_name0, image_name1 = raw_pair.strip("\n").split(" ")
path_img0 = os.path.join(self.img_path, image_name0)
path_img1 = os.path.join(self.img_path, image_name1)
- img0, scale0 = read_img_gray(path_img0, resize=self.img_resize, down_factor=self.down_factor)
- img1, scale1 = read_img_gray(path_img1, resize=self.img_resize, down_factor=self.down_factor)
- return {"image0": img0, "image1": img1,
- "scale0": scale0, "scale1": scale1,
- "pair_names": (image_name0, image_name1),
- "dataset_name": "InLoc"}
\ No newline at end of file
+ img0, scale0 = read_img_gray(
+ path_img0, resize=self.img_resize, down_factor=self.down_factor
+ )
+ img1, scale1 = read_img_gray(
+ path_img1, resize=self.img_resize, down_factor=self.down_factor
+ )
+ return {
+ "image0": img0,
+ "image1": img1,
+ "scale0": scale0,
+ "scale1": scale1,
+ "pair_names": (image_name0, image_name1),
+ "dataset_name": "InLoc",
+ }
diff --git a/imcui/third_party/TopicFM/src/datasets/megadepth.py b/third_party/TopicFM/src/datasets/megadepth.py
similarity index 50%
rename from imcui/third_party/TopicFM/src/datasets/megadepth.py
rename to third_party/TopicFM/src/datasets/megadepth.py
index e92768e72e373c2a8ebeaf1158f9710fb1bfb5f1..77516327ebed8ca4ea8be9692a7077d94f03ee5b 100644
--- a/imcui/third_party/TopicFM/src/datasets/megadepth.py
+++ b/third_party/TopicFM/src/datasets/megadepth.py
@@ -9,20 +9,22 @@ from src.utils.dataset import read_megadepth_gray, read_megadepth_depth
class MegaDepthDataset(Dataset):
- def __init__(self,
- root_dir,
- npz_path,
- mode='train',
- min_overlap_score=0.4,
- img_resize=None,
- df=None,
- img_padding=False,
- depth_padding=False,
- augment_fn=None,
- **kwargs):
+ def __init__(
+ self,
+ root_dir,
+ npz_path,
+ mode="train",
+ min_overlap_score=0.4,
+ img_resize=None,
+ df=None,
+ img_padding=False,
+ depth_padding=False,
+ augment_fn=None,
+ **kwargs
+ ):
"""
Manage one scene(npz_path) of MegaDepth dataset.
-
+
Args:
root_dir (str): megadepth root directory that has `phoenix`.
npz_path (str): {scene_id}.npz path. This contains image pair information of a scene.
@@ -38,30 +40,38 @@ class MegaDepthDataset(Dataset):
super().__init__()
self.root_dir = root_dir
self.mode = mode
- self.scene_id = npz_path.split('.')[0]
+ self.scene_id = npz_path.split(".")[0]
# prepare scene_info and pair_info
- if mode == 'test' and min_overlap_score != 0:
- logger.warning("You are using `min_overlap_score`!=0 in test mode. Set to 0.")
+ if mode == "test" and min_overlap_score != 0:
+ logger.warning(
+ "You are using `min_overlap_score`!=0 in test mode. Set to 0."
+ )
min_overlap_score = 0
self.scene_info = np.load(npz_path, allow_pickle=True)
- self.pair_infos = self.scene_info['pair_infos'].copy()
- del self.scene_info['pair_infos']
- self.pair_infos = [pair_info for pair_info in self.pair_infos if pair_info[1] > min_overlap_score]
+ self.pair_infos = self.scene_info["pair_infos"].copy()
+ del self.scene_info["pair_infos"]
+ self.pair_infos = [
+ pair_info
+ for pair_info in self.pair_infos
+ if pair_info[1] > min_overlap_score
+ ]
# parameters for image resizing, padding and depthmap padding
- if mode == 'train':
+ if mode == "train":
assert img_resize is not None and img_padding and depth_padding
self.img_resize = img_resize
- if mode == 'val':
+ if mode == "val":
self.img_resize = 864
self.df = df
self.img_padding = img_padding
- self.depth_max_size = 2000 if depth_padding else None # the upperbound of depthmaps size in megadepth.
+ self.depth_max_size = (
+ 2000 if depth_padding else None
+ ) # the upperbound of depthmaps size in megadepth.
# for training LoFTR
- self.augment_fn = augment_fn if mode == 'train' else None
- self.coarse_scale = getattr(kwargs, 'coarse_scale', 0.125)
+ self.augment_fn = augment_fn if mode == "train" else None
+ self.coarse_scale = getattr(kwargs, "coarse_scale", 0.125)
def __len__(self):
return len(self.pair_infos)
@@ -70,60 +80,77 @@ class MegaDepthDataset(Dataset):
(idx0, idx1), overlap_score, central_matches = self.pair_infos[idx]
# read grayscale image and mask. (1, h, w) and (h, w)
- img_name0 = osp.join(self.root_dir, self.scene_info['image_paths'][idx0])
- img_name1 = osp.join(self.root_dir, self.scene_info['image_paths'][idx1])
-
+ img_name0 = osp.join(self.root_dir, self.scene_info["image_paths"][idx0])
+ img_name1 = osp.join(self.root_dir, self.scene_info["image_paths"][idx1])
+
# TODO: Support augmentation & handle seeds for each worker correctly.
image0, mask0, scale0 = read_megadepth_gray(
- img_name0, self.img_resize, self.df, self.img_padding, None)
- # np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
+ img_name0, self.img_resize, self.df, self.img_padding, None
+ )
+ # np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
image1, mask1, scale1 = read_megadepth_gray(
- img_name1, self.img_resize, self.df, self.img_padding, None)
- # np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
+ img_name1, self.img_resize, self.df, self.img_padding, None
+ )
+ # np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
# read depth. shape: (h, w)
- if self.mode in ['train', 'val']:
+ if self.mode in ["train", "val"]:
depth0 = read_megadepth_depth(
- osp.join(self.root_dir, self.scene_info['depth_paths'][idx0]), pad_to=self.depth_max_size)
+ osp.join(self.root_dir, self.scene_info["depth_paths"][idx0]),
+ pad_to=self.depth_max_size,
+ )
depth1 = read_megadepth_depth(
- osp.join(self.root_dir, self.scene_info['depth_paths'][idx1]), pad_to=self.depth_max_size)
+ osp.join(self.root_dir, self.scene_info["depth_paths"][idx1]),
+ pad_to=self.depth_max_size,
+ )
else:
depth0 = depth1 = torch.tensor([])
# read intrinsics of original size
- K_0 = torch.tensor(self.scene_info['intrinsics'][idx0].copy(), dtype=torch.float).reshape(3, 3)
- K_1 = torch.tensor(self.scene_info['intrinsics'][idx1].copy(), dtype=torch.float).reshape(3, 3)
+ K_0 = torch.tensor(
+ self.scene_info["intrinsics"][idx0].copy(), dtype=torch.float
+ ).reshape(3, 3)
+ K_1 = torch.tensor(
+ self.scene_info["intrinsics"][idx1].copy(), dtype=torch.float
+ ).reshape(3, 3)
# read and compute relative poses
- T0 = self.scene_info['poses'][idx0]
- T1 = self.scene_info['poses'][idx1]
- T_0to1 = torch.tensor(np.matmul(T1, np.linalg.inv(T0)), dtype=torch.float)[:4, :4] # (4, 4)
+ T0 = self.scene_info["poses"][idx0]
+ T1 = self.scene_info["poses"][idx1]
+ T_0to1 = torch.tensor(np.matmul(T1, np.linalg.inv(T0)), dtype=torch.float)[
+ :4, :4
+ ] # (4, 4)
T_1to0 = T_0to1.inverse()
data = {
- 'image0': image0, # (1, h, w)
- 'depth0': depth0, # (h, w)
- 'image1': image1,
- 'depth1': depth1,
- 'T_0to1': T_0to1, # (4, 4)
- 'T_1to0': T_1to0,
- 'K0': K_0, # (3, 3)
- 'K1': K_1,
- 'scale0': scale0, # [scale_w, scale_h]
- 'scale1': scale1,
- 'dataset_name': 'MegaDepth',
- 'scene_id': self.scene_id,
- 'pair_id': idx,
- 'pair_names': (self.scene_info['image_paths'][idx0], self.scene_info['image_paths'][idx1]),
+ "image0": image0, # (1, h, w)
+ "depth0": depth0, # (h, w)
+ "image1": image1,
+ "depth1": depth1,
+ "T_0to1": T_0to1, # (4, 4)
+ "T_1to0": T_1to0,
+ "K0": K_0, # (3, 3)
+ "K1": K_1,
+ "scale0": scale0, # [scale_w, scale_h]
+ "scale1": scale1,
+ "dataset_name": "MegaDepth",
+ "scene_id": self.scene_id,
+ "pair_id": idx,
+ "pair_names": (
+ self.scene_info["image_paths"][idx0],
+ self.scene_info["image_paths"][idx1],
+ ),
}
# for LoFTR training
if mask0 is not None: # img_padding is True
if self.coarse_scale:
- [ts_mask_0, ts_mask_1] = F.interpolate(torch.stack([mask0, mask1], dim=0)[None].float(),
- scale_factor=self.coarse_scale,
- mode='nearest',
- recompute_scale_factor=False)[0].bool()
- data.update({'mask0': ts_mask_0, 'mask1': ts_mask_1})
+ [ts_mask_0, ts_mask_1] = F.interpolate(
+ torch.stack([mask0, mask1], dim=0)[None].float(),
+ scale_factor=self.coarse_scale,
+ mode="nearest",
+ recompute_scale_factor=False,
+ )[0].bool()
+ data.update({"mask0": ts_mask_0, "mask1": ts_mask_1})
return data
diff --git a/imcui/third_party/XoFTR/src/datasets/sampler.py b/third_party/TopicFM/src/datasets/sampler.py
similarity index 74%
rename from imcui/third_party/XoFTR/src/datasets/sampler.py
rename to third_party/TopicFM/src/datasets/sampler.py
index 81b6f435645632a013476f9a665a0861ab7fcb61..131111c4cf69cd8770058dfac2be717aa183978e 100644
--- a/imcui/third_party/XoFTR/src/datasets/sampler.py
+++ b/third_party/TopicFM/src/datasets/sampler.py
@@ -3,10 +3,10 @@ from torch.utils.data import Sampler, ConcatDataset
class RandomConcatSampler(Sampler):
- """ Random sampler for ConcatDataset. At each epoch, `n_samples_per_subset` samples will be draw from each subset
+ """Random sampler for ConcatDataset. At each epoch, `n_samples_per_subset` samples will be draw from each subset
in the ConcatDataset. If `subset_replacement` is ``True``, sampling within each subset will be done with replacement.
However, it is impossible to sample data without replacement between epochs, unless bulding a stateful sampler lived along the entire training phase.
-
+
For current implementation, the randomness of sampling is ensured no matter the sampler is recreated across epochs or not and call `torch.manual_seed()` or not.
Args:
shuffle (bool): shuffle the random sampled indices across all sub-datsets.
@@ -18,16 +18,19 @@ class RandomConcatSampler(Sampler):
TODO: Add a `set_epoch()` method to fullfill sampling without replacement across epochs.
ref: https://github.com/PyTorchLightning/pytorch-lightning/blob/e9846dd758cfb1500eb9dba2d86f6912eb487587/pytorch_lightning/trainer/training_loop.py#L373
"""
- def __init__(self,
- data_source: ConcatDataset,
- n_samples_per_subset: int,
- subset_replacement: bool=True,
- shuffle: bool=True,
- repeat: int=1,
- seed: int=None):
+
+ def __init__(
+ self,
+ data_source: ConcatDataset,
+ n_samples_per_subset: int,
+ subset_replacement: bool = True,
+ shuffle: bool = True,
+ repeat: int = 1,
+ seed: int = None,
+ ):
if not isinstance(data_source, ConcatDataset):
raise TypeError("data_source should be torch.utils.data.ConcatDataset")
-
+
self.data_source = data_source
self.n_subset = len(self.data_source.datasets)
self.n_samples_per_subset = n_samples_per_subset
@@ -37,27 +40,37 @@ class RandomConcatSampler(Sampler):
self.shuffle = shuffle
self.generator = torch.manual_seed(seed)
assert self.repeat >= 1
-
+
def __len__(self):
return self.n_samples
-
+
def __iter__(self):
indices = []
# sample from each sub-dataset
for d_idx in range(self.n_subset):
- low = 0 if d_idx==0 else self.data_source.cumulative_sizes[d_idx-1]
+ low = 0 if d_idx == 0 else self.data_source.cumulative_sizes[d_idx - 1]
high = self.data_source.cumulative_sizes[d_idx]
if self.subset_replacement:
- rand_tensor = torch.randint(low, high, (self.n_samples_per_subset, ),
- generator=self.generator, dtype=torch.int64)
+ rand_tensor = torch.randint(
+ low,
+ high,
+ (self.n_samples_per_subset,),
+ generator=self.generator,
+ dtype=torch.int64,
+ )
else: # sample without replacement
len_subset = len(self.data_source.datasets[d_idx])
rand_tensor = torch.randperm(len_subset, generator=self.generator) + low
if len_subset >= self.n_samples_per_subset:
- rand_tensor = rand_tensor[:self.n_samples_per_subset]
- else: # padding with replacement
- rand_tensor_replacement = torch.randint(low, high, (self.n_samples_per_subset - len_subset, ),
- generator=self.generator, dtype=torch.int64)
+ rand_tensor = rand_tensor[: self.n_samples_per_subset]
+ else: # padding with replacement
+ rand_tensor_replacement = torch.randint(
+ low,
+ high,
+ (self.n_samples_per_subset - len_subset,),
+ generator=self.generator,
+ dtype=torch.int64,
+ )
rand_tensor = torch.cat([rand_tensor, rand_tensor_replacement])
indices.append(rand_tensor)
indices = torch.cat(indices)
@@ -72,6 +85,6 @@ class RandomConcatSampler(Sampler):
_choice = lambda x: x[torch.randperm(len(x), generator=self.generator)]
repeat_indices = map(_choice, repeat_indices)
indices = torch.cat([indices, *repeat_indices], 0)
-
+
assert indices.shape[0] == self.n_samples
return iter(indices.tolist())
diff --git a/imcui/third_party/TopicFM/src/datasets/scannet.py b/third_party/TopicFM/src/datasets/scannet.py
similarity index 52%
rename from imcui/third_party/TopicFM/src/datasets/scannet.py
rename to third_party/TopicFM/src/datasets/scannet.py
index fb5dab7b150a3c6f54eb07b0459bbf3e9ba58fbf..b955c4fa1609625be2c6c1a0ed6665109908bba0 100644
--- a/imcui/third_party/TopicFM/src/datasets/scannet.py
+++ b/third_party/TopicFM/src/datasets/scannet.py
@@ -10,20 +10,22 @@ from src.utils.dataset import (
read_scannet_gray,
read_scannet_depth,
read_scannet_pose,
- read_scannet_intrinsic
+ read_scannet_intrinsic,
)
class ScanNetDataset(utils.data.Dataset):
- def __init__(self,
- root_dir,
- npz_path,
- intrinsic_path,
- mode='train',
- min_overlap_score=0.4,
- augment_fn=None,
- pose_dir=None,
- **kwargs):
+ def __init__(
+ self,
+ root_dir,
+ npz_path,
+ intrinsic_path,
+ mode="train",
+ min_overlap_score=0.4,
+ augment_fn=None,
+ pose_dir=None,
+ **kwargs,
+ ):
"""Manage one scene of ScanNet Dataset.
Args:
root_dir (str): ScanNet root directory that contains scene folders.
@@ -38,78 +40,88 @@ class ScanNetDataset(utils.data.Dataset):
self.root_dir = root_dir
self.pose_dir = pose_dir if pose_dir is not None else root_dir
self.mode = mode
- self.img_resize = (640, 480) if 'img_resize' not in kwargs else kwargs['img_resize']
+ self.img_resize = (
+ (640, 480) if "img_resize" not in kwargs else kwargs["img_resize"]
+ )
# prepare data_names, intrinsics and extrinsics(T)
with np.load(npz_path) as data:
- self.data_names = data['name']
- if 'score' in data.keys() and mode not in ['val' or 'test']:
- kept_mask = data['score'] > min_overlap_score
+ self.data_names = data["name"]
+ if "score" in data.keys() and mode not in ["val" or "test"]:
+ kept_mask = data["score"] > min_overlap_score
self.data_names = self.data_names[kept_mask]
self.intrinsics = dict(np.load(intrinsic_path))
# for training LoFTR
- self.augment_fn = augment_fn if mode == 'train' else None
+ self.augment_fn = augment_fn if mode == "train" else None
def __len__(self):
return len(self.data_names)
def _read_abs_pose(self, scene_name, name):
- pth = osp.join(self.pose_dir,
- scene_name,
- 'pose', f'{name}.txt')
+ pth = osp.join(self.pose_dir, scene_name, "pose", f"{name}.txt")
return read_scannet_pose(pth)
def _compute_rel_pose(self, scene_name, name0, name1):
pose0 = self._read_abs_pose(scene_name, name0)
pose1 = self._read_abs_pose(scene_name, name1)
-
+
return np.matmul(pose1, inv(pose0)) # (4, 4)
def __getitem__(self, idx):
data_name = self.data_names[idx]
scene_name, scene_sub_name, stem_name_0, stem_name_1 = data_name
- scene_name = f'scene{scene_name:04d}_{scene_sub_name:02d}'
+ scene_name = f"scene{scene_name:04d}_{scene_sub_name:02d}"
# read the grayscale image which will be resized to (1, 480, 640)
- img_name0 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_0}.jpg')
- img_name1 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_1}.jpg')
-
+ img_name0 = osp.join(self.root_dir, scene_name, "color", f"{stem_name_0}.jpg")
+ img_name1 = osp.join(self.root_dir, scene_name, "color", f"{stem_name_1}.jpg")
+
# TODO: Support augmentation & handle seeds for each worker correctly.
image0 = read_scannet_gray(img_name0, resize=self.img_resize, augment_fn=None)
- # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
+ # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
image1 = read_scannet_gray(img_name1, resize=self.img_resize, augment_fn=None)
- # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
+ # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5]))
# read the depthmap which is stored as (480, 640)
- if self.mode in ['train', 'val']:
- depth0 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_0}.png'))
- depth1 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_1}.png'))
+ if self.mode in ["train", "val"]:
+ depth0 = read_scannet_depth(
+ osp.join(self.root_dir, scene_name, "depth", f"{stem_name_0}.png")
+ )
+ depth1 = read_scannet_depth(
+ osp.join(self.root_dir, scene_name, "depth", f"{stem_name_1}.png")
+ )
else:
depth0 = depth1 = torch.tensor([])
# read the intrinsic of depthmap
- K_0 = K_1 = torch.tensor(self.intrinsics[scene_name].copy(), dtype=torch.float).reshape(3, 3)
+ K_0 = K_1 = torch.tensor(
+ self.intrinsics[scene_name].copy(), dtype=torch.float
+ ).reshape(3, 3)
# read and compute relative poses
- T_0to1 = torch.tensor(self._compute_rel_pose(scene_name, stem_name_0, stem_name_1),
- dtype=torch.float32)
+ T_0to1 = torch.tensor(
+ self._compute_rel_pose(scene_name, stem_name_0, stem_name_1),
+ dtype=torch.float32,
+ )
T_1to0 = T_0to1.inverse()
data = {
- 'image0': image0, # (1, h, w)
- 'depth0': depth0, # (h, w)
- 'image1': image1,
- 'depth1': depth1,
- 'T_0to1': T_0to1, # (4, 4)
- 'T_1to0': T_1to0,
- 'K0': K_0, # (3, 3)
- 'K1': K_1,
- 'dataset_name': 'ScanNet',
- 'scene_id': scene_name,
- 'pair_id': idx,
- 'pair_names': (osp.join(scene_name, 'color', f'{stem_name_0}.jpg'),
- osp.join(scene_name, 'color', f'{stem_name_1}.jpg'))
+ "image0": image0, # (1, h, w)
+ "depth0": depth0, # (h, w)
+ "image1": image1,
+ "depth1": depth1,
+ "T_0to1": T_0to1, # (4, 4)
+ "T_1to0": T_1to0,
+ "K0": K_0, # (3, 3)
+ "K1": K_1,
+ "dataset_name": "ScanNet",
+ "scene_id": scene_name,
+ "pair_id": idx,
+ "pair_names": (
+ osp.join(scene_name, "color", f"{stem_name_0}.jpg"),
+ osp.join(scene_name, "color", f"{stem_name_1}.jpg"),
+ ),
}
return data
diff --git a/third_party/TopicFM/src/lightning_trainer/data.py b/third_party/TopicFM/src/lightning_trainer/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..95f6a5eeecf39a993b86674242eacb7b42f8a566
--- /dev/null
+++ b/third_party/TopicFM/src/lightning_trainer/data.py
@@ -0,0 +1,399 @@
+import os
+import math
+from collections import abc
+from loguru import logger
+from torch.utils.data.dataset import Dataset
+from tqdm import tqdm
+from os import path as osp
+from pathlib import Path
+from joblib import Parallel, delayed
+
+import pytorch_lightning as pl
+from torch import distributed as dist
+from torch.utils.data import (
+ Dataset,
+ DataLoader,
+ ConcatDataset,
+ DistributedSampler,
+ RandomSampler,
+ dataloader,
+)
+
+from src.utils.augment import build_augmentor
+from src.utils.dataloader import get_local_split
+from src.utils.misc import tqdm_joblib
+from src.utils import comm
+from src.datasets.megadepth import MegaDepthDataset
+from src.datasets.scannet import ScanNetDataset
+from src.datasets.sampler import RandomConcatSampler
+
+
+class MultiSceneDataModule(pl.LightningDataModule):
+ """
+ For distributed training, each training process is assgined
+ only a part of the training scenes to reduce memory overhead.
+ """
+
+ def __init__(self, args, config):
+ super().__init__()
+
+ # 1. data config
+ # Train and Val should from the same data source
+ self.trainval_data_source = config.DATASET.TRAINVAL_DATA_SOURCE
+ self.test_data_source = config.DATASET.TEST_DATA_SOURCE
+ # training and validating
+ self.train_data_root = config.DATASET.TRAIN_DATA_ROOT
+ self.train_pose_root = config.DATASET.TRAIN_POSE_ROOT # (optional)
+ self.train_npz_root = config.DATASET.TRAIN_NPZ_ROOT
+ self.train_list_path = config.DATASET.TRAIN_LIST_PATH
+ self.train_intrinsic_path = config.DATASET.TRAIN_INTRINSIC_PATH
+ self.val_data_root = config.DATASET.VAL_DATA_ROOT
+ self.val_pose_root = config.DATASET.VAL_POSE_ROOT # (optional)
+ self.val_npz_root = config.DATASET.VAL_NPZ_ROOT
+ self.val_list_path = config.DATASET.VAL_LIST_PATH
+ self.val_intrinsic_path = config.DATASET.VAL_INTRINSIC_PATH
+ # testing
+ self.test_data_root = config.DATASET.TEST_DATA_ROOT
+ self.test_pose_root = config.DATASET.TEST_POSE_ROOT # (optional)
+ self.test_npz_root = config.DATASET.TEST_NPZ_ROOT
+ self.test_list_path = config.DATASET.TEST_LIST_PATH
+ self.test_intrinsic_path = config.DATASET.TEST_INTRINSIC_PATH
+
+ # 2. dataset config
+ # general options
+ self.min_overlap_score_test = (
+ config.DATASET.MIN_OVERLAP_SCORE_TEST
+ ) # 0.4, omit data with overlap_score < min_overlap_score
+ self.min_overlap_score_train = config.DATASET.MIN_OVERLAP_SCORE_TRAIN
+ self.augment_fn = build_augmentor(
+ config.DATASET.AUGMENTATION_TYPE
+ ) # None, options: [None, 'dark', 'mobile']
+
+ # MegaDepth options
+ self.mgdpt_img_resize = config.DATASET.MGDPT_IMG_RESIZE # 840
+ self.mgdpt_img_pad = config.DATASET.MGDPT_IMG_PAD # True
+ self.mgdpt_depth_pad = config.DATASET.MGDPT_DEPTH_PAD # True
+ self.mgdpt_df = config.DATASET.MGDPT_DF # 8
+ self.coarse_scale = 1 / config.MODEL.RESOLUTION[0] # 0.125. for training loftr.
+
+ # 3.loader parameters
+ self.train_loader_params = {
+ "batch_size": args.batch_size,
+ "num_workers": args.num_workers,
+ "pin_memory": getattr(args, "pin_memory", True),
+ }
+ self.val_loader_params = {
+ "batch_size": 1,
+ "shuffle": False,
+ "num_workers": args.num_workers,
+ "pin_memory": getattr(args, "pin_memory", True),
+ }
+ self.test_loader_params = {
+ "batch_size": 1,
+ "shuffle": False,
+ "num_workers": args.num_workers,
+ "pin_memory": True,
+ }
+
+ # 4. sampler
+ self.data_sampler = config.TRAINER.DATA_SAMPLER
+ self.n_samples_per_subset = config.TRAINER.N_SAMPLES_PER_SUBSET
+ self.subset_replacement = config.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT
+ self.shuffle = config.TRAINER.SB_SUBSET_SHUFFLE
+ self.repeat = config.TRAINER.SB_REPEAT
+
+ # (optional) RandomSampler for debugging
+
+ # misc configurations
+ self.parallel_load_data = getattr(args, "parallel_load_data", False)
+ self.seed = config.TRAINER.SEED # 66
+
+ def setup(self, stage=None):
+ """
+ Setup train / val / test dataset. This method will be called by PL automatically.
+ Args:
+ stage (str): 'fit' in training phase, and 'test' in testing phase.
+ """
+
+ assert stage in ["fit", "test"], "stage must be either fit or test"
+
+ try:
+ self.world_size = dist.get_world_size()
+ self.rank = dist.get_rank()
+ logger.info(f"[rank:{self.rank}] world_size: {self.world_size}")
+ except AssertionError as ae:
+ self.world_size = 1
+ self.rank = 0
+ logger.warning(str(ae) + " (set wolrd_size=1 and rank=0)")
+
+ if stage == "fit":
+ self.train_dataset = self._setup_dataset(
+ self.train_data_root,
+ self.train_npz_root,
+ self.train_list_path,
+ self.train_intrinsic_path,
+ mode="train",
+ min_overlap_score=self.min_overlap_score_train,
+ pose_dir=self.train_pose_root,
+ )
+ # setup multiple (optional) validation subsets
+ if isinstance(self.val_list_path, (list, tuple)):
+ self.val_dataset = []
+ if not isinstance(self.val_npz_root, (list, tuple)):
+ self.val_npz_root = [
+ self.val_npz_root for _ in range(len(self.val_list_path))
+ ]
+ for npz_list, npz_root in zip(self.val_list_path, self.val_npz_root):
+ self.val_dataset.append(
+ self._setup_dataset(
+ self.val_data_root,
+ npz_root,
+ npz_list,
+ self.val_intrinsic_path,
+ mode="val",
+ min_overlap_score=self.min_overlap_score_test,
+ pose_dir=self.val_pose_root,
+ )
+ )
+ else:
+ self.val_dataset = self._setup_dataset(
+ self.val_data_root,
+ self.val_npz_root,
+ self.val_list_path,
+ self.val_intrinsic_path,
+ mode="val",
+ min_overlap_score=self.min_overlap_score_test,
+ pose_dir=self.val_pose_root,
+ )
+ logger.info(f"[rank:{self.rank}] Train & Val Dataset loaded!")
+ else: # stage == 'test
+ self.test_dataset = self._setup_dataset(
+ self.test_data_root,
+ self.test_npz_root,
+ self.test_list_path,
+ self.test_intrinsic_path,
+ mode="test",
+ min_overlap_score=self.min_overlap_score_test,
+ pose_dir=self.test_pose_root,
+ )
+ logger.info(f"[rank:{self.rank}]: Test Dataset loaded!")
+
+ def _setup_dataset(
+ self,
+ data_root,
+ split_npz_root,
+ scene_list_path,
+ intri_path,
+ mode="train",
+ min_overlap_score=0.0,
+ pose_dir=None,
+ ):
+ """Setup train / val / test set"""
+ with open(scene_list_path, "r") as f:
+ npz_names = [name.split()[0] for name in f.readlines()]
+
+ if mode == "train":
+ local_npz_names = get_local_split(
+ npz_names, self.world_size, self.rank, self.seed
+ )
+ else:
+ local_npz_names = npz_names
+ logger.info(f"[rank {self.rank}]: {len(local_npz_names)} scene(s) assigned.")
+
+ dataset_builder = (
+ self._build_concat_dataset_parallel
+ if self.parallel_load_data
+ else self._build_concat_dataset
+ )
+ return dataset_builder(
+ data_root,
+ local_npz_names,
+ split_npz_root,
+ intri_path,
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ pose_dir=pose_dir,
+ )
+
+ def _build_concat_dataset(
+ self,
+ data_root,
+ npz_names,
+ npz_dir,
+ intrinsic_path,
+ mode,
+ min_overlap_score=0.0,
+ pose_dir=None,
+ ):
+ datasets = []
+ augment_fn = self.augment_fn if mode == "train" else None
+ data_source = (
+ self.trainval_data_source
+ if mode in ["train", "val"]
+ else self.test_data_source
+ )
+ if str(data_source).lower() == "megadepth":
+ npz_names = [f"{n}.npz" for n in npz_names]
+ for npz_name in tqdm(
+ npz_names,
+ desc=f"[rank:{self.rank}] loading {mode} datasets",
+ disable=int(self.rank) != 0,
+ ):
+ # `ScanNetDataset`/`MegaDepthDataset` load all data from npz_path when initialized, which might take time.
+ npz_path = osp.join(npz_dir, npz_name)
+ if data_source == "ScanNet":
+ datasets.append(
+ ScanNetDataset(
+ data_root,
+ npz_path,
+ intrinsic_path,
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ augment_fn=augment_fn,
+ pose_dir=pose_dir,
+ )
+ )
+ elif data_source == "MegaDepth":
+ datasets.append(
+ MegaDepthDataset(
+ data_root,
+ npz_path,
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ img_resize=self.mgdpt_img_resize,
+ df=self.mgdpt_df,
+ img_padding=self.mgdpt_img_pad,
+ depth_padding=self.mgdpt_depth_pad,
+ augment_fn=augment_fn,
+ coarse_scale=self.coarse_scale,
+ )
+ )
+ else:
+ raise NotImplementedError()
+ return ConcatDataset(datasets)
+
+ def _build_concat_dataset_parallel(
+ self,
+ data_root,
+ npz_names,
+ npz_dir,
+ intrinsic_path,
+ mode,
+ min_overlap_score=0.0,
+ pose_dir=None,
+ ):
+ augment_fn = self.augment_fn if mode == "train" else None
+ data_source = (
+ self.trainval_data_source
+ if mode in ["train", "val"]
+ else self.test_data_source
+ )
+ if str(data_source).lower() == "megadepth":
+ npz_names = [f"{n}.npz" for n in npz_names]
+ with tqdm_joblib(
+ tqdm(
+ desc=f"[rank:{self.rank}] loading {mode} datasets",
+ total=len(npz_names),
+ disable=int(self.rank) != 0,
+ )
+ ):
+ if data_source == "ScanNet":
+ datasets = Parallel(
+ n_jobs=math.floor(
+ len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()
+ )
+ )(
+ delayed(
+ lambda x: _build_dataset(
+ ScanNetDataset,
+ data_root,
+ osp.join(npz_dir, x),
+ intrinsic_path,
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ augment_fn=augment_fn,
+ pose_dir=pose_dir,
+ )
+ )(name)
+ for name in npz_names
+ )
+ elif data_source == "MegaDepth":
+ # TODO: _pickle.PicklingError: Could not pickle the task to send it to the workers.
+ raise NotImplementedError()
+ datasets = Parallel(
+ n_jobs=math.floor(
+ len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()
+ )
+ )(
+ delayed(
+ lambda x: _build_dataset(
+ MegaDepthDataset,
+ data_root,
+ osp.join(npz_dir, x),
+ mode=mode,
+ min_overlap_score=min_overlap_score,
+ img_resize=self.mgdpt_img_resize,
+ df=self.mgdpt_df,
+ img_padding=self.mgdpt_img_pad,
+ depth_padding=self.mgdpt_depth_pad,
+ augment_fn=augment_fn,
+ coarse_scale=self.coarse_scale,
+ )
+ )(name)
+ for name in npz_names
+ )
+ else:
+ raise ValueError(f"Unknown dataset: {data_source}")
+ return ConcatDataset(datasets)
+
+ def train_dataloader(self):
+ """Build training dataloader for ScanNet / MegaDepth."""
+ assert self.data_sampler in ["scene_balance"]
+ logger.info(
+ f"[rank:{self.rank}/{self.world_size}]: Train Sampler and DataLoader re-init (should not re-init between epochs!)."
+ )
+ if self.data_sampler == "scene_balance":
+ sampler = RandomConcatSampler(
+ self.train_dataset,
+ self.n_samples_per_subset,
+ self.subset_replacement,
+ self.shuffle,
+ self.repeat,
+ self.seed,
+ )
+ else:
+ sampler = None
+ dataloader = DataLoader(
+ self.train_dataset, sampler=sampler, **self.train_loader_params
+ )
+ return dataloader
+
+ def val_dataloader(self):
+ """Build validation dataloader for ScanNet / MegaDepth."""
+ logger.info(
+ f"[rank:{self.rank}/{self.world_size}]: Val Sampler and DataLoader re-init."
+ )
+ if not isinstance(self.val_dataset, abc.Sequence):
+ sampler = DistributedSampler(self.val_dataset, shuffle=False)
+ return DataLoader(
+ self.val_dataset, sampler=sampler, **self.val_loader_params
+ )
+ else:
+ dataloaders = []
+ for dataset in self.val_dataset:
+ sampler = DistributedSampler(dataset, shuffle=False)
+ dataloaders.append(
+ DataLoader(dataset, sampler=sampler, **self.val_loader_params)
+ )
+ return dataloaders
+
+ def test_dataloader(self, *args, **kwargs):
+ logger.info(
+ f"[rank:{self.rank}/{self.world_size}]: Test Sampler and DataLoader re-init."
+ )
+ sampler = DistributedSampler(self.test_dataset, shuffle=False)
+ return DataLoader(self.test_dataset, sampler=sampler, **self.test_loader_params)
+
+
+def _build_dataset(dataset: Dataset, *args, **kwargs):
+ return dataset(*args, **kwargs)
diff --git a/third_party/TopicFM/src/lightning_trainer/trainer.py b/third_party/TopicFM/src/lightning_trainer/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..cce4839b536eba974426309eca10415547479f50
--- /dev/null
+++ b/third_party/TopicFM/src/lightning_trainer/trainer.py
@@ -0,0 +1,310 @@
+from collections import defaultdict
+import pprint
+from loguru import logger
+from pathlib import Path
+
+import torch
+import numpy as np
+import pytorch_lightning as pl
+from matplotlib import pyplot as plt
+
+from src.models import TopicFM
+from src.models.utils.supervision import (
+ compute_supervision_coarse,
+ compute_supervision_fine,
+)
+from src.losses.loss import TopicFMLoss
+from src.optimizers import build_optimizer, build_scheduler
+from src.utils.metrics import (
+ compute_symmetrical_epipolar_errors,
+ compute_pose_errors,
+ aggregate_metrics,
+)
+from src.utils.plotting import make_matching_figures
+from src.utils.comm import gather, all_gather
+from src.utils.misc import lower_config, flattenList
+from src.utils.profiler import PassThroughProfiler
+
+
+class PL_Trainer(pl.LightningModule):
+ def __init__(self, config, pretrained_ckpt=None, profiler=None, dump_dir=None):
+ """
+ TODO:
+ - use the new version of PL logging API.
+ """
+ super().__init__()
+ # Misc
+ self.config = config # full config
+ _config = lower_config(self.config)
+ self.model_cfg = lower_config(_config["model"])
+ self.profiler = profiler or PassThroughProfiler()
+ self.n_vals_plot = max(
+ config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1
+ )
+
+ # Matcher: TopicFM
+ self.matcher = TopicFM(config=_config["model"])
+ self.loss = TopicFMLoss(_config)
+
+ # Pretrained weights
+ if pretrained_ckpt:
+ state_dict = torch.load(pretrained_ckpt, map_location="cpu")["state_dict"]
+ self.matcher.load_state_dict(state_dict, strict=True)
+ logger.info(f"Load '{pretrained_ckpt}' as pretrained checkpoint")
+
+ # Testing
+ self.dump_dir = dump_dir
+
+ def configure_optimizers(self):
+ # FIXME: The scheduler did not work properly when `--resume_from_checkpoint`
+ optimizer = build_optimizer(self, self.config)
+ scheduler = build_scheduler(self.config, optimizer)
+ return [optimizer], [scheduler]
+
+ def optimizer_step(
+ self,
+ epoch,
+ batch_idx,
+ optimizer,
+ optimizer_idx,
+ optimizer_closure,
+ on_tpu,
+ using_native_amp,
+ using_lbfgs,
+ ):
+ # learning rate warm up
+ warmup_step = self.config.TRAINER.WARMUP_STEP
+ if self.trainer.global_step < warmup_step:
+ if self.config.TRAINER.WARMUP_TYPE == "linear":
+ base_lr = self.config.TRAINER.WARMUP_RATIO * self.config.TRAINER.TRUE_LR
+ lr = base_lr + (
+ self.trainer.global_step / self.config.TRAINER.WARMUP_STEP
+ ) * abs(self.config.TRAINER.TRUE_LR - base_lr)
+ for pg in optimizer.param_groups:
+ pg["lr"] = lr
+ elif self.config.TRAINER.WARMUP_TYPE == "constant":
+ pass
+ else:
+ raise ValueError(
+ f"Unknown lr warm-up strategy: {self.config.TRAINER.WARMUP_TYPE}"
+ )
+
+ # update params
+ optimizer.step(closure=optimizer_closure)
+ optimizer.zero_grad()
+
+ def _trainval_inference(self, batch):
+ with self.profiler.profile("Compute coarse supervision"):
+ compute_supervision_coarse(batch, self.config)
+
+ with self.profiler.profile("TopicFM"):
+ self.matcher(batch)
+
+ with self.profiler.profile("Compute fine supervision"):
+ compute_supervision_fine(batch, self.config)
+
+ with self.profiler.profile("Compute losses"):
+ self.loss(batch)
+
+ def _compute_metrics(self, batch):
+ with self.profiler.profile("Copmute metrics"):
+ compute_symmetrical_epipolar_errors(
+ batch
+ ) # compute epi_errs for each match
+ compute_pose_errors(
+ batch, self.config
+ ) # compute R_errs, t_errs, pose_errs for each pair
+
+ rel_pair_names = list(zip(*batch["pair_names"]))
+ bs = batch["image0"].size(0)
+ metrics = {
+ # to filter duplicate pairs caused by DistributedSampler
+ "identifiers": ["#".join(rel_pair_names[b]) for b in range(bs)],
+ "epi_errs": [
+ batch["epi_errs"][batch["m_bids"] == b].cpu().numpy()
+ for b in range(bs)
+ ],
+ "R_errs": batch["R_errs"],
+ "t_errs": batch["t_errs"],
+ "inliers": batch["inliers"],
+ }
+ ret_dict = {"metrics": metrics}
+ return ret_dict, rel_pair_names
+
+ def training_step(self, batch, batch_idx):
+ self._trainval_inference(batch)
+
+ # logging
+ if (
+ self.trainer.global_rank == 0
+ and self.global_step % self.trainer.log_every_n_steps == 0
+ ):
+ # scalars
+ for k, v in batch["loss_scalars"].items():
+ self.logger.experiment.add_scalar(f"train/{k}", v, self.global_step)
+
+ # figures
+ if self.config.TRAINER.ENABLE_PLOTTING:
+ compute_symmetrical_epipolar_errors(
+ batch
+ ) # compute epi_errs for each match
+ figures = make_matching_figures(
+ batch, self.config, self.config.TRAINER.PLOT_MODE
+ )
+ for k, v in figures.items():
+ self.logger.experiment.add_figure(
+ f"train_match/{k}", v, self.global_step
+ )
+
+ return {"loss": batch["loss"]}
+
+ def training_epoch_end(self, outputs):
+ avg_loss = torch.stack([x["loss"] for x in outputs]).mean()
+ if self.trainer.global_rank == 0:
+ self.logger.experiment.add_scalar(
+ "train/avg_loss_on_epoch", avg_loss, global_step=self.current_epoch
+ )
+
+ def validation_step(self, batch, batch_idx):
+ self._trainval_inference(batch)
+
+ ret_dict, _ = self._compute_metrics(batch)
+
+ val_plot_interval = max(self.trainer.num_val_batches[0] // self.n_vals_plot, 1)
+ figures = {self.config.TRAINER.PLOT_MODE: []}
+ if batch_idx % val_plot_interval == 0:
+ figures = make_matching_figures(
+ batch, self.config, mode=self.config.TRAINER.PLOT_MODE
+ )
+
+ return {
+ **ret_dict,
+ "loss_scalars": batch["loss_scalars"],
+ "figures": figures,
+ }
+
+ def validation_epoch_end(self, outputs):
+ # handle multiple validation sets
+ multi_outputs = (
+ [outputs] if not isinstance(outputs[0], (list, tuple)) else outputs
+ )
+ multi_val_metrics = defaultdict(list)
+
+ for valset_idx, outputs in enumerate(multi_outputs):
+ # since pl performs sanity_check at the very begining of the training
+ cur_epoch = self.trainer.current_epoch
+ if (
+ not self.trainer.resume_from_checkpoint
+ and self.trainer.running_sanity_check
+ ):
+ cur_epoch = -1
+
+ # 1. loss_scalars: dict of list, on cpu
+ _loss_scalars = [o["loss_scalars"] for o in outputs]
+ loss_scalars = {
+ k: flattenList(all_gather([_ls[k] for _ls in _loss_scalars]))
+ for k in _loss_scalars[0]
+ }
+
+ # 2. val metrics: dict of list, numpy
+ _metrics = [o["metrics"] for o in outputs]
+ metrics = {
+ k: flattenList(all_gather(flattenList([_me[k] for _me in _metrics])))
+ for k in _metrics[0]
+ }
+ # NOTE: all ranks need to `aggregate_merics`, but only log at rank-0
+ val_metrics_4tb = aggregate_metrics(
+ metrics, self.config.TRAINER.EPI_ERR_THR
+ )
+ for thr in [5, 10, 20]:
+ multi_val_metrics[f"auc@{thr}"].append(val_metrics_4tb[f"auc@{thr}"])
+
+ # 3. figures
+ _figures = [o["figures"] for o in outputs]
+ figures = {
+ k: flattenList(gather(flattenList([_me[k] for _me in _figures])))
+ for k in _figures[0]
+ }
+
+ # tensorboard records only on rank 0
+ if self.trainer.global_rank == 0:
+ for k, v in loss_scalars.items():
+ mean_v = torch.stack(v).mean()
+ self.logger.experiment.add_scalar(
+ f"val_{valset_idx}/avg_{k}", mean_v, global_step=cur_epoch
+ )
+
+ for k, v in val_metrics_4tb.items():
+ self.logger.experiment.add_scalar(
+ f"metrics_{valset_idx}/{k}", v, global_step=cur_epoch
+ )
+
+ for k, v in figures.items():
+ if self.trainer.global_rank == 0:
+ for plot_idx, fig in enumerate(v):
+ self.logger.experiment.add_figure(
+ f"val_match_{valset_idx}/{k}/pair-{plot_idx}",
+ fig,
+ cur_epoch,
+ close=True,
+ )
+ plt.close("all")
+
+ for thr in [5, 10, 20]:
+ # log on all ranks for ModelCheckpoint callback to work properly
+ self.log(
+ f"auc@{thr}", torch.tensor(np.mean(multi_val_metrics[f"auc@{thr}"]))
+ ) # ckpt monitors on this
+
+ def test_step(self, batch, batch_idx):
+ with self.profiler.profile("TopicFM"):
+ self.matcher(batch)
+
+ ret_dict, rel_pair_names = self._compute_metrics(batch)
+
+ with self.profiler.profile("dump_results"):
+ if self.dump_dir is not None:
+ # dump results for further analysis
+ keys_to_save = {"mkpts0_f", "mkpts1_f", "mconf", "epi_errs"}
+ pair_names = list(zip(*batch["pair_names"]))
+ bs = batch["image0"].shape[0]
+ dumps = []
+ for b_id in range(bs):
+ item = {}
+ mask = batch["m_bids"] == b_id
+ item["pair_names"] = pair_names[b_id]
+ item["identifier"] = "#".join(rel_pair_names[b_id])
+ for key in keys_to_save:
+ item[key] = batch[key][mask].cpu().numpy()
+ for key in ["R_errs", "t_errs", "inliers"]:
+ item[key] = batch[key][b_id]
+ dumps.append(item)
+ ret_dict["dumps"] = dumps
+
+ return ret_dict
+
+ def test_epoch_end(self, outputs):
+ # metrics: dict of list, numpy
+ _metrics = [o["metrics"] for o in outputs]
+ metrics = {
+ k: flattenList(gather(flattenList([_me[k] for _me in _metrics])))
+ for k in _metrics[0]
+ }
+
+ # [{key: [{...}, *#bs]}, *#batch]
+ if self.dump_dir is not None:
+ Path(self.dump_dir).mkdir(parents=True, exist_ok=True)
+ _dumps = flattenList([o["dumps"] for o in outputs]) # [{...}, #bs*#batch]
+ dumps = flattenList(gather(_dumps)) # [{...}, #proc*#bs*#batch]
+ logger.info(
+ f"Prediction and evaluation results will be saved to: {self.dump_dir}"
+ )
+
+ if self.trainer.global_rank == 0:
+ print(self.profiler.summary())
+ val_metrics_4tb = aggregate_metrics(
+ metrics, self.config.TRAINER.EPI_ERR_THR
+ )
+ logger.info("\n" + pprint.pformat(val_metrics_4tb))
+ if self.dump_dir is not None:
+ np.save(Path(self.dump_dir) / "TopicFM_pred_eval", dumps)
diff --git a/imcui/third_party/TopicFM/src/losses/loss.py b/third_party/TopicFM/src/losses/loss.py
similarity index 62%
rename from imcui/third_party/TopicFM/src/losses/loss.py
rename to third_party/TopicFM/src/losses/loss.py
index 4be58498579c9fe649ed0ce2d42f230e59cef581..e386bb557285a290962477179e9a3a36b665368f 100644
--- a/imcui/third_party/TopicFM/src/losses/loss.py
+++ b/third_party/TopicFM/src/losses/loss.py
@@ -13,10 +13,10 @@ def sample_non_matches(pos_mask, match_ids=None, sampling_ratio=10):
return ~pos_mask
neg_mask = torch.zeros_like(pos_mask)
- probs = torch.ones((HW - 1)//3, device=pos_mask.device)
+ probs = torch.ones((HW - 1) // 3, device=pos_mask.device)
for _ in range(sampling_ratio):
d = torch.multinomial(probs, len(j_ids), replacement=True)
- sampled_j_ids = (j_ids + d*3 + 1) % HW
+ sampled_j_ids = (j_ids + d * 3 + 1) % HW
neg_mask[b_ids, i_ids, sampled_j_ids] = True
# neg_mask = neg_matrix == 1
else:
@@ -29,18 +29,20 @@ class TopicFMLoss(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config # config under the global namespace
- self.loss_config = config['model']['loss']
- self.match_type = self.config['model']['match_coarse']['match_type']
-
+ self.loss_config = config["model"]["loss"]
+ self.match_type = self.config["model"]["match_coarse"]["match_type"]
+
# coarse-level
- self.correct_thr = self.loss_config['fine_correct_thr']
- self.c_pos_w = self.loss_config['pos_weight']
- self.c_neg_w = self.loss_config['neg_weight']
+ self.correct_thr = self.loss_config["fine_correct_thr"]
+ self.c_pos_w = self.loss_config["pos_weight"]
+ self.c_neg_w = self.loss_config["neg_weight"]
# fine-level
- self.fine_type = self.loss_config['fine_type']
+ self.fine_type = self.loss_config["fine_type"]
- def compute_coarse_loss(self, conf, topic_mat, conf_gt, match_ids=None, weight=None):
- """ Point-wise CE / Focal Loss with 0 / 1 confidence as gt.
+ def compute_coarse_loss(
+ self, conf, topic_mat, conf_gt, match_ids=None, weight=None
+ ):
+ """Point-wise CE / Focal Loss with 0 / 1 confidence as gt.
Args:
conf (torch.Tensor): (N, HW0, HW1) / (N, HW0+1, HW1+1)
conf_gt (torch.Tensor): (N, HW0, HW1)
@@ -53,30 +55,30 @@ class TopicFMLoss(nn.Module):
if not pos_mask.any(): # assign a wrong gt
pos_mask[0, 0, 0] = True
if weight is not None:
- weight[0, 0, 0] = 0.
- c_pos_w = 0.
+ weight[0, 0, 0] = 0.0
+ c_pos_w = 0.0
if not neg_mask.any():
neg_mask[0, 0, 0] = True
if weight is not None:
- weight[0, 0, 0] = 0.
- c_neg_w = 0.
+ weight[0, 0, 0] = 0.0
+ c_neg_w = 0.0
conf = torch.clamp(conf, 1e-6, 1 - 1e-6)
- alpha = self.loss_config['focal_alpha']
+ alpha = self.loss_config["focal_alpha"]
loss = 0.0
if isinstance(topic_mat, torch.Tensor):
pos_topic = topic_mat[pos_mask]
- loss_pos_topic = - alpha * (pos_topic + 1e-6).log()
+ loss_pos_topic = -alpha * (pos_topic + 1e-6).log()
neg_topic = topic_mat[neg_mask]
- loss_neg_topic = - alpha * (1 - neg_topic + 1e-6).log()
+ loss_neg_topic = -alpha * (1 - neg_topic + 1e-6).log()
if weight is not None:
loss_pos_topic = loss_pos_topic * weight[pos_mask]
loss_neg_topic = loss_neg_topic * weight[neg_mask]
loss = loss_pos_topic.mean() + loss_neg_topic.mean()
pos_conf = conf[pos_mask]
- loss_pos = - alpha * pos_conf.log()
+ loss_pos = -alpha * pos_conf.log()
# handle loss weights
if weight is not None:
# Different from dense-spvs, the loss w.r.t. padded regions aren't directly zeroed out,
@@ -86,11 +88,11 @@ class TopicFMLoss(nn.Module):
loss = loss + c_pos_w * loss_pos.mean()
return loss
-
+
def compute_fine_loss(self, expec_f, expec_f_gt):
- if self.fine_type == 'l2_with_std':
+ if self.fine_type == "l2_with_std":
return self._compute_fine_loss_l2_std(expec_f, expec_f_gt)
- elif self.fine_type == 'l2':
+ elif self.fine_type == "l2":
return self._compute_fine_loss_l2(expec_f, expec_f_gt)
else:
raise NotImplementedError()
@@ -101,9 +103,13 @@ class TopicFMLoss(nn.Module):
expec_f (torch.Tensor): [M, 2]
expec_f_gt (torch.Tensor): [M, 2]
"""
- correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr
+ correct_mask = (
+ torch.linalg.norm(expec_f_gt, ord=float("inf"), dim=1) < self.correct_thr
+ )
if correct_mask.sum() == 0:
- if self.training: # this seldomly happen when training, since we pad prediction with gt
+ if (
+ self.training
+ ): # this seldomly happen when training, since we pad prediction with gt
logger.warning("assign a false supervision to avoid ddp deadlock")
correct_mask[0] = True
else:
@@ -118,34 +124,45 @@ class TopicFMLoss(nn.Module):
expec_f_gt (torch.Tensor): [M, 2]
"""
# correct_mask tells you which pair to compute fine-loss
- correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr
+ correct_mask = (
+ torch.linalg.norm(expec_f_gt, ord=float("inf"), dim=1) < self.correct_thr
+ )
# use std as weight that measures uncertainty
std = expec_f[:, 2]
- inverse_std = 1. / torch.clamp(std, min=1e-10)
- weight = (inverse_std / torch.mean(inverse_std)).detach() # avoid minizing loss through increase std
+ inverse_std = 1.0 / torch.clamp(std, min=1e-10)
+ weight = (
+ inverse_std / torch.mean(inverse_std)
+ ).detach() # avoid minizing loss through increase std
# corner case: no correct coarse match found
if not correct_mask.any():
- if self.training: # this seldomly happen during training, since we pad prediction with gt
- # sometimes there is not coarse-level gt at all.
+ if (
+ self.training
+ ): # this seldomly happen during training, since we pad prediction with gt
+ # sometimes there is not coarse-level gt at all.
logger.warning("assign a false supervision to avoid ddp deadlock")
correct_mask[0] = True
- weight[0] = 0.
+ weight[0] = 0.0
else:
return None
# l2 loss with std
- offset_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask, :2]) ** 2).sum(-1)
+ offset_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask, :2]) ** 2).sum(
+ -1
+ )
loss = (offset_l2 * weight[correct_mask]).mean()
return loss
-
+
@torch.no_grad()
def compute_c_weight(self, data):
- """ compute element-wise weights for computing coarse-level loss. """
- if 'mask0' in data:
- c_weight = (data['mask0'].flatten(-2)[..., None] * data['mask1'].flatten(-2)[:, None]).float()
+ """compute element-wise weights for computing coarse-level loss."""
+ if "mask0" in data:
+ c_weight = (
+ data["mask0"].flatten(-2)[..., None]
+ * data["mask1"].flatten(-2)[:, None]
+ ).float()
else:
c_weight = None
return c_weight
@@ -163,20 +180,24 @@ class TopicFMLoss(nn.Module):
c_weight = self.compute_c_weight(data)
# 1. coarse-level loss
- loss_c = self.compute_coarse_loss(data['conf_matrix'], data['topic_matrix'],
- data['conf_matrix_gt'], match_ids=(data['spv_b_ids'], data['spv_i_ids'], data['spv_j_ids']),
- weight=c_weight)
- loss = loss_c * self.loss_config['coarse_weight']
+ loss_c = self.compute_coarse_loss(
+ data["conf_matrix"],
+ data["topic_matrix"],
+ data["conf_matrix_gt"],
+ match_ids=(data["spv_b_ids"], data["spv_i_ids"], data["spv_j_ids"]),
+ weight=c_weight,
+ )
+ loss = loss_c * self.loss_config["coarse_weight"]
loss_scalars.update({"loss_c": loss_c.clone().detach().cpu()})
# 2. fine-level loss
- loss_f = self.compute_fine_loss(data['expec_f'], data['expec_f_gt'])
+ loss_f = self.compute_fine_loss(data["expec_f"], data["expec_f_gt"])
if loss_f is not None:
- loss += loss_f * self.loss_config['fine_weight']
- loss_scalars.update({"loss_f": loss_f.clone().detach().cpu()})
+ loss += loss_f * self.loss_config["fine_weight"]
+ loss_scalars.update({"loss_f": loss_f.clone().detach().cpu()})
else:
assert self.training is False
- loss_scalars.update({'loss_f': torch.tensor(1.)}) # 1 is the upper bound
+ loss_scalars.update({"loss_f": torch.tensor(1.0)}) # 1 is the upper bound
- loss_scalars.update({'loss': loss.clone().detach().cpu()})
+ loss_scalars.update({"loss": loss.clone().detach().cpu()})
data.update({"loss": loss, "loss_scalars": loss_scalars})
diff --git a/imcui/third_party/TopicFM/src/models/__init__.py b/third_party/TopicFM/src/models/__init__.py
similarity index 100%
rename from imcui/third_party/TopicFM/src/models/__init__.py
rename to third_party/TopicFM/src/models/__init__.py
diff --git a/imcui/third_party/TopicFM/src/models/backbone/__init__.py b/third_party/TopicFM/src/models/backbone/__init__.py
similarity index 62%
rename from imcui/third_party/TopicFM/src/models/backbone/__init__.py
rename to third_party/TopicFM/src/models/backbone/__init__.py
index 53f98db4e910b46716bed7cfc6ebbf8c8bfad399..72a80de20ba3f6bc02454f4930b25d6b18f4b34f 100644
--- a/imcui/third_party/TopicFM/src/models/backbone/__init__.py
+++ b/third_party/TopicFM/src/models/backbone/__init__.py
@@ -2,4 +2,4 @@ from .fpn import FPN
def build_backbone(config):
- return FPN(config['fpn'])
+ return FPN(config["fpn"])
diff --git a/imcui/third_party/TopicFM/src/models/backbone/fpn.py b/third_party/TopicFM/src/models/backbone/fpn.py
similarity index 72%
rename from imcui/third_party/TopicFM/src/models/backbone/fpn.py
rename to third_party/TopicFM/src/models/backbone/fpn.py
index 93cc475f57317f9dbb8132cdfe0297391972f9e2..7f38ec13f196793a00cacbaaa3eb7c0a5d8e9605 100644
--- a/imcui/third_party/TopicFM/src/models/backbone/fpn.py
+++ b/third_party/TopicFM/src/models/backbone/fpn.py
@@ -4,12 +4,16 @@ import torch.nn.functional as F
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution without padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False)
+ return nn.Conv2d(
+ in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False
+ )
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
+ return nn.Conv2d(
+ in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
+ )
class ConvBlock(nn.Module):
@@ -22,7 +26,7 @@ class ConvBlock(nn.Module):
def forward(self, x):
y = self.conv(x)
if self.bn:
- y = self.bn(y) #F.layer_norm(y, y.shape[1:])
+ y = self.bn(y) # F.layer_norm(y, y.shape[1:])
y = self.act(y)
return y
@@ -37,14 +41,16 @@ class FPN(nn.Module):
super().__init__()
# Config
block = ConvBlock
- initial_dim = config['initial_dim']
- block_dims = config['block_dims']
+ initial_dim = config["initial_dim"]
+ block_dims = config["block_dims"]
# Class Variable
self.in_planes = initial_dim
# Networks
- self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False)
+ self.conv1 = nn.Conv2d(
+ 1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False
+ )
self.bn1 = nn.BatchNorm2d(initial_dim)
self.relu = nn.ReLU(inplace=True)
@@ -72,7 +78,7 @@ class FPN(nn.Module):
for m in self.modules():
if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+ nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
@@ -94,16 +100,22 @@ class FPN(nn.Module):
x4 = self.layer4(x3) # 1/16
# FPN
- x4_out_2x = F.interpolate(x4, scale_factor=2., mode='bilinear', align_corners=True)
+ x4_out_2x = F.interpolate(
+ x4, scale_factor=2.0, mode="bilinear", align_corners=True
+ )
x3_out = self.layer3_outconv(x3)
- x3_out = self.layer3_outconv2(x3_out+x4_out_2x)
+ x3_out = self.layer3_outconv2(x3_out + x4_out_2x)
- x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True)
+ x3_out_2x = F.interpolate(
+ x3_out, scale_factor=2.0, mode="bilinear", align_corners=True
+ )
x2_out = self.layer2_outconv(x2)
- x2_out = self.layer2_outconv2(x2_out+x3_out_2x)
+ x2_out = self.layer2_outconv2(x2_out + x3_out_2x)
- x2_out_2x = F.interpolate(x2_out, scale_factor=2., mode='bilinear', align_corners=True)
+ x2_out_2x = F.interpolate(
+ x2_out, scale_factor=2.0, mode="bilinear", align_corners=True
+ )
x1_out = self.layer1_outconv(x1)
- x1_out = self.layer1_outconv2(x1_out+x2_out_2x)
+ x1_out = self.layer1_outconv2(x1_out + x2_out_2x)
return [x3_out, x1_out]
diff --git a/imcui/third_party/TopicFM/src/models/modules/__init__.py b/third_party/TopicFM/src/models/modules/__init__.py
similarity index 100%
rename from imcui/third_party/TopicFM/src/models/modules/__init__.py
rename to third_party/TopicFM/src/models/modules/__init__.py
diff --git a/third_party/TopicFM/src/models/modules/fine_preprocess.py b/third_party/TopicFM/src/models/modules/fine_preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cdce2d327ebc88371769946a292824f834729a5
--- /dev/null
+++ b/third_party/TopicFM/src/models/modules/fine_preprocess.py
@@ -0,0 +1,75 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops.einops import rearrange, repeat
+
+
+class FinePreprocess(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ self.config = config
+ self.cat_c_feat = config["fine_concat_coarse_feat"]
+ self.W = self.config["fine_window_size"]
+
+ d_model_c = self.config["coarse"]["d_model"]
+ d_model_f = self.config["fine"]["d_model"]
+ self.d_model_f = d_model_f
+ if self.cat_c_feat:
+ self.down_proj = nn.Linear(d_model_c, d_model_f, bias=True)
+ self.merge_feat = nn.Linear(2 * d_model_f, d_model_f, bias=True)
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.kaiming_normal_(p, mode="fan_out", nonlinearity="relu")
+
+ def forward(self, feat_f0, feat_f1, feat_c0, feat_c1, data):
+ W = self.W
+ stride = data["hw0_f"][0] // data["hw0_c"][0]
+
+ data.update({"W": W})
+ if data["b_ids"].shape[0] == 0:
+ feat0 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device)
+ feat1 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device)
+ return feat0, feat1
+
+ # 1. unfold(crop) all local windows
+ feat_f0_unfold = F.unfold(
+ feat_f0, kernel_size=(W, W), stride=stride, padding=W // 2
+ )
+ feat_f0_unfold = rearrange(feat_f0_unfold, "n (c ww) l -> n l ww c", ww=W**2)
+ feat_f1_unfold = F.unfold(
+ feat_f1, kernel_size=(W, W), stride=stride, padding=W // 2
+ )
+ feat_f1_unfold = rearrange(feat_f1_unfold, "n (c ww) l -> n l ww c", ww=W**2)
+
+ # 2. select only the predicted matches
+ feat_f0_unfold = feat_f0_unfold[data["b_ids"], data["i_ids"]] # [n, ww, cf]
+ feat_f1_unfold = feat_f1_unfold[data["b_ids"], data["j_ids"]]
+
+ # option: use coarse-level feature as context: concat and linear
+ if self.cat_c_feat:
+ feat_c_win = self.down_proj(
+ torch.cat(
+ [
+ feat_c0[data["b_ids"], data["i_ids"]],
+ feat_c1[data["b_ids"], data["j_ids"]],
+ ],
+ 0,
+ )
+ ) # [2n, c]
+ feat_cf_win = self.merge_feat(
+ torch.cat(
+ [
+ torch.cat([feat_f0_unfold, feat_f1_unfold], 0), # [2n, ww, cf]
+ repeat(feat_c_win, "n c -> n ww c", ww=W**2), # [2n, ww, cf]
+ ],
+ -1,
+ )
+ )
+ feat_f0_unfold, feat_f1_unfold = torch.chunk(feat_cf_win, 2, dim=0)
+
+ return feat_f0_unfold, feat_f1_unfold
diff --git a/imcui/third_party/TopicFM/src/models/modules/linear_attention.py b/third_party/TopicFM/src/models/modules/linear_attention.py
similarity index 87%
rename from imcui/third_party/TopicFM/src/models/modules/linear_attention.py
rename to third_party/TopicFM/src/models/modules/linear_attention.py
index af6cd825033e98b7be15cc694ce28110ef84cc93..57b86b3ba682da62f9ff65893aa0ccd6753d32af 100644
--- a/imcui/third_party/TopicFM/src/models/modules/linear_attention.py
+++ b/third_party/TopicFM/src/models/modules/linear_attention.py
@@ -18,7 +18,7 @@ class LinearAttention(Module):
self.eps = eps
def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
- """ Multi-Head linear attention proposed in "Transformers are RNNs"
+ """Multi-Head linear attention proposed in "Transformers are RNNs"
Args:
queries: [N, L, H, D]
keys: [N, S, H, D]
@@ -54,7 +54,7 @@ class FullAttention(Module):
self.dropout = Dropout(attention_dropout)
def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
- """ Multi-head scaled dot-product attention, a.k.a full attention.
+ """Multi-head scaled dot-product attention, a.k.a full attention.
Args:
queries: [N, L, H, D]
keys: [N, S, H, D]
@@ -68,10 +68,12 @@ class FullAttention(Module):
# Compute the unnormalized attention and apply the masks
QK = torch.einsum("nlhd,nshd->nlsh", queries, keys)
if kv_mask is not None:
- QK.masked_fill_(~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]).bool(), -1e9)
+ QK.masked_fill_(
+ ~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]).bool(), -1e9
+ )
# Compute the attention and the weighted average
- softmax_temp = 1. / queries.size(3)**.5 # sqrt(D)
+ softmax_temp = 1.0 / queries.size(3) ** 0.5 # sqrt(D)
A = torch.softmax(softmax_temp * QK, dim=2)
if self.use_dropout:
A = self.dropout(A)
diff --git a/imcui/third_party/TopicFM/src/models/modules/transformer.py b/third_party/TopicFM/src/models/modules/transformer.py
similarity index 58%
rename from imcui/third_party/TopicFM/src/models/modules/transformer.py
rename to third_party/TopicFM/src/models/modules/transformer.py
index 27ff8f6554844b1e14a7094fcbad40876f766db8..cef17ca689cd0f844c1d6bd6c0f987a3e0c3be59 100644
--- a/imcui/third_party/TopicFM/src/models/modules/transformer.py
+++ b/third_party/TopicFM/src/models/modules/transformer.py
@@ -8,10 +8,7 @@ from .linear_attention import LinearAttention, FullAttention
class LoFTREncoderLayer(nn.Module):
- def __init__(self,
- d_model,
- nhead,
- attention='linear'):
+ def __init__(self, d_model, nhead, attention="linear"):
super(LoFTREncoderLayer, self).__init__()
self.dim = d_model // nhead
@@ -21,14 +18,14 @@ class LoFTREncoderLayer(nn.Module):
self.q_proj = nn.Linear(d_model, d_model, bias=False)
self.k_proj = nn.Linear(d_model, d_model, bias=False)
self.v_proj = nn.Linear(d_model, d_model, bias=False)
- self.attention = LinearAttention() if attention == 'linear' else FullAttention()
+ self.attention = LinearAttention() if attention == "linear" else FullAttention()
self.merge = nn.Linear(d_model, d_model, bias=False)
# feed-forward network
self.mlp = nn.Sequential(
- nn.Linear(d_model*2, d_model*2, bias=False),
+ nn.Linear(d_model * 2, d_model * 2, bias=False),
nn.GELU(),
- nn.Linear(d_model*2, d_model, bias=False),
+ nn.Linear(d_model * 2, d_model, bias=False),
)
# norm and dropout
@@ -50,8 +47,10 @@ class LoFTREncoderLayer(nn.Module):
query = self.q_proj(query).view(bs, -1, self.nhead, self.dim) # [N, L, (H, D)]
key = self.k_proj(key).view(bs, -1, self.nhead, self.dim) # [N, S, (H, D)]
value = self.v_proj(value).view(bs, -1, self.nhead, self.dim)
- message = self.attention(query, key, value, q_mask=x_mask, kv_mask=source_mask) # [N, L, (H, D)]
- message = self.merge(message.view(bs, -1, self.nhead*self.dim)) # [N, L, C]
+ message = self.attention(
+ query, key, value, q_mask=x_mask, kv_mask=source_mask
+ ) # [N, L, (H, D)]
+ message = self.merge(message.view(bs, -1, self.nhead * self.dim)) # [N, L, C]
message = self.norm1(message)
# feed-forward network
@@ -68,18 +67,33 @@ class TopicFormer(nn.Module):
super(TopicFormer, self).__init__()
self.config = config
- self.d_model = config['d_model']
- self.nhead = config['nhead']
- self.layer_names = config['layer_names']
- encoder_layer = LoFTREncoderLayer(config['d_model'], config['nhead'], config['attention'])
- self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(len(self.layer_names))])
-
- self.topic_transformers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(2*config['n_topic_transformers'])]) if config['n_samples'] > 0 else None #nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(2)])
- self.n_iter_topic_transformer = config['n_topic_transformers']
+ self.d_model = config["d_model"]
+ self.nhead = config["nhead"]
+ self.layer_names = config["layer_names"]
+ encoder_layer = LoFTREncoderLayer(
+ config["d_model"], config["nhead"], config["attention"]
+ )
+ self.layers = nn.ModuleList(
+ [copy.deepcopy(encoder_layer) for _ in range(len(self.layer_names))]
+ )
- self.seed_tokens = nn.Parameter(torch.randn(config['n_topics'], config['d_model']))
- self.register_parameter('seed_tokens', self.seed_tokens)
- self.n_samples = config['n_samples']
+ self.topic_transformers = (
+ nn.ModuleList(
+ [
+ copy.deepcopy(encoder_layer)
+ for _ in range(2 * config["n_topic_transformers"])
+ ]
+ )
+ if config["n_samples"] > 0
+ else None
+ ) # nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(2)])
+ self.n_iter_topic_transformer = config["n_topic_transformers"]
+
+ self.seed_tokens = nn.Parameter(
+ torch.randn(config["n_topics"], config["d_model"])
+ )
+ self.register_parameter("seed_tokens", self.seed_tokens)
+ self.n_samples = config["n_samples"]
self._reset_parameters()
@@ -94,9 +108,9 @@ class TopicFormer(nn.Module):
topics (torch.Tensor): [N, L+S, K]
"""
prob_topics0, prob_topics1 = prob_topics[:, :L], prob_topics[:, L:]
- topics0, topics1 = topics[:, :L], topics[:, L:]
+ topics0, topics1 = topics[:, :L], topics[:, L:]
- theta0 = F.normalize(prob_topics0.sum(dim=1), p=1, dim=-1) # [N, K]
+ theta0 = F.normalize(prob_topics0.sum(dim=1), p=1, dim=-1) # [N, K]
theta1 = F.normalize(prob_topics1.sum(dim=1), p=1, dim=-1)
theta = F.normalize(theta0 * theta1, p=1, dim=-1)
if self.n_samples == 0:
@@ -106,18 +120,28 @@ class TopicFormer(nn.Module):
sampled_values = torch.gather(theta, dim=-1, index=sampled_inds)
else:
sampled_values, sampled_inds = torch.topk(theta, self.n_samples, dim=-1)
- sampled_topics0 = torch.gather(topics0, dim=-1, index=sampled_inds.unsqueeze(1).repeat(1, topics0.shape[1], 1))
- sampled_topics1 = torch.gather(topics1, dim=-1, index=sampled_inds.unsqueeze(1).repeat(1, topics1.shape[1], 1))
+ sampled_topics0 = torch.gather(
+ topics0,
+ dim=-1,
+ index=sampled_inds.unsqueeze(1).repeat(1, topics0.shape[1], 1),
+ )
+ sampled_topics1 = torch.gather(
+ topics1,
+ dim=-1,
+ index=sampled_inds.unsqueeze(1).repeat(1, topics1.shape[1], 1),
+ )
return sampled_topics0, sampled_topics1
def reduce_feat(self, feat, topick, N, C):
len_topic = topick.sum(dim=-1).int()
max_len = len_topic.max().item()
selected_ids = topick.bool()
- resized_feat = torch.zeros((N, max_len, C), dtype=torch.float32, device=feat.device)
+ resized_feat = torch.zeros(
+ (N, max_len, C), dtype=torch.float32, device=feat.device
+ )
new_mask = torch.zeros_like(resized_feat[..., 0]).bool()
for i in range(N):
- new_mask[i, :len_topic[i]] = True
+ new_mask[i, : len_topic[i]] = True
resized_feat[new_mask, :] = feat[selected_ids, :]
return resized_feat, new_mask, selected_ids
@@ -130,8 +154,16 @@ class TopicFormer(nn.Module):
mask1 (torch.Tensor): [N, S] (optional)
"""
- assert self.d_model == feat0.shape[2], "the feature number of src and transformer must be equal"
- N, L, S, C, K = feat0.shape[0], feat0.shape[1], feat1.shape[1], feat0.shape[2], self.config['n_topics']
+ assert (
+ self.d_model == feat0.shape[2]
+ ), "the feature number of src and transformer must be equal"
+ N, L, S, C, K = (
+ feat0.shape[0],
+ feat0.shape[1],
+ feat1.shape[1],
+ feat0.shape[2],
+ self.config["n_topics"],
+ )
seeds = self.seed_tokens.unsqueeze(0).repeat(N, 1, 1)
@@ -142,18 +174,20 @@ class TopicFormer(nn.Module):
mask = None
for layer, name in zip(self.layers, self.layer_names):
- if name == 'seed':
+ if name == "seed":
# seeds = layer(seeds, feat0, None, mask0)
# seeds = layer(seeds, feat1, None, mask1)
seeds = layer(seeds, feat, None, mask)
- elif name == 'feat':
+ elif name == "feat":
feat0 = layer(feat0, seeds, mask0, None)
feat1 = layer(feat1, seeds, mask1, None)
dmatrix = torch.einsum("nmd,nkd->nmk", feat, seeds)
prob_topics = F.softmax(dmatrix, dim=-1)
- feat_topics = torch.zeros_like(dmatrix).scatter_(-1, torch.argmax(dmatrix, dim=-1, keepdim=True), 1.0)
+ feat_topics = torch.zeros_like(dmatrix).scatter_(
+ -1, torch.argmax(dmatrix, dim=-1, keepdim=True), 1.0
+ )
if mask is not None:
feat_topics = feat_topics * mask.unsqueeze(-1)
@@ -163,35 +197,57 @@ class TopicFormer(nn.Module):
logger.warning("topic distribution is highly sparse!")
sampled_topics = self.sample_topic(prob_topics.detach(), feat_topics, L)
if sampled_topics is not None:
- updated_feat0, updated_feat1 = torch.zeros_like(feat0), torch.zeros_like(feat1)
+ updated_feat0, updated_feat1 = torch.zeros_like(feat0), torch.zeros_like(
+ feat1
+ )
s_topics0, s_topics1 = sampled_topics
for k in range(s_topics0.shape[-1]):
- topick0, topick1 = s_topics0[..., k], s_topics1[..., k] # [N, L+S]
+ topick0, topick1 = s_topics0[..., k], s_topics1[..., k] # [N, L+S]
if (topick0.sum() > 0) and (topick1.sum() > 0):
- new_feat0, new_mask0, selected_ids0 = self.reduce_feat(feat0, topick0, N, C)
- new_feat1, new_mask1, selected_ids1 = self.reduce_feat(feat1, topick1, N, C)
+ new_feat0, new_mask0, selected_ids0 = self.reduce_feat(
+ feat0, topick0, N, C
+ )
+ new_feat1, new_mask1, selected_ids1 = self.reduce_feat(
+ feat1, topick1, N, C
+ )
for idt in range(self.n_iter_topic_transformer):
- new_feat0 = self.topic_transformers[idt*2](new_feat0, new_feat0, new_mask0, new_mask0)
- new_feat1 = self.topic_transformers[idt*2](new_feat1, new_feat1, new_mask1, new_mask1)
- new_feat0 = self.topic_transformers[idt*2+1](new_feat0, new_feat1, new_mask0, new_mask1)
- new_feat1 = self.topic_transformers[idt*2+1](new_feat1, new_feat0, new_mask1, new_mask0)
+ new_feat0 = self.topic_transformers[idt * 2](
+ new_feat0, new_feat0, new_mask0, new_mask0
+ )
+ new_feat1 = self.topic_transformers[idt * 2](
+ new_feat1, new_feat1, new_mask1, new_mask1
+ )
+ new_feat0 = self.topic_transformers[idt * 2 + 1](
+ new_feat0, new_feat1, new_mask0, new_mask1
+ )
+ new_feat1 = self.topic_transformers[idt * 2 + 1](
+ new_feat1, new_feat0, new_mask1, new_mask0
+ )
updated_feat0[selected_ids0, :] = new_feat0[new_mask0, :]
updated_feat1[selected_ids1, :] = new_feat1[new_mask1, :]
feat0 = (1 - s_topics0.sum(dim=-1, keepdim=True)) * feat0 + updated_feat0
feat1 = (1 - s_topics1.sum(dim=-1, keepdim=True)) * feat1 + updated_feat1
- conf_matrix = torch.einsum("nlc,nsc->nls", feat0, feat1) / C**.5 #(C * temperature)
+ conf_matrix = (
+ torch.einsum("nlc,nsc->nls", feat0, feat1) / C**0.5
+ ) # (C * temperature)
if self.training:
- topic_matrix = torch.einsum("nlk,nsk->nls", prob_topics[:, :L], prob_topics[:, L:])
- outlier_mask = torch.einsum("nlk,nsk->nls", feat_topics[:, :L], feat_topics[:, L:])
+ topic_matrix = torch.einsum(
+ "nlk,nsk->nls", prob_topics[:, :L], prob_topics[:, L:]
+ )
+ outlier_mask = torch.einsum(
+ "nlk,nsk->nls", feat_topics[:, :L], feat_topics[:, L:]
+ )
else:
topic_matrix = {"img0": feat_topics[:, :L], "img1": feat_topics[:, L:]}
outlier_mask = torch.ones_like(conf_matrix)
if mask0 is not None:
- outlier_mask = (outlier_mask * mask0[..., None] * mask1[:, None]) #.bool()
+ outlier_mask = outlier_mask * mask0[..., None] * mask1[:, None] # .bool()
conf_matrix.masked_fill_(~outlier_mask.bool(), -1e9)
- conf_matrix = F.softmax(conf_matrix, 1) * F.softmax(conf_matrix, 2) # * topic_matrix
+ conf_matrix = F.softmax(conf_matrix, 1) * F.softmax(
+ conf_matrix, 2
+ ) # * topic_matrix
return feat0, feat1, conf_matrix, topic_matrix
@@ -203,11 +259,15 @@ class LocalFeatureTransformer(nn.Module):
super(LocalFeatureTransformer, self).__init__()
self.config = config
- self.d_model = config['d_model']
- self.nhead = config['nhead']
- self.layer_names = config['layer_names']
- encoder_layer = LoFTREncoderLayer(config['d_model'], config['nhead'], config['attention'])
- self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(2)]) #len(self.layer_names))])
+ self.d_model = config["d_model"]
+ self.nhead = config["nhead"]
+ self.layer_names = config["layer_names"]
+ encoder_layer = LoFTREncoderLayer(
+ config["d_model"], config["nhead"], config["attention"]
+ )
+ self.layers = nn.ModuleList(
+ [copy.deepcopy(encoder_layer) for _ in range(2)]
+ ) # len(self.layer_names))])
self._reset_parameters()
def _reset_parameters(self):
@@ -224,7 +284,9 @@ class LocalFeatureTransformer(nn.Module):
mask1 (torch.Tensor): [N, S] (optional)
"""
- assert self.d_model == feat0.shape[2], "the feature number of src and transformer must be equal"
+ assert (
+ self.d_model == feat0.shape[2]
+ ), "the feature number of src and transformer must be equal"
feat0 = self.layers[0](feat0, feat1, mask0, mask1)
feat1 = self.layers[1](feat1, feat0, mask1, mask0)
diff --git a/imcui/third_party/TopicFM/src/models/topic_fm.py b/third_party/TopicFM/src/models/topic_fm.py
similarity index 54%
rename from imcui/third_party/TopicFM/src/models/topic_fm.py
rename to third_party/TopicFM/src/models/topic_fm.py
index 95cd22f9b66d08760382fe4cd22c4df918cc9f68..2556bdbb489574e13a5e5af60be87c546473d406 100644
--- a/imcui/third_party/TopicFM/src/models/topic_fm.py
+++ b/third_party/TopicFM/src/models/topic_fm.py
@@ -17,14 +17,14 @@ class TopicFM(nn.Module):
# Modules
self.backbone = build_backbone(config)
- self.loftr_coarse = TopicFormer(config['coarse'])
- self.coarse_matching = CoarseMatching(config['match_coarse'])
+ self.loftr_coarse = TopicFormer(config["coarse"])
+ self.coarse_matching = CoarseMatching(config["match_coarse"])
self.fine_preprocess = FinePreprocess(config)
self.loftr_fine = LocalFeatureTransformer(config["fine"])
self.fine_matching = FineMatching()
def forward(self, data):
- """
+ """
Update:
data (dict): {
'image0': (torch.Tensor): (N, 1, H, W)
@@ -34,46 +34,65 @@ class TopicFM(nn.Module):
}
"""
# 1. Local Feature CNN
- data.update({
- 'bs': data['image0'].size(0),
- 'hw0_i': data['image0'].shape[2:], 'hw1_i': data['image1'].shape[2:]
- })
+ data.update(
+ {
+ "bs": data["image0"].size(0),
+ "hw0_i": data["image0"].shape[2:],
+ "hw1_i": data["image1"].shape[2:],
+ }
+ )
- if data['hw0_i'] == data['hw1_i']: # faster & better BN convergence
- feats_c, feats_f = self.backbone(torch.cat([data['image0'], data['image1']], dim=0))
- (feat_c0, feat_c1), (feat_f0, feat_f1) = feats_c.split(data['bs']), feats_f.split(data['bs'])
+ if data["hw0_i"] == data["hw1_i"]: # faster & better BN convergence
+ feats_c, feats_f = self.backbone(
+ torch.cat([data["image0"], data["image1"]], dim=0)
+ )
+ (feat_c0, feat_c1), (feat_f0, feat_f1) = feats_c.split(
+ data["bs"]
+ ), feats_f.split(data["bs"])
else: # handle different input shapes
- (feat_c0, feat_f0), (feat_c1, feat_f1) = self.backbone(data['image0']), self.backbone(data['image1'])
+ (feat_c0, feat_f0), (feat_c1, feat_f1) = self.backbone(
+ data["image0"]
+ ), self.backbone(data["image1"])
- data.update({
- 'hw0_c': feat_c0.shape[2:], 'hw1_c': feat_c1.shape[2:],
- 'hw0_f': feat_f0.shape[2:], 'hw1_f': feat_f1.shape[2:]
- })
+ data.update(
+ {
+ "hw0_c": feat_c0.shape[2:],
+ "hw1_c": feat_c1.shape[2:],
+ "hw0_f": feat_f0.shape[2:],
+ "hw1_f": feat_f1.shape[2:],
+ }
+ )
# 2. coarse-level loftr module
- feat_c0 = rearrange(feat_c0, 'n c h w -> n (h w) c')
- feat_c1 = rearrange(feat_c1, 'n c h w -> n (h w) c')
+ feat_c0 = rearrange(feat_c0, "n c h w -> n (h w) c")
+ feat_c1 = rearrange(feat_c1, "n c h w -> n (h w) c")
mask_c0 = mask_c1 = None # mask is useful in training
- if 'mask0' in data:
- mask_c0, mask_c1 = data['mask0'].flatten(-2), data['mask1'].flatten(-2)
+ if "mask0" in data:
+ mask_c0, mask_c1 = data["mask0"].flatten(-2), data["mask1"].flatten(-2)
- feat_c0, feat_c1, conf_matrix, topic_matrix = self.loftr_coarse(feat_c0, feat_c1, mask_c0, mask_c1)
- data.update({"conf_matrix": conf_matrix, "topic_matrix": topic_matrix}) ######
+ feat_c0, feat_c1, conf_matrix, topic_matrix = self.loftr_coarse(
+ feat_c0, feat_c1, mask_c0, mask_c1
+ )
+ data.update({"conf_matrix": conf_matrix, "topic_matrix": topic_matrix}) ######
# 3. match coarse-level
self.coarse_matching(data)
# 4. fine-level refinement
- feat_f0_unfold, feat_f1_unfold = self.fine_preprocess(feat_f0, feat_f1, feat_c0.detach(), feat_c1.detach(), data)
+ feat_f0_unfold, feat_f1_unfold = self.fine_preprocess(
+ feat_f0, feat_f1, feat_c0.detach(), feat_c1.detach(), data
+ )
if feat_f0_unfold.size(0) != 0: # at least one coarse level predicted
- feat_f0_unfold, feat_f1_unfold = self.loftr_fine(feat_f0_unfold, feat_f1_unfold)
+ feat_f0_unfold, feat_f1_unfold = self.loftr_fine(
+ feat_f0_unfold, feat_f1_unfold
+ )
# 5. match fine-level
self.fine_matching(feat_f0_unfold, feat_f1_unfold, data)
def load_state_dict(self, state_dict, *args, **kwargs):
for k in list(state_dict.keys()):
- if k.startswith('matcher.'):
- state_dict[k.replace('matcher.', '', 1)] = state_dict.pop(k)
+ if k.startswith("matcher."):
+ state_dict[k.replace("matcher.", "", 1)] = state_dict.pop(k)
return super().load_state_dict(state_dict, *args, **kwargs)
diff --git a/imcui/third_party/TopicFM/src/models/utils/coarse_matching.py b/third_party/TopicFM/src/models/utils/coarse_matching.py
similarity index 59%
rename from imcui/third_party/TopicFM/src/models/utils/coarse_matching.py
rename to third_party/TopicFM/src/models/utils/coarse_matching.py
index 75adbb5cc465220e759a044f96f86c08da2d7a50..0cd0ea3db496fe50f82bf7660696e96e26b23484 100644
--- a/imcui/third_party/TopicFM/src/models/utils/coarse_matching.py
+++ b/third_party/TopicFM/src/models/utils/coarse_matching.py
@@ -5,8 +5,9 @@ from einops.einops import rearrange
INF = 1e9
+
def mask_border(m, b: int, v):
- """ Mask borders with value
+ """Mask borders with value
Args:
m (torch.Tensor): [N, H0, W0, H1, W1]
b (int)
@@ -37,22 +38,21 @@ def mask_border_with_padding(m, bd, v, p_m0, p_m1):
h0s, w0s = p_m0.sum(1).max(-1)[0].int(), p_m0.sum(-1).max(-1)[0].int()
h1s, w1s = p_m1.sum(1).max(-1)[0].int(), p_m1.sum(-1).max(-1)[0].int()
for b_idx, (h0, w0, h1, w1) in enumerate(zip(h0s, w0s, h1s, w1s)):
- m[b_idx, h0 - bd:] = v
- m[b_idx, :, w0 - bd:] = v
- m[b_idx, :, :, h1 - bd:] = v
- m[b_idx, :, :, :, w1 - bd:] = v
+ m[b_idx, h0 - bd :] = v
+ m[b_idx, :, w0 - bd :] = v
+ m[b_idx, :, :, h1 - bd :] = v
+ m[b_idx, :, :, :, w1 - bd :] = v
def compute_max_candidates(p_m0, p_m1):
"""Compute the max candidates of all pairs within a batch
-
+
Args:
p_m0, p_m1 (torch.Tensor): padded masks
"""
h0s, w0s = p_m0.sum(1).max(-1)[0], p_m0.sum(-1).max(-1)[0]
h1s, w1s = p_m1.sum(1).max(-1)[0], p_m1.sum(-1).max(-1)[0]
- max_cand = torch.sum(
- torch.min(torch.stack([h0s * w0s, h1s * w1s], -1), -1)[0])
+ max_cand = torch.sum(torch.min(torch.stack([h0s * w0s, h1s * w1s], -1), -1)[0])
return max_cand
@@ -61,26 +61,27 @@ class CoarseMatching(nn.Module):
super().__init__()
self.config = config
# general config
- self.thr = config['thr']
- self.border_rm = config['border_rm']
+ self.thr = config["thr"]
+ self.border_rm = config["border_rm"]
# -- # for trainig fine-level LoFTR
- self.train_coarse_percent = config['train_coarse_percent']
- self.train_pad_num_gt_min = config['train_pad_num_gt_min']
+ self.train_coarse_percent = config["train_coarse_percent"]
+ self.train_pad_num_gt_min = config["train_pad_num_gt_min"]
# we provide 2 options for differentiable matching
- self.match_type = config['match_type']
- if self.match_type == 'dual_softmax':
- self.temperature = config['dsmax_temperature']
- elif self.match_type == 'sinkhorn':
+ self.match_type = config["match_type"]
+ if self.match_type == "dual_softmax":
+ self.temperature = config["dsmax_temperature"]
+ elif self.match_type == "sinkhorn":
try:
from .superglue import log_optimal_transport
except ImportError:
raise ImportError("download superglue.py first!")
self.log_optimal_transport = log_optimal_transport
self.bin_score = nn.Parameter(
- torch.tensor(config['skh_init_bin_score'], requires_grad=True))
- self.skh_iters = config['skh_iters']
- self.skh_prefilter = config['skh_prefilter']
+ torch.tensor(config["skh_init_bin_score"], requires_grad=True)
+ )
+ self.skh_iters = config["skh_iters"]
+ self.skh_prefilter = config["skh_prefilter"]
else:
raise NotImplementedError()
@@ -99,7 +100,7 @@ class CoarseMatching(nn.Module):
'mconf' (torch.Tensor): [M]}
NOTE: M' != M during training.
"""
- conf_matrix = data['conf_matrix']
+ conf_matrix = data["conf_matrix"]
# predict coarse matches from conf_matrix
data.update(**self.get_coarse_match(conf_matrix, data))
@@ -121,28 +122,33 @@ class CoarseMatching(nn.Module):
'mconf' (torch.Tensor): [M]}
"""
axes_lengths = {
- 'h0c': data['hw0_c'][0],
- 'w0c': data['hw0_c'][1],
- 'h1c': data['hw1_c'][0],
- 'w1c': data['hw1_c'][1]
+ "h0c": data["hw0_c"][0],
+ "w0c": data["hw0_c"][1],
+ "h1c": data["hw1_c"][0],
+ "w1c": data["hw1_c"][1],
}
_device = conf_matrix.device
# 1. confidence thresholding
mask = conf_matrix > self.thr
- mask = rearrange(mask, 'b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c',
- **axes_lengths)
- if 'mask0' not in data:
+ mask = rearrange(
+ mask, "b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c", **axes_lengths
+ )
+ if "mask0" not in data:
mask_border(mask, self.border_rm, False)
else:
- mask_border_with_padding(mask, self.border_rm, False,
- data['mask0'], data['mask1'])
- mask = rearrange(mask, 'b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)',
- **axes_lengths)
+ mask_border_with_padding(
+ mask, self.border_rm, False, data["mask0"], data["mask1"]
+ )
+ mask = rearrange(
+ mask, "b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)", **axes_lengths
+ )
# 2. mutual nearest
- mask = mask \
- * (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0]) \
+ mask = (
+ mask
+ * (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0])
* (conf_matrix == conf_matrix.max(dim=1, keepdim=True)[0])
+ )
# 3. find all valid coarse matches
# this only works when at most one `True` in each row
@@ -157,16 +163,17 @@ class CoarseMatching(nn.Module):
# NOTE:
# The sampling is performed across all pairs in a batch without manually balancing
# #samples for fine-level increases w.r.t. batch_size
- if 'mask0' not in data:
- num_candidates_max = mask.size(0) * max(
- mask.size(1), mask.size(2))
+ if "mask0" not in data:
+ num_candidates_max = mask.size(0) * max(mask.size(1), mask.size(2))
else:
num_candidates_max = compute_max_candidates(
- data['mask0'], data['mask1'])
- num_matches_train = int(num_candidates_max *
- self.train_coarse_percent)
+ data["mask0"], data["mask1"]
+ )
+ num_matches_train = int(num_candidates_max * self.train_coarse_percent)
num_matches_pred = len(b_ids)
- assert self.train_pad_num_gt_min < num_matches_train, "min-num-gt-pad should be less than num-train-matches"
+ assert (
+ self.train_pad_num_gt_min < num_matches_train
+ ), "min-num-gt-pad should be less than num-train-matches"
# pred_indices is to select from prediction
if num_matches_pred <= num_matches_train - self.train_pad_num_gt_min:
@@ -174,44 +181,55 @@ class CoarseMatching(nn.Module):
else:
pred_indices = torch.randint(
num_matches_pred,
- (num_matches_train - self.train_pad_num_gt_min, ),
- device=_device)
+ (num_matches_train - self.train_pad_num_gt_min,),
+ device=_device,
+ )
# gt_pad_indices is to select from gt padding. e.g. max(3787-4800, 200)
gt_pad_indices = torch.randint(
- len(data['spv_b_ids']),
- (max(num_matches_train - num_matches_pred,
- self.train_pad_num_gt_min), ),
- device=_device)
- mconf_gt = torch.zeros(len(data['spv_b_ids']), device=_device) # set conf of gt paddings to all zero
+ len(data["spv_b_ids"]),
+ (max(num_matches_train - num_matches_pred, self.train_pad_num_gt_min),),
+ device=_device,
+ )
+ mconf_gt = torch.zeros(
+ len(data["spv_b_ids"]), device=_device
+ ) # set conf of gt paddings to all zero
b_ids, i_ids, j_ids, mconf = map(
- lambda x, y: torch.cat([x[pred_indices], y[gt_pad_indices]],
- dim=0),
- *zip([b_ids, data['spv_b_ids']], [i_ids, data['spv_i_ids']],
- [j_ids, data['spv_j_ids']], [mconf, mconf_gt]))
+ lambda x, y: torch.cat([x[pred_indices], y[gt_pad_indices]], dim=0),
+ *zip(
+ [b_ids, data["spv_b_ids"]],
+ [i_ids, data["spv_i_ids"]],
+ [j_ids, data["spv_j_ids"]],
+ [mconf, mconf_gt],
+ )
+ )
# These matches select patches that feed into fine-level network
- coarse_matches = {'b_ids': b_ids, 'i_ids': i_ids, 'j_ids': j_ids}
+ coarse_matches = {"b_ids": b_ids, "i_ids": i_ids, "j_ids": j_ids}
# 4. Update with matches in original image resolution
- scale = data['hw0_i'][0] / data['hw0_c'][0]
- scale0 = scale * data['scale0'][b_ids] if 'scale0' in data else scale
- scale1 = scale * data['scale1'][b_ids] if 'scale1' in data else scale
- mkpts0_c = torch.stack(
- [i_ids % data['hw0_c'][1], i_ids // data['hw0_c'][1]],
- dim=1) * scale0
- mkpts1_c = torch.stack(
- [j_ids % data['hw1_c'][1], j_ids // data['hw1_c'][1]],
- dim=1) * scale1
+ scale = data["hw0_i"][0] / data["hw0_c"][0]
+ scale0 = scale * data["scale0"][b_ids] if "scale0" in data else scale
+ scale1 = scale * data["scale1"][b_ids] if "scale1" in data else scale
+ mkpts0_c = (
+ torch.stack([i_ids % data["hw0_c"][1], i_ids // data["hw0_c"][1]], dim=1)
+ * scale0
+ )
+ mkpts1_c = (
+ torch.stack([j_ids % data["hw1_c"][1], j_ids // data["hw1_c"][1]], dim=1)
+ * scale1
+ )
# These matches is the current prediction (for visualization)
- coarse_matches.update({
- 'gt_mask': mconf == 0,
- 'm_bids': b_ids[mconf != 0], # mconf == 0 => gt matches
- 'mkpts0_c': mkpts0_c[mconf != 0],
- 'mkpts1_c': mkpts1_c[mconf != 0],
- 'mconf': mconf[mconf != 0]
- })
+ coarse_matches.update(
+ {
+ "gt_mask": mconf == 0,
+ "m_bids": b_ids[mconf != 0], # mconf == 0 => gt matches
+ "mkpts0_c": mkpts0_c[mconf != 0],
+ "mkpts1_c": mkpts1_c[mconf != 0],
+ "mconf": mconf[mconf != 0],
+ }
+ )
return coarse_matches
diff --git a/imcui/third_party/TopicFM/src/models/utils/fine_matching.py b/third_party/TopicFM/src/models/utils/fine_matching.py
similarity index 51%
rename from imcui/third_party/TopicFM/src/models/utils/fine_matching.py
rename to third_party/TopicFM/src/models/utils/fine_matching.py
index 018f2fe475600b319998c263a97237ce135c3aaf..7156e3e1f22e2e341062565e5ad6baee41dd9bc6 100644
--- a/imcui/third_party/TopicFM/src/models/utils/fine_matching.py
+++ b/third_party/TopicFM/src/models/utils/fine_matching.py
@@ -27,39 +27,57 @@ class FineMatching(nn.Module):
"""
M, WW, C = feat_f0.shape
W = int(math.sqrt(WW))
- scale = data['hw0_i'][0] / data['hw0_f'][0]
+ scale = data["hw0_i"][0] / data["hw0_f"][0]
self.M, self.W, self.WW, self.C, self.scale = M, W, WW, C, scale
# corner case: if no coarse matches found
if M == 0:
- assert self.training == False, "M is always >0, when training, see coarse_matching.py"
+ assert (
+ self.training == False
+ ), "M is always >0, when training, see coarse_matching.py"
# logger.warning('No matches found in coarse-level.')
- data.update({
- 'expec_f': torch.empty(0, 3, device=feat_f0.device),
- 'mkpts0_f': data['mkpts0_c'],
- 'mkpts1_f': data['mkpts1_c'],
- })
+ data.update(
+ {
+ "expec_f": torch.empty(0, 3, device=feat_f0.device),
+ "mkpts0_f": data["mkpts0_c"],
+ "mkpts1_f": data["mkpts1_c"],
+ }
+ )
return
- feat_f0_picked = feat_f0[:, WW//2, :]
+ feat_f0_picked = feat_f0[:, WW // 2, :]
- sim_matrix = torch.einsum('mc,mrc->mr', feat_f0_picked, feat_f1)
- softmax_temp = 1. / C**.5
+ sim_matrix = torch.einsum("mc,mrc->mr", feat_f0_picked, feat_f1)
+ softmax_temp = 1.0 / C**0.5
heatmap = torch.softmax(softmax_temp * sim_matrix, dim=1)
- feat_f1_picked = (feat_f1 * heatmap.unsqueeze(-1)).sum(dim=1) # [M, C]
+ feat_f1_picked = (feat_f1 * heatmap.unsqueeze(-1)).sum(dim=1) # [M, C]
heatmap = heatmap.view(-1, W, W)
# compute coordinates from heatmap
- coords1_normalized = dsnt.spatial_expectation2d(heatmap[None], True)[0] # [M, 2]
- grid_normalized = create_meshgrid(W, W, True, heatmap.device).reshape(1, -1, 2) # [1, WW, 2]
+ coords1_normalized = dsnt.spatial_expectation2d(heatmap[None], True)[
+ 0
+ ] # [M, 2]
+ grid_normalized = create_meshgrid(W, W, True, heatmap.device).reshape(
+ 1, -1, 2
+ ) # [1, WW, 2]
# compute std over
- var = torch.sum(grid_normalized**2 * heatmap.view(-1, WW, 1), dim=1) - coords1_normalized**2 # [M, 2]
- std = torch.sum(torch.sqrt(torch.clamp(var, min=1e-10)), -1) # [M] clamp needed for numerical stability
-
+ var = (
+ torch.sum(grid_normalized**2 * heatmap.view(-1, WW, 1), dim=1)
+ - coords1_normalized**2
+ ) # [M, 2]
+ std = torch.sum(
+ torch.sqrt(torch.clamp(var, min=1e-10)), -1
+ ) # [M] clamp needed for numerical stability
+
# for fine-level supervision
- data.update({'expec_f': torch.cat([coords1_normalized, std.unsqueeze(1)], -1),
- 'descriptors0': feat_f0_picked.detach(), 'descriptors1': feat_f1_picked.detach()})
+ data.update(
+ {
+ "expec_f": torch.cat([coords1_normalized, std.unsqueeze(1)], -1),
+ "descriptors0": feat_f0_picked.detach(),
+ "descriptors1": feat_f1_picked.detach(),
+ }
+ )
# compute absolute kpt coords
self.get_fine_match(coords1_normalized, data)
@@ -70,11 +88,13 @@ class FineMatching(nn.Module):
# mkpts0_f and mkpts1_f
# scale0 = scale * data['scale0'][data['b_ids']] if 'scale0' in data else scale
- mkpts0_f = data['mkpts0_c'] # + (coords0_normed * (W // 2) * scale0 )[:len(data['mconf'])]
- scale1 = scale * data['scale1'][data['b_ids']] if 'scale1' in data else scale
- mkpts1_f = data['mkpts1_c'] + (coords1_normed * (W // 2) * scale1)[:len(data['mconf'])]
+ mkpts0_f = data[
+ "mkpts0_c"
+ ] # + (coords0_normed * (W // 2) * scale0 )[:len(data['mconf'])]
+ scale1 = scale * data["scale1"][data["b_ids"]] if "scale1" in data else scale
+ mkpts1_f = (
+ data["mkpts1_c"]
+ + (coords1_normed * (W // 2) * scale1)[: len(data["mconf"])]
+ )
- data.update({
- "mkpts0_f": mkpts0_f,
- "mkpts1_f": mkpts1_f
- })
+ data.update({"mkpts0_f": mkpts0_f, "mkpts1_f": mkpts1_f})
diff --git a/imcui/third_party/TopicFM/src/models/utils/geometry.py b/third_party/TopicFM/src/models/utils/geometry.py
similarity index 59%
rename from imcui/third_party/TopicFM/src/models/utils/geometry.py
rename to third_party/TopicFM/src/models/utils/geometry.py
index f95cdb65b48324c4f4ceb20231b1bed992b41116..6101f738f2b2b7ee014fcb53a4032391939ed8cd 100644
--- a/imcui/third_party/TopicFM/src/models/utils/geometry.py
+++ b/third_party/TopicFM/src/models/utils/geometry.py
@@ -3,10 +3,10 @@ import torch
@torch.no_grad()
def warp_kpts(kpts0, depth0, depth1, T_0to1, K0, K1):
- """ Warp kpts0 from I0 to I1 with depth, K and Rt
+ """Warp kpts0 from I0 to I1 with depth, K and Rt
Also check covisibility and depth consistency.
Depth is consistent if relative error < 0.2 (hard-coded).
-
+
Args:
kpts0 (torch.Tensor): [N, L, 2] - ,
depth0 (torch.Tensor): [N, H, W],
@@ -22,33 +22,52 @@ def warp_kpts(kpts0, depth0, depth1, T_0to1, K0, K1):
# Sample depth, get calculable_mask on depth != 0
kpts0_depth = torch.stack(
- [depth0[i, kpts0_long[i, :, 1], kpts0_long[i, :, 0]] for i in range(kpts0.shape[0])], dim=0
+ [
+ depth0[i, kpts0_long[i, :, 1], kpts0_long[i, :, 0]]
+ for i in range(kpts0.shape[0])
+ ],
+ dim=0,
) # (N, L)
nonzero_mask = kpts0_depth != 0
# Unproject
- kpts0_h = torch.cat([kpts0, torch.ones_like(kpts0[:, :, [0]])], dim=-1) * kpts0_depth[..., None] # (N, L, 3)
+ kpts0_h = (
+ torch.cat([kpts0, torch.ones_like(kpts0[:, :, [0]])], dim=-1)
+ * kpts0_depth[..., None]
+ ) # (N, L, 3)
kpts0_cam = K0.inverse() @ kpts0_h.transpose(2, 1) # (N, 3, L)
# Rigid Transform
- w_kpts0_cam = T_0to1[:, :3, :3] @ kpts0_cam + T_0to1[:, :3, [3]] # (N, 3, L)
+ w_kpts0_cam = T_0to1[:, :3, :3] @ kpts0_cam + T_0to1[:, :3, [3]] # (N, 3, L)
w_kpts0_depth_computed = w_kpts0_cam[:, 2, :]
# Project
w_kpts0_h = (K1 @ w_kpts0_cam).transpose(2, 1) # (N, L, 3)
- w_kpts0 = w_kpts0_h[:, :, :2] / (w_kpts0_h[:, :, [2]] + 1e-4) # (N, L, 2), +1e-4 to avoid zero depth
+ w_kpts0 = w_kpts0_h[:, :, :2] / (
+ w_kpts0_h[:, :, [2]] + 1e-4
+ ) # (N, L, 2), +1e-4 to avoid zero depth
# Covisible Check
h, w = depth1.shape[1:3]
- covisible_mask = (w_kpts0[:, :, 0] > 0) * (w_kpts0[:, :, 0] < w-1) * \
- (w_kpts0[:, :, 1] > 0) * (w_kpts0[:, :, 1] < h-1)
+ covisible_mask = (
+ (w_kpts0[:, :, 0] > 0)
+ * (w_kpts0[:, :, 0] < w - 1)
+ * (w_kpts0[:, :, 1] > 0)
+ * (w_kpts0[:, :, 1] < h - 1)
+ )
w_kpts0_long = w_kpts0.long()
w_kpts0_long[~covisible_mask, :] = 0
w_kpts0_depth = torch.stack(
- [depth1[i, w_kpts0_long[i, :, 1], w_kpts0_long[i, :, 0]] for i in range(w_kpts0_long.shape[0])], dim=0
+ [
+ depth1[i, w_kpts0_long[i, :, 1], w_kpts0_long[i, :, 0]]
+ for i in range(w_kpts0_long.shape[0])
+ ],
+ dim=0,
) # (N, L)
- consistent_mask = ((w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth).abs() < 0.2
+ consistent_mask = (
+ (w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth
+ ).abs() < 0.2
valid_mask = nonzero_mask * covisible_mask * consistent_mask
return valid_mask, w_kpts0
diff --git a/imcui/third_party/TopicFM/src/models/utils/supervision.py b/third_party/TopicFM/src/models/utils/supervision.py
similarity index 60%
rename from imcui/third_party/TopicFM/src/models/utils/supervision.py
rename to third_party/TopicFM/src/models/utils/supervision.py
index 1f1f0478fdcbe7f8ceffbc4aff4d507cec55bbd2..86f167e95439d588c998ca32b9296c3482484215 100644
--- a/imcui/third_party/TopicFM/src/models/utils/supervision.py
+++ b/third_party/TopicFM/src/models/utils/supervision.py
@@ -13,7 +13,7 @@ from .geometry import warp_kpts
@torch.no_grad()
def mask_pts_at_padded_regions(grid_pt, mask):
"""For megadepth dataset, zero-padding exists in images"""
- mask = repeat(mask, 'n h w -> n (h w) c', c=2)
+ mask = repeat(mask, "n h w -> n (h w) c", c=2)
grid_pt[~mask.bool()] = 0
return grid_pt
@@ -30,37 +30,55 @@ def spvs_coarse(data, config):
'spv_w_pt0_i': [N, hw0, 2], in original image resolution
'spv_pt1_i': [N, hw1, 2], in original image resolution
}
-
+
NOTE:
- for scannet dataset, there're 3 kinds of resolution {i, c, f}
- for megadepth dataset, there're 4 kinds of resolution {i, i_resize, c, f}
"""
# 1. misc
- device = data['image0'].device
- N, _, H0, W0 = data['image0'].shape
- _, _, H1, W1 = data['image1'].shape
- scale = config['MODEL']['RESOLUTION'][0]
- scale0 = scale * data['scale0'][:, None] if 'scale0' in data else scale
- scale1 = scale * data['scale1'][:, None] if 'scale0' in data else scale
+ device = data["image0"].device
+ N, _, H0, W0 = data["image0"].shape
+ _, _, H1, W1 = data["image1"].shape
+ scale = config["MODEL"]["RESOLUTION"][0]
+ scale0 = scale * data["scale0"][:, None] if "scale0" in data else scale
+ scale1 = scale * data["scale1"][:, None] if "scale0" in data else scale
h0, w0, h1, w1 = map(lambda x: x // scale, [H0, W0, H1, W1])
# 2. warp grids
# create kpts in meshgrid and resize them to image resolution
- grid_pt0_c = create_meshgrid(h0, w0, False, device).reshape(1, h0*w0, 2).repeat(N, 1, 1) # [N, hw, 2]
+ grid_pt0_c = (
+ create_meshgrid(h0, w0, False, device).reshape(1, h0 * w0, 2).repeat(N, 1, 1)
+ ) # [N, hw, 2]
grid_pt0_i = scale0 * grid_pt0_c
- grid_pt1_c = create_meshgrid(h1, w1, False, device).reshape(1, h1*w1, 2).repeat(N, 1, 1)
+ grid_pt1_c = (
+ create_meshgrid(h1, w1, False, device).reshape(1, h1 * w1, 2).repeat(N, 1, 1)
+ )
grid_pt1_i = scale1 * grid_pt1_c
# mask padded region to (0, 0), so no need to manually mask conf_matrix_gt
- if 'mask0' in data:
- grid_pt0_i = mask_pts_at_padded_regions(grid_pt0_i, data['mask0'])
- grid_pt1_i = mask_pts_at_padded_regions(grid_pt1_i, data['mask1'])
+ if "mask0" in data:
+ grid_pt0_i = mask_pts_at_padded_regions(grid_pt0_i, data["mask0"])
+ grid_pt1_i = mask_pts_at_padded_regions(grid_pt1_i, data["mask1"])
# warp kpts bi-directionally and resize them to coarse-level resolution
# (no depth consistency check, since it leads to worse results experimentally)
# (unhandled edge case: points with 0-depth will be warped to the left-up corner)
- _, w_pt0_i = warp_kpts(grid_pt0_i, data['depth0'], data['depth1'], data['T_0to1'], data['K0'], data['K1'])
- _, w_pt1_i = warp_kpts(grid_pt1_i, data['depth1'], data['depth0'], data['T_1to0'], data['K1'], data['K0'])
+ _, w_pt0_i = warp_kpts(
+ grid_pt0_i,
+ data["depth0"],
+ data["depth1"],
+ data["T_0to1"],
+ data["K0"],
+ data["K1"],
+ )
+ _, w_pt1_i = warp_kpts(
+ grid_pt1_i,
+ data["depth1"],
+ data["depth0"],
+ data["T_1to0"],
+ data["K1"],
+ data["K0"],
+ )
w_pt0_c = w_pt0_i / scale1
w_pt1_c = w_pt1_i / scale0
@@ -72,21 +90,26 @@ def spvs_coarse(data, config):
# corner case: out of boundary
def out_bound_mask(pt, w, h):
- return (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h)
+ return (
+ (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h)
+ )
+
nearest_index1[out_bound_mask(w_pt0_c_round, w1, h1)] = 0
nearest_index0[out_bound_mask(w_pt1_c_round, w0, h0)] = 0
- loop_back = torch.stack([nearest_index0[_b][_i] for _b, _i in enumerate(nearest_index1)], dim=0)
- correct_0to1 = loop_back == torch.arange(h0*w0, device=device)[None].repeat(N, 1)
+ loop_back = torch.stack(
+ [nearest_index0[_b][_i] for _b, _i in enumerate(nearest_index1)], dim=0
+ )
+ correct_0to1 = loop_back == torch.arange(h0 * w0, device=device)[None].repeat(N, 1)
correct_0to1[:, 0] = False # ignore the top-left corner
# 4. construct a gt conf_matrix
- conf_matrix_gt = torch.zeros(N, h0*w0, h1*w1, device=device)
+ conf_matrix_gt = torch.zeros(N, h0 * w0, h1 * w1, device=device)
b_ids, i_ids = torch.where(correct_0to1 != 0)
j_ids = nearest_index1[b_ids, i_ids]
conf_matrix_gt[b_ids, i_ids, j_ids] = 1
- data.update({'conf_matrix_gt': conf_matrix_gt})
+ data.update({"conf_matrix_gt": conf_matrix_gt})
# 5. save coarse matches(gt) for training fine level
if len(b_ids) == 0:
@@ -96,30 +119,26 @@ def spvs_coarse(data, config):
i_ids = torch.tensor([0], device=device)
j_ids = torch.tensor([0], device=device)
- data.update({
- 'spv_b_ids': b_ids,
- 'spv_i_ids': i_ids,
- 'spv_j_ids': j_ids
- })
+ data.update({"spv_b_ids": b_ids, "spv_i_ids": i_ids, "spv_j_ids": j_ids})
# 6. save intermediate results (for fast fine-level computation)
- data.update({
- 'spv_w_pt0_i': w_pt0_i,
- 'spv_pt1_i': grid_pt1_i
- })
+ data.update({"spv_w_pt0_i": w_pt0_i, "spv_pt1_i": grid_pt1_i})
def compute_supervision_coarse(data, config):
- assert len(set(data['dataset_name'])) == 1, "Do not support mixed datasets training!"
- data_source = data['dataset_name'][0]
- if data_source.lower() in ['scannet', 'megadepth']:
+ assert (
+ len(set(data["dataset_name"])) == 1
+ ), "Do not support mixed datasets training!"
+ data_source = data["dataset_name"][0]
+ if data_source.lower() in ["scannet", "megadepth"]:
spvs_coarse(data, config)
else:
- raise ValueError(f'Unknown data source: {data_source}')
+ raise ValueError(f"Unknown data source: {data_source}")
############## ↓ Fine-Level supervision ↓ ##############
+
@torch.no_grad()
def spvs_fine(data, config):
"""
@@ -129,23 +148,25 @@ def spvs_fine(data, config):
"""
# 1. misc
# w_pt0_i, pt1_i = data.pop('spv_w_pt0_i'), data.pop('spv_pt1_i')
- w_pt0_i, pt1_i = data['spv_w_pt0_i'], data['spv_pt1_i']
- scale = config['MODEL']['RESOLUTION'][1]
- radius = config['MODEL']['FINE_WINDOW_SIZE'] // 2
+ w_pt0_i, pt1_i = data["spv_w_pt0_i"], data["spv_pt1_i"]
+ scale = config["MODEL"]["RESOLUTION"][1]
+ radius = config["MODEL"]["FINE_WINDOW_SIZE"] // 2
# 2. get coarse prediction
- b_ids, i_ids, j_ids = data['b_ids'], data['i_ids'], data['j_ids']
+ b_ids, i_ids, j_ids = data["b_ids"], data["i_ids"], data["j_ids"]
# 3. compute gt
- scale = scale * data['scale1'][b_ids] if 'scale0' in data else scale
+ scale = scale * data["scale1"][b_ids] if "scale0" in data else scale
# `expec_f_gt` might exceed the window, i.e. abs(*) > 1, which would be filtered later
- expec_f_gt = (w_pt0_i[b_ids, i_ids] - pt1_i[b_ids, j_ids]) / scale / radius # [M, 2]
+ expec_f_gt = (
+ (w_pt0_i[b_ids, i_ids] - pt1_i[b_ids, j_ids]) / scale / radius
+ ) # [M, 2]
data.update({"expec_f_gt": expec_f_gt})
def compute_supervision_fine(data, config):
- data_source = data['dataset_name'][0]
- if data_source.lower() in ['scannet', 'megadepth']:
+ data_source = data["dataset_name"][0]
+ if data_source.lower() in ["scannet", "megadepth"]:
spvs_fine(data, config)
else:
raise NotImplementedError
diff --git a/third_party/TopicFM/src/optimizers/__init__.py b/third_party/TopicFM/src/optimizers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4e36c22e00217deccacd589f8924b2f74589456
--- /dev/null
+++ b/third_party/TopicFM/src/optimizers/__init__.py
@@ -0,0 +1,55 @@
+import torch
+from torch.optim.lr_scheduler import MultiStepLR, CosineAnnealingLR, ExponentialLR
+
+
+def build_optimizer(model, config):
+ name = config.TRAINER.OPTIMIZER
+ lr = config.TRAINER.TRUE_LR
+
+ if name == "adam":
+ return torch.optim.Adam(
+ model.parameters(), lr=lr, weight_decay=config.TRAINER.ADAM_DECAY
+ )
+ elif name == "adamw":
+ return torch.optim.AdamW(
+ model.parameters(), lr=lr, weight_decay=config.TRAINER.ADAMW_DECAY
+ )
+ else:
+ raise ValueError(f"TRAINER.OPTIMIZER = {name} is not a valid optimizer!")
+
+
+def build_scheduler(config, optimizer):
+ """
+ Returns:
+ scheduler (dict):{
+ 'scheduler': lr_scheduler,
+ 'interval': 'step', # or 'epoch'
+ 'monitor': 'val_f1', (optional)
+ 'frequency': x, (optional)
+ }
+ """
+ scheduler = {"interval": config.TRAINER.SCHEDULER_INTERVAL}
+ name = config.TRAINER.SCHEDULER
+
+ if name == "MultiStepLR":
+ scheduler.update(
+ {
+ "scheduler": MultiStepLR(
+ optimizer,
+ config.TRAINER.MSLR_MILESTONES,
+ gamma=config.TRAINER.MSLR_GAMMA,
+ )
+ }
+ )
+ elif name == "CosineAnnealing":
+ scheduler.update(
+ {"scheduler": CosineAnnealingLR(optimizer, config.TRAINER.COSA_TMAX)}
+ )
+ elif name == "ExponentialLR":
+ scheduler.update(
+ {"scheduler": ExponentialLR(optimizer, config.TRAINER.ELR_GAMMA)}
+ )
+ else:
+ raise NotImplementedError()
+
+ return scheduler
diff --git a/third_party/TopicFM/src/utils/augment.py b/third_party/TopicFM/src/utils/augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..068751c6c07091bbaed76debd43a73155f61b9bd
--- /dev/null
+++ b/third_party/TopicFM/src/utils/augment.py
@@ -0,0 +1,65 @@
+import albumentations as A
+
+
+class DarkAug(object):
+ """
+ Extreme dark augmentation aiming at Aachen Day-Night
+ """
+
+ def __init__(self) -> None:
+ self.augmentor = A.Compose(
+ [
+ A.RandomBrightnessContrast(
+ p=0.75, brightness_limit=(-0.6, 0.0), contrast_limit=(-0.5, 0.3)
+ ),
+ A.Blur(p=0.1, blur_limit=(3, 9)),
+ A.MotionBlur(p=0.2, blur_limit=(3, 25)),
+ A.RandomGamma(p=0.1, gamma_limit=(15, 65)),
+ A.HueSaturationValue(p=0.1, val_shift_limit=(-100, -40)),
+ ],
+ p=0.75,
+ )
+
+ def __call__(self, x):
+ return self.augmentor(image=x)["image"]
+
+
+class MobileAug(object):
+ """
+ Random augmentations aiming at images of mobile/handhold devices.
+ """
+
+ def __init__(self):
+ self.augmentor = A.Compose(
+ [
+ A.MotionBlur(p=0.25),
+ A.ColorJitter(p=0.5),
+ A.RandomRain(p=0.1), # random occlusion
+ A.RandomSunFlare(p=0.1),
+ A.JpegCompression(p=0.25),
+ A.ISONoise(p=0.25),
+ ],
+ p=1.0,
+ )
+
+ def __call__(self, x):
+ return self.augmentor(image=x)["image"]
+
+
+def build_augmentor(method=None, **kwargs):
+ if method is not None:
+ raise NotImplementedError(
+ "Using of augmentation functions are not supported yet!"
+ )
+ if method == "dark":
+ return DarkAug()
+ elif method == "mobile":
+ return MobileAug()
+ elif method is None:
+ return None
+ else:
+ raise ValueError(f"Invalid augmentation method: {method}")
+
+
+if __name__ == "__main__":
+ augmentor = build_augmentor("FDA")
diff --git a/imcui/third_party/XoFTR/src/utils/comm.py b/third_party/TopicFM/src/utils/comm.py
similarity index 95%
rename from imcui/third_party/XoFTR/src/utils/comm.py
rename to third_party/TopicFM/src/utils/comm.py
index 26ec9517cc47e224430106d8ae9aa99a3fe49167..9f578cda8933cc358934c645fcf413c63ab4d79d 100644
--- a/imcui/third_party/XoFTR/src/utils/comm.py
+++ b/third_party/TopicFM/src/utils/comm.py
@@ -98,11 +98,11 @@ def _serialize_to_tensor(data, group):
device = torch.device("cpu" if backend == "gloo" else "cuda")
buffer = pickle.dumps(data)
- if len(buffer) > 1024 ** 3:
+ if len(buffer) > 1024**3:
logger = logging.getLogger(__name__)
logger.warning(
"Rank {} trying to all-gather {:.2f} GB of data on device {}".format(
- get_rank(), len(buffer) / (1024 ** 3), device
+ get_rank(), len(buffer) / (1024**3), device
)
)
storage = torch.ByteStorage.from_buffer(buffer)
@@ -122,7 +122,8 @@ def _pad_to_largest_tensor(tensor, group):
), "comm.gather/all_gather must be called from ranks within the given group!"
local_size = torch.tensor([tensor.numel()], dtype=torch.int64, device=tensor.device)
size_list = [
- torch.zeros([1], dtype=torch.int64, device=tensor.device) for _ in range(world_size)
+ torch.zeros([1], dtype=torch.int64, device=tensor.device)
+ for _ in range(world_size)
]
dist.all_gather(size_list, local_size, group=group)
@@ -133,7 +134,9 @@ def _pad_to_largest_tensor(tensor, group):
# we pad the tensor because torch all_gather does not support
# gathering tensors of different shapes
if local_size != max_size:
- padding = torch.zeros((max_size - local_size,), dtype=torch.uint8, device=tensor.device)
+ padding = torch.zeros(
+ (max_size - local_size,), dtype=torch.uint8, device=tensor.device
+ )
tensor = torch.cat((tensor, padding), dim=0)
return size_list, tensor
@@ -164,7 +167,8 @@ def all_gather(data, group=None):
# receiving Tensor from all ranks
tensor_list = [
- torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list
+ torch.empty((max_size,), dtype=torch.uint8, device=tensor.device)
+ for _ in size_list
]
dist.all_gather(tensor_list, tensor, group=group)
@@ -205,7 +209,8 @@ def gather(data, dst=0, group=None):
if rank == dst:
max_size = max(size_list)
tensor_list = [
- torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list
+ torch.empty((max_size,), dtype=torch.uint8, device=tensor.device)
+ for _ in size_list
]
dist.gather(tensor, tensor_list, dst=dst, group=group)
@@ -228,7 +233,7 @@ def shared_random_seed():
All workers must call this function, otherwise it will deadlock.
"""
- ints = np.random.randint(2 ** 31)
+ ints = np.random.randint(2**31)
all_ints = all_gather(ints)
return all_ints[0]
diff --git a/imcui/third_party/TopicFM/src/utils/dataloader.py b/third_party/TopicFM/src/utils/dataloader.py
similarity index 55%
rename from imcui/third_party/TopicFM/src/utils/dataloader.py
rename to third_party/TopicFM/src/utils/dataloader.py
index 6da37b880a290c2bb3ebb028d0c8dab592acc5c1..b980dfd344714870ecdacd9e7a9742f51c3ee14d 100644
--- a/imcui/third_party/TopicFM/src/utils/dataloader.py
+++ b/third_party/TopicFM/src/utils/dataloader.py
@@ -3,21 +3,22 @@ import numpy as np
# --- PL-DATAMODULE ---
+
def get_local_split(items: list, world_size: int, rank: int, seed: int):
- """ The local rank only loads a split of the dataset. """
+ """The local rank only loads a split of the dataset."""
n_items = len(items)
items_permute = np.random.RandomState(seed).permutation(items)
if n_items % world_size == 0:
padded_items = items_permute
else:
padding = np.random.RandomState(seed).choice(
- items,
- world_size - (n_items % world_size),
- replace=True)
+ items, world_size - (n_items % world_size), replace=True
+ )
padded_items = np.concatenate([items_permute, padding])
- assert len(padded_items) % world_size == 0, \
- f'len(padded_items): {len(padded_items)}; world_size: {world_size}; len(padding): {len(padding)}'
+ assert (
+ len(padded_items) % world_size == 0
+ ), f"len(padded_items): {len(padded_items)}; world_size: {world_size}; len(padding): {len(padding)}"
n_per_rank = len(padded_items) // world_size
- local_items = padded_items[n_per_rank * rank: n_per_rank * (rank+1)]
+ local_items = padded_items[n_per_rank * rank : n_per_rank * (rank + 1)]
return local_items
diff --git a/imcui/third_party/TopicFM/src/utils/dataset.py b/third_party/TopicFM/src/utils/dataset.py
similarity index 77%
rename from imcui/third_party/TopicFM/src/utils/dataset.py
rename to third_party/TopicFM/src/utils/dataset.py
index 647bbadd821b6c90736ed45462270670b1017b0b..f26722dddcc15516b1986182a246b0cdb52c347a 100644
--- a/imcui/third_party/TopicFM/src/utils/dataset.py
+++ b/third_party/TopicFM/src/utils/dataset.py
@@ -12,8 +12,11 @@ MEGADEPTH_CLIENT = SCANNET_CLIENT = None
# --- DATA IO ---
+
def load_array_from_s3(
- path, client, cv_type,
+ path,
+ client,
+ cv_type,
use_h5py=False,
):
byte_str = client.Get(path)
@@ -23,7 +26,7 @@ def load_array_from_s3(
data = cv2.imdecode(raw_array, cv_type)
else:
f = io.BytesIO(byte_str)
- data = np.array(h5py.File(f, 'r')['/depth'])
+ data = np.array(h5py.File(f, "r")["/depth"])
except Exception as ex:
print(f"==> Data loading failure: {path}")
raise ex
@@ -33,9 +36,8 @@ def load_array_from_s3(
def imread_gray(path, augment_fn=None, client=SCANNET_CLIENT):
- cv_type = cv2.IMREAD_GRAYSCALE if augment_fn is None \
- else cv2.IMREAD_COLOR
- if str(path).startswith('s3://'):
+ cv_type = cv2.IMREAD_GRAYSCALE if augment_fn is None else cv2.IMREAD_COLOR
+ if str(path).startswith("s3://"):
image = load_array_from_s3(str(path), client, cv_type)
else:
image = cv2.imread(str(path), cv_type)
@@ -49,9 +51,9 @@ def imread_gray(path, augment_fn=None, client=SCANNET_CLIENT):
def get_resized_wh(w, h, resize=None):
- if (resize is not None) and (max(h,w) > resize): # resize the longer edge
+ if (resize is not None) and (max(h, w) > resize): # resize the longer edge
scale = resize / max(h, w)
- w_new, h_new = int(round(w*scale)), int(round(h*scale))
+ w_new, h_new = int(round(w * scale)), int(round(h * scale))
else:
w_new, h_new = w, h
return w_new, h_new
@@ -66,20 +68,22 @@ def get_divisible_wh(w, h, df=None):
def pad_bottom_right(inp, pad_size, ret_mask=False):
- assert isinstance(pad_size, int) and pad_size >= max(inp.shape[-2:]), f"{pad_size} < {max(inp.shape[-2:])}"
+ assert isinstance(pad_size, int) and pad_size >= max(
+ inp.shape[-2:]
+ ), f"{pad_size} < {max(inp.shape[-2:])}"
mask = None
if inp.ndim == 2:
padded = np.zeros((pad_size, pad_size), dtype=inp.dtype)
- padded[:inp.shape[0], :inp.shape[1]] = inp
+ padded[: inp.shape[0], : inp.shape[1]] = inp
if ret_mask:
mask = np.zeros((pad_size, pad_size), dtype=bool)
- mask[:inp.shape[0], :inp.shape[1]] = True
+ mask[: inp.shape[0], : inp.shape[1]] = True
elif inp.ndim == 3:
padded = np.zeros((inp.shape[0], pad_size, pad_size), dtype=inp.dtype)
- padded[:, :inp.shape[1], :inp.shape[2]] = inp
+ padded[:, : inp.shape[1], : inp.shape[2]] = inp
if ret_mask:
mask = np.zeros((inp.shape[0], pad_size, pad_size), dtype=bool)
- mask[:, :inp.shape[1], :inp.shape[2]] = True
+ mask[:, : inp.shape[1], : inp.shape[2]] = True
else:
raise NotImplementedError()
return padded, mask
@@ -87,6 +91,7 @@ def pad_bottom_right(inp, pad_size, ret_mask=False):
# --- MEGADEPTH ---
+
def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=None):
"""
Args:
@@ -96,7 +101,7 @@ def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=No
Returns:
image (torch.tensor): (1, h, w)
mask (torch.tensor): (h, w)
- scale (torch.tensor): [w/w_new, h/h_new]
+ scale (torch.tensor): [w/w_new, h/h_new]
"""
# read image
image = imread_gray(path, augment_fn, client=MEGADEPTH_CLIENT)
@@ -107,25 +112,27 @@ def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=No
w_new, h_new = get_divisible_wh(w_new, h_new, df)
image = cv2.resize(image, (w_new, h_new))
- scale = torch.tensor([w/w_new, h/h_new], dtype=torch.float)
+ scale = torch.tensor([w / w_new, h / h_new], dtype=torch.float)
if padding: # padding
- pad_to = resize #max(h_new, w_new)
+ pad_to = resize # max(h_new, w_new)
image, mask = pad_bottom_right(image, pad_to, ret_mask=True)
else:
mask = None
- image = torch.from_numpy(image).float()[None] / 255 # (h, w) -> (1, h, w) and normalized
+ image = (
+ torch.from_numpy(image).float()[None] / 255
+ ) # (h, w) -> (1, h, w) and normalized
mask = torch.from_numpy(mask) if mask is not None else None
return image, mask, scale
def read_megadepth_depth(path, pad_to=None):
- if str(path).startswith('s3://'):
+ if str(path).startswith("s3://"):
depth = load_array_from_s3(path, MEGADEPTH_CLIENT, None, use_h5py=True)
else:
- depth = np.array(h5py.File(path, 'r')['depth'])
+ depth = np.array(h5py.File(path, "r")["depth"])
if pad_to is not None:
depth, _ = pad_bottom_right(depth, pad_to, ret_mask=False)
depth = torch.from_numpy(depth).float() # (h, w)
@@ -134,6 +141,7 @@ def read_megadepth_depth(path, pad_to=None):
# --- ScanNet ---
+
def read_scannet_gray(path, resize=(640, 480), augment_fn=None):
"""
Args:
@@ -142,7 +150,7 @@ def read_scannet_gray(path, resize=(640, 480), augment_fn=None):
Returns:
image (torch.tensor): (1, h, w)
mask (torch.tensor): (h, w)
- scale (torch.tensor): [w/w_new, h/h_new]
+ scale (torch.tensor): [w/w_new, h/h_new]
"""
# read and resize image
image = imread_gray(path, augment_fn)
@@ -155,6 +163,7 @@ def read_scannet_gray(path, resize=(640, 480), augment_fn=None):
# ---- evaluation datasets: HLoc, Aachen, InLoc
+
def read_img_gray(path, resize=None, down_factor=16):
# read and resize image
image = imread_gray(path, None)
@@ -174,7 +183,7 @@ def read_img_gray(path, resize=None, down_factor=16):
def read_scannet_depth(path):
- if str(path).startswith('s3://'):
+ if str(path).startswith("s3://"):
depth = load_array_from_s3(str(path), SCANNET_CLIENT, cv2.IMREAD_UNCHANGED)
else:
depth = cv2.imread(str(path), cv2.IMREAD_UNCHANGED)
@@ -184,18 +193,17 @@ def read_scannet_depth(path):
def read_scannet_pose(path):
- """ Read ScanNet's Camera2World pose and transform it to World2Camera.
-
+ """Read ScanNet's Camera2World pose and transform it to World2Camera.
+
Returns:
pose_w2c (np.ndarray): (4, 4)
"""
- cam2world = np.loadtxt(path, delimiter=' ')
+ cam2world = np.loadtxt(path, delimiter=" ")
world2cam = inv(cam2world)
return world2cam
def read_scannet_intrinsic(path):
- """ Read ScanNet's intrinsic matrix and return the 3x3 matrix.
- """
- intrinsic = np.loadtxt(path, delimiter=' ')
+ """Read ScanNet's intrinsic matrix and return the 3x3 matrix."""
+ intrinsic = np.loadtxt(path, delimiter=" ")
return intrinsic[:-1, :-1]
diff --git a/imcui/third_party/TopicFM/src/utils/metrics.py b/third_party/TopicFM/src/utils/metrics.py
similarity index 70%
rename from imcui/third_party/TopicFM/src/utils/metrics.py
rename to third_party/TopicFM/src/utils/metrics.py
index a93c31ed1d151cd41e2449a19be2d6abc5f9d419..6190b04f0af85680a0c951f74309c0b66c80e1e5 100644
--- a/imcui/third_party/TopicFM/src/utils/metrics.py
+++ b/third_party/TopicFM/src/utils/metrics.py
@@ -9,6 +9,7 @@ from kornia.geometry.conversions import convert_points_to_homogeneous
# --- METRICS ---
+
def relative_pose_error(T_0to1, R, t, ignore_gt_t_thr=0.0):
# angle error between 2 vectors
t_gt = T_0to1[:3, 3]
@@ -21,7 +22,7 @@ def relative_pose_error(T_0to1, R, t, ignore_gt_t_thr=0.0):
# angle error between 2 rotation matrices
R_gt = T_0to1[:3, :3]
cos = (np.trace(np.dot(R.T, R_gt)) - 1) / 2
- cos = np.clip(cos, -1., 1.) # handle numercial errors
+ cos = np.clip(cos, -1.0, 1.0) # handle numercial errors
R_err = np.rad2deg(np.abs(np.arccos(cos)))
return t_err, R_err
@@ -43,30 +44,36 @@ def symmetric_epipolar_distance(pts0, pts1, E, K0, K1):
p1Ep0 = torch.sum(pts1 * Ep0, -1) # [N,]
Etp1 = pts1 @ E # [N, 3]
- d = p1Ep0**2 * (1.0 / (Ep0[:, 0]**2 + Ep0[:, 1]**2) + 1.0 / (Etp1[:, 0]**2 + Etp1[:, 1]**2)) # N
+ d = p1Ep0**2 * (
+ 1.0 / (Ep0[:, 0] ** 2 + Ep0[:, 1] ** 2)
+ + 1.0 / (Etp1[:, 0] ** 2 + Etp1[:, 1] ** 2)
+ ) # N
return d
def compute_symmetrical_epipolar_errors(data):
- """
+ """
Update:
data (dict):{"epi_errs": [M]}
"""
- Tx = numeric.cross_product_matrix(data['T_0to1'][:, :3, 3])
- E_mat = Tx @ data['T_0to1'][:, :3, :3]
+ Tx = numeric.cross_product_matrix(data["T_0to1"][:, :3, 3])
+ E_mat = Tx @ data["T_0to1"][:, :3, :3]
- m_bids = data['m_bids']
- pts0 = data['mkpts0_f']
- pts1 = data['mkpts1_f']
+ m_bids = data["m_bids"]
+ pts0 = data["mkpts0_f"]
+ pts1 = data["mkpts1_f"]
epi_errs = []
for bs in range(Tx.size(0)):
mask = m_bids == bs
epi_errs.append(
- symmetric_epipolar_distance(pts0[mask], pts1[mask], E_mat[bs], data['K0'][bs], data['K1'][bs]))
+ symmetric_epipolar_distance(
+ pts0[mask], pts1[mask], E_mat[bs], data["K0"][bs], data["K1"][bs]
+ )
+ )
epi_errs = torch.cat(epi_errs, dim=0)
- data.update({'epi_errs': epi_errs})
+ data.update({"epi_errs": epi_errs})
def estimate_pose(kpts0, kpts1, K0, K1, thresh, conf=0.99999):
@@ -81,7 +88,8 @@ def estimate_pose(kpts0, kpts1, K0, K1, thresh, conf=0.99999):
# compute pose with cv2
E, mask = cv2.findEssentialMat(
- kpts0, kpts1, np.eye(3), threshold=ransac_thr, prob=conf, method=cv2.RANSAC)
+ kpts0, kpts1, np.eye(3), threshold=ransac_thr, prob=conf, method=cv2.RANSAC
+ )
if E is None:
print("\nE is None while trying to recover pose.\n")
return None
@@ -99,7 +107,7 @@ def estimate_pose(kpts0, kpts1, K0, K1, thresh, conf=0.99999):
def compute_pose_errors(data, config=None, ransac_thr=0.5, ransac_conf=0.99999):
- """
+ """
Update:
data (dict):{
"R_errs" List[float]: [N]
@@ -107,35 +115,40 @@ def compute_pose_errors(data, config=None, ransac_thr=0.5, ransac_conf=0.99999):
"inliers" List[np.ndarray]: [N]
}
"""
- pixel_thr = config.TRAINER.RANSAC_PIXEL_THR if config is not None else ransac_thr # 0.5
+ pixel_thr = (
+ config.TRAINER.RANSAC_PIXEL_THR if config is not None else ransac_thr
+ ) # 0.5
conf = config.TRAINER.RANSAC_CONF if config is not None else ransac_conf # 0.99999
- data.update({'R_errs': [], 't_errs': [], 'inliers': []})
+ data.update({"R_errs": [], "t_errs": [], "inliers": []})
- m_bids = data['m_bids'].cpu().numpy()
- pts0 = data['mkpts0_f'].cpu().numpy()
- pts1 = data['mkpts1_f'].cpu().numpy()
- K0 = data['K0'].cpu().numpy()
- K1 = data['K1'].cpu().numpy()
- T_0to1 = data['T_0to1'].cpu().numpy()
+ m_bids = data["m_bids"].cpu().numpy()
+ pts0 = data["mkpts0_f"].cpu().numpy()
+ pts1 = data["mkpts1_f"].cpu().numpy()
+ K0 = data["K0"].cpu().numpy()
+ K1 = data["K1"].cpu().numpy()
+ T_0to1 = data["T_0to1"].cpu().numpy()
for bs in range(K0.shape[0]):
mask = m_bids == bs
- ret = estimate_pose(pts0[mask], pts1[mask], K0[bs], K1[bs], pixel_thr, conf=conf)
+ ret = estimate_pose(
+ pts0[mask], pts1[mask], K0[bs], K1[bs], pixel_thr, conf=conf
+ )
if ret is None:
- data['R_errs'].append(np.inf)
- data['t_errs'].append(np.inf)
- data['inliers'].append(np.array([]).astype(np.bool))
+ data["R_errs"].append(np.inf)
+ data["t_errs"].append(np.inf)
+ data["inliers"].append(np.array([]).astype(np.bool))
else:
R, t, inliers = ret
t_err, R_err = relative_pose_error(T_0to1[bs], R, t, ignore_gt_t_thr=0.0)
- data['R_errs'].append(R_err)
- data['t_errs'].append(t_err)
- data['inliers'].append(inliers)
+ data["R_errs"].append(R_err)
+ data["t_errs"].append(t_err)
+ data["inliers"].append(inliers)
# --- METRIC AGGREGATION ---
+
def error_auc(errors, thresholds):
"""
Args:
@@ -149,11 +162,11 @@ def error_auc(errors, thresholds):
thresholds = [5, 10, 20]
for thr in thresholds:
last_index = np.searchsorted(errors, thr)
- y = recall[:last_index] + [recall[last_index-1]]
+ y = recall[:last_index] + [recall[last_index - 1]]
x = errors[:last_index] + [thr]
aucs.append(np.trapz(y, x) / thr)
- return {f'auc@{t}': auc for t, auc in zip(thresholds, aucs)}
+ return {f"auc@{t}": auc for t, auc in zip(thresholds, aucs)}
def epidist_prec(errors, thresholds, ret_dict=False):
@@ -165,29 +178,33 @@ def epidist_prec(errors, thresholds, ret_dict=False):
prec_.append(np.mean(correct_mask) if len(correct_mask) > 0 else 0)
precs.append(np.mean(prec_) if len(prec_) > 0 else 0)
if ret_dict:
- return {f'prec@{t:.0e}': prec for t, prec in zip(thresholds, precs)}
+ return {f"prec@{t:.0e}": prec for t, prec in zip(thresholds, precs)}
else:
return precs
def aggregate_metrics(metrics, epi_err_thr=5e-4):
- """ Aggregate metrics for the whole dataset:
+ """Aggregate metrics for the whole dataset:
(This method should be called once per dataset)
1. AUC of the pose error (angular) at the threshold [5, 10, 20]
2. Mean matching precision at the threshold 5e-4(ScanNet), 1e-4(MegaDepth)
"""
# filter duplicates
- unq_ids = OrderedDict((iden, id) for id, iden in enumerate(metrics['identifiers']))
+ unq_ids = OrderedDict((iden, id) for id, iden in enumerate(metrics["identifiers"]))
unq_ids = list(unq_ids.values())
- logger.info(f'Aggregating metrics over {len(unq_ids)} unique items...')
+ logger.info(f"Aggregating metrics over {len(unq_ids)} unique items...")
# pose auc
angular_thresholds = [5, 10, 20]
- pose_errors = np.max(np.stack([metrics['R_errs'], metrics['t_errs']]), axis=0)[unq_ids]
+ pose_errors = np.max(np.stack([metrics["R_errs"], metrics["t_errs"]]), axis=0)[
+ unq_ids
+ ]
aucs = error_auc(pose_errors, angular_thresholds) # (auc@5, auc@10, auc@20)
# matching precision
dist_thresholds = [epi_err_thr]
- precs = epidist_prec(np.array(metrics['epi_errs'], dtype=object)[unq_ids], dist_thresholds, True) # (prec@err_thr)
+ precs = epidist_prec(
+ np.array(metrics["epi_errs"], dtype=object)[unq_ids], dist_thresholds, True
+ ) # (prec@err_thr)
return {**aucs, **precs}
diff --git a/imcui/third_party/XoFTR/src/utils/misc.py b/third_party/TopicFM/src/utils/misc.py
similarity index 79%
rename from imcui/third_party/XoFTR/src/utils/misc.py
rename to third_party/TopicFM/src/utils/misc.py
index 9c8db04666519753ea2df43903ab6c47ec00a9a1..461077d77f1628c67055d841a5e70c29c7b82ade 100644
--- a/imcui/third_party/XoFTR/src/utils/misc.py
+++ b/third_party/TopicFM/src/utils/misc.py
@@ -24,7 +24,7 @@ def upper_config(dict_cfg):
def log_on(condition, message, level):
if condition:
- assert level in ['INFO', 'DEBUG', 'WARNING', 'ERROR', 'CRITICAL']
+ assert level in ["INFO", "DEBUG", "WARNING", "ERROR", "CRITICAL"]
logger.log(level, message)
@@ -34,32 +34,35 @@ def get_rank_zero_only_logger(logger: _Logger):
else:
for _level in logger._core.levels.keys():
level = _level.lower()
- setattr(logger, level,
- lambda x: None)
+ setattr(logger, level, lambda x: None)
logger._log = lambda x: None
return logger
def setup_gpus(gpus: Union[str, int]) -> int:
- """ A temporary fix for pytorch-lighting 1.3.x """
+ """A temporary fix for pytorch-lighting 1.3.x"""
gpus = str(gpus)
gpu_ids = []
-
- if ',' not in gpus:
+
+ if "," not in gpus:
n_gpus = int(gpus)
return n_gpus if n_gpus != -1 else torch.cuda.device_count()
else:
- gpu_ids = [i.strip() for i in gpus.split(',') if i != '']
-
+ gpu_ids = [i.strip() for i in gpus.split(",") if i != ""]
+
# setup environment variables
- visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
+ visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
if visible_devices is None:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
- os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(i) for i in gpu_ids)
- visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
- logger.warning(f'[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}')
+ os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str(i) for i in gpu_ids)
+ visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
+ logger.warning(
+ f"[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}"
+ )
else:
- logger.warning('[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process.')
+ logger.warning(
+ "[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process."
+ )
return len(gpu_ids)
@@ -70,11 +73,11 @@ def flattenList(x):
@contextlib.contextmanager
def tqdm_joblib(tqdm_object):
"""Context manager to patch joblib to report into tqdm progress bar given as argument
-
+
Usage:
with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar:
Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10))
-
+
When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing)
ret_vals = Parallel(n_jobs=args.world_size)(
delayed(lambda x: _compute_cov_score(pid, *x))(param)
@@ -83,6 +86,7 @@ def tqdm_joblib(tqdm_object):
total=len(image_ids)*(len(image_ids)-1)/2))
Src: https://stackoverflow.com/a/58936697
"""
+
class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@@ -98,4 +102,3 @@ def tqdm_joblib(tqdm_object):
finally:
joblib.parallel.BatchCompletionCallBack = old_batch_callback
tqdm_object.close()
-
diff --git a/imcui/third_party/TopicFM/src/utils/plotting.py b/third_party/TopicFM/src/utils/plotting.py
similarity index 52%
rename from imcui/third_party/TopicFM/src/utils/plotting.py
rename to third_party/TopicFM/src/utils/plotting.py
index 89b22ef27e6152225d07ab24bb3e62718d180b59..189045409c822f2e1d79610b29ea7e2825ae4bbd 100644
--- a/imcui/third_party/TopicFM/src/utils/plotting.py
+++ b/third_party/TopicFM/src/utils/plotting.py
@@ -9,37 +9,49 @@ import torch
def _compute_conf_thresh(data):
- dataset_name = data['dataset_name'][0].lower()
- if dataset_name == 'scannet':
+ dataset_name = data["dataset_name"][0].lower()
+ if dataset_name == "scannet":
thr = 5e-4
- elif dataset_name == 'megadepth':
+ elif dataset_name == "megadepth":
thr = 1e-4
else:
- raise ValueError(f'Unknown dataset: {dataset_name}')
+ raise ValueError(f"Unknown dataset: {dataset_name}")
return thr
# --- VISUALIZATION --- #
+
def make_matching_figure(
- img0, img1, mkpts0, mkpts1, color,
- kpts0=None, kpts1=None, text=[], dpi=75, path=None):
+ img0,
+ img1,
+ mkpts0,
+ mkpts1,
+ color,
+ kpts0=None,
+ kpts1=None,
+ text=[],
+ dpi=75,
+ path=None,
+):
# draw image pair
- assert mkpts0.shape[0] == mkpts1.shape[0], f'mkpts0: {mkpts0.shape[0]} v.s. mkpts1: {mkpts1.shape[0]}'
+ assert (
+ mkpts0.shape[0] == mkpts1.shape[0]
+ ), f"mkpts0: {mkpts0.shape[0]} v.s. mkpts1: {mkpts1.shape[0]}"
fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi)
axes[0].imshow(img0) # , cmap='gray')
axes[1].imshow(img1) # , cmap='gray')
- for i in range(2): # clear all frames
+ for i in range(2): # clear all frames
axes[i].get_yaxis().set_ticks([])
axes[i].get_xaxis().set_ticks([])
for spine in axes[i].spines.values():
spine.set_visible(False)
plt.tight_layout(pad=1)
-
+
if kpts0 is not None:
assert kpts1 is not None
- axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c='w', s=5)
- axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c='w', s=5)
+ axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c="w", s=5)
+ axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c="w", s=5)
# draw matches
if mkpts0.shape[0] != 0 and mkpts1.shape[0] != 0:
@@ -47,99 +59,112 @@ def make_matching_figure(
transFigure = fig.transFigure.inverted()
fkpts0 = transFigure.transform(axes[0].transData.transform(mkpts0))
fkpts1 = transFigure.transform(axes[1].transData.transform(mkpts1))
- fig.lines = [matplotlib.lines.Line2D((fkpts0[i, 0], fkpts1[i, 0]),
- (fkpts0[i, 1], fkpts1[i, 1]),
- transform=fig.transFigure, c=color[i], linewidth=2)
- for i in range(len(mkpts0))]
-
+ fig.lines = [
+ matplotlib.lines.Line2D(
+ (fkpts0[i, 0], fkpts1[i, 0]),
+ (fkpts0[i, 1], fkpts1[i, 1]),
+ transform=fig.transFigure,
+ c=color[i],
+ linewidth=2,
+ )
+ for i in range(len(mkpts0))
+ ]
+
axes[0].scatter(mkpts0[:, 0], mkpts0[:, 1], c=color[..., :3], s=4)
axes[1].scatter(mkpts1[:, 0], mkpts1[:, 1], c=color[..., :3], s=4)
# put txts
- txt_color = 'k' if img0[:100, :200].mean() > 200 else 'w'
+ txt_color = "k" if img0[:100, :200].mean() > 200 else "w"
fig.text(
- 0.01, 0.99, '\n'.join(text), transform=fig.axes[0].transAxes,
- fontsize=15, va='top', ha='left', color=txt_color)
+ 0.01,
+ 0.99,
+ "\n".join(text),
+ transform=fig.axes[0].transAxes,
+ fontsize=15,
+ va="top",
+ ha="left",
+ color=txt_color,
+ )
# save or return figure
if path:
- plt.savefig(str(path), bbox_inches='tight', pad_inches=0)
+ plt.savefig(str(path), bbox_inches="tight", pad_inches=0)
plt.close()
else:
return fig
-def _make_evaluation_figure(data, b_id, alpha='dynamic'):
- b_mask = data['m_bids'] == b_id
+def _make_evaluation_figure(data, b_id, alpha="dynamic"):
+ b_mask = data["m_bids"] == b_id
conf_thr = _compute_conf_thresh(data)
-
- img0 = (data['image0'][b_id][0].cpu().numpy() * 255).round().astype(np.int32)
- img1 = (data['image1'][b_id][0].cpu().numpy() * 255).round().astype(np.int32)
- kpts0 = data['mkpts0_f'][b_mask].cpu().numpy()
- kpts1 = data['mkpts1_f'][b_mask].cpu().numpy()
-
+
+ img0 = (data["image0"][b_id][0].cpu().numpy() * 255).round().astype(np.int32)
+ img1 = (data["image1"][b_id][0].cpu().numpy() * 255).round().astype(np.int32)
+ kpts0 = data["mkpts0_f"][b_mask].cpu().numpy()
+ kpts1 = data["mkpts1_f"][b_mask].cpu().numpy()
+
# for megadepth, we visualize matches on the resized image
- if 'scale0' in data:
- kpts0 = kpts0 / data['scale0'][b_id].cpu().numpy()[[1, 0]]
- kpts1 = kpts1 / data['scale1'][b_id].cpu().numpy()[[1, 0]]
+ if "scale0" in data:
+ kpts0 = kpts0 / data["scale0"][b_id].cpu().numpy()[[1, 0]]
+ kpts1 = kpts1 / data["scale1"][b_id].cpu().numpy()[[1, 0]]
- epi_errs = data['epi_errs'][b_mask].cpu().numpy()
+ epi_errs = data["epi_errs"][b_mask].cpu().numpy()
correct_mask = epi_errs < conf_thr
precision = np.mean(correct_mask) if len(correct_mask) > 0 else 0
n_correct = np.sum(correct_mask)
- n_gt_matches = int(data['conf_matrix_gt'][b_id].sum().cpu())
+ n_gt_matches = int(data["conf_matrix_gt"][b_id].sum().cpu())
recall = 0 if n_gt_matches == 0 else n_correct / (n_gt_matches)
# recall might be larger than 1, since the calculation of conf_matrix_gt
# uses groundtruth depths and camera poses, but epipolar distance is used here.
# matching info
- if alpha == 'dynamic':
+ if alpha == "dynamic":
alpha = dynamic_alpha(len(correct_mask))
color = error_colormap(epi_errs, conf_thr, alpha=alpha)
-
+
text = [
- f'#Matches {len(kpts0)}',
- f'Precision({conf_thr:.2e}) ({100 * precision:.1f}%): {n_correct}/{len(kpts0)}',
- f'Recall({conf_thr:.2e}) ({100 * recall:.1f}%): {n_correct}/{n_gt_matches}'
+ f"#Matches {len(kpts0)}",
+ f"Precision({conf_thr:.2e}) ({100 * precision:.1f}%): {n_correct}/{len(kpts0)}",
+ f"Recall({conf_thr:.2e}) ({100 * recall:.1f}%): {n_correct}/{n_gt_matches}",
]
-
+
# make the figure
- figure = make_matching_figure(img0, img1, kpts0, kpts1,
- color, text=text)
+ figure = make_matching_figure(img0, img1, kpts0, kpts1, color, text=text)
return figure
+
def _make_confidence_figure(data, b_id):
# TODO: Implement confidence figure
raise NotImplementedError()
-def make_matching_figures(data, config, mode='evaluation'):
- """ Make matching figures for a batch.
-
+def make_matching_figures(data, config, mode="evaluation"):
+ """Make matching figures for a batch.
+
Args:
data (Dict): a batch updated by PL_LoFTR.
config (Dict): matcher config
Returns:
figures (Dict[str, List[plt.figure]]
"""
- assert mode in ['evaluation', 'confidence'] # 'confidence'
+ assert mode in ["evaluation", "confidence"] # 'confidence'
figures = {mode: []}
- for b_id in range(data['image0'].size(0)):
- if mode == 'evaluation':
+ for b_id in range(data["image0"].size(0)):
+ if mode == "evaluation":
fig = _make_evaluation_figure(
- data, b_id,
- alpha=config.TRAINER.PLOT_MATCHES_ALPHA)
- elif mode == 'confidence':
+ data, b_id, alpha=config.TRAINER.PLOT_MATCHES_ALPHA
+ )
+ elif mode == "confidence":
fig = _make_confidence_figure(data, b_id)
else:
- raise ValueError(f'Unknown plot mode: {mode}')
+ raise ValueError(f"Unknown plot mode: {mode}")
figures[mode].append(fig)
return figures
-def dynamic_alpha(n_matches,
- milestones=[0, 300, 1000, 2000],
- alphas=[1.0, 0.8, 0.4, 0.2]):
+def dynamic_alpha(
+ n_matches, milestones=[0, 300, 1000, 2000], alphas=[1.0, 0.8, 0.4, 0.2]
+):
if n_matches == 0:
return 1.0
ranges = list(zip(alphas, alphas[1:] + [None]))
@@ -148,14 +173,18 @@ def dynamic_alpha(n_matches,
if _range[1] is None:
return _range[0]
return _range[1] + (milestones[loc + 1] - n_matches) / (
- milestones[loc + 1] - milestones[loc]) * (_range[0] - _range[1])
+ milestones[loc + 1] - milestones[loc]
+ ) * (_range[0] - _range[1])
def error_colormap(err, thr, alpha=1.0):
assert alpha <= 1.0 and alpha > 0, f"Invaid alpha value: {alpha}"
x = 1 - np.clip(err / (thr * 2), 0, 1)
return np.clip(
- np.stack([2-x*2, x*2, np.zeros_like(x), np.ones_like(x)*alpha], -1), 0, 1)
+ np.stack([2 - x * 2, x * 2, np.zeros_like(x), np.ones_like(x) * alpha], -1),
+ 0,
+ 1,
+ )
np.random.seed(1995)
@@ -163,7 +192,9 @@ color_map = np.arange(100)
np.random.shuffle(color_map)
-def draw_topics(data, img0, img1, saved_folder="viz_topics", show_n_topics=8, saved_name=None):
+def draw_topics(
+ data, img0, img1, saved_folder="viz_topics", show_n_topics=8, saved_name=None
+):
topic0, topic1 = data["topic_matrix"]["img0"], data["topic_matrix"]["img1"]
hw0_c, hw1_c = data["hw0_c"], data["hw1_c"]
@@ -188,27 +219,38 @@ def draw_topics(data, img0, img1, saved_folder="viz_topics", show_n_topics=8, sa
theta1 /= theta1.sum().float()
# top_topic0 = torch.argsort(theta0, descending=True)[:show_n_topics]
# top_topic1 = torch.argsort(theta1, descending=True)[:show_n_topics]
- top_topics = torch.argsort(theta0*theta1, descending=True)[:show_n_topics]
+ top_topics = torch.argsort(theta0 * theta1, descending=True)[:show_n_topics]
# print(sum_topic0, sum_topic1)
- topic0 = topic0[0].argmax(dim=-1, keepdim=True) #.float() / (n_topics - 1) #* 255 + 1 #
+ topic0 = topic0[0].argmax(
+ dim=-1, keepdim=True
+ ) # .float() / (n_topics - 1) #* 255 + 1 #
# topic0[~mask0_nonzero] = -1
- topic1 = topic1[0].argmax(dim=-1, keepdim=True) #.float() / (n_topics - 1) #* 255 + 1
+ topic1 = topic1[0].argmax(
+ dim=-1, keepdim=True
+ ) # .float() / (n_topics - 1) #* 255 + 1
# topic1[~mask1_nonzero] = -1
label_img0, label_img1 = torch.zeros_like(topic0) - 1, torch.zeros_like(topic1) - 1
for i, k in enumerate(top_topics):
label_img0[topic0 == k] = color_map[k]
label_img1[topic1 == k] = color_map[k]
-# print(hw0_c, scale0)
-# print(hw1_c, scale1)
+ # print(hw0_c, scale0)
+ # print(hw1_c, scale1)
# map_topic0 = F.fold(label_img0.unsqueeze(0), hw0_i, kernel_size=scale0, stride=scale0)
- map_topic0 = label_img0.float().view(hw0_c).cpu().numpy() #map_topic0.squeeze(0).squeeze(0).cpu().numpy()
- map_topic0 = cv2.resize(map_topic0, (int(hw0_c[1] * scale0[0]), int(hw0_c[0] * scale0[1])))
+ map_topic0 = (
+ label_img0.float().view(hw0_c).cpu().numpy()
+ ) # map_topic0.squeeze(0).squeeze(0).cpu().numpy()
+ map_topic0 = cv2.resize(
+ map_topic0, (int(hw0_c[1] * scale0[0]), int(hw0_c[0] * scale0[1]))
+ )
# map_topic1 = F.fold(label_img1.unsqueeze(0), hw1_i, kernel_size=scale1, stride=scale1)
- map_topic1 = label_img1.float().view(hw1_c).cpu().numpy() #map_topic1.squeeze(0).squeeze(0).cpu().numpy()
- map_topic1 = cv2.resize(map_topic1, (int(hw1_c[1] * scale1[0]), int(hw1_c[0] * scale1[1])))
-
+ map_topic1 = (
+ label_img1.float().view(hw1_c).cpu().numpy()
+ ) # map_topic1.squeeze(0).squeeze(0).cpu().numpy()
+ map_topic1 = cv2.resize(
+ map_topic1, (int(hw1_c[1] * scale1[0]), int(hw1_c[0] * scale1[1]))
+ )
# show image0
if saved_name is None:
@@ -219,28 +261,57 @@ def draw_topics(data, img0, img1, saved_folder="viz_topics", show_n_topics=8, sa
path_saved_img0 = os.path.join(saved_folder, "{}_0.png".format(saved_name))
plt.imshow(img0)
masked_map_topic0 = np.ma.masked_where(map_topic0 < 0, map_topic0)
- plt.imshow(masked_map_topic0, cmap=plt.cm.jet, vmin=0, vmax=n_topics-1, alpha=.3, interpolation='bilinear')
+ plt.imshow(
+ masked_map_topic0,
+ cmap=plt.cm.jet,
+ vmin=0,
+ vmax=n_topics - 1,
+ alpha=0.3,
+ interpolation="bilinear",
+ )
# plt.show()
- plt.axis('off')
- plt.savefig(path_saved_img0, bbox_inches='tight', pad_inches=0, dpi=250)
+ plt.axis("off")
+ plt.savefig(path_saved_img0, bbox_inches="tight", pad_inches=0, dpi=250)
plt.close()
path_saved_img1 = os.path.join(saved_folder, "{}_1.png".format(saved_name))
plt.imshow(img1)
masked_map_topic1 = np.ma.masked_where(map_topic1 < 0, map_topic1)
- plt.imshow(masked_map_topic1, cmap=plt.cm.jet, vmin=0, vmax=n_topics-1, alpha=.3, interpolation='bilinear')
- plt.axis('off')
- plt.savefig(path_saved_img1, bbox_inches='tight', pad_inches=0, dpi=250)
+ plt.imshow(
+ masked_map_topic1,
+ cmap=plt.cm.jet,
+ vmin=0,
+ vmax=n_topics - 1,
+ alpha=0.3,
+ interpolation="bilinear",
+ )
+ plt.axis("off")
+ plt.savefig(path_saved_img1, bbox_inches="tight", pad_inches=0, dpi=250)
plt.close()
-def draw_topicfm_demo(data, img0, img1, mkpts0, mkpts1, mcolor, text, show_n_topics=8,
- topic_alpha=0.3, margin=5, path=None, opencv_display=False, opencv_title=''):
+def draw_topicfm_demo(
+ data,
+ img0,
+ img1,
+ mkpts0,
+ mkpts1,
+ mcolor,
+ text,
+ show_n_topics=8,
+ topic_alpha=0.3,
+ margin=5,
+ path=None,
+ opencv_display=False,
+ opencv_title="",
+):
topic_map0, topic_map1 = draw_topics(data, img0, img1, show_n_topics=show_n_topics)
- mask_tm0, mask_tm1 = np.expand_dims(topic_map0 >= 0, axis=-1), np.expand_dims(topic_map1 >= 0, axis=-1)
+ mask_tm0, mask_tm1 = np.expand_dims(topic_map0 >= 0, axis=-1), np.expand_dims(
+ topic_map1 >= 0, axis=-1
+ )
- topic_cm0, topic_cm1 = cm.jet(topic_map0 / 99.), cm.jet(topic_map1 / 99.)
+ topic_cm0, topic_cm1 = cm.jet(topic_map0 / 99.0), cm.jet(topic_map1 / 99.0)
topic_cm0 = cv2.cvtColor(topic_cm0[..., :3].astype(np.float32), cv2.COLOR_RGB2BGR)
topic_cm1 = cv2.cvtColor(topic_cm1[..., :3].astype(np.float32), cv2.COLOR_RGB2BGR)
overlay0 = (mask_tm0 * topic_cm0 + (1 - mask_tm0) * img0).astype(np.float32)
@@ -249,7 +320,9 @@ def draw_topicfm_demo(data, img0, img1, mkpts0, mkpts1, mcolor, text, show_n_top
cv2.addWeighted(overlay0, topic_alpha, img0, 1 - topic_alpha, 0, overlay0)
cv2.addWeighted(overlay1, topic_alpha, img1, 1 - topic_alpha, 0, overlay1)
- overlay0, overlay1 = (overlay0 * 255).astype(np.uint8), (overlay1 * 255).astype(np.uint8)
+ overlay0, overlay1 = (overlay0 * 255).astype(np.uint8), (overlay1 * 255).astype(
+ np.uint8
+ )
h0, w0 = img0.shape[:2]
h1, w1 = img1.shape[:2]
@@ -258,19 +331,25 @@ def draw_topicfm_demo(data, img0, img1, mkpts0, mkpts1, mcolor, text, show_n_top
out_fig[:h0, :w0] = overlay0
if h0 >= h1:
start = (h0 - h1) // 2
- out_fig[start:(start+h1), (w0+margin):(w0+margin+w1)] = overlay1
+ out_fig[start : (start + h1), (w0 + margin) : (w0 + margin + w1)] = overlay1
else:
start = (h1 - h0) // 2
- out_fig[:h0, (w0+margin):(w0+margin+w1)] = overlay1[start:(start+h0)]
+ out_fig[:h0, (w0 + margin) : (w0 + margin + w1)] = overlay1[
+ start : (start + h0)
+ ]
step_h = h0 + margin * 2
- out_fig[step_h:step_h+h0, :w0] = (img0 * 255).astype(np.uint8)
+ out_fig[step_h : step_h + h0, :w0] = (img0 * 255).astype(np.uint8)
if h0 >= h1:
start = step_h + (h0 - h1) // 2
- out_fig[start:start+h1, (w0+margin):(w0+margin+w1)] = (img1 * 255).astype(np.uint8)
+ out_fig[start : start + h1, (w0 + margin) : (w0 + margin + w1)] = (
+ img1 * 255
+ ).astype(np.uint8)
else:
start = (h1 - h0) // 2
- out_fig[step_h:step_h+h0, (w0+margin):(w0+margin+w1)] = (img1[start:start+h0] * 255).astype(np.uint8)
+ out_fig[step_h : step_h + h0, (w0 + margin) : (w0 + margin + w1)] = (
+ img1[start : start + h0] * 255
+ ).astype(np.uint8)
# draw matching lines, this is inspried from https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/master/models/utils.py
mkpts0, mkpts1 = np.round(mkpts0).astype(int), np.round(mkpts1).astype(int)
@@ -278,24 +357,53 @@ def draw_topicfm_demo(data, img0, img1, mkpts0, mkpts1, mcolor, text, show_n_top
for (x0, y0), (x1, y1), c in zip(mkpts0, mkpts1, mcolor):
c = c.tolist()
- cv2.line(out_fig, (x0, y0+step_h), (x1+margin+w0, y1+step_h+(h0-h1)//2),
- color=c, thickness=1, lineType=cv2.LINE_AA)
+ cv2.line(
+ out_fig,
+ (x0, y0 + step_h),
+ (x1 + margin + w0, y1 + step_h + (h0 - h1) // 2),
+ color=c,
+ thickness=1,
+ lineType=cv2.LINE_AA,
+ )
# display line end-points as circles
- cv2.circle(out_fig, (x0, y0+step_h), 2, c, -1, lineType=cv2.LINE_AA)
- cv2.circle(out_fig, (x1+margin+w0, y1+step_h+(h0-h1)//2), 2, c, -1, lineType=cv2.LINE_AA)
+ cv2.circle(out_fig, (x0, y0 + step_h), 2, c, -1, lineType=cv2.LINE_AA)
+ cv2.circle(
+ out_fig,
+ (x1 + margin + w0, y1 + step_h + (h0 - h1) // 2),
+ 2,
+ c,
+ -1,
+ lineType=cv2.LINE_AA,
+ )
# Scale factor for consistent visualization across scales.
- sc = min(h / 960., 2.0)
+ sc = min(h / 960.0, 2.0)
# Big text.
Ht = int(30 * sc) # text height
txt_color_fg = (255, 255, 255)
txt_color_bg = (0, 0, 0)
for i, t in enumerate(text):
- cv2.putText(out_fig, t, (int(8 * sc), Ht + step_h*i), cv2.FONT_HERSHEY_DUPLEX,
- 1.0 * sc, txt_color_bg, 2, cv2.LINE_AA)
- cv2.putText(out_fig, t, (int(8 * sc), Ht + step_h*i), cv2.FONT_HERSHEY_DUPLEX,
- 1.0 * sc, txt_color_fg, 1, cv2.LINE_AA)
+ cv2.putText(
+ out_fig,
+ t,
+ (int(8 * sc), Ht + step_h * i),
+ cv2.FONT_HERSHEY_DUPLEX,
+ 1.0 * sc,
+ txt_color_bg,
+ 2,
+ cv2.LINE_AA,
+ )
+ cv2.putText(
+ out_fig,
+ t,
+ (int(8 * sc), Ht + step_h * i),
+ cv2.FONT_HERSHEY_DUPLEX,
+ 1.0 * sc,
+ txt_color_fg,
+ 1,
+ cv2.LINE_AA,
+ )
if path is not None:
cv2.imwrite(str(path), out_fig)
@@ -305,9 +413,3 @@ def draw_topicfm_demo(data, img0, img1, mkpts0, mkpts1, mcolor, text, show_n_top
cv2.waitKey(1)
return out_fig
-
-
-
-
-
-
diff --git a/imcui/third_party/XoFTR/src/utils/profiler.py b/third_party/TopicFM/src/utils/profiler.py
similarity index 88%
rename from imcui/third_party/XoFTR/src/utils/profiler.py
rename to third_party/TopicFM/src/utils/profiler.py
index 6d21ed79fb506ef09c75483355402c48a195aaa9..0275ea34e3eb9cceb4ed809bebeda209749f5bc5 100644
--- a/imcui/third_party/XoFTR/src/utils/profiler.py
+++ b/third_party/TopicFM/src/utils/profiler.py
@@ -7,7 +7,7 @@ from pytorch_lightning.utilities import rank_zero_only
class InferenceProfiler(SimpleProfiler):
"""
This profiler records duration of actions with cuda.synchronize()
- Use this in test time.
+ Use this in test time.
"""
def __init__(self):
@@ -28,12 +28,13 @@ class InferenceProfiler(SimpleProfiler):
def build_profiler(name):
- if name == 'inference':
+ if name == "inference":
return InferenceProfiler()
- elif name == 'pytorch':
+ elif name == "pytorch":
from pytorch_lightning.profiler import PyTorchProfiler
+
return PyTorchProfiler(use_cuda=True, profile_memory=True, row_limit=100)
elif name is None:
return PassThroughProfiler()
else:
- raise ValueError(f'Invalid profiler: {name}')
+ raise ValueError(f"Invalid profiler: {name}")
diff --git a/imcui/third_party/TopicFM/test.py b/third_party/TopicFM/test.py
similarity index 56%
rename from imcui/third_party/TopicFM/test.py
rename to third_party/TopicFM/test.py
index aeb451cde3674b70b0d2e02f37ff1fd391004d30..7b941ea4f6529c2206d527be85a23523dcf0e148 100644
--- a/imcui/third_party/TopicFM/test.py
+++ b/third_party/TopicFM/test.py
@@ -13,29 +13,43 @@ from src.lightning_trainer.trainer import PL_Trainer
def parse_args():
# init a costum parser which will be added into pl.Trainer parser
# check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags
- parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter
+ )
+ parser.add_argument("data_cfg_path", type=str, help="data config path")
+ parser.add_argument("main_cfg_path", type=str, help="main config path")
parser.add_argument(
- 'data_cfg_path', type=str, help='data config path')
+ "--ckpt_path",
+ type=str,
+ default="weights/indoor_ds.ckpt",
+ help="path to the checkpoint",
+ )
parser.add_argument(
- 'main_cfg_path', type=str, help='main config path')
+ "--dump_dir",
+ type=str,
+ default=None,
+ help="if set, the matching results will be dump to dump_dir",
+ )
parser.add_argument(
- '--ckpt_path', type=str, default="weights/indoor_ds.ckpt", help='path to the checkpoint')
+ "--profiler_name",
+ type=str,
+ default=None,
+ help="options: [inference, pytorch], or leave it unset",
+ )
+ parser.add_argument("--batch_size", type=int, default=1, help="batch_size per gpu")
+ parser.add_argument("--num_workers", type=int, default=2)
parser.add_argument(
- '--dump_dir', type=str, default=None, help="if set, the matching results will be dump to dump_dir")
- parser.add_argument(
- '--profiler_name', type=str, default=None, help='options: [inference, pytorch], or leave it unset')
- parser.add_argument(
- '--batch_size', type=int, default=1, help='batch_size per gpu')
- parser.add_argument(
- '--num_workers', type=int, default=2)
- parser.add_argument(
- '--thr', type=float, default=None, help='modify the coarse-level matching threshold.')
+ "--thr",
+ type=float,
+ default=None,
+ help="modify the coarse-level matching threshold.",
+ )
parser = pl.Trainer.add_argparse_args(parser)
return parser.parse_args()
-if __name__ == '__main__':
+if __name__ == "__main__":
# parse arguments
args = parse_args()
pprint.pprint(vars(args))
@@ -54,7 +68,12 @@ if __name__ == '__main__':
# lightning module
profiler = build_profiler(args.profiler_name)
- model = PL_Trainer(config, pretrained_ckpt=args.ckpt_path, profiler=profiler, dump_dir=args.dump_dir)
+ model = PL_Trainer(
+ config,
+ pretrained_ckpt=args.ckpt_path,
+ profiler=profiler,
+ dump_dir=args.dump_dir,
+ )
loguru_logger.info(f"Model-lightning initialized!")
# lightning data
@@ -62,7 +81,9 @@ if __name__ == '__main__':
loguru_logger.info(f"DataModule initialized!")
# lightning trainer
- trainer = pl.Trainer.from_argparse_args(args, replace_sampler_ddp=False, logger=False)
+ trainer = pl.Trainer.from_argparse_args(
+ args, replace_sampler_ddp=False, logger=False
+ )
loguru_logger.info(f"Start testing!")
trainer.test(model, datamodule=data_module, verbose=False)
diff --git a/imcui/third_party/TopicFM/train.py b/third_party/TopicFM/train.py
similarity index 61%
rename from imcui/third_party/TopicFM/train.py
rename to third_party/TopicFM/train.py
index a552c23718b81ddcb282cedbfe3ceb45e50b3f29..9188b80a3fb407f4871b8147a2c90fa382380e25 100644
--- a/imcui/third_party/TopicFM/train.py
+++ b/third_party/TopicFM/train.py
@@ -23,32 +23,43 @@ loguru_logger = get_rank_zero_only_logger(loguru_logger)
def parse_args():
# init a costum parser which will be added into pl.Trainer parser
# check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags
- parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter
+ )
+ parser.add_argument("data_cfg_path", type=str, help="data config path")
+ parser.add_argument("main_cfg_path", type=str, help="main config path")
+ parser.add_argument("--exp_name", type=str, default="default_exp_name")
+ parser.add_argument("--batch_size", type=int, default=4, help="batch_size per gpu")
+ parser.add_argument("--num_workers", type=int, default=4)
parser.add_argument(
- 'data_cfg_path', type=str, help='data config path')
+ "--pin_memory",
+ type=lambda x: bool(strtobool(x)),
+ nargs="?",
+ default=True,
+ help="whether loading data to pinned memory or not",
+ )
parser.add_argument(
- 'main_cfg_path', type=str, help='main config path')
+ "--ckpt_path",
+ type=str,
+ default=None,
+ help="pretrained checkpoint path, helpful for using a pre-trained coarse-only LoFTR",
+ )
parser.add_argument(
- '--exp_name', type=str, default='default_exp_name')
+ "--disable_ckpt",
+ action="store_true",
+ help="disable checkpoint saving (useful for debugging).",
+ )
parser.add_argument(
- '--batch_size', type=int, default=4, help='batch_size per gpu')
+ "--profiler_name",
+ type=str,
+ default=None,
+ help="options: [inference, pytorch], or leave it unset",
+ )
parser.add_argument(
- '--num_workers', type=int, default=4)
- parser.add_argument(
- '--pin_memory', type=lambda x: bool(strtobool(x)),
- nargs='?', default=True, help='whether loading data to pinned memory or not')
- parser.add_argument(
- '--ckpt_path', type=str, default=None,
- help='pretrained checkpoint path, helpful for using a pre-trained coarse-only LoFTR')
- parser.add_argument(
- '--disable_ckpt', action='store_true',
- help='disable checkpoint saving (useful for debugging).')
- parser.add_argument(
- '--profiler_name', type=str, default=None,
- help='options: [inference, pytorch], or leave it unset')
- parser.add_argument(
- '--parallel_load_data', action='store_true',
- help='load datasets in with multiple processes.')
+ "--parallel_load_data",
+ action="store_true",
+ help="load datasets in with multiple processes.",
+ )
parser = pl.Trainer.add_argparse_args(parser)
return parser.parse_args()
@@ -66,7 +77,7 @@ def main():
pl.seed_everything(config.TRAINER.SEED) # reproducibility
# TODO: Use different seeds for each dataloader workers
# This is needed for data augmentation
-
+
# scale lr and warmup-step automatically
args.gpus = _n_gpus = setup_gpus(args.gpus)
config.TRAINER.WORLD_SIZE = _n_gpus * args.num_nodes
@@ -75,49 +86,59 @@ def main():
config.TRAINER.SCALING = _scaling
config.TRAINER.TRUE_LR = config.TRAINER.CANONICAL_LR * _scaling
config.TRAINER.WARMUP_STEP = math.floor(config.TRAINER.WARMUP_STEP / _scaling)
-
+
# lightning module
profiler = build_profiler(args.profiler_name)
model = PL_Trainer(config, pretrained_ckpt=args.ckpt_path, profiler=profiler)
loguru_logger.info(f"Model LightningModule initialized!")
-
+
# lightning data
data_module = MultiSceneDataModule(args, config)
loguru_logger.info(f"Model DataModule initialized!")
-
+
# TensorBoard Logger
- logger = TensorBoardLogger(save_dir='logs/tb_logs', name=args.exp_name, default_hp_metric=False)
- ckpt_dir = Path(logger.log_dir) / 'checkpoints'
-
+ logger = TensorBoardLogger(
+ save_dir="logs/tb_logs", name=args.exp_name, default_hp_metric=False
+ )
+ ckpt_dir = Path(logger.log_dir) / "checkpoints"
+
# Callbacks
# TODO: update ModelCheckpoint to monitor multiple metrics
- ckpt_callback = ModelCheckpoint(monitor='auc@10', verbose=True, save_top_k=5, mode='max',
- save_last=True,
- dirpath=str(ckpt_dir),
- filename='{epoch}-{auc@5:.3f}-{auc@10:.3f}-{auc@20:.3f}')
- lr_monitor = LearningRateMonitor(logging_interval='step')
+ ckpt_callback = ModelCheckpoint(
+ monitor="auc@10",
+ verbose=True,
+ save_top_k=5,
+ mode="max",
+ save_last=True,
+ dirpath=str(ckpt_dir),
+ filename="{epoch}-{auc@5:.3f}-{auc@10:.3f}-{auc@20:.3f}",
+ )
+ lr_monitor = LearningRateMonitor(logging_interval="step")
callbacks = [lr_monitor]
if not args.disable_ckpt:
callbacks.append(ckpt_callback)
-
+
# Lightning Trainer
trainer = pl.Trainer.from_argparse_args(
args,
- plugins=DDPPlugin(find_unused_parameters=False,
- num_nodes=args.num_nodes,
- sync_batchnorm=config.TRAINER.WORLD_SIZE > 0),
+ plugins=DDPPlugin(
+ find_unused_parameters=False,
+ num_nodes=args.num_nodes,
+ sync_batchnorm=config.TRAINER.WORLD_SIZE > 0,
+ ),
gradient_clip_val=config.TRAINER.GRADIENT_CLIPPING,
callbacks=callbacks,
logger=logger,
sync_batchnorm=config.TRAINER.WORLD_SIZE > 0,
replace_sampler_ddp=False, # use custom sampler
reload_dataloaders_every_epoch=False, # avoid repeated samples!
- weights_summary='full',
- profiler=profiler)
+ weights_summary="full",
+ profiler=profiler,
+ )
loguru_logger.info(f"Trainer initialized!")
loguru_logger.info(f"Start training!")
trainer.fit(model, datamodule=data_module)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/third_party/TopicFM/visualization.py b/third_party/TopicFM/visualization.py
new file mode 100644
index 0000000000000000000000000000000000000000..73ec7dd74e21ac72204484cf8d4f3c6fd56a72a2
--- /dev/null
+++ b/third_party/TopicFM/visualization.py
@@ -0,0 +1,153 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+import os, glob, cv2
+import argparse
+from argparse import Namespace
+import yaml
+from tqdm import tqdm
+import torch
+from torch.utils.data import Dataset, DataLoader, SequentialSampler
+
+from src.datasets.custom_dataloader import TestDataLoader
+from src.utils.dataset import read_img_gray
+from configs.data.base import cfg as data_cfg
+import viz
+
+
+def get_model_config(method_name, dataset_name, root_dir="viz"):
+ config_file = f"{root_dir}/configs/{method_name}.yml"
+ with open(config_file, "r") as f:
+ model_conf = yaml.load(f, Loader=yaml.FullLoader)[dataset_name]
+ return model_conf
+
+
+class DemoDataset(Dataset):
+ def __init__(self, dataset_dir, img_file=None, resize=0, down_factor=16):
+ self.dataset_dir = dataset_dir
+ if img_file is None:
+ self.list_img_files = glob.glob(os.path.join(dataset_dir, "*.*"))
+ self.list_img_files.sort()
+ else:
+ with open(img_file) as f:
+ self.list_img_files = [
+ os.path.join(dataset_dir, img_file.strip())
+ for img_file in f.readlines()
+ ]
+ self.resize = resize
+ self.down_factor = down_factor
+
+ def __len__(self):
+ return len(self.list_img_files)
+
+ def __getitem__(self, idx):
+ img_path = self.list_img_files[
+ idx
+ ] # os.path.join(self.dataset_dir, self.list_img_files[idx])
+ img, scale = read_img_gray(
+ img_path, resize=self.resize, down_factor=self.down_factor
+ )
+ return {"img": img, "id": idx, "img_path": img_path}
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description="Visualize matches")
+ parser.add_argument("--gpu", "-gpu", type=str, default="0")
+ parser.add_argument("--method", type=str, default=None)
+ parser.add_argument("--dataset_dir", type=str, default="data/aachen-day-night")
+ parser.add_argument("--pair_dir", type=str, default=None)
+ parser.add_argument(
+ "--dataset_name",
+ type=str,
+ choices=["megadepth", "scannet", "aachen_v1.1", "inloc"],
+ default="megadepth",
+ )
+ parser.add_argument("--measure_time", action="store_true")
+ parser.add_argument("--no_viz", action="store_true")
+ parser.add_argument("--compute_eval_metrics", action="store_true")
+ parser.add_argument("--run_demo", action="store_true")
+
+ args = parser.parse_args()
+
+ model_cfg = get_model_config(args.method, args.dataset_name)
+ class_name = model_cfg["class"]
+ model = viz.__dict__[class_name](model_cfg)
+ # all_args = Namespace(**vars(args), **model_cfg)
+ if not args.run_demo:
+ if args.dataset_name == "megadepth":
+ from configs.data.megadepth_test_1500 import cfg
+
+ data_cfg.merge_from_other_cfg(cfg)
+ elif args.dataset_name == "scannet":
+ from configs.data.scannet_test_1500 import cfg
+
+ data_cfg.merge_from_other_cfg(cfg)
+ elif args.dataset_name == "aachen_v1.1":
+ data_cfg.merge_from_list(
+ [
+ "DATASET.TEST_DATA_SOURCE",
+ "aachen_v1.1",
+ "DATASET.TEST_DATA_ROOT",
+ os.path.join(args.dataset_dir, "images/images_upright"),
+ "DATASET.TEST_LIST_PATH",
+ args.pair_dir,
+ "DATASET.TEST_IMGSIZE",
+ model_cfg["imsize"],
+ ]
+ )
+ elif args.dataset_name == "inloc":
+ data_cfg.merge_from_list(
+ [
+ "DATASET.TEST_DATA_SOURCE",
+ "inloc",
+ "DATASET.TEST_DATA_ROOT",
+ args.dataset_dir,
+ "DATASET.TEST_LIST_PATH",
+ args.pair_dir,
+ "DATASET.TEST_IMGSIZE",
+ model_cfg["imsize"],
+ ]
+ )
+
+ has_ground_truth = str(data_cfg.DATASET.TEST_DATA_SOURCE).lower() in [
+ "megadepth",
+ "scannet",
+ ]
+ dataloader = TestDataLoader(data_cfg)
+ with torch.no_grad():
+ for data_dict in tqdm(dataloader):
+ for k, v in data_dict.items():
+ if isinstance(v, torch.Tensor):
+ data_dict[k] = v.cuda() if torch.cuda.is_available() else v
+ img_root_dir = data_cfg.DATASET.TEST_DATA_ROOT
+ model.match_and_draw(
+ data_dict,
+ root_dir=img_root_dir,
+ ground_truth=has_ground_truth,
+ measure_time=args.measure_time,
+ viz_matches=(not args.no_viz),
+ )
+
+ if args.measure_time:
+ print(
+ "Running time for each image is {} miliseconds".format(
+ model.measure_time()
+ )
+ )
+ if args.compute_eval_metrics and has_ground_truth:
+ model.compute_eval_metrics()
+ else:
+ demo_dataset = DemoDataset(args.dataset_dir, img_file=args.pair_dir, resize=640)
+ sampler = SequentialSampler(demo_dataset)
+ dataloader = DataLoader(demo_dataset, batch_size=1, sampler=sampler)
+
+ writer = cv2.VideoWriter(
+ "topicfm_demo.mp4",
+ cv2.VideoWriter_fourcc(*"mp4v"),
+ 15,
+ (640 * 2 + 5, 480 * 2 + 10),
+ )
+
+ model.run_demo(
+ iter(dataloader), writer
+ ) # , output_dir="demo", no_display=True)
diff --git a/imcui/third_party/TopicFM/viz/__init__.py b/third_party/TopicFM/viz/__init__.py
similarity index 100%
rename from imcui/third_party/TopicFM/viz/__init__.py
rename to third_party/TopicFM/viz/__init__.py
diff --git a/imcui/third_party/XoFTR/src/__init__.py b/third_party/TopicFM/viz/configs/__init__.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/__init__.py
rename to third_party/TopicFM/viz/configs/__init__.py
diff --git a/imcui/third_party/TopicFM/viz/configs/loftr.yml b/third_party/TopicFM/viz/configs/loftr.yml
similarity index 100%
rename from imcui/third_party/TopicFM/viz/configs/loftr.yml
rename to third_party/TopicFM/viz/configs/loftr.yml
diff --git a/imcui/third_party/TopicFM/viz/configs/patch2pix.yml b/third_party/TopicFM/viz/configs/patch2pix.yml
similarity index 100%
rename from imcui/third_party/TopicFM/viz/configs/patch2pix.yml
rename to third_party/TopicFM/viz/configs/patch2pix.yml
diff --git a/imcui/third_party/TopicFM/viz/configs/topicfm.yml b/third_party/TopicFM/viz/configs/topicfm.yml
similarity index 100%
rename from imcui/third_party/TopicFM/viz/configs/topicfm.yml
rename to third_party/TopicFM/viz/configs/topicfm.yml
diff --git a/imcui/third_party/dad/dad/datasets/__init__.py b/third_party/TopicFM/viz/methods/__init__.py
similarity index 100%
rename from imcui/third_party/dad/dad/datasets/__init__.py
rename to third_party/TopicFM/viz/methods/__init__.py
diff --git a/imcui/third_party/TopicFM/viz/methods/base.py b/third_party/TopicFM/viz/methods/base.py
similarity index 74%
rename from imcui/third_party/TopicFM/viz/methods/base.py
rename to third_party/TopicFM/viz/methods/base.py
index 377e95134f339459bff3c5a0d30b3bfbc122d978..1dfc23efb5fb49bbf510364599489c9acf1df263 100644
--- a/imcui/third_party/TopicFM/viz/methods/base.py
+++ b/third_party/TopicFM/viz/methods/base.py
@@ -14,7 +14,9 @@ def flatten_list(x):
class Viz(metaclass=ABCMeta):
def __init__(self):
super().__init__()
- self.device = torch.device('cuda:{}'.format(0) if torch.cuda.is_available() else 'cpu')
+ self.device = torch.device(
+ "cuda:{}".format(0) if torch.cuda.is_available() else "cpu"
+ )
torch.set_grad_enabled(False)
# for evaluation metrics of MegaDepth and ScanNet
@@ -33,11 +35,15 @@ class Viz(metaclass=ABCMeta):
f"{self.name}",
f"#Matches: {len(mkpts0)}",
]
- if 'R_errs' in kwargs:
- text.append(f"$\\Delta$R:{kwargs['R_errs']:.2f}°, $\\Delta$t:{kwargs['t_errs']:.2f}°",)
+ if "R_errs" in kwargs:
+ text.append(
+ f"$\\Delta$R:{kwargs['R_errs']:.2f}°, $\\Delta$t:{kwargs['t_errs']:.2f}°",
+ )
if path:
- make_matching_figure(img0, img1, mkpts0, mkpts1, color, text=text, path=path, dpi=150)
+ make_matching_figure(
+ img0, img1, mkpts0, mkpts1, color, text=text, path=path, dpi=150
+ )
else:
return make_matching_figure(img0, img1, mkpts0, mkpts1, color, text=text)
@@ -47,11 +53,11 @@ class Viz(metaclass=ABCMeta):
def compute_eval_metrics(self, epi_err_thr=5e-4):
# metrics: dict of list, numpy
- _metrics = [o['metrics'] for o in self.eval_stats]
+ _metrics = [o["metrics"] for o in self.eval_stats]
metrics = {k: flatten_list([_me[k] for _me in _metrics]) for k in _metrics[0]}
val_metrics_4tb = aggregate_metrics(metrics, epi_err_thr)
- print('\n' + pprint.pformat(val_metrics_4tb))
+ print("\n" + pprint.pformat(val_metrics_4tb))
def measure_time(self):
if len(self.time_stats) == 0:
diff --git a/third_party/TopicFM/viz/methods/loftr.py b/third_party/TopicFM/viz/methods/loftr.py
new file mode 100644
index 0000000000000000000000000000000000000000..29046a2aa95596cbfe9656c3bda6dafcb1a55058
--- /dev/null
+++ b/third_party/TopicFM/viz/methods/loftr.py
@@ -0,0 +1,123 @@
+from argparse import Namespace
+import os
+import torch
+import cv2
+
+from .base import Viz
+from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors
+
+from third_party.loftr.src.loftr import LoFTR, default_cfg
+
+
+class VizLoFTR(Viz):
+ def __init__(self, args):
+ super().__init__()
+ if type(args) == dict:
+ args = Namespace(**args)
+
+ self.match_threshold = args.match_threshold
+
+ # Load model
+ conf = dict(default_cfg)
+ conf["match_coarse"]["thr"] = self.match_threshold
+ print(conf)
+ self.model = LoFTR(config=conf)
+ ckpt_dict = torch.load(args.ckpt)
+ self.model.load_state_dict(ckpt_dict["state_dict"])
+ self.model = self.model.eval().to(self.device)
+
+ # Name the method
+ # self.ckpt_name = args.ckpt.split('/')[-1].split('.')[0]
+ self.name = "LoFTR"
+
+ print(f"Initialize {self.name}")
+
+ def match_and_draw(
+ self,
+ data_dict,
+ root_dir=None,
+ ground_truth=False,
+ measure_time=False,
+ viz_matches=True,
+ ):
+ if measure_time:
+ torch.cuda.synchronize()
+ start = torch.cuda.Event(enable_timing=True)
+ end = torch.cuda.Event(enable_timing=True)
+ start.record()
+ self.model(data_dict)
+ if measure_time:
+ torch.cuda.synchronize()
+ end.record()
+ torch.cuda.synchronize()
+ self.time_stats.append(start.elapsed_time(end))
+
+ kpts0 = data_dict["mkpts0_f"].cpu().numpy()
+ kpts1 = data_dict["mkpts1_f"].cpu().numpy()
+
+ img_name0, img_name1 = list(zip(*data_dict["pair_names"]))[0]
+ img0 = cv2.imread(os.path.join(root_dir, img_name0))
+ img1 = cv2.imread(os.path.join(root_dir, img_name1))
+ if str(data_dict["dataset_name"][0]).lower() == "scannet":
+ img0 = cv2.resize(img0, (640, 480))
+ img1 = cv2.resize(img1, (640, 480))
+
+ if viz_matches:
+ saved_name = "_".join(
+ [
+ img_name0.split("/")[-1].split(".")[0],
+ img_name1.split("/")[-1].split(".")[0],
+ ]
+ )
+ folder_matches = os.path.join(root_dir, "{}_viz_matches".format(self.name))
+ if not os.path.exists(folder_matches):
+ os.makedirs(folder_matches)
+ path_to_save_matches = os.path.join(
+ folder_matches, "{}.png".format(saved_name)
+ )
+ if ground_truth:
+ compute_symmetrical_epipolar_errors(
+ data_dict
+ ) # compute epi_errs for each match
+ compute_pose_errors(
+ data_dict
+ ) # compute R_errs, t_errs, pose_errs for each pair
+ epi_errors = data_dict["epi_errs"].cpu().numpy()
+ R_errors, t_errors = data_dict["R_errs"][0], data_dict["t_errs"][0]
+
+ self.draw_matches(
+ kpts0,
+ kpts1,
+ img0,
+ img1,
+ epi_errors,
+ path=path_to_save_matches,
+ R_errs=R_errors,
+ t_errs=t_errors,
+ )
+
+ rel_pair_names = list(zip(*data_dict["pair_names"]))
+ bs = data_dict["image0"].size(0)
+ metrics = {
+ # to filter duplicate pairs caused by DistributedSampler
+ "identifiers": ["#".join(rel_pair_names[b]) for b in range(bs)],
+ "epi_errs": [
+ data_dict["epi_errs"][data_dict["m_bids"] == b].cpu().numpy()
+ for b in range(bs)
+ ],
+ "R_errs": data_dict["R_errs"],
+ "t_errs": data_dict["t_errs"],
+ "inliers": data_dict["inliers"],
+ }
+ self.eval_stats.append({"metrics": metrics})
+ else:
+ m_conf = 1 - data_dict["mconf"].cpu().numpy()
+ self.draw_matches(
+ kpts0,
+ kpts1,
+ img0,
+ img1,
+ m_conf,
+ path=path_to_save_matches,
+ conf_thr=0.4,
+ )
diff --git a/third_party/TopicFM/viz/methods/patch2pix.py b/third_party/TopicFM/viz/methods/patch2pix.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d2df36f35c5b06ea8d45980e0b6b91e7482c718
--- /dev/null
+++ b/third_party/TopicFM/viz/methods/patch2pix.py
@@ -0,0 +1,131 @@
+from argparse import Namespace
+import os, sys
+import torch
+import cv2
+from pathlib import Path
+
+from .base import Viz
+from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors
+
+patch2pix_path = Path(__file__).parent / "../../third_party/patch2pix"
+sys.path.append(str(patch2pix_path))
+from third_party.patch2pix.utils.eval.model_helper import load_model, estimate_matches
+
+
+class VizPatch2Pix(Viz):
+ def __init__(self, args):
+ super().__init__()
+
+ if type(args) == dict:
+ args = Namespace(**args)
+ self.imsize = args.imsize
+ self.match_threshold = args.match_threshold
+ self.ksize = args.ksize
+ self.model = load_model(args.ckpt, method="patch2pix")
+ self.name = "Patch2Pix"
+ print(f"Initialize {self.name} with image size {self.imsize}")
+
+ def match_and_draw(
+ self,
+ data_dict,
+ root_dir=None,
+ ground_truth=False,
+ measure_time=False,
+ viz_matches=True,
+ ):
+ img_name0, img_name1 = list(zip(*data_dict["pair_names"]))[0]
+ path_img0 = os.path.join(root_dir, img_name0)
+ path_img1 = os.path.join(root_dir, img_name1)
+ img0, img1 = cv2.imread(path_img0), cv2.imread(path_img1)
+ return_m_upscale = True
+ if str(data_dict["dataset_name"][0]).lower() == "scannet":
+ # self.imsize = 640
+ img0 = cv2.resize(img0, tuple(self.imsize)) # (640, 480))
+ img1 = cv2.resize(img1, tuple(self.imsize)) # (640, 480))
+ return_m_upscale = False
+ outputs = estimate_matches(
+ self.model,
+ path_img0,
+ path_img1,
+ ksize=self.ksize,
+ io_thres=self.match_threshold,
+ eval_type="fine",
+ imsize=self.imsize,
+ return_upscale=return_m_upscale,
+ measure_time=measure_time,
+ )
+ if measure_time:
+ self.time_stats.append(outputs[-1])
+ matches, mconf = outputs[0], outputs[1]
+ kpts0 = matches[:, :2]
+ kpts1 = matches[:, 2:4]
+
+ if viz_matches:
+ saved_name = "_".join(
+ [
+ img_name0.split("/")[-1].split(".")[0],
+ img_name1.split("/")[-1].split(".")[0],
+ ]
+ )
+ folder_matches = os.path.join(root_dir, "{}_viz_matches".format(self.name))
+ if not os.path.exists(folder_matches):
+ os.makedirs(folder_matches)
+ path_to_save_matches = os.path.join(
+ folder_matches, "{}.png".format(saved_name)
+ )
+
+ if ground_truth:
+ data_dict["mkpts0_f"] = (
+ torch.from_numpy(matches[:, :2]).float().to(self.device)
+ )
+ data_dict["mkpts1_f"] = (
+ torch.from_numpy(matches[:, 2:4]).float().to(self.device)
+ )
+ data_dict["m_bids"] = torch.zeros(
+ matches.shape[0], device=self.device, dtype=torch.float32
+ )
+ compute_symmetrical_epipolar_errors(
+ data_dict
+ ) # compute epi_errs for each match
+ compute_pose_errors(
+ data_dict
+ ) # compute R_errs, t_errs, pose_errs for each pair
+ epi_errors = data_dict["epi_errs"].cpu().numpy()
+ R_errors, t_errors = data_dict["R_errs"][0], data_dict["t_errs"][0]
+
+ self.draw_matches(
+ kpts0,
+ kpts1,
+ img0,
+ img1,
+ epi_errors,
+ path=path_to_save_matches,
+ R_errs=R_errors,
+ t_errs=t_errors,
+ )
+
+ rel_pair_names = list(zip(*data_dict["pair_names"]))
+ bs = data_dict["image0"].size(0)
+ metrics = {
+ # to filter duplicate pairs caused by DistributedSampler
+ "identifiers": ["#".join(rel_pair_names[b]) for b in range(bs)],
+ "epi_errs": [
+ data_dict["epi_errs"][data_dict["m_bids"] == b].cpu().numpy()
+ for b in range(bs)
+ ],
+ "R_errs": data_dict["R_errs"],
+ "t_errs": data_dict["t_errs"],
+ "inliers": data_dict["inliers"],
+ }
+ self.eval_stats.append({"metrics": metrics})
+ else:
+ m_conf = 1 - mconf
+ self.draw_matches(
+ kpts0,
+ kpts1,
+ img0,
+ img1,
+ m_conf,
+ path=path_to_save_matches,
+ conf_thr=0.4,
+ )
diff --git a/third_party/TopicFM/viz/methods/topicfm.py b/third_party/TopicFM/viz/methods/topicfm.py
new file mode 100644
index 0000000000000000000000000000000000000000..e066dc4e031d47b295c4c14db774643ba0a2f25c
--- /dev/null
+++ b/third_party/TopicFM/viz/methods/topicfm.py
@@ -0,0 +1,267 @@
+from argparse import Namespace
+import os
+import torch
+import cv2
+from time import time
+from pathlib import Path
+import matplotlib.cm as cm
+import numpy as np
+
+from src.models.topic_fm import TopicFM
+from src import get_model_cfg
+from .base import Viz
+from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors
+from src.utils.plotting import draw_topics, draw_topicfm_demo, error_colormap
+
+
+class VizTopicFM(Viz):
+ def __init__(self, args):
+ super().__init__()
+ if type(args) == dict:
+ args = Namespace(**args)
+
+ self.match_threshold = args.match_threshold
+ self.n_sampling_topics = args.n_sampling_topics
+ self.show_n_topics = args.show_n_topics
+
+ # Load model
+ conf = dict(get_model_cfg())
+ conf["match_coarse"]["thr"] = self.match_threshold
+ conf["coarse"]["n_samples"] = self.n_sampling_topics
+ print("model config: ", conf)
+ self.model = TopicFM(config=conf)
+ ckpt_dict = torch.load(args.ckpt)
+ self.model.load_state_dict(ckpt_dict["state_dict"])
+ self.model = self.model.eval().to(self.device)
+
+ # Name the method
+ # self.ckpt_name = args.ckpt.split('/')[-1].split('.')[0]
+ self.name = "TopicFM"
+
+ print(f"Initialize {self.name}")
+
+ def match_and_draw(
+ self,
+ data_dict,
+ root_dir=None,
+ ground_truth=False,
+ measure_time=False,
+ viz_matches=True,
+ ):
+ if measure_time:
+ torch.cuda.synchronize()
+ start = torch.cuda.Event(enable_timing=True)
+ end = torch.cuda.Event(enable_timing=True)
+ start.record()
+ self.model(data_dict)
+ if measure_time:
+ torch.cuda.synchronize()
+ end.record()
+ torch.cuda.synchronize()
+ self.time_stats.append(start.elapsed_time(end))
+
+ kpts0 = data_dict["mkpts0_f"].cpu().numpy()
+ kpts1 = data_dict["mkpts1_f"].cpu().numpy()
+
+ img_name0, img_name1 = list(zip(*data_dict["pair_names"]))[0]
+ img0 = cv2.imread(os.path.join(root_dir, img_name0))
+ img1 = cv2.imread(os.path.join(root_dir, img_name1))
+ if str(data_dict["dataset_name"][0]).lower() == "scannet":
+ img0 = cv2.resize(img0, (640, 480))
+ img1 = cv2.resize(img1, (640, 480))
+
+ if viz_matches:
+ saved_name = "_".join(
+ [
+ img_name0.split("/")[-1].split(".")[0],
+ img_name1.split("/")[-1].split(".")[0],
+ ]
+ )
+ folder_matches = os.path.join(root_dir, "{}_viz_matches".format(self.name))
+ if not os.path.exists(folder_matches):
+ os.makedirs(folder_matches)
+ path_to_save_matches = os.path.join(
+ folder_matches, "{}.png".format(saved_name)
+ )
+
+ if ground_truth:
+ compute_symmetrical_epipolar_errors(
+ data_dict
+ ) # compute epi_errs for each match
+ compute_pose_errors(
+ data_dict
+ ) # compute R_errs, t_errs, pose_errs for each pair
+ epi_errors = data_dict["epi_errs"].cpu().numpy()
+ R_errors, t_errors = data_dict["R_errs"][0], data_dict["t_errs"][0]
+
+ self.draw_matches(
+ kpts0,
+ kpts1,
+ img0,
+ img1,
+ epi_errors,
+ path=path_to_save_matches,
+ R_errs=R_errors,
+ t_errs=t_errors,
+ )
+
+ # compute evaluation metrics
+ rel_pair_names = list(zip(*data_dict["pair_names"]))
+ bs = data_dict["image0"].size(0)
+ metrics = {
+ # to filter duplicate pairs caused by DistributedSampler
+ "identifiers": ["#".join(rel_pair_names[b]) for b in range(bs)],
+ "epi_errs": [
+ data_dict["epi_errs"][data_dict["m_bids"] == b].cpu().numpy()
+ for b in range(bs)
+ ],
+ "R_errs": data_dict["R_errs"],
+ "t_errs": data_dict["t_errs"],
+ "inliers": data_dict["inliers"],
+ }
+ self.eval_stats.append({"metrics": metrics})
+ else:
+ m_conf = 1 - data_dict["mconf"].cpu().numpy()
+ self.draw_matches(
+ kpts0,
+ kpts1,
+ img0,
+ img1,
+ m_conf,
+ path=path_to_save_matches,
+ conf_thr=0.4,
+ )
+ if self.show_n_topics > 0:
+ folder_topics = os.path.join(
+ root_dir, "{}_viz_topics".format(self.name)
+ )
+ if not os.path.exists(folder_topics):
+ os.makedirs(folder_topics)
+ draw_topics(
+ data_dict,
+ img0,
+ img1,
+ saved_folder=folder_topics,
+ show_n_topics=self.show_n_topics,
+ saved_name=saved_name,
+ )
+
+ def run_demo(
+ self, dataloader, writer=None, output_dir=None, no_display=False, skip_frames=1
+ ):
+ data_dict = next(dataloader)
+
+ frame_id = 0
+ last_image_id = 0
+ img0 = (
+ np.array(cv2.imread(str(data_dict["img_path"][0])), dtype=np.float32) / 255
+ )
+ frame_tensor = data_dict["img"].to(self.device)
+ pair_data = {"image0": frame_tensor}
+ last_frame = cv2.resize(
+ img0, (frame_tensor.shape[-1], frame_tensor.shape[-2]), cv2.INTER_LINEAR
+ )
+
+ if output_dir is not None:
+ print("==> Will write outputs to {}".format(output_dir))
+ Path(output_dir).mkdir(exist_ok=True)
+
+ # Create a window to display the demo.
+ if not no_display:
+ window_name = "Topic-assisted Feature Matching"
+ cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+ cv2.resizeWindow(window_name, (640 * 2, 480 * 2))
+ else:
+ print("Skipping visualization, will not show a GUI.")
+
+ # Print the keyboard help menu.
+ print(
+ "==> Keyboard control:\n"
+ "\tn: select the current frame as the reference image (left)\n"
+ "\tq: quit"
+ )
+
+ # vis_range = [kwargs["bottom_k"], kwargs["top_k"]]
+
+ while True:
+ frame_id += 1
+ if frame_id == len(dataloader):
+ print("Finished demo_loftr.py")
+ break
+ data_dict = next(dataloader)
+ if frame_id % skip_frames != 0:
+ # print("Skipping frame.")
+ continue
+
+ stem0, stem1 = last_image_id, data_dict["id"][0].item() - 1
+ frame = (
+ np.array(cv2.imread(str(data_dict["img_path"][0])), dtype=np.float32)
+ / 255
+ )
+
+ frame_tensor = data_dict["img"].to(self.device)
+ frame = cv2.resize(
+ frame,
+ (frame_tensor.shape[-1], frame_tensor.shape[-2]),
+ interpolation=cv2.INTER_LINEAR,
+ )
+ pair_data = {**pair_data, "image1": frame_tensor}
+ self.model(pair_data)
+
+ total_n_matches = len(pair_data["mkpts0_f"])
+ mkpts0 = pair_data["mkpts0_f"].cpu().numpy() # [vis_range[0]:vis_range[1]]
+ mkpts1 = pair_data["mkpts1_f"].cpu().numpy() # [vis_range[0]:vis_range[1]]
+ mconf = pair_data["mconf"].cpu().numpy() # [vis_range[0]:vis_range[1]]
+
+ # Normalize confidence.
+ if len(mconf) > 0:
+ mconf = 1 - mconf
+
+ # alpha = 0
+ # color = cm.jet(mconf, alpha=alpha)
+ color = error_colormap(mconf, thr=0.4, alpha=0.1)
+
+ text = [
+ f"Topics",
+ "#Matches: {}".format(total_n_matches),
+ ]
+
+ out = draw_topicfm_demo(
+ pair_data,
+ last_frame,
+ frame,
+ mkpts0,
+ mkpts1,
+ color,
+ text,
+ show_n_topics=4,
+ path=None,
+ )
+
+ if not no_display:
+ if writer is not None:
+ writer.write(out)
+ cv2.imshow("TopicFM Matches", out)
+ key = chr(cv2.waitKey(10) & 0xFF)
+ if key == "q":
+ if writer is not None:
+ writer.release()
+ print("Exiting...")
+ break
+ elif key == "n":
+ pair_data["image0"] = frame_tensor
+ last_frame = frame
+ last_image_id = data_dict["id"][0].item() - 1
+ frame_id_left = frame_id
+
+ elif output_dir is not None:
+ stem = "matches_{:06}_{:06}".format(stem0, stem1)
+ out_file = str(Path(output_dir, stem + ".png"))
+ print("\nWriting image to {}".format(out_file))
+ cv2.imwrite(out_file, out)
+ else:
+ raise ValueError("output_dir is required when no display is given.")
+
+ cv2.destroyAllWindows()
+ if writer is not None:
+ writer.release()
diff --git a/third_party/XoFTR/LICENSE b/third_party/XoFTR/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7
--- /dev/null
+++ b/third_party/XoFTR/LICENSE
@@ -0,0 +1,202 @@
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
diff --git a/third_party/XoFTR/README.md b/third_party/XoFTR/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ec750d470d115f36d3d4a93fa5fa646e57e525e3
--- /dev/null
+++ b/third_party/XoFTR/README.md
@@ -0,0 +1,115 @@
+# XoFTR: Cross-modal Feature Matching Transformer
+### [Paper (arXiv)](https://arxiv.org/pdf/2404.09692) | [Paper (CVF)](https://openaccess.thecvf.com/content/CVPR2024W/IMW/papers/Tuzcuoglu_XoFTR_Cross-modal_Feature_Matching_Transformer_CVPRW_2024_paper.pdf)
+
+
+This is Pytorch implementation of XoFTR: Cross-modal Feature Matching Transformer [CVPR 2024 Image Matching Workshop](https://image-matching-workshop.github.io/) paper.
+
+XoFTR is a cross-modal cross-view method for local feature matching between thermal infrared (TIR) and visible images.
+
+
+
+
+
+
+## Colab demo
+To run XoFTR with custom image pairs without configuring your own GPU environment, you can use the Colab demo:
+[](https://colab.research.google.com/drive/1T495vybejujZjJlPY-sHm8YwV5Ss86AM?usp=sharing)
+
+## Installation
+```shell
+conda env create -f environment.yaml
+conda activate xoftr
+```
+Download links for
+ - [Pretrained models weights](https://drive.google.com/drive/folders/1RAI243OHuyZ4Weo1NiTy280bCE_82s4q?usp=drive_link): Two versions available, trained at 640 and 840 resolutions.
+ - [METU-VisTIR dataset](https://drive.google.com/file/d/1Sj_vxj-GXvDQIMSg-ZUJR0vHBLIeDrLg/view?usp=sharing)
+
+## METU-VisTIR Dataset
+
+
+
+
+
+
+This dataset includes thermal and visible images captured across six diverse scenes with ground-truth camera poses. Four of the scenes encompass images captured under both cloudy and sunny conditions, while the remaining two scenes exclusively feature cloudy conditions. Since the cameras are auto-focus, there may be result in slight imperfections in the ground truth camera parameters. For more information about the dataset, please refer to our [paper](https://arxiv.org/pdf/2404.09692).
+
+**License of the dataset:**
+
+The METU-VisTIR dataset is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
+### Data format
+The dataset is organized into folders according to scenarios. The organization format is as follows:
+```
+METU-VisTIR/
+├── index/
+│ ├── scene_info_test/
+│ │ ├── cloudy_cloudy_scene_1.npz # scene info with test pairs
+│ │ └── ...
+│ ├── scene_info_val/
+│ │ ├── cloudy_cloudy_scene_1.npz # scene info with val pairs
+│ │ └── ...
+│ └── val_test_list/
+│ ├── test_list.txt # test scenes list
+│ └── val_list.txt # val scenes list
+├── cloudy/ # cloudy scenes
+│ ├── scene_1/
+│ │ ├── thermal/
+│ │ │ └── images/ # thermal images
+│ │ └── visible/
+│ │ └── images/ # visible images
+│ └── ...
+└── sunny/ # sunny scenes
+ └── ...
+```
+
+cloudy_cloudy_scene_\*.npz and cloudy_sunny_scene_\*.npz files contain GT camera poses and image pairs
+
+## Runing XoFTR
+### Demo to match image pairs with XoFTR
+
+A demo notebook for XoFTR on a single pair of images is given in [notebooks/xoftr_demo.ipynb](notebooks/xoftr_demo.ipynb).
+
+
+### Reproduce the testing results for relative pose estimation
+You need to download METU-VisTIR dataset. After downloading, unzip the required files. Then, symlinks need to be created for the `data` folder.
+```shell
+unzip downloaded-file.zip
+
+# set up symlinks
+ln -s /path/to/METU_VisTIR/ /path/to/XoFTR/data/
+```
+
+```shell
+conda activate xoftr
+
+python test_relative_pose.py xoftr --ckpt weights/weights_xoftr_640.ckpt
+
+# with visualization
+python test_relative_pose.py xoftr --ckpt weights/weights_xoftr_640.ckpt --save_figs
+```
+
+The results and figures are saved to `results_relative_pose/`.
+
+
+
+## Training
+See [Training XoFTR](./docs/TRAINING.md) for more details.
+
+## Citation
+
+If you find this code useful for your research, please use the following BibTeX entry.
+
+```bibtex
+@inproceedings{tuzcuouglu2024xoftr,
+ title={XoFTR: Cross-modal Feature Matching Transformer},
+ author={Tuzcuo{\u{g}}lu, {\"O}nder and K{\"o}ksal, Aybora and Sofu, Bu{\u{g}}ra and Kalkan, Sinan and Alatan, A Aydin},
+ booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+ pages={4275--4286},
+ year={2024}
+}
+```
+## Acknowledgement
+This code is derived from [LoFTR](https://github.com/zju3dv/LoFTR). We are grateful to the authors for their contribution of the source code.
+
+
+
+
diff --git a/imcui/third_party/dust3r/croco/datasets/__init__.py b/third_party/XoFTR/configs/data/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/__init__.py
rename to third_party/XoFTR/configs/data/__init__.py
diff --git a/imcui/third_party/EfficientLoFTR/configs/data/base.py b/third_party/XoFTR/configs/data/base.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/configs/data/base.py
rename to third_party/XoFTR/configs/data/base.py
diff --git a/imcui/third_party/XoFTR/configs/data/megadepth_trainval_840.py b/third_party/XoFTR/configs/data/megadepth_trainval_840.py
similarity index 100%
rename from imcui/third_party/XoFTR/configs/data/megadepth_trainval_840.py
rename to third_party/XoFTR/configs/data/megadepth_trainval_840.py
diff --git a/imcui/third_party/XoFTR/configs/data/megadepth_vistir_trainval_640.py b/third_party/XoFTR/configs/data/megadepth_vistir_trainval_640.py
similarity index 100%
rename from imcui/third_party/XoFTR/configs/data/megadepth_vistir_trainval_640.py
rename to third_party/XoFTR/configs/data/megadepth_vistir_trainval_640.py
diff --git a/imcui/third_party/XoFTR/configs/data/pretrain.py b/third_party/XoFTR/configs/data/pretrain.py
similarity index 100%
rename from imcui/third_party/XoFTR/configs/data/pretrain.py
rename to third_party/XoFTR/configs/data/pretrain.py
diff --git a/imcui/third_party/XoFTR/configs/xoftr/outdoor/visible_thermal.py b/third_party/XoFTR/configs/xoftr/outdoor/visible_thermal.py
similarity index 100%
rename from imcui/third_party/XoFTR/configs/xoftr/outdoor/visible_thermal.py
rename to third_party/XoFTR/configs/xoftr/outdoor/visible_thermal.py
diff --git a/imcui/third_party/XoFTR/configs/xoftr/pretrain/pretrain.py b/third_party/XoFTR/configs/xoftr/pretrain/pretrain.py
similarity index 100%
rename from imcui/third_party/XoFTR/configs/xoftr/pretrain/pretrain.py
rename to third_party/XoFTR/configs/xoftr/pretrain/pretrain.py
diff --git a/third_party/XoFTR/data/megadepth/index/.gitignore b/third_party/XoFTR/data/megadepth/index/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/XoFTR/data/megadepth/index/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/XoFTR/data/megadepth/test/.gitignore b/third_party/XoFTR/data/megadepth/test/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/XoFTR/data/megadepth/test/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/XoFTR/data/megadepth/train/.gitignore b/third_party/XoFTR/data/megadepth/train/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32
--- /dev/null
+++ b/third_party/XoFTR/data/megadepth/train/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/third_party/XoFTR/docs/TRAINING.md b/third_party/XoFTR/docs/TRAINING.md
new file mode 100644
index 0000000000000000000000000000000000000000..a44e364567091da617e6cf9b176751ecbd8afd0e
--- /dev/null
+++ b/third_party/XoFTR/docs/TRAINING.md
@@ -0,0 +1,63 @@
+
+# Traininig XoFTR
+
+## Dataset setup
+Generally, two parts of data are needed for training XoFTR, the original dataset, i.e., MegaDepth and KAIST Multispectral Pedestrian Detection Benchmark dataset. For MegaDepth the offline generated dataset indices are also required. The dataset indices store scenes, image pairs, and other metadata within the dataset used for training. For the MegaDepth dataset, the relative poses between images used for training are directly cached in the indexing files.
+
+### Download datasets
+#### MegaDepth
+In the fine-tuning stage, we use depth maps, undistorted images, corresponding camera intrinsics and extrinsics provided in the [original MegaDepth dataset](https://www.cs.cornell.edu/projects/megadepth/).
+- Please download [MegaDepth undistorted images and processed depths](https://www.cs.cornell.edu/projects/megadepth/dataset/Megadepth_v1/MegaDepth_v1.tar.gz)
+ - The path of the download data will be referred to as `/path/to/megadepth`
+
+
+#### KAIST Multispectral Pedestrian Detection Benchmark dataset
+In the pre-training stage, we use LWIR and visible image pairs from [KAIST Multispectral Pedestrian Detection Benchmark](https://soonminhwang.github.io/rgbt-ped-detection/).
+
+- Please set up the KAIST Multispectral Pedestrian Detection Benchmark dataset following [the official guide](https://github.com/SoonminHwang/rgbt-ped-detection) or from [OneDrive link](https://onedrive.live.com/download?cid=1570430EADF56512&resid=1570430EADF56512%21109419&authkey=AJcMP-7Yp86PWoE)
+ - At the end, you should have the folder `kaist-cvpr15`, referred as `/path/to/kaist-cvpr15`
+
+### Download the dataset indices
+
+You can download the required dataset indices from the [following link](https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf).
+After downloading, unzip the required files.
+```shell
+unzip downloaded-file.zip
+
+# extract dataset indices
+tar xf train-data/megadepth_indices.tar
+```
+
+### Build the dataset symlinks
+
+We symlink the datasets to the `data` directory under the main XoFTR project directory.
+
+```shell
+# MegaDepth
+# -- # fine-tuning dataset
+ln -sv /path/to/megadepth/phoenix /path/to/XoFTR/data/megadepth/train
+# -- # dataset indices
+ln -s /path/to/megadepth_indices/* /path/to/XoFTR/data/megadepth/index
+
+# KAIST Multispectral Pedestrian Detection Benchmark dataset
+# -- # pre-training dataset
+ln -sv /path/to/kaist-cvpr15 /path/to/XoFTR/data
+```
+
+
+## Training
+We provide pre-training and fine-tuning scripts for the datasets. The results in the XoFTR paper can be reproduced with 2 RTX A5000 (24 GB) GPUs for pre-training and 8 A100 GPUs for fine-tuning. For a different setup, we scale the learning rate and its warm-up linearly, but the final evaluation results might vary due to the different batch size & learning rate used. Thus the reproduction of results in our paper is not guaranteed.
+
+
+### Pre-training
+``` shell
+scripts/reproduce_train/pretrain.sh
+```
+> NOTE: Originally, we used 2 GPUs with a batch size of 2. You can change the number of GPUs and batch size in the script as per your need.
+
+### Fine-tuning on MegaDepth
+In the script, the path for pre-trained weights is `pretrain_weights/epoch=8-.ckpt`. We used the weight of the 9th epoch from the pre-training stage (epoch numbers start from 0). You can change this ckpt path accordingly.
+``` shell
+scripts/reproduce_train/visible_thermal.sh
+```
+> NOTE: Originally, we used 8 GPUs with a batch size of 2. You can change the number of GPUs and batch size in the script as per your need.
\ No newline at end of file
diff --git a/imcui/third_party/XoFTR/environment.yaml b/third_party/XoFTR/environment.yaml
similarity index 100%
rename from imcui/third_party/XoFTR/environment.yaml
rename to third_party/XoFTR/environment.yaml
diff --git a/third_party/XoFTR/notebooks/xoftr_demo.ipynb b/third_party/XoFTR/notebooks/xoftr_demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..661969acb8d009679b59a46bda9aa1ab4a312fbe
--- /dev/null
+++ b/third_party/XoFTR/notebooks/xoftr_demo.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Demo notebook for XoFTR on a single pair of images\n",
+ "This notebook demonstrates the use of XoFTR with two different data input/output approaches"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run once\n",
+ "import os\n",
+ "os.chdir(\"..\")\n",
+ "import torch\n",
+ "import cv2\n",
+ "import numpy as np\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.cm as cm\n",
+ "from src.utils.plotting import make_matching_figure\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## First Approach\n",
+ "Using a data i/o wrapper"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from src.xoftr import XoFTR\n",
+ "from src.config.default import get_cfg_defaults\n",
+ "from src.utils.data_io import DataIOWrapper, lower_config\n",
+ "\n",
+ "# Get default configurations\n",
+ "config = get_cfg_defaults(inference=True)\n",
+ "config = lower_config(config)\n",
+ "\n",
+ "# Coarse level threshold\n",
+ "config['xoftr']['match_coarse']['thr'] = 0.3 # Default 0.3\n",
+ "\n",
+ "# Fine level threshold\n",
+ "config['xoftr']['fine']['thr'] = 0.1 # Default 0.1\n",
+ "\n",
+ "# It is posseble to get denser matches\n",
+ "# If True, xoftr returns all fine-level matches for each fine-level window (at 1/2 resolution)\n",
+ "config['xoftr']['fine']['denser'] = False # Default False\n",
+ "\n",
+ "# XoFTR model\n",
+ "matcher = XoFTR(config=config[\"xoftr\"])\n",
+ "\n",
+ "# The input image sizes for xoftr\n",
+ "# Note: The output matches and output images are in original image size\n",
+ "config['test']['img0_resize'] = 640 # resize the longer side, None for no resize\n",
+ "config['test']['img1_resize'] = 640 # resize the longer side, None for no resize\n",
+ "\n",
+ "# The path for weights\n",
+ "ckpt = \"weights/weights_xoftr_640.ckpt\"\n",
+ "\n",
+ "# Data I/O wrapper\n",
+ "matcher = DataIOWrapper(matcher, config=config[\"test\"], ckpt=ckpt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Paths for example images\n",
+ "img0_pth = \"assets/METU_VisTIR_samples/cloudy/scene_7/visible/images/IM_04525.jpg\"\n",
+ "img1_pth = \"assets/METU_VisTIR_samples/cloudy/scene_7/thermal/images/IM_01139.jpg\"\n",
+ "\n",
+ "# Load and match images\n",
+ "# Note: images are converted to grayscale before matching\n",
+ "output_data = matcher.from_paths(img0_pth, img1_pth, read_color=True)\n",
+ "\n",
+ "# Matched keypoints\n",
+ "mkpts0 = output_data['mkpts0']\n",
+ "mkpts1 = output_data['mkpts1']\n",
+ "\n",
+ "# Confidence values for fine-level matching\n",
+ "mconf = output_data['mconf']\n",
+ "\n",
+ "# Original images BGR or GRAY\n",
+ "img0 = output_data['img0']\n",
+ "img1 = output_data['img1']\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Mask outliers using RANSAC (Homography or Fundamental Matrix)\n",
+ "\n",
+ "inlier_method = 'F' # F: Fundamental Matrix, H: Homography \n",
+ "\n",
+ "# RANSAC types: https://opencv.org/blog/evaluating-opencvs-new-ransacs/\n",
+ "\n",
+ "if inlier_method == 'F':\n",
+ " F, inlier_mask = cv2.findFundamentalMat(mkpts0, mkpts1, cv2.USAC_MAGSAC, ransacReprojThreshold=1, maxIters=10000, confidence=0.9999)\n",
+ "elif inlier_method == 'H':\n",
+ " H_pred, inlier_mask = cv2.findHomography(mkpts0, mkpts1, cv2.USAC_MAGSAC, ransacReprojThreshold=1, maxIters=10000, confidence=0.9999)\n",
+ "\n",
+ "inlier_mask = inlier_mask.ravel() > 0\n",
+ "mkpts0 = mkpts0[inlier_mask]\n",
+ "mkpts1 = mkpts1[inlier_mask]\n",
+ "mconf = mconf[inlier_mask]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Draw\n",
+ "color = cm.jet(mconf)\n",
+ "text = [\n",
+ " 'XoFTR',\n",
+ " 'Matches: {}'.format(len(mconf)),\n",
+ "]\n",
+ "if len(img0.shape) == 3:\n",
+ " _img0 = cv2.cvtColor(img0, cv2.COLOR_BGR2RGB)\n",
+ "else:\n",
+ " _img0 = img0\n",
+ "if len(img1.shape) == 3:\n",
+ " _img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)\n",
+ "else:\n",
+ " _img1 = img1\n",
+ "fig_org = make_matching_figure(_img0, _img1, np.zeros(0), np.zeros(0), np.zeros(0), text=[\"Original\"], dpi=125)\n",
+ "fig_match = make_matching_figure(_img0, _img1, mkpts0, mkpts1, color, text=text, dpi=125)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Second Approach\n",
+ "Process inputs and outputs manually"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from src.xoftr import XoFTR\n",
+ "from src.config.default import get_cfg_defaults\n",
+ "from src.utils.misc import lower_config\n",
+ "from src.utils.data_io import DataIOWrapper\n",
+ "\n",
+ "# Get default configurations\n",
+ "config = get_cfg_defaults(inference=True)\n",
+ "config = lower_config(config)\n",
+ "\n",
+ "# Coarse level threshold\n",
+ "config['xoftr']['match_coarse']['thr'] = 0.3 # Default 0.3\n",
+ "\n",
+ "# Fine level threshold\n",
+ "config['xoftr']['fine']['thr'] = 0.1 # Default 0.1\n",
+ "\n",
+ "# It is posseble to get denser matches\n",
+ "# If True, xoftr returns all fine-level matches for each fine-level window (at 1/2 resolution)\n",
+ "config['xoftr']['fine']['denser'] = False # Default False\n",
+ "\n",
+ "# XoFTR model\n",
+ "matcher = XoFTR(config=config[\"xoftr\"])\n",
+ "\n",
+ "# The path for weights\n",
+ "ckpt = \"weights/weights_xoftr_640.ckpt\"\n",
+ "\n",
+ "# Load model\n",
+ "matcher.load_state_dict(torch.load(ckpt)['state_dict'],strict=True)\n",
+ "matcher = matcher.eval().cuda()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Image 0 shape: (2160, 3840, 3)\n",
+ "Image 1 shape: (512, 640, 3)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Paths for example images\n",
+ "img0_pth = \"assets/METU_VisTIR_samples/indoor/scene_8/visible/images/IM_02798.jpg\"\n",
+ "img1_pth = \"assets/METU_VisTIR_samples/indoor/scene_8/thermal/images/IM_00006.jpg\"\n",
+ "\n",
+ "# Read images\n",
+ "img0_raw = cv2.imread(img0_pth)\n",
+ "img1_raw = cv2.imread(img1_pth)\n",
+ "\n",
+ "print(\"Image 0 shape:\", img0_raw.shape)\n",
+ "print(\"Image 1 shape:\", img1_raw.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Change the sizes of the images\n",
+ "img0_size = (640, 360) # input size shuold be divisible by 8\n",
+ "img1_size = (640, 512) # input size shuold be divisible by 8\n",
+ "\n",
+ "# Or the sizes remains the same\n",
+ "# img0_size = (img0_raw.shape[1], img0_raw.shape[0]) \n",
+ "# img1_size = (img1_raw.shape[1], img1_raw.shape[0]) \n",
+ "\n",
+ "# Resize images\n",
+ "img0_raw = cv2.resize(img0_raw, (img0_size[0]//8*8, img0_size[1]//8*8)) \n",
+ "img1_raw = cv2.resize(img1_raw, (img1_size[0]//8*8, img1_size[1]//8*8)) \n",
+ "\n",
+ "# Convert images to gray and tensor\n",
+ "img0 = torch.from_numpy(cv2.cvtColor(img0_raw, cv2.COLOR_BGR2GRAY))[None][None].cuda() / 255.\n",
+ "img1 = torch.from_numpy(cv2.cvtColor(img1_raw, cv2.COLOR_BGR2GRAY))[None][None].cuda() / 255.\n",
+ "batch = {'image0': img0, 'image1': img1}\n",
+ "\n",
+ "# Inference with XoFTR and get prediction\n",
+ "with torch.no_grad():\n",
+ " matcher(batch)\n",
+ " mkpts0 = batch['mkpts0_f'].cpu().numpy()\n",
+ " mkpts1 = batch['mkpts1_f'].cpu().numpy()\n",
+ " mconf = batch['mconf_f'].cpu().numpy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Mask outliers using RANSAC (Homography or Fundamental Matrix)\n",
+ "\n",
+ "inlier_method = 'F' # F: Fundamental Matrix, H: Homography \n",
+ "\n",
+ "if inlier_method == 'F':\n",
+ " F, inlier_mask = cv2.findFundamentalMat(mkpts0, mkpts1, cv2.USAC_MAGSAC, ransacReprojThreshold=1, maxIters=10000, confidence=0.9999)\n",
+ "elif inlier_method == 'H':\n",
+ " H_pred, inlier_mask = cv2.findHomography(mkpts0, mkpts1, cv2.USAC_MAGSAC, ransacReprojThreshold=1, maxIters=10000, confidence=0.9999)\n",
+ "\n",
+ "inlier_mask = inlier_mask.ravel() > 0\n",
+ "mkpts0 = mkpts0[inlier_mask]\n",
+ "mkpts1 = mkpts1[inlier_mask]\n",
+ "mconf = mconf[inlier_mask]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAABNgAAAH4CAYAAACYIlDtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAABM5AAATOQGPwlYBAAEAAElEQVR4nOz9aZBlW3Yehn1nukPO81BVOdTwxm70QAwNNQGCgkCLJmDaMjzQP+SR4UmOsEwrZNOWREphmwqLVNgOkSJlMmDBNukg4UHhoC2FKRIggB7Q3XhDv37v1auqrMrMqqyc58w7nGH7x7nr3HXWXfvcrPcaDeBhfxE37r1n2HvttYez13fW2tszxhg4ODg4ODg4ODg4ODg4ODg4ODg4fCr4v98CODg4ODg4ODg4ODg4ODg4ODg4/GGGI9gcHBwcHBwcHBwcHBwcHBwcHBw+AxzB5uDg4ODg4ODg4ODg4ODg4ODg8BngCDYHBwcHBwcHBwcHBwcHBwcHB4fPAEewOTg4ODg4ODg4ODg4ODg4ODg4fAY4gs3BwcHBwcHBwcHBwcHBwcHBweEzwBFsDg4ODg4ODg4ODg4ODg4ODg4OnwGOYHNwcHBwcHBwcHBwcHBwcHBwcPgMcASbg4ODg4ODg4ODg4ODg4ODg4PDZ4Aj2BwcHBwcHBwcHBwcHBwcHBwcHD4DHMHm4ODg4ODg4ODg4ODg4ODg4ODwGRD+fgvg4ODg4ODg4ODw2bG0tAQAMMaUjqdpCt/34XlecSzLMnieB2NMcZz+UxpZlhXnPc9DlmXFOXmvMaa4NwiC4rdMQ8pA8oVhCGNMISedo/vDMESSJKX7fd8fkJP+U3myLEOSJAPnsyxDFEUYGxtDFEUAgEajgbGxsUIOAMW9aZoWcqRpOlAWqQ8JKhfpheuN8iLwdKieqCxUZp4O1xt9ut0uWq0WLi8vi2OUj5SBn6M8+H9Zf3QuTdOS3HSOZKX7tHajlZeX1dbG+DVcf7xO6B5qh7Z6McYgCILSMZJbysLLKNPQ/sv2QXLwuqa6pN9cdpkPLy8/Jtu8vCfLslJ/tKWltUMaN4IgKOXFP5ospEc6Ltsbz1uTgY7xOtXGDS1vngbVIV3Dx0B+vSa/lJdD68O8HrX8qE9JHVC+vK1K/cjfUvc0RtGHxiopL+lUjiFcbu1clT74eOr7vtrvuQwkG93DP1R3Wlu2gdqn7/uo1+sYGxtDEARI0xTtdhtZlhVjoS1d+XzTzmvtRNPRMIRhiCiKUKvVSmN2GIbF/3q9jlqtVhyvkpn/rnqey9/tdruoCxoHqV54G6BnHp0j3dI1sk3QM4HnxfshPY/5cWMMkiRBmqbIsgxhGJbGHLqWdEH5pmlaPLv5eG2rryAIEARBcW0URUU/5c8JKifX9TvvvPNq9fxKVzs4ODg4ODg4OPyBhCQjpHHKiQ5pyNPEmBu40rDlxIo04KqIJYI0TDTDm+SU8tGkmE+GuXGmpWMjVzhRlCRJQbZxI4PLAaA0MZfEGi+LrUz8vDTMuQFiM7A1UkTqjMpkM+aJsODyaSSBRp5QHqR/mT6Xg67jRBtP16YjjVCzGfr8OK8vMowk6STbgVY/tjZcda0k47R7OMlC90jY2qlGlGjpSHKMoBnoMl0C70dEXFA+8vqq9iqNfy0vSdJKY5qXmZeb1yvvx5qcGlFJ8mnlsfUFWWb+fxjRovUzKQ8AxHFcSk+Oe1x2TS7b/6oxUMoi+6ocu6WeJXkpIdsC0K9DTrTxccFGcmljhhw76V7f90tkTKPRQBiGyLKsINqGjUm29q7B1sa1+zkhSHrTxodh6ck2woldre7lNz376Jo0TQuCi8gtqR+NNK/Shawr+vC2wp/1NN6QHNR2JPlMMvKXYHK85c9tIuPoXpInTdPiuc5lk22gqu5tcASbg4ODg4ODg8PnAJp3Dzdy5IRRei1wooJDM1TlRN52XE7uuQxcDj4Bl0aPhpsYJbK8ciJtTP52vlarwRhTGBz0xp6XhzwrJEllI0GkzqRRqskq0+JeZPINvtSFJLzIcCBDVjPobPJIw5XrkBvHsk1pxrzUgTzG5agyZKTRKCGJGd7ubLLx9LTyVMki2zQ3HG+aTlW6NoJKy9sGWx/V8tTy4tfIdLU8tPz5t9ZPNHKcE7Q8fdl/q2SUcmi6shFw9G2rO+2c9p+3QXmfbB9EJHDCQcpuG3dkfWh9zqYX3m+kJ89NIPOTpJutfUjyhOS4SVukeykP7u1ELxdorLy+vobv+6jVakiSBGNjY+h2u6WXRNyDjstW1Weq/lfdQ+SRfBlAckjd2Dz6tLbMn1Py+cjvk/2J50n6k9fx85SO1pYl+W9M2TuO/ts86KWM9LzhL274PVI33ANZ0w1/FmrPMX79Z4Ej2BwcHBwcHBwcPiewETfSK6zK6LEZaDbySMuPT5A1Dx9Kp8o4txGGPByJJvUyXJHLwz/SO4ZP+vkbdJ4vEVVA2cNPM1TommEGKk305dt8TQ88T3mOdEgyU1lIBjJmqsJQNUNdthV5rSSDuFEkPZ9sslcZMVWkmzSGJPHA05d5SI8XrX3JvGyEw7D+JGXVQou1vKQc3DjU5NTIRZm3TT5uuEryVZJZkgSTxzhku5Fy0DlJInPPFBvJSPdxwm2Y51NVG5fX83FQ06G8TxIEmgwaOcDvl15V9F2lQ1ue2vkq0ozXlVYW2V+0e2R9S7KZhw7KurgJmaHJTmlLsqbRaBTniWgjDzbyWuIey51OB51Ox/pS4aYy3gQkLw/dpnziOEYYhgPkI0GO49pzlT/ntf4MlD3E6B66hhOPfAzlnodcN/LZYHv20z2c2OIELM+bP9cJsp1Jbzs+hnFdU5rSO53mDPwcLwdvU68SskxwBJuDg4ODg4ODw+cAcg0czVi2GWRVBiC/ThogNpJBM7p4Otq3JIE0MkK+yeb32+Tk1/AJPf1OkqSYnPOJtXa/JBZkulxOXg6t7FwGfr+c2EvZbJBlkwSE5qFSVd9SFnmt5mkkjRNu2Mnyavrlepbro0ldDkvTBlpDiJeDE0lcDpmPhmGkmSa/NER5Olpfld4tPH3Zfl4FcpyQsmnpkvGr6Ui2LXkfv4bXm6wPLX2bnjUdaHJXhTHytlqlT94+h7U3W1+zEWQyBE8bx6vGFU33NtJe3sP7rUa4yft5v+b5SbnkMc1bmteZlg8/p8lE7ScIAtRqNYyPjyNJErRarYLQSZKkyJ+eIfxeWlvThk/bvzTIcZHy5+M8EYFam7X1AWBwGQh5nu7neuB1LUkv+bzizxZOAPI2xddok89T2QeIKOPneHvkZJvneYjjuMibntu0ZhvlXaUb3u4kGSl1Iom3V4Ej2BwcHBwcHBwcPieoWidHM2Y5NKOfoBmuVQaHRhoMS4+Oc5kB+4Lp0gjX3kDbjG5p4GRZvvhyFEUD3jEcfMMIzfizlcWmI2m4SEhjuCodTRZe39w41YgVm46qSJOb3K+1AU5oUV1pXpbcINP0I70ZbeFU/F6+PlwVOcbLpP2vInOkXug6Xi5OaPM8tP+2fHlemo40QkcSPjbvRi4jP25L29YOpKw2HfH+bqsfGxljw7CXBlIWWsSd/msEGP9vG0u5rCSvHBPpGk5aaGGV2jiurUcnofUjm5yyjw8bt4AyYSnbMf3WxiMub1X/42OELBf/Ju9j8lIDgLGxMfi+j/Pz84KwkiQfL4tM/ybHPw3pxvsb30TEmPLSBEC5H3CvYG08lH2Vv5yR5ZT9nu6VnthSZklS2p5J2nGZvxz/5UsOSbrxDRB43XGvN03H5KnGxxN+LT33iWCTm+XIlzE3hSPYHBwcHBwcHBw+B+ATVM2g00Li+GRdkiPyOhuRRNfIc9wAfZW3wDYjghuXdB2f6Ms8pMHACSNOstAkmoeYaAYhL4/00pIGJdf7sLLK/1LfMqxVgudN9yRJUuzMBgwaw7ye5aLWw+SR10jvNtk+JNnJ65dfrxmNNuNfpqfpR6alETVa+5Dl4HlqHo5cBmnQyjw1zydOlti8DHlaVUa9dq2mU6kjmbam+2FtuYpU045rBjXQXz9Qu5fLL71u+bds47b2VzW+8DyHkVP8fi1tOi+PDRs7ONHAvWx4GbX+oRGLWpviZbCN91VjjnzGcJJH05kcs+U5DpsXowwF5HK2222cnp4Wa63R/XzdLyJSpB61PiPr19bGq/qkLCOvI1nPfD05TWdSPpv+bPUsz8v11rSxSSOlSCZNHtl3eb6+76PT6cDzvIKwo2s0Ao90wj3daFMDbazmJJoMayUilgg7/nzV5ipVHo3D4Ag2BwcHBwcHB4fPCTQDkZ+TkEalZnAPIz00smSYcab914w+jZDhJIY0euT1VUYlN9a4VwHlIQkczRChY9JLTJOZwn5sOtA8CGRooCyTTa9xHBdyaWFDcn0em0FtIwroWxqM/F7p8cPrk8sliQV+PZeBG0I2sstW7/w+6aUgy6fJLf9L76VhdSOhEVr8W3ozaeQLz08zjG1hjFL+m5AEwwiXm6QjxwgpnyQbbOnJOuf64eScNhZJPdvKIUkB2SaqyioJmar2YGuHcoyzlYkTRryMsgxSbpkW75ca2aelIcMseb1KPcv2qD0zpD5knvLZJsf9LMvQ6XTQ7XZxcnJSkDZyHOEy0m/Z5rT+rOmLy8zLawMnpuT4m2X5btakW56uVv+ynjUdau2f3yvJM97W+XORtw0+fnKyUqtbWXbarZRv6ME3MeAeY1zHdJy/zOFjJV/Xjd9D13FPN7qGyifv48Sf9Cq8KRzB5uDg4ODg4ODwOYFG/PBJPYd8Ay09kSg9zaDl56WhJEkSm4E5zFCnayTZYDOYhhmztrzozbZcT0wLL6H85fozkmgCBtf20kiZKkg9V11Dho8xBrVarWQoyfWFuEEMoDBCuYHE09bC1nj+vD60dqIZ6NJAtV2n5VNFZsn/UibtGgm+Bo9mPMsycuNT5qXp1Saf7Ev8Go1ss/UHm6x0Dy+TJpN2rqpvDSPAqmBrd5IAtpVNIzmkZxt5wNh0ocljI3xkvfBjMkxN5qV5ZBE0T1N5v9QXl4NIBLpWeonZ6k7TPSdf5L1VxJvMS8oqPY/5PXQf6YmPt1wWLbyTZE6SZGBjgCRJrISt9Ba1tTstzJLSs41DNvD+p7UruS6Y1LMss61fVtV71Zgt8+Wy0HXcG5Cuk8SX1CGAEglML4D4s53fS0QcXUNhspReHMdFfXOSj19D4zfVIfdo489Lkkl7jvN+9SpwBJuDg4ODg4ODw+cANjJAM/ptBrhmrGv52MgGPkmuIm2qDHCZtzSepQHHJ+c0Ief/bTqgxa9pkk4TcUpD3s+JNi4b1yk3NshbjRt48n45sdcMTqlzfq80Fm1pSePeZqRWGWu2eqLzmheIhGwHsixam7GBG8CkK+59IfXDjS1bnlIHVQYv5Sl33tMg617mOQy2fibzqCLBZNviv7W0eL5a+5DkhNZvpcw2eXm/1fLlsJHplIa8n+fFZa2C7Eeajqruk/3W1o+1/qsRL5rcXD5aR0obY24CbfyUpK5N5/xbS1M7roVSSz3ZvIJt4yf3pJL9n3TE+yu/hv7LlyNyXJFl08ivKh1zvWo64uut8et4+trLMhtsfVTrX/wZyHWrQe7KKXeO5n2U9Mc91OQzkYhQzSuZe6hR/QEo1kylnVf5empyDJd9gtKjdfvks5YT3Y5gc3BwcHBwcHD4I4qqibE0LuUkkq6h+7WJOE/HljefAEsjx5aXDfxeOekmY4iTJnzCz/PRDFq+MDZdQxN5aQjFcVzcz9eDkSSJnNjLMtp0ZyM+eD1oRpXUjeblJ9On/9zg0MLCbPlokMa3jSzjx0mPWjvQjD+t/VYZ9XLdN/m/ioDQjHj6ln1MIxi0+pbEj61vcfDjvL6qQj9tfVPqUuahpcXLwGWV+tFIsaq0pQxV6Wtk0zAyh9qWDPuWfaOqTMDgWlBcpqr8NTm5V51My/O8gdBWLoMmM7/O9/u7YNK1tr6lyavJbGsv8lp5TvOClmnKfGW/5h/Zr/gxGd5vk5/rnRNo1K/kc0WG5WuwPdeqoOmBE370ooe/8JGEkdQRP0Yya3nK/PkzTl5nW5aAjsnnHulNG6c4OUXliuO4JD/95/nbSDr6Jhn52mw8TW1pBE4Gktx0jHuXy+do1bPCBkewOTg4ODg4ODh8TqAZeXKCLtdzkRNJjZC5iZHGiS5bOJQ0vIblWWWs83Vc+DVhmE9v+eReGlL8Tbr0ViOQdxuf2JNRIg0OrkNuFHMjgRv9VWSVVmbumWUjUfg9ZNxwjwipX37vMNJAM7iryDypTymfjdTQDC1+LemUh29K40kjRrR2Jo14bZ0jIj+kDjiJYQsh4+XRZOF6sN2j9REb+SH7NoE8d3jZNEKjKk8Obcy4iUFq6+OSUJF9VUuD+qN2nLcROscJgCpihBMe0tPRRiza5JShdjbvV43442Mo5W2TndIh4oDGLQAq0SbrSMpRVTbZp7U2p/U33mboHlkeKZfWxqSMsu34fn+3S07M8PJRn5VjB6VLY8iw0PmbQCMaNZ3y8Eb5vJbfw54hMn+NjNPKVQVe59RHtLGMt9kwDEv9jp5HzWazaKf0jOY7wEoSTXqT8Rdh0tOQyhbHcbGeXZqmqNVqRZ6cZOUEpqZrvgHQq8ARbA4ODg4ODg4OnwNUGV/SA0qSAjxEg9/Hf9vS50YXfyMs7+dp3NRIkYY//aaJtgy34ZNvmadMlxsIQRCgVqshiqKS0ZVlWYmw45N4zQiS3jISNiNbllGelwaSnu4Ijo7+m2i31xFF78CY/wN8v9rDgcukGUzDIMOsKA9u/JBeZAgWv98mm9SjNO5le5LGNJeHy8GPyXKS3FxezTDV0udyy3qW5zXI+24KWb/aWlVSHkms2NqGzUtIk0GSQ58Fw/oJl5mPN8PGOp6e9k1EhxxXbP1T07PWNmTII4et30vyhZNtnCDiHjvaLqzD8uN5aoS79l/2HyKu5D3yBYYkzDQPM36eXyPHbE1vVZ5IWlp8jKrql7ayV+lyWFq8zJxskrrk4GOd9jyykXP8fmo3Wh+zjcM8LV43Wnuna2W/CoIA7Xa7KCff1ZNIYSK96LnLx3StTvmYw5+/cRwXeVxfX2NkZKRYFoJ0S2Sqpl9NdzeFI9gcHBwcHBwcHD4nkAQGn/xKjwyOMAyLN/aStNJ+V72N59fZiLZh6UjZ6T+F0HB5ZCgKL6NmVFMZaXLNJ+0UDsrTDoKg5BVBeXEDjxuJcl0aglwcmsuplVszarOwjuu1r8EzKUY2vwOk3d45H9vb/ztcXf2J3tV/FlG0jJGRf3VgkwPNcLsJgaSBk1HS6OR64CSFNKqlocav4XJw3XA9c2OMEw6ajjVPP8qP0uAhwdK44wSdRvra1k2ykYQ2okIz6Hn9aP3H9lvzxOL64WRNFdFjI+SG9WEOLX1OFMm8NGhl4+WTRG4URaX1ucjYl3lRW5DtVXoMaR5bGolh04dWv7Z7tT7C7+EeRUB/bSmtPjU5uQ40UlBeo+mBCCE+FvN1xGQ5NH3I35Qml1vzeON1Ln/ztbdINg7p/cS9rbSww6owc63+q9owv0eWVZaXP0ekvmSZZdoaEcbvkbJqbVH2D0DfyZvXCV3Dr6P7x8fHcXl5CQAl0s3zPNTr9cKzrdvtFoSYMfnmPfQc5uCe7LwO+QYIQRCg0+mUxr8kSVCr1UrPc/WZqyzPMAye+bTUnIODg4ODg4ODwx8YLC8vFxNE6aljCy8iYgDokwjGmBKhBJTXWONrpHDyhu6XIZVyQs8ntFIuPsHVZNYm+/w/n+zTTmTyejJams1mQc7UajU0Go3SDpyaV4zmGUTnpWEqDVHN2OSGHV+Lh+uLkDansfVf/78inrsPAKh/8n3M/71/B8n1Mlqtt3F29t/gNQsgxsTEEjyvbKjJ9XK4QcqNMUkWaQSZJAtkuJU0WLSwH7pOhubxe7n3IPc04W2O36ORBhphoIWuanJr6XKSjvTBN7aQJBaXg8KXpAELoOTVwXXF0+NhsLwueZ/Sykgyc4KJf3P9cLKJ64WTV7a+LQkPqQPKg7c5eUyrBxqvuN60hcplvWnneH1oxKPs31IeSb5IAoKHyEkvQL5IPJ3j7Ui2Md7+icTiY1G32x0goTiZwsvEdW2DTf+yrmTd2vLhslEd8vO0qySRTTKUj4dt8hcsGmzehiSvFp4qwWXmZZeegVr/0cYQ3r6azSaiKCrqj9oJgbypa7VaKT3+POfl5C8XNI9uGhe43n3fLzzJpPy8bHw8k+lr+pP9g0PWue/76HQ6AMrrsGVZhsvLy1J56T9/Ccjvkc8B+k3rMdZqteJcrVYrdELf0kOUyxkEAXZ2dgbKWgXnwebg4ODg4ODg8DlAlfGnTRzpOhnmx40jbhjK3RI5OcehkRqS4ODGis1QorQ46SPLqhnHkoCR11PeFEJCYSRkqGj6lPJoRquWF4ABDwQAuJj6MzhZ+O/BeCEmj/4uZo7/3kA+uYE3gk53Dd2Rr+Bk/c8h/vB+Pns/Bjr/4MfwHL8qJQRAcifwPLvXAycE+X9pyHHZbWlxUmyY4S51p5Fq3LjVDB5+jrdvgi28iuerGYq2tqgZ0Fx+rZyabmUa8l7ZTuW1st2T7qSBqJVF3ivTtMlfpRfpOcPv09qLTIeTh7y/aOQFb5+UvvRMk2ME76+a/nnZbF6PkniTkP2cy6J9ZPlJPukFK3XJ+wgnybV7uf4l4STrVp7T6kq7RuqRYAs95KSwDOej0HwiJsMwLNLm62QN65+2ts+JPSkX/dbKwoloSSJWjR/82Utlt42tXHbaFZMT5UQO8f5OxJDW9/iHv/CQ4wd5b2kvWig93pbks1YS3ZSu7bnBn730mz9zjemvHzg2NgZj+h7ljUajSCsIAsRxXOiItxW+lhv3OE6SpDhH8wMeksqPSdK46nlmgyPYHBwcHBwcHBw+p7ARAHLiLyfRWgiZ5oFWq9WK0AvKj761iTXlqRklUk4b8WEz9mxGo5YulZs8JigUhQw9zUiQeWokG7+3MFR8H2kwD5Ncwk8v0Rn/Keyt/++B3v2Hy38ZuKzDvz5Ea/LLiGffQDx7B9n8LMxCA1gCUEcZRwB+AGAcmDz9exi/+oc4Pf1zuLz8JZIY9fpfQ5rGJSNGGkpV0Mp3k3Lb0pLkWRU5BQyuJUUeiTYPFkkgaUY+/dZIimEy20BpSWNUCy2SBJqmM62d2Yxyfl72CxtBpelFu590o5EV8reNfNGMfH5e66NVepFEk/Qw4mlyz7ZhRrLs37Leb5KGTM9GqgEoGfTUVuTab1oZh+lAXke/bXWm1a3maSvLXkWyaWXmJBuvJ1n/RIDIdievt+Vna6vy2SPbCr/Xlod8EcV1wz23tXZEMhhj0O12S3VIBJFsy9zLTQPlK/se91rTPIXpXl4mWzlJZ+QJppVNtg9b3RN4myevTfIm4zrhBF4Yhuh2u6VlFsjzr91uF0tcUL/i69nxcYjLQqQblZHKQd6gkgB8FTiCzcHBwcHBwcHhcwg+QdYMGWmAcW8SThjYQtbSNC3CNDTjRxIDGhlWZdRwY0iWSRqaZJjZrtd0QvfQRJ2+beWwlYGgGYVJuIidtb+DuPljQJrCT58jm50FZgFMeEADQAIc/sJfBMYGVABcJfBfXsA/OwfgIxlfAWLkjmr/+fySdGcBo9/6LprN7+Lq6jeRJPeRJN9Akvw/kSRZKaxKloHXu9SRZqxqxpMklPgxjVDRPtJYs5FeMm2NkLCRAXSOvCK00CCpAy0t7RwP2+NhVZquuYHPy6ORU9yAl/lLIkirH1ke2/lh11bdJ/sxL4/0WpJeq1qZ+XUcPA8+VnFigaclyRUp67AySHLEJrfWT6p0opVHymkbF6v6IIXP8WO8H1WNi1oeWr+UerGRKbJ/8rZM1xFZwkP56dnD+7gMjbaRaJSHbD88/6ry87Jq3ktcp/I+ylfKxsvC9Si9JbmHG3lX83GE7pXkpww953Umw4W1MlA9SEKN7reF/NvIPQ7ZjjjRRfnwjQwoTR7Gy8Nn6T+FExM5p4VZd7v52qR8/VQuB1AOu5Ztjo7Hcax66Q+DI9gcHBwcHBwcHD4H4BNkORGn3zQRlsYAP07/6RhNRDXjmU9geX6S6OCTVi2kT8JG0nA5pRFdZTTy+7mMPGRnGPnA5S8ZnvAR126hW7uLbv3L6DZ+DPHca0jmFoCZZk6cjQIYDZA11wYTjgEcdBA83Yd/fQk/68B4IdL6HNLmEjJMI6tP59d2FbmS696vDNPT/xBJkuD6+hrn514RXlRlsA8zkujbRg7cRO/8Po0E494f0nDTCCW6BxhcnNoWrsrX4JFkgdTDMEJKa3vce4LaFl1HHiAyDf5b67Navrbj3ADnxzRSgOtDC3/k5IZGTGiECYVkcfKEzvF7tPuroOUvx4QqEoIb9pLw0fKyjU+26226keeGjXcakSq9h7X7eH+RYX78OkluVBFltry0cvJjPG1OVsgQcL4OGiel+YsPrQ9yryxO4hF42bW2Iu+RZSiN68qYJ8sp9cLLZ9Mh/8h1ybi+6GN7hlM+vC55++btR8t7WPuWnnX8Wcl3heZyV7U9Sl9u/iPrgf6TdzmVoV6vF+HDSZIUO5/T7qCcWKX16yi/LMsG1r3kfUfKzNuQrS6r4Ag2BwcHBwcHB4fPATTjUTPqNUOcPjaiik+m+WTeZgByY0QaCFIWCW4Mauu08TJymV/FaKeFj6vW9uFGYmYM0nAWSeM+WsFbaNe+hHj2TSTzyzAzY8C4n5NoYwCaAKQ9nAE4A/AC8M/3kHmjQDAGeABCA3h1pNEK0klSTobwegcje7+F6OIpootnqF0+Q9Taw85P/VXEU2/mMsaXmHn4f4TneSWjggw3WZ9SN5ohKaGFCWrpcf3L469CwklPCjpWRRZp93PDjx+X7Uoj2zhuSj5wTwu+EyUZd5Iw4MajpiObMT2svPK4JA24vBp5LfPXCApp1NuIVklwDiMu5T2SsNBID0ncSTkkacXLJcepqvGoqh9xGTUvWNm2ZGg+v86mS63d0AsOTXbZxjVU9UtJxtjGDluavBxcPiLUbOUkjzZOshHpNkx+bdzh+WjekTyPKuJRI1BlmyIiXdYj9+SkshDRL0kc7lnLQ4d5/+Hh7ZpOKH9JAhLkCyrteUrXydBYOV5qaWs6lKQW14tNzxTmzcdUAEX4LNUb94akY1SfrVYLURSV2p8cTyQZSeeGPRttcASbg4ODg4ODg8PnAHzRYzlxpmM2Y5EmqJr3B/2Wmwfw37bdC7UJKjeabZN02+Sfl0Wu0WWT25YmycAJg8wfQbe2hk7tLVyHX0Jn/C3E83eQzU4CEyHzRgMw+CIeuASwC+AyA1odIPWAoAFEKEi3bGSxuNzvnCE6fIToYgO1i6cIzjdQv9pCeLmJ0MSluiKZ1379v4aLO/9ZZOEIRnf+EaLrHaTIDYc4jgu985AZbmxRWjbjQSNyJAlpM461upDtbxhINr4IOk+Lt2OtHDaj23af7BMa6WaTUWtT3PjVSEGqC60tyv4qw7zkvdIIlH1GEgqaB5NGoEk9VBEZXFb+u8rDRxunNB1LAoyDPOa4rLzdS9JNlov3e63skjyRZZdthNKrIhj42CjLJEkPea/UJY2BvK1JEtPWR239V5bnJtD0oD0neB7Sc5hfQ+QI1a0x+eL33W635OnK26ZG/FSV4SZjl6ZHLQ+u/6pxkYc1cx2RVx4n4LQ2rz0reR/QyDQ5lvP0tJdj8jkhZefjCl+SwTZmyvatvTSRXmRSj/JlBe9fQRCg2+2WdlIlzzYi3pIkQaPRQBzHpXFBI7nlHObTkGuAI9gcHBwcHBwcHD4X0Cb+HFVhRlUhllWEAzf4qwxy+dZ4GDTDW4aj8DJLo4LLPQA/ghm7h3TmjyEd+2lcT30Fx0uvIZufBaZrfU+0UeTEmEQbwCGACwO0uvDiDjyTwkR1mNoIZQLUm/nPJAHaIdBO0Dj6pxjd/48KrzQ/Pi8ZLTYjpVQHyRUmnv1a/xqv763IyRdaH0/zQJMGI69Hfk4jYmxkSNV5eS19y3aneVbayD6pK0k8SZKE5ynltBEbvNz8uDSAuTHIwb0sNLLIpit5XpI8GqRebGnZym9rIzxt7bwkbulbkoAaESP7ssyX5JUkoPZN11bpSZJ2kgjU8tHKrOmLk2vap0pHUh9V4yk/pnkFVhFsr0IY3KTt8bLJtqeNCbwtSOKf92MKbU+SBJ7nFQSJMaYgSigdIuO0dsJJFsC+lEAVoaXp0DYmDiPEePsjgoo2bqG2I4koG/HEdS37kiybbXyW4zsnnaRuSDa5GYdsJ/xZL9uCnCvY9MXDzUlOqQtOsmkv82gsaDQaqNVqRWgp0N+ZVuqT8uJt69PAEWwODg4ODg4ODp9DaEaDBpshxifE0rtNIyckcWIzUHge8neVwcL/y3V4ZLqZMUDjFrLm6zCjPw0z+ePArbeBpWVgtgkz5qNDJFpDES5GHtJ5AeA6gddtw8timDCCqdNuBB4Q1XNiLe0gutxEtJ8TZ+H5U4TnG4gunsJvH8B4dXgmBkzf48amgyqdaeDESRRFA2vmaXqykXgE6QWkkTL8Po3Y4vnayCvu1UHnePibJGlknvRbeohpedlk1HRVRXxRf7ARKFy/VYTfTQgzgiRPZZm5p6LUlcxPW9uJIIlwG0EzTH5ej1pb19qEHDNs5BIvM79ftlVJUtE5TgQMI/mqxkZ5rmrMMsYU5KscTzXyw0aUyPR5WXjbkjJIopdfaxtLtb6jtXPtvzb2SKKGdoXku71Kufh41mg0SqQLgGLnSBoryHuJzmVZVqzlpelO6lHKaxs/+DE5nkgySNMhJ4RsYZ6c8KqSfdiz2DbmEFmm9VPuGcrnArb+QOnR9drmMbwN8L4gPVG5fFpZaWMirkcCEapcHk60UTg1n9fwlx/0zcOTX+VZTHAEm4ODg4ODg4PD5wA8TJMmilrYJ4GTVbbJJL+GT0olOSHvtRFImjErf2tpafkBgAknkI49QNr8SaSjfwzZ4heB5TvA3DgwEZTXRZM2awrgHMA+gKsU6HTgp10g8JHVRgG/540UhDDNMRiTIbx6gej4ezmJdvEU0UVOpIXXO0Cm7zZmjIFn2sVv6ammGetEqNgICqkPjfCguiLjnudNoDy4BwgZHZphrNUPP28zwKqILvJAkAub8xAkri/5n+7h4UOyTUpDU5ZfGrGy/UnCRZZJC9vkfUYSA7KepBeYjWDhckrD3BZyLY1omY6mnypSzFafUr824oq3S6lfboBT+fgaVFxHPKxOM+I5qWYjvzikbqp0L3Vju1Zep8nEyyHl0fLRCDTZbykPW/5a/+Bp28pmk0c7V0VmERnGZfU8r1jEnnaL5OGTdC+FDPIyc6KexjIC9UMtjLZKB7xMvE9rxBG/Tt7H8+F1SjKRB16Vh7mE7GNaGGjVmMahPf8lWajpRAtz5+dtcwMAA/OUqmeIHOfoGu7xJ8c3uo6Tq77vFxs30Hlj+rtK800VeJip20XUwcHBwcHBweGPOGiiqS3aa7tWrtXEYSNltPSGhYsOk0eTz3g1pM27yEZ/At36V5HOfRnZ0howPwVMRf1wzhEMrotmkK+LtoP+umhxKyfbGmNA2NsYwAuAxggyjCBo72Pk+GHukXbxFOHZE9Svt+CdPUVgYp0YgIGpICV4eaThIY0Q3/eRLL+O9lt/AsHlMcbe/f8AaawaMTwNToBKA0zmIc9r5Aigh+RoMsjfVddVHefycC8IKldVHjxNjezS2mzVf1v7tBEfPA9ODnED2NY2bGSUDbLepGzyfklIaUZrVVm1vOV1kqCyEft0rXadJE74dfzbJpckKzW5beSiLNswHd9kbNXaXdVYK/uT1KkkNPh5jSDi/UYruwyT0/QwTE4JjXTRSJPJyUnMzs4CyF8oJElSkGpZlqHT6RReVhTa53leiYgHUBoneFmJMKFnoSbnsGcQH3/k2CrJeV42uWYZryMibWw7onICT7ZpSQ5WkZ0a+PW8bWpkmdYWKQ+uZ06W8fkEyStf1lC4Lyc+NRllf5fjOF9njeqav5ShepBe+PRsIfANaKh8fKdSvnnQTeEINgcHBwcHBweHzwG0Bb9txrqNSNCut729toXAcHLGFrY2YBjAQ1ZfRtL4CpLmTyKe/BKypQcwC7PATB0Y8/pEmjZ7bQE4QE6mXXeAbgtABtQaAF8XrdHMP50zeIfvIzh7gvrVJpqt52i2tlG/3kKYXg+UrQi7xKDhoenrpiQJBxkF16//cez8d/6Dgvw7/4l/Abf+9p+HyQbDaPh/MibIcNM2rpC6N6YfDqOtAURGC79fymAjO4aVVdOX5glGMvHrZFiPJBek0Uh53oSgk3JKkoXLSu1EelNR3rSGFMkmyR8ujzTeCbLsmr40gkTTo21c4Plyr0aeJr9f05Msj5a+RiLKNinztuWprbUoiRxJosn/Gmkgy6SlxY/xMnHDnRv1Mk2NjK2CrGNeFiIa+G/NQ1ArG/f4soUj2mCrc1sf42lfXl5ienoaq6urxQL1RLK1Wi2cn58jCAK0Wq3iPiLauOeXJHlo91HKj66VayNK3ct6kPrV2qAce+Q5SkcjSek/9yym8Zo8qPjOl3Js0PoNl1vWozYuaES0dqxqDOUe0pJUo3s5yaW9aNDy0sY0z/MKbzPaaZWu5c8o/gzkafFQZO4xLUOISX90vWw7N4Ej2BwcHBwcHBwcPkfQCDF5nhuu0gjWDAlOwPB0tXwkIcCRhVNIGl9A0vxpJONfQbr8JszCAjA7Akz4/R0660rBYgAnKNZFQ6cFmDgnohpj/etq9fyTtIGTx/BOHgMnn8AcfgwcfwKcPILfPkKjXke9Xkej0UC9XketVssNHgx6btFEW5twc73J8CUbqTVg+AQR0vk1tKfv4OiX/1Lfsw5A642fQefB19D45BsDeXLjk+QEUAqTqjKwuZFI8vN7qsLriAjhZeY6s7UBqQupa94u5bVSZqBM5PI86CM9XmzEAB2rWjupiniQ6XHyjaelkUzy/mEeTrx9Sdl4/5bGJNedDK+sIsmk4c3zIHlt5Jb8LdPRQG2K6o/XtUY08v/aemRVa5Rp/ULqk0Nrk3Rcu4/LZSPJND1opIwmW1U/42SlbBc8X21s579tOrARJVp4qrxveXkZv/iLvwjf97G5uYnLy0u02210u13s7u7i6OioINiCIEC73S76OifU5BgiSXoZOs3LI70sbXWn1YkWsqyNlbzNcTKKZJPebpzk5m1WQrZVPuZpkLJRurZlJKQetXFLy4v6LulIlp+nQfnw8Uie5+2Wb7DACVZO7vFy0DUyzJzPVfhvLjPfmfRV4Qg2BwcHBwcHB4fPAfhaS8AgeUHHbAaiJAJe1VguJvdBE3HtPrq1ryEe/SrShS8gW7wFzI8Dk2JdNIkU+eYCuwCuMqDTBtIOEPhAfay/LloYAuE4kKXwLrbg730T/skj4OQRvJNHwNEnyM63inXROCHheR48ZsTIMBbShWZs2QxazbDjnyzLgKiBbGEdyfw6kvk1xHP0ex3J9K1+2TgyAD6Q1UdVY4bXBV8rRhJs/Jv/tpVFM+qqjGnN80EjEDTCwFYeaSxq5bBdr3mjSIJBI7hsxItNXlleSoMbdpLssvUlbR0l+ZsTvFI/si658SkJsaqycN3ZSB7+36ZzTceyTPwaSTBKYk+Ge9nIDC4HN7g5CaKFpVV5F8pyav1GI3K4N6gsr8xD6sfWL7Wy8jathaXa6ruqL9h0UUWu8XyriJ56vY75+Xl88YtfxPz8PE5OTnB1dVV4qH3729/G48eP4fs+Op0OgiBAt9stka28/NLrlo/3st/byEpJvskyyHZlIyu1Psav0cIW+RpsdF6uLUb5anVWNbbb6pZ/a2HaNp1o5agi6nga9DsMw5LndBWBqJ2T3oxcd1wW3ido7KRxma73PK8kC1//URsrbgJHsDk4ODg4ODg4fA7AjVq5HpdmMHFDpAqaUQsvQBzdQbf2U0hGfhzJ3JeQLa3BzE8A01Ee0knroknuJUMeynkE4Mrk66IlbXgeYBqjQBBRxkBzBMAIvKuXCHa/j+DsCbyTx/BPH8M7eQTv7Bm8rFtMhEsGWOAj8/TQWfm2mibWdA2fuCdJUlrjha/tMqDTxhjiuVUk8+uI59bQnVlBd3YV3dlVJJNLeZkEwpMdjGx8B/XjbURHW2gvvIHzkV8CXgIwgH/nHM0n37YarEC/jGEYolaroV6vo9PplIg2eiMPDBIn8hhvH9JQ09KQ+tUMQEmmkMzcUNNILc3A40aTXJfI5mFI93AiUiOP6JgW3izJEi63bEuSINJ0Jw14GULFQ6u4HJLckjLy8FWuZ97fJTGmkUHyv60epD61e6TRbfufZeXdcOleHgYpy87bFx+nuJFtu95myNtICzmG2Ig4W93YrufHtN+8fLa+wcsk2wiHHP+4bJonkSSTpLxVY5NElmVotVpoNBqYmJjA5OQkarUaWq0WjDGYmppCmqb4+3//7+Odd97B3NxckQ8RUbzfc1JRLkjPiRcubxXBrNWP9szgRA2vE2q3tnR4Htzbi8rFN5zRSGWtvdraN89LllkLC+bewLKN837M75PtTY57tlBUzYPW1qYJ/CWi7Oc0PlCdcK9uItAotNTzvNKOolz+MAxL5KYLEXVwcHBwcHBw+CMKWp/EGIMoihDHcTH5DYKgWJ9GI4b4ZLWYkAIw0TzSka+hW/8JpDNfRrJ4D2ZhBphpAONef100bQ56DWAPOZnW6gLdNuD11kWLGpQ70GgAaACdMwSH7yM4y8M6g7MnCM43EJxtwIsvi2QHCEExuSYDRU7gi3XUmBHEd7DkBgw39vnkOwgCJNEIksW7iGdX0Z1bQ8w+6eTioB6yFNHZLsY2vo3a0TZqR5uoHW2hfryN2skLeHG7qAfP87D14N8AbtPNBtn2BLr1uxjpfGA1DGm9GFoMvNvtotvtlt7Ykx7ozT9/iy89gqSRyI0frh9poGmeDZzIlUanRjhIIkkjdjjhIvOShqj0suDkHPeeoHbA24tGrCVJUlpYnctJbYZ0Jr3BuOEsDW6bES2JY76+kJST+rdGKpJBqRn/dJwTFNSXOImmedlp7UJCqz+CNNSBfCzj18gQMGrPFKJMOrXpTzOYeX3dhCCS8khCg6epEWCSiCDIUG45DmkEnOa5xvOleuOLvmsyauWW/SbLshI5L/uNTIN0zEknuofSCcMQ3/3ud/Erv/IrmJubw+joKJaXlzE/P48sy/Dxxx/j448/Rr1eR6vVQhiGaLfbxS6QvC9J8Bcgvu/j6uoKjUajGPc4AST77U3asOd5RTn4WCG9VuV4yskbypdkqtVqpfTpvySubDLx9ibrV7Y/rQ3wcU62TVm//LkqxzMZoinzkP1Ujt3aM4eP89QWiYTneuHPfjle8HxI57xNU7vieVN6NK96FTiCzcHBwcHBwcHhcwA+EeYECk3weYgE0J98Zl4T8ehX0a3/FOKxLyNZegPZwjwwNwJMsnXRtM20usg90S4BXKdAtwUvS2CiMA/pJNRq+Sdp5cTZ2QaC8yfwTx/DP82/vfYRNDPX8zzA8taeGwZEqgEoEWd0H5/A0wLSNOFvNBolwzBtTqA7u4p4bg2dmRXEvd/x3BrS8blBIdME0fFz1D/+p6gfb6Nx8hy1oy3UjrYQHj9HaJg3DoAkmkEysobzuS+i21xBt7mCTmMFncZtpLUZXvpczWNvYOT8g4GyU7loV7YoilCr1dSFm+k+zftO81qRxv4wA4ru1Yw4/p9gI2u4Iabdx0k8Tk5IUsJmkEoDTsrASToNnFzhxqLUsyw/XV9lMGvlpfYK9L3RJLmgEQZaWjJPbuRK0oiT8RqZpqXH8+T38HPyuDTAeRo2AogTLJwYlEY7HdPytYHLI8nIqvs5EcmJPF4nw+qHpyHz1IgIHmIoPTMloSf1oP2vah82nWj38XGZgxO2W1tb+NVf/VUEQYBGo4FGo4Fms4larYaDgwPEcYyRkZHSiwNOVlWRTvx62p00TVNEUVRqN7IP8TFFvqDR+q70Br3Jb9nGoyga2AmV92+SgYcucv2SPm1kHJeBl5t7d2qQ53ia9HzVPOS19mFLp6p/02/5gsQmN9Wf1g808l0+B/hzUf5/VTiCzcHBwcHBwcHhcwA5AS8RLAgQ199Gt/bT6Ix8Gcni28gWl2HmxoCpsL8uWkNJOEG+LtoFgOt8XTQv7QKBD9MYA7zeJDQMgHAMJksQXGwh2Ptm4YHmn+Yead7lC8DoC2BDMWA0EkSbrHOjm3tY8HAS/tYdnodgehHJ/F1c3X4N17deQ7pwNw/rnF1DNjql6KGbk2hb7yM62kJ0+AzR4Saig02EJzsIwMi9IELcWESnsYLLhR9HPLqKbnMV3eYddBsryMLRgeS9rINa6zn8tIW4eZsKCHge6peflK6VRhQZ8931ZcRffgPpex/B/ONvFrqSnkjSSyFX/6BXFV0rvQarSALptWUz4DRj3lZGaRDzMCpuKNlCTWW/0OQaZrTzb+kZwT3M5D10XPNm08gfSRoCuUci9yKTZKesT0kycXk4eP5EwPM0aRdUTthquqmqZ64DW7vR0rARY2T8cn1x45rICikjz0cSTlpew9qjjQirIh/5camTmxB63PDn4xkd58eq+qyWtqYrrhcZLjgsPV4uvkMm0PeiPTk5UcnCOI4RRVGx/hoPxdQIPcqHk2O+7xftl851u12rTqntSF3JPiHbAU9H1r8WYk3XEOnDPUt53+ZexFKvsr++Cni92tqgHIekTrjXrK2da6gaI20kHCc9tbTkmC+9K+UHGHzmcchx9VXKR3AEm4ODg4ODg4PD5wAGQBLeQTv6aXQafwzx7BeRLa0gm58AZsS6aNI2SpF7oR0gD+1stYGkA88HTH0UCHpTRj9fF81gBP7lDoKd9xCeb/SItCcIzp7AP9+Eb5JS8oVHkAcY9N8qk6cBMGhQD5RPOc4n1zK8xQDwZpZhlu4Di/eAxXvIlh4Ay/fRWX6AjkKieXEbtaNtRE+/i+hoE9HhFqKjTdQOtxCc7ABZPxTI+DUkIyvoNu/h8s7PIR1bRzyyhnh0DUnzNow/6PLnJxeoXT9Drf0ctdYWatfbqLW20eg8Rz05QpYmyMJxPHvr38HV7NcBE2P+4V9D4+z9vDwKAUa/kz/zM4j/t/8yEARoAEj/7b+N1v/qbw4QTxoxMMzrwLYmkDQgeV3ZiFGbB4M8rnnLcdk0rwQZGkTXc0PURghVtTvNEJUGtI3UqdKLLJP8zb3LSOe8z8hNFDTj2ZaHVs4qj0V5L79GM9ClTvh9nBzV+rzN8L9JWaT3j1ZOjRgZBukxKclqm761uqiCTR88HxvZx0ksm774vZKc4OXix7S2zXd05Pnw3zJEldIJw7DwtqXrABSEWLPZRJqm6Ha7JYKPpyU9vSS4p5MkaOR4JAlYbWywjbsSXB4eCilBx+XamHQuCIIidNHW7mS6Gnmryag9R2SfsbVdHpLNx4xhfVgelzLbwOuDk+e2sU4e42OpfF5pdcn7l3x23BSe+TS0nIODg4ODg4ODw+8Ljs+BX/8e8I3vA997DjxqAYcB0Bk1wISXe6KNQF8X7RLAOYArAK24t/5XBlNrAFF94HKvc4Lw7AmC86c5kXb2BP7JY3inT4DeumgUdgPczFi96dTTRlgAzEDwfaQTC8gW7iFdvIu0920W78Ms3QMaYzJZoH0Ff28DwcEzBPsbGDnbwcjpDhonzxFe7AN87aBgNA/hHFnJybOR1eKTNJb73nsMQecA0fUWwqstRFfPELW2UbveQq21Da97XHCb0mAEyp4X4eg8kLYQIimtLVMyAoMA2cI0zOoy4n/vLwJjTYARLQfrvwDv8LQwTHiIMOlRhvqQUcENO9K/vJ57NnEjmn80coB+82+N+OD5kqGshazRdZqHngabcSqPS7KCG5VZlqFerxf/hxFG0kjndcnJAF4/0nuDG7ZSt/y81JGtzFRGm7GqhYHJa2QdDDPYteM2MrLKGOfhYlUeWxoRrMmskQlSHn49b2tc7/yj6Use421DepFK0pO8rLjXkzH9JQA46SRJKOrTfJ0+G/En5SNvKZ7nMOKB50/5cXIY6K99laZp8ZvKxscfLjORdrxsvD+Q91cYhqV1SbXxga7nbUKS81q5tHq1EXAyfDsIAtRqtUJ/NIbSsg58nToKDQf0nZHl2mHcM0vKavuW4aU8D60/yn6rjUOSVOR6lWlqYyXVoaZ3zZuNp2Uj9mT/4nLL5zAvr+d5eOedd9Q8bXAebA4ODg4ODg4Of8DQ7gLfeA/4rfeA39kAPjoH9nzgehQw4+hvLvBFfldvctgGcIje5gIp0G3DMwkQRjD1kf7ltQimFsFLWgjPH/cItL4nWnD2BH7nZMAoKYxZr+w1xifQ0qgmyEmtnNhqxncGD9nMLWQL95At3kW6eA/ZQk6mZQvrAC8T4foc3s4nwMsn8HafwOw8gtn5BNh9ApzsIghDjI2NwfN9NKbuwJt5HVejP4Fkbg2dxgq6I6uImytI68p6ayZD2NpB8+hbiK43EV5vIbreQq1HqnnJZUkfhU6Qe9VVGSQl46CTh08l9RqS1UVg7RbS1SWY1SWYO4swq8swtxeAWgSJghCamgCOzgb0yiGNMm70SKOFp2FbY0qWu4pwqjLwbUSMXB/NRtDZyqvJI8/ZZJP3aga+zFsjkLTQJE1eTlDQeV5+bnTKPnST37KfyvJpHkjSa1Cmq5FJNn1LvWiGvpRRC63lepBhfjy0Vsql/db+0zGbcU9y2nQs+7ZGAsh2LMdJWvdK65/8GJGiMnSU56P1P60uCDf1auNlIJJREiayXxDpRKDQUHpxw9fXk22fE95cDklYa88brq+q8VEet40pGnkl9cyJQ0lyctKQdMdJUZ4Xl0UbW7XnqfS2vcn4xtuhTFcj5Ph1w/qUTdd0rWzrWvrSy1nmy39LL2aejyw7H2M+zTpsjmBzcHBwcHBwcPh9QJYB730C/Mb3gG89Aj44Bp5nwGUTSCcAjCMn0d5Ubo7RXxetBdQBdFvn+bpo9RGU10UbzddFu9xCdLCB8PxpTp6dPUbt4hn8612YrPy2uJj8ismqnLhrRgXtviWNcW7QDBirQYh05jbS+fWeF1qPTFu4h2x+TfeuuzpF8OIjeLsbCPY34O0+gffyCfDyMcz5ATz0CBB4SBqLMGPrwPIvAm/cQzL9Os6nHyAbuwtTmxhMO+sivH6O+tn3e95om3lIZ2sb4fVz+CYeIKbQyw9eec0ibuTwSX5h+AHAzASSOzmJhtVlmNUlJKvLMCuLwOKs0gAAHJ7C++AJvO2X8Lf3kP7xr8B8td9Ykg8eIX28BZJOMz40A5C+bxJKqhnZ2hpiNqNHGkY8f9s9tnYor6kie2xGMjC44YPNSJdr/cg+oZFl3IPCph+6l66V4b0yHJBfy/PQZJDlHXYdD3Gjdao0nVXlUQUtX0mi2NoET8NGqGj3SZ1zEoGOS6KBk3W2dmWDJFNl+5ftwEaGceOfy8zHIRkiqrVJ7uEoPXq0Mmvr2lWNDZKU4TLza4k4CsOwtDuo53lF6KgxptiQptvtFjJTO5djDS8fUN7sRi5HwMsidTaszUnQPdqYoJFTsl55+9X0KmXUwNuQ1m8kccZDU7V0tTFJ6zu8rFIfMj1t/JX38jLzNs+vrdKVrFv5m57D2rNDLjsgw0pvCkewOTg4ODg4ODj8HuLpc+CffBf41ofAOwfAsxg4qwExJ9HuArgnbkyRE2h7AK6BKAUaYc6dXWVASnNSH+gAwOgEgqsdhLvvITx/gvB8A+FFHtoZXj7Pvdgw6CEhjWa6hoNPqG2hWFoYFv32PA8Ia0hn7yCmcM75daTkiTa3CoSKJ9b5AYJn7yHYf1p8vN0n8PaewL867XsEeCGy0RVk4/eQ3vllZON3YSbu5Z/xu0BY3r3BAEjjCwQXz1BrbaPW3ka9vV2siRa2dwHT97jgxkqe3/DQIDrn+z4y3wOW5pCt38pJtJWeJ9rqErC6DIwpXnhJCm/nAPitd+Bt78Lb2oW3lZNp3os94LJVyiv42/8PpP+TfxHZV95A5+EGTv9nfy1ncRkhoxnzXFYyPnj98R3uJPFWRVTZDCC6TxpLPFRL8/DgMnNIjzabh5UWHiYNY2mcSn1Jjy4uszRopZ6ryC4OjfDhfZb3P0l8aIanVs9aOXkePH06L9PSDHqtjciy0bdNxzwPzWNMhtwBfQNc81Lh8ktZZH6anPIeSYpIJEmCMAwHQiil/jVo7YSTK3Qv5c1DJ7X7bKSZJOi0tioJHa4v2Q/5cUmG8PS1Z4dsU8YY1Gq1gX5ARBz3hCMPN2P6oaXk+cUX4ufedHxDEr4xkFZXnwXaM1Qjh7TnB9UB38nXNo7I/mNLs0pOTTZtzJCeglX9VOtTWh5STq0/ShJMys2vsRF28mWfNk7Sd9X6fTeBI9gcHBwcHBwcHD4jjk6AX/8u8I0PgO8+Bx63gKMI6IwDmEAe0nkLwB3l5gsAOwCuAD8GmgEQBEDbAF02t4sDIDbAbB34qTngdfrM5t9/5uuvI8g6AHRDzXZ82GRfQk5iS2+uwzri+TWkC3eRLNxFMr+eh3Mu3kU2uwL4g+v2eKd7CDe+h2B/I18bbf8pgr2n8A+eAldnfXnCUaTj60hG15CtfR3p+F1k4+vIxu8hG7mjpo3WAXD4LnD2BDh7DJw+Bs43MBLvYMS7AoxBvV5HvV4vjDpW0AFDlOtG6sg06zC3F3LSbO1W/r2yBLO6DNxeAKIQAzVyeQ0QeVaQaLvwt3fh7R7CSwdDvQryQhgqQTeB91f+DsIwxPnuLrKrq8F7RD3Tb0my2YxyeW+VYceNGpmXRrRwOTXjSjP4pDwyFI+nr9WfFuIpQ+ronPSykaSUjXDgssk60MrDPYykbvl1shxSXjpHZIPqOWqBVkZJxkkZ+LeELEPVPbb/GgFIx22estri9jzdqrRtxr/tN2/HWZYNLF6v9QMNWh6SHJYbDPB2LPOQ/UPmr7UpSpPnJcuq9T/ZP6Q3qHaf5mVEv2UIaJZliKKoRB4SYZYkCeI4zsfC3ksCPv5Jwr7qRRGXTbZ/7bkqdVnVtiVxxnWnvRwYVj82WTTZtP/8tya/fH68Cnknyy3LorUrTtppzy4uP/c40+Y2NiJNa3ty/LCleRM4gs3BwcHBwcHB4QZotYDffhf47feA33kKfHSZr4vWGgXMJHISbRKAFtF3DWAf+bpobaDpA1EIxF6+TBohC/L9B0Z84G1OovU+r80CM4qzEwB4SavwVgJ0wwfQDVZpoJfSFRPyLGr01kBbR7Z4D/HcOtKF3Bstm75dkoHgH79A9Mk34e8/RbC3AX//KcL9Z/D3n8LrXPUNj2gS2fg9JGP3kd37BaRjOYmWjt+FaS7p5b7cRrD/DXjnG/DOnsA7fwJz+hjm9DHQPVeNz6zRQDYyUng7SG8TdWF330c2PQGztgyQ59naMsxKHtKJ+Wm9YvaP4b33CbC1A2zmRJr/fC8n007OAaHvdvttdDo/g7B5BDM5DmSXGGn/BgDdS4D/zrL+LpPDDCC5+DtQDoHTjHVuOHOj1UZcSBn5f82AsRmokgCTXpeaEaQZgpI4st2v6U4acpqXHTecbUarTRYyGDkRwckFYDDcUCO9eBkk8SPLKg3QKtJFEiT82mHha1IuCRu5pd1D+XHju9S3lbBB3ta1Nezk9doxXidVRrfUoUYqDNOTpguSm9qYDLvkkGWQ57R6riq3LW1ZXnmvrVxV68PJvGTfp29OAGpjk2yfpD/ygpMEIl//jM5rhItN37KPl54djKjifZ3IQe1e3nb5epeSFKqCRmwNG5fkNfyb32eDdt6mQ+24bRzgbVESnxoJR+1E6l2el+tXynpwIaIODg4ODg4ODp8BSQK8+yHwm+8A334EvH8KvDD5umjZFHISbQzAF5SbuwCOkTNk1/m6aPUQyHzgKkXfeykEWsiXUbs/XSbP6PetCeAVXhAXsE1+NYOST175BNTzPKS1kR5pdq8XxtkP6cymlwczzjL4x89R+/g34e8/hb+3gfDgGYL9p8DuBoK0mxs/Bsiai4hHVpFOfgmdN/5s7ok2dhfp+DpMbUpJO4Z/uYng+B8huNiAf/kM/sVT+Ocb8C6eIYuvS2+8qWxZliETxhiVLwzDwggjT5Msy+DVImRLc8juLCBbXQKIPOttLKCGcnZj4MU+8OGTwgsNWy/hb+3CbO/Cb3cHDB05iafjp6e/jP39fx1oesC9OhDl1zU738Kd0z8Pz4tL5dAMljRNS2FQ8vqq9iK9fjTCQd7PyUjpJWVbQFwae5qHhDSueflsBpE0ouhbIxE0eWzklSQ2qIzccOaQhDW/3paPdq80PLnRKGW0oYqskoRPlfFcRSppJJIGrT1JkgXQ1/iS6ch2S3IMI79k++LpaffKNqgRInxdMCnfq5COnJSzlZf3C36vzFO7RjtGx/m3jXiRepHX3KSsXH4pj2yLWh3ZiBM6zkk4SofqJgzDgkhLkqQguYhQS5Kk2LmTE3ayzVa1MU0fPC3aKZRkJjk8zyvWjONEoa0vVOnaRvTRt3yBVIWq62zE2E3T1fKwtVHeRyUxxvXL24nt2cbPEUFLL21I//SiathuuRocwebg4ODg4ODwRwrGAI+f5psLfPMj4N1DYDMBzhpAMoE8pHMUwOvKzQnykM6XAFpAmPTWRQuAa74uWpCvi9YxwOo48NO9UM7XmDfa+lS+B8EPC3KCWmX8ZFmGrDmBeK4Xzjm/joTWRJtfRza5MJhBmiA42kbtB/8E4cEz+LsbCA6e5r8PNhFk+YQ0yYCkeRtm4h7isa8j+bE/l6+NNraOdGwNCLX1xq4QXG4i2PttBJfPEFw+g3f+BP7FUwStHSBLVCIgMxn8nkcC91DhxtmAB9toE+bNu/Deuo/g3h0kD9aQ3b2dk2m35vVKubgCNl/C23qZk2fbe30y7eUhPOb9UDIcsgxGGL6SDMiyJuL4NrrxKvZP/+fAhAdM+kBiCoKtVf9pXNZ/ARPd/7hIR6ZJ//mue1pImO14IXMF4cJJBE50SY8RjcSReZChq61VJck1Sk8L+ePlv4mRV0V+aISLlof0nKpar+om/ZGTcJJspHWiSGdcPlsYlVYWm16064aRZbycsgwaqcOvkW2xSiYNVbolffH1tzikN+AwTzBeXo2k1ggAadwPK6tMX4O27p628L2sLx5iyckavhmCvO8mfUOWWYMkzvh4zI/z8GVbX+H5yj5J5SGdSGKNZKSXKtQ2+FptFFrq+36xqQLv23Ecq+HumnyavBQyTDJz2YhUI3kAFF7VfOdYqs+qdmTTP9cVpTOsPfNrZV/mfd3W9rS0bkLqaWQm9WugvPEKjYF0n61MtjLy66WeiHhzBJuDg4ODg4ODA3ISbW8f+M3vAd/8APjODvCkAxxHQGcCeSjnGIBVAOvyZuQk2hGAK8CL85DOIMzXRYvZvDoJ86jP+QbwJeGF9voccH8GGKn9KEpcfjMNIA9pHJ1CMreOmAi0+f5vMzYzmEjSRXC4hdrmewj3nyI4eFasi+YdbsHP8jf9fm0UycgqsvF76Iz/PNLlPIwzHV1HOroC+INTTK9zjPDsYwQXT+FfPkV4tQn//GnujdbZB017OZlSIsYwaLRwcoEm41mWIVieR7C2DH/9NoK7dxDcu4Pw3grC+ysIFgZjeA0A7B7Ce+djeFsvS+uhec93YY7P4UE3IrjxQ7LRufx8gDheRrd7G93sPrr+W+j6d5EEy0ijWZhGE5jygIYHPLAbH4k3VaTPvTeKMjCSptvtDoS+8GuknBoJwg1Mfs0wI4pDhkBpXinc8JayceNO8wrjetZkq/KGkKgy7nl6PF++NhQ3nnma9G3z5rOViaetySnDRWW5ZN/h93EPQ143lEYVwWHDq5BkUiZJFPEQYC0s1kbm0Rpdkjzi8mhEjW2Mke3B1r40Ys3WRnk6w9ocXSPJe17/RBhVjZVSHk0f/Dhvf7ys/D6t/dnSJg8xHoaptV3Nw4j/5+M9gIE+QiSVJPFk+kTYeJ6Her2OMAyRJEnhXUZ9hLzLut3ujck12zn5MoETwbyOKQ05RspxVOunw/qodu2wOtT+28ZPOncTOWS/k8dt5+mZIsO+bXkAGAjH5fJy3XPPOKorbROoYXAEm4ODg4ODg8MfWpydAb/zHvCb7wG/8wx4eA3sBUBrDMAUchJtHoC2fNcV8h06rwC0gYYPREG+LlqbzddMmC+hNhYCP6aQaK/NAdPN3+OCWmAMsN8BHl8Blz/1y4jn1pDMrSFZ6JFoI1ODN8VthAebCJ98B0EvjJM2FvCOn8M3PaMlmkA6dhfp2Dq68/8FpHfXkY7dRTK6imzkliqPf/0S0eHvwO95ofnnG/n3xVMEyfmAkVNMoj0PmWIs98spjMQohHd7EeG9O/Dv3kZt/Tb8tVvw795GsH4b3uhghZhOF8nT5+h85wN4Wy9hNnMiLdo5QOPgDEGcWEPNjDGFfIPr+xhk2RSSZAXdeAVt8ybi4AGScAVJuICsNg40gpxAiyzkTdJC0DlCdt1AdjUJ1AAsRXkFex4808Jo/JvWN/P0zUOPuDeH3HmPG+tkpMu6kaG1PF+uB43Esh2j/zKURzMwZd3ztEq6EzLw+6oMNJkGX9uNG+wESQLxzQM0koWXUTPyZV1KAoTrhWShOpEhuDK8ietAGuW8XDZSTDNwbfVgM7ariEpN/zxPIskkISzrU3r7yTXxZBuUHly8zcnwaJvs2m+NPJU6lISbjeDix3hf5vlI4q6K1OHl5jqU5dXqbBhJKtuERk5WjRE8nLOqvfDfkngCUHiHkSeaHAOpnksvoIRcdC3tAMtJlyRJil1KtTYybPyzhbLyPLg+qDzSa06rkyr9aNfYjkn5qggwoOwprdW3vH/YeCfzoDT5Ltba84/rSZLCVc8LXm55LdWzTPOmcASbg4ODg4ODwx9otFrA+7Qu2mPggzPgBYCr0d66aBTS+WXl5g5yT7RLAC0gMv110a75vCkE2gBSP/c6K20u0CPUlsaBV3hB/EODMcDLDvDoMifS5OeSXrD+i/9ucY/XuUZw8Azhw28gPHhWfIL9pwjP9+AZgyRNkdbmivDN1vJPI32whmRsHenYOkxd8XDLEvhX26i9/CcIrnISLbh4huDyKYKrLSC5Vg187rXAJ+g2cgQAvIkx+Ou34K/fhrd+G/7d2/B7397tBXhK6EZ2co7k4VNkT18g2dhG/GgT8cY2kidbiLd3YXp5NptNNBoN1Ot1wPfhj4zA6xlSOmHQRLe7hG58G7F5E13/DcTBGpJwCWk0DYxEOYFW9/RGkiUIuqcIO3uIsk3Usseom49Qy54jTLYReu3epL6Bo/N/Ca322/DSCGa+gcCcYe76byBKtgFmbJB+CTxMlm90QOFJ3MiTRphcPwpAyeiTHiHkjSG9WaShxWXgIKMVGCQFuf45cSRDv7isdFwj6DSvGI34knqRJA33bpDGF5eT56uRbfSR5JCNGOMyaOvdcQPeZlRycONWtglJEGjhUVoemlGvGdC285rxrbVTfi+Xj+tFI42kHjV5+H/tPq4jSagOA/e2lQSGPt7oHo4AijFKeodqhIgkdiRIf1xvmly2fCSBchMCypY2P2/TAR3n/cpGhvOxgDzReHgtlZ/3XQrL5C8kOPFGYxbJqslpa/fcs470xHdEzbKsRNhrhJdEFUElz2ukl5S3Sud8HOTy8f8yDY0MrapfbTyW45u8VhKZskyaJ6YttJr3Uf7/VeEINgcHBwcHB4ffd8Qx8MnjfJfOb34EvHMEbKf5umjpFPKQzlEAb2s3AzhHHtbZAvwEaLJ10YrpVZBfmnjA6sTgDp2vzwGrkz/cddFuiswAz1s6gfb4urzTKGE8BF4bAx6M5p+/+Zf+lXw9tL0N+Gd7CHwfBj6S5i1k43eRjN5H+87PIxldQzK2hnR0DSYcHUw4beXroR18G8HVM0RXm/AvnsG/2EDY2oFnEpXgAACDwR04ZRhdQRYYA39pNvc4u3sbwVqPPFu/DX/9FrzZqQHRTJbB7Owj+ca7MJs7yJ6+QLrxHNnT50g2tpEcnZaMFYAZB2JCnyQJwjBErVZDkhh43hLi+A66yRpaeBOxfw9xsIw0moOpjwKTXk6iBYpRYwy8+BJh6xBhso1atoFa+iFq2VPUzAv46UERYiqNS4qNzY2wDhYW/t1+ONFZr0qY4VW0GebdQN5XFM5ERkEcxwPrDvF7yFDnO+mRocL1Vy7q4Po7kkDi90mShO4jYo3OUUgW149cf8i2dpTUC5XFpjPSBV/fTJI6lD4nFTjRxY142ihDGmlcRq4rKjvVMzf8JOHA5dDqQuqCg3t/aOSjLKdG3kljl+dtaxsyL83QJl2QjHSOExGSiLLlR+2J6kXKpskq26vmMSevp29beDYd4+1BtmEtL5mH1Bknvz3PK/o1H+u09quVg1/DZZZtll/PX5DY+gcvkzH9tdWItCKii28WQp5a9JFkhiYnXUvjE6XNPVP5rpvUpoCy1xWvn9Jzqdf26PmgvQiqIockGSbDQgnUVqMoKuSQHpWyH/H2qoVHynbJdUfpUbgrjUH8ej4WyTR4vcpy0thN9U/pkGx8HCLvVOqvdD+lx9uz53nF84zaDfUtHvZpGwcl2cvHW14GOUZrx14FjmBzcHBwcHBw+JEgy4CtLeBb7wHf+AD47i6w0QWOa0A8AWAaeUjnfQAP5M3ICbQD5CGd3ZxEC4Lc8yyh+ZUHZFF+yUIT+IpCot2fARrRj6bMHEkGbFlItI1roKNEIkxFwBfH+yQa/8zXcmepdgw8PQL+1sYB4vEfQ3z3F5GOriEeXUc6egfwBxeB87pnCM8fI7jaRHjV25XzYiNfF621B5iypwxQJnQIcl0ZQjFxr0XwVpfgrd1GcO9OzxvtFvy1W/DWbsFr1gfvbXdgNneQfucDpM9eIHv6AubZDrJnL5BtvQQ63ZKho02mZThQlhnkMcN3YXAfSfATiGtv4Tq6h9RfhKlN5jHCDR+oWSbUSQdB9xhhsoMofYaGeYiaeYQofY4gfg6TdUqT97LC7QSJzagmY0MzPDV9k8FBi3bX6/VCFzJ9qTtpWMhreRiTlNFmgGgGimbcV627xJEkSakNauSEJD/IkCTI9XQ4cUT3yjwk4afJLdsbXcvT5r8lkaaVhyCNbq0dyfuriACZtiQEh5FiMl2b3JxssZXTRlTYyidDMSUBSKSFzE/WoUxDK4fWD2zkJ1AmmXk74/qVpDgnr2z5yj7K5dDGB05C32S8uQkpystoG4u43LxPcLKeyk3n5XjDCUn6rckty6SNYxopK89r9S/rR/a5qj4lZaT65ffzDQxs5eHtWF6nycH7BtcrJ5s50cbHMNs4aiOAeVvgBBmXidc3yUHef5xMpHv5eCrz4eXjsmk6r3oWST3xDz3feHvjhOmrwhFsDg4ODg4ODj80GAPs7QHvfAD89vvAt7eAT66B/RBojwOYQU6i3QKwoiRwiXyHzt66aHUPiKJ8XbQSAVUDWgDGa8CXWRgnrYn22iww9fuwLlqcAc+udRLt6XV5gwTCXA346qROos32uLHzFvDkEHh8CPzTj4FfOQQeHwBPjoDnpz3nrK//h6V0/fY+aifvIrh4mhNpl5sILp8iut4COsfFpgLcOC0msLAbeoRioj0+Av/unZw0W78Fr0eieWu34N2ah6dMUM3RKbKPnsA820H69HmfQHv2Atg9gsfkshnb3Fsll68OY+4gy1aR4A2k9S8AjdeAxh2gMQ806kUYZ9dXJuJZCj8+RXi1jyjdQj17hJr5GLVsC2GyDS89G3jbX8jiZTCCpKgyTiTRZTNqqjwlZNrca4Z7c1QZqtpbe/6fe2pJUkkasJpxI/UhjSHuQcZDTukeTTfk+WBrl1WkgjQwNbJGhspqsOlM5qnVqfTQ0tLlaUs5NSLERkDYdKMZpdwot3mH3BQa6VFFCmqkj7ynajyQJIaWj41MkbrQCDoug3Yfl48Tq5JEktdr0Agv3g6orwAoea5x0sSmp6p8byKHphMNGnHG09DaBv+teR7R9VU6rSLhCNpYaCPQ5HFJoleVmZNIWjp0jHvxcaLH5sFWVWZtnKD/VF4eVk364KQTJ92rysrTlf2U64n00Ol0ijSjKCq8MMlrmcZF+QwicKKWk6C8v9meq3SPFhrK604+c7hOXwWOYHNwcHBwcHB4ZZycAD/4CPjGe8A3nwAfXAC7Xr4umplGHtI5BWBOubkN4BA5mdYGwgyoR4CR66JF+RJqxgceyI0Fev8Xx3Ivrh8lOmnucaaRaJstIFXmY0t14GvTgwTa/dHcS80Y4OCyR6LtAP/fHpn2pPc5uBxM0/eA1Wngn3sduD8H/N/+1v8aYW9DgfB6C6FpA9B3wTIYfNvMDcTSRN7zgOU5BJxAW13OwznXbsGbnhhMP01hXuwj+6138lDOZzswmzswz14g2XgO7+KqmMhSiCIAmAoPCWM8ZNkismwFqXmANPwC0vrryOorMLVFoDGeE2gNDwgtjaJzAVzuw+9uI4gfoYGPMBq8wGiwh7p3VHjuScPBGAOjTPoBqEYUh0a0UZrl8hn1ty09aRRQvdFOeJSGNFpspJ0mpwxpAsqL7WvEWVV5uMxU7zxEkJNt3CAaRqLRMa3MPG/pYSgNX608GskjvXAoLZtRquneVj/J+gNc/Hf/pzDTc2h+4x+j8X/5m5W65enL33StRiRIHWiG7WcxNLU2pREF2ppdUibSOw8L1NK1yWwjT6r6pkZwaeXiRIKNVNXSp2uHEZCSeJJ9ScpSVddS/zT+yrZf1Z6kbBrBwskc+qYNCIAyISrJJdIlT096HdI1PC9NPk4SyutuQhLK66ruqSL3ZL3wsXXgeQu9brWy2f5TvlWy8d12pfe3loaWtmy7co05+q2FzMdxXKxrx+te5sP1wJ9BvF1ofZP3W62vyfr9NESaDY5gc3BwcHBwcFBxdQU8/AT49vvANz8G3j0CtgGcN4FsGjmBNgrgS8rNMfL1oy4AXAMeXxfN5AQPACAAkgBIPWB9UifRVqeA4NW99D8TrhM7ibbVYvIz3G4APzOjk2jjIZBmwIvTHnG2Cfz9gz6B9vgQuOwMplkPgXuzwNfWchLtwXz+fX8OWJ8Bamwm9//+C3+jNNHOKowOOflOwwDe6hL8u3cQ3L3TWwvtFry123mIZ30wzNRct2G2dpB9+33mgbYDbO7AbO/CdONSnsUkP00L/clJbZZNIMvWECerSP23kEZvIa2tw9SXYerT/d04a57OrKZdoH0E7/w50H4EtN6H1/0QaG8ga20AaQtRFKFer6NWq6HRaKDRaCBEVPJAI7mljDbiQZvMy3sKvVkm8pKo44aJDJ/R7jXGFJsZ8I0DyNiRYYAa+SCNHNuaSDchEkkum/GondOIO7m+GzfuJPlB4EQaETNaGGRVPUpIPcn/knjk5ygfvk6R2tYmp3D0N/4+spn8zUTnq1/DuOdj7Ff/ekEUaLrWCJJhRJqNALXBVo8atLQ1AqAq32Fl0cgwvvaWJoskozRy5ia61eSiNifJOCljVdk0WW0kIfVrWWZNz7KtDSONtLqVutMg7+NrZ8k1HSUJI+Xl454kWDS5tfFEk0s+/2zlkWO9RnoNI2V4GjI0k29Kw9Pj52zeepq++BivjXNa++Ht0kZUyrLIsZPSIdmpjtI0HZApyzLUajWkaYpOp1MsaUDnuDykM/LM1vLTiF1beTVSnqfneWUvcFnuV4Ej2BwcHBwcHP4Io9MBNjaA3/0+8I0Pge/sAs8S4KQOJFPI10UbB/CWcnOKfHOBPQDXALpAPQDCMPc8K9ZF8wFTyy9ZGgN+fLYfykm7dN77fVgX7SIBnmibClwBL9qD13sAVpvAz88Nkmj3RoCREOgm+XpoTw6BJzvAbxKBdpAf72qbFdSB13rE2YO5PoH2YB64PQncdAkQvmi0nIB6ngczNQ70vNCC1WX467fy/71QTg3m8ATm+4+QbeYeaNh8CbO5g/jxNryD475ulAmtlCOfqNaRZUtI0xUk5gGS8AtIo/vIaisw9Vmg0ejvxqluJpABnTP4F3vwu5vwOh8iiD8A2o8RdDeB9AimZ6DwHd+MMfDR3/CCGwvcm0Iz5Hka/Bsoe3NJA+wmhi7/TrN5pNks6tFT5D2o7BlgI1dsRgc3VIeFVdmMK42I0tZBsxkk0ijiJBHdJ/VJecj05NpqVeSOZjhqnneaQcbTkOXkhqzWbrS0eZqe5yGrN2AWl5EtLCOdW+x/zy8hvv9GQa71bkLrF34JY7/61yvLyv9/WnDCQx7X/g8zPG96nutLG0eq7qG6sK13R9dKUlMbq2SeVW1N1juXm0gvLVTU9r+qzfA8NaJJysGJKU2Xsk9q44pG4EhoegXKIZj0Lb20tDGrag2uYbJocmvlqRr/5HVafQyDJp/n9b0E5SYCGiF8k3IMk0HmTzJw8FBi2QaqnoNau5Uy0jki0MhbmW+GIPOh3zxklmTj33StfGnE9SrHZ7rH1p+055RNFzeBI9gcHBwcHBw+50hTYHMT+ODDfHOBb28DjzrAYQh0JpGvizYOYB3APXGzQR7K+QL5umgdIAJQ662L1uVzj1pOC9RrwFfn+x5o3CNtovF7XtwSzmLLzpxXwK7iMeYDWB8B/jPzgyTa3ZHcgeqy0/c8++RZHs5JXmjbJ/mOoBIL48CPr5S90IhMm/shhbkGUQTcmgdWl4Eegeat34K3upwTaZNjA/eYJAVe7CH9je8ge5qvgWY2XwKbL4GtlzAXV+pk1yQJPEZYADTB9ZCms0jTFaTZXaThF5CEryGtrSKrLwL18d5mAh4QWQodt+C3D+B3t+F3HyGIP0AQP4bffYYg2QFM3wuIjIZil030vQu5EUsGTqnsprxekWYoaEY2pSlJL5vRoRkr0jA/uvjzOLz4CwB8RME27sz+txFFO6Xd1Oh6bV0ebkxoa9Jo5JhWrmFr/tiMErpHpm27jkOu7ybvt8lhg+d5pfXceD3wcCSNgJHgxielxdsFN+Qyz0M6PYdsYQlYvIV0bhHp/BLM4nLvd06imfFJq+yIY1kY+JfnqgzaekJVbU2rJ42gq9pYQzO0NWgEhUZiUnpaaC2XTWuPkiTioeaEIAgG+hDlJ/OSxJzMT17Pr+HjoDF9T1Kpa9u4IPO2EX78eBAEpTA/DRo5o40NWt3wNs5llPLZyCg+HpEnUhUxqIHrlf7LsMBXIUO0869CWlVdq+mGZNbk5Otb8vM2woqDrpEboGhtiH/L6/kGA7yugMENLXjZbSG7UnbeLrgnI738arVapfZB1xNBTTLKdQWHjXmaHqVnNNdh1bPPtmRFFRzB5uDg4ODg8DmAMcDODvDRx8C3PwC+tQF8eAG8DIHWGIBZABMAFpFvMCDRArCPYl00PwMaIWACoMXnL1Ee/emHueeZJNFenwPmR384hNFNcdS1k2iH3cHrQy/3OPtjk4Mk2tpIzvscXfU9z975BPi1o96mAofA3sVgmp4HrEwBP/egR6DN9sm0e7PAxA9pw4UWMmwhxha62BTfweN/CK826AZorlrA5ktk33i374X27AWyzR3g+R5MnE92abFhOTHnyCe7Y0jT2+h0V5H5byEJ30IarSONlmFq03ksMHmhaQ0hS+B1TuBf78DvbiCIP0QQf4Qw2UKQbMHProq8hhnwGiFGk2huKGiGHE3ibcaiJF40Es0GmyEqyTxjQly2v4bDi38FRA3G6Qr2z/81rM3/D0sGgC19SWR1u92CbLAZSFJ3vHxSbn4/1/sw47hKL/w+6ZnGZdHqWSP1eKicJGukfmwkjU0vBoAZHUc6twAs3UY8Ow+zcKsgzLKFpfx7diHf1tgC7/gQwcvnCN7/LvyDXfj7uwgO9xAc7ME/eIngcB/e2QnO/u3/AO2f++fze9otjP+tv1oi+mSdSG+oUp4V/cRWJ5rROVAWS51XkW7yXk3XJA/lXdUugDKJXkVQSXJDEqdamSShVaUDSZ7xfPj1mlxVdcGvkWOSJHDkWn7avbZ+Lcsr9UDf2nhpG2OkzFp6GqlK7U87J3WjEaJVY5iGqrR4eloIv8yTp6OOJaa/Nh0ng8mzzff9Uni7rY/cpAzaWMDloPq0ycvJTLmmpaxDmbbMR5OBE21819RGo4EwDBFFEWq1WpEOJ/woTT5noVBT7vEmX0zIl0+y7Uo9fBpyDXAEm4ODg4ODwx8aGAMcHQEPHwLvfAj89kPgvVPghQEuRgEzg3xdtDEAP64k0AVwgnxdtBaAOCfR/DAn0YppUJiH0rU9YH26H8bJSbQ7kz+6ddGMAQ4qSLSTePCemg/cHwH+mekygfbaKLDSzD3Vds56JNpeHsr5K7Qm2hFw1hpMMwqAu7N9TzTujXZ3Jt+o4TOXFQbHSAfIsy3E2ESMfSTqfXMIYN57CLP5Et72bu6J9uwFvK1deEdnpbfAxSQSgyFt+cQyQpIsI8vWkGT3kIRvI43uI43uIKvNA/Um0OwRaNpmAsbAiy/gX+0j6G4iSB7B734fYfIUQbwF032JoDdxpQkyTYbTNC02ExjmLSDfoGtGpM0Qshkx/Fq5nhYdBwbDoPg5npcxPpJsAUm6gm5yC0m2gji5jTi9gzi9gyRdBECGFt1vECerA2Up7Vpq8fbii4mTwSGNcVlezYC7CXmgkTo2g5enoa2rpBEDsh5tMlK74UaT1I80KAs9+kEeojm/hHR+EencEszCUnEsI6+zkdGB/Au0W/D3XyJ699vwD/bg7+/mhNnBHvyDXYSHe/AO9oBuR/Wsk6TA9P/iv4/O138e2fQsGr/7TXjPN5EphjPdJ3dYlDq7SV1r69xVkWi24/z+m3pWae1ZI2+rSFPe1qWX1E0IJ00fw2Abn7R6eZX+ZiuvJAfkmlU8HY300cgCWYZhctpIKKlz6R1F64rJa7Q2LeWyeRvaysDHGvmcsOmHt/lhfcdG/Glkms07jfLh7Va2Ta2Nyjw0wlPTE9cJ32FayibzsMkgx3Mpr3zJwT1J6VlP5Bpvy8YYXF9fIwxDNBqNgeeC3KSG5KFnI3ko801y0jQtvCf5889Wz1XLKdwUjmBzcHBwcHD4A4bzc+DRI+D7HwLf/Aj47j6wmQIntLkAhXRqmwskyNdFIxKtk5NNYW9HzmKK6wOo5xt6Lo8BPym80F6f7ZFGP6KZgjHAy46dRLtQeKVmADwYAf7k7KAn2u0mkGXA5nHP82wb+I8PgX+vF865cQS0FWJutNYjzl4fXBNtZfqHQyomMHjZI8w2C/IsJ9O2EeMSg8ZdCOA2IvwsRrGGCGuIsIoaVnvfo/Cx+sv/XK5LAIYMHs8rbciQTyyBLJtFmq4iTu4g9t5GEr6OtL6KtLYI1CeAug80/XwzAbUQHfjdQwRXLxDETxCmH6GWPoLf3YIfbwNZpzTB5pPblE3o5WTWZkxIY1Ee46CJs/Rck4YWNwiI+OCEIxlHZBhqE+4sM0izuR5xdgfd5FaPOCMSbRnA4AYRQIow2EWz9l2E/jEu2n8KOdHmAfDQrP2ulbzj5dEMLu65JgkTjSyzpUc64eSVlEUarMPIF37tp8Gw+yiPzBhkE1M5ObawhKS31pmZz8mzbGEpD9+cnrUvcphl8I/2EWw+RrC/C/9gtyDN/INdeHs78Pd3gYszdffbgTYt2pzNMPeNQfO3/9O+Acu8SGS9Uh1VGbycXOT5UZr8vzTKq+pUqwsuh1ZGSTZp4ISpRsTJtDUdyr5ua7Oa7Pw6rQyaYc7zlGv/VUGOTxq5wY9LbzX+u4pAu8nmD7xctrLJ35Q/hxxziPQn+WXI7jBCVP6uIlqlnFobqxqrJMklx0M5Ftrul/lrzzouv1yjTo7jN4FWX7YxV/PcsrVp2e75SwJ+XBKblBbfzIGPT0SC0RptXE+SIOPjVxiGxfW+76Pb7RYh2UmSIAzDEqEXx3EhF9Uf371aLhlA5PCn9V4DHMHm4ODg4ODw+4JWC3jypBfS+QPg2y/yddGO60BMJNoEgDcAvCluNsgJtEMU66KFyNdFS3yxLlo9d1wbaQBflCTaHPBgNl9k/0eBzOSbB9hItGtlA4CxYJA8ezAKvDYGLNeBVtxfD+3JBvAPDvrroW2d5Dt3SsyOAl++Nbgr54O5fK20z/DissAVssIDbbv3Td5oLxCrfmhj8HuEWYS1Hnm2hhrWEGEZEUJUC8bXpsmyEaTpHSTJHcTZa4iDN5BE95HVlpHVeu52DS//+Eq6WQqvewz/YhdB9xmi9CGC+AcIky1E6XMgOSkmyXwCLQkE7W2/NDy0j0aC0f38W+Yj34bTNVJPPG+gb6Ry4yH3vDBIs2kk6R3E6e3eZwVJ2iPTktsw0BcWDPw9NKLvIwqeoxa+QBg8RxS+QD3aQejvwvOSQp6L1s9i9+RfR5LOYaz5G1iY/CuFjNy7r6geZaMBGxlAeUjyReqGk6DDCDkNVQSaZvDR8SpSTjOms6hWeJklc4sw80tI5hZgFm/loZrzS3k4Z8Mel+1dXuSE2ZOPe4TZHoIecRbs78I/3IN/tA8vHRyUSOZizS30CL1emKrNQKWwXWpntMue1K9GMhTtG4D3Z/9ZjPzs2zA/eIz23/1PgTgt5UFGI5GjtnUCJblM4CSOLLesn5vUNb9e/tfSlL95G+aEonYfjUl8N0N5jU23soxVBI0sL9e5TIv0PCzctqoNEGz9l69VJUMZJUHCx1m6TsvLRiJpsspyaGlK8opvBsHLbGtTnAypIgJtxJkcH6vIRr62IqFqyYJh4GXnoaFcF7a2z3U5jATVnglaGaVc/Lz0dOfn6Ho+rvCxhq8rR/XFiSy+Y6j0pOPPbiLDeNg/7/t0XRiGiOO4yJPuieO4FHZL9/C2xsk27ZlH7fPT1Lkj2BwcHBwcHH6PEMfAs2d5SOd3fwB86xnw0TWwR5sLzAKYBLAK4K6SwDWAXeQkWhvw0nynTRMAbf7Mr+WOa2EIvKGEc742C8z9iNZFSw2wdS3Is97/J1dARyG8JkPg7XFGoI30fy/UgZPrPon2eBv4LbYz58tzXY47U8DP3Ct7oRGZNvlDWA/NwOAQaUGikScaeaMdQmELASwgxFfRLJFoqz0SbQYBvCEkGiFJDLa3DTY2DB49Nnj3Y4Pdzv8ZSXgbaTQPjI3kIZwVmwl48RX81j787hbC5DGi9EME8Qa8zlP4yW7JW08aJxmblMt1kAg8VE8z2vnEXjOMbAYuycAhQ/804o2nxSfyWTaG68460mw1J9B6nmjdXiinMXp4YOAfoR49RC18gSh8gdB/jih4gSh8jjDYAUx7YOLOdWFMX69jjX+KB8t/auBaXh7NgNcMH40YkSQLT1szzIbp/9NCplkiADwP2fRssaNmvjnAEtKFJWS9Y+nCEszktD2DJIZ/uI/wkw/hH+YeZ8HBLrz9Xfj7LxEc7sHbf4mgda22kwHDWySvGboamcANOW5s8vaorTHGjdwBshlA8O//Zaz/0ioCLwPwFVz9V7+OnV/6N4EkLRnqWgizBKXN8yLDl4cj8nS0tiN1Y8v3VY5pepZ9QyMmSXeAvpvlMHA92IhP2W64QS6Jb+mZpBnyUsf8Ou2/rf9aSVlL+SWhZavLKsixW7Yp6YlIO0jKsZDSkvrSZJcvG4bVr6ZfW7uTO5nK+gUGiWeZhvzP65sTPfQ/DPt0jByHb9JmZfmr1t3k5aryqLtJmeRxPu5IIlRuwiOJL20cpLGIn+cekDz0mNcJX4O00WgUJFmSJKjVaqW2SGQ8ycTJQPq+Sbi4hCPYHBwcHBwcPgOyDNjezkM63/8Q+OZj4P0z4IUHXI0jJ9GmAcwBWFYS6AA4Qr65QG9dtHpvXbQ2UFoXzYRA1wfuTpdDOV+fA16bA+5M2KOdfpiIM2Dzuk+c0efRJfD0GoiVOeFsDfjKZJk8e20s/54Ogd2LPmn26HEezvm4R6SdXA+mF/rA+izwz7856IV2dxZoalF5r1pOGLxgpNlWiUjr4nrA/M53WL2DCF9AowjjXOuRaSuI0MTNKsgYg8NDYGPDYGMjw3sfGXy4ATx5AewcA2cdD6bu5SRa3c/Z09d+tpxIGsPvHiFovUSYbCBKP0KYPsrXRYu3gey6ZABw4yiXoXpBeCmvjfwhA0IaSXzyLY0rPqmWBqS8Zpg8adaA8dYAfx3w7yIJ7uLavIasu4qsswaDKbUOfO8UtfBZjzR7UXii1cIdhP5zeN71QH4l4xCDRo1GIvIySj3JtCWxQ+Br7PHFnnmYp82TScuT56sZVFo5qgxZMzLa2wxgGfHsQhG6mfaOZfOLyOYWgMjecb2zkzxE88N383XODvfg77/Mf+/vIjjag398CE+QMlxeSVJqZdbuk7+riAW5JpxmPFYdB4A4fhOpdwvNtw4RvTUF//4tBG/fx+if/gKuTQLPGDS8Nka/eAujf/KLuPz/vVuUT+bBiQFJblQRKbb2yI/biJybhFjJ9G3jC+/v8hgnPoaREpK0sl0r++QwYkaTlZNrkliV8lSRGto5eZ82DsrxU0KSgbYyDEPVuCbbCdD3sOS6pXWzqogeTnTYNq3h/7XnkfZbKwsnWHg+nEC6CenCn6k0/vJ0+CY78hnH5XnVUEXtWQGUPbelnPJbG5/4c1p7qca9EXnbIiJL7vLL86vyOuVkFw/f5OGjvE212+0iX9JpvV4v5KIwUvJQ4yGrfPyOosjaP4fBEWwODg4ODg5DYAywt5eTaB9+DHzrY+B3D4EtA5yNAGYOOYk2AeAnlARi5OuiXSL3SusAkQ+EtTx8s3gXG+SfDoDbE4NeaK/P5eRa7Ufw9O6kOVmmhXI+a+WeahKLdeCnpgfDOe+PAONBHrJJJNq7HwO/dtBfD+1a2e2zGeWk2c896BFos30ybXUaCO0b9t0YlwMbCvR/7yBW/dDG4eMeagVpRmGcq6hhGSGCG3qhXV8bPH2afz76xOD9TwwebgLPD4GDCw9J0PNAawRAwNKczL88GEw2gFszBvduGfzGf/LvI0g+Qhg/y3fkNCf9ZfOFUcPXwQnD0Er0aBN/YNCjTV7H89LIMvqtefFw2IgdTh6kWQjPX0eKFWRYQ+avwWAVJlzPiTVvsZRmhjxcGbhC6G+hFn4XtWgH9ehlL3zzOcJgG753UZJj0NAvG0PcKNOMVLrGRghwHfJjVE5pzHJ9c+NJLnzdJ0x1Ekwj3Ii40K4rIQyRzszn3mW9DQFStrNmOreIbGEJZmwCVnTaCA73Ef3g3f4aZz1vs3yts5cIj/bhddoDsksDGOgTmxqBI+tEI3M4aSPD16raKhmYqky8r0QR/NdW4L+xiuDuMqL1JUTLswjmxxFOjSAcG0XUAKIghu/Jgfas+JWYfACMpseKMCfZF21yS7LVRpZxvWjtWhsbbPrh6VRdK2Xi5Iy8XhsbeLgkpSH7hA0aWWi7RuqA5889eKRXm40AGQbb+CjrVyO7NNn5p4pElXnLtsV1K2UhQqXb7Q6MYfzDySe6n/el8vIH5XUDbWveybYhPceq9KURTFr5tbogealcGpHNy8K9qugctd+qkHPZP25CBGnP9yoSSabP60PqgNcj96QdGRmB7/u4urpCHMelkNI4jgf6tdQ71wc/J/XKryG9JUkyEOrJj/u+jyiKSiSmLNer9FGCI9gcHBwcHBx6ODnJSbSHD4HvfgR8+yXwJAaOG0A2i3xdtEnkmwvI+UiGfF20AxQhnT7ypa4SP+fYgN59jfz/WBP4siDQaF20sR/BumitFNhQCLRHV8BWC4p/FnCrAfzxmfKunA9GgfujQJgBT496nmcv8lDO/9NBvivnsyMgUV76To8Aby8NhnHenwOWJ3LHrM8CA4MDpMUmAjykcxsxjiyhnEsI8eNolsgzCuucgn+jUM40NXj+PCfQHj02eO9jg4+eAs92gb1T4Cr1gIaff/hmAs3eB0AzNFiYANaXMryx5uHLbwKvr3i4dwtYWfAQhQA1xjt3/oqVECPIcAvtTTR/C20jzqShIgkJoGyIaCFXmrHO76P8jAmQ4Q6MWUeGVWRmDRlWkJpVZN4ajL8EeP5gnzQtwGwC6TuAeQrPPINJn8LDMzTre2jUrzA+PoaRkZHSJgf9sg16E3D5qKxEVvJr6W04laEkloXc4tDIDmnI8/S47PwembYkL6jOeFggkPf9bHQcZmEZZnEZZmG5CNXkBFo2Mw8EdqbbPzpA8GIr9zSjdc4O94pNA/z9l/DPT+ELI5KMpJKho+iwqp1z41TqqKrt2SDrYSAcsxbBf/MugtdXEN67hXB1EeGtGYTzE4imRhCORYjqPqIgrRzXMhMjjiNcX4yie1ZDfJzAO34X6d4p8J/7k5gZbyFEgtBLkXZStH/r4xJJzom+LMuKc1Tn5EnCyQnuRcR1L9twFVmjjSHSaObfNvKGvvn4w8/LvCShKK+1jTscVWOmdp/mbUttjpePr+2olcM2Rmj5cgKBXy83aJHpS10BKC0Ez/Op0qlWl1y/Mn8qm1xvkJfF9/3Cu8hGoPIxTT5HtLF1GEEm64DA+7NG1lHfsRFSGonL+wN/sSIX8adjXDa+EL+UW8tbq2etnVV58cq0JUmsjXuyvJQH7wtJkqDZbGJiYgJXV1cFqdVqtXBxcVEQXlwO27NOrq1GG2hIIpB7u9FvvstoFOXbvvNrKE3yupdjyk3hmU9zl4ODg4ODwx9SXF3lJNqjR8B7HwHf2gI+agEHERDPIA/pnAIwCqjRfFfInQh6JBrSnEQjzzOJZjRIoNFnduT3powcl0m+9pnmifa8PXi9B2ClqW8scG8ESOLerpyH/c0EyCvtxdlgekBOlMkwTvo9oy9v9UrowuA5I804mbaFGG2FKqzBw4plQ4E7iNC4QSinMQbHx8DTpwZPnmT4wScG33+Uk4svjoCTlgdT8/qbCSgT48AzmB0F7swBD+4AP/Y68MX7Hu4uA3eXgYlX0M/KykppEs/JG06ERVFUTErpWjqnTXIl+PX0n09muWHOr+cy8Im+MR4yLCEza0jNCtIsJ89Ss4rMrCLDLejvhGP42IaHTfjYArKnQPYMHp4B2TMkyQt4vbrnu441Gg00m01kWYaRkRFMTk6WDAhpOJPscl0smy7omxt62ppc0lCT5Jgk3+g3kXecICMjJggCnJyc4Pz8HN1uF/V6vWR4BUGQh2H2NgkgAo1+F8TZwjLQrBigWtc5Wbb/Ev7hHsLDffh7L4sdNsOj/XzdszQp9MONeQL3vOLeB3SPDDWUxr30GNMMbulVQRhGqBCyRg3B62sI31hDeO8WgpV5hEvTPeKsiXCshqjuIfKHEWce4m6A+NpH9yxE97CGzssmOs/G0Xkygs7DBtrfbyJ5UUOfLTYIgm0sL//xPI1bSxj9m/8qJu6NI9s7wfn/+K8j/fhFae0jSUDJstrIXDl2EBFH5AgZpnJzBs2UpLZI6x4lSVLUBzeSZdiY7/sl7xJOTMj1kCR5aiunXEyd91kiMHi/14g6jfzRwvtIZn4NX0SdjvE2qYGPD7LctOsiyc4Xead6obXO+H/eRqh+pB7lmC/l4G2LH+OycdA13W638GAyxhTyR1GERqNREC68T9uIJT6eEjQ9amnw/5IkJb1QXqRbYwyur6/RbrdLIYkSfDz2PK8gcIAy6U0ETr1eL9pms9ks8q/X6wXxKD1NeV7Ufnib489wToJppCDJxL3ltDLx8YHrne8Oy8krfp3neSWCn4/xfC21q6srJEmCOI7R7XZhjEG9Xi/pgPRF6dL9VGfU5nm5iUimDyfRarVaiVije6jeqB6TJMGv//qvD+imCs6DzcHBwcHhc4dOB9jY6Id0fnMD+P458DIA2pMA5pGHdN4BsK4k0Ea+Q+cF8nXREqAWAH6UnyoQ5Z/UB+7N6CTarfHf+3XRzuJBEu1R73tXYf18AGsjwJ+aH/REW28C59eMOHsO/Boj046uBtMLfGBtGvhTb5S90B7MAffmgJEfwnpo50iLNdA4ebaJLl4igWamTMLH66iLDQXy30sI4d/AC63VMnj2LPdCe/jI4L2HBp9sAVt7wMGlh67n9cM4Q5beRO8Dg4k6sDQF3LuV4e17Hr70BnD/Vu6FtjTj/dDaR5WBxIk2zSDl63bRpFcLUaE0eX588s4Xte4TagZJOoPUrMJ460izFSTZCjKTh3Sm5g4ArZGk8LCDAN+C720h6BFpvrcFH5vw8BJAWkywSx53gQcYf8DjgIw7WmOFjBoeosQNJY1wlESNZtxLTwKSgYdJyXt4PlrdybQJZNCEUQRMzSBbvI04asIsLKO7fDsnzRaWgcVbOaE2O6/omlSewjvaR7jxSb6j5sFuvilAL3QzONhFeLgP7+oChumGe32U2horjyRaZduSxiStkyONekpDGtkaJNlUIuNGRxC8sYbgtRWE95YRrswjXJ5BODeOcLKJaDRCeCPizKDb9XF5VUN8FqJzUEN3p4n21gS6j5poP2yg8/4Ikr0IKIUVXyAI9uH7uwiCPfj+Hpr+OS7wLyF/w2MAeGg0fqOvo51dtP7sX0CL6UwLQeO60dba4m1QtkVOFnEShvcLjWzhdU/X8F1T6TgZybwOJQEm+0BV+9cgxzmpEy0NjYSQOrkJQcvTkR5sPF/bb56+HCv4LrE0dnHSnXTL1yuTclX1HY1o1epCOyY9Q+k4r2/65n1fjgucEJXe1DztqjHUplPpZcbLyn/z9sPltpFrkjDNsmzAs4rGNNkPeH7a81bqiX7z55F8VkhiTWu3PA2tfdryo/LJ9sC98rgupZek1FutVitt9JAkSbFRgdYHbPXOX8zwOiE90Ie3R0rP5oXO15V7FTiCzcHBwcHhDyXSFNjczEm0jx8C33kEvHMMbAG4HENOos30PktKAl0Ap+ivi9bN+ZGwBnQ99AmbMP90AdyZHNyh8/U5YH0aiH4I64FV4bire6E9vgIOlPXLQg+4OwJ8dXLQE22lAeydMQ+0p8BvH/Y9066U9OphTpp9/W7ZA+3BPLA289nLn8FgD4m6ocAmujhVKDQPwC2E+CmMDGwosIoIkxguVJYZvHiRE2iPnxh8/2ODH2wAz14CL0+By5gIND/fUIDAwjhrgcHCOLC6aPDmWu6F9uZa7oW2tuihUYT73sxj5tNCC08CBg01m4Eq1wrSJrf8WoIxQJpNoZve6hFnuedZkq0iNXeQmhUAujeUj11E/rvwsQ3f20TQI88Cfxsm3YIx3VLeWZbBQ/mtupSPjskdyKSRztOQ4YQ8Ha5TjegZZihXQfO80AzGrFZHMruAdG4R6fwi4pl5JHOLiGfnkcwu9D7zMLWK2PKL89zj7JMfFCGb/v5LePsv+2Ta0QF84YHA5SLSjIgzIiEliauVSTNCZZvlxj8Rp3RekjoyvK6ok/FRBG+uI3p9Nfc4uzOHcGka0fwEwskGotEIUc1DOIQ4SzODuOvj8qqO7lmA7n4N3ZcjaD8dR+dJE+2Pm+h8v4n0YJA48/09BMEL+H5OoDWDfQRTOYnmeS8RBPsIwza7p28oNxq/jrOzv4w0XUKj8RuYmvq3rART0RY9oLYwhvSyC7TLhIY0sDk00oSTG1r4ms1LlnsTyt34ZJp0np/j5A9Pl7cdWznkcZVQVQg0eb30LrURSnQtv5d7Y2rkkUbsSD0OtGXlmByTpZzSi8wWnq8RaZqOtJBCeUwSUZSvJFTpuJRDbszCZZf64LCRR5qno0xTO0Z5S3JHkmK2+zXZwjAcIGkkGcbDLqU3ouwjdD9/Pkld8PSk7ug7juNSm+Xpa+M4T8f24k32DQ3yRQpvu5zYIvJY6p2TYfL5y71tqS3JlxDyOSWf7fKljubZNwyOYHNwcHBw+AMLY4CdnZxE++QT4Hc/Br6zDzxNgNMRwMwjD+mcBPCTSgIpci+0PeQhnV3Ay3rrogVAYb75ABr5/8kR4Ktzg7t0Ppj94XhiVZX1sKt7oT2+Ak7iwXtqfh62+bXpshfag1FgMQK2T/rhm48f5jtzPjnM10mLlXniRAN4Y2HQC+3+HHBr8rN74nWQYVvsyklrom0jRkcJ5azDwyoi/ITwQFvthXLWbxDKeXKSE2gbGwYffpLh+58Aj54DLw6B45aHLGIkmt+bNPrIyVkAPgymR4DbsyYP43wNePsecP92TqJNj3uVhvqPCjYDUk5kNSOOEyXcwKXr0mw0J83S3PMsJ89WkZk8nNNgXJXJwxFC/yECb6v4+N42fGwi9F/AmJbVuEy9FICnTva5gWEzIGiyzg1a7lXDDXryltL0xv9rOh9GXGpGdlFmz0MyOYNkbgHp3AKS2UUkPRItmc1JtGR2EdnEpJo2ACCJER0foPnkY4RH+0hfbKO9uYHayRHM7nNg90W+/lm7VdzCDRppxFEpNMNOK5PUA6VpC8nUQsC0NsDvTbMMZmocwVt3ETy4g+DuMsI784iWpxHOjiGabCIcjRDVgNAbRpwBcddD+7KO7mmA7kENnRcj6GxOoPOoifZHPeLsJCrd53nn8P19BMELeN4ufH8XDX8X/vg+gmCv5422B9+/VkkT7o2To08u8rLWau9jfv6/WNyT67F/rbyntjCGL/yd/wpGv7CIrJti83/zj7H3d99Vy24jKriRqxE62r22finDVanM1B5qtfxByne2pfSyLCv6ojS86RrNq6mqrFI+7fdN76fjGrGjkXn8Gm0s0Yg9fl4a+1R2Whye9zUax+g+oPzShci3KsLA1s9txziJysunHaNQQR6mx8P3+DXas0y2fTl+cVk0OblcWln4OpRyyQBJ8GptZ2BsF2WQsnEyjZO18oUP/7Y9K22oasMa5Hil9RdjTMnLUJbV9i3TkC9nJOlImz/wNg70iWwuhyQ+eT3yfsPTS9O05DlH4MSwHKNuCkewOTg4ODj8vsIY4OgoJ9AePQI++Bj49gvgYQc4rAPZHIA55OuifQmD66IZ5F5oO73v3rpotQiF51mBWn65HwFfnBv0RHttFpj5PVwXzZg8ZNPmiXaeDN7T8HPC7OdmBz3RJr1884DHB8CTl8C7R/2dObdP8/wkFseBn1ztEWiz5bXRZkfxmYmiM2VDASLSXiJRN06YRoA3UWcbCvS90RZuEMrZ6RhsbuYE2qPHeRjnw2cGm3se9i+ADvw+gcZd7YowTmA0MliaAtaXM7x918OXXgce3MnDOG/PeVXruP+BAV9nDEBpEgroHgT9/yNIstv52mfpam8ttNViTTRDbKOAhzME3rOCOAv8nETzzCYCbxtB0Comx7oBpO+mZzOSbMaLnKBrb6dJDlovJoqiQj/c200zZHga0rDgv0uhec2R3OtsfgnJ3ELf06wg0BaQzMwBYZnI4fDPTxEe7iJ6+D6Cwz1Ex4eIjvYRHu4jOtpHcLiP8PwEPvrGw8HBAbrHx/BrtcKIzbIMRjFINU8iGerFjRveviRxKQlPW91QukEQAJ4HMzOJ4M111F9bhb++mIdqLk4hmh3PPc5GQoQ1IPJ17xFCmhnEHR+tyzri0wCd/Rq6L0bRfjaB7uMm2h820PmgifRMI852EQTbPQJtF01vD8HkXo8w20UQ7MPzrkt1L9sLlYuXm6PKa8kWxin1V0rTB+oL43jtr/4SRr+Q75TrRT7W/41fwPm3t9F6cjTgQaa1XRt4e9bav2wj8jf1Le6Nw0khuka7VxrEGiHLdSzJLo0MkW1TEmBVetDatQT3PNI8lmzEHC+fJOn4f+qHGsFFuzNquuCh7pLg4NfL9mA7LsdY2R44uca9Vak98G9OjtBvG4FG6QAYGLfkOCx1xu+VfZh0ROfoGSHLIcMIpa64HARJIFGa/DmoPXN4vpp3NYGPuxq0cYXfy9OU/7VvkpHLxNdS43nKdkhp8GcPT5/S5jLzsSMMQ3S73WJdNWpHYRgijuPS+nk8L63cvE6lFx+l4Xke4ji+0Rgh4Qg2BwcHB4cfCc7P+5sLfPQQ+M4z4IMrYDfsbS5A66K9DuAtJYEWgH30SbQ450r8COjw51/PyyzzgfszOol264ewO6UNmQF22ron2pMr4EqxEUeDwbXQHowC90eAKAU2ep5nT7aA3zoA/sPDfGfO/YvBtHwPWJkGfv61chjn/Tng3iww3viM5YPBSyRsQ4FyKOe5EsrpA7iFCF/HSIk8o505J4aEcmaZwcuXwNOnGTY2DN7/2ODDDWBjB3h5Apx3vTx8s+EBdb9fuY3eB0DoG8yPASsLBq+vAl9+HXhjLSfQ1peA0SY1iD8ArmifEjQRlERHbjSEiNNbyLCKNFtFkt3J1z/LcgItw4Il1eue59l3EfjbfU80f7sXynkxkDchn+CWU5NEDTe6NSNJ+0/HpOElQxU5tFAZuZaUjbDj/z3PQ+b5SGbmkM5RqGZOmiVzC0hn6fcizOiYRaeA1+0gONxD48P3EB7uIehtFBAc7iLg5FmalAxFjfyi32SM0ZpyGiEhYTOq6ZwWakV6kGvUldL0PGB+BsHbdxG+toJgbREhhWrOjPWJs2g4cZakBnE7J866JwG6B3V0no+g82wC7U+a6HzUROeDBrJLSZydwfd34ftbCIJd+P4+mv4ugsn93vG9gjiT5auCzSDlupFGvjzHdTtAFjVCNJbGUV8YR31xHOHcKOoLY6gvjKG2MIbawjjqC2OIZkfh+eV69TwP8IDm/Vl0np4MnhP/tX4l/0vii/czma5GWkkSQxrU/HotHFGCk5tSTpsMlLZWxmHll3nYSDCNgLxpv6sa73i6mscTv07TCycpNGJTwzBSQdOh9LaTBBi9yJD9gI8nttBTSeLZ5NCIHDrOPfdk2+UEJL+Wt9th+uDPIikXvcyRbZ6vMynlJ2/OqjJLHcrzWh+ja2Q4NIctzypC2LbGIb+GyirDYGWfsbV/vlkEXU9rqdZ6L5SorSVJgjAMB0h9Odcg8JdFvE06gs3BwcHB4fcVrRbw5ElOoj18CHzvMfDuGfDCB1pTABbQ31zgrpJAB8AxchKtBaCbkzNRDYj9wXXRYgCrU+VQztdoXbQpIPw98jpKDbDd0r3QnlwBbWXF/YkQeHNM2ZmzCSRttivn434o5+MD4ELZpKAW5JsH/OTq4K6c6zO9XU0/A9q9UM5NFr5Jv58jRlfxQ2vCwypq+BqiYh20ld73bUSoDSGuzs76YZwfP8rw/ifAo21g+wA4vvKQ8jDOgKU1nX95MJhsArdnDO7dBr74APjifeBebzOB+ak/GGGcv1dIEvTWPbuDOL2DJF1BYu70jq0gM0vQt8VtI/C2UfM+6JFm+RpoYfAcUfAcMIcA7KGlREjShF2SfP3rdK8dPpnlE3BpvGrkmM34kpN/koefj6KoeINdGHYAzNg4utNziGcXEPfWNYtnFvoeaHOLSKdn7fHSWYbg9AjRzibCw32EB3s9z7MDBAe7+WYBh3vwzk4QWAiHkq4UQ4d0zcvM0+l2c79d7j1EOpNkCE+Lp8/TK+XjecDiHPy31hG9voJgbQnRnXmEi5MIZ8cQTjQQNQNEERDelDi76BFn+3V0no+i/TQP1ex82EDnB01k12VzxfNOe55lmz2SbA8j/h688ZfF/zA8gOe1BupdGmf5ueFEg0aI2AxT3naLdeeMQW12BPWFcdTmR1FbGEdjaSInzRbHUZ/PybP64jjCsYp19ADEZ2109y9w9ckB4sMrjL29hJHX5vpypgatTw4LeTRjl5eNn5MkjLxOM4D5vVI/fMdbjUyT/Z23V83jjx/XZNHGKdtvzXDWiAotD/6R5dM+NqKuiliQedF/2aepn0vyjKdFhNGrridlI+L4GCFl5h58PLRSpqWlW9VetXu0Nmx7Ztjy5PdRG6R2yzHMW8ymJ9pJl9oKkOuoVquV2j9/HlJ6cjkD2WYkmSnztoXpa/81wlPTqVxCgkNbtoG3X9Ij93qzveiS/YeHa/I+R2nK9MIwRK1WK4WP8nXdpA44QaqRnq8CR7A5ODg4OLwS4hh49qwf0vneJ8D3joBnKXAxjpxEm0O+ftWfUBJIkK+LdoF8c4EO4GVArZavi1Y8CnvromUAJkYHvdBoXbTmZySTbEgyYLOlr4e2cQXEyhxrJgK+NDHoibZaBy4ugY2jHnG2CfzfexsMPD0COkpo6Fi9T5zxMM4Hc8DtqXznzk8LA4OTYldOHsqZe6LtQhEIwBwCfKEXyklroZEn2jwCeBWGardrsLWVE2iPn+ReaB8/A57tAnvnQNv0yLOGnzOIhPHeB0AzNFicBNaXMry5nu/G+eB2TqCtLnqIQuAPswdaFbIMeLkLPN0Enm32vrfy76ebwPZzIE2/odwZI/BeoBb8NgJvG1GwnYdyelvwsQWYXXhe2SApJqSGjDZ9UW36Lw1iG7llTH8XNQrJ5Plqb+MJcsJOv/k3n6APGJm1GszCMvxbKzC3V9G5vYrWYr6z5unS7dwbbXYeptG01oHXukJ4uI/a1hOERwcIe15n/sFuzwNtD8HRAbw0KcnDfxfyKKQFL6csM0HTDRmFfDFozaDV6qZI3/eBW/OI3r6P2usriO4uI7ozj2hxKl/jbLyOcKRHnHk3IM5aPq4u6ohPQnT3amg/H0Xn6SQ6Dxu5x9mHDWRtnTgLgmc9D7N9jCgeZ8D1wBpJNkNcll+2OX79MD3TcW5k+/Ww52k2htr8GJpLk6gv5iRafXG88DarzY/Br9gJxqQZugdXuN44Qnf/Ep39S8QHl+geXKKzd4nu/iXae+foHlwC3bIXaH12DG/99X8Bk//MGtKrLjb/8j9Cd/tswBDXysLPS2Kiqs9xgpu3P+2+wbXndDLP1v+1utRIOC6fvF6DrQ3YCG+ZjxwTOUliC6mWhJmtjFrf52nxa7SQOp6XRkBKYoXnJwkU7XqeniSxbWOPpk86Jr3f6JzmFVWlI5vHLS8LL58koIhc4952RLho5efgZZchoORVLMOHjTElMo++ed60ThiXl+tYK6et7mUfkv2W5JHltJFQ/D6NoJOhyTIPKTsnGfkaaVXjl20dNy4XhZTShhPSq5anyWWuCrGtgiPYHBwcHBwGkGXA8+d9Eu3Dh8B3doFHXeBkBDBEok0B+BoGOY0M+aYCR73v3rpoUQggyj3PCtQBg9xL7QuCQCOPtGm7zfuZ0M2Ap9e6J9qzayBR5lELNeAnpwY90ZZC4OSc7cz5OA/nfHIIbJ3koaMS82PAV++UvdCITJsfK9ngr4wUBjvFhgJ98ozCOS+UUM4AwG1E+FmMFOGba2xzgbGKUE5jDHb3DDY28jDOHzw0+OAJ8OQFsHMMnHW8chgnhTbVkYcHAwg8g9lRYGXe4MEK8KXXgbfW840E7i4Dk2OkkM8fiWYMcHAIPH1WJs6ITNvcBrrK7q6eB9y5DXz9a8D3vvMPEPrP4RdhnFvwsQugHDLIDbNMaZh8u3tukNiMLAIPYdQWdJbkjjTOyvrQ1yMaMPo8D2ZqBtn8Uv5ZWIJZvAWzsAzTI9CweAuYzRtZ1vuUxqA0RXhyiMbmE4S99c3Co73e9z6Cgz0Eh7vA5QU86MZ28dZbebvPDQFtbSn+X4PNUKQ06X8Yhuh0OqVQK+N58FaW0PzS66i9voJwPfc4q/XWOIsm6gibAaLIIPQU11uGODHoXuXEWfc4J846z0fR2ZhC+5Pc26zzcQOmWx4nPO+k53H2BPkaZ3sYCfM1zoCXCMN9RNEhsuwKQNmzicpfXutt0LtBI2XoPo0kkW1UIpxsoLE40SPLxlBfHEN9caIgy8jbrDZdvWhnet1FZ+8CZ7/7HJ29C3T2L9Ddy4mznEi7QHxwheS0XTwkNLIrDEMkSVIYi9zAzs47ePjf+jUEzQhZJ4HJdM8rjpsQRzwNjZDh1wZBUIRjaffyb3lOJcRZGvI/1Z0MN+TesDIdG7Ej85FtROpDpqn1WR4GBwyuaVgVqqqRdvJa3t61utBILxs5xctRRfLZCDPf99GN1tBtvoZa6xHCztNS/rz/Eckh8yXySZIpNv3ajmvjrq3+bEQUETtUdzzEsKotcBn4mCzJPA7SERFs8jmrfWtrImqoarPaXECWQZJLWr/g/6k8UueyfxOoLUiizUbucrnl0hJ8rJflIL3S0gkaYafNi7ie+W7WN4Uj2BwcHBz+iMIYYH+/T6J9/BD47hbwUQvYrwPZAvrron0ZwB9TErkGsIs+iRbnnlVBDejy5zbtvhkAD2b6YZw8tHNp/LMRSja0U2DjGnh0KUi0a2DrGgrNBNxqAF+fKRNo90dyp7z9s96mAvs5efaf9MI5d88H0/E84M4U8CfuD3qh3Z8DJj4jcdhCViLN+M6czxGXSYQeRnqhnGsl8iz/fwsRogry6uIiD+N8+tTg4SOD9z/J8Mmmh+0D4PASSEK2mUDI0pnu/5yoGyxPA/duG7x9D/ixB/3dOJdnPWvk3R92GAOcnjLibKtMpj3bAq6v9XuXFoEf/wqwvgrcXe99r+WflTu59ycArKz8hQECJv8uT/rlJF7eo5EXWrgIGSMyHf5fGolSDrkIe4mIa44gnVuEWVhGtpATaOn8Uv5/cRlmfglmYQmoVYTUnZ0CezvAD94FdnfgHewiONxF/fgQtZND1E+PMNa+Rr33ZptklBPwLMuKseImxAWBGzO2RcjlMQ7btSYIYO4swHt9FVhfgre6ALM8g9rMKNYm6gjH66iNBIhCg+AmxNmlj8uLCPFxiM5uDd3nY2hvTKLzsIn2DxroftKAiXXiLAieFKGZY/U9BCP9zQF8fx+e11ZDh+gTBCE8Lyi1K41Y4Dq2GYbSOOTt1Q+D3jpm42gs5uub1RfGc7KMrW1WWxhD0Kh2je4cXKK9c4bTd56js3uOzsElOrsXSI6u0N2/LLzQ0qsyK24jVKVXi9Y/q3QCAFm7bAhyI9xmFEtDXRrDNtKK+q709qCxggzaKhkkgW/rOxqR6nleaWF/Xiapa3msqs9xI1uOVRJ8TCwTwFkptE8jSqQu5TgswyzlundSF7IMUmdaGTUd8XZh0xNP93z6l3G48lcALwBMivntv4ix418rXcPzkB52Upe0qDzvx5oXm63sVcQRl137rYUeE4l7k3XYbHJI8qiqbNo6dNJbV/MUL41zFS+35HVEVPN+fxMikdK2lZWO8/5EO8RKb0DZlulTFYoq9awdJwJP8xzkbZuXg3v0yZDdV4Ej2BwcHBw+5zg5yQk0ItLeeQK8fwnsBEA8hzykcwb55gJfUBJoI/dEY+uieeitixagvxpXb120zANWpspeaPRZnfy9WRftKsnXPuPkGf1+3oK6c+VKE/i5uR6BNtJfD200BV6e9ki058C7B3k455ND4LQ1mE4U5OueffXtHoHGdua8OwsMsdMqYWBwhLS3Dlp3YEOBA+ihWvMI8GU0BzYUWEOE2YpQzjg22NjO8PRpHsb5wcN8M4FnuwYvTz20Mq9PoNVZGmO9D4BaYLAwAawtGry5lnuhvbaSE2jrS0Cjruf9ecDFBSPMNgWZtplv9KFhdgZ4+w1gfa1PnBGZtrYCNF+BiJXkhJww32SyKCe7clLM09Emqvy8NnlOAWBuEdniMrLeDpu599ky0rnF3AttYRlmYsouZNyFt78L/wfvwtt/mZNnezswezvwdneA3RdIdraB66vSbX4QIKrVENZqqDUa8IMAGBnJCSsz6BUgjUOC1LH0UqkyyjTj2vf9nJAIA2DtFvzXVuDfXYa/Og9/aRrB3ATC6SaCsRrCho8oyCqJM2OAJDFoX/iILyJ0DwN09xrobI+ivTGJ7idNtL/fRPdJHSaRxNlxjyB7BN/PwzPHGvvwmhSmuQfP20MQdMV9+s6N8rx2TpILXMccMpTK9334I1GxIUC9t55ZfXEc9aXe2mZEpM2OwKswlLNOgs7+BS5+sJt7m+1d9EI0c88zOtY5uEQWl3fOo7rWdqSTOropOSsNfX6cf/O1BDVSRPMSlQSI7KOaTBrxqZFHvE1LY91GGGvQQp7lb+2/lFUjYTQiRuu3Ntm0vDX9UX78etvacrZxQxIjMg8Zkir7oY1IHEZGFX0SAUw4iSycRBZMwoRTSIMJpOEMTm7/a+iv7enj8M6/idGT/wgw3VIb4N6GnHwlMlLr85JM0wikYWUYdo7S0EKcqa40cqoqH1mf2hgo+4jWLyTBJNPn8hIhrPVX2ReqntVSjmG64/LwNOQxyoPqm6fPCUQuj6YTKbOUX7Yj0jGvR/kMok+n0ynJZYxRN5m4CRzB5uDg4PA5wNVVvjg+kWjffwS8cwJsAmhPA1gEMIt8c4H7SgIJgHP010XrIg/prAFZyNZFCwA0c8Jqekwn0e7PfDZSyYbzWA/lfHwFvFQ2AvABrI0AvzBf9kRbawBhB3h+3AvnfAL89iHwq7310dqK29dILSfM/tnXBtdEW5n6bKRh0gvlJPKMbyiwhRhXio9dCOAOIryFRo88KxNpI+pi9vlk4uAA2NjI8PRphg8fGnzwGHj8HHhxDJy2PBgK42ywMM4IRRinD4PpUeDObB7G+WMPgLfu5uug3V0GZiY+v5sJtFp5qObTZ2XijMi0o2P9vokJQZyt9cm09VVgfPyHI582eeaTcdtEnaNqki0ntZoxmo6MIptbzMmy+UWk80sDXmjZ7AIQ2qeg3skR/Jcv4L33Xfj7L+Ed7MLfe9kj0l7kHmknR/BRniBLIyhIU2QWQoMbODysVa4rQ+DllGSj1JNq8EcRsrUlBK+tILi3DO/OPIKlaQTzEwinmghGI0QNH+FQ4ixDknjoXASIz2t5qObLOtpbY2hvTOQeZx80EG80YFI5DhzB83bheQ97u2ruoR6+BMJd+P4ugmAPQXAAz+uUSENJjPUNz8EwS5sBqrUjueB6SX8AanOjRVhmKVSTvM56xNnQTQFOW+jsX+Dq4X6fLOt5mVGYZmfvAslZu5DPRubIMmoGs2YocmP7JsbtsLy4vuR1wyD7NyfCZD1RHXECgqdRWo9OEB+y3BqBaCOzbCQd6TdJkgFD3UaqVRnnUhZO6EnjnI5rxjnPR5OLiFeZJ5FPklSTuuPHiKTi+tfaKZfJeBGyYBKJP56TZUHvE2rfE71P/t8EN3xAeR6M10TmjwBJe2A85CQHX+eL64vINq0tav3qVdq9vEdrj8b0QwI9L9+hMkmSys0NNMi2WDUukHcX/SavK7pGW4NNyi3HYZu3tG3MkAScLAO/xzbey/bKwZ+zVelocw1ZVp4mb1vc+4/LQvUnX0Dwvk4vKTzPQ71eL+Y2r9q+CjnNp7jzl//Lf7oU/0rCUWHoHJ+kcDdX3oBkQ6H70zTtdTgPUVQr5UPX0QOY36s9eCg/W6XwfHmsLr+H2HY5aPJ7SR+8UWsDpjZJ452Yd0Qpt+d5yGBYufuNha9ZITuv1AMvV5qm8E25I9FxbYDTOgFdQ3H0XF45IOTH0lIb0cAfXHLSy8soF4S0TVCozXH5qV1Rp5Jut9KtFgDCsIbZ2dmBwZPnId800sBMD1ib26k2keIDRf7wCRH2wmG4vEmSIO50Cvdq388/fKAu55HBiEUebRMsKSPdQ+2V3wug6D/8zS6d19akkGtF8Hs4qh60pPdhEynbQ5q2yrY9HLQHkXYMwMBkSXOJH/bQkpPKvN2EODmZxcnxAk5O5nF0uoi9aAWnk3OIF0aAJfTXRRsdKEK+vsuFl5NoV8h37EwAz0vg1YAsGDR4a6aFaexjBgeYMnuYwQFmvANMZXuIsutKF27ZH/vXeciy8pjeuwJXXg1nzQWcjSzgtDGP05EFnDbmcNqYx3VtYiAPz6SYbB9hqnWI6fYBptr7mGofYrx1AtPycZHN4AzzOE5ncWbmcWLmcG5mYJS1xBq4xLR/iCnvCNPBESZxgJngGJPeAUa9C3je4O5lvGxVE9y4FuJyegJXsxO4mB7H5cwELmcn8mNTYzDKjgVhu4vxkwuMH59j9OgcE2eXmDi5xOjxOUbPr4Ak16Gsg04nxOnpNM7PZnB4NI390yUcXc/jvDOF63QMWS3sbSbgAZHFcyC9RhDvI8qeo549Rt08RC3bxu3OM8y0d1HLkgEdcFmA/rMR6PcJDx58Uw6NoLGRDA25TkuaZci8vo5pfC0m6D0RjAF8P39We8IoMr3r5LgK9AwUL5c9SQNcp8u4Tu/gOruDVnYHrWwFV+kdtNI76JgFXV+4RtPfRsPfRsPLP3VvGw3/OQJ/EyY7RuYBbQ9ohUDqG3hhBD8MYXplfvzwmZr2q2B1dXXA+Bv2rCUdSA8YSqMwAvwA2ex87nU2t4h4dqFY7yydXUC2kHujmdExe0btFoKDXXj7L+Ef7MHfz8M1vb2X+UYBB7vwD/dh2i3rBFx7E82fLfw3zQt4OmEYol6vI4oiNBoNhGGI0dFRRFFUjLuat578FOdqNZi7t+Ddvw3v7jL823MIbk3Dnx1HONVAOFpDWPcQBSl8zz7lNgaIkwDxtY/43Ef3KEJ3t4725hg6T/LNAdrfH0F3s567CTMEwQnC8BBRtA9gF0myhTjeQhDsAsh31vS8PXhep/QM10KJeP+QpApQDouj++TYZ5uHAYBXCxDNjaC+OI7m0iRqLDQz9zgbLzYJ8CveXGRJiu7BVc+j7AKd3YuCLGvv5eua0f+M7SIj58Ga7FVzAD6n0+YoURQNGJpSR1q75raDfLYA/TWL5DyBn+eEKE9fqw9ZXu3Zxsdwyl+2mSzLirmgFhbJy6yR1/L5xUPWuK55WYwxiOO4eGbwORYPe5OeXTxvPrbJ+/gxacdo8z9+LemK5pV0jP/m5SD5oigqxiveHjhSROiaUZhwCok/jsTrffxxJN4YsmACaTCJ1B9H4vcIsh5RZoLqtQJLMCn89Bx+cpZ/p2e93/n/ID2Dn53DS85wuvQvI2m+lg9gnof61few9Ml/CX7PJiS9pWmKVquFTqdT2Ir0oXFYtmHZlgiSLJHtTB7X5tZaH+X3pmlahCAbY3B1dYXr6+tSHUlIgpnGAt/3Ua/XB9o5LbgfBAFqvXUesiwr7ouiCEEQDDybuIw2G46P77bxjPLWuAvKS+pZ9keevsyLzz1IHuJSbHLxeRn9t42dvH/yeQDNKfk1/F4qK7VD4l/a7XbJHuQ7v37ve99T69yGT+XBxheK44wrVao0dKnQVIFU6G63O7BTBR/I8/v7aaRpiijK3SKyLF9Iljcqyps/EKSiVddyNohReWRH5RNPPsmj8sgGzV0gAfvbE/rmlZkkSamxy4cAPJ5eeaFP7QEtO6Qc2D3PKxbtJXloUOEPOFkO/kBJkgRRFBVbDssOwO/hE37ekXid8UGV0tJciGXn5u1Jdl5utMkJNCdUuT6BMllSr9fBn9W8LDb9kA7kBECWg8td1Xb4pJA/EDzPQ6qsayB1xQfjzJTdojVXfGkI04cmVFwHWjryXl42baLL64cGee0hweuPv3UkYk/qmGQlA14+MORDU5K6vJ64fuQEUJaZ9CHbNK/z8kPCx/nZNE5PF3F8PI/jkwXsmTs4mVhEe24UWPaBryAP6RwDBhyVDHLybAelddE8L4UXGmQRG/Zr+cc3BlM4xLTZxwz2MW3yz5TZx6h3AV/pb0EQwFA5M8DzymO5QX/MlETHdTCKs7EFnDTmcdKYw2kzJ9BOm/NoR4PMYJDFmGwfYelyKyfRWoeYau1j5PoSSSfARTaLU8zhJJvDo+wrODXzuMTUQDoAMIYT3PY3MO0fYco7wJR3iCn/AJM4QNNv9/oRSvXeb3+D41kxIfd9tEcbuJqd7JFoEzmJNjOOy9kJdMb0yW3z/Apz2/sYOz7D2MkFxo8vMH5ygdGjM9SvO6UF1fvjLXB2PoHTkymcnMzg4HQBhxeLOG3P4DKZQOzXex5oXh7GWffyjQQYfJNiJLjARP0Uc2OHmJ/YxezIESbqh7g6/gCPP34P55fneX81veelAcaSDM0wQJqkeWidMTDZ8JdbxXPS95DFaak90TOW2hV/3hYvTthzlY8hOZnmAb35QmFUseccAPiehxR5/2ply7hKb+M6vYPL5DZaZgXXKZFpi4BCvnrooOnvYC78p2j6z3ufLTS856h7m6j5x/C88suZJDVIswwdk+Z5AzCeB/j52Bt6XvGSYxgBdlPwl2yaocHrp7jHGGQTU0hm5orNAdL5JWQ977Os9zubmc93mtQzhn98gGBroyDKgoM9+Ae78A924e3twD/Yg3d+iuAGxq/n23cfk/LTNTYdykm8/sJpUEdpvQazfgt4cBtYXYR/exb+4iSC2XEEUw2Eo1FOnPnDiLMUcRygdRUiPgvQPQrR2amjszWG9pNJdD5qoPODEXS3deIsCPYRRRsIw32MhwcI5vYRRQcIwwNE0QGC4AC+31+3KI5jnJ2d4ezsjM1ncvKZykbPqSrjhf5X6YgfC6eaqC+MobE4UfIuqy2MsnXOxhFNVcc7J5cddPYvcfbd7Z6X2QXauyxUc+8C3YMrxMfXMOKlqJz7aG2lyujU5iLyHH9RKufXgB6OyWXkz0Uup01eI8ayYee1uuV9Q5ZJ9iv+rZGrfF5Gi8GTUco3OeBzU36/tGls+dJ5OZ/i80PyNLLlRelx3VTpUsrB25UmJ28H8j4+zyzqwRgYv4ksnEDq515iOUE2gbT3O+397n+PI+2RaMZvWOUegEkKkiyK9/oEGSPK/OQMfpYf8+JT+OkZwuwCSC8Q+OX+bdNh4+LbOL7zl9AdeRu11keY2f7L8GAnNuRcmObf9GznL7t5/pq9pPVTjZi0pUXXa84A8v6qNqPlJ+f73KahNs7tL34P/2jPSQLvK1zf9J/bajZbksCdUCSJJq+XdgvZXcP6FXc+oWO2eYnUp/af26myfXKdyTmp5Erkh7+Y014y3QSfimDj5JH2gOCDPWep+4xlL3O22J02oOZKiFTyIk8LpYk4QG+w+5M3amCaUZ1f3z9H/+U373CczZVviviDgO+OIR/8eUPI7QE5kCRpAvQeVMbkE3IPebhNZkx/1Zze/V4v38APiIscYOdJ1yQjneMTcendJTsq9y7o102ZmKwa9Hi76OsQpW/ZOfh9/A1IyMJKuLy8fKRnOcDLB6T2FlIjK0m+brcL3w8H2iTv0JQvyay9CedtntcJH0h5nXCPQiPqsVSvA7ook8+URpqmgMng9x6g9EZBThJ5HfDyygcUL5t8GyKJMK57Pphp9a55Fso2QmXn7Vi+cekL2H/Lq012OSHPJ03aJIu3Z6knAj9WTgu4uJjA6ekiTo4XcHwyj4P2Mg7Hb6E1OwGzHABvIA/pnIA+UrcAHCBfF62NPKTTZPDDDFkU9AgH5KF9EWCMhwmcFOTZtNnHNPYw4x1h3BzBh0GWmXxcKeT2gMzA+IDn+fAAZKav44Q8qLzyWzsD4Lo2gdNmz/us2SPSeiRaJxwkm8K0i8n2IW6fPcFU6wBT7QNMtw5Rb18jaUc4zeZwms3h3JvHC3MPp9kc2oqbnocUk94x1r2PMO0fYgL7mPYPMYkDTHqHqPnJQH/rGwxBz7Ou3K/pdwLgemYcV7OTPfIs90i7nJ7A1cwEkvpgXKyfpBg9vcDMzhHGj88xdnxeEGljpxcIusnAQ98YoNUawcuTOZyeTOP4dB7754s4uZ7DRTyFlhkB6kGfRAu8vJ1wBz9j0PBaGK+dYbp5hMXJfcyM7WFm5ARTjWOMN66RZQa+74E32yRJcH14VepzvucjMxk8DzBZhgQZfHqGMD3RtaC2AwOf2pLn5SRXkiAs9O3BmEGymr6Lvoycd+C7eVG9RFEEAyDNUnhevrvgVTKbk2bZCi6T27jObuMqzf+3siUYDNaThyQn0KLfQdPf7hFo2xgJnmPEf44o2Ifv58/jLC1P5jIYAOT54CPLkvzNfc/D3IeHLH8zBgODNDPwgyAn23qakhPPz4LSmF1vIJ1bQDy7gGRuEWlvrbN0fgnZXC98c34RaNhJD+/qEv7BLmpPHyM43IO/n3ubefsvex5oe/AP9+GlCeTcQM59pJxy3BxGkmkTeG2s14zyIo9mA/4XXkPjy6+j8foKorUlRHfmEMyOIZhoIhgNEdYwlDjLTIokDtC66BFnhyE6Ow10NsfQfjyJ9sMmOt9vIN6po3Cz7CEIjhEEBwjDJwjDA4xH+wjnD+D7ewjDfdRqRwjDQwRBf5F4riPteclJ6W63i06nU3hNyLmPNJakAUb/g1qY76TZ2zmzsVQmz/Lv/FzVpgAmy9A9ukb7xVm+m+Y+W9NsPyfOKFwzuSqvg8NfTFL5pMEjy8Wf2VJ/fE7L9afNq2QeNuNTmxNVGYz0/JH61upEzjnov5xPlfStzHd5PrK83G7gGNitlL2M1EgQ3jb5vF6rC812sp3j4wT3liMbk67nESTa/E0Dn6/ztlF6BrJ5YpplQDAGE03lnmREfoVTQDTZC8OcAKKpnjdZTqQRYWa8m6+f4ZkuguwcQXaJZvclwuwcQXaOMLuA1yPHgh5RZrrHJW8zk1wUNqKtL2jPXs/zCnvSGAPjeTj/0/8DXH/1TyO4OsHk/+uvor71QXFvkBxi7un/qKQ3LwhwPflFHP3Ev4VkZBkj+9/A9Lf/l0iSfbU98zk8t0ekjVfoRTw7eFraC5QqUp3S4/dI2Xw/93JqtVrWNGyypWlachDiedDvkG2wI20SoGxjc2cN2Zelk4mmDzkW8nNan+V65Ndz/kWz97h9zcdymsfZ+rzMW0IjuzQnB+4ExaGNizwNTnzyMe1V8akINk0ZXEA++HOPsCAIkGQpjOl15IIUMoDnwwBIkhRBEPbJEd9HkuYT7ahHqhkYGJNXVhiUXW5zZQBBEMKYTK1ESR7xRso7E33zAXsYk8nf5EjyhgxWY/r6AYhgyBCGQT4JRy8kJsvJJAMvd1LxgAy9hmqAoGfUBn6QexDAK4JHZQeSRCjQ99oK4CFDubNoD2t+j2z8XJ+afviA05/Y5Iad5/kwhnRFxi4fCPpEUU6ulTsHXU/p5NeXva54/dIx7e0Wl1cOZnSMCD7eaeVDSssrjuMBcsc2IPTbxyDRlWUpfBOy/31CrjxwZD3CZPBNYZ42eV6ip7N+eWnxUa398PLy8kt5+G+pY9k++UAm33BqOpb1VC6X3k6zNLf7c/tff0DKAVUSv7xMdIyXrSwv0GqN4uxsEUeHczg5mcfh5TIOR27hfHoGZjkE1gD8OIBJDHgYAQBiA5x55XXRMgPfT2FqfuFBRuuiAT6a5gLT2MdUtocZc4gp7GPG7GMSh6h5g+ODyXrjptcbaXrciO97MFlvTDEeqL/6XogsNUhTg8vadO591iPSTpgnWhwMFihKOz3y7BNMXe9jsnWAqfYRwusYcbeek2hmDqdmHpv/f9b+PN6W5LrrRL8Rmbnn6cx3qJKqJEtWybZsyZKhsS0PeMDGI8Z4BDMa6Mf70DQ8gxteY7rf+wDvYwwNzWO2McYGt7E/NmB4bhowsmVjWZYlSypJpZJqunXvPWfvs+c5MyPeH5GROzJ27nOrqjvOZ3/2PrlzZ8aUK9b6xW+tpd/CmFOSko4J2dIVA+6KZ+nJAUfimp64oicGdOSIQKg9oGZniOxvCNmxVxrWUcTq7IjFcYfZcZvFSc+AaMcdlr3Wrs/ddq02tAdjA54NpwY8yz7Xp8ucD+U+J0kSMZ4cMRz2GI+PuRrfZrg8Y7I+ZqFaqCjauXFWBDQwL68fmsGMXm3IabvPeechvfo1R/UR3dqYQKZ7z4hrkLnHlCpuMrjnmjUagtgwwYQQoDUiA+gseBAIaWJiZUBtLuO0RqeKIAyztdkw2dI0Bozb6P5mmSNLAIRVKgUbdcw8vctSPc5CZe/pYyzUYyzTu6jSh0lRl5cchx+kLu/RDHYAWjN4mYq4jxBp8XknyzCb44We7AFSvQt3EQQBqYIgrKC1Jo1jlNC79Q2dAdTSxDQ0C1eudL6aorRmoOB+Ci+nmpdTeDnRDP/8X80ANAOe6d7x4YskCfL6ivDZjyGvHhIOLnPXzWDwkOAqA9GWi7yNvnuuHSshRM4a9HVDV9HXehesvWyDyz3fl7f+umk3U/P/e12iz3yCylNPEL7hDtUnbhPdOaZy3qVy1CBqVYiq8pUBZ1vJammBs8gwzp5vsfpkl83H62w+Wid+WCkFzsLwijB8ljC8ohP2Cc6uCIJLomhAGF4RRdcIEZcq7rZ9rneIPe4DAa4OY4ZzF2IkTVMqlUpBH3N1jLBVpXarQ/WiTeN2j+qFyapZu9Wher57f1RSgHQds72aM/vIAzaXc7Z9L7bZ5YzVwwnbwQKd7HQVd/PM6tm27u7Yu/qq2w6/lAEHh/QHOwdv0mduKmV6m3vc1S/L9B5/3Mv0I9cGcdvj9hkUXcTcdc7KE3e+lDFMyvQX95wkSahUKoWNDZel5rNB7LvVdS244NpSrp7n6q9RFLFerwueSm7bXTligRj3uLGldraab3QXbEAZoESTNOgQZ0BZGnRQ0rhYqkKMMve4Act4NSCZWmcg2ZhK/KIByPSMUM0I1Iww+z9Qs5xdFqQGSIuCJAfJbHFdWF0QxvZHfkzKQn/bviwDL8psOnvP6Vf9cca/58/n69X6jZ/PYz/wlajJkPEX/hXWj38JcnHJ0Xt/gFr/g2ZsKkc8/LIfRUcdEIL567+RJN5S+w/fm4OiVnZXKpV8zN0QEb7NfmjT39qVZW2xv3Hnm098cfvWnm/XO2vLb7fbwrUObQT5epUdKx+0LbMd7O/c56WMPOLWPQgCttttfi8bIsiP5+YTTnxZYn9jr+tvZPpywwXWrKs2GLnj2492rK0N7OrkPiDnE5hcO86VFe4YuDLB9o0v3+1YusCZZb/6MSbde7vA56strwlg8xvjCtejo2N+6Af/Hs1miw9+6AP81b/2lxHCie8RGNcNC5R8w9d9M9/x7b8fgB/8ob/G+3/jfdm1jZtOKHeMj1TbSWFYT3Zy2ofLfYC+78/9Rd7ymW99Re35K//zX+Tbv+27+cw3P/VaugOAv/m//HWe/dQzfOabn+JP/9//H3vfK6XYbDYMR9c8+6lP8t5ffQ9Xl5cYrV1iulB6k92J1WbHVmRgEgKlgGwXPNGpYQ6YmxVAnDKgwE4e04cQ2DgX2hozdgfDKM27BSxTbhxB7AIiPmhki/3OZw6Bu3MvkNJOSReAkUhpBa17j91DJqU1EN172we7uMNl3SXStBwoUaoI7Jn+ExlYtetHX0nygRcoKlpu/D7/HPfdfvYVS/O1QApZGFN7/TAMSbZbp58Flp3iLySuYFKabF7t2EtlSqPb5rKFyZ9XZQtQ2cLoCy4/9oYPhLr95vedb5CU9acPNpQpnP7vXAPGziWrzGw3VYajszwu2nB2i6vwLtOjE5KLqomL9hRwxB5AYhqMAdCu2MVFS0GIFCqgreIggRqAINQxx1xypK44om9e+pIjBlT0ald3isaK9najC+01WhxCSHJQW0imlQ6j+hmT+jmj2gmj2hmT+hnj2ilJUNlrTiVZcbS8pLceGDBt1ae9HBKuUtbbBhNtQLS+foxn+Twm+gRV4pJXZcmJuKQnjPvmkRzQk9ccyWuaegIoRCaKtCrKC50B7lZeFRRMYN1usjjtMj/pZkBaJ2Olddm2Spg8ShtXzucf0rqe0BrOaI2mtDJ3zspyFxh7t0MH81mHl6dPMBodMxifGTfO1QnzpMNW1h/pxim0oi5ndCpjjpsDzrsPOa5f0auP6NauqUebPUW5OLezqeNtElglwpWZtmhtYtukaeJcJ1MshUBqQKtsXyx7hpV7jf3dafcZst9pZZhcmZdpYec11h3m6WMsk9cxT++yUI+x0IaNtkjvkpYGGISq6NMLn6YZvExD3qMhXzJsNPESjeAhgYyxcxtZNDq1I+/tPDGiUYAyzE2/jzW6wN502032W61UHjdVZ781G2XZ7zQEHmCwVBlglgFnOYiWmPf72bGSXBzwjd9h7j2dmHhmH/+wcdccmHhn4uoB4XWfcHCJHA1QsbmKOz/cNvrtKmMC+OuAC4S5/x+St4eulZduh/CpJ4je/HrCN9whfN050e0jKmddol6dqBkRVQWRSPCqXShKa+KNYrEIiMeSbb/C9kGN9QttNp/ssvl4jfWH6yT9CnYe2xIE14RhnzD8JGE4oB1cEp33s2NXBMEVYThAyp3L2qFidVr72VfgXePQ3yBwz/GPBWFA/byDPK6h9RHyqErjTo/67a4Bzy461G51qF10HpkUYDtasrmcMfvYw9wlc3M5dT4bBloyWZcaYmUbia5M8EEZ97nx55gPVpWBJmX97c89/7dFva88qLp/rTK95yYwzS9l4/yoY+5v9+So1zYXoLTtKuunMrm8pxNkxRrMfl+5m8C+YWzv5YcEcdvlnmvXJBtrSimVA3u2Ti7wppRCI4lFlZgWOuyhwq5hiAU9VNAmsUH6XaAs/78N4pVnJhLpwgBf8YBo/SkTgyydGvZYOsn/N6DZFKmmRHpOxJyAuNBW2163uDJASgkStNgBC/642f71ZYUvw905rgFVqaEaPVSzh272SBtd0mYX1eiiml1Us5d/nza6qFqPtH3H6KiJgAR03Ob6Xd9PXHkT8fFbQWvSzh2uvupnCBaXqKiF9uPlas32zhfTcIBgq0cnScJ2u83jjfkhVVyQ2JUp7vxz7Q0LitnPYMIewX4cw0PPmX9PW/x7lRVfbrnHfeDKnrfZmNiYFhhz5Y1vz9ux9evq9o0F6dzn1V/by+RJ2Wf/3EM2Uv5cOmua+xu/Xbbtvt3mt7usf/01wm2nez+3LbZfbPFtUF+Wl21IvJrymhls/m6zFbDj8Zh/8S//GX/0j/y3fN7nvoMv+qIv472/8ouY2A8CoUWuAJ2f3eKbv/lbAfi19/0qv/nB3yCKQqPESztwOz/2sgH2H8D8u1fRJ0Ls74a92rJb1A5/X6/XuVt/jLt3HuML/5sv5md+9qd476/+ErayShddUP3B11rn7IFcWfcmjGnPvjuAW/aUDSF2OepEfpFsNxqElPkOitI6Mxp2QI2vXPgP702Khq9Q+UpM0VgsPghpau+1Y67tmmvHYyfMdgu1W98dQ2P3vwUobVwvy7Qr7pT5wsJv6yFDxN+Vc/vuJgVKiMCrc1ERd8d9Ny6i9Pyy8VHaeJr5Cr8/ru54HKrHIcab++73iw8CuPcp65Ob6uLPOXNMgTDg9KE5Wfa8AMRxwHh0ynB0zvD6jNH4nCvuMu6esT1vwB3gMzAgWhvfNiOPi/YyOxAtBoRChArtxkXL7B6pFUdccaSu6OkBR/qSYwb01EMaYlGIzWX7xbQze2YosrfK+lwIQaoF09pJFgvtjEnjnFH1xHyunZDK/Z3aWrzgdHGf3toAaEfrAY3FBLGCVdxgrE6YcM59/SRP6y9gTpf9YHHQYMJt8bwTB21g3Dr1FQ25Kh0LKSQITDsz0NnKUK01KEilZHncYXbSZnna2zHRjo0rZ1opceWME5qjGccvXRr22fXUgGmjGY3hlCApsqG1hvW6zmByxHRyymh0wtX0NsPlKbP4iJVuFt04Q7HvxglUWNEKpxzVB5y2LzltXdKrjziqD2lXpwhxeG77z2mZIVW2e70ruzXL/jaO40zZ2wHWYRiirFs5oIVAZH0gMiAfpQlKlDXXKLO7l5u0xlI9xkI9znxjGGjzjIG2UI8R6/1kFgAVMaIdfJpm8DLN4B5N+RLN4D5NeY9acI9QrAuKd+KsjSk6Z4gLuZNXO/li5pE1CuNU5edpofM4c7mukTXVVSiVUkXALdvgUVqhNKRByPbkhPWt26QXd9EXd0hOLjLXzQs+60HCywlMbtBfKsCdAL6gCncCwd0A7gaCO9n7t777dxBe95GbdWG9ccchV/w9+WzPc9/L1gH/urb4G1nucX8dUVqjjzqEb3mSypseJ3jyNuHjZ0S3johO24TdOlEzJKq8AuBMKbZbyXwesh0HbK+Mq+b2+Q7rZ3tZjLMayfU+cCZEP2OVPUOlMuAo6hPdGWSxza4y8MwAZ67CXejHQj8drmfxvuXAuG8wAIhqQHTaNK+zBtGZiWkWnjay4w3CsybRcQMRHtZnVZyyvpox/cSlAcuyBACrBxM2D2esr6aGedZfoLdFI9fWyT/mHi9rWxlLzzWeyoxQ9zw/eZVrBJUZu4cMRt9msOwFV/f0r+22rezdL2X6intP1xg+1F9luuAh49r9rqgrHgZybjKi/fq657nMDttPvhufWx9bLGhij7sxq40cDUlEi23mQpnUTAyyRLQNu4yWAc6kiUVmYpTZAP6vLvWySGcGFNveJ3JAMZmDZCZ4v7AsMjXNmGQzUNu9cXCBACBfK127RgZOuA6nf93+8u0If2x8/U0hSOtt4lob1eiS1DukjS7bSpO00SWptw1wZl/1Dqp6hIq6QMVkjI/ZvW+zd3ssAa6Bq6K94Zbl6TeaDwnOOQEEEZX5Cwi1ZX36+WSKBAhBML+Xk23sWmrb5cdYd0G4V1LccfFjSbt9XCaP3OLbovY6RRvyMAju62C+vejW19flfBnn6062b9xyKKaZa4v57SiTi/5nv31ufcvWwLI1okxPsNdx5WBZvez1y9Yce9xfj8uu69fXJbq4ctO2JYoiTy8s05tfWfk/5SJqhUlBcRKC//Aff4Ev+qIv4y2f+RR/4Lv/MB/6rQ8yn88yUCjIWUh/9A//cWrVGovFnB/75z9SYBhIIXLmvbtAlA2iPV7ozOzz4LrP//gD31/oTH/B2W63/O3/9YecaxXl4d/+m/8QgPf9+q/yL3/yn+fGVX6+hO02y8zkXPtf/m//nF9///uyNghOT8/4vM99B1/x5V9NGIb83t/zbdy7/xIvvPRCVuf9LDO2vv5g237xlV5bfPTd7zf/9/41yhbhQ5PspmM+8HlIMfHvV/Zw+AtRsf4GuN39v79zat9N3LF9xcV9QMvQdzsWflbOQwKrwBLzdhjKhPehcdp9t3vGysbK3gdRfC78PvbniW13IX6CFKTWvTTvq/0xKxsjF1gsY6CVFVcQ+33i17lM6D9qEXbngv/c7BaigPH4iPHonGEWF+06vsOwdYvVeRPuSPhsdnHRyjY/lxouhQHRVhiFRWtEmKJL4qIJDV1G9NRVnqnzSBtQrc3YOH377RPZc3UA2HbbnC9yMmTSOmVUPWXaOGdYO81YaAZEU3K/MfXtjPPZSxytB3RXV3SXA+rLOawE67TNSJ8x1ie8oJ/iQ/qUFfvKrkDRYcjrxDMGRMuSCnTJXFazwNz+fDdzoTi3/AV7W6uwPO3lcdBmxx0Wp10WJ12W3RbI/TlRWazoXA5pXU9MJs6BceNsDifUJnOk536eJCHTSY8XJ5/BeHzMYHKb68U5k80xy7RNGlUyAE0a5KMp9rK3Sp3QDGZ0q0NOWlectR9y1BjQrQ3pVIZUo7Tw3Lj399caX864MsZ9uYqHb8i6ykPZDqFVdi1rraD8ayBXSsiEwr7imOoK8/QOK/16FuqxjIX2OIvsfaPL3RZDMaclX6IZ/BpNeY+mvEcreJmGfImavE8lWBT6x+0TLcj7qmxOIXYMVL8fhRAovWNEa62N3NOGueavPfl9899q4maLZe+EzcVdtme3WJ2esz49Y31yzubsgm2WdZMbXEGvUngiNEDZ3cAAaXfDIoh2Kg9vBABU7r9kxsc7fkhuupt6N63xvuJcpjtorU0ShNNj5JseJ/iMx4jecIcoA87Ck5YBzhpZjLNHAGepUsQbBzi7DNner7N+vs3mkz02T9fYPF0nGUfsG4KXwAPg05iML+YlxGXurlmtjqlWBdVqlXq9nht47hjvgJZiPx7Sn3z9xe97e64QAtmpUjmzwFmTykWL6KRBdNYiOjVAWnTWJOzcHNg8XWyJ+wvmLz4gGSxYX84YP3/J5MUB28s568sp68sp8XC121LUxeRfVj74G7xunQ8V17Bx21f2e389O2R8HrpvmV7nX69Mvtnf2f99l2z7u333+P06l7XpUH3KfuN/7+u7ZXUu07fcupVdY08Gsj9P/fX1UbpUWXuRFVLZYSs7OzDMBueXHXTUy0ExC5TZIP5KlrORy2+sCNScQM+oJveQmwlsx4h0TJi7V44RyYRIzyEeIZOpeU+noIvB3g+Nqb+ulo1LGbDgXsP9fdl9NKCjGnG9jaobFlla34FjSb1DWu+QNjok9S5xrU1a7ZJWuqigtQ+SlQFlM2Ck8d3YH1msiSTF7rN9BYBaE05eIm3eQlfbWO8HpOANv/wnqAw/QfyW17H8iu9l0fkdTH79GEZTOv/1LxI7dhYUwy65stXvW3duurLGB6zL9Bl/jhea6j0DNv6b+1u7MVjmHfPIrvTq5eMP9v6u7e+32WIt7nX859geE2K3KeECmP66XSaDXD207Pru/C+TJ27dy1hzN43Roe8e1ae+vufLTFvKNsMOPf/2uL/Z8mrLawLYwjByOhp2CrYJYoyQ/ON/8nf5q//vv0m32+P3f9cf5O//w79bmJxf/IVfwud8zucC8C9+8scZjUdOw4zCSq4iFgfOZbq4x20pdJqGzWYXMHU3kK4SpNluN87vzbubtRKMsrfOaJxli5rPhDO0101e35dfvse9+/eYzqZ86+/5dqSUfMkXfzn/7Cd+5KCh477bB81fIA4tCP5vyxQb/z6lC4G9RtY5+b3Zf2DKlExXSPgT130oDlGmyya3X1c7D5VKkQ47wa4KSeL6Y+8L2jKB4bfHHQP/AbXHXYTcv7YvRMoEnPu5bE4rrTNcJ1eTC/dBiDwJArvAQc755f1nj+U2c5q5CJYpDZhnpKy/XAPVL2VC1P2t7Wd/R9k9v6yvbrqX36dpqpnPj7genDAaXTAen9Nf3OK6cZvFSdeAaG/CgGhHUBILHTYaRsIoLyuyuGggZIquiB2wE2YvBA29MBk6s8ychpl2SU9cE2LjTGnQO1DFjmXZ3PH7DCCVkYmDlrHPzMuw0qbVY3QJU7e5mXB79lzOQuuurqks1ui1YJF0GGVMtGf15zHWp8TsG3oBMV0G3JbPZ+DZDkTryTGSpOBy7H5227C3oEvBqtNk2msxP+myPO2xPO1mgFqXbbPE6FSaxmTO+afv07ye0BqauGjN6wnN6wnRauudLlksOgzHZ0zGb+Z6csH1/Dbj9SnzuMtWNh7pxolW1OWCdjSmV+9z1n7AcbPPcWNEtzakFszxHrViHdS+ogCHFfdDctw996ZnQsqA3dflRliapoWMxDoD3YUAoUDLiEV6i4V6nGX6OKuMjbZUBkBbq4vStgasaAYvcyw/RFO+TCtjobXCl2nKe1TEBHI2ovmNlIIkTckifxW+syxr04/7bAKkRNksd/IA8Gb7hqIiZTxDDTi9OT5jfW5AstXJBZuzC9ZnF2xOz1mfXrA5vSBtHDYU5XpFdPWAxofeZ5IDXF8SDK+oDK8IrvuE1w8Jrq947ulnD17j1ZayeeTPJfd1aK21RQHi/ITwLU9Qe9PjBK+/RfjYqWGcnbQIuzWiRkgUQfhI4CzdAWejgO1VxOZenc3zHdbPdNg83WDzsRrpdBdz1dZCiAFCPESIDyLEQ6R8SLX6AAOmPUCIByj1AKWKep8LLAdBhJQhUlbReh9oKesv/38oB9lkFBCeNKicNQlPmwZAO2sSnjaoWODs3LzLymEVXCtNMlyyvT9j8aGHbPtz0usV2/6C7dUMNVwT9xck1yvUqugsvNlsuLq6Yj6fmyQg3rrqAqqmPw67ytn27W2me9+7nw/Ns0PGk61T2feH3Gf93/uy02XKuAaXK+Ps9f16+G0o65MyOfKo37nnlIF/rs7iu0zZY66e5NbZj9fkM1fL6uq2W4kqKnOp1GGPNOyQil02SxVkMciCIkBmMluWZ8gub3xKkLlRVrfP5/HITOD+zKVSTQmSCRWxNFkt9QwRjwlZopVhHtqYTuu1Yeq69po/D/N54bmvlvW7r4u4Y+b3WRAEaCFJaq0cDFu3j6F5ZNwuMzdM1eyS1LuoRoek1iWpdEnDDlDZB8lcoCzBJLGaYGIkHWCTHSxWWbfOLPaYWiOSJXI7I4inyM0QuR4SrPsE6yFyM0ZuJsjNmGAzQazHUIvYvPFzkdNrqh/7ZeM9dfE2Lr/2x0irRyAEpy/8OBxVSN7yefD//bM0woAGDzl+6iNsvuH7SIf3SaXMY/hZm8mOm2WVViqVQhKSsrGEosux72JbNrZ23Mo2823xWZmH9O1DpUxfs8fdzSwfRCzzNLDz2pXPrg1t2+dvCvhtdOt2SL/05aTbN3477P1tf9n6+9fx7Xxb57L45WVrS1nd7XNn543fZ25dfZa5PeZ7CvrPf5kNe6heN5XXGIPNxOgpu7kFme7ff5l/829/ht/zzd/Gl7z7y/ml976Hj3zkt9Ba0253+O7v+h4APv6Jj/GeX/rPewuUabBE6xSc2Ci28WXgkB04vyNcummx2AHdBzfKJqd1LbMTp7igS1v5wtV1pqQjBTpLQvCe9/4Xvv5rv4larcYbnnzj3n0OKTHu5PHr6ytM7vk+G8vtFzcrW5kCXnjQNJhWCOM6o4vsiEMuIX79XYFS1tYyhcdfDN3f+f76YPvBArHG+AoCd5f+MEhm6+v3l/1tmqYkSVJQOA/V3T3upjN3S5ny6PaRuS9orQr1PwSeOVfOfhPcSIsuG5ND1H+cO2qdPUF6x1az2X3JeG9aYzLmWbcpx93N4lDufcqUpNKW3bjgCZbLDsPhGePROaPROdfTcwbRXWbHR6g7IbwOeAcGRCtLnmd3/2xygSwuGkIhKrokLhpU9CZnnxUzdQ6I9Lr0eZZSutEGd5+cvnF/kwQVA6JZd876GaMsqcCs2oMSEK29GXF38uwuHtpqSDjfoNchC3XEGJOd875+kiknpCWoYoUVRzn7rJ8z0XpyQCMdIh1WTcH4yIBCl8FZoHVHIYujdg6gzU86OxDtuIOK9peoYBvTHE45eeEBzcGE1rUB0NrDKfXhlNCNByYU222d0eiI/uTNDEenDKa3GS0vmCVHrFQLXQ12yQQiYRJOdIv3jFjTDKZ0a9ecth5y0rqkV7umW7umW5sg2GVXfiUKnX13ZaJvtJXJp7LiMm7LjNF9Vz4jN6xMcb+z45MkmvX6nDi5S5w+TpLeJU4eJ4nv8tHkLit1izIKp2BLM7jPRfTLNMWLtML7NOVLNMRLtIJ71IMROwBtf+c1j38pBEoIApz1Pg8DsDvPiJr950prTZKmRvab6GqotJgpWwPbdpfVyRmrk3NW57fYnN1ic3Ju2GdnF6xPL9j2jg0rq7zzqQ77NF/8NNX+Q6qDKypX2fvgIWH/IcHlffR0TKxSYmBNShxJZLWaG3yvRXm7qbhysSyEQ4GlJCX61inyza8jfONdoiduE949JbrVIzxpEXVqhI2AKHwFwFmaEq8l61nEdizZXhrgbP18h80zXTYfrbP5eI107ssYhRAmg6aUzyHEQyrBFbL5EMM6e4iUlyj1ACl3GQJ9fWd3bD8mjdsH9plw49TY7w8ZHrJZITyt5yBZeNqkct7KgLRGzkKLjm8GG9QmIe4vWHz0ymTMHCyIB0viwYK4v8iOLYmHS0iLzHI3eZbraueWMAyJ4zgHmCzzwZdJN7XZnuM+n27QaFvKjFt3bMqu496/7Lh/7bI1vsxQ9OtfZiC5hq2vx/s6c9kzWdZP/vUP3bdMzyvrN9/gc+vq67+F9guBEnVi2THxyIIsHlnUIxWtPJtl6mSzVPY96KLlzTH5CkXHBiRLp1TjS4LMndLGH7PfBcq4X0Z6bkC0dIrUCwT7bbZz2nXhsuuXECJXj5QwLCPfS8c1nP255dse7r2BnE1mwbC02TNulfb/RjeLS2ZilSWNHqreQ0VdE8T/USBZAoyAgeb/MjaZTpDpCpkuCOI5cjsh2FwTrAbIdZ9gM0asxoj1CLmZoJfXiPWYIJ4RsK+PuP+X6RDBKiD64MukzTriyTvQaRIdVXh8/hdI2m9CvfVNbP/wlzIN302zPSEMd/cIXn9K+KVvJf2p+3t9b4sLEr+W58y9boEY4yQBKbcXis+11eN8ZrfMgEHf1n01pUwWut9Z+9KVATed57bZP893OS17LxvnR/WtvZcPQvnyvkw+l12v7LPb7jJ5asfAXwP9z3bc/N+XrRk+ruT3qX+dV1L+T7mI2sq6lXa/+9mf+yn+m9/+Rdy+fZc/+of+ON//l/4c2+2WP/Ddf5B2u8M23vLD//Qf7nVE/kAHAa9/3RN85Vd8LU899VZ63R7bOOby8iEf+MD7+Q//8f/HZlMM8px3XslgFRUKl1rqIsnmfxv35pBRXNbusompvT6yscuuhwPu3nmMdqsF7BQ9v5/LJmcZYOYrg7b4QKFVlHz/bgsWlU2q/NpZNkGtFDIISFVaUNRfiaAGcnDKHxffxcCtv8+AceNylLHh/Af3kEunnWv2cxlrzq2LvbevuB56yG3xldNDQufwdTQ6Y3bKwhzbfW/nvTVQ7TXKUHx/3vqGhgtylS1MFsMQ7IRyFNmAtCI3fg0AlwHNWufupmWKjv/8P6qv1ut6DqINh+eMJmf0xR0m3TPSOxW4DbwVOMa47fmXUZjdwSHGpXONUYSEzuKiOQBCpnuGOqFHnyN1xYkY0E0f0stio9X03DB4nTbd1IZDi8I2qJqsnFlGzlH9NAfSFtXefkdoRWcz4nXjZ3IQrbUYIVcpalNhlvbyzJwv6rcw5YjyeGhTzsVLOXh2JAZ0xYCeGFCnyMLKx0eDFkWQ132O0jQlbtZYnHRZnR3l7LP5aZfFSYdV54Ar53xF7/4gZ541rzNXzsGE+mxZGMokkUynR1zPLhiPnmIwveB6fsFke8Iy7ZKElSILrSWg5Y2FTmnIKe3KkOPGJSet+5w0h3SrBkSrBDvmW5nLfsEQ8IfHM+7LDABf/pcBdYcMTbff7fN+SIkz8suM13zRZjI9Yjw5YmzfJ0eMxl2m8yO0LqNvJtTlA07C99EK7plEAuIlmsFLNOQ9mkEfgUJovUu6gyuby9evQv+ILP5nECD0PmPDKHaZa6O3s2vPTSsVFue3WZ/fYnV6wfr0jNXpBZuzW6xPL1ifnrM+PUdVD7vehYs51f5D2p9+htr1FdX+JbXBJdWrh9Sur6j0HxIN+4TOuCRJgiZLIIFmk8QGyNMqY3xj1lCtswQJh5XZ11ry+RYEcOsM3vJ6ojc/jnz8gvCxU8LzLtFxk7BTJaqHhJEiEjcZDAasTFaS1TwkHgZsLits79VYP9dl80yX9UdrbJ+pkS72gTMpLXD2PFJeUpUPEU3DPJPyEiEeEIZDtN4W6m+fKbt+GZljstCX6Rt++91rlBnYUkpkGFC/1aX7ulOajx1TPW/l7pnhSeaimYFpQePmrIHJ2LDLlp/oE18tcsAsHizNe39OPFiSzjZ5PV0dxpUTUhpndTfBlAsCue2z7+55Vn+2Rus+yL7rq4Ogg2cg3WQwufXy3Wp8Gebrzv59y465bSzT73yDr0wvtJ9dEOaQUeffu6wfDvVVmT1yyLD326y0JqGOjno5k8xkt2yjwyNU2M2zXeo8y2UHFfZMZku5n3ToUBFqm8cZi+J7WbB+A47ZDJcynezetXHBDPUM0gWBo4fbNecmVsguEZ21s4phWew6aDf9XTDZ7WcrE9xnJ9dXhUQ1e2yrLWgfkda7WbB+E6NMt3qkjV4W0L9HWu+hqr3y2GSuu6V7bEmmAL8WoEzs2GQCQCPUGpmuCJIZQTwniseE2xHhakAUjwm2E6J4aoCxzYQwMe8iXZNmjC+/byy4njjf22PUq+ijHrLbQvTa6NMzkqfegWiERMsHyHYdui3oNBGdJnRbiG7LvNfLQdjdrLs+2HztAFdWN3Tnv0tA8J/zQ7L+0HFXfzrk5nfoeXT70ffIOWSTP6r49rm9jkv8OeQ6esiG8/Ui/7eHjh2yjf21xCcRuXX01xqfbXtIT3XHuExWlo15WVvsmERRlGdNLdvgdsfcnVtlbXPr6YKovk79asprBNiKxoARmjshaiu53Sb8ox/++/yl7/8r3Lp1m2/6hm/h45/4GF/0hV8CwM/965/h/v2XnUa675qv/Zqv41u/5TsK94qiCm948o284ck38qVf+jv5G3/zr3F5+XB/wXT6wve93Sk09iSj+BcX/h0TJy9i3zhQ1q0LbYI/u8ZiCaCyq4v5f7kygbzdlNZ+lhP3d76RZo/5DwkUA877E9lORhdY8a/n/l8wcLRhRlnjwFXuyhSKciPPDxi++30ZPfOQEWKvc4gO6/e//VzGkjoEqlkF1Z07ro++Pd9Xtvy62D4suCGV7Er6Sqg9FmRsJwvaWhaHe46UMjf4tBZY1ptbfCqv+7lMmXaP5XU0B0CQLxTudX1g1O2TQAp7Baxbl62XX484rjIanTEenjEaXTAcnzFIbzNqnRPfqpvkAm/AgGhdynAjWGh4KHZx0RJTdwOiyd1zWjEvoRVdrrMMnVf01CXHXHEs+nTEFK0ysFSDEAZoLhvrsr6zx9ZB3bhw1k4ZN2yGTsNKW/rZlzAAUHc95PXDp40r5/KK+nxKsIZkEzFTx4z1GQN9h2f157L0I+mbHqbNmMfFJw0TzbLQMpfOitgUFim3vla+l7rrSMmq2zQZOU+6zE8MiGZeHeJGmSunojGacfbsPePKeW2YaPX+iN50SbDa5EoXGSNxOjnhhclbuB6fMZjdYrw+Y5702IjGjoGWu3J699OaqljSCsf06gNOW/c5alzRqw/pVQc0opkzJ4vGAlCQAVDMTGrrWbZg743jATlxiDHhGoPu74tjsw+qaQ2rdYPp7JTJ9JjR5IjxpMdkesJ4csRkekSSlgEGimZjQrvxNOjnqYT3CIN71Cr3ieQ9OlxyZ7UgCCDIWKkqTRGY/pIGRgKx2ySzirR1VXM3GwrsZJXVXRj7RStFiEAoTRgGpEKQalj3jlhnMc5WZ4ZptspYZ6vTC1an58Tdo8NjkMTUrvt0P/Vxav1L8xpcUh1cUuk/pHk9oNq/IljOzQ8CubdO5vVUilTv2PNmHCVxkpIIZz5l4QuUABmYDTz3Wv58uKnEWnOPhE/qJc+z5mW95iEbhmLDjA23P/YjhPWAMHwlwFlCvJSsZhHboWT7MGJzr8HmuS7rT3RYf7TO9pM11MpXFS3j7CFSfpogeEhFXBK0LgmCK4R4gHHjvCIIypXmQ4qrO6/zmjprr3+u6x6TK9iVgNpFi/qtbp45s36rQ+12h9pFl8adLo3bPapnLURweGdaxynx9ZL1p4fEWfD/HXBm3pPBkniwRG2TvTG9qU2HivtbVy644IV7rtuHrmHt6i7+vX1DxJcnN9XrUd+7/5fpR36f+MUfd/fa/jX9cw7VzR8T12D112pX1yury6H65/VBoIM2OuqZbJahBcG6GSjWQ0fmPT8W9tBZlkvEKzfLRLpCpFOCZEglc7e0mS0DNUUmTnZLnbleZsdRKyjR0aWUpeuRfdaCwPCfytY8O6/KPCD88fCBbzuX0zRFRzU2zR5xrZUH8o/rHeLMFTOpd0nrbZJGl7jWMXHJqj100IRUHg7gX3C7zHZ+X02xwFggiv/r2LhcxnNkPCNwQLJKMiaKp7nLZZTMieIZwXZCEC8Q7PSIQ0CK7X+tNUiJblRJm8ekrRp0W+hWHd1uoNsNVLsB7Sai0yDsNCF75WBZSYKnQ9xFnWrURpMmgnQVkK4qpLqCUhFpGqCSwLynAWmSvacBUXXN4296Pr+OejAg/T9+bS+OmMsWsy6/sM/Kd/vCBUh8fcp/lm2/ufd0j/nfu/Wy9bDvOUDJzbLGvW6ZbHG/s8+BtYltDFC3vmVy0rezfRvK/f2hdrv/u/qsL/NcW9lvi7v+uH1lr+H/1i9l2IRff9eWtO214+Df022P2wb3Hm6/uvaO368+5lKGMTyqvEYXUZf9YmKk7HYinMlOwNNPf5T/8p7/zJd96e/kd3/tN/DFX2TAtXv3XuLn/92/LlzXNMpc+4u/6N1827d+FwDPPf8pfvpnfpIXXniOaq3Gb3vXf8M3fP23cHpyyp/9M3+Bv/xXvp/lclkAK4S3u+AbTWag8jsXfIJtIOS9wceAi7tsjoAQyCADEfyO8pRnV0k8PTkB4OrqshRhL1Mo3H6y776iWga8lRnFZZOu7J7u8Zz5lmdWI/f4s3OiLKOJX3eXBWaNLreP3Dr4D42/S1kmMNxF6iawzp7r77Tae9q2uBTjsnb5gqeg6Dt9XaZ82/+t8LYUeTfD1a6t2mSyw/q+S+d5M+ckaYqySpDaxaBz7+cKD3tv1xD22+wr6Ob52gF9ZQCtLzT9RUA6QPR2C7PZbYbDU8ajC8ajc/rrC4a1C9YXHbiLSS5wCvQol1przOaZjYsWUx4XLUsuAIKmnnKkDYB2IgaZO+cVHTUkFKkB3rS76505SIsArUwyCetiZ/s0f4aFYBU2GWWx0EY2qUD2WkcehQqQKqG7HnAxe5Hu6oreakBtsUSsIN5UmagTpuKM++oJntbvYlvi2ypJ6DLgQr5kXDnp05MmuUCHYebitQ+MZ5UuLPiubNGViPlxx4Bnxx0WmTvn4qTL8riDCvfdBYNtTHMw4exTL9O4ntIcjDNXzhmN0QypdmnlN5sqo1GP4fw2z01O6U9vMVxeMIuPWaoWuho6yQSEmQf+/fTWuHFWBxw3H3Laekivek2vNqRVGRLK3W7u7pEQdpjzZwj2AS9/kXePF5/RfTZofidHvviL9yFD05Ul/nXWmxqTyTHj6RGT6THj6XHGSDPv27icndVszDg/e0CvM6LXHdHrjjnqjei0h3TbI2azaz78Wx9mMLgmTRVCGKYVGkRCJv/dbE0yZ25qrY03i5SorD9ceVZm6IIBMDf1Oqvjc5bnt1ienuWA2frsguXpOeuTc1Ynp+joMEsjmo6pDy45/sRHd8BZ/yH16z616yvz/+g6y4BalEnaCIys88me7/KNEaPf7CvnIpMJZnx3O+Aij1dqmGu2FORrFPGs3vKsXvEca+7rFZcOcLYmJmWLEHEmn8r7oN2BJIF4ITLgLDDA2Ut11p/usflExzDOPlVHrf3nNmXnqvksUl5SEQ8ImldI+ZAgMO/mnKLss+uXuwbnLr8UN158g77M5aYMOKmetKhetKictajd6lC96FA9N59rF22qGZhW6Zb5/e9KPF2xvpyx+NSAeLBADTeo0Zp0sGQ7MKDZ9mpOOllnGyk7XcGds7Z+ZYbBofb4bSr7jX8P39A8dE17Xcv+seNQqs+WrNGHim+8+kbKIfDpJjDspraXgYk3Gc/+fd3zy5hsZb93v9dIUtk2LLLIgF86OsqZZSrsGkDMfheaY+Z4B8ThmHZ+EenCZK3cPiRcPYNMxogMFAtSE6A/cLJc7t6noNbADhQrA6Ddd1/3Ew6I4epvSZIUxtB1U/ava6/nx2LSQhJXG3kcskIQ/3qHpGFikqWNLnGtS1LrkEZd0uhAbDI/gP8So++9WjZZru6IIptMK0S6QqZLxHaG3E4IN0Pk5ppw1SfcjhGbMXK9i08mLaNM7YAhIHcl9D/nczgKEZ0mun2GajdR3Sa63YROA91uGrAs+0y7ge40s/8b0GrADZsCbjMFoGKF2oJKJOkiJJ1FKF0hVeEeMKaSkDSV2f8hWpmriM3aYJGVTKfYrGh/+L0Eg/swGRLGWxa/90+hqzUQgmRb4fLnBpws3o+azkn+8c+hR7N8zvjPsp1b/hrrFldntaWMXea+fNDMtZXLdC7fdvPttiiK8pjuN/Z9yfX9+rl1cr/3z/fbbs8vI+K4z3KZvHTr5o6DlHIvAdQh8MvHK3yZ4vazf9+yvvHHubDp6tmV7n3dsSpbV9y67ts6xXLou0f97lHlNQFsrouhXwoLrzAgwI//xI/y9s97B73eEcfHJyil+Ec//PdJkm3WgJ3RrZTZ6f6Ob/8DALzw4nP81b/+l9lkWTqZwb//hX/LvZfv8Wf+9PdxenLK137NN/DTP/OTBydTtVotDKJB0XUG8iQ5i8adEK4fdKEEgQE4nAkvNQhhgikXFB65m1Dr9ZpKxbjQvfuLvpRq5pryy7/6S/nEtkUpRRRFexTFskCHrjBwmWGHFD7fILDH3Id3T5HL2iiFJCPsmUkni9eydfaVK/eByPszi+Gm7GeEsXC1zkENt772Ovb6FhDy2+capfb37gNv62op6H6/2/PLdgLs5yiKcmWmrA5Qzjj03UR94egmZxDCZcVYtD7LcBa4AsXOj2xnxesHMyaKMAzy7vVZc1a4Hqq/29e+YHaVMVvCQBAnmmymZMC0YDo9ZjQ8Yzy+MHHR5udcR7dYnPTgMWmYaGeYuGhl2EAMTCnGRVNg4qKBtkpHQB4eqqrXu7hoTqbOnhhQ0ZscITYMEwe8tvOR3QJSfHbM+6rSyRIJnOfJBUZZbLRNuB+LJ1AxvdWAO5NPc7Tu01kOiOYbWGq22zpTThnrU17UT/FhTkvjoUVsDGCms1hockCPAUeyT1tMkGJfnrnPd5mSnCrFtlkrZOW0jLTFaZd1dx8QBKjOlvReuqI1NG6cjb5JKtAezahMF5ArTgHz+THjcY/LyZsZTW8xWNxmujlhkfZIwuqOfVYTB9w4FXU5ox0N6dWvDAutPqBbHdCtXlOP1gcXWt9Y1zZDboYUSyFQVjQ58tPO+5tiErpyxZd5bh1sKTNIy5SsbVxhOjs2jLPZSQFMm0yPWW/KYz3VqkuOjwb0OiO6nSHdzpBed8hRb0K3MyYKN4AoACJuPVSaKS1CQCCzuJuaQCuqWhAKgdI72ePWW0phWGxOX221Jrl1h8npBcuTM1Znt1jm4JllnV0Qt/az0OZ9tt1SG1xy8rEPU7++onp1SX1wSX3YN++DK2r9S6IkJrXAbSBIUpUn58nnfhAYIE0L0lShhUmAIGWUtx2xA9H8tTJVKnPv3AXsFdIaoGb9ilFZwieBqtXYPH7B9s2vI33jHeSTFzTunBCe9wiOmoStCmFNEgWKPyb+00FbUQKoEL2tkKwqqEmV+LLK8oUqk6drzD9SY/3hBtvnq6jNPnC2c9V8FikfUg+ukO2HCPGQIDAZN4XoY6zXcvCiOF/3s3K5777B5F7D6jJpmqIk1C66VC5aVM/bGVDWpnJu3mv2/aL9iKQAivXVnMVzAwYPp6weTFg/nLJ6OGF5f8zmasbi5RGrBxNkArVajTAMqVQq1Ot1qtXq3iaanQul9yuRBe7/rnx9FNB0CNzyjayya5fp4Y9i9R8yct06+XVw27G/HhZ1yTJD2L+2b8i5ctf9vV+vR9kd9vtUSwh66KgHUY80c7lUYQfCXg6OqQKDrIcOu+iwjAF+uIgkY45t7iOTpw1glk4QyThjkU0R6RgRj5FpxiDLMlsKvUtQ4SebsOu4y+YpbIA57beb1mVGsd/Ph76z13I3lNKwgmgeo5o94moL1eqhm0ckjQ66kQFnjexV75BUjkgrHbTM2GSu2+WGIkgWYzZGX3MAf/bZZGqDTA2bTGwnBJsxcjMkWPZNjLLNmGA7zQCyMWI1Joyn6M2UwJvX/nNW0NkrIbrdRPTO0N0mottGdg04xlEH3W1Bp0XcaUK3CW3DJNOdJrRu3gDwi9oqVCwMSDYJSVVESoU0LYJkFihz/9c6k2kqe2nns9o/Xv/QL9B+/88ZvV1KtJBs3voFLH7Xd+8qVK1T2a54/U//L7ntMH/+w7z8h/4y25M7ND76X+n93e9DLiakcQyZHmXd+XzA1hZXjyoDwtwx8G1VX94e0nfde5X9zrc5Xfaa/XxIXvvX92W/L2/dY2Xy2r2fTxyxx137zG+XL6Ntn72STRH3d2Vy21+bfPnhX9/KJn+z2h8XHzR173to88XW36+zu67YPvO9z/x10O3vQ+vZKy2vCWCzrhAuelgmgIQApRKWywUf+q0P8iXv/jIAPvCb7+fZZz8BWBaZKNCS3/H2L6DTNgvcv/zJH2e12iJlmLtmpanmIx/9MB/4zffzjre/ky/+wnfzr376J5zB2nX8yckpf/dv/6ODbfmRH/2H/Mqv/nI++HEcU6lUSgFEjWXoqRxgAwqgj1ukDKhUDPm23Y44PTnjHZ/3+XzFl30VAO/55V/kN37z1/eUFchiuTiGvTvgYRjmoJw/AXwh4T5Ufntcd96yB8WnTrrHhBA5EGa/s8LTnmff3TZYow5rwKodILdTAAQ6LRdirmD2f+eDRrbeLjssvz/sXd9H3n2h4o63i7L7gKT7QLtjV8ZUdIvJzmsFUGb8IwmCkDTNjJ7s2rYf0nS30+0DmW4/aV1cSNzx2e//fbaGyx4sMyws6LRcdk1igeEZo9E5w8k5A3GL2dEx+rHQsNE+D+PSWYbZpMBcw0AYl84Nmb2nERWFtkwpQQ7ChTrJs3Ie2Syd2jDSmnK1N355n5heNv3kfF8AQ4VkVukYV8762S42Wv2Mce2MONxHAsN0Q2/V53XrZ+iu+rQXQ8JFDCvBOm4wyeKhPcvnMtVH6JJg8XVmnHHPuHFmLDQbE63BDKXSvQXKn/NGVu4ypSVo1kdtFqfdnQunk5UzKYmvIVJFYzTl/OMv0ByaOGid0Yza5ZDGYEIlsSxPxWrVZjo9oT+5xSenn8PV5BaT9Rmz+Dhz4wyKIFoJiFphRSsc060OOG094LhxSacyoFe7phGNc3cKd0zLDC5/Id1fm3a/SdOUarVKmsR7c8SXZ64rv3sdyxgxx8y2uHUD9BU4e16aRszmR4ynJ0ymRzv3zZkB0JarcsCpEq3pdoY8dud5A5x1R3RzMO2aem1/h9Wum0YGGqa4n3XO1i1VxlUjN4BTw+YUaYrQklQlCBGRpjFJp8smi222yrJp5q6amevm+vh0lxSkpFRH1zQfvER9cEW9f0ltcEXjup+5bz6kNrikNpua2G5CkCSOguYNfeyMm1lbdhshVhndzYkAGYjcxVOl5nw3fIEFXl2ZGgSSVCnSeo348dts3/wY6ZN3SB8/R93qwVkHcVQnaFUIapIoSKmLm9wLUuJYE88FpF3UuMr2ssrquRqTj1WZ/ladzUfrbF+oobf+WgO3bsHt2/D2O3D7S+Bf3P9Bgrp13zQAmhB9hFMHd223a5M1INw54xbfYChTgt3/w3aVylmL6kWb6nmb6q0MODtrGRAtA84qJ4ezrwKkq5j15ZTRB15iczVj83DG+nKagWdTNlczVvcnbAZzkq0BK8ri+Vi5qLXZJLM6lJQmeLWrD5Wxxfzi90eZHndT/xwqhwxK95hvWLryypX5NrbuK0nm8Kg6HZKhZQadW8oMZf867v8Fo05WULKTxx7TUS8P4K9Dwy5L7efQAmgZSBaUbwqVFq0Q6RSRTAjWzyGTCSJ7yWScA2YymUAyIkynhmWWTAy4JlSh3q+qrxx9zNUny9aqMv3L19211uh2B5RCLhfmOyFI621U6whaxyT1tmGWtY5QDRvMv5eBZF0vNlm1PIC/eyxnk73yLjeNy973AviniHSJTJcE2xlBMiXYjog214TrIeFmhNiMEesRejlErEeE2xliPUbopNAnbp+5QdG11ohmHd1pIHunqPbjiG7buFN2W4hucxd7zLpXdlo7l8sD8cjysXE/K43ealSMAb+uQxKduVumYambpft/3lElgFjpa7tEbubI7RK5WUC8RqrMrUMCoUSHIWnnhPT89eZY9lp92Vez+rKvfuTQbXtnhQ263gtP0/6B72RdeQKx6SO2S0SlghCCreySUCEOU+bf+v8iPXsCPv5exI/8GVhO8rFSShUSyB0CU3x5eMgmcc859Hsf5PGvV2YTPaocurfVQ1wbEYqYis84tn18qI6vBPBz5VGZXLmp/u7/ZfauXV/8Orm/89fhVzM27md3LfHHzb/eoba+kjq493bxi1dbXnOSA5e5c0gZsZ38hje+mS/+onfnxz/7sz6Hk5NTrq/7kLFcTCPM9d78ps8EYLGY89GnP4IQdvfGuJpEkQETfv39v8Y73v5OOp0ud+88xoOHD7I6vZrWCIJg9zC7naiUKsZUc+LMUKL0aCBwwL3v/H3fzXf+Pgf1z8o23vJPfvQf8uGP/lbheJnS44Iz+6DJfmahQ8zCMtDMfTCtb7xPL5VSolNVSGhQdu09gSWlSYbg0dZzEADyAL62LpApxoCkiPxbg9llW9m6WODMV0xygzH7zhUC9iG17KtHCStXGPqCsEx42JfN6OVfo+wBt8CaOxd2glYWdirS1EX5ze+UUoZ14rVxX7m1Cl4RkPXjTNk2+NTq1arBaHjOeGwydA6HZ1zrW4xbp6i7FRMX7c0Yl842+3HRNAY8u08xuYAmi4smdopXFhdNakWXQc5Cs+6cR/qKFpM8K5VSKmOWZMCqLrruuu+mfYCQLOrHDKsnjGqnWVKBc8NEq52SBPsuaZVkxdHaJBQ4Wg9ozMYEi4R0LVltG0w4ZaLPuc8TLMr8GYEWI+6KT9GjT9fLzlmTm9JFzVfi/bm7DaVJHnB2xOK4w/Ksx+zYunK2S4GOcL3Nkwk0BmPaoxmN/pjmYEJzuiDQ5tpxXGEyOWY6OeHe6Cmu57cZLW8x3R6zVFnGz3oGnlUE9PZlhdQxDTmlWx1y3HjIcfMBvdqAdtTnqD4mCuKCkQj+Tle5DCor7m7+/kIrsCqwea7Kqeb++uayjd0xMc+GiXsIVrYI5sse09lp5rbZYzI7zcG0+aJbWu8wiOl2htw6v2fYZ50hve6OjVavLTFrplG8d3WG3abVru92AOyuPWXKkAWf4u2WrYbN6QXp2W2S3jHJyQXpyRlXx7dQx4aFtj49J60fzpgYrJbUB5ec/db7qfYf0hz0qQ0eUhtcUb82zLPa4IogTQqyZjf+NlYZGWPOtDlX6sXOiMnXLRzZaVQLVJIU3DnNoJkNM3OuJAgFKs3mWLvF4vEL4jc/TvLkbdQTF6TnXfRZB9GtIVsVgpogkimBUIfj1+iUJNHEU8FyGhJfB2weVFi/1GD9bI/1J7psPlJj+1INHReFZBDAxQXcuQNvvw13vtyAaHfu7N7v3IGzM3OuW37+5/9Wvp74iq4tZSCAPa8MDMmLFEWWWQag1W51dm6b54aJFjRuDri+vV6wuZoz/fB91g5wtrmcsbmc5ceS2XovOLJbL7eNfv3L2i2lzOeQXV+NXhntbbLZ37i/LdPD3L4rK75R4BuB7nUfZfi45/hrvMsMtPW1Rl0puONc19dj/H5072vnlnvPMiBIaw1BPQO93JhjXQckOyKVnfxz7n4ZdiG4OSNrsRFpBohNkMtPFlhkIjZMMplmx2IT3J/tyLDM1Gwv147VhXzGlz92pt3l/VXW3zeBA3tN8s8NqyarZevIBOyvdzNGWQ/VMMfX7/oS0ovHMnbYArHW6LD16NhkKcZDYGS2Hl9VscCYgwUhyGLErZDxnCCeIDcjgs2QcNUn2AxNHLLthHA7Q27GsB4h12NCvc02PIqggq/DKa1J6xVErw2dLnRMdkvRa6PaDaSNQZYF6rdAGd0WtBuFeGSPcuo18cgMkyxdBqhFSKqrpFQPAmM2TplS1heVG9ljhVcaI7dLos0SkayQ8RqpE4RQIDVaCnQYoqMIVa2iKnVUvYmqN4EGCvPsPCrfpVgv0WmAiGPC/gtEVy8g5xPkfIJYTJHzCapaY/IHvh+k3eAW9J7+tfz5D4KAVXiXj73ub7Gt3AWtuLj3tzh9+CMM3vDnGT3+R4wd8IVraNWMEXLrM6DeRv6N37cnV8oyFr+S56hgt5YALP56d9M6YfUQG9MyjuNCzO9DduKh4+6GhxAi96ByXatdQNG2wY3Lbs8r0//9Z8Mv7rXLsIZXUsqex7L7+f3vlsLawGHQzr+vuy65dvQhRq6v19h6lW34uPUoW/sOtfNR5TUz2OyN7QQoKvJGmASB2dX/Y3/kTyJlwPX1gGazRa1W5w99zx/jB3/orzkdYiUMnJ6eAXD/wf3CfX1q4/37L+ffdXvHPHj4MDcubNcOBn3+wl/8s3l9D6U2tw+T/4C4gBlao5OURAqznmRgm40/o9AG4HhEqUQVvvkbvoWX7r3IZLpD78tAPn8yQtFw9I0l/yF0v7cPuJ18ZYqonbw52ypNEXpfeXL7zf1t3g4hIDSAkMtSI5AkaYoUcv9hUZpIFhlqPnBlj5e5n1jBbOvi9pV/L9uOMj92V7i61wIKOza+Yl0msCyA59bXF3Dme3udndBIkiR3ZU2VIlEmzp9SKVqHTt97O8DZeXYu7RRg89tdf+0Ahl39jADabipMJheMRmdcX58yHJ5zvT1nVD8nuV03TLTP5ea4aCtggAkou8IodBqQygAwe3HRoKXH9LTJ0mnioz3kWFxxFIwhjdHKMv2y9gqFjZdkn/pUpQQyKgIgSMaVI8Z1k1BgXDvN4qOdMqmdksqSwK/xkpPFfXrrAd1ln/piRrBUqFXAMmkz4YyxPuUF/RY27BsCkpQO1zwhnjbJBMSADpcmwQDXSG2YRmU7JPajO4+lNEku4k6L+UmH2XGb5WmPZZZYYH7SZdMpZ4PUJnOOXriklQFp7eGMxmBM43pCJcvKKUTEdNphPD7manyH4eRtXC/uMN2eMU+6xEHdsM/qwrw3hcnO6hatqIkF7WhEr37FSfMBx/U+vfqQVnhFI1qg9U7+7Ge4K8YE9JnSdjzL5JG7cNpMZP5zmXnwAeZZyzNdCoFWAhtTz9bHPEP5KYVnN01hvugymR4xnZ/mrpzT6QmT2TGzeRet99V2KRM6rRFPPP6MiXvWueaoO6LTvqbXHdGoT5GO+/0h2eLW1cj3EO1Ruizgl8sJYFVtMGu0mDW7zBodZs0O80aHWaPNtNFhWmuybpWDfwCkKbXhgPYLn6J29ZDG4DIHy+rXV9SvHtK87hPNpru10WXKmmhnuzqK3UaJrxQbgEARRSFKJXkEswxLLPSHu64ppRBhFmZBmIyMcbPB9ok7xJ/xGOkb75A8doq6dYQ+bSG6dWQrIqgKwgw4CykXa1onJIlkOxck04h4kAFnLzRYfarH+mMdwzh7uYpO/N2FhEBeIYNLwugZovCKeushQdgnCC759z//oweBs1daypTVQ4puvh5WA+pnHaKzZp4YoHorY5llTLPqeeuRSQHUNmFzNWf28Ss2lzO2/RnrhzO2VzM2V3MDnl3N2PTnEO821crqWgZklIEc7pj7oLqVrTbov+0fVzF3NwFdL4EyHcMtvg7p6iaHlPEyA8w1qOx1fBl36LPfX+5moh1fV2c5tOngGjl+/TSAbOwYZE6GS6IeunKUB/PXbtD+DEhDHs7Uu1dUnINi0fJ+gUG2Y5GNIR4j00kOkAVqikjnhRAZtpR5JrgsEb9P/d+5fV3WP2XFNwjd4wrQjS5ps4u2LLIsTpn53M0ZZarRQzWOTQD/qAOiVg6SJdoAGJGGPiaAfwp8qr0fG/pgpbPXHpssNgBPYgL4y+2YcDMmWPcJVn2CzRixzrJdJnPEaoRYjwiSOXgb82V6NoEkbdaQvTbisQ66c8swxVo16BlGGe0muttEd1pgmWUdE58sPCAoy6SUihVqo1GJQE0DUh1lIFklA8Z2bDI/gL9dS9Eak+GGRwNlAJsFQbxGblfIZFUEyTI7SUcRqlJFVWuoRhNVrQMRighVmrDKbZQiWM6QiwmV8RVyOUXOp4j5OAfK5CIDzLxjYjFFqF0ylDIvGyvT6vefY/SdfxZd79L+zV+i+fFPMnzT17KuHhPXjrk+/UqSxrnR5UPB5W/77xno7yZtnkOoIRIQ1OxFzXx4+9cU5oWVue6z5hNB9uRTyfPoetscWi/KruHaiFZuuqGE/M0Kv9x03N0kMhvWO+IFGHmz3W7zjR4ry8uY1WXg4aMAIFfu+dcowxnKfl/22dbLtdfdc/xxKNPj/Xv7XmmH2nnofvZ/V/6+EoDMn2+vhrVYVl5zkgNrnLuluBNuGvP1X/fNPPH6JwH4xz/8D3j88dfxnd/++3n7530+7/z8L+D9v/G+ggBWSuXxyTab9d613cmxXC3z72vVWjH+mFffQ4CJvbbPzvIVuexo0SBUGi3NOmbOLV77n//EP+V9v/5fQQgqUcTZxS1+55d+Je98x7s4Oz3nD373H+Hv/P2/VaiLP+Ht7oA7SV3E1j3uTqQyVqFvqLpt9Xc68+t6/aUtU0hnWS31PqpsFReb9qFMudtDkQ8IQPseBMGeQCoTkr7BbvpOIaUVjgZEMnVIIa9jsOdWYq/hzhv72dKY/Xr488YHOrfbrZNtzgUNd4a8bzQkSZJnbnL7r8ikKS4QBVAh7w/y843AjxheHzPM3DlHwzOul2cMw3PW5214HHgT8DswcdHKqBoxMGEXF20LNi4aFXbBWAOwMfnrekFP9zlWfXqZW2dHXXIkBkQkCK2yvsjmu8geMgJSleTzVAiRZx5EQ4JkVjtnVD9lXLPunAZEm9ZOUbLEFTOecz6/R3fVp7fsU5ktkStI1wHLtMsYk5nzU/ptJOwzMkK29OjzWJaZ80ia2GhdrmgzIpD7CrodJ12SfAJAScHquMPypMf8uM08c+lcnvZYHHdIavv1EElKYzil+3LfMM+uJ7SGJrFAYzgl3CaAYLlsMJ0eMx4f89LkKYbzu4xW58ziI9a0oCpNIgHLRCuxjSLWNIIJ3cqAk8yNs1s1LLROdUwgi89Q0aVSFuSk687kyhBXQbL9VdpXnjJkz7XPTJmRbv8XAtCaJEu24u4YSmkU6uWqswPOZsaVczozANp0doRS+0uoEIp2a8zd28/R61zT7WTgWcZEa9RHSFlULgwbzSoh+32+u/ZhA30rJfNGj1mzw6zRYd40r2mjw7zRNsfrbdKwLHuoKZXNmtpkQPTpjyOuHhJdXxFeXxL2H9C4vuLipQccj4eESqFUAposDI5JfmNrJpSZ3b5rWhAEJsEARVDEN2Z3a0MGmmeyW0qZB+bOTmTTarJ54jbxmx5n8/oL1OvOSC96cNqCbh3ZCgkqO+DMhmj0nyKtE5JYsp0JkokBzrYPKqxeaLJ6tsfqY202H60TP6iiUy+YOAmVoE9VXlIJPklDPqDa6BOJS8LgIYF4AOI+CQO2YYqKAkQUobHGjbnOO95xeOxfabGhGrTWRMcNquetLJ6ZSQ4QnTXz5ADWfTPq3AyCxNM1m8spi08NDHB2NTeumQ+mrC+nxP0F68sp6WSTM9fdZ88FM8p0E3/N9YGNMrCizCAoU+JdOWTXQ7vxZePibrfb3E3U12X8+7lupmVg4CGgyr1GWXv8712AsMw48A0Kt502xEgud6VEBy1U1EVXjqFyBFEPoiOoHGVg2VHugkkGpOkMKEPezEYsFLXJXSyj9YuZK2UGksVjwxjLwDEbj0zHQ2Q8QbLKdc6b+jG/lccy83Uf//dl88Lvd9uHbimdZ1E1A8K66OZR4bNuGUaZysAz1eihaseoShfCdjE2WVkA/0Sbzyt73wxAqyfQ1UaXqmfvDW3WaX+KDAW8YHUehVDrLNOlCeAfbEbI9YBw3SdYDxHrcR643zDLpgTbKcSrgs3he8FIKVFhgOi1CY7a8FgLuqfQMdkt6bUz1liWybLThAwo050WolUvsMdsMw7tL6hNSrrFMMmGAamuGBaZrpSyx3LAzI1HpjUosQ+IlYFlGkhjxHaB2CyRyRqhtghSu/ihA4muROhKFV2to5tNtE0IQJOU5s1MMpUSLGeEyxmV8SXBYkqwnBEspoTLGeFqhpxPsuNTgsUUuZgQzKeI1QztxAczzdvJKmu3KKWM11ClgW50STqPk965Tdq8IG2dkzZOUM1j0nqPtNFD19qoWsMkLqhGUMlil67h+qnv4Pqp77ihQVa/O8/mtDCb7D1Mh4oMqBw92Hu2tNb5GuaSalw702WDHQLNfDvxkG1ZGAaPwOHaklZXuUke+ff31zKlVA7YubquS/yxstval1AMTeIyh93Y2f7aWNbGQyCTv+7c1Cb3+rb+rrwsY/eVgbcuy81fs/262XuX6b/+2B9qe1m7yjbDXJv5pvNeSXlNABtITDZNiZT7C48tJydnfPM3/T4AfvW/vpcPf+RD/NaHP8Tv+O1fyBNPvIHv+f1/mKef/gjrzbow+ddrEzfJAm1WEXInk5SSWnWnFG42myIF0quxq+T5i6cP8LkBrQudKnaDqcmUFsqD2drr6izD6jrecu/ll/hnP/EjpGnKb3vXb+eNb/gM3vX5X8Cv/8b7So0LX+n0+9dti1tX3yXSjw3n3sf+zmXv+Wi025dKQBAGqDhGpymBPJAQwOtPrXUe9Nk1jguZuNhHu+13N01wH3GW0gJXO+XED8S/MwCskCu6VrnX9sshw99tv6v0mf8NaJQLTa1JlcJ0idkZ85McuLusaZoiMkNMyrAkUKNxG12v10ROnDqtA8bjYyZjw0YbDc+4np0xkBcsu214nTRA2ucDJ1BCxMriogFX7OKipaZfqWgIs3ZKckAm0jE9fUUv7eeunMf6kh4D6izz+ZBaQFNYRUAhZWiCjjszz/Z5GkS7rJyNDEDL4qNNa8fokuxdjc2E27Pn6K0GtOYDKosNcgXJpsIs6TDljAf6SZ7h81Elal2VBSfiPj0GuQtnhyt6YkCTHduozLizCVTc+SCEII5Ck0TgtMviuJ3FQ+sZIO2ovUvY4JRwtTGZODMAzWWi1YZTJJAkFcbjHpPJCQ8mtxlO385wdZvZ9oRF2kFFUQacZa+ugG7xPkKnNOSETmXIUeOSk8Z9evVrOhWTUKASbApGaP67/JkVBTDdBagP7QgV3UApnF8mi+0xn2liZZnPfNvJJ3L2sVaK9abGdH7KZGYYaNP5CZPpcQ6ipWm5Ydlqjrl1/iLdLPNmtzOk272m1xnRbAyJIvtcayxjbxc/cuf2bxWuHV2AfO5gkwUgmNeahmXW7DFvtpk1u8xzIM0w0da1w+5UUqW0FlNuXT+gvZzRXk5oL6a0ljM6yxmd1Yz2coaaT3nhhRf51LOfYr5Y5AB2IKGiFM21hiRFu7R800p0qiHLAm77VzibADmwISWI3fpV5s4hpGTdabN54hbbNz1O/ORtkrvHqNtH6JM2dGvIRkhQhUgkyCxZSdkeQA6cTXfA2eZ+lfULDZafPGL9sTabp+tsH1bJM13YeUZCNbiiIq+oh5+gJ6+oti6pBFdUg0uq8pJQXFEJRpgMnGa+JWjCKGS7TUhUwjaJSQQkeeRHiUo1MgyyeRocXN/8kpAyZsWEJWOW2fvu/y/4d99r3DdPmzcnBUgVm8Gc5fNDNpdT1pcz1g+nbK9mrC3T7KFhm+n1Tvfy12U7t91j9jzfuCkzFA4p9WXKdFkd3M9FNnZRJvu6kgXI7Bx0X7AfR8Zez9XNykAa97yb6uz2TZlRsteHCFLRJA06JKIN0REq6JIGJkZZItokso0Ouqx6NeI3tMzxqAdhD+SrUPfTZeZaOSBYPZsBYzYuWeZumWW7DDKQzLpfkq72DJ4yFplvUBm9uxhvqMyrxO9Lv+/LigvYudfQQqDrHeN22ejmTDLax/ln1TIAWdroohsn6NoROmqDqN8cwN++hphA/oELjKVG1+o4IFnd+b7Bo30WlYZlgpiskS8tEJMJOqqSvv5JQJv7fdeGox/6S3T+959GiqLN4M5lIQRpNTIB+I8amVvl6xHddhaLLGOSdZsEnR2DLMiAsrB2czyyQr8rjd6YzJbpRqCWsgCS+cCY/3++TmqdgWGvACjTgFawnSO2K8PIU2aghDDrFqFERxYkqxlXyzxjdYSmh6Z3OMxcmhjQazFF9u8RLCaEKwOaGUBsYHRUvwABAABJREFUmv8fLqc5oBYuZwTbFaLEpvBlj9YaJUPSWpu42mJ1+hbixgXb+glJ45S42iWpd1H1I9J6h7TWRFdr6EoVqiFEkj1f6BsHi2LWVj+zawzECWK7hThAp9XsmIZtSmfwC0zPfnemeAl4EnhLdv94jfzHf6pwOxfIsuE4XHvc3RwJCnbOru/KNnD8EANlTDjb34fkur3nTeuSfy23PrlukCR5nHcbT92VY+597DG7AeS2x34XRVEBGHT70rcTfWKL+17mSeOuVWWy1scj3Lr5xV7DJ7H4NoE97jPt/D46BMiVFfcaLtZThjX4dXgtwJotrwlgK6KmtgJGwkmHJfJH/tCfoFqtMp/P+LEf/6fZUc0/+uF/wP/8A3+V4+MTvvVbv4Mf/Wf/pKBoX18PALhz+06umLu7smA64LG7j+f3Ggz6+eJhzilHWA8NRr6L7oAcfsl5cUIYsE0YYEa4AJNz39RjYZifCn7mX/8Un/3Wz6bZbPG7vuJref8Hfr2ghPhg4KE6u4qe/xmKO7a+cXpIaXEFWT7pgp3PucjqFSBQASTZZ98ABoO3CCEhy+QmhMk+J4U0bqMIkjg2cwjDirP1c+t00wPk7zBAMVuo/yCb/3eupOY3Ip8zZQ+uKxTsA+eDsAVDU2twlEmtFSIztIXYxaALsudG6R0A6D4HrgGapim//qvv5r2/9NUkScjnvv2DfP03/VukTNFaMpk0GfSPeXC/xfD6hOHonFFwm1nrCB4PjEvnW9nFRfNlhsKAZy9TjIsGECpD77bjkulSblw0k1jAvJ+IAQ09Ma6qObBgNR2MUosJLo6QZg7JAKWMm1sSVJjUTk0ygQw8m9TPGNXOmFV7ZhfNK63NiLuTT9NZXdGej4kWW1gJ4k2FuTrKXDk/kzm/vXQeNRlzWzyXuXL26WJYaD0xoCZWhbnkgzcuiGbPU1qzaTdYZO6bi9Meq7NenlBg0z6QAXI85/j5BwZAG5iMnDYeWmW1QRAwGTeZTk+ZzU55efw6rhd3GK/PjRunbBQBtJJsnGhNVSxpR2N6tT4nrfv0an26lT7d6oBmZY5SSYHN4SoLaVpUPuwzAvsZat0Fq4yllPfXAWO2WO0iE8a/h8tEtq/1ppqxz45NLLTpcQFE28blzJ16bcbZyQM67euMfTbKPhtArVIpGsR2N9EAfjuw3tTPxBdVyshDU0dzQlqtM2l1mTbaLNtHGdOsW2CgzRvtUvZlXtf1kvZiwp3+PdrLqQHQFlOaizGd5Zz2ckpzvcxk0H4funNXC0GaxRtBSGPkqRStJNa/VimNFBmLVIo8UUi2Q2KuHQTobPMgsN0kJYlWxEdd1m98jPhNj5E8cYvksVPSiy76pLUDzipF4MzxIrezAa1jkq1ks5AkE0ncD9ncr7B5ocnimSPWH2uxfrpO3C8DzmKqwYBaeEkj+ATH8pJap09FXlILLqmFfWrBFYG8Rkpv7gny+KE5EKBAiB17Wgob39PIffKEFyBTiIVC1q2bv5FnQavKfcYOaLZkwoqx93nBfhILt7TecsHmcsb0gy+zuZqzvZoXwLNtf8H64ZR4uCTebAvPuCvbfJ3BLYfOc/vE3dhzN9Psvdz1zU3aZM+x93HXZDf+qr8+uvPZr7PbRvu/m5TJZ6rZc1xZ5t6jzLDzFXW3f3KmbhCRyjZp1CENOqSyQyrbqKCLCjuk0gBmadBBZ+BZKs3/SrZL176DJZ0bF8r1A4ifNm6VyRiSMTI278bFcgeaEY8I0imh3BlwZUarL2cfBW6VGTi+MeP2vz8G7n3LgDodVQ0Y5rlZ0j6GVg/lMMxU4whdO0FVuzs2mQUOtjhul85rhUm+5CpOQhtdqOGAZA0yoMwFzrJjrwSDWm2Q0wXiwRQ5GiGHA8LhFcHgIUH/HsGwTzC8Ro6uCWYThDMHZRBAt834+/4Sm6/4KqRIaf3af6bVGyO+95sQ3RYyZ5FZV8t2HrQ/jF65OaiTzNUyBjWXpJMSJtkBoKwYj0xn64o87Ga5Ux1NiTcmgH6yRuoYoRMQyhgcQYCOQnSlYkCyWgNyxrYEWmha+S32SrIlmE8JZkOih89nrLEJMotFZhlkcj5GLmY7Ntl8Aqt5noXUxs0qA+6FDEiiBmmtjWqdMO+9hW3jjLRxxrbWI60fkdS6JLW2OafaQFWq6GoIUbDb0H6lxQJiG4xu72dxjRVsE8Rmg1ivkOsZcjkmWA6Ri0s4rrH5/LchatD+4H/isU//umGfxhNCtSQKjSxNgxbP3/1/Mm5/IWHc59an/yc6s19hMPsQV6//02hRpfaenyH4ib/DunWKeO43SfsvFqrqe0FZVrEF3ezmSRRFbDblyZx8uW+vVchc7YBB1p6z4J39ncswcz+/muLrs7Y+1i6yOqM9bgE3N4amO498Xdmvpw/QufLTxRd8sMr+1t0Y92W1/xv7O9uv0pn7rnyGYvgP35by1wMfoCuQTJw6uPaJf7+yIsTOs8W+u+Nux8HvE/s5ig57fhwqr5HBZncD7Q697fBsd0EIvvgLv5TP+ZzPBeDHf+JHGY2u84F4/vlP8wv/4d/zNV/9u/mKL/8qfuVX3sOnPvVJDDNO84lnPs5Xf9XX0my2eOqpz+LDH/7Q3gBrrXnnO78AgOl0wlX/sjB5XYHsGnZlaCXs4lBZ4zhNU2QgC378e2CPZ5zYc9zvy+iTi8WC//iL/4Fv+N3fzMnJKe98xxfw67/xa3uKRpkB6bfBfeDLXGBdAWPr5/eHnbw+0u73t9bauMUKSLUy8eekyagGoC1bLZsjWmuENgkLVBYvyNZMZhsaUgal7XZLmVG+b4jr/J4GyNpX/GxcJ3NI5OcVh2z3G7tIugaB0oowjDKQVWBvre39szkUhDsfeqV8hd9kuLPxiaQQVKJMAGVn3K+9jk83P5NWOuNtsw/w0nOfwS/+p6/P2in44Afewb2XHkN3YVQ9Rt0N4THgXeziopXZ5EsME83GRYuzSwZZXDRbxyy5AEBbj01SAdWnZxMM6D5dMUKyH0zSBaJ3AK/dxDL9HQdVZvVTRlUDoJl4aMadc1492q+3VrQ3Ix4fP0NnOaC9mBIsYtRSkGwqTPUxE875tP5c4+roFUFKhyGvFx8zAFoWE61Hnw59IhGXAkO2uIu0lROpFKxOdqDZ/LjN4rTH8rTL4rhLWt0XyDJJaAxn9F66pDmY0Lie0Lqe5i6dYapZLauGdTg95v74LoPZOxmvLpjFx6x0C6pZNs6aNLHQShT3QG9pBhO6tWuTTKDxgF5tSDu6olsbE8oiI8U3kg9954L+ZQa4bxy53/sgmm+gPQpcg/2YipttwGxukgjMFqc5mGZdOteb8ph01cqSXrdPp2VioLXbw9yds926phJt95QTtx5+rDNfhqYIlja+WbPDotk1n1s9ZvU285Zhnm0rh13zgiSmvZhy5/JF2ssZrcWEznJGM3tvL6e0FlMqeqdg+f35qEzRLlABxjVju93uxhKIwhCdKCoyQGubFGa3WWCBuc1Jj80bH2P7xrukT94mzYAzTtrQqZYCZ/txzjRKx6QbA5zFY0mSAWfrF5osnzli9XSHzceqxIMqfhpRQUwtMOBYK/gYJ8EV9U6favCQatCnHl5RC6+I5DAHvaSUBjgsKICmP1KtsUusz5S2dyzU3ipkWqGERkuNOGtSu2hRP2/CeZvgVofwokPldofquUkUEJ22COoRf4GfOjgfWlQ5osGTnNKlTo8GXRr0spc99hmf8eRuXBy9xP1cpkNovdvocZ9XHxjf04NsT3jPu38/ey/X0LDfu8wzF1SzYSHsefbaZeu7/b1/X/dePrDsu2C6bS9ro3kmQhLZ2rHHsiyXadA14FnQJZUdVNDOzzH/d1FBeWbgQ0WmM2Q6pRLfJ1BTAjUz7+kMmU4I0gmBmhHqmQHG9IxIz7n/4tOoZMNyuSSOYyqVyl7f+X1a8ADRRUPVHbdD13hkWzwQcw/kFwJVb+cxyVSj68Qn6xqQzLpeNnqoxgm62kNXuhyMTeaCCSkwwjDKSos2KL4Fxro3AGd1DTX9aDZQHCPGU2R/aICx60uC60uC4TViOEAOrwlG1wTjIXJ0jdQqy2LZhk7DgGJHHcMYe3MTuk8hbWbLXnuX3dIG7ZeSWwC8ZO7/9Z9pXiVFbVPUVpNuBWokSVUGkokaqaqUBu3fi0cGZucoxchiPzaZ9j7bqaIVxEtkskGoTZb504BkWgqIQnRUQVerBiQL3FWiiqZ6ECQT241hko37RDb+2GyMzFhjFigT8zHhcmbAsnkGnG3XoIuM13x25LJdm2QTjS663mXbfh3xxTlp8xzVOiepH5HWe+hGD1XvmsQD1boB/SoZi+wGIGB/oNjNY58lmc9xhdjGiM0asV4QrGcEyxFi3ifc9Am2feT22rhhJxNkPEVsx4TpDJIZwQECyuYz3sqDH/hZyNhTw695E8f/n2c4+eBHEUIQ1aLcTgrFms946S+w3W7zNSJRiu69H6V3/8eYL1bEmxWr1QqxWu2tQ0BB7rsbJ+73Vs4/CkzJ41dnelkcx7kN6BIsLCDl/ta1A9z7lLHjHlXcdrr6s4sNJElCtVrdY7NZ8KdSqeR96oN8vhwt06v9l9t/7prr9nEZOOj3h62nvaZfP/eeZW6x/v3K9BV/HXLtML+4G4VuHdx2uH3r3sfXPdx6+7rIKy2vGWCz8VDcCaO1YaR02l2+6zu/B4CPfeyjvOeX/hNhILHxr5RS/PTP/CTveudv4/TklD/8B7+X//EHvg+DVAg++KHfYDqd0Ol0+fZv/U6eeebjuV+yLZ/z2Z/L57/jXQD88nvfU3jwymh9/oDkHZjtsoeWiadBC40M5J4Az++vDcjhXi8XBrrwgz0BAuZ+73nve/jyL/1KWs0WX/FlX8n7P/C+fDCtAPAVXR9os3Vy22qFiR2TQ3RZWy8rgOy13Ifejq//MO5uLpxMbPuGsv1NqlIDCx1QxNwHzDfEyxRB98EwD6nGBP53WFKeEbnznZekqTUiDJPEgm6+wM/rnwlqrTVaaRDmfkEgTLhuAULb3alsDjl1MdcxLqLWcDN6maLIABUolfLx5tv42Tvfg84M2I92Pp/TTzyEdwOPC5Ol8wwGR+f7gYTALMIjDTOxSy6gMDuuFb2Li+ZYt3W94Ig+R8q4dB7rPkdiQEddIdN16TzSSpcm9bD9tw7qjBvHTJsXBkBz2GjLSmd/HmhFZ33N64Yfo7fsU5vNkYsUvZZs4zozfcJYn/JQP0lcgioFbOlyzR3x6RxA69KnK/q0GRLJIn3ajpPOnmcXbMgX+mpk3DhPusal88SAadaVk5J+iZZr2lfDgitnoz+meT2lMV2gkpDJ5IjJ+Ijp7JR7kzdwvbjFdHvKIu2ShpVX4MapqMsZ7WjIUf2Sk+Z9erUBvboB0arBco9p5y4WWu+nHt+BC6Kw42T7y/ZP2TNif++Cb1aWucqK++4/3/6zrrUmSUNmc9NPOfNsbj5PZycsV/vzCCAKN3Ta19y+eI5ue0CnfU03Y6N1OyNq1VWRjCAs0G4ViHJmjgY21TqLZpd5y4Bmi2Ync9m0rzbLejt3oywrjeWM3uTagGTzSQ6gWRCttZhS2yxzEH5Xx32XY+X1uy1lwIh73L2e+R5UmrLZbMx6iyZFkJweoZ64w/rJu4zunMOdY9RFB07aiE4N2QgIIwgz4MzGOCsWjVIx6VaymUuSsWR7FbC9X2X9fJPFJ46Nq+bHa8TDSglwtqUWDKgHV3SjjxuWWe+SWjCgFlxSD817wDWWHagw8tjOwxzI0VksmgLzFFKldwx0XQQUXIULDaIeIc9biLMWwUUHedoguGgjz5oEFy3EWZPgrIU8aTwiKUDKdjBn+cwV26s5v/crv7EUOOtSJ3ykz9hunrjjXXbMnUNlO8aHWI5l93KfV/eYD1o96pw8lETJTrsfvzXvPw/42etfTwnPlWkRIaJTttVTRPMWqnnGKjoyAfvDHolso2SHRO4YZKk0LDIVlIP2pUUrpDLgVyV+EbmeEqQTZDolSKcEyn6eEagJIpkikphIXxHqGVrFBQOwzAhy2x4EAVEUUa+GzLarvD99xqHbV/Z/f433x93vf7tO7smZsELquldaZlnGLtNN426ZNnvoxjG6doSq9iBsQRrcDJJZVzWfTXaoyAwQc10xXcZZTZlXXZu4o9EjDGilEJMx8oEBx+RokDHKhsjhADEcIGZjonRDoLfIikB22yaDZS/LZHnUQjzRgu4t878DlonWq8iaCqi1AclUDGlfoFRIQpVU1FAqPBCPLCRNJWinranK2GQCcNhkPlDmgmQCSDaGSZZugKQYlywMcpBMV+te1hbDJlMlm6EAYrMyQNjwksrCBuWfEiwmMBsbJpmNQ7aYEDhsMjarUnABnI0yGZBW29Dssa00UWefhWrdIm2ekzROSBtH6EaPtNZB1Tt5LDJdjSAKIXgVAJlmN6dXmHjFpSBZgtjEyO2aYLMgWE0JV2PC7TWV+JpwOyBMJwTpDBGPCZIpQTIlVHOEWqMyt0rXFs6BJK1JK1XoHJF026h2l7R1h02nh2q2Ue0eqtVBtbvm1eqgWh2S8zs5uGbL4F1fSuvX/nMOrNmXlQWubImiKJcjUSCIKcp6lzkF7MmyfD5kx9341WU2pz3XvqdZfF3bL64nnHsNV5a68wTI7XHbn690Q6Gs+IxG2x53DXVlva9Pu/p3mR79SoprC/j96/froXXff65cfb+AB5Wc764jZXZFma1ZphO4xdfV/Xu77XDXLRdvsHVyx8K1B19NH9vymgA2fzIXFSzN7//uP0yn02W73fIP/8nfAwQKg/yr1FDx1us1/+zH/gn//X/353nd657gd3311/Hv/v3PIUSASlN+4l/+GH/ie/8Ur3/9k/zF7/8B/tVP/yTPv/Ac9Xqdd73zt/HN3/gtAAyu+/ybn//ZPQPNnxIuclpQCLKg264haGAYE7i5JJdD3m4bk+1gP6H3lBr7iuMtv/ie/8TXfc03cHF+i7d99ufxoQ//5o3gWdmkKTNw/YfCFQqu8HEFmy3urrI/Ud222+/KFNjCQydM1jhN5hKpiwpaWZ386/kPgD13Jxjta/+37kNp52dRUSyCLi4C7tcTnbm6KkUgzBqbTyM3Xhg7t0EhdF5HEKRa8J/738DHpm+jHiz58rOf443tT+W/jHWd90Rfg74UxpWzAi833sDLX/eG4iAkGBbaQ4zCmcdFAyrqQFy0Dcf0OdJ9jujT01ccM6Cnr6jq5b5RhECh0B6qv2snrII608YF4/opo5rj0lk7ZVXZ362XKqW7HnAxe5H2YkB1viZYKtKVYBM3mWYg2j3ehCoRT1WWHHFJVw7o6j49mbUlHNFQYw65NgL5s2wBFKVNGo51p2XcN3PwbAeibVv1vTqgNPXJnNNP3adxPaE5nNK6ntLIALVwsWGx6DCdHDOdHBs3zvkXZG6cR2xFw7DPLIDWEKWx7yqsaIZjjmp9TpoP6NUv6VaHxo0zGiHFvkvzTsYUlZOyhauMEesbXu7C57Nx3Xv617Sfy4Azt6SpYLE6Zjw5Zr48YzI9ZjI9yQG0+bJLWT6wIIjptK45P32JTmtIpzWg1x3nYFq9tiALtlYKMimHfJDPjyBg1jAg2aKVgWX23bprNrsk0eGA31G8obWYcjL+NM3FlPZi4rhsTugsp7RWC2SalP6+oGx5/es/n/bdpcjbctNvAFIpWTYbTE7bLM46LI6arLs11s2QdeXrqDUCmvWQKNREIuawbmHunWwk64UkGUniq5DtyxVWz7UM4+yjbTafrJEMK/hGsWRLLehTD/p0g6epBVfUO5fUwkuqwRWtytDEQBMjlEoQAkRglSIDirrKV277CcMudmV/PpfBWxOM2ysCxFGD8KKNOGsaAC0DzsRpE3lujsmzFrJ9s7+XmqxJ+nM2z/ZJLmdsL6dsLmesrqYsBjM21yuS0Qo13RSUuD/0zA/eeN1XUg7pBod0AH+j1L+W7Tefxfmo+/vHfHDN7kS7CYzc67vrtz23cJ2wgczAMCIDjBF20WEvy2jZ2wXyj3pZYH/z2oY7gTt/ZIcmhjmWTgm3VxkwljHI1CxnkQWqGIuswgKSKSpNCpuePiBp+ySOb3Pv3t9hs/kspJxw587/QKfzi3v94v6mrCgF6/U3sVq9jjT9EJXKz+JuiENRz3ONt7xICY0OWDZZswftowwgO0K3MrCsdYRq9ND1E1Sth466hk1mwTDX5dIFyVJgjGGUvZoitNFnbMwyC5hVY6gmUEsNs6whoBFA49HJGcRiacCy543rpRwOkLMJwWaB3C4J0jWBjgmChCASBDaLZa+FeKyFeGsH0buTgWVtREkSokNFpzoL2q9RK4GaQ6oi42opaqQq3Mtm6TLLCvI0STOQDCDgxkyXxUogknXGJIuB1PRzICAMTVyyqolLtr+haNhkZVGXxHppQLDBIAfEhJPRMlhOCeYTxNzEKgvmBjBjPiZMk4LbuKurKKVACEStjWp0Saot0sYpm6O3krYuSBqnpI1jVL1HWu+i6m3zqtbR1SpUMlfLV1PsvI0x+rYLjBXi8MWIzQa5WSLXc4LlmHA9pJKOCeNrgu2ASM0yYGxGpBZGrqg5UhudwJd/OdgRBOh6k6TVITlukzTP2NSfIG22SZyXanbMe6tD2uqQtruoZtuJJ/cKSrw1mUg3G3SjCILGg0uur6/z8bAgVhhVmHzmd7O8/TsIl1ecfPh/paGmeVvs2MVxXJpQDnb2uY1R5gJeN20E+P3mrm3b7RYpZQF89O9bZs/acy3AZuPCvZLi69FuHX29275cD4iy9rrrpw+4+ffz6+K+3LLTf27efHHPdYvvIeG2RWtdSEThE2d8HMEFFn2Qy2/7IWzklYCDfibYMpzDHxfb1ldbXhPAZhk9LrPJNuyzP/vzePcXfxkAP/uv/xUPH94HVBakZPd7KSUf+M3f4H2//l/5gnf9dr75m34fv/a+X+V6eI1Wivf+yns4OjrmW7/l23nDk2/k+/7c/7BXj8Ggzw/+zb/GcrnM67AzJIsPz06BVOTuPQJznjCxwXSWUlo71pdwKeCOwq6yJA/m+ioDVKBk9crrsDvffPOe9/4iX/buL6fZbPGVX/5VfPC3PoB1vTHXKn8YbFvdUkaZ9SeYGSOJ60LpAkpaZzv8qTFU4jhGKZ35hCe5UIzjmCRJ2Gw2uYC1gmi9Xu8ADGVia9leCITYBa6UAbdunXNyclJYSPy22e/KDPvdA2Jf+8Cv+wDbeeF+Z0GwXf8adprdqkvTBClAK6OZaK2JY5Wd4z5w2XVdFkRWrtfnvL//buI0REUBH5m9E9DMkh4/ee+P81T7t5glXa635yzTbDGrAF+J0f4vMYt6IS6aNi6dtuRx0RIDPKXGjfNYXHPMFUf0aUuTxr6UYuyBMHlfCsEqahlXzoZhoY1qZ0yyTJ2baB8ZClRMdzXg9vQ5WvMhlfkasdSoTchq22TKCX19h+d4K2XgSYMJt8QLdOhzJAf0xDUdfUWHPg25yvt6t1ABqpwlBZAGklUGmC2O28xPOiYjZwakqZLYIzJOaF5POX7hIY3rMc3B1GTlvJ5Qu56ynQVMp6dMJ8dMpqc8P/0MRkvrxtlGV4IdgFYVcLxvDEmd0AgmdCrWjfMh3dqAbnVIpzIgCmLQRYPI3SjQJUZSmQGXOmCE8eCXqAwstqUARDoGv+9O7st71wD3QVjzWTCbdzLQLGOezU/zmGizxRFa7y9cUqS0W0Mev/MMnZZhn3U717Rb13TaAxr1GVorgmDHzgiCwMS9VBqNLLgqr2rNHCCzoNkyY6DZY6t6+W46GFC9uZxyOnxIazGltZjQXkwyEG2aJQyYEm1WSE8BOORWUAaE7N33BqWo7JxUSpadFtPTDovTNovjJptejW2rStII0TUJFU0QKCL2gbOdZ7hCqZh4I1jMA+KRJLkK2b5UZZMxzlYfbbN9tk4ytkkanDqxpR70qYWXdIOPGsZZ95Jm0KceXFIL+jSjPpEYkW96ZDJcCSNDZWCuW5DV0uxqiCwphHTYYRoIEKTZMxMIQaI1idSEj/VIj2tEt7vo04ZhmGWMM3HWQpw3kadNxA2Gl04VarAgfWFEfDlH9eckV3P01YJ0sERdzUkeTlCDBdvVllQrUq1MbO0kZitgJVOSQKDDEBlGhblR5vLwWkoZWH5obYRiPDWX0e4+02X32Nctyl0ry5RfC4Zq2SStHKNrHXTUQwWdPHOlinqZ+6UBzAh7qLCHjnoQdiG4OfNpoagY4hFsR7B6yWSvTCcEakpFz6nIJTW5JFBzAmWM31DPIB4h1SJPwOSz+X1l352rUppkHrrEKCyOAyjV4OWXf5DN5rOya3d4+eW/Qb3+FUTRKL92mY7nvgO8+OL/jevr32P+iWDb+mKC7t9D9s5Qja4J4N8+Rrd60DpCt44NWNY4RjdOoNKDSnufTebGJstwGMMme+XDYCrrvEsg0maDqbqFygZR3UJli6glGbvMgGW6XUO3Gx4TyhZJTuePYwOUvTw0rn+rGcFmiYxXBGpDIBPCUBFGmqAeEHRqBhh7UxN6HUTnDuLVxiNbm8yW6VSgRpCqCqmokor6LvZYBoy5bDLtxiMDSBJILUgmQQeUxiSDoqkhAJUi0jXCDd4fCHQQGJfLetUATHtx/AI0TTT7zEyxmiPnU8LxwwKLTFpgLHO5tMH9g8UUkWW3DPQOdvN1BAAdVlB1A5DpRpdN5wnS2xYgOyWt90jqPXSjg6q1UTZgf7ViALJXE6xfsZu3C8oBsjwemXG1lKuFYZwuRwSbYQaQ9QniIUEyRWYMMvN5gkzmoHfeQNYgD8MwZ3IpIRwArM22fYu0+SbSlj3WJWm0SFttkmYnA886pM0WabMNN8Ri3Ru7zdoAxeMh0b3nkLOJ+X8xg+mYYG7+l/Mp4XyKmE+z/yeItcnmqxotHvytnyR+41NmpvQf0Pnf/jFQjJOmtab/WX+Sydv/u/z+i/Mv4M7PfS06XuXrit1QscCFD564a5Krg/qso4KMpbi+2XrZJDb2Ozfgv7uh4F/Ld9P0deOy9dAvh9p0qFgAsFqtFuxW+1sbI8xfa+xvD9XJ19/Lfu+DaO5aUoYr2N+4xfdUcb3f/H4+BGb5feef49+3zN47RN4ps4tc8M5dR10Q2L3PoeQNN5XXBLC5xq2L6kVRhT/2R/5bAO69/BL/+t/8NKAydo9GCmkC5meDoZTiR3/sh/nsz3objUaDP/g938sP/a2/bgLgC/i3P/+zfPgjH+Srv/Jrectb3kq32yOJEx5ePuD9v/E+/o//+At5vJiyjnTLjmppADFTo0xR19Y9yFBpLfMhNcG1CkVl7n1KK4TaATxCsDMA8pLdxYJyKs0ALtNvy+WS//Rf/iNf/7XfyGN3H+etb/ksPvL0b6FSE7MqTnZBeLfbLUlidnWUUiyXy7ztaZoSx3EOeJWh0VpppAyQMjAAkN4JC52NjQwkUux81e33SukcwdfaxCFTqSJJ4rzPtFKkKiWJbZBImfVpZghlYQfsvAmDgE6nzcnJLtZZmSF5k+BwA1JaoMac7mYQtX1gjkm5vzPkFgsOWsXXVEnlAJ4Qlqa8DzSY5oocQdVaM1p1+Kcf/9Ns0gyIqmizOxhm40LA07O326my22HcAD8DvBHjzgA761drenLMibzmWA45Fn2OGdBVl0SrB6TxJp+PYRgS2HZq4/a8Z3gJwbzSYVw7yxILWHdO83kb7hsyYbqlt+5zd/xJWvMxlfkGlppkHbFKWkw55YF+khVv2/utQNFmyOPik3RFnx4mJtqRvKZDn1CvnbHAE9a7/3djJ4hrxpVzmWXiNNk5OyxOOqx67VJlrLJY0bk/oDWc7Vw5Bya5QHi9ZD47YTo9YTw+5t70SQbzzI0z6ZKG1aIbZ0eA762oNTU5pxWOMjfOB/RqfTqVPr3akHo4z3TrHf/RBTkNaC8RHjvTX/D9BdN9boQQpMrKPSPvjIxy3ATZN5bda5UZ6rt7CJbrDrP5aQai2Qycp8zm5n+lyoKDKtrNMXcuPk2nPaDTMu6bndY1nVafVnPKLuFuccdLaSO5QbCVEYt2j3mry6LVM+6azmfLRkuDw0tddbOitZhw3n+ZZg6eTXPWWXM+prma58lsyozcHJT06P6uPC6Ty74MKgL/uzanQcCi02Jy2mZ53mV51GDTqxO3KySNCF2TiEgTBikhyR5wFqII2WZzRpBuAjbLKuk4IO5HbO7VWH2qwewjHVYfabL9dJ1ksg+cGcbZFbXgkq78EPWwT617aZhnWeyzqryiFgyRwsSX1Jj1BzRhEBaUoIKMzzaopBCkQJKkhZ1cpRQoqzxpdLuKuNVCnxjWmT5rIs+aRGcNxFnLMNDOm4heCQvVKXq5RV0tSD94H3U1N5/7c9KrOXqwyI7NUaMVpDp7XoweYTZVQARRJp9Ss/ZIQSBCUhWjdIp5xs2fEFYO7zOJ/q8qvlLtzzF/p96+lymw/m/ctVMDIuyQZuywNItHpqNeBpRZcKxrwDHnXYc9kK8icHC6zjJbjpCr5yAeQ2z+1/EIEY8hHqE3Q/RmaAC1eITaDCEpokDVep0wDAnDkHq9TqPRoF6vF4wWIzt3SnWZEeBv1tli1idJmp6QJKek6TlJckqSnBHHJ6TpWf5/kpyitb9RJdC6xmbzemRwja63TRbLlskOmNS7xPIWiT4nSU9J4mPSTYdk2SYNT0xG8KmG76qi3vYnUPGf2AFkfhD/FJhmr1dTRPYqEKkU6BWES2RliYjWUFkbwKyaGmZZs4Ju19GdJqrbgZqrXxhGFHj40TwDdiYvE2yXBMmagJhApgRhSlgVBM2AsBURduuIxxsIGQBH2evmojYpKstsqa4FqSIL2l8jFfUbg/Zr19VS6wwkU5kuJ0GElGa6tI0UzruQiGQNaosgIQtiDEGW4bJWgeohYLmCprK3xS8WM9N3g5cNe2wxydlk4XKWB/CXuSvmJM96SVIed0hrDTJA19uoehfd6BpA7PxJkuY5SeMU1TgmtSyyWitjkTUMQFaJXn2wfjdb65wbQLIUNhvkZmVAstUYuR4SbkcE8YBgOyBMJrAxCT1EPCHMADOpdmEZfPmXfw4j0mY7d6fctp4kzVwq05YBydJWB23dLZsd0naHtNkxTLJXUeRqQbCYEV1fUX/xWYLF3DD95lPCxdQAmQuTpZTJCD0ZEcyniNkEsd3kZBYXzCpjLPtAlRYhSWTiTB7/5b/M8m3vhkqb8IWXWN35ZtQTHXStRxq10dUuaaXL9uStO2MYSHpvZHv+dir3f7XAWLP38UEk2K1FZfiCC6ZYO8wHbd01zMZbc+0IFzQpY4b57LayTaTXWmxbbwq9cJMNXGDql/Tdod/7n2/6TUHPdupZBmRakNT2mwtg+sCae31/vTyk+/ht9Ovp68pws1eOPd8FOm1d3aSFcRyzXq8LDPsyrOCVFKFfw6++7Tu+rhAjzG20rYjvarhTlLNYbEIixC57hspANSmDjB1UUlnhuj7ZFWn3nW2KS1v0v5Myu4fWOXnGgDHmHNcNwbaxWq2y3W4RAhPgHsvuUqRpQhwnbDdbNpstSZqwWhrEfrVekMRG4bagXiADdgw1p38C4xprjgnCMCBOYqSQxuUwixOmdOaSkyQGiJMGsExVikpVwVFReMaRBY/yM7J7mwdD5r+z7owmAUGRU+4+7Du3DQtA7QJfG2BrxyS0LDG3Lp/91rfypje9seB6ViY8fJTajFm5y6/70PnC0jV03eILvuK10nxXys71KIo4OTkhDMOCj7wx/jO2UHatX7v8Mv7jvW9kr1TIFa6GnHGrfo/Tep/j6hVHtQG/1X4nH03eWbBvX8+zfIX4WU6jKc1aaMbczlml2G62rNdr4tgG7DfPQiglGsG82mNUO2FcPzcAWs26dZ6QBPsuT1Gyprce0F1e0ZjNieZb9AqSTcQy6TDRp0w4Zcu+ARsQ0+HaAGhiQJcBXdGno6/oyRGh3I2fLYeEuPlSsGw3mB93WJ8fMT/umGQCGTMtbpYonUpRH88z980xjYFJJNAaTqn3p2wGFWZTA6KNxsdcz24xXp0zj4/Y5G6cmStnpXxhDVmbZAJV68Z5Rac6oFu9pl0ZEQbOYqLS/LkpCH3IACNTrBzNKeOZPHTdJexz67pOlblkCiFI9e55da9vP1t2JiVKh9YQxy0ms2PG0xNm81Nmi1PGk6OMjXZCkpS7yzUb4yJw1jbss257SKc1JgiKO0Tuc5sCm1aPZavLtNlh0cpAtGaXWauXu3Buaodj1sg0oZWBZK2FiXXWsp8Xk+z/KZU03pt37oLtuir4yoB7nh0HH/D0lQe3j9MoYt5qML3osjztsjxusOnWiNtV0oxxdhNw5halBOk2IF0EJJOQ+DJie6/K6lPNHDjbfLpOOvNci4AgSGi1ptTqI9L0JZLkJYLwijAaUI2uqASXnHHFUTygKiSgcsa3bYv5LECZMI/akbdlyRb8IoQw7LWTJly04KyFOG8hL9pw2jBgWcY2E2dNRO1mcEZdL9FXc3R/ge5nIFk/A8z6S9TVDN1fwmJbkENaaxOvG2cHWBiX8j0DQQFCIGSYux4opSA0sd4SlZCqlG2SshWapUxQYYgOA0QQFsBDKSWf/uSL5Y15FeXu3bt789DdkHDXuzAM0QhiGrmbZSrbEPVIgy5kQJlyADOdsch02EGHXQMgvNKSLhHJBJmMC5kr7TG2I0Q6RSRjY/wmY/RmiNpcQ7rK5Z4QYi+zPOwyxLlreZkhUq1WCYKASqVCo9Gg2WxSrRo5Vtg0c3670zkaGUh2TpqeEsdnJMlJDpZZ4CxNjynPNJQVkRA05ojGEtnZENcv0K26cXk8V9BVsFrDNMrcKTWMFUy0Ac9uIjwKDJj1FTV46w3PiasSCueVriFZIrYz2E4QYoYM54hoBdHGuGc2Q2hVod0wYFmviz46Rnd7N1Qsu1W8RW7mBPHasMpETBgogoomrAmCZkjUDAlkQiDTG+WeLWqVoDaKNBaoRJKqMAvaX0dRy4AxSZp6bpd+PDKlDEiWZHHJtAQRAGF5TDK3L8Go0mqLUFvQMYjMeyeUxuWyUoHKK0kpuquPzJhiIge/dnHJ5HxSAMhsAP9gMUUuZwi1M4Jd+0xpTVBvG8C22kLXO6SNY5LmOWnzjLR5Slo/QjWODDjms8gqr5KfkbIPhj3C1VJsliY5wWZMsJ0gNwYgC7Z9gmSCTIy7pYwnRHqO3oyQaoPWOvey8g1xrTXU6iTNNrrTI220DUDW7hpQrNXNATHd7pC2uhl41kW1O+gbdI7ysZvnQFi4yACx+ZRwOSdczJDzKZXVHDmbEC5miNmEcDUnWs4RaRHgPwQ6WJlnSRcmEUOTJGyRVjqkFZO5OAlbqGoXVemgqz1UtYOqdFCVbna8bb6LDrP5y4qI5+igatYC52G9+2+/iaj/odzDSWvNbDZju90Wkg247bFxJK18tvPVMris3eXqsWVgits3Njune9z1CrH3cOvjgke2ruv1ujR76cF+8TavbFZUi3tYdqOtT9n3UkrjghuG+Daqe+2yz4dAJvdcX6f1Nz7981wAzvattVVsH9uQDzuSTnniQXcc3HHLN7ic8FauHl7Wt+79XHlnr1+WhdSNd+9mFd1sduC0jZOnlOITn/jEKx57eI0MNuNRJrEB2qUUpKlNQiBAyAzeMR0ThpFxU9LaAEJKodMUGYhCJxpA4LBvrfnsGmWuK6jtbEmcJqRKEWYdEydxFv9NZWwvA4Jt4i3r1YrlaoVWiiRJCQLDvLIPlelg09Y0TRHSsLxg52estGmtnVx2YA3VlAx8KqLQxWLa4hrA9n3X7iKgWPh1bqDuBJQ7mYp9uR/wz7j8igI1VghQqhgTwK1XMZihVeI1UrpZSt24CeZ4HMdOOtxdEE7X/9y9py22noeQbhd4cMfAljLwzC+2XwKZGTyBQKUCpd0HuzzmVC58KO5+SLEPFstKghIhBPC23n/l6+78FEFQBDaeFC8ySU+4x5MANJnzu6Ofpaf7hEG0ExyOoBNByLR2wqDdY1w9ZdI4Y9o4Z5Kx0dIStkA1WXGyvKS7vKY+nyNnMWIt2W4iFmmPiT7hOf05pOz/tsKKLiaZQEf06THgKLimra9oMQa9Y6DkAluQz1W3n4QQpGHA4qi9x0BbnHZZHndKXTmDbUzjesrJc/dNUoHhlPZwRuN6QvBgw2zYZTI+YTw+4sWZceOcbo9Zqo7J6OS6cR6Jvc1uoVMawYR2Zchx/SFHjYcc1Ye0K3061Wuqwbp0IdotWrv/gyAzwhGEhQVrl4kXsqdcpYSB3O0IeguGbzAX3nHOodjHBYe6DAzfxjWmWfbN6eKM2cyy0EwctG1czgCq12Yc9x6Y5AGtAZ32kHarn7l0jqhU0sKCZ/tmG1UZt4+Zt3rGVbPdM8kCMhBt3uyyaHbQN7hD1FZz2rMRtx4+T3sxpTEf05pPaM7GtFczWvMJ9dUCdBH4KszFbMysJPAVDdin9buLuX/cVSK2Ycii22J63mV51mF91GDdrZO0K0XgTO4DZwKooCBjnKVKkK4D1ouMcXYVsXmpxurZJrMPd1h9pMX2hRrpvOT5CBJa7Snt1oyz9oD2U1OarSnt9ox2e0arPaXVmtFsrkmThOvrIU8//TT9/iB/RmUgCZWiujVEUJ1Zl9Z1oyCXlUJmclC4NP1GZOKaXbThrJm/RJYowH7mpOGxwItFbxL0YIH+WJ+0v0D3F6jLWQagGfCM/gKul+g4zRXnxLpEa/L8CRoQsjj+7hhYl9X85cl+IQQiAKW9zZwwA7xTZeKParOZt3sWDbtJePcsW5NeaUlTGC9hNIe49Xkmu2XQ3b3CLirI2GWBwyKLeuigw77b2A0lmRuAbPOAYPExiE02yxwkKwBnY2MIp1NkOoXM+C17HsuMBmvoSK3zhDo+YOhusrnX9nezXb3H35lOUzKQzLLKTjLgzABmcXyaf1bqEUkOalvkyRbZW0BPoHoSmpFxa9PaADdrDQtNOqlkAJqGewlsZiUXdFhEFaAp4a6EljCfmwLaAlrZsbYw4JoAEg2bOWxfQmxGiNUQllcQXyOiFaKmoS6hGUCrBp0GutuG4xP0yRn65BR9/AZ0GN2I56EVQbImZEuQXBNEirAiCCvasMwCA5SFMiWQCVLuz3Wd6l3Q/k1CupRsVYSibuKRUX3l8ciUcfUjtXHJMpBMROzFJnNLxiJDVECkkG4ROgtwmzPJQqhUILoJ4DdsMrc+YjFBzq5zMEzMxkWQbDFBzscEixliPkbMTJbLYLNAOLptQU+WIWmtbRJH1Nqk9S5p+w2kty5I22eoxglpvWeyrtZahnFWrZt4apVwl/DqlRTNDgBbYRiPB1lkGjZb46a4niNXM4LtmCAeEWyvjbtl7mo5IUgMkyxIpwTJzLh0U1xXgR0AAqTVOrrTQx91SNpHJJ03kLbahimWgWVpFodMtYrB+/WrATjTxLgaz6eEz1/mn4MMHJOzSe5uGSymyPkMMR0TreZEmxWh2I+B5X+Goh0jhEDLiKTSQ1W6JFGLJGib97CdAWYGDEuitjkWtUmiNioygBryVZj3KkZup8jNlGj5KQNmbqfI7RSxmRA4n8V6QRBfE8Qzgu2UKF2i0y2L1/8uLr/kb2M3FRov/AK10UfRcpdwz5XP/iZQYappvWcPwz7I4upc7nXcZyTMXCEOZZ5072HPjeOYKIoK9p2U8lWBa/61bZtcEo/bF36MUR9YOuSe6oJI9v+ydpYBs/76Wwakub9z7TnYJzO5BAJ3fF0ArAyXcK/pu3m65z2qlM0ntz0Wl7H1tF5wtv+tThBFUQHE9fv4lZbXBLDZtN8WaAG30wUQGJBKpwRSkKTFwNfksVMyd5/MZQKl2G7NrmQYRrlylaYGQLC0vTiJWW9WbLZbttstm41R2sgnSWDiXmQdbv2XzQQFY/SqjLlmA9JDmpY/rO5kcw0Kq9i5k6ZM0ZOOgLGlaMztA2oFQav3GYH2s4se22uUgUhuHV2h5LbFbXeZMNyNX5HC6+/++/d022rBNYB4m2bAYlp6fg68HADf/DoVgY3DD6QPgrjX11qTOsbYvkEtSl1aC4aYM28+6/j9/NrllzLdHpv2iy3f8eTfR4mAerDkrPoAIQSTqMdHjt+OQPM5ww/Qjqf8gegf8Kx6M1uqvFE8S12sUTJiWDlm3LjgunLEdXTMqHrMsHLMKOqhShbVWjznfH6PzmJIdb4iXCSopWQb15inXcb6lCv9GLokHlqdGefiHl3Rp8uAIznIM3TW9AylioE5LYimtUZpT4ADSaOWJRPosMjioC1PeyyOO6y7rXJXztmS7st94745nNLMmGiVh1OS+zWm02OmkxNGkxOenT3FdHvGPOkaVp6bTKAtTOIIr1RZ0K6M6dauOGk+oFvt061d06kMaEZTpNwZcfb5d+eZuwCWzTPYLUQyA7+NFDQl9RltyiTFCKUkQZjfJAlSBsbRrGShLMiXTJ4GQZDFT6wwnZ8wmx8zm53mMdAmM/O+3pTvVlaiVZZ5c5C9GwCt1xnRavSpVreFfoiBVbPLrNXlueYt47rZ7LJo9wx4lsU6iyuHYycFyZbWfMLt+88Z9tlslLPQ2oupcddcTIkclvNBZh5mQ+dR8sD9bRng4f4+rlZZHXWYZcDZsldn22sQtyLSZghViYyMURmJYkBcEyXIAc5SSbqSGXBmGGebl6qsPtlk+pEu64802b5YI12UAWcxrdaMdnvKefuK1lPmc6s9o9Uy793OnGptiZRFN9VDO4Fuu7Ur+zRkcJABArVGpwodSBP8/6gG502C8zbivGWSAlgA7awF501E62aDRk9Whmn2TB/6SwOiXRkAjcECfTVHXc4R822hngZEzp4lsWMOu23T9l3p7PnJ2ix2bS2bA1IKkiyem72+r3QaeZcBJ0KiKDLSSJM8yY/K2EHm3jYJw+7eMqhyNYbRAsYLA5aNHvU+N8DadOk0+rN+/sa+BrJslWOC9UsOk2yMSAybTMRjA5ilk9z1UmbZL8kyW7ryzuoBZet3mUFk22z6eRfuwY6rfS/7XBhbb733z9e6jhC3gQu0vg3cAm6xiV8P6jF0fIfx/BSd9KAkFuSugdpkojwSJhRAF8PiEtlGotIGzForWGjUXBoA4r6GxY4FXz4YGJDsSBrArCUyoExCE2jGiPqGoDonEDPC7YRwOyTcDAnXfcKtYfKwUQYwFlWur9/BqnYLfTKDO/cJ7t5BnZ6hT96K7n4R1G52mwaQJIY5G8QEcp2zycIg3X3OwDMpTFgTHac5SJbGErUOSFVEQpWNaJJSKXWzLI1Hlqaw3SJs8H4t0ASZW3F4GCSDzFWlmrPJUDGCGOQKpDQxUiuVvQyJ3qADdbRl6KcJYj4hGF+bYP3zMWI+QcxGkIFlxuVysscqYzlDOPPXrs0iCJCNLnEWh0zXTXbV9OxJ0ifP0a0zVOPYAGT1TgaStTIWWfW1Bet3QbKbGGVxakCy9coAZJsJQTwhSAxIFmz6BMl4l9UyzjLjpjMzH3VxYPZCAkhJWm8axthxB9W+zbb1lhwEs8DYDiTbvatWB8JX4V6+3RoQbD4levgScp7FjpvtgDHjemmOBzlwNkWsdjEYy2SaX3IXzCBAV9vEtSPDEsuYY0nYRlWz90qbtNI1TLOoQxpljLOojQpfBVMOkPECuZ0Srq+oTJ9FbhyQbD1GbCbI7QS5mSI2Y4J4htxMEJsJod449nP5BuIy/iL6m7+B0mdU5G9yVv2ThNE4XxfbL/3vVP/972V58dsIlg9pPPfzRl9wrmdjd7s2gxvbyq7p9pglfbibpIcAGLeu/tpgj++Hl9kHOP0xtoQVy6Z7NcXXIWHn7eCz/33Qy2IXNpacr5/46527nroum3673ev4fVEGTPn3cuvsssVdgovFbnxCkQtY+XqU2w9l9rR7DVt393p+Px8CCW2/uOw4e283k7lfbnrmD5XX5iL6bV9vgvfaTsknf1AQohJBohK2SUKQ0TvXqxWr1YrFYoHSmvVqzWYT5wOldWqCG+fbzDo3FneASkoYmh0rY0Aag/T/T9x/h0uznPW58F1V3ZPjSm/YQXtra6OAMiAhCRACFABZGYQQSCDy8XHA2GBsDsb42PBh+/AdbHxsAwaDSRKSkBBBZJAECBEkFLd2jm9aYXLo7qo6f1RXT03PrDcIf/7qutbqCT0dqqor3PV7nkcqmSuINlctrbW5Es2NctcrlijagDJ82QbbypW6/ICetn8IuE5ryEJgte1cYfIVIQREWmtk7uh5Wx6UgeDqtxKbDwK1zc1/yWcA+aQK70/NghAKH7FRswKJwNo2kgqwRFGMIYhIhuDxt9/OU5/2FGC9Yd9G37etZpTl3z5Pynlfdla4Td3mvgPBShocwhSf/O/6/T6NRm2tfFxayY6FcObHC93mw1c+l8xWeFLvr9itXXLnyo81rB/w00/5RyzyDrWRjnnd/T9PGlU5ru5xUt3jqLLrwFq1jxWbg6pmOqa3PKY5OiIeTJEzjVlIkrTB1O4wMHvMNpyEARjaYkhPXMnB2RFdrrg/cUhVLNbub1sdDPPeAMtceTbutXO/aN1ClZbVNyfaQhvqg3GhQGseOl9ojcMh8mHD9HLb+UIb9jkeneNkfsAk3WEhmlD1AE06FdqWpEhyM85DdpoX6Tcu06kc0o6v0KsPiGRW3F9oShkm3/B7s6RteVFOYb0p+1oqny80k/YTbyUlWcnh+AbEA6yNGU/6DMZ7jCcueMBw3M9VabvM5t2t1xdFy8B088hF42wf0u0c024dUq3MCkCR1ltMGi3n26zdY1xvO7VZK4dnrQ6zRvt0JYw1NGYTWpMBLe/XbOqUZq3JkM58TG14TGUxRQWdcvi8butIy+1beZ8yNAvLpfgeSKtVpj2nOJvvd1jsNFnmijPTcODMKc4y4i2q1DBpLdFLiZ4oshNFdqnC4qFccfaxLvOPNkkermHmm8+xUqkDZa2V0swBs1EO1JzqrFZbBfs4bUBcvk//51fwyvmrM83ly1f41L13M2RBtFundraL7FdpHnRo7jSo7jVQZ1qw10RcKyhAZuDQATJzcYy9PEEczYvXHDqAZi9PkJnG5r5QDZsroVJKyEEhNnd/ILzP1PWFjgImQmm+rtdAUFFvWO+zw8Uj7RVojjNu9DfGkOvSDKDIqGAqPdK4z0I0WYgmS9lmqdokUZck6pDFfWxlxznvVx2M6mFUF6uuoYxaK1/oNaHfhH7LbXv561/47z9eKMg8IPPvbXLs4Brrfkd8vdgowy2D46K+BG5CtpVX+dnb1o+GZeP3EVvKc72fERjTJ9MHmPg2THwbRLdixE1YcwB6D9I+LDqQXkOpUscBs1b+17QugJByC7a5My5ILEwNTCxMLUyM5+SnpworVVkLaGhEI0FW58jaiCg+IVaXicyjRMkRKhkSpWMirxSJNbZZwfT6pLt76LNn0QcHZHt76N4uWbdH1uqg62109dqTcVfmDo45JZmDY8VrmaGURqZLRJJgUotJBUZHaBs7f2SyjrGVTX9kuZpszR8ZOFPLJEF45/0WB8lEDKq6bnJZnonI4A+dq5o0QlisEhA55/1cxb/mRsrSNTgmx4Mcgo3yz/LPc3NMD2HEZICYTwoI4ZMzxati6h1o9aHZwzZ60NpFtw4wzTOY5g660cc2epha2wGyWjOPaBnfuLP+a5lXhp8tM1gukIspcuEURzIdoNIjZHLoQFk2RKbDXEk2Ll5L4wL2+Ge9PIYBsFJh213noL/ddYqyVoes2cG0V4BsBc2C9832lqikpycxn+XgModg4zzowniUm8aOXVmNBsTzKWI8QE3GiPEAsVygtozBwvlCaDJY3KuM0ZU2ptJFx87M0lSdeaX/3EMyZ2rp/nSlg4k7NxSUAJMSpWMHKtMJUb5VyWj1WTYmzqbIZAiLAWIxIMomqHQMJivgVNhulrdFfgbtbOhze+2SjATZJTU3c3H+VlwIX/e7uno/51vfUNQN3w+U55b+enRnj8HzXsPMCPj9X8Lc9zdFeZSvy5tGepNJv/XlFP6FfX4ofCjDpDJU2gbh/Pm9KMe5hlpZdw2Hw+tSsZ02/gIK359CrJRSHrj5z8N7azQaG/fmymZl2hqepwC8wbO1AbfZvoDs9w3v4zS+4duF8v1a60xEfd6FYKs8xto2ft8mRCrDsvL1XW0MHI5V/DH9NYb7VqtVarVa4ec+zGN/jo997GNby/u09BkBtq/4O1+GiBTpYkmaGUbjMZPJhOVi4Rzxa99jrkvrPAkuGi8BJtsa4Bk/efA3vw06hb6Hysoxb3NdlmOuBpXuPN5/mF+dj+N4DaqsNbblidkp0C1UOPnrC38XFnhZfYUOHGLLfNXdVx6xAmflc4YAIFaR8yFTakzKYWbLDY8rG4XFOWXe3u0LhPXf5tAukrmyXhZL8k5uGRFFaq3xLRpxK7jp5vM861nPWGskwrIKP/fHDM1wfSoP7ssQYlv0j/BB9eWtnOOgNXC3zaRXKcX+/j6VSow3HfbJaK/gWnVqq788OqmIsNZ3JIo/vv3V/Hn7RW7BNKbkNHiV2smQ3uKIzmxCbZoiF5Zsrlgsq5ykXU70Dku7uTItyehyRE8e5cEEnE+0njykwxHSJkU+wro/qbDT9fUwU5LprgNnLiJnl+lO16nRdjqYaAs4WKZFIIFm7hOtdTSicmFO9lCVwVGX0XCHwegMR7MzjJa7zHQHU4nXgwlsGYwKa6jJMe34mJ3GZXaaF+jVjmhXDunXj6mqGUKsnmV/f+tmztthWFGuW1bNyg39aSAnzLu1PMmfx3CVLmzYV+UhmUy7DpyN+gwnu7kSzQG0ybQLW9SHUqZ02sd0Wsd5BE4XTKDbOabTPqJem5BJyaTZYdbuM2l2VqqzVs+9z/2eZVcJ8R4nC2eemavLmpOBA2mTIc3JiPZsSGs2cpMsIQplY5hPXom7DUCGHfs2aBYO6NbaOCFYVqtM+m2mZ3rM9jss+w2Sbp2sXXXgrCaQkQNn0bXAWRaCs4j0YszyoRqzu5tMPtp1wQEerWEWm/U/ilJarSHN1gqYtVsj2u0JjeaAZnNIpztdA2fl/ibMr7Dt3rZK6Pcrfi8luirQLYVpR6RNiW1H6LYiayl0U5G1JFkzN2e7SrLTJPBt5gDZ2vtLE9ThFHM4QVjWnjfvR8UNDxQiB1ca7eCsPWVxSjrFu8Wp6by7Bt9Oaq1RQR2y1iKi1YB87fONZ1UUkM5YixZVUtUljXokUYdUdUijLqnqkKgumeqSRh23j+qS5d9lqotR11YGrTIyK6JZ+r9XvuyFBTDrtzYhWr/l/jr10+em58+f33qvYT8Vjst833k1wBY+f+VBa7kOlvvtMG0b01kVk9XbmPpZdOU2TJzDMs5hsj1MugPLLnbWgkkNxnEuBzwlCQJglv/VrItWKYxTnRnrIgbOA2A2zV9fbUQscBCuoZGNBFmbIytjosoxkbxELK8QyQtUxGNE2RXMPGIwfiM626Ox+xc0b/sTzE1nSQ/OYA7OkO3sort9sk4X3eyi6y10pUEW1U5frFiVTg7GNFEOx5TMUEIjTYJIHdgSWmONW4gxooaRDbSJt5pZ6mzL4CNNHCRLnfN+a0CgsCJykExEm5BM5K9l6c+mYEPn/R6S3QCISJcF9HKmlvnrwPTSQzI58e/dn1zOizFq0aaqCFvvYHIFmXPY38E09zDts5jGLqa563yRNTpYryKr1aFaubFrh6sDsm3vlwkkzmG/WA6J0hEyPXFmestDZHqETEeIZOCiWuqhc9ifjVB2ZWoZzpX88yelxEQxWaOF7faxuZ8x7U0q290cmLVzk8vczLLddT7Kmjfoq2s6KZRkTjE2LJz2r96P833Gq6iW46Grz2JdHLHNNM6PJ6zNlcNRE13pOGf8OSDTuRml9eCs2sVWusV33jeZvWEV2cSpxJJxAcjcdkyUTpDpKDernObgbEzFOOWZMk5FVlaZl8eVa9CqNI/0gM239c7iK8LSIzMttO5gRRdDF2O7WLoY28HQw5g22vrv2vn3pwdlkIx4fO9ztsKREEporZmdO0v8jv9CfTclTSo89snzLN/4MvjUn2/cYwiaKpXKmolnuPX7wQrmhRAnvAZfP7bND8vQKKxbfmzuodpwOCz83F0tnQbYPEjzfte8Ks6Dtmq1SpZlxedCCOr1+lo/61M4Ry2Pj8Ox8GmgsQwXy/v5PAiPG57X719mMz7oYjkaaxlohkKW8DjbUtnqYhtkK38X5lVZVOTHO/6194XnzYI3uEx+nv8lgO3MzQfAutROKZXHyMoPDIU/qvDG/EAsiiIyo8GAkLkCzpug4OhtkiTuARL58cRqEBc2Qg5mrNtKh8mbgnoAVG4QPOwgUFghBDoHdP58voKEq+Dh/ftUfrDX8iiANuWK6QFbeJwyaLLWFiv3ls3JFNat7tt8zmANEMhFIVcIqJVaxqSaOHJKM6zNAynkygAPI20+3rOuHMijooEt/IeFyVdav/IQDubJy/LWm8/z1Kc/bcMc1d9TCAdD1YGfsIUPSJiPZWWbb3D8Pr7uhJ9prRFQmANuW83xKY5j9vZ280ZxHURpY1ksliyXS9cQCydH8mAwiiqF6qJSqVCtVvn1c9/MfQ8/E56Lg2tL2Mku8bijBxBz0MuY6bLBQPcZ6B2yLf7QYpHQV8d07GU69jJte4WePGRHHdGLxngTFVWqL+XOCFzdSpo1pjud3JzTwTMfUGDR3a60qI6mNI9cEAEfkbN2eYS+x7K82HQAbbjH8fQcg8U+07RHqmorBVpNQLx94lRhTjMa0Ku7YAL92hValUM6lUO69TGC1YCiPOjydcHXGd9uhMDW1ZM8L4ylMOXK0woOgJ+AlMHcxnOapxBY+mvyn7m6BvNFr1CcOfPN3CfaZI/JpI/ZYr4khKbdOlkPIpBv2+1j1J5h2mrnfs76TnHWaDNp95jm0TXn9dMHyMJoB8zygADNPChAY3xSBAxoToZU02WRz2Vgua0D9q/D92FfYoxZc+i6scAhJct6nclOh+mZLvO9Not+k6RbI2tXsQ1VmGpG4nrBmUKPJdlxbqr5YI3p3U0mf9Nn8fEGyWNVzHI7OGu2hg6aFeDMqcyazRHN1pB6/YR6fekZzlYTzfJgZ72c1/NqTYElQTcdNDPtCN2O0C2Fbit0a/XatCJsdHWlhJxk6OMFk0ePWVwckh3OWF6ZkF6ZIq9MaTw6pnlxhJimrmtgvd0NB1GR2FwjKNTLXm1tDOTwz+Z+SGHTXUH4vJQhkRDCtaeuY0LnJqNaNnLFWJc06pLIzgqGFbCsU0CzEJQZef3+eYRJiPSQOBsSZUMiPc63I1Q2QCTHkB4T6SE6OybLTkBOEGKGtNMij3zZPnjfo9d97tPS+fPn1/LOl01Yb2A1UQsB29UGsX5b7pPzLxx4aPUxzR603VY3etjoHEacw5ozDpgt+9hZB6Z1GFVgqOAEZ7J2tVTBwTIPzxoWqhZk/mdKajMPzK5HbRZnyPoCVZ0QVYZE8QlRdEgsLxOLR4ntw8TiUVR7gew1odNEdxqk+/vogzPo/X2y/i6600O3u+hGh6zWJItcBOMsk07Ncg1mJkWgJrMp0mZIkyFz5aaLeB9hRQVjFHbNzDLCaLnpjwxguYRkAWngvB/l4JisQFRfQbIwifyvrCazmYOVUjg/XnF0Q2oklvNcJTZyvsgKs8oBjAe5/zEHXjwcU/m+5JCsaCMAU2lgG11Mo4updZxarNHFtM5gWvsOljX62GbPwbR6C2qN3BfZDZga5rd/TTC24Y9siVjMEHPnlF8kJ4U/MpUeIpOBC/CRDoj0pPBLJrMRkk3H5muL+AD1BrrZxrZ7BRgzBQzrFO+9M39TqMq6DhRebzImV/uNClC2BsfGLjqp8j7LZmMYDZCjIWo2KQIulPu6bWPtYlwe15x/sdzPmK60sdVeHr2yg6100JVurihzyjHtHfbfqIpMJ4W/MZmMitcqHRU+ymQyLOCYSsfO3DIZI9Mxwq7meR4She2tHx+F48FwETGEoD4PrBUYmmjdIcmaGNsm1W206ZBmTYzt5O/dZ9q00LaDNh2M7eCj8l5nBiAZIcUQayJ0en4FzqXNg325a6uqv+R8/03Q7OULJG1sq4fOX5tGF9Nso7sdePEXIJtukTZZxtz01Ie45+fGiG/6ojWw4scHZUWXEE5h5Mftvv5vg0s+XQuwbauD4TgWVotSy6Ub445GIxaLxQ3k5/p1eT9fXqHnXW35uuEBm//eGFME5PFBeEIusO0+fL8eBebv2+61DNS2gbiwHoZ5GI4nwnG6Mc6vmf9bYxusi4HK6rcw38tjXn/88vVuu79wLF0+BrA25vHmyh6wVSqVAlJvy7cbBWyfkQ82n0LKF160lBKEINPZGkUNpYve2b2RoS8TB3O0zoqbMxg/dnZBEwQQ+DlZVV7fSDlgRlhRfL8vwKJdW5E3cIUiTDpH/FaAsQYpJDJSpFmGjBRCCqRYKbDK0tSNzi+KEHkeaaOJ1CqijU++koWTSYQo7P23Nxb5jbidWI2I/MPmvi66FAkiIP7hxKUSO2CgcSq0b/uWN3OmX+MXf+qnePTSCXMTkQlFlpvB2FxdIIUGq4oxXHnSXAaIBYASTr0iyQ8mIqSMsHbdFroMSfy9e0ViGY6F+V4GrL6elI+xpjrLzTmLyyo9VOUOz9V1l/fGuEZEODyMMRmXLl3CGEOl1liBRSRSSbSv6/kzI4VgeqHvMvjPVuc85gzHnFm7jrqYchBdZCc6YS8e0FfH9NUxO/EJdTtCCDg5OUFnTikXSeVMdI11Pgnzyai1FpRk3m0x2e0w3mkz23HBBCY7DqRltU3FktCaxvGYg7seKgBa43BI9GBK9kCFyWGbwXCXy6MDjuefzSTtM7ctqKr1YAK1zYm+JKMpR3RyM86dhovG2akc0qkcEatkrS6sAdtAdBAOZsplX14ZLEehE0LmgDo3QQvUNFIotA6fwdX5yh1e+dlVQjKfNRiMdxlP9tf8n3nfaFpvG+QbWs0h587cT7t1RLt9SK9zTLM3Qp1JMWcEs26HabPDpNXlsHmWB1tPdGabzQ7mKqYz1cWM5mTI/uWHaXp4lqvQWtMRndmY2mwEej1QRZHnvtzytr54zWZnt808ttxxaq1BCpbtFuN+m9lBl/l+l+WOA2c6N9UUFZGDs5RIrK94xUCMxgfC0ZlELySzcRV97BRniwdrzO5qMfloj/nHGyQXa9hkc2IYRUmuOBtxpvUwraeNabaGtFtu22yNaLWG1GpLvOLMO8bdtiInxLoz2LCO+P4z9JlhKgLTjgs4lrUUpq0KgGb8tqGubmKUWaKJpnJxiRxroolGTTRynBFPDXKsiacWNdUsZwseeughHr3nXiaTqYNaUmFtRstKbJJi5oZYisLhfPhMFQtIgOT0Z8K/VqV6Vfjs0U6rlqkWaW2HRHWY00RXdliKlgNjcY9UOZNLB8j8tk0adbHi+ifN0iyJ9ZA4G9BYPkSUDYj0kIoeEesRUTZApUOq1sGzih4hsxOibITUTnHo+keH+W1AKRbpAoMl0RmpFMwV2Fhh48iNVfLxQxRFayYL/7NTOMgvA8ywbGSllk+MuphmL//rQquPafXdttlDV/uY9Cx2uQvTDkzqcCIdKDsB7geGwAT/OJ5yYUADZ6Z5DgfOvNpM5sTH5AuPc5ubaBp4+DrUZmiIBiCOoHIZzKOgH0HUTqjuLIj3E6pnNLWbBPXzLdROF7OzQ7azS9brk7XPoZtPZFFrMa3W0apGZiMXndIo9ELBsYAj4baP4LbHuM+O8tfHAkaCyjvHyJsypE1xA7MItAukhJboTGGziExXScv+yACxXDgVT5o5v2Q2CHQjJEYpQG2HZLIKoupG/BVWajJMriRLHSQTV2lLipSfZzHLlWODwuxSToa5X7IVFPORLRmdYMfHyPHQwT6CehlXHRhr5r7G6u51Wj+HufWZmNY+1qvI6h2nIKu3sNVcRXYjcM+wAmAznK+8a6rKNCwWiMUUmTgzSpGcIJMj5PIKYnkFlQ4R2QiZDHI/ZENkOoJ0lIsH1p+50B+xBWyjVcAx3TpH1n5SYWZpOj0Hw9pddLOdQ7NeAcu4isJ8I2UpcuwAWXTlYuGo36nFRoU/MpH7JosmIxi54AtyNil8W/t78dvTJsXGgo4a6MY5Z15Z6RVRLT0UC5Vla2aWlQ42ugEACK58kiHR9CIquauAZd4nmUqdTzLpVWXLISKHZtIsi3lbGc5sAzTbFsXKc0KXNxXSrIvBg6+uU5TZDtZ2SXWrAGKZdp9nxkOzFleNRlxKggVKjlBySMxDSOGAmWCIZIgUo/z1HKsEtlLDVhrYqIWWbYzYRdsei/kXgs0QVYP6Iot6tkHuaVTHojopsnWecfV3kbFGxRoVub9qlPtljDRK+THQ5eL6Ll85i8BQfdqtG+seYf6GQpYQyoRj+LK7h2u5JijDlm2QyivX/DzRL8KX3QVdV1lsAVihSiqKIiqVSnE+Y0zhc61arbJcLguQXn7m/Ovy/NuncP5dBpHh9V3tnsrj9XDsEELD8HxhGYS/DVmQF8aU9wmvvbwQXb7f8H257Snng2dP/vq8b7swaqtXsKXpymXZtjbtRtLfCrD5FGa0vzBPZD3UKDdQfgJirMWYFcUPjwfO5E4phcnNTsPJWliQQkqikh35dqq5qhzh9ZbDBftVBiklEoGxhsw7qrVuAi6EQEarSuNX2bHOrXHi1TJSoo0Gu6K84erFmjRSipxkreetc6a7gmjKCEyuOIPVUMxaW6jz/e/9uUKTDmstWZqCXJnQ/tRP/iSxXfKk83s89UmfxZXxkpPZgkuHw+KI1oAVDkKAKEy7wjIpd07+OpxSURcXtwqRu8o7X1+2AbSySV35e5+2mf6Fk/vwmtYeHGvWozluOYevFz4C6up+c5iaGaqVCkmaYrV2IM0YZJBP5WOKNDcdDQbUt8X3cHt0N315yH48pB8dYSuGo5v3acwTDk5mbuXWen84q84nI0MKwTKSTA92mOy2mex1mXkl2k6Hab+F3WLaEC0Sp0A7HFI/GtI+HlN5bIK+R5I8UmU82OF4uM+FyZMZLneZ6i4mrqybcfYE9EoHtpaanNCOT+jXL7HTvEi3ekivfky3dkyFkzyC7/rgbFvjW65j2+pA2HCHK4R+37BDW+t8pMRaUTw/Pn9Pa2x9ezSZxozHu3kAgT3Gk12GHqCNd0mz7auIjfqI/d2Hc9XZCbXdCdFBgjivsTfHTDvNwu/Z5eZ5Jq0uyVX87Sid0ZwMOXPpIQfMpkPak2FhuukjbsZZutExh3lWfh+CstDh7GnQKHxvhGDRbDDZ6TA702O+116Bs5ZTnImqM9WMRYoqgTM3dXDgzNoVOEvGMdlRbqr5QI3Zp9uMP9xl8akGyaUaNt0OzpzibMhB8yFaT/O+zkZF0IBGc0Clsliba5brkB/sudeykPWXIVLRzxnj3Iq24jWFmVeeheoz046w1atPGMVco8aa+HJSwDIxzogmBjXJUBP3vVraXP29Haj4/rU4brlNFIDO+1jjIoaTBydSQq71x8VChO9vjCWRLUx1l4VsO5PKuMdStMniHon3S6a6K0gWdQtl2TZfk6clpWdU9IhadkR7cR+xHlE1Y2R6QsWMiUNolg2J9dC9N2OkWebji6B/Fr4tWPUfRb4J8sjhbsHErOn2XTI2XxiUEqNT5+7B5iq9fDEPsXJt4Rco/2ckC9hay5m8tfroZm721uxhW7388x0Hyuo9TH0XW+1jow7M63AEHOLg0AkwAB7EgYgJMAautYgf49RmHpp5tZkKwJm2sEhhksFYu/M+LCG9RrmLESI6RDZPkI0RsjVG9meonSVqP0GdN0Q3g7o5Ru11Mb1d6O1gW7dg620y49Re2ihmRjH2wEwr7FKuANmjW2BZCM3G14BR0jpwuCPgDkgutUBb53Q8TRF64WCyFYDCyhgrq1hV2QrJrKxhZc2N2mMcGBO5EzOZ562y1wnJ8kKaTRCjAeRRK8X4pABkDpiNAlNMb4Y5Qk6HkDizKaWUA3y1FuTKMdvo5iqyDmn3SSzO72Obe5jGTgHRnJllA6o1B/duJIUAzDvrvxYkS1IHBZdTZDpGpgOnJEsOHShLjtxnqfNJttoOEHq+ocjdmKxLiW120O0OdPvozu1k3qyy3cV2uoVqrPBXFijNbsjUNFmgxiPU6IT40QcCFdmoMKuUuW8y76xfTV10S7mcF4v4/j7KgKE8z9IiwlS6pO19TLULtZ6LWFkJoFg18EcWe79kbUzcviEVmdBJbmY5Ip5dyMGYM7tUydA5509GqPy9zKNZqnSMSEZgNl1NhP01bAK0oiyFG/uVxQiuvW6gTZvMtFemlba3Bs2MbZOZTvG9V5JZbszUVIkJSo6oqItElTFKjFByjJJjIjUmkmOwQ4RKMZUqplIllTW0bKLpom2PzHTQWQvNzYj6ExC1GFlTyJpANS3yQKMODGpfo3Y1qm+QXY3qaFRTIxufRtU0N9ANu/GZcebmSVrNg5xEGCOw1o2ttXG+GtOHHt2wbFo/1rprI2ttYYESjkHD8jsNoIXvi3l6nsrzPF9PkiQpFGdaa6rVKtPp9Pozo3QN/rr9a7+wHM5FQuuq8ry6zCjCdLV5kN+G1xH+pvz8l48Xni8EnuV5QWj55f2Y+fFUKHTwz1Y4pi6fx38fmmuGvz2tvMv3FR4/hLCwUicW8+Ycuvnfh23g/18AWwjK1hozubo5rbXr8EsUdUVCV+99CjPXp7AQQ3hXJrjl6/OfW2sRReAEFzHU6Dy+l29bpZ88SbTOcnK9DmxW53H+0TLjHw5n3lqYoOXwyR/binzRsDRJK0/EhPAmL/nCo5SFog/WCzuM4LgtXQ0oGWMwuIG+BNIkQ0tDQsSnL80QYsEilSxKjgCVNzOyK1CxjRaH97Sh6LBujd9LbcMHp1xm66bA2yf8/r03+fPXET64Zfv98oMtpSv38Bq2QdBVua3uNzyntZZer8NysWA2W5ImC2bzuQvcgSDJV0P8tY9rNZ7QfR8Xo8cXedgSQ76q9XPU5dxdE5LjMx1+6+texrLpIjA+9S/v5vP/4G9Y1GoMug3G3Sbjfpsr9YjZbofZbpdlZ7spZ200ZfehyzSPRjSPhzSvjOE+Q3qPYnapxXC4y6XxWU7mT2OS9khkfd2MsyHYNl6IWNKKBnRrh+w2L9CruWic7fiQVnyEknqjXvg8dZGH7RqwDfM+bDfKJpfrjZ9F+qjE4aelgZZ/nsvHL7cn7nmEYfWA9LDGdNhnMNxhNNlZmXROdkmS7QOoWnVCv3eRdveE6u6M+CBBnk0xNwv0La6spq0ujzVvZ9p8OvYqA9H6fEJndEJrkkfYnAxpTQbOhDM33azPp8hSh+X9kfj6qZQqHMmX79nvX27Ty6pQAC1g3mox3e0wOeix2G+z7DdJezVMs4JpKKc4iwyxyNbAmYDccCEDsgKcZXPJchyjjyKSC7ni7FNtxh/psfhUg/RyFZttA2dLZ6LZHHCm9SDNp41yM80h7faYRtNBtUplQRSpjXL29WDlB2/dtCNULpcHg0aB7dZIOhGmswJoph2vq81aEairTH61RU410WGCGmeF0szBsnyb/5GuB2oJn6ULs/N8fPA0qirhc3Y/RKsyxdrNug1ibU4nhFPhedgjhQQUWnWYmwaq3mFSb6OjHmncJVU90qhDIp2SzJlehuaXVwl6sSVFekJFj6gnF+nqT1PJ1WMVPXJ/ZpwrzdxfxYyomJFTktnEKQ+2tQlKgjYYESiacf09+IHW+rjEhq+DPtO1O3m55/BdCemAmrsCrDUI4Vwt2FwNb6xxIhvfp8UrZ8ahH5AwpQZOUjjO4DjNX6fB6/zzoyVcmcHRDAZzGPzbh12lTHGmkTMcKDvGwbJLOFg2xgGzabC9ujX1Sm12HgfO6haiDEQCegnZwil9Ztodb6Swl2uQNDjdPlI62NgYoXYnqN4ctbdA7adE5wzqZogfJ4luj1BPqCF3mmSmhjY3o02UAzNVKMuWAUAzVjkYeAW4qwTKQlh2lG8n1wPNLPQ14lYNHY1oaWgb6FroSExPQa8K3Sgf9OVVQwiYC4zsrVRkwmeLdouoStzgqFwi5zMHW3KTyspiwvLwEosrF7CDIyrJHDs8hvGJM7EcnyBGA+fE348thSBqdAr/Y7bRRbT60Oxjm7tk556CvXPPqciaO9h6B1tvr3yRVSo3APbc7Rbwa8L1O+1fLGE5ywHZqIBkcnmIXF5BpifOrDKHY87sMn+tV87JyxPSYiyhIuh0sZ0+pt0la53FtHMQ5lVjrQ501k0xbdv93UgS86mDX4cXiR/4dAmODQMzzNx5/3hQRLcUyXJtHlEeH0PJpApBphrY2h5Js11EtHTO+1u5YqxbgDJbvHeO/G10euTvbcmpxsbEs8eQyxUIk8th4YfMR7H0ZpZR7r9M6PWIluFYsbxQGo79/Xxz28KrH2Nq08bSQ9sW2nhI1kXbTv46fN8tPtO2A1tcs5ye0gKKxfFlIjlGyUmuLBsRqQmRHCPUFFGP0HEVG9fQURMjmmS2R6o7aN0hNS3Syj5UK4hqhKzj/roGdcYBMrmjiXY01Z5BdjSqpZENjaprZDzBPWTXl7SWGKPITMxyWccYhU4kZi5gMcJMljBdYEdT7GCCPBkhBkPE0Qny6DhXO46IphPE6IRoMiL6nCcjf+4HiapuQeHCo+fR/+Z1yACg+OTLNOx/w3IWYmWJFPo6D9M22OQ/3zb3Lo910zRdU44ppQq/YjeSyvW1PJcJz+FNFX1994DPm4yWjxnmVTg+L89Vw/P534THCvOrPPcpH38biAvBGYSBE1d8IJzDbwOF22DYade3ZikZXEd4LH+vIUwtQ7dwTuTH9X7sG9a5UPl2o+kz8sG2d263uJG1DM8HEeWb9xcaRRFGrypw2GiGGVAOlRoOcMudSgimthX+qoAjwGCsxvmx2K7AKMsSnTNlX4AWaykilfpzhHkRVshwMlZco3S+nsr3LMCBP+NWd61xUVgBrDF4q1A3ObA4318lgLhlwBC+L1fe8LdrYMtokMqBCms29t12zBBi+Xv3xF5K59TRGshMXoG14ey5szz3uc9BytVDuW2gEDZSvuHzKczfMIXvQ5IeUvNymN7ifqxByvUHMDxmpVKh3+8TRUH5alMEpHAPagaoHNQ6B96LJM0ndQLyRrsSRcRxhbvF53OPeQZNNeMLar9HVw2QeQRWieC9b/wyHrvjvM949/s0Q29Z+RWZppH7QWsej2kejYkfWmDvjUkeihle6XI8OuBkdsA42WFuWtiyGee2YAJoGnJEp3LMTuMiu63LdCpHdKqHdKtHVNR8rS6WwU1ZkVaGGtsGT2GdC+tvWO7hM6scy8eX/rZjhY2q/365FEznB858c+QicY4mOxzPznA8P4sdbgdfcTyn1R1Q35lQOVigzmZwkyW7JSK9rcL8TIdJs0taOd0PRpQmhT+zVhEkwAUGaI7zqJuzEVHgqD0EhOU6vK3tDev9afsV+yvFstth3G8xOdNlseeCA6SdGroVYxsRogJKGaISOCsnD870XKFHkuxQkV6osLi/zvTTLcZ/3Wd5d92BM30aOHMmmc3msIiu2WoNaTbzv9aIOJ7n6qpNnyY++fflxZJw36JOCEFWsdhO7KBZO8K0XFAA2wnAWTvC1q++xCuWJgdlmVOVTTLkOEOONTKAZ3KaOWX06oLdJn/rEQ6l8vRJSslD09v4uXu+CZOblHTjE775if8JrGCu6yx1g1lWY2nqzLM6C11noWvMdZ1pUuFkApNlTCbbGNnFqBtzYh1nI2I9pJKNchNLpxqr5lunIBsQp+511ThwFiVDhM1NtnNNmERi1DbFgffXZgtfpD4Zu97mhL9LzbqvVAPO3yKrNiXMT5Nnfrkds7nPCoHM25rVQMyoVZ3LspQMQ1ppsmx2mbf7zNtd0vYOdPbRjQ6m0UM3u5h6D93Y5Y6nfj4ncwfKFgkrSBZuPXSYsh2UlaHZtXybRUDLQstAI4PaEuQU7BSpJ4hkiljOIdHYRQWWLWzaxmR92BJUJ0yyuXTKsoOU6JxG3QTx44RTmJ3V7u+MwfQkBpUryXJoZhw0yzKF1pLMRBibmzEuWEGxNVCWw7Lws+l1QLO2RrQNdAy0LXQEtqegH0MXBxV7OKgYse6TrABluTJPCQd1byDJqTPNk9MhajZ272cj4tk4f+/M9dTU+bVSs9yUbzZGmJyKqhhaPWZUOElhamOWUR3V3cM2dxDdc9jWPra5A60daHSwjTbUmlCr3ZiCynJtGLbV1NI4AJtMEdnYwa/kBJE4h/0qO0GmgyKSpUiGiCR/r8dI1ifkYb9VLIRUqtDtY1pd6PYcFGu20S2nIrM5HLM5LHPwzEE027j+SL4AYuyjjI6Q4wFiNCwc9zvFX+6wP49uKcZDoukYMRkhsnRtrrQxn2I15lRKYaNq4XvMVLukqlm8dw7824EvMg/LnE8yU7mxxQ6hl7mvsXGhHvO+x7xvMpHDsSgdI5JhActUNkVYszHeK5cZrFsuwfp4bdvU1NhKbkbp/I0ZPAjrFq917qDfBH7I3PenO+zfmgfMULl5pZRjVG5mqcS4AGVCThCNDFuLMXEVGzcwqoWVXbTtO4inW9i4iahVoRojqxJZB9W1yF4OyHY1aseg+roAZKqlkXWNqt6YstkY4aCYdm2oMRE6BbvQmHmKnS6xkzl2NEOMppjjAeJ4CEcniMMj5MgHlkgYPfrVJEefRVXeS7//o1Qqk0Ktf1pU7rCc/ZwtyzLSTpvkq7+O5VKQvuutmLs+ujb3XMt74XxUA4XbDW86GUIvP88M4Vuo9vLHCuGO37cMosLr9p+7/DQkSUKWZYzH4xuGbOE54jheiwZarVYLeFar1dbGoUBxj/67MJXhT3muU35fXigO51M+z8KgEaHrqm3H9HkT5qUHVJPJZOMawzz1ijH/Wim1phj0yR+/rFzz1xL6efN1wv9uG2T1n/vf+Xri61e9Xi9EM17M4fPHWssnP/nJ6ynu1f1+JoBt//xe8XptooLlu7/re/ju7/ru4vsf/nc/zI/+2P/lT4cUzgGrr+xCCG65+Rb+8s8+VPzm7nvu4Qu/5IvWgIjPhDCTww4pJKy+UGWkyFJHugVsffiLjAgepm0TtPIkNfyN3+dtv/hWnv/5z+Otv/I2/uE//s61PCvOFZh6lid54bVZa7G65LPNUqzsSCnzD1YprFzXMjfx97GhPhQCyTqgCBui8Pf+MyEEtVqNF33xl/KiL34Rz3rWs3ncrbfRaDQYjUfcdden+J3feS+/8o63MZ1Ocaoswe7uLs97/ucDq2sIG0f/2Yu/9GW8/CtfWZz77/3Db1srh5A4l+GLv77v/75/xflzNwHwJ3/6Pn7mZ39qrRFeg3rWmXyGZR9ulVJ0Oh3q9VpuKpRDC+0mXiaXqCMU1jiz0yRNnYmssGBXIZdVACEjpYiUdAokmTfC1tWXd33HKxnsd9dWiduDCXsXB3QGU6oXJthPw/gTS+aPNBgOdzmanGW03GOSdTFxvFKg1QSc4vC8Kma04xN69cvsNi/SrR3Rqx3RrR3RjIYFHC7XWZ/CBimEZrA5mPL7h+rU8JkqP9NlQBQCdnd8UQgGbMmgQxvJZLLLYNR3vs8CM87heJfZ7JTV56qBmwWct3CThfOWveqjZI+LWD6+wfxs83T/L9bQmI0dLJsMaU+HecCAEc3xCa3pkPZsRGUxQ4b5KXKAL5yaNTS7s8YgpCzuzuawdRswKr73+8YR43qd+Zk+k/0Oy71ccdatoZsxtq4KcBaLFCk2y7c4lgWdSvRcokeK7DAieazC8oEa0086xdny7ibJlQrbIv7F8ZJmc0ijOczNMoe0mt632SiPrLkCZ+XAMuUUflcGsWsgUQKdigNknagAZ155Zj1MayuIrjIhMRY5y/2Zjby6LEOMUqKpQY4y5CglmhlkYovycz8N6u5anm6C5GJwaAJDRLtu1ul/K4TgVx58I58aPvX0675WsgZpRig9QuoRkR0hsyH1bEgrHVJLhzk0cxCtmg4LeBbrMQrrXXKvIktj1srCqePWV2RXz7Yp/LsJgEBt6AbGEmMCUx+7yk+Xf86dg5LSBTwI2wx3QQ5hCj95XZkE2Xzxaq3PUxJjLZmQJPUOWbNH2uyStnqkjT5po0vS7JI2u2TNLstGn6S+S1bpklW66KgFJtoEZVnwt3Ib6LYzNkFZGZpN899eLdUyRCOB6hQRD5HqGKEHCDNBZAukSbEIrG1izQ4m28Us+5hFm9PVZkBkic5q1DlNdFavvfbQjANgH2zFQbI0EehUkmWKzERoG2NEhCZaTfznrKBZqCo7FIF5Zv56dj3QzDi1WVfmf6z+evlfHwfPPDQT4Jz3c2OQzBjkfIyajlFTF80ymo0dbMmBmJy4z8R4QDQfF77J5GzszNqEwFZbzgdZ7qxf19qYepes3kY3dsnqO+h6n6zew9Ta6FoLU21gqjWIb9BZ/42AsTVTSw3LGSKbuuiVmYNkcnmETK4gk2PnyD9zcKwAZNkI0nE+vhJrExg/ZhBCkGkNjSayt4vt9Jzz/k4Pm0Mw3XbO0227h+l0A5PL3NSyegM+u7R2/uPGwwKW+T9RfDZwfuO8H7IclsnZ2P1+ywS9HNTIvZDYysqc0oMyryQz1ZV6zFR7hXqs2F/dgLN6a3JfcStH/XI5QqYjohyOSb9dDmExQC6HRHpCnE2QJjkV/G0DYuHnYf+1OUYUaNvAmACImY6DYqaDLgBZoCTz39sulhtR0pnc/5iDY87/2Bgphu69GCPVGOoLREtApYKpNNCyjpUtjOihbQ+tOxjZRFbriFoFUVXIqkR1cSaVPYPayyFZLzezbHszS+3MLG+gKbF2ZWZZbLXELg12kWFnKWK2gMkChhMYjBEnQwfJLh+iht4XYm4yPB0hkmUBl0JoElqMlMty2zjKmPUAVLDpfL6c/HmzLCNNU5bLJdPpdON6whSaEXoI5f/COWsIqMu+ynwKry0EUqF4oswCyovW/rpHo9HWe7xW8tdQqVSKe/P+v3xEUa9gC+/F+wmrVqunKv1CsBSC6XCuVAZqft/QItCXLazA1mlujcLPQt7goet0OmWxWKy1gz6F/tf8MTxw84BtG9jzv/GuWDwk8+cI5/9loZQ/VhhBNDy/DyIRx/GGea7fJ8sy7rrrrusp7iJ9xiaiYQUvCo7Nyv3aV72WH/2xHwX8BNsNrsMK/FWvfV3p6OuNQLg6VVa3Fb+QrKKa5eaZGRZZcY78bz1/M3/2vj8B4Kve8NX86Qf/rLiP8H62qer8+21A7rSHbdvKjIM3FJKENRgo1wMDKCGxauWk3Zjcp4s/t1hvAMJK5o+9bWXhtM4xLEfrdgTWzWROu1drLR/7yKdotzdXiHZ3dnn+817A85/3Ar7ujW/iW7/9W3jggftBrB40Y+zW67PWsru7x0te/OUbx9328ISfhxOrL3/Zywu45m8tdGK5AV6D44ZwsWzWKKUzAS7yRzrYJomcksKAEQaBRkY5ABUWrO+QIElq/PWffRHjcYfuzRNuetwht/bup1FdBue1nP30Yxwud8nuU2T3x+hPS2q/q/nE8CyjRYcF9cAPmjzVjFOR0oqGdGuH9BuX2Glcplc7pls9ol09JhbZKj/F6SAtLIdNc89NRaffLzyWX7nw+bxNlXNaBxju69oGwWjSZTxyJpuDfDsa7zAa7TKZ9bB2c5SjZEa7fczeTZ+k0z6i2zl2kTg7R3Rax/zMP/xnpLX1Qd0hN1FZLmhOh+w9csEpzPJIm2uqs6lTF2xTT2ICiFzKL4LXXrEE/tlfFxcaIIsjJo0Gs/0eszNd5rstkp0mabvqFGchOGMdnEm8qaabPTlwpkhnUa44y8HZ/XWmn2wz+us+y/vqpIdXB2fd9hH17r20bvLAbEizOXAqtPaISmVlrlNuy9eh/vrgrVwfXVsooO7MMG3Xw7MY23bgzCvNTDvCtq7R5aUGNc6oPLZEjDPUKC1UZt5cM5po7CghKjknKfeJ5efGq2K9CbOxNgdQmwNavy36ESFW9WDL8VefbZbJXvUyZxqXqKkZdbmgpubUo3yr5tTUgqqcY5bH3H/3R3jg/vvQRiPIB27a0NUZ/YUmMhohFOT1WlqPqAQObuFeC9dZWavXnv3VRNqpfl39z5G4cOowifeBuK4q8wBuax3w32cGGW36c5XSOdtO4hZZu0/SaJM0umStPkmjQ9rssWx2yBpdlvUuSWOHtNYnqea+hkQ9AAysgwf/lwBLnDlmmGz+3TZgNi19NtsovrWkYujswe7jYO8M7B8YWl1NvW0g0qRC8873vBdEC5P20PMuetxCH++QLQ+uemzZ10S3a9TZJVEJmMkzBtsz2J5GNyXaxmQmRlNhLmoY4YCZYRX0pLifEI6F0OyI1fsTAfNrQDOFg2H7bAdlfrsDtHGLSDcSQdBoB8NyZZjKVWRRrixzjvyHxefF97Mxcj5BWIuNa2TVFmmlCc0+utZG1x0o07UdljufRXa+j27uoGsdTK3l/qo1p7y6ETMUw6oeTtmEYVeDZYsE0ikimzgn/MsTZHIMi8uI5RVkMkCkAwfKvC+y/DNhFshg0rNtXCyUwjbbziH/ThfdfhxZp+dUZK1ODs46hZml7fSgvVKXEd0ALEyTAoqpC4/kyrGRg2CF8/4hjHJYNhkhczWZmE02fFT6e9qwirAWGTcw1S463kcffFYBv6h2MdUetuoBmn8dml3eoIosWxS+yKLJI8jlIDenHBJlzjl/lE1gcbLmxN+ZYU6QojSut3ZrH+pNo4r5j1IuIFnp/lfHidDW+SHTgVllZtrYksmlU5l1nbP+3EfZjTnsT5yCTIyI5aOFw37JCClHKDVGNhJEM4M6mLiGVnVs1MSINpoexnRdZM3qGWz1VnQ1RlcVqoUDYp1cObarifsa1c3NLJuBmWW04NqOJ1dpzcxyUXfRfZcWu8xgnsA0gekcMZohxzMYjFDHQ9TxCdGJa2MqU6dejeYT5HKxtkjny9OrgMpAKJzXZFm2pvIuFqcCMBLOs8MFrbDubFvkXB/vCIbD17FMnkS18mna7bcixDpA9+cNr+W0/hzW3Zj4IED+GsIIo2UIU86ncjoNxIXHL4sEfKTJJLlWSOrNc512X/584dxg2/zHl1VYPqG6NVTVlfN729zav9/GccrXer336Oudn9eFwhd/z2E5lU0vPcQKVXa+3oUwzdcDbzrr80IIBzDLIHUbqwnPH27LLMCn8nfXmz4jwBbe/LbKAy5CX5qm3PmEO3nW05/Fhz/64dVF57v6jH3da14LwGg0otPpAOvmOqdVgPWHSWCFWzd3fZhwLCOnUuVKW77204CNT9sm/2HaRpe3HWdbKpssle8x/Fz46zerfAQ2VF/brqN87LAcr7ZveKzTyrvdbrNYLPjN3/p1fvt33svf/M1HGI1G3HLLLXztG76er33DG7njjifwM//tZ/mKl7+UxWKxMfEKHwyfXv+6r6VSqXB4dIW93f2N6wgfrm2N6/7+AV/xsr/DbDbFWkuz2Vq7/22/8ao0SvkT2nI71Zk3E1p9v6pboLNcuSIlSojcp1HOWAUYI3nn276FSxdvgSfGcKLgoqWSznjmzp+zmNY4muwzXPSZ/rcWthKtBRMY1G6ivKAnMNSEM+PsNy6z08oBWu2IbvWIejzP5+r5deLvETd5LaLxUkyUyqBsG1QLG1FY95Xo91k389oO6TfAeVE2kumszXC0m/s/28t9oDkF2njSx5jNgZsQmnbzhJvO3UOndUQ3B2edzpGLyNkYIcT6ik947jOHD/PIzXf6D1BZypt+9t/QnQ6L+9zWKYZ5Vq6v2/LAvw7fZ5WIWbvN9KDLbL/LbLdN0qvnirMIW/PgTBOTXRucJSVw9miFxX0Npp9sMf5wn+V9DdKjGLZAmjhe0GoN6Tav0NoZ0rxlRLM1pNFwvs1arSH1xoBKZVm029sGY9eTivwTYJqRU5q1FLZTKcCZ7njlmYNoVK4RFGCWA7KLS2eeOUrXfJzJceYg2nIdcG4rF6UURqi1ewuvO7zXYh+Rg8BQvbxtYloafGyT1/vPt7XRn7v3QT49ejI2VyC1ojFvvvMnaFfc5CCw+N+op+PlFJ0Hv7DG5q4I5Ib6WxT34t8HbUbgPsHvW24f3SonudbNLTR4s0spJNZ4mGjBegC3umYjIwfDml2SZp+k1WdZd8DMQbMey0aHZaPPstonrfZJKl0y1QQbOdgVQjIPHbySzEOvayXDSm1WhmVTYGxc1MuJgC1+A8Mk4jGqdswzvuRmeucyqjsaGpos1iyFZpEapnPLZGCZXJA8dlHwwEcU2RW55Xl9yepl7NRm1Wdo1Jklai9F7qWIHQ07FrsvMfsKvVfB1KtoWSVFsUQ6dZsQnp3mBcAmNPOvr9hVcIRjCQMBi2sABYUDZWdZwbIeDpT1bQ7MxMo881p8QmcwGsBF5wuI8QmM/N9x8b6azInmE+LFhGaWUM8WVHWClJFThdU7ZNV2DsbaJNXzzCuf5QKynE/QjT663nMKs1BFFt3gkNrXvyUrSHbdirIlSs+QeoLKxqhsRKwHxPqEyeH9JOOLsBxgFkeozClRWTpIZrPlmlogNLValY1CdPvQ6WPP9LCdJxWmlXR7K1VZx5ta9pyKrN3FNm/Qaf9i5kzTTg5RD91X+CFzfsfGhX8yMR4U4EyMBsjpCOaztbGab9fKkyxjDFZIyM0pbfPxmJ1uDr9WajJbc+91oCazubqMG1SRieXIRbQcPViYWcrcnNI77ZdrCrPcJ9nC+SLzE2U/2S+Pk8Ix9Nq4PWgSNudPAkutAGEOfuUml6aLzZzjfmNWjvpDtZnlxsxopZjkgOwiUnzaqcekc9wvxAhRmyObKbQMtiaxcR2rmhjVzK/PndfETWxtF1OJkdUI2RDYjs0d8xtkTxPvaGo9g2znKrKGN7PUOFv68XVdc2hmmaZVzFKhlwa70Nh5gp0uoTCznMDJCI4HyKMTxJUjKvMp0WxCZTbGDPoML74YAezv/hrV6qMbsKVsrljub926lVir22V46rfl+VsIispznvAY2+ZRIeSw1rKsvYSL8VsARSf5H3TNbxT7Xr7y/QyHbyzOu1g8lYOD79s4ZwhdTptnhtdWViMppQqwchpQu9qcPhzzlPOgnLy5oA+8dy3LsNPSaccvzwvC5zqcE4d+zfz3Zeu+8nHDfCnDtvK5y785jUNcDV761z6fQiYRwrWwLpbravh5eDz/+9BFlLV2zUQU1v3MlaGlh84h5AvnoN6SrOyCwO9X9pF7PekzBmxlAFbOeG007/3d9/LqV7ya17zqNXzkYx8BfGORO/xF8vSnPo0n3PEEJpMJf/BHf8gr/84rgHXaGb4OvwuTmwTkKgGZd64SlBZkVhfOjwHEtsFEcG/bJr/lyZO///A7ERxjm0zRWruxgLUB0Hx+mnW7aSnySYdYHUsKuVaxtk1qy/e27fW13pfhyrZ8+28//ZP86P/9f3F4eKXYTynF7NMz/uW/+n4uXHiMf/KPv4dbbrmFr3vj1/ETP/kTG51KOa8+93OeyxOf+GQefOgBPvHJj/HlL335Wl6t5f2WTsMYwxu/5k1UKhXe9vZf4qUv+QqazdZVG1SX3Iwix2y5os+9shYXxAOJkHFh2mdzEYcrtxzqKuvKzRgi/7ADxri689gjtzi4VgXuTWHPQE+S1Br8efKiU1VoFbGgWxuz1z5hv33ITvOEXv2EbvUIqS+QpbPCX5+XH5frmaPPucrO4iLlkk9mRX4/hnzyu7F4dmrH5rflzgA2QfK6SSgslk0Gwz7j6UFuxplH4hztMpr00XpbKHpDqzni7MEDdNrHdNuHdNtHdPK/VmOAVOsdSBlUONaxfRXtxb/zi7znK9/ClYObqS2mvPS9P08zmfLwU+/E1uDcx+4nXmRrg5ONziiHBcbmvqDimGmrwXS/y/xsn/lOk2Wv7nycNWOoS0QMkdJEW8GZZSs4G3hwVmVxr1ec9UgebJAebwdnlcqCZnNIr3WF5t6Q1m1Dmk0Hz1rtEY36gFZ7XCjOtrV9623OOjws+9cD53/ddGJMJ8K2Y2w3xrSd0sx0Ikw7V59dT1CAcUZ0aZH7NMsh2jgAZvmf0Js+JE5b2OCUdq486Ng4TrlPCvf3ZuRQ4KJyO1Tu8MupyNf8GsMVTGPcw3p7+x6+7o6f4BODZ1KRS56z92c0ozm+TRNidc3l5y8ciPjzGWMcajH5YgHODBspETZvQ8JrJ79BCwQ+PMOFCJ8PWa1F0uyQNHokzR5Js0Pa7LOsdxwsa/VYNHrO7LK6Q1rtkkQtjGyslGTb1GQaByyuX3DgC8n9Zaxg2VgjRilyrN3rscVOJXYWYxZVXxrbD6c0UWtMdH6C2pkj9pbInkG0FdQjRCXGEmPnEfqKJLtY52/uH5D9qcLOffvpnXytJ9U3xGcN9SdnRAcWcQBmV5DtCdJdgdlT2D2F6Uq0lGTeiau0m4e0+b1eCGDZMQ6YXcFBsxPpVGYDCcur9Z24UWUPuDnf9q2LLN1nU3HW2pKFWeoiWY6OEZMB4sIA8ekBjIeIycDBMg/OxifI8RA7PobRCXoywsR1RKuPrXeh1cc2utDsQ7OLbfRg7/GkvbNk7T0WrR3Gja5z2F+rOxXZjSTNqv6NuGF/ZMIskHqK0lNUNkJlA1R6QpQeEekhKhsR6ZEDZHqcvx+j9ATB5qq6EM5dx913342eTrFxjOrurGBY9zZE1znwN90d0u4Ott1B+H063QKa0ercUFaI6cQBsUuPIcafdJBsNFwzwRSjQfFnRycrlVmWFhOccpu8tggMWFUrVGG6ewtm34OxnjO7LCJadnMw1ivUZLZ6Y/dEOndAbHGCGj7gnPAvR8jlwJlYLryp5aAws1TZmDidQDIuVjS2OWPfBg/859bawlQ+3NcnF42xBaJHZto5/GqvFGU+imUR3bKz5sjfsm0sdVrKULlpZVXdi/dH5tRkI2R9jmimzn9jw0ItxsR1rPQO+120TU0LU9/HVm5CxBG6IVEdkC0NXeOAWE/nfshMyQ+ZRsgZ15T4FvkTRLM0VfQ0woR+yHITSzuercwsj06cmeWVQ6Lhqt7K6Yh4MUOUTArLY2rfb2utC1PAJLmdex/4KYxxC/uHR6/niXd+HfXao1v7eH+cq9/baj5WtuQoz9vCOVbY55ddA203B5YY03KBIUyXOc/mUP8LmCswlkX2uUyXX4lIBxjTZzb7UijU7JbR+PXs7f8wMCqOfy1z0vK1p2lawDQpV8EHyu6iYD0YYXkcFY49Tiu/8v2HSqpruSi5nlQeO4fgZhskDwUdPu82wGtpbl6+1/DPt0HhfYRAKtyWr7ucwnNbu+7bzptYhmUQWuglSYK1ztTTl3EY0TO87/C3YYALa515s68PPg/9d/44oamxP0foXsXv639bBoBheZfdslxP+owAW/kCfWaEW4C3v/PtvPoVr+aVr3glP/CvfwAIaL0QWGN5zaudeu03fus3tkqSy5Vl22cAOs3ppO+EBCAFEomSChH40/Dy9jIY3FYZyg9f+DoM+1xMLlhvcEN7YilFEW3MAzljVz4ciuspVWgpJTpXFORH3mhcwpXIbddaztdyOu0+y+/DBjv87ff+8+85dQJqreWn//tP8c3f9K30+31e+MIXFYDN3ftmBNF6vc6rXvEajDH8ytt/iac8ebtfoW2dit8+9znP4ylPeSoPPvQA73v/H/LSl3yF32tjtbNcFwqbck/OCCehCpAotXp8vLNt71VJCoE2GUJKVKnz8lYrhfVKJNyTuK+g5zovRcbN7YfYa19mp3FIr35Mvz6kXzuh0YBKzfkZ8Y2Gb6hGowRtLJVoU2Hj791aXxclInfvtQpmYYutB4HhM15uzEPfDGHntj55d79fJo0CnHnlWQHRxjuk6Xb/Go36iP3dx3JwlkO0zgnt9hHt1glKpmv7SyEKIzObg9LwOtb2zW3/y4MVXx86o2Pe+Ev/niSuEqcJ81aD3/vuN7DfO0EKy2H2RJ7wn/+E9v1HpJUKs26byUGP2X6H+U6TtN8k61TRzQhqClGxRFITk67xGAWowpbMraDqpVyBsysxySMV5vfVmX6i7RRnD9bJTmK2TfIrFac422leoXUwpHn7kFZ7TLs1pJ4DtGZzSLW6LrvftjLqXouNehDmobEWms4MM+3ELjhAOyq2uh0VJpvXDAqwcIAsfnCGzE005ShFjNIcnqUOms00wm5XF5eBexl+nQZ6w2OUB3Eb4JT1fk8IsUmi/T7WM6dNRVi5XwvPUQbVRTuZtzc+7y24YDjAzY37ubX5YKGYXaN6pXOunmmXRWXFnLsOp4dTfsEhN9MUFoyKWNY7pK0+abtPUnc+yZbNLlmzx7KZv6/vuL9qjzRuk6qSb7LQgX8Iym4sAFp+4fmf96mFQaRLovECNVwghktXr0YZjC1MBXpeQydN0mUHo8N2SGFRa8E1VWtJfH6JOjNFnTeoAwstjYgtQhhsCnYOeijRFxTZxQbJo230R7apzYIUW6IzluiJYPcN7AnsrsTuCey+xO4p2BdwALou0NI6gZiHZmsFjINmlwNodmjhish9muXA7ES4yKLXgmZxDsluZdMks8c6OGsC2TL3ATTITfYGDpqdDBAPuffkES2dQ+2BM8McDzBJAs0eptbBNp2vMdvsYeodaPYxjTPY80/F3rmLbe1gGz1so+Oc9dfr12UWasIXHnrNgCEOlA1ZCV68InGGMz+9hUD1mCH1HKlnKD12CqRsQJQ5SKayPGqizsFYHuGSdIDULuJzGbKsjcUAW6k5U8teF93aZ9m8A9PqoFsddLOdb937rNmGTo9Fpeac/N+I035jwJtWPnTfmqmld9C/MrkcFc78nZP/IeRmSmUYBOvjMoNwUSvjNrZ2k4tQWu1BrQf1voNloYlloSRzfspuSEVmdO6Af4Qa3o9cDhG5GaXzQzYsTCu9PzKZjhCLgVORmWSjLywrffxn4aK62OaDrVy2NkabPpqVmaWlh/VwzEexZD2apTGd3GH/DZibMkeKEUqcEMsHUXKIIAdk1Sk0lthGimwZRFNgKzEmbmFFEy3aWNtzoKXawlT72EqMqCpUR6BaBtnSqE7uoL+bm1nmfshUQyNrGhn5hv760oaZZSowS42ZZ9hZgp3MEJM5djiFwQiZQzIOj5BHx6jpGDEaoGZj4tnEqVvZ7MfL1hiuC6yjTRWtq2R2n4wG1tYxpo4xNaytYW0DY2tYW3ff+demhs33XyzvLOAagDZtjo5fxS03/aer1o007XN09CVYK9nb+0Pi+MpG/pw2bljdF6Rpw9UX00HrNmnWxJgeqdnFiH2M2HFmtLKHEW0HRWUdK2sgY+eHsoIfoLo/ASQCPgFTXhxe0cY1SiEhcPMRQg+f96eBtjIgKZtHblXdlvIx/CwcI27bp2wqGI7//jbqtW3XdbU5eHkeGgK28mK1v76wDofH28ZLtrVLp1nilFN5flQeC/t2MARmIfiCVWTRNE2Leau/h9DnW2gB5VmKlLLwG2itLY7hr99DuHBe7H/rzWhDJZu/7jIA9vv5c3+mZf8Z+2DzJ/apKFz/mYU/+KM/4OjoiDMHZ3jhF76Q933gfWuUEyF41d95JQC/8s5f4TWvek1xPD83CCfut9x8M694+St4/vOex5Of9GR2d3bJsozHLjzG+z7wfv7zf/nP3P/A/cVkSkWKRGf8xZ9+iFtvuaU49i///C9t3MutT7ituCefsXc8/g7e/PVv4gue/wWcO3eWSEVcuHiBT37qU7z7Pe/md3//91gulxuNHLhCe/7zns+3f/O38oxnPIN2u82Fxy7wG+/9TX7sx/8D4/EYgoIN7xMh+JxnPZs3vfFNPOc5z+Vg/4Asy7j/gfv5jd94D//1J/8L0+lk7Vw+NZstvuWbv5WXvuRlPOGOO2k0GgyGAw4PD/nYx/6G3/+D3+NX3/XOgjL71YHwGsqS5auVf/n7cgNWAFCteeCB++n3+xwcHBSV2j3Um8d8xctfQ7vd4Y/f/4c89PCDfPZTnrZx/nIDEaZGvcHrXvN6jDH84i/9XEn2uR4Y4WrmWD6Cqy/eEARu6+Bgc5XGl3EIZK21nDn3KLc87j4evnAHfG41V+tYkIIvfPwf8gW3/X7RWEip8P7eBNW1vC83mFKsA4dNAGHxt+o+W0HdlfhEQqkh8jLR8LNwtSFNKwxH/QKcOR9oOUwb7bJMtsjxgFp1Sr97hU77MAdoR7k/tBM6rSPieAXQvJGtEALtpDNYs1lPi3IRFL4Zw2TMKvqNl7+XO60QQiyOWsy04FOvejZ73RMO9S6xSBHC8pHveDGKbCs4i4AIQxmcJdOYbCDRVyKSR6rM760z/XiH0Yd7JA83yAYR28HZnFZrxG7rEs2zI5pP8CaazteZU5+NiOMEJTdhkIXcT9bqs21QqXgWIknWVIW6zHpY1vGKs3zbjq4ZFEBMMuRxUqjLnDJopTRzAC1DZqev4q6Xs1jLohD0wvpzUV48CRUF5ZW9jefpVLPl0vV4wJbTqjJICyFl+VjlAV1ZtW2MIRMRDzfOUzUJ5xeXwJiNdrpo14TFCK+sFmAcVL9HP4EPZC/AoPhc9SGeHn8MCyxFzKDSZbR7O9PoANPsYfMIl7bR5bjaRtS6LOu7zjdZ3CWN2+tqstCBf4oDZBkrNdnJ1iLdnjwkU6v3wiYovSBKZ1TSEVEyJJqMiIdzouECJgabVcjSBlnaJl22SRYdklmXdNomtXXS084XWaKzKfHZjOjcCHWQItsZoqoRKjeZTcBOBfqyJHtMkjym0H8dY69lBtnFgbHHC9gVsC/d+30BZ4ADAfsWuhmiGmOlRYstLM7iTE6PBNyfb68Ah2rdl9lJntfJNaBZBQfFHsd2X2bNBKoThDxGZkfIySD/y4HK6AR50ZnpifHAqc1GA9RigkYhmk49ZhpdbL1bQDJT76CbT8Ds7mKau26fegcabWytAfGNqGtYV4adsKkiW+IgWQjL5jhgtgSWBpY2B70Fld2eHgI+AefUP6crfpNIZMXeoSnP2rNrLabewLS6ZP02y9ZN6NaTMc124aA/hGQmBGetDvZG8kNnLkrl4Bhx8VEIFGOMTmB44sDnaLD6LnfiL2fOj1x47b5v9G2jlNItEsRNqPewtR5p/Q7o93Ng1oNqF1vrFb7IHDDrFo79beUGozmmMwfJFkcOkuXATCwHqGRcqMdUuoJkKh1jZ8eIdFr4LC33B2E767/fmGAGfUfYf+RXhqWZO+nvYEUPm0e0FLa//hkdDD1sAcy6WG4gGAPGOeVnRCQfRskxUoyI5AjiCbK5hGYGTY1oWmxTYKM6VjUwtDC00bqLjVvY6h6mchNUFTQFom2RTU3kIVl73Q9ZYWZZMXxmZpYRSVrFzAVmaTELjZ0lMF1gJzMYTRHDMRznZpaHR8ijE+QoB7yTIfFkDGmCtRHaVLHGQS1EowBc1taxOACmze0sbS2HYTkAMzVM8Nra/H2+zwqW3UgghWuXWzl5QcG2JIRgudzlE5/4cdJ0F4ALF97I4x//w0i1RGdtMt0iyzqkeoeUXTLbJbMdNG20aGBEAyNqoCpuThGxDsg8JLvuW0gRZglGYE0O7C3wBGic+R3ajd+g8chfcPTo/8Zo9AZ/J3Q6b0PKCVqvK6nKC5qnjfWK0wfzrdAJv//9afMg/1n5XNtAVjmdtkhwrWvdlq62QBvCpziON+BVGRyVj1E+dvh72LT2K/8u/G35/bZ9wmOXx65AAbGyLCNJEtI0XbtuP84OFWXhthx8IAScPiiE39fXhRDC1nJ/2V756I8Tjve9qKJ87eH8X2td+Hrz5fC/TMHmL7p8gVIGDm4Bk2ne9Z538ZY3v4XXvPLV/PEf/5H7TT5Rf+EXvZCDgwMuX77M+z7wPl77qtfmv7SFqZ2nlUIIfuc3f5tet7d2HdVqlTufcCd3PuFOvuarX8+3fce38Vu//ZsuM4zOwcG1H4oywf3fv+Pv8o/+wXdu2N3e8fg7uOPxd/Dyr/jKrcES3Gv4377tO/hn3/O9aw/p7bffzt/99v+NL/niL+GVr3sV0+nU+ZsJ/PIIIfjB7/9BvukbvmnjGp/+tKfz9Kc9na99wxv5mq99Pffdf8/aQ3Du3Hne/c73cNttt6/9bn9vn/29fZ78pCfzVa97Pb/zu7/NcDhcI71/9Rd/w6233MoHPvB+Xvmal28AhjCVG8VtE8JwX5+/e3su+uxkMsGYVTSaKHdK7fPq9tsez3Of8zxGoxG/9d73uHMEZRiqAv35y2Xwmld/FZ1Olz9+3x9w/wP3+T3X9lsDCafcF4FPodX3Gcvlkla77fLJ7bwGTLXWaxPsbQ1iFMHr3vDzfOSvns2F8a0cVm+GSPLE/Y/xgtv+sNjfBUcQWOuiShrWGyB/D97p5wqSZGsrc+X79L6T3KVLsMZbwjoBSGGe5/JB69gBtFGf0XSXca4+G44cRJsvtg+c43hBt33ETe17C4jW6xzTah7S7RxTqy5Xq1PeHBoKfrLqNFbXHyb/W99oCyndsEYU/9Z+5/M0LJMyGF7lk+I33vMm7vrU5wBw09fezZ3yXjITcaAOi32NEeiFZDmNMQNJetkpzhb3Nph8rMP4w12WjzTQo6uBsyG7rYu0zo9o3emhWWCy2RpTqWyuAIcDf22cileKCCWli4KoXYS6SCnHgBBQlWQdBa0Y3VbQjsiaiqyt0Pk2qXFdQQHkKCN6ZOGUZSMPzgK12ShDTDOEWUH88FkLFcPylE5s2+AiLC//HJQXfUI/PL6jPk3uX46OFT5f5UmYr2/h+SIpyfzv/XMenFtaVzYmGDD445UVEOX7nMQtfv6p38JR3Tmqf+LRR3nNp38JazRDuvy5eD4LUeeO6C72xCPMZJ1F3CCpNEmqLRZxk5PogI/LpxVmtw9yG7/GK0hthM2UgxHn2Yx06X2T3aiaTARbzy2sRpklUTYjzibEyxGV5YDq8oja4pA6x8RyijUR2jRIdIssbZMsuyTJDot5j3TaZTk8w2RwO2Z6dWfosqtRN6XUz81QZzLkTopsaITSoDUsNGYm0CdOJaqvRMzvjTAnMXCVY8fAnoA7hdvuC9gDsZM5/2Z7Bs5Y7IGEpsJWI4i21G2LU9EdC/hUTHoo4FDmKjPhwFkIzdJrzIyqOEB2e75tZ9BaIqpTZJRHEhWHKHsZuTxETh1gYXyCemAIHx3AZAjzObZSL8AYDWdeaXIYZpo9bP820pt3sY1+DtHa2HoTqjVuKGyeB7Eprn5dj6N+77PMQ7L5EhYpzDNYagcXEwm6AqbG1dU+EsmIWB4TRUdE8phYuK3kkEn6XEbZS5y7hg6Izhi5N2bc/qJCSWbaXXSjjW610a3uSmGWwzLU9Q+1RZqgJiPUZER86RGiychFJ52MkNMxapwHWvCf55Et5WRIJU2oxDEPP/wwJycna+1jCP+QUQHCbH0HvXMH5nwPW+8jaj3nb6zWL8wui7/8PeoGoF+hIhsiB/chlwNE4lRi0qvIch9kIhkiFitwJpMxinVH7j6FbfCpC8Jic8K4PllUTjGWO+I31r1GdDH0crVYN9+nh8UrzRw0u2obsZGSPJLlkFg+5tRjjBDxGNFcIJsZNDNEU0NTQF05U0vRworcR5pqIypNbHUHU3F9tmo4Ba1s5iaVHe1UZR33XjWdgsyZWc5xdPnaydrQzLKGnkhsYjELg5llMHMmlmI8wQ4ncDJDHM+wh1O4PEWepIhxhh1liInFLiKsqWFMtQBaIfSypo+xT1p9ZmoOepl6vk+Nv6UmZC0Jlgg5R4gFUsyJ5AAp50i5QIgFQsyQInw/R4o5Qi6Q/i//3H2fOJgrLEZrEJIsfQqPXfo+sBXIXXss5me4+54fJNNNBz5FEy3qWFHFyApWVmBHOcsWJdCqw938mLvoKitIdiNJL5FmgdAzZDZBmBGKIcqeIDkk4grSXiEWIzADlB0h7RDFGJE/f0a0eaz5SyTqSS7/vn3C7AUvZsaLiQ4f4+Z/+SZq93ycJHky1eqnabd/GVgBCmtXQRnKi5mnpdPGQ2UV6TbLraKcS2P6ELSF89xtFiz+Gsqw7TNJZXBWFkGUx5Oh6MaPXcNjhdttqtpt34f5ES4y+M+3mwpv5l34WTimDo+bJEkxzg35QMiLfJ3weeIVb+Xxvr9/X3+UUiRJsnY9xhhms1nxWXhe/zs/J/BBDsv56IVHy+Vm1N0wkMT1pr8VYAsfkm0FAvArb38bb3nzW/jyl34F3/t938t8PgfrvFt91Wu+CoBf/bVfdRUuAAdSShdlzNrc2lPwwAMP8P4/+QDv/8D7uXDhAieDY1rNJk958lP41m/+Np7zec/hP/2H/8TzX/h8Ll26WFSeL/ziL+Dmm2/mA3/kooh+zRtfzwf//IPhzZAsfacj+Ht/9+/z3d/1TwD4+Cc+zn/4T/+Rv/rwXzOdTDlz5gzP/bznuMAMQSVI07SYTzzn857Da1/9Wt7zG7/Of/2pn+D+++9nZ6fPN775G3nLm7+RJz/pSfz9v/v3+KEf+eH89BLyyKk/8H0/wDd9wzehtebnf/Hneeuv/DIPPPAAURzx/M9/Pv/0u7+XW299HP/jZ3+BL3vJi5hMJviIov/i//gBbrvtdmbzGT/yb3+Y3/nd3+by5Uu02x1uvulmnve85/O613zVVYl6WL6+bLfJT8Pf+xQ2SOXjPOUpn80tt9wKwEc+8hGEEEyn05wKrzc0r3vt1yCl5Nd+/Z3M53N3jABKhPbd2wj+42+/g+c/7wsZj0e841fftmUyvU7etx1nraEtTbClFMXDa/L6aWHjObBQhP09Lc+iKOPZn/dBhPhzoty0RWBz4LXazxiDsStfHj4ffJ6FlD0ss3AVxO8bqvfC8jKpYDztMxr1C2g2Gu8yHDkTzumst1EHAJRK6LaPObP/MJ32Id32MZ3OMZ2WB2izkLtvlJ1X9tgcUjrfIxa1MTAOoaczW/PmnyFoLHccm9er1hrxbbDFv//Ex59bwDWAC99xG9M7+ohbMo6TA6Yf7JA8WkOPty8LVqtOcXbQuULr1hHt9oh2Z0ynPabVGtHpTOh0JlQqCd5Md1WHViZ69Uo9dy7cREaKTK+ik+o8G2UkyeqCZUOSNSXLloNlaQN0JyZtCHRLoVvqmkEB1NygRiniwqIICiByszo11Yihg2g+KED4/GyDT/670EFpuL8vl3DfMswKyzbcJ1wIKJe5P2YBX/NyDZW7/ljen8NpMM+n8LpOg70evKvcJNS7LjDGIPLzh+f2eRP2qeGx33/rlxVwDeCu3afxk0/fB2s5jM9glYQIPm6eswJiISDz2wSnwsnHCgnXYW4l8r9QTWaWRNmcSE+JkxGV5YhqckxtfkhNDqipEVFliaplyFqGbAh0XCHRfeYcsBQ7LPUOy2WXZHg708Fnkx7VyC5W0Jci0FcBScqizmTEn5USHUyRvQTZSCHSWGmxWqJThZ5F6EGMPpRkdwvsn0pYXuN+u8CORTw+Q/Z1DsssYs9iz0g4pzDnI8x+BNV4bUERwKIcWx3hANmD5BEzxQqaHeOg2THOPPM6oJnqQ/wEaOxAexf6e7CzBwd7cHYfbj6Axx3A+Q68+steiDyawRWLjevOEX89B2MekNU7mOZZsoOnYm7fyQFZB1tvORXZDQChwiWkVytO2A7G1vyPBe8NYDUsZzBbIOZzxHwB8wSxzLBLIIuxWRV0E2vbOOdtpUwK67I1YI7APAL6kvvjEOIR1GZU+wbZs8gdi9yrEO8/kUX9GZh2FbtTYdG+Hd18BrrRImt1nG8p6fv1No/yf181S8RihpqMiE4OqT58Xw7HRkSTHJRNhmtwLJqOC2gmk1WEZd82hG2db1uMqmOrXXTcIavchN55MuO4ha32mJwRaGoFKKPeg6pTnFHrQfXGVGQkE+d7bHYFMbgXuRgUppbevFLkfslUDspU/jnJeM0/sb+vsJ/epjr2723xustg9EMs0+cRqYfotr6XOP4E1tZyhVgHa3tFdEtrnbN+Sy8ws3RwzEM1yw2q6Zg400ouEYlPIxgh1BDZXCBaCTQybEMjm0BTYOoRqCZGttw5RRcqLUTlACo3Q10hGzgI5gFZywEy1dIrM8u6RlY1Mspw9svD67redTPLGmaZq8hmBjPV2HGKHaWYYYI91pjDFHtZYy4b7GVgJLBjiRhFmFkFCtVXA2N3MKaBM5f8n6v6EmKOlHOEmKPUgEhcKKCXB1pSLlA56HLvcxiG+x3MkSIFYdzYyho3V7KuQzMmBur59VcxpgZ41Zt7r7MmmeljbTM3D13tb20FQ+RMKaPImVMqkavGRA7CBNwkILL5d1UG6iWr/a43WYuwS5SdI+0clU2IGKEYEIkTYnGEtFcQ5gRpRyhGCDNA6AHCjsDqtbFTeb7gx8Fe0OLmNgakhHYfXW+jmx12mv+WRee5JE+8k9kLXlRcXrZ3nqM3fCfn/sM/WoMtXq1XHs94kFJebF2/5U0TxKuNLU8bk4XHC19vm6f61z6Fi7bhPp9pKo8d18RJpWvxJpH+d/6ay78v31e43bbwUL730/JsG4S8GhAN3QWF6jL/5++pzBU8PPOLzKfNx0NT0PB8Yf0Nf5MkyRrE8/nl5wKVSqUY93vFWxzHW63bPlPA+hkDtnCCGk5eZFBQUko+8jcf4f4H7uf2227nJV/2Et79nndjjKFer/Oyl7wMcOah5QqANQhhCeW0L/3Kl61JRKWUHB0d89DDj/Cb7/0t3voLb+UFz38B3/imb+CHfuSHAPcgzxdzFouVx+P5YsEkMLEMafItt9zCP/0n3wPA7//h7/N1b37jSo4oBIPhgLvu+hQ/+/M/RxRFZEY7XzhxVPCQx936OH72f/wc/+z7/3mRN4PhgB/4V/+S82fP8bKXvoyvft1X8UM/8sMFAZZS8oynPYNvecu3APAvfvD7+W8/89/WGo63v/PtvP9P3s/v/dbv8/jHP55v/Ia38B9//McKuPMlL/oyAP71v/5B/vN//X+KfDs6OuLBBx/gA3/yfv7dv/+RtXIsHq7Sc3MjD2G4Xyi39OQ4imL+6Xf/8+LzX/rlX0QI54w3yzLieDXpftELv4xzZ89zz71381d//RdbG7VtDemqDkre8PqvR0rJ29/51oJou4l2WZ203nB6Yh0+sGEjuFKZRBizvm/YOBf1yecbikwna3Cs3HEI4QIMKCndWrsFcJ2cMxUSSJmr05TESAeXikbFBscUBovB2vUVHq1hPOk6k81hn8Foh9G4X0C0ybSHtZsNiZQZ7dYxt970KbrtY9rehLN9RLdzTKM+3gBo4T2etvJR3q9sfrEtreClWLlT3dJZhscq158syzb2L0Mcfz2Dwf7ab83dMYO791DdlOr5JfW9GY9v3UunckynM6FdQLMR7faESiUtQCxBXfEATXr7FVbBKNYApLEQCdKWImnASTbB9qroXtVt2zFZU6LbyqnN5FUGANoiJ5rocoKaGOTQRdJMD8cc3vUQPVvjbGOX/UoHkxlOTk5YJOlG2YWvpZRrTcdpZb3tt7Du/6Q8eArzaptvhvIAYNv5wlRuz/zWDzLDzn/bNZQHR+X6Gw7GQlP78mQyM6aoC/734TUX+Sdw/rWMYBRvOue+0jyLSpdYI+E+nOXO1ReE8xNQ9PxSZDTEnApLomxEOrpANr1IRQ2J4hEqmlGvZjSihEZdQMMimjF0m2TtBrrTJmGH5aLPcnoL0+FTGF6ukF2M0BdisnsjsgsR+mKMGV592V22MtReSvzsOexqRNdi6wJTUS4ASyawM7BjgT4EfWhZ3qdg6CZGp6bYIHsZ8nEa0ffATGDPCMy5GHs+wp6P4LyEmjdCZ83vGhY3pz0S8BjwUenUZWVo5pVm2TUG4nGGaCyQOxNEPEJGJyhxyFu+/iu5+QBu3Yezu7C7B1TgJIVB/neS5NsUjhbw8QX88SEMHoNhBkff+dtwo876Qwg2ZDNwxDYwFn4PzpQomyGyCSIdIpIBYjFCTKewSBGJxaYRpDHoGiZrYm0HSx/DLog20N7mMjAvgwxhDxHyQUQ0gsoYUZ9BcwntDNszsCOxBzH2TA3R62A7feg8ATqfB60VTFluO36e/FKrzKFX5fJjuYJsiBw7UKbGQ9Rs4sBY4I/MgzTy1fXyhGItyZgsaqIrHUylQ1o5wBx00Ll/Ml3pYPJt8Zl/H7fdxP56k8lgMUAsBnD8acTixEGxxQAWKzjG/CRXkw1Q6QQzOyLKpki7UpGFE5VtypFwLHS1CRl4hYrC2JYDXrmCzNqeg2S5OaW2HRbJS7HyPFQhkQdc0b8FNoPrWSQokkYwRDJEcR9SDBByhGjMEc3lyll/0zoVWVNiaxWsaDofVXQh6kC1iazuIKoVVBMHwOqBYswDsua6HzJnZnn9UmBjBDo0s5xK9EI4c8sJmAnoEZgTgT6WmEOJvlRBX4zQl2rYkwqMYgf8Z1xf/3CVJMTSwS+xQMgFcTQs4NbaViwQch4ovOYouch/n+BaV5OXB7l/aZn7ElVr0MuYSrG11ingjK6iaZLZXZxZZ7UAY34fa73c6xpJsgJeBRwTUNkCy8r7XG28VU7WIEyKNEukXrjAJoxRDIm9alYMMGnEYw+9GbQCbUFnPOVJ/5BW81MbY52wXTHGYIRBm5XyxmKxlQq6sYtutEnrLUwjN0dvdrDNjlPcNjrOXL3VxTRy0/VGB9NoO8h2HSnrH2yMj8Jxlm024SteRWV6SPSud5NNr62k9HBESlnM/be1KWUrgxtJV5uTFddeAlefSQrbzRCWlZ3vlwUtHoRuW2TeBhrL9WPbfVwNFJ52/G3ngtXcOxz7+jrp+7+ygs0YQ6VSwVoX7GC5XF6VMZS323yilcuxfK++HpVVbX4/Hy3W338IAf+XArYynIB1ZUyYrLX86rt+le/8B9/Ja171Wt79nndjreWlL3kZzWaTu++5mw9/5MPrEwxWFcNPfpykz2lWwmvw5zVG865f+1Ve8PwX8IVf8EX80I/80NbJUJi20d9vfNNbiOOYJEn4+//w7xU+1sIJHuS+KUyGklFhWuTPMJvN+KF/+8NYawsHfr4SvvXtb+NlL30ZB/sHnL/pPJcuXiqO/5Zv+EaklHz4Ix/mp//7TxfXFFaoS5cu8VM/81P803/yT3n1q17Df/zxHyvuzT+gFy5eOHVws20gZK3lWZ/7tI2y3VaWYVrzG5cnL730FTiOY771W76N533+8wD4xV/6Be6661MALJfLPMqlu7e93X2+7EtfSpZlvP0dv7x+/KD4vMPCUPHiH5aXvPhlnD9/E/feezd/9sE/KeqPB17uPlbHKa9Q+Pssw2OvJlrRc1uYL5YfbH9dWUDkwwbmVBhgceeBwkG5EorUrCKraK1RNg/mkUMBgCzNsBZmszZHJ22Goz7j8boPtPGkhzGbj7wQhnZrwE3n7qXTPnZ+0DrHeSTOQxqNIVJahAVROAZyZeLyx5uZbsKIEDiWO4hQohvmn0/arq+i+PzKeWNRnmWFYDjA35bKjeVp7QPA2bPr5sUg+OIveRvPfNYfI6WFhkJ+wU1oBd27F1SO0hL4daY0GypCazF1SdJSpE1F1pRkLbdNm87vWdaUpC2FqV6jcZ/nkQ4vL50fs4lZi6pZWVjUKEMtIEa4VZpcBm2tRUWC2+uPI67ECCFB2zUlVwiZw3Dhp3XY28BzmPchbCp3qmsDsqB+l+tWeX/fV5Q73rJCNTyW/z5M4eCmXH/Dz1btwqZpaXjeAq65D4vABOH1h21oqLiz1iKsRAi4Y3Qv9+w8edV4YXnjh/6/PHLc5I/2vm3t3LE+Zsc+QC8b0J5fpvfZN/GJ591C1Fu6iV7FkFyuYi9LHt+6H1MTJNWYeUWxrMbQvBOzEOhLEdmFmPnFiPGFiOxijL7LbR00i8guRZBepX5Ki9jViJss8pkp9C22DtbH5rBAYmFhMAOLObSkjwj4iCr5w97ELqKdIXoGHm9dQIAzCs4q7FmnNOMccFZAB8y259vgJp5XhAsG8Ikclh2SQ7MAmJ1wdVUdINSSOB4SVQfEtUMie4jiECmP0ZUpuqWxbYPtRdhOHVN3Tvx1vY9u7JLVP4t33ARjDdMrYDf9Wp+eDAH8qq4g2fVGtfRZqxc5JBsj0iEyOUEmR4jlITI5gWSIWKbOBDONsFmMzepY0wKxi2Evd569h+YOENeAHzJFxENk9RAaDzsVUEdjewaxqxwsO1vD3tSEcx1s4wxw5ro4gdUacl9jfPJueHQGFxZwWSMHAjFUyHFENnoqdrLrqJsEYjhf//9w0P3ltbas7OjaAjZu5g77O2TxLsvmbeieA2Amj2i5DsY6BTgz8Q0EIABkNkMlI6LFEdXR/ah0jEpGqMxto3RClI2J9ZRLD3yKZHSZbHIFlY4xi5FT0wZt+Wn9ZKiSUFKiosgtEAVpcwIdOQf9IvcvZv1r53vMRa50ijJj+xiz42Ca7YBpgFWrR9z/mdL7MFUBqYi4CykfQzRmDpK1AjPLBtimhHqErVadLzLRBtVBVNvIyhlk5RZkU6CquUqsYVCtDNnQqGYAyTxAq2iEXHC9IYqt9WaWEYmJ0WOJWSj0TGGmEXqs0COFGSj0kUIfR5jLCn1JYS4p7KF0bZT3H3iqr20TgK85UoxRYoGUMwrzxoYHYAEMYw5igRApAo0lQzosA6ysM6RQWCsAD75WQMvBLA++aljbxdoza5+HAOxGgjNcX9LFc4uSDggpCUo5v7AFFCOAZKy2NwTJMqSZIezEBTXJhigGKI6IOCYSI6Rx6rFIjKmoKbGcEOXlEcXr46qy6sZai6xKurd8iMuXXwFI9g/eQ333YZb1M+hGh7TRQtfbZI02aa2JbrTJGh2yesuZqefvddMBMlu9AV9/xiBnY6e2vfwwcpovKExHqNnqta3UOHzDd62UzkLQ/OgH1toXfz8A6fk7uPwN7yCZuUWO/ms+yuQbX4CdnA7Zts0//fizbGngLn27+j/M3/L7bXCtbKLozQZDE8UbTeVx9Lbvts29/b2WIyyHadsCbfn78vn8NpyXwabiLVwcLsO6EFaVrcpCaxGf/L2Ev/dmpOHCzTb3T1fL0/LYfNs9+G2oGBRC5PzBjbsrlUrgtipam6dcz2LRtvQZRxH1Fc1nbkGXg/18RX37O3+F7/wH38kLv+iF9Pt9jo6OCvPQt//q29cqmD/GalCT5ud0Tf3nfs7n8bWvfwOf8zmfy9kzZ2k0GhuV9o7H31FkSBhxwichtkNCay1f+AVfCMD73v/HPPrYo2vgsPwwO9q5KOi6P95f/vVfMRwO1x4Sn+5/4P7i9cHeHhcuPOqOLeGLXuDO/cE//zOazWZuk7zZo9716bsAePKTn+JgYOpmIh/7+Ed5/vNewD/7p9/H5cuX+eCf/9l1kf1tE9zyw1e28S7fVzn5SvqiF30p3/Wdztz2rrs+xb/5of+z+H71ELqH4rWv/moqlQp/8Ie/y+Url9YetrKj+nKjIISg39/hpS/+CrTW/PLbfmFD/myL364fK4QG4b37Yzs45/xXuc8ognSEMDgED94HmzcR85Nsa+1KyQSkWcQnPvUMFssWdzzuLs6dPSzu1fkhDI8Ly6TFleM9htN9hsM+xycdBicdjk+6DAZdsmz7qnarOeTsmYfp5vCs3Tqm13UQrdk4IY5XCj1XzvlcXoAVOZzUjmq5yEBg7To48ymEBWUZuM/PEHyetnLhy8Jvy427BxxxHG90TKcB9W3+tMq/Cz+787M+wQu+8N186IMvxhjFM5/9Pp717A8ghcQ0BCf/+xPQe24SefQCw+N+8TLxxDhY1pIsa5C1XGAA3VI5RHPbq5oIGEs0M1QHGjVeUplZ1DgtTDUrS4EcW9TMUCUCLCK/B/9cuTyPsFagogpJJXHPk81QWFr1GBk51ZsUCgNIX3eD57MMqK7W0XioHnZ05fz3+Rx2YOVjn9bWhNDNfx5GDAp9PvjncJtz0m2DhfI1bAPh6/B01TaWYWG4KOLrP6W89AOO0+q/FKJos5576YPMowYf2X8WsU74wgd/h3OTh6kkCX+5eIRJ7WZ3fbdo0qf0uSR2uGI0b7rvvbSeNOOhJ46wFsyJInsoxl5UZBciPnHxKejHIvRjEdljEenFCH05xgyvoY6pW9gDPhvYtdAG6sZFm7TWNZKzBDlIEUcajsA8qLDTaww5KhZ2LDwxDwRwIOGMcMEAzuCA2YGAfbBxdRO0GJzZ5aFwf58WTnV2JPJomtb9nQAjBebqkyspplTUwIGz5gRZnSPrS0Qjg7bFdAS2H2N2m6SdDlm9S1I/YF6/HRvdiH8mIIULQ7Yrxq6lIsvXGARANkPoKSIdI7MBMhkgk8Mckg2QeWRLmQ4gHSCSCTZV6GWEkA6QafYw7GPEPlbsofksB87YBVEqQ8n6nDlKEI0Foj2B7tAFTNhT2IMKnK3BfuQ+6wPNCCt2sexuz5MsdVEtRwO45yEXTGE0xB6N4FKCuJLBscAOJHYcw6SGnTexSQuyPoh94DyIJ6wd1rdMa8umAqgD52C2/2wu9RpkkYdjbfc6VJXFbefL7HqTyVDpyEGxxZBl+gRYKlhYEILW8g/oLN/n9knHqGSc7z8myiZgtofoKI/fKpUKw0sPko7HiCxDB+3baVDNPUeNHJLtODNK0UPKHVLbQ4su0MeKLogexvYwWQ7SbBds4+pwLPz8+jMMqgmilkAjxdY6UIugBvL2DHVHiorPIhvnHSArzCwNqr0yr5QtpyBTtTyapdK4RmJwXVdRmFnqCsuZxCwVeh6hpwozCQDZiUIfKsxhhL6k0BcV9pKEcQLjJWI6Q2kPuFZmjjJ/L+UCRWACKfJIMUKDzBAdm49BbT5GFDn0klgbAyvYFSq5jK5hbBVtd9e+X0XD/J9p3umSg3pLhEiRMkGIMUoNXJn68SQCZzEhMTbCyhpWVvJt1UWL3TC5ZAXGouD9DTAyTIK0M6T1gGyI5JiIYxTO95g0A6cusyOndrQjIjFGsCyidbv73JzE+zFmpKIiwJCIK5j2DstWl6zRLkwudaNTQDHdaJN6QFZffXZUf+WN+3DMgVh85VFUDszk1AWnkZM8MM1kSDQbu+jOkwHxfAKzMYr1MVE49wvvs/boPVz5mn+EbnbpfPC32Pu9n0Oe24Ven6y7Q9bexXR20Y0e06d/BVVSquIYGWtOPvU0dv7+P2b0b/7VqfBifY64StvG7T6dJmQIfxsufJYhXThGDZVmZfcdn0nyrMSPYf3YNI7jjfa5rA4uj0WvNvfeNpbcBsxC4cPV5k3l44TztjAyZ+ieCNYFLKE/M2tX0T8Xi0WhGts2ZzjtPraVQflZ9HWhnFdxHFOpVNasTMKFbT+nCOvBtkX060mfEWALK2Z4cVmWBSvsq3T/A/fz4Y98mGc+45m88u+8knf92rv4ghd8AQDveOc71grMJ4PNJ/m5ki1SfP/3fj/f+s3fes3r63Q6a/R5W6UpKyX869vzAAEf+/jHNgp82yTTF9ByuXQ268CFC485n242dzgeFPRstpKG12s1F10PaDabnD17FoBv+5Zv59u+5duveZ9KKfo7fS5fvoS18H/+6x/kV9/xazzhCXfynnf/JhcuPMaf/OkH+NM/+1P+8I/+gPvuuxdYf4iuRbtD9UZIc7elsqnX05/2DH78x/4foijiscce41u/45tZLOcIVvbYlUqFNF3yOc9+Dk984pM5GRzz27/zm8VxrgZeyhPhN7z+66hUKvzeH/w2Dz/yEFLYNQC2usd1hYkDDAKExVrhHMWL0GeTBGFyxdo6+S7nYxm2uS+NM2n014ubPGeZ5K3v+CYeeezxAPzxB17Cq1/+P9jpTxgM+wyGPYaDPiejvjPpHPZJ0u2KgGZjypkzV2g1ruTBA46KiJy97ggpkzV5cljv3eqM2AjoUWSZyZWjBZg2G8Az7LB8Kitay/kSll+5AwjzNNw33KesTrpaxwqsLQqEqdzJlM/3nOf+Ls95zu8D3k+56yiWT98t4BqArUgeePPZrecurj01RBND/UJCPDVEU0M81UQTTTyz7vXUUFmAsOvm61mWMRwOnZRZKVdeUpJEEXGlCsai0wSURBuDiqLC94CIIpQS6CxjmfsmiKKIyEgkisykWCCKY6JIEVcqbhBg9Vo7WM7fVZ4DFiIVg3Xvy/uWO6hyh1jO/9PamrDjCwcJYUe49TkMzrvR3+SQzC/IhCA27LBD8O4/g3Woedr9Uarj2wZ4a/tb52fQWKco+OJHfo8veuT3igWBRCnaNclXPvrD/HX6RcyzAx6ufCF8Ege0DiU/c/TlqMspehA7ZdbV/H0JHPQ4AzwF6FuoJkixQGVzVLJALDKYG5go9LCCvr+C/mgV9PYBn8nj6NIHzos8cqaAA9yfB2YHuG2boOHJU4YDYoe5Web9FnHZIK5kiEODOLIwADuK0LMabDFzD5NUc6LKDNVbIOoJommcyq0XY3oV9G4VvVvH7tUwlSYLmiy46arHBByt8dBrxLX9kJVf++sT0KxCpwadOuw0YK8NB23Ya0G/Cf2G++s1Vq/7Tfebxz3uiViqaHYx7JPZXTR7aLOLEftk4slocYCRZzDsYW3PnfhqYrN6Bj3j4OeugR0BO6xAWR/3vgfUY2zo/H05R46GyPHlPILlEPHoII9gOURNhjCYYA8zxEBhBgLGVcy0jk07GLuHtXtuy51odoFNk+mNFC0hnkFlBLVjqGtoSmhHeebmGdzA/QXd34CXMOAlG4eU6RSZjIjml6kO70GlE1Q6cuaVS8sgezkm2YEUqouP007/BJs5yKFpo+mjRZ9EnAcRODUErG6wk77t9PZgS9sLYT8q0bYNeo+MLikRotIH0QO1S0YHRD//62FFz23p4ZwPVjbBmFc3bgAzi1fcX1eKMqhqiA1EFlEVUBPIBsiORfY0ak+jDjKisynR+Qx1LiPq5oCsqVH1C8iqN7O8/rRmZrmoYhKFnucqsolEjwV6AHZgMMcZ+jDFHibYi1PspQliNEFORjAeorIl1iZIqcEaLKlb2BJuTBQLgffxZa3EWuXylQZWVLGq7iBX7u9LZzUy2ylAlwdj/7+DXg58SbkgikbIwmzTf74M9luwqnoyh2ER1kYYG0MB8WoY28DYJkY4X3hGtjByz41FwuiV2yJaRlAEwbneezELpJ0EkOwYxYkDZGaIIgdiZoA0QwfN7BBlxyiZFWOabbDFg4C1sSpgKnV0fa8wpdSNNrbVxTS7Ti2Wm13qRhvT7GJb3dX72g2qVedTovmYeHCF+mP3ombj4i+ajVGzEVEOztR0hJqPkZMhjAfYxQwCqAGreW44Bi6Pe1Wrgd7vYnb2sP19TP8A3d7BNHcwjR6m1sFUO+i4hVFNjKgTLWvIeZXJ476W4be+CbNUzmVFOd23etm+/QQA07ttYxxWTh6yheAMVkCnPKYsH+vUtvQar/15vehgm3Xe3yaVBR3+2sM5WWjSGM6tywqtsurrtBTW+TLD8d+fNoe6Wj6GisJw0Tw0efUKQH++NE1ZLpdr9xjmwfWCzG1j+DIEDOuOH9f7awkjjcZxTBzHRfTQMD+uxj2ulj5jwOa3GyCrlPF+n3f86tt55jOeyate8WqkdNT2Q3/5IR58+MGrElclJZnWvPqVryng2vv/5P387M/9dz716bs4PjpimSwxxvCaV72Gf/vD/26NNm6bTG07l/++1XLOcyfTydpvypP5baDAL6frLHMr+AiMNlhjc8WPJU1Wq5BGrxRDrWbZae/1pWoAev7iLz/El7/8JfyT7/oevuRFX8q5c+d57Wu+itfmasE/++Cf8n3/xz/jrz/8V2vXfb3U2v+m3CGVGychBHfe+Vn87M/8PK1Wi6OjQ97yzW/i4sULrlHMvbL7UL5KSV7x8lcB8K53v4NMZ1uBTTmF9evpT3smT37SZzMYnPAbv/me/MFxvo6MMYWSLPxtsZqQK7K0tvlAAnRwDWm6pFqtrj38kI8HxLriKZzYhxN/IUR+rytp8/0P3VnANQBjIt7+7m/Yeq+16pyd/hHd7jE7/TF7uxN2dif0d4Z0uwOUWqC1ZjockSZpcI1+oLDZgPn78+rLUKq7pRY45d+WAf62tO04p5nRlU1Ft/0+bGe2dU7h7zauPGgkQ0ASApCyLLzcWbgHGLIsRam8LYg3719NNa37FwU4i6cGNdFUps5sM8oE1hjG4zGtVmvj3kMoZIJOwu9Xr9eZTqdUKxWkhUajiZACAxirQRim4zHNVsv53dHamTML6SCyNkQITKoRCJI0B3BCggStU2rd7lr9LcNMIQQikKwIb0iypTqs8m+zfPyxwnpRnjSW5do+H8K6U4a6V6sL5fOUU+iDoZzKMO9qILg8YJBSrplZXW3A4n4Hy2WD8ajFZNxkPG4xnPW4vDxDOohJTqpMJi0mkzaLeTB4/8W1owKgqzFiz8JnWzgwVOpLmnrqJhoYyCx6YVmMLYsTiRlVsY9UYJYPkBAYGqQ01jOkigNmd+SAbF/kSjMB+34rnNKt/KykFg4N4rJBXsxQ92TISynycoY4MtgTiRnF6HmdNGlTNivy8/ziTispspGhblpgOwLbU9h+BB3poF0Ht227iVLCFrMZwzoIG3F1JVn5ddC8CZMisykqG7soiekJJEeI7BCVDVF6hMxGyGyISAeobIRIh9z11x+iWd1kjACTKVy6ApcO3d+FBy1/fgUeuQwXDuHyIRwdwsX4bsiCCbo/VpiFwjqm0sNBszIo6/vv3Fakc6d0GA9dxMrxEEYniNEQecH5H3PfDRCjIWI8haGFUYxNuxizh7G7aLOLtbsYu0dmHle8dpDnGklqqKUOlDWm0FTQrjho1gCarGBZA4hLgQ8AdArzE0RyiFgMiJIJ0WyCTBtk05tQyYy2ej9x9jAqy7CJwC4FVlfQaQVNBy36ZOyQih6pOGDOk9CigxXrk+gln82Sz3ZvAhGtsgMkM0zJwX7ECZYqadZC5w75tWnnrzto00GbNplpY0wHXXyfR5i07XUIVqcAY6YMyQxg8w/sjdAN6/xCRiAiCxWLbBpk1yB3NWo/Q53RqPMp0XlNdFNKfFOC6oV+yPTW+n3qGW1gZqkj9FBhFhIzE2Rjizk06CvOQb+5soDDGebKFA7n2KMpjOeI2QIxT7BpBiiEiHCqr5WKa/tfP4dedW7M19v1pVUEyiXOfHOwAcI8APPwS0r/ecIqwuVq39U2ze+zgjZVjG6hdRutXf0xtuPqkX9vdkmTDlp0MTKvz3nwnA0odhosu5F7N1OknaDsEKkHKHtcqMYUTjnmYZlTlQ2RdoQwQ7DpxqR32yJ3CEYMAtvqkLQ62FYvN6kModjKH5kDZx1002/bEN1A5FydFeCr9tghar6CY9lJFzOs0kjvp2k/5WDZfEI8nyCnQ+LFDGF0cS/bxsZhMsa4sWI1JuvVMZ3z6N4uWXuXrNnDNPvoehdT7zrTdQ/IZB1D3dWNrIJOnIoZe8rD6fu6UpIqQ6qESE6ImglKLFDLISqdYMdD5HzA4uZnsohvx2rJ9JEWSMPyvW+/ahaW4UY4J9i2KO6/C0HL2nWesn85b6NwYXrL3PZGQVt4zaG5Z3hNZeWdf+1FKN75/mluuK7GUMI6VBYjlO/tasq4074Lx/EeSmqtSZJkg5mEC+HzuTMP9haR2+6jPB/wKZw3ledt5fv070N3Y+12u/ABn2UZ1ep62x5CQ18Onwlg/VvFPA4p4TaQ5V8JIXj3r/8a3//P/wWf+zmfy8GBi4b29ne+fe044Yh5ldHu5r7+a78OgA9+6IO8/utf7wJn2vXKU6tdv615SPDDSdlkMqHf79NqttYepm2E+LQM9+MXIUAY65Rswk1DTeAvKtMOMEkhOT45Lj7/zn/8nfzyW3+xUL8VlQ836TbGEBXXrQFZDFg+9rGP8vVv/lqajSbPec5zec5zPp8vfdGX8qxnPZvPf+7z+LV3/QZf/vKX8Dd/85Hrzqswv077LsyPm2++hV/4uV9id3eX4WjIm9/y9dx7371rlTQ016tWa7TbbkX6G970zde8lh/9dz8OwD33fJr//BP/Ea01/f4OAL1en3//Iz921d8//3lfwPOf5xSUP/3ff4L3vf+PHICQKxlwSN8rlUpwfwFYDABfmbqHD7T/PDRlBMiycodtqVYXPOOzP0Knc0S/N6TfOaHTOaFWWxS/jSsx1XoDIRVWeNPOvAzMesMiWG+w/XenNcplkLLab+XHrlgNsCu/duUGe9sqRPm84ffbGvttnWpow38tMHyaxNoYsxHdNbzu4jq8ozdL/k8QRTEWpw6rfXTE9MsOsPXI/5ibfv2Y1n2Lor0IAZO1ljR/fqvVKlmWFStx28wYy/nnYG8Q6RJYLObIKEIqhbCQJCkC4aAxboHCBHU2imOEFIiKuxelvI+HyAXPsBaEYLFYuPwNnldrnJLKXVcAwaxBik1/auGcbVuHd7V6uM3sN/xdWJZhfTqtTpXrUTl/rzZACX8Ttgs+Xzz0LcO18Jnw32VaMp20mE7bXJmf4UPq8xlPOtQPZ/QunjCbNJiM20wmLfQp5t4+1RsT6p0pvZsOiZoLaBgunLkVHVcAA6lFzDLUIMUMFOZR4GOwzGC5DTDBSsV2q1gBs1BhdhDAsxYrEmQsYpYQXUxQjy6IHpujPpogLmVwZLGjCmZcIZs1yJIWadqC3Km1D3q6eYMW+qIAYwUkC2CZf23jGE18dVPKY+ASp0Oy0kVIvSDKpig9IU6HxOkJleSYSnJElA6J0yFROiBOR8jkhCgdEqVDB9RMRmI1mU5JrWaJZREJslgiohghVa5C3CWp38ay/mR092b+8c9aLlyBK4dwfATDI5gcw/wI9KJcR0vvZQ7Kbqs69WEZlFWnyMoIoQZIe4iaOTMhMRoU0ExcHhQQTYzz7yYjhDYY0yUzOxizi2UPY/exdhdtD8jEM5xJqd3Dmj5Wb0LRjSStcybfCABZnU1Q5j+vKKf6Wo4QiwFiOcTOT/LXA+yhc9xvZyeIpf/+GD09gsXU+Y2TbVB7RPVziPgAVTuDiM8gKgqlxmRilyt8HZoOG6awN2Lxm1fsVvoHNLKPYrUFrbCmgrEttOky4fPRZjf3924YZi9iyF+sBpBwuqml75N82xj0Udd9gUI4UFY1iKZxKrJ+DsrOZERnM9TZjOh8SnQuJT6fEO1kqKpGqtMnG1aDnQvMTOZbQfaIIp1W0FNFNpGYoUSPBGZoMSOLHWrMOMWOMhgvsZMUpho7s9iFgEy5SLK6gnN7UMmh1w1Aj+tKhlDBJeUUOFqDXqvvvC+2GV795SBYCMTmhMowmKNUAixwppPrZ/cTbTeRFkAbbTwA64DtoU0ba7povUOSdjA6B6z5n7YdjOhgZSv3Rcb6X4XtwEzZG4xsaQoVmWKA0kMHy6wDYZEYF++FGRKLMZGYIu0QzBAf2dKn8iQZNpXhOo6dz8pmZ+WIP4dhfqsbbUyrh250MM22g2Q5QLuRJBYz5GyEGh0RP3ZfYWqpZiMX/Xc6DN6HvsrGROmCOHDK76HCww//AwaXXwHAiM/n1lt/lN2zv+eexVoF3ayTnr+NrOsAWdroohtdsloXU+uQxS20aqJlHS3qaFtD6wr6/+Xtz6NtWfK7PvATETnu8ZxzhzfqvSpVSaXSgGQJEBKWjEDNICSwBLZYnjDGpm2EDKtXuz0ttxuZNthmsGUEFgszNINRQ2NkScY2MpKMhDBC84Qkq4ZXVe+9e++Z9pRzRPQfkZE7dp59zrn3oe64K28OJ3cOkZGREZ/4/n6/LkY3EaZR7wGQtQ6QiR1JUiL1zg0ONRtkvUaWK1RxjdhcojaXROsL1PU5qloRtwUSzeS1M974S78D+Sjns/kZXuVdAH7x717xt77x52h0yvm/8q1sF78SFdVMfvaPUfxP3znkz7EUgtKwvzhuK4eDnmGfK2x3h3wibLuNXXv4/cfnDv/2vGl8TA/Ixtc/HkQOQeFtoO/Y9dwGo8bXNO4LhddzrD0d9pPGbecxzI6iCKUUbdtSluWgmAwhaXhf/hj+HOG9j9NdfcWxWMOD0vC3vt+klBoijKreEsgYMwhg2rY9uJ5j9dLzpn+iIAfHiO5B56f30WS05dn5M77/738/v+bLfw1vfNobNE3Dd/6P33lwnKOFSLiO7oc/9GEAvuO7vqNvR7ierxACiXu4n/WhDx29XjnOmKBzOMyNAQsf+ehH+KLTL+JzPvtzbxTUYyZpxwqz6CcP0yR9Pvk/+v2sA3DGdqyuV1xeXnJ2dsbnfPjDtE3jAIoNCrx151QId2zhjn5MIrordvzd7/lf+V//7nfzh//IH+JXffGX8Ne/7W8ymUz4/d/4B/jd/8bvOppX94G0Y38LJbwPHz7ir/2V/zevvvoaZVnyr/6uf5mf+ZmfPlopvheb5tEFHZz7vaaxGsW/jKFU1FU8e7gDfSVwBBaFRH9/qfvy7be//81fZD5bs9n6D7/g13353+ELP/8fHq1EQ7CH6ufWlW+D+8jESXIgDRfi+MjDsZGC8Xt9+HzE0Bgc2vfDBnvjWGOfBeFxfX4fe/53QZDbrjtcfh4FUwhFwv1uwB7LjcaKGToxgmjV8eBPf4Tu176KiQWnP1Uw+1iNBaqqGkafwtEpL4n2H5Wmaciy7EDpGN7PuIL3/hosziXefOYGAkzXYXVHlqboKKJpGrqu68NPJ6RpihCConbOmZVSricEWCswpkPgHFpLqdDW0AtNg/Ivhj6c6Otl93uXWY7H7fP8mIrrsANx+FzHKkK/3f/2WJkYP8P7TVlvjnodM10d/z1ssPnt+1FIRVUl7LYLtrtFrzibs9vN2W7mbLYzdlu3XBTHTUW2zHnGS8hIky4rZm9skYsOMTWYTLCezTFKARZaA4WlvBKUlxN4mjm1FeB663vHwRboUC7I5mMBb7A30fTmmo/Yw7MHDGozUZbI1YbokyXxp0qid1vETwAXArtO6LYZupigy5yunmCqOS3iWBt+nyY42OPhWAjL5uxhSo57924zq+yAp8CnRtvCV95alC6I9I6oXZO0K5LumqS5IGmuiNtrEr1B1SvibkXcrYm7Naq+IurWxBIsHda6ctP17Q3/ITfWtRl0PqGZL2gfLClmH6RIX6VOXqeUj6nFQ1p7RtudoLsFppxgixw2Mawl1MJF9+vTt/6hUX7FPSh7H3BqSU9h8gBmD+CkjzL6+CG88ghePoGzCP6Db/i3HCT7SK80W13Bbo0MvktaG4Q4wfamo519jJavoeUHMOIljHmEsWdYvcR2c2w74T7T28GH2RiQjaesQURrhL12MLJeQX2NqBycFNUKcX7dw7Mr6LdTXSObDViNtjOseoiWpxhxhlEPQD2A6CGoNzHqITZ6DNPHsHgA0aGFwJ1l9K5kDZgOYTpEZ7AaRCewncR2SQ+C+hM0sDVfwZavCD6aHJbRIUleXCHVtzSlRWQWMdXIhUGcaNQDjXrkFGXRSy46rzrrUMsWNXeQzNYCWzol2BiIDds/Lmj+saDapegixxTKbd/2+xUCUwClwNZgawndL70T+0NV1xZ4ho9oGUIvIUocvKqBMgBbPpplNfzdAy8hKpRq+vU99Bp36MJvS2ieNm4/+L9bK9B62oOvJVq/5II9mCW2Vyhau8QatzwAMuPMLAkB2VgtFkKy4W/2vTnttz0IY4Xq1s4nmV33JpVuWbHe+ySzK2d6yQ4lxdCWGbdhQqChlEIgMMZi8hlm+jLdZD5EtBwgmVeNTRwYG5Rl/d9s8gKms8Y4QLbbED9568Bhv/RQbLsa/JPJYX1FVO4QXRhlRwA+YIOLWmptPqwbO0Fnj2mXS8yjB9iTE8zJEhZz7GyKmebYLKeNTpklW2wssFLylN/LE/t7MV2MbhSmuaVLbjj4TgQPEBm1KNWQiC0qrZCmQHVbZLN1dWpxjdheIrdXyNU5XD2Fi3dR5YpYl9jeWidMN9qhWUS0zIkWOdEyI/q0jGj5KulZzuLVGY9/3QfgUcuE64Pq7QO/9pRf8bte5Qf+1Fu89Od/J4uy7KPTN881SL5/lIeuqDyQGpc3D27CgfjwfsL2ZPj38YCp3xa+8/P5nPV6aGjdm461Vz3k0VqTJMnwPT5mQeP9s4XtzXFAtzCN+28hFwj7XeM2+rivGcKw8Hzj/pzf5gMOxnFMWZbDPY1dEoXnquv6wKzUQ66xCXPYNvf55f8e+n2LoujgPsL+Qtifj6LoYN/wtz6vfXTTsUuhF4GrPv0TKdjG5kMHEEXs9/EX9ze//W/ya7781wDwfX/v+zi/OD98uMGrGUIKKeWgIvJSyfDcIMiynN/4G37TwfUJKXptvDPtGm46Uge+uazt4RXw9/7e9/FFX/hFfPmXfTmvvPQy7z551/3d/QCsHQE7MSjLDs7dH9PnxfACjToA++2W7/u+7+Vrv/br+Jqv+a38wW/6f1BV1QEIcKftHdDj1UO2N208BBljUPIP/vcf5Hu/7+/yVb/pq/mMD37mwbUe6xQf3Muos3rsHOB83/3Vv/xtfPqnf4Cmafg9/+bv5kd+9Idv+Pbyx/AVRtPUfPO3/HHMSAkS3tMXf/GX8iVf/KsB+C+/+b/AYvcqG+BHfvSH+OhHnY85bZx/jHFl8Hv+jW/kZHnCT/7kj/Ht3/m3EMCz86cHL5B/tNoYvEP/Q38Me1CyLxg3QYB3Yhneb/hBEUKQZxW/61/8M3z/P/g1FMWED33Gz/B5n/3jRz86URS5a+oLgvcFt18XCEI5tSsbY3PKcZ6EwGMMgKQUwZwBoNxW4Ywl+WEK73u8/diHdvyBD9N4ROrY/d324Q7Lcnit4Ufl4Lz9ex+8ysNcSsl0ZZn87csb6kw/SuI/lHXtzIz9fn6eJMmNj+t40GH8AfTX6T8aUkoEzgxUKTHUiVHkPnzz+XI4R6ycjzGsRXcGKRVSCjptENaSpKlrCGtvxuPOPQBTeXgt42cYvgPHwNVYvh/eWyjJDvc7tu8x6BbOj/lQG5cRf823mRxrLdjtpg6cbT0wWwymmftpRtferaTIJztmsy0vvfIOk8kOO7H8zKPPdSoea6EzUFnM1lJeW8rLCN5SgW+uI8PPAgfEXj8CzMbwbAax1iR1h76WJO9qXr5eEb9zzc++9WHMjyu4lHBt4VrAVYfdLdDFEm0F9V03N8GppEJIFoKyDMcOYjzxuwnNKlykvBFzF6Yj6iFZ0q5J2mvi5pK0vSTtVkTNNUm3JunWRO2auFuR6g1JuyLSO6wNgnX0eebKVj/SSW8CO11Sz2a08yXV/BUHzGYL2sWSZnJCJR9T2wc09gFtu0Q3M3SZY3YpXEvnI+4SeIt7AyeQGVh0iNdLxKRApltEvCKKV/xzX/vP8OojeP0hvPkYXl/AqXLTXNw+iqoNvHsJv/hRMP8woRNfgBEvY+xDtDnDJCeYbo5tZ9gmgzrpfWvek/L+mY4hWVoi4gIZrRByhVQXCHOOaq5R7QZRr5DNGlmvUBcb+JRTl4nqGrpyqEettWgTYaMHGHmGlQ/R8gQjH2Dl52DUA7Q8RU8fYOePsNFDUKfcUJe912SsC1fdAZ3oy6XYl1HNfrmj9zWYYEnu5mXvJSmLyC0iN4iJm2SuEblBZhqRGUSskZFBKO2+8cYpvA5A2VainwnaHxXYQmLKDNoXiCT4XEnjYFag0vKwKxmDrf3k4H+4XqBUy00TSAfIhKiwtsEPpvs09hsU9hf8t+HYgE2YjrUdDuGQwNq5g2B2SadnWOMinwpO0MbBMm3m0EdE9X/XZgFi3kex5Dgcu2FiaSHqVWTPCR7cxdY9+Fo7INa5uTOtdMvCrIjEZgBn3uRSUNwQIBxr34X5TBSj53P09DFt74vMAbDloBjT+Qw9W/Yqsh6c9Yoy5PPbkoq2GeBX/OxTPSDzUGyDKrbIbYXYlIhtjdh1iHWD2GnYgW6dGbAxbgrBWGcfYczrOFPgHJulcJYjHuTwSganCSxjxDyCmYSJgExAIrCRwEiJRWKsxHQK0yhMe8+9+XpkuEGLihqUakhVhcorlClR3Q7VbYnqNapao4oV8e4KtblyCrL1BVG1hmqDHZmTemATDrSbSCDnKdEiQ52miNdT1OINsocT8ocp2VlCfpaQLmOyRUQ6k6RTSZILksQSK01ER0xHFEwuNNYO+Ilbb/nBByYHbpvG7brnScf6FP5+x1ApbH+Oy7LfZ9zWH5/HB8vyvra11rRte+vvbkvj+/T9K89KPNgJfXqHbd+Qdfj9x8cPQdI4b0JQNu5/+u0DAA/f8SPHH+dTCO1CmBf6vQ/vJXzuHpYdG5w4dq/jbT6QmedBYx4x7n+MQaM/pn9XjvUBfP6PB9hfJL1nBVvYSQmJ8JAllgPzNIBv/45v58d+/MfAwvnF+c0HF6yOC9InPvkJPviBD/KVX/GV/Jn/9s+MCpPhP/oP/yMenD04+L0FlBBYY1lfr4aMfOnRS0jCj5jF9Pfz5//Cn+cbfu83kiQJ3/xf/kn+5X/lX6BpGgYXQ3Z/jx6iWGsRdm8u567puClcmLyPJZ/+9Lf+Kb72a7+Olx6/xB/9z/84v+/f/r1uP2MQ0vMcNwqklOLNN97kIx/9CFI630yLxZJ33nn7oLEQFsw33ngTgKurqxvXEl73sTTuRB88cyFI05S/8Of+Ep/3uZ+H1ppv/P3fwPd+3/ccVCRjRVf48r399icPohaG+wgh2AQjB5/81CeGfPGQbrPZsNlshucp2L9ge8eG7su2K3Z84pNvOXByAGP6Z6jEALPGz9Fvk9KpfXQAacI88nAt/P14dEQpxenJmq/+Dd8e5PvhczsACT3gQhx+pDqjkVIgrOzBT4QxHSCwViDl3RXz+Nr9+uGzuFk2hNvY5xw3KrRjgOPY+cZlKwQ1YeXp8yPM1/A8950z3H7YkD5+Tn9/g2ZNOM9j+7K8b9CHjQc/EuNlyEIIyrIc/rbdbjk5OQE4cLI5/ogcU0B2XXcUDNJP1ux9qhkjDnwLaK3dqC4OHEdS4j0TJmlyAAmt0UigO1K2x/kbPkNfJ+7VXa7MjwdGxp2ZY8c8Vlb8/sfA8fg5j+X//jxNk7LdzNgO4GzRr/cQbbvozTin3GXmplTnoNlL7zKZbsmzgjhpUKoFDMZItFY0dcJuO2Gzm/L220vq8nF/hKND0s408rGATxd788xjarMTzbQtmJRb8s2O9GnLW299kPrJBH4UuBRMdzvOmhX1Jme7nbDbLaAVVMCaE+DN0cn7fJzEzgT0MYHyCAfKUlwn0XcUx77LfGrcJK8aoq4g7hz4SrsVWXvllGTtNUm/PW5XJO2KTG+ImhVJt0bZEmucA3H/hDusE5baYARaxbTzJe1yQTU7ZTt/n4Nl0znNbEE77WHZzIGzJjul0Se07QJdTWHVQzIPyq6Aj/Xz9f0dAZE1qKxAPdoQR2sicU5kn6G6d1D1p7D1W8jyLaz+JKpuEBcSLg8bxGk+4z/7Mz/PdQVXJTx5Av/bj8On3oV3njqz0YtzJ0hbX0KxgmoD3QbMLsz7O1wkpP2zPDOQ1oi0QsQ7RLwFeYkUVyj5DMkzIvsE2V6hGufEX5TXiOsVoln3xr2HDVdjLIgM5Cs05hEmeplWvkktH2HkmZsmJ1i1QCtvwjYF9RxmfnY/mHVn8mXRl8cxIBtPAwy1IFyEQ2TQyLPCTUbcr+DbX2wPSixCmf1oqzX99Vlsa921Wcsgb+twKrDrY8f0flTvaLJL68w9Y41VNVCD3EG2QSZb6LagNwhb4BVdh7DLR7Y8VH0p1bL39eXNJDvX5rilHj9WJ48VIWEbMNzml4fbCjqYPh37jodztyywdjEAMgfCTjBmge0hWKggswEgc/vMj5tYxtzcFtEryEy/Tb4QJBO23PsZ6wIIZldErJHWOexXdoUSG+QAzFZI6hvfyDA/fftkAA/WYtNJrwx7fXDG75z1z9G9DzLvn8wMk/u7fUGH/WK3QxYF6nxF/LF3HQjb1ohtg9i0sOkQWwNr4wZZNmBXAruWUKf4iKfGTujMKT6wggsE4YGWdWbODzXqkUG+zyDPLJxYxALE1PaADGwPyKyQWOEixJpOousIe0uwniHdAGQGFXVI1RKrHUrUSF0huxLV7pDVhqjaoIprou0FF7/4myg/+T7MuYSt4MHiu3j0ed/Jbv65pNUnOL3+/kF4cQCiEomcpchFijxLkW+myNkryPmnkZwlZGcx6UlMspAkM+Xg2FSQZJCkECvTQ7H2AJKpYUSr/1jfk9pOUncZm2ZKu2vprlaYSpAtzig+eEJCxSu82wtJXHl8+8c3CCGY/9oP8mm/8cMU5yt+9j/7LjYfeXontBj3DcbJ51Hoh9c70x9DrNvqj2NJSkme50OES9+f89YjL2J5Na4TvTsYX/f5Pq9XV/nrDiFhaKZ6G0sI/3Zsn1BJNq5bx2q/sG8xPv64H++P6fN9rOwd96/8FF7vscH42565/x3s1XPAgYlteK3h/uF3yb9bPs+9si2MfBoeL8yXF0nvCbCFo0TjzmmYZN/pc062XefwF/tIlsP+1gGycfKjKta65e/8ru/kD/zbf4Bf/aW/mm/+Y9/Mt/6338rbb7/N+958H7/nd/8efstX/xZ+/ud/js/8zA8Nx3cdTnfuqqr4hV/4eT70oc/iX/tdv5uf/pmf5mMf++gNKv2JT7zFH/pP/xP+4H/8TXzlr/tK/vb/+L/wX33zn+Af/dAPsSt2vPzyK3zxF/8qvv6f/3r+8B/+f/IDf/8HgNsBxnh7+IykOKSpP/IjP8yf/Jb/mt/3Dd/I13/97+ADH/gAf+q/+Rb+0T/6ITbbDfP5nA9+8IN82a/+cr7ua387f+e7/2f+3X//38EYw9nZA/7hD/4w/8vf+Z/5H//2d/LjP/HjPH36hCROeP/7P51/8//8e/ncz/k8AP7mf/83buT3j/3wT/LGG2/y/T/w9/itX/vVz12Y/HP81j/9Z/nSL3EKsz/4Tf8xf/d7vps8z4eCG5aNrtWDlDSKbkaCGVcAbuPNvL1N7Sawg9LmWIUjcCr6G07H7f65CNk7fe+jvFr6cmodvOgPfuNa/btxX+Xgr+QmXDgOZT1YEyLsau5Vd1rbG3kEOH9b4jDfjgGLEAJ6+GfM4UhEmMf+uXifXf444WjouKI+1gi+LY/u+oiEy7fBlTF8O3avx+qt8Uco/Mj7ewnzL1RjhZFyfAAD/5vZbDa8B7vdblCzjY83TmPY7K819Ccw3K/u0F2HUhJjNG3rZPjb7Y7l8uQw//qGNkZjur6utF4uDW3X9Pm3/zAf8xM3zq8QnoVlKjz3OE/D+w7f62OS9tugnNZQFFMHzTYzdruFM8vcLnpoNu/X57S3ROL1KcsKZvMNjx49YTLdkiQ1UeQix7nGgKRtYooqY1PMud7OePvtV49HzxouHOfs//3C+TV7dByepcuaudoxrQomm4L4aYN4p6F5t+Wt8w9QPn1M9L0tDz/yNvIaimrOpnzMs+Z4p2fHnJ13ph7jAMuCPSjzsCzDqc58B3J0K1KXRN3OwbByRdpekdU9IGtXpN2apFsRN05NlrTXpHpD2m2JaA/qxYOy3leu2hhMHNPMl+izU4rpK1zPP0QzP6GZzmjmS5r5kna+pJ4vaGZL2tmcZrakmS/oJnMnjrniJii7Aj4x+tvuvs6vIYp3xNE1yfSSxDwjM09I27fJzFNSzontUyLzlExeQdlgS0ETTWmTE+p4ThXPaNIFzcmCKvsgTfwraOXLGPHIRfTslr2ibOJUZWXEK1+A464Fz9PfQSQQzyF9HaYnMD+Bj33iR5HyGiUvUOIp0ryDNO+iuieIusQWNXITY9dOuaG168BqnSDkDJi47fY1Wj4DK0/QcgnRAivn2GiCVTlWpaBSbBSBiiASbnoeruBHLe5Lg7oM6Hoo1dn9up+0BqPdgeV+sMqNSkk3GQnG+yg6/A7TB7E58H8GhwDMdvvlwf+ZCZb9xNAJt5FFTgwis8hJr1DLgMwi+vdPpCBS68yBmwLRVQi9Q7ZbVHNFVD0jKt5FtdfEzTXKbAn9fSnll93gnVKKt956i6urqwPFtLEGFe87nX4e1sfHzOCPdbR81hyr7499a8f19bFvw/g74HyQLYAT54jfLjC6d86v5w6KBUDMgzPTm2Ba2/sBvM0h/43tFpTutwnXSHx+RtY77XdqMdmuBhNLYfZmlsJcI8w10YG55QpBc2PQyOdZ2AEeBgGFQMxO0dOX6GYnTkV2AMS8X7JFryibY6Yn6Gnvi+yIVcmtqTOITQPbDvGkRWwvEWuDXVvYClhLB8PWCruKYRPBxjq3BTtcgBAS9J2BTHpA9kijHhvkawZxZhEnFjk3qJmBid0ryJTByBprFcYodBdh6gRt5HF/nsO9cADIhNKoqEGKkigpUbZE6gLVbpDNFlWvkMUKubtCbS7h+hnq+pxofY7YXSN0PQy++hSayg5tvEShFhkvLb6Xqw98HfZzHzN9/BbZ51V0H/jXeTTbMpu+j2X8aSziT5FMJWkuiHOIE3sPIOth+vM8yg6aCorS0uwM9c5QrVqq65bqqqU8ryif1VSrjmZjqDcd1VpTbSwXy6/k7S/6E/tOrBB82o/8e7x6/b38yl/5K/nA7/hsPvFheCJ/hkf5RxFYfuLbnvET3/aMs6/+HN74Y791uI6Xv+YL+Dtf8H+nPt/ccqXHB079dp+stQeqsLFaKvzNbbDkGKT3sKht26Eeq6rqheDasev2/auxis8PPoR95RAGHasbjrW5x3l3rP87rotDaHiszj6Wfz6Ffbtx/+7YcwjBVTjYEp5nnGd+Hj5fD9R8/ydUSI77qOF9Wmup63owCw0HfY7BxhCGvpf0ngFbFEU37IE91PLJWov3EeVeSrGHBP7mrBmAwZCZ7MGYALDwLf/Nn+TXfsWv5Zd93i/jt33tb+O3fe1vO7im7/rb38V3f/f/wp/4Y//VwbGGIiHgz/75P8t/8Uf+KF/0Rb+c7/27/9vB7x8+Ph0K17d8y39NnuX8O//X/xtf8PlfwJ//b//i8XwY09nwvrkJdzwMCVNYgIwx/MFv+o+p64o/8Pv/L/zyX/4r+HN/9i/c+hzqpu5vzRWkOI75zV/11fzmr/rqW3/zV/+7v8xf/Eu3HzO8rvF9hNcbpldffZXf9Bu/alj/pj/4h/imPzh2JrNPn/zkJ/mKX/fl/kwHIxHHXlJfju67VleuGEaAxvsEK0gBUkm0scM1HGsMSm/T659l//vhnCM44xU7YyWUe+nBw7mhfyn2jf2wAhvLUi0WOegG/L62/9Z5/1c+m8K885DtJvgaP8sxNHL34I49llWHDfKDcj6CavuDC2QfrdUGFdr4+YXXEV7juII/XO8rUOH8Io0r1nE6BndCUHTsY7XvQB2absZRRCQVyMMOhvdJ4POtbVuMNaQqYTaZDB+L8EM7/liNR538O+I/CiFck0KgVIQSEm218w+lFBiDSiRdbzJt6MGa7UG0db9DQBT3I4Gi30dIvB864SmzD/yAK2x3fRDDPA4DAYQNjXHjYKxwaNvERdLczSl2S2eeuRlBs92cYjfD3qEwkdKpzR4+esp0siHLC+KkRqkO0b9HbRdRdRmbYsp2O+NTT05pB7XZLWmCA2WfyRGVGagzzWReMJ8UzLod+bokedainmnEOWzP55QfnZCc14h1b5JaTHlavEZZT46esiHlbT4TMOTpjtlkzeMHb5PnFT9nPtcBs1RAYlFpx5e9/kOczQt+cvMZ/ML2zYNjvZ59kjfyT/Kxyctcxhlcv8XpD/8Vkt2niPSatNvwoLjmpK5IkCizVxmHcZl93Wiy3MGwswWb+fs5n85pFwvqea8em5/QzBc004Wb9+CsmS3Q4yBFFthyCMqugLeBS4t41iEuLVwJp364xzxH0JGIS1L7jIwLUs5J7TkJ56TigoRzInGNjRtUZmjTKV3qHEzX8ZwuXdImS67SN2iTBW28pDGntOaUrp1hmgmUcg/Idv217/r15+gDyQiyGeSPYTKF6RSmE5jmMMshT2GSQBY7Bto0UJRQFP20gY/9Yoo2b9LZz8L5B8pwBJVDqBD1UxYsh9v9+n0p6HDdu98AyI5Nxtm6DmpI4RxNCmfyzmB14KdQJXRPBIIQgAkDyiBig0gMIrWIXCMnBjnVyLlGLjTqRLsImR6M5da9V70Khgis8pDNgG4RbQV1gSjXRNsL4uaSuLkg0VckUYXKBHY5R7/52dgv/DxmD7cgLenU0dT6O36Y7X/4bcfrVQU2G7WLgr+H39xxQCXYdxbC3xybxn8fD5SNt9/WQQoVZCZ0wt876TfB35yJpVeTBQoyXJvhuSJYKgtRt/dFpoxTkj13EoeQrLveL9u1M6nsAZmyK4RdEbEBfYW0G7Dt8F0f+wIdwFjYJkxzdP4QPf1c9PQhzB6ip6foyQl2doqZLZxz/lnvx2s2wcxy7DSD2Qv66yusU4c9A37RwsarxXoQdtdyCbbPZDv4Cew4jFpaImYV0csd8rFGfhjEqUAsJcwVTCJsHmOTGBPHWJVgRIKxMVrHmDZCNynairsB2chTgoz2ESxl5swrpd6hup0zUS9XqHKF3F4h1hfI9Tny6hny+pkDZ8Yd7Fjb8gBqxA6QiXmKfDBHffAxavl+4rMp+cOM7EFMdhKRLRT5QpJOIZ0Y0tSQJJpYdQEg+8ERIPvh536MXQdtI6gqaApLszU0G0216qivW4rzmvJZRbt224rrhnLVUqw0RZnTMsckC3Q0p4sWkJ1g4gUmmaPjBSaeD+tmssCcLNx6vPdhKZQlThvivEF80T/FZPMRtp89Y0vDa88WvHXxCv/gEwVtAtGr7+MDf/FLyT/nlYP7yF5Z8spX/TI+9v/6gVvv1T+TsQnf7cD/mKBlX0cd69/dBZ+8mai1doh4+V5Ay/h8Y9XdYR/xcEA5DKp1F4gaixj8PuExx/fr56GvsTFgCtOxb8TYXZdf98KaQ3/g+2/UGBgOgo078tf3l8LzeWYQmnKGxzzWx/BAzsPMYyagx9bfS3rPjizCj+5BAT0CNvYP1TqY1pMA0cO3/e+PP1StNUVR8Nu+/uv4xm/4t/ma3/w1vPbqa+x2O37h//gFvu2v/zX+u2/77/jtX/fbRxdJPwrlzvEX/9JfpNgV/Ev/4r/Mhz/rw8zn8wNVRvgC/tE/9p/znd/1Hfzrv/vf4Mu+7Mt55eVX0LrjnXfe4ad/5qf59m//W/yjf/RDN0BAmD/3dfTHeQmugP3hP/Kf8tf/xl/nd/2r/xpf9k9/Oa+//hqTyZTNZsPHPvZRfvAf/H3+h+/4H/ihH/rf+wuHT7z1CX7TV/96vuKf+bV8yZd8Ka+//mk8fvwYJRVPnj7hh3/4H/FX/9pf4Xu+53997sJybL9xJfZeC154LA8L/PHCTviQNwd87NA2/eAaetB5V6UVvvBK4hw+97a/+77CHrqFsKP/46154wCMxFoOHCtae3jMYw3UY3biQ354CD06p2c/g2meEPjAGPtKRSClODj+8WsfN5Zlz2h8xeX8fHmw54/tFKgBhLHg/bf57f6ZuOclB2hqLRgMSiq8+bXleAjusBI+kFCLfV55F2HHRqh8GpulhPsdyxshhIOUIxDqKvhoyBfvJ9KnPM8PzTwRdG3LyfIElcRDRNBxNFNfXj/60Y/yyiuvkOf5cP9exhx+qJ0S2CL7iKAYhRWdC4RA63xO+g+wEC6/lUIIyLKMJM1AhAq1/oMsRR8HIXhmMPic3PtjOyxzPo/DvAw/ZNYKtpspu11vnnlDZebWd9s5TXO3U+M0LZnNNjx48IzJZEOcVCRJhxAd1lhaq6hMTlFP2O2mXK1z3n3y6t2mIBKnNvt0cdOn2UNLtqyZLAvm0y1zuSPfFMRPNercwLnFXim6jyraVUJZTNgVM3bFjLfKl6nqu30hCSyTfMdituGVR+8wm2zIkhWYJ1TFx3nttZhpvmExK5jmJUpYZKSGhsaj3a/n+ze/fjjWb3npu/jC059GSsX7Z5/gT37kX6A2rpN0Fl/zO1//G+R2y3q95qd+5Kf4P37xF7GTmXM6PZtRTB4j8jmbfI6dn9LNF9QTpyhrZw6QedPLZrbAJPd0/DSu43YFnGuij5aoJw3x+Ybkco29Fth1hN6l6DLHmtsIj8CSIKjJeEbKORnnJFwQqw0q2qKSGpm2kGrIwWQRXbagSZZ0yYI6PWMbv482XdCmC5p4iW3TPSDbBpMHZSXejRQ0/aDhncmA0IghVqodoLW1Cmxvb9ZXV6btgdkzuLjnyAgCEGYhApWBfvB+iEz/N9H/vYNI3Q/BgL5yf779OgvtCJQN64z8l4VwzMMyGSwfSePHL3ozyFw7GDbrYdhS91EwO9RDjXrQET1uiV7qiF7tUKcGkRtsIjFSYVqJriWmck767c5gdxq7bmFVIlY7xNUKeXmBuniKPP8UafuUNK+RKYhljjhdwKOHiLMTeLBAnswQyxy5SFGLmGj2AKEe3pGBF0M2+iwXyb5JPu4M3TaY4ZfDtlPYWfK/H3dMx22isJPmjqHQetb7HDsFcYK1S7BLrDe15NBRvw0g2QDIYA/JjoGxYZvpFWS6X2+dMvK5fXYJsBLhTS3NCtH18x6ODWaY9hr0dWCGuUXYNVgDJBiTAjmhI3u37kwTjX2A4dNclMfpso9oOYfFAjubORA2m2BnGcxT7DzBzmKYRzCXbkpfoO2se0C2Bd4KANkaB8M29Nta5LZG9H7J5K5A7Jwqy6keQ1PfkjAwhJi3yEcp6pUcPifDLnJYTjDTCUymmHSCSXJMNMHICZoMY3K0XtJ1MaZW1HfVh5YbYisZtaioIVIb0rxC2hI1qDc3yHKFLK+dg/71BeL6HHn1FHF1DnWJkDGoDCNTrMqGZVSGVSlGpNgow6QpYrZEnb6MeCNBzhOiWUyylEzPLPmJZrLQTGYd2bQhnzRkWUOaNKRxQ6LawPfYXkWmhg/E86VWR9RtyradU9Y5ZZX3bYQpu+2M7WbG4me/DXv+tIdnhnqjqXeCsnKATEczWjmji6Z00ZxOzegiN+loho4dQNPxDP3qHP3mAhvvFe4y0iSZA2Rx5qZhPW1J8oY4rUiST5BkFXFWk2Qt0cQSTSxx2o3u6jf3XtgssAImzL/k8/aPXRusudm378q7JdqhyumYu4/b6r+wvzb2+RseewzXwmN5dVTXdYOaLYx8+V6Sv55xvQwcRD/15wih0bFz3nUd437vbQAu7EuPLUaO/SZMY2g19EWApmmcqGDUDxgPPPjlY9YqYzHHsb/55RBYhs82PEYoSgh/G34v/X7+N+NrftEk7HsoLa+88fJBJ9ff4LEXICwwYQjVkKqGHczbZNJjU6rwRRJCDC+Cz5Dxbw+oMIeZPy6Mxx7o+J6OrYfHGt/LMYAx/s2xY43P7Y8Rdtz3nVf2HemR2W2YX+Njh8e4Lx0bPRg/67FM10OB4VrM/nf//Nf/c2h9aKY77qj7NL5nT97D63ANpEMJapi/ft07dxyDtHDf8bIxpu8HRaRpyuuvv+4MRA7KTljWwt/fzC9/LeG9+vXQFjx87tZaZByRTnLiOB3O7Svj3fWauq4PQF6knKOk+yqsY2Vs/M4dS77i0lq7CLcj+LkvizdNN8fvscu1Q99j4TMfP6PhA+HVcUf+Hpb7297V8bMZ1xmSm/tJKcnyhEzFiGhfOY+VaNZaiqJw9Z+vrKXk+vqax48fUxQFsz4aaHhNP/dzP8f73/c+JpMJKoooigJjDJvNBqUUi8ViCJJgvd8AbbDCmdxdXFxwerpEKQk4RcOucH6/4lghhSCKFVJFWKuJ4wQpnfLu6bPz3urqZuAUi+UAqFkHRbsuDqJmLnrfZr3CbLtg20Oz3W6Gtbd3moTQzGYbptMN09mGNC1I0holOyyGVsVUJqPqMopiQrnOqVcpurhnvGhKD8o4MNGMTluyZcV0XjCfbVlkGya7kuhphzi3cCmw1xHtdUSzSdmVDphtyxnbYkbd3APNhGGS7ZhNdswmW2aTLdN8y3xaMMnWw/b5dMckKxDClZGPPHqT/+kLfyPbdMqn/fyP8MZf/mN88I1PQ0g5RDyKhAJ1WN++Yz6dZ+o1zpYrlouGOpu4Kcm5VCe8ZV6iSySTSUGpBFWcslUxO5XQ5lPXqX3OJOua+HpF/PaO6J0S+bRFXmi4tNhridnEAyxrmzlNN+c+WVTEjlRckagVsdoSxSUyrhFJi5hYbOZhWU4bz2nVklbMXOeD3EUx7KM4UuJgmYdiNXt3My17ENRb+t0LzOyR5WPjg5b9aE2PuhElAmfWJ2gQsgXbIoTG0hKnMR/40Icx0tJa4TgVgtpAawSNceudEBgpni9a4P7Dc/++ZgTJtHEde2PBGPbmk3bvk8xPnXTBARq4Hzq6JBfaRbZ80Lmol2cd6szBMbd9vE3DzGCIMK1CNxLdAzK9s5h1h1k36KsCc7lBn1+hnz7DvPM23Tsfx3zq/4Drj0G1Yvb4hOkrD8lfPiN/+QHZy4+IXn4MD07hZI44nSIWOXKZoGYRaiYOArzcmt0G2iqmLRO6ft4WEr3R6E2LWdXYVUFbpWzOvpB5tuLJt7+K+bmY01/1jDfKfwf7D3+0f2SHAzrHtoXtLv/9SZKEt956i+vrNVpPcRFITkCcIMQp2JMBjGFPgCWWZb/9BKc6O8FFtwjae5J7VGQGoraHZNaZWSrlZJkv4Nge2+6d8NsVyq4RZoO0O4TeIUwBukKYGnSD0K2LhNhpdGcRZAgxwZh9tEcPxvbLbm5Uhp1NYZFjZ7lThi2kiyjiIx375TmwCJZnuHt83lQZ2Brnc2zTIrYNbGrnpH9bIrcFYlcgtxvEboMaHPpfI6troj7wg1L7CKlSVv22BmYx5sFj9MlD7Mkj7OIhZn6KmZygsyUmnWPiGUbN0DJHk2NsiukSdBth2hfUWwiLVC1StShqhK1Ruka0FbItEXWNqP28hrKEskZUNVStazeIZIBjVqbBcoKRDpJZmUIcE80l0VQSTQXxzBLnLdm0ZDZfM59vmc42TCY7JpOSPCvI0oosrkjj6g5A9mKKpE4r6iamriOqKqYqFNVWUW4k5UpQXlmKS0uzslSbjnajaXeGrrBURUSnI6x0Cj6rUp49+hp2i182VJlKXzHf/TSdmqHjGV3kgJqJPCCzRElHnPdQzE/DetsDs4okKRwoy2u3Pe+IJxZ5j9h3nEyj0esSvW2ozMtU+gFtlWCvV0w+8j1EVcOjx1M+/ddHJNMWtpof+eOf5OPf/zbtqqTbViSf/zKf8Rf+RWQ/gHD9Y2/xvV/2h+mK+l7VkrUuGJh3jB+qoEILkAOLjmAwfqwWO9YXH/cN/W+tdSaobduitX6hKKLjc/mgCb7t5reN/YCFbW2l1PC7Y8AozKfwd8cEKMfg2jFLq3HehOvj8/p8EkIMAde6rqOqKp49e3bD0ibc1/82hKF+e3i9/rrSNL3hB8+bhoYRSYGhfx/2Bf230eepjxbq8z7LsuF44763X/6pn/qp537+8E+gYLvroYV/vwHF6NuhUqCEpOv0URDlly2gIjUoYKTqM1cIhD2EIOOH748VmlRB3zZ0F+d2NBZr7EH0i4MO/wh+HYNgYzjxInl3jN4e+82xQn/jhRgOIYblYR+Eg1vyJry87dxjWDg+3zEweHA9Bwfbt/f30ONwZDUEFGMT5LAs+dGE8FzOAM6pCvxvx9fhX+Qoigb/e+ORW38d/mUbnjH+Zd5X5gRAJixn7jcCIdx8f9+HzvDHeRqCtXF+GgMu+p0bXRa9csnqoefjPhB9sAYhRG9d4zpHY4ATvi83lKhHIOOxcj9eN4A9VtH74ikEUnjQ5NRtKpIYHRzP7t9B8PlnHewZ/N8ItPajU04xKIJrCe8nBPs+jUe2bsJdO5xbCK9eO8w76KPMCte/GCp30/vq0nuQmKapUxgGfthOT08BpyILwZ7uOjCWtqp58s47vPnm+5xiLCjv0+n04ENgjKGsKuqqwuJMhJRSlGWNELaPJOrKK8L5xTRYqqYmzydM8mkP5wVSJSgV03UNCEG5m7INFGZebRaaaO62C+r6frXZdLrh7OycfLIljiuiqMFG0EYRjUiodUpZZVSbnItVzrsfWdwdlVHiFGZBQADxwJCeNA6aLQpm0w2LfM202RGdW8S5wV4J9FVM97MJxXbCrpyyK+dclq/yiXJGfY9yTgjNNC84mV8zm3yK2WTDdLJjmm97iLZfzrMCpfp3in05HSsy/bPVGtaTJX/11/xLtMq1hv/xP/UVnKdTird/gW46p04nNPmUJp1QpRPqNKdOJ1RpTh1nzwdU+pR0DUm1I1ldEn/8F5CbFWK1RT5pUOea7ByiKwHbBLNL0dWUtplTd0tq+4BavHSv9aMSFUpWpPElUmmQYKXAKoUVCiMjDDGGmE5P6bopu/Z1B2x27IHZOJACQYaO5+PlY2moEgzCaoRDWEjbIGiRNA6G9cugewfOTolp8ZHkFJYYYyOMTTDE2Ch3CrJY9EqyBBulWO+rbNjupk4IfqpncTfuz0OMFwFm1uIkqBowII1zhC/7IBFCAr1vsi6CFkQBdiNgI+E5O9wiN4cg7EGHOtV7cHbaDYBMnnWwBJZgjURXElMKB8i2BrNusaua9mpH/YkN9kev4NlT7LufxL77cbj8OHL3KaRpnPmJFMRnc1jmxA+XxKdT1KNTopdeQr30gOjzzogffIDo7POJTzLik4RoKp8TlgnaMqYuE9q3e2i2E+itRq8bzLrGXO3geoe9uIaLS8TlOVG1IqpXqOqqD9ZR9Y8saAvLCcUH/n3e+fjXDOe7+sFHYL6ON8SP9d8SiTZTtJ7TtbPBD5nu5mjdT2a/bMzc/c3MadspBwoynyS3+yNTBqKmVzsaUNveyX8EKgZxHyST4E0IbYPQBcKUvU+5GoyDYeiuh7cGOoswEts5SGt1hNUp2Axjc6w9pbUZd5oAT3DA6wEOhi1w06xfngfbh2Xr5vkLqhK2pYNhTwvER3eI7QaxWfeRLq9Ru2vk7hK5u0DtzpG7c6LCRb0UraslQ8UIBG2pxRIevoQ5fYx5/BC7eATzM/TkNUx2Qu0BWTRDixxDhrEZuovpmtiZyPtmi5/8wIEP4BpuwyCEQWAQtiGyJcJo0AahdW+y3bcvDVjtoLrt6zorE4zKMCR03B34QCpN/LAHQXlDPGkCQLZx03TLZHrJJC/I84IsKcmSijSufwkBmaCpJWUFdSGot4Jq3VGvWqrLiuq8orqoqS4bmq2hXLXO/HIrKMqc2k4xyQKbLDHJAhPnzrQycaaUujextOnCbU/n6NkcEzl3D0Ia4rQdoNgkazjJPt6Dsl49liUk2ZokeUKcVSRZTZx1xJOOKLcIec9NjpLeNehNhX5SsVtXbnlTo9cVZtPQbap+uabbVLTXBXrt9unWFaINVEhC0C4/BEqyNB+FZcTkNOUL/9QHyCeR62NOLf/0N2R89Nt3mLoDC9t/+BY//Vv+DPOv+CCbdy/5yJ//e3TF/T4TfJ8odB8UbvfLYR86bNcfU6/d1T8P+5d+PZxeJN3sUxxCwfBaQsjlU6gIC/um4X0cu7bxgPh4/3BAZgyxxtd1W/8+PFd47SHU8n3s8PxjoHfMldNtoocQpPq/hfAuzOswT8ZlQMp9sMHQXNdf+7i/eJ+S7670ngBbGC1i/KBu67j6BxGnyVB4tTF0uhv290oUFdjYChWACBwICzsmodnWMfo6LtDH970JBcP9xvczVvbcB9QAhPRmeofAwkO9MUAa38c4jaHkeF+jzQ0V1L7g7dvnYxB6cM2jezu2z3i/Yy92sKPDFAfHOLzX0Ab8oFISoLUze5MSoiimrqreRC+0v94fZ3yt/sUKHSTC8aAdYcU3zie/rLWG0ciBUhJrDeGphfC/D2DR6LhjyOrncRz374srPwBxFBNHsYNsWAzdUK78NcfDiLE9yOOxCuy+yuNWX4ujfbyj0X2lKRzIseYg4ISLCOjyZHjPhURGhwq+YX87IPkBdIlerXYUDFuBwEG3AbgZ8Kat/sEIJD4Uof+bFJJQ+elKk/vNOHeGBvLofXVQsy8P0eFHyuej1ponT57woQ996MZ7Yq0liWN+8sd/grPlkldefsV9lPoy40eHrLUsl8t9PSYl0+kUIcQwyvOxj32M9XrNl3zJl/TH1oieu7dtRFWdststhvl6PWO9doECLi9iNhunNjO3mumBEIbpdMvJ6QXT6Zo0LYiSBpsKuljSyJjGJtRNSrXL2F6nXJ0/wBaPbj0m4NQBjwR8BvAQorOO9LSHZrOdU5rlK+Z2g7rQiEuwV5LuOqb+eM7uH8/YlU5l9rR4k4+WM5r2eaDZjpPFJbN8y7RXmjnFmVOZTSc7ZpMNk6xEiBFslWGAkJsNh3CkTrhKAXD9Hf9b2T/Ptx++Thsdmhyff/av5Ps/+1cevfa0qciakpPdmqpp0Y1i0e34gP0Uc10x05p4ZeieKdqLhPpCcf2Optqk7HYZ15uY67Wi1e/HMMeKe0w9/XtpcC+YxpkAdmJwSeW5DgY0GZpb8n+g435uAlgWkHPvc+u+VosHZdY45RV9fWwcEHPgye7vwf8sWNNDmNS+46iOQ7HBBDMGItmvy+fz/TT+SNyTZGKJMoHMQMRA5NizNtYFuWkt1NYByTWwFtiVhPIww25tJiqLPOtQjzXqs3pgFirMTt1cnBg4MYiZ60whQO/AbC1m02FXLea6xFzusBcr2p87h2dPEU8+ibr4BHL3CZS+Roh9fS+tRShJcjpFnc2RJxOiB3PE2Qnycx8h/5kz1MP3o87mRKcT1DIlXsREs+dTRplO0FYJdRmzvU5oy5huC91GYzYNdlXDpoKrDVyu4PwSeX2JKC+JqmtUfU1U72GZe2QCYwVGLDHylNYu6FjSipepxGfR2iVaPKKVp2hxhmaJFnO0nLoH+PHRRf6i5br+DWy7L+qB2YwbgAyOm1jGDIBMRh1WalDrHpCpAJLdVS4lhO+o6VzwiE5DXe7NfLWAToGO9oEnNPuAFBqwAssUew94Ge5nBpxpWNaIeQfLDjHXiEWJmBe9eqz35zVT2EWMnSUwS5zp8/OmrkVsV8jtCvnxa8R2RVRuEJtrZLFBbq9hs0IVa8T6GrG9RhUbVLGB3Qr0YeAn337qtEaePoBHr2BPX8U8eAXzvg+ipw9pslNMssTGC4ycosUES4YxCcbEmC7Ctj3o9vDLTxpndXc92u5MJ/rnxDAo+WJJ9gME4Oq640mYBklBkm1Js4IkLUjygjiryac7N4g1L5jMSyazhnxak09a0qwjSzuSpCOOugPn/BEdMe2LA7IO2saJ4ZrCUG8N9bpzEOyqpbyonaP+y5Z63Tnzyo2mXFuKakInFoMPMmdGOccmC0yyRMez3t9YD8qyBWY2R79/ju0VZDLSe5PKfsry9mDdmVhuSNInJGnVw7SOeKKJsheENMZitjV226A/1VBvasyuwWzqHpLVmG09QLNuVaI3tVOOrUqaVYFt9dCW9v1NFbtoo/kyJpkr8nlEtohIH0ekH1Ck8znp4pR0rsiWMek8Il2ofh6RzhRCnh0vL0IweRARzwV1ua/jtz/zDpc/9nG22y02CB5wWxr3JUORzLEB/jGAue34x/ow435X2Cf3Yo4QEL3XdExV5s/pzxOq8o799hhMOwamwns9Bh5vS/43twmfbgOWobubruuG9fBawvwL8zi8x7HwJLyGY/dyGzAc55G/n8GdUvAsQnc74bFD6HYsyNt96T0BtvAhjWGEv1B/YwCm9zsilMRqQyQVne4OoIZP2jgH3U7d1iGtRPsPmXU3r43Zq7ACtU6Ymf46QzIZXr9/GINdteiVNzjn385hOiAOTRPdOft78p0CTzrBEfzhIQcZLRVpllOWJZ3uDvLvtsrCF6ZjL+Oxewl/KyQYqz3nOHJcvw3nGP6I1H1MwsdQZryvn3uQFea/VyMJ9p3Q/f0f3sfBuYZrD5RkFoR1klHfYfWd2hC+jp+bX3fXZjG90kjK6EY5CfPeb4+UU49ZGF44PTzrEM7tHS/uK8XDiiZ8dscqx5vP3w7PTXe6B0n0wNE7vwcVRQNcGxSCPZDy9xWOoIZKvzDv/PWM/YON8zOsjMfEPywDYcUnA2d3/m9jufO4ohzDt/A69s8rWDf+Wtw7bewewFpriJO4vz6nnvP3oqIgYldft4Rd0vAj4VWOsA8TPVyTuPkeu0huzl3wZ33WZ934CPiHVDctb77//fzcP/7HLE/POJ1OXJ3YvzMPHjwY8qRpmgGCbjZbNpuY7TalKE4oit/CajXjv/9bDyiLUzbbGdfXE3bbGVV13IG+T0lSM51teO31j5PnO9SkxaagY+HUZsQ0OqEpE+rrlGdXE558bNb7WbolKZxvsw8I5AND9KAjPanJ5iXTxY75pIdmckOyaRGXAn0laVcpxcWM3Sdn7Mo5u3LG2+VjfqGc09wTEVQKzSTfcrq8GKDZLHfgbDrZMsm2zGc7ZvmOPCvxptQOZod1ku+b9nWwPWx8hPXEuM4e5sKVUfccfeMDhJU0laJpUto2pusS6iJ2YpDCQi2ghPTJBY9+4kdRFdjCYAswTUJX5dRtTmemPIs/DWNTMLC1M962j1yxkuLwg3Qs+Y6boTcNpIdk/bKxbnvvl29wLC7dJNBYNNIDMtXnmfVgS+7nQuGAWe+DC4L5+Lr8N1a7azgAZOYoMJOidKo5UaFEjRIVUrUI0WGFhTjGRjE6SrBRglEJJkoxKsX061ZFWBU5uPYcAOx2cnXLzqmBxELSgzvpgL4wPSircFH41gJ7LTBr6dy+3Uj7gSWf5LIHY6/fBGXyxCBODWJhYWb76HwGi8FuNXbdYa4r7FUJF2vssyvMj51jn72DefpJou0nic0FsHXA3lpE/00ZBl8jgTydoU56YPZoivzwKeLRZ6Me/dOosyXybIpaTohOUtQ8Ipo8nzTDdJK2jKmKhPayN8PcWrp1S3td015sac/XtOdXdE8vaN95F3P+BIoLZHlFt3qXmWyIhSHLMiaTCXGSkE8f0rEcptYsqfSHae0jOvuAjlM6lmjhQImRE2e+dl/yykPNXoHp3y//XgHEYFNFF52Bsoiocu+tklgl97Ds1uQA2UH3TwfnLW2vHOMQhvl9umC5dxfoUsPeZ5eztZbS+++qQFSIrIEzXJnyMGwR9T7HIsw0wc4S7NyZYtr5BDubuWniHajHQHbU4jpMotwit2vE9dvITzpYJrbXyN3GzbcrZLGGzQZR1siigqrCFjVWg4xyiHJnjihTVDpDixjmDzGzlzGTD9OeLLAvLbDR1EXNlZnz50WMJQarsEa6yNFG7CHX+KJ3/fRLkWyHsA1CV4iuROoKaWpnKttVCNOv6xppm+Fv0tRIPOhpiaeaeKqJJpZ0rpmcGvITw+REkOaYod4AAQAASURBVC1dkJVsJkgngiSzJCnEsfklBWR1CesBkGnqtYtiWV22lM8qqqs9IKvWHdVGsCsz56R/UIqd9vP5gYrMrc8x+QK9dOs2moA3rxz7GxugWOCXLF2Tpu8SZ/UekOUadTuHPP7IWo3e1JhtQ/duTbNxsMxsG8rzM5pVTlS/haqfoNc1dtdgd00Pzhps0RyUqaHNKC3xTBBPJfFMMpm5efy6JJ0p4tmUZDYnmUmSHoili8gBs0VEMnuxrr/RlnrjnsfqExX1RtOsO+qtpl53fPbXPSad7830iouWzbMSgfsmhM7uj0GQY2nc7x0ruvy3Bg5VoWO/X/5Yd/UdfAp/H/Yr/eD2L0UK+0khAwj71WM3M+HybbAr7Gv7/PLbx78LIdb4GOP9b4NW4+P4fAr72b4PGDIBKzK6/Mtcf6v6PoQtD/rn4bIVAnv2ELFdH/QFw/7q+B7Cax/fi5Ry8Ivun3X4d9+n8v6xx0DwRdN7AmxjwjcuIMfUOdA3AfsGmRQSa/ThDbgfIYSgM73SiD1xVD04MNY1FIQQA9AaF4JxAQjTQWc/gAwDseghmr9gK/bqJ38PR8EYoK1T6CjhIzC6/WrdUtXNjQoivObxtY0L+HjfY78Z7+9v7BgYE+Kwk3jb8cZ/D5WLt+3jfx+qNqSU1E1NHDm5vwMD3QEA9Mffn991mo5R8BvPj/3LNa5khdgriNw13VQO7v926HPMwxN3XA0i7hVbXsXJAdzx5xuPpIT5Fd5ruH0Mig/Vkg4GJkniYN8teR9eh5TqoG/or8t/8EKYNa6Ixs/7GAgLoei4LByD3m4D7h0Lto+vw1+r38eDrDDizRhgHT0Wh2XZgZNDhdFwfHEsH+2gkg3zIxyd0VqzWq2G/PA+G3zQAx/eu+s6OqNp+3rAX9NyuQSgbX24cffh/eCHPpPJZELZwnozZ72acXWV8uRJhDGP2W4XrK5zdrsl2+2czWaG1nepzSyz2Y6zBxumiyfEsxZyg0kFbSSpiWi0oq4TqrVifRlxdf4Adnc56sZ1qB6Ber8hPmtIljX5omQ63zGfbljmK+ZqQ7qrESuBvo4pdzlFOWd36VRmRbngE+Vr7MoZbXcPNJMd03zH2fIZ0+wQms2nBVm6YjrZMMm3zCYNg4Kpf/6HfhIdO9HaAPLgnXDbJW0b07YRbdsDsDahaRVdl9I2EW0bo3VKXUf9vv3UxYfrbUzbJnT9ctPEPVAbmT/JcBKugy6hlg/4pPzKvS8kyR78+P1tcAzYd/q0BRvCKNODs77T3ZsD7Tv+YZnvAZOfvJLMCHf8ft2KFIS4vcs1HNI4M0xqlK1QVChKIlEQiy2J2BCLa2KxIpOXxGKLEjWRbEFIDBJDhCZGi4RGLqnlGU20oFantGJJq+Z0ckorz6hk2oMyuc+ne1P4LXuOhpWwTvyTWETszDBdfAOLbUBUYD0su1JQ9QTyyFkPzpwbB8bePPRFJk6MmxYWMbPIiUXmEOWCqlzDtsVeltjLLeJyDU/PUb9wjvz7n0JefQLVPEOKFUIUCOGU0k3T3Pi+AthEIU9nRA/mxF84xy4eIR58JuLRA9TDM+SDBfJkijzJUYuEaB6hsudrjOpW0ZYx5SahfRrTVjHd1tKtO7pVjb4u0edruotr9LMLzNNncH2BKC5gdw67CyguMW21VwgbgREnaPEAEb+BVa9h1edi1UuY6DGcPmCXnIFaQDQHlWNVwlFzymNlJYRW2uxBmeUA8roHKPp3WOwVZzfMEsN1iR03y71KrGYfZdV0CN0hbIs0TQ9XnJN4yY7i7CXsq284R/pmjfyHfxVZvIMQNVDgHdxDP5cVch7BPMbOU8Qig3mOXcxhtoT5CXZ+gp0tMTM/f4ydLjHzJcS31Nk+OwaYBzQatlvk+Q758XcRRYkoK2TVQNlA3SAajWg1tAbbGoTu6xojsSJ2frpUjpUZnTrBqs90ICzKsHEGJzGcvqAt3bHkA3SMk2AYWEAZRGQQUiNE15uZNw5u6RLR7RDNBllvEOU1YnuJ3Jwj2i1SVwhdga6Rtiaidea0PTyj/3ukNNE8Jl5OUIsMOU9R85ToJCN/lJE/TMlPY7LThGwRkc0lyUSS5OKXFJBpDU0NdQUbD8g2muraA7KG6rweFGTVuqPZGKqtoKgmNNY54zc9ENPxHBvPAxPLhXPOHy+cgmw+R7/pAJkQhiiAYoO/sbGz/qwlzq5J0nd6J/3O/1iUmxdyBQigi8YBsmc11bqi21R79dimGqCY2TZ068r5KlvXsGuxRYutusEH9b69Cp968h9QdF9BviyZnK754Gd/M49f+XmSx4p0rohnE5LZjGSuSGaKZO4UZ8lMkswVUfZiZVu3pgeWHRcfKYflZuMAWbl2y+V14+DZVlOtWtqte8bN9ohLnqBN/3PfccFXf8tnMn85pThv+a7f//POO4HY+xILfZvdNmAepnGf6Vg/KbSaCZnEbSq2Qbhxh4IrVIr5fpJf/idRr4X94LBPFU4hiPTXMragu69/Hv7tBqMIfuunsaJsvP+4fzfuL477huP8PYB7csHupe9Cx5/rVpufZfLub3RBZ4J9rbXw/g9i/txfhw9+CL1eYf6934f63757cN00vo/w3u+DYeN+fngvWmvquiZJksFf3rjv+7zpPQc58Dcy9lsWFm6/D3IPeCT7kf7QZMrvOzxMKQKH/X1GGC+fZHhxrT5U24zVaocdpuOZPi7Y4Qs2psLDPXE3iffO3odCHL40QcEe/3aQ8HoZo5JgGX7vleC9ddKg3huDmvtfOG8a5wu1Ux0I99+taWxSGJ7TK3r8Nl8xSLmPfugt8Hz+/vNf/8/hLO1ugkV3f+7ajpH20E7ez31ehC/gsQrBB1YYqHoAvHwf9FjSWmOsIssy3ve+99P0Ckh/LXdVhgcVDYcfqrCcjkn+IQSURHHCZDZDxlFwXS1N07C9WtE0++g8URQ5Hxujchbecwgoj5m43Qapw/3Cyjp8Dv6+jsG3XhCER2FG783LfXn35fvY+Y+90zfKuWcC4T5hGe+f9VBv9fsPdcGwq4e9h8/t5Zcesdls+MEf/EFmsxlf8iVfwltvvUUcxzx79owkScgmOdN8gjGGsq6oy4o0zbCcUuwW7HZLyvKUqjphs5mzXk/ZbJy55no9pSjuVpvFcct8sWV6UhDPa+RMO7VZKmlkRGMVdRvT7CK6S4U+ly6A3V3fCwU8tEQPNNGpU5rli5LZbMdsumGZXbNQW7K6RBXQrJNBXeamw+W2u3vo10GzrQNm+db5NMv3vszydM1i6paztAQEbato2oiuTWg7N3VNRNsmNDqma/eKsAPoNYCtZATEkh6g7bfdZR7rys9YbxHMbbAureuERRYZ0XfMhIPkUmKldKN1Qt4ssOPkoZhXlA3KsvE2c4sMxMMySxSBjCwWgzYdXlVmiYEYe4fp0P5oHYlYk4hrEnFNJi/J5AW5PCdX5+TyXSbyKZm6IGKLIaI2c1pOaFjQ2IWbs6Syp1TqEbV4QC1PaOScTs7oZI5R6SFcHCDjvZcILwrMpIXUIiI3sAEWoRkCJ9hCYFYStnIPD+5Kyg5wTJ11yDONPDWIRQ/JpgYmlngiSDJBmsEsFSxkxImWPELxsoh4I5G8byp57UTy4BROTyEfAZvP//zPp+u6oY4GBngWL6bYRYY6nTnH7ssc8fDMTQ9OkKdz1OkUucxRy4RoppDPGe2wqxVt1Ztf+nmvLNOrmu6qwFzvMOfXmPNL7LNz5PoS1Vwjigvs5hlRswMTAzmdztH2dTreRPMKnXiM5gwjTzFygZVTrJpgoxRUAlEPUaPn7HxaG5g30qsjOUI5+0mKvQ+z5wkwAU6FmlpINCLtsGmHmBvsSQynmTONjIBnFp5aZLHjpX/w36DsJam6RnJFpCqcaqxCqRoh3KABMsJOFuh0hs7mtJMTusVj1q9/Hldf+HXuJoSADMQ7P434ib8D84fY6RlMTmFygs0WkM4hmTkTRZ8XIRALJ7+96aDuegim+6AY1imYjVer9rarL+o06rmT3b//rqEQLDP623jd18caqTqErFHCKb2E2SG7LaJZIatLZHmJ3D5DXL+LuPwk8uId5NUFdM2NfkPY4TtomyhJvMhQy4x4kaOWOdEiIz7JyB6m5A9TJg8zJqfJoDRKJ5I0FyQZxLE98D22n+6reA6TB2RNBfXOA7K9iaUHZNVVS7lqqdfagZetYFfmtHbe+yDrfYz16jHve8yBsr35pd/HRrnzw5Y1I39jgbllAM6StCLJSuK0dqq7vCPOX7ir6nyJbZ2/Mb3em1J6H2R6U9Gt3NyZWtaYTTP8ji5sBwuMSbA2JZlOSeYZ6Twjnacks5hkHpMuIrKFA2HZQpItBekCsoUhnRuypSZbdETpCyr/KkOzM7QbTdM/s6Y3f3UQTO9NZdcd1bqlWneU142DnEV30DcOy2bo1ik0h7TWDgPb43QMfAkpyE9jqut2CI4FeyWT1pq2bVmtXB9l3H87dg5vKTQ24fNTGBQgvK9xYINx/+OYud8YdoEb0O+6blA1lWV56/Xedg9+HkWRC1gWwD/vbD/sM/tn4YMeeJcv42s7dt3hccaChcGtzei3IbMZg8lQlDLuw1qrsCJH25Smi9E2BTmhMwllJdiVFm1TDBnIKYgJXfrldNmv9meHCOLyj5O230oXJ+g0hSxHpxnmm/4ofMaH3a7GQFNz+n/6FSS7zY3+dtjv831Pv0/Yf03TdLhPpdQA0HzwCaUUu92OpmkOAlL43/zkT/7kcz97eI+A7dU3X7nhZN4/vIOCLnqTy+DBxCpyruj1XkYY+lgTIzvhkPpiPbBxnQMs+6h84jCSqE/HOuHHiGzXdTdNQe1NE1b/tzH5He8b0tExlAoLfHhNUkpM25GmKUop6qYeolQK1+sH4x0UC+eexro8u+8x3oCDvTInfDZDPombxxrn2bG8iOP44L5CuDa83AHQSNOU3/rP/hacuaY5+vIP5+hpwFjaOjbr9OcPQU8o7d1X+m1/TU5FZ8y+3LhG02H5CJVyFlfpvfraayAO1Zx+Pn62Y+jmjxfuF1ZuY7DmDm0QQhGlKflsNoyKucqkxRhNURRU2+KAECq5/1gdq6DHKtThPkdgcvy8xx+t8Jkc+6iNK8HxMx7vO1T+UuLDDIyv2e83hq+Hx2LwRGd7M+UxeB1+FwC2MQCFfdAwIQQWzaOHj9C9X7TdbkeWLSmqBdvNjOvrnNV6Rlksub6esN3OWa2m7Aa12e2Om4WwTKYlk5OC9KRGzBrkTNIlgi6SNELRdhFNpWhXCnsu4al1ERPvSGJuUQ8MyVlHflozWdbMFyXL+ZbTyYal2jGjoV1t2awzNrsJRTVnWzhYVpQztj086/Td8EXJjjwtyNJiH80rbohVQyRalNQoOqSwWCMPwJYHYE0IvAIg1t0F7J4Xeg1zg5S6n5zjZ+kq1+A37rDWCIwRaCsw9NHxvEJlUKrI0bbn6GB6OGYs0mokHZHoiGRLohqSuCFRNXlSkSQNcdKhVEsUaYQEa52j/Y927+OpfqU3RRM86C5Jm4a6TinLnLLKsPa+6+mI1BVKXpLKC+ack+GAWcolsdwSyQalDAKNJqYTSxqWNGbu5nZJzYJanFGLExq1oJMzjEwOAdkYlj2vuiwA5M8FzFQPy2T/TI1FNGArgd0I7Ea6KJhesXJH/0eeOCf+HpLJpUHMjYNkuUHEGiEbBBWi20FxhV1fwLNzxPlT1PnbiO3bxPIaIVdIuUbKlg9+8IN89//8vc+TAVgsBZoVDWtaVjSsbMsT3fBf/5W/jD1bwHKKOp0hFylyEaFmChk/JyyrogGWtWVCW0Z0W9Cbjm7VYa4auosac1mjn9XYZw32uoZdhyg77NZgtharZxizwNg5Ri7RYoaVk16B1CsLo7gHY+LQr53i+UHW2KT5AJL1Kx6ySEBKNz1PEvSArERmFSKtEZMG5gYWIKYSO4sgjzFZgk0TTJJAHLlzHIM9x1IBtHD6Y99Jl84xkwVdNsOkE2ycYeIEG8V7Nebz1CsA/zvO3PjF+cTzJaudCss0Tr0lNEjjYiIogej9F1qlen9wCqTCuB2wveP8G3l0FySTFpV2yKhByhpFr97TO0S7RTUbKK+RxRVicwXrc8T1M7h6hrx8F3F5Abo7aM+MVQ0HbYpIoWYJapGhFhlRD8iiRcrkpYz8Yc7kQUJ+kpCdxGTziHQmSXNJmkKc/BIDshLqXkFWbTrq69apyC4aimcV9VVLszE0W029MRRbKIrJAMi6aIaO5nsgdgDIFoHpZe+0P8pQcXeHeqz3Q+ahWVoRZyVJ2pul5hqVvFgB9OaVXi3mnPA3dKvWTWuN3li6laVbQbcWtCtBey1o1xF6G2O6dIBixqRYmwIx8VSRLRXpXJAtLekc8qUhWxiyZUe+bPt5Q7asmZxU5Ccl+bIkPylQ0YsBsmqbUl5PKFc55fWE4nri1vtt9VbzcP7naHeWdqOpt3p4fu3WoAPfAL58jvu6vv8bDtKH5nrH+hV+n7E/atgDoHEaQ+Sw7R+aVHoo1jR7EF3XNefn54PP5rFia5w89DgG1uAm1D52fbf1W30am2OG+Rped9M0bLfbW6/1WBozkjASZgjY/HII2ELIFvbfxil8ju64IGRGZxKQExcMhQwrciw52rrgKH67IQXh93PBU7TMMOkUneSYNMOkOSZNMWnqvm9pApnaDyCl7KcsXB/9LVzPuC+g/I00+x2/iexnf/LGoMZYFDVmOn5bHMfDOxDH8cBa4jgeLNXW6zXGuIijWZYdKA5/+qd/+oWu9z2ZiB4DTuGyv1EV7WWhvrB4iHRbxzjMoPA8xhgi5cwuw9Z1WNmEL3b429sg23j5WKf7rpf/tvv2D/y2890GF4wxoCR11yKNdioe//LA4O9ZSuf0HCkweu//xBy91kMwop3Hd4TBKRXs2In/aPD2yLUegyK+Yj0W7SUEOF6C50Pluv31jTC7wOg5Kgj28/kVAjS/3ZeF0O7b38P+oxCaM+/VduHH6dgzE0Kie/Nk51Pr0KR1yOcj5x1XCmNfhWOwFQJEIRVYn4f24HrDcxtj6IwmVgpr/P3KQSUYVkTjexznUQgpbwOroR8Ev8+xsn8MgB0D2ONzDSNNR4r2+JpuTz2cFmNM1w+uGxzYxw8IjK8T6jpjs5lTbGdst4shguYnJ5/J2+VD7LlEvWNoN3dDpyjumJyUnH36NdFJC1MwuaWLJa1StEbRtopup5wF1NMEfia5W20WWdRpS/x6SxYozRbTDctsxUxtmJgKaQzbcs5mt2S7W7Ap5hQXM55Wj/h4/SZlk2PM3Z8EgUEJjRSaVBRunEOD1QLTKXSrsFo5x/Y2YsuCrZ0HR3ge6LUnWkKaHnpphDAI4TpTKnL7utdBYK3oR5gl78XhszG9VaRSIOO93y0lnS+xCBe0QgqsUE5pdmeyJKoh7cFinpZMsoJJWjCb7JhPC+bTNYvphpPlljRpiOKWKBI0Tcx2N2G3m1AUM3a7KevtlF/86Pt5tnqEUoZIana7CbticqdZ8AUPHazNS2bzgkcvX2CWkiSreSyuSKIaIVour8555913nCWpTbEs0HaB0Au2+k2u7S+jEUtaueg7ydxUk3mH6xHPCcr6JOzevNXA/cBM9B14i7A9WGnAlgK7FVDLPSjzkxU476rBUSYGeapRJx3yDQ/JLDLXiKRDyMaVtW6HKq5Jd8/Irj9J9u7bpKsnJNeXKFYkak2k1kjR0XQdRhi0EbTWUJmWFstOWXQUYyPlgqAkwbcIiE5m8OopP8M1KxqubcvTruFJ13JhGq5oWdNQyJZKtbRR41R2o2whgunv/OUHm9vSmV4Wzzwwi+k2km4l6a4l3aWkuZB0TxTtk4ju3RizimCnMNsEdomDP8Gn2AEwsX/eUbCsBOS4KI3htudJ1u4hWWsYzJoxCOH8YiIECAUyYh/ZUgSA9rZzVRCvIN4gsgqyFrUAeRrDMoFZis1jbJ5ishibxNg4dso4obByhhazPeh53uSBn++33VY/SSCFqy/+6luOw748V9ws49ruTb0JIkomApqniGcfRTQbqLeIZovoSoSuEaYC2yGyGJFniDyFSYbIc8hzyHLIJpDmEOeYyMFRI1KMTTE6xuopMH1uhiekQSYdKqqR0inHpHGATHZbZL1GVivEdgXrc+T2Erm6QFw+wZ6/i714dtA3G7cbfbvQty+0NU4xtpwQvZwTfeYbRIuc5DQjf5w79diDlOwkJj+Jnc+qmSKdCNJUPCcga/rp9rQ3sbSsd5ZqUB61lJcN5XlDeV5RXTROWbZ2pnrVTlGUExr25pWkZ9hkifa+x3rn/cabXGYL9MIryNIhemVoUplmDUnWBuoxryh7lyT92N5vW65f3LyyNHQrQ/fEUK/ZQ7BrRbuSdOuY9jqmvY6or1LaVUJzndNc57TXOe124r5FAxxLgAipNNnCDT7mJwWTk4JsWbI4KchfL4btHoa59bWbL0uyZYkc1533pHKVUK4S1u/EPP25M6pVRLWWVCtBvZHUG6hWhmrtfMp1haXdaupNjS4NwnZIeYEQNUJIfuEX/gRt+9Jw/Jdf/qu88cb1Dfg1TmH/5HAAfj/gHQKHY8ca91HHA+jhPrddw21pDOi01iRJ4qw3ynJwfu8ByF0p7E95WDeGZmMxx7i/Oj7ebVzgYBA9EC74v/tzh6au7yU5A4MMoRYIOcVGcxo1xZDRRjMsOUJNQEywYoLolV+WHGsmaFJ0MsGkkx56ZZgs78GXg182TSATx+FWxvPBr3sNFyzHbeePJG2grBFVDbXBrh84dwcVUAvU5gcR249jiwLKAlPsoNhhv/53wpvv7zPOQF0hPv6Rod8Y+kjzz9+Xv9CKDfaim1AIlCTJ0E/3+3qg6S0A7noPnie9JwXbS68/PuhMh4dwZlZ7B+zAEFXPO+MWQvQNadtLR50DC2MdQVIhHJCuc2xsL+vsVWzatMieBvlradt2IM4hhAnloAe+RYLrHsMNuL0DfwhqDl9S/6DCfAkru2MmdceO7/fpm5ROwWOdYs0KMbQhrbWBu51DiGOMGdiC7I/hB/zFMPJvD5qj/pwh0bDWDmZ1d8GW8QiCp/H7EQg1HFYIQZqmfPXX/GaUugntxmooY0zgp9tdizEG0zuq92UpfJbh8/EOxY13KNz7ZXL77Ndd4AN3+x6ked9c/sVuO/dyvvHGGxhufpD8Cz6WXY+f7fj53/lxE9I5wBYKFcdOwdYTdwcUHa1fr9dsix2JUMheoYi1xPH+HbgNroVBCeDmx3L8+/saAsfu69g+t933wfH7Mij8dr9tKK97Z/SHHSwbwLXhyH2ZEOx2U7a7Odvtwplrbhdst7M+guZ8AGk3/GSN05mFx5bpZE2alZgpmEzQxopORHRdhK0EXNreHIj71Wa5IZ53RHlHkrUkcUOqGmLREFkNWqBtTGsymi6laVMandKZCG1itFUY7y/rruQhxYGDe7/N+xiy+6nPVyE1UriOrxDeCdH+kNYK5+rLPLcd33MnKTVx3BLFfYQyv9xPUeTWRa+CMcIpJoyN6YjpTB+koU2p24yqzdH3wEXv+22W75hNdkyy3T7CaL5lNu2DJaQb5tMaKV090HYxZTGjKKdst1N2u9yBs2LCbjdzMK2YOmC2m9xf1gAVdSwXG7LMKdqSpCOKNVI5GGEBYyWdiWm7iKrOKOqcdTPr/X9xHJD5Blbcrwv25eO5wKUNfkcPFZ7jd9K4r5MGGoEtgWoEynSwHFYlkUWeaKckWxjkxCByjUw7pGyQtiTqtsTlNfH2Gfn1p5itPsZEPyGWaxK1JpYrIrHGR7YcvhLB4FBYN7mozQzQzlqDFdDZiGaxoD15QHu6RJ8t0adLzMsP4aUz5MMF6kFOdJoSnSiipSSa2T0nuitnDc5HWagsK2LaTUx7ndBexc7p/7OY7llM+05M+ySCjeidrVs392qmHsgNgEzZETCTPUwLQRr31yf7TAKjXTRXNKIPK+uaHo7SWdEXtEGZxKGacTyJGqIKkTaItHPBGRILM4mdKMgV5BFkEaQRxAoSGShL+/P4cz1vtWRHE0fm3LM9iKp74LPNt8WMRpVrZL1F1juiZkdUrVHFNfHugri8QhVXRMUlcbWC4hq6EkWLiCPMfIl8+ArN7IR3Tj9A9wW/wUGxq6fIq3cgmWOTGUZNsWLi1Ao6wXQx9j5T+FESyqCSFhm1SFEjKVG2QOgdsnGATFQrxO4KubmE9QVi9RQuniEu3kFfXAwROcOOc9ix9e12KSUGi5wlDpAtM9QiRy1S4pOcyaOMyaOU7DQlP4vJlr2CbCJJJoI0EySJ7YHYGJK9NwVZXVqawlD1vqrK65bisqZ4VjkfZBcNzUZTbfq/byVlNaUZTCyXmKRXhyULbLrcK8eSZR/ZcjFEtRRpPKjGDqJVjtVjvaP+JK2J07rf3hJl+oUtdduVor2OevCV0F6nNKuU9jp19c0q7rcnwT7J8DfTHi9TKu7Il0UAv3pYtizITzZMTjdMTrZMTnf9Nq8cq8iXNdn8xZzNG00PwCz1xvmMq9eaetNSb1qadUO9ranXrXtm67aPPtrRbp2KzOibVhewBzt+e9gX8oKSsK/sl+v6FT71qW+gaR6zWPwQr776F1DqpnujMB0TZIz7sH6bnzwo8CkUI/j3bezmKYR4Y+FD2Mfzyffxxv2e0NeaMYa2dW5sNpvNsHxbH8Jfn1eweUGG399fV9i3HlvRhSaRx/qt4+dlrMLYDE2KJaczCZacVsd0JkHblM4ktF0McuImMdkvpzPI+2kyhXwK+QTyDLIM8hSypIdc9r2Br1sCsb/XJKoGWTeIukYOU4mqS2RToeoC2VTIuur/ViGq/bqoSkRVohq/XKGamu2zp2yePaVdr1DG9ZK977Qm/2epZv8WIIjXf5qk+BsHZdcrz8Sbn47+s38NPutz4fqS/N/9fWQ/8D0kSUJd16RpOvSvfRmx1tK27YFIZsynus75RTw7O6NpGpIkQUo5gLf1eg049WSWZQghhsAIP/VTP/Vi+fteANurb74ymLeNC61UvcNj60yxgKFiV0o5xYMVPcgwGGPpOn3wArsPqzk0NZMg+7DSjqr1+w/9b5exx4DAAGiOmASEtPN54dr478cqiWOmpbcdLzzWfddwF7gYoNqRvx2rXPz2Y4M5t5FbIXtQ4TtQvoLCL++vLYRrzhfYYZ4kScJv++1f24OvQ6eP4QfgNsXVMEJjXCd//HE52Me6jqcQhyF6tW5xwSgCc2T2HzDd6QEQC+XyVxv3Mr755vvQ9viHdwzcwmd23wjQsB99X8YygGaEM091Ptji4Tdt26B1x3a7pSxLROcpv3QmluKm/7fb8nScd8f8Gt5VNsP7vrUcBc82/E14/GMjTuHvx/u740HbZuy2c3a7BbvNzM23M7ZbD8ycAs35Nru91alUx2y2IZ9syfOCKG5QUvflBp7OXmatFrC1sLawss8VMWwIoEivqjEKbN8Yld68kN7EkNHUb3suaGbBGIQxwzLGYrV1jrM9QHvhL8DxpFRH1MOuAXTFLZHqiBMHwqLhb22/r4Nhh2DMrau4detRS5zoftnQ6Ziinu59vFVTdsWMXTVz5qy9KeuunGLs3R3HSDV7WDZEGd0Nvt8m3idcvmM6aRAC2lZSlFOKXX/eXkm2200piinb3bRXn03Z7aY07f1+zNK0ZjrdMZ2UTKc7ZtMd02nBbFoQxZpv/zu/0e0ocB+9GwCe280ulUXFGhNLbCRd5Mra/+ieJO0eRBjujhI7pL5AaesEHR6UhYBsPPnbmGvkwiBmBpH1kEw1RLZykKy6Its+ZbL+JLP2HfLoHVLxLolcM4m3YFbIaN9Rl0LQGUljU5Azmi7pTSEmaJvRGWcm0ZkUY3M668wkOpOhZYpZThBnmfOX9SBBnMWIlxLkSxHyoSR6ZFFnhvhUEy86onn7XB1Za8QeklUelkW064RuFTtYdhHTXSTYywguEuSlIm4EcQNRBap2vMlaaI2gNdAYWO0qjIqdwmsMx0KV4YuoynoFqVMbujJhVW9mGUWBqq2fe0B7FJQZd4wIFz01kfvf+o5G0h8jGR3PTyEg89Pz3ouFg4iPzwPKPJAkOJ8EhAHTugADbYlst6h6Q1ytUbtrkuIatXlGsrlAbq9Jqh1y55RZqthguxaDwM4ewGyBPnlENz9Dz07dNDlBZwt0MkcnM4yc0skcTYYxGVrH6DaGe82+D5OMNTJukapFigpJiTQFstsh2jWiWiNKB8jM6inR5gp7/Qxx8QT99B3UZk00dgkTpNDSQGuNwRIvcuQ8ITmdouYpcp6RnOZMHudMHuVMHqTkZwn5MiabK9Kp6iNZ/v8GkDkTSz046S8vG4qLivJZTXleO+VYD8i2a0tVzWhxMKxVc0z8AJs+wMSnmOQEEy97ZdkSE88w6RSbTLFJjsrl0WiViYdmeeCLLGtd9Mp0ry6L0he7T9OJoxCsvQ5A2C1/69aWbmMQ1pnfOtWV8/0nZUUyKciXWyYnWwfHTntl2KIkX9bkJw35SUO26MgWuvc7ZsiWlmxhiW8E+bjnmbUeinXDvOkBWN37IavXDobVm875juufW73uaApNpPbijvHgfTgI70UgvvyGKshjbVJgMFcMxRU+hf6nfZ/Iiz7GA9rADVB1TIXjjx864PcQYMizwPVSCBvC7f444TnGQAJuwqmxL6/Qrcx437Bv7U1Yt9vtYHJ51zk9VEMI4niGiKZINcOQIeQUGc0QverLihwhJ84Msjd3tCIHkaPlBPIZNp9iMqf+slmGzTJIU0yWQha7AZkD80ZG8Mu671G4Hu77ggrPO1PdIuoGUTcoD76aClmXyLpEVDtUUyGrElFXPeRqkHVF1DZQlqhmD8NEVQ6ATDU1cddC3Tce2MMvX0bD5xmWuZA1+Ofr3xP/rI0xFEXB5eUl1toDv+wHAK3/XVVVB6abvpwO51ueEFUl0yxju92yXC6dReMIYMdxfGAmba2laRqMMUynU8qyJM9zmqYhTVPyPMeLZry56NisOSzPSil+/Md//IUe43syETXWOJMq/5IFnWhjLcrL9qT/+PagLfjfWpDSmQeGVHxf8d3sRDt/HPZGQyiEKmFFGP7NL99H428Da3eluwj8scoj/N34WsfODG+r1O+65mMj7nddu2s7HlcZ3fwBg0WXEEEjtV8WopeZDccRPbwCrwuQwfV1nSaO1UEeDNcVXMsNFaDw6kac2Za9qQoLn7eb35QV+2s8AI7eN0B/W6ofQRVS9edxL7Pl0L+eb4MfezZ3gSbgoHILnw30Pkz6ZxTmT6jGdBFNXZRRcFFSfQRR4Snd6Jx3Qdfweu8qR+P3bPzhvav8j4/hl8d55u5VOqARqMo2mwXbzQnr9YJiN2e3dSohfY8CSEUtcdQymW4R0iCs6X0dgtHSRaEzTuG2WkWsVktgecvRwoavuDl3BaLvGDoFhfWmhlL0wKyHave9r15B1o6c2Ztw3exNknDQK1R6RWm7h1q3QK8QkkVx6/aJGpLEEEUNUdyRJB1x7KFXQxxrlDosQ64asCCc+ua28q+1cpCsnLIppuyqBRe7Gdty2gMz97einFPUE+7zIZbEFdN8y2svXQVBE3ZM84JpvmGa75hPdkyyDWnSYoygLB0M2xW9WWYx48n5S+x2n86ucOBs1wOzur5/GDGOWqbTHY8ePXPgbFo6aDYpmEx3zGcF02nBZLJjPiuJoi6oCwRFk3O9nfGTP/3ZXF6cwNz2kEEM0CyfVGgh6Wx0D0gUaCKcuqwHq5EvT/eVObH3b9XQ+3bjblimBWQGGUAylTlzy6jbklbXJLun5PqcNFmRpldk6ppYrlCixhaCbpsOEEyT0eqEzmZ0NsfYjMYsecbL6OpX0dkcbVI0zuGuW88cQLMZlgQRGeIHLdGDlvhhS/ygJX7UEr/cT4/cFJ21ZKcN8bIlPqqU0EB5mEVa0JYJdZWy/dScroxpi4huLWlXgu7K0l4YuguDfqZpn2jMM40oDLawzm9ZIUhsxiuvfCbFDtY1lC0uCmTMTcjUmxOSD484SNPbn6e0gVLNOsDlQVfM/lx+iunrpuh2Di+5CddCU5MYiA3EtlfK4dRxw2ABe2D1vMkGUyietaN9wk9fOEARwj/TIjsHyFRboKoNqlgTlSvi3SVqe4HaXBDvNsjdGrlbIXcOkKm6REUxbTRBL87QJw8xy0fo+RndZImenKJf+Ty2b85cdEQ1RYu8L6sJpkvc9+o+dailh+L9rSQdKu6IkoZ0unGmzLZEmRJJTXP5NnJ3idBbzOUT7NN3sJdPMefvYJ+9g71eO/+9429vOGguBFIpTNdBHCMnEenZjPiNKfHyIdEyJzmdMHmcMXk8cXDszEWxzBcR6VS9oILsOU0sGwfI1oWl2hiqlaG8thSXhuKZoXwm2T4VFNeKchVTrlKKzZRdeUKtT5xiLJlj0zkmmUI6xaY5pBk2yXpTqxg7jeFMQSaJJ/rAjDL3ppPZCIzlDUn2jDj91F5NljfI6MVGsXSpaK4juncjytWU9lr2PsacqWW7tui1pltrupVGbxq6dev8lK0rbNkMYEypxil5ZYO1JemsIV/WLOYt+UlH9lCTfdD2jvghW6g+0EJEuuhNZYN1lbwYzO3q3in/WnP+rosqWm+6AZi1W0O1duvNtnfi781mVy1dZW60I2/ru40Hjp3QY6+mCpUuYTrW3g37YCHU8vscG6AOk7+ecArdrhyLdDkGGOGxwv5g6AbHQwuvzgl/H0LFu/qToZjF/33s2P5YvhgL1mZokfaga4KVzteXwanBjEyd36/I+QWrpNw7v89nkM/7+aRXfeXYPKfLM7o8hSyl8uqtsaorw31jwvUx+LrfIKBPz6GM7DSUNZQVVBWsSiidaSPVDootlJv9trIc5qIuiduWqGuJmsYpxHooJqqSRHdEbYuoK6S1pGmK91U+Li/j4Hy+jIT912MihqEM+PI16m+Fc18u/LawbITwNuyz+XN7MF1VFba/l+l0OoA8D78mkwld1w3BBHwgjNDfnjvWKXEc89prrw3natuWJEkOyqTzR+6CUvgy3LYtVVUNAD2sA4QQg0+88bs9rkeOWRvel96bDzZcX9DY/QmFBxz9eliJ7cGIaxEJId2H2+xpo097k0CL8JFC+xaSsQbZmwNa3EXYnvh48ji2j94Du9uB1FgW66853HdcSO8CWMcq3/Fxjl3HseONz3VbJ/XYizQGinelwRQ1eIb7vx3u54IjeLYV+LUR4J3+C7mPAOPMOe3AGwg+WE6uuffhdQOqju55eJGFwOCJdw9sR5VFeN9u+eZHSwiFNwsafgu9Ty4DQqE9OLICrAu4IKXESxUGO3B6RmJvPpPwfsLnchP2+WuTYLzSLmxQ9PdmLMrRs+E4e1MLSxSrwe8aAqS4qW6Ew9GrsQLy2If1eWAbCLSOaJrIRWpsYpqmj+DYJnRtTKdT2jaiLHOK3YxiN6Uqc6pqQl1nNE1K07h9dRc9h5lh2MPytjj2yNyiO4sevqWD454jydx+zgGOiX3kunDdL98HzaxBolGiJRItsXJBAJKoJI1rsqQgTwrydEealsSRN41sSWIHw6K46WFXAMailjjWt/oaOdaQCxuTx8rtMX95h/vsG2hNq9iVM7ZDJFEHzHaVD5TgFGbbakZV3x0lFSBPC6b5loenT/toozumk1511k+TdMN0skNJTVVPKAtnflkUM6csO5/xtHjcg7IZReFMM8vy/vMr1TGd7jg7veqVZjsm08Jdw3THZLJjNiuYTgpms4Ikaft8gc4k7OoJRT1x82bC0/ohxcWE3dv77W6fnLLJR4b7wONwxf2tJnGdZGuRaIy9zxxY9A78LXTidkAWTsZCYpCRRsgOaVuUrpFtRdQWRKJG0qCokaJFiAapDLZVdFcZ2uYD6NI2ozQnbO3LaL7g3jy/9S4SB8vih00/b4kf1UxfKUleaogf74getsRnHdGJJlpqounzNY5MJ2nLmLrM2V55ZZnCbECvLfraTd0ldJeS9iLGrCNME2EaB0xMk6BNBio9jHQaVjMePC0Yqqu6s2ysuJOP7TOBQwg2hmLRLduscNC0h/VCd2A7hG4RqkNIN7eqQ2YWMQGbSmyuIJUOPCQxJkmwceTUaz7Usr/Ho0Xwljo2BGWh6e8YlIV56OeD2ah1TvbrAtlsEOUGtleIzSV29QSuniDWl+jrc8z1BarYkHcVWVMRlVumWUZy8hhx9hLm9BHd4gF6dko3OUHnC/SjD9O8OmcbTwdAZsgc1O2cieW9yRKwI4tMNSpuidOKTKxQwjnpV3qHaneoZoOqHeRTxTVqe4VanaOuz5GX57DdIsy+XhZCsPn0L+Ij/8p/jp6eED96l1/x6v+H5T/1Gla/wsf/yMf41Lf8IOkyJ359Rvw5j4iWE9IHE+Yvz5g+cn7I8rOE/CQmXzg/ZNlEkmSQJJZY6H9iQOZc6URUpaLaZZTryEGw64TiImP3LGP7ZML2yYTiek55PWd3PafYLNgVJzRmgU1SSJPeDJh9xzucZ8Ap8ArO1cLMgbA08DcWRq9MsjVxdrGPZpk0gdP+51OlhqlbG9qVoT3XlKuObt3SrRv0uqZb9REr1yXdZofZFOjNFr3eYLYb7G6HbfYBGA6LkSVfJCRzxXTuwFf2ICJ9fw/D5lEfybKPROrB2Hz/dxn5DHq+1Ow0zVZTXndcf6IawFgzqMq63uSyo93ZPorlHpi1lR5UXqH1CNzsD40HWLW+24XOfek+n8PhccL5UVA3ahsdaxeHv/P73+YbbNyvORAQ9Nu8b2u/HvoZD69trDCy1mKsojWBA3uZ0emETiS90isHOcWqDCP2UMyQoeMJNpthsgm2n0yWYdMeQGfe11d8U+V1l3nj+O/PlZ5DvWkMVL3qq6oR68qBqsqBLVkViLpAVFtE6U0aQ3WXm3sgJqoSqhK729FtN9hiR7tesbu+fq7+tE9huZBSouKYOEkG9ZW38LLWOt+2UTSIlzyTGPuVDy219rdvbrw7x4Cp/93hQPge/I7Lty+/t7nQCoMMjF1CnZ6eDn+Loog8z4eoqB4eXl9fM51OybKM1Wo1qN28H7Sxnz4pXaAIb7JpraWqqgPz4dD8uCiKgUVVVQXAdrsdzEL9O+bzOgzUGPqUP1YXP9fzty9SWvr06vtfvQGj/IPxmekvOHR+b7BE0kcrsVhraJvW+boC+nBornAZZ44ghEAqiTFOkeNO2IMeKRECpN1LD8cy4GMwLIRu/m+hdPa2NM6q2zJ8DE3G0OJYuu1axy/O8/x2/BIc+yAd7BuM8nq8Fp5tPLg63J8/Zn99x5xQjs8rpSRSzsYjSxK+6jd/FVmaIqS98SEJr/PGNQOCvX8278cvTGFFNP6AhecIoezBB2rkg0zgyrK2LXk+59XXP60HwR7M9UPmN8CyCUj8IaS4HWbsper+rwO8jiImsxlRkmKxB/ex2+3Ybtcos3/2URS5SI3WYq2kaaIhGmNdq37ZRWjs2pimjfr1xO0b7N92MW3jojiGER+bNqFr3X5dEx2WoBudJhHM76u0PBy74bwmWP4nT0JqVGyRkUHFIGM7+O2xQqGJ0DaiMzH3SS0i1ZCnxQCFZvmG2XTLbHLNYrZ2fruyDdN8SxK3gCWsFm57n8d1wW0NtDAdq/vua6Aea6yBoGlTtoWDYmVvirmrHCg7gGnVjKa9u/UkhOnNMne9ysyZZE4ne2Dmt03SgraNe4XZXk02gLPdhLKcDSaaRXm/yk0IzbQHZNPpjsmkYDrZMpkUvXnmjtmsJM+3TKc78qxFCNBGUDY5uzqnqKcUzYSimbKtcspmStFO2VU5RTNhV08p6rwvM/clQyQ0fZgNjJHP5z8P7laTHUzW7evN/Pp3R1gLrcWa/n188SbBrUmJkkhUwbw6mEd5RfKwcsqyRx3qgUY9BPFSjHisEA9jxIMYcRohTxRyLpH5/ecF0K0KomA6R/9tEdPtFN02pt1GtJuYbhvTbFLabYKuvbl2D8fHar2Wm/k63tbyXH2CfSbZwPwxUFiFQGlcVVrHtaYJzDKYZ7CYwHIKP/aTP4CIa6KkxWYt5AIzS9DTFD3J6aZTdDZHZxN0kmGiBFQAyuSR892V7GhitExwvAGKcagks51TkDU7VL1FVWuiYoXaXRFtLxDra1S5IS63yN0asb1G7dbY9RWybWllRjM7pV08wJ48dOaV0xPM5BSTn0DmfF7ZaIpVU4yYYG2G1glWv+A4s3ARLFXsolgqH8XSlER6h+q2qMbdgypXRMU1anOJWp0Try/h8hxZlAizV6GPnZKPO0gIEHmMmCUwjdyDPjvDLpfIh2eIRyc0n/2F7hsmDa+dvsXp5BohLRklD+UFsdiDsZj2PZlYGiOoqpS6yKh2OeU6p1hNKC6nFOdTts9mbN+ds30yp7ieDtESi+2EsprQGO+Am+NgLARkqYXMoKaGeN7e9Dd2VD3Wr4f+x17QvNJqQ7eu0OtqmOtNRbsqMduG9rqgW5foTU23Kl2Ey22DWde0qxK9rbHaHP1OIyzpPHJ+4hYehsWkC0W26LfP3foelB0CMvG8kXX7VG+63h9cP193gQ+y3h9Z73esGSJYuu3NVmO6vc+r0OTvmMudIQ+DNoY354J9m3w8ED4MnI/cDoVKmTEcgENT5GPtprssgu7rzx3rhxxz/RMeM2xfeRNHD1N86rqYd67+TXbV55Kk7/L4wXcQqZ1TtAa+v4zIsN59QW/y2ImULplhswk6ydFZhkmyIcKj6Z3c2zSGVN7h22sEusamkS8IlO9MdYuomh5+VZjdDsoSW3rF1xaKDVS7waeXN2fcTw6Iub+VTunVgzFZV9A0yAD4jNvJYV98DEzG5W2sDvOKrLIs2W637xmwKaXIsuzAXNgrqmBfXsK/WWsPTBd9uR2LJcIyHZoSh+bAt/UNwuOGy2OIO85L/1t/fH/Nfn1sOu3X27ZFCDEEv/D3WNf1cL5BnNPnS1mWRFE0KNP8ccemreFzDtVy3hfhfD7nyZMnB8pP7yM9vKamaaiq6qAeMcb8/yeKqE9j0j92MhjevBDiQGEkhBudkEo6H2yA6E1P+x0QvqAgByWGUgrtM6S/DstNJUY4Dyu/8cvX7+i9xbkom8H9vWh+hMvj899XoY87w+NCfez3xz464b7HOt233dvBuY/8LZSohoqpY/R8fP0h4fadRuOPpQ59hB02OF1L3Zt3ugkHYAn9HvhgBYcVwtiPwfhejwG8Yz7H3FyjIgVd5CzxjFM4HT6XI+o05SK8iv7eRQA09+eArnOKr7qOaLsE3ca0XRJAMAe2Op0gxIzOZDS1omki6kpR14qyFFSVHKBY1zr1mINfEcYoCO18OTK3d20fw7Fwel6nOL73FXp9DoHZHqD5Imnt3ccUwhAnDWnSkGY1WVaSpM4MMkoMMrYIJTAyotUpVZNT1BPKakpROV9dRwXifdFJ4opFvhkAkFdOTfKNMz/M1oPfrkg1/TXdrAOA0QcQRB/Nd9wgONZYDI9127t9mC/Hg5L0S5R1zracsS2mFNUekLn5fO/jrJzR6btBkZQd02zHg8X5AMdChdlsUjDNnD8zSUdVOyf/u3LCdueA2fbZnCfFywMs80DN3OuE2zCZFEwmBQ8fPmM63TLJnR+z6XTrTDMnO2bTgumsIEtLwNLqhKLpQVk9YddMKZsJV/UpxdMpu3pC2eyVZ9Uxddk4WRBW47W9Eo1F9MFpbvutpPNQ8HlA2QBzRu/ybcthSiwyNkS5Jkpb8q6gKy5omxVC1khRIWRJRMXE1kzsjoTClWtRoWKDigwiMsjYwWkbCcRcYR/EyIcZ9jSBsxRzMsE+nGNOczhJEMsIMZeoGUQzkMnzDWV3taKtnN+g7kkPzIreyX+R0O5i2l1Cu43pdgntJsY0al/F6H7yMMzPm2DqbA/HbDC9IGlyo043OyiH1VqwvUK1KyKxYZLVfOkXfA4nc1guIFtAMgM5BZ1BkcBawiVwaeBaw6qDrYYrE3C9L/3Vx1Vzz3H5d45dHFOP+UmB1BWqKXpAtiEuV8S7a1SxIio2ROWGqNwie/9jbK6Iqh1R02BlRj1dYk8f08179djslC5boOcfoDubUakpWk2cgszuFWQv5KBfu3amV5BFaoOSDREVihKlC1S7JWo2yGqN6iGf2lygVudw9Qx5dYnYlc4VXa8wtzZB6xjIcVEP3boQE/ddFxltdoY+fQN7usB++gxOpvAoJ3k1IXoQoU4UciYhl4hEuHcqgsEnp7AIaZHKONP/aG/S79LT4TZLppS9FPIl3uWX4ToGxkiqKqUsJr2CLKdcTdhdTiguZmyfztg+6QHZ1ZTiOqJcRZQ7RVFFdBZINWQGkVlEbrGpQGQCm0rIFDaNII/hJMZmCWqmSKaaPCtY5NdH1GPNTQf+WU2c1SR598LmlabuHPi6qChXFd26pNvU6FVJuy57YFaj1xXNdQFFS3tdYDYNZtdgi3YASeN2d5xFxBNBtozJlzHzeUR6FpG9PyJdzMgWpy7AwmKvJPMgzYOyF0nW9IqwjWb1yZp6s6PZmt6/mKbdOWDWbDTVqhsc9Dc9JOsK5ws79Cc09s0lhDj4+3iAbTxAPgYUt/Vz7hoI9Mv3tV8G65AjfaXwGONrOXZ8f6xjfaXh9wgsGdqmwNRBLunqG4tzV+DVX1bkmN7/l5E5Jp5g8hk6zbHppFd/ecVXhknjPfzyCub087iYfuXtqq+xA/znLj6+fX1HanUPvXrV19arunrVV12i6h2yB1+yrqAqkV751SvEVG/uKCu3TdW1mzcV3W6HYg+26rrm4uIC5zfaq/z3Aw0eOHkwGT47b0Y4fu5CuLbV0b49N8uKb0uHoqCwjIV9QGt9IMabpsEvkvy5QyA87mMe6+cf9JtHxwv3CxVmYZkOAfNt79qYjYzheGhaHSrFACaTySBO8kAQ9haMY+gVinCMMSRJwmazIY7jYdmzha7r2Gw2AwCbzWYAg5DKRwH1+RTmQ3hNwADw2rbl8ePHBybV3iS1rusBsPr7va1ued703nywHbHLHUsLjz0kKaXz3yb2oxW60/gGbOj/YajI+3/e9NB6UOFvuO/zmxFU8S/tbYDFJyFEH3jBNbzUoCFwvsNcoegrZtE7nrf0/uD6PLA4R/jBcceQJ8yvcJ/wOo6l8ccuTHc98NtA3W372QAM+fOOP5DhC3jjwxRsC0n4uCIM7a0tlrZtEGLC5eUJRZHz+PG7xLGHZmCNrxCFKzuAUpLOhlFAb1ZOIZA4ds9j6e149Cm8b63B2oy69sqtDMScpnttrwZrYrfc9YquxsOtiKaNaXt1V1O7vzWN+03bRrRdRNeqXir4XqDXLX87SCNv4oc5EkwhJFNHtt+epNREUe38dCUVkWpRqkVKjRAaF9xEoLuIqk6pqwx9p3pAkmYls+mW6WzHdLYlzRqipEVGFiEFRkRoYqpu4gBRMWNXPuKimmJ36s6gA2nvq+tseeGUTNmW2WRLnq17R/eb3gH+hki1B+9B+GE+np97UByawYcf9fC9OGYqfBs03+f3zcauMYKimlLU3neZc/zvFWaFV5z1EO15ggBM8y0vnb3j1Ga9P7NJD8qcueaGJKoxXURRTQezzLKcsdlMePfpK86v2W7izr2b9p3Ru1OWOYXZ2eklk0lvlunnUwfLJpMtk8mWPCvBStblgnWxYFvO2VZzttWUd+pXKM+nFE1O1eXUXUatUxqTYO7yTOtBg+k/ELZX+AjbQ4Vb3gcBVqg93zrqpwwHxwJoJoxBGU2kNEppiiJzqrL+QK++9pRX33jSg2ONTAwqtcjEQGwQkXWd0RhkLLDKghLYSDpXa0pihKRD0dqYzioarShqzbOrHW0rMHKBVo+Qkxi7iCjminouUAtQc4jnHfG0IZ61bj5tiSduLqPnMcO0dJWgrWKqi9DBv1eXJbSbiHaT0K5T2msXIdPupPNBVeKi71a49Y49QDMGYVskGkHpHo8Q7lnICCvua+54mh8sh2XhlnVpS2JzTWxWROYaZddIc43QVwixoUtr9ESjZwJO5/9f3v402Lolze/Cfjmtae9z3uHeW7emruqunjV0q1tTq5EEmhqZkIQw6sCAhcAyNvCBCGFshx04wEHYEQbkCAgbZAYDIeQwAgkBUsgSbmE0j63W0OpB3VVd3dXVde99p3P2sIac/CFXrp17nX3e99xb1cqIHWvvtdZeQ2auXJm//D/PQ3j7Kf7xE+LVI8L2CbG7xjVPuK07/mRdWEquIdkl6zvB3V5cCcpcsa5sytcBCBQgPHI6osYdetih+9sEyfod6rjDHHeY4YAZDlTjEbF/ldRko0WiCdvHjNvHuO0Tps1jfPcY11zjqk8yvL3F6w1OdjiSubD3FcE/MIJlnoexILRH1R5VW4y6RYkREWbnz8cD4tDD/kC42RFvdsRXe/zLHf7lHv9iT9xbjK/RaoPWV2i9RakN0BBjjfUVU6wI4eOE8JkEz0KFfKKRH9PIdwzqGwT6UxHzrqd+11K9NaEfWdQmIBuPqEDogNAgZESogJQBqcLst9KhlZ0njkfOHKy9KSuCIHhJ9IIwCCZriBMEpwmjwvWa6jhSuQk/an7i9pv42x98J5//D/4c+5/ZQW2g0cS6QjQ1NMn3GE0L9ZZQddB2xEcpIp6+7jCbSLeKVlm1dlGOpd9lVMth/u4+tHml3yc/Yv7FwP6Ckszdjml50y/b0vae6aaHyc/5dK7yCCGgakn3uKJ7UlNtJVdXivbjFfU3GZpH3QLOqit1Uo8tKjNN1X04T+behsVk8vDseGY+OcwmlKd1JzA23CZYNh08Sp5PZJdp3Udf9/Wzi5ZL/fScyvFcuc996ZKY4RKYy98vjQ3XiprXjVFON2sIJDWXF8n00YoUyTEwq71Ek6DYbPIYZ/Dlqo7Ybgl1i686gmmJVUvUDUFVRDWreJVKEyQKlgmT5SIp+gRi9k0qTu/wifQYTyK9p16S3lPTvC5P5FjAzgf+fg/fG+GbInz9nG8hIgaLnJLJo9yPyOdTMmvMER7HI2o6oqe+UH0NxONxiebIMPv+mqFYPB6I/RHRH1PUyHAKVFiCx9dBy3UdyGO9/H0NanRxjOxfq1RB3nf8PCbL13dpPL1mDw8Z+62Pf2l9eey83/qaH5rWz1Iec67rfpl3l3yI52ta3+cdXlJcd3mc9TN4xmD8yYS7zLsMmUoxTWnSemnckutDOc7JZR9CYBzHRamWecDxeFzA2DRN9H2/XH9WDwohuL6+PlO8ZXPzpmnOzp35QtM0y/eqqpbgClVVLdFD89gsg918rXVdU9f1kmcZxH3Y9JFMRD/+2Y8v9K+s4LkAcioreS5UY8ySaSnTI876s/0uVQYh73fQCKkdTOamd32wXYImZ0AoZMfgCQLkvujJDvcEoYiCgMjB5tN9IpJ7rqJCXZoFehMkWwOrcgD/pkH9m16K921fA6o1RFs3tOsBfXnMdcOXrzc/vNlcuKwL/+Bv+k38pb/8j/EX/+KvBuDp02f8zv/pf8ijRzfc3lzz//njv5Hnz57yqU//LL/p+/4kTXOSkYYQCUEzThpnK5yrEryyOi1HPfv6qmbTR4Nz9RkI865mnAzTVM371Un55Q3eVXivCT7X6YdAr7hSjrwOen31Kflb81SVT364jEWpEa08yIgUCZTFoPBB4Z3Gzb7QpqliGmteZ04nRJidtO9oN3uapkdri5RpGOi9xE6Scao57rvFt9XrUvZntb1K5nd1O2KqNEBPKjOJj4YpNAzjln1/xbHfchw2r71WgLrqZwh0Upp1zZ5NtzupqZodbbOjMn6ZdYlREOPdGb/7OoGXoO59+1xalse/9OIrX5DOSQ7DZjHBPA5XCZotsOxqUZ4dh9dHRoUTWEx5UYCyIr/aqkcCw2Doh47DcTs7/8/KsuzDbMvh2GHfYBIKUFUjbdPT1D11PVCZiaqaqIxFygmtfIKxeIgC6yt61zC6ltFVDKHGxTrBIQxeKLxURClTREN9T/u3dIrz93h6HM8gwwOUSoELTv5nOBYDCoeZ1SV1NWIaS91ZdOvQXf549MYj6kicoZcXGhs0NipcTF6OBltzs7/CoVB1Amg2alzUxAdLkyK6TVBMbwootp0h2fb0W3fz9tY+WDWSAJlZTDHdMCvJdhp7MwOy5xX2ucG+V+HeM8QbCTtgD9ySBiMR0A5tepQeUcYmx/gaolYErQnKEGSFlzUPHrW/BoyV61Q8oMOOKr7ChFdU8RVVvMXEG7R/RWV6YhsJVwZ73TA9fcT0+AnToyfY7RNse41vt8nk0lTEPFj7WphdltddQrISjs3ArDIQDy8x4w6TIdlxhx72s4psnxz0H27n7zt8P+BHD90Wf/0W/vop4eotXPsI3z7G11ucToDML4CsxvvsoH8uixmAnakE178diJB6UNJ7hPcI55GTg8kjRw+TQ4wBhgCDJ44RhkicIE6KaNUM5ipCqIixIcbXAHsFbCLqbYf+pKX+uoHqUyPmXYt5Z6J+e8Q8tqitQ7UJXIsqIFRyGZDAmEfrOaiLsij14R0ex8gMxuQMhSO4gLDzPfcTHAa4PcCLHfHlK3hxQ3jvBcNzz/G5ZNy1eLGZy+MKr69wJn0frz/N7Tf8SoLqoO+p6UE3CBExtTs53S8UY1XhuP/0O/mGqz6qeeVuhmG3Y/p+02NvZyXZq6Qic7cD08sDHOxibulvB6abZDq7HvDlfmPVqRPweqQxG0FznaBY9jnWPDLJDDN/X5RkBl1/eAf9ORplf2tP0SxnKDbc2sUXWYpemZz4D7eW4cZi+8suXi4N5OF8Yjuvy4PbMsplOb5Yq0TWY4gSgJSTymvVS/l9PXZbBwY4G8sIAaJOSq6Y2oZF3ZWd3ceaEGsCG5zvCOKKqK9w+oqgO2K1weuOqGqiqgmqJsoUCTkKRZJynpT3acKLpDDOwXryOzm3ORMJZJ2plEn+Ry+1wx913UP/97q0TTDvOz72T9DwBSjA1XJIca4+zOWbFVbl9xLCZD9WJSQq6+MlOLuGVut9yvqxHuuuoVg+p/eevu95+fIl1tqz8XKul8aYxaQ2O58v6355vnVk1nLcva7D5TWWkKtkF/l5yOqmfM3jOOKcYxxHDofXzNjfk/J5MvzJ95fX5XNVVbVE0yzvvZp9tmUf8+Vzewku5v3K+lNC1Hzu8rnOwAo4u76cd2trxJxnpbulrBiTUtL3/RLBs+97xnFECHEWcCAr2vJ3axO/OR6Pd9pFrTXb7fYM6OZz5fOW11+OtbTWZ6a5bdsuwK5sP8t6kfM01wVrLc45/vSf/tMfruw/CmB79zPvnjlfXCu0ygIsK0C+2fy9JNoAAokQxc2qFcmOEqUVQcxjoRDPKk22s5URXAxL5uQKlq9Rzk7HspFpSVhP+2bMlnq5n/zUJ/hX/vf/Cl/4qZ/k9/5f/228D7OcOKJE8gUHYK1DKHkmb83HvETNyzxb+wZbD75zus8HwpsG8GW6RPhLieela4dTVJqS/pa20qUN93pdrsD5mFVV8R2/6F/gD/4X/2KR3/Dk8XM+9emf5sd/7FuYhmqWDAq0majrEed0AkU+q77gMvQqvv+9hl7aobWfIzG6pDoxlqpKDuhTFEaXTBi1IwTF3/nb38bNq0dU1cSv+J4f4tOf/VmMTvskx/UeZyU+bPH+Kf1wxdBX3N623N403LxqefWy5vZmwzi83oltVY1cXe3ptnva5oipJoy2QMCHSAiKcTT0x5rDfsN+3+He4My5bY9stwc22yN121PVDmUCQkEUKZKhDQ297RZzxP4B0Kyperp2t4Czrt2zyaaZs5nmpt2xaQ5n0Rghwe+lhqxAF5ybDazBGXD2TF7qZK7Xlw10+f9yoCClZJzUCYwVZpiHYcv+WJhp9huG6SFBAA7J8f/KLLOrUz611REp0kTCMLQcDlfs9tfs91fsjynIxLHf0A8tw9BiXfXGcwrhUTJ90rRD8iUXQzJF8k4mOJ3HoovDdTF/F4UjdjE7Yi/WXfI1s6iVmB/xmD551wzK5D3/v3S8M6f+AUlEiKQ2ETqZU4oqQgOhkdAIfK3wtSI2Mpl7fC19lgCSkIJeCJ98J0mHkZa6G6k2I2YzUm0TOBOd4ygaRllhHk2Ya0v9ZKC6npCtfxCLiiHBMjdUJ79lg0lKspukIrPPDe5ZhX3fYN+vcO9p4q1MirID0Du0O2DEAW1GpJ6QJhCMmCGZIagKr2q8TJ8oHhji6x4wVt6Aij0mHjDhhiq8ognPqeJLanbocIPUR8QW/HVNuG6YrjaMjx8zXj/BXj1l6h7hmi2+bgi6Jih9ClgixMMt4M+uMU3K3fW5wF1ANn+uKnhSwds1PNHwRMFjlZZP9On7YwXXMnHI6CIHG3jv6PnX/53/iF4+xulHWPkIG7c4Orxvca4h2Ao/VYRREweVBpxrKOa4f52PJ3Pa5SNmc9qvYRIk86gNsLFUnznQfvZI9ekhBbN426KeOMxjh956VBuQdUSapOSUOoExpfwFc8oPl/ysGMsBIoQPCO+RziHnyG/iOCB2+wTHPnhBfP8V/sWB6dnI8DzQ39SMboaV5gqn0vdgrvEmwbK03OL0Fd5sEJsOua3QXViZVdoLYKwAZ02C+8p8OBAYJo+7OeJuk8+xuJ8W/2MZmiUVWYJoYT8S9hN+NzK+2MPgU685niwZ8rvUB4/pBNVWLRCsfWzoHldUV8kpf3Otqa7UDMwK9dgMz+R9kyj3pOl4DsTG/fz9xtG/mhh3Dt8Lji9Hxp2jv3HYnU8w7dbjxvOBYB73lIO4vL30hbse68DJRCp/z+Aj96XXE9dlv0FKeebYO28vTaTKAfByLCFBtAQ6Alus3xK5JnBNiBs8W2LcENngY0eIXXJ4r7d41YFu0oSGrAmimoFXCmccUQmuR8milg2kduDeoDlfBdz6aqDY1zxZBBNCWKSYEIwIMSX3CgxIOSFF+ijpEGJK1hxiZJi+nle7v2816RJR8sAv/YbfghRJRHCfdU0u91zHcl0qx9UliMv7lgDikhrrEmDLdeoSpF1DiZzWY9V8jAyrnj17tlxjqbLKACpDqGyOmLdnB/Rl3319vvIaymepZBTlmHU9gb42n85AKKuvjsfjG+rF5ZRVXdnJf1ZW5fVCiGVbvrdLgC2X2yUF4FoIU47ny7ZorUhTSi2+x0puUx67HOPkuliWX+nLzDnH8+fPORwOSzmv62pu77JQq7T0yQCu5AZCiCUKaS7bki2suUM+5zRNZ7A250VVVTx+/HhpkzebzR1ulYFghp7WWn7gB37gQ5X7RwJsn/j6T1yEOPlGywejfPnkG8j/zRmdXzRibm2knO131bmDPDcliu2IKJFB2Skt9rRwipooxHmky6KS5VSS/Ut0PxHlis2mY7IT/fF44jhSJifRpMr49ttv8/Xf8PX8rb/1N5ncSape0uSLUu+Y+vBpNkicvczXpLq8xqXzMu9fUuX7irZ8gMoZrQwpSzhYHucShCgBYa64TdNwe3t71oDECCJHgJ3vtaoqPvb27+FP/5n/xcXrTBc4ps9XmYRIfktUnoXWNnW6zYTRI9qMGD2h5m1aTyg1ofWsCNMWtXx3NI3gYx9/TF0n9ZjSs78v49A6wb2lw4OYJ91OEObsBRcF/9l//D/hx3/sm+arTY6Kf9F3/jDT0LLbbdjtthz2G7xXF+8v3+Nme6Tb7Og2B6pqoK4sUgaECElt5hRjbzgcWva7Dcfj6z2GK+3Ybg9stweaNjkO1lWC30FAEBobKkbX0Y/Zl9ebVVTNHABgM4OzxcF9k/2aZR9n+6TGu3Ovd+t3+VxHSE5PC8B23//KZyDX6zXkXpsT35VkQz+aBKtmWHYcr9n3G445IEC/eXgQAAJN3dOY9KnMSCVHKj2hsEhhwesEXEfNMLVMU8M4VVhXY53BO4MLye/egxRPsVB25e9LHc2/59wVnMEyWfk0uK3i4l8kKkmQkigkQazqbeTkF8tzOrYIZP/yUZDgxmLC9oCec6ksK1U/+RjV/KmBdv6UjrVXlykICXDNsEsLixZuXudSRD05/8aihaeSaZvGYqTHSLeAMiMdWqSosbILiE2ANhI3kdgJbKvoW83U1kytZmoVvpGEVhDr+EBYJs4hWV7eGtxNdVKVPTPYDyrsVwzufQP7gOyPiH6XIhnaA1pOqI0k1oqgFFEnFVmQFUE0yWRH1ATeHPzjdIH3/c6Q7IiJe6pwQx1fUrsPaMQLTDUg6wlRe8JVjd1W2G2HvbpmfPSEcfuUqbvGNVtcNUMyrZOvgUuQ7KP6J6M4Rlm38seOyN0edfMK+WqHfHkg3h4RhwGxH5GHCfYj4mih92AVV+1TlL7il//K38irY2R3hN1xDmo2wDQI7AB2EPgJwiiIlmR6VMKvn585o3TLwiGVRcoRKcY0iJQTUs7rZBpECjEiagcbj9iAuA6IdwTmE6A/IVBvS+RThbyWyG3yxycbiaxJcMxkP2N+hmIWrRz3RUR+XQpBEL0kBoEIMUXdDUk9J71F2Qk5jeihRx4OyJs9PH+JeLbDPR8Y3z9iX0wMO83tvmL0V4Tqmkl0yO4pVnY4nWBZgmab9Ftv8VWH2DTIrcG04bJ6rC4c9re2UJmNHyl6ZegdcT8SdiN+l+AXR0vYT8TdSDzY5IR/NyXH/IcJDnZWkg3E0S0Aqe/7M/MbqRIcq7YK1YHuWNRhuhPojZj9jZ32SftLqiuJ2cgP7aDfHgLTPmD3fl5G7DFgdwF7iPQ3ljAI+psJRsX++UAcJO6Y/iuKybs82Mrv7ux7p2kahmE4c2tSTpxldVDZNxZCME3T0uc2xizHL0FXUj4EQjA4r5CyQ8gW7zXDqHFhi67eYpwqfNziQ5tgV2gJsUt+BmNDDA0uGqKoU7srDFFURAwhamJUaaIyzMArw+DlHTvn+88nyPp7AraADLXElCAXIyL2M9QaZtBlYV5HHJHSzhAsRb2G1IYJYYmhR2uHd0eMdstxlbTAMP8eEYzE2EOc0iulKOvS59Qa4FRVdTZeU0qxO34bL/e/jvd2vwUXnqDkjm/71L/Ko/Yv4QO8UL+JkU9xFX+I6/jX7wg0yrFYOZ6+BNnytixmyeZy63HuGj7ldAngZEiz7ieXcLcEQfn5GceRly9fnl1rNt3L0SVLf19lPztDoPJacionti8BuAx4MsQxxlyElznv8ng6BzfIgO1NCrZLApcSoOX7MsYsY+MS7OR7Lu+9VL2VACmnDE7z8fL2cnKjPGYJRHN7liFSCV9LSLmGj7n9zPmZzSdzPXvx4gXH4/GsDMp6kf+3Pn4J4Mr6J4Sg67ozs9X1ccuxWwlI832uucjTp08XuJcjmebjG2NomuYOV/mjf/SPvrb81+kj+2BbQ6q1aSQkFZkp1E3rtH5IIsnXVkyhzgh+7hQzE9lKEWNAzwPapGuSQPLHJaVCSp9C9nJ3BiCd467KJF/bpVC0OVk7cXNj5+uWSDkPwuPpXoQQPH/+nBc3r8AHkIVM1vvZV9sJC549iABR4IJHIRAx4oNbBFrrGYfXPWhlo7Sm1mWDnPfN9PgSaLjUAJWdlHXFHsdxib6ROxhaK4RIZVc+tN4HHj/6UaT0hJDrjeC7vusv8z2/8s/y7/3ffzcnfzgRYyy/6nv/IlXlUGrCGDsDrQTCjHFoNaGNndc5jLEoNSGEW66/vJey4VjKf/a55L1Pg/2l0XKEGAFDVTd8/BOfPJVfrl8SXFbqKbkwigxh7862pKAHX/rSJ87WhaD4m3/9OwAwZuLqes+nvu5nkSLwU1/4JKfpQvjYuze89c6B/mi4eVXz/FnD+1/Z8LrUdT3bqyNvf+xF6vRXHqUjaImPEhcrRt/QTxsOx6f87PAp2Mtk1nVPausjXbvnnafvFZEg98m/WXtL1+y42hzpmj1K+bOyWM8k5XXp+7nZ+LrxT2WzmklbPStCCJCCAEzzrIQggW0pZx9V4dS5Tkly7Fte3j7iZv+I3eGafX+dzCKHDcdxQz929FPLaJs3+jODiMIhY6AKA4RI9BCswFsJTs7mi4IYoBcNvWhAPIEZ0iYAPx/uTcApVTyEPMFlaTy69ikKXp1MFlXrkE0kaEmUkhAVPip81Hivlk/wCm8VwRXK0QiE2f9PqSzLTHwNIkoYkZVqZ+kESIHCHDOe7kdFpPZoE1CVo2onqnak2Q7U3UTTDtS1naHYDLtEippnpKOSvoBklkp4tPTIMCQQNgMyIxySgCwGhetOHKSxS9zWjLVkahSuM4yNom9r+rZhaCuOrcG1GtdUxI4E8x6QghcJkB0M9lmFy+DsYBA9NJNFD56f+uufZnqvxn2lwr2v4Sh49+p9NmqgjiOV8FRKEFTAYrERbAyMzjPYkX4SCUhdtXB99bDYgpeEwDGg4oiOR0w8UIVbqnhDJV9SmVu02aObCdF4aMBua/zVFtttGK8eM27fYmqvmZotx+odgq6IWoFQ5/Vo/f2hKdfPMuBBhlMDSYm3B55NqFcW8cIiX1nkzsMYEEOECeIkYJIEp4heE7whhIoYNFDPvoLeevBl7eblz/ylN+yoY3pmKhBVRGwDqgJVR3QTMW1kGG9QakLKFL3ViCMq7FHuBm13SLdPzvzFBAzE2CPESFV5rD+grmvoDGGjEW+1qLc64qMG3trAky086hDbBjYG2Rpkk5zyJ4uuiNIBrWMyqfxqzSmDBA/RSogBGT0qOFSwaD8h7YAee9TxgNrNcOzFwPiVPf1XdvhdZLh19HvNZK8J8gkDbTKppE2QTG2w8uOz2eWsHtNbQt0hHleITxtM62f1mKUqoFjXzGqyRUF2S9U8m/2SfTi1XAyReLDE/UR8MeEOE/Eww7H5Ew4TcW8J+xHmZdhPiN7hd2Oan5gnaBdft/N7VtcKs5F0G4HZSPQG9OMEzcyVodpcoTowG0F9rdEdmK3EzPubzYejfTFE7CFi94HDex67t7hDnEFZwB4SMJv2HruPuGOcYdm83yEs6ue1WiW/7/N9OhcwRmHt7CBdKXQNMUp8SM8mMqZIjKHGB40LmuhrptAyjgJEQ4xVcqHhK3zY4HyNDy0+tDhvCLFJZpDeJPchsSIEk5Zxhl3IAnaJtPxq1FdfLdzK79uvNomIUB4hPUJl2O5myD4ktRYjSoxIRgwTgoEYeoxyM+QaUMohGFHSASNKWbS0GBOJ4QiMaOWoqoh3B7TyxNhjtCfGiRjPVTwZ0tR1mrQsFVN5WY59ykFz2f/Pfpmy5VNpagkZAqROS/lcrQf4a0FEOV7Kv683P8aj7Y/z2Xf/Q6x/C61eIIUDIfkJ8X/gufy+5bzfGP4N3o3/9QL0gDOLrXzPOWVIUVpfTNO0rCuBzHpSOSt1yvzJ156ftXVfPC/X48R8nSWwyXmbzQDL/57y+HQvJdAuyyGnNURbj/dLaJT/W4LR8vy5TcnwsQRKpaXdQ1IJtnJdynA1HzsDt3LckreVYH99v+txU1kv1oEhSsVX/n2p7Nfj/TKvMxvIQqhs3jtN03LcnLJgan3sfK2XBDtl3bukzCvzM5dHeY/r7Se+cILMOf9LZWZ+HgCOx+MCYfM19X1/pjq8O3Z/c/pIgK0s9Exdy8oNcwEGgYthHledbGXhlLml9LA0OfQujdy0VkuhVdW52VIIIRt5LpmitSZYd9YQrdMaMq0LdV2p832uz532T5EOpVKkaD0O7zxa5sFyTMCsHPjHNBVeniGUJqsfchq6bAjzPawrNZwDuLLBuY/il41BPlZuDC7NWJQPbfky8T7MMz76rNyllGyvvsz3/47/F3/yv/uH6I8d3/4Lfph/6H/031IZx/d935/iT/6J3zBfc+Af+Uf/W37BL/ix5WEpr39dnuU1pHX3590aJCacN9edIpgFUS/bWNWTJS8C6CiQcT4/EDO4JAXEYC7rHFhCAB979xk/9fkMxZKJ2u/+X/5nfOKTz6jqcTnPn/8zv4Sf+sLTs3t4/70r3n/vCq092+sjH3v3RZoBrwNaB4QWJO9QFZOv6KeO43DF+8O7b/Sv3DZHtu2ed99+72SS2aRomttZdZbA2UDyy3YXUJd1Lef5fSm/jPL38iV3+p9YgkY4a2afcvoUZXX2wee8wU0G53QKQjFHYk1QrGayLaOvk+IrGFwwuKgJKIIoVC+vSyGe/OmEMM8Wl+s4g0xeCoKSCyeLUSRH9kGe1Fr3JZEc2osmINtTFDdaoIHYCnwj8JUGMweo8IJoJd6Bv8/86zB/1v7Fst8yyLMZIONpucAy8ZBAkKnd8yBjQDmflF9qojYjbTXQNXuuuz3X3UueXL3irasPuOp21NqhRAJdeVB1li2rF3fuJK0V07ktK9vGvFyefy2xrWGqNX3X4FrN2CiGrqFvG8a2YmwNrlH4ThKz8u0BKTiZlGW72cyyT+aYcRCoHlQfMYOj7h1NP9ENIxs70MWXNNHy45//ev6Hv/Zdp8ihQvDZd76ElBH3rGEYuhPENJH3xnf4SLKCEBBMyJgiK2p1RJsjuj4i6yNmM6DaI6qziFYSakVoW3zTYWXD1DzCNdeM9SOO9ccIqkoqshK2rsFr/v6QVAKyDMaOJH9uL4EPSAEVn82/d/Mn1/EHpQrPPWbSZoZcBmhEWhpO6/Ly0roqINuIaD2y9sjKIuqRqrboauSzX/cWj7aRx1eRt67gY1eCj18JPn0t+MwjxccrxbVQbFCoorGIEY4hRRX9bf/47yE+foS72hIfXyEeXxGvOuJ1C4+2iO1T5KZCtDopxyqBMnNk2RmKvdmcMjs1upvOzCm9IAaJih6FQ+PQYUofN2CmHj3sUfs9vNrhXwxM7x2wH4zY7PR9Hxn6mptDw+iSOixWj3B6yyQ6vHmEVZ8imCvsrCTzekNsW+Sna8RGY9pw5m+saiY2bWlSmc0vX2Ka96ia6UObV0YbTjDsKxPjfkzfD3YBZ+GQlGR+NyVIdnSQoVpvkzh41U7lgZ+uJapN0EtvBPWVxHxKJpXYVYXpmgTEthLdgejexmwV7dVAczWh2w8HyIJLMMwdArsvJV9jGZhNMyBzh8i48wmYHSPjjcMeAsOtZzoKlNwwTgJiTaRegNQSaTWYBVDFmEIlOq+JMfnXC/Mykv7nQ40PDSHUuFATwuzba943RkMIioBO71TU1xZafZh1XwuolZOKoFOwDKEDaI/UDqkdQiXrCiEsSibllWRKKtM4oBlRDBgGdBwR7oAhmTIm64ABo31SfTGgtUtmjYyFAmxCyZPPr/zOLEEMnI8n8jgjBzZbm9+tJ+fXIoysIMr/SeOLajl+BhDZfHbtimYNTtbgphzU53sor3MtwsiCAQApDdarub9Y4aMB0eCjQaiGKGp8MLigcLIhxGr2nWrwMdXnKBusV0QqkA3WSxyPeR6+q8iFyJfk7+Yb2x846/dkxVc5tiuB4dpX19pMNOd7OSYsoVzZl8rjuGmaZpidVF2bzWbZlvtbSqkFdMYYF/VXKcDI0RrX467SdLDM83wd19fXSxmU5VKaX+brKZVYpelhPtalvmGZXyU/yH7jS1i2rh9Laa3gUMkW8pi6fC7KZ6iEVdnnXN6/9FmWx8+5DuRryvuXIC8fr1RUlmW79tWWn98MZPO9riFpXleaduZ196kM123EOq/WYG8Nt8/H8+fP+HrfspzL8We+vuxXLa8vgXq+x/zf7Xa7wMQPmz6Siegnv+GTy8WWcCVf7JrU5m2XbjY/WOWxcsqZeEktBSAQSKHw/hQqa5qmpER5zfWvoVJ+uF5HqMvzrvdTWvK7ftc/zQcfPONP/Ik/wTQ79BNCgDidaxxHtDbIle4/Qx0hEno5P2+6Tx/jHXdjZ/uUxxSna17PDpTr4a6DykskeF2xSsVfCd3KfXN5lkAu/dZL4yel5Dt+4S/gO77zFxOCK64rOeePMfCVr7zDyxdv8e7Hv8KjR8/PGsXyoSjL5NKDfOmBXkPG5RMi3rmkqFwgj0izkUIQYkSbik9+6tNn+VnS8fPySXnSDxV/7ge/h5v9Nd/4mc/zHd/2d9J/QuSDD675/f/Pf5wXz99Ca8dv+x//Mb7rl/7wnM+SQ7/ldt/xEz/5WX7gv/veBHYkICXXb080V4H9oeY4vN7kUxBp255tl6JBps+eTXvgqtuz3fZ0zc2sPjsihDuDt+v8Oq0D7xUuR0ud9BkEm6xO4MuaOSDFDL0mhfPzrPGkZ0BmZliWwJmbA1I4a7AuLe8kRQJUavX9zpI3QzMRZ/9bAWkC0nhkHVKURBVnMZkgBIheEkZFGCVhkMRREgYBg3iYT6JsnjhDsuV7/l2TTBpzQNfCka9wHmEjwpHMxZwgWMkS2Srvm5eRU9sh4vzh5K9MiZOq7CGDgxhR0aNxVHKiVgOtGdhUBzbtLsGyzQ1Pti94fPWcbbNHiLvt+Olwd4NB5JffejvcHYiuJ0ZipRZFmW11AmeN4tg1DG3N2NXY1mBnM8zYgXiz6zkAvFUrM0yD6w30AjGA7gNm8FS9pe0nun5k4we62KNGh5gjjoVJMdiWw9BwHBuOUzcvWw5jSz82HMYG6x94YUX5KuHRws9KXoeqPKZxyMaj24DsArENDOLIrRvwbUXsGmLXEdqG2DbEWoPRJ+XkiXHfryh7aMpANwmpEiTbATckMPYKeM45KNsDfUTorKCYP8ohpEOYkD6VIFaC2ChirYi1JtSaYBTRaILRBKVAy3Mg9iZQ1nh0M0exVBNKjmgGdDii/QE53aLHHfLwEnV8Cbtn+NvnhNtncPMMbl8Rbg9w6EmST/DK8Au/+5fD9pr/07/9H/KBtzz3lhfBcYPlJlh2WPZYDkwMwjEKixeWIB0oh1QOpb56c8rg5/YjJBAuY0DnZ5wJIyZMGDB+wLgeMx7Qhz1+NxBuJtyzgenFxPg8OYa3u4A9Qn+sOB47etvhZ59jViZn/aV5pddXWL2d/Y81iG01m1j6i/7GlqiWBTgzs6mlVB8uD8LgztRiCY4l/2Jxn0wpw2Ei7EZE7/G3AzGbV+4GpD31K9bQH0C3sxpsK6mvFHojqDaS6kotYMx0Iv3eCKoMyzaSaitR1Yfr4NtRc7ztGG477GGC4wumnWLcK8adYrw19LeK4bZivG3obyuONzX9q4Zxv6XfzSAs1qRIqmYGXwbnExDzoSZGkxzbRzODLkOMGnigcutrre76WicTZzhOUpBWEUw8tTXap490c1uUfG0pOaFEUnQtyq44oGOCXSocUb5P4MsfqOKAdntE7BFM83OczB2FOFex5AHg2vn4ejKzNHXNffH7rGHg8qB4PcGer6GEJCVEyfuWx1r7qFsrj9b3sJ68zv9JY7WwQCwhG1xQjFYm89kZdkVRJ0WhSL9DnMGWSP7kfEzuMyLVvL4izFAsigofZnDGrEykWo4dMMSPpk35CClixIHf/OifuDOenqZpUdeM43g2Ts75lvO4BCklwCpFLSdBxClQQgY4GZZlqJfFMjkiZAkgqqq6Azqz+bS1lnEcmaZpgULZdLQ0G12D12ma2G63Z2aJOZVQp7zfU305wTFgAURrNVhpFlrms3OOaZo4Ho+JL7wh3QdzlVK0bbsAO2PMEtjgUpl1XYeUkrZtzwI8ZJ9s5TNdPitVVS1tRF53iavkY+Z8zyqu/Htt0ZfLJCv8yv54ZgHZRHS32y1Ac80UMlxcjylzOb7ORLSM9rnmC+X+a65Tms5CqvulyakQYsm3ss4Mw3AWeOKv//W//sbyL9NHBmz5AjItr+v6DDSUGbt+sHMDL6VkHMd7oUTOwPXLYJHxMcvqYxGtxHuId18a68ZmrWzLjcq6ot/JsAvU+tJLYl3gEBFSoJXGWnfxumBWr0WQUmCU4Hq7YbSe/XE4u67T/0hmp4nOpf+Lk8+5dXlcuq9L1wF3fcfB6UVZSq7LF/daepr/Uw6C83mEUPzy7/4uvu3bv4UYTy/m06A5yWZCCCjJ/N2fXW/5IJXXvb7Pkk6vZ63y/usXuhBJXRSZzxfT/bngMbrhE5/+1L35uv7tnODf/4P/DF9+/2RW+g/8yj/Nt33jT7A/bNkfNuwOW168esxoK459x/644XDc0I9vgGYisukGHl31dO2eurph2+256o50TU/XDCmioRyptE++2KYTvJomzThKrDUEXzGOKm2bQVmGYdZlYJaWtojG6qzmTcEKllSaBy7fxen7DMmE8UgdlwjCKWPnMvGC6CTBCvAPOG/2u1V+stsowcl5fo4+NZCiG5bLh1j8mJCcbbce3Xp0a9GtTYPCrJgwFqU9QkZCkIQpgckMFqepYrI1/VjjxvrkRyWbs+XfOUVABISEKEWCZUvwgPnzgCwSMdyBZV11YNvsuWpveXR1y6PuBY82N1w3R2o9JggtRDKxLY9VALJLMA3uQv3yPyX4j0AwEttq/KZiajW21UydYWgbhq5hqDV2U50pyx4Ky9ykUgTM3mAXJ/8GepA9qCFQ9W6BZc2xZ+MHGj8hBg9jBCsJ1tAPdYJkU8NxbDmOHYeh5Tilz2BrHjoq1Nm8FY9UgdFVjKFO5VkDW6CL0AZ0G4gbCFtJ7ARsBLQigdp1PuTTy+L3V2N2WarJRtKzMgJDhD4gjg51sMh9jzkOmH6P6XeYaY/yacApSA7ZYyWJjYJO47oGd7XBdV36NA2ubnG6xccaFyp4aJuTb9kk82g5AzJdAjJ3wNg9ZrzF9K8wh5dU+xdUt8+pXz1Dv3iGub1FvNqhjmMygWu22GaDba9xbz1heOsa/+Qa9/gK/2QLj68I1y1cN+irBrWtUJ1GNRJdgari1yg6pZpVYyBiTCrR6JOPQCyVmKjFSCMGqtBThSNmOiL7Ab+3+JsBf+sZX1rGl47x+YS99dgDTPvI8aA59C2j3eKrR1g5wzCVgJlTm6QeyyaWVYu4apHbCrlVVCUga4q2sFSPzaaW2U/Zh01hn80px2Q6uTsBMg4WOXjCbkwO/F/1qCngbwdkH5KazN8d3MHcKRdQb/ViLllfJ3PL6kpRX6XvGYaZjUqfGZBVW6i2Avkhx+TTUXF40XD73hW3H1yx++Ca3fMrbj+4Zv/imsPLK463W/p9x3BosUODndJkVYp8PkPxI+nZ/FqCrL8XYEvEGWpxen9X8TQpVa7TKeorOrWVyYxxNmGcfXYpMcygq0eFA5UckeE4P/89Jg5It59hV4+2B9S0Q7uJyjtEoTgp+41wPpmdxy3rCdy1UCBvW/cfy/dgmfLvPOArFTHrMVS+hnLgW8Kx9aTpGozlvnw+T2lmF6JEqJbJyRk4VSBSEATn9QK43AypAkmxlRRcFS6YBYYl8KWT4mv53wy5MtiKBs9shjsf7+c7SaZZBWhnn7cTEosSab0SNv2WDlXuK5J6UMQBLV16rwmLiCNK2LQujog4oqWf/ceN1CZCGFAi8Od3/xo34VuXa/nG+g/z7c1/fEctWNah0owzbytVWKVYpawPJWBrmubMWX2pAsv1IW8v/aoveVZYFwiR/BJmlVA+Xt4nq9zytWWwMk0TMaaoltksM/tIzMAjB0DIPhAz3Msqxgx5ymvMQKsEeevnLI8JSxZwOBwWp/slNHpTyvBSSknXdez3e7TWXF1dsdlsluczQzYhxMJQMjzbbDZYa5ftJVQrLf+As/ttmuYOdCyf93z/0zQtkT5zPuaAK8AdkJnrW1nG5fFLwLbf7+n7/oyp5LYlWySWDKFkOLncc93Ix85BDspAL7lO52vIy/V4v6yjeb98vFz/M2DLKYPWXE5CiL93gK2EEHCy6S3ll2XGrxuHUr5prT1r2EtQVxZiSWuXxsWfd0iDc8mHFudSwXWlWFeWvP8leXB5rNP6BLIE57BnPYtQ/ne9/uxeltUnUPd1n/wY//L/6l/iz/zZP89/9d/8MWJIAE0gmKYBpSNap+n18xe8TH7AMuwD4tkLHeAk8S6vM30CUp5UI+tUNly53O67t7IxKF/waan5hd/2rfzSX/ZdhHCi5SU8zIBNCIGS3CmT8iFaP6jraynr65rOl0q4cxibgGcJT13wKFnxqc983Z2HuzxneZ1f/PKn+Q/+4D+dV7xZRUWkNhOVHql08lGh5WwqR0AJBUGiRMRbgbOKaVKMg2QcFc6qBMzCa6RIa19YJezSl7YJhAnIKqCqgDDx9Mm+gTSgBHE2HQxSEqXAC5V86lh1GoBf+uRtb3ICJTh1sJfOtkepgBQBGZOvM+VjMqkbBLEXhF7he4UbFGF684hHG0vT9bRtT7c5smkPbDZ7tt2OttlR1xY5K8yClAyu4TBuUqTUacNh7OinLgU76Dui0w9Xl0mxirRZlMUDuILEp4G1Hmn0ka46sql3XHc7HnU3PNrcsG32SXFWH6n0qTOUk+f8Bbu0MfEuyF//99LvECOhVriuSpCsTQqzqdXJX1nXMDYVtjO4ThFaSWxBPHBw6gZ97ti/r3C9Jh4Fsgd5iKhDQO0Deucxrzyq98RDJB4EcdTYo8IOmsk1DL5i8jVTMNhgsFGTkJciPtQDeWlqKzg9VxUnxWJHAmbXwCPgMXA1b0uxYc4HtZeUZPepyh6SIif/ZEuEViB6RHTIMKZBqL+ldq+ox5fU/UuqwwE1WeToEFGhhuRP0+kGV2+YusdM9TVTtcXpTXIKT4uLNd5XH+IC00XK2qOqk4JMyyENkP0R4w+YaYcZb6n6G8z+JfX+JfUuAbL65Qeol7eo3ZE4BsZ6g++umZ48xj59jH/rMeHJNeHxFeFRR7juiFcNXNWIrkJsNKJVqEYgq4iuwgPNKe9PpTlldsKfVKE+gTEmKjHRyIFW9LSyZ6MmOulphKbD0NDS0NGypWXD7//3/gDjC4vbBfpbhz3A2Ncch45h2uB0UpBl9ZhVmyWiZVaSxa5DXtWITYXcaEzrCvWYPYtoeaYmmz+6epAXv1PJukDYuwWOZfVY3M/O+HeFr7HbIYG0w+yLbFaQCZI7gEjysQUNi3lh1OhNhe4U7aMKvVGzmkxQXwnMJlBfRaqrSHMVqK899ZWjubY015Z6a9OEiFPY0eBGjR1Nirw7pe9uMNjRMPUV/W3HzfvX7J5ds3+x5Xizob9NIGw8VgnkjxXeZj+Xyd9ciMVDe9Yv/JApFsuRh00OlUkVqq28rONpcqohga0FchX7VMVv5dPkj/II4ZBYhLRIRmRMpovJjPGIDgc0B7S7xYQjyvWoaUTbCTWN8yfBLjWNKTjFNCHsNFuA3O2H5rHI2kLlEvBaQ6+yj3hpIFfuS3H+cp9yQvmSaWTZh73Ut837n86lcEHOUKtJUCukOn9SWSXfcFFUuGhm80M1q7o0Pp7Dq6zUyuaLJ6VWRcDc2Td+TW1fLyWfoJWYAzrN3yUF7BIWGTPoSvBKMNcrYZFxRObfc1AoLd3ch55BFyOEARFGlHAoaTHKUxvBMByX8U2pfinLpRQOlKZnJcBcjyfKsiz3KRV+Nmz4u+P3c/Dv8rb5O3yu+eMIcW5eW6qv1sdcj8Mu1b3SpDaPAy+N50twkv3ThZACJeSotuv7vG9itYS+pTqszOOsXsoKq6x8K01c89gzQ5E8NsvHWqvX6rpegGJmDaWrqjLlMWC+ngz8cn5Ya5fgL1l9l4FQ2WaUqsLHjx+z3+/PnPNnZVp2pH84HM4A6DiOC+y65KB/3SaFEGjbdlE1lgFYrLULTMvXWkYYLcFk6cPtUvsD5wrUfO4S4HvvefXq1RIQojSBzduzuq48VwnYLqnnMhArzV/Xxy7rSAn81qa0GWDmfZqmWQBb/m+ue7m8pmniR3/0R/kw6SMDtlL+WX4vpZjli6SEY5mW5sy4lJn5ppzLvsAgO8qXhV8skdVqQhBDIDgPqxfWffCjrLQZLOVdsx+0UwzC0whn9qyV7quIVsq8RwSkmBvAWZF2aQCa7il1otaNUlJtRbRShCgIMe8PQkhCtITgkCJFEErHixhTLxLdSK60MU1mCnnW0J4DNoGUAmvdcl25bJ3zZ9e3ViiuYWhJiS/tf6LGhm/6hq/nV/zKX8Z+f81f/ku/FGsNv/g7/gaf+MTPoWQawC+Kx6KzuabT6wbokp+2soNVvhDSLF3EWoVzVVJtWU2ILeOgsLbCWoP3Fd7XWKsJcUPbPS1UR3pRfVmnk7rLnnyDDUPN8dhBJ6Gd/fN50hKST6u8XJzBiwvwa7VeAzqiqhTBMYGu9BEzLMuwKxbAK8ivQScpwhyQ6ewjxpA+MyyLoyCMs+nRa5KQgaoZqZqRphlomgGjR4xMkRtF9AgfwQn8qBj7lqHv6I8tx2OXfFC9ISll2WwObLoDXXdMvuS2R5rmQN0OyCqZgkYtiEIxuJqjTcEMjtOG/dBxOCRYZsfmLijLy+WmSLDsTplxXqYPGEhpYanVQKP7GZYlZdn15pardkdXHehmWNZVRypzT2Rk5mdCphAxIp7arnUK4nywcDYIAHytmFqFbQ2uMzMsM/Rdw9A2TF1SnLlWEdvZDPOBVS+bXmZTTDcY7LHC7xX+RuNvFP6Fxj1TuA8M7iuacKMIB4nfS0KvCKNOF1rm9TrvH6osWQBo8f86QhORXYQNcBUTJLsWhEcCHsk0QC3/n4+x/r4AsjhDMnEOzT7MNRbHFgSU8CgRGGNFcrwN+Mi3jj+K/Nm/ywc/9YX0rlMadAO6RZkOuXmEax9h9RYnWxwtPjS4UBH8BVPt1yURUbVHVg5V+xSQRsyALBww7kA17aiGW6rhlvrwknr3kvr2Gc2rZ1QvPkAdJ/wU8cEwdVfYt5/inj7GvXWNe7RNcOy6JV61cJUc8dMlMCbbFKlSVxH1NYhOeW5OGZHBoziZU1ZiohEjtezpZE8nD7TygLSWOFhcPzHNURKVN4QD/IO/5rfQccWWK1q2VHSY0GGnjmPfsRsabnrBq4H06bnz/Qf+3F9BXG2Q2xq5NahtBmSFSeUSrfKkJDv9/nDmlTGC30Xs84B/6XEvPf5VwN+Av4m4XcTvwO8F9jbi96SJjiO4o8QPimhV8qWVfW5Fc/KtFQxRKqptpN6C2UDVBUzn0W3EtB7dBHTtMLVDGYfUqR1XKiBkcvGQQViCYvNyTIBs6g1TXzP21bxPAmB21Hin8VYTwkyuy+dx/Xx+mGf1TkYWGVoeN0+EZVPlEmzVsXArEBOo33CKjlwCr7r4r0qSbSkcQlhkmJU2YUCGHu0G1DQgxx41HlHjIX2m47x+RI0DchqR4wzBxpHp9hV18LjdjtAfEbMCqpy8zI7S4VzZkscOZYAGOAcZef/S1Gc9+X0+2XzuR6uchL00RsiRBst0Gt8A4qTKygAqUuMxyZ/capsPZjErzCaKgfQ7xAS27sCuWBHQC+wq9418yDb3I6QTlLoEuGa4NUMsNX/P60UcZ4WXg6ziiiOKOYpnnCAM8/EmREiQK/jj/F9LDPZsjFaWTx4EZyumPCCu63opt1yG9wGtcmBemil67xcVV3nsUv1VQrYS0uSx7LqultZa+X5yPcyBGcpjri2ryuPDSWRSChzKMdBalFL+v3xOMhy77/m5ZOq7Bmg5P0of7JfuuQRUpSIuA6/S7DTvn8syX2c2R83j1TIYRd7/0vgzn2udJ+sxar7eUtGX61IIYfE/l00KSzPSXA9CCAzDQNu2C6CZpommaRZfdIvrLKn5/K/6R3n16W+ne/YzfP3/7/fT+XM/X9lP2NoMNP82JrUF2WxVa81ut8MYw+FwWOpuWR6lz711nVibUpb1uFxXqstyKtv4EAK3t7ccDoflOSnrammCXJ4vg7FL7rqySWd+DkvWtJ78KCFbCf/yPkopxnHknXfeOVPIlaa4lwIkKKX4W3/rb/Fh0kcCbB//7MeBc1vjEqSVM0Pr7yXQyjdf2vSWBDHBo/nlqAXeJX9neXq9BHsiUapUcN4TQ7xTMfJ1nt2yOH/BrmWRZ6l0gia4U+kv/W/dOK0HukKI2V/1uXnUeqZiPcu2roB5fQ7pG+V5g50rSAwk4HbhmstGMKdEm0/EuSynsqzWsxXl8ctyLxVtWld86uMf47u/+zfz//h9/xz73VXqLH6v4N1v/4DPbX6Cv7/97wmjT765XMU4SJzPEMwsEGuyBpsd2ttqAVv5k/13Zd9eJ39eyaTCuSpBrXuUW3cG5uV6tf6fSIBrUXclBY6XKpnwzfsKkyAH4qP2yF+fFP4skmJaeoywqOX3KZqiJkVni5MiDAI3KPyosL1mmtVA41AxDg1D3zAMb46cafQ0+3M7sGn2NFWPURNSeEQMydzTJfPUsa/p++0MsDYJSL5BqiWln2HZ4bTcHGi7Hl1bVOWXfPZCMflkwncYO/bHKw6HDX3fMQxNUtddAma5v52VQWtQ9pGATaSWI41JsGxb77nq9lw1O9p6P0OyA119YFv3tNURKe52/GEN7c9n4U9LOT8bCu+zma/C+xQUwnqNq07ml26j8Z3CXinclcI/UgkYXYO8DsjrgNr4BHTfdKeRBMf66txv2aHC3hrsywr7wuCezc7/36+w72nYyWTelP2pW1LTfyf/Y6Hse9izJOTsX68KyGYOGNFB7CB2grCV+I0ibNTJJ1627rwAsZalmKVf88TJ4vPvQ5pdihBQwWOio/KWJky0YWTje5o48IPvfjtRy9m0OlKHkd/xUz/Axh3ZuIHGDnR+pA02PWcx8h999rfyI1/8VqrHPXZXEx9iWr3Os3pWj1UOpS1KjpgZkhl3oOpvqI+v0vIwq8duntG8fIY4HMHK5N+m6bBPH2GfXOOeXBEfX+GvO8KjlrhtYJsiWYpOIRqJbASyjmiTfMr9fJhTajwmq8bkSJMVY/JIpw60ckBFB5MlDBHbR+ze4249041lejkxPhvpPzhyfK+nfzmwv7Hc7D23Y0M/XTOFK5x5hDdXBPOIWD1CdE95/LGvw6kt3/2rvo9Xo2cXLHsmeiZGNSvDansGwUwzoesJJVPAEIlP6mYCYVL4QS1LP/uJXL4PCj/Ny0HhDgmA+b3AHyEcBb5XhEHieokfDWFUeGvwVhOdJgRDCJoYZpOtdf0ufgvtMZVL9cZ45GLKN7sAgNkVA8Qg8S5FK/ZO4UaNm14DFh4Cux76/aOkrPZaK71aoIknheomJpXqZobwW+BRhOuYVKvXJN9e1iFGj5wccpyQk0WO06LakuOAngbUdESOR/R4QNvDvG5EjANymHjx6pcxHN5hGz7Px+SfxNi0TdkJuXLoXfYD133W8vsaXsH55GZWJ6yhWt5WDralTP6Eykh6eZyQAUoJvkAiVLMALBeSUkuoNqm2ZrPCrObyZ76z9Ky+SoDLFTAsclJxnUOuEwhL2w1f28gFd5PAzeaJ2RzRLjCqBFzZVFGU65iS7zbpkGJCCz9DshSoIJspJhPGad4nKbqUtEnNFe0iYCjLdT3YvjSeWdeXXJ5Z8ZLVJKVvqNLv1nrsk2Frrh8ZbuSBcx7TlMtywJ4d8sMpmmOuV6UJXVnXcn3NkG7tCufSfeZrXW+7lNbj4of24cpx4KV8XoPiNSAsj7t+bi/9vgSf1nWizLu1aV/pp3sNA8sxYS67skzXY/F12eT8bpqGzWZzJtYp/YqVdeN4PNJ13VIHc/nm+pfPne8nm6tWVbUsy3LIbRSwmJ2WjvPXQSfy/eY6/aP/8L/Ez/7Sf2gpx6df+hG+9z/73xIL4FM+P2vQnNvYfL78O9fZEs6tQdrr0pu2l/WuVNeVkVettQzDwG63u+OnPYPSdf3KeTTO/uuzqi7XgRgj2+128Tm4rkPrely+c5Lve720CRmoX11dLe1ShqdlJOI1mI4x8sM//MMPyp+cPrKnxvVDeB9kKalo3l4SxZIslsdOn5PCCxJky8qxM7Azw7WQM2PVxpXXVj4kpx3SooRrd0BcsV8eZb1231U+rc+5NLCril8GhFh3Zi41vGXjl7cpJbHhnNqWDa1UEoEgzIq9fOw1LD2BybsKwPU9rxv5Sx2x8lqkmBWHIfLDP/yLE1yDNBD9C5H3/uLbvBff5i/o7zkHWm/y35XXt8CVuAC/IsKk5aIqUiJ9vsagax5qo/Ao4ajFNDuhjzR6YFsfC8CVIqwt34VdYFjaZ4Zl0mNU4GrTcN2Z5MS8AGXT8IrxcINmwsSAkBLnFftjx+G4YX/csO/n5bHj0G/YHbfsj8nnWz90dwJtrFNdDWzbPW8/ekZb91R6QkuLFCH5RPQCbxXTUDEOLYfDhpv3H/Nz/Sd5rckqIESga5Oy7J2332ezOSS/cu2Iqh2qCkvZJWBWcei37A7X7Pst7x8/xvCVBjdVl/2W5eiYgvsBWXdh2xuSxNOYYVGWbdsdm1lRtp1hWWt6KjlSyQkVJ2JMQNi5pHpM4FdjDwp7o3nhnvK+e3cGwMkXXjL91Xiffejps+3ealxQxCvgUUQ8DshHAfU0YN6eMJ+wmI9NmHcs5q0J89SiH0+YK3uPsiyicKhZlheDwPaGcaixXynMMA8Ge2twNxr/UuNfKOILSXguEC9B9QFlA8p6pA+IECAKvJBEoRGoNIFRRlUVJDXG5tJ15QqTVFGqCYg2QAOhk4RW4DsNnTgPGjGrPaKW+CjPLZEFCDyCk4JEiDBDcM4VZfdf0GmHGd7o4DHWUQVL40faMLDxA1t/4CoceOT2PAk7ruwtmzDQ+gHN3aA/cHpPfLP8Gf7gN/+DBCGovOV3/fgf5VtvvpjU21IurgRAzCJZQZgZRf1kQDUJcijjqcIt2D3S7ZB2jx5u0f0N7fGGq3BExwFtJ4J1IGq80oS2JW5awvUGNi1xU0OtoZKItobuk4T26xgbga1gqPxiSpnNKTOTuJzyg3tK3stFNRachCjQc3RKg8VgZz9jI63saeSRjTzQqSPVPIEg8XgPdgDXR9w+YHcO+8ribhz22YB7NuI+GJleTtzcej64cfTHmmPfcbRbJnOFax4zmcdY9ZRRP2JUT7DqEVZdYastNB00DeJRhTQmmfaLBMBkTBBMhoiYlcw2RJjgr/3AF4hOLCDMDwrfVxyHDj+mgCoZnAU7m9xfqo9fy3X31festH5AiiQXBkz1+THzc6b8DN5CAm8qKa1EFdCdReGIURKCSKaUYTat/ahUrJ7B1yaezLM3swq1I/k23JRLwMzO7I1NERxVUutol1RcyYQxgS819hjXz8qvPdrO+xxH1KsR+fmk/lLz/5IKbARrz8WtF/qW6wH2pb5njJGn/MUzxUzZZ/TFAKWcKL10HJh7gMIkCMUpYmJyIJ+iIAZRJY9/UeKEPgNcCVzVRJFMFtP3GmQz/86RFVO00RirM59bwc7qrUuu+T68u757U1JvTQuI0hyR8qaAWzP0WkwYM/DKYKzw0bUowOzyO8Gt8WypZpWXxKIfyO8uvRdKtdWlccfrxigAUcSznn75/rlv4L3u41+qmxlylUrFPCiHcx9hpbAgD3zLceJ9Y6ESzmRlVq775fizFESs86xcn/e7D4Ctj1Pe+yVAldMa0q3HcWvhRE5rVVj5v3K8Vu5TAqr7INy6rNbAroR0JYjKv3OwgnyNa2hX3nc5xizzeS0myaq4NTTJy91ux263I8ZI13VnADir3crxblZ69X2/XHMGLcv+MWID9NJQXT1CtRuaqsErw+QCo6mJVYNoOqhqhijxykDd4HWFlRonNUMApyqcVDhVEasarwzB1ARdEUyN14bx6u1ZqZyu+8Wnv50v01A9/xm6ruN4PJ7df76ntcorA6x8j6Xqqqyv6++XoNt9z/GltB7jr4VU5bWUKQPA/Gzm68zfS1Vg2SaUx7/kQ7+8vxLGlsBRCMEwDGdBOPK51sKwfC2Jp6iFW33Y9JEB2xquXHqo8n7rBiRncPlgrOk1QIinMMznzvLuQrl8HYuUu3hoy4K+BJMSCbkbFnZ97PP7utt4Xqqc5YzIffl36fhr5dq6ob/UcJ5eXqeoKYsa8Ew6nAaQMQSk1GfHKq/v9HLOn7sv7fseyPWLuSzrDEGlTDLaNvvwz5HafpGE7/7azxRKPAqLxs6KLYtmQuHSMqZOlMKiY942d6zC3KkKE0Y4GgNvPeqodMDIMAOxdByNo5YeLdP5lDjPm/Q8pHp8RwyTy6Csj+u6oTVV8w52esrtvuGD3Zbbfcvtvub5S8mrG7MESDgcNwxT89p8EUTaJkUWfefJM5pqxKjUaRQxQADvFHbSSWV27Nh95YpX/ePZ183rU9se6boDT58+p+sOtO2BqrOoyiErn/yXCYGNhmFoOQzXHA4dL/snfGX/CaZnNdHJ+80xJXcVZPnTlr9npdMDVE4Sv8AwI1KZahwqelRIgEi4UwTP4CR+hl1H23HrrvAz+LIz/IpvgIsXy0YH9FOLecsmQPbuvHzbYj4xoT9m6d45pnVPJ8xji944HuIeLHiB7Sumoeb45e2iLHNHQzwI2IM8RtTeUx0t5nbC7CxmcrTesbETwkbwinGq6KeOfmrop47jpDlOLb1t3whVl3s1AdGmga9oA7GT51FV158GqAVJewQQUHgkMYEMYYlCEIUgyLREFiZeF6pBRN15JokRFQLKJ0hWBUvrR1o/sAl9AmVuzyO/44m75Soc2IaR2g9I7rbT6ZDn6183SXOpQ/wrnv0I33LzRZ7Xj3m3f87GDdgaDh+vMMfIZj8SgmKwDUdjGHTNN8mf4Uuf/ThSRdrHR0w78RnzeezxBZOKqI1BthrVXqPqR6gmm1NmJ/xvMqeMnEj2uKxN5pQqqVWtwI06gUccJlqMsNRiohYDjRhmP2MHOnmglhOGBMeEgijBjjD1iv6lZnipGF5IxheS8TmMz+D2A/jgGUyvJONeYo8bhnFLP20Y3BVWXuPkFis2ONHhRYeLDZ4GL+aIcrNaJUaVogF7QXSAE8T5s0Dgdfqo0Oqh68ptXwN/31I5lE7qMlNZVJ3NSS1mDtCia7uALyFnBX9C0USSuWwIkuDV/K5IwVrsWDGNBjfObcugieGCGdSM8Rd/hZegSR1PSrAFgoWkEmsCogqI2iOMQ2iLNBalBqQc0KpHyyOKHYZdUnzZZPbIUbN79o2I0fKW/atUL16gvpLMHrP5I+OAKga18DDokDvrZV+s3G/9n3J9OYiOUZ47fp8VWtlB/LLuzExxdjgfZoAVT/sFMTuPL/cvgFZWdQUxq75ExZ2XyqXbz+asHyGJOKurmJAxgSsjjkxvPSE+rpGiZ/vBT9Pu3j8puuSs3IrTovgSjAvwUuLkqF4U4CwviUPq70mHwJGDba1ByOtA0qW+c16/TmuQUo5vLo0p7kv3Qdb1+6SEJvelcux1aQx0qW+/Pl6p+Fj/LpVCpWlYaY5VWrZImZyKZ9O3sixKZWQ5xihBW5nP633XgKc8bk7l2HKt6ioBVv6+BmpvEh/kT+mXfH0N+X9lWdxXnpfqwqXrXad13V4fI9//OqBhztPyU5rGrq+9hC75+CUAKU17Y4x4IXHSIJoOrwyxahZANUTBq37g6CLBVETTEKsaUbfEqk3rqoaYgVjVEKoEwZjXxaommIaoK0JVL/vztXCbcyEJ75BuSp9pIB73sH16ak9n0PbsSz9NtXu+1NumaRAimS5muJOfo5yvpbnxWiW3tjDL/1k/z5fePXfu4R4+UULwNYAt25WsHsv1MfuDy9e7hrblfeVzX4q2WoqI8rYS5ubryubFXdctyrsMWkvl4yUQnb8/pF1ep48E2C4BmfIG17/LB74ER3lb3jdnyvphLwtq/aBmWCPKdcUQaV3Il2Bg+n6isOvMvPRyWgOkS8v7OlCX8md9nvsa0nXenzfouWKc8q2cnVlfixACRFI6lGVzpzLNg9HS7Dan/BCX97CeZSlfbku4YU6Rin7hL/ph/sz/8Ks5+C38zrJKRhSe727+yknVhcNIi8GljpFIYGwNyIxwyRQypllGFZPCqnwBl+D1NEsDxKTuK9cLIfBzvkohqWXNx9qPI1R6mF/cPOan3/skj69e8dlPfjndczh/gZ2XcyovOUO2ECLDVHPotxyOHbtDx6HfnqvNZvXZod8w2dePrOSsBHt0dcPHq/epzIRRDkmAWWVmJ4UdK4ZjzXHX8fK9x7xvP/ba46bqEIhRQoTHT1/wma//aarGImuHNIEgwXnDZJMZ6bHvOPYtHwzvMD7/NMHe4+g/F8d9sKzhHJQt/sseoGLwMZ3jKE7iGMvp++p3CIqBjoE3+3XLSSp3ptIxxtK2PcrMv1tL9faQ1GNPLfoti3rikG8FeCvCE+BxRFwDVxGxDcj2YY16cHJWlrXsX57MMX2vEH1SkOmjw/SW6jhR3TrUzlOPgXYaYRrxo8SPmsnWHKaOw7RhZzuOU8cwNjj3QP8vYlaHbMXr4VipKmtAqICODkVYhu5RyBmQSYKQy3JRk501bclI7j736iIEZHAY7zDBUoeJxg90fqDzR678ga3d88jd8NjteBQOtL6niklVUraj63Z53bEWQiQV2WpQdp/Jf5kiYIVm1A29qjk2FX3TMNaGvjHYWjPVCldJbCX5oeqT+EbgtwIhA0o6pAootTanvOXX8gOvKbhsi5vvK4H1ECQ4gbMGGWYFlvdo79HOop1HW4caPXqy6N7DMRKPCr9XuL5wCt8bxmPFdDQcDh3TYVZsHnT69IrpoJNJ8WRwtsb5Cueb5M8IQ4xz+N+fT5D1piblI09NrlNyyq2UR0nHu+++hTFQGTAVKAVKRYR2YBzRODAWUU+IakI2I7IZ0O2AbnuqbkBWN3RXPXU7zo69PUQxK1wVbnavMA3J2f54aOj37bxsGPcNx33H8bBleL9l3NcE9xEHHyYmENZEuAqIZkJWHlE5pLFIM6HUiFQ9SvZotUeLHVrcUnGLCntMuMGEW5Q9oq1FTgNyGJDHkfoQoJhZXk9ElrPQl1xgDMPH+Gs/9O8y2acAPK9+Lb/su/4Fqupl8WwKnFdE1awiIyZgtfjVmgGVm53Mh9nnlo+GpMg6mR+eYFn+79p/V+F/602+t173injTRMvScQ4z3MrqqgnFHlOosk7rT/62RBwRTETfJ2AVBoI7QhhnVbtFhBERh9QuxfP/5uNWJqLV3H4KgWw7Yrfhi9//u/nKr/8tqSyjZuc/w3f8a/8X2q986Y46q2xP1yZtS1YVA7NyrCDueeAvjVXKdfe14etjlGntczp/X/fVy37j6wDefSDvzvvowrhmPZi8NGa5dB2vG2yugVQJmssxSamgLP+T15XQprz2cvk6gPA6Rdg6X9bLS3l2KY8ujSnXebjO71KNtR6HltdbjndL4FWKJsrrKIUr5f1fKqvXjXHztiAkQVVYo/G6IpoaKzVeGbyp0jaliaaeAViCYN5UxboKPyu34rwtrasJ2uBVRTDV2f+j+vkBXXiHmIYUFGUakHZAH14h3YSYxnlbWjL2CDsShiMMR9xxjzvs8ccdzNvvLMeeT779Fp/5+Ds0AuJ4pJWCYKclj8dx5Atf+ALv/5rvp//n/82c+Vz9if+Eev+CEE9BJTK8ylFSs9JqDVBz+ZewqgS/6+d2XVc/CjhaH7dsd7XWdF13VvfyOzjD9gzc4BQQIsZ4ZtYNJ4s+7z3TNFHX9ZIPWZ26fsbLPkAZdCLGuPj8gwR0sylqvvZLkC7f51ok9ZD0kbqJAomUKqlsOHekvW7oygpRSv7OG/fzRqbcF7LaB9Y9ibMGuahM2ewyzziuG7K1oi1dR6ZIpJHF/MKVIg3kLjVG5XEvwcPyXvI+l/IoPxDrirJ+6ZQprpZ5/xBjCvxQVO4z89CVfLwMwJBVgusZreAjMZzuOxPfct98f+V9nl1vPPneSPeriDEJDa+2B/7Zf+4/4K/+lV/GF37um/jZT35m+d9vvf4jfFfz1+7MWOVj5s86GuxSJ+XsWDF44mq2aS2lZi713MjdKV8EQkpEEcQjxsiPf/Gb+AN/7Hcsaq5f891/gd/89/0pQoR+aNkdOvb9lmO/ZTebaR76jv1xe2aq6fzrH0clHZvuwNNHL+naQFtHtAwIAsELghUMR8FhLxmODcf3Ot4b333tMQGMmWibgSePX1HVA7r2SBMQmjkgglgiJz6/ecpkG9gBMfLKPeXVjz29C8zK9FpYtvoo3jy4pYBkA2eQTHifVGbRYUhQthITJkMu7VDGolu3rFMrKFZCMq0t2rg760Tj4BHIRwK/hXgl8RvN2Br6rmVsa2xnsK0mdIrYiuSX5wHJW5V8ld3OPsl6gxsq/FEiepB9wPQOc5wwe4t+6VGHgJkCZrSE0eEmx2irFM3UdtzOkGycauxoEiB9SKqYza15g5osYuqJqh6Tc3ERlnIMIsMxhZcqRZSVCZYhxOKfLMz6zzeWfgioDMr8RB0mWj/Q+Z7OHbkKB67sLkEy94qrcExml+HkR7Js94WQZLq77lCXnf/837xcD3QAPJJBVfSqpm8a+qZmbCqG2jDWGltrbK1wtcQZSaggGgFVABOTiZzx90SnPDkElJx8l19KzimCTWaGw9QSJkkcJXEUxKMkHAVxL4g7SXil8C8l/rnGfqCZnjf0zxvsbZ0cvQ/J75abNH4yX5366qPCra9CGXMxyYg0HlUFZOVmc1mL0hNSTmg1IsWIDEm9LIIFlz7CWXAerCO6AFritSZUGldpfK3xtcE3Bt9WsFWYJ9A+jbSPHc1jS/tkons00m1Gum6gbXvauqfWA8o65BiRQ4QewlGdINj+BMOG/fx5v6XfP2I8vJsA2aGl3zV4+xEdoqtZCVY5ROVQ777E6BGpBrQ+ouURrQ5IDmj2GPbouKNih3Y3aPeCJuzQcZecnVMMaEcBI2cd19fB6tR/EwuokrrDeomXTxi8YjQbXND42ODcBus7vO9wvsOHjhBanG8IviGEluArvE+BFII32OkRoelS22xg/My7/KXwnyDdVECuueH+8C7/7k+rMY3IZofFR7MrzBHL7ScIlqImusK31jm4SqArRUzU0iHCiEyRhyAMyzKGCVVMsMKpnVxDq3S9c3Q6IDQdU1sR6obQdvi6JbZd+t48wtcNoUnALDYtoe0ITYfP3+f/habFNx2haS6rSYQgasPLr/8Wmp/7meVa1xPDazVF2Z6XY5F8j+t7Lo+V//fG4nwNrFinS25b8uB4/Z9L17Y+9vp5Ka/jPnh0X7o0+F6fZ33+S8c+Pbvnk9jrMU0e563LKStw7oNV+Xc+flY/lZP7pf+n8jrL+nEpX8t98/a8rnwG7rPked1xy/+tVYzrPFjGmQh81eKkximzmBzGql7Al5OGcGaOaPA6/fYZculqAV7ptzmBrmKfvIzqazaLdJ6CR7kJaSeEHZFuour3i8pLFcuz796ivWX37H36Vy9R3iLtiHQjYhpRPh1T2gTRQr9HeYe0IyY4/DTeKcf1+yYvs7rKWou1lv1+D9YSZn9v96Wt+haePG0X9aUTJ7dYOTiCEILr/+730335J/Df+svQP/d5Nj/0p/CFmWIZOKI0X8xp3T6VgSWz7zW43yT5Uj82/y7T+vkvj5XXZWUjnJ7xuq7vmHbnlOFh3reqKoZhWI47DMNy7WWwizLIQd6eywouB7HJwLGu67N8yWKfcrJjDbfX+bHOm4ekjwbYRAIzIUSEPLfTXktFCRGp5BlgKW8oxjx7JIjxfDYgZ3Byupl8spUPRAjJl1furJQU13uPJyKlIIaY9ruQypdw6ZcMVrNgxX9iLOe9EtQqAd+6Ib4EnC7NTKxfkmczFOX/Vg94BmsIiVTJJ4tALruFEE95FOc8iRC8R4i7L50yf9Os07mjxvK61/eV68Daxn7dWQuhlMoLnjy55fu+708hxA/whfGzfODe5tPmZ/m4/vJynvte5OV1rK+/zKO8vfzfurEMMSzxYiMegSrOmYEjTNOGv/uT30zbHvljf+Y34RczuMif+cFfxQ/+yHfQDy3hDSDD6Im2Hni8fZlMSoWfzWQE3grcpJLaYDRMY83t+4+45dFrjykISBlARLSZQCVQlmCZTGZyOSqZAmsqrK641dfng9jIuZN/SKY5FvgKpyAP9bw+RzkrFWbqIY1SRJNMxSo5UcuBWo80uqfVPW3V05ljCpRQ79nWB6pqmuHYNIMxj1aexP3vAv9762uMBCNnx/4VtlELGBu7mr5tOHQNU2twnSa0yb+XeKBplpuSgse+TGqR7OQ/9GJWlnn00aOPE+ZVQN045CEQJgFOISYN1uAnw2jbBZJNQ4Ub9cOVJZJZVQa8wx1AJpqAqSbqesRUFt04ZOMQShCReCEJUiffElLhRfoepJrrlMCKGiseSBFjRIbZkb9zVH6iDuMCyjZuz5U7cO2TouyRu2XjDjR+QIv7O6/n8Oy0LsY0S7MehGSa46NgkoZR1/Smoe8a+rpibA1jU2Frw2RmOFZJQiWIFVDFFMikSj6k7o9Oacl2b6/zPeadxE0Gd9SMQ407GtzB4HYau6twtxr3yuBemRQY4oXGPTO4Zxr30uB2ad+LlOqjqrvK9DUwSUQFpI4pwETlUc0cOKGz6Pb0OwdUkHVaJ6sw+9tL66QOEANxcjB6GCxisMRhgt4TbcA5gQ8CHzReKLxSaTCiDU7XeFPjmxrfNLimYqwNobkm1oZQa2gkup1oNz3d5kjb9bTNgbbp6ebgI00cMH2P7j3iGFDHAAcBRwgHiTuYEyB73tB/sWU8POLZvmE4tAz7Ezyz40fLYKkizSbSbcHar1B176PkMX1mEKbjDh1v0GGHZI9RA0oc0LJHiQTMjO4TdCGrMCHG1P5EMfvNEg1R1Liok9+s2UQRUTPFd+jjJ9nJ+sycMVLhYkXwNT60eN/gQ0PwNSHUhAV6aWIwRK/TJyjwco56y/3LDz8JfzfNkz4RMcOtNdg6h2DZafzJGf3d7Sn4RPodQ4qqmJzJz/7bpJsh5N2JylwGOV2CAEs5ZaARV7P5BJRQ+Ag0G3zTprpePcbqClvVCYC1Ha5uCXWTwFfTLgAsA7LQtgsY83VDqFui+SqjWjqHGo7IoUce91QvnqGmAT0OyKFn943fzvixT+QbBiFoP/jKHWj2urSeDHkTACvXXwItD0339VPXE/733ce6D7O+htflwXrM8bo8ujQ+ua+urdOlyfVL15Tvu+ybrWHUepnBBJwD+fKYr8vH8vyXxi4lmL0vX/J50phP4EyNn2FWAlbJp1asGpwyOKlX8Ko6g1dOm6TkKsBWuc/ptzkdW/08RY6NYQ54MqWlmzDjEelGpLOoGX5JN80gKy3FNCLtgHQ2KcH8hJxGtLcFDEuwS4ekGlMuwTTlJmQMxBDOAgKU3KBUNub8L7f9zM/8DK9evaJpmjuQqYS2ef8QAnF1rPuej3IMfxr/Xg6SeKmfWaq2nHMLKMuClNIcVv/YX0H8+F9dxssldC6VW+UzU15red5y/XqsfVbkq2fj0vNz6b7eBIsvgey1ArU8X2muKaWkaZple9M0ix/FnF/ee7quO4HLwiS2PPY6AmlVVYQQqKqKvu/PhEFVVS2KtjJCbA7WUd77R00fCbCVswYhuosvAphJsbhcsS9R1BgiyHPotHY8p/UppK8UMimOVlOLZ7Mm80u5vCa4a+5TVv5yX5EB3ureRPltOcf5sdcNRnmudQGWDcPyQEtJDAGEODNRWgdGWC+9D3cAXgKUabtzOTpOuuzcEJQvvXIppUizyPc0LKV/kUudivJBKB+4HAUlZ22Mkd2u48/+V7+Wn/7pz/L22x/w2377H+bdd99bjlOG3y4bwrW5QLm8TybuvcD7GucM0ySTyWThNN47wzQqQmyYpjn6ojPs9k/50b/7D+BsA5HkXF0IeDzXhwDDsZ79dTligOhn3z1BpOh9IdUrS4V9E60p2zXF3WinK5PKqAVeqwebTwr8HIhhXsqAEPGkQELigsIFg4sm0YFvvf94UoTZ2f+Brj7Ozv6PdPVhWd/OAQAavWPTTMglmm9Ybnj9zNxX/8pscpXCNhrbaWxrcCUs65pZWaZxrca3ktiKFPTiAcmNOgGyF0U0zN4QMyw7BnTvUDsHrwTxFuKQlETOGuykmKYUuXYaa6axwo4aP2jCmJy2PyjlCHVXLIBMNg7TOExtqaqRqh6p6gnZpgiZ0TADMj07ZDU4abBK46XGS8UkaybRPNhxecr0iAoe5T3GW6ow0fiRxvV0vmfrDlz5Hdd2x2N3wyN/S+eOVH5EvcZ/yen5Bh8k3qdndO+v0rPpDc4rJqsYQ0UvW3qdynfcmFTGG4VrJWEjoYPYgug8oonIdoY6tUNVHl3Ze6JTRpI/sfG1irEQRAJjg2a46ZJZ5EFjZzjmbk36vJpB2AuDe66xzwzuuUng7GCI9sNF9ryYXsNchU7RU1N044B0Du9HoowpAEwlELVIUTsbgWgUqvXo1qEah24durOYzmI2FrOdqDZjWl5NCcxW/lSHIoQoIEqih+Al3urkm2sy2INmOkrcqHCTxFuJ85JpdgIQZIVXOgVWMEklFipFrCXU8hSxseH0PX9MuoBKTLSip5VHOtFzLY+0safq95jhJerg0MeA7AMcQBwhHgThRuK/rJn2VVKF7RvGQ8N+/5Tn+4Zh3zIcGqb+gVD5TmF4tD5izEClX3HdHPnuX/HNPNpKrjaC6y1sOmgbaOZPXSXTUWNAG1CaeVJBMFrBMMG//n/+A8RZebUALlHh2WB5ejI7FPVirhgLs0QfZ9gVKgj168HWevmmfb7qFBHSI5RDSIusLFJZpJqQckzmpnJMvtdms1Mlj2h9QKsjWh7Q6oCSO5S8ZRw+yd/5kX8VUDCA+EHHd37H7+Gq+rvA5ejweQL1dYOU3I+TophoFAElFaGw+pArv2YhxuQzqGnw9Ql0uWVdg6vTtgS6EuzK+7u6Sf8p9glNS6g+Yh1dLiyghh459qhhQN++QvZHRH9A9DMgm5d6HFBjjxoHZN8jh7SfPB4RwxE1pG0c96i5P5cHmfmT+6P904/xt/53/xbDxz8FQvDpP/5f8OQn/06ag1y5+8gDuvxZuyrJ5VK+Yy4NpC4NZF83qbuGcZcGw/dB09IUaX3MSyCgTPcB2UvXc+l/ZbpvAH8fkLx0zEuD93JMkcdY5fNTlk0pwCjHTuvxZRSCoMwCumTTMdYt1C1WKqja5FOrbmcfXoUJY1Z3leaLavX70tJUP3+gC5B2RGWw5SbqY39HzSXthHIjwk1od4JZOiSVlrQjwo7oYJftjMMCu5RP/xNuTEAt+iQkWEHGddlm0JTzv1RersfoZdndBz9jYbq6Hhtf6tevAdtZXSjgUwnWyuvK29fBD8r7K5+1dQTktRnypXzK26uqWvx7lRZbWTFVjstLNVZuv8rItWvoWN5vmV/lfefjXsrPhzzH96W1WOVS+3bpU5ZLvp61mAdYfKGVx82Ks9yWl1FSy/fv+ne+vhwV1HtPVVVnqrqcv7mcch7n6yv9vV0q74cmET/Cvz79DZ9KmbdSUwk5m9CVA6UZTkkpYM5sEe/akseYBtdnD50QsyP+onKRzDZDDCRH+fJORTsPiJBmQUyWXMLsSyz5AVtnXFmYJUkuZ18QqZFfTCw5j9ySVWb5HqTML9CV/DznIXcfnLvS6UgIJ5YXY5iPe7LZznbJKd0l62W5rF/sUqpZxnrKvwTOUkW/9ELNx7qjWuR+yLiUdxAordi2Hb/9t/9WEB5i5D/9j/9nfP7z37Qcp+0O/Jbf+kcIQeCdwVqF9zXWKpzTTJM6RVKcoyqWERmdz0uNd9VZ1MUYv0qbo/y+FfNHwRKh8rUpoqRHyIgLBto4q78Em+2eph2T438lCEJho2HyM9x6Ywq0ZqAyE1VW0ogwK/Ik3ius14yuYpjqdP43JC1tAmX1kdYcefnBE26eX1NJyy/6tr/NN379T7KZI2VuqgOVHhDiozXoy4tagK/lCZS1mrFRuE3F2NYMm4aprZk6g2/VCZY9cMogRb88mV+WyjLZg+gD4hgJryTxRhIOcwS/Kfkw8lYnH1GjOUGyQRIHAe6BoCxHu20jsgmoxqNri6ktdTVS1wNVPWFah2g8ooVQC5w2WGGYVI1VBisrpjx7KnWKepl9lD0wJf9kAR0cxltqP1L7kcYNtFNPPfV0Q8927GkOe9rjiOodyqv0LPr8HGqcU+m5cxrv0yftk77n/WxUjLJiMgZ/LYmPBeJxRDwOiEcB9cijrz362qKvHHpr0Ztk3qvblE+6cmiT6vm5OeXDk5uSfzA36qQY6zVub5bIqP5WY1/NcOzFDMc+SMoxe5sAWujfZNsc0cqngAHKo7VHq2SmbLTFGI/ROdKmx8xLrRxKOYz2PIuP+SH77aDibDIpeJefRA5f4na0mG/+BPLJNZWMBDSjbxnGlpHmBOYFfPuv+xu89XXPMM1E1U6YZv7Ul/Pvx37/L+D4c5sU0XLQuF7je40bFP6o07PUGqbfpoilqjV/LsGvB6eYAh+InoaedhyojhOmnzC9Rx09aucQ+4g4RjgIYi/xh9n327FiODSzaWUzm1a2jMfXB365PwWMGdBqQIkBJcbZKfsIfiT6iRgtAk+QDi8jQUJQINT8zhSSKCVRpuiLVX21RFJ8/PQTDBPLZzXnV2bLKSDAQ8DWvcuYPss68XDQ/5okxAl6STWi5IBSPVL2Ke/kAaUS9FLqiJy35/20HpGyT6BMHBHyiJJDOp4s+hFFR37dz4HLg/31wP7VzS/lZ3/utwGBT33iD/H40d9e7mM9wbucE6BOCshsFhm6DmtqXFUTuw2+aha1l5+VYRmKuarCN90MwWaYNu+H/OoguxyHGWL1yCEBsdP30/oMxUR/WEBY3ictj+hpRI1DGpAX0CqEFAUyq1AuwZVy8FwqCy6ZLWmtqapqcT+SB0IhBKKp2H/666kOO7rn7y9R4Mr+RR7A5u/lpG85aM792rUJ4ro+5bQGTyUMWk8O5eV6svlSXVzXy3X//L6B8aV1a6BWDoZP44G793Np4nmtrnsdbIxwMjucHc5nk0QrFUHXODVP5ikDs8or+edK8CpWzRJdkapJyi2TnM6X/rvC7Lsr+/L6+Uoyq6zWcOvMZNGewbBSsaV8UnVl88UMv5SzSDfO++fvaSlc8vF6CYis60K5vlRpraNGlmPi++Dp+ve6npcpH18IsYwHL9X9fK61WWO5f2lOuL6vcpxY/s7PnDGGL3zhC7x48YK6rs/+C5yN19fHyevvy4/y+nJ7kds4ay3TNNH3PfclIQSf+9zn+LZv+zaOx+NyzbmMYoz0fc/P/uzPLkqsrJrKKU8swMmpf/ZnVloKlu+4UnRStie5DTwT7azyaw3213lxqZ3J68t2rmQuOWUmcYk3lMfJ/ysDGpR1KbffdV0zDMNZO5/3Lc3r121rFhB572ma5l7V7KU6vU5/+S//5Yvr70sfOchBjJFYPHQhBEQQSHnK7FywWiZ/O0u0GCEhBuQMi9YRXPLxL0mpIc3yrSWC+VNKCnNlVUotcTATGJwr13LE02DUh3RdUYBQEheTGWogEgXIHOkmVwxBclQ/n/+s8Odrc+4ECcuHIOdled/5gSgbzdOxE4jMIC05QZTEmO81P6jns3Lrl+qpk5F61bnDcpo9FIgi8mUZAri8j/J37rSs168r7GInLVjyK0XTTMqlL3zhc2dl3R83/Bf/+T95uSK+IUl58pmltEWbkaY5pO/KLuaFSlmUmlDKpn3n31JOp21ySs7qpeMv/NXv5+b24yfzxwhSe4SKVNXEpz/xZZ4+fsV2O9B1e14Mj/nK7mNstwd+zS/8C7z79DlSwh//G7+eP/tjv5JyYH5gy4HtLMcCIyeaauBxc0wDbumQAqSSICuck0xO04+G41jhg6S3Hb293zl/pUc2dc/jx7d09WGGY8cTJGt6tk363tVHjJyQgjsdx5zWptl5c65TIUZso3CtmYHZ/OkMQ1cztDVTl3yWuVYRWpFg2QP4Z4wkh+h9hb01SyRM21fEXhAHQTxC3Ev8K4XfadxeEyaFt/oEzCZ1UpL1AnqSf7eHpiYi64B+y85KsmRuWVcDTTVQVSNqkyBZbCC0Cmc0k64ZVc2gGsYZmB3khp28TqBMfAhQFmMyyfcBOQSkDcjBo0aPOgbk3qP2Hnkb4blHvIzEl4pwI/CTwYUEx8ZQcXQtIejL5xYs/tjkI49+x6HfSUEb9BOHfmLRjxz6kUVfWcw2qZ50Z9HtQN0UYOxB0SkvJ+9S2dmDYRiakznlfgZktwZ3o3EvdVIjvVLwSsBLkHuBGEBNES0TwFLaJVCnPJV2bLRH6ykBr9kEOS0d+uMO83UJiCkZqEyY72WGZ7O/PykdlQnJUX1yKXrW9i/fieeqaDGvjSeVxh+7/fvhkAsgpff4RuAbT5kSQHhPp0ZaOXK9eUUjRjo1wVPH8DnP13/XT3L19o4YIFiBfW/P+KWBftcTbo6E2wPyZmB3/BV8sP9VfPGPfQ67u0vFBB4lBvx1TbwW8OscZxM7k0f0lm5/oH1+pN73NMdxhmMW1QfEGBG9II4zwB5m4D3UTH02oWw5Hp7wqn+XB/sOXCWpXPpID9JSb8Y5s1InJCLmiLOKIBRRaqI0C5RclkJiRYd9aPCTEoKVMMzOv53FhoCIHuEjh/dBBmjmT/QQHQQH3oGfkis4O91/yocnP4OtGV5VfYJcc+RNJY/J75oakPIEx6Tsker0XesRJXsQB7QaZzgW7rz783et9Vlnupy8XCsU8vac0rzkadC2Vh9cgmheytn31yapv6p6NnVssFVD7DY8rn4IVze8qH8Fz5q/fzaLbHFZJda0+Ko+qcaaNkWg+CqSsFOCV0OPOexQL95fYJgaetQ0IvvDYi5pphE5JiimpzEpxvpjUooNPXoaEH0P4dQvz/lU5k+Z5xl8rQeaS9+9cOwsikEqnBxal6qQ++BMaTGyvqbyd7luWdqJqy/8eCrPecCU+xv3jRHKvvT6fOX4Ip/nvgFVeS8ltCjP87pB6PoZKM9536TjfXl4KW9CSC5Nwgy2EojKvrcMVC0+wyxd47RZTBud0meO6L2uCh9e1QK1fKn8Wnx5Jej185WEnRb/WtJO6OPtotIqfXdJOy4wywS3AK4q+qTochPGW+QMs7S3KGdnc8ZxNltMoEx6exYs7xIIvQSk1uWf4UYJPMv/34ENqzZrbaVTHhvOgxesIVxZ3y891+X1rseFl8BT2bau3eusj7X+T3kfd8r3NW3DOr/zOLE8rylM0y/9fw3P19e8fk+U+6zzLV/rpfOUKcYU+TPDnrJ9yj7Yyrwswdp9QKv0K7ZmB+vruwTJ7mtnHtI2r/NqDcgu1Z0SAl5K6/t73fO0Vo+v6+f6WGtz1fI61vXn4numuM/1++Mh74hL6SN6MZTkc5UXJ4RAxIgWmawXHZAQUXMQgVj4iQghLuMFEUGEOKttSPsjwQeiFMhZDYc8haddO+bP8Ozk3+tuyi8lCpVN4kmlYizDM7Hsv1ZlXap06xf+6x5KUZgH5LJXytwp3DxoiavBmdZZNbEyMwiQfKtJclndbYBT+ZTBBhKgE3PZ5vOc7u00I5n2ycrDEHJDmq5VKVmc76TAA4n3MVk4ColQgnHsUVoSfIIJjx+/4uXLJ/P5I0JEfsNv/BM0zRGlp1nZ4TCVn2FYgmRSzhBMW5QaMSYgxCmMdK6fpU192dG61DDk3yc6n+rvB68+w1/7G799vieBNhP/0r/47/P48e2dh/gHf+o7+HN/5XvSigN8/vaz/JPf+0dmKHZ30Lqt9xhtGWzNMDXYkHx37e6tRbBpRrbtyLtPb9nUPVq8oqv2bNuBzaw8u6oH2mrPpj5i1OUoVut1+XcIYXE6HAVJrdIq3AzKpnk5dg19VzM15gTLOklsQDxgXByDSEqywWBfnUCZm6NhhkEQjiopiXazeqg3+FHjbYqAGSeVLPoyIOvnz31KkFUSKiRTuC6BospYjJmo1EitR6TyxDYSW4lvFaHRTK1hqhtslSIu2Uozmppb/YioxElN9mFAmRPp+sfiPg7AnhRg4tX8eTF/XpHAYBQEAWFtJ5jPrUj+8rYxQbFvcgkKPrW0j0f0Y5sUY9eFYqxLEVB149C1fY055cNSMqfU+Ekz7Rv6QRF6RewlsRdwEIgjiGNEHSL66DF7hzla6uNENXmMDRgxA3LpFgWYMSkaYwbnRoc0Dn4K8cn5ZMalDqyU+s7Aa2kzy+wsyjK3y6/rAAiR/HVGLkd1Skro4tixgAnzYd5WL+7k5bdOf44nX/pDxEdfZPu0572/8jN8uvsUv/43/EaU0ku7ppTiuIG/+RuvCGZ+50lo/98/wo/8W/81MQZkni+wnjZuOaprfsL8U9TtyNWTHm0cb3V/k8bcJqfyYYNzLb15C7vf4P8ZhR8UYVD4XiWTWduyj4/Yf+hawumZyXCrWv1+6FJAEJqw6u4IApWyaGmpcKjo0HFMUVKjRwUQISBiRLik8ooB8Cdz1+AE3iXTVu8VziqcM1irsVYTwpsgjDmznHx1YY+mge0WHl/DZgPbTVpuNmn9piu+b+D3/b7fizGzAmwGY1L2SHFclGGwR0pLjCfTlXJgeJZPqw5o2Skvf5/+JziNHc8HmFn5lE09yhSFwDUtsYBYbjaJDIW548kssl38h50pxTIQa1p81RCrrxIEBJ+A1wy/qpuXiwmkGgb0NCygK4OvZfuyX1pmWMbxgOG8/1EqDcrB3npgfmnwvOTv7EM2l025vDQAK8vwdYOI9fp1G/m6wUcJzy5dT/6d+1mX6t+l/6+VHGvwlZcZxq3HCvfd3xqk3Zc3uX+4jEVCgCJSYqhqoqlnKHVSbVmhzpRbJ6VW9ulVgC5zMlX0qkqO7VV1tj6Yr9L89zVJuGnx0yXthBlmZ/T2pOySblr8dSk3R2icQZeYRuJwgNlHl5gGhBthODeBzIBM2BEdfRJlFHV8HcSstH7Kz0VpKlbX9fL/EhJfMhsuf7MCCuu2rryGUp2zNm+7O4Y7T+UzcQnEls/4uu6V5y6vaa0YW8owj2HvgSkP/b2+v/J4uXxyGeR1lwD3fXlQnu9i21ZM5BtjlkAEF/ta4q5aKm9bl/t99/lR0jRN7Ha7pc7lyYB8PWVAvfW5LkGfcvz5urIp8/KSRdl9/dJL2y9d1/rceZ9Lz1CpFr7vWl8HCu+7nkvP0335V24rVYRwzmjKdqSsY/l5yee89Ow8JH3EIAd3qXwuVCWYneenl72UEjWbRspZDpnUZ6nnW2b2OjNLElnuE2OkMtUdpVwUwKoxycsQwinQQSJOEOYKqWdnekoR48lRYqae3tuzArpY2U7jIII4v7d8yrwOMvThDsU+5YNc9slRVtfnjzEBrLsdgbxfGtQhzv05rB8MOM//8+tOEE2I3LCvveBHqkrN4W71fI/FHsX3BGBT/UALFKkxds4i5mP99n/kD/EH/sDvZBobhIj8tn/4D/Pdv/Sv3WmIy45n7rSXMtFUB1jyrVQEljbYuZFeN8Zn9ab4f0Tw3d/xXxNjwxe/9Mvpup7f/H3/PY8f3y73mJcxRv7q57+bDOIAdv01v+8H/qk79Senw9jR0bNtDrz76IMFkJ1MNA9cdSPvvK355MdqurpHq0jgFG3l+fPnxDD7RkSkceaqTEIIRCVwi/llcvQ/NYqpUfRdk5RlrWGa/ZWFdoZlD3j/BC+SmuyYfEyVyjI/KFyvsTuNvamwtxXTvsb2FUwyu7w6waUSlI1vPvd8l4gclbEJyUw2BoQP4CNRC2Ij8Z0kXEnitSBeK+KVwG0MrjXQtmlQn4M3SD6cf7LAKdLpNC+PJFB2AG6BG06g7Nm8brqQwYKkGusi6qmfFWMO/c0W/cRismIsm1NuJvRmBmNtBmNfA3NKqwiTIhwV09QQR4GcQI4BNQb06DGTpbGWZhhohommH2mHkWq0qDEgXaEA5vKsf7nNGEOoAkEHuD7ts57JhdOsYfkS9f48ytL6BZ+ebTdv86RJBYGUermGu52S8kXOcpzXDWYvHae8hrzd+0BEMTmJ9RUuaKxXfJ1/j28ZP8+PD5+DCO+Gn+ZTh7/D5nu+gatv/MVMh5rq61u++N9L/su/+A1YVzNaw+Q01ifgM/2nijFqvFWEQRImgQ//GjEook+m+4n4iKSyGmDc6eWxe8mvf30FWUMxw73gS2mHMZHKBCqTgK2RI0aPVHqiUhajHFpOaOEw0qYJuBAQUSRzRh/AJzgfbJqo8V4SnMJZic9my1ZhbYW1hmkyTFPFOH8OdpuEgV9FktLTVCNVNbFpDtTXQzLzrrKSdZo/A838vWkmqmqkriyVmWgbx+/4Xf/iAsq2W+i6Dy+U+iN/+D8/i0J2fp1yeV/mxqyECWsl0qW+DkIkiNC0yUl+00GGXIu/sJbQJOf42S9YKMFXdqa/mE6maJJfbSrNIOvnH6CmGW7N67Laa71ugWBDj5gBmbETehpgHInhPOpY7g9ka4L1YLlsY9aDo2UwdGHQdGefVVt46RzrcloDuryuPHZuIy+V97qdKvudZf/rUv26737ycdYQrMynbHVyX37kvlg5iC+PufT7lQJlUiRfZYimOUVYLJzSnyIsJqVXMLOqa+VwPplA1icgVqi8Sh9dQVcPm0n8CEl4u/jgks6ixwPy8OrMEX36flJqZQWXmAa0d8l/1zTO0RinBXot0RjtCNOw+OmSbkKsQMql+laCsLI8crRA59zS316roO4MpKVEzG3XUteQfPGb/+e8+NivxdhXfN2P/t/YvvzbFwHb3bHNeXrT+ksgoLzOcp9cX8u+SNmveNNzuz7nfe3EUgdWz2TpAL+0gFrDi/L3+vjl9ocAnEv5tb7+NcC+JHIoJwvW51pf/9qke+1rcV1WlwDOur697v22Lp/1tb4uJasyc+Y/TAjBOI5U80RPCXTW15b5SHmP+Z7L/LlUd9b3us6XS+m+fFg/35fyYf2fsn5fenbW+5fA7JJ11KU+dNnPv48JrMcQJU9a+9lf3195nes69FHSRwJs3/u934MQYva/kAYnSqWGrtIGcoND8lUhYqBp6rmDEjAmgRitDHaaFtBkjCH7YauqKkmffeB47FFaYypNVRm0VAzjSIhhdugvkEJhbboOXVf0x34x5yRGJKcHUimJc54QUoRMrSSmMkipcCFwe3vLcBzZbFqEELza3SKlZLPZMPUD0zTN+0vGYeR4PC4vkTDfy8l5fwpQ4LzHWYt3ydeY1Arr7GymWTZYyYRiHIc5dG0q6GxHDBQdCjdvZ/aHd6qEunhJlZU3mWSmcrTWXeyIn5RxEENyNC7kecNTwqkYI01j7pUQLwAriuTMXkokAucmVNuglCaG1OH/3Dd+kX/5f/1v8t57b/P48Uuur3fLccrjljS5vN8yf8rta2nrGs6Wx8v5kddnm/ycH1JFfvX3/Ld8/6d+8I67mvIahBAo6Ze8zOlbP/F3+fTTL9PNppn9VcX0nYonj17xDT/5c3TH6ew6zgfuEqEk14+fcrW9JgPNICWDiRw6zU23ZaxhrCRTqxmamrFNERFto5Pj91YkYPOAFJxMcGxfYT8oYZnBDYbpYJhuGqbbCrercHtDOMoEikpQ1q+WDxU/5WIvTaxy5LgOuCYFmHg0f78GrgRxI/CdxDecQFnhi+rB586gLAO+EpTtOIGylyRQ9jygBo+JySRNdw618agnjuodi357Qj31qM851COPug6IrU8mpF1AdT5BwdqjcnRKc190yocl7yXBSuIk8VbhJ42YInKKyYR09JjBY4YJM1iafqQeJtrjQDVM6MGh7Cka87ozs5Zz39epW7L1nhf/2oSmjCS0PtalzsWlqEsnuJZVIhl4x2LAPJv+S00kYp1kChUhGmxQWKewQSfY5dQS9MMHhfXJv9xpnWFyyVm/9SqZ3oYEuVzI++q03Wkma3BOYa3GeYV3CuIMunL9u7B8L36G98I/C3+Bhz9Ll5KICDXDaO2Rs7msMm42rZ+QalrM/4weMGJAywMq7FF+hxqe0Y4v+dTjK77tW76FJ4/fAqHY7w7c3BzpB4d14Bw4q5mcYZwahtHQj4bxUDNONcNYM04V/VTxarxitDXjmPxFvlkN9uZUmRl4mZHr7Y5mgV8jdTXSVFNaV09o3dNUE8ak9W3dU1UTlR5oGpeCE1QDdTOhpCVHLA8xTfaJmKdWMnXMBSjO3j0h5mkQ+CW/5Ku+xbOOpZcqmTrOkCt2G6ypsVUF3XaJEFlGjoxNR+i6RQ0W2u5cQdY0i6L5oyY5jYuvMHN7Q/PBVxYH+XLs0cOw+AGTs/Lr5BssOdtPZpMDZhoR/TGBgdcMlMt+wKXO+6X9hRBp0vbCQC4POEr/RJcGuJfarBKolwOE9T7Pjr+cn7j5PUz+CW81f45vffJvIcXx4oDodYOkS4Poct2lNvrSIDbD2bUK4NI1hBgRyuCr0sdW6Tw++dsSTYtsNnhdIZp2dlJfQd3NwGtWf6nZB1dVE6smRWOcTRezA3s/m0B+tfXzviRypMQ56qKaeszxBrX47rJIm3xwpciJpQ+vcTZpdItD+uynK0daPMGu5NdLzSaNBH+mprivPHN5lO/jsn+7hhuX6sBSd4VY3OqU4GwNctb1Iz8T5TFLAJR/ryfHSvCwQKwQ+alv+t18+XO/E2JkEIIf/RX/Dt/5P/xjNPbFnWc6H+PsPl5XnsW1r/skZf97vX+Zf+tnZw36Xgdn1tdYBoG4rx1Z51kGM5cUW/f1vdbXsLbqKe+xzIv72pi1uvZS36/MjzUEudPmchciQlKIrdufdf7ed63l/T50fVknLz1zZZJSLpExp2lanOoLkYIflArLXFaXoqdeAjwPOXeZHnrvl+55nZeXANvrQFjZVqz3v2RZuK5j5f/Kc6zry30KxfUx19eXn5WyfcxK5PuOt2YID00fCbB1zVxxunaOSFk0BjMQQiTTQaU0wVuMUQgBWkmknKGQcGgTl0hLRE+KXhixbnaUjqLrqnQ+CcFPjC5FtpREjJbEmMyA6jo7Pw3I9txRas6knGFNcy6jjDG9TIxQbNq3ieSXkeKtd5+eCnjZP546X94jS6pOcuAfvEcJed5QpMgMicCGE9FfN6blOaSUCE7KPDHnhZCScRypm9ms1BYvSClPkUfTWxJE6uqHGKmMSWO1cB7IQcz7RE6VSytFJOL9KWzx0PeM08TNzQ0CST8kIDj0fYKNJKVaTDfC/nDgsN9B8Hif/Ph6L9huruaoKy6pEoC2GfnsZ39mzoXzmYScL6X8uByslHlZRoM5G9AUMyolwCpndMtOiZTyNCMxm7w65xjHEVOfzE/WDWKMkV/7bX+eLz77NGEOqPCpJ1/mn/j7/ghGp3P9xXd+Ca/+MQ0eduO7fOmb3uEX/ZdfBCcZasVQa9ymxrUS2ymmjcFdG8KVJl5B3ATYJCBzSm+v6hFEKwijxO4N03s1065iuq2TqeW+wu4M9jD7Mbs1+IPGH5LpHpNIJouO9Cmdan+YNifDgbVPIg1sOcGx8nMFbEjBAGoSKMsRU/O49UHnjuBB2GTuJaeAnDx6dOjBUQ0T9TgktVV/YDMc2IwHKmUJTSQ+EoTHEh5J/FYQ38nRKQWhEcQGYi2SM/cqInREmoA0/qs2pwxWEZwk7iXW1gsYk2NATwE9OkzvMKOlHiaaYaI+DrT9QDVFxOAQPqzau8vKiPs6RuV/L3UCSiCdrvv1s7mXZovSeSQ+SqybfcINGhs01kl8SN9dAa0WWDWDK7usm6Peztt9OO1nvZ73nQMvBHU6rtf4NwU/iZzX5RJ+vQaInS/jSSn2EZIQIdWrxtI8HqjaiaobqbuRZjMkhVQ70LYjbTPQNSNdPdI2PZ2ZqKNjvB346Z/4El/44S9zfKUZDjXxtsOIK2p9hTJXTKHB+ZoQGhBXtO07VNU7+NAy9Iqbo6AfNONUMYw11n31fnmU9NT1sCi+Hm1vFqVXU52UYQmOjVS6T9vNRF31GJ3W13qibgaMHOiqCWPSZB6RWT10UjnnuquVwoUUgCb4QGRWcBMJMak+U39F4mKCad4qAum7iIogHFEkaCYgBX8Sc5+HpDCPEYJ3xJj6SlEKBqn5sovsI+wjHALs4/w7XF53KLbtY2Qf4PP/7h/Ct10CavqrjHznXIJcQ4867jEvP0iAq+9PvsIKH2ALBJuhWLlOTyN6jh4p47kiaa06LZelmQ1cHpyXsCvve596K68r271Lk3Jlm5b3KTvueb/1Pvl6Lp2zTCFARIOoCCG1OzEaIoYQNaP/OH/7xb9Oftl9MPwG1M2Bb3n8ey8Oti5CkvlaotI4U2OlxqvkdP6k1MqKropoquRkvkpRF0Xdpm0m+fDyZo7qq+vFWX1SdjUJfpnsiL5OkUt/3kCXW0wLE/AaMP3tKdqiL7aVoKtwPM+UgjeoYJdtS2RGb2E2cUyKMEecetScv2WfuVSjrNO6PMp3ZK57WY2xVuLl/2XIVX4ulX/5DK1N9kqLnNwXLn0838nf1XO4XEtRx2KMhBiJqsbLBidrnKiJumWMGisqnKjxsiaoNm2XNUE1RN2l9bolqAavWoJuibolqBav0n+CasqLSvepN9w++U6qr/zA2Rhg3X+577m/tH293yWwdN9AvTQ/Xufnug90X56X/a9Slbcu95zWgoH1sS7dx333eknVUx67HBetAdml9nB97flZyQFR1hMTlwBm2X8sFZF5LFYqxMr/rid6y2O+Lk/WAGf937JsLuVX+R6apomu6+j7ns1mwziOy5jVWnvHTHSd1/n+y7p9qQ++VgPf17+/L10qu0vpdc/ReqLlvjK4dN5Lx1/vd6lu53PlccS6jNbHzfuV1mjrvkHZn7gEez9q+kiArXTel5Vry03JstMhkDKpA9YPZX7wlJorUeHQOTt4BoFUc0UScekML51VkeDaCZTcnQlZm+2UhXJ6SQmUOvkKSx1hUEbhQ0REkTrec0cvHzPGOANFuYC0nOKsVEOdR2ARQi4O/hHpBSWESMoQIWAGaDkbFqg3+8gRuXJECSHOisGUXVnh5r1fLNmC98mHjBCZmiFixNlpuX8fTg1GKCpXfrF7WzwEMaKEoGsbNm3L00ePlsis6Z5YYOCdTh5hdpYfUVrPs3IQnEUJkqPpGPmJL3wD/82f/O3c7B7xyY//LL/uV/9/+dxnfnIGsOcdi9eZtpTllKFY3m6MIYRIjJpxmiMhujkSojdYKwkxRR3NERBjrJmmNCB/0X6O3QefpXaedw8fEJ1IPnjm/XO0xMkq3h6ecxxbQpDc/tyWf+Nv//PzedKx+D8KPve/+THUxiFk5L1PfYIwyvQZJPH/z92fR9uy5Hl92CemzL33OXd49933Xk1dXUVRQ090NYJuiqEnwAsj0AIELWHZYEAgL7xYMoMEQkYyQpaFZBlbCCHZkpYAGbHQAllYyEB3070aME3TNF1d9FBzv6rX9eZ3h3PO3jszhp//iIjcsfPsfe65993X9HKslTszI3PnnJERn/j+fr/XswPwtC15Y86PG0vcZH9HcWt264zZ9EuChljuu1ZZuVUVXMemr1uezEFZArqEuplQNwV1K6FugroF3JCsKFspZKGQTmcTTVuOST9GIZYElQQTIjYGXBzp48gibljFNat4wak/ZxnXLGWgN1tcNxJ7g18tGHqDv+0IC0voDanXpE4hncpgzIFyWckjLhBNfCJzShFIwWSztY0m+I44kuFYMac0Q8ANMSvGBk+/Gek3m6waGwJmG9Bx5wey7b0+BJ3nvTa7Mi4Dhd2xzaFW22MMIVrAMXrDKAVuiSPR4aMhJkeUAqmiIYrbwa1kSWIZff5PVW61gGsfjO1gVyzT8oTQ6eobwgS3tESMimgShoRG0JLoJbKQMX9nEkhSSFSkpElRE2P2s5XSEzra1zGbBy5GFv1I3/mimvIZGE1mhAML5+k7jy7Hmd34STb1TuRIwFvD6C3ytffxyWYV2HnP9su3Gb5yh4vBMQy2mETupofB8aTBAtrUdwWCuYFbN+7z3LPl2LtWFbYtirECxFyOjrvoPItupHMb+n6kMxusvijRObOyW2KClL+xKUUQjTMOoxwxJVKMbNYb1psNIUai98hZ4GK9YS3C/W02941RMFoRQsxgzEfQTcNBlW+0Llozo0lKMFqTYvmGGo0yQhKyCeTyhLQ6JSxP8cslg+tJixuE1Qnx5JRt15NWN0irU2R1kk0lFytCvyJ0S3y/ZHQLxm7B6BaEEhnv93/lms4iZ0kBpwpOFKjg6V9/pYkYuUVtLnbzm83kLJ+L88lvWLo4Q2/WuHHAjkMGYY3rhXkFuoVJ1Y9bnZ9Xetse+zT7NtdG6qEK/yHo3zZ06r52UdQtSRQhGlBdhlXJTtAqiZ3mYzIIDqV7kuRvcV2G6vK85HnBEcKKkE7QegRlEbGI6vI64kiUbZf/JBxS9jntXzmSs4hz4GxWVXfsOo9cM3bNfAc44Y3+21A34+R0fmeq2AKv6u9rB8AwT+hu+VEpZWfyehxQPgMpd/Fg54i+KrWKCSPjdoJbDMUPV1F7VQf0TkJ2bJ98dkLvx2l9lwIdCaf3G0Jz1Vau3+1D2JpaEFvn4bJCox1y/RV0o36oz+4hsDJPc8jStkFqOdRaVRzbxrF91O3XwG712GJKJCzRnuC7FZ6uAKwFQXVEvSCaPCSzIKi+AK/Fbpnum/kM09plT8UsVhI6bjBxi45buuEtTNqiwob16c8j9Hf2VrfbN/aUJ606CPaFA1ddrzrd3vd5mdO6nrl02LLvK619Jg7t4xCsaNeZuwyaQ6Bj/22hbLu8/qc9h/Zcj6mJjm1nfryHzmH+jTh0XHV6bu7Z/h92z3NtQzvnpm9Fayo+h9Nz8cWx85qnQ+vVd/MqBVNbztR9d13HxcUFfd9P77lSagoM0z4v9VgPwbtD1lWHypyrYNAhmHjda3IoGEO7v/a7fWi77XqH2i7tMbXrzmFuza+QbP7f9t639YnauTCvZ8zL//nxtsf8pOmJo4ju2wRXgVTuHlZINmMqFWNd2vj14Oe269PFLIEF6nbqv+aOdyuwyyd/mfLWj232M7Lvg6slmHX9KQpoktLbrPb2BZedbNZ9ttCuddzbFhLzG6ZRSEpZxlUaOtX0SgBpnUOmlKHYgYe0AjCVisJN0qWHW88e0HmDfP4AH/q4P/JlFplAqAi5J1+31wliCYQQYzafkTGiVFmYKSUKxfnFCX/xv/9f4n0HVvHSq1/NX/grv4v3PP8SH//af0BKhhgd3ptsvjWaDMRChlq++NyJxeeQDy7DrthloFXUKylaYiq9+/P359D7dEXe5/kgez6GpuvC7sE/lCrcOocv/FsfffR6x6CYySB3ml8Cq+sVCoqE1YFO++zzyI7YU58h2Q2ySu4U0okhnhjC0hB6S3CWYCzRGJLWxZG/zoqP6+w4CTolbAjY6OmSp48DfdzmMQNOjTiTlSddP2L7gNzQhN4Qe03qNdIpxIHqBFzCVDhmA6NNeFVbJ9OOMQwYBo65Bt43p7STOaUaJYOxbQZj3dbnYb2l3wycbAe6zYgdYo7gOftowOFeo937BSFlsDvGPjuRL4orHzSJbh9MJUuSnUprLMqueABatf/ZKbzmKjA7KS3fqaSQ7ANOl8EEFm7LaZm2OuJMDlxgdURL6ZxIMinAJCpEFJIMMSpS1IRgSKkA8pDLAe8dYxlC3Cl5Mla73nkqlbLZYDewOCkAzFVAlqc7m02BDQGjBU3KMAyFRCmgThO8xvuOcXA76HXmOB+XvDXenPKH0TEOjhDefqPY2kDXBfres1wM3L55Nqm/VqvAjVWi77ak+Bad2dB1G3q3pXfFPLL4EOvcht55VouR3m1wbkTrlH08KpWVDCiUqCk4UQyJ4D3jZksaPehi7iuglMEpjXhBtkIcIvfuP+Thg4dst1tiSozbLd4HlBdkTCQlRRWmizP3fI2TEgRNioItstYgEekWDIseOblBXJ4w3lwRVieEfsm4WhGWJ4TVirg8ISyW+OUJcXlCXK7wyyVxdYJflOXLJXGZIRlHGsDXTaqYPprNGnv/PjfCiBu32GHNd/7Sb+NUZ1B2quBUqzK+Om+pdt/kT/yW/8Xe/qpZ31yJVdUANbVlVVtm5R53hTYLRHX4tINOMRmQDiX9lKd0n+GWZKhUQVeFTLHAr5gM4CZIlQrwSkXFJeTyTVpAVZe14IoCuCT/51qOMhX7MKvjMNiaA69HQjDApQmI5WWqKK9VHp44KQK3eJnfeHlRimg/lqiLGWh1m7OSt3MkrxvolRVa2z1zRe1HtM9O6vU44CQ2zuyzc/q0XU//Eb/vzmLeebN39M33MMY4uT2pz2Xf94jI5Kto7jfTe4+IEI3BdN1e3bttVFWFyNwypK47BzPtO9Gu2zaC63HM91nXbRvyhyxS5u/WXkNUW4JZkboMvrzqSA34inqnEIt6kZVddjef8zIEC3oxKcYqGJPrhGO/RtJxi4lbTNrg/AMWw2uYuMn5aUCH9bSOjlt02GBlAH9R/jfgZAR/gQ4bTBro8Mh4ThzXmAOKpJQSF6c/jx//xX+a0N0G4O5Lf51nHn4KmUHKep3be1rzroIJh+pox0DrdC30zudaLVsPWUrNlV/HUrv+vI54qE3Wtp3nYK/9z/w85sdy6JgObXPebp6bCLfvRrvt9n2r67SK5Pm1rsde88ZxxDk3MYOqBmvvawvl2vvSbqt9x+f3qN1Wez9auFaHqyBbXbZYLDg/P6frOhaLxZ7ApnKJcRzRWtN13R4kbBWx9XxaIDd/P+bpEHdo34XrpENld3udajoW2ONRULZNV1m91PXnbGd+fG35W1N9xmp5XJMxhnHcfbPmxzVnUm061vFxVXriGnz+SAZAyL3h9SXLjXYFe7ChfQlbOLcnddXtha0nI9O2DxVSLWCrqV6gtid1XtjuS4vzbpTRe9uIMaGaIALzQmn+wZz3EsyvV1sY5Hl1idu0tL1usy2EpuX15pfKSntN9wrechuyj7bjVLgdH3rB2ut2SC2Wr3ELIVXZc91OKeSRKSv/PSsT8zHDq6+/gPd9/msgQyMFX3ntfXzltfc1F3R24a4LxWr+0+rQNWVbtaJtHzEugzIJbRNRafA6O7ovJpiGgJFI8ro0sh9VKc/Ll93IajGwcGv6foO9EVE3Epwq0qkmrQx+afELg+8tvnN45/DastELLvSK8gJfL4mgRDApYcaITR6XPE58dkqufAZk1mO7EbcY6VYjZuFRS7JqzEkORvAY5pTHjJ5ac8p0bhi9gZHJnNIMCTeZhHq67chiM7LYbFlcbDDbCFuVHaNPfrOq2aBrTBOzMmtMJ6yLOstHQxC7W17nG3i1A1/78zvY9TbNua6RjIrYArBcGZZuuwNeOk7ga5cXCvTK/3U64EzEaI/TdXtxWjcmkKL6Ssnm61nVncExjB1jKD63xo5h6BjG7HvrzC+L8/me8QnNDRVCV/xpnS7X9LcyCHM2n4c1WcFmdEKr/AwrkR28SwoJihB09hc2dvjRMq4dw9BxPq4Yx5w/jpa3qwZTSug6n4fec+vWeZkf6btA13v6PkMy2430fWDRh7x84Vm4rAY7vQE3TxKKBxh1niGZ9dxY9UgKPLz3Bt6v0SkhWcqHpvjuJLtWSEqyQ/fsgwCAbCgJCo0xDh8T3ie2w4awHZGQMigbAyqASQaDZn2xwW8G3nj1Dfx2JCVh3OZABaAmB9rKOsLiBnFxwri8wbh6nrg4xa9O8MtFVn4tV4yrFX51Qlhk4OWXOb+O83AyzcsRh+nXTXocsZsLzGZN9/AB9tWXMesL7Gad1WCbdZ7frlHrC/TFBbI+Q87PYLslrh8QxoE4bsBv0NsL2G4aRXni9q27vPu978eZJTEq/r1f/J2MI4z+8LAd4eGRZfV/L57/y4g4UD1CLmdamCWS81CuQKqclwoEgwqxCryaPmDvQKqgy5F9gh4FWxHVRZRLedpJVkw7ycrjzmOcz1CrU+U/GqwqnTAanEacydO21JeEXSCaYTY+Nv0/I7svOJRSypCr8dOl1wP64c4EscIsUyCWKv67qgN7U9YxceSNB7+E1x/8SvDQxdf5yMmfYikvTdvSfkCGDUbSVL9rG6pzhVVtMNZ0CB7M65KwX9erZk6q6bg+ZK7UNpwrVKvjeaS5EMLks6gCi7q8Ov6u9Vzn3HScrT+m1sJkft67Omraa0C14LnOt9cgn6clmEVRfy3wriuQKw9BdeBWjHQFdi0ms8eod4qwCr/mKjDRT+e7r+OASQMmbbHhnD69MYEtk4Y9GGbTtlnWTMctOm2xcZgUZY4RFYdiZbRrI1S/0/V+HgJL7fPQptZPGQpU8elcQUq97yklFg8+yzd8/2/h/JlfgB3uc/PsJ/ae13oP2/tYA5LMTUjrdHts82Ocg625MmZuFty2N9uIjxWOzOFW24ZqwUHrj2tnIbYPgOeQr757c8DQnt/8+T8UbGJ+bIfaiPXdqPevbUe37cM5EJmbxc4hYKs2bAE1QN/3eyDeez+tX8uQ9r/zdn8Lfeb39xDArNtrFdf1Ob9OqvDs9PR08hendXbl1JaHreqylnktVIsx0nXdnrL1kNnsHKzOleB13UOm6e39nZfVcyDZpmMinXr87TM3v9ZtMMJD+z30nLTT9drUvD2BVvPczd/P9hvR7rdOt/s+JqZ6kvREtSZdKocqeyaZ2uSKVHiYQPE/IrROIqsvEkVKUgCLKso1VZwCJ0Q0gpTCqT4Q+2qsfMKtiorJP5au8KoAO4mCtvvQbf+5qb42aoGvp5fKGjPBwjy+bH9e06GbsFeA1V9pwN7e/ySbTxqTVWAVkGk1AbW9ArYehyoqgnJFcuW9vMhaT3n1n8cK5ENg7RBNnr9g7UO9I8yZ1eTNVSlXS6fr0bbXKnH79pt5XUVWRV3xhGpdGss6YMzOCbw1HmtGjA1Y7bHWo/UueqJWY1bIGI8xHmNynjUeVcZa++K4uswrj1IDRgX+35/4wzy49W5wauos/6rtSzzr79OnLYs0sExZibVIu2ElA4u0pY9brEQS8Ce+8Afw39HDf052lk+OmrRcbFmtNvSLDfZWQt9IyAkZlJ0Y5EaPnPSMnWF0Gm8tXmvuqROSOn08s0spZpcp5oE8WFWvX8iQbDHQ9cXX0+kGtxrRXcLYlJ3wG4+1j2/iJJLNKaPXyMbgfc84KBhADQq2oDag1oK6EFgL6gw4U/AA5FwTzh1hsPjieH7nd8td9tV1QLkV0tsHJY9KWb3lsToDKacDy26zp+baTZfrX/JMgVpW+wKzPJ1NGOVxdreuMxGNx5qsBHMqoI2nMwmjA9pkE22kGmIqJAlDqJEV+x0A8z2jX7AdO8YhA7ELf5odz48dgy/r+wzKRp+nnzT1bqBzI50dOVmsy7WKWcmmUjGXLOWnCCRFisWMM2hiMARv8aPFn3WMo+NsOOHN8TYxPi01WAZhy9XArdvndM7T9xmM9Z3HleV976d1p+k+sOjzOotFzA7zu1w+1WIxa7Hy9zCXxVVlDUpFUkz0Xc+NkyVKApvzB8ThAkmBk9WC01XH+vw+6/MzZBOJJO4/DIhkJbTTeR85wrcFidRILZoSXEEJKcF6vebiYsN2Wxwv2464PGVtes5Sz4PQsRHDRgzROqLSxNMlYXGCXywnhVhY1vkVfnmCXxUwVtaJ/ZM/MwAqRuxmXYYL+rfewGy3mM0Ws95gNgNmM6C3I3o9YrYjau1RG48aImod0JsIm4jeCGwTsgGiIdFl0z8cUTmiFMVVNlRj4Dm2qiOKJeLyIJaoumwOSEdSXQZbFV7Vad3DGfzoT+3O5f3f+rYuRUm/65rrJRQjCg/Ko5xHdQHlRnS3QXURXIZaqpeizBJUAV/KCTLBMI3qdIZaTiNOIZ0BW6BWZwvcsoi1xUzSXu0jLtIEyTHI1iCHoNc5h4HYOqK2CTYpf0O25PmhDKNCRg2Pa/L9NwTXv8U3fPRfxcWQfXgV9Vgah10djMMdpsfgT12/QiitNS/I97ANf5Yot1naL6Euxr3/t/XhtuZ2CBwcSm0D4pALgvl/2zrfXMUC7IGAeaNlrvqqjccKzbz3OOf2FBxtY3KudJhMLbUh6R6vXPbtNTNnFJf9fe1MIpd7yjCvb+LVTXCJpF02l2xMIJN5e+VTTSr5ArMy1Or8gwzDJrCVxyZu8noFjNm0RYctugKyAr1sGrAyToqxHCis2Z9Se/evfdYONTAPAYj6zPoY9gBQTfWetuqmNtV7deiZbffV3uP5cQB04YxnXvu7U3un3Vb7rMzftfmzON9um1pY0K43f77r9NzfY3sd5vuqx9a+H237a67eaZ/9Q+9Zez5tmdHu69C682eiPdf2+h1SqLXn1Iosjp3zoTbyIbPtY8dXz6ndxjjm8q/rOrz3e8/ksXvXnt+hZ2t+HhXat0MF/FeBNqXUBAK32y2wgztd113aRj2/Fti251JBYvtczRWO9T8V5B5SUV71LTj0XrTt+UdBt0PXcw795vs5lneMPRza3/xY5kxmvu9D923+flwFG+FqX4XH0pO1Oopz4EmfJDL5XpP2M5+yuahQHiARUgxobSZIlylMUVepCmKKyinmf2uTTSAzNKrbLvutF1IXsyHJirqsSsiNSa00SjLyU7XRQvUxVkFP3XferlYQUkSih6I0Uyo3RoXs5FgA1fgfqIc2FcalEbu7Li14kgkAJhJGtU6/8/lrnQ2NaBpadWgfBlOPGVBaEwsYnI7rwAMKlx2uzu2a23RsXql9KtzCNagVzEMy6f2HOqsh4dln7vFrf+X/yP/0/f9sMaco50nif/Ub/0ve+8LPoHVEK0/1sD8n5Yd6e+aFfj2W1nZ/vjylDIzzdG2IKr7x9b/BDzz/O6dtv3f7FX73z/wFDPuBE/I293t02mP5gvtq/Cf6/Bb+HnZqOCVc6BXn+mR3Ia+TGlCmU0KrhCngxdiA6zyuG3HLLYvVlsXpmpPTMxaLAef8Y+2qphiKGeVg2A4r4mBIW0PcaNLaEteGeGGIZzYHT3hgGB90+HuO8d6C4axnuFgQ48+GemsfYnVmZNVtJjWX2QNdOxDWqrhsAV1aeXqTioqrqqIyrHUmYFTeXmcTWo10NqGm0Kc5te/yvEI0r5TW/N1zDDF1DezK0RbP/Yrt4CYw5n3PpoyHsWfruwLO8v+2vmf03RPDRWc8znqc8Zwuzrm1fIDREa2KqaTIrmiNihizuWT0muAN0Vv8UNRgvmOQtwlbVNpTg62Wm0vAq5umA32BXDXPuYHlMuHcSF+UY/V/Sl92hJ7vxeHytV0278zYdUbUP1K+qVIswAVjBGfh5kmPHy54eP/N7Nw3WIxYDInx/AFhuyalyNlac072ZTqgGe2S0a0YuwWDXTB2S9aqY3BLRteztQsulOVh0qyxbG2f/YH1S3y3xHfZR1joloTFktgv39a9ATLg2ozobUC/4ek399CbgNpkGKI2CbURqMM2A3bZKNhoZKOQjUE2GtlY4taSfFZcjTzLlnc9HZOoaob/BEnJiGIE8Tt4hUexLuMCtcTTd4rFQmN1QCvPd37Hd2AWmb3pHnTxz6Vmiq7kQCwkC8lAbIa/9X3fS3IGOkuyBWo5R7KW5Lo87bqdQ3rXI+7G9U9QgJEdyDoEvR7s5tU6ZNi1iahNgo2gth699bDdIAMwKBg04jUyGmQsJ/O2kkEmU/CINgNabzFlrO2AXQwotcWYLVqXfL3F2l1+SpYXX/yXESlq8s8pbt39FMvbP83Fxddx794vpO/v8cILfxut93vy5+ChrZe0jaFDUGoyObJvotRbBwHGoYbAfD81PQoyHGu8HWugzAHEUTMkpVBuSbRLRp1NIMUsSHYF3QnJLDHLG0S9QPWnRQFWnd6vJlDWOsPfqcTyuk8lJSnAa42OA/1YFGDVBLIouqwMEySzJd/KiPLrvCxtMWGLipuy7hYrI1bt+xtsVTLtfL22bd18fn/qc9X6mpuroOb3TURIotHqst+t9hls68YtDJo3Xts6yryBeyjVdcKtO3zlV/5mwskN7vzw93PrJ354TwXZHke7n/Za1DS33mn3degcD12X+Xr1+s6/63OA1+bD5cADh97VWjZ85Svfylde/hU4e86HP/xXWK1e2oNk8+OZ77Nt51y1z/k1OfY+z+/foWs/bx8eUkS1ar/5eRw7tvl253XeuVoupTSZWB5Sq86Pqab589We86F2cNtum2/r2Hm07059NyskM8Zwfn6+dy3qszu/BlXFeeiaHrq+c6uy+XEduu/zd/s65Xzd17xee91vzRx4zrd96Dt41XfrULv6qvs0P/9D73Q7fcgK8nHSEwE2K9lvhyAoiWg0Ok3kKPsx0xplDCkmCAnTdaRYZLPl3/mgs5mpUgotgjGKZd+z7BeISA4yoMr65aXWKvtbEfL6KQkhFZ8sSuV9SoZH0QhGASkiKivEtNFESaQCvYzKPqSyHzQBlVAqw0GSRwMiCqNNPuoCTrRSoKU47TVQlQFKZyWazWaPebvlZdXlypXGlFINpFTFvYtEtNJo8raZHoLdw5d7Qus8zTKNaQqG9gGcv8TzD8I8Har4Ue/8lNfKMfWkAqzgMgPICsraCmUNqVe22BzvJ37xD/J1H/sxvu8HfyWf/9KH6fuB7/iW7+FD738RREhoXj+7w6rbcNJtjkqQ221f9eGZAzlJCUnlBVMZxCaBz371J/ipD3wrNo18x3v+CvbDDrOFr/veL2L3gnAcMmee8CsJGHXPq4tnsc+NxAuN3LD1YKmAOEOyiNZZfWRtwBZ/ZP1yy3K1ZnVyxunpBcvVBcYeP8fL56wI3hFGy/bBivPB4TeWsHGEtSNc2KwKO7OEM0d46PD3HeG+JdxzhHNLuHBIvF4rVJEumR1aHViYM+yN+5NK67J5YjFBVPvLMsTyxVTx8natiTiboVfeRpxEfa3MOF/yGiCl/cAcNnGoH6oYYzaj4/IHZHpvqM9iBbYQk2UMCzZjhmAVkI1hkYHX2DOGnnHsGXxXlGGLRi1WzCd9T3pCKGbqdVSRXufIkhopTvQFKUErJBbn/r6ow0ZD8I4UzOQPzePwR412jydr/WQGebLa8Mzth5Oiy7VArIAuN5vui4lk50as27JYRPo+YIwv5ejlStLldNyZNOwqMnsfYKWqyGuv8nXsw3+ooiBk59MikJRiqxZs1YLB9KR+yYUozoKwRjHanqhWbJeGi6VmsI7R9Yyuw3cd3nX4zhE6h+8csbOE3j6egvVQGiQDkg3wFrBRGXZtSt62HTf5h/LqugMkWZFYPfbhKAloPJoRg8/TMtKxReFznuTlWgV0gVym/EeJxxDy8vJfpfJ/VBqxOqIkL1NpABXQnZCMgk4Qp3IwFKdITsMimxpmaGVInSV0Fm80vuvx3QLf9cR+SVwsSf0yg6xuAa4ndQvE3WJYnjL0C5Ltibbjz78doWUFX//Mr7wMvc6Bc5+VexcBtY6wjujNFjabovIq93xQMOoyNsioEW8QbyE4JDze+y4l7POxr1OFWsasdwDspMKwbVm+g18ZgI0YM2LMgFKbCZrtxluMGVFqg7UjME7lQlunmasv4HDF+/T0i3zmM/8643iH27f/ER/5yP+Vt976Vn7yJ/91ajny6qu/nG/8xn8LrXd1jkN+f+YNtkONw3ljul1+qMExhxLz9ebndwlUYBC32ovwGO0S09+YfH2JW+LpCcpNYKuaSdaIj9EsCzxr8u0Cngb0lrTzAxa3dOO9Ar6GYh7ps++4GEqU0YAKJYJ2iBAFHQUJCUYh+p4vv/mbSoR0BQkW3Rv8ig/+nr3rOW+Mz02cakd1G7wAdh3YEwBozE7rs9Hek7np4aGG3xzett+fOSRp59fj83zqK/97Hm4/xtK9wte/509y5+QnLz1Lx9SR8+No0zFTsnnSWjO4nk/+kf+U7fPvBeAr3/4b+Nif+Te5+YPfc+mY543uQ9djvv9DZpJ1u+15zrfdrnPo/Tj0//m1ny+fb6fOf/nL38Ynf+wPTfmvvPqL+bZv/d/S9/f3gErdfqvOm+/rUDkyLzOuA4fmdZhD5c4h/1fts95ek7lp5aH/HDqmednbWltpren7fvJJVt+B1iy3jlsV7Hz7h/Y9r9+3eXN12aNSa95Z7+fkP7IA4dZEvt3PsevVPkuH3vsW5NX/zzvo222112P+zrf7b5c/Cno96ttz7Boeq0fPn4X5/g6VS/N3o31GDu3zUc9Du53r3v82PVF17t03i324NcSYVVyqRsREZ79lkrBWEyMgCmez2aV1llQUTUar3EhUtSdfITHR9wnNOmvflBTTT0rvoMaIApshmdIaTAYiSSRH+YnF3JOsCrA6v6gxhGwqqiU72q88DZ3hYExoA4ImSkDZHAChWjIqZRAphZ4qL7Cx+BDQOgO1lAJJDElUBiNWEWMFkkBx0qwUKFULkQrcFFEyOEQpjIb6XEQp/gSmQqz4yynRzkIE0QokoJUBiVn9pvJxU5QN2QxXTfcMXdR95XrElEGeKg9Wor58GUZCXa52Z6SbfJWfhykgrFDUe6UgkLwVo/cLld1Lmn9unF7wz/2qvzbtVgREEg+2t/hvfuR38vrFCygSv+rD/x++5X3fN5nvtrBseinK+1ehWU3ZRCGRpEQ8jNl31hA1sZgWJukIyfLS7Y/xj09/PbwKz955mS+90sEb+Zp/9rkPc+IG0oIcYGAJaplQi4ReREwfMX3AdBHbeexkTnnBr+Z/ZHO+5GJ9wmq1ZrFac6SOMF2f4C1hdHnYWjZvnPBwc5uwthmOnVc45ggPLOGBIz1UyEMF5xq9AR0S1qQDpokBpyIr47Fmi1UeU0wTnS7KrDsBe9fvAbD6/xaSZZPGUNQZ16P/VxVkVxWsNbWF4bEPQv247fteqO9jHXYpSfYbth367Ig+LIoCLJtEDr4qyYoyrM7P1xn7Jw4ioNXOb5gisTSbfNAVhqVsJllNJaO3O6Zdx2U6YolYhkftU2clV9eNLLst3Y0c7bLCrkkRVp3hTyaS4755ZA1S4XbgTOv9j+IhE4m52ciuIr0Prvc7DjQhpqysTJaYih+4ZAkxR/qNyeSIvyXiaQi65OVxiIaYcsTQEHPwBJ80o3UM1uGdxVuHd4bROnxnCM4SO0PoDLHTxL4MnSYtNNIr0kLlKLo9sFC5rHi7ws0IrMkA6z4N+FL7cGvDDnrt5QXUNmRzyW02m9SbARc9VvmiFq5gKqAYi8luVmBp5bHi6dIWO24wKUOqCraMZJhVtzHBMXxexohhREleZlQGXkaHrPaSETEK+g7f9Yy2I3Y93i1I3SIr61xP7BaEbkGwjtgtCbYjLXJ0ztiVdbslsXtmWv/SuFsQu35a/6mmwM7c8XyAsxF14VGbhBkibn1Gt0m878570QPoLagCyKQMqQxxC2EDfgt+A2OZHta7+sLhVIMHXC8pNaL1FqU2mAq3ui16kYFXBVtV/aXUFuc81o5YO86A125sbQZfsMYYj9a7crtChrbMbsuB1q9SO56DrPn/RPK1iTGrpbNiekGMPTE6UupIqSelnhi7Zn43fffudxNjT0qOT3/693L//i+gLYsePPgFPHjwNTzzzE8Ahxsix+DZvIFxqQGFxuu+mDsuJjPGYHaO7+Pe9IJkF0SVQdmk/tI9ofoJa/LRT8fHngoDKmzRcUQNAzaco6JHBQ8hgvfg4wS8VAQdQbygk4aQBxUNBE3yBgmm+FZ1INk8O8gJI88g0jUqxbd99Gz8CwcVhxUaHPKDBPvPWv3ftNUjDcG2E6gdQnT81Iu/hzce/GI6d4+Pvf8/4/bpT+5to9Zf8n4NSTqSuBzlWly5Ll3+3onlJ77yv+PCfxCAzeYFfvhH/yR3l/+IF971/+Xd7/s+knNEkwNXieugX5CMy/NdRzSuRK3tSNYRbZm3HdFYorHN8o5Y1LLJuqyUtQ5xPePNOwzPvXvvqr/8nb+Jmz/4PQcb+jUdamQfahRP1zBElMr1d0nFn6TYaRrVI8kRoibFXWThlBwp5qApIjbPi0NSnS7rlrZBijXycDflx7puiSicYo1enPO831cLe3+L11//Rbzvfd8zPUMtgJ3DrkMAcA5rr4JYh9K8XFJqp6ab+yib349D6sbW1HIOqef3bO6yqK7bKta01ozjyDAMkz+zdhvz8RxIziFJu9+6jxbqVUVl9S84P+dDSSnFcrmczFeHYaArwVi6rmMcx+m9bc16a9nSlhPe++n850Ee22Op1+kQQJo/J4fA7DEg3V6ruUK0vZ7H4Pp1gPsxaH4I3s3L0JTSnlls2547BgDb7c9h9VXTren146Yn+qouuwFdHgrpFBJHbDEFVEpVly6gE2KBlFDKU128pJSKqitgrG56txRJC0aNpJRPzijwJSJfTAmNRmEy1EmCBEBJ8VkGRjQWIfoMqTSglcGhEZ0pr5cBpTUpZWWN0oaYyGauxai1m/iRzhEPtQJ0Pgad/cYZrUlBICaICgKoulyy7zblBQmxeiBDJG9fJFJoFDFkxVpnbKlMgaRETBFjLFqZHCCyvgwqA7Lqx05rjSOrITJEc1AieSqdfYzkB7Acs6jJOlcpi9Yqg0kBtJogqCA416Gne5sLueBjJnLaIFIilKnykqscKbRCyIQQUiCRyKa2Ci1gi0mvkqwOS6kcUFERJZHcMazydUdlxcff/PSv4/WLF4CM+b77s/8sr53dpbfD5FfLl3GsTuejmxzOR3E7/1ziiMkVo6xrpH8M/DF407yb9eIm3/5L/+ZjvTfVR1TYWoahJw276bC1PNjeoTsb6dYBdSGYi4RdR9xFpF977JBwIXBqhJs3F9y6uSxmix5nB5yJKEYePHhlUm45GzF3I/q2f2TBOO89gH3Qodj1aFTQK4/Yzm6f+9eiBavHKgRX9WAc6vFotxGTLmaSrgFfrQJsB8F2sMw1QKysM/bZIfgTJEU201WS1WFGso87KX7DYij+f1oIdmRcI1/6A/txzuNchmCu83SrAricp+uy+W+FXn01E3ZDY/6Y4Zi1w16+c40JI4qYdoAqREOSHYDKfu8KuJIKsAxDdKTxBvHM4JPJijixE+SKyU5gaw92JUOIulmvBWM7GNYuC1ETks6+nxrYnadlP28JnBzIO7TuogxvJyXJPqCGhNkm9Fs56IbeRvQQMEPADgE7+mlww4jzI93o6fyAG0c6P9AFjxu36O2WzsJyaVmtOrRRaCOlY0dwXe7YMjZinGD6gL0T0TqSwoaXvvzTfP/3fi8pkRXTSqFILMRzN0RuSJq+swDKdETXQ7fMZqMFZlFMDYPNy2O/IFToZU8I3Z2s5mogVnAt2OoJZXlooFco4yt7HN5G0mFEb7fo8wFzPmLON7iLh+gLj1pH1HmATUJtYjFXzfdQBmCrSKOGMZs0Jm+QYvKcgiM1kEbSguysrKYe6BHAl6GmT1/nuDWcnMBqBacrWD2fp+vwD/7B96HUZgJaSm2B9QTLYD3BsTpO6ZyUzgswG3AuYMxhJUZtIFTY1TYAnHOTH5o2tQ2c/N9EjJoQFohkqCWyIKUOkSUhOER6QrC76yj9lF9h2ByEZQBWp/eXZRPPp52ab1MHY/cMm8W7ELsi6L44wc+O7oPK89W3l9jlBLtiEzUyFf9hFZRlR/hPFvDlUopV1RVQY0QFjw1bVEgQBIKgAkhQEEGFYrYbylCUjBIcBDsFZqqD0CP0XK877Topu1hQjGg1ohmx6gJt7hcIP2J0WaZ8M4zZh67yZbnfW0cxgCR+8o3fT0g74PHM8pPAZaUjgIgipo6Qsg/FGC2oXD/I0XF38zWqbSrQRugKwMmRcGNyRN0RbDadjSYrWJPtuZCvwn/1M9AJ2+4Ffqj/j+hW96HTJGOQziDWIiVoR/Z7yOXgIMei31qFuI7Xu0/wev8J/on+g0/tbh1NMaF8yB1CG/I4AH8Ntl98gS+/9HtydOFy3aAD6XbXMZkGlLkCwPaH+o7n6WtGE36HktbZj7NSoUwHtB6x9oKUHCGc7q2fy+AdpG1hwXXUM21d+RCsP6Rmav87Ty1gaH3ZzZfDLmLjvG3RbmN+fIeUSu12W/VRO933/aQAg+Nme3Wfrdn0sQ6MQ0O77UPKvUOpfhOttVxcXNB13SUwesivWXssrTptDusPgf2ad8jM/JjY4Fh6FOw6lneoHXYs79j2D3U6PSrtdUA1z9z8fWnvX3sPrnOubzc9UY3DB5PNQGMxq1Q5GAEqK7NSiRIWNoHOOYTmAceXhjkgQigqq1TmBcEaizUKScJbbz1gseyJpZczIiiVTTKziquEU0iCoPBBFd9mpjT+A4PfYjBYYwgi+JQwOqs/lFaIHxGpVaXsDy0JaGMQCUhKGGtIMZ+XF6ay22nJMAsybAOKR2qS5O9I00ZFFcCmAVV82Zl6LtGXGy7FRx1EPyJFRZeUyr7mUsoAiqzYsLgMGVUgxYTVHSoJCSlA0KCBpCAKWV1nTFbAaMEqw1hgokERC/hDK4KCthKZpNBiU54BUViy+ayV+tCXdeNOIhtTIopkmBki+BLBTmmcUoQYEAHnOow2+VrpHLVOaYXrlsSYuLd5np2aLl/ZT77yLVc+r1aPxcTQY1SgN1uMy2oJq3IwA6Oy6sLqXb41AWSLxvPi+z/Oqy98BB7AyelDTuwZb736bI44KIobr2xZPhjph5F+M7IYtpwMG062F5yMHjNEyiNXH4XJzEyUKqeyT+Cnl39Z1C71v1pz85k73Llz99KHahgGTDwnNT0aWhWT7RmhP/QBnDeoDtH/rKQsz0WWDl5qgM19c8xT+8GvBV5MitFnE0kflqy3dvIv5kOfzSd9N40rDJvg2bjLe1IoRoFhFWxJgsl12iEAdgUUEzSRqrKIO7VXVX+djAWOeZwLWBdwNmBsxLqYg2+YiLGSK2hW0FZAJ5QWlM4dEIkdjArRlF7sDK5q/pB6LuKKVEw9J7iVWnhV4Vb5bwO1nvh6Hr/QucI/QS12cOsS8Mr5aiVZGbq8vJ70ClmA9JIlEW8jGR+xY8D5iAsBdy/QB5/BVvD0YWSZPH0c6fyWZRp4bml5YQmncc0bX/wsXNzHDRcs0paT6FmGkRQHYvQ5YIMoouxKA0HKM6NyeQCgcifQeuvx0bK8cYfTZ55FLU8Z7ZIhGUbbEaxiNIpgLAHHoCzeWLx2BDRBG0btCNrgVR62SfHWc2te+cCvQVxPcovii6tD2QWqQLNUQFfqekQ/LaXIflLBY8ctxg/YccBtLugfvIlZj+hzj1579DqiLmIGX5ud4/rJFHIAGTRpUMWk0Rb1i8vAK7hJmRRjT5QFUZYEuflUz0VzgVYbtNpg1D2M2qDcFlXgltKbaYyseeGFGyyXEaMHtNnyR/7wH9iDZYcG5+CquuAnPvFH95Reh9QEteGxUwd4vNfAgpRWeJ+HCrVy4zXDMFggsphgl1LLAsj6MiwnILYPwPoGfHXw1JRHu6SUR+sBY0a0HnHuYTEvHdEmoIxHmYh2AiaBTSgrFCtWxKkcpKHT4Ax0juQM0mc/drHvSV1H6ntGe5tkb+RgRy7fkJ/k3317J5BSgVzZjLGCq6wAAwkavLkEtq49iEWuMNfdu5YMOzDFiFIjigElw96yCr9wdVmeV2pE715OjPIgAzFegGyxOmJMdvPgXPZnanUERpw1aFO8UYrDB40POkNStUDoMe5GhrKyIKYFMXUIC5L0eQgdPp0WQJNVXYEOKeqr6DrM3fMMO51FdYH7/cf43u6vIjYH7BBbnoUuPxdHIdajxvO8a/HS+pIbRp69zh8uJy/ZbHzMt4FRZXPxoeSNAj7i0nleVtbJfhE1MigYFDLoMhgYTPP/dttczq95USNHTnrgfbx87cAsYYJW+V2v8OoC5x6U6TwYHUD5Zt6jSr7WZTumALC6TR2maWPyOmpaXtY1WV2tdcCYmC08bCTGDVrnF81as1fGtunNNz/CD/3D/xMpZX+zt25+mhde+KGpbt2Ckrae3qZ5h0VbZ59DqkPpWKf1vN5ft/XKK9/OZz/72wlhyXve8z38/J//X0xWCPN2RWu2eeyY5+2Fdt25shP2I422MKpVLh0DVnOFXQta5ua4rbCgjq8DOAGstVMk5GMBQeZKtWrWWe+5LZHP2+Ocn/v8/s0j+B66f/P5ek3nwOnQ9uewtD2+R6X2uOaAsx0fUokdE1Acmj7kh7Eeb+v38tDyR51nPY6ftSAHX3mQ/ZYZnU0mM8jKqiZVTawkgWicUaAiw+hx1mKsZRxHnHUkAqI8Siu0skWbFrl727HoLC+88Dw/8AN/NxdSEcbRFwfGWW11drFhO4yIJGIKpCj4kAhJSFoRR48pyjaUyb30RcVlrUIp6JzD2myOqbXBWU3fd6xWS6y2dMseazV971j0PVopVqsVznWAwivZdbDrXe+sMRqjNVoyDNRaMY7FVDSlrOgqN1JCAFV6CoqwLcaENg5RiUgs/t40ShQqtT1sCVQEiSgk1/MkIiQQwaDQxfQtSiwAQCFSXkoSIXkiUnzH7T4GWamXgznEomgzWoMkfEp0NoObEBPKWmKqclqFVrWCJFitsboAPQQx+dwoprGidDZ3TZIrcFEXYJtfjiRCICv2vmr1aV4+e6EcoaCI/Esf/U94/uQNNANOhaLe8lg1ImGLcw5FBn226xm9R9sSHZYcpCH4QNc5dLYRzqDX2vziKnhz8Sn+44//a5x1NzE3Ih//+A9jXH7hPvS5C37Fp+6jlC7KyMh22GaXOMmDTqSFIoQMGhFFkkiM+ZoKBq0NKYUMmRWTf60cKTYr+GqDJESPCgM6jaAsqdwjSYJSBi0lAIeWEpQDMNl/YH52svIQcvCGSWKspOyjfNyL6e8OBILoiURn9SGmQK+OTVWLhR7ve7ZDcVw/7ptMboZFdrg/5rEPHWNwbwuKZailrgW+roJl2sYMuUzE2IQ2sUCuhLaCauCW0pLFlTmCygRKKyQRlU17kmgSegJaYzrlYVFepaBzw2fzZKf+dpPRMQ8m7k07O2JNQquAcRG1SuiloFZkuLUSVFF2ybLArUUxg+w1qVekhdmZSXaa0FlCMaMMziLm8XqYS9fD7thjBl8u+gzANiP9maeLGYZ1YaQPnj56uhjow0gfRhYx0NX8EFh4TxdHujSyiB6DlIdboUloEZyBWzdWPPvsLbQEPv+Zz/Dm/bc4Gz3KKXRRYj00HUmyOdfF4g5eO6LpCcZmyGV7Rhze9Iza4nVHMD2Dcoy6Y9Qdg+5IbkHqlgTT4XX5rzJ4bXOHy9NILwAfauZjQPsBPW5x44Ad1pize3vwy/otZpPNG80mZV9em5gj/FYfXlvJjbJRTw2zDLssEiyx+PEKoSOmjpgWBFkQZEmQJYPcIsjyKZp+gWaLVVuM3mLUhk7fx6g8bdQaq7ZYtcGQ8zRrbPXlJWu02qC4KP/Pg9Vb4CIHL5A1orZsoicgxX2ZYmM0wUj+3pU+IZHsYkEpxYc+/FGWqxW6fMu/67v+AJA7KbdbWK+FzQbOz4XXX4fNJs9fNX7xxd9VgMKygYpuUnS14KuqwUQ63gnFxwS39IjWA85dsFiMJb9ZZjIAQ0dUhV5OUFYhVopaJ0cdTc5C12UTtK5H+o7UZfWkuBz1MZoVwdxGTDZjE2OvppLXTYl9YFX92z0p8GoHD4hGEUrU8pnyiiFDANr8sfgdzD4FFQOaAaVyRyFqi+k8qs8gTNKmqLcCSMjlqehcj8eCGGpEDaWWCBlcJXp8yKqtUJREohYgPQlHkBskuZPzJ1PkAvPEIrqJIttrpLNZedWZAq4KpKww60nB1XXGR5LQEa9Hvh6dRtkHThvggToOpIZy/48tuwpm1f8eWj896pnPAgnP7fy8VUA1galGhaVCBlA6lHWzCwEoQV0qcFUlGnFf/qNGVIl+rtRIWlgefMMvJJ123PjCJ1l95dOkuCXGDcbkupfrBK0CIgPGRES2gEepHRDRWue6faPigWzSZozBWjupq65yEH/pijyinKjLvel47YO/BGzHCy/+Q7qtLx3H+wES5gDszp1P8yt++e/jtde+GecueNe7vh+tPVfxiqtgRkpp6ihpVVdzn9vHVGT52EDElE6TbB4bi/nr2dmH+LEf+8NU4Pvii78F5+7xgQ/8d9N1ryCjBVzVvLJVhLUKt7lvs6sASD2X6susBVHtObawrd1GKyI4tK/2GKt5aKskq89UDeh3KFVYdn5+fhBWttut+2qPvT3WFnq19+/QsR+CUoeA2aFr0m7vkCCiXe/Q9ToE9Q79f35ch/Y3X/eY4u2QG4l5sAvYPW/1Hh663u3+j/mfPBZg5TrpyRRsZkXSmhFFSgqMYDQF9lSlWn4do8pGndisVhOdUGZJQqFEo4qVlBcwStPpbKupNGy3W05OTtiuB9bna5JYvAgxjcQYGCNsthlahJgYQySKENEEEoYOUsCgSGRQkmMMZF2JNmCMYJUudd+AMyqbVqgzjNJYl8/L2gJBJGC0wdSbryhBDlR2EC7VP1m+ITFFnMuXOaSsvqsqtQT4caQzdnqJvR8ZxwFrO5TROGcJo8e5Dtc7qj8yqw3GaPquo+t6nDNoZ1ksFlmtZzWm9AA6ZzFGk8RzcnJSnESGYv6jcK4rD5Gmau5EF9llihNgS5JyHbAAvhQSmoCYXfjkbG5qGYMnxAzkNBnWWa0JMeRrrU2Gbs5kQKcUKYUMLmO+gqk2SBCMzf//TV/zl1inm/z467+Albvgu77mL/CL3vPj+eUg34MYa3QmTUoLYiyFFoaYttxYGmLIMCuEmCOuGk2t6UomShAVfsz/vbV5yO/9nt/HF977zSwl8ZHv/gnO338Lez5w56ff5CVVlSfZ7FXp/NHKLu2qrzybgVpSWV0YKt1RaG1RCpTRCIkY8jXxMbJYnGK7DKGdcySl6AZPur/h5PQmp6e3UKqGS+95Vq/QJoe7D0EYhwEfYgZ/mzWDH9kOHVvfsV6fcDFYNmHJemPxccHgF5yte7bDiovtgu24YDtmBdngO7zv8DGb3sYn9Ck2uTp7BPA6uKy8P6gM/6hxNFQxT69+E1WzzHE5bz5WkJRluE6xWI/vQFJIrijOwFXnPFptcsRRnTAm+7eyJpV1ynSZtyWAQ54PE/DK68z/m/cliwK3ek3oFVL8gYXOEHuD7wzRWbwzhM4xuo7RdnjbZQf6tmN0eXxhs8+rYN6eek2lNAGvk7CmW1cINmbQFUZc8Bl8+XHK72KFYH4yjeyCZ5EyIDOpVN50Bk/BOIK2BO3yfBlGZfK0sXhlOVcZWoXO4ntDUJqoHXZ1glmeELThIiTub0a2CbxxeZvGFSDm8N/ckd4pRZckXAo4CbgUcRJY+nGatxLK8ohNni7FvF4MqG1CbyWbM24ENapJ5SVbhWwVaVD4i8AbrzzgZ774KuIdKViIC0SW6NhjQ1YsRVkwyLMZgKUlURakp9UIJX9zrdpMgGuh38TpLYYNVm8wapPBVwVguoAy1gV0rTF6g9MDzgwZjskFmg1ardGs0WxY9Bbvd8aY2QfrfmXSql2DLKXs3zVocEozeCGQlW8h9UTytYjcxKeOkHo23uDpGaXDqyUDC6JaEtUSSVlpAwsk9UiRZt5742ZW3cQMwZ555iGbDQyPcpB4ZfquI/kJpTLYyuMNWj9AqWxGmoehKMBCCRKwBZUb2aJSdtTuLLp3iLUkY6HPCiDpigJy2WeoVYI3JNMX0HUjvzvWkYxFjEWsQWz5/r7dwBw1eXbQauQxIFcq5pMeHUK5+w23AAEAAElEQVT2IxY9Oo6YuM3O9GXIPgRV8RGoYh4T0SqiVI4WnSNGp6KGzp2dIip/mzCklMEWohEMIpZkNCKGyRdUygQpYYtZnCWyJMmNsp7Fh5sk6chWHQP565M/agkNRu0AVkcDr9ROSfWkqqyO3IK4jlrrnbDOPZQG2Sm1BuACuMfjA6vHUWcdgl3SPsu5vqJULONUhggqlecmktVSEUSz8c9n2An0/avcWL1YIJUvYNpnlWZ5R63dovvs59DZAaXGEvAn+9DVOnfib/xtvvDp387Dhx/Nh6YC3/TxP85zz/1DlNoBqLbR2zZqq1qmVVhVkHLIPK0dWlPD57/413ag4WaOClm33YKzdn95+/sKpTYwRF2/RmC8CkRMd+aAQuaYYqldHvoT/v5v/A84e/YD+R6dv8kv/3/9a/QPXm3+oQlhZ7Iq4ghBZTPhZHj22R9DxPHw4YdLXs7fmbza6X8Vdu18w9m9der/YrTNcls6WmyzXrusDsUv3WOke/c+zgc+8N8dBTD1ns3B1lxpddkH93600FaZFGOclNgtTDuWjoHS9vlptzP3YwdMgRquiiDZquXqdup0VZLX57cCvHZ5C/HqNalDzZs/58eUXe17OT/f9lgPLWvT/J619/G6JrPXSXModgy2PQq4tefc3tv5uR8CsHPIBpcD4V3neTuUnuizJ1hi+YCk6LHWkSQrN6qfrZxirkOoDKasJjfI1a59DVksoNFYrYhEfIrcsA4h8g1f97X86I/+GK5bMI6+uOhVKO3YjJ5tSKQUiVK+a2QzSKMM1c9ZLMdSf5XKPmcAYhSwpZ0tRaFFNjNVCmJSKJUdZJuYFT5e531pleGbEFHUQj2rw4wpN1FbxlChmyIpPV0jIwqje3Q575gMSnf0i1V5cDwhKNA9PkJYR0Tqx2YEFJotRmt8SihjJ4BVowyoYr6XYuBbvuWbeN97n0VZ2G42/O3v+QHOLxIpgTMGQdhsB9ajx0t5+IvSqt6sEDzaGWJIGTzqHvoT+sWC1ckCazN87BcLnLPZ6WPf0TnHamFZdB3GapzN0WD92YixlmXXsx0HrC4VEDKo0xg0ic2Yo81Gf87v+Pr/hNFH4jhgrObBvYRWWaJLVTMVMBeCR2uDcUJKCiOaSIKiYOusKU+NISuRUlYyAZJSNhsR8H7gjh549tW/w/31V0HneM/Fz+T9GNk1DgRSyD75lCk+8pIClVCE7OuvefuSKGLU+BCJ0eKjwgdLrH7jkikgy7H1S7Z+xZhOGcMJG3/CZjxhO5yw8ctJJRZizxiLn4+UfdEl0SQxRV31NpQKj6MOa1/yStxrOga7FFlEZy8vMyarqewUOTT7k8rTGUIp5XPwhgKl8pABlFYBZwWtI85ls01N2IEsnQMJWCNYnbA2m7AYHTFEtEn0Nqu+tIkkm7BqLGYtvuyzOvAvUnAVizKzmHiLAqPx2hB0x2A7vNOMLvuhGgroGir0KgBsbZbZwX6TtwfGXF72dlMGXBlu3Vyf72DXNJ5N+yGrxILHBo+JCUtCp4hKoJIgShNUhl3eOEIxYQzKFoVWBmTeWM7UIps22gLAJlBmGMu017t8r+w7A7o6UC7h4oiLIzbl8WI8K3keJx6XynQa6ZLHpUiXAiaOaO+RcYsMW2RQLPUJC7VCxQXKG9KmOK0fFIyWNCjiYPCjxo8O7y3j6PCjxXuL945xtAyj43xcMY4WP1pGv1vnaaas7Mrwq1NnrOxrEwizegfFdsN2AmItNGuBmdPb3bqsi0pBJjVxBeWwU83m72rOT5A7C7AF9i0ILAjSE8mmYtt0F596Egt86ojSE9cFjBVoGCsskz6bmElPlJ7QTMdpeoG87WgUh1LAjx5jR6wZsXbDBz+oWS4VyyUsFoquh65TuA6cU1hHVtwb0FZhbFGa61y+BOAv/ZW/SrIFeFWH5J0jGofYnmQXiO0Qc5tgu+zPyVjEWqjAy2qwqhl4ehadLdSqECSkArgSxIgKOfLj3hBjAWARHQLUKJEh5XkfIcTshqPUs0TKB2QCWSrDKcmWEEKBWqInBVfCgBgiS4TTncqrfoja7+f8m1ZTC5l6Hg9cHcu7AmpJB7FzzT4lj81TgpZXpcQugEcFT2c8Hph6Utg1SFGLSTatVRlqKpUrKkpFlK4Aq0LQosQqKi1VTf+qyWExLzbGoyfgPGDsmE259QbrRnS3Qd9c42xAmwxdM8zKUrRqQljbHpAbbt77yVTsmIokxFMeXHwYZ97McO1AQ7L+71i0wLq8bRiepsRz3/IpXn752xmGm9y9+8PcvPni3n8PKVBqA/9QAJJ5sIe5+mZ+rMf2deicWpCWfU5XyOQKsFqAWoBdIX5JlB4VT1B2RaInJEeiJxbFpdARkyOR/btFqTC7KC1bsJUskgrYSiYPUSPRENSC9COLqRwbwrN83/bPogc/QS150s7np5iyQjjXi7XOz35WHm6YzGQnlWKeR0kJapOXbTZ3efPNb97Vz4G+f+3Svuaqnzq0cLWmQwqqNr+FInVc1XItlJv7qmuVcfNja/e151t6BqiqD9G5W4VjJoLz86hRTr33E5iZQ6n5eR0DkHPoe0zxdxXsav/bXvdj72ndb3usx+7XIfPU+XYOTV91zO29bP93SDXXXpP58R3qKJir4+bH0ZY3sF+eHTvHq9IT1cinCA7VtpXs/0ztNdyl2J9HlClwK+YWt9bVhE3tOSwUEZQ1JBWIKSBiuXHzNrefeZZhfIOYwMeIFsXgIxebDT6EvH8RVBSUyWaNiTSZN6KYgNN0DpIQKC9SxCiFcY7sZk0gCqIV0UiN+4ky2a8bko89kkGd0QpJ+fxN6YWMxZ+a1oqUBGOK48r6gTEmR1OVbGaJRLK9GRjtcuAA7SZVWLbT02SlEpCEbAha/NiZLivBihouxkiUlEGi1FC1iRgixihu3brFcrliOwx4Hwmx0Hfp6ZanxDGDgUAi2li2YVASEWMYNx5RmvOgSWnJeC7orcqQSoEyuRdNqQ0oVRxoF9M5hE7pDGQzDcwPv9TKUY5CqxXFF11Cp0BURX49Dhhn8/2VgFGgJaEk0fUOpTWdXaBNj7ELjPQsVid0dslqcQvB4RYnoHqUdoyDKmaaPaiOmDQhaFLKEZlC1IRoONv2fOpnvpX1cAuAd996kXff/uIUNKF1dhulw0dDSNkUygdHCNm8IhXH8CllhWF5XQ5X1tVseKIXlivUYaVwqYVXqZxqlT2IVWWVMTXyaR3naHGdG3E2+xVzdqB3I53bYO3AohM02a+KwWdTSx1wRb1llMfYlMfFDMYUk4LOpFxBVR5b1FqQTcB3F0JKZNiqWFMFdKrpmUoxlejGOeV3Sug6i/cBrQ0hZgibgWzMgFQpFoueYDRb7fi2X/vrUTdvEboTttrx5XsPeeViy6vna268+718Tk75jHsWtPBCuE8EttqyNZa1MmyNm+DZaHvkbTput9HT+az+Oh0u6C/8pP5yweNCwIaIjQEdIyoGrAg6JXQCXUx/s1ltVj0k9ASwgqoQy2XVie45Uyd5Whl87whLk2GYzuP4jim6BCeBnkgvgRMSPSMmnGH8FsY1dtyiw3YCYiZusX7LAsHGLS55ujTQRY+JI30aMGHEeI8aIQ0ga0EGlYfQ4zfZCbuPC/y4mEyfB9/jYzaFHscFY1jwcLxZzKEdIitiWuJ9xzBmMCbypC/v5WRtwHU1QEVgdbql6wKdCyWKa5gG10W6LtD3EevGaT3vH/Dqa1/gc5/5ZPYZZrZQ1F+3ZeC2Cty8uWR5coJBkJ95FTdmf6T5VWsrKopIdv4dWeKjI8gCHztELwmpZ+QOIe4AVihwqwKsIP00v1u2A2chdeW/+f9P03y0Js1YVHEDWg04fcZCvVnM9YrPHxVhUpyk0pCP2cWBlhwFXEFUimQU0Zjs1kJbRFlwHdJ1SLdAdUvs4pRke6I5wWvLZ05Pp8AHksVOtTjLZXZ16trm1fXqJfkXjynYrpmiTA7vCSkHeAgJFZr8CjXGlNVCQVBeIV4VBZlCeU0KCuoQNUS763SpScHkf1QZqJFOj8Grq5YdU2QdA1ZzoHWd+T3ll+wAWh0vfhagFuwAZQug7pfxoJ4MaO1NSzMvMKRsajIkGBPKpykiqFbZa/2kxFLZ5rW+N8bkg01pi9JDNm2tEKv4zDMm+yRVeoOzI30fQZ2hWJNDJT9E2xG7iMBA1ym6zk0N2rbx3ZZPh0yT2ul5w2u+/nx79f/HfF9dbrDt37ZHNRhFBKPPuHPjR8o6h9UnbUOypkOROC/vb+R97/ueAg90jqIrjkhPSB1JL4n0pfPCEaUHsySGbLmQ6IiUyLvkjtwabTOmHAAjA6scdCCJKbDKkqIBsUi0SNJIMjCBqzxQBok6lxupKUP+aafa+WvJ73rteFiC7jwn65f3wFWejvumtjrPh7DizTc/RggLTk5e4fnnf7hEW85g9pXzb+G1i18ylfdL9zLf8O4/jdHDpEpUkynvbr7m5e/T7tAfBTTeOvs6fvTzf5Qx3GHlXuKbPvTvcLJ4kZTgBz/5ZzgPP7/+ga88/LWMP/E83/Cxfx+lzi9tqzUPbd+RFmq1pqWHguK0/srq/BxM12XzNDcJnJ9v/V/d/6F3HrjyGOepPWalFM7tOuVan3StkrNV+rVRMZXKasy5z7Zj6rOryqBD597OzyFeO93mHVJ/PSod2t+hbRxTxB0CW8eOYz4/N0WeQ8Kr4GJrFnoILF43PRFg248suLv4OeBBlUsqJIW9B7R+CKcLoXahgKuqDZFiMplP6PTGktMbC966lyPEETRDypEpL7YD1vUoJYw+sFouef7ZuxijefX11zlbD2hjSmCEvJvppZOscEopTfAMpUrwgOy3yvtIpzqUTojWDCHQ2+zLI6SUzUS1Bq3RprwoWqEpMK1UglsnhtraKZJqbaiEGEuktFxbznbeQiz+soLIxAdFdqGTBcMYA+shMPotPkaCDySR6aOeQQk4JZyfbxDJ98xZx+npCRebQAgJVezLl6sFyi14cJ6B5jYlMm9Q2YebWhDFoswzpOhwi5sM3pB0l01gTHYyKymHwtamB6kSZIeoXFOV6evUgdrNx/GrSPEEbc/QZjP58UB264g4ZLDkELXV14fLH+6hbvcxAcZceXWVKqu8NS9vvpqXX/3q6fndg2Ja9tQYe0mx3yA6lPb2KyAFgUiOTKlVwKiI0yNWjzgz0Jktnd2wdGuW/ZqlO+fG8ozeXbDst/TdmqXbsHBnLPsLFv2azsZispthtCnPlaSEMhVgFTWfykA05+3yp95Mdj2milpOBEgRpXRWg2pdVKK7AhB2BWwrg04p5l5LKZVUUcXHYPZdh0AiEaNHWctoF2xdR+iXebpbMLoFg+vxbsHgFoxuyehyvu+WDK5ncD1jt2R0fR7qdLcgmvyR/Pfn92d1/NZ9nrt5QkClSD8M2afVxYab4QEuBEwIaB+wKWFSRMccBEMlQWJEJcnXW1S2ZUeTlCGgicpk8KU7vHGMxnFhCvzSjujclb5m3k7qJdBlrVABXZGF8izUhp6ES74ALl+UX4GlSnQpkLbnGL9FDRf48wfI5hzZnNOpSCcj1g/ouMGFLX1MhAuPjY40akIxTR7DIkOvEvhi8A4fHd7XvLzO1p/gw50JiuXAGT0+LBh9N+Wlpxi8QeuUo692ns4FTm5c8ExXYJjbQbE8n9dzroKwGRhzeT3nAotFxDbBMLTer/y0zurzO6UIwRC8y6o3n6MXe28YBk3wls02cn62wI8fxHsDxTk4qeN+7DF6RXpjgaglVp8gW0UcTQPAFsTUF7PJnnfCf5dRQ4Fe2d+P02t69bD49clmeEpXc6scNRslU1mczcVN8YeoQVmStiRlSaprxo6ke6Ja4s1NvOt2jagnGRy7hljbCDvwTo6z+UtRgoUM1epQfYANZL9OI7tQpO10aMbxwDg140uK4+Zj1jbQjkEtnc9NWiC1KvNzxdVcfXXIxLDm7ym/DoCsnmzq2PMYjuOfQtpTY6l8L/Ycx/MEPrTkEb61ZHeP63QwzX3I34sbq0/i7AO0LkEG9Eg2Ac4N75Qir776LyJySu3Vu3nzB3jve/8faL1GmzXWbOm6/PBorTGnhmEY2OjNFGypKjNqY7o1YaqpNbepgTbGcZzKLMiNzb7vp29+nTbG4JxjHMdpW3MfRkp1U2OrdSZ/SHFwSMFSzb/qucwjm7f/a7czj9hX14kxZkWkycrZpJYktSSobgJWonqCcSS1IEpHUn2JNtpllVUqCitxJLFN5MzGvG9SVhVwFVt1lS7zFVKpHaiKKkeHnYA31/DR9g4nxWVgVfwvYgWMoEwq0ylPm5RNaXWeVyaLOLTOasXqN1fpEoFWx9xJogNaRTS5g0SC5pWXvwWCm+rad5/9EW7e+CIqFVPvUu+WFNFS6qJRsV3c4cWv+XUgZiqbn/upH2H14DViyibfQu5wl2LiHWXJmG4i4oip48GDn4c8m82n7nOH8/hBnL4gFV+lo74BN3aAdcO7cacDzyw/A9RmQQaYKRkg129iOkWmwB/Zx2aIJbhPyn5PU+qLhUuO4BziglfufVtutwHr9fv4Bz/xf+HZmz9MTEvO/YeYVAARUJo37n0zn/78v8LXfeQ/muruh1REc59mNc1h8DFVVy1bvPeTKfHcqf0hmHwdEHQMULVO9733j3RwX8uQxWIx/aceey3L6n7mxz8H9nVbLRQ6BKAOlW+HlFnt9TzWAdGmY7Bxvo+5z71HAairFHfzZ2MOFQ/tu85XePkoxdu8rXksteq1Y8ETrpuezES0OeFErtNqKRJeXZiC1IdjH8iZArLyNpgARPa9LzgUQm6YZ1vlyN1nb3HvrTcZx4hXAaUNymQTQuUy8Ioh0/KFFlQKGIGFMUSyk3/RJgcOUAZS8TNGbrgnBZRCwFjNGCNGaVCSzRKUKdAsn78CugNRQqabobKaLaVsDxik0O96ccrNjfU66j5/QEutMZaCOYlDUl/ManuCZHM/oYPYk1RHlI4NmsE4IooghkQNC76DWc4s+Dsvv4tP/d3bZbnj3oPfynod8cESakhxyb1SYh1KZ5gWY/YLgjryuMwrtvWyCLlC/7ipWm3O/yue6pxXSQnsICkDHPGQirv/0mioUEaVSJ8kk5WGkiskiC09aLVFdI2kgPcdWSaSFWFSzFyk9OJK9t1y60Rxc+VZuAGd7nN278s4dYbTDzHcQ6U3Sel1jH4T4X72N2TO2Qz3s7rK+/Ie5Q+dMRpnNJ3JH72u63DKZb960bJanWBEML3DscLSoVSHTwYlOWx8SA7vfYbfktUJxmiiCEkkqzNF0EV5GlNEYlUZ5nfYdR0Sin8Eilqs+LFTpiooycpS0fiux3cFZPVLfNdl4OUWhOWK0fUMbonv6njJ2C0YugWjLRBsAmYLRtfjXf/Yj9nebZWEGwc6v6X3W1bbMxZhYBk9nd/y/mdvcqoSqziySCOrFFjEkU//ox/i73/sN/OlGx/Kja2SdIoknStXW3q282eoNkSfMNk4ZNPEYr54Mj7AJV/yxwy66hBGbPTYsMWGoZg1jjjxdBImk0ebRroUcGnExpEujrgY8n/TiEkekuCDw8eOcVwW6FWit46WmJZsB0eIPcPo2IaO81CivvquwLG7jP69EySrQ4VhPuQK4dNKioRzA86OdG5gudhw68aDorrMyktjR7TeZjPGznNy6liuYLXSLJaCcn5SjtkCvlw34mygW0Scqw6fD1VEFN5X4GUYR0OMjnEweG+K+WdefnZm8GO/l++9nQDZlDfaS+v4sawb3gklYWoUpqWhYgO9GlHqQfmWS2HCuaOtqklFa0RpRGU1V57PwRqSttmFhDL5+1I7phREdZNsfN0kzdsDXxV2zWFYVYrBPoCKB+bTgbwKvOp86wesBV3zIe2+V9mno5rBriZJMxYmsHVUkbViH1w9yin83KyxOzLfHVj+jid1GUBtgAc8ga8s2Ydjh9Zrh9aBfPVDUg5pGkszz4Hper/m603bUvv3d768pZtSftp6Frleud28j418oJip5Yb+8bpNPrDN5ms4Pf30TK1gqe5IRGTyTXhVw+tQmje4W5iltd5Tn1W/W6l2KGuHrLKKKqSOIRlC6sCckFSPdqdoe5KDLagFPlpEdYhaEFOuA2dQlSOI5jp1Vk+lUHxPtSaAsSqtCqSKegJXFVYRyrjCqliBFWX4pwysoCnfpBkLqsuwSjWwShnJFkamgVVqH1gpnaOZV9PaXK8NaCKSYq7vSh2nUqaVemAqwctyAy93zkrp/Uia3P+qSb5caykd5zpTdpEaHKdCK5MHlU8y10xdcXtSzLhzT0uxkKmNNnW4s3uW3vjyN/MG35xn5uXwfPqT+/kv8637/zn2v5rqu1RGgRPCXs+t2l8f+Htv/p93/3kMJdGTpBBPefXet0+HMqWkyrdKeHCWffm16q4K1CocadVfc9hxCPJUmDUPNDBXkV6lYpv/t+a15dB8vu67+j5rVWyHzLDbVM/14uKCxWKxp0irxxpCmMxOD+2n9c02jzh6TD13SLF66JoculaPgpHtNb4K9LXLDx3HddL8u3JIidd24ByDgIfOpzXPbc/92Hlc5xivm54IsF19Q4XzzR2++8f+FV69//O5uXqDX/WN/wUv3PpcXl4KBqV24G2itiqruEIIJJcvirOWmzdvcOPGKQ8fjoQkiDVorwkpQFqgjM4KNISud1hR3L55h9ff2hLFYbqOd33VB3n1tXuEaFmv/U5JpTq0XhBUj4jOkf2UQ6lsLqhlgQ4dpB6tM6BAdcXRc4YULdBKBVRl57R5vq7TTkvptToWuvrxbwq7RsOBNACfu082IdhLqUSh8mRnvXkwbFl0I9ok1hcPERmRsgzJUagkbRF8Bl8qMJnO6IhWiUT5IBdfWUoiwhZrQKR2tXsUAQkv8ODFf3vXcEFABbR9E6RHigPuWps/9L26XoqgNyi9RemHKLst8xu03qJ0MZUyW9BbYJ2XmYEh/HLG+K2lxZe7vG4u/zi9+ScofR/FZlIl5oOLaEncEsUiDfzSb/wgH/+m96IIPLj/gB/9kX/Cw4cXCBofEuttYD0mfBQSiZQUG4SRrgTltYgSVImGi1YknS+Z1pqQVI5KKTD6xGZ7kaGwEpR6CJSopNOV2ykdK/c1qpjyAtFa0nJFWJ4iyyWxXxAXp4TFkrBYEZdL0uqU0K9IiyXp5JTYL4nLE9LyhFDGabEiLk+IyxWxX8GRj8V1k/UD/bil81tOLu5zxw+4cYsbNvTjFucrKBtwwxozrHHDFjts6YYBPZyht2t6P2DHDXa7QW/XKHRxpZe4cbLg7p2bdE4TU+Bbv/WX0S+6EpV190H8H378e/mJd/9q6PfP6ZmL13lm/fqk6MrO6LOfLhvHne8uycu7OKJDHls/ZFgWRmwccgTHxt9Xl0L2/0gOQEICiUJIJkOquMgmijH3aFZ4tR0sMfSMY47aeuZPsporLDPk8hV0dYxVDeYb8FUUZE8zWTPg7EDn8nC6fFjA12aCX50dsGabFVw2r++6bJ7cF2VXV9Rhq6WQ2NK7rAJTZk2MA0ly3Gbdr1icPINzdxB9g2Gw+FEzesc4asbi62zwMI6G0SvW57ZAL8t4CW5VOHY8L8Z3AHgpyUq22qFV+2966BZpB7ka4FWDsIgidy5R11E7sKUVk/K2jsujnZQ6FtejOS6uhlxZjLmDWroZVDOeDzVVRdch4FWnN7O8Y1CsVYRd1SCaTxsebWrYAQv2AVVVa+2ZLKr9vBZgzcHWPO9nw3F84jKg2tCYInIYTF2V7w/kzyMpHvrvvN6u6gHWG5Pytw6YbloNWNRmVeA1yRxh569NNdP1Yfy5lbLrjUiSQozLIVpzhnMPMwhRHnRA2RyVFRdRNpD0grP1L8zQRav898XAm3e/g0Q2BRTpEZ3VVklyHdZHQ4i5czerfvsMPIoZYPVfJWlnDijJQCzQpEIrV0wAk0a8hu1OXbWDVS2wgkcXOu9wUtKUYZJVVVZQLqEWFVYVpZWJGVTpAqv0PqyiqKgmH3EU9W2FU0WZr5CcBzsrhsmqQZESqAKqJMIUCTaVjuQCV0UMooqZplSfg5rkHeLzd2mCVOgCqup3orwP05gngzqPKlfb+1un2zL5yv/L1Y0BOTpzOL3D0Gq2s8N5V51HC9oPvRcTfC/lnsrz+TmTZnrn3iB3DCYG/yzZL0EuII1Z88Izfw+lRl6790vx4U7eR63qJsXJyUu7XcvOPLTCpDnkquvVvArX5wBnHgG1rn/MRPQqtep8vUOgZA6TqjKqBs2o01elCtGcc1O00TkY6/ueGCObzWbqVMjuoPSev68Y49ThMIdcc1VXe86PA8COzR9SjM2Xz7c9/991YdujTDbnKr75tq/afhtcovXVN//P3Ay0hXFX+ZS8TnpbVbTpQHYZKITv/dTv5pX7HwHgwfo5/ocf+v1869f+t2ilyke6REURQxSzi3xC/hAbZUoPmkObBZtBWK8TF5vIGPKHPiTLuKw9Vh30+UP/2Ve78rEwk5wd4HMvNwd+hXnXlekxPvQ5XHoGUjnEeh2vM8RS45SnGsDV/kcxNtNlKNtSyoOM+UOuhYvtOev1Q2LKARZOb5+gdcRsH6LXb/HCnRW/6Bd+kA+8/wadi9y60fPmW6/y05/9Al/84iso5dj4LetBeLgeuH37WV54z3u48JEXX3qFzcbnaJxkZRNK4SmmeloRlcEnQancc4rSoA3KmqkRZ8QgSqaCso2wEry5bB9TnIQqcx/TbVFqAwWCaVMhWRnMhhzxbJ2X2wGl87RSBZyxBjOWtuNORZm/ScJ+V3OZlITKNIOFfB9n6z/INnwHWp1x2v0Zlu5v7wo9chRTTX5RDZaFRD64WmKiB39RTCdhtVpy9/m7/PCv/T381K/7naAU7/5rf467f/qPM3ohoQhFaelTkeOVl1yrHHFXkb+j0RnC6gasVqiTU9LyBFmdIssVaXWSx8vTDLkWS9JylWHX4oS4WBUAtiJWELbIwEzs25MmqHHAbDeY7QX27AH9619Bb9bozRqzXWM3F6jtBXZzUfIvMJs1enuB2a4x6zV6c17yL9DbNXa7gZjBUnUfR3keM6zPBeuYEgMgUnrSkpDQKGUY+hVeNMYaxvWGuN1mQC+gJGLSwDf/M1/Du9/9LiQOSMqq2uzoutZy666F73jxb/BP7n4TUnyQnT54md/61/8op2kkRE/wAmoJapX9UMUFKWWoFeOCGGp01hN86AiTiqsnhKwEq4qwcXSMoURy9TsQ5n1HeopOdY0OGWQV33qnJ/foTJ63ZsgmjnZbBj/Br86NBYbldTo3Yt1A34dseuRGOpvBmLUjSiXGIeC9Lue8ZBN6jLvFdnTZj5m3jGPHZliw2ToeDh1he5OUlkRZEuKCGHtCyOYPw6CziWSwZWwIQRODzmrcp+gPbS9NQEjtYJVRxWJdXV52DGhdBbqa+Ss/R3Jguo4r3DKyg1sVdk37mQ004/l+2qEFWJ4cwbQ2kuv4cWBWO92aE84jFLZgazVfTy6bFl6CWmqX344dl/OfvjXs5VRVcS2Qah3HH4JVe47f63rN9CBlfSk+tWTnS81TfGvVsWSzxezzuqQmGEVthMPUuM+Nw7lDuHc6Xb4Zx6vCtQMwUP2EZQf3zbxq52Pp5Asom1BdQruE2JShlY1FaJOmQEfKKMRk6p10hdfFakMV9wtKgRQlZ8p5UABIqi4aqqKnjJPOsCrpSVmloyBBssIqCSGuCOHkMqgayS7MWmjVvNQj7+MLP/knn+5teVQyMsGqPfM/J6hlma4mgDpCGVQFVSqRgwnlcbFHAZVQtQFW8rJqSjKckNblRGVaBbSKLuvqArE0qKKYqgExqAEx8nxVTU2mmWoXnUlqQ65VTz0ppJqnQ1BqXmamRyxvAdWhcrdNcmni+ukdhVaP2PYE0w+kQ/np0Aq7i6IqvKqgqj5zKs9ngcgSiW5vozdvfh5rhxJIY0SrgfOzr+L87H2l/up5//u+n9PTl/J6OgfUWPvnOPdfxbJ7k+dOfgSthxJMI/sxVnhQORCX1rntJ7Lz4TU3iztmJicivPHw43zy8/8GPt5k0b3GN33oj3Nj+XkAPuz/PJ/58u/krYcfZ7t5HpLmZPVFvuZD/9lk8li3d0gpVmFbu+8K1yrAEpE9dxdK7UzJU8qRZquJaFWHHUtVGdvCmbnpad12VZXVvLr9EMJ0XBUYHvPtVo+1bdPWdm5VUFWYtt1uJ7PGCu3q+XZdt+e/7phiq+5zDpmOgao20MKh1IK7NrW+yNpxnT4EM4/dl2OKw/m+D5nGHjNpPXbc9Xq293UewbX9/3y6XqsWAP+sRRFtD256WQp5V2heefChZi1FiEv+9qd+59vZVd0SWgV00eqLGnKFSK2x+hwlA70pjtmN8PDhWwiemzcWfPybvo7XX3+Jl778RTabM5QMGJ1VWIqRzgnOBCRsGMdzlg68v8CP5xg8zgZWvcaZAspUjlCo8TiTUIwYldVcunS/VEVfDTBgjMnyd4qDcZ0rqCKCSnW9XWElQEg7e20pIAFAaUNIiSSaxY07/Mz6ZWIcSSp3lS/su+g7R7p4g1U848Pv+igfeI/hxskWqwWjoXeK05MFi4XDe6GzjrX3hBDRxnJ2dsE2Rm6cLBm3F4jEXYRWNCYlfEpY50gpokUXTiWl46t8vKkBDnYvZGs3rbXGLT6L6b9EHN4/3e3V3b/MjXf/3/M69nIPg0RQ5vLLOj0vB2h+ShmkyVRIZi+CmV/VvJ1DSlLIPYWAIXCz/xPc7P9Es7+6lTIu3+Lc3NAgiQ/9/Pfw5itbYtoCWUq/6Du+8olfy0/8c7932sjP/Obfw4PTO7jP/ARplRVfaXWCLFbI6qTAsjywPMl5yxXSv01VUYwZaG0y2Oruv1EA2Bq9XRfolZfb7cVufrvGrC9Q63Psdo0ZNpjNGrO9wI0DjCMJDcSpGTS/P7vnoFpI5QAl5cVgR0Dzdc1VmlqBZTJTTYCqilhU9q+EZFMCyY7HExalFcvn3s9icROUZf3Sa2y2F5CWkBYQO1IyvPTWx/ixz36IcSMMo+XFtz6KcMpma9huDdvBsN4aPve5r0P9rZt8lfw09+Q2fg16Y/jz8n/LESB9Dp3+tJJSgnPj5MPr5HSg686ziqv46MqKLl8cRQeMGdFqxLmE0iVohM2VNK2LubWOGCWgIzHpYsro8KHLKq/Q471mHA0h5MAdm7DgbDglrHPUW+9NDvYRzBRWPiW9N0hSpNLTXhs5j6wkP/HFYh9UaTLwOgSwHgd0tfmP6tmfw6153eNQ/lVqrkP/a/017pkb8ghopfbzjx3rVZEN5yBqHjVxDqwOKbUOLTsEu544PcbzdUhBdZ8DQEtmYxqn8LIDVPN163pbwCvUqGBUyACMGkabl13dWf4Y5/v23638GNQIc8U8rDjtzo7sq3P7kRqpzpjUwKoCRmxEdQVOuTxWLmUVlUuISagiypIKf001Ly4STWNKR4kt+bmMyaZhLsMPKZFBKeZ+YjMAkcYUULK6KsWemFaNGWD28yvVsXprAtgC4/nwc0Jl1cKqBlotsqKKoqxCp3K9QJUogxUSMEGDDKWUAokxB+RKGTBkYUz+LouQTcZqeVLVf9P0TqKayOBRMChdIGy9t9VGW2lSnS4BeKYyF5qy8Cl9M9pyN83yK5y5CmbVespeGbqr9+bp5lhl+rl8HI9zSk8NWl1zO/PVroRws5RkttL8g9Q8dwiKuAexaq9NnVZlgKrCEnQZK2L51Fc1YG20l5Moz6fClHqIJYQTUjLk6K8gorNKU4r58GRq3Z6i4upAO/sF+Me+/r/m7t0fu+S36sGDD7LZ3OH27c+zWNwv+9+HQsdS9SNY4YEIxHgZRMynD7WLlFI8d+uTfPs3/laG8ZTOPkCpNLVvFv0ZX//z/hRKKTbbHrhD514u138/UGFVYNX5FlS05npzc8kWOFVfjdZanHO5jejc5KOxhViHTB7n0S3r+bbgpY2AW/ddtzkMwwTNttstKSWcc5NvtXmq53Tz5s0pSnAL4kSEcRync66RStvjbq9XNTOtSrZDUOmQI/759ubLD0G29l5Ym5/zFkRdpS47NH3o2ZqnYzCtpkOmr/PrWdc5FAiivhet+qw+D4fcHbTHMecMdf1HKRgPpSc2EZ2f7EQYteL26lXeOn8v9QOjVOJXfOwv03ebDKVUDpFtTMJVIFYinyzMyDPLkbs3hb6LvPDcTcK44eL8Hl/66S/z1r0zHjy84N7DDV985SHiTjDdEqUdaXvOx977LhZO8/4PfIS/9j99Nw8utnz8az/O//rX/3bevHePP//n/p9c2MCzz9zk4YP7jMOW0W8Ytlu63kFK3OgUmsi9i3uYUjB02tJFx9IYjM7RPOtN1RM+UEXtVG9ehjcxFVgWY643lA+yZvdgpPKBEYSYGqd9CpKkRnGVe+FijIjWhJDwIeKT5B5UBdoYbt2+yco5Xv3Kz6CSsOwXLFc9KW6y02cRnHPcuH3KculQOhG3Wdba9T2uc2y3G7rTU05vnfLmGy9x80RhreP+2ZqoF6gQMaKIfsjgyrrcO4jNPavG5CugBNE6q67UfgFXX2jUyO0P/H7Wr/0OUngBd/KjrO7+5el5k5SvhYiglZ6uzTHK3+YdKsAO5UNbkOR17IxiHyLoO0MSQVStgOZ7KimincYtHYvTjlqTUlrz6vu/tr5Q0/bOf81vhl9zxbu3vkBt1+j1Beb1l+mq+mub1V6mADE3bDDbDXpzQTdssUMGYHa7yett1thhg9pcoMeh1FkVNZqGLme0CzJafCbOroGkeKkgnO7ZVEFiL78CNYFS0YZadREo/vM6YlqSZJmdtbIixmV2AiyrrF5KeVmSvF6a1l+SZFXy2uUrRJakl05JaYGk4632v/fDeXh0+tppyprsVNrbhOuE5WrLibkoICuijaB0Lg+VAqVlB6Mp9XBFbvwI2UeVQEwZSKVYTNjL9Jg027QgrVekCNKAK0mUhg67RgBHxnuNgncozSGVnc0fhFo1r8AxZvn6wH/nwOsSwJLZfJNqI2dqRM220Tau2v8cBViz6UMA69h/FJfNEOfg6jpqq0eBq6uAVt3Xz0Y6ZFp4zg5IzX1oHVVv6Uc7lJ/71WpVYdcGJWo2viq1MCorDhQB5wRjJdeJCpj6+l/4NTgHXQfO7Q9dB8aB6bOLIt010w5UGf6bv/TnwQpiZeePyQijRNAZUAlZYS7Kgsq+jaKospFd8CClip+qYgaIOCQV53XRIKknpSU5MmCJCJgORAKMeh9IjcAF+3meXSCGJ/Hd+rSTSWArrCrASpexk2wOWMrz7Ih4Z4aVTVXzOFt5lHoJlHZ/U6drgvjsIACQagOnKgRrx1IFiqpEo1YNqFLTOvm+0pSZ5bzmkOq6z/yjHvl5uVjH7XTZl9TpSWXVQCpkVjbOyuzHgTvz478EstTBycdL8z+q48uO7aMeW02HzvWdyLMJdKTv3iIDKtmNJZX6mDRgNfsUVVDgtcqwWnQOuiC7IUc5uU5SgGkO8Z126pjIkT3DNM5t0topEJvxbr35MlQi0WPNGme2ZR3Pyy//cjab56azAeHk5PVLjX2tNc888yK3b//0pcZ9XedR6dA687bKowBHO2+NYBYPi+BgV69vj61za7Te7gGIdrsVfFzyvT7b1qFji3E5dd50XXdp3zXQQat2gx04a6FanW7dUM2Prx5zO9TtV8BXFXbHopICEyicA5oWAjrnODk5Ybvd7gVYaXlKhTm1zTlX3rWAcn4vD80favu2y1rw2F6L9j/zdvVVqrVHCVyOtdfbZVf959AxzGH0IVjWqipbtWX739ZlWU3tc/Uk6YmDHLQvTM3L0QOF7/z6/4q//iP/KpvxFkYHfvU3/jl+/rt+aO9AU/Rok22Md22qhEW4sYjcWkHfGW6sVmyVMI6W1WrF2dkGYy3G5B7N6hHJWocYl+sopUKUIpAsN26c8oXPf4YXXng3EgPPP3eXr/nYh3j9tVfYbC4IMfKlL73EerPGjxGlhNWqw61OchQjScSUCCFCZwE1Ed6qOtuP+lF78aqfHD1FY5yuIfUByPNJQGJC6axyq36wRCAHTCgfOCkmmuz8I791/z5Rsl8GURGnNUYnVPQwDIBgnMW5su0CqIzR9Iuersv2QaOPOGdZ6R4M+NHz7rt3uNiuSSLcuXObm7d6wpdew65usN2M+KAJwfPgfEMKHmV18bFl0KUWlZhQK0Lag5N7DgjNm9x83586/JIhDWTUiMTiv/Tyy17n53l7PR5HC4n6wpVnO+16C9ren6nAIfs1Q8qTONnTKCQFFJFEZHWy4s6zN6Z3xhjDe85fv/Ruvf/v/w0Wf/OvskiJzetvwPYC5wfSwwcM995C1YIjBLQojAar6zFnwG1N7h0x5XoZk32LzQuKqZBR+Vlrr3skmwPVy6MU5Rm0GWIVM8ccMXZBlCVSoVaFXGlJTCtCOiGmFYk8jiW/rpNkkYfU5+3J07bDKg0gZFfB1YCpjj7Yb3BMz4dqwA271hHstiW7+YAGydBu46U4xJYZwHrE+BL4uub5PW5qoZRRu3M+BLyOwaxDY5p53TZeGlg1B1XHIFM7/ST/kUPz6ur1qn+tJwVWb0el1ZHNGXvAPnFr7/HSlsOO449FNLwKWj1J9MS54/hZysrw0LhTqI2hUHyHhuIyIaAJKJXXV9R1q2uG7Jeqqq20CWBDNknrpSiqBDHZtxJG5XkraAveSP62TQorRbIKUYaoHFFpPJaIIeLw2hJ18cdqFkB2tC7F4Tri0G6F4PCyQkJuoP7kCQQPsR02ZGfgbYTQY2DKA+G3HQ6w8HNBZVXqZtlfVRnrkufKtMqDUvV7Wr/LsPfS7kEDNY1zH5Eq9aUCnFofa6jiJL2Y8ela1uumLKNUStUepJrESHNIdeV5P8G1OgRH6nQ77Lmgk2aZ7L4hMtuONBury+cdEo+b1PRzIP/aG7jenw4tfhT0u+pbMl9vnveo5U+cd51v/HVuyNtYx+f84eLOI7eQy8994JQtiLbZ7NruTKvrstq5oHVWy9cCqTXJFhlIaUDEZ7NHFTBGpum8Ti3IxuIzrJpzR5wDYxLWyt60tcKO5gceLr+BH3/mD+HdHW6Gz/ILz/6PLNPlOvjl8961Ver82fjV/NAr/w5DvIsi8PXP/Vk+cOu7ERHe976/zQ/90L/NdnsXEL7ma/5LFouvEOPhOnjbnmihlAh87nPfxZe//KsxZuAjH/lvefe7/94l6FKPqVU2HVIAHQIicxhxDDDMQUT9bwVHVd3TWia1KrIKh9r2cgtOYlzy6U//27z11i9DqZEPfvA/5z3v+cuTgq0yB+ccy+Vy2uf8WhwCMTWvgpXWdLa9HvW6OecwxkyqrhpF9DqRRCsYm5sh1v9XE9fKS+p1mEesnN+fQ89LCw7b+3wozYFSu632v/PIyPN9tMsPtdPn+2yfnauev+uk+bG3UHC+vD3WQ+dx7Pzn3KB9Vq97nG16YgXb/KLtpoXnbr7Ib/u2P8LDzV1OFw9x5oKUdmFoq20ysxdXkmT3LdGQYgYpoKZoQ6vVisXiArcZ0bqQcy04rbHOoXy2m6a33Lp5St/b7I9n9Pz4j/846/Wah2cPuXHzebRRPHPnFvq+MHpP1/WcX6zR2pGSB3a9hrm+kkgJYiwqFL2TtKtS0aqXX9EUpEpQOwM5FGrXdi/gTUTQKtN4o0yW40terlIiCqRsA5eVRaoQcxFiEryPpCSklCuld555Bqc1b736GlZrsqQatNIoYws3UBhjMc5x6/ZN7r/1kN4qFs5y6+5zrMeRkxsrnnvhLuGV14ii+MprD3njoWW9FtRwnxATaEeMgURCK4tSmph29VSp16I40U6SkCRYY4u5ZjGZVZcJ+rzAqIV7TKFc092LdSgdfLFThpht/m69/f9CNUG8fBxwWW4rIlQBTVkRYwyvvvoqt09Mjqak8lXRWvHtL/4Q/+jk3fzUN/0qAN7/qb/LJ/7if8jLrz/gy196hdWNO5xfCMHcIKTb+PEuKXUklmjpULKE1KPVgsQS4QQ4JckJUNRaZUAW2WFx6hE6JHVFleCYnOFOPY/Zz8jkW6c6Pp0aKE85tZtt/UqrR0yX6703Pa/NtvlyjfEccD0R8HqCdAlkPQJ4TQ1A9sEW5Pt0sPFXH061f5kuTavL+fGK9Q814A5ut0nz+atMCh8Xaj0q/6plPxuuoyKXAdQF8BZXRzacw65DIKsxV1Q+ob2gY542MWFCxIRIGrYMZ2dkh+nV7+eIU54+eXrJZsVGeUyBWqaALSMZbCkVwUZwCW0T2u2glLKS1REWcCr7Q7UKZSGZPI1ViFUFVunss8q6bN6tHFE7Aj2JjkhPEEdUHUk6YgkalMQRpSdyiqQSZbs6WhebVRU1GmDjy2oPPM2hlSeDxkOmgO26b8uM87LLUYA3HnMbykoOvlqDsBoYwwBasvLKygSsphdxYimyV2Tu+grqx3tX5kstF9C778AEpJqyam7W3KpRm7JN6vf+upDq7aSroEmFVNO87JRV4QCgmubr92AGpvbKtWuclxxbTe2NDi26esE1r+n8k36dvx0618eFVk8Vbsnx9Q5u8Fh6J9ZplE86TpAp+8tq1FDszKmrSmrnE7A1t94pqyrwqq4eRPye/0CtI1onEhZnLqb179//Wl588TcQ44rbt3+cD3/4v8K58z3FltaJqrgVGclt/119ulWGVDXOsaiMrSCjBS91O1WV1Kbqi6r9n6CIotCmR7CgLWiH0g5lO7TtUd0S0T0DDm9XhGiI4hjVKZ++9fuz1cIAD+TD/Ij9N/iG8/84fyMoJuWSfYOj8rcjic75GFIyoLKa9wsP/nmGeLfcbcOnXv+9rNwrDPFZbixe4tu//fey2XwVXfcWff+A7GNxHxDka5MVgbUerlRHjNmdxs/8zK/iM5/5bdPz9CM/8of5Zb/sD3DnzhcuwYK6zQodWhVbez/qevO8Oj33MzVv49d15uaJLUDLlahlOa8cCCWfl0GpnhAUSuUKVza97Xj11d/IW2/9srJNxxe+8Pu4efOnuHv3Mzs/3SHgvd9Tf7XH28KfuenoIfDWHnsLu+ZqtQrbHgWVWtBYr0ULI+v+5mqxKriYX/fKPeYO+ueQc76t+XNxKMDEIWg3v9+HUrvsENC7DjB71PJjz+ycOx0632Owrd3nvJyag7r5sbSA+HHTEyvYYB+Y1YNBBFEKZyLPnLxc4FGh4M3/1eymp7SrayQRYkrEoLDGEXS2Q+6XHbbLThGNs/Sd5SJ6FlrTdY7gLSGMOLfk/r3XWS7g7GFg3Gz58Ec+xDPPPMM4eh6ePeSll17i/r03Jpvq4LcYrTC2w5oe70dSFFbLE7bbi+wkXTLs0kkwJp9PSvVaVJl/9m82nZcISZUXHzWp0+pDMim48gUkSiKlCEqhxSAogl8S5Fk6/RVUDpZa6rMKnyJJgU4Kg0WU4c6duzz73LO8/OnPsVQJ0xt0p5HiE02VSFtaaRau4+T0hM16y3rwmAi3bp6weXVDt1py48Yp62GgWy64/+AhnKusnIvbfC46w5gkClEB3TjGF3SxMRSERL0wSqkMEVVT8FBhVVX85QAKNcBAvVYUR7OII0UNBQqllKeV7nJeMqRUWu3S47fvwZ99CyLgTn8Sbc/KckdWHVXYVPPyBw/JvhiqdCGJRUnx51LD4kkOF47k+TydVYGviuYLr9vSq65zkyY1PeqNOdqX5Nv4Et9fGjbk1tYxUHEIbDztdF3QNa0rl6epjTt2Y2bLgR3AOgK20oHl7yTw2nP4Xk60KrJqY7KlqWr6aa6L3s3vSR/q/M60/Mp7fEy9dd37f9XyFi4teEIwJY8Ju9R+/tvyr/UYqfWvVWHUQw6oraRALEGNoIoTeOUFPSZUAO0TOqQ8jgkTCsCKERMTLgasRGyKOAlkVOTpksfZSOeKDzwbMRbERbRV4KQ4TgflVPZJZUB1GVLJKWAUhYNnSGVU9sttNNjsXD3pHAs6iiIkm6MABkuMmmFQPDwbeP2Nh6RkUXSILIjplJgsY7KcV7PAZHfjxn9V9Vl1EDwdglXHls//+06+09dJSrKKSkvxV5XzVM03ktsPi7Z8m6amCTlUULZlQH3/Wxi1B6LaeXV5edPnIQrSXmV3sX88V6THutzzleflz1ReFUglsiu3i+/TvbJrrwxX+9s/ti81v9hHkmJXoTxEjeZZ6tCCxwB/s+L9sdMxiCQH8g6t91Ty5BHL5wuPpbe7To6SsgNJra+/nRJVlwAV1qa9dfdBUYVX7TK/t3xz63m2z7xAN7zF7Td/qnQe7MwH874ryMpE3pgMoOp26/EZIw0Mi9N5HuukPaYUqctaC4+2QTlvsAsQEwi2+LpzPNh+hJ949Q8xxGfp3Jt89F3/KSeLL3P3+R/k2Y/+CDF1JGXZyjNseBdJOQRHlKy8TWKnAd3n7wCWhCGVunBSlmiKz0NjZ5Cq5EkN5FHXKx24JaKpWAVLPeUhqtShd4C/FHL7dSLPDobLbPpQHWk2/0A+yt+VP3N1veoa2ylmU/yDL/27U92912/i1Hlpm9i9cZ6uUV6v0wTfFS6vvPIJFosLQlBAV3zdGkQcIWiyUtqSUh2bEtjJkX3i5n1miFfX2eW38ynt5nfbNHv5++u4DCHfdi9lPt/1+sOk9FPTM976YqvsYa4WuwqYzP3Czd+p1kSw/veQv65jyVo7+Yjz3l+CWK3Pt0ORKeu5tHCw/q9VurXnOt/GIcA2h2rtebfr1Os5j7w5h6zHQOOj4NuhYzq2/vz8231fVwk3v8dzwNnu56rzmp/b46Qn9r7d3sg9FRr7H4ZsNkoGRns3WfPqG1+ND5b3PP/TaBsyhNAZqlQ1XEuXV8sFJycLHpytMUZjjKI3Dq2EcdhC8ohYrDGcnqzodPbxtn74kI9+9MPElBAJrDf3WSzezQc++D68D6QUWd99huATMUVSHElRMfotL774In3n8NtsNJcVTfv25MC+PzGa+iCXb1yuy5QHwxTnjiGAyQq1HDQgEZLw2vk/zxfv/QmEnt58ia95/nexdC8iogipKNu0JqZIxIJdMMoJRj1L8rcQUbjTO6j+o7zy1h1i8KBUiXLTsd54Xn/zXdx/uOWtBxvW3nL2ynO8/Op9uuUzrP/xB9kOmq38IjZpS4y5oPZBo1iQSqtZ6BDpwK9AOYQOVfy4CAVUqQKkqK3D5qOKKvmHWhyXSM7bSuH8l+7fpEMf0Kc4/dTdyVwbfJUD0KUho+vBxd1y1dRIVNqraKtJxdVkt0NiqlBcagg87TSHWro54bZxtNegOvLstAqNmi8H5p/EpOo610DzGD605Apg1YCqJ/W/9VTSI97JvUiGZLOU+zRKq7xcjSlDrJBhlgqCCRVgRWyMmBixMeBSwCWPk4gTj5WIY8TphNUBpwPWBDoXcMZjTSoR/nIxJCW6pxhVgJXOSiqtSCtNPNFENAFN0oaoDFFpghiiaHy0hJgjlMZo8TFHKA3BkKJhE3tSNDlyadDZf17UpFCGqElBIaGBVPNIf9eFUnNTwKeosnoqqYCqCVIpaAPhiAZ6UH15fSocKe+zyPx9PpCOgSrYh+XTPHugageydvtonVg/9aLtGEyaylLZL4OS5HspZbqWu7Vw3tvGFRDqUt7jfk/V8b/MIdXjKtOekG0dTVcBpblD+2PrPVGeXGM9ucZDdZ2n7op1VLPO9Ao1F7bxFzf5j1PFpxx1nB3MoyJJThBWe9u+sfhbnHY/MEGmNuhFVVOJeFLamQFmaJXBmDGCtQlrQeuAcwpr9RRdsLYlagRB2CkyamN7sVhccnQ9b8Bm9yRZ9ZTIyiS04+WP/Cq+/Et+0/SuDV94jp/3D/4iMVmSyurZxCKbetMRinIW1eV1yIOIK/DJTiAqu9MoHRUUoDIppXQzLpFJpUQqFZ2PT0qU2Zk1gaRSRtUgENPArG62G4/hWT710h+7XIeDR+c9av5prXNw/kD59nM+CYO6g1c3qAev2vo4+bunSsCY9vtWzdh37aL99PnP/wt8/vP/wjt9ArOUGvVkhdsRrT3GbPfylaplwG5+p9QMGFPl+ztArlTgzTc/wcOH31j2J4Di5s0vT8qu+n5Xc825Hy3Yh9Xt+z9Xe83NU+dKv7r+XIX2KDPBav7aKq3mEKlVZM5BWt3XFOSw2d8heHRoen4+bd6xZYfSMUXqfB/tcR6CcHXdY9ftUZCu3c6hc35cqDefv871uO41O5SeCLAds0ndk90VqdXeRSG/Om89uM1f/Vv/JpvtTQBu33yV7/qf/4cs+4copUkxlG0V00KdlT9Kgessi0VH33Us+g6Jhg5Y6URQofgaEazS3Do54a3XBx68eY8khocPPaPvWalneeuexdqe7J/RkUSz3UYESxgTSQzeJx6u34WxHSEqRnFsNz1d6FG6Lx9EN308UX2m/KW1nErLNxXfK9mkJf8HceXDXHqFxML0UdbT/8b47un6DfH9/OjLfwNFQC53c5eV4I25c/aX4AdeepI7DXzxMdf/p+3n5ar0kF3F46knAZV7XlEJag+mCpyuHCkNKB3oe4PSgjGgtBC8Z/SBJAmdM9lsBrp+wWY7olSOdrRTPBVH0lNvXx0aSDSpvd7uGV1VqLQNrTLdNE4PjqWZnyu6jo7rdrncSHncczRcT511XQfwT2qq+Fg+fN9GS7P1m1V9XW2o9ZsMujwQMszSQVBRUCmhk6BTxEgqQ/YuZVWYBqc9Tgc6G+jMiDMjnQm5f1siWoEkSAVORW2IaKKyJKUJShNTwm+3hKiIyaH7Ewa3wPc9IRpSiUgao8YHyzZ2E7jKoCoHfKCCqoFH+KfiMJS6avl8+Kdd2VcygSE1ASv2X61LHckTtTq0wR1wb0SVByHV/JOjZ/PzdQCqOeCBPR/jTI+Vjm0kyg52VIl8hVOJ3b2fN+6mbanLB3XsJOZwqV1PHVr3ClDYTszL2OukA0XoU0s/q9BKrrGeXOMhegxQVd8D1exwCljQRNqsUErHDKJUdpyuVFFTFSildQJVFFWt36qisqIE91LVp5TxGB0RGbE2N0Z1WUdrjzYepbL5dm7UDuhi5g0epEZ5TdM0BFIcJ/CliIjEvc+qUtncLmHQtiOJQRnHxXpk8BFRlshX89L5f4BwAxRo8xonN/4OSgUiDtRJBlKqB9OT9AJRHUl32XRbdYh2iC4fwRqdo0ToEEonrDJlelfPmYATCoplB9V9RYUSUe3ebV8AVGWEdVrY+SMU4CXgu5k44hvpl/AGv+QyHLoKGh2KLMoV/zu2jCPjq5YdW/fnfCoHO71vs8UtED5Yjs0yj5XTP6vXY7+um+RyD2YLni+PfQFXoZj8wsOHHyLGrEju+ze5e/eTE6SqkGsXtTnDrKq63Pmwy8BL61jUnruAO3X9y8dT1ZkerXcmoNVc0hgz+aeqDv4rGJpHBq2mk605ZrUcs9YW88/v5h//4z/Gm29+HKUiH/7wX+S5534SpXJluVV+zS3n5jDrGAxpwdoxRVerYmt9ds9NOOt+WgZS1V9VidYeUwVncyhV9w9M1/UQPKrHdQycHcs/dqzteo8yF71qH3OodggIHgNUczh2iCkdU+TN01xlN1+n3tND6rVj/zukbnvcpOQJtG+/+3/zO3Y7rJFG9P4BV7khlJdC5daWCPzX//2/x/nFs3vbvHn6Os/cehXEQKovkcHYnhgNPkAIihCYpn2ghPQ2KHa9P0rVHiFVIMHTdJj+/4/p8Jdaqfrg5d7MlKNGTPOSIlJi1KsKl+qgipSiVDalFNyoWtELKB1BxjxWHkVk678Tkffmw1AXrJZ/EaNfQcoHJfeqBlLyZN8UCZHsy0DrHElLkmbz2r+Ev/iWvB1zBunGrnGsyJVm8zranJHCbSTeYnKGrwTdvQoqlecnP2O1J3FS2pXoSUy9ifXSyf543gh4R9J1gdXbHc+nr3FIV0U+nDuEf1yH8Y8Du342/GslKTBL1Q79y4CmviZpNhyqvBNzfC2VMEpQKmJ0QuuENgktufGnydO5PZiQBKrCpwgpFcVUUU6lVJRVyZB8UVRF8/bUUo+ruvq5pLJqoNW1H/3aOGgX7P1fDoCqGYCawFQ7fWCd9ljeCXBS0xxWiOyGBLuwwhx4ZtWB57edL5Bvmr5i39Pq1zlZdeXslfPv5LW8bjrWYH5U3rX/I9dYTw7kHdjOPFPtPQAZNFUoNcGo3VBhkyoR+9Tkbypm0zztdw08UwJX1IbftDyiGPN6ugS/UB6ts2NCa4uigi3GZOqu9Yi1HsUAbOlcXj9GnwNmwV7jas90j6x+EpVdUERROZyrKup8nacj2QdglA5URxAHuifQEekQs0J0l03wVEfUPYIjlWAYiQyaUjHDy9NF8VTrIFV9RfUbVUzqJpO7DJ2kUTbJXOWUmnECorr8LargeT5uOyjn36uafwg4HfvPPO+q+UPTV+UdGx96xn9Op/qusT+eTwMTNS1F7cF1j533VWXIz8lrloOi1Ci+SqVs1q/aMieidIXfeRqjMGpoQHc73pkfG52D5mTlVVVjBYz2aF3B94g1ns89/I088B8t33zFzf4L/OLn/w9o7bE2TcBK612DPoRTPvvZ38B68xx37/4YX/W+70HrfVCRUsf9+x/DmJFnnvlcAeOXQUAFYG3edJWaAG2HzIvrOnP/ay1kqvPOObz30zYqlKrKLa01IYRpvsKmuq2aV7fR+nMDxTjeROstWm+aa5AmcHXv3j3u3bu3pwCbD7Cvcp2rrdr8uu26LKXEMAyXfL4Nw8Bmszka6EApxQc+8AHe+973TscxV9mFEHjjjTc4Ozublju3622vEK5eszpdr9MhtdixY6njY/CsLq/3CfZNaA9ds6u23S6fX+e6zTbNodYxRVq7rVbZV7fdPufHQOB8WYW09dmeR5k9dBx12Sc/+clL274qPXGQg92LWYIWFMfx+SUi+84iO7AXEbL3LFhvTzlfP7v7AJT08Pw5Hp4/d2BvT5Ae9RE4Wqneb+0qFUFyS9haiGGcKoe69ARmJ6Zx6oFU7JySQpknQKkAqhpVRwWUlEoiBTqVqGgQ0CoSU8+XH/xBsl+wfNC9/QLPnfxVhJGkPNvtc6zXHwPO0e5vcfP5nhdeuMm9L/0kz3dbPvBVd/9/5P1p1DXJfdAJ/mLJzHuf7d1rlVSlklRabMuyLHnBtgCz2LQ9NtgDdINphqYZA21gGNycobs/NN0cMNvQLB6GpmlmMNAcPDDgOZg2xjYYeZdl2ZYll1yqUu3Luz7rvTczIv7zISIy4+aT93kXlapkE+fcm5mRkZGRkZGZEb/4Lzz29od4xzvexLPPfpztynHp8hZVBU2lEek4Ojrk+GjBi8+/yK19xfkHHuGnPvKzYBTVfIsLl6+ws3uOn/mZn+Xw8ICqaTDWcnR0wP3338+qVbz86jXQ0aaDtlU0YqwNWsdOYDAGYxPh0OtkOK+vut/I4vj3Ffdjm0X4FgwvIFIjrkKkgRBVUAk10e5Zc7rjlKGLAyTOuPatPQ3cRO7Du/tSRyRAezK0hNWF2zSiHMajyRzGI+LPEfBKNurWduWQqmYjeKqItoQqBlXEU2BKjZasA607gV2vB9/2Am2ALqC6AJ2HVUAdefAB5UJcpkGGBIWvLk13HMfgIy+z1Ev+tURD2FkSrFMJGKnXEEoZfFr3k/sntkupnDcsyGZIRLFuWf8KTTXztZ8MAGpK5a9PtwFSbXws75W0nHFcr6onnJKk6iE86+o8jnVwhTrdRjcOekaDq7WyjPbD6TY/eT1FJZ05sLvNvtcbZJ01MLxng+xyB8fIhnyKNpBvtorrulDL29mZYywYk34WrAFrwVZQVVBVQlUp6pr+1zQwn0E9g3/9/f8cWGJMi9YtihVBFnh/jFYdSNzOElAqASjo8H7JYHvKJQmHgEiLcwuU8jRN3Q8QjE2S98pEKaegMPWMZradpJMsStcJRM3wUuPMjKBnBBq8mSOqIei6l34KuiaoOsGnGi8NopIqnoowqwRPUZMgbxe2nSR5Ci2knyREAAWqt4UanzVVgKe8TXyn9CCKdQC1aYJk3DY2/YqmMJm2zOd2UlJnnZcz0mxafl6DlbPCBIxSaeWs79Cm7bPip+pk/NzfSd3eNuTvyN0edxdBST/5o1SH0csEpHyxjOMS1duxi+MY1AC5M+jOznNMv95iTJfeNR3WdEQX1hGoex8NdRrj0y9KWjl3QtNoRKKR1GgDL0+200MIoDcInz1BlhCplFwqPWMCfZoMgDLwKQHJJumnPFh/4P6f55eu/T5urd7OTvUcX3j5H9CYgzVTSVJMFokYfuIn/wdu3Xo7AC+++Bvp2ss8/vj3jsrquHLllwoIoYr8hnQZQI2N8+cylmEM1UpY1TeHAlSNx2ljBwJ5mdfzPRlDi7I8JXgrYZi1xwl0DdJwpdTa+B6WZcj3Mp+zBCl3AobK+lFK9V5ExyqlUyHvz+2u9FQ6bi+5bjI8G3sezfcw25rL+d9N2HS9A6OZlvjaJF1X5jkuyxigjvMrj9tUh5vKszbRNYqbklIbt/Wpso+vbVzWseTgpvPdSbg3wIZORttjKBtTbNQKKfQF+kpC0TTxReudZRgJKB564Anuv/wclfbUZsVOs2JmOx544CJarfB+CcrTro5Ynhxx8+Y+z7z4MofHR2glXDm/gwkLQnfCo48+zGp5hG5bPvPEk4gVdt58mW/8lm/gmZeu8rEnXsD5JVtVQMsCpVYE3yIOus7jXKCeVbzy0ku0y0O8W7K7PeP48AZ1ZdluahpbYYxOYyjBed/bjKi0id9Avd5QSpFTEUmQ53QjLF862/WTPHntu+jCFXbrn+Ztl/841l7DBbh5+FW89NJ/299Gbb6Y+s1/gne941E+8tTPcm5e8fJzn+C+Kx/gwvm38gu/eJMwq9jZPs/JiaGqt+k6xeHhBY6OWq7uX+L6LQjb7+TaQcWqhXPnL7N34Z28+ILj1s03sVwF7GqO1jMQePE5Q5AGt1S0riZQoVT0VAkVSkVPMZJmZyMQqyCkkbUkdQCpCOHcKW+FwiWcXOSeR2i3O2xtcFqxriNFMfBWG+JHPSqlozOHXUk2spK66EyhalAjG1pSgdQglYoQKy9fC1XFOwrj67vL4EICTAHlJAInF2AlqFuSDF6nXwkVyONNnQYPSf0j2+cLdlBvzJJgpcpjFESIyxUJbhlwJmrGJPAkY/B0txJZWZUynNXzfp3oQe8sAk4PIFIoTXdshFQbfpuMq0/lsSnfPv41rBMhNZZ0/WNIlQ2q50FxORjtB76jQUq5fmpZlF1NpL/j+z2qqKnD7hRUjev39Q63A0t3Ba3kDtLJ+jIBquRaCJV+SJJUkDjJpVW0fm2y9z7l0HRp6VBqlbZbjGoxaolWKwwrtFqg9TKus8SoFsUSbVYYlpAnxugQVihJLwvtad0CT4cox1K3rIyjVQFVC8po0FH9LSiD0hUXLl3h8n0PoOwcUTWdrumC5U//376Lkw5OWli0cbnqYNHF5TItVw5aF5edg/0Orvk4r3D867+o934X1e2SI6KQpK/J0v2FiQHWpZ5iPCPwVCx7KKxOg6ZAVEUfg6jxPZ5qO1OQ6SypqU1xd+IY5o4BVG6v6jbpzrjGz7uQvyUT73gYvgXj+LPeT0xsy8S+iWJMbk/em9yHkPV3+iknQhvy/5yHLDU1OFoYnC+U0phZ7Ter+a1SXGHjyqSHR7uopmcEcNEsgBZQAW2E3hFLdsqiSWkUGI2TGqOX6ErhiU7ElFEEJaA1L9W/gRPzllRfgqiK95/8t1zwv9CPR0rYFJOtD1TLQfaUl0TnXK8OWIKgPB7K0jNTBt7z2Mk5j9YVIWyjVPQEFILpDes7N8N7hXOKqppHA/9BpbFFckwWPQP16yE7SCM6TcsmekLQKKrBGUH2Ippt6uX8yI4LLCFoGjlkFS7xkSe/s8g/TQIU6943rNYm8YWnP/P1PP74906CozHwymHK6+cYYowB1JQa3ljdMq+Xx/k0xi29bWa74xFk7fALn/w2btx6K7s7z/Ped38Ps+agv4+lWmkJysZgZSxtNJYwK8fR5TVMeU7dBIimIE7JMLqu6+skexUdg6lxyNJvdV2fKlsJQnNcbvNjNlDWd1lXm66lBI9l2ATCzpIaK69lDE83tYvyvkwdN1Wmsu43QbdNacZtYtM5puqsbGPjtlemG4fyGbmbcE+ArXRbWwKjdRJefhWHQhrj+Nqv/B5+8Mf+T/2g9fG3/gy/7UN/H2UUVmBLO85vrZiZjrc88lZEhMViyUnrODk5Yf/6LV7dOcS1L/BCdwuFIqwM+A5DYK96HK8dezsNy/kL7LslM47Q8gxvfvMev/j0IW6xRIDzF3Y4OfIcrzqC95jKghIODw/puizSalmcnGBsRRBP5z1GGzAK0jVnOl8SYqXWZ0rG1F1phQRiZ7doKOWDcGH+o7z/oa8A1SDSEhCU0qCEG9d//9otDP4Rnv+Ff8i//KSmXf2PfCZ5lPmxT1r+9j8C+AP3crv5uY/d02G3CQ6Sqii9y/EOUSFCKgSUQusXUbMD/IOPwHwOqsW++An0yfXYiUnSgWRvU8oR3HlWN7+evkOMwm79LK5+DLoLcDxVHgXMT0dv6qCd1XF7GPjBsqdaTzwNn2XoYRZReisPZhbAEYPdEdHDIKdclmqKY++KGWZlz4olzFoWvwXR9lXHALYKaCVjUOWkAFhvBCUYBzl7EJGXBrBy56DqTiHV3cTDZw+tysFJuZ1t9vU/WR9Er/1U0ZCl2C5GUmuDpA1lzgPWSYCVgirS3SukGm+X62+05YDbQabbQiuZiEsreV0RnZUoUGoAVBI8ErJNpig6p/AYOqx0WFo0HSbDKBwmSSrYZBfK0GJ0i1EdVrcoWWF128Mpq1YYvcLKEmtWGFlgdRxImiTtHe1Vxld+0EIwGq8V1BXB1jhd01VbdGZOZ+Y4PcPpOU7XOD1D7IwuST153eBVRdBN9G6nakRV0TuernDSIGo7ectL4EkVHu9Ek81MZPgkZAmoYUkhBTsNoMrfNIB64QR4qohL4f/4bXfQTqZg0hqMeNcdpPkstnP7kvH61LEbgMcbBkDuJahhcacwavzOOQtQ3e79tSms1V26wSLxee/fDVK87zl9b3Jbzm27l/D7fAiDfSilsmH0boBUeZm8e2qTTJUojzbSr0fwlEB84SE4rtMvy3VRcNS8lf35e4Z9rLh/9WEUXfyuaxCdRE21QrRCVJTmzBKWFOshS3pKhVNzomEH27+H+jQYst3KOw75vobbLPO635QmtoeXVh9i4e6PpiWCQak4SZ63S1gkCUJFMJUAVk4jNsF9A8npAymthAFcxXxG22tw6k49bn6+hNKzrT+1HtXZ10Nsz2cP9DeFqbRTQCavl7BnSsqvTJvtq2VVUFhX14tgRfipn/sOXrn2QUC4uf929g8e4qs/+J1ozRpYLW20ja8h51dKFY5tj42vdQzbssPBKWmqsYRWmV9e5rJlgJjLW9bZVMjqsfP5vIeJmZOU9Zdhc4ZodV2vlSlLtpUgbEoKbJPk1RQEnIJlU1JqZT3l9fI+bQJ64zodS4neLoyh3fhcWRox19+4TFN5bZKYGwPYnH+pLjuGvfca7umNNSa5ZSHzdroUhOxBZQiPP/rTPHTfk9zaf4CL519ib/eAXlQfE219FfRaa43SQyXXdU1dWWprUaJQytB2saNnCBydHNMul2yfu4zVBhcCN27d4qmnn4bZBdxqha0sQToOj07oukAXhLZdMq9nCML29pydrTkvPv9pnOswSpAQqKwmT54JRNtyrDdcm14keXyYH7ZcN8MLDUD6/s+4wZYNKwSPYNBKkDTQDBM9MWM76tkBmgPmVaCqFDt7M86d3+LGjZeYVY5zuzVVHWjq+FKXsAJpWSwOOTo+ZtE6rl2/ymyr4vLlXd7xjjfz0qvP85nPPIn3y15k20uLD0tC6Oi6FV4CYgRlFcqAqCjurbQgOkAVO0DBnePkpf8LfvkYdv4kuw//LXQV9ep9+EKOVr8PkW1m1b9lPvuX3Pyj/zM8lOCXVLj2nWz/m78b69BEg+liKpSxBN0geo4JT+DbR8BY9Pwl9M4RlfkVXHgb4i7BzwIfA2qBSqBZQqUH1claQa3jrzExrvxlCfepzvMM+BSDxFQpOdWyLoWVl2OItSICrAyyFkQvgwtgIQlUqQFWOc6Qsno9g5wNqxSxw9rIsD0JplL6sU2qTcfcU/w91NcYTvVLik6sbJCiKuIyOOqlI0b7x892Caom3/kT+ycvbzz6OyPtvYCqN7oJTg3cy/VJaCWn44qIWGPxpikFSoUob5mkBrQSdLE0JvR28ozxWJOXDlt5KuuxlaOqPJUNGOupqoC1nqryWOuoqoC2Dl0JqhZ0rdC1IHVAGWAGYjQBHb2bYvEYHJYuJC+nYui8wUmMC8HQec1i5bhx85jFqoPk6TkOoAw6DZAc9QChsv1JCgBVACdBn1K3k1PSTlOgaRo+rU0EZFXsO7nnm9pA+QyW23l9/GNi/zhuanvyWZ6AG6feA+W6mi7/1HW+YeE2D/nUO38TnDrrXb1pe6oIG7cH42Gqv/FxXa3F53sXklm54b6pvCu351O2zUoPj+V6lBZ8fUIx4JjcH+EUykd1Pp3hU4LsOqoCKx2QZENXVF5PENyQ4FRkcHGpIgsyKkImFeMifIr99ritEaNBRY/NaN2DqbiukT4uplUq2VVGI8wRFD5tx2/3HdRt+czdDjpNLZdDGgkNL8tvust8AoiggoCE5LRdeon+YTsujXRIaIf3ZXqfkibic/vLcL+Pe43b2XN8I8/xja9pnjlkib518BSlj5X2WJtN8RRSgKS0OvRmdJROYDWZ5OnzJUJXhRvaNcO5ovH/4hjtMFpSnnnSKZr60boEZNn8j8cYQRuP0VLEp3Ipx2C/eh0MlWO/H/+JP8Mrr3wg7+Wdj/+z2wK2dRChcC46z/PeEkKN8waRGu8tPlTR6V5IHm9DjBdqRGqcs/hg07EV3sf0Qap+W6TCpbxDSOlSvmXaEEqVmVju/cPHuX5jzqx5tR8bb1JbnZIQynBqLNGYjx+Pl3M4S9JoClqV0lBAbyPOWkvbtmitmc/nHB9PSmacKn+WeMusIl9L13VUVdXbXSs9ik4BwlL9OZ9jU9gEk8r9OY8pCb8pkDSGXmPptBw/ljrM1zAlJVgeM1bfnYKC4/zKspTpxtexqT5y/BTDKtW5N9XD3YZ7BmxlKGlfWUitNRLWDeZlUri3c4vd7ZsoBRqNpA6Ox8cukA+EyhKIEmQog1UAGqUt81nD3nzOvK5oO48xGlttsVocY2YNR9ev43cvUtUWlhodFLWd4/AcXH+Oi/c9jELx0kuvIIDrWsQ79t0+RitC8CxOjlFhEQdKWmNVRfAdyqQHFA25gTFcoyHpVSuF1oMoaK6reINBCnHRKYKbxVQHGKdRRNiGKC5e+MccH/568sdVV1f5db/jb3Dr6Q9z7uQ6D53b4uKD9/M1X/dboDH8m+//Fzx8ccbjjz1MVVnm8zkuCCcnJ6xWK65fvcUzLxzwsV85oG1vcP+DF3nHY4/wwS//AB/+qR/n4PBTnKyWBFG0XRdfIkYhHdSNohNNwBB0hTY1aIMyBqcU2li0itdx9Myfwy/fCQht+yC3Vm9i+6H/O0oZoGJLvjd2VDvLcvEh3BNvhVcCfEnqWDVzjv8Pf/zO2yuP0PLIeuQXSaE2KODrdUmrHl4BzsftUsLr54AfO6NT89fvuHi3CXJnA5YK1oyqn/rdAaTatG9T+smy3AFhGQ9ES0g13vYp0amOsnAKOk0OSNV6XF9oYXIwq6bST4UJULXp0u8UVE0NQM/K9/UKUwP8yTg5HUf2RZuWSYpKJ2ciSnxS34tOG5rKEAhonWGVT8DKYWwCVZVHVWm7EUwdwDpsQ++YTmwEU2gIRiVtdJ1+imAVohSiNZ743up8NCDug8YHg0+qIkF0vy6SPJcmwBSCQoLGiUHCAJZCPxAixvUDJOJ6BlCl8fAM1TNcEjUtfVrCqHv59k8dc7cA6qw0k3GyHg+n28sd5TOxPn6O770/9FmGe3hQ7wVGjdOdFacn9gHRCVFqRMqnexHXlaRl2eAkDlKVeOg/JUlNF4g2QyTBqSS9T2znKnvA7qHUeD3aS5P0fPXbUkrI3CusGgYoU6+vewrKDYbTs3SUiR7DtRbIqnpK0uRiglIqQSoViyU9J1I9lIrrDGDKpKVSRbrhJ2vxRFjV7ydJUCtINuygEJi8HVy6EwB1uzRROPb2+dzpvjspyxq0eqM/nknoIG2d3e5CYeNsCkJ5tM1QNIzSxDjdOw6JXmuHY0MPj6IdswiCjIlt9BXzfg7No1i14AH/Y+zxdK8horQfytWDpzhz3JdPR+dLxgjQIdIlO2qxnCIRmtV1HL+URvHHql5Tal2l6mkpSTSGJL2QQ1JdLMdem+xZlRIseXtqgB7jNT5UiNQgDT5YRLZ70ORcAlYFpBrWE6hK2zvnX8KrX6TrtqmbA165+cW8dP2DEWSVxyeYVYKteN67ckf/WQWtoz3PrMYcnTcc9dsiS/YPv5j4vs3964DiYE3VsfS0ebpuT9+XHLK0V3Y+UNpmK4VbNrWp8TlKlcypNBmy5bTZrp9z7sx62tvbo65rjo+PT0lg5fyzwE2uh3yuKU+lY+gzJVl1OxA7FT8Gl6XkYMl3xvBsLDlWat3l/aXU4ZRq+Tjfs8pbqp5PHXcnUp5T8A1OQ7tN6e/mXOPwWcvcjklyGXoDgQy0ML9Ye9HDosIiqIodjyAe30XvHVFkMuIlaypsVSFEAry1u83i1j6zeU3btpysFpwslzjv2d47h5hXaDvwi46PfeRjXD3Yx/uaW6HiwTe9mVkz5+DwMMIyZWjmNduzhuA75o1hdRKwleHk+IhZ3bA66QghegSRIJheFDIgqT5aCQgh2jsI6+KYwwchQsThxaBxziOyblSxFMGNdu2iVKAoze7uj3Ll4d/D0a1vwOkV9eX38eEf+e/QXON99/9VOv8fMMZw8dJFPvnUr3D16lXedOnN/b0REQge8Y7KRtt5gcDx8Um0q0e8X9dv3MBYy+7uDspWLLqAVy1+teLchQu0y5bl8QnORQkPraNtOkV6MHR86YYgEM4nuAa5t++Xb+Xgqb+5uZH9FYHLHr40bDYG33skFPDZfgyse/cc5asooJQq4tTpbYolxIHwubx/9OO1jN/wUG+SlugHyjIaBBf7ShHMIEMndG0wPIZPE+BprT5H+yVHqfV0ivV0ijTyUKPjyv13GDde37Qcr78RYarHPQYPa/FJEliSFBUCagBVWXoqq75Emy1p3QjKSpzVtWndCtgoDaUqwIS4rCA5sENZIVhQVqdxsOp/QemkEqMIaIKKkkwZOIU0gJZQLLOUUyCqiHiFiIXQ9BJNR5OSTvT7T0GmlihlMAWhXivQMs5n7bnasM2oDFOgqHw2x+eRifhxHncTv5bP+Nl+PcJr8MDdKXTatH5H7+BA9EKXBgjZ+yXJGx3RuVEe2I6NPSo6RDxBeYQova2IzpJUQQBmdcWsmSUIJf19+oov++r4ehYQnwRgAgSffgF8Wvc+Oj7xLq47B66DT3/6abJKVVbfijaCTBqU1WTP2FPhdW0aOiSpqPgeI9uXKlT2emmrNHYbYFL5U0lqKtG/LFWl1Nr+/qeKdKf2cbpdSLHEJtMe3BnwuZt97rM4vvzGj2HT5LFv9EfwLkIp0bQGoXKcQ9nB5lkveaQGCb0h/WCsv5d+IttDS3BZOVb2Es/v/BZEW9Cw7Z/h0cX3YZKNNNQgWbUmMVWcp19qj1ZRIkqko6pB4XFugTFCVYFSw0Cy7Pv3VTDqC44HmuXgc6yiNVYxy/FZle4d4Z+eyrPcPkvda2rQXhqHj+vZw2SEFqX0zvja1m24QbSZVtN1FZ3TiNRIglveW1ANSJNglk2SVzVBapzT+FBHSS6pR5Jatpe+ilJghWRWBllSEQrJrgj6PwfhlHCUx2iHNsmLqeqwZomuDjDG9ZBL6+hIYgBfMX2MH9Z1cjIRPaImT83J4YRSq/TcRPMN1vr+mOigwiESelXRUlAmhMByuUREuHrjG3ji6T9Nfnk+8tDfoapOyKaSciilnjbBmynpNGstVVWtqX+WkA0GGJbVLMtQtt9c9hxfqq4653rpOeccbdv23kTPCiLCrVu32N7e7tt/WZac/2q1Ijp0sP15S4A2ZXarLH9Zb/cCgaae8Vy2cv9ZEmA5TalWOQZ1Yxi2STJs/J4qwd24TJuu4azynvUO3GR78G6g5VnhngDb1E0HRi/UwYOHtQZhePEXGSWWUdopUwnsBMRCcLFXaZTG6Qi0KmtpZjNsPaNlidna4+Ubr9KtVlgVHQ6o7MHSWByKuprxu7/pd/DJTz3BD//oT3ByeIurrzbMtraIxgkXKAnUdcX9Vy6yWhxw6dwlFicNxmqeOTlBRKGtRQhIiLOSgxHOaDw0+NwxhxAEazTeh156C5XHTQpdeF4tqiTWoazH5VnjkHp6s/kcaT1V/RPsnv9Jbvq/yPFhFDcOPMTPvvgXCQ/8RczyIj/yM2/mlz55wsef+iqwb+f5W5cIwVDVW/hgWCw7nIP9gxX7R8LLJ8esBE5ebXj1+Bw//8wuzdbvYP/ohKPjFeiGxdLRdcLixhylajoXcA6CGCQZIKV3Lz9av2hiR3IVRvBDbYAhKg6if7yMm1gXiYDtwTv8CGbQBOtwY22wKqO41IkNEmEERdo8Y5rTqWJ77byjC5h6bqdUAnPcxnfoVOUVUcJ0vd3NtmJzHm90f/3UyLC4b5AfvCJRGtTlZXL3DiF676t0HOAZwVQm2inXku2EoyrofBsHZRaUjXYZgxHEJDVpo/r0YhVi6FVmUBIhFemnivtXSj6ldiUJMkkBnPwmz3ZnST5lG3ufy3A7ADQFfzZKRKVE5f3r4VNq2P1zN8q3hFib0myK6/dNwOXXNHwOHpxNoAnWIcJUmk3gCQb4pCQ5FIjb2ZZblHIIyaFAGuTSoZVPNtw8Otlwi7bdWqzq0KzQrACPxmEQCDG9ENX2hk+iQnzoC5m0sHqQq5SKYClJTQXRKNH4bKxaTBqAGUKyexQkqsoKdRxUSY4r1pNRay8zspHse7ULtNwQ/y+evKfsClgEqEcjpDIqmj84BZFk2C6PKwHTpu2p9lFKyTFaH8Op8TI5U5ANAEnK9U2Q6RSUkjsAUjJ8rzPg/NUInoDs0VGpQbIoq9Up7RI0Kj1AltJOETD1Uk1J9c6kbVuBd8teEkmpLkrt0WGMINL1MKuqAMl2dWNeMU20nxbzE5AofRx8S9NovF9hTCCEFmtznqGXgLKWOIE9kgbJIUucZEmZ0gh2HoiXg9VSRWiow2kj1u8w/5yr9fuowyFXTn4cq4dxzJTk1RiQlYPlUkIqA6a6LuEYp7wJloa4xwO8sgw53ynoUNZD20Z9+6qq+nPlQX7nNE+++nu4dvRetqpXePuVf0JtDyLEosE5uyaNJdIM0lTUSY2w6iW4kAbnIxxzPto1CIXqYpBS7TBBsASySmmt10vNOj4HCVilX2WOMXXedmv7jO5QRXpjoq3AtTRmOEapFmscsERrh7UepVYpPgI1pVYJoIUzyqn69l229ykIOgVhc3wJf8o8S7gRfzHvLBWYAVHOr6oquq7j4Qd+kL3dT3Fw+DZmzWc4v/crqMKWYG6vY2ms8hnN8bmM5XNZepKcat/lszIOY5BX8ovyXVB6wszPX2lz/qyQn+tcp/kc2WnCahU73aUkXHYaUZYFhvfA+N6MwdX4HTR+p22qi6l6Ku/F+L1Z1uH4Hpb7xnUxfieO78VZ0Kq8tmkhpfVnYOr6xvVStrlSkg2m1ZY3wbY7DffUO8wXNG7QUw+3MRGujQuqlIpSGEIEKES4prT0zvIkykagJC0VaBUwWmG1oW4atncv4I4OUDrablAEFouWptnm4GiFtw26mtGJ4tkXXqRpZjjlce6Eo1vXIXRU2hNUotN+xad/5ZeBDi2ebrWMUK+uaFdLlIRojFlAiCqgIsQXiYrXIiS4Zi1B4gAALShi2vzCMkZHqS6G2areEwo6wbTiQVEQEm2bVRUHx0vEB0Q0QaJxydzzFSo++vJ/x0dfBj4M8DUAPPORu7jRJ3D9BLi6OclyzVZnspwvg/h4H6eyQ4IVmICEyzCLdab2rqKrI9RqP3pmUwGkQ6k0m4jHd5dwR18SPVSm9mGap9H1yxAqusMvAQJ0HTy3KgbMUgy2ZdR5vlNSpKJn0FmT8iFCigRWToW7AVebBiWbjn29wxrgGO0rvVoixcArAyviYK4foJXro58hvgdMGvxljaDxei+ZkJZZGkH3BRnKOpZ48urOYFQYfECcCvkYSJBq605q8fbhLAmo8fZauql7I3cGkM6CWms/NZ3XuLxr669lw53K624fkjPSnAWabrsu/VKpBGAVETrpuEQnCUMT7bZlj3C6t9MWpQu1Sfbb0s/aaJctrsdBaWU9VeWorKOufdxvfYpPx5lInPJjGYJKkoWKrgu8+uo1PvGJX4Gk4goGTY0RzVYHjcsPaUVIjnIgzeaTVFyoCNg1GBXEIt6m7cHItZMGSWp/pQHrrBJYqgZ+XoTxO8oU65veX6rYN4ZQUz/OWI7LMtUHnQTBFJM8qgBI5b70c7K+PYZMYZxfWn/DP0h3EnIZMzgqvDlS2B3LEka2lDhKElAJDjNhKwocxgREOowuJBu1G6SXdHFMcuikk92nUn0Ouj5/lOvz673hmmjjKYQWk4z4ZxgWj5Ue1mSAU6pglaAn7882gMqBWw5TA6Xc13fOrQ2YgLVBYD4+ny/nXdohyuOAsfHwnFe+jnJ8kZ2AbZLKKgfDuQxjAYBxGU+NRUYDr3zclrzMW7p/HdPpQZUwD5Az2Mv5lYPKEqzla2mahq7rUEr1+VRVhfeCtdv4YDlZKLpO03UKpec4ZzFmi67TOG/Reo7zJgGpJtnXKtUIS8msCLEGia9oVysku1zSqyzWOL9NVjvc59289NRv2PyIvUZBqRJYtRFq6SWNKlQSC0hldN5ue+gVbQom5zm6S2Yluh52kcBWBl7WDGDLGAesqGygbRdrHiDHQKoED2Ovp1naaUqjK+czVkmbAg7DMzndZsvz5mcsn3dKwmkMf0sIm/fn57IsL7B2rnEeOZ33vi+bc45Z/ST1hU+l89i1c47fHfmc5TNU2l0vz5ml11arVf9MlRCqvO6p+za+H2W5y3dVvu7yfRlCoEsmkTaFfOxsNuvLkdU+RaLGWym1mfMvoU4Gczl9fndWVdVfcw6lEFO+likeM772nsmkc+T7m9fH7Wac1xjwQYSEpTptfrfndlmqB5d55WNyGcbtMl/zFEQr1WvHtu/Kb8x4YiWfP4PhXO/ltZfXkCUNS8B8N+HeJNiIklQh+ATKFLpo1Kf1biOAUqh4pKSL0SNq2N9UQSuFFsViueTc+fMYW+FpMXZO0zRUzZxOz1lxjfsvznjz5RnLxRJ8G23Ua8vCL2FLY2aGRbfk3/7wD+JCoK4teI90Byz3Twh4dHBxwOECjYmz38F7qsqSZznjOD6qtQYRvAjZuLPkAQ1JHkUrxINHAE3025B6y0r3fVqQvo8cYb9EPRBiXz2qWmYV1FiDGM18axt1cIxSBlGWanYVf/xYcYcUb9/7xzz+7jlvefwtPPmpT/DEL32U973nLTz8wB6VFXZ3GowJOLdAXMfJ4pCr1/b5iV96gaOTQ+aV8NB953nHOx/l/R98Hx//+C/wUz/9kyx9R+eWqNDh3IKmrmjdkqq2+OA52F+gzAxsg5s/wuKtf4owewh79DG2XvjbaGnp2ofw7aO0f+R34d79fvwT98O/btGvXqOWH2CuvwetQUIEq8cv/0Hc0XvX2qFfPYrn0dNNerVppGK5Z61oz2kx7gpo7iGvMbUfs4J+wFVCq+K3NqDLkEmKAaAaoNPtBnjjbSmWPdgZgarJgn+WIec7SbVG4Y4BlBQD1BJEZQCVyp8MDseB5JBOofr8VSnFVEApydC2AE/C+vaZcKpf3mtd3g4+vcZhEyA4FTduu0nFNYOoZJ8IhKyGp7RgrIqASmcAlVRbdXIcYCR2oG3pRCBuZ9AUO9Oeqha0jTPFpsoAKq4bW5YndR76MjI8ZxAnSUQRvEmwKtps817jvcF7g/MG5xTemWi/zZloy81pgo8OBryLxwSvYpzX+LTuvcEFS+g0YakQr1K6pFrrEyTz8UcgrifgKyU8zlDk8yGMgZSaWJ4FptiwXS7zuozi4DScOgsMj39j9bognAZQ+Vn/PKnvOw5Z6smTIU8GQXGZgJPJaQb30Er5AQRpD9L16zk/a+ifUcXg5TuqySW1vGzjqfciPsCprG6Xy1XCLa091jIAsrQvS1NlCSit1wenpfRD7mRn6YQSyow1MHIe5f5SVWqs2jQ18z61PSVxMQY849n79XTlQ7EOw6aOmdpXap2UceNBail9VUqijCXCSrWosv7KwWR5fXnwks9bXvMYppUwYwwNc5ocpuprHLxXyZZVg8gc52tCmBFkRudqvK/xIaoe+jBLwCpt+yiBJdIkia1CGstH+1giNsEvi4Q86ZBVtZMEbNDECYzXa3Ih1cvaOzR+c3uN8WLfha2foTbXKFUSszRWBmBZkktnkJUkuWyCV9Z6kBV1HWK89THeZFX8VLICeOQwvqcldFq7qqJNjY8fA648oB6r344H8Lldl+Uo2335DJfP3vjYfNy4TOP48l0zFcaqg2WdbAJoJRCZAmNl3lPqeGNAV5633F/WQwksSqBT7s9SXeU7sJRMKt8BWS20FOwp66Osh3z8WBppyqh9XmZJvbH0aV43xvRSepsgWwl6xu96Ywx1XZ+q8/IaIU5AZMBbwrYsdTr1LSnrfuq+jMP42sr2PLZzVsKqsq7H7XQMYksQNb4/5TOavaduEtQq29K47PkcZT2UqsMlrM7fo3w9GViOv5Hldznfz3z/cj53G+7NyQGC+NSI8w1ieHhhvfErVFKpVGRBlz6vsgODRtv08BCl0U4WC27cvJkeSIex0UuTtZoH799mb0fjXUdwlyP08o7VYonWmtbD/YtdHnv8IhoTHR5og7YVShs6HwujjKCNxrtA1/nUV48eREII+DbQtivadknXOrxzdCHSV3EB7wJt1+F7436A1rguIFLelAjToleq6JUU6ZVnE4j0fR2ZIIjWCB6tNEGB0hqvFIvFKrGAaIz7ymP/iJd+5THc8gqgeNv2d/OVj34/X/sN30h9+QjrPszRCz/Ce9/6Bbz90fsxRnP+/PmkHrui7TyHh4fU3GBWPc1CHbG3u8dD98EDF27y3i/Y5slfeZWqusZitSQEj7SxDlarJe2qZXkSrcRpbRBxBJlz/AX/D8LsIRCh3XobobrC9gv/CzQV/p1vx115P7xK9JD55TXBP8iSP8CSb0GpJdChRBNkG1xqrv1gKumGZGPJ2ZNNhitrs+8KngCe4/YDuTKMB27loCyPOcZh0yCyP8+dDsbuZtA2kXYEg9btUhUf8R4qZUKkhjrM8Rk+TQ5EC2B1ar+aGMiO0pVAioljTi3vpf7ufgAsG9bv6tRT4KlUqep/YWgWpfRTIRmVoVReZkcAKoOoZH9NJRCl9SAVlde1iSAqgyptAqYKaJvTerQNEUJpj6lDfD9aleogqsJHIZlUYJ2bWbLVhkKUQVD4oAmowVGA17ig8c5AMKxOVvjWI2JxzmDqXVpv8d5EwBQ0wWnEG9oQYZR43cOm4BXSqR5KRRispiUUx+vj36Z9ny/hLCiVNfFh4n3D6eZ/u8dhqvFvAsWFDbG190y/rT6/6vGMoLIXOFxSc012llRHtL8WwZROL39lkntocYiKXnM8HZLshmH8AJiSvTZbwe7ujNnMJrWgAXL94W//dqyFqgJrWVu/3TKvf+u3fjPa+F5iKoQWkZauiw6bRFwvOVNKJZXAZCyZNB4s5s5sliQYq7ZlFbRxhz4vN8GQ8cCzHLidBU3GA4S4PnSSx1IKuZzloGW8nc9RzmyXg8apwd2mwfF4QJTrfZy+vNZyWQ4QyvzGg+TxOcsB0vg+lIM1rS0+gIjBJSnTEDSorAodJ3LBkh2/5AnLQHyvk/b7oFC66vMQ8tIiWR1QEqBKqoFBknpgbxvL9ksJsQxBLPSAyiRbn8kBhmTvxsOPNU/Hquh3TBH61yFMvY8noNbmvsOmNB50dDgySFOuQ2qdvGVClqTM8HoA2PvLLyLIjLLT+/Y3/TVm9vn+3fDK/V/FM+/8A3gz44HnfoA3feqfcrP5Cg6qL2LuX+Sy+0GsjoNua20vueO9XxvYxkG5oWz+5XMGFOnW3x15OQUTxmAhn698dnL+U5KY5THlMzuGYmO4kOPHNrXGYSzRNX53lHBsDDjKOhpDtnH+4zAGciVMy+/Hcn+Zp3NuTeJrqtxT11tORJRpxip5U+/aMXjKEm6lHbQxUBy/N8d5Dt+F9Xf2WOoww7R8jnwfyns9DqUUcQkBc71lia5xmyq/OxkklrAvf1fL71f5vRrf76l3fLldxuXvUJYknoJc5fbUszfOexw3ru9x+x2DvHHfoGxv43Y3BrJT7SvnXb5TStXSKQg+1Y42Xeftwj07OZgi1Zs+4DmEkF2ig7UGkLWLBZAQMDZ+PJWKnYLj40NQKrq6Vvn7qAgCjRG0ATWLFnBFDHKuAqVSh2EbqxVGqQjTlI72jwAhnkMZUBq8k2SIWwjaELwgUQyN4APOOzrnkBDovEcpjSaqfIYgSQJCY7TCVDXOZdHEJCKpNaIUPomcrpZLJAgniyWHRycsFktC61gslnH8tPQ8tHcR3zmOF0sOlifst0uOg+dk/xZ0XXSyYDwz+wL3v/M76J5yfOgdO7zjMY1393H1xsu89PQNnn7qSZq6jt/mosPVN5oQMMpgdbRXI16xu73DxQsXcMuO1eFJFFe1FRcuzGldh1u20QCk67DGMJ/Powiv0ihTc1S/PcK12CgAcBc+xP6FDw0N6d8BHwK+BPhfgU/lRnwO4Vy6T5vCWV50NjwMszMO6QeNMvQx1iATxXrq5ViVBpg+pQ8oUUivgiMoUT1k1tm7mhDThXSKICkuza6vSWNl4HQ7MDW+jrNeCJ8dfHpNQwmf1rYpYFS6J5TLBKCK9QiAAkorjFG40CLJbpS2FlHR1gshSl3Us4ZVuwC16iU3UI7dbcvF8zOUiZ6vds/NsLWKrtmtoKtAVStevfYy2njsrGLv/BbLEFgEFb1VoiCB8WxjTaK/TLzouC7JSUAGUMRBQY4LqBSv+3gvChfSoMVF75Y9xNgEivxE/IrNx5wFpbLny7Ng1VT6zyfIoiZ+ZXwJrODOHpFNUGrtvVHE3YvU0xtYh9GWWoJPCTL1ywSjNMlAMn49bbLFpvAY5VG06OQsIOdTQi1TQCetosmBbBeKsKL3nJfsRpHsvCk6FPF4rX00OUCHKIchoCRfQzpeHIoW30tfTcAcpfPUWOwbiCAS8CIEhDZ4nA8stCAoThBabfDGIrU61XRm8xlvfvOb2N7ejt8L0mAqBP7YH/v2z/o+bW29DJzuJNe1TfHVWud0LJE11Rkf26Mp1XryDD8M2gvl4KEcfE31E8frU9tjCaoyXTnwGJ8rD1amVLdyWaM6iBBCmhAI0V5f56PNPqWqHiqF5E3Yyiz2NdOLQsT0MCrDJ8Gc3k7ASbDQ2wM0fV556YPu8xVi3tGLsYkAqci/X0rUvCjzCyHt7yGVgRChWG+nMCQ96I39iA3Le9n3uoX1fkJU1U8OS5T0MCoYS6hmvYStZsmsu04pGTmoB6d3h3YY7UB1KNWhaLE2vkeiHa4VWrdY06V+RXzvVRacWxFC9Kjp3YrgW4wRgl8hEgG8VlFFuKpUev9FIGbyJFpatxYUAWMGycEMT8pBJQxqeOUzPpbgevXoq/jktT8bQalRPLD3fZjtY5ZcRNuG4/Nv5xMf+LPkj+Fn3vUHODx+G9fboT9/vf0gX7L6C2uwuiwXDEBmDAc2vXvK/VMD/1z+qVC+G3K6TXmUMK7cNwYDU++SUir2dmXaBC/yuacmIMYD/SmQP54cGJd/DBjH7+mcxxSAH6tCl2BkbRxZ5L/pXo7b3TjNpsmUctxanqNsS2dBkvEEzPg8ItLDvLZt+2WWXJuq86nrGJe1vLfjSZJymaE0rJvhymWfAqrls3RW+cowrqMSdOW4KWA6hlzjdppD6TW2PN94OQXcxkC7lBzb1C7Oatdl3jn9GNpvus5S+nrTM3e7cE+Azajkhl1ip1OrCA/GuqxwesYhqzkGSbBLRfNWZYfPO8EbhQ8eqwSjqjhAJUl3aY0EhdGRgkgQQucxVQXikUjLsBjEalSII8HeWLgGH1Ilo8kqm4qoXSciWG3xRCAStMdUUVxA0SBJzVURPW+iMmCL16zREQgqlZwbqMRCcgMG7y3e76JUFM3tvIs62yE6TNBopPNc2NrDhUDwHm0sGEs9m+NtA3XN8crRiadDc2t/SfeFN2gICB4xnlovCYfXuPnysyyPjxDehqJ8CQfqeoYPUGlPbS0NHcavcMdHHO/fogGOrl3jC9/5Dp55+jPcvHnIjaMTlG9R3lEh2MaggqNW0AVPoEW3yXibSARsIuCPmF37fggOv71L92XfCJ9W8K8U3AT20o1YUXTUjlBynMBUFtvIL2k1sDB/DlwNz+fjGAb3uRwlmKI8RxnuFj6VJOh0duUpbqcBedtH+LYwSgpgENJ6AZ/KbSWpwwm9gf/eiHkY9uu8DMmOWuycxngPJkItSdJT6Cj1hJFob0+DsmnbKrBCNvqP0ckhgI3PoUoDB2UhqT+LMohKAxg17Es61ZP1PdT1Vh83ZhNCMjae1ewKILTvYX8MmBZMgKe33ZmU1JmQSu4MVJW/zyeVNLVhWe7P7XOqgZ8ajOW38eco5Kwnn/17yC6rryZ11myg24zW47ac2qe1RBXXLGXYHyOF9KFgtC+OT/tKCUUtWDvsUzmfIt+2Pea55z7DJ3/pYyiiWl2GXLt+xYPdij3pUNJR0WFUwOAxaui8gCQp5YlOPgqlSLZC6Y8pO8GnZsHT61gJUTo+Vioh3SAvxO8HAXSUNEepJFQrCLJ2vr4voQpAJILRJj7nZdkUBAkY0iSIgER7Dn3TCOLT9z41mfQ904CT7OkuvhqXInjtETQej2FQZcjnLTvEOn8XWa+zzyaMO85lZ72EXuPZXK0tnZN1cJTgjmiDV0nlTeloe09FOT4VapatjQPzboBCIdndEwz0cWYtf1gHSyLpvS8RQg2gKEklKTvEZ9Ak0SNqCbKClOeMangyca6Yx5imf5bhbmHUnaS57T7ZkOZ1/E6o7MU69R10QJnUH8jx6b2kTNpn8rqkdR89XZsAVlA2oGyIfYxaoufr5AGbKkTnQhXRmYdVKCNIpeIkuoqTXKLSerEtqd9/tPOW1O9J7wxmvP/Df46dw8+kz7IGHfsdTkgeRSOg9CjQFQGNshVeouR2SP2VfhktSNOZms4bAtvJBKJFdGzPQdcElderBLkaRFmUblJ+Ver72Ngv0lX/fKJSWydKjocEWyGeHxWfRfK1pwk7GRuWzO1J4GX5Jl6Wb1qL45NF2xK43n710L8Hrta/nuPV38f4p9ckf8YAqQQ0gyqWTpKhhfbTBiA2HuCPIdmUpFC5v2+yI6BWppmCR1Ppyu2p8433lfBn6rgcP6U6N55A2ASxxuBhfNxYJXSTxN2myZCybsu8yusr8y6/PeV1T4GaTecqbR+e4grFtZbXk8FglgzL97sEoqWU0xjeZcnh20EWrQdbXaWqewnJxm1gDKDH97BUUxyfq7z2sSTWWRNX47rbpJY/BbvG5dv0bIzvb9kPKSHj+NqmnuWxdFsO5Tul3F+WaQrCbgKR4/te1kFuL2dJMG4K99SriLaJ8iBo6GBO6auX230F6GTcXwlGK8p7pBI0C0rhPNS24kd/9Cfp2kAXAu0qsFiuUKqidZ7jZYt3nrmt2a1rKq3p7C56e05zruLiDjSzLS5sbzGrLdvbW2xtN3RhBQq0Mhhl0+UIkmbLO+/R2uC8x2iLd54o/Nal/osiSPImqlI95IfQJHVPohRNcICAc3FUrVWUqDNGxY6/hbrWMG/AOUyCbvHD2cXPplKgHSp5bzNmgYhiz6oEzAKXzjk4X+O94HyEgHV1wnsePc+jV74S7wNbWw1BhOAc169fZzabMZ/PmW81bM8azl+5zNve/W6WzjO3FVYb5rMZVy5ZDhYrvu6r3sPh4QkHB8ccHB2yWLUsThacLJacHK9YLTtOlks6HzhePQcv/D+5+fAfjjdXWi498Z1sH/0k3hi8E5z+QW597bfjfulRuLoz2OpZCzsIOxNj4Y418RwVVZY5UdOMrAdPMiEZVayXS8o4Geyd9dvSp88wCp0HgBaUxNlNE+PnOzVxPKCi2p0WghZEC14pVG3o8LhsW81qqAzJreXQF9rw0b9dh3oTjLqnIKP1/CuB0lhyKq93DlYCIaS4gHIywCYvPYBSAaTPw6NCWAdNSSVQsnpgAIJKkpijdLl85fobLcH3hp//swhl0ccDPEBr38OfUlXVJAP/OixQrLAmSizNt2dgdTT8r0t4NIZSpeqrP5XejECVLkBTD6dUVpWN9qOsJRknXwdkAwATopv7wb5UlChIl110ItZnuaPH6JxmqrOS1eqmZlbhdOd7qnMTBymbPSiLCDdu3GC5+jTPPv3J9dnUIMx1YB5aahcGp5NKJZafv+F5ffi2rw1QFHGAVJRxPGAY21iJfCB1diVKn2ZnPkKWHQNlRrZGRp35sj5CCIjRmNSxlhDShJ1dr9tUXgmBIII2DSEoOhVtJqEsnuhtVLAEDEFbvBi8KHwwdErTAZ2qWClDazRBz3C6QkkdvZiSBuJUOLcNBxfRxw0qAR8fNN7Df/uXwDlwflh23fr27ZYfv/r3WJeImgJZA0AbgNfrZQfqcxAEUP3LPk4SkaSb8wtJ5Z5b2lRRjVdLMvex5i04fR/6OBW/LzmuMD0hsv7ded0mPxRR0rYiTVyx/jNMx1Vn7BvHjfdvSmPo4c1kn+KsjoaMfpBv4/r6nfxyfr3UdOpH9JOro/yuq1N5fPjK34Irit7pyt2ee9PPT6T9fJLqvtvQfwvWw8myo1ouezABcOuLP8BT3/6naC9c4sLHfoa3f/d3weEhSimq2vC7v+MWH/rmE1yn+N+/5xI/+i8vnYJHYymrEqxMfQPy93gTOMhpyomGNZNFIyhVrpfQYPy9HkvOTH2nN0G9Ej5MlXMKNo4B3e36CWeFcR0ApyTXxvmdBczKuirLCKclFsfXWXIDoPfEmSWknHM91Br3m0pnI6UkErAGWsbrSqlees25KPSSf+Pyj8tdqkSXfZ4xZCrrdur6RaLdtSmp8im2Mp64HNflOJRxJZwbg7dNoHP8zE2db+rZHEuTTjnRyOnGdZ3Pf5ZK7JSduJxmCo6V5TlLsrBMc7fhnqftsk59LkBelg4OSrWCkjgOFRS7z7GvnC4mCF48Ho+oQG1nGCqOlicctYGDg5bWuSjk5APHnUJJjTRzLrgZ52fbHJx/C89fOUfwR/zyEx+hrucsb94ktC0KE0Fa34EHCURpvJBn2jw6CJevXGRWWbabmt29bR64/zJXLu9x4fwOEHjllZfpuoDVNVVdsbMzR2sTJ8+UQtuKpUvSbRKwpuqv31iLEodPs+MhSbcpkxqV0SgtSIh620E8yoM3joBCd7FDGCBK0klAI5HvKJI7dEBarIJ6t4ozdyHOyFsVJQRdu+DItXgRtETJBKUVMwLaQUBxtILDm0/Fl4eCc1uKnQbedN+FZEdOsfKeys5SHgqMRVc1Wj3P893/wrXuIm/dfoX7fsPbMeaddF5YOofvVqD+JfJbNf/iLV/DDzz11TAn/iB13CZ+QJy6HPXdBPj1ZUMtj5v60Lx2neFNHcveakDoEC0YFe+VSaoKOnhEAsp7KmswrWPZrgiuQzqBRUCLipKRngSfFOIlwSkVIVQPmFLnMKio/pE8CBJMglE5To/2F+tS5qVOQ6o0uJjsZN5ROP3q2XT4XWX7Ghz3ugSVpYqkl4DqvUlqPwCiHkj5aKA/GfY3VhKk8j200tr3jgB0D4qiOomxkoBS3s4e6QLWyJCfWT9nBlBGZ4moAUCtl29d+goc1k5/3EXiwNeEjqMXPoU7uoa1ceb9/sc/yIGaxYHaRAehtK8w1QkuO2fj/WUZJm+JUr0UURw7THcqpzqUpR2IKegzhk3jmbtToGqivFMdnhLmlZ2Xsq5PXeNI7D7Wp0K5gJIsKWHwUqjIiU6QKUqQhl4iqMKrZAtJWSSr0yVbTKLqJOVh+jQhSVoEbPypCp/y9ElKw+saEYvDEFRMJ8rGdZOOXcsvSS5lYKQGSSaxBjEJLPVASa+v6wIwbWaU6yHDirPC1C09gRdPppP/+b91h+eeCjrNMal3Jzoq67ZAc+eg3x7/QnKao07vGx8zBdZhHYz034nReijixhMv48mY8c9NbLt8zkyA1ovwmoakTdmDpSYtjUTJbCNRusqQJLBSXNofJbWIklhFPCbt0+lXSYS/KU4ZheT9uamqpH4sGVLHjo703+IMAhMEzPuK+Lze2ygDED3kJwpaoC2g4p3ApDE8uh2Y+pwFNVreSbjTFwDEHnKS2OvhbhjiJdlESw06xjl0v+1RkpdRxZ7gQKJdxxyncSjxaHEY5SB0mBRvGNIQOoJbIaFDQlRNJUS105ivg5A95QZUyGr2ASU+li2nJZYz9lMdAfj0V/1Z9t/0NaChPn6WvY/9LNd2fgckT9d74Rew9QqxbyLMLKbShHN7HP6RP8wDdoHRz2K+8hK6+e088k/+LtoIX/l1S37z71ogAlUtfMsfucrLzzY8+fN78c7dwTd0KpTQapzPuK8wlm4qJWfGkGisol7mOWUHsUwzpbJ6u+tTSp1yHJjjx+XaVCflGH0svXbWMoexama5fyxNNAZtJbwq6yrnOwVNSlBS9m+cc73R/2xHdKwimusrl80Yw3K57O9zTtO2bV8fJYTpug6R6AW0dIBwFqjM+W4CpVrr3uZptu2e72F5z3Pa7IEzm2/I58hpyr7iWHrtLMA7rtucb9nep/Ia3/Py3m4Cv2OoNa6X0q7o+HpyPZTP51Q7gnVAl9fHavCb6mIKrOUylOe/GxXcMtybF1GtUMqwch02qVeicyNJhS0upnyYc4PPF6TCoOpB6vYG0WAqLC2ihA982Zfy4R/7CPtHR2htmc9maG0wPrA8PCKYGj/fotHzKP0lQnPpfo72FW9+7N288uyncceHdKsugof84QtJHcVUoC0qRMcK2hAHFG2DDxpXz+nCFvtHgb1zWY0Ebt085IVnX8H7qCIbgsOYaCPp6vUjDk5aVp1DaUMIHhFPCELwgeDjTP1sbmmaaNzwyuXzWK24ePky5y/scfnCeWazmrqpaOo69Y1jJ0cU2MpE+2/icT7WX27StnwIVUKJIbnlJaqhguBcQPTQyDWgKotIoAsqzrAjZGmt6C02wQCd1X4NJmiidyGDSLRzpdwCAd6krvFQrdDe0h6lj0AIUSTcBYy2VHXNIxefgtVXR1W81AFvqiVv23sGnW1QEIGARqitptJEQKCFyhief+E8L720S2NbLl445Oc+/ijidMpP4DGgVqnxH7Hb/TLBqejxNcGl6LkvzeonuydklRRJvdsQt8lqKSGJ3QcNUoPY6FChpRhkVDgBNx50THVYRyGPJ/6jCFkSQQWifaXBKC8kj3N4BhsocQnRVfu5vS0eefQhfuEXfxYvLVSGnYuX8FpYndzCH+1jdMt9ly9xsjjm8OAqRi2iq3dx4Fe89U17fPHj96NVy4MPXeLS5fNR/U4HKhvQymFN4BMf/xhGebZ2Gh5+9E1c21+x78ArQetoeNyYgEhHVXFK6qm/5DPAS4wLk/vyMUpFm5brTlXW8980QzPkMW1gdPzOzuk3zZBFia1SGmtdyktEUBpcewK+6+Vmds7vIaaCYNM9XzeyXn5Qp2bsxmUZygqDalD0COpFyOox2SFD9hbaehBqglg6H6WUfIjOZDqfbTSZYhklkJyHkOLzMh8Xsm2n7IU0REcNMV20/SS9DSiFSNrfp0/vprSd19eWScqmjIs/EMnvNqJtUR/j+kF0hvLx9q0vx+unb/d02vE4ddNYV0br48H5nYYMgVSxvgkQnRWvGCZ27uaYO4n7bI5X8TPccxgYtPY9mBCXB9cPEngKRMegAk4NMMoLyinEEeM90fO2A7KXWqeK9WiTFa9TnAZ/NxDiswy9qqH0aoXYpDJoCjiVfzqDKSKUMqBM7LdmBidZI84oMDleRahu1BCvo0ZCjo9mCQYwNXyzS0BF34ZlBBV726p5mSHhFJj8VbMto32pXjTwXlCqw+qTCHEI6dudJngSaNIJPmXYpFOPR4sr4JNL6aLUocoAShxaugShurQv7jcqnIJY0WlJPJeI4+aVL+Fw7zGa5VUeeOEHqcKyP4+EBLqSx2tJsFNZhSeAsShbITbaWjW1QlmNyhKFqb2ZRoMGXSmUAVNpTBW1GLCxL68tGKvQltjXNtGrtom+2fqlrXWSqiZKU5uUVkd71Eb7pOWSpLp1nDzLNtxMGadCsfRYFTAqmgSIy/izeZ0/GddxGDxWOQzf2ZsQ2Bx+YH3z69OvCOXn+9F3L/jUz+1M9i/uRppkPCgv44fznh50T/WX8v6pftRYCqoEN7DefxkfO853qozlgH9TmrI/NgYt41Be0xhyTUGVEsKM6z73D0u1y6ny5evI9ZUBy1TZp+om20NbLBZ9fKm6OXZ0MAYsGdTkay69R+e8xlJupXooRAcIly5dwlrL/v4+i8Wih0UQPVmOoVNeVlUVzUF1XW/rbUoNeAzAbgd2pvq9Y+A4vrflfSwB4hjOjUHg+HwlSJtSh53SyMjH5PZU2ocs72UJvcfXVfb/S/CY8yvLn+PKkM81BWfL+y8ivWbJ66YiWr5IjDFRtcIONFIpBWEdsOXKz+5qe3pJ6oQIGKNRWsVfWGCagISOixfPc/niHl/3276ZZnsbow2vXr3OjZs32T8+5hefeJoXnr+FMw5Mg9Wa5XLJfKfm+V/8DG+7MOdbvun3sH/9VZ576gXak5aTo0N816G0pfWB/ZMVh0crdi/ex4l4luJotEcHz/HhPqIC87lFgsF7hbUVDzz0MIujjuvXbtEFD2JwHSy7juOFY/+4jR6VTOxtBCxOJMItBUjgZBHQy4BIy7Ovvpw6cS8Dni1bIxI/PFqb2CkguTlW8WWgglAbaFdLjFFUlWVrPqe2jt3ze2zPt6mqinPn97jvyhVmsxm2slhr2dvdw7sWY2NjrmqD9gHXtQSx6NTBDJKNP4N3AVHR7h1hidEarzqUUkT1+GzvJc+4hKhmFGInJ/SN20CnUARC8KxWK75k5yO8uflanuNNsXGqju985G/ztq1PxwZCVCdOiDQCMFLbEY0Wj7wTwuNxNvH7f+Sr+ehHHitaroJP0B/jwg43+cC9PAJ3+KC81hlmYFr00lWeQU0dV6Lai0pwSqn4i8ZzCyClo3FerVqM6hKsWqU0LUa1cTsZEkfFWdXsYS8a/w097IqAK51LonFyT7tehnQ8RR7eL6NxciXACqWE4NdnCctZBGDtw5z3D/DFsrNznve87Uu4fOkGNw4OefbIYx54DLu9w6tP/DI6vMzejuXLf91v5Kd+6iME9yqaE0R1UbjAC297+C181fveg4SWRx97Kw+96eFo+zENbIP3LJdLVreegCBs757nre84h3n2EBYaZ+okVaOTtqulTXbjvGRNWJ3GI8k2DDppGiUHCdqkuxwHfoJKeUUHL0GiyIpXCunzikMYyT+lh+01GzTpfP02ZKcLXlSCQRkmaWRlerjjQrQrGcIQF6FQ3o5eQ6WHSKpfL+GP9wHpPMGnAev1CnnSpmMTHBJ6ycyQVLAyNCI7CUmSlJLUtPKyH8iWDiDGkjPjuNf8mX2dw+0gTQXUE+nKtIXqu+pV4JPaXfZiW9hrXF8vDImn9ejVTpIkhyQPdwmapwF3XtfJsx0qxAFz7+UuxMGxSoNvyU4UPEgbt/HxKQgeFRsGBhDvonR6Pr8ExDtMmimLtl0l2moVH48HCD5KCXea4A3iiN5v/WBIvvMa38VnoQuWNhg6DKJqgjRAjYRY6SHUiNTU1TZ1vZ08KCaPiT7+Hrj/EboW2hbaFXRt/Lku/oRpx9Xr4fxr0I4GWNULhtUM0lMmSWllaFVKvfUSVqP2p4ofKY6JuFNpslfI9HjeKxAqpd0+J8BJ1rfvSmrq8zHkZ7P0OpnXQ/JQGYb+hgo4dujkQgJEgXO7N7my97O85dL3oyuFsZpkRgxtFbrSaAM6bycYmuFSMnmGzvDIxokrnaBSXM/S3/S2MIdlNgdQgqa8ncHSsxEU5e0CLOVti8eoBJWSE5UBMEUA9as5dGKS2nucEPISJ5Vc+v67oFmJjR5gQ3S85L1Oyzgx5IsJohin8B6cqjl++LFUc1HlXr96jfM/+iOEoHjXl7S880u6tfJce7E5NdmXwxTkyutjwDQ1CTg+fgyvxsdtgkTjUA7SS7tnYwm6shx5uQlwjSFTecwmibWp6xhf79Tk5Div8fWU8GEswVQeMwXvStAzltoqyzrOo4QlWWUzq0+Ow7hcpSfPs6DVWIppDO6mJJ9ms1kPYbquw1rbM45s821sey6E6Ek0e+6cqo8S5J2eRB+0D8b3tCznGCxOwcsyv6nnqYRTU5Pu43zztZbSdiWsLMu2qX2W96SEdmVdldJt5f3YBMQ3BaXUmif0Ep6NHUvk/XeS7zjcM2DLDdjJuocYrRRaWJuOyJ4xyhfJmCwrFQdiSsB4F2cQ9YyAUG/NeMtb38LR0XWquQFdgXFYawiuY17VWKPRStHUBtyS81XFSWh56yOPs3ru4zzxcx9lph3N8YJ2/wZ1Fw0Rh6BRymA6T+gCFUJlwZhd9hrD4f4hQSU5LpfU+LRCa9jb2Wa+1WCtpnVRWROJ9nacD+zt7mLqmitX7uPWrZvcOjjmpGvx0b1p+pBpOp97ZAqFJoqVGxZdwCqFTTbdAtGhRJAIEqy1GCJsaLZ3uHjhHDs7WywWC1btgsOl4dbREqVXqBcPcN1ThPSiEqBdrpg1M5zrQAm10oTQYepoaJQgaCWE4KmbmqaZAcLu7g7GGq5cuojWmnMX9tje3mHezKiqOPdnG4u2mhA8wTtEDG2IdnB0JBQ8Yd/F9zS/n1vqIu8Jv8QflP8Xf+aRv8xP7H8FC9niS3d/ngerFyEQ1U6VRI+cEnGCp0OQ1LE1cfgUIoRzPkk5nWrxAltxMLBVHfPg1isYHT1FVcnukjU+qeQ5KuMwpouqdoqkGhe93hkTom0mEzthWkXVvas3rvCvfug3M3Swhd//LT/MO9/6MkeL62jdsrtT85a3PEhTa4JrWa1O6PySLrQcHJ1wsDgGHO1in/1bN9g/PMS5Fc4HfICDoyNWrWOx6ui8YG2Nl4CgUKYCFUFwK57K1mgliI7GV0SbCD10dFYSDelGY7geUKYBpQh4jInqYaIUQWuUTiMopRAMQWlQDahZckCgkkOCaFQYNKKTMV1t1gwMY0wCPDFeZWPE2oAogtIpr3ScSpIE2haGixX057MYY9k5d4GrsxnLznN4dEJnanYvXqZtPYt3HiPBs9KGf2Eq7OMNy9UCpdWQJ5rvr2p+qGpie7pRw/VYnyKx8xiSpGPUrNVwpOEn2Axubhd3N2nv5vh7yftXc0h2EpWOUnIRCMXtKAGT9qm4P6aTPk6X2zptq2Fdm7CWLkokxoEdvZRAfG8q7QepAlU6K4gDPF0O+Hp1W8HmtNpjtUSJXZ3UhHVU2bEmYLSL3uRMfHK1ClgDAz6N78bolQ7Ee15+4QU+8jM/xY1XX0WFgPLRDEEdPPctHVeWS+ZhmBHUkjq8qB4gqKRmmN9u404XKoJRraOjAh3lp+P7RRswNaINnSjEVIipcEqBrXFYnKrxek7HjE5vsZItOuLPqzleZjganJoTZBsnFwjS4BPEClIRJEoghlBFddZgEbERlIWkGho04k1SlU8q814Pqva+kEp6DcNy0w4Nnz5gWpptTvTXMgZX6ozt8S+dA4rtcl8ZT3HtdwKYsiQWxHbyawgwMQZL5SSWLoFTgk2FurxKRv11Xtq0z0Tj/dqECI2SgX9TCcb6mMam/oUNmMqnbZ/iPLbymCrEZd5OS2sdtgpp6bDWUVWequrSuqOyXVxWjtq2VFVHbR2VbeM+EyXATkssRfgUnaBE0FSp22Pfz+fQQ6V+mdaDwSUp5FXQ+GDxMk9ASffSx35NOrkATA580o7wLkkQJ/DkHbEP4RXBS1w6om3iLuBb6aWNg4PQpW2n8K3vbcz6TlBBQSf4LtCerFidrAhdIHRRijU46LoQy5PPm8oRVYinwc4wcBVEBjtUZ4EaGAAQRFW+q9/8FTz3X/wx0Jr61Zd453//p5i9GCXUZlvCn/yrt3jX+6Pq30/87zt89N/toBS9pNM479vez9t4ChxLweRQjm2njinTTUEpWAdwmyBXHguPVdrGoOGsa5gCLFPpx9d3VtgkxTSWxirzGUstDcHgfW470qs9lmUpj90EgnJYrVYcHR31gGVK9bfMe+paS3gyVrkcqwXmfWWdtG3Lq6++ys2bN4lCJYMkWll/U/BwDIBLW20wcJLyvGOYeztVxU37xnVTQqoMmrqumwRXU0A0lz+vj+2flWZISmGIsWmXXIfjcpXScGUY1+E430HQQk/WYdl2BucqA8Qtn8t8TKlKfLfh3pwcFI2uLOD4BsBp8j6Vl8kFVwqIjgOceNA11sZB/d7eHq+8eo3H3vk4y9WKbtXyykuvQGVRRmOtieqmXWC72cJvb3O06Ng/PmKmDF/whV/A/ivP8dL+M1RaRxUlETw+wgbRuGDoOg8CJ27JzNQR7ACddwQCLnhMZfBhRd1Yzl88x40btyLldIqgBEL0haaVwnvH1auvIALOO3xwUT1JhBBAJPS2+SNFC7EeVJIeA7yE6GHNeURH8XMvIamj+uSpVDg4OuTyfZcQBU4l8U6rQUE1s+guSsRZH6l75xUrH72G6aAJVYWpmuRUSWHriujRJ8K/Ls3+3tr3aO25deMlQvBYo4kv0Qh4jNEEkeSNLV6X0ooQokE0pYQD9vjJ3/038WoLlOIXzPv4H67/Z7zv+X9EPfsoVTPn5/w2n5h/AVVVU281+BAIaHTyzNR5jzJ1r2TgfJL+UbF+Tj5Y0egTVt1WLIcJVA92OCp25kd88C0fw+po3DpK+pS/KIXTUuNVHfMVTVBRyifkIawa1oel5oHf9Aq3js8Bip3dI36oeRc/yLv78wiK0GpCq4ZtrRGtCOc14XweIhf7k7TR5zyU4OX1hkH+s8//YC1OIMD10XY8TtGuxSVJJxRLzhgIf54HpQfbbhkG5W2tA7rKACikfQGtMixK69nAfwJBvYq2jlJGxki/NCrZfEtqJhEceazO2yFBoyQVYOK+1eFVOHqFWrXUNTzy1rdwy1U4U2FMlIDSyTZdlnKKNuPoVV+0HmzJaZVg2kSHa9xxOlVnxQc4S1aXx5X5TXU44o44MZLTKpWc3Ux0COKESa/wBNriAvHdoqJ1nfwOcpIs7Ugc7DmxnEhFkDnOG1yn075onsCLxaXJGx8MTqLKaufhcLHg6oNfRXs5FB57KxDLZ5xFeYMKNtpf88lbYzADmAoJSGU1+pDUCENSI8zbngiofHqmXF4S1eanbG7l3+sZNkEpS5T2KwHUGFaxYRsi6M0U8qw8xuuvRSjh12cVBmnCLMlEkjBcA0pJwonkNESpCJZ6G5MZKOU4I2jrh20bbUJqEyGStnF/hE9+DTBZK2k9g6S4z1iPLSFT7bDWU1Uuvm8SSKoqhzUeW3VUtqOuHFWVQJNtaTJgquLk2rrkUrE+CZ1e78b72oYu2B4suQyZvCb07xJNKxU+1BEuiS7gUgGbvGbV7eCDQckCJOAcw6SUj9LIoQBOES5FyOSd4J0MJsO8IF5FwOSSeruD4AKhE3wnSBe4efxunr/1G+nahkv2Z7i//mHwwv6Vt/ELX/8nWGxdQb98SPfvKpbhIj5YLu3/B77w5b9C8KfvXRYcGEtZjKUqRAIi/tQ3QSSqtWWwUKqEjYHLWFolnzfvL8dYeT0PcEWiGpPsbnHrT3wLq/e9DfXCNZrv+kfozzxbqFuNvfb1JV275hwW597C8x/8Drqty5x7/id48KN/D4KbVFccQ56xGuF93/dPufDv/w3uwiXmLz6P6tooUqAUyxPFd/3RC9z3JocEy8H1GXUtGDMMeMfAY1x/Z0mJTdVzme948L3pnGWe4+2pUI59pyBLTlPChk15jMuxKa9NY+1NMHDqfOW9HN/HqToZAykReOqpP8Czz/4uQPHgg9/HO97x3TjX9m07S3hNwdlxnnl7Nput9cVKeFIClU3XOq7fsvylJNftbK6FEFitVgDUdb0Gg8bcI69D7Es2TbMmfZef49Lm2lgtcwx2cnkzoBtLF47LUtbJ+D0GUVpubOOvBGFT0pVnPYdZQi+XJ+dXPl8lnMs29ca25Mp33vh5LNt/zm9cf2WbmLrv+R07Vm/O0mqlhFuOv9twz04OxgbgALTSqDRbrYtKGL+0xgQ1JLtBiiiFJgqsrZAwOEBomob9/X2a2YxgLM3OLisCL77wPNf2lxyvVhyFwJX5jNXxMs6cNw3NvObms0c0Wxcw+jrOQecaWudpg0eUovWao8US5wW3glaETgeOT1aslifo+Yy5iVI72iiCc9SVpa5n7G7P2d2Z4ztHi0Rvo/nmGMP23i6r5ZLjkwWV0oi2OEI0U+EdWhuUMrSdxwUXRd4ZbKShk0pZFzuyRoDgsZoocWds7xjBuY4rV67wxBNPRPtyqc6DD7SLFd53QBbjTI0zpeu6JcaCuGjLTItCQkCZeGdI+hkKwAcQjdImqoqaKt1/8CEgIXqR69u1ypeSfcIJ7v4vw1fba23q+n1fzg/d9+VnNLrR9lhKeNyaZ8BvWs+gowHgkD1+mA9Nn6ecob9jsBPQXlBBov3BMPyObm1zJNtrcSp5t1KBuB6IEiUBjPgeBqmzzilEVbleNS6uS0qXJTF6G0xJAquPSx7QBltNcUD/unlB+xwFowNIhySjv9EmD4BH/ApCh1bRscT5SxfZv/YcyAqSSppRngt7lisX5mg8Fy7sMp/b3qlAViVpV0cc7t/A6kDTGC7fd4mDRcuiC0iSYuqlkxIkWoNYGVT1IKvwWllIMWXJSKvjNZTHKRXLEw0ru+SV+fT9m+qMjfdlIBTjcrrhQ1l2ZIbj1wlD+QEf3vUx3fBhB4Vw65Vn8TeeZ45DVZYvftcWTx1YDkMTYZO2A4QSjaNKqiwRAHU9VIrQyYuhTfbPnJjo/VFMgk1JIoHksVGSxGYYBpABPdhL6/fFZyJC9aQam+IkGQ8PIYH1tB3nScpni/6ZW/M22NvEYtqAe/nLUkJTUHrDu+HUerkcrxNNX74uYRNwysbj7xRK3cn2nYCtjcVMKqiUaqwlcMq2ItPNUx7RAXSER1F6SVDGJ5W2GFc3hmam0baETgFtPW97+6NJsskPPxuXyri0nZbGxZ+NS5PW92+9gql9L7WUpZgiRErrIymm2rZUddxukgST1QkqJZW4X/NqcmGkIpcBUvbyKpq2l2Jal14a4FKUXAqFily00auj5FCKC3ndQcjSTEnaKbgInyJkihBKvI6STW1IoCnBKacG6aZO4bqAdDFvFQzdKtB5RdeB94bWxYlI501vWzLqbcT+qjE1rX+UJz/9h/BhBxQ88PAPceW+H439vcJTqNIVQUXzA1kCPppRiMrYKkmdR4c1IJhoNCxLpaMJAhQS7yGJbPbS72liUeW43ryBAhUl4EHRhV322/fEZ7oB1G9hx/5uZrPrXL/vfcjPJ1r+8hVYDA/+i1u/hftm/54rJz99CmaUkhj9xEsakOWB7HgwWw6O+/53MXgv8yjPJRBtBCqFMiZ25tIEuzIaKpu0AxQ0TbIbGOtTGU1nDAf/1TfTfunjAMiFXRZ/448z/+b/BtOte4Fc/zYXL8E8+asMrt7lk//J38FvXwIRju/7QoKpefin/uapwfYYRkxNcIUQ0DeuM9+/dQqI5XQvP2uw1mDtNDwq18cQZQwzzkoz7seM1eTGacfrZb2NoUVZx2V7mIJJU/20TVJZU/2q29XJOJT7z4KjuRynzwFQJ02OOq0bRKKpA+8tStVcv/5Bnnnm28gdixdf/Fa2tz/D5cv/rIcWZRsYX+e4PRljqKqqdz6Q1SfHkCUfP+VoYnzNUwC1BGFjIDV2CpGPzZ5MMzibzWa9xFMue7b3ltVbcx3kNBnSlecrQVE+b+kYoCz3VPsbg+OzxgQ5fVVVtG17ShqvbA85lAAulzdPIGQ7dGM12BKylfCs5EFjicZ8rjGULKX9jDFYa08BurEkXAkMM5wsn6nSuUR5nvl8jnNuI3A9K9wzYIsgbSic0brvqIsCHz+BKNZtsMG6faVMvPMFiQKldBIt1Sg0FqEykWg2O1ssj5Zs7e1hZxXnL1xmf3EDUUuoLMHEmeOt+Q4vn3QsVp698w/y4Z/+NBd0xYuvzjg+dqBqjpcL0IFlEI5bSycOJ55Dp+jqivPes3DRWLWSOFvfecFJMo9tFHvnd9nZ3eb4aIn3HRaDKI9rO1SwLA6jSOt2U1PPK/a25rQnh8x2GvRMceHCeUxV85Gf+zQnJx0oFzsmXliI4IOPnu1CQKUOifcOUQprNXqm6doVxsSOTV3XLJdLvPN0zrNYLAjeRzioFEEE7wNaaayxoIXaWoKS5Gldo4zpvYqq4kExSXoq3/fyAYvxg777qY93ajOomG5+9FwiQqofzc8Pn+XRJ/5h9OgqPkIrBCWevZ2aR978ANYE5rXluac/w8nhMa5rSQQJ8R7XeTrnWCyXnCxbOucRPM4rrprvg4WGwwwMFUpukQ3HSG9s5nau4aaCft2FLzYFleyG9GpnOg5++m0VVUt6WJQ9Tq4dEzDKJfCT4/2QRmVj/b43qmuT+lsZp/t9ASQeL+KYNSbaNzEx/Xxe0VSGyoK1gWamqUxUj5s1htpqrFHYKjq18G6F9y2+W9Itj1A4jo8OOTw+YrFccmv/Fp13rDrHtRsHHLYaT5w1a51jtXJcvWF5/we+gc/8xP8bKw5TafCeujK85x0P8MVf8AjBad773veyd2E7tfuQxI+EZ555hheffQ6A+c4uj73jC/n0qwuuLnR0ZMHwvhvPBMLpWboUC8QhfehNQMfBC9qkQZ6NQ1yJwMmJiZJMoZepiMPiECFTlmgSLF0aKPrkFTIPJqOXyDSQRCeolNL1kEkPsKlYFxJokgJATcDbDHp72CQkNTwicHqedSg0BYJK6Zy73S6lJM8CUlOAahOkyttMxJ9Vptc7TAEmM9o+BaACgy21OBugyl8vPegHVTldgKEeCAW07XoVN5W95WbAZIufkbTuo6RlL6E0qMzpKmCqJN1Uxf1ZcilvV9bH+FKSyfpeWslWfl1NzrbUlaOu4tLqdg0w9RJLagyd3K8ZNbleaikD6vwLw3YbmgizQ5GudwKS7TYlCJ7WQzC4kLYLsOO9jWmDwQcb93mL90M+Lm97i3cpH29wzvbHO2fxedsX2yHnb2M+fRlsUZ4qQiYpHobklTMNT2JUsR43ive2jPdxet/4+d/4rlGb3zGb3oeb3kt3vC93wNOfABcACeg3CVff/iGu6g/Fd4CmX6LW1fDLJYpkv3G0nDoub4/ynzxudJ58XKXhin4pgT9Jt6MB/TCXeTWZgBDUexUoQfRwB/fbr2MVvoys/ZBNVkiCehn2hdLsRQKE2aTEsFS9bdSsgQBJG0FUjBdFr8gvOu1P38V0/3u7ovl7KaldJin79XRJxfMzCp4e4kVA/c5/Di7019Sb29BmfV2fMRRUCkS4+ejX8qafHtwclwP5MfAYD17HUjY5PkuHlIPvKeATi6Emz13uG/epbgexxtAg5zkGKuP1TaGEHGNYNj7fFPQMQSNSIVIRQoX3hpBseHqf7HWGql+O063Hl+mriWPtZPz6MUM6kbsdFw11v7//OBcvDvVR2lMe110JyYwxrFYr5vM5IsL29jbL5XINkIzDFNQct6kScpUeKkt1xVIKa3ye8j5noJYBYFmOsh2Vbb4sWz4u26jPEm0ZImUnADmfUn2x3M5lHqs+jp+R8hnNdZ2vtQRJpZTZ1OTDVHvOZckSirmMufzltWf4mD3BlsBt/D6o6xrn3JoN//Jel+Usr6283rLsY0GBEmjmvDOzynD3XsI9ATabRI0V0QaRUmnmRQEosndJpVSSZFq/IaVo89QLNWjQypDEerDWMps1nDt3jq35FserwNFiyeUrD9E0Hc+/uI/3jjZY6t0aG2DlHCsEt2ixpuZGZ3j51Rtc2wfvGjrvksFNT1BEVVMsi7piRRogqgijbPy+xPISYWL0pGmYzefM5zPqmaXrOoJSdB6qyka/SOlmuiB43yLLwK2b13nb3kO8912PcOXKHtV8i4//8qcIoebkqOXhhx9kXlU89eIrtK6ldV2cuQoBI5pzu3N25g3LkwUEIcR5LrrOsVgsWC6XuGVLEBAfHRIoBVFxNUoahhCip6OYLVFFNkrd6JHo9NSMQL6uEEJsA5Ls703ocKejOPCP08kO8+1nWF6+j3f84t/gV77ojwGG2fFLfOCHv53dg6f6/EUibIXAlUs7fODKF6GtZ65mfOH5Yz725M/TJZCY9f2XrefoxKFOloSVRwfBI2ipo5HmWBRyD9qEl1GsUEl8RCWxEqWyge3kzSp7sZQ4oMrSDaFq8M1u9OLWXqXyNyB7uRSHpkUpTwir6NFKBZAWjYsgqTLMasXWTNNUsDVv2NlpqExgNq+obGB7u8LgEEmDWelSGVagWoK37C8ucGHrFRpzQl1bUBqrNULANvHDWGmDtiY5yYgVICraV7LWRt7h2r6NRPXo2L5CCAl8JsCUO5NEFQkUGIaXo6R5ZxJURWQYYwRSpw2yKpX4aGdPCYgKBBV66T3pFCdi8FQEokRSJ5YOS0eF97ByFV49QNdEON7tRiknh6V+FM5R4UOFp6YSg+4Uu0dzXjx/me1v/JoosaQt3mtaMfyYwI/uR8mp+S9cQOmKLih8IHqMDIrjxSraYxON3LKYl2esHLixp8cMmgqJpgyd+s5yHlz1g57RQOu1gkz3un2vx342ebwW1zAuy+sSpLedlQeHKhmNj8IbycabDSgbjXX361VAVz4us2pc7TF1AkaNw9QO28RfNeuwTUc166iblnqefrMV9XxFPW+pqjZKLtm47G0sZTU520bAZLsImGyL1R2VihJLt1OTswVsiu+VX72hDVUBmbLKnE0quJZO6gSc7ACeMqxJyxLsTMb7YekTGI/ro/gCLjmXlgk0ZZjUgydXxOdlSFDJF2XIUCoBpwi84r41aDQVNr2HzgLPU+untmX4FkjEDVGKOiWQomR9+miLtYxTxPcqSPysbCrn6Kck+myYKm905pTfy1KAsBgvvVTpkE4Cg2p0iH2wNcnVz7ugNqzD7PFDdr7toP9OSfo2TQGezSBoYn86LhT5bM5TgWczdCrj70n6/k33Vm1veIhGSSD1K210Epcd0aBA+YDyHUpWyYlLQAXXO4FR4of4vC0eb7c4euhL185mVvtrcCyHKfCV48eSQsCaGls5IB6Dg/JXDrwnJ+8nIEJejqWwoqRV04OpDK5EakQqvLf9cgpoRYktQ3RaEwGU9+uAKuZxGlgN4Gt6eVot5/UJWrdoHZ2Qad2hdYe1J2jdprh2bd96WodSLdBirePo6K1cu/bVa/lvbT1zCoqd1oZYhyG57cznc7a2tvr7n1WexzbAyvyzmuc45HNtMsBvjIlCKUX8GNKN13O+paTTGG5lKavsQbS81imHBONnIddXaZMwpy+lYbME1hhAr99rvXYuay1t265J6pb1Wp5/zAJKad4MBDNEK22blSyhrMO6rnvANoZw+R6X74dSFTSfzzmHMaaXdBxzi9xu8jmzSmz5TslqreU9z+XK+WZV1rsJ9+bkACKqSVBtUPVMNnAmZv2UTg+DHgbacYwt/eAng7oAoAwq2ccy1qIM7O3tYIzl+OiYf/Ov/y2LhefgYMnNWwcoU2F0hTaaygtOGySAc4Ld3eEVLGp3l5O9llXo8K7D+xU+tHgFJGh2pBStVsxDFERvfYdVW70aqzYmSoCl96DWmq3tOVvzhq7t6JaRPMeHwKQPnkYJXLxwiUuXz3FwdEgwFi9gbEXwwtasZrnwdMFzvFjQuo75rEY7BSuwWnPx/B7ndxoef+wBrlw+x0c/+otcvXaMD7C7u0vTNBwcHCS1UU/wAaU1XkfQEYgNKDtKCBLAC5JEMARFpRSdSy8vFYgqpRmGpA9in59AUkkTJHqT1dH+GiJRXYDYSf149+d53n0rvBP4kMAzCoxw/6e/n8d/+e+wtf80yq8QHYFNyFAwRA+TR0fHfXtyznPxwgVmWw0sVlGU3kVnDEYHmipwbscwawJeDMcnS4I64Tj8XW7pP9w3vt3wj3kwfAeCS0xD9/cq9vkDStmo+pzsK0Up/mhz7tYX/Ge89GV/GX7Z9gaer7zwl7jvlf+JNExI6nAqdfgTWTcRUiul0RqMB30sOKU42FccENVpldIoCVgTn6jo3EIjCForvNJcc1/Ox7q/hpjzoI94fP6XuTR/AqnnYLYIZo6d7yHVHFVtoes56AbRDUHVBB2loND1mopMUBaHISQ1uyDRLpQk1TpJ+yStx2W0U9erywWdfmmGNeg11dRejbXvNLMOmtYkg0r41L9cXlsY81puv1HhNb2O0YXIaH2cflKy6/UZUPaSUFUEUaZKUKp22Npj6y4CqToCqXrWUjUt9aylma+oZyua2YpmvmI2XzCbrajrGDdrljTNkqaJ67PZglmzZF4vmM2WzGcnzJtF/M0WWOuY6Nf8qgiDHSZbSDHZHiR1UrEIsxi3JqmUpZrWpZK6UnIoZOmkAuz4AfaUsKmHRyMppvX1cjk+biizy5JPzqwDKheBevSElyRSiKAGEZQoojoo+YNABj8qgx8ZOsAqPzNFvz6bAiifiZgf689MgjZG21MQJ86Ule9H1iSg1lSPBXwXTscn6NPH+XJ/wHhZA0OytlRJ6vNz1ajV2nLq9fmGvVK1DF5Us9RV6XzCFOu2WE9p0WqQqOrjiH2pbP+vX6r1dTXOr/idte9O95vbpz3Z2+XkJ3dfyxo9FZRkNeykEBobYtyW5HpJ4rrCJxAUZbx7SCTCorsf77cggGbFrv00Wlp83XD0wNvwtka7FfULN+nkHDp07C4+xay7ik4ehJX30euw68B3ECKAUhLI3ol1LqPEbatASSD4DnFdH5+PDa4jGpSLZRXvkmZGgl0kcxIIFJ6MQ9f216kkUGmF0cR8EUwxqFVK0b3nUa7/hT+I7MwhCPX/9L3of/gDZ0qFlYPdctn6huc+9Ge49QXfGOP9iod/8q/3aYZJ8HUj+PE8pgBREVw5p1FqhkhF12m0nhVQKXlblhql4rpSs7RsCtg15LkugVVKWk3tH5Z3L4X12oQSRpVwqqpOUKqb3Be327R0/X6to+M1pRywStsepdoij5hnGV/uL9NFJy1DWyilusaQabiedfXaEnqEoPjlX/5OXn756wG4ePHf88AD39vnDYPk0ViNsJQcK6XMDg8Pcc71IKiUbCo9dmZwUqqR5n1jKaycn3OuP28Igfl8jve+l1oqbbJlL6A5j3xua23v8GAsQVXWaVmvZT2WdZPjc1xez3mPweEYYE23vwFalscBveqq956maXqJuvKdkQHW2OFIWa9jydDS82ZZT7k+p+CbUtHZQq7bUsIu55tVUEuNyNKeHAySeCVoHUu0jaFZVVU9wBxDTYCdnZ3Juj0r3BtgU4Ban0kYGkcaPCfAoAqKmI6OMytK9X1MvdYQiapHkbXFP6Uw1nLh0nmqqqGuZlw4d4HgD+PLwli0rSEZvrcmQqLaWpYYQmW42VjU1i7HqxUr1xJci/I1rl0hwcWOsBp+LjjayuIEXAj44KKTAu/TNQ56yzvbO+zs7HJ8vMQYCD42HlsZZrOaVedAFJ7AsnV4ZegCKF0hKval9rZrjhYtQcH+8SHb21s431IZw+6FCyDCB9//Ph57yxUWhy8R3BGGFaHrkBC4ePEi73vf+/ilX/olLl++zM1XrtHR4UN8WGJdDyDMWoMQCD69yFAE8XjVV3w0/KpUcnqQ1H7zx1VCgj5RlbQXTR1YXEqjuOq/OsI1iHbRflrBu+LmK3wDN7/sA5iwjEMbpfpllrAChdLwb+sm5q00yinaL3XDuEdShzQtlVJpPKB6MCcotCwRDIrAif7tfFp+O8NoWOXhVVpKastlKAYD2sTN9wCvxsZ79b4/zVX5r/umfibI2BR3Vnw/yCvSLYEDgB0+JX82GhFfTeTxem7/qg/DQGh9EFQsy/1rY8T4DlEqKYgkcYq4vf6Lr0MZBufQ2+QbD6TxKtrRC4CL68FF4/LiNMFpQvdazoCePaCu6gSp6pa6aWlmK+pmtb6d1usmQqxhu6VpVvH4YrtPu7Ys9xX5FXHGnJ6tvJdwCipJAZGK9V7KKVgO5Bw3w0X8yuAXWVopq8YlON2rxcXjgh/SeEnLkOy/JSAVQnRS4FuTQFQV4ZAzeGfj/fbRM2aO74/xhuB1mmmPIMl5OD5qOTg4QXyUwgwS0ymnMV6wopOkqiJKh4DK6k2S3rP5+x50hDCJFkgC4SK6lzyJEkUpbbZRl6A72Y5d8uAZ7UTqlJca0oQYP0h9qsLRglpzriDJ+2e/dOqupYZktPxVGZSswx9zBhjSMsChEXjJ3ndPQZi0LflTrUk2pPJ+NSzvAOS8pvvNa5jXqXegmm4XSSII8QX4GX4UoIQQQUuadYrxIR0fov66kgA+qWaLDPGkbULfL1P5/AokxPxjfzt/f9b734qYRX9sPxjMhl8sJ8fvoO3Oo09ado6eoqqO8uX3fSQAb+fcuv999L0+EZrjl/nSH/pTcepNxanY6OUgSuETPCYDJgZTI3kwVaoNnmW8frytdcX1xbsQGs7XH8foVdEvVXTNNnZ5NEg+FseWY5msKjZl1LoEAWMpqRI4rZlOKZZjaDEe9JU2jbKKWW9vSGuUtXgvaN3Quoqua4AGpRrcT3U0v+17WN5/H90txYsP/Wa69/9Otp/7FDtP/hzBV8lmVgRVeQl5uybInIPVV9C6B+GJQNW8iJkdoRcnvOj+KKX6Yl7PeQ1qhG+EFFY4BY60dlh7XEhdrUtcZWgV00YJLWPcGsAqYVX8eYyJx1jr+zyhTcfG9bw/l0OpdZM6Y9W2qXU4DV2m4ExuO7m9jiHLlMRgjs9xUaBh3alHmVcp3TPOa6zmqZTwjnf8eR555G9Hm5JNy0sXvgGjFZdu/ghV2O/b9Cbbvlpr2rbt99V1zUMPPcRsNqOuoyrSzs4ObdtydHS09lyV6o4ZhuX1EsyICE3TMJ/PT0mRZY+a+X4tl8s1mNh1XV+vTdPQNE0Pfsr72LYtTdP092g+n/d2zuq6XlNNzIBnXA8ZcI3hWCkxlsPY1l+pSjll1y23xSwUFELoy1ECrAwus7RXliTL11hC97I9lO0vw7MM6kovriV4zTbpcp6ls4Fx/hmGVVW1Vg+ljbZcz8aYHt7lcpf3qny3l9tKqTW7encT7tGLaH6A40d8/UOR40Gjh0FjvvGi8CHE2Red3NQWx5PmkZWKhvKN1pg0czObVdjKUFcN589fYLmC2WwPZV7meNHhvELVFukCqu2o5oYbzmHrPWxVIcHjbFTx6nzq9Kgsah7QAuKj/zZBuLZcEfobpU41UIiNbzafMZs31FVN3YFrV6njBJXViLIsVx2C4PILLEl3xQ7b4J00iIDSeImd2vm84cqly1y/foPj4xO01szncxYHS/bqLa67E/aXHVZprly5wtbWFlfecoVntcFqS+s9re8IQfAhsGpXuK5DIImrRjttxlTxboaAF0Vlk3SixA6d1gaIVsqcj9fWeR89pYbBxfFQP+ljgLDkoaHCfh74QuAq/cCn5f47aner2ye54/CaDZ7OA3PgZRhRljsLYzgzta3L7QSB+9OoaJ38xXsp/Fnlkmm4tDZ4kdGAZNiORr6lN/Qd7S0Nhr6jvaXCc1xaN8n+kkm/cj17ijPJE5ypPNbGjowpjHcb20UbS7ZL+5OnOOt673HtrZqf/2cfQJwiOIV0htBpfBchlV8ZXGvxK4trLd2yomsr3Couu2VN21Z0qxrXjWdFM6R97QfpdbMaQFaSwipBVjNbUZUQq993GlqV4CqDsqppo92q/Ju5pJ4YJcFM02GTGqOobIfNjJbJ0UCyhxR/VVoafAJF7dLjWsF3ESjZ5iJH7VZM5y2hM7hFgknB4F0yMO51L4UUJZciSOpV6yQBpwy0SpjVb+seYsV4TfBR4jIEeqATEtAJfgA4/TKv+6gqtrYvedIc9qteIiinz5A0pyPHZzD0OZUa+jwOWWoovWPUGhAq3jF5PTtGMNK/h3rV3A1gKEsQKQViCjCUoJCUkEgreumizxXIuVdoNAJi5TWshw1gaBzE97BIJXMAUcJIIhgixyXqH8IAfSRK4kQpeWK6/BaUQTIwvxljkjzZMLwrVdmnzHBiqqwqIqF+v1KJ8xTXmutB6SJO92bHyPA4Q1qVKlJHSew4URcbSjaynw3yr8XrTCjtrw1Am4KnZp/33kHKob05u0137QIKYW/702twIdZnBg3rqkowAIVSRWpKgqYMw4AocHH28WIsMcAA7w1yCKJD0sAoSj56VrwPcaLCn1YNjBJV0bg7NL1UVLZ3lY8ZANZpm1bDMSM4RQ3SEKRCsuRVhlh9Ge5eCuuE93OV//SujolB0y0eIlsfitJOHUq1xfoKY476uCyBpdQqLdvRMWW6Lklo+V7SypgIsOo6+nKAtpfEGuDXIM0VYVZLtAu6DpDGKm7l/b6dJN8UBC3bZrks95VwdQBWANPeSssy5TA2Yp/jNj0D+RmagrolNChh3BiilXmMpazKfMpyniUtFcHIddr6Ih9/zz+gq68A8PyDv58v/uU/RM3BGjgqnYbk47NqXoYoh4eHqC99K6vf9sXIquPwf/sw9up+f3zOL19D0zTMZrMevmRbWqWE3HK57GFXtnuWQZG1lpOTk76MGRCVKoUAFy5cWANVWXrLGNPbDBMRdnZ2+jxWq1UPhnI7yVBoCqrmusnnLCWzSmA4bkslRCvhaE43ZfC/VM/M15nvQam2mes7A8/cTvM58vVkScBShbPrOubzeX9NJVfJ1zq2y5bBXJYWLL8RGa6VHkHLcpdlylJu5bNSOt8oy57PnyUl7zbcE2ALoRS7XPdQA+WMWVazIHW+0iyZqL4/yKgxRZijEHSUoBJBgscYhfawXCww2nDp0mWaZpe6aXCh4+VX91HOUs23CEZTaaGxFoxmbiv2NJwsliyWx6yOltB2gMe3LeK7KEmiFV3XYpTCWNh3LQ0BkVlqfGlWKQQq1gnrbNYwm1Uslx1VbdFKsEbhXMdsvk3bxQei61oEiWqXAEpjaoOuonMEL4EuBLatxXWrBN+iB5L9g0MWyxUzY1DATj3n4vYOYTYjdA7XObZmcx586EHQiu2tbY5PFiwWS7z3rFYrjk9OaFdLVm3b64MfnxyjiR5bRSl88CgPdfJc5ENSIxWJg5EQMNpEDKlU9Iqp08BUhegJFulnRs/LR1B0CBZ+K3AzDjbzoGHv6GfZWj0HIUofWgMnxwva1ZIgHhUCVnve+65Hscpjrebc7oz96zc5PrhJt1oiwXHywGWOHnuYRpY88vTPMzvZp9IeoyOQqW2yHaQ9WlqiOXfB6vRytnu0TrO7m6QHtcLIEr96GUKLJqq/alWjrPDX5n+P/ZceiHBtK7Zlqxb8pw98FzvqFo0WfvDd38In3v7rYA927S1+5+73slMdUOsOrYUnP1bxzJNvxhjNfP4idX2EtrH9mWR71liFsYKxCm1651FoI7TqIZY3t7n1mYuYqqOqWmbVreg5rg5xmY1894a9M4xKBr5NS1111CbaX6pMh1VnGfbe7E0ubr820kSfy/DUE2/jr3/3n7ltOq09VR3tW9m6o6oddd2ytXMSoVMynm5qFw2sG49KxtwzOFRGeoPuaOJ6DwoEZaSQ+GBQHVJxYB+S1EAgAiAfkgRUBktJcmnhZxyFLYJPkkxeE04M4Ugnaaa0TOviElTyA0Aa4E4Be/qlAjcBg0ZQKO//jzKYCIfUFAgyo20N1HnMLpTqY7eFMf26Wm8zOkkS9W0pSfKqESR6rUDOnf7MxLmm8oIRHEqwZKI5nQkwZJAS6kFRD4eS5FCGPRkE5T5LsR5PIkU/JR4/nDtLh2cQlMub+z3r16PybHAxwZaPQ8H58xfptUpl0BQV6OOjeYfC9Jfcpi7uKmR9yCHPXwugqAeGSb2Owu5UVv2jlDbzHTp5YomqfINk2VgyrW9rITpmWpNeQwiuYbl4M3hN07xAZV6BkBw4ZTtaIURNitwugy+k3Ia8lQTEO/zeFW7Mvh730YvgFYolVy79f2iqpwi+68ugU9vVCXCGEFULJWSJs2iDNZtwAY2S5AE09dWVGl4M8bGIXj0VmqArnv6Cb8PVF2JDWSnkhw0/c/jdcYLYPcdDl38wjgd0bFtClJyVMBhmjxApSuNKsEQ1wyilK2KRENOLz9vZ/IQppF51kqrNS5UmTGqCb+KErng0Lu2L15+lbNfUqe9mkvS1DOPJ1byuJ/ZtSnsv22fse2D+V7hv6x+h9TB4H9uumgKlefBcpsuhTF8aFS9VyQ6+4qu58Z4PMLtxjTf/0A9Q+UE1ryxDLmxeLUHD2GbcuMxleTaBtzHonYJupfRUjhtMBdFLI5b1MAXMyrzH9TSVtizX1L4yxG9F7HSIrpKX9ugAS1XN4NSGuBRdEZRF1PoyqHic6ApRVVpGx1t52yeTMkfzd/RwDaCt7+PlK7+dt137B6egYVShDLx041t48fo3YHTLow/8bzx830eot3d45bd9GdUf+wYu7N7CaE/zn7yf69/0F9Av3eqlrHJdzedz6rpmNpsB9KAup8mqiiXEyeAsA7HZbMbh4eEauMySTCLSA7nt7W1msxnL5TIJq8R7Xkq5lXCsqiqqqmK5XPZ5ZamtDLdK4FRCq/y8lHAp23Yr1W1LFcycJsfn656Slsxgaqy6m8FaCdpyHeb2V0oBlnlnqFbCvQzpYJhMKY8p23R5f/J2KVmZ0+djxmqh5fM2tr02hpT5mc1eScfg+m7DPQG2oaJk7WadIvI9DXVobfoZS6NjJ9QHj9bJBXffuY2dVe89iOp1abVSiDie+czTnLvwJs6fP8+rV5/BuS6R6h30SaCyc7wExDl8qFDWUAPvevA+XnrxRV44PKBeCX4Z4Q3BIT7poGuNkQAhgiNB48lOAlTq3OaGVDR4bdja2sJaTV0ZVp2nnhl8suEVUuMs66nrunSNqTMdJEqEFQ+jNfGhWCxOcM7RzBrqukbcIuYFbM+26DQsF0vEe2ZNlKS7cv8DbG1twfUbeGC1alHeRxfg2kSX6+IARds6lKoIPhsrjCAppN68gn7WMTXb+ABqDRJVSbWJXad1TxwBEUXDk7zX/hGe9H+axX+4n3C0FzszGnYf/3ne/+LvwWxZzMmKnb2aCxcv8OTTL/Lsizfokp2u3Srwre/7JrQKNE3Dm9/0MC+q53j6+tMcHB7w8gffy8f+mz9Jk9rf1Zs13/Q/fgcXwwLoENUxr2yc6TICuiEoIYhFVRqHhup+Dl3D9u5FXFhhG4PWjtC+ymq1jPb3tGHpK6zV/EHzJ/mXD3wnL7/6NtSJcMk+z2+9+N08OPs0LiiUtSy/fMb2xUOO210OOc//yh/if979g3zN7MOxoL8x/T7bkGyKioBzdgAwa8sk2ZPUx3xnCMthfzxO0zpL5yqWfk7nKjpf0bkK5ys6b6I0krdx28WlK2wk9b/k2a03sl16iOvLGM8/QKMEjDII8oN0UfCakPeFDIfKfRkW5fgsLaSjlFqCSVG1UqFrN0gVZTtD2eZQCgHD6tiwYvYa3KTP46DWwdApQGToPb+RBTmMrKueqWF/f2wPffL+4qcE6T3SKZTREQil/ZKgY4REashH62k4U9pKyqDIjNY3gZ/bQa07WSry35oa1W0/zWeO5Qp14vI0/RlkbTxEkW49/9R5Sp3Y8bmVqLL4cUUVZ0iTY6Lieq/Or1RqGJ9vIXn14/MEEAXXS3uFAnwMtpwiZLGA0UTVOpWab7GehelMyUvzdlr/6Ed+JgKWkAFRVscDgkcSLJIQYt8nFjAJRieAKAIZSEICPbkmw9BMRgNATVQDtrZKEAd6leJe5VghEvsOpWF7RbSbmx7+eLpgijRZVVmT7XIO2wmoCHEdVdj6THkn78Z5H8kDclZV7l9SEvt+IjaVW6dOmh6uR5J92awynSaSs03RPh/REJq0DSt5kGiwNfT10os65+My4Onj8n61nk5iH5sWZN7wqv29w/7StIBM5AXr5920b5xuKs2/Wt8fZLvfv1Bv5tPyX0zn+4YEQ1BmGiopBmnYvP+sd/5Zkwnl92Zkwy9up+V4O5mWiGWQ/qeK7fX1Ik4LZkujakUwhtbPkpSlYI+uceWnvxfp7d3FxhHNVyTYrAL77ddy5L4yXXyEsFv1E0QpxKEWp+BUXt8EsUpJlfGAuJTqEhFe+Lpv5DP/5X/VH3v9Sz7IB/7Sny3eTQNoK/Mbl2+8LGHFprRlujFsKAfcKgkfBAwBC7rGGT2AKV1Fqf4RnBJlCXm7h1IZUA37RFUE7JBXv9/28UFVQ1y5ThkfjxN1p8N96bVPtJVe40QlZ0xr2ijJ87fKcdkzuBGMDVw2AWWfXstjp72Ea78uOuWoLViLWAuVZcUVmvAmHquvoitB1V/H/u6X0F3a45I1KHUAAY4Odlgc3M/8G74U9/f/HUopZrNZL6GUIVdeZim20l5XHpOXIDRLs2WglCFShjPAKa+hTdOswb2sQlkem9t3aWA/q8FukmQs40oYXYLofB1jIFVK2OXj8jmzlF2GYuO8cx4lNCufzSlnFWNV3zFoH6cvn6Wp53Ys5TmGlGPoXZavPD7XT74P5bH5mDLfMXAry3ovkO2eAJtvVwhQVzUaFWffAAS8BLQ20ZA+xE5cUUmxoMNLOfbBBl3cvuLIKpkao2ucagnS8uSTz/LBr3iUrb09nPf4kAltnAskCKHzhJVDX7DRMDwB7Tzn5tsY51EdWC+44Ani0UnqijBIX2kMgtC5jpUL+ORB0CUJNhgaj7YaU1Vsbe9yfOwxJlDbhpNW6Hygw+FDlCDLwDAEwfnoKfPkcEnwQmNrfBhmQspHra5rjo+OOTg85tyORev48DazGf7kBB881hhsU3Nj/yZ7F6+ArcBaTtqWznV4Bcoa0BqtDcYOOskiIY2TAkJUVfXpvmod7fL4YgZJIWhJ4sQq2p3DxFnNxFZRysSBoVJcMP+OL7U/zo8d/Az5w00Q5nsX+cxv+P8RlKFeHaH+9UewzwYOly379TLeCWVYqMC/+IW3xz6IFs49ucfJ8YLD/X0WJwteuP4BTv7qJS7/4VdRCo7mV/gnf+4fx45BGsDTqwKdOaK9q1ADb+HpfvvDfPva/gZ4szzH4XI31QX8ovkiDvwenqhS933/12/l2Z98dE3CSLzGu3WINMCjbHurlD6K6//RqpQlYDOAHRk6tFOdY6OgUmm/oFTo81BaFUCJ5KiE2G6U9BJCoiRWd+5E23ic5JFwafQ6O4jK+/LIeQr63O6XO+9THf4x8Nm0fuo3xjavYVAUg/N7zHytWcv69kSn5NdOKPHaPQaRYWCbn5OUYfa22MtkZfbWD6plSFfEZ+jSexamOEf2ALlhkJ7NSuV8RJKkez5fyOAiHZuN/JfwYGq931Z9kQigQspqbNewd5ySDPyPgcSp9MV6YJB4GafJpkB8EZfocOz7JEdOE+Dixal6m0p76j6V6T9wBiBRp+OZSjex706XakMeZz2mm/Z9Ph9ThqkHdKoOUFCq+N2uPm+7LxXuhtpcjjciJAikdgLKAFZQVlC2XBeo0nq1/qOO8boOqAqoA7oWVBVQjaDqgB4vZ4KqPHou0RPzXKAJmJlHzQTTeJQFpWOZ4lIGr85GCEpxrbuE/MpLyM8+Hd9TSlCo4iUYPzlx/BII+avZf0PLl2QaBOZnQqVvbAJW2QmaNjrOaUQvWv1jqiD2E1Co5OwqptUondaJ/Yidd+7S3NfESVapOPbbPHXtHYhA8+zTLL75kdQ81PDNzxMmKp1fLan8izi5CEqozfNctb+Xq3zbkB76iZa+MrSmn4gp8h7OV2z3afTa8UMaxerKg7CS+I71ipsPv5dXH/1ytl94cYBGVHhlEAaglUFU3D8ApvXtCnRVgKoJ+MVpqLUmwZVg1p1Dq9SOCjMp0YN4glRjkFWs6wl4Zfs4F82smBZtFmk9mlnRxqf08VxKZxgmsf2n53FYpt9n4QdCJErJiSRHZ8lkiEiaFBeNyIPclIfX9xVpfbYn7OKku/iH8TeiWRHlhQd3nuNkf4enPvpuHjXvZlt+pLevZoxZs3nmnOuly8YqkaU6bBZ4Mcb0trkyryi9WpYAKqspZpg2lsgaS57N53MuXLjQl+v4+JgQQu+4IUOcse3HErDlc5flgwgQ8/qUB9UMwEo4VjpryGlKGJih4dguXHlMeb2leuYm6dasbpvvVekRtQwlBB979ywhX3lfyvtTljFrGZZ55HKMnTaUdVRKA46v+U7DPQG2D3/4w31jzBVQXmym/lk3t67r/uKy3q0xhu3tbS5evEhd172hQO89wUskFyhC8FR1xaqD4IWnn3qWr/yqGRcvXsC3HVu2YrduuBGO6FwXoZtzuOWSCqiaBhQsThY0dc3e7g5Lv6RdLpN0VpSgIwGmfKM736ElEmfRhpXr8CIslytWqxU72/EtZIyJnksry87ODkdHS5ZtQBub4B10rScEYbZVsbU15+YtiwRJapsttq5QaLa3d1AqSvwp1WC0ZjabMZ/NuXl9Pz74Kn6plVI0s4b22OOcR2nFwdER8+0tPvHJT/Llv+4yznu2t7fZ2trm+PAIvGCNpbIVSISHaEXV1HT3P4y/735mT30KOTmJDRFN7B3FmeAMzkTA2jgLqHV6YCjob3KAEQ3zDmHpt/FsFzGKV3/6zbz5wlNceu812AX1n38RKGEGXFEJlMSk3CDP3kUTbqgK1B4oeEDtI3qfRTfrB43l4LBUBwL6gV5cHwY1Ku3LA8S1NDnpSkUHqwKLw3k/0AoJcA1mbHRSSwCbYKqI4v8bvmUw/i2K6z9+P+3H52tSQINBaimgUdq2RLtviiTRIyjrk60iOT1zqtR6fHLlnsUeRIMyapjAL2dhx2plZZxiEK/YBHrOkvYZA59N20zsU8W+sqOXb1e/b625Ta+X28X483R6OR33axruvAahhDt3+8t9hDFEkVHchKfGU+vhjPjx/imYMpUm20gLE2n9KM7fJt6N9k+l89x5mcu4jY39jPH46JAxI7iTY+5o3+twzGQVfDbneaOv83P1yinfqWshfgiz5IzSxfpUHGenI3/X07Jc7+OSt/kSLceeD33b7gFElnbLv5RmzTHHGngdVB377V5lkMG+Yu9QQ03aXnyjJrMGKZKAruLPND6u13FpqtF27dF1WlYB3SRPy8VxpvHo2mObnNYV2x4zS56Zm2QSoUnrs2ifU8+iDdXIb6JUS7XnhhtCbl5y6lNKhlTF9pD+s0lzd3FOKpxYuCyorxzaX54cXZcmlrW8yjR5/zitVmtv0s9Z+PdHX8Mvrt4L5+NE/cn5L1j/jnjovQNnyX0/2u8VbXgzrX/zsN8BTvo04kCdOm76HOIVKtkqHbQFxku1pkFQho9V/z08OrFDjaStMlDKkMqOpK2KtDlO24DNNoNNtOmrjUObZYJW2eRHCa3G0lsJ1loSwCLBZNLvtXlfxC6V6qGViOpt3w7QShGkJojGpW3fmai1kuCVb00/ke+9QXy0cxsn+k3KP55HSOskZ0Sx4sny9XEzfUBu16f7bPqCwHUe6Nf399/Kla2tHqCU3CHDtrGX0XEojfR3XdfbXcuwpfTemaFaKf21XC57ibEMbzIIysCnaRouXLjA9vY2JycneO+p65qu63q4N5YoyyFzFeAU7JpSHy7h1jg+C/iUeW9Sz8xqkvmYUqptDLfK40tYOJYoG0uLjY8twVYOGV6O1TvHdbBJyjQ7bsh2+DJAzfcqs6y8PVYdLSVY7zbcE2A7OjoqKsOkilJoPXghiVQ4ql4eHx/3De3w8LAvcGkUUOvozEBEsEazpTp+7zd9NfN5zc2b17GVxtr44Fhdc2l7j22nqVfCsQ/sbO0gx8c476m0Rgls1w121mCsZbY9x6BomhprV1SVxrcaSTd5Pp+xWC6jWkWamVLJ4K3tG4bgfVjzfpFvgtIabRTz+Yyj4+g5pDvYB23SbE9sKBkwOu9xhWj0weEhF++/PzWm1HjSA19XNUpFTxbL5ZLdWYJ7WlNVOqrZVlGibXd7h/39g3icMVhb9TrQANZYQmVxrqOqKpxzHP2Xf5yX/89/Kl7LjWu85U/858ye/ASiB+k1yA+LYG0V7eQVD5BOTiliXzZ1fZUiWxNWStGom8zVUyzkMQDe8k1Pcun3XuPBdz3Pydbdu8CdCrscvib5bAphpfjR2dednagB/mvgFe7s4/Eu4J3rcXInxz0GfNFrc12vT7fvDQi3gyR3st7HqbuHM/ey/17LdzfQ5fUq31kfpc8JjBjDgLycGOybcv9EXJGuBwC6AAA6DZyKuFJ6UhVAgXR+VUsBkFOcCuuAoS9rPD/pPKXqkFIq7RuDDYZzqiGf7NFWchz5OtM50ut6XT0pxSHJW2T8JmUo0nuQzIPGfO0M9UJ//HAcueyU58v3TkGZjlzeob56sqKKfUUevcZq0Sb6+uSMdLkOGaVN5+mbs8rfN+lfnMP9KvIqjsmmaIcZm7IOcjpBUOt1unYdo/Xi2D7Lfj2Wceq9nifKUMSJIKcJnUa6pGbfKXynEafSPkPo4rpvk+3GViFuiA+dXlsXp9JyU/ywjB6QFZLjXY5/gyCWGiSsyl8euPcSWGZ9P1kqxOTtQVqENPDGnE6LoTiGmH8fn7aLybM4iOgLyzvf9wm+8rd++PWvp88m3OFtzRO6uSFLuRzFZejKKE0Qi+vVdD2GBbE/G58Fu2vpdB1BgQITHHLzBPEhdl+LydW+74UQAninei/OwWt88ursXIQUwdt+PZvn8MFEwNGbv7DJbqpdM5XRL5NtOp88Qg+eoU2/HoKBpo7b3uCcofaa9ih6hRU9GCK/83s0SE317T9LUtkBZK0BLRNQswForUlg6TZBK98vVZK4UqWzKyswt8h23Ut8aeWwbtmDqwiv0rr53LwjzpbGqhBRONHRy3cX72noNH5VQCyX77XubeMGnxxBeROBVUj2dbPKewGuJEst9S/3tCwlus/63SnQegODCg7Eo5ItTN/NwRnwoMOSZn6TxZUHItzt1Fp5z0vHQw891EutZWGdLLVUVdUpFUagdzKQx6/OOVarVbKTHo3yZ4cGeX+2Y55tteU8thLgg3UYloFeFjo6OTnh4OAg2ZrzzGazHsyN1aVLiatS0m6scjmlplmqapbSYzDArxKslefJyzKv0uvn2BlFvsbMc6YcL4xVQPN9Km22jctQSuuV8Ky8hyV/KZe5/sbSZznv7CU1S/2VjjHKayvhYGkz7m7CPQG2+XzeN4S2bTFG431UITxdkIFWlhc81gH23uEAJQpvwGqHd4LrAt4H6roCFfWsBYdWmnloOD5esqgtemahOkHXlqZpowRbpVF6G6c8h+0JO02FnVna1QInHUoL1hjOnd/l/Pldrl69ysHBQVFyj0tCWIMOr8HoCoXqxVLj9XhMbTA1GBv4/7P35/G2JNldH/pdEZG59z7THavqVlV3VXf1oB6klrolIYRAA4OE+AC2sI0MZnrm8YyR4RlhBtvYlgF/bGzrY2NAH+BjzMMY7AcPPVliMMJCoBkNqNVSt3qoHqqrumu4Vfeee6a9MzMi1vsjInLHzrPPrXtvVw/iOc5nn8yMjIiMjIyMjPWL31pLTGBvf5d2vkBMw+VLV7h1eIuzsyVdPxB0RtRAlIiPsFx6bt25tdEuURhBNZWIOJvihoj2kajKrJ1jEFYaeeX2LS6bqzTtjMXuLkX6cFnvO2ZapLEOIxaNA6vHn0jgWp5txKvXefY7/yqXv/97ESPJdl6mb2vp5Pl4wxPqOPhv/hRBTKagA7u8nxBneC5x67qje+oRDAOX+kNAMNEzvHBnXKWB9Yu0u7MYJ2bOuUTPj0rwgSACl+YcHV/i9p1rqAqDb2j9WfpoyeTjpbne0w9aNgAVtUxkk5CiEXxoCYOl+RXJn6kKxM6NplSSgATqBH7QwPO5stsEpAkgUJqOSsDNrP0qblMAFgM0gOqmPFaz9KiEwLJfH1OONadnnUDrqusoA5f9IueOcQJ1dsbeQZVa13OE+lwlryYhWjfbrW629UUvOHeu8hvzknXaSlDeKORV9stxeSZms9xaHaRukloYLkm2ln3upqY5plL2RXOk8wXrxjndSHOuDF3fQHlftpcn04hz8RfmnwjQ9bFObmyEP6qy1gLXmmFbHoZyfn/dNYVY0RxrgXXcL+k3yti03/IvXfgCmHD/SxWKkFOzR+5lP2zJN2U3Pmj8lCH5+Qg1w9pU2waYb4m3F6Sfxr0W++Y1UMu+r5ClYA/iSapxwHoULd/w9cuppqGMgceH+3z0/W/hsU/8ORZHP5/mSwqqMaleI8RRQAM/+GzTGDRmD2plrM1pbLavOzIBINvPS1UoZmBA0iK1FPVnHRdVyydcSHZ6dSxfxzhjzHhbmlHfGKUynt4QsGv1vFF1z45qegGbVf7WBtdjnQ9LkBmBOWIiatrRPlYQh1nMaF+3h5/t091RhpdWyQFDsWllijpgs2H4/YFAKxSMjsynNQOqeFZXztmzmhVmVa1KmIAnZwasWybWVRtwOyT2ootoGFA/VCqJNbtLMQ1rxlXFshq3nyXgaqM18jx/BLFi8kCuavBdJAyOoW8Iwy7RX01AVrEf7DPjKrgRwEpOLcya0ZVZV4XVtWZcSbazvQau0rSimrSN78Q9/C4Csj6fITtfSb+IRI/RgESf9ss2DIjv0zb0mLLvV5ihx4UeCT3ih5xnwASPUY+EARs94nskenToiP0KfI8OHTqssBow+XqOSCOKIxL7Ja1NJJcSEsNsl8PTL8XZwLVL76dp4KV3/nI+8g3fytHx6wkhWdre627zzeEDHLz97SOYVgA1SDJi0YobNdOySaSjo6MRROm6jtPT01GeL0CX936MOz09RVW5ffv2iFl0XZIDd3Z2WCwWNE3DfD4f2VDACGZ578e8xdHAcrkc09U23GqArf7VgFathgprAKlmbtX4ymjOqjo/dpMKpJuqc5ZtAaKKXTlYayaWMrapedbOCGrAqr7Hgg3V7VXnrZ2DTNVO67Tb1EfretfXmdrIK3H1dacqqdvYj68WHghge+jaNWRCW/S+2PAqD8sS/DB+NMWsDUbWHbgE1YggiCbvlQvpieMHN1Mxh1Nu3XqJ+XxGNwjdI9c4XBzx8WefQb1ihsjh0QmPX9tnp3W8FHq6maUPPbP5nP2DfRaLnfEhxZj0cg7v3GG5OmXoe/b29vDdKrllnbX0qyWqayqjQlKtrDp06fRN42hnjrad41xL152yXB1i3IyjoxP29nfzHEYzSq7J0QNK0zYs5gsgoeUxOIIosza9tHcOj7Cm6HpDQDDWcXZ2wvJsibmyy8t3biPzlmZnzmw2S9ROY5jNWkxj8V0C5VQV4yyubfA3XkfuTeOzCA8/yiu/Y9OW2GcjnPEQZx3c7B7ePLG4IEP90eqr+FL1O/CbZ9/HFz/8/vEzWlgJkvVGx/WgPFM8R+sX3dhPxesmM+MH87bPwFa5htGx7L/zl347z3zkqXtohYLUnP8e39P3+eReEn2Owr1OKLal0y37n+8Jyi+FIJWtxko9Cxj7+EX7G+sgY//evp+O16Dl1nP5xAZkJ9NzFdBaprtj3SS/Q2zc07lrTO+H6f1UAPCW/Jtx6+tIna66j/V968abKtOypI6f1vd8ns3r6ObcfvJ8ZHKvBRiW8Zysx7yNdOv9btVxeHgbjcm4tSrZAL7SxEhTPBtmgVpG+gigFeRQ9ovgnQGAFBdHQb3UJxURx7IEoEyqUyUgxgpTr64Lqb6anAPU14+xXItkgzJ7ziUYYnSQbVUGtVmtL6nBJO+FAtGtPRPGwgYxOU3y0EuOL14Mo1o0JC+HsZwrXg7VEtVB9oBIdGzY2/pcBgmIBMT4/AuI5H3rkVlBc/p0zgwYExAZwHiEAcRjSn5Jx2KGVI54jBlABkSGxEoxHqXHuYCzStPGlM6m/GM5DPk7mxDxqGmSayRpQZRnrpqAmwScx413A8rEvLxTCUQCzcChIp7sdCGlV03zyAQ2RXxIQNOQVUbKmGIAMYmRXzQz0PxO5zpaa/L1GBeEEsuh6HqDHfOtx7sQEhOibVK/KAIGrCf0Jb5mT0yFopcf+x08/9QfA+D48BL9C4dc+4WfYtmfbrXN44OCbfHRMETBuPkIWLnZzghmRUnG2sXNUNNg3JygFmy2R2XbBC7RgDRgWqKrjK/LupwCSsXqeG2IfW2Efeql8MGcpiS28IZq4AbbajO+BpuwkS7HzWxkYW1mWG2qCEpRFczlYCpV3RG00i2gFZlxJQm4eg1tAN9z6yioZoZdZ/C9ZSjMq9PCwHL4PrPtBjey7BJ7LjOwsrfWc8yu8UtUmFdrerBKAa9YyxnraS8PxMb6fAZVkkfqgNkAsRILy0SPaAawQgKlTBjWQFYcMEOHjUMCs4YOG3rMsMIRcxoPocfGsC4jA1omDFgN4HvwPY1RtO9oRGHosgfkZKemBm2mbKYyRtSOHGANaNSsrykQAjAFaUqaovZYp6ttYxnnElkDwKRxsGY8Wbvkocs/BYC1DSEoj33op/mVwy1ktstzj3wZUSxPHX2Ig3kkNDsMw0Df9yNoVcg/TdOMGnelbiGEsX4hBFar1YYjg5KmVmUsjgtEhNlsRtM07O/vj/dXwLzCZKvburRlOa7T1O1Y202r1RdrUKwGgGr1yvrZ1ADR1JvoFNCrWXNT1lkB12rwyjk3svm2Mb1MhfPUcdPrTW3hbeubU8CulFez+oottc3+Y8f2qNVLi5poiTPGjOrENahY17uOe5DwQADbwd4eUZOHSI2REH1mO2Vqn0hWpVx7LpNcWe+HjRdznCTJupMYES41gnGOgBLCgDFzduYNNx65iliDN5GTYckqeOazOXbhODq9hTRzzMoRZ5bZ4irNww/zULjD2dmKp17/JE3jMNlduLWpbN8PBN+Nni+xgg8D3Z1TFjstXgeUBZJX6aDZAAtLp5+1C6w5BQJRPdYIhpaAQUS5deuQru9QMdhmhh8EIy2C5axb8chsJ2mheY+GyDJ6MAYfIxqV2aLBNobOD0nudAbXtIiPLPuB24dH7Fy9ikd4/uWb3Lj+cF6QNYi1RJSgimZWmjGG/Wc+ij07ISzWjLfdH/unPPw//vdJBdUZ1Ic06SwCYCVgpUlkWeHU/Avj91OjYkWyy/tqAqyCEeX0638Zp3/034a2wbxyi8Uf/jO4p5+jHwInq4GgYI1jx8Bv/5ZfzZVFJPied7ztzZyeHnFyfMTRK7d59iv20N/wOCFajJSB0eD6Ll1TGbejgFfiyMJkmlIj2fscWoSAiMSek+6A49V1PvQDbyP0LhuzLvnW+6qwc3bKU498mCKUF6FaiyB6DlGaCtBj4kpQXwN6UVso3s1GQEE276m+SrlEfoZaVWFde4XsTXANg+iYVyB5fRyPK0FYgJjt5uT+MbZMFtS1AgDWhtG1uodSXkx9RIrAvp5dSTmWtK+hlJPoGIbyQckUDY159S73P42ohjxBSnmswOte9xjPPfsc/WrJpYNdnnj0GhaPEbh+7RpNKyktGYQQ5ZmPfZxFaxMAYAzXHnqE28vAEA2gySfCCCKlKWjx+IeGnGadzohmk3ZpAjeutUpIjlqMJN+IOY8x6/YxGRg2hqqNy6OR8Vg4722rponfLdQfvOnkoJ7ETcPU4KpqhH6Jf/njDCcvImKwzR5Xn3oXJ9pkg8ubE7ltH+BpfXVMm70za/Z8lpkWktXYwxbbD9OVv/repvHT+6/zTic59T0A46RgY5IrSfuYzDyxyOiYRcY3qapPNIgYEL953ZjypvfDoOor0waBT9z6BP/4B79/dOkOKf1c4YYPXD/rEQkQI1YN1uZ7iKA0RG0I2hJoiMyI2uC1JWo+psVHS9AWpU1pcx5PQ9SWQEuIDTGfj9rgSWWGmPL52KTtWHZ13RwXdEbMx/pg05jPOAgdIj2GHqRHWGGlTyCS7RE3IPSIpPPGeBY7hsXCYmVA7IChyyBVAqz+3d//+5jNoG154O2v+lVfhXOyMXGuV7zLCnpZ6JuqZ8CasT/tx1vfu6oPll9jGyw2MZtCPh83x536HZq+W3Wd6vht+WFTRWZapzrdhqpN0UCYeBgTEZyk1XUrFombajMGQ2ObbDoqeQkMYvHO4K0ZQSoKU0oWnPJOIo5WPpZUne1sg22lxVNgBTaN+QtbK6cnG3OfdR/HN1cQAmIiH/jSv7UGtTJgpiaXJQ8qJOh2j4JTttUFHgWNjTRWMXZYg1auGwHZYteqVhOcMrWSWmDcNMg+2rjKjKvm88ssVg+xN4ReiL3Fd5awcsRs98oPljg4wpC8qUef1Di1qIlmcD8xr5LHW61YVxGT1AU1A1aln+dJ+cjEqgGselvmhvfy+3yGDAwJed5GHIGrBFqFDfbVyKaKHpPZVzYmIMuGAeO7FDesMEOH8asUHxJYJTGBVSas2VmJlRXSvh8SIJZBK6shz//OG7GH9bgzBUbG26uAjpplVPan7Kd6W49nUwCsBhvGtBXQftEYW5c/DTXwUXtVLeemNrSm6oHbmEfTuWaMkaBzToZ34UzHvnv/enzOZRdA6rnnnmMYBtzTH8R7z9PVtbuu2wBPnHOcnp7Sti3GmFHdc01u2QQIyzVqZwC1Af7i/bNt23OgyxRMq+erJUznyXUfmIaEk6xtpd2LemJdbm1XbPo8yjMq91uAw23fzrovln5Wq8KW+yyLOIWRVr6x9fOvHSbUfaYGb0vdp44Kattr9ZzAOTeqmtbMtfL+1aax6nIvsrdW+k7pP3U7vFr7XxQeaGb6Fe96M8fHR5wtV/hh4PT0DNs0OLemDIYQx8ZUkt2uEAN9KDTNFB9DTFhH6YxJNqAJK87OlrTtbJwEOmk52FvQWME5y/7eglu3buNXK8xiF58f1M7uLrEfWMz3aGZ7hKM7nKxOUYS2nY3AT/ARRLh0aR/rJKu6BmazGZGA1RbfrfAa8THQ+4HVMhk0ZIswWe63aVts4/BhyY0bN3j58A6971ESc0xC8rbazGcgBnEW01g+9KEPEwfPTjtLq7kqGLEs5nNWp0uSkGiBQEQQawhR6QePsY7js1PurFZoY/nYM8/wyCOPIMYibZMxjCRoxhjApAFksTzlS/+LP8b7f/e/x/DIo+z+8x/m0T/7n+CWpxl5NjhrqLtXmrAzotBQfwTWiLJmYEVI4GuMmwNKuLRD9yd+D84JSIDrB/Tf+cfgr383GqAJioSIMQ6vng+/553stAkgufnQdbzv6VYrlienHOuK3j+BNEUIFcyPfpz9Vw4RPM5I8qIjAWMAjSRsMNXbAJEZuEtEmuTEgYgxc0Q7tLvDD33kG/nwK7+M439wGe3/JVcV+/+z8L73bR7/ZNGdkjWwLLX9qrIgQDnWcayrw8hWKvmAKUOs3pZ058+vgdaaDVXqMuYZ59drcHZaznieijW1weCqjydsz7EupR7b651YvzKWUZcHQBjQ7gRijxihcS08vQPWomy21/n7m95T2cq6jUq8KlSvqkzacV0/ufjcxn3nMc1I1TZr2caY0mZS1S2XPbLd6n6QzkeUWJyiRCFEg0aS8eFsaDgG1iow2eB68KBqiUGIXiCzuGIkeyROXoZDEM5OO+7c+Z3EYEgsKwvqIFo+EA2SGViaGVixHPP5YWEJiVFl8BgZMJK21qxoOEqgVAGn6DHiM+BV0g/5fAG6Biw5jSRwK5XRg3ZY60EHoMPSY61HtAPpMDKArjB4oCNoh6J4IBDoNNKJMliIjd14X0sfco3j4Uce4trVq9n2K8Tg0/c4T8i/7d/7fWNfqn+wgTnfNRijiGyudG+0azXZr22PFKHmIqFwenwR0AabE9ptq+zbALa0OGsSgCSOkNUBk6pfYlthWqLJ29EL4PpYi9rgBjOqeA5cpwnY5MWbNWC19i7YjHE6AlXNWHYCsAot6QFDtu244VHwHLj0Kh4FzYCxr2DtgBTbVsZjXALckifBGrjSDSPwahaIMxjnse1qu5rg5xu4GtIv9hbtJdkHPDOEwSTAqrcMgyMODWFo8IPLts3Wds7WHgzXhtvTMlkBsMzIskqOvdgKYq1fzFy5cyAW3JWN9XkN64XKtEgZqm1RG0ygEtEjIYNXfhiBJvHdqEIovkdG0KpDhg7/yMMcfsu/krypGzDLU25853+Fu3UThn4EsyQzuSQMkLcmejQMzLLa2Ww2S6SFvN1YkKpCPQZNx6MahJqC8cAGGLUNkJ/mK8cBmIJNYytnmai+7nTxrb6PKdA1XYysw0ULjDXgVi+K1OVNx+JpO03vu3wb1p4mHU+f/rs83/1GjAw8ufjrPNb+nXNlFftipX3qOkxVAkWELjzE+47+Al18DIBrsx/liy//Z5gK3Cjlnp6ejg4Jyj3VwE4pM8ZI13UbYFvd5kVdsL7fEretbevv4mw223jGdTtv64vTb+lFC0B3s412ESg3LXcaX99f3ebT73+dZ9pHp/2iZoaV51Lia0CuXpCq268+X/LW7Qybc4c6/bTeBSQr163Bv5o1V9+zc24rwFa3Q21Trgbxpt5d7yU80AzhqScfRvUGMSbGRGqcvHCeQYsEsCV0M2hS/1gulyxXPV3XE2NCWJfLjpOzZWpsKiqlXzCfz7DWjC52jToaI4hGBt/TDx1Df0YceobMyvLDwN7uHsvnj+jPlnSrwNlpz3ByhLMNl/evMF/MWfWBYUiDwe3bR4gpA1sSeKzTVEcRFnOHxAQGIgktJ9NG6w6QhDRD4xyztsUawyu3buFj/nRbm5lTeaUCEJvsmFlrGYKnMYYri13mbcOdrsMixNye3gf84BGJo02NpOYAiqLiuHN6jFWhHyIRg53NmC8WuKYFY7DNenAJCM41XP7w+3nH7/9Wur5Lk31AJdUrxJjscbD5kpUQ4/rjkELKX1YMRCQDqUVYTgyvqBHzxOPQVF1QBH3kIbo/ltRTDdmZbA7//H46qQG+7svuJ8c9hRs8x943HKF9mkipsp5UlVBNqkabUvUkK+dR5cJzUMqenl+Xtz6//fqqm+k3z8lm3Ea9i+2pLdcuOLhupq+vfbdzr1avV2uzV2uXc9eetItO63WuXe5Wt812MdPnWawsT9Pnem0+r9eyXdKotXVCX9plaiOEzbLOtcu5a+c8FzyTL/gwbZttv4vsqNzNUPCrGRG+17yfryBbfvaCeOFib773cv6icxfEq1gU+3kzE/bZCM8cAUcXn599+T0UsuG8oorOcTH+GAQd4zToup2Njob0R2/SpRxT/SRdJ9Nrzz8nU29TmuKFWiQxMCUDFRvghFnvaw1Y1NvPJMiEbTUBr6YeBe3Uo6CJFaAVMG7A2G6DpVXbwNoKjm2AYQW0yuyuJtfpwbVOXoOQnVQMgTjE5Fxi2CUuldgnJxfaG3xnCH2D7zPzanAEn37JoL/N6teVzauyJQNXFBVBkz97qTNoUZPMfSVVq8Sx2WeAjUFyCmLdTyjj2/2Eu15H1++KAERGJy3l3cpx44KK5DQoSFLXT6YeFEw+JqZ0KGKqc/m8mOwYx2QZzORrCujMojcubbyjcusIfe5limFfNfmkkfwu5kGgGP416dklQ20tKiazrPOLbarnmJ9p3NvDtrUQOiO++RK7f/f7z6lxQZEFMoBh1+N8Lcj3fT8KxdvYtFMwpOS/CBwr6WowZhuDq4BEdf76ulNb4tM6TetZwhRAmwIq2wC2ixYytoFi9fVqcG0KbMWoRDUgzRp8Fgc0+CjJ1IHM8DGxxF/qfzXPr34zoESd89HTP4hlYM99KNkyjI7j172Nlduh+djTSLdETIuqSyrmvkFiS1EtF9MCLbeGrxnBNYBXuq/h5upreXT3hzbutQZN6udZA1v1ViQZsx+GYcO2WgFiCkBWm6wqbbWt7es+OQV86zpOn9+0TlP23lQtFDZBqxqorEGgGhyrn3dhb5U61uYCCjBW96fSrjXbfdt7U1+/ZsSX7RQsq9uyXLd+BtuuVd9nAcFqJxDFG6z3fgS96mdRALa6PWvWWg3IFvCsXKcGAAtbsWawFbD/fsMDAWwGj23WKH1Nl4whedfRRogRUE82TYVbOPYWDtWd3ACBEHez8Jm/BKrJ/sZwRvQnGIrObrKd2jQO1YAPPWerE6wTnIvcfOkldmaXWC47FvNddpoTbp2ccnBpn0++9xluXJsTJRBi8sqJnKISQCLWCK1rMTYjvHlVR216APOZQzTiu1NefGHJpUWEcBVCJPiwnlQiqG2wTcNisYO1x7jZHOM9QxRWfoVqwBnh6uXLnK16zlYDu41DfTIqG/yQV5QcwzBg7Zy+84i1GGMJKlhjiAawDnVCwOMEojVYr1y/eo0Xnn+J+XwP2zTMF3tJlVR04+Utqw3OtczmM47PTlDAkTyilq4fNIxsr8a2aEysQyQpU6oqURVbOinlxYzU9BGRMgBJmmA88yz25JSwuzNOpOQDH8F+51/FNi2rQRlixBpLA3zD17ybgx1B1fP6J17H2dkp/TBwenrK8dERn3zicT7+675uvF6zPOPLv+evsTusgJAmLMamlVwpqlcGFZMYKTJDmwN6b2nnOwzdCbO9y2joCf3L9MszfvrFb+XFl385GMW2PnmnEviSh/8p737kH2Kd4mNWhTVC10PTzFBx/NC/+K186sV30H+sHY1L77U/x97iGUKM+KhEEYI8xqn7ZcRsZ04M7OlP0nBEmnABo723pEYWzJyja19F3G0oDKP54SdYHH4MWK/GQlY70/EwxUmJKzPbFPJQuZG2OpEmazVFaOO8bMTXdUiozfpaupF/W9z5443yR2cVOvalTQP6Vb2qD0zjGmazGaenS0JI6upN45JDjdxIs7bNNiTZCN2qq4oUjGsZPGM71fOszTlXsZo1ja/udQqEjQVtaZP6WRRAbJJ0oy1k3RYlfn16s53q6qkyqU496cz3G0iMqwh4SfFRkkZsZl0lI+tStEHQAKIGDQIlb06DlrzkvJI1f6Uy1J7zBKrzOX9cp/m8BNFNo+yO9O7WcfW+TULTRec2t9vKrsvN5VjZTCPrrcjWVxXqZ73lNSoH216xzfu/y/kNdOiCfBfF38+5+ypDLohne6jl/9JuF4Vpmm1p9VX281Yn283zdpLu1fv+q+IUU4+CBVCa2LM651HQabJflYEn09S2sbaVeb7sDRXEsWxdG363VZx51Tv5rIbY115TJXlmXSW2lfbZ22qXAKzQW0Jn8X22dzVk21eZharREYrNKyoAqwBWyBrAKsBJAUsy8LkBYm4A2TL91K/DvQJXny2QsFy/gFKlnhvAb72tz6/jilfnMS6PiaP35HLe6Dq+ihvHYWH0JD0t7zPDg4VtjXi34eHeg44bCQoHM3jTZcpKX7KvmRcE86L/aHOzpIl5BSgTBCTRosd8o7mOtGKONg3abjp+kNFO4XYTEjUbqY7bxvzaBl5N00/L3XbdWtC/KNTgRZr3GMQ0xGhRHKpu3Ipp07spDh8EhgYfDGJ28NGNJhFUZxsmFFRdNrXgkikETbYNo6ZrxLyPuswkd2nBqbDPi8GSvJ8YmSbPx80oU2erkgkwHefcD9px1/k+fPTtm6fuTJJOCT/3SADqwtUNAKawjWD9rGvsoVZFrNl8NeOoBraK+mXNVippalBmynCcMpjqPlQDbBf18Skwug0QrdlVpS4FINzG5NzGbtvGPKvz1PW823swfQenLHfYBLVqFuCUsTZl202xoxoUrOte1E9LmTUgWNezZrSVtqzTb1MBLgBb6T/FQUbNpHPObdzX/YYHAtiOb7/EYr5IqGLbYEh2zVQV9QH1gTB2SNn4UgTNKLoRNCqNyeqHYtDoiSEQiESbBu2mcTiXH1aMNE3L4eEhMUbms5b5vGG+07A3LFieepZ9z0c+8AHsfJ/H9vZ4KXquvO4xwslN+m6FEUMIcbS0pUE52HM8duMK165dpnEW71cJDNKy6qJoCDRGcBZ2d2YwoupCiGtjiDHG0ZtJjIHV8iyXFTBisqokiULdOBQhxhRvjKVtZsSgmJkdQaBaT3roA7bJVFxNrLZ5MyNEJfaeg3bBTjOjdQ5VcK6hnS1wrsHYhohHJel4F3R/5hrmi0Wyl2bMRiczeaWrfEd9PcioonVnn34YNa/WkV6qUIwZG8Gosofn8nf9TT74+38X/XOXWH5/z+7f/IfMbv5z3M4u4iF0yVCljZEnr1/j2kGEMPAWEfphxenpKSfHJ9y6+TIf/Jovx1WjuC5m/PRv+/0P0sXvGt7IRwHwh44X//pjiIGP9L+cpz/1VWnSlSdpm1tF3gLzqyvufNdDY1lLvpabF13obVC0s5b8uu1p6smwAl8KvCEdnl79Mrj6Zeng1YTf+4l7rfLdb7mvxTVeizI/2/kuis8AFoHkstxfcOy3/Kbxd8vzIGWsbXt/7oOb/NrJsX2V43tJs+24eZV0ljUrYyPcfXL7uWzGzy8cMQn1OHa/vwfN96D5X6sb3uJRcCvANFEfrNlS621mWFVA1xSY2gZgnQPFRtXFKu7zqCmoEeJgUJ/YVdEbdCWEwaJDg/aSzvdC6A0xA1exN8TOEocMZlVqg1Gz3SvWAFYBrYq9qzXryow2EQvTR0eQRtgEeSrgqpZjayD2buFzpY29UT/drGudZgx6HvkZ3wVd78d0LGVhQ/O5qAkIiAoRFl96THN1YN69iEik2BkVm5hbae64tuNaVPMLQi1kgZJ8DTKrs64PWt1CBo1yicWpmhoIb3qsQsoUOVkx+8f/PN9T0r4xmpxkECIhq3SLJsccBQPUDDhpCBl0imPdUMVoTlOE25zOmnUZonkBOmYnHzEtXhNjsompikUy8UySHCOkuuS26a/uMfz+37ApxP/tf0b8M3/zwu6wjf11EQNsG1AGsHrb23j2f/guYjMDD+5Tz7P7A/+cINeI+YOZ3rMEFkGT9sUh2ibyQJyDNGhs0TDHxh0Y5iDJfmfUhhgdmoGpBERlsEqbfOxQbdZgFMXsQWJEKzYDUTaDT+W4yDoZoKqR2y+oD+UDhgJYF5bkeKyjjIKkcX8NMKf00cyJdrFewHMwa56naY7wu7usHnvdxtxn/qkPc/0H/jeM8YgOGBOxJiYzDqQf2nMnfgPP97+DdQMHrrT/YqMfFpClttNVAK8alKmBpKlNzmEYRgYUbKoJXwT+1O9PuW4BZ6YMuJodV+owBdRq1mO5Vs0ubNt2g+lV6lnbGSv5ajZmXVbNjqvj6/xTsHkba28bkFef28aWq4Gpup51G03ZhhcB43VZBVALFVhfq/8W23sFKKtZcJuszWzCKzs7qI+n7Vr3lRrAvd/wYADbrRc5EUkfE0m2ZozNoExMDg7EWkQM1jpiDIiAc5ZhCMxm82xAWbDzWcpbGBk+EIYBkbTwHmNAaLLXJcvDDz0EscP0Kx7eb9hz+xzMld32iOU88jALFM9eO/ChFz4CreXOnRd4w5XL+K4Djcxbw9WDHWbGM/TC3tzx2MPXuX79Ent7C2wrxODpuo6u61h1K4jJu6kTmM9m2c15UY3N1ML84pWOB+CMQcSyVv1SIgH1kb3FTv5QFq9aJJt0SLLxLsnhQ4ipEy+XS06OT3GXdghe8AHOVl1xmkVrHA9fvsbx8SlW4dbLL/OQu5FefOuw2XuLGk1233xCdhVATLaRpmumfp4s5tdyowNObQ2ke04rXmtEOU+M8n2brHaKgGsc5uplfvi3/GFO/9Gj+D+5gCjc5H/kSvOruOH/Y1argcSASRMtH4Zs4Du9AP2Q2keMYJuGpq9di6Zw6dPPsHtymOuQafX5fkbF11hebEl2VjS9nMEPNLMZxEAMK+Kwwpl0A7/4yq/k5NOXufm/PHbumncNHjhgvZpzt3f2jPXkvU5bbDmdiweuAa/7jJZVP79BL9i/W7qt8bol7h7yF9Do1UCmAAwXpJ/G3e3cNO4i8CwzwD4vwegaMCq/Gsiaxm/b30ijr3L+Ho5rxtaDBtXN96/0ibs180X9JpL6w3QIKkLfuTJfZcb+QI9az+errx2ruI1qTDJNQaTpfsmzTeAGNlSztwrlUu3fpR4PHPScR8FN74ETG1cjyBQrtb8qX22IfQJ41Xk2QKymZldpBrt0bbS9xH8+gatAsm81COpJqoFdsnM1ZHtXOjTEXoidWYNXg0M7S/CW2FviYEcGVgyOENe2/KJKMtJfjLNjiCYJrVEETGFcZbaVkRHMWqurypqBKWwCWvDq3cYA889KE54PlUy+2cHzQKABYUhASkwG3ClG3IOHYlw9JDtYhGR0XYZ+tFtl/DDawTJDhxn67I1whfQr6JbYfoX1PWZ1humWON9juiW6PMV3M55++v+D6u5Yt3b+Md7xtn8d0FGgKfavYC2I1cdFeCtCiqrSxwW/0Pyn3LZfidGON/m/wuP+u1Gx/Oyb/xxH73gntLD8ics8/lN/kSdf+J6t6knb7EjV8bV9qDpPzW7ZeCyV4FkL4KrKya/9ck7/w98JjUOOT9n7D/8izfs+unHtWlCvjYWr6mj4vITadEwdXwuZBSBQwLg5xsxAmvGnavEekDWjybl5Ut0zbkxv7AwxLSJtsg+olq5XVj/xKeJXvzuBUR+7RfiBx1H94yhtBqTydQrIlZ3JMDKlCgiWAamY2FOIgViAqgJEZZrfBwz82vWEdeCAD/F/5Jt/lfcmTLZfcKEAUEX9l6yyS2Y9ZrXvcT/PcfI4T/llRx2aPc2mJs7zq4Zkv64BbSSDVQKt5GNBG4O2BlqDOoPOLDT2/ALfdFvG0VH6OXd39x06HqXj0S1NpdinVzz+E/8AYINtVNhE5T2+Gj9MY5e81H0TVpa8ce//xZWd5zZAnWPzAgABAABJREFUn5pZtu2drN+nab5yrgAyNcg0BcGm4E891tRAVck/ZattA6um16qZaHXewqSqWWzTMbCuR6nnVFXZez+CdInks5bPa0+e9f1tA+GmoOM2Zlx9XLPKpqqyddvWoFVtx27qyKCu64bjjmocLzhLCIG2bTcAuJpJV/pFbQOwOECovxeFATftDzWge7/hgQC2oe9QwIqhGHHWUDpWUkGTqEnN0PfpxYjK0CnGWIbVMb0m0GR1VvLp6Gk0BMXZxOqy0jAMPYeHr6DqwLY8+/GfJwwdj+x06K5Fr18hvuEKw8pjo0Fi4EnreKs7o/MR866nUHpkeJnXP7rDlb23MQyes7Njum5F3/dcv3aFxhlmziJG6GLg5PSEl19+OTHmgnLlylVed+MhXONAiv7+uuM31uCszbq6inOWnfmcpm3ph47VUokKQYV4tuL08A6PPXqJ45Ob9MMSYYYRJRrBSwKWYP3yWOsw1nG2XKGD4qPh4PFHWLYNt1dL8BGCErqOxlg+/dynuHrtGs5aFju7WJtXAkLEugZpYbVaElHE2uQAQpRIzM+CcWVvKiNOJzCpLWovKmskuiQNSmLzReXgYI+jX/YrWV59CDXg/top4S8lHedDfjt35FspNivSBZX//LZB7iQh0nyqXFnHdPqXILRNWQpEiDT9WxLIN9q/SPml2MCAMW4EEQFrhKgBa4ViE0PDQDK1Fznur7Ic9pEvKR5pIpfaFzF4om25s/sw8hUe8xV+bDijyS7e2JCGBKWuX518rrZRwljfcYU8h/Cjjvj3N/XCdQA+yGbY9vWUC+KnAvG2uLKdCuwbQvS2eJmcy71qa9pJWdv2t8ZNr7FlfwB+NO/Hye81E/LvM9TC43R/Nonblu6ivBvndb0CKZxXOSz7tYphXU4t0Bagenx3qvj8Lm2Pp3qv87uIYmxmCpdyAxAV8UCfgfrySpR3OJcl5f2gzCFzXCn/XBqt4lJdM7yejjPwJrq+jqyXgMZbm/ArGEvK/bCOG7kUWo5L2nSBRPyQzXNVXtUtcVX8+jc9fi37s24aXd8Aprao8xXwyniMrbY2bHoOHFX9avZWxaoqwFVTgVXFm6DTtWfBLGSY9lVv5LMa4qAZnKoArCUEL4TBZJtXCcAKKzMyrLQ3NGaPMJikLljUCIfE1grepDK8ySCWoGKz+qCAyeqDUoFWBcCyjHHrd13Ov/8bABF3B7EKcPW5AK/kot/5gV6ywclRDS7GUZ1NQsggVgSfDLurD0jIAJfPBt5znPEeM/jkqdAP4AOm75EuGX7XVYesumQMfvDoMCBDgMJOqr3NxUhx9pS8c+v63kr1qQWgojkQN4SHtM2CUTFvkdsgLUYKgQWRBSKXERFWqqiJ6I7CDjz07v+R27e/hRAu0TTPceXK/5tX3G9MZWiEZi0Mp/nceWPppQ4iAjEJWc+338htkwwJRlo+0vxBDuL72Y8f4ks//Id46VNfy2rnMR668yMchOcSO4vzQFq5301QCgYfEzCkjqhzYgaARFoIDSF74UzsqMRgSkBSctoSgkmAbyxbC9/XEP7Pn8Tv7CKHK467r0X5tai0hOhGT8aqCewq+4l91aB9WvwvYNUISmWVPs0f1cSMSi9bcqaQX7qykHa3oKSFtnsNP5l/ADwBfNl9ZH6VejxIqNhSa5VcBRureckEjCqeYmswypEW6FwCoLSAUg4YQSiBxqBt3jYGWnsx+LRtfysT/Tw49aDNsTXEmDykhjweRT+OSyYGTBm7Qh6PNCLeY/qArHIeP6T4UBxVpHwFwJcQkifWMEAImOAheKzm8dAPibvn1/ltzOXFyKq7RCOehbxMf3bKzBhO2wU//wf+K+Ism1UR4eFf+DEWi8XGe1yr+xUwwznHF+3+Hd5hv3scW5xbAMnWeckzZUxOgabUfHEDZKsN2NdAWwhh1Aqbem2t7XTVY1JZSNjGbqtBo6J+Wq5Zg0MFUNrG/tzmEKBOO13cKGFaZvne1OSXDaeDk/YqIFVhitXs1SmwVttGq1Uxy72XML2H+tpTJwnTc9N7my5Q1MSeouIJxXHmGiSbehCtn1N5jrXX0JoJWd9TXU5hut1PeDAbbFnITwqM5a88OCXGxJjyfhhXaWKMBO9p2hafDeCXCp/z9qAwRFjM58To6brIiy+8iHXZG2YxsqP5IWRDm65NcU3TImZgj1tZLdMQjCCcElvh2l4DzNIHOgZC8KhGRAxo8syJ2+fKXsubn3iM5WqFtYbd3V329/eSB7zQoVnQa9sWEHrj2VdBdwxvfpOyszhg0S5oZ47l8ozlyRINkVXXESUym1n6fqA7Ux596BG6vqFbdbTNPC2Y2F0UJaowDIEYPHcOb4MoMycY43joxjWagwNu//wHCDqwPDtBESyO55/7NF/yrnelts3PIM1BBWcswcYkhMXI2ekpQgLfNH3+iZrAH5VEQ4+SXx5Zz3CjxnxchNXNmXhh+Rlj0jMjeTE9OzujPzpNiZ4T9JZBf3zdgbd5rnsQLeiB3VdP9BmEemp/myc2zhmn8NZi54JsQ4U1sANorOjno9Fz2UgzOoCq8yroL1r0xbtQIC76+t9v/GcjDfDABvI/01lNBXCOq3vjL00QNuOqn7xG8RexMT6r4X4vsgaFtrX5Z9KN7nrJCTB37nfRudcqz72W81qF4lFwA7SqwaXkBXDKvDKmVuebqA9OWVoXeifcVC8cgTJX1adSRfx8htGm1SDoYEdVwXWcyUbaDaFbH4fCshpMZlul+Bgs0eeygkn70WTvq9nDoBiiZJVBI1ldMC+AZNaVFtBqtHUnW+zhsfneT/vUNAgJWL9/u7r3Fy4Erl6ljrrlF6vtaBOx2p/+puzgYbI/TOK2MX/1fAWnazCfs1AE8sXn+sJbwhQF2LYwdTVtOy7xgv6p8+e3lRPvkqaex8D4ff/F8O208VYCpe4kMOoV/boEjmWPxYV9pdSqfMW+VFHfs+frdL9tcVE4vc8yP9fBTICoor6X2bCFLVW2mhci1IFY0MyMGllRG2BU+Rlw5v7Bp7udK8dy8Xv6anHTIH6oAKME/Ij3mAJI1WBS8BtA1QgwLUMGs3wCmqJPoFIGk0xM8QXMqoEqfAKkagDLxIDRCMOQyq/qV4AwqYAsq8nUjYkRh+JXy/R5qICZqTpeDTYUsKMASrVtbWAT2K+AohqwKqp1qjp656zVDEteYwx932c5F/q+Zz6fjyzS603D3l/5j/ngN/5b9DsHPPJzP8wbfuj/i5vPNwAgay1t9hJbe/IsdS/3U67Ztu05oGp6T9O6To/rdhCRDXtmdVsC47XrOpf2KQDONttiNQiW7KavgaFNW37r3n2RWuR0fxu4WOep92uwqM5TgMfacUD9nAt4V7dLDfhNF3emQN20b9bg4Da1yikbblpO3Q+n7TQFwooThNrGWp2+9KnS7wqQWIOFNeBYcKkalKtZh/cbHghgaxoHmYFW30ypgHMF7SydMT3g6BzOWZy1iZ1m0+VrWl5poNVqhQ8exbBYzJNKZgwgklb2yA0N2WNWXrlUCEOXzhmIJLVAFZNsHsDItCo2E1xe8U12YpVZY1AGdvcsxjpitFjjMtp7SvQJkOt95PbhbZwtLms1sc7Ecv3Kgiv7j+IahzWWGK7ibIOxhm5YEaLSzGYYJ6g+yusefZK+9xAjwxDpVp6z1VmaGyl0qwHrHO1shgAhJPDShyV7+y2PPXadvu8YhmM0OLqzFd6csWgsR2fHnBy+wunhLU6P7rBcLrFiCD7QdT2oEnyPcxbv0z3EwScPpzHmVfEC8mj2KkrybhpD/jDYcwPB5iAUsSajwnPHfHefV/7B32P2zd/K6fO/gvh317SDhqe5uvwmNEasc1hxzNTy277l13Ew71GNvO1tb+Ps7IxuWHF21nFyfMrh4SF3Do8Z/Anz+SmzhfLSjTfz97/lP+dscQXbd3zjP/5vedszP0GMJBZLaBiCQ2xiBQSZE2TO7t5VfO9p2h0Cc0SWdCcvo8MpahW1DT/f/Gp+6PLvJUjLwfACv/7wv2OvewHXGv7hV34bzz/8DvS5hEy+6xe+j3c+82NEWs5WPSKGw+PAJz8tnC6b1J9Rrj50nTtHR4w2IbJwB8lLrjFJzfeVvW9h5d5IURfd7d/H5f4nkmpxth8h5H4ta/syiTaXGYayNpQM1bEKYpKXW0QozJo0m1unz5+VvJ+2guT5dYlfS2k6SpdsxLNh74KUdu1aLF8nnx/rwvoeNwy35nrpOn19TdU5IV6Cr87RMWI5TCVlJxzWWhrnRs+bxmbKl6axA4WIJAcnWuorCIZYCRtaMfY0AH59jOY2qIWfjMWeY/rVAsz0nNTpLvgA3I3Zdq/7dztvq/37KXfb9rUMMgGRJjaoph4Fxay3YiPGFAbWJpA15qvLmtrJGkGrtUH383GJlfV5B66KDatsxyoOCbgahiaBVF1hXCXvgyNo1RvUJ6ZV9BnA8slWVvAW9ZLigyGGBF5pNMSY91USC8vkMc7KGsgy+bhmWhWmQq0uPO1/UwCrDiXP5yqsh7d7BLGqfpB3N8ie+Tc6JYzVLx+bAMuT0zQn8hHxiviIhogMCkNIq5d5a4YAISbG1RCRoIm95QP4OLK7TIWiKTt0PI5iaXkRp8+nkV4UNNn3MZIWYtejdcyM/DW7VGNC39aPKazvNSM5Amnel0+MI3yeV4quvwZlPle+QOXaKT6uFw1z+uSJPRBVq29Its5rXFpwxYAkcyciLn1H4/p7GBWiCoJNgGz+dmkGhUQsK32KTt4MGBwvMosfzt9ow9re07pDp4W4hIyk825kPyU1vZoNZatz00H5gvDZAKjuEs54K2efWRGfWSjMqQJCGU3qfFk1T1ubACabuH+jyp5jU1WvAFONuZgF9SDg06ulNTDVJZ/inudCZiZJZmkan9SNAfRgD20d0q1wL7yA6boRtKJiLknOj/dItwajUvwa5KIAXT7tm2k53sNY/pDrVINe6Rr4gdbIyKhqAIkBp5qOjaDBV+Z5dENuLGDItrBNzaw+NwVEgHNl1YyebXHl+lOV5yl7qMwxy7G1FlMZi58K/7X6XF236fnak+O2bQ16FMCgXKcAZlM1SFVlNpsxn8/HvPP5fARtCkvt4Rc/wUP/858Zr+dns5GpNFX1q+tZ7JVP1SALcFjabvrMtjGzpvbVCptsm0pk3R51mdbakd1W6loDTzVDblt/qL3d1uroU1CpDlNVy2m/nrK/StgGvJbypvdUnsE20LHkqcuvPaZOf1Ogd/pcpsy/i8DAOn2pwzbwbhuLrwCZ5V2p3/26b09BuRogrd+hbe0+fc4PEh4IYLPWroU72HghNxHYBLzAmq0WY2JulQZIHYhzg0hCJJVZO0NV+cjTH+fo9hmDD6gmm2Pee85WK3yIiLE427K3t8u1a1fY39tjb29B2zbs7e/hwynzdicz6XI9RZNKpmYjfKRvWYwxCdWqDF2HMRBirCZnuWPFSN91dHTrtjEGUcEQsY2A9Ggk21pLLgbbRvPkbYUqiDgOdgS7O8NaoVv1JI8zC5q2zQwmxTiHIMxms9T8Rug7TwDe8tQNvPc0TUvwwulpB+2c3aan3VHso/tcnj1O112j6wNd3xFDZHmWVGSjBk7PTlktOzQqZ8sV3icnFV3Xr5Fskw3VSmJjKWAlwypiGPyQQAnjssygKDFNzSWrLyx7lsuB/mTFw7/vt3Pyzf86R+/8VwhPX6Y5/SDX5b+mi7cwYrFqSX+GuTtip+lxDq4frDgxp3R9x9yeMZNjbDjE9Lc5O7vJoj1ldwF/+//+F1hdP8BIRNXyj5/6D+CHvws7LDE2gV+DT6rLQYVk88IwW+wimibL1s7A9IT+OsPyiKYxLM0+P3rld8FSMauBE7nGP4p/mLee/RDWRh7jw7Tznr7ZZe/kRXaeOuaZN31xajMfEPWEqNjbkXAnTUQgcOyU1u7R9X2aqOBBA0YU0YAxYER5XP4njuZfzWCuMQufYr/7aSRJExkJzdjL+E4mMDqW6Ziu5c/CQAXWZUgyvBu1QHBFjBkzjbnKuYzAZpAIagi+pImjEFQPWKOItK5RBSxpvr5WdU/sEk1eraQC38pgjVaVlY1y47v+BDft9XQm9ux/8K9yOT7PMHj80PPGJx7lXW9/kmFYgiqPPvooi8UORciLCo2d88EPfoQQwAfLcOkqq+uP0t0ONCeJrRg0AQsxmvTTvK/l2BKDJJXxkiam/VCACLX5eJI3Sk6zjgtRqvPp99qqCJbmPA8mXeRR0LjMvmry1oX0sx7rQmZLRVxLBqDIYNWUbaUjuFUArbUdq7VqybhfvMJ9HkP0ggZBx61JoNPSJG+BXrK6YGJgxeJlsLeZdWXGbRzs+rzPLKzqpyF5YY0x78cE+qqaBGEU4MoW8Io146oAV1MB724MrDq0+fe5CtvAqXsFsWogawO4hvGDtsaSGA2cTlXJR1aWnmdlRTDT+I2frvP7dVmyHi5fNdS4+/nmabdhdZvp8zMMwvq5T9ly62Fz89pVG/V6QM9b1u1Vg/3n4tiI0+liwbm0WxYaJj+NdyujqgNbyqfev49x8jMAqDyP4rfZLfpCDiM4xXYbm1OnL0VtbxuAdK/AVF3e/QBT020Z5ybPVyG91xt9R9BoMV0HIYHNxJjShTj+JCoaLcFf2WBqNvEmO7xvDThllhPLAkxlBlQGpSgMKI2Z9RTz+SGrL0eIfjxnNIIfEpQaI0aV1pp0rAFRRYee1ekJQ5eMgRY14xADIoab/+V34N/45DjHa/sXefT3fluak21hotQ2jOA8GDUViKfC/dR+Xn08lReBkWkCYLMKl7FpcVkLUDARfC8SfreBMrUwXd/LlE0zBXUuAhXqe6rLKsJ/beB9ClTUQn7Jt80eYX2PZb82vF7XpwYUak+KwGjjq/b8WIM6NZBUt0cx5j997iVNDRaWbVHfK+kKiyjGyHw+37inGoiqQY+6X0wJOBeBSnW9t9lD2waO1fddg0jTPrCNXTgN28DQ+tmUcrapik4Bzqldsov647Z61GXX5W5ThazbY3qvdZtOQakpy64GE6csum31q+syLXM6Dk3f9dlstgHc1u/CFCybvj+lzClIXc5Px6mp44n7CQ8EsI30wZhWDdNYLYCO21RZm28mycA19TI1cEFS8+ojmw1ubTbAH6FtZ5yc3mK59HivrHxk2QdWvWfZeUIEFcGaOxj7aQShcQ4QrDGj0B9jwFmwTrASSe9VoHXCrG2YzWbs7e5w6dI+e3u7XLl6jWtXruL7FVE91kpyyhCUqGlWnF5OyeuQwuCTk4bkNEAqLDKiPkEc1tgEeKiC9IiBIUYGrRB5FO0rne6QPprDapUYY5JUN40qpgFpDSF2GCPMnCLGc/zyR2ms5aCFvRt7OHeZpmlRTQNt3/dYYznr+vG47we6rqfre6Iaum6FxkCMMHiPHwKnyw4fIjpEzs6WeO8JKIe3b2NMw9B7og8M2QhhGaSjCiEMHN8+IgZFVmfs/J2/zq78z+NcOMSY7PsRMGoxJia1YgSDwRqh71f5Zcor1ZInE4lOh1jFOuGV609kAAYQQ7SGf/T1/88H6fbnQsuK7r07DM8mIzR3eJSf4lvXCY7voZD9/LvfMNpkS/svym/J7ue1EjpTu6yFz3UemeQvaUQ2j8cypvFCUlOoyyj5zWYd1vGvUocx/vz9ibmgDg+IH5kf94SnGzTMufPYt3PoZRSAXxyEH/9ZkvTp01aLk4FA0pUG4Nc+2MW3hgQKbXoSnHj8mxUD6gFpI6YZcK0iTUhMqCYm4+pNxZRqKjW/c4bdtfISqONWyrFRRHKcZJXEbLvs8xW0gKsZlExbQYu6XxC0qPuFxLwKQwa2epfYVgWs6ovtK5sBrkp9sABfmZGlPgFkMZg1YBYzkJVfkDgFrtoJcDUVELcBWNuC4QtHXfCegSw9N7YkQu5m/HhcbPIIGSglC/ab8WZjjMvwcWVX89Xi4cHHjAcJqlSqjWk8Ub/ex+exxdfn5XyecPc8m/km1xkdpcjk2uevFYd8fqiuP71WzZT9DMCme05fLnfRc9uKIH4BhYoRtQkCyYOBTw/Kino1+1Nby6q+5VTf8nGFrpofVKt2xd7luffxXNyWfPW1Sr6xD5wva+Nad6vjpKwHGwfKzSpwa3LOEnj3gxT6uQ/55vsvfgdxbxd7crpV8IbzoMSUWQNsCKBTMOheBNNt6WqheRvz5V6YJTXQsi1vLZRvs1W1DcgpoFIBqmoVx2EYRrtNNYBU8k7LLftTAb82AD8FzKZAQq1uWRvJnzJ4SmiahiGbaVLV0atibaS+rttYprF88D3fws3XfymL01u88yf+Js3RS2OamkU0tXs1dQxStrWNtmkb189w2zOYgjM1eFJA4dIOtZpkDchMgbQpeFafq9PXqp/TtFPQrr6Pmhk2fWfKc5+mm9Z12galnaeAYA1AOedG+3M1AFnapAbzpizOi8DObeEiYHpar9LW02dfA8J3u1ap+5S1Nm2jmkVavz/AORXR+vp1vaesxvsJDwSwdV3HbDbDWkMIiqkMQCbvoOTKFeN3aRWl7/uNAWwYhjGdZiTtHMIfU6O9+c1v5vlPv4xGy2noCNFxuho46yKDl6QKihIZCMGDCjarLUq2kVJAQStJ1c0YaIykOYWJzNqItQFjV1hzC+vAGLC2xaokd9r59srA0DgQYmIjGaFtG4yx7O3vMJu1zGcz9vb2aVvHpUsHNM4lFo81uKbBmmzAVockg0h2DqE6tokqSH5RkGSTjewJXEzeFzLTbr3CHP0KYxy+MoLrB0PoDKoRI4ZYhA6EEBIbbTEzLNpkFyOqoHEGKohxtE0yrtw0c5rGoT5i2wbfDwwxsOpW+F6JQfFDz3LoOT4+SR5ZVz3eB4ahp/NKDDAMPSdnS7puIISYvLb2A4OPDEOg6z3GCNa1SKYOeJ9enqjpWYtJnkSdczSN4yQofmhAGx579hf49BPvSg9NlVl3wm/5378D41egybF5iIH+ylWef8M7kaBc+8gHcUNk/9JlwtDRtnv8vTf/63ziytsTAijKXneb5Sd28b1DrqR+fMU/z7uOvw/EYoxFnGPVQ4guq1xa+igIcwYfEdtwOjhu3VECDkp7q7J/sMfRyRnJq1RWCxGTt/m4mRHdLKnzaCSyQKUFCYhoNnbtQGp101LO/Rts/IIMGkFDVg9fbyFuxAkRjYH57iVOdA+eFbhVD9xK0b8SoyQnx0lN0GbQRC04G2lcxFrwwxIjyvLSnNnDZ9z4ik+vPQ/2K4yN2CaRPk1DVjOsGFZ5m9RWPreC/7kQFYlAlLQNJI+YBVQMkgV0kwR8LzAIcRBC79bMq8y2SsbZMwtryB4HM/sqeEPowfdCjOkYaYjBEkJiEaxZfqlhiodBbBZKGyqbMpPfNgbWvbRtKfO1tqF0T4DUlt89gVkRQbHFcQMB1CN4rFEaY5M37mFFtzwl+gHRIdmcUY8JA43vWfgeG4dRncjGbKsmMyhMDNhiFFmTzZq1bRo/MjNSPo+hWiWs215kXHCDtbfsNBHUrH6XjJMnL5S5L/ikChcxoC71kShodEmlDpvtphk02nELJvcnuxGvarOaaj6Oyd7TOt6CGmKs8xnQqozcT8fzapOzJ7UQU370VSZlryVAtZ6Gbe7frRydsHju6UX5HAbRV2dDfaYqeA8MPlXHJrI22K6QFylUDNBkVd/U1iaeYvU4sa9jyM4YyrcrfbeKl1FiRAhoyOq0qtnzqK4dN5D3yXkorHPN+cscMk8a+3wdspputoVgCyiFjirBRgQZTbiYgnjlFzhzy5M+8FoQg3RepOquGS0rZYxCG9SOm8Z4WX8Qy3Lzupw0hhRkL40vpspf8hUzGeQ8RfgqeYTlG55C24Y12zFw5Z/+IJKFtzKXTo+uzMlBZZ+j+HVVvWAhv8CefW/Kx3pxvdxoqrKM+5SaGoOMaGK+XklRsEMRxJh0V8ak55KfiTFmzKsxmW2JUYkxjPUFUCOcfMtvAmtHoUvOltjV6q5gWB1fC6U1Q6oI8uW4tttUAwZ1XC0ApybaZJRcxESZgkbTNFMgYAqilfsooQBI2wCXi8CdWg2tABcismEUPT1as0EsqQGDqRH3Un6dt76PKVBWP58aCKjTT9OVuOn9lvao9+t6RIX3ffXv5Okv+01j/CuPvJVf87f+EHboRrBjJFJUAGxpswLqFePx24CoGmCZ9oFpO9Xg0vRZbQNbpv1h2t7T8rcxtC5iXsGm/a9pWRex4qbPe/rctgFW29hU98JGm7K1poDtFCCfgp4lz/SZ3Q1w29YW24C6u4F30/YSkXF8qZ1U1OnLe1fufzrWTMeTul7TZ1wD3fcbHlhFVFWzCmF5IUvH2OZSNo4fgnrgUl27ay03NjYSFlh3iPl8zrXrl7j50h16D/3pQO89USPWCnM3Q8XQtA3GwbAKtPPsrrZ1BL8i+Jgm8SrEmFRMQzLGlbyVBkMg4jTJkzYajBVk5VFJABpRKkAQnEv2RooKqXPJ1sitwzOsXbsGtrZ45UzzGQGMCnt7LV/5VV9M4yzHx8e87+feR4wke2jB085m3Hj0BruLOZevXmP34AquSbbhjKT2HroVIXTEmCpubAJRYv5oq3oKgBA1IpmBEzTZIAkhgY5lAiAmTzZEKDZSRA0SBoaQBKbQn7DK6Tk1GBQfIs4ISAQn7M0cl82CG9dmiFh2d/dTmwDRGEQMGpVV17Fcrlitek5PThiGgc5HDg/vcHq6JPhIP3hs41GUWZvcD7umoY2RIQzM2hl+PuCsQ2PD8XFkuVS+6jv/BD/+7/83vPDk27l08zl+3f/8Hbz+xY/jDIiJWAm88vDj/L3f/e0Mix0Abr71Hfya/+Y7eP3+PneOD3nsda/jL3zxO4nd+nU5khvwxuqlULilT/BD4f/BznBIoz0udBjf0cQBFz0udpjQ00ZFulMcA+bOCf3RIfQD0q0wQ8dea7gxb3jCRT79sedhCMjQYYYBEwZs6Dl8zzdz81f8nvSchp6DP/ujHD37DdkwdODJa/8JL/+Rb+X0qS/FrE556q/9h1z9sf+9GkzSOxZdsukmrk12CsURENRYxDTJwLdxqLGocWBsBukcUfKxsclgsUlqkaZpiWJz2pzX5mMcMZeHdcQCGNbHxqVrGAvVvpZzpR4m26kZ6+Ym+wbG6zvcbJflKo96jwo8SqX2tkY1Cunk1cPOuNde7bn+tpvnUiR2lYzG04dg03G/ZlnFmiGVt8mulUG7BEAlxpXNtq6EONisWmhGA+14IXoDQRLYEDaZVjEIZLVBVdK2CDujuiDr7TYAy05+r/bNKfk+W+GBgan7yINiJWKJWI0YIk4iTgOOiCXQ5H2Hp9FIk+Ma9SmdRlwMyZaMRmzwtAI2epoYcUGx6tflxoBDaTTkdAEbA022e+PUYwIMg/IjP/Kj7O7t85a3vAPb7BKicvvskE986gNc23mSG9e+iI994lP85E+8l8ErIRrUNJhosXHGjo8c9IqYhqA2ecZLVyfmO1RpUGnwNETZyfENUWzed0Rp1vs0qFhCKUPqeLc1zzbHNq8a7heguvAZb9meu0YBoyag1IYq4ucRoNrWFoZXAYu2gFcPCky9Wn4JID7ZOMQjEhHx6YdHJKQtHmFApBgur4yMZ+93EobRvlSy8zSMBsrrfRN8ArEqO1DSexgG1A+EbgXDkL2J9mk/eCzJ7pNTxWh6721R4fPJkHnse/xqmconCR+1Ee0EfAgvuP+AV9zvBmAn/DhPDv8+Rk/zHNqfE+brVfkyl/bej2mmQkItQBVhoHhI22a3p85fmB3GrD2GTsuuGQ4lTEGXWqCpbSyV+7kIDJkyobYJy3V9p+DO1AB3DaTUqo2lnLodY4ycvuOdfPQ/+1OE/X1kGHj9f/edXP6B/3MERYZhGMGDWghzzrE7fBUv+D9K0Csc2B/g0ea/xsiwcZ9TkGFb2MZSmaavn7NIWUxuRvmobvvVaoX3fjQAXrOI5u//IC//yT8KzoEPXP8vvxMJ5xlrNRhVM0DqPlGr69WATdkvwFNJ45y7EJyoj0va+to1SDYFVgooNAWGahCvpK9Bhm0soIsAtjTMG4xrxrnrgME3DWKbROzN8QEZ56JRDGpdnkfbzZ+YNOet8olrxvk31o1po5hUZpHtJM2pIwacS56kxSR7pqaeL1tiqYdJ8+wgJjvrWZddtnHMZ8YyojFpfj4JZwcP89zuo1z6xHs3ALbyXtbv6fSZlHFvynqbOheYvkf1uDQFlabvSn3tGgiu1XfrMqf9sB476zF5OobV/XQ61k3rtg2YqlVwp2NzDX6VvNvUdetxpi57Ws/pcf3eTt+BadvU5U/boq5H3T7TMawuZzrmbavftraellfabwqA1d/hegwraujbAL5pKONE/a25nyB6Ucl3CX/2278ZYzY/2mt33uc9LkwbJl2yAFXrjrseSBuEPAgCrnFcu3aNZ575OO//hac5Ohp44ZUl1288zm/9N38rL958kQ/+4gc4OT3j+PiE05Mz/DDQDQMaAhEYfIc1Tf7opDp3XTdS/xrrcEYQAs7K6Oo1eReVLINJYibJunNBQIkYY9FYzkeUkJwbKBAFg1SLbAJBsSaymFm+9mu/nL39GYvFgh/54Z/i6M4ZMaQF8Hd+8TsY1HP1oet85JlP8ZM//SFOTzucM5wdr1L9jNJ1S4IfmNlksL9tZxwc7HHlyh7Xrx3wxBM3eOjKJa5dukwzcwwxYDD0qyPQSCOGIEVmyGt/Umij5YUWfPUttkWuCJsTQjHp2RoBsTIKIFHWfaGsZlhrR2ajkFfWJNuv8gPOGGazOW3b4PsV/WpJ1614/etfz5UrVwDFh4HVakXX9QQfCNHn/qnE0BOJDEEJ3uN9j5H1izsMnu/7tb+On3/3ezb66N7zz7P70ovErmNmLR9+8mtYygFnZ/sUFbWrh89yZq5w5i7hn8sogkBDjzGBiKDGrD9c1iSa4b0I99O4agF3DH3+lfBjwA/nNBaYA2+CUZ0iJqZb0pzQvK+JR6LV8bhN6SWWFfWQVtpjRDTbFYkh2QnJXpEkeIgeCZklE3Pc0CWhKGaGTalLTPboJPrxHNkALjEJR4TEthnz5XJUO4wC6tMzzYwbNKCxx2iuL6lu/9q/8a/y9//e9/Bid5Wzd/5h4vwqixf+CTsf+cuoiewukiHXtzzxEL/h13wZXRzo9GH+07/1pxJw2ac+5VzkS99yk0/fCbwwv4GeWbgaMV8ZKXaFkmFuA9Zk1tUWQfZu7Kv7ZyO/tuEzBaUeJH0JWh2PzJrqGM7bVartK8WIiZq0EGPq1+IjJmTbNSGRTcQrEnL8oJigKZ1XtB/oT85occxsi4TM7Ms2ucq+ZAJIOdYxTT4OklhZUfJ5Kd0+v44CmgHQbPw/5v2Y84LDBwihxGf7fFMw536/5Belr5/VvT6zc6CUrlehCiBVrjk+K1m3lUqlev0FFD5TxtT9MKUsSFobwDmwDlyTty6x5a2Dpsk/m7c5rnXp9xM//s8SQKUDVqfe9hKwlAyLD/jVCouifb/2eFcZLddslDyN7RlcUsNpeDcaZiz6n6SNL2WPfCHZCQ2B1ghOSMzH7DmPSnCBzVX3i1aZpxP2ku9uE+RacJmGeiU+xkjf9xtxJX+Zj9aC+3S1vAiKdf6iCnVOzcpcwccGG1/YeFWmYNSU6VOnu+iei8Aosl7Z38Z22XafNRBVg2S1UFPm9jUwUbd1PXevn9H0HqZCbKnTNoF0W1nTsE0IrkGXKbukZtbU/YzdXVaPPY578QWa4+PxmZTnW6uxlXO1Wl5d1rb+W64/vedpW9bPrDalU87V5wvAVp5Z/fzPzs7O3W/dFsOjj+Df8CTu48/QvPDi+AzrZ7DRPmRbvBmEkaZJi6isQRo3mxPFjCBRzGBUzLaN1VpsMyOYpEVR0tQgUv0zTUPAIC5dqwBO5MXSkg7jkhO2cYE2AVo6LgwX8KiUXS8YmwwgJeApmk1gaVqnX8phXIiIaYweFyN0bRtQNI7OJxjTF1uBkePXvYPYLpKMlsO7/vzvZPapDyEizJ66zPzt1/HPHXPy3hfOAUMFQKsJNU3TbICpNUA79cY5BVen34+N+52AqyVMPZbWAE+5fiH9iMj4fSgLEFNAv9S73tZhG1i7LU1d11KfGuCuvxNTYLC+Rp2/TjMFJadA2rSu5frbQLCpGu02EG7bosr03HSMr0G/bW24DTQtecpYWINqNTBfLx4Vhx1lgWJ6nzXoWPpmSfcn/+Sf3PoMLwoPxC0o4FqpWI0I1w+uxNWgW70CVPe3dEMm64InDxGHd47Z3dvDurxC1jbs7u9xcnIHEaFtW/Z2dpi97jHmM8eHPvQhgg503Sl953EGjpZL5k2LxSQvohnc26AzDx6JCk1GZI1DMAx5tcuYpMri1BDjEmcdEguarIlJJkD0NI3gfap/IBusFEMEvB8o+jJWknpil22fOZdW769fv87y7HlWvscYoZ21GGOZHyywsxl2vsOl+VWatuV49SlW3TIZRHU72BmsgtIHhZXwyvKET7x4RGOfZ/7TH2FuG2bGpBVkkziCIp7FDH7nb/vNBBPxPvCjP/JjidVmLQcHl3nooWtcunSJ2WzB3u6ldM/G0A8JyLRO6Ptl9gprEEmst7ZpKV4fRRxWDBo9KLTWoRpR72mTPl5i4GmyvyMxqX4i4PszQq8JeMEwmzW88OKnuXnz5vrFJakiG7EokeK5tkjrly7tocZxcP1hFosFbdvSzmd4Dfzgw4+c6+Mnjz7KyaNrY8Q7dOxwk2vXKpbSk7DPC8ALfORnvzipzwH9a65jVoXsEUsE9DJwufqAfAL48Srt64Fvg7X0u37dt2EWv+RCWVDYAFcmcdXxdyrwzf/RRtpBv5Ij/eMbgv/HFb6/Lu9dwD8C/i6A4DvLz7z3Rir3GqmNdwzxUv0xv2hCphPAST7HIFYBWwsAouv9sN6KKurzvk9pJNuoM1EwsWwFiZLPKZJtOzEoePKxJrXSQZEhn195wmpIaqedknFZ5vMdgo8QBR8CgkU02VrTbP9OsAkLzqBVDNnepSZASjJgpVEIEVA7jrt3Dff7Ipgtv1d7BlTbeylfWAOOiQyeXuMayCvG81/LF/n+F+smQVLfflD1vFdjUZmQvmMSwUREQvqZgBAorCiDz6wSj5WAMR5iVpPVPgH3scdoj2HAao/EDtEe8X1aKMge8Qgehh4TImEY8GFFDAPRD/RxQIOiQzJubmJAvUd8DyEwc8JDVy6zP18kICuzoUz2oJcYU4G//7//g8+04fmK3/1HNiab08ltmTyrKl3Xbaw+T9PX7J16or6r/3wDKKvt/ZQJ7WhMeyIEbBMIpvtTUKVmR0wFg5Jveg81+2HKACg2h6agR7mHuh71Kvg0lIn3VCCpBRljDFYPk03dCYAyvfZUaJoKWtuuPxV2LhKOLmIWlLBNVW7btaZhylzYxsiqn+f0vqdCaC0c1syOKbC37X6mdZkyQwoToWa3NX1P84mPJ5M1F9y7MWlxNNrEqPeuGRlEbjbPrCMDNoNLGRQaWUKYpLVRgKXMRsKuGVEj2GQzc0gM2AQOiUvMfckMKnENkhH4EldYUV6FaBJLKY5AlB2BJWxmR31tBpdsBqrGdLkOFYil9rNJQ/8chBjSmKthAjYVBxMBl5msY1wBooq5hCqtaMRoclBRl0cMWE2yytpxhd/casCio4OLtGiRyhudWcT0jSB6rKbF7rG8fA9GI6HrEA1pSuB78B4Nw4YTDWJAq/egyOUFPCqg0WyWjLxuU+8FuPWWX877v/XPoDbJqo/87D/k0q1PorMZ+9/0Rh7+j746yWzAi3/5Z3j5r/7cxjXrd7PGBqbjXw2S1O/5RQylbd+Pu6WH89+X+vqlXoVks039cjqu1cBfKb/cY832rNPW41xddn3/28bpkre+7ynwtO1bOB0/p9+Zad2n7VNCvTAw/aZfBCJO77fgRjXgWkDPixYpahZt3dbb2nP6Ha/b5m5pp8f1nONzZoNNtTRosuVVqPFlVafu3KXR6oYoN+j9piteY9YvYojKfLEY88xmM3YX++zu7dHMV+zvJlXPfhiwTaDrj3niyRuIeAzCzqLj9OQU2zSsVkskRqL3CQTyA4ZUf+MaOlUCigalbYsaHMSs7mpNsv8VsnQTAiRX9AYxxc6CgjGsBo8VkwW7RFgKWgxKAgIaFTER75Vmlty7Y5JdiLe89Qlu3nyRGBts09Jn9Rphjm0WrIaea9euMZ/PWdyeM8Se1Sp1Ut93iTJMuk5EMDhCVDqf2HYILMQl0BKwNMzbOSoOIdA2Mx599AleeP4mQxd48dN3ePYTr7Dse7oh0PeRw5MzmnaOj4F+GJIdjxiJMeAamLctj954mCdff4NHHnqYSweX2T9YsDMzNLMZq9UZCslWnpgEQMaAkZiYc2R1WkO2r0ayPSImWRYRh4mpI6rJKqwxPZNkwyRltgaGwWNEOTo8xRjDyfEyyX7IKOi++fCEH/8tj40rM64f+Hf+17/N/tExfYw0OwvUtRwfXOf5q0/yMCc8xhnSOH4wvI2/sfoKZpfPxnb/JvuzXDGneHF0Uegx9GoZMAxq6dUxxDT58ViGaBii4LEENXg1SfGseKJMPmkpNogUSzAz4iBwM6v7RUG/UtAvkQQmhCzgfpy1gF7G13p/yhbaFl7tfJ3mwvOTzHK3fbk4zd3i7mVb9mtAZMOI8pb0AvxWgd+kyXB9o8lrYpPL2JO1zbI6/7S+RYVswnIqXsjOeSP01X4gsehGAKva1r/Caqz3e6Ar8VLZVGP7L24HYKfksY1wUd8oaqRmsn8BIDWs2Gz3ug1LGXVcdf0yf9Apo62AT1NAatrm9wtQfcYg1KuEqXqfJfW7e2VVFe9/21R7bVV+/Xwsm8/KyvpYOP8chfPPtAYYYfNZTH9TQLz+UaWZnpNk01Kn71o9xo1xSVBJ34aw/pUV/LxqX/ZNORd9XsFPKoaiWQDzgRj6tFgUAxo9IQ6Z+b0WtMjsXtGAOsPR3g5LK5CFtrEeMZlqsDHwv30skV2dOb+1F8RPzw+LK1gUgkclCWyiEdV1h51OPFWTWk6Zx00n9tsAsqmazXTSepFwMBV+toFhF+W/qMw61KDKtJxamJk63ZrmL3PTup1qgXMq5NwNwFJVhvkl+mtvYnbnOTj89Nb7mgpD9X651tSDYA0G1vW5iJU0FYKmgsq0rUoZUZOdROwa1CmgEdYhGSxSawmLfdTNkO40mYBwrgKaMmhjzAgsYRz97gHDpYdwx7cxQweuqdTcEtsIazcZSGbKgNqMG01YZPBpzWZas6fIoFkNKm0wlwowNRE6f6kF8UNmJvkROCqaAxIj1neb4FPRHhjZTWugSbKpgsJ0MhVwZVSr/Zg1EBKTNR3n62rM3lDD6DE19HN0aNiZvUIcOhojGBID1uT849gZApq9qkoGu5xA6LoxTmKgMQZ0rYY3FdoLmFLk1Hr8qMeA6RhVWDElbwFcpqBLfd3aS+g2MLyEmrEkRQvCCiqKcZYoYFtLbwaMNYjL5j5sBnrNDLHz7PRNEGeSjGlIWkVGaLKDPs3fc3F2LIucpqTFClfdkjcu/zT95Yew/ozFW1/G/ImvRqww//rXjd9cVeXh3/cebv/dDxHvdFvHsHocmmIGNdA1ZaTW342pemO9f7fFiG2LAXWeKX5R59/m2XNqo6t2VFDi6+O6viVMx/xpfab3NL3nKThX13Fa1rZvRA1ebWvPaTtO63PRgledfsrQq79HU5MGdR2m3+Opevq2doG1ZmVdv/p51iqf9XiwDWybAo73Gh54WSJVPs26RWQ0Wlifh81JQUHLizFGYNzWjW+MQUxiOBWK/snJCTs7C65evcwrLx/x8s1T+k4Z+p75jmOxSCqWTzzxBM987Dk++qGPcWgbXnnlFSKGjmTUPwwBJ4lV5nXttrjUzftkdHYIQutMYlJ4j6AYsQkgA6xRIsmgaO5GaeJsk+0zyXbWJDtpiApG84RUEggiManihTCgsQUsly8fcO3aFbruldyujuNumajPAlEDly7vI2LY2Z1zeOcWQQeCH0gaiOnaUXKbiyGi+BhpNK/CCenDZBwiyqCKMQ1DCMxmjre//e3cfOkVglWMj4BBs5rSyoNpduhCUoFTM0/AlgSwQi8QouETz5/wqRc/guFpyO1tDbSNJNfiFqyNzFxDCJGogauX99nZ2WFnZ4f5vGFvf49Lly6xf7CLRZInU0KyDRYCxigh24grhu2EGo02CW7UrO6KABEjWW2L9Cie+uRz/I6/+72894vfjguBX/6zP8dDtw8REeaqcLIEVuwe3eHhTzydtZnSc/1a/Tle3jnhn8x+JY16vmX59/n6/sdIarUme+xJNvCsdfyv3/ev8L0/8GtQNXzz1/8sf+IPfD9tm6iqGpNKwtlZl2wDooSQHGj0Q1KDjJrYOs+ZS/y1J/5VXppf46k7H+cbf+Yf8Pd++Jt4+fg6r7v6i3zNr/wR/pdf9e/wwrU34IYlX/Ujf52HP/qTBNtyphZvHd629GIYjINmzuBagjiia4muJdiW6BrUNgRJ22gS7T/adrSHFiVPtiXbRxM32mnTIgiXn1qSCWUDmo2Ml19MqrefkarYT7EGpKZAVrwgPsj5NAWYiVqdy6BUdjIyCv5X2AS/ssnDew4FlChgRw103A2QKmP+dH8DdMiIhJBUhuelbpruQ7UCO/JxlOr+OX//Md9jaaviuXBb+GyDUHcLFzGjZvWxnj9f/8ozmQJY29LV223gWL3fTOId0EgFjrF+pnfrS3ftZ5/Be/QvVTAkZHITu3vNwqjmWh1PQMLjCC9PgcMtYOJve/p83NY8226mHH/7P96eFyoUOv87Fz+NK+PDel/Gfa3SawYw07mNrRazA3FEv40qybB/Ok5zoWL0vzLmXwT9IsBnULII7ZIdboyqTDpkNVUPocdqQH2HhC6py0aP+G40JeBXZwl8DX0uPzNL0OSdXZKndpvsSiCSDH6rSJq3lMevmYFv1w6Fkov3BPIsb3wJN3/FHwA3Bw1c/um/wc6nfjqzmWzFYkoAENYhriWKjDaaCgg0soyMy+maMW+tDifWjep0xcbpmp3kqnLW59J2XdbGuV/qanLFfEVcq8WNxzEgvl8zlCpwqZjCGM1YhAQOFTMWViOaWUg1k2kTqEq2A01OmwDwYWQsjYB/jCMTieC3sqTKbCpZnVBaaxKIPyTm0tAt8atVWZZNNgRFKqdymzaTtrGFYBOQLSBSrdI3DAOqSdVqm9B7kcBegye1zPjBD/4ePvaxfwOAS5c+xFd81XcwW5xkcAjEGcRaTFlVmEmyKe0MYg2mSQsuptkhZiBKRbGNq/Ib1AjiMnBkhJjTqCji7ObCkjGIS+QHFTKQpbmM9I4bm0gAxRxIAaXKebEmxVXnR9As55PiwKnkt5Kubb9Qv+GX8u98EBEQcAcz+jsdcB4sKvs1mDVlbk3t6pXjixixNWA3vda0fhct8ADnVFZr0GoKlE3BncLAqoG8Ghjc1g7Tuk2BwCkYXJdT5ynxdZopuFXSb7P5tm0Rrb7uNhBwel/Te6nbcBsAOrWHN/WiWpdTt2Ndtylbewpc1kzC+pq1Cmld5+k9TFmS9xMekMG2dmxQZmNTWwgiZkIlLAhwSmPt+qHVIFxpTCtrlVNjDIvFgrPlMhn7v7TPi/MTzs6OuX37kIPLD+Nck9USLDdf+hSNGXj48j5WWp4brhCGE+zLTzOszvAhZrUo0laUZPcGhqF43knCvqDYjKoJCpIAmhBByWAZ4JsbIJFZeCkN6mIIMWZQJBnBdVltJq10J6CEOCBi8CEwMzMwhv3Ll3Av3UGNJahycHBAO5vnOa7hzp1jDg722d/f3+gANhmkwut6hTVqHOfOq2EXKz2mjRhJoJYaw+mqy7eRPJhaa9jZmxPCEj8YIgMhBnxUuhDwIea5tSYmgUSatqF1Lc5ZrBUMxQtsBsBQxAgeD0FoheykwWEtOBFOzyKr7pQ7d5bJfptk9J+1Sq1C9phqEAKRNAl2jUGJzGdzFvMdFvMZOztz2rZlvjOnaZsRfEOysdsiKAA37nyYb37/h1HAOGFpsjFXYxMQaZKKmijp+Wl+/qp8y/H38i3H35sdZKVVptR/lL7vQQ0ilh/96S/je/7xN47X/Ac/+B6Ojjre/Y5f3BwgU3MlVp/N6nCanDpojIixWGv4De99X+rzjaVxX8m3fNVtjBwSYwOn38D/7fvez0n8II3vMXqDqL+J0CUnHFDsr3iMNXif3oniuCSS2mgYzogaCT7gQ2AYBvqux/tIDJEhBIIPRI34kJ51CAmURYp7bkVzP0zjaFzHsZbRSr5IBOOgnRGdI9oGbZq0om0bcG1S1XAOXIvahrPZVxOax/HPJLXkjXAFeIq10f6LQJSlwi02Qarxu1EBUdNfDUDdCzutZqmV/dqW3msapojbfWQ7Bw7peRbVCAzmc9kjavKml4+zWnNSbU5jqDWKsUnFT4hpkUEDrROGYYnLDkjUdxhNRsVnVvDdimF5yuW9BX51Sn+2xEQlDEuIHeI7HnnkCgeLHXaalpvPvwghcnp4xMnxHa5c3uX1Nx6m65a8/MJNYuh5+JGHiD4Qzgy3X7hDvwq0s5bdnZ00cTawmC2YzxdcvnSdECL9cEbXL3n42mNYGs6WZ0RN3uOWyzN2dudYlzxZ931655xzOGuIqnlcsckznIFIx1l3i76PHBw8igaLN3C6OqWdJScxXRdxbs7Nlw+5cu0aJ6dLdg8uJc/DGHxMrOveB8ByeHSIccLu7lWOT5e8dPNFFru7uGaGD3B21KFDcurTZFX+xNKQZGuneB82JnkkJm0pRpJz+vS5zE51TI4XyV6MTR6wc/li1gtSxuTvISgy5kufW0Nhs2pOkz5SMuYp3951PLmMnG8jzSS9yBpf2nhFZL0/bqsy6nepXtTcmnayncZti5ct16/3zasc12D71rCt0BzuEXnUUudyrep449z9/mqm5AXlX5jvM7nm/aR9TYLl8Df+Hg75Pa9VgfcWxjkvm9+ucauT75ZOzofNeXM+J9V+0sZo8kc9fQ+lX2K6Dg2O6PeqRSvBcQvLMd311zN2IFWk63joJ/8PCBEJaeIgIbGfCNkubEzXTgBWBm1DTKYKYsyAlqA+pNFAlSKXb7KU0mIsUMkkrOe4kzAVGmt7Tuv057+5awGdBNTGhqiawMxa8LaaHMc7UCfEvI8B2xpiiW8E6wyDGowTWGj+/uZvbWtQA8Eq6gSMYpxgGsE4QRyIZdwv3/DseB5sSl/qQkmbjxub8pRyJM8TxjR57iB53mBcOWadx5R7E77a3uFr3F9BXPK2Dl/3wF39CyWoT/1Rc78kaj7O+16h98mOdYgpvpyr0pb89XkNcV1ulU593Ewfc9lpgrCRRn1SV9AhQATfJ3NJcQhoVIwK0QckgkFSfEjyT6qDYlW4/Ie+jPlXrU3qrD70CuGFs60MoikbaZvDiSmDDdgAZacMtPr9qR0k1Ncu4SJW1hRAK/Wt2ValHlOgsAaApky4bYywi+p0Ud1Ke20zLTBNU7fJtL2m152Ch9vUM7flmQJZ2+q9DRCbgl/TOk1VY7ex5C6675J/GleOC8DmvX9VBuk2sO5BwgMDbHUHXyO6hUUtwLriIQTats3HpYzkGKA2WFteRpMn3qmBDMY4jHG4tqFdzFjsL5gtHK+8dMztwyOeNI/jjIOozNuWt775jVgGvv+DO/zNJ/4E/oldcOCWd3jn9/wedj71LxADzikEk1aLSAbKI5EQlBhiNjANYvrxIzwypAorhJZnbvwFDveT++Irx3+X17/wh2hcJHjFGZsm/aoEiaPZpajKUh9ilyUqa09Axlj29hZY5zjrBpbLM2YGhn5IX3xZI/TOOWazGWdnZzzyyCP0/Yprly9z+/YhJ2crBp8FF3mUm93/wqDvwSyPePLSH+Ha/O9lYcCkSYlk9ptEXDPj0qV9utVA8EpcCmdd5LTz+JjByJAmBkaEp556UwbsTFKd0WTLbejTipkzlt29HeY7O3zy4x8haKDrhWAS4BpjYNZYbAbT0lNPziQ0Rkx2DlBPXUQEZ/NEjjT3A8PqtGd16rmd5bNhGHAZsU558wAqEENyUCEmOadI2gCG+WzGfDFnPlswb2c0bcvO3i4xxLQyaVP/HW3QZETM5PsRs2mHRkQRo3z8uddXd5DCj/zMV/MjP/PVD/Ia/l9hW9jN2xoA+7XAH72HvP9U4C+8RvWomWhTtTtHAqmmjLTpr4R6f40Jbwf6NgC/6jdRAd1QQa1VUgfOq6SO37h7+djcPU0p6iIvrSV+eJWrHN/l3Ct3OfcS8KFJ3PT4/wr/V7gwCNvVbe/2m44Fn+3fvdTxQer0S9wM0zZVeIYt8Q/yu2gR5bNVpn+Asl51/H4wYWI7LlsJUiwIG3Zp1+c8V/FcBVHERoxTxEWME+64b8K4iFhNWzfdrtPLfPs5neRvpvnOlV3XoZyvjqd1sHc5d9f8+TrT/PYeUe4L2/xBnuFF19xW1r3UTwm9QX3yZB5HD+nJW3r0Bl3lbdhyLm+dnHL54MMJLJoAUxKUWECiAjj5SBwSCBwGn0GklM/3QzofIhRQySewKA4Bg+C7oQKk1iBWHALRxwxSVeCSj1hjUZ/A3lKOFQsh0rhmQ72tBmtqIKGAOrW9J9iUs8eWrcCP4olzygicso1KmLK+ahZScfZXNMzK+SKfO+c2WIojcBYjOMfRn/1Zwu86o/miKwzPHnPrL7+XeTsbr1XU8abeZqcquLWWW6nzVHW0vufpfU7VMku+bUB5XWbZr9t4m+p83f7bQLZtDKj62Vykpl+XOXXeUOpRO9SZAkDbnvfdQg2qTa8/ZaVNmYXTc3X+bfczXZCoQb3Sx+rnfTdga8p6LXHbrl/iCv5UA6S1qu+0ruV4ylyb2u27l/BAU6aaIjmlRIbsUbLYY6s7R0lTbH2org2NnnshkBGMG4aBtm3pfEfTOHZ3d1ns7LDqbnN4dJi8Zs7b7HFyhnWW1hi+e+eP4G/sJGmsBS8HvP83/g2+6sN/Bf/8J9DY8OzBr6aPjkvPfA+XPvk9DDF5GTVGCD4ibUNsLXbZg0T81XcirmVx9IvEqLx89feO4BrA7f1/jdDf4eDk+7B2iY1nWD3DxBMcpzjxqDng4+1f49j8CjCeD//0S+wvlnz169/Hb//yn04MrJ2WPijG2Cw/K7D2iHHp0iXOzs42XGL3ffKK2TYtVnsG0gB0Z/jvGPQ96dmxx8fvfBeqDY19CWc8Vpd86qXHMPaEPW/Y3W3Y2X8M+0qHcSdgHd0Q6YeAz6uUYpP9vWvXrnP9+nVefuWlNOBJmt3bxuKaWRowQuDq9Ye5cvUKt1+5ydnJccLFjOBDNgapZWDJdOxML7hY9znZ6VNVJP/lYTEBh14TSGvb3Gep1JoZl9qtJa2YqkmaIkEZuo7jOyvQw8xyUMSY9DEp7EDy4GLyy1lYKdbg2sSm3N3ZYbEzxxrLbLHg0vwjwK/PuVPnftebf5wveuK9iXaucbyOGIOxQuMaxBia1mGzioYxktlza3BvPRlSig07VZCiIpjP5V5UtaKOedIsPE/ScpLUrmtoU6ioy+jIMHSNQxWss1jX0DpH0ziGLtkmSqquMa3Yaior+D4PsMnDqxbWI0ldNsakHhtjYgBFjQxDwIeB4H16Vj4QYsT7yMdOvp6XuncjBG40/4zX7f0c7/3N30Z3bQ/eJ6kTDJ6DH/weZEgqGwwehsAd+TdhtQfXUsM56XnPoz/Fwe6M/cWMRhRHoEFxRPzqhBef/SQzURqBvcWcSwd7iCili5VVcVh/ZENUgiZWnx9S3QefmIEh939rLSGu2X3GJjuFPmZvxZQPtOS2InnPDQEfA4MPdHEgxCFd2yjO2uS0pU3s4mQ/M6m3a9Rs54+kUq5CsI7oHCHbrwmmeOeyBJsMNEexxGxLpxhTjtlYcnSbasOjXZzs3UsyC4riJh5DCJm5lBGCmPtJTYvR8o3L3lofVCh8oHBP8qluAUurOPI7WWwSSlp0koyOiihrcz9Z1ah4ctBkp9IJhKEf7cw0ohgJtFZpbaSRiI0BGTxOAzp0OAJdd8bx7ZdoXGB/0XL60in2aEVzeMp8iNlbpE9eVX2AmGzkzF1Dv+qgjAVWiNaiziCzhvbyDp2LaUH+NCBkz24u2TqK1mS1NEmelJ0BZ5F5Sy8RtzvHW+h6z6zZQTF02bzBRn6XPDKrS/FqLbhkn8k0DdK2DDFkO0ySmCE222WyJqnfWYNpG8Q5+hgxzhGNrMvLaUs+bKprMjw+riD+0g3eJyZQjCPLJ7GEch8LERnKuXw+FnW4OOYlhHU5ZeEp1swhMqOo2GTSsaykcqepr+U80WfHD0quUxjrsN4GJGR107BmLUnUrFJX+qxCjOjg0dHQdxpZauGztrk0nbTXHuxqQbAWjgaf2fxSVEINQUGyLbCRnmMMr7znd+KvPLEu/+QmD/+ffzq924U5ZBO7KKmUGUyT1cgagXlSizONRaxFG0n2khqDNBbjLKYxo3qZlOMmq5+5VG5iESUVOclMI0xaWJTMZEoMpMJUkorlJGQLECNDqbCdElAE0lh03mJMBsxMxGiPxWcVOYMpoJJVjMvOSr5gVeHuL8QhsZKSA/O0JQ3d+VihAz1Nc83R5mkAzfmIed/ruTLI5URfwCbJIGoVV+yrRjIzKuULQ0CiQEzaGHhN39MKhI2DZoaSEL0iUQhDsmschwRYGTVjeYWJGIe4rmP5tE2E6TpuCvacnj7KBz7w+6g/lG9849/mTW/64dGMUA2oTFk2pRxVHWXNGtQqcSVPna921mJMMuvS9/1GmXXYKCd7IkTSDNpaQ4ypvkHWthKnwv9FKod1m9TXqwGl6ThUM43uBrTU5+s2qNuvtpde2q9mZ07vZ6xzFzj5K+8fy3KqaHYQsM0uXQ1ibWNCXcREqtNNbYWWdqk9Lk/LrcM0bht4NbW3OWV8TZ9LDR6WPjfV7nu1sA0QLP2yvrfpezBtuxK/zZlEXe62607rvS1NDYzV7bmtb03zblMtrUHdbYBXvZ2CdqUu0z46fR5TRl8BjS8CBy8C+u41PLCTg3KTSfVr3blM3TAT1BLSQ/HeX4i4izFplUHW5YtYQkgIu3MNi8WM2aLF+57VcoVzDbu7exAjTdNytlyhuwccu8tphXJFUhND8Ozxo1/+7dXNpN+RfhPPhr+ADEPyHtavcHNorrW4NmBObnP40lXi3lUA5PYd1M/gxTm8wIaK19G1f5uja//2BY2X7C5onMEZYCwvnj3Gi0vl6dtv4p89+9W84dLH6ZfPo/EWz9w07O9GHmKHm2eXOHbwyKXH8O2cVVwSmIMUdb/I6VlXGbtMKow+fklVgcQP+8TRd21U64//pYuft5EOoUMkWUw35GPTc3y75eiZfW7fep6+P2Exh7ZVrBmwZkB1RRhOeeZWjzEdq9MvQsMJIh2t6WnsQNtEnPS0tsfZjsYONC7ibE/bBIykOGd9mpCKElWy/RYSC03WQC0kuwpkI54SSz8CHwO2DJxZlVPEpImGJAgpdz5Uk8qVUQgx4kzyZljOp3cvgmb7aSHiB2W58kTg1q2j/J5kMEA/wFsfu86HP/3bAHj9tX/IFz30x7DdkAbOkCaaGiPYBuMaFvt7GDHs7+wxb2aJyekEJeQPlxJjep80/0lcq5e28x3auWN3p+Fgf5d5O0vgWwFpMqAllAHMjMB2VE3sQcJ4XjOooRn9ibkdUlkQs008JY0FWrC/MnhqYoc626R6a8xl68j4a5o2A8ktzjmMdYBmkC2p3fphSPsFnPKR3v/VvFKf+kLTzvjYQ3+D/+E9v4uT2R7zvuPf+md/idft/SR933N8coy2wlF/zKcP/yk/euu/ZRUfZc++yB/+uj/PG699lCeefIKHH3kktYsCalGF559/gQ8d/OKobgvJHkiSq2Tsh6mtQmqrmPpUCBFrk32OdjYHhPk82R5cLOYsdnZwTUvTZtfTEcRall03gm+JJWkTo9RHzs6WLLsVXd/TD4FVPzAEj3Mti8WCg919ut5jG0fwMZmS07VNoWLAOir4YWDV9ZwtVyxXHf0wsFyu6FYryO/CkNV7U39J6vQaIxIyiW5FWkARg0bNWruKMdBEz47paWXFoB23+p4n3/ZOPvrsy4iDPixxraHr0th+584dLl26xOWrVzkdVjx241HCEAgh8snnnuHM9/g4Y75/hdnOHtE4gjh0Nufg6kOceOhtS980LK4+xJ2VcrgMXLnxGHfOAqe9IO0Os/19lr0yqCVKk34mqSGLbYjiCFiiGoIKIo6IoJpVKZNlHYrn5BEYHIHC/Ako5/J7Nmo019/yadxnNY3eWzqAz3DC8fkO4odk1yhG1PfEUGwfBUxIRr7xnkXrGFZJVXmx44ABwkB/dEZ3fAZDAJ/yBh14+NFrHB8ec3Z8Mtpraq2A9sxmloeuXsKvVoTeI0MAnzxoF0cKlqRy9Uf+4LcnpwUP+jPwm3/DNyebUMQEYhXj4JUAWsJqtTqndlIvetaGgIENA/v1BLoIO0UgWrO3N9V4ys/YZBtWnMG1DqwhaEz2lWyyX2ScRVsoxrZVGG0tSQaRxEoCbiUBvwkoSraait2lkl+cZcN4d7aFlEwSpMYr9pGSGp1NZWXVvGLDqeQbbShJvo5d5y+Gwov9JXHfjc53wBmMRAwesW8fve59bkO96FZC2Jbw3krLDKOiohZNi7dzFIN0Hfb4TgJrO2XVP8TQ76PBYOIZc/kExJBswz78RoZ2B3t2ys4zH8asVhtlT1XsCgNJlFHlTVTGdBI1g0Apj6gkxlNWcwu9RxAshugDTdaC8d1A9Em9lPx9NSr5/pK6nAalX3YsZnN8P2Ax+GGtelSAiW3CWx0KSaEpQA2bgmb9Po7vzkQtbpSjJu+mMQaX69L3fQKjJ8J1/V5OWTZ1msJwKnEY8OLHe6hBn8K0KWX3fT+SKhaLBd77UdgtHl339oQQ7vDRj/5+Qtjh4Yf/KY8++lfo++EcOFa3RQ2C1G1aeySc2vkuIEwNiNTjXO1deHrNcq+1dtbU+2Ehl0wZaTVQVZdf16Vuz3JcA1TlPurrbSMhTAGaclzbMKufewEonHPjcan/Nu+J2xYq6r5X+v5UBbTWeCv3XtepLGZMr1GrPk7bs+6vU5teF4EkU1yitGtp77qtpv2gfv51+XX6+t0tbL+acVjnmwK9pR71AtBF9gzvBqjWbTgFq7aB1HV96j61DfjaVv9tYN90XlG/E1MV4PLe1vmmz2t6btvzqcuut9NxblrXuu9OQdT6udxPeCCAzcgm6lc3PpllFWJGbysEsV4pqENNoxUgqGKkdLA0GYgxvYhGLG3j2Jk7REMe+B07O7t0yyXGOu58ydfx84PjxtEtXmivJvWGM0CVGQOv6z/Ji90eJ/MbcAgsoRi0VDtDzQzsAaGH3esvMZt1eLPHpUWkPz0idBa/vyD0Dn0D8CbgTl4p8pJWUzMzRoaQtzGxqrwS4+VE8T8GENgD5qktP3n7Bp+8dSM1jAK32ZwLWfjQs8Cz+fgqcFV5iQh7HmFAGODyCvQMiWfEsEhA4zFpohNh1/4QVo8RjTQY3vSG12WB2GKkYbn0nBwHOu/wcY6PLSE0DMER4wEhOLpeeOVozkuHLfB4qs/ddLdegyB4jOmx0mNNjzUDRnqs6XLcMIkbMGUrHUZ6nPVYs8pxPc70OX3eN0OO6xAZMPRYu8LJgK3SQr/mjWliZiUmWgZdpADIhfimvOepP807X//niMHQNLc4dm/ksHkb+/1H2Y9P04cEBnofiWcdt4+WDDEQNGJD9qKa2S5RQTVmkCoxxNQYxCfwwViLmc1ws4aD3TnXLh/whtc/ytve9jpiHHDW8clPfpLTkyUhxMxOS4yqGANgMpCoLHYW7O4k5uje/i6uaWisSyyF1AKoFiYOyTh0Bp2SEFFWezRJgtljrCWvQklE1TMMnsFnV5JaQKpUfu4ACcgrg7MRhIB1lk/uv4WzZpd3LD/Ovp7Rto5f5p/lb773z/HK4gpXVofM9z1nb3+Svh9Ynp6AMZycnNL1Hb+6+zbOuj2u755w6WCGArN2ltS8NXP9NIGGq1W3nriVsSurDWssE4Js20XTxzd5uk8sLOeSfUU0jX1dd0bXnfHKrcJ2ykCngqgSTWKaCUIfB5xLdQqa1KljiMluihha17A3b5nvz2nalv29XfZ35plVIUTv6QdPjIqIBSP0fhhZc90gDL3hbGnpuxZrdzHGEhF29/doXEswjmgcPsKApfORs97Te1Bj6YPSo3gMZ6tAFwLLITBopBt67oQOJRCMYdCGl+f7LN/qcDszvEYWB3t0fuDWnRNWQ+TR1z3OJ7uBo9WSX9zdY+U9bjZneFyxs+QF2bYLZju73L5zzGLvANPMGILS9R6PgLHYdka/E/EqHLZz4lzANmAcRwpxxxKCjIbCR9ZdMRAu5yebv6TCdDI2Hq4BwA21Y7bs3237oOceJP/IAtRs3y/tF5V88laMIpLs/Zn8ExsQExLQoeln8Ikh6JRZC77vaWxA9ZjWCTqsmM1P2TlY4Y+XSO9hGIjDksbe5rEbjrPdI85uvYL2K4wf2F0Icxd4w8FDmJ2B4xduE4fEIgu9x5CM+lsn7M4M777xSsZs0kJFgW41LakkExbouO2ILCfnrn3DVWzjEgjkJC10GkbwJ2YwCoEgOoJAI6CUDXKvjXOvQaM1YJQAJGPNmHYEt0q6UpbZzCPFqPcv8VDU09QXEKiybZTjY+czINSh/mgEbMa0dfpiN8nHUSVtVE3LdpHG46BEHyowidG+Uuj9hspcAZbINpWGrl8DThmkGgGyDDT1qw6jMoJJaKq3E4tRxvtzxuZFphREErNkQBDrMEN3jpHQhRuAo5FncW4tS7Rtm76F3tPn9MWQPlRAUiU0AjRNsxV0qAGRGhgp+WezGV3XQRGAjaHve9q2hZjsy5ZyImshMWT5pbWWuPJIXlisGVNTgArOC4NT0KMWRuv61mEK9tQMlVrAr69bwLEip5V6Tm1N1fJZnbcGUmqwYlrnOr4GXWqA6PDwcOO+6/PXr38v169/H2kClNInUupaKK/BryloX9dhGIaNBYJXq3+pi7WW+XzOnTt3mM1mo2OI+ro1UFbAk6KCVhYwxn4zAdrqZ+acG8HVWi2yBo5qoKbE18+ttEkBEkudSvopoLDNc+IUqKr7Sym77k9TAHYKBNfgVg0aFWC11K8GL2qbiPX9lYWbMq4sl0t2dnbGepX7qK9d7qWcs9aOz7HUq96fvnfludRad3UQWTt1nLIK62ddgNYaaJv212ldtgHJ07avAbGu68b4bWE6dtZxpcxSz9Jvy/3X/bW0Y83oK6EeL0pfKf1sOmY3Fatx2seBjXe0vs96bJ167a3rNgW/y/3V70I5v21sLarQNYjbNM25e77X8MA22Na0TTtObCEzgCRxUtZpyouYHAjU8esOtEbuG0ngBBisTYJqjB7E0rhkHNpZIWa38846jJsTY+C/33k3f2v+Ran8xyMHP3PMUb8HrXAQDvm2s+/i2q0P8l+88qcSJiQkAC6A8R1OBwazMwpSb3z30zz8jhfGe/8n3/vNdMsFoFjnca7D2oC1AWMChmy4W3U0/qqZsq3eEAch9ieEpSE+7/DHFv+pBj2xmW6ta1tKUwFjCxKfT1AMpSizc2uTY7gOCTSBU/36ddkKL288YLLHwYigGKNYiVgbcSYws4G28SxPbnLlwHFpN/Lip57G6SlX94TrB0JYvUJcHUIcCKuO1dmS48M7rJY9IVqMWWCYAy3WzhFmqMxR2rSff5GWqDNiTNugDSHvx7w/xF1Ww1WCNsTYop9DYzFGug3Qzcp638iANQnUM9Jj6Edw0EjPSfN6Xlj8yqR2ZOHG8h9xrfsxRFcIK6DHaE+UJSI9VgdEepQlhj6BqboCWSF0qHiEpHrXaSD2kW65JOgpooKTFxl6eNc7n6LzHcbBiy+8xIuffoXBK6JK7xPIFUNItgjzADabzdjd3WG+mDGfL3DOJu+nxmYVNsVaQ9d3OJdUENNAlieCRpItu8WCxc6cxWLGfLaDdXkwM4kNpXnyPn4wNMvSUtRiBZECdmXBQIU//9Tv42euJDXoS8Md/pMP/rfcOHmJhKwf4vSTHFPw3wCanIKIRC7ttuhuA2S2WBRUPWjc+KABCQhE+Phznya0C3wU+qh0Q2A1CIMaelU8grgWXEu72MW2M6RpkWaWPK9mdcpAUnGLktUxs9pllKyWKUmFMkhW1RSzcT6IRXEpvsRhCJJAooBdb7FEDKGxxNaMeaLkcst18jVTfLpuffz5CJ+4n8RX7n5aQmYxaRqvixc34kBjwMUB9WfZQ2FSR5vPHFYivltB9GgYiL6jseAwtFaIfUd3epYcgRrBidI2wnxmODm+DT6waGcQPK212CjcfPZ5bFTiMCDRY0SROHDp6oL5Lly6toMQuPPyK/QnS/Cew5u3+GVf8W7e+sY38Tf+p7/O3LW89YueYO+agXZF0DNaa+iODqFTbHA4cZjo6U7OeN/73sfly9fRzvDQpYf5yC9+mLZpaRdzHnry9QwKg039cvfKJZ576WW8NFx57BF653jz29/Dd3/3P2HwDcx2kN0FBzceYuf6dexiF9vscNIrPQ3LoAw00LREa5Gmod1b0CwWyRmPSarlyXkC+Zc8N4qRBIap5u9q3qIpnpjaq44raSRiKMBadc7kc/ndT8d6wbm8FUVMBuaMZpb+Ykwvsj+WY8w1RK5Oylpf67I8dte++d/wd+6np28Nj//JX/UZl3G3oBVbqd4SY1Zri+gQYeVHEAmfnevk9N7NkRAxy2ViFFXnNNs6Iu/HbCg8ZjCoBniS2puOxrqLDaYEMOkINKkPxBDx3ZDV3QLR+5wfQu+T5kTQBIBmYElUcMZmny2S1ejSNfzgRwConuwXoaQILFMWSMlTg0CwudofY3JqFPcWyMkSyYLvlO1QWIK10FcEw3KuhFrwrMsogvRslmwmmaYhDkM297EWPKL34BSMyez0BMrXTIHCILHep/cvs2JqjZWmeSVfM31b5/P5OfCpBk9qgbycnwpXwMjA2Sas1QJtLUAVgaqkrZ9TDQLU91gLaSXdNiF8ygKpBehp2ulxXdcpoDTtK6UO9XF93U15TcY+UkCdqepl3SenfTNpymy2Vd3vp2Fa1vR51PHpOP2m9zdV1dt2r/U1p9e4qF1qALZux93dXdq25fT0dOxr07rXfaIGd6fPpOQpoNq2vlwDpSVcBDzUz6VOM+2HUzCxjqvfrwJ49H1/rv7TMqYg2BQom4bSR0q+wmwr56aATQ3qTNsPEpA5bbdtoT5fxs3p/WwDz7b1/+l7vg0UK+XUjL2Spy67Bh8vAr1KX5sy6KZ5rbXMZjOMMSOgPL1m3Scvej9rYLG+rymzvX42df+rVTNLKOPwNvXnbe03be+6PbaBhzXjctq36jx1e5Z85TtZ97O6nbYxKe/Wx+8WPmMkIlVQsppeRXEVM55LD6og1BFjJA8Ypopfo+upnDW6lBg9nrZ1ia7vBGMtQ8yGE43FNY5nfJPANdW0aouweveMr/jkP+FTH/wAX/bkQzwbWn5y7zdx+5E3JnZYIHkXNMq7bv9DHv3KY2ZPNfgQefHxJ2h2O45f3OPOrSssT3dZNGe0tkMRYjTEaBmGhtVqTgz30JwWWOTf1dKGEed6nPMYk8G6IkCQVg8l20pQDzoI2glxaYlnhnBiCScWf6chntrMpNNsuDxNXPEZuCvgXZFmRsFGtgB4ybFAMdGwcdADcpXDglrYtwDw7IqkklsDhALs5F++n1IZ0YAQMCSmmNEOy4pGljRyhtNjZuaImd5hJrdYmJdZ8BL75iVmHOE4w9CBanKSIEJUS4wNXh0xzkdQTmnphwTCRWaE2GSgrk37sSVomwG8Nse3Of9sfRybnK7Km49DnNHr3lhW1Nnd+8MBkE2zvNB+Ey/oN51PM51HbJtXKLlNU3vWFo4lG+8QPM+eGn7gextEPNZEhm6F9ys0Box4cAGx2fKzekSTpXshYKJHTvNPPNbkZycBKwFrIsQea2MqK/PtihW35KFWcDZ7mjWGTLqqVsEjGpPX3aT7kX8Zc2+spWks8/mMtmmYtS0vXH4jP/PCe6AD5nBHLvEdj/1xHu5uJhtOIqgkNm2UpFIUsw2wKGY8TunMOp1ku01iiC6nIZXFr/9Dd3+uX8DBxDCqjSWgKWIzmGQ0YmOkUT+eq9Ml9bq0LzFgc5zEZEPJZPU40ZDU7jQQw0BjBTHKzAj0PU6Enbbh5PA2fugwc4fHc/jKTd721FPszWZ8/GNPs+qW+L5j5iwmDqzOTlC/gtjTnx2z6k65cnkfNHLl0gHv+uK38Qs//ws8cv0qvj+laQyNsTixdGenDP6YF29+irZ17Mx3mc12OTtbISK8cus5rl97hMPbt9nZndM0hpnb5fROx/6lS1y+fIUYIx99+mN86bvezSee/QSLxYz93QOOj+5wdHSHO3eOeetTb+Ho8AgRg5kJ+4/M6PSUoXe4Zo5zlv3Ll+nOPIfmjOihiz4xfhqQmbJ7paE5aFlpz7WHrnO6t0RMRA0MfhdrP8lL/R1+1b/xZpbdKTI7Q/cMna5oF3M8DqsLlquefuVxzTx7Bj7gsV/+MM2sxbUNO3t7fKl8Kd3QY62lmc84PTtjYdJ7Md+Z8+Z4IzOfElOjnz3DN37xE8kDqi3qdytM8+lkY6nJ3kaNZHW8fzkYS1ETqBDVoGrycd5GQ1SDV5f2Q4lbn9OY88ecP+b8MZ1L8Tl9rLYlja7TlPLSdl2Xddzk3N3ylfqpXHwuSrKJKHkxxGiyuyV5WzwXTn7UDMIMfGphxhnF2AE7nCISEOsRExAXkGZIzMIYkJDAZwlDAsejh5BMeZjgIfRI32PiAENiDpphQGwy9YGm9KhHZcisa0+MAxoGCJHQrdDBgw9pbhOSCrANSkRweWVfRIrFgw0hu56E14BGDUL0D72ZT33jd9BdfQOLT/8cj/8f/xnm+CXgPNMpvOlx4l/8o/D6R9CXDzHf/ueQn/j5jYl/CTVIUOpYC/MF6Cv1qAGmUuciGNYr/bXAVsqq2Sw1QFHb4yn1KEJLbf9mKoSV8mtBrL7H+t6manKqOgrcRbgqMkRhtU3VmqYCbbnWlMUzFYCnoMj0NwWZtoEbJb4GsqbhIoG4ZvjUQEddZl2HeluDVLWgWdetmO3ZBoLFGEcndbXQPdVImoJf24Cw+jlM2+KidirtuzIPEdxVdvzHEb03t+t3AySn7QupHxWb3zWjbGqHrJRVA8el3vV2ev91XP08Spj2xal64EWAxbZ2n4JD2/KWc23bnlNJrIHE6T1su8+Sdwpw1u/HdLHhoveg7ns1s65un1L3u4V6PC7tUbOzpvnLNWrgedt9bgOAtgGQ0zw1aDYFFafvfM1CFFkvoGzr02W8vahdp2BifZ0pG7Bum21j0bZ61mYnCkuxZvOVcmoQbKp6PB3bpv1kW/+dAn/1N6hcu+5z08WRurzpO3SOXPEA4YEAtpq2mbZrBto0lLrV+rY18r852K7zlRehrMb9/9j786DrkvyuD/xk5tnuvc999net7a3q6lXdrW61hBBSW0Zgg6QREmBgxoDHEzCewRGYMcvY4bBjhhiPHQwThAfwH8yAw8Y2MEiAxCBAIdAG2nqRequu7qrq2t+qd32Wu501M+ePPHlvPvme+9ZbpUb2RExGnDhbnjy5n/x9z/f3+9V1zWg0xuI+nkVRkCYJSiUomaCkZCFz//B636iMzz39O+Dp38Hbceb+ERu2gxV84dLv5UvnmsnzC+qyoPmV7IIR0T5RBxhZDbUAK2F1l9E3/gF5dYdE1chxwye/61uxY8krd2/R5mP0aESX72Amh+hiF5NNMEmBVTlGZhiboI2ibTIqnWDMOzBF8n7bv3hZSE2SaJRyAIiUHqxzDCUP1tlendV0EtsKTKPQtcRUiq5M0EvlALoGRGcQrUVqi+osqjXIDrqyJhMpVgvqssWa3g6RFZsFf89EdJQE3zYCbyfL43APTkePENYgnt0kYj2jwSDwAFCLVC0JNVJWJJSkYknKglzMyTil4JRC3GeH1xiLW4zEfVJKJBuKuVLJBcr5GgLux4CrXk/Bdv3ZGIslo9MKYzNanWLtiMZM+ZnLf88BrhO+ScHVtSV9oJp8WPXbOux8s979Gwxqy/FQaLnoZvIW8DWc/4heu3rBDotthQuB5vhYR9fDe0Pxtx2/m7i/mc9Zeibbv0om2kbAlsr0wnjP5lFmfU0qg/DHwb073/D3vh2ZW8RoE1deejCNWf/sUlpuCoP85O/krf7exfebTTrKciaD+/3xqXJxK2VplGUl3f1TZTjv0xlJy4vKINWnWErDqgcbcmm4oiwz5dhQMnEjbxHUjHfWWvXn/ez4wId4/VODYa+oOrpucUNCctFDazKQdhh8XN8bWiDj6EK6QtOzsAXCCDrdszy16H0vCPfjp3bXrAVrnAI4RtBUGqudaQlnK1OAcc9iJF1taKoWYSVKpdTLFtOBsJK6aTk+PEa3lru37zu2OgrTGsqyYne6S5qmLOZLMpmRygLTWs5OzqmrFikSqmWN6CRd62xJKplSlzW67ZAy4dKVS2RJyq3XbyG1dGp8jaGqKq5cvYI1cPvWXUYjpwa0Kmv2DvapqpZF2XBw6ZhiZ0JZ14g0Y1FVtFpTTHYY70y5e3IfVEJajEAqms6AVM4xQ5Iy2pnQ6A6ShBtPf+BB5779/7Hefvr6f5n/j3Zh/Tf0M2bbD5qh5x6233zw3C8TOxAPWDsgideDYsvxtjiPcj50Le238L4wAQDIBgD06sXx9QcAQgBnkF8GP7KENe5HFAZhO7Duh5awHRj3k0LYFqE7yisfIlMpGRrxsQ9x/um/yu4LPwNaozuN6JzjHdt26D/yu2F3ipgJyA7R/8X/keSH/5xzPtL/dHbraev0F3rPOlJJEqXpuhaZZoBFonvnThazVpvpvbRb5/1c9uvwNE0fYHEBDwicIVssZK/FqkXAoDDl0w7TC5kI4fU4LyFIErL1PGAXqsGFAteFLiMuGlD3+d8GEIWCeizoDQnfIegUA2EXiQQ8cN/X8xBY8bC8DbGswhCrKobxfBttAxL887ABRmPG1ZDQGqaxTVjfJsCG4MWbu3+cm3t/HIC8e4sP3fmTFPrm4POxUBwDeCFQFQJtnhEUguK+T6+9ZkbCdwzcxsBdmKdtIJh/bhvAGY+lON34OJSpY4BpqL1CQCWU030e4jTjsA0k9eUK3xfG2cYIDd+zZsRGfXMIBArfFwNgMQgatnEYL2Rxhem8Ux8Lf2DE+QxBym1M0zCdGDQauh/WQXgc12NcX0Mh7sPxu4bAQh+G2GBhWkN5GGISxiBjeD/+geXBxvB8aByGdR32qfAnU1hn4bW4v79XoO29AWypM+hJr/5l0WsPh5sPbKgKuungSe+6OAybj5yzWWSMQQJ5nuPtBIkkdemozSCVSoGSPZwCT+kZT5s5r8jpOu0Pn77I+9/+HF/4+gs88b4Po6RhJY743N7vhA8KmLEG0IS1HB3c5Q//of8WcAvXpsmp635rcuq6CK6Fx5+mqXLqMqcuC/7FKqcpc7o8cSDdDOdRq6uQ7QpVz1DlXbLqhI9cHXNYLJhmDdcOU1I9o1reoTQVKwV7N56gHe9wt9W8dGfBSu3AzgG6mFKKFJFP0DJDk2JI0FZhTELd5OguwdqHoPwCyPotwiOk6tbqr8oDdUKvWXWpAas70IKiE9jWYFqLaSS6UejKomvRo2eOBYVuEW2NqhtUVcFqCbMFqqpJWlCdRGiF1QnWphjGdKLAiBxtM6xwZbRCYb2FGg/aCUAKrEi2q8m+22DtRnhoN+AdtjdUTIfqVTmVdeCdYklql6Sck4tzcs4YJffJzB0ScY9CzMjMWxwlv8L90W917xCCnfrrfOyt/62zF2AVbacBhQ6ogEI4JsGHP/oxssmU81VLaRPu3r5Nc3qHrinRRtJqaI0BqbFIpHVssY88+yS//bd9kqo2IDLunpS89toJ83lHoxO6TtFZZ2tPG0XXZRibYkWGtRmWFIvbG5w6rrEpiNTFI8Xgjg0J1iZOhdH0z9ikv54+AAS+J2EqPH8VN6OFYJkHl7QG24Jx7DuBRgjtjoVj7wkcq29cwGScoKRhMsnIc4mSTkVaKYuSlrPTe1hbkyiL6Nl7QnSOvZdaVKJRqUGlDriRibP75I9JpBNSle0ZtBKhrDM75416S7u2leQZHygLit5LIo4Z5PSXHIitcEKjohcOwfYsEyQb728Kxz7qnxXSuvf1x/hN2X5cBcKmupiOWKfb3//N09D+VxqM7gEjDyaZi+fWSEwne5DJ7c36vmMfYSTWKEyHO7cKqwW6s+6HhFVY3TOGOtCdYzlZoxzrSPtjge7A+LhaYLXqnxNo3TOk+p8cuhOOtKMF1iaYTmC02DxvVJ/vpGdQSWfCwPSbFhjtADL7rxSM/Z8+vP3KwMUE2IF7Hh3dDe5lcGpYfzcXDXDyYBKLM5yN1z6UW+yTLoPjLz33iJn+nzT4n2T/vxQu/tD7n0e4ym0+NnzrPxq49swv/CvNzTpY9zcmgEEIa05cuBafBwLVUJy1kGLfIZ04TrS3PehpXXwnCD343NDzYiiPCWvAUVwoi+jT6fMevnOTCCgzHH/gXTb1I2igrEH6D+TZDudL9Hg2xvTLYNtXsweGrTPdk9v1M+s0rAcYAoHYbuQzp33g8mYT/z6xyYcN8xm2TyjjbfLu63dTz5s41poHrmENjTrizuRH1uvkOrnOawf/Bz54789tYgagw1AYEpjD4IHPkD0mhLMLGDpwCcFfz7LxjK9YtS8Gcf17Q+BwKC8etAhVcUN16Bh83ZbOUB1sizPEQB0CJmLmY1zvQ4BPyHwNwe+QDTu0xczI0M5W2G5D5YzzFoNPYdwYBArzOAQEDT0bg4shA9efhz8jYpB5iFUXgz1D4HQYYkB2iFk3VFehDcgQaN0G9MX5iMlT4Y+POA/hD4qhMAT2xX0q7BshSy1u77AfhKBuzPaLyxWz7sK+927DexKFjDGoRD3QCL7Am0I/iEj7STb8AxbqrwuxMUro/qa5LLZt208CfsIKOouwpFlOJgV/rf4sfzn/EK+1ko83b/Kxm59lkUluvf1FnimWXNEzLlf3GC/v8wtP/iH/VSVJKn7oh3+cyWjOcjlGpR1CQFkWaKUgtxzv3SVPa7bNY6uzEeXphGxck+3UZKMGYyV1k9P0gNzm2AF2TVNQ1xmv1dd6cC6nKQvqcU69dCCdeVGtAQOha1RbkrZL0m6JWdxFVq9gVnew1Qk0Z6h2TtqeM9YLRqqmbM5QuylybwoHxySHV5GHV2D3EDPeh/GUydEVKpXTktIJp17ZWYU2qndoUNC1CQ9dWPs/x+MHbzlwwv1hVTJHkCIZIdh3iyUDthPOqGkrsZ3A1ArdCkyV0NUS28o1sCVtjTI1iVmRtjOK9pxRe5fR6i0my7fJqwWiNOgmodYH1OKIRhxSm10admjthI4RmhGaogcns4eDd/TgXX/JD7f2weI+Wij7zQLWsuBZfmXyz6FXm8V2SNsgTE1iK1Lh1GcVC2yZk3Ylq/nbCFWhxSXU5C5V/Sq6PGGUdhRyiTRnTk2vqZForqqKDx/s07QVaZrRHRk+d/YlTrozVlVLa1ssksa0aDSddWrc1lxcnEgEVkCiwDkgcfe8EcvNhOcWe0N/9qwFYzMsOZ1OsORom9P2Krq2V+PtdN6r3yZY6+zybdR4czqbstx7itPLv5VL11ZMdkqe/IFX1+9p768wnVkb4JaJQCRetY3e+LdAJhaROOaRUGZzrvzeIBKLTAxXkp5VlRiEssjEItP3xMP8n1/QBtF7YcNYhN4c+3toC42zWeTjrp/RBqyzp2S0cewMnN2jrm2RQqJUgjWWk/snKCHIkoxUKMqyRCA4OzlhPB5z5fIlynKB1BIlEnYnU269dZNUCYw2jPIxl4+vcPO1myhruXx0idPzM8q6JEkkxwcHFEmG6TRKSO7dOeHtm7fY3Zmyv7vLm6+/RlOt2J/sMZstmBQTJpOC115+nffdeJbpzpSbb73O9WuXqVZLbr7xJlVZI6xgZ7fgySevc+veG6jC0KwsQua8/cYtjHaL0ceuP461cO/uGVp3HB1Pma/mrJZznv3gsyRZyuWjKzRlx+tvvE7XNUiVsbt3yHg0odMtbdswne7w8msvYG3D5ctXOT2Z86EbH+Vw75BKl6R5xr35PbLdDKHgrbffQAk4unKF5XLG6ewe+aRgOtlnZ7THnbfvc7BzCG3Hl7/8OR574jKKjHYuyOQIqwzj3RHJOGG2mvH6y2/QlQ2/63f8G9y+c5ePfPjD/Pw/+Xm6RpMohRGgpCDNJTJPKG3D+HCHsqzZ3dulbhpWy5b5rGSym7FztM/OwR5W7HB4cIXbL7/Ba196jceuXKc9Lzm5d0orDOPDjOMnr3D75A7v/8CzLOcVs9mCm6/eRJcGYSSHlw9o2hJhU1ZnLXXVURQFtdWkec5qtaStG2TmVFeL0Yj9S/uoTFJ3LdevP4a18NVff47z+3PScUKSQW0afs8P/X5e+eobfOnzz9G2HUmmODjeo9hNuPL4Vb7xjVd547W3GE9GXL58Bdsp7rw9o1zWGASXjy/x9q3bCKlQiWL3YJdinFGWFfPZOdWqZbq3x3Rvn/snJ/wP/8+/s/6n49eY4fn62kPO/+yf/bNOVVAqgnUq2lq09kKSE2WNtnShp1Dh7Gf2J+t4bsHZX/PfQut+nvr7/jmL6Bl6GSQpKGcj0krF6v0fZ/nx7woEc8HRT/1dsttvujMhHGLv0+nL7t7f/2xgo67qbf6u8+rTFeEC2hkq8D+n1u9B0q3VjXoh0tepVAiVgOy9WEvl7AU6mhsIicGp0bp7br1g/d8GqbDS2dBESqzKKI+eWb9HSIsyFdObn3c2WL1DHKWwCLoPPQV5ytoDeavJf/nLAdVfbPZR/v2PuIv3pS/4uq7itmQtkGze4X6gX7z/wLstvb024RvrQv9Zt2Ww93Uewk++neJ282GdRuB22V5IJ+iv8bV+nR/GWSNTQf1ZsXkmfOfFNeBAnPU1n+/gXDwozP3/w3sIvl9ZS5083Jaliz4M+njhOQZwhkAIpdR6TZskyVoO9bJvmqZrG1+hyuwQIBGz3ELQJ1ShjO1IPYyN97Byh89si/Mwllx4LVYPDtMeYqmF98P6eBizaCiPoY270KZkCAhuA1fD81DzKLwe5yXMYwioxGUK62eofreBudvAvyG22BCjLAR+wvSGAMq4n8Tv3vaeOM9Dnj3DdvB5C4HfISZamM+4PHFe4vqLx23MBvTtGzKhh/pFOOa9DcqY1RuDvmGcdxveM9cgnixitFOIi/TL0IaAfz6mHG8qpUfqYT2w1nYkjFNHdWqiOWmSkPYeXEBwbGv+s+rLLO69SaYW/NN7c4qnn+TGs+9jNl9xkApUmvBDs7/Bp37qb/M1rnPLJPz6j/wV/vGP/gj1ZBQW0q1LCsFaa8ZalO1IdE1KQ2IbumVBojXdcwmLX967UE8q70hGLdmkpphWFDslxU69AeHGDdPJjKNJTTatya42ZOOafNKQTWqySY1IDE0bsOUiwK6uD6nraxcYdM0qp65yFsucpsqdd1MDdB2iWSLvzFBvzEnqM7LmnBtP3GN3VDFNao52YJo0ZHbJNFcUoxG5ahgXmm5kOc/G3OkKfuJffoZPft8PcL+zvD1vKFWOTgvaJKeTGZ1I6EjQNsFYhTYJXZfQthm6i9hLca9McI4WHggSkTi2o1QKREHHPksMKy9laEALB9I1Tg3W1BJdOzVY2wgwFmk6lG1RpibVC7JuTt6eUrR32SnfYmf1GvvLF9it3kR1NdZaWjOiFYes7AElVyjtEaU9ouGAxu7RMKUTO7R27MA7kTvnDCRY0h68U/2iLF7o9l4KZToI4pVBLdwLaQ+9w09GrJ25PhCs+xv4Qmn5uz9hUMI5rEhkh22XSLNCiiUqXaD0OUk2Q5kZIpmT2BkZMxJzRiZWJFQouyShwoqaRC2RonRawEJcANjFel0vEVykghtjelsaDUps7D14bez1B8EIjOHC5HphwsU9dyN9ktl/8D2kH7seLPIfPZhWgzZIAwqnKqaEQloJBoQWvcq4YHVeIqxyqnF9X+u0YxPZzjGbrJHotmc5dao/VuhWuX0n0W2CbhW6TejaZLNvUlfurgeb/V73TKOuZ0mFe//ennnlngnjBGnpzXNCW6TtUFajREei3JYq73HXeeZNZEOiWhK18dSbyAalGhLZurjCnUtRI2RNlgsmRxPeuv8ql68eYnVFOb+PkC1lfYaRFavmDJUZytUpZX3OYrFgf3+KVR1Pfu9vpZ3NeOapD3LrrduQZBx2Fbfu3gShSPYOWe4vmL32JrpumGXf4Pf+gd/Hj/29H+fweJ8rH7lCuywx1vD151/k9OycG+97lm7Rsrz7Ft/zLd/Ky6+8TK077pwuGB9kfObXvsC1a9d5+esvMhlPUInkC5/9ApPRiIP9A77tY8/y9a89z3Kx4HO/+AU+/LGP8pWvfoEPvP9ZIOF4z5CnKZPdHe7cvYtMFFevX+Lxx6+hdcu8rFjMZly/dJ23b79FIhWvvf0mN27c4KUXX0R3lju373L71pe5euUyaZpwdv8+168+wfNf/yrLxeskqeLl177G6cERp3fvcbh/iE0lZycLTk/ucffOLaSUNK3h8evX0ULTmnss6uf5xMe/jdFejkxabt1+kxtPPMn8fM5olLFzULC3u0PXaaySTA6mGKX5Pb/vB7nz1lt85OPPIr9co1nyoe+4TlkuwXZ86YtfZCeb8vi193F+umI1n5GOoFmVnM9O2NvfZZKOOd6fUuo53eltzsr73HjfhzkYGWaq5Hf+rk+QCMGXfvnXOD42PP7Mk9RywbK8zZRTbr/0eVrdko9G/K5Pf5Dnfu15mkXJQZZTMmexrEhGAnJBljlbclIKxjuaZ779aU5npxRFznJ5TtPd5ezuCR//+MdRyZscHx8zaUa89cpdPvkdn2QxP+FXv/DrvPHlf8CHnngfs1fucHL3lN3pHll6n9nZfe62ig88eY3v+c7HsLqla+fUZ4pb6i5dJXnrrVvos69zWWqqsqEYZ8hz6M5rhNZ84PpVbjz2Pr74pS9w/xsnTJKM3/nxdz1tPRD+89XPrj0yhi7nvcmN0DNg0zQXBJmYITAkJPgwtIC9+IN1c7xmP7wlefv6n+T+9/0+RFtz9Uf/Gscv/P0H1pBD7wnTD72UDS2gwz/a4fPeyPEFUw/9e40xFxwJ+D/d3j5ZzELwNr+8eqJnwITlBmeTJk1T7qgf5MVP/zmQCaJd8eGf/k/Zu/m5QeG2Ftc4/y//PfTVI8TZguP/y99EnT1/QYUo/OMeCt4eDAjTDOOGYYhZ4gWPWHgO2ydmXIR17NXqYoDA22Lz6cYCYPhtD9kyPsR9IxaEveF+KeUa+PAh9Ojn8xQyYmKD4uE7Q9nF11VYtth2XdiOYTp23d82oLmLfxGs9rKM0+ZZL4aQUuFBO22ME096D+d+LWnWgqno01d92sJ5ipfOlIkDKJyDGbFmZrk8eODR9FBlCLgCa8DVlVet322tdz7lTKP4utCmBzY9xono73vBHcwa/GSdjl881hzy4tH/GUjdu4Rgt/7iA/UbH4f9MxyP4drR254LWVYxoyccN/EYCsdiKJjHcnE8RmOBPrazFoJtcbliwG7dv6K04/l5G7Di48bsvaH5M4wfn8eAXNwWDyvDUJq+nv3YDZ8fUgXd1h5w0ZMucGHMxm0RphGDP2HcGCQL8xGWI2yPWDU1jBN/d0OgNVa9H2JZxQBR/I5tQGQ4b4X9MOyvYf+P2y60bxam9zDgM/52hOmG7R97Dfb78PsWYlFxufx7QvXPh83TYR635e3dhPcEsPVsYhKVYK1ZT1Rw0TOImwxdQT1bzXds038gXLTNYgkEbVs5t8frxQqkUuDYbZIkUygFuq3ptKbtNEKl6FpT54JF2bCatRwfCXSzQmcFSbHDnVt3Sa5eRukFNlMczF/hQ/e+DOoa/BzUYuQACtXXTApi3zi1m1ys1bC0TNEype7NyXAA/FfAr/TPpqxBIl0m6DShfmLE3Bvc8c/5/eZnmAuBvST/iy8ZtWTjhmxSUexUZJOLINxoXLI/OXPnBzXZpAfqdmrSUYNIwUhB0/Xg3AV11zFVlTCrjqjLgrpyzLlqlTug7jSnq9K1PbqMjrHsqM4+hP2Xu4xlTXd2i6RZsCPuUp28xrV9SSFXpN05V3YzxmLJnjqje+srmNM7NFnOfHqV+5OrzCeXaA+uU44PWGQ7LLMJejRF52N0OkKrAiNTtEjXtuq0Ueguoe7y7c4lJBuQbsAclxUJViUYmaPlhEoc0fAkCyz3jF2X13Y9WNcKTCOdvbpKYhoB2iJ1hzItSlckuiTtZoyau+T1XcarN5msXmE6f5HJ6jZJW2OxtNbSNC3L5QqZpqSjAxZdxqrZY9VNkeklGrGHTi4h1SWuTp/k3OScyR0qXSDTXawsqBqBJkWpDGNAmxC0CzqVECAUVuDUR0XfzwTA3qZfArHm5iMHaxDWIOiQtuudVmxUZxOWJHaJsitSlqRqSWJnJMzJ7DkJCxK7QlKS2BpF6YA/WYJZoajxjlEufPwEWGu4ffttPrEseFm4lZwFbNPx3B/+7+lemzn2RmdotKapGucptenQnUEZV/yDSc6//h3P8O0fcUbc3//BD7C/v4+168pCCMGP/diPceXSJXfFr5hxf/KFEGirAdEz//qFZiDsuscsMmAChws9a8GQ0ZGiZUYjFZ1M0HJEq1JqpdBdRiMSGhJa67aOhE5kdDJ1m0rp0pS2S+l0Qqc3+1Y75x+b6+5ap1OqumBhprQ6Q+sUY3/z1ASVanlD1kDN819osLp0NhRVR5Lp3hlMi7ElXbfg2vVDstRQ12csVie8/toR33j5d3Lt6gE/909PyJIxk5GiGF1FqZafef5t2mZOkoz4xZ/7Ct/9Pd9Gq5fYRnH3rQWyO6RbKSYFFMoJG13dMddzqrpivlrxxptvcH56RlMvySYj3vjG26Q2RwB5nqGmu5zcuuc+zIkkSSU3336TtmmAlLJsOTtdcTS9zi/8s1/i0qXL3L19zt7uFeq65uTeTXaneyzLilwnJP3i8MMf/Ahv334brTXLpoHZGY8/+yTT6ZTz8zNOl3e5fOmA4+NDQDIej1FK8cM//MP8s3/+z3j+q8/z5ktv8kP/ix/g5NZ9kuuP8eZrb9JoQz075/z8nPnsHKGgrFecfvmc0/P7PP/8r/OtH/sWfvpf/BNmiyXT0x2+/pXn+V2/89+A1vCJj38HTz/xNKuzJe97Imd6uMevfvYzfOp7PknbtgghODk946vPP8fv/t3/Js+/+DxGGE7efIlXv/o8H/3It3FwdcQbb7/A+JmazObM21vkyYiT+3ewRqCU5VOf+DZe/NrLvPH8myRtAqnm+Moe46OrtNZy6+4t9o93WdYVZyeSq9cfQyrF66+/Sj7J0LpmPEkxy5YnnngSgOnuiJPTWzRmTrGf8s9/9mdp64qPfvKjvPHGa+hnnuBT/9pHWJQd1krOl+eUby5oZc28nCHvW44u73Dnzl1e+uJdDkZHnNw740d+3w/zD/7BPySdZEwmGUjDaGfEY499iOVqxauvvsHPfv3noXeMkGaDf5TedQj/6MaCUrgg9/fj/bYFqw9DzAoPNA397fULXC/gPP53/iqP/e2/snln8Nc5DmE6obAaxvfgVgzAhGmEwkAIKoWL/dAZQCiY+zL7cjRNs9bA8EyKGMDx+QJomoYkSbj+jZ/i+O3PU+9eJ7//MiNTYgIvoGE+s5ff4vh/9edhfwrnCzKpnI3FQFAfqudtIGVcnrj9fH2EHjlj4S02Lh7+8PKCkAfR/JgH1kBkCOSFZY3BzjjfXjjy4Ic/DtkSPp3QS2GY9xC88+mGAmict6H8bAMohsq0rT9jnTMurZ0jJ+GFUUcnuAgyIJAiSCPAG5VwypuhVqYQAmMNgkBFym7S0mhEz/6zwq6PMbi0elnNPxOzaXz9OfKDRRuNEsoxSqO+736ebvpNLJTHgElY13EdWmtR9/9jXtn/07TygMPqF7kx/68HgZBwDRX21VDA9iHuz+FYD/tKTA4J8xbbEr/Y1PZC+cLr2wyt+/fHz4Rhm8Af1l9cL9v6Zhw3fPdQe8VhCOwLAZ7QU21c1m0gWTznx3kO3zFUf0PXtv2ICdP27RJeH2r3+NrQN9X3oVDVNgbQwnYcYkj5eovBqFAFOe4jcfvFdRFf3wYSbpvr4rYaynf8vYjbNbY1uK3t4joK6zYsS1g/4fFQfYc/ER9mb289zz1k/n+U8J4ANg+SucEj13+MYPMXyncGx0i5iM4XRdEvZjyaq4OOph6oxPWfKNFPllKhVEKWZaRpRprkCGtpu5bUKP72Z57gc197mo/caLmS/V12ZMLewSFvvvYa+XiCWOXITJEoQaEU1FfgLZc+t3G2VQzOnpdUDjTzW2pIEou2FkRHktW0Zm/zXAOU1jlAgA1wcTeqxKeA97EB0SJD5OtruONumdItUlZMWJtg8CEG6vx5nJ6wpKOGbNKQT6oLIJwD72rG46U7P6rJnqz7uDVJ0UIKNgUjJG17UeU1rR17rqp2qctneKHKqVc59apn05U5plFwFeRxS9otydo52eqU7PSU7LUZI7MkNaeIxQtMqEntktzOycySVJ+T2QWFXZLoOVK4JYkZjWn2LlHtX6Peu0Sze4lmckA72aMdTenyCTob0aUjdJKhZe6AOpRj1vUqsF2b0HVpb8MoCpKNnboBhwSGDCs1Vo6wahctLuEUhGzvWAKnUtcbAzetxPZ26kwt0JXsLVl3KOPYkUqvUM2ckT5H33+NZ67P4c4vc+fsdaZ6wcefucKosPz8r32FVXbMSEiW994kL3KcAmfDH/tj/zZZeoU//xf+O1AHlDrnypUrfOxb3s+sSim7KYt2wq37hvNlSqlHtGZMa3I0xdqWmrNpp7iAwsVzogB6z5xWJK7rhn3xmxWsRvbeTaVtkNRIW5FSM840L/yljObYoI81UizpvvIS9uYfQtgzTDfH6BVGr2iZ0Zo5sMKqBYlYkagOkzhB0YID6ewG5XZz/MDHQEoUvbUSIRBSIlFY49RS/aSepsmFj5dP2U/kbk416K6f4GWHUg1WLRil/YdEKTcvWYkVBmEtRl/8EPmPUSi4hOyBMBhHDbz4jBCo/nez6Vwa2khak9B2zp5e0yk6naJNSq0Tms555G1aSaszqlaAHVHrlMakzLWCfJdVbWlqidUZRqd0JgUKpCgQoqAuAcbM5g1SjenaBKvGGJNTtil6lWIjz7ynkf2rN14F+EHu3Xq0LvXc89vvObt6LUq1WFsxnkjSVGMpKQpomzkvfj0BKr763AnWrtB6ycHhmDw3dO0SqVqMqUgSzc5Uce/eTbIUvvbFOdNpjtb7nN2Zc3J6GyFalKrJs12uPXHA+z54jc7MeeLxI55/8SuoRPGtH/0EQkqeeuopfuZnfpo7t+/w6quvslwumS8WzGYznn76BodHB2A1Vy5f4a/+lf8Hn/jEJ/jgB57lS1/6ErffusPzzz1HZzR3b9/jqSef4Ynrj/H8l5+jrGtUAleuX+bo2iUMz2C05u79GWdnZ3z7p34Lt27d4gPf8jF0kvDLv/wvqbsV91enZCJjuSxZzpdIJbn1iz/Ht3zkI/zMz/0cdVPz5GNP8ff+/j8kkYpRXjC9tMO1x3a4ffoS37i9YFmekYmEtMhJpWR3OuZTn/g4168/xksvvUxdrvjIR55lL58yzgrSRLGqZ9w5u41oLE9cuswXXvkyB8cHjC6l3F6+ySibcOXyMV/70tfY3d2jKTv29/fZ3d2lbTtuvvY2VVVRrVY0Zcf7nn6WxWLG22/fYrKzywsvv8KqXPLp3/695KMxn//855gcTJhMD5hMRrRNx0c/8ttoqy8x+o7LqFbw/T/yO/iJf/ATfPK7Pszrr72B6QwqSdjd22HZzDHS8Hv/wA/wcz/z89x565yyrBhP3uufjYshFKzCRafrzw8uXLctvuM4oRAyBHSEhofjdw2lGTOK4gVxvLiNhZuwbKEXzHAhH4NpPoSqJLG3sqF683nyeQyFH/8uHycugwf1jDEU1QlFdeLKExnVDwX5NStgXtIZC3JjmylM34eQBRP/nfcAw1A9+PV12A6hp7fQe2IIQMaCt3/G10vokdDvY+EsBgD8Pu43Pt6QQBXWnS+H71txO4TphQyL0H5W+K5teRtiPwyVLU5vSJD0z8SASwxux22xDQiJ8xQL0EN1EV+PBcywb4d1EXujjd8fPhMLr3G9xffi8h3Uv8LB7T/4gKDv+31oliQcS2EbxGPTt6WvzzivwAXQOR7rYbrx+ipkAsdjOwSdQlAvBinfjUAf1/tQOmEewrKEc3lY52Hbx30mrsf4XfE4jgG6sB23fYdCkD8ETX1aD/PIGYOkQ/0zzvtQ2eK5wIeQ8RVeC/MRA9RD9Rc/u+1bHH7H4hCPy23v2Db/hPGHALptz8U4T/jOof4cz10hSzrEjYbKs43dF/bbobqI50fP7g7bKex/8Q9Cfz9kUb6b8J4AtrZtgoJuKjMsoLFuQamUwnYdUnrKnwk+1A/SD6WUTvUvzKQ3Mikkpmt7cXdjj00IQAiSRPGX//n7+dHP3wDgS6fw9OERf+LTXyAtcu6dntI9/RQmLRjtTKhySSLAikBg86xyyQbgqgkALEkngcZCqmhV7q5f7jdXkA3AtezTzPtz44AWRkH6/n3vxlSDz48/joG60IMg/bEWtPOcdp6zNDubvIaAna962R9vAeqysQPnHIPOgXDZxAF1u5Nzd77bkF2te7Zdjcw1IrXYRGBTgUHRdL3aazWhqQ6py4K0yqmqnPMemGtWOfWyoCkzbOfU9VS7ImkX5O2MtDknK+ekywXpGzNSMyfTr7JjFqTdjFQvSPWc1MxJ9ZzMLJCmdEU2FqskOh/T7h7R7B1T7V6mmV6imR7STvZpR1P0aEKXjdBpgU5ytMrQ0tlsMzj1V2NUrwKbbncu4dVfRw/egsw5sZQao/bQ4jKdMAg+zj/Ggv3XEdpiteBnNNhOYj/kWHVe/ZUWhNZI3fH516AQHYvf/kFUc45c3iFPTtHXMi6bO+ynr3OcVJy+/jz3Xn+Z5XxBXdfUjaZpOspVizGbv9NWCOfsQO1g1Ljf76LFmFbs0KkDtNinFftosUvLLi1jWnbQ7NAxRosRhtzZTyPtgTu16aNh/46PBSAURigQOToAlUsBMwu37uPcK37dP/wd78pL6y3ga3c6kruaRLQUn7WMUkORdBRpxzhtGaUt9299hP05jNWScVqSyZJMVhSqIVctmarJREWuGjJVu72oyGRDKhvnxMDYtTHQrmtdHRs/pypgY5DTWsd6lNB7NO4X2/3gF1IirAoG6WYRrLsO3VPkdde5acEv6E2fpl/I2P64r1ih+g8/llSUpMribeAYY+iMcX/NpZvA/IdLa01rDShJ1RnqcY7Y2yEdjWmrhldeeonxuEDJhFdffJFpkfPY1csUQnE2K3nr3l0WtqNIC3Yme9y4cYO2a1iuSjpjWDQdn/r238aXvvA1zs4rnnjs/WTqiBe+/jJSFVidgB2T53vMzmuqGozJEeRYk9EZx9objQ8oa0FXAzoFcoQY0eneMYcs0CbFUqC7lLZKqFcZbbeHkCO0foLbAx6f79x79D63LbzwIvz8z1y8JlWDFA1pplGqAf4wUraopCVJNIgVnVny9ksVo5Ekzw1C1Owf/CF+4vkTstxSZL+Pf/6TmtOTD3BwUHD92iH3b96iOk+pqifRrWb/MOOlr99kubrP009f4+T0JpOJJLGKr375JarVOdoaPvqxT/Dk+z/M1772VW6fzlFCk5CgEzBCg7B85etf4dO//Xv46X/208zKBdcef4y6aZBIbt4+ZQ/B5ceuszfeJc+PSFvJqy+9zP0796CF3d0pWZawM5mCsaxWq/WPuktXjlnVJWmWcHx8maZqWJyUzE6XLLs5Tz97A2OXPPOhG3TVjPOzBY89cY161XDz9VcpRhNWZYWwMComTHcL6npFS8tTN57i7r177ExzymbGv/zlnyPPJjRtx8n9+yyXiscffwxrLT/9Uz9FmqZIqThbLXjlrZf48Ld9gBdeeJHJ0YT3P/MBsqxgPltQrSqOjy+BNHz3934Xi3nFG6+/SVXXv/FOw4OLzFjIDBfDMWASqkL66zHgEdq/8WmEa7h40euvD10LQaGHCQmxEBa+K1yEDwnpYR5joTgU2oYEbP9s13XrRbZXT3kUoMLXqT+P2Qe+7KFqayiIaK3X6r5SyjUzbIgVFAYvHIeCxJAgE8bxdfCAGYfoOAZGfQjz5OMPtZt/7lEAhBCcCNs3fDYUfELWRGiCJmY1hcF9f7u1nBG3bZyf+NmHlSPuy9uE1lig2/beEPQI74Vy1LbnYk+uQ2XxfS5+XwzAbCtbmP+wjeP5KExr2/M++LixSt9QmjGIHOcxHGf+vgfosiwbrMOHhXjuiPMUz1fbAJs4Tlwfcb9/GJAS3o+PtwFa2wCVeC6Kfyw8LD9h/oe+M0P5C+PEoI2/FwJrQ9+qd6qDbfeH6uBR+nac7/D4YX07/haHfWabanD8zoeptsd5ifuTD+G8F6r3x3NBnLehOSu8FpctzldYpm1lCNOO+1C47ojbJQSN4zQ82z5sb89mC78T8Th5t3MDvFcVUXGRHqu17heXm0lLCaeb7xYkPmPb3QVvCnJxweINTGodei+xZFnGeDxG6w6tDYkQtG3D/+dLjxMiRq+cPMlf/ptTiskZJ7On+cWXxhzb2+w3b6HUFbLsLQ7Ltxg3r7LKbjj1ON8H5rb3RDiA4H6x2qjYKSARm30iehXTYJ8Kl3YmILUBsDWQdgAc9NUW7O3m2IYPBIePpM0lHjwM3xcCdkTXjaCZ5TSzDOy0v74lPY+fyCjdNVDnQLnc26Xr1V5H4xX7k1MH2h1umHYiMxsV3BSMlGgUTZtTV1Oa8pi6yqnKnFnpGHR12bPplhtVV9G1JO2SrJmRdjMy3YNv5Zx0Me/PTxjpV9nVC1I9Q+k5uZ4hm3PSnkW37svCsYqabEK3c4DeP6LePaLaO6abHtHu7KF39kj3D+iyEW3ibNU1vZ06I5x3TeMdS3SKrktp2gzdDTiX8PV6kczTN1OCJmeGZSE1Sj2JFgbBh3kNy2u2Z9ZpgdVgjwT2o1791YF1trYIbRBdi2yc59u0nZPUp+TNfbL6hLQ5I2tOScp7jLsT9uqXyNp7JF3lWGbSID0AJAQId+ZQHJBCAhpLRmuL3nZdgZY7tGKMlhNaJrRmQiv2HGBn9mjFDi1TOiZoJhgxwsgJRhQgCtpOYWwSjZGgD0fdPx5vRiRO9VLkrLoozjo8DdWDdf9owTpbZ6IjES2paElFRSprMumAuUKV5KqkUEsKWVIkLVKUZKohlzWpqMlU7YC/pCaVDQk1uarJZIWiwf/AoGepmV4tBZyKibF2M+SjxYG1npnQ9vfcHwf3cd18dJSUaxURr2KyVpnBqaXfun+Pg8kTPHXjBl/92te5e+cuCsE0H1HOFxRScXx0zPWrjzG7fRdlLNeOL3FrdsYz73s/92/d5aUvf4VUpZBIGgmny3PK5lkuPSbZPZBk4nWe+/V/wv7+PsK2THYyEIJXX3kDheTSZEJRZGDdIjvLMxb1iuVizpXjHSZpzjRJUcaAlUgF+U7B3rXL5OOCrqlZns6QMmE0HnFy+x65TMjSDJlkZOkOi6VhudAsVhopHZA83b/C8ZUbZON9zuctZSu5f3/BcmFoO8npWcVoesxbt06Qaoolp20VdSOxJkOoEXm+S5LuUBT7NI3g9LREqjHW5iwWAmOmOFZfjtbZe+2UW8PPb7kuRMdP/rgmTTTGlghRkyQtRW4xZoWxFVLWGFvyz36qRco/QJZZ3jzeodHnWF2ipCZ/UzJ5KUOqhk4v6KozPvzB72e0U/LS81/m9M371OU5RW75tk98EMtdxhNF3Z7z9Rfvsn80Im01N9/+CvPZDJUrklHKB595FqxBtC3PfeXXmU6nqGwHbZbUXU1nOqqyQljL/HxBkuSUsxl7+1PO5jNeePFFtO64f2rYO9phZ0+QpSNuv33OJz/+caSy1F3DfL6k7WoWixnXrz/OwsLrr73G+559H5/85Ceoq4rz2Tmr1R26tiNXKS+//CIyVzz51A3euPcKagfy6Xt2l3MhxAybcD9khyoUEv0+vh4v/sM/uj692O7XNoEtzEv4PnhQFWnbQj1eZHv7W0PvjcsQ1kGctj8P2R7xX/WQ0RIL0WG+hwTLEOQaAlk8+6tt2wvqmnVdr7VEhNiwmUIWUfgef+zrOARhhuoyFChCW0xDQF3MTAjT8PUd1rlnl8X53Cbgh3nbFjfMY8gECvtx6DwtfDYU6EKhyec7rtP42RBgjusyzn/4nqF+EreZL1N8Lx6Dj5JWXL44DJUxFJ5DMGqI5Tn0vqHzbaDL0PgM564YFAvTDMvlgfIweEZrCAiFQHZcxhgsiOs5Blf83o+XobJtAzmGgIFtIbwf5j0GCt8JaPN1tk0972Egnp9fw7HvGXtDYzZu7zjdkOkasnvj+hsav+FYD9mF7/T+uE4eBkpt699Dc7avn7Cf+3LELO/4XeGcEBOU4vkszN/Q+7exq4bG1La5KixnDG6GfcanMTRWwzTgIkst/gaGZd32DR2a6+JvcpifMK2huTesbx8/TCsuV5jv3zQGW6i7Gi/moB+s9AKWMXSdxt0WCHHx4+GeF0jpG046Fau+YE1r+ZnXvp+v3P1Wrh+1/JHv+CkOR7ddpUhnBFSIXj3LGIrUUtVsMDYBN+/uY+8cAE/zS1+LCnOl3wBshxSAtCSUSLukaq85BpqxYGCvu8P52SGM5foaVjtWWi3gHSbMB8IalAPUO4BzqXBqiv58aMAJHrTx5q/7vK4ZacHNIcDuHcG64QH/ADg4BNThrjXznGaes3hr2t8TF0ERn07CBqgL0w2AunyysT3ngbqdydyx7S67a+m4RvYgnfAqr1JgpaQzCU1zmbp8gqbKma9y6qpXce0BumaVo5vEseiaJWk7J21njiGnHTCX6DnpakY6n5O+PiPpvsGOOWeHJR97aofjqWZHrcjQfOPlW/zCr7/GvVbw4U/8Vt6+d8r90znjUc7v/l/+W/z8v/xF9h+7zI2nPsC9nQNelyndeBext48eTViR0KoUo1KsdOwu591MObBOK2c0v0tp2xwd26t7iPqrq94cww5g0UojhaYSBtGrJ3qgjt4LrLNVJzF1D9hVYm2nTrY1ql2RtguSZkbanpO2c7J2RtqckjWnZM2JA/DK24zq50m7JcJc/FtvjMXPdV3X8W//u/8Ov/aFL/Kd3/kxLh2MqeuO195ccHT1fXz1669x861bJKNLiJ3H+MUvvMh49zLPfvAT/PTP/yqdnIDcYzy9ynTvOudLGI8PQUxodE7HmFan1G1C3Sow8iLgPTTcYxBPRBsCLZxNwVqMHoz3TQmWRLQbAE82pLImlzW5rHrwriSXKzLZOGBPVmSyJpU1KQ7sS0XpwD9Rk4oVqexZeJQI2/ZThEVIQaf7BZCQCCmwxtA0LYd7B7z6jVd54dVXUUlC27RM8xFZB4lMOHj6GXb39/jcZz/Lpf0j8qIAoznaPWB274S2rMhU5v6mYRBKME4KsrTgqWef5XOf+QzdsuSDH/wAN195jb3dCXdu3eLSlcs8c+MJ2rpzdmmArulY1TWz81OSIuNwuke1WKGmKdZoWq1RMiWRCjowy5rTeydu6lGS/StHJEVOtVyigFGe0xnD+5+9yutvvMGkWHHQNHTdCVIIkvRNWH6Rcm5QwnI4HqPlCbsj16ef2pXsHx8iP5pTNw03btzgxRdfYDZbsFysWJYrpITp7g4qkRwcHjIap8jcgpS89MLrjIodzmcz8lFOaxqyfIKxBfvH17l7f4ZSY46Pn2C+6FjMNGfnJVoXlCuLlDvsTI45OV0yHh2wKi15sct4dEBVWupGgi3Isj3u3DrH2hytU9pWkWZT8nwPYzIWc03bSepuTLlKMeYyxmRY++AS47VX3rn3/tLPDF///Je3PyNljVKdc66RdaSZIcu0U/22S7RekmWGPLcYU2J1iRAOEKzrOasvn9HqGfv7Yx5//Ig7d99E6yVKVdy5LXjqqWOE0Ny5/Tq331xQ1qfs7GUcX7pOknbMTl+nKe8yzhL29w54/ZU3OD465vT0lNWy4ujwiHJRkRUJiUpIVcLi/IxEGCwdq/n9RxjX7xziRalfS/l1Wyg4hYtIIQRt264F6iHj7bARaGNBIHZWNQSaDIEJIeskXDSHeY3L50MsdIXCbcxk8XG2CQ9hGvG6NlYTC6/F133+QxV9H9q2pSw37opiod0Ys/ZUGKpIhcBP1jv1ktKZZkkCO27+vduAGR9C0M2fh+w6fy2s47BsYfrbQL24TeIyx/Xm78V53/bMNtBjqP/FwAJs2i9kr8VtEecxjrMtb+8EQIbxh9Ia2od1Gqc9dP1R6nxojG4Dh+IxGT+7rVxDgn34jphp6u+HQEz43lgtL+z7/pnY8Lp/PgYvPRCy7afEw+oxnie2gSlhGjG4tg002da3t82HDwPqwrkoLkfYNvHc6+/7fheWq2maNfllaGzF6cdAqw8+zXi+8JuvrxD0Du+FY9W3ZZhuXE9xfYbXh+IO3fMAI3Bhro3tVg6FEFCK+87QNylkP8dj3LdJ6HQlrJ+htLeNxfBnTvxN9+NqqI3juWBoXMfXQhZZ/I3eBo6FoFrYP2JnR/G7wm+ilM7umndyFLZDOA/EJLCwLd5teE8AWzzgw8G3zphg7Z3GhU08n9lQaNbaI+waYZzKkRCCn3zph/nJl/4tAL52D7745pP817///46xFm2MU3myBmtch/ij33mTv/JjN9aATp51/Id//B9yP1/yuV/6Ch947Fuw5x2yMpy9WrM6yTntdjixe9zb+Rh18bgDfmwK+W7AEHKNds4VuAp8qL9qDJPsBNmdYNoF0nToskEvK0TZkhrJbrHP0dHjNHXCailYzKFcSpraeQ+0WjkdN7N9khwMnsUUsub8ccpmH4J0ueyf2wLOeWDOg1kiOMY6VpK2oEG3zsui0WANG8BuCKwb/JH2CGhCGMUObMaChWbhgLrNPeHYhz4ND7j6OvPlDNMV1qmzjgOgbqdmd3zmwLuDhuzxmmTUIjODyGxfz72aqZQYdunsIU3pvLfOegadB+l+YVnQrJyqqzAd6aUZfHrGSNX8qqyRR0vK2V0W+ox/8eoepWwY351xkGl2uzdZvPEihawQwvL4E0+wTDO+8uJN9pIDbOVwMmFK/uT/7gf4pV/+Rf7FZ1+gHB1h9vaps4TD63s8/YkPcK4yqmKHVVKwIGVhE9okw6gUo3qgTigsEm0UXv3VgXUJbVtgQtU4Qd/XGFR/NaQYRnTs0wiDlBopDFIEtuo8YO2Buq4H6WrlHEp0FtFpRNsg24qkXpJ0S242E/RT9/m1heCahKlsqLMTrnaK86xG7p7yqQ/tMxpLXvz8Z/nUUx/jt3z0mM/8+H/j2K9pwu/46HcjdcXbq5t87wee5fpBgRWC9z37Pg4ODjBYjIXOpKzqjL/1oz/J5cfeT6Vzqi6nsQWVLqiN2ypdUHY5pR5RdjuU3ZiyG7m4ZkTT5bQ6ozUpxiQbEDocO/Hx0Nh4CIjXiYxOZFRD8b4JwSl5tySiIRU1iahJRUUuKzK5IhUliVihRI1IK+r2HF3NSETFHM3prYZclKSyxtybMzr8NAs9p9UW3S1ZLU97z6UJnXUqrjJPaXRHYiyvff1lfsv3/laefPxxnv/il/nWb/8gt156AdnljFSKqDuMlMzOz9g/vsTR0SGr03PneTpXTKc7mLZjN8sRnaZqa9AGhSZRE0yrqc4WKOXot23dkmvFyc27NKvK2f/S7vv1tee/7oBF4+yIpolAKYFUEiUlaZLQGMPHPvZxfu7tn0FiEQKs1qzOZogsYz5f8varb5AnCaMsY39vitnfoWk7lsuSVAu60wW0Y2QmyPKMjz7xDGVdcWW8Q1nX1F1DlmSMp2Oa9i5PHhiuXp9y99YXmRQSnXUk1xRNq7FWUdYNu3t7PPfcVzg8PGLvYJ+D4yOOD4/44he/iDPtkJKKlJu7b/PkU09xfj7nbHbOU089SV23fPGLX+LxqyOquiLNMqY7UyyaNEkoywZLQVlqxpMdJnvHHF97jPNZiRQ5TStY1RpBQZpOMW1GUysSNaFtE9omRZgCzISuSzg7a8AUaJ1gbIElw/bMvaaRGJMhROHs+7U55TLHmIyuu4oxA3TfgXD/LnzjxQevf+Fz72Z0dBvAjxpEjRQNKmlJkw6VdEjZIERF2805vjRBpQ1/4j+EIoc8hzzr9/mjXSsKd9yZq0jbImiwNEizmUhCkC0Ujvx+NBqt74cLzXC9Fi6CvZCzTa1iPVcMgCL+fEioj0Gwofhh8O8PgaNQuBgSEuK8+ufjY8/UCNUzh8oWM9P8eZhmkiSUZbkGDsI6jIGdEKDzwmzohTMU6n36IfMwBiZCQSMWLn3asBEUw/rywlDchj7N0I5oyDCJwYS4TeM2igXmWMjc1o/8u0OBPQQrQ9A4BDnC/hOzReJ3PUzojus5TsNfH2JMhfdjQXQInBiqgzhPsZAdpr8t/6FgHT63rc8MASrhO6QcdhwR12cMgsVtEAr5YV8I3xmO/5h5GcYL3x+ex8JzDECEZQznhqFyxGWNQ8y4HAL3wnERn8fg0tAcEoeh60N9eqifSelU05MkWc8RIXN0aAyH6Q3hBeG8tW1OCL9L/ln/MyFmvMVtFqf3qCHuW3H9xX0pnoPid4UsPR9ixlaIh8TfKh83nNviZ8O5Yltets0B/l3hmAm/o/EcHNeHL0/ICIvrMOxjcdvH+Rvq80P5jgG1MO24X3jnS2G7heUxxqx/WoV9FBj89rybIOy2EfmQ8Jf+3A/1mX9wchLB31DEgzROHzbU3Yu0vvWg7a/9n37hL3J7eZ1QKrw6vcd+9haL89e5cpDwXd/xDMe7De38VZ678yl+/JeuXXjXY1fucOMjrzO7/xLHux1XsxOelG8weukr5C+/wWy+4uay4n/83hfQagJCrAXao/IXuTJVCF1jdUeWjSjLhLfvTcmLXfaPx9ybaxbViNY+mrGnUdowTpZknEN9h4N8wbX9lkJUdJWlaRNEaWnKlOVqxJ3FY5zePUI3CiUMSrW0TUrXJOhWYjtxkSH2KEECCeQ7NX/ub/7FXq2ycI4LqoKqd2BQNSPqtqBqC+q2oO7648p5G60q90zTZiDFxpac98Tav8eBWtoJGaZFdAaMxHYS0ymsVlgjsVaAjVhCcKH933N4GFjXA3UXGHceqIvjxiCkByK3AXWBfTpvq04VGplpRGYRqYUEbCIc+0w4tddWZ7RlvlFx9V5dFwVtlYIWJKZGtEt2MrD1jIKGpJ3xiQ9dopvf4bkvfI7EtKSs0Mu7PLVb8iPf+yQZJxyMLJNccX4+5/mvvsCd2yeUZUvdaOqmo65bVnVDKwtaMUFPDpD7B5j9Q+zuLmb/ELN3iN7dR0/30JM99GiMyXJsmmJVgpXSAXVWYjxYpxVdm/aOJRK6NmXQXt2jNKnQKOkYdRLTO5XogToNVgtsK7CtV3+V0AmEMdimhbZGNhWH4xSzOsPW51ybSvaTFWNWPH6QcHUK+2nJUbrkOK84Tlf85N/9mzx74zEkAq9OCcEHGByLy7oOE06z8R9BTUZlC0qd0ZgRLWNqXVDpcQ/W5TR2RNWl1GZMbTIqXVDpEWU7ccBeO6LWOXWX05gMbbIHbSe+E4j3QOVG26NcIzr+DQeNsg1SOK+yihLBCmGW5GnN3l6C1UuEXqAX913/lxubd8KWCLtCZR2XnjwgtTWrOzcR3Tk05xR2RSo1mUpplWGcZ9iWNTtTSYUxmq7rWK1KOq0pRiOKUU4xKkjTDCEkGE3TNmCgbGtGRU6eZeuPe2s65lWJRqA6aKoaJSVGG+R4jCZBa0u5WmCakoPJhHGRIBMBMuX8bEG5WiGVYGc0Jk0FQgoQwn1zNSAEVri5syxLqrYmGyVMd8c8/dQz5FlO09ZYoVFJitbQeXUnbRhPJggl0dYiVMKyKjk9O+HsbIYxktdv3qTVLUmaA5K9vSmz+YLbt+5QNQ2tgbpuUVKSpAl7e3usVit0ZwHNM888zmh/hKFlUZ6zMxmxf7iHpur/LOYs53OadkleCIS0JDKlyC6xMz7g3r17NFWJMG6RvSyXyESQFjlHx8cIIZnPzrh75z4CwWy+4Hu/99Ocn51y863X6ZoWa1Pun6yYTi+xd3CFtkuRasylS0/zwtff5NatM5aLjmc+8C20tWJ39xJtKxFMeeUbd9CdYjw5oFxpZmcddZnSdQlJMqWqIEkcEGh65x3u3DkEoVfjNSZF6wz7m+iZV4gaKVqEaJHSHbvzBin7vWiQskOIBiX76zR9nA4p6v75FiVbrK2RskHJFik7oO7TaFF9OlK27ppokKpDihbYCOxDAuoQ+yAULGMhPFY38cKBBxO9UBgyq2KWV2hfyL8nZleE+1C49PmL8+rLFApNXkj1i/bwOAYj/No6FGpjASNJnLOvkGEXCmohYDWk/hiDer7efN5CQSjMR1x34frd12NYX0MCa9j+QwJnLGSFwmSojuzrtixL2tapWYdAsq8rb8YmFsrgIqgYs2V8uWKhLWZshXUfxg2FTt/eYT5CATcMcTuFdR6Om5jtGIYhECNmCoXHPu2YHbRNJXYIZAiD7wc+7SHGUlzmIbAuvB/OEzGDJc5T/I4wHyEI7R20eDXsMP/+mh+jMXgd110IjsT59vYAfR8P8x3Wz1Bd+vwM1bcfhzG4FcYL5zSf55C5GdddWPcegPfl8Ol581BD48WnMZRuDGTEYEm4+XEb2siLf5YMzWnbAJEh4Ceso3hcxHUelzUEp/y8EIJmeZ5f6B/xe4fm7Ji96evZvzdmRw+lEYNT2+oi/gkSflPj764fc+E9/00LAbawfcJ+Hdd1OLf4+zF4OjTf+fshWzUG4EJmd8j+9u2SJAl5nl8Yr6Fquf+++vxaa/kTf+JPDNbjtvAeGWxgrAMkpHLnCIcwmN4LHX3DK5UgxMb46NAAiSfUTWVJRkn5wPvPyim35h8HPs6bd+Hz/9jf+ajbpUBgzuTm7cvcvH0Z+PYH0hJSM5qekY/vot8cOTW5Q9agyf3xd3Pf/7BUOFZUBlx3l+4akDuGfLok626imyXSaiQaUy/RdUkKjEcj9vYvkRf7LErJbKW4V12jNTd4u4SvPlhMV469fjvuy9TCbn7Gt934EpNkziRZoI1gxIKxLWnqrPfmWTBb7rJYTVmVE1bVmKoeUdcZbZ3Rtgmmk4zyFb/1B3/1Ia39aMEY4d67csBbXRbUtctHVY8cWFf3IF1wr64Ltw/vNwW1zh2QpwsqU1Db3AmGrEhshTENshOYLsXqBNulGJ2AVljjVfkC4DFk160bnw0QMMToe6fgQTWfbgDUNcucZhEw6ozYABxhPkJ1Xr/194U0pOOmB+qcCux0MnMqr4c1yRqkM2uQDiUwUvCGuINGMfnE99C2KVXlVFx/bVXwK3MH1uk6QWrNRJYk+Qx17Zykczbmkm6GaGZQnSLaGbI9I+3mpGcz0ntvkukZWbcgsXMUBiH7ybQ3TOhtr0klXRVY6NSELtmhK3bpdo/Q033M3h5mOkXv7tHtHtJOD9CTXexohMmzHqiTIBWmBxCc+qtEa4XuAbo1WNcFRhQ9W3HALJVdI78jDHAPkGIfKTULLJLeu2bvMMRqga3BLqRTgf3UH8c27p7Uxnl/NR2ZrslMQ05NYUtGlIxYMRZLxnbBDjMm4pw9ZuzKGbucMRYlOyyZqIXrVsIBJ7ZX3bbW1a8xGikSjGmR/fzo+7XF9AxYv3hUlDqlMeM1q642BWWXUeuC2hY0ZuTGlsnX7LvGjlzcHsCrtQP7Gp3TmmIDfoeAXQzkxSDeur8HF/wH/x1BPIVmhBYjWrF/4d5cw72TB9t2nacwdEBsHmD9ug5FTUJNQkkmyrVqbCKq3j5eRVLUpJSOsdeWZLRkVYUSFcpWWL1AtHMSUbKrBLtKkooaoVcYOiaTKa+/9iYJ/cIiScmShHpZ0miQSUZV1QijaY2l1RapDRaDFJZUKay0WKtRskAKS55ndFrjWYtusWfIxjlTmyKkA/Ruv/YmSiikEhjbIKTCWhiNJxweHrC/d8D+4VEPGDqnG6uqpBzvcNO8hUoTru9N6XRLp437rluwly6hn36a1hq0sVR1i5CSLHOsw7rtMNqQJorjy0eczs5o2oZre/vs7e3SNBV7B1dASO6fnHD/dEZjVig7Yjwa0zXOXt6tN96mKApkkpNmGVJIlss5urWkKuHeW3coignCKDJGtJ2BLmE1azm9t2CUTqHQzBdzWnNG2dTsmIqyKqmbhsPD20wmtynGb3L9+gEf/TgoJWl1S6c7jg4v8y0fK2mbmjzPuH//lNP7M8pFzWJZMp3uoNKEb3zjZQ4ODpju7LKqSg4OD3ns8eu89NKLHBwc8v5nnubll17k7u17jPIdnn7/h7l1e8Zrr95iNqv50b/+y9Q1bmugqjbHW6/VUAXP/PiP/xOMzbA2ResUS4pT182wZBiTYmyGNhOMyZynaPto7L5vTuhQa1BvA/I5MK7tmX0OmBMe1FvHa/rrm/iCag0MCtE4xqCt0WZFovQaEMRWQEOaGvecrJFCr9encFGADYXR8H4sJIRxwwW+F5TCELJqQjUjv6gP3x0Lpf5aURQXbEvFAFSYX59Pn48hpoN/1o377gGbUuGzYRnDdbu/HwOnocAUpuHLHQpvMSDonwnVvmLhNFaN2iZIhoJWnHefRw9++Lz5soTvihk1McsslGniPhAK3UP1F5d1qH1iIdPnK5SjwjL7/TawKtz7d/o8NE2zPo8F7rA+wnHg8xy34zZQYdu5D2EbhIJ0DOKGde7V3OO0PEAc10c45uMyxmDFUL35eCGAFYa4XYbGX9x3hp4Jr20bo2FacQgByfDaEKgXAhdxG/uxEQPDcf496Bernvs4Q+qcYTzv3CUGAcNrMcgd7sPjh7VBeB6DaXH9+LghKBXfD1lacdmGmFthfsN0pXS278KxHoO7HuQMx38M3se2SePyh44NwnyFAJ//poTzXvyN8e8YAvDDfIX5jOPFbL+QARjm34Nn8bfDX4sZgyHY5kGzLMsufNtCZ0JhXcffoXcT3hOD7b/8U9/vHhYglRP0BAKEvZAhn3EpxQMTrXveSUrhh+piRUqev/9R/toX/mM669Db73nmy/zpf+2/45W37vNT/+J1rtz4NL/le34P984Fr79VcWt1g3/yuStALwgKixCatS2YB4Q+v/cnwcQk2AjpwaaShjRtsKJBplBMRmgBpbZ0ZFjxaLilEpqRKilESZJ03Jpcg2MLIwEdyDsVZlbAGfAK8Fk2zLBtm7+vDFlWM8pXjIsVk3zOdDRnp5ixW5ywPz7jysfucv37blHkK7pSsbw/5vRnn6AzhzTk1EbQGLCJgsSAbEjShiRvyPKarKjIRzVFz9YqJhX5uCafVOTjZn0u5bvuYoOhrrMelMupq5Fj0NUBWNf0DLw2p2oK6i6n6pk9VVfQtJKmEXQtNLWlaRT1KqUsd6hWU5p2gtEptgft6JQDxrTgASYbXOxL36ywTt9uAS4CoC5+vwcfQvXeEKibXHQkkY2bNZNO5gaRmjVIZ4XASAdmddZ7R816VddibY/OthLZLZ0TiG5G2jkbdEl/7G3TJd05STcn0TO372bI7py0naGoENZPkgYbIS2eCWZMhxWKTk1okx2uPvthDt/3IXYff4pXTs944qMfYSYF59mUO62kSjNKDJ0SiCxBS+FAOil6CE1irMSYAKjr1WC7ztmte6+N6FVgvfqrU4HFsepCW3WNxLbSAffaooxGGU2iOxLTkJmanJqckoKSkV0yYcmEBTssmNoZO5wzNadMzBkjlmSiwfZqYda4jhAueMKP3NACLfw7Zq3AmK7/2Foak60BucaOnWpsD8g5dl1ObUaUJl/fr40D8xrrAbwRrR3TmjGNHUHokOKdWHfxNQBr/F8etwnxEMDuUa/ZDQj4TQiCjly1SLtCmRXK27aTDdKWKFGBXqLrczJRk8qKUdIySjsy2aAoSXDPZD1Db5Q0jPMWZVYUSYegX8z6ESQEUgpU4jxzu+8sJMnGVbSQLo4QyrW1BtPbPLRosJauMxhtSPIMpdzCI89zpBAUxZh8NCFNJTKwowqghaEL2J1dpxlPJiilODo+5MqVK6RJQlWXLMsVp2en3L7/NrNqgVACaQVtrdndPeSVl1/l8OiI1him0ymL5YL79+5TFAVCSs7PZtR1R7kqKesGqRJW5QopJVeuXObJJx+jSzV37t7mS1/+qgMYcYusJE04PNzn+tXr3D+9y950SpHmNF3L+fyMqlxx48b72N05ZrIzpW0a3njzDbq2Ybo/QihX5nm54vadu1y//jiXL13j7skJ9+7d5/HHH+ftt9/m/r37PH7tGnkq+MbXX+LuW+d84js/wY2nn+Yzv/oZZrMZ//xHn/sN97Xv/u7vfuBaLMDBZmENkCQpdW2QaoS1OdZmdFrRdQopx72X7BTwIJ2z56lNgiXDOdhIHVhnUrRNHaBn8z5+gtap88jb3zN+34N8xmZY0z/33v79vqewAfF6tp5oHjyXG5BPiBpBswYJfVzPBBR49l+NB/2U7LC2BFtjqXpgsEGpFnpAENxaybNF/EI/FA7gInst/NMeCltKKdq2vQA6hGwcH8J5PwQtQsHfCyyhYB0KNeG3xD8zBFL4Y2MUlgIll+trQyo8nr3n68GX2wOMoYDWNA1N01DX9YXrPh1fh6Hhe1+uUA3IlyMEXmKWTSiMhnGGBL1NH9t8S2I2XBjCdw0BLf7ekEOPGPwK22MInAxD3Ibhc0OyW/zcEJgYgsQhkOnzD5s+F/eBsJy+z8bqoWH+wjktTMPvQy/AQ/nz9ZXnmx8NISjr3+NBhhhQD5l0IdMrBDu8YO/BodBzYfjOsK7Ceo6Bn7jeh0De2NtwOEeEAFkI4Aw5HojLHYINYTnjNgnl+phU40HtsL3DuS3P83W7ZVm2znfoATIE/eJ2i9vZ12M8NuPn4vgh2zIG+uIQglKecSeEY+GFTiHCvujTruuaLHNsAB/Xg5NVVV1gh4WsqyGQLwQ2w/eFdSiE+6ESs1+llNR1fYElHKuo+jZs25bRaHSBiR2mE/alOHgP2X6Oj8eUTyMEZMOyeuZk3GeVUmuTCr5vhizL6XRK0zSMRqML87kH2Py7QtVSX59/7I/9sQfK8bDw3lYxwvbgle+MBtUvWOlZF26F7we4m3TyPEPri3ThcFA9+NERfGD/C/wn3/XneHX5rTx1TfFdz7yEYMQT1474vu/s2N0v+ZYrz9EcNCyPFsj0y3xgpPi5Fz5FogSffubnOdx/hdX4Ci++vmAxzxFij+YclvcUXX1AwzEze8CZ2WcurmHEZM0MWbMhWtYCnbYZmhRHdxM8QD6LgTnZIlSNSmsmE8loJwNp6YDWCpZ6StVmUAIfFfCsf3XhDv5bYIxzxtCxZrKx6vfP9Hl0WhpuryVNN6LRI867o95RQ5TPB8A6i0o1SaYpio6iaBgXFft7gv0Dze6k4uig5WC/4eoly9/563+Rv/QX/lNu3lny1/+rf0bbXWe1GrNa5hT5NcqqoCwzOqNASbQtyXJDmrdkWevAuXHVg3NDIN2D14sdtx9PFhzs3acYVySp5psR2jahbpxjg7opHEjnwbo2p25SmkZd2OomoW5SqmZEWU6oqh3KasqqmlKvdqiWY+rlmGo5Wqt1bgXqAowAxBa7de8i9NQxa4WzCbfKHw7UxRhzxKoT0jj23KRmZzwnm9wnHbeookXlGpkZZFYgkhyrjh2QJRyQtTKKziS0jWNQNmVOvchpywxhuh6Uc8Cb6uak3YxEz1HtOYleoLoZid/0HNmcspdILi9e56ONxTz3y/z73/s4ZXWH3b1rvPrmiv3pJb7665/hq1/4WX777/4Rfu2rr/P2vTOeeOwKf+tv/b9h54hstM8P/L4/yIk2fOnVV1lIwfX3P0szmjAvJnQ7+9RpRp0oOiUwSmIkGClAOiDSCrEB6nQP0F0A61KMUa5e/VgbDALHfU1pgkaUUjv11x6sw1rXj3qwzrRyrQLrrlswFqk1SmsS05J0DRk1ua3JbElhSwq7ZMySiV0wNudM7Iwdc8pEnDDlnIKSlI3AIqWgkC2ZqZkm8zUgqvXmr5rRmtbQgywCYw1GayygjUZ3Gmsshs3Ctpjs08oJt04b5pUiyQ56MG5MawpqRtS2oBJjOiY0psCICYYJHSPniIIxLSO0mNDYEcYWjw7Y6aE4Fqx29Q09m7AfDO8BsLMioeocYxJx1AN48ICH6pBtaSHoCO8YJK1zRuGdVfQ28daOK3oPs5nyTi0aMlWTJQ15opmOBUXaoZtTlF6hxJzEVqTUKFEhTY2wbq6tyiXWGKpyBaf311ikED3WaS0WuwY7pVQUowJbOW+It2dzzt++w6go6HRH12mqpiKzil0zQnca3bYUIsWeL3ny4Ig8G7Ez3WUymaAuXyf9YMpkvINKU5q2oes0s9k588WCunHePcuq4sqVK+TjnA7NkweXePLwOk3TUpVV34c1Vlr2RzvcuHSZTht023IyO+Xqk89QVxXCKFb3zrn3+i3SLGUkUmpjOH3rPlmmaLsOoyTHu0dMix1M2/LEY1d4+snr3L93j2dvPMblgwmHewdYbZkfn3MwPqJbNdy9eYud0Zjv+LZPPnpjPyQM/akOBY5QqNkIuIYkMShV9Y4dFCN50YZSLJyGQn4sFIZC2ZAwGwqD1toLwodLM6HrFJbepp5x4BzCe8tNenaeA/cs2QWwzh2na3ae7T35eBDP2NwBfbhnHODnWH8uzmQT5zeZ3eeBvQ1Y5/ZCtgH4V6Nkh+oZeg7cqx1jrwcEnXqvAwMdeOfVddvgmR5ApMFSbVR7RYMQHSpg9odCdczKGhKgYsBNCMGds9/Lm3f/DNYWTEe/ytPX/iPg9AIwED7rhbQYBAj7nJchvHAag1/xT/1Q5oiZgz6EacfjKQZE4nhhueO04vqJ44bvCdOKr4XbEAgX5z9km/k0YhBqW/62AYVDYaiut6kuhm08BFyEc8VQ+cP5ZKjuQjZnzBwMgR9vcN0DPr6eQgAkBl+G2iVMO2TRhP0tBHjifhenG14Py+lZTTFTZ8g2ZFh/IeMvTDeus7jfhKxWH3w6oSp32HZD/TMs3zaQK8yX1nqtwufBmLAewnrZpsIcMyvDuonfGQNIQ3HD+S4ed75/eKDGz1O+jsbjMVVVrcvuARxr7QXAx6cRljEE5EI7dDGD0/fh0DxC2JZh+/pxGa8FhBBrtVbf1kMswrAvp2l64XybabBwHHm7nmmaXqjTcGyH/cin6ZlnPl74oyTsH96hgQfL2ralKAqsdWq7YX2Eqtu+7rwzBA/OvdPcNxTeE4PtL/zp7183CAQdd+2V0uLFEh/CThKj5nDxb0E4cKSUqB7BTtNel1k64a3TFiEy0iQlUQkqTRmNxnSt7ju5Jk0ltYaSjJPzs75hDKJeYuclWW0YpQW10ZwsV9ybz5i3mpo95uWERbfLeTVGc8DJIkeryyy7fVpxiU5dYVZP6ezkIptoiOHkWUkPM04kcWDZFZyh+EkHuXLOCkJbZrLr03H2pPhH/emYBwXFFVDxAKvJMWZwwJsH7Zr+OLz+sONwTaUsJBaZGpLckKQted5RFBXjomY6bji79xwfeqwkWz5H2t4HW9IYRWV2aJlS2UMa9jldTTmrJnTmAG2maD3G6BFG5xjdO4XoxLp+VdJRTKo1GLcB56L9tCbfcVsx6Y8nFcVOD+L1YF8+rihGPTuvqMjyQN/4NxC0kTRNRtNmNE1GVSdUdYplQmtGVE3GbC5ZViM6s8tyNaKuRlTlhHpZUC0LZ4utLKhXhbOZV/a283pmn2MscbHvfTOCtQ/2LctG9XUbaAjrrur7XwjUZeOGdNygio4k37DpSGzfZ53Kq7G9owWT0PYAlnMeUaC7hMSUHE5gJ6nYzS25aDmYJLSzm6zuvsBT1454++YbSFPx2PGIn/unfx/Bgn1R8yf/938IXZ3yS7/0i5zdv8Xv+s6PMS1ASsv73vc+dvd2neKrlAhpyZKCf/qT/5jxeEJDxsqOKE3B0o5YpWNW2Q6zfJdFvsd8tEtVjKizgqZI0VmKSSQ2ldje2YiVogdhAladVnRdr/rag3R+v27jdxkEvVOJ0AOswdnvilh1aNkz7qzzAmscWKe6hkQ35DSktiIzJbldUZglhZm7rTtnrM8ZdfeZmDOy5h6FXiD0AqMNVjvGmRWQFzk7kwnGWqqyom070IbWeFDO0hpDJywqd3blqrJEWFAIhHUsaukXsfSLJZui5ahXey0Y710n373MrXsrlpVag3IdzuadFhPa/ri1ozVg19oNeNcyfmew7qHXDNIaRM+4EwiMcQC4s0GoNqy5GLB7N2w8YUE4FpsVgHhv/WUoqN6hRSZ7z7KqB+pE1R87MC9XDQkezNuAerlybLw8achlQ6Ya0t6TbdpvUhpXDCEQAoTqbWOIhLzIkUqSJOl64aqUwliLsRYp3IKwbZ0qa7laIZMEozX5uGC6M2Wys8Pu7i6joiDNMlTap5umSODe6Tkn5/eZVSuqsuTypWPq+Yq6bGjqjvFkzGgyxiIoV0u00ZRlydlizvR4n3t37jJfLUkyxdUrlzHWcPXKZQ73DzjY3adtDCf3z0mTjLKsyfKMk9kZTVfyh//gn/8Nt9GnP/3pBxapPoQCll9UlmW5XryGDAzPghpiv4TqfV4Y8iwDH7TWg6p2saAdsx2GBDO/gA7VnGLBLlych8/HQl0ojMT3hwV9QARsO5NugD+brq+ZgLXnric9aOeueRagjwuOKag9ay8C+fDnZMF970noNydsbPc1F1l6Pci3Yfo9yAAUPTAoRQ00aLPP7dM/Sjh5HU7/Po8f//kLQmAoqIUCU2hHJwaZvPqfd0Sxra95mcKzFGLh7mLZLwIUQ+BUGDe+Hgqv28C0UOAMQYchYTZmLW0DY4byFabjz8NxvU0MjMv+sDhDafqyeSP5Q+ymIVDEPxczWOLx623u+eshmOfjtW17Qb3U72PAyQMeIcvLz4O+bLHKns9/rKoYA2q+74ZsmvD+UP8YqvvwWlzvsV3AOG8hy86fx30iZIv64OfxOH9CiAfAB/++MI5nKPlvQVg34fgK68GDSOPxeN1GHsQZAkvDeojTehQQM2y7MA3/bQvvD80XcXt6sCwsiweUQhZtaPszzGNoB9Pn189xvo6bpll/w7eBw+G8ErNt4+DbCFgDUiH4NjRWw+OQ3eiZcT6/IZ4DG5tyZVmS5/mFdopZj74Mvv2HmI8eJPfPhYCZr/s8z9dpFEWxLqdnvYXj3W9+PZTnOavV6jeHwRZT1jed0gk6iIsTQFz4uJOGH5XQWJ4xBoQg7Su8XK4waOQFgUHR0gtZSlIuk149qqcQWoHBYFWO6hqUEEgsShrSw4JMpWRpzthadu0eT9grdLYXTIzFWENd3UNwD911IARN02EN5HnO7XsnvPr6OaerEd/7/X+Ut080/+CffJ478wmyeJKTyQ9ikiMHmDnKWq92yAakCn8OvNRvfdkeqdlGwMQ61dI4vAJ8cSCJmL12GWfCrg02D7i1fT69Yf81My9IzwrQAqMlTQdNk7HqAL0Hc5yaa/csrz7PRaAuBO78fCU1ImmRqkGqEqWW5Mk9kmxOIhck8pxUzfo/r6CtQtsRmjHNsqBcjNF2RKenGHMV3XlwLnXgnBbvyimElJp8Wrttt3GA3G5FvtNQ7DrbaA6824B1ec+0K8YVxagmzyuKoiLPa/KiIi9qpuOK44Mz8vxNvllqtE2bOrZdO6JqC8q6Z+N51dqVs4EXg3Nu7x1d5NTlaO3Eoqr6uNWIC55D30uwToHNqfnmzviZhbW9vBioIzoPwDqhHFA3Hi9Jxy3YljLXNMax6W4tLWQTePLbeFOCffpptE14TivaH/7dtE3GnSbj3/tyjjCW7GhJsjfjC3PNzmrJWKy43EqOdmAnqZlmDbtZxdFI8HzzLewrZ79uJFYcccploZFGYksLK7vOs1AX1TXcfOkK5XBLSSNGlIwp7YhKTFjKMbN0l9PigFmxT1lMqIqCLk/pMoXOFDaT2FQEwLsD4q1wzjIsjlV3AaALjtsucEyyVWZzwpBzIZHS9qzapdDOE6Z3LOGZvlpgO9asOtvJYJ6zYCxCa2TXoXRDoltyGhJTkugVqV6Q6yVpNyNrT8nNjLQ9QVV3mIoZRXefVCqSbgk4kCqREoRASbnuKMK0znkB5wCo+ibiRHK1tysjpHSSsxBg9YWPvx+K7lu1MYMAEi3HaDleO6LQckKLU3VtrFNHL01Bax27rmFE299zKrOjtSOLVozRZoT1E6nv4w+zaTdwTZoWsQbvbACGC4zxatCJA9reC2AXXNMixYiEWoxd1Ql3034TQbxENKRrhp1j4DkgrqFQNalqyGRD7ll5qiFPWjJZUSQtuWpJZEWhGhLV9Gq2FaPSYLqOanGX5ekZQsp+vbJZgDZdR13XLFZL8v0dhBXcr1tkh2NeGijbhvLs3Alcuhe4LOymGbdfeJXd/Sl5Oubo0hG5zNnd2+XapcvUZU3eCq4dX+ZotMOoGFFWFXdu3ebDn/hWvvKVoQ/1uw/xX+BQkPFCabigDxfjoSAlxMZzZqje49/h4/h0w3f69GKQLxSafTqhkPsw0Mx70PT3wnyEgmoYYgbDEFAwBP754NIE6EB0SLFcL8u2gTj+WvyXPbbjFrZJWFdhPfnFfwg6CZEAOUI6L7qCAiFHsPao2wN5Nlsfa5M44K9X6XVquPk6nulVdz0gqE3aM/02gJ+Pa/Vko9L7G2L3WcrmQxfafqitwnr090LhaSjEwFgIIMTMiqF0h/rJNmAtTmcofjx24vE5lP84zW39LLwWAybbALRHAc6G0ntYXrfND/EW53+IZRSDInF7h3NOuIXgYwy0heUJ6yYU6Le17zZwIc73trL74OfY0IYUbMCv2OB7PHdu609DLNKwXUKgJvwmxHn389bQvLptfA6FsP/FrLiwneK48Xu6rlurWsZtuI2oM5R2+JyvmxC8CdsnjB/WRwwkDX0rQnV0bwvQs6WstReYXkVRXFBd9umFKugelPMAtQfkvNqslJKmaS4AYyFgFLPOQxDMv8PH93vfV8J8hd+qcEzFYy6sB++cwoPS/r0+TlEUF/Lq+1WYPw82+nWItXatxh2yKP0PvnBMhWYBfBv4Z71qaugMJ8sysiyjruu12ivAeDxmtVptZVw+LLwngG3og+iuCwQCIcFaAdg+4xvkcdPxN0bmXOP4Tu8/OmbdkbAG3fW2maxFdx1S9B1EaKywaG3RRtA2FVIIrBU01cq9S0oQyw2vzjpbTK0oKf2AFw4Y1NqAFKQqdZiSUGQCOqMpMkmap+TFFItAyoTjy3t88MPu/lNP3qJuGj540LBYzGiat/jPXvgRlusxuwU5t4ZcNDRthi0F3BYX44vo0RiAKC1UAua2Bx/EhjGkgBtcBPN6e09rYKsCngD+8+3IPuDYcGW/hcfhed1vzp7wxS0G7zQb8C5k2KGwVqFNgda7tNsAuXc6toDskEmLVDVpskTlDrCTqkLSgfB9UmCNcAtJm7vN5P3CMqOsU8qygLfkuwLngIB9KBwbMWGzTwWkkE16sM4DeTs1xXTDossLty+KkrzYgHTFaHMvL6p13LyoGGUrDib3KbIKpd5tpodD2yXO1l0TOKkInVZU+cbDbBne74G6+F4VPlvQtoGTgncI1gqn0lvncMIGqIvBiHC8hMceqEsM2bglHUMyGlFaTZNnnGW7vF1aRGNB9cCVkGgjMdNPOLt0dUbbprQ6I6VmolaMxIoRS8ZyxVj052LJWJb9+ZKRWFLgQLyxLJnIFYf2BGczsv/YNBZbW+zZxn6aFArLRQ9D/uOmrWJlcipGrOyYijHncp/z0TEn2QGLYocq36EtMnSRYHLpHBtmElIQKRvgXdDbquvt1fV2+LaBdbpLN/18q8zlJjLbq8FqRk4xaW2rzgEVa1adFpjWseroxAYYX89jBqE7pO5QbYsyDampSExFalZkekFmlr1DjjNGesZofMaoPSFvTxh390n1isQuUTjnEUpIXCZwbdEDbw5kg8SuEKLEintung0+6G1Vk44KrO2wJqK3W42x7i/FqmmZHh9y35R83w/+AKenFZ/9F19kea9F2gmdKHrW3A6tHdOY/lyM6Bhh5JTaFD37zqnEdowdmGccmKeHPXs8EmCHBWm7ftOuXYxXT6YH7jx4pxx4t9Yjj7aha0PX+3MhNUZIGlHQiALEruuDQm6AyG9CSHv2XCZ7dVnVq9HKureN51Rnd8YwTjV50jnPtKohE6Vj6vXsvFRUSCpnS9IcUi01aWZ5/RuvMBqNkVLw2svfIEkTdnanrFYlt2/f4WC6h+k6xsWY119+haqqvyllC4WYbSytkNXgF7Nt215Q7fDPh3a7hgAzHzcUDEJBJjyP0wkFuXiRHrLjvBDp58FQ0IhVS3wImSixIBsL7A8DImKBYhvQ8U6gycOE/lhoHFK59MGYFiE6JBVZmpKm6QPslRCwiwFPL5TE9RXGDfMRC7Fhnt163anzbsA59xGI2X1VfYM37v4ZWGtyCEbZiw/U9dB7QuEuBGTDtozb7WFg2DagZwiQfRgYMRSG4oTC+lAZ3216/nr8zBDo5ONtU4EbAp8eBp4M9duhOEPjI35POJbifL1Tm4Z9Mx4vMVASC/IhqB0SQOL8DM1TYV6G2jK8FzPZQlDDz2nbyhqHkIUUj4EhoCkGy+I8hulu62NheeI0HtZW29Lyed02RsK0QxJOOG/FNrtCJmTY74fSDNfMcR4elsa2NOO29vk0xql5jkajNdgU9wMPQIVgX8jU9v3Dx/Hf1xB88kBY2H89K9d/J32e4vz6fuTjt227zpMHA2NHBjEw5wEwz0iL1xv+mRBQDtmhXhXWA12+HkJwTkq59v7pz8fj8Rp08/Xn3xmy1/y5Z6TleU5d1+v+5fda67UNz67rODs7w1qnutt1HV3Xrdmy7ya8Ry+iG+OhvqFiHW0YZqv551zwE5BvkM3kt+mMm8Ewn6147rnnMZ1Gd44GOB6P2JmOme5OmU73yIucJPETju8MAht8eDpTI3oMynBxIgYnxDVthxUglATrVJHa1tI0FcvlHCkVgv7PqtGoJOHrz524NCzsFgnkko/sfYPPnn5yXSepaPk9T/wcrS04rTLOq5xZnVOLPe6YK+hRArtsBEkvTPrjwe9eDyrUA5OcxHkgvRA2CTkYVHLtxltUP15QMaIWOSZTTlC+sFnEyB3baX+teDQw5KFBcxGs84BdCOLV0RYDd34f2qhrAZNgbIJhtKlLy6ZOh8A5b68uVqPtwTqhGqRqQXYIoRHSBOCtBCOxphc8jUKIDNMJZ8e4HF44N0gaRjg6YhAEG0BO9fsQnEuEs9uUOKCOVEDWHysckg0kSbth0Xn113G4r5wqbeFUZEOwrsgr8mzFKF2SpxVFVpJnNXm+ZHfnlDxvyNLu3bT41qCN7NltkafZ8iIQV1cBOLe+N9o8W0Veavtnmzp3bRQESw/UNTmc8s5AXQzYCUBCm2Qsx9COU1bFhNNMO9t0vfMIYa2z2dYzi7RJ6LSi7VLaLsUawSgpGckVE+mAubFc9UDccg3cFcKd53bBmIqxcvbUcrMgx2271iKlm8vUSqLn3kDvRU9EQ3+fOpIeoHNA3ULucyKOOB9dYTHaoxxNaUYFXZFhC+XmgJFFFEBqEQkIr+LbM+tCW3W6SwfBOu3BOp1syLvRcLgYJJYMTdaTgC31A7bq6Fl1wo3BVm6AunBe1QbRaQfW6ZbUNA6oszWZWZHZFblZUJgFIzNnZGeMulPG5oypXTKxJ6RiQdsuENoZvTfW9iCdQQgYFSM6Y9kd5aAsmYWv/cIvMjufs7eqOMwEcA8pbQ9sggMke6ajdfN2URQsFou1+oRAkkgByqs/GZAZje2BuR6k03KnB+smrNrUnTOmCTzItr0TisYGjil60K61xfameAfADgvCaCQt0rRI24HpwHh1ZRBInLMFhbUJXiXcAXh92AbaPcI1ITRSGoS0YCyNTmlEhmUH7/Tkmw7iyUCNNqkp0o4i6ZzdO1WTyZZcNY55l9Tc/VEYFzApYJy743EOk1F/nm/uFxkM/VQdYnuEAm0IUIXnwAOqF6GQNgQEbBOUY0HUL/LDhX6sFvcwIRk2f/ZDYSZUc4oFJF8XPgwJSfFxGIYE6KHybquTIbDuYcfb8hGGsL5824QCixfSwvoI2yRsg6G0QzAuvB7Xx+bYImVHkjRYu3pAeL9QBzu/ihCWN+78KSwpk+LXuXb4lx5INwZRQgE3BMHCto4Za6FgGZchfEcMuIZxYkE6fM+jtFUctoEyQ/GGwhDw8U7PbAOH4mceBqjFcYbG97b3hvPHO+VxaKyFQEoYYoBnWxligCkUxEPAxY+bmMEb9rWHtUvYR4YAGB9Cw/ExSBbXa5hmWL5wfPvrHpQI8xD3db8PnTb48/i9cf+K0wzfG9tIjOvfx/HphHnZNg954Cic1zxIFdrUi1lF4Xdl6H3xty2cT7b91AjrIp7nw7kpxEM8WypJEoqiIMuytapyqOo8Go3W7/dMr9A2WjifG2PWLO6u65yDJyFomoaiKNaOEMI5L7TlFv+88fOkB7c8WAY88KMtbhsfP/ymeKAt7itxXYdaih7cCvMAm3Hi+7QPnrUHrIE7n+9wfBhjGI/Ha+cMQgiWS2db1oNodV2vwTZft03TXMh3VVXrdhmyKfdO4TfEYPMdwVf8pgFYd7iw0sLziwPlIu3Qpe8kVx83SVIuX77OV597Ad1a2lZQ1y2rUnP33gJrbq+RRmMF2nYY0/9RtZaqbjBGMypyDo93OT7e48qVSxwfXkYpQVFkWOOYc8b2nUQKurZDqRSL6dVhLBiD1j3l1roluW1bXn/7Bj/2T/8Q8+Uen/jwr/N7vu8n+F8/9jfo9B/jucVHOEpP+N889d/zwZ0XsMaAkM6ElTCkIuWle+/jv/jMn6K7m8MZqGnJdzz+F0EuWDGltLuUdp9aXqIWh7TiiE7s0YldLMVFgXHrZp2A2QsSFhDC8N/8jT9yoY2rKqdajSjLEVVZbI5XBeVqRHV/RFX215peHbEbUekRZTei1CMqm9HJhE4oumSMyTJMptFKoxOJzpJe1U1hMoXNpVNz3RdQ4ICi32jwziCG2HYVG7bdNuCuJgDsEkyTQD1CV1wU0m1w7kG5hzHvrEYojUpBZQKkRWunumNxXt261mI7wEgHnho/0fgPwTssigRrUK5T0CUZyySHZG/NnHMAXQ/YeWDOqx0+8PFzgAG9gf9QXVhmmnzS27+bVA608wy7vGaUzRglM0bJnDxdUeQleVZRZDV53jqQLu/6Y+8Eo2F/74wiqSjS6p3b+hFDVeUBiFds1GC3gXPBeV0VVOt4MQPPgX3L8xzOeU9A3Yopq2TCbNySFC3JSKMyB9I50Mo6NVCcQwRjErRRtNqBVYnoyNWKiVpSSAfEFSwdMMeCgiWFXVDY/tysGLFgJFbkdk5OjRAdWbegkJKptlwyhhvWolaRzc0+hMwIYwzaAFJSdykrU1AyYmb3mCVXmGdXmI+OWU33aMc72J0JdpxCIRE5iMKiMo3IQKzBOuPYnz2L0ArHrHMOJYbBuq5L0W2CkeoRTRbJiFlnehVYB8asPcBqgW1Fr/o6BNTRM+t072SiJbUNqa1JTElmS5RdkZg5RbZgWp+Tp2eMds6ZdOdMzD1G+pTCrMipMaYBa0mTDKs7lHKLmOl0uvk2Kvezx+A87SqVAC0j2TEWywulXAsNIy94eMDDYMyDnri00QgEWlukymlMRmd7e3WiB96YrNVeGzuhMfnaScXag6zxHmQLGuMBPeew4h1BraExs55zDdLWSNOQ0KGEQWFRwvZkbtl/1wXWSEwn0UahdT9mdPLg+98TiGdQ0iCkcSBe7wiqNQltl7Jcq1mrh5b3R7/68KqIwyh/EIx7Sf5lFBWKGikrpC0R/lyU2GKJ1UuEKZGqRFIhTUkiW6QtwaxIRIM1KyzthQV5uE4bUpXZBiptE3S2xQ0FC38tXtwPvTsUBmK11VhYHkp/W96G8hmexwJmLGSHYZsAPnQeviMW5MP0vMD2MObbkJA8FCe8NyR8DuXrUYKUkmvHP8al/Z+g7XISdYa1BmsfzoSK8z8Ervg8xipMMRDkn4vLFfePsC6G0hrq849aD6Ga9DYwIwxDYyeug/jaUD/y/eMigeHRQN2hPheCGUMsqDC/MZMr7oPh83E7xG0Xgg9xHYXXQpZ/KLPGIJi/H5cvjrOtXsKyxD8NhsoUAhkPG/Pxu+K+HedzWzpDaYZ1H9dtCGptG5ePUi9hnob6bJyfOM9+nISsLg8axe0Vjt+h/hX20aH+Gtbb0E/noe9YXJ7QO6u3L+ZBL5//GFyKgSEPnvl8hICsUmqd3pA6Z9qzmWFjk8+rQobjLm4X763V16sH+TwYNtRmPv/+XT798LvtMaCYnQesGWqe+eaPfb59XsMfIOGY9+qbUso1cBnajDPGUNf12hyGvz+bzajreg3s+WeGmKS+TxVFcQHrejfhPQFsYSfb0Pk2rpd9PkLXreGgdXR2d81XjrNJdPHvgTvcvCvLUvb29zjRM1pjEFZijQbhwDBtLdomaCPQVjqBwQisBSsSkJaqFdy+U3P3/m1e/sZd0uQFEilRSmBtx2hUoLWhaRvGI6frlBeKosg5ONinGGWMJ2MmkwmJEiiRgrCcnI/5v/2//hNW5Q5gee3mDTCCH/k3/x7/wdN/ydccYDFaIKXC0IEzM4eWHUeHdzC3EngDUBZ9OuKzb/1pfv8nP03bLmjbjq7VdJ2haTQiWKh3JDRyH50coItDTHoZm+7Tpgd0ySGNOqCV+8xHz7AcP+MdoEIH0mr+h3/07zAdzZmOzhG7hnI6ZiedM03n7O2cc5zfZi+fkdLyiOuIB4IxwoF15ciBdD1454C7Hqw7y6hLRbNSVCU0LbQaKiOpjKK2CSUpldxhyQFLjqnVASabYLIcm6WQKkilY9hlwbYLXMKBd+/Nf+7FUDMM3oWAnT+O1WUbhW0UXQ3dmpmXrZ9vQ+Dundh21kmdQuEMSPWqttbajTDaWWhs4JwiBukGJg9v2ysRG/VWD8p5Bl0PyplUUKYFZTpycR7oJD04J+jBObFRR1T0wF6/+WuBrT+pFxTyjHFyTpGeMylq9nYNeV6xd5AgxIrJLqi0QaYtKmuRSUua1eRpTZFW5ElNoSpyWZEnFXlaMxkvOEzvUaTfRDXaJg3Ydw+CdRt23ugiOBeAeJ6Zt74339jE65qUtc0633T91qJoTc5CHIAyqFFLOtKovENlG5BOKAfSmZ65o42i0wlaKzJZUsgVhVxSqDkjtWDUM+YKD9RZx+bKzILMzBmxJNNL0vYca1vXaLZiZM7JtGXPuA+U7gFkJBhr2dvbJc8U1rRUVYPpNHVjaK1FCoG2UKkJtZxyZvc5l8es8uuUxVWa8THdzh5yZ0I6yklGCWIkkDmowqB2tCtzapCZhcQBdkh6D7ds1F9jz69tio6AO5s8KrspBOsK2pBV1//ksD2rbs2oi0G6mFlnerDO1CTWOZhIzYrcrMhZMrZuK+wZE33OnlwwsacU+owRJZmZO3VP4fInhFwPUbdwcMCPWxTJXvC1pPR/p41CppC2JUKUCCH7acdgTCDESdchQ5UGT9kHLhxrbZBK0ZJRdTlVV1DqnI4JZZdR655VR68maydUOgDozGjNsqvNiFoX1GbMoiswW+2XRiGc/i7YtatRtiIVHQpNKi2J6IE7ARjjgDvTmxYwEq0VunXjqDOOlartlg/NuwHxBKQJJCko5TYh+ylWQNvAaQ33z6HTUPMx1o4thr7V7/LbJ2yJ9BsV0rptc71aXxf9uaJE9OeKGmFW6+ckJVJUCFxcbLVxUrIFmIrBlVDAj1X7fAiFqaF03rHcA/FioXsIGIlZUz7EKk/b3hm/e0j4FFF9xaDhkCAYCrohEDAkQIZARZh2uI6Pr4Xxw3rw6W3eWZNnNbZXNR8SzOLjsL1DgMqnHb9zqH7CvPj2iMsaC+lhnT8qeBG/c9v50DPbwJL4/dsA3ViQHuo38RgYAhfj4xgc89eGGEThPmZUwoOs+W15DYGE+H4INoX5C+tgqH7jvuTT8vtQjTRMKxw/cTuEIG0Y4jEWMoJCZt7Q+BmaIx7GpotBI5+vkA3l3xGCjDEYNdSPYtA+rPOw/X3cuH58+WOnCnFfj/teON5jL5px/HDcbhsrvg2GwMW4DcK6GgLl4nYP3+eBGz/fV1V1QQ0znr/jtvB9w6+VPHPPM8R8XYROhLynV4+p+DQ8qBeWLxxXWmvqur7wLY3HQNjOPs2wbbwqqdZ6DdT5vh86ZvB1b4xZ240L5/AQxAr7jX8m/PaEwGLoRMM/HzpX8uPN5887YPBlDcdGDBz66zGT7lHDbwhm8AV0SKr35tIDWtailEciL34AXce8SO0O7zs7bpuKd4v0lLpecXx8yHJRUbcN2lqESLDW0BmDFQqDdfb2O0PnmXEGLF7d1NB1glRIOmsR3vAsgkQoqlr3nXpE17p3N5WgbRpW89sI0Q8+4ZwnSAxWWN689/09uAYeSPuHP/tD/OLnv42d8ZzdnRXTnTnTyZzd6ZLpZMlkfM7O2F3b36v4hnoa85KCTwCf6Qc4O/zdX/o1yA0i08isQaY1KimRaoFScxI5Q4lTSDWzK7+NbrLPpHuRD9/6v7LbPU/OikQpJPD1D/77fPVDf+ZCO2qd8j9W/y48zcYm22IDxojeILuwXnHJOqPawomRSmgUHaloyUVNrkrGasU4KRknS6bpjGm6YKKWTOSSaTpnOpqxuz/j+uguk2T1GwLuuk71LLucqsypVin1KqWuFE2pqEpJfapoaklVJ5R1QdnusLJ7LNlnaY9YimNW9pBKjx0LT48c+8WDQGm05daBdh7EOwCu4dTlvhlaRh64C9l2XlXW27db27kT0AhsCObVwTMh+Odt3w2pwHrbfJYNGCbYMB9beuP9HigwvYOOGKSzG3tcoUqrPw5ZcpnYsOk8g+6BjmAxjFmJESt5DZTsAT82WxrsVbRhyGUL7YKEhiuX9nnr5itAh5CWK49dZVEuMaojn1jGOxKZa2wBad6tAboiq8jTHqATNYV0x4Xqwbukv5868K7IKvKdmt2DGXlWkaXfJG+0WkYqtJHqbMTOu+CsIraD1zNV6zqnWrnzphpTmx3OfXMqgyq021Ln6RVlkcmGUaaR7ueGVAirSUVFJhZkYkEh5qT2lNyck5k5uZmT2wW5WbBvO3bNisLMKLIlWTInkUuHrFvXB8d2SdrO0ctvoNqO61mGElAkCVIIMpWgrftoKgQqeVDw7KykVbus1DHz5DLn2XXm6WVW2SWafJ92vIMeFdhRSlJ0JEWFyi0y1yS5Ru10vbqvRfQqvx78DW3VbVh0DwfrSB91sovBuslG/TVi1dEJMOJBkC5k1hmD7DoS25HahsTWzhusLclt6dRgWTA2M0Z2xtjMmHLGjj4l7+5TUJHpFQktCDDGkqX5+rturemvb9Rf/F9CKZ1B3gtAihRYSlILKZZp0i80k80C0xujdYsiBWzWFgBC2PVPPb+20KTUZkTZ5DR2RNXl1KboQTgHxjVmRG1HVD2YV/eqspX2cXOnXmtHzExOZwO7do+qLRCA38I0KFOhbIWyDdK2pEIjMSQSUim59tiz7jEDRoPWoDtoWzck6gaqFZQVlFtNtkXg2gBgd/F6ixAagTN5gDA4W5AWRK+qLCRaTNFiHysSrEj5pnmntQZJhbBVANiVEYhXOxt3AaDnz4VxIJ4H9WTP1hNitQb+lOzWZkJgGKQIBb0hgW0ICBgCZGJh34cQNIjXvLAR9GOAbkggjcGG8FqsehaWYyh/7wQmxcJgnK7Pc8wYjOtpCJyIQZawrLFazhCbLgZrYlZizFQJwY6hfMbCZFzWuJ7C/VAYAqy2MSfjd8TvGwKO4nIO9ZGwLh+FcRaXaVubbns2LOMQoBwzYofS8m3kjbuHAnqc3sPqf2gc+H0Muvl3+vraVi9x28V1HZbRh7hPxuM5rI+hehkaj++UB1+OEOwM04qPw2d9ejFLOe4LQ+UI48dg4lA+w2d82bxdsbCsvh/E8yTwAHDm78dzdzi/x/kN+6p/zrOq/LNDJgn8PZ+fEAj1pKMQ8BmqhxBY88CXvx96oB2q5/C6dwjggacQjIzB0DRNaZpm3e/DdZafJ3ydhKyyMO9N0zAej2maZg18eW/iYf8Jga4QBPPphOCYf3fsNXgIYPb5i1W7/doxrqsQPIzHUthnfL2H1x40g/bO4T0CbGFFSLquDTrk5iPr0cvNhL5Z4DggztkJcn/Dw8ERot0ClWRYBGmWMplOGO8UlJVGWQHa0ugWY+0aI8BaZ4/GQNf1De07Br38ri1CKm/1jcQKjJTu77TsKZZr738OMDNIEpUA7i92KhJnH8dAIs+iOhIoWbEsC05m13r36g8PSjlGG3ctXBdr4UjoDlqBXSbokxRtd9gqqo+BHZjX38Znmr/nABVpHUCXa8RNDb8CFJa1ya/r/bbCARRjuwFEcEyXTau/i+C9kNbAkrVjBWFsT2jqJ28swhs6xyCEQaFJREciWjJRk4masSoZJU79bZwsmUhno2qiluykC3ayJdPpjL1L51wq7jCRKxLx7gfFOvt1QlWm1GVKVWUOvKsKynpEVY8omx1WzS7lfJfq/oiyKtz11qnJ1sYBdV51tuoKKpNhpQzYYXLDsEvZHHvgrgCmuPb6Ztu782y7cKtFD9KJi0BeHcULbeT5Y28Dz7PtPFjnWXuB8XgndNpekqRXXQbXMXCqkEr8f6n702DLtjyhD/utYU/n3CFvZr7MN9RcTRVdXdDVdAMCWtBACwMyGCkkYSkcBlsR/iAZGaOww2N4kMFYtgLZH0Q4QgoZYykMFhZIgIwaEYih1RN0VTVVXVVd8/DmzLzDGfawBn9Ya52zzrr73JfvVXXTWhk79757XPPZ/9/+Dxk4E3soF6TxvXlrDufkbTgHglFWIO4xKsFXvyJAfWIH575+Ges615xL2wIkjoUe6dREqw2t9gzDNYIojCqJUwIrFZOumFTNoBsG3eGU3gmyUtrgu66KUE70tL6nYaAVEdbpLY0KJrFNNcSlp62GAPGqqH1X7yHexckzmjoEuvhepb6f8YOXaeDt/d/tNeuGvmGYi0q7vc+2f5nLoWJ0FZOvQYlg9hq1Kr2Qe206JKoeqcSGWmxp5ArtrvD1m1RcshSr6H9uRceK1t3Qig2dX7FUPY1bo4UJASxc7A/WUvtrTuwNL9iv4gcRkJhML2eCqtLk73y7l0chdjPgRMWlv+BGvchl/TKX6gU21Qus1AVbdcrUSGyn8K2E1qObkboZUI1BNxZ9OqEqg2xcNPsFdADSXgWNOi9EAHIzJq+lSaw17+YnPME6jcUz+sXeBDaBOnMHqDvY9tEM1qCcoWKMprA9DVsa+p2WY+tuWHJNZy85Fdec+Gcs3A0tayqzohIGKQSCGNUTcFLhbZgvqqoG0ldTu3vPCC9g4taLtnOOWloquWIhr8PXSxMoV3L/4F2cgpzHxSV8fLOx7QM8fvbkKe9//ytY55iMYLA1RiyxYhkBXcvgmgjt9j7rJr9g6xoGEzTuetfsIN7oGnp7ymAbVvbQr92333yHJky/D6eE7xgClg0sKuhqaBR8+5tfRjoLziCcgV10WcDHsjuJ9wrvFJ4q+DJMESVpg8/S5/lSNAvs8n0GIYJTfiHCFx0hXIR46bwwxryQOLHEynM8Gi/q7yHEs3vtOwLkFAm+kTTwErwbkH4b9w+HmnvxPCWGvUZe0tJjiu+X88Ak31dCFthDqnRslC/x2vJ/wqjez9J8mhfXfwbFOAtlUirNyEptlTIPMK9pddczSiErF4DydZ6XUtjKn1Nqr6VjuTPv/L5zAKoEG8fKMredn1vCg7zMt7pU8eyyjfN7zj177tq5e5b5SG2X+6IqQUJZz3PH7jp+V/sfq4e5ss+VqWy3Y3lLECBv2xLY5WVPf89p25R9ME/5vhJOz5VhTnAv85JDiVILrwQtZR7K+5b75vrisbaaGyNzae7Y3LyQ1mX55+ojbSftpBzc5PNDarN8f/n8OW3Uct67qyzpnFwDFjjwP5rvy/Oerk3z6Zy29Nz8mPpD6SornV9ek2vqJW21/Lr842Q+JvLo2rnGWCrrXFkSMEr70nYOAnP4lTv4z9sh+SfLrRXTs8v7lHNzym/++5dr26X7J62y/CNOyn8qd4J3d7VLaofS39ux9kv3TXmfG3fvlN4TYMsHBoQInpA61KGaf/5DH2CaOLAJDuftC7n/AQkgy1i/c+InhGC5XNItOhYLSz9YHBZpBUqoAGakZ3I2VkiU4QFLavwgZiA81oGSCusdEoFH4T0468N7nRe7l8BU6aHTe6yQ2GSmgeDFi8/w0Zf+M77y2u8DoNIrfv9v/B/w4OwX8B5Gs6AfHtCPZ4zTPQZzn+14wWTusxnP6E4+wna4xzeevI/hq4dh+HzpREgQAEwFot4gqwEqh20fwAeBX1M2GDBK/CjxQ5VpPRGWLwOfJzNfjMdsBG0dcOYRFw75wKLuOdS5Q5w7OPFwAizAdxLfCVwj8I0MSyVxlcB3EY5EbY9Qrnf58uzY+0MbCGaPuZadzzTs4hd4Id3e+42I2nZ+Ck6nxUCnBhqxZVFt6cQ2RnzcslQbltWKk2rFab3i9N41Z4+veLl7jYbhPYO7L/Ixfs7+cDAtshJvYzQ+G/wDOSNxVmGNwkwKazR2UthJM10rpqnCmAZjNeNUM5mKcQp/eyGj6VtcknZNXNDglcRrgW8EfinwlQzbOmrheBGEMATey8jB0nY8DuwlqCwlf3dJc64XBZwTt01n++z8HNqts/Ua6H34O5lxlVpz0QH8DtzBgRadr3wAdI2IMDMHc3FbzbzQRG28razYygqnRASk9w+15/IInJlpa8iGpRXbHSjWwqCkxUvPVtZsVY2T97BKMynNpBoG3dDrLmjr5cMkmv/u1pbduBWjofHbqFkXNOpqPQWoV0dIVw80uqdVAdw1KsI+mWvpDTTNlqYJAS5OTlc8fPg2TfvLY0Z7GKyi1LArYN1wn2H7EoOpGUzDytX0Y8M4NQxjy2Zq6YcF06jRDFGTbkOtV7RiRSuu6cRlWLiiEzdRa2vDQvZ0YkXHmsqHCNRKqRCF2nu8s7TS8dg/4QX3FvS/gBD7l05rk8C4f0mY/eGXmpU/55l4gbfFY67rF7mWD1jr+2zUOb08wTQtvtXBN10LsvFUzUBbb9CNQTcT+tygaoNMpr9VNP0NzscCqLPH/dOVsM7bd+PEVeDRO1g3+cXOBNZbwERY58QdkI49rHPBZ13lDTrCupqeyve0bGj8hqXYsBArlqxY+mvOuObUXXLiVzTxHO2H3fjfvxDl0ati3v3+i7GQAimiCZu08eUw+Ed59OID6iZ8DRY4KrXF+y1SPkUqiVJyNxVKKdO3uPAuIwXGWbRUwexWgBABIu5nL8nkGgbbMLqO3/sv/WlWPay23FrfbOf35+una1i3Hyb4C3jvKZh0blGuR9geYUekGxHeINyE8DZoMMcIs94HQJrMZfE6QDJf46lxNDg6HC2Wjud0jJia7wjEswFq7QDeXgMvjEsfXtAEIGT4fRQKxwIrznFC4/neQrw9rIvrzKT2EOL1ONeydR9G+IlT8bPUvIpiALvB0vPt0z+J0a+A9/T64xhxxoc3/4udAHMYMCykUnMo35cDgHxdQia4LQDCIbybgxDlOTnsSPlIglH+vNJ0DPZOtnNhN5n25L617hK+jwnaxyDaXHnmUq6R807pGBBJaQ4MlGUoBeoyz3eBsDnglm8/j9CYt8uxfM6dN3efvFxzgmy+vwSwOSAo+1lutpbuk5tkp3PnzL/S8/P8HDMXTesSSuZ5K7XLyuvT9pz25V39pYSyJWSca/u74NocYDjWt47193fS8izPOQZy8/yX81HZ3nN9MW/PvD+k/pM/NzetzE0TS423vH3yOatMx+osPTvvk2XbldckTbI8Wmc6PzexzN8t8zk0f3ap8TVX77nmX64hmlJ5jzz/KU/p2nI85X0hH1/pPmXk3hwe5r8z5T1KDeayztN9SrPS/ANQXoby+rJ/3fVb8k7pPQG2PcUVtyaUlPEyk0mDDW4TxENgBz4KyoLDBnbO03Ud9+7d4/qqR4wWhEcqifQgTPwsjcTjsN4FzTbrsX7/DIUIX+1N9DOjE6WcqLRCeNBShuigCHxWt867CApTvgEfyvdPfvJ/ysfe95fop/u8ePEPOenexlpQStKpLYv228QCplIicCA9v+FHfj3nF/fQVc0Xr3+If/i5l3hRfxG//hzf/JbnarVk05+z2Z7TT/cYxgvG6YJxdZ/JXjDZB+G+K+CGwv+YyPyQeaj9nmrembJzLPhJYUeFvRLwFjN+xbIl9zvmA9AU2lGdGfQ9gz6f0Pcm6guDPDfIE4s6F/glsJCwrHCtwjQVvdaMWmO0wmqJi4CIJTtTq/esZZeijqa19TuNqhTdjqRZJ2M5pEPKoHUnsUjhqJhQyURWRl9fDHT0tKKnE8FsdnxQ8eSFB3u/Y+8iSRwNAw1HbYN+xZKzIvAsH4SqAOGSqVy2iCAN7Y75+W0O9snb5xoRnMvHhbQ9xvWQrQcBg8D3Ar/Nlk1cr+N2Wo+EaxxBi8cSnudE+DuVT4qgeSsjpKwk1PJWxNZ9Cn0E6RlkSy9bvBJ4rW7DuRm/c2n4aSYaAqCr5EQlJmSMXhs0nwRWSqzSGF0xyoZn6oRRd+Fejj3ITMKpzBZPNA23qMEiJ4efYl241L5JTd8E8Fb3QWtObWlU7O8+auKJoGXX6sxUNkWwrQeaNvu7DZp35/euaJueqv7emtEe+LfrG4b+UOOuHx4wTC8zjA3PpobXh5Z+bNj2HcNQYyeJmwRu8vjJIieDGAdqv6Z1l5zqSxbiilas6FTwddapG1o2qOAg8daLiXCGE57QmTd5bD+LHIKbAyHYAexwzV4hUwgBQiBjUCDvYeM7Vuoxz6pXuKxf4lI+ZK0CpNuKEwa5wFQttBIagax98E1Xmz2oqw36dEK3BtWYGFgimsAmUCfBOX2nyevBtqnw7t1AiwzW+Rrhu/CbkSLAWhG16rgb1O006xzSGSoMmhBkohZDMO1mQ8eG1m9YckNX33DCNUt3yYl8Su2egbmkdT217Vl2HT7+5isdx4FI7zThPUDIQzMdqeKXt3QOcSj70J42c3QMDiW3NGKD90/51Ee/u34P8Lt+9+/BUWPpGGyFEwu8XGJoMb7Fig5Lt1s7scDSYugwEYBZWqxY4GSHUQ/CPUT9zg+/IwnfI92Gyr+JdBukGxB2QPgJ6cadxp3Yadw5gsWxBC/D74JX4Cu8r3C+2mnbeTocHYZTPHdEuz2aOW5BPCFGhBhJgX2EGFFsEAng7dwoxN84IcNHWqGwIuZF1Ljn0MS74Z+4I28hY9f174QNB0JNEsTy9+QkEMLep0wpgJZAKzzmdsTYYwJ6CQ7yL/1zwkguwOTRaxPIkFLufAel++T3TkJTKk8u+CQBM12TQ5QcQOYC2xzEyJ+Rl32+SeYF5blr5ururnsfu0cpoOcC6Ts9t9zO23UOlB3LU9538mOl4P1OUHFOk2lO0M5hR/68EiqlvpvuX5qTlqAtz/OxflGOj1IgT2AmL086L8m4qTz5tXcBj7xNyuNlPeXHypSPlTyPZfuV0HNu3Ofly+eSXMO0zFcu26e5KG/zNC6PBZ1IWlxzgC/tSxwi13yag4ZzWq15vspyl/U/1w/eCbiUfSr9ncOx/Dl5naSUn1+2UalZlTOY0tS1bM/y77INj835+Rg81ta5Rm2CaGVb5OOvbPv8Y0k5VnPIXtZDnlL7lsAuH5Nz5bsLpr1XyPaeAJv30UFx1sjlD3x42w2OkPcV43d0NnWEstGCIBGe4SLckEoiRPK/Am1Ts1x0jIPBGoNQMgTXkxKLxdqJaTLhC753wTLRRpME7wMu8dFMDxn8wAgPCoQXSO/CwyU4E6OSyaDNIwHnRaF7FUCeEILHFz+122ttNE21ezNVuWuo+IPgQVcamaKMeMsPf+DLfPz+p7l8csUbr76Fn97k7GbLMBimccTYYMpip2TaEur19Zf+KF/94J+CQSNXWz7w1X+Xbv0UY+8zuQdM5j6DuWCY7jOI+4zyHGp92yyxWERjka1FNB5fg+8UTqvnhHSQ3li9E4yjYhzbPXz7DvA17oZ10QeclA5VWdpmom5Hmm6kabd090a6i4Hu3sjivqc6B7dUDG3Ftq65kTU3YsFKtQyqZqoUtpL4WuCT9lJFMK/dmcYmXbh3mTy3gV30bSY88LZDfjGYwaaIc1KFRWiLUgHaKWlRIvi1U9LQyIlKDlQYaj9SMVGJkUYONGqkUQO1Gqj1GDSRItzQMjjp9mLv0N6TzPH2+/b7D/dZp5hcFR13a6zXGKdiMBEVgol4Edy4RzYZnMdHMC1E0JqTAqQEHfwrfpcKFr9qkt9BuQgBswUjDo+lbb9fE+FVcpa+g3p2D/xwAbx4F+atIGjuISaA9B6BQXtL40dO/CreD6SzaGeQzqC8RTgXhFj8Dp5ZJbEnGnNSMcmKUXZMqtlrLWYQdfANPW0QKH2CqICb0INBbB1M4CcZtTTj4hM1FIeQL5riYkBaSzNFWOeiGa3K/Nw10Ty2CdFp2zattwHWxf0HEK8ZuP/gaTiv+x6a0W5zzbsA7cbxIf3wPoahYT20jEPNOGjMqDGTwI0CNzoYLUwTcuxR44ZqukH1a/TYU/UbVL+i8T1C7OGa0AKEx1mHVJIlW07EN3iFb2O3duY3OIzJ/CUuQR0hBB7JKBasxClP1cs8VS9xJR9yrR6wlRdsxAm9PGESHUZXiEYiG5CtQ9XBN52uxwDrahOi3p6HtawtQntE5XdtHDSIxDv6pyth3cEXrndMAo/CoiKs8zsI542IME68A6RLaw/OoawNsE6MVGKiZqAVA63f0rFlYTe0rDgVNyy54cxd0/lnnKkNLVs6OdD4gdwXbfk1972+wM3XACjG6LcsvGMItzerKF/aS58x+Vf9XDj1osKJDuNbnFzg5QmTb/BqiROL3X4nFjjRRUC3CJprchH+FgHoGfUApzu87L67wvoJ5bdIv0H7Z9R+g7AR3vkB5Q3CTsGc30Wfuc4Hv7zxY6xA77TunA9ad84H+DiKl5iIHy89WH/BwYfHd9swO5BnQaiDfefyv+RE/AxOtIxc8KT778SPk+GFWLtnu3baFf+IsJjDi/z4QXaOCKD5vUsBvDxvTohLae7De35OLj/kgCydW5rR5rCgruuDMXRMoJwDJqWAV47JY/WRUgkRjwGp571+TpAt81Q+J9fqygXPEkgdy9sxIFdChbk+UZbxLqBWapOk55SCcZ7vuXrLAVEJmnIBfy6vpeCejuWwo4Rxed/I58C8nsq6KMuSylNqzqR75OmYsH+sHOXYzJ9bjtO72rs8p+w3cxAwv3/5+1U+pwR1c2bec1pIcOijq3xWOVfkdZJ+58r+mz83+Sor57cyLznkmmvvuWcnM80UHTP/IDBX13kfLCEVcAsWl+2Z53tOKzLPfz5nHOsTZcq10+aen7dvXpdz7ZOnMh/e+1uALu1L+U7Hko+5HL6lNDfWYW8GmyKtHivv3Fg+du47pfcI2PYTWtlo+8F2WOBwncPaNFGlyUIeDLrdRCwOKzUtSimatmWx7FittsFniguRzKSUeGHDe7IA6xzWRT0xD97FhhQBuAklwTuEBec91oJWHqWgEg6UQMtwLc4F044I+eKdwvtPvi0g7bGpYV34Ag4i7ksNKAhaQPt6zQef0pqqVuhKorVkHA8nISEFuP2L04uv/znuPf3/YU8/wsn6y7Tqivqh3kUdU1IghEdKhRRBuvXcYztdgHiBX/sDP8r1uuXyZsG3v2N561sV2/4+/XiPfrpgmC6w7mzfEZLPsBkwp7oe3Y2ozuzgHDU4LbFNhVk2GPm85hkRSCJxTjGNDesSwpWgLtegsxDMRhxaWnQ1ctJONN3AYjFyshw4W44sFiNdZ5HtyKAJcE63rFTHtVyy1h1DXTM1Fa4V+EYegZJ+D+3auI6ANhgMvsvkYnkm9maxlkLLzkVhPHxdF8ohVNBGEVWAKTKayGoxUYngN6lmojITtR8D1Egad3LLQq1Z6jX360uW1YrT+obz5oqzxRWdCBp6UtyepJ8n9UPNdtvQbys2fUW/VQxbESLHbh1Dbxn7iXFrmPqeaYDeLxk4pTp/meXFB3i60eizD/P2TY2rH7KaakbRMliwMgRnRHrQIDRRO8cHU7qKUC9VgACi9nsgENciNzWmWGfbpG0VlzYdd1HJIXi+unW98Ajpv2cWSv+4U/DtlGn8uQjgfL4vLKT92UL0zehj/3ZGRtgo2VrFxpzg3dnevDrdL0FAEe+1FfgrsdNm9KPA9yIEAhlBOYPGUskJLQ2VGqnkRF1NVGqiqkZqHWB1rcdgMquHzO9dNLlttnTNlrbu6c42XLzwlK7doL9nZrTLELBiaELwibFmHCqmIWoSjwI/eezoYrjlCTX1qKFHmwk1jYjR4QaBHXwMhCDxo2fqLd54Ora0YsML9g1wPw9EYWLnlyz/oKbYbHratsY7l36+cPqEXp5yqR9zVb/MtX6Ja3mfjb5gG7XpRtEx0WKkRjQCWTtkbdG13WnSHZi/LsNaNQbZ2MAiNKD8LqhEMH8NsG7O5LWEdda8S5XhiKqsD37rhGsD7M5B3TFIV/5tfAgwEcNVVExx/h1CwBS2tKLn7/5/4aKFhwt4sIBHS3h8ErbPGjhtYFmFbxXvmPtCOEnbpSmI93uwNvfSvxMe/ITyE9JfBbcMSegxhxohpfCa9pUvzs658FEnQrcA7QKsS9p1Xi2xoouQbhkBXtLACzDPq0X8+5RRPsLp7jnfK44k76JftS2TuA/J1DQOhh+6/G/i7YTzHU50eBb4BBVdi/VtMIX1LY4FxjV4kdYd1rds3PdheHjw2IX6Ai+qv7BrD+2veWPxr4T2wPDK5k8ftG0p8OYaWHl9p7qeM41Jf5f9puwzB9UzI8SXmm8pP7eq1u/Nto7lP9/OBbdScCyFYmDnbLs0UZqDFGWd5NeV19wF7ubSMYDyTvV67Hlz9VvCnzkTq1IrphR+y/qdE57LcftOaa7+Up/OwcKcNk85b8C+HyV5cM5ErOwjCbLMmW7O1UfZJnPtV86ZdwneqRzHTBtzM+cS1hyDE7mmarp3CVqeN5WgLPfdVcKXYxpOpcboMQA0Z5abrs3Lk4/tMmpjee8ESkoYXvbtuZQ/O2/X1EdT/yvNCsu6nitrAmTp2XmfLwMFlP2/hGM5GMrH913tmNfpHHQrj+XjMWc55Xbeh1O+yg8gud+4vDx3tUu6Jo3X5BZgrl+Uvzt3tXleZ6k/pfYp59E8lWV9nv50V3rPgC0HQXOkOW2nwRsm+0OaG16w9h1JCgXS7/YlrTfw8f3GoVRF01S0bU3dVIyTwY7BB0iYfD0uOsy2dsI6v9u/m8iEQMng2No6kDK8OBnncYBywaxDScfN+Yf40m/9YwyLB7zvq3+Tj3zuL6KkOPpRPY01kXIezXl2hiIHP9BByHbe747v6lVItFLUdU1TV9TVyCAFRgiC9+507307OO9pxzeorp8G+KJVqDdEdB7tIfpKgQDblLzmVF3R1N/iRz55D+sHpISnT6/4zKc/R792jJMJGnPGMYwV15uTANzsQ4bhHpO7z7C5YLq5YHIXWPcQ4+5j3QNG9wDvm5maiilCOtFMNCdr6uWWahHh3MIiG4NowFcCXylk1zG2NX3b0fuWMWrSvHMKGloTmsk1bCcOQdwKeMq8Bp0htpVFS0ujBpp6YrGYaLuJqpmQlQPtcRKMUhihmIRmEJreNYyqxtQa18rg064tliZbl8Cu8odBEFIUzgMtu3epEpabxhq/N6/yhL5zy5ddgnYuRFLUFlHvfdtJTICXzlBZQ2UmKhfAXRud+LdySyeDf7tFteJUrzipbzhZrDhvrnhfc0XDQM14BNxdxuVbwE/PFsubGm8qlGswvWfaGM5PXuCtN1Y8OH/M1MPP/cxneevpQG+X/Lbf+Xv50te3WB7y6S+9yfLeRxjHE56tznm2fsD18ALr/oTe1hirMNEJvZcEn20pEEMZoCJv05bQ5ou4vmVpdQjeuAPmpW2sR4xxmRxi8oiRsG18+Nt4hPVBuLdxcT5or+3uyR4UEttZO6R2iDrC2YoAH7XfRYQNf7s9VMwXuV+QHimDOauSPvhEjE79w7mEbel/1Wg1GgSGls17MTWDnVbj7kuPI8z7jgBcvd9pEOb6pCEis9uBcCXDopcGfWrQMi7KxGuP6Z8eHpvTXZU4cOCSubUBptBX5GSQo0GZETlZmBx+CAEpTgeLmBxua/Gjh97jB4fbrnmh/yXE+CX84BGDh22usc2tl63dfmDwLVtOuBT3eKpe5FI9ZqVeYKUfcMMZG7lkYMEkWiZqrNQIDaqO2nT1FDXqJnQz0dbboFG3iJp1jUE1U4TshCAqUoAE6/Q7+qcrj1v7bl+domadl1hf0bsmfAGIZrAJxH3mdd5Zs86BdFBJqBW0OizLCpY1fOUH/i20XVPZNWq8pjLXqPEZjbmiGp+hpyukWaPMOs61t3NbCuulFtQcMMvbOL3Q5h9GYf+Cu7sWj5bBft2at3bPTtfmfafsP7kJVH7MebC+DmBOBPiGPsGKDi+XWNoA6yLIO9C4y9csmNSD9NU0rIVAKkEtV8DqQGjZv7PuNW6SIJ0iIqa8b9yv4fPjv7czZ1Xc8FD99QMB7PH2/87p+HeY9AdY2M/TuO8c1Mtc3aS/y3fytD+HFKXAVQq9eZoDL2VKQCcJNyVgyM3L5vJXCurpnGTak4Tq1LeSgJ1rNpXCa5I3yvyXcspd4Gyuro/VxZwQVgpppbljec4cwMzzOSfE5+Mrv25uDN0Fh8q5ugRKpTZPeX6Z8vPyvjGncVcKtumaVMa0ttYewIq83KmP5NfmkOpY/seS6MwAAQAASURBVObGVJ6PXKM3z3deb3ldpjZJY6B0i5TyU67n8pZSCQdKjaG8/+dtk8OOub6bA6Qc4pVy/F1A4665IfkVS/fO58JUL3ParHPPzef+fC6bM6+c68s5HErPy/tKXsf5HFaaOublnxvzqY/nsLDUgsx/O1Pdp3pKbZaCHuQfwMrnpbky+TSDwzlhDryVZSzn5DyPc1p9B7+3WRvkdV5C7/x3f649j33wSH+ned8Ys5sPyt+MvG+kusoDWeT1cCw979z2POm9ATZH9JsUtoO/isOMlB05VWAZSQQhghNpsq9VAnLP3lIKQvQwSYoa1nYNXVczjYZpMDgbtOMma7Be4Fx80bLhS7yNX1wF0fpTepx3CC9xTiAEIbiBd3gBEoXpTvnJf/EvMJw8AuD1D/8OrG75+Gf//OFEF4FYSkKEfeFYuHfQsnA7Xyz78nuCj5cUYCEOJBFN6YSnqlTCbzugEpQ99n/7iPR2HTPWq8fjY7mDPxiJ9Z6kvxbMzRwQJkGhQEjBYtGxXC4w4xpjBcQyCTHQVBukeA3lPa10IXqiNTuQmXzTVErSVAotz/C8gHMXWP8AYx8ymgtGc5/J3Wcy9xnNfYa3LhheveDaPD5o/2Op1htOm9fpTm5oFiuqZU/VbVHdiGhGZOsQNVjtEW2FqxpcfY9Bn7HVZ2yaJf1Jy0j9HJBO4NEB0NGw8fDseX3QJUCHiwKypZIWpUMkQSqPVwKnBTZGozQy+JzzWgVBMHOaH7LjDzUII7/dAZ2Fg86HpRUR6om9U/88cmkrdlE50wh+V1NK6oQDe2CXa9lFE1/hYRd4QiQgcwjtZO2gTrEkHVpYlHHoyaCtobITtZ+o/bCLvhm07bYs5JpFteZU34TgFM01ZyfXDNXbtOc9I28iBPzIh1JFWeCv8egToRi/4/cB/O3Z8gmjwGiEUQijWF1u8YPATCHwxGgq+qlhYxaspxPW0ymr6YTVdMbN6oyrt8+4Xt/j6vKCq5szVuszhqnDOg1eRP897AMl5P7Zci3RHNqV4O6M9xwX+iAN7CPN9sCNP/x7t4h9pNmRCGxdADXWI00wRcW7YKKsJDTV3m/dLb9zfg/b4iKFoZZbKrmlkgNSjkhlUdqCCrDEVwJXKXylcXWFrWpMXYe5LAd+B1qDCRrvIaCK5tgymm4Hs0YCJAt2z7sk4Pj90jN17OOlBiThmQfjQLDXOv2Vgo2S0J/u+P7x3SbhM9DnXTBl9g7p49/O7aFv2raOE+s4tc8Q9kmYPyYboLJxCOOQ1hPdGyKdRziHdxLrNJOrGXxL7xcMvmPYdgzrhsE1TL5mchXWBUhmXfD3KKUPZvoyuiJQE5UakNqiOos6s8jKoiqDqsP6MJhJMN/ea9NppqnCThVmrA5MXktY59y7b2wHDN4zOLhJ8C2Buk9+6rkg3e5va0OQCT/tghkou0XaHmU3SLNCTzdoc4Psn6GmS3T/FNm/jRovaRgR0wo5rfFu77AeDgXz/EU9vQdaaw8ih5WCZvo7F6byF/f8XTKZ/YTrDJhrtJTUUoLZv9Dngm96Rg4r0vO01nx5+b/j7fr37PJzPv0U2j1BZECqNE3L856O5RHhhBB04kt8f/VHedv+AfCGR9V/TCtfCx+Is7paui/A+IWDekmgwjlH0zS7fXm9pXOSgJHyWQpNZT5THvNjxwTIXLBPKddgKGFALhQnIbt8Zglw8jZOx621O7iWnlFq2eSpNAXMBfiyD6TjqQ7n8pSX71i9lPvya/Jnz0UBzYHTXN3kzvtzP1hlOcp+WWr15fktQdwc8JsDf8egU6rv1MZl2+TAJ5Utz2Pqnwk4pHNSmfP6LKFCLqynMVBqsJUAMS9TuldejlSfZWTHuXrL5VzvPU3THNRbqS2cPy89K7lSKus6v08OLNJ6mqYdDCrNrnOIXpY3/Z3yn9+zdOmUgJMQ+4CFOaRL98kjQKbnK6UYx5Hlcnnr2aXWUoJLc/NIVVUH9y/7Xvlbks8T5byR2iR/fl6vc/dP2wlqzWnkpWtSm5QfN8pxVkYFTuXN2y6fw3PGku6f98G8n+Z1kIPU8vcp1yKbG/cpT/lvbX58zpw1/w3K+3Tqn0mLrZwLU9vkH0ny8qdn53NCHpU018RL55Y+OfN75O2X97W5Y8+b3pM4No5TVlEJKAU4JKWM0MjPdrb871g6jIuAKkGv3UD3IBzWCoQIEEqpKg4wzWLRsN0OKK2wfmJy0eWVczhvo6aapOsapNLBrCU8NPjkSBXpLcIGrTYhApQyk+fZ+37rDq6lvH72t/xx3n7pN9Ct32S5eYPF+k261Rss16/Trd9Ce3PQqYmmo7tBReyQpK+tAm89zkmECMBSIfHCo0SclHR0liwliWr6QDl39bkn30FZwgswzhFczYn4Pi1CkAZE9MmVoF1uIhLy27Q1Z+dLtuseYzzTZLDeMVrPaB2D9YzeY73H2gkTQaazwXxFKvAimN8qeU1T9VTqVWotkJKYoxCFTWsVwKmQEdZqxumc7XDOaIPPuNFcMJgL7j/8flabJVerBdc3S65uFjx5+/0Yu3jHfiuw1NUVbXXJon2DB80VbXNF11zSLG5QbU/VGeoTj1qAXmhku2CUJ4zylEmdMHDC2i5YuTNWYklfd/S0TFTvAOmCBoNBYajoQ9e42/dcDi58gFQSz84MVICTYKnhCvhq/rwjQlvmButgW/joEysK98lvkiJoaSXok8BPvr3Tqov7arH/uxLxHvFeCaqUcEUX28k31676/D6fgggxIkBJZp86mnzWDtG4AFhbH0yUO4fsgpAs8GjjUZNFG4O2lsqN1G4M0I6eTm5Yyg2nes1pteJEh4iyJ9WK5eKG9mxNJ3taek4JvrLebfIG3AhuELhBYEbFNGmmSTNOFYNpGEzLelqwNid7cGdOA7jrz7h+es7N9Tmrq1NWlyf0qyVu0uyirYqiXjWH2pA1e227tHTZcgo8FuGc5/p9yX6gykOOEA02RYTdiiziLLvIsn4APwqYPM4ojFsSop9IvNT7vkssY/zIM+caSXpDzYaaHs2IFhNCWoRwoDxOSpyWOFVhdMdYtUy6Zaq6vY+4YykbR4oJTY8WE1K6GIBC4GQIQOGMxg1xGVUsn8xAdFGFygez5dZDE9YCH7QQh2zJNRVtPMfHZQf/3H6cSA41DzMwuN926HqiakzQCKtjMIQqRC1Vld0tUscPBUnrUbsdKJQRXooMnqYlaZnP69nlfiC/e9JYEQIehHDE/3iS330JOwS1858yRPgNTybW0bzaOYl34YOgs8Fdgov+DW3aLq45tk15LAVzSX4ibQd2gbcP8FZgjGAqfU1m26Tt9BJmDBiDmEaEGZBjj5w2qHGNmlao6Ro5XKKny6BVNzxBTzcos0aYFcpsQqRSbgvDJUBIwl4uMOXnTFN4X62qiql+wM29H6Ad32Sx+vKBMDJ2L+FlRb3+5m4q+ejmT9Lab7BWv5aF+zqv9P+PaA2wf2HPBfDyZTwHODlUBFiqr7KQ//au1RNcK+HGXNnzspaC6ZwWSi6A5EJwvpTPzOvwLpB1F3SZg3Pp2lQfpaZDOpanXCgsoVJ+3xIi5HkotaSO+aXLBbqyfCUQKoXdvBxz1+YgYu7Z5bH8mhzapHQMmMy1aVmeY312LpXtmN8vXZuPybwvAgcmiCnfJWzL+3RZjlQHc7610n3L/prvz+HRsf47J2jn16S/5+BcnnKlkjlgltpyLj+l5laZp3y+mgMqOTjJAVlK+XaulVf24XJe8d7vYFIC3ABt2+7aN4dpAHVdhzk3Bi+RUrJYLG7BkLJ8qQ0TfMnLk+b6EiCVY2YuleXLNd9S38rzcKwflvWYg9/yGfl6joGUYyjdr9TKy9sjz09+bQ6TU1uVILusj3x+Sc/MIXIO9kotwznt4Hyd9/9jeZ6mafeblGsql+XNrynHdn7vY6BxLh0bx+VvZzmXvZv0ngBbKkDqjPkXSO8Jgq/Yqy7eJsDsNLaSewtk1MjKwJuzNmgo7Mqf1CYrqkpTN3X4OqBG1oNh3RuUEiwaTVufYZ0I9xAZrfWeWuv4AmtxNgQOGIaRcTsglcbhUM5gb54dFlwIbNXxrY/9vqN1026e0K1eZ7F5k+XqDdrV6yzXb7BYvUG3fovl+g30tDkwjRVSYq3Bo5AofPR9k8xrVHqpyEPheh803NzhF8rQPuFlTSmCOZsPkrZzcSKNChkimpqSfsCyzqq1YrFo0FoG33OEF+ztOLGdLMYLRuOYXFC/9C40YoKJwcxQIoVDApUwIBVuVzYLBHAKUatRiajhZ2iqp9T67YOJTQjPj/7ob0HX0Pc9Xd3xcz/7D+j7kcnUbIczNsN9NsM5/XhBb+4zThdhe7rHOIUgD/14n+vt+/H+nX3yKNnT1Zd0zRVdc8WyvWLRvMb7uhuW7TMW7RXL9pquvqRebKnPJGpxgasv+K+++OP8/c/9WNAIUh7ORNA2UkANuh1YLjeMssbIimmh4VTu4VJaxzZKWPIWPBsJVpOOoEFmiyWZqO2EPPZyXbJ19mIftW/k9rnldb/iaeYrOt9FdpTf1+/s4ndQKphGstOYIvltqzzUfufIPfg3jIErpEUpGzUWJ6roz6vWQ/TpNVJV0b9XPVLXY3DKX4/B31f0AaaqoK0lleWe7nmg1yj9HVRrURcRcCgXQUdYhLPgfdAu9kGL1noZ4a5mcC29b9j6jo1bsplOuZlOuenPWW3PWG1OWT85ob9esn3W0V+2DM/aoCGToF2quxy4VgRAl2vd5eayHbAQe3h3nzAedpaYoljL529f4/ewLgG7AVwv6YcT+ukkjg15Gz6K+LclwOwNURMT5DjR+BDBVYsBjUFIg5QelMDLoJlndIXRNZNu2OolRjf7ex/0O/blT0WVoH1PJQakMChhEcpHMC1xSmFldNw/aJxQ+KDqtzNzxMR7++y+OVhNbZHOsaF+uInlzepsB/WT/0pDgCZprigBPRRzypHtXfJU1UTT9lmQihiUotsHqeianqZdheAVXU/dDjTdQN0M1IuRuh2p2pGmGaiakaoNMFDVFqn9AbgrDWqTge7d+wLUslbhvAxQyyuck+FvF7ZtDFISAr4Ef22TVxgfPK1NaAx1WIsYLVWokC8hdtBxZ1adtBoj7JRVgqXHOv9/vZJ3Dd49wvtH+yjF2cIuejPhbxs+PGKjpqJxCOvAWDAGZwzYCTONMA0I0yOnkWoaYBzwY4+wFts85vqFH8dVDb0W9Jdf48FX/wbSWN5+9OOsTj+Jd4L25hs8/vpfQEwDwjre734KzE+iENjx5fCNxznEOEVI6fHG4o0FY/HWhjxGbUxvLNbYoIlpHEyBN++GTiFEl6ZeB3WXCUQlQEnvSXOBKnJBOqV30mZJ61L4Lp85l+b2zwmt+XYJ2ubuU2p2pe1cu/GYsF3CmdykKS9rmee5v0tBb+7c/L4lmEvnlyaTuXA3B3NKOFTmbQ6Upu1jYC8dP3aszHdpMjbXv0qgWD6j1MQpy1tCwPT8JGvmILVs9xyU5EuurVT277n6KgHDMbCZ5zflMYcjVVWhtd6ZruYgtSxvDjLyesmhz1x9ltp5CazldVICt7yO5sqU9zkV3RWl6+u6PtDwSZq05QeFBMPruj6A+8DBPcrnlu2f13vKc16+uTGR2q5s3yQv58CwBNjleM37Swlc87yVZciBXTm+jo3hfJ3vL8f+MbiVH8vBbfnc8nekBEv5sXLeTMfGcbw1hyaNuncqW9n/8j6a6rOEuznIz8uRAG+pCTs3tvNUHp9rw2Pt9zzpuzIoSsSzbdtMldfjk25UzFDue8IT5YHJorRGJB0q66P2kgjCoXAggqnhOI4H/haklDRNQ9e2dN3END1j2S340Csf4Qd/6MO8/sZXeemF7+Mbr77O1772dU7P79Nve8ZxxBvL+nJN3/fBXNSEqKPCTijvaLVCVhVtc4b84t/m/lf/Hk8/8qMASDPw2/7Sf5+Ly6+wPX2R7elL9CeP2Z68yPbkMZuTF9mcPObm3od49ugHjtZbNdywWL/BYvU63fpNuptXsfeXPFbXPHCXSGDpVgFESYnIvkgKGbTZyAZ66FiHL1JSBp9swhGCOaSv4p4gFFqHVhJvBEqD1grrLFrvwzsvT06oao31A6MR9COsestgLJOVWO+CxmFsWGcNdaVY1BVaQltpKiHw3iJ9MDENJqTEZyXHg0GASD9MUoZ8CiF2b6FJO886iya8GJ2fn8eJ2wEjSr5JW7/Oaed3MBGC6W8KrhHODeayxp3hxEOsf8gwXTCZ+0z2fgzocJ9hukc/3aef7nG1fom3rj7+XOOi0Tcs2kv68TQIqaHlwuoE+HCoL+MbrnxzN8wKJDbeYS+tJiaAiGadXkRNs0iskxC8E4TFXiBOa5WtYQ8ePIcmRLs8ZD8Iewayhy01e020HBLeurdHumAeFpyGg5sk3qhDKFiuPYCl0hPonsWJZvIDXli60y70ayFxQgVWYlyIvEnQ+gCJ8CI40d/5xmL/TJOtnQiQoSf404rHkgN+pnnB4r+OSWkTIZ7bQTqlA7xr9ECnNrvjUtmgoRUBANLv/Fl5KYJGmFABIIiggWSFwjmFtQprgoN+M2rsqHFGhrrOoV2ubTenKVmay3ZAJ/amsgtgSRhrD8ShVmSpKamy/TMAw1GxHTXb/jQCKHEIoQ4gNntw58JxOU1UZkDbET2NaDchJ4NwJkS4lBGgRUA3Vi3b6gynq0OAVaZUL4uQ78pvqcSAFgahbIAxCpySeK2wKrSDVQo7KfxWQS+DuXjH3h9jXpY0fyiC773UBrlrOs8eSqZlS6ijFJglwboE6pxgmmomU7Nan32XsG4+KWVCpNl2uAXxmnZL2wy7dddtadsNXbdh0W3p4nbbbll0EQK2PXUd/G2qxgbT/u9B8p4YfELgJsG90/ehfAu+wdmW0bT0Y8tmbLgeWq62Lc+Glqu+4apvuRlrVmPLzdiwGWu2U0M/BT+jk62wQqC0DwEjkiZibVBNXGobl2T6apF1GPtyp3kodlDRO4GzKkSOdllEYx8js3vCPgAv9rxXwE6jUridyTa5mXVaa4/caWnH87LjUuadIA3m57NzXvKdYs8/BcCLAHwh2/8HZq/PP23lL8+iOPY8yccPkRgHNi0BzmEdOAfG4W1+/PCYiC4YvIvv0C4c98Yi4hL+jtdYH02C/f45nvC3tUETNt4bG54tnEc6j5/MTD58AJ1x2xsbPq4GU5Lwt3Xh+vTcyYb7WocYQRbCSy6450JqOpYLYrkgNwcsSuhUArpd+x0RQt+xDWfA5TFINydglwDznQDYOz2nPG8O0M3BumOC6Nyxu4T/uXylfWV9l/vKe6Q2SX3gmLP3UvhNfyfQPFdvc/3kWJ7LfjUHP/Prc/9wOfjNTStLoJrLtjkoKCHNXfWZ36+s4xwelSaKJbSZGzvpvlVVHZi8Jt9fEMBigomlFlaCi2k73T/3oZWDqzkt0jLPZTuU7XisX5Rtm4O6Yxpfd/1dwreyT90FaUpAlwPUY/PI3LFckSmNlTkT47zsaV/6O9fiy5V20rH0d6qj3CQ7HSvzdtecnMqbIoKW2q/5thC3/SDmx9O4KSFh2n5e7dycn6Rjd32YeKf03gCbSBUGCIfzJn5ddUgV3uxz6phTyNSAcrd/V7Tdy7MHlBIY57Bm/6M4TQPOLePfEqUqlAomqYyecdvz6N59vvnlL9C9JPnhH/x+/onf9Clef+sZ03bi6ZNLLt96my+8+YTHFydUbY1zlqrRKCWRQjNZxzAMDNuR1998ld/4//mXefMTv5epu+Dx1/8u51dfByE42z7l/K1fjNpdcWASooUKJLY6YXXymOHkRbYnL7DqHrE9fYnN8hHr5WO2y0dc3f++XZV+rqjiyk/cP7vk7P7bdC++gXr6beSTbyGffQf15Nvop68jr17Hm4HIDnAuNMrkLI3WCCHRMu9swawQbwOg8x6tPQLBOE44u4coUkratqXtWsZhw9UN3PSafmoxfowad+Gl8t7JkkVT46eBVkoWdYgyF/zieIxQGO9xk8HXoe0QddBaFGC9Q2cvUAgVwKsP+VUyTU7JZ4VDKUFVKU5OO8ZpxNro68Un9JQNsOhdPDh33/fbrlkjuEHqb+4GqpISLRVCRplbhgiuHodzFYO5F+HbBf10wXa8x3a4x3aMfw/nYd94EbRmymQJWiM5AEuCRjKjivt9Gvgx0z6Tto8P93d4MUxACaJvuOdIIuYhXZ8AnI/3yGEgR9YHGRZBEyQRjV052QvbkiDQJwAS20wIsKLC+ZorBIhQx9vVTD5iUmJNwyVds+LD7+v41jc+jdu+ijff4g/+vh/g5z77D3n80oI3V69y/6UFn/yh7+dKnrNqH9AvHrDSJ6xUy1rWbFXNygtGWTEpjVMaJ2SAmzuwKTL/RtyGd3P7y3MGYPSZbzOi6RWI5NvOBi01EQWkFIVTeA8uAlnvY+AKwAi8YR8EIS0mBkmwgPF4Q/C7aCu8qUL0zY0AK3BW4E3U5jHRPM18Lxy//SpJOTDeAT9xG/jly5wGZNzvVMWgKoZjZtFzA1lk62jOqqRDCIsiREMWMSptGi9OyuALUzRMugal5mF3WuflVI662qKrEaVN8EUWtTKdVji1X6yK2ldewlbCRoQyJD+QNQFslsBxV2YR4agP23lKWnVrbmvVJVg3EsZFguB5nWVzhxWajTlhc3MS5tu7oN17SEI46jrCt3qga7e0Ec4tl2sW3YrTxZplt+JkseKkW7Hs1nTdlq7b0LUDTddT1wNVBF2ickxcsxVvYkUf4FYFogu8eAm89B7yakbBMEjGsWIcK4apYTu1DKZjOy3opwX9Nq7Ngn7qGKaOfmwZpgD4Jlsx+QrjVNTAkxilcFoiKnaBJHQ7odsYCbbNIsHqoJHpvHpXwSR2x0eNMRrn0lyTQbkM0B0CO7ffziI6C+/D3Lj1iHVmGp0tJ9ef5fFrfwWhJF4KhFagFEJnf0uJ0BLUfvEqaJUKrUK029218TcinSsl5NfK4nhatEK0FSgZPpQqtb8u3Xuuf/KObwKz1xzb5+84T7zLY7mok7a9dTQJ2iUoGKGdOIB6Hu8cNoJFa90O5jljQlCWBP7iNcLtr3XpdzI9J90/3s85j0vAMWlK+nC+sAES7rbj9dg96BQHz43nmwg80/EIIgPMDEAzHX+n+SgHj3maAwDvBMaeJ931rGOQId+Xg7EkVOemhHfBvgPBt1KYf/5HEK9c4H/xVfxf+3SoO+bhY37PJHjnoCPXVDp2fWlunIOCHMTMQZwEblKZrbU7DbBj9ZvDglLIz8FLylMOVHIwVbZNacae13F+Tp7nsg3Stek+6fzchDM3fU3wJdcSTXAxPS+ZjuYaimXK81HC8RymzMGdsowlqMnb6y7ImLfVMR9gJUSaK0cJt/P1HEjNYV0+No494y5z0FLbL89XORbKuig12fL6KI8dmxuOAeC5e+V9rcxrDozz8Z3uMTcnlVC2PJanu+ag95Leo4noXPjnsM4njATT0r5cHTqfGEq66CKcESKYks5NNMlsMkTZrFnR830f/T7uP3jINMaBj+Dx40f8o89+iY9/5OOc61P+xs9+jk41PHp0wegmrm9WPHt6zbOrFcYEVddhmJAemqZD2JGXP/+f4t0UooEIgRLiNkPwHqX2kWL0uOL82Rr79i9RFaq6qSxUHZvFIzbLR7zyw7+F4fQFnlXnrBcv8RYnvC3O+criY7jl98PjmYZwlvrmDaqr16ivXqO+fp3m6jXqq1fp1m+zXL2J2rxGLaJatRBZjg87mVQSpfc2RkEzsaFuGpRu8bLCMyKYePnxI7Rw1MKi7cRJ03LWNvTXz7DWMIxbrBcIqbCVZzCGwThUVaFclIBkMCGF0BcmGyd3BMKDw0X2lDo8gMBYT91IhsHEPHYIcYWuVHgx8g7hbvsw8DEah2fa9SlrHVJFIBGTdeFlXEqBQVB7cDY8WynDSfuEZfN2bO8qaJI5AvxKkRgB7yu+9vpv4ue+9i/zxuWnAMfDl3+Jt+XHd8EAHshf4p74Nv24h3Lb8ZxZVZqQhd2i1Yamuaapb2irFU0dlkr3VFWP1n0QluUQfE5psK7GixYvF0i1ROoFlhrnaiajGIzAUmGsxvqKydUYrzEuE6xcEK6tD1oMnqjBgJh5my72zRboOZMHombEu31NtH6JZcmwgZ//IuA/QKLDf+5vAP5f4nOvxhv/IvydvxXBlAhGilI6lPAo4VDS4m1PzYaFmNByoJLDzkG+EhNKTEhl8Qp8rXC1xjYaUzeMbcPYNExtg+1qbFthFxrXaexSwomEpQiQ4iSuk8ZWrK9jHPPOlIJPRNNHXAblyATVnXla8KcllUdUNpip1S5o7yQfd/EnwEeAJ3uD3gxU/UA1jOh+DOtxpJpGKjPG6LKG2hoqP9HgqLBUfgprLJWwKOuQxqGsjRBP4UzUgDPB35TN/i73T2PFONSMU8M41kymYpzCeppqpikK82N0Sj9q7KSwaT0FgOgmhTMC38ud+RoRYAYKDt8LYHM8KYLvxpCmO899r0kysmQkczycQzE5sxyYsPvgdy35clM+aNLKACZ9BGs+LVIEuJbA+U5LFm4N8FzrNmkaVgKWHmq/C85yCx5GDUIm9sFXci29iX0wltz0NS+fZj/nFnDOIxnMguFmcbD/u0rphVGAVp5lN3HvfMv984H75z337w3cO+vDct5zfjJwdtpzetpzuuxp2wFd9/zEf/FXqFuHbqBqQTeOqnVUrWHRbDk/haoBVX13L48pGSsZpiaAOdMyjC29acO+TQvffCvCugW9XTCKBSNLBtExygVW1BgZzaClwmjJ6dXfxmuDbTvsvQ65bBEnDXQttj3B6cUumMQezGWwLm7bSTN8e0v/zRX9+UexJ/fi73Qsu4KX/8v/J6+++N9l97VXCNrP/we4b/2D3XtrLiAkwTJpaOTCq8/usRM2uC0E5k61D7vAodZLvj9dmwSP3Vf7SqPbGlVV+GS27j0GhwtfKAOc0wHK7UGhRFbBYkNWCql1MEmXHEI+Ge9R6Rg9O4OCUgSoGJd0X7EDjnG8K4lQCq+CL2GvEoTcg0UvsntHKCmUxOvwLNFUO4gptMQrFc30JVRRW+Md+urcEP2eT9nfbTI2OZPeaSvuNAttgn2ZBmHanwPDW5Ax2840HRN4FPEeASDm99gDzPz5pTZlgI/ZsyIw9Nbh+jGYTVuHnwzeg3DgjcUZE2FmyHtya4HLzEaB8X/zh/C/+aOhff/pH8K97z7i3/mbR6FBPt5SmoMad6X8HnNAJn/enOZY2pdAU6n1k88ZJQAoYcsc/CjBTqllVGpCzUGadI80n5XAIs9XVVUHjumTxlquxZaDvjzP+Rya6iCZjOZ5TDwgL4sQe3PTORj3PMC3PJbnrVznZZ8DwHP9YA4olcCnBGBzz0uphGslIJ5L6Zq5iKJl38jrOu1L7VJqEKd7pOeXJrtlKqHksXqfK/dc3ebPyftTumdev/kz5/rDsbzelYfnnS/m0nsDbEcelEfdOeYjIlWClJJaqKgEc6jGm9RJJ3MY9SgtiWAmet40DcZtUJWialo++MGPBDVUD53qePurr/Hm575NvxmY1iPWGL78+a9QnTSoVjNOHi00jp5p2vLBD30AKRRf++bX4kuUDC8f7Bs2+UPLG3UXaECGCKbeOVQxyR2otNuRs9WrfKQd+dj1P2DpAOF4JB/hnGez2fL200u+emn59rjkNXvCM3Wf6/pBMEs9fcxw+iKblz7B+gM/fLS9qs1T2pvX6VZvxPXrdDdvBD9x6zc4Wb9J6zah3bKxW+uGRnU8uCf4tT/4Cbyu+Ma3v800Gb7+xa/y/R/6IJunr1FXikpX2PaU1dXbjEgm75msxdoQxGK0BmENlT5FCIXQDu8sOBGi/Yn91y2bTwLsB7dUAiEU1hpEbIvz8zOePbuk3w64SmD9hLO3oz75ZIYGGGvRPk04RFdGgpX/ID+3/TdY25d5sf0pfsviTyPEiHAeJXXQtLNuB5ODPwiZdP7Aw7PVB/h7X/jjvPrsh2PgBcn54pv8od/0P+Te8lVeG389bww/wEX1TT68+CkgwD9B1MBDsekXjOY+/fSQzRj8yW3Ge2yH873WXFxWVy/x1Mxoyt1KjrYKvuJ2S/Msri+5qC9pq0tOuiuq+gld/YxlN6EriaorlqdndF1L2zaHIdKj1gAIpFJ4/I62T06y8aes7XK3bOyS6+mUy/6cZ+N9roZT1tMp27FlsA2TqSPEi9Ju8g3njiw+W/vs73Lfri+Ig9Ut8zRByH98sQvKZeoQbKjDCEhZFe9TIiKb52iad0rxS+1hObIksjKVQOJAY0oc+Ezzlci2OYxaWgZCaGaWimAemgInaKJJWYAsMm7L1sEyQLvgCN8jKhcCTjQOWTtkayEohdxKKvpTqqeB2vTUdqCxPY0baH1Pw0hDTye2LEQfgk+ogU4NtLqn0z1tNbCoQ1CKFMhCincvWvVTy3Zq6W1H7zq2tqV3C/qxZTt0bLcdm9WCzfWCfr1gc9OxvVmwXXVsbzqGq5b+JizDqmFYN0zbGoyI8FPs+25qyzmts7J9c+24OU21/Ce79NE457fxzv0ZqI1+2ryVe/PpFM3ye2NFeSR9L+DQc94jr8NMw3h/mz1UOdRM5nAszh0r5h0vQvVZBIOoeSpqvnrsurnnAIh/NZuXg+ajEBNKDCjRI9ki6Wmqa9r6kkV7Tdde0jVP6ZonLLoVbbelXQQz2W7Z0y4czRJ0C7rx6MZTtYKqJS4T4qxGn0wsz1e0uqfR0T/Cx56vmo8l5zxT75m2MG49041n7D3jAMYqxklgvWZyGl5+gP/4y5id7zvN6m98ietv/iLboeJrv+l/yfUr3x8AoGkRb7/Jr/nan6e+ueRbH/qjeFnxwrf+Mo+/+R+RfAKnd9Aywltal6aF+btuKUSnF/fS7HFO8C3vme/P8yU9yMkh7BR+H7wHG/zT+fjulM90+cdtL+XBIoQ4+OB4KxXHctM2kX43nTv48Jx/UM+FvVzYTOeUH+Xzek5CdvJnlb9LW2sZzYT1HqEDQHQCZKUj+FNBGzBuiwjnEgQkageqWu+uQQZtRa8EcqeFuIeL6boEEG8tUhwcC9qOKu4Tu/uTIsXr/Lp0n/31BzCy1gEuFhB0t/wqTmVoifzbxcFPRgJ43ofo47sLPPwzPwJ/9r8Ifc0fanGl8ZLLmiXUTvJGKaOmYzlIOAYESsWPdHwO5GmtD7TD0v3nYF1+/2OwLM9zruySK62k6/O/c8iT3ysBsxzmlKaksNeGy7UCk2VaCTUTjMs/PKQ5IC9jWf68nst58hhcK+tmrl5L2JVHtkx5LP3Fl3VQgrY8D3MgJt2rhGLl33Nzf/57MXd92S/zOi5/U0pQmwO7/PetrKvyPiWQKz8SzUGoud+0Ms9zZZ+rm9y0umzjYzA0/426K08lFC3rtlQqe5703mx74hcG8CBA7UxB2a2DVsThBBQKuQcphkjM2XeIXJVRq0OC6aXMXEN5pBJUjaJdNMja84tf+yI/9fM/zbe+8nX+tX/lv8dZu0DoilpV9JsRtzG06PAFTNZ4YRnGgYv7F5wuX8J7wWgt603P1dVT+u3IvXOFMxIpPVaCUBITNexUAdl2DRTnAEEw16xiPeRTg/MhpIF3hvPTM7y36KrCufSOFCMHCTjZvsWLV9+gvd7w4Lqn344Mo2EYwxeAyVjG+pTh3iuM917CnL+MvXgf09mLTPdeZjh7ie35K9w8/sTxjjCu+c/dNQ/9JQ/9NY9YcS6e4l74CvfOG/7K3/vtfOkzp9T6q/zAx/49BqGYlg9hc8NrN0/Zbp4GwbltMDYOAqCuah49fsQrL79EpTWf+czPYsyIIr5oAEIpJKnMYt+PpEAIv5NfQuSzMJHXTQ0KVK2QlcCtHQIRtdPAOYv3oU2U1IjKMxmDJ/wwWB+j0sj4VYSWn7j+S0z9GQj4ys0H2Fy8yI+f/asBsMb2tNaGF5goTAnvUNGcdbBL/vLP/Fk248N8oHC1eT9//ef/FB948DNoOaDlxFP5fi7Vi2g5oOSEkiNCjmg1gByp5IiWV9xbPkGfjGg1oZVBiB6tJgTRH4WSjKZi25/STxf00/1gpjoFMBe04gKM68foT27zCu45AjxIMUYY94y2ekZbX9LVT4NQVl+xaK9o9BOa6ildfcn5yZppWqO1plk01LXi/sV9Hp+f0XUdWoUfYkf88cWjxH6i3K1F+lETGC/pfceWjrVr6d2SjV3w5vYBrw+PeXt4yLU5Y+OW9D5Ec7VS43YvwERTtHcpkCfNl7QcmGpmSwnxfOine59x3AaCHnAOKSwCu3OvjoimS2FUEBSkQt59WoTcbQcImMG19GOzJ4O/XCpPWZqvVw/Y9AWtPH0OPpQQ40BLyu8hUgw4IVJwicbvom2K1iM6i1x4xNIhTiyiA9E5RAui87CI241H+5GagVoONPQ0YksrBxq/pfUDLXE/PZ0caERPq3o61dOogU6PdN2a+9WTHbRrGN41uHNe0JuOrevY2qAFtJ06tmNH33f025bttqPfBIDXrzv6dct2Hba3645+1dKvwr7tqqNfdZi+ikBM7DW4bKrHWL+5b7sle5haE/y7Jb92aVkQ/N0d/B3X5ZQyZybtmDeLTsvog5+7uUAL+TLtt8VkUJNDRB9SQTMzflSx0T+Yi4EOnAxaiFbcztNULKbI3yzc9xnk9/He2fllhNhfsbQfYB6Np8FxcjAdrN71PX02dvcfV5LJMsIFOKAVQjmUW6HdCikNSk1IPaHVhKosWlukDj4fZQXu5AGy1VRqQ+PfotIjVT1QNY6qsbu1bhx166haS91ZdOUOA718xaL+q70fyUpNnOgP8PLvfQWlLb9Z/y2U/om9v8n3W+Rv/DDefRpv/xh2CoGv3PR+bNSwccZix+AbzUwGOxqmAcwAUw/TlgAAexH+7j1mEJgexh7GjUBYtRNAc99Ec9oMJaRLgu9B62YCcA4C5rQH8vXcV/5cMD72tT4HAvnxdH0Oxuaen183JyTnICEXnnKhsbymBCaSAPcYDYzx50RM4R65UCz2bl0gY9zi9of8vM5yaJrylWBBDjFy4be8R9LcOQZpS7iT139e9hyk3KpP2GkQ7rUXVVC+lgKhozahJEBVnTQBxS1YeAgI1YHGYjrmpdiZUh/AxQgIXfxwcHBNAoc7aKgyCFksjcb/mhcPyoi93U9zUHLrA/sMBCn7cDn+jo3J/PoSCOXjMsmypZZaDqtcJkfmfSxv99zPVXpWbopZ9tXSbC8fW3PgolzyPpafu3PvFPtcriWVzwM5CCuBY77MAYu5+szHQ9nfS2iTpwON4pnnl/nLgWQOAPMxnqf8/Lln59slLMvbIf8QU7ZDqpNyPknX5c8o5825PjkHwEptyfL6fG4qfczNlT0HfOVvwly58nzO5aGMzlrWaX6/uTKWv3l5++T1Us4RZZqmdy9MvWfnObsXACGCw9K49hAd8Uu8T7biKQLE3hdbOcgCjLv9ow77qCyVFAc/LOEHAZSGkYn3f/wjfLxd8OztZ9hJonVNv9pws9nwh//FP8x/8P/6D/FYumrB+bJieb9j8pY3Xn/Kd775DczkcDh8nCAq3eAteGVw3gYoZEOkTKUUXgR19/RuLUXY2P9gA54AnGT4kclJ6oSjaWte+fCHmPqrfahjLXGjw/tQr1VdhUEQ/znnsM4iRPjAI4BquEK+dknz6i/sJt9aV9SV3v1++faE8ewxw9lL9KcvMp68yJP6U1y2vw530bB68ZQ3m8f4FK5sAVzEwvx64NLD8DH+/vbH+I0feYN/+OqXudi+iXnzy7i3vs6ZecbHLzqWdcVmvWazXmOMYZpGXn31VV5/4zW8G9FSM3kTfJ2J4FPNpTr1+UCJfY2AGDw+/tjsX8hOTk6oqypci0UKiRAe5+2eb3iPmYKzsPwlV8dosgCvTv8k0/YsCJax+K9tfzt/vv+FoJWjHJXfoOlRYghgTITtSm6p5YZhWrB58RE8dVF62dEU3rr6+DsESbgLAM0dC4BGShMjD5ogyMgRJQ1Sjig5UakRKXtqtaY7eYaWW7QakCKYzDofzD6trbG+xrgaYxdMpmWySyaz4GrzCm/b5wvwUOsb2uoyQrhndE3Qlmv1E9r6GW31lLZ6FqPEPmHZbtFacHJywmJxQte1NG1HXWuatg4mhP6GJTc8wAVfj8Ljz5NWoojVk3/pi1DKhz4zWsGluc+bwwOeDg95MtznyfgCT8b73Jhz1m7JQM1EjRMSL9VeG2gJnPOcM2XRTknDp3SIb4EYcRCq9K54mPZdJ5q7WRo2tFyzkM9YqisW6oqFvmEhn9HpS1p5TSOuqGRPJQzOVkyTwrNgdDXDVGFMg3EVjgXGtaHNbRXa3VVYVzG4htF1YfEnjK7D+Bbja6zXOK+jabDcAcBdnue6arkvwcCce9750f32TVP1/LIk7zOpKy2eEOmRqKUHKII5pCKAP0X4yKBcCAihQyTZFERCVxNaT1T1hK7COkSPHUPUzG7LchF8eJ2fX3G6vGbZrLlfv013uqW+P6Eqg9YTukr3M+jKoLWhfHcyTrG1Hb3t2JqOfuzoh5Zt39FvI6zbtAHcrSPA27T0cf827X8rALvtKgA9N6lDAJXgFGQglEN4lzQhO2aAXba9EHA6c/yOj4ce/XzuJNMYPAjm4MNHEueQziK9O9TgEQKHwAq5C9qRtJXD+BR70J7WI0FztefQh9zIIbBLUD71sZ22qTiEc+W2yZ6xzZ7TA4PfA8KkSZh/BJhLB+D/rm2R3Wu/7eOyOzZG1ug7Jv9C8RHieRrqV3+S0u4iN5fBYZR2MUhE9rcy4RoZQKR0FoRFCIvQJgBKHxtaGHxl8FiEt1hvwFu8M3hnQ5AAZ3HWBhV85whxr5K2osV7g7XJNtrE/QYhPN5PKOUBg4y+HaUMrhDS+Uqlv+3uvHQsXJOe5Q4EzVwwz4XM/L09pdykCfYyRSn8zkGBXKsn3T83IZsDhPn1KZUCXQkty+vza/Jj5fm5FlB+TQkHyvLm+ZvTsii354TP8DrkwVmE2T9PZdeXGl/pfqXAOgdD5kBuCQHyY9M03QIbZXlL4bqERsP/7J+GH/9kyhjiP/zJMG1mfS0HV7mGUl6eshxzfSmdm0OWY1Akz2taJxO9dMw5t/O/lvppukfeb9Pxsj1yAJc/M+UxaZDl98nbtYyWW0LgvJx5W9wFIHMtphLEpPYo+0qqizKfJeQon30MDN0F18rjc2O2vGc+RnOT2bJ/H7tf3n93VldFnc7Nael+pflm3i45yMuPlVDuLg2/fN/cnJMHSijLVZ5TWimWY38OsJXPnPudOAbZyrp+J9PU8vznOV7mIYe6c4D1edJ7AmxKHaqrHlQK2cdNYqMbC3LfofMvOIdfoSC9haWyCGFRIjQodt+IUkiU1NR1Q6U1Wihe++ar/Av/3L/A3/urP4EWivXlFn8GZ9053/jaNzDTyGRG/GS5Wt/w6luGyXnGyWFtyLyLb4oOaOoq5EPUGGei/zWHlMG01VkPPk0YntTmQuWUFxw+/ODFdswp+TQY8AFKhjp0mMnuOvDOLME7pJBIGbS0hLBYLCZqEjrvo7AbIiWGfyF/wfJUoIY17VtfYfnkqwBc3vw2PveFP0GSbr0e+Tf+x3+S+x82bE9e5rVpwRdft/ynv/RH4ErAX46DWrb89OKDsPhgABBJ02EJf3XlqdqeWq9oVDJDfJOFex1rfpFu82VOhq/Q+jUgUUQhINP68D4zFYtCSJDdBc6Z2Af3JsKLxYLNzYCNJrZCCqSSIbKW91hnSEYL5SQQfggV3sehkDQaUqqB+wImxWROmabTvXA5N94UQcMDN2OmGAtXChtp/9E0f8x5hbPPH0Ht3ad84ilF2PzYviCjaRnNi1xvX+I4NcmvdSgxotWWSm1pqjWVWlHrFXV1Ta2vaPUlTXXJon5Crd9i2Q60C8HpmebBWcfyVHN6UtE0BqEmrDEIwNoQ5dZ5xxnf5qJ6FVEJ5Hmci0T+hQ4QjkqBQ7OeTpn0iwzqMZfbU95an/Fmf8ZP/6OnDNVLDPIhozzHyAXIat43VR7gbumD76h3mywR0CmG6ZTBnnJlXzk0j4U94Mj7nB2p2dBxxal/yol8xqK6ppOXdPqKU/U6rXzGUj2j8W9T+RtElJuVkFgMXmqcHcO87mSYI13yjxhfGET6YLBg4x/wTLzIDS+y8i+wlg/ZigsGeY9BnDKKJUZ2GGo8VRTUxX7c3dL+iX0rNZV07H0oxWM7gV8cQkkHu2ix77TkQIFsnfqqDEDQ53kR4H9VWeOEhhcC9s7fHVKGIAlKG5SyaGVQKkC5SgVAp7QJ0K8yO/h3r7rkUf0m+v5E/dIYgGA7oCq71yCUIDR45bFOY43GGLXzgTVN0ffdVDNMDeNUMfYN/bZleNLQf7ujXy0YNg1+lEe11IC9CXMO73JwtziynoN5GdALPu6SOt97qXYfg4/4sMR3CLE7HDRNXVLPzOf/1D9LmGYIwGzNHp7lmnX5OBcE6JvqJUXafR6LBss++uua8GFo5cPfOyiYjaGUcq3T3Bz54DdtZruEdvn4g8Nx+FxLFvRjGpB+QtgR4QekG5CMKB8W3Ii3I84MSO9QUlAriXeWFATBOYkxYe29wouWXn8cLxdUYk0rXkPiQYTJ3Wc22t4rPBI7KcyocK7CuxbnFN5JnA2RlF2Mpuzduzc5+dWZEsBLS4Bvx/env038e69CmsBfOMftzpPKcvbIo7Vj/SSOJOlQan9P5yacG+N9EzgMx0LkWROv8bs87fMSFqV8PJ6ud7v9oTx7iCmlw/uJ1HlD5PrbQv1OZim0OfJUCtMlJMm1R8p9+XklkJgTTtP+Y5olc+lYvo9pscA7a+Xlx8rypnvvBPU/9Z/gfu6r8P4HiM9/B/mTX95ZTiXFBAClGrbbjwMDy+XXSG2T8lgCtjwPpYbXXF5LLaEko+WgLNdISmbMSqkDP1bpnKZpDiI+1nV9UK8llM7rKAHDdG0y8yzrcU6DZw4wle1QAth8X2myOgdR5mByAmw5aMr7UBn5snxuOabKOpkrZ3ldme6CUCUUmoNsc1A51VF+/TGwVdbbHIg+ZuKYjucg71g9zMHtlM+5/JZ9dQ7wHQP15XPmIOxd9VTWdfncu56X6mMO7s3Nr3fNg9baA5Pmd5u+6/BvQoiDsLyluuOuYOL2V4CU5ibiPbRyqORNOwMNXoCIJqRaV1RVxRe/8EX67ZbTboly8KVPf56f+It/jdXllt/4iU8hhaSSEmMtopJYEd4OlbLYzYAzPgjdPhhhWecYrEHjQfr47ueRkhAtVUikEyFSZnTGL4VAmL02QfBXF7SxrHV4AhzEJ38rju1moO9XdMsFQsisHlJnA6klSsWvH+nHwFuUlFjnsW4fVtcJSaUUEXEiVYiOmcwviff85lv/LXIIMpmaf/PP/o945fEb3Dub6NoNbvMa9ecnxqliJ9Q6eLi4YjIVw5sNw0bhk6NoBBMdEx1rXjjecapoztWMqGqLrlbU6oZGPKUVT+jcmyzEW9TqhkrdUOkbunbD05sXOLMrFt2wg7Onp6fcXG4YpySJRdV5f6hWnPe/XLXaOcdL+u+ixAbrFvs8/hEHrYT3H+n7xoZlcgjjwXjsUOFHDZPaOZYXxuMnwr4kIOXCUtrOo0aW64nbcC4XXMiOM3OOO3Ktz+9bnpNuNje5zL9UHR4vvW3MJ+s11pwymFNWwzudfddEl445QqAKgyBp9VmUMlTaUleeSlu0HqmUQamRWo9UaqCuR05PoK0HTk4lZ2eCWk+cVhvO5TW/+OW/Tnd+glooZAVCTwihcLLF0GFYYsQJk1gy+VMm7jH6e4zunMHdY5T32EUHKEHcTmD1UWuSaD5BmKk73iWkqxmpGe09rqYP3jZxTcJ8khG8Q3iL9BO139JwyZK3uBDf4cS+yol6RqeukO5tGv+URl4GodVbgrn0FdJdcl/8Eg9EgPreO5xIZvN+1xWFDy5nHty7h69f4Jl+xFP/MiteYOUesuY+G+7RizN6lky0TKLBigovos+a50zCO5Q3aEa076lFj6onVOsQrYWFwjYS12pco5maGtNobF1hqxAp0Yu9WfietGV1OXKorZQvu+iX3B7Tpti3M0v0e39mPv59AB/EzBwgQIjdoUO/XMXyqwgMCkK0RyUngkaMDz766ijo5tEiU+TUtHYC34PvBf6pxHkRAIZLQS8UBybbaVrKl3wcZj4KaZg3hV0Qgo/s9glYgO8EPj//e/HdozQtT+kWpBLcgsUJ1G1nlqTclNTvEdD5UK4cYDbPOc48oQ8nSHfFbVA3iUOzemAXuCJvg9yYIT93djtq0vrQYLPawM+bvEdgkH4IfurchlG8gGcBeAbu0fuaTn8DpORk/Cxn9hdRfoNmixIblF+j/AbFBidaviT+V6zlx2i23+H7+V9TqcudEOCc52n9e3j19E9Qt3CqvsZH/Z/hpL7CK4+qFLrWCCVRTfDhpOoKVSmUDr7CpFIoXSGURmkJIvgXE0KFbaFwPgsCc0egGHdHABlnFGYSTEOAh2lJAWHMFLeNPLxfBIrO1rtx6azCexnBo8I/h7sKgLfe/C7a9lcoJWhYQsUc1u3hnkPKw3OT1uAeDKb94fz9fczB/nR+gor5vXLQmOfp8Jr9+blm4x6U3i5H2l+ev5und3Vy24dSSnMQaVawdh7+2qd3UCk3eUzX9n3LZz/7f+Hm5tcC8MILf49PfvJ/j9bckk3hdlTL3Gy7BH/5OSVQKE0yk1l3DtTy81L+S02p0jx6ri7SfdK9khyTnpM05Uotp2Mm4HMaSMdgbXpe2s7vWWpYlSk/J1/n956DJ8dAyNz95657p/PzlMO+Y5Aur5P8mSXDyPfPwaK5Z5fQ8hgkz1Pep49p7+Z5z+9bbt+V8nq5C+SVecuPHwNpx+o95a8E9Mf67vOmObCZ9uf9Mi1zARWeN70nwJYmqwDC5IE6JETfWR6EyJztpUKEnO4K530ETmJ3FCH2gQycEwgpduZ8pepl0mSq65qXX3qJ17/zHaS1tLrmheUZi/d/mC/cfJlHp2dc6Ir10LNVgl4JXK2wTjAOBuc9QimUBGuC6aWMkopxFhnL5ZIpq4tqgwKscwRn9wFAeS8im/DBt0EEF0HIFFhih/BgvGXdb1Hex3IT5bcAwZIjfe8S7xDxpckENS9vsUF1K9R97IjOO7wTWCfAyp0GlxRB6FVaofT2Vtv2Q8MXvvpR/FHVjJDvt9+4t9vTNCNts6HSW6pqi5YjQgw4DFYpJqkwsmZwGisXOFqca3GmxvYL7M2CsX/wXP7g/9rP7re7bmDRrKn1Ci0vUeIKJa/QaoUS11TqmkqtwD9FcomS1yieodWKurrB+2HXP7Xa8Dvv/2F+7vpPs7Uv8aj5GZ4++gHWZ++LZQ7Q44W3foFquMELjVUNVrcY1dA3F9hFg5QWp6KZYayv53/nv3sAC+Mi0Aswj8lHAV3ABH6S+EngJwWTnId4x3wNzZ2XQ7ZSwEv78LePHQhCBbTbCYEuAh0fI7/6/bmOAGy9CGagXkSAK54b+nkk1gX1FpND0+86/SF4rdx3V7vtv4YLYdHiBpKJjfR7UCAjHJESLxReKqyo99pxKj4mBRUQBNimgMogtQ3QehetMfg2CYEMxF5gXhK2Z38sgp8qS8WWBVsecMlH+Q6ENksmbrmJXYxEGsCcQbmeyq9o3FM6+RYn4jucmi+zNL9EZ79J5TeEOdKjJHTjxIl6k5f1m7zCPwLhkVoinQ1+KpUK1aOSF0TwCHp/ypV/iae8xLV/zBUPWbv7rP05G3/K1p8w0jESoJwRFUbUoGJAEEPU1jnWbKEmtJ/o2NCILZ1YsxDXLLjkRFyxFGs6f4X2hlHWbJolq/oeq+4+m+acTXPCcLJgrBtMU2EbjasVvpb4RkAt9iaUt5ayfZ7jx935/fjdAr3fmyoOHgaPGDxycvjRIyaPGMMHAGE83ogY+S2MNzERtMaNwEdA4sc014g4Twh8HqDBEoIdGBGDHhTgJ2kaZrDPI/ESnFD7oh5bvhdgMIGcYymvw8s7zvMz61vQM6YcCsoMmqcl9YMGaOPSZX932XoRt/PgFvk6326Be8U5qQ5LWOvEfo4+mKtjfZQmr7lvypHwe+RiWV4kg3SEDwXPmyyh7q8I9Z9A3ZY9qNv9PmX1W2rV5e38jqCO+DtTYX2F5aToayH/hhe44QVwcKM/xWt3vUF7t1PFH5oX+bnxP+EeP4PyK5Rf43G8Vf0hmCRbI7jigivzx/nk8Md2mije+50ZFhyaceWAIWnz5yBhHEfGcURWjmahaDoRAlN0gmYhqeO6agXVKVStoOkEuhXULVRdCmKRB7MQ6LTdQNWB0u9e8CiTc4JxKxk3imGtGFaSYaMZ1pJ6oTl9fAj9fuGvK37+L2mkrPBeYQyMo8MY8D40vHOJ3kbtQq8QQiNEFTUU5W5/fm4aKN7rg3PCO7GO1wYq7Nz+mHNJk1EcXJvO2Z9fZfdUxTO+a52HXyXpECbuYaAt9t8NI/dwbw8Fw3b4OpBMl6W0XF19gpubvSuTt976Ud5667/B+973N3eaZPkYyYFa+XcOPICdrJkL4UlGTaAul4u11gcAL9fQyjWG8nFbQqTc9DS/T37fdB7sLaPy55X3Ks3ecoWDY7CrhKB3AY05SJLunTQNE7CYy0sJf+bulz+nBLfPC9bm7n1MIy1vn1KRqKyzMl+p3o5peZWaXSVkS/vyvB4Dk/kz8nvmx8q6SAApV2yapumgrfO6Tb9HpdlxSrmrgLm6LJ+fw9+5dkiphGpz2nz5s0pgV96rPLc8p2zvcj543vSeZvN9IQUpUpX3MshsIoChEE0wTlQKwoQLeIFLzhKFAOfxyTmuTz62ghgFyceAu+XQNAhAYTtNUp/6wR/irF0ymYlm0XJvueDm9ScsFjXWTCzRODSDn3AIpsljrMG5QLWSDzljbDD9cT76/kqVHv8WHi9tMEP0HokIjq29w3twPv9aYnflBkh+6XZ/O9huNtTaYE0wPw3XhONNU6G1il9PAijzOLzYD7bdILXBl5kQYI0NAnZsC+c9Soa8JuH6/Q//fb7z9h9gshcAPDh/k//Dn/g/cnFvom5e4Omzis//gzdx9oOYx7+bn/35CWuuqfRE173Cet3i/QVPnjquLiXrzRnb/jHvRgrS6gZVrZFnPbKx0Ap8o3Fdja1rbNPhdBPuubckgAG2m4btpkas7+E37w/C3LtIggmtbqj1NVqtqNQNC/0d7ukvUPc3PPx3eja/7jH+tIYXgEeSt05+kPr8ih/7q/86Lz79eYSY+Pvv+9/y5fv/7O6+v/n1/zOffPYXmFSNkRW2WmBUjdULjGowqsHqBqNarO6wumGSDUbVON3toJ3R7e7Y7nzVYFWDqTpMF7dVg5ff+xezpJ2XtPCYfBCyJ+Ja4icJRs4DuzuBXtDw81NYbpnc5kS+3PZ+XsNh9/cMFPREGMRth+X5397v1iKI/vHRAQBaYw8z5UV88c4zepjCC3Twt/bu03N5l8LNjrk5SZ9DYV8Q/YqlRQQrNkmMdMY+UISO6+QEP2n6aIGvNFZrrO4Y1QXr+v2HQm4ChLkmpfNgPdJa1DRR+Z5arFlwxYl8wrl6jYfim9yX3+TcvcmSG2q/RQhJrW54JFa8KL8cv+iClPtQ8cGNQXA1IKXAecnWdlyb+zwxj7h0j7h2L3Dj7rPigrU7YeuXDH7BKFoMNU4oRqEZZcdKXOyrNF+nlEzt1h79bKJipGFLI9ZciLdYyitO5TPO5VPO5VssxVNO9RZpepR3WK8ZbMfgOjZmwcqfcynvc9085Ka9x7Y9o2+XjG3L1NTYpsK1CtdIaOS+TXJgtxQBrmgCsNO8C73Sma6UgH7UsAsmkQ7pXHBhgI2aow6pbPBFVzuoPXQevxCITkBlkd5DhHWMEdyN7Na39wXI57fACsQKxAbYOuQAog/AUCZwaNLCIRSO4M9PEmclLmns2MyEzwVB2Lo4KHLAB4dagcc0BGfBYDY/JGA9vpfG+B6kPF9Ja3YH4WYAYO5XL4370r+eEvtz0phPcM5yCDXLjy6WOPf63bsRJw7OfXB0XkUgfdvK7HjyBAj4jOA/9oa9ye3AXqPOZueXoK7UrMvz/o7bcr9tAK255LfemeWt/MjBC3/+op8LTklQzTVq8rVzbu9wfAA3efrrkJkgLITnSXnbt1QufOTCfW7KlvLnvUfXkqqBeiGQlaNdKnQDVRsizaoYdbZdKFTt0C3UnaRdSFTt0W2KSmvjImgfe6omRq2tb9fT5lXB1//WPi/OOYZhuOWIuhREE9TI6yrVU5IlkjyRjicBG6CqqlmToSTM5ZFm5wTdVI95HvK2BoGUCeLJOBcFsGcMSFljLQhR7cBgMmd2TpAA4CFYzM85XCcQmQM/F+e9BPzS/dJ7TAkeAzSU3IaQIU8BGt4+z/sK59qD++yfm6Dkd5em6SWqqrql7Za0wJIAndo5951XCvGpv+c+23Kts3zc5qCt1IzJgU2ugZY/+5iGVVqXpqCp75XaNu+kDTeX7oJB+XyUQ7v0ESBp7uX1kfp7DiBLaDVX5ykP5XgqfTbm581pQOUycg5N0j3K8pbalHP5mTs+l5/yGfk670clZEvbc9p+6e85GFfm9djfKa9lvo6Z/pblK6FrymuZz/JepVbnXMrHYV7esiwlpCzzmrd7vs77wlzfzueF9Ntwl3n/Xek9SeX7iguBDMLDw3uEPCC5ETi5rHFnqC8iBkuQtzsAO187t8l8Ql9KKaSQmGniwx/6EA/v34+TW4X3nvv37/Ptb30bnEcisRZGbxmnEe8cImpieGOZXDbgpMBYg5Lxx9CHNzAhfIRyMSmJsz5alzrwRF9ukEKK3qbbcS0k600Prd3L7AcDM9RzLPCuvnJCmyYd54PfsaBgFxpEes/O5sKL+A4ZNPcWzTf4Jz/5+3n92e+jEhN/+A++yslSABUny5G6WrN5+VswXvLRf+oRv/PHDZ/5zKcZ+4Ef/W2/HWstDx484OryCV/74i/x2pe/yvWTZzx9atiOSwazZL1tWW07jLvg8qbG2DOMPcf5C4w7x9h7TPaMaXUfc3X2rvqhWljk0uHug32/ghOxf9lPX+tjF5LjFtVvkNsetR0Qaw8bcL3GjB3D8ABjlxzAwVeBz6U/BPxO4JMwDuf85yf/LlQghgl/XQUh4jz0o58++df5bPNHULZH2y2V3VLbNXV/Q+VX1Kxo5A2NuKLhmoZLpBvQrkf7IZiyuQHlB6TrkWaLZozdQ+bKnqFLevBKY6tmD+ZUewjyVIORddzfYqt2B+cm1WZQL5zvdLyXasP9qgbTtvF+vzxAj6idJ4w7gHFBWyYAPSYZNGWSNtUc0BvFPORLzsGTwO0J/6UKLWBeMOHe/30L9OV/J8kqn4Nt0NLDGyQGhUH4CelHJBOSEeEdxDnIOx/XYmfy5Z3AOwnxhZv0Mhz9+uxeWF35JfwO6FdwN3/r4PyR7z7N5EUSnMeLlkm2bOQ9LuUrhX8nkfm284cwUAVNQKUMUhsa3dNUG7p6xbK+4bR5i4ftE8712yzVJSfqmpf1t/hQ9bXgi0wO4VrhbzlFdl7Sc8rGnnJtH/DUPOLKPeTaP2Tl7rFy52z9kp4lIy0TDU5ojKgxomYrT4AX9lTrDrIlvEMzUtPTijWdXLGU15y4Z3xw+DLn5i1ONtcsxDUt15yoFY27RjOAqNlODYM7YXQn9HZBbxdsp47BLtnaJRu7ZC3Od7Bu050wdAumtsF0AdbRyr3WVA7sdtBOZD6+BL4CL9XzMQ9LABw3eQVzAO2EcbsooNJF30e1RTQOKRxCuuD6qvKI2kPrEUuPOImRYSM0PoKVY0pHogkWU7BSNFG7b7RoY1HWor2hsiPVNFFZQ21GGjtS27QeaKaBxg40tqeeRtTkkKNHjg5GcJPCjJppqBiH4H9uHGqmoWHoK6axZhpqxrFmGjVD3zKONWMf/NZNY/BlZ6bg3y6Y3Els8uvlgn83fAEC52DgnYu4+/ivSCofNiNg56b0B3ME5MFHDj4O7Mx/ffhAcCrCb3U+RYq5+3EI1nofItwmzb3U8TOAW1+/hhq3WLlg1C9GU38B18CL8P3f+NdYnHwFJ5cMPObz7b9FeJEI5V66L+2KOqdhMac1kWvf5MJ+2l8KM6UpWno3zc3kcjO6HCaUTrCdc+AkphfYIXxs2mZaOrlwU8K6lA9jzIEAVYKOx99v+Wf+zb3bFSHg6z+9BySlcJjXzTHNjbk0pzFR/n0MUuTlzIHNnDBe5jN/lw+B4EJZpRRovT9eVfvz8kBxJQTM+8VBO91R3rJcxzRQSi2PlHIgmYOetD6m/XEsD+EeDiESWKywVhC0ECukrG9pIb766m/nC1/459nP8YIXXvhFuq67VZZjdZT2a613Yyffn9d5gmf5eGya5gCe5VpvORhJ4yjBqLlIoWld1n/ZT0voUe5LZSv7fV6Wcn9eXzkwy0FaCq6Q+6lK4G+uj8+Nmzmgls8Tad9d6/JeOVzJy1nOjXN9oYRhc/Amf375nNyHXD4f5Pk61oY5sCrLlrdrrvFXtmEJwY7V1RzkKgHjsX5VakyWdZTPRfm958xX01yfv3uX7Z+eld8rh25zMDCvtxL03TXPz2nUpTL/immwHdioEl/wBIDDuaJho9noLrMShJc476OmlwLvdo6yE1QKnU0FoVMG7bi5CRJE8EMhJV3bcXp+xv37F0xDv/OdLaVifXmNdGHbWsWwmRi2mxCBUiqsS7jOp1rFOItCIH0AZzKWKTgzDWagIv6dNO2SGaj3DoRHCmJ5wj4PwQlyrLvz83Oub1Z705pKxPzkdUzQWiNEi1Iy7vPpk2ucRHEBrjkfTFhl8M9mrEO5oM2mlNxFO/Xe0+i3+MDDP0enoG1+BPxJyjCC8G44ToZKVxgZNOhUVXHv/gWr1YrP/MIv8MmPf4y333qbm+srvIBuOaK7NQsMp9PEME1cXt1Qsdr502sqTVXFF6loBiaEwvozjDnF2HOMOcO4C8bpjMmeYewZDx99nH7oWPcnTOac61XD6rWO7XC3dpCjw9HFtmXvR+chwWxuCbQTlbyiYoW2a9b+/Viz3JsL/bPADx3e1yetpC8RYJwNfXLLozvz816S8JbgI2ZCMaH8gPY92m0DmHMJ6G3Qdk3lB5Qb0NOAGgaU69F2oPI3tO5JuDaeI/2Itj2KAWl6NOF+Mpq5hDFPhLxg0Ux6D/NsVTOpFh+17owM5rNpmXaae+1ubXW7O28H9xIcrBtsl7T8frmAngsaejtTW6L5kdxBvXc0pd0dE7fPm1TU1Ktwhp2//lvCa9IWuQvgPecxIUakGFAyOPpWjAHkuSnABGcQPi0W7wW44JDb+SqsTY11TfCb42qcbXCuAqdva/qVTsujLfsuEmMCwAeOwbIftd1v2XuHeUFBJHyxH2m54d67uDrLSwQ04Wcl+AILkXktWk4oNaHlSLVbD9zTKyo9otW08+mH9MFMVwomWTPKALEH3THKjkF1jKpllAF6e6XxWjLplkm3rFXMf4Jyd0QHl95QiYGWDa1Ys5Q3nMgrTuunnLVPOJHXnMtvc6LWtFyxUDcsxBrFFJy6b1zQvENgfE1vlvSmozcLBrtkM4UopINdhrVZRoC3ZO1P2ehTNosTxm7B2DXYZb3zSbYza2zZmwo2HEYXzaCdXyiSO6bn1rJLDvpTMn43BoXx+8ADNpqhu9A3ReyYQjqQPkR/bTyidcilQZx41IkL+78bwGQ8cvLIyaKcRTtLxUTFRC0GGjHSiD4EIJFbFnLDQq45kStO5Q0n8oYzeUPDQMNAS0/DgBaHNeQ9WKMwY8U0asxYBbA3Vox9xeZmyXq1ZLNeslkt2K4XbNaLEE1204WgE31L37eMfcPQ14xDE0DftmLqqwD7eo0ZNXZSYOXerLRcyjp7R8DH/Bz3rhZxBxi8teM9pHe+fuSl49d83fEl/hTiJjr4x1BdXzPJCxAKyRbjT/k8/zcEI4IJ/IgUBu9GRPpIExcpLMoZlPUoY5HC7CKLC29AjDg5IMQU9ov4oUc6tLLhw49yCBn8bkpp0cojxIS3I2Bn/VTlglQSrKuq2gngx7QTSgFTCME0Tbtrk2CT4Fv6+80vaP7Gn4Qf+ucsQsHn/zPFV39SIKXY5SeBizxPcChU5QJp2pdklQQ4SmBSCr53AbZUJ3kq6+Pgg3jmg6v0EVxqTZRQsxTcy/LNgZcEAI4Jr8cgYn6vUsOx3Jdr1iSQlEeaLCFS+czU39J1Ukrq2lNVKULniBATTbM3M0z3ePToP0YpwVe+8uNIOfGJT/wlXnrpi4A40DzMAVfZH9Kx3EyuFLDzuiihQn6v3L9aquMcpOX5yTW68jot+1Numlr2jdzMNB8PZcr7cgl9yz6Ub+egrTQFvGt/0gTK+00yF82fkddDWc/H+nsqd9lP8w8F5ZjItcJKrbES0qQ2KNsxz19+fQJFqX1yWDTPLw7h0ZypcJny+bM0s83TMTCezs/PSXnN2zjXUCw1ynIgmd9nDh4m0JyDuZQHay3GmNn2Sc/M55BSQ66c5/I2K+e/vF7mxn6+5P35WB0+T/qufLDtBw3B75pIBRB451FaIRC7CJlCCKQIcA3BLsQyIvqpOpi09/bwOBGEFr+PzJLUqb0XCKnAE3xOmIn3feQDbM2WwQ68evU296pTllVDg2LrBdoLTnSNrhdBRnTBCXfQVgkUITwLfAJtUgSVllReEYIQCADvDjTadvWAxboYaEAIiL7lvBCIGFns0eOXePrkLdw44UWoL5fMqACBiLDLgwgegkVWd875ILgLiRc2mIOKAMhUrFKbBowI8mx4StCFmlzwg2W8ZxqnXTs46xB4nJsYe8AFf3Hn9+6Bh/OLC1abDZ//whf4DT/0wzSnZ5y//DLT2OMvn7G9fMo4GbyA0Tlu+p7BOTQCLQWjc3gLtRTIaJorBFQxcqQQ30IgUT4EZ0A4BIbf//t/D44NUkpeeeUl1us1V1dXPHuy5vXXJ15/U/HkqmK1XjBOZwzTGf10Sj+cMppzBnvBIF5gcheY1Rn26RI3JW/UFRMXTFzsO/sngFOCYPjngf937CLS7/1hVUCdm2kN6LpHNC760JH4muAPSyu8UqBk8LclBcn/Fk4cOp8vFm+DppKzDaY8/r1WNopJ7LSvYjS2qFmnbAR7rqeyW7TbBi08PwTtO9Oj+wHlRyo/0PqnaDcg/YC2fYB/fqTxI9oPCDOg6JH0CDfh7X5Cc87hZYXRLZMKWnhJM89VLZOsgxltFUHdDtrVEdAtItDLzHP13tQ23c+2Lb4K4M+Ld/+14h3TznceGcyb2+Y5gN78tp9qrKmxJkq6O6FV7CemuwRaRejDd8G9g+3kKyWXrIMfuXlTcZ8BOYcSod01PRVbNFsqNmh/g3IblHdIB3iJ8zWjO2Ww54zmhNEsMabBGh3MSzLNv1vwz2fP9cX+LGvspl4Zh5a6i299D5LnGEETmRkvIvwWRb8IO1NeJwWDbBlUy5V6cNss98A/l9hti8pQ6ZGm2tLWaxb1NWfVJafVU070FcvqitPmkpPFJY/l11nIDa24QonbYeHzlxJjwVy2bJ8uGO2C0Z2yNQsMp6yHlsEt2E6LHcDrbdjemgW9W7JmwdAtAqg7ZR9MYMkhsGs41LTbQbsM2LVBy+5dv+VYgv+vq9Q8Pots6sPPcDKbdyCsj6aN0Z+k9AjpkZVF1A7ZBU1rubCIpUOewruaXubmduORxiGtQzuLxqC9CWFN1EjT9XTLnlb0dGLLUqxZihVLseaefMoH1NdZiA0t/Q7aNQwo8fw2mNZKttsuQLq+ox9a+j5sb4eOYWjZDmG7361btpuOzc2C7c2C/mbB9rqjv2npr1uGVYMb9GHAj2Rimpv67oKAsB/nWf8+8DeXNNXSOgeA5VzgRPbx4Aiwk8U23D5vdpFYcXL0uBMda/GJ2xwvb/+yed6TvffzphCdVcZooJIY0ZMJIQxKGKQI2zIuAfyl4EIW/BTOYQ8AlTQILIIJKQLwU9KAN3g3oLVHTBNaerwbEEw8/TuWT/+dABqVCnmRMWqoEhatHaO5YhzjrJ2ZsgKzAlYpyBpjdpEgYS+kz8G1Uts5v0cuIObPy0FIOjZN084/WC5cpmeUwmrK4ziOB3nMoeRuLjZmNzfn/sXy8pd+yPK6KgFAAqgJhJQO//Nn5TDPOUdd1weabXl9Jk2odN8Et/J7pXaYg0N5HX3qU3+ZT33qL+80rJzTu/ophfg8L3VdH0Cfqqp2zzwWeTRvy9zUM39e3nbpuaXmX6rX8hlzfydgUYKzEqLmsKoEmXOmmCUoy/tPOreE2TkgzKOlJjPRY+XJ3xdy8JbMs9M5pUZaaovcJHWuDksQlu/L6yONlbyflsAph4Op3pPGXt4PckCVR5PN1zmwKYFqfq+y3vIxnee9nNPyOstNTPM6KuevEqTl6Vgb5veZa9/cx17+HGBXN6k+yzosQXQOJ+fKlT+vHGOpjvM5Ls2f+Tg+BhFTvaU56r2k93SVtS5qcQEItFZILwNkyyQwZ4PKs/D7twgbTRiV3H9BKL8cpYIFfOWCIJF1NogV7lOlhneQt58+5e1nTzDSI7Vg1PC6WSOGCrv1SF+DMAhdsTAWrWEzDZiYv5CfKMj4oDlHBGHBaXpwB4IgRO0U8cfX79+z3E5CC/d03uOdRSofzGCT0CvCfR8+esRbT96GehECxZU/HNm5Ebch8CgBQqoI8YKWWigDJI0R70CovF4TOLRIZPDP5j1KSIwzrDcbhLvYdTgPyPg1ZxoGzh+eIazg7OIC4QSdbri4eMST1ZaLxy9jzMTmzWuury+R0uHdiI3vqqqqcf2EFyFQhMNjnWcyFi0CdFRKYKyLLCDoRjrhwYTQ6oo4ISsdajoO9LquaZotZ6cbpuEGyYrTZtrVw2Qt02iwRIjoQSHp5Yv8wsn/laf8MN34Kmq1YTu9zFJ8h/b0Td5sfwecEQS3Wyl/42b/8r8GaDBzIeTmwFke1TFnFEkgkA60Q2iHUA5qh9iZbnl89L/kahlMvBoJtTr0lZM7vs6FgzIvM/nzVmOdxtp2/vxfjuRd1NAbA8xzfdDEcwPabSLcC6BPux7pIvAzYS3dQOV6Gn8VjsXj0vYoO6JcD3aDsBuUH5FmC8ZgjeXFR6f82I/9BpwQ1GcvYLtz1lawdpqbSfLv/0c/Qf3w/cjFKb5a4KtgTpsWm2nxOd3gVINVdTzW4NJ20+AW+/27yKLfsyQyDTy/gwPzmna8Z6DHJPBGBYE30/49DubyYworFlixCK6oymtk8fduHaLEqijkVXJLJQZquaVWPbXsqfSEiOanlpqJGkPDJBpGaiZfY1yFdRXOCvDqsF8noT1p65VgLk/p+NyYTiBgzvdf8gm4e052L8LcNQ/O5zLx7pKHEGGWmhvOgZfvviD9hsh4tfQE/03BebVSQXBWyqDlFKLy6oFGD6FNqoGqmtAq7q+2nOhLmtpQ1z1tN1LrLU3dU9c9VdXjpMT1CtPXTE80Ey2jX7KdFox2GaCc6TKtuy7Cu46tWbAxC5zU0PoA6xKwO4gAyj6QwIFprM/8DIq924GTCO3ke2iB5HPtbQ5BewbvsIQgNtFUVvjwoUskE9nKISuH6Cyqc8jORWjnEafvMj/HwJ11KBci71beBI07MR1o0XViS8eGhdpwcrpmeR407s7kNY/F5S2NOymev7amsQqQrg/Aru8zSNe39Dmw6zu2Q9y37diuO/p1x3bV0a9btjcd/U1Hv2qjiwEOwd1unSBq/AhiRAzaIPYRnHO4W3PbjLoMMKEIv9/JrD3VgdgRtcP5eBdNnOPz8s6XIIdzTBE45LuHgRA+2kqsqJ7jXL+fI/5xJ+8QvYlwMABAwbTbDgDQRvBnIgQMHxKltAcQMADEBBkNSuyvE7FRVLqfDABRpPvJsI2fIkwMUFErH56FwTMi8XgGggnLIbDKhWzvPVVVHQCEUrjNNZ9yYTs3f0sag8ABhJsDQvn2XSaoeUpaaAmcJaE6BQRIMEQptfOPNgzDAWRJoCbla7BLJnvCafMEwaHbnTzPKeW+8PJ8pnopr82F6zntnlLQzwFlXre5TJvOKzVrynvn5+QwrzQnPAYF0rEcHJQApsx/mYfymalcOeRNwKTU6kl1mvKU2nROQzMHF3k+EvzI6yAHUKnMuY/FEkjldTUHmvL+kIOltOTmzGX/yAFZCS/LNiz3lVC7zGt+TVkvZfnK++cpjancN2dZxrwuy76YHyu35/I094E1r/MS4Kd+kK5J81AJgPPzy/3H8jV3fQ7V83pL2/lHgfR32V75/Phe0nv2wXborK6EZKmTJ9IaTBNTIIOcpuZkOC+wMQYhg2+1kG7baiNAyAjuvOMzv/BZPvp9H+HFV17EPnmKUJYVhtFKNienbD72Pra+p6k9p9crvvrpzzIZx4Rl8ia8s0iPxRFsWUXQMoJgqioE1tmgUZUaRIDHB/cfQuxkpQSzJCDiPUx2H4/HWkPd1FjrOT27F+Eeux+jVLeeaBvsfXiR8VEaEyLIaM6TIrb6CPec80wmmLUKF9YOkHisDQanOta5xSG9ZzIT3jpkU0XAGSCj9yZ+nQ9gr/aCN77zKvdOT9luB9brgWa5oKoV3mwY+6dUtaTSBozDuxBMwuMxxuGlQWq9A4ooffiy5qIvPylwBA0771IXkDhrSGHA0yDXWlE3DUqvswkydVh2fcRFB8oWz2cW/zaX6jeDh617PzRBkLr2Zxi7QJyt8Xq5h16fgeb6bX7z9//Pqbst2/YRq+ZlVup9bOVjeh4y+HMme4KxHc7WYIIz/50ADXt4oAmaGXeyFRlbLZOHUpS120FgZ16+iyWPAJmDPEXQykt+apImSC5M5BHuyv2VD0vt9gLGTjtPHs/PrLaexNoG6xqwp7eP/zIl4UY0I3/xqaMWA+2lpVWWSoxoDMqPPHn8IZpaIZ1BDyOyD1p90o1oVtQ8RbngZ034Ee1HsD2aKfjRs8EcV/kR6YbwsigETiicykCdrGKU2ganW5yqsXHx8e9JthhxxiROsPIEIxZYtcCqLoA9XeNVjas0XitYyH2bfs8U9DJpbhcIgz3cOwbxjvnGuxP+JSFY4K3GoEMIiKMw77s8Brt6yrXJwvcYkQ7sB2a5zqsofVhJGjV5ZNdcG0unZ/o9iJsbM7t6BkaPGC1ycsjJhqARxqCsQTmDdgZpDViBNQpnFc4orAvb1ims09GnlyRF8D2YdNJkmsafFbhYIIv+ZdbyO0xChE9eQjqE8EgRAytIh5IWJS2NWrNQ18E0ToVjQgU/qUL50J4y+n2Nv/MWiRUaJxQTGuMrBl8z+hqkPIz4eUIIIJEDuwTtdjDGQSOCv7ikwZxATD6/nhDjn8QXwVjO50ZSA+HDToLpB+Mm/C1MhHY2vD8I75AigjttkbVFNB65cMiFQyw88qQOPu7ebRDmmYwL45DOI71F+xCdt/bBVDbAuH4H7hZsWNQbTpoVJ/dWnMgbztU1FzzZAbuWnprxXYG7PprAbg+gXbvfzvdFgLftA7DrN22Adqs9vOuvO4Z1A1sRfAuWICz/oLE7FqFammAa9qC3XN9j359uHbeIhYWFQyzA10At8FqAUmEpwd27XUaH6C2yN4i4LQYXtkcf5p3Jh0AkgSPFSMIiaNtbGfyE7hzexxeDOUAHhwDwXcPAfJF4UYd3adHNgMDy/F8lYDBpDO40/qYMBh5qB+41Bg/PSVqCCQYmuBjAoN1pDOKHEPRMBE1D78b4PLeDg0raCCBHmlqidNinlCNEnAngUOBiUKE96MlhUg49SpO21eqUp08/Sts+5eLiKzuhOmkCKqX4wpM/yGfe+G8DknvtN/mxD/2fWFSXtzRcSuCXC8U5YMnhWAnFSoH/rlRq9cytD1q3MH1M2yVES+UqNXzy80ttozkQMgdt8lRCkdLUN8/3MUiTc4AcnqU85sA3v2/p67EEnCUgmwON5Xou73PQp6yf/Pp0rITYOcxM2pFleXOoMwfcynzl9TbXTnN9cK7P5X0lN7HOzVqPAakyP/nfZb0cu/7YOJiDgKm+0jklBMvvUwLk/L7HNDjz/M09Gw79rOVzwlya09Z9N+k9RhEFrRVCJBK8/1oQTCX3JpM53RZqPxkmMpj+PgR0MSJpsBPbg6YZGuw9OBc61+/+Xb+bH/x1v543Xv06m2FAEXy7VU3Db/hDv4unS3j7rbcYxp43P/eL/NI/+gWcF4zOMziHFdBbi1CKSgTTyskGHy1Ka3w0cXQuCMUSsBFeIYh+0yKYEiL6mcsdF0oQYZ8QAiElzy6vGE3UHhMCJwSTO1QB3neeuIhodkrUivMem5Hh3cAAvBM4l3iKR6V6xgdNMmeppALv2aY8JLXYCEi3256hHzhRmnunZ9RS80tf/BI/9MlPcHN5xeWTFeePllg7su0vOT+vuF6tubleI2WFmwTOjhhn0FREPb6QT6kxeJQPUVClD2DN4pE+qAkIF8CYF0EDUoi9+m76Kqa0oq4rqloHjUohMd7iffYj5vcq+c57rvSPsKuopQhCfvTns/EfDEEOQguE1T3HdHHKT27+DHJrkGIMJm5iQNOjxZZWPEWLNbVYoVmh6zFERj0R2NMKd75kOFsw3LvPZvGQrXqRaToLTpBXhBf1DUFg2gJbi9wYxODCC6yVQe1dqnlzsPzreUUQ9FQQJp9jZLN/281S0ry44VDgPwB5Ii7yNuDz2a3zPOZR6WqgcdBErYzWItpsfeKCJkrtofEholwl8FrhlMJLjZMVHn3UxPZuqAfe1kyuZjoAkj6WIdbLKz/wHPX4/El4E4IeuL0vvPS3dCMymtIqNwYz2rhPxnM79xpLvz9XujEExvAj0kWQ58O5wgwIpxj7jtFcMLqHDP4+ExeMXDBxDyPPMOI0LGqJk10wf841NXLH92m7sYjGImq72+dPBFQSX+kAKL772jr80/gMzH2vgZ7fLTs/XpnJrRdBq9mjnhPgibvh3mxZ50hFXKTcg3EFvlOBRSet1dI07tiSjjuLxtJEk+5GbGKwhTWtXFP7AWknvFPYKTjnH8Zg7rkdl/TjgmFcYEwdNXM4HGcOdurMc+PR+RntQR/nfx8A3+4nMMI9m75M/HILyClTx1LU5hfFtiBonscPb4HuxWMyA65pPsw/YCSwshRwAvKEoKF2Av4U/FlYcx720Ym9T7sl+/n1Dmh357cKT4i++SY7iLsfHxGkW4+Ivu4StAvAzgeo2YRFtB7ROeTSIxZx6cLy3OnIqSmKrXIO7YOZbMVI7acdiNuDuzXLZs2yXXMiVpyqa07UNQ9588BMtmF4bubinNiZxc6ayPa3TWT7Pvq920Qtu1XUursJy7BqmbYVXIs9rNvNT373oSFF8PYG/v/M/XnQdct+14d9untNe3imdzrn3Ple6V7pSmjCEggksEIYQolAQeykgnAoJ0Xs4HKCXVSlHJWTckgMGeykiCsJiYkcsIOTCoQIMOUBBAZjyQLpoqvpSle6g849wzs9057WWt39yx/dvXbv9az9nve898iVrlrP3s9eU6/uXr26P+v7+/2wGnzWxyruwrmjsC6tE1gIah78EDIPcFVm4GcFvmmCSr76Ou639KZ56zGbDcWmRa1b9LpFb1v0pkdte9SmxexsAHytha1l9cZn2Dz81EE5fOxv/yj19S3el3Qd9L0iRLms8L5ApIxL+O79/v/wvcD5MG4QyrCPKpEUCvdlYOAHtowVg3IECBKfJ///AgYJcC5BPj0GfnaAe8l0WGGx/YxnTz+N+ODi5/zsqzx88IWg/ItQcGeX/Mrl7yR1AFe7j/HT7/wzfP/H/q39uScsoF40YT6Y42QqnDyNJ+v57zkMmlKKjRVeY/AzpZDKYc2UuibPxzFVUT4vzvd7ERjIgeN4nymQNQUxxnPy/LrH5f4iAJNf31i9N4aM4+3H1zhlIjqGXOPyG/+WQ5gcwOa/vRfAGrfNY3U5bg9T9fci6JvWjc1zE3Cb8u02BQHzlObYebtNKb/mvB1MlWO+fqrNTG0/Vd9p+9xE+hhwPAb90rHHdTm+T/N6fVHbea/0SoAtRRbKO4X01iI5rTMmRBbN32I4F4iktZnvtgGShYGp1gEE+XRBRiHeBdlplD96H4MiRFAU8qTYbVdY3/Pzv/B5zk3JrFnyXd/4aX7tF57imwpKx5fe/jU+/NpryLziidux6VtEK6x4emdRWlEVRRhOJ0mj9Tjr0Sr4zDJGRYGBH/yaDR2It8G0EkMyCXBJop3szQkgspnNudms2ew63nn8LqiP45wFpXAqNcCscmPEsBB2OymyMrtxRSy3oHkS5bGikWjaaowh+YGDvfyxcyFq0XbX4+OsyyMpXiXtdoPb7aLLMUO/67h+9pT1ak1dllztOuZ2Rj2fcf7ggl/9hbd48viS+fyMq8tVtF7wOBdUB0YU1gleKyTZRCMYpQijRRMUdz69HSFMtvrog68M+a+qiq7rBnNWlMKYGBDDBKAW5sSC06BFxYiwIB5m/its9KcABW2c/BVpYitUPMdThcFZDHnupcJTv7x55Oa9NhCUcqAcgxmQkaAkKxVSafx9Ewa3tTp0FJ6y6izFdkW1XlGuNhS3O/R1j1qDbAxuW9J3M3p/SisniK6ngdwkrHPoog/+hIpo7mJAjIZS4xuDaPMBBSHQ0Gv8lheb0r5oXVI6JnPYwkMZwI+qLLpx6MaiZw7V9KjaQmPxZUcxh9mZYf5wFhQBswJfGWyh6I3BKc1N56Bo8LoEXb0UuHsx1EsmuPMA9mwGIfyv0wBa3ADgEqjTvh1UdbU8Y+bfQvkd2vdgPViF7xVsCuS6wHcFvqvwXYNra1w7x7dzbLtEutnhxNDG8x4AOo+ZbzDzNcVsg5ltMbMdZtah6i4oaWqLriVA11pDXSDlDF82Qe1ngtLPFgWurLCmxOkKayren5OrqbSfTSWu897msq+6TvYgdzABU6NsZIAu//2l4N7o+2QyWGWwumKnl9ya+y+Gc/m6GQHqKI8hmoqqYCpa6pZCdxTKYsITEycFnavpfMPONVjfHEL4kc+sFwXEHcBbuscsKGuZy4babmmkpXIdhe8prMU4h3bBd5lyEk14FXiFswW9LbGuCJE7XYFzBdaFwEjOGbzXuKj0816HFz+iQ7RficvRgn51m/pX2jMvz7RMvYTJg06kZ0wDNOoQzCziMlNhmYMsQJKPvGW2TcldAOAIL2mes4d2gznkHhwpuzePJSkVjaALHyLKVh7dgJq5AdbpmcXUPb45Qb0fADQ195Rwfi3Rv90A7npqaanoArSTaCbLmkW1ZlmvWJyvOC1vuTDP7pjJVnQvzUSsNYPSbhdB3S4p6pIvu/bQfHa3Df7tdutkKpup7m7Cp9+ZUP53oB3xJYVCrMZZjfI+mP/LKNOGl4B1+//VwiKNjm0mtSlN/eAKluDuNXTVKbaeBeXdS6XQuJfNl/n4T/5HKKVo25bVaoWIHJjajSfkUyZoORxIPqVC5Eo9QDrngoNSazVaN3hfYK1GqRrvk7+vatjH+3L4/fD/EFQISpxL64oI/fb/393fDFDQ+zBuEELeJgHgrysMzBfBa5PBwTEUDM8uSW8e8pvgdP/1io9x9fxjL6hvAOGmfeOgfqd85N3Ze/T7WL2VTBlfNHE/dqz3One+31ixNj7mGFSNodlY0XUMxr0on1Pr09w9B4gJMIzNDPP859uOj5mbUuZQI1fE5aa8+XWPfXElsHKsjKfqaFyWL7r+8fpjEG9K8ZSbPY5h0xQgPJaXqX2OQdjxMfO2mwemyaO6TrWrVL/j314Ey/I6elkwna87ts0x4Dr1f7quY+cZ5zOlXEU7hphTecnZ1nvd31PplWbFeSSaqcoOFayHbdNNZXRQG+XqtbTv/iYGVNaQceBjtIm+R5tgvmh9aoygtOCl5fS0wRTCvYf3cZstN3bL53/hZ5Buxk3Xwvk5n/y27+LNN3+Vv/dTP8muELSpUCKUFCx0BSoo15RSwTQxVbgQYVYYRCfVXLr+4I8mBDPwGly3wxhFUQRnmT76KApqt3Dtr73xBl1nubq55rOf/gSFKRAJkE9ggIm5CtC5lCcVgaTBax/BpgoPO3GEKyJAOOsRrQfhQ7oerYIKzxiN8z2gEPGgiwOnlSjFbrfj2bOnPH7yhIvTMxSam5tblosFaMW7T56glcf3PYUpUZR87c0noDWbdsPy9Iz7F6c8eXKJKgt0HcphX7xCZ3uKsgDvYt4USPTjFx/O1vWYIkSrPaToYAqDifA3RXwV8fQxiIVXCvT+QfLNqz/B50/+HXp9H7RF7RRCgabju6o/wUfqH0NjUBH0JTdZYRo5o5MlO7nIVEBn9JzQyym9nGA5oZc5vcxx0uCYhU+p8ISojeHNqgkDpRTk4H3bWxVYzrHj6IkzwqRnmFiFiGFGbTD0QR3l4mJbTNeirEOsQrYa3xf4vsb2M2y3pOtOEPeCaK0jOKfLlqLaUFRbinKHrlpM2aELiypsAHZFULaKIarQCrwqsWYWFjXHqRqn6leEJsnEttybbycT2xV3gNfq/cI8RVSLeKg6TNOhmh4zs6jGhklgE5eZBPVd7fcT2EpBrYIvvcrgK4NUBqkKXFkgpsTrCjH1+wJ3L15n8H6Gt9EEd4B6KkK99/8geankLMp6kl8ybyt8X9P1D4MCw2b18i53IJ1yFsMWo7aUekOl1zTlJctZy3LeMiuvaaprCv2MprjCFM8oS4dzglMGp0q80XhTIrpEigpvGqSaxaAYJa6YYU3DTp2wVQta5rR6TqebYJKrK5wpcVWBnxvEqADlvy6ed6TMe+FO8IuvF+jlZery32QawmUTpPcGeBqXJlnj4xy77DvQLqg2lJZg1hm/p5dVHo3H7JWDed7i8USXrPUZ6+Zsr97NJ5lHUqValsWKhVmzLNYsiyuWZsWiWLE0G5bVhoXZsDQ3LM2ak2LNvNigJXTYySzDe8F5jbUlnQtmibu2Ydc1dF1D29ZsdxW7rqLtarqupG1LetvwiW/6AbZbYbuFtoXdLny2LXQddJ3QdfH/HqzdL86Fl2vWeg5MfN8XqJcj37+OlOo5fQ4vPwj3TgJ+UX0ppUIqlSln1aGfvPQ5426Qiyo7VilQbinNBtufIN4EsOqFQl9xdvJfYIqOshSoFL4u6asCWczDMp/Rzxr6WY2vZlC8j5v8BeDOiA8RZcVSSk8pAdzVsqPx2wDuJIK7YsOyXrO8t2JZ3XJfPzkwk61pKelfGtx10b/dHRPZ3UhlF2Hedhd82+02TYB2g4ls8G0XFHc133D7l9GrHuca+r7G+znWNljb4NyMq6tvQORwuvGt3/NnOT//3DBx7q1FVTW2rHFVg6sa+rLG1Q0ymzH/w9+H//BrfKX9BE/610Aplk+/dhRKjFUKeVJK3TH7ySPdhd+Db0no70yUc+gwFhtMwYbxuXPn22mil++bjgV7ADSOxrkHGBLGkBEEQjXAPu+L+GIgQDmlaqzVJFC43yYAPueKYdu0OKcPYF+ChCG4UAKGU/vdXSeSfC4wCQB/6/f/OR4+ehPrNF4MV5sH/Odv/nPsOxDFg/mvHpTRFNR40eR8St00pcIZT7rH200pk/I2cQy25Nvl9X9MXTT+bSpP4+3G6UUAL5XJWMnzIriTr3/R+Y6BifR7HiFyCrAksDZVNuPj5sBn7CPtWD6n8jsFnoCjgSvS/Zgg5TifU/X/ftO4zsbfcyYzpTibOta4zb4of1Nll0O98bbj+2MMl8dANaXxdU3B0Zdt7/n1TZnfTsH0fN9x/R4rzxelVzQRvVtQ+TrgDvUz5d6cI1+XF/KYZscDYpRGZXJF7wOwszaoxU5OlvzW3/qbMbrmza/+CnVl2HaadrPhGz/zCfqu5M//e3+O8v4F777zFt/wxiO+4dEpH24+Rdf39K3D9hbrPLuux4uw7XZx/B4gkHhAaZx3WNvfcW45RPGBwfZCnKeXUQha5wkjSsXl8yu63nH/3gPe/tpb9P2nUCbkpa6rfSMQQBTOe6xzeDci3hi8CxFExflgiqsdXhTOq8H/XXqsqAFegpWwj9Gatu1B6WBeGX9z3rNrW54+eYabV3S7DnUGu82W26trTFXxj372CxRlx6fvKy6fPuXq8ppt23Jx/xy05lTCayoRzXLe4JzHWo8qw0jbWxeDNqRAEsG3HEj4Hm9kL1CWBjiUcBpTYEwZpOlao0wMuBHLKASaEJzzB535ov0pvk++j63+OI28DaphZz7J0nyJWj0LbyJVMFsVHGJDuRVaMHJDww2NfjvAvAz4kkyEB1nu4Vud/f0SVZsk330lVmZ4taCVM3Zyj45zes7oOKf1S3o5oWdJL3MsC5yfYaXBSY0jLF4qvBhEYmRFp8JEWjSCxlFOmweF8daRoA55Ck7mlQo+QxTRPAgfbpTBd5TGu4Jdf4HflnBb7CFV7gsuNwuLyegtVXHDvHxMaW4oi2vKckUZgZ0ut+iixVQOZboQBKIIbd/rkp4Gp2b0LOjklJ4TLEt6Flg1w6mG2elDbtYd3lSILuIb1ldNGmhwNCCyj/K6BW7Ve8O6l1mXJqVVF5Resx4979EzGwFe+KQJSo8A9EAaQdWESWutw2dT4EuNVEVYygJfVbiiRIo6yIbeN7h7wTrrwSnEKpRTg0JPUtTPl5jLCwWWAK7bl62WqFJSXtDeoa3F9H0ImkFLKVtKttRqRaVW1HpFpbYs1A33tMW4LZXqggm4tNTKhWAbdJR0aLdFq4JO5qz9GbfulNbcZy33WMkZa3XGVp2ykWUAdnoeJuq5M/Q7i8eUFl04VCHhtwZkYfBG4wrzirD5RUl9sAq9g8ib3FU0uhEYGyZc4YsocMoMWXsh3Ms/p65r/G+uiMu+d7rmuap4ru8FKHds29FnXeyYF1sW5YZFuWZZrjipbjmrbjmptiyLFcsmfN4vn7EwG2b6FqKP01wF8Ef+hd9xtIZeNv3gD/7gwVv1NGYKKpwZfR+WEGH7FOvO6e0pzp/Q2QXOzXF+jnMzvK/xUuNdiUjFoLbxmvhWZK8iHC/A8AbtqD3qy0C8rwf0NfQ0w3+eYKbr9EMeqx+8C/wMoe7TfTeo/SQEGzIeCo8uHBR9fFHUYqoOVbaYaouZW9QyBZ7QcM+g7hXIhcFflPhlQX9SwkkFizmqeB/PnSPgbvBv5x2FD+Cu8h2V75h1Nyy7axrV0egdlZYAkmdrTqpbTqsbTrg+MJNt2FFgXx7cbcG2in5LWNL3ncLuFE/f/SRv/dpvDGBuNUPsmo9+6GexO2G7BruD7a3CdZbduqPf3NJfpjkA/KE/BZ/5rT8Z6lAU/6s3f5g3//I73PvVn70DBHKlTD7By8fr+W/7e8QfTKSm/O6MQVv6fjAeH00sQxXJcMyiKOi6bjhXOn8OzdI+ydF8shpKDszTdYYlXXd42IpsD64tTcDzYAcpv8mX1BR0SnlIc5tjUClPg5gigpOxn7TAH0qs1fzDf/jP8Ku/+tvj2FnRNNd87LWfoiy3w7ku6i9gjPBfvPlH6dycj559jn/iw//+ARjK0/icU4ECxpPvfB56Z/45UR75b1Ow6tjEfaqtjI89Na/O1+fXdgyITeV/nOcpWDEFtIY5VGYqOL5fxgDjWJ7G15u3qbGqKL9fx/7hjtXHGOy8F9DKAdiUT68c0qVzTynBxvnJ1+d5OWZKOgZx42uaAvbjbXPQlu6/3Cx0/LJhXPZ5mY/rcMqn3YvqYSqv+f2Yt5vxNuP2lpu4vgwIHOfzRXkaPx/G1zTVd7/f9Ip2XUGZFQpKHdyQ+7cyhLfJyJ2LCGZ8CmOKg4IXEXRGYkOBxkic1mJtUC6dn55y/+Ieu64Lrlu8p+16vCjqSqguliwWFffvn/Lo9Qs65yiqc3TdoNynsO2a3eqG2+sZm82WLh7bOU/bdkCIbuk8OA/W20GS6Kyj7xzWBcf9TkIlda0NQQgEvFgEHcI+p0aDxnoXykUcSmmss1xdXXF+esbjy3dBeWpdoH3w+TOYhwpoZVCi0WhQnhjjE09UuRXgowphuEl08OmgVAB6KX8qKsMIHmLCw9AKXgVTzd72hDgOghhFLx2zpgZVsbVCaxW973n87ClvvfUWv/hrt/zwn/pX+dG/+G/wsUcP2G23NIs1VnyIcGqh7x1d21NVFbe3K+q6xnuPkzh4UIn+e5zSGKXxOpgOgwfr0BIiIFm742rzkP/jX/qj/OLXPsrrZ+/wz37/j/Bg/qtUZRlgrETLNh/K3YmEOb2EoAvB1LTE9yvm5hdRImijqfxjlAdvolIx3pNVVeCsQwG9T2UnmD4q48JtgY/+5QzBT17sjiC2A+tBGwUudX5h7hHaeotRHUauqfVbnMQ6BtBKh/1IgwoPWqGVxjsVRBrR0azzXRa+3aNMQ2trepnRs2RjPsyq+QSr8g025jV25gGdOqOTU6zMca5BfHk4cT5QbinEG0SKw8lULINBxfYCsdt02oNHJxVbHrD1D4neig9h3NiMNFPkaLaU6pZK31Lqa2p9SV1esTRfoTS3lOYaba754f/J/4Af+Xf+t3zrt32E3/q9n+H/+e/9WT76xgM+8Y3fROsMqjpl52c8Wzdcrpdcb+b83Z/8KtRv4PQZXi2xMserOU41CDVOV8GHiy6j6awOZlepTL4ehjekCufKMDDNlUg7/WJw935+NwRQ17ToeYR5TYeeR2Ve4yD6xwvO3AXVCDJEYlRIrZDKQK2jMi/APOoaqUp8VSNFFezX5UjeXgXqpWALMSBCCIqgojP/iv4lgd6rJiU+wDtCkIuCjlrWLLnC0GHEobwEpWgf7iVrNc6VWFvSu5reNrR2Rm9nh+0+dwcWlV9lsWVW3zKrbmnqLXW9pap2VFExWtYWbTpU4cAItqjoVIU1Db2q6HVFr2taNafTczo1o2sa3KLEmSKYg7+UD8eDUnjvTT5IM9uxMm+qfb/MJagM/KXLmPqepZaGloZLLiaOly0HYC4o9ArTUxpLZTqqouff/MewnMHZIiwXS7h3AvdP4UFc5g3M6tESfyuLuw6dw8AZwKL1DUVxTdMIc+cOnFQH9x1hnDNl/pEPwJO63jlF7xtEn9D5GY4F1i9wLPDqBKfC/14tcCyxfo5TJzhZYGWJ5wRvF+DLw3s5+eVL/0v2ObyQ8eBdMCWVoOIJ/guyehIfn1GaOxGBo0nx3TTVOYQHm8SIRGNe+L5F53cOLwdtAx1MY9ESoJ7xqJKolCZEEm9ANSr67FMhGMVChe8Lv/d3twgLC0EtBX0isIzrGgnHq8NLGGqBMt4GzlH0LaVtkb6kcELpe0rpaFQIMjFjy5l6l6W+ptEt8/mO+dmORbljXgRQt6TlAc/4Tv4aNS2FOkpbyY2hvQvgzluYn2cTdIQ/xr/OX/yb4aWYaMW7P/g7uP7sN6DffIuLv/BXMKvNMCEzxhxMjnLlWPpMUSsPfEYfgSHjCWIOA+BQMTGGI8n8NK3LJ9/5Z75vepmf/k9qtjHgSedJ15P2SWZOaY6Vzj8GIFPXOp54jif0eT5Tys+b8p/ya4wQ7hbHb/pNP0JV7Xj77W9lPn/Od3/3/4Oy3B6cX2vNJy9+go+f/TiCpjApH3cnxuP/j8HAY3Amv56xuei4bnO14dhh+hi25OmYL6z8enOQMAUKxiab+THGoPBYGY0/x0AsT8cA25Qq6Fh+xuceQ5scLk8dd6o8x3B1ClDl68YwKd/2RYER8nzkZrPj/KXvqX7GgQbGfUaexs9bpfZquHGbmErjOhyD5bG5b9rn2L2SQ888v/m1jM3w8/rJ4Wh+zPG1j/OS153Weog0moKe5Pvl9TAFSXO4OP49vbRI/6eXF+NyTC9EtNbUdT1Z9i9KrwTY0uAqdPSHBTh0pPHNSOrQp27wvQJpL83WWkc6l1KYfLXtDsSxWa35ype+hEiImKmS6aQIXWeZzRoevfaIpq4pyoZZVdN2PdW8wZiCrvNIPaerNM1M0/UtzvrwRklp+t7Sdz2dDRXQWR8dB9rwFtiG6JzbtsXZngADwNkA+qzt8S76mrFBjdbbnt766LvFokVjTMkbr7/B2+++y73793n8tS+FQaBKHWR6UIU3xL0NyjoBbPS9pqKZaMCYh407lXMqaycS5kgDZAvJGEVnLbUytLs2QCGR2ABLClNQmAJtDKvVhq53eIHrqxUP7z/gtYcP2fzMl/j//Lv/bwo3xzaOL7/9VT7x0YdsNzv6HrxolFMY0zCfaW5uV9g+UHZRCmeDv7h9Xh3ehMG1kmDWavCI9ygdboY/93f+Ob7wzmcA+OLuk/xv/ua/xL/2+//lUAZeEPFoo1Fe8N7RO4cQ/AI6BWiDj37frHdogklqCtxhvSB9AKRFWdL3SZ6/L2MnYGNEWSPB55vEsrcARiPeg1IY60FrRAnOCgWEdiJ7QhWuP9Spc/FhHe8tr4K5lMRtjQoTBq88IrG99S7OHQTvItzVGi09FTtKrtBac+6/iNr+p6jtvrPRem+uq7WmVwWbxWtszl9ju3jE5uR1tsv0+RrrxWvsmkdgY7SyDtixj2668ZjVhmK1RW969NZB56ELPl6CmWL0aSfRXJaoukvmTfBKQMozo2VGy6P9j/kzNxb5P/VvAPJ/4m/8LeA/Bvw/jfplS/X3LZXumBUds3LHvNwwL1csmg3mya9RLb5MVV1TF8+o6ivK8paiWGOK6bcm4gpcu8C6JW13Su/v0bszOn+GlTOsnGD9EisB2Dk1w8kMr+q4lDEyHNmi4hL/r7J1H0QSCeovX+JdARacA7EKVhqujijc3o8qL6UCaDrMvEPPOvSsR817VPKVN3MhiMJMYuALYsALgpltHRapDDIPJra+NEhd4ssCX1ZIWeKKGsnfQqVJ9tcD9Sx7B/BWBv9RydSm9QGQ7X10fWCUdUg9M3pm3PiHhyB6w10wJ0IhPQUtldpRsaFmTaPW3FNfY65XNFwyV9fM5DlzfYv0K6pCBYBvYgTq2mDVnI06ZcMFaznnlgtW6h4bfcZWLWn1nN7UWFMFUFfoQ5PAvD037CMqJ0XRB50+SDPbY2265277zpNi6Is8hk4ZOhVi2qDg3bc4BHrvMwU++P9F04cgPOwwaodmhya8wNGqRdMGH4vsYrCeLvzGDiU7tOwwug+/SdxXx6AptOC3KNmh1I5Sr1BqjZEI5ySbfEkcnUSFd4IYaawyn4cQpbfrjtbXUJ+BOUX0KaJO8OoErxZBfexmOJY4AqwTtcTrEzzLoOam5lU6QOW3GNYYWaP9FuU3KN/jJfjDFV2gygopa8RUCHV4iSLBBE58AU4jLirGU6TQvH3kn3m/4gn9rKgM+MWJTryWX8d3AcdTDvyi31WVXppFpa0qJZrmSgB+dfhUjUBUTdMEH3mkwBZzQc8cpu4omo6yaSlnO6r5jqbcUZdbmmrHrN4yrzcs5htOTjfc9xsaFYNQyA4339F+xGPanrf+G7+bJ3/gdw1Z337Xt/LxP/6vYthPsMa+oMYwK00Yc//SuXubtE3aP81pcug1hgrjCWtK+bFhD/zShBy4k78EqdJcKQc1xyBeri4aA5upPI0/88lruu5jjr5z4DOenA9NKrsmpRRFYfme7/l3jwK8w7wSLCQ4VL6M8z91nPTbeNup7Y6Vzbg+8/IfT/anINqxMhtDjimoked1DC3ydAzcTeVxqhzy6xybjE6VV57fsQ+ytP0UEEr75VFcp6Dni5RTeR6mvo+Bzri+xvWQp1yxmd9rx/IxvrZxPSR4k77n9TCl4ku/51AsFzEdA21juJXX39Q1T4FkuKv6HF/v+AXeuJ2N4WDaJweOafsxxM7PmwP6qfpIS67qHdf7lDJxXFdTz4C2DTYyRVHQNM3w/f2mVwJsySeW924w9xzfVAMIMCZO3KOppRxWjDEmDL50VsiEt8haB0ChFIgECLIPSBd8D4QBXDArrJsS8Y6nb70VTExc8N8VAE2BVyoU2KzBGMOiqVg2ZVAdxfFEJ4q+t7RdR9M0eOcRJ/TO4mwAaNZZXO+CuWjfBx8/ztPZEOBBnGD7HoQQzVRpdp0NKjEHYj1Prj7Gj/1n/zOeX5/xtce/wpLP88yd8TPFb+JL8hk+JLf8QfUfA7dRkRUjp0lQ+Yn2UICWgqoB14PvbfBhFrdJEUb3wQLSTRzqRwl4rzAoOrF47xDvEccA2ESBiKLdefoiwKLb2zXlbM5ut+OTn/gIyv19vL1idfVznH644nf+zk/z9lu39H2J8xpVVihl8G2H7RxVPWO3azHRRCaAKPZB1pTCpzdUSDAJBsQ6drstSgm/9M6nD9rk1eaC/9G//yM0xZpKX1Gqy7BwifZXKHmG4Qr8Ywp1hXHPKPwltb6mNDvQmtZ6jA9mqUZr0BI1fiFAQ2r7oa1Hua+PbVorUCq5Cgpt3kdfgxIAsPGCjxv0gPbpCoPvvv1Iev8gKEzYP5wzKuJEsGT5wSDOZQq3UF5GK1wbzDyUir72UuhvIpyOHawbOk2DdcHsc3H9VZY3vxauJ2VOVLwfBdGGdnaf9fI1dqdvsF48Yrt8jc3yNbb3XmOzDDDOl3sTnXGqt5csb7/KfP0us9W7zG7epVldUl5fUa9vMOs10mk6dU4n57RyTiun9P4k+rtbYGWBJUApS40f/NxV+BigAokTL8mWiSQUBOPBhtu0fYqiugbuf/9+46SkS4EsYrsIJrMhUpYmTGjDRHdNoVeU6oZSX1LXzzkxX6AsLin0hkLtUDpMiI3aopUd7lfvK6xd4uwCa5fYdont5zh3gk2/9fFTTullGZUkSxxzPE2MdMKLg1oM6wPAk+gjj5rgb+wVZNJHk3eoKBNWXgKIdEXwJWkVstOwKqZBxsuo8qbq2Hj0rMUsLXrWUywd1QlUpwoz9/iqj1BPULUglcIZsEbhCoUrDL4o8FWBLw2uLPCLIir0NFKbTMU3cf7U9l4F6jmPth7dh3LT1rOPcgoS1aXeaSQ64Rev8ZKcnAEoLBWWih0nr1536WjeDSq9ipZStZS+Y8aaE3kezWs7Suko6MEL3ikQjbcG6ypaN6O1Mzo3o/UzWtfQUWNVFdpbbj6bnPLn7VUf+W5AaYfWHm2CeZ9KIqe5wmuNNwavX0Wh9xLphdBOvRzQOwZppj6JzUuFvi8Qy5ji8+OF6rxxepHQKEuKBOfaAOx0u/9fdmiV1rWoIkA9xQ5NR2F7jOqw+hYptjEitw2Rk+VdjP41ChVAn5MVSnY4u0URg22JjpP1ELzISk0vZ9HX6QzRJzi1RNQJrf0orX0DU3XoUtHaOvSPejFAu948wOflNi6Po2Xi0WaNMStMucKwRsuKQq0xrNB6i9It1KBqja81fjnDL2fIPHy6ZoabzXFlgzcNXjcIBaLLPbTrNbTs20/L/vnUSXjJ1bL/PFhPFtCA44rPMZS3Cfgdpv/SwZ+SO/e4KoD/UOCPExR+lUD5W7h69y9g7Aaleozp0bqLnz3GhO+woSx7iqLFmBatt9R1T1m2GNNjTIiIqXWPUh1FEXyyGWMxJtBQpe76fhpP4vJJZhImHPoPvuurKB1rDHXG6rocfI0njGMgM/5trCgJitS9ieUYmqVtczA0/n08UU4T4CnF07FJf0pTE/3xflOg4aDJjK5xCkCN10+Br6n85qqXtN0wvh7V5xjsjOsqz9O4DU2V2zFwNVWn6XNctunYL4KLxyDXFCSZKqNxWzjW5vO2k+f/GEQc11F+b+QwMW/v43thnM+UxqB0fB2Dy6LRscZQ0lpLXdcotff3mIOysfl0Oma6B9P/6TxTJuspb2M4lYBWfp+O2+J4+3SOMZ+ZArqprSfoNAXU0j5j89507lzZmtd5rmIUCQKtsiyHssmPm8oo/xyXrbWWsiwPFHciMhyzKAqMMZRlGYJpas16vb7Tv+WBFV42vSJgO/Q9NtVZJGf+YVtIj2OldLZdRuN9eksR3oB66wN8iclZi44BAwYpt1IBgsRJKCo8gyPPCyofJQHIOQde6NsO221BhX1UBEjahCe3LgwuVqp2mqaqUKZExJPUTc4HFZf3QfmWbjrbe6x39LuO3a5FxO+jgnaeru3x3rNZV/wHP/lvsesuAOEXvvIa1T/7n/GTxaMwYVehZv4x38D/VP0vAkQUjxDKpDCKqvRUVcmirpGlwkaFXYrQmhq0taF8RDzehVmnMgZrXQA3wf4U5YWb21ucs+jM7tlai3c9pSlx2mCd58nTp5ydntL1PWVZYOyWT/6Gj/ON98Bffp6r5yeUVU3R9DgMHRbxOyxC5z3NbM521yIESCsOdKHQPrYhL2jizZJuSkCrAk9BQce9xSXP1vdIkgCF8JnXf5nbbcXtds5V9w04mU03YGE/3xRQtqXgioIrSi6p1CWlDt9LdUXVX1GqK0p5zqxcUchzKnOFUZb0Rq33sZ2lTkgFMKe1hti2rTiIgE6r6BMvtleJhFcpjYYB+sJetebFI85TaDOcx0f/FUqBtS4DYdF3Bvu3RP7AT5xCxyht+weFQujCPTpILdJnvMUSXJMwua5Xj2nWj+Hdnw1tNHt4iATz5q45Y3fyOtuTPXTbnrzOZvGI7cnrrE8/zNWjz07XFVB0a2ard5mv3mF2+y6z23c5X32R2epdmuu3ma/fpd5cgnicpI49f0AHYGtlQecbHHPm9z/Mn/gf/8/5q3/j73K1rXn9w9/G537mS3SuAHPK1ja0bs66m7Fpa3Z9Se8qti2EgAlppJ98JEKIthZUeBLtY+/Mx9K2R82TxhtHYIdFKRtgnWoxaoupArAzekWhNhTqlka9TcENRt1SmKRe2aBoI4ApEFvh3Bzbz7F2gbNL7C6Dd/HTJaDnwnfxEZTmEOOFsM6jyh5V9KiyB2NRhaBKiaBDRfNDgxQGr8ugDFEfRERaQHwIUOIjxHMSzM4ceGvwrqC3is1KI5cmqDFfVn33wsl2SMoEiKdTgIto1iW1QUoVIF00oR2iA+eO3Kvs/9pH8OdRZwIN+NoglcJXBl++R5m9CtTr/T7QQhcn+CMljjhD72f0fsY2dGQZzMkmAelr8nX19SQJ/qZULwEgCIhPkTz1wcxfxOAwQbWcyiEH7Pl3FfKndDDZbIodTbllXm2YVS1V1VMWPUXh0NriRdOrkk4adlKz9TN2NLRUWEqsNjhjEKORIphMD4Dw1xvodRwq7t4L6OX1egDufGgHzqOsI0UUFlcBCkWHkU0oRKVwzLHqBJFwL6PL/WMkX/Ik7MvlRSm1m8INwG5YfLdX2yWFntphklJPdRRFz0n9K7h+hbEdC9VjdAd+C36zh4D0CAprQ0AN6wqczNDlBao4w7LAswwmsMyD8pg5jiWeBZb7tHwsROvOr9ERXsa8ILK4og9qOlZUvIthNfyvZTX8r2QVo3uDmteYeYOfz/CnSy6r7+S6+uwQ0VPPdjSLG1xV4aK/TakMUmikVDAs7BVqeX0khXo7+txKCEiTrmlLeAmVlOwbCb/vJPy/E9gJqpMQtb2TcA9Hn41eqqAAHPqk6KNFdHi2ig7m/6Oms1f87dOGj75HY/qgUvRFqyR+erR2KOXQOl8CqNPaZqBvv2jdUhQdxuw/jdlF2NeiVI/IbjiOMY6isBSFoygc0FGWHq0P/XQN5TQBlsZ+r2A/wc59Hg1Xmpm7jf0Rjcd9OfzLJ8UiisvLjwCW09Ov8eTJp3n69JtZLJ7y8Y//BFofwpEx7EvpGGB60fopODFe/14mgPn/OahIKpspYDDeZ6pucmAw3nfKVHG8zVT5TIHe8e9jWDVVRmPfYXn5jIHusTyOIdzLgtG8TqbKfWrbcX7GQHjqOl8EWKfK60XXksqkLEuqqjow6R6ruPLj58raMfAb36tjYJbD3dQW0/HywAv5OacUc6mdjGF5Ovd4m/H/4+uZ+j9xhbGyNsGutE9RFFRVdXCP5QEyUztM4DG/f9Lx67o+MO3UWjObzWjblvv37+MyNxnpGouiGBTA3nt2u90L1bsvSl9XkIP0Pe8IU0pwINHYuOdQSUVh4oRf0CoMihWCeBf9TaWOPlSy7S1F9Nl20FmJDKonSZPr5P9K+ZRhlIJCq32o6EwlFMBIeF3ne4sg9K7HdjtufTDF1FpHP/bJ/rigMIYqSgqLokJ0cD5te0/fdRChB0pRoOi6Dmcdv/zlT0S4FsqEFrr/SwO/bQcnCn5XBbfCl+2H+Jf6/yUnzVOqjzyjaR8zt0845V1m3VtU7XPK3SVme4W4DtcHk8PAajS9t/joy0QggrZg0ioieOfCdr1FCzR17FSyG8cYjS4UEiNxdl3P5ZMnzD/6ESoNF6ennMxKPnp2xr3Gcl0+ZHn2Go+8wunnPHtyidve4rymKGbYYou1luXpCavVKurDPDiNV1G9CGiC416lAFE4HUQ1rfMYI/z3fvv/jf/D3/oX2XYNWnn+2A/8Rb7nE3+P58+ueefd5zy7vOF2Ldzslqx3S7bujJ2/YNst6P05lntYLnAqLJYLeh6w5RtBzPHJc5ffPDcU6pKSCOCiYq7S19n/V1Tqkko/p9JXGLVBJLQJrTQMIC11mOkBH3299BZF8J8X2rnGJpMSEvDyoY5EMnYY6zKWqcBeFSjJpJjh/ME0O8JmrYJ5jGjAZR2lH/jhvi/Qw313OMSN1wRU22uq7TWnj7+Q7ReW1M76YhbA2/I1NievsVm+we7kNXYnbwymqbf3PnWkUkC5jtnt46CCu32b5vYxs9t3aG7eobl9THPzNs3qKXPZ4HnO937Lx/lt39rx1Z/+ZS6vr/ld37fgm/m72N1j7p/VgFDOZvRotq2jsz3eFfz5v/Q3kOU5RVGhtMFT4NUMr2aIiv7YZEbvF7TuIZ17iPX36Pwp1p/iZIGTGY4GkQqvomlsgnVD/8QhsJMwUXVy8vX7+wHAoVWPqoLKTqkuwDi1Q8dInbX6GkZtKFhh1E2Y2HmLJLWUM3hnEFfiugJvK5xf4NslLkI55xY4exL+d+e8lGO+cNkR4PUU9RZd7TBViy5bdGnRZY8qXQxu4VCFCs01g3ZeGbwq8UWDK1IQkAavjisq32/S4kJfJR7lI8RLiwWxydeaRqzG9wa/KV9elTfcUjo4aOdI16Q4BHINUDvMzKHnlmJm0YvwaeY9Zt4HBd/coWdhUY1ArXFliGZrFwWuMLiywhZh6U2I8nc85e2X4/Bu/HvvUZ2gehdUejZ+Ood2UbHnBbyKoHivzgum5ib42JMi3kuvnvYq1jnXsFf/yMSSrlPCS6ECS6k6GrWl1sGpfKN3zPSWudnGKKQrar1Be0trKzZ2yaafs+kXPPj4b+F6DbdbWLew6WDXQ+v2bgV9AtsDfGUfnCaBkSRgS9u+cpHs1Y93pl49SD/D2tkE0OO4Ks+6UN+9C75VnUX1Pcr20FuwDtV7tPNIjDqsHITAHioGKjB4VeBVhTWvxRceEi48h3j5d4nnz/932XbjVO4/lXRBdSft3tRW2tB3El54lFyhZTdAPnEdyZZDqxAAS+nwRkKkCIGIVIWnRlSDp8YzxzEL4I7XcSwZzF7zPIaB016ldjnOvAw7eGZ87+s/zHn1lTtqGIiTKxGsaFpmdNUFXXHGrjhlV5+zay5o6xPaZomtF9hmhq1mEdiV+LMCeVggpUZKvQd2RQbtskVGuTyeRmppIcC7HaHeugD/P/tjf4G3/sDvRXYK2cDix3+ZD/3V/zxGM53hXD0s1paIhA6y7wu830fRDN9T1MtYR94gohExQREsYdk3rLT+hRfyX3LyDBGYlRug313gZyPwswewLwG/APuCwq8oukHpZ0xPWXqU6of9A/RLx+yy4+4DZXRdxY/92J/k8eNvBuDi4le5vNyD0Dff/A6+7/v+z+St4piKaGpCn2+T/z/eBg7nsOM0BmPj848/x6a86VxjWDJWcaXfjgGvY5B06vrydEydmMOhHDSN1U5TYPVFkC8ph/JrmQJsU8ArB0fjax7XXf45vu6pMj1mbvtegGzqGvNrHef1jrhIa7quG1RTxpgBFCVzxjGs8t7T9/2gopoCfXm+xteTBzFJ63KT8jyveV2MTVbz40/5hYO9WjCPajyui3Se9H+qj5SXfMkhfDp+URTUdU3TNOx2uwOYBtB1HYvFAhEZ/LknRVxZlgNYS/fjcrmk7/shD7PZLLr+cgdBZxJQW6/XWGsPTE/fC9BPpVf2wTaumJzCD7NnxlLitP1hJ4UiRH2U8NDKO6Jg1ubjZNyhtRmggpCRcRXBmlKkiI4QG4WoMPEabtApOXXYXlTCAh6FDs7jdYB3Kl4PUQ3WWkvyfXb45iH48iIYYlKUJaYomZc1Wms+7nMgoeAT0Qzmd8eJ3ypeEHCt73M9vw9LhjfsB86Sk8PkzlK2G6puRdPeMOsvmfXPWPTvMLNfY9Y+oWqfU22eU2yfhsGsqAhNavA986ZAF3sCXFUeXSowgi6DT7n1asPjJ0/5+Ic/zNWzx2AtdeXpnryFOVlSzh6hmwf4YkcnM1bbDrVaUWiDUYodoa7Pzk7ZbraISxLmANMGvZZSiArlE9+3oYXwRtPAt3z4l/iRf+F/zVefPuC0eotldcVuF+CQ0QqlDKVpWVRP0PYtCm+Zec/MtUO5e4KZbKEMygQIq9B4dYLlHk5d0HOBk3sRwEUQJ+dYuaAnfK75NF7m2Q1y/N5RdBHIBXVcoaJSjr1Ja6WuIqSLajp1A7KPXJvazsEDyx92hFoJgsYqh/GCUiGyqni1h25qfww/3L8qKOVExSGkwrkkv47nis0z+T/0LgQ78dH3XWiUUQmnDgcGIhD82AlJEaq1Qrdrlt2XmD/9FR5qQ95xi4+Gurpku3zIdvkau9PX2Zy8zu7kNbYnb7BZvsbu5DWev/HtyEf+ienCF0+9fkpz+5ivzOFLXzjn6uK3U5l3uOcfcHn2Ogt/C+xVcD7BadFsdhuU0gP0lziQ1axRaoNSz0Oe0715pHedGrQdZlPT9ud07h5t/4DeXdC5C3p/RudOsXKK82f0LKL5Zx0maqoEVRz2EeNlgAIBSkBgQbz/lzP785SwD9SX1HY9in5QkpS8SyVfCmZhukdjMWJR3gaFmVeI08HnW1/iXYW3Nb5vcH2D3czp+ns4d8R86wXJmC1FsaYqrymKtzDlmqLaYqo2QrsOlaBdIVB6dCGIAV1EM0Kp8XqG1zFSrJ5DsUDUDFENvS/pXEHnCnpXRrPMrz8pcWixIQKqOLQPARICiFOIVXi7h3diFWJNCJ6w1biVwbmK/kW+8d5H0spSFhvKeUs531EsO4q5xcwsZmEx0WeebjxSa3xTYEuDq0tcWWDrCluWuGEpcFWBLwpkQfSfxyEser9F+R5QT/eOwvZo6ygizFFWQnn2KpZhKNdhyUxuD3xEDhWlgk81DB0169z8Vnjvsk6A9O28sMHMoFpCU8JZCYsK5hU0BVSaEJDHBhcRdgf9NXzxl74SQG5f4G2J7wp8XyC+QKwBFzuHSoVzJkB3RzUZlxzajRWrSc06m/j9aAobHVX5Hks9x9V3k0BPAqyzPap3qN6irEM5h7YO6XuUc0GZ13twURXpPLg4BlUaEYWoEODAqxKvSpxe7E1xVbmvwxzovciaPt9uMnk0LQW3KFqU9KFfjfQ9jEWTfiv6jo153PiP3Dn58+6zWDlFq5ZC93EJiuhC+nguh5Fb5u0tqjucwOUmZflvY6VHPhHxaHpV0xVndOU5O3NGW5zQze/Rzi7o5if09ZK+mtHNTtmdfmgfHKkAjKVQW3xV4usSSrNvi8l4QcFHf/6vc7/9HKtv/TSzx8949LmfxnxCDRO1qUn8WJGQJqnjSeT4uvNntbUgUmGtQakGawu8L7HWIBICEQWXByXWNljb0PcVfV/hfYO1Jc5VcX2J9/t9vI/ReiX8v4d9+8X78ADeA7+U0jp4hUhTvy5JqTSm0qSQIAGu7UHwV77ym/n2b/9RTk/fArhTR3nZj/1AhWMfV7hNQa6p7acA9HibMfCZAg7jfB2DOOm4YyiXvucwJM/3GLxMtc+pvKe8pvzmKqLxOfKUnzf3pzVeNz7n4fz/EFTk0G8KKObQcgwE0/pU5knx9KL6PQZJps6fYE1evlMAcgzWUt6S3675fE4Qqpg7ftTGoC/BuPR7ft1T7T3/XylFVVUZNzFDeYwDBORqtTwfCQCm9nFXNBU4R1J3JdPLVJdjs+MxjMyViLkaNrXzXN1XliVKqcEHWjIzT+Vzenp6UJ7L5ZKyLOn7fii3qqoOyl1EqKoKay2bTZCRp+Om86Ztkw+2cZt4v+mVANu+kR3acocCC6qZYCGy7wRSB5AXVOJwCvbRNkc3e7gwnUkHD1U0484u7OPxkkDZYRoT2f3NFX7zY3AxugatVFB+RYfzinQNidClfaPsUATbOVzfxuNp5tUtv+8HfpS//nd+f7i+bwP3vdnEMfqkO7PXnLsbrlmyNXNaU4Oa6kAVnpKWM9rylNvqwxMQLvuuQfUdxW5H1d7StNc07XOW7inXCh4Uz3jElkfesik29NVXKcuSbtez3my5vV3T9463v/YWt1eXOOn4yle/wrd+6z/JunWUxYyithRlzW674erZM7rNmr7vsV1L13XsboN6Tevgo08SjA2Go+FtbwSAOsJZ8QG4pnZ0tuz5puor7HYtXbuvI5XMh2FQHwLRj/Den5lHYQCHgE8PBI8ezEUV8wEWM7RpEoxSaXCg8FJjOQ8QTiKMk3Mc97CcRzCXlHLndPKAtXwjL+uU2XBDpa7uKONyBV2lLqlUAHONvgLZoKPFlJKg1PQIaAnRaH2A1kEZ4ECC37ZgOKpQLgSVCJ2PH+5VRGI7kmGiqUTQhDDsWiuULg7u1f39qQNCFUjKtwBX84eXO7jvhmRbmus3aa7fhK/l90E22Mewm1+wW77B9vR1dievszt5g83yEbvT19mdvs7q/qf4fDXn88+Be78L7sFfugE+8r3wEVi4NffsJff9DRf2mpP+ktP2OTP9mO1r76K7W4zbBV6V9VfjN4fHBjvjB+vQioYXDo66ekatnrOUXx62TQ+AqUHl/q2VDiaf9gTbnwyfzp7S9Sf0/SnWB1hn/QlWTqKqbh5MmqZMP/XEkpxgD1mJ0EE0QoVIFRR3cBfejWfU6ZjpaXTEslth0WqHUjY4cY8TToUNPu8kwadwYrEKbIQMtqLtL9h0byDb6tDH0Hu+mHIU5ZayWFOUadlQVtfU1Yay2lJWW+qmZTbvOT2Hi/sNZxcNRaMwDVgv9JQoM0eo6WzBeuNoe8WuhW0Lu07R9prOFVhfYn2FlZLeV7SuxEqNlZqOGcGh+weTVHSfoIYyU4hTe0ft2eJdgbcFvZuH39YCN2m9mlblvZK6QzCFRemorJuBNAppNK4x0JgAgvIlN7GtQTc2wL/GUtQumOrWwbeeWkgw0y0NrjB0uqQzNaJeEYzmEO1AmSfRrE7dhUDHVIxZ23Qeth62O7jcZud6EZz56Mdf7RqGaxHwATpKm/IZ20OfFvZ1O0D7LF+p/8jNDmuCj6wcnqZALQnUjV8IjPudtG0O9Ipj5aGA4IPzlZrg+wJ6HnqHshHi9RHqWYtyFmVtUOdFuEcfFa9WgtltVOhJVOdJWggm854KrxYIDaLq+MzOUl4PE+lzz//Ye16uwkb3Ax1G9/vvE4vOv7MLKr5krpsFzQj/t5R6Ta2eD78XJvTfzlm6LpgF/PJn/nm++vF/CgDtdnzbT/0wF89+CoCuOOcnvuv/jvv+E/gU6YHJR/7Bf0S523L2c7/Mg1/68jDRBoaJYK6mSCm8QK4GlcdY/ZH2mQIP+8mwR6kufm6oqmlFVdo//y0HMtbag2PnE/4cuOTzkXS8dI1FUeFcuMlEKkTqCOfKCP0O4Z+1FX1fY22FtXVcEvArouIvwL10DO9LnAtwL0DAPegL0E8P0G+cQnCf974LnavuRCcczwePgbTxmPEYKBrvO4YMwB24kO8/BmIJMoT8H6p6jgGRqXaRw9/8XPn3qTIYmwKm9vVefv7yJd93vH3uv2wMusZ5PVZm42VcHlNg8xiMm4JkeR7GZftegDNPebnl/uvS8cZ5v8sqQtsty3KAOKmfycHTGJqmfAJDf5CAWdpuDNkSQEumk33fD+fOYVEOzFKd5cqsHG7lecrLbWyynfYZX9v4/hvfR6nMuq7DGDOASO/94P9MRJjNZszn8+AHPwO6qWzSdbz++utsNpshMEHyeeeco21b2rYdzEBFgt+1tC4p38b3y/ilyvttQ3l6RW8oeznEYScFQaWm4lu1pP4QvIdCR0IJeOdJijaPjz7YDhvT1A0rEhU0EhtaUj5lnUHKYb7PmJiOC2xoQEoFOHfgI0XtzwvBN1wELcegYPgt/z9G/ZSgfPm9v+2v8Z2f/UmeX13w8I13+Svuv8vnzfdQSM83FT/Dd+sf53u6v0e4xDgAk4JWn7A299iYc27khBVnXLpTntsLruw5N5xyq07YmjldUR0BciBU9FT05Qnr6sNwBmj4QqpaS/CncQ/4ffDnvaMsOsx33FJ++oo39Q792Td567nmax///fyDleY3lN/B/FEP2ydcf/nnWV0+w25umOugBumcwsxntGXoEIJj/UCkbW+jGWsoHy979ZoWUDiMApzFKz8MjKYGJVoblA4DVOftMId24hEUzvuhbYY5QghOoCS1pf2MIYe8MgJBmlS8grBFs6VR7+zrXQTnfQiYkNpOBpLFazwndBHA2QjgXFTL9RLMVwOYO6eXC1byjXgytdwL0lgtVxF9y6X/02/qGiPBhLW0V2FSq4ZCA1RQDwZSFwCZDxfvY5kBaEIsVmsFbWKpJ8A2lKcfYNJwPAlm4iIhiMbwtkUF49aDt3jkA86gVExHD6DOUd08pbp9ztnbP4f32QMj1rUUhm/67u/n+//QD/ETX3qbd2XB6Se+hZ9/94rnquGqPOdZccGvmY/cLdRP/8vhWvsd5eoJ9eox9foJ1eop1eox9fop1eoJzfop5fYyOPJXezP6oSxEYPQgU+nhlAp09OBNafzwOxxUWExxSVFeBSgx6ufC5KCMfeK+nYu4YCpjl1h7St+f4uwpfb/E9RHY2RNc/LT29OC7MJt0Mj/tr81D0aOMC06qtQSfVCq0ojCxDJPjfOIoEt7kk5rl1DgrV9W9dBIULlsEoslniAAKOE3XL2ntGb6roNPBVGnsEHy8pGyZnqre0TQts3lHM+up6134Pu85OxNePxFmC0sz21JXz5g1O6pyRVWssf1jXLem29zStxuU92w7xbYVWqsRqempsFLRS4WjwfmaXs3wzLA09FJj1Sw6gm/oabDSYIuGXprs9/ApLwX/X2LQkfwNDgDJo+OixMXvLv6fwEPYVhxIb3C9we0K7HXmKy8d84j60lPQvcwQRxHaoBFM02PmDjO3lMuect5TLHqqRUe56CkXHdUsbEMNzBRU4GuFVAopdfSJp7FFgSsNdlnQndV0uqLTFb0p6U05RJx+5SQcqvUs0QE+0aH9aLFBraWsj0ouG7675KcwAOqhv9EK0Tr47FIVnhKvK4IDxVdNsb2kPOVpDBiFg/s/PDjkUJ2bxmlTYO4YoFMeVBcUtsqiVCSFOr4cNjoc1+hgbm5MjFCskaIISqo746qoJqJ8NZgHh2a2Y6B3YILroe9iXVq0tWjrUc6iY736vsbZmsK1NFyjnQXr0U4COHVxnB37Wy8Gj0EocCrAmFaWeGqcVMjX7ThxKgXNp1YRyv2jltnnvgZGKN01b8p/k7f4AwO0u//jP8nNL3wz9kMnmPmWi+svcP6VL/I19TswqmOGgN9SiUdLS1lYtNtRGEulHEp2oWozdUaaoObPyKIoBtXH+NmbJpe5M/spCDI1cc4/hzGk3osHchVIEiKkNKUmOTyPj8EbHFrvMsGDn8xjfpw8T/mEOuUhHSflczzZH/ta8l5wzkQgtze//dKXvpfPf/6fZn9DHz4k7937EhcXvzZMkMcwKR9DTV3DuGyOgZhjaap8x2Wdny+ffySfT1Pzy/T/FJDK14eyu6vySdvnUGbcHnIVzhhE5eWW52Gs0Br/lraf8i+X+73KAfAUlMiB3BTAy9N4vYgcqIzGeT1mKpy2z6HW+Hqn2kcq25T3cWCBMSwam1amPJ2ennJ+fs4nPvEJlsslX/va19hutyil6Pv+DrhK+Uv+2tK6/P7Ny89aO6hztdaUZUnTNMN2Y79iqW5S/hJgy9tb8j2WHyPlLw/Mkrfl1Hfm9TVWv+UQK5nN7nY7Tk5O2O12w7GSusx7z8OHD/nQhz7EYrHg9vaWm5sb1us16/Wapmmw1mKtZb1ec3NzMyjVUj+VgGPKb/otlUtuFpraWP49KeESRMx9v72fpOT99kLAv/4v/q6Dgr0Lq1LDi5NIhGBuqe6oNcZgKqW8E3PO03UtyewSQEtu4imISt4uOGiQU+Br6nz5TRWg35Tk9W6ndji5nYJs+851iuTm+3ZUIYJWhD9kVSMJOImEN51xcqyVJlkHaiFACaXQRqOKkrXMWatzVpxyLSfcygnP3DnP+gtu3Bm36oy1OWFbzOlfAOSymgnbHFHGDf+Lp+x3VO2apruh6S6ZdU9o2neou3cot0+p20uK9hqzfQ7XT5AYqCEEh3BY5/E+lINyFi2KP/QHf4CqdNR1zSc+9Uk2mw3b7ZautbRty83NLU+f3vDk2S23247r6xW7tqXtHU6ga+0d4p63xRgM9KAuw0PmmO8ENYwXVHyrioA2IUKnj9BWD9BtwLSxegNcRqVdA2zaV0P6IsNcwkmDjWaqlrMI46JKjnu4aMqalHTBtPWc96OWKw9MVwOUqwZYd0nBJbW+oZDnFFyGaGnGDH7+GNR9Ie8JvgNDdFulFJrsHohgfriP4945604mrKHco7m5ErwPvuqU1vsyVgGOhHPFAYYIzsC3fut38rv/a7+Hd5++SzNb8ImPfJSf/9xP0BQrai045bH1kne54G055TmnvKPO+NzbO7anH6I/fY3u5CH9/D7oI+XqLdX6GfXqCdXqCfUmwLd69SSCuMeUq6dofzjTnOrI88HC1MAp70d8NAl2UWmbft9vcyi539dRTirCTDUBytBx7yXZ3rshX+GzjIERTnH2bATjgoLO2hO8P0PkfPjf2lPEv0CNlQM6DRQWU28x5Q5dtqiyR5cWijDvlwgFko8miQoWT4mXsBAh3Z0J/Pj7B5FiIBElATaLV9GRt97DkRySvADWVUVHYTacLj3NrKWptxTFirpaU1cbqnJDXd1QlmsKfU2pbyjNFaW5xnBFYTbx2eKGlwPBpUPI255PSBQBFjgqHAs6qfB6EXzYscBS00mFpQmL1LR+wc6esLEntG5B6xb0MqfzNb3UOEqsKvHKhDoy73/AMl3GgsFS0FOonpIeg8WIi77xJKrvDN5qXG/wEdrZvgjfO4PvNb7XAZ4mmJHA1QeQlPJUZUdZ9lRlS9UEeBcWS7HwfOyzn6VYAjW4EroCOhOy1OoghNupPd9tVVhvNYe+2HKVWIrA+kElT3A23zpU26PbDtWFT1qP62bQC2V/ie7XwYea9RGISvTfqILJqlRBlSU1QoVnf8+KMqB1WD6otjJOQ/+XXmrk68bbjv6RDi0pgmo3+GPTdMFnmw7rhR6dFN+aoJI0BikqpGjw5Qxf1EhR4YsKKUqkKIPZdHkM6H0A6WWAXoSxOi5YixngrA0+EqNKjz7607NZdGgbox17FV5uofAqdNaeAichcJWXMqj0ItDz1Lx/2/D3ThqL0ZkaT/fD90KHOkzri7he0w6/lbpH0Q7rlOyCy4Oo9iuNBb8N+5sefIcxhwqjHFLkIG+scsn9CqX98miEOZzL3YfkTs7HJoYQxgqda3i8/Syl3vFw9ovBnmMEtsYmssfgzHj+l5/rEB4JP/Mzf5AvfvH3oJTjW77lR1HK8fjxN7FcPuXbvu0/oK43B2Aln9in60/nTxPnMbAcQ6m8TMfb5/lM157PBcdj/vG8MpVNuD5/Z9/8eO+Vz7ycx+vGQCaZ6CXIkStw8rzl0GG8Ph9H5jAkv17vQyC/sdIn5SWBh9yEMq0bA5d0/LFZXt62E4DJAU8CUsfA8Hgul/I1lfLryFNez/mYOq+v/Dw5eEop+Q1bLBYsl0u+93u/l4cPH/L06VO+8pWvcHl5Sdu2B77A8naTrjWvOwiKtOQPLAlLUhmJyACSEkBL/t6891RVNSi/xuWQw6a0Xx4oIY8onKskU9TN/J7Jyz5vmykllWceUTip1/q+H9pROl7TNLzxxhucnJxwdXXF48ePKYqC9XqNUmpQpqX+sG3bYf+cHY3n8WMYmNalz/zYENSHubLuT//pPz3Zro6lVzYRzSsipZRR53xW4Knww7TZi8dkPpYO+Z46+J4mi/kEWYTBOXyoqFRYfhiEhII8vOlUVMp4EcRzcLPu99l3oNoEn1OKbL6uBOsiVBt1smHjw44r/z7uUPOHZTp3Jd1wqCMlH+GPIDqWlvIUSkfokPKp8c6B65mxYaaecV8kqsMIEC5OWLUOgFIrTdd6VrJgrc/Ylg/Zmguu5YxLe8GVPOTaX3AjS27VjI2Z0Rcvkoloeub0zFhXDxnGS8dMV5Vguh3lbkXTXlO3z6nbxzTdOzTdU6rNM5rtc75WPOLC3HBPtlknpYZYi+gCUxSgBO96CqMpjcYUBucdlVZYG0AOSuOiFCbUkwympR4fJ50qgB8VlHHhwZAGIdGHXagaEI1GQASbUX4nguhgsBwi3ob2fPjAU0hsW2gfzISVCgNRiXlRiuA/bYuRLYZ3qJK/sCApi3nVUYWjhmvTGBynwW+curc3Y43KOJtBufS55tNBLfcSsCGo5aJfuQjmisyvnJErKh3NWfUltVxiuKI0ghKFUz1amXCdqSnrpPjUSLw3nQ/X5Zyj0EFBp3WAbULwMafiPYYEsBcG9iECrxNhu2mZLxf845/9OR48vODRw4c8e/6M9WpLc6ajwkFT43nknnOyfZe+63m23nL9t/8hfTlHpQerLrEnD+iWr9EtH9IuHtKdPKJfPqSLy+rRZ5A3fsPRsis3l1SrJ1TrAOIShKvWjylvg0qulPbgTWH69N4HkBjvfyUapfZvFw/72btv0Pd9EgRF4H7Q4mNbDZAtRZY9hP2hnwWte8ryOcY8RTVBjZi/XMgHkfP5/OBtuPga505DBNP+BOeCcs67c/qonnP2hL4/Cdtsgtlrax8hvnrvxpnaqOqoqmtMsaKsN5hqg6l26KrDJFhXOCg8ynhEKbwOjtQ98xAp0M9CkAqpcdLgfbUHd7nibgB1wUZ730e8dHbvpI6KjioEIUxwP6mVgohhv0yaHHpK01OaHaXZUZkNlVlTF7dUxQ1Ncc2suKQqbynLW+pyRV3dUldrSn3DrHo7RK/WBQ5PUQQYblQwmUKll2c6uD3VGqPCNqn1KAOxd0Kk4LZdcNs+4HZ7n9v2Aav2nE1/xro/Y9ufsrVLdnZB62d0vqH39d6scFBEKpwpcaakHX6XCGXVofnxKyaNQ+MDKPUBlHqn8E4fh6VDRE5BuxCwQZyitwW9LVi3CnercZ1B+jCO+sX/5OvLZ0pKWZSxKO1R2iMFIYrpTCMzgzQFzHQwtWwIESfr+HlgdivQSDS7VcFvW0n4jADILWZBAV9wAPMcDz6YiwFot+hti+469K6Dtkd3FmkV1p0hvUG3O1QnSF8gNixYE3wSOh2Ujz7WlY8vu/KXhWPT+Be2GQXUMTDByYs2vJvS/dhDCq6lfDRxlx7te7TfUkqLdju0Dwt+SyEdRgWgZxLM01BUBarQYIoQOKeo8LqEssGbKixFjTU13lTD/eJNgTclvjb4RYEUIbroHugFs1VHOVj2v3KQnWNA747C0mHsOqjxbI/q7QDx9KDSC8BP+RAExcQgKMoRo0VD8KcbIsGKxKAYFLgI85wv6dwCJxc4H+DuB598BHj9yLy23ZvRqlCXAfgFyJdMa4sI8QxtgH3ZfqWxlGIpjaXSFuPD8fMJ5JS506p7wH/4lR9mY+8D8KHFP+a/8tH/PYidnIBOAYkp4HF3PneYtFZ8+7f/Fb7zO//qcAyAz372b98BPIPQYaTAS/+PlUX58V6Uh/G6qWOMYU2+TT5mz0FpPs4ZHz+f9OdzzHGaerE6hkc5DJiCRIcvTffXNwWc8vIaw64c8iZwk19H2n5sijp1jimQmF9bvi4/bsrHlL+3cdmkdAwi5mWWjjm+P3KIlI+fgQEq5aaRaX2CWOk4qay22y2Xl5d86Utf4s0336Rt26FMx3kUCeaL+fWPQV+CbynfY4ieWEyuXk3HSGaj+XUmsNU0zUE5p7zlkU/3bGffBsb+7xKQS9eY11nKe162l5eXQz7HUNoYw+3tLXVdD8qzdCylYsDIDCam6xwr7fL7ZryklLehpF5L9ZGXxaukV3qi5JWbU/+U2bDNyB662L8hzCWQwDBJy/uXXAqcv9FIB9kXSlJ17d9ChJuHIT85iXVub9KWN/L8nKmhGRNIrCebcEqcHIwUbgHZHHaaeaPPy2Z88091rGl9/sbiML+xM0Ix+KwaJtCjt05pHw0kIMH+Rh0ao7OcmltO5RbVvx38x8Rr09qgCoNJjuuVpqdgp0+4liU3csItJ+zKRzzrL7j0F1zZE679gpWaszEN9oVATsXoWQ276gE03zgJ5P5+9r287pnbHUu/4kTdsqwvWeh3qaqnFKdPMJunVJvn1LePmblb1O52//aEEO3VOaF3djBZ9l6w1iNRdRUAq+CdoIzCWY9L5itKIRIyJEIwPY0aTQksLAQbCKwn3is61leqxRwGRRPVZLrnfayzVEJhcd6l6WqyshzghydAkgSZY5eLQ/BcBVULX40BDtKEOEPbKnvj5wShwamzAN3UPRz3olougjm1h3I9F7TygDWZb7n8uetGn4CxNwdKuSIL+FCl/zOTViPPMBLeYhADZCRH1OGeLXDexc4kAEYk4kcJvv2KouZjH/8En/+5n+P+g3MePnzET33lH3Jze8uDi1N8NENKQRrSPWeto3cWV0QTN63BO6rbJ5Q3j1lmA5SUJHZsdn5Bt3hAt3xEu3hAf/oa7fwB3clDusVDdmcfYv3oM0fvDtOtqW4fU62fUN4+DiaoEcSVt4+pN88w28sBqg7nzj7T9337uDsgTm01f0DGxsg+2EU+yJp6E3hX2TsexB0MyNWOomyp6mfDcaYG9Pmx9gPVejBltf0JvT2h7xbZb8nkdTlsY/sTdps3EHl5OKd1S1GuKMtbimJFUz0O/xe38fcVulgFZV3l0GWPaIsqQUyJY4b1TVSfzrCyxMkSJydYN8NKiD7rpcZLjfM1XkpESkRMBuzSog7/h8N7bfoqgvknNYGIjJJwCOwStBMyUOeCOoyOQrWUuqXUWyq9oSpW1GZNU9xQlzfM61vm1XOWzTWns2vm1XOaZk2h04sLYV5esaiveP30i/F+k/DCKE2sCk2IrKwjODf0/ZLV5oT19pTN7pz19pT15oTV6pT19oz19oTN9pTb7Rmb9pTOzaZ9C+ZL4SmbnrLuKOseUzlM5VCFBGWkAa81XgWtnJWSzlcvGcwiBEDwL1IQy+FiFJQ6iJcqDbU5XCoNtQ7BGpUDH8Qy2BZ+9me+ADYGOehKXF/i2grZNPhnFe9PGZT60Rek3BQ8AbYZAdbNPcwzkJc+c795QyRUhypDZGNVxqVwUPq9Eq80+NM5UhT0VYVU+4jARyyF33/qHXQeWgkmt1uCbHCrwmcbv/fqEBqNfdLBvuiGcYvcNXHVBtEm3tJz3HsU90snT8j/8H8MwyE9WnoKWkrZUPiWgg7tO7RvY3RUh1bJZD74YEX5FMg1vMBUCq9LbspPsC4eoZVlIW8BDm8KpKhwJqjyEtTzRYGYAl8VyNwEoFcmoBeCNKTie7UpTXbtCXBPBr8IyjplA6Ar3QbVW4xLIM9hrEW5fohkrG0IiqGdD/5pnUOsR1tBnICK0UaT2S3Bl6ZQ4SSY7zup6PwCF7//eqv0iqTQ0wHyFbrjun2DTX9/aKtvrb6Dr95+D584/fGD4+TP7zTPy2HKGIaMgcHB/OgIkMu3H287hi9pXZ638fdxvsdQKk/jMUoOdcbnmcrfsfON8/Je87ypvI/zOXXN4xeYY9h1DEBOnXN87DEIy80L0/p8Hj9V18fKaZyXMTzMf8+h0vhaxrBtDEPTccfKpfx4Ke/Jl6FIiEo5Vt3l/ycAlFRgyddaUqYppbi5ueH58+csl8vhOrquG5Rd+TWn60iO9dM5ktpyDMvy9pxDsVRHfd8fcIMcWqe8pCAIWmtWq9WQh7wuxya96TMxlrFJa262esxcOW8fwJ385/nYbreDYi1dW66sy+/TcdvP7+V87nGsPeTnTsri/P4at9mXTa+sYMttUsedU16I+4twg1Q6jToShFPqsMGnc+Q3dUrpeEFhFp4QPoGIwawpUUcfG5E5oLFpv3SOXPacqCXsqX+hM5MqIlwDkDA+EgmKJ+Xv+jrIb6g8Tb2ZSDLQqYYR/g9O49P/Wt99YORtIClQwn75jbInNioz0RvMfiG+WU2qKcKAynfEosPHt78zvaGRt3kUy6NwBlNqvJKDKKCh7CrWnLEpzljpoIbbFK/xrLvH0/aES7fkxs9ZqRm74r2AHPSUXFNwrZZg3gj5TFHQLrgD6JTvmfUbZv0N8/6apXvGvH9C3V8y76+pds+pt8+Z9TeUm6eo3RUGzbbdBHUVir63IaR97/De0VuL8ypG4Yxl490enKEi8AnBF7zzeG8GFaWXEIVTqWCq5RHE98EsRqk9bBvaVKr/bCATa1SFSs86kKDCSYEihOT7kKH9D3UcQZCW/C2QBlq0f4eKd0iMPDUhg8KgcfhonxzMP50HL6e0/hTR97Hco+cCr+4Npqz50pMisWaBPl4ACxRdBHHBjLXo93DO2EvKaLaaoFzJJUbdoH2PQvGD//U/wHd8x3fy4z/5k2itKcqCx+++y9XlFXzsdJgUDZAxltNut0MpQ9dbZvqwH0nb559DhyxCsbmk2FyyePrFwfRj/CbLlXP6k6CCa5eP6JcP4mdQwrXLR2zvf/J4udiWav2MavU4QL/VE+r1E8rboIoLIO45wUaL4WGTmzbkD6r0/1QKzWXfVyeT0fxlyNSgDLjTnx/7bQoAjh+OWreY+ilF+XhygHyY59wUQhA/w9oT+n5B30eVXLeMv53sgZxdDrCu687YrD9C8gX3MknrHUV1S1muKCKQK8oVZbWiKZ9hiluqakVRrqN6bIWpVhTVCqV7UCWeGVe3Fl0s0cU5ohY4aWIU2RmiF3garJ/TywInS6wP0M75GdbXOF9ho8Nq502IiAl3Id3U/0MyOAyOmm5KuZMgXQ9BajdaFzrGMGkXHyaDylLQUeodld5Smw2V2VIVK+blmlm9Yl6tWDTXLOpLFvUN86rjpHmH++bL1MWOIrKrwpjo23P/rLOuZnXbsNmds9mdsd4uWW/P2GzP2e7OBii3fnbKanPC7foU79+7foui42z+nMXJivliQ7Pc0Mx3VLOWumkpZ5aysujKo8sA63qpWPdLVv2Sbb9gY2e0bsbO1fS+gvKUzoJ1sBPYJbD5fsxUv/2bJn/W0lGwwkiLlg7nazp/P6i5vGPhf4XSP4/+2NLC4OvOW424GrEVzja4rkRsje8rvK3wbgZtFYDUdazr9wUQDgHL3QtgWmXWWGh6VNOjmx7VtJimRdUdqu4wVYeqW1TVo6sOVfUh2EJjoDLQFPjSIFWBr0qkLPFVg68aXL3AnZ0FVdcAgr6eNNq/J9r6jpaOvR+9BIaiuwy8HNxLB9A9LWMfdrEuHA1ONXe56ddzWVmE3A1vHF6tOLS0GB/MZUtZYyTCPOkwvkX5NrpJSEFrMqCn4stOpRFtwMSgD6YEU+J1EUxqTYUvS8QUOFOFOjRliABdFPi6QBYaKXRU6CXgXX59IC+lo0Avh3l+CIJRug21vY4mtRZl+wD3+qDSU66Pqj03+EvERr+VFsRJ8DOMBpV8JIZgGE5qfAR4Tio6t8Da82B6K6OItx2whk13jojwxQ99P7/4yd+LKMU3/erf5NNf+08P6zN7No9VWFOT6fR72mcKmqWUA4R8/JE/t6fGBsee92OYMoZABy/5Rsc5BtbG54ZD0Jj/n7adAkf5umPjxjGESccepxeN1aYASb7Pi64r/TaOQpmudTznz8+ZKw2P1U1+/lyNNR7DTbWbVC5jZ/1T2+RjzLye8vPkgQHGICuNZ/fuUDxt2x7UyTgf3ntubm7Y7XaDQmxsgTKuIzj065fyk19Tfp15GafvST03FYE03yb3WZbKKP2et6N0vLy888Aw6fiJByW4l8p76h4fg8WUt/H9l6tUUzkkBVwO9dL2eV3v5wd3o0UncJmXY17fef7S/jkXej/plXyw/ak//jtGhQGKQ8XZYSMVtNE4twdISqkDM6FxGneuuc0wpPl81rHq6ONhaJz7CSBE6CGHhHaqcxsrxrQOTtQT0FAq0dLRQyODXfkxxw12vD6/VuDgBr/b8R++VSCqlaYobA5h8o4kv5Hyax533Mk0MgEcRg+9kLHs+ocy5aBOJdYTgDYBUoXJuB78k6XtFfubzWPoVclGnbItLtiYC1ac8vaq5ll/n2t/j135kBsWrPWMnamxxUtMekXiQF0N4O2o2SqOWb9l1t8yd1ecuOcs/SVze03VXlJtn9O0lzTdFeX2KXp3hfQW54Wut+A9fXyj4FzwQ9j3ljx6poiE8bLsH0wiAbSlcrG9RUQhOtVl/lDzJL91YeIaFWxp/RGH1IdtywDxLYIy0dw0wleEVDMSzayN0iGAqISBbwpUYIxmMJ8Vghs27t5rSkXsqiJcJzAfr2qsP8eZ+/gUedUHk9ZezoNvOXUvgrn0eX70GsfJyA2FuuKjr9e88VrNm1/+aT75sTnf8PEzfuFzf4eZfs53fcsp5/Nbls2a0/kWx5bWepy1fPErb/P3fuqXsMUsi1BmDq9Naw7v1Ok07vPC//uXBvsjJH984ajelLTzewG6nbxGt3hEtwzKuPD5kG75APSRe8E7ys3zAbhVt48HJVxx+zhAuPVTtG2z9nH3YZjnX8QdrNc6vdU6lGqnh+Pp6enBi4300B4/O1K5pP1TnzUeOI0fYflgJx0jfwN4rB7yvnMorpGJStgOnGsijAsKOXsA5ML3BOtsvwzmrdHcNURUe7mkzZYiQjfhkqpa08w2lNV6D+qKFSZuE36/paq3GC3BVLMocPHtotYKU5RAcGyuzDJEJ9RzTHGKUzNUsaSZ38cUpxT1GUV5RmdLbteK27XmeuXZ7AzbnabrCtquoLMF1hU4FzvRY5BuCuB9IEmiGacLwE53lLqlNjuaYsOsuKWp1szKFbPqllm5pTRbqqKjKVsa0zKvOxa1pS47XA/tVrHZ1Ky2p6x3AchttiesN6estyesNlE5tzlhs12+VC5nzYbl4pblfMVyccvJYsXJcsV8fsvZcssf/CP/HR48gAcP4OFDqGp46xl89TG8+SR8f+c5PL6CJ9fw7AYuV3C9httNFPWqbGH0v5IILgTBMEiYP5AqcAST6NSpC7X/EoW/RvkO5QM4wEdg4Pb+8ZQXlNPBN5vTYDXO1nhX4e0M39d41yC+wUuNuBLxJeJMMNH9ugjRnQtBmR6le3w5g0JHk2NFZd5iVn45wLxZD43HzARmDt0IagZSeVSjoSGYy1cKV4KeVVAXuEIjdYkvK1wxxxYzXBX8sPmyDKCoMgdjyg8kWQELhW8H/2gpQqy3CukV0geTWnqzB0Z5gJKDYCWjRcLnxe7nqfwNTlV4XeFUWHxcnK7wqsbpDy4S8kESj6HHSFDrhc/4Pf2uLEp6FB6tHFp5lI7jKFRwDxAjuXql8Tr69NQGpwPgExO/l0VU7BUB5hUGb8ze3LZUH9w9ltIU0BvDvGhGG0xoA7gzrme9vke/XRz42/v4xY9z71Nf4qe/+b+dBu2gFD/wj/5NPvzuTwF3X4KNTbvgLtyAwwnseD6SUm7Wlj/PxzAkfZ8CY+Nn+XgeNU7jvIz9YqVz5dc8/j4eiwDDWGYKkE0Bu/z3/Hhjn3BT5z5miZCOmc/ZjgG8cVmM902ALY+QmsZjeR7G5ZDynp9jbDqYX7PW+kBJBtC27R3VUr5vDqTGpozpeHnKYV7aDu62v7xsxpYXeR3ndZ3yntdbDpRyf4rjOhozkXS8HCDl5ZXm7Dlgy/OXK/rydYNV20jIlOBYDubG7SEvo3zd2Fz4Re31RffoeP34xXy+3fjYef7G7TI/R85XpmBu8pEnEkx/c9Ng7z1/5s/8Gd5PeiXA9q/98z8wfDfGRPO0Q8AGh44wld6Tw3wCNIZxd+FcOEeyuR1urAgShk5fq4Ox+mHlsQdR0cGT+LtS4Pz//cQufzbetXvPO9CMJ4QsZQ1u3LDy86Tf8jJLxx06+mQEqPfKv+xqJxrd4RumcUedIAgQJl9lEZ6r405YMZnXKTqdb5OX4bGJcIJveZtIKSnC0i5aa8QH08pUd6YsDm5005xgZ49oqwfsygsu7Qlvrxe8uznlWX/ClVtyI/MI5CrcBwXkMrOJ2m6Y2xWz/ooTf8m8f07TX9O0V8zsNfXuOU37nGr7HLN+ivI91nraXY/zjqQ23HUepaDr+r25Nfv2IS75FwPrXZg0CRzCtzjt9CFyp0MO21c8gGTX6lBA3gGrYdAZfH2ltpO1k0SUVPKFGDtem0d92t9HIX8R3mlQolAeRAdw53AYrcPka9+Ywr7hQtCocC8AntMQeVXO6bk3mK06dbEP+KD2UE6VD+nsyw3ujbYs6jWL6hYlV2w2b2PULYW5oTC3VOaGQt1g1DWFuaHUNxh9gwoOdoAAnJKaNO9LEiDfj2fTStmX80umfCDlUfTzC7rlw6h+e0QbwVt/8lr4XD7Cl83R45nt9QDfytt3KSN8K28fU62eUNy8i2lXQ3fnvRteoEB6+Bus7Uj+L9PbrZOTkztvE4dSGQ1MxgPdYy8rjg2E0z7jB3X+ey5zz8tx6vzjvOZ5GA8qxucLv0Hf1xHEnQ7QLYdzCdYd/hb+f9lAJQDGbAKES+q5qKCrqi3aXFPVAdJV9YqiWFHVW8p6Q920qBjQom4ayhhdqqqqMOioas7PzljMaz7+iU9w8eA+TbNAac1ms+X6esPzyx1Pr7dYV7FtDZ0t2LYK2xdsd4q2L7G+YtdqNruSm3XFtp2xa2taW9O7CutLvARH6IJ5f9DugwR3ylLqAO3qYkddbGmqDfNyQ1PuaMqOynTBpN+D8gZnNX1f4PuadlfRtRW77YztZsZmNWe1WmLte5soGwP37wfYlsBbgm/j7/fvQzWDP/xH//t0cs7WntBzzs6d0fkzWjmnkzM6uaD197C5AlEBWL5T/WG8qhEzD4oXNccSotJ61eDVLH42OGqcaoZ1omdYTujUA0SVBAV1idezD6oygj8q2aH8DsMWLdFHWQw4IM7Gh0Sw6/U+RMREKrxUiKtwPnwGeBejSfTq0NRzDJOiL70hotQHlLTugwrXhEXrDmN2aNMF35Bzi256aCzlEmgc1D4uCl8W2LrBlQ22anBVja9quvqM3pyG+6Ai1G8tmEXL4t4TXFHhihpXVNiiQswH7H/MO0zfYmw3fGrbYvoW3XcUtkX1HbprUV0PrYPeonsffbHJHgAeAD0NYuIL/QIvBnQVFFy6OQB6TpX77+y/y9cVBfd40hnM0wdgL/yvlUOJjaa3yVljcjmjEBV8xoVPg+gCr00Ad9oMEE9MAHq+NFAGoJdg3gcN9D7zlf+E7/7ZHzmYqx0bz0/5JxMRnGiel5+kUJ7z/svR6ueuc/oxRJt6fqbPY2BsvP3dOceLoVIOXqag0fgZPzW3m37u34Vb4+sa75ODq6lxTm4hMH7pmc6R+wY7NhebKoPcV1gKMjCGY+Mx13g8l2DHuGyO+bPSWh8EOfDeT5o75sAtD1px4NdX7gZXGIOa/DOHaePfx9+Ptf+8reRtZzx/Hn+m70n4ko5tjDlQhKUymrpf8jR1D+a/jyFcWjcG4WPhzRiGpd/Gppsp5cc7ZgmTH3fq95SvKYufvHzzviABsXHd5tc1Lq90nlypmIIwlGU5tNn3C9he6al6R8rL/mWbFwfxInK1Gmpv3paOMb5px1ApN+vMzysieyfwpJuHEEEwS3lDCs7oJelpDjrHvAGOO56Q3wAE8m0GqJM6EAHh8AYed0Jju+TxDTAFwfSgwhM0OjqIDmqo8Ii2JJg2lsjm57nT2Xo5CNTg7EQI4zj5H9+Y+UNtSib+sg9LkX10m/yBAolXyd6lVPRbplRQ1iHB512u2PK7a0x7y4xfZa4U99B8MkIrLx5lFHVdh4gmRlOf3Gejz7lyC9bqjCt7yjubE97ZnvC8X3Jpl6yYs9YNu6LCm/camClamdOqOZfmUYByBbDgqEqutDvmdsXcXrF0lyz6S2b9FY29pt5dMmsvKTdPqHfXlO0Vqr2lt5a+7eljnQmKbdsG6BzhRnrzYUUQH8tHQYjQur/fQv2F8hQJ7cBHExTRasioDFcYqGeKTinEezGqr1T8LcE/fzCAGmo2tLn4xwDibWCZKhSQdxLrXKGyqLnhnMG4w7uwvZdLlLqi0ZpaBPGyj7ajAkhOUG/XdXzfb/kt/Ik/+a/w//rLP0Yxe8jy/FP8tb/+DyirB9y//zHW7Yyb3YJVt+R6O+d2u+Bmd8qmfQMhBit4D0/PRq0o9DVG3VDqa4y+plBXlOYmBndYUegbjLqh0JeU+hatNgcQcmoQdAzO5w8YEcHcPGZ285i52vsvOHhgFQV9uaA7eRRA3EkAcUNwhpNHdCeP2D769PHW3m0pb9+lun1McfOYcrWHcMX1O5ibtzGry9AixCMSTKxVpK02Rng0SkchTd6fy0F/OPWmdmrgm+8z7vdg9AyJaQzX0vexei1/NqV85uefGmCOz6VUAF/GbFgUzw4e7oeFO/wZ/v3Sl77Chz/8zSEIRLfEyTldt9iDuuhfLgSLWNJ3Acy124esbz7J+zHXM8V68C0XPtcU5Q11s6WskoLuhtnsCzSzlmrRslj01E3LYl6znM04OTvlwcUF56+fsjw9496D+xRNhXEe2/Vc3dyyazu27YrtbouzHi1C8lEpXY82But6pO+wzrBZC7vesNkpVmvHdgdtb9jsFuy6Jdt+SeuWbLsZbd/QuhmtDVFOe1/hpMQlU0SVOmReAOkUXkpaKWntjLU9e3V4l3yNXYR/tXIU2lJoi9GWAsfp2T2MAB5sF5Z2C19aw88/BrExXwk45N/jovWfjdFkb6iqa6rqmqK45qT6MmV5RVleU5ZrPm//JF35EIrQN17wE5yYd8L910+beuX3XLpv8rHHwUvVYXyoEFXh1CxAORpENXg9x6k6fFdzvGrAhE+v53uQFyGe6BzuNTi1pNcPcUUD6gMCROLQ9Gh6RCmcjsrEeD8W/pqH3U9iCEGklA+tSXkJETdjfUgv4BQ4he80YhViS7wtEF9jbY33Nc5VONfgXIX3NV23iL/9OkXRLB22bCiKjlJvKIoI9qoePbOYWY+aeWgcqnGoxqMaCUCvAalANQpqhdSaTf2Ad5ffEUxtS6jMFWf+V3BlgHe+qHBlTV/PccsLXFEj7+H6430n8QPIK1wfvseltiuM7ShsF9a5Dt11qN6hOxvNNiPQcxJUfi7VHUGU6YNtsvcawcSADykico2lPIR5qsKrkl4v2KmL4f9XuzZeONZQ4jDSUcg2KvQi1ItKXo2LZrf7MdumveBy8/FgBqQArXh4/gW6+w3X5x8/HKeuHSvzkEKFIAoFFi19HAMeRsiceuneqTl/696/wmX5KQA+1H6Of/L6f4eKNu/HXiJOjXemthkDsIOiOwJRxr9PiSDGIo+pvKVjjIHG1DZT+ZrK7xj+5eUwzsOLjnHsnFNzzvG15OvGY8xUNlNA70XXNj7XOI1hT2pbOSxJEKeu60HZlquREoRJ+UvjqnH5jWHbuFymAFheHnkZ5+PTBPnysWruq2wYm08EhoTgoH+cj1yJNgZtU/Uyzk9e7vl1pe2mYOe43R5jFVNtNLnZOqYAHbetF7GQMVDLjzf+zK1b8vF0fo6xsi0vi/FYP9XRsXy8bHqlUck4c+F7AjO5I7mk2NhPz9MFJ39j+cUMF0kIQ2t0UsMEHz9hfWxYGrQJZoaiDysnfU8FNpiWxkl/nqdUEXlecvCX2xynihu/xUmN3ygNCmzXH9wAg2ooqW5GbybuQD0iLMyUat4Hn2Yugi+l/AExFg7fUOgIoULZxRslHW/k1yt/MHrvg3mhutvJ5585XHvRTTS+ifM8jiMB3ZGWmuTY+rAz1Ebdreuh7FQWpTbWp86iw3hL3wUT1X77NZA3ORFYiPBI4FM2KI+UgmpeoVVQbpycnCLNOdd+xlqf8aRreN6d8Pb6jCftKc+6Jdduzq2as9E1bVHjzXvfkL3UXKuaa3M/ALkU0e2ISs74jnm/YuGuWbhL5v0Vc3tD0z6naa+CUq69pG6fU+0usasnOGtDcAYn9L0FNM7uw297dFC/KbDeYq1GK/ASJc1DOw33o/ceROOVAm/ZR5JMSi2Fx+F1CI4h3mO9G0W6VSgJ/hJdBtHilI18pqtIQQti/SsV/EmLoLTCWsc+cAdD2wKFk3DfuQjh799/ADgqs+Obv3HGzeptPvPpE7740c9xNiv4lm94jcoIohxiKnadZ7vbsetaPv/zX+Enf2lHXzzCyRmSFHL+DCfnWDnH+TOsnMbfzti5j7Nh/p7tAEDRY/QNhbqm0Nfx8yZ8j59GXQ/r92o5e2cABfuBUIKtBw8REczuhtnuhtmTL96BPAN4N1U0O31Ef/oa/clD+pNH2fIaq3sfA3VkMuh6itsnFDfvYK7fwVy/Td/fUm2fBnPU1RP0+hnKTzuZmnoAj/ub/G1zerbkA//Ux00Neqce7Olz/LBOz4Op84+fD1P5T+vSMyUfCA3nis8zlZuHeQnKTtfTqyvqxYZ6GZ61+fWMleCHwFAhfknXRdPVfol3p7TtHB+DQDh7gu2z4BD9kt3mEda+PzhXlOtorrqhrIK5alWtqWdPaWZb5vOOerZjcdJz/77hwcOC196oOb9nuLg44/TkFFNodrsQLr3rWmzXY0VwfU/Xteg4JnDORnN1h9jnoJ6hRFAIXd9itEI8dO0G8RZvA6Hqdy3dboeg6F3JZley2Z6w2p2yaU/ZdMsA2jc1vV9iZcGub+hcjZWazlVYKbEUQW2CDm8Z9+8kXgDtwqcXQ4ehc3VQSAlcPp4oUA2cxOUlkqdh62u28jD6V2OvAkoBLJJD/kFIY7lVn+Uf6r8YFLr6mtJcUZpLSvOMqnhKWTymqS4x5jlFsR3UA7A38x5PTPeTnZ6C/mB97jg93348CRnfw3nQqmESIxrMIsK5GdZXeD1D1AynGjw1YhY4KkQvsFLi1AjYUR8q86RGfIVnrzS0nPF2+TvDP6/ITIzsQhRJ2uF7LVfBKT3737X0wUehCMo7xFqUiypJJwG2OoXvAWfwvUJ6jfgA6Lw07Pw92u0DpFMY0wMK5yq6boZzZ2E7/8EBr45znulvH6nyWqriZviuyxYzc6jGomY91C4AvaTMayRGr1VIpUL020pBbfCVQTUFUpX4KvjKk7LGFTU+Qj1bNrSzU2wEfB90GpR5tsXYPvveUfbrAe4VtkXbjqLvoNthrEP1wXxTWx8CI3QumnsKrpcwsfEB6okEmIepsVShr9H1oNLzKnz3OpjbelVhVU2rTwbV3uRzeR6X7AXOE/kmeEpYsvRL/B5+6cHvGR1A0FgK30aY11NElV6RVHt0GLFcmQ9xXX5s2POt+jv5lea38Q2bv3133pM9b49BnvQ5Bgz5M3ksNsifhXlfko6fb5fvmx9zap/x/uO51BhsTIGyKYiRH3tq+ylF/hgEjOdS+fUdS+PjHdsvnWcstBmXyxj45PPGKbXj+Jy5sCb18+mYeRTPtCRT1nH55PP5dL78uTN+vuTXMMwbJ9rN2Aoj5S2HhTnsS/vnFhrjtj0+9xjGjdt2Xv7jeyE3SZ1qN3n5K6UGP2rHxtp5O5lSk42VYXl5jj/zfKcyykHiuG2kPOcqxrEQa2qeMGYY4zzkKTeDzsVh6Xyvkl75tV9eMTmcSWncEKeoYfo+FJ4xocsXwbkwcYcUnSJFdpA4md83iLDLvtGMaea488nBTspX3hDz6xvnuyzLYbv8+M75ZPR1QHCNDj6Ukr8tFV8aISDusLzEh5XeBwfDeR7ycs870Xy9Tr5vONQ+GBNARgBzGTFXGptBjwAs9ADXiP8HTHdY9zlonGrYY/g27gxgbyc/1W7yfcZpSva8vy7BSzATEYkF4YLSLV2IiICSZHQLSjAqwjnTxHMDEsDMdrtlu93i/dsoBdoL953lNVPwzanjrw113bBYLmhmMwpT0OkZz+2c533Dc7vgWX/KW6sTnnVnPO+XXPs5K+ZsTE1bVshLhA9zUnLLBbfmAopPBkVEA5wyqZLTYpnZdQBy9jmz/ppZexXMVrtrqt0zys0zivUzmv6KcntLt9vixdM7S28t4gXrHM766C8uqB+d92FiAyg04sDaUOhD5F0CFNYmRNY6eOhHP11KRVNfYN8cFT76gpNYIUNbYt8ee2sRgh8770Yqp6z56Ai/f+iH/gjzmeHLX/4yWmtub2+pqopHDx/Rrq/inNNHJV0yO1d4D23XgVxRsqXS8f7TOnBsD8bs/SfAvu17KXFyhvVnWDnD+TMc58P/AcadZv9fsHMf52XNAbVaBeimglIugLegmDMk9dw1pbmh0DdouUKr9VBAxx6iANq21Je/RvX8qwf3an5/e2WwJ8EctT8JIK5bPsSevkZ/8gh78hq7D38bfOw3AnAzcQ3l+hnV6klcHlOtn1KvnlBvnlKtntBsnlHYXcxrPvA99KWQ8jTuO8ZvXMcqt7wfyYHdcI2j605pvN0YVOblOZ5AjN8eKqWGvvYAhuqo/NOaWmm0C0BJV9XRlxd38+DR5pZmdksz25fBWMY/lScRje0XIQhEv4hKueRf7nQwX7XDEpR069UjnP3knbI4njxltaGqN9TNlrrZ0cx2LE8ss/mO0zPPyanl3oOCs3Ph4kLx4IHm4r7i7Nwg4lBa0e5agmLd03c2BqJxWGsR76Ia1tG7Hu8chdIYbehtH/rttgN5gtJRHW0dWnx46eB7vA0qfe8dNzfXYZveopjR9jPWq4rVqmC1XbLenbBpl2x2CzbtjK2dsetCUIPO1fSupvMl1pc4ir3z/tSHh/cEmRsC7gK7SYCn7q47mhRQRBP6ey+uoo7M2b4PJm/SoWnRbDFqi1GrqOC9CWBFXVLopxR6HQCS2mLYIbKiMh1abzGqRal9YKuxuU9+b+eqhtRua61JkQG0Xg3ZTWM95xxVUdF1HaUpEfYuS/L7Mo0JxKfJkOGJ/jY2cs65/SVKWUUQNxtAHHqOUw02RYqkCT7GCMpJSz0o98I++/97tWTHA6yqOejvD7s5oj/+l0pKeoxqI6zrKFWLpqVU7QDxigT4aKN5bY/ywYl+CG7hEBtETkGFJ+CCEs/1RTCv9TV9Hz6trQblXfoeFHpBlbfd/nqo8nyEeB3GtBRFT2F2NMU1xnQURYcuOsysR888euZQs6jKm3moBDUDalANSK2RCqg1rlRQGXxV4Es9KPKcKbFFFU1ra1xR0jXLsK784H3K6dzEtm8DyOsD0NN9R+VuMLaj8jZAPddR2J7S22CK6zOo50CsQ3uNtx7fe5Ro8BrxCkeBVQ3XszfwqqDe3eIxgyrPUvH00WfY1Wd4V2I7qG7WiBi2+iya4YYgFC9KG33vzjM6BxzpGTZlbnYMnB1TVHnvD14EjJ/VU8car8vTeJvx93TOKT+weToGu8bbT0GO8Rhj3D9OHWt8PVNjpKn9k8nce4Gdqbzm9ZKzgmNjjTGEgkNlWkp5ZNGxZcP4ez5uy+es4yWdCw4t58btLR03rR/vOwVi0rNsCvzl+yfR0dS5xlZ+KY+5e5O8TMZj2rGYJa/rMdTKy3tcRimv4zyN4Vq+z5SgaGzpMXXvpZTfT+PymAKL6f/8pXfab4o7pWONzY3zcybu837TKwE2kzyCyeG4bSxhzBv3VCXkFRt/QVQY1AZF0v5iw7b7yH1K6UHVNj7uvrOd7kQTmBsr0cYVBJmSIzOHSNeX33xa5zesyW78VHkhMIJSCjMMpPZO9xI09Jm6bEyU84YyZYqR7oFhoKgiKOLwZkr/O+SgnobjxLoNAR7CkLMYySjzG+7OJDPBzH2tAtONe+8Qcq+SCtcSAYdkbzrCzkP5Tz00hk9F1q5Ce83NDJGgfkrBBMQ7RHRQQ0qamWTwUIEeItXp6CgcvEsqPct2u2a7XR+0JxGhEeHDSvERpfgOFKYoqBcNs3nDfLGgjJFub2zJ07bi2i140jU86c55sr3gWXfKpTvhRubcMmNrarqyRN7LH4wIXgxrdcranELxseCPJY1zp8xW8czsmrm9ZmGvmNtrmu6KOgZzqHaXlJun1NtLqt1z3O07+G2LczYoRXqL8xoRh3UuKNlKj3jBoxBJQS4ESfdzLHIFiASo5cTjRMVrCB2NF4+GEMEWz93Busd7lbW7fXISBwri+K7v+m7+7X/7/8pv+b7vpyxLlsslb7zxYX71F68wqkCLjQA8mptaT9dZ2l14QyaZggLvh/M4Z+M9k4o/4bmOQj3B6MdUIuji7sMo/x720zEK5NkIxJ0NvwXV3BnWn0a13Cfw7n2o5VRSxV0NyjijrqLpagR06hrNZQBz6galPeL9ADzDSwNLef025uotZirU2fCmMt6/Xjxdfcpu8Yj5xz4TlHCLh9iT14YADduLj7J+7ZuP5tm0t9SrpwG+rZ8y2z6nuHmHZv0kAOLVU4rddWbQfDzlb7/G9TX+Pq6jtH/e/0+pecfHyQeJ40Hjvq1AeL4pNCqYLcX7wztHJ54yvqXxtiMF2UiDjmNAYqwKyPOT6mrsxDhtZwwodUtZ3dJw+BIrP974GRrypPFuSbub49wpzp5ibYrYughgLkVu7U6w3ZK+X3B9+RBnX64thzx4qnpL3Wxpmh3NvGW22LFcWmaLlvNzWJxYTs8cp2eee/c1p2eOk1PH8kRRFBqtw4uyve+T/f/Ohj4hDO4jhneOs77n5vaG29sVRVFy/3RBURrEeQpT4HyLkh3I43DfDC+AAOcHtxJaHF3n+aH/1v+Qqyu4vmb4zL8/v4Snl/D8Bi6v4WoFtzHAwXoLvWcP6AyHwG78qdmbiaVecwrYHQV4hqCyqXHH5HVpn2PCiTarQ3o0HUZtKdQWo1cUaoNRu7hsMbql1B2aNYotVdFT0qJli2FLoVsKadFs0GqLogt9XZwsVVVQNOXqg6kJqNZ7B9mP/OeHviwcZ4P3zw4mgeNJQvo9HXPct4zP50XwFDhqrK/opcLT0LpiAHpWSnqpgxktDb1UUZ0XVHtJieepcZL849VsZImlwstLAKDULl44n/AYWgo6DLugVmIXl5ZaVnF9i5YdhQqfRnYosRhxwTrDecR6pPeIDYq8YEqrkAjn9vAufPe+wfsGkfA9/e5cTd/P2O0+eFUegNZ9BvI6jOmozJqiCH7ziqJFmxZVW/TMomYOPXPomex95jVEmJdUeeArjS8VvtT4ysQgF9XeR15Z4com/l9hm8Ue5h1Tjb/qNUbz2sL2gyIvmdca27GrT9m9fjZs7yl5/ed/nt/8d/784XMOjZgGpyp+pfqt/PTJD+UTEV7rfyF+VXeej+MJ9DFwBof3UW6idzAP4O6zfMoCKc9PDu1SmgJiY2A0BcPycxxbP7V9/jlVBnn+ptRqLxrXjMsnL5epue/UfC9PaSw0Lte0LncSn/evOWgZzyvTHHc/jtgDsnyslQc+TPUxnpunY6S2kcozj76ZA7I0vsnXp2fB2El++j4W4rxMmgKWYxA1Lv9Ut+OyzlV+uc+8MRtJ15g+cxiXImWO8zEu3/F53yvl904exGF8zDHoy8spb8N5ULRcvJVvlysF8/aSxE93x7iHfv/SMYqioCzLg37l/aSvW8EW/9kbgY4G73nB5evz70NjiS9diZAnL8C8MNLFJoCVN/L8uDmMystm3OHmFZyvO9ZZpm3HYW73Mse9eWkCbOFY+fECwNlXXLqW/XmMSR3emMyn55UME4CwLr2JjQ3VJ1XeCD5x2OkbY6JKcN8cBvDhfeYHDpLyTu8zegi7klIv20HHCK8B/IHSBmTvsS6U5WE55cCQCFgkXXtURaUyVvEY6fGX32wiEkystD5oo4qMgquI4BSghOA1RpEmuuHke2g3tAuR4KRfaYJPuEOok+rsIGIrGnGO7fqWzeaWZ0+fDNsEv2OwFKF2Pd/YzFBAfVKzXCwxVYExgYQ5J9y6ihtZcOkXPO+XPN7d52l3xrPujCu35FrmrJmxLQKQOzrJ2Vc6iGLLkq1Z8qz4cHCYW7NXyOVQLjbpENjhill/xay7po6mqsXuWTAF3D6n2oWgDmbzDLe+CaZwPvh8c9ZFs+SgOnTO0rtQWiKQ/Hd5FZwMiRPARBAX2piPdYGPHgpjfeTtvSwrfuof/SO+5zd+O2VZsNls+MhHPsJ8PqeuG2azWYDgRkeAFM7t473e9z3OOqzvDnwq6OFNh0apYCKrMxO/vB/JB0bjh9dhH+TRBKhVFW/eGfRMHRuCWs76qIbzZzjO9t8lKugySGf9OTv5GC/7KNDcYtR1XK6iau4Ko28GEKf8FaW5RqkrDFcoblEiFOvnzFfPuPBPgahETs+REKEDX85pl8H0tF88oD95PZqoPqRdhCipm/ufPJ4/2w7qt2r9lGoVoqJWq8c066fUm2fU20tUBu6nBr75uvy3VOZTLwvGg4XxsfJzjN+8Hmyf/mb7DINCrdFqr0ge52vqGvJBFNwd2I3hW56n9PwcD3LGZZTvc9iuHWV1S1HeAO8M2wxQYqJ89m+SVVTNLfH9GdYGIOeiaq7rwmL7BX23DMCuW7BePcDZ4wE8xkkpR93sqGcRzs12zBYt83nHbNGyWHacnMJiaTm/KLi4UJxdwNm5Z7Gc82B+wtk9x+r2lrOLM6qqDG1bBzvRoUxTmcRrdjF4jPcOfPB9eX4O5+cvnfU7qW3h9/2+H0LklK6f03Uz+m5B1zVYt6TfLej6WSzLBX0/p+sXeFliZYmTRXiTNAXnjv1vPGgfI20SrlOrCADUS0M7kTL6yFvQCTBtNf6+k8KhVRvgm9pS6Ajr1JbS9BgdFHVG7yj1DrzQtTPm9VOW8yu02mLYUJUWI1uU3VAWHbBD67uAbXzv5/ffVH+Svis6StWjvaOM6xpnh3t/eN6MTG9Sf5RPnJxzVFV1sM45gomhmgW1XQRxVupo7tzgpMIS1XgEyGcJ2ziq+FmHbePvHUssD3DpzV3eDanR58vWmXRRYbdDS4uR7aC4M7QUak2hLjGypdSWyvQov6EylkJ1lNohfY+WaDXiBCzBrNYrxBU4W+F9hbXBF14CeUGJF363tpxU5/X9+bDPr6cqLwW+qMx1/L8fgmCY2qLnDj13mDkDzDNzhZqpAPIqGfzl+RKoDdQlrtS4osSacg/vigpnKmwZPpOZrStq5MhEerO4uPP8NAhKdlS0fHb7N/Gq5Jdn/1U0lt+w+VEetT8Xx/J3fXxNKVvy/1OaeqbmY6g8jZ9NLwIbwMH9mv82ttiZyt/U838qT8fSVB+R9y1TEOvYOd5rLpvvN943P97Us38qD/l+4/nmWBCS0oFSn31d5GOSXNwyPu7Uvmn/8Zgiz3O+bQIx6feqqobvaZyfjj+lBEvHP1a+KR2r25Ry1eN4rDZud7naLldoprnk+L7K83Ns3JnX1RTsyoHV1P03VR+5iiw/1zigw7heU33lY88UbCBXlqXIrWVZ3nkOp+de+j5WswIDQCvLks1mM8C1FNQrrVPq1VRsrwzYxiY2WgWIkhcS3B3Ej33Y7DsygrkVEF1eH20EY3XYsQkGUekWKiSBKoDpRpZXzrjzHzeA/GbP9wmNk2GbsN1+UnYovYR9RNAc6HBwrqSOy2+6oG7TcUk33f4GCWahBjXAuKxUsjpJ5ZmW/FptHySrXu6+QcnfHIyPu7++WJ7RdE+rCL3yAacKk+yw06hDkDDpTpOV/IbTsczE+SG4Rajauw8MUdBnb3tUvOE9YZKqdDDP1UqDuGEydPgg3ZuPDG0CFSNx7gGgsFfbjd+AxZIL7VAzkNJUB+nwWitKXQAB7mx3G7a7TTDDjMqtkB8wSnFfhIda8R11g1lqFvNFeGtkCoRQ9tYJ167m2s654YRLe8o72/s868552p3sgZxq2JqKnvL/x9ufxu7WbPlh0K+q9vAM/+mM73jfO/Ttvn27+3a3BzJYduyWkIUFCSJEwp8SPgQJI6MwCCuAQIIPCIiQCIqwFCVCWBGIDohYQFCEDQ52bEcxSsd2d9+e7vjO55z/9Ix7qCo+rFp7r72e2v9z3tOGffQ/z/PsoXbVqlWr1vrVWqsS4TB/pHY3WKKxS9yU7wG1AZYYQbgMIFf4hsC47pa8446UM67cv0J9vIXbv0C1eYFyf43ycA0cbhE8eUEFz5uiEMjQx4jO+wTMB8ACMSbeGHwnyZD/tV/7NXzve9/Dp59+iu9+97vY7Xb4+je/gR/+8IeIMeDs4iyFsloYZxBTpU0k4PhwbHF2fg6kUJAYyRu27Vq0XY8Yk0dEAmuNsSmXVkyh4uPkoZWXh+SNlFMPuerT94DCvEDhXsAUU0VG8qGU3d5HRHOGPjxCwBU6fw5vruDjFXy4oNxyE3DuCk34JuIb5pYDOjjcwZobONzi1as97cKacsqVQ565OxTtHYrjHarrjykJfFHSGAkAbZRjEF2NZk27orbnCXhbP0O7fprOP8Pde98bXU71ETyq/XUC4V6g3r3AYvcqfR9DU6MfQxGmbD9VegesILNCy/ezQTHerX5zH8bpewIovNnHiKquYVLouwGGfX5y819OAdYGgz5ySrj09JNt4rlCh+4M7RLvlTnxJh5DQ5vH73pOLspblNUdED8F5XS1g5eoodUN+g4Cwzk/ZwwF+u4cbbNC8JcJZFqhG0A5CmslUO4MbbtGc1xje/8M/g13GKZ2eyyWR5TVDtbdY7Fq8OgRcH7hcX4VsD7rcH4RsFq3WK1bXFx6PHlisTrr4coGxgKFcYCx4zz4hzjqGlguN3Bun8b21Djp+34ib9q2HXYqo3CTkJLun6P3a3TtCn1/Bu/P0PdnCPEcXbtEd1jDh/MBqKPra/T9Gl560cpQ11mADjCuB4oAwyCdNYjpj1CsBNpxeTMgXQ7AIy+7FXxcoQmPEnBHM8ODxxHA3cO3WNMScGePyeuOQbxm+O5Mg8Ly5xEWB5hIHnZF8s7j76VrYbBD9A2z9sS4kp6vuVAqYJrwmfuevEgiCtvCmHFSl+PNe5/CoqcyQhvh2kDmOcn7gD46tL4YNqXoI+W160I57DZLAB2FyAa7QNuXYkfaCh5j+G3gUFp7QZ56RuVUG9UqOuZAWea1mh8jLzwZMkufLYXPpvMVtmNePDQozBGF7Sh3Hpq0g21PYbUhAskTzwSL4Et03SlAF+MCXVcmD7wKfV8OwB6BfTW8L5NX3gohPPr/qVcee+Tx5zKF2DLIZ12LH338JxGeVMA/a4B/LALfAmAM3v/xfzLRXU6jYYBf3P1V/OLurw5zgFdzkjzm7LLcdb5H607ac0cvZsmy+FNeZ0OceVvne5LPyDoM+oCod66+/Cl1hTl7Un7yNe/9iReTnJ8lOCLlQA4UkTqpdhbJAUm5PsjZ+3w+B6LI+9gu5nslPjDaz2Yi03Suz5wXnOYduTjB78nhEbIcDVTJdkyj18YIgIdydWmPKvk+kp+nIKTsC/nHzzC/y37Tz0uay7mDQaNcZAW3Q9ZJO0nJ9zEdJFgp68o05+f05hUPAdj8Wdf18B7WY8boxmmePlkGj2cAk/u5Lgyy8QYN1lrUdY3FYvGHDhN9a4BtBFgS48FMEjMT0TzIOrWDwcmgF3fKpAxOxp8AKQItpkkMmVDTjh3BNsnA1GnsxTYFtui6VPjHDRQYrDoBalTHaOE3gmV5Y0Uzq5wMdHl68Iz1spO2c3vk+0ZGlOenKzFcB5MAR+3qOv6NAB1/J6b14CTbOSBp/Jz2sxR4BEZwv0s60y9rqf85f50cuHMu0Kk5yVtOGrBI+2UkTzeTgJc4AoARkcICASDl+iMAzlB4D7/fx2Tdjmq6MfSCwUiKFtYQgBjZIo5IlUDaCTVQ2YmOFgDlyjaUTw+Uwy8MRiiBgDEIM52vxYim2cPvyZNiAgbSqwBQruB3igIfOIc/WtVYXC5grUVVlIBJoZl9jxAN7uMaW5xjY85x3T3GF4dH+LK5xMvmDDf9GvdxiZ1Z4FhW6MoC6N4AkAtAjwob+xyb8vkIyOXCVdOfDR0W7RiqWh+vUR/JK67YvwI2L1Eer2FTqKDZXcOGCB88fCJP3we88/w5fvVXfxX/xr/+v8b3fvmXBhn267/+6/ilX/pFXF1doCwbeB9hjIMBGfPGGLS9weLyCr/y0XcRXYmu6+FDj+Oxwd3dHbb7PYHrYQSTy6KCcQY+eMTe43Dco2s7HPYHWEv3SE+3nGeCnmx47Ejel9f0woe8T58fZUOAjVuU2MGYT1AVPZDCWK2PCG46cQ/1iOUAwvl4RXnl/AUCCJDz4RIh5Zrz8Qo+PkaHb+LYvNm0w3mcGIyjjR7uUbgNytu74fyF+ymFtdo7OLNH5QoY53BcXKFdPkFz9hTNmQLizp5h+/TbiO/+wuz7y8PtmAtu9yJ9f4lqR3ni6u0LFP0BwQJARKEA+An9AUTLQ5XkQUAYFhWIuJjY/CHNlT4SkBxs8uiEgTEWFqcrzznlnA+pyOWMl9cd2pDQCtacISTnsYmsjrTYAkMLFRPQ7aQckrQxhmGzHoMwASSpPly/Bq5ssShensyvXCc+PxoZNNa7zqFv1xS22q3RNmu0zSrtzLoecsxxiGvXnWG/LdG1H+Lm5QKf/eTN6GmdHzzmFqsDlssG/+7/Dnj0aPx7/Hj6m/+WywFbPC039bEMo8gp5gyqsQc7ABSFQ1F0MOYGxtxO+ER+l6EorJOxftb3QIzn6LoVecn1KwRPYFzbrtD1a/h+ja5fo+/P0B2WCZw7Q9cToPdaMEGCdBbkRVdEGBcInHOWVrEUkDc+J8A6F9Ocw3O3+MPM9/Q7xAptrND683T+DYC7Nzo8bYRgDuNfCoG16ZOBudIeURYdStvAGgqbNWGHuuzh7BGFPWIBTyBSPMLaU+NYG5LaMNOAgJZvHM1h4VHhiBg3sDHxWVqwkmUN80k0aLs2a8xLWTOCMwbGrQC3hkcNW57DG/KkY0CvR4VoVgh23LyCPoXXHnvjpRx5x3gxnH9rjzQDCq1NrGvRDqGyI0CX8t/FZtjoooob2PgyeeuNIbXyOYcjTGhhvAc8YKOF7w2sWSGEJWJcgEJlCbTj3+SFV6HviwTwSQ89+VnjcLgYQMAsDT4F8K8B+OcN8OeA7/7k/4qf+4d/7UT2cz/nACbt6aOBKjmfyEX8HDDFR26xSD6TO68BCg0o5eZVrZvNlc1AC7ddy2Hp2CDbrQEpTZM54EiDSLl6y/7QhzyvwSNJ/xyAlQvpy5Ury5Sgi5QtPAdpjzn5rhz9ta4h5QUwblwowVG2ox/yCGSa6kVt2Zf8/SG7X57TvCrLl7TKnQ8hTAAx6b3G91UpLy+3V++Gyd8lwMhgFQNWQ2SYmYKXsg7sJS15mcE1zTMSQJNAcF3Xk/OaX6yllA593w86qwzX5N+sxwAYwlzbth1ow7nt+D5ZV93nkjZyPL7t8VYA2ygkx3BEaSiwQmwMJTdHtEqwTgeINDKHOwwlFgcwSeSnBaxmACIMefjIwasVBvkMlTPGiY9CxJ8MnFxZ4ztOPVNkPaUAnDN+JTgo3yffIwe8ZHxZ3qlb71TxkwAa/U7eOmasr67rVJhSe8ew2LzXDWuiowfjCJBO6y7bSU/6MBWm3Eadm07SGABMIGCOQTUjeZNBUDudrCYCO9038GyMlENMhdyGBAwOYJkZ6WwMrd4lZ6qEr0UYJLCZvT3TJ284Sl5X3Jdx2PCVn42Ry1cTDMbVnkl4YqpStBi8aILv0fQd2qbBdntP76cBjMjgpHNABM6qEo+qAt8uSrizAtUVrSB77xER0feBdvM1Bfb2AsfyEe7CGb44XOLz/RU+P1zgVbvGrV/jPi6wtwscyxq9K2i1+aEwoBgBDwQU2Lsn2BePgaUF1ngQkDPBo+ruUR2vUR9vUB6usWju8e+eLfHJZ8/xd9wv4Hh8Bxe2xXtdjf/kN7+P58+e4fHPfB0h7FMxtFNp7wOazsO7JT781s/hiAKwBZx1KE2NarHCxdWjAcglGhOxiX9IwerbDvfbexz2ezRNA2MsKlcAJnmWdB22uy22m+1gcJi0aEH/aEMFk8q0hiBYGb1tTALzQszIslO5w79jjOD9W4m9DHw/bjIj80JOZJrpYM0LOHwx8mNxKl+n3k4Wq/U7CGn31T5e0KYPww6sl+g95ZTrAl07dB8hxDfPLVe4DQp7T5s6pM0dnLtHae+xtB+jtPeo3AbO3gMLj3i1RHv2CO362QjEnT1Ds36C4/m72D399uz7XLsnwG33EosUllrvXqDapNDU3QuUx3sAcZIDUiuC/F3TmRd+uq4jJYJzRFrmUtF2c7qiqBc/pNzVx5yRK+urARfJX3oO0GVIRUzXZaCBmmdzPCyvPzQvS4OGD01jObfRxkoG1vaoFg1qczNck/OcXHEvywLnZ1f4jd/4/9BKqVvDmCdwxTsI4QKh57xyZ2gbBuiSx1yzRN+usd+vcXvzDoIv8Qe/k+2ak6Ou88Db48fAb//2fwlFsUFRbFBVO1TVDtbe0s6u5RZF0Q/01Imadb9KY0LqHqzIslLOXooAUBSAtTuU5eak3+h6MSj/zjm0bQtjpvlRus6h90yrJXnU9Wt03RJ9f46mXaDr1giBPOrajrznumaFrluh71Y45ea5g/kmjl5yApR7+u5vo1ruYJyHKTyMA6Kje6OxCNYhGtrxsY81+rBAH5f0GWpEVG8G2snvwutOKAGirm9/kNfdMXljNSlk9pi87tJ51w7edYVrhu/OHFAWLYF86RlrDjBhB4MWMtqCx6/kG+YZNlykh5AcjzljF6B1H2uOcCaBu+aa3hMDnKGNStjwLAyBxxxGpA9+x9S+ADzKYffZpneDFx6FytJGFl2ohhDZHtUA1J2E0vJ5LNDgkoDAWIr+VIdRn/Jgnhwa4IfNKrT3XWkaFKZLgF4LF49Y2B1cpByGtNlIuoYjCrSw8QCYEn9t95fQ+sth5+FfLP4d/OQ/+Mex2bxH7/0rwPr/9CV+9Z/5P8AVBjHm5q3TcEwpQ06arUAe7Uihy9eySXooaQAlp/PIeTJnY7E8kzwsy5ELRto+4fPa3pv7y82fuu1ybpcAwxDRIxbONACm5+QcoCTvlcAKgAlAmOsvWebrDt1vshweqywbuq4b7pFea3I3UT4k7bT9W1XVCZAkgRTpXMIgTNuSDs4gEHs3lWWJtm2z9NThhw+BbtwGBqnk89JDS/YJ80Df96iqahgjx+NxGDPyXgaW+H1af2L+PDs7G3hJexxKsI3pwPKUwyk1HXN6oKSrMQbL5XJSJ60PM5DGdNH8JnUIySPSG5+f1xskyDpZawe+67pu0EmkHOE6cb9/lcPEnFb9muN/+l//s+lpjEQUjR8T/jNaTMny2cuMniHvjZgAEL2arAVp1/XYbreoqhrBp1DG5AE1oAiYCg+TLmrhoI0EKYTGZ4XSpc6P75DnqU3amB2F8FiX8fd48LPsdacFbu67/K2NFcngo7CdDjLuo7Ge1BfS/Vi2QQqtkT78TumWPdJunJim9Tn1euN78wOUFXk5acl7rASdIim/A7g0vh0M5IUYB0/JybMpSJn7iO+RE+q4uUCEYWAQiXjpbYMyyXSe9LWenIZvExpp42Q4n0Ieh75OSKLkMyCtQcbUDyYBc0htl/VKBDKpS4xN7YkY2xYjedQkII6PkIAkk4Taol7AOkNeeInf+H2IET5E1IsatlyiLR9jVzzCdbvCx5sLfLJZ48vmAq+aFW7DGlsssbO0w6qv3mAtIMQxz5yNtHxQmdFokoCcYtWy2+ISBzyyW1zEe5xjj5XfomxuYXc3ML6F6/Yo+gNWcY/QHVFYg74PiDYpYZga8KMSF4a8WfTdoUjeMiEmoLLrsd9vsdvt0bYtTWxJ+CfoGz4EdH2HV6+ucdjv0TYNkPrQWkP5+YyBCXEMm04hqlbw1sDHZiI6p6QcmII9qRRom39sMr61pwPLkNVqdTLZ8bP6O9/jQ0GbOYRxUwfOLcc55zp/Tt5y4QK9v0AXLvCmO7E6s0WRQLjSbsg7Lu266oo94hrAmUM4r+Avl+gfnaG7vEJ7Th5x3erxbNnGdyPwtntFu6TuacdU3iW1PtwCvjuZF2jeBG5uCOy5urp67aqrpLVUTPleXn3U/SNlIMvsnIEi3yH5SXtKTVqh5iZdJ22sDPdjyrP6uwQRc+3X75c8l1N8tS4gn9F0cs6hLApcXT7G3/xb/z6WyyXquoZzFlW9QLWo4YxNnqoOo46SlDaYIdWBAeD7Ev/av/Jv4OYGuLkBrq8xfJd/ufNfRfeztkFZEgBWlltU1TaBcfsBmOPz9LkbzjnXDyEXubEt6St5TPZ/WZY4Ho+DkcCecBymIfmWz3H5zjk0TYO6rid8IPUfMo4LeE+ecU1D+efaZom2XaFpF/D+DF27Qtut0LUrdP0KbbvC7e1HY/6+t8CyimKHstyjLPYoyh3KYgdX7lFUDYUElx1s0QGuB5yHcRHRBpjCIlqLYBJIFxfw6a/HksIm4yKdX8FjAcQyA8yp3w9ei+L7Hx64AwIBcRwOm74XZgTubALpCNhrYMKOQmaxh8NhCJWVYJ+Je3AMKI95GXLEOXLYwJJGoXNuCPXR0RksNySPaVmmI0xyYAwwGoisn0qZId8LACE6BFOjS2BdH2s0vRO70y4Gr7wxZLZCJ7z0AhKwh8Vwv49j6O0/uiPisf0h/mT8X+Fv/+2/gOvrb+Lq6hP8iT/xl3F+/vE0v7A9DXWUycOHEjPziZ779fym7UEtj7n/ZB5paYDLZ2U/SRAmBwbrOkpPM/m8rrvkJ9kWDXJoPV/yqa43P8O/pZzM2Qr8yf2Qm8Mkv096PY5AY9d1J+CLBG90O2WdGRyRieP5XeyxJvtUbkTAz7BXVk6P0W2Ri2ByIYw9uhnAk95NDBgxiMaLmQx08XPcr7Ku3F+6LZoOus8kPwHT5P9yB20Ag0yT9JPvXCwWAyDcNM3AyxzSqOdISSMuU8otBs/4GUlLyWPs6MLv0YvFzHe8AMdt4/JlmUxrvpf7R8oTviY3oOD3MOCmcQqeC2R/yHZLIG2z2eD6+hpVVaGu68FTj/9VV5DGAAEAAElEQVS89/iLf/Ev4qscbxcimgxKzqmllVdjpAfUCNxMO2j0VALGCWtaznieBIRDDKxQG/iO85txqBWDJqnMIes9EM1UmM0ZKXyM7WLQRLu5TtFZXY42NLzXQlsbA9MwGr3CAshQUJM8BzFcG+nMA/xUmLNwGOlwGmo2KAFqouRz2ngyZpqn58RAimP/SjrJiYTvHyfT6Wp6CD4N9mn+g0HgifoOfBMwhB1Fw4YabToxTLqR+CKIvgQwJl73HrAGHtP8gTGFeVqkMNAYB1+SgJFmzpjU79R3fSBwJQ40fY1xmZlgh3rKcZSAL/aYGoC61D4Mz9DfyNEJc4opp1uMCUwb/esQeZOKtHuwUXwr7uvbBttWbAsnaMF1CBEod0WKe3+FdVliFSM+BOCXEb7ycFdjfoDFYoHVaoXgStyFM7xqF/jysMLH2wt8vr3Al80Zbvoz3Po1dmaJQ7FAU5YIRUFg2xHzR4hD7pbOrvCyWOFl9XQaPrTE1GMuHUVosfRb1H6Hhd9h4bdY9DssA32v+y3qboNl2KHqNihiAzOExdgh32RM8btF5XC5WOD8EYGZERi8eSyPr+DR9x6Pnz5F0zRpRSXdYwyMRcojCBwOB2w2G+y2G2x3O4S+R+lG5cYYFpQj70hFGUju6yxzDABDYDQzT0zyloE4LRvkxM7l5YAVfb++HmOENR0q9wqmuB65K6Mw8kHtMfBhhc5fEAgXr9AnEK7zF/DhYrwWLtD5S+znvOVenp5ib7nS3mPhPoGrDjCLDlhGxJVFvCgRLmr0V2v0jy9w//gXgOdFHvOLAeX+ZghFpTDUF6h316h2L7D338dZezeZE6UCJGnGR271X8qanJc0/SaPYppnZBksg5j+0+TAuv8mMtVOwwC04iMNqQkPqH7V82rOaNHt1c/rQ9ZdKqLakMsZMDAGTXMcFFx+tu97mNYCRYFoKX/kOMY8bEyJ53lRA4BxPd5/H3j//ZMqPnjECBwOI/j2F/7CfxfH4wp9f442hbG2LeVQ45DWrjtH111it/sAMb65Guhck4C5XQLltqiq/QDS1fUOVbUfvOUYqKvrPcpy6gXPhjd7rkkFXCZHlkYBG0EavNNGKnXPDmV5wGJxamADeS+U/f4Mf+/v/Vfx6tV3cHb2GX7lV/4trFZ3tFlE2jSiTXnp2m45hsHyhhIdedAdmyt02/cxeCu94WFMj6LYoSi2KIo9nKUNQhbuM5TlDtZtURZ7VNUetjzAFR626mHLAFelecst0i6jC3gsEc0KHkv0A0BXD9/pr0bn0/ewAGIal28E0slrFj1W6ONSeN2xDv2HO3h3WSdAutI2KF3yxrMUTou4h8UBpeNrDRZtj7rsB+CuKjs4c4DFAc50gzEpD8kTOoyJ+mnq2STtCm1ksucH87E1HhZ7OOxoASsGeDNNeRJDzIJXzo4GfV1TQjkJShDvW8At0YUSrS8R7RJtKMkLL9YIDN6l3HgE6BJYd+ef43P/q4ISBoU5YL36En/2z/6PJ/SQqVuAUf+X3qwMYkj9mc8zHeVcxNfmPM9yABEb+uylFEIYAPhTO+rUXosxDh5LXB/+zffKBQXpvSP7uyzLoS45RwDdbnkw/0gbDTjNpyh5TepVDPhI8Ebzpny/zLGl5zw+uC25hdK6rgeAh69JkIgXYdhDS6Yh0HWTXmvsfcR0Ph6PwxyRs9lzwHlZllitVmiaZqjrbrcbwHYe71ye9KaW5XKS+6ZpBhqsVqtJri7uAwZkJC+yR5TsV7lpwvn5+YSPQgg4Pz9HjBHH43EAjHjnaz1+5BzJcyLTkgHFuq7Rti2stZPNG6TuyPXu+35ol04BoW1uXtRgWST7Uo7NQW4loE2+c4qnxEkfy36QgBiXIeujF/H4Xtk/EgzmMmU53GYG1dgDjs+zDPiqx1sBbBEGMA4xprxjkOCLJQ8WO4YS0s44Y8J9IiAgwQBuPNhbwlrKz5LCnXwfgMhx1FKAMVCUjEUDGMvhe8IIM6ysWQyAngRs7Cn6LEFCqh/fcwqKaOU7J9jnDEgI+hEzcR24TdPVND7G0K0cQCgwmKGs6aRC9ZFlJa+txITTHAiipIFOAswxDOox7UYAT3v2jXWUg8IMz8l+IJpPXUmpzLENgX8zYBUCEC0BZ5aUFR8DnKH2SffbGCNi6lNEk+AqDxMIZDKBnrHGJqVx9AYCAiZ592BgBk8+AiKsoXJiiHCGc85h3ORATX5cr1HhoFxLQy+n0NMRUAPlaFO0AYAw8FYceBYGsNGMGTaMQXR0jyssTMqcbowZ8iON/Tvtu/QNAbTZAmEtlCeKvVOpPWJMAYjBo2s92uaY6p688jiBu+V2WNjCoXAljCWPuGUI+Fr0+CgCvoqwS4f1eoWzs3PUdY16USN4j30HvGxKvDzW+Hx/jk+2j3Ad3sWPbx225hJ3vsaxOMOxqNGUBVAWROAG84ePBNrFiN4W2BRX2FRXFLdSAVgAEy85QTIbeyz8Hgu/xdLvUPdbLMIOi55/b7AIe9TdBot+h8rvYJI7XmDesCVsWeC8WmCdNt0IwScjhu5kz8Pzvsejpx3atoXvWgJKTdokI4V2d80Bm809rq9vsNnt0HVt4pMCRVGSFyKDycbAWAvPgJyxzOLjeI1x8ORk6C4aJGFE/R4wBYVyRq7kN60Yz8kQLWMHw9xsUBcb1PhkMvHLscb1GAyiWCYPuQTA+XN4TyGrfbwczrH3XOsv0XdfA44OuH+Af5gX7AGuOsLULczSAyuDeObgL2rsLr+F7fnPAysAH4I+V6Dk3AZwx/sh9LTevUS5fYF6/2r0htt8iaLbEf0FbbitrCDNgXJEIwLqpzJYztsAzzm5cJ/ckQP7ZncWi5FkT3pxLg9GztjSc3AWEMMpz+TqmQPVZFlcnrUWAYE8UaMHh1U7l3bKVkaOrMfEGAKmu3V/hcMYYLWivw8+AJ4+/e3J4hW3S3oOjAYZbWoQwiXadj389f1FAuLWabdW3nX0HG17hqZ5hO32Q8T45jlKnDsmQG43AG8cwkq/96jrffq9Edd2sPY0efND3pXAaVSE7gP5ydcWi3v8yT/5Pz+RRXJ1XK7qAyOPSMCF/gDvSzTNEm23RNusyHOuW6VcdPQp89H1PQF2fb/G4fAe+v5n8FVzgjnXoCx25EFX7lGWe1TVAVW1x6LcoCy/SN51++TJuEdR71DXBzi7A2yTwJjkRZfAudZX8HGBLtb0GZL3FH+PddqBlIA6P3wn7zvAfnXQLn0PsUKIBTqckx42AHd/OPDOIKQ8dU3mbwydLV0zhtQ6CrN0Zg+XcuKVtoHBfgD8LA4TLyYZ1qdli+Th3HX+zvMagxcMZHHImAyds7ZDYVosbATnUtSLIRI44Xf5YPDvH/8SPuu/B4CAze9V/+ehHjlQUZ6T4wLAZK7h54FTkEWPUd1+SYOh70zeE5rHqy5D67A5GnMdJbAl/2R7+V55XspZ3d9zc06ujnP6j5RhErCQHk5zZcn35eZKppn2ANJglAQ82FaU9JF9IMEMHRLI7+Z3SACIy+ZQQumBx8CdBj11PTiNQVEUWK/XE/7gZ5bL5dB3ALBeryc5v4wxgwcsv0svCMlwZgaJ+D6mk/yTnmXSC493feZxzeCh7rup80mYzEkAJuGVEpTT3pyyHJmjjH9L+uoxKmksQ0RlP+X4Xo8npo8GpOXY1vWX/SV5+E3tCGC6Uyr3weFwGBYuZJioLP+rHm8FsIUQwe0zMcLDD95ibEyToTLAOsM5yTjEiNMVShpwBMqFEGAC5QMC3KDo5xpMz9J370/DJQdGUSvzg9EQIgJO3cGlAOHy6C9k6yFdyuX13AoBH9pAoM8ReJICVj8nvcq4rnrSzAlw7gsGxab0oh2hmKbaIJGCgft2FMpTw4vK9Sfvlm0aB3D+XZrOI02nwm4QzkO1COQLIbk3C4VtwCSMHTzA2LUrxEggXQJ3Rw84k623dukO/lSJsDaBa+xZGAZGHjf1ELQZ6Jm8yYwx8ILvECN8AsA4hG/ynOrvSdkpVJTOpSTjZgRRjXg2qnKkABtoES0cOIsXJv0/FWyyjnTG8vhL3nEE3HhQDsUA30X4hoAjYzBs8GDSTqpwQNs0uL+7HyeNGOH7gBiBVQz4uaLA91YLnF9d4NH3nqJelmi7Fh4lNvcbfP7JDV5uDrj3NbB+gpf9Y3y6f4qP91e4iU+ws1c4mBXacom2rNBXJWLaBABt+ssdPhLrR9oo4FCssS/PgCoBchKIU4CciQF12JN3XL8lr7hu9JSr/T0Wfo+yu0fdkiedjR1MJPlcVA7VYoHap1XGEEb5adK46D0unz7De18L8L5DDD2NDRjUaVwFH3F/f4/PPvsUd3e36NoWrijgjIWz0qsWoF1UM3zPCtyMspAb81KeSWVCy08pX7XioRUE/Z6ctwHxZAdrXqIwL7BwSbGFGcaulEWsmFpboA8r9P5ikkeu7c5oF9ZwgS6co/cppLW/QNdcINy94U6sxsMsPMJqgf3ZR9iffWsE31YAnoE8LVeAKRtUeIkqfInF8eUAvjEYV+9eoDzcwujQ34kiJBV17i85/k/7QR458EMqQXxF51yTT1ix6qjrJ985J++A0UCQ85FWxLRSpvlQK4ja+857jzqFZXRdh7o+zRmr5zyum5zTeYX/D3vkZHSu/XQuoiiOKIoei8WLCa2k8q+9GHgR6XBwiPEqgW4rNM0qbQ5xJgC68wG467o1DodHuL//GmTuptcdRXFAWe6SN9wufd8JEG6TPV9Ve3Ciz5zirb9LGuW8+Pl6jHHwPtDlTOnUYLlsUFWvEFcyosEPsiPXZ/zb+4i2reH9GdpuBd+fofdniOECvV/D9+fo+xV6f47g6VqfNpYgWj+H92++Ky4dAWV5SODcDoUA4srkXVdWByzKWwLnihQK67YpRHYHa1tAjOYYgT4U6OMCwazQhzoBdRT+2vRFAuVSiCR/Rvl7OTnfxyUClq8H6R64FqNFjwV8qNEEPscT8h/usKZLAN0xgXWN+E3fS0seeZzTzprDFNhLobLyOcTDMH5lqCyf08Zt7yP6uESNI4CItq/wH33yz+Pz7fewKl/hj7//v8Xj5Q+S/hXxa6v/BX7c/TEc/TneLf4+Lt0XYP0MGOVXjt9zNgcfeu4FxiTvsr76yJWl2zp1UJjaYbJ8flaPa90WaVdp+07XQ3rwyXtybdE2XK6doyfiCCyQLDjdwVRuqKDbwe/T8w8/p/tDerPl5sBc/2v65W3ZEXzieyRoJv8YHGIasqeU5HEO49QeWRLc7bpusiEAv1vmEJM04XdSfmQzvDPGOHhFyjDi3JzB/KZpqb2l2CON+1Z673FZMkR1sGuVxxe3N8Y4uV9jABIskraqfF/O5pa8J/tbbmokD0lPCfhq2123NQcKy/pL2vHY0CCvfFYfUn+RNJC7gjKd6rqe4Dc63+FXPd7Ogy3UiKEnMM16hORNAQAhGpgYwT4yPkQUlsJ1Yoi0iCUHryUrk2xsk3I6mWG7cMr7RPf4lMhUEkoznVZaiVCiA9LPk05JxrsEBuZWB7Rg0uCZFk6T90g6CiZnxm3b9mTFXk8OOcEv6zJt++hGmxN62mjJlS8HhTZW+dACW9dB0k0/x3Tg87oPZb34O7WJvcMMrNilihPBx0hAVlGyYEx1CAEVJ6RMv41JRmWIyVtn3L2Uw99cJE7ygnbSTVoOXG3UyX5hOg6fwlgtOLF8AomN6C8TI0yR4LHkOUdp4ylUM0YgpVrD0A1p173A4y4ylpP61zC4FgBMx5Pk65xBOhjLIcA7wIBXMgxcQbudhkgALnlayb4EgCmvAAbDLqvGjF56jrzbjAmI0cIg7awKO3jPRe/hPQ1vKY8AoPUturbDbr/Dl1+8gE1QYA+P2Eccj8DSAJfLA9aLFsfjD7ErA/6g3eHLY4SpFohwqMoKRVliEVYI3QqNu0BTXGGHZ7jrrnDTnmHTr7AJSxywQFMs0JUl+roECgoNRof5nVb7BMghItqIpljiWC6B6jlQGvJimgHkAKD0hwTAJRCu36BOv8vuHoueQler9h51v4EzLaxzKGuTvBp5/EYEw2BwQLle4/E7zxHT5guD/PAB+8MRL1+8wPX1K2y2WyBGFM6gKAuEblyJA0YFtO86FGqMSKVB8hhwGq4ux46eNAdOUuOPP7WiKXNAcfmyDP1+/i3lfjqLwm7hTPKWq8zJ5K/bYa1FiCV6f44uXKDrz+HjFdr+DD5eDTnlen+B/XEB0z1Bf3OJ/tXFDAPREVGjwQdo8AE2NaZA3BrABYB3AlyxpZxzuEaNF6j7z7HoP8Hy+Al5w+1ewcU+a+DoPpLtzM0heu4Z7hvm3yl9mWekZ6HuH/l+yTfcNzJkZq7Oc4aW/K6vaT3gcBi9VejPw8DR7olmBGNkKBeAyULjnHL4toc0NGXd+Tcr5Ewn/s7KP4e3cLtyC4TGAFXVoChe4ezsetAxZI4/yfNTuhm0bYW2PYP3F2ialfCgY2Bu6j3XNGscDk9xd/cRvgoAwkCR9JiTnnPkNbcl4KjcYLE4pM0gdjDswSv0EamvaENNHnqs6zBiLje3Gg/win0/gFgpInDyXv7NxkE+51QJ789op1a/Trvfjl50fbemXHRDmOsafUf56Q6Hx+i6D/FVwojp3d0EnKOwVwLfymKfQmB3JIOKPSrzEktLXozObeHcFtaeLpSzUTka6SX6WMJjiYAVWl9id3yGn3zy53Dsr3B+8VM8evp9RLtCtKsEzC0GUK2TobKhHvPfhUTsNwDp5r3uLCJW6MMS8KDIn5iZuL/yEQhwM+RpVxYNLI4o3RGFpbDYqqBrh3CFn+7/GDxqLItr/NLl/wU/vf2j+Hz7PQARh/4x/l8/+pfxT//cfwN1cUh17/CR+ztDKgPWm7M1ycyNei6nMk7nB/6dKzs3H+eOnNzWC2fSLpHv07qHtk3kc3runtOFc+3Qc5a+LvUTCRzI6xJQkeHGOtxN6+y5eVe2Weslr5vzJOAiAS2+X84ZXA/5x0BFbrdKbd/JcEPprcdjXwO9kn5sh2m7S97PZUqPZKlb8jN1XSPGMXeoMeMmHFxffl4v9HIZmj/5ugS/ZJt0X+TGmKa9DJ/M9V2uTF2OpIPW5WQZsr4S/JSAmv79ELgsy+f3yf7h9/Gn3OgwJwO0/jl15DET3meelGCmDlfl937V460Atj/zn/lzCDGgqkr89m/+Jl5++QW65oC+71A4MiS99+QRAVolMimcMfQJTnNkIDu6YdIJMQLRAzwRESEiXGYwj0RNHi3sZWAZHJkqmgwADEIYesvkAJaBmoEHZRUp/E3s3EiORZRoPGLMD6Lj2DWzTQzWE2DJIsapgfDQpKWPOaNG36PduPVgynmO5Jiaf0t6yiO3+iNdh+VgohDjablt2w5utBz2GKJPidwBQNMZgGXwj3bvJN4QSq6aCGyZYuYDJaY3kcCGwliAjbeUoFG2X09mOvkot0/2u94pRtIPQEqM75MRGgFrEmiV+DCFKxpjUphrEgaYqnDDpgXgzxRWqvqQ6RrCyA/SaJKbb0zqarkoserQcjuoH6wpABNhDCf7ZsEtk34SEMfPyfEz1nMcszEKcDLS7qnGGCCMY8zBIEGi6GOP6APaGOFigIsWPRwWaUHJhIj9dgffeezv99hvGsph0jaIAPZmT/1SOJgI8uIqC6ytw7kr8K16CecsVucrXF5doqpr9J1H5yP2vcHeV7jtz3DdXOK2ucB9f45dOMMeKxztEm2xQFdV8HUxAnIP7bTaR/KSiwBsRO9KbKvH2FRPgNpSyKr0klN6rAvNALot+i2qfodFvyFgrqN8cq65xdLvUbX3KLsNAZjgFV6gWJ3h/OoSX/ffBCJxl2VwGxF3mw0+/fhTvHz5EiFEtKGD3R2xWvHq+zgFsdzQvCfHxBzAwvdLXpH3alnOBydb1SAC/2bel+/U789N7PJZLksexvCKtKed+PAllotTI56/f/rpp3j69GkC7y18WMPjijZz8BcJiDujMNbkJdeFC7TxMfrdJfr7M8SJJ4uFxwU8LnDEh9hMGAOUe3AFYNHBVQ1csUPp7lGaG1R4hdp/iUX/CRbdx1iEL1EWGxjFqDnwQB8TAEgoZQz8SJrqsuWh51EZXiF/z614StmtjUM+cvM492MOZAkhTMJk5Hs1YKW9LP5RHppH5SEVYAYkpQLNz0sezs1XUiF/yFtwrFNEVR1RVUcY82rS9tePd4u2XaDryGuuS7uytrxba7tC06wn4a1tu8Zu9wy3t1/HVwPnGIzbDiBdURAoVxQbLBaj1xxvBEEbRewAnC4E6zbN9ZFc/GQem1vglLyoy6J+DCjLDYDNg/qjNlZGvga8r4d8c223JFCuXaHrCJDr+vXoOcebR3Rr9P0Ku+0H6P0beuqKw7nDkI/OOfKSK8sdinKPMnnR1dUBRblLIbAHIB7wD//efwXH47sAgHt8G+uf+xy/8N1fn+T9mRvfTJcQDfpQoY8LdIFDYmt4LNEF2oyg89UQOivvI5Aun/MuPJTfbu775DdF4YRYoY0l2nAGNAbkDfowXx/wGP/R5l+QLQYAtP4MN4dv4J2z35ro7jzO5SK4PHLGtzwn+UmCDcBU3mnbg8t4yKaZOycBPgmWyDbNtSMnp3LtyYEbsu7afpJ8pcdYzn7Sz/IznM+Mf7dtO+gvmhZSFusy9X1Mt9H2muY6kzaaBrpy8l4vPvBcwl5snBvMez/x5pK5vtiTjenNIdFVVU0Wq7SOx+fYTuQ6SHuM7+V2yPk5l/tT9ivfP7VJpuGyAAYQSHqn6bGlZTzrOzze+D7ZX/yMpI3Ubdi5Q4476WCj66l5TdZT8l2OT6XuoPlP0ob7n++VgFnOyUdiNbqs3JGb9/i31uu4fJkTjmnOOqeOfNGLlV/leCuAzS0WMDGiCwHf/M538c1vfwcxhSKF4BGCx263w5eff477+3scDlsYQ55BIUZ43yE0HoUz6NsWzlkUrgCsRR9Gd/4QgL5vYUzaiSYaBB/IXjTj4JfKKzFOEh52NORjEMzjRyCOc0UBFtZNJwApUCeCJ4LCnYKcGPi5BBr0AnDB6JGEFOrmMca7M0/4EIb8cTFEGBOG3HCAwbjt39h2PuSqjU6MKe/Tq6fyPj0B8DktcPWAkkgzn5PXx3ZOBwkPsDngbaQtfTLjnw429ojSE7CcRAhgkscILI5hshNacdioGUOXYjhNzq0VWl0PuULC17QCMV0hA4IHoiVAypowUXJs4kUfRh6S79STiZxM9b20ux3UOTsZU1xOzpuBLunPsU1cHtEJiJHDPw2sKQEz9XTIgRmaxnLCjDEM4cx0JAXaGPQ9hfeWCXirixKBeSumVaMoQNAQEYyBcwahD+i6Hh4czhoAk7xqkqzzAOIhIBqDyABsyhtXlhXKskBV1VivzvH4+RM8WQPfLO7R+Hu0bYu27bKhZCFGtL7CAWvctWe4bR7hrrvEzp9jG89xwApNsUJb1OirCn5RAC55AXsAB+SPjgE5WoAIhcW+vMS+ukKs3WsBORt68oDrN8MmDnW/QdVtUHcbFO0dyuYOVXOPorlB3e2wOrvAt769wHe+8/PUh9bi5uYaP/7RD2AshvwW3Ley/yWvznmRST57CKCYy/eln5NzinyXfk6vfstypIzSYR1yjGplVD6byzfBspbu2aE0e7j6iwnAo8f31GgsU+64i2EH1s5foLXPcbTP0Jmn6OIjyjnXrRA2NeKLAj6W8DhDi3dm6QsAKD1s3cCVBwIbzB0qXKOOX6KOL1DYe5TunjaGcPco3QbO7icyQ8oCCa7LftFzlDwk3TVPSX6R86U2UObmQ61Qyh0vYabgIF93BSbyUper++kfxaEXf3LXpEzNAclMJ0mrnCEtD6knaSNSv4sNFT3X545TozVSPrHFEev1qYIu267ndO8jum6Fvr+Y5JwbveTOFDBHAN52+y667quARCGFVY755JzbJCDuftgAoig2sPYu/d6ka82E/lpfkPJJzvM5L0Omn/6uDXFtzEi91xjA2hZF0WC5vJnUQ9Ja6zOy7oBDCEzP1ZBvrkkec227pFx0XcpD15/Bs8ddd4HD4T2E8FVDXQEg4uOPfw2/8N1fz8qMOSPOmojKNSjjEUt3Cp5ouSx5W5YtdSCAvNp4N1gC7AigG8G5xRSYw3IIk+WceP2wo2w95McD57ejJs+Cdib2WPZ32HdPxEWDZfFqwmdzfapplbMLcnPn6RieygNZjgYTHuoreU9uEUt+z7VPlz3le3PybO7QY2luMQ+YJlvPtXlufOrv/B7WB7j9EhiSNMjNBVInkfoGQH3IoZFSpuf6m6/zPdK7i/uYvcmWyyVCoE0o+LwxZgjplOXoeUQCcNw+XW+uuwZdckBOTr/k52QYqQz35LbpxQ5ZpuYfWQ/5x+/g9kjAT8oSKXteF92Wq1dubpR9L+dtfY7blMMANJ/l5hD+rukjF+9yc5a8JvUu/tMYh35Of9d6XIxxAKflpklcNrdLns8tyrzueLsQ0QggWljjYJyBNwHRpUFhKjhj8OzqCZ598CFiDIi9hw8dClcg+Ajft9jcb/Dll59hc3eHvutSWFdE8F1i9mTlmTLlnjLwPU/aAUVVwMSQQugIhDGGdm0kgiaDnlLgn3TSKFSQiG4A6ZGWUUA4txZNSUkQgMIL6ZiGPOVACi3AeVANA4pz23FbfCSQDQbGOiBwe6YgmRQQuUlLH7lBm1MS+D6ZAFO3J2eAasbXCoqkUa6+o2Bnb7XpKoI8phP/NBccv9sYi77vToQv95v8HCasOIZphhgoHJOYClBCyJgExkUBZKXfDCBwm3KAAPWjQYw9jCkRo4f3FG5kXQJObUqcHSKNCYOU6F7zLO3wxGSaTjADVQizHTYIOf1ObTsVjvpd4ySUX9UbvSIACvU0MCYixJ7yQGFqaOkxkptAeddg6h8DY1jR8OS0Gul9BuSNGINH7BIQbqSC4KmTIhCjBVxJPnKRdscJtiCAPAIw3Zi7TlDGh8BdjWAMrHfwTYdjqvMr+yV++vGPsFqdYblaYLla4p1330W9XiMCaNsOXd8N4yCEgAIey3iHx4s7mOWnA41J4Ce5xSQJBk0osQtL3HdXuG2ucN9eYRvPsIvnONo1GrdEV9bo6wqhLhBTSPWw06rebTVGCmX1MdE5AAVwLNY4VmeIdfFaQA4xoOq3qLoNqvaePrt71O09rr53j/b6Y9x+8nuwh2sUzR3q7h4LGyfAhRzzWsmb87yV/CJDDefkYU651oqqPHKriHNlyrKkfJ5L2KpXOnV7pGu8XtGWddNyFgAMWlTFNSpcn7SJ6yrLGjYUimdo3Ds4Vh/hWL6Pxr2L1j5DGx4RUNevEdoFYlMi7FcIuxW69skszjt9qafQMXuP0t6itHcoXALi7OYEkKvKLUp3P/GWy7VVHnpRSc9TWiHW85RexZ7oBCHgeDxOVo6ZZ4pkVEhekYdW+N5GgcsdOkyHz2ma6HdKXeShuXxOkdfzas6YlSGMujx9TpeVO7Ri/5DeQ7oOeUZZ2wB4MbR7rixZtxAsmmaBw2EB7y8mO7SyFx2HunLuua5bY7f7AHdvmmsRAJDGRLmBcwS6kVfcJnnJbYZwVtqdlUJfV6sGzo35BzQ/5xY5JdDMz2hen+oPeUBK0kzTnhfCnNtgsbjHYjGVc3KBgH/zc9OyK4Rwjr5fw/tzhHCOEC7Q9ZSbruvX2Gzexaef/lOT5wp3FGXkjbLcoXXpnOGsPVEeOqy1NFX6HUq7xyJt5hTdNBcff8oxxh4xwNQ7ho102Aoheddxnrqjv8TffPkvIaAET86Pqh/jH7/6K/jrf/DfQevPABh87/m/jYsF5VmT/J6b16SM03Iid+jnc7p+br57CKSaO5+Tz5q35X2yjXIcaP1T2nH63VLmSTtK0krbkrKM3Fyt6y+vse6gaarL0Hw+J6clj8l2SocA3VYGgLRXOOe0kp5JvNsoA1PssSbfy8AZg0ty3HE9GNxiUE5fy8ktWW8ZSivbpn9Luujn5TifW8iU75bJ/2UdcyAT00TK6Vyo7Rzvsdzkd02j+077UPKOXnSTdZE0le+S9WS+1GXP6aHyt9SFczqW5BXd5twY0jrJXH30eGdayw0i+Jk5R6A3Od4aYONQrmBTor5YoKzK0fgDAENgmF069CHAwaCwDq6weOdrBb71C9+llEs+onAWQMB+v8WLz7/AT3/6CV69eoWubWAMsKhrnJ1d4IvPvkBEQB86EMxlYAMTnt4HADF4uKKgid1aOEueIVZ10GQwcRghAzoh0u6FJrkS8sARyDbR41RQzK3MyAE2GkXzq8GEBgAwZgD4gIgYgAg/5A9iUFJuMkF98bCLsF6hlgOIr0sa6frrtugBIsvJGS7Sw4N/y/OMUk3LJRpw3abbp8fJ+6jeFHIaPHkSTldIQnqGaJdaQXn/zPQ9xtD1YZdaI+9HSoQOIAbKvwMDa2i8yPqfesAVILQsZVTLCPBRmIjzIVLIpzGCXmMfe8/0ZoN5FLxUXgoYjfK5kZf0kTM+xlWX0d1WCkH+DAkYpk0nxnKIFmMoFxG4ANDD+4CiGCcpLcypOgaAG8AdM6JhqVxDfesIwOQbjeGGG8BQ8rboI0LXwQePrj+g7Y/w1gG2GMDSGEeALaaCBnpGzjkXJ8nbEQAbLHZdj/19hLEFPv/kc5Rliaqu8fjJEzx7/gzWFmj7gKZryTuRaSx4R259boaxHrBAg4Vt8Li6AapRvrFyQ+OENi2IR4MGC+ziCvf9I9y1l9j0V9iFS+xxhqNdESBX1fB1ibAoEK0bIevcxg4xAXJ9BBBgTIBxEX1VoyuX2C7eA9aWuyMPyAEwzT3c4QbF8QZFc4vyeIuyvRvOueYWZXOL4niLqr2DjdOEdtpY1gsPc4q75tncPdPmxtmk9Foh4fmAf2uZpeuvlQheSZ7OGUGM8ynQ+DpDj++R8pvfp+W0MUBhdyjwA5x1P4TpT3Pb8eHrAt2Tp2jWT9Es38Wx+hDH4j205hlaPEHnL+H9GXCwwB7pz6HfXaA/XOCw/wDD5i+vOZzdoXQb8oorEgBnNwM4V7pNOr9BVdBf4Y4nCiq3RXpT5XbCYoVYnmPF/P7+nhY/RB/GGMnbWe0emFPeH1IK3+aYMyxle3OK8ZyHir5PG366LTLMVI+nnLGpdYVce+Sn5Puc0aHfmVP85wxm/XvKBwFVtUFVbaDBOQ1KMM34nhBsJq/cmfCeOxt2bqVNItYpBPYjhPDm4JwxPnnNbUVY6w51rTd/GHPQkSfdPcqSdpHWfC95V4+LXP9oGaX5S5Yh5VruXVOZ1KIsb1BVt2DvHR5PrB/0vUcIBT7//E+k2gX8/M//70/mghz/zvGBHAv6ux4zmndY9j/kjTVHz9x7pNyWgKlFD2e2qIrd+Fxl8Kee/qv426/+a+jiGmfuc/yTj/4yLqvP8M9857+J68M3sCxe4aL+/KSdkn/50B4/c/SSn3LOlfSZkylzcoXvyclLzauSbnou1vWQ8jhny+g6aJ6W9lJubs7xnZYZ8pBjRNJO0lDaYBMemNg1eTtM86+1dtBjcoBa7l0y3FPmw2KwV94vc6nxs1L/4d8y35oEXSTQI4Ep2T7df7oNmm9k2+TB75O8pg/9jtw8p8eJ7odcv/BzEsjR1x6apxgUys11+rweZ3pcvG6+1ONxTibmaCyf105PD9VBvo/vyzvLIPub36fv0eNa6naaNm9zvBXAZqyBMw7WWVhXnOTqoD/AuALGANYAi6KmQSSAC0ML5AiGwk1jNHD1BZ5/sMTT976BEAN88m4rywrN/oCv/8wWX3z+OT7/7BM0bZN2FaSNFIAI73tEeATfIyDtKBgsikj3GWMotxZARqEBXCQwCjHVHxQ6F0F1KhQSLZUA2VF8PpuzIDOhjoyKCf34ntyEzJ9sVFkzbqsbfICBoxxd0SCGhDWoXTyBabgMMxevfHPeGF0PPQlq9FneIxlWDyipeMr2yfPSyJH50/hP5ueRaP1k4mZgCxSya62D933KfWbgfQdjTpM8OuMI7LIRDNgWrhj4hxyeWGiTEWrAQB7VlQHoaMSqcRj7fRgjhkIOrXWQY1jSTJ5j/pKeAEwD2V855YMntrEsFnDk7dn3Xr0LJ+8m+o504TJzHo65vpcCLafIRESY6BMPj4JvWu8IxALspRdjP/IWAN7zEZHxHAJQYyQwSk5WnLcRGHnNe4+u6xG8B2waH0Da8TTxrE0lTyYTOwCunONOKlXOEU191yD4Dn1jcNgCt9cv8NOf/hBlXWNRr/D48RM8fvIEiEDre3jeLCIEWiSwo/uyVsx133MuCj5YllXocY47PC+vKd9WwhpjYI+81E8+IGyBo6lx362x6Z9i0z3C9XGJPS5xtOdoi3N05Qp9VcMvKsSlQzTFCMjlNnYICZDzASYGIAFy0RXoq3fQX3wIlO61gJztDyiOtyiaWwLljgmAa+7SuVsUTTrf3sH0+wG41PJVKxLy/OSdmRXtE3mPKaDB92lPBb4v9y7Zj9JVnZULrezLeUUq2tk2pLHCz+UUuFw7Zdlc90EpCR0Wm89R3X2KC/MPsgZENBbt6jHa9VO07z2lz/UzAuVWT9EUz9Cap4hNLUA4ALvxu9n2iNsC7f4RmsNTxObN1BiDfgDdBgAuAXRVsUVVSFBui0W1S7nlpvmv9Epz0zRpfE8Va+eKIaGzpCEbTswfr/PW+KrHnG4i3/GQIZEbCzmj4E3HED+v2yjrIueD3BiYM0S0Ej+nCOfqLtv1Joc2AricXJv4+2iURli7wWKxhfefDhseSBku68hyomkaeG/RdbQZhPcXCOESfX+ewiz5+zlipO/sUXd//xG8X7xR2+j9fQLdpjnnGISTgFxZboYNIpy7R1G0w9wo5Yk2UHK8xka69hKQ+h7rS8hsxDTV3wz+U3/8X8Hnn/8JHA6P8eTJP8DV1Y+y/ZzjLXlogEnqQHNlzZUv9dvcWJDt1fSS44TplMsVlXv/B8vfwH/xg7+AY79Cbe9BTggWldvjnfVvntRDljlnfObokAMvpQEsF/Llu7R+m6Mfv0cDV5o+Obmkx6r2zJEyKyfj9KeWm9qemer3p0C1lm25RWn9rOQfCazKvpeLbLmyuO481mQ/cC40po/sp6IoBu+v1WqFGGnBj58xZkwIz/dLAFHOk1wnDQpKejBNJU/L73JThDmey+lW/D3njSQBOtm/0mvNmBEE1KD6Q3NQLveYvleP34fmI/1srq05XU7fL9uck8163Mh2zo0bfY9sa44muXbl3pVr00PyPEcbeU+Or6R3cI43v4qeII+3BNjsJDkhWz4xRjhrwBsL8CqTdWMn9oEMXyTPrJQSCCGQ8RkjEFPIGAyAogRCQOsDUBY4u7zA+aMrfPvnvzOEabm0s2LwHofDHp9++glevHqJzd09DFKsegIguq5D9CQgDAIQwgDEWSYkIvoh7prABANL3h+Qgg9IkACMdUNuJmcMuuSxg8iK4HxegsFox/zkMFA6nXPOUV2Qtk6EgXVAiD3YGjWGwIaI08GSUwzZG07uAsbXci6g/IxMKJ0bgPJ8jnlzbZeTDAnTvPHBg0S+cwCbrJl4rBkbacfJCBjjYP3Yt0NZDM/EBCJFerbppzsNnk5mDL5RyLCBQbAGQD9tcyDvNsAgBouACPb0hE+GrzDAmL6SZtJg4/qM37kfiGanE8L4jLXsTUYrvzkBCeRy+DkAZgQRlXKiAUvNZznlgZ5PNI0RRUoGHAx5BYYQELxPoI1NIWJ2aG/XMXARUDiLkj1KrIGPEbQD6UPhzAQcGgvAlgAsCSdL4A+MSTuXEhGp/twuUv4JVCPxZ4QcBI9U4bXE4bi8WUe78+j2BzR2g7uXL/GTskBRlKjrCk+ePsXF5SOYqkCERTAU9up7zk2Z6JlROmX/sWt9jHK11g7PxghEG+GSrAshwDog9hHmeI148yM8Wy7xjrXAIr2HGkObvDQWprWItwG9O8M2PMZd/wjb8AQ7f4mDucTRnKF1a7TlEn1dIyxKxFWZqMQVBoEp8ggRaCN5yEUPYzzgAmIBdNUl2sVT4KKcgnGZ+dD4lsC34w2KhnLGMQBXtXdwCaRjwM419zBx9IaV40krzGz46CNnAEje08pKTjHg5/ga81IuybEcd9pgNcYg9h5VUcIA6OPpQohsD7eZQX1tbOn2aFkg20mFelS7l6j3rxC//P5EyRnmKAB9fY5m9WQA4NqnBMK1K/KQa9dP0dfnVGaPKRi3B8x9C3d3gLtvYLcdsI/AwSIcSzTtI+yaDzBsk/eao7B7VMWWgLhym8C4LZb1HnW1wxd372JR7lCVGyzMFpXZ0OJgiHAubzjLOTVH+38UhwQ5tOzTim9OudZ15TJzCmoOANaHVtC1Eq7v4XfLTx5/OSNYftdjaI5f5bNaJ9J1159ynOXqnKtHbo6dM5IATvzt4f1LFMX1xNCUxlFRFCjLckgePl6r0PfnQ+iq95fCc26V2bGVrh0OT+D9m+c8411Dq4rCV3kziBGU4/DW7eTPuTsAzYnRK2nn/QLf//5/Gy9e/FMoyw2+852/jPff/5sDnTS/GhPwwQd/W/TnqXGoZY7mG32/PjTvsle57scpjU6BYz7PIMlD9/G1ubE8f1/Awt2l8k758yF+zfE0H7kFntyn1CO1h3YONJP1z9XzIVmZ60d9ba5vX/d+/Z6c/OF7c56tbyLj+T1zCzp8D9vWfL/0+NL2iZY5coMA9jDjUE7aeGp0mmAQzZjpJjh1XU/sLq6zBLDke3S9GIiTehWXIev7EM2lrjU3DmQfSPrKZ/R8Jucm+bzmH308pLvpOTHXNjkf5Hhv7p258ZMDLmW7pIONLIfv0fZ+jo902fK3plFOruR0jlz7ct9fN77n6CXfJenG2AcDyrrNb3u8FcBWljW09UKKF+2iOSaJY28TBs8iQhDMECIBbMmzh3zHgtjhUAI1oBxkhjcFGHcVJawuwhUlLlePcfX4EbwfV8QiMJT55RdfYHt7j7vbO8RIHm/W0V6DXdOi7xu0bUvgAbct0vUYPCxCyvGE5MVC3+MQjgZ00cIWFtFExGgQEy0IuhuFkexgbqcGM6YCAQNN2fPG2NHzZnKvTdcSoJkbIBIYo/LHiTBntPF3PRh0qOzrJn+ZxFErKlqIyXfJa9LLjs9NhcbYRqaHnIx8H1P4oZjIjEkeSYnGiebOugHokUqBrP+gDCbPK4B2zo1gDwYC+8IQkkpgXCIcgTJxDGr0wRPPA5PJWobFSOVS8oycbAHpPiwEnaFxySG1mpajcjt6tQ19BQNjx805ph5hpzvDzglDqYhMFKgYYdL3gAifwkpjEO01LB9GQJE+KU/dUK5KMq8PPTmFEOB78mDz3gMl9U+IcUAnQ2TgIUzGijECJJpMIHzWiO8yEbtJbTHwAQTohB590+C4i7i7fom6XqBaLPHo8VM8fvoEVVnCW4cAwp5831OuOcGPc8qNVoBDIJ6NMQxgvA8ddrsdqqqC76msq6ur4TlryVsvGj2pEl+Y0OIcX+JZ+emEzsH7wXvYHwNwBPyNwd4vsPVX2Phn2IbH2IYrNOYCR3OGo12ir1cIyyXiqgaWlaBiIukB080dPANyASZ6wHgYR3nk+nqFvrpAPK9fC8gheBTt/eANVza3cMcxTLVs6ZOAuRsU7T1czOexlL9zQFXufnkvyzg5vvRW83PGijTgbOHQx4Ah+4C6n5+XY9RYC+j6xlNvBz1PwJiBrHKMSwXuZA6KEcXxHu5wh/X1Dyf8LL/7YoFm9QTdWQLf1s/QrJ6gOX+K9h0OVX0KZMLdEQHsO1TXd6huNijudnB3R7htD7MLiHsAxwK+XVC4nj/H9vg+/H6ZYRIAjepD9Cl3HHnFVY7CV6tii6rk3xy+St5zdbXLl/0WB49R7a2Qm2sfMoy1IppTvPlTemPJMrTyrt+vAbO59uj65nhdHzn9QZY3XfTIh7DybzlXPfRevjenM8mxSvrnCMxIr31pYPP45bEvvfdZFkxTP4zvdM6jKO5QVTcZHelhA6brrMgxt0bXnStgTuaeWw+/9/t3EIJcgH/4oA0UCJijz/shx1xRbHB7+6u4u/tVAEDbPsI/+Af/Mi4ufozLy08m5WijVBrwOd7N9fdDXsFzfZyT9Zo35ozDwUZRejDXRep4Wu+UfMRlzuntOd5/aNzoNsjFI60TynY/ZFTnzmue1fXL1VO/JzfGtZzRvCE/pV70UF/m5Feufjm5+RAfyU9guiCq3yV1bT6nN4uRHr78m8vhXRL5PVVVoa7rQY+Qdeecapr/eKEWoJxqcsMm+X5tn0h65LzbZJted8gy5nhc95E8p/NsPTQ/cXv40HU+0XnU+Vzdc+3M8SjXQ+vv+l6ZvkKXfaLLzfBg7pB8l2tvrg9y/ZFr51x/PVQ3TZdcXR+SvfKcbpMElx969qscb+fBJpQmALQLKCfbHLzZJCOMAsYHdvUmUIwcsCj8iXYXDAjBJ+whDoAUooGx5JVFxBG5F9L5QZABsEVJXmkDMxBA8sHXvgHzIQaPqIhI3m0xYHN/h+ubW2zu7tA0RyASaOcihYEcjwcc9lv0vh8S5jtjEA1gQxoEhoz9GAEDD0QKt+IE+SYZ0xOjwSYPOmtPJsyBUlRgaicQgxkMwhiFZT/kiIonSt60/+yQa46vTwWR9NLhWudDg2S5k0EnvRUTKApMAaOcUJJl6Lrz78IVRIhZJTsl+Z/w4XgUhUsgjTDwjAHvdJ7wm+TjEOAsEDhxPg9MS3Q3xgz1MCYO2BmdduSlBEPeaqLvwQLRUr8FQQvDNEsdbmDQM5CpFC0pWGPkUM/pJgdEbzP0rU3hjNbmvU34XM4jZ9zNdgrqaqM5R/c5QUzC3w/lm0Btj8bQ2I25MqTyQ/HmJgLRpjDHzMREdMiHzoVAO5L2fUDbNImOSbYAgwebdVMjhcphOnN9TidcCBBeXh/vi6kdaUyDGNgY4Ljf43g8YHt3j08//Rh1VWG1PsfVo0co6wVgHcqiAO8KKz1tg6KFDGngUKW+63A8HmGsQ+9pp9eioOTI1lmYCJhCJShn/sTUAOW2GGNQoJhct9bSSIgRJoHbLhoUtsF58RneNZ+OigCA0FO4f79tcPvxDb74/HMEWOzjCn7xdfTV19GX76Gv34Uvn6KvH8MvzhEWZwmQqxAXdaIJMxAIEJGgiI9AEwHvYYIE5CJCVaCp38Xx0dcBZwcZkQXkALh2I7zg6JPCVjmn3C3s4QZlc0f55XwzUX608iS9hHlcyZ2t5L3yGH7HOHh/WmPGTTlYNhqW79NmDUYkAKNAkBijgosxyuMYRxkJTMKljVC+59qbU6r0/GSMgeuPWN1/Atx/MpFZUjcJxqFbP8Fx9QTd+hmOq6doz56iWT3BcfUUzaMn2L3/IaIr850JoDrcYHm4xsXxx1hsb1De7VHdNyh2HYqjwY9/6wew8QLGPkYwj+DDJfp4BR8u0flzHNoPEN/QW+7iV4Enj4AnV+Lvgd+Pr4CLs2EYApiGf0iaSrrM0ThHa61E68UtqeDz8TpDSRufc9dl/eee1+3Ivf91Sv/r6iiNLbmb9lxbckaQPCSwJ+cj/pP5xbgPyrKchAhyH/CCtpQLucU3bcjqumtwydoOdX2DxeI2S2P5W+uaXVckj7mLFLY65p7j8NbpBhGUe+5weA8xPgTOGQAOd3c/i6urT0/amaN/rh9zbZgzzB7i5Tmv0IcWUGZbJuqWM6Z13XMghR6/8jn5bM4LdI6nNbCcq+8cSDjVb07znOXoNVcP7Ukk5ZM8n5Nzrxv/sk5yIXvOiJfP5XhK9omem2RZfE7bQmwLak90uYENHxJcsdYO4Z7yPVVVndCJc6i1bXuyKM/f5aZKdV0jxoi2bSfhktLrjH9rrzteGBhSG6lF35wnqqRdrh8eskV1e+Y26ct95z/tTaeBNq1vzNlR+h25OWruU/e/fk7TRF7XPK9pxuXn2q9tMy0rXkfLh8bbQzTJPZO79lC5c0CkLE97eGo6yL6fq9ebHG8FsPGErnOveX+a3FEb2jFGRM7vQyeA4BPARqFPkwYlAIPCTEmRHFbsrBb6CbwzNj0ncnfBJAMAiDaBGgwcWcCagEfPn+Hy6VMyDqggFK5ECB6bzRZ3N7e4eXWNw/GArm3hUsf4vkPbdWiaBof9hgBCHyknSyAA0hikMFTyYyMDkv8NtU8gTAJxIhATUsOeRsOAA4WdhhDgCirBGbnj5qliMZlEYwLZ4ulq1NBPIfUtBnIgxumKrwYrYqRNICi0lgUQ9yd9Tr1+piu23Ea+DwwURD5PQJRLRro3px5w04FAIK0WhoNpGOOI/XFdY+IMS5tocFsLBqhE8YNnx8DbkUJA7dhuPj+CL+ndiZ+R2sff6R2Sd4l3ohnHhFX9JWnJhevrIXBDOSR7BPz4ec0HWWVpsJ1PBRBPsjpHnBTU+h0j/4g2MJgeqd5U9jhpj2WOE6gxgI3UZ0Hkg5mGzIy5y/j3hB9BAaghRPS+T/nBAKRyY+KZ1PrURyNdZdtyE7/k9Ry9p2XQeefIG9aAQsDD0cM3DfabA+6uX8EVJerlCudXV1it16gWi2H1MYo6sILQti3RIAS0bYcQyZPXWIsQAWMdDByFh0besKGYjCGt+HO/8/VgEogOc/IcjIMxBDlQOQCSLKPwT4A9ck1hYF2JuizxzmqNJ0+f43g84ubVK3Tdj7Hffx/393coCocq5QLRypE3BXz5BE3xERr7PrryPXTlc3TlY/T1I/jqHGG5RlguEFclYq1CVoHTjR36CLSBPORCDwpZ9TBFBCqDrnqM5uI94HExesnNHJM8csJTjj3j+tvPEbcv0JY9TL+BbbfDRju5cSVlqVifAkDAM2Kc8MUgY1J9BqA/M1YncwgyPM5jOUx1gRinHtNzRqD8PTdOND/JcXyiiIcei90LLHYvgBffh4WDdYZm2hjhXIEYDdrlBdr1M+wXj9CtnuG4TiDc8jGOqyfYnn+Au8c/M9+Hxw2q7Zeodi9Q71/hbP9DLI4EzFXbF6j2DXC0BC6EC3T9BTp/jrY/R+fP0fkLdP05vv71X8WrW+B3fghs39ChrSiAx5cj+PYHP/xLqNwGi3ILZ+5QpQ0gqmKDutiiLrao3BbOTfMBSi9H2W+6D7QhJr2qZHnyuh6Tkmd1rht9vI1iK5/LzTn8e07JnytvzuDLlTsH/gCnm5noZ2W44BgRArGQPX2fNHTZgJXXdVtzxulc++X4kvfJZ3lu4QWbouhQlncI4WZSjnyWy+OcTlQ24H2VNnqgjR9+8IP/8uDBhiSplsuPB6BX04//ZJga13/OYNXfc7yfo5H21H/IAJTPS/7ncZfzKuTfOV1e8liOJ+b1iumnbnPuftlfWt7K+V8+l5PHuk2ah4wxQz4k2a6H9CYpT/R88lC7+E/uYjsHTOvnJb21DaRlmW7nXH8y/eqaFgTHXeOnAFaMYw5qzvEpo4L4mqyX3B0xxpiitKZgGMsa2besR/I5phGDeF3XDR5tkh818AacRslIOy8HSOrvsl+1HNbjST/LtMjJi9x407yWe+/cXKXHaO6e3G/dbn28Tr5oeuSOuXfP/X7d++fo96ZHbqw9dO+bvOd1dGKQGiCQmXmd51ktU99W/wDeOkT01HUUwOQ7g2tS6PI9xpgESqRdHCMBYAEgYMHa5BxBYJRzjOZLBcEM/m0xGbgxAhEOxvCOdwYxGcgGU0YwVih8NvmYmdE7BTEiRINj08OHDrascPn4CS4un4xedRQbir7vcWwadG2Lu7sb9H0LxIiyKOG9R9+32G23OO53aA5HhNBReGvwsMbAGWpj7yM4vbhFAsmSVw4bnNScRLsQEw0ZvLKwdm5Vxw5Qng/kmWONAIYmRpKZ0HQ8n7AgkzyyYvI8BBlnAQTcTRO/j/Ch5gNWxlihlIbhWB96F1IuvFGA0y6LnJXrQYEnJ3r+ZCASEc5YsSOjAtBUmdobyhiC8AaULk4nzXGwaxqL1wzgWvoZImDCQDFjGNjB8FBOfGqlSRs6ZngJfXLTtJKsJzQ9hnN00ZOZPCeVj9dNALotAFLfGBhYGgeI8D2vSCXAMYXhmtQPr9umWoc1DwpXgrnZm61ITM/VsQn4TrUcAfIkiAwSeR+YfLWSeMorQ4EA9GYgAQB7URGobzuL9njA7v4ORV1hfX6Bi4tzrNZnKKsaZcqv0Xc92q4dkrLHSPIGxqEoSKpETEMBdZgN00DzBBsz8h6pcMpzegzRZVowoPx+IzDqHJJHHRlKpS1Q1gucX17Ce4/dZoOXL75EUTjcXF/j7u4Wi3qRjDyXvJt7FP4zVPYLnCkFPKe8eVOidc/RuA/RFR+gKd5FVzxFXz1BX13CLy4QFqsJIHfCzT2AjfjdMSDnEyDXwzgPFBEoDfr6DO36MXBVzwJyn3E9hzxyY4hqmTZ1cMeb4Xs5eNDdAzEQyBaS51mS2w+tAucMbz1XaJnDNNQgu+bvufGvx+Pr6iXv5XPam0oaT1Q3Hs8GIS1pVYdbLJp7nEfyrISiDaxBX63QLp+iOydPuGb1GNvqEi99hf78ObqLd3B8+gAI1zeo96+wOLzCYv8qfb/G2e6HWB6vsdi/xF//K/8buNT3bQtc3wGvboFXN+lTf1fnfvdHgPf/2Gwd5FHYPaoUxlqnMNbS0Sd9v0dpU1jrcO4wiDat/+USBM/14dwxZ9hKYETLj9eVw2XN/Z7jMQnIAKegmHxOfspwuly9JI9Kzy9p5DLIwPSTspXpIdsgATlZL91uXV85XufoJtv8kOEmF925jrLNWpeQdRjpHAHs4dwOZUlt/KVf+p/gN37jf4bd7psADL75zX8Ljx//DmjTqtMUEFoX1PJBnp+j1YlOrI6HZNPr9Bz9zkH3EPKR+UKCq9Lrcc7om6uTlH+5tmgelOM653Umn8vpyHLcsp6fu1e+U/I/ly/tST4nZYzWo6S+otumn9eAYU4nex29cnXSi8vyM+dtJ+mgvdEYUGd5UNf1MF5YzqzXa3RdN7RJLzLye0mn9VgsFgOQoOsr+5CvSZkr29913SCXcvOtHlfcJglG63umsmA8craFvj4n03NgyUOgyUMyj6/NjaXXvUPLhRyfafnxOn58qA58aHkjz+XordvxpvP3XDveps7y3Nx89lXL1bKD+VjSOG+Pvd3xVgCbVDjk9zmPtezEFxkeAzDkT2Lb1MA6A2ctjIlp9704eJYAgPcp8XjAUA55s1BIGVsouXdrt9QpEU1aURuN7JCAJWMpubvA+WCtRb20WJ0TKPXs3Xdpd0prYY1F33fouhaHwwG+7xFDgO/aBCdGhM7j+vol7u/vsNtu0PU9YkjJJ42FNY7AQaTwUwEoMTBDABIDX/QdJiWqRASBazZ5cnFYIIWf2WTQyl00xj4lQC83WcUY4WwxOW+NBXm5nSozGmDSk7O8NlldTLwiDwJk+TvdZOLIUwR5ELg6wHSRdpgKMcD3PUwUKyjGq8E1KnvaaNST1uj5lnpGDuYBRDODFx3jJsYk78lInagnhJwSpg89xqTruBQgp89y205zp/GzWiF+SPF+nSDKTd45esr7TyewNKZ5E4nSwftAIdocimwsSbQIINJmEqPSnniOvkwEqFzRIH6j1WQqVtYtH36hJ2DN2zkBn3tm0loz9QScgloRtBsYffc+EA/7Hl3foGmO2NxcY7VeoV6uUC3WMDAoygJ96OFcibIokwRi8JKAxYQRMyUQ4+gBKPtHh4hpnpHtnOR6NGk4TxSwOC6esPzgCc9a2JD4OgLBefY7hgkBl/UCZ5eXsAAuLh/h9voVrHW4vb3G/f0dyqKgBRqxXbyeTE9pHrEIH2PRfTzpk5yiE0JAdEs07l009mtoinfRFu+ir56jrx4TIFefISwJkMO6QCxVDjmANnreit9tBFoP03sgdAB6WBdgygCUBqEucVx8iMPlz77WQ27II3e8SV5yd6ehq3LX1eMNhcni1OjMhUho+khaynseUkontOUhawzGhKcY+j3EJKPSKgUvnhFfmWHOIviYUyFYRB8R7cDgqf94DsUAOMZ4qqAjRJTtHov+E9jdZ4Pxs9tv8YMf/gDWGJRlAVOtEB99gHD5Drrz52hWtCFDkzzimtVT3Dz7BcDmAZj63wHeWwAfLIAPl/T3wRL48D3gg28Bv7IE3l8Ai5mI0xiBf/af+xdw7M7Q9Gfo/AXa/gyHlEeu6c/R+vPBe671Z9i17+PuuJrtG3kY9KgcbfJQp/xxpb3HotxhkTaAKN09apc+yy3qYofCjYYy93XOa515QfKE1DOH/hDfc/PPHGiQM/rmjDl96FD73Pu1Z59sD98v5z9tjPJ5aykkVHqa6PL0eJNhYPx+mZtN61taJ9bl6/H6kAEkPW1CCIPxrdufe7YoisGLjWkr21FVL/HH/ti/iOPxayjLLRaLW8ToTu7lQ9JHhvrJv6IohsTW8jnJk9Kzkq/PGaW5+YTPa9rL+hpjhrBffl5/z/WbrLPWyyVtZT8/ZKzLcvg684ReZGO+kuCKBE74Hlmm5tXceJZ6hn63PnLjS7Yr1z5NF/mM1Je1x62sJ3sbyvOSz3P50OR93KbcXCj1Kt60hOvEedL4Od6UwHs/cXjh8SBpx+e5z7QneYxxsoGB9I4DpuPAWjuEiErvNr2rbW4DghDCEOYuF1u1vcP10va5lgu5fuXv0qNPAraSF/VY4udyfJQbc3PjaO6QMkLbafydy5izCebKfej3nP2lx6V8NkfXuXfLsa/f+7rnc2N3rl5z84/umxw/yXcBozclR/TIts7R86G+nTveCmDjwcluzVqgDpUSYW4T4k2SDpNpBxtRJE81A0NJ3w1d5+TdfYyUewxmUJIZTBsAF2NA3hAObBCzYTX+kSdMKgExjvmIYhzdZwdCRwvLjGRHIg8CAEAwoDw+oHDWGA1gHJwzqOoal1dXNICtRfCUa84kGj7dfQ1Nc8TxeERRFggxoHAFou/Rtx2ur1/h+uUL3Ny8Qtt0cIY8dayxcJbyaVljYGyAQwFrQR4USB4+IaBHAEwcQh5N2n3UDp5xhvEL4aEnaXp6SCVDC1U9ucoypaeUHEwc7y95I6FWGHbppMIBFpBg/G0EsIxJRpMIk4wjw1B/CY+44FMbbAIqo008NHpi2OQ5NQ0zYHiC6Ee4ZlKefRjyEBnDxiEGAIFbOaeUaUUrt4oohcjceXlNv0cbwNwPevLNPZtT2HU99Pu5nFySXLmaKOs0Cj2Ae5GASzKWTQKJIyKMdfDwABx8cIimhHEp55NhcM4j+hbA6U6L/G4Xk0zQ9U91l+FQrBRqb4ehnzOCXtIip4TmJvOpoh3TEBhDwkMC2REsYtcidA3a5oCIV9juj3hy9Rir8zWMAYpqgaKqsVitUdVVkn/Je2104MUQppmZeLTnhaSTVoK0ohqpCQPIxjstGmPShhbJI6agfHBVMSbSjaEY+MCGgOhD8jqOePL8XTx++hRd1+Hp8+fYbzcIIeDFiy/x8uVLVFVJIIiZeltIZU/TOtd+yeshBDhzxCr+GLX/gezccU4S/W2MQYcarfsQjfsauuJ9tOVz9OUzdNVj+PqCALnFEmFVI64roEwhI5J5AsZdMwHKH9d6mL6HCR2M6WGsB4oAVECsHNrFMxzPvg449zAgh2keuQF8G77fpFxytOtq2d7D9scJTSRNpRx7nQIaY8QQzxpJjtJY5XQUliaptPtuHEapGf9PAB3xdcoXluYRrYDx/TxHiSj6aZ0SzQNGPacoCrRp9ZNCbgEXOhR3n6LYfT4YK5KPjDGI1qFdXKE9e4ZmNeaDa9dP8LN//E/jkwPwG3fAf3gz3z9PKwG+MRCXQLnd5SMsdi+x7D4bFg+0kcxjkkENHxy6cI62P0PTU8hqHy/RdGu0/gKtP0PrU1irP0fbX2DbvIv4hipkYfcUmprCVCu3GTzj6nI3eMgRILdN3nOHNJdT3zIf6d0Wmb4yz46UyfysPGR/sMGnz8vd9FgPkAaylBE5Oa5lip77ZISHfI7lEgMv8nlZf2mks+EvE9FLw1/TSgMiks+1NzK/n41wWZ6cByTtcjudyygFrS/k5O5UbwhYrX6c6v2wR5amp/To4YM9ftg7SLdHgj3SiJRzgNxwiv+0sanpyn0j2y7vk/qePC8BA+0FJFP1yLbnaPyQsSh1D80nsj6SDyWYo89LsIKva080WQ/NG3LMslcn00HvaC1lugTBZHu4TiwPtQE/hjcXkz6XXmVMC66r5HMJqkkZIQErYyjscrFYDO/TO4BKvpV05HqyrcrjnT1wtD6bW7xmXmHwTvaBbJfkFclbLAckL0j5JN/J5cqx1Pf9JKRU9wM/x/3AvzUvS/BN8p+mgdTtuf26bbJc/i7pLflIyx15Teo58sjJiFyZst91vXUd9Tted+Rk5UQPyoz73L3cTlmmlHU5W0GXI9sj6zAnCzTvS1msF700n+h+5O9d16GqKlRVhc1mM4DXDEBr+f1V6c3HWwFsPKBHIU+5e8AEsQ6E8hjARkKfGMgRib6pARSWaS0TgbTjiAhaRE/eJTGtSBuXFOLTFSRgwF5gbURRWBgj0X03vDNGA+97dF2PvieB671UwnhCIePNOQcYAyffBbGSYlgQANaUsIXBarVCYO8LxOE6td9RHjXncHZ5gQtziRAigXMJLAreo+97XD17jq9942fQNEc4V8DT1quIMeD+/g7XL7/E7fU1uu6IXdOm3U4trImUk9ua9GlTqxLzgIGLdMQ47KJpOIeYIaM3AimWi4A4Mxg3BCpOhGwymOGlJ1WAddPBpXcdywk5ppezZtihcxwo6XtEAs0ImEwQxADCjQPEpL6PIMAOiNGn+wKCRwpDTJ4NFrCRPCB6E4Zye5PAs9S3jBcYE1M4Iw32pmkmk5H+nCgYMSnJySDUCpBUciwIGA1p10ekcGkuN8ZTsEzSVAu0nDI4d+gxl5sg9SrfnFKn36fvo8/kiRgjDBxiQmfYcy0aA5gID8BGD8Bi/fgd/Oqf+c/CVWtUVQXnCjhjsdvc4Sd/8Hv48T/4m4Dfw0Q/0CqGAERLAWM8SUcCYTkJfKrkCa9OvHpAOayiaA/ly6P/OCw7CMBQT8wmAcaI07CQoSyuA/M4WCH1sDYipLyPIfRo2w73N6+wXpTo+wPJQOtQliWqeoFqtcJqtUa1WMKVNXygoLkCDp3vTlYY5SQ30CEByEwzm+73PoJ9SmGk5xPAnmwmjp6cxhiYaBCEJ0SljIck/sd3F0BIG1zEGBF8j2JRY7FeY311CfiAqydP8bXDAW3X4sWXX+DLzz+HAbBYLuFc8pJTY0Xy5JwRxde88BRJPZf6h+XrFMQuzBG2/V0sze9NAI/cmPAh4NV9D3f2HWD1LTTuPfTlO2iLJ/D1I/j6En29RliuEJcV4nmNWCxO2gEAOKY/ADgGBch1MNbDFAEogVhb9PUFmqt3gaJ8LSBn+8N0U4d2DFN1KYS1bG5RNfcom3u4fsd7zFCbERH96FU7jB2eA+Ioz5lfrLFApHypJAkp5NOl0DFpfM4pTFo5DH5U4LQBNTJFJL0GwGGXxhQi7UZuGACfKs3CqR4meCwO11g1twgvfgdsQMcY8f/4b/3pVC/gVQt8cgA+PgCfHOnz48N47od7AuJOjv/c/xIAUHR7LPfXWOwpJHW5e4nl4QaL/UucNbdYHW5gt9eIoYdBh9rdoLLXOEs55mVCban8A+yNERFwhrY/Rx8vBxCu6c8G7zgG7FpPn5vje7gJX8FbjnPGMRg3gHNjPjkG5Qp7h7rYwdqpsaUP3R5tSPI1brs0ymTInnxmbk6V98h3ac+qnJGS+z13aNBPepPxfMz1ksY7P2uMmXgkSd0hR0td34f0uNwxZ2jpsjUdJBih80Xp37IsDTxwGXNgrczdpus86qaniy+5vtI6X26e0c9K8E562POhjUnZD3Ju1uCYpHnuvbosyaO6P+R3fY+cL+f0SnmP/q3H6ET/VWCfnHs1L+i5m+nDz1prB7CVQac5r0XO/T3aNKdtk+9gby1+drlcDn3L91RVNaGlDrMfFviF7OBzMmeUpmWOdyVNc3050UEf4OccDzx0Xc4b+r1Sls7JFHlurt4atNL36/7MyVw5niXNcvWea6+eC3L1mJsbXnfk+ik3rnLjOfduKS/mnp97hzw/1y+5dur35mSS5H/5rDyvy3oTfpUHb/DBu+qyfHndXPxVj7cG2OTBwJdJ4AWM0CYjARZg1yKkBNfsPeAI5KBnA4EGISJGmwzqCCTDOgLkKTQjsFnokJACYPwQAkkHKb99H9C2DbpuTCwaI04GGLfBGDMgUXrCDoHCmwyoXuuzM1hLbeHQJ875QuUM1aDwT8FsDiJsIIW1OFdgsXSoFwss/ToN3JHyF4+u8Pz99xF7inUpnMPV1RWO7R7O0u6DL7/8Ep9+9ilevXyB9niE73oyiEOPAg5VRV4hrrBwlpguxACHiBD71B8RsMlLK70/8GRiAB8DGccDMJrox4LGnoI+WpByKKsxZDTTc2yVjELLGgIfmbestQliIICEYBkzeI0Zkw/VoHdzvdIEgGn+giK9NxigMBZdoBC1EAMsxGoNI3Jx7GuDAl1L4Kp1bJiT5xsZbhTeZ1NCf6tAY731Mq/UhAg4m/YkJUQHmNBydJGeW7GRbfSek8oCcljpSUmvXOhVFllHTW95SOVWK4ocWjIVvLoECgPjcEYP4v02hXbjfodPP7vGoX8Jk7xJu0C8fHX+DD0cjPdAGF2DIzECvQ/S49IgJo8aI8DkkxqxoLe0I7CJIz3pqannpgMolM0QYKbpY9QzwztCREoRRWHOiYc9b+oQAnnJ9uShagxQFAS2eU87tRpjEPoGbbOH3d1jV9SolyvUqzO4qsRqfQbraNMAr7yR5CfAYGIcPDwRAu3kGhPYZlOIn5oIh+8GI5GQFExMlWs2MjRYPNQhjBOxt7RYE0KALQoYGFSrNVZdh4iAx0+e4Ge+9W147/Hl55/hJz/5EUIMqOt6UFbln568pfzi8cUe1gMfD5yTmm4lL+c9P+XKHCCS8loL091h2f4mlu4H9Nwhv1rO7+5wibb4Oo7mfbTFe+jKZ+iKJ7ShQ30Bv1jTDqvLCvFygeiWJ/wMYLqxwyEAbT8Acta0AyAXywhUDmFR4XD2DcTHy9cCcpxHjoG3Qn2WzT3K9g5Vew+7f4Wq38GZqUwZv5thrLCc4r7y3sO6YtRN1LOyLyQdgWm6hKmyle6PFAqHNE+N9yoDJYLmCpYdg5foqQfUQB8DPK3p71eu5um47UfAjcG3f/Ov/ns4LB/jsHqMw+oJNpcfzj5vfYfF4Zo83vavUG3pc7F/hdXhGvX2BardNUwcc4IxrYg/t1iWW1j7xUgjY3H7+FuI1uHR9R8M4cZ8hODQxQs03VnyhjvHsVsNHnINg3I9XW/6M2yOfxhvOfo+flLOubrYoS7pXDG4gp7Oa7Ld2njSh1T4pW6j+1h7MWk+fJND10EbjxI8kuAB35vzJsp5+nEZss7yfM7Az5Wh26vPSfmX0xFlm/gZzkmVMyK1Ia1/s/eapAe3k0GtnOGrZTW/V7crdy4n/+doouutQVTdbin/pEePrHPO+J7zRMmBz3MAgXxeAlSSr+R7HgIB5t6Ve/dD/MZhlBJ0k6AVH2XaHIk9GauqGsKHNZgn+09el/oJ0409tfi39FbjMtgDje/RwKD2CJP9rnlA8qwGxOViydyigu5P+Vufy/XZXBk5ean1O/1M7rzm0zl5kxsbPH6m9mb+e47uczyvf2u66rJzvzVN5t6RkxMP1emhesrfc/WRc5l8VvLoQ3PVnNdcTn7puun+y52T8lzL49cdMcZBNvAuujk+/8MebwWwAQr9NAYEjiRmhB2MjAie5Om6KwhMAFJogg8nDBwDbVQA45JBSCvEiFxdAw6YsZZ3NWWUn7GwCGdLsEEcI9ClBN8AUjgolzUecuVPghMJEwNiTEm3yQh21qCqS9SLigxFw3dOVzQlQ+QS4MpJ3ySDnid/RAPfR4RAdHaOcrOREWlQ1SRUV6vVoIAsizViDCiKJVZnV/jgo2/B9x0BQ4WFCQTs7Lc73Fxf44vPP8dnn3+Kw+6A7eYVjAGWyxoGQFUUKEoH6wGYAs6SB5EPPW3WAMqVl1SiRNeIAQEBAJ/CL30asBagHdz6hEQA0ZMnA7EU599zg8IzDCBDXoK8UcHwNmNAqfkSGJvCzWwkL5UweAMlwR6I7s6l3H047StuU+LY5JXC+0mOu7COE4IlmqT+dC4pRCBwxCTe9X3KH5JCnmKM8JH6xHuP6AOC9ZReiSd2SxtLWBgEY8ioS+0xCTix1sBjnBSkS7bkNSlYpSCsqnJQNPSkoCdtqXBIrzWtbEuhKF17NVDHdBzCB81UuTQY6crVMMYAIcIjUH7D3iN2LdrdHUKkVfm6XODdZ0/x4uVLNPe3aI5HFH0Da3UoiB0AfAoLJr4MAIa9T5BCgzO0A1Sy6cQlku7DZ+TyCKydU2ISbjY+K+WVASiU2cCBvG+MMSkEPY2NQBsBvPv8OXMsAQLGAr2H7wP6pkVz2MHc3wLWoqwWWK3PcX5xgeV6jbIsyMtXTWLUJ3EE2tNcQCCbScB9qrsR4RwgYE7y4sAzIOAQZqooSVprQ0B6KZESy5um0MYJABBKArTrxQIh5dc8u7rCR9/8Jnzfozk0+M3f/Pu4vb3FakUeNrxYw3wp38f17vs+7cLqaT5QYfJDPwrjQwOVcq7JKZG8Gi7HH9/f9/0ASDMvV7hD1f99rMJvwLRScZTAO90fItCbp2jLb6CxIyDXV0/Ql5foFxcIi7TD6rJEvFoiuhWyqkyX/mIEDgGm7WH6jgA5dIDrYZ0fPOTiosJx8S72V99+LSCH6FE29wTAtQTMFSlMtWwIiCubW5Tths51G9jYA5YAUGMMXEbBl+OOQ3ZyRs0pr1JfbrdbSulgRlVKyzXn3DCn0wXAWPKMHozRJMe+6nFWAN85pz8+fuN/+G9O6tvD4rB8RH+rJ9gtaGfUw+oJjgmEu3n2HVzbGXUwBtT7m8ETbrF9ieXhGvXuBVaHGyx2L7FublGGDsGV+Dt/5r+PL9/9ZQDAo1e/jz/11/9HqEMzFGdMj4W7RWWvB9pKAECGwnE7AIO2X6CPlzh267T76hka3ok1sOfc2eBB9zbeclWxRWXvR6853vzB3g+AHeecq4sdjBkXm/W8N9Z9+v0hQ+AhAOGrGGbaONR8rD2igNGzRr9btmnOuHvdod+vf3OfyyO3gMd6CH9qefkmdeR3a+9MSSu5q6eug6Sf9j6fM/rlkeub3D1S99Eg5lwf59qf45ucvjH3W3r65Q7NJ7n+5c+HDFjdLimfdUio1rv4HHth8g6cxpyCyzxf6jAz6a3MIWPyfRJY4Ot8ng10CW5Kb1fJU1wOX5ft1kAdv0/2n5yjpF4+x0+yHyVd9T1vIlt0mfJ7rr/1Ar/s05yNId+ry37dd1mO5hOmLdMr15ZcHfU75uqr35srU/fNHB1yY0eWIe95aDzpI0dvTefcoQFzWU9Zlj6kXM29Xx+v86TLlal/v8nB46WqqkmYNo9flhdvUufXHW8FsA2EMAZIO3ZGRPLKEEalswbGEejljEHnIxA9fJCKRoI1Au8gx8ppRIychFQm9x6N6/Eg4ImqxB5zbvDYaJpuQCv5iHEqILTboc5rMEwwxsA4UtarqkRVcaLH5PUyM9nwuRBCVpGXdZEKB4WtGgpzE7icdYArLBaLepKfgd9DFouFcRGFLVEUJWIcQ4dC7GEQcVYusL56jK996zuACWjb4wBYVkWF42GLm5trvHzxEi+++AK77Q53d3cwMcA5m7zeyJurdOPGCjECBgbWkBEOYymqxg7wI4EV0aQNCkDAHz2YgAWL4IEIR8BZtLCFSwAQyP3HEMAUUj61GMacJWP/UVyZMQ7ecx6HOPBa30fEFHJrzAgEjR5efsIPvuddRwioYC8ducttEDwuywNA+fUiBt5mz01KyE0giBmETUAICbT1BoW1CAD6GGA8ED2FrlJqwqlQYNdXSQ+ZEFUrkOyRKc/N8ac0hJjnpLIy0v70kGPCmOR9EtMIisQbBgYmurRTLu30CsNKnkl/5I1qrYWNEcFHuOARERBDh1cv7vDy1St88MH76Loj2r7FoizgSocilIiIKFw5tsMYhOjRdR3atiWvMENyjYC2tLtx8lAFTkNk9WTMV6TI0nSxQDKuKUScAGRppCfwl4FrPwL5cagLhryVg0IcaRwFT7ShiWWkINfMAAihR2x7ABa+adFs77G5fYmyWqBarPDo8SNY57BcJXDFWoQgjJJUIIE8JYUtx4i+9+TNZ92ULqkiEuCcGKCReJWv65wqc4YJKdgB1haQeRtDIMCen+v7HlVRAMslQiBA+08++zV0voOBwReffYE/+P3fxf1mi7IYkwNT0+1J6Df3Ma+GRcELOm+RNoSkYqE9D7j9MrcSgEldZILxOeOVyqM0CAYG0VsgGlgTsbDXqLqXODcGpjMIu2nuR6pYYj9ToInPcbQforHvoS3fQ1c+RV8+Rldfwtfn0x1WLyoggf4nR5/+NgzIdTB9Bxs6GLSwLu20WkWgtgh1hXbxCIfzr2FukwB5uHaTgLc7FO0dyuYeVXuXPOPuUKTfdbdB1W1g4hGwFj5GFKlbWb5Lw8dZBwODsqAwg17kwJkaEBYWNqXIEAqpmeYRM4YWX16PMr75IceHjT3WuxdYbr4YrmkQsPcBx8UFdvUjHNdP0Jw9wzFtznBcPcFx9QTbq49w9+znZt9ZNhs43+K4ejKcu3nybfzed/9p/NJv/h+H9spPro8e16dGIlAVByxsg1UpPHoV3fVzPjgKT+04bJU84o7devCeo8/Ra27bfzVvOfaMK+0GlbtP3+9RWtqJdQDoHO3IauK4vXAOFMmt/M8ZbVManfLVKP+mCb+BUYZofUkCBTpdhZz/c0bjQ8ebACxvYvDNlallnzaSdH1PFm7TkfP61zp8DvyYo8PrjLXcvKb5WZ6feLSr67I8+Vsnndc01OfnjOKH+lAuHGlvoVyb+V7ZNxqgmKOtpJHURWV5Ojm/LFN6eXGYGDs0aA92PidBNw24yXpLYE16mDP4p+UW6+S6DfpTvovrID065f1z3kMTHUzVe+7IjanceJiT7bqv3gR0eUjGyefm5L6sIx+SN3J0ycmIh+qVG0cP2QIPtU33sf4u52xdtrZBHjrm6Jn7Lfl5Thbquui2yiNHn7l6vEkbtAzRDiC59vA51pmPx+Ogs+vcgrKMXH+/yfF2u4iKsMuhOZE2ITAACscVAoAAnzyFEGPyXDCDsUjmK4NreUbMuXby4T0ZmF3XABjdTkPgFTnKyySfkwMz2744hiTJBLgA4JxBtaS8ThanSV51G+RglRPjdFKXiRnTZMEAUDzdbrkoLJarJYyJlOdIeFFJ2pGhGoB42k6bctrRI6xQGdTVKvWbIw+g6hzP3jnH0+cf4Ts/H+AjAZXORFgAbeyxrGrc3Nzg5uYOL774HK9evaLwM0MggDM2QRMkiJ2JiKGjUFJTwKXtNo1xCNHThhKU7I48IH3EkNi2jymuGImmTF/yQiPPR+7rxC8cKpo81xADDBLIZoBgQrJtyEPMGQyTITDucnU6gGW/ThOyysEPnObSov5OQsEbOBRwpkBADx87GBRwhUltMokWae/ZSFCPRxzqLcPQeAzISVzWQx6yjXoVWQPAPG4se+iJoSyVqtzkrp8fAWQPZ+xUjvQ9XNqxl9sDxJQfEMN5ChEWK8qhp+c98aj3PZbLJYwxuLl+hcWqRKhX8F0H23vyAMRUptCEYlGUBQyUd0KidwgUEmlFbhQpM7gcLa8enLS5SQzciWT/1tqBh0WFaGEj5ENO2ECKCPChh3UGMXnKUSQn5S0bngODdrQ7aYxAfzigOzQ4bnbYXL9CtVxgtVrg8tETLM7OKRSVKji2b8htSNV0xsAVBXoRJqaVFGn8zYXpyclPT5SaxjGOHsjjO1PzRF8hedvy+0tjsLIWXdfgo9UaH37tI8QQ0DYH/PSnP8GPfvQj9H2HsiyA43HwKpNtMYZysrH0Gc6nPuPKMM9KRVkCBdroa9sWi8ViQhup/Ms5RiobUtkfyuX+iaCdoKnbKMw3edkyTw0A6LDxQI8qfoIFPkP0EdZbxMOpAT70T7BozAdo7NdwtO+iK99BWz5DXz5CX9MOq365QlzWiKsK8bJGsIrX+fAAdgDuI3DoYdoetm9hYwuDDtZ2MIUHypAAuRL9YoXD1XPAlfkyxWH7AwFyLQFvVXuPutui6hII129QN3dY+h2WYYcy0qZIMYy7sE3zeUwV9pyRKxdv/lEeOaNDnjsBZBGxONyi2LzE5cvfO1FiY9LdfH2O4/oJ2rPnOKweJSDuGe2SevYUm7P3TupyWD87MZ5ZcX/IuOLvMpRR61byU34f9CXTo3Y3qN3NxJBi+ZgzukKI8HGVQlcTEJd2XuXdWftwMezI2vZn2DbvoQ8/82Z9gx6lYxAu/bn74RznkyvtPWy8Qek2cLiFMeGkrvLIASF6Pmeaa71VG3A5j7KHDCem+Zv0pz60Bw7fJxdecjq11HHm3p8ziHPgzxxv8fecAaznXM17et6XYIO+nnu/NhzlHKO9q3I0mAMU5mil25C7ngMV5mgoxxsfGnyT7cmdB8bwS5axWl5KfZvLyuXqk/OtBFGttUNOJpZLXFc5J/M5qXPosnhsacBJ0kLykuRfSQOui6yzpmuOzjma52g6N06lvM3ZyHOyJ1cXOa7neJDbnRsXui0P1Ud/8qH1M902WWaOdrl2596px758t6b9HN10n+lD02juvW9yPDSO5TU9pnP3aDrMyXtN67n3z9WXx4EcJ/r9uedyc0JRFMOu26zHS0clLW/f9ng7gG1Qykbj2hiDwkJ48aRVoj4mrxgOAUphctYhxnq4LybD7sH3DcJBXmUPASYm5T4zxpAXzEzYQ27gy4mOveuAceeU84szBPjk8eDRj9vhnUyi/Jw+qMPGUAiyO8kopdsJtZAMxc+VZYHlagFrMewiw/fSpGBhTKrfMHkUg8EkJwGq4+muM/QuGXIlJuyygEtAljUEClSJfk+fL/H0+Qf42e98N+XR8yidw2q5RFmW2Gzvsdne44vPv8CLLz/Hzc0N7m5vYQsH03sUVUEhnNHARqAu6wTiFDCO4DMbI3zwMDFS2rFEf6q3zFXFQMUY5sk0AByiIYvSOocYAafpYyKCCShQjJ5fVQkfKf8aAg/mKWDL/CLzesh+z+0e5VM4po9ABwozs8bBWIe+6wETAOMAE8jwpMbB8FjiyWwCRllQwvsiCSIOa+SxypO19PCZKtTk5RLgk0Huk6dcYS0l/bfJm8sQeOD7kGzwtHlFCCnpdwrbTmUaAAgpzBUYNqVgj6aB5yMQI41nAor8ZJxR9al8hAhrA4IxCeAhGj1/5x389m9/H3/jb/w/8Uf+6B/Bd37u54Zk9jpEVfYPgfaU/9Ak0HUEAJOcgQQracxS3QRwOtSVnucde6djTXvBmQnAMfCLSVcm4zc/ufI7ZNJneR+3hutDZaZ2RADwcMYg7XOM3vcw3sF3LY6bDa5fvsRyfYbLqydYrtao1ivyVIZUdmmBxduA4Medp06NiamCKfuDQTLtoWvM1MNCT/AapKMJ2So6Y0ITDimh8PsaKGvEBYUdL+IZvvvoMb7zi99D9AHHpsGPfv/38Qe//7swNqKu60GhZFkc4wio8FzkilFp5vvZYJCKgN4ghMBiP9ntjhUN3vlaGzFyXEueGPhXzFGjJzl7S053fPPegzKEnh65OU7S2RqPJX6CZfgJyU1vYJpT/uY6932Fg/kAjf0AjXsPbfEMXfUUXfkIfXUJX6/hl0uEBe2w6leLvHccgGGn1fsIHHvYpoP1LWxoUw45AuRMGYHaIFYF/GKJ3fpDbK5+dq7Ukc6hg/3lW7jDNYrmFlU3AnNVlz4bAuzK5g5lt01O12O7y7Ic+uAPo8jJI2dMSWVx7HNMPmWIIN83AT9CgDve46zZwN39dOJ96pxDXdf44r1fwd/6tf/BBEx+58t/eGJsaQVZXssBClqJ1vwuj9w9+hh1pmnYdqoyCrNHUe2xqqbjheWLc+5k8bXtgKZfn4Bvnb9IO7FSKCvnluvCOfbdB2/sLefMHoW9R6UAOfKOox1YF2XKK+c2WFZ7OLOb0CHnqSbbNQeivKlxOJ2f8kaXLjtXjjaqc0a2zpGoy5szPrVs1fzH597E2M6VOdc+PiQAw3OA1Av0hhR8n+y7nPH+On7nT03nh54d9Eu1uKztJn1eAxu63nrsS+NZzw18TX7K+U17FOb0AmPM4IUu3yXvZ4Nbvl/OpUwHGeLJn6zzs1yR4+sh2SWf155vmn6SHpKeWn/QvMHnmMclDXX5ub7RZel+y9VR3ifljG5b7p36mDsn5wouV9NczyUaqMzVQ+tPrwNbdDlz9JRHrsy5BQVdjpaFr6P/3LjNye25eut3yLrM8cjccznsJde2XPu1jMjJm7l26LHV9/0wh3NZWm5oefNVj7fLwWZoQiiKggowAMNtOcVtDMc0MKZEtJadX4b/9cSoCU6dQsoPXTsFN6TwGIzSDOPJgSZXI1jQek/eLcYAdVWjrFNcf+iUAjAF1U6NR26fAYUnjWGxiCJcK1WR6hQA2PR+AriqqkJdExjpPSUrH8sGRgOft59OQGcEtPfaVNni7+OkIduTEzy0SQWBONQmBrL4+QhnHKyl0NUuRHRNB9gK5xdPcXH5BN/+zs/TpgygUMdFWaDrW3gfcDwe8PLFC9xc32B7v8HhsIP3PfoQYaOHM4APnjx3fA9rS/hI9TADMSl00xiL3gc4Y+FjC2Mcoqf+sBYUkhnZsOQ+oOsxhLRbaALOTEwbZkSMqcDMkFtO8iTT8KFJY+zvNEYSDSMMfIgIaAkkc0kghQgbp15ihhNnmxGcDoE29oiRyorRJgiYwFhyaiSwjfJijXWZTMwAnLUoeEKyIzATYwTSDqaD0ocCJtGJgCBLucAMj/UIy1CnIcTNTgwq5mPqgxAikjNXujY18Pu+J1AnOhhL+RRjCPBdB9/SOD3sd/j2t76BX/6lX0SIEY3vgCHv0cjnBFR0iOgRvUHXE/hsrBtyhSWESxxx+M1LBOOGAEEk+B8YYqAfycu082VA6pfEa2GkMx+DfAPLQPYOzk/czFNy8s0ZKDFtDhFjHPJTyT62hvIOjuALeX2aaHDY3KHZ71AUNerVCmeXj3B+dYGyqii8mZUqa+EsAW4wlBied4Ud5d/U+Na5S7ThrZVufkaGYsrVaAam9Eq39OasqgoxUn5NY5PhFmPKMYnhNwDU3uMXLs/x87/6vQEs/smPfoA/+IPfw36/h7MFqqqgBPjGDLuqcq495jnfU/5JnUxbrtIBo5dTzlMtCj7jsnMA49BWNSeMcsoM4Js1InfewITjpg5zCqB+35xypQ95viw7FPGHOIs/II81D8RjnNBkLNMgxDWO+BoO9j0c7Lto3XO05aPkIXdOO6wulgjLCmFVI6xmNnRg2hwAbCLMoYNJgJyLDZwhDzlbetgqAJUFliX20aI9ew/N0+9gN19yKt9TjriGQlTL5m7YyKHqaHfVv/4J8GwBPFsCTxdAmXcGfvDI0VnySe6cHj86ob0GX/g38yiPs2ef/Mf4I3/3L+N3f/G/gGgdvv17/x4++vHfmtSNjxygo/XAh5R+Wc5ErimFn3U77YXE7dTv16CDlKGyvnoBw9mA2t1gUdzivD5dyJG05fPeBwSs0fRn8PFqAsJ14RJNf4Yu0OYPrb9AHy6w7z+A7+ZDdSf04txybpN2X53uwsrfS3OPqtyiwB0W5Q7OjbuZ6zlEH1MdPd9/OQNK3is3ZZK2g/YE4jKlvsqeo7pOkldlOfo+aXjp+k31/dM2Slsix4OynVJ+SZ6Q3hOSVvJ5PsfjUi7m6Prl+FkvXsny9XdZ37n75LiaK0eOO0kbbczmZJPkOfk+CShp2vCmBhIE07TgME+OUJHe85Kuug+1kc515mdz4IKmofZc0/SQ9ZUe+Lqduef0+bl5lj9lnzw0l+vnZbu1jJSHnDekHsPjNJtiQ/Q1/5bvntj24hn9Xnn/Q23JXc/RmD/1Odknc30rD62/Sp1WlpubF3VdJdCkbffXje+5tmq9MXevBtpl3aRsyfGU1um13qrHW27OZZnC1/SmISxj9YIEly3nmaZpJpuCaFkudaNcf77J8ZY52CiMCojoYqRk88k7hQdUCBEhWhhrURTVyHzpP9p10UDmfBoPCvMByPgiAvFK2xTconOngzzH5DlFzhh6h/QOKAqLxaoe7tWr9Hqy1V4CI2NQmrDkIzXsNBb8CCRIQUWftIkBTMBqtRwS1HvfD+2OkWnENEhGFQzlqgKGhMnJH2Go6zipnII/WmDMCVD6PoKBfFg7zU0gyz05V6aV4Ah0IQC2gjXA6rzGR+eX+OhbMYErBs4WKAoHEwj42e92uL65xic//Rjb7QaHwxHwHYXCGUM+dj4g9D1i6OEBBLjkGUTXaTOEBLIZBj0pF40dhMTo2ccAAaIB78RIGzskgBYGBjbtLErht4hCCY8URkqGtU9gCfEECYkAGA/2AI3RwhV0nnleAg/MlwYjuEwhwx4B3ZQX0/3OWsrTNXhIYVKW7HckMJjCxtLkWLJwZTCRgEyqMNcjcV3waawnw94C3hAwZ40dACVjaIdLa4okC0zaFZSBOh5PLGEMInzKKYihrsZYlJUBDPWnv/0SpS3hYXC/u4d1BYqqQt83sKEfdoElo3KULcaYIeeWNX0CPm1aQBj5WY4FbYTICWuqYCeyif7i3IFEx8hk5RuzSpN8h/6eU1K0IibDEXNtku+dyjTexIJyefXBo/Utjt0B2+0dXn5ZoCxrPH76HMv1GsvVCsY6dLFPYzmBQ6D+YqA6RvLe5UlX00/TWSsoEkBixS23wp9TKKUREGNElcYr7+x10t+g3HB1XSF6jxgNogG+/Qvfw7e+810gBvRth+Nui+//1m/h5fU1Ot+jSGOc+a0oSnCYrk0ywKU6BDXG2SB/SGmRbdMrfBNlWADlLAe0ss1zhFYuH1KeHlII9TysQ4H40GGyfC8f3CdTQ3WHM/M7ODe/S+HzKa+bORpgM+WpGA2a/hxHvI/WfYjGPUfjnqF1j9CWlD+ur1cIiwXCskJcLxGWK/QnreUKpc/bAOxa2LaF7RvY2MChgXUd5ZErA1AbhNrBVyt0i0vszz8EzKlX4H/6/zb9fVUR2PZsMQXe5G/5uSxGsEzSPmeIcB+wN7yktbyulV9Znuw/7pNv/sFfw8/++G9MyppshAKclC35JVfXQXZkdC1ZnpaTuTI1/+k2Spki6yd1vdxY094UfEianRpPEYUlbzljXgxl8uYlvJsh99NQtqng42UKXb1AFy7gcYUuXKAP5+jC5cRbrvXn2DbvvbG3XGEPApRLGz1wPjnHO7FSKCvfV9j90F5pTOb6IWewah1Ty+u5+zX/aBkvaa/5TxpkOd7Q9dQGn36f5AVZjpxj9JyV88rQ5XH9ZYivnsNzHjDaMJX1yb1rjr56LuD3cTkS6OPrUg7J8iSgKcuQ9NXzt7Qf5BwvgdWyHDfo4vyt+t2sQ+tFIK6HLE/zquZFDZjm5rScnJT9xdelEa9B0Bz9+U/KwzmdT94n+V+WnwOvJO1knzDt9IYMD+knHImwWq2GZ4ZN/MR75j5zOtxD+oluk6Szft/c77my58a+fkYv/Oh35PpSj+fcfMLHVDc/Bfu0LNN9NEdH4HSBPtde+V3KL10/fbAckzIwJ3M0nWS5uQUTPV4l+MZjVQPXepOwGOMkXJx5tK7rIVfiVzneOkRU76KjgTJXOLjIKxQAAzHkZcVA0WiQy4ONdin45xQkuZIj79HG0fxgsrDWDAQsCguk3QVzzEhlcjgnAYWDwWI4t9QIQMTokAfFpoqIMSxkLdbrFYwdw4LGZ8hI4IT8CBEBYchxdiogAkIcoUgZBsIAm6axnHCY8TSDp5YjRikkQLmJxLu0oJerRcO52IHALQKoyKvKgT3JAIcYzLjbbIhw9RmevXuGZ+9+HUNeKUOWeug9+v6IzeYeN9evcH9zi/1hh+P+kBK/R1h4LOoFQtejb1t0Pe28SKZ/QEjBcbRbqBn82ywsookpHI7CMLlreKc6g7G/DCxiH2AsEJIXkPUGxpLvWTTklWQBGAtYK1fCaIxYO1VcpCB2jvuEgFmDUUEIQ7gxMQCNE/odeGtKPzWiWeANAtoHlIbBcYOm6RCiByKHCVtKyh8BY/xQN3pXAXKi9DCWnh+8bkyEQZk2MDCJ5j1iJLBh3KVzDNMmj0kjaJPKjpxHEDDGwbmIptngH/6H/2/sD0d0HoiwWK7XeP7ue9hubnC+BKwzcJbAZ2tABqCn8eRDRI8IbxKol8bunDKaU4a1Qsb8D0glhydNtYqVmjjwgiH65crU45bfw/dJRUjLyhgZNjxVLOYmx7GtCdz1IPDfW3SNQWv3aPc7wDlU9QJn5xdYrFZ4/Pgxeg8K0UVENCY5UxnYGBEt7QA87EVspjtqSfrI+rPhqVegpDGsQX896ZZlSXUJgXYbDmEIF5HAlqRSiBbB2AGMd0WBGAJC6FAua6wuzvBPPHtKG2UE4Ljf44vPPsPHP/kpjscjCufgg0fbNGi7Hmh6lIVNobkAnB0BN+8nnnTasOc+l/OkXsEDxtU7BpRDjDDJKzYXhsvvY+VDK39y1+A55VSWoflIK1O5sTTQOwQYVyCC9viI7Noa0x4Conhdp3F8RCzKeyzNBs79/lh+AMIhwBzNhFbeG7T9IxzNhziYd9DY5zjaJ2jdJdryEm25RlfWaUOHCuF8jbA4O6HD5OgA7APMvoNt97B9AxcaOBxhXYdf/uWfQ7EAUBm0DtgGg1cN8KMN0J1G454c6wLAP/Gvom7vUXUb1N09quYOdXePetjUgc5VzR1stztRmoGpci/ncm1o8Sf/aQMmZ+zLsSvnNFnmvO51qsTzdb2gId/B7dA8pcOrdP30OXnohVe+T8ss/bwGn+bGj36eaUFyzqMwN6jc9YRnWT5K+Tn2pYGPqxSyej7sutp62oG18+fDrqytp00htu176I9fJbfcFlUKX63sPcpig8puBBC3FUAdfSJ2Aw1Y/5jztJJ9yW0rigLb5jnujt/AqvgJzqqfTvhY8mGOf3J9rd+n+0cbq3PzZa7+Dy2K5PQL/SfL5DHHujrznwTvcvWWer7mY/0e7T0tn8kZ7dorR4PnPN65/2TbZQhn31MOXW6j7hdjzJDzkvld5l6TbdZt53blaJqTL/o61zHXXtmfup9zfMT3yPdJmsg2az57iDd0G2RZEtzQh54PcrrE3LiQ5zWIIsENvVCh66/5VLdF68K552XZGnzOzTNzNM61U1/L8Qi/O6fHSh7JYReyz3hM50C2HB34U8uZubpzXbQ+xt/l83L8ar7TY1SH8EswOsf3uv65c7KsqqoA0MKrzJmeawuHgDdNM9SZN7PjZ1arFay1WC6Xk3zHVVXh7Ozs/38Am8z7Qo2NQHQDSDAYwDGCDeORwQACBKbGGnAqFPgzx/BagZYGgGaKXGcB5IXnHANJqZzoh/BF+S5+3nveNMAObRnvM0Cc7iyjPQF0fYBIwJQJWC5X6ZkOsY8nE1+M5P0wCAnj4IaQTbViBwCw8IFysvGENtJ1SkcpeBnB1bTW/UADhXY4Hd9/KvAG5deSj9fgpWNAOdcs5UJDNIjWwRC6RcZrcGmnRANjHEwxKhGpFlSvmPgwWDi7xPlljfPLp8A34mA82xR2G0KPuixxd3uHly9e4PrVK7RNSxtlxB7WUGiYsw7N4YDj8YjQt2h9TzuZGgeYiBgMTNo7F2z0GaZCgCX/JwQPBBNhjCNjMLU9BvJyIz9Ai+hGGlrj4EOPopiOBRbYAE9SDJGQlUkkN7A2YARwqL/ZY5IER0SM05wF0khnwd/7fuisyJ5cJoFfhjcXiKLfgdj5QeBZl3aGZRg69VPwPYxhg4jBpinPT8esNP5THeFh4tSTktu2WFo4WyB6gjCNabB99TGK0gGoQHnSClC4O/O9Q0jWunEjn0ZMJzspp3R958aKnHCZ3t4/vNPWOG5OJ/45xSGnxHXduFmDVpSGMmPmHNebPe5ESLq1acdeSIORckq24YAIoDnusbm/hXUOLz5fYbk6Q71cYblcoV4sYewI0IYYYBxo1MRRAcl5Umk6yglVLiLIifykTUKpMIb6PAgZyMcgB4lJh3dYZRRZa2EKl3iNgFkC7SkUf7E+w9XTp/jZ736XyjUWr169wvd/53ewubuDRYS1QNe22O128E2DoqAQZe892ralUZjq4D1tBmMEjWTf58AGzVvUkwSy9n037Fxs1GJNbkxKWTGnAGsjWYL3ueOheUb2uVZSZR21YSLHrTY0JI/kFD5rI5b1Nc7cHYz5raF8Y4CqLLHf7/HlF1/g0BxQVCVqd44+voPWfQ1t8T7a4l0c7WN01SP05SW6+gy+XiEsa8RlCX9RwdfnYisV4D/48pQupououoirGLEqgHUFrBb0V68MigVgEiC38Qbf3xjcrz9EXyxOC1OHDV3KFXeXdly9JUCuvRvO2f0rFIcbyi/X7WhBSBgO2sjTupsca1KXmuNX2QeaJ3KGgkx8PshM1ffyPs3Dut9zOqc+J3UQWXdd9pzRIuWwblNO980ZOrky5oxPfsaZHYpyj1X1xQkdc3UkGVzg2Etgbj14zTX9GTrONxcuhl1Zd+37b+wt58w+5ZO7Q2Hu6XPILXc/fC/s3RDWarEdnv/0/k/h7/30X0rvC/jFd/51/MyT//tgp0zkvKKvvDbXD5r+DBzMeRXLftDyUfOJPLRHlC5Pelrw/SwTcx5tc2XneFuPW11nTTdg5PscUCUdFPq+HxKJs8cIj1eZm5UXuxhQ4+dijINHmpzv5d8USJ6GI2rDXHvbaftH0yVnl+oFvRxQOXfoeYjrpuuTG/MPHbm+lzJAy2OmUY4/5/hI1l+/VwMb8rzkGWAEN9njUMsqSctcXXL6cK6O+ll9Lld2DsjSz8/RaE4WPzSH8Xlp18n7Jb/nxmouj69si9Z9dX0fqreUR7ocrSNqoFDrh5IfZJ2lDJNlzzlJDQ4gSf7WdT3wEnvNsvzh1C/S67WqKnRdN5mjz87OsFqtsFqtsF6vcXZ2Nsgpzi/70JiYO97ag40rSy81BDYYk+zsIRZquF8rC68bHJKoOZCKj5xwlEoWCxP+LIpxdY8YOgKgkFY2OWSdtGAABXuK1k0FshS0cwIzRgKbnLWwzmC5pPxqIZwmrKZyeCIXqwopxxjjWuSFM76TmUx7Nkha8mtyK9B6opH05mvOkbEx14/ynLU2eWxxvjCTjPP0PmeAYFLYIPFUeuGJQOE2ntAZhpyrItIGCCYFyDJIAHQdhRe2vUFRn+HdD9d498NvpFdFlM6iLAs4Z+H7gM+/+AzXr15hu9kCKQS1KgqE0CN4j+ZwxG67w263BTx7h3AePArbTQ56IA8y5QZMpCD/NsLb4CztRAgYyrcXGZVLQJWcoAf+i6A8cWYEspjuaSfdONmUA+AcbkVhT2jMfR3tmKcNMYrw2QSmJc/DEDBsIAATyUspcg4GP/RnBGBiHEA3YwDvI9h7z1o9xgk0NEYbHiM4N/LBCBTawqFyi7Qbb8qDZRmAHHNDhECbMSBG+OTu6fvRWA+poZo2OeOF+8Ek3s5NqK9TkiYTtxgGEgSVypJW0HT50ptBKwWyztynfAxABQfHxiT5DMm/YcwlmUPYMgOhqRAfEWKP0Btsmgbb+3sY61CVFVbrM6zOz7Fan6Gsa9q11Tn4lB+QgKW0ws08jXE3YWlwMH2Y3zjk2rliMnlLI1/K6dw1OWfl5hy9ysh0jJFDuMj3lXmUuDACVU3jIUY8fecd/Innz2GMQde2iMHj5YsX+PzTz7Db3iOGgJuba+z3BzRNO/SUtW7wBLQK5JA8pHk173FGEpIGxgi6IY3nJC0GGH9iRJg05jDNA5TjxZzMznklcBt5DAEYwwtVf8j+0f0s+0p/l7SRXtXyvhw9R8WP6nZ7e4fjsYFz7EHeYl1/gTPzJaz9j0d9pImwnQX2Ul4Avq/QhHewx3s44jmO7jn+yT/9n8fnW+DVEbjtyYPt6AyayqBZW9xWqT1N+rvD6dE8hjt0qNo7uL5BEQ9wpkVhG5iC8siZCvCVgy+XaOoLHFbPcX/5rUxh6ogeVbsZvOLK5g51e49Fd4+622DRbVC1d1j0Wyy6DepuA9KvpmBWrg/loa/r+V8ailoeap1Q9zWXI3lBG3SaV3Xdcnyr35d7Vr9bL5DMGWTamMmdY+BF56jK1eN1v6e6VYfa3WBV3YMNulxbZD/ECHR+MYSwDjuxhgv04ZJyyqXcck3aCGLffQAf3zy3HIeqEpjnhiu/9cW/iI+u/gYMNpPxrcezNvA0EJcz8l8nW7h8PbdIA1IDaZL2D3nchTANYZQLSfLdcr7XvDKdr05lnTwvFy8keKIXlvi9sj48P/POn1VVoSgKNE0z5EGT7WXDWOboYu80PlfX9aQ9PJ+xBx/3MzsJcDuk4a495nS/5q5puszRTL5DjmV5bU6WPPQ7956cjJl7p6yT9kbLla3rJz91HbhsveCm7U3JwxIgyck7/Q75Hnl/Tg7qMa5pJK/rZx5qry5LjltN99xvYDo35PpF8iof8nxuR1lpu8vnc/OZpuccbfT7dd5dqS9pXS8393rvsVgshj5n7zNZR36P5A05VuXimDFmiDBxzg2pQ3izMQbGWO4YY7BarYZ6OOfw6NEj/N2/+3eHe66urvD+++9jtVrBOTfID+lRKx0V3vR4u00OMK7kxJAsbYu0Kx17p/EnAITspDz3W597yMiRjCUnNmn0UAholerMHlNk/NFtkf1rJu88XeE0oLBB8rIwZsrIchDIQSWZpigSgFM4FAUj1j5Tb/7OA2Jst2HLW1TZB4DDbxlc0+VxGC+f4rrJFSV+JifA5SCg8wycnBo9rGQNZSAOeff4DLVHrH45edkCydtNC22tdA+8IVkkMr1YgE3jsWl1E8n7DnBOJFc3Bn2IiMbinfe+hnfe+xq1H1SOtUDXNPBdh81mg+vra9zd3gLBw/tu8NryfY/mcMBuu8P+uEfoesSYvH5SeFnhyAPLJgCOeHncndIEmwxYGS7JTbVD+KpJxn1gNCTRxYc48pEFYNiTzyDCEchmiL+Yx8fccw7WxoFvLKOBiGCwzgAIoYcxFGYHnvxiRDRkpFsn3HdNAqd9SGPIgOcLbvt0kmPARnqqjbwTEQFDO5HCGMr9FkdIYACoEGEiGe5t28NYwPsWMLSpAXn3Ee1CpKettQjJO5H5jj+JH8dchtbyuEz0n4iTSH0Vp+NkTkEf+j4BGNwIYwwBp3G6u9/0MAn9HPtKhv/lVsbpbTFtfjEqQsYQgDbUiCdnO4JG3Dxu+1RuJICJx30wCNGj9R2a4w53t9coqxqr9RnWF2co6wXqxQpVXcP3PZbL9bAjbYgeISCNlxF8ofpG0K65U49k3gxGGlNcR+3xxNQaqZaUbhb0qZyYrg/fjUE0ieYwgBVAjbMCkB5l2LBqD1oUMQDKuoL3PT5cr/HBRx/BRKA9NvjJT36En/74x6jKAtWCVuOiB16+eIHjcY++P8I6WjjSmzjwoRW7oS0zSjDPp0EqZgBsTOAbDAFwBQPqgIljqgOf+Ig3iKHzNG45/6I+JkCAsRjWAhK9aQFsGtIi51VdVm6cSRrI9/GKqjZUc15tMdKihgXQNkd0XYva1unM6fv4PTrvIS2yHbAqfoK1+enwvn/7v/fPndCGy7q9BX70qcfvfhrxwy+An15HfHoHfLEjQO6uB7Yw2CPAryr4RzVQPmxAIQC48XDHFtXxBoVv4MIBzhxQ2Aa26GDKHigDQlmgq1bo60u09SW2M3nk9FF2WwpZbVOIanuf/u5Qd1tU3R0WLYWzLroNithl9b05o1IaPFomSkBizoiTBl6uDH1v7pysz0Peo9LAkYaiPqcNL/l8zjjS3+Wh3zl3fa4Mfe51xjDdA5TuQHnZyi8mdZegjDGjFwttLlZh3yzg4xUO7WrY+KGPBMr18ZLyy/kL9PECnb9QnnIGEQX6cI5FsZ/IQ2mAPgQcyLrxPTn+y8kTeb/8ro3OOdrq53UeMZkOgctj8ErKQs0jct5hQETaT5r/Na/oOYJlJn+Xxi87L3BY1vF4nBjFsgwJbDGoJmkqPVAk/7CRLwFJdijgfpbye64/cnOD5pM5HpH9osevnm/nbNi5spgm8r5c2Vqnm3OMkPRmGurQ31y9tL4gwWE+dDiyvMb9Ke1ROW/L8EnpAZXzXpKykeujN/nggz0f5fzAMoafy+lEOXksrwGnqQakTOPrmq48LnKpNiQdcnXiTx2iKMfqHAiu6y7rm5vncvJJ5knjca/bIvlKLihwvXlsMhjOYZ1SzmmnIAbiWa/lTUykd2vf94PXWQgBdV2jrmmDRQ7zrOsaVVWhbVssFgtcXl7i/v5+kEl1XePRo0dYrVYoimIA5aTNxOV81eOtALbCEfoYgwGSFwySBw4wAisx5idv3bGSAbTwkIJSrzBJhteKNhGkgjGcYLFIdUtgnwE430+McVipJxzDgC3LybuimxjbQy4btTIqGZ+ZpigK1HUFVxgBrOVBNZMM65C8OYww3hPMhwSppHeTAUrMGRFDHsTkUzLRp8zdoO+XtGQBwWGGTIjTdnOfMF1TGzEVdGO/p/JTqGUEAwqnCo2mV3ZyTDWbTD7RwFgkAIHDiZE81VgZyO8eNL6fzvfew7ceoY+I0WG5vsD7q3O8/+FHQApddK5ADBFN01CY3GaDQ3NE7Psh+b8zBGyF3mO7vcf93R322w2OxyOcow0JrAVK5wbPIQprNMlrbewDEORLuM6kHyPSLhuwoB1EYeTqGhDg026ejCNwm20CuyymrMFjl8AeA8BEj2jGfHFSGctNmIZOpCEWURRUN6K3VG4410pM44F4gwBBC2unRjKxpUngWvK+MQYxJNAkYSURhjwc04kAwMSYvN2Q8v0JMHCgcxTtIW+rYRzIRFC5iS0BZZoe+sgpuFK+EC/PewgZELiZXAURAq3aaGVOjttxPM4bT3yEEGiXWpyOkWlf5AwwT2BJjORtGzxC16I7HnB/d42irFBWNZbrNSKAs/NLVPUCy9USRVGmiTui6/2wuyvJyjgATCMNx/fKlXhJf0lnfZ7zn4H7gWkEwCjlxT5AM1n+RDlmOWgMur5DYQuUFckN4j2LxWqJx4fnWJ6d4+L8LM0bFHr62eef4bjfoWsJ0AciurbBq1cvcXtzh5gAR1ZStBImDQhtTOQU7BgjghmVJgPaAZHpagYklkE1mqeG9vIiTBwBrZzBSgscwDjm6GdutGjDWdaVy9ZhF5q/+Vl5n1yRn+QrHOgzXWzSq8i6fpK2WjmXOstD484Yg0ePgEePHP7IL87eBgD483/+zyMEi7Y7w7Z9gm18Bzs8x948wd5e4ugu0FXn6OoVfL2AX1Tw6xr+6eL/y9ufM9u2K2ti2AdgNHPO1e7m7HPuuf2t9+oxWBVUiREMsYuQIUeOIuRJFiNkKEKWHP0B/QLJUMiUJZnyaSjkkKKMUrDYVBVFVr2q25+z27VXO5sxBgAZQGJ8IyfmOs19FO7dZ6015xhoEolE5ofMBIbmOwA5AOZugt0P6Pa3aKY9mrhFYw5o3AFNO8I0I0wPhLbF2K4xdJc4dFd4unqDaL9b5XTTDv34kMJUMxgnP1fjA7rxDv3hrnjPtWFfaMRzxaffTGM5YDNmBizkHxsTz81HrbDuV6WbMuC0TNDP8k+ul/dSXtPCkzxO+f1Uf5/7TPeNDapToENNt5fP9e9S/3q9RowR+/0e3h/g4iMa8wl9j4UBruWTtSlfzn/22/89Pm7/EfKmh8v+d1g1H8rca4+ZOXpluQ/LuudxMA20jHmu6Hc0TVjGnNpDuS3m5RrQoL3jGLRgz95a3Xo8eg8QEI/pz/o1G8HaeI8xFm+Vtm2PxqT/lvY1yKEvwBHvNm5H5x5k/tM8yHtybdw1sIjreW5+n7OlanOt1xT3oaYL1urRa6NmOzH/sCOI1tlqdZ/qK8taBt+kLamf1x7rHPxdjVZMi1MHYDV7kIFonh+xd7lNTbdTPFH7qflJ+nqKfvo7Pb9CI9mLautU6tHh1XodSH01OSNtSsi2ngcG/fgWe66zxgvMs0AKDxfZ03Uddrtd+f1wOKDruiKPZUwsmwXYElkvHmca8JL8aUI/mWsZC196stls8Nvf/rbkXzMmgXd8UZr0i2m9Wn13yg1dfmSIaFJ5Yyy2ZB5IVqzNcuEwwWvorlYe9ALXi4sFg2a8tm3RdQ2s1aAJCeOERBTQJ4o9Kn1FGldpM9pifKfnT+efKf3JoYlt1yaGMPlqaMolVaFs7kPeHFPypWJrGGOKp46fZrQ3xIAYR4SQgAUGAVN9AGCysZU8+Fg41fqzVNpkYaU+svGjN+hE3+QxBPGvMsnYOhYuJvFSBCSsMkbp97HC+pziWhuL8E3MwG8ghUAWYhK4trSnBTJAAO40IXhRZiQWVSA9IAE/IQPNyWDu+jOcnb+EbSym6YC+bXEYB3RtkzeCEYf9HvvDHvvtFuMwpHC5mEJF+66DnybcfPqEj58+4u72FuMwQGAk5yyc2P8xwpkMyOVbT0MwORQ0zUlKeZWBmMyLAcgedFlImgRiJZ6fPecSTZISGzEDliZaREthoLEOsiwUs8blSxYifAyIltZ8uh0BMQZY69JaiiJ3BHxd8kCSNzMAKTxvjUEwec3F+cKKxDNGFj6myeehZvAHpoS51nhLK4Bs6GhjooyffnlOCauVUh8i/OQXm92iPZkvkz3HsMxzkkUfyvC53YSWJrlNc8nrQWSQSeTMPJBoyM/WZHS5wEJoEWP2TJuA0cIPAw5Pj9je3yIag9tPn9D1PdbrM3R9h9XmDJv1Bt16nYBiRPgwAXCpPwB8mI3ppITPivA8hqWCL/uG3BjEfHpK2arR/tS8aQXQGFPoZY1BY9PBhbHJy9S5Bn3b4fHhAU3X4mq9Qts2eV8yaNcWv/j1r5PXnjEZrAwYDnt89XCPp8cnxOjR2KQg7bZbvHv/Dne3t/A51NQ2GVQwdp7TijJ5SvZauaBFGW28v/MeWWgg0sciXZgCpAODKHu0yfkyl8p0CCFf9BBntz8jsiDDvdRXNvS4RPVMbaxa0WTDwRiDaNPt6AIGhnzrsWvEu1Kks7STPy/6ktCC6Gm/fw6f7ypp7AGr/h7r1QNex9+W7xYG2gCMT2NWpFvs9ud4HF7jyXyJrfkCT3iJffMCB3eJMd+w6vMNq/5sBf9mjcGd5v1MAJg7D7c7oDvcop12aOIWrT3A2T1cM8A2I9BFhLbD2G0wdJcYukvcn/0c/ur755HrD3foxocSrtod7rDKYazdcIf19JjCV/0Oxvii2IcQFrfIazoxL/BargFY8nzNkOJ39d86DAfAkTGq+Vm3ob2MTj1T817QemxNRw9qXcrnNeNSHzrX+iJtDsOwoJkAoGwwaiOcafg/+uX/Af/s3f8aN0+/xnn3R/xbX/9fkPIVL0NBT4ETNR341DOnZP2pvUDbLqcu22FeYNrx82JcCm1YxgnIpW2p2lxznXoMxpiFkbvUk2cayt9N05RbiGd7YQZ5pU/iZKB5QZ5hg1/Gs9lsFjzA/MRrQugyjiPatkWKFGpK37gdBgA0raT/NQ8lTSee81q43He9z8+cavOk7kCFgS0ZZ42XT8kq5s1TXnvcNv/TdBQgV/hHgAmWHdxXDgFmfq+1z7qa1t20zK3JCvbuZ97Rc8mHbaf0eKblKdDPmGUIs5b3jI1IewL4CA8zb/J61/01xhSPPT4gqs2bzJHUL2uG9TWu11pbgPEQAsZxvpCGvU1lHWqcRrzI5Pmzs7PFOpR1Kt+L91nbtgUUE889WeOr1QqHw6HwkAZNWabK3/v9vtSXDnI8Hh4e0DQNLi8v8ebNm+ItyzbBZrPBarX60brZjwLY+MbQ+TbKGWzj77VSwgwO1FF6vYg5n4C4JgryKnV0XZsX9oxa61tSQvS5n7F4YJQxZIPRlPAjA04Cn55b5jNLn4knWyz/MwbYrNeQSxSck3xG8w1uXJYCmYScPrePkptoWiTyS18ZxDCHz6GMwRSG/a4TTZ6TmbYyD6HMsbwuXkXLZPrJ+DEGJWn24jvuW/ZyC0jgjFWeOcwXetOSRXS8sSSvHRYoyN5daSx2ITSe20hEYA3DkOqfPFLOMVP4J2YgxxSAKo0DSJ5zQITrHEKY4KzD5BNoJGBfv1pjvdlkegQgZs+ymMCnNMaI6y9/gp/tDzk5oy+gmrUW0Qfsdlt8vvmM+8+3uLv7jP0u5W1Ktxt69G0H1zjYADiTgSek0L0mpAsWYjb+AkzKkRZlTFiMlecyY5ew0WIc04UIaU0LUJxoIH2VdwNCzqkEBJd5PjYAHBDTLa7wWXhnRCbNCc/X0qvRlDx1pvATzydg0kUN3gtLwGQejj7ATzGDkpL3KWQQwACqbjbIDAFTtTU286548UDJn6j6eazQ8wailQdWxNJ0xAKuidt0WS9+5ltjjg2rVF+uJc6fS1me7Apad3ySWJX7PizHkNuKMc2DXHk8+TEdGkwjht0Tnh7ukpLfJvfvfr3BxeU1zi4u0LUN+s06r9Ox5HCUdb/su8iRWZED5rABrTSe2o++j6Gl3yvfy35HtCxGkM/excZiGEZsdzus1+vEfpmXYxBlxSLm0Gg/RdjWoul6rM+vMp+luYlZKfrq57/EeBgQsvf25FMY3sPtHT69e4+Pnz7CjyOQlSXXzN7KrDSV3+0yJQIrrcJ3NWUnxCkDalmhbiyC90mmZrknnrjM59ZaINJtllnHMA6zgCq+3WmlyrNa8WN+13N5KrcIGwcS6i3e47L3p4MkC5h5daftMM1DaScKCWdeE1r+XRRtiADLPEqsYM9zd8BmPWCzvkEI/y1ijOX0OYwBZjKIj3xCfoZhvMbd/gV29qfYujc4uNfYN9cY2iuM3Tmmfg2/6hHWLaazNaarDQ7fC5Cb4PYHtIcHbMa3aOIODbZo3AFtP8K2I2IT4ZsOY7vGmAG57eYN7trvl0euHx+zV9wd+iHljZPLHfrxAe3hNnnOHe6w8k+I01BoVQPDlvoGDycWQ0LoqdeF6BnamOM50pEG3D7LVJ08Wr4Xj47avrTUced62XOBx32Kz3gcLC9l7egcXdxH+U54U+9Luk3xaDDGoGt2+Hd//X8uBqAcgEpdtXUl+7f2IOU50DmPpN86zItpL+MEUICGec0sQXvJTybfc2oXBhAEtBK7RwxwlkkM0ohBqr2Ey8VTWf/Vc8FGr3wvBrUYnzJeKcn26soYmCeZbzi3muY9lsMyNplLGUttHAw6MH8aM4Ma7MXC88geP7wuvo8hzTqQ0II9oJkvtJzQ7/E4NH01UMR7kYybwVamR02v0wC5HqvOQ8rtMjDP7XM/mYfE5qzRR9t3zOPMF7U+LWwIhSMwUKfHxHM/jmMBfMVJReQr75HyvbUWu92ueDDV9ECWn0wTDn+XtgVI0jqmyFoB0QWArB2KnDo4AVDW4ziOxXuU9xFpT2yDEAI2mw3u7u5KjjKhBYd2Wmtxfn5e6hMZIXSSkHCtMzH+YK0tFwYYY7Db7fDTn/4Uj4+PJUfaMAwIIRSvWZGJEv7pvS/ebDK/vFcxnWTMMq9nZ2c4OzvD5eUlhmHA2dkZfvWrX+HLL79cAJsyHyEE7Pf7sq+8evUKP6T8yBxs88KLcWlMyXfIoVwz4GOOFtb8bCq8CPg5ibuViRSiNY1F1/Ul5HLZr+XpWQhBnFWQDK85ATXEMybOBlAIAcjeEHJbHpAXVzbcDEhBCCm/2mazgbEBzq4g3j8xAsYs45LTezMAIXRKCrp4fmXUL5dpmspp0VKQZECn/J6K0IwF7rHAF/DMoG0dKWeFoke05NxTAG2qMWDG2mavMC2M0t+gUMdUp/fLjZfbXLZ//DPGBJqEIMayJdryldrz6dspIxmYjUfvk0eaMRbRJEPQp84ngCBgweeze3oCr0LwcLAIYcoIeuKJtlMnNTEi2uSx6KepgJMhM+3Z+QXOzy8TAOQDQpiyJ2YKW74IHi+//BmGYcA47RGngNF7hGiwXq3QOIenx0c8Pd7h3bff4ObDRzw+bNG2DfqugTU5HDWHI1trchhlAvMiInzmxwSMAjCheMclQM1mwx7FoAxxNj6nOKU1lt/x8l3IG19RhPO6CBHOiPIze3cak+dDbdBp7jNXG6kH5d3EjyHd5AqDYCNMABrrAOcwNcDkPcLoMY4DwjTmC4NzTr0olzyY7GQXEWHg8/QbACFOuUkDAZ4TZJm8hQrfRp/CBGMCQoqClT0PAYELUhsJdDTF0xZYgjnzOjhW1njTMdYs1mKN72X9mAx+scwqhnruX/Jiy2vEzUZWLYRDNtvafhEBwJMCGAE/jRDEc/IThuGA/c7CPtzh7vYTum6Fvu+xXm9wdnmFbrXCarVC23QY/QQfJlgbEX3IfGgLH3gvuQVng4aNhBlAqRsCNQWZFU6hvZTFSaeivTGmXChg8j708PCQLzqxkHQF1uZbfJG81tKlIvmmXmNgaa+IWUZZZ2CbDv3qDHL7bvo+8ZP/yYTDr/8K4zRi8iOmycP7CcN+h/v7e3x8/wE3n24w+RERQOMcuuY4HAgxhbX64BFsyOIveXj54BGz96yxDaKAqQDClPnRIsuW2VhlI1zmgnWDxT5W1rfwuXjYp7nw/thjjeed54zXjp5v+aycFNtlH42dys3GNss/CQeWfS+atJJDjLAwR3rPX1pEOWRgQZ8sazrqNamf08+27R7r1Tv03bdo23+5AFEBAAGI2whsU8i99+c4DFd4Cl/iMb7B1rzG1r7EobnG0F5i7M6yh1yHuGkxnW0wXZ9hb794frAhwjyMaHYHNMMT1sP7HLK6Rev2aJoBthtgOovJdZi6DYb2HEN3hUN3iYezr/F988jNgNx9uuBhnwC5drhDf7hHW0C6e8AfCg1rOqnmReYxNvgX+ivNj5bfeq5YZvH32oiXwsYzy2rug25by0WpRwqvHb2exdji+kX+avmp6cWgCtfNIDLn49JtWWuP+s3tCa0ZFDtFZ+6TpjG3tzwITPbLMAwL7wv2TpPnxINDPmeQDJD0N24BXHH/a/o/f87rtpZXSYxc+fvU+Bnoq/Fireg5XeZ3PgaC9Zrgdk/VqT9jXWR5WHjag0sX5nXmb63fLPaFCi9rPazWXy7shcXzrGmmx1ujiR7PqTb5Pb3e9ZrXddRCyrUskXHptSv1chvaE5Xza0rdTHem1ziO5XcGprU84MLyQ8LYxYZnQI7rkrG2bYtxHItHlrwn9Qg9OM0Eh03ywbjYohIaKe3IcwxADcNQwCwG4RkwlyLg9Wq1wjRNuLq6wmazKbnKnp6eFrIFSOGVh8MBzjkMw1BymIlskvZl7qWP3G8ZS4wRV1dXeHp6WsyB9qhjGSbzyBiIBtakXeaLtm2xWq2w2WwQQsBut0PXdViv11itkh2x3+8Xud0EWNO8+EPKj/RgQ1Zes8FaQ/1jAt+S7i23Ks6bgjZMtPAQwvBnDK6tVunmTb0pynu6ngAPA1fAMfFggZFgqHxqlTeyMgzUhb0AhjJ5Z/05rBOwxSBGPjVbIqpSJPGzXtxl08vGlPe+oO71E5YygiJY+BQzzZMGO2fPM4k3nuugfgvYkYHUaZrgcp6WKImdTc6NRf2ZBaEY6xksiAn8SH/Ob9QUyO/amFHGNIMPkgcsARyzcJcTjpoNw20Jn4ki4lyDpslCziKd5iMDsSHAWBmjKFhA03RATCfW0QQYOyuOqU6Dpj1WXrWCE4OBsXGhhInhHE3KW2dMRLo9tIftOtiuQx9XCCPQ9CuMw1SSvvdnV7j+4ku8+dmvAAS0TYe+a9E4gxg8pmHEbr/H3c0n/PlPf8peLQF+GmEyn1trMxtF2JDCUa0xGEOECRHWAVPwMKZJ1xJEmZOIGBJAmXKELT06Qkyer/M8JNAueMnhJ5wVE7hlpnxzH8+zBnBkQ5W1Ks9lbjXLE9DGZaA6An4Y8PndB0xw6FY9+q5Hu+qBnJNusiGF18W8DgwyEFbLfZPl0MK7NA1Sw+Splnk9i9eqS6IAUiUrClo+CU/zBqT5XcpJpTISzZWyWlPkBHhMqeiysYMEX/HaqnknFBmPdFCxPIHN3j8iU8YJiBHDNGLY7fBoDbpmBfvxAxqXTt4vr1/g8uIKTetg8ulZzDwwBY9pTMCdtYC1TdXrRErNmND9ZuWTwciacZbkOgChGb3j8unehw8f8OHDB5xfXKTngxgspvCGKALPGc3S/9nLPNDekp83DpuuT8ArZF8MiH7COHr81d+f4KcJIYa8FiMO2y3u7u7w4d1bPDw84OHhAUAC34zNJ5fGwJqUMy6aAGMcgp9grHij5ZDuzNf5PCIduoTZEBRa80nlPK5ZJ4gxIpg5/8Y0ptDHSebJHK8VlhV8ci3t8PfaqNtut+k02s0eEkXxtxauGH7ivYaSq0+gVBgD70PZH3+MAneqFJpUTsAXelHFOD1lqElhntcGrn4+PQu07RO6bosLfLv4rpQx/3tMcn8cL7DbX+IpfIkn8wZb8wX27iWG9gUO7UUG5NYI6x5x4zCenWF8cY6dPV4Di+Ij7MMAtz+gOWxxOX5AG57Q2gTKNc0BthuAxsG7BlN/hkObPOSG/gqfr/4K0bbPt4GUR647pBxy3eE2ecXtb9Ed7tAcbtEe0u/yr/G7ap5BPR/asNU8o71K5DPmT23kS93yt+YRBsdq/eLynCH9fYx7DabUinwvBznL/WLJtyxDZPxjlg1sf2ha6D7xPlCTG8AxAMVrRJ6VeWGDX/5JOCPXKYAae3aJLivf84GxtMW34DGNa0AF95cvE5DPuN8MKvH8ndLV2autRlMef832e66N2t9at9Df8zycqoNB3lrRew8na5dSA4m/T2FQl+nBdbB+oS/B0OWkDkJz+V1942dr9XCfuLBXVW0cumgPOz544vrZG04+Y/uR+ZsjTQTA9t4XTygNtFprC6idDpLaBb11/+V7WXMxxhLKKJ81TYPtdluALk13YxIgt91u8fLly3IxiLXJ4+3q6gq73a6ETEv/BBhrmga73Q4XFxdFhrDeInzCfC9J/+V58Rzj/GPr9RpN02C/3xdPNwbkBYwTsE0coYSGgltwiKkG9nme+ZZhBjFlPDLf3BehhdSRUoR1C5kdY8RqtSo0lSLg2nq9xsPDQ/GcE8DvlPfgDyk/CmCTRX1KCIUQBL0CABi7PNmR9xm9ZeathewYY9B1bXHTlEsAasAagBx2iGygBljbQE7IUzH0XxrLkSDI/2KcjZBsoDdtj65NSKkADyGmBPoM/tUEvRGQaQFL2QwU5fCuGDANHt7HklC6lu6ZhZws8uQiHxf9EMABiFnIOHSduIzOdS3HnYGOcpOkQ6Ss8QsokoC5ICgsLJC9YZYgHLKBMSfC1IrJKYaOMS7nKcw0FW/EmD2p5HZQHfpTUxZFCCzBXiDdpAk0zRroAcnvEbxH9NNRP0MI8NMOJucBtMbAtam+pj0Gk51ziOmaAlhYuNYiTANs05RLCbXRb12bvNzyWhu8B2DhXAtjGgxhhDFA1ydhOXmPxjkALVbrM3Rdi+JhFQIiImwf0Z5HXL/6Gl//5m+SQY4EDJmYACo/eey2T7i/vcWHd2/x6cN73H6+Qde0iAhwxibDOqQcckDM0ZXZEwvIjmWGuCGAI728rDdYRGvKZQOJXhZDCAgmwkwejbWILnmvpcT002LjXdJuBpVZVoiyCwAmJk8g4yP8fsAYAH/YY7QOfbdC2zq0qw6NMSmPnG0SwGwAbyKMTWOPtE5TeF8olw/MbS2VqVMKpNQUY4Sl0z4ZJ2+kwn9cNpvNSX5n2cHvSnitgODayNLKsrwXQkZKbAI8TZxD6tKsI/kvJoSnvMvGwfG6D9mZOKHzYczzaCbAAIfDgOiSd5e1Bne3t+l+3M6i79fYnF/g+uUrXF1e4uriCodxwH4Y4P3sFSBUZmWKaatP4eTZRX47nA4jYuWRjTAgXWgidd3f3+Pdu3c4Pz8v9ZVnMy4jShzPozy79JCe+5nKMiePMQaW5GKZy+xd2TSAXRvAGpiI+YDn5St8+ZOv8ff+6q8RsmHo/YQYEhh3e/MZb99+iw8fPmLY7zH6AYBF37cwZlqEIASfvddk/yl5FpdhprJP1BLyct9Foeb9I8SUNoDpwQYtz5/Mg9QvJ8SirIv+kcLhhd+XcyqKcZnv1ADpKWkyk7GLEr73d1XYwGDeYF7W65jpoj8X+ojxyfXpZ/in1MPrSMudGsBjTETX3aPr7nFt/gzg2GCddhOasUF8iIixwzCcYzdcJ0AOb7Czr7B3L3ForzFkQM6v0oUO4axBOGsxvrrE7ruI6QPs0wh3s0d7eMLl8Fs0/h6d3cLiEV03wjYHoHGYXIuxP8shq3LT6i/gX/z972oFxo/phtWcL67LFzs0uxu0h7sEzg3pZ7P7jG56OqKj6MI6tJELzxvPhZ4znjvRiWr6fk2fkne4Du6P8AGHjPLzzrkjjxNgNrIYbBcgifsq4+S2GPySn/pGTnn+5BzRWNnrhr9j+klhYFN7++jfdX+lHfEEEUBNwAIZK3t/sUEt+qV8x4AMh19qXuD5Yrpp3mCasScJeyRJYaCR7Tumwym9v8Zjui9af3huTnht6DZrIcC1evT60nQsexx5tH1XqdmKtc/197Ux1p7lsYhdxPvgc2uhNpe1NjT9+e/aHAqPyvrVl2Hw8wyKaQ9T5gcJS+QQybZtF+uJ+yDviFzYbDYlxFLmTurisQn9ZL+X/nZdVzzSrLW4uLiYHVTI+UX+NU2D6+vrot80TVO8xSSkUm7R5Nt2BcBer9dHQDhH/Am9ZK7Fy4zXuIxPxiYHEcaYAhj2fV9ykjHNhE5Sx3q9XswJ90f4TuQEpyE4Pz9f7Dmsi7FMbJoG6/W6gGaCe0iUH/O06HCHw6GAlP/kn/wTbLdbXF9fYxgG3N/fL9azyC/tkfpDy4+85OA47E86JUVyxnjv0ZjZrVnyErAQC2HKG+5c3zRNhUnPz88zWpnyT3H7rCzElJY8dxKIUQwTBtbmRSUhgPLZQhAg2YnG5FxVuVjjslukXOVqSm6hQh+TvCO0oY+Fd9gMKBTBExOgM40eIfgcWiFgF5CMwKXSLD9l8Qlz8maO4kGX6mkah9VqDc5RNT9Dpyfwmd4WBk2C3MxMXyBDZjwmH+HJey/jcAvhWss3ohX+2oYWFZgKAM66BDqwx6AxJWm/CNWacq+VBBFsywUVS/9jzg0moNM4DsTDy5w2TSt5Iubxrjdz7HkE8mUCBFAYAZMi7GqN6ANg5nCqQov8PiJgbIPJexTPvTw5/cqBvXNaOvFkxWpud6kMOYjAGhFjftZ7oHHoz1t8sbnEqy9/ApiUS8g6oG0c+jbdhPjwcI8//OEP+OabP+Ph4QGH3R7BT/AhJJAKBq1N4dDeBxiZP5PWSKJlApynmIApS/PWWofYuJyzKRaWHEJMoXJhuTGn8XrMYdkBNhoEONjGweewtWgMgvUwLnvZ+ADEBOJhGhG9R/RpvNY5jAjpVmVnUr47ExGMg9xwGozNYZ04KsaG3HWbEtwjrSfJ8xcSUgdDCETMOeF8SLyoDQoeM5/4SP4f/p6BsSWdkkxLfJryAmoDi9tlJYjBIJHFNk1rvnAiXR5hImDoggTdb92nInBiescg8aMx4hGZ5CdcixD2KST0EHF4esLD3S3ef/snuKbB9fUrXL98hc35Obq2x3pzhin4fLNpxOTnjVmPlY0CvRmfosspucZGDI/ZOYc3b95gt9st6tSK6JI2czllpHDfF4puuf0zc1+WBchwf9mjbM4Z5mz6vIloVtLOfIu3McD1F2/w87/397Ixl3ioMQbjsIf3EY9393j77i3ef3iHu8+36RbVxsHAobGz4sgGoig84zjOHoDWZtm8pIXObSR7qw/zwV16aWnwyT4xDMNi/iRvSmnfGBwOhzQ+ta+JEmuthQ8jnG3h/ZQ86E3yUgxiRINA8qOZ/PFF6zKnQAD5J3tXzcOJi7wrCqt8xgdGzxX+Xutv0qfa8wuQuVKXcxNWq89YrT7jBX67+L70awAwAPEuIoQVxvEa28MlnuIMyB2al9g3Vxjbc4z9WbnQIWwahPNLjK+unh0fAGAKsNsBzecdmsMTLsbfop3u0JgntPYJzu0R7R5oGsSmw7g6x9hdYewTILc9+xL313/vO5sxwaMd7tEebhMAl0NTV+MDVtMDVuMDNv6p5JJr4h1MOD4M1PJioU/H4xxdNfrLd5xgu1a3PhDitQeg5KnTALesf5ah3CctG7VxJc/pMT4HstRkt4yRx1WTqVp3ZVBQxsh5luQdGbfIPqmT86XJGDknn/zNOi7f+snzIrofj1M7OMhn+nM9Ps1DTCf5p8MEF3uP4iV9wM46DM/hKZmhf5f39TzzT6ZZDZDW/WWaaw9n3jN4/rjNGtjEY+Lx1sajaVajo6ZFrej50n1k3ta2k+YF7q8eX22/0f80zXQ+O/m867oFQMyRGbI/y+fsFSWergICcb4uGYPwawgpH1nbtiWcU+QJHwgIhiH8zTnVpN/SrozJWovVanXEc3puxXtO3pumqeSbFB1FvMz4Eg+hkYSnslOJ9oyUz9jDV9oR/YdBPI5yE/1HLiKQOWJZJHRgWc45KWVu5BmeZxmH0FS82lhX3u/35VIIay2GYSieZ8I7wluSR71pmpJL7dOnT7i4uMDFxUXhEeZTbc+f0om+q/y4EFFKlC0n6qnMxofJIVhCPLlNIr0XM7gjgtVlphdBavDy5TUxhD9KEMtCuZzc5YTDc5FwtmOBHrOHlYRtIM5hM7Mgn3OvWQusVmvaEEMGwHxW8g3m2zlT7q2lgFz0YNEnQbZDCECwOe9XWDxf2+wFLGEvvtlQszDFUFq617JgEm+sQpdEinwhRIA1TTb6I2DnsNVEVTFUsvAJc1uFJwzK+3ICKW3JOPTGURM6ErrL47TWJoCl0HgG+xLKvcxzpn/OY543gVMekYsNJ/jFCave/PmUHybAwaFbrxBDLJ5qMs4QE/BpcgzgOHn0XYeIKXlDxQawMfNqhIQJSlspN8ccgitENybxqLUy3jkWX9NfF6lbDGQh10zvCFgL51b5OwPrDKI12McIxAB3dolf/xv/AL/5m3+ADFPOCmPjMBwO2D494M9/+BP+/Oc/Y/vwgO2TnMoDDkgAnMDmUyg+fmmCAYSYxzfLFedcSmNGoKjcLOmMgEQZDAgG3iznOAbAh5jnCJgmD2MCTABGY2G6gDB5dLFB8CFfmjDCGJ/m0Dg4OFjXANbCW4vQdphChG0S6QwMogWm4hXl062xzhXgNIFyeb7zJSAxAggp5C8gXVah+VbzuzHz6ZNWeE6GF8SYHdfknbrRzUYLb0wL/jJW8dM8zhi8DBQwyEDZacVxKfvDcjyJbIghrZvJ56TeIcCEgAiDYRix2/4ZH969g3Ut+r7H2fkFrl+9RNutcH51icvzM6QQtRE+ziEGWunjIjJYxsbPiZzSco7leIzpdHW/3+Pbb79F1/clb1cNWGBFSQobkjWAsvZdmp/jxOUGZNAaMgzMfNJYFGSgeGs6pLWPGOHaNueQS3NjIuD65P21uXqBL37+S8QwwcUJVxdrBNPg8+0DYjDY7/d4enrC58+f8fHjR+x2O9x//oxhGNB3HRzReaarQWNTDkhWhNlA4PmKISIaxZdmNg69B4IxSLk3DUIMaDqLGFq0sS0hEcamvY8BbA8P4wHxWtWJzI0xCNHDRlf2xikcA7s/tvC6ZiVR5kx79+h9jn+eMrzks4Xn6omy0AdUu7XntI4nHoR8oMhFt10zPtkD1bkDnHuHvn+LV/ZvF7Iz7QERcRthdgb4bDCOa+z3F9iPL7E1X2Jn32DvvsDeXmPorvOFDmeYVj3iukM4azGc9xhev8D2JFVymTzc9gB3J4Dc79H5O7TmEY19hDGPsI1HtA6+aTF15xj6BMiNq2sM/TWeLn4BLA7Y6qUdHtCN9+gP99lDTkC5ezSH25RH7nCbc83dw/j9whDlORI6ayOc51eeZ52qBhrUAN5Ta5eLjgTgvY/3PDa0gDkHr+4HlxooI5/rd2vgA68zMS6lz+z1IXVqDx6um/VhprkYnbwmRT4LUMf6KBc21rW3IQNkfFhfo5OmUe1ZvdfV3qsVrVtwPc89L+9ouVDbT7mwXHyuf/o5BjBOXVRzinanPqt9XtPv+PPnitb9tIx9ri/MY8IPtbxXtf7rdXRKh2T9SfOhrBPJjcX8z+Nf2F35cwnX1H3V7/Lf8qy0K56yGphjHpM+yzPSP7n1UvKTyRg1WG6tLQCX4AFt2x6FsmrAWPotYD17gwnNeJ4FN2Cwij33JKSV9d4areQyhGEYircaywo9j9z2fr8vPMQH9xymymDY4XDA4XA4ijgYx7HIealHe7sxX03ThO12i+12u/BgDCEc2fPMn9q2+SHlLwoR1Ys2DSQBTNam8JM5lGUGV1KnHQAhCNB1iTn6fg1rsxcFnQ5I0Ru3/CuQlZHTYAe5kZFBtxgjImclV0U24nRD4YSmbXB2fp5rN4tn5skUYQJIYnVjomq7fnJSwDUfs23yXI41ZBq74jbK9Uj9SdBYzN4688bOz6qRkzeQyTmPZGM2xcMmAgRKZuNg4fUhT4nQiYhxQsRxoldNDynaWExKL3kIRp2Alk+zJf/cDGrWlDtp57lTuppit396rCp/8q51dhEIm04Sk8EmMAArPKm9jLojYL1eIYUaO0ThoXisXA3jkHMAtYX/pLvzRiybTeJdTljLSi8XHo+cQC6eM6ANBslT0DkCugyijXAAEMbi7RBhECYPGAEFgXZzgV/+9d/gF3/19zM/ze10roGxFne3t/jbf/Ev8O0f/oinpwcERLTOphBAmwxflPBlA1n9cmMw4+2T8TCRgjdZyTDJmE7LJgFawQBDjDCIiH5EnIBDsGgbCx8DjGngEOFd8saz0cIDcC6NHQCca9GGAS5aILQISPSbEPMFATlPVUyXQFjrAJOAIQdTkqjPQFJibFP+rvO3yMVThoqW4Qvli3RQY0zxRJNndb16c5sDx5cHCUWxI1lVxiBTZUzhA22wCF+ygsiFlZy5HQGCYsapk5wLwWM47PH0+Ij3799i1XdYn63Rdiu8ePkKq9Ualy9f4erFJZ62T/AhlMtudCLqU+0Dx55s7M3A4zscDvjTn/6ElXKt57qFVpoO3I5WmE/tESxbNW01Xcvn9K6eh/KOscXzshGjP8+pxbyfpMcbPD3c4/Gbd/jNX/01XLvBw8MjuvUa59dX+OpnP11477548QKfbz5n+eiBGHHYD3jabfFw/4Cbjx/x+eN7PDzcYxxGtF2HcTiglRuUizxDTvewVJoOfsph9LOib51LukgMGMaAvu1gIjCOOX+JTXJHkvtO+YKarm2Th6XNABvk5kKLxjnEOGEM882Ozxl+P7TIfHJIhnzOOev086fmXz+n9yL+rPb8qb2Gn2MZ9V1GJ7dVe742Pq1nyHqo1Qnwegro+z3adotL8x7e//NjsN0Ddm8RdxHhEzAM5xjHK2yHF9i7r7EzX2BrX2HvrjF0yUNuWm0QVivETQN/toK/XGPoX303IDd6uN0B7mGL5vCAzfB7dONntHhA1zyh7waYJsA0LWLXY2g3KVy1u8ChvcShu8Tn6zffO49cn4G4Tm5UHe/RUu64fkq55VbTAzA9FC/z52QJsDSkmZ4CQnMIpMwPG2ryT/Q9DhuV8DDO18g/RQ/+Lo+pmjeReF0wn8jnDBbMeujsXScyl8cu7TPAxu/x+2J3sDecrGk++NAGsfSTQ8J43LX29N7LtNJgBa8tLcc4TI3b5Z/6c+YVTX9+Tq9x9l7R885733OgktalanJDj5HrZN5i2tfqrvWB6Vwbo35Pxs38qp+V/jwn7071R/qk55p/6s95/Zx6tqa/yDPn5+clv1iMseyrDCDV9BYGlNlzTdaJgG2yRlh/rckI731Jir9arcqY5D2+oVaKgGTsQSb6cd/35W9ecwKoSzsMkMsz2rNVisgj6evhcFiMT/ok8lJkiNBUPhO6PHcYLLSUPosXm4BtQhvJu3bqMEJ+F9tScp/t9/siK2Quma+1vSGAJeMx2m7VfLZarfD73/8e2+0WX3zxRQlJ1TJP6uX+/hgd7UffIipoZyGelcWeQhDnsLw5lEAr48aYgpiyZ8001cENHvhMQBYGtniIsGEXERDDfIpVFnxR6tJTxswM27Yd1pvzYoSLCahR0tLGor/HANJx/wNGPyFMU6JReF7hMWb2QOJFVxPAacHP4NFz3hdAyq8Wo4cPE5x1hXbFZsq3RxoYRBPgYzI2gx+Pxpj+nutO/TQlvPPURqs3BJkrVA3ZGn0SuNu2s3sq85vuIzB7QdQACO5f4skJw3RIOYsqm3DIxnwIAc4mwdiukosrTApZrdFK6nfOwRmXvQABuWkTwBxql4VhAmQD2qYFMIdI1JQi+azr5htpanOgaQ9QaFxQwkacyJxNoJZaI/JZCOlZl2+ajaqfIQREm8KuIqa8lg2s6TBFBz9EGLfGr//6H+LXf/0PC90bZ+HHAXd3n/H2mz/h/dtvsN1vsV618Nagy0YxfM4N5X1JNJ68wwKss4ijnxVWm0CACIOYb/ydgk/UjwZjNIhjgLMB0wSMjYWLHk1jYYPBYCKsdbCwaKyBN4kp7TTBDDnX1bRPIc22QTRA23bwxiNkj0BIUnhEwMTkAZSX7LxBTjAJeitrA2ZGk3metNLAuS15vvl3Nvhl4y4hOjaHjtoEcFib+rHwmBE5GUMONUz50orsMQYJbkXh8BhjAW/kGb1faAVZyz3ma1tR7sRYttYhhpQ7EYiIwSLGgN1gcHi6R7QOn96/Rb9ao2169Js1Lq6ucHl9jVcvX8Nag/1hn0J0rcMwTYghLhQMVgD1OPQJo3w2jmMJA4CsXRkvrU8JKzcmh8bK5xWAbTG/rCiQougSMr54VvoEmhceAxsyyxPoHOIepjyHeX6yI3mMASYu96HLq2uY6xe4fzykfcUoPmyaFIoPg5vbz4gmrX+bYXLbtlhfnOHLn3wF/M1fpzBQksExpltWM3PCGYPDcMBhf8D28QkPD/d4fHzCp0+fcP9wj/04wk8e1nn0/Qr+kMJXp4PcWDhiHAPuHx5wGAZ0TYPoHGCAMaT8q8YBT4cdQgyZvhFTvrV1luaJ/8thFUw+nPy7KZoHZV7jCZ7RugHzp9432KDX39UMUb3ncBvaYK/9030CEk9yvj2uj/ugi65Xt6H1BaaNVvh1valfEavVI1arR5yFP6Jp/ptSZwpvNohDhJ0swn1ACA7jeIVxvMJ+eoWd/Ro79wV29iUOzYt0w2p/Dt9v4Nc9cNbAb1bw12sM3euj8elihglud0Bzu0U7POF8/BNeTp/Rmzt0bofGbWGaiGCSp/XQZECuv8ShvcS4usbD+S/gm9V3tmVzHjkG5DrJKXe4Qz8+oDvcoT3cYjU+oJue5j2M5ph1GE7vARx7L0n4dggBk1vBmUN5XwNo8rvolvo2eb2n1LzD2ABlbzTgOGStFs6vZSh7phynJ1nu4dI/HTL7HNghPxnU0/TUeiOvkVN1ylhqOryWMc/1tUZ3tg9rtDulp2sD+dSe+Fyp9bNmP+i9nT9/TseuecnpUjPka/JTPv8uPjj1HBett5wat/ZClPc0HeRZPXZdt/wugJjcvCkJ9AVk53dqwLhuj0OkZ5u+LWtWh2bLuDgH2jAMJVyUnRNqc8K0lWgEsdc59F1opOWD4AoSiskgcbGV4gweynvimSXzcHZ2Vtrg0FaRM8Ccc5plnKxTicaQPkjdUo/0i/POyQUOMkfDMJQbOZmm7OVZdEzMa0bWqkQEyPywzJT3a/TX9chn8rxcjvD27dtFRCSPl+tiHq+the9TfjTAJhPjnIWxtoAb4jKyFIbJO8T7AOeWrp/C/DW0W8qsyKV8bXORBZryJZV3zBKAmvwI8baKMYpZh2Q8i+eRyQjyBk3bwph04ygikAz/OVFybZOpeVTVlDBxRyxhoDHCmGTkCbClhYkxs/fRqU2mNj9awEkf+HeeI2fbbBTpd1BolYxnXzyDEmn0BpnqSHUDMSyZ8zQwuWxUb7Tz8+IdmNpJrqfLUIbaSZ1u6xQtF8p3nq/oPZxt8ljj0eadBFNENMkoZGFaG4M2CnjT0AYCCw9J4GitA98+Jz/5JFT4Mq2xJS00X8wemTnDnNQZAAGMjSEeN7EAbDwmNrqSSenQtA7WGkzZnXcem4H3EwCTPblE4NosL0ZMPuacRQEGMeU5s4DrO7z48ku8fPMl/ofrfw+bzQpAgMmm935/wM3NR/yrf/m3eP/2LYbDAc4BrUteZ9EH2BgxhQOGId2SKGGlY5hwmDyMsXBtC+Q8ix7ANCWj+TAd0MDB+Zyg3lnkOzPRNglks4jpghcYWJdOdGw8wNoGAQGubWGtS7e4ugbROETnAecQi1dsulSEDT1rEgCbZgZZfiz5SnizeBDSBlUH2mc+EL4UfpHPdJ4CY5BDf5frhvk9ytyZSoiiIR6PkqMtSR8fZ8O9tj61Uc77xtJLJwOrxUcuZP5OYI7J8stYgxAnhGHCNB4wHJ7QNCvgHvj4/tvkidh22Gw22Jyf4eLiEr/6zW+wO4w4HA4JMO16jH7C5BPoJgBYxnVL3znUoO973N3d4f7+vvRfvE4FhBTQiRUiY5IXo84fUWhB9LfGAJXT0JJHi5Q37325dCGJfUmjkH4XMo7TCOeamax5nGn9z2NN3XAZNjZwjS2h2jHE8m4oMsYuDj7S3rcqBjHzAJB9fxPzAIhwrkn9DTPtASSPtZCCrZu+Q7ta4fzFFd7EnwDGFKBYeDVGObRxsMalscUAP034w+9/j//qv/gv4doOX335Ja6urpIOZC2sc/DDgMfHRzw+PiEiYBySl52R+R8nRAMEUzQFTH5CZ+ar5f+SwoqlBgkYsNDGLMsY+Vz/rY24U/vWqTb0/OnnWT7o9/Q+J8+yosxroAaEaR2Kc8nxM9rA0e/yPqplkDYetWHIeoNzAU1zi83mDtfmj7D2vwawNGb8ziNuI+ydxTT1OBzOMQxXOITX2NovsTVfYGhfY+xeYeovMfYX+YbVDnHjMJ2tMV1vsG+/wMNzjAPAHEa4/QHtwxbt+IDN+Ge89P8MffyM1j6iafYptNpZRNdi7C8wdJc45Esdhu4S282XuL/6nnnkxod0scN4j/5wh+Zwh54AOvGgc9sbdOM9nFnOlXMO+9Vr/Cf/5v8Wny7/Ct34iH/7n/6f8NW7f1zmj+dF5lGHvkpdx9EF85rROYRkH9IJw6Vd9hphvmB+4PAseY/3N20PyTvyuxxyaD1QgxlSal55zI/8ndTNY+K+LPYbKhqE4b7wPBzxgxqrlhH8Xe05TWcem9Z5v6svul+njGu9Jz+nW9Vo8Fy9NdpyPdoGOUU/XRf/POXdd8p2Aerh05q2uo96/Py7Bn+BGRwSO4ovG5DP5V32dhJHHb4IgC8HECCb3+H8bcmWbIrTj5YfmkYsp4H50FE83uSihKZpSrgne6Nx9Acw30rKc8sH5AJaCb3E+8tai4eHhxLZxvnhautD9lq9VhjYknJq/zvFn0wLbX8wP9QOwbkdDbppekl0JPMdOxMxrwl+8uHDB/z5z39G3/elP8MwlDnSB+BCd+7nDyk/CmBLua1SZ5q2WRjZxzdXiqJqSmgju1nrmHWePPk3jqPqQcp3potMkM9X8QbPis+chDkaOgm1BjARm7OzklQPsAgBKfl6vgEzwh+1I/Umjx2yNFSZXSen5PkVYw5fmj1RuF5g3pRkwdRASH6WN1X+XNcrf7NgFpqe2mv4+Zgtj1qdtZ/AUngd1Z3pkXqw3BxlrLotY+Z/ztkjw0sLcq3w85hqYwBm4TANA5DDiY0xMDkvnzxrrckeGh7GGjQZNDlV9GYoQrwGMPMG6L3Hfr8nRd0gAS/LzVLKfEIiHonH+Sj0WFMHAcRl0mI9bz54tI1D285Xusv77MlTFLUYM4hh55DGEJKBbpJHmBEQJKYNcxgGjNOQxzgn63fOwVihDwB4nF2eYxgOeSzJ6yxYh831S/xb/86/U9aXnzwam3LA3dx8xuebj3j77i0e7u7gR19A1CYavAgd/ro7xzAFHA4Dtrsddk9PmKa0cfnJADlfmzE5TNoAgIEPCWAzMPlAwcD4QzbADYyPaK3B3iePNjQWMe5z3jYH03UwTQJQvU0wZcyOk8Y02f8ry1lrAPicy8kkDzxi69pGqXmgpqCdUhT1Jqi/Y75K35sCcpQbjSptFIXCBxilsNaUuNqa5j7o76SuhcINpItE6KdBTHn/PeDjDsZYxABMBphsg/32AbefLRrX4Y+//S1ggM35OVabM/zVX/8V/BSQUvFZNPk0MISAvC0mmMnOt4aGEHBzc0MGmSveVxICnWcJAhcl5XDeP7XMX9CE5piBNQYlmD4nvZ3l0CQEwKR8hsXHNssM4UlrJcGtyx4m2TjMhy0x5oMIa3NeSlnPFsAylEn6yTdcHfENI5iQNWlzuFrI4aCpA3M7EbL1mpxOwroEXMszmo7ONGjaFn/9N3+Dv/77f4Pf//536PJlR13Xo+1aWGPQuabsaSn7n0EQxdq61B9SMkVnOhwG/F2UeW86DtFg2Sy/y3faKGKQSerVxj8rzlwf9+OU/sHK8ql1ruvUOo3uBxskpwxj3S8NCNR1juV+fArA0HqHNmZPyavaZ9yWfN40B3TdCOAzYvxtOXSTNdI0DVrbwnoLu3UYbnscDpd4fNpgO77E1rzBzn2BvX2JQ3uVPeQ28ALInVlMZxtML86wa98c0U4XcxjR7PdoHrdohwdspm/xYvpnWMUbdO4R1uxhHRCtRWhbjF3yjBvypQ6H9hKH/hoPFz/HIjfBidIOD4tbVs/jDu+u/008rr8EAAzNGf7x/+B/h//pf/K/wXq8X/AD68h8AM2JyBlAYwBA81QNOOBQL2Bef8zbNY8uBuO4Xd1nqVv6LM8y70thnZv7w7zLbfD6r4EgZb7VdzVdW4/hu+o69ZleP6fqqekvui+nZMGp/a4mi7i9mp0hpXbooMd0ij41WfJd46w5UvDztf6xjNF95udq/eLveEx6zHrvqfGO7ANSh4R3y23dmkbi4SXPy+UFUgQs4zUroZlyaQGARf16HE3TYLvdYr1eo+s6PD4+wnuP1WpVdCfWoSQCgT3JkqxuFnYbMPOZhDfqW9ElP5msb32pit67ZH9s2zkvLIfLyzMM2PFc8D5fs4m5zzEu8yjLwcCpeRaZxilVNCCp+aH2j/dpHrPkX2PZKWAtO7iIzit9H4bhyDtSvCd5zEI3bveHlh8FsLXdfNKT2mbA5thtWNwkeeJrwhOYJ5x/8jM8efyMTBwzvzVCmNSvEGO+pRFwrYNztixYY8y8QIyAAvPY5tvWjgVHrSR6IHusDcgYQ7ZBbDZIksHFY5S+iDCQ8ei6ddvPbdD8c+7b8yc3Gmwq9eT8OmX8R2Oe+2YLuFOvmxdPjLOhpY3n2gLW7vlaoa2Nmfv43LNy60iuGBBvPO+TV5KxxYiEjMWmxPY1zzn9N28m3BctSLg/ya03gb2Ss026zButVu5S7sP5Od0vORUpRmckT4Jsuc5jSDzddx1cYxff1YQQe3XGGNOlDekhBOMRA2BMC98g9zPC2Q77/aGsGSDRqWvbOdQrj6HvO3Sdw+gDrBWFMwER1kbI5QdCPx+TR12YIlaX1/jq/BJf/+LXCfwzBtEnL5thGPG42+MwTtjvDthut/h8+xnv3r3D3e0jHh8fsdsd8PT0iMMhfT+M6STJRGCYAkaTaTclzyxnAGcsjA0wcOhcMrSdCWhCAgOMB2BGYEjJpduuA1wD2AYx2gSsuew5ltztAKSbjGXc4gVYMxD599o6YD7Un2nF/JRBKgrTfOqTvw9LPgrheA2yIqD7XVPmmO+41GSX8PxzXnH80zq5jCRAUg/4EIBphPERU9wjTj3GacLD4y2Mdfj2T79D16+wXm9wfnmFN1/9JK3xEND1XQaJIrJTKKy1ePf2HVrXABZwrkke4WV4Ea54NR97/fLBAocB6Hnl+eD3WGFSFMxtzkB2NClc3zUZ0LUxJ+q3iY9lbrBUmvu+h3Fmzvfh6bDCmBIwWTMOUx/MYm+Tz/RzBevjvdGYDJ7FPJYsb2ESvmaRdQSDEAFrG1gDeD8WGZ/aUeGJmca/+NWvgAgchkM6PbZpT2qzIh5jykU59wUFJ+W1JnV3Of/e31WpGZq17wRYre0hLCd0qekWem0xH9ZyMbFnIrfPBzas59WMU22E6P6dMj6lPa5Xj5WfY3CkVqemtbzHXnKn9A4xFKToeQCW3kea9tLWcl49nHvC+fkOfT/i1TP9xQGIe4Px/QV2u3MM4yW20yvs7FfY2VfYuRcY2msM7UUC5FZrxHWLeGYxnp1hfHmOXfPlEU10sfsBbr9Du92iGx+wGb/FS//PsQqf0OIWbZsOqqI1CE2HQ3ueLnNoL7Frr3Effomn1Vd4uv4pYA3eHU8Cguuwv/4VLm//vxBjlYEE9oTg2wV5TTIAJ/zHgFkNdOb50HnW2ADk3HJ67ph3mBdO8b4Gu+V9/lnrl1733Bd5XtsT3H5tX9Zj0muhpqPrd577WaONrk/TvLbmdD+ek5PfJTt1/0/pL8+NuzYu/Z3MB//kNrmvus7vS39+h3UxBlhq/ayB0lx4Lek+8BwJiLVarXB9fb3gU7mxUoATXs9936Pv+/KMpomAPHJIx15fMc5AjexH/A9I3nQClPElC7W9RvoqifSlTmttCRtlWqRoNn80x3xDpuyH7LWtaSfts4eb3AYq/ePnauv21F6v9Wl+n3N0a6CXPW3ldw1qfRfP6P4Ys8xXKbnu+JIpa+0iRDjGmCJPNht88803eHx8RNu2OD8/L/QVHUSAUj3eH1t+FMCmDWf+WQN6jElJnJkwQnRmVvm3HJRsPvm02Ugox5zUThaABuRC9nwJMXtOWIOua2GdQb9axlQvhCqSFwwouKcumOqnjwDyApVFR98JaIe5CWMNrMm3gnYNkEPnYvZ8qG2cerM71Y9TP/XvUg8/NzN3LJiagSF8bXkSLgqgzSF+2Vclv11xVaV5rirrKQsRjSuibcUIPd7wajTQyjMXbpOFOQtRAIsFZ5yDdQ4hJsBIDDFrLeQE9hRfSWGBo+nPcxojcDgMGIYD5AIBoUcsDHSsVHBuxDSG47W6UBxLiCFoHpGMwtKf1FpjU7hn+viYptwfDX6UZ0Xo+6FsHDE2aBqL3XaHcZzymol5zTawxhZQxuX3z842OAxzUk/ZiGIUcMCVd5guBjF538SYeVT4M2CcUt6z1cZgBeDy4hyTv8JXX32Bv/qr32C/P2AYRtzfP+Dz51s8Pjzh06ePuL+/x263x3a3w363w+EwYPIewXv44LP/a8pd2Dpg8h7WWDhr0PjsbWN9AuKcBULy5krxoBZNvizDWAvjGsSmQTAA5FKEFDeax2TmgMjKIcUpmcDKT83IfM44fK5waP1cR1TPqFMtpPXPtwTzGBIb1b0seQ09B6jxuuExzgqjgDFZoYvpNlnYdJP24XBI/fYAYBGsxWG3xdPDHT5+fIc//v53aNse6/UGr16/xNn5Ofp+BescLq+u8dvf/w5nZ2cwCDCODTqU9AS5twtq6bGEMOd2qyl/s8fEvBZkjScQab6oZ6aFyM6l0c/7fA30qCnPYcrGKUy6YVP6CO15Ecs/qce5pZKf+kxgI5aFgYkiCHKKCuGHVFESeBHSV5PO0IxNnrWLwN6lAcy8GhFLvhgJxxelj5XEmiEs81QDif+SIm2JYs+ynueLPVpOyfHaZzwOPoHX+4su/Ll+R3uF6XdkTTLIofvFxmFtDLXf2YjUPHyqr/IeJ7Ku6R8sh+SnzDWDgvrggtuv6XlFz1Iyj9e91sPZS7Cm96W2A7ruHl13D+Cbkgy7apjtI+KuwfD2Evv9BQ7DJbbTS+zcV9iaV9jn/HFDe4Gp38DnCx3CmUM4u8D46hJb9xM8W2KEPQxo9js02yeMnzeYbtdAG4B/6IAuj6MxwAZAl94x0eOLcIu+78sButBWdC8xyhg4BZZ51GTMYrSLbih1aMPv1LyzHBBdhfmUgdtanWxo6jV7KnfQKZ1YxiV9YX6s7aGnp+b02Gt8qd/RRdPsVD90u6fW9ykD/VS92kbQv9eePVVnTU86Na7n6qm1qcd7bDM8r4vV9urvklnMK6cOWaXU5p4Pb4rNQfpDjLGEWXddh1/+8pfYbDYYxxHn5+domgYPDw94+fJl2QukcP3TNGG/3xesQfbjGCP2+30BrQQvkD6KE4uMS5L0Pz09lXxlvM64fd5TWa50XXe0R3CftV0qv/OBp/RNnhcPK97PNe1ZNpziC83rLB9re11tP6z91GPT+oauR+sdPCZOb6HHIv9kbmXuZezjOBZ9jPPtTdOEb775Bufn5wghlIMVAVxrOpvwBR+q/JDyowC2UwurprTIwOSkUjrddd0Rk+tqZd2HsJzINOjZU41vjJD60nszsCbhZV3XISJUJ22uI39n6PcSiof8me5jyqHGfeHxFGYEb24u5Q5zFs42cPkGNu+nbGckw58ZWyPgNcbnNmsIbG1h8KI9qif/bzFgzONmujvnYMrFEyEjMwr0qijArDgCKAZZGn/CZJxrFotKl+fowEZBjSbs+ci8JJ/ziWQRtI3LfasrFLqP2oA49RwAjOOULsIYJWxoqYDX6uQNhQ1SJge3HWMy4uV7LfiJgoiQa5VN4W0tqOtK17xu5nUZgRgQYwKh9/sDzs4ucNgPiwSYs5diCu8MSJ5FsEDTtvDBwziHRgntmSeNYHlqjlI+x6ZpwLkZgQiYET5Y9P26rNVpmuCngMlP2GxWCCHi5Ysr/PwXP8E4eOx2Wzw+bPHw8IjPn29xf3+Pu7s73N3d4fHxEYdhyFdKe8QpwCNinCJi9HDOwo6JPs6l0NLWWTQuhd67fGGE8elqAJEZpmlgnYNrO3jnEJAAXgOPaFPydB+Xc61PwGSetGGsN73aSdkC5FCb4OLSA1UX/0y8KXn/BB/M3yHm21yZhyMgl3pguRZqbvCn+ldTHurFJBlkgJSMMPN99uz0cnuyTzfKBrnQJ3j4EGDtHoOx2D894O7mPQxSOGm/WuOXv/oN/sn/5x/jP/gP/kNYY3Bxdol+1cOYWab6bNB57zH5KeciPD7Rs5aNL20gaBmUgGRAjM3jMG/9DpfneEXaZeXKWovgk9ezsXSyjHj0DsvD4nUeBCQ6NUPUL2MQM89kAQVgvmAGMZZLIkJM+oaVCzhihHWUN9MkmJoVQx4/9z3GiMvLywJI7Ha7hQHOp+1a/j63B/zYovdSzTMxxoVBLTqZNvhZQZb3dD85yTKvRb3+pF592gzM8oLlBodu1eplI411In3CXqMNFy3fpJ96Xmo6BQNYmoe1zOQ9lcehDWSpW88Zfy/GqvzOBhyHixqTvBhqBo0eH9cpf/Oz7O00j8djtfqMvr8pn2swqDw7GowfWwzfXOBwuMRuf4FdfIOteY29e419c42hucLQnWPsVvDrFbBxCGcNhrNLDK+vgDcn1sgEdNsthm6DJo74H//h/4rX7YDJrBZAo+ZlbTDVPDBk/2cvmppXiDwrRlqNvqdkZdGbT/CvlrnyvNZVGUzX86q9kHgM3D+mS41fWO5refBdv5/SjWtjfa7od/Wa1p+dor1u87vqOdWX7yu7dZu1d/V86HdP8dAPLZo+z4GRnFSfebY2Fs13vDb4cwbdjJmjx37zm9/gV7/6VVkLIt/Pz88RY8Rutys3ZXLUmubJcUy5caUIOC7rWdNU01328PV6jd1ud3RRgA6VFBmpAXtuh282ZrnBa1F7Kevx1Q5XOBRUXwpR04VZV9a6ut6zaryiP9OyRH7nulm34L7pOoVX+KIDeVfATgALLInDQoX2+/0ebdtiHEc8PDzg/v4e5+fn+N3vfoenp6dy8YRc0LBer4/kpOjeEmJ8nKrsu8uPAtj0BANLIjNhmCDPeb7lmpFNK3HGKM9oIE0mQU+WPB9jwHrTw2VDtWnbcntayLmLWKicAqKMAWLO42IASG4WYUBeMN7XlUzuX4wRxlq0XYvWtYsTeh8i0p2CsbSFuBSy8k8rA7W+P/dZbWM9RQcJ6dHjKF/Lhiz1QSmVMT8fj0/RtTLJY0zjS14McjmGfq4+93Xl+NTmLe7APHdMGw5hScLJ5T7VT/xmIzLlK4phmcfjVJH20nXFMg7xFpvHxwqz8IFsWlLPvBEs57O8nzAuxIgjeh0J5Li80ODUc6zozaavzMGyD8F7jGPA+/cf8fLlC8QYizHQtg7WOrRtB8nBZp0FTMT+sMfZao3N2RrRoABQwPE17SGk23oZdEz0Sp456UZJibf3yUOvbdAjVi5fCXDNfItr4xwiAh79E16+uMbF+TnevHmFYfgaw+GAp6ct7u8fcPdwj6ftDp8+fsLt7WccDiO2T9vk4TalyxWiMSnPl48YTcDoIqxJlyk4l0JLQ5Mlo5sQfQMzDjDOwYwD0LRwTQM4B9gW0aTwRgeHMYR8K+Jy09cKM5eagcxzy2vlSLZVFK/vKpJPDCaBPybTw1gJhgYg3pqVPtWU6VOKKo+d62EvgjL+GEu7kevKjoWewCzkPSfJvwAEnw54YBDGMfOewbDfoul63N9+xu7xHn/47b9Cv1rj4fYeq1U6BHLWwnUtNmdnePXiBSIiDvsDDtOY98HkCemDR4xzvghnLKzLeVANr+sEvM1eaceGhC56j2f68Ds1eV47WdVKrZ4nBgy08aH7cmofA1AuyUh1msI/vpxA5vaMyftaAv7SPr7cayOFMdeUVe6DKHhAusGMbzc85aGkafp3WZh+vJ9p5R+Yw0p4LvnASf7WY+afTAemE+/vGuCXdSd7LIfmiL6ow5t5fKysSz+1Us9h1M/RuWZE8Ng0AAIcA6b6nVobpwwX/VwNaOH3WV/jz8RLmw+0RS9gA6EmF7le9vTSOlltPUgeIjYAOcdeWh8DnPuIzeYTXtDYRI/nBN2t7YD9BQ53lxjHlEPu//1f/C+wdxfATx3w73fAT+e5/Uef/h/46z/8Z1iHPVYuXYYkckfLEp4nMepq8kf+5hsEOQk4ezu0bbtoS88t665sFGr6MyhZkze1PVa/w+Or7ZPCt6eSj9dopcf03L56aj859Vltr9Zty2fP7QU1Wv3QUuvDqXpr8wwcHz6eWmu6/hrv1ejO7Wh58Fz9eoy8lrnfmv80oKzr57XD78tnfBAihW/MZWCEo28A4OnpqdQnN1eKl3hNh+D1oHVT4XcGe3TeNPknoIp4w+nx60MNKd57rNdrPD09wVq78JI6BWqzvBdZJPuY7IsiQyXfHIe11uRxrV5ND+7zqf2ceUZ/dqqc0u1kDhiIrLWp+yl8wvaY1Ct7hxx8sK4hdcjNqJ8+fSqea6w/TNOE3W5XLjtYOmkA2+32O8d8qvwogE0TRwgnf8uk6zwEMUbARBgLBIR0Q1qqMde83OAjEVYjrceKqSi5yTB3jUHTHiPvzrqUqD7WmWmuU4yrBKzNedlk4pOHnAAYqU91JmVGW+SjI+UwxuwlFqPkSgciMTZS8m8JJ9VCWY9D/67z3mnU/NmFE8WAzCRBAgnztYtLL4Jc5jlPifhjjBnQmZ/RSiTzlchiuRwjzV9906st/pqiob+TBatP6k9tOjKetu0LUJNCjVIIUnrZI10okDwgrLVA9kSS+UVOAGRgjtqcky9+/0S0rKzpU379fhFgYfawrCkACRhMYFLXt2ia0+EoXH8qTLOlsIwhIviAaQy4ubnF+fkFVqs1pslnb86SzR/OpstGgknr77B9wmazxtnZWcnHljxylvMqczYMB4zjdCRQ50tZDIzNYHaWRjFGrFarxanRrMg0ALLnhE8g/XAY0VgL27aITYPNqkcIG1xfXuLN61cYxgHDNOHh4REPD48IIeDm5jNubm7w+fYOt3d3CWwbBoSQ6DP4FOZirYUZIwwipi6FxlofgTbCRgPrJ4RpBMwe1jYwTQM0LcwqHSwEA4RpTFGmMQLRJ74jmZLW8wyGBsUHvEmfUuBrpWYc1Irm8fJ7DpFc1mGyM1mcj2HMXE9Nia31QyvRS8WR+yFrJP2UcMoYxYOTx5vDqmNMecZigZZTfTHxPDBiHAZ8eHqLtmnw8e03CRw1Yqh1Wcly6FZrXL+4xmq9wubsHMZYuKbBy1evsNls8Pj0hN1+l9apTzdgppDocOTtlpaERbpVtQ6YnDLutHwUuulDJE1jPoHWSirn51gC2bPuwM/ruqVf/E6t77KZWgNwGCx9CUAUZslb5SCXwrDhKf2pgWLSBwF1rLX49OkTrq+vi/fPd5Xq4daPKJoW+iCRvb/kGUkuzSAJeyxxLrQaGCHtyOen6AbMnmz8PYfescHPUQ41g1b6yUCgyHltHGperRmYbKjoA7GlnDiWW0wLBpq0UQwkw43pwEYEj0PTUY/9WH6ZYoj1fb84kZfCa0vzMh/YDcNQ5pUjB2oGndSrQXn2YpE227ZdHAbqvskaahpgmm7QNPfw3uNs83/Df/wf/68wfeyBv98CP03v/Hz3z/DvHP5TODciWqBpEh+LgVujP9OE5V4NDBfgTOaL80DJ9zxGXX+Nz5gep56X32vzzSHGGriu6cY872xPyVhO7de1Pun+6Pa0fOD6azpErWiZrsej9+9av0+9V6Otbvc5T9ga2H7q2ef6zX2t2co12vJYmW9P9aM2b3qNankjvzOPybh5ffD4eT3r8co7bKfIwYd4pvV9X8L6xAba7XZo27Z4omkPcaYf90kS/TMwZYzJUSTLfGu8bgX4ds6Viwmkb9JnPe+87wEo/QaSjPfe4+LiotBaxif9ZRrzvis0ZPnPezj3Rw5OamPhz/Sa0ryueVOvU71+ZUy195gm/A47SWnMiOtivpS9kQ8HuU3JY8c0E/6SsOCzszN8+vQJ2+0W4zhis9kUoJYPg/b7/UIXkrYE6Pyh5UfnYJOGteLEg6wWwq+SbWeAeCx8vPcpB1lYhiDyBMg71gJNM+dbkHRVegMqm6KfDf/nhJi1wlBzmKoPYsQc5x3RP6Vea+18AyIL1opCvWDu7HGAoiwmcE3Gxu88d8JcA9BqRvOpojdJm2+549tYue/JCAWiF1plw5hyhmlFnWljbQJKpVhbnyO9Iek5OOWVyDSpuchqQZLaCAV4SYZaVkyNWNK+wMUxg7NJSGQWl1toF3bfvBmFEIpATnSZldO5zXnsbLhqgcjK+pz8v64QnFKsjAF8DOj6tnjqPccrUk9qYhaCtTmJPmAYRrx48TKHDCWvhbZr83NZ0c23+jmkU4ar60tsNpuKgXcM3Ip7r8gELbzTBsRXRk/4/PkzXrx4UeZfTqbYYEs0BdACu90+1Q8y7ACEGBHagBCSgTPFgIuL87y5ALv9Fk+PWzw+PeHxaYv7u0fc3d3h5uYzHh4esH3aYcx5AUKMMDFiGg5obYPGAIP3aK2DNRFN45I33eThphGuGWGmAbFpYW2DNgIIE+BsAcd5kzI2eeoAgLHuiB9OGQD6O/0OKzG1/YCV71oxZnlCNc8t8VNeW/iO/nL5LiVCj0m865jHTtFHwDcB12R8GrgKwWO/2+Li4gJhGjAO+wIYT8MOLt8Uax/ucffpA0xj0fU9EBt0XYvXr19jfbbGYUxeKuv1Bl3f4cXLtJ4OwwGT9/BTyv8XfDaojJkPrUwKw5Qcd1ohYsNb5oFPiIXfeVz8vvwUHqiBFfqnrDNuQ/4WZZlzQul9gN9jY9Jm7/MYl6foej4XyiJQPLP1mDRfiazVSvPZ2VkJTRDjgG+3YqPgOePsh5YaqCJrUL4rh3oxFpryO/wsr1W9d2tDnUEjmTduW95nA6m233KyZn6flXE9Rga1uE9M65oCL3/Lc2yY1fQB7ksNKJG/tW6s6wZmYFEby6yfsBefgE/Ma2zQjOOIs7Oz0gfOCSheVtxfDRZoeaWBNZlPyTMk45ST/xiTQSpt63Utf4snBusyAI7yxwpYbYzBz372O/xH/9H/ER8+/ATn7garDxGDD7iOn9HkA2uee6G71utYvgntxShnXVf4QY+Db87jscj8aL6WsXD7ug2WL1I0bzH9mB9ZFjOgoGUVf8dj1e1ze6f3ujpoxe0Iffh5XbduW9epPRB126fe4/Zqc8B8ocPRWYbpdjWgUNsLtWzUskMnd9d8x/JAHyJx+6wbaVpyu1r3rX2m5aHstfI7vyvv81pl+a7lmTGzh1Hbtliv1+VykfU6HZg3TbPwNBKPLwaHxEYSkFsOAEQuMQ0FlBMwWfol8pQP9ViGylzwoQLTS+S1lBjjIjE+e9Y1TTPn6cVSNogMZx4TOcR1Mw/IM3r/0zzBFzRoftNyXMs17ievG7GpBGjS3vnynegTGjjkcbM+yQAaj1vLKD1WBtUAHKWdkH1NAFMgeUMeDgesVqui88gYJezYObcILRZnqMPhcORd+X3KjwLYmAlkY2ImEaJVN46EqiVfBJbXQsiY4QlSANPX9bCfrnPo+g7WyaIujaCxTc5pNisnp4GlOf9MTYFLTLv0fDvVJ/nbGFNuN2HhVNuENSNqhdiYZE16pETbKY8cSj+E9jJO/pwVsFNAHP/OCoAW/MvNypYLJGYyphvZYsgefWLU43iRLDcHwJjljU/cf6aVVhpOFT0fMg45QWBF4FhBzh4p2Xul7/sErJiUv8gYk8CJwm+xeP/IQp0FcVzwTTbfcj1pcY+j1JvQYb1R8vhZSZr7znRokBLLL42GRTF5DVbolL9G3zcot4sqOs5FFOMGh8MeBWSgektc/eThxwnBe6zXG8SY1uYwjWjaFhEGMQMrs7dmRPQem1UC0BEDok9Klw/JUzABW4l2ERKK7uBcW/qhFRD2epC19vLlyyPFQ5/Ky3cfPnxA17RonQMi0HZt8lmMAt5mWYGAxgc0zqZ8T3HC2abDZrPBF3gN7yfs9wO2ux0eHx/x6dMNbm9u8fHmMz7d3GI4HHDYHRBjwOB9ugnVRwzGo7EWTd7sjLFoXQJM3DQAroExDZxr4IYBwVlMzqbbbpG9gxERJ8CWk8l5poVmIctjk7lAbotkmuo9QPhEAwe8jk+dmnE5Ns6LOJn5MHcu3eobEc3xbaSnvAV4DPJ7mrf6CZw2jObTorm/iddlnxDFyqvxptxIbdsAMRYACDBAAILxCAbwU1pdcQCG7RbGWDwg4v7uI5x18DEB8V3XoetarDZrnJ9fYL3q0fUr9JsNXr58ifV6jf3hUBRBMfKHfJorXl4hZCUVs8d24ukA27Tpp3XwMSzACxk7zyMrSacOyRgwkTr4lJiNDdE5OLyKFVfemxgsYYX/Wb0kl1MGIfNNba/X/GWMwWq1KuORE3F+Xu/x/30Vra/UjDGRW3rv132u1QUsT6a5aFovD42WRgv3gdMySHtsCGp+q41H583RNKnpcVy0oSE8V5uvmvyq6Vannme9kMci42BgTetDbBx3XVeMHzH2+r5P+2z2EGFjkg0TDkcSQwlYehyK91lNpxSQTfLbsDGv54C9rpK32gyIaUNJSnruEefnfwtrk4fduXMlHFTLcyksD/gzzZf6e9YDWN7wePgZlg21Nmvzr/mWS82e4O+O9fHZKJWigWCpi9cu96dm6H7XGFgW1OSlrkvXewrMrs1LbX5r46j9rumn9dRa/Vp+abrpfU1kk9bVuT0x2nUfZKyn9jHuo5bjmpZ63+X1p+0vrlfzCYBF+DOD4ex1Jp/LIZJ8Lzd3vnz5EtM04csvv0TTNHjx4gW6rsM0TViv14gx4unpCY+Pj0UPkPFovVFCRVkWy7inaTrKQ8a/i7yr0YtvkgRQPhOdg8EzmTMB8GT/k9xhsub4b3GykQOmruvKBSoM3Ol5Y31JzxfrSsxP7AnI+o/MJ+tVm80Gh8NhcWiy3+8LT3K97Lwh42DQEkDJa8a6l9arWKeT73ju5HsNvkmprR+d0oLnUZxXAGC9Xi/WjPTfWls82nheZSw/JlT0L77kQDolbpTyGT8rf8/MkgCGmTjZ+CBEGaiBJxFiUbVtk0EPA+OMekbaAhDTLaKyMLkvuq/GyEaQPCWEiabp2CNHF160xhhsNpuFsDrFKFyvNjYXCi4Mog/luoEYk0HJJ/osBLXy8H36oH+ywquRc1Y4YgHPkG4/jPOJwkxbsxDEPM70M4VHsYIijF7bxGRB1RSDU3/rBctClvs58yiwWq3y3yGF6arNdW5/eSK13LwETJuflXI4DHlRO4i3mRbgUoRujMrP43SL59hFmE9cy/hiLHmlNL2MFcATYHBtOd6l4pQE47EhEnIuKiDCTwE+C3QBrKdpgiPDoXibhQCX4By0XYOmEQGe1sA0zYCh9yJwPaI5DpvgcDRg6ekgvCbriD0HeCzaBf7FixeYDkOmpSk0iyGS8A8IcaI5swjBYJoMutYhZhAmXKwx+iuM04Sf/vQr7J4O2O32+Hx3iz/+8c+4+XSDp8c9Hh8fMY0TxmnCME6YvIfxEXaMcM6gsQZ9ky59cM4gwqNpA3Z3N4Br0G7O06UIJmd6NAYGS88QGJNyQOaQRQ56mWICkWtGUM2o4VOt5wzMGhCnFVNW7hhkm19CCRvlUMClcjsDY5z7stY3lm/Cy1pJ4PqkDp9DNRNNZtouTwkjPn++wddf/6TQSjZ0Z5vZMzgCkkLRGJNSK0wTRu8xmXTYEqPBsHeIxsB9TpeQ2MahbXr0qzUuzi/Q9S2GccT55QXW6w36vsPFxTXevHoF730B34IoiMFjEp6I85wUT7R4fPLKCo+mlXjRMCjHsl3/47Up61hORRl4qRk8usgzes1rHtQGshT2HhJeYKOG1w6/KzQo6SAAvH//Hq9fv17spd9nDD+01Awnva/rZ7VRx0ALj1PKKV2ltj9IvWz885jZg07KqUM2rVBzaBXrHNrw1LTQhucpmuh5lWcZ7OPPNR10u8yDR3uyKrW5kTY077BOJR5r7CUm3tzAMucOj2EZnjkbuGJ08ZilbQbE+TCqRh+93ng8mgZ6TMCct0nrkhqY0LJBy/NT8yK0qtWl+amqN51Yw7W9kb+r8UqtP5pOeg1qjymRnzX+0vNSk0O19VwbJ89VTbbWZDw/o8EfLiyvn6ON1hV0H7l/vGZ0XbXx6XZrMkF4EsDCKOf6ef547xPvUgGm9V7Ie2ptfrgN2WOlDzUvXpHD7J0qwALTicd4fn5engsh4OzsDIfDAZeXlwsQd7PZFNu373tsNhu0bYtXr17BOYfValXCNOXWz91uVwAR0YMYvGGayrsipzQQIkU7/fB8MGDO8yY0Wq1W5XM5PBbPOT4E4b7KP5GTfMAh/Zc6anpCCClCinVB+V7sXAYUud8Mesn7LIeNMUd8BcwHKrvdrnzGOelk7Wn9R/Ov9JEvcWBgT/okYCKH5vL7Mc7eb6x76zX53Npjj1eeh67r8PHjR4SQvCDPz88BoGBXTBMZd9u2GIYBFxcXuL29LeGmP6T8KIBNinQkAV3LhZkIosNC5LdjIaM3uQRIzMCNfNf3q0IQY9JtYEF553jv0boOAfnUO/qUfijm8FCLYvSHsDwpiAaI02xU8WTz2HhRMj2S+2ubxyohkoCAg1p4a6afx18DNEwJ6Wlci9bN7p9ageZNQJ/26qLf5XxtLJj4/VlZSSFms8dhAv9qCqM2cqTOlNR+Vhg51p+BJBZWp5QCTUspsjg0TWvPAhHGxuRhggBjYgERa23VlLFlMQAkt9hScCagKHmtneqb0EyAtVPzwO+KMBuHEQZzeGBio9r7rPCo3qs+zTQ7Pq2TInMYYAAf4McR0Uecn5+XkwrZ1NumRbTqBC8kD7vDYYcRAecXKezFWAPE5amy8B2iSbct5s2qdhqnBXOMETc3N/jiiy8WiiLPp960fvvb3+LFixdp88iXJRhLYVch8UuMyUNsve6RLlHwMH6eZ+ccJj/BmIg+GIw+YLNq4TfnmHzEl19+gV/87KfY7g/49PEzPn78iHfv3uHh4RH77TYn5vSYvMcweFgAY+PhXIPWejSuRQQwSm6L7RNs06LfnKNd9QjWwltTQDNADjkMXJM3Qhq7VRe8aGVf84k2qJnmUvT65c/1Mwvl2whsHZH+HwGTLr2YwvFaXfLzEiiRok82uYjBeVo2s0HsFnWwoidK2zyWCD9mpSBGhDDBWZfyzBkLWFI0YkDTZpkfDRDTRRgxBpgIBA+EfCPtHo94uLe4+ZAvY8n0Oz87gzEG3XqFxrVomhbr1Qar1QpX19d49fo1+s0Gu8MB+8MAn9fqRIpkazMYjRlctzAIeb/WKSSstXRQcXxQwr+zLNX002EVNf7iudFGwnNF18PKpcgyHa6keVeUce4zMHvmrddrvH37Fq9fvy65oViefp9+/pCi69P01/sXGzD8rj7Fls9q7S31v2VuHgaqWf8TRV+DrlKnBnVYtmiwn8eo5+kUffk7eY91lRqIKp8zv2p68b54qg9igHBID/dDA5MMKnK7DNoyuCaGqBihApgJT/M+aq3FxcVFaUPaE5BYe1ZI3TyfUhj41LKS54b7LQYp6zmsezO92dv1VGEa8rxwH/Wz3CdpT9pmY5Hfq7Wryylgv/a31se1bqzXCL/DcpHpLoCF5kG9B9b+PiWfdV/lZ218ei1ocIvHUeMVXS/3U+vfNbrWZJeWKUwrTsKv29S01+3wmuDC86jrDCEsEuEL3+q6T41ZnpdnGCDndS601zTXeytHErF8EPDM2nSzp4TbbTYbdF2H8/Pz8rfs+THKxW0et7e3AGYAhoEptmVjTJFGvP7ZGyyEOdea8Lw+uNLyk2ULezfrW1LlYHgcx3L4cwqM5bli8EmAHDmcEHsHQAmPZf3COYfdbreYG9kXGGxjXpX5YRkvn8l7Wm7Ld8aYEmIr4Jj0s9Y2j5XlougLEh2h+8Kyl/WjFMHRLurVeyAfBMlPaZv3ZG5HngGWOScfHh4Kz3CfxLOb1wuvKfEEHIYBT09P+KHlRwNs0smlS3Ly4phLAoXKX8R8NdCKPW2WbZkSt82KhryTDCubEqQjwRXCHMakk3/AIBoh3twvnqwQAD/mCYRfTHptk+C+XlxclL+nkQWrScnE4VES4RMtylNKyct4ASyFVgYf4Gy6FdVPHh7LE1v5ycbBc5uCVmTZoJF4eK4bqIeYhnybnfwTIJGV4SXtPAC5kOLYvb+2SdY2wucME3m2tkhPGzLJ6ygpqP08JwYQsJfr599lMR6XLDCzJ1MMDj6MGA4HhCDzNSs+mr4sQPRJf8hgCK8xptNSOBrEmLG1IqyW/NB1HcZpD2uXRuKCL2MsdbHCC6R1CsyGG0xMIIhJYcN935WrkcWjRdzE57ojbAbUx3GPw2GHq+vLua2IRd8SfdJ68dHg8fERL1++rMzD8j0pj4+PePPmzWKMpxRoYwwO2x3evHqdvMdM8lyzTuYojd/7KckSn7xoz87WOBwOiLHBZKYFn6TcWxExGDQO2B08bG+x6XoM4wGb8w2macLLl9f4xS+/xtPTDp9v7vD+/Xvc3Nzg5uMNHh4esdvtMY0e+ykgDHs0xmDVB/S+SZ5zma7Bj8B0wPjUoOk3sJszmNYB0SLCIvgJxjlMPoUsJ/aScNLkIRiNCvmJctPsceJqVlqZjix7WJ7WgK/jsvTsFfYvshooF1YIz5gCkVMtIcArI1n6qU/VtVIj/dfyaJYxWNQpe1XNWOGLOhAT4OacW95UGmOOAo9yHUe58KHk5DTSh5TUHzF5fIac0WHywN0hK60NEGLKmZjCvhr0/Rqbs82c1qBtcL45xxdfvMGvfvYz+JiMgP1+X4BbHwJ8N+dqKXMbInzeEEzpFwBj0mEM0YzfYwXzVA6nU3uwFGcshmlczKsUvYezEWuNyVfT5DnM3HQsS5fGlgC8vM9psMhaixcvXpQT4zS/ttxmXvOu+bGFAU7ewzXPin5TM475dLm21+l1oE/3a4X3MjYsuV/8PivVeu+W8B0ZA9NdyxfdV61TcP80z2ivAea7U95BNf2iRhOts+m+1p5nxZ/rkPck/EX6Zu2c/5eNOc7NJoWTZjdNg/1+X0JOpQ02nE7plQxQcR+1jsdrozZ3PH/fRVP9u+YlTT+msfAv85ueBx5zbSzP9akmh2qfa31K83Tt8K/WLq8HKXybbq2c1omX/dVrpzamhWzE8Trj9mRcp+arNlb9XO0z3TduX9MRWHo81eZWj0n371R5btzftw55j4GE7+Jr7aAgRficDyhYpur6WMadnZ3h+vq6yIT1eo2vv/4aQOK5zWZTHAf2+xRxIbSUCwvkWWttuehFvIw41FKek7+ttQsPImNmEHFpPyznT4BCAe/lH/dF53sTOSjhrVounMIEGPASegpAI3WM44hhGEpOL54nyUMHLMFYTr9QO4CS34dhKABoTc4x37B3oICBwhdCUy2fpV/ymfwtoJXMq7zLBzV8OCnvSx49a+fbVln/13qR1hV4XmS/YeBNvpe83X/6058Q4wwiCt/pfcyYGWT9/Pkznp6ecHd39/9fgG0pjI5Pq+bJmV0peXBa8MwCgw0ei7N84s5FCCOLIgmLgFhy3QiYkN+LEdFEOOuKERxCQPDicUXMi5hzO+GIkXRfrbVY9Rs41y4WQ1HNyyQbwDhigu93G4VBqSoLizbdwDgtT2z1RsKMqp/RC1P6pDcVbehqpgZQ8vmkBTi77LOCz6ebQg/nbHGHlbb0aY0up65L1mPhfspC56KVGCzyo0WsVj2a1iEGUhD46Uq7ANB1EiLNt77Y+V2T+DlMBref79H3qwJkMW14vqSf4ziWm2jmdk1ua84Jx/O8EDTZy0d/Xtq2qf8h+gWNawqLtDMrz2lw83dkKCaSYvITbNMgRIO9ug5ZBJlxJnunRSCm9b3b7XB1dQXx0AHluYsxjcnYTIfsz/Ty5cuq8qF5Js1Zh5cvXx55Ls3r+PgUmaazyB7edOc20jrfbFZljXA9NUUrhAjXXGDyAdPk0TUtnAO6tsV61WMcJ1yeX+Hl9TV+8YufJbDt8y3evn2Ht2/f4vb2Hvf3dxjGEX6csB1GHIzHKuenNA5orUNoADdFhPEJYb9H23dYb86AroU3yHnXEtAcJg8jl1w4IPgJFstE8wYpZ5ceEz/Dp5U1Y5Kflc1fGx41hbmmVKb8clzmfsYYSyqCEEI5lJFSO93Vf5d37Zw7gj3cdD95nDIO8SaZlbIZqGA+ZPotlADyWA0hQFKahpwnDTHmHJFpzRhjABMLoBinAAODBg5xGDBhwLTfYnt/kypyFsZZONdgtUqJiI3tYJsWDsDZ2Tmcc/jqq6/wk6+/gmsa7HY77Pf7dFIY0gULssYX+wxa+CA51QJa1yzoJeVI1lgFnJDyn0TNTJv1er1QyNkjieUsF6GpzT9jfiYgFiXdWlv25fIMYvHutPkQLfrkXRhiAIwpOWdfv3yFvu/x+PiIcRhSao0YgbDM+/eXlAXQqcao6ct8Vdt/tVJf0y/42ZquIb/L59pA4bWu39X7Go9R+sFGpx5fzTDmdrh+DaqwMaL7xn2q9VH39/v8LbqTGB8yTnmW04HwfsSyUaI7pA42cjhES75nWasNk7ZtywGYGPcsz3gMfODO/WJPOHlO76XSLgOm8qxugw9AaoZujabSBpdT88WHKfIcj5112lP6quZlXZi/amuIP5fftfF+6rNa0YBnrS+n+neq3tp60utAnmEPIulvjYdO/f2cXcDPad1KPj+lV9c81OSnBtVrcknqFr7kZ/T81frAMlrToya39HzonxoYZnpo/j4FsLHsaNu2hHmen59jvV4XgE3qkIMV7z3u7++LJ57Wn9kTXP7mvKoC8HB+d+998ZBiW4GLyDDODcl6g+hZcpjA4ArrFgxiS/TP2dnZAuDjtDu1gyEZL9sEzB/iRcqeYcJr4mzA4Bzzfc0eZ+BN6Mq3U0vdUr/Wt2Xc7OxU21fZ8Unr5pzTV8I7pX4dAirtydzyLZ5ap6gdqjGNeY3I8wzOMtgmlxbs93s0TYPz8/MSusygtfDadrvFMAzY7Xa4vb3F/f39IpLxh5S/OERUwkBrypXEVEvi6DRwIQ4A8jKIUepL+YPOzi6qSqIwrjDrPOjkfZTaRm7DABDXaJeZIUISwseA7G0iSamBgOMbz6QIAzRNU075ER0kX5sUvXHbfDuf9yOQvfrkkRoTxRgTgbISn5TzpWcSC2n5p40y6XPthIhPZtngrSmjPAfyU4TNfGPUHJIg865PeGOM2aV4CZSdUoR4wbPLNgvRI7oBSMnzlxse/zMZFJppMp+IJFp5xGhhLGBMfVFJXXOoj0UMYkDMObtiuUhAEkAesFptqnPHYxCahBAW4Fp6ppafMBW5wGFZdyz5qWSdpGZSG13fJnajaT+1BngTnG82ReFnrTjH6BH8hHGcSsL0vu/LxijvRloPcnvw9YtrhOBhYFI4GpbzaWARg0N0QNN0ue2ll6hW0OSzEAJ2u91iw2bhrOcCSKGhr65fHCVllne0In539xEvXrxA3/cL12TuF/cnRo8IoO9WaF3ANB3gYBA94E1IoZ+tR9938H7Cqu/Qr1q8fv0KP/v5z/Du/XvcfLrBx4+f8HB3j8PhgMNhxMMwAHFEax26FnDBoHVAbz1sDDj4AeNhj6btsDo7R9f3mEK6TAXGAj5JTGOzlw4MfJSDjCw4aZ0zb+rNk/mqZqTr9/X6rhkk+m8tf4/X/9JwnkhG1gzNWlu8Zlm+1mRtTfaKEjvL57DgCW5D8y/Xxb8L6CvdjCGDO3kvKeCPwNERiCYb1wJPxQQ0IXjYYDANIx53ezzcfka0KUy4aVsYAF3f4+3bP8D90wZd06FfrRLoZR26VY8vv/wKVy+usDk7S/kDpwkBaW0P4whnLXy+aVo8x3iOWCnTtJ7nKR9cIM634Zpj47yc6iLOwooOBoAEignIJqkYjDUp/JYUXAbYQgwwcWmYMVCgAVtjkofQ+fk5fve73+H8/BxnZ2cLRe8vLdqIW/AIGVo1nYEVd66P35VnuM+smGvZxu9K0etE5ph1EN7f2ZjlPrExoNvjtcP00GAJG9O1NvW6rK13vdfUwLqaQc191IaRFAbdxHDRJ/08XmOWOWmkDtGb5XttgMszbdsWI1PmmIEx7h+3Je2wTsfgg/4n9WlDcbEmia7LA4k5FInrY5rX6K7nyhizOIjlZ7mPet64/uf+1kXzkubZmlxnnmRg8bk2j/WwY6DmuT5+n+fkGb1PPtd//flz7dRkZ40vTrVb66fmQd0/5klus9aWHrueO01HLTP0WDSop9uvjUnzvl7fmo/5PV5rxpji6SpglQDyZ2dn6Pu+gPOHnLO173scDoeiC/O+IaCMAGMxpjBRfVAoe4nYoywLpE6eDwFmaro6Ow/I72KLShgge8VpvVToI3Yte/jK4YaEecrca6CO55MBS9aDmQa83zKAKDqE7AliT2sHCBkr3/A5TdPRDa8M6kn7OkJAxq/BYqYxh+iKbNdylT+XuWVaCU/s9/sSNir9lvFyP3kcTEeZBwbTeA7kGTnQ5AgS4Ss5QBKb7nA44ObmBrvdDvf399hut4u8dD+0/CiAbfbYAoDlRiGTwIRN36MQRJ7nd621BVXkxcWTlj4bMU0+KwCZkDHnmVr6GiEp0OIKubw4YZo8xGtAPAB0/3gRAsh9PMv5LQwQ6znQ9Aa62BAre4l+x5gZ7fD5Jk5jk+FTU0Ckj7IwWFHVm4BGhlm5qykPNab13i9yXPFCDQGwti/0nwGdOS8Y1899lO8YIed+64UvdcwCIMD7CQmomJNxa0Ga+iPhlR7WWfT9KgE2yELaBqTQLenHcXsixIiVq7fdCbA7DLtcx/Lyi9pmKvPBuYtSu2ndsZCbFUAPDn9ebACRjSaTO2zRr1aI8fhmtNo8iTu1nFacKmU+gRRW6NMtoDKHSYjaBU2NQQbTgLbvsdmscRj2JGdU36KFdRHRpEsOQkAKhQzL01G9TqRvwzCU24t0ESHOCrtzDj//+c8RJ7/YKKQ+OdVL9aU2f/GLXwGYT65YlrFRIvPatg2sFwA9gYbBAN56dHYGY6wJJSlu16Ub4s7PNnjz6jW2+z0+f/6Mb7/5Fjc3n/H582c8PDyUzWw3jmh9wGgNBmfRWgfXODQuwo8jpsMOznUwrsH59RWitYjGIdiIGGwGZ5K8NMYgwsJHADGCV4rmH+EvWdt8kiU0rym8Qmfh5WVKgpnX2OjU8k4rulo+I4QEIJqlEc8KDB+u8N/6e634syIgP2VDF4WCLwhhZYH5Ve8P/P1irFlelZVO+5rNfc0SPz0VsfCUlg8M5fsEEjgXpwnRGIzjAMBg3G3xZEwBpYw1MK4BbJIrf/7j7wAYrNdnAAzOzjYwiGi6Fm3f4uXVS7y4fokm76fDNOIwjgWw9SEAMeZ+CH2SZ5xB9rQMyVPPxiQLhPeCn7DqOkxTAoJ8OA6FnPcDSZrcFllZvP4Q4XIIrzVy6+tszDhRAqMo5xbGzDdk8XxphfinP/0af/7zn9H3fVH2/vsq2riTfulnhCaivx175i51Hd5fmU9rxiLTgNe4rpN/cn28hviAVXQsVsC57do4hRZaiT9FF1103Rqo4z2zplvpG0G5L2zwca4xaYM9BbhuHQ5mrS0HgAzeaVnL4+BDUQbH2FiUunnsIoNZdvI88Pj13sD6o9TD88zP6/nVXitMp9rc8Vjld+0Vo8EI/S7LXb0X8We6Dp5rrc/WaFNrHzieZy7cRm3PqLVx6nO9TjXfPNdnvQ/Xnq3paXpu+Fm9/k+1L/07NYff9R7zIveR+6X/roECWt+oyUJgXnNs94gM1mC+pi2vZ14POopI2hAgQUA0IIXQ8Zpnr1MJZ+TxyTNd15V9glO9OOcWEWYCouiQdNYBZT5Fpmu9SeZFe8aJzNEgudQh//hiBfFsZ+BHZKyAWCy3JXyTvXm5D2xTi1yW97UzDL/LfCyyQAA8bp/1DT3vYhMU73ozH5Kx/GSdkudA/mZ9sgbiyd9aHzBmvrlVDme03JD3GYgVfmee55BV5g+pg9eGXrssU/k96dPt7S2894vLOIWvxVmq73vc3Nzg06dPBQAU4I91mh9SfhTApgWHFCEysAxxEYIsN5Q5Z4kg5UIQrbgBKfE6T26qJynABlrwy4abcoPJ57LAUr3JEE7gWiy5fIyx8JNHRHqmbRMjX16mpOaIKZk0A0eakWTs2qU1dS0LSIRcx7ECwMxujAjP5P1nFcPrBX7KuJTPmUkEzOSFzwKcjUYej9yyojea+dSnjDjXfRqM4aJRhqjRuwABAABJREFUaR5jzKGchpQplDHPJyDp7ywwUor9ozHOfQOsbdB3PajLaQDRIl3WENKfuR8CLDnbFss0CtAcl0oDkLzZBGCTUGpguRHznIlAFcW4dCsmfk39SF10TuYKOBx25AbMvKnBJQkptej7FiHMqL6UmhIVY1x4JvJz/MxCGI/phlThF23MaINLQJumTfkZakpajBHGWhjXwJgAH4DPnz/jyy9/spA10r+akh9jumyBQ2P4GTZ2YkwuxtvtNn0PA+ts9qYToDLRkzfZlMfGoO2aAljIppluRF0qcd6PGMeAtu1g88UDAODjTO8CEhtxiZ7Q9/1sdK0C1psVLs7P8OrVCzw9bvHhw0d8/PgRHz9+SuGjQ8r/Nw7Jm2gKAc5PaJxL4Idz6FwKlR92W7i2xdn1FUzbwNgWwWRvNqSk9glXsEDMoBvSmgIAH3zme0C8eLWRosO+2Q1/yfvHa4b5T8/lKb5kI1aKcw5eKdZStKKnvXu0AsUKm7TH9cj3rAyJnJfCSrM+NNEyg8He2pi1DOfv5N8SHEHiaaGXkQOJpddYkosCkif/2OAjECYEAMOwSznkfMwHcmlc6/UmAbpdgz+Gfw1Ei6bp0HaJ723jcH31AtcvX+Li8gJ9t0oAnp0P7w7jATFEWOuyrEbpYwLdskd6OmZJwF+Y84VkIpQjIOscXDFSHEK0eYzI4cYx8TcsmL3S5RGzEVP2P5iyLhM/p+eKESF7BCx+/ouf4/7+Hn3fY7f/4dfAnyparsm8Mx/Id5rfeCz8vtTBMlbv06y8ax2BP1seeM3rW+ridmvrWmQH91fvvVoRZ7lS65NeJ7o93hO46P2CQzJjjAtPK/lcDmR0H+Y9oilArISysOFWo40GXsSg5vFqWch04kMDTf9TNNOfsyySvum/a7oje0To/vF78lPowwcrp/rGc67Xgn5G00MbmbWi29b8zLSp8TH/5Pnhn8+t51p/9Oc1ME/qkL4yYC2f1Q6tTtGA+8vrmo1ePY+18dToWTP6a7KlRgctc/R61/1g7zXmBaEJ/9R05KgqBnr5eVkTrIuwXOUxa57nd9njhw8ujTHFtnPOYb1el4tNuq4rFxLs93vEGEto4n6fDrQlH5rMncghqVvkE6d7EtBKy1E9R3wwwD+ZtwXkEntT9lHpk865JjyrPbz4J+tmDGpJEVyAQzP58sa2bQtIV7zhVZ3yuzFm4ZnFzzEPsJzT+yjriMxXQ76wTPYEaZuxFBkb65hMM/YGlHEwTqHnRO/R/D4fcOlnZO+T8WggT+af5QPLXKadfGatLd6FnEaB555pJvwcQopYuri4OFrz8vc0Tbi9vS38LxdT1OTS9y1/EcAmg5MFoE/ehdjLzUsEV7tIoCp11RaCEHtuPynAXH9tI5DPYzSYpkM6Cc+5q0IIyVB2AGBgYj5ZzvnR2rZDsAGvXqWE6TE4xDADeMtQ12ODRxZizbAB+XowM9UvFsgATj6ez4E8R2g205D7wz/5lEAbgFz0IpDfJW+aVny4Td5I5dnaBq1pojfZGBOAVp4D5lxispAjEMIE4z2ciXAhwk8eCJnGJhkzwVhEMwNyIUQ0roHr8k2XzJ/RIMDkS/qyCRYNInyZoxjFcDwGAngs0xRK/DeftLJA5vnj0xKZyyWNRSjIRpZoLbHluj+5V5lHZ3AOiGgaS/UdA2U8F2ks8waklYPlvIUMToe8efcnFcv5/QSgTtMAZx32++McHTON0n9CAALSpvPVV19XN1Q9HqEpsARknlMgjTG4u7uDMclonvJpnbEWyXUrAbxMB2uBy8s1rBNFyaRLDBqXQF8LOLO82MI1Dbo+e0dFwGRg1MIt5sBai+iQXd7bMq9N0yB0AW5s0LU9ur7DxfkFXrx8gS/evMb9/T3effsOw3DA58+fF+7Pow+YfALvxxCzh5BBYz2sBe4/foC1Dbr1GrbvAOtg2hWiiYjOwJiI5DmZcnw1+VDAZl4NMS4MzNqa18ruqd914Xljo6jGn8wDWjF3SKGiXK/eu/SeppV9WR81BY55TJRa/TmDBKdku1ZO5Rn5VwNH2LhgBU4Mf26rtBeyB6qAa25WWo5keZbLMSYeamRMEbAxIPqUyDYC2D4+FC9Ia1LuRGPbtBcboG87fL75APM7m3KmOgfnGmw2cjt3kmcXF1fYrM9wfnGRQ6ZNeja7+Qvtu75B03VwLu0JSWFKvQ4CzOU1F7PXnsPysg45DANM3hNiCic1CWQW+cq0ds7CwaKJx0ql5uurq6tyyvp3UdgIPHXarvlI72Mss3mvknr5e82jrOzy92xs6u9r64x/r+0HQj/xPuB6WMkWA4DXxnq9LsqzrBsZO68Dpo20w8aL/NReAbK+Zb+UG+lqxjePV+aMPUZ0qJH2uJIx1bx7df1MS/lOf3/qPc2//LfQpVYH01gDq0wPmUuWz1qm8btMQz0WTd9T/KHf5Xb1GFh+a36pta9tEzaY+fvaPJyag1qfdR81mPNcPbxv6M/kXb0nnervKf6QOow5vpBEvtM6ou6DLkxL5kEeL3siAVisKW5Hr/nauPReK7+LvKt59mge5r7K58LvwBwJxDKG+1MDIYSPxLvq7OysADAXFxd48eJFAdDEThc5JPJCPJCkbuZL0S01384pXkyxcWTNiaMN73lSjyS253ySHK7OdNXgVt/35aZN6SvrlcwPvA60vsdADCe5l2dkLnUCfEmHpMEvjX/IO3y5Acsv6SMDY9ozTwoDd3zIorERAQOZL3hN1HRRoYuAmXovrNXD9jwDm0xLqU9orVMTyPzKu/KsllEay5B9wBhTbizV9OS5kfZijLi9vcX19XWxr6VdOQiNMeVe2263C5qfOsD+vuVHAWzCBBw7zQTnzZY3w4S6oiQ41/VpZkjEm71x5MTc2uOTd56cGJO3jSjTEgo6t5lOv40YfyVEJ2LV93BNi6urK0w+uWuGEBH8VN5N3lR5Ykujx7mDagqE1AC1WWvDSMZvjMlW6tKA1MyuFyYLSL3p1DZgKXohSt+4/zVeqCmoLOC4TTZCpZ2aQm9iAtEaY2EicNjvsL1/xLTbI+wPwBRgvEcPh8406Zq8QwpfwpTnxFmgsQgWQGOBEDFMA8zmDHvjgcYi9g7ri3Osri4QGgPEZFIh5xhLXgoGXbtCjFkBts3RfPNPBtd4PbBg43eFPqeuvU/1Lk+5ZDPjky5ug+nJQrNp2oVBUCvcvggz3pg0z8UYgRx6G6IH4mzUez/BueWVzfOY0/vjmIziVb9O7xNv5F/yD7nQIPHXet1XQ4q5X3pD4M3i1M3FPMaLiwtst9sj2iNnjpIbZvmd9Jzk1kISL8bAyoUwWHqljdME27QwzsHBpNxrJEulv0nuBoR9yMACefvG5CXpbbpIxHcBTdugaxwuL8/xi5/9DG+//QY3N1e4+XyDm5vPeHx8xNPTFsMwYgoRmDx8kzx6vAsIe2S+GzGOe7imQes6dJtN8mprexjXIJgI8UAUgK6mIDONmL95znitaJpKHbyxSuFwU3mOgaWaIsZ/y0UNMRxfBa7b4j7zM1op0M/JfiknkFKEF5lOtbbYSOV2WPliOtf6IIoNt33KkCnKVf5bryFt5Gia6O9j9Hy8lGRtGBF8ov/2MKR8ixlci9k79Nbx6XmLD+8+LOaz5I5pWsQQ4Zp0S+fl5QWuX77E1dVVGrP3sAbpsCyEdOgWApxr0eTbUJ1LnnFOPAZiClNN+o548GVezUdesi4Xl7xknULTTA420nyacvGE3Mj2lxZtaGpwVRuQwlOB+L6my8jzeu6Fj3WEgX5Og1C6H7oNfpaf0/qlfoef51xAbChyHp8an4rhIvuxMaaEv0iok/Bdbf/RtOE+M100DTQtGGDjOlkG8L50qr5a3/QcaG9i3Uc24LRs1u/W2q19rg0XTQNNR2CZD5H3SNYrtXzT8rB2eFKjf41mun+1tRXj8pIt7YX+nM7B7fDnNZ7R/a/ppNxHHkttbLxWeT0/R69Te0eNVpoOrA/X+KUmB56bJ+7fKRD71Jj1u7UURbU+6MK0Yp1Zr805l/F8uQmD5XqdybN8AN/3fQHVuq5D3/clibvwTC1cUG5vZI8yqVPaYZkiNOC1JH1lWSp18H4ieaHFVmG7RZ5n+1J7YwEo+pKEULK+xPgD91ensWE6sz6mgfqik2fASI9H6mKeqO2XnK+ND3gkSoMPadgm4rXOezGDxQzeSe4waUvb6lq2SR+lTuYBAV/l8In3v5ps4rBPARq5TqGhPMPgLoPLmn56XoQe4zji6ekJMcaj2185RFnqiDEBZ7vdDl3XYb1el+fPzs4W9BmGYZGzTy7rEN7V+Mz3KT8KYOOYVGaueVKTUWkMh7sho4VLzxFZbDyJvFGmyZyV0lNCnY0XQeRDkIkBjJGFBQAxJ1KPJWfLqk+3R56dr5HCRDwMLIIP8GECN5cmxM5JmUPGyyIAG3M+OBLeJkISmCMmQ9Rak4HC+dQi5u8kgbKBKZ4BMSwVFwZhWPgyAMebmj49KwbCCSNXntfv8jM1JZk3I+6r/j7GOCe2DiEZVSECAWhCxP7uEffvPsJsB2yiRTsBh/sn3L59j93dPcaHLfwwobUW1gONSaCARb5II0TAB/iQFrWPEbAWEyRmfYJpLdA4NJsVrt68hv/6FabzBn7d4uLrN3DnK4TOAdYg1WxLODLzJ/PiOMqNeXXloKa4ibDhmwhrGz8XPsXQ76TfZf3NNxaGEIqreK1unnvpG3uu6efS70AyKkMKD6M1GDyKlyo3p2kw5YtG1us1Srj2gkgCqiEF/cbZ+OFN59R4ZCwitNnFuabEMR3v7x8QYyjGVdmoJB4ye+DEYJKcybeaWudyBHEsXqeABUQBgEkXCZjEX2dnFxnIzSKiohAXheUwIEiIXPZwjTHCxAhrAqyJsAawwQMxwpoIH0Zszs6w2fwSX331BW5v7/Hx0w3ef/iAm083uL9/wHZ7wHA4wPsJ0xQwGKB1KUehtekGUh8iJkwYhwNc26Ht13Bdi+gc0DawrkFAAIzNq9vIgAC6YRemfntxjT/0PNbmSr7j9ajXpq6T20vyKM1XMVZiTJ6K+XPpN5dTirpe7yx7p2k6yv/n/QS5NITr0zTR4BqP+ZTc0KfSuo1Tskn3o8iH0n5+BnHxfI2+upQ+GkA20JBllgFgQk7TkC9/QLA5HNlgGA7gkNpUX5J1MdNYUil8/PQW9vc2g2cpD521+ZApHwK0TYPN2RnW6zNcXF5gtVqh63pMeW35aUKMAV6MreyhCZPAMR88zjZnaNu8V0L2uQxMxkQja2254dZIegtjEAww/kgFrlYYgNEG0imjW+aJT+O1YXlKUefv2PCQ5+QZ0SN03kW9TmtrUxsgeqz8Dtc7X0KU3hE5zqGWoqSzUSt1aq8FeUc8RsRg0GPkA6kYY3mOv+ffpd9anvHnvC/rv5mOp4yr2t9aPul50O9p2VJrt/Ye2wh6fKfaq/2syVVtiHEdp4AR/TvzINNT97e2L+v+ayOe+8BARm0uavozf69trRo9Tu2Xp+jOf/N64vpqc3qqrlp9/P1zfHWKh2q/19rToaT6UPcUX+l+yTts+2he5mcYBDfGLGxavV6B+fZL6SsDmvI5e3PKM23b4uws5QGXvFHX19eQfGkaJNEeOCKXGJDh8EFpR/qtAULmZaaneATJu7vdDjHGAraI3iOgl9gJOrw1hFBsIe5LjMuUS1pWcz4veU/vdzwuzXdis5ySf0JLkdvSxxqYBWBhc7HsF7rpPUO+0/QWG4flG9st3G8Gm3jM+nPmd83/AqxJEZBJbNT9fr/Ikw+gjE9ymbGMknyhLFfGccR+v8fFxQU+fvyI9XqN9XpdeFaDiEJD+Y5Do+UdnkvJEy7tX11d4ezsDJ8+fcL19TVevXpVQF/RB/b7PR4eHsreLrTe7/cLXv2h5UcBbBxWomOqU8eAtm2KMT8jiscXAjDTssK/FIDHQpAFccp5NOWLC+YcbsbMiDWQTs9TsvcRxnhYF9H3LfreoVuNcO0BiHfJgyECsAYxBliTPQtMHhwcYmiAGBBCgxCarKw3iHBATPmKjIlAtIDJXj05l0wSJvXNOuY2YoyQxMlsPGmFgenCixfAYnFpJYzb5Pr06YEWINwmv8ebCT9TU8pilFP/gBg8rAfaweP+Tx9w9+179GPE9Oke999+RHja43b0sCHNp4kRjffoYIDoYEICIRE8Et4Qk29QJk/jGpgAmJA8EVxMjmltMMAhwgwe8ekRj+8fcPfP/zXQO9jzFcLPvoL94hKv/tFfI74+gzcOkm+HaT8LPuTFPi02Uh5zjT464aisBxagepMXgXpqMwCWGwcLXz7Fr73Lm9Zutzu6/np+Tn46ACZ5b1D/p3FC368hhnDMGAvzTeKzBhER63Wz4F3ujwDsIguGYST+XD5bU0CZVgxQsPLC7zBd+q5DjPn0KLcfJYZT6AUDWMBngDGFkBqgtJXBOJsFtLXIwXEwxsGSYSY00Aofy9hhGHF1dZ1O8NJVn4gxJXoP3sMghb3ZDDj3rUPXOnTrHn6wONv0uLo8w6vXL/DmzWvcfr7Dh48fcfPpM+7uHvD4+IjtdocYIvbTBOOzt59JvN04i8l7NJNHmEaYXQYUux6m7eD6Lk2MTQnfE+CWbh+NMVICeVOcRJFzekXijdqa0XPF3/N7LJeYD7gO5oUlv+W6hHHLPBexclSX7ht/r/s0DEM5YT4Gv5a8WvNOrfGq5mstg/R7tXVWo23pi18qhGn9xXmvyjCbtbYIh9peXTOguA8iI0IIKd9mjJAca8ZaxLzeYkz5NY21GcT28N7kCyvyvmNt6dNh2KdDqjylhvrlnMPd/WdYm7wATM6zOOeAQd6vbQZB530tebsB5+cX6LsOrmlgYdGt+jIe5xysa0o+0AAB6kwB29quQ/sjFLha4dN2zXu1eam9y3PN+7jwrPZmlr+1h7w8w3pere2a3sCf136yAi9ty3dszACzZ7gYeHKrnBh0vMfy6bnUJYaDjIX3FaaNPC+8IV4WcpO0vM9hrdKuBkbFgBADUt8MV9PnNNioizY69bt6rk/xzXP1c2EasZGlwUWtg3BdrBfoz/Q+Ie9+F1h9rM88HyqoaaC/5zGKTcL1yr+aoct0AI6jSGptP1c0LfQ81sbG+4v0vza+52yB5/6ugcmn2uc69E/m+1pfGEgCcKS7aqBNt8F6d01n0DSRA1tZq7z2tUyQ96VukV3iEDJNU7nUjEPZ+r5H0zRYr9cFEAghYLVa4enpCZ8/fy5zJva27NGy5gQ4kf5KmKaAN5JWgeW9BoGlT9rWc84twikZVJLv2RtS5J+MXeZFnmOvJ2ttOeQwZnlBlOZp1pu0jqdlNIOYMc6eWsDyojrNT1KvPiTS9fMYefzMv8x/Wm4LD2h5pPdlDu9kkFGe1/sxyyjeu2tynvds4ZkYI3a7HV6/fl3CdSWfqAYHxdPMmDmMVe+Hq9UKFxcX2O12mKapXJxhrcXFxQUeHx8LfTjclPvN/KplV9d1ePfuHR4fH+Gcw263w93dHbxPFzUKn242G9ze3pY9WurgNSoeiT+k/EUhojwZMunpdjtXQsKe2/zl99qNhBKCyaf6erMSwZHc92RRzQCWayyM8YhxgDE7rFYBxu3g3Bau3cFZj7Y1aJqIiAnGjDBxQgg+KdTSF5MnLEYAHhEOJrbp1B0tYugRAfjggHiGGM4RcQnvW4xjh+A7WNtCktwbBdQshH02xtn40mPXyg/TJNFsPlXRi59pyZ+zQK4pGaeUqxqw9tznISTj34wT1sbh9//0b7H95iM2u4j9+8/YfrzF1gNNBFYCNsKknDc2IsYAV+ok5dS52dsBKMZM+jLNnQ8eiICPlDwxj8mGlBA77jzM9hEPH/4VQu8w/f4Dpi/P8cv/8N/GcLXK6d2OjeukBEtIZN2rimkopyB8s6oINC56HuQkpfbdbGTPPCbt6pA03SctYDmvQn3uM9wQPYJP/xApb0LXkzIpdS83uCL8zfGNWskwbjJoF8FhmLwZ8vjld70eZEy8AZ5SOGMEDCz2+z3GMeWEEzqkfsUS2lZTJEvfDBCNSbcq5vEbCjm2SCDbMIxFgdDy4JSxwrTzwaf8USGBIMYClm4xlBsQry7P0wUFJtG571t0qx5nmw1ev3qJN199gfu7O3y8ucXHdx/w8dNnPD5t8fjwCETxMswnkd7CGSC45L1jnUVjHcwwwliH0HeIBuj6FbrVGsYaBAREk7yQMuSY+WcGa+dQ/byeK0ABz7H+XWhU+1zPU81Alu9kfR2BtemPKt9w26fakmf1jVe6rlMGigbjvouXuehnNQCxGKdaK7XvNf2A4z6fqpvb1/2tyaOiYBkDEyOCobWR00fIZQyyShEjTDQpT2CMiD4u3gEgt3Vgih7TCMAYHA670qb00xSleA4jBmYZZozBw/3dvKcihZcaaxBDgGtcBgIzn5l5Tq21iAG4vr5GrMjnH1tq3k3MR7LnM41FYZXvtHEiz7H3sCignD9I80ytDpljfbJfe4+Vd52MWfILAcfhWJrnOXREbqyXZMnyufSHDWa93sTQ1HTmIs+uVqsSXiJGV81w4rkRGsu4RE+w1hYjR3sqyE99qYH+XR/Qyjhr86bf1fLg1LNavmr9Qes4XHdN9mj5r3mzNg+nZA+vAy1/anJX9xGYDz70uzUdWPdfP197/7l967k+6n3mhxShSa0fbICfal/P8am1XKOF5slTvCjP1vY7TdtTYzlFGz0PItd0X1g+1Poh/WNAkdc3y19Zx33fL6JYrLV48eJF4THvfdHHn56eigwahgH7/R7W2iLHpB4BGhhoFpDNe4/9fo/1er3IaSYgivRV+sNgocglGYu8B8yXHIos5bBXDhuU5zg9i9BM/vEc7HY79H1fDkWEnjI/fCOz0Fz6wHMvNGenIPkndPfe4+zsDMaYcnFDjLF4TzGfycGH9Ed4QujCMqLv+4VnuM5JrA9v5D2Nmeg1wwBg7WBM+shyj+dO6MdAJ/NtjCkUU0KR27bF/f09drsdHh4eyuVvu92u4DEytsPhgKZpCkYj9BG6yN54fn5evN9kX764uEDXdbi6usJ2u104ociNoKwTs1wQ3QSYL6oIIeDy8hKr1aqAe8JTFxcXABIYt1qtFvqN8JC+L+D7lr/Yg00ms21brFarDKyZBYPrn1owzaguM9exoi1/p8WaQjwT4OABk0IN265B20ywbod+vUfb3yHEBzRuQN97mLiHsQNgRsB4GAPYaCSTCqKJaFwCukzSowsIgwDAioFtYCUECg5ATmCOFjE0CFgjxBVCOIP3V0C4RghXGKc1EBykRQYdACD67NkClBDUGt34cyl8Ra7ewLUCK/MhC4xPcIHlxqk3Rn5XnuUNY34eKGF0ISD4gA4O+w+f8dv/1z/B+Whw96d3wHZA8BEuGqzzkCSwzhiDOGWhTv2xpfLkjhBistSNyWE4MXvKADAhe1fFkIyumMGhXIMJSPMR8ychG2jjhMd//jv4v23wpz/dYfUPfo0v/71/gEc3lqDlcRrzYndg8j63ofMmXlOqaopYCAHb7bZ4hdbamYWmhIgC0zSffLPwrylxMoey8clGLe+xApd+zkZJCBEWBsY6II4zOF2hQwKgIg7DPt+oeQwqimGbxhEyoGWRwrsSEBZCGmMkYPUUaKCVpNPGAoocklDv1GeTw5ljfmap7MlqnoWwAYzQLMu4DJ4bAMHkSw9iRBIsc9/mC1SWhnGM6RKPfr3O4dWAjelG0uAjAjw80lro1x1iiJjGgBiAts03hHV9CX201qFtHdbrHmfnG7x4cY3Xb97g6Wc/xaePn/Du/Ud8/PAJu6ct7h8fcdin8NHBB9gITAHpghHn0BqP1jVwNmCcDrDGwu8PmJ62KVdb36PpVimvVkqFmNcrkC4RyesTyeuthB0jr2nFe0wfPhXXcusUj9cME9mINS/q38UD75Q3ZA3I1krLarVa/M11zCfJ8lkOe5eUBFHWt+QwlL0Sap1mPqT2WYFierEMz1RR/Uf1udr6Sh6edRrquWB5xH/zPDIIEEK+GdqHdPiWm4pFYSWjNacdMGbeB+Z9NZEmxgzyInvG5dt9y34QkQ8PMrgkNUdgmkZYl/ub63DOJQ+1gU9Vw6Jv6VIQA2dzwt8Y8XB/M4cj/4WlZlQyfcWQ0M/U6tCn3azYc87AGOcbw7hNAZXkmZqhKYmG5T15RnQJBro4xIjHwzJS6mFPCg4vGsdxcVIuOqUxppzGa8W967riWSH6L3udc9oF+U7nGWKasIeHvKP38hqYwOOtGfJaV2T5Iv3QdJK+a6BTy0ieH/05/87zLG2ysX9KhrBHggY3NH/ynMuYhAYMftToqoveDzRgq3UmroP1Y25Phyrquk/VWdu3TsnP2jNa72da1fY+XT/regCO5uFUm7WDWM3fwBwhU8t9e2q+9Z4v9dXGqL+Tvkl7Nf7V7fJ64nkElgC/9Iv5ltcjA1MsHwVQ6/se6/Ua5+fnAJL9dnl5id1uh8PhgP1+j/1+X+aBQRCWyeIlBaDkkWLASJ53zuHp6amEl4o82+12izFzvjCun9eaPMd0YoCHw06lHXlP30AqnkmsBwnYB6RcWdvtFjGmg//NZlPANvbQek73E884qfvs7KzsC6xn85zy7c1cD8tdKfrQSnuOCd9oj2ttizCAyWBbzUZnwErvR9oOkr+13if8pMfHYa0CON7c3ODjx48YxxG73Q5v374FAOx2u8JzvP/IYRDzizwjbcicijfZarWCMeniJ7nt9tWrV3jx4kVxRHj58mVZT7e3t4U3eP543d3c3ABASc0iAO/hcFiEnt7d3RVaS147Bk31Xvx9yo8C2IBZcK3XPaxtYC3gnCRf9AuG0yg+C+0lM4jxWgeCJNn67LEGAAFNE9F2B7TdPVbrPVzzAGsf0LgdXPOEBNaNgHEwMSDGCYgBJua8WlaEq0Us9yAE+BzGFRFTDq5sHOdljAhZHAHAlD8f4RoLZ3bwIcKiQcAKMZzB4xLN+CXGwxtMw+aI/GI4aWVJ6D0/cwyWsVeP3vCX9S8VK42E1+aZFUPdLgvTWTGSd/N/QsC56/HHf/IvcP8v/gh3v0d8f4tdANw0pYTuQPKGEYDMIN1CaHMoDW2yRdCXqUq5etKsBHEwOxqztRata+H9fANJ8CHxBDJIh5xDRyA0Y2APHvf/3R/x9PtPuPnDt/g3/pf/E9zbEXGKOSQ0gWunFBiePyAJRrnog+ezdrIr5XA4HF0OwvUetxtLMubvOtmX9wHg8fFxkSBVt8HPmxhzWLb02ySwJIpymsL+Uh3A7Pnnsd1uC8ggbSwUNAgvzeNNQlyU5GSoprHYQv9aqSnn+uaamqI1b1wWxqKqFMq7yYW/LwoR182KHbchSkK6hVQU7IBpWp5u8XxZ41Iid+FpANYkr57oHGwIyRPMWITJA2bCxeYSw7CFtQ2MSV5vPlg0zqGDg/dAP6VLEzarNTrX4MXlFX7y9U/w8cNn3N7c4NsPH/H4+JBuH93t4McJ4+STZ5AHBhPRupBukbQGxkY4HxDGCXYwMIcdxqaDcRbdeoV2tYZP2EbyCs0ey+lW5wxKCBwSUEJHgbqnAitnWmE4ZaCwxw7PrVa4yjyT4sNGi+Y1Xsu1foqyLMa8Nsj0Zs5yl0/IZb9gxU7SMKS2E0g3K88zTYCIdBOyvMfGsYB1x/uIrJ0EcovCtlQUEw0A1yxv0OR6uGg5owufiseY9+SYw47N3A9t6DmX8iAGP9+YJu3FPEYgcVQMIedNTMi9hJ6aGAvYJntMMe5jhPEZhcu546IPgBwA5C1ELkYwxmSAPoOyZpZBHsvE7H9JkVNjpqveN/gwToevCPgkn4nOJXMvxsJqtSq3Y1pry4m1yMMiswi4lsNYCSuSeWGFXuaODQbutyjl4tnFHgvacNCGsjFmAW5JvhUNLuk9UwxmLU+kLg2cyHt8yQLPjwYB5CfLAV5zQgeRFfqAUxuZbDCzvDgVDqTbPNqPK4aL9EkDCbOMWM6lpm/t7xrPSOH9k9uRsWqwXupbHIRpHUZ9z/SWOhgY1HPG8rj2jN4jGIxgo5PpqfmF7SemDa+PU/RayDw1/wJ+a3CA5/27PCL5d/H6YJCC6cxF1pI29jUNpbRtuwD0OReXzBHTQ3ug1NYHfy6yiMErBtOY3tx/ud1SrzeWr+L90/c9Li4usNlsCkgk7U7ThMPhgJubGxwOhwXgxDm2mJYhhNI2002DtayPiJwWnhbPOHlenpN8bvKPQTsBJQSoE3ksa1EADOFzPsxhUE76x6CMxgastSUPloxrt9sd8ZWAZxypo/mYwwufnp6Ocl4LfbTMjDEW4EeS32uQUd6XvVfoILJPh7T2fb+YW+YxzUvAMl80R5rxZ7V+8z4utGUaG5MAVt6HZQyvXr3COI745ptv8OnTJ3z+/Bl3d3cYhqHwEAOGwzAUb0PZE8XDXHReGZe8JyDXdrstnnLjOOL9+/cpJ3fu+3q9xpdffom+7/HmzZsCvF1cXODq6goxRtzd3S344PLyEm/fvsXDw8NRiKrIvWEYFn3kfUielzx0p+zL58qPAtisNTg/PyuCbFaeZ3fE2oYmjCIMcOxJIl5E5GViDSKmBGSYDj6MACxcE2HsDv3qAavVA5ruMxp3A9s8ZaXY5/w+edMxETFM2dIX8CS3iQyuxYA4hYyrZGGNiEa8zMSdDciKff1kJ8YAkwEgYwIsRhi7gzU3cO4tXPsa0/43OOy/RIw9vA9wxqY88b7urbasf1Ym2UhL/TLV53g+OLRFb5anDCARgrzZ1/sWgHzOHyeDcJgQ39/jv/3P/zs8/f4t3G7E5DNNfQr7WcTnI9HCZaDNT5THCnnTFs8CRIQpwDq78DbKPYGzFiHON7uFEDBFESzJCrImP4Pk1WNgEHMoaYwR8BGNtanNxy0O//m/xL9sLX7+P/v38RgOsK6+hDRd5R9fMiB01YaBVpLFqGGBWqM9zxcwG1Gn5kvP9+PjYxFqp8aypLDBLp+IOety+F82si2No/wnfbbdbsspnu6PvOODR4SFeLJ6Lx4Fx7IieZwdn3xKzLxsjpxbQYoO593tdnh62pZTGxlADIA1LvOKrKmZnt6PmCZX3dw1LbURB3CeLQvnZuCkzHlI9G67HgGiZMcEaJpQAA8AME2DECdEM+HThw+4+OUark3grImACwY+2ARMBw9ngckGuKZD3yYvnwCD9XSGs7Mz/OQnb/Dzpyd8++07fPj4AXd3d7i/f8Dj4xOGcUScAiafksCHENC3HawBjBnRWYvWNzCThzEjogP8YYdD8wjX9WhXPVzTIxogiBeoiGbx2DIm8ZaJQExh4iHOub6ksIySedXGk1ZaZH74hFbPG8vOEFJuMElWz0Ubp1oJkjaHYVh4vMg65/mu1StFG4x6fS/luhg7TT5YOPYiSe3PiYZT3XkicOx9wyevNbrKFht9OigJSKHKafx5P/bLm800nZk2/PkxwFj31pafWt8odIlFJJV20i6T65Axy36HfD6QaeMSg+d2kA9ksnFnm+yNmZ4xFojeI0agpRvgTD6AMkihp/CnD1h+SJGTf2uXXmBc2NhhI7XruqI8s0EnhztSjyiobdsWwEwUauYj0TfEEJO+yXoTQ5V/lvlQhiPrOWLcMejHc1zzWmHvEuYrlhdsdGldB5gBPnlPDD8OP5U+673gFICqjX7WCWpAGnvQCU2kbQYSNHjBRisbsWxc1vhEfvI+yXXo/gsdpX7t9SG8puUdH/bWwCJ9qMEgzlL+HHsqndJ/anKGy6kDTf6c+fNUO/yTaa6/0+tVh/zpd3Sf9ZrRY2egRfMK97tGO+4705jrZ5ksReZJj1+HXNXmjYusMz12rl97B2k66NBeWfccSlYDTTWYKe02TVPCyoA5zCzGiLOzM6xWK7x586YY63LwIfMgIIMAA/K9pqXmbw5hl+9qobyaX1erVcm/JiCZgDx6HlmfEnnK7Qo4LMAJF5H1zLfazpHn+OCAP5f54LXAoCmABZCvZaMuvH9If6Rt9hyLMZY9TXsgA7Mzi9CMD3JkHhmAY09Yfpb3G74gQuuhWk5KX2Uv4vak6PUh/QZQPLMEOBXvbNFN37x5g/fv35fcZTc3N/j06dPiGcmfJmMWfpLQZRk776PicThNU7EBWVcW21jA0IeHhzI/fd/j/fv3BWyz1uLVq1f44osvcHl5idevX5cbdGOM5dZQOci7uLjAOI7lMoUYY464nG8z1R6ZLBv0xQ7ft/wogO38/GwB0khhcI03DD3RzDAsINLXy0GEMCIBJAbRH2BtQLPaouveoes/oHN3cM0B0e7nywgw1xOzF5o1ruQfApgBk1E7F4OSVFkMPRNLgmRrly7CpTUC3nJqoaLApxCVABcnOHNA2+wwrSNi6LHffQFEg3Eaqwqdph2HMnA/asYFC0qhtQgHfaLD7wKzkqOFUu20j/+OMYXuRe+xGoA//uN/hrv/6l+j33m4YYLLSZiMSTewMdi0cDMmISp9L8I1zJ/HGEvo3VwiTEw3eaavli79McY5YT1Ou39aaxGnlFssjhn1fxox/de/RfPv/lswX/SL508pBjI/DF4xXfV7PJ+8gch3siHUNhHZNBj4rilC2sAQcI03jpqSy+X27g6ttQUMMUghf+kij+NNHkigl4BXWh6EEBCN8APg3NIbRZ/Mye/pZ0Bax9LnOaE1G4uavnpT2qw3sGZ5imRMNqLz5pfkgEXwyRPGOQvnjm/LEtrWjBz2BDGZF5NgR+kzy1BEg/u7W3yx2iRj3+RbCgNyNrfZSJE10TQNvnj9CrZtSt5CEyKmKd1eapqIEFwCPWxEjAZu3cC2DQ6HEa336ebQs4iLywu8eHGNp6ef4+PHG3z79lvc3t3h9vYOD7ePOBwOOAwHRAD7cci3NVqMJuW4bKeYPIQySGHHCf6ww7Tr0LY9mr5D268QrEV0BiEawImRPuUxW0SI4pH2hDQPnnL1HV/OonlNFJLaSW/NWOC5K3/HDOAL1yUGOVLgNL/KGmBeYYVLrzvN5zr3hG7rWJ5LkvRj70t+X4crcLuiFPHf0pbm87RW0g23Zb35kMJGM63kdk3WFbQhy2PQBrdWXGvPcJ943RdltNIWg03yNwNFhWbUb66bjRzxgU6/C+9WQBvhxx+R3+NUYYNDxlEz1LiwYi9KJxtn8jmwTFot3zdNg/Pz86pex8+JUi1zyCCblBr4XPYHqpvbKvu6+p3Hxn/zPwYin1Oi9T6qdbXa38Dzh1o1g/OUsSRzyCFWNSBGy63afqnHIvMjddbGyGtAAwDcRk2OsHzT64tlV01ms2w4NT9axn6f72pzJc9z/2tzwfXW6v++vFAbs25b/mbQiHmE1wPrbzVeO6X3nKKd0F7AFJYL2rNPr00pDR0q1J7X65L7zB4l/L2WK7KGuS4xilkWyHfPAfnGmEVuR/mOae6cK6CcePJeXFzgxYsXuLi4KLSS8MvdblfCpBl4EI8o+Yz3WXlG/pa+ie0t467JEP69tn41EKkPuvT6Zjkse694qrFs1mCPHgP3SfOmBvu1fcN18R7CnwkteZ/RuiD/1LxnrS1zoiO89AUPzL9CT14TQlc+YGBnEp4/Xts1HYzfETrw87wmWF6wHS99kkMwAfWkT7J//O3f/i0eHh7w8PCADx8+4HA4IMZYPPcYlHp8fESMc446vXdwpJB2vmKPNt57hCYCVEu0h8zJfr9H3/f45ptv8O2338J7j9evX+OLL77Aq1ev8PXXXxfvtsfHx9L+5eVlmUe51ANAAQTZy1HL5pp++X3Kj9LqtPeTMBEXLYTnkkCtZI8cn5rEmBFvE1E8oeIEY0e0qyc0/Qe0/bfo3T2sPcAg5H8m50wzxdiUFF3SR25rKYQ8ckBiZbNLScSXFc4AkDEGiKacfGd3uKSEA4BJYI+MBfAwcYQx32K13mDYO/jhMoVJYbnx8d/CmOL5w5/VnpPP+MS5tvnp9wEsFjEL6NrmzCUt+pz0/vYJH/+Lv8Xdf/N7dHsPMwUYn0Jl/ORh6IRT6uQTZ16oS8E0z4QIEa00CkBaDB3aKCQvm48pZxgDUsJ7TrwZ6RkY4LDfwzUO/naL3/+n/yVe/8//fUyuvlkwbQEUocb8p5VFXtRa6Gm+1YKT546BAzGMTimCMaYY9fPz84XizAZVbZ4/f/6M1WqFVduCw9JijOliAFMPV2CvOhZYZdNVz38Xzy3pHAp/WDuHj2ohyfOQvkvrN/iIh4f7Rehqqn85DmD5/jhOaNvVIoRBvtNzI5/rwwkoPi2AR44zc87hpz/9KcYxh1HTQqjxEYJF37c42/QY/XwLctpI2tyeuIrbFBhtHO7vH4py2hgL06U5G4NPIaSbDS4vL/Dll69xf3+P9+8/4O23b3F794Dbu7vizh99ulQkBotpGuFdvkhkAkaXwlMbB7g4IPoJ07BFs+8SwNo26NYrILiU+N0YwIiiLTyRQ4N98lK0AKJadzVwk5Uj3ki18vYcn/HJqvCsgYSYH+95XJ+AqzzXug1WNvU+yxu9lvV6/TKoLMqDyFJttGpFWup8bn+Zx5f+8bGSeMSZWL5MtDMJPIfR+eKOZYJuU+Qif860kc9EaZR+aiNNG10sI2uF16OmC8/rsYydvWzLJ9SuGCry3il5+0OLPtTUfeUxMS36vl9cbsP9KZemuPmWceGl1WpV2mJa6Pll+nEoDwPebAzpNaAPBYXPdV4k+akPVbgw//NBB/OXNqZ43qVu1pWk/5rXavpWbT6k1PZ17jPvEawrsQzj55/bQ7XM0LJF72Gn+n20b9H7NaCX6VHje2631n5NVvE7Wl/i+dD167GwjHhuzM+V5/YQ5hGeUy56Hcjf+ie3VwNbeNwaVOI2uR81GrGXq/yt9xDdB54DXhssl5nmejx6HFo+aJrxQaocTGta6zkWu0jABgHl2HtJ3hPvtK7r0HVJF2qaBpvNZuGZdjgcsN1uy3qSPZtD89i+ku/l0FVCR59zuGC+YBtB6/u1fcpaW7yNQggl/Fbvv1q+cZ16bvW8yRzoNan/af7R+zv3gQE9fkbmh/su0Ssy7zqsUstzblvq5ptReS/h34Wm0jbbZcw7AtxwzjeZK17TAI4ufZT+zreaL72tuD3N6ywn9QEV271nZ2f405/+hIeHh6zXvy8AsKwHTsfA64vpwfsIyw4JEWXdgfdN0YnlbykCSEudnD9NeH+/3+Obb77Bu3fv0Pc9vvjiC/zsZz/Dr3/9awzDUC7zEC82yX0otJT5kT4yv2vP9B9a/uJjU871ASzDdGrKjXMu3QYZksdBhM/eYWSkI+R8NyZ5pblbbDZ/Rr96D+seYMwWxsgpdMiXDoh6z8JIfnI/ZPKOT7vTM6WnSMAbl4jo8013BkhxS5G05/lJ+cjEDMjl/HLpGw+HLYx9D9e+wng4g0W6MVGEOhsIsig0SCNj0gJPfhcFvhYOygJGFpB8z+77NaWF+8DKVAgBzgD4+ID/7v/+/8TFDuimmJJLh+ThUYCyyWfvQFM8ApwxcxhuRZjTLOSJonmNMSevzkCrlRC+9A5vtDFG2IiFR0EMHtGIV1AWEAAmn/o5HUasug7jOCGEiN0fPqOZDLxcekHzIUXalJxmLIhrpUZ/LlqQ6tBRrWDNStVMC/HMFP7/9OlTAde0V4Iu0qf9fo/NqkfbWhgbEIN4k8TiLZjCp5dKruT24bwAMaYLS6KNsEjAD7A81VyGwhzTQgtlccVPsfky7sopNgHjMUY8PDyUzU1vojDCXzTXFhjHqbgdH72DWXFkUPzx8RFnZ2e00bsidxi8iSJnjMEf/vRH/OY3v0n1QeRYKECFPkBwjcHbt9/iJ19/tWgbDrNHZ7SwIcCG5A0WYDAMB6zXGzSNRcwXI7iuRfv/Y+/Pem1JkvRQ7HOPWNOezpxzZrGqui7VHES2CFxSehMg6E1v/Cv6N9KrAEEPwpWgNwpXgu6FROiKRLPZZFd3dVdWZeV08px99rTmCHc9uH8eX9jytbMyuzSg7vLEyb33WhE+mJubm31uZh4naBqH2TRgMZ3gbDbF1cU5nj9/ipevXuDNm2u8e3eD+/t73Fy/w8PDCl2fbxXqOuwDgNDDuxZNSBc59L1D4/rs1dSg2YXk/egbxN0WcA5+MkUznaFpJ4BrEDwQ86FFdAEhRvg2Gw4AQtjBZS/jPLNCFzeSB3rAU1Nia0ae8p2+G2NMyfAJcFM59h697Imq/FjZxjWoxrrdR1XZ0rA9VU5U6VLjVvvAfvNz5RFVjIBx7sFjyjR/pjUwNlatIstJiH1IoZQyBmu0WRpr2zo/KldV8bT1KK21z/qu0trmFFKlX4s1BtivtjKHnCc14P6QxZ4iW4NFx2fpqMmvaWwydJS8y3r4OcNMrMGnc6/tqIHD8asBbQE6PqPvqAKs36uBoe/WxqzF8ryVB7pmtT2l8QGf45BPtc6afNHvFMTTPa7Gz/pebY5rvFDrt/alJgdVL7XGt/6z4V527ll+X96v7t/S3xqNtX77TO3ZY/X/vuVYH/id5XsWSw9roCsgf2zOanuSri/lKX2m1n/LwyrX7KGVtm3Hz/aOgb72Wf3c9kXXwvfRk6BZCKGk0NGUEQQWlD7n5+cAxp5pCqTFOIBxDC2LMZbwznKwGFN+L/bRetUBgw5hva9ijMXTTXVbBersGla61vQEna+aXce+2f1f69B6rDyKccifWTvQtHyl9qKO0dovShet28ov/q68oHxjv9N50Xa+zwmBoI72wwJ3lpZ6kGnb1/4paK005rzyObt2SJcaKGn7z89Uh+GefX5+jtvbW3z55Zfo+x43NzclLxvzsRGwZH/5vnrnMfSYupICgXa+FCxUPUtpz5BUpa8eqPFZhoLudjusVivc3Nzgiy++wOeff44nT54AAK6urkrb2ofFYoHJZII3b96UMSmQqx5+P0ZP+1EAm6Kw7Kwa6HYTG09+QOgSKBaDQwj58gGXvktVRkR0iO4efv4VzmdfYdq+gW/6ktckGU49EtiVBUtqJf92uNGNb6azykc2WDPwEmO+sSWHHKVu9YOHTfTF6EZRbPJ3+UKE1GburovgtaSx9G2Nxt/Duw49Jgl6M0IXQEk8aIXYMUWL7+otKCoA7HMqnHWxHyt2M+/7HjEEXM7PMH27wX/zv/s/42INhH1AEyOQDeqx8Bvg0IjDMACrDFkkOS1QCRFy4vkVc9JuCkKMN+qu61IeK/gUylbmNNcfM632HfLtB2gFZY8xIKw3OOsdhhShY5o6N+Rb037b5+zf7969w9nZ2cEGwN91LinMagrtUD8NbAt2J0Xg6urqwOMDOEzyTKG83W7hnEPbtHDuENwi2I2c5Ij9CyGMLgAA1J26AVxECIBzg5G+26W2FotFVZm0NNXN9Pz8Evya8smCDWW95vHR1R8YkpBauthN/MWLF6MkrKrMqQKna0wT09bkFPvkXQIq27bFz372M1BuASiebSOQWdrq+j0+/uQjNE1KdJ/6dRguED1zFDpMmhYffvgh1utNGWeMDs2kRdh3mDUzhD6gz7w3m82wmM9xeXGJjz78CMvVEu9ubnB/84DPP/8N7h4e8PDwgO16i32/R98lD9euC9h1Dq33mDQuhQL3Aa5Lp6qt8+j3W7Sth9ts0TQbNNMp/GSCdjYD2iZd6hCRcnTGWGR/mmN6lmUZ45CT2pNm5D1VOA9DUXQujinzyl8os3EYesF3CV5QSfAAPJ/tGAp7qEBTwSfvMkWAKogKtNl1y6IGnI5H925rYOm6sUqzejYNxtSQvPuocpK71DY5d1Yfyo2clrZ2PlTJ135apZrtDrw8jMsafpRROj6r0KoyWVPoLfjIdyzwbnnrD+W5xmJPlGtt1tonPfSQwaYiULrRG5m5Vaz+Z+Wt6hrHdJFjv+saUnpbHVPXAf+2c2+NMH5u55Q8UesT6+Y71hCqGWuWT2vrXD+vGXq2v/Y95c9jzwBjDyn+XetnzRBmvWoUkS+ov6rXaM3gYzu1ebC2hV2X1u6o0aMmw/U5pdNjz9R0NDuGY/p3rS81sNf2pSZHajpxrV19R+dGaWbrqO1p2hbf1fyE/GkBBCsLdc86xteWZsfoVXuf4yM/cz9iEnW+Z0G3+Xxe3plMJnj58mXZY5kqhXm4FotFMfaZn5Kgg64Ntqd7LD19qjZTHC48ijGO8q8xfE73cj7HvZX0IdBjQSk+yzYt0Km50WqHGapPaHif/tMDc90zrax4jG+tvNHnVHfWOq0+oL/XDiMs+Md9jfXZgxW7brRu0t46QtScWew6VFrrmLSv9vBNachxaP41LXZu7J7G9qmXMIfq9fV18bq8v78/mEdth/XZ8M8QwujmTY5H6aT6g73BVeeG47a2qd2Tub4Yku29L7ecfvnll3j9+jUuLy/x/vvv4+rqaqSTOeeKJ5yuPQXia/rTDyk/CmDTDQjASMABY6+mgdEAICVTTh9kxm6TFwuA7OXVw7ktmuYbzGZfYDp9g2m7BnLOnegj4Dx8BsMGk0ZBtQHAGf6GAHGHp+SuuMAZZaB8pHWmROfpytHkqRNjRM5EDsfwOAcAKcTJlW7krD0RQNgihgfA7dB3E3g/LM4YYzlNUQavCSMdR4zDTSCjcZhFrHPJz7uuO5rMr9YWBS/6DouzM/Tv7vDf/q//K1zsgdgBDWK6ZS1GNEghVEXAZiDT9oVjtQJPE9RTkA1zl0I+QX7zgOtzfWDoVhZoXbrpEBEI2YurR0Sf88N554HQF6Ao5VVXIQU00aHf73D/9gbuk8scCowSjkrBpYJUSercoQIYQsDNzc0oVPOY8NSxW0V5TM/D2zXT2gzYbNbFMLJeOaQzPRFK6CPbDiGhG9EhAdME1TIdHGclfUjQSm/LGm1AcOgRkADrgNg5eN8W4a/j13HUNtkYI2azRXXNkF58Lsg79/f3BRDlBjSq37wPoITJpjkYPEBrp4Es2+0Ws1m62MQ58sIhuEzPQzibbwowUzXaPFmapkE78WhGht94raf3PBR0inEcYtY0DSZNiz5EBDhEP1xMUjbBSYvZdILpZILLqytsX23w6tUrvLu5xZu3b/D2zVvcE2zbbrHbdYh9QBd69D3QOA/vuXkC0fXo2xb72GHqI9AD3W4P33h0jUczm6GZz9FMWsTeJa8x5xB8g3wfMNKlF67IAGTvNa7hgSY+8UHkjbcB3Et4cYY9ZVaeIh8WfskT1OdnfH6uz5NHhYG3F7EfhX9xCFpzHmrKGIsq0jVZzT5bmaLynwqQ5og5BtbYU+dBUTo0eK0R1vd9AWfIR03TFA9RXS/qxs8+HTOAa0aYKkraD3uL8DF5ovXquNWYsHXzJ+nI/coqikonKyf+PsWOw7apf9uTe1UuVRFXelrjl0ou67Lgp/Ky3uBpDTprlChd9XPtB+mrbdWUdksbC7Da+o/91L1ZjT/7nNKnRn976MLP1Ci1fbKfaT012WT7rP2ya7rGg7X1QJ6xxq59T/dQ7Zv24TF+t9/9PmvDriW7xpR23wf61P6286fPWT74vjrIf8fop2NR76Xfp27929Zv27R7zWM0rNHR8p4C6Nbrid8pEHWs36SzHYultbatdgFlEb3ReONw27aYzWblYHk2m6FtW1xeXiKEdCvnZrMpe9N2uy23EALjfVbXAvV954ak79xnLDhB+cGf6olk6XDsoF0Pe6y8tSCOtqt/H3PYUH7g9wrgWRll+8U6dL/UcdUORqzsUl5UuijvaH0cW+3g5pisUxpqyCWA0SUtNdmvY7bzqnxt90v+0/f1JlHLH0prvqd11PQO9snSXvtKG3u322Gz2eDt27dYrVZl7KybwDLHo2AZf7c3pNp51L1Nx0Q9UEFoK6cs31obUkNmY4wFaOPYCYTf3NzgxYsXJf/3crkcOVHozb01ntU944eUHwWw6WK2DK4EHMIOuRkDfZ8J7jy60AHo4RwnYQf412ja32A6+xaL2RoDWOUAMFSlByJPVcmsBMD4u5O/UX5PIAiNKP06yg+GcwIxdCWt2iFoV8kLBgBM5B8j4HIoJBwcshGBHogOm80Su/09gtujaScI4sbMyWexNCb9dUECOPD2qc2bvguguGvSndpu3nzOGjpd16FtGkzOztAtV/iv/1f/W3y4maDv86y4nPMsJoPTA4h9yHl4Do02tjsKaZOfgBF8/M57IApcFxOYEGNI7ZVk+TE/51Ji+HKLac4bBiQADgBCzLcsplsDi2BpGuy7DbCeYvn2HrNPLhM9eiouHtPpvGx6dlOCGJEqHHe73SiUtEZ7FfCcB3VBT8+S7wkSjNv33mO5TIlX6eJbExxWKee73W6bbtbzORRsBGxnD1HZNCngNCRyXH/eOHogea9N4Dyy59VhklO7BlhUYRmUvAbeD7zE+RiPyxfQy24Khdcjc6sNPNp1HT744IOBr6R/j5WhjQEAyl8UgHh4JN362ccIyrnyuB+DPzFG+MYBMXlu3d8+YDZ7Aufbsk5iXiMuxoPbh2OMePPda8zni1FS4hh7xOjhW4/GDTfrtHGCPm+sLYA4dWinHRrnsMrX0r/3/gt8vHofd7cPePs23UR0c3OD29t7rFYrbDZb9F3ivwKQx4CJb+FiB4+Anesxa/NNwTGiDQ1ct0OzWcFNpmgmk/xvDjQ94DyS1126vCEgywcksLwPeQ+h3GnS80mEjJVngo+1k0xrhCiIY/dBlale/mn4oG7gnkqyK2h19TRNlS5dVxZss3zN57QelevAGGTVoocfdi1zLVilWdclgXtVIDkmBtsrLZQ21hirjVHHoZ5XXPvWmNQ6auAMFfymaQ4unKkBd/o5T7trxry+Z+fy71us3Naixpjyte5HFjisGcFq2PEQRPMWaT90z7b7vTXU9POaMcL+aOgu90HdG4/xL9uwc6xt6zNKD7teOL8WeLDv8R0F5Ww/rBGm71nZY/tiaad11+bvWH01elhwvPaPc8P2ysGL8ELNiLdjs/TVz2p912fs848Z7sov9v1aeYwutXVh69U+69ySPrU1o8WC4JbXLAhTWzvaR6XR9z3HedRxWZ3TzpfKkRhjWSO1+VJATv+u8Rf1EoI9Sm/nXMkFSSBtOp3i/PwcZ2dn5XPePsxnVacOIaW0YB7KY/ut0sGOW+dE9z+OQemlYJTuV9xnVL7p78AANGk+KpXDLDWQxTlXxkiZdGinjGWWPWS0/dE928pnu7YeW7e2n9pGzS7i7/yn79WKPqfzU9Pp7H7In9qG7mvqMcbnVQaQh1kPeUR1Nt2bLQBK3fuYvab0tYejOnfKO6vVqlxiQD1HLwHQ/bXGIzoXXOuW3tq+gpkcL9vmMzXe0nnTz1TXAgZHHG236zo8PDzg17/+dbJL3nuvjJnf8596NWr/eHnGDy0/OgebCpSa4kpvCQ6WzNI0Dbp+jz4AztHTIML5FaL/EpPprzGfv8G0NfHciABauBxamhJaRziGicIuqPFnDj7lxfEKROjjeUJHm5guYj+EfprvkHMluRwWl/oV4TPQFtn/GIuX037fYb/pEfYruLADPG8HnUBzA1mhogZZjABvh9Mrb62AqQkbZVo1qI8pi5bBy7Ohx3YT4L67w7OtA/qQxp3nycdErxAT4OHcOJcE27FjtGMYLbI+LzKfaR/lmZC8CFMGq2RIhz6W5Np8po99Booyn8S8geY+9F2f52ssBPp8IhX3e6xXK8wRi1BKxkYCLuwtXzqO9K9uDKqwr/3NfDecN+uOm8rhqQZpnFx4PWazKUIY07e8faA8Zlf2PsA7buAEoQ95S/vSNE25xjymCQAA9CHk2zqB3SiHQJpTK1NqvKfKEecnXQJCYKSBXnJg6ZpkV4+u36PxDWI8PN13DvlSgfH4bm5uRrfCOjeuXxVIvsNbcNJG2ZYXKXcILsC5EkDdhx7zxQIx6sZ6uPlApiLGgI8++hBdtxvWhBsAvNION++8BJ49f555IpbcdJrnj/NZgGPvELYpnHQ6maCZJHqficIwX8xweXGO9957gdX6U9ze3uLbb17j9evXeFiucH+/xGa1xm63Rx96hD5gs9vDecB7hwYphKKdtHBwaJuA1jv4LsD3Ab7bwm0d9u0Uznm00xka36KJ6eCm6zv4yRQhUQ0O+UbpvPa98wixB73VCujp6G2tJ/SDhxYL127NkBrL6lh4gAaA9ZIs8hAYDiFivjdV1o5hxsF51PCcfnZMkVOlR/9W2cT3+ZP/dIz67NggTCM6dkA02mPy4KMBFzkmu7bsutb9j/KYIbmqMA+kiwc0U6VZ21B5ZIE4u9dSlmtI1TEjQ3//Q4WKeu+Lh6qCmdaYtrxBPiCtmAhbn68ZR2xD9UE1gu18az+dG4eaWHBPn1W5b/NRaVGjR5/R8er4rSGo76mBo7yiN3nqO8d4TOuy/KIggTWK9BmVI3bcxwyex+jPsdi2tM5jfFvTz2qgvT3UPdbHY/qgPlPrp/281lf9W+msc2vHW2vzWCE/WsDF9sHKDPZBf2q7Vm4pH/FvlUt2/7G6zGNjtJ8r8KVtWL3MAuD8zPJ1bRzaJwVlNUrFhnkCKN5h3vtR1MF8Pkfbtnj69GnZYwmq6bpR8MLmiLL7dm2PsYCU0oJ6ue5DfM56dFowRfPF6b6jz9bWAd8Bxl582j/Vk8mvuh8ruGDrt+NjsfJU+cnKzWP1WhlTO/zhczVgieM6Jt91jDX5ozTW/pAetT1C97maLmLnTNeB3eP5meUzW6fqWOyDrsmaF7fuNwrW2b83mw222+3oFk3l75ruZlPnWJla4wk7NvaD/Hhoex1elFTja6WVlW18v2kafPvtt3j69Ck++ugjTCYTbLfbkj/b2t+MMmHoKNMA/dDy97rkgINTwqRJjwhhmCglUtd1iOhQDJXo4P0Gwf0Gjf81Js0tWheyeZfMInrJON9n6yMiXbvJf8bId/ldtUOQcnMhDMZj8vZAAddiDBkIo3KvgsEB8PlyRF8+c8XoSt4mafw9vANC7OFCyuMWYzKEYgS6fY/VssN606EPe/hmj+nEofFzJOO5Rwgu07F+atX3zMUzK2Cm3bisULNCSMGZmjJVF9LDjauJAhF+2+P/9r/5P+HlKrXRxR7eOzh4ROcQQ4QPKfyP/dA+1drhwgYEpIoRkeAaAEYbM7TIe4+UuB4l0jfRLwCNRwzJw8V7DxcCAlknhDSvGTelZ46LEX1IidThHHa8YSRGhL7DdrlO77U+h3ulUFArjJSeg7IxVpaVHgoksdDYIdjB/BDpmQEIs0Xr3m436PsOZ2dneW3mGYwJzFBFt/QrRvgYgdjnZeELiDzkGRwttJFCNdq0HdvLiqJ0OdENcO7wRj+lpQpaGpH8eX5+juFGU24why7V2t9vv32Np0+fFlrZdZYHVDrK7z/66CNwrQ5yaOz5o7SM8TAZbIyxgF+lPZc8pxx6hADsdns0TQvExNupEx707B1vMA5wEdvVBrNZOyh6VIJ9uhgmZGI7N3gNxQi05ZagHI7fh+Gkt/EJ8BOF1HuPJ0+eFEWVhr2fzco427bFfJZuWD1bLHB5cYbnz57gk08/wt3dHb57/RY3N7e4vr7OG/2uJBkNXUC6AKNBt+3y4UxA4x0a5+D7Hk0HOHjsmw4T32C3XKGdTDCZTdFOJnC+gUcH5x2CS/SJ+wDXJHne913ZsxI7OnQhgXM+Gj7A46e2j8le8h7XBGmjss4qC+Vk26V9SgFlADnUXYx1HIY6atvq2VZTZrTf2i+7j2sf9dmxkaytD8Z207SF1pCUEYnn3YEIC7E+nmNlaGe4YfFADpkx67hHss88q7SxJ6783SqjVrbXDEtdO3+Iwnl7zFAH6mGStUPTGv0tnTQEVpV89od9sjys/bPKsW1Li+a6qRn8tXd0LNY4sJ9x/BaAsqCBlQf8zK4vDW2xBtExo1lBh5rhat/RsTxWbH/1M47N5nRSWXdMBuhzuhZq7SoIQ7paGh4zYo8VyzP299r82r4fe6dGq1rbds0pL9fa0LWosqP2vL5n+RLASObV6M++27ZqoLv2XflP17X2ke1Zg1g/s7LS/lNgnmATZTdBNHqgAcB8PseTJ09KmCf3VKYfUABBQXlLDxYLvCmorQdTKt9rvMB67b5AGW89l1SOMLxU2+IcqNy08kv7qbJF1xGLhkZyrDpGW2xuTbvfPyZ7+L2CTUojC47Z74+1VZPf31e+T0Ye093s2rYep8fkBsdnD1pVB+TvVnbyHabXsTTRAy3OnfIZ+6L16v5MRw3mE2TKEr011NoqbN8CuFx3wMCzCo4rnSxQfEy2H5Nx/K52WGXnTsffdR02m03Jy7ZYLDCfz0drTZ/bbrfYbDbFm9Re5vn7lh8FsHHgCtDoBQVADsXBGLkvHXTDc3A9QnwN536NyeQt2iYZUAAt7gjAJzMwIINryIZF8kkAkkfQYPHnH1VFAACyN1p0GEJNAcQWXdih7zxCcPlnCkeNocntNHBxghB9siJihEMD71u4fBOo8zukGxT36WZElxkPQAzAbrfDbpvyD0X3BJOZx3p/B+e3AJKnV7a7M6M1GZDJDAOg8fR2c8VAtoaSMtgxgcxn7emZCoThH+BcC5fcAJM9H/aYuBabb99h5xaIkxatbxCQQYCYMt7R+OOmoic7xzZ63RRiTGAEQzldTDd8Usg4oHivxTj26kjTlMfa9wjOpXBc8kqfLmGIIQJxCCdCTDcDRjckGI0xIsQw/AsB8+kUbTtB3+/zPBzmNEt9IZA1ePFZA0c3cOXd+Xw+EmbDSVupPc+PA8NClXYU1AmEopBL76VwzMppHSK6PiD2feLBRpXwQ+VNi7q7RwGgHFwO2eWcjJUuCme2o2OwY9INbAhvZmjpoUEcYyz8QcWL4NowrnHYQcS4D7y9cZgbV2hRU6RVcdIQBzU0Qxwnww2hh/cRu12HWQarYlDl8FAJLsUB5+dnVZCS68SPwIYEJN8tH4oCy9DIPoc9+6aB8z7fiHxoZNqcKlzL3JR4MjabTTHbz3B2tsDTp0+w273Ehx+8h/v7Jd589x1ub+9wfXOH+7s7rNZrbFc7dH26tbffB3T7Dm3j0fWA8xGud2hyWOykadG3Kby7iwHb/R6t9/BNUmDa6QSxaeCaFDYcXQN4B3pMFfL5CB9z+HkGfGL5y2cwPz1bOznl78cAKea55DMsx05n9aeejFrjMM3k8Kz1aqjxilWOtR37mbrO676gbehYjivbqmyOc4IM8n84MEhyCPC+QR/GClutfrZNA+ngkEaefayuY8/aonuXGgN8Dzj0vtPvrRL6hyoKlAGHaRce0w0sD9UUXa1blf2aDFQjq6aTWV3A9s3Waf8peKzy5xhd7b5Sa0ufqwEVdu3rmPQ77VvNY7XGW4/xgbZh6Wif+X2LruPj63a81r+vDSsfauPWuhVo5jvHAP3avB7rzzE+sDS2stD21X6m4DT/tn2xstLy9rG6lWZ2fdr1YPO92jHXaHNsPbEe3ccfo4f2UelQ06NsPUoP3lBMnWWxWJSUItRJzs/PMZlMcHl5WYx3eqntdrvRHPAmTh2H5pbWfqjc1v2Va3a/35cDP91LrO2koIYenFm6WR6yMkbzIlsvIaWltalZfwihgHg13tC2a+uQz1teqckbu9/VDjr0OdZzTN7Zzy3gbN95rM7H9tSRzlR0kXjwN20+leX8TkG2mpzSda/9oDOAgrjUB7Udq3fVdEUFW+2ew/rsgRn7xPyDDw8P6Pse8/kcq9VqtA5t+gO7f2k/7N5un7My4zE+OPbzsXnUovNEepPWDw/JzmHEn3PJtqetslwusVwuR5eY6GHTDy0/GmCzhO/7vXyOZIzEgOQdhsFrjDdpxogQOvT9OzST32Dqr+HRpdAYIId+ZoM4420gyDaitwBtUQ12evY4IHrE2ACxRegbdF2L/c6h3zfo+xYxTIDQAHGGLuyx2wIhOuz3QLfvgNAixgxwNU02uJIRmlqggKUREnO4aAbgMq1C7t/4sgWP/u4tnL9HCkdK/4CU/Ltp22Tceo8QE8AXYwpbcz4Zvr5pcHZ2DucTsNA2HgzHTCQagKIh/1kh6sGpe34l/x3y78g3qrrkiZbnFc5j+d014m6PVYiYdVPMphM0IRmzLhuyLtOCG9IIgc6kGvKY5bksYEjqqs9eNXBAviQUiGEUwjcsRi52pIc9ym2iISUCRAx5vATW+mGD5XMRwH63y2AaQZf87L7DdD6D9w26LiWIr3kEsF+80ZFgl4ZS1YQJgRiNK/fej07DE4+hAEExCriYf+72eyAGzGbTTG+X6ZVpW9bMGDzY7XeAc2idK2sWMEZI7oPKwBAimphBreTjWXgueaok2cCLAewmlqocb3qH/BlKnUnwAyH0cM4j9Lmf5Dvh9Zh5e7lcou/7clU1lbZk7GW6OGRajTd5npCmfufLCDAOi3JuGAPrdS4B9iN37qZBUPCWmxVUubAG3oEQLGWzWePi4qysf8tfcEUSZx5xgEfxakw56/I6bVu40MM3eYwx5qT9ybN2vVmj63pMp3KpinMp9Dpv0CGGDLB12Hd7wAGz2STnDmlwdrbAixfP8eq953i4f8D9/QrX1+9we3eH19++SUDbJl2MsN+lDa/v+rJWm97BeY+uD+hCA+88fN8nmdC2wL6Dd4DfbOCbFr71aCdToG3gmgl8BqtjnvCQPWApu0PYJznruLY473XvDFUyrCKX5mdzwOM6R/YzW6zSxM8AlNDKIj+lDnvyXVMotQ3+5Nqwiqf1btL1acfO9hWIsQqffpa6nw5nWj4TQjlEiUaJrRmibNP2zxqS+t0IWBf6WE8vfRcY9k+b/F37qO/qfFsg9A9RbD907HavV36yfbQGso5HwTf9W70gLFhi512fscaU1TG1DvaDfbQGjv6uxY5DvQiUj9RYtfxl5ak1Jmp90D7b/IM1PrGAnh1PDcx5TGbYZy1gatdPbf3az227dj3ouzX62n7WDCVLu2PP2vbs+8pLKg/ZJ6uHWfpaGW7b5DM2AbvWRwBR6+ezNpzaHp6okW3ltB7U2b5Z2tVoacdhPWP4jPanRpPaWrcX1Nh1SjCJXmlN0+Ds7AyTyQQffvhhAR4oU+hlwz2I3jc2pJJ1U25bA9nKOrbB+WJ4mPIEgSttS8elskTXmZUVumfrYZB6tbEPMY7DTY95c1r5xN81x5v11FGeJW/aw/2aDNL2ap/Xntdi5WJNX3hsrdf202Nt2jXBZ6wMs/VrXVb2kq52r9Bn7frh+7YPCTcZg6j2EqBauG/td+2DfV95R2UK87zHGAvAprqe5nZUPUj5RvtoPdmU9jrfx/SNY/qbTSlV4y2lAdehyiHnUtQXcyzyQoflcombmxvc39/j/v6+eK1p/7nuf0z5UQDbALpQ6NswmYobYMyLAwNY0PdbANdw+BberQAQyAqAC0No6DjW81inQEAtxgYOU6BvsFm32G1a7HczhDBFCDPs9wlgC31E33vEmP4lgCuNL0RdfBkUcwOQkMaZPo8AhvlwcHHwsgJDXDOQwTFEF3MbGbAKuwQAhZABvPRmt43oM/DRx+RZt9/vcmhZg6ZpMWkmOL+8yICWw7Rt4P0k3YiZ++x8Az9JnlZN4+HQpDxHTT7ZcWRcGtD8W+CakALKAjKIGNIlBp//6m+x2e3Qxh4IAbHrMJ3O4KYOaACgQZORDbs4YowFLUv9cBkVG+Y1ZnCt4IV9SDRxiTtcGMKBE89lRB95oTsPv0vhxS4kkM45DB5vIQABZROll0rXdYADupA3X1In8vIGYD5fYBd6aD6zoR9pKIM3USw3FI1AMhwaqPv9vtwgS5CNJ3bOuXyfQBIgHg1CD0zbGbr9Hn2IiBlkuru7w/n5BSath+tdAgqRLhdJPJ3ZGwPAFkKPzWqNpm3Qep7UjMG1IvSHCuBc8gqbzWbpc9eAC4a50NI/P6qLm0/NU0K/H+TN4K2w2WzR+Aab1QbOO8QgHrZti8l0irZJ4BZBub4PRVnjJmJPuoeLMYb21+s1zs7ODgwGZJ5Ijw8/ubFxmtM7Q3h1zaAclPGAxeIMgEcMABprAB0aRpyjVN/YoLDh4Fq4MaZNZQBf29YhxsNQrxgjvItw3qNpgUk+gY4xwrcJqERMACeBEu9TaLH3Dm3bIMZQAOeu69BMPOaLGZ4/f473Xr3Acr3C9Ue3WD4scXN7i4f7JVarNVbLdUrMutuh73r0IV1kEvqAfddnr7XEb11IF5S03sN5h6bv4TuP3TYBx75JBxjT6SwdTjQtXNui8T5fysIw5pABqyZxOz2sE1HA/HjWkKspDeoBad3cawqfVZrtcyNjbXhp4GPOXQjQ2vl7MDLZKrlqGFrDQhUa/Vz5Stf0sRxjtl72PclxrqNeX5BnDosFcagEquJpDZLH3te+2Xm1a6NGn9q8ahsKMv0hSoxH9tkjfVFDwRpAOh59nuOj0q3Kt75LYJV7oPWEBMbeGGzHgqO279o3NXrtvNmx8Bk7N8orKjOP0ewYqMe/LXDKsT9GV5Z6XtXjRp8d4zGjt0aLx57V52t8oe+o0aXjVyPtsT7afcmuT32Pfx8DTez4LN3s+j8GqNW8FO0zOo5aPce8O/UZ24ca77DYvI6Wh5T+ZU+uyBU1oo/xkconjqUmK2iAH4tK4fPc9wisLRYLvHjxAs+ePSv5cwGUBOTqRUIdmMAEZQr7rDIGSGDcbDYrHizaFyvDKaM0VJJ/09jWmxJtHc4NYKL2ScENSw+24ZwbRS9Yeus86Tua7kHHV2srxuFQjO/SA5CyZjKZjACfmjysrYtjvKWlJmPt5/rP8ryVl9qPmpystaWf19451D0OZZ3KG91zbJ5SYHyoYOeddek4+C55X/MMks7k5ZpM1nHp95a3qAuR7/h93/dYLBYFTJrNZoXfSBMrr5qmKTma7R5u5TnLsUM7O2/2cyv/HytKV9KNziree/zn//yfcXZ2hs1mg81mg91uBwBYrVYjW8j++zHlR94i2o+YggIAOGRo75oERnjkCUsL+Pz8DH3Y4fbuDZrJAxA7xJg8x1xIwTiusniHIkYOgBgbuHiG3XqOh1uPbj9HCA32uxm6rsG+8wixRUZ80iS4oaphAgE4j9oUxmx48iWnoBkAF5UhhndiBtkKX8QxTkg4DRm+SpCijJ8AT3CIISbvvoxDtd7BuYj18hreAfAOe+cR4BI44j086O2WQk3hkmeZcw6+maBpU66iZtLC+xbO5TCqEv6W+xSlv3msLjZ48/Ya990OwASxj+hcj75PlxFMpi0a59E6X7wrDgSuMcRq7sguP6fvuexV0zOIi8pExChXW0RA3/XJo6nL6c7DkIsqhFBCTnf7XTI686JyjUeICYjr8zz66Avwt9/3UCx2rDylOfI5Yft2uy9rxW4qdgEnYeXg4NDtkgvvtFnA9S1uvnnAu+8esLrbIPZA6+ZwnUPbTBH2fQYWEoi1Xq9wfu4QkPLQAT2i2yK4HSbTCaaXEzx7/xIXVwtEH9Bhj4f1HbyfYNJM4BwQXciEzOBQFAaWOdput5hOp8MpRhySV2tyeM6TntbZRJ+2DLyQ6vI+hQJ47xE6KgYodXnv0fZp3vbYpYsuWoZ1o1xQsFqtcHZ2NtqUCADqRuy9H10qoaeOVunh8yEgh1nSAyYegAzHjSX9vq6Y243MNx5XV1do28Hj6jgdh5I2dWQFeAA/08abbuRlXxQUoteb3fBHYB5QbgptWo/tdgMg4OnTJ1itHtD3Hfq+RTNpksLnGlxeXmC73eDF8+fY7Tus12us1xt8+81rbLc7vH3zBu+ub/CwXGOz2WK336PLBzf70MOHBkBAH3ZwABq4dMMqUt6yxjdoWw/fO3S7HbrtFt43aJoJ0Hq0voGbtvDtBN61iC6D+c0Aspf5Q/Ly5YKwoYk1BY+GxvjzIWw7xsM5UmNAQTX7vRZ6K3vvs/f0mIdqbvr8rqZkU5GqtVkzHvVd+5l9j79bJVrbUmPx+xQtqyzbBNMHeooooQPvj5VGC8yo0sk2WJcdmy3WMDnmhfNjSg2cOuZdx/L7KK1qPHC/0tQJ+j37YWn0+5bHZJe2pbzJPqmBat87Vo/20QLGNYPg+9ag5QnWoZ6c9v3aWLVepbt9r7Y3PFa/LdYw0zps28fqre3hxwzcGu/VgE816mrP1/jZ6pjWKOXvGvZXe8+WWl8eo0dtjavOUDuoY132c+qN1K0UzOR3NR61PHkMPKjRU/+2hwB2f7NAGtd+0zTl8oFnz55hNpthsVhgMpmUBOPb7bYkFGfIp81ZaOV27cCQz9FjSwEv7Z/lbbvWCKwRXFOA7bF92MoNG6apberFLgROLAAGYGTsq16hfGplgvZN54d9SBeBNaWPs9kM+/1+RCM+e5DeCWM5WuOvY3uzfl/bg+3nlv9r9R37+/t0jtr61j7U9Bit3x6Okpb6fu2ffcaCt5y39Xpd1pLOsb14Qd+xqVoIllogkvy0Xq+x2+0QQii5/wi8TqfTkvebQFvXpZQ1m83mIIeoAm6af400Ik/W9BCOQXlN5VdNLh/TXSzPsB49KH779i1++ctf4uLiojxLsBnA6MbQGp/+kPL3ukWUP48tCnpvhLjLQsTj8vKiGBnb7R022xXOFh4hpMTxofFgvrEhl5QDQ0sHwz6mULPQoNsvcPNuhu3yAmF/ie3WoQtt7k9T/DroC+dZZ64rKc/0ColwrhkJqfQMjaDxIj4QMuKlpuWARjEHsbr8cIzlpruYDdyYPclCTB5yGT+DazxizIm+PeCzt58rrUekoC56dkUgdOg7oMcwBAeHgAQ6eOfSrXpNi+S10iBdCDCBa5PxOZvO4Zr0e+qfg2/TIth2O7gYEFyLMz9BHxJ63u4aTCYtZr7FJAMUzrniXebd4S0h+vuweXginAmVppcM0rykMC6X86jFEv4ZQkgebgDQu3IDaQi8vCABbbuuQwg9+phdpXM9KbzUod+nMEQm+W9i+uedQzCT7QqoNoBIRMktuAYge5shhQI7Bx8ymBNnWL/r8PqrG9xfr9CEGXw/xX4N7FYd+n3yvHPYZiebFTx8AtMyBta2E6xerzK7Z4DQRQTn4X2En+zxcLmEny4Rmx5+GtAsIj782Us47IG2S0CvyzkEY8zeaR7qqUoBXXMR5oKImaRNMygKBOVqm6NVnihPGgfsu30C4SPQxXz5RjwENfbZ02m73aU8XEh56Ki40EtwBDYkphrVs1qtsFgsyqZI5YPzzUtHdDMlDdh35e9y2pg6LCA7ckgpw1BTh45tNFpub25xdj5D05xVlZdjZTabl3aAsWLd+AkiBjd2tk9ln7c02nXLcTRNAtZD7AG0xUW7bSeYzxdpzfUBk2mfeTii6/ZYLBb52R2u8p5xeXEORGC1+gxv377Ft9++xrubW9ze3WH5sMZ+3+VQ7QSg912A8xF9DHDBpXXrG7jQo8l9a71Ht49o2h5tGxA2AV3ToN026L0HfJvCSdsWTd6Ee4cUKlrGmsK/+7DP851ClQHO1zBnNCKsgROj7jcJtBvm+tAbjHOh/HTMqD72uXPJ6805V/j9+5RjVdRrdWqbNUPRKpfWc1TfP3Yin+R7SELWGI3c7EPoizzV/vA5q7/oWtXQJh0/v7cnwgqssb/q2fXY2rVGxx+iaP/Vg0gNNB2T1d1UVqlxrWOzSj7b0VxoJWwcgwJtjT8q3ZY/jvEhn9H+qSzS9+zvVnG3IIUFWZUWHJPOkU0sXZtf/dyCE7ZoH7RYcEDr+LEn69qe/q0HJna+j60J7Yc+Z40py1N237AAj+XLx+RYzbDSPdgW9QxhW5bXle412tmx2j7X6GxBK+VlC3DUALra/PGnrnXgUNfkAYnqpTVZyO/0fUsvpTHH3zTpkOzs7Axt22I2m+Hp06fFGwZI+t52u8VyucRqtRrluWqapqxhlal8Rm8zpyGveW3ZV+VhDbljFMgxOcxxU3arXFBgVAEwnUvyDJ9RkJF009BWhm1qH3ibo+5LysPaXm0fUv7h3wQ++A77wH7xGdZL8E/XtI5Z+1LTBWq8q/xFuujnWid/qrcz+2PfO9Sj6sD+Y4cmx9a47XuNb/i31QusbsOicoJ7IlMGOTeArUDi/c1mU95V/jwGtmobCt4pDfVyOPaT64IeaeR/OhUQgFWbSfklxljAQIJzSgPdB6zMfUz2Kv31ndr8KJ1YD+dCIxi22y0uLy/LOMjbHIfqnFbP+aHlR4aIDgxWQyN10aUBBFxePhGBl8CM7XaLu/sVppM9Ls8d4ALQDWFUrIcXjzHJf8qL1GC7ucLN2zNsHy6x380R4iy9l3MoRedG0Ad/510JgE4cnzp2CpbBFeCA+Mdo8730cyjj1L4MbSB5TyGBEuxnjA4uxgS22T7EXL/LP2OPcicBsT+Ces7Dh5joiWzgBpfy0eXK+ghE7xDRYJ1zvjnXoG2m8E3aCJ68nOLJn77Euy/fYPuwxSZ4zN0Ui9hiEVr0+xb9dIK292ibCaZNW2757GI3yjOgAsbBAXmh78MejRqRyOFNfYIlu5DDgPqQQz5jyQUVYkx3Y4S+gEQJgIrYdz0CIvZdl8Mnk7dKcAno9MjC2See6WNA6zxa5Nhudkb4ScE1braccmtIEWRxwaONUyxml/j6t2/xm19+iWa/gNtPsV3t0e+bnENuD+8aODTw0Bh5B8CnMOHg0YecW6ELhS+4Nnrn0LgpYojY7wJuV9sU9uYiGh8QnMPDl9do5z3OXrT45L94ATfbYu+2w7oGcqhcGjO9ctRYHW9gvgAPqvgy2aSljf7Ufx4RjW+w7cm3aZ0kWscBuB0ZKhGTSQt4j/OzMwDjPHi65gAkcBXjeTo/Pz8Q4mkcXV6TCcJXZWy73R5sFGrIK63S3z1iDrsmwOYcQeXDE1tLq8urS0wmfmBGjI1Ca0AmmZD4dRjD8F6qOwG/9NJifZoAVNvQ/hSFoEkXJMSYwkP7fpJzr8yywt/DuSQHHh4ecHZ2lk/wSOvkXTebtQghousCXr33Ap9+9hFubu7x9u1bvPnuLd6+u8F6tcF6vUHX9djvO4TsPZe8UiP6MAAATUieqxPfwPURkz4meDr06PpsULh0UYLzHnvvUkjpbAY3maX9xSHdRhxiDihNedySIpRCbAegLaLv95hO2zyvpJkzfydeGg50Ipwbg25KZ/u7LTW+KV6jGImvElKqimiZR7YjShHbtWueSo418qjMq4FS41U1ELQ+VTCLHOG4gCHqPadH0Pdtkl4tFlioKWx2z7eGqTUONJTJtqvySZXAP0RRg0+NEgtg1MZJw1XHp+/bMTO/pMoyYPCwYXs639qnGGPJl0R5aUEfrfv3+V7HpUAOP7eGqM671gNgxKucI9uenX8+q/XYZ2oGmz6jn1kwTflR61L66/jsXCr9rGeEBdC1Lcufdl9jH2jMaN2277X+26JjV/ro35q/TJ/Rn7Y+Ww+Ljv0YQKA/awCcBa5qgLY+VwOsyMPaZ5Wzk8kE3vuiw7AfBFH4U9cl15j2Td/lfE2n01KHXiDAkLWppINg7rT5fI7FYlG+o3Hdti1ubm4OcqCxXXrpKM2999jtdiPAnkCQXmbA96xHT4yx3Cq62w03kisYYPsB8GBvACOm0+nIs4hjIj25n5AuPGjcbrfld5sknvPHena7XamH60Y953Rdq+zVtabP0AOJtFbZqwcbHB9BBQKg7C/nizdN2r3dAhAcn+6vGs5oC/th84/pHHJMVg5ZuabF8hH/VuDTyiwrv9VzUtcS29X8s7qW+J4FIXX/t7RgXxnKTH4mCFbrL2nctu0ICLOym2A350JlIdtYrVaj5/u+x8VFOsxeLpdlDSroputSDxfIx9wf1aNcPYUtfVQfJK+yPs4/QXSlIdce67Z2nPJoCKF46nGOuGaVt2r7rgW8f2j50R5sOghVXp2LGdBJBoNvPCa+xX6/RdO06LotqNKvt1s83D8ghh28m+DyDNjHPRje0zYzOO+ywEqhhgCw3Z3j5voC97cv0O/PgNCWAMuC3ERCZeNSGDZGyevlcp77QenTE4kyqSFk5d2GsuTfwwCIZTyjJNanYQLGdub+eiAnbR4raOlihwiGr7rc5zSGXDmACLk9i+3mMfU5H1UUSpQRuwxENkMoJQnnGl4wkcDMZPvt0ccdXB/hosMuuhw+GfH800v8lx/+jxH7Dj42iF3A7mGL6y++w9tff4Xubo3ptsO8bzDd7TGLE7QRQOuTh1gfgDbTOcTiaejggGxURufQ7xPIpnkVkrdCQOMcun2XPDJi8nCiQergEfrk0dB1Abt+eJ98E5AuoQhUxCMA+DTn3gEIyX0wAr0LCB7oY0Q/3MAB54CmGZ9m6gUFpb3oEPsAjwgPj8XkCte/e8B/+ovfIG6ncJsJut0ZtvuU6yqGFulm2SxwvcshXwO/Jj5MIBOAo+FcCViO6F3MHpFpTHSnDGgQELF7C2xixPp1j9svXmP+xOP9n51j/jLCNXsEl3IFthMPTY+khpNzDk2bcoglcA0AAphXiXyoyr8VYjGGlGcrxsy7Djc392iaFkM4IwHGBM57b3KaNA2cj1iuN5jP51UDQ/tO+cUNhQqTKorORfR99lwqnhDDBSUDsDrOhXFMWSkHFd5hPj+Dyxd3OOfK4nQYC/lh3lMo8Gw+xdnZ+VHwwhbngPv7e/R9wNXVk9F3Rf41KP1gdTEm8LDve5yfn1cVjtR28qdMJyRp47168gST9nneFFukdAN9ua1rNpsV0DXGWE7wrq6uisLTdT0WiykuL8/w7NkzvPfeKyw/W+JhucTd3T2++eZ1OiFfbrFaJTf4zSaFoDD0O51aAREBIYP/MabLeiZt2uCnTYMeHVyXvPB80yDs9+g3Wzif8vo1kxaT+TzfUOqLV6xvM/2p2Oaw8nRiucV0Oh/xeAKgqSC6Eb05f4nPeAnH+IZCVaB1DSqvUH7UQAf9F2P2EOP7otBLhaPDq4P3Rem2Rrs1EFRRtwa4NZL186JQu3w4B5e9a3kQhwS6e4wUY+XVWpiYbUvHYMdCuXCsj8fAEEsf9XL4+5YiewX8pmGn9K4BDuyH9rcGhOhPGy5ieUFpU+MR/Y79twq53S+0XjW+VL7a2/zU8FK6HB48DJ8rmKv0sn2wY7E0URraYuljv6u9Y585xme2XvuZevjU3td/QD3sV40gy1usq5anSNuszW+tT9YYsga4ba8GOirf2LnXduz+acesa0xtITvfdizabi0noV0/WhQ0Ozs7G4Uutm2LyWQy8oxq2xbz+bzk/mUKD/WwUo9qeqjM5/NiwC4WC8QYcXFxga7rCpi2WCzgffKy2W63BTDjONimDZ1XPlJ6ae4zlq7rRhcOWDqpBxuf6boO2+0WwPhCKusMwjngYSG/U0BS+UND5RQ8qI2J7+m+zD5rUf1PZbSuN3u4YNcw9w+CDhwDQcEaUEP66IGBOjconRR0Xa/Xha59348unqjxO+vSsevaqT1n9wadOwtq12Scbe/YgZHSmH8rSKhA5bEDxGN7mz7PudG93spnnVd6ZpKXVW8jbRT8tQCU8rINUabu7r3HxcUFHh4eyrxPJpMSaXV+nuwIPqv8qfsk14UCXuQ99o1gP0FcvUmVdZNvFTQjjUkDBbMZ0ql7ggXvSGvLT2rHKW/VaEiZqvT+IeVHe7DVhF3+VpSWZDwMhn8HZI+TtDgDlg891qs9Yu+wf+ZweRUA16MNAEIybBEbOBfRo8dq9QLv3jzHbvMUfWAi/ywsjaJQ23yBDNx45ATWhJ8OBZ/+BDc9HJ4CAhGNc+g9Uj6zEBGcKHwhX9gQUG5AY51DEKwrAN9gUCONDwP4d0zXGr4f5qF8M7KE0leDAK0g6yVElbm3HBwc2hIMGZOHWK6jnXlMphPEmIxOxIjZsxkuPrnCZ//lT4F9wKRvsX/Y4uHbG7z9u69w/26JuO3glwGzrsMsZAC1z/3KuEIMGYTkXLnBJZ0nQDHGFM6Vc24l6Cd52u27Hvtum0CpfAth5yJ8SN5owCBwixDP6GiI2QsFA2jqkEDU6B2mizn6kLzbnB+HPqqgTryYgLW+C2i8R4MZ4maC3/7VN/jm736HtjtD6Bbwkfn/GgnxHRu3qa7ECwSU+n7IA6UnDfp3FKEfY+KY0aaXxwwHhD4gBCB0Af3GYfO2x+2X7/DyZ1N88A8v4M/3cJMmg14h/8OIB10GiWO0p+8Ru92+JLUdaGQ8Efq+jCmtPYfbm1tMmmlq1wF6uYTmeVOXducc7u7u8OzFy2qeBG1fleUYI25vb3F5eVmE+2Bkp77ZjRdIYaxULgttZfPU+nXMAHB3d4f5vMP5+cUwbueKdFLFvGwSiGjapnhwphfT2rF9GG8SDpeXFyW81ZZjylLTNLi4uDg4gTxUJLMXSBxk0nw+w3TSlts0uy4BxQTWqEzwVHU6nRYF+/z8fJR0OF0YkMJJr64u0fcdlusVPvnkY9zfP2C5XOH6+gbr1RbfvXmLd9c32KzXpd99TArDbt+jcz1C2KL1DfouoJ1M0JdbjfNJXRfg24gGHhHp2dg36LYb+LZBzDcmz88uEs87Bzif5CmoKAfMZzPonSiWhoknxqd+A20DmCOQ+oSV38fmEojouh5tMwGXivVqsHKrrGUcPt/HOn/Zfdd6L9ixWsXUrkt9Xg0OVa5CiIjo0aBNveV7bgCIa0XXoxq7usdbHccavzXQQde4zkFNJ/lDAWss6oXGYgEEbVcVZQss6PN81hY79xags3JE5Qaf1RCmGj21L1ofeeAYaKvPWiNbD1D1HaWJKt8qsx+jzYE+VXn+WB06xmPP1+hv6W3rPvaZ0g+on+Zr+8fWsu2rrk2799i5tfuG0rxGn9o7/MzmjDrQ/bmfGhpYuVPb+2z7yjNq5Fr9QvunbWmf9Hn1ylNeZR30zlIDW+dH+z6ZTLDdbovXmzoO8DMa9DzYomfa2dlZSdUApEMuHoI9PDxguVzCuSExu3ND2Bk9wKzepIA2+6tFQYUYIx4eHkZ0VMNX507Hx3ErP1gPTfZV+YQyyHpFAsM+oZ/reNh3ylEdp/bLAroqjygXH5M3lq9ruoP2mWNWjyMtfd9juVyW93XP47u6LzKsTvnL7h/a95r3ak331vFZGtii39v67d7Anzofur/rXABjfqQ3Fcel3+lBlG1Pv1Ne4BxbWaPzxn6pd5qCsLV9getF22XINnkfGDzR5vM5drsd7u/vy2dcZ9pvesFtt9vyHWmnnmzWk075QuWdBcE16qkmSy2wbA/SbJ0qY6xnPX+yz9bbUnne7um0NY7pkI+VvxfARgNIS2+AqTQxAJBBsp5EA2LosNls0Ic99ruAzbbFk5XD0yceF2dT+HYL7yMQe8Qwx3p9hfvbDxD7J4Br0DbJiIsYK92pfj8i7mjjA3OONRxQ9n6rj5XP+KaBz7l8CELB+QSChYiGe3kz3OjmvUfkJh9jCVUCDpsriwaAO7K5P6Zw1RS14dl80h8Hf7aYDY/oXPHMA+uIsYwhGYjJQOO7zue/DjYABVqQPNMmLeAcmssFLj9e4Mk/+xC+B9ABkw1w/bvv8O2vvsLmzT3ccod2B7R9SkLuI830YYNtfHIXLzfgOQDcRPqQwyObAXxxLmOCEX0AuhjQYvD4M74Y6YbEGPI8c+QRiEMIVegD1gRRPOCbYbFvt9sigAbBHBH6iBYt/GaOL//qBl/81Wv4/QxNvESIAb7NRmtMYZBNO0Xojl8P3PcRQH+g3FF5qCmTh/M1NqwoqIPMYyKxQ9w0ePvLPd5++S1+8a/ew9l7Dr3fo4/7DGgeGmdJKRkMIecc1usNFouzo0oCuS6gz40nN6oun5J4CdUmren9Yw3LgIhm0uL9jz6Ed+ppWz8ljnne2Zf33ntvJLT5vG5+w6lhgmBjDJjNhlOP5G030NvSR8vLFy/hm4pXgYsFGKcymKYz/f306VM4B4S+y96uh8aFNXTGsnJ8omcVO2B8InYsnE3bjBkIhhsOD/bdHs6nCw/6LsD7JtOsH81f7QYxKgYxxuJ5MZ3usdnsyiY/nU5xcbbHi+dPsd3ucffqDm07xZu37/Cbz7/Azc0tbm5u0k2k3X44ye8j4AKic9juO3TdHg75goRyIt1h0gNtky6NCbEvnqRu3ydA3Dv02w2aSb61eTJBO5kBGZR99fQJut0WrvGAm4zmgYAwlSNVRK2hNcj6sYfAsM8FqCcn32tbj77vsjd44tdUVQKGZasq/bCesIkfx8Z0zZC3cuZgbRo+4zrj2Gu04WfWkIyI+dbaCIR+2MdcD4dJWbO1k2cFedQTRxP/qvJsjZix/HJIN4v3AAJC7NFkPYMHGs4nnaX0wQF96BDxhwkRVUXbnqRrUZ1IP2N5bG71fW3XPmMNmFo7fM56JwB18FF5ozaP7ghvqlFFw8weEti9yMpLrU+9Kmr0sGOtfVd75nE97vFSMxBsUZmg/FHjB63H8sJj7dT21sf6rAaaNT6PtUPgwJbHQlmP/a28d2yMx2Scfb/G8xYU4HMWKKQc4vhYr3rkERArYf5uDDKTt+k9Qq99giXUVy4vL+GcK0nNr66uijzj8/QGa9sW9/f3iDEehF7SmNY22Q/u01b2Kk8ruMB61EtMv+N+bYEpjp3ee2yfuq2G1tl9VAEQjk/n2Xtfwk4nk0nJI6vzb3lEbU8FCBQIsPKZ/zQflH6me56G/GqIqT184mfatuqvShsL8GnoH78j72gYLPU0nasa3ytwbgGZ2npTnXu018v6qoXE6jpVvrM00GLlrwXjakX7o/WQVnav0Lm041X9yMo/1Yts3aSR9VTT5+w+td1uMZ/PEWPE69evi4ygzmOdi/SiBa2T/+bzeeHX/X6P+Xw+mjdgHEHQtumAXW9K1TZrslHDRoEhxQ/7pXJB56c23/qczrOda13bXEs/tPwogM05h80mhVupsCvfe4+msQtmCCVNHjcRgIeLLXabPfa7HXZdg+XS4/5hisUsYjpzWMyB+XSK3fYV9tuPEPszpBsuAZT8NIcLk8SpGSpAMrwRunzzZzJeG+eAUnfEAIElIyoE3s4IAaKQrcjxKyN6DBZ7UqGbgTFSZ8xiz/WrMB51B/VTPX6uikdRDksH+X82TaDweBgDISZ+RwOL/fz+EtGz7oCUt2jiEKcO+3nE2dNX+Omfvg/0AU0AwnKP+y/e4fWvv8LDNzfw6w5xG9F2yVsr7AJ8BLZxj32IaNAUkJCedTFkb7s+h3YiwrU+wWrOITvOZAe9BGr5mD6I0Q3hw3EMvEaXfbW8g5s2mE89ep9CE2OMuL+/x3w+N4s1h3ttPN7+bo//9P/8FSb9GWI/Qwqjy8BMFzHxDfocNud9RJqaQSG2i17nWHlKDdUYI1wYJ0G1iiD5zJ7eWWWxC4B7N8V/+r98g5//j97Dk08bNIsOEQFtOx0ph6krXeY+hxTG6XB2dv6oMAOAvu8QeobXRTTOo+93ozBJbp7q/WCLh8NmtU6KVXuYXyYBcyg3nqbZSvRhjgLmX/MZJLFgHje09IzD3V1SRFOo57g/GpoxWiFxHHYJ9tM50GPJNwzbTEZ63ycA8u7uBne3Ee+//z50dav0snROiuQW+32XL28YxNh4biLSxS+Jh0MY8hlMp9MD+aohB8xBFkIondjv9phPp2nNOQ2nGDavdItPxGw+AaLHarXCcrkst4/p/LNstxGz+RzbTQrB7PuA+TytgbOzM1xdXuDVi2d49+4d3r27wbt3d7i+vsZmu8Xd3R12XY99HxC7pJDv93s0zmMfIyJPrxzQoYHvA1pEuHRJM6btBKHr4PMtzb7vEToPbPfY0tCYzuDbFhdNg6bvEGODznVwvkHK/MZ1CTgE9H2SSREhHwQ5KBQ7nBZHDGxP3o7D9iQygSd66fnh2fRc4jO+x7XY92n9eg903aCUA4CPpZEMGI6VOXtiPtrzMLRjlUmbB0x5Vg05LR4eLla8qEID+Ax4uQTbxxjheJEP3zeKrO6jqb4IXn6ka1/Bt9RhCTeQ23eVpqP+yfchHD9M+SGF/bfePEpHpSffsSAB+2wNAZbD/h/mPrPzRrpynlksEGy9HWpGjvKi9sPSWQ0u1qVGqQVdVbm3Cn+tH8foq2OzBsOxcqB7Gfprm7aNWr+swfkYrZQf9B0LDNTq0H8WTLF9PTammixgOwqAWD7T9y2QUaOpba/WTu1ZSy/ud7W5tu9pe8pLlj8tbS0dKcMJXtn1SxnGHE1Nk26JPDs7K7odwaJXr16VcRAk4SGDpjZhiKjaFHqhlfK/pm9REEjlJtu0B7tc++pVrPRW8EgjFCx4wb7adCCDXnKYskAPSthPK4M4H9STrc2pRflU+6egqdKO39XsWP2btKXOq2uSc1ZLN6AgUwihAGTKm3aOWbd9l3yvvMbv7Vw+ZkvY322xY6itZ3vgyDqt7vHY73b9azvKJ7Z+rcu2qXKw9rmOnX/r2lE9irxDcFgBOpUPymfahvI5+zSZTPDu3Ts0TXMQ2q1eibqmlIfV5nHOYbFYYLfb4fLyssgHPfhV4J4/6eVqeUPpy36TDnooxjFq/3Qfs7xY2/OU32tyRPOv1Xj0+8qP9mCj55pFvb33yHeTIdnGHCzQ9/sRI7Rti67v0ult32Hfd9isHVZ3O7TTBtNZi8nUYd5c4OXzzwDM4BuPaMLnnKsrVTVlQYU33yfdQgQQgoBGnHAe6yPX48cglcB9/CLGKJ42uQ/ykoODzzdfKuBmGU0Xv1Vo7WbO38upHr9zKMBETVkdKUExCxFId934OW2Ln1uBVitWcDk4OO/QIwI+0KEDWExw/vw9/Ox/+D7iPiBuA3Z3W3z+l3+Lz//mc2ATcNZNEHYp/NaHDq5P+cWCj0B0cC6FL7oYU74zB/hdKGBnhEuf8XZaB/Qx0amBQ3AoSb1dTLcPJgMo9dlRkUPyWghhQO85B2XR7jz8for/+n//32G2f4VpeAoGgpb5cUAMzD/BE8l0m+SQm+nwBMZuXnxGjaxGhJpF+LU0TXM01lw3lAjA72b43Z/fYrVp8PLnU/hFi67bIcaxV8nAI2Ol+TH+AMbu5n3fY7l6gENE00xHwpNyhN4mVqHf7XY4Oz9L4Yf92IsmRsHGA5Dyog8bzZMnT0xumQHl1o3TKoZnZ2eylgcj3J6w2DngCWlaeQENATXeXNt4NI2CCGlenj5NJ8/0cuN8lbHEsY8m+66Kok65Nf5VHrHu6+trnJ+fZ8+540ZlkWEhYt/tcHd3i7PFvPCS8iTlFnPA9GFbwBaeeNHA4AkWczFMp1PAdZi0M+x2bc7pFrDZbHB+vkDbeszmEzx5eomPPv4Qq9UKD/drvH37Fu/evcP17S3W6y12uz2WyyUeVqthX+h79PsuecB2STa2zgHB5UtQstK/22HStGg80PgI5/YAIjo4dNs1fPFqbeHbFu10Ajdp4ZoW6bzJJ0/cgLLHubxP+aZFk9deTYlMP+vGP9fI2FunZtRl+SZ1DXtK2vfSXFMO+iKnkC/ocLlm5RnKHlUM7V6kYJaOTQ0i9qd2sMC2lE/L931atylMPg7e2rof9QExr+/hIDB7YnAc+ebyGp+Xf90Qnl/bZ1mOneD/IYrVx2z9Oi/6uTX8LC3HvDYuNUBFFfvqvMRD0MnSqNan2rMqh1UHVENFP9f3rQ5l/9lnlKdrddh5tW1ZI6L2vH3vmGx9TPG368H2VcGoWpu138fy4nBvUF5S+V97RvnQAp3U1Wt90+gA7Qs/U/qyfQuq2L4CGD2vddr+PkaX2jzb9aVzV1uf+pmdazX4SJ9Bb0DJsaa3eTZNg/l8XkJL6aF1c3NzMDcARsC8Jjiv8buCL+yTelIpaGB1CgUEazcAMw0MPXRqhzP8qWAbwXuCcNQPVAfT39VoZ53WK9yCGho1YsEAfq4y5JjMZVFZSb2MY1Caqe5rQQCVs9qObYsAS61vNRpof+0ByGPrRduze7WOydLAtql/a19HF+JJnZT5FlixNLayi/VYG0lpe2w/sOtex0wA14I9KpPUljnGQ6qTaLF7lfbT0pLtEvy2udO4JgnAqp3DOhXk5/cEWm1f1N5ixKPWTbnVNMNNpVrI55wD1WHtPFsZb9effmb5vUYvS7P/r3mwKeqoTM9/KUwCCIFuxEPy7zQxLZwDnGvgYgPskyEK57ANAVsX4F2Htu0wmc4wbz3ee75AzMCdQw9kZd8hK75GqFjFl9/Tm6KYAk4WPe1Tq7y4HFLK9pD7CyMEBjwLDYGcut0DIAE4vRhJVqGwzJHomwy6UscxZUBDNQUusycKdsMfnh0rUo8VK3SswNLNeCQ8iDlmzybnE5gFB4TMma718LMGiycT/A8++Sf40//ZP4HvWrg98Le//Dv89q9+jfXbe2Ad0HYe/a4H9gFN6OGjQ+M8AjLYGZMnV9M0ea59CsnMXg0NHFqXPkvmbfYgg0Mf+kSbALQZ0G2oxIvCUHgmgzfbu4Df/sV3eP2rDRbde/CxgXeusITL5E4kSrckJpolXk8/+xEP1ISC8neMQwhdGmvd8NZ3qeTY+dNnRu/3Ed2Dx1f/vsPMn+P5LzzinN4e5OMoYHUtvM3Wz81mbESvVkv0+w7z2bwMxa4Tntg6OHR9VwC3i4sLTGbT7O1lN2x66Hj4ZjAEQgj5hNAB8Oj7cHDKyWKNguVyid1uh6urqzze8cmgnQOtbzqdYDKZou+7QrYEgAFJtueLIpzPiKBD4xu07aQoZLX5CkbZYrtJGU48p+udz3IO7Zjn8zlevXp1sPFqmyPlLYPSaUMd8itYPiB9U4hEQEoPMJz48kYrtqeKd4wRIe7Rd77kjVit0gUJZ2dnZU+YzdJyuLy8xPPnHd5//xXW6zVWqxXe3d5it9vj5vYev/vdl7h7WGK72WDf7dG3CURPqfciYoiIfcrO2PU7eOcwmTTY9x32XVdkg/cOHhF9iHB7KjBA3O3gNx6zSZsuT/CAaybwrsFsMgGaiC5nk3TOIYYOQfnXHXqL2HnQv5U3aoqtnTs7h6n0SJdVDCezCQD1SF6OGbSWXIgxH1ipoVDWURyA38ED1I+UOjVEVcGzoX3kKTt+1UtqQM8gTz0CenikMIzGRYR+j+jSrbCDx0Iaz9DvDtwyvW/Qh+4gxDX1nbRF1n0OgRnK3z9ksYeNSh9+bz9XGh2jpRadH92f+L4FDNSIVI/pmpGi8uoxsK3m8VgzGnV8qpeM5JUbg0QKHqhxrnNs6aXPsI4azY8ZeceK/e6xZ20btq0aLWv1HeiWR561PK97Zq0OW496P6vxRloCYxBY67eeIgr8WD6y7av8tPz6WJ+t/D32rH5neesY7/CfTbZP8IzfnZ+fJ2/rHDZKry3nXMmfFkJKar/ZbAqNqC+wnlqidH5H+mq4JTDkMqLHmxroChjoGtC9hevfhsQqwEcPG5WpCpJZWnIM3Jt0jnQMrCfGwRuMc69eyZoPivRXzySOj+2ow4nyrV0jlh+Uj9gPDbfleNhf5ReVRZbfdFyP8SIwgKEKelo5pz/teFiX8pCGNfL5Y/21ddc+tzJL9T9bp+qJnNfajZDHZLfK/2PrvNZP6rDK8zb3mt2DLC9YmWZ5Vz0oVV6yTg3JtNFsMcZyWQXrUp7SvVZ5vAYOst/7/b4A+LyhtjZfXE88LOeYFMBiW84NnqqcX64xqzfod3Z+VG7wd23L2nC6fmvz80PKjw4RBZzpJAA4+MYBIEGAlIS9QeiBpp3Cu5jytMUGDSZoJwuEAHR9D0SfQ0cdnIvYuw7bdYPzlzP0IXkcxRjhfDI6PXwGSuqGhfbX0VAp3ma+/D461ay8HyNyiGNEXxYHhpNwPudQLjHVD3VehglDvrhxWCSlr2YBDoZqzB5TY682l+tzUUI+46Fyq0xzzBhDriceQQWPKeq2HFMYfZNuqSRUSrqBf7sB3uO40HBcHvBA3wZgDnz2L36Cz/7sHwAhImx7vPv2Db745W9w/9Ut4iogLvfoNn1yTcuhyQDQI8DHBPC50oEhtBQuedXFmG6a7WMo/aHHSowRftIiYjhJSbTNYJ2bYHcT8N/+H/4T5vvnaPsZnGvg0Qw5+JwrBlear4gQEyCdqY2+HzZzYHw7aG2j4Bx3XZcAxTh+zipx5AUrUB4rzrl0c6gH0Dn8+r+7xWL+Hs7/4QQxrtNMOqSJS5RF1/UHQvSwLyHPQ19kR993mE5mCK5FjAmwoFDU01EK49DHfGobsF6vse96XLZDOIMNm6IC4JBuO4VDuTknJd0fNgBr6KkSz7qp5IK8CxrV9dOvVGhE9MXLVzeNtLnG0e2aKjdubt7hyZOnVQDTKngJTIvldq7F4gypOwN/6EbKTcm5sfFkFd7HQJ4kd4HpbI7ZfA7n4gEfsF3+Pp8Piqv3Hs+fP8/9GU7gMjeWNQdM0TdUTlrs92kjZojvCIwLaR0tzme42l2i6zu89/4rdF2Pru/wk88+wtev3+Ldm7dYPaQbSperFbq+R7frsO96RABd6OBDWsf7fVZknEPTh3TzqPPJc63rk8ds06BxXc4B6oGQZEvyQNyhbRrEXZMAtMajbVoE7xFdvnmq8Yi+TSGlyfU53YwqazPRtS95Rq0XWG2d19a//T3NBRAC123a93kLKut2GNZJH3jLJ/LhQVYIAfh8+EGaReFXPZ3XdcB1bvlM+8hi14EqSXYtJnr5sq9jyPyJdjK+jTJS6W0dQgwFQA5xX/pHfuv7Hi74sgacj2XMAIocY16SP0RRoKIm+x+jE/+uGWTKI2p01OqryTpVZHVt1+awZszV9A19To2CmtFUMwZr9AAOvZmOjemYsWS/t3StGVuPjVM/r+m3LNrH2thrxkSN1nxWw/iO6Ru1Nsj//PsYzZQW2q4+U/PasGPX56xRpHu95Uflt1od2h9dT7X939apc0IdQudI84bGOHiEKKjG/Gj8++LiotAsxpS+YrValf70/XDzH3N5sV8WDGL7IYTRhWHqTWgN3BjHt24qGMU+8KCAXmO1ObN6lAIG7IOl8zj6oL4GFAjgmJTnWY/uI3xH5Rl1S4Ja/FzXrV3DGrKrRQ9XtS3n3GhOtO92jKyf4IINbRv0xPHeqIAn6WzBaP15zLZgW7oW+FlNxtl1qPs6UPfCtfXa9WXlr6ZyqNkx+rzqqtrv2hi0TbW9HrORbP9Vtlkg6Jgc5/P6rpaa/FVZeZA2w48PiNT2IeA3irQSvmb7dn9Q8E/pTj5mZKO9VVrHRZBTc1GObehhTfKCAfINZY963Nk9z/KdBdVV/uiao9yxe70FiX9I+XEAm6erYgbDMiTinTI6gBx+6dAkD4wIpITlyQMNjm7gDfreIwZeAhCA6AGkNs4WT9PguAAikgeBS+1EUegBLo6EWhC0Se+l3FoQRreLtr4QysjTcxE5XEQVJSrn4p1UAaoKsyK/n/uY+mwNmtx1l6Eo9nWIQwUcSuL96DMI2fUj5rfKG4XG6PsweOs5A8qxfa0rxDgQxmyiUQUKxmOIufd9oYOTcQ1lgJj40zJ3CuN0bX5o0uDZ+Ss8/9kruM7DbSJuvnqDL/7mc6zfroBNRL/co7vdpv71uc6Y+uiRwvD6NIAyfyHPk3cOrXMIEQgugXM+b0i+obdZ4scJptjfNvhv/o//DpPVUzR+WgYSzRxzLLr5Dx5U480MGHuPHtYzCOGYw5sb7w8EtW44doPTzcZu9vpsEWwu0fA//N9/g3/1yc/h5k2aPRdGz5cwPqnLKl8qN7wD9l2X8qD1EdPJdNR35WPlNd94tJMWwQETIIMr49MLpS1ABTp5k/Z57fCaaufGypsqbLZst1s8PDzg6dOnIlfG47Ml0SjRSW/eAcahpZq8l5v2drvF+fk5nj9/jq6T2x9Nw1x/2qcYedqTVmjNM0PeRgJRXFHgNQeLKr4qU8eGU8yAjwOzibE+VQQTr/jsWZiemc1mePXqVX4mXZrhfUrYbxWWphlCUxaLBYB0WsYQGU2GWhI1T1wKz44L9CF5z03aFq/ee4X1coXVaonb2zu8e3eD29sHLB9WWK5XWG932O13iH02OvqQDo8csI8B6c6DCBcCHBJY75PIRuvTZSp9zkOHfgfvPCZNn/JJ5tuBvXcFqAs+HVK17QTBe8A1cIgIPoWdcq6GFAZJRo/kv/zkvjnMsXj+cO+UeVWerCnfnGf+zVtYU0jpsE04ADH0CPlSCfJk+vUwl4jyFdupgSPHABOrwNs6rKJNpYuKlc1BUmgUIgmZ95I0ONKu7/o8F3G4YAWuAI5KtxADvvvuO/whiu79HJ9VXu3nxwBYS0ertNfAF1Vglf7aP63b6ifWOOI7yg8W1DhmOOk4a/tpzStZ39G/a98f2wsee8fykpYa3Y/1zT5b0/WsgVRr3xqFrNcmSD/WttLB8pQ1LoHD0FS7fvU7NSDZN93/7disR5vlDQAlbFLXtW3b0o3fWeCn9j7bUpBKPUzobcbUFpeXlwVoa9sWT548wWKxKIYoQXgg7WW73a5cArTdbkeeG2yPRquCV+rxpyCavl+TnfRm0nV+LL8jvdJY2L7OXe2f5RfOjx6msc8MMRsOy8apN3SvYn2kheYLs/otZT11A4at6Xoh7yqIpF56HKt64zMkl7RW24uXNmkOPY5Ji8oZhs4q2MFx811dw1pIG/ICx87vFGTV0EHSU8EaC3zbtVhbEwoO2vRSNdmlfKOf19Yfx64yrcbP+tP2XftvaV+TfdZu0nasXNO9za4b0qZWB9eyyjwdt3pv830NAVbdh/3hmrC6jd0/7SFEjW52T7HOQtqmfdbmZtM1vd1ui5zj+qXcUfljgW5La6Wl9kH1AbuHWnlU45nfp/wogK1tZsmYzYCa80nZjJHKZYsURtLkm72yawgA3iIaYvIAmM/mgE+gV4hA6PPJQgbZvHPJYyj0yapiiTk0z+WQT2WOfMLuXZMUXzcAU46gX0WxOaZQNY5GiwcQcgJ9V5TqGOMAKPgEVqX3K0pJTG8Wxss9G5lAWf8uC3no1Ajoika3cwA8HKKgsI8pYiMaNENi/xEjOQUoDbDDvjlXwtB0TOkzAdJYh3clIbWJTJPQyQS8xdwfHWpWBUafJ6MlbyZTIE4cnvwX7+HqT16lF7qIN1++xpe/+i3cfQe/cdjfrrB89wC3B5oAoM+ejnl2AVc87VJfE693cAjeo/W0rxJPhQC46LC+Dvi//ld/jnZ3BRcc+tijcW16hp4lCRk9EBIcu13UNeFmhSGFdLkB1I1P6Wo8b5UbVUrt82VeckkeKQkQ8f05vvj31/jJv7xA34QC0lKIqtJsyyBYB5AthA7ddod+15erwbUPo5OuHC5OXg0uomk9JtMMmGTDlu+OFM88B3AR3jt0XUAIHeixpYqN3UxVMaIi9uzZs6KA2NDL2kafPvM5f91hAmedNz1FoTJ2c3ODq6snUJC20Io/nRsA3jgYGRcXk0Jv5QdtI7UPDAByusjj8vKyAFh2bCyDchfR7Tvstpt8c9lYYWFJtB7filTblJ2jYgk414AhycrPzrlRfjZuqORxPcGPMQJ9CnHpQff2rFQ+ucRulwyZ7XaP1XKF+/t73N8/4O27Gzw83GO92+HhYYntJuW36UNE6IbLLCgb4BmqGbHPWRibJrvHZ8Bt12XPt6bJn6Uwd5+Bm9j2CG6bhKxLt5n6qUfbTBEiEL1H9A28B2LMQJl3QPRl/0heZb14YDtECA2R5DLXltJVf1pw4ZiiqfyRfoacHiCW/Sb9c4jOwyN5+nWhR76WBjEENOK9a0E4GjraD/V6qylJ/L0G6HEN2zKSuQHQC2icc/ATh4CQLk+KGRh1YdSXGGLy9C9oZ2p3uVoetPdjCulpFfuaEqrvcE3UnqntH9pW7TBG267VYZVx/dzud7W9kXXW+mnn+xjf2r3RtlNT0GtFn6/tc9rPGv1ZamvqWHv29/HePOzj1kiy49B+Uxeo6QzH1re+b4EqNQitoap95l7KeuwhmK3Dzof9qcYqD6f0PQAH3kNKf12v/Hu4LTyUsCoN41M6EMTTttiXs7OzAvZMp1N88MEHZa76PiXnn0wmBVjTQyHe4Lnf70feJyrDFATkmFVXIR2Vlhyj9RpRWcpndN+080MvfhbvffFkq+VRq3li2WKTu3Mc6qFn9Wf1VNHxKZ9avlX+t+Grdl+IMZY50vZr60vzldeK7mH8m21Zjxq2p5EspEVN5isIpzqr0pw05dj5vvJuLfKjJte1TvIYwUo9TLXAhYJ4Wp+CvxZEs3JW+187+NF2avuD1mOLrvHansViZZRdIzUZqu9aHrDFgmR2ju1c6VpQnZr06bquRNwoLezvNXltx6Q8Wpsbuy7ItxwX150eHlDOkQ9ULltvNK2XddbkgNXzlP42nFnHf2xOHis/8pKD9DNkr4YohqR3RPnTzWeps6LQIBtsMaBtJzi/uITzPv1zGdiIDqHPLsgx4Pr6HT744KPDBe20TxnscigJ5NPzsQBwziUQSUW4LkD7u3Q6e14AgE/hLCNFOxupMSL0fWmP9YyYUxe0bk4hGfgDpFYHIx5bpPp5TclTWj3GLKPvnPRV6+Q48vdR3ov5UYcx8+pYIw6F5Pf1iQtDhWfJL8Vxpy6n7nqH6GO6RGIKvPj5e3jx01dAFxF3ATffvMGb334LLCM210vcf3uLuNzDdwlUczFhtb7QtE+grQNC49DOZwBybqaYeGCGc/y//h//Cc3uHOiSJ0rycpNwiZDAgf4RQW3nS38qPezfXAfphYAQxpsff9YEplWcj/UDSNBjkwHN2APwDl//5ho/+1fPEf2uzG8Zswirod1BqQoSNuucg2taNE2PxWKGGMf9Us8u51wByXz+bLfdou96LM7O8ksYeQ4OoRF9ppUoxk2LJy+eVfNc2DWtysJ+v8dutxuFCaTnj2/krDeEvlxtrW1Z5cDOzxnHJ3VRxhSgOj1cPJq4GW+3W5ydnY/GbvlwrOwOitXTp09H18hr/+xnfLXve7x79w5PnlyCYZ7H+I3KtBqIzCloFUgLYOppPRU8nsaqgqZGSwgB6HMekgLyDs/NZjOcn5+h6wK6Z3vsdi+w3+/xsFxhvV5iuVrj7u4Bd7d3uHtYYrPd4eFhhfV6jW7fo5eTwhhcCml32RsshyC3PssHlwHjPh0eND7lhfQZzPcMyck83zgPt3OIzS490CTQzXmPxrVA2yKGBFy5tsm3ZqPch+G4R3o/OsxIqzP/PzERuOVG1MOC9ffH5Irdb4dnXDq88g4x9Mnbz6V9JDrkvTV9WNs7bFuqfFnD7TGZW5OPNUUSGNbckFcOQEh7YFqKIa8/s89lssO5dLN2BMLu+J79Q8oxhdAqwLUxPqZMWsWbRRXe0RjlndGeLUaCNSItfW3/9KcazMeeteOycpw/1Sio9UHHXKOP1b1qfFMbk/792P5be/6xwjGRN3VPsX3W/VkNNwCjudGfNSBKP6fxVPMSsMCZgj4FqPbjpNLaV7ZLgEzpobK/Rld9Rvfq2pypt5Ll3RhjOcBhcm6Oi3sN86VNp9OyV/Mm7CdPnmC1WpUbPjebDebzefFM4g3mHAtznGl/CVpYOlkAQMdTWx9aJy9BUE8r1YV0Tjlfuoa1XuvxZENGySM6LuUT7r/ODTcoAhiBfwoaWhl0TKch4KOAn4JmBE55aRlpQA831SPoTTebzUr9Vr4S4CVPEyDVfal2GMQxKJiptOea0IMlnU/LL7rXWpmknyu/kL4K9LLQM88CX1x71kNN8/mxb/xpPeXsc3ZsVme181yzOXS/qNFYZYaW2jri3xxzTW5ZnUNlqI6h9kytH7bo+wS2VW/WCxNYby13mdLc7ttWN9C5VprU5LAtdu+hvcO1vdvtSj5k1dFtiDrXrwUbrVelyhE7X7W8dMf6quP8oeVHAWwjZEuCThiWFuXjGCOtPNAeIojUti0WszM0rgGhr5QsORkUISbj/fr6Lfb7riTIZr3uoB+p8j4OMbdABtyKrcf+jN0FLYOMxmjoOhJE6QNh5JC99gaGGCt1pr9IynUEUj4el7w48D0Tapm10OSIYjmi2yOLYETX8n0mnpd6+liMraHONMGDYeb07VJ/zYywAj8MLxSeitkgqSm/IN9Je2UsLgFmKeo4Ai3gZh7PLz/Aqz/5EOiA+7d3ePvla/R3O/R3HW6+/A7rNw+I24AJPHxMRl4PYOIcggspN1zok/EXHXx0+O1ffYOHbzpg3+ZbZB2cG04OAMD7scef5T2rDB9b/LUNEijLLY+/DqxZhdkqqMf7hmGN67qJDk3XoI1zdDEZ+9vttngP1ddYLKGAcBEODZyLCAF4eLjHPLuop/C3YSMkjYB6Xo7WN5gvktfbgM2Naea9R0pRPxi7TUYxuu4wZ0bNiONms9/vsV6vcXl5mTcDtncIJNjiXFIYJ5P2QDHjhlbLZUIlnApgYnI3Wl8OOldjPrAnOFaJSG0cnrhS2dV3SQutZzTuTOAB1IoHzw/vNWgaQjzICZypzPd5mGMeVqWHfWmaBrPZDE+fPi1/6zwQEKHhgja7qsehjs1mM1JAptOIvp9gNks8fXV1iT5farBZb7Fab7Bar7Hd7nBze4ubd7e4u7vHw0MC4Xb7Pfp9PnWPSHm7+pSRa9/3siPk/cJFND57dDmgcQ2aNkln5wHnG7SuzwpwDvf1Dq33Kfeb83BtixAjfNvCtW05/PC+RcpJ6VKetpgu1hh4BYBrEEMKk05eV74EjraNR4yD4qIKmeWpwnche9PlcijXkPsUC0+nOpH3AV++874VjMqlvrjhHW1DPQceU55VEaeyak/qVcnUcZbcNTEdqhRIu+gdY0DSaxKE3Ad6Ovx9i86Dghd23Fbe0OC1a9LuzVpHzeiwe4vNt6gn6Zrzx/a/Ni7bL/bbKsr6rJVJdt/Tea/JQds/289af5Te9t3DffBxBd4+p3XXDDQWnV/7nm1T16/qALU+2jFbrxH1suH3NTrp+2zbAgj0FmMflc803Expr/XqDYuW//QAR4EqXQesl20RlOPf8/kcs9msHAJNp9OStkFBJ4bbnZ2dlZxp/Gy9Xh8AGBxrjSeUnnYN8H29cdvSXHlF6c56lE9qOZoIuNU8APVZBegY4qaGsfKa0lv7bL1RtI+sW/mCkQQ65pruxDnmXHKOm6bB/f09/vZv/xYhBDx58gT/7J/9MwAo4ONyucTV1RUmkwlub2/R93255ZwyrbbeyDsMD6buqHyp+aLYV5VXLDr/us6UD0if2pxbwJvfWfmn/KAho1qH1ZGtvk9QUQ+RWaeCUeQD/mNd+lP7NrIXZTzHnrdrpNbXY7TWYkEclSmUV3V7Z1jXVv+1hxw6Bj7DunXdKN/oHKgnlt2feQnYcrkchSZrW1YWWVrY8dt1a5+v6Q+8UZRpZzabTcqbnXM4cm1b+0tlkfKf9v1Yf+yeWHvOylU+UxvD95UfDbANp00SJkoABKJsQzdq3jAYEBHR+BbzxQW8a+HRJNAC1DcdHAKij9hsV3j79g1m849SEmgdqHq3GI+pslD0cSDlXneD8ltVbGK62MBnz7U0lnF7jlgdIprifTa4Z9vNJ/Ur5y/DoTAr9IuxeE1970wcEQTKSHaBHFOW0zz6Qhw1YOyzvvE5B026LoJkLjU7gmSPj+GYAB0JuXgclLO/l/oqY3Uy58jzGZ2DmwIXHz7BxQdPgOjQbfa4/eYam7dL9Pc7vPviDa6/eI1mG9FGoEE25nzy4owMP4wT/PaX32L3kFgs+rGSX06snIPzvuSqUxromGqbhn2+Rkv0OX9QCOXSC8uT+g4VDruZFzCt9q7ZkGKMwN4DXYMwAXY50W6N/3R83DS8d4n2fYf7uyX6rsueO37UT7tp6EYTQsDDwwOeP39ePHSSt2S6QEA3QZ/BNAcgupQfbL6YI92IKHyCsZLDtm1oA08wh2cGAMJ6WgwbZo+u2+PJk6tSj57WaN4WVRCAdJPnzc1Nfpa8LXQWHs8TVvrnfYPF4iz3Kz1d22S8HzbyGNM4qEjq3OvcHpM5k8kUr169hHMeJi3IiG6DMcWwnGkB5ZxLAYPRq5fCodHHuiaTCRaLxehUkd9Z+aj8FWMst7HpdwxJ0dtMiyJ73qULEPYdIhL/Pywf8PCwwmq1wbvbW7x7d4PVcoPVaoXVco3Ndov9bgfeUpv2nJSvrQ8BMUTs+wDnEvjrYkQTPRqkywac9+jQwedLf1LUeYPgI7wLiDHAN/lCoH26bKGkc3DZAIZHbNokx13yzob3gE8e5cFlGNrxtN2jiT36CPi2KZ6RA98poIscnlpWEpjXwHufQ9ndiP8MVxReSN8PO0oMfZKjiHDwCCCPRCAel3fKb1aW6hrVNX9MzmrdFgjQz47JcJW5VHz/EEWVbvLosX5YDwFdR/ybP+16sfXYd20dNRBePVzsc1pqgM9jfx8zUuzzOhZrOB3bt7TUQBA1gPRvbfOxOo+NqybnajSvGSXHwBpty86LeuMoAAPUw28s0KnGoOapsnK6xm/qlTMCpo8Al0obGoxKA91H2Xf1qLE/uWfo2Bj2SRCNN5XP5/Nya3iMceQ5wqL04t9KS+oUOnfaV91vvPejiwa43nXe9Dv23faJdNOQUoL8zO2m87Xb7Q7AA86znUuds+Uyhb5box8Ywj3plVJ05PwM2yQ4pSCbjlf5i3Wx/bH9NciwY+vTe4+/+Iu/QAgBi8UCb968KRdMdV2H7XaLFy9e4MmTJwDSLeX0sKdHG/lHdQg9WJhMJuX2V869AgNWvvKf8gD7qvqNPhdjHN0Eav8pP9iDarsfav36LumturiuO+Vt51zx3NOQ1Nr+wLosAGd5l334vqJzrDxWA5FqunqtLZU1VibrflYrts+kk8op2w+7p/F3vS1Xw5rJX1q/8g/XvtJW5ZOOwfa3tj8eW3O1PgMocqHrOmw2G2w2m1EUj8pMlY/2YPCxNnQ8wCHAZp9TeW/ntiY/v6/8yBBRUUQjkoKbDbW+R1Kx3dgllQIm5HDKtp0gRo/JhDfuZSHiARccXDEYe/Rhj9/+9jd47/33k5eJ9OP7FlmM4tHhMvhXwubqOWTSSEYOW8M4HIqXAzjBMWZlf1x+H6XOKvgNJxO8g/HHFWWcmkJzTKCw7TSemEOIpN78D5kWadB+dDPaeOHV+2b78EO+f2zOH1PAbb1O/hczzwJAe97ixc/eA/5BROwCnl6/jw/f/RT9ssP2uwd895++QD9DvkU05wKMEb/99e/wcLuFizNSadRPFeY1xVgVWjsG7fv30kaeqyn2+izr0hs5h+eTYZ36Rt7J/OLGKFQIAT4C6+UazXk6nVDFqzYXBTTJydr7MORKm88X+Xc3unWVP+2pC4XxYpHeiy7m0O20NtMpH9tmP4iwAevNGmfni6pgtnOip4xUIK+ursRTJoF0upHb/gPAZDLFbJYUWoYNqCLJEBSOV/vQNA0uLi7qp0ycKKG1KlV3d7d48uRJfqe+SaVNZrzJhxDKNdxWvijvDHK/9AZN02CxuDwqs+2Gye94wr/ZJJrS029oux72ws3+7OxsRD9VJKiYq7LFz+fz+eDdlvtDJZi/az/Jfzx9i/ECT55dYr9Pp/er1Qb39ytsNlusVmvc3z/gu+/eYLvfYvmwxmq5xGazwa7rEPqAvk8AWyyGVUKs9ttkPDjkHHRxD9+EnMbUw7uIxtETDPBdj8aTTvu8JtMtnqHZA3DwzSSFxHtkYM2nsGvPhNUBEQ1cOwVcyiEZEeFiAv8Qs/Ln0q2aEN4IfbrQKHkSJwCMOfNSPQqgce7VgBp7hwM532YMCRxERLpEIebDhJhF76Dwe+8R+5Tz1RmgQXlR11hNDlve1Lm3z7NeG8Jl66AcI3D7hyhWSWd7dr9Rz4TaIUutvzo2/e779hq+ZxX52lzYOqz8O9Y3W5e2d6y/Vn5RRtQAQzs+uwfV9tdasQZCbW5sPVaPe0zP0XeO0bD2Xs2LggYbn7N7ugIc5COtw84BDTGdf51XbU+NSvKnBfz0RjoW1lNb0xbQVV2CXqv0RAJQvNLm83m5jXo+n+Py8hKbzab0n3sjwS+lOQEF7jXOOWy322LcxhiLd1CMsST15lh0b9EDHjU8lUZ6MMfP1di2xiQ/o9cb3ydIz/HYA06+r7nR+JyuP86DBRn1ez0EUP6aTqfFACefHdN/WYfyG/tnn1fwkPocZfXl5SX+7M/+DP/u3/07fPfdd7i/v4dzKQx0Op1itVrhb/7mb/CLX/wCn376aek/L51i6KR6V3KsmqoixiG8k7rGKC+smSP223omqjywc6y0ImiivKK6j8o08q59n/VyvWi4nspV0oR0jTGlJeGYmLvQgoXkPQBYr9elD3qTpPVatXxsS03ftPLssb1eZZm+TxopT6rctIewdv9Q+aTv2j6yKBimck33BtKbn5PGnCfOw8XFxehWWgveWdulZnc+tjdq+yortK4nT57g9vYWd3d35XkLfHI8XNsxpjB2zTVn90X7t11L1u7Quanttcd44/cpP9KDbQDURifWGbyKzKUGKnsNQuhEANKwBtpJg+iTpxqTyDsfEtAWsxdb7HF39w5//cu/wj/+J/8UjR+8Q2qLyxILAHKH0+fRwWVwzcUIhh2FEIYQFodRCKpz2dxPWjqQ/ZaSfeH4CiIwGCBuAAfKA6kyxDh4l5T+wwjTXF+GGarCwy5cR6MlewX40RhyK65gSUMJvTA4vQ8dEZXSfxp6zrlixAtcNfThEcV+NObvURRrpfbusYVfM+KVL8r8k5+dS7zsItAArvG4+vApnnzwDAgR+/UOz//Jh1g0U/gXHt1uBeenQO/xu7/+Crfv9jj3c/iYZkHD4Y4J2Zrg4ue62VoPCS0USJOmRS+bW4zjEz1LBz1xcpkvkQPBtK8hhPwZ8riSV1MK6Xbp8oYIxF0HF9zR8AQ1YgCHphme877B3d0dJg0DqHJIGManbVYJoDJaQD0kngxAyYXkXAIQYoywHNY0DZ4/f44BFB3zMjcbzb+hG1wCuripJa8qe+Jm5zgBXTd48eJFUa7UYLO5xHTT7Psev/vd7/Dhhx9hyLFnNhccrok0X66AToA/WB+PbVKaoFm/OzbGVH+iwW63wWx2Wa1Xf/LEmrTuug53d3d4+vQpAJdDIQdwa7tNJ/mLxRzT6bSAfzQYBoPQgXlB07sBaixacEFPzTlf6hWk61PXad/3WK1WOLs4Q993OSn1HpcXF3j+PLnCd12H3b7D8mGN/b7DervB3d0D3t3cYLvd4fU332K9WmO722G/z4BbvjTB0YCFQ+hSWKfL+5ZDSACYePV5BDSNQ+OTQtI2DZxHCh/tOjTwaJpQZLlzQOM8GufQOYd2ki4scq4B/CatK58uYYB3cE0C4lpM0iEL870BCAjlkgQ4ekR3QIjD4QYXawqiBK+V6XsaAClkHLnGGJNMjiEkmeN9qYuX9IQY0+2tCAmCC4lOiMif+WGJC7uqoReyTGmaJsmROA6lUl5RA1KNeKukWb1E6+Bp7h+i6Gm4GkUsVf0IYwPKjteOwRY1ECij1Nizxpm+UwOz1Ngb9ouxAqx0rI1P61IDSE/m1RBSWWDrsXVag8s+Y40lW7cFGmpKfU2ftT9rc6KGn35f65Odb31fx6mGINeIJuIngMP55P5FI1zbVhqwPfLKfD7HZrMp82TfY/1KT76vBhTHoqATC73QKM/5O/cOXjIwm80wn8/RNA3Oz88L8LfZbHB3d1foz7HzJ+tU4zKlgZiUPrVti91uh7Ozs5K/lUAS66RhaRPtc33pHjSbzQ7GatewznvNW46fc5zkH+oes9ms6Co2pJNrS8FTfm77wzr1uRqAHmMsuegIetZ0X+2Hpq9QuaE8o0VlNul/eXmJV69e4Z/+03+K169f4/r6Gre3tyOAarPZ4O/+7u/w2Wef4eOPP8br168xnU7TJUcSSsx2CVwC4xxjzPWmYaMKCFpQjGtL6Wm9/FR2au46rYv1MwxPDw4J8hFYZd9UZimvkfd13yHP81mGA3Jdb7fb0bplX9ie974AvnyOfdTQQc6/nVtre1g5WQOoWKfl62P2q7ar/Ka053xrmgq1ufiuXQ+2T3xPwTz+PZvNyhzxffIU/yb4773HxcVFCcnU8FIdh6VFja527N/3jKXf119/XcKrOT7V+1X2UOZx7pQXKVu5fuzhg4K4CtxpqekMOg/k8x9afpwHGwbwyRIudQhFYecbavh4TwUjG5LejR+nUeyRsk67iBB3+Prrr7E4O8fPf/azpLhjrHTZfiDnWIqsM//H70NMSr+LCTQKiPAxGeiThJwBiOhd9nWLKBGiQyLowWB3Ltc+4FHwTTISRpp8Jk/AoPjEOIBhRbgC6PshTJXFC3lVyQoM7YyDB4NpVQg8njt7MhgEXtQ6lMx2AdWM7dri1PJ9zzym0FshWiu2TyrEgGSqO2SvCD5Pw09Cl6KLcI1DezHF1cUMDh7L/TWWrx/g2wUW0yvslrfYb4Ewe4roHLp+i8a3IOCim5cu3mNGjs0RYQWZHZdzyWNEn9GklwfPmpOVpBzlpL3OZ7s3AmEM1CJyk8heSjlLd+zzGDEWUMfmTfvT9z2+/fZbXF5eIq1AGmHDu+qZwbooQB8eHhJY1fcIMaQQO7YfA/qYwMcYYwklJ4++efMGH3/88UgQK4/wJ5UBzeuSEvc/QdcRfEsEqvG13dSvrq5GCome4FJ5sXzunMNiscDLly8RQo/I23gxDkGor4+YQagZdrt96SefOebhoIq6AqffVwp/5Hm0NFHFwfI8+VY3UxrDgyEITKc0hht0XcBuly4X2G43WCzm5iKI5MmVnk8HPCFENE3qn8seVkDiNeYMofLDTTaEDgmwc3lcA4+kPC1bvFg8L4o2x5kU+BYh9Oj7gGdPnyLGgNADm+0Wm+0Ou12H67dvcXt7h3c3N7i/T2GmDw9LbLYb9H1A3+0RQgae+wAXsgGBkAGr9NN7n9blPqD1CYTrYtp/I/oUdhq6FE7qkwx03sMjAXGNd+j6dKOqS+h0mv8A+EkCqUJMe9zeN9lDrM1AZkDreeFCg9h4dIEpJQJCzrVKujH3mnMhQ+qxyGDvMo2RvelCUvwbn3OdOULxeR4zr8fAoybKvCx/MvCngLrLTMqftfWadIbh2It1AflgUJRuq/RH8x2VYyqN7969K2Hmf9+iQIcq6lzD5EcNSVKZobcAsu+kgQUPmRDcygQN2WKxnhtqDB7bw9Vos30iHbVvavir3NBcRASGttvtqH/2EMqWQe4c7tu1vik4aP/WMdt0A2qMKx/V3qF84V5ujQA14Al6Ku/ZcfB3730BC+idZo0Vzh/BJO9T3srFYjGS1eqlox5GnK/ZbFaMbwXjqCupbsr67CGP3bvVE857X0L82F8CaLzJc7FY4NmzZ4UvtC8hhJInTdcCv/feFxrbtA5KU45B54J5vWgEM1+Z3uSp87vf74ucIC3IN6T3brcrz+hBnUYpcH51T7e5w0II5VCN3nO63tQrhjxJAIj/jgHoquuohw3pxPZJSxtypzpSOVzOPMrceAzX5LMWTFT9juNrmpSD7dNPP8XLly/L/H755Zf48ssv8bvf/a7kXXv9+vXokKTrOpyfn5c18+7du0IH7QNlkvIjP+e7qtuq3LR7EvtMXlQ6altWhnDMejDENaqXdgBJpyEt+U/zvpGXdUy6duiFqGC97kW1vZF8wb7TA9QCUerVZkGRGi/perRyz/uU71jr0jqO7WXcA7UefZ604XrWg1/Ooeq8CsATlKcX7Ww2Q9M0Jfcy1867d++wWCwK/08mkwK48rIOzi+QwPJnz54VL1xesNJ1XckTqbqC0on0Vj5TGV17R/fEvk8Xnm2325H3KOnKPqrs0jmwYLzOq92flOcJxCnvW16gHLUelvaQ6PctP9KDbcx0zgEhyrXaJSQEeYA1AMUBcIghoHE+nV27HHYYgKRsoxjIiCl++7eff4HZZI6PP/4438yYkVdkA0GVoCAnpxEYoVSiTA/vo4SPRqQcSd77lM+JSmVICnX0XoDGMWAQs1LPwmTSAQEoSeJdCXVBAemSETAAbjnxeiSqPIw1xCzssiecLwJloPMBuKFjFzrFmEP+shEV5Rm7qVmlWcf9fZ9Zo2PUtwMw4Pjn32fg1wybmlDVZwLGIFvNQCJIS/iz67dAtwP2K3Tra/ziH5/jH/x0jutvlrh/7dCvFwjBw4eI4JIXSEgx1aNNxQJd+p0VFio09OR9JMQwbD4q1NL76dZTvuu9R+OblDsqACF0cEARKONNyw9WbAw5hDqFsvX9HtE7PH35Aht3nQ3RerEGU4wR3XaLJxdP8hwchusMQtQX4zhiOM3iBQOhwGfsZoRvklAZK2nJG6brenz44ftwRxKk203b5f7xpJd5OJK31uFY7abMsex2OywWs9F3/Mf2qGypQsibdhaLRVaYh7Gmucu3NyfqpEMKl0CRGCNev/4OL168GM0Ff2o7abMaj+PNmzd49uxZdXwHayXTmJvtmzdvcHFxdmAAKo86N+SAoZKpV3WrLGA9bduMNlx+17bJiKZxMJzQad91/MPn6ebfgOjC4O2V4+F9DEBsZA03KRenrL/5YlGUZgUSaLiNFbi0ec9mU1xGoNt3ePbsCtvtFuvNDtvtDqvlCu9ubnF7c4Pbmxusdzvc3z0gdB22+z126+1I8YkxZg+vBMDFCGz7Hj56oA9lH2u8yxcM5f2n9fAu7YNdF9K+FQG4LCdcxD4kMN0H7vEJAGubJu1lNNBdyqvovYfzLQI6NI1PoJhrgaaFg0MPj+hdwuidRx8jomvgkG5Zjhk4juQrny67SABpkkfcsx2SZ13XJ5nQeB7m9YBvRmvYHnIge3bDAX3s4BqXPfAcYh9SblJd396B4aiRBw4xADHfuIcEvjZ+AJ8o+/uYvfOyDhJiwP3yAYvz8a3AP7YoSGL5wh60AOPDEAsOaJ0qD62xpMB3rS9sm3JcvTj0OV1H/PuYkm9lF8fMUgP0uJ8AgwLP/h3rt21DacbfKbtVUVf+UvrafVW9TKyBovyqB25q0GkaAQW19FDPgk/6szZnWq/2zY6Tz3MMBNd0LHyHHj4KpBCcI+ipPEX6KNjIf/bwUUOdaMDSKG2aBovFooBpTOdwdnaGs7OUi5Q5xxTYUsBW6aWgCsfO/pPmejimc06Qi/oVQUyCbGx/v9+Xf9b7hV5u6rGkh34KtLAd9arSdaBAhq5jBWG5j7Ifu92ujFv52K4b1VVZj+p+ajjrWtO1op41tgy3wYcRL6vurKCyBYaVFgQa+c7NzQ289yUc+Kc//SlevHiBn/zkJ/j666/xV3/1V7i7u8PNzQ1CCHjvvffgnMN6vcZf/uVfFhDh008/zd73gycT1wL7oiG2Ku80rxsBLisHVKZrvjWVD3atsl22pzm8tE/Kd1qHAob8zupg5HnN16tyTZ/XfqpNZNcbvZQAjMBz9ln3Nms3WlCGn6m8Ud7TtrXftT7rHNq5UZppeDDfVZBexx5CKCBYjMmTk5ep0fbYbDYjeeHcEHpOcJaebXyG9fIgIcbkPb9YLEahzUwFo/ygctDSx+7RHLsC4Kz75uYGy+VydHhwbJ3bOVPZS7rxfU0toGtb9zGCoTUbVHmTz+vvNRn0feVHA2z1TgHJcE03jqVnxgxvGT/0VMizQC0TmU6fUw6WmG8/2+Fh+Q5/93d/i9lshlevXqWQzoSUHSpDDvA4DNew/S/CI4NwMcaSHB4YcLkRgQkgGKXrGH140l7ayu14tu/qxmppxyejQxnHAUh+fAExHN/gRmNEfT7SPKXxHKrJw3tW0bbC7PuYsKaEH+uTVWgfq6/2jr7Lvo4FAUOVEthZm2driAAw9IkAsrEwiZhfdXj/3OOjn02x3zS4/nqL29crdKsZmrBAgzaBoqHLycNrxt5x4IL9H4DjlLfPO58My+LXOUbl0+9pLanw4Iad1p7kx3Fj5T49S2BuEIb7/TYLqgg/aTGdzbByMYNgQz9UiNo5SgLbF1lBwasCsmxMmfoE8PTENGLMo5QJqf9jI5BdODtbHChgo7kWQTwoEg5Ajzdv3uD999836+Bw3lTh4LiWyyXm8+noWQu08l31NGiaBpvNZmSUcTzDLYth8MhxHl23RwzO8A5wDFC069hl2US3fjUurFKs/U4//YHBoe1oXXzPtq853w7nh6HKSofBCNO5q8lDlWcA4HyTlexuODGNMe1TTttOlwBkMucvgOl8gucvnhXFksqJGi7qku99i65LFx2s11usVyucn59jMZ/jyWW+WKEP6HK46Wq1wma9wz70uL1+h29ev8b9/Qrr9QrrdToR7LseXZ+MxNiHnNNtUGaoDHYxookx3wjtgC4r+ZmOrW/gPY2dmNIINC1i35WbbZOio7ne+C/ChwROJV5NROobj4i9rDWXJw2AT3eJO+/hG49pM8G+7zNo5dDH7EfbuJz7tUmXyURqCxF959E06eZUhB4hy6EYupzKwRd5ACSAlPklvffD/kdZR8MtM1nKKQc4tOmQy4/5fyxHIlCk8ZBWwscIZA/E6BxaeLTOo/nh+lu1PGZMWB1JeV/3FzWIdV+yBpsqr4f7Dco71oC3e5AWbYttjNaoUaB1renY1ZDXvvNzBT4saGJpacet/VD6ah32nx2b1Vn4nM3DxM+sDOS+ZY0OBU8AjIASzoUeoFlDVues9p31XOAcEMwjPdq2HR0q8Dkr4/msykpLT/UOY5vsB4Ej7hPMm0YDlSAevfkIbNGg1JscOXbr3aD90YT8Vj/Q2zKtxwPrUyBE9wP7t9ZtATE+oyAIAURdK94PYZvWM0fXBue1pt+rkcxwWnq76JrnT31fD3ftGrKHx/xddYuaXmFljcot6zGmuoeVB/qe2gaqkz88PBRQeDab4Sc/+Qk++eQTvHr1Cl988QXatsXNzQ3ef/997Pd7/OVf/iV+9atfYbfb4ec//zmurq5GcoZ9Wq/XBSjXtV0DquiNZOdd+2zlheqMbNfKXJUFNXrX6KF8qPTUCDXykYbJKtjGejXPoO5BFjTjd1yvlFlsj/S1Hpa6JysPPWajUtZSv1ZQVN8l8F0DRZNdtC8yR8E2riXSfLFYjHIcEnylHL25ucH9/T0eHh4KDfb7PbbbLXa7HW5ubkaHDOv1+iDcWEFX6vBM9UKaOZdC3+fzebngiznz6DVnw6+Vt3TeHqM9AUH28xhAV5snu//a7/i97ud2XfN7K1tVXtmDN+X7H1p+FMCmAko3kZSHKKJpJmD4BxNlHxgyLp3wwk/QNIOx6DxSPrYYmWpnGGjYw7kJHpZ3+OUvf4kYI957/72in7MURUA2YvWAsM9pifkUPmTQronjiUnvHQKHthwoY0jhJYfwVc0odYaJD78vQjK6Sp2P98UqdrXvjn2vn9sTIfv+71uf/f77njtW/w97Ng6AJYDe+QTyOpe9FHM//BgoOqhvwEtBsM23EdGt0UzWeH/e4oOfnGOz2uO7LzbYvlsgbKZwbnzCaQEHK9StUsRNxsWYAegx+GdPXzlm5aVhHWe+z0VPQNhul5OvJw++uuecpY1VnI6Vr7/+Gk8vL0BvHmsc6CadPEYSINCHHm/fvsUHH3yQx4Ti6ZI2+LH319jwBJbLB3zwwQcHpyfHFGN+13Wpfx9//PHItZnzVlsLVtF59eoVQhhySVgvBlWIuBGtViu8fPmyGAq1/mn/m2aC1WqFdpqAisvLS5yfX2C9XiOBa4cnlFZGs7Rti48//hhdx41reM9uXMPvic6TyQR/8id/gt1uM+qfKmTAAJayxBgxm83SxRXCr6pccd4HekdR5sZJj5PcrHimYpADMQwu4l23B9AAEWCuwRg9UMJIB0B1aMPj/PwcTZNCw1N/A0IYQjgmk1l5xzmH2SwZnev1N3A+4PmzZ+WkkuPs+g59CHj6NIUjxxix//gD/GL7M2y3HTabNR4eHnB7c4fvvvsO725vsNvvsdnsEPpBMSM9yKspHFzA+Jhzp+Q11rYphYN3Do3zJTfZPu7yhTwausUcdz2aZoIYerTOYR9Cuv07pBBQ12TvbxcxQTpc65yDj8nbu3dp609ecHnNNh6tbxB98iifeI/YK5jSFL6MYVc86EO+6tvDAS55vDde5F2+lKFpmgTKGVkW+sPcKHAODgEuud2hDxGNS+DcJMtO5L3ExQymxnRrOpxDzN/F2KPJ4bSIPdr2h4cg1Aplfw00qO3HKsPVu8mGFj0mwxW0snXatqx3j1XCtdDI0b8t8Gfn59gY2T/db62Rpkq0BVlq+p4dp91D1LCy/VQPFJ0f25YaxsAY2KJBoAYz556gFz8f9oXBw8zu3Uon1e91PPqdPXzS3/XyDHpDaGiR3Zt1rOrlxjGShjRaz87OiqcaP6MnGuduv9+X2+nspUHAOGzV5oBSvYOAJPdp9sPyLdvQOWc9LAqOsG0avwRP6EWnBp72T+tVMMCCd8zNVusH6c3vtV+knc6D6i/soz0U5HP6T/nZ8rRdn1qfXc9WH9b1qeuKvxMUsEYzv1cdN8ZYvAgVeGYbfG632+H8/ByfffYZXrx4gdevX+Pzzz8v3//H//gf0TQNPvnkE/zkJz8pHpKqHyrdCdRwLdETSfUgBcapC2rSf31X61YaDR7/A28rD9g1aQEFnQdtV3+Srjq/CgyyX+yvPQywfbJgmQLhum+w/7pXkNaqdx+zU1Re6Pjss2xXAS3lYYJxyrfqgaryQHPL8SZiHpwvl0t8++23BUC7u7sr9VBGkBYqTylL2A8NS+XvHKt6zHKfAFBys+nanc/nWCwWWCwWowtg9ACGXq06dpXr5L31eo31en1wIY6lc40fdO/n3OleyvmvyR2+p/xj9R/lCf2b+9ePueX9RwFs/5P/6b/+Ma8dLf/z/8X/8g9a36mcyqn8/658gH/0g57/0/8P9eNUTuVUTuW/j8UCNApKAWOPJGt8qPFLA0QVVj6v4LQF6WrgGttSABDAQc43HYP2me/bftaet31TcErHreCW0sbWXxuLBYasga+Gm3pJW+PRhmLaObPjU88cHSP7QJCN41NPC5ZaeKwa9Qqu6pxaemq/6b2h4BSNMNJZ803xedavtLJ90TBMjnM6nRaDUPuj9NfQUf49m83KWDSEkKCZghd2rmtG3/fxn+ULgiRarxr1BCOdcyMPExtuBQwhaQx9UgCM72vfrPeWgj6sT+fb3m7JZ2hwklb2QFbbsKGMCuhYg9euL/0JHB7O2VID2chDbFvBaQI8Whd5QgEaFob0Nk2DDz74AOfn57i+vsbr16/x9u1bXFxc4LPPPsOf/MmfYLFYFA9OK0+1v0o/C/TbAwGVMxyLld8Kbmo7OucKxmmd9qIF8owFQew4mKrFHpjooah6LPE73Uu0XV1vKof0Of6tPMvntG+PeZPWaGT3BQXl+XM+nx94X+k+aD9TUJL8Q2/QyWSCL7/8Em/evEHXdbi/vy+eZgTXlB5c6yEM3tsa0ssxqUxT8EkBTwKzKU3JIBd1v1utUl7jGCMWiwXm83Sh2MXFxchrmW3afZE0SbmRh0OEmketfaf2ee173XOsrONeozxtwXpdS7YoIPxDy98rRPRUTuVUTuVUTuVUTuVU/v+jWCXyGFCj3ylwpkAHgRo1HoABgFFgwAJtNaNG37cGtfadz7OP/M4W7beOwz7DooqyGrRKlxp4ZPupfbO0qRms1mjT/qixVwMNdJx2jtimgmhqENAA41wq+GnBRcsjtj71MLY8RDCDHmH8zgJn9IAIIZTbOgk6zWazkWeD0kW9pWjU0khUcMd6jZHWaoQruKteFCGEknjbGunsDz2MCPZp2CrrU/rUADkFFDQpvYI/NFrVgFcjUsEjnTcNu2NbOgfaR67dEELxQKGnHgEY8tputxsBxKybebFqFzJoX2uAhs4V61Te4U8LPtk5UfqyXfU01Od1TJxjDZ+OMZbcVAra2P4453B7e4vJZIL33nuv5PV7+/Ytrq6u8MEHH5RnlA62n5xX9st6TylgwPe896MwRuUjC8Aqn+vcKNigvK6yz8oYK4dV9vB3BWItr6inHgE3BVpslBn5kW3p2mL9CqooQKb8Xsu/WZPnSiuln8o/OyZtS/lW6awyR8G+h4cHLJfL4rG2XC5HkQYMxWbb/F0BO8pFPRDRtiyQz/FZGadApdLAgp080ABS+Cq975qmKT8tT+ncUb5aOtXkg50nXQe6LnSuans2x6bjq7Wn9dtiAeXft5wAtlM5lVM5lVM5lVM5lT+Coso0cOj1YcEaFgsS8acawdbgY73q8aHtWvCoFn6j/Tim4GofdQz8XT0a1DtGx6VjUlDIGvnad33H9tEq+7b/6qGhN2LW6leaHAMI2W/WTaOKBrcCADVDR8N+7Rhqc81+q5HFcRB8UPCQRjOTaPMfvZ0uLy9H3mj0flCvDhqVNMQY9qj5vnQ+bJic0suG2amRrWPabDYHwEPNa0dpRTCQQIcCOseMd/5TYIs/FZTW3y3QxrotkKbeevzchuXZonygfdIbd9W7rwYAKxClHmEKetYMW63fAoUKWpA3tA82ZNzyrK5T8qUCdJb3dU0550ah1QqsaB8VoOr7Hi9evMDTp08xmUywXC5xfX2NDz74YHQTuZV9HJcdh37G8du+W5nOtCH0JNN1r88RjCON7M242hf+U+9V1qVglMoU1s95pAyxPKMyxMpf/tObRwEUkJfFriM9xNC1YmW97kkWZOR8WsBc/67Je46F9OC80fuM7RAIXS6Xo/xqd3d3o4sF1KuRfdU+qNzlT9379BZXpbndhzkG8vxkMhmB6RyH0pdyhnKawN92u83pUYZ9RueePMM1cgwEV0BX18SxPUuLnZcafey7dl+x8qTWzu9bTgDbqZzKqZzKqZzKqZzKH0GhQaEn7N+nKKpyTgNBT65Zr1VGFTCywJ62Z70abH/VaLAGkB2P1qtGte1LrZ8KEhwLzdR+8TNth/XpuwqMWHpqe0of9TrQdy0IoPS3tFUDmPUoMEPvI/V2UfCiBprwO+uZxbb0FjZ6o/AiAYJpvH1R5+n8/Hw0R7wdkf2gQclk5tpf5QF+RkOe4K4NP7XzonMYQspjRIPSJlxX4AFA8byz4A0NTn7OnFsWkAAGUMaGcum4lP4KnmifrHGt/EKAjN6ECqDRY0iL9lUBRl236rljb2+0fGoBEgV7LDCrhrt9365nu9ZUXlhjmMW2b9eVrhdtR4FCBdcU+LJAwMPDA548eYJ//I//Mf7zf/7P+Pzzz3F+fo7z83Msl8vRnJL/CMgoCKl5tKxMVTDTglT6vfVKsl5b+r3ygQLdLDXwTcFqPqvv8znlNfKvgk+8KEkBepVF5Ht+NplMimdlzXNa/1YgTg86avKA7ykdrbxR0K1286e+p58xP2SMKaT94eEBr1+/xvX1NdbrdQFFmWOt5gFK3rB7k92fdU2RD3R8dn3Y/Zoy0c6jzjc/Y31WxpB/ZrNZCTlVeTGZTErYtF1fOi4rO3XvBcYXH9XATv1Mvbftd1Z26V5jATpdF79vOQFsp3Iqp3Iqp3Iqp3IqfwTFGt+2KEDEv6lsqzJvPa7UMNG6gLE3jBqgNQONpea9UAN99OTZelTZuq0hoWEySgvWaRV0YDDI1EOL73wfwKZzoAY0P9M+23BNCy6qMVEbp/2cBje9wpirx4aGqqcH6ao0Utrw8gD2j15bBNLorXZ5eQnvU24ijvf8/LwYYdvtttxKZ40ozVOlxnCMsVwaoCATDdzpdDrygqNBBwz54BSMsGASQTUCesDYc8TysdJDvWf0ZlT13rLzomGX2peaVyELn9P6AIzm1Rp/apQqCKg0sWsDGLzilAfV6LfArPJizXBVcEJ5Wo1WFr6jNxXa8EyVRQps1gAWBXfUq0mBCe0j16tdT6SJ9aZSPiYozHC/N2/e4M2bN9hut/gX/+Jf4Pz8HH3fl8s2lO6kLdtVLyMFWVQ+KqChc0XaEcTS8EHlI82bZT18tD92j9A50L91rVgQknyv86u3GluetfsD55HPK7Ci8lb/PgYsWeDs2HzydwWPtE72RwFBuz8qKHh1dYXlcom7uzu8efMGr1+/xv39/chjjv3WywLYZwtE1fbf2lqyfbbvWtkGoFx6wPeVv7RuK5Ms35D3QgijG6C5b/R9Xy5a0LYsaKhtKl11jdgx6XvKazpHCiTr/q7tkT+snvBDyglgO5VTOZVTOZVTOZVT+SMoVFKt548aZ/aEmMaM3rSo9QGH3l1qsFqgTJ+3RrA91VYl256qa721ZNX0irCny6qs82+rSKtxyGf4nAI+mltMwQwFHGrKvgUQlZ4KbOlcabGGrs6VPfVX4IO04tjoXWb74Zw78MyaTqfoug6LxQL7/X50ucBisSh10didzWblGfUoCyFgvV6PPJf4u3owWbCB79O41VsT+d3Z2RlijOXGTACYz+eFtuxbjb+OGY4KJqmhTtrorZ5qQJLuzJvEG/iUB/V3jsuCQ9YQ1rnm89aI3W63I160fVcASAFFfV5BGaWJ9QBSeligmD9ra4HzTS9KYJyzS3+3PFpbS6ybckpzK9XoDuDA647rzx4o2PVhvZQU8KqBO5vNBtfX11itVjg/P8e7d+/wb//tv8Vnn32GTz75BLPZbJTfjW2pLLDrU73NdFwqf/U9lfEEpVkYslgDgJXnFBBl3wjWWz7Qudc6tP8K8HvvSw4vriOls9ZTO9hQ2aF8wnaUZy0gYte13av0bwXzajSyXl78js+R9vP5HPP5HF999RV++9vfFuCVPMT5sMCStlvzOLT8oWPUcds9Vcdr90O+y/WidK7JJ32P/9QDbr/fYzqdYj6f4+rqCs45PH36FMvlEjHGckCiNFdaqzxQAFzbVLmlQJrWx7VAHuZ7mu7A8oLqTD/Gc43lBLCdyqmcyqmcyqmcyqn8ERQbMgjUjUXr3aXfsR59V41La5AeA3DYB6u0WuBPDUa+WxuDNR4YFmi9IWpeWvxOPTqO3WBKGhK8Uw8eCwBYBV2LAg81utn3tC7rQcKf9EBRbw4dP+nC7xmmpHPNcZE23g953OiF1vc9njx5UoAcBcdYF8M86XlBkJbABwE8DdfjrZ8K3Gw2mxEgqV5f7BswGFFq/HC8NpRKaU1e1pA5BSEt72qpgboWrKaxbUEvC8ioRxn7qEY0P9PbHFmYn0mNZsuTWnQtEwRUMNaCugq4EEDU7+z6ZxvKr1bOcI4UbFutVqP+6rpQg9cCY+yH0kdBNh0z3yNP6Pf8XL2tLN3sGlR5ZedS+efJkyf40z/908Ijf/3Xf40///M/x2Qywccff1z6S56kVyPXJ+fcAmkKItgcYVZmK3jK3/n3dDotifQtGFYDTLQPSksdM+lj5bDSUwEl7XONj1mv5pW0QDJlTI3f7b6hdFIeULraQyWlr8pNfsaf/IxzRvm43W4xn8/hnMMXX3yBr7/+Gnd3d8WL0YaD1niR/bV7gH5n51Dn0YKo/J791n3NArhalwXgtJAf7J7O8XRdh91uh7u7O1xeXiLGiBcvXhTZ3rbtKGRU97Jj+5vOqc6XAr+6/tVrUr27FbSvHRrY/foUInoqp3Iqp3Iqp3Iqp/Lf06KKKYsqilRYFXTRzyxYRYXZe19uebT1KTBklXT+TgVajUAL0ulz2i9rDPB0XA0xNVgsQGUBPa1bx6r0Ii3VE0RBmZpXgNZlPTx0rFT2Wa+GQBHc0HBEbU+NAc4NvcjYDsEYDV+czWaYTqcFcFNeodG6Wq1GXnUsNlG78pQa8DWDj9+z3vV6XYAENazX6zWePHlSbmLcbDYjHiI9WOhZp6GNSgMFWBWUINg0nU5Hxp3eNqhGuIIACpqRZrvdbjRP9K5S41lzzen7wKGnohrD/KxpmgKOKF/rGtZQVzs3CrpreBuNUAVdLdhi17auUfW6seuf65wyg0bu+fl5AaD0dlhdg7r+tA31uuN8aGgnQ4gtiK191T7qetXxaqmBfOwLaUGgTGXSxcUF7u/v8dd//de4vr7Gy5cvR2Gbdg5CCJjNZqPwOuWFGnikwIzlHRYNtbQy5tg+waIy1c4Nwwlrhw9KG/4EMFoH+rsFc1W+sd/KJzpmO34L4Nk9xoaAK2251nT9cQ/gcwow2j2QcxtCupF4Mpngyy+/xFdffVU+10sEagCWBdx0XMfmyRY7VzVAjm3Tw1TlisoMXX+28HINu6+yfwRUuSYvLy/Rti2ePn0K5xyWy2UBbxUQ177XgEXqKToHKi/5Dr1GVU/gPOh+VdNV9Fm2+UPLCWA7lVM5lVM5lVM5lVP5IygKMAHj02s1SvkZwydqgJE1VKl0KlihxgKfqxl6FsxSIFD7wFxRCiypkel9yrflnMNutytAC9tQo5v1ADgAgOxYlXbqwca/gSF8UQEoSzcdnxpKShPtnwUD1SvPuRTiqfWxbZ7Mez/ktnHOYbFYYDabYbfbYTab4fnz5/DeF/CMBhyNGhrbDOmcTqfFK01DM5VHFLxRQ9R69Wy321I/QYjdblfAIM4PAYfr62v0fY/b29sR6KO8Y0EjBXxoKBLkUr7VeVIAzYIylm+tIa6gnXODRyGLXkagfKUhqDWjWoE8/c4afLqOFTjUPum/mleYDc/lOBUwYX8tqGhBGf6tFySoFwnnijzLz/lsjS6sV3NCsY/aHzuv5BkFnsnTepigbVjvNn3GGvDKg5xzveGWgG3f91gul/jwww+xXq/x+vVrXFxc4MMPP4RzroQJ6jzTc1RlM8elfbKAJOdcwR3yA99l3XxmNpuNaGzBcwWaLehYo9ExDywFjYHhIKEGgiktauH12peazGUhT9WK7lG2zdq6s2NV2a28qGGeDI/03uP+/h739/clhN7KMu2HDXEFcAAC1caqtFGaq1y049d1r4cfrNMCkMeKBTEtMKigJPMQXl5eFiC6aRq8evUKn3/+efmbIaRcu9oXbdOO365RyoHFYoG+7wt4TTlEr2fuS7pe2KbNVfhDywlgO5VTOZVTOZVTOZVT+SMpahzY0AY9+bVAFsP6WIc1cPg5lU8qwjRqjoFz/IzFKsjqVUMjQQE4Akc8LedzNBR1vNqGghHWaLXgnaWR9o/eSgoSHDvVV+NCgQQF0SwIoO2oARdCwNXV1SjX3Pn5eQFD5vN58W6aTqeFRkAyzjabTTEqYozFC4r0Ua9CgpybzabkWiM4xvYVEOU/6yGlxhaNdwAFtKN3B9smIEWvOwUaOQ962YGGT6pBSf7VcC0a8zo3NJbUk4jhet77chmDNRJZCE7quiDIwtA1awTaEEZdGwoyWd7UBOHqQcf51X6o8W5BC5tXS0MEFahUAI+AHW9v5Hzq+tKfpIH2g7Tm3AEohq7KH/6twAvpyLVnPVUtGGnBdYI5zI/HdaH8UAM3rAyzoIz9jHPEvq3X6xGg9/HHH+OLL77A9fU1Li4uRjfckg85H9YbzHrpsP+WH3UuFQhVEJCgwWQyGSWyZx4w/ce+28MZCzgqYG+BXUtLfd6CODoWO0f8m/OotLOHSNZLUXlF+13bl+yY7WGCnRftG9cw1yPnn966Dw8PB2tS9wXldS02V6MWpVtt363JGkvT2j5t37e55mpyyzk3kvUKkHFf4WUXBHjn83nZmz799FM453B7e1tkKPcf1mllAw8DyBtci8oT/Pvs7Azr9boAzZpnzvLefD4fAdCkBz/7IeUEsJ3KqZzKqZzKqZzKqfwRFHvTphp/wPimSAXGVLFV4Iu/W6NCDQZgfLpvv7MeW1q3ejmo8WIBB3qH0OBneAqNGa3DjlNpouCWgjPWeLYGlnqxWcOeP9lfCzYpaKYGgnrnKDjAGzoB4IMPPiigGC8VaNu2hHzyNjbvhwTiOg6ddw0FUgNLn6cRst/vC2BnvZ1Yr9LbGis03AFgs9kUYI1ginrErVYr7Pf7YnSp8URDi4YoeVYvV6DBRG8lfkbwDMAIJGS/+DdDPTXkUnNhsaiBR8DHuSFXHL0v1ajneBUgUyBQ1xPnSb20+L71bCJQo22QNseMavs8PTsUxCOfKpignnE1UMV6xGieMdKVScZ1LWlbCqop3dVDiwazArj8W9cljW3yEOmqOfz4mY5B+VnXsQI2yjecBwII3qebdNXj5fz8HM+ePcN3332HX//613j58iVmsxkeHh7QNA2urq6Klyrr1vkniKE0tV537L9+rsC4AtVcC+Qp61VlacX6OGYF7FU2qOxWea9yll5DNU8u5R8rQ60MVv6z/VZZrt6Fth0LsNh1qzyve6LyLPtEXmN+yRgj1us17u7uyrt6WMG+6pwd60+t6L5ji8oSCxSqXKjtXdYT0YLMfE9/6vol3biWKMdjjOUCGN6sSlpR7ocQcHFxUejCXIEKhrFN5uzkoQ3XHsE721/NI8o99/z8fCTb9HvWxUOsGq/9PuUEsJ3KqZzKqZzKqZzKqfwRFAVR6F2hoX48XVZwieXi4mKkyFrvNxogBH9YL5V0KtOsU400YOxBpB5r6nXEoh5INlSGSrBNsKwGNkEsBSdIG2DIqRJCwGq1wtnZ2chwVqPq/Py81EEAiAYQ62Gb/J1KuwWOFGRwLoWGee9LaGff97i6usJyuSweakp7Bao4Hgt0adJ0C6iyLnuLGj2mgORtxts6CbjVDCpLJ865ehYoqKaGv4JHs9mszAP7q+GeNHIsqKPgCf+m14N6CFmvH64JNTg1cT6fV75UA5/trtfr8q4a0cqHylNcM+yber3UQCb2TwGYtm1HoGrf9yNQUde0DftTXmE/CYSq953KBh0PgNIX3ohpgXCuA9KYADiNbQVJlH8ImquHJWmtNNGwbQU5FLBVL1yGo2koKuuznn2cDwUvNMdfDZDWEHT+rSANADx58gT7/R53d3f46quvcHl5Wfj9ww8/xKtXr8o60D5wDrz3hY7qxVvz7mFf+YwFe+kZSrppW5zXruswm82K56ICfTVaqAzQQxvlHd13OFatXwFrjkfllY6Xa9jKIeV1zomufZXXChRZQI/8q3sIn9NnLVDIfYl75LfffovNZoPJZFJyspGGyoesywI5fNYCbSpfWJeCoPYARdem5REtFsjSwx/tE3UL/s21yz1Eb3jW9UvQMcaIs7OzcmCkOQz5HoF/laEE6R8eHjCbzbBcLkuOtcVigYuLi1GuNd5iquHzbdtiuVzi6uqqpFEgD7dtW25+5aHPxcUFdrsdLi8v8UPLCWA7lVM5lVM5lVM5lVP5IyiLxWKUCFzBHvUGst4J1kOGxSrkasyxnqZpsF6vi2FLZZbAkiruvOGSirENDVSPB/V2oJEODCCihhFaDzINKVMPCnuj5vn5eTkpV5BOQbsaoESFXE++NYQpxoiLi4tiHNC7ZbfbYT6f4/LysuRF0/6HEHB/f18AwWOGtyblV0NI50fzaClAZUFUS0MalTWjlb9bbwfOu/IG+coCMgqa1oAzHQsNNPVsU2Oc/wgw0Qgn+KTAooY4su+cS/XuId3Im/ou553vaogg6cl5JG0VqFOQQb0p1aOTfWO/SAvWy/lhexZwUkDAzp0FG+186vMWaLfeX7V5VD62HkH8XI19/q5guF2/2i8F7e2lLAp2K6DIdvRyD/ZHvSMteGaBBu2H9l+L0ohjmc1m+PTTT/HmzZsSBkfD/te//jVevHiBrutwe3uL5XKJtm3x/PnzkVGvY7Dt6kGH0pa0tqH1usaUnk3TYLFYHOQRVDDRypNa0X1BgTfSRoFDyjX1OFTPLjsu8pDKQ37G9aa0UUDXen2RFtbLi2AzZYKdVws4sU2Chrp2yduLxeLAw1jD3bXfyoNKT/JyTfbyPX6u8kDlvr5nZYTS2PZBv9f5p6cX61evbADFu5PeZQTO9DCNY7d7oV2/PEiYzWaj/WU2m+Hs7AxnZ2eFVtxv/+E//IclrcJisQAwHBL87d/+LW5vb0tONn6/3++Lhzjl8u3tLX5oOQFsp3Iqp3Iqp3Iqp3IqfwTF3rClwBnBAavMU1E9prgDA2hmPaGoACuQpIauhkcquKP95PcKaKnSbU/V2V8bDmsBnpp3Ao0zfs/QFe2jAh1U1lknle75fF7ozecUQKHSv1wuy3fAYNxvt9uRkc/2ddycDxtOo4YPjW713FJvE4JT/N3SyoJA7KP1xGKx4IM1vGw+IYJfbF8/UwCG7XIcaojXcobp3KsnEoFZDQNVA1UNe0tzPs95tV4fOlaOST3CCAJyvtk3NUoJOCvIoHOpc895JL/TA0kBVet1xZ8KvBJk0b9jjCMgReWEAu7KAxw3n9H1pSCFgngE0nVt6/s6Bxyr8qjKGj5LQKYmP1g/jXDlXc1JxjETsNTbYGsApC0WkNCiMksBlPPzc2y3W1xfX+O9997D5eUlvvvuO/zFX/wF1us19vt98eJ877338Omnn+Lp06cFtFBZyvnnGOkFZEETnVs75xwXAQeuDwviKdjFdjlHutfo+O2c8HPtm50/XYtW5ljAif2yAJHyotJKf2rdtk8WjDrmBWr3FO0X54PrVWUR1zH/qfyw/VKQsxY+bcel82r3Ph0Dn1ePPgW+lea6rvUdBTft+rX91ZydCqTrXldbS+wrATo9ANK9gTSmlyDb0L1yuVzi6dOnxSPtm2++gfe+3Fb98PAw8pTXQwU94Pgh5QSwncqpnMqpnMqpnMqp/BEUBYnUYFHAQkGy2sm+Aguq9PJzBTSAwauHRl7ttJ/t0tAnEKEedWrAqLJujS0LmNRCh9Tw0LAWBY8UJFCjlaGc6oHANulNpt9R+edp/m63w2KxKGGe0+kU19fXJSSG4Z2ay0s9Qwjk0XggbfTkX2lswQfWwXAvGiJqBClARE8va9RZ7w4tasSq0WZDsBiqo7mkjtEdGAxa5QEa0swpRZCHY2CIkjXmNWRSgTOdT+shpeOy41DQU8NCaUSqJ1rNK4W8pXNm6WkBTNKVZb/fF54jXyt/8F3rKWYBHwumK/BnjUwLWOsaU0NewTUFZ7jOLUBkATaOoTZ2BXIsuKGebDoXfN+CdLrGuaYsiMR3dY2pDK3JRRZtnzSg583Tp0/RdR3u7u5wdXWFs7MzfPPNN5jNZnj58iU+/PDDkmidHjsKgtcOQZS/tT818KkGFFgwie+Sz0hz3iysNLHAJuWG1mv7a/tuacr29OBBvdv0WQuIqbyotaU/7dxRHlO+qwxSmajgk/aZc7VYLPDw8ID7+/vSL/IEaUj6WqBe50v3Wp03u64t3Th+Cz7bYmUD69AQarsv8UDKucFjT0NpVY7YOSCYrekYdE1pP9TzTj39KP+ZVoB7pILnm82meIhyz51Op1itVvjd736HyWSCp0+f4he/+AX+5m/+Bl988QXu7u6w3W5HIfD0Nu+6rni3/ZByAthO5VRO5VRO5VRO5VT+CAoNxtopPhVlqwAD44TZ9lSc79r8SSz2ZJlKs4aP6TNqrFMRpvHCvqtRb73ZLDjCttSI1zxc2ndtWw1sm7OF+VgIHurtf5Ye9ETQsWpeGiDltyNdF4tFyctDQ0uBGfWE0zAzjl3H2Pd9Ac9oGOmNifT+opFPAEL5wdJTDUwFPI8VAijaPzWW2N58Pi/zRJrZetXbhrl8NOxTeYxzrGCTzaFFOiiP8TMF5SxQoN4M2gfST2/FY7sEiKxRqmtK69VSA/f4Nw1z8qF6amii/9q7pAWNZTWqWWjgal+sxwz5ywIiCobo+mU9Sv+a3NB+69zzfQVt9F/Nq0oBPOXfY55unA/vh8sJmqYpodnaDwtAqPeY7YfKPaVx13U4Pz/H2dkZfvnLX+LLL7/Ezc0NYox4+fIlPvjgA1xeXpaQe7a32+0OwGhdczoOlZHH5lrnGRh7gmnRfYBzQ2BZ5YnylgVPLf0s6MJ3dJ4UONa5BzCSbXY/4zhq+5cCOdovC5Jp35Q+BMj0fdsHPgskWad7BvunMkc9eHW9qmxSWui86LgsPe1caDv6vD6r82TXKeeXB0PkB813BozTDdRkaAgp72DXdfjoo48O9hw7d6yLoKmuO64L1k0gjf1ZrVb41a9+hfV6jc8++wwvXrzA9fU1bm5u8OrVK3z44YdYLBb45//8n+O9997Df/gP/wHX19dl32Z6gNVqNcrd+kPKCWA7lVM5lVM5lVM5lVP5Iyh6i5Y1Zqicshwz6LXUgBX1UnrMUGfRcDP1plDjSvtpDW1+Z/O2KOBnQ9rUY4nGAJV4emYsFovy3WQywXQ6LTniFOhTg0gTeKsBr7SmkUU6AcDl5WUBjVgnT+HZXzXgaKiz0KPNgo00wugJwdBTBaiAwYuGhj7rsbm7rOGlQEbNCLd8oOFT2n8+p94I9NDRd3UOSWP1yvB+CNMlyMDxAUNOLc1Lpf2qeXgocKfzqN5mFlRiH9geQcWap5aCHzVeVjqrsatgIP9tt9vCQxxjLWeeAmE02G14qgWBavRiXaSBjo2gG8eovKjeijb/mtJJ+c16xulFHBbktPTR+VIATcE1ygKVQbr+9BCA7dl5UT7iP13/Os8q77z3RTbP53P8/Oc/x7/5N/8G3nv86Z/+KT755JPiNaNzaD3WlG8tOF4D1bSfFkjW+VOeOwYc6hqw/EVaaRs1L03tfw1s41xQpqvXs+VZK58AjDxkLb3YN/uZBdksyMUDB50bPqteXkCSP8vlsvSD86R121BEBRAViLOegBYstGOyRetU/j32ndahBwcM2+fvugY055rmIVXaapg+6fPdd9/h2bNnIxnLgwvdz9WTTffazWaD9Xo9kmkhhHJZ0NnZGfq+x5s3b/DmzRtcXl5isVjgs88+Q9u2uL29xcPDAy4vL/HJJ5/g7OwMn3/+Ob788ks8PDyUAw2Gmb558+aAvt9XTgDbqZzKqZzKqZzKqZzKH0GxSfyBuiFjlXRrKLJYUKym3FMhrgEkWp/WZcEwNVL0HTVcbd94Sg5gpOjTiNH8MjT0CdDEmC4h4LtKLwIiGp6lXgDHAELS3Yaose8MbXHOlZxPalyRjrwtjeANb1dU4FGNuxBCua2OBqh6INkk6AoCcd71AgCOUw1dpX3NO0WNZx0722Rya/Uws0asNX417IfATS2nWgiheHMpKFMDRViPJlVnaK/lPaWPhuey0IhWvtWxqSeXrkm7JmoAlF2TNHatR5byt64LC/ItFgtst9sRnyoPqZegzqt6DtGjk+MmOK200jkhMEKvFwVcamCOevOo7NB1xrmwQBfr5DvkFfWuUp607+i4La0V4LTFzrWdZ8vTfOZnP/sZPvroI2w2G1xdXeH999/HV199NaK9zhHn1PZf2+HfCszpmlewkrLTyjV9Rw8RFIC3+bSAw1yPOv8KJnH89rCHhX1Q+UdvItJCQTcF2XiIorTWYvcjCy5ZmUhZTLof8+Bmv9iP3W43Ci8MIZS1s16vRyGYvASA9FZPa+UrC/Tq78dAMjtefVbXWE0/0Oc0pYCCz0xzwLXCWzwpT/UQR2+DJj/u9/tyoQCAEjrNsM/ZbHawr6sOwv2D+7WGNM9mMzx//hz7/R5ff/01vvzySywWiwJk39zclJuyp9MpLi8v8Wd/9mf4+c9/jm+//Ra/+c1vClBq5evvW04A26mcyqmcyqmcyqmcyh9BofJpDWxg7Imihq0q8jReWexpv9bDzxRgUADFAjPqPaLvqseAnlLXQr40RKlpmpLjjAYgjTJgALpo0Kixy1N5noY7l249VYNNjYzaRQJqhNqwSDVc1PuK7+uNhmqcKwBIEIO00HYtyBZjurCBOXEUEFDa2PlUb0QdrxpZylv8acFGtmPHboG6GGMJwdlutweG3jHPEhabZJ9eQWoEKtBV8+Sh8U/e0JBZ5XMLHmqfFCRQw78WsqkGofK/egHVvDaVp9hvpb0CJgoU6bqxc63PKwhrQTulm/KP5rVTIIL10MBWOUBwWMEr1q2htQqO2L5b4FGBQdapAKWCN+plpaG9VgbquGaz2ShUdD6fY7PZjAApemGqt18tbNnO6263w3a7xT/6R/8I//7f/3tcX1/j/v6+AFYK2tXAbOUpO0aVqXatqgypzbX+zTFxHByXtq1yNsbBY1a9nBRYte/pmHTNkfc5rwqaK5CvtFA+0XmwvGTHTdlt5YzuQaQf+2HlPNeGepZ+8803JQenrkvrzcrDAb7HVAUci/W41P7rmtDPtO/2Hc6X0svSVd9X/iXfaJ8Jsinozf1NLyNQ0E0PThh2utvtsN/vsVwuEWMs+UPPz89HeoEeKHGeGS6v/Sddnj9/jvPzc1xdXeHt27f4+uuvEUIouU1DCPjmm2/KhRTn5+d4//33cX5+jjdv3qDve9zc3JxuET2VUzmVUzmVUzmVU/nva1GlGBiHSPFzKsiqYFtvJau429NwGtI0ChTkUKWa9dAI09sZaUxofW3bFi8vPTWmcTOZTLBarYpCT88AGjdq5G232wNwQw0FJi9mfzT5PWnHsSvwyBN4YPDWsF5faqBYA1PDh9QAUsPTGkhqLKrxXgNaFHTT72ueKhwfDVImf1eg03oAad0WtKzdvqleWtbTjfxD3lH+4fPqiajGLI1XmwsIGG6K1PEqEKJ953zSA0a95CzQx793u92IL/i59XZQsIJzYw1i8rcCH+Qp0kPXr45LecuCdCoDuB7VG0m/q3lpWYCV/bNJ/JVO6vHCvvBiBlssH2mf1KuV/GPHZ723LOhh51vXKsF61sNxkY5nZ2dYr9f41a9+hclkgp/+9KdFLinf1jwV+U9lgNLT+5Qs/rPPPsPvfvc7rNdrPDw84MmTJyX3mvZdx6egnf6z86WyREMm1WtW67fehTpHOs8WuFT+Jd2ZU5KynWtRQ9wtmGrljAKWBF/prWTnXOWkrhcW0ryWt0+Lldukl36vfK08RtpwP3rz5g3u7+9xd3eHvk+3Tp+fnxcvLoK3PLTh2uSeqMC/vQVa5YDdI2o8Y+eMY9K9+9ghg+7lTdOU0EzdG+k5pjlNFWjTQyL+zbYYykma0HONOdXIL5PJZHRjKGlBuhI0I3/wkKrve8xmM/z0pz/F+++/jzdv3uDu7g6z2azsI7xB9Pz8HE+fPsV8PsfV1RV+/vOfYzqd4uuvv8af//mf44eWE8B2KqdyKqdyKqdyKqfyR1LUu0SVbhr/VFw1ZFK9PGon/wp6KEhApVeBEmv4Mr8ZgBIqqLd7aT+0f865EtZGMIw3fTFsEkDxQKMBpeE6NbCMf2uolIYAWhCKHk+kxX6/L6fuNLD4U8NNaQCpQcPTdvXcsV6DNSOJfx8zhjXMUMOo+EzN60Lpoe2psavP8XMF8HSsnEftpxqECkRoviwFttRIJ80VNFDQhnXSuxEYe0nqOrB0IxBLI1cBNaUFiwU4SXPrnXds7fB3DcXld8orbMuGJrGPFvxQ0FfBNuuFokCT9tHOq6W18g+f43rQNaSePsAAmDMEjB6g2g+lj45PPXz0wMB6ZSn4qQBKzXNT+VIBCQUyFOAhgPv69Wu8efMGq9UKv/jFL/Dy5UvsdruSrJ3v6di5FnTu7YEDDwA+/vhj/OpXv8LNzQ0+/vjjcnhAUIlzb2WBAraWx3S+LDjJ7/USCNKI32ldLAoG6fzWvNp031EwTw8cCMCRz7Ud0oe0JfBuDyAsP1sPKpWNdj3bserasetWgUx6l5H2ynMqF7z3Jcfm7e0tVqsVlsslzs7OSt8I8CjIpjRUr0wdk4LFnEP2rxYiq38TtFegUg8H2H+On/2IMWKxWIwu/NlsNuj7HqvVqqxxgm3UKdgmP9d1rHNydnaG2Ww28sImXbfbLS4uLgrPdl2Hm5ub4iGot23rPDjnygUFMaaUEN57XF9f48svv0TTpAtNXr9+jclkgg8++ACbzQaffPIJNpsNvvjiiwLI/fKXv8S//tf/+oCHHisngO1UTuVUTuVUTuVUTuWPoNCQ01BC+x0wAAXqIaQgAw0ha9RQgeW/tm1xcXFRlHMq2fQG421qmmD84uIC2+0WT58+LWFXbF/7q8aTehIwPIUKtz1tZz/UcLKGlHNDSCiNP3vrGX+qIUtghoq+htkxdIWeFjRk1GCuGW1qhOvY2U/+rQaJehUo0KDPKFhaq0PbUaNcP1PaqsGs79uwVaWxAjU6V2r0EohRcEXBG9sfGraah4kGnHq7WK9LC5pqnxWgZf90DGq40fuJRp01hmt0rnnnaL8UvNL3le+tB472364bO2blDQXXaQzHOIR76RwrT9TWJ9cBgVLrFaS3+wEpPJLPUx4oUMF6CUbT2Nb1A6DIFG2bgDIBKQuAEEyv8Y9zDuv1GovFAvv9Huv1Gs6l8PEvv/wSb9++xXfffYd/+S//JV68eIGnT5+W9b5arUZA6bF5VnoSpHz58iW++uor3NzcYL1el1xVBJE5NqW3BSEVKKM8Il9SpingqkDffD7Hdrsd8bb+rK1rW5RHdf3+v9s7s9/Ikuy8nySZK3dWlbu6e2rcs8ntGXhsvxgSJBuCHvSo/1h6kABBgDCAZpHGck9NV2+1sIpkkcw9mZl+KPyCvzx12dPleSvHBxAkM++N5cSJuPd88UVEtoeX1zcRuLSvfdV1zv0r+xqH1JiAYgkh+ZO303Q9sn83jb+5fIB2Is/9/f3Sx+bzeYxGozg7O9t47nrJMoQV95iI8zjId+6nfv66z5p49/PCz3/ajbr5f9K077AsGltfXl7GZDKJ+XxeDhlgIs2EIe3SpLKnfFtbb9Sdw+Ewbm5uysme+/v7cXx8HI8ePYqrq6tS99FoVGzBoQS5/OTLtfv7+3FxcREvX74shy68fv06nj17FicnJ3F5eRkPHz6ML7/8Mr744otYr9dlou9dUAm2ioqKioqKior3ALwMe5+ViE31mlUrVpcwu81+KRGby7AgzXgRRknGy633SfO9Vh6hdmu323F9fb2xbM0gmKAc3rOMmW7vWUR9HLCjhCBIZW8gB3kOkKwIyGlGvAncUcsRzBPEOT2CdxNzKB/Yj4rghDqRvhUhbr8cjAIHSJCOJhWaVD8O1rL6JhNqrtNdJBv5W92W293+6Xp7mZ1/e+mR8zTh5Wvtdzmos81IAxKDz+2D2b74QLY9vskJr957ym1F+U0q2N8ocxMBAhGTVXYmpY1MsmYCmjy418qXrHLifvzJ5LztDAnANZSJz8iHPpCX8mYVGn9b8dbku5l0o/1pD/ogB2BABnItY5mXE0M07uzsxNOnTwuJfn5+HrPZLFqtVnz00Udl/8D79+/H/v5+4+Ey7iP2aeq5WCzi8PAwHjx4EE+ePInRaBT37t0r5XTfNfFpn+G3fdNEmwkr8rV61Ion+5f32wJOjzr5IIImgtG+xrX4k/tFJsP533XLExT2V+zA+OctDHgO8AywYjir35qQCSv7vsnP6XQae3t7JX+ew/fu3YvFYhE7OztxcXFRlljO5/M4OTkpZdrb2yukrtXUTASh5HM7s9WDn23YBR+L2JwYyks2Scv9xzbFRvgOBNt0Oo3Dw8M4OTmJJ0+elPeBXq/3FnmXl4b6eeMxljT29vbi+vo6Xr9+XcjJVqsVx8fHsVgsYjqdbpwYCjkO8b67u7vxzGOZOvX+5JNP4pNPPomXL1/G5eVlUao+ffo0ZrNZPH/+vPj36enpWxMm3wWVYKuoqKioqKioeA9AEOL9hHjZ5mUT4oKXfhRnvPQOBoOyXwzpQQrxN8Fmv98vwR+BLAGGX8wJhkmPl3Ve+mez2cYhAw588gs+wVLEbZDJyzv1pW4EFg6kI94oaXzSGekbmdhhQ+adnZ2YTqcb5FPTMiSTSQ70MwlKEGTVnQOQ/HdW15mMoX0yucZPViQaq9WqLKnBlqR9l/LD+WDriM1lo02KBatQaFf7hlVoDratxKG9UF55iZJ9Itsp28uEjpe/mkDieis9THTRniZx3I4Evi6L7e7AOAfv+IZPNMztZ3K5qax8B4mSyc+skoJYgwzHd7L/5HpAPBHMshTQNsh2M8mefQiix0T5YDAohw6MRqN4+PBhrNfrmEwmRRGEqtYEjhWni8XiLYK31+vF1dVVsf3HH38cu7u7cXh4GK9evYrLy8t4+vRpnJ2dxXA4jE8++STu378fDx48iNVqFePxuCjuMhmI/ejrLG3b2tqKDz74IL766qu4urqKR48exfn5+cY+iF6KTJ1yv8+f0R6Mg5zkauVUxBuSmFMcnYaVg26vTHbRP8jHfuXTiu2r+Ackk8uMz+UxLxNsHlvzuGhSBRv2+/1C8OVxxT6c94A0QcmY4vE0Tx4sl282xY948xylLu12O+7duxedTieurq7i/Py8kGIo3axew25sR0A96JdMgjFe+VlCW1ghatU5Y6cVkkxc2fZZ7U477ezsxGQy2fj9wQcflGcqBJztbGIvj1vklcejDz/8MLa2tuLx48cxnU7LgQfX19fFvx4+fFgm6549exYREcfHx6XcjD8mIw8PD+Pg4KC8C33/+9+P4+PjuLi4iP/9v/93PHnyJJ4+fVr6ayaavysqwVZRUVFRUVFR8R6Afcx4keXlmRdeiAheMAmQWFqxXq9jMBhEp9Mpe545SOX0Ll6ivSyLF1FvzsyLLUEVQYmVVltbW2WjYu73kkGWz3S73ULs8cLrvdisaPIsPQEXJ04SVFi94xd9B3xWQRGQmVzy3kUOfhz0QSS6PgSwg8GgBFd5qSXpkldElH19rCbJy1T5nD2OMrkFbKOI22ASYpUNuR3ckQZ28UmLLruvpw1MNtm2+AsBet5/CR+znbNShh8TmNTJyglfn5e22TY5eKZ8pGl7WSmR6+7gjO+yvQwTDfRPqx3dlq6/g1b7Du1nH2cMsA+Z8MX29CMrjJoCTRM97g9uc/o8Qa7blmtNUkMM0Dbcw/XT6TR+85vfxNdffx1/+qd/Gj/5yU8KYbG9vR2Hh4flAIFOpxMnJyeFdHF53WcYk9h/bb1ex8OHD+Pk5CSm02k8efIkXr58Gaenp/Hxxx/Hz372s5hOp/Hb3/42Pvroo7h//35MJpO4vr7eaHPbNCKKAme1WsUHH3wQ7XY7+v1+PH36ND755JO3FK7eI84kGN9bbZYVZxGx0ScgQa2U9BjlMcFks/N2+tzDeGaFtP3GhBV9DfKWv7nW9c2kYa4z/Zj0ecaRh8c2LyH1s8uEdFOfsq1NXNs3yXtvby9evnwZs9msLPn10tH9/f04ODgoCi3SHwwGJX2Tojs7O9Hv92O1WsX19XWMRqNYLpdxdHS0ocSjb93c3MRkMint0uv1CoFq+5tE9XuCCWePxSbNPTkxm83Kdg8sFb25uSnKMq6dTqdFdc5BECZ8IVYpA0T74eFh/OQnP4nnz5/H1tZWnJ2dFVKv3W7H/fv3i+3Pzs5iNBrF0dFRaWuUa7u7u7G1tRX7+/uxv79f1Ojj8Tg+//zz2Nvbi93d3dJOl5eX0Wrd7gVYl4hWVFRUVFRUVPx/isFgsBEsdbvdjdO12u12nJycRLvdLsssCG7YV4oXYsgTB255FpqXYi/htMKA/cgibhVaXi4EHLiZFFqtViWoICgnAEC1QjCcCR1sQDBG8EAAdnNzU9Q1Dkxz4G01T0RskEpWduWlmZBm2NTEBicSegPzvA+VfzvYta2syKIuri9pZgImqz8IRFEhzGazDULWadg2zPI7KDYxYkKV+7wBtskV7rEiLhNl/p962q4sr6I+DhxN+mBLSB6rnYxMjkXc7gGYFTVZeWP1jwkj32tVIDAxRVr4ciYpnR/3mcB0fu579C8Hzc7PKsas7GwC/gAgqkyAm9Cxz1npRH6odrAv/QwS7MWLF0Vt0ul04ic/+Uns7+9Ht9styqhf/vKX8eLFi3j06FH85V/+ZfR6vRI4dzqdooTBd8bjcbHh69evi6J2a2sr9vb24ic/+Uk8fPgwnjx5EhER9+7di3/5l3+JX//619Fut+Nv/uZv4tGjRzEejzfUUSb0ZrNZvHr1Ks7OzqLdbsfV1VXs7u7GJ598El9//XUMh8PY29srJypmvzBRbqVYJkya/MdjDZMN/E1enhxwf7e6023Nd1ZFQZp4DDMpbzWYSVzIOdLBb+xj+GVeZmqimD7l/krdUS568sXpmHT3OEm5s9LKxJufMYw7eWKCZwjLGUejUdkHDNKI+tHGHmexy8uXL8uJm+z7ube3F/1+P/r9ftnPjTK7rp5Ewu85PAgiympqxlqPod1uN/r9fpydnRW73b9/P16/fh2np6dxc3MT+/v7EfHmneTevXtFgZb90vvQ5bGu1XqjRn348GEMh8OyVJu6jEaj2Nvbi+Pj42i32/Hy5ctYrVZxcnKyodabTCYxm83i5cuXsVwuYzgcxvX1dcxmszg7OytkZ8TbSmyW6L8rKsFWUVFRUVFRUfEeAGXaYDCIfr+/oRwYDAYbSyWZSWamf7VaFfKIz3ix5iUTcoGgyuSC0+JvEyIOrniBJyhhCRMv9RBpVn1YQUfQ3ev14vXr17G3txcRt+SaX4gdfBGsmJBysJT3dELBZ7WSA0wHAv6hbgT7EBmuHwGACQ6IPC9tdUAHaWfyClid0+v1YjgcFrLMARVwEGoVD21J/ihcqK/3AsrLWh1IR8RbaZkY+zZyibQJ2CljroeJXu5nnztIBJOnLq+XjOUlWFmx4nYgIEQhiE3sSwTqVhdB6FoxZBtTTn6776AyMRFo8jSrxbKtrEra2toqS7KxQSYWyNPEF0oop50VkpQBdRBkmfPyOODxAkI3Ispeje7H2B5ymrQ/++yz+Pu///v4H//jf0S3242jo6P49a9/Hc+fP4+zs7OyrOzw8DCOjo7i/Pw8Xr16FcvlMnZ3d8sSMxPAnDh4enoau7u7cXx8HNvb23Hv3r3odrtxenoajx8/jl/84hfx7NmzaLfb8fnnnxf1GTbLqlT2laL+X375ZZkgePr0aXS73fj5z38el5eXZfyG0LAtKOdisYh+vx+TyaTYBV+DEMsKMtqH/sHBE3l/RLdvRJSxxGQ57eXljZAmXk5JP55MJkUpRLlcL+dnBS7l82nOHgOwMfubee9OVM8er6gjf5v4YQx2n/VBA5lg5G+ILzb8j7hVD/Jcxeed/3g8jvV6XdqDcYj8GSs4tToiYjKZlIM45vN5fPDBB7G7u7sxoYMCDr/wc288Hke/3y9tfHV1VcrX6/Wi0+nEbDaL+Xwe0+m0nPKZlWEffPBBvH79euPZuVqt4urqKtrtdjx48CB2d3fLxB/t7XcG2gXb8Ixj7Ol0OvHw4cNYrVbx2Wefxenpaczn8/joo4/K/ocQhO12O54/fx6j0ai8VywWixiNRiXty8vLkq/HHp/oGhHlRF+X5V1QCbaKioqKioqKivcA7E+EkiMTXSY6eLnmGl6AHaBHRHlpJ2ghwOBF2wREJrEc2FnhErG5DMwkA3udeVkGig6rYVAkWH2COirnRX5WlFAXkBVaftnOhI5JmEwA2T5WMrBvDoSOiQzKZfKJ/EykeakWaXIfJAj3eO8sk6eQNVlhkomdrLICJl9NjmUyi3RtayshbNOsFAEOfN1e+V63q9vPewESJKEwIciz8oYyUzf7BAEr9svqlKx+M9lIMEcbNS1Jy2ll0or6ZvKLoLZJaUaaJgncHi63y2Ryz0tssRl90/dmuP0hw6ymyb6Izekzy+WycRP3fr9flrQfHBzEbDaLzz77LD799NM4PDyMxWIRn332WZydnUW3242XL1/GdDqN//pf/2s8fvw4vv7663jy5EkcHx/Hp59+Gu12OwaDQazX69jd3Y2XL1/Gl19+Gdvb23FxcVGWiVLuhw8fxocffhi/+tWvotvtxv7+flmKev/+/UKO5/2bUNY8ePAgRqNRUfh1u92ynPXZs2fx85//PLrdbiGpvEE8vpvVsew9CDFi0tXKWtosH0pBO1tta0VXnjRBScVycvobPmOlJKR3t9stz5jxeFz6UJ7UYBxfr9dFiQU83uUxI082mJg2AU2Z8iSFbQXZNZlMih/zvQkwE5E8IweDQVxfX28QxhCkPBuwKyTq1dVVGZNMnjPWM+4Mh8PSj6jf0dFRWRY5Ho/LBM3NzU28fv26qOX29vZKvzo8PCw++vDhw9jb24vxeByj0SguLi7i/v37ZZnn8fHxhvo8j59sW8AS0AcPHpSlm61Wa+NEUS8hZp9EJvDoC7Y3RB3k9MHBQRwcHMTl5WU8e/asEGe8v9Dnzs7ONk4opx8xXrGPKu8NfvewqtL+9q6oBFtFRUVFRUVFxXuArG7xy6eDAgccEbdLfRxY54CbYLFp1jmrqXKQTh7+n0ADtQtqMZY68cILCUKgbUWE62ZC0AFUJni8nIcXar9Am1TyizdBi+tpWNWWyUbs6CVWJjK9FMfl5B7bzqqqrEAzOdftdgshSXtlpZXhpaX4Q1Yhut1s66wqgwDAP3y/64a93HZ5+ZdhFVaTYstEm9VoJpKs6qJ8TYSs60uekGzcl5WcTUQbebG8sWm5aibarDzKbeCy2o9yfrYFfuC6YB/qwLhhohp/p829TNcqpkx45vZwPhBG9qNMxi2Xy7LnoseDiFtVECTbarWKf/3Xf431eh3/4T/8h/jZz35WDitgSSiEzsXFRVk6enBwED/96U/j4cOHcXp6Gt1uNy4uLuLx48dxenoa6/U6Xr58GT/+8Y/je9/7XjnQBUXPn//5n8f29nb87d/+bZycnMT3vve9os5i/y0vn7Y6p9frxdHRUYxGo+h0OvGDH/wgLi4u4vT0NL788ss4ODjYIOguLy/LBvImO7LfmlAwIeT+RRtZcRTxZrndd1mqSF0YgyJu1WYeu6gvfQYfQLHntHP/ZAywr3h8tK/nZ49tANlptZL7p8cCrvHEgpfHe0l603hFv4UUXC7f7LH5+vXrDYKU9PiNDUajUSFLmaBZr99MHrHMEZ8fj8eFvBoMBjGbzeLm5qacYnpwcFCWIF9dXcWrV6/i5OQkPvroo41TdSHH7t+/H8vlMk5PT2M6ncbV1VUcHh7G4eHhBunlMR2S/ejoqDy7UdVB5s1msxgOh7G/vx+9Xq+oLQeDQVnyenFxUQjl4XAYV1dXMZ/Pi3oOshRfgpymf9JOfm67jdvtdlH/8R35sRTdEzdbW7eHkWAn7wP6XVEJtoqKioqKioqK9wAER8wG5yA5EzImnzzj7uDcJA/3W+GTVVgRb5++BnLQDXmFQigiSrDgAJ4AI5fbSzi8zIllMYYJM0g6ymBCwAot7iNQgxTI5KTvNeHEfXmjen67TA4gvQwz2zUTml6+mIkz24B6OZjIfmFyxIEu92cyjc8jYsPP7C9WqEREWQ7MvS6HSV77G9+53LmeJoBNSjqoblrqk+tvv87+bH90IO52so+a5OV+B4M+rTHXLRNpJrdNTOS8s29asWQS1nWlLhAK7HPkfbIy8XuXn7k/uV8ZDoJ9byaSIZUguK+uruJf//Vf4/nz52VZGGX793//9zg9PS2blpsY+uabb8ryxFarVfZj+rd/+7f48MMPY7lcFjLgyy+/jGfPnhWC4He/+13cu3cvjo+P4/r6OubzefT7/RgMBmV53tXVVfzmN7+J9Xodx8fHcXBwEOPxuNQJ++ZlmbRhp9OJ73//+zGdTuPLL7+MTz/9tIwBELNWDvb7/bIELiLKElzUQlxH2+blpbQL5YLEwFdYCooPuE/iJ/Yf75/lCQkDBa2XbFqlSDtC+hfBXsgAAFMnSURBVDjdrJhlPOUzk8y5fpQVlZ+Vm3kSxnWzGtjli9hc+g1ReHNzU8gufAzSLo8H9nfSHg6HpW79fr8sbRwOh2UiaX9/PwaDQTx48CA6nU6MRqOIuD39dDQaxb1798qzcHt7O/r9frEFWyxkQpMxeTAYxMcffxyTyaSQ4fgI/5PXYDCI4XBYlNn7+/sxnU6L/+zv75fl1998803pLyyZ9SEnr1+/jq2trQ1FGn7J/6PRqJCGnGhOO3gyyDanL6xWq7KvKAp57OZnFPcC7137rqgEW0VFRUVFRUXFewA2+uYlPge2vIzyd14q6Nl8Eww5CMkky10Km1wGkzEO2Alws2LNChkHIwQMLL1BXUMZuN9ldbDvwMjBmD8jrSaVhL/LdcwkHWnloC7by2RfJimsUMsKOQcFbjsHHdQplym3eyZ1KFdWZTn4zftk5XJkQs+29RJQK3NsG+xl5VdW9fHbqh63s+tnMo20IXEy6ZSVh6hg3Kb4cy5Hbmvq6DLbb7NPcE1TXbPvcL/JTYPl1VYWQezZh1BpUk+W9pkQM8HcRBbnekDSkg+2yssPgVVuXhrKiYqQbu12Oy4uLsp+UK1Wq+wldXR0FI8ePYrHjx9Hp9OJy8vL+MUvfhEXFxeFPJrP5/HrX/96Y6N5SANUse12O8bjcfzTP/3TxtJmfLfX6xXlzddffx2vX7+OP//zP4//9J/+U9mzLOKN4siKNPyAJYLL5TJ+8IMfxGq1itPT07i4uIh79+7FcDgsS0W/+eabePnyZfzH//gf49NPP42tra24f/9+IVZoX5a7QkJxomXui/aVfFIpbUWfpO72W+7xXlpWfa3Xt/sVco33hvPYZj+wL2Rfgmw0kW2f83MrT57g24zN1M19CMLP5JzJaf/G3qSN0pD0OUDIfu++6zH54cOH8fjx47i+vi4Kq9FoFOPxuCxjhFzb3d3dGEdns1nZL8z2YQsHJniWy2VcXFzEer2O/f39WK/X5fTM9Xpd1HCoJS8vL+Pp06cxGAxif3+/EIzszYa9ptNpUT7O5/MYDofx8uXLjf1as+3weXyFtoGc9F6Z9Bd8Z71+o+pDheZTWj3OuG3zhIKfRfxtBa2fa/8vJ4hGVIKtoqKioqKiouK9AOo1B9gmpAhYI2Ij0Iu43Sjeey4BAhyC3fzCnAkm/+1r8t4y6/WbfXZ42TX554A0v5g3kQl5KRHXmygykUbaPkWStFxPExkm2/x5/h5Qdupl1RMBloPM/NKff2gn8raqwm1mMsRLZh3Y5nrlumQS1TBxR50gJWx/k3PZdm5LgvjsX03kZhOxZHvzGUFVJhRN2tleJnzsf/iZ7zO5CTGX1WWUqym4434UElmJk9vHdXVemYR0P/U9+Ao2dr1MEkJy+AAI+qXHiqzSc9n8WS63+zHpEmjTf/FV5zmbzcpm6vfv34/79+/H1tZWDIfDuLi4KPugsan5bDYrBPxisYinT5/G119/Ha1WK6bTadmsnf3b+v1+XF1dxf7+/sbYYRIDtSG+sr29XTaoXy7f7OP3q1/9qvSFs7OzaLVa8cknn8Tx8XFcXV29tczQRO729nZ8/PHHcXFxEefn53F4eBiz2SzOz8/je9/7XpycnMTp6Wn88pe/jN/97ndxcnISf/VXfxVHR0eFVHv69GkMh8N4/vx53NzcxIMHD0o7o95jn7WsMrTqNI/ZtImVPPiLx2d8mvZDgQdJCKnLuOuxOyuRm8h19x/3N5chjxXU1WORxxrXoalv5ImGPBbY/yOiLGuk3LaVxzPbG1tBqDFmdbvdQizzN+nM5/NCCG5tbRXiazgcljIeHBzEyclJUcSZCIyIQiZDuPE8Xq1WcXFxEdfX17G9vV0OPrAa2WMrakU/X32wQCZo3betkm61WkXd5vajP3rJr98PbEfIRcYVDvTwhB5lcLuTDj7YarU2Djl6V1SCraKioqKioqLiPQAvrV7SRRDHi7D3jfEeXd4I2Ms0CJrAtxEvpNWETJp4rx7UALm8VjRkEgiFBsFyflE3keSglsCecnq5H4QJ6hVsSaBg8sQv9667vwdeguelrHm5qctussWBZLazyZW87IlA1aox7smk4l1lcJvltnQetocPAXCZDZOZdxFpTeUysXgXMQhcb9ow4nYz9UygWVFGwOZ8W63bZUuk7yV0Jq+dr9sA+7jNHORFbB6iwT0m4igfZXadXX4C0UxI5v+zL7s9s0LI/SrnyTXuy7aV/87t7Xy5jwCZYHlvby/29vZiNBrF6elp2dvp008/jX//93+Px48fF4Lj8PAw9vf3iwIIcuLs7CyePXtW8ru5uYnr6+uIeEM4eHzyNRBnTfZhPBoOh/H73/8+bm5uYjKZxGKxiBcvXsSf/dmfxcnJSbneG7DbvoPBIB4+fBgXFxeFaHnx4kV0Op348MMP48c//nF88cUX8etf/7psdv+Xf/mXcXZ2Fi9evIjPP/88nj17Vvzy66+/jnv37sWHH35YVHsoANnnimeA/dMEOe2blWU+SCCr+yJu90+j33FSK6dKQiiZtKJOPqnXRHNWuDURyx5XaKfc160mg0T2vpIe07a3bw+PgOTBP/FpCNZ+vx8XFxcbkzjkQRmaCEk27OdawF6IpIPScr2+XWpLWVBT+nnGibU817AvvkV6e3t7hYzGNhByKCCzIhdb41fuE8BbK+D7jFuUNftMnmzivcXPP4+HWSEH+Yi9INXJl2WqlB8fZ4KI+/yMadpa4A+hEmwVFRUVFRUVFe8RHDDzkrtcLsvssJdeZVIEssoz5jmQz4G4883L4jIJwo9fXAnOvPdZxO1LvIMsp8s1lD2TDQ5ivTTJS3eabEbQlP+nDk3lMIligoX8IjZP4MzqH6siXN+m3yYnqAv75RAoECT69FUThFYQ5SDOajQrsBxEW+URERt7Rdln2Hspg6CWNLNiMd9DPl4+lG2W1ST+235lf7fKIqvPvu3vTEjl4N4EapPP+z73C7d1toHrkgkEp5HbzT6XlTSZbMsEAISCg2fydLu4Xla0eL8uB9/2Y5PgbIJvdebu7m7s7u6WMenFixdxdXVV1GyffvppTKfT+Pzzz6PdbscPf/jDuHfvXukHLNnrdrtxfn4e//RP/xTPnj2L4XAYnU6nlBU/dLu577oNMhnMBMV4PI6vvvqqkBdXV1dxdHQUP/3pT2N3dzd2dnZiOBzGs2fPYr1ex2AwiFarVVR5jx49KkRdRMT19XX8y7/8S5ydncWjR4/iww8/jN/85jfx6tWr+Id/+IdysuSrV6/i6uoqWq1W/OhHP4qPPvooer1eHB8fx+HhYXQ6nfjlL38Zw+EwWq03e2jdu3cvjo6OyobynuCwP9KGs9kser1eaRfaCFKIcWOxWMRgMIjVahVPnjyJ58+fx/n5eczn8/jkk0/ixz/+cfT7/TIGcT976nmrAIhuDnjg70xq23cZh2g3yJwmQtvjpscQn8Jsoq/b7Zb0GUO8Ef/e3l5cXl4W8ggFFeNgnvRZrVZxfn5e/mb/M8ZTyLvValVUa56osY22traKIpxyXV9fx4sXLzbGAqvVF4tFnJ2dvbUcNk94WWFKH6Gvs1Szidz03ybDPAbkv02G5kkHj20eT/LkE/ezjQT2tmLdzw6u4Tlku941SfRtqARbRUVFRUVFRcV7AL+4EwT4JCyfhMmssAN0XoD9Au0gnOt48czqrruImIhN0oF0IzbVOg7sycN5Z+LkLiWTy+BlqSYkrGSwUsdkiYMwf+7yZvKwaekQaTlQibhVK3gpFOWx7b2cynUzOZq/MwGSlR/Og2tMDmZCJQezbguuhWTwMjAUYV4CaXi5KwFizjO3P+WyGmpnZ6fslWPCqsnPss+77RyAo3qyL9h2uX1NwPrvJoI0L2FuIlbdhzIx5XZrIudMQrKEzLa3H+fA1ASp90sz6ZL7d+4PEMm+hvQc2Lpt+Gm32+XERMaXXq9X0oD8f/ToUWxtvdmv6vDwMH7605/G7373u1gsFvHJJ5/E/v7+xob6Nzc30e/349NPP43Ly8uypBSVXKvVKiSIx4y8x1geb3KbrtfrmEwmhTB58eJF/N3f/V18+eWXcXx8XE4zffnyZUREnJycxO7ublxcXBSC6eLiIn71q19Fv9+PVqtVyKmPPvoojo+P4+c//3lEvCE3fvvb3xYyKiLiRz/6UTkd1Tbc3t6Op0+fxunpaUS82a/zf/7P/xnf//73y750lBm7mHD08kDv3ecxFX/c2dmJ/f39mM/ncXV1FZ999lmcn5/HeDyOTqcTP/vZz0qaX3zxRVxfX5fN+7e2tsqSRo9j7hfkYb/meWaiF8WW+0r2XZNYkFZWMNEHrL7KS7r5bLVaxatXr8regGzkz5LlXBcIMya+WIo8nU43+pKvdV/35ID9k2cn5ZxMJsV2Jtjw9fV6vTEJk78jP+zitDjMwc8nk5l5wiyT/NzjvuQ+lscp6mm1m9vEbW0lZt4/07bMkwp+znni5F1QCbaKioqKioqKivcAvFAyS+vfvOz7pdOkl5dJbm1tvbXHi/dM8d5RBM4cruCXVeCAnkCDF2SWXTEz7hdvExBeSuQlXyyjyURBxOaBDPll3QFAftH395BsBJVN6jPqZPLABBCBXyZSfB3Y2toq6gnbsYm4zNfkgIvfnKDnejpYNgFIGrZXxO1m5tQJ+2ab50DW6flaBza2dU43E0gmDq0wNNnbRPDSTrSllwnfRVTlYNAkSiabMhHnAC8fbpDrlZde5bSdt8ua/Tn3FYJhyBIv+WsKXIFJVp8ubAIjqzRdn52dnbLpeRMhxX2Z1I14WxFDHzeJgNqL68fjcXzwwQfxp3/6p4WMMkmGMmi9XpfTlff29qLdbsdwOCz7Vtmfcr0iNoNybOY+wlibyddvvvkmXrx4Ef1+P/b29sqhBzc3N7G7uxvHx8fxi1/8oizVGw6HMR6Py+EE8/k8vvnmm/jnf/7nOD4+blzq12q9IeKfP38ev/3tb8uec7PZrBwOMRwO46uvvionKo7H4/iTP/mTolpmrGJc9nhmotGHy1jBaFvs7e2VJbmvXr0qhOZnn30WDx48iJOTk5hMJvHFF1/E06dPyxLgg4ODOD4+3hgDPXb5OeGxMxOgEGWQVfZ/n2TMZ5BbjA1WpkFAoVK0X0NmebLg6uoqxuNx6Ts8h+3rfl5A5JFuHiesJvM4yUEGlNNp5+etx4xMEOZnmccDE9T2acpEG/kZ6P6Q4TxIn2tzHd0PfeKnn8V+pnkMs/rM5cFG/pw6WN3oslWCraKioqKioqLi/1N49pqXS4g1vxRzrYmBiNhYegIB4Rn9TASs15tL+5qCaZNk/p6gCbJuOp1uLDmyii6TM6Q5Ho9jMBhs7ItjMs1BTURsBBt+Ief/JuRg2vVxHR0geAbcduPlnuVIOYh3+t4TxnZzAO8ZePZXirhdukWeXrKJ3R185NP9HLzaNtjPe+J4v7Umf8xqPJMSVpE52GlqI9uaQMuboVM/55tJnZx+tq3bzPZ1+b3RuJexNRGJOZjLPuoy2ccc/GcFhb9v6r+ZbMv9rcmm2TYuj8lW96tMRNpP+L/dbpdxpKk9XV8TvVlpsl6v39oT0cQf49PDhw/j6Ogo9vb2NpbckQd+ASkfEWWJ5mQyKXunubw5iM/BPWlmdRPjMLi5uYnhcBjX19dlmWNExOvXrwtJcn5+Xsgrj9P4+ZdffhlfffVVyYt+6FNCT09P4/z8PNbr25NEIY2Oj49LuXq9Xjx+/DhevHhRlnNaZcXvvG8ktqae+JzHuOVyWU56ZfzZ2dmJ0WgUz58/j3/4h3+IwWAQ/X6/HOSAyqrVerM5/2AwKLazKo26uKzOPyLKfmndbjeur683lnXik3mstu9C4vj3ZDIp+9c5HyYvVqs3J2x2Op2YTqcxn8/LSaJedohNvRQWoKajX1gB7Mkwyk09qBN9xGO783Wftc/m8aRp3Mtp4QvuJ7SBl4X73pwmbZfHO483uV9xDXY1yZtVaW5TCFJ/5/GuaexpGqu/KyrBVlFRUVFRUVHxHsD7yWQSJS/ViNhc3kbAxgsyy4t48URV4pMF/WLsGfwcdBMYWGFEUE0aVqzcNfOdZ/a73W5R2Jk8irglYig3S2AywZjJhkx2mOwhXZMxlCW/hHsZVb4mKwoIJGwbgiUC1hxgmZjLAWZeFus96lw+fMRqjaxgsMIBMsFLhfL1TeTNXaSSAyLbw6o/bOd2j7jd7wnyhfvn8/lbJFFuZyvZ8He3mX+yKjCTSRFRCBBv6g3cfplQNAFJ3ejDXE/b0Keb7JsDQvsJ+blvWL33bTCJ07Rfots3B+W2p/uAiRg+a1K9NClNSMvLw1hGiRqs1+uVgxC2tt4sNxwOh2VcZNP2y8vLYuO9vb3SF9hE3uUx6UDbZGLACh8TA5QNe9DPPKGAfzHOmgwkT48PkDzr9Zvlf5Bw6/W6HFywWq3KIQK0F8sRt7e3YzQaRav1Zvnp+fl58WP7EnlSdv6n7+UJAHyM+pgEtV1QtbXb7UIOoapbr9dxdnZWTrp0n+MHYsV2tj+hDmRp8WQyKaSb68H1JoqcHvYzccX+gvTL2Wy2oe5Diea9T/P4bjLKbW6Ci7+9RBXf8/Od/sCEg9PKv92/IHmtKAcmifNnwEo/6pivwb7uz3mcb1KE8x1lyCri/Ly1bVxX+yTPLE9QYQePQ54Mwb4e674rKsFWUVFRUVFRUfEewSQYL4l+Cc2z9ry4QlbxUmuCDHCtX655gfXJcTkgj3g7EM8EU95w3C/N3O/7ImKDjDApQX0dnDsNf+a6+btMGjnAz4Ef5STvTBYZVjKYwMvLrKxmc+Dql3+uy4QK5W0KRAlSaKNMZDUFK1zvoJqgHRuZ2MpBcW7LJlLSyISi2x7s7OxEu93e2IPOS5X5uYsEdMDGPfgvefKDnagLAS02gMzO9XJZnA/t5qCen6xacT/M5LPr4v3TIAK63W7xQYhAE6nOJxNd9nHby2Rp7jPuD5lAdRCd76X8tKsDX7cTbcR3PpRge3s7ut1uRET827/9W9zc3MTPfvazODw83FA3Xl5exnA4jJubmzg5OYnlchmDwSCm02n0er2NcfOuMSATCfn7rBqyPzqY995nVmXZprTTcrksqi/GAhM8Vt+YuGOchvTxlgDtdntjCaPrbeIZEoxlie4H+DKkEyQgG/ZD/HkSZLlcltMdTdZjRyZErIbK93sMzD7aarXi8vIyer1eKScEttvMfY3P1uv1xrPQ9qaubmPKzrJdK6qxE3Zx33C5SAOfMGHlcZXrKAft0mq9WQ48n8+LD1vR6rz9vPZEk8cAlwn/IV9ODs1KP5ev6dlKuvZL+3sel/258/CYZAVgJvPy5EbuZ36+5TLaJrlPfhdUgq2ioqKioqKi4j0AL4O8QHqJVp45z4EJwUdWmvnFlkApBwqZRDERA4nE/ZlgsxrDyz/84uz68Xu1WpX9lBxccY33jmP5jMkUq7JycJzJRQccEZuKLAddTfuA8VkmWVxe/vdhCgQFEEi2Pfn5OgIgBzrkRcBnBaKVAw4qHCQ7IMoKOBMiTUoJPndbG3epzDIZ6OtsYxOm9oXVarWhVswEmn0wlwF/MJGJn5jAxado/8ViUVRDfN6kGMUv+L9pPzP3Gy+vdKCYyR0Hlu5rTQo5fMqKtiYfN3Ja9jv3PROSTQExP+47+SAFrjep4T62tfVmSeTW1lZZ+gd2dnai3+/HP/7jP8Y///M/x2KxiNFoFH/xF38RDx48iNlsVhRsV1dXRfl1fHwcy+UyTk5OYmtrqyjeXH/3U8pk8taERCYl8730G4+xJtFdV++F2W63YzQaxWAwKEopSLImEpe0IOAg5EibMcX3uI34jvFzvV6XTe3zQQSQdBA30+m02Nfk/Hw+L8opK2EhS6mP/Y7y0B+ywgqy2xMtnU6nqMN4JkGCmwDyWOP+jjoNeFlsbk/GRx8WEfHmGTSdTksa7j+ZOHabYFc/O+xTeRxD4RoR5aTd6XRa1NseVwwfOuIJKUhIk7EmcT0uURb7Au3tcdt9A5Wxnw158u3b2p/v3NfoA/mZxLW2I581kf5NY2Aeb78LKsFWUVFRUVFRUfEeAHVDRJRlkT5FMM+4G7w481KOGgRCweoIzwYTWFh91e123yJXTCb45db76SyXy6JiQ3lAkJfJMUiniNvlS5TN+RJ8LZfLEpCbMOQ6K6RMQmYSyssRyc/khIkGkyl8ZhWFbUYA6usiYmOpqNUueamuZ9nJ24dRmPR0QMf//LZihLQJWqxcyYq5JtLHwbr3v6H8tG3T3jjYOisNuB5bOLCGCGCpFHV0nQjy3WaUMxNCTSoH2oE+lINt2hbCrUlxllURmYwlPfqC7e78MplpIsykN3/bv+yf7s8mA52HfYf/3b5c72XfJjLc51xHL5vM+3y5/5jkdZ9kGeB6vY79/f3odDrx9ddfx1dffbWhYPlv/+2/xYcffhi9Xi8iohBE/BwdHRXF03g83iDebWMTV1n1ZP/0uOa2w089/m1vbxeFl0kFfI2JEhM0Jpqm02ns7OwU4sp+YsUWy1Ldfs7XfmEiNCIKsUfa2Khp7FwsFkVJaJVTxKY60SeBMhZAoGay2RMI9iEvfTWxQxooOVHL+flDWTIBlNvS6sOIeKtN7a+TySQ6nU45YKHb7ZbDhUw+ezzCD/ibPE0W5bGI+60GY8zBHyBhm55R2C6T64yPtn+euPCzg6W99GX7JfD1kHaUw+UxyeYJJJ4DJoOdh9vHZBplYgwwOWqCPD8/MzHYRE7+IVSCraKioqKioqLiPQCBasTtxt8OKHjZNOGSFTFeRkbwwYs+ZIX3y8mz0p4RzwRXJju8TImXdxQQBAsRt0SGSTWrQFB45NMSHZhkwiDi9uU7k4GZXPN9+bscYDoQc178TXmxq8nLJvLOCsROp7NxAh73Wz3goAOylba2qoDPTLZ5WRR5sATNp8hm4oayehmUiRUrB+xzBIGUzQGwA/KsykOZ4sDR9XMZTH5ge4gIrnOw5j6zvb1dSC4vT6Me2IRTLk2+sJTO5TF54qDcga9PHMWXuJZ+5DqZjLFfkN50Oi37lOX+Qpt7jHAdKAfldfkdqFKO7E8m4ejfDqBJA7J0vV5v2AyQFnlhb5Su9HvIjKOjo+h2uzGdTuP58+dxenoaNzc38dd//dexu7sb/X6/+BiHCgyHw0Ju9Pv9WC7fLGE0MZaDdMqEL5rUBN4Hk3HCJCR2IP18uAt9OBNmOW/aOhMTkDtcT1lQMbpfZiLZ7b21tVVOxvT+m7le3DMej8t9qNZardYGyYet7H/cz0EU7KeWxxA/B0yI+Lllgi4iCrHODxND1AOf4m/sb0LO/Ytnap5M4cCGiDfPZAg3lwfyz8Q1Pukxnj7q9OnH9jWryqibiXW3I/ayQs/qNI8jJr6cPwRyVp7m8kVEUfFBdlrpbTLNdvcY4PZ3H2QyME8u2ZYcWuJ3B/LOE0/4gfuAJ17eBZVgq6ioqKioqKh4D2CiJy/ng9QhuMoBI+RXJk0ILPIyLRNFeSlSntUmiPcLOeSFyTsUcw62CXq5xoECebD00UGjZ/utzJjNZiVdlycTjaRhWxhWOWS75xl1k3fYuIl0sUrHATvXmJyyWsP5NxFc2J97HAj5HvKFVCI/iD8rGrBBk/rA6ZuYcqBDngSPzi8Twla4LRaL6PV6G0osymLFAeU2iUd57BtZWWXiwPax/d037Ctu6ybFCmVzn/LSq2xLPjcRbPLRAan7suEliZTVpF0mQiEGI6KQ5i5f7gNWVZlod95ZddakcLF/ka4VlCbrrQBDqXRzcxNffPFFfP755zEej+PevXtFTXRxcRG///3v48mTJ/GDH/wg9vb2otvtxmg02lCIQmwcHh4WMoZxDqIl+6d9yMSLfSiT9PaNiNsDO0xeQhy6v5AG9jO50zQemdjzAQYRb8iOyWRS9uvKvsH/PryGMdlKK/u3FXvYAF9yP8kTEPYNvvMhAXlvTt/nsWW9vlXCsRSWMYOJiFxG96t8eAukLUskKZ/3XuT/+Xy+sacZ7b5arcqBEp488IREHk/zszv3CxPYmXjzmGFf8fPGvuTxz+lkwjU/izzWOd+cjuuRJ95cF/uR/cfji8c78vQ1eXzyuOL6uNxud4/ntqGv/66oBFtFRUVFRUVFxXuA/GLrQMIz1A6AUTZ42ZP3YssnrHnG3bPFfnl3kGbyh2AHkuSuQMunMVo1lIMDfntvnazC8su678lEF/UmKGkii2zjTIA0kXG2l+1Lfv6d88vkDf972aTzwe5WhDhf6puJHOxh9YbVapCSOUhxGck724Hv7CdNp9A2BYm5bUwomOhyOQy3hUmoTG5YQURZCAAj3j5Z1H7UlJ//d3mtmMj+YF+3f9k/3OaU1weNOE98GNUY5YCwATmgJi1vZN9E2pkUzsG6A2KXDTu4fraliZeIW2IGn2E8QQGViZHhcBjj8Tja7XY8evQo/vN//s+FRJtMJnFxcRGPHz+Os7OzOD09je3t7Tg8PCx2pJwO/J0vdbJNTEBlxZfJCP73tRDAkJq2JXbyWI0Ns5rYfpJ/XI5MLjgvVFbuB01tYzLW5TTxw31Ox9fbpiYsPSblCYomP2rqix5rvBeanymZAMsEpkkz7ytHPSCR7Q/sF8gSWhPYfp5kgtBjGeWyasx285hFXU20Z5uYoPJz2X7G976niZBrIsO8j5qJQ7e34fHOylOe3Uy00E9M6LvM9p0mIg/kMZt3A9/HM8v9iLHFqrlKsFVUVFRUVFRU/H8MvwzywshLpGe0rRCwaomX36xEyCoDXvYJhHlpddCPwoQ9eazYsXqgaSaaclOepplt8uYFOde7yS6ZhMg2+7b/TfhQfgc+DqJM7OTAivLzXVYs5GtNkmbSyaRFVrOQj5eZcj/BZVOdTUiZ7Foub0/Dc7lcBrdTtlsmDV3eJnKRslAPK4UIgn2NA0vSNsnVarWKevEuoqepjXI/Iv1cVgJ7+5YVOc7D9qKcJj1y/yN96uNldsDKMNqMvuig0eoo9z8rxZxuLmv2Jfu8/ZMli6RBvexfrq/L7aXFJiiwJf6AKqnb7cbu7m5ZArq/v79xCMJkMol2u10+G41GZWyhf0wmk+LjXvbLoQp3kY2ZHMy2MlmciRzuY284VL1eKkgbWeWTJx7cn7zMkXbFXij2uB7CAoVe07JR9x+TELaJ64LS0P3aJAxl4fljH8t58V0mqTwumVyymg7f8OQBBI6JLZN62Iyl8e6vmaR0OVer2yX5LosnUkjH2zfweX4OZiKO8adpTLAPZHvcBfuHCVGXx/7ltmQMyoRfUx9wvfjb47wnT/jfKkMTbS6L2yJPuGTb8Jv9BrFpfja7Xbe2tt7aS/ZdUAm2ioqKioqKior3CH4x5+XcqjAHahGbL6necyoHk37BBibdcrB1V3AZcUvM+QWcF/2mJax875dhysE+LCZ78lJA55EDfaedA8JMwJjYyORaJol8P9+5PE2BkoPOHCA4cKL+KE+8qbpVU7ZTDlgcYLgdfHCAgzbKkAMcL1VtskeT4i7bJAexWW2Rgy+TQLZ79k2nldvAeTkfk1v2B/uPAzU+516TBS4LZBH33xUo2s/dlhB8VpPg+94rzLbK/mhip2lJluvsfeRyMApMrJjItQrU5AoEpetnIiKPE4Z9FdKw1Wpt2GRvb6/sVcjeX7RZt9st+z2ynLzX68VwOCykwevXr8v4Y7Wh94a7qw+DPGbYP/BR6mD/8X5SqKTIJ49jEbGhMqQfuu1N6rh/ZgKt1bpdUgn56mW92Ni+zZiDfTKpt16vy7JJvs+TDlZGZXuZyMvX2f9NDtnOqG6xk6+xX+YDNbIqzMQedjCJ1Wq1NiaZsk+4jci7acz1GJMJSrdlPtwHsjA/r+yDJr5dBtvM6kqXxb5nMoxTWrkuk+aZsMuTaPzt50vT+4avpawev+3jTaScbW0buGz2B9qy3+8XZSk+8S6oBFtFRUVFRUVFxXuCpiBlvV6XQNMvmpn0yESKA96sQCHgzy/OzMz7pdfBmJVylAlkgobAKKvvXF7SopzM9Ht5EHahTiZHcj3vCu5t30x8ZEIkB05NhE4mAJ12Jqb43IRiJqM6nU4JKh1kOEjLs/yQBlZluR7eAB8bOxh1AOqArokYyySOg76ma8BdyiCTBL7GasbsWw4AnV4uv/3AbYUdXcesbGoKdFEOWUFBXk3kggNH9yNgAiyTge4Xmah1u0HIrtfrjcM1+N792X3J9ve1tg15ezzJpF8mMl3O7Kf+3LZ2e5q0oa7b29sxHo9LPWgL14/6r1ar6PV6JaiGHIK0W6/XMRqNNoJt2sXEd5OSJquUmtSQJlxbrdbGoS2QkE7bS4A9kUKai8WifA9B473MmEwZjUZFvcZ3TMZ4qbgP36AeVkZ6r7ZW61YllpVn1COPX3nsM7Hmsb2pn0EIQuo5bWzmfTfxI5O0EbenlTb5q59zWV2d+3Am9PjO44L7h8kpj2vY1OO90/AzhnvcRva/TK7Zb51XLq9t6XHPfZvnQ55csB9AWtueLqeXPrtfu71dtkzMkXcmW5uezX4H8vsOsE9Dyr8rKsFWUVFRUVFRUfEewCSFCQyCE59Axwuvg9JvUwSY7DFywJsDTgf3Xu7HUrqsqoh4+yRT78fkl/RMipnQc7DhsjqY9RIqgqsmVU8mTPzjtDOaiC5vIO6XfAcxzs91yHYl0IMw8OmYbhMrInKAa3vlNssbSm9tbZVlV3zWRJ7lsrutrIzJqgW3mTeYdvmoT5PC0u3e1BZO1/eaXHJa9gOX/9vys02chk/SpX95ORv3fpvNbFv7cSaOKJP7EaRJ7huQEuSdFXK2fQ5gc71dH6txIuItu7tdvVSZ+lp16WXnJl/4HmTCxD7tZYJWz9I3sBunj7re7XY7BoNBrFart5YOYves9PE4zLVuY9/rTfNpA/sIaZrQzySS/+50OmUJMIc1ZPVYJjlBXopIuUin1+uVcmdiFqLF9livb/e79HJN2yTD4xTIBAjlps1tr2wb7AZRkn0w29P3WTEGGWZS2X7Nz83NTTnB1XawyirXnfxpb49x9lfayOXMaWTbmlzKy2M97jU9c2yriM3TQ8kfH/F47Xq4/kzK2R5ub78jZBLP5KDrlp8hnjS86/5Wq1WUrhBq9i+X+a5nyrehEmwVFRUVFRUVFe8BeCnMQR+BlD93AOxAz3ukoR7zniv5JdckmdON2AyiHZzkPbMc9Lm8BGaZDMrkWtNsfVa7+R7KlgPMJtIsl89pWNXgl/ocPEGwUDYHXa6383VAmPN3eXNZICIol79zMOL0rFa6a2+npnJkNZKDmUws2K5Nn+d6ZcLPtua3VRP2Z/tkTq/Jds4jL52+K8jDj03E5H3Rcvv0er0NQmy5XJaledlfctlII5OgDvJNMtCvXR/XM9vX5JXrnPfps39k37D9bCsTFC67g+km5Z1JP9rTCjLqTPtnMtL5epknY4vTwkeYiLAN+JxlcXnJGHYzmsYLEzNOf2trc9N4+4OJTS/vdZuitvNm/NQRm0REUee5XCY4PPHgtsNPaTcOAGA8t09lQgLCyWM/trE98pie/cZ1th/xeX5+QG556XyTSstt1USkZKLPZFyTEi8Tv7aHn1VNZFj2lUz42c883tqG7tf+2/sr2rcyEekx3/7B9e4nLmfON7eh7ZL9m/zx2/xc8m/S9CFF2Yd4NqAchkTPz13XLz9HqB+KyHdFJdgqKioqKioqKt4DWHmSAw8HlVxnws2qgBw0cy0vwHkGPM+OOxB34LNYLKLdbhfiKM/+myRy0MlLciZw8ou8X7ibyDcvX81knYPdHNw4rfxj2/NSj+1yAGB7OdDJaiyTBS6TlQsOgpoC+UxIZNKH9E0OOuBz4JFVINlmDmxdd1/TRCRyveuabeW/M3EJmeNN7932oElFlNO6q/0hGMgfQmy93lxamcucSQMrd8jH5TUp2kTwERznvbFMeuf+uLW1FdPp9FsVf+4v2caZDMr9Kfuc06XuJu4ol6/NShqThbad+wDlYUxw+7he2U+alrvarvRfDhxArcl41u12N/qCyVzb6y6yhmtIA8KPNuUgGBMBHm8zIZTHLZNlXOu902xD7IWSOfuFCWuUfxGxceCD/cDKQRMtOe9MMrq9XCf7Zl4a6jbN43/Okz5nOzX1gzyee6mx69dE9jTVebFYvLWU332Uv3O72qbuz3wHYQh5aoVdbr+mZ8pdPuXnovdAzT5mtadtb4It18dtzXuCJwv8nPG46X6Q+1seB21P+u3Ozk7s7e2VPRczWWa1I+VrWoac7ftdUAm2ioqKioqKior3AF7+55dnB187Ozsl6Oa7rCwwYbJa3S4n5QU7B0JNy1ocpJmkIFiNiLeCaa41IWgSJ+LtQCEHEVnlk8k36uzvrQZoemnPAZjtlQNsgvQcsFFfB2tW/BD8O6jx0lVs43I7CMl1N5mTiRF/7gCCdByEA/sP1zp4d0BL0JeD4hxIO4DysrhWq7WRj8mQpqAcG+aANgenTUSYy8f1+HJG9kUIEi89s0rI17pOqI2s2MjtkgnUvE+e1UP2Wa7hb/sc9cKmeY+rHEw2kc3fZkd/T/r+Pvua/ZLrrSCjLZqWFZtAzvuqmXQjLz7nx4Q91zjApz/69EkTD5PJ5K3TjbO/kWcmjXI/phyz2WyDoP+2caiJ2LGN8qECvs+kk/sVaWZfQa1GfX2ggZ8TOR2Xy8uHc9tngoP78sSCnzWZ3M3p5QkgP6ua+lt+NlB/6pbJc7eJ78lt+ofybyJic3/M45fruVqtyjOCcYL3ANvb+VCWbLdMXlJ214/62odcn9yn83Paz9pMxNHf/BzI5afM9kGPR/P5vBBsvV6v7DHowyAiomxRkW3qvkp+LNF9F1SCraKioqKioqLiPQAKo0ywRdwGLrzAMtNrwstBoPdB8kt2DubzDDcz1FZRZGWNySOCAKfp4MQv6yjZcgAQEY0vy03Ek5GD6Ryw3RUsemlitrGDU9Imb58Mhx1c/kwkOSgzcZoDmlwX7ncZmgI5E2pNJEtT8O42pMzevB8iyISh7deULnD9fT33ZKIn28ABlNvEvnrX3w7ibZv8Ge1PednDh3y8nNr5mHwxceATG02cNbXrXT7eRGz4Owi97H/ZBtTZbZIJjayiJJ9McJCOSXIvXaW9svLJeeel5FbrtFq3pzdiV9oF8pJxyHVratP1+nYfuqZlaigkyZcx1ks28/hCnrabx0qTHNR1Pp8XUqHT6RRCjzTYB839z/t0tdvt6Ha7G21jgshkPL9pE5eZ/Le3N0+fbhoTbUO3vYlq/O8uIs/ltT/kvuyy58/z35noMuGYr8t1c/lt60wkkk62Xx4nmyZI3K5N9fi2+tk3eTZgZ9syK7Ca+mcmCHk2cy1+kNsjq9/4zJNNuS1cdj/H3QecjvuLJwr4cdt4goYx9ObmphxMwv+UwxOFnvAif+rviap3QSXYKioqKioqKireAzS9tN8V9BKQ8jKdv8sBiIPOTDhERONJWw6I8my61UZWKTQRg54xJ6B0sJkDk0xA5CAiBzrk6zwdjDtY5p5MKuYg1grBTCI52FssFhuz9q4DadrOmYTw9QRYpEdgC5FqO2VC02oyt5mJIdvMqiK+8z4/DoKyT+R2ymQR/7tMTfXNBJ6X3HmPnmyz7GNN/pJJgBzY5nT4zsuEM8nmfpMJOQ6oaCIVmgLiTELnvuB+yWeQnlZvONDN+boP5CDcQbB9E5ut1+uNjd6tAKL++X6fAEtZ7We+ZrVaFUKp2+3GcrmM8Xi8cYIjxAPKGBPUkPURb/ZZ6na75dRNruOgABQxDuAHg0G0Wq0Yj8cbhPtdfdM2dt/ywTMeW0zcmhijHHligb8z8U/ZGO+zss9lsB95DPSEidsrqzwzaWgCpGmstr3ytZQPotS2N7Hja5uef9l38322Rd4CgfYwMWMypukZ43bEhnmZfvaNfF0ToZb/9nOBvOkr9JHpdLrRpm5j7JJVrBk+ydT910Sa2536NNmZH8gwn3Tr9DzG2p8ibtW/bku+39nZiV6vV773ybFWonlfSdvZBxDZHk3X/yFUgq2ioqKioqKi4j2ASaxMGjiQXq9vTybzi3EmEAD3mrQweDmG9Iq4VWQ4iOJlmBdeAhp+A5eb/ykXgWcmE3LA5M+bSKKmOlidY6Ilk02ZuMnXWZlDkExgiB2xRV7Cl1Vc/jvbx2X9NgLJZc6EjesKPNtvu5hg9KmrrdbmpuJZSZCDYLdBE5HTlEdWv/E3AaDrTJpWYlg9lpVc+V7XNRNj2U7Oz/fk/md78LdVGLksObB0MO3vXIZsm4goNux2u9FutzcI9YjbZeUmSzK5SD7O23Vx3/CJkfneXDZ/BjFJGzm4zeNZJq0ZEyDuGXtIh33zTDjzYzIgq9tcJ9vD5YDc83hmVVv2a/uLSbfsMyYtICN8gmMmkzIxZX/J/RSYwKQMtgd7DLLsLvuel3E3tXUuTx5z8/Mp+5NJQhOifo41TUDwWZ7sgETJNufvrESD/CENk3vuu7l9PSaZTDKabJHJ0bvslUnO6XRayGCnb9Ivj595wsm2xJ5NxCvpmTjnPj/DvFeifdvjP6fdopJsIlwjNg85YB9EfNl9joM4+I73HPLEh9wvaPemCQueZezJ+C6oBFtFRUVFRUVFxXuA2WxWNlcmKPELayYsmjaP9uw9L9vck5VbXMfpek1BRkYm1BxQ3KUOaLVapS5W2o3H443gzYqYTH74Bd4v+hAF3lPNAX2TiswqhRw4+sWdl3cC10weeLmS99Gx6oS2sy3uIhRpA9SEVspl8i8H5CYo+D/b1kEZvsAytrxPVla35LK6zJk8cTuacHSgyLU5H8qYyQu3Tw6mcrnsh5k8tTKF/NxXcn39Y5+0X/vznGcuNyRmDn75zvV1PTudTvENAk/8znllZUyT3e4iE2k7CGSfbum9BJ0nZc2Kvkx+NREmJq59CIEDduyCSpBr2SsPO47H440xyT6UCT4AqQEZw3UmdDxm2p5WpUVEIT+z70REURq7jTzekgb2dvvY7nzmveNMirhtM8FkUs1jretlf8ukm9O2j/qaTJSRdibg/L0JeRTBfk5QR491WWnF504HG6H4pIzZRyiLl8CSPyQvfpLrmOviZcR+7uR99Nznu91uKRd9B8Um5fLkmu3l53r279VqVYg73+NnQ26DrEq1fe0vEF2MP95P0s8n6mnb+XlKuuv1GxJ9vV5Hr9fbINwiYqOOJtVs2+y7ebx7V1SCraKioqKioqLiPQAvoN4HK+/JtV6/WbrlY+sj4i3lkYNhv9DnYM33r1ar6PV6GwExL/l5dpyXWgcEq9WqEAFNwb2XSxGANxFsebbdL/cm8HKg46CjKQBzPk1knA844B7v10XbNBGVmWj00hbqdBf5AlxnlkmaUM0BVA7Ym4i0u0gWbIOtIFkhPnPQYlLT5bXt7aeUy/fmoNuBmcuYfdTtnYnFrK4yKeJ7rHygH1iViL9k8rWpvncRpN5nzeQRZBg2IXi2jZ1WVuSZcINkclAOgUMa+I/7rH3BbelA2MQyPjGbzcoYYkIZYtH9gHHA/kVZsh2zL2IryLx+v79BehFsM7ahnKHuEHOZTOAe7D4ej2O5fLNHWafTKcoklGz2I5M57rsee+zTHqvdxowH7XY7ZrNZ6WekSV28jxxl9rLX5XJZ9nkjD/7v9Xobkw0QkO12O6bTaam/SdzsFx53PU41jVVcn4lhE2omQvJ41QTKy3UQOVaPUTaPN+7rLAd2f3DdXN88fuVxIRNfXqJrH7B/Y2c/E7kHos5jmgkp2g6VJnl6iavtnAnaPF772e/7KaMnrdy3m8bAPHbn8dhtQbn9/LSimt/Zh5bLZVGr0o5+J9rd3S3jEnmY9MQWpN+099x3RSXYKioqKioqKireAywWixL0dbvdEkDmmeWtra3o9/sxHo/LvSbVHFjzsmzVCGQSG2rzAruzs1OWhXqfN6ujuJ5gxsoDqy0iboNOl8sKF16Iefn2LHgmm5jtd/Dm5WcoXAhm+TwvqcuEhQNNPuM36oXpdLpxXy6jAyATltgd29Cm1DWTpKSVg9y7iEL/T14sOdrb2yvBIvYySJd8IDNQ0TSRd5TLKjPSxUccQDmdJmIClaYVR5lkwlb4GoGYfctLlU1s5LzxTweEJoQoP+oJ+qOJa5No9hO3BSQBAaDJO/yZPuHyUm+TRCC3X7fbLYF4xBsyYj6fb+wnRj9rakuILPwGcsN9KyJiMBjEer2O6XRayuP+ZWWJCUpskUkviGOUcVaZoaa0L83n87J0jPHOKje3h/s/xAR1NmmBr1OPg4ODmE6nERFl3yvak/awHV0+t09eMmzyAMLAKiXGM+/7N5/Pi+LKPky9+JvyMY4Oh8Po9/tvEW2ohD2uZLWdFWImmKyGpb08HpuU8TMm9wcOcMi+TNpud5NY19fXb5H6TWq61Wq1cYAOzzHKMpvNNiauvA8gNlksFjEajQrZubX1ZmkhZYy47f+2B+QvSi6TXh7XUHFZUWdfyopjjw9uK/wR3+x2u+X5ZAKVds5kFs936sznjJ0cikBaHg8ggLnf7yQek/EllnHT5oz59oFMBnofTsAS54iIfr9f2oi2N+nJfYwz30YQfxsqwVZRUVFRUVFR8R6AIJZgsUklwW+/TJpU8stkViE40Obl2S/sBBBeqpP3L8qBNZ81kXouA/Wg3IPBoAQovNA7KLE6wAEAZXUQ6kCUv/mNMovAiPtzGUFWVDkPkBUbmRTy/7aPFSqZQGnKy9c4Le8BRwDlgMgnP+a9sXJwS1kgJzqdzsYm07ZL/tvkoL+3uuQuG9qX/duqxnyt64sPmCTM+TWRkVZKmch0+X0fhIf75F3LsrJveX8h2sjpZx+kLzpIdYDsa+8iEWlHK77oW/yfyb2dnZ1ot9uFpHDQShl9gIHrbYLX/pr7fFaW4cdcb8VKJnKp59bWVkwmk1IPEyaQsJDZHrdYfjqbzWI6nW6opEw6MV7kJd4m8sibe33QjMdlyn6Xwo/DHSBxqbuX5mMnJg9MFEMcobxjeS0+u1wuYzAYFHKJvEyOYB/7L+WMeLNlAXW1utfkShPhRVtybT4d08pR2xU/nEwmxX9zP8g+Rj+CZKKPOg/GDfu2bbBer6Pb7Uar1Sq2oy18qmsmVClPJt3ZqB9iyOS9ScncH5l08fYQOzs7pQ2trub5x5ht9VvE7emhLlfut1awTafT0sa0A9dtb28XAnp3d7ekmSdf7A+5D1NWtzX2IB0f8gNpenNzU0i14+Pjoq7udrulvt5LkPp0u93o9/sxmUw2CMrvikqwVVRUVFRUVFS8ByC48YbQnU4nIpo3b/cSkLteInnpJOhDMeQg0UEtL/x5VjgTdVZwOJjKZFMTGegXctfpLnLQ12R1CHkBX5cDY671zL3TIZigXHlpDqQAL/SQJ5k4+jaikzLSHrYZ33GP2/ou+2TFIPXDBk171Jj0yKQV5YFwYAma8wAOmCI2VWCZHPa9lMfkkwkuK0ZyOxF4rdfrQgA40M0+4jL4M5OAuU7+zsTCZDLZOI3P7Z/zy+3YVA77lfuo++QfSsdBaa/XKwoVlgZSdueZ+w3ED35jko+8rPpxHb7NR/2ZlbSQjVaJejmXiQ+TEv1+f6Nv5jwyOQnwcXwKsonvbA9PbDQpi+zPjKeoB5tsQhlRF3J6KUtCTYbbH+jbnghh/KGtrICdTqexWCxiMpnE1tZWnJycFN9FBYid1+t1ecaYVPPedxA7AHWTST9PzlB2nh2eMEEF2uv1Cinp9uWZtFgsinLUdqfO+IpJnTwGLpfLGI1GERGFpOI0XG+Y7+cSbdXr9WI2m20QXPgqPpiJdT8rKZvH0iYFnn0/+wp5+D7bmu0hIBQh2CDERqPRW88b9z/3MYjJvHTVk2muO8R9v98v/ST7bn7+ut74FTaz4pGxDx9FyejvhsNhRNyqd1Gi5rGRSSbsZdt+V1SCraKioqKioqLiPcB6vY7RaBSDwWBDpUNwcVcAm5cB5lnqTIqZGDChlEkCXradRsTmKZUETf7MyMG3VQhe6pHLzT1ZmWUy6K7gnuDRdiMfz/JnZOIjByUO5hzI+xoHf7abA0KUbJCpmQRpqt9dNv22wMz1MLHpJZUut8tsUoJrmtrT9TJ5k8mGTL46yGu6xjbP/0MEUUfq3LTJfLaR77Wdctt7Hybu4T6IRPvIXT/2gSYiznAfgliwksrl5H4TX1aEWo1oNZX90yS6P89EqceHpmCVtsvtTpldXtcrq6jIz0sR8VPqYR+xD3l8y+pO+j/lpO9RLi/lzTZv6sPZtyOikL4mFTy+opBCpcxPPt3T9qW8lMFqVAgv7Ed7Mu6dn5/HvXv3CmnmPfMoC+l4IgK1FN/RNplohAixghR7mZizb6HCIj0TWaQFEcl97pdWetlP3T+xw2q1islkEq1WKw4ODjaeJxGxQWzS5hDUbM2AKhDfNGlEf3OdUIG1Wq2yaT/1s99QPmC1GIQkNvNzBv/2QQD4MuTnZDIpeeSx1H9DTkGUeYzht8dAqxC51wcoOA/X0X7j/0H2e9qo1WoV/7aqkN+kZZKXNCBZUR96uel3RSXYKioqKioqKireAywWi7LvCy+h8/l8Y6lZxO2ykhy85qDWATMvpbPZrLzQW/HjQIl7rKTL5B7ps+TKihcTeTmIdjnZl8wBndPPf2cVTCYsfC0EngMN2zAHFL7fASd5eq8uL5/L6ivycaBHvUnfS8qcrwmATGL5Gq6zghECkSVJeRlgDvIIFh3Eeg8ttzHBTLZdU91ss7w8yD5iUpE0TL66jM7T9fc1JniyasR2blI02ia0m+8jgIfs9hKmTPrc1Vb4FLZwHWz7TFBmIofvrEClXiaDvEyROjSNFU4ztyEEldsgK4Dyvf7/rs+yitXjEMG8/4dkoz+zLMx9G4KLsYG8s01tk0wM+HuUQoyZ9i+XG1KL8dKTDJQZUoJ67uzsxGQy2Qj8TRiYlDIJDZmE7XwAxdnZ2QbxtFwu4/z8fMMPKJsVgxBb1Gk8HheCLJMYXjqblbMmf+0bJva8T6dJE4+X7teU1USw+xfl82b3bidUnT4l2/nx3HLb7+3txWKxKGQZRBs2JU/7MAdXRNw+d/JYZxLS/mn7A+99xyQMdYeUtbLLExGcSopiz0pp9wePeev1upCJTGS5b1ux7bKaGM/PAv+N3+VnXr7WYwLPMz/jsFlElENK8Ev7gPeWNDn4LqgEW0VFRUVFRUXFe4DLy8s4OjoqM+gRb2/Ez0uuCRIjk1ERtwF9Dnb9eSYZCDjyUrH8ousXdgJJXtLZyyUHtTmf/AKdCULSc5Dk+jlgoV7k5eAsK6dMXnG/g8Rczojbfcm4z3vBgRzg+++sJCCQMmnWtA9Ztr8/dx6ZILJi0SqgJsLAQRWBldvLChK3jwkAt2FO/y4ilKDRaVtRlUlG+1Emt+xrue+4PE12dDoOoJ2eyQjazIGny5pJP/tVzsd2sT1NMN3V/tTbqhza23toWcmWybFsbxMeroPr7bam3HeNF7Yh10GKWV3GckDs6SDeZD1jJAG2l+5yfR4b/b8VOfi260g5vKStqY0yAZHHj2xnFGIE/vZ1p+32tRIX9dD29nYh2y4uLuL6+jq2t7c3lnHO5/Po9XrR7/c37Ml9Vvqu15uKNZMd7jP2r2zjPEZ4yfNisSj7Z+Hz0+m0LDs8PDwsyiru5W9Iquzzmah1W0IyQVCxh1in0ykKMPsKftRut+Pg4KCop5pIIq4lP9s2t1+2Ze7rVsvxvMwEv+uN/08mk9jf3y/lyocG5eW+WdnpND12R2yehuw8IeC87DKP1yZ0fb/7msdtp+PnNUuu8RlI0lbrlhiFWMvEqvsWZX5XVIKtoqKioqKiouI9wNXVVYzH47KkJeL2hdEz5HzO7LfVMZmcMAi8eBknyIrYXHbj2XYvI+S3X6ojbpfmmbjyKaR5KQnXZlINgsDBDOViWaXLxQu9g/kc/HsD8Tyb3UTAcJ/T87WoVXxao9vFaeV2MMmXbZkVDlby5O+bAvg/hBzI56DZ6g4HKNybTxYlDb7P5OVdxJbrF3G7PMuEciYoM2HjQN7+2WSnJiLuLpINuA5Ox3XO7eClYiapMiFI+g42c3pcn9UmWYHjtssqudw3UVOtVqtyKqeR/cH9z3bOhEb2ybtg3yFvnwRqso2g2ETKcrksSxtNgrm/ZzuamDGBYCKAvBirTIZC+EXExn5P2CgTlS5b/g7iJO/f2ETIelk+fpUnTKxQ9UEL2f9ZMri/v/9WP3M/injTF3u93oYqzBvIc18mM/y3/ZPN6bvdbszn87i6unpLbUbZqScKbgDRYj93X/ZefHm84dkGocg+dRFRtmHIxHir9WYvtqx6grAjfy8JdntYqevlxm5n2tV+aZVZ/p3He9puOp2WctGHWBZr/7Bv0W9cdsYF+gu/OTnUvuVxnvLbfq4ffsy9+HFeNup3CfJ3u65Wb5b7Winv76x8p1ycXGs197ugEmwVFRUVFRUVFe8BvvnmmxiNRvHDH/4wjo+PY3d3tyyxiYiNF+dMsPllPCI2ghATBBBDBB1stB2xSVaYJGATb7+kGw4UHXySrtUeDhQoc57pzsSFlQJZacLLeE7LpARoWi6ZbeR8PStPvXL6Vpc4LdvJwamDCYhDgjEvlct76NguTW3gfAD+4vx8X1Pw3PQ395rEyQRDVm1EbG4I7r9NBHG/9zXL5E4mqWxXn7roNnYZnM+3EZa+r4mEM9FNoO66UQ+3hxUU2eect8vI5zkQdV5WmXCNl9N1Op2N/b0gi1AwNRGR+e9MirsseYlv9r1MMLofuo9lH6MeDuojblU7bEKffclK1VxWEzq0o6+1ytb9xQSMyT4mDfC/9Xq9ccpn02SC28/kWbZfLvdsNisnQmID6rC9vR39fj8Gg8EGGefxq9V6czgHyxy9fC4TZOv17cm8Tf079yWX2TaDCGUJHwdBkAenuTK2enylruTDcwfCBD82kel+aft7KwTv1zYajWK12tyLjnuzr2Tfxg62i5V0Hkvcb5smJfwZZLOJ5Hy9l6nShigiISsh2zyGunwmKPMPZKifQybzrWLzGN5EEHspM+3gvrBerzdUdtjDhwhhH8ZP3kNQuKL6w08g2yjHcDgsh4q8KyrBVlFRUVFRUVHxHmCxWMRwOIz/83/+Tzx48KAQbX7JZaYaFRob+VqxQgCdia+IWzWSCTeCD3/uzyJuX559upfzdcBoYsfBXN6w2YG8g2oHKs7DwYrTzgE//1spENGsIqIMJkGcJ3Xz35kIagpuHbh47x7uy4SE7W+VAERIDoQzKWRVmEkV8vFSO9uBfEyqWr1heznwxf/ynmaU30FrJtiaiCan3UQuNhF4mQCjns7Lfu86ZaVRk99l4in7gIM5K6Fy38jEXf475+d+mwN1I6v3bHv6Q1bDRETZ2Dwve/N+W7msDqhd1kzMufzOn3rkNsv70mVFjG3oJYc5TRNA4C4CPxMh+Lv3C+M6kymQRF6aan9oIhnv6gfUOftCHjfoa7SVT/WlvkzEmGQwgUVe7Xa77LXl9vHhGBGbBwDYv03cQSrm0yS9NI97IH1QE8/n8/LcoswQh+yZxrMNgsQHUXisdz72KZNs5LuzsxPj8bgcZIDtdnd3N0gl+4rbz5MAud3cnozltL+JKI+7ebIMUgqi0T7q5zJthi1JLx/yQrt5nF6vb/dNZaxnbLdCzeWCVJ7NZmW/uZ2dnbfIKz+bXW6n6Tairuz5Z2LZJBv3sqSYvObzeQwGg42DMvyspe+w3PddUAm2ioqKioqKior3ALyMzmaz+Oqrr2I4HMYPf/jD+Pjjj2N/f7+8UDsgd7BkNRuElcmwHOz55ZeAzoGGAwPUL71eb2NmOS/7QC1gksZp5mCf7zMR4hdr73dDEAwxyKENqEqslHDAj01MulF/8s0ElQNhEzD+PysemsiQnJ7zctsTILnNCISa0nHZsCN1zXtGuX1cL9rZG4FnApG0nR7l+0NBp23s/512Jhea7NNE6OQyNpXP35k8cnlyWbCDiUxsh697+W72s0zg2R8yKerlWi6z62fC2GqP7FMeB1AOURb2tIrYJEFsZ5eLemYlrPun7eq28fjSBLeB60HaHCqQxzYvUyYd28cnsOZrssooE9akweSFl9Z5rETlQ/CeJxHcd/AXyCEfgNBE+vE/ZSFvVHs+FIYyttvt2Nvbi5ubmxiPxzEej8uyS8rf6XQKGZG3FGi1WoXMwVePjo7i9evXRd2M3SBXGC9QO2VyyuOIJ2aoj8lc9kezIhTyjSWmEE/j8XjjICCIqFarVdKwD2Iv54u/zefzGI1GRQll1V7E5pJokMcl9zX2Hr2rH+Bn1MVLoHO/4hmP70KCkSa/8/PKeXqyDD+gfSDo3EbUPRPY+C1LPnnu+nTzrAx1v6I8XvppQo/2hGCkHPhHv9/fsK33lWRfvf39/Y2TV3lPODw8jPV68/Ck74pKsFVUVFRUVFRUvAdYrVYlSJhMJoVQOz09jf/yX/5L2XwZsosXY/a48UmBDr6zWqyJ7HKaeeac/yHUrN7IS/LIm1PzILwcvES8vWTP//MCzks95IIVbtPpdGOWn3wziWTSKRM7ORDPdsvBlQP0byPbsj3ykqImVYMDfu/nlQNJl9HBakYO3PjM12aFXFaz5SW5Lp/37ck+l1VLWV2U6+vPs+1dT/viXcgEUCbtXA8H3Nmuzh80+S/kSVZ/9Pv9t2ydfcO+jl2tAHH/9Gbe/h0RG/6eCUTayaSafYpgdrlclmWlmWT1mJCXNeLLLoPLketrf7fdI24Je/qSFYGMTZCErn8TWWtis4mYzXuzUd68PM2kg79jzMx+YbuY2MsKN5fJ9vV1pN/r9Tbymc/nhSDa2dmJ3d3d2NnZib29vbKMcr1+s/8azwuTDyYjJpPJBomIH1Iuq6VsM8Z3yEYTbPi1n0fUOfs37ew935hEWa/XG88Plk+anMMWtqcnA3K7M/FkP/UpsZ6IsA+Tv0mqPMbmcf4ussrPAE9QQODaRh6Pm3zLtiQNk6d5fDfBBuHnpdoQshBeVsT7uYXNIfGxlycZ7EfA5UWhSL2Hw2FcXl5u9D3an+XGrrtJZIjYm5ub4ptcl8fE74JKsFVUVFRUVFRUvAfgZXu9XhdCZTgclr2HHj16FD/60Y82luU4cCC4YnkQn5v4yQQDe3+x907el4v7nDbpRrytQPGPgxbScDCWYcLBL9MOpj1rbmWPg/xMzmTCy0vBbEMHvE2fk4/JB36jMLHNrHAweep2Jv1M4GHbu8oITLhQhlwWE2EmmmxH18kHU2QFj9s1KwOyuuKuoNBkZiYtbbementfsiZkW2ZiL+/Xl8udg+ect9UauW5cY6VGU/q+jzZ0uwMrgnIgnfuPVaDuX5AQKFoyKQp5ghKUsuUlbra7SV3XB7/KZXF7UHbXNS+dzj4OvJcX6tVMDpmssA/4NNDc7lyfFXsmHzwG9fv9t0jBJn/JPmK/jrhVEmb75XY06cs47DHbtuz3+4W4GI/HpYxuLzavd/vk371eL4bDYfEL93vK6QM0aJ/t7e2YTqfFnvY7xhPSc5qz2SxGo1GxHc849mAbDAYbp4LSPnlSAFuSt8vb6/ViNpuVvFleSD/Ifc3KNxNamUjDD1mKyL0m6TLhRLrePsHIxCC29kElLJn0mEn75ucj9rIiEcI6L203UclntHe32y3qtU6nE69fv47FYhFHR0cxGAzKUl/7sW26WCxiMBjEYDAobXRzcxOTyWRDnU/+3jbBdVkul9Hv9+Pg4KAQ0PggfnJ+fh6r1Sru378f74pKsFVUVFRUVFRUvAdw8MuLPy/1Z2dncXp6Gi9evIj/9b/+VxwfH8d6vY5Xr15tEBcO1HJw5r8dQDgAy+qjiM3gjyDKm21HbCqFCNoz2eXAwoGKA2PycvkyMZL3jWOmm3sc3OSlksCBU1M7ZIVVtmNOz0RWE0xWkYYJtOwDfOalgU7LSh8jE3QmBTM5w/40TcTGtxERfJbbyZ85YAMOyHOwbv+0vXO+9sncdk3/mxzBFk33O8B1v8jEicnXTBqZiCOQ5DqWveV6kp8JkExWUx7yaFIsus5WsJC+yVHSz0osbMweWSbqMwFKPXNf8DW5nk0kqZdu5npzD2OgCa+m67jfdczXWQ3r8bGpf1lxlP2i1WqVPagWi8VbB0o4z1xHE7kmkTxB4P6KWsd17PV6ZWkoyi63I0oe0s6kL6ea0s74jA9wmE6nMZvNYnd3t3wHkeOTS7EnJInt3+/3CyHDuD2ZTDZOM4UsIm3stbOzU1SVPN9McvHc8hgK8bpYLKLf72/UiSW17Cd2eXkZy+Uy9vb2NpRfpOd+AzmE8svKOuByQoTSPm5TbIPf5NNGc7+xL8zn8xiPx3F9fb3x/PIzZT6fb+SPuh1y0pM+2dfwZ8hsgO3wO048Z2zDrpTXfZ66rlarjUMUaOvz8/MypnlPuX6/X/bPy88abOYDSmgz7qHfnJ6exruiEmwVFRUVFRUVFe8BeOmNuFXL8BPx5iX38ePHMZ1O4/vf/3789//+3+PevXvx8uXLjeDAs+M5uMzEGTPRgO89W+wNmHmxJZDykhuTUiYyMmHhGXYTKuRpO/g+YKKIvXv4HEVeVoI4qEVh0UScOODOChTyzMoTK8Ga1Ai0X7fbLQFNE/npwA5lDKo3gh4vfSHQsO0jbpUxkAQmBXKdvPyHcqIaIACyDW2vrFRy2+Qg0SSh07FtM0Fmgsjp5v3gmn5ymdz+9q8MX+f03a8cJLvcJvHcdlZH5XqTn/d0MyETcUtMumzkk9V4lMXkJn0i2y/v34c/8He32y3+YZu47l7a575DGt4/yn7g+jMuQQJltR5lZZ8tiAPyaCLxsp2zT7gObjvfi9LVdvLBEBFvlFaDwaCQNXcR8/4sq+IgazzWMxZQT5M0HntRZWWCqNPpxMHBwQYpNpvNNmy9vb1d9jlzW6D2Wq/fLPWH/JpMJhv+xbI9yokdTbJdXV2V/doWi0XZxw1bUT/22uJZZJKTPK6urgq5Z7WYnzGM7e12e2P5K/nSb/C1169fF5IG21M274/o0y29LLzpWddut4tisknJSh6QSZBfrVYrer3exiET3kuN5wB1cboeQ8ifvsjySezAHnrc60kc25Nr6Ae0z2Qyifv378fV1VU8ffo05vN57O/vbxzAQRtbgWbilX0F8eM8yZBP6e33+7FYLIofTSaTMs5ia37TRltbW+WZ+66oBFtFRUVFRUVFxXuATqezseyOl+OINwFHv9+Pm5ub+P3vfx9Pnz6Nr7/+Ov7kT/4k9vb2yt40kDAEola1gRyQm3jxcg6+39raiv39/bJPCooAiJumAMJ5ZuLKJIThwN/kHmmzt0oO2B3cZ8UXAZyJOiuuSDsv7dva2iqz7nnJKvUgcMhKBmySlQK0o4P7Vqv11ix9JgFcPsqfgzoH5qhTTFiYyHBwZhWAAzLbtomsNMHmzeXtVyaLmlRFfwh5SRDp2dfscyAr53ydCcZMSvlevs9LoZvuN4FLGiad1+t12deIAJj7IBEy2ZyDUl/TpEYzeZMDb/uNCTBIAwJn6re3txevX7+Og4ODDZKC+mSFSiar3D+a2gi11WQy2diny0QSNnb/o14mWawwy+WxDRkT8R+TJyYhctub1KDt7MOQROxrRl9wX4ZsaBp3sg9mO7IUlLQ5vZE+Tn4cbuA90SBWl8tlDAaDolYz4YKKzbbvdDpxeHgYu7u75bN+vx9PnjwpvgKJ5oMeKGMenyD52MbASi9sfHNzU0gUxhzuo/7sOdfv9zfIIJNQlN/KMduWMYVyMF4Cj1kmSxkzvdQYf/FJnL7eJDRpkzfkD22IX+b+y/hsAnQwGLzlvy4vJ7N6wmp/f7+QWKjalss3hxb5HYG+QxrYv9/vl/rz7H748GFRC2IHTwa4L0Gyki72y88Ck+YcVMA44X7NeOp+cXR0VJR13W632OXDDz+Md0Vr/f9Cy1VUVFRUVFRUVFRUVFRUVFRUVFRERMS7H4tQUVFRUVFRUVFRUVFRUVFRUVFRUVAJtoqKioqKioqKioqKioqKioqKij8ClWCrqKioqKioqKioqKioqKioqKj4I1AJtoqKioqKioqKioqKioqKioqKij8ClWCrqKioqKioqKioqKioqKioqKj4I1AJtoqKioqKioqKioqKioqKioqKij8ClWCrqKioqKioqKioqKioqKioqKj4I1AJtoqKioqKioqKioqKioqKioqKij8ClWCrqKioqKioqKioqKioqKioqKj4I1AJtoqKioqKioqKioqKioqKioqKij8ClWCrqKioqKioqKioqKioqKioqKj4I1AJtoqKioqKioqKioqKioqKioqKij8C/xe94kQFLYRfiwAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Draw\n",
+ "color = cm.jet(mconf)\n",
+ "text = [\n",
+ " 'XoFTR',\n",
+ " 'Matches: {}'.format(len(mconf)),\n",
+ "]\n",
+ "if len(img0_raw.shape) == 3:\n",
+ " _img0 = cv2.cvtColor(img0_raw, cv2.COLOR_BGR2RGB)\n",
+ "else:\n",
+ " _img0 = img0_raw\n",
+ "if len(img1_raw.shape) == 3:\n",
+ " _img1 = cv2.cvtColor(img1_raw, cv2.COLOR_BGR2RGB)\n",
+ "else:\n",
+ " _img1 = img1_raw\n",
+ "fig_org = make_matching_figure(_img0, _img1, np.zeros(0), np.zeros(0), np.zeros(0), text=[\"Original\"], dpi=125)\n",
+ "fig_match = make_matching_figure(_img0, _img1, mkpts0, mkpts1, color, text=text, dpi=125)"
+ ]
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "5b8911f875a754a9ad2a8804064d078bf6a1985972bb0389b9d67771213c8e20"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/XoFTR/notebooks/xoftr_demo_batch.ipynb b/third_party/XoFTR/notebooks/xoftr_demo_batch.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..aaaa82609b25d6c1f27e1a4722a3f391cb12d3d6
--- /dev/null
+++ b/third_party/XoFTR/notebooks/xoftr_demo_batch.ipynb
@@ -0,0 +1,311 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# A demo notebook for XoFTR using a batch of image pairs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run once\n",
+ "import os\n",
+ "os.chdir(\"..\")\n",
+ "import torch\n",
+ "import torch.nn.functional as F\n",
+ "import cv2\n",
+ "import numpy as np\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.cm as cm\n",
+ "from src.utils.plotting import make_matching_figure\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Functions for preprocessing images\n",
+ "def preprocess_image(img, device, resize=None, df=None, padding=None):\n",
+ " img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
+ " h, w = img.shape[:2]\n",
+ " if resize is not None:\n",
+ " scale = resize / max(h, w)\n",
+ " w_new, h_new = int(round(w*scale)), int(round(h*scale))\n",
+ " else:\n",
+ " w_new, h_new = w, h\n",
+ " \n",
+ " if df is not None:\n",
+ " w_new, h_new = map(lambda x: int(x // df * df), [w_new, h_new])\n",
+ " \n",
+ " img = cv2.resize(img, (w_new, h_new))\n",
+ " scale = np.array([w/w_new, h/h_new], dtype=np.float)\n",
+ " if padding: # padding\n",
+ " pad_to = max(h_new, w_new)\n",
+ " img, mask = pad_bottom_right(img, pad_to, ret_mask=True)\n",
+ " mask = torch.from_numpy(mask).to(device)\n",
+ " else:\n",
+ " mask = None\n",
+ " img = torch.from_numpy(img)[None][None].to(device).float() / 255.0\n",
+ "\n",
+ " mask = F.interpolate(mask[None][None].float(),\n",
+ " scale_factor=0.125,\n",
+ " mode='nearest',\n",
+ " recompute_scale_factor=False)[0].bool()\n",
+ "\n",
+ " return img, scale, mask\n",
+ "\n",
+ "def pad_bottom_right(inp, pad_size, ret_mask=False):\n",
+ " assert isinstance(pad_size, int) and pad_size >= max(inp.shape[-2:]), f\"{pad_size} < {max(inp.shape[-2:])}\"\n",
+ " mask = None\n",
+ " if inp.ndim == 2:\n",
+ " padded = np.zeros((pad_size, pad_size), dtype=inp.dtype)\n",
+ " padded[:inp.shape[0], :inp.shape[1]] = inp\n",
+ " if ret_mask:\n",
+ " mask = np.zeros((pad_size, pad_size), dtype=bool)\n",
+ " mask[:inp.shape[0], :inp.shape[1]] = True\n",
+ " elif inp.ndim == 3:\n",
+ " padded = np.zeros((inp.shape[0], pad_size, pad_size), dtype=inp.dtype)\n",
+ " padded[:, :inp.shape[1], :inp.shape[2]] = inp\n",
+ " if ret_mask:\n",
+ " mask = np.zeros((inp.shape[0], pad_size, pad_size), dtype=bool)\n",
+ " mask[:, :inp.shape[1], :inp.shape[2]] = True\n",
+ " else:\n",
+ " raise NotImplementedError()\n",
+ " return padded, mask\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from src.xoftr import XoFTR\n",
+ "from src.config.default import get_cfg_defaults\n",
+ "from src.utils.misc import lower_config\n",
+ "from src.utils.data_io import DataIOWrapper\n",
+ "\n",
+ "# Get default configurations\n",
+ "config = get_cfg_defaults(inference=True)\n",
+ "config = lower_config(config)\n",
+ "\n",
+ "# Coarse level threshold\n",
+ "config['xoftr']['match_coarse']['thr'] = 0.3 # Default 0.3\n",
+ "\n",
+ "# Fine level threshold\n",
+ "config['xoftr']['fine']['thr'] = 0.1 # Default 0.1\n",
+ "\n",
+ "# It is posseble to get denser matches\n",
+ "# If True, xoftr returns all fine-level matches for each fine-level window (at 1/2 resolution)\n",
+ "config['xoftr']['fine']['denser'] = False # Default False\n",
+ "\n",
+ "\n",
+ "# XoFTR model\n",
+ "matcher = XoFTR(config=config[\"xoftr\"])\n",
+ "\n",
+ "# The path for weights\n",
+ "ckpt = \"weights/weights_xoftr_640.ckpt\"\n",
+ "\n",
+ "# Load model\n",
+ "matcher.load_state_dict(torch.load(ckpt)['state_dict'],strict=True)\n",
+ "matcher = matcher.eval().cuda()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Paths for example image pairs\n",
+ "img_pair_paths = [[\"assets/METU_VisTIR_samples/indoor/scene_8/visible/images/IM_02798.jpg\", \n",
+ " \"assets/METU_VisTIR_samples/indoor/scene_8/thermal/images/IM_00006.jpg\"],\n",
+ "\n",
+ " [\"assets/METU_VisTIR_samples/cloudy/scene_7/visible/images/IM_04525.jpg\",\n",
+ " \"assets/METU_VisTIR_samples/cloudy/scene_7/thermal/images/IM_01139.jpg\"]]\n",
+ "\n",
+ "\n",
+ "img0_raw_list = []\n",
+ "img1_raw_list = []\n",
+ "img0_list = []\n",
+ "img1_list = []\n",
+ "scale0_list = []\n",
+ "scale1_list = []\n",
+ "mask0_list = []\n",
+ "mask1_list = []\n",
+ "\n",
+ "for path in img_pair_paths:\n",
+ " # Read images\n",
+ " img0_raw = cv2.imread(path[0])\n",
+ " img1_raw = cv2.imread(path[1])\n",
+ "\n",
+ " # preprocess images (resizing + pad + to tensor)\n",
+ " img0, scale0, mask0 = preprocess_image(img0_raw, device='cuda', resize=640, df=8, padding=True)\n",
+ " img1, scale1, mask1 = preprocess_image(img1_raw, device='cuda', resize=640, df=8, padding=True)\n",
+ " \n",
+ " img0_raw_list.append(img0_raw)\n",
+ " img1_raw_list.append(img1_raw)\n",
+ " img0_list.append(img0)\n",
+ " img1_list.append(img1)\n",
+ " scale0_list.append(scale0)\n",
+ " scale1_list.append(scale1)\n",
+ " mask0_list.append(mask0)\n",
+ " mask1_list.append(mask1)\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create batch with padding masks\n",
+ "batch = {'image0': torch.cat(img0_list, 0),\n",
+ " 'image1': torch.cat(img1_list, 0),\n",
+ " 'mask0': torch.cat(mask0_list, 0),\n",
+ " 'mask1': torch.cat(mask1_list, 0)}\n",
+ "\n",
+ "# Inference with XoFTR and get prediction\n",
+ "with torch.no_grad():\n",
+ " matcher(batch)\n",
+ " m_bids = batch['m_bids'].cpu().numpy() # batch indices for matches\n",
+ " mkpts0 = batch['mkpts0_f'].cpu().numpy() \n",
+ " mkpts1 = batch['mkpts1_f'].cpu().numpy()\n",
+ " mconf = batch['mconf_f'].cpu().numpy()\n",
+ "\n",
+ "# Seperate matches for each pair in the batch and scale them to original image resolution\n",
+ "sep_mkpts0 = [mkpts0[m_bids==ii] * scale0_list[ii] for ii in range(batch['bs'])] # bs: batch size\n",
+ "sep_mkpts1 = [mkpts1[m_bids==ii] * scale1_list[ii] for ii in range(batch['bs'])]\n",
+ "sep_mconf = [mconf[m_bids==ii] for ii in range(batch['bs'])]\n",
+ "\n",
+ "# sep_mkpts0[0] and sep_mkpts1[0] are matches for the first image pair in the batch\n",
+ "# sep_mkpts0[1] and sep_mkpts1[1] are matches for the second image pair in the batch\n",
+ "# ..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Mask outliers using RANSAC (Homography or Fundamental Matrix)\n",
+ "\n",
+ "inlier_method = 'F' # F: Fundamental Matrix, H: Homography \n",
+ "\n",
+ "for ii in range(batch['bs']): # bs: batch size\n",
+ "\n",
+ " if inlier_method == 'F':\n",
+ " F, inlier_mask = cv2.findFundamentalMat(sep_mkpts0[ii], sep_mkpts1[ii], cv2.USAC_MAGSAC, ransacReprojThreshold=1, maxIters=10000, confidence=0.9999)\n",
+ " elif inlier_method == 'H':\n",
+ " H_pred, inlier_mask = cv2.findHomography(sep_mkpts0[ii], sep_mkpts1[ii], cv2.USAC_MAGSAC, ransacReprojThreshold=1, maxIters=10000, confidence=0.9999)\n",
+ "\n",
+ " inlier_mask = inlier_mask.ravel() > 0\n",
+ " sep_mkpts0[ii] = sep_mkpts0[ii][inlier_mask]\n",
+ " sep_mkpts1[ii] = sep_mkpts1[ii][inlier_mask]\n",
+ " sep_mconf[ii] = sep_mconf[ii][inlier_mask]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Draw\n",
+ "for ii in range(batch['bs']):\n",
+ " color = cm.jet(sep_mconf[ii])\n",
+ " text = [\n",
+ " 'XoFTR',\n",
+ " 'Matches: {}'.format(len(sep_mconf[ii])),\n",
+ " ]\n",
+ " if len(img0_raw_list[ii].shape) == 3:\n",
+ " _img0 = cv2.cvtColor(img0_raw_list[ii], cv2.COLOR_BGR2RGB)\n",
+ " else:\n",
+ " _img0 = img0_raw\n",
+ " if len(img1_raw_list[ii].shape) == 3:\n",
+ " _img1 = cv2.cvtColor(img1_raw_list[ii], cv2.COLOR_BGR2RGB)\n",
+ " else:\n",
+ " _img1 = img1_raw\n",
+ " fig_org = make_matching_figure(_img0, _img1, np.zeros(0), np.zeros(0), np.zeros(0), text=[\"Original\"], dpi=125)\n",
+ " fig_match = make_matching_figure(_img0, _img1, sep_mkpts0[ii], sep_mkpts1[ii], color, text=text, dpi=125)"
+ ]
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "5b8911f875a754a9ad2a8804064d078bf6a1985972bb0389b9d67771213c8e20"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/imcui/third_party/XoFTR/pretrain.py b/third_party/XoFTR/pretrain.py
similarity index 100%
rename from imcui/third_party/XoFTR/pretrain.py
rename to third_party/XoFTR/pretrain.py
diff --git a/third_party/XoFTR/requirements.txt b/third_party/XoFTR/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4bea9d7587f83b1d6d5915e9a1d85191ec15e0d2
--- /dev/null
+++ b/third_party/XoFTR/requirements.txt
@@ -0,0 +1,19 @@
+numpy==1.23.1
+opencv_python==4.5.1.48
+albumentations==0.5.1 --no-binary=imgaug,albumentations
+ray>=1.0.1
+einops==0.3.0
+kornia==0.4.1
+loguru==0.5.3
+yacs>=0.1.8
+tqdm==4.65.0
+autopep8
+pylint
+ipython
+jupyterlab
+matplotlib
+h5py==3.1.0
+pytorch-lightning==1.3.5
+torchmetrics==0.6.0 # version problem: https://github.com/NVIDIA/DeepLearningExamples/issues/1113#issuecomment-1102969461
+joblib>=1.0.1
+wandb
diff --git a/third_party/XoFTR/scripts/reproduce_train/pretrain.sh b/third_party/XoFTR/scripts/reproduce_train/pretrain.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a36b0e19635e0e8981ba293d2414d49021dd4894
--- /dev/null
+++ b/third_party/XoFTR/scripts/reproduce_train/pretrain.sh
@@ -0,0 +1,31 @@
+#!/bin/bash -l
+
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+# conda activate loftr
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+data_cfg_path="configs/data/pretrain.py"
+main_cfg_path="configs/xoftr/pretrain/pretrain.py"
+
+n_nodes=1
+n_gpus_per_node=2
+torch_num_workers=16
+batch_size=2
+pin_memory=true
+exp_name="pretrain-${TRAIN_IMG_SIZE}-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))"
+
+python -u ./pretrain.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --exp_name=${exp_name} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \
+ --check_val_every_n_epoch=1 \
+ --log_every_n_steps=100 \
+ --limit_val_batches=1. \
+ --num_sanity_val_steps=10 \
+ --benchmark=True \
+ --max_epochs=15
diff --git a/third_party/XoFTR/scripts/reproduce_train/visible_thermal.sh b/third_party/XoFTR/scripts/reproduce_train/visible_thermal.sh
new file mode 100644
index 0000000000000000000000000000000000000000..95bb4fb09ce6266fa9d79b2cf568d9e4179c7866
--- /dev/null
+++ b/third_party/XoFTR/scripts/reproduce_train/visible_thermal.sh
@@ -0,0 +1,35 @@
+#!/bin/bash -l
+
+SCRIPTPATH=$(dirname $(readlink -f "$0"))
+PROJECT_DIR="${SCRIPTPATH}/../../"
+
+# conda activate xoftr
+export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
+cd $PROJECT_DIR
+
+TRAIN_IMG_SIZE=640
+# TRAIN_IMG_SIZE=840
+data_cfg_path="configs/data/megadepth_vistir_trainval_${TRAIN_IMG_SIZE}.py"
+main_cfg_path="configs/xoftr/outdoor/visible_thermal.py"
+
+n_nodes=1
+n_gpus_per_node=8
+torch_num_workers=16
+batch_size=2
+pin_memory=true
+exp_name="visible_thermal-${TRAIN_IMG_SIZE}-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))"
+ckpt_path="pretrain_weights/epoch=8-.ckpt"
+
+python -u ./train.py \
+ ${data_cfg_path} \
+ ${main_cfg_path} \
+ --exp_name=${exp_name} \
+ --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
+ --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \
+ --check_val_every_n_epoch=1 \
+ --log_every_n_steps=100 \
+ --limit_val_batches=1. \
+ --num_sanity_val_steps=10 \
+ --benchmark=True \
+ --max_epochs=30 \
+ --ckpt_path=${ckpt_path}
diff --git a/imcui/third_party/dust3r/croco/datasets/habitat_sim/__init__.py b/third_party/XoFTR/src/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/habitat_sim/__init__.py
rename to third_party/XoFTR/src/__init__.py
diff --git a/imcui/third_party/XoFTR/src/config/default.py b/third_party/XoFTR/src/config/default.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/config/default.py
rename to third_party/XoFTR/src/config/default.py
diff --git a/imcui/third_party/XoFTR/src/datasets/megadepth.py b/third_party/XoFTR/src/datasets/megadepth.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/datasets/megadepth.py
rename to third_party/XoFTR/src/datasets/megadepth.py
diff --git a/imcui/third_party/XoFTR/src/datasets/pretrain_dataset.py b/third_party/XoFTR/src/datasets/pretrain_dataset.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/datasets/pretrain_dataset.py
rename to third_party/XoFTR/src/datasets/pretrain_dataset.py
diff --git a/imcui/third_party/EfficientLoFTR/src/datasets/sampler.py b/third_party/XoFTR/src/datasets/sampler.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/datasets/sampler.py
rename to third_party/XoFTR/src/datasets/sampler.py
diff --git a/imcui/third_party/XoFTR/src/datasets/scannet.py b/third_party/XoFTR/src/datasets/scannet.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/datasets/scannet.py
rename to third_party/XoFTR/src/datasets/scannet.py
diff --git a/imcui/third_party/XoFTR/src/datasets/vistir.py b/third_party/XoFTR/src/datasets/vistir.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/datasets/vistir.py
rename to third_party/XoFTR/src/datasets/vistir.py
diff --git a/imcui/third_party/XoFTR/src/lightning/data.py b/third_party/XoFTR/src/lightning/data.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/lightning/data.py
rename to third_party/XoFTR/src/lightning/data.py
diff --git a/imcui/third_party/XoFTR/src/lightning/data_pretrain.py b/third_party/XoFTR/src/lightning/data_pretrain.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/lightning/data_pretrain.py
rename to third_party/XoFTR/src/lightning/data_pretrain.py
diff --git a/imcui/third_party/XoFTR/src/lightning/lightning_xoftr.py b/third_party/XoFTR/src/lightning/lightning_xoftr.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/lightning/lightning_xoftr.py
rename to third_party/XoFTR/src/lightning/lightning_xoftr.py
diff --git a/imcui/third_party/XoFTR/src/lightning/lightning_xoftr_pretrain.py b/third_party/XoFTR/src/lightning/lightning_xoftr_pretrain.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/lightning/lightning_xoftr_pretrain.py
rename to third_party/XoFTR/src/lightning/lightning_xoftr_pretrain.py
diff --git a/imcui/third_party/XoFTR/src/losses/xoftr_loss.py b/third_party/XoFTR/src/losses/xoftr_loss.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/losses/xoftr_loss.py
rename to third_party/XoFTR/src/losses/xoftr_loss.py
diff --git a/imcui/third_party/XoFTR/src/losses/xoftr_loss_pretrain.py b/third_party/XoFTR/src/losses/xoftr_loss_pretrain.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/losses/xoftr_loss_pretrain.py
rename to third_party/XoFTR/src/losses/xoftr_loss_pretrain.py
diff --git a/imcui/third_party/EfficientLoFTR/src/optimizers/__init__.py b/third_party/XoFTR/src/optimizers/__init__.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/optimizers/__init__.py
rename to third_party/XoFTR/src/optimizers/__init__.py
diff --git a/imcui/third_party/XoFTR/src/utils/augment.py b/third_party/XoFTR/src/utils/augment.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/utils/augment.py
rename to third_party/XoFTR/src/utils/augment.py
diff --git a/imcui/third_party/EfficientLoFTR/src/utils/comm.py b/third_party/XoFTR/src/utils/comm.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/utils/comm.py
rename to third_party/XoFTR/src/utils/comm.py
diff --git a/imcui/third_party/XoFTR/src/utils/data_io.py b/third_party/XoFTR/src/utils/data_io.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/utils/data_io.py
rename to third_party/XoFTR/src/utils/data_io.py
diff --git a/imcui/third_party/EfficientLoFTR/src/utils/dataloader.py b/third_party/XoFTR/src/utils/dataloader.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/utils/dataloader.py
rename to third_party/XoFTR/src/utils/dataloader.py
diff --git a/imcui/third_party/XoFTR/src/utils/dataset.py b/third_party/XoFTR/src/utils/dataset.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/utils/dataset.py
rename to third_party/XoFTR/src/utils/dataset.py
diff --git a/imcui/third_party/XoFTR/src/utils/metrics.py b/third_party/XoFTR/src/utils/metrics.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/utils/metrics.py
rename to third_party/XoFTR/src/utils/metrics.py
diff --git a/imcui/third_party/TopicFM/src/utils/misc.py b/third_party/XoFTR/src/utils/misc.py
similarity index 100%
rename from imcui/third_party/TopicFM/src/utils/misc.py
rename to third_party/XoFTR/src/utils/misc.py
diff --git a/imcui/third_party/XoFTR/src/utils/plotting.py b/third_party/XoFTR/src/utils/plotting.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/utils/plotting.py
rename to third_party/XoFTR/src/utils/plotting.py
diff --git a/imcui/third_party/XoFTR/src/utils/pretrain_utils.py b/third_party/XoFTR/src/utils/pretrain_utils.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/utils/pretrain_utils.py
rename to third_party/XoFTR/src/utils/pretrain_utils.py
diff --git a/imcui/third_party/EfficientLoFTR/src/utils/profiler.py b/third_party/XoFTR/src/utils/profiler.py
similarity index 100%
rename from imcui/third_party/EfficientLoFTR/src/utils/profiler.py
rename to third_party/XoFTR/src/utils/profiler.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/__init__.py b/third_party/XoFTR/src/xoftr/__init__.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/__init__.py
rename to third_party/XoFTR/src/xoftr/__init__.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/backbone/__init__.py b/third_party/XoFTR/src/xoftr/backbone/__init__.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/backbone/__init__.py
rename to third_party/XoFTR/src/xoftr/backbone/__init__.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/backbone/resnet.py b/third_party/XoFTR/src/xoftr/backbone/resnet.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/backbone/resnet.py
rename to third_party/XoFTR/src/xoftr/backbone/resnet.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/utils/geometry.py b/third_party/XoFTR/src/xoftr/utils/geometry.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/utils/geometry.py
rename to third_party/XoFTR/src/xoftr/utils/geometry.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/utils/position_encoding.py b/third_party/XoFTR/src/xoftr/utils/position_encoding.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/utils/position_encoding.py
rename to third_party/XoFTR/src/xoftr/utils/position_encoding.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/utils/supervision.py b/third_party/XoFTR/src/xoftr/utils/supervision.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/utils/supervision.py
rename to third_party/XoFTR/src/xoftr/utils/supervision.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/xoftr.py b/third_party/XoFTR/src/xoftr/xoftr.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/xoftr.py
rename to third_party/XoFTR/src/xoftr/xoftr.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/xoftr_module/__init__.py b/third_party/XoFTR/src/xoftr/xoftr_module/__init__.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/xoftr_module/__init__.py
rename to third_party/XoFTR/src/xoftr/xoftr_module/__init__.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/xoftr_module/coarse_matching.py b/third_party/XoFTR/src/xoftr/xoftr_module/coarse_matching.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/xoftr_module/coarse_matching.py
rename to third_party/XoFTR/src/xoftr/xoftr_module/coarse_matching.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/xoftr_module/fine_matching.py b/third_party/XoFTR/src/xoftr/xoftr_module/fine_matching.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/xoftr_module/fine_matching.py
rename to third_party/XoFTR/src/xoftr/xoftr_module/fine_matching.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/xoftr_module/fine_process.py b/third_party/XoFTR/src/xoftr/xoftr_module/fine_process.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/xoftr_module/fine_process.py
rename to third_party/XoFTR/src/xoftr/xoftr_module/fine_process.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/xoftr_module/linear_attention.py b/third_party/XoFTR/src/xoftr/xoftr_module/linear_attention.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/xoftr_module/linear_attention.py
rename to third_party/XoFTR/src/xoftr/xoftr_module/linear_attention.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/xoftr_module/transformer.py b/third_party/XoFTR/src/xoftr/xoftr_module/transformer.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/xoftr_module/transformer.py
rename to third_party/XoFTR/src/xoftr/xoftr_module/transformer.py
diff --git a/imcui/third_party/XoFTR/src/xoftr/xoftr_pretrain.py b/third_party/XoFTR/src/xoftr/xoftr_pretrain.py
similarity index 100%
rename from imcui/third_party/XoFTR/src/xoftr/xoftr_pretrain.py
rename to third_party/XoFTR/src/xoftr/xoftr_pretrain.py
diff --git a/imcui/third_party/XoFTR/test.py b/third_party/XoFTR/test.py
similarity index 100%
rename from imcui/third_party/XoFTR/test.py
rename to third_party/XoFTR/test.py
diff --git a/imcui/third_party/XoFTR/test_relative_pose.py b/third_party/XoFTR/test_relative_pose.py
similarity index 100%
rename from imcui/third_party/XoFTR/test_relative_pose.py
rename to third_party/XoFTR/test_relative_pose.py
diff --git a/imcui/third_party/XoFTR/train.py b/third_party/XoFTR/train.py
similarity index 100%
rename from imcui/third_party/XoFTR/train.py
rename to third_party/XoFTR/train.py
diff --git a/third_party/d2net/.gitignore b/third_party/d2net/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..fda64312542ac8b636532f580c7648708dd0c1ba
--- /dev/null
+++ b/third_party/d2net/.gitignore
@@ -0,0 +1,13 @@
+__pycache__
+.vscode
+checkpoints*
+train_vis
+log.txt
+hpatches_sequences/hseq.pdf
+hpatches_sequences/hseq-top.pdf
+hpatches_sequences/hpatches-sequences-release*
+hpatches_sequences/cache
+hpatches_sequences/cache-top
+.ipynb_checkpoints
+vlfeat
+*.d2-net
diff --git a/third_party/d2net/LICENSE b/third_party/d2net/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..5d50329f25f288161a596172f69c84b9dc465b27
--- /dev/null
+++ b/third_party/d2net/LICENSE
@@ -0,0 +1,33 @@
+The Clear BSD License
+
+Copyright (c) 2019 Mihai Dusmanu
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted (subject to the limitations in the disclaimer
+below) provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice,
+ this list of conditions and the following disclaimer.
+
+ * Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+
+ * Neither the names of the copyright holders nor the names of the
+ contributors nor the names of their institutions may be used to endorse
+ or promote products derived from this software without specific prior
+ written permission.
+
+NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
+THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
+CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
+IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/third_party/d2net/README.md b/third_party/d2net/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..741c88dffcea55fc482d823d585421fbe0996cea
--- /dev/null
+++ b/third_party/d2net/README.md
@@ -0,0 +1,121 @@
+# D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
+
+This repository contains the implementation of the following paper:
+
+```text
+"D2-Net: A Trainable CNN for Joint Detection and Description of Local Features".
+M. Dusmanu, I. Rocco, T. Pajdla, M. Pollefeys, J. Sivic, A. Torii, and T. Sattler. CVPR 2019.
+```
+
+[Paper on arXiv](https://arxiv.org/abs/1905.03561), [Project page](https://dsmn.ml/publications/d2-net.html)
+
+## Getting started
+
+Python 3.6+ is recommended for running our code. [Conda](https://docs.conda.io/en/latest/) can be used to install the required packages:
+
+```bash
+conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
+conda install h5py imageio imagesize matplotlib numpy scipy tqdm
+```
+
+## Downloading the models
+
+The off-the-shelf **Caffe VGG16** weights and their tuned counterpart can be downloaded by running:
+
+```bash
+mkdir models
+wget https://dsmn.ml/files/d2-net/d2_ots.pth -O models/d2_ots.pth
+wget https://dsmn.ml/files/d2-net/d2_tf.pth -O models/d2_tf.pth
+wget https://dsmn.ml/files/d2-net/d2_tf_no_phototourism.pth -O models/d2_tf_no_phototourism.pth
+```
+
+**Update - 23 May 2019** We have added a new set of weights trained on MegaDepth without the PhotoTourism scenes (sagrada_familia - 0019, lincoln_memorial_statue - 0021, british_museum - 0024, london_bridge - 0025, us_capitol - 0078, mount_rushmore - 1589). Our initial results show similar performance. In order to use these weights at test time, you should add `--model_file models/d2_tf_no_phototourism.pth`.
+
+## Feature extraction
+
+`extract_features.py` can be used to extract D2 features for a given list of images. The singlescale features require less than 6GB of VRAM for 1200x1600 images. The `--multiscale` flag can be used to extract multiscale features - for this, we recommend at least 12GB of VRAM.
+
+The output format can be either [`npz`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html) or `mat`. In either case, the feature files encapsulate three arrays:
+
+- `keypoints` [`N x 3`] array containing the positions of keypoints `x, y` and the scales `s`. The positions follow the COLMAP format, with the `X` axis pointing to the right and the `Y` axis to the bottom.
+- `scores` [`N`] array containing the activations of keypoints (higher is better).
+- `descriptors` [`N x 512`] array containing the L2 normalized descriptors.
+
+```bash
+python extract_features.py --image_list_file images.txt (--multiscale)
+```
+
+# Feature extraction with kapture datasets
+
+Kapture is a pivot file format, based on text and binary files, used to describe SFM (Structure From Motion) and more generally sensor-acquired data.
+
+It is available at https://github.com/naver/kapture.
+It contains conversion tools for popular formats and several popular datasets are directly available in kapture.
+
+It can be installed with:
+```bash
+pip install kapture
+```
+
+Datasets can be downloaded with:
+```bash
+kapture_download_dataset.py update
+kapture_download_dataset.py list
+# e.g.: install mapping and query of Extended-CMU-Seasons_slice22
+kapture_download_dataset.py install "Extended-CMU-Seasons_slice22_*"
+```
+If you want to convert your own dataset into kapture, please find some examples [here](https://github.com/naver/kapture/blob/master/doc/datasets.adoc).
+
+Once installed, you can extract keypoints for your kapture dataset with:
+```bash
+python extract_kapture.py --kapture-root pathto/yourkapturedataset (--multiscale)
+```
+
+Run `python extract_kapture.py --help` for more information on the extraction parameters.
+
+## Tuning on MegaDepth
+
+The training pipeline provided here is a PyTorch implementation of the TensorFlow code that was used to train the model available to download above.
+
+**Update - 05 June 2019** We have fixed a bug in the dataset preprocessing - retraining now yields similar results to the original TensorFlow implementation.
+
+**Update - 07 August 2019** We have released an updated, more accurate version of the training dataset - training is more stable and significantly faster for equal performance.
+
+### Downloading and preprocessing the MegaDepth dataset
+
+For this part, [COLMAP](https://colmap.github.io/) should be installed. Please refer to the official website for installation instructions.
+
+After downloading the entire [MegaDepth](http://www.cs.cornell.edu/projects/megadepth/) dataset (including SfM models), the first step is generating the undistorted reconstructions. This can be done by calling `undistort_reconstructions.py` as follows:
+
+```bash
+python undistort_reconstructions.py --colmap_path /path/to/colmap/executable --base_path /path/to/megadepth
+```
+
+Next, `preprocess_megadepth.sh` can be used to retrieve the camera parameters and compute the overlap between images for all scenes.
+
+```bash
+bash preprocess_undistorted_megadepth.sh /path/to/megadepth /path/to/output/folder
+```
+
+In case you prefer downloading the undistorted reconstructions and aggregated scene information folder directly, you can find them [here - Google Drive](https://drive.google.com/open?id=1hxpOsqOZefdrba_BqnW490XpNX_LgXPB). You will still need to download the depth maps ("MegaDepth v1 Dataset") from the MegaDepth website.
+
+### Training
+
+After downloading and preprocessing MegaDepth, the training can be started right away:
+
+```bash
+python train.py --use_validation --dataset_path /path/to/megadepth --scene_info_path /path/to/preprocessing/output
+```
+
+## BibTeX
+
+If you use this code in your project, please cite the following paper:
+
+```bibtex
+@InProceedings{Dusmanu2019CVPR,
+ author = {Dusmanu, Mihai and Rocco, Ignacio and Pajdla, Tomas and Pollefeys, Marc and Sivic, Josef and Torii, Akihiko and Sattler, Torsten},
+ title = {{D2-Net: A Trainable CNN for Joint Detection and Description of Local Features}},
+ booktitle = {Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+ year = {2019},
+}
+```
diff --git a/third_party/d2net/extract_features.py b/third_party/d2net/extract_features.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebcac0889d084c59d86bb21ed80d1e1ed8f17d8d
--- /dev/null
+++ b/third_party/d2net/extract_features.py
@@ -0,0 +1,144 @@
+import argparse
+
+import numpy as np
+
+import imageio
+
+import torch
+
+from tqdm import tqdm
+
+import scipy
+import scipy.io
+import scipy.misc
+
+from lib.model_test import D2Net
+from lib.utils import preprocess_image
+from lib.pyramid import process_multiscale
+
+# CUDA
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda:0" if use_cuda else "cpu")
+
+# Argument parsing
+parser = argparse.ArgumentParser(description="Feature extraction script")
+
+parser.add_argument(
+ "--image_list_file",
+ type=str,
+ required=True,
+ help="path to a file containing a list of images to process",
+)
+
+parser.add_argument(
+ "--preprocessing",
+ type=str,
+ default="caffe",
+ help="image preprocessing (caffe or torch)",
+)
+parser.add_argument(
+ "--model_file", type=str, default="models/d2_tf.pth", help="path to the full model"
+)
+
+parser.add_argument(
+ "--max_edge", type=int, default=1600, help="maximum image size at network input"
+)
+parser.add_argument(
+ "--max_sum_edges",
+ type=int,
+ default=2800,
+ help="maximum sum of image sizes at network input",
+)
+
+parser.add_argument(
+ "--output_extension", type=str, default=".d2-net", help="extension for the output"
+)
+parser.add_argument(
+ "--output_type", type=str, default="npz", help="output file type (npz or mat)"
+)
+
+parser.add_argument(
+ "--multiscale",
+ dest="multiscale",
+ action="store_true",
+ help="extract multiscale features",
+)
+parser.set_defaults(multiscale=False)
+
+parser.add_argument(
+ "--no-relu",
+ dest="use_relu",
+ action="store_false",
+ help="remove ReLU after the dense feature extraction module",
+)
+parser.set_defaults(use_relu=True)
+
+args = parser.parse_args()
+
+print(args)
+
+# Creating CNN model
+model = D2Net(model_file=args.model_file, use_relu=args.use_relu, use_cuda=use_cuda)
+
+# Process the file
+with open(args.image_list_file, "r") as f:
+ lines = f.readlines()
+for line in tqdm(lines, total=len(lines)):
+ path = line.strip()
+
+ image = imageio.imread(path)
+ if len(image.shape) == 2:
+ image = image[:, :, np.newaxis]
+ image = np.repeat(image, 3, -1)
+
+ # TODO: switch to PIL.Image due to deprecation of scipy.misc.imresize.
+ resized_image = image
+ if max(resized_image.shape) > args.max_edge:
+ resized_image = scipy.misc.imresize(
+ resized_image, args.max_edge / max(resized_image.shape)
+ ).astype("float")
+ if sum(resized_image.shape[:2]) > args.max_sum_edges:
+ resized_image = scipy.misc.imresize(
+ resized_image, args.max_sum_edges / sum(resized_image.shape[:2])
+ ).astype("float")
+
+ fact_i = image.shape[0] / resized_image.shape[0]
+ fact_j = image.shape[1] / resized_image.shape[1]
+
+ input_image = preprocess_image(resized_image, preprocessing=args.preprocessing)
+ with torch.no_grad():
+ if args.multiscale:
+ keypoints, scores, descriptors = process_multiscale(
+ torch.tensor(
+ input_image[np.newaxis, :, :, :].astype(np.float32), device=device
+ ),
+ model,
+ )
+ else:
+ keypoints, scores, descriptors = process_multiscale(
+ torch.tensor(
+ input_image[np.newaxis, :, :, :].astype(np.float32), device=device
+ ),
+ model,
+ scales=[1],
+ )
+
+ # Input image coordinates
+ keypoints[:, 0] *= fact_i
+ keypoints[:, 1] *= fact_j
+ # i, j -> u, v
+ keypoints = keypoints[:, [1, 0, 2]]
+
+ if args.output_type == "npz":
+ with open(path + args.output_extension, "wb") as output_file:
+ np.savez(
+ output_file, keypoints=keypoints, scores=scores, descriptors=descriptors
+ )
+ elif args.output_type == "mat":
+ with open(path + args.output_extension, "wb") as output_file:
+ scipy.io.savemat(
+ output_file,
+ {"keypoints": keypoints, "scores": scores, "descriptors": descriptors},
+ )
+ else:
+ raise ValueError("Unknown output type.")
diff --git a/third_party/d2net/extract_hesaff.m b/third_party/d2net/extract_hesaff.m
new file mode 100644
index 0000000000000000000000000000000000000000..5f544a49512640304df006e6704de5aaa14b0e6c
--- /dev/null
+++ b/third_party/d2net/extract_hesaff.m
@@ -0,0 +1,25 @@
+fid = fopen('image_list_hpatches_sequences.txt');
+
+tline = fgetl(fid);
+while ischar(tline)
+ disp(tline);
+ I = im2single(imread(tline));
+ if size(I, 3) > 1
+ I = rgb2gray(I);
+ end
+
+ [F, D, info] = vl_covdet(I, 'Method', 'Hessian', ...
+ 'EstimateAffineShape', true, ...
+ 'EstimateOrientation', true, ...
+ 'DoubleImage', false, ...
+ 'peakThreshold', 14 / 256^2);
+ keypoints = F';
+ scores = info.peakScores;
+ descriptors = D';
+
+ save([tline '.hesaff'], 'keypoints', 'scores', 'descriptors');
+
+ tline = fgetl(fid);
+end
+
+fclose(fid);
diff --git a/imcui/third_party/d2net/extract_kapture.py b/third_party/d2net/extract_kapture.py
similarity index 53%
rename from imcui/third_party/d2net/extract_kapture.py
rename to third_party/d2net/extract_kapture.py
index 23198b978229c699dbe24cd3bc0400d62bcab030..bad6ad4254238b9c9425243ff80f830bc4f02198 100644
--- a/imcui/third_party/d2net/extract_kapture.py
+++ b/third_party/d2net/extract_kapture.py
@@ -13,9 +13,21 @@ from os import path
import kapture
from kapture.io.records import get_image_fullpath
from kapture.io.csv import kapture_from_dir, get_all_tar_handlers
-from kapture.io.csv import get_feature_csv_fullpath, keypoints_to_file, descriptors_to_file
-from kapture.io.features import get_keypoints_fullpath, keypoints_check_dir, image_keypoints_to_file
-from kapture.io.features import get_descriptors_fullpath, descriptors_check_dir, image_descriptors_to_file
+from kapture.io.csv import (
+ get_feature_csv_fullpath,
+ keypoints_to_file,
+ descriptors_to_file,
+)
+from kapture.io.features import (
+ get_keypoints_fullpath,
+ keypoints_check_dir,
+ image_keypoints_to_file,
+)
+from kapture.io.features import (
+ get_descriptors_fullpath,
+ descriptors_check_dir,
+ image_descriptors_to_file,
+)
from lib.model_test import D2Net
from lib.utils import preprocess_image
@@ -28,68 +40,89 @@ use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
# Argument parsing
-parser = argparse.ArgumentParser(description='Feature extraction script')
+parser = argparse.ArgumentParser(description="Feature extraction script")
parser.add_argument(
- '--kapture-root', type=str, required=True,
- help='path to kapture root directory'
+ "--kapture-root", type=str, required=True, help="path to kapture root directory"
)
parser.add_argument(
- '--preprocessing', type=str, default='caffe',
- help='image preprocessing (caffe or torch)'
+ "--preprocessing",
+ type=str,
+ default="caffe",
+ help="image preprocessing (caffe or torch)",
)
parser.add_argument(
- '--model_file', type=str, default='models/d2_tf.pth',
- help='path to the full model'
+ "--model_file", type=str, default="models/d2_tf.pth", help="path to the full model"
)
parser.add_argument(
- '--keypoints-type', type=str, default=None,
- help='keypoint type_name, default is filename of model'
+ "--keypoints-type",
+ type=str,
+ default=None,
+ help="keypoint type_name, default is filename of model",
)
parser.add_argument(
- '--descriptors-type', type=str, default=None,
- help='descriptors type_name, default is filename of model'
+ "--descriptors-type",
+ type=str,
+ default=None,
+ help="descriptors type_name, default is filename of model",
)
parser.add_argument(
- '--max_edge', type=int, default=1600,
- help='maximum image size at network input'
+ "--max_edge", type=int, default=1600, help="maximum image size at network input"
)
parser.add_argument(
- '--max_sum_edges', type=int, default=2800,
- help='maximum sum of image sizes at network input'
+ "--max_sum_edges",
+ type=int,
+ default=2800,
+ help="maximum sum of image sizes at network input",
)
parser.add_argument(
- '--multiscale', dest='multiscale', action='store_true',
- help='extract multiscale features'
+ "--multiscale",
+ dest="multiscale",
+ action="store_true",
+ help="extract multiscale features",
)
parser.set_defaults(multiscale=False)
parser.add_argument(
- '--no-relu', dest='use_relu', action='store_false',
- help='remove ReLU after the dense feature extraction module'
+ "--no-relu",
+ dest="use_relu",
+ action="store_false",
+ help="remove ReLU after the dense feature extraction module",
)
parser.set_defaults(use_relu=True)
-parser.add_argument("--max-keypoints", type=int, default=float("+inf"),
- help='max number of keypoints save to disk')
+parser.add_argument(
+ "--max-keypoints",
+ type=int,
+ default=float("+inf"),
+ help="max number of keypoints save to disk",
+)
args = parser.parse_args()
print(args)
-with get_all_tar_handlers(args.kapture_root,
- mode={kapture.Keypoints: 'a',
- kapture.Descriptors: 'a',
- kapture.GlobalFeatures: 'r',
- kapture.Matches: 'r'}) as tar_handlers:
- kdata = kapture_from_dir(args.kapture_root,
- skip_list=[kapture.GlobalFeatures,
- kapture.Matches,
- kapture.Points3d,
- kapture.Observations],
- tar_handlers=tar_handlers)
+with get_all_tar_handlers(
+ args.kapture_root,
+ mode={
+ kapture.Keypoints: "a",
+ kapture.Descriptors: "a",
+ kapture.GlobalFeatures: "r",
+ kapture.Matches: "r",
+ },
+) as tar_handlers:
+ kdata = kapture_from_dir(
+ args.kapture_root,
+ skip_list=[
+ kapture.GlobalFeatures,
+ kapture.Matches,
+ kapture.Points3d,
+ kapture.Observations,
+ ],
+ tar_handlers=tar_handlers,
+ )
if kdata.keypoints is None:
kdata.keypoints = {}
if kdata.descriptors is None:
@@ -99,28 +132,29 @@ with get_all_tar_handlers(args.kapture_root,
image_list = [filename for _, _, filename in kapture.flatten(kdata.records_camera)]
if args.keypoints_type is None:
args.keypoints_type = path.splitext(path.basename(args.model_file))[0]
- print(f'keypoints_type set to {args.keypoints_type}')
+ print(f"keypoints_type set to {args.keypoints_type}")
if args.descriptors_type is None:
args.descriptors_type = path.splitext(path.basename(args.model_file))[0]
- print(f'descriptors_type set to {args.descriptors_type}')
- if args.keypoints_type in kdata.keypoints and args.descriptors_type in kdata.descriptors:
- image_list = [name
- for name in image_list
- if name not in kdata.keypoints[args.keypoints_type] or
- name not in kdata.descriptors[args.descriptors_type]]
+ print(f"descriptors_type set to {args.descriptors_type}")
+ if (
+ args.keypoints_type in kdata.keypoints
+ and args.descriptors_type in kdata.descriptors
+ ):
+ image_list = [
+ name
+ for name in image_list
+ if name not in kdata.keypoints[args.keypoints_type]
+ or name not in kdata.descriptors[args.descriptors_type]
+ ]
if len(image_list) == 0:
- print('All features were already extracted')
+ print("All features were already extracted")
exit(0)
else:
- print(f'Extracting d2net features for {len(image_list)} images')
+ print(f"Extracting d2net features for {len(image_list)} images")
# Creating CNN model
- model = D2Net(
- model_file=args.model_file,
- use_relu=args.use_relu,
- use_cuda=use_cuda
- )
+ model = D2Net(model_file=args.model_file, use_relu=args.use_relu, use_cuda=use_cuda)
if args.keypoints_type not in kdata.keypoints:
keypoints_dtype = None
@@ -138,7 +172,7 @@ with get_all_tar_handlers(args.kapture_root,
# Process the files
for image_name in tqdm(image_list, total=len(image_list)):
img_path = get_image_fullpath(args.kapture_root, image_name)
- image = Image.open(img_path).convert('RGB')
+ image = Image.open(img_path).convert("RGB")
width, height = image.size
@@ -162,30 +196,27 @@ with get_all_tar_handlers(args.kapture_root,
fact_i = width / resized_width
fact_j = height / resized_height
- resized_image = np.array(resized_image).astype('float')
+ resized_image = np.array(resized_image).astype("float")
- input_image = preprocess_image(
- resized_image,
- preprocessing=args.preprocessing
- )
+ input_image = preprocess_image(resized_image, preprocessing=args.preprocessing)
with torch.no_grad():
if args.multiscale:
keypoints, scores, descriptors = process_multiscale(
torch.tensor(
input_image[np.newaxis, :, :, :].astype(np.float32),
- device=device
+ device=device,
),
- model
+ model,
)
else:
keypoints, scores, descriptors = process_multiscale(
torch.tensor(
input_image[np.newaxis, :, :, :].astype(np.float32),
- device=device
+ device=device,
),
model,
- scales=[1]
+ scales=[1],
)
# Input image coordinates
@@ -196,7 +227,7 @@ with get_all_tar_handlers(args.kapture_root,
if args.max_keypoints != float("+inf"):
# keep the last (the highest) indexes
- idx_keep = scores.argsort()[-min(len(keypoints), args.max_keypoints):]
+ idx_keep = scores.argsort()[-min(len(keypoints), args.max_keypoints) :]
keypoints = keypoints[idx_keep]
descriptors = descriptors[idx_keep]
@@ -207,42 +238,65 @@ with get_all_tar_handlers(args.kapture_root,
keypoints_dsize = keypoints.shape[1]
descriptors_dsize = descriptors.shape[1]
- kdata.keypoints[args.keypoints_type] = kapture.Keypoints('d2net', keypoints_dtype, keypoints_dsize)
- kdata.descriptors[args.descriptors_type] = kapture.Descriptors('d2net', descriptors_dtype,
- descriptors_dsize,
- args.keypoints_type, 'L2')
-
- keypoints_config_absolute_path = get_feature_csv_fullpath(kapture.Keypoints,
- args.keypoints_type,
- args.kapture_root)
- descriptors_config_absolute_path = get_feature_csv_fullpath(kapture.Descriptors,
- args.descriptors_type,
- args.kapture_root)
-
- keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints[args.keypoints_type])
- descriptors_to_file(descriptors_config_absolute_path, kdata.descriptors[args.descriptors_type])
+ kdata.keypoints[args.keypoints_type] = kapture.Keypoints(
+ "d2net", keypoints_dtype, keypoints_dsize
+ )
+ kdata.descriptors[args.descriptors_type] = kapture.Descriptors(
+ "d2net", descriptors_dtype, descriptors_dsize, args.keypoints_type, "L2"
+ )
+
+ keypoints_config_absolute_path = get_feature_csv_fullpath(
+ kapture.Keypoints, args.keypoints_type, args.kapture_root
+ )
+ descriptors_config_absolute_path = get_feature_csv_fullpath(
+ kapture.Descriptors, args.descriptors_type, args.kapture_root
+ )
+
+ keypoints_to_file(
+ keypoints_config_absolute_path, kdata.keypoints[args.keypoints_type]
+ )
+ descriptors_to_file(
+ descriptors_config_absolute_path,
+ kdata.descriptors[args.descriptors_type],
+ )
else:
assert kdata.keypoints[args.keypoints_type].dtype == keypoints.dtype
assert kdata.descriptors[args.descriptors_type].dtype == descriptors.dtype
assert kdata.keypoints[args.keypoints_type].dsize == keypoints.shape[1]
- assert kdata.descriptors[args.descriptors_type].dsize == descriptors.shape[1]
- assert kdata.descriptors[args.descriptors_type].keypoints_type == args.keypoints_type
- assert kdata.descriptors[args.descriptors_type].metric_type == 'L2'
-
- keypoints_fullpath = get_keypoints_fullpath(args.keypoints_type, args.kapture_root,
- image_name, tar_handlers)
+ assert (
+ kdata.descriptors[args.descriptors_type].dsize == descriptors.shape[1]
+ )
+ assert (
+ kdata.descriptors[args.descriptors_type].keypoints_type
+ == args.keypoints_type
+ )
+ assert kdata.descriptors[args.descriptors_type].metric_type == "L2"
+
+ keypoints_fullpath = get_keypoints_fullpath(
+ args.keypoints_type, args.kapture_root, image_name, tar_handlers
+ )
print(f"Saving {keypoints.shape[0]} keypoints to {keypoints_fullpath}")
image_keypoints_to_file(keypoints_fullpath, keypoints)
kdata.keypoints[args.keypoints_type].add(image_name)
- descriptors_fullpath = get_descriptors_fullpath(args.descriptors_type, args.kapture_root,
- image_name, tar_handlers)
+ descriptors_fullpath = get_descriptors_fullpath(
+ args.descriptors_type, args.kapture_root, image_name, tar_handlers
+ )
print(f"Saving {descriptors.shape[0]} descriptors to {descriptors_fullpath}")
image_descriptors_to_file(descriptors_fullpath, descriptors)
kdata.descriptors[args.descriptors_type].add(image_name)
- if not keypoints_check_dir(kdata.keypoints[args.keypoints_type], args.keypoints_type,
- args.kapture_root, tar_handlers) or \
- not descriptors_check_dir(kdata.descriptors[args.descriptors_type], args.descriptors_type,
- args.kapture_root, tar_handlers):
- print('local feature extraction ended successfully but not all files were saved')
+ if not keypoints_check_dir(
+ kdata.keypoints[args.keypoints_type],
+ args.keypoints_type,
+ args.kapture_root,
+ tar_handlers,
+ ) or not descriptors_check_dir(
+ kdata.descriptors[args.descriptors_type],
+ args.descriptors_type,
+ args.kapture_root,
+ tar_handlers,
+ ):
+ print(
+ "local feature extraction ended successfully but not all files were saved"
+ )
diff --git a/third_party/d2net/hpatches_sequences/HPatches-Sequences-Matching-Benchmark.ipynb b/third_party/d2net/hpatches_sequences/HPatches-Sequences-Matching-Benchmark.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bb9c93165c3325c70d22290cc53f55a34b28c1f3
--- /dev/null
+++ b/third_party/d2net/hpatches_sequences/HPatches-Sequences-Matching-Benchmark.ipynb
@@ -0,0 +1,441 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "import torch\n",
+ "\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "from tqdm import tqdm_notebook as tqdm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "use_cuda = torch.cuda.is_available()\n",
+ "device = torch.device('cuda:0' if use_cuda else 'cpu')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add new methods here.\n",
+ "# methods = ['hesaff', 'hesaffnet', 'delf', 'delf-new', 'superpoint', 'd2-net', 'd2-net-trained']\n",
+ "# names = ['Hes. Aff. + Root-SIFT', 'HAN + HN++', 'DELF', 'DELF New', 'SuperPoint', 'D2-Net', 'D2-Net Trained']\n",
+ "# colors = ['black', 'orange', 'red', 'red', 'blue', 'purple', 'purple']\n",
+ "# linestyles = ['-', '-', '-', '--', '-', '-', '--']\n",
+ "methods = ['hesaff', 'hesaffnet', 'delf', 'delf-new', 'superpoint', 'lf-net', 'd2-net', 'd2-net-ms', 'd2-net-trained', 'd2-net-trained-ms']\n",
+ "names = ['Hes. Aff. + Root-SIFT', 'HAN + HN++', 'DELF', 'DELF New', 'SuperPoint', 'LF-Net', 'D2-Net', 'D2-Net MS', 'D2-Net Trained', 'D2-Net Trained MS']\n",
+ "colors = ['black', 'orange', 'red', 'red', 'blue', 'brown', 'purple', 'green', 'purple', 'green']\n",
+ "linestyles = ['-', '-', '-', '--', '-', '-', '-', '-', '--', '--']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Change here if you want to use top K or all features.\n",
+ "# top_k = 2000\n",
+ "top_k = None "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n_i = 52\n",
+ "n_v = 56"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset_path = 'hpatches-sequences-release'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lim = [1, 15]\n",
+ "rng = np.arange(lim[0], lim[1] + 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mnn_matcher(descriptors_a, descriptors_b):\n",
+ " device = descriptors_a.device\n",
+ " sim = descriptors_a @ descriptors_b.t()\n",
+ " nn12 = torch.max(sim, dim=1)[1]\n",
+ " nn21 = torch.max(sim, dim=0)[1]\n",
+ " ids1 = torch.arange(0, sim.shape[0], device=device)\n",
+ " mask = (ids1 == nn21[nn12])\n",
+ " matches = torch.stack([ids1[mask], nn12[mask]])\n",
+ " return matches.t().data.cpu().numpy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "def benchmark_features(read_feats):\n",
+ " seq_names = sorted(os.listdir(dataset_path))\n",
+ "\n",
+ " n_feats = []\n",
+ " n_matches = []\n",
+ " seq_type = []\n",
+ " i_err = {thr: 0 for thr in rng}\n",
+ " v_err = {thr: 0 for thr in rng}\n",
+ "\n",
+ " for seq_idx, seq_name in tqdm(enumerate(seq_names), total=len(seq_names)):\n",
+ " keypoints_a, descriptors_a = read_feats(seq_name, 1)\n",
+ " n_feats.append(keypoints_a.shape[0])\n",
+ "\n",
+ " for im_idx in range(2, 7):\n",
+ " keypoints_b, descriptors_b = read_feats(seq_name, im_idx)\n",
+ " n_feats.append(keypoints_b.shape[0])\n",
+ "\n",
+ " matches = mnn_matcher(\n",
+ " torch.from_numpy(descriptors_a).to(device=device), \n",
+ " torch.from_numpy(descriptors_b).to(device=device)\n",
+ " )\n",
+ " \n",
+ " homography = np.loadtxt(os.path.join(dataset_path, seq_name, \"H_1_\" + str(im_idx)))\n",
+ " \n",
+ " pos_a = keypoints_a[matches[:, 0], : 2] \n",
+ " pos_a_h = np.concatenate([pos_a, np.ones([matches.shape[0], 1])], axis=1)\n",
+ " pos_b_proj_h = np.transpose(np.dot(homography, np.transpose(pos_a_h)))\n",
+ " pos_b_proj = pos_b_proj_h[:, : 2] / pos_b_proj_h[:, 2 :]\n",
+ "\n",
+ " pos_b = keypoints_b[matches[:, 1], : 2]\n",
+ "\n",
+ " dist = np.sqrt(np.sum((pos_b - pos_b_proj) ** 2, axis=1))\n",
+ "\n",
+ " n_matches.append(matches.shape[0])\n",
+ " seq_type.append(seq_name[0])\n",
+ " \n",
+ " if dist.shape[0] == 0:\n",
+ " dist = np.array([float(\"inf\")])\n",
+ " \n",
+ " for thr in rng:\n",
+ " if seq_name[0] == 'i':\n",
+ " i_err[thr] += np.mean(dist <= thr)\n",
+ " else:\n",
+ " v_err[thr] += np.mean(dist <= thr)\n",
+ " \n",
+ " seq_type = np.array(seq_type)\n",
+ " n_feats = np.array(n_feats)\n",
+ " n_matches = np.array(n_matches)\n",
+ " \n",
+ " return i_err, v_err, [seq_type, n_feats, n_matches]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def summary(stats):\n",
+ " seq_type, n_feats, n_matches = stats\n",
+ " print('# Features: {:f} - [{:d}, {:d}]'.format(np.mean(n_feats), np.min(n_feats), np.max(n_feats)))\n",
+ " print('# Matches: Overall {:f}, Illumination {:f}, Viewpoint {:f}'.format(\n",
+ " np.sum(n_matches) / ((n_i + n_v) * 5), \n",
+ " np.sum(n_matches[seq_type == 'i']) / (n_i * 5), \n",
+ " np.sum(n_matches[seq_type == 'v']) / (n_v * 5))\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def generate_read_function(method, extension='ppm'):\n",
+ " def read_function(seq_name, im_idx):\n",
+ " aux = np.load(os.path.join(dataset_path, seq_name, '%d.%s.%s' % (im_idx, extension, method)))\n",
+ " if top_k is None:\n",
+ " return aux['keypoints'], aux['descriptors']\n",
+ " else:\n",
+ " assert('scores' in aux)\n",
+ " ids = np.argsort(aux['scores'])[-top_k :]\n",
+ " return aux['keypoints'][ids, :], aux['descriptors'][ids, :]\n",
+ " return read_function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sift_to_rootsift(descriptors):\n",
+ " return np.sqrt(descriptors / np.expand_dims(np.sum(np.abs(descriptors), axis=1), axis=1) + 1e-16)\n",
+ "def parse_mat(mat):\n",
+ " keypoints = mat['keypoints'][:, : 2]\n",
+ " raw_descriptors = mat['descriptors']\n",
+ " l2_norm_descriptors = raw_descriptors / np.expand_dims(np.sum(raw_descriptors ** 2, axis=1), axis=1)\n",
+ " descriptors = sift_to_rootsift(l2_norm_descriptors)\n",
+ " if top_k is None:\n",
+ " return keypoints, descriptors\n",
+ " else:\n",
+ " assert('scores' in mat)\n",
+ " ids = np.argsort(mat['scores'][0])[-top_k :]\n",
+ " return keypoints[ids, :], descriptors[ids, :]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if top_k is None:\n",
+ " cache_dir = 'cache'\n",
+ "else:\n",
+ " cache_dir = 'cache-top'\n",
+ "if not os.path.isdir(cache_dir):\n",
+ " os.mkdir(cache_dir)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "errors = {}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hesaff\n",
+ "Loading precomputed errors...\n",
+ "# Features: 6710.137346 - [296, 26021]\n",
+ "# Matches: Overall 2851.679630, Illumination 1585.803846, Viewpoint 4027.135714\n",
+ "hesaffnet\n",
+ "Loading precomputed errors...\n",
+ "# Features: 3860.754630 - [89, 16326]\n",
+ "# Matches: Overall 1959.996296, Illumination 1098.419231, Viewpoint 2760.032143\n",
+ "delf\n",
+ "Loading precomputed errors...\n",
+ "# Features: 4608.236111 - [1196, 10939]\n",
+ "# Matches: Overall 1912.400000, Illumination 1973.100000, Viewpoint 1856.035714\n",
+ "delf-new\n",
+ "Loading precomputed errors...\n",
+ "# Features: 4590.001543 - [953, 12696]\n",
+ "# Matches: Overall 1940.288889, Illumination 2031.873077, Viewpoint 1855.246429\n",
+ "superpoint\n",
+ "Loading precomputed errors...\n",
+ "# Features: 1562.611111 - [90, 6422]\n",
+ "# Matches: Overall 883.440741, Illumination 667.830769, Viewpoint 1083.650000\n",
+ "lf-net\n",
+ "Loading precomputed errors...\n",
+ "# Features: 500.000000 - [500, 500]\n",
+ "# Matches: Overall 177.475926, Illumination 183.073077, Viewpoint 172.278571\n",
+ "d2-net\n",
+ "Loading precomputed errors...\n",
+ "# Features: 2994.067901 - [641, 9337]\n",
+ "# Matches: Overall 1182.574074, Illumination 964.588462, Viewpoint 1384.989286\n",
+ "d2-net-ms\n",
+ "Loading precomputed errors...\n",
+ "# Features: 4928.163580 - [1009, 15230]\n",
+ "# Matches: Overall 1698.377778, Illumination 1384.215385, Viewpoint 1990.100000\n",
+ "d2-net-trained\n",
+ "Loading precomputed errors...\n",
+ "# Features: 5965.117284 - [1309, 18974]\n",
+ "# Matches: Overall 2495.900000, Illumination 2033.250000, Viewpoint 2925.503571\n",
+ "d2-net-trained-ms\n",
+ "Loading precomputed errors...\n",
+ "# Features: 8254.473765 - [1797, 26880]\n",
+ "# Matches: Overall 2831.638889, Illumination 2313.957692, Viewpoint 3312.342857\n"
+ ]
+ }
+ ],
+ "source": [
+ "for method in methods:\n",
+ " output_file = os.path.join(cache_dir, method + '.npy')\n",
+ " print(method)\n",
+ " if method == 'hesaff':\n",
+ " read_function = lambda seq_name, im_idx: parse_mat(loadmat(os.path.join(dataset_path, seq_name, '%d.ppm.hesaff' % im_idx), appendmat=False))\n",
+ " else:\n",
+ " if method == 'delf' or method == 'delf-new':\n",
+ " read_function = generate_read_function(method, extension='png')\n",
+ " else:\n",
+ " read_function = generate_read_function(method)\n",
+ " if os.path.exists(output_file):\n",
+ " print('Loading precomputed errors...')\n",
+ " errors[method] = np.load(output_file, allow_pickle=True)\n",
+ " else:\n",
+ " errors[method] = benchmark_features(read_function)\n",
+ " np.save(output_file, errors[method])\n",
+ " summary(errors[method][-1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt_lim = [1, 10]\n",
+ "plt_rng = np.arange(plt_lim[0], plt_lim[1] + 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.rc('axes', titlesize=25)\n",
+ "plt.rc('axes', labelsize=25)\n",
+ "\n",
+ "plt.figure(figsize=(15, 5))\n",
+ "\n",
+ "plt.subplot(1, 3, 1)\n",
+ "for method, name, color, ls in zip(methods, names, colors, linestyles):\n",
+ " i_err, v_err, _ = errors[method]\n",
+ " plt.plot(plt_rng, [(i_err[thr] + v_err[thr]) / ((n_i + n_v) * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name)\n",
+ "plt.title('Overall')\n",
+ "plt.xlim(plt_lim)\n",
+ "plt.xticks(plt_rng)\n",
+ "plt.ylabel('MMA')\n",
+ "plt.ylim([0, 1])\n",
+ "plt.grid()\n",
+ "plt.tick_params(axis='both', which='major', labelsize=20)\n",
+ "plt.legend()\n",
+ "\n",
+ "plt.subplot(1, 3, 2)\n",
+ "for method, name, color, ls in zip(methods, names, colors, linestyles):\n",
+ " i_err, v_err, _ = errors[method]\n",
+ " plt.plot(plt_rng, [i_err[thr] / (n_i * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name)\n",
+ "plt.title('Illumination')\n",
+ "plt.xlabel('threshold [px]')\n",
+ "plt.xlim(plt_lim)\n",
+ "plt.xticks(plt_rng)\n",
+ "plt.ylim([0, 1])\n",
+ "plt.gca().axes.set_yticklabels([])\n",
+ "plt.grid()\n",
+ "plt.tick_params(axis='both', which='major', labelsize=20)\n",
+ "\n",
+ "plt.subplot(1, 3, 3)\n",
+ "for method, name, color, ls in zip(methods, names, colors, linestyles):\n",
+ " i_err, v_err, _ = errors[method]\n",
+ " plt.plot(plt_rng, [v_err[thr] / (n_v * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name)\n",
+ "plt.title('Viewpoint')\n",
+ "plt.xlim(plt_lim)\n",
+ "plt.xticks(plt_rng)\n",
+ "plt.ylim([0, 1])\n",
+ "plt.gca().axes.set_yticklabels([])\n",
+ "plt.grid()\n",
+ "plt.tick_params(axis='both', which='major', labelsize=20)\n",
+ "\n",
+ "if top_k is None:\n",
+ " plt.savefig('hseq.pdf', bbox_inches='tight', dpi=300)\n",
+ "else:\n",
+ " plt.savefig('hseq-top.pdf', bbox_inches='tight', dpi=300)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/d2net/hpatches_sequences/README.md b/third_party/d2net/hpatches_sequences/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2a0b5e0f154d1717087c35f93cd02a0f54fc6027
--- /dev/null
+++ b/third_party/d2net/hpatches_sequences/README.md
@@ -0,0 +1,22 @@
+# HPatches Sequences / Image Pairs Matching Benchmark
+
+Please check the [official repository](https://github.com/hpatches/hpatches-dataset) for more information regarding references.
+
+The dataset can be downloaded by running `bash download.sh` - this script downloads and extracts the HPatches Sequences dataset and removes the sequences containing high resolution images (`> 1600x1200`) as mentioned in the D2-Net paper. You can also download the cache with results for all methods from the D2-Net paper by running `bash download_cache.sh`.
+
+New methods can be added in cell 4 of the notebook. The local features are supposed to be stored in the [`npz`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html) format with three fields:
+
+- `keypoints` - `N x 2` matrix with `x, y` coordinates of each keypoint in COLMAP format (the `X` axis points to the right, the `Y` axis to the bottom)
+
+- `scores` - `N` array with detection scores for each keypoint (higher is better) - only required for the "top K" version of the benchmark
+
+- `descriptors` - `N x D` matrix with the descriptors (L2 normalized if you plan on using the provided mutual nearest neighbors matcher)
+
+Moreover, the `npz` files are supposed to be saved alongside their corresponding images with the same extension as the `method` (e.g. if `method = d2-net`, the features for the image `hpatches-sequences-release/i_ajuntament/1.ppm` should be in the file `hpatches-sequences-release/i_ajuntament/1.ppm.d2-net`).
+
+We provide a simple script to extract Hessian Affine keypoints with SIFT descriptors (`extract_hesaff.m`); this script requires MATLAB and [VLFeat](http://www.vlfeat.org/).
+
+D2-Net features can be extracted by running:
+```
+python extract_features.py --image_list_file image_list_hpatches_sequences.txt
+```
diff --git a/third_party/d2net/hpatches_sequences/convert_to_png.sh b/third_party/d2net/hpatches_sequences/convert_to_png.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5b82fff606b4ef60bad32cfef463a601cbfd4586
--- /dev/null
+++ b/third_party/d2net/hpatches_sequences/convert_to_png.sh
@@ -0,0 +1,9 @@
+# DELF Extraction script doesn't support .ppm images.
+current_dir=`pwd`
+echo $current_dir
+for dir in `ls hpatches-sequences-release`; do
+ echo $dir
+ cd hpatches-sequences-release/$dir
+ mogrify -format png *.ppm
+ cd $current_dir
+done
diff --git a/third_party/d2net/hpatches_sequences/download.sh b/third_party/d2net/hpatches_sequences/download.sh
new file mode 100644
index 0000000000000000000000000000000000000000..80eb0e3c9f24345c17177cb9d3ab0834f8d58a27
--- /dev/null
+++ b/third_party/d2net/hpatches_sequences/download.sh
@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+
+# Download the dataset
+wget http://icvl.ee.ic.ac.uk/vbalnt/hpatches/hpatches-sequences-release.tar.gz
+
+# Extract the dataset
+tar xvzf hpatches-sequences-release.tar.gz
+
+# Remove the high-resolution sequences
+cd hpatches-sequences-release
+rm -rf i_contruction i_crownnight i_dc i_pencils i_whitebuilding v_artisans v_astronautis v_talent
+cd ..
diff --git a/third_party/d2net/hpatches_sequences/download_cache.sh b/third_party/d2net/hpatches_sequences/download_cache.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7a5a34acc75af5c2f398d3ec8cea367be404cdeb
--- /dev/null
+++ b/third_party/d2net/hpatches_sequences/download_cache.sh
@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+
+wget https://dsmn.ml/files/d2-net/hpatches-sequences-cache.tar.gz
+tar xvzf hpatches-sequences-cache.tar.gz
+rm -rf hpatches-sequences-cache.tar.gz
+
+wget https://dsmn.ml/files/d2-net/hpatches-sequences-cache-top.tar.gz
+tar xvzf hpatches-sequences-cache-top.tar.gz
+rm -rf hpatches-sequences-cache-top.tar.gz
+
diff --git a/third_party/d2net/image_list_hpatches_sequences.txt b/third_party/d2net/image_list_hpatches_sequences.txt
new file mode 100644
index 0000000000000000000000000000000000000000..edee04fef9a4bdadba7b10015a3f0e20cd3e10fc
--- /dev/null
+++ b/third_party/d2net/image_list_hpatches_sequences.txt
@@ -0,0 +1,648 @@
+hpatches_sequences/hpatches-sequences-release/v_vitro/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_vitro/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_vitro/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_vitro/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_vitro/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_vitro/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_apprentices/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_apprentices/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_apprentices/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_apprentices/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_apprentices/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_apprentices/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_miniature/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_miniature/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_miniature/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_miniature/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_miniature/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_miniature/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_churchill/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_churchill/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_churchill/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_churchill/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_churchill/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_churchill/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_soldiers/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_soldiers/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_soldiers/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_soldiers/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_soldiers/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_soldiers/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_nijmegen/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_nijmegen/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_nijmegen/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_nijmegen/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_nijmegen/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_nijmegen/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_wapping/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_wapping/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_wapping/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_wapping/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_wapping/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_wapping/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_bip/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_bip/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_bip/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_bip/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_bip/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_bip/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_fog/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_fog/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_fog/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_fog/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_fog/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_fog/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_nescafe/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_nescafe/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_nescafe/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_nescafe/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_nescafe/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_nescafe/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_village/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_village/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_village/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_village/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_village/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_village/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_table/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_table/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_table/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_table/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_table/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_table/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_calder/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_calder/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_calder/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_calder/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_calder/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_calder/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_partyfood/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_partyfood/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_partyfood/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_partyfood/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_partyfood/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_partyfood/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_bridger/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_bridger/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_bridger/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_bridger/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_bridger/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_bridger/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_dirtywall/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_dirtywall/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_dirtywall/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_dirtywall/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_dirtywall/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_dirtywall/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_parking/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_parking/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_parking/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_parking/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_parking/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_parking/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_wormhole/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_wormhole/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_wormhole/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_wormhole/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_wormhole/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_wormhole/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_tempera/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_tempera/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_tempera/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_tempera/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_tempera/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_tempera/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_greenhouse/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_greenhouse/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_greenhouse/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_greenhouse/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_greenhouse/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_greenhouse/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_adam/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_adam/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_adam/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_adam/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_adam/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_adam/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_smurf/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_smurf/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_smurf/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_smurf/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_smurf/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_smurf/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_posters/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_posters/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_posters/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_posters/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_posters/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_posters/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_cartooncity/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_cartooncity/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_cartooncity/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_cartooncity/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_cartooncity/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_cartooncity/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_melon/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_melon/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_melon/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_melon/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_melon/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_melon/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_resort/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_resort/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_resort/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_resort/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_resort/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_resort/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_coffeehouse/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_coffeehouse/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_coffeehouse/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_coffeehouse/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_coffeehouse/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_coffeehouse/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_colors/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_colors/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_colors/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_colors/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_colors/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_colors/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_underground/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_underground/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_underground/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_underground/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_underground/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_underground/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_pomegranate/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_pomegranate/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_pomegranate/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_pomegranate/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_pomegranate/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_pomegranate/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_eastsouth/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_eastsouth/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_eastsouth/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_eastsouth/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_eastsouth/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_eastsouth/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_tabletop/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_tabletop/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_tabletop/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_tabletop/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_tabletop/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_tabletop/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_crownday/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_crownday/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_crownday/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_crownday/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_crownday/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_crownday/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_leuven/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_leuven/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_leuven/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_leuven/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_leuven/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_leuven/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_tools/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_tools/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_tools/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_tools/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_tools/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_tools/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_ski/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_ski/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_ski/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_ski/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_ski/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_ski/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_ktirio/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_ktirio/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_ktirio/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_ktirio/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_ktirio/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_ktirio/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_duda/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_duda/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_duda/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_duda/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_duda/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_duda/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_pool/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_pool/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_pool/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_pool/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_pool/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_pool/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_woman/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_woman/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_woman/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_woman/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_woman/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_woman/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionnight/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionnight/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionnight/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionnight/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionnight/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionnight/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_pinard/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_pinard/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_pinard/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_pinard/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_pinard/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_pinard/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_wall/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_wall/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_wall/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_wall/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_wall/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_wall/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_sunseason/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_sunseason/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_sunseason/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_sunseason/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_sunseason/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_sunseason/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_bees/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_bees/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_bees/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_bees/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_bees/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_bees/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_brooklyn/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_brooklyn/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_brooklyn/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_brooklyn/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_brooklyn/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_brooklyn/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_strand/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_strand/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_strand/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_strand/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_strand/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_strand/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_dome/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_dome/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_dome/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_dome/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_dome/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_dome/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_samples/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_samples/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_samples/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_samples/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_samples/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_samples/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_bricks/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_bricks/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_bricks/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_bricks/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_bricks/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_bricks/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_home/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_home/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_home/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_home/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_home/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_home/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_beyus/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_beyus/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_beyus/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_beyus/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_beyus/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_beyus/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_porta/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_porta/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_porta/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_porta/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_porta/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_porta/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_weapons/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_weapons/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_weapons/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_weapons/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_weapons/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_weapons/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_abstract/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_abstract/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_abstract/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_abstract/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_abstract/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_abstract/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_gardens/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_gardens/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_gardens/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_gardens/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_gardens/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_gardens/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_veggies/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_veggies/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_veggies/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_veggies/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_veggies/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_veggies/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_circus/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_circus/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_circus/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_circus/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_circus/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_circus/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_santuario/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_santuario/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_santuario/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_santuario/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_santuario/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_santuario/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionday/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionday/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionday/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionday/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionday/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_lionday/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_boat/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_boat/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_boat/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_boat/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_boat/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_boat/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_salon/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_salon/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_salon/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_salon/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_salon/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_salon/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_steps/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_steps/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_steps/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_steps/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_steps/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_steps/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_ajuntament/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_ajuntament/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_ajuntament/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_ajuntament/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_ajuntament/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_ajuntament/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_fest/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_fest/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_fest/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_fest/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_fest/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_fest/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_kions/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_kions/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_kions/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_kions/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_kions/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_kions/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_wounded/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_wounded/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_wounded/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_wounded/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_wounded/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_wounded/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_indiana/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_indiana/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_indiana/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_indiana/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_indiana/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_indiana/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_yuri/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_yuri/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_yuri/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_yuri/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_yuri/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_yuri/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_boutique/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_boutique/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_boutique/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_boutique/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_boutique/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_boutique/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_birdwoman/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_birdwoman/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_birdwoman/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_birdwoman/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_birdwoman/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_birdwoman/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_grace/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_grace/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_grace/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_grace/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_grace/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_grace/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_man/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_man/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_man/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_man/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_man/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_man/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_kurhaus/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_kurhaus/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_kurhaus/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_kurhaus/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_kurhaus/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_kurhaus/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_busstop/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_busstop/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_busstop/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_busstop/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_busstop/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_busstop/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_machines/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_machines/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_machines/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_machines/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_machines/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_machines/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_castle/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_castle/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_castle/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_castle/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_castle/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_castle/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_bologna/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_bologna/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_bologna/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_bologna/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_bologna/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_bologna/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_blueprint/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_blueprint/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_blueprint/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_blueprint/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_blueprint/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_blueprint/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_troulos/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_troulos/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_troulos/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_troulos/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_troulos/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_troulos/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_gonnenberg/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_gonnenberg/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_gonnenberg/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_gonnenberg/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_gonnenberg/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_gonnenberg/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_war/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_war/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_war/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_war/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_war/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_war/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_autannes/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_autannes/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_autannes/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_autannes/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_autannes/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_autannes/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_bird/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_bird/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_bird/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_bird/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_bird/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_bird/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_london/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_london/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_london/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_london/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_london/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_london/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_fenis/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_fenis/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_fenis/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_fenis/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_fenis/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_fenis/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_graffiti/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_graffiti/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_graffiti/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_graffiti/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_graffiti/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_graffiti/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_zion/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_zion/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_zion/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_zion/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_zion/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_zion/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_toy/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_toy/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_toy/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_toy/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_toy/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_toy/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_objects/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_objects/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_objects/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_objects/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_objects/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_objects/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_charing/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_charing/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_charing/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_charing/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_charing/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_charing/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_maskedman/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_maskedman/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_maskedman/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_maskedman/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_maskedman/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_maskedman/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_chestnuts/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_chestnuts/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_chestnuts/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_chestnuts/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_chestnuts/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_chestnuts/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_school/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_school/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_school/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_school/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_school/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_school/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_nuts/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_nuts/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_nuts/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_nuts/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_nuts/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_nuts/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_feast/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_feast/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_feast/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_feast/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_feast/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_feast/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_courses/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_courses/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_courses/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_courses/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_courses/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_courses/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_yard/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_yard/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_yard/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_yard/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_yard/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_yard/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_azzola/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_azzola/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_azzola/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_azzola/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_azzola/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_azzola/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_books/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_books/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_books/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_books/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_books/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_books/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_yellowtent/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_yellowtent/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_yellowtent/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_yellowtent/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_yellowtent/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_yellowtent/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_bark/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_bark/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_bark/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_bark/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_bark/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_bark/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_laptop/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_laptop/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_laptop/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_laptop/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_laptop/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_laptop/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_fruits/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_fruits/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_fruits/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_fruits/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_fruits/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_fruits/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_dogman/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_dogman/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_dogman/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_dogman/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_dogman/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_dogman/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_greentea/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_greentea/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_greentea/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_greentea/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_greentea/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_greentea/6.ppm
+hpatches_sequences/hpatches-sequences-release/i_londonbridge/5.ppm
+hpatches_sequences/hpatches-sequences-release/i_londonbridge/2.ppm
+hpatches_sequences/hpatches-sequences-release/i_londonbridge/4.ppm
+hpatches_sequences/hpatches-sequences-release/i_londonbridge/1.ppm
+hpatches_sequences/hpatches-sequences-release/i_londonbridge/3.ppm
+hpatches_sequences/hpatches-sequences-release/i_londonbridge/6.ppm
+hpatches_sequences/hpatches-sequences-release/v_there/5.ppm
+hpatches_sequences/hpatches-sequences-release/v_there/2.ppm
+hpatches_sequences/hpatches-sequences-release/v_there/4.ppm
+hpatches_sequences/hpatches-sequences-release/v_there/1.ppm
+hpatches_sequences/hpatches-sequences-release/v_there/3.ppm
+hpatches_sequences/hpatches-sequences-release/v_there/6.ppm
diff --git a/third_party/d2net/image_list_qualitative.txt b/third_party/d2net/image_list_qualitative.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f8e4916b50cf13aae6ad847403127752bf062025
--- /dev/null
+++ b/third_party/d2net/image_list_qualitative.txt
@@ -0,0 +1,6 @@
+qualitative/images/pair_1/1.jpg
+qualitative/images/pair_1/2.jpg
+qualitative/images/pair_2/1.jpg
+qualitative/images/pair_2/2.jpg
+qualitative/images/pair_3/1.jpg
+qualitative/images/pair_3/2.jpg
diff --git a/third_party/d2net/inloc/README.md b/third_party/d2net/inloc/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..598368ba5c361770c8bc571d1793a613854babfe
--- /dev/null
+++ b/third_party/d2net/inloc/README.md
@@ -0,0 +1,15 @@
+# InLoc evaluation instructions
+
+Start by downloading the [InLoc_demo](https://github.com/HajimeTaira/InLoc_demo) code. Once it is up and running according to the official instruction, you can copy and paste all the files available here overwriting the `Features_WUSTL` and `parfor_sparseGV` functions. `generate_list.m` will generate `image_list.txt` containing the queries and top 100 database matches (run `sort -u image_list.txt > image_list_unique.txt` to remove the duplicates). After extracting features for all the images in `image_list_unique.txt`, you can run `custom_demo` directly.
+
+The feature extraction part for D2-Net can be done using the following command: `python extract_features.py --image_list_file /path/to/image_list_unique.txt --multiscale --output_format .mat`.
+
+In case you plan on using your own features, don't forget to change the extension in `Features_WUSTL.m`. The local features are supposed to be stored in the `mat` format with two fields:
+
+- `keypoints` - `N x 3` matrix with `x, y, scale` coordinates of each keypoint in COLMAP format (the `X` axis points to the right, the `Y` axis to the bottom),
+
+- `descriptors` - `N x D` matrix with the descriptors.
+
+The evaluation pipeline is live at [visuallocalization.net](https://www.visuallocalization.net/). In order to generate a submission file, please use the provided [ImgList2text](https://github.com/HajimeTaira/InLoc_demo/blob/master/functions/utils/ImgList2text.m) function.
+
+We have also provided the `merge_files` MATLAB script that was used to merge the solutions of D2-Net Multiscale and Dense InLoc based on the view synthesis score. It can be used as follows `merge_files('output/densePV_top10_shortlist_method1.mat', 'outputs/densePV_top10_shortlist_method2.mat')`.
\ No newline at end of file
diff --git a/third_party/d2net/inloc/custom_demo.m b/third_party/d2net/inloc/custom_demo.m
new file mode 100644
index 0000000000000000000000000000000000000000..91057ed63bdc3d1b9284e0ed24f74cf83b431839
--- /dev/null
+++ b/third_party/d2net/inloc/custom_demo.m
@@ -0,0 +1,13 @@
+% Startup
+startup;
+[ params ] = setup_project_ht_WUSTL;
+
+% 1. Retrieval
+ht_retrieval;
+
+% 2. Geometric verification
+ht_top100_sparsePE_localization;
+
+% 3. Pose verification
+ImgList_densePE = ImgList_sparsePE; % Force dense PV to use sparse PE results.
+ht_top10_densePV_localization;
diff --git a/third_party/d2net/inloc/functions/wustl_function/Features_WUSTL.m b/third_party/d2net/inloc/functions/wustl_function/Features_WUSTL.m
new file mode 100644
index 0000000000000000000000000000000000000000..88551e076799ef0eb30d995c90c89fff448105db
--- /dev/null
+++ b/third_party/d2net/inloc/functions/wustl_function/Features_WUSTL.m
@@ -0,0 +1,6 @@
+function [f, d] = features_custom(I_path)
+ data = load([I_path '.d2-net'], '-mat');
+ f = double(data.keypoints(:, 1 : 3).');
+ d = double(data.descriptors.');
+end
+
diff --git a/third_party/d2net/inloc/functions/wustl_function/parfor_sparseGV.m b/third_party/d2net/inloc/functions/wustl_function/parfor_sparseGV.m
new file mode 100644
index 0000000000000000000000000000000000000000..04cdadc5c447dabdde708c1ac50884802e5a045d
--- /dev/null
+++ b/third_party/d2net/inloc/functions/wustl_function/parfor_sparseGV.m
@@ -0,0 +1,73 @@
+function parfor_sparseGV( qname, dbname, params )
+
+
+[~, dbbasename, ~] = fileparts(dbname);
+this_sparsegv_matname = fullfile(params.output.gv_sparse.dir, qname, [dbbasename, params.output.gv_sparse.matformat]);
+
+if exist(this_sparsegv_matname, 'file') ~= 2
+ %load features
+ qfmatname = fullfile(params.input.feature.dir, params.data.q.dir, [qname, params.input.feature.q_sps_matformat]);
+ if exist(qfmatname, 'file') ~= 2
+ Iqname = fullfile(params.data.dir, params.data.q.dir, qname);
+ [f, d] = features_WUSTL(Iqname);
+ [qfdir, ~, ~] = fileparts(qfmatname);
+ if exist(qfdir, 'dir') ~= 7
+ mkdir(qfdir);
+ end
+ save('-v6', qfmatname, 'f', 'd');
+ end
+ features_q = load(qfmatname);
+
+ dbfmatname = fullfile(params.input.feature.dir, params.data.db.cutout.dir, [dbname, params.input.feature.db_sps_matformat]);
+ if exist(dbfmatname, 'file') ~= 2
+ Idbname = fullfile(params.data.dir, params.data.db.cutout.dir, dbname);
+ [f, d] = features_WUSTL(Idbname);
+ [dbfdir, ~, ~] = fileparts(dbfmatname);
+ if exist(dbfdir, 'dir') ~= 7
+ mkdir(dbfdir);
+ end
+ save('-v6', dbfmatname, 'f', 'd');
+ end
+ features_db = load(dbfmatname);
+
+ %geometric verification
+ if size(features_db.d, 2) < 6
+ H = nan(3, 3);
+ inls_qidx = [];
+ inls_dbidx = [];
+ inliernum = 0;
+ matches = [];
+ inliers = [];
+ else
+
+ %geometric verification (homography lo-ransac)
+ [matches, inliers, H, ~] = at_sparseransac(features_q.f,features_q.d,features_db.f,features_db.d,3,10);
+ inliernum = length(inliers);
+ inls_qidx = inliers(1, :); inls_dbidx = inliers(2, :);
+ end
+
+ %save
+ if exist(fullfile(params.output.gv_sparse.dir, qname), 'dir') ~= 7
+ mkdir(fullfile(params.output.gv_sparse.dir, qname));
+ end
+ save('-v6', this_sparsegv_matname, 'H', 'inliernum', 'inls_qidx', 'inls_dbidx', 'matches', 'inliers');
+
+% %debug
+% Iq = imread(fullfile(params.data.dir, params.data.q.dir, qname));
+% Idb = imread(fullfile(params.data.dir, params.data.db.cutout.dir, dbname));
+% figure();
+% ultimateSubplot ( 2, 1, 1, 1, 0.01, 0.05 );
+% imshow(rgb2gray(Iq));hold on;
+% plot(features_q.f(1, inls_qidx), features_q.f(2, inls_qidx),'g.');
+% ultimateSubplot ( 2, 1, 2, 1, 0.01, 0.05 );
+% imshow(rgb2gray(Idb));hold on;
+% plot(features_db.f(1, inls_dbidx), features_db.f(2, inls_dbidx),'g.');
+%
+% keyboard;
+
+end
+
+
+
+end
+
diff --git a/third_party/d2net/inloc/generate_list.m b/third_party/d2net/inloc/generate_list.m
new file mode 100644
index 0000000000000000000000000000000000000000..e7680cbefe98421b242e77007d4bc2773acfc6f2
--- /dev/null
+++ b/third_party/d2net/inloc/generate_list.m
@@ -0,0 +1,25 @@
+startup;
+params = setup_project;
+
+ht_retrieval;
+
+shortlist_topN = 100;
+
+query_dir = fullfile(params.data.dir, params.data.q.dir);
+db_dir = fullfile(params.data.dir, params.data.db.cutout.dir);
+
+image_list_file = fopen('image_list.txt', 'w');
+
+for ii = 1:1:length(ImgList_original)
+ query_image_path = [query_dir '/' ImgList_original(ii).queryname];
+
+ fprintf(image_list_file, '%s\n', query_image_path);
+
+ for jj = 1:1:shortlist_topN
+ db_image_path = [db_dir '/' ImgList_original(ii).topNname{jj}];
+
+ fprintf(image_list_file, '%s\n', db_image_path);
+ end
+end
+
+fclose(image_list_file);
diff --git a/third_party/d2net/inloc/merge_files.m b/third_party/d2net/inloc/merge_files.m
new file mode 100644
index 0000000000000000000000000000000000000000..789a8974d5e7b9ac67a6c1982a332b7be2042975
--- /dev/null
+++ b/third_party/d2net/inloc/merge_files.m
@@ -0,0 +1,82 @@
+function ImgList = merge_files(file1, file2)
+ f1 = load(file1);
+ ImgList_file1 = f1.ImgList;
+ f2 = load(file2);
+ ImgList_file2 = f2.ImgList;
+
+ PV_topN = 10;
+
+ n1 = 0;
+ n2 = 0;
+ ImgList = struct('queryname', {}, 'topNname', {}, 'topNscore', {}, 'P', {});
+ for ii = 1:1:length(ImgList_file1)
+ ImgList(ii).queryname = ImgList_file1(ii).queryname;
+
+ sum_scores = containers.Map('KeyType', 'char', 'ValueType', 'double');
+ for jj = 1 : PV_topN
+ name = char(ImgList_file1(ii).topNname(jj));
+ if isKey(sum_scores, name)
+ sum_scores(name) = sum_scores(name) + ImgList_file1(ii).topNscore(jj);
+ else
+ sum_scores(name) = ImgList_file1(ii).topNscore(jj);
+ end
+ name = char(ImgList_file2(ii).topNname(jj));
+ if isKey(sum_scores, name)
+ sum_scores(name) = sum_scores(name) + ImgList_file2(ii).topNscore(jj);
+ else
+ sum_scores(name) = ImgList_file2(ii).topNscore(jj);
+ end
+ end
+
+ max_score = 0;
+ img_name = 0;
+ for key = keys(sum_scores)
+ if sum_scores(char(key)) > max_score
+ max_score = sum_scores(char(key));
+ img_name = key;
+ end
+ end
+
+ id_dense = 0;
+ id_sparse = 0;
+ for jj = 1 : PV_topN
+ if strcmp(char(ImgList_file1(ii).topNname(jj)), img_name)
+ id_dense = jj;
+ end
+ if strcmp(char(ImgList_file2(ii).topNname(jj)), img_name)
+ id_sparse = jj;
+ end
+ end
+
+ if id_sparse == 0
+ n1 = n1 + 1;
+ ImgList(ii).topNscore = [ImgList_file1(ii).topNscore(id_dense)];
+ ImgList(ii).topNname = [ImgList_file1(ii).topNname(id_dense)];
+ ImgList(ii).P = [ImgList_file1(ii).P(id_dense)];
+ continue
+ end
+
+ if id_dense == 0
+ n2 = n2 + 1;
+ ImgList(ii).topNscore = [ImgList_file2(ii).topNscore(id_sparse)];
+ ImgList(ii).topNname = [ImgList_file2(ii).topNname(id_sparse)];
+ ImgList(ii).P = [ImgList_file2(ii).P(id_sparse)];
+ continue
+ end
+
+ max_score = 0;
+ if ImgList_file1(ii).topNscore(id_dense) > ImgList_file2(ii).topNscore(id_sparse)
+ n1 = n1 + 1;
+ ImgList(ii).topNscore = [ImgList_file1(ii).topNscore(id_dense)];
+ ImgList(ii).topNname = [ImgList_file1(ii).topNname(id_dense)];
+ ImgList(ii).P = [ImgList_file1(ii).P(id_dense)];
+ else
+ n2 = n2 + 1;
+ ImgList(ii).topNscore = [ImgList_file2(ii).topNscore(id_sparse)];
+ ImgList(ii).topNname = [ImgList_file2(ii).topNname(id_sparse)];
+ ImgList(ii).P = [ImgList_file2(ii).P(id_sparse)];
+ end
+ end
+
+ fprintf(1, "%d file 1 poses & %d file 2 poses selected\n", n1, n2);
+end
\ No newline at end of file
diff --git a/imcui/third_party/d2net/lib/dataset.py b/third_party/d2net/lib/dataset.py
similarity index 100%
rename from imcui/third_party/d2net/lib/dataset.py
rename to third_party/d2net/lib/dataset.py
diff --git a/imcui/third_party/d2net/lib/exceptions.py b/third_party/d2net/lib/exceptions.py
similarity index 100%
rename from imcui/third_party/d2net/lib/exceptions.py
rename to third_party/d2net/lib/exceptions.py
diff --git a/imcui/third_party/d2net/lib/loss.py b/third_party/d2net/lib/loss.py
similarity index 100%
rename from imcui/third_party/d2net/lib/loss.py
rename to third_party/d2net/lib/loss.py
diff --git a/imcui/third_party/d2net/lib/model.py b/third_party/d2net/lib/model.py
similarity index 100%
rename from imcui/third_party/d2net/lib/model.py
rename to third_party/d2net/lib/model.py
diff --git a/imcui/third_party/d2net/lib/model_test.py b/third_party/d2net/lib/model_test.py
similarity index 100%
rename from imcui/third_party/d2net/lib/model_test.py
rename to third_party/d2net/lib/model_test.py
diff --git a/imcui/third_party/d2net/lib/pyramid.py b/third_party/d2net/lib/pyramid.py
similarity index 100%
rename from imcui/third_party/d2net/lib/pyramid.py
rename to third_party/d2net/lib/pyramid.py
diff --git a/imcui/third_party/d2net/lib/utils.py b/third_party/d2net/lib/utils.py
similarity index 100%
rename from imcui/third_party/d2net/lib/utils.py
rename to third_party/d2net/lib/utils.py
diff --git a/imcui/third_party/d2net/megadepth_utils/preprocess_scene.py b/third_party/d2net/megadepth_utils/preprocess_scene.py
similarity index 59%
rename from imcui/third_party/d2net/megadepth_utils/preprocess_scene.py
rename to third_party/d2net/megadepth_utils/preprocess_scene.py
index fc68a403795e7cddce88dfcb74b38d19ab09e133..5364058829b7e45eabd61a32a591711645fc1ded 100644
--- a/imcui/third_party/d2net/megadepth_utils/preprocess_scene.py
+++ b/third_party/d2net/megadepth_utils/preprocess_scene.py
@@ -6,78 +6,63 @@ import numpy as np
import os
-parser = argparse.ArgumentParser(description='MegaDepth preprocessing script')
+parser = argparse.ArgumentParser(description="MegaDepth preprocessing script")
-parser.add_argument(
- '--base_path', type=str, required=True,
- help='path to MegaDepth'
-)
-parser.add_argument(
- '--scene_id', type=str, required=True,
- help='scene ID'
-)
+parser.add_argument("--base_path", type=str, required=True, help="path to MegaDepth")
+parser.add_argument("--scene_id", type=str, required=True, help="scene ID")
parser.add_argument(
- '--output_path', type=str, required=True,
- help='path to the output directory'
+ "--output_path", type=str, required=True, help="path to the output directory"
)
args = parser.parse_args()
base_path = args.base_path
# Remove the trailing / if need be.
-if base_path[-1] in ['/', '\\']:
- base_path = base_path[: - 1]
+if base_path[-1] in ["/", "\\"]:
+ base_path = base_path[:-1]
scene_id = args.scene_id
-base_depth_path = os.path.join(
- base_path, 'phoenix/S6/zl548/MegaDepth_v1'
-)
-base_undistorted_sfm_path = os.path.join(
- base_path, 'Undistorted_SfM'
-)
+base_depth_path = os.path.join(base_path, "phoenix/S6/zl548/MegaDepth_v1")
+base_undistorted_sfm_path = os.path.join(base_path, "Undistorted_SfM")
undistorted_sparse_path = os.path.join(
- base_undistorted_sfm_path, scene_id, 'sparse-txt'
+ base_undistorted_sfm_path, scene_id, "sparse-txt"
)
if not os.path.exists(undistorted_sparse_path):
exit()
-depths_path = os.path.join(
- base_depth_path, scene_id, 'dense0', 'depths'
-)
+depths_path = os.path.join(base_depth_path, scene_id, "dense0", "depths")
if not os.path.exists(depths_path):
exit()
-images_path = os.path.join(
- base_undistorted_sfm_path, scene_id, 'images'
-)
+images_path = os.path.join(base_undistorted_sfm_path, scene_id, "images")
if not os.path.exists(images_path):
exit()
# Process cameras.txt
-with open(os.path.join(undistorted_sparse_path, 'cameras.txt'), 'r') as f:
- raw = f.readlines()[3 :] # skip the header
+with open(os.path.join(undistorted_sparse_path, "cameras.txt"), "r") as f:
+ raw = f.readlines()[3:] # skip the header
camera_intrinsics = {}
for camera in raw:
- camera = camera.split(' ')
- camera_intrinsics[int(camera[0])] = [float(elem) for elem in camera[2 :]]
+ camera = camera.split(" ")
+ camera_intrinsics[int(camera[0])] = [float(elem) for elem in camera[2:]]
# Process points3D.txt
-with open(os.path.join(undistorted_sparse_path, 'points3D.txt'), 'r') as f:
- raw = f.readlines()[3 :] # skip the header
+with open(os.path.join(undistorted_sparse_path, "points3D.txt"), "r") as f:
+ raw = f.readlines()[3:] # skip the header
points3D = {}
for point3D in raw:
- point3D = point3D.split(' ')
- points3D[int(point3D[0])] = np.array([
- float(point3D[1]), float(point3D[2]), float(point3D[3])
- ])
-
+ point3D = point3D.split(" ")
+ points3D[int(point3D[0])] = np.array(
+ [float(point3D[1]), float(point3D[2]), float(point3D[3])]
+ )
+
# Process images.txt
-with open(os.path.join(undistorted_sparse_path, 'images.txt'), 'r') as f:
- raw = f.readlines()[4 :] # skip the header
+with open(os.path.join(undistorted_sparse_path, "images.txt"), "r") as f:
+ raw = f.readlines()[4:] # skip the header
image_id_to_idx = {}
image_names = []
@@ -85,19 +70,19 @@ raw_pose = []
camera = []
points3D_id_to_2D = []
n_points3D = []
-for idx, (image, points) in enumerate(zip(raw[:: 2], raw[1 :: 2])):
- image = image.split(' ')
- points = points.split(' ')
+for idx, (image, points) in enumerate(zip(raw[::2], raw[1::2])):
+ image = image.split(" ")
+ points = points.split(" ")
image_id_to_idx[int(image[0])] = idx
- image_name = image[-1].strip('\n')
+ image_name = image[-1].strip("\n")
image_names.append(image_name)
- raw_pose.append([float(elem) for elem in image[1 : -2]])
+ raw_pose.append([float(elem) for elem in image[1:-2]])
camera.append(int(image[-2]))
current_points3D_id_to_2D = {}
- for x, y, point3D_id in zip(points[:: 3], points[1 :: 3], points[2 :: 3]):
+ for x, y, point3D_id in zip(points[::3], points[1::3], points[2::3]):
if int(point3D_id) == -1:
continue
current_points3D_id_to_2D[int(point3D_id)] = [float(x), float(y)]
@@ -110,12 +95,10 @@ image_paths = []
depth_paths = []
for image_name in image_names:
image_path = os.path.join(images_path, image_name)
-
+
# Path to the depth file
- depth_path = os.path.join(
- depths_path, '%s.h5' % os.path.splitext(image_name)[0]
- )
-
+ depth_path = os.path.join(depths_path, "%s.h5" % os.path.splitext(image_name)[0])
+
if os.path.exists(depth_path):
# Check if depth map or background / foreground mask
file_size = os.stat(depth_path).st_size
@@ -152,32 +135,22 @@ for idx, image_name in enumerate(image_names):
intrinsics.append(K)
image_pose = raw_pose[idx]
- qvec = image_pose[: 4]
+ qvec = image_pose[:4]
qvec = qvec / np.linalg.norm(qvec)
w, x, y, z = qvec
- R = np.array([
- [
- 1 - 2 * y * y - 2 * z * z,
- 2 * x * y - 2 * z * w,
- 2 * x * z + 2 * y * w
- ],
+ R = np.array(
[
- 2 * x * y + 2 * z * w,
- 1 - 2 * x * x - 2 * z * z,
- 2 * y * z - 2 * x * w
- ],
- [
- 2 * x * z - 2 * y * w,
- 2 * y * z + 2 * x * w,
- 1 - 2 * x * x - 2 * y * y
+ [1 - 2 * y * y - 2 * z * z, 2 * x * y - 2 * z * w, 2 * x * z + 2 * y * w],
+ [2 * x * y + 2 * z * w, 1 - 2 * x * x - 2 * z * z, 2 * y * z - 2 * x * w],
+ [2 * x * z - 2 * y * w, 2 * y * z + 2 * x * w, 1 - 2 * x * x - 2 * y * y],
]
- ])
+ )
principal_axis.append(R[2, :])
- t = image_pose[4 : 7]
+ t = image_pose[4:7]
# World-to-Camera pose
current_pose = np.zeros([4, 4])
- current_pose[: 3, : 3] = R
- current_pose[: 3, 3] = t
+ current_pose[:3, :3] = R
+ current_pose[:3, 3] = t
current_pose[3, 3] = 1
# Camera-to-World pose
# pose = np.zeros([4, 4])
@@ -185,38 +158,38 @@ for idx, image_name in enumerate(image_names):
# pose[: 3, 3] = -np.matmul(np.transpose(R), t)
# pose[3, 3] = 1
poses.append(current_pose)
-
+
current_points3D_id_to_ndepth = {}
for point3D_id in points3D_id_to_2D[idx].keys():
p3d = points3D[point3D_id]
- current_points3D_id_to_ndepth[point3D_id] = (np.dot(R[2, :], p3d) + t[2]) / (.5 * (K[0, 0] + K[1, 1]))
+ current_points3D_id_to_ndepth[point3D_id] = (np.dot(R[2, :], p3d) + t[2]) / (
+ 0.5 * (K[0, 0] + K[1, 1])
+ )
points3D_id_to_ndepth.append(current_points3D_id_to_ndepth)
principal_axis = np.array(principal_axis)
-angles = np.rad2deg(np.arccos(
- np.clip(
- np.dot(principal_axis, np.transpose(principal_axis)),
- -1, 1
- )
-))
+angles = np.rad2deg(
+ np.arccos(np.clip(np.dot(principal_axis, np.transpose(principal_axis)), -1, 1))
+)
# Compute overlap score
-overlap_matrix = np.full([n_images, n_images], -1.)
-scale_ratio_matrix = np.full([n_images, n_images], -1.)
+overlap_matrix = np.full([n_images, n_images], -1.0)
+scale_ratio_matrix = np.full([n_images, n_images], -1.0)
for idx1 in range(n_images):
if image_paths[idx1] is None or depth_paths[idx1] is None:
continue
for idx2 in range(idx1 + 1, n_images):
if image_paths[idx2] is None or depth_paths[idx2] is None:
continue
- matches = (
- points3D_id_to_2D[idx1].keys() &
- points3D_id_to_2D[idx2].keys()
- )
+ matches = points3D_id_to_2D[idx1].keys() & points3D_id_to_2D[idx2].keys()
min_num_points3D = min(
len(points3D_id_to_2D[idx1]), len(points3D_id_to_2D[idx2])
)
- overlap_matrix[idx1, idx2] = len(matches) / len(points3D_id_to_2D[idx1]) # min_num_points3D
- overlap_matrix[idx2, idx1] = len(matches) / len(points3D_id_to_2D[idx2]) # min_num_points3D
+ overlap_matrix[idx1, idx2] = len(matches) / len(
+ points3D_id_to_2D[idx1]
+ ) # min_num_points3D
+ overlap_matrix[idx2, idx1] = len(matches) / len(
+ points3D_id_to_2D[idx2]
+ ) # min_num_points3D
if len(matches) == 0:
continue
points3D_id_to_ndepth1 = points3D_id_to_ndepth[idx1]
@@ -228,7 +201,7 @@ for idx1 in range(n_images):
scale_ratio_matrix[idx2, idx1] = min_scale_ratio
np.savez(
- os.path.join(args.output_path, '%s.npz' % scene_id),
+ os.path.join(args.output_path, "%s.npz" % scene_id),
image_paths=image_paths,
depth_paths=depth_paths,
intrinsics=intrinsics,
@@ -238,5 +211,5 @@ np.savez(
angles=angles,
n_points3D=n_points3D,
points3D_id_to_2D=points3D_id_to_2D,
- points3D_id_to_ndepth=points3D_id_to_ndepth
+ points3D_id_to_ndepth=points3D_id_to_ndepth,
)
diff --git a/third_party/d2net/megadepth_utils/preprocess_undistorted_megadepth.sh b/third_party/d2net/megadepth_utils/preprocess_undistorted_megadepth.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c983ee464bb36439d68f52d60f981414e2c6e84b
--- /dev/null
+++ b/third_party/d2net/megadepth_utils/preprocess_undistorted_megadepth.sh
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+if [[ $# != 2 ]]; then
+ echo 'Usage: bash preprocess_megadepth.sh /path/to/megadepth /output/path'
+ exit
+fi
+
+export dataset_path=$1
+export output_path=$2
+
+mkdir $output_path
+echo 0
+ls $dataset_path/Undistorted_SfM | xargs -P 8 -I % sh -c 'echo %; python preprocess_scene.py --base_path $dataset_path --scene_id % --output_path $output_path'
\ No newline at end of file
diff --git a/third_party/d2net/megadepth_utils/train_scenes.txt b/third_party/d2net/megadepth_utils/train_scenes.txt
new file mode 100644
index 0000000000000000000000000000000000000000..635c8dfe5d0f1814d92f3a891a4b3d48ba8da93f
--- /dev/null
+++ b/third_party/d2net/megadepth_utils/train_scenes.txt
@@ -0,0 +1,117 @@
+0000
+0001
+0002
+0003
+0004
+0005
+0007
+0008
+0011
+0012
+0013
+0015
+0017
+0019
+0020
+0021
+0022
+0023
+0024
+0025
+0026
+0027
+0032
+0035
+0036
+0037
+0039
+0042
+0043
+0046
+0048
+0050
+0056
+0057
+0060
+0061
+0063
+0065
+0070
+0080
+0083
+0086
+0087
+0095
+0098
+0100
+0101
+0103
+0104
+0105
+0107
+0115
+0117
+0122
+0130
+0137
+0143
+0147
+0148
+0149
+0150
+0156
+0160
+0176
+0183
+0189
+0190
+0200
+0214
+0224
+0235
+0237
+0240
+0243
+0258
+0265
+0269
+0299
+0312
+0326
+0327
+0331
+0335
+0341
+0348
+0366
+0377
+0380
+0394
+0407
+0411
+0430
+0446
+0455
+0472
+0474
+0476
+0478
+0493
+0494
+0496
+0505
+0559
+0733
+0860
+1017
+1589
+4541
+5004
+5005
+5006
+5007
+5009
+5010
+5012
+5013
+5017
diff --git a/third_party/d2net/megadepth_utils/undistort_reconstructions.py b/third_party/d2net/megadepth_utils/undistort_reconstructions.py
new file mode 100644
index 0000000000000000000000000000000000000000..822c9abd3fc75fd8fc1e8d9ada75aa76802c6798
--- /dev/null
+++ b/third_party/d2net/megadepth_utils/undistort_reconstructions.py
@@ -0,0 +1,69 @@
+import argparse
+
+import imagesize
+
+import os
+
+import subprocess
+
+parser = argparse.ArgumentParser(description="MegaDepth Undistortion")
+
+parser.add_argument(
+ "--colmap_path", type=str, required=True, help="path to colmap executable"
+)
+parser.add_argument("--base_path", type=str, required=True, help="path to MegaDepth")
+
+args = parser.parse_args()
+
+sfm_path = os.path.join(args.base_path, "MegaDepth_v1_SfM")
+base_depth_path = os.path.join(args.base_path, "phoenix/S6/zl548/MegaDepth_v1")
+output_path = os.path.join(args.base_path, "Undistorted_SfM")
+
+os.mkdir(output_path)
+
+for scene_name in os.listdir(base_depth_path):
+ current_output_path = os.path.join(output_path, scene_name)
+ os.mkdir(current_output_path)
+
+ image_path = os.path.join(base_depth_path, scene_name, "dense0", "imgs")
+ if not os.path.exists(image_path):
+ continue
+
+ # Find the maximum image size in scene.
+ max_image_size = 0
+ for image_name in os.listdir(image_path):
+ max_image_size = max(
+ max_image_size, max(imagesize.get(os.path.join(image_path, image_name)))
+ )
+
+ # Undistort the images and update the reconstruction.
+ subprocess.call(
+ [
+ os.path.join(args.colmap_path, "colmap"),
+ "image_undistorter",
+ "--image_path",
+ os.path.join(sfm_path, scene_name, "images"),
+ "--input_path",
+ os.path.join(sfm_path, scene_name, "sparse", "manhattan", "0"),
+ "--output_path",
+ current_output_path,
+ "--max_image_size",
+ str(max_image_size),
+ ]
+ )
+
+ # Transform the reconstruction to raw text format.
+ sparse_txt_path = os.path.join(current_output_path, "sparse-txt")
+ os.mkdir(sparse_txt_path)
+ subprocess.call(
+ [
+ os.path.join(args.colmap_path, "colmap"),
+ "model_converter",
+ "--input_path",
+ os.path.join(current_output_path, "sparse"),
+ "--output_path",
+ sparse_txt_path,
+ "--output_type",
+ "TXT",
+ ]
+ )
diff --git a/third_party/d2net/megadepth_utils/valid_scenes.txt b/third_party/d2net/megadepth_utils/valid_scenes.txt
new file mode 100644
index 0000000000000000000000000000000000000000..42503496535a13b9426db28a22c6df891191c9f2
--- /dev/null
+++ b/third_party/d2net/megadepth_utils/valid_scenes.txt
@@ -0,0 +1,77 @@
+0016
+0033
+0034
+0041
+0044
+0047
+0049
+0058
+0062
+0064
+0067
+0071
+0076
+0078
+0090
+0094
+0099
+0102
+0121
+0129
+0133
+0141
+0151
+0162
+0168
+0175
+0177
+0178
+0181
+0185
+0186
+0197
+0204
+0205
+0209
+0212
+0217
+0223
+0229
+0231
+0238
+0252
+0257
+0271
+0275
+0277
+0281
+0285
+0286
+0290
+0294
+0303
+0306
+0307
+0323
+0349
+0360
+0387
+0389
+0402
+0406
+0412
+0443
+0482
+0768
+1001
+3346
+5000
+5001
+5002
+5003
+5008
+5011
+5014
+5015
+5016
+5018
diff --git a/third_party/d2net/qualitative/Qualitative-Matches.ipynb b/third_party/d2net/qualitative/Qualitative-Matches.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5ae18faa46ee3ab4efddc48eb6455f7f1341fb40
--- /dev/null
+++ b/third_party/d2net/qualitative/Qualitative-Matches.ipynb
@@ -0,0 +1,217 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import cv2\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "from PIL import Image\n",
+ "\n",
+ "from skimage.feature import match_descriptors\n",
+ "from skimage.measure import ransac\n",
+ "from skimage.transform import ProjectiveTransform"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Don't forget to run feature extraction before running this script\n",
+ "```python extract_features.py --image_list_file image_list_qualitative.txt```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Change the pair index here (possible values: 1, 2 or 3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pair_idx = 2\n",
+ "assert(pair_idx in [1, 2, 3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading the features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pair_path = os.path.join('images', 'pair_%d' % pair_idx)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image1 = np.array(Image.open(os.path.join(pair_path, '1.jpg')))\n",
+ "image2 = np.array(Image.open(os.path.join(pair_path, '2.jpg')))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "feat1 = np.load(os.path.join(pair_path, '1.jpg.d2-net'))\n",
+ "feat2 = np.load(os.path.join(pair_path, '2.jpg.d2-net'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Mutual nearest neighbors matching"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "matches = match_descriptors(feat1['descriptors'], feat2['descriptors'], cross_check=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of raw matches: 296.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Number of raw matches: %d.' % matches.shape[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Homography fitting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of inliers: 69.\n"
+ ]
+ }
+ ],
+ "source": [
+ "keypoints_left = feat1['keypoints'][matches[:, 0], : 2]\n",
+ "keypoints_right = feat2['keypoints'][matches[:, 1], : 2]\n",
+ "np.random.seed(0)\n",
+ "model, inliers = ransac(\n",
+ " (keypoints_left, keypoints_right),\n",
+ " ProjectiveTransform, min_samples=4,\n",
+ " residual_threshold=4, max_trials=10000\n",
+ ")\n",
+ "n_inliers = np.sum(inliers)\n",
+ "print('Number of inliers: %d.' % n_inliers)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plotting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "inlier_keypoints_left = [cv2.KeyPoint(point[0], point[1], 1) for point in keypoints_left[inliers]]\n",
+ "inlier_keypoints_right = [cv2.KeyPoint(point[0], point[1], 1) for point in keypoints_right[inliers]]\n",
+ "placeholder_matches = [cv2.DMatch(idx, idx, 1) for idx in range(n_inliers)]\n",
+ "image3 = cv2.drawMatches(image1, inlier_keypoints_left, image2, inlier_keypoints_right, placeholder_matches, None)\n",
+ "\n",
+ "plt.figure(figsize=(15, 15))\n",
+ "plt.imshow(image3)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/d2net/qualitative/images/pair_1/1.jpg b/third_party/d2net/qualitative/images/pair_1/1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..30e969e4214b17724749421acbde8e25d2378ec1
--- /dev/null
+++ b/third_party/d2net/qualitative/images/pair_1/1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca3fbf5145372316ed0d7b3e5c23183e05094ee95b60d5f669e2a03d0783bc43
+size 63747
diff --git a/third_party/d2net/qualitative/images/pair_1/2.jpg b/third_party/d2net/qualitative/images/pair_1/2.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f289909ce7520aa712b4d92c2a16867f6466d1e4
--- /dev/null
+++ b/third_party/d2net/qualitative/images/pair_1/2.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4cc4ee1bd7b2c342a9e4d3ce5a66850d1b8b77d8113642de55338f02ddaa9e35
+size 40726
diff --git a/third_party/d2net/qualitative/images/pair_2/1.jpg b/third_party/d2net/qualitative/images/pair_2/1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..588806f2ad92391585c289aa1e2c7b96313ea0f9
--- /dev/null
+++ b/third_party/d2net/qualitative/images/pair_2/1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb840ffd7e84d42fcb51338c5299ce18b07bbe183f764422616c034a14bf0e25
+size 81310
diff --git a/third_party/d2net/qualitative/images/pair_2/2.jpg b/third_party/d2net/qualitative/images/pair_2/2.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f2737214e4c8ad776262006d556e1ddd1922b6be
--- /dev/null
+++ b/third_party/d2net/qualitative/images/pair_2/2.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8dff3a9db9e38ac796fa96144c6f7fbe212852559cba864e3319f826fa1c4ff0
+size 77962
diff --git a/third_party/d2net/qualitative/images/pair_3/1.jpg b/third_party/d2net/qualitative/images/pair_3/1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a08411d75a88034d4b48ab47813bbb9821aaab6f
--- /dev/null
+++ b/third_party/d2net/qualitative/images/pair_3/1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4393bb1531361b180dc1def1213bfae22aabafe8696a956094d4ae9cfe3328d1
+size 565714
diff --git a/third_party/d2net/qualitative/images/pair_3/2.jpg b/third_party/d2net/qualitative/images/pair_3/2.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bfa7a264d640c74c1620bfb293d6182891e0f4bb
--- /dev/null
+++ b/third_party/d2net/qualitative/images/pair_3/2.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae9c4b91e00446bf45a30c0ecb65abc17328aae10eb21286b4205e959898cec3
+size 199241
diff --git a/third_party/d2net/train.py b/third_party/d2net/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ca584e131c14930f86c3252f93b89f1aea40713
--- /dev/null
+++ b/third_party/d2net/train.py
@@ -0,0 +1,285 @@
+import argparse
+
+import numpy as np
+
+import os
+
+import shutil
+
+import torch
+import torch.optim as optim
+
+from torch.utils.data import DataLoader
+
+from tqdm import tqdm
+
+import warnings
+
+from lib.dataset import MegaDepthDataset
+from lib.exceptions import NoGradientError
+from lib.loss import loss_function
+from lib.model import D2Net
+
+
+# CUDA
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda:0" if use_cuda else "cpu")
+
+# Seed
+torch.manual_seed(1)
+if use_cuda:
+ torch.cuda.manual_seed(1)
+np.random.seed(1)
+
+# Argument parsing
+parser = argparse.ArgumentParser(description="Training script")
+
+parser.add_argument(
+ "--dataset_path", type=str, required=True, help="path to the dataset"
+)
+parser.add_argument(
+ "--scene_info_path", type=str, required=True, help="path to the processed scenes"
+)
+
+parser.add_argument(
+ "--preprocessing",
+ type=str,
+ default="caffe",
+ help="image preprocessing (caffe or torch)",
+)
+parser.add_argument(
+ "--model_file", type=str, default="models/d2_ots.pth", help="path to the full model"
+)
+
+parser.add_argument(
+ "--num_epochs", type=int, default=10, help="number of training epochs"
+)
+parser.add_argument("--lr", type=float, default=1e-3, help="initial learning rate")
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
+parser.add_argument(
+ "--num_workers", type=int, default=4, help="number of workers for data loading"
+)
+
+parser.add_argument(
+ "--use_validation",
+ dest="use_validation",
+ action="store_true",
+ help="use the validation split",
+)
+parser.set_defaults(use_validation=False)
+
+parser.add_argument(
+ "--log_interval", type=int, default=250, help="loss logging interval"
+)
+
+parser.add_argument("--log_file", type=str, default="log.txt", help="loss logging file")
+
+parser.add_argument(
+ "--plot", dest="plot", action="store_true", help="plot training pairs"
+)
+parser.set_defaults(plot=False)
+
+parser.add_argument(
+ "--checkpoint_directory",
+ type=str,
+ default="checkpoints",
+ help="directory for training checkpoints",
+)
+parser.add_argument(
+ "--checkpoint_prefix",
+ type=str,
+ default="d2",
+ help="prefix for training checkpoints",
+)
+
+args = parser.parse_args()
+
+print(args)
+
+# Create the folders for plotting if need be
+if args.plot:
+ plot_path = "train_vis"
+ if os.path.isdir(plot_path):
+ print("[Warning] Plotting directory already exists.")
+ else:
+ os.mkdir(plot_path)
+
+# Creating CNN model
+model = D2Net(model_file=args.model_file, use_cuda=use_cuda)
+
+# Optimizer
+optimizer = optim.Adam(
+ filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr
+)
+
+# Dataset
+if args.use_validation:
+ validation_dataset = MegaDepthDataset(
+ scene_list_path="megadepth_utils/valid_scenes.txt",
+ scene_info_path=args.scene_info_path,
+ base_path=args.dataset_path,
+ train=False,
+ preprocessing=args.preprocessing,
+ pairs_per_scene=25,
+ )
+ validation_dataloader = DataLoader(
+ validation_dataset, batch_size=args.batch_size, num_workers=args.num_workers
+ )
+
+training_dataset = MegaDepthDataset(
+ scene_list_path="megadepth_utils/train_scenes.txt",
+ scene_info_path=args.scene_info_path,
+ base_path=args.dataset_path,
+ preprocessing=args.preprocessing,
+)
+training_dataloader = DataLoader(
+ training_dataset, batch_size=args.batch_size, num_workers=args.num_workers
+)
+
+
+# Define epoch function
+def process_epoch(
+ epoch_idx,
+ model,
+ loss_function,
+ optimizer,
+ dataloader,
+ device,
+ log_file,
+ args,
+ train=True,
+):
+ epoch_losses = []
+
+ torch.set_grad_enabled(train)
+
+ progress_bar = tqdm(enumerate(dataloader), total=len(dataloader))
+ for batch_idx, batch in progress_bar:
+ if train:
+ optimizer.zero_grad()
+
+ batch["train"] = train
+ batch["epoch_idx"] = epoch_idx
+ batch["batch_idx"] = batch_idx
+ batch["batch_size"] = args.batch_size
+ batch["preprocessing"] = args.preprocessing
+ batch["log_interval"] = args.log_interval
+
+ try:
+ loss = loss_function(model, batch, device, plot=args.plot)
+ except NoGradientError:
+ continue
+
+ current_loss = loss.data.cpu().numpy()[0]
+ epoch_losses.append(current_loss)
+
+ progress_bar.set_postfix(loss=("%.4f" % np.mean(epoch_losses)))
+
+ if batch_idx % args.log_interval == 0:
+ log_file.write(
+ "[%s] epoch %d - batch %d / %d - avg_loss: %f\n"
+ % (
+ "train" if train else "valid",
+ epoch_idx,
+ batch_idx,
+ len(dataloader),
+ np.mean(epoch_losses),
+ )
+ )
+
+ if train:
+ loss.backward()
+ optimizer.step()
+
+ log_file.write(
+ "[%s] epoch %d - avg_loss: %f\n"
+ % ("train" if train else "valid", epoch_idx, np.mean(epoch_losses))
+ )
+ log_file.flush()
+
+ return np.mean(epoch_losses)
+
+
+# Create the checkpoint directory
+if os.path.isdir(args.checkpoint_directory):
+ print("[Warning] Checkpoint directory already exists.")
+else:
+ os.mkdir(args.checkpoint_directory)
+
+
+# Open the log file for writing
+if os.path.exists(args.log_file):
+ print("[Warning] Log file already exists.")
+log_file = open(args.log_file, "a+")
+
+# Initialize the history
+train_loss_history = []
+validation_loss_history = []
+if args.use_validation:
+ validation_dataset.build_dataset()
+ min_validation_loss = process_epoch(
+ 0,
+ model,
+ loss_function,
+ optimizer,
+ validation_dataloader,
+ device,
+ log_file,
+ args,
+ train=False,
+ )
+
+# Start the training
+for epoch_idx in range(1, args.num_epochs + 1):
+ # Process epoch
+ training_dataset.build_dataset()
+ train_loss_history.append(
+ process_epoch(
+ epoch_idx,
+ model,
+ loss_function,
+ optimizer,
+ training_dataloader,
+ device,
+ log_file,
+ args,
+ )
+ )
+
+ if args.use_validation:
+ validation_loss_history.append(
+ process_epoch(
+ epoch_idx,
+ model,
+ loss_function,
+ optimizer,
+ validation_dataloader,
+ device,
+ log_file,
+ args,
+ train=False,
+ )
+ )
+
+ # Save the current checkpoint
+ checkpoint_path = os.path.join(
+ args.checkpoint_directory, "%s.%02d.pth" % (args.checkpoint_prefix, epoch_idx)
+ )
+ checkpoint = {
+ "args": args,
+ "epoch_idx": epoch_idx,
+ "model": model.state_dict(),
+ "optimizer": optimizer.state_dict(),
+ "train_loss_history": train_loss_history,
+ "validation_loss_history": validation_loss_history,
+ }
+ torch.save(checkpoint, checkpoint_path)
+ if args.use_validation and validation_loss_history[-1] < min_validation_loss:
+ min_validation_loss = validation_loss_history[-1]
+ best_checkpoint_path = os.path.join(
+ args.checkpoint_directory, "%s.best.pth" % args.checkpoint_prefix
+ )
+ shutil.copy(checkpoint_path, best_checkpoint_path)
+
+# Close the log file
+log_file.close()
diff --git a/third_party/dust3r/.gitignore b/third_party/dust3r/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..0eb590aa03e6840537cce148886a74de6dcce096
--- /dev/null
+++ b/third_party/dust3r/.gitignore
@@ -0,0 +1,133 @@
+data/
+checkpoints/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+__pycache__*
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/third_party/dust3r/.gitmodules b/third_party/dust3r/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..c950ef981a8d2e47599dd7acbbe1bf8de9a42aca
--- /dev/null
+++ b/third_party/dust3r/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "croco"]
+ path = croco
+ url = https://github.com/naver/croco
diff --git a/third_party/dust3r/LICENSE b/third_party/dust3r/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..a97986e3a8ddd49973959f6c748dfa8b881b64d3
--- /dev/null
+++ b/third_party/dust3r/LICENSE
@@ -0,0 +1,7 @@
+DUSt3R, Copyright (c) 2024-present Naver Corporation, is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license.
+
+A summary of the CC BY-NC-SA 4.0 license is located here:
+ https://creativecommons.org/licenses/by-nc-sa/4.0/
+
+The CC BY-NC-SA 4.0 license is located here:
+ https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
diff --git a/third_party/dust3r/NOTICE b/third_party/dust3r/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..31d92d26f1b665d0f06b23378ef1e1d558b648d7
--- /dev/null
+++ b/third_party/dust3r/NOTICE
@@ -0,0 +1,13 @@
+DUSt3R
+Copyright 2024-present NAVER Corp.
+
+This project contains subcomponents with separate copyright notices and license terms.
+Your use of the source code for these subcomponents is subject to the terms and conditions of the following licenses.
+
+====
+
+naver/croco
+https://github.com/naver/croco/
+
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0
+
diff --git a/third_party/dust3r/README.md b/third_party/dust3r/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..013646478823a1ac77f3c70603abb35650b58304
--- /dev/null
+++ b/third_party/dust3r/README.md
@@ -0,0 +1,360 @@
+
+
+Official implementation of `DUSt3R: Geometric 3D Vision Made Easy`
+[[Project page](https://dust3r.europe.naverlabs.com/)], [[DUSt3R arxiv](https://arxiv.org/abs/2312.14132)]
+
+> :warning: **We have removed the checkpoints temporarily**: We apologize for that!
+
+
+
+
+
+```bibtex
+@inproceedings{dust3r_cvpr24,
+ title={DUSt3R: Geometric 3D Vision Made Easy},
+ author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud},
+ booktitle = {CVPR},
+ year = {2024}
+}
+
+@misc{dust3r_arxiv23,
+ title={DUSt3R: Geometric 3D Vision Made Easy},
+ author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud},
+ year={2023},
+ eprint={2312.14132},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV}
+}
+```
+
+## Table of Contents
+
+- [Table of Contents](#table-of-contents)
+- [License](#license)
+- [Get Started](#get-started)
+ - [Installation](#installation)
+ - [Checkpoints](#checkpoints)
+ - [Interactive demo](#interactive-demo)
+ - [Interactive demo with docker](#interactive-demo-with-docker)
+- [Usage](#usage)
+- [Training](#training)
+ - [Demo](#demo)
+ - [Our Hyperparameters](#our-hyperparameters)
+
+## License
+
+The code is distributed under the CC BY-NC-SA 4.0 License.
+See [LICENSE](LICENSE) for more information.
+
+```python
+# Copyright (C) 2024-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+```
+
+## Get Started
+
+### Installation
+
+1. Clone DUSt3R.
+```bash
+git clone --recursive https://github.com/naver/dust3r
+cd dust3r
+# if you have already cloned dust3r:
+# git submodule update --init --recursive
+```
+
+2. Create the environment, here we show an example using conda.
+```bash
+conda create -n dust3r python=3.11 cmake=3.14.0
+conda activate dust3r
+conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
+pip install -r requirements.txt
+# Optional: you can also install additional packages to:
+# - add support for HEIC images
+pip install -r requirements_optional.txt
+```
+
+3. Optional, compile the cuda kernels for RoPE (as in CroCo v2).
+```bash
+# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.
+cd croco/models/curope/
+python setup.py build_ext --inplace
+cd ../../../
+```
+
+### Checkpoints
+> :warning: **We have removed the checkpoints temporarily**: We apologize for that!
+
+You can obtain the checkpoints by two ways:
+
+1) You can use our huggingface_hub integration: the models will be downloaded automatically.
+
+2) Otherwise, We provide several pre-trained models:
+
+| Modelname | Training resolutions | Head | Encoder | Decoder |
+|-------------|----------------------|------|---------|---------|
+| [`DUSt3R_ViTLarge_BaseDecoder_224_linear.pth`](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_224_linear.pth) | 224x224 | Linear | ViT-L | ViT-B |
+| [`DUSt3R_ViTLarge_BaseDecoder_512_linear.pth`](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_linear.pth) | 512x384, 512x336, 512x288, 512x256, 512x160 | Linear | ViT-L | ViT-B |
+| [`DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth`]() | 512x384, 512x336, 512x288, 512x256, 512x160 | DPT | ViT-L | ViT-B |
+
+You can check the hyperparameters we used to train these models in the [section: Our Hyperparameters](#our-hyperparameters)
+
+To download a specific model, for example `DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth`:
+```bash
+mkdir -p checkpoints/
+wget TODO -P checkpoints/
+```
+
+For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0. Again, see [section: Our Hyperparameters](#our-hyperparameters) for details.
+
+### Interactive demo
+
+In this demo, you should be able run DUSt3R on your machine to reconstruct a scene.
+First select images that depicts the same scene.
+
+You can adjust the global alignment schedule and its number of iterations.
+
+> [!NOTE]
+> If you selected one or two images, the global alignment procedure will be skipped (mode=GlobalAlignerMode.PairViewer)
+
+Hit "Run" and wait.
+When the global alignment ends, the reconstruction appears.
+Use the slider "min_conf_thr" to show or remove low confidence areas.
+
+```bash
+python3 demo.py --model_name DUSt3R_ViTLarge_BaseDecoder_512_dpt
+
+# Use --weights to load a checkpoint from a local file, eg --weights checkpoints/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth
+# Use --image_size to select the correct resolution for the selected checkpoint. 512 (default) or 224
+# Use --local_network to make it accessible on the local network, or --server_name to specify the url manually
+# Use --server_port to change the port, by default it will search for an available port starting at 7860
+# Use --device to use a different device, by default it's "cuda"
+```
+
+### Interactive demo with docker
+
+To run DUSt3R using Docker, including with NVIDIA CUDA support, follow these instructions:
+
+1. **Install Docker**: If not already installed, download and install `docker` and `docker compose` from the [Docker website](https://www.docker.com/get-started).
+
+2. **Install NVIDIA Docker Toolkit**: For GPU support, install the NVIDIA Docker toolkit from the [Nvidia website](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).
+
+3. **Build the Docker image and run it**: `cd` into the `./docker` directory and run the following commands:
+
+```bash
+cd docker
+bash run.sh --with-cuda --model_name="DUSt3R_ViTLarge_BaseDecoder_512_dpt"
+```
+
+Or if you want to run the demo without CUDA support, run the following command:
+
+```bash
+cd docker
+bash run.sh --model_name="DUSt3R_ViTLarge_BaseDecoder_512_dpt"
+```
+
+By default, `demo.py` is lanched with the option `--local_network`.
+Visit `http://localhost:7860/` to access the web UI (or replace `localhost` with the machine's name to access it from the network).
+
+`run.sh` will launch docker-compose using either the [docker-compose-cuda.yml](docker/docker-compose-cuda.yml) or [docker-compose-cpu.ym](docker/docker-compose-cpu.yml) config file, then it starts the demo using [entrypoint.sh](docker/files/entrypoint.sh).
+
+
+
+
+## Usage
+
+```python
+from dust3r.inference import inference
+from dust3r.model import AsymmetricCroCo3DStereo
+from dust3r.utils.image import load_images
+from dust3r.image_pairs import make_pairs
+from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
+
+if __name__ == '__main__':
+ device = 'cuda'
+ batch_size = 1
+ schedule = 'cosine'
+ lr = 0.01
+ niter = 300
+
+ model_name = "naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt"
+ # you can put the path to a local checkpoint in model_name if needed
+ model = AsymmetricCroCo3DStereo.from_pretrained(model_name).to(device)
+ # load_images can take a list of images or a directory
+ images = load_images(['croco/assets/Chateau1.png', 'croco/assets/Chateau2.png'], size=512)
+ pairs = make_pairs(images, scene_graph='complete', prefilter=None, symmetrize=True)
+ output = inference(pairs, model, device, batch_size=batch_size)
+
+ # at this stage, you have the raw dust3r predictions
+ view1, pred1 = output['view1'], output['pred1']
+ view2, pred2 = output['view2'], output['pred2']
+ # here, view1, pred1, view2, pred2 are dicts of lists of len(2)
+ # -> because we symmetrize we have (im1, im2) and (im2, im1) pairs
+ # in each view you have:
+ # an integer image identifier: view1['idx'] and view2['idx']
+ # the img: view1['img'] and view2['img']
+ # the image shape: view1['true_shape'] and view2['true_shape']
+ # an instance string output by the dataloader: view1['instance'] and view2['instance']
+ # pred1 and pred2 contains the confidence values: pred1['conf'] and pred2['conf']
+ # pred1 contains 3D points for view1['img'] in view1['img'] space: pred1['pts3d']
+ # pred2 contains 3D points for view2['img'] in view1['img'] space: pred2['pts3d_in_other_view']
+
+ # next we'll use the global_aligner to align the predictions
+ # depending on your task, you may be fine with the raw output and not need it
+ # with only two input images, you could use GlobalAlignerMode.PairViewer: it would just convert the output
+ # if using GlobalAlignerMode.PairViewer, no need to run compute_global_alignment
+ scene = global_aligner(output, device=device, mode=GlobalAlignerMode.PointCloudOptimizer)
+ loss = scene.compute_global_alignment(init="mst", niter=niter, schedule=schedule, lr=lr)
+
+ # retrieve useful values from scene:
+ imgs = scene.imgs
+ focals = scene.get_focals()
+ poses = scene.get_im_poses()
+ pts3d = scene.get_pts3d()
+ confidence_masks = scene.get_masks()
+
+ # visualize reconstruction
+ scene.show()
+
+ # find 2D-2D matches between the two images
+ from dust3r.utils.geometry import find_reciprocal_matches, xy_grid
+ pts2d_list, pts3d_list = [], []
+ for i in range(2):
+ conf_i = confidence_masks[i].cpu().numpy()
+ pts2d_list.append(xy_grid(*imgs[i].shape[:2][::-1])[conf_i]) # imgs[i].shape[:2] = (H, W)
+ pts3d_list.append(pts3d[i].detach().cpu().numpy()[conf_i])
+ reciprocal_in_P2, nn2_in_P1, num_matches = find_reciprocal_matches(*pts3d_list)
+ print(f'found {num_matches} matches')
+ matches_im1 = pts2d_list[1][reciprocal_in_P2]
+ matches_im0 = pts2d_list[0][nn2_in_P1][reciprocal_in_P2]
+
+ # visualize a few matches
+ import numpy as np
+ from matplotlib import pyplot as pl
+ n_viz = 10
+ match_idx_to_viz = np.round(np.linspace(0, num_matches-1, n_viz)).astype(int)
+ viz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz]
+
+ H0, W0, H1, W1 = *imgs[0].shape[:2], *imgs[1].shape[:2]
+ img0 = np.pad(imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
+ img1 = np.pad(imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
+ img = np.concatenate((img0, img1), axis=1)
+ pl.figure()
+ pl.imshow(img)
+ cmap = pl.get_cmap('jet')
+ for i in range(n_viz):
+ (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T
+ pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)
+ pl.show(block=True)
+
+```
+
+
+## Training
+
+In this section, we present a short demonstration to get started with training DUSt3R.
+At the moment, we didn't release the training datasets, so we're going to download and prepare a subset of [CO3Dv2](https://github.com/facebookresearch/co3d) - [Creative Commons Attribution-NonCommercial 4.0 International](https://github.com/facebookresearch/co3d/blob/main/LICENSE) and launch the training code on it.
+The demo model will be trained for a few epochs on a very small dataset.
+It will not be very good.
+
+### Demo
+
+```bash
+# download and prepare the co3d subset
+mkdir -p data/co3d_subset
+cd data/co3d_subset
+git clone https://github.com/facebookresearch/co3d
+cd co3d
+python3 ./co3d/download_dataset.py --download_folder ../ --single_sequence_subset
+rm ../*.zip
+cd ../../..
+
+python3 datasets_preprocess/preprocess_co3d.py --co3d_dir data/co3d_subset --output_dir data/co3d_subset_processed --single_sequence_subset
+
+# download the pretrained croco v2 checkpoint
+mkdir -p checkpoints/
+wget https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo_V2_ViTLarge_BaseDecoder.pth -P checkpoints/
+
+# the training of dust3r is done in 3 steps.
+# for this example we'll do fewer epochs, for the actual hyperparameters we used in the paper, see the next section: "Our Hyperparameters"
+# step 1 - train dust3r for 224 resolution
+torchrun --nproc_per_node=4 train.py \
+ --train_dataset "1000 @ Co3d(split='train', ROOT='data/co3d_subset_processed', aug_crop=16, mask_bg='rand', resolution=224, transform=ColorJitter)" \
+ --test_dataset "100 @ Co3d(split='test', ROOT='data/co3d_subset_processed', resolution=224, seed=777)" \
+ --model "AsymmetricCroCo3DStereo(pos_embed='RoPE100', img_size=(224, 224), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --train_criterion "ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion "Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --pretrained "checkpoints/CroCo_V2_ViTLarge_BaseDecoder.pth" \
+ --lr 0.0001 --min_lr 1e-06 --warmup_epochs 1 --epochs 10 --batch_size 16 --accum_iter 1 \
+ --save_freq 1 --keep_freq 5 --eval_freq 1 \
+ --output_dir "checkpoints/dust3r_demo_224"
+
+# step 2 - train dust3r for 512 resolution
+torchrun --nproc_per_node=4 train.py \
+ --train_dataset "1000 @ Co3d(split='train', ROOT='data/co3d_subset_processed', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter)" \
+ --test_dataset "100 @ Co3d(split='test', ROOT='data/co3d_subset_processed', resolution=(512,384), seed=777)" \
+ --model "AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --train_criterion "ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion "Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --pretrained "checkpoints/dust3r_demo_224/checkpoint-best.pth" \
+ --lr 0.0001 --min_lr 1e-06 --warmup_epochs 1 --epochs 10 --batch_size 4 --accum_iter 4 \
+ --save_freq 1 --keep_freq 5 --eval_freq 1 \
+ --output_dir "checkpoints/dust3r_demo_512"
+
+# step 3 - train dust3r for 512 resolution with dpt
+torchrun --nproc_per_node=4 train.py \
+ --train_dataset "1000 @ Co3d(split='train', ROOT='data/co3d_subset_processed', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter)" \
+ --test_dataset "100 @ Co3d(split='test', ROOT='data/co3d_subset_processed', resolution=(512,384), seed=777)" \
+ --model "AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='dpt', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --train_criterion "ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion "Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --pretrained "checkpoints/dust3r_demo_512/checkpoint-best.pth" \
+ --lr 0.0001 --min_lr 1e-06 --warmup_epochs 1 --epochs 10 --batch_size 2 --accum_iter 8 \
+ --save_freq 1 --keep_freq 5 --eval_freq 1 \
+ --output_dir "checkpoints/dust3r_demo_512dpt"
+
+```
+
+### Our Hyperparameters
+
+We didn't release the training datasets, but here are the commands we used for training our models:
+
+```bash
+# NOTE: ROOT path omitted for datasets
+# 224 linear
+torchrun --nproc_per_node 8 train.py \
+ --train_dataset=" + 100_000 @ Habitat(1_000_000, split='train', aug_crop=16, resolution=224, transform=ColorJitter) + 100_000 @ BlendedMVS(split='train', aug_crop=16, resolution=224, transform=ColorJitter) + 100_000 @ MegaDepth(split='train', aug_crop=16, resolution=224, transform=ColorJitter) + 100_000 @ ARKitScenes(aug_crop=256, resolution=224, transform=ColorJitter) + 100_000 @ Co3d(split='train', aug_crop=16, mask_bg='rand', resolution=224, transform=ColorJitter) + 100_000 @ StaticThings3D(aug_crop=256, mask_bg='rand', resolution=224, transform=ColorJitter) + 100_000 @ ScanNetpp(split='train', aug_crop=256, resolution=224, transform=ColorJitter) + 100_000 @ InternalUnreleasedDataset(aug_crop=128, resolution=224, transform=ColorJitter) " \
+ --test_dataset=" Habitat(1_000, split='val', resolution=224, seed=777) + 1_000 @ BlendedMVS(split='val', resolution=224, seed=777) + 1_000 @ MegaDepth(split='val', resolution=224, seed=777) + 1_000 @ Co3d(split='test', mask_bg='rand', resolution=224, seed=777) " \
+ --train_criterion="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion="Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', img_size=(224, 224), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --pretrained="checkpoints/CroCo_V2_ViTLarge_BaseDecoder.pth" \
+ --lr=0.0001 --min_lr=1e-06 --warmup_epochs=10 --epochs=100 --batch_size=16 --accum_iter=1 \
+ --save_freq=5 --keep_freq=10 --eval_freq=1 \
+ --output_dir="checkpoints/dust3r_224"
+
+# 512 linear
+torchrun --nproc_per_node 8 train.py \
+ --train_dataset=" + 10_000 @ Habitat(1_000_000, split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ BlendedMVS(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ MegaDepth(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ARKitScenes(aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ Co3d(split='train', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ StaticThings3D(aug_crop=256, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ScanNetpp(split='train', aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ InternalUnreleasedDataset(aug_crop=128, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) " \
+ --test_dataset=" Habitat(1_000, split='val', resolution=(512,384), seed=777) + 1_000 @ BlendedMVS(split='val', resolution=(512,384), seed=777) + 1_000 @ MegaDepth(split='val', resolution=(512,336), seed=777) + 1_000 @ Co3d(split='test', resolution=(512,384), seed=777) " \
+ --train_criterion="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion="Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --pretrained="checkpoints/dust3r_224/checkpoint-best.pth" \
+ --lr=0.0001 --min_lr=1e-06 --warmup_epochs=20 --epochs=100 --batch_size=4 --accum_iter=2 \
+ --save_freq=10 --keep_freq=10 --eval_freq=1 --print_freq=10 \
+ --output_dir="checkpoints/dust3r_512"
+
+# 512 dpt
+torchrun --nproc_per_node 8 train.py \
+ --train_dataset=" + 10_000 @ Habitat(1_000_000, split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ BlendedMVS(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ MegaDepth(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ARKitScenes(aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ Co3d(split='train', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ StaticThings3D(aug_crop=256, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ScanNetpp(split='train', aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ InternalUnreleasedDataset(aug_crop=128, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) " \
+ --test_dataset=" Habitat(1_000, split='val', resolution=(512,384), seed=777) + 1_000 @ BlendedMVS(split='val', resolution=(512,384), seed=777) + 1_000 @ MegaDepth(split='val', resolution=(512,336), seed=777) + 1_000 @ Co3d(split='test', resolution=(512,384), seed=777) " \
+ --train_criterion="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion="Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='dpt', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --pretrained="checkpoints/dust3r_512/checkpoint-best.pth" \
+ --lr=0.0001 --min_lr=1e-06 --warmup_epochs=15 --epochs=90 --batch_size=4 --accum_iter=2 \
+ --save_freq=5 --keep_freq=10 --eval_freq=1 --print_freq=10 \
+ --output_dir="checkpoints/dust3r_512dpt"
+
+```
diff --git a/third_party/dust3r/croco/.gitignore b/third_party/dust3r/croco/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..0eb590aa03e6840537cce148886a74de6dcce096
--- /dev/null
+++ b/third_party/dust3r/croco/.gitignore
@@ -0,0 +1,133 @@
+data/
+checkpoints/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+__pycache__*
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/third_party/dust3r/croco/LICENSE b/third_party/dust3r/croco/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..d9b84b1a65f9db6d8920a9048d162f52ba3ea56d
--- /dev/null
+++ b/third_party/dust3r/croco/LICENSE
@@ -0,0 +1,52 @@
+CroCo, Copyright (c) 2022-present Naver Corporation, is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license.
+
+A summary of the CC BY-NC-SA 4.0 license is located here:
+ https://creativecommons.org/licenses/by-nc-sa/4.0/
+
+The CC BY-NC-SA 4.0 license is located here:
+ https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
+
+
+SEE NOTICE BELOW WITH RESPECT TO THE FILE: models/pos_embed.py, models/blocks.py
+
+***************************
+
+NOTICE WITH RESPECT TO THE FILE: models/pos_embed.py
+
+This software is being redistributed in a modifiled form. The original form is available here:
+
+https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+
+This software in this file incorporates parts of the following software available here:
+
+Transformer: https://github.com/tensorflow/models/blob/master/official/legacy/transformer/model_utils.py
+available under the following license: https://github.com/tensorflow/models/blob/master/LICENSE
+
+MoCo v3: https://github.com/facebookresearch/moco-v3
+available under the following license: https://github.com/facebookresearch/moco-v3/blob/main/LICENSE
+
+DeiT: https://github.com/facebookresearch/deit
+available under the following license: https://github.com/facebookresearch/deit/blob/main/LICENSE
+
+
+ORIGINAL COPYRIGHT NOTICE AND PERMISSION NOTICE AVAILABLE HERE IS REPRODUCE BELOW:
+
+https://github.com/facebookresearch/mae/blob/main/LICENSE
+
+Attribution-NonCommercial 4.0 International
+
+***************************
+
+NOTICE WITH RESPECT TO THE FILE: models/blocks.py
+
+This software is being redistributed in a modifiled form. The original form is available here:
+
+https://github.com/rwightman/pytorch-image-models
+
+ORIGINAL COPYRIGHT NOTICE AND PERMISSION NOTICE AVAILABLE HERE IS REPRODUCE BELOW:
+
+https://github.com/rwightman/pytorch-image-models/blob/master/LICENSE
+
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
\ No newline at end of file
diff --git a/third_party/dust3r/croco/NOTICE b/third_party/dust3r/croco/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..d51bb365036c12d428d6e3a4fd00885756d5261c
--- /dev/null
+++ b/third_party/dust3r/croco/NOTICE
@@ -0,0 +1,21 @@
+CroCo
+Copyright 2022-present NAVER Corp.
+
+This project contains subcomponents with separate copyright notices and license terms.
+Your use of the source code for these subcomponents is subject to the terms and conditions of the following licenses.
+
+====
+
+facebookresearch/mae
+https://github.com/facebookresearch/mae
+
+Attribution-NonCommercial 4.0 International
+
+====
+
+rwightman/pytorch-image-models
+https://github.com/rwightman/pytorch-image-models
+
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
\ No newline at end of file
diff --git a/third_party/dust3r/croco/README.MD b/third_party/dust3r/croco/README.MD
new file mode 100644
index 0000000000000000000000000000000000000000..38e33b001a60bd16749317fb297acd60f28a6f1b
--- /dev/null
+++ b/third_party/dust3r/croco/README.MD
@@ -0,0 +1,124 @@
+# CroCo + CroCo v2 / CroCo-Stereo / CroCo-Flow
+
+[[`CroCo arXiv`](https://arxiv.org/abs/2210.10716)] [[`CroCo v2 arXiv`](https://arxiv.org/abs/2211.10408)] [[`project page and demo`](https://croco.europe.naverlabs.com/)]
+
+This repository contains the code for our CroCo model presented in our NeurIPS'22 paper [CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion](https://openreview.net/pdf?id=wZEfHUM5ri) and its follow-up extension published at ICCV'23 [Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow](https://openaccess.thecvf.com/content/ICCV2023/html/Weinzaepfel_CroCo_v2_Improved_Cross-view_Completion_Pre-training_for_Stereo_Matching_and_ICCV_2023_paper.html), refered to as CroCo v2:
+
+
+
+```bibtex
+@inproceedings{croco,
+ title={{CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion}},
+ author={{Weinzaepfel, Philippe and Leroy, Vincent and Lucas, Thomas and Br\'egier, Romain and Cabon, Yohann and Arora, Vaibhav and Antsfeld, Leonid and Chidlovskii, Boris and Csurka, Gabriela and Revaud J\'er\^ome}},
+ booktitle={{NeurIPS}},
+ year={2022}
+}
+
+@inproceedings{croco_v2,
+ title={{CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow}},
+ author={Weinzaepfel, Philippe and Lucas, Thomas and Leroy, Vincent and Cabon, Yohann and Arora, Vaibhav and Br{\'e}gier, Romain and Csurka, Gabriela and Antsfeld, Leonid and Chidlovskii, Boris and Revaud, J{\'e}r{\^o}me},
+ booktitle={ICCV},
+ year={2023}
+}
+```
+
+## License
+
+The code is distributed under the CC BY-NC-SA 4.0 License. See [LICENSE](LICENSE) for more information.
+Some components are based on code from [MAE](https://github.com/facebookresearch/mae) released under the CC BY-NC-SA 4.0 License and [timm](https://github.com/rwightman/pytorch-image-models) released under the Apache 2.0 License.
+Some components for stereo matching and optical flow are based on code from [unimatch](https://github.com/autonomousvision/unimatch) released under the MIT license.
+
+## Preparation
+
+1. Install dependencies on a machine with a NVidia GPU using e.g. conda. Note that `habitat-sim` is required only for the interactive demo and the synthetic pre-training data generation. If you don't plan to use it, you can ignore the line installing it and use a more recent python version.
+
+```bash
+conda create -n croco python=3.7 cmake=3.14.0
+conda activate croco
+conda install habitat-sim headless -c conda-forge -c aihabitat
+conda install pytorch torchvision -c pytorch
+conda install notebook ipykernel matplotlib
+conda install ipywidgets widgetsnbextension
+conda install scikit-learn tqdm quaternion opencv # only for pretraining / habitat data generation
+
+```
+
+2. Compile cuda kernels for RoPE
+
+CroCo v2 relies on RoPE positional embeddings for which you need to compile some cuda kernels.
+```bash
+cd models/curope/
+python setup.py build_ext --inplace
+cd ../../
+```
+
+This can be a bit long as we compile for all cuda architectures, feel free to update L9 of `models/curope/setup.py` to compile for specific architectures only.
+You might also need to set the environment `CUDA_HOME` in case you use a custom cuda installation.
+
+In case you cannot provide, we also provide a slow pytorch version, which will be automatically loaded.
+
+3. Download pre-trained model
+
+We provide several pre-trained models:
+
+| modelname | pre-training data | pos. embed. | Encoder | Decoder |
+|------------------------------------------------------------------------------------------------------------------------------------|-------------------|-------------|---------|---------|
+| [`CroCo.pth`](https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo.pth) | Habitat | cosine | ViT-B | Small |
+| [`CroCo_V2_ViTBase_SmallDecoder.pth`](https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo_V2_ViTBase_SmallDecoder.pth) | Habitat + real | RoPE | ViT-B | Small |
+| [`CroCo_V2_ViTBase_BaseDecoder.pth`](https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo_V2_ViTBase_BaseDecoder.pth) | Habitat + real | RoPE | ViT-B | Base |
+| [`CroCo_V2_ViTLarge_BaseDecoder.pth`](https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo_V2_ViTLarge_BaseDecoder.pth) | Habitat + real | RoPE | ViT-L | Base |
+
+To download a specific model, i.e., the first one (`CroCo.pth`)
+```bash
+mkdir -p pretrained_models/
+wget https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo.pth -P pretrained_models/
+```
+
+## Reconstruction example
+
+Simply run after downloading the `CroCo_V2_ViTLarge_BaseDecoder` pretrained model (or update the corresponding line in `demo.py`)
+```bash
+python demo.py
+```
+
+## Interactive demonstration of cross-view completion reconstruction on the Habitat simulator
+
+First download the test scene from Habitat:
+```bash
+python -m habitat_sim.utils.datasets_download --uids habitat_test_scenes --data-path habitat-sim-data/
+```
+
+Then, run the Notebook demo `interactive_demo.ipynb`.
+
+In this demo, you should be able to sample a random reference viewpoint from an [Habitat](https://github.com/facebookresearch/habitat-sim) test scene. Use the sliders to change viewpoint and select a masked target view to reconstruct using CroCo.
+
+
+## Pre-training
+
+### CroCo
+
+To pre-train CroCo, please first generate the pre-training data from the Habitat simulator, following the instructions in [datasets/habitat_sim/README.MD](datasets/habitat_sim/README.MD) and then run the following command:
+```
+torchrun --nproc_per_node=4 pretrain.py --output_dir ./output/pretraining/
+```
+
+Our CroCo pre-training was launched on a single server with 4 GPUs.
+It should take around 10 days with A100 or 15 days with V100 to do the 400 pre-training epochs, but decent performances are obtained earlier in training.
+Note that, while the code contains the same scaling rule of the learning rate as MAE when changing the effective batch size, we did not experimented if it is valid in our case.
+The first run can take a few minutes to start, to parse all available pre-training pairs.
+
+### CroCo v2
+
+For CroCo v2 pre-training, in addition to the generation of the pre-training data from the Habitat simulator above, please pre-extract the crops from the real datasets following the instructions in [datasets/crops/README.MD](datasets/crops/README.MD).
+Then, run the following command for the largest model (ViT-L encoder, Base decoder):
+```
+torchrun --nproc_per_node=8 pretrain.py --model "CroCoNet(enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_num_heads=12, dec_depth=12, pos_embed='RoPE100')" --dataset "habitat_release+ARKitScenes+MegaDepth+3DStreetView+IndoorVL" --warmup_epochs 12 --max_epoch 125 --epochs 250 --amp 0 --keep_freq 5 --output_dir ./output/pretraining_crocov2/
+```
+
+Our CroCo v2 pre-training was launched on a single server with 8 GPUs for the largest model, and on a single server with 4 GPUs for the smaller ones, keeping a batch size of 64 per gpu in all cases.
+The largest model should take around 12 days on A100.
+Note that, while the code contains the same scaling rule of the learning rate as MAE when changing the effective batch size, we did not experimented if it is valid in our case.
+
+## Stereo matching and Optical flow downstream tasks
+
+For CroCo-Stereo and CroCo-Flow, please refer to [stereoflow/README.MD](stereoflow/README.MD).
diff --git a/third_party/dust3r/croco/assets/Chateau1.png b/third_party/dust3r/croco/assets/Chateau1.png
new file mode 100644
index 0000000000000000000000000000000000000000..295b00e46972ffcacaca60c2c7c7ec7a04c762fa
--- /dev/null
+++ b/third_party/dust3r/croco/assets/Chateau1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71ffb8c7d77e5ced0bb3dcd2cb0db84d0e98e6ff5ffd2d02696a7156e5284857
+size 112106
diff --git a/third_party/dust3r/croco/assets/Chateau2.png b/third_party/dust3r/croco/assets/Chateau2.png
new file mode 100644
index 0000000000000000000000000000000000000000..97b3c058ff180a6d0c0853ab533b0823a06f8425
--- /dev/null
+++ b/third_party/dust3r/croco/assets/Chateau2.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3a0be9e19f6b89491d692c71e3f2317c2288a898a990561d48b7667218b47c8
+size 109905
diff --git a/third_party/dust3r/croco/assets/arch.jpg b/third_party/dust3r/croco/assets/arch.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..894c58e25c2d9ee0b579c6f5a6ce78d12217d106
--- /dev/null
+++ b/third_party/dust3r/croco/assets/arch.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05fbf12896a79819a3864a800b174896bd3b6fa29b4f4f580d06725ff7c30dc7
+size 74842
diff --git a/third_party/dust3r/croco/croco-stereo-flow-demo.ipynb b/third_party/dust3r/croco/croco-stereo-flow-demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2b00a7607ab5f82d1857041969bfec977e56b3e0
--- /dev/null
+++ b/third_party/dust3r/croco/croco-stereo-flow-demo.ipynb
@@ -0,0 +1,191 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9bca0f41",
+ "metadata": {},
+ "source": [
+ "# Simple inference example with CroCo-Stereo or CroCo-Flow"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "80653ef7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Copyright (C) 2022-present Naver Corporation. All rights reserved.\n",
+ "# Licensed under CC BY-NC-SA 4.0 (non-commercial use only)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4f033862",
+ "metadata": {},
+ "source": [
+ "First download the model(s) of your choice by running\n",
+ "```\n",
+ "bash stereoflow/download_model.sh crocostereo.pth\n",
+ "bash stereoflow/download_model.sh crocoflow.pth\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1fb2e392",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "use_gpu = torch.cuda.is_available() and torch.cuda.device_count()>0\n",
+ "device = torch.device('cuda:0' if use_gpu else 'cpu')\n",
+ "import matplotlib.pylab as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0e25d77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from stereoflow.test import _load_model_and_criterion\n",
+ "from stereoflow.engine import tiled_pred\n",
+ "from stereoflow.datasets_stereo import img_to_tensor, vis_disparity\n",
+ "from stereoflow.datasets_flow import flowToColor\n",
+ "tile_overlap=0.7 # recommended value, higher value can be slightly better but slower"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "86a921f5",
+ "metadata": {},
+ "source": [
+ "### CroCo-Stereo example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64e483cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image1 = np.asarray(Image.open(''))\n",
+ "image2 = np.asarray(Image.open(''))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f0d04303",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model, _, cropsize, with_conf, task, tile_conf_mode = _load_model_and_criterion('stereoflow_models/crocostereo.pth', None, device)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "47dc14b5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "im1 = img_to_tensor(image1).to(device).unsqueeze(0)\n",
+ "im2 = img_to_tensor(image2).to(device).unsqueeze(0)\n",
+ "with torch.inference_mode():\n",
+ " pred, _, _ = tiled_pred(model, None, im1, im2, None, conf_mode=tile_conf_mode, overlap=tile_overlap, crop=cropsize, with_conf=with_conf, return_time=False)\n",
+ "pred = pred.squeeze(0).squeeze(0).cpu().numpy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "583b9f16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.imshow(vis_disparity(pred))\n",
+ "plt.axis('off')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2df5d70",
+ "metadata": {},
+ "source": [
+ "### CroCo-Flow example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ee257a7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image1 = np.asarray(Image.open(''))\n",
+ "image2 = np.asarray(Image.open(''))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d5edccf0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model, _, cropsize, with_conf, task, tile_conf_mode = _load_model_and_criterion('stereoflow_models/crocoflow.pth', None, device)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b19692c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "im1 = img_to_tensor(image1).to(device).unsqueeze(0)\n",
+ "im2 = img_to_tensor(image2).to(device).unsqueeze(0)\n",
+ "with torch.inference_mode():\n",
+ " pred, _, _ = tiled_pred(model, None, im1, im2, None, conf_mode=tile_conf_mode, overlap=tile_overlap, crop=cropsize, with_conf=with_conf, return_time=False)\n",
+ "pred = pred.squeeze(0).permute(1,2,0).cpu().numpy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26f79db3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.imshow(flowToColor(pred))\n",
+ "plt.axis('off')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/imcui/third_party/gim/hloc/extractors/__init__.py b/third_party/dust3r/croco/datasets/__init__.py
similarity index 100%
rename from imcui/third_party/gim/hloc/extractors/__init__.py
rename to third_party/dust3r/croco/datasets/__init__.py
diff --git a/third_party/dust3r/croco/datasets/crops/README.MD b/third_party/dust3r/croco/datasets/crops/README.MD
new file mode 100644
index 0000000000000000000000000000000000000000..47ddabebb177644694ee247ae878173a3a16644f
--- /dev/null
+++ b/third_party/dust3r/croco/datasets/crops/README.MD
@@ -0,0 +1,104 @@
+## Generation of crops from the real datasets
+
+The instructions below allow to generate the crops used for pre-training CroCo v2 from the following real-world datasets: ARKitScenes, MegaDepth, 3DStreetView and IndoorVL.
+
+### Download the metadata of the crops to generate
+
+First, download the metadata and put them in `./data/`:
+```
+mkdir -p data
+cd data/
+wget https://download.europe.naverlabs.com/ComputerVision/CroCo/data/crop_metadata.zip
+unzip crop_metadata.zip
+rm crop_metadata.zip
+cd ..
+```
+
+### Prepare the original datasets
+
+Second, download the original datasets in `./data/original_datasets/`.
+```
+mkdir -p data/original_datasets
+```
+
+##### ARKitScenes
+
+Download the `raw` dataset from https://github.com/apple/ARKitScenes/blob/main/DATA.md and put it in `./data/original_datasets/ARKitScenes/`.
+The resulting file structure should be like:
+```
+./data/original_datasets/ARKitScenes/
+└───Training
+ └───40753679
+ │ │ ultrawide
+ │ │ ...
+ └───40753686
+ │
+ ...
+```
+
+##### MegaDepth
+
+Download `MegaDepth v1 Dataset` from https://www.cs.cornell.edu/projects/megadepth/ and put it in `./data/original_datasets/MegaDepth/`.
+The resulting file structure should be like:
+
+```
+./data/original_datasets/MegaDepth/
+└───0000
+│ └───images
+│ │ │ 1000557903_87fa96b8a4_o.jpg
+│ │ └ ...
+│ └─── ...
+└───0001
+│ │
+│ └ ...
+└─── ...
+```
+
+##### 3DStreetView
+
+Download `3D_Street_View` dataset from https://github.com/amir32002/3D_Street_View and put it in `./data/original_datasets/3DStreetView/`.
+The resulting file structure should be like:
+
+```
+./data/original_datasets/3DStreetView/
+└───dataset_aligned
+│ └───0002
+│ │ │ 0000002_0000001_0000002_0000001.jpg
+│ │ └ ...
+│ └─── ...
+└───dataset_unaligned
+│ └───0003
+│ │ │ 0000003_0000001_0000002_0000001.jpg
+│ │ └ ...
+│ └─── ...
+```
+
+##### IndoorVL
+
+Download the `IndoorVL` datasets using [Kapture](https://github.com/naver/kapture).
+
+```
+pip install kapture
+mkdir -p ./data/original_datasets/IndoorVL
+cd ./data/original_datasets/IndoorVL
+kapture_download_dataset.py update
+kapture_download_dataset.py install "HyundaiDepartmentStore_*"
+kapture_download_dataset.py install "GangnamStation_*"
+cd -
+```
+
+### Extract the crops
+
+Now, extract the crops for each of the dataset:
+```
+for dataset in ARKitScenes MegaDepth 3DStreetView IndoorVL;
+do
+ python3 datasets/crops/extract_crops_from_images.py --crops ./data/crop_metadata/${dataset}/crops_release.txt --root-dir ./data/original_datasets/${dataset}/ --output-dir ./data/${dataset}_crops/ --imsize 256 --nthread 8 --max-subdir-levels 5 --ideal-number-pairs-in-dir 500;
+done
+```
+
+##### Note for IndoorVL
+
+Due to some legal issues, we can only release 144,228 pairs out of the 1,593,689 pairs used in the paper.
+To account for it in terms of number of pre-training iterations, the pre-training command in this repository uses 125 training epochs including 12 warm-up epochs and learning rate cosine schedule of 250, instead of 100, 10 and 200 respectively.
+The impact on the performance is negligible.
diff --git a/imcui/third_party/dust3r/croco/datasets/crops/extract_crops_from_images.py b/third_party/dust3r/croco/datasets/crops/extract_crops_from_images.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/crops/extract_crops_from_images.py
rename to third_party/dust3r/croco/datasets/crops/extract_crops_from_images.py
diff --git a/third_party/dust3r/croco/datasets/habitat_sim/README.MD b/third_party/dust3r/croco/datasets/habitat_sim/README.MD
new file mode 100644
index 0000000000000000000000000000000000000000..a505781ff9eb91bce7f1d189e848f8ba1c560940
--- /dev/null
+++ b/third_party/dust3r/croco/datasets/habitat_sim/README.MD
@@ -0,0 +1,76 @@
+## Generation of synthetic image pairs using Habitat-Sim
+
+These instructions allow to generate pre-training pairs from the Habitat simulator.
+As we did not save metadata of the pairs used in the original paper, they are not strictly the same, but these data use the same setting and are equivalent.
+
+### Download Habitat-Sim scenes
+Download Habitat-Sim scenes:
+- Download links can be found here: https://github.com/facebookresearch/habitat-sim/blob/main/DATASETS.md
+- We used scenes from the HM3D, habitat-test-scenes, Replica, ReplicaCad and ScanNet datasets.
+- Please put the scenes under `./data/habitat-sim-data/scene_datasets/` following the structure below, or update manually paths in `paths.py`.
+```
+./data/
+└──habitat-sim-data/
+ └──scene_datasets/
+ ├──hm3d/
+ ├──gibson/
+ ├──habitat-test-scenes/
+ ├──replica_cad_baked_lighting/
+ ├──replica_cad/
+ ├──ReplicaDataset/
+ └──scannet/
+```
+
+### Image pairs generation
+We provide metadata to generate reproducible images pairs for pretraining and validation.
+Experiments described in the paper used similar data, but whose generation was not reproducible at the time.
+
+Specifications:
+- 256x256 resolution images, with 60 degrees field of view .
+- Up to 1000 image pairs per scene.
+- Number of scenes considered/number of images pairs per dataset:
+ - Scannet: 1097 scenes / 985 209 pairs
+ - HM3D:
+ - hm3d/train: 800 / 800k pairs
+ - hm3d/val: 100 scenes / 100k pairs
+ - hm3d/minival: 10 scenes / 10k pairs
+ - habitat-test-scenes: 3 scenes / 3k pairs
+ - replica_cad_baked_lighting: 13 scenes / 13k pairs
+
+- Scenes from hm3d/val and hm3d/minival pairs were not used for the pre-training but kept for validation purposes.
+
+Download metadata and extract it:
+```bash
+mkdir -p data/habitat_release_metadata/
+cd data/habitat_release_metadata/
+wget https://download.europe.naverlabs.com/ComputerVision/CroCo/data/habitat_release_metadata/multiview_habitat_metadata.tar.gz
+tar -xvf multiview_habitat_metadata.tar.gz
+cd ../..
+# Location of the metadata
+METADATA_DIR="./data/habitat_release_metadata/multiview_habitat_metadata"
+```
+
+Generate image pairs from metadata:
+- The following command will print a list of commandlines to generate image pairs for each scene:
+```bash
+# Target output directory
+PAIRS_DATASET_DIR="./data/habitat_release/"
+python datasets/habitat_sim/generate_from_metadata_files.py --input_dir=$METADATA_DIR --output_dir=$PAIRS_DATASET_DIR
+```
+- One can launch multiple of such commands in parallel e.g. using GNU Parallel:
+```bash
+python datasets/habitat_sim/generate_from_metadata_files.py --input_dir=$METADATA_DIR --output_dir=$PAIRS_DATASET_DIR | parallel -j 16
+```
+
+## Metadata generation
+
+Image pairs were randomly sampled using the following commands, whose outputs contain randomness and are thus not exactly reproducible:
+```bash
+# Print commandlines to generate image pairs from the different scenes available.
+PAIRS_DATASET_DIR=MY_CUSTOM_PATH
+python datasets/habitat_sim/generate_multiview_images.py --list_commands --output_dir=$PAIRS_DATASET_DIR
+
+# Once a dataset is generated, pack metadata files for reproducibility.
+METADATA_DIR=MY_CUSTON_PATH
+python datasets/habitat_sim/pack_metadata_files.py $PAIRS_DATASET_DIR $METADATA_DIR
+```
diff --git a/imcui/third_party/gim/networks/lightglue/models/matchers/__init__.py b/third_party/dust3r/croco/datasets/habitat_sim/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/lightglue/models/matchers/__init__.py
rename to third_party/dust3r/croco/datasets/habitat_sim/__init__.py
diff --git a/imcui/third_party/dust3r/croco/datasets/habitat_sim/generate_from_metadata.py b/third_party/dust3r/croco/datasets/habitat_sim/generate_from_metadata.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/habitat_sim/generate_from_metadata.py
rename to third_party/dust3r/croco/datasets/habitat_sim/generate_from_metadata.py
diff --git a/imcui/third_party/dust3r/croco/datasets/habitat_sim/generate_from_metadata_files.py b/third_party/dust3r/croco/datasets/habitat_sim/generate_from_metadata_files.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/habitat_sim/generate_from_metadata_files.py
rename to third_party/dust3r/croco/datasets/habitat_sim/generate_from_metadata_files.py
diff --git a/imcui/third_party/dust3r/croco/datasets/habitat_sim/generate_multiview_images.py b/third_party/dust3r/croco/datasets/habitat_sim/generate_multiview_images.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/habitat_sim/generate_multiview_images.py
rename to third_party/dust3r/croco/datasets/habitat_sim/generate_multiview_images.py
diff --git a/imcui/third_party/dust3r/croco/datasets/habitat_sim/multiview_habitat_sim_generator.py b/third_party/dust3r/croco/datasets/habitat_sim/multiview_habitat_sim_generator.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/habitat_sim/multiview_habitat_sim_generator.py
rename to third_party/dust3r/croco/datasets/habitat_sim/multiview_habitat_sim_generator.py
diff --git a/imcui/third_party/dust3r/croco/datasets/habitat_sim/pack_metadata_files.py b/third_party/dust3r/croco/datasets/habitat_sim/pack_metadata_files.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/habitat_sim/pack_metadata_files.py
rename to third_party/dust3r/croco/datasets/habitat_sim/pack_metadata_files.py
diff --git a/imcui/third_party/dust3r/croco/datasets/habitat_sim/paths.py b/third_party/dust3r/croco/datasets/habitat_sim/paths.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/habitat_sim/paths.py
rename to third_party/dust3r/croco/datasets/habitat_sim/paths.py
diff --git a/imcui/third_party/dust3r/croco/datasets/pairs_dataset.py b/third_party/dust3r/croco/datasets/pairs_dataset.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/pairs_dataset.py
rename to third_party/dust3r/croco/datasets/pairs_dataset.py
diff --git a/imcui/third_party/dust3r/croco/datasets/transforms.py b/third_party/dust3r/croco/datasets/transforms.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/datasets/transforms.py
rename to third_party/dust3r/croco/datasets/transforms.py
diff --git a/imcui/third_party/dust3r/croco/demo.py b/third_party/dust3r/croco/demo.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/demo.py
rename to third_party/dust3r/croco/demo.py
diff --git a/third_party/dust3r/croco/interactive_demo.ipynb b/third_party/dust3r/croco/interactive_demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6cfc960af5baac9a69029c29a16eea4e24123a71
--- /dev/null
+++ b/third_party/dust3r/croco/interactive_demo.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Interactive demo of Cross-view Completion."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Copyright (C) 2022-present Naver Corporation. All rights reserved.\n",
+ "# Licensed under CC BY-NC-SA 4.0 (non-commercial use only)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "from models.croco import CroCoNet\n",
+ "from ipywidgets import interact, interactive, fixed, interact_manual\n",
+ "import ipywidgets as widgets\n",
+ "import matplotlib.pyplot as plt\n",
+ "import quaternion\n",
+ "import models.masking"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load CroCo model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ckpt = torch.load('pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth', 'cpu')\n",
+ "model = CroCoNet( **ckpt.get('croco_kwargs',{}))\n",
+ "msg = model.load_state_dict(ckpt['model'], strict=True)\n",
+ "use_gpu = torch.cuda.is_available() and torch.cuda.device_count()>0\n",
+ "device = torch.device('cuda:0' if use_gpu else 'cpu')\n",
+ "model = model.eval()\n",
+ "model = model.to(device=device)\n",
+ "print(msg)\n",
+ "\n",
+ "def process_images(ref_image, target_image, masking_ratio, reconstruct_unmasked_patches=False):\n",
+ " \"\"\"\n",
+ " Perform Cross-View completion using two input images, specified using Numpy arrays.\n",
+ " \"\"\"\n",
+ " # Replace the mask generator\n",
+ " model.mask_generator = models.masking.RandomMask(model.patch_embed.num_patches, masking_ratio)\n",
+ "\n",
+ " # ImageNet-1k color normalization\n",
+ " imagenet_mean = torch.as_tensor([0.485, 0.456, 0.406]).reshape(1,3,1,1).to(device)\n",
+ " imagenet_std = torch.as_tensor([0.229, 0.224, 0.225]).reshape(1,3,1,1).to(device)\n",
+ "\n",
+ " normalize_input_colors = True\n",
+ " is_output_normalized = True\n",
+ " with torch.no_grad():\n",
+ " # Cast data to torch\n",
+ " target_image = (torch.as_tensor(target_image, dtype=torch.float, device=device).permute(2,0,1) / 255)[None]\n",
+ " ref_image = (torch.as_tensor(ref_image, dtype=torch.float, device=device).permute(2,0,1) / 255)[None]\n",
+ "\n",
+ " if normalize_input_colors:\n",
+ " ref_image = (ref_image - imagenet_mean) / imagenet_std\n",
+ " target_image = (target_image - imagenet_mean) / imagenet_std\n",
+ "\n",
+ " out, mask, _ = model(target_image, ref_image)\n",
+ " # # get target\n",
+ " if not is_output_normalized:\n",
+ " predicted_image = model.unpatchify(out)\n",
+ " else:\n",
+ " # The output only contains higher order information,\n",
+ " # we retrieve mean and standard deviation from the actual target image\n",
+ " patchified = model.patchify(target_image)\n",
+ " mean = patchified.mean(dim=-1, keepdim=True)\n",
+ " var = patchified.var(dim=-1, keepdim=True)\n",
+ " pred_renorm = out * (var + 1.e-6)**.5 + mean\n",
+ " predicted_image = model.unpatchify(pred_renorm)\n",
+ "\n",
+ " image_masks = model.unpatchify(model.patchify(torch.ones_like(ref_image)) * mask[:,:,None])\n",
+ " masked_target_image = (1 - image_masks) * target_image\n",
+ " \n",
+ " if not reconstruct_unmasked_patches:\n",
+ " # Replace unmasked patches by their actual values\n",
+ " predicted_image = predicted_image * image_masks + masked_target_image\n",
+ "\n",
+ " # Unapply color normalization\n",
+ " if normalize_input_colors:\n",
+ " predicted_image = predicted_image * imagenet_std + imagenet_mean\n",
+ " masked_target_image = masked_target_image * imagenet_std + imagenet_mean\n",
+ " \n",
+ " # Cast to Numpy\n",
+ " masked_target_image = np.asarray(torch.clamp(masked_target_image.squeeze(0).permute(1,2,0) * 255, 0, 255).cpu().numpy(), dtype=np.uint8)\n",
+ " predicted_image = np.asarray(torch.clamp(predicted_image.squeeze(0).permute(1,2,0) * 255, 0, 255).cpu().numpy(), dtype=np.uint8)\n",
+ " return masked_target_image, predicted_image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Use the Habitat simulator to render images from arbitrary viewpoints (requires habitat_sim to be installed)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.environ[\"MAGNUM_LOG\"]=\"quiet\"\n",
+ "os.environ[\"HABITAT_SIM_LOG\"]=\"quiet\"\n",
+ "import habitat_sim\n",
+ "\n",
+ "scene = \"habitat-sim-data/scene_datasets/habitat-test-scenes/skokloster-castle.glb\"\n",
+ "navmesh = \"habitat-sim-data/scene_datasets/habitat-test-scenes/skokloster-castle.navmesh\"\n",
+ "\n",
+ "sim_cfg = habitat_sim.SimulatorConfiguration()\n",
+ "if use_gpu: sim_cfg.gpu_device_id = 0\n",
+ "sim_cfg.scene_id = scene\n",
+ "sim_cfg.load_semantic_mesh = False\n",
+ "rgb_sensor_spec = habitat_sim.CameraSensorSpec()\n",
+ "rgb_sensor_spec.uuid = \"color\"\n",
+ "rgb_sensor_spec.sensor_type = habitat_sim.SensorType.COLOR\n",
+ "rgb_sensor_spec.resolution = (224,224)\n",
+ "rgb_sensor_spec.hfov = 56.56\n",
+ "rgb_sensor_spec.position = [0.0, 0.0, 0.0]\n",
+ "rgb_sensor_spec.orientation = [0, 0, 0]\n",
+ "agent_cfg = habitat_sim.agent.AgentConfiguration(sensor_specifications=[rgb_sensor_spec])\n",
+ "\n",
+ "\n",
+ "cfg = habitat_sim.Configuration(sim_cfg, [agent_cfg])\n",
+ "sim = habitat_sim.Simulator(cfg)\n",
+ "if navmesh is not None:\n",
+ " sim.pathfinder.load_nav_mesh(navmesh)\n",
+ "agent = sim.initialize_agent(agent_id=0)\n",
+ "\n",
+ "def sample_random_viewpoint():\n",
+ " \"\"\" Sample a random viewpoint using the navmesh \"\"\"\n",
+ " nav_point = sim.pathfinder.get_random_navigable_point()\n",
+ " # Sample a random viewpoint height\n",
+ " viewpoint_height = np.random.uniform(1.0, 1.6)\n",
+ " viewpoint_position = nav_point + viewpoint_height * habitat_sim.geo.UP\n",
+ " viewpoint_orientation = quaternion.from_rotation_vector(np.random.uniform(-np.pi, np.pi) * habitat_sim.geo.UP)\n",
+ " return viewpoint_position, viewpoint_orientation\n",
+ "\n",
+ "def render_viewpoint(position, orientation):\n",
+ " agent_state = habitat_sim.AgentState()\n",
+ " agent_state.position = position\n",
+ " agent_state.rotation = orientation\n",
+ " agent.set_state(agent_state)\n",
+ " viewpoint_observations = sim.get_sensor_observations(agent_ids=0)\n",
+ " image = viewpoint_observations['color'][:,:,:3]\n",
+ " image = np.asarray(np.clip(1.5 * np.asarray(image, dtype=float), 0, 255), dtype=np.uint8)\n",
+ " return image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sample a random reference view"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ref_position, ref_orientation = sample_random_viewpoint()\n",
+ "ref_image = render_viewpoint(ref_position, ref_orientation)\n",
+ "plt.clf()\n",
+ "fig, axes = plt.subplots(1,1, squeeze=False, num=1)\n",
+ "axes[0,0].imshow(ref_image)\n",
+ "for ax in axes.flatten():\n",
+ " ax.set_xticks([])\n",
+ " ax.set_yticks([])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interactive cross-view completion using CroCo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reconstruct_unmasked_patches = False\n",
+ "\n",
+ "def show_demo(masking_ratio, x, y, z, panorama, elevation):\n",
+ " R = quaternion.as_rotation_matrix(ref_orientation)\n",
+ " target_position = ref_position + x * R[:,0] + y * R[:,1] + z * R[:,2]\n",
+ " target_orientation = (ref_orientation\n",
+ " * quaternion.from_rotation_vector(-elevation * np.pi/180 * habitat_sim.geo.LEFT) \n",
+ " * quaternion.from_rotation_vector(-panorama * np.pi/180 * habitat_sim.geo.UP))\n",
+ " \n",
+ " ref_image = render_viewpoint(ref_position, ref_orientation)\n",
+ " target_image = render_viewpoint(target_position, target_orientation)\n",
+ "\n",
+ " masked_target_image, predicted_image = process_images(ref_image, target_image, masking_ratio, reconstruct_unmasked_patches)\n",
+ "\n",
+ " fig, axes = plt.subplots(1,4, squeeze=True, dpi=300)\n",
+ " axes[0].imshow(ref_image)\n",
+ " axes[0].set_xlabel(\"Reference\")\n",
+ " axes[1].imshow(masked_target_image)\n",
+ " axes[1].set_xlabel(\"Masked target\")\n",
+ " axes[2].imshow(predicted_image)\n",
+ " axes[2].set_xlabel(\"Reconstruction\") \n",
+ " axes[3].imshow(target_image)\n",
+ " axes[3].set_xlabel(\"Target\")\n",
+ " for ax in axes.flatten():\n",
+ " ax.set_xticks([])\n",
+ " ax.set_yticks([])\n",
+ "\n",
+ "interact(show_demo,\n",
+ " masking_ratio=widgets.FloatSlider(description='masking', value=0.9, min=0.0, max=1.0),\n",
+ " x=widgets.FloatSlider(value=0.0, min=-0.5, max=0.5, step=0.05),\n",
+ " y=widgets.FloatSlider(value=0.0, min=-0.5, max=0.5, step=0.05),\n",
+ " z=widgets.FloatSlider(value=0.0, min=-0.5, max=0.5, step=0.05),\n",
+ " panorama=widgets.FloatSlider(value=0.0, min=-20, max=20, step=0.5),\n",
+ " elevation=widgets.FloatSlider(value=0.0, min=-20, max=20, step=0.5));"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.13"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "f9237820cd248d7e07cb4fb9f0e4508a85d642f19d831560c0a4b61f3e907e67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/imcui/third_party/dust3r/croco/models/blocks.py b/third_party/dust3r/croco/models/blocks.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/blocks.py
rename to third_party/dust3r/croco/models/blocks.py
diff --git a/imcui/third_party/dust3r/croco/models/criterion.py b/third_party/dust3r/croco/models/criterion.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/criterion.py
rename to third_party/dust3r/croco/models/criterion.py
diff --git a/imcui/third_party/dust3r/croco/models/croco.py b/third_party/dust3r/croco/models/croco.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/croco.py
rename to third_party/dust3r/croco/models/croco.py
diff --git a/imcui/third_party/dust3r/croco/models/croco_downstream.py b/third_party/dust3r/croco/models/croco_downstream.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/croco_downstream.py
rename to third_party/dust3r/croco/models/croco_downstream.py
diff --git a/imcui/third_party/dust3r/croco/models/curope/__init__.py b/third_party/dust3r/croco/models/curope/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/curope/__init__.py
rename to third_party/dust3r/croco/models/curope/__init__.py
diff --git a/third_party/dust3r/croco/models/curope/curope.cpp b/third_party/dust3r/croco/models/curope/curope.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..8fe9058e05aa1bf3f37b0d970edc7312bc68455b
--- /dev/null
+++ b/third_party/dust3r/croco/models/curope/curope.cpp
@@ -0,0 +1,69 @@
+/*
+ Copyright (C) 2022-present Naver Corporation. All rights reserved.
+ Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+*/
+
+#include
+
+// forward declaration
+void rope_2d_cuda( torch::Tensor tokens, const torch::Tensor pos, const float base, const float fwd );
+
+void rope_2d_cpu( torch::Tensor tokens, const torch::Tensor positions, const float base, const float fwd )
+{
+ const int B = tokens.size(0);
+ const int N = tokens.size(1);
+ const int H = tokens.size(2);
+ const int D = tokens.size(3) / 4;
+
+ auto tok = tokens.accessor();
+ auto pos = positions.accessor();
+
+ for (int b = 0; b < B; b++) {
+ for (int x = 0; x < 2; x++) { // y and then x (2d)
+ for (int n = 0; n < N; n++) {
+
+ // grab the token position
+ const int p = pos[b][n][x];
+
+ for (int h = 0; h < H; h++) {
+ for (int d = 0; d < D; d++) {
+ // grab the two values
+ float u = tok[b][n][h][d+0+x*2*D];
+ float v = tok[b][n][h][d+D+x*2*D];
+
+ // grab the cos,sin
+ const float inv_freq = fwd * p / powf(base, d/float(D));
+ float c = cosf(inv_freq);
+ float s = sinf(inv_freq);
+
+ // write the result
+ tok[b][n][h][d+0+x*2*D] = u*c - v*s;
+ tok[b][n][h][d+D+x*2*D] = v*c + u*s;
+ }
+ }
+ }
+ }
+ }
+}
+
+void rope_2d( torch::Tensor tokens, // B,N,H,D
+ const torch::Tensor positions, // B,N,2
+ const float base,
+ const float fwd )
+{
+ TORCH_CHECK(tokens.dim() == 4, "tokens must have 4 dimensions");
+ TORCH_CHECK(positions.dim() == 3, "positions must have 3 dimensions");
+ TORCH_CHECK(tokens.size(0) == positions.size(0), "batch size differs between tokens & positions");
+ TORCH_CHECK(tokens.size(1) == positions.size(1), "seq_length differs between tokens & positions");
+ TORCH_CHECK(positions.size(2) == 2, "positions.shape[2] must be equal to 2");
+ TORCH_CHECK(tokens.is_cuda() == positions.is_cuda(), "tokens and positions are not on the same device" );
+
+ if (tokens.is_cuda())
+ rope_2d_cuda( tokens, positions, base, fwd );
+ else
+ rope_2d_cpu( tokens, positions, base, fwd );
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("rope_2d", &rope_2d, "RoPE 2d forward/backward");
+}
diff --git a/imcui/third_party/dust3r/croco/models/curope/curope2d.py b/third_party/dust3r/croco/models/curope/curope2d.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/curope/curope2d.py
rename to third_party/dust3r/croco/models/curope/curope2d.py
diff --git a/third_party/dust3r/croco/models/curope/kernels.cu b/third_party/dust3r/croco/models/curope/kernels.cu
new file mode 100644
index 0000000000000000000000000000000000000000..7156cd1bb935cb1f0be45e58add53f9c21505c20
--- /dev/null
+++ b/third_party/dust3r/croco/models/curope/kernels.cu
@@ -0,0 +1,108 @@
+/*
+ Copyright (C) 2022-present Naver Corporation. All rights reserved.
+ Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+*/
+
+#include
+#include
+#include
+#include
+
+#define CHECK_CUDA(tensor) {\
+ TORCH_CHECK((tensor).is_cuda(), #tensor " is not in cuda memory"); \
+ TORCH_CHECK((tensor).is_contiguous(), #tensor " is not contiguous"); }
+void CHECK_KERNEL() {auto error = cudaGetLastError(); TORCH_CHECK( error == cudaSuccess, cudaGetErrorString(error));}
+
+
+template < typename scalar_t >
+__global__ void rope_2d_cuda_kernel(
+ //scalar_t* __restrict__ tokens,
+ torch::PackedTensorAccessor32 tokens,
+ const int64_t* __restrict__ pos,
+ const float base,
+ const float fwd )
+ // const int N, const int H, const int D )
+{
+ // tokens shape = (B, N, H, D)
+ const int N = tokens.size(1);
+ const int H = tokens.size(2);
+ const int D = tokens.size(3);
+
+ // each block update a single token, for all heads
+ // each thread takes care of a single output
+ extern __shared__ float shared[];
+ float* shared_inv_freq = shared + D;
+
+ const int b = blockIdx.x / N;
+ const int n = blockIdx.x % N;
+
+ const int Q = D / 4;
+ // one token = [0..Q : Q..2Q : 2Q..3Q : 3Q..D]
+ // u_Y v_Y u_X v_X
+
+ // shared memory: first, compute inv_freq
+ if (threadIdx.x < Q)
+ shared_inv_freq[threadIdx.x] = fwd / powf(base, threadIdx.x/float(Q));
+ __syncthreads();
+
+ // start of X or Y part
+ const int X = threadIdx.x < D/2 ? 0 : 1;
+ const int m = (X*D/2) + (threadIdx.x % Q); // index of u_Y or u_X
+
+ // grab the cos,sin appropriate for me
+ const float freq = pos[blockIdx.x*2+X] * shared_inv_freq[threadIdx.x % Q];
+ const float cos = cosf(freq);
+ const float sin = sinf(freq);
+ /*
+ float* shared_cos_sin = shared + D + D/4;
+ if ((threadIdx.x % (D/2)) < Q)
+ shared_cos_sin[m+0] = cosf(freq);
+ else
+ shared_cos_sin[m+Q] = sinf(freq);
+ __syncthreads();
+ const float cos = shared_cos_sin[m+0];
+ const float sin = shared_cos_sin[m+Q];
+ */
+
+ for (int h = 0; h < H; h++)
+ {
+ // then, load all the token for this head in shared memory
+ shared[threadIdx.x] = tokens[b][n][h][threadIdx.x];
+ __syncthreads();
+
+ const float u = shared[m];
+ const float v = shared[m+Q];
+
+ // write output
+ if ((threadIdx.x % (D/2)) < Q)
+ tokens[b][n][h][threadIdx.x] = u*cos - v*sin;
+ else
+ tokens[b][n][h][threadIdx.x] = v*cos + u*sin;
+ }
+}
+
+void rope_2d_cuda( torch::Tensor tokens, const torch::Tensor pos, const float base, const float fwd )
+{
+ const int B = tokens.size(0); // batch size
+ const int N = tokens.size(1); // sequence length
+ const int H = tokens.size(2); // number of heads
+ const int D = tokens.size(3); // dimension per head
+
+ TORCH_CHECK(tokens.stride(3) == 1 && tokens.stride(2) == D, "tokens are not contiguous");
+ TORCH_CHECK(pos.is_contiguous(), "positions are not contiguous");
+ TORCH_CHECK(pos.size(0) == B && pos.size(1) == N && pos.size(2) == 2, "bad pos.shape");
+ TORCH_CHECK(D % 4 == 0, "token dim must be multiple of 4");
+
+ // one block for each layer, one thread per local-max
+ const int THREADS_PER_BLOCK = D;
+ const int N_BLOCKS = B * N; // each block takes care of H*D values
+ const int SHARED_MEM = sizeof(float) * (D + D/4);
+
+ AT_DISPATCH_FLOATING_TYPES_AND_HALF(tokens.type(), "rope_2d_cuda", ([&] {
+ rope_2d_cuda_kernel <<>> (
+ //tokens.data_ptr(),
+ tokens.packed_accessor32(),
+ pos.data_ptr(),
+ base, fwd); //, N, H, D );
+ }));
+}
diff --git a/imcui/third_party/dust3r/croco/models/curope/setup.py b/third_party/dust3r/croco/models/curope/setup.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/curope/setup.py
rename to third_party/dust3r/croco/models/curope/setup.py
diff --git a/imcui/third_party/dust3r/croco/models/dpt_block.py b/third_party/dust3r/croco/models/dpt_block.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/dpt_block.py
rename to third_party/dust3r/croco/models/dpt_block.py
diff --git a/imcui/third_party/dust3r/croco/models/head_downstream.py b/third_party/dust3r/croco/models/head_downstream.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/head_downstream.py
rename to third_party/dust3r/croco/models/head_downstream.py
diff --git a/imcui/third_party/dust3r/croco/models/masking.py b/third_party/dust3r/croco/models/masking.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/masking.py
rename to third_party/dust3r/croco/models/masking.py
diff --git a/imcui/third_party/dust3r/croco/models/pos_embed.py b/third_party/dust3r/croco/models/pos_embed.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/models/pos_embed.py
rename to third_party/dust3r/croco/models/pos_embed.py
diff --git a/imcui/third_party/dust3r/croco/pretrain.py b/third_party/dust3r/croco/pretrain.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/pretrain.py
rename to third_party/dust3r/croco/pretrain.py
diff --git a/third_party/dust3r/croco/stereoflow/README.MD b/third_party/dust3r/croco/stereoflow/README.MD
new file mode 100644
index 0000000000000000000000000000000000000000..81595380fadd274b523e0cf77921b1b65cbedb34
--- /dev/null
+++ b/third_party/dust3r/croco/stereoflow/README.MD
@@ -0,0 +1,318 @@
+## CroCo-Stereo and CroCo-Flow
+
+This README explains how to use CroCo-Stereo and CroCo-Flow as well as how they were trained.
+All commands should be launched from the root directory.
+
+### Simple inference example
+
+We provide a simple inference exemple for CroCo-Stereo and CroCo-Flow in the Totebook `croco-stereo-flow-demo.ipynb`.
+Before running it, please download the trained models with:
+```
+bash stereoflow/download_model.sh crocostereo.pth
+bash stereoflow/download_model.sh crocoflow.pth
+```
+
+### Prepare data for training or evaluation
+
+Put the datasets used for training/evaluation in `./data/stereoflow` (or update the paths at the top of `stereoflow/datasets_stereo.py` and `stereoflow/datasets_flow.py`).
+Please find below on the file structure should look for each dataset:
+
+FlyingChairs
+
+```
+./data/stereoflow/FlyingChairs/
+└───chairs_split.txt
+└───data/
+ └─── ...
+```
+
+
+
+MPI-Sintel
+
+```
+./data/stereoflow/MPI-Sintel/
+└───training/
+│ └───clean/
+│ └───final/
+│ └───flow/
+└───test/
+ └───clean/
+ └───final/
+```
+
+
+
+SceneFlow (including FlyingThings)
+
+```
+./data/stereoflow/SceneFlow/
+└───Driving/
+│ └───disparity/
+│ └───frames_cleanpass/
+│ └───frames_finalpass/
+└───FlyingThings/
+│ └───disparity/
+│ └───frames_cleanpass/
+│ └───frames_finalpass/
+│ └───optical_flow/
+└───Monkaa/
+ └───disparity/
+ └───frames_cleanpass/
+ └───frames_finalpass/
+```
+
+
+
+TartanAir
+
+```
+./data/stereoflow/TartanAir/
+└───abandonedfactory/
+│ └───.../
+└───abandonedfactory_night/
+│ └───.../
+└───.../
+```
+
+
+
+Booster
+
+```
+./data/stereoflow/booster_gt/
+└───train/
+ └───balanced/
+ └───Bathroom/
+ └───Bedroom/
+ └───...
+```
+
+
+
+CREStereo
+
+```
+./data/stereoflow/crenet_stereo_trainset/
+└───stereo_trainset/
+ └───crestereo/
+ └───hole/
+ └───reflective/
+ └───shapenet/
+ └───tree/
+```
+
+
+
+ETH3D Two-view Low-res
+
+```
+./data/stereoflow/eth3d_lowres/
+└───test/
+│ └───lakeside_1l/
+│ └───...
+└───train/
+│ └───delivery_area_1l/
+│ └───...
+└───train_gt/
+ └───delivery_area_1l/
+ └───...
+```
+
+
+
+KITTI 2012
+
+```
+./data/stereoflow/kitti-stereo-2012/
+└───testing/
+│ └───colored_0/
+│ └───colored_1/
+└───training/
+ └───colored_0/
+ └───colored_1/
+ └───disp_occ/
+ └───flow_occ/
+```
+
+
+
+KITTI 2015
+
+```
+./data/stereoflow/kitti-stereo-2015/
+└───testing/
+│ └───image_2/
+│ └───image_3/
+└───training/
+ └───image_2/
+ └───image_3/
+ └───disp_occ_0/
+ └───flow_occ/
+```
+
+
+
+Middlebury
+
+```
+./data/stereoflow/middlebury
+└───2005/
+│ └───train/
+│ └───Art/
+│ └───...
+└───2006/
+│ └───Aloe/
+│ └───Baby1/
+│ └───...
+└───2014/
+│ └───Adirondack-imperfect/
+│ └───Adirondack-perfect/
+│ └───...
+└───2021/
+│ └───data/
+│ └───artroom1/
+│ └───artroom2/
+│ └───...
+└───MiddEval3_F/
+ └───test/
+ │ └───Australia/
+ │ └───...
+ └───train/
+ └───Adirondack/
+ └───...
+```
+
+
+
+Spring
+
+```
+./data/stereoflow/spring/
+└───test/
+│ └───0003/
+│ └───...
+└───train/
+ └───0001/
+ └───...
+```
+
+
+
+### CroCo-Stereo
+
+##### Main model
+
+The main training of CroCo-Stereo was performed on a series of datasets, and it was used as it for Middlebury v3 benchmark.
+
+```
+# Download the model
+bash stereoflow/download_model.sh crocostereo.pth
+# Middlebury v3 submission
+python stereoflow/test.py --model stereoflow_models/crocostereo.pth --dataset "MdEval3('all_full')" --save submission --tile_overlap 0.9
+# Training command that was used, using checkpoint-last.pth
+python -u stereoflow/train.py stereo --criterion "LaplacianLossBounded2()" --dataset "CREStereo('train')+SceneFlow('train_allpass')+30*ETH3DLowRes('train')+50*Md05('train')+50*Md06('train')+50*Md14('train')+50*Md21('train')+50*MdEval3('train_full')+Booster('train_balanced')" --val_dataset "SceneFlow('test1of100_finalpass')+SceneFlow('test1of100_cleanpass')+ETH3DLowRes('subval')+Md05('subval')+Md06('subval')+Md14('subval')+Md21('subval')+MdEval3('subval_full')+Booster('subval_balanced')" --lr 3e-5 --batch_size 6 --epochs 32 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --output_dir xps/crocostereo/main/
+# or it can be launched on multiple gpus (while maintaining the effective batch size), e.g. on 3 gpus:
+torchrun --nproc_per_node 3 stereoflow/train.py stereo --criterion "LaplacianLossBounded2()" --dataset "CREStereo('train')+SceneFlow('train_allpass')+30*ETH3DLowRes('train')+50*Md05('train')+50*Md06('train')+50*Md14('train')+50*Md21('train')+50*MdEval3('train_full')+Booster('train_balanced')" --val_dataset "SceneFlow('test1of100_finalpass')+SceneFlow('test1of100_cleanpass')+ETH3DLowRes('subval')+Md05('subval')+Md06('subval')+Md14('subval')+Md21('subval')+MdEval3('subval_full')+Booster('subval_balanced')" --lr 3e-5 --batch_size 2 --epochs 32 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --output_dir xps/crocostereo/main/
+```
+
+For evaluation of validation set, we also provide the model trained on the `subtrain` subset of the training sets.
+
+```
+# Download the model
+bash stereoflow/download_model.sh crocostereo_subtrain.pth
+# Evaluation on validation sets
+python stereoflow/test.py --model stereoflow_models/crocostereo_subtrain.pth --dataset "MdEval3('subval_full')+ETH3DLowRes('subval')+SceneFlow('test_finalpass')+SceneFlow('test_cleanpass')" --save metrics --tile_overlap 0.9
+# Training command that was used (same as above but on subtrain, using checkpoint-best.pth), can also be launched on multiple gpus
+python -u stereoflow/train.py stereo --criterion "LaplacianLossBounded2()" --dataset "CREStereo('train')+SceneFlow('train_allpass')+30*ETH3DLowRes('subtrain')+50*Md05('subtrain')+50*Md06('subtrain')+50*Md14('subtrain')+50*Md21('subtrain')+50*MdEval3('subtrain_full')+Booster('subtrain_balanced')" --val_dataset "SceneFlow('test1of100_finalpass')+SceneFlow('test1of100_cleanpass')+ETH3DLowRes('subval')+Md05('subval')+Md06('subval')+Md14('subval')+Md21('subval')+MdEval3('subval_full')+Booster('subval_balanced')" --lr 3e-5 --batch_size 6 --epochs 32 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --output_dir xps/crocostereo/main_subtrain/
+```
+
+##### Other models
+
+
+ Model for ETH3D
+ The model used for the submission on ETH3D is trained with the same command but using an unbounded Laplacian loss.
+
+ # Download the model
+ bash stereoflow/download_model.sh crocostereo_eth3d.pth
+ # ETH3D submission
+ python stereoflow/test.py --model stereoflow_models/crocostereo_eth3d.pth --dataset "ETH3DLowRes('all')" --save submission --tile_overlap 0.9
+ # Training command that was used
+ python -u stereoflow/train.py stereo --criterion "LaplacianLoss()" --tile_conf_mode conf_expbeta3 --dataset "CREStereo('train')+SceneFlow('train_allpass')+30*ETH3DLowRes('train')+50*Md05('train')+50*Md06('train')+50*Md14('train')+50*Md21('train')+50*MdEval3('train_full')+Booster('train_balanced')" --val_dataset "SceneFlow('test1of100_finalpass')+SceneFlow('test1of100_cleanpass')+ETH3DLowRes('subval')+Md05('subval')+Md06('subval')+Md14('subval')+Md21('subval')+MdEval3('subval_full')+Booster('subval_balanced')" --lr 3e-5 --batch_size 6 --epochs 32 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --output_dir xps/crocostereo/main_eth3d/
+
+
+
+
+ Main model finetuned on Kitti
+
+ # Download the model
+ bash stereoflow/download_model.sh crocostereo_finetune_kitti.pth
+ # Kitti submission
+ python stereoflow/test.py --model stereoflow_models/crocostereo_finetune_kitti.pth --dataset "Kitti15('test')" --save submission --tile_overlap 0.9
+ # Training that was used
+ python -u stereoflow/train.py stereo --crop 352 1216 --criterion "LaplacianLossBounded2()" --dataset "Kitti12('train')+Kitti15('train')" --lr 3e-5 --batch_size 1 --accum_iter 6 --epochs 20 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --start_from stereoflow_models/crocostereo.pth --output_dir xps/crocostereo/finetune_kitti/ --save_every 5
+
+
+
+ Main model finetuned on Spring
+
+ # Download the model
+ bash stereoflow/download_model.sh crocostereo_finetune_spring.pth
+ # Spring submission
+ python stereoflow/test.py --model stereoflow_models/crocostereo_finetune_spring.pth --dataset "Spring('test')" --save submission --tile_overlap 0.9
+ # Training command that was used
+ python -u stereoflow/train.py stereo --criterion "LaplacianLossBounded2()" --dataset "Spring('train')" --lr 3e-5 --batch_size 6 --epochs 8 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --start_from stereoflow_models/crocostereo.pth --output_dir xps/crocostereo/finetune_spring/
+
+
+
+ Smaller models
+ To train CroCo-Stereo with smaller CroCo pretrained models, simply replace the --pretrained argument. To download the smaller CroCo-Stereo models based on CroCo v2 pretraining with ViT-Base encoder and Small encoder, use bash stereoflow/download_model.sh crocostereo_subtrain_vitb_smalldecoder.pth, and for the model with a ViT-Base encoder and a Base decoder, use bash stereoflow/download_model.sh crocostereo_subtrain_vitb_basedecoder.pth.
+
+
+
+### CroCo-Flow
+
+##### Main model
+
+The main training of CroCo-Flow was performed on the FlyingThings, FlyingChairs, MPI-Sintel and TartanAir datasets.
+It was used for our submission to the MPI-Sintel benchmark.
+
+```
+# Download the model
+bash stereoflow/download_model.sh crocoflow.pth
+# Evaluation
+python stereoflow/test.py --model stereoflow_models/crocoflow.pth --dataset "MPISintel('subval_cleanpass')+MPISintel('subval_finalpass')" --save metrics --tile_overlap 0.9
+# Sintel submission
+python stereoflow/test.py --model stereoflow_models/crocoflow.pth --dataset "MPISintel('test_allpass')" --save submission --tile_overlap 0.9
+# Training command that was used, with checkpoint-best.pth
+python -u stereoflow/train.py flow --criterion "LaplacianLossBounded()" --dataset "40*MPISintel('subtrain_cleanpass')+40*MPISintel('subtrain_finalpass')+4*FlyingThings('train_allpass')+4*FlyingChairs('train')+TartanAir('train')" --val_dataset "MPISintel('subval_cleanpass')+MPISintel('subval_finalpass')" --lr 2e-5 --batch_size 8 --epochs 240 --img_per_epoch 30000 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --output_dir xps/crocoflow/main/
+```
+
+##### Other models
+
+
+ Main model finetuned on Kitti
+
+ # Download the model
+ bash stereoflow/download_model.sh crocoflow_finetune_kitti.pth
+ # Kitti submission
+ python stereoflow/test.py --model stereoflow_models/crocoflow_finetune_kitti.pth --dataset "Kitti15('test')" --save submission --tile_overlap 0.99
+ # Training that was used, with checkpoint-last.pth
+ python -u stereoflow/train.py flow --crop 352 1216 --criterion "LaplacianLossBounded()" --dataset "Kitti15('train')+Kitti12('train')" --lr 2e-5 --batch_size 1 --accum_iter 8 --epochs 150 --save_every 5 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --start_from stereoflow_models/crocoflow.pth --output_dir xps/crocoflow/finetune_kitti/
+
+
+
+ Main model finetuned on Spring
+
+ # Download the model
+ bash stereoflow/download_model.sh crocoflow_finetune_spring.pth
+ # Spring submission
+ python stereoflow/test.py --model stereoflow_models/crocoflow_finetune_spring.pth --dataset "Spring('test')" --save submission --tile_overlap 0.9
+ # Training command that was used, with checkpoint-last.pth
+ python -u stereoflow/train.py flow --criterion "LaplacianLossBounded()" --dataset "Spring('train')" --lr 2e-5 --batch_size 8 --epochs 12 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --start_from stereoflow_models/crocoflow.pth --output_dir xps/crocoflow/finetune_spring/
+
+
+
+ Smaller models
+ To train CroCo-Flow with smaller CroCo pretrained models, simply replace the --pretrained argument. To download the smaller CroCo-Flow models based on CroCo v2 pretraining with ViT-Base encoder and Small encoder, use bash stereoflow/download_model.sh crocoflow_vitb_smalldecoder.pth, and for the model with a ViT-Base encoder and a Base decoder, use bash stereoflow/download_model.sh crocoflow_vitb_basedecoder.pth.
+
diff --git a/imcui/third_party/dust3r/croco/stereoflow/augmentor.py b/third_party/dust3r/croco/stereoflow/augmentor.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/stereoflow/augmentor.py
rename to third_party/dust3r/croco/stereoflow/augmentor.py
diff --git a/imcui/third_party/dust3r/croco/stereoflow/criterion.py b/third_party/dust3r/croco/stereoflow/criterion.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/stereoflow/criterion.py
rename to third_party/dust3r/croco/stereoflow/criterion.py
diff --git a/imcui/third_party/dust3r/croco/stereoflow/datasets_flow.py b/third_party/dust3r/croco/stereoflow/datasets_flow.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/stereoflow/datasets_flow.py
rename to third_party/dust3r/croco/stereoflow/datasets_flow.py
diff --git a/imcui/third_party/dust3r/croco/stereoflow/datasets_stereo.py b/third_party/dust3r/croco/stereoflow/datasets_stereo.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/stereoflow/datasets_stereo.py
rename to third_party/dust3r/croco/stereoflow/datasets_stereo.py
diff --git a/third_party/dust3r/croco/stereoflow/download_model.sh b/third_party/dust3r/croco/stereoflow/download_model.sh
new file mode 100644
index 0000000000000000000000000000000000000000..533119609108c5ec3c22ff79b10e9215c1ac5098
--- /dev/null
+++ b/third_party/dust3r/croco/stereoflow/download_model.sh
@@ -0,0 +1,12 @@
+# Copyright (C) 2022-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+
+model=$1
+outfile="stereoflow_models/${model}"
+if [[ ! -f $outfile ]]
+then
+ mkdir -p stereoflow_models/;
+ wget https://download.europe.naverlabs.com/ComputerVision/CroCo/StereoFlow_models/$1 -P stereoflow_models/;
+else
+ echo "Model ${model} already downloaded in ${outfile}."
+fi
\ No newline at end of file
diff --git a/imcui/third_party/dust3r/croco/stereoflow/engine.py b/third_party/dust3r/croco/stereoflow/engine.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/stereoflow/engine.py
rename to third_party/dust3r/croco/stereoflow/engine.py
diff --git a/imcui/third_party/dust3r/croco/stereoflow/test.py b/third_party/dust3r/croco/stereoflow/test.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/stereoflow/test.py
rename to third_party/dust3r/croco/stereoflow/test.py
diff --git a/imcui/third_party/dust3r/croco/stereoflow/train.py b/third_party/dust3r/croco/stereoflow/train.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/stereoflow/train.py
rename to third_party/dust3r/croco/stereoflow/train.py
diff --git a/imcui/third_party/dust3r/croco/utils/misc.py b/third_party/dust3r/croco/utils/misc.py
similarity index 100%
rename from imcui/third_party/dust3r/croco/utils/misc.py
rename to third_party/dust3r/croco/utils/misc.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/path_to_root.py b/third_party/dust3r/datasets_preprocess/path_to_root.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/path_to_root.py
rename to third_party/dust3r/datasets_preprocess/path_to_root.py
diff --git a/imcui/third_party/mast3r/dust3r/datasets_preprocess/preprocess_co3d.py b/third_party/dust3r/datasets_preprocess/preprocess_co3d.py
similarity index 98%
rename from imcui/third_party/mast3r/dust3r/datasets_preprocess/preprocess_co3d.py
rename to third_party/dust3r/datasets_preprocess/preprocess_co3d.py
index e287b85ddf8791bd6f2d53a6992d13b916c209b6..27b2eee0a954f5cec539cba35ce69af6a8c0d77f 100644
--- a/imcui/third_party/mast3r/dust3r/datasets_preprocess/preprocess_co3d.py
+++ b/third_party/dust3r/datasets_preprocess/preprocess_co3d.py
@@ -36,7 +36,7 @@ CATEGORIES = [
"mouse", "orange", "parkingmeter", "pizza", "plant", "remote", "sandwich",
"skateboard", "stopsign",
"suitcase", "teddybear", "toaster", "toilet", "toybus",
- "toyplane", "toytrain", "toytruck", "tv",
+ "toyplane", "toytrain", "toytruck", "tv",
"umbrella", "vase", "wineglass",
]
CATEGORIES_IDX = {cat: i for i, cat in enumerate(CATEGORIES)} # for seeding
@@ -199,8 +199,8 @@ def prepare_sequences(category, co3d_dir, output_dir, img_size, split, min_quali
camera_intrinsics = camera_intrinsics.numpy()
cx, cy = camera_intrinsics[:2, 2].round().astype(int)
- min_margin_x = min(cx, W - cx)
- min_margin_y = min(cy, H - cy)
+ min_margin_x = min(cx, W-cx)
+ min_margin_y = min(cy, H-cy)
# the new window will be a rectangle of size (2*min_margin_x, 2*min_margin_y) centered on (cx,cy)
l, t = cx - min_margin_x, cy - min_margin_y
@@ -225,7 +225,7 @@ def prepare_sequences(category, co3d_dir, output_dir, img_size, split, min_quali
# generate and adjust camera pose
camera_pose = np.eye(4, dtype=np.float32)
camera_pose[:3, :3] = R
- camera_pose[:3, 3] = tvec
+ camera_pose[:3, 3] = tvec
camera_pose = np.linalg.inv(camera_pose)
# save crop images and depth, metadata
diff --git a/imcui/third_party/dust3r/dust3r/demo.py b/third_party/dust3r/demo.py
similarity index 88%
rename from imcui/third_party/dust3r/dust3r/demo.py
rename to third_party/dust3r/demo.py
index c491be097b71ec38ea981dadf4f456d6e9829d48..c57d6d27c985f175c247803ab5875f87d8e8cbd8 100644
--- a/imcui/third_party/dust3r/dust3r/demo.py
+++ b/third_party/dust3r/demo.py
@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
@@ -6,18 +7,18 @@
# --------------------------------------------------------
import argparse
import math
-import builtins
-import datetime
import gradio
import os
import torch
import numpy as np
+import tempfile
import functools
import trimesh
import copy
from scipy.spatial.transform import Rotation
from dust3r.inference import inference
+from dust3r.model import AsymmetricCroCo3DStereo
from dust3r.image_pairs import make_pairs
from dust3r.utils.image import load_images, rgb
from dust3r.utils.device import to_numpy
@@ -25,6 +26,10 @@ from dust3r.viz import add_scene_cam, CAM_COLORS, OPENGL, pts3d_to_trimesh, cat_
from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
import matplotlib.pyplot as pl
+pl.ion()
+
+torch.backends.cuda.matmul.allow_tf32 = True # for gpu >= Ampere and pytorch >= 1.12
+batch_size = 1
def get_args_parser():
@@ -50,19 +55,6 @@ def get_args_parser():
return parser
-def set_print_with_timestamp(time_format="%Y-%m-%d %H:%M:%S"):
- builtin_print = builtins.print
-
- def print_with_timestamp(*args, **kwargs):
- now = datetime.datetime.now()
- formatted_date_time = now.strftime(time_format)
-
- builtin_print(f'[{formatted_date_time}] ', end='') # print with time stamp
- builtin_print(*args, **kwargs)
-
- builtins.print = print_with_timestamp
-
-
def _convert_scene_output_to_glb(outdir, imgs, pts3d, mask, focals, cams2world, cam_size=0.05,
cam_color=None, as_pointcloud=False,
transparent_cams=False, silent=False):
@@ -149,7 +141,7 @@ def get_reconstructed_scene(outdir, model, device, silent, image_size, filelist,
scenegraph_type = scenegraph_type + "-" + str(refid)
pairs = make_pairs(imgs, scene_graph=scenegraph_type, prefilter=None, symmetrize=True)
- output = inference(pairs, model, device, batch_size=1, verbose=not silent)
+ output = inference(pairs, model, device, batch_size=batch_size, verbose=not silent)
mode = GlobalAlignerMode.PointCloudOptimizer if len(imgs) > 2 else GlobalAlignerMode.PairViewer
scene = global_aligner(output, device=device, mode=mode, verbose=not silent)
@@ -169,9 +161,9 @@ def get_reconstructed_scene(outdir, model, device, silent, image_size, filelist,
confs = to_numpy([c for c in scene.im_conf])
cmap = pl.get_cmap('jet')
depths_max = max([d.max() for d in depths])
- depths = [d / depths_max for d in depths]
+ depths = [d/depths_max for d in depths]
confs_max = max([d.max() for d in confs])
- confs = [cmap(d / confs_max) for d in confs]
+ confs = [cmap(d/confs_max) for d in confs]
imgs = []
for i in range(len(rgbimg)):
@@ -184,22 +176,22 @@ def get_reconstructed_scene(outdir, model, device, silent, image_size, filelist,
def set_scenegraph_options(inputfiles, winsize, refid, scenegraph_type):
num_files = len(inputfiles) if inputfiles is not None else 1
- max_winsize = max(1, math.ceil((num_files - 1) / 2))
+ max_winsize = max(1, math.ceil((num_files-1)/2))
if scenegraph_type == "swin":
winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
minimum=1, maximum=max_winsize, step=1, visible=True)
refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
- maximum=num_files - 1, step=1, visible=False)
+ maximum=num_files-1, step=1, visible=False)
elif scenegraph_type == "oneref":
winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
minimum=1, maximum=max_winsize, step=1, visible=False)
refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
- maximum=num_files - 1, step=1, visible=True)
+ maximum=num_files-1, step=1, visible=True)
else:
winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
minimum=1, maximum=max_winsize, step=1, visible=False)
refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
- maximum=num_files - 1, step=1, visible=False)
+ maximum=num_files-1, step=1, visible=False)
return winsize, refid
@@ -217,9 +209,7 @@ def main_demo(tmpdirname, model, device, image_size, server_name, server_port, s
value='linear', label="schedule", info="For global alignment!")
niter = gradio.Number(value=300, precision=0, minimum=0, maximum=5000,
label="num_iterations", info="For global alignment!")
- scenegraph_type = gradio.Dropdown([("complete: all possible image pairs", "complete"),
- ("swin: sliding window", "swin"),
- ("oneref: match one image with all", "oneref")],
+ scenegraph_type = gradio.Dropdown(["complete", "swin", "oneref"],
value='complete', label="Scenegraph",
info="Define how to make pairs",
interactive=True)
@@ -281,3 +271,30 @@ def main_demo(tmpdirname, model, device, image_size, server_name, server_port, s
clean_depth, transparent_cams, cam_size],
outputs=outmodel)
demo.launch(share=False, server_name=server_name, server_port=server_port)
+
+
+if __name__ == '__main__':
+ parser = get_args_parser()
+ args = parser.parse_args()
+
+ if args.tmp_dir is not None:
+ tmp_path = args.tmp_dir
+ os.makedirs(tmp_path, exist_ok=True)
+ tempfile.tempdir = tmp_path
+
+ if args.server_name is not None:
+ server_name = args.server_name
+ else:
+ server_name = '0.0.0.0' if args.local_network else '127.0.0.1'
+
+ if args.weights is not None:
+ weights_path = args.weights
+ else:
+ weights_path = "naver/" + args.model_name
+ model = AsymmetricCroCo3DStereo.from_pretrained(weights_path).to(args.device)
+
+ # dust3r will write the 3D model inside tmpdirname
+ with tempfile.TemporaryDirectory(suffix='dust3r_gradio_demo') as tmpdirname:
+ if not args.silent:
+ print('Outputing stuff in', tmpdirname)
+ main_demo(tmpdirname, model, args.device, args.image_size, server_name, args.server_port, silent=args.silent)
diff --git a/imcui/third_party/dust3r/docker/docker-compose-cpu.yml b/third_party/dust3r/docker/docker-compose-cpu.yml
similarity index 100%
rename from imcui/third_party/dust3r/docker/docker-compose-cpu.yml
rename to third_party/dust3r/docker/docker-compose-cpu.yml
diff --git a/imcui/third_party/dust3r/docker/docker-compose-cuda.yml b/third_party/dust3r/docker/docker-compose-cuda.yml
similarity index 100%
rename from imcui/third_party/dust3r/docker/docker-compose-cuda.yml
rename to third_party/dust3r/docker/docker-compose-cuda.yml
diff --git a/third_party/dust3r/docker/files/cpu.Dockerfile b/third_party/dust3r/docker/files/cpu.Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..c9ccc39682dd7c7723f447ff47f12531a593446f
--- /dev/null
+++ b/third_party/dust3r/docker/files/cpu.Dockerfile
@@ -0,0 +1,38 @@
+FROM python:3.11-slim
+
+LABEL description="Docker container for DUSt3R with dependencies installed. CPU VERSION"
+
+ENV DEVICE="cpu"
+ENV MODEL="DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth"
+ARG DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && apt-get install -y \
+ git \
+ libgl1-mesa-glx \
+ libegl1-mesa \
+ libxrandr2 \
+ libxrandr2 \
+ libxss1 \
+ libxcursor1 \
+ libxcomposite1 \
+ libasound2 \
+ libxi6 \
+ libxtst6 \
+ libglib2.0-0 \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN git clone --recursive https://github.com/naver/dust3r /dust3r
+WORKDIR /dust3r
+
+RUN pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+RUN pip install -r requirements.txt
+RUN pip install -r requirements_optional.txt
+RUN pip install opencv-python==4.8.0.74
+
+WORKDIR /dust3r
+
+COPY entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+
+ENTRYPOINT ["/entrypoint.sh"]
diff --git a/third_party/dust3r/docker/files/cuda.Dockerfile b/third_party/dust3r/docker/files/cuda.Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..a1d2edce1a5e7cee2fa3d66faf4f6ee019595267
--- /dev/null
+++ b/third_party/dust3r/docker/files/cuda.Dockerfile
@@ -0,0 +1,27 @@
+FROM nvcr.io/nvidia/pytorch:24.01-py3
+
+LABEL description="Docker container for DUSt3R with dependencies installed. CUDA VERSION"
+ENV DEVICE="cuda"
+ENV MODEL="DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth"
+ARG DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && apt-get install -y \
+ git=1:2.34.1-1ubuntu1.10 \
+ libglib2.0-0=2.72.4-0ubuntu2.2 \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN git clone --recursive https://github.com/naver/dust3r /dust3r
+WORKDIR /dust3r
+RUN pip install -r requirements.txt
+RUN pip install -r requirements_optional.txt
+RUN pip install opencv-python==4.8.0.74
+
+WORKDIR /dust3r/croco/models/curope/
+RUN python setup.py build_ext --inplace
+
+WORKDIR /dust3r
+COPY entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+
+ENTRYPOINT ["/entrypoint.sh"]
diff --git a/third_party/dust3r/docker/files/entrypoint.sh b/third_party/dust3r/docker/files/entrypoint.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9637072a0af071f927ca0481bcaa4b600644b8b5
--- /dev/null
+++ b/third_party/dust3r/docker/files/entrypoint.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+
+set -eux
+
+DEVICE=${DEVICE:-cuda}
+MODEL=${MODEL:-DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth}
+
+exec python3 demo.py --weights "checkpoints/$MODEL" --device "$DEVICE" --local_network "$@"
diff --git a/third_party/dust3r/docker/run.sh b/third_party/dust3r/docker/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6c920363d607fc6019f10780d072edf49bee3046
--- /dev/null
+++ b/third_party/dust3r/docker/run.sh
@@ -0,0 +1,68 @@
+#!/bin/bash
+
+set -eux
+
+# Default model name
+model_name="DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth"
+
+check_docker() {
+ if ! command -v docker &>/dev/null; then
+ echo "Docker could not be found. Please install Docker and try again."
+ exit 1
+ fi
+}
+
+download_model_checkpoint() {
+ if [ -f "./files/checkpoints/${model_name}" ]; then
+ echo "Model checkpoint ${model_name} already exists. Skipping download."
+ return
+ fi
+ echo "Downloading model checkpoint ${model_name}..."
+ wget "https://download.europe.naverlabs.com/ComputerVision/DUSt3R/${model_name}" -P ./files/checkpoints
+}
+
+set_dcomp() {
+ if command -v docker-compose &>/dev/null; then
+ dcomp="docker-compose"
+ elif command -v docker &>/dev/null && docker compose version &>/dev/null; then
+ dcomp="docker compose"
+ else
+ echo "Docker Compose could not be found. Please install Docker Compose and try again."
+ exit 1
+ fi
+}
+
+run_docker() {
+ export MODEL=${model_name}
+ if [ "$with_cuda" -eq 1 ]; then
+ $dcomp -f docker-compose-cuda.yml up --build
+ else
+ $dcomp -f docker-compose-cpu.yml up --build
+ fi
+}
+
+with_cuda=0
+for arg in "$@"; do
+ case $arg in
+ --with-cuda)
+ with_cuda=1
+ ;;
+ --model_name=*)
+ model_name="${arg#*=}.pth"
+ ;;
+ *)
+ echo "Unknown parameter passed: $arg"
+ exit 1
+ ;;
+ esac
+done
+
+
+main() {
+ check_docker
+ download_model_checkpoint
+ set_dcomp
+ run_docker
+}
+
+main
diff --git a/imcui/third_party/dust3r/datasets_preprocess/habitat/habitat_renderer/__init__.py b/third_party/dust3r/dust3r/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/habitat/habitat_renderer/__init__.py
rename to third_party/dust3r/dust3r/__init__.py
diff --git a/imcui/third_party/dust3r/dust3r/cloud_opt/__init__.py b/third_party/dust3r/dust3r/cloud_opt/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/cloud_opt/__init__.py
rename to third_party/dust3r/dust3r/cloud_opt/__init__.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/base_opt.py b/third_party/dust3r/dust3r/cloud_opt/base_opt.py
similarity index 88%
rename from imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/base_opt.py
rename to third_party/dust3r/dust3r/cloud_opt/base_opt.py
index 4d36e05bfca80509bced20add7c067987d538951..62c1ea0666c058bb944176e9b821754f32ff90da 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/base_opt.py
+++ b/third_party/dust3r/dust3r/cloud_opt/base_opt.py
@@ -231,17 +231,43 @@ class BasePCOptimizer (nn.Module):
def get_depthmaps(self, raw=False):
raise NotImplementedError()
- def clean_pointcloud(self, **kw):
+ @torch.no_grad()
+ def clean_pointcloud(self, tol=0.001, max_bad_conf=0):
+ """ Method:
+ 1) express all 3d points in each camera coordinate frame
+ 2) if they're in front of a depthmap --> then lower their confidence
+ """
+ assert 0 <= tol < 1
cams = inv(self.get_im_poses())
K = self.get_intrinsics()
depthmaps = self.get_depthmaps()
- all_pts3d = self.get_pts3d()
+ res = deepcopy(self)
+
+ for i, pts3d in enumerate(self.depth_to_pts3d()):
+ for j in range(self.n_imgs):
+ if i == j:
+ continue
+
+ # project 3dpts in other view
+ Hi, Wi = self.imshapes[i]
+ Hj, Wj = self.imshapes[j]
+ proj = geotrf(cams[j], pts3d[:Hi*Wi]).reshape(Hi, Wi, 3)
+ proj_depth = proj[:, :, 2]
+ u, v = geotrf(K[j], proj, norm=1, ncol=2).round().long().unbind(-1)
+
+ # check which points are actually in the visible cone
+ msk_i = (proj_depth > 0) & (0 <= u) & (u < Wj) & (0 <= v) & (v < Hj)
+ msk_j = v[msk_i], u[msk_i]
- new_im_confs = clean_pointcloud(self.im_conf, K, cams, depthmaps, all_pts3d, **kw)
+ # find bad points = those in front but less confident
+ bad_points = (proj_depth[msk_i] < (1-tol) * depthmaps[j][msk_j]
+ ) & (res.im_conf[i][msk_i] < res.im_conf[j][msk_j])
- for i, new_conf in enumerate(new_im_confs):
- self.im_conf[i].data[:] = new_conf
- return self
+ bad_msk_i = msk_i.clone()
+ bad_msk_i[msk_i] = bad_points
+ res.im_conf[i][bad_msk_i] = res.im_conf[i][bad_msk_i].clip_(max=max_bad_conf)
+
+ return res
def forward(self, ret_details=False):
pw_poses = self.get_pw_poses() # cam-to-world
@@ -364,42 +390,3 @@ def global_alignment_iter(net, cur_iter, niter, lr_base, lr_min, optimizer, sche
optimizer.step()
return float(loss), lr
-
-
-@torch.no_grad()
-def clean_pointcloud( im_confs, K, cams, depthmaps, all_pts3d,
- tol=0.001, bad_conf=0, dbg=()):
- """ Method:
- 1) express all 3d points in each camera coordinate frame
- 2) if they're in front of a depthmap --> then lower their confidence
- """
- assert len(im_confs) == len(cams) == len(K) == len(depthmaps) == len(all_pts3d)
- assert 0 <= tol < 1
- res = [c.clone() for c in im_confs]
-
- # reshape appropriately
- all_pts3d = [p.view(*c.shape,3) for p,c in zip(all_pts3d, im_confs)]
- depthmaps = [d.view(*c.shape) for d,c in zip(depthmaps, im_confs)]
-
- for i, pts3d in enumerate(all_pts3d):
- for j in range(len(all_pts3d)):
- if i == j: continue
-
- # project 3dpts in other view
- proj = geotrf(cams[j], pts3d)
- proj_depth = proj[:,:,2]
- u,v = geotrf(K[j], proj, norm=1, ncol=2).round().long().unbind(-1)
-
- # check which points are actually in the visible cone
- H, W = im_confs[j].shape
- msk_i = (proj_depth > 0) & (0 <= u) & (u < W) & (0 <= v) & (v < H)
- msk_j = v[msk_i], u[msk_i]
-
- # find bad points = those in front but less confident
- bad_points = (proj_depth[msk_i] < (1-tol) * depthmaps[j][msk_j]) & (res[i][msk_i] < res[j][msk_j])
-
- bad_msk_i = msk_i.clone()
- bad_msk_i[msk_i] = bad_points
- res[i][bad_msk_i] = res[i][bad_msk_i].clip_(max=bad_conf)
-
- return res
diff --git a/imcui/third_party/dust3r/dust3r/cloud_opt/commons.py b/third_party/dust3r/dust3r/cloud_opt/commons.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/cloud_opt/commons.py
rename to third_party/dust3r/dust3r/cloud_opt/commons.py
diff --git a/imcui/third_party/dust3r/dust3r/cloud_opt/init_im_poses.py b/third_party/dust3r/dust3r/cloud_opt/init_im_poses.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/cloud_opt/init_im_poses.py
rename to third_party/dust3r/dust3r/cloud_opt/init_im_poses.py
diff --git a/imcui/third_party/dust3r/dust3r/cloud_opt/modular_optimizer.py b/third_party/dust3r/dust3r/cloud_opt/modular_optimizer.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/cloud_opt/modular_optimizer.py
rename to third_party/dust3r/dust3r/cloud_opt/modular_optimizer.py
diff --git a/imcui/third_party/dust3r/dust3r/cloud_opt/optimizer.py b/third_party/dust3r/dust3r/cloud_opt/optimizer.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/cloud_opt/optimizer.py
rename to third_party/dust3r/dust3r/cloud_opt/optimizer.py
diff --git a/imcui/third_party/dust3r/dust3r/cloud_opt/pair_viewer.py b/third_party/dust3r/dust3r/cloud_opt/pair_viewer.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/cloud_opt/pair_viewer.py
rename to third_party/dust3r/dust3r/cloud_opt/pair_viewer.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/datasets/__init__.py b/third_party/dust3r/dust3r/datasets/__init__.py
similarity index 76%
rename from imcui/third_party/mast3r/dust3r/dust3r/datasets/__init__.py
rename to third_party/dust3r/dust3r/datasets/__init__.py
index 2123d09ec2840ab5ee9ca43057c35f93233bde89..cc5e79718e4a3eb2e31c60c8a390e61a19ec5432 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/datasets/__init__.py
+++ b/third_party/dust3r/dust3r/datasets/__init__.py
@@ -1,16 +1,8 @@
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
from .utils.transforms import *
-from .base.batched_sampler import BatchedRandomSampler # noqa
-from .arkitscenes import ARKitScenes # noqa
-from .blendedmvs import BlendedMVS # noqa
-from .co3d import Co3d # noqa
-from .habitat import Habitat # noqa
-from .megadepth import MegaDepth # noqa
-from .scannetpp import ScanNetpp # noqa
-from .staticthings3d import StaticThings3D # noqa
-from .waymo import Waymo # noqa
-from .wildrgbd import WildRGBD # noqa
+from .base.batched_sampler import BatchedRandomSampler # noqa: F401
+from .co3d import Co3d # noqa: F401
def get_data_loader(dataset, batch_size, num_workers=8, shuffle=True, drop_last=True, pin_mem=True):
diff --git a/imcui/third_party/dust3r/dust3r/__init__.py b/third_party/dust3r/dust3r/datasets/base/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/__init__.py
rename to third_party/dust3r/dust3r/datasets/base/__init__.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/datasets/base/base_stereo_view_dataset.py b/third_party/dust3r/dust3r/datasets/base/base_stereo_view_dataset.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/datasets/base/base_stereo_view_dataset.py
rename to third_party/dust3r/dust3r/datasets/base/base_stereo_view_dataset.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/base/batched_sampler.py b/third_party/dust3r/dust3r/datasets/base/batched_sampler.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/base/batched_sampler.py
rename to third_party/dust3r/dust3r/datasets/base/batched_sampler.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/base/easy_dataset.py b/third_party/dust3r/dust3r/datasets/base/easy_dataset.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/base/easy_dataset.py
rename to third_party/dust3r/dust3r/datasets/base/easy_dataset.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/datasets/co3d.py b/third_party/dust3r/dust3r/datasets/co3d.py
similarity index 78%
rename from imcui/third_party/mast3r/dust3r/dust3r/datasets/co3d.py
rename to third_party/dust3r/dust3r/datasets/co3d.py
index 2ea5c8555d34b776e7a48396dcd0eecece713e34..9fc94f9420d86372e643c00e7cddf85b3d1982c6 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/datasets/co3d.py
+++ b/third_party/dust3r/dust3r/datasets/co3d.py
@@ -24,7 +24,6 @@ class Co3d(BaseStereoViewDataset):
super().__init__(*args, **kwargs)
assert mask_bg in (True, False, 'rand')
self.mask_bg = mask_bg
- self.dataset_label = 'Co3d_v2'
# load all scenes
with open(osp.join(self.ROOT, f'selected_seqs_{self.split}.json'), 'r') as f:
@@ -38,30 +37,13 @@ class Co3d(BaseStereoViewDataset):
# we prepare all combinations such that i-j = +/- [5, 10, .., 90] degrees
self.combinations = [(i, j)
for i, j in itertools.combinations(range(100), 2)
- if 0 < abs(i - j) <= 30 and abs(i - j) % 5 == 0]
+ if 0 < abs(i-j) <= 30 and abs(i-j) % 5 == 0]
self.invalidate = {scene: {} for scene in self.scene_list}
def __len__(self):
return len(self.scene_list) * len(self.combinations)
- def _get_metadatapath(self, obj, instance, view_idx):
- return osp.join(self.ROOT, obj, instance, 'images', f'frame{view_idx:06n}.npz')
-
- def _get_impath(self, obj, instance, view_idx):
- return osp.join(self.ROOT, obj, instance, 'images', f'frame{view_idx:06n}.jpg')
-
- def _get_depthpath(self, obj, instance, view_idx):
- return osp.join(self.ROOT, obj, instance, 'depths', f'frame{view_idx:06n}.jpg.geometric.png')
-
- def _get_maskpath(self, obj, instance, view_idx):
- return osp.join(self.ROOT, obj, instance, 'masks', f'frame{view_idx:06n}.png')
-
- def _read_depthmap(self, depthpath, input_metadata):
- depthmap = imread_cv2(depthpath, cv2.IMREAD_UNCHANGED)
- depthmap = (depthmap.astype(np.float32) / 65535) * np.nan_to_num(input_metadata['maximum_depth'])
- return depthmap
-
def _get_views(self, idx, resolution, rng):
# choose a scene
obj, instance = self.scene_list[idx // len(self.combinations)]
@@ -69,7 +51,7 @@ class Co3d(BaseStereoViewDataset):
im1_idx, im2_idx = self.combinations[idx % len(self.combinations)]
# add a bit of randomness
- last = len(image_pool) - 1
+ last = len(image_pool)-1
if resolution not in self.invalidate[obj, instance]: # flag invalid images
self.invalidate[obj, instance][resolution] = [False for _ in range(len(image_pool))]
@@ -94,22 +76,21 @@ class Co3d(BaseStereoViewDataset):
view_idx = image_pool[im_idx]
- impath = self._get_impath(obj, instance, view_idx)
- depthpath = self._get_depthpath(obj, instance, view_idx)
+ impath = osp.join(self.ROOT, obj, instance, 'images', f'frame{view_idx:06n}.jpg')
# load camera params
- metadata_path = self._get_metadatapath(obj, instance, view_idx)
- input_metadata = np.load(metadata_path)
+ input_metadata = np.load(impath.replace('jpg', 'npz'))
camera_pose = input_metadata['camera_pose'].astype(np.float32)
intrinsics = input_metadata['camera_intrinsics'].astype(np.float32)
# load image and depth
rgb_image = imread_cv2(impath)
- depthmap = self._read_depthmap(depthpath, input_metadata)
+ depthmap = imread_cv2(impath.replace('images', 'depths') + '.geometric.png', cv2.IMREAD_UNCHANGED)
+ depthmap = (depthmap.astype(np.float32) / 65535) * np.nan_to_num(input_metadata['maximum_depth'])
if mask_bg:
# load object mask
- maskpath = self._get_maskpath(obj, instance, view_idx)
+ maskpath = osp.join(self.ROOT, obj, instance, 'masks', f'frame{view_idx:06n}.png')
maskmap = imread_cv2(maskpath, cv2.IMREAD_UNCHANGED).astype(np.float32)
maskmap = (maskmap / 255.0) > 0.1
@@ -131,7 +112,7 @@ class Co3d(BaseStereoViewDataset):
depthmap=depthmap,
camera_pose=camera_pose,
camera_intrinsics=intrinsics,
- dataset=self.dataset_label,
+ dataset='Co3d_v2',
label=osp.join(obj, instance),
instance=osp.split(impath)[1],
))
@@ -159,7 +140,7 @@ if __name__ == "__main__":
viz.add_pointcloud(pts3d, colors, valid_mask)
viz.add_camera(pose_c2w=views[view_idx]['camera_pose'],
focal=views[view_idx]['camera_intrinsics'][0, 0],
- color=(idx * 255, (1 - idx) * 255, 0),
+ color=(idx*255, (1 - idx)*255, 0),
image=colors,
cam_size=cam_size)
viz.show()
diff --git a/imcui/third_party/dust3r/dust3r/datasets/base/__init__.py b/third_party/dust3r/dust3r/datasets/utils/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/base/__init__.py
rename to third_party/dust3r/dust3r/datasets/utils/__init__.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/utils/cropping.py b/third_party/dust3r/dust3r/datasets/utils/cropping.py
similarity index 91%
rename from imcui/third_party/dust3r/dust3r/datasets/utils/cropping.py
rename to third_party/dust3r/dust3r/datasets/utils/cropping.py
index 07a331847cb8df997b3012790f5a96f69f21464d..02b1915676f3deea24f57032f7588ff34cbfaeb9 100644
--- a/imcui/third_party/dust3r/dust3r/datasets/utils/cropping.py
+++ b/third_party/dust3r/dust3r/datasets/utils/cropping.py
@@ -12,10 +12,8 @@ import numpy as np # noqa
from dust3r.utils.geometry import colmap_to_opencv_intrinsics, opencv_to_colmap_intrinsics # noqa
try:
lanczos = PIL.Image.Resampling.LANCZOS
- bicubic = PIL.Image.Resampling.BICUBIC
except AttributeError:
lanczos = PIL.Image.LANCZOS
- bicubic = PIL.Image.BICUBIC
class ImageList:
@@ -53,7 +51,7 @@ class ImageList:
return [getattr(im, func)(*args, **kwargs) for im in self.images]
-def rescale_image_depthmap(image, depthmap, camera_intrinsics, output_resolution, force=True):
+def rescale_image_depthmap(image, depthmap, camera_intrinsics, output_resolution):
""" Jointly rescale a (image, depthmap)
so that (out_width, out_height) >= output_res
"""
@@ -63,16 +61,13 @@ def rescale_image_depthmap(image, depthmap, camera_intrinsics, output_resolution
if depthmap is not None:
# can also use this with masks instead of depthmaps
assert tuple(depthmap.shape[:2]) == image.size[::-1]
-
- # define output resolution
assert output_resolution.shape == (2,)
+ # define output resolution
scale_final = max(output_resolution / image.size) + 1e-8
- if scale_final >= 1 and not force: # image is already smaller than what is asked
- return (image.to_pil(), depthmap, camera_intrinsics)
output_resolution = np.floor(input_resolution * scale_final).astype(int)
# first rescale the image so that it contains the crop
- image = image.resize(tuple(output_resolution), resample=lanczos if scale_final < 1 else bicubic)
+ image = image.resize(output_resolution, resample=lanczos)
if depthmap is not None:
depthmap = cv2.resize(depthmap, output_resolution, fx=scale_final,
fy=scale_final, interpolation=cv2.INTER_NEAREST)
@@ -120,5 +115,5 @@ def crop_image_depthmap(image, depthmap, camera_intrinsics, crop_bbox):
def bbox_from_intrinsics_in_out(input_camera_matrix, output_camera_matrix, output_resolution):
out_width, out_height = output_resolution
l, t = np.int32(np.round(input_camera_matrix[:2, 2] - output_camera_matrix[:2, 2]))
- crop_bbox = (l, t, l + out_width, t + out_height)
+ crop_bbox = (l, t, l+out_width, t+out_height)
return crop_bbox
diff --git a/imcui/third_party/dust3r/dust3r/datasets/utils/transforms.py b/third_party/dust3r/dust3r/datasets/utils/transforms.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/utils/transforms.py
rename to third_party/dust3r/dust3r/datasets/utils/transforms.py
diff --git a/imcui/third_party/dust3r/dust3r/heads/__init__.py b/third_party/dust3r/dust3r/heads/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/heads/__init__.py
rename to third_party/dust3r/dust3r/heads/__init__.py
diff --git a/imcui/third_party/dust3r/dust3r/heads/dpt_head.py b/third_party/dust3r/dust3r/heads/dpt_head.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/heads/dpt_head.py
rename to third_party/dust3r/dust3r/heads/dpt_head.py
diff --git a/imcui/third_party/dust3r/dust3r/heads/linear_head.py b/third_party/dust3r/dust3r/heads/linear_head.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/heads/linear_head.py
rename to third_party/dust3r/dust3r/heads/linear_head.py
diff --git a/imcui/third_party/dust3r/dust3r/heads/postprocess.py b/third_party/dust3r/dust3r/heads/postprocess.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/heads/postprocess.py
rename to third_party/dust3r/dust3r/heads/postprocess.py
diff --git a/imcui/third_party/dust3r/dust3r/image_pairs.py b/third_party/dust3r/dust3r/image_pairs.py
similarity index 66%
rename from imcui/third_party/dust3r/dust3r/image_pairs.py
rename to third_party/dust3r/dust3r/image_pairs.py
index ebcf902b4d07b83fe83ffceba3f45ca0d74dfcf7..571d834f0331cbd7bed3e79adbf7bf2c954cdcef 100644
--- a/imcui/third_party/dust3r/dust3r/image_pairs.py
+++ b/third_party/dust3r/dust3r/image_pairs.py
@@ -15,41 +15,14 @@ def make_pairs(imgs, scene_graph='complete', prefilter=None, symmetrize=True):
for j in range(i):
pairs.append((imgs[i], imgs[j]))
elif scene_graph.startswith('swin'):
- iscyclic = not scene_graph.endswith('noncyclic')
- try:
- winsize = int(scene_graph.split('-')[1])
- except Exception as e:
- winsize = 3
+ winsize = int(scene_graph.split('-')[1]) if '-' in scene_graph else 3
pairsid = set()
for i in range(len(imgs)):
- for j in range(1, winsize + 1):
- idx = (i + j)
- if iscyclic:
- idx = idx % len(imgs) # explicit loop closure
- if idx >= len(imgs):
- continue
+ for j in range(1, winsize+1):
+ idx = (i + j) % len(imgs) # explicit loop closure
pairsid.add((i, idx) if i < idx else (idx, i))
for i, j in pairsid:
pairs.append((imgs[i], imgs[j]))
- elif scene_graph.startswith('logwin'):
- iscyclic = not scene_graph.endswith('noncyclic')
- try:
- winsize = int(scene_graph.split('-')[1])
- except Exception as e:
- winsize = 3
- offsets = [2**i for i in range(winsize)]
- pairsid = set()
- for i in range(len(imgs)):
- ixs_l = [i - off for off in offsets]
- ixs_r = [i + off for off in offsets]
- for j in ixs_l + ixs_r:
- if iscyclic:
- j = j % len(imgs) # Explicit loop closure
- if j < 0 or j >= len(imgs) or j == i:
- continue
- pairsid.add((i, j) if i < j else (j, i))
- for i, j in pairsid:
- pairs.append((imgs[i], imgs[j]))
elif scene_graph.startswith('oneref'):
refid = int(scene_graph.split('-')[1]) if '-' in scene_graph else 0
for j in range(len(imgs)):
@@ -79,13 +52,13 @@ def sel(x, kept):
def _filter_edges_seq(edges, seq_dis_thr, cyclic=False):
# number of images
- n = max(max(e) for e in edges) + 1
+ n = max(max(e) for e in edges)+1
kept = []
for e, (i, j) in enumerate(edges):
- dis = abs(i - j)
+ dis = abs(i-j)
if cyclic:
- dis = min(dis, abs(i + n - j), abs(i - n - j))
+ dis = min(dis, abs(i+n-j), abs(i-n-j))
if dis <= seq_dis_thr:
kept.append(e)
return kept
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/inference.py b/third_party/dust3r/dust3r/inference.py
similarity index 95%
rename from imcui/third_party/mast3r/dust3r/dust3r/inference.py
rename to third_party/dust3r/dust3r/inference.py
index 90540486b077add90ca50f62a5072e082cb2f2d7..95a7eaaa778bb8c6ec869635670a939da00018b5 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/inference.py
+++ b/third_party/dust3r/dust3r/inference.py
@@ -31,10 +31,9 @@ def make_batch_symmetric(batch):
def loss_of_one_batch(batch, model, criterion, device, symmetrize_batch=False, use_amp=False, ret=None):
view1, view2 = batch
- ignore_keys = set(['depthmap', 'dataset', 'label', 'instance', 'idx', 'true_shape', 'rng'])
for view in batch:
- for name in view.keys(): # pseudo_focal
- if name in ignore_keys:
+ for name in 'img pts3d valid_mask camera_pose camera_intrinsics F_matrix corres'.split(): # pseudo_focal
+ if name not in view:
continue
view[name] = view[name].to(device, non_blocking=True)
@@ -64,7 +63,7 @@ def inference(pairs, model, device, batch_size=8, verbose=True):
batch_size = 1
for i in tqdm.trange(0, len(pairs), batch_size, disable=not verbose):
- res = loss_of_one_batch(collate_with_cat(pairs[i:i + batch_size]), model, None, device)
+ res = loss_of_one_batch(collate_with_cat(pairs[i:i+batch_size]), model, None, device)
result.append(to_cpu(res))
result = collate_with_cat(result, lists=multiple_shapes)
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/losses.py b/third_party/dust3r/dust3r/losses.py
similarity index 95%
rename from imcui/third_party/mast3r/dust3r/dust3r/losses.py
rename to third_party/dust3r/dust3r/losses.py
index 4f8febff1a2dd674e759bcf83d023099a59cc934..7d6e20fd3a30d6d498afdc13ec852ae984d05f7e 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/losses.py
+++ b/third_party/dust3r/dust3r/losses.py
@@ -25,20 +25,18 @@ def Sum(*losses_and_masks):
return loss
-class BaseCriterion(nn.Module):
+class LLoss (nn.Module):
+ """ L-norm loss
+ """
+
def __init__(self, reduction='mean'):
super().__init__()
self.reduction = reduction
-
-class LLoss (BaseCriterion):
- """ L-norm loss
- """
-
def forward(self, a, b):
assert a.shape == b.shape and a.ndim >= 2 and 1 <= a.shape[-1] <= 3, f'Bad shape = {a.shape}'
dist = self.distance(a, b)
- assert dist.ndim == a.ndim - 1 # one dimension less
+ assert dist.ndim == a.ndim-1 # one dimension less
if self.reduction == 'none':
return dist
if self.reduction == 'sum':
@@ -64,17 +62,17 @@ L21 = L21Loss()
class Criterion (nn.Module):
def __init__(self, criterion=None):
super().__init__()
- assert isinstance(criterion, BaseCriterion), f'{criterion} is not a proper criterion!'
+ assert isinstance(criterion, LLoss), f'{criterion} is not a proper criterion!'+bb()
self.criterion = copy(criterion)
def get_name(self):
return f'{type(self).__name__}({self.criterion})'
- def with_reduction(self, mode='none'):
+ def with_reduction(self, mode):
res = loss = deepcopy(self)
while loss is not None:
assert isinstance(loss, Criterion)
- loss.criterion.reduction = mode # make it return the loss for each sample
+ loss.criterion.reduction = 'none' # make it return the loss for each sample
loss = loss._loss2 # we assume loss is a Multiloss
return res
@@ -190,7 +188,7 @@ class Regr3D (Criterion, MultiLoss):
# loss on gt2 side
l2 = self.criterion(pred_pts2[mask2], gt_pts2[mask2])
self_name = type(self).__name__
- details = {self_name + '_pts3d_1': float(l1.mean()), self_name + '_pts3d_2': float(l2.mean())}
+ details = {self_name+'_pts3d_1': float(l1.mean()), self_name+'_pts3d_2': float(l2.mean())}
return Sum((l1, mask1), (l2, mask2)), (details | monitoring)
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/model.py b/third_party/dust3r/dust3r/model.py
similarity index 93%
rename from imcui/third_party/mast3r/dust3r/dust3r/model.py
rename to third_party/dust3r/dust3r/model.py
index 41c3a4f78eb5fbafdeb7ab8523468de320886c64..40ac37fc8b538e11f27c85766e3937084e22ad10 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/model.py
+++ b/third_party/dust3r/dust3r/model.py
@@ -20,9 +20,7 @@ from models.croco import CroCoNet # noqa
inf = float('inf')
hf_version_number = huggingface_hub.__version__
-assert version.parse(hf_version_number) >= version.parse("0.22.0"), ("Outdated huggingface_hub version, "
- "please reinstall requirements.txt")
-
+assert version.parse(hf_version_number) >= version.parse("0.22.0"), "Outdated huggingface_hub version, please reinstall requirements.txt"
def load_model(model_path, device, verbose=True):
if verbose:
@@ -78,11 +76,7 @@ class AsymmetricCroCo3DStereo (
if os.path.isfile(pretrained_model_name_or_path):
return load_model(pretrained_model_name_or_path, device='cpu')
else:
- try:
- model = super(AsymmetricCroCo3DStereo, cls).from_pretrained(pretrained_model_name_or_path, **kw)
- except TypeError as e:
- raise Exception(f'tried to load {pretrained_model_name_or_path} from huggingface, but failed')
- return model
+ return super(AsymmetricCroCo3DStereo, cls).from_pretrained(pretrained_model_name_or_path, **kw)
def _set_patch_embed(self, img_size=224, patch_size=16, enc_embed_dim=768):
self.patch_embed = get_patch_embed(self.patch_embed_cls, img_size, patch_size, enc_embed_dim)
@@ -99,9 +93,9 @@ class AsymmetricCroCo3DStereo (
def set_freeze(self, freeze): # this is for use by downstream models
self.freeze = freeze
to_be_frozen = {
- 'none': [],
- 'mask': [self.mask_token],
- 'encoder': [self.mask_token, self.patch_embed, self.enc_blocks],
+ 'none': [],
+ 'mask': [self.mask_token],
+ 'encoder': [self.mask_token, self.patch_embed, self.enc_blocks],
}
freeze_all_params(to_be_frozen[freeze])
diff --git a/imcui/third_party/dust3r/dust3r/optim_factory.py b/third_party/dust3r/dust3r/optim_factory.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/optim_factory.py
rename to third_party/dust3r/dust3r/optim_factory.py
diff --git a/imcui/third_party/dust3r/dust3r/patch_embed.py b/third_party/dust3r/dust3r/patch_embed.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/patch_embed.py
rename to third_party/dust3r/dust3r/patch_embed.py
diff --git a/imcui/third_party/dust3r/dust3r/post_process.py b/third_party/dust3r/dust3r/post_process.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/post_process.py
rename to third_party/dust3r/dust3r/post_process.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/utils/__init__.py b/third_party/dust3r/dust3r/utils/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/utils/__init__.py
rename to third_party/dust3r/dust3r/utils/__init__.py
diff --git a/imcui/third_party/dust3r/dust3r/utils/device.py b/third_party/dust3r/dust3r/utils/device.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/utils/device.py
rename to third_party/dust3r/dust3r/utils/device.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/utils/geometry.py b/third_party/dust3r/dust3r/utils/geometry.py
similarity index 92%
rename from imcui/third_party/mast3r/dust3r/dust3r/utils/geometry.py
rename to third_party/dust3r/dust3r/utils/geometry.py
index ce365faf2acb97ffaafa1b80cb8ee0c28de0b6d6..648a72ec6498c481c357b732c1ef389e83c7422f 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/utils/geometry.py
+++ b/third_party/dust3r/dust3r/utils/geometry.py
@@ -26,7 +26,7 @@ def xy_grid(W, H, device=None, origin=(0, 0), unsqueeze=None, cat_dim=-1, homoge
meshgrid, stack = torch.meshgrid, torch.stack
ones = lambda *a: torch.ones(*a, device=device)
- tw, th = [arange(o, o + s, **arange_kw) for s, o in zip((W, H), origin)]
+ tw, th = [arange(o, o+s, **arange_kw) for s, o in zip((W, H), origin)]
grid = meshgrid(tw, th, indexing='xy')
if homogeneous:
grid = grid + (ones((H, W)),)
@@ -64,13 +64,13 @@ def geotrf(Trf, pts, ncol=None, norm=False):
d = pts.shape[3]
if Trf.shape[-1] == d:
pts = torch.einsum("bij, bhwj -> bhwi", Trf, pts)
- elif Trf.shape[-1] == d + 1:
+ elif Trf.shape[-1] == d+1:
pts = torch.einsum("bij, bhwj -> bhwi", Trf[:, :d, :d], pts) + Trf[:, None, None, :d, d]
else:
raise ValueError(f'bad shape, not ending with 3 or 4, for {pts.shape=}')
else:
if Trf.ndim >= 3:
- n = Trf.ndim - 2
+ n = Trf.ndim-2
assert Trf.shape[:n] == pts.shape[:n], 'batch size does not match'
Trf = Trf.reshape(-1, Trf.shape[-2], Trf.shape[-1])
@@ -81,7 +81,7 @@ def geotrf(Trf, pts, ncol=None, norm=False):
# Trf == (B,d,d) & pts == (B,d) --> (B, 1, d)
pts = pts[:, None, :]
- if pts.shape[-1] + 1 == Trf.shape[-1]:
+ if pts.shape[-1]+1 == Trf.shape[-1]:
Trf = Trf.swapaxes(-1, -2) # transpose Trf
pts = pts @ Trf[..., :-1, :] + Trf[..., -1:, :]
elif pts.shape[-1] == Trf.shape[-1]:
@@ -143,8 +143,8 @@ def depthmap_to_pts3d(depth, pseudo_focal, pp=None, **_):
# set principal point
if pp is None:
- grid_x = grid_x - (W - 1) / 2
- grid_y = grid_y - (H - 1) / 2
+ grid_x = grid_x - (W-1)/2
+ grid_y = grid_y - (H-1)/2
else:
grid_x = grid_x.expand(B, -1, -1) - pp[:, 0, None, None]
grid_y = grid_y.expand(B, -1, -1) - pp[:, 1, None, None]
@@ -207,16 +207,13 @@ def depthmap_to_absolute_camera_coordinates(depthmap, camera_intrinsics, camera_
pointmap of absolute coordinates (HxWx3 array), and a mask specifying valid pixels."""
X_cam, valid_mask = depthmap_to_camera_coordinates(depthmap, camera_intrinsics)
- X_world = X_cam # default
- if camera_pose is not None:
- # R_cam2world = np.float32(camera_params["R_cam2world"])
- # t_cam2world = np.float32(camera_params["t_cam2world"]).squeeze()
- R_cam2world = camera_pose[:3, :3]
- t_cam2world = camera_pose[:3, 3]
-
- # Express in absolute coordinates (invalid depth values)
- X_world = np.einsum("ik, vuk -> vui", R_cam2world, X_cam) + t_cam2world[None, None, :]
+ # R_cam2world = np.float32(camera_params["R_cam2world"])
+ # t_cam2world = np.float32(camera_params["t_cam2world"]).squeeze()
+ R_cam2world = camera_pose[:3, :3]
+ t_cam2world = camera_pose[:3, 3]
+ # Express in absolute coordinates (invalid depth values)
+ X_world = np.einsum("ik, vuk -> vui", R_cam2world, X_cam) + t_cam2world[None, None, :]
return X_world, valid_mask
@@ -246,7 +243,7 @@ def opencv_to_colmap_intrinsics(K):
return K
-def normalize_pointcloud(pts1, pts2, norm_mode='avg_dis', valid1=None, valid2=None, ret_factor=False):
+def normalize_pointcloud(pts1, pts2, norm_mode='avg_dis', valid1=None, valid2=None):
""" renorm pointmaps pts1, pts2 with norm_mode
"""
assert pts1.ndim >= 3 and pts1.shape[-1] == 3
@@ -270,10 +267,10 @@ def normalize_pointcloud(pts1, pts2, norm_mode='avg_dis', valid1=None, valid2=No
log_dis = torch.log1p(all_dis)
warp_factor = log_dis / all_dis.clip(min=1e-8)
H1, W1 = pts1.shape[1:-1]
- pts1 = pts1 * warp_factor[:, :W1 * H1].view(-1, H1, W1, 1)
+ pts1 = pts1 * warp_factor[:, :W1*H1].view(-1, H1, W1, 1)
if pts2 is not None:
H2, W2 = pts2.shape[1:-1]
- pts2 = pts2 * warp_factor[:, W1 * H1:].view(-1, H2, W2, 1)
+ pts2 = pts2 * warp_factor[:, W1*H1:].view(-1, H2, W2, 1)
all_dis = log_dis # this is their true distance afterwards
else:
raise ValueError(f'bad {dis_mode=}')
@@ -304,8 +301,6 @@ def normalize_pointcloud(pts1, pts2, norm_mode='avg_dis', valid1=None, valid2=No
res = pts1 / norm_factor
if pts2 is not None:
res = (res, pts2 / norm_factor)
- if ret_factor:
- res = res + (norm_factor,)
return res
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/utils/image.py b/third_party/dust3r/dust3r/utils/image.py
similarity index 97%
rename from imcui/third_party/mast3r/dust3r/dust3r/utils/image.py
rename to third_party/dust3r/dust3r/utils/image.py
index 6312a346df919ae6a0424504d824ef813fea250f..7a709713291cd312d83eabd10f84076be84a0c88 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/utils/image.py
+++ b/third_party/dust3r/dust3r/utils/image.py
@@ -23,11 +23,6 @@ except ImportError:
ImgNorm = tvf.Compose([tvf.ToTensor(), tvf.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-def img_to_arr( img ):
- if isinstance(img, str):
- img = imread_cv2(img)
- return img
-
def imread_cv2(path, options=cv2.IMREAD_COLOR):
""" Open an image or a depthmap with opencv-python.
"""
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/utils/misc.py b/third_party/dust3r/dust3r/utils/misc.py
similarity index 96%
rename from imcui/third_party/mast3r/dust3r/dust3r/utils/misc.py
rename to third_party/dust3r/dust3r/utils/misc.py
index 88c4d2dab6d5c14021ed9ed6646c3159a3a4637b..ab9fd06a063c3eafbfafddc011064ebb8a3232a8 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/utils/misc.py
+++ b/third_party/dust3r/dust3r/utils/misc.py
@@ -36,7 +36,7 @@ def is_symmetrized(gt1, gt2):
return False # special case of batchsize 1
ok = True
for i in range(0, len(x), 2):
- ok = ok and (x[i] == y[i + 1]) and (x[i + 1] == y[i])
+ ok = ok and (x[i] == y[i+1]) and (x[i+1] == y[i])
return ok
@@ -81,7 +81,7 @@ def transpose_to_landscape(head, activate=True):
# batch is a mix of both portraint & landscape
def selout(ar): return [d[ar] for d in decout]
l_result = head(selout(is_landscape), (H, W))
- p_result = transposed(head(selout(is_portrait), (W, H)))
+ p_result = transposed(head(selout(is_portrait), (W, H)))
# allocate full result
result = {}
diff --git a/imcui/third_party/dust3r/dust3r/utils/path_to_croco.py b/third_party/dust3r/dust3r/utils/path_to_croco.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/utils/path_to_croco.py
rename to third_party/dust3r/dust3r/utils/path_to_croco.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/viz.py b/third_party/dust3r/dust3r/viz.py
similarity index 75%
rename from imcui/third_party/mast3r/dust3r/dust3r/viz.py
rename to third_party/dust3r/dust3r/viz.py
index 9150e8b850d9f1e6bf9ddf6e865d34fc743e276a..a21f399accf6710816cc4a858d60849ccaad31e1 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/viz.py
+++ b/third_party/dust3r/dust3r/viz.py
@@ -9,9 +9,9 @@ import numpy as np
from scipy.spatial.transform import Rotation
import torch
-from dust3r.utils.geometry import geotrf, get_med_dist_between_poses, depthmap_to_absolute_camera_coordinates
+from dust3r.utils.geometry import geotrf, get_med_dist_between_poses
from dust3r.utils.device import to_numpy
-from dust3r.utils.image import rgb, img_to_arr
+from dust3r.utils.image import rgb
try:
import trimesh
@@ -19,7 +19,6 @@ except ImportError:
print('/!\\ module trimesh is not installed, cannot visualize results /!\\')
-
def cat_3d(vecs):
if isinstance(vecs, (np.ndarray, torch.Tensor)):
vecs = [vecs]
@@ -120,93 +119,40 @@ class SceneViz:
def __init__(self):
self.scene = trimesh.Scene()
- def add_rgbd(self, image, depth, intrinsics=None, cam2world=None, zfar=np.inf, mask=None):
- image = img_to_arr(image)
-
- # make up some intrinsics
- if intrinsics is None:
- H, W, THREE = image.shape
- focal = max(H, W)
- intrinsics = np.float32([[focal, 0, W/2], [0, focal, H/2], [0, 0, 1]])
-
- # compute 3d points
- pts3d = depthmap_to_pts3d(depth, intrinsics, cam2world=cam2world)
-
- return self.add_pointcloud(pts3d, image, mask=(depth=0.22
\ No newline at end of file
diff --git a/third_party/dust3r/requirements_optional.txt b/third_party/dust3r/requirements_optional.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c7fd52ab30ab0499f6fd7b59bb6e9e1f4e833d5c
--- /dev/null
+++ b/third_party/dust3r/requirements_optional.txt
@@ -0,0 +1 @@
+pillow-heif # add heif/heic image support
\ No newline at end of file
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/training.py b/third_party/dust3r/train.py
similarity index 95%
rename from imcui/third_party/mast3r/dust3r/dust3r/training.py
rename to third_party/dust3r/train.py
index 53af9764ebb03a0083c22294298ed674e9164edc..4deb01b97c011d462bc0b49638720828cf485b77 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/training.py
+++ b/third_party/dust3r/train.py
@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
@@ -68,8 +69,7 @@ def get_args_parser():
parser.add_argument('--amp', type=int, default=0,
choices=[0, 1], help="Use Automatic Mixed Precision for pretraining")
- parser.add_argument("--disable_cudnn_benchmark", action='store_true', default=False,
- help="set cudnn.benchmark = False")
+
# others
parser.add_argument('--num_workers', default=8, type=int)
parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
@@ -89,12 +89,12 @@ def get_args_parser():
return parser
-def train(args):
+def main(args):
misc.init_distributed_mode(args)
global_rank = misc.get_rank()
world_size = misc.get_world_size()
- print("output_dir: " + args.output_dir)
+ print("output_dir: "+args.output_dir)
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
@@ -113,7 +113,7 @@ def train(args):
torch.manual_seed(seed)
np.random.seed(seed)
- cudnn.benchmark = not args.disable_cudnn_benchmark
+ cudnn.benchmark = True
# training dataset and loader
print('Building train dataset {:s}'.format(args.train_dataset))
@@ -169,7 +169,7 @@ def train(args):
for test_name in data_loader_test:
if test_name not in test_stats:
continue
- log_stats.update({test_name + '_' + k: v for k, v in test_stats[test_name].items()})
+ log_stats.update({test_name+'_'+k: v for k, v in test_stats[test_name].items()})
with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
f.write(json.dumps(log_stats) + "\n")
@@ -190,12 +190,12 @@ def train(args):
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
train_stats = test_stats = {}
- for epoch in range(args.start_epoch, args.epochs + 1):
+ for epoch in range(args.start_epoch, args.epochs+1):
# Save immediately the last checkpoint
if epoch > args.start_epoch:
if args.save_freq and epoch % args.save_freq == 0 or epoch == args.epochs:
- save_model(epoch - 1, 'last', best_so_far)
+ save_model(epoch-1, 'last', best_so_far)
# Test on multiple datasets
new_best = False
@@ -216,9 +216,9 @@ def train(args):
if epoch > args.start_epoch:
if args.keep_freq and epoch % args.keep_freq == 0:
- save_model(epoch - 1, str(epoch), best_so_far)
+ save_model(epoch-1, str(epoch), best_so_far)
if new_best:
- save_model(epoch - 1, 'best', best_so_far)
+ save_model(epoch-1, 'best', best_so_far)
if epoch >= args.epochs:
break # exit after writing last test to disk
@@ -330,7 +330,7 @@ def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
log_writer.add_scalar('train_lr', lr, epoch_1000x)
log_writer.add_scalar('train_iter', epoch_1000x, epoch_1000x)
for name, val in loss_details.items():
- log_writer.add_scalar('train_' + name, val, epoch_1000x)
+ log_writer.add_scalar('train_'+name, val, epoch_1000x)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
@@ -372,6 +372,12 @@ def test_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
if log_writer is not None:
for name, val in results.items():
- log_writer.add_scalar(prefix + '_' + name, val, 1000 * epoch)
+ log_writer.add_scalar(prefix+'_'+name, val, 1000*epoch)
return results
+
+
+if __name__ == '__main__':
+ args = get_args_parser()
+ args = args.parse_args()
+ main(args)
diff --git a/third_party/gim/.gitattributes b/third_party/gim/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..f9d1720c8a2012d548e3c5cac888b488a7405510
--- /dev/null
+++ b/third_party/gim/.gitattributes
@@ -0,0 +1,3 @@
+gim_dkm_100h.ckpt filter=lfs diff=lfs merge=lfs -text
+COLMAP.glb filter=lfs diff=lfs merge=lfs -text
+GIM.glb filter=lfs diff=lfs merge=lfs -text
diff --git a/third_party/gim/.gitignore b/third_party/gim/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..1d0381bab030aebcf64e7cf84889fe8a90bb0286
--- /dev/null
+++ b/third_party/gim/.gitignore
@@ -0,0 +1,6 @@
+
+.idea/
+
+.DS_Store
+
+**/__pycache__/
diff --git a/third_party/gim/LICENSE b/third_party/gim/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..69ec044fe829bf0a4bd4515fffa92808de214f03
--- /dev/null
+++ b/third_party/gim/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Xuelun Shen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/third_party/gim/README.md b/third_party/gim/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f8008e5686885577c718cd148a3e8d9fbfb82170
--- /dev/null
+++ b/third_party/gim/README.md
@@ -0,0 +1,195 @@
+
+
+
+
+
+
GIM: Learning Generalizable Image Matcher From Internet Videos
+
+
+
+
+
+
+ Click to show
+ a1_a2_match.png.
+
+
+
+
+
a1_a2_match.png is a visualization of the match between the two images
+
+
+
+
+ Click to show
+ a1_a2_warp.png.
+
+
+
+
+
a1_a2_warp.png shows the effect of projecting image a2 onto image a1 using homography
+
+
+There are more images in the `assets/demo` folder, you can try them out.
+
+
+
+ Click to show other images.
+
+
+
+
+
+
+
+
+
+
+
+## 📌 Citation
+
+If the paper and code from `gim` help your research, we kindly ask you to give a citation to our paper ❤️. Additionally, if you appreciate our work and find this repository useful, giving it a star ⭐️ would be a wonderful way to support our work. Thank you very much.
+
+```bibtex
+@inproceedings{
+xuelun2024gim,
+title={GIM: Learning Generalizable Image Matcher From Internet Videos},
+author={Xuelun Shen and Zhipeng Cai and Wei Yin and Matthias Müller and Zijun Li and Kaixuan Wang and Xiaozhi Chen and Cheng Wang},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024}
+}
+```
+
+## 🌟 Star History
+
+
+
+
+
+
+
+
+
+## License
+
+This repository is under the MIT License. This content/model is provided here for research purposes only. Any use beyond this is your sole responsibility and subject to your securing the necessary rights for your purpose.
diff --git a/third_party/gim/README.zh-CN-simplified.md b/third_party/gim/README.zh-CN-simplified.md
new file mode 100644
index 0000000000000000000000000000000000000000..eaea4462631d9d1ebf037795c7e0cbb1f5f81e65
--- /dev/null
+++ b/third_party/gim/README.zh-CN-simplified.md
@@ -0,0 +1,186 @@
+
+
+
+
+
+
GIM: Learning Generalizable Image Matcher From Internet Videos
+
+
+## 📌 引用
+
+如果我们的代码对你的研究有帮助, 请给我们的论文一个引用 ❤️ 并给 gim 的仓库点个小星星 ⭐️ 吧, 多谢啦～
+
+```bibtex
+@inproceedings{
+xuelun2024gim,
+title={GIM: Learning Generalizable Image Matcher From Internet Videos},
+author={Xuelun Shen and Zhipeng Cai and Wei Yin and Matthias Müller and Zijun Li and Kaixuan Wang and Xiaozhi Chen and Cheng Wang},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024}
+}
+```
+
+## License
+
+This repository is under the MIT License. This content/model is provided here for research purposes only. Any use beyond this is your sole responsibility and subject to your securing the necessary rights for your purpose.
diff --git a/imcui/third_party/gim/demo.py b/third_party/gim/demo.py
similarity index 84%
rename from imcui/third_party/gim/demo.py
rename to third_party/gim/demo.py
index 4af940a9719931852f0d517c2f44732a3d724846..3a7980c5227f88dd5297f69a0563914b30998420 100644
--- a/imcui/third_party/gim/demo.py
+++ b/third_party/gim/demo.py
@@ -10,13 +10,10 @@ import matplotlib.pyplot as plt
import torchvision.transforms.functional as F
from os.path import join
-from tools import get_padding_size
-from networks.loftr.loftr import LoFTR
-from networks.loftr.misc import lower_config
-from networks.loftr.config import get_cfg_defaults
-from networks.dkm.models.model_zoo.DKMv3 import DKMv3
-from networks.lightglue.superpoint import SuperPoint
-from networks.lightglue.models.matchers.lightglue import LightGlue
+
+from dkm.models.model_zoo.DKMv3 import DKMv3
+from gluefactory.superpoint import SuperPoint
+from gluefactory.models.matchers.lightglue import LightGlue
DEFAULT_MIN_NUM_MATCHES = 4
DEFAULT_RANSAC_MAX_ITER = 10000
@@ -302,14 +299,13 @@ def plot_images(imgs, titles=None, cmaps="gray", dpi=100, size=5, pad=0.5):
def fig2im(fig):
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
- buf_ndarray = np.frombuffer(fig.canvas.buffer_rgba(), dtype="u1")
- # noinspection PyArgumentList
- im = buf_ndarray.reshape(h, w, 4)
+ buf_ndarray = np.frombuffer(fig.canvas.tostring_rgb(), dtype="u1")
+ im = buf_ndarray.reshape(h, w, 3)
return im
if __name__ == '__main__':
- model_zoo = ['gim_dkm', 'gim_loftr', 'gim_lightglue']
+ model_zoo = ['gim_dkm', 'gim_lightglue']
# model
parser = argparse.ArgumentParser()
@@ -326,9 +322,6 @@ if __name__ == '__main__':
if args.model == 'gim_dkm':
ckpt = 'gim_dkm_100h.ckpt'
model = DKMv3(weights=None, h=672, w=896)
- elif args.model == 'gim_loftr':
- ckpt = 'gim_loftr_50h.ckpt'
- model = LoFTR(lower_config(get_cfg_defaults())['loftr'])
elif args.model == 'gim_lightglue':
ckpt = 'gim_lightglue_100h.ckpt'
detector = SuperPoint({
@@ -358,11 +351,6 @@ if __name__ == '__main__':
state_dict.pop(k)
model.load_state_dict(state_dict)
- elif args.model == 'gim_loftr':
- state_dict = torch.load(checkpoints_path, map_location='cpu')
- if 'state_dict' in state_dict.keys(): state_dict = state_dict['state_dict']
- model.load_state_dict(state_dict)
-
elif args.model == 'gim_lightglue':
state_dict = torch.load(checkpoints_path, map_location='cpu')
if 'state_dict' in state_dict.keys(): state_dict = state_dict['state_dict']
@@ -402,22 +390,16 @@ if __name__ == '__main__':
image0 = image0.to(device)[None]
image1 = image1.to(device)[None]
- b_ids, mconf, kpts0, kpts1 = None, None, None, None
data = dict(color0=image0, color1=image1, image0=image0, image1=image1)
if args.model == 'gim_dkm':
- orig_width0, orig_height0, pad_left0, pad_right0, pad_top0, pad_bottom0 = get_padding_size(image0, 672, 896)
- orig_width1, orig_height1, pad_left1, pad_right1, pad_top1, pad_bottom1 = get_padding_size(image1, 672, 896)
- image0_ = torch.nn.functional.pad(image0, (pad_left0, pad_right0, pad_top0, pad_bottom0))
- image1_ = torch.nn.functional.pad(image1, (pad_left1, pad_right1, pad_top1, pad_bottom1))
-
with warnings.catch_warnings():
warnings.simplefilter("ignore")
- dense_matches, dense_certainty = model.match(image0_, image1_)
+ dense_matches, dense_certainty = model.match(image0, image1)
sparse_matches, mconf = model.sample(dense_matches, dense_certainty, 5000)
- height0, width0 = image0_.shape[-2:]
- height1, width1 = image1_.shape[-2:]
+ height0, width0 = image0.shape[-2:]
+ height1, width1 = image1.shape[-2:]
kpts0 = sparse_matches[:, :2]
kpts0 = torch.stack((
@@ -426,33 +408,6 @@ if __name__ == '__main__':
kpts1 = torch.stack((
width1 * (kpts1[:, 0] + 1) / 2, height1 * (kpts1[:, 1] + 1) / 2), dim=-1,)
b_ids = torch.where(mconf[None])[0]
-
- # before padding
- kpts0 -= kpts0.new_tensor((pad_left0, pad_top0))[None]
- kpts1 -= kpts1.new_tensor((pad_left1, pad_top1))[None]
- mask_ = (kpts0[:, 0] > 0) & \
- (kpts0[:, 1] > 0) & \
- (kpts1[:, 0] > 0) & \
- (kpts1[:, 1] > 0)
- mask_ = mask_ & \
- (kpts0[:, 0] <= (orig_width0 - 1)) & \
- (kpts1[:, 0] <= (orig_width1 - 1)) & \
- (kpts0[:, 1] <= (orig_height0 - 1)) & \
- (kpts1[:, 1] <= (orig_height1 - 1))
-
- mconf = mconf[mask_]
- b_ids = b_ids[mask_]
- kpts0 = kpts0[mask_]
- kpts1 = kpts1[mask_]
-
- elif args.model == 'gim_loftr':
- with torch.no_grad():
- model(data)
- kpts0 = data['mkpts0_f']
- kpts1 = data['mkpts1_f']
- b_ids = data['m_bids']
- mconf = data['mconf']
-
elif args.model == 'gim_lightglue':
gray0 = read_image(img_path0, grayscale=True)
gray1 = read_image(img_path1, grayscale=True)
@@ -473,16 +428,16 @@ if __name__ == '__main__':
data.update(dict(scale0=scale0, scale1=scale1))
pred = {}
- with torch.no_grad():
- pred.update({k + '0': v for k, v in detector({
- "image": data["gray0"],
- }).items()})
- pred.update({k + '1': v for k, v in detector({
- "image": data["gray1"],
- }).items()})
- pred.update(model({**pred, **data,
- **{'image_size0': data['size0'],
- 'image_size1': data['size1']}}))
+ pred.update({k + '0': v for k, v in detector({
+ "image": data["gray0"],
+ "image_size": data["size0"],
+ }).items()})
+ pred.update({k + '1': v for k, v in detector({
+ "image": data["gray1"],
+ "image_size": data["size1"],
+ }).items()})
+ pred.update(model({**pred, **data,
+ **{'resize0': data['size0'], 'resize1': data['size1']}}))
kpts0 = torch.cat([kp * s for kp, s in zip(pred['keypoints0'], data['scale0'][:, None])])
kpts1 = torch.cat([kp * s for kp, s in zip(pred['keypoints1'], data['scale1'][:, None])])
diff --git a/imcui/third_party/gim/networks/__init__.py b/third_party/gim/gim/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/__init__.py
rename to third_party/gim/gim/__init__.py
diff --git a/imcui/third_party/DKM/dkm/__init__.py b/third_party/gim/gim/dkm/__init__.py
similarity index 100%
rename from imcui/third_party/DKM/dkm/__init__.py
rename to third_party/gim/gim/dkm/__init__.py
diff --git a/third_party/gim/gim/dkm/benchmarks/__init__.py b/third_party/gim/gim/dkm/benchmarks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..57643fd314a2301138aecdc804a5877d0ce9274e
--- /dev/null
+++ b/third_party/gim/gim/dkm/benchmarks/__init__.py
@@ -0,0 +1,4 @@
+from .hpatches_sequences_homog_benchmark import HpatchesHomogBenchmark
+from .scannet_benchmark import ScanNetBenchmark
+from .megadepth1500_benchmark import Megadepth1500Benchmark
+from .megadepth_dense_benchmark import MegadepthDenseBenchmark
diff --git a/imcui/third_party/DKM/dkm/benchmarks/hpatches_sequences_homog_benchmark.py b/third_party/gim/gim/dkm/benchmarks/hpatches_sequences_homog_benchmark.py
similarity index 100%
rename from imcui/third_party/DKM/dkm/benchmarks/hpatches_sequences_homog_benchmark.py
rename to third_party/gim/gim/dkm/benchmarks/hpatches_sequences_homog_benchmark.py
diff --git a/imcui/third_party/DKM/dkm/benchmarks/megadepth1500_benchmark.py b/third_party/gim/gim/dkm/benchmarks/megadepth1500_benchmark.py
similarity index 100%
rename from imcui/third_party/DKM/dkm/benchmarks/megadepth1500_benchmark.py
rename to third_party/gim/gim/dkm/benchmarks/megadepth1500_benchmark.py
diff --git a/imcui/third_party/DKM/dkm/benchmarks/megadepth_dense_benchmark.py b/third_party/gim/gim/dkm/benchmarks/megadepth_dense_benchmark.py
similarity index 100%
rename from imcui/third_party/DKM/dkm/benchmarks/megadepth_dense_benchmark.py
rename to third_party/gim/gim/dkm/benchmarks/megadepth_dense_benchmark.py
diff --git a/imcui/third_party/DKM/dkm/benchmarks/scannet_benchmark.py b/third_party/gim/gim/dkm/benchmarks/scannet_benchmark.py
similarity index 100%
rename from imcui/third_party/DKM/dkm/benchmarks/scannet_benchmark.py
rename to third_party/gim/gim/dkm/benchmarks/scannet_benchmark.py
diff --git a/third_party/gim/gim/dkm/checkpointing/__init__.py b/third_party/gim/gim/dkm/checkpointing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..22f5afe727aa6f6e8fffa9ecf5be69cbff686577
--- /dev/null
+++ b/third_party/gim/gim/dkm/checkpointing/__init__.py
@@ -0,0 +1 @@
+from .checkpoint import CheckPoint
diff --git a/third_party/gim/gim/dkm/checkpointing/checkpoint.py b/third_party/gim/gim/dkm/checkpointing/checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..715eeb587ebb87ed0d1bcf9940e048adbe35cde2
--- /dev/null
+++ b/third_party/gim/gim/dkm/checkpointing/checkpoint.py
@@ -0,0 +1,31 @@
+import os
+import torch
+from torch.nn.parallel.data_parallel import DataParallel
+from torch.nn.parallel.distributed import DistributedDataParallel
+from loguru import logger
+
+
+class CheckPoint:
+ def __init__(self, dir=None, name="tmp"):
+ self.name = name
+ self.dir = dir
+ os.makedirs(self.dir, exist_ok=True)
+
+ def __call__(
+ self,
+ model,
+ optimizer,
+ lr_scheduler,
+ n,
+ ):
+ assert model is not None
+ if isinstance(model, (DataParallel, DistributedDataParallel)):
+ model = model.module
+ states = {
+ "model": model.state_dict(),
+ "n": n,
+ "optimizer": optimizer.state_dict(),
+ "lr_scheduler": lr_scheduler.state_dict(),
+ }
+ torch.save(states, self.dir + self.name + f"_latest.pth")
+ logger.info(f"Saved states {list(states.keys())}, at step {n}")
diff --git a/third_party/gim/gim/dkm/datasets/__init__.py b/third_party/gim/gim/dkm/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b81083212edaf345c30f0cb1116c5f9de284ce6
--- /dev/null
+++ b/third_party/gim/gim/dkm/datasets/__init__.py
@@ -0,0 +1 @@
+from .megadepth import MegadepthBuilder
diff --git a/third_party/gim/gim/dkm/datasets/megadepth.py b/third_party/gim/gim/dkm/datasets/megadepth.py
new file mode 100644
index 0000000000000000000000000000000000000000..c580607e910ce1926b7711b5473aa82b20865369
--- /dev/null
+++ b/third_party/gim/gim/dkm/datasets/megadepth.py
@@ -0,0 +1,177 @@
+import os
+import random
+from PIL import Image
+import h5py
+import numpy as np
+import torch
+from torch.utils.data import Dataset, DataLoader, ConcatDataset
+
+from dkm.utils import get_depth_tuple_transform_ops, get_tuple_transform_ops
+import torchvision.transforms.functional as tvf
+from dkm.utils.transforms import GeometricSequential
+import kornia.augmentation as K
+
+
+class MegadepthScene:
+ def __init__(
+ self,
+ data_root,
+ scene_info,
+ ht=384,
+ wt=512,
+ min_overlap=0.0,
+ shake_t=0,
+ rot_prob=0.0,
+ normalize=True,
+ ) -> None:
+ self.data_root = data_root
+ self.image_paths = scene_info["image_paths"]
+ self.depth_paths = scene_info["depth_paths"]
+ self.intrinsics = scene_info["intrinsics"]
+ self.poses = scene_info["poses"]
+ self.pairs = scene_info["pairs"]
+ self.overlaps = scene_info["overlaps"]
+ threshold = self.overlaps > min_overlap
+ self.pairs = self.pairs[threshold]
+ self.overlaps = self.overlaps[threshold]
+ if len(self.pairs) > 100000:
+ pairinds = np.random.choice(
+ np.arange(0, len(self.pairs)), 100000, replace=False
+ )
+ self.pairs = self.pairs[pairinds]
+ self.overlaps = self.overlaps[pairinds]
+ # counts, bins = np.histogram(self.overlaps,20)
+ # print(counts)
+ self.im_transform_ops = get_tuple_transform_ops(
+ resize=(ht, wt), normalize=normalize
+ )
+ self.depth_transform_ops = get_depth_tuple_transform_ops(
+ resize=(ht, wt), normalize=False
+ )
+ self.wt, self.ht = wt, ht
+ self.shake_t = shake_t
+ self.H_generator = GeometricSequential(K.RandomAffine(degrees=90, p=rot_prob))
+
+ def load_im(self, im_ref, crop=None):
+ im = Image.open(im_ref)
+ return im
+
+ def load_depth(self, depth_ref, crop=None):
+ depth = np.array(h5py.File(depth_ref, "r")["depth"])
+ return torch.from_numpy(depth)
+
+ def __len__(self):
+ return len(self.pairs)
+
+ def scale_intrinsic(self, K, wi, hi):
+ sx, sy = self.wt / wi, self.ht / hi
+ sK = torch.tensor([[sx, 0, 0], [0, sy, 0], [0, 0, 1]])
+ return sK @ K
+
+ def rand_shake(self, *things):
+ t = np.random.choice(range(-self.shake_t, self.shake_t + 1), size=2)
+ return [
+ tvf.affine(thing, angle=0.0, translate=list(t), scale=1.0, shear=[0.0, 0.0])
+ for thing in things
+ ], t
+
+ def __getitem__(self, pair_idx):
+ # read intrinsics of original size
+ idx1, idx2 = self.pairs[pair_idx]
+ K1 = torch.tensor(self.intrinsics[idx1].copy(), dtype=torch.float).reshape(3, 3)
+ K2 = torch.tensor(self.intrinsics[idx2].copy(), dtype=torch.float).reshape(3, 3)
+
+ # read and compute relative poses
+ T1 = self.poses[idx1]
+ T2 = self.poses[idx2]
+ T_1to2 = torch.tensor(np.matmul(T2, np.linalg.inv(T1)), dtype=torch.float)[
+ :4, :4
+ ] # (4, 4)
+
+ # Load positive pair data
+ im1, im2 = self.image_paths[idx1], self.image_paths[idx2]
+ depth1, depth2 = self.depth_paths[idx1], self.depth_paths[idx2]
+ im_src_ref = os.path.join(self.data_root, im1)
+ im_pos_ref = os.path.join(self.data_root, im2)
+ depth_src_ref = os.path.join(self.data_root, depth1)
+ depth_pos_ref = os.path.join(self.data_root, depth2)
+ # return torch.randn((1000,1000))
+ im_src = self.load_im(im_src_ref)
+ im_pos = self.load_im(im_pos_ref)
+ depth_src = self.load_depth(depth_src_ref)
+ depth_pos = self.load_depth(depth_pos_ref)
+
+ # Recompute camera intrinsic matrix due to the resize
+ K1 = self.scale_intrinsic(K1, im_src.width, im_src.height)
+ K2 = self.scale_intrinsic(K2, im_pos.width, im_pos.height)
+ # Process images
+ im_src, im_pos = self.im_transform_ops((im_src, im_pos))
+ depth_src, depth_pos = self.depth_transform_ops(
+ (depth_src[None, None], depth_pos[None, None])
+ )
+ [im_src, im_pos, depth_src, depth_pos], t = self.rand_shake(
+ im_src, im_pos, depth_src, depth_pos
+ )
+ im_src, Hq = self.H_generator(im_src[None])
+ depth_src = self.H_generator.apply_transform(depth_src, Hq)
+ K1[:2, 2] += t
+ K2[:2, 2] += t
+ K1 = Hq[0] @ K1
+ data_dict = {
+ "query": im_src[0],
+ "query_identifier": self.image_paths[idx1].split("/")[-1].split(".jpg")[0],
+ "support": im_pos,
+ "support_identifier": self.image_paths[idx2]
+ .split("/")[-1]
+ .split(".jpg")[0],
+ "query_depth": depth_src[0, 0],
+ "support_depth": depth_pos[0, 0],
+ "K1": K1,
+ "K2": K2,
+ "T_1to2": T_1to2,
+ }
+ return data_dict
+
+
+class MegadepthBuilder:
+ def __init__(self, data_root="data/megadepth") -> None:
+ self.data_root = data_root
+ self.scene_info_root = os.path.join(data_root, "prep_scene_info")
+ self.all_scenes = os.listdir(self.scene_info_root)
+ self.test_scenes = ["0017.npy", "0004.npy", "0048.npy", "0013.npy"]
+ self.test_scenes_loftr = ["0015.npy", "0022.npy"]
+
+ def build_scenes(self, split="train", min_overlap=0.0, **kwargs):
+ if split == "train":
+ scene_names = set(self.all_scenes) - set(self.test_scenes)
+ elif split == "train_loftr":
+ scene_names = set(self.all_scenes) - set(self.test_scenes_loftr)
+ elif split == "test":
+ scene_names = self.test_scenes
+ elif split == "test_loftr":
+ scene_names = self.test_scenes_loftr
+ else:
+ raise ValueError(f"Split {split} not available")
+ scenes = []
+ for scene_name in scene_names:
+ scene_info = np.load(
+ os.path.join(self.scene_info_root, scene_name), allow_pickle=True
+ ).item()
+ scenes.append(
+ MegadepthScene(
+ self.data_root, scene_info, min_overlap=min_overlap, **kwargs
+ )
+ )
+ return scenes
+
+ def weight_scenes(self, concat_dataset, alpha=0.5):
+ ns = []
+ for d in concat_dataset.datasets:
+ ns.append(len(d))
+ ws = torch.cat([torch.ones(n) / n**alpha for n in ns])
+ return ws
+
+
+if __name__ == "__main__":
+ mega_test = ConcatDataset(MegadepthBuilder().build_scenes(split="train"))
+ mega_test[0]
diff --git a/imcui/third_party/DKM/dkm/datasets/scannet.py b/third_party/gim/gim/dkm/datasets/scannet.py
similarity index 100%
rename from imcui/third_party/DKM/dkm/datasets/scannet.py
rename to third_party/gim/gim/dkm/datasets/scannet.py
diff --git a/third_party/gim/gim/dkm/losses/__init__.py b/third_party/gim/gim/dkm/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..71914f50d891079d204a07c57367159888f892de
--- /dev/null
+++ b/third_party/gim/gim/dkm/losses/__init__.py
@@ -0,0 +1 @@
+from .depth_match_regression_loss import DepthRegressionLoss
diff --git a/third_party/gim/gim/dkm/losses/depth_match_regression_loss.py b/third_party/gim/gim/dkm/losses/depth_match_regression_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..80da70347b4b4addc721e2a14ed489f8683fd48a
--- /dev/null
+++ b/third_party/gim/gim/dkm/losses/depth_match_regression_loss.py
@@ -0,0 +1,128 @@
+from einops.einops import rearrange
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dkm.utils.utils import warp_kpts
+
+
+class DepthRegressionLoss(nn.Module):
+ def __init__(
+ self,
+ robust=True,
+ center_coords=False,
+ scale_normalize=False,
+ ce_weight=0.01,
+ local_loss=True,
+ local_dist=4.0,
+ local_largest_scale=8,
+ ):
+ super().__init__()
+ self.robust = robust # measured in pixels
+ self.center_coords = center_coords
+ self.scale_normalize = scale_normalize
+ self.ce_weight = ce_weight
+ self.local_loss = local_loss
+ self.local_dist = local_dist
+ self.local_largest_scale = local_largest_scale
+
+ def geometric_dist(self, depth1, depth2, T_1to2, K1, K2, dense_matches, scale):
+ """[summary]
+
+ Args:
+ H ([type]): [description]
+ scale ([type]): [description]
+
+ Returns:
+ [type]: [description]
+ """
+ b, h1, w1, d = dense_matches.shape
+ with torch.no_grad():
+ x1_n = torch.meshgrid(
+ *[
+ torch.linspace(
+ -1 + 1 / n, 1 - 1 / n, n, device=dense_matches.device
+ )
+ for n in (b, h1, w1)
+ ]
+ )
+ x1_n = torch.stack((x1_n[2], x1_n[1]), dim=-1).reshape(b, h1 * w1, 2)
+ mask, x2 = warp_kpts(
+ x1_n.double(),
+ depth1.double(),
+ depth2.double(),
+ T_1to2.double(),
+ K1.double(),
+ K2.double(),
+ )
+ prob = mask.float().reshape(b, h1, w1)
+ gd = (dense_matches - x2.reshape(b, h1, w1, 2)).norm(dim=-1) # *scale?
+ return gd, prob
+
+ def dense_depth_loss(self, dense_certainty, prob, gd, scale, eps=1e-8):
+ """[summary]
+
+ Args:
+ dense_certainty ([type]): [description]
+ prob ([type]): [description]
+ eps ([type], optional): [description]. Defaults to 1e-8.
+
+ Returns:
+ [type]: [description]
+ """
+ smooth_prob = prob
+ ce_loss = F.binary_cross_entropy_with_logits(dense_certainty[:, 0], smooth_prob)
+ depth_loss = gd[prob > 0]
+ if not torch.any(prob > 0).item():
+ depth_loss = (gd * 0.0).mean() # Prevent issues where prob is 0 everywhere
+ return {
+ f"ce_loss_{scale}": ce_loss.mean(),
+ f"depth_loss_{scale}": depth_loss.mean(),
+ }
+
+ def forward(self, dense_corresps, batch):
+ """[summary]
+
+ Args:
+ out ([type]): [description]
+ batch ([type]): [description]
+
+ Returns:
+ [type]: [description]
+ """
+ scales = list(dense_corresps.keys())
+ tot_loss = 0.0
+ prev_gd = 0.0
+ for scale in scales:
+ dense_scale_corresps = dense_corresps[scale]
+ dense_scale_certainty, dense_scale_coords = (
+ dense_scale_corresps["dense_certainty"],
+ dense_scale_corresps["dense_flow"],
+ )
+ dense_scale_coords = rearrange(dense_scale_coords, "b d h w -> b h w d")
+ b, h, w, d = dense_scale_coords.shape
+ gd, prob = self.geometric_dist(
+ batch["query_depth"],
+ batch["support_depth"],
+ batch["T_1to2"],
+ batch["K1"],
+ batch["K2"],
+ dense_scale_coords,
+ scale,
+ )
+ if (
+ scale <= self.local_largest_scale and self.local_loss
+ ): # Thought here is that fine matching loss should not be punished by coarse mistakes, but should identify wrong matching
+ prob = prob * (
+ F.interpolate(prev_gd[:, None], size=(h, w), mode="nearest")[:, 0]
+ < (2 / 512) * (self.local_dist * scale)
+ )
+ depth_losses = self.dense_depth_loss(dense_scale_certainty, prob, gd, scale)
+ scale_loss = (
+ self.ce_weight * depth_losses[f"ce_loss_{scale}"]
+ + depth_losses[f"depth_loss_{scale}"]
+ ) # scale ce loss for coarser scales
+ if self.scale_normalize:
+ scale_loss = scale_loss * 1 / scale
+ tot_loss = tot_loss + scale_loss
+ prev_gd = gd.detach()
+ return tot_loss
diff --git a/imcui/third_party/gim/networks/dkm/models/__init__.py b/third_party/gim/gim/dkm/models/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/dkm/models/__init__.py
rename to third_party/gim/gim/dkm/models/__init__.py
diff --git a/imcui/third_party/gim/networks/dkm/models/dkm.py b/third_party/gim/gim/dkm/models/dkm.py
similarity index 96%
rename from imcui/third_party/gim/networks/dkm/models/dkm.py
rename to third_party/gim/gim/dkm/models/dkm.py
index 62fbb9a1000995d940ba816ab9c9c5bf9b5d0895..0cc6d35d5165c797ef7fdd4bc16ac405efa9b02f 100644
--- a/imcui/third_party/gim/networks/dkm/models/dkm.py
+++ b/third_party/gim/gim/dkm/models/dkm.py
@@ -1,11 +1,13 @@
import math
+import os
+import numpy as np
+from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
-from networks.dkm.utils import get_tuple_transform_ops
+from gim.dkm.utils import get_tuple_transform_ops
from einops import rearrange
-from networks.dkm.utils.local_correlation import local_correlation
-from networks.dkm.utils.kde import kde
+from gim.dkm.utils.local_correlation import local_correlation
class ConvRefiner(nn.Module):
@@ -589,7 +591,6 @@ class RegressionMatcher(nn.Module):
if "threshold" in self.sample_mode:
upper_thresh = self.sample_thresh
dense_certainty = dense_certainty.clone()
- dense_certainty_ = dense_certainty.clone()
dense_certainty[dense_certainty > upper_thresh] = 1
elif "pow" in self.sample_mode:
dense_certainty = dense_certainty**(1/3)
@@ -599,19 +600,17 @@ class RegressionMatcher(nn.Module):
dense_matches.reshape(-1, 4),
dense_certainty.reshape(-1),
)
- certainty_ = dense_certainty_.reshape(-1)
expansion_factor = 4 if "balanced" in self.sample_mode else 1
if not certainty.sum(): certainty = certainty + 1e-8
good_samples = torch.multinomial(certainty,
num_samples = min(expansion_factor*num, len(certainty)),
replacement=False)
good_matches, good_certainty = matches[good_samples], certainty[good_samples]
- good_certainty_ = certainty_[good_samples]
- good_certainty = good_certainty_
if "balanced" not in self.sample_mode:
return good_matches, good_certainty
- density = kde(good_matches, std=0.1, device=dense_matches.device)
+ from gim.dkm.utils.kde import kde
+ density = kde(good_matches, std=0.1)
p = 1 / (density+1)
p[density < 10] = 1e-7 # Basically should have at least 10 perfect neighbours, or around 100 ok ones
balanced_samples = torch.multinomial(p,
@@ -719,17 +718,10 @@ class RegressionMatcher(nn.Module):
query_coords = query_coords[None].expand(b, 2, hs, ws)
dense_certainty = dense_certainty.sigmoid() # logits -> probs
query_coords = query_coords.permute(0, 2, 3, 1)
- if (query_to_support.abs() > 1).sum() > 0 and True:
+ if (query_to_support.abs() > 1).any() and True:
wrong = (query_to_support.abs() > 1).sum(dim=-1) > 0
dense_certainty[wrong[:,None]] = 0
- # remove black pixels
- black_mask1 = (im1_path[0, 0] < 0.03125) & (im1_path[0, 1] < 0.03125) & (im1_path[0, 2] < 0.03125)
- black_mask2 = (im2_path[0, 0] < 0.03125) & (im2_path[0, 1] < 0.03125) & (im2_path[0, 2] < 0.03125)
- black_mask1 = F.interpolate(black_mask1.float()[None, None], size=tuple(dense_certainty.shape[-2:]), mode='nearest').bool()
- black_mask2 = F.interpolate(black_mask2.float()[None, None], size=tuple(dense_certainty.shape[-2:]), mode='nearest').bool()
- black_mask = torch.cat((black_mask1, black_mask2), dim=0)
- dense_certainty[black_mask] = 0
-
+
query_to_support = torch.clamp(query_to_support, -1, 1)
if symmetric:
support_coords = query_coords
diff --git a/imcui/third_party/gim/networks/dkm/models/encoders.py b/third_party/gim/gim/dkm/models/encoders.py
similarity index 100%
rename from imcui/third_party/gim/networks/dkm/models/encoders.py
rename to third_party/gim/gim/dkm/models/encoders.py
diff --git a/imcui/third_party/gim/networks/dkm/models/model_zoo/DKMv3.py b/third_party/gim/gim/dkm/models/model_zoo/DKMv3.py
similarity index 97%
rename from imcui/third_party/gim/networks/dkm/models/model_zoo/DKMv3.py
rename to third_party/gim/gim/dkm/models/model_zoo/DKMv3.py
index ab527fa25c2fd39f755398a7d891e45e39fc8774..57f8a8bce35a8b499ece5c11ca42659f4197a95b 100644
--- a/imcui/third_party/gim/networks/dkm/models/model_zoo/DKMv3.py
+++ b/third_party/gim/gim/dkm/models/model_zoo/DKMv3.py
@@ -1,5 +1,8 @@
-from networks.dkm.models.dkm import *
-from networks.dkm.models.encoders import *
+import torch
+
+from torch import nn
+from gim.dkm.models.dkm import *
+from gim.dkm.models.encoders import *
def DKMv3(weights, h, w, symmetric = True, sample_mode= "threshold_balanced", **kwargs):
diff --git a/imcui/third_party/gim/networks/dkm/models/model_zoo/__init__.py b/third_party/gim/gim/dkm/models/model_zoo/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/dkm/models/model_zoo/__init__.py
rename to third_party/gim/gim/dkm/models/model_zoo/__init__.py
diff --git a/third_party/gim/gim/dkm/train/__init__.py b/third_party/gim/gim/dkm/train/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..90269dc0f345a575e0ba21f5afa34202c7e6b433
--- /dev/null
+++ b/third_party/gim/gim/dkm/train/__init__.py
@@ -0,0 +1 @@
+from .train import train_k_epochs
diff --git a/third_party/gim/gim/dkm/train/train.py b/third_party/gim/gim/dkm/train/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..b580221f56a2667784836f0237955cc75131b88c
--- /dev/null
+++ b/third_party/gim/gim/dkm/train/train.py
@@ -0,0 +1,67 @@
+from tqdm import tqdm
+from dkm.utils.utils import to_cuda
+
+
+def train_step(train_batch, model, objective, optimizer, **kwargs):
+ optimizer.zero_grad()
+ out = model(train_batch)
+ l = objective(out, train_batch)
+ l.backward()
+ optimizer.step()
+ return {"train_out": out, "train_loss": l.item()}
+
+
+def train_k_steps(
+ n_0, k, dataloader, model, objective, optimizer, lr_scheduler, progress_bar=True
+):
+ for n in tqdm(range(n_0, n_0 + k), disable=not progress_bar):
+ batch = next(dataloader)
+ model.train(True)
+ batch = to_cuda(batch)
+ train_step(
+ train_batch=batch,
+ model=model,
+ objective=objective,
+ optimizer=optimizer,
+ lr_scheduler=lr_scheduler,
+ n=n,
+ )
+ lr_scheduler.step()
+
+
+def train_epoch(
+ dataloader=None,
+ model=None,
+ objective=None,
+ optimizer=None,
+ lr_scheduler=None,
+ epoch=None,
+):
+ model.train(True)
+ print(f"At epoch {epoch}")
+ for batch in tqdm(dataloader, mininterval=5.0):
+ batch = to_cuda(batch)
+ train_step(
+ train_batch=batch, model=model, objective=objective, optimizer=optimizer
+ )
+ lr_scheduler.step()
+ return {
+ "model": model,
+ "optimizer": optimizer,
+ "lr_scheduler": lr_scheduler,
+ "epoch": epoch,
+ }
+
+
+def train_k_epochs(
+ start_epoch, end_epoch, dataloader, model, objective, optimizer, lr_scheduler
+):
+ for epoch in range(start_epoch, end_epoch + 1):
+ train_epoch(
+ dataloader=dataloader,
+ model=model,
+ objective=objective,
+ optimizer=optimizer,
+ lr_scheduler=lr_scheduler,
+ epoch=epoch,
+ )
diff --git a/imcui/third_party/gim/networks/dkm/utils/__init__.py b/third_party/gim/gim/dkm/utils/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/dkm/utils/__init__.py
rename to third_party/gim/gim/dkm/utils/__init__.py
diff --git a/imcui/third_party/DKM/dkm/utils/kde.py b/third_party/gim/gim/dkm/utils/kde.py
similarity index 100%
rename from imcui/third_party/DKM/dkm/utils/kde.py
rename to third_party/gim/gim/dkm/utils/kde.py
diff --git a/imcui/third_party/DKM/dkm/utils/local_correlation.py b/third_party/gim/gim/dkm/utils/local_correlation.py
similarity index 100%
rename from imcui/third_party/DKM/dkm/utils/local_correlation.py
rename to third_party/gim/gim/dkm/utils/local_correlation.py
diff --git a/imcui/third_party/gim/networks/dkm/utils/transforms.py b/third_party/gim/gim/dkm/utils/transforms.py
similarity index 100%
rename from imcui/third_party/gim/networks/dkm/utils/transforms.py
rename to third_party/gim/gim/dkm/utils/transforms.py
diff --git a/imcui/third_party/gim/networks/dkm/utils/utils.py b/third_party/gim/gim/dkm/utils/utils.py
similarity index 100%
rename from imcui/third_party/gim/networks/dkm/utils/utils.py
rename to third_party/gim/gim/dkm/utils/utils.py
diff --git a/third_party/gim/gim/gluefactory/__init__.py b/third_party/gim/gim/gluefactory/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d83f92d44af898b99adf75f45900efb4178b096
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/__init__.py
@@ -0,0 +1,17 @@
+import logging
+
+from .utils.experiments import load_experiment # noqa: F401
+
+formatter = logging.Formatter(
+ fmt="[%(asctime)s %(name)s %(levelname)s] %(message)s", datefmt="%m/%d/%Y %H:%M:%S"
+)
+handler = logging.StreamHandler()
+handler.setFormatter(formatter)
+handler.setLevel(logging.INFO)
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+logger.addHandler(handler)
+logger.propagate = False
+
+__module_name__ = __name__
diff --git a/third_party/gim/gim/gluefactory/configs/aliked+NN.yaml b/third_party/gim/gim/gluefactory/configs/aliked+NN.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3490ce3c4864b1bcef196658835cec22bf3074a7
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/aliked+NN.yaml
@@ -0,0 +1,24 @@
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.aliked
+ max_num_keypoints: 2048
+ detection_threshold: 0.0
+ matcher:
+ name: matchers.nearest_neighbor_matcher
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024 # overwrite config above
diff --git a/third_party/gim/gim/gluefactory/configs/aliked+lightglue-official.yaml b/third_party/gim/gim/gluefactory/configs/aliked+lightglue-official.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..47bd826621ed9253d72e74f6f8a5714aac90dadc
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/aliked+lightglue-official.yaml
@@ -0,0 +1,28 @@
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.aliked
+ max_num_keypoints: 2048
+ detection_threshold: 0.0
+ matcher:
+ name: matchers.lightglue_pretrained
+ features: aliked
+ depth_confidence: -1
+ width_confidence: -1
+ filter_threshold: 0.1
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024 # overwrite config above
diff --git a/third_party/gim/gim/gluefactory/configs/aliked+lightglue_homography.yaml b/third_party/gim/gim/gluefactory/configs/aliked+lightglue_homography.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cf54aa31348b33e35f59bee526cd83a873d56f1f
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/aliked+lightglue_homography.yaml
@@ -0,0 +1,50 @@
+data:
+ name: homographies
+ data_dir: revisitop1m
+ train_size: 150000
+ val_size: 2000
+ batch_size: 128
+ num_workers: 14
+ homography:
+ difficulty: 0.7
+ max_angle: 45
+ photometric:
+ name: lg
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.aliked
+ max_num_keypoints: 512
+ detection_threshold: 0.0
+ trainable: False
+ detector:
+ name: null
+ descriptor:
+ name: null
+ ground_truth:
+ name: matchers.homography_matcher
+ th_positive: 3
+ th_negative: 3
+ matcher:
+ name: matchers.lightglue
+ filter_threshold: 0.1
+ flash: false
+ checkpointed: true
+ input_dim: 128
+train:
+ seed: 0
+ epochs: 40
+ log_every_iter: 100
+ eval_every_iter: 500
+ lr: 1e-4
+ lr_schedule:
+ start: 20
+ type: exp
+ on_epoch: true
+ exp_div_10: 10
+ plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
+benchmarks:
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
diff --git a/third_party/gim/gim/gluefactory/configs/aliked+lightglue_megadepth.yaml b/third_party/gim/gim/gluefactory/configs/aliked+lightglue_megadepth.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..12e27a845123eb63a5313d82da5d198e4c6a1dc4
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/aliked+lightglue_megadepth.yaml
@@ -0,0 +1,70 @@
+data:
+ name: megadepth
+ preprocessing:
+ resize: 1024
+ side: long
+ square_pad: True
+ train_split: train_scenes_clean.txt
+ train_num_per_scene: 300
+ val_split: valid_scenes_clean.txt
+ val_pairs: valid_pairs.txt
+ min_overlap: 0.1
+ max_overlap: 0.7
+ num_overlap_bins: 3
+ read_depth: true
+ read_image: true
+ batch_size: 32
+ num_workers: 14
+ load_features:
+ do: false # enable this if you have cached predictions
+ path: exports/megadepth-undist-depth-r1024_ALIKED-k2048-n16/{scene}.h5
+ padding_length: 2048
+ padding_fn: pad_local_features
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.aliked
+ max_num_keypoints: 2048
+ detection_threshold: 0.0
+ trainable: False
+ matcher:
+ name: matchers.lightglue
+ filter_threshold: 0.1
+ flash: false
+ checkpointed: true
+ input_dim: 128
+ ground_truth:
+ name: matchers.depth_matcher
+ th_positive: 3
+ th_negative: 5
+ th_epi: 5
+ allow_no_extract: True
+train:
+ seed: 0
+ epochs: 50
+ log_every_iter: 100
+ eval_every_iter: 1000
+ lr: 1e-4
+ lr_schedule:
+ start: 30
+ type: exp
+ on_epoch: true
+ exp_div_10: 10
+ dataset_callback_fn: sample_new_items
+ plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024
diff --git a/third_party/gim/gim/gluefactory/configs/disk+NN.yaml b/third_party/gim/gim/gluefactory/configs/disk+NN.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fa6054a67a60a5c95b8b61320af6c5ed666e6cd0
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/disk+NN.yaml
@@ -0,0 +1,24 @@
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.disk_kornia
+ max_num_keypoints: 2048
+ detection_threshold: 0.0
+ matcher:
+ name: matchers.nearest_neighbor_matcher
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024 # overwrite config above
diff --git a/third_party/gim/gim/gluefactory/configs/disk+lightglue-official.yaml b/third_party/gim/gim/gluefactory/configs/disk+lightglue-official.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8d0fdb0b4bee9fa6aaad56cb3c5206ad5b4a4f96
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/disk+lightglue-official.yaml
@@ -0,0 +1,28 @@
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.disk_kornia
+ max_num_keypoints: 2048
+ detection_threshold: 0.0
+ matcher:
+ name: matchers.lightglue_pretrained
+ features: disk
+ depth_confidence: -1
+ width_confidence: -1
+ filter_threshold: 0.1
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024 # overwrite config above
diff --git a/third_party/gim/gim/gluefactory/configs/disk+lightglue_homography.yaml b/third_party/gim/gim/gluefactory/configs/disk+lightglue_homography.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..867b1a2b53c1063cfc5bc62e265e151f6c9a716c
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/disk+lightglue_homography.yaml
@@ -0,0 +1,47 @@
+data:
+ name: homographies
+ data_dir: revisitop1m
+ train_size: 150000
+ val_size: 2000
+ batch_size: 128
+ num_workers: 14
+ homography:
+ difficulty: 0.7
+ max_angle: 45
+ photometric:
+ name: lg
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.disk_kornia
+ max_num_keypoints: 512
+ force_num_keypoints: True
+ detection_threshold: 0.0
+ trainable: False
+ ground_truth:
+ name: matchers.homography_matcher
+ th_positive: 3
+ th_negative: 3
+ matcher:
+ name: matchers.lightglue
+ filter_threshold: 0.1
+ input_dim: 128
+ flash: false
+ checkpointed: true
+train:
+ seed: 0
+ epochs: 40
+ log_every_iter: 100
+ eval_every_iter: 500
+ lr: 1e-4
+ lr_schedule:
+ start: 20
+ type: exp
+ on_epoch: true
+ exp_div_10: 10
+ plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
+benchmarks:
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
diff --git a/third_party/gim/gim/gluefactory/configs/disk+lightglue_megadepth.yaml b/third_party/gim/gim/gluefactory/configs/disk+lightglue_megadepth.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0beb37948c43ce5df2a3f30fb35442e94f4e6f97
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/disk+lightglue_megadepth.yaml
@@ -0,0 +1,70 @@
+data:
+ name: megadepth
+ preprocessing:
+ resize: 1024
+ side: long
+ square_pad: True
+ train_split: train_scenes_clean.txt
+ train_num_per_scene: 300
+ val_split: valid_scenes_clean.txt
+ val_pairs: valid_pairs.txt
+ min_overlap: 0.1
+ max_overlap: 0.7
+ num_overlap_bins: 3
+ read_depth: true
+ read_image: true
+ batch_size: 32
+ num_workers: 14
+ load_features:
+ do: false # enable this if you have cached predictions
+ path: exports/megadepth-undist-depth-r1024_DISK-k2048-nms5/{scene}.h5
+ padding_length: 2048
+ padding_fn: pad_local_features
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.disk_kornia
+ max_num_keypoints: 512
+ force_num_keypoints: True
+ detection_threshold: 0.0
+ trainable: False
+ ground_truth:
+ name: matchers.homography_matcher
+ th_positive: 3
+ th_negative: 3
+ matcher:
+ name: matchers.lightglue
+ filter_threshold: 0.1
+ input_dim: 128
+ flash: false
+ checkpointed: true
+ allow_no_extract: True
+train:
+ seed: 0
+ epochs: 50
+ log_every_iter: 100
+ eval_every_iter: 1000
+ lr: 1e-4
+ lr_schedule:
+ start: 30
+ type: exp
+ on_epoch: true
+ exp_div_10: 10
+ dataset_callback_fn: sample_new_items
+ plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1024
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024
diff --git a/third_party/gim/gim/gluefactory/configs/sift+NN.yaml b/third_party/gim/gim/gluefactory/configs/sift+NN.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..67f296924789414f39d8f91cd9456bca38cc838e
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/sift+NN.yaml
@@ -0,0 +1,28 @@
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.sift
+ detector: pycolmap_cuda
+ max_num_keypoints: 2048
+ detection_threshold: 0.00666666
+ nms_radius: -1
+ pycolmap_options:
+ first_octave: -1
+ matcher:
+ name: matchers.nearest_neighbor_matcher
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024 # overwrite config above
diff --git a/third_party/gim/gim/gluefactory/configs/sift+lightglue-official.yaml b/third_party/gim/gim/gluefactory/configs/sift+lightglue-official.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7d22df58106fe974779646bf23ad55a9bbf509f8
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/sift+lightglue-official.yaml
@@ -0,0 +1,28 @@
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.sift
+ backend: pycolmap_cuda
+ max_num_keypoints: 4096
+ matcher:
+ name: matchers.lightglue_pretrained
+ features: sift
+ depth_confidence: -1
+ width_confidence: -1
+ filter_threshold: 0.1
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024 # overwrite config above
diff --git a/third_party/gim/gim/gluefactory/configs/sift+lightglue_homography.yaml b/third_party/gim/gim/gluefactory/configs/sift+lightglue_homography.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2822a4f8e0f1f1dd0b383ed505caac9ca6ee38d6
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/sift+lightglue_homography.yaml
@@ -0,0 +1,51 @@
+data:
+ name: homographies
+ data_dir: revisitop1m
+ train_size: 150000
+ val_size: 2000
+ batch_size: 64
+ num_workers: 14
+ homography:
+ difficulty: 0.7
+ max_angle: 45
+ photometric:
+ name: lg
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.sift
+ backend: pycolmap_cuda
+ max_num_keypoints: 1024
+ force_num_keypoints: True
+ nms_radius: 3
+ trainable: False
+ ground_truth:
+ name: matchers.homography_matcher
+ th_positive: 3
+ th_negative: 3
+ matcher:
+ name: matchers.lightglue
+ filter_threshold: 0.1
+ flash: false
+ checkpointed: true
+ input_dim: 128
+train:
+ seed: 0
+ epochs: 40
+ log_every_iter: 100
+ eval_every_iter: 500
+ lr: 1e-4
+ lr_schedule:
+ start: 20
+ type: exp
+ on_epoch: true
+ exp_div_10: 10
+ plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
+benchmarks:
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ nms_radius: 0
diff --git a/third_party/gim/gim/gluefactory/configs/sift+lightglue_megadepth.yaml b/third_party/gim/gim/gluefactory/configs/sift+lightglue_megadepth.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bc8c87b34c53622496e2ba95ca7f588d947fc12b
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/sift+lightglue_megadepth.yaml
@@ -0,0 +1,78 @@
+data:
+ name: megadepth
+ preprocessing:
+ resize: 1024
+ side: long
+ square_pad: True
+ train_split: train_scenes_clean.txt
+ train_num_per_scene: 300
+ val_split: valid_scenes_clean.txt
+ val_pairs: valid_pairs.txt
+ min_overlap: 0.1
+ max_overlap: 0.7
+ num_overlap_bins: 3
+ read_depth: true
+ read_image: true
+ batch_size: 32
+ num_workers: 14
+ load_features:
+ do: false # enable this if you have cached predictions
+ path: exports/megadepth-undist-depth-r1024_pycolmap_SIFTGPU-nms3-fixed-k2048/{scene}.h5
+ padding_length: 2048
+ padding_fn: pad_local_features
+ data_keys: ["keypoints", "keypoint_scores", "descriptors", "oris", "scales"]
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.sift
+ backend: pycolmap_cuda
+ max_num_keypoints: 2048
+ force_num_keypoints: True
+ nms_radius: 3
+ trainable: False
+ matcher:
+ name: matchers.lightglue
+ filter_threshold: 0.1
+ flash: false
+ checkpointed: true
+ add_scale_ori: true
+ input_dim: 128
+ ground_truth:
+ name: matchers.depth_matcher
+ th_positive: 3
+ th_negative: 5
+ th_epi: 5
+ allow_no_extract: True
+train:
+ seed: 0
+ epochs: 50
+ log_every_iter: 100
+ eval_every_iter: 1000
+ lr: 1e-4
+ lr_schedule:
+ start: 30
+ type: exp
+ on_epoch: true
+ exp_div_10: 10
+ dataset_callback_fn: sample_new_items
+ plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ model:
+ extractor:
+ nms_radius: 0
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024
+ nms_radius: 0
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint+NN.yaml b/third_party/gim/gim/gluefactory/configs/superpoint+NN.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9822ab2c5e595af8491153d09c5068aa6e61f76c
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint+NN.yaml
@@ -0,0 +1,25 @@
+model:
+ name: two_view_pipeline
+ extractor:
+ name: gluefactory_nonfree.superpoint
+ max_num_keypoints: 2048
+ detection_threshold: 0.0
+ nms_radius: 3
+ matcher:
+ name: matchers.nearest_neighbor_matcher
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 1.0
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024 # overwrite config above
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint+lightglue-official.yaml b/third_party/gim/gim/gluefactory/configs/superpoint+lightglue-official.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a03d66f2f1fe1f2d7ccea949e03fdcbb15dd9a18
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint+lightglue-official.yaml
@@ -0,0 +1,29 @@
+model:
+ name: two_view_pipeline
+ extractor:
+ name: gluefactory_nonfree.superpoint
+ max_num_keypoints: 2048
+ detection_threshold: 0.0
+ nms_radius: 3
+ matcher:
+ name: matchers.lightglue_pretrained
+ features: superpoint
+ depth_confidence: -1
+ width_confidence: -1
+ filter_threshold: 0.1
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024 # overwrite config above
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint+lightglue_homography.yaml b/third_party/gim/gim/gluefactory/configs/superpoint+lightglue_homography.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1f353b33f8f995b55e1194b237bd209fdb780768
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint+lightglue_homography.yaml
@@ -0,0 +1,47 @@
+data:
+ name: homographies
+ data_dir: revisitop1m
+ train_size: 150000
+ val_size: 2000
+ batch_size: 128
+ num_workers: 14
+ homography:
+ difficulty: 0.7
+ max_angle: 45
+ photometric:
+ name: lg
+model:
+ name: two_view_pipeline
+ extractor:
+ name: gluefactory_nonfree.superpoint
+ max_num_keypoints: 512
+ force_num_keypoints: True
+ detection_threshold: 0.0
+ nms_radius: 3
+ trainable: False
+ ground_truth:
+ name: matchers.homography_matcher
+ th_positive: 3
+ th_negative: 3
+ matcher:
+ name: matchers.lightglue
+ filter_threshold: 0.1
+ flash: false
+ checkpointed: true
+train:
+ seed: 0
+ epochs: 40
+ log_every_iter: 100
+ eval_every_iter: 500
+ lr: 1e-4
+ lr_schedule:
+ start: 20
+ type: exp
+ on_epoch: true
+ exp_div_10: 10
+ plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
+benchmarks:
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint+lightglue_megadepth.yaml b/third_party/gim/gim/gluefactory/configs/superpoint+lightglue_megadepth.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6e3a982ab453a839783dc0985c9522866d653544
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint+lightglue_megadepth.yaml
@@ -0,0 +1,71 @@
+data:
+ name: megadepth
+ preprocessing:
+ resize: 1024
+ side: long
+ square_pad: True
+ train_split: train_scenes_clean.txt
+ train_num_per_scene: 300
+ val_split: valid_scenes_clean.txt
+ val_pairs: valid_pairs.txt
+ min_overlap: 0.1
+ max_overlap: 0.7
+ num_overlap_bins: 3
+ read_depth: true
+ read_image: true
+ batch_size: 32
+ num_workers: 14
+ load_features:
+ do: false # enable this if you have cached predictions
+ path: exports/megadepth-undist-depth-r1024_SP-k2048-nms3/{scene}.h5
+ padding_length: 2048
+ padding_fn: pad_local_features
+model:
+ name: two_view_pipeline
+ extractor:
+ name: gluefactory_nonfree.superpoint
+ max_num_keypoints: 2048
+ force_num_keypoints: True
+ detection_threshold: 0.0
+ nms_radius: 3
+ trainable: False
+ matcher:
+ name: matchers.lightglue
+ filter_threshold: 0.1
+ flash: false
+ checkpointed: true
+ ground_truth:
+ name: matchers.depth_matcher
+ th_positive: 3
+ th_negative: 5
+ th_epi: 5
+ allow_no_extract: True
+train:
+ seed: 0
+ epochs: 50
+ log_every_iter: 100
+ eval_every_iter: 1000
+ lr: 1e-4
+ lr_schedule:
+ start: 30
+ type: exp
+ on_epoch: true
+ exp_div_10: 10
+ dataset_callback_fn: sample_new_items
+ plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint+lsd+gluestick-homography.yaml b/third_party/gim/gim/gluefactory/configs/superpoint+lsd+gluestick-homography.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..62bc883ed117121ac5c16c63a56640e6dfe72523
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint+lsd+gluestick-homography.yaml
@@ -0,0 +1,73 @@
+data:
+ name: homographies
+ homography:
+ difficulty: 0.7
+ max_angle: 45
+ patch_shape: [640, 480]
+ photometric:
+ p: 0.75
+ train_size: 900000
+ val_size: 1000
+ batch_size: 160 # 20 per 10GB of GPU mem (12 for triplet)
+ num_workers: 15
+model:
+ name: gluefactory.models.two_view_pipeline
+ extractor:
+ name: gluefactory.models.lines.wireframe
+ trainable: False
+ point_extractor:
+ name: gluefactory.models.extractors.superpoint_open
+ # name: disk
+ # chunk: 10
+ max_num_keypoints: 1000
+ force_num_keypoints: true
+ trainable: False
+ line_extractor:
+ name: gluefactory.models.lines.lsd
+ max_num_lines: 250
+ force_num_lines: True
+ min_length: 15
+ trainable: False
+ wireframe_params:
+ merge_points: True
+ merge_line_endpoints: True
+ nms_radius: 4
+ detector:
+ name: null
+ descriptor:
+ name: null
+ ground_truth:
+ name: gluefactory.models.matchers.homography_matcher
+ trainable: False
+ use_points: True
+ use_lines: True
+ th_positive: 3
+ th_negative: 5
+ matcher:
+ name: gluefactory.models.matchers.gluestick
+ input_dim: 256 # 128 for DISK
+ descriptor_dim: 256 # 128 for DISK
+ inter_supervision: [2, 5]
+ GNN_layers: [
+ self, cross, self, cross, self, cross,
+ self, cross, self, cross, self, cross,
+ self, cross, self, cross, self, cross,
+ ]
+ checkpointed: true
+train:
+ seed: 0
+ epochs: 200
+ log_every_iter: 400
+ eval_every_iter: 700
+ save_every_iter: 1400
+ lr: 1e-4
+ lr_schedule:
+ type: exp # exp or multi_step
+ start: 200e3
+ exp_div_10: 200e3
+ gamma: 0.5
+ step: 50e3
+ n_steps: 4
+ submodules: []
+ # clip_grad: 10 # Use only with mixed precision
+ # load_experiment:
\ No newline at end of file
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint+lsd+gluestick-megadepth.yaml b/third_party/gim/gim/gluefactory/configs/superpoint+lsd+gluestick-megadepth.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5946826d4e8ec86f6c18e023cee62a8e0cfe2d56
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint+lsd+gluestick-megadepth.yaml
@@ -0,0 +1,74 @@
+data:
+ name: gluefactory.datasets.megadepth
+ train_num_per_scene: 300
+ val_pairs: valid_pairs.txt
+ views: 2
+ min_overlap: 0.1
+ max_overlap: 0.7
+ num_overlap_bins: 3
+ preprocessing:
+ resize: 640
+ square_pad: True
+ batch_size: 160
+ num_workers: 15
+model:
+ name: gluefactory.models.two_view_pipeline
+ extractor:
+ name: gluefactory.models.lines.wireframe
+ trainable: False
+ point_extractor:
+ name: gluefactory.models.extractors.superpoint_open
+ # name: disk
+ # chunk: 10
+ max_num_keypoints: 1000
+ force_num_keypoints: true
+ trainable: False
+ line_extractor:
+ name: gluefactory.models.lines.lsd
+ max_num_lines: 250
+ force_num_lines: True
+ min_length: 15
+ trainable: False
+ wireframe_params:
+ merge_points: True
+ merge_line_endpoints: True
+ nms_radius: 4
+ detector:
+ name: null
+ descriptor:
+ name: null
+ ground_truth:
+ name: gluefactory.models.matchers.depth_matcher
+ trainable: False
+ use_points: True
+ use_lines: True
+ th_positive: 3
+ th_negative: 5
+ matcher:
+ name: gluefactory.models.matchers.gluestick
+ input_dim: 256 # 128 for DISK
+ descriptor_dim: 256 # 128 for DISK
+ inter_supervision: null
+ GNN_layers: [
+ self, cross, self, cross, self, cross,
+ self, cross, self, cross, self, cross,
+ self, cross, self, cross, self, cross,
+ ]
+ checkpointed: true
+train:
+ seed: 0
+ epochs: 200
+ log_every_iter: 400
+ eval_every_iter: 700
+ save_every_iter: 1400
+ lr: 1e-4
+ lr_schedule:
+ type: exp # exp or multi_step
+ start: 200e3
+ exp_div_10: 200e3
+ gamma: 0.5
+ step: 50e3
+ n_steps: 4
+ submodules: []
+ # clip_grad: 10 # Use only with mixed precision
+ load_experiment: gluestick_H
\ No newline at end of file
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint+lsd+gluestick.yaml b/third_party/gim/gim/gluefactory/configs/superpoint+lsd+gluestick.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..edabb2ffd726fb0df2183b69c470019fb69f7ed5
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint+lsd+gluestick.yaml
@@ -0,0 +1,49 @@
+model:
+ name: gluefactory.models.two_view_pipeline
+ extractor:
+ name: gluefactory.models.lines.wireframe
+ point_extractor:
+ name: gluefactory_nonfree.superpoint
+ trainable: False
+ dense_outputs: True
+ max_num_keypoints: 2048
+ force_num_keypoints: False
+ detection_threshold: 0
+ line_extractor:
+ name: gluefactory.models.lines.lsd
+ trainable: False
+ max_num_lines: 512
+ force_num_lines: False
+ min_length: 15
+ wireframe_params:
+ merge_points: True
+ merge_line_endpoints: True
+ nms_radius: 3
+ matcher:
+ name: gluefactory.models.matchers.gluestick
+ weights: checkpoint_GlueStick_MD # This will download weights from internet
+
+ # ground_truth: # for ETH3D, comment otherwise
+ # name: gluefactory.models.matchers.depth_matcher
+ # use_lines: True
+
+benchmarks:
+ hpatches:
+ eval:
+ estimator: homography_est
+ ransac_th: -1 # [1., 1.5, 2., 2.5, 3.]
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: poselib
+ ransac_th: -1
+ eth3d:
+ ground_truth:
+ name: gluefactory.models.matchers.depth_matcher
+ use_lines: True
+ eval:
+ plot_methods: [ ] # ['sp+NN', 'sp+sg', 'superpoint+lsd+gluestick']
+ plot_line_methods: [ ] # ['superpoint+lsd+gluestick', 'sp+deeplsd+gs']
\ No newline at end of file
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint+superglue-official.yaml b/third_party/gim/gim/gluefactory/configs/superpoint+superglue-official.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..090ff5a10601f1105ce76ff3d0f32fbbb2d309c8
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint+superglue-official.yaml
@@ -0,0 +1,26 @@
+model:
+ name: two_view_pipeline
+ extractor:
+ name: gluefactory_nonfree.superpoint
+ max_num_keypoints: 2048
+ detection_threshold: 0.0
+ nms_radius: 3
+ matcher:
+ name: gluefactory_nonfree.superglue
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024 # overwrite config above
+
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint-open+NN.yaml b/third_party/gim/gim/gluefactory/configs/superpoint-open+NN.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..681f1171c782be799f43ad797fb06a262d8bb0d2
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint-open+NN.yaml
@@ -0,0 +1,25 @@
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.superpoint_open
+ max_num_keypoints: 2048
+ detection_threshold: 0.0
+ nms_radius: 3
+ matcher:
+ name: matchers.nearest_neighbor_matcher
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 1.0
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024 # overwrite config above
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint-open+lightglue_homography.yaml b/third_party/gim/gim/gluefactory/configs/superpoint-open+lightglue_homography.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6368544d107ec84c466328747dc7bc8fd7aa6ddf
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint-open+lightglue_homography.yaml
@@ -0,0 +1,47 @@
+data:
+ name: homographies
+ data_dir: revisitop1m
+ train_size: 150000
+ val_size: 2000
+ batch_size: 128
+ num_workers: 14
+ homography:
+ difficulty: 0.7
+ max_angle: 45
+ photometric:
+ name: lg
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.superpoint_open
+ max_num_keypoints: 512
+ force_num_keypoints: True
+ detection_threshold: -1
+ nms_radius: 3
+ trainable: False
+ ground_truth:
+ name: matchers.homography_matcher
+ th_positive: 3
+ th_negative: 3
+ matcher:
+ name: matchers.lightglue
+ filter_threshold: 0.1
+ flash: false
+ checkpointed: true
+train:
+ seed: 0
+ epochs: 40
+ log_every_iter: 100
+ eval_every_iter: 500
+ lr: 1e-4
+ lr_schedule:
+ start: 20
+ type: exp
+ on_epoch: true
+ exp_div_10: 10
+ plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
+benchmarks:
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
diff --git a/third_party/gim/gim/gluefactory/configs/superpoint-open+lightglue_megadepth.yaml b/third_party/gim/gim/gluefactory/configs/superpoint-open+lightglue_megadepth.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a99d139dd1ee22ac5614d3b2b7efefa7f5012691
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/configs/superpoint-open+lightglue_megadepth.yaml
@@ -0,0 +1,71 @@
+data:
+ name: megadepth
+ preprocessing:
+ resize: 1024
+ side: long
+ square_pad: True
+ train_split: train_scenes_clean.txt
+ train_num_per_scene: 300
+ val_split: valid_scenes_clean.txt
+ val_pairs: valid_pairs.txt
+ min_overlap: 0.1
+ max_overlap: 0.7
+ num_overlap_bins: 3
+ read_depth: true
+ read_image: true
+ batch_size: 32
+ num_workers: 14
+ load_features:
+ do: false # enable this if you have cached predictions
+ path: exports/megadepth-undist-depth-r1024_SP-open-k2048-nms3/{scene}.h5
+ padding_length: 2048
+ padding_fn: pad_local_features
+model:
+ name: two_view_pipeline
+ extractor:
+ name: extractors.superpoint_open
+ max_num_keypoints: 2048
+ force_num_keypoints: True
+ detection_threshold: -1
+ nms_radius: 3
+ trainable: False
+ matcher:
+ name: matchers.lightglue
+ filter_threshold: 0.1
+ flash: false
+ checkpointed: true
+ ground_truth:
+ name: matchers.depth_matcher
+ th_positive: 3
+ th_negative: 5
+ th_epi: 5
+ allow_no_extract: True
+train:
+ seed: 0
+ epochs: 50
+ log_every_iter: 100
+ eval_every_iter: 1000
+ lr: 1e-4
+ lr_schedule:
+ start: 30
+ type: exp
+ on_epoch: true
+ exp_div_10: 10
+ dataset_callback_fn: sample_new_items
+ plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
+benchmarks:
+ megadepth1500:
+ data:
+ preprocessing:
+ side: long
+ resize: 1600
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ hpatches:
+ eval:
+ estimator: opencv
+ ransac_th: 0.5
+ model:
+ extractor:
+ max_num_keypoints: 1024
diff --git a/third_party/gim/gim/gluefactory/datasets/__init__.py b/third_party/gim/gim/gluefactory/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce05e9a63b6708c69d4afb45351a8c7ef9481300
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/datasets/__init__.py
@@ -0,0 +1,25 @@
+import importlib.util
+
+from ..utils.tools import get_class
+from .base_dataset import BaseDataset
+
+
+def get_dataset(name):
+ import_paths = [name, f"{__name__}.{name}"]
+ for path in import_paths:
+ try:
+ spec = importlib.util.find_spec(path)
+ except ModuleNotFoundError:
+ spec = None
+ if spec is not None:
+ try:
+ return get_class(path, BaseDataset)
+ except AssertionError:
+ mod = __import__(path, fromlist=[""])
+ try:
+ return mod.__main_dataset__
+ except AttributeError as exc:
+ print(exc)
+ continue
+
+ raise RuntimeError(f'Dataset {name} not found in any of [{" ".join(import_paths)}]')
diff --git a/third_party/gim/gim/gluefactory/datasets/augmentations.py b/third_party/gim/gim/gluefactory/datasets/augmentations.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd391294c1227cdf789386e2fa1bfe41b0213ab4
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/datasets/augmentations.py
@@ -0,0 +1,244 @@
+from typing import Union
+
+import albumentations as A
+import cv2
+import numpy as np
+import torch
+from albumentations.pytorch.transforms import ToTensorV2
+from omegaconf import OmegaConf
+
+
+class IdentityTransform(A.ImageOnlyTransform):
+ def apply(self, img, **params):
+ return img
+
+ def get_transform_init_args_names(self):
+ return ()
+
+
+class RandomAdditiveShade(A.ImageOnlyTransform):
+ def __init__(
+ self,
+ nb_ellipses=10,
+ transparency_limit=[-0.5, 0.8],
+ kernel_size_limit=[150, 350],
+ always_apply=False,
+ p=0.5,
+ ):
+ super().__init__(always_apply, p)
+ self.nb_ellipses = nb_ellipses
+ self.transparency_limit = transparency_limit
+ self.kernel_size_limit = kernel_size_limit
+
+ def apply(self, img, **params):
+ if img.dtype == np.float32:
+ shaded = self._py_additive_shade(img * 255.0)
+ shaded /= 255.0
+ elif img.dtype == np.uint8:
+ shaded = self._py_additive_shade(img.astype(np.float32))
+ shaded = shaded.astype(np.uint8)
+ else:
+ raise NotImplementedError(
+ f"Data augmentation not available for type: {img.dtype}"
+ )
+ return shaded
+
+ def _py_additive_shade(self, img):
+ grayscale = len(img.shape) == 2
+ if grayscale:
+ img = img[None]
+ min_dim = min(img.shape[:2]) / 4
+ mask = np.zeros(img.shape[:2], img.dtype)
+ for i in range(self.nb_ellipses):
+ ax = int(max(np.random.rand() * min_dim, min_dim / 5))
+ ay = int(max(np.random.rand() * min_dim, min_dim / 5))
+ max_rad = max(ax, ay)
+ x = np.random.randint(max_rad, img.shape[1] - max_rad) # center
+ y = np.random.randint(max_rad, img.shape[0] - max_rad)
+ angle = np.random.rand() * 90
+ cv2.ellipse(mask, (x, y), (ax, ay), angle, 0, 360, 255, -1)
+
+ transparency = np.random.uniform(*self.transparency_limit)
+ ks = np.random.randint(*self.kernel_size_limit)
+ if (ks % 2) == 0: # kernel_size has to be odd
+ ks += 1
+ mask = cv2.GaussianBlur(mask.astype(np.float32), (ks, ks), 0)
+ shaded = img * (1 - transparency * mask[..., np.newaxis] / 255.0)
+ out = np.clip(shaded, 0, 255)
+ if grayscale:
+ out = out.squeeze(0)
+ return out
+
+ def get_transform_init_args_names(self):
+ return "transparency_limit", "kernel_size_limit", "nb_ellipses"
+
+
+def kw(entry: Union[float, dict], n=None, **default):
+ if not isinstance(entry, dict):
+ entry = {"p": entry}
+ entry = OmegaConf.create(entry)
+ if n is not None:
+ entry = default.get(n, entry)
+ return OmegaConf.merge(default, entry)
+
+
+def kwi(entry: Union[float, dict], n=None, **default):
+ conf = kw(entry, n=n, **default)
+ return {k: conf[k] for k in set(default.keys()).union(set(["p"]))}
+
+
+def replay_str(transforms, s="Replay:\n", log_inactive=True):
+ for t in transforms:
+ if "transforms" in t.keys():
+ s = replay_str(t["transforms"], s=s)
+ elif t["applied"] or log_inactive:
+ s += t["__class_fullname__"] + " " + str(t["applied"]) + "\n"
+ return s
+
+
+class BaseAugmentation(object):
+ base_default_conf = {
+ "name": "???",
+ "shuffle": False,
+ "p": 1.0,
+ "verbose": False,
+ "dtype": "uint8", # (byte, float)
+ }
+
+ default_conf = {}
+
+ def __init__(self, conf={}):
+ """Perform some logic and call the _init method of the child model."""
+ default_conf = OmegaConf.merge(
+ OmegaConf.create(self.base_default_conf),
+ OmegaConf.create(self.default_conf),
+ )
+ OmegaConf.set_struct(default_conf, True)
+ if isinstance(conf, dict):
+ conf = OmegaConf.create(conf)
+ self.conf = OmegaConf.merge(default_conf, conf)
+ OmegaConf.set_readonly(self.conf, True)
+ self._init(self.conf)
+
+ self.conf = OmegaConf.merge(self.conf, conf)
+ if self.conf.verbose:
+ self.compose = A.ReplayCompose
+ else:
+ self.compose = A.Compose
+ if self.conf.dtype == "uint8":
+ self.dtype = np.uint8
+ self.preprocess = A.FromFloat(always_apply=True, dtype="uint8")
+ self.postprocess = A.ToFloat(always_apply=True)
+ elif self.conf.dtype == "float32":
+ self.dtype = np.float32
+ self.preprocess = A.ToFloat(always_apply=True)
+ self.postprocess = IdentityTransform()
+ else:
+ raise ValueError(f"Unsupported dtype {self.conf.dtype}")
+ self.to_tensor = ToTensorV2()
+
+ def _init(self, conf):
+ """Child class overwrites this, setting up a list of transforms"""
+ self.transforms = []
+
+ def __call__(self, image, return_tensor=False):
+ """image as HW or HWC"""
+ if isinstance(image, torch.Tensor):
+ image = image.cpu().detach().numpy()
+ data = {"image": image}
+ if image.dtype != self.dtype:
+ data = self.preprocess(**data)
+ transforms = self.transforms
+ if self.conf.shuffle:
+ order = [i for i, _ in enumerate(transforms)]
+ np.random.shuffle(order)
+ transforms = [transforms[i] for i in order]
+ transformed = self.compose(transforms, p=self.conf.p)(**data)
+ if self.conf.verbose:
+ print(replay_str(transformed["replay"]["transforms"]))
+ transformed = self.postprocess(**transformed)
+ if return_tensor:
+ return self.to_tensor(**transformed)["image"]
+ else:
+ return transformed["image"]
+
+
+class IdentityAugmentation(BaseAugmentation):
+ default_conf = {}
+
+ def _init(self, conf):
+ self.transforms = [IdentityTransform(p=1.0)]
+
+
+class DarkAugmentation(BaseAugmentation):
+ default_conf = {"p": 0.75}
+
+ def _init(self, conf):
+ bright_contr = 0.5
+ blur = 0.1
+ random_gamma = 0.1
+ hue = 0.1
+ self.transforms = [
+ A.RandomRain(p=0.2),
+ A.RandomBrightnessContrast(
+ **kw(
+ bright_contr,
+ brightness_limit=(-0.4, 0.0),
+ contrast_limit=(-0.3, 0.0),
+ )
+ ),
+ A.OneOf(
+ [
+ A.Blur(**kwi(blur, p=0.1, blur_limit=(3, 9), n="blur")),
+ A.MotionBlur(
+ **kwi(blur, p=0.2, blur_limit=(3, 25), n="motion_blur")
+ ),
+ A.ISONoise(),
+ A.ImageCompression(),
+ ],
+ **kwi(blur, p=0.1),
+ ),
+ A.RandomGamma(**kw(random_gamma, gamma_limit=(15, 65))),
+ A.OneOf(
+ [
+ A.Equalize(),
+ A.CLAHE(p=0.2),
+ A.ToGray(),
+ A.ToSepia(p=0.1),
+ A.HueSaturationValue(**kw(hue, val_shift_limit=(-100, -40))),
+ ],
+ p=0.5,
+ ),
+ ]
+
+
+class LGAugmentation(BaseAugmentation):
+ default_conf = {"p": 0.95}
+
+ def _init(self, conf):
+ self.transforms = [
+ A.RandomGamma(p=0.1, gamma_limit=(15, 65)),
+ A.HueSaturationValue(p=0.1, val_shift_limit=(-100, -40)),
+ A.OneOf(
+ [
+ A.Blur(blur_limit=(3, 9)),
+ A.MotionBlur(blur_limit=(3, 25)),
+ A.ISONoise(),
+ A.ImageCompression(),
+ ],
+ p=0.1,
+ ),
+ A.Blur(p=0.1, blur_limit=(3, 9)),
+ A.MotionBlur(p=0.1, blur_limit=(3, 25)),
+ A.RandomBrightnessContrast(
+ p=0.5, brightness_limit=(-0.4, 0.0), contrast_limit=(-0.3, 0.0)
+ ),
+ A.CLAHE(p=0.2),
+ ]
+
+
+augmentations = {
+ "dark": DarkAugmentation,
+ "lg": LGAugmentation,
+ "identity": IdentityAugmentation,
+}
diff --git a/third_party/gim/gim/gluefactory/datasets/base_dataset.py b/third_party/gim/gim/gluefactory/datasets/base_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef622cbc6c45c69f39ecf5e31b74bffc78e125e3
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/datasets/base_dataset.py
@@ -0,0 +1,206 @@
+"""
+Base class for dataset.
+See mnist.py for an example of dataset.
+"""
+
+import collections
+import logging
+from abc import ABCMeta, abstractmethod
+
+import omegaconf
+import torch
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader, Sampler, get_worker_info
+from torch.utils.data._utils.collate import (
+ default_collate_err_msg_format,
+ np_str_obj_array_pattern,
+)
+
+from ..utils.tensor import string_classes
+from ..utils.tools import set_num_threads, set_seed
+
+logger = logging.getLogger(__name__)
+
+
+class LoopSampler(Sampler):
+ def __init__(self, loop_size, total_size=None):
+ self.loop_size = loop_size
+ self.total_size = total_size - (total_size % loop_size)
+
+ def __iter__(self):
+ return (i % self.loop_size for i in range(self.total_size))
+
+ def __len__(self):
+ return self.total_size
+
+
+def worker_init_fn(i):
+ info = get_worker_info()
+ if hasattr(info.dataset, "conf"):
+ conf = info.dataset.conf
+ set_seed(info.id + conf.seed)
+ set_num_threads(conf.num_threads)
+ else:
+ set_num_threads(1)
+
+
+def collate(batch):
+ """Difference with PyTorch default_collate: it can stack of other objects."""
+ if not isinstance(batch, list): # no batching
+ return batch
+ elem = batch[0]
+ elem_type = type(elem)
+ if isinstance(elem, torch.Tensor):
+ if torch.utils.data.get_worker_info() is not None:
+ # If we're in a background process, concatenate directly into a
+ # shared memory tensor to avoid an extra copy
+ numel = sum([x.numel() for x in batch])
+ try:
+ storage = elem.untyped_storage()._new_shared(numel) # noqa: F841
+ except AttributeError:
+ storage = elem.storage()._new_shared(numel) # noqa: F841
+ return torch.stack(batch, dim=0)
+ elif (
+ elem_type.__module__ == "numpy"
+ and elem_type.__name__ != "str_"
+ and elem_type.__name__ != "string_"
+ ):
+ if elem_type.__name__ == "ndarray" or elem_type.__name__ == "memmap":
+ # array of string classes and object
+ if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
+ raise TypeError(default_collate_err_msg_format.format(elem.dtype))
+ return collate([torch.as_tensor(b) for b in batch])
+ elif elem.shape == (): # scalars
+ return torch.as_tensor(batch)
+ elif isinstance(elem, float):
+ return torch.tensor(batch, dtype=torch.float64)
+ elif isinstance(elem, int):
+ return torch.tensor(batch)
+ elif isinstance(elem, string_classes):
+ return batch
+ elif isinstance(elem, collections.abc.Mapping):
+ return {key: collate([d[key] for d in batch]) for key in elem}
+ elif isinstance(elem, tuple) and hasattr(elem, "_fields"): # namedtuple
+ return elem_type(*(collate(samples) for samples in zip(*batch)))
+ elif isinstance(elem, collections.abc.Sequence):
+ # check to make sure that the elements in batch have consistent size
+ it = iter(batch)
+ elem_size = len(next(it))
+ if not all(len(elem) == elem_size for elem in it):
+ raise RuntimeError("each element in list of batch should be of equal size")
+ transposed = zip(*batch)
+ return [collate(samples) for samples in transposed]
+ elif elem is None:
+ return elem
+ else:
+ # try to stack anyway in case the object implements stacking.
+ return torch.stack(batch, 0)
+
+
+class BaseDataset(metaclass=ABCMeta):
+ """
+ What the dataset model is expect to declare:
+ default_conf: dictionary of the default configuration of the dataset.
+ It overwrites base_default_conf in BaseModel, and it is overwritten by
+ the user-provided configuration passed to __init__.
+ Configurations can be nested.
+
+ _init(self, conf): initialization method, where conf is the final
+ configuration object (also accessible with `self.conf`). Accessing
+ unknown configuration entries will raise an error.
+
+ get_dataset(self, split): method that returns an instance of
+ torch.utils.data.Dataset corresponding to the requested split string,
+ which can be `'train'`, `'val'`, or `'test'`.
+ """
+
+ base_default_conf = {
+ "name": "???",
+ "num_workers": "???",
+ "train_batch_size": "???",
+ "val_batch_size": "???",
+ "test_batch_size": "???",
+ "shuffle_training": True,
+ "batch_size": 1,
+ "num_threads": 1,
+ "seed": 0,
+ "prefetch_factor": 2,
+ }
+ default_conf = {}
+
+ def __init__(self, conf):
+ """Perform some logic and call the _init method of the child model."""
+ default_conf = OmegaConf.merge(
+ OmegaConf.create(self.base_default_conf),
+ OmegaConf.create(self.default_conf),
+ )
+ OmegaConf.set_struct(default_conf, True)
+ if isinstance(conf, dict):
+ conf = OmegaConf.create(conf)
+ self.conf = OmegaConf.merge(default_conf, conf)
+ OmegaConf.set_readonly(self.conf, True)
+ logger.info(f"Creating dataset {self.__class__.__name__}")
+ self._init(self.conf)
+
+ @abstractmethod
+ def _init(self, conf):
+ """To be implemented by the child class."""
+ raise NotImplementedError
+
+ @abstractmethod
+ def get_dataset(self, split):
+ """To be implemented by the child class."""
+ raise NotImplementedError
+
+ def get_data_loader(self, split, shuffle=None, pinned=False, distributed=False):
+ """Return a data loader for a given split."""
+ assert split in ["train", "val", "test"]
+ dataset = self.get_dataset(split)
+ try:
+ batch_size = self.conf[split + "_batch_size"]
+ except omegaconf.MissingMandatoryValue:
+ batch_size = self.conf.batch_size
+ num_workers = self.conf.get("num_workers", batch_size)
+ if distributed:
+ shuffle = False
+ sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+ else:
+ sampler = None
+ if shuffle is None:
+ shuffle = split == "train" and self.conf.shuffle_training
+ return DataLoader(
+ dataset,
+ batch_size=batch_size,
+ shuffle=shuffle,
+ sampler=sampler,
+ pin_memory=pinned,
+ collate_fn=collate,
+ num_workers=num_workers,
+ worker_init_fn=worker_init_fn,
+ prefetch_factor=self.conf.prefetch_factor,
+ drop_last=True if split == "train" else False,
+ )
+
+ def get_overfit_loader(self, split):
+ """Return an overfit data loader.
+ The training set is composed of a single duplicated batch, while
+ the validation and test sets contain a single copy of this same batch.
+ This is useful to debug a model and make sure that losses and metrics
+ correlate well.
+ """
+ assert split in ["train", "val", "test"]
+ dataset = self.get_dataset("train")
+ sampler = LoopSampler(
+ self.conf.batch_size,
+ len(dataset) if split == "train" else self.conf.batch_size,
+ )
+ num_workers = self.conf.get("num_workers", self.conf.batch_size)
+ return DataLoader(
+ dataset,
+ batch_size=self.conf.batch_size,
+ pin_memory=True,
+ num_workers=num_workers,
+ sampler=sampler,
+ worker_init_fn=worker_init_fn,
+ collate_fn=collate,
+ )
diff --git a/third_party/gim/gim/gluefactory/datasets/eth3d.py b/third_party/gim/gim/gluefactory/datasets/eth3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..44fd73f8037867807d5bc51adfa7ace11dab3cc3
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/datasets/eth3d.py
@@ -0,0 +1,254 @@
+"""
+ETH3D multi-view benchmark, used for line matching evaluation.
+"""
+import logging
+import os
+import shutil
+import zipfile
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+
+from ..geometry.wrappers import Camera, Pose
+from ..settings import DATA_PATH
+from ..utils.image import ImagePreprocessor, load_image
+from .base_dataset import BaseDataset
+from .utils import scale_intrinsics
+
+logger = logging.getLogger(__name__)
+
+
+def read_cameras(camera_file, scale_factor=None):
+ """Read the camera intrinsics from a file in COLMAP format."""
+ with open(camera_file, "r") as f:
+ raw_cameras = f.read().rstrip().split("\n")
+ raw_cameras = raw_cameras[3:]
+ cameras = []
+ for c in raw_cameras:
+ data = c.split(" ")
+ fx, fy, cx, cy = np.array(list(map(float, data[4:])))
+ K = np.array([[fx, 0.0, cx], [0.0, fy, cy], [0.0, 0.0, 1.0]], dtype=np.float32)
+ if scale_factor is not None:
+ K = scale_intrinsics(K, np.array([scale_factor, scale_factor]))
+ cameras.append(Camera.from_calibration_matrix(K).float())
+ return cameras
+
+
+def qvec2rotmat(qvec):
+ """Convert from quaternions to rotation matrix."""
+ return np.array(
+ [
+ [
+ 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2,
+ 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
+ 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2],
+ ],
+ [
+ 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
+ 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2,
+ 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1],
+ ],
+ [
+ 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
+ 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
+ 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2,
+ ],
+ ]
+ )
+
+
+class ETH3DDataset(BaseDataset):
+ default_conf = {
+ "data_dir": "ETH3D_undistorted",
+ "grayscale": True,
+ "downsize_factor": 8,
+ "min_covisibility": 500,
+ "batch_size": 1,
+ "two_view": True,
+ "min_overlap": 0.5,
+ "max_overlap": 1.0,
+ "sort_by_overlap": False,
+ "seed": 0,
+ }
+
+ def _init(self, conf):
+ self.grayscale = conf.grayscale
+ self.downsize_factor = conf.downsize_factor
+
+ # Set random seeds
+ np.random.seed(conf.seed)
+ torch.manual_seed(conf.seed)
+
+ # Auto-download the dataset
+ if not (DATA_PATH / conf.data_dir).exists():
+ logger.info("Downloading the ETH3D dataset...")
+ self.download_eth3d()
+
+ # Form pairs of images from the multiview dataset
+ self.img_dir = DATA_PATH / conf.data_dir
+ self.data = []
+ for folder in self.img_dir.iterdir():
+ img_folder = Path(folder, "images", "dslr_images_undistorted")
+ depth_folder = Path(folder, "ground_truth_depth/undistorted_depth")
+ depth_ext = ".png"
+ names = [img.name for img in img_folder.iterdir()]
+ names.sort()
+
+ # Read intrinsics and extrinsics data
+ cameras = read_cameras(
+ str(Path(folder, "dslr_calibration_undistorted", "cameras.txt")),
+ 1 / self.downsize_factor,
+ )
+ name_to_cam_idx = {name: {} for name in names}
+ with open(
+ str(Path(folder, "dslr_calibration_jpg", "images.txt")), "r"
+ ) as f:
+ raw_data = f.read().rstrip().split("\n")[4::2]
+ for raw_line in raw_data:
+ line = raw_line.split(" ")
+ img_name = os.path.basename(line[-1])
+ name_to_cam_idx[img_name]["dist_camera_idx"] = int(line[-2])
+ T_world_to_camera = {}
+ image_visible_points3D = {}
+ with open(
+ str(Path(folder, "dslr_calibration_undistorted", "images.txt")), "r"
+ ) as f:
+ lines = f.readlines()[4:] # Skip the header
+ raw_poses = [line.strip("\n").split(" ") for line in lines[::2]]
+ raw_points = [line.strip("\n").split(" ") for line in lines[1::2]]
+ for raw_pose, raw_pts in zip(raw_poses, raw_points):
+ img_name = os.path.basename(raw_pose[-1])
+ # Extract the transform from world to camera
+ target_extrinsics = list(map(float, raw_pose[1:8]))
+ pose = np.eye(4, dtype=np.float32)
+ pose[:3, :3] = qvec2rotmat(target_extrinsics[:4])
+ pose[:3, 3] = target_extrinsics[4:]
+ T_world_to_camera[img_name] = pose
+ name_to_cam_idx[img_name]["undist_camera_idx"] = int(raw_pose[-2])
+ # Extract the visible 3D points
+ point3D_ids = [id for id in map(int, raw_pts[2::3]) if id != -1]
+ image_visible_points3D[img_name] = set(point3D_ids)
+
+ # Extract the covisibility of each image
+ num_imgs = len(names)
+ n_covisible_points = np.zeros((num_imgs, num_imgs))
+ for i in range(num_imgs - 1):
+ for j in range(i + 1, num_imgs):
+ visible_points3D1 = image_visible_points3D[names[i]]
+ visible_points3D2 = image_visible_points3D[names[j]]
+ n_covisible_points[i, j] = len(
+ visible_points3D1 & visible_points3D2
+ )
+
+ # Keep only the pairs with enough covisibility
+ valid_pairs = np.where(n_covisible_points >= conf.min_covisibility)
+ valid_pairs = np.stack(valid_pairs, axis=1)
+
+ self.data += [
+ {
+ "view0": {
+ "name": names[i][:-4],
+ "img_path": str(Path(img_folder, names[i])),
+ "depth_path": str(Path(depth_folder, names[i][:-4]))
+ + depth_ext,
+ "camera": cameras[name_to_cam_idx[names[i]]["dist_camera_idx"]],
+ "T_w2cam": Pose.from_4x4mat(T_world_to_camera[names[i]]),
+ },
+ "view1": {
+ "name": names[j][:-4],
+ "img_path": str(Path(img_folder, names[j])),
+ "depth_path": str(Path(depth_folder, names[j][:-4]))
+ + depth_ext,
+ "camera": cameras[name_to_cam_idx[names[j]]["dist_camera_idx"]],
+ "T_w2cam": Pose.from_4x4mat(T_world_to_camera[names[j]]),
+ },
+ "T_world_to_ref": Pose.from_4x4mat(T_world_to_camera[names[i]]),
+ "T_world_to_target": Pose.from_4x4mat(T_world_to_camera[names[j]]),
+ "T_0to1": Pose.from_4x4mat(
+ np.float32(
+ T_world_to_camera[names[j]]
+ @ np.linalg.inv(T_world_to_camera[names[i]])
+ )
+ ),
+ "T_1to0": Pose.from_4x4mat(
+ np.float32(
+ T_world_to_camera[names[i]]
+ @ np.linalg.inv(T_world_to_camera[names[j]])
+ )
+ ),
+ "n_covisible_points": n_covisible_points[i, j],
+ }
+ for (i, j) in valid_pairs
+ ]
+
+ # Print some info
+ print("[Info] Successfully initialized dataset")
+ print("\t Name: ETH3D")
+ print("----------------------------------------")
+
+ def download_eth3d(self):
+ data_dir = DATA_PATH / self.conf.data_dir
+ tmp_dir = data_dir.parent / "ETH3D_tmp"
+ if tmp_dir.exists():
+ shutil.rmtree(tmp_dir)
+ tmp_dir.mkdir(exist_ok=True, parents=True)
+ url_base = "https://cvg-data.inf.ethz.ch/SOLD2/SOLD2_ETH3D_undistorted/"
+ zip_name = "ETH3D_undistorted.zip"
+ zip_path = tmp_dir / zip_name
+ torch.hub.download_url_to_file(url_base + zip_name, zip_path)
+ with zipfile.ZipFile(zip_path, "r") as zip_ref:
+ zip_ref.extractall(tmp_dir)
+ shutil.move(tmp_dir / zip_name.split(".")[0], data_dir)
+
+ def get_dataset(self, split):
+ return ETH3DDataset(self.conf)
+
+ def _read_image(self, img_path):
+ img = load_image(img_path, grayscale=self.grayscale)
+ shape = img.shape[-2:]
+ # instead of INTER_AREA this does bilinear interpolation with antialiasing
+ img_data = ImagePreprocessor({"resize": max(shape) // self.downsize_factor})(
+ img
+ )
+ return img_data
+
+ def read_depth(self, depth_path):
+ if self.downsize_factor != 8:
+ raise ValueError(
+ "Undistorted depth only available for low res"
+ + " images(downsize_factor = 8)."
+ )
+ depth_img = cv2.imread(depth_path, cv2.IMREAD_ANYDEPTH)
+ depth_img = depth_img.astype(np.float32) / 256
+
+ return depth_img
+
+ def __getitem__(self, idx):
+ """Returns the data associated to a pair of images (reference, target)
+ that are co-visible."""
+ data = self.data[idx]
+ # Load the images
+ view0 = data.pop("view0")
+ view1 = data.pop("view1")
+ view0 = {**view0, **self._read_image(view0["img_path"])}
+ view1 = {**view1, **self._read_image(view1["img_path"])}
+ view0["scales"] = np.array([1.0, 1]).astype(np.float32)
+ view1["scales"] = np.array([1.0, 1]).astype(np.float32)
+
+ # Load the depths
+ view0["depth"] = self.read_depth(view0["depth_path"])
+ view1["depth"] = self.read_depth(view1["depth_path"])
+
+ outputs = {
+ **data,
+ "view0": view0,
+ "view1": view1,
+ "name": f"{view0['name']}_{view1['name']}",
+ }
+
+ return outputs
+
+ def __len__(self):
+ return len(self.data)
diff --git a/third_party/gim/gim/gluefactory/datasets/homographies.py b/third_party/gim/gim/gluefactory/datasets/homographies.py
new file mode 100644
index 0000000000000000000000000000000000000000..08f7563ca21856fbe32357690c20b6ef0fa5cb68
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/datasets/homographies.py
@@ -0,0 +1,311 @@
+"""
+Simply load images from a folder or nested folders (does not have any split),
+and apply homographic adaptations to it. Yields an image pair without border
+artifacts.
+"""
+
+import argparse
+import logging
+import shutil
+import tarfile
+from pathlib import Path
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import omegaconf
+import torch
+from omegaconf import OmegaConf
+from tqdm import tqdm
+
+from ..geometry.homography import (
+ compute_homography,
+ sample_homography_corners,
+ warp_points,
+)
+from ..models.cache_loader import CacheLoader, pad_local_features
+from ..settings import DATA_PATH
+from ..utils.image import read_image
+from ..utils.tools import fork_rng
+from ..visualization.viz2d import plot_image_grid
+from .augmentations import IdentityAugmentation, augmentations
+from .base_dataset import BaseDataset
+
+logger = logging.getLogger(__name__)
+
+
+def sample_homography(img, conf: dict, size: list):
+ data = {}
+ H, _, coords, _ = sample_homography_corners(img.shape[:2][::-1], **conf)
+ data["image"] = cv2.warpPerspective(img, H, tuple(size))
+ data["H_"] = H.astype(np.float32)
+ data["coords"] = coords.astype(np.float32)
+ data["image_size"] = np.array(size, dtype=np.float32)
+ return data
+
+
+class HomographyDataset(BaseDataset):
+ default_conf = {
+ # image search
+ "data_dir": "revisitop1m", # the top-level directory
+ "image_dir": "jpg/", # the subdirectory with the images
+ "image_list": "revisitop1m.txt", # optional: list or filename of list
+ "glob": ["*.jpg", "*.png", "*.jpeg", "*.JPG", "*.PNG"],
+ # splits
+ "train_size": 100,
+ "val_size": 10,
+ "shuffle_seed": 0, # or None to skip
+ # image loading
+ "grayscale": False,
+ "triplet": False,
+ "right_only": False, # image0 is orig (rescaled), image1 is right
+ "reseed": False,
+ "homography": {
+ "difficulty": 0.8,
+ "translation": 1.0,
+ "max_angle": 60,
+ "n_angles": 10,
+ "patch_shape": [640, 480],
+ "min_convexity": 0.05,
+ },
+ "photometric": {
+ "name": "dark",
+ "p": 0.75,
+ # 'difficulty': 1.0, # currently unused
+ },
+ # feature loading
+ "load_features": {
+ "do": False,
+ **CacheLoader.default_conf,
+ "collate": False,
+ "thresh": 0.0,
+ "max_num_keypoints": -1,
+ "force_num_keypoints": False,
+ },
+ }
+
+ def _init(self, conf):
+ data_dir = DATA_PATH / conf.data_dir
+ if not data_dir.exists():
+ if conf.data_dir == "revisitop1m":
+ logger.info("Downloading the revisitop1m dataset.")
+ self.download_revisitop1m()
+ else:
+ raise FileNotFoundError(data_dir)
+
+ image_dir = data_dir / conf.image_dir
+ images = []
+ if conf.image_list is None:
+ glob = [conf.glob] if isinstance(conf.glob, str) else conf.glob
+ for g in glob:
+ images += list(image_dir.glob("**/" + g))
+ if len(images) == 0:
+ raise ValueError(f"Cannot find any image in folder: {image_dir}.")
+ images = [i.relative_to(image_dir).as_posix() for i in images]
+ images = sorted(images) # for deterministic behavior
+ logger.info("Found %d images in folder.", len(images))
+ elif isinstance(conf.image_list, (str, Path)):
+ image_list = data_dir / conf.image_list
+ if not image_list.exists():
+ raise FileNotFoundError(f"Cannot find image list {image_list}.")
+ images = image_list.read_text().rstrip("\n").split("\n")
+ for image in images:
+ if not (image_dir / image).exists():
+ raise FileNotFoundError(image_dir / image)
+ logger.info("Found %d images in list file.", len(images))
+ elif isinstance(conf.image_list, omegaconf.listconfig.ListConfig):
+ images = conf.image_list.to_container()
+ for image in images:
+ if not (image_dir / image).exists():
+ raise FileNotFoundError(image_dir / image)
+ else:
+ raise ValueError(conf.image_list)
+
+ if conf.shuffle_seed is not None:
+ np.random.RandomState(conf.shuffle_seed).shuffle(images)
+ train_images = images[: conf.train_size]
+ val_images = images[conf.train_size : conf.train_size + conf.val_size]
+ self.images = {"train": train_images, "val": val_images}
+
+ def download_revisitop1m(self):
+ data_dir = DATA_PATH / self.conf.data_dir
+ tmp_dir = data_dir.parent / "revisitop1m_tmp"
+ if tmp_dir.exists(): # The previous download failed.
+ shutil.rmtree(tmp_dir)
+ image_dir = tmp_dir / self.conf.image_dir
+ image_dir.mkdir(exist_ok=True, parents=True)
+ num_files = 100
+ url_base = "http://ptak.felk.cvut.cz/revisitop/revisitop1m/"
+ list_name = "revisitop1m.txt"
+ torch.hub.download_url_to_file(url_base + list_name, tmp_dir / list_name)
+ for n in tqdm(range(num_files), position=1):
+ tar_name = "revisitop1m.{}.tar.gz".format(n + 1)
+ tar_path = image_dir / tar_name
+ torch.hub.download_url_to_file(url_base + "jpg/" + tar_name, tar_path)
+ with tarfile.open(tar_path) as tar:
+ tar.extractall(path=image_dir)
+ tar_path.unlink()
+ shutil.move(tmp_dir, data_dir)
+
+ def get_dataset(self, split):
+ return _Dataset(self.conf, self.images[split], split)
+
+
+class _Dataset(torch.utils.data.Dataset):
+ def __init__(self, conf, image_names, split):
+ self.conf = conf
+ self.split = split
+ self.image_names = np.array(image_names)
+ self.image_dir = DATA_PATH / conf.data_dir / conf.image_dir
+
+ aug_conf = conf.photometric
+ aug_name = aug_conf.name
+ assert (
+ aug_name in augmentations.keys()
+ ), f'{aug_name} not in {" ".join(augmentations.keys())}'
+ self.photo_augment = augmentations[aug_name](aug_conf)
+ self.left_augment = (
+ IdentityAugmentation() if conf.right_only else self.photo_augment
+ )
+ self.img_to_tensor = IdentityAugmentation()
+
+ if conf.load_features.do:
+ self.feature_loader = CacheLoader(conf.load_features)
+
+ def _transform_keypoints(self, features, data):
+ """Transform keypoints by a homography, threshold them,
+ and potentially keep only the best ones."""
+ # Warp points
+ features["keypoints"] = warp_points(
+ features["keypoints"], data["H_"], inverse=False
+ )
+ h, w = data["image"].shape[1:3]
+ valid = (
+ (features["keypoints"][:, 0] >= 0)
+ & (features["keypoints"][:, 0] <= w - 1)
+ & (features["keypoints"][:, 1] >= 0)
+ & (features["keypoints"][:, 1] <= h - 1)
+ )
+ features["keypoints"] = features["keypoints"][valid]
+
+ # Threshold
+ if self.conf.load_features.thresh > 0:
+ valid = features["keypoint_scores"] >= self.conf.load_features.thresh
+ features = {k: v[valid] for k, v in features.items()}
+
+ # Get the top keypoints and pad
+ n = self.conf.load_features.max_num_keypoints
+ if n > -1:
+ inds = np.argsort(-features["keypoint_scores"])
+ features = {k: v[inds[:n]] for k, v in features.items()}
+
+ if self.conf.load_features.force_num_keypoints:
+ features = pad_local_features(
+ features, self.conf.load_features.max_num_keypoints
+ )
+
+ return features
+
+ def __getitem__(self, idx):
+ if self.conf.reseed:
+ with fork_rng(self.conf.seed + idx, False):
+ return self.getitem(idx)
+ else:
+ return self.getitem(idx)
+
+ def _read_view(self, img, H_conf, ps, left=False):
+ data = sample_homography(img, H_conf, ps)
+ if left:
+ data["image"] = self.left_augment(data["image"], return_tensor=True)
+ else:
+ data["image"] = self.photo_augment(data["image"], return_tensor=True)
+
+ gs = data["image"].new_tensor([0.299, 0.587, 0.114]).view(3, 1, 1)
+ if self.conf.grayscale:
+ data["image"] = (data["image"] * gs).sum(0, keepdim=True)
+
+ if self.conf.load_features.do:
+ features = self.feature_loader({k: [v] for k, v in data.items()})
+ features = self._transform_keypoints(features, data)
+ data["cache"] = features
+
+ return data
+
+ def getitem(self, idx):
+ name = self.image_names[idx]
+ img = read_image(self.image_dir / name, False)
+ if img is None:
+ logging.warning("Image %s could not be read.", name)
+ img = np.zeros((1024, 1024) + (() if self.conf.grayscale else (3,)))
+ img = img.astype(np.float32) / 255.0
+ size = img.shape[:2][::-1]
+ ps = self.conf.homography.patch_shape
+
+ left_conf = omegaconf.OmegaConf.to_container(self.conf.homography)
+ if self.conf.right_only:
+ left_conf["difficulty"] = 0.0
+
+ data0 = self._read_view(img, left_conf, ps, left=True)
+ data1 = self._read_view(img, self.conf.homography, ps, left=False)
+
+ H = compute_homography(data0["coords"], data1["coords"], [1, 1])
+
+ data = {
+ "name": name,
+ "original_image_size": np.array(size),
+ "H_0to1": H.astype(np.float32),
+ "idx": idx,
+ "view0": data0,
+ "view1": data1,
+ }
+
+ if self.conf.triplet:
+ # Generate third image
+ data2 = self._read_view(img, self.conf.homography, ps, left=False)
+ H02 = compute_homography(data0["coords"], data2["coords"], [1, 1])
+ H12 = compute_homography(data1["coords"], data2["coords"], [1, 1])
+
+ data = {
+ "H_0to2": H02.astype(np.float32),
+ "H_1to2": H12.astype(np.float32),
+ "view2": data2,
+ **data,
+ }
+
+ return data
+
+ def __len__(self):
+ return len(self.image_names)
+
+
+def visualize(args):
+ conf = {
+ "batch_size": 1,
+ "num_workers": 1,
+ "prefetch_factor": 1,
+ }
+ conf = OmegaConf.merge(conf, OmegaConf.from_cli(args.dotlist))
+ dataset = HomographyDataset(conf)
+ loader = dataset.get_data_loader("train")
+ logger.info("The dataset has %d elements.", len(loader))
+
+ with fork_rng(seed=dataset.conf.seed):
+ images = []
+ for _, data in zip(range(args.num_items), loader):
+ images.append(
+ (data[f"view{i}"]["image"][0].permute(1, 2, 0) for i in range(2))
+ )
+ plot_image_grid(images, dpi=args.dpi)
+ plt.tight_layout()
+ plt.show()
+
+
+if __name__ == "__main__":
+ from .. import logger # overwrite the logger
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--num_items", type=int, default=8)
+ parser.add_argument("--dpi", type=int, default=100)
+ parser.add_argument("dotlist", nargs="*")
+ args = parser.parse_intermixed_args()
+ visualize(args)
diff --git a/third_party/gim/gim/gluefactory/datasets/hpatches.py b/third_party/gim/gim/gluefactory/datasets/hpatches.py
new file mode 100644
index 0000000000000000000000000000000000000000..baf4ac8e5a015fe3678d36ad46a159609d08a13a
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/datasets/hpatches.py
@@ -0,0 +1,145 @@
+"""
+Simply load images from a folder or nested folders (does not have any split).
+"""
+import argparse
+import logging
+import tarfile
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from omegaconf import OmegaConf
+
+from ..settings import DATA_PATH
+from ..utils.image import ImagePreprocessor, load_image
+from ..utils.tools import fork_rng
+from ..visualization.viz2d import plot_image_grid
+from .base_dataset import BaseDataset
+
+logger = logging.getLogger(__name__)
+
+
+def read_homography(path):
+ with open(path) as f:
+ result = []
+ for line in f.readlines():
+ while " " in line: # Remove double spaces
+ line = line.replace(" ", " ")
+ line = line.replace(" \n", "").replace("\n", "")
+ # Split and discard empty strings
+ elements = list(filter(lambda s: s, line.split(" ")))
+ if elements:
+ result.append(elements)
+ return np.array(result).astype(float)
+
+
+class HPatches(BaseDataset, torch.utils.data.Dataset):
+ default_conf = {
+ "preprocessing": ImagePreprocessor.default_conf,
+ "data_dir": "hpatches-sequences-release",
+ "subset": None,
+ "ignore_large_images": True,
+ "grayscale": False,
+ }
+
+ # Large images that were ignored in previous papers
+ ignored_scenes = (
+ "i_contruction",
+ "i_crownnight",
+ "i_dc",
+ "i_pencils",
+ "i_whitebuilding",
+ "v_artisans",
+ "v_astronautis",
+ "v_talent",
+ )
+ url = "http://icvl.ee.ic.ac.uk/vbalnt/hpatches/hpatches-sequences-release.tar.gz"
+
+ def _init(self, conf):
+ assert conf.batch_size == 1
+ self.preprocessor = ImagePreprocessor(conf.preprocessing)
+
+ self.root = DATA_PATH / conf.data_dir
+ if not self.root.exists():
+ logger.info("Downloading the HPatches dataset.")
+ self.download()
+ self.sequences = sorted([x.name for x in self.root.iterdir()])
+ if not self.sequences:
+ raise ValueError("No image found!")
+ self.items = [] # (seq, q_idx, is_illu)
+ for seq in self.sequences:
+ if conf.ignore_large_images and seq in self.ignored_scenes:
+ continue
+ if conf.subset is not None and conf.subset != seq[0]:
+ continue
+ for i in range(2, 7):
+ self.items.append((seq, i, seq[0] == "i"))
+
+ def download(self):
+ data_dir = self.root.parent
+ data_dir.mkdir(exist_ok=True, parents=True)
+ tar_path = data_dir / self.url.rsplit("/", 1)[-1]
+ torch.hub.download_url_to_file(self.url, tar_path)
+ with tarfile.open(tar_path) as tar:
+ tar.extractall(data_dir)
+ tar_path.unlink()
+
+ def get_dataset(self, split):
+ assert split in ["val", "test"]
+ return self
+
+ def _read_image(self, seq: str, idx: int) -> dict:
+ img = load_image(self.root / seq / f"{idx}.ppm", self.conf.grayscale)
+ return self.preprocessor(img)
+
+ def __getitem__(self, idx):
+ seq, q_idx, is_illu = self.items[idx]
+ data0 = self._read_image(seq, 1)
+ data1 = self._read_image(seq, q_idx)
+ H = read_homography(self.root / seq / f"H_1_{q_idx}")
+ H = data1["transform"] @ H @ np.linalg.inv(data0["transform"])
+ return {
+ "H_0to1": H.astype(np.float32),
+ "scene": seq,
+ "idx": idx,
+ "is_illu": is_illu,
+ "name": f"{seq}/{idx}.ppm",
+ "view0": data0,
+ "view1": data1,
+ }
+
+ def __len__(self):
+ return len(self.items)
+
+
+def visualize(args):
+ conf = {
+ "batch_size": 1,
+ "num_workers": 8,
+ "prefetch_factor": 1,
+ }
+ conf = OmegaConf.merge(conf, OmegaConf.from_cli(args.dotlist))
+ dataset = HPatches(conf)
+ loader = dataset.get_data_loader("test")
+ logger.info("The dataset has %d elements.", len(loader))
+
+ with fork_rng(seed=dataset.conf.seed):
+ images = []
+ for _, data in zip(range(args.num_items), loader):
+ images.append(
+ (data[f"view{i}"]["image"][0].permute(1, 2, 0) for i in range(2))
+ )
+ plot_image_grid(images, dpi=args.dpi)
+ plt.tight_layout()
+ plt.show()
+
+
+if __name__ == "__main__":
+ from .. import logger # overwrite the logger
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--num_items", type=int, default=8)
+ parser.add_argument("--dpi", type=int, default=100)
+ parser.add_argument("dotlist", nargs="*")
+ args = parser.parse_intermixed_args()
+ visualize(args)
diff --git a/third_party/gim/gim/gluefactory/datasets/image_folder.py b/third_party/gim/gim/gluefactory/datasets/image_folder.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecbd3abf2067840b3fff10388f299814c6f98a01
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/datasets/image_folder.py
@@ -0,0 +1,59 @@
+"""
+Simply load images from a folder or nested folders (does not have any split).
+"""
+
+import logging
+from pathlib import Path
+
+import omegaconf
+import torch
+
+from ..utils.image import ImagePreprocessor, load_image
+from .base_dataset import BaseDataset
+
+
+class ImageFolder(BaseDataset, torch.utils.data.Dataset):
+ default_conf = {
+ "glob": ["*.jpg", "*.png", "*.jpeg", "*.JPG", "*.PNG"],
+ "images": "???",
+ "root_folder": "/",
+ "preprocessing": ImagePreprocessor.default_conf,
+ }
+
+ def _init(self, conf):
+ self.root = conf.root_folder
+ if isinstance(conf.images, str):
+ if not Path(conf.images).is_dir():
+ with open(conf.images, "r") as f:
+ self.images = f.read().rstrip("\n").split("\n")
+ logging.info(f"Found {len(self.images)} images in list file.")
+ else:
+ self.images = []
+ glob = [conf.glob] if isinstance(conf.glob, str) else conf.glob
+ for g in glob:
+ self.images += list(Path(conf.images).glob("**/" + g))
+ if len(self.images) == 0:
+ raise ValueError(
+ f"Could not find any image in folder: {conf.images}."
+ )
+ self.images = [i.relative_to(conf.images) for i in self.images]
+ self.root = conf.images
+ logging.info(f"Found {len(self.images)} images in folder.")
+ elif isinstance(conf.images, omegaconf.listconfig.ListConfig):
+ self.images = conf.images.to_container()
+ else:
+ raise ValueError(conf.images)
+
+ self.preprocessor = ImagePreprocessor(conf.preprocessing)
+
+ def get_dataset(self, split):
+ return self
+
+ def __getitem__(self, idx):
+ path = self.images[idx]
+ img = load_image(path)
+ data = {"name": str(path), **self.preprocessor(img)}
+ return data
+
+ def __len__(self):
+ return len(self.images)
diff --git a/third_party/gim/gim/gluefactory/datasets/image_pairs.py b/third_party/gim/gim/gluefactory/datasets/image_pairs.py
new file mode 100644
index 0000000000000000000000000000000000000000..08bd76031258331b5d8c770da67314ab67df6c86
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/datasets/image_pairs.py
@@ -0,0 +1,100 @@
+"""
+Simply load images from a folder or nested folders (does not have any split).
+"""
+
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from ..geometry.wrappers import Camera, Pose
+from ..settings import DATA_PATH
+from ..utils.image import ImagePreprocessor, load_image
+from .base_dataset import BaseDataset
+
+
+def names_to_pair(name0, name1, separator="/"):
+ return separator.join((name0.replace("/", "-"), name1.replace("/", "-")))
+
+
+def parse_homography(homography_elems) -> Camera:
+ return (
+ np.array([float(x) for x in homography_elems[:9]])
+ .reshape(3, 3)
+ .astype(np.float32)
+ )
+
+
+def parse_camera(calib_elems) -> Camera:
+ # assert len(calib_list) == 9
+ K = np.array([float(x) for x in calib_elems[:9]]).reshape(3, 3).astype(np.float32)
+ return Camera.from_calibration_matrix(K)
+
+
+def parse_relative_pose(pose_elems) -> Pose:
+ # assert len(calib_list) == 9
+ R, t = pose_elems[:9], pose_elems[9:12]
+ R = np.array([float(x) for x in R]).reshape(3, 3).astype(np.float32)
+ t = np.array([float(x) for x in t]).astype(np.float32)
+ return Pose.from_Rt(R, t)
+
+
+class ImagePairs(BaseDataset, torch.utils.data.Dataset):
+ default_conf = {
+ "pairs": "???", # ToDo: add image folder interface
+ "root": "???",
+ "preprocessing": ImagePreprocessor.default_conf,
+ "extra_data": None, # relative_pose, homography
+ }
+
+ def _init(self, conf):
+ pair_f = (
+ Path(conf.pairs) if Path(conf.pairs).exists() else DATA_PATH / conf.pairs
+ )
+ with open(str(pair_f), "r") as f:
+ self.items = [line.rstrip() for line in f]
+ self.preprocessor = ImagePreprocessor(conf.preprocessing)
+
+ def get_dataset(self, split):
+ return self
+
+ def _read_view(self, name):
+ path = DATA_PATH / self.conf.root / name
+ img = load_image(path)
+ return self.preprocessor(img)
+
+ def __getitem__(self, idx):
+ line = self.items[idx]
+ pair_data = line.split(" ")
+ name0, name1 = pair_data[:2]
+ data0 = self._read_view(name0)
+ data1 = self._read_view(name1)
+
+ data = {
+ "view0": data0,
+ "view1": data1,
+ }
+ if self.conf.extra_data == "relative_pose":
+ data["view0"]["camera"] = parse_camera(pair_data[2:11]).scale(
+ data0["scales"]
+ )
+ data["view1"]["camera"] = parse_camera(pair_data[11:20]).scale(
+ data1["scales"]
+ )
+ data["T_0to1"] = parse_relative_pose(pair_data[20:32])
+ elif self.conf.extra_data == "homography":
+ data["H_0to1"] = (
+ data1["transform"]
+ @ parse_homography(pair_data[2:11])
+ @ np.linalg.inv(data0["transform"])
+ )
+ else:
+ assert (
+ self.conf.extra_data is None
+ ), f"Unknown extra data format {self.conf.extra_data}"
+
+ data["name"] = names_to_pair(name0, name1)
+ return data
+
+ def __len__(self):
+ return len(self.items)
diff --git a/third_party/gim/gim/gluefactory/datasets/megadepth.py b/third_party/gim/gim/gluefactory/datasets/megadepth.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2c6d932ca475978b02f8d6cfcc6cd3b0c75ef9f
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/datasets/megadepth.py
@@ -0,0 +1,514 @@
+import argparse
+import logging
+import shutil
+import tarfile
+from collections.abc import Iterable
+from pathlib import Path
+
+import h5py
+import matplotlib.pyplot as plt
+import numpy as np
+import PIL.Image
+import torch
+from omegaconf import OmegaConf
+
+from ..geometry.wrappers import Camera, Pose
+from ..models.cache_loader import CacheLoader
+from ..settings import DATA_PATH
+from ..utils.image import ImagePreprocessor, load_image
+from ..utils.tools import fork_rng
+from ..visualization.viz2d import plot_heatmaps, plot_image_grid
+from .base_dataset import BaseDataset
+from .utils import rotate_intrinsics, rotate_pose_inplane, scale_intrinsics
+
+logger = logging.getLogger(__name__)
+scene_lists_path = Path(__file__).parent / "megadepth_scene_lists"
+
+
+def sample_n(data, num, seed=None):
+ if len(data) > num:
+ selected = np.random.RandomState(seed).choice(len(data), num, replace=False)
+ return data[selected]
+ else:
+ return data
+
+
+class MegaDepth(BaseDataset):
+ default_conf = {
+ # paths
+ "data_dir": "megadepth/",
+ "depth_subpath": "depth_undistorted/",
+ "image_subpath": "Undistorted_SfM/",
+ "info_dir": "scene_info/", # @TODO: intrinsics problem?
+ # Training
+ "train_split": "train_scenes_clean.txt",
+ "train_num_per_scene": 500,
+ # Validation
+ "val_split": "valid_scenes_clean.txt",
+ "val_num_per_scene": None,
+ "val_pairs": None,
+ # Test
+ "test_split": "test_scenes_clean.txt",
+ "test_num_per_scene": None,
+ "test_pairs": None,
+ # data sampling
+ "views": 2,
+ "min_overlap": 0.3, # only with D2-Net format
+ "max_overlap": 1.0, # only with D2-Net format
+ "num_overlap_bins": 1,
+ "sort_by_overlap": False,
+ "triplet_enforce_overlap": False, # only with views==3
+ # image options
+ "read_depth": True,
+ "read_image": True,
+ "grayscale": False,
+ "preprocessing": ImagePreprocessor.default_conf,
+ "p_rotate": 0.0, # probability to rotate image by +/- 90°
+ "reseed": False,
+ "seed": 0,
+ # features from cache
+ "load_features": {
+ "do": False,
+ **CacheLoader.default_conf,
+ "collate": False,
+ },
+ }
+
+ def _init(self, conf):
+ if not (DATA_PATH / conf.data_dir).exists():
+ logger.info("Downloading the MegaDepth dataset.")
+ self.download()
+
+ def download(self):
+ data_dir = DATA_PATH / self.conf.data_dir
+ tmp_dir = data_dir.parent / "megadepth_tmp"
+ if tmp_dir.exists(): # The previous download failed.
+ shutil.rmtree(tmp_dir)
+ tmp_dir.mkdir(exist_ok=True, parents=True)
+ url_base = "https://cvg-data.inf.ethz.ch/megadepth/"
+ for tar_name, out_name in (
+ ("Undistorted_SfM.tar.gz", self.conf.image_subpath),
+ ("depth_undistorted.tar.gz", self.conf.depth_subpath),
+ ("scene_info.tar.gz", self.conf.info_dir),
+ ):
+ tar_path = tmp_dir / tar_name
+ torch.hub.download_url_to_file(url_base + tar_name, tar_path)
+ with tarfile.open(tar_path) as tar:
+ tar.extractall(path=tmp_dir)
+ tar_path.unlink()
+ shutil.move(tmp_dir / tar_name.split(".")[0], tmp_dir / out_name)
+ shutil.move(tmp_dir, data_dir)
+
+ def get_dataset(self, split):
+ assert self.conf.views in [1, 2, 3]
+ if self.conf.views == 3:
+ return _TripletDataset(self.conf, split)
+ else:
+ return _PairDataset(self.conf, split)
+
+
+class _PairDataset(torch.utils.data.Dataset):
+ def __init__(self, conf, split, load_sample=True):
+ self.root = DATA_PATH / conf.data_dir
+ assert self.root.exists(), self.root
+ self.split = split
+ self.conf = conf
+
+ split_conf = conf[split + "_split"]
+ if isinstance(split_conf, (str, Path)):
+ scenes_path = scene_lists_path / split_conf
+ scenes = scenes_path.read_text().rstrip("\n").split("\n")
+ elif isinstance(split_conf, Iterable):
+ scenes = list(split_conf)
+ else:
+ raise ValueError(f"Unknown split configuration: {split_conf}.")
+ scenes = sorted(set(scenes))
+
+ if conf.load_features.do:
+ self.feature_loader = CacheLoader(conf.load_features)
+
+ self.preprocessor = ImagePreprocessor(conf.preprocessing)
+
+ self.images = {}
+ self.depths = {}
+ self.poses = {}
+ self.intrinsics = {}
+ self.valid = {}
+
+ # load metadata
+ self.info_dir = self.root / self.conf.info_dir
+ self.scenes = []
+ for scene in scenes:
+ path = self.info_dir / (scene + ".npz")
+ try:
+ info = np.load(str(path), allow_pickle=True)
+ except Exception:
+ logger.warning(
+ "Cannot load scene info for scene %s at %s.", scene, path
+ )
+ continue
+ self.images[scene] = info["image_paths"]
+ self.depths[scene] = info["depth_paths"]
+ self.poses[scene] = info["poses"]
+ self.intrinsics[scene] = info["intrinsics"]
+ self.scenes.append(scene)
+
+ if load_sample:
+ self.sample_new_items(conf.seed)
+ assert len(self.items) > 0
+
+ def sample_new_items(self, seed):
+ logger.info("Sampling new %s data with seed %d.", self.split, seed)
+ self.items = []
+ split = self.split
+ num_per_scene = self.conf[self.split + "_num_per_scene"]
+ if isinstance(num_per_scene, Iterable):
+ num_pos, num_neg = num_per_scene
+ else:
+ num_pos = num_per_scene
+ num_neg = None
+ if split != "train" and self.conf[split + "_pairs"] is not None:
+ # Fixed validation or test pairs
+ assert num_pos is None
+ assert num_neg is None
+ assert self.conf.views == 2
+ pairs_path = scene_lists_path / self.conf[split + "_pairs"]
+ for line in pairs_path.read_text().rstrip("\n").split("\n"):
+ im0, im1 = line.split(" ")
+ scene = im0.split("/")[0]
+ assert im1.split("/")[0] == scene
+ im0, im1 = [self.conf.image_subpath + im for im in [im0, im1]]
+ assert im0 in self.images[scene]
+ assert im1 in self.images[scene]
+ idx0 = np.where(self.images[scene] == im0)[0][0]
+ idx1 = np.where(self.images[scene] == im1)[0][0]
+ self.items.append((scene, idx0, idx1, 1.0))
+ elif self.conf.views == 1:
+ for scene in self.scenes:
+ if scene not in self.images:
+ continue
+ valid = (self.images[scene] != None) | ( # noqa: E711
+ self.depths[scene] != None # noqa: E711
+ )
+ ids = np.where(valid)[0]
+ if num_pos and len(ids) > num_pos:
+ ids = np.random.RandomState(seed).choice(
+ ids, num_pos, replace=False
+ )
+ ids = [(scene, i) for i in ids]
+ self.items.extend(ids)
+ else:
+ for scene in self.scenes:
+ path = self.info_dir / (scene + ".npz")
+ assert path.exists(), path
+ info = np.load(str(path), allow_pickle=True)
+ valid = (self.images[scene] != None) & ( # noqa: E711
+ self.depths[scene] != None # noqa: E711
+ )
+ ind = np.where(valid)[0]
+ mat = info["overlap_matrix"][valid][:, valid]
+
+ if num_pos is not None:
+ # Sample a subset of pairs, binned by overlap.
+ num_bins = self.conf.num_overlap_bins
+ assert num_bins > 0
+ bin_width = (
+ self.conf.max_overlap - self.conf.min_overlap
+ ) / num_bins
+ num_per_bin = num_pos // num_bins
+ pairs_all = []
+ for k in range(num_bins):
+ bin_min = self.conf.min_overlap + k * bin_width
+ bin_max = bin_min + bin_width
+ pairs_bin = (mat > bin_min) & (mat <= bin_max)
+ pairs_bin = np.stack(np.where(pairs_bin), -1)
+ pairs_all.append(pairs_bin)
+ # Skip bins with too few samples
+ has_enough_samples = [len(p) >= num_per_bin * 2 for p in pairs_all]
+ num_per_bin_2 = num_pos // max(1, sum(has_enough_samples))
+ pairs = []
+ for pairs_bin, keep in zip(pairs_all, has_enough_samples):
+ if keep:
+ pairs.append(sample_n(pairs_bin, num_per_bin_2, seed))
+ pairs = np.concatenate(pairs, 0)
+ else:
+ pairs = (mat > self.conf.min_overlap) & (
+ mat <= self.conf.max_overlap
+ )
+ pairs = np.stack(np.where(pairs), -1)
+
+ pairs = [(scene, ind[i], ind[j], mat[i, j]) for i, j in pairs]
+ if num_neg is not None:
+ neg_pairs = np.stack(np.where(mat <= 0.0), -1)
+ neg_pairs = sample_n(neg_pairs, num_neg, seed)
+ pairs += [(scene, ind[i], ind[j], mat[i, j]) for i, j in neg_pairs]
+ self.items.extend(pairs)
+ if self.conf.views == 2 and self.conf.sort_by_overlap:
+ self.items.sort(key=lambda i: i[-1], reverse=True)
+ else:
+ np.random.RandomState(seed).shuffle(self.items)
+
+ def _read_view(self, scene, idx):
+ path = self.root / self.images[scene][idx]
+
+ # read pose data
+ K = self.intrinsics[scene][idx].astype(np.float32, copy=False)
+ T = self.poses[scene][idx].astype(np.float32, copy=False)
+
+ # read image
+ if self.conf.read_image:
+ img = load_image(self.root / self.images[scene][idx], self.conf.grayscale)
+ else:
+ size = PIL.Image.open(path).size[::-1]
+ img = torch.zeros(
+ [3 - 2 * int(self.conf.grayscale), size[0], size[1]]
+ ).float()
+
+ # read depth
+ if self.conf.read_depth:
+ # depth_path = (
+ # self.root / self.conf.depth_subpath / scene / (path.stem + ".h5")
+ # )
+ depth_subpath = self.depths[scene][idx]
+ depth_id = depth_subpath.split('/')[-1][:-3]
+ assert depth_id == path.stem
+ depth_path = self.root / depth_subpath
+ with h5py.File(str(depth_path), "r") as f:
+ depth = f["/depth"].__array__().astype(np.float32, copy=False)
+ depth = torch.Tensor(depth)[None]
+ assert depth.shape[-2:] == img.shape[-2:]
+ else:
+ depth = None
+
+ # add random rotations
+ do_rotate = self.conf.p_rotate > 0.0 and self.split == "train"
+ if do_rotate:
+ p = self.conf.p_rotate
+ k = 0
+ if np.random.rand() < p:
+ k = np.random.choice(2, 1, replace=False)[0] * 2 - 1
+ img = np.rot90(img, k=-k, axes=(-2, -1))
+ if self.conf.read_depth:
+ depth = np.rot90(depth, k=-k, axes=(-2, -1)).copy()
+ K = rotate_intrinsics(K, img.shape, k + 2)
+ T = rotate_pose_inplane(T, k + 2)
+
+ name = path.name
+
+ data = self.preprocessor(img)
+ if depth is not None:
+ data["depth"] = self.preprocessor(depth, interpolation="nearest")["image"][
+ 0
+ ]
+ K = scale_intrinsics(K, data["scales"])
+
+ data = {
+ "name": name,
+ "scene": scene,
+ "T_w2cam": Pose.from_4x4mat(T),
+ "depth": depth,
+ "camera": Camera.from_calibration_matrix(K).float(),
+ **data,
+ }
+
+ if self.conf.load_features.do:
+ features = self.feature_loader({k: [v] for k, v in data.items()})
+ if do_rotate and k != 0:
+ # ang = np.deg2rad(k * 90.)
+ kpts = features["keypoints"].copy()
+ x, y = kpts[:, 0].copy(), kpts[:, 1].copy()
+ w, h = data["image_size"]
+ if k == 1:
+ kpts[:, 0] = w - y
+ kpts[:, 1] = x
+ elif k == -1:
+ kpts[:, 0] = y
+ kpts[:, 1] = h - x
+
+ else:
+ raise ValueError
+ features["keypoints"] = kpts
+
+ data = {"cache": features, **data}
+ return data
+
+ def __getitem__(self, idx):
+ if self.conf.reseed:
+ with fork_rng(self.conf.seed + idx, False):
+ return self.getitem(idx)
+ else:
+ return self.getitem(idx)
+
+ def getitem(self, idx):
+ if self.conf.views == 2:
+ if isinstance(idx, list):
+ scene, idx0, idx1, overlap = idx
+ else:
+ scene, idx0, idx1, overlap = self.items[idx]
+ data0 = self._read_view(scene, idx0)
+ data1 = self._read_view(scene, idx1)
+ data = {
+ "view0": data0,
+ "view1": data1,
+ }
+ data["T_0to1"] = data1["T_w2cam"] @ data0["T_w2cam"].inv()
+ data["T_1to0"] = data0["T_w2cam"] @ data1["T_w2cam"].inv()
+ data["overlap_0to1"] = overlap
+ data["name"] = f"{scene}/{data0['name']}_{data1['name']}"
+ else:
+ assert self.conf.views == 1
+ scene, idx0 = self.items[idx]
+ data = self._read_view(scene, idx0)
+ data["scene"] = scene
+ data["idx"] = idx
+ return data
+
+ def __len__(self):
+ return len(self.items)
+
+
+class _TripletDataset(_PairDataset):
+ def sample_new_items(self, seed):
+ logging.info("Sampling new triplets with seed %d", seed)
+ self.items = []
+ split = self.split
+ num = self.conf[self.split + "_num_per_scene"]
+ if split != "train" and self.conf[split + "_pairs"] is not None:
+ if Path(self.conf[split + "_pairs"]).exists():
+ pairs_path = Path(self.conf[split + "_pairs"])
+ else:
+ pairs_path = DATA_PATH / "configs" / self.conf[split + "_pairs"]
+ for line in pairs_path.read_text().rstrip("\n").split("\n"):
+ im0, im1, im2 = line.split(" ")
+ assert im0[:4] == im1[:4]
+ scene = im1[:4]
+ idx0 = np.where(self.images[scene] == im0)
+ idx1 = np.where(self.images[scene] == im1)
+ idx2 = np.where(self.images[scene] == im2)
+ self.items.append((scene, idx0, idx1, idx2, 1.0, 1.0, 1.0))
+ else:
+ for scene in self.scenes:
+ path = self.info_dir / (scene + ".npz")
+ assert path.exists(), path
+ info = np.load(str(path), allow_pickle=True)
+ if self.conf.num_overlap_bins > 1:
+ raise NotImplementedError("TODO")
+ valid = (self.images[scene] != None) & ( # noqa: E711
+ self.depth[scene] != None # noqa: E711
+ )
+ ind = np.where(valid)[0]
+ mat = info["overlap_matrix"][valid][:, valid]
+ good = (mat > self.conf.min_overlap) & (mat <= self.conf.max_overlap)
+ triplets = []
+ if self.conf.triplet_enforce_overlap:
+ pairs = np.stack(np.where(good), -1)
+ for i0, i1 in pairs:
+ for i2 in pairs[pairs[:, 0] == i0, 1]:
+ if good[i1, i2]:
+ triplets.append((i0, i1, i2))
+ if len(triplets) > num:
+ selected = np.random.RandomState(seed).choice(
+ len(triplets), num, replace=False
+ )
+ selected = range(num)
+ triplets = np.array(triplets)[selected]
+ else:
+ # we first enforce that each row has >1 pairs
+ non_unique = good.sum(-1) > 1
+ ind_r = np.where(non_unique)[0]
+ good = good[non_unique]
+ pairs = np.stack(np.where(good), -1)
+ if len(pairs) > num:
+ selected = np.random.RandomState(seed).choice(
+ len(pairs), num, replace=False
+ )
+ pairs = pairs[selected]
+ for idx, (k, i) in enumerate(pairs):
+ # We now sample a j from row k s.t. i != j
+ possible_j = np.where(good[k])[0]
+ possible_j = possible_j[possible_j != i]
+ selected = np.random.RandomState(seed + idx).choice(
+ len(possible_j), 1, replace=False
+ )[0]
+ triplets.append((ind_r[k], i, possible_j[selected]))
+ triplets = [
+ (scene, ind[k], ind[i], ind[j], mat[k, i], mat[k, j], mat[i, j])
+ for k, i, j in triplets
+ ]
+ self.items.extend(triplets)
+ np.random.RandomState(seed).shuffle(self.items)
+
+ def __getitem__(self, idx):
+ scene, idx0, idx1, idx2, overlap01, overlap02, overlap12 = self.items[idx]
+ data0 = self._read_view(scene, idx0)
+ data1 = self._read_view(scene, idx1)
+ data2 = self._read_view(scene, idx2)
+ data = {
+ "view0": data0,
+ "view1": data1,
+ "view2": data2,
+ }
+ data["T_0to1"] = data1["T_w2cam"] @ data0["T_w2cam"].inv()
+ data["T_0to2"] = data2["T_w2cam"] @ data0["T_w2cam"].inv()
+ data["T_1to2"] = data2["T_w2cam"] @ data1["T_w2cam"].inv()
+ data["T_1to0"] = data0["T_w2cam"] @ data1["T_w2cam"].inv()
+ data["T_2to0"] = data0["T_w2cam"] @ data2["T_w2cam"].inv()
+ data["T_2to1"] = data1["T_w2cam"] @ data2["T_w2cam"].inv()
+
+ data["overlap_0to1"] = overlap01
+ data["overlap_0to2"] = overlap02
+ data["overlap_1to2"] = overlap12
+ data["scene"] = scene
+ data["name"] = f"{scene}/{data0['name']}_{data1['name']}_{data2['name']}"
+ return data
+
+ def __len__(self):
+ return len(self.items)
+
+
+def visualize(args):
+ conf = {
+ "min_overlap": 0.1,
+ "max_overlap": 0.7,
+ "num_overlap_bins": 3,
+ "sort_by_overlap": False,
+ "train_num_per_scene": 5,
+ "batch_size": 1,
+ "num_workers": 0,
+ "prefetch_factor": None,
+ "val_num_per_scene": None,
+ }
+ conf = OmegaConf.merge(conf, OmegaConf.from_cli(args.dotlist))
+ dataset = MegaDepth(conf)
+ loader = dataset.get_data_loader(args.split)
+ logger.info("The dataset has elements.", len(loader))
+
+ with fork_rng(seed=dataset.conf.seed):
+ images, depths = [], []
+ for _, data in zip(range(args.num_items), loader):
+ images.append(
+ [
+ data[f"view{i}"]["image"][0].permute(1, 2, 0)
+ for i in range(dataset.conf.views)
+ ]
+ )
+ depths.append(
+ [data[f"view{i}"]["depth"][0] for i in range(dataset.conf.views)]
+ )
+
+ axes = plot_image_grid(images, dpi=args.dpi)
+ for i in range(len(images)):
+ plot_heatmaps(depths[i], axes=axes[i])
+ plt.show()
+
+
+if __name__ == "__main__":
+ from .. import logger # overwrite the logger
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--split", type=str, default="val")
+ parser.add_argument("--num_items", type=int, default=4)
+ parser.add_argument("--dpi", type=int, default=100)
+ parser.add_argument("dotlist", nargs="*")
+ args = parser.parse_intermixed_args()
+ visualize(args)
diff --git a/third_party/gim/gim/gluefactory/datasets/utils.py b/third_party/gim/gim/gluefactory/datasets/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3aef0118c624d81e1bb4921041f34b73e9d8ac89
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/datasets/utils.py
@@ -0,0 +1,131 @@
+import cv2
+import numpy as np
+import torch
+
+
+def read_image(path, grayscale=False):
+ """Read an image from path as RGB or grayscale"""
+ mode = cv2.IMREAD_GRAYSCALE if grayscale else cv2.IMREAD_COLOR
+ image = cv2.imread(str(path), mode)
+ if image is None:
+ raise IOError(f"Could not read image at {path}.")
+ if not grayscale:
+ image = image[..., ::-1]
+ return image
+
+
+def numpy_image_to_torch(image):
+ """Normalize the image tensor and reorder the dimensions."""
+ if image.ndim == 3:
+ image = image.transpose((2, 0, 1)) # HxWxC to CxHxW
+ elif image.ndim == 2:
+ image = image[None] # add channel axis
+ else:
+ raise ValueError(f"Not an image: {image.shape}")
+ return torch.tensor(image / 255.0, dtype=torch.float)
+
+
+def rotate_intrinsics(K, image_shape, rot):
+ """image_shape is the shape of the image after rotation"""
+ assert rot <= 3
+ h, w = image_shape[:2][:: -1 if (rot % 2) else 1]
+ fx, fy, cx, cy = K[0, 0], K[1, 1], K[0, 2], K[1, 2]
+ rot = rot % 4
+ if rot == 1:
+ return np.array(
+ [[fy, 0.0, cy], [0.0, fx, w - cx], [0.0, 0.0, 1.0]], dtype=K.dtype
+ )
+ elif rot == 2:
+ return np.array(
+ [[fx, 0.0, w - cx], [0.0, fy, h - cy], [0.0, 0.0, 1.0]],
+ dtype=K.dtype,
+ )
+ else: # if rot == 3:
+ return np.array(
+ [[fy, 0.0, h - cy], [0.0, fx, cx], [0.0, 0.0, 1.0]], dtype=K.dtype
+ )
+
+
+def rotate_pose_inplane(i_T_w, rot):
+ rotation_matrices = [
+ np.array(
+ [
+ [np.cos(r), -np.sin(r), 0.0, 0.0],
+ [np.sin(r), np.cos(r), 0.0, 0.0],
+ [0.0, 0.0, 1.0, 0.0],
+ [0.0, 0.0, 0.0, 1.0],
+ ],
+ dtype=np.float32,
+ )
+ for r in [np.deg2rad(d) for d in (0, 270, 180, 90)]
+ ]
+ return np.dot(rotation_matrices[rot], i_T_w)
+
+
+def scale_intrinsics(K, scales):
+ """Scale intrinsics after resizing the corresponding image."""
+ scales = np.diag(np.concatenate([scales, [1.0]]))
+ return np.dot(scales.astype(K.dtype, copy=False), K)
+
+
+def get_divisible_wh(w, h, df=None):
+ if df is not None:
+ w_new, h_new = map(lambda x: int(x // df * df), [w, h])
+ else:
+ w_new, h_new = w, h
+ return w_new, h_new
+
+
+def resize(image, size, fn=None, interp="linear", df=None):
+ """Resize an image to a fixed size, or according to max or min edge."""
+ h, w = image.shape[:2]
+ if isinstance(size, int):
+ scale = size / fn(h, w)
+ h_new, w_new = int(round(h * scale)), int(round(w * scale))
+ w_new, h_new = get_divisible_wh(w_new, h_new, df)
+ scale = (w_new / w, h_new / h)
+ elif isinstance(size, (tuple, list)):
+ h_new, w_new = size
+ scale = (w_new / w, h_new / h)
+ else:
+ raise ValueError(f"Incorrect new size: {size}")
+ mode = {
+ "linear": cv2.INTER_LINEAR,
+ "cubic": cv2.INTER_CUBIC,
+ "nearest": cv2.INTER_NEAREST,
+ "area": cv2.INTER_AREA,
+ }[interp]
+ return cv2.resize(image, (w_new, h_new), interpolation=mode), scale
+
+
+def crop(image, size, random=True, other=None, K=None, return_bbox=False):
+ """Random or deterministic crop of an image, adjust depth and intrinsics."""
+ h, w = image.shape[:2]
+ h_new, w_new = (size, size) if isinstance(size, int) else size
+ top = np.random.randint(0, h - h_new + 1) if random else 0
+ left = np.random.randint(0, w - w_new + 1) if random else 0
+ image = image[top : top + h_new, left : left + w_new]
+ ret = [image]
+ if other is not None:
+ ret += [other[top : top + h_new, left : left + w_new]]
+ if K is not None:
+ K[0, 2] -= left
+ K[1, 2] -= top
+ ret += [K]
+ if return_bbox:
+ ret += [(top, top + h_new, left, left + w_new)]
+ return ret
+
+
+def zero_pad(size, *images):
+ """zero pad images to size x size"""
+ ret = []
+ for image in images:
+ if image is None:
+ ret.append(None)
+ continue
+ h, w = image.shape[:2]
+ padded = np.zeros((size, size) + image.shape[2:], dtype=image.dtype)
+ padded[:h, :w] = image
+ ret.append(padded)
+ return ret
diff --git a/third_party/gim/gim/gluefactory/eval/__init__.py b/third_party/gim/gim/gluefactory/eval/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d451e062329d4a87d5b440e5c961bc62e148842
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/eval/__init__.py
@@ -0,0 +1,20 @@
+import torch
+
+from ..utils.tools import get_class
+from .eval_pipeline import EvalPipeline
+
+
+def get_benchmark(benchmark):
+ return get_class(f"{__name__}.{benchmark}", EvalPipeline)
+
+
+@torch.no_grad()
+def run_benchmark(benchmark, eval_conf, experiment_dir, model=None):
+ """This overwrites existing benchmarks"""
+ experiment_dir.mkdir(exist_ok=True, parents=True)
+ bm = get_benchmark(benchmark)
+
+ pipeline = bm(eval_conf)
+ return pipeline.run(
+ experiment_dir, model=model, overwrite=True, overwrite_eval=True
+ )
diff --git a/third_party/gim/gim/gluefactory/eval/eth3d.py b/third_party/gim/gim/gluefactory/eval/eth3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2fe3a5df628abed729ed753cbb0491a18200a11
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/eval/eth3d.py
@@ -0,0 +1,202 @@
+from collections import defaultdict
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+from omegaconf import OmegaConf
+from tqdm import tqdm
+
+from ..datasets import get_dataset
+from ..models.cache_loader import CacheLoader
+from ..settings import EVAL_PATH
+from ..utils.export_predictions import export_predictions
+from .eval_pipeline import EvalPipeline, load_eval
+from .io import get_eval_parser, load_model, parse_eval_args
+from .utils import aggregate_pr_results, get_tp_fp_pts
+
+
+def eval_dataset(loader, pred_file, suffix=""):
+ results = defaultdict(list)
+ results["num_pos" + suffix] = 0
+ cache_loader = CacheLoader({"path": str(pred_file), "collate": None}).eval()
+ for data in tqdm(loader):
+ pred = cache_loader(data)
+
+ if suffix == "":
+ scores = pred["matching_scores0"].numpy()
+ sort_indices = np.argsort(scores)[::-1]
+ gt_matches = pred["gt_matches0"].numpy()[sort_indices]
+ pred_matches = pred["matches0"].numpy()[sort_indices]
+ else:
+ scores = pred["line_matching_scores0"].numpy()
+ sort_indices = np.argsort(scores)[::-1]
+ gt_matches = pred["gt_line_matches0"].numpy()[sort_indices]
+ pred_matches = pred["line_matches0"].numpy()[sort_indices]
+ scores = scores[sort_indices]
+
+ tp, fp, scores, num_pos = get_tp_fp_pts(pred_matches, gt_matches, scores)
+ results["tp" + suffix].append(tp)
+ results["fp" + suffix].append(fp)
+ results["scores" + suffix].append(scores)
+ results["num_pos" + suffix] += num_pos
+
+ # Aggregate the results
+ return aggregate_pr_results(results, suffix=suffix)
+
+
+class ETH3DPipeline(EvalPipeline):
+ default_conf = {
+ "data": {
+ "name": "eth3d",
+ "batch_size": 1,
+ "train_batch_size": 1,
+ "val_batch_size": 1,
+ "test_batch_size": 1,
+ "num_workers": 16,
+ },
+ "model": {
+ "name": "gluefactory.models.two_view_pipeline",
+ "ground_truth": {
+ "name": "gluefactory.models.matchers.depth_matcher",
+ "use_lines": False,
+ },
+ "run_gt_in_forward": True,
+ },
+ "eval": {"plot_methods": [], "plot_line_methods": [], "eval_lines": False},
+ }
+
+ export_keys = [
+ "gt_matches0",
+ "matches0",
+ "matching_scores0",
+ ]
+
+ optional_export_keys = [
+ "gt_line_matches0",
+ "line_matches0",
+ "line_matching_scores0",
+ ]
+
+ def get_dataloader(self, data_conf=None):
+ data_conf = data_conf if data_conf is not None else self.default_conf["data"]
+ dataset = get_dataset("eth3d")(data_conf)
+ return dataset.get_data_loader("test")
+
+ def get_predictions(self, experiment_dir, model=None, overwrite=False):
+ pred_file = experiment_dir / "predictions.h5"
+ if not pred_file.exists() or overwrite:
+ if model is None:
+ model = load_model(self.conf.model, self.conf.checkpoint)
+ export_predictions(
+ self.get_dataloader(self.conf.data),
+ model,
+ pred_file,
+ keys=self.export_keys,
+ optional_keys=self.optional_export_keys,
+ )
+ return pred_file
+
+ def run_eval(self, loader, pred_file):
+ eval_conf = self.conf.eval
+ r = eval_dataset(loader, pred_file)
+ if self.conf.eval.eval_lines:
+ r.update(eval_dataset(loader, pred_file, conf=eval_conf, suffix="_lines"))
+ s = {}
+
+ return s, {}, r
+
+
+def plot_pr_curve(
+ models_name, results, dst_file="eth3d_pr_curve.pdf", title=None, suffix=""
+):
+ plt.figure()
+ f_scores = np.linspace(0.2, 0.9, num=8)
+ for f_score in f_scores:
+ x = np.linspace(0.01, 1)
+ y = f_score * x / (2 * x - f_score)
+ plt.plot(x[y >= 0], y[y >= 0], color=[0, 0.5, 0], alpha=0.3)
+ plt.annotate(
+ "f={0:0.1}".format(f_score),
+ xy=(0.9, y[45] + 0.02),
+ alpha=0.4,
+ fontsize=14,
+ )
+
+ plt.rcParams.update({"font.size": 12})
+ # plt.rc('legend', fontsize=10)
+ plt.grid(True)
+ plt.axis([0.0, 1.0, 0.0, 1.0])
+ plt.xticks(np.arange(0, 1.05, step=0.1), fontsize=16)
+ plt.xlabel("Recall", fontsize=18)
+ plt.ylabel("Precision", fontsize=18)
+ plt.yticks(np.arange(0, 1.05, step=0.1), fontsize=16)
+ plt.ylim([0.3, 1.0])
+ prop_cycle = plt.rcParams["axes.prop_cycle"]
+ colors = prop_cycle.by_key()["color"]
+ for m, c in zip(models_name, colors):
+ sAP_string = f'{m}: {results[m]["AP" + suffix]:.1f}'
+ plt.plot(
+ results[m]["curve_recall" + suffix],
+ results[m]["curve_precision" + suffix],
+ label=sAP_string,
+ color=c,
+ )
+
+ plt.legend(fontsize=16, loc="lower right")
+ if title:
+ plt.title(title)
+
+ plt.tight_layout(pad=0.5)
+ print(f"Saving plot to: {dst_file}")
+ plt.savefig(dst_file)
+ plt.show()
+
+
+if __name__ == "__main__":
+ dataset_name = Path(__file__).stem
+ parser = get_eval_parser()
+ args = parser.parse_intermixed_args()
+
+ default_conf = OmegaConf.create(ETH3DPipeline.default_conf)
+
+ # mingle paths
+ output_dir = Path(EVAL_PATH, dataset_name)
+ output_dir.mkdir(exist_ok=True, parents=True)
+
+ name, conf = parse_eval_args(
+ dataset_name,
+ args,
+ "configs/",
+ default_conf,
+ )
+
+ experiment_dir = output_dir / name
+ experiment_dir.mkdir(exist_ok=True)
+
+ pipeline = ETH3DPipeline(conf)
+ s, f, r = pipeline.run(
+ experiment_dir, overwrite=args.overwrite, overwrite_eval=args.overwrite_eval
+ )
+
+ # print results
+ for k, v in r.items():
+ if k.startswith("AP"):
+ print(f"{k}: {v:.2f}")
+
+ if args.plot:
+ results = {}
+ for m in conf.eval.plot_methods:
+ exp_dir = output_dir / m
+ results[m] = load_eval(exp_dir)[1]
+
+ plot_pr_curve(conf.eval.plot_methods, results, dst_file="eth3d_pr_curve.pdf")
+ if conf.eval.eval_lines:
+ for m in conf.eval.plot_line_methods:
+ exp_dir = output_dir / m
+ results[m] = load_eval(exp_dir)[1]
+ plot_pr_curve(
+ conf.eval.plot_line_methods,
+ results,
+ dst_file="eth3d_pr_curve_lines.pdf",
+ suffix="_lines",
+ )
diff --git a/third_party/gim/gim/gluefactory/eval/eval_pipeline.py b/third_party/gim/gim/gluefactory/eval/eval_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac56237705180132428ff2eb9631803a3c34d8ac
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/eval/eval_pipeline.py
@@ -0,0 +1,109 @@
+import json
+
+import h5py
+import numpy as np
+from omegaconf import OmegaConf
+
+
+def load_eval(dir):
+ summaries, results = {}, {}
+ with h5py.File(str(dir / "results.h5"), "r") as hfile:
+ for k in hfile.keys():
+ r = np.array(hfile[k])
+ if len(r.shape) < 3:
+ results[k] = r
+ for k, v in hfile.attrs.items():
+ summaries[k] = v
+ with open(dir / "summaries.json", "r") as f:
+ s = json.load(f)
+ summaries = {k: v if v is not None else np.nan for k, v in s.items()}
+ return summaries, results
+
+
+def save_eval(dir, summaries, figures, results):
+ with h5py.File(str(dir / "results.h5"), "w") as hfile:
+ for k, v in results.items():
+ arr = np.array(v)
+ if not np.issubdtype(arr.dtype, np.number):
+ arr = arr.astype("object")
+ hfile.create_dataset(k, data=arr)
+ # just to be safe, not used in practice
+ for k, v in summaries.items():
+ hfile.attrs[k] = v
+ s = {
+ k: float(v) if np.isfinite(v) else None
+ for k, v in summaries.items()
+ if not isinstance(v, list)
+ }
+ s = {**s, **{k: v for k, v in summaries.items() if isinstance(v, list)}}
+ with open(dir / "summaries.json", "w") as f:
+ json.dump(s, f, indent=4)
+
+ for fig_name, fig in figures.items():
+ fig.savefig(dir / f"{fig_name}.png")
+
+
+def exists_eval(dir):
+ return (dir / "results.h5").exists() and (dir / "summaries.json").exists()
+
+
+class EvalPipeline:
+ default_conf = {}
+
+ export_keys = []
+ optional_export_keys = []
+
+ def __init__(self, conf):
+ """Assumes"""
+ self.default_conf = OmegaConf.create(self.default_conf)
+ self.conf = OmegaConf.merge(self.default_conf, conf)
+ self._init(self.conf)
+
+ def _init(self, conf):
+ pass
+
+ @classmethod
+ def get_dataloader(self, data_conf=None):
+ """Returns a data loader with samples for each eval datapoint"""
+ raise NotImplementedError
+
+ def get_predictions(self, experiment_dir, model=None, overwrite=False):
+ """Export a prediction file for each eval datapoint"""
+ raise NotImplementedError
+
+ def run_eval(self, loader, pred_file):
+ """Run the eval on cached predictions"""
+ raise NotImplementedError
+
+ def run(self, experiment_dir, model=None, overwrite=False, overwrite_eval=False):
+ """Run export+eval loop"""
+ self.save_conf(
+ experiment_dir, overwrite=overwrite, overwrite_eval=overwrite_eval
+ )
+ pred_file = self.get_predictions(
+ experiment_dir, model=model, overwrite=overwrite
+ )
+
+ f = {}
+ if not exists_eval(experiment_dir) or overwrite_eval or overwrite:
+ s, f, r = self.run_eval(self.get_dataloader(), pred_file)
+ save_eval(experiment_dir, s, f, r)
+ s, r = load_eval(experiment_dir)
+ return s, f, r
+
+ def save_conf(self, experiment_dir, overwrite=False, overwrite_eval=False):
+ # store config
+ conf_output_path = experiment_dir / "conf.yaml"
+ if conf_output_path.exists():
+ saved_conf = OmegaConf.load(conf_output_path)
+ if (saved_conf.data != self.conf.data) or (
+ saved_conf.model != self.conf.model
+ ):
+ assert (
+ overwrite
+ ), "configs changed, add --overwrite to rerun experiment with new conf"
+ if saved_conf.eval != self.conf.eval:
+ assert (
+ overwrite or overwrite_eval
+ ), "eval configs changed, add --overwrite_eval to rerun evaluation"
+ OmegaConf.save(self.conf, experiment_dir / "conf.yaml")
diff --git a/third_party/gim/gim/gluefactory/eval/hpatches.py b/third_party/gim/gim/gluefactory/eval/hpatches.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcd799c3e2adc14140b1b2d5c341f7833a0a1370
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/eval/hpatches.py
@@ -0,0 +1,203 @@
+from collections import defaultdict
+from collections.abc import Iterable
+from pathlib import Path
+from pprint import pprint
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from omegaconf import OmegaConf
+from tqdm import tqdm
+
+from ..datasets import get_dataset
+from ..models.cache_loader import CacheLoader
+from ..settings import EVAL_PATH
+from ..utils.export_predictions import export_predictions
+from ..utils.tensor import map_tensor
+from ..utils.tools import AUCMetric
+from ..visualization.viz2d import plot_cumulative
+from .eval_pipeline import EvalPipeline
+from .io import get_eval_parser, load_model, parse_eval_args
+from .utils import (
+ eval_homography_dlt,
+ eval_homography_robust,
+ eval_matches_homography,
+ eval_poses,
+)
+
+
+class HPatchesPipeline(EvalPipeline):
+ default_conf = {
+ "data": {
+ "batch_size": 1,
+ "name": "hpatches",
+ "num_workers": 16,
+ "preprocessing": {
+ "resize": 480, # we also resize during eval to have comparable metrics
+ "side": "short",
+ },
+ },
+ "model": {
+ "ground_truth": {
+ "name": None, # remove gt matches
+ }
+ },
+ "eval": {
+ "estimator": "poselib",
+ "ransac_th": 1.0, # -1 runs a bunch of thresholds and selects the best
+ },
+ }
+ export_keys = [
+ "keypoints0",
+ "keypoints1",
+ "keypoint_scores0",
+ "keypoint_scores1",
+ "matches0",
+ "matches1",
+ "matching_scores0",
+ "matching_scores1",
+ ]
+
+ optional_export_keys = [
+ "lines0",
+ "lines1",
+ "orig_lines0",
+ "orig_lines1",
+ "line_matches0",
+ "line_matches1",
+ "line_matching_scores0",
+ "line_matching_scores1",
+ ]
+
+ def _init(self, conf):
+ pass
+
+ @classmethod
+ def get_dataloader(self, data_conf=None):
+ data_conf = data_conf if data_conf else self.default_conf["data"]
+ dataset = get_dataset("hpatches")(data_conf)
+ return dataset.get_data_loader("test")
+
+ def get_predictions(self, experiment_dir, model=None, overwrite=False):
+ pred_file = experiment_dir / "predictions.h5"
+ if not pred_file.exists() or overwrite:
+ if model is None:
+ model = load_model(self.conf.model, self.conf.checkpoint)
+ export_predictions(
+ self.get_dataloader(self.conf.data),
+ model,
+ pred_file,
+ keys=self.export_keys,
+ optional_keys=self.optional_export_keys,
+ )
+ return pred_file
+
+ def run_eval(self, loader, pred_file):
+ assert pred_file.exists()
+ results = defaultdict(list)
+
+ conf = self.conf.eval
+
+ test_thresholds = (
+ ([conf.ransac_th] if conf.ransac_th > 0 else [0.5, 1.0, 1.5, 2.0, 2.5, 3.0])
+ if not isinstance(conf.ransac_th, Iterable)
+ else conf.ransac_th
+ )
+ pose_results = defaultdict(lambda: defaultdict(list))
+ cache_loader = CacheLoader({"path": str(pred_file), "collate": None}).eval()
+ for i, data in enumerate(tqdm(loader)):
+ pred = cache_loader(data)
+ # Remove batch dimension
+ data = map_tensor(data, lambda t: torch.squeeze(t, dim=0))
+ # add custom evaluations here
+ if "keypoints0" in pred:
+ results_i = eval_matches_homography(data, pred)
+ results_i = {**results_i, **eval_homography_dlt(data, pred)}
+ else:
+ results_i = {}
+ for th in test_thresholds:
+ pose_results_i = eval_homography_robust(
+ data,
+ pred,
+ {"estimator": conf.estimator, "ransac_th": th},
+ )
+ [pose_results[th][k].append(v) for k, v in pose_results_i.items()]
+
+ # we also store the names for later reference
+ results_i["names"] = data["name"][0]
+ results_i["scenes"] = data["scene"][0]
+
+ for k, v in results_i.items():
+ results[k].append(v)
+
+ # summarize results as a dict[str, float]
+ # you can also add your custom evaluations here
+ summaries = {}
+ for k, v in results.items():
+ arr = np.array(v)
+ if not np.issubdtype(np.array(v).dtype, np.number):
+ continue
+ summaries[f"m{k}"] = round(np.median(arr), 3)
+
+ auc_ths = [1, 3, 5]
+ best_pose_results, best_th = eval_poses(
+ pose_results, auc_ths=auc_ths, key="H_error_ransac", unit="px"
+ )
+ if "H_error_dlt" in results.keys():
+ dlt_aucs = AUCMetric(auc_ths, results["H_error_dlt"]).compute()
+ for i, ath in enumerate(auc_ths):
+ summaries[f"H_error_dlt@{ath}px"] = dlt_aucs[i]
+
+ results = {**results, **pose_results[best_th]}
+ summaries = {
+ **summaries,
+ **best_pose_results,
+ }
+
+ figures = {
+ "homography_recall": plot_cumulative(
+ {
+ "DLT": results["H_error_dlt"],
+ self.conf.eval.estimator: results["H_error_ransac"],
+ },
+ [0, 10],
+ unit="px",
+ title="Homography ",
+ )
+ }
+
+ return summaries, figures, results
+
+
+if __name__ == "__main__":
+ dataset_name = Path(__file__).stem
+ parser = get_eval_parser()
+ args = parser.parse_intermixed_args()
+
+ default_conf = OmegaConf.create(HPatchesPipeline.default_conf)
+
+ # mingle paths
+ output_dir = Path(EVAL_PATH, dataset_name)
+ output_dir.mkdir(exist_ok=True, parents=True)
+
+ name, conf = parse_eval_args(
+ dataset_name,
+ args,
+ "configs/",
+ default_conf,
+ )
+
+ experiment_dir = output_dir / name
+ experiment_dir.mkdir(exist_ok=True)
+
+ pipeline = HPatchesPipeline(conf)
+ s, f, r = pipeline.run(
+ experiment_dir, overwrite=args.overwrite, overwrite_eval=args.overwrite_eval
+ )
+
+ # print results
+ pprint(s)
+ if args.plot:
+ for name, fig in f.items():
+ fig.canvas.manager.set_window_title(name)
+ plt.show()
diff --git a/third_party/gim/gim/gluefactory/eval/inspect.py b/third_party/gim/gim/gluefactory/eval/inspect.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b7a3929eedd275b7ab7e257afecc8ed131cdfbc
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/eval/inspect.py
@@ -0,0 +1,61 @@
+import argparse
+from collections import defaultdict
+from pathlib import Path
+from pprint import pprint
+
+import matplotlib
+import matplotlib.pyplot as plt
+
+from ..settings import EVAL_PATH
+from ..visualization.global_frame import GlobalFrame
+from ..visualization.two_view_frame import TwoViewFrame
+from . import get_benchmark
+from .eval_pipeline import load_eval
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("benchmark", type=str)
+ parser.add_argument("--x", type=str, default=None)
+ parser.add_argument("--y", type=str, default=None)
+ parser.add_argument("--backend", type=str, default=None)
+ parser.add_argument(
+ "--default_plot", type=str, default=TwoViewFrame.default_conf["default"]
+ )
+
+ parser.add_argument("dotlist", nargs="*")
+ args = parser.parse_intermixed_args()
+
+ output_dir = Path(EVAL_PATH, args.benchmark)
+
+ results = {}
+ summaries = defaultdict(dict)
+
+ predictions = {}
+
+ if args.backend:
+ matplotlib.use(args.backend)
+
+ bm = get_benchmark(args.benchmark)
+ loader = bm.get_dataloader()
+
+ for name in args.dotlist:
+ experiment_dir = output_dir / name
+ pred_file = experiment_dir / "predictions.h5"
+ s, results[name] = load_eval(experiment_dir)
+ predictions[name] = pred_file
+ for k, v in s.items():
+ summaries[k][name] = v
+
+ pprint(summaries)
+
+ plt.close("all")
+
+ frame = GlobalFrame(
+ {"child": {"default": args.default_plot}, **vars(args)},
+ results,
+ loader,
+ predictions,
+ child_frame=TwoViewFrame,
+ )
+ frame.draw()
+ plt.show()
diff --git a/third_party/gim/gim/gluefactory/eval/io.py b/third_party/gim/gim/gluefactory/eval/io.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a55d59ed8fd8decf2beaec39eac353a735b03fa
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/eval/io.py
@@ -0,0 +1,109 @@
+import argparse
+from pathlib import Path
+from pprint import pprint
+from typing import Optional
+
+import pkg_resources
+from omegaconf import OmegaConf
+
+from ..models import get_model
+from ..settings import TRAINING_PATH
+from ..utils.experiments import load_experiment
+
+
+def parse_config_path(name_or_path: Optional[str], defaults: str) -> Path:
+ default_configs = {}
+ for c in pkg_resources.resource_listdir("gluefactory", str(defaults)):
+ if c.endswith(".yaml"):
+ default_configs[Path(c).stem] = Path(
+ pkg_resources.resource_filename("gluefactory", defaults + c)
+ )
+ if name_or_path is None:
+ return None
+ if name_or_path in default_configs:
+ return default_configs[name_or_path]
+ path = Path(name_or_path)
+ if not path.exists():
+ raise FileNotFoundError(
+ f"Cannot find the config file: {name_or_path}. "
+ f"Not in the default configs {list(default_configs.keys())} "
+ "and not an existing path."
+ )
+ return Path(path)
+
+
+def extract_benchmark_conf(conf, benchmark):
+ mconf = OmegaConf.create(
+ {
+ "model": conf.get("model", {}),
+ }
+ )
+ if "benchmarks" in conf.keys():
+ return OmegaConf.merge(mconf, conf.benchmarks.get(benchmark, {}))
+ else:
+ return mconf
+
+
+def parse_eval_args(benchmark, args, configs_path, default=None):
+ conf = {"data": {}, "model": {}, "eval": {}}
+ if args.conf:
+ conf_path = parse_config_path(args.conf, configs_path)
+ custom_conf = OmegaConf.load(conf_path)
+ conf = extract_benchmark_conf(OmegaConf.merge(conf, custom_conf), benchmark)
+ args.tag = (
+ args.tag if args.tag is not None else conf_path.name.replace(".yaml", "")
+ )
+
+ cli_conf = OmegaConf.from_cli(args.dotlist)
+ conf = OmegaConf.merge(conf, cli_conf)
+ conf.checkpoint = args.checkpoint if args.checkpoint else conf.get("checkpoint")
+
+ if conf.checkpoint and not conf.checkpoint.endswith(".tar"):
+ checkpoint_conf = OmegaConf.load(
+ TRAINING_PATH / conf.checkpoint / "config.yaml"
+ )
+ conf = OmegaConf.merge(extract_benchmark_conf(checkpoint_conf, benchmark), conf)
+
+ if default:
+ conf = OmegaConf.merge(default, conf)
+
+ if args.tag is not None:
+ name = args.tag
+ elif args.conf and conf.checkpoint:
+ name = f"{args.conf}_{conf.checkpoint}"
+ elif args.conf:
+ name = args.conf
+ elif conf.checkpoint:
+ name = conf.checkpoint
+ if len(args.dotlist) > 0 and not args.tag:
+ name = name + "_" + ":".join(args.dotlist)
+ print("Running benchmark:", benchmark)
+ print("Experiment tag:", name)
+ print("Config:")
+ pprint(OmegaConf.to_container(conf))
+ return name, conf
+
+
+def load_model(model_conf, checkpoint):
+ if checkpoint:
+ model = load_experiment(checkpoint, conf=model_conf).eval()
+ else:
+ model = get_model("two_view_pipeline")(model_conf).eval()
+ if not model.is_initialized():
+ raise ValueError(
+ "The provided model has non-initialized parameters. "
+ + "Try to load a checkpoint instead."
+ )
+ return model
+
+
+def get_eval_parser():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--tag", type=str, default=None)
+ parser.add_argument("--checkpoint", type=str, default=None)
+ parser.add_argument("--conf", type=str, default=None)
+ parser.add_argument("--overwrite", action="store_true")
+ parser.add_argument("--overwrite_eval", action="store_true")
+ parser.add_argument("--plot", action="store_true")
+ parser.add_argument("dotlist", nargs="*")
+ return parser
diff --git a/third_party/gim/gim/gluefactory/eval/megadepth1500.py b/third_party/gim/gim/gluefactory/eval/megadepth1500.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9cb10a7e8bdf82d58b54559f2a35167114da21c
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/eval/megadepth1500.py
@@ -0,0 +1,189 @@
+import logging
+import zipfile
+from collections import defaultdict
+from collections.abc import Iterable
+from pathlib import Path
+from pprint import pprint
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from omegaconf import OmegaConf
+from tqdm import tqdm
+
+from ..datasets import get_dataset
+from ..models.cache_loader import CacheLoader
+from ..settings import DATA_PATH, EVAL_PATH
+from ..utils.export_predictions import export_predictions
+from ..visualization.viz2d import plot_cumulative
+from .eval_pipeline import EvalPipeline
+from .io import get_eval_parser, load_model, parse_eval_args
+from .utils import eval_matches_epipolar, eval_poses, eval_relative_pose_robust
+
+logger = logging.getLogger(__name__)
+
+
+class MegaDepth1500Pipeline(EvalPipeline):
+ default_conf = {
+ "data": {
+ "name": "image_pairs",
+ "pairs": "megadepth1500/pairs_calibrated.txt",
+ "root": "megadepth1500/images/",
+ "extra_data": "relative_pose",
+ "preprocessing": {
+ "side": "long",
+ },
+ },
+ "model": {
+ "ground_truth": {
+ "name": None, # remove gt matches
+ }
+ },
+ "eval": {
+ "estimator": "poselib",
+ "ransac_th": 1.0, # -1 runs a bunch of thresholds and selects the best
+ },
+ }
+
+ export_keys = [
+ "keypoints0",
+ "keypoints1",
+ "keypoint_scores0",
+ "keypoint_scores1",
+ "matches0",
+ "matches1",
+ "matching_scores0",
+ "matching_scores1",
+ ]
+ optional_export_keys = []
+
+ def _init(self, conf):
+ if not (DATA_PATH / "megadepth1500").exists():
+ logger.info("Downloading the MegaDepth-1500 dataset.")
+ url = "https://cvg-data.inf.ethz.ch/megadepth/megadepth1500.zip"
+ zip_path = DATA_PATH / url.rsplit("/", 1)[-1]
+ zip_path.parent.mkdir(exist_ok=True, parents=True)
+ torch.hub.download_url_to_file(url, zip_path)
+ with zipfile.ZipFile(zip_path) as fid:
+ fid.extractall(DATA_PATH)
+ zip_path.unlink()
+
+ @classmethod
+ def get_dataloader(self, data_conf=None):
+ """Returns a data loader with samples for each eval datapoint"""
+ data_conf = data_conf if data_conf else self.default_conf["data"]
+ dataset = get_dataset(data_conf["name"])(data_conf)
+ return dataset.get_data_loader("test")
+
+ def get_predictions(self, experiment_dir, model=None, overwrite=False):
+ """Export a prediction file for each eval datapoint"""
+ pred_file = experiment_dir / "predictions.h5"
+ if not pred_file.exists() or overwrite:
+ if model is None:
+ model = load_model(self.conf.model, self.conf.checkpoint)
+ export_predictions(
+ self.get_dataloader(self.conf.data),
+ model,
+ pred_file,
+ keys=self.export_keys,
+ optional_keys=self.optional_export_keys,
+ )
+ return pred_file
+
+ def run_eval(self, loader, pred_file):
+ """Run the eval on cached predictions"""
+ conf = self.conf.eval
+ results = defaultdict(list)
+ test_thresholds = (
+ ([conf.ransac_th] if conf.ransac_th > 0 else [0.5, 1.0, 1.5, 2.0, 2.5, 3.0])
+ if not isinstance(conf.ransac_th, Iterable)
+ else conf.ransac_th
+ )
+ pose_results = defaultdict(lambda: defaultdict(list))
+ cache_loader = CacheLoader({"path": str(pred_file), "collate": None}).eval()
+ for i, data in enumerate(tqdm(loader)):
+ pred = cache_loader(data)
+ # add custom evaluations here
+ results_i = eval_matches_epipolar(data, pred)
+ for th in test_thresholds:
+ pose_results_i = eval_relative_pose_robust(
+ data,
+ pred,
+ {"estimator": conf.estimator, "ransac_th": th},
+ )
+ [pose_results[th][k].append(v) for k, v in pose_results_i.items()]
+
+ # we also store the names for later reference
+ results_i["names"] = data["name"][0]
+ if "scene" in data.keys():
+ results_i["scenes"] = data["scene"][0]
+
+ for k, v in results_i.items():
+ results[k].append(v)
+
+ # summarize results as a dict[str, float]
+ # you can also add your custom evaluations here
+ summaries = {}
+ for k, v in results.items():
+ arr = np.array(v)
+ if not np.issubdtype(np.array(v).dtype, np.number):
+ continue
+ summaries[f"m{k}"] = round(np.mean(arr), 3)
+
+ best_pose_results, best_th = eval_poses(
+ pose_results, auc_ths=[5, 10, 20], key="rel_pose_error"
+ )
+ results = {**results, **pose_results[best_th]}
+ summaries = {
+ **summaries,
+ **best_pose_results,
+ }
+
+ figures = {
+ "pose_recall": plot_cumulative(
+ {self.conf.eval.estimator: results["rel_pose_error"]},
+ [0, 30],
+ unit="°",
+ title="Pose ",
+ )
+ }
+
+ return summaries, figures, results
+
+
+if __name__ == "__main__":
+ from .. import logger # overwrite the logger
+
+ dataset_name = Path(__file__).stem
+ parser = get_eval_parser()
+ args = parser.parse_intermixed_args()
+
+ default_conf = OmegaConf.create(MegaDepth1500Pipeline.default_conf)
+
+ # mingle paths
+ output_dir = Path(EVAL_PATH, dataset_name)
+ output_dir.mkdir(exist_ok=True, parents=True)
+
+ name, conf = parse_eval_args(
+ dataset_name,
+ args,
+ "configs/",
+ default_conf,
+ )
+
+ experiment_dir = output_dir / name
+ experiment_dir.mkdir(exist_ok=True)
+
+ pipeline = MegaDepth1500Pipeline(conf)
+ s, f, r = pipeline.run(
+ experiment_dir,
+ overwrite=args.overwrite,
+ overwrite_eval=args.overwrite_eval,
+ )
+
+ pprint(s)
+
+ if args.plot:
+ for name, fig in f.items():
+ fig.canvas.manager.set_window_title(name)
+ plt.show()
diff --git a/third_party/gim/gim/gluefactory/eval/utils.py b/third_party/gim/gim/gluefactory/eval/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b89fe792f5d2129cbdac20da5908a5adb62b4048
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/eval/utils.py
@@ -0,0 +1,272 @@
+import numpy as np
+import torch
+from kornia.geometry.homography import find_homography_dlt
+
+from ..geometry.epipolar import generalized_epi_dist, relative_pose_error
+from ..geometry.gt_generation import IGNORE_FEATURE
+from ..geometry.homography import homography_corner_error, sym_homography_error
+from ..robust_estimators import load_estimator
+from ..utils.tensor import index_batch
+from ..utils.tools import AUCMetric
+
+
+def check_keys_recursive(d, pattern):
+ if isinstance(pattern, dict):
+ {check_keys_recursive(d[k], v) for k, v in pattern.items()}
+ else:
+ for k in pattern:
+ assert k in d.keys()
+
+
+def get_matches_scores(kpts0, kpts1, matches0, mscores0):
+ m0 = matches0 > -1
+ m1 = matches0[m0]
+ pts0 = kpts0[m0]
+ pts1 = kpts1[m1]
+ scores = mscores0[m0]
+ return pts0, pts1, scores
+
+
+def eval_per_batch_item(data: dict, pred: dict, eval_f, *args, **kwargs):
+ # Batched data
+ results = [
+ eval_f(data_i, pred_i, *args, **kwargs)
+ for data_i, pred_i in zip(index_batch(data), index_batch(pred))
+ ]
+ # Return a dictionary of lists with the evaluation of each item
+ return {k: [r[k] for r in results] for k in results[0].keys()}
+
+
+def eval_matches_epipolar(data: dict, pred: dict) -> dict:
+ check_keys_recursive(data, ["view0", "view1", "T_0to1"])
+ check_keys_recursive(
+ pred, ["keypoints0", "keypoints1", "matches0", "matching_scores0"]
+ )
+
+ kp0, kp1 = pred["keypoints0"], pred["keypoints1"]
+ m0, scores0 = pred["matches0"], pred["matching_scores0"]
+ pts0, pts1, scores = get_matches_scores(kp0, kp1, m0, scores0)
+
+ results = {}
+
+ # match metrics
+ n_epi_err = generalized_epi_dist(
+ pts0[None],
+ pts1[None],
+ data["view0"]["camera"],
+ data["view1"]["camera"],
+ data["T_0to1"],
+ False,
+ essential=True,
+ )[0]
+ results["epi_prec@1e-4"] = (n_epi_err < 1e-4).float().mean()
+ results["epi_prec@5e-4"] = (n_epi_err < 5e-4).float().mean()
+ results["epi_prec@1e-3"] = (n_epi_err < 1e-3).float().mean()
+
+ results["num_matches"] = pts0.shape[0]
+ results["num_keypoints"] = (kp0.shape[0] + kp1.shape[0]) / 2.0
+
+ return results
+
+
+def eval_matches_homography(data: dict, pred: dict) -> dict:
+ check_keys_recursive(data, ["H_0to1"])
+ check_keys_recursive(
+ pred, ["keypoints0", "keypoints1", "matches0", "matching_scores0"]
+ )
+
+ H_gt = data["H_0to1"]
+ if H_gt.ndim > 2:
+ return eval_per_batch_item(data, pred, eval_matches_homography)
+
+ kp0, kp1 = pred["keypoints0"], pred["keypoints1"]
+ m0, scores0 = pred["matches0"], pred["matching_scores0"]
+ pts0, pts1, scores = get_matches_scores(kp0, kp1, m0, scores0)
+ err = sym_homography_error(pts0, pts1, H_gt)
+ results = {}
+ results["prec@1px"] = (err < 1).float().mean().nan_to_num().item()
+ results["prec@3px"] = (err < 3).float().mean().nan_to_num().item()
+ results["num_matches"] = pts0.shape[0]
+ results["num_keypoints"] = (kp0.shape[0] + kp1.shape[0]) / 2.0
+ return results
+
+
+def eval_relative_pose_robust(data, pred, conf):
+ check_keys_recursive(data, ["view0", "view1", "T_0to1"])
+ check_keys_recursive(
+ pred, ["keypoints0", "keypoints1", "matches0", "matching_scores0"]
+ )
+
+ T_gt = data["T_0to1"]
+ kp0, kp1 = pred["keypoints0"], pred["keypoints1"]
+ m0, scores0 = pred["matches0"], pred["matching_scores0"]
+ pts0, pts1, scores = get_matches_scores(kp0, kp1, m0, scores0)
+
+ results = {}
+
+ estimator = load_estimator("relative_pose", conf["estimator"])(conf)
+ data_ = {
+ "m_kpts0": pts0,
+ "m_kpts1": pts1,
+ "camera0": data["view0"]["camera"][0],
+ "camera1": data["view1"]["camera"][0],
+ }
+ est = estimator(data_)
+
+ if not est["success"]:
+ results["rel_pose_error"] = float("inf")
+ results["ransac_inl"] = 0
+ results["ransac_inl%"] = 0
+ else:
+ # R, t, inl = ret
+ M = est["M_0to1"]
+ inl = est["inliers"].numpy()
+ t_error, r_error = relative_pose_error(T_gt, M.R, M.t)
+ results["rel_pose_error"] = max(r_error, t_error)
+ results["ransac_inl"] = np.sum(inl)
+ results["ransac_inl%"] = np.mean(inl)
+
+ return results
+
+
+def eval_homography_robust(data, pred, conf):
+ H_gt = data["H_0to1"]
+ if H_gt.ndim > 2:
+ return eval_per_batch_item(data, pred, eval_relative_pose_robust, conf)
+
+ estimator = load_estimator("homography", conf["estimator"])(conf)
+
+ data_ = {}
+ if "keypoints0" in pred:
+ kp0, kp1 = pred["keypoints0"], pred["keypoints1"]
+ m0, scores0 = pred["matches0"], pred["matching_scores0"]
+ pts0, pts1, _ = get_matches_scores(kp0, kp1, m0, scores0)
+ data_["m_kpts0"] = pts0
+ data_["m_kpts1"] = pts1
+ if "lines0" in pred:
+ if "orig_lines0" in pred:
+ lines0 = pred["orig_lines0"]
+ lines1 = pred["orig_lines1"]
+ else:
+ lines0 = pred["lines0"]
+ lines1 = pred["lines1"]
+ m_lines0, m_lines1, _ = get_matches_scores(
+ lines0, lines1, pred["line_matches0"], pred["line_matching_scores0"]
+ )
+ data_["m_lines0"] = m_lines0
+ data_["m_lines1"] = m_lines1
+
+ est = estimator(data_)
+ if est["success"]:
+ M = est["M_0to1"]
+ error_r = homography_corner_error(M, H_gt, data["view0"]["image_size"]).item()
+ else:
+ error_r = float("inf")
+
+ results = {}
+ results["H_error_ransac"] = error_r
+ if "inliers" in est:
+ inl = est["inliers"]
+ results["ransac_inl"] = inl.float().sum().item()
+ results["ransac_inl%"] = inl.float().sum().item() / max(len(inl), 1)
+
+ return results
+
+
+def eval_homography_dlt(data, pred):
+ H_gt = data["H_0to1"]
+ H_inf = torch.ones_like(H_gt) * float("inf")
+
+ kp0, kp1 = pred["keypoints0"], pred["keypoints1"]
+ m0, scores0 = pred["matches0"], pred["matching_scores0"]
+ pts0, pts1, scores = get_matches_scores(kp0, kp1, m0, scores0)
+ scores = scores.to(pts0)
+ results = {}
+ try:
+ if H_gt.ndim == 2:
+ pts0, pts1, scores = pts0[None], pts1[None], scores[None]
+ h_dlt = find_homography_dlt(pts0, pts1, scores)
+ if H_gt.ndim == 2:
+ h_dlt = h_dlt[0]
+ except AssertionError:
+ h_dlt = H_inf
+
+ error_dlt = homography_corner_error(h_dlt, H_gt, data["view0"]["image_size"])
+ results["H_error_dlt"] = error_dlt.item()
+ return results
+
+
+def eval_poses(pose_results, auc_ths, key, unit="°"):
+ pose_aucs = {}
+ best_th = -1
+ for th, results_i in pose_results.items():
+ pose_aucs[th] = AUCMetric(auc_ths, results_i[key]).compute()
+ mAAs = {k: np.mean(v) for k, v in pose_aucs.items()}
+ best_th = max(mAAs, key=mAAs.get)
+
+ if len(pose_aucs) > -1:
+ print("Tested ransac setup with following results:")
+ print("AUC", pose_aucs)
+ print("mAA", mAAs)
+ print("best threshold =", best_th)
+
+ summaries = {}
+
+ for i, ath in enumerate(auc_ths):
+ summaries[f"{key}@{ath}{unit}"] = pose_aucs[best_th][i]
+ summaries[f"{key}_mAA"] = mAAs[best_th]
+
+ for k, v in pose_results[best_th].items():
+ arr = np.array(v)
+ if not np.issubdtype(np.array(v).dtype, np.number):
+ continue
+ summaries[f"m{k}"] = round(np.median(arr), 3)
+ return summaries, best_th
+
+
+def get_tp_fp_pts(pred_matches, gt_matches, pred_scores):
+ """
+ Computes the True Positives (TP), False positives (FP), the score associated
+ to each match and the number of positives for a set of matches.
+ """
+ assert pred_matches.shape == pred_scores.shape
+ ignore_mask = gt_matches != IGNORE_FEATURE
+ pred_matches, gt_matches, pred_scores = (
+ pred_matches[ignore_mask],
+ gt_matches[ignore_mask],
+ pred_scores[ignore_mask],
+ )
+ num_pos = np.sum(gt_matches != -1)
+ pred_positives = pred_matches != -1
+ tp = pred_matches[pred_positives] == gt_matches[pred_positives]
+ fp = pred_matches[pred_positives] != gt_matches[pred_positives]
+ scores = pred_scores[pred_positives]
+ return tp, fp, scores, num_pos
+
+
+def AP(tp, fp):
+ recall = tp
+ precision = tp / np.maximum(tp + fp, 1e-9)
+ recall = np.concatenate(([0.0], recall, [1.0]))
+ precision = np.concatenate(([0.0], precision, [0.0]))
+ for i in range(precision.size - 1, 0, -1):
+ precision[i - 1] = max(precision[i - 1], precision[i])
+ i = np.where(recall[1:] != recall[:-1])[0]
+ ap = np.sum((recall[i + 1] - recall[i]) * precision[i + 1])
+ return ap
+
+
+def aggregate_pr_results(results, suffix=""):
+ tp_list = np.concatenate(results["tp" + suffix], axis=0)
+ fp_list = np.concatenate(results["fp" + suffix], axis=0)
+ scores_list = np.concatenate(results["scores" + suffix], axis=0)
+ n_gt = max(results["num_pos" + suffix], 1)
+
+ out = {}
+ idx = np.argsort(scores_list)[::-1]
+ tp_vals = np.cumsum(tp_list[idx]) / n_gt
+ fp_vals = np.cumsum(fp_list[idx]) / n_gt
+ out["curve_recall" + suffix] = tp_vals
+ out["curve_precision" + suffix] = tp_vals / np.maximum(tp_vals + fp_vals, 1e-9)
+ out["AP" + suffix] = AP(tp_vals, fp_vals) * 100
+ return out
diff --git a/third_party/gim/gim/gluefactory/geometry/depth.py b/third_party/gim/gim/gluefactory/geometry/depth.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca68bc5f4d712e11b8a0ee3e4f930e1a3c196b4a
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/geometry/depth.py
@@ -0,0 +1,88 @@
+import kornia
+import torch
+
+from .utils import get_image_coords
+from .wrappers import Camera
+
+
+def sample_fmap(pts, fmap):
+ h, w = fmap.shape[-2:]
+ grid_sample = torch.nn.functional.grid_sample
+ pts = (pts / pts.new_tensor([[w, h]]) * 2 - 1)[:, None]
+ # @TODO: This might still be a source of noise --> bilinear interpolation dangerous
+ interp_lin = grid_sample(fmap, pts, align_corners=False, mode="bilinear")
+ interp_nn = grid_sample(fmap, pts, align_corners=False, mode="nearest")
+ return torch.where(torch.isnan(interp_lin), interp_nn, interp_lin)[:, :, 0].permute(
+ 0, 2, 1
+ )
+
+
+def sample_depth(pts, depth_):
+ depth = torch.where(depth_ > 0, depth_, depth_.new_tensor(float("nan")))
+ depth = depth[:, None]
+ interp = sample_fmap(pts, depth).squeeze(-1)
+ valid = (~torch.isnan(interp)) & (interp > 0)
+ return interp, valid
+
+
+def sample_normals_from_depth(pts, depth, K):
+ depth = depth[:, None]
+ normals = kornia.geometry.depth.depth_to_normals(depth, K)
+ normals = torch.where(depth > 0, normals, 0.0)
+ interp = sample_fmap(pts, normals)
+ valid = (~torch.isnan(interp)) & (interp > 0)
+ return interp, valid
+
+
+def project(
+ kpi,
+ di,
+ depthj,
+ camera_i,
+ camera_j,
+ T_itoj,
+ validi,
+ ccth=None,
+ sample_depth_fun=sample_depth,
+ sample_depth_kwargs=None,
+):
+ if sample_depth_kwargs is None:
+ sample_depth_kwargs = {}
+
+ kpi_3d_i = camera_i.image2cam(kpi)
+ kpi_3d_i = kpi_3d_i * di[..., None]
+ kpi_3d_j = T_itoj.transform(kpi_3d_i)
+ kpi_j, validj = camera_j.cam2image(kpi_3d_j)
+ # di_j = kpi_3d_j[..., -1]
+ validi = validi & validj
+ if depthj is None or ccth is None:
+ return kpi_j, validi & validj
+ else:
+ # circle consistency
+ dj, validj = sample_depth_fun(kpi_j, depthj, **sample_depth_kwargs)
+ kpi_j_3d_j = camera_j.image2cam(kpi_j) * dj[..., None]
+ kpi_j_i, validj_i = camera_i.cam2image(T_itoj.inv().transform(kpi_j_3d_j))
+ consistent = ((kpi - kpi_j_i) ** 2).sum(-1) < ccth
+ visible = validi & consistent & validj_i & validj
+ # visible = validi
+ return kpi_j, visible
+
+
+def dense_warp_consistency(
+ depthi: torch.Tensor,
+ depthj: torch.Tensor,
+ T_itoj: torch.Tensor,
+ camerai: Camera,
+ cameraj: Camera,
+ **kwargs,
+):
+ kpi = get_image_coords(depthi).flatten(-3, -2)
+ di = depthi.flatten(
+ -2,
+ )
+ validi = di > 0
+ kpir, validir = project(kpi, di, depthj, camerai, cameraj, T_itoj, validi, **kwargs)
+
+ return kpir.unflatten(-2, depthi.shape[-2:]), validir.unflatten(
+ -1, (depthj.shape[-2:])
+ )
diff --git a/third_party/gim/gim/gluefactory/geometry/epipolar.py b/third_party/gim/gim/gluefactory/geometry/epipolar.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f7bb9ce8b8f1c117f64b30ba4cd9afa846eeff9
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/geometry/epipolar.py
@@ -0,0 +1,155 @@
+import torch
+
+from .utils import skew_symmetric, to_homogeneous
+from .wrappers import Camera, Pose
+
+
+def T_to_E(T: Pose):
+ """Convert batched poses (..., 4, 4) to batched essential matrices."""
+ return skew_symmetric(T.t) @ T.R
+
+
+def T_to_F(cam0: Camera, cam1: Camera, T_0to1: Pose):
+ return E_to_F(cam0, cam1, T_to_E(T_0to1))
+
+
+def E_to_F(cam0: Camera, cam1: Camera, E: torch.Tensor):
+ assert cam0._data.shape[-1] == 6, "only pinhole cameras supported"
+ assert cam1._data.shape[-1] == 6, "only pinhole cameras supported"
+ K0 = cam0.calibration_matrix()
+ K1 = cam1.calibration_matrix()
+ return K1.inverse().transpose(-1, -2) @ E @ K0.inverse()
+
+
+def F_to_E(cam0: Camera, cam1: Camera, F: torch.Tensor):
+ assert cam0._data.shape[-1] == 6, "only pinhole cameras supported"
+ assert cam1._data.shape[-1] == 6, "only pinhole cameras supported"
+ K0 = cam0.calibration_matrix()
+ K1 = cam1.calibration_matrix()
+ return K1.transpose(-1, -2) @ F @ K0
+
+
+def sym_epipolar_distance(p0, p1, E, squared=True):
+ """Compute batched symmetric epipolar distances.
+ Args:
+ p0, p1: batched tensors of N 2D points of size (..., N, 2).
+ E: essential matrices from camera 0 to camera 1, size (..., 3, 3).
+ Returns:
+ The symmetric epipolar distance of each point-pair: (..., N).
+ """
+ assert p0.shape[-2] == p1.shape[-2]
+ if p0.shape[-2] == 0:
+ return torch.zeros(p0.shape[:-1]).to(p0)
+ if p0.shape[-1] != 3:
+ p0 = to_homogeneous(p0)
+ if p1.shape[-1] != 3:
+ p1 = to_homogeneous(p1)
+ p1_E_p0 = torch.einsum("...ni,...ij,...nj->...n", p1, E, p0)
+ E_p0 = torch.einsum("...ij,...nj->...ni", E, p0)
+ Et_p1 = torch.einsum("...ij,...ni->...nj", E, p1)
+ d0 = (E_p0[..., 0] ** 2 + E_p0[..., 1] ** 2).clamp(min=1e-6)
+ d1 = (Et_p1[..., 0] ** 2 + Et_p1[..., 1] ** 2).clamp(min=1e-6)
+ if squared:
+ d = p1_E_p0**2 * (1 / d0 + 1 / d1)
+ else:
+ d = p1_E_p0.abs() * (1 / d0.sqrt() + 1 / d1.sqrt()) / 2
+ return d
+
+
+def sym_epipolar_distance_all(p0, p1, E, eps=1e-15):
+ if p0.shape[-1] != 3:
+ p0 = to_homogeneous(p0)
+ if p1.shape[-1] != 3:
+ p1 = to_homogeneous(p1)
+ p1_E_p0 = torch.einsum("...mi,...ij,...nj->...nm", p1, E, p0).abs()
+ E_p0 = torch.einsum("...ij,...nj->...ni", E, p0)
+ Et_p1 = torch.einsum("...ij,...mi->...mj", E, p1)
+ d0 = p1_E_p0 / (E_p0[..., None, 0] ** 2 + E_p0[..., None, 1] ** 2 + eps).sqrt()
+ d1 = (
+ p1_E_p0
+ / (Et_p1[..., None, :, 0] ** 2 + Et_p1[..., None, :, 1] ** 2 + eps).sqrt()
+ )
+ return (d0 + d1) / 2
+
+
+def generalized_epi_dist(
+ kpts0, kpts1, cam0: Camera, cam1: Camera, T_0to1: Pose, all=True, essential=True
+):
+ if essential:
+ E = T_to_E(T_0to1)
+ p0 = cam0.image2cam(kpts0)
+ p1 = cam1.image2cam(kpts1)
+ if all:
+ return sym_epipolar_distance_all(p0, p1, E, agg="max")
+ else:
+ return sym_epipolar_distance(p0, p1, E, squared=False)
+ else:
+ assert cam0._data.shape[-1] == 6
+ assert cam1._data.shape[-1] == 6
+ K0, K1 = cam0.calibration_matrix(), cam1.calibration_matrix()
+ F = K1.inverse().transpose(-1, -2) @ T_to_E(T_0to1) @ K0.inverse()
+ if all:
+ return sym_epipolar_distance_all(kpts0, kpts1, F)
+ else:
+ return sym_epipolar_distance(kpts0, kpts1, F, squared=False)
+
+
+def decompose_essential_matrix(E):
+ # decompose matrix by its singular values
+ U, _, V = torch.svd(E)
+ Vt = V.transpose(-2, -1)
+
+ mask = torch.ones_like(E)
+ mask[..., -1:] *= -1.0 # fill last column with negative values
+
+ maskt = mask.transpose(-2, -1)
+
+ # avoid singularities
+ U = torch.where((torch.det(U) < 0.0)[..., None, None], U * mask, U)
+ Vt = torch.where((torch.det(Vt) < 0.0)[..., None, None], Vt * maskt, Vt)
+
+ W = skew_symmetric(E.new_tensor([[0, 0, 1]]))
+ W[..., 2, 2] += 1.0
+
+ # reconstruct rotations and retrieve translation vector
+ U_W_Vt = U @ W @ Vt
+ U_Wt_Vt = U @ W.transpose(-2, -1) @ Vt
+
+ # return values
+ R1 = U_W_Vt
+ R2 = U_Wt_Vt
+ T = U[..., -1]
+ return R1, R2, T
+
+
+# pose errors
+# TODO: test for batched data
+def angle_error_mat(R1, R2):
+ cos = (torch.trace(torch.einsum("...ij, ...jk -> ...ik", R1.T, R2)) - 1) / 2
+ cos = torch.clip(cos, -1.0, 1.0) # numerical errors can make it out of bounds
+ return torch.rad2deg(torch.abs(torch.arccos(cos)))
+
+
+def angle_error_vec(v1, v2, eps=1e-10):
+ n = torch.clip(v1.norm(dim=-1) * v2.norm(dim=-1), min=eps)
+ v1v2 = (v1 * v2).sum(dim=-1) # dot product in the last dimension
+ return torch.rad2deg(torch.arccos(torch.clip(v1v2 / n, -1.0, 1.0)))
+
+
+def relative_pose_error(T_0to1, R, t, ignore_gt_t_thr=0.0, eps=1e-10):
+ if isinstance(T_0to1, torch.Tensor):
+ R_gt, t_gt = T_0to1[:3, :3], T_0to1[:3, 3]
+ else:
+ R_gt, t_gt = T_0to1.R, T_0to1.t
+ R_gt, t_gt = torch.squeeze(R_gt), torch.squeeze(t_gt)
+
+ # angle error between 2 vectors
+ t_err = angle_error_vec(t, t_gt, eps)
+ t_err = torch.minimum(t_err, 180 - t_err) # handle E ambiguity
+ if t_gt.norm() < ignore_gt_t_thr: # pure rotation is challenging
+ t_err = 0
+
+ # angle error between 2 rotation matrices
+ r_err = angle_error_mat(R, R_gt)
+
+ return t_err, r_err
diff --git a/third_party/gim/gim/gluefactory/geometry/gt_generation.py b/third_party/gim/gim/gluefactory/geometry/gt_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..21390cd79722535445b19036bab0c8bab3804715
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/geometry/gt_generation.py
@@ -0,0 +1,558 @@
+import numpy as np
+import torch
+from scipy.optimize import linear_sum_assignment
+
+from .depth import project, sample_depth
+from .epipolar import T_to_E, sym_epipolar_distance_all
+from .homography import warp_points_torch
+
+IGNORE_FEATURE = -2
+UNMATCHED_FEATURE = -1
+
+
+@torch.no_grad()
+def gt_matches_from_pose_depth(
+ kp0, kp1, data, pos_th=3, neg_th=5, epi_th=None, cc_th=None, **kw
+):
+ if kp0.shape[1] == 0 or kp1.shape[1] == 0:
+ b_size, n_kp0 = kp0.shape[:2]
+ n_kp1 = kp1.shape[1]
+ assignment = torch.zeros(
+ b_size, n_kp0, n_kp1, dtype=torch.bool, device=kp0.device
+ )
+ m0 = -torch.ones_like(kp0[:, :, 0]).long()
+ m1 = -torch.ones_like(kp1[:, :, 0]).long()
+ return assignment, m0, m1
+ camera0, camera1 = data["view0"]["camera"], data["view1"]["camera"]
+ T_0to1, T_1to0 = data["T_0to1"], data["T_1to0"]
+
+ depth0 = data["view0"].get("depth")
+ depth1 = data["view1"].get("depth")
+ if "depth_keypoints0" in kw and "depth_keypoints1" in kw:
+ d0, valid0 = kw["depth_keypoints0"], kw["valid_depth_keypoints0"]
+ d1, valid1 = kw["depth_keypoints1"], kw["valid_depth_keypoints1"]
+ else:
+ assert depth0 is not None
+ assert depth1 is not None
+ d0, valid0 = sample_depth(kp0, depth0)
+ d1, valid1 = sample_depth(kp1, depth1)
+
+ kp0_1, visible0 = project(
+ kp0, d0, depth1, camera0, camera1, T_0to1, valid0, ccth=cc_th
+ )
+ kp1_0, visible1 = project(
+ kp1, d1, depth0, camera1, camera0, T_1to0, valid1, ccth=cc_th
+ )
+ mask_visible = visible0.unsqueeze(-1) & visible1.unsqueeze(-2)
+
+ # build a distance matrix of size [... x M x N]
+ dist0 = torch.sum((kp0_1.unsqueeze(-2) - kp1.unsqueeze(-3)) ** 2, -1)
+ dist1 = torch.sum((kp0.unsqueeze(-2) - kp1_0.unsqueeze(-3)) ** 2, -1)
+ dist = torch.max(dist0, dist1)
+ inf = dist.new_tensor(float("inf"))
+ dist = torch.where(mask_visible, dist, inf)
+
+ min0 = dist.min(-1).indices
+ min1 = dist.min(-2).indices
+
+ ismin0 = torch.zeros(dist.shape, dtype=torch.bool, device=dist.device)
+ ismin1 = ismin0.clone()
+ ismin0.scatter_(-1, min0.unsqueeze(-1), value=1)
+ ismin1.scatter_(-2, min1.unsqueeze(-2), value=1)
+ positive = ismin0 & ismin1 & (dist < pos_th**2)
+
+ negative0 = (dist0.min(-1).values > neg_th**2) & valid0
+ negative1 = (dist1.min(-2).values > neg_th**2) & valid1
+
+ # pack the indices of positive matches
+ # if -1: unmatched point
+ # if -2: ignore point
+ unmatched = min0.new_tensor(UNMATCHED_FEATURE)
+ ignore = min0.new_tensor(IGNORE_FEATURE)
+ m0 = torch.where(positive.any(-1), min0, ignore)
+ m1 = torch.where(positive.any(-2), min1, ignore)
+ m0 = torch.where(negative0, unmatched, m0)
+ m1 = torch.where(negative1, unmatched, m1)
+
+ F = (
+ camera1.calibration_matrix().inverse().transpose(-1, -2)
+ @ T_to_E(T_0to1)
+ @ camera0.calibration_matrix().inverse()
+ )
+ epi_dist = sym_epipolar_distance_all(kp0, kp1, F)
+
+ # Add some more unmatched points using epipolar geometry
+ if epi_th is not None:
+ mask_ignore = (m0.unsqueeze(-1) == ignore) & (m1.unsqueeze(-2) == ignore)
+ epi_dist = torch.where(mask_ignore, epi_dist, inf)
+ exclude0 = epi_dist.min(-1).values > neg_th
+ exclude1 = epi_dist.min(-2).values > neg_th
+ m0 = torch.where((~valid0) & exclude0, ignore.new_tensor(-1), m0)
+ m1 = torch.where((~valid1) & exclude1, ignore.new_tensor(-1), m1)
+
+ return {
+ "assignment": positive,
+ "reward": (dist < pos_th**2).float() - (epi_dist > neg_th).float(),
+ "matches0": m0,
+ "matches1": m1,
+ "matching_scores0": (m0 > -1).float(),
+ "matching_scores1": (m1 > -1).float(),
+ "depth_keypoints0": d0,
+ "depth_keypoints1": d1,
+ "proj_0to1": kp0_1,
+ "proj_1to0": kp1_0,
+ "visible0": visible0,
+ "visible1": visible1,
+ }
+
+
+@torch.no_grad()
+def gt_matches_from_homography(kp0, kp1, H, pos_th=3, neg_th=6, **kw):
+ if kp0.shape[1] == 0 or kp1.shape[1] == 0:
+ b_size, n_kp0 = kp0.shape[:2]
+ n_kp1 = kp1.shape[1]
+ assignment = torch.zeros(
+ b_size, n_kp0, n_kp1, dtype=torch.bool, device=kp0.device
+ )
+ m0 = -torch.ones_like(kp0[:, :, 0]).long()
+ m1 = -torch.ones_like(kp1[:, :, 0]).long()
+ return assignment, m0, m1
+ kp0_1 = warp_points_torch(kp0, H, inverse=False)
+ kp1_0 = warp_points_torch(kp1, H, inverse=True)
+
+ # build a distance matrix of size [... x M x N]
+ dist0 = torch.sum((kp0_1.unsqueeze(-2) - kp1.unsqueeze(-3)) ** 2, -1)
+ dist1 = torch.sum((kp0.unsqueeze(-2) - kp1_0.unsqueeze(-3)) ** 2, -1)
+ dist = torch.max(dist0, dist1)
+
+ reward = (dist < pos_th**2).float() - (dist > neg_th**2).float()
+
+ min0 = dist.min(-1).indices
+ min1 = dist.min(-2).indices
+
+ ismin0 = torch.zeros(dist.shape, dtype=torch.bool, device=dist.device)
+ ismin1 = ismin0.clone()
+ ismin0.scatter_(-1, min0.unsqueeze(-1), value=1)
+ ismin1.scatter_(-2, min1.unsqueeze(-2), value=1)
+ positive = ismin0 & ismin1 & (dist < pos_th**2)
+
+ negative0 = dist0.min(-1).values > neg_th**2
+ negative1 = dist1.min(-2).values > neg_th**2
+
+ # pack the indices of positive matches
+ # if -1: unmatched point
+ # if -2: ignore point
+ unmatched = min0.new_tensor(UNMATCHED_FEATURE)
+ ignore = min0.new_tensor(IGNORE_FEATURE)
+ m0 = torch.where(positive.any(-1), min0, ignore)
+ m1 = torch.where(positive.any(-2), min1, ignore)
+ m0 = torch.where(negative0, unmatched, m0)
+ m1 = torch.where(negative1, unmatched, m1)
+
+ return {
+ "assignment": positive,
+ "reward": reward,
+ "matches0": m0,
+ "matches1": m1,
+ "matching_scores0": (m0 > -1).float(),
+ "matching_scores1": (m1 > -1).float(),
+ "proj_0to1": kp0_1,
+ "proj_1to0": kp1_0,
+ }
+
+
+def sample_pts(lines, npts):
+ dir_vec = (lines[..., 2:4] - lines[..., :2]) / (npts - 1)
+ pts = lines[..., :2, np.newaxis] + dir_vec[..., np.newaxis].expand(
+ dir_vec.shape + (npts,)
+ ) * torch.arange(npts).to(lines)
+ pts = torch.transpose(pts, -1, -2)
+ return pts
+
+
+def torch_perp_dist(segs2d, points_2d):
+ # Check batch size and segments format
+ assert segs2d.shape[0] == points_2d.shape[0]
+ assert segs2d.shape[-1] == 4
+ dir = segs2d[..., 2:] - segs2d[..., :2]
+ sizes = torch.norm(dir, dim=-1).half()
+ norm_dir = dir / torch.unsqueeze(sizes, dim=-1)
+ # middle_ptn = 0.5 * (segs2d[..., 2:] + segs2d[..., :2])
+ # centered [batch, nsegs0, nsegs1, n_sampled_pts, 2]
+ centered = points_2d[:, None] - segs2d[..., None, None, 2:]
+
+ R = torch.cat(
+ [
+ norm_dir[..., 0, None],
+ norm_dir[..., 1, None],
+ -norm_dir[..., 1, None],
+ norm_dir[..., 0, None],
+ ],
+ dim=2,
+ ).reshape((len(segs2d), -1, 2, 2))
+ # Try to reduce the memory consumption by using float16 type
+ if centered.is_cuda:
+ centered, R = centered.half(), R.half()
+ # R: [batch, nsegs0, 2, 2] , centered: [batch, nsegs1, n_sampled_pts, 2]
+ # -> [batch, nsegs0, nsegs1, n_sampled_pts, 2]
+ rotated = torch.einsum("bdji,bdepi->bdepj", R, centered)
+
+ overlaping = (rotated[..., 0] <= 0) & (
+ torch.abs(rotated[..., 0]) <= sizes[..., None, None]
+ )
+
+ return torch.abs(rotated[..., 1]), overlaping
+
+
+@torch.no_grad()
+def gt_line_matches_from_pose_depth(
+ pred_lines0,
+ pred_lines1,
+ valid_lines0,
+ valid_lines1,
+ data,
+ npts=50,
+ dist_th=5,
+ overlap_th=0.2,
+ min_visibility_th=0.5,
+):
+ """Compute ground truth line matches and label the remaining the lines as:
+ - UNMATCHED: if reprojection is outside the image
+ or far away from any other line.
+ - IGNORE: if a line has not enough valid depth pixels along itself
+ or it is labeled as invalid."""
+ lines0 = pred_lines0.clone()
+ lines1 = pred_lines1.clone()
+
+ if pred_lines0.shape[1] == 0 or pred_lines1.shape[1] == 0:
+ bsize, nlines0, nlines1 = (
+ pred_lines0.shape[0],
+ pred_lines0.shape[1],
+ pred_lines1.shape[1],
+ )
+ positive = torch.zeros(
+ (bsize, nlines0, nlines1), dtype=torch.bool, device=pred_lines0.device
+ )
+ m0 = torch.full((bsize, nlines0), -1, device=pred_lines0.device)
+ m1 = torch.full((bsize, nlines1), -1, device=pred_lines0.device)
+ return positive, m0, m1
+
+ if lines0.shape[-2:] == (2, 2):
+ lines0 = torch.flatten(lines0, -2)
+ elif lines0.dim() == 4:
+ lines0 = torch.cat([lines0[:, :, 0], lines0[:, :, -1]], dim=2)
+ if lines1.shape[-2:] == (2, 2):
+ lines1 = torch.flatten(lines1, -2)
+ elif lines1.dim() == 4:
+ lines1 = torch.cat([lines1[:, :, 0], lines1[:, :, -1]], dim=2)
+ b_size, n_lines0, _ = lines0.shape
+ b_size, n_lines1, _ = lines1.shape
+ h0, w0 = data["view0"]["depth"][0].shape
+ h1, w1 = data["view1"]["depth"][0].shape
+
+ lines0 = torch.min(
+ torch.max(lines0, torch.zeros_like(lines0)),
+ lines0.new_tensor([w0 - 1, h0 - 1, w0 - 1, h0 - 1], dtype=torch.float),
+ )
+ lines1 = torch.min(
+ torch.max(lines1, torch.zeros_like(lines1)),
+ lines1.new_tensor([w1 - 1, h1 - 1, w1 - 1, h1 - 1], dtype=torch.float),
+ )
+
+ # Sample points along each line
+ pts0 = sample_pts(lines0, npts).reshape(b_size, n_lines0 * npts, 2)
+ pts1 = sample_pts(lines1, npts).reshape(b_size, n_lines1 * npts, 2)
+
+ # Sample depth and valid points
+ d0, valid0_pts0 = sample_depth(pts0, data["view0"]["depth"])
+ d1, valid1_pts1 = sample_depth(pts1, data["view1"]["depth"])
+
+ # Reproject to the other view
+ pts0_1, visible0 = project(
+ pts0,
+ d0,
+ data["view1"]["depth"],
+ data["view0"]["camera"],
+ data["view1"]["camera"],
+ data["T_0to1"],
+ valid0_pts0,
+ )
+ pts1_0, visible1 = project(
+ pts1,
+ d1,
+ data["view0"]["depth"],
+ data["view1"]["camera"],
+ data["view0"]["camera"],
+ data["T_1to0"],
+ valid1_pts1,
+ )
+
+ h0, w0 = data["view0"]["image"].shape[-2:]
+ h1, w1 = data["view1"]["image"].shape[-2:]
+ # If a line has less than min_visibility_th inside the image is considered OUTSIDE
+ pts_out_of0 = (pts1_0 < 0).any(-1) | (
+ pts1_0 >= torch.tensor([w0, h0]).to(pts1_0)
+ ).any(-1)
+ pts_out_of0 = pts_out_of0.reshape(b_size, n_lines1, npts).float()
+ out_of0 = pts_out_of0.mean(dim=-1) >= (1 - min_visibility_th)
+ pts_out_of1 = (pts0_1 < 0).any(-1) | (
+ pts0_1 >= torch.tensor([w1, h1]).to(pts0_1)
+ ).any(-1)
+ pts_out_of1 = pts_out_of1.reshape(b_size, n_lines0, npts).float()
+ out_of1 = pts_out_of1.mean(dim=-1) >= (1 - min_visibility_th)
+
+ # visible0 is [bs, nl0 * npts]
+ pts0_1 = pts0_1.reshape(b_size, n_lines0, npts, 2)
+ pts1_0 = pts1_0.reshape(b_size, n_lines1, npts, 2)
+
+ perp_dists0, overlaping0 = torch_perp_dist(lines0, pts1_0)
+ close_points0 = (perp_dists0 < dist_th) & overlaping0 # [bs, nl0, nl1, npts]
+ del perp_dists0, overlaping0
+ close_points0 = close_points0 * visible1.reshape(b_size, 1, n_lines1, npts)
+
+ perp_dists1, overlaping1 = torch_perp_dist(lines1, pts0_1)
+ close_points1 = (perp_dists1 < dist_th) & overlaping1 # [bs, nl1, nl0, npts]
+ del perp_dists1, overlaping1
+ close_points1 = close_points1 * visible0.reshape(b_size, 1, n_lines0, npts)
+ torch.cuda.empty_cache()
+
+ # For each segment detected in 0, how many sampled points from
+ # reprojected segments 1 are close
+ num_close_pts0 = close_points0.sum(dim=-1) # [bs, nl0, nl1]
+
+ # num_close_pts0_t = num_close_pts0.transpose(-1, -2)
+ # For each segment detected in 1, how many sampled points from
+ # reprojected segments 0 are close
+ num_close_pts1 = close_points1.sum(dim=-1)
+ num_close_pts1_t = num_close_pts1.transpose(-1, -2) # [bs, nl1, nl0]
+ num_close_pts = num_close_pts0 * num_close_pts1_t
+ mask_close = (
+ num_close_pts1_t
+ > visible0.reshape(b_size, n_lines0, npts).float().sum(-1)[:, :, None]
+ * overlap_th
+ ) & (
+ num_close_pts0
+ > visible1.reshape(b_size, n_lines1, npts).float().sum(-1)[:, None] * overlap_th
+ )
+ # mask_close = (num_close_pts1_t > npts * overlap_th) & (
+ # num_close_pts0 > npts * overlap_th)
+
+ # Define the unmatched lines
+ unmatched0 = torch.all(~mask_close, dim=2) | out_of1
+ unmatched1 = torch.all(~mask_close, dim=1) | out_of0
+
+ # Define the lines to ignore
+ ignore0 = (
+ valid0_pts0.reshape(b_size, n_lines0, npts).float().mean(dim=-1)
+ < min_visibility_th
+ ) | ~valid_lines0
+ ignore1 = (
+ valid1_pts1.reshape(b_size, n_lines1, npts).float().mean(dim=-1)
+ < min_visibility_th
+ ) | ~valid_lines1
+
+ cost = -num_close_pts.clone()
+ # High score for unmatched and non-valid lines
+ cost[unmatched0] = 1e6
+ cost[ignore0] = 1e6
+ # TODO: Is it reasonable to forbid the matching with a segment because it
+ # has not GT depth?
+ cost = cost.transpose(1, 2)
+ cost[unmatched1] = 1e6
+ cost[ignore1] = 1e6
+ cost = cost.transpose(1, 2)
+
+ # For each row, returns the col of max number of points
+ assignation = np.array(
+ [linear_sum_assignment(C) for C in cost.detach().cpu().numpy()]
+ )
+ assignation = torch.tensor(assignation).to(num_close_pts)
+ # Set ignore and unmatched labels
+ unmatched = assignation.new_tensor(UNMATCHED_FEATURE)
+ ignore = assignation.new_tensor(IGNORE_FEATURE)
+
+ positive = num_close_pts.new_zeros(num_close_pts.shape, dtype=torch.bool)
+ all_in_batch = (
+ torch.arange(b_size)[:, None].repeat(1, assignation.shape[-1]).flatten()
+ )
+ positive[
+ all_in_batch, assignation[:, 0].flatten(), assignation[:, 1].flatten()
+ ] = True
+
+ m0 = assignation.new_full((b_size, n_lines0), unmatched, dtype=torch.long)
+ m0.scatter_(-1, assignation[:, 0], assignation[:, 1])
+ m1 = assignation.new_full((b_size, n_lines1), unmatched, dtype=torch.long)
+ m1.scatter_(-1, assignation[:, 1], assignation[:, 0])
+
+ positive = positive & mask_close
+ # Remove values to be ignored or unmatched
+ positive[unmatched0] = False
+ positive[ignore0] = False
+ positive = positive.transpose(1, 2)
+ positive[unmatched1] = False
+ positive[ignore1] = False
+ positive = positive.transpose(1, 2)
+ m0[~positive.any(-1)] = unmatched
+ m0[unmatched0] = unmatched
+ m0[ignore0] = ignore
+ m1[~positive.any(-2)] = unmatched
+ m1[unmatched1] = unmatched
+ m1[ignore1] = ignore
+
+ if num_close_pts.numel() == 0:
+ no_matches = torch.zeros(positive.shape[0], 0).to(positive)
+ return positive, no_matches, no_matches
+
+ return positive, m0, m1
+
+
+@torch.no_grad()
+def gt_line_matches_from_homography(
+ pred_lines0,
+ pred_lines1,
+ valid_lines0,
+ valid_lines1,
+ shape0,
+ shape1,
+ H,
+ npts=50,
+ dist_th=5,
+ overlap_th=0.2,
+ min_visibility_th=0.2,
+):
+ """Compute ground truth line matches and label the remaining the lines as:
+ - UNMATCHED: if reprojection is outside the image or far away from any other line.
+ - IGNORE: if a line is labeled as invalid."""
+ h0, w0 = shape0[-2:]
+ h1, w1 = shape1[-2:]
+ lines0 = pred_lines0.clone()
+ lines1 = pred_lines1.clone()
+ if lines0.shape[-2:] == (2, 2):
+ lines0 = torch.flatten(lines0, -2)
+ elif lines0.dim() == 4:
+ lines0 = torch.cat([lines0[:, :, 0], lines0[:, :, -1]], dim=2)
+ if lines1.shape[-2:] == (2, 2):
+ lines1 = torch.flatten(lines1, -2)
+ elif lines1.dim() == 4:
+ lines1 = torch.cat([lines1[:, :, 0], lines1[:, :, -1]], dim=2)
+ b_size, n_lines0, _ = lines0.shape
+ b_size, n_lines1, _ = lines1.shape
+
+ lines0 = torch.min(
+ torch.max(lines0, torch.zeros_like(lines0)),
+ lines0.new_tensor([w0 - 1, h0 - 1, w0 - 1, h0 - 1], dtype=torch.float),
+ )
+ lines1 = torch.min(
+ torch.max(lines1, torch.zeros_like(lines1)),
+ lines1.new_tensor([w1 - 1, h1 - 1, w1 - 1, h1 - 1], dtype=torch.float),
+ )
+
+ # Sample points along each line
+ pts0 = sample_pts(lines0, npts).reshape(b_size, n_lines0 * npts, 2)
+ pts1 = sample_pts(lines1, npts).reshape(b_size, n_lines1 * npts, 2)
+
+ # Project the points to the other image
+ pts0_1 = warp_points_torch(pts0, H, inverse=False)
+ pts1_0 = warp_points_torch(pts1, H, inverse=True)
+ pts0_1 = pts0_1.reshape(b_size, n_lines0, npts, 2)
+ pts1_0 = pts1_0.reshape(b_size, n_lines1, npts, 2)
+
+ # If a line has less than min_visibility_th inside the image is considered OUTSIDE
+ pts_out_of0 = (pts1_0 < 0).any(-1) | (
+ pts1_0 >= torch.tensor([w0, h0]).to(pts1_0)
+ ).any(-1)
+ pts_out_of0 = pts_out_of0.reshape(b_size, n_lines1, npts).float()
+ out_of0 = pts_out_of0.mean(dim=-1) >= (1 - min_visibility_th)
+ pts_out_of1 = (pts0_1 < 0).any(-1) | (
+ pts0_1 >= torch.tensor([w1, h1]).to(pts0_1)
+ ).any(-1)
+ pts_out_of1 = pts_out_of1.reshape(b_size, n_lines0, npts).float()
+ out_of1 = pts_out_of1.mean(dim=-1) >= (1 - min_visibility_th)
+
+ perp_dists0, overlaping0 = torch_perp_dist(lines0, pts1_0)
+ close_points0 = (perp_dists0 < dist_th) & overlaping0 # [bs, nl0, nl1, npts]
+ del perp_dists0, overlaping0
+
+ perp_dists1, overlaping1 = torch_perp_dist(lines1, pts0_1)
+ close_points1 = (perp_dists1 < dist_th) & overlaping1 # [bs, nl1, nl0, npts]
+ del perp_dists1, overlaping1
+ torch.cuda.empty_cache()
+
+ # For each segment detected in 0,
+ # how many sampled points from reprojected segments 1 are close
+ num_close_pts0 = close_points0.sum(dim=-1) # [bs, nl0, nl1]
+ # num_close_pts0_t = num_close_pts0.transpose(-1, -2)
+ # For each segment detected in 1,
+ # how many sampled points from reprojected segments 0 are close
+ num_close_pts1 = close_points1.sum(dim=-1)
+ num_close_pts1_t = num_close_pts1.transpose(-1, -2) # [bs, nl1, nl0]
+
+ num_close_pts = num_close_pts0 * num_close_pts1_t
+ mask_close = (
+ (num_close_pts1_t > npts * overlap_th)
+ & (num_close_pts0 > npts * overlap_th)
+ & ~out_of0.unsqueeze(1)
+ & ~out_of1.unsqueeze(-1)
+ )
+
+ # Define the unmatched lines
+ unmatched0 = torch.all(~mask_close, dim=2) | out_of1
+ unmatched1 = torch.all(~mask_close, dim=1) | out_of0
+
+ # Define the lines to ignore
+ ignore0 = ~valid_lines0
+ ignore1 = ~valid_lines1
+
+ cost = -num_close_pts.clone()
+ # High score for unmatched and non-valid lines
+ cost[unmatched0] = 1e6
+ cost[ignore0] = 1e6
+ cost = cost.transpose(1, 2)
+ cost[unmatched1] = 1e6
+ cost[ignore1] = 1e6
+ cost = cost.transpose(1, 2)
+ # For each row, returns the col of max number of points
+ assignation = np.array(
+ [linear_sum_assignment(C) for C in cost.detach().cpu().numpy()]
+ )
+ assignation = torch.tensor(assignation).to(num_close_pts)
+
+ # Set unmatched labels
+ unmatched = assignation.new_tensor(UNMATCHED_FEATURE)
+ ignore = assignation.new_tensor(IGNORE_FEATURE)
+
+ positive = num_close_pts.new_zeros(num_close_pts.shape, dtype=torch.bool)
+ # TODO Do with a single and beautiful call
+ # for b in range(b_size):
+ # positive[b][assignation[b, 0], assignation[b, 1]] = True
+ positive[
+ torch.arange(b_size)[:, None].repeat(1, assignation.shape[-1]).flatten(),
+ assignation[:, 0].flatten(),
+ assignation[:, 1].flatten(),
+ ] = True
+
+ m0 = assignation.new_full((b_size, n_lines0), unmatched, dtype=torch.long)
+ m0.scatter_(-1, assignation[:, 0], assignation[:, 1])
+ m1 = assignation.new_full((b_size, n_lines1), unmatched, dtype=torch.long)
+ m1.scatter_(-1, assignation[:, 1], assignation[:, 0])
+
+ positive = positive & mask_close
+ # Remove values to be ignored or unmatched
+ positive[unmatched0] = False
+ positive[ignore0] = False
+ positive = positive.transpose(1, 2)
+ positive[unmatched1] = False
+ positive[ignore1] = False
+ positive = positive.transpose(1, 2)
+ m0[~positive.any(-1)] = unmatched
+ m0[unmatched0] = unmatched
+ m0[ignore0] = ignore
+ m1[~positive.any(-2)] = unmatched
+ m1[unmatched1] = unmatched
+ m1[ignore1] = ignore
+
+ if num_close_pts.numel() == 0:
+ no_matches = torch.zeros(positive.shape[0], 0).to(positive)
+ return positive, no_matches, no_matches
+
+ return positive, m0, m1
diff --git a/third_party/gim/gim/gluefactory/geometry/homography.py b/third_party/gim/gim/gluefactory/geometry/homography.py
new file mode 100644
index 0000000000000000000000000000000000000000..f87b9f9031efb270236786d9f09bb9b048aedc8b
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/geometry/homography.py
@@ -0,0 +1,342 @@
+import math
+from typing import Tuple
+
+import numpy as np
+import torch
+
+from .utils import from_homogeneous, to_homogeneous
+
+
+def flat2mat(H):
+ return np.reshape(np.concatenate([H, np.ones_like(H[:, :1])], axis=1), [3, 3])
+
+
+# Homography creation
+
+
+def create_center_patch(shape, patch_shape=None):
+ if patch_shape is None:
+ patch_shape = shape
+ width, height = shape
+ pwidth, pheight = patch_shape
+ left = int((width - pwidth) / 2)
+ bottom = int((height - pheight) / 2)
+ right = int((width + pwidth) / 2)
+ top = int((height + pheight) / 2)
+ return np.array([[left, bottom], [left, top], [right, top], [right, bottom]])
+
+
+def check_convex(patch, min_convexity=0.05):
+ """Checks if given polygon vertices [N,2] form a convex shape"""
+ for i in range(patch.shape[0]):
+ x1, y1 = patch[(i - 1) % patch.shape[0]]
+ x2, y2 = patch[i]
+ x3, y3 = patch[(i + 1) % patch.shape[0]]
+ if (x2 - x1) * (y3 - y2) - (x3 - x2) * (y2 - y1) > -min_convexity:
+ return False
+ return True
+
+
+def sample_homography_corners(
+ shape,
+ patch_shape,
+ difficulty=1.0,
+ translation=0.4,
+ n_angles=10,
+ max_angle=90,
+ min_convexity=0.05,
+ rng=np.random,
+):
+ max_angle = max_angle / 180.0 * math.pi
+ width, height = shape
+ pwidth, pheight = width * (1 - difficulty), height * (1 - difficulty)
+ min_pts1 = create_center_patch(shape, (pwidth, pheight))
+ full = create_center_patch(shape)
+ pts2 = create_center_patch(patch_shape)
+ scale = min_pts1 - full
+ found_valid = False
+ cnt = -1
+ while not found_valid:
+ offsets = rng.uniform(0.0, 1.0, size=(4, 2)) * scale
+ pts1 = full + offsets
+ found_valid = check_convex(pts1 / np.array(shape), min_convexity)
+ cnt += 1
+
+ # re-center
+ pts1 = pts1 - np.mean(pts1, axis=0, keepdims=True)
+ pts1 = pts1 + np.mean(min_pts1, axis=0, keepdims=True)
+
+ # Rotation
+ if n_angles > 0 and difficulty > 0:
+ angles = np.linspace(-max_angle * difficulty, max_angle * difficulty, n_angles)
+ rng.shuffle(angles)
+ rng.shuffle(angles)
+ angles = np.concatenate([[0.0], angles], axis=0)
+
+ center = np.mean(pts1, axis=0, keepdims=True)
+ rot_mat = np.reshape(
+ np.stack(
+ [np.cos(angles), -np.sin(angles), np.sin(angles), np.cos(angles)],
+ axis=1,
+ ),
+ [-1, 2, 2],
+ )
+ rotated = (
+ np.matmul(
+ np.tile(np.expand_dims(pts1 - center, axis=0), [n_angles + 1, 1, 1]),
+ rot_mat,
+ )
+ + center
+ )
+
+ for idx in range(1, n_angles):
+ warped_points = rotated[idx] / np.array(shape)
+ if np.all((warped_points >= 0.0) & (warped_points < 1.0)):
+ pts1 = rotated[idx]
+ break
+
+ # Translation
+ if translation > 0:
+ min_trans = -np.min(pts1, axis=0)
+ max_trans = shape - np.max(pts1, axis=0)
+ trans = rng.uniform(min_trans, max_trans)[None]
+ pts1 += trans * translation * difficulty
+
+ H = compute_homography(pts1, pts2, [1.0, 1.0])
+ warped = warp_points(full, H, inverse=False)
+ return H, full, warped, patch_shape
+
+
+def compute_homography(pts1_, pts2_, shape):
+ """Compute the homography matrix from 4 point correspondences"""
+ # Rescale to actual size
+ shape = np.array(shape[::-1], dtype=np.float32) # different convention [y, x]
+ pts1 = pts1_ * np.expand_dims(shape, axis=0)
+ pts2 = pts2_ * np.expand_dims(shape, axis=0)
+
+ def ax(p, q):
+ return [p[0], p[1], 1, 0, 0, 0, -p[0] * q[0], -p[1] * q[0]]
+
+ def ay(p, q):
+ return [0, 0, 0, p[0], p[1], 1, -p[0] * q[1], -p[1] * q[1]]
+
+ a_mat = np.stack([f(pts1[i], pts2[i]) for i in range(4) for f in (ax, ay)], axis=0)
+ p_mat = np.transpose(
+ np.stack([[pts2[i][j] for i in range(4) for j in range(2)]], axis=0)
+ )
+ homography = np.transpose(np.linalg.solve(a_mat, p_mat))
+ return flat2mat(homography)
+
+
+# Point warping utils
+
+
+def warp_points(points, homography, inverse=True):
+ """
+ Warp a list of points with the INVERSE of the given homography.
+ The inverse is used to be coherent with tf.contrib.image.transform
+ Arguments:
+ points: list of N points, shape (N, 2).
+ homography: batched or not (shapes (B, 3, 3) and (3, 3) respectively).
+ Returns: a Tensor of shape (N, 2) or (B, N, 2) (depending on whether the homography
+ is batched) containing the new coordinates of the warped points.
+ """
+ H = homography[None] if len(homography.shape) == 2 else homography
+
+ # Get the points to the homogeneous format
+ num_points = points.shape[0]
+ # points = points.astype(np.float32)[:, ::-1]
+ points = np.concatenate([points, np.ones([num_points, 1], dtype=np.float32)], -1)
+
+ H_inv = np.transpose(np.linalg.inv(H) if inverse else H)
+ warped_points = np.tensordot(points, H_inv, axes=[[1], [0]])
+
+ warped_points = np.transpose(warped_points, [2, 0, 1])
+ warped_points[np.abs(warped_points[:, :, 2]) < 1e-8, 2] = 1e-8
+ warped_points = warped_points[:, :, :2] / warped_points[:, :, 2:]
+
+ return warped_points[0] if len(homography.shape) == 2 else warped_points
+
+
+def warp_points_torch(points, H, inverse=True):
+ """
+ Warp a list of points with the INVERSE of the given homography.
+ The inverse is used to be coherent with tf.contrib.image.transform
+ Arguments:
+ points: batched list of N points, shape (B, N, 2).
+ H: batched or not (shapes (B, 3, 3) and (3, 3) respectively).
+ inverse: Whether to multiply the points by H or the inverse of H
+ Returns: a Tensor of shape (B, N, 2) containing the new coordinates of the warps.
+ """
+
+ # Get the points to the homogeneous format
+ points = to_homogeneous(points)
+
+ # Apply the homography
+ H_mat = (torch.inverse(H) if inverse else H).transpose(-2, -1)
+ warped_points = torch.einsum("...nj,...ji->...ni", points, H_mat)
+
+ warped_points = from_homogeneous(warped_points, eps=1e-5)
+ return warped_points
+
+
+# Line warping utils
+
+
+def seg_equation(segs):
+ # calculate list of start, end and midpoints points from both lists
+ start_points, end_points = to_homogeneous(segs[..., 0, :]), to_homogeneous(
+ segs[..., 1, :]
+ )
+ # Compute the line equations as ax + by + c = 0 , where x^2 + y^2 = 1
+ lines = torch.cross(start_points, end_points, dim=-1)
+ lines_norm = torch.sqrt(lines[..., 0] ** 2 + lines[..., 1] ** 2)[..., None]
+ assert torch.all(
+ lines_norm > 0
+ ), "Error: trying to compute the equation of a line with a single point"
+ lines = lines / lines_norm
+ return lines
+
+
+def is_inside_img(pts: torch.Tensor, img_shape: Tuple[int, int]):
+ h, w = img_shape
+ return (
+ (pts >= 0).all(dim=-1)
+ & (pts[..., 0] < w)
+ & (pts[..., 1] < h)
+ & (~torch.isinf(pts).any(dim=-1))
+ )
+
+
+def shrink_segs_to_img(segs: torch.Tensor, img_shape: Tuple[int, int]) -> torch.Tensor:
+ """
+ Shrink an array of segments to fit inside the image.
+ :param segs: The tensor of segments with shape (N, 2, 2)
+ :param img_shape: The image shape in format (H, W)
+ """
+ EPS = 1e-4
+ device = segs.device
+ w, h = img_shape[1], img_shape[0]
+ # Project the segments to the reference image
+ segs = segs.clone()
+ eqs = seg_equation(segs)
+ x0, y0 = torch.tensor([1.0, 0, 0.0], device=device), torch.tensor(
+ [0.0, 1, 0], device=device
+ )
+ x0 = x0.repeat(eqs.shape[:-1] + (1,))
+ y0 = y0.repeat(eqs.shape[:-1] + (1,))
+ pt_x0s = torch.cross(eqs, x0, dim=-1)
+ pt_x0s = pt_x0s[..., :-1] / pt_x0s[..., None, -1]
+ pt_x0s_valid = is_inside_img(pt_x0s, img_shape)
+ pt_y0s = torch.cross(eqs, y0, dim=-1)
+ pt_y0s = pt_y0s[..., :-1] / pt_y0s[..., None, -1]
+ pt_y0s_valid = is_inside_img(pt_y0s, img_shape)
+
+ xW = torch.tensor([1.0, 0, EPS - w], device=device)
+ yH = torch.tensor([0.0, 1, EPS - h], device=device)
+ xW = xW.repeat(eqs.shape[:-1] + (1,))
+ yH = yH.repeat(eqs.shape[:-1] + (1,))
+ pt_xWs = torch.cross(eqs, xW, dim=-1)
+ pt_xWs = pt_xWs[..., :-1] / pt_xWs[..., None, -1]
+ pt_xWs_valid = is_inside_img(pt_xWs, img_shape)
+ pt_yHs = torch.cross(eqs, yH, dim=-1)
+ pt_yHs = pt_yHs[..., :-1] / pt_yHs[..., None, -1]
+ pt_yHs_valid = is_inside_img(pt_yHs, img_shape)
+
+ # If the X coordinate of the first endpoint is out
+ mask = (segs[..., 0, 0] < 0) & pt_x0s_valid
+ segs[mask, 0, :] = pt_x0s[mask]
+ mask = (segs[..., 0, 0] > (w - 1)) & pt_xWs_valid
+ segs[mask, 0, :] = pt_xWs[mask]
+ # If the X coordinate of the second endpoint is out
+ mask = (segs[..., 1, 0] < 0) & pt_x0s_valid
+ segs[mask, 1, :] = pt_x0s[mask]
+ mask = (segs[:, 1, 0] > (w - 1)) & pt_xWs_valid
+ segs[mask, 1, :] = pt_xWs[mask]
+ # If the Y coordinate of the first endpoint is out
+ mask = (segs[..., 0, 1] < 0) & pt_y0s_valid
+ segs[mask, 0, :] = pt_y0s[mask]
+ mask = (segs[..., 0, 1] > (h - 1)) & pt_yHs_valid
+ segs[mask, 0, :] = pt_yHs[mask]
+ # If the Y coordinate of the second endpoint is out
+ mask = (segs[..., 1, 1] < 0) & pt_y0s_valid
+ segs[mask, 1, :] = pt_y0s[mask]
+ mask = (segs[..., 1, 1] > (h - 1)) & pt_yHs_valid
+ segs[mask, 1, :] = pt_yHs[mask]
+
+ assert (
+ torch.all(segs >= 0)
+ and torch.all(segs[..., 0] < w)
+ and torch.all(segs[..., 1] < h)
+ )
+ return segs
+
+
+def warp_lines_torch(
+ lines, H, inverse=True, dst_shape: Tuple[int, int] = None
+) -> Tuple[torch.Tensor, torch.Tensor]:
+ """
+ :param lines: A tensor of shape (B, N, 2, 2)
+ where B is the batch size, N the number of lines.
+ :param H: The homography used to convert the lines.
+ batched or not (shapes (B, 3, 3) and (3, 3) respectively).
+ :param inverse: Whether to apply H or the inverse of H
+ :param dst_shape:If provided, lines are trimmed to be inside the image
+ """
+ device = lines.device
+ batch_size = len(lines)
+ lines = warp_points_torch(lines.reshape(batch_size, -1, 2), H, inverse).reshape(
+ lines.shape
+ )
+
+ if dst_shape is None:
+ return lines, torch.ones(lines.shape[:-2], dtype=torch.bool, device=device)
+
+ out_img = torch.any(
+ (lines < 0) | (lines >= torch.tensor(dst_shape[::-1], device=device)), -1
+ )
+ valid = ~out_img.all(-1)
+ any_out_of_img = out_img.any(-1)
+ lines_to_trim = valid & any_out_of_img
+
+ for b in range(batch_size):
+ lines_to_trim_mask_b = lines_to_trim[b]
+ lines_to_trim_b = lines[b][lines_to_trim_mask_b]
+ corrected_lines = shrink_segs_to_img(lines_to_trim_b, dst_shape)
+ lines[b][lines_to_trim_mask_b] = corrected_lines
+
+ return lines, valid
+
+
+# Homography evaluation utils
+
+
+def sym_homography_error(kpts0, kpts1, T_0to1):
+ kpts0_1 = from_homogeneous(to_homogeneous(kpts0) @ T_0to1.transpose(-1, -2))
+ dist0_1 = ((kpts0_1 - kpts1) ** 2).sum(-1).sqrt()
+
+ kpts1_0 = from_homogeneous(
+ to_homogeneous(kpts1) @ torch.pinverse(T_0to1.transpose(-1, -2))
+ )
+ dist1_0 = ((kpts1_0 - kpts0) ** 2).sum(-1).sqrt()
+
+ return (dist0_1 + dist1_0) / 2.0
+
+
+def sym_homography_error_all(kpts0, kpts1, H):
+ kp0_1 = warp_points_torch(kpts0, H, inverse=False)
+ kp1_0 = warp_points_torch(kpts1, H, inverse=True)
+
+ # build a distance matrix of size [... x M x N]
+ dist0 = torch.sum((kp0_1.unsqueeze(-2) - kpts1.unsqueeze(-3)) ** 2, -1).sqrt()
+ dist1 = torch.sum((kpts0.unsqueeze(-2) - kp1_0.unsqueeze(-3)) ** 2, -1).sqrt()
+ return (dist0 + dist1) / 2.0
+
+
+def homography_corner_error(T, T_gt, image_size):
+ W, H = image_size[..., 0], image_size[..., 1]
+ corners0 = torch.Tensor([[0, 0], [W, 0], [W, H], [0, H]]).float().to(T)
+ corners1_gt = from_homogeneous(to_homogeneous(corners0) @ T_gt.transpose(-1, -2))
+ corners1 = from_homogeneous(to_homogeneous(corners0) @ T.transpose(-1, -2))
+ d = torch.sqrt(((corners1 - corners1_gt) ** 2).sum(-1))
+ return d.mean(-1)
diff --git a/third_party/gim/gim/gluefactory/geometry/utils.py b/third_party/gim/gim/gluefactory/geometry/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..4734e341fdead0f4883347e82792f3b080adb562
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/geometry/utils.py
@@ -0,0 +1,167 @@
+import numpy as np
+import torch
+
+
+def to_homogeneous(points):
+ """Convert N-dimensional points to homogeneous coordinates.
+ Args:
+ points: torch.Tensor or numpy.ndarray with size (..., N).
+ Returns:
+ A torch.Tensor or numpy.ndarray with size (..., N+1).
+ """
+ if isinstance(points, torch.Tensor):
+ pad = points.new_ones(points.shape[:-1] + (1,))
+ return torch.cat([points, pad], dim=-1)
+ elif isinstance(points, np.ndarray):
+ pad = np.ones((points.shape[:-1] + (1,)), dtype=points.dtype)
+ return np.concatenate([points, pad], axis=-1)
+ else:
+ raise ValueError
+
+
+def from_homogeneous(points, eps=0.0):
+ """Remove the homogeneous dimension of N-dimensional points.
+ Args:
+ points: torch.Tensor or numpy.ndarray with size (..., N+1).
+ eps: Epsilon value to prevent zero division.
+ Returns:
+ A torch.Tensor or numpy ndarray with size (..., N).
+ """
+ return points[..., :-1] / (points[..., -1:] + eps)
+
+
+def batched_eye_like(x: torch.Tensor, n: int):
+ """Create a batch of identity matrices.
+ Args:
+ x: a reference torch.Tensor whose batch dimension will be copied.
+ n: the size of each identity matrix.
+ Returns:
+ A torch.Tensor of size (B, n, n), with same dtype and device as x.
+ """
+ return torch.eye(n).to(x)[None].repeat(len(x), 1, 1)
+
+
+def skew_symmetric(v):
+ """Create a skew-symmetric matrix from a (batched) vector of size (..., 3)."""
+ z = torch.zeros_like(v[..., 0])
+ M = torch.stack(
+ [
+ z,
+ -v[..., 2],
+ v[..., 1],
+ v[..., 2],
+ z,
+ -v[..., 0],
+ -v[..., 1],
+ v[..., 0],
+ z,
+ ],
+ dim=-1,
+ ).reshape(v.shape[:-1] + (3, 3))
+ return M
+
+
+def transform_points(T, points):
+ return from_homogeneous(to_homogeneous(points) @ T.transpose(-1, -2))
+
+
+def is_inside(pts, shape):
+ return (pts > 0).all(-1) & (pts < shape[:, None]).all(-1)
+
+
+def so3exp_map(w, eps: float = 1e-7):
+ """Compute rotation matrices from batched twists.
+ Args:
+ w: batched 3D axis-angle vectors of size (..., 3).
+ Returns:
+ A batch of rotation matrices of size (..., 3, 3).
+ """
+ theta = w.norm(p=2, dim=-1, keepdim=True)
+ small = theta < eps
+ div = torch.where(small, torch.ones_like(theta), theta)
+ W = skew_symmetric(w / div)
+ theta = theta[..., None] # ... x 1 x 1
+ res = W * torch.sin(theta) + (W @ W) * (1 - torch.cos(theta))
+ res = torch.where(small[..., None], W, res) # first-order Taylor approx
+ return torch.eye(3).to(W) + res
+
+
+@torch.jit.script
+def distort_points(pts, dist):
+ """Distort normalized 2D coordinates
+ and check for validity of the distortion model.
+ """
+ dist = dist.unsqueeze(-2) # add point dimension
+ ndist = dist.shape[-1]
+ undist = pts
+ valid = torch.ones(pts.shape[:-1], device=pts.device, dtype=torch.bool)
+ if ndist > 0:
+ k1, k2 = dist[..., :2].split(1, -1)
+ r2 = torch.sum(pts**2, -1, keepdim=True)
+ radial = k1 * r2 + k2 * r2**2
+ undist = undist + pts * radial
+
+ # The distortion model is supposedly only valid within the image
+ # boundaries. Because of the negative radial distortion, points that
+ # are far outside of the boundaries might actually be mapped back
+ # within the image. To account for this, we discard points that are
+ # beyond the inflection point of the distortion model,
+ # e.g. such that d(r + k_1 r^3 + k2 r^5)/dr = 0
+ limited = ((k2 > 0) & ((9 * k1**2 - 20 * k2) > 0)) | ((k2 <= 0) & (k1 > 0))
+ limit = torch.abs(
+ torch.where(
+ k2 > 0,
+ (torch.sqrt(9 * k1**2 - 20 * k2) - 3 * k1) / (10 * k2),
+ 1 / (3 * k1),
+ )
+ )
+ valid = valid & torch.squeeze(~limited | (r2 < limit), -1)
+
+ if ndist > 2:
+ p12 = dist[..., 2:]
+ p21 = p12.flip(-1)
+ uv = torch.prod(pts, -1, keepdim=True)
+ undist = undist + 2 * p12 * uv + p21 * (r2 + 2 * pts**2)
+ # TODO: handle tangential boundaries
+
+ return undist, valid
+
+
+@torch.jit.script
+def J_distort_points(pts, dist):
+ dist = dist.unsqueeze(-2) # add point dimension
+ ndist = dist.shape[-1]
+
+ J_diag = torch.ones_like(pts)
+ J_cross = torch.zeros_like(pts)
+ if ndist > 0:
+ k1, k2 = dist[..., :2].split(1, -1)
+ r2 = torch.sum(pts**2, -1, keepdim=True)
+ uv = torch.prod(pts, -1, keepdim=True)
+ radial = k1 * r2 + k2 * r2**2
+ d_radial = 2 * k1 + 4 * k2 * r2
+ J_diag += radial + (pts**2) * d_radial
+ J_cross += uv * d_radial
+
+ if ndist > 2:
+ p12 = dist[..., 2:]
+ p21 = p12.flip(-1)
+ J_diag += 2 * p12 * pts.flip(-1) + 6 * p21 * pts
+ J_cross += 2 * p12 * pts + 2 * p21 * pts.flip(-1)
+
+ J = torch.diag_embed(J_diag) + torch.diag_embed(J_cross).flip(-1)
+ return J
+
+
+def get_image_coords(img):
+ h, w = img.shape[-2:]
+ return (
+ torch.stack(
+ torch.meshgrid(
+ torch.arange(h, dtype=torch.float32, device=img.device),
+ torch.arange(w, dtype=torch.float32, device=img.device),
+ indexing="ij",
+ )[::-1],
+ dim=0,
+ ).permute(1, 2, 0)
+ )[None] + 0.5
diff --git a/third_party/gim/gim/gluefactory/geometry/wrappers.py b/third_party/gim/gim/gluefactory/geometry/wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d4a1b1038a05e6d0900695ddf72c572d06a777c
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/geometry/wrappers.py
@@ -0,0 +1,425 @@
+"""
+Convenience classes for an SE3 pose and a pinhole Camera with lens distortion.
+Based on PyTorch tensors: differentiable, batched, with GPU support.
+"""
+
+import functools
+import inspect
+import math
+from typing import Dict, List, NamedTuple, Optional, Tuple, Union
+
+import numpy as np
+import torch
+
+from .utils import (
+ J_distort_points,
+ distort_points,
+ skew_symmetric,
+ so3exp_map,
+ to_homogeneous,
+)
+
+
+def autocast(func):
+ """Cast the inputs of a TensorWrapper method to PyTorch tensors
+ if they are numpy arrays. Use the device and dtype of the wrapper.
+ """
+
+ @functools.wraps(func)
+ def wrap(self, *args):
+ device = torch.device("cpu")
+ dtype = None
+ if isinstance(self, TensorWrapper):
+ if self._data is not None:
+ device = self.device
+ dtype = self.dtype
+ elif not inspect.isclass(self) or not issubclass(self, TensorWrapper):
+ raise ValueError(self)
+
+ cast_args = []
+ for arg in args:
+ if isinstance(arg, np.ndarray):
+ arg = torch.from_numpy(arg)
+ arg = arg.to(device=device, dtype=dtype)
+ cast_args.append(arg)
+ return func(self, *cast_args)
+
+ return wrap
+
+
+class TensorWrapper:
+ _data = None
+
+ @autocast
+ def __init__(self, data: torch.Tensor):
+ self._data = data
+
+ @property
+ def shape(self):
+ return self._data.shape[:-1]
+
+ @property
+ def device(self):
+ return self._data.device
+
+ @property
+ def dtype(self):
+ return self._data.dtype
+
+ def __getitem__(self, index):
+ return self.__class__(self._data[index])
+
+ def __setitem__(self, index, item):
+ self._data[index] = item.data
+
+ def to(self, *args, **kwargs):
+ return self.__class__(self._data.to(*args, **kwargs))
+
+ def cpu(self):
+ return self.__class__(self._data.cpu())
+
+ def cuda(self):
+ return self.__class__(self._data.cuda())
+
+ def pin_memory(self):
+ return self.__class__(self._data.pin_memory())
+
+ def float(self):
+ return self.__class__(self._data.float())
+
+ def double(self):
+ return self.__class__(self._data.double())
+
+ def detach(self):
+ return self.__class__(self._data.detach())
+
+ @classmethod
+ def stack(cls, objects: List, dim=0, *, out=None):
+ data = torch.stack([obj._data for obj in objects], dim=dim, out=out)
+ return cls(data)
+
+ @classmethod
+ def __torch_function__(self, func, types, args=(), kwargs=None):
+ if kwargs is None:
+ kwargs = {}
+ if func is torch.stack:
+ return self.stack(*args, **kwargs)
+ else:
+ return NotImplemented
+
+
+class Pose(TensorWrapper):
+ def __init__(self, data: torch.Tensor):
+ assert data.shape[-1] == 12
+ super().__init__(data)
+
+ @classmethod
+ @autocast
+ def from_Rt(cls, R: torch.Tensor, t: torch.Tensor):
+ """Pose from a rotation matrix and translation vector.
+ Accepts numpy arrays or PyTorch tensors.
+
+ Args:
+ R: rotation matrix with shape (..., 3, 3).
+ t: translation vector with shape (..., 3).
+ """
+ assert R.shape[-2:] == (3, 3)
+ assert t.shape[-1] == 3
+ assert R.shape[:-2] == t.shape[:-1]
+ data = torch.cat([R.flatten(start_dim=-2), t], -1)
+ return cls(data)
+
+ @classmethod
+ @autocast
+ def from_aa(cls, aa: torch.Tensor, t: torch.Tensor):
+ """Pose from an axis-angle rotation vector and translation vector.
+ Accepts numpy arrays or PyTorch tensors.
+
+ Args:
+ aa: axis-angle rotation vector with shape (..., 3).
+ t: translation vector with shape (..., 3).
+ """
+ assert aa.shape[-1] == 3
+ assert t.shape[-1] == 3
+ assert aa.shape[:-1] == t.shape[:-1]
+ return cls.from_Rt(so3exp_map(aa), t)
+
+ @classmethod
+ def from_4x4mat(cls, T: torch.Tensor):
+ """Pose from an SE(3) transformation matrix.
+ Args:
+ T: transformation matrix with shape (..., 4, 4).
+ """
+ assert T.shape[-2:] == (4, 4)
+ R, t = T[..., :3, :3], T[..., :3, 3]
+ return cls.from_Rt(R, t)
+
+ @classmethod
+ def from_colmap(cls, image: NamedTuple):
+ """Pose from a COLMAP Image."""
+ return cls.from_Rt(image.qvec2rotmat(), image.tvec)
+
+ @property
+ def R(self) -> torch.Tensor:
+ """Underlying rotation matrix with shape (..., 3, 3)."""
+ rvec = self._data[..., :9]
+ return rvec.reshape(rvec.shape[:-1] + (3, 3))
+
+ @property
+ def t(self) -> torch.Tensor:
+ """Underlying translation vector with shape (..., 3)."""
+ return self._data[..., -3:]
+
+ def inv(self) -> "Pose":
+ """Invert an SE(3) pose."""
+ R = self.R.transpose(-1, -2)
+ t = -(R @ self.t.unsqueeze(-1)).squeeze(-1)
+ return self.__class__.from_Rt(R, t)
+
+ def compose(self, other: "Pose") -> "Pose":
+ """Chain two SE(3) poses: T_B2C.compose(T_A2B) -> T_A2C."""
+ R = self.R @ other.R
+ t = self.t + (self.R @ other.t.unsqueeze(-1)).squeeze(-1)
+ return self.__class__.from_Rt(R, t)
+
+ @autocast
+ def transform(self, p3d: torch.Tensor) -> torch.Tensor:
+ """Transform a set of 3D points.
+ Args:
+ p3d: 3D points, numpy array or PyTorch tensor with shape (..., 3).
+ """
+ assert p3d.shape[-1] == 3
+ # assert p3d.shape[:-2] == self.shape # allow broadcasting
+ return p3d @ self.R.transpose(-1, -2) + self.t.unsqueeze(-2)
+
+ def __mul__(self, p3D: torch.Tensor) -> torch.Tensor:
+ """Transform a set of 3D points: T_A2B * p3D_A -> p3D_B."""
+ return self.transform(p3D)
+
+ def __matmul__(
+ self, other: Union["Pose", torch.Tensor]
+ ) -> Union["Pose", torch.Tensor]:
+ """Transform a set of 3D points: T_A2B * p3D_A -> p3D_B.
+ or chain two SE(3) poses: T_B2C @ T_A2B -> T_A2C."""
+ if isinstance(other, self.__class__):
+ return self.compose(other)
+ else:
+ return self.transform(other)
+
+ @autocast
+ def J_transform(self, p3d_out: torch.Tensor):
+ # [[1,0,0,0,-pz,py],
+ # [0,1,0,pz,0,-px],
+ # [0,0,1,-py,px,0]]
+ J_t = torch.diag_embed(torch.ones_like(p3d_out))
+ J_rot = -skew_symmetric(p3d_out)
+ J = torch.cat([J_t, J_rot], dim=-1)
+ return J # N x 3 x 6
+
+ def numpy(self) -> Tuple[np.ndarray]:
+ return self.R.numpy(), self.t.numpy()
+
+ def magnitude(self) -> Tuple[torch.Tensor]:
+ """Magnitude of the SE(3) transformation.
+ Returns:
+ dr: rotation anngle in degrees.
+ dt: translation distance in meters.
+ """
+ trace = torch.diagonal(self.R, dim1=-1, dim2=-2).sum(-1)
+ cos = torch.clamp((trace - 1) / 2, -1, 1)
+ dr = torch.acos(cos).abs() / math.pi * 180
+ dt = torch.norm(self.t, dim=-1)
+ return dr, dt
+
+ def __repr__(self):
+ return f"Pose: {self.shape} {self.dtype} {self.device}"
+
+
+class Camera(TensorWrapper):
+ eps = 1e-4
+
+ def __init__(self, data: torch.Tensor):
+ assert data.shape[-1] in {6, 8, 10}
+ super().__init__(data)
+
+ @classmethod
+ def from_colmap(cls, camera: Union[Dict, NamedTuple]):
+ """Camera from a COLMAP Camera tuple or dictionary.
+ We use the corner-convetion from COLMAP (center of top left pixel is (0.5, 0.5))
+ """
+ if isinstance(camera, tuple):
+ camera = camera._asdict()
+
+ model = camera["model"]
+ params = camera["params"]
+
+ if model in ["OPENCV", "PINHOLE", "RADIAL"]:
+ (fx, fy, cx, cy), params = np.split(params, [4])
+ elif model in ["SIMPLE_PINHOLE", "SIMPLE_RADIAL"]:
+ (f, cx, cy), params = np.split(params, [3])
+ fx = fy = f
+ if model == "SIMPLE_RADIAL":
+ params = np.r_[params, 0.0]
+ else:
+ raise NotImplementedError(model)
+
+ data = np.r_[camera["width"], camera["height"], fx, fy, cx, cy, params]
+ return cls(data)
+
+ @classmethod
+ @autocast
+ def from_calibration_matrix(cls, K: torch.Tensor):
+ cx, cy = K[..., 0, 2], K[..., 1, 2]
+ fx, fy = K[..., 0, 0], K[..., 1, 1]
+ data = torch.stack([2 * cx, 2 * cy, fx, fy, cx, cy], -1)
+ return cls(data)
+
+ @autocast
+ def calibration_matrix(self):
+ K = torch.zeros(
+ *self._data.shape[:-1],
+ 3,
+ 3,
+ device=self._data.device,
+ dtype=self._data.dtype,
+ )
+ K[..., 0, 2] = self._data[..., 4]
+ K[..., 1, 2] = self._data[..., 5]
+ K[..., 0, 0] = self._data[..., 2]
+ K[..., 1, 1] = self._data[..., 3]
+ K[..., 2, 2] = 1.0
+ return K
+
+ @property
+ def size(self) -> torch.Tensor:
+ """Size (width height) of the images, with shape (..., 2)."""
+ return self._data[..., :2]
+
+ @property
+ def f(self) -> torch.Tensor:
+ """Focal lengths (fx, fy) with shape (..., 2)."""
+ return self._data[..., 2:4]
+
+ @property
+ def c(self) -> torch.Tensor:
+ """Principal points (cx, cy) with shape (..., 2)."""
+ return self._data[..., 4:6]
+
+ @property
+ def dist(self) -> torch.Tensor:
+ """Distortion parameters, with shape (..., {0, 2, 4})."""
+ return self._data[..., 6:]
+
+ @autocast
+ def scale(self, scales: torch.Tensor):
+ """Update the camera parameters after resizing an image."""
+ s = scales
+ data = torch.cat([self.size * s, self.f * s, self.c * s, self.dist], -1)
+ return self.__class__(data)
+
+ def crop(self, left_top: Tuple[float], size: Tuple[int]):
+ """Update the camera parameters after cropping an image."""
+ left_top = self._data.new_tensor(left_top)
+ size = self._data.new_tensor(size)
+ data = torch.cat([size, self.f, self.c - left_top, self.dist], -1)
+ return self.__class__(data)
+
+ @autocast
+ def in_image(self, p2d: torch.Tensor):
+ """Check if 2D points are within the image boundaries."""
+ assert p2d.shape[-1] == 2
+ # assert p2d.shape[:-2] == self.shape # allow broadcasting
+ size = self.size.unsqueeze(-2)
+ valid = torch.all((p2d >= 0) & (p2d <= (size - 1)), -1)
+ return valid
+
+ @autocast
+ def project(self, p3d: torch.Tensor) -> Tuple[torch.Tensor]:
+ """Project 3D points into the camera plane and check for visibility."""
+ z = p3d[..., -1]
+ valid = z > self.eps
+ z = z.clamp(min=self.eps)
+ p2d = p3d[..., :-1] / z.unsqueeze(-1)
+ return p2d, valid
+
+ def J_project(self, p3d: torch.Tensor):
+ x, y, z = p3d[..., 0], p3d[..., 1], p3d[..., 2]
+ zero = torch.zeros_like(z)
+ z = z.clamp(min=self.eps)
+ J = torch.stack([1 / z, zero, -x / z**2, zero, 1 / z, -y / z**2], dim=-1)
+ J = J.reshape(p3d.shape[:-1] + (2, 3))
+ return J # N x 2 x 3
+
+ @autocast
+ def distort(self, pts: torch.Tensor) -> Tuple[torch.Tensor]:
+ """Distort normalized 2D coordinates
+ and check for validity of the distortion model.
+ """
+ assert pts.shape[-1] == 2
+ # assert pts.shape[:-2] == self.shape # allow broadcasting
+ return distort_points(pts, self.dist)
+
+ def J_distort(self, pts: torch.Tensor):
+ return J_distort_points(pts, self.dist) # N x 2 x 2
+
+ @autocast
+ def denormalize(self, p2d: torch.Tensor) -> torch.Tensor:
+ """Convert normalized 2D coordinates into pixel coordinates."""
+ return p2d * self.f.unsqueeze(-2) + self.c.unsqueeze(-2)
+
+ @autocast
+ def normalize(self, p2d: torch.Tensor) -> torch.Tensor:
+ """Convert normalized 2D coordinates into pixel coordinates."""
+ return (p2d - self.c.unsqueeze(-2)) / self.f.unsqueeze(-2)
+
+ def J_denormalize(self):
+ return torch.diag_embed(self.f).unsqueeze(-3) # 1 x 2 x 2
+
+ @autocast
+ def cam2image(self, p3d: torch.Tensor) -> Tuple[torch.Tensor]:
+ """Transform 3D points into 2D pixel coordinates."""
+ p2d, visible = self.project(p3d)
+ p2d, mask = self.distort(p2d)
+ p2d = self.denormalize(p2d)
+ valid = visible & mask & self.in_image(p2d)
+ return p2d, valid
+
+ def J_world2image(self, p3d: torch.Tensor):
+ p2d_dist, valid = self.project(p3d)
+ J = self.J_denormalize() @ self.J_distort(p2d_dist) @ self.J_project(p3d)
+ return J, valid
+
+ @autocast
+ def image2cam(self, p2d: torch.Tensor) -> torch.Tensor:
+ """Convert 2D pixel corrdinates to 3D points with z=1"""
+ assert self._data.shape
+ p2d = self.normalize(p2d)
+ # iterative undistortion
+ return to_homogeneous(p2d)
+
+ def to_cameradict(self, camera_model: Optional[str] = None) -> List[Dict]:
+ data = self._data.clone()
+ if data.dim() == 1:
+ data = data.unsqueeze(0)
+ assert data.dim() == 2
+ b, d = data.shape
+ if camera_model is None:
+ camera_model = {6: "PINHOLE", 8: "RADIAL", 10: "OPENCV"}[d]
+ cameras = []
+ for i in range(b):
+ if camera_model.startswith("SIMPLE_"):
+ params = [x.item() for x in data[i, 3 : min(d, 7)]]
+ else:
+ params = [x.item() for x in data[i, 2:]]
+ cameras.append(
+ {
+ "model": camera_model,
+ "width": int(data[i, 0].item()),
+ "height": int(data[i, 1].item()),
+ "params": params,
+ }
+ )
+ return cameras if self._data.dim() == 2 else cameras[0]
+
+ def __repr__(self):
+ return f"Camera {self.shape} {self.dtype} {self.device}"
diff --git a/imcui/third_party/gim/networks/lightglue/models/__init__.py b/third_party/gim/gim/gluefactory/models/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/lightglue/models/__init__.py
rename to third_party/gim/gim/gluefactory/models/__init__.py
diff --git a/imcui/third_party/gim/networks/lightglue/models/utils/__init__.py b/third_party/gim/gim/gluefactory/models/backbones/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/lightglue/models/utils/__init__.py
rename to third_party/gim/gim/gluefactory/models/backbones/__init__.py
diff --git a/third_party/gim/gim/gluefactory/models/backbones/dinov2.py b/third_party/gim/gim/gluefactory/models/backbones/dinov2.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf828523f70c8c96941ff3b29a75299765809037
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/backbones/dinov2.py
@@ -0,0 +1,30 @@
+import torch
+import torch.nn.functional as F
+
+from ..base_model import BaseModel
+
+
+class DinoV2(BaseModel):
+ default_conf = {"weights": "dinov2_vits14", "allow_resize": False}
+ required_data_keys = ["image"]
+
+ def _init(self, conf):
+ self.net = torch.hub.load("facebookresearch/dinov2", conf.weights)
+ self.set_initialized()
+
+ def _forward(self, data):
+ img = data["image"]
+ if self.conf.allow_resize:
+ img = F.upsample(img, [int(x // 14 * 14) for x in img.shape[-2:]])
+ desc, cls_token = self.net.get_intermediate_layers(
+ img, n=1, return_class_token=True, reshape=True
+ )[0]
+
+ return {
+ "features": desc,
+ "global_descriptor": cls_token,
+ "descriptors": desc.flatten(-2).transpose(-2, -1),
+ }
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/imcui/third_party/gim/networks/lightglue/models/base_model.py b/third_party/gim/gim/gluefactory/models/base_model.py
similarity index 100%
rename from imcui/third_party/gim/networks/lightglue/models/base_model.py
rename to third_party/gim/gim/gluefactory/models/base_model.py
diff --git a/third_party/gim/gim/gluefactory/models/cache_loader.py b/third_party/gim/gim/gluefactory/models/cache_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..b345a997e8287d136292624280de9f4a9d97700a
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/cache_loader.py
@@ -0,0 +1,139 @@
+import string
+
+import h5py
+import torch
+
+from ..datasets.base_dataset import collate
+from ..settings import DATA_PATH
+from ..utils.tensor import batch_to_device
+from .base_model import BaseModel
+from .utils.misc import pad_to_length
+
+
+def pad_local_features(pred: dict, seq_l: int):
+ pred["keypoints"] = pad_to_length(
+ pred["keypoints"],
+ seq_l,
+ -2,
+ mode="random_c",
+ )
+ if "keypoint_scores" in pred.keys():
+ pred["keypoint_scores"] = pad_to_length(
+ pred["keypoint_scores"], seq_l, -1, mode="zeros"
+ )
+ if "descriptors" in pred.keys():
+ pred["descriptors"] = pad_to_length(
+ pred["descriptors"], seq_l, -2, mode="random"
+ )
+ if "scales" in pred.keys():
+ pred["scales"] = pad_to_length(pred["scales"], seq_l, -1, mode="zeros")
+ if "oris" in pred.keys():
+ pred["oris"] = pad_to_length(pred["oris"], seq_l, -1, mode="zeros")
+
+ if "depth_keypoints" in pred.keys():
+ pred["depth_keypoints"] = pad_to_length(
+ pred["depth_keypoints"], seq_l, -1, mode="zeros"
+ )
+ if "valid_depth_keypoints" in pred.keys():
+ pred["valid_depth_keypoints"] = pad_to_length(
+ pred["valid_depth_keypoints"], seq_l, -1, mode="zeros"
+ )
+ return pred
+
+
+def pad_line_features(pred, seq_l: int = None):
+ raise NotImplementedError
+
+
+def recursive_load(grp, pkeys):
+ return {
+ k: torch.from_numpy(grp[k].__array__())
+ if isinstance(grp[k], h5py.Dataset)
+ else recursive_load(grp[k], list(grp.keys()))
+ for k in pkeys
+ }
+
+
+class CacheLoader(BaseModel):
+ default_conf = {
+ "path": "???", # can be a format string like exports/{scene}/
+ "data_keys": None, # load all keys
+ "device": None, # load to same device as data
+ "trainable": False,
+ "add_data_path": True,
+ "collate": True,
+ "scale": ["keypoints", "lines", "orig_lines"],
+ "padding_fn": None,
+ "padding_length": None, # required for batching!
+ "numeric_type": "float32", # [None, "float16", "float32", "float64"]
+ }
+
+ required_data_keys = ["name"] # we need an identifier
+
+ def _init(self, conf):
+ self.hfiles = {}
+ self.padding_fn = conf.padding_fn
+ if self.padding_fn is not None:
+ self.padding_fn = eval(self.padding_fn)
+ self.numeric_dtype = {
+ None: None,
+ "float16": torch.float16,
+ "float32": torch.float32,
+ "float64": torch.float64,
+ }[conf.numeric_type]
+
+ def _forward(self, data):
+ preds = []
+ device = self.conf.device
+ if not device:
+ devices = set(
+ [v.device for v in data.values() if isinstance(v, torch.Tensor)]
+ )
+ if len(devices) == 0:
+ device = "cpu"
+ else:
+ assert len(devices) == 1
+ device = devices.pop()
+
+ var_names = [x[1] for x in string.Formatter().parse(self.conf.path) if x[1]]
+ for i, name in enumerate(data["name"]):
+ fpath = self.conf.path.format(**{k: data[k][i] for k in var_names})
+ if self.conf.add_data_path:
+ fpath = DATA_PATH / fpath
+ hfile = h5py.File(str(fpath), "r")
+ grp = hfile[name]
+ pkeys = (
+ self.conf.data_keys if self.conf.data_keys is not None else grp.keys()
+ )
+ pred = recursive_load(grp, pkeys)
+ if self.numeric_dtype is not None:
+ pred = {
+ k: v
+ if not isinstance(v, torch.Tensor) or not torch.is_floating_point(v)
+ else v.to(dtype=self.numeric_dtype)
+ for k, v in pred.items()
+ }
+ pred = batch_to_device(pred, device)
+ for k, v in pred.items():
+ for pattern in self.conf.scale:
+ if k.startswith(pattern):
+ view_idx = k.replace(pattern, "")
+ scales = (
+ data["scales"]
+ if len(view_idx) == 0
+ else data[f"view{view_idx}"]["scales"]
+ )
+ pred[k] = pred[k] * scales[i]
+ # use this function to fix number of keypoints etc.
+ if self.padding_fn is not None:
+ pred = self.padding_fn(pred, self.conf.padding_length)
+ preds.append(pred)
+ hfile.close()
+ if self.conf.collate:
+ return batch_to_device(collate(preds), device)
+ else:
+ assert len(preds) == 1
+ return batch_to_device(preds[0], device)
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/imcui/third_party/lanet/__init__.py b/third_party/gim/gim/gluefactory/models/extractors/__init__.py
similarity index 100%
rename from imcui/third_party/lanet/__init__.py
rename to third_party/gim/gim/gluefactory/models/extractors/__init__.py
diff --git a/imcui/third_party/dad/dad/detectors/third_party/lightglue/aliked.py b/third_party/gim/gim/gluefactory/models/extractors/aliked.py
similarity index 85%
rename from imcui/third_party/dad/dad/detectors/third_party/lightglue/aliked.py
rename to third_party/gim/gim/gluefactory/models/extractors/aliked.py
index 74870cb31b304931d89eca9ec47ed41a47aa2c61..80cd348ab192cc978d7bc997aa379f24ab774cd1 100644
--- a/imcui/third_party/dad/dad/detectors/third_party/lightglue/aliked.py
+++ b/third_party/gim/gim/gluefactory/models/extractors/aliked.py
@@ -1,48 +1,27 @@
-# BSD 3-Clause License
-
-# Copyright (c) 2022, Zhao Xiaoming
-# All rights reserved.
-
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-
-# 1. Redistributions of source code must retain the above copyright notice, this
-# list of conditions and the following disclaimer.
-
-# 2. Redistributions in binary form must reproduce the above copyright notice,
-# this list of conditions and the following disclaimer in the documentation
-# and/or other materials provided with the distribution.
-
-# 3. Neither the name of the copyright holder nor the names of its
-# contributors may be used to endorse or promote products derived from
-# this software without specific prior written permission.
-
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-# Authors:
-# Xiaoming Zhao, Xingming Wu, Weihai Chen, Peter C.Y. Chen, Qingsong Xu, and Zhengguo Li
-# Code from https://github.com/Shiaoming/ALIKED
-
from typing import Callable, Optional
import torch
import torch.nn.functional as F
import torchvision
-from kornia.color import grayscale_to_rgb
from torch import nn
from torch.nn.modules.utils import _pair
from torchvision.models import resnet
-from .utils import Extractor
+from gluefactory.models.base_model import BaseModel
+
+# coordinates system
+# ------------------------------> [ x: range=-1.0~1.0; w: range=0~W ]
+# | -----------------------------
+# | | |
+# | | |
+# | | |
+# | | image |
+# | | |
+# | | |
+# | | |
+# | |---------------------------|
+# v
+# [ y: range=-1.0~1.0; h: range=0~H ]
def get_patches(
@@ -177,6 +156,7 @@ class DKD(nn.Module):
sel_idx = sort_idx[: self.n_limit]
indices = indices[sel_idx]
indices_keypoints.append(indices)
+
wh = torch.tensor([w - 1, h - 1], device=scores_nograd.device)
keypoints = []
@@ -185,7 +165,6 @@ class DKD(nn.Module):
if sub_pixel:
# detect soft keypoints with grad backpropagation
patches = self.unfold(scores_map) # B x (kernel**2) x (H*W)
- # print(patches.shape)
self.hw_grid = self.hw_grid.to(scores_map) # to device
for b_idx in range(b):
patch = patches[b_idx].t() # (H*W) x (kernel**2)
@@ -228,7 +207,9 @@ class DKD(nn.Module):
keypoints_xy.view(1, 1, -1, 2),
mode="bilinear",
align_corners=True,
- )[0, 0, 0, :] # CxN
+ )[
+ 0, 0, 0, :
+ ] # CxN
keypoints.append(keypoints_xy)
scoredispersitys.append(scoredispersity)
@@ -249,7 +230,9 @@ class DKD(nn.Module):
keypoints_xy.view(1, 1, -1, 2),
mode="bilinear",
align_corners=True,
- )[0, 0, 0, :] # CxN
+ )[
+ 0, 0, 0, :
+ ] # CxN
keypoints.append(keypoints_xy)
scoredispersitys.append(kptscore) # for jit.script compatability
kptscores.append(kptscore)
@@ -605,11 +588,13 @@ class SDDH(nn.Module):
return descriptors, offsets
-class ALIKED(Extractor):
+class ALIKED(BaseModel):
default_conf = {
"model_name": "aliked-n16",
"max_num_keypoints": -1,
"detection_threshold": 0.2,
+ "force_num_keypoints": False,
+ "pretrained": True,
"nms_radius": 2,
}
@@ -617,23 +602,52 @@ class ALIKED(Extractor):
n_limit_max = 20000
- # c1, c2, c3, c4, dim, K, M
cfgs = {
- "aliked-t16": [8, 16, 32, 64, 64, 3, 16],
- "aliked-n16": [16, 32, 64, 128, 128, 3, 16],
- "aliked-n16rot": [16, 32, 64, 128, 128, 3, 16],
- "aliked-n32": [16, 32, 64, 128, 128, 3, 32],
- }
- preprocess_conf = {
- "resize": 1024,
+ "aliked-t16": {
+ "c1": 8,
+ "c2": 16,
+ "c3": 32,
+ "c4": 64,
+ "dim": 64,
+ "K": 3,
+ "M": 16,
+ },
+ "aliked-n16": {
+ "c1": 16,
+ "c2": 32,
+ "c3": 64,
+ "c4": 128,
+ "dim": 128,
+ "K": 3,
+ "M": 16,
+ },
+ "aliked-n16rot": {
+ "c1": 16,
+ "c2": 32,
+ "c3": 64,
+ "c4": 128,
+ "dim": 128,
+ "K": 3,
+ "M": 16,
+ },
+ "aliked-n32": {
+ "c1": 16,
+ "c2": 32,
+ "c3": 64,
+ "c4": 128,
+ "dim": 128,
+ "K": 3,
+ "M": 32,
+ },
}
required_data_keys = ["image"]
- def __init__(self, **conf):
- super().__init__(**conf) # Update with default configuration.
- conf = self.conf
- c1, c2, c3, c4, dim, K, M = self.cfgs[conf.model_name]
+ def _init(self, conf):
+ if conf.force_num_keypoints:
+ assert conf.detection_threshold <= 0 and conf.max_num_keypoints > 0
+ # get configurations
+ c1, c2, c3, c4, dim, K, M = [v for _, v in self.cfgs[conf.model_name].items()]
conv_types = ["conv", "conv", "dcn", "dcn"]
conv2D = False
mask = False
@@ -644,10 +658,35 @@ class ALIKED(Extractor):
self.norm = nn.BatchNorm2d
self.gate = nn.SELU(inplace=True)
self.block1 = ConvBlock(3, c1, self.gate, self.norm, conv_type=conv_types[0])
- self.block2 = self.get_resblock(c1, c2, conv_types[1], mask)
- self.block3 = self.get_resblock(c2, c3, conv_types[2], mask)
- self.block4 = self.get_resblock(c3, c4, conv_types[3], mask)
-
+ self.block2 = ResBlock(
+ c1,
+ c2,
+ 1,
+ nn.Conv2d(c1, c2, 1),
+ gate=self.gate,
+ norm_layer=self.norm,
+ conv_type=conv_types[1],
+ )
+ self.block3 = ResBlock(
+ c2,
+ c3,
+ 1,
+ nn.Conv2d(c2, c3, 1),
+ gate=self.gate,
+ norm_layer=self.norm,
+ conv_type=conv_types[2],
+ mask=mask,
+ )
+ self.block4 = ResBlock(
+ c3,
+ c4,
+ 1,
+ nn.Conv2d(c3, c4, 1),
+ gate=self.gate,
+ norm_layer=self.norm,
+ conv_type=conv_types[3],
+ mask=mask,
+ )
self.conv1 = resnet.conv1x1(c1, dim // 4)
self.conv2 = resnet.conv1x1(c2, dim // 4)
self.conv3 = resnet.conv1x1(c3, dim // 4)
@@ -683,22 +722,12 @@ class ALIKED(Extractor):
else self.n_limit_max,
)
- state_dict = torch.hub.load_state_dict_from_url(
- self.checkpoint_url.format(conf.model_name), map_location="cpu"
- )
- self.load_state_dict(state_dict, strict=True)
-
- def get_resblock(self, c_in, c_out, conv_type, mask):
- return ResBlock(
- c_in,
- c_out,
- 1,
- nn.Conv2d(c_in, c_out, 1),
- gate=self.gate,
- norm_layer=self.norm,
- conv_type=conv_type,
- mask=mask,
- )
+ # load pretrained
+ if conf.pretrained:
+ state_dict = torch.hub.load_state_dict_from_url(
+ self.checkpoint_url.format(conf.model_name), map_location="cpu"
+ )
+ self.load_state_dict(state_dict, strict=True)
def extract_dense_map(self, image):
# Pads images such that dimensions are divisible by
@@ -733,38 +762,25 @@ class ALIKED(Extractor):
return feature_map, score_map
- def forward(self, data: dict) -> dict:
- # need to set here unfortunately
- self.dkd.n_limit = (
- self.conf.max_num_keypoints
- if self.conf.max_num_keypoints > 0
- else self.n_limit_max
- )
+ def _forward(self, data):
image = data["image"]
- if image.shape[1] == 1:
- image = grayscale_to_rgb(image)
feature_map, score_map = self.extract_dense_map(image)
keypoints, kptscores, scoredispersitys = self.dkd(
score_map, image_size=data.get("image_size")
)
- # descriptors, offsets = self.desc_head(feature_map, keypoints)
+ descriptors, offsets = self.desc_head(feature_map, keypoints)
_, _, h, w = image.shape
- wh = torch.tensor([w - 1, h - 1], device=image.device)
- # no padding required
- # we can set detection_threshold=-1 and conf.max_num_keypoints > 0
+ wh = torch.tensor([w, h], device=image.device)
+ # no padding required,
+ # we can set detection_threshold=-1 and conf.max_num_keypoints
return {
- "keypoints": wh * (torch.stack(keypoints) + 1) / 2.0, # B x N x 2
- # "descriptors": torch.stack(descriptors), # B x N x D
- "keypoint_scores": torch.stack(kptscores), # B x N
- "scoremap": score_map, # B x 1 x H x W
+ "keypoints": wh * (torch.stack(keypoints) + 1) / 2.0, # B N 2
+ "descriptors": torch.stack(descriptors), # B N D
+ "keypoint_scores": torch.stack(kptscores), # B N
+ "score_dispersity": torch.stack(scoredispersitys),
+ "score_map": score_map, # Bx1xHxW
}
-
-class ALIKEDROT(ALIKED):
- default_conf = {
- "model_name": "aliked-n16rot",
- "max_num_keypoints": -1,
- "detection_threshold": 0.2,
- "nms_radius": 2,
- }
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/extractors/disk_kornia.py b/third_party/gim/gim/gluefactory/models/extractors/disk_kornia.py
new file mode 100644
index 0000000000000000000000000000000000000000..e01ab89dfae7ffbb9b1309d4db02cfe5b3f956d0
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/extractors/disk_kornia.py
@@ -0,0 +1,108 @@
+import kornia
+import torch
+
+from ..base_model import BaseModel
+from ..utils.misc import pad_and_stack
+
+
+class DISK(BaseModel):
+ default_conf = {
+ "weights": "depth",
+ "dense_outputs": False,
+ "max_num_keypoints": None,
+ "desc_dim": 128,
+ "nms_window_size": 5,
+ "detection_threshold": 0.0,
+ "force_num_keypoints": False,
+ "pad_if_not_divisible": True,
+ "chunk": 4, # for reduced VRAM in training
+ }
+ required_data_keys = ["image"]
+
+ def _init(self, conf):
+ self.model = kornia.feature.DISK.from_pretrained(conf.weights)
+ self.set_initialized()
+
+ def _get_dense_outputs(self, images):
+ B = images.shape[0]
+ if self.conf.pad_if_not_divisible:
+ h, w = images.shape[2:]
+ pd_h = 16 - h % 16 if h % 16 > 0 else 0
+ pd_w = 16 - w % 16 if w % 16 > 0 else 0
+ images = torch.nn.functional.pad(images, (0, pd_w, 0, pd_h), value=0.0)
+
+ heatmaps, descriptors = self.model.heatmap_and_dense_descriptors(images)
+ if self.conf.pad_if_not_divisible:
+ heatmaps = heatmaps[..., :h, :w]
+ descriptors = descriptors[..., :h, :w]
+
+ keypoints = kornia.feature.disk.detector.heatmap_to_keypoints(
+ heatmaps,
+ n=self.conf.max_num_keypoints,
+ window_size=self.conf.nms_window_size,
+ score_threshold=self.conf.detection_threshold,
+ )
+
+ features = []
+ for i in range(B):
+ features.append(keypoints[i].merge_with_descriptors(descriptors[i]))
+
+ return features, descriptors
+
+ def _forward(self, data):
+ image = data["image"]
+
+ keypoints, scores, descriptors = [], [], []
+ if self.conf.dense_outputs:
+ dense_descriptors = []
+ chunk = self.conf.chunk
+ for i in range(0, image.shape[0], chunk):
+ if self.conf.dense_outputs:
+ features, d_descriptors = self._get_dense_outputs(
+ image[: min(image.shape[0], i + chunk)]
+ )
+ dense_descriptors.append(d_descriptors)
+ else:
+ features = self.model(
+ image[: min(image.shape[0], i + chunk)],
+ n=self.conf.max_num_keypoints,
+ window_size=self.conf.nms_window_size,
+ score_threshold=self.conf.detection_threshold,
+ pad_if_not_divisible=self.conf.pad_if_not_divisible,
+ )
+ keypoints += [f.keypoints for f in features]
+ scores += [f.detection_scores for f in features]
+ descriptors += [f.descriptors for f in features]
+ del features
+
+ if self.conf.force_num_keypoints:
+ # pad to target_length
+ target_length = self.conf.max_num_keypoints
+ keypoints = pad_and_stack(
+ keypoints,
+ target_length,
+ -2,
+ mode="random_c",
+ bounds=(
+ 0,
+ data.get("image_size", torch.tensor(image.shape[-2:])).min().item(),
+ ),
+ )
+ scores = pad_and_stack(scores, target_length, -1, mode="zeros")
+ descriptors = pad_and_stack(descriptors, target_length, -2, mode="zeros")
+ else:
+ keypoints = torch.stack(keypoints, 0)
+ scores = torch.stack(scores, 0)
+ descriptors = torch.stack(descriptors, 0)
+
+ pred = {
+ "keypoints": keypoints.to(image) + 0.5,
+ "keypoint_scores": scores.to(image),
+ "descriptors": descriptors.to(image),
+ }
+ if self.conf.dense_outputs:
+ pred["dense_descriptors"] = torch.cat(dense_descriptors, 0)
+ return pred
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/extractors/grid_extractor.py b/third_party/gim/gim/gluefactory/models/extractors/grid_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd221d97c50afaa5c9fa826a54eca0e7413721f9
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/extractors/grid_extractor.py
@@ -0,0 +1,60 @@
+import math
+
+import torch
+
+from ..base_model import BaseModel
+
+
+def to_sequence(map):
+ return map.flatten(-2).transpose(-1, -2)
+
+
+def to_map(sequence):
+ n = sequence.shape[-2]
+ e = math.isqrt(n)
+ assert e * e == n
+ assert e * e == n
+ sequence.transpose(-1, -2).unflatten(-1, [e, e])
+
+
+class GridExtractor(BaseModel):
+ default_conf = {"cell_size": 14}
+ required_data_keys = ["image"]
+
+ def _init(self, conf):
+ pass
+
+ def _forward(self, data):
+ b, c, h, w = data["image"].shape
+
+ cgrid = (
+ torch.stack(
+ torch.meshgrid(
+ torch.arange(
+ h // self.conf.cell_size,
+ dtype=torch.float32,
+ device=data["image"].device,
+ ),
+ torch.arange(
+ w // self.conf.cell_size,
+ dtype=torch.float32,
+ device=data["image"].device,
+ ),
+ indexing="ij",
+ )[::-1],
+ dim=0,
+ )
+ .unsqueeze(0)
+ .repeat([b, 1, 1, 1])
+ * self.conf.cell_size
+ + self.conf.cell_size / 2
+ )
+ pred = {
+ "grid": cgrid + 0.5,
+ "keypoints": to_sequence(cgrid) + 0.5,
+ }
+
+ return pred
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/extractors/keynet_affnet_hardnet.py b/third_party/gim/gim/gluefactory/models/extractors/keynet_affnet_hardnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..419ee972cd4c859074a4fe5bdb62e03ef1cb08e4
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/extractors/keynet_affnet_hardnet.py
@@ -0,0 +1,74 @@
+import kornia
+import torch
+
+from ..base_model import BaseModel
+from ..utils.misc import pad_to_length
+
+
+class KeyNetAffNetHardNet(BaseModel):
+ default_conf = {
+ "max_num_keypoints": None,
+ "desc_dim": 128,
+ "upright": False,
+ "scale_laf": 1.0,
+ "chunk": 4, # for reduced VRAM in training
+ }
+ required_data_keys = ["image"]
+
+ def _init(self, conf):
+ self.model = kornia.feature.KeyNetHardNet(
+ num_features=conf.max_num_keypoints,
+ upright=conf.upright,
+ scale_laf=conf.scale_laf,
+ )
+ self.set_initialized()
+
+ def _forward(self, data):
+ image = data["image"]
+ if image.shape[1] == 3: # RGB
+ scale = image.new_tensor([0.299, 0.587, 0.114]).view(1, 3, 1, 1)
+ image = (image * scale).sum(1, keepdim=True)
+ lafs, scores, descs = [], [], []
+ im_size = data.get("image_size")
+ for i in range(image.shape[0]):
+ img_i = image[i : i + 1, :1]
+ if im_size is not None:
+ img_i = img_i[:, :, : im_size[i, 1], : im_size[i, 0]]
+ laf, score, desc = self.model(img_i)
+ xn = pad_to_length(
+ kornia.feature.get_laf_center(laf),
+ self.conf.max_num_keypoints,
+ pad_dim=-2,
+ mode="random_c",
+ bounds=(0, min(img_i.shape[-2:])),
+ )
+ laf = torch.cat(
+ [
+ laf,
+ kornia.feature.laf_from_center_scale_ori(xn[:, score.shape[-1] :]),
+ ],
+ -3,
+ )
+ lafs.append(laf)
+ scores.append(pad_to_length(score, self.conf.max_num_keypoints, -1))
+ descs.append(pad_to_length(desc, self.conf.max_num_keypoints, -2))
+
+ lafs = torch.cat(lafs, 0)
+ scores = torch.cat(scores, 0)
+ descs = torch.cat(descs, 0)
+ keypoints = kornia.feature.get_laf_center(lafs)
+ scales = kornia.feature.get_laf_scale(lafs)[..., 0]
+ oris = kornia.feature.get_laf_orientation(lafs)
+ pred = {
+ "keypoints": keypoints,
+ "scales": scales.squeeze(-1),
+ "oris": oris.squeeze(-1),
+ "lafs": lafs,
+ "keypoint_scores": scores,
+ "descriptors": descs,
+ }
+
+ return pred
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/extractors/mixed.py b/third_party/gim/gim/gluefactory/models/extractors/mixed.py
new file mode 100644
index 0000000000000000000000000000000000000000..5524cb6ec6f28c3d28f2f3b648a56e44960ecb97
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/extractors/mixed.py
@@ -0,0 +1,76 @@
+import torch.nn.functional as F
+from omegaconf import OmegaConf
+
+from .. import get_model
+from ..base_model import BaseModel
+
+to_ctr = OmegaConf.to_container # convert DictConfig to dict
+
+
+class MixedExtractor(BaseModel):
+ default_conf = {
+ "detector": {"name": None},
+ "descriptor": {"name": None},
+ "interpolate_descriptors_from": None, # field name
+ }
+
+ required_data_keys = ["image"]
+ required_cache_keys = []
+
+ def _init(self, conf):
+ if conf.detector.name:
+ self.detector = get_model(conf.detector.name)(to_ctr(conf.detector))
+ else:
+ self.required_data_keys += ["cache"]
+ self.required_cache_keys += ["keypoints"]
+
+ if conf.descriptor.name:
+ self.descriptor = get_model(conf.descriptor.name)(to_ctr(conf.descriptor))
+ else:
+ self.required_data_keys += ["cache"]
+ self.required_cache_keys += ["descriptors"]
+
+ def _forward(self, data):
+ if self.conf.detector.name:
+ pred = self.detector(data)
+ else:
+ pred = data["cache"]
+ if self.conf.detector.name:
+ pred = {**pred, **self.descriptor({**pred, **data})}
+
+ if self.conf.interpolate_descriptors_from:
+ h, w = data["image"].shape[-2:]
+ kpts = pred["keypoints"]
+ pts = (kpts / kpts.new_tensor([[w, h]]) * 2 - 1)[:, None]
+ pred["descriptors"] = (
+ F.grid_sample(
+ pred[self.conf.interpolate_descriptors_from],
+ pts,
+ align_corners=False,
+ mode="bilinear",
+ )
+ .squeeze(-2)
+ .transpose(-2, -1)
+ .contiguous()
+ )
+
+ return pred
+
+ def loss(self, pred, data):
+ losses = {}
+ metrics = {}
+ total = 0
+
+ for k in ["detector", "descriptor"]:
+ apply = True
+ if "apply_loss" in self.conf[k].keys():
+ apply = self.conf[k].apply_loss
+ if self.conf[k].name and apply:
+ try:
+ losses_, metrics_ = getattr(self, k).loss(pred, {**pred, **data})
+ except NotImplementedError:
+ continue
+ losses = {**losses, **losses_}
+ metrics = {**metrics, **metrics_}
+ total = losses_["total"] + total
+ return {**losses, "total": total}, metrics
diff --git a/imcui/third_party/dad/dad/detectors/third_party/lightglue/sift.py b/third_party/gim/gim/gluefactory/models/extractors/sift.py
similarity index 86%
rename from imcui/third_party/dad/dad/detectors/third_party/lightglue/sift.py
rename to third_party/gim/gim/gluefactory/models/extractors/sift.py
index d172e7444743f400ddb45944dd72e74eb19944ce..9f07725df20301934eb403c124742e8299e22611 100644
--- a/imcui/third_party/dad/dad/detectors/third_party/lightglue/sift.py
+++ b/third_party/gim/gim/gluefactory/models/extractors/sift.py
@@ -11,7 +11,8 @@ try:
except ImportError:
pycolmap = None
-from .utils import Extractor
+from ..base_model import BaseModel
+from ..utils.misc import pad_to_length
def filter_dog_point(points, scales, angles, image_shape, nms_radius, scores=None):
@@ -76,7 +77,7 @@ def run_opencv_sift(features: cv2.Feature2D, image: np.ndarray) -> np.ndarray:
return points, scores, scales, angles, descriptors
-class SIFT(Extractor):
+class SIFT(BaseModel):
default_conf = {
"rootsift": True,
"nms_radius": 0, # None to disable filtering entirely.
@@ -86,16 +87,12 @@ class SIFT(Extractor):
"edge_threshold": 10,
"first_octave": -1, # only used by pycolmap, the default of COLMAP
"num_octaves": 4,
- }
-
- preprocess_conf = {
- "resize": 1024,
+ "force_num_keypoints": False,
}
required_data_keys = ["image"]
- def __init__(self, **conf):
- super().__init__(**conf) # Update with default configuration.
+ def _init(self, conf):
backend = self.conf.backend
if backend.startswith("pycolmap"):
if pycolmap is None:
@@ -134,7 +131,7 @@ class SIFT(Extractor):
else:
backends = {"opencv", "pycolmap", "pycolmap_cpu", "pycolmap_cuda"}
raise ValueError(
- f"Unknown backend: {backend} not in {{{','.join(backends)}}}."
+ f"Unknown backend: {backend} not in " f"{{{','.join(backends)}}}."
)
def extract_single_image(self, image: torch.Tensor):
@@ -181,7 +178,7 @@ class SIFT(Extractor):
pred["oris"],
image_np.shape,
self.conf.nms_radius,
- scores=pred.get("keypoint_scores"),
+ pred["keypoint_scores"],
)
pred = {k: v[keep] for k, v in pred.items()}
@@ -193,9 +190,27 @@ class SIFT(Extractor):
indices = torch.topk(pred["keypoint_scores"], num_points).indices
pred = {k: v[indices] for k, v in pred.items()}
+ if self.conf.force_num_keypoints:
+ num_points = min(self.conf.max_num_keypoints, len(pred["keypoints"]))
+ pred["keypoints"] = pad_to_length(
+ pred["keypoints"],
+ num_points,
+ -2,
+ mode="random_c",
+ bounds=(0, min(image.shape[1:])),
+ )
+ pred["scales"] = pad_to_length(pred["scales"], num_points, -1, mode="zeros")
+ pred["oris"] = pad_to_length(pred["oris"], num_points, -1, mode="zeros")
+ pred["descriptors"] = pad_to_length(
+ pred["descriptors"], num_points, -2, mode="zeros"
+ )
+ if pred["keypoint_scores"] is not None:
+ scores = pad_to_length(
+ pred["keypoint_scores"], num_points, -1, mode="zeros"
+ )
return pred
- def forward(self, data: dict) -> dict:
+ def _forward(self, data: dict) -> dict:
image = data["image"]
if image.shape[1] == 3:
image = rgb_to_grayscale(image)
@@ -214,3 +229,6 @@ class SIFT(Extractor):
if self.conf.rootsift:
pred["descriptors"] = sift_to_rootsift(pred["descriptors"])
return pred
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/extractors/sift_kornia.py b/third_party/gim/gim/gluefactory/models/extractors/sift_kornia.py
new file mode 100644
index 0000000000000000000000000000000000000000..699e5a26da2f620fe049b35b83bab239d0d615d6
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/extractors/sift_kornia.py
@@ -0,0 +1,46 @@
+import kornia
+import torch
+
+from ..base_model import BaseModel
+
+
+class KorniaSIFT(BaseModel):
+ default_conf = {
+ "has_detector": True,
+ "has_descriptor": True,
+ "max_num_keypoints": -1,
+ "detection_threshold": None,
+ "rootsift": True,
+ }
+
+ required_data_keys = ["image"]
+
+ def _init(self, conf):
+ self.sift = kornia.feature.SIFTFeature(
+ num_features=self.conf.max_num_keypoints, rootsift=self.conf.rootsift
+ )
+ self.set_initialized()
+
+ def _forward(self, data):
+ lafs, scores, descriptors = self.sift(data["image"])
+ keypoints = kornia.feature.get_laf_center(lafs)
+ scales = kornia.feature.get_laf_scale(lafs).squeeze(-1).squeeze(-1)
+ oris = kornia.feature.get_laf_orientation(lafs).squeeze(-1)
+ pred = {
+ "keypoints": keypoints, # @TODO: confirm keypoints are in corner convention
+ "scales": scales,
+ "oris": oris,
+ "keypoint_scores": scores,
+ }
+
+ if self.conf.has_descriptor:
+ pred["descriptors"] = descriptors
+
+ pred = {k: pred[k].to(device=data["image"].device) for k in pred.keys()}
+
+ pred["scales"] = pred["scales"]
+ pred["oris"] = torch.deg2rad(pred["oris"])
+ return pred
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/extractors/superpoint_open.py b/third_party/gim/gim/gluefactory/models/extractors/superpoint_open.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f960407897e9695240078e138fffec7d4467e91
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/extractors/superpoint_open.py
@@ -0,0 +1,210 @@
+"""PyTorch implementation of the SuperPoint model,
+ derived from the TensorFlow re-implementation (2018).
+ Authors: Rémi Pautrat, Paul-Edouard Sarlin
+ https://github.com/rpautrat/SuperPoint
+ The implementation of this model and its trained weights are made
+ available under the MIT license.
+"""
+from collections import OrderedDict
+from types import SimpleNamespace
+
+import torch
+import torch.nn as nn
+
+from ..base_model import BaseModel
+from ..utils.misc import pad_and_stack
+
+
+def sample_descriptors(keypoints, descriptors, s: int = 8):
+ """Interpolate descriptors at keypoint locations"""
+ b, c, h, w = descriptors.shape
+ keypoints = (keypoints + 0.5) / (keypoints.new_tensor([w, h]) * s)
+ keypoints = keypoints * 2 - 1 # normalize to (-1, 1)
+ descriptors = torch.nn.functional.grid_sample(
+ descriptors, keypoints.view(b, 1, -1, 2), mode="bilinear", align_corners=False
+ )
+ descriptors = torch.nn.functional.normalize(
+ descriptors.reshape(b, c, -1), p=2, dim=1
+ )
+ return descriptors
+
+
+def batched_nms(scores, nms_radius: int):
+ assert nms_radius >= 0
+
+ def max_pool(x):
+ return torch.nn.functional.max_pool2d(
+ x, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius
+ )
+
+ zeros = torch.zeros_like(scores)
+ max_mask = scores == max_pool(scores)
+ for _ in range(2):
+ supp_mask = max_pool(max_mask.float()) > 0
+ supp_scores = torch.where(supp_mask, zeros, scores)
+ new_max_mask = supp_scores == max_pool(supp_scores)
+ max_mask = max_mask | (new_max_mask & (~supp_mask))
+ return torch.where(max_mask, scores, zeros)
+
+
+def select_top_k_keypoints(keypoints, scores, k):
+ if k >= len(keypoints):
+ return keypoints, scores
+ scores, indices = torch.topk(scores, k, dim=0, sorted=True)
+ return keypoints[indices], scores
+
+
+class VGGBlock(nn.Sequential):
+ def __init__(self, c_in, c_out, kernel_size, relu=True):
+ padding = (kernel_size - 1) // 2
+ conv = nn.Conv2d(
+ c_in, c_out, kernel_size=kernel_size, stride=1, padding=padding
+ )
+ activation = nn.ReLU(inplace=True) if relu else nn.Identity()
+ bn = nn.BatchNorm2d(c_out, eps=0.001)
+ super().__init__(
+ OrderedDict(
+ [
+ ("conv", conv),
+ ("activation", activation),
+ ("bn", bn),
+ ]
+ )
+ )
+
+
+class SuperPoint(BaseModel):
+ default_conf = {
+ "descriptor_dim": 256,
+ "nms_radius": 4,
+ "max_num_keypoints": None,
+ "force_num_keypoints": False,
+ "detection_threshold": 0.005,
+ "remove_borders": 4,
+ "descriptor_dim": 256,
+ "channels": [64, 64, 128, 128, 256],
+ "dense_outputs": None,
+ }
+
+ checkpoint_url = "https://github.com/rpautrat/SuperPoint/raw/master/weights/superpoint_v6_from_tf.pth" # noqa: E501
+
+ def _init(self, conf):
+ self.conf = SimpleNamespace(**conf)
+ self.stride = 2 ** (len(self.conf.channels) - 2)
+ channels = [1, *self.conf.channels[:-1]]
+
+ backbone = []
+ for i, c in enumerate(channels[1:], 1):
+ layers = [VGGBlock(channels[i - 1], c, 3), VGGBlock(c, c, 3)]
+ if i < len(channels) - 1:
+ layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
+ backbone.append(nn.Sequential(*layers))
+ self.backbone = nn.Sequential(*backbone)
+
+ c = self.conf.channels[-1]
+ self.detector = nn.Sequential(
+ VGGBlock(channels[-1], c, 3),
+ VGGBlock(c, self.stride**2 + 1, 1, relu=False),
+ )
+ self.descriptor = nn.Sequential(
+ VGGBlock(channels[-1], c, 3),
+ VGGBlock(c, self.conf.descriptor_dim, 1, relu=False),
+ )
+
+ state_dict = torch.hub.load_state_dict_from_url(self.checkpoint_url)
+ self.load_state_dict(state_dict)
+
+ def _forward(self, data):
+ image = data["image"]
+ if image.shape[1] == 3: # RGB
+ scale = image.new_tensor([0.299, 0.587, 0.114]).view(1, 3, 1, 1)
+ image = (image * scale).sum(1, keepdim=True)
+ features = self.backbone(image)
+ descriptors_dense = torch.nn.functional.normalize(
+ self.descriptor(features), p=2, dim=1
+ )
+
+ # Decode the detection scores
+ scores = self.detector(features)
+ scores = torch.nn.functional.softmax(scores, 1)[:, :-1]
+ b, _, h, w = scores.shape
+ scores = scores.permute(0, 2, 3, 1).reshape(b, h, w, self.stride, self.stride)
+ scores = scores.permute(0, 1, 3, 2, 4).reshape(
+ b, h * self.stride, w * self.stride
+ )
+ scores = batched_nms(scores, self.conf.nms_radius)
+
+ # Discard keypoints near the image borders
+ if self.conf.remove_borders:
+ pad = self.conf.remove_borders
+ scores[:, :pad] = -1
+ scores[:, :, :pad] = -1
+ scores[:, -pad:] = -1
+ scores[:, :, -pad:] = -1
+
+ # Extract keypoints
+ if b > 1:
+ idxs = torch.where(scores > self.conf.detection_threshold)
+ mask = idxs[0] == torch.arange(b, device=scores.device)[:, None]
+ else: # Faster shortcut
+ scores = scores.squeeze(0)
+ idxs = torch.where(scores > self.conf.detection_threshold)
+
+ # Convert (i, j) to (x, y)
+ keypoints_all = torch.stack(idxs[-2:], dim=-1).flip(1).float()
+ scores_all = scores[idxs]
+
+ keypoints = []
+ scores = []
+ for i in range(b):
+ if b > 1:
+ k = keypoints_all[mask[i]]
+ s = scores_all[mask[i]]
+ else:
+ k = keypoints_all
+ s = scores_all
+ if self.conf.max_num_keypoints is not None:
+ k, s = select_top_k_keypoints(k, s, self.conf.max_num_keypoints)
+
+ keypoints.append(k)
+ scores.append(s)
+
+ if self.conf.force_num_keypoints:
+ keypoints = pad_and_stack(
+ keypoints,
+ self.conf.max_num_keypoints,
+ -2,
+ mode="random_c",
+ bounds=(
+ 0,
+ data.get("image_size", torch.tensor(image.shape[-2:])).min().item(),
+ ),
+ )
+ scores = pad_and_stack(
+ scores, self.conf.max_num_keypoints, -1, mode="zeros"
+ )
+ else:
+ keypoints = torch.stack(keypoints, 0)
+ scores = torch.stack(scores, 0)
+
+ if len(keypoints) == 1 or self.conf.force_num_keypoints:
+ # Batch sampling of the descriptors
+ desc = sample_descriptors(keypoints, descriptors_dense, self.stride)
+ else:
+ desc = [
+ sample_descriptors(k[None], d[None], self.stride)[0]
+ for k, d in zip(keypoints, descriptors_dense)
+ ]
+
+ pred = {
+ "keypoints": keypoints + 0.5,
+ "keypoint_scores": scores,
+ "descriptors": desc.transpose(-1, -2),
+ }
+ if self.conf.dense_outputs:
+ pred["dense_descriptors"] = descriptors_dense
+
+ return pred
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/imcui/third_party/lanet/network_v0/__init__.py b/third_party/gim/gim/gluefactory/models/lines/__init__.py
similarity index 100%
rename from imcui/third_party/lanet/network_v0/__init__.py
rename to third_party/gim/gim/gluefactory/models/lines/__init__.py
diff --git a/third_party/gim/gim/gluefactory/models/lines/deeplsd.py b/third_party/gim/gim/gluefactory/models/lines/deeplsd.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1aa57df4b7f3a218018dad2762880076934e03d
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/lines/deeplsd.py
@@ -0,0 +1,106 @@
+import deeplsd.models.deeplsd_inference as deeplsd_inference
+import numpy as np
+import torch
+
+from ...settings import DATA_PATH
+from ..base_model import BaseModel
+
+
+class DeepLSD(BaseModel):
+ default_conf = {
+ "min_length": 15,
+ "max_num_lines": None,
+ "force_num_lines": False,
+ "model_conf": {
+ "detect_lines": True,
+ "line_detection_params": {
+ "merge": False,
+ "grad_nfa": True,
+ "filtering": "normal",
+ "grad_thresh": 3,
+ },
+ },
+ }
+ required_data_keys = ["image"]
+
+ def _init(self, conf):
+ if self.conf.force_num_lines:
+ assert (
+ self.conf.max_num_lines is not None
+ ), "Missing max_num_lines parameter"
+ ckpt = DATA_PATH / "weights/deeplsd_md.tar"
+ if not ckpt.is_file():
+ self.download_model(ckpt)
+ ckpt = torch.load(ckpt, map_location="cpu")
+ self.net = deeplsd_inference.DeepLSD(conf.model_conf).eval()
+ self.net.load_state_dict(ckpt["model"])
+ self.set_initialized()
+
+ def download_model(self, path):
+ import subprocess
+
+ if not path.parent.is_dir():
+ path.parent.mkdir(parents=True, exist_ok=True)
+ link = "https://cvg-data.inf.ethz.ch/DeepLSD/deeplsd_md.tar"
+ cmd = ["wget", link, "-O", path]
+ print("Downloading DeepLSD model...")
+ subprocess.run(cmd, check=True)
+
+ def _forward(self, data):
+ image = data["image"]
+ lines, line_scores, valid_lines = [], [], []
+ if image.shape[1] == 3:
+ # Convert to grayscale
+ scale = image.new_tensor([0.299, 0.587, 0.114]).view(1, 3, 1, 1)
+ image = (image * scale).sum(1, keepdim=True)
+
+ # Forward pass
+ with torch.no_grad():
+ segs = self.net({"image": image})["lines"]
+
+ # Line scores are the sqrt of the length
+ for seg in segs:
+ lengths = np.linalg.norm(seg[:, 0] - seg[:, 1], axis=1)
+ segs = seg[lengths >= self.conf.min_length]
+ scores = np.sqrt(lengths[lengths >= self.conf.min_length])
+
+ # Keep the best lines
+ indices = np.argsort(-scores)
+ if self.conf.max_num_lines is not None:
+ indices = indices[: self.conf.max_num_lines]
+ segs = segs[indices]
+ scores = scores[indices]
+
+ # Pad if necessary
+ n = len(segs)
+ valid_mask = np.ones(n, dtype=bool)
+ if self.conf.force_num_lines:
+ pad = self.conf.max_num_lines - n
+ segs = np.concatenate(
+ [segs, np.zeros((pad, 2, 2), dtype=np.float32)], axis=0
+ )
+ scores = np.concatenate(
+ [scores, np.zeros(pad, dtype=np.float32)], axis=0
+ )
+ valid_mask = np.concatenate(
+ [valid_mask, np.zeros(pad, dtype=bool)], axis=0
+ )
+
+ lines.append(segs)
+ line_scores.append(scores)
+ valid_lines.append(valid_mask)
+
+ # Batch if possible
+ if len(image) == 1 or self.conf.force_num_lines:
+ lines = torch.tensor(lines, dtype=torch.float, device=image.device)
+ line_scores = torch.tensor(
+ line_scores, dtype=torch.float, device=image.device
+ )
+ valid_lines = torch.tensor(
+ valid_lines, dtype=torch.bool, device=image.device
+ )
+
+ return {"lines": lines, "line_scores": line_scores, "valid_lines": valid_lines}
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/lines/lsd.py b/third_party/gim/gim/gluefactory/models/lines/lsd.py
new file mode 100644
index 0000000000000000000000000000000000000000..06f1c12d222f2c66f4ded070fea6d1a8c66b5422
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/lines/lsd.py
@@ -0,0 +1,88 @@
+import numpy as np
+import torch
+from joblib import Parallel, delayed
+from pytlsd import lsd
+
+from ..base_model import BaseModel
+
+
+class LSD(BaseModel):
+ default_conf = {
+ "min_length": 15,
+ "max_num_lines": None,
+ "force_num_lines": False,
+ "n_jobs": 4,
+ }
+ required_data_keys = ["image"]
+
+ def _init(self, conf):
+ if self.conf.force_num_lines:
+ assert (
+ self.conf.max_num_lines is not None
+ ), "Missing max_num_lines parameter"
+
+ def detect_lines(self, img):
+ # Run LSD
+ segs = lsd(img)
+
+ # Filter out keylines that do not meet the minimum length criteria
+ lengths = np.linalg.norm(segs[:, 2:4] - segs[:, 0:2], axis=1)
+ to_keep = lengths >= self.conf.min_length
+ segs, lengths = segs[to_keep], lengths[to_keep]
+
+ # Keep the best lines
+ scores = segs[:, -1] * np.sqrt(lengths)
+ segs = segs[:, :4].reshape(-1, 2, 2)
+ indices = np.argsort(-scores)
+ if self.conf.max_num_lines is not None:
+ indices = indices[: self.conf.max_num_lines]
+ segs = segs[indices]
+ scores = scores[indices]
+
+ # Pad if necessary
+ n = len(segs)
+ valid_mask = np.ones(n, dtype=bool)
+ if self.conf.force_num_lines:
+ pad = self.conf.max_num_lines - n
+ segs = np.concatenate(
+ [segs, np.zeros((pad, 2, 2), dtype=np.float32)], axis=0
+ )
+ scores = np.concatenate([scores, np.zeros(pad, dtype=np.float32)], axis=0)
+ valid_mask = np.concatenate([valid_mask, np.zeros(pad, dtype=bool)], axis=0)
+
+ return segs, scores, valid_mask
+
+ def _forward(self, data):
+ # Convert to the right data format
+ image = data["image"]
+ if image.shape[1] == 3:
+ # Convert to grayscale
+ scale = image.new_tensor([0.299, 0.587, 0.114]).view(1, 3, 1, 1)
+ image = (image * scale).sum(1, keepdim=True)
+ device = image.device
+ b_size = len(image)
+ image = np.uint8(image.squeeze(1).cpu().numpy() * 255)
+
+ # LSD detection in parallel
+ if b_size == 1:
+ lines, line_scores, valid_lines = self.detect_lines(image[0])
+ lines = [lines]
+ line_scores = [line_scores]
+ valid_lines = [valid_lines]
+ else:
+ lines, line_scores, valid_lines = zip(
+ *Parallel(n_jobs=self.conf.n_jobs)(
+ delayed(self.detect_lines)(img) for img in image
+ )
+ )
+
+ # Batch if possible
+ if b_size == 1 or self.conf.force_num_lines:
+ lines = torch.tensor(lines, dtype=torch.float, device=device)
+ line_scores = torch.tensor(line_scores, dtype=torch.float, device=device)
+ valid_lines = torch.tensor(valid_lines, dtype=torch.bool, device=device)
+
+ return {"lines": lines, "line_scores": line_scores, "valid_lines": valid_lines}
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/lines/wireframe.py b/third_party/gim/gim/gluefactory/models/lines/wireframe.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac0d0b5a9297e9a401e33744f06ee1af8e96c2b5
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/lines/wireframe.py
@@ -0,0 +1,312 @@
+import torch
+from sklearn.cluster import DBSCAN
+
+from .. import get_model
+from ..base_model import BaseModel
+
+
+def sample_descriptors_corner_conv(keypoints, descriptors, s: int = 8):
+ """Interpolate descriptors at keypoint locations"""
+ b, c, h, w = descriptors.shape
+ keypoints = keypoints / (keypoints.new_tensor([w, h]) * s)
+ keypoints = keypoints * 2 - 1 # normalize to (-1, 1)
+ descriptors = torch.nn.functional.grid_sample(
+ descriptors, keypoints.view(b, 1, -1, 2), mode="bilinear", align_corners=False
+ )
+ descriptors = torch.nn.functional.normalize(
+ descriptors.reshape(b, c, -1), p=2, dim=1
+ )
+ return descriptors
+
+
+def lines_to_wireframe(
+ lines, line_scores, all_descs, s, nms_radius, force_num_lines, max_num_lines
+):
+ """Given a set of lines, their score and dense descriptors,
+ merge close-by endpoints and compute a wireframe defined by
+ its junctions and connectivity.
+ Returns:
+ junctions: list of [num_junc, 2] tensors listing all wireframe junctions
+ junc_scores: list of [num_junc] tensors with the junction score
+ junc_descs: list of [dim, num_junc] tensors with the junction descriptors
+ connectivity: list of [num_junc, num_junc] bool arrays with True when 2
+ junctions are connected
+ new_lines: the new set of [b_size, num_lines, 2, 2] lines
+ lines_junc_idx: a [b_size, num_lines, 2] tensor with the indices of the
+ junctions of each endpoint
+ num_true_junctions: a list of the number of valid junctions for each image
+ in the batch, i.e. before filling with random ones
+ """
+ b_size, _, h, w = all_descs.shape
+ device = lines.device
+ h, w = h * s, w * s
+ endpoints = lines.reshape(b_size, -1, 2)
+
+ (
+ junctions,
+ junc_scores,
+ connectivity,
+ new_lines,
+ lines_junc_idx,
+ num_true_junctions,
+ ) = ([], [], [], [], [], [])
+ for bs in range(b_size):
+ # Cluster the junctions that are close-by
+ db = DBSCAN(eps=nms_radius, min_samples=1).fit(endpoints[bs].cpu().numpy())
+ clusters = db.labels_
+ n_clusters = len(set(clusters))
+ num_true_junctions.append(n_clusters)
+
+ # Compute the average junction and score for each cluster
+ clusters = torch.tensor(clusters, dtype=torch.long, device=device)
+ new_junc = torch.zeros(n_clusters, 2, dtype=torch.float, device=device)
+ new_junc.scatter_reduce_(
+ 0,
+ clusters[:, None].repeat(1, 2),
+ endpoints[bs],
+ reduce="mean",
+ include_self=False,
+ )
+ junctions.append(new_junc)
+ new_scores = torch.zeros(n_clusters, dtype=torch.float, device=device)
+ new_scores.scatter_reduce_(
+ 0,
+ clusters,
+ torch.repeat_interleave(line_scores[bs], 2),
+ reduce="mean",
+ include_self=False,
+ )
+ junc_scores.append(new_scores)
+
+ # Compute the new lines
+ new_lines.append(junctions[-1][clusters].reshape(-1, 2, 2))
+ lines_junc_idx.append(clusters.reshape(-1, 2))
+
+ if force_num_lines:
+ # Add random junctions (with no connectivity)
+ missing = max_num_lines * 2 - len(junctions[-1])
+ junctions[-1] = torch.cat(
+ [
+ junctions[-1],
+ torch.rand(missing, 2).to(lines)
+ * lines.new_tensor([[w - 1, h - 1]]),
+ ],
+ dim=0,
+ )
+ junc_scores[-1] = torch.cat(
+ [junc_scores[-1], torch.zeros(missing).to(lines)], dim=0
+ )
+
+ junc_connect = torch.eye(max_num_lines * 2, dtype=torch.bool, device=device)
+ pairs = clusters.reshape(-1, 2) # these pairs are connected by a line
+ junc_connect[pairs[:, 0], pairs[:, 1]] = True
+ junc_connect[pairs[:, 1], pairs[:, 0]] = True
+ connectivity.append(junc_connect)
+ else:
+ # Compute the junction connectivity
+ junc_connect = torch.eye(n_clusters, dtype=torch.bool, device=device)
+ pairs = clusters.reshape(-1, 2) # these pairs are connected by a line
+ junc_connect[pairs[:, 0], pairs[:, 1]] = True
+ junc_connect[pairs[:, 1], pairs[:, 0]] = True
+ connectivity.append(junc_connect)
+
+ junctions = torch.stack(junctions, dim=0)
+ new_lines = torch.stack(new_lines, dim=0)
+ lines_junc_idx = torch.stack(lines_junc_idx, dim=0)
+
+ # Interpolate the new junction descriptors
+ junc_descs = sample_descriptors_corner_conv(junctions, all_descs, s).mT
+
+ return (
+ junctions,
+ junc_scores,
+ junc_descs,
+ connectivity,
+ new_lines,
+ lines_junc_idx,
+ num_true_junctions,
+ )
+
+
+class WireframeExtractor(BaseModel):
+ default_conf = {
+ "point_extractor": {
+ "name": None,
+ "trainable": False,
+ "dense_outputs": True,
+ "max_num_keypoints": None,
+ "force_num_keypoints": False,
+ },
+ "line_extractor": {
+ "name": None,
+ "trainable": False,
+ "max_num_lines": None,
+ "force_num_lines": False,
+ "min_length": 15,
+ },
+ "wireframe_params": {
+ "merge_points": True,
+ "merge_line_endpoints": True,
+ "nms_radius": 3,
+ },
+ }
+ required_data_keys = ["image"]
+
+ def _init(self, conf):
+ self.point_extractor = get_model(self.conf.point_extractor.name)(
+ self.conf.point_extractor
+ )
+ self.line_extractor = get_model(self.conf.line_extractor.name)(
+ self.conf.line_extractor
+ )
+
+ def _forward(self, data):
+ b_size, _, h, w = data["image"].shape
+ device = data["image"].device
+
+ if (
+ not self.conf.point_extractor.force_num_keypoints
+ or not self.conf.line_extractor.force_num_lines
+ ):
+ assert b_size == 1, "Only batch size of 1 accepted for non padded inputs"
+
+ # Line detection
+ pred = self.line_extractor(data)
+ if pred["line_scores"].shape[-1] != 0:
+ pred["line_scores"] /= pred["line_scores"].max(dim=1)[0][:, None] + 1e-8
+
+ # Keypoint prediction
+ pred = {**pred, **self.point_extractor(data)}
+ assert (
+ "dense_descriptors" in pred
+ ), "The KP extractor should return dense descriptors"
+ s_desc = data["image"].shape[2] // pred["dense_descriptors"].shape[2]
+
+ # Remove keypoints that are too close to line endpoints
+ if self.conf.wireframe_params.merge_points:
+ line_endpts = pred["lines"].reshape(b_size, -1, 2)
+ dist_pt_lines = torch.norm(
+ pred["keypoints"][:, :, None] - line_endpts[:, None], dim=-1
+ )
+ # For each keypoint, mark it as valid or to remove
+ pts_to_remove = torch.any(
+ dist_pt_lines < self.conf.wireframe_params.nms_radius, dim=2
+ )
+ if self.conf.point_extractor.force_num_keypoints:
+ # Replace the points with random ones
+ num_to_remove = pts_to_remove.int().sum().item()
+ pred["keypoints"][pts_to_remove] = torch.rand(
+ num_to_remove, 2, device=device
+ ) * pred["keypoints"].new_tensor([[w - 1, h - 1]])
+ pred["keypoint_scores"][pts_to_remove] = 0
+ for bs in range(b_size):
+ descrs = sample_descriptors_corner_conv(
+ pred["keypoints"][bs][pts_to_remove[bs]][None],
+ pred["dense_descriptors"][bs][None],
+ s_desc,
+ )
+ pred["descriptors"][bs][pts_to_remove[bs]] = descrs[0].T
+ else:
+ # Simply remove them (we assume batch_size = 1 here)
+ assert len(pred["keypoints"]) == 1
+ pred["keypoints"] = pred["keypoints"][0][~pts_to_remove[0]][None]
+ pred["keypoint_scores"] = pred["keypoint_scores"][0][~pts_to_remove[0]][
+ None
+ ]
+ pred["descriptors"] = pred["descriptors"][0][~pts_to_remove[0]][None]
+
+ # Connect the lines together to form a wireframe
+ orig_lines = pred["lines"].clone()
+ if (
+ self.conf.wireframe_params.merge_line_endpoints
+ and len(pred["lines"][0]) > 0
+ ):
+ # Merge first close-by endpoints to connect lines
+ (
+ line_points,
+ line_pts_scores,
+ line_descs,
+ line_association,
+ pred["lines"],
+ lines_junc_idx,
+ n_true_junctions,
+ ) = lines_to_wireframe(
+ pred["lines"],
+ pred["line_scores"],
+ pred["dense_descriptors"],
+ s=s_desc,
+ nms_radius=self.conf.wireframe_params.nms_radius,
+ force_num_lines=self.conf.line_extractor.force_num_lines,
+ max_num_lines=self.conf.line_extractor.max_num_lines,
+ )
+
+ # Add the keypoints to the junctions and fill the rest with random keypoints
+ (all_points, all_scores, all_descs, pl_associativity) = [], [], [], []
+ for bs in range(b_size):
+ all_points.append(
+ torch.cat([line_points[bs], pred["keypoints"][bs]], dim=0)
+ )
+ all_scores.append(
+ torch.cat([line_pts_scores[bs], pred["keypoint_scores"][bs]], dim=0)
+ )
+ all_descs.append(
+ torch.cat([line_descs[bs], pred["descriptors"][bs]], dim=0)
+ )
+
+ associativity = torch.eye(
+ len(all_points[-1]), dtype=torch.bool, device=device
+ )
+ associativity[
+ : n_true_junctions[bs], : n_true_junctions[bs]
+ ] = line_association[bs][: n_true_junctions[bs], : n_true_junctions[bs]]
+ pl_associativity.append(associativity)
+
+ all_points = torch.stack(all_points, dim=0)
+ all_scores = torch.stack(all_scores, dim=0)
+ all_descs = torch.stack(all_descs, dim=0)
+ pl_associativity = torch.stack(pl_associativity, dim=0)
+ else:
+ # Lines are independent
+ all_points = torch.cat(
+ [pred["lines"].reshape(b_size, -1, 2), pred["keypoints"]], dim=1
+ )
+ n_pts = all_points.shape[1]
+ num_lines = pred["lines"].shape[1]
+ n_true_junctions = [num_lines * 2] * b_size
+ all_scores = torch.cat(
+ [
+ torch.repeat_interleave(pred["line_scores"], 2, dim=1),
+ pred["keypoint_scores"],
+ ],
+ dim=1,
+ )
+ line_descs = sample_descriptors_corner_conv(
+ pred["lines"].reshape(b_size, -1, 2), pred["dense_descriptors"], s_desc
+ ).mT # [B, n_lines * 2, desc_dim]
+ all_descs = torch.cat([line_descs, pred["descriptors"]], dim=1)
+ pl_associativity = torch.eye(n_pts, dtype=torch.bool, device=device)[
+ None
+ ].repeat(b_size, 1, 1)
+ lines_junc_idx = (
+ torch.arange(num_lines * 2, device=device)
+ .reshape(1, -1, 2)
+ .repeat(b_size, 1, 1)
+ )
+
+ del pred["dense_descriptors"] # Remove dense descriptors to save memory
+ torch.cuda.empty_cache()
+
+ pred["keypoints"] = all_points
+ pred["keypoint_scores"] = all_scores
+ pred["descriptors"] = all_descs
+ pred["pl_associativity"] = pl_associativity
+ pred["num_junctions"] = torch.tensor(n_true_junctions)
+ pred["orig_lines"] = orig_lines
+ pred["lines_junc_idx"] = lines_junc_idx
+ return pred
+
+ def loss(self, pred, data):
+ raise NotImplementedError
+
+ def metrics(self, _pred, _data):
+ return {}
diff --git a/imcui/third_party/lanet/network_v1/__init__.py b/third_party/gim/gim/gluefactory/models/matchers/__init__.py
similarity index 100%
rename from imcui/third_party/lanet/network_v1/__init__.py
rename to third_party/gim/gim/gluefactory/models/matchers/__init__.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/__init__.py b/third_party/gim/gim/gluefactory/models/matchers/adalam.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/__init__.py
rename to third_party/gim/gim/gluefactory/models/matchers/adalam.py
diff --git a/third_party/gim/gim/gluefactory/models/matchers/depth_matcher.py b/third_party/gim/gim/gluefactory/models/matchers/depth_matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..125ded2b8aabdca100898c352a4d631d03134ea9
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/matchers/depth_matcher.py
@@ -0,0 +1,82 @@
+import torch
+
+from ...geometry.gt_generation import (
+ gt_line_matches_from_pose_depth,
+ gt_matches_from_pose_depth,
+)
+from ..base_model import BaseModel
+
+
+class DepthMatcher(BaseModel):
+ default_conf = {
+ # GT parameters for points
+ "use_points": True,
+ "th_positive": 3.0,
+ "th_negative": 5.0,
+ "th_epi": None, # add some more epi outliers
+ "th_consistency": None, # check for projection consistency in px
+ # GT parameters for lines
+ "use_lines": False,
+ "n_line_sampled_pts": 50,
+ "line_perp_dist_th": 5,
+ "overlap_th": 0.2,
+ "min_visibility_th": 0.5,
+ }
+
+ required_data_keys = ["view0", "view1", "T_0to1", "T_1to0"]
+
+ def _init(self, conf):
+ # TODO (iago): Is this just boilerplate code?
+ if self.conf.use_points:
+ self.required_data_keys += ["keypoints0", "keypoints1"]
+ if self.conf.use_lines:
+ self.required_data_keys += [
+ "lines0",
+ "lines1",
+ "valid_lines0",
+ "valid_lines1",
+ ]
+
+ @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
+ def _forward(self, data):
+ result = {}
+ if self.conf.use_points:
+ if "depth_keypoints0" in data:
+ keys = [
+ "depth_keypoints0",
+ "valid_depth_keypoints0",
+ "depth_keypoints1",
+ "valid_depth_keypoints1",
+ ]
+ kw = {k: data[k] for k in keys}
+ else:
+ kw = {}
+ result = gt_matches_from_pose_depth(
+ data["keypoints0"],
+ data["keypoints1"],
+ data,
+ pos_th=self.conf.th_positive,
+ neg_th=self.conf.th_negative,
+ epi_th=self.conf.th_epi,
+ cc_th=self.conf.th_consistency,
+ **kw,
+ )
+ if self.conf.use_lines:
+ line_assignment, line_m0, line_m1 = gt_line_matches_from_pose_depth(
+ data["lines0"],
+ data["lines1"],
+ data["valid_lines0"],
+ data["valid_lines1"],
+ data,
+ self.conf.n_line_sampled_pts,
+ self.conf.line_perp_dist_th,
+ self.conf.overlap_th,
+ self.conf.min_visibility_th,
+ )
+ result["line_matches0"] = line_m0
+ result["line_matches1"] = line_m1
+ result["line_assignment"] = line_assignment
+ return result
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/matchers/gluestick.py b/third_party/gim/gim/gluefactory/models/matchers/gluestick.py
new file mode 100644
index 0000000000000000000000000000000000000000..b46af1361104a4ceae24236fdaf5ab9582b128a4
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/matchers/gluestick.py
@@ -0,0 +1,776 @@
+import logging
+import warnings
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from ...settings import DATA_PATH
+from ..base_model import BaseModel
+from ..utils.metrics import matcher_metrics
+
+warnings.filterwarnings("ignore", category=UserWarning)
+ETH_EPS = 1e-8
+
+
+class GlueStick(BaseModel):
+ default_conf = {
+ "input_dim": 256,
+ "descriptor_dim": 256,
+ "weights": None,
+ "version": "v0.1_arxiv",
+ "keypoint_encoder": [32, 64, 128, 256],
+ "GNN_layers": ["self", "cross"] * 9,
+ "num_line_iterations": 1,
+ "line_attention": False,
+ "filter_threshold": 0.2,
+ "checkpointed": False,
+ "skip_init": False,
+ "inter_supervision": None,
+ "loss": {
+ "nll_weight": 1.0,
+ "nll_balancing": 0.5,
+ "inter_supervision": [0.3, 0.6],
+ },
+ }
+ required_data_keys = [
+ "view0",
+ "view1",
+ "keypoints0",
+ "keypoints1",
+ "descriptors0",
+ "descriptors1",
+ "keypoint_scores0",
+ "keypoint_scores1",
+ "lines0",
+ "lines1",
+ "lines_junc_idx0",
+ "lines_junc_idx1",
+ "line_scores0",
+ "line_scores1",
+ ]
+
+ DEFAULT_LOSS_CONF = {"nll_weight": 1.0, "nll_balancing": 0.5}
+
+ url = (
+ "https://github.com/cvg/GlueStick/releases/download/{}/"
+ "checkpoint_GlueStick_MD.tar"
+ )
+
+ def _init(self, conf):
+ if conf.input_dim != conf.descriptor_dim:
+ self.input_proj = nn.Conv1d(
+ conf.input_dim, conf.descriptor_dim, kernel_size=1
+ )
+ nn.init.constant_(self.input_proj.bias, 0.0)
+
+ self.kenc = KeypointEncoder(conf.descriptor_dim, conf.keypoint_encoder)
+ self.lenc = EndPtEncoder(conf.descriptor_dim, conf.keypoint_encoder)
+ self.gnn = AttentionalGNN(
+ conf.descriptor_dim,
+ conf.GNN_layers,
+ checkpointed=conf.checkpointed,
+ inter_supervision=conf.inter_supervision,
+ num_line_iterations=conf.num_line_iterations,
+ line_attention=conf.line_attention,
+ )
+ self.final_proj = nn.Conv1d(
+ conf.descriptor_dim, conf.descriptor_dim, kernel_size=1
+ )
+ nn.init.constant_(self.final_proj.bias, 0.0)
+ nn.init.orthogonal_(self.final_proj.weight, gain=1)
+ self.final_line_proj = nn.Conv1d(
+ conf.descriptor_dim, conf.descriptor_dim, kernel_size=1
+ )
+ nn.init.constant_(self.final_line_proj.bias, 0.0)
+ nn.init.orthogonal_(self.final_line_proj.weight, gain=1)
+ if conf.inter_supervision is not None:
+ self.inter_line_proj = nn.ModuleList(
+ [
+ nn.Conv1d(conf.descriptor_dim, conf.descriptor_dim, kernel_size=1)
+ for _ in conf.inter_supervision
+ ]
+ )
+ self.layer2idx = {}
+ for i, l in enumerate(conf.inter_supervision):
+ nn.init.constant_(self.inter_line_proj[i].bias, 0.0)
+ nn.init.orthogonal_(self.inter_line_proj[i].weight, gain=1)
+ self.layer2idx[l] = i
+
+ bin_score = torch.nn.Parameter(torch.tensor(1.0))
+ self.register_parameter("bin_score", bin_score)
+ line_bin_score = torch.nn.Parameter(torch.tensor(1.0))
+ self.register_parameter("line_bin_score", line_bin_score)
+
+ if conf.weights:
+ assert isinstance(conf.weights, (Path, str))
+ fname = DATA_PATH / "weights" / f"{conf.weights}_{conf.version}.tar"
+ fname.parent.mkdir(exist_ok=True, parents=True)
+ if Path(conf.weights).exists():
+ logging.info(f'Loading GlueStick model from "{conf.weights}"')
+ state_dict = torch.load(conf.weights, map_location="cpu")
+ elif fname.exists():
+ logging.info(f'Loading GlueStick model from "{fname}"')
+ state_dict = torch.load(fname, map_location="cpu")
+ else:
+ logging.info(
+ "Loading GlueStick model from " f'"{self.url.format(conf.version)}"'
+ )
+ state_dict = torch.hub.load_state_dict_from_url(
+ self.url.format(conf.version), file_name=fname, map_location="cpu"
+ )
+
+ if "model" in state_dict:
+ state_dict = {
+ k.replace("matcher.", ""): v
+ for k, v in state_dict["model"].items()
+ if "matcher." in k
+ }
+ state_dict = {
+ k.replace("module.", ""): v for k, v in state_dict.items()
+ }
+ self.load_state_dict(state_dict, strict=False)
+
+ def _forward(self, data):
+ device = data["keypoints0"].device
+ b_size = len(data["keypoints0"])
+ image_size0 = (
+ data["view0"]["image_size"]
+ if "image_size" in data["view0"]
+ else data["view0"]["image"].shape
+ )
+ image_size1 = (
+ data["view1"]["image_size"]
+ if "image_size" in data["view1"]
+ else data["view1"]["image"].shape
+ )
+
+ pred = {}
+ desc0, desc1 = data["descriptors0"].mT, data["descriptors1"].mT
+ kpts0, kpts1 = data["keypoints0"], data["keypoints1"]
+
+ n_kpts0, n_kpts1 = kpts0.shape[1], kpts1.shape[1]
+ n_lines0, n_lines1 = data["lines0"].shape[1], data["lines1"].shape[1]
+ if n_kpts0 == 0 or n_kpts1 == 0:
+ # No detected keypoints nor lines
+ pred["log_assignment"] = torch.zeros(
+ b_size, n_kpts0, n_kpts1, dtype=torch.float, device=device
+ )
+ pred["matches0"] = torch.full(
+ (b_size, n_kpts0), -1, device=device, dtype=torch.int64
+ )
+ pred["matches1"] = torch.full(
+ (b_size, n_kpts1), -1, device=device, dtype=torch.int64
+ )
+ pred["matching_scores0"] = torch.zeros(
+ (b_size, n_kpts0), device=device, dtype=torch.float32
+ )
+ pred["matching_scores1"] = torch.zeros(
+ (b_size, n_kpts1), device=device, dtype=torch.float32
+ )
+ pred["line_log_assignment"] = torch.zeros(
+ b_size, n_lines0, n_lines1, dtype=torch.float, device=device
+ )
+ pred["line_matches0"] = torch.full(
+ (b_size, n_lines0), -1, device=device, dtype=torch.int64
+ )
+ pred["line_matches1"] = torch.full(
+ (b_size, n_lines1), -1, device=device, dtype=torch.int64
+ )
+ pred["line_matching_scores0"] = torch.zeros(
+ (b_size, n_lines0), device=device, dtype=torch.float32
+ )
+ pred["line_matching_scores1"] = torch.zeros(
+ (b_size, n_kpts1), device=device, dtype=torch.float32
+ )
+ return pred
+
+ lines0 = data["lines0"].flatten(1, 2)
+ lines1 = data["lines1"].flatten(1, 2)
+ # [b_size, num_lines * 2]
+ lines_junc_idx0 = data["lines_junc_idx0"].flatten(1, 2)
+ lines_junc_idx1 = data["lines_junc_idx1"].flatten(1, 2)
+
+ if self.conf.input_dim != self.conf.descriptor_dim:
+ desc0 = self.input_proj(desc0)
+ desc1 = self.input_proj(desc1)
+
+ kpts0 = normalize_keypoints(kpts0, image_size0)
+ kpts1 = normalize_keypoints(kpts1, image_size1)
+
+ desc0 = desc0 + self.kenc(kpts0, data["keypoint_scores0"])
+ desc1 = desc1 + self.kenc(kpts1, data["keypoint_scores1"])
+
+ if n_lines0 != 0 and n_lines1 != 0:
+ # Pre-compute the line encodings
+ lines0 = normalize_keypoints(lines0, image_size0).reshape(
+ b_size, n_lines0, 2, 2
+ )
+ lines1 = normalize_keypoints(lines1, image_size1).reshape(
+ b_size, n_lines1, 2, 2
+ )
+ line_enc0 = self.lenc(lines0, data["line_scores0"])
+ line_enc1 = self.lenc(lines1, data["line_scores1"])
+ else:
+ line_enc0 = torch.zeros(
+ b_size,
+ self.conf.descriptor_dim,
+ n_lines0 * 2,
+ dtype=torch.float,
+ device=device,
+ )
+ line_enc1 = torch.zeros(
+ b_size,
+ self.conf.descriptor_dim,
+ n_lines1 * 2,
+ dtype=torch.float,
+ device=device,
+ )
+
+ desc0, desc1 = self.gnn(
+ desc0, desc1, line_enc0, line_enc1, lines_junc_idx0, lines_junc_idx1
+ )
+
+ # Match all points (KP and line junctions)
+ mdesc0, mdesc1 = self.final_proj(desc0), self.final_proj(desc1)
+
+ kp_scores = torch.einsum("bdn,bdm->bnm", mdesc0, mdesc1)
+ kp_scores = kp_scores / self.conf.descriptor_dim**0.5
+ kp_scores = log_double_softmax(kp_scores, self.bin_score)
+ m0, m1, mscores0, mscores1 = self._get_matches(kp_scores)
+ pred["log_assignment"] = kp_scores
+ pred["matches0"] = m0
+ pred["matches1"] = m1
+ pred["matching_scores0"] = mscores0
+ pred["matching_scores1"] = mscores1
+
+ # Match the lines
+ if n_lines0 > 0 and n_lines1 > 0:
+ (
+ line_scores,
+ m0_lines,
+ m1_lines,
+ mscores0_lines,
+ mscores1_lines,
+ raw_line_scores,
+ ) = self._get_line_matches(
+ desc0[:, :, : 2 * n_lines0],
+ desc1[:, :, : 2 * n_lines1],
+ lines_junc_idx0,
+ lines_junc_idx1,
+ self.final_line_proj,
+ )
+ if self.conf.inter_supervision:
+ for layer in self.conf.inter_supervision:
+ (
+ line_scores_i,
+ m0_lines_i,
+ m1_lines_i,
+ mscores0_lines_i,
+ mscores1_lines_i,
+ _,
+ ) = self._get_line_matches(
+ self.gnn.inter_layers[layer][0][:, :, : 2 * n_lines0],
+ self.gnn.inter_layers[layer][1][:, :, : 2 * n_lines1],
+ lines_junc_idx0,
+ lines_junc_idx1,
+ self.inter_line_proj[self.layer2idx[layer]],
+ )
+ pred[f"line_{layer}_log_assignment"] = line_scores_i
+ pred[f"line_{layer}_matches0"] = m0_lines_i
+ pred[f"line_{layer}_matches1"] = m1_lines_i
+ pred[f"line_{layer}_matching_scores0"] = mscores0_lines_i
+ pred[f"line_{layer}_matching_scores1"] = mscores1_lines_i
+ else:
+ line_scores = torch.zeros(
+ b_size, n_lines0, n_lines1, dtype=torch.float, device=device
+ )
+ m0_lines = torch.full(
+ (b_size, n_lines0), -1, device=device, dtype=torch.int64
+ )
+ m1_lines = torch.full(
+ (b_size, n_lines1), -1, device=device, dtype=torch.int64
+ )
+ mscores0_lines = torch.zeros(
+ (b_size, n_lines0), device=device, dtype=torch.float32
+ )
+ mscores1_lines = torch.zeros(
+ (b_size, n_lines1), device=device, dtype=torch.float32
+ )
+ raw_line_scores = torch.zeros(
+ b_size, n_lines0, n_lines1, dtype=torch.float, device=device
+ )
+ pred["line_log_assignment"] = line_scores
+ pred["line_matches0"] = m0_lines
+ pred["line_matches1"] = m1_lines
+ pred["line_matching_scores0"] = mscores0_lines
+ pred["line_matching_scores1"] = mscores1_lines
+ pred["raw_line_scores"] = raw_line_scores
+
+ return pred
+
+ def _get_matches(self, scores_mat):
+ max0 = scores_mat[:, :-1, :-1].max(2)
+ max1 = scores_mat[:, :-1, :-1].max(1)
+ m0, m1 = max0.indices, max1.indices
+ mutual0 = arange_like(m0, 1)[None] == m1.gather(1, m0)
+ mutual1 = arange_like(m1, 1)[None] == m0.gather(1, m1)
+ zero = scores_mat.new_tensor(0)
+ mscores0 = torch.where(mutual0, max0.values.exp(), zero)
+ mscores1 = torch.where(mutual1, mscores0.gather(1, m1), zero)
+ valid0 = mutual0 & (mscores0 > self.conf.filter_threshold)
+ valid1 = mutual1 & valid0.gather(1, m1)
+ m0 = torch.where(valid0, m0, m0.new_tensor(-1))
+ m1 = torch.where(valid1, m1, m1.new_tensor(-1))
+ return m0, m1, mscores0, mscores1
+
+ def _get_line_matches(
+ self, ldesc0, ldesc1, lines_junc_idx0, lines_junc_idx1, final_proj
+ ):
+ mldesc0 = final_proj(ldesc0)
+ mldesc1 = final_proj(ldesc1)
+
+ line_scores = torch.einsum("bdn,bdm->bnm", mldesc0, mldesc1)
+ line_scores = line_scores / self.conf.descriptor_dim**0.5
+
+ # Get the line representation from the junction descriptors
+ n2_lines0 = lines_junc_idx0.shape[1]
+ n2_lines1 = lines_junc_idx1.shape[1]
+ line_scores = torch.gather(
+ line_scores,
+ dim=2,
+ index=lines_junc_idx1[:, None, :].repeat(1, line_scores.shape[1], 1),
+ )
+ line_scores = torch.gather(
+ line_scores,
+ dim=1,
+ index=lines_junc_idx0[:, :, None].repeat(1, 1, n2_lines1),
+ )
+ line_scores = line_scores.reshape((-1, n2_lines0 // 2, 2, n2_lines1 // 2, 2))
+
+ # Match either in one direction or the other
+ raw_line_scores = 0.5 * torch.maximum(
+ line_scores[:, :, 0, :, 0] + line_scores[:, :, 1, :, 1],
+ line_scores[:, :, 0, :, 1] + line_scores[:, :, 1, :, 0],
+ )
+ line_scores = log_double_softmax(raw_line_scores, self.line_bin_score)
+ m0_lines, m1_lines, mscores0_lines, mscores1_lines = self._get_matches(
+ line_scores
+ )
+ return (
+ line_scores,
+ m0_lines,
+ m1_lines,
+ mscores0_lines,
+ mscores1_lines,
+ raw_line_scores,
+ )
+
+ def sub_loss(self, pred, data, losses, bin_score, prefix="", layer=-1):
+ line_suffix = "" if layer == -1 else f"{layer}_"
+ layer_weight = (
+ 1.0
+ if layer == -1
+ else self.conf.loss.inter_supervision[self.layer2idx[layer]]
+ )
+
+ positive = data["gt_" + prefix + "assignment"].float()
+ num_pos = torch.max(positive.sum((1, 2)), positive.new_tensor(1))
+ neg0 = (data["gt_" + prefix + "matches0"] == -1).float()
+ neg1 = (data["gt_" + prefix + "matches1"] == -1).float()
+ num_neg = torch.max(neg0.sum(1) + neg1.sum(1), neg0.new_tensor(1))
+
+ log_assignment = pred[prefix + line_suffix + "log_assignment"]
+ nll_pos = -(log_assignment[:, :-1, :-1] * positive).sum((1, 2))
+ nll_pos /= num_pos
+ nll_neg0 = -(log_assignment[:, :-1, -1] * neg0).sum(1)
+ nll_neg1 = -(log_assignment[:, -1, :-1] * neg1).sum(1)
+ nll_neg = (nll_neg0 + nll_neg1) / num_neg
+ nll = (
+ self.conf.loss.nll_balancing * nll_pos
+ + (1 - self.conf.loss.nll_balancing) * nll_neg
+ )
+ losses[prefix + line_suffix + "assignment_nll"] = nll
+ if self.conf.loss.nll_weight > 0:
+ losses["total"] += nll * self.conf.loss.nll_weight * layer_weight
+
+ # Some statistics
+ if line_suffix == "":
+ losses[prefix + "num_matchable"] = num_pos
+ losses[prefix + "num_unmatchable"] = num_neg
+ losses[prefix + "sinkhorn_norm"] = (
+ log_assignment.exp()[:, :-1].sum(2).mean(1)
+ )
+ losses[prefix + "bin_score"] = bin_score[None]
+
+ return losses
+
+ def loss(self, pred, data):
+ losses = {"total": 0}
+ # If there are keypoints add their loss terms
+ if not (data["keypoints0"].shape[1] == 0 or data["keypoints1"].shape[1] == 0):
+ losses = self.sub_loss(pred, data, losses, self.bin_score, prefix="")
+
+ # If there are lines add their loss terms
+ if (
+ "lines0" in data
+ and "lines1" in data
+ and data["lines0"].shape[1] > 0
+ and data["lines1"].shape[1] > 0
+ ):
+ losses = self.sub_loss(
+ pred, data, losses, self.line_bin_score, prefix="line_"
+ )
+
+ if self.conf.inter_supervision:
+ for layer in self.conf.inter_supervision:
+ losses = self.sub_loss(
+ pred, data, losses, self.line_bin_score, prefix="line_", layer=layer
+ )
+
+ # Compute the metrics
+ metrics = {}
+ if not self.training:
+ if (
+ "matches0" in pred
+ and pred["matches0"].shape[1] > 0
+ and pred["matches1"].shape[1] > 0
+ ):
+ metrics = {**metrics, **matcher_metrics(pred, data, prefix="")}
+ if (
+ "line_matches0" in pred
+ and data["lines0"].shape[1] > 0
+ and data["lines1"].shape[1] > 0
+ ):
+ metrics = {**metrics, **matcher_metrics(pred, data, prefix="line_")}
+ if self.conf.inter_supervision:
+ for layer in self.conf.inter_supervision:
+ inter_metrics = matcher_metrics(
+ pred, data, prefix=f"line_{layer}_", prefix_gt="line_"
+ )
+ metrics = {**metrics, **inter_metrics}
+
+ return losses, metrics
+
+
+def MLP(channels, do_bn=True):
+ n = len(channels)
+ layers = []
+ for i in range(1, n):
+ layers.append(nn.Conv1d(channels[i - 1], channels[i], kernel_size=1, bias=True))
+ if i < (n - 1):
+ if do_bn:
+ layers.append(nn.BatchNorm1d(channels[i]))
+ layers.append(nn.ReLU())
+ return nn.Sequential(*layers)
+
+
+def normalize_keypoints(kpts, shape_or_size):
+ if isinstance(shape_or_size, (tuple, list)):
+ # it"s a shape
+ h, w = shape_or_size[-2:]
+ size = kpts.new_tensor([[w, h]])
+ else:
+ # it"s a size
+ assert isinstance(shape_or_size, torch.Tensor)
+ size = shape_or_size.to(kpts)
+ c = size / 2
+ f = size.max(1, keepdim=True).values * 0.7 # somehow we used 0.7 for SG
+ return (kpts - c[:, None, :]) / f[:, None, :]
+
+
+class KeypointEncoder(nn.Module):
+ def __init__(self, feature_dim, layers):
+ super().__init__()
+ self.encoder = MLP([3] + list(layers) + [feature_dim], do_bn=True)
+ nn.init.constant_(self.encoder[-1].bias, 0.0)
+
+ def forward(self, kpts, scores):
+ inputs = [kpts.transpose(1, 2), scores.unsqueeze(1)]
+ return self.encoder(torch.cat(inputs, dim=1))
+
+
+class EndPtEncoder(nn.Module):
+ def __init__(self, feature_dim, layers):
+ super().__init__()
+ self.encoder = MLP([5] + list(layers) + [feature_dim], do_bn=True)
+ nn.init.constant_(self.encoder[-1].bias, 0.0)
+
+ def forward(self, endpoints, scores):
+ # endpoints should be [B, N, 2, 2]
+ # output is [B, feature_dim, N * 2]
+ b_size, n_pts, _, _ = endpoints.shape
+ assert tuple(endpoints.shape[-2:]) == (2, 2)
+ endpt_offset = (endpoints[:, :, 1] - endpoints[:, :, 0]).unsqueeze(2)
+ endpt_offset = torch.cat([endpt_offset, -endpt_offset], dim=2)
+ endpt_offset = endpt_offset.reshape(b_size, 2 * n_pts, 2).transpose(1, 2)
+ inputs = [
+ endpoints.flatten(1, 2).transpose(1, 2),
+ endpt_offset,
+ scores.repeat(1, 2).unsqueeze(1),
+ ]
+ return self.encoder(torch.cat(inputs, dim=1))
+
+
+@torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
+def attention(query, key, value):
+ dim = query.shape[1]
+ scores = torch.einsum("bdhn,bdhm->bhnm", query, key) / dim**0.5
+ prob = torch.nn.functional.softmax(scores, dim=-1)
+ return torch.einsum("bhnm,bdhm->bdhn", prob, value), prob
+
+
+class MultiHeadedAttention(nn.Module):
+ def __init__(self, h, d_model):
+ super().__init__()
+ assert d_model % h == 0
+ self.dim = d_model // h
+ self.h = h
+ self.merge = nn.Conv1d(d_model, d_model, kernel_size=1)
+ self.proj = nn.ModuleList([deepcopy(self.merge) for _ in range(3)])
+ # self.prob = []
+
+ def forward(self, query, key, value):
+ b = query.size(0)
+ query, key, value = [
+ layer(x).view(b, self.dim, self.h, -1)
+ for layer, x in zip(self.proj, (query, key, value))
+ ]
+ x, prob = attention(query, key, value)
+ # self.prob.append(prob.mean(dim=1))
+ return self.merge(x.contiguous().view(b, self.dim * self.h, -1))
+
+
+class AttentionalPropagation(nn.Module):
+ def __init__(self, num_dim, num_heads, skip_init=False):
+ super().__init__()
+ self.attn = MultiHeadedAttention(num_heads, num_dim)
+ self.mlp = MLP([num_dim * 2, num_dim * 2, num_dim], do_bn=True)
+ nn.init.constant_(self.mlp[-1].bias, 0.0)
+ if skip_init:
+ self.register_parameter("scaling", nn.Parameter(torch.tensor(0.0)))
+ else:
+ self.scaling = 1.0
+
+ def forward(self, x, source):
+ message = self.attn(x, source, source)
+ return self.mlp(torch.cat([x, message], dim=1)) * self.scaling
+
+
+class GNNLayer(nn.Module):
+ def __init__(self, feature_dim, layer_type, skip_init):
+ super().__init__()
+ assert layer_type in ["cross", "self"]
+ self.type = layer_type
+ self.update = AttentionalPropagation(feature_dim, 4, skip_init)
+
+ def forward(self, desc0, desc1):
+ if self.type == "cross":
+ src0, src1 = desc1, desc0
+ elif self.type == "self":
+ src0, src1 = desc0, desc1
+ else:
+ raise ValueError("Unknown layer type: " + self.type)
+ # self.update.attn.prob = []
+ delta0, delta1 = self.update(desc0, src0), self.update(desc1, src1)
+ desc0, desc1 = (desc0 + delta0), (desc1 + delta1)
+ return desc0, desc1
+
+
+class LineLayer(nn.Module):
+ def __init__(self, feature_dim, line_attention=False):
+ super().__init__()
+ self.dim = feature_dim
+ self.mlp = MLP([self.dim * 3, self.dim * 2, self.dim], do_bn=True)
+ self.line_attention = line_attention
+ if line_attention:
+ self.proj_node = nn.Conv1d(self.dim, self.dim, kernel_size=1)
+ self.proj_neigh = nn.Conv1d(2 * self.dim, self.dim, kernel_size=1)
+
+ def get_endpoint_update(self, ldesc, line_enc, lines_junc_idx):
+ # ldesc is [bs, D, n_junc], line_enc [bs, D, n_lines * 2]
+ # and lines_junc_idx [bs, n_lines * 2]
+ # Create one message per line endpoint
+ b_size = lines_junc_idx.shape[0]
+ line_desc = torch.gather(
+ ldesc, 2, lines_junc_idx[:, None].repeat(1, self.dim, 1)
+ )
+ line_desc2 = line_desc.reshape(b_size, self.dim, -1, 2).flip([-1])
+ message = torch.cat(
+ [line_desc, line_desc2.flatten(2, 3).clone(), line_enc], dim=1
+ )
+ return self.mlp(message) # [b_size, D, n_lines * 2]
+
+ def get_endpoint_attention(self, ldesc, line_enc, lines_junc_idx):
+ # ldesc is [bs, D, n_junc], line_enc [bs, D, n_lines * 2]
+ # and lines_junc_idx [bs, n_lines * 2]
+ b_size = lines_junc_idx.shape[0]
+ expanded_lines_junc_idx = lines_junc_idx[:, None].repeat(1, self.dim, 1)
+
+ # Query: desc of the current node
+ query = self.proj_node(ldesc) # [b_size, D, n_junc]
+ query = torch.gather(query, 2, expanded_lines_junc_idx)
+ # query is [b_size, D, n_lines * 2]
+
+ # Key: combination of neighboring desc and line encodings
+ line_desc = torch.gather(ldesc, 2, expanded_lines_junc_idx)
+ line_desc2 = line_desc.reshape(b_size, self.dim, -1, 2).flip([-1])
+ key = self.proj_neigh(
+ torch.cat([line_desc2.flatten(2, 3).clone(), line_enc], dim=1)
+ ) # [b_size, D, n_lines * 2]
+
+ # Compute the attention weights with a custom softmax per junction
+ prob = (query * key).sum(dim=1) / self.dim**0.5 # [b_size, n_lines * 2]
+ prob = torch.exp(prob - prob.max())
+ denom = torch.zeros_like(ldesc[:, 0]).scatter_reduce_(
+ dim=1, index=lines_junc_idx, src=prob, reduce="sum", include_self=False
+ ) # [b_size, n_junc]
+ denom = torch.gather(denom, 1, lines_junc_idx) # [b_size, n_lines * 2]
+ prob = prob / (denom + ETH_EPS)
+ return prob # [b_size, n_lines * 2]
+
+ def forward(
+ self, ldesc0, ldesc1, line_enc0, line_enc1, lines_junc_idx0, lines_junc_idx1
+ ):
+ # Gather the endpoint updates
+ lupdate0 = self.get_endpoint_update(ldesc0, line_enc0, lines_junc_idx0)
+ lupdate1 = self.get_endpoint_update(ldesc1, line_enc1, lines_junc_idx1)
+
+ update0, update1 = torch.zeros_like(ldesc0), torch.zeros_like(ldesc1)
+ dim = ldesc0.shape[1]
+ if self.line_attention:
+ # Compute an attention for each neighbor and do a weighted average
+ prob0 = self.get_endpoint_attention(ldesc0, line_enc0, lines_junc_idx0)
+ lupdate0 = lupdate0 * prob0[:, None]
+ update0 = update0.scatter_reduce_(
+ dim=2,
+ index=lines_junc_idx0[:, None].repeat(1, dim, 1),
+ src=lupdate0,
+ reduce="sum",
+ include_self=False,
+ )
+ prob1 = self.get_endpoint_attention(ldesc1, line_enc1, lines_junc_idx1)
+ lupdate1 = lupdate1 * prob1[:, None]
+ update1 = update1.scatter_reduce_(
+ dim=2,
+ index=lines_junc_idx1[:, None].repeat(1, dim, 1),
+ src=lupdate1,
+ reduce="sum",
+ include_self=False,
+ )
+ else:
+ # Average the updates for each junction (requires torch > 1.12)
+ update0 = update0.scatter_reduce_(
+ dim=2,
+ index=lines_junc_idx0[:, None].repeat(1, dim, 1),
+ src=lupdate0,
+ reduce="mean",
+ include_self=False,
+ )
+ update1 = update1.scatter_reduce_(
+ dim=2,
+ index=lines_junc_idx1[:, None].repeat(1, dim, 1),
+ src=lupdate1,
+ reduce="mean",
+ include_self=False,
+ )
+
+ # Update
+ ldesc0 = ldesc0 + update0
+ ldesc1 = ldesc1 + update1
+
+ return ldesc0, ldesc1
+
+
+class AttentionalGNN(nn.Module):
+ def __init__(
+ self,
+ feature_dim,
+ layer_types,
+ checkpointed=False,
+ skip=False,
+ inter_supervision=None,
+ num_line_iterations=1,
+ line_attention=False,
+ ):
+ super().__init__()
+ self.checkpointed = checkpointed
+ self.inter_supervision = inter_supervision
+ self.num_line_iterations = num_line_iterations
+ self.inter_layers = {}
+ self.layers = nn.ModuleList(
+ [GNNLayer(feature_dim, layer_type, skip) for layer_type in layer_types]
+ )
+ self.line_layers = nn.ModuleList(
+ [
+ LineLayer(feature_dim, line_attention)
+ for _ in range(len(layer_types) // 2)
+ ]
+ )
+
+ def forward(
+ self, desc0, desc1, line_enc0, line_enc1, lines_junc_idx0, lines_junc_idx1
+ ):
+ for i, layer in enumerate(self.layers):
+ if self.checkpointed:
+ desc0, desc1 = torch.utils.checkpoint.checkpoint(
+ layer, desc0, desc1, preserve_rng_state=False
+ )
+ else:
+ desc0, desc1 = layer(desc0, desc1)
+ if (
+ layer.type == "self"
+ and lines_junc_idx0.shape[1] > 0
+ and lines_junc_idx1.shape[1] > 0
+ ):
+ # Add line self attention layers after every self layer
+ for _ in range(self.num_line_iterations):
+ if self.checkpointed:
+ desc0, desc1 = torch.utils.checkpoint.checkpoint(
+ self.line_layers[i // 2],
+ desc0,
+ desc1,
+ line_enc0,
+ line_enc1,
+ lines_junc_idx0,
+ lines_junc_idx1,
+ preserve_rng_state=False,
+ )
+ else:
+ desc0, desc1 = self.line_layers[i // 2](
+ desc0,
+ desc1,
+ line_enc0,
+ line_enc1,
+ lines_junc_idx0,
+ lines_junc_idx1,
+ )
+
+ # Optionally store the line descriptor at intermediate layers
+ if (
+ self.inter_supervision is not None
+ and (i // 2) in self.inter_supervision
+ and layer.type == "cross"
+ ):
+ self.inter_layers[i // 2] = (desc0.clone(), desc1.clone())
+ return desc0, desc1
+
+
+def log_double_softmax(scores, bin_score):
+ b, m, n = scores.shape
+ bin_ = bin_score[None, None, None]
+ scores0 = torch.cat([scores, bin_.expand(b, m, 1)], 2)
+ scores1 = torch.cat([scores, bin_.expand(b, 1, n)], 1)
+ scores0 = torch.nn.functional.log_softmax(scores0, 2)
+ scores1 = torch.nn.functional.log_softmax(scores1, 1)
+ scores = scores.new_full((b, m + 1, n + 1), 0)
+ scores[:, :m, :n] = (scores0[:, :, :n] + scores1[:, :m, :]) / 2
+ scores[:, :-1, -1] = scores0[:, :, -1]
+ scores[:, -1, :-1] = scores1[:, -1, :]
+ return scores
+
+
+def arange_like(x, dim):
+ return x.new_ones(x.shape[dim]).cumsum(0) - 1 # traceable in 1.1
diff --git a/third_party/gim/gim/gluefactory/models/matchers/homography_matcher.py b/third_party/gim/gim/gluefactory/models/matchers/homography_matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3642fb7b71797e8043dfeca0cdfda712dc2f25f
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/matchers/homography_matcher.py
@@ -0,0 +1,66 @@
+from ...geometry.gt_generation import (
+ gt_line_matches_from_homography,
+ gt_matches_from_homography,
+)
+from ..base_model import BaseModel
+
+
+class HomographyMatcher(BaseModel):
+ default_conf = {
+ # GT parameters for points
+ "use_points": True,
+ "th_positive": 3.0,
+ "th_negative": 3.0,
+ # GT parameters for lines
+ "use_lines": False,
+ "n_line_sampled_pts": 50,
+ "line_perp_dist_th": 5,
+ "overlap_th": 0.2,
+ "min_visibility_th": 0.5,
+ }
+
+ required_data_keys = ["H_0to1"]
+
+ def _init(self, conf):
+ # TODO (iago): Is this just boilerplate code?
+ if self.conf.use_points:
+ self.required_data_keys += ["keypoints0", "keypoints1"]
+ if self.conf.use_lines:
+ self.required_data_keys += [
+ "lines0",
+ "lines1",
+ "valid_lines0",
+ "valid_lines1",
+ ]
+
+ def _forward(self, data):
+ result = {}
+ if self.conf.use_points:
+ result = gt_matches_from_homography(
+ data["keypoints0"],
+ data["keypoints1"],
+ data["H_0to1"],
+ pos_th=self.conf.th_positive,
+ neg_th=self.conf.th_negative,
+ )
+ if self.conf.use_lines:
+ line_assignment, line_m0, line_m1 = gt_line_matches_from_homography(
+ data["lines0"],
+ data["lines1"],
+ data["valid_lines0"],
+ data["valid_lines1"],
+ data["view0"]["image"].shape,
+ data["view1"]["image"].shape,
+ data["H_0to1"],
+ self.conf.n_line_sampled_pts,
+ self.conf.line_perp_dist_th,
+ self.conf.overlap_th,
+ self.conf.min_visibility_th,
+ )
+ result["line_matches0"] = line_m0
+ result["line_matches1"] = line_m1
+ result["line_assignment"] = line_assignment
+ return result
+
+ def loss(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/matchers/kornia_loftr.py b/third_party/gim/gim/gluefactory/models/matchers/kornia_loftr.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fbd47b0c067d5f1c28bf720530c8e75247689db
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/matchers/kornia_loftr.py
@@ -0,0 +1,66 @@
+import kornia
+import torch
+
+from ...models import BaseModel
+
+
+class LoFTRModule(BaseModel):
+ default_conf = {
+ "topk": None,
+ "zero_pad": False,
+ }
+ required_data_keys = ["view0", "view1"]
+
+ def _init(self, conf):
+ self.net = kornia.feature.LoFTR(pretrained="outdoor")
+ self.set_initialized()
+
+ def _forward(self, data):
+ image0 = data["view0"]["image"]
+ image1 = data["view1"]["image"]
+ if self.conf.zero_pad:
+ image0, mask0 = self.zero_pad(image0)
+ image1, mask1 = self.zero_pad(image1)
+ res = self.net(
+ {"image0": image0, "image1": image1, "mask0": mask0, "mask1": mask1}
+ )
+ res = self.net({"image0": image0, "image1": image1})
+ else:
+ res = self.net({"image0": image0, "image1": image1})
+ topk = self.conf.topk
+ if topk is not None and res["confidence"].shape[-1] > topk:
+ _, top = torch.topk(res["confidence"], topk, -1)
+ m_kpts0 = res["keypoints0"][None][:, top]
+ m_kpts1 = res["keypoints1"][None][:, top]
+ scores = res["confidence"][None][:, top]
+ else:
+ m_kpts0 = res["keypoints0"][None]
+ m_kpts1 = res["keypoints1"][None]
+ scores = res["confidence"][None]
+
+ m0 = torch.arange(0, scores.shape[-1]).to(scores.device)[None]
+ m1 = torch.arange(0, scores.shape[-1]).to(scores.device)[None]
+ return {
+ "matches0": m0,
+ "matches1": m1,
+ "matching_scores0": scores,
+ "keypoints0": m_kpts0,
+ "keypoints1": m_kpts1,
+ "keypoint_scores0": scores,
+ "keypoint_scores1": scores,
+ "matching_scores1": scores,
+ }
+
+ def zero_pad(self, img):
+ b, c, h, w = img.shape
+ if h == w:
+ return img
+ s = max(h, w)
+ image = torch.zeros((b, c, s, s)).to(img)
+ image[:, :, :h, :w] = img
+ mask = torch.zeros_like(image)
+ mask[:, :, :h, :w] = 1.0
+ return image, mask.squeeze(0).float()
+
+ def loss(self, pred, data):
+ return NotImplementedError
diff --git a/imcui/third_party/dad/dad/detectors/third_party/lightglue/lightglue.py b/third_party/gim/gim/gluefactory/models/matchers/lightglue.py
similarity index 69%
rename from imcui/third_party/dad/dad/detectors/third_party/lightglue/lightglue.py
rename to third_party/gim/gim/gluefactory/models/matchers/lightglue.py
index 37c65adcef928ef8bdfb8a10bd2da1f6327430f6..f344871b964f5ab06719e054b737fbbd3accdf77 100644
--- a/imcui/third_party/dad/dad/detectors/third_party/lightglue/lightglue.py
+++ b/third_party/gim/gim/gluefactory/models/matchers/lightglue.py
@@ -1,27 +1,24 @@
import warnings
from pathlib import Path
-from types import SimpleNamespace
-from typing import Callable, List, Optional, Tuple
+from typing import Callable, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
+from omegaconf import OmegaConf
from torch import nn
+from torch.utils.checkpoint import checkpoint
-try:
- from flash_attn.modules.mha import FlashCrossAttention
-except ModuleNotFoundError:
- FlashCrossAttention = None
+from ...settings import DATA_PATH
+from ..utils.losses import NLLLoss
+from ..utils.metrics import matcher_metrics
-if FlashCrossAttention or hasattr(F, "scaled_dot_product_attention"):
- FLASH_AVAILABLE = True
-else:
- FLASH_AVAILABLE = False
+FLASH_AVAILABLE = hasattr(F, "scaled_dot_product_attention")
torch.backends.cudnn.deterministic = True
-@torch.amp.custom_fwd(device_type="cuda", cast_inputs=torch.float32)
+@torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
def normalize_keypoints(
kpts: torch.Tensor, size: Optional[torch.Tensor] = None
) -> torch.Tensor:
@@ -36,18 +33,6 @@ def normalize_keypoints(
return kpts
-def pad_to_length(x: torch.Tensor, length: int) -> Tuple[torch.Tensor]:
- if length <= x.shape[-2]:
- return x, torch.ones_like(x[..., :1], dtype=torch.bool)
- pad = torch.ones(
- *x.shape[:-2], length - x.shape[-2], x.shape[-1], device=x.device, dtype=x.dtype
- )
- y = torch.cat([x, pad], dim=-2)
- mask = torch.zeros(*y.shape[:-1], 1, dtype=torch.bool, device=x.device)
- mask[..., : x.shape[-2], :] = True
- return y, mask
-
-
def rotate_half(x: torch.Tensor) -> torch.Tensor:
x = x.unflatten(-1, (-1, 2))
x1, x2 = x.unbind(dim=-1)
@@ -78,6 +63,7 @@ class TokenConfidence(nn.Module):
def __init__(self, dim: int) -> None:
super().__init__()
self.token = nn.Sequential(nn.Linear(dim, 1), nn.Sigmoid())
+ self.loss_fn = nn.BCEWithLogitsLoss(reduction="none")
def forward(self, desc0: torch.Tensor, desc1: torch.Tensor):
"""get confidence tokens"""
@@ -86,6 +72,21 @@ class TokenConfidence(nn.Module):
self.token(desc1.detach()).squeeze(-1),
)
+ def loss(self, desc0, desc1, la_now, la_final):
+ logit0 = self.token[0](desc0.detach()).squeeze(-1)
+ logit1 = self.token[0](desc1.detach()).squeeze(-1)
+ la_now, la_final = la_now.detach(), la_final.detach()
+ correct0 = (
+ la_final[:, :-1, :].max(-1).indices == la_now[:, :-1, :].max(-1).indices
+ )
+ correct1 = (
+ la_final[:, :, :-1].max(-2).indices == la_now[:, :, :-1].max(-2).indices
+ )
+ return (
+ self.loss_fn(logit0, correct0.float()).mean(-1)
+ + self.loss_fn(logit1, correct1.float()).mean(-1)
+ ) / 2.0
+
class Attention(nn.Module):
def __init__(self, allow_flash: bool) -> None:
@@ -97,27 +98,18 @@ class Attention(nn.Module):
stacklevel=2,
)
self.enable_flash = allow_flash and FLASH_AVAILABLE
- self.has_sdp = hasattr(F, "scaled_dot_product_attention")
- if allow_flash and FlashCrossAttention:
- self.flash_ = FlashCrossAttention()
- if self.has_sdp:
+
+ if FLASH_AVAILABLE:
torch.backends.cuda.enable_flash_sdp(allow_flash)
def forward(self, q, k, v, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
- if q.shape[-2] == 0 or k.shape[-2] == 0:
- return q.new_zeros((*q.shape[:-1], v.shape[-1]))
if self.enable_flash and q.device.type == "cuda":
# use torch 2.0 scaled_dot_product_attention with flash
- if self.has_sdp:
+ if FLASH_AVAILABLE:
args = [x.half().contiguous() for x in [q, k, v]]
v = F.scaled_dot_product_attention(*args, attn_mask=mask).to(q.dtype)
return v if mask is None else v.nan_to_num()
- else:
- assert mask is None
- q, k, v = [x.transpose(-2, -3).contiguous() for x in [q, k, v]]
- m = self.flash_(q.half(), torch.stack([k, v], 2).half())
- return m.transpose(-2, -3).to(q.dtype).clone()
- elif self.has_sdp:
+ elif FLASH_AVAILABLE:
args = [x.contiguous() for x in [q, k, v]]
v = F.scaled_dot_product_attention(*args, attn_mask=mask)
return v if mask is None else v.nan_to_num()
@@ -315,69 +307,32 @@ class LightGlue(nn.Module):
default_conf = {
"name": "lightglue", # just for interfacing
"input_dim": 256, # input descriptor dimension (autoselected from weights)
- "descriptor_dim": 256,
"add_scale_ori": False,
+ "descriptor_dim": 256,
"n_layers": 9,
"num_heads": 4,
- "flash": True, # enable FlashAttention if available.
+ "flash": False, # enable FlashAttention if available.
"mp": False, # enable mixed precision
- "depth_confidence": 0.95, # early stopping, disable with -1
- "width_confidence": 0.99, # point pruning, disable with -1
- "filter_threshold": 0.1, # match threshold
- "weights": None,
- }
-
- # Point pruning involves an overhead (gather).
- # Therefore, we only activate it if there are enough keypoints.
- pruning_keypoint_thresholds = {
- "cpu": -1,
- "mps": -1,
- "cuda": 1024,
- "flash": 1536,
+ "depth_confidence": -1, # early stopping, disable with -1
+ "width_confidence": -1, # point pruning, disable with -1
+ "filter_threshold": 0.0, # match threshold
+ "checkpointed": False,
+ "weights": "superpoint_lightglue", # either a path or the name of pretrained weights (disk, ...)
+ "weights_from_version": "v0.1_arxiv",
+ "loss": {
+ "gamma": 1.0,
+ "fn": "nll",
+ "nll_balancing": 0.5,
+ },
}
- required_data_keys = ["image0", "image1"]
-
- version = "v0.1_arxiv"
- url = "https://github.com/cvg/LightGlue/releases/download/{}/{}_lightglue.pth"
+ required_data_keys = ["keypoints0", "keypoints1", "descriptors0", "descriptors1"]
- features = {
- "superpoint": {
- "weights": "superpoint_lightglue",
- "input_dim": 256,
- },
- "disk": {
- "weights": "disk_lightglue",
- "input_dim": 128,
- },
- "aliked": {
- "weights": "aliked_lightglue",
- "input_dim": 128,
- },
- "sift": {
- "weights": "sift_lightglue",
- "input_dim": 128,
- "add_scale_ori": True,
- },
- "doghardnet": {
- "weights": "doghardnet_lightglue",
- "input_dim": 128,
- "add_scale_ori": True,
- },
- }
+ url = "https://github.com/cvg/LightGlue/releases/download/{}/{}.pth"
- def __init__(self, features="superpoint", **conf) -> None:
+ def __init__(self, conf) -> None:
super().__init__()
- self.conf = conf = SimpleNamespace(**{**self.default_conf, **conf})
- if features is not None:
- if features not in self.features:
- raise ValueError(
- f"Unsupported features: {features} not in "
- f"{{{','.join(self.features)}}}"
- )
- for k, v in self.features[features].items():
- setattr(conf, k, v)
-
+ self.conf = conf = OmegaConf.merge(self.default_conf, conf)
if conf.input_dim != conf.descriptor_dim:
self.input_proj = nn.Linear(conf.input_dim, conf.descriptor_dim, bias=True)
else:
@@ -385,7 +340,7 @@ class LightGlue(nn.Module):
head_dim = conf.descriptor_dim // conf.num_heads
self.posenc = LearnableFourierPositionalEncoding(
- 2 + 2 * self.conf.add_scale_ori, head_dim, head_dim
+ 2 + 2 * conf.add_scale_ori, head_dim, head_dim
)
h, n, d = conf.num_heads, conf.n_layers, conf.descriptor_dim
@@ -398,24 +353,32 @@ class LightGlue(nn.Module):
self.token_confidence = nn.ModuleList(
[TokenConfidence(d) for _ in range(n - 1)]
)
- self.register_buffer(
- "confidence_thresholds",
- torch.Tensor(
- [self.confidence_threshold(i) for i in range(self.conf.n_layers)]
- ),
- )
+
+ self.loss_fn = NLLLoss(conf.loss)
state_dict = None
- if features is not None:
- fname = f"{conf.weights}_{self.version.replace('.', '-')}.pth"
- state_dict = torch.hub.load_state_dict_from_url(
- self.url.format(self.version, features), file_name=fname
- )
- self.load_state_dict(state_dict, strict=False)
- elif conf.weights is not None:
- path = Path(__file__).parent
- path = path / "weights/{}.pth".format(self.conf.weights)
- state_dict = torch.load(str(path), map_location="cpu")
+ if conf.weights is not None:
+ # weights can be either a path or an existing file from official LG
+ if Path(conf.weights).exists():
+ state_dict = torch.load(conf.weights, map_location="cpu")
+ elif (Path(DATA_PATH) / conf.weights).exists():
+ state_dict = torch.load(
+ str(DATA_PATH / conf.weights), map_location="cpu"
+ )
+ elif (Path('weights') / (conf.weights + '.pth')).exists():
+ state_dict = torch.load(
+ str(Path('weights') / (conf.weights + '.pth')), map_location="cpu"
+ )
+ print(f"Readed weights from {Path('weights') / (conf.weights + '.pth')}")
+ else:
+ fname = (
+ f"{conf.weights}_{conf.weights_from_version}".replace(".", "-")
+ + ".pth"
+ )
+ state_dict = torch.hub.load_state_dict_from_url(
+ self.url.format(conf.weights_from_version, conf.weights),
+ file_name=fname,
+ )
if state_dict:
# rename old state dict entries
@@ -425,92 +388,62 @@ class LightGlue(nn.Module):
pattern = f"cross_attn.{i}", f"transformers.{i}.cross_attn"
state_dict = {k.replace(*pattern): v for k, v in state_dict.items()}
self.load_state_dict(state_dict, strict=False)
+ print(f"Loaded weights from {conf.weights}")
- # static lengths LightGlue is compiled for (only used with torch.compile)
- self.static_lengths = None
-
- def compile(
- self, mode="reduce-overhead", static_lengths=[256, 512, 768, 1024, 1280, 1536]
- ):
+ def compile(self, mode="reduce-overhead"):
if self.conf.width_confidence != -1:
warnings.warn(
"Point pruning is partially disabled for compiled forward.",
stacklevel=2,
)
- torch._inductor.cudagraph_mark_step_begin()
for i in range(self.conf.n_layers):
- self.transformers[i].masked_forward = torch.compile(
- self.transformers[i].masked_forward, mode=mode, fullgraph=True
+ self.transformers[i] = torch.compile(
+ self.transformers[i], mode=mode, fullgraph=True
)
- self.static_lengths = static_lengths
-
def forward(self, data: dict) -> dict:
- """
- Match keypoints and descriptors between two images
-
- Input (dict):
- image0: dict
- keypoints: [B x M x 2]
- descriptors: [B x M x D]
- image: [B x C x H x W] or image_size: [B x 2]
- image1: dict
- keypoints: [B x N x 2]
- descriptors: [B x N x D]
- image: [B x C x H x W] or image_size: [B x 2]
- Output (dict):
- matches0: [B x M]
- matching_scores0: [B x M]
- matches1: [B x N]
- matching_scores1: [B x N]
- matches: List[[Si x 2]]
- scores: List[[Si]]
- stop: int
- prune0: [B x M]
- prune1: [B x N]
- """
- with torch.autocast(enabled=self.conf.mp, device_type="cuda"):
- return self._forward(data)
-
- def _forward(self, data: dict) -> dict:
for key in self.required_data_keys:
assert key in data, f"Missing key {key} in data"
- data0, data1 = data["image0"], data["image1"]
- kpts0, kpts1 = data0["keypoints"], data1["keypoints"]
+
+ kpts0, kpts1 = data["keypoints0"], data["keypoints1"]
b, m, _ = kpts0.shape
b, n, _ = kpts1.shape
device = kpts0.device
- size0, size1 = data0.get("image_size"), data1.get("image_size")
+ # if "view0" in data.keys() and "view1" in data.keys():
+ size0 = data["resize0"][:, [1, 0]]
+ size1 = data["resize1"][:, [1, 0]]
kpts0 = normalize_keypoints(kpts0, size0).clone()
kpts1 = normalize_keypoints(kpts1, size1).clone()
if self.conf.add_scale_ori:
+ sc0, o0 = data["scales0"], data["oris0"]
+ sc1, o1 = data["scales1"], data["oris1"]
kpts0 = torch.cat(
- [kpts0] + [data0[k].unsqueeze(-1) for k in ("scales", "oris")], -1
+ [
+ kpts0,
+ sc0 if sc0.dim() == 3 else sc0[..., None],
+ o0 if o0.dim() == 3 else o0[..., None],
+ ],
+ -1,
)
kpts1 = torch.cat(
- [kpts1] + [data1[k].unsqueeze(-1) for k in ("scales", "oris")], -1
+ [
+ kpts1,
+ sc1 if sc1.dim() == 3 else sc1[..., None],
+ o1 if o1.dim() == 3 else o1[..., None],
+ ],
+ -1,
)
- desc0 = data0["descriptors"].detach().contiguous()
- desc1 = data1["descriptors"].detach().contiguous()
+
+ desc0 = data["descriptors0"].contiguous()
+ desc1 = data["descriptors1"].contiguous()
assert desc0.shape[-1] == self.conf.input_dim
assert desc1.shape[-1] == self.conf.input_dim
-
if torch.is_autocast_enabled():
desc0 = desc0.half()
desc1 = desc1.half()
-
- mask0, mask1 = None, None
- c = max(m, n)
- do_compile = self.static_lengths and c <= max(self.static_lengths)
- if do_compile:
- kn = min([k for k in self.static_lengths if k >= c])
- desc0, mask0 = pad_to_length(desc0, kn)
- desc1, mask1 = pad_to_length(desc1, kn)
- kpts0, _ = pad_to_length(kpts0, kn)
- kpts1, _ = pad_to_length(kpts1, kn)
desc0 = self.input_proj(desc0)
desc1 = self.input_proj(desc1)
# cache positional embeddings
@@ -518,9 +451,11 @@ class LightGlue(nn.Module):
encoding1 = self.posenc(kpts1)
# GNN + final_proj + assignment
- do_early_stop = self.conf.depth_confidence > 0
- do_point_pruning = self.conf.width_confidence > 0 and not do_compile
- pruning_th = self.pruning_min_kpts(device)
+ do_early_stop = self.conf.depth_confidence > 0 and not self.training
+ do_point_pruning = self.conf.width_confidence > 0 and not self.training
+
+ all_desc0, all_desc1 = [], []
+
if do_point_pruning:
ind0 = torch.arange(0, m, device=device)[None]
ind1 = torch.arange(0, n, device=device)[None]
@@ -529,19 +464,25 @@ class LightGlue(nn.Module):
prune1 = torch.ones_like(ind1)
token0, token1 = None, None
for i in range(self.conf.n_layers):
- if desc0.shape[1] == 0 or desc1.shape[1] == 0: # no keypoints
- break
- desc0, desc1 = self.transformers[i](
- desc0, desc1, encoding0, encoding1, mask0=mask0, mask1=mask1
- )
- if i == self.conf.n_layers - 1:
+ if self.conf.checkpointed and self.training:
+ desc0, desc1 = checkpoint(
+ self.transformers[i], desc0, desc1, encoding0, encoding1
+ )
+ else:
+ desc0, desc1 = self.transformers[i](desc0, desc1, encoding0, encoding1)
+ if self.training or i == self.conf.n_layers - 1:
+ all_desc0.append(desc0)
+ all_desc1.append(desc1)
continue # no early stopping or adaptive width at last layer
+ # only for eval
if do_early_stop:
+ assert b == 1
token0, token1 = self.token_confidence[i](desc0, desc1)
- if self.check_if_stop(token0[..., :m], token1[..., :n], i, m + n):
+ if self.check_if_stop(token0[..., :m, :], token1[..., :n, :], i, m + n):
break
- if do_point_pruning and desc0.shape[-2] > pruning_th:
+ if do_point_pruning:
+ assert b == 1
scores0 = self.log_assignment[i].get_matchability(desc0)
prunemask0 = self.get_pruning_mask(token0, scores0, i)
keep0 = torch.where(prunemask0)[1]
@@ -549,7 +490,6 @@ class LightGlue(nn.Module):
desc0 = desc0.index_select(1, keep0)
encoding0 = encoding0.index_select(-2, keep0)
prune0[:, ind0] += 1
- if do_point_pruning and desc1.shape[-2] > pruning_th:
scores1 = self.log_assignment[i].get_matchability(desc1)
prunemask1 = self.get_pruning_mask(token1, scores1, i)
keep1 = torch.where(prunemask1)[1]
@@ -558,33 +498,12 @@ class LightGlue(nn.Module):
encoding1 = encoding1.index_select(-2, keep1)
prune1[:, ind1] += 1
- if desc0.shape[1] == 0 or desc1.shape[1] == 0: # no keypoints
- m0 = desc0.new_full((b, m), -1, dtype=torch.long)
- m1 = desc1.new_full((b, n), -1, dtype=torch.long)
- mscores0 = desc0.new_zeros((b, m))
- mscores1 = desc1.new_zeros((b, n))
- matches = desc0.new_empty((b, 0, 2), dtype=torch.long)
- mscores = desc0.new_empty((b, 0))
- if not do_point_pruning:
- prune0 = torch.ones_like(mscores0) * self.conf.n_layers
- prune1 = torch.ones_like(mscores1) * self.conf.n_layers
- return {
- "matches0": m0,
- "matches1": m1,
- "matching_scores0": mscores0,
- "matching_scores1": mscores1,
- "stop": i + 1,
- "matches": matches,
- "scores": mscores,
- "prune0": prune0,
- "prune1": prune1,
- }
-
- desc0, desc1 = desc0[..., :m, :], desc1[..., :n, :] # remove padding
+ desc0, desc1 = desc0[..., :m, :], desc1[..., :n, :]
scores, _ = self.log_assignment[i](desc0, desc1)
m0, m1, mscores0, mscores1 = filter_matches(scores, self.conf.filter_threshold)
matches, mscores = [], []
for k in range(b):
+ if self.training: break
valid = m0[k] > -1
m_indices_0 = torch.where(valid)[0]
m_indices_1 = m0[k][valid]
@@ -594,7 +513,6 @@ class LightGlue(nn.Module):
matches.append(torch.stack([m_indices_0, m_indices_1], -1))
mscores.append(mscores0[k][valid])
- # TODO: Remove when hloc switches to the compact format.
if do_point_pruning:
m0_ = torch.full((b, m), -1, device=m0.device, dtype=m0.dtype)
m1_ = torch.full((b, n), -1, device=m1.device, dtype=m1.dtype)
@@ -609,11 +527,14 @@ class LightGlue(nn.Module):
prune0 = torch.ones_like(mscores0) * self.conf.n_layers
prune1 = torch.ones_like(mscores1) * self.conf.n_layers
- return {
+ pred = {
"matches0": m0,
"matches1": m1,
"matching_scores0": mscores0,
"matching_scores1": mscores1,
+ "ref_descriptors0": torch.stack(all_desc0, 1),
+ "ref_descriptors1": torch.stack(all_desc1, 1),
+ "log_assignment": scores,
"stop": i + 1,
"matches": matches,
"scores": mscores,
@@ -621,6 +542,8 @@ class LightGlue(nn.Module):
"prune1": prune1,
}
+ return pred
+
def confidence_threshold(self, layer_index: int) -> float:
"""scaled confidence threshold"""
threshold = 0.8 + 0.1 * np.exp(-4.0 * layer_index / self.conf.n_layers)
@@ -653,3 +576,57 @@ class LightGlue(nn.Module):
return self.pruning_keypoint_thresholds["flash"]
else:
return self.pruning_keypoint_thresholds[device.type]
+
+ def loss(self, pred, data):
+ def loss_params(pred, i):
+ la, _ = self.log_assignment[i](
+ pred["ref_descriptors0"][:, i], pred["ref_descriptors1"][:, i]
+ )
+ return {
+ "log_assignment": la,
+ }
+
+ sum_weights = 1.0
+ nll, gt_weights, loss_metrics = self.loss_fn(loss_params(pred, -1), data)
+ N = pred["ref_descriptors0"].shape[1]
+ losses = {"total": nll, "last": nll.clone().detach(), **loss_metrics}
+
+ if self.training:
+ losses["confidence"] = 0.0
+
+ # B = pred['log_assignment'].shape[0]
+ losses["row_norm"] = pred["log_assignment"].exp()[:, :-1].sum(2).mean(1)
+ for i in range(N - 1):
+ params_i = loss_params(pred, i)
+ nll, _, _ = self.loss_fn(params_i, data, weights=gt_weights)
+
+ if self.conf.loss.gamma > 0.0:
+ weight = self.conf.loss.gamma ** (N - i - 1)
+ else:
+ weight = i + 1
+ sum_weights += weight
+ losses["total"] = losses["total"] + nll * weight
+
+ losses["confidence"] += self.token_confidence[i].loss(
+ pred["ref_descriptors0"][:, i],
+ pred["ref_descriptors1"][:, i],
+ params_i["log_assignment"],
+ pred["log_assignment"],
+ ) / (N - 1)
+
+ del params_i
+ losses["total"] /= sum_weights
+
+ # confidences
+ if self.training:
+ losses["total"] = losses["total"] + losses["confidence"]
+
+ if not self.training:
+ # add metrics
+ metrics = matcher_metrics(pred, data)
+ else:
+ metrics = {}
+ return losses, metrics
+
+
+__main_model__ = LightGlue
diff --git a/third_party/gim/gim/gluefactory/models/matchers/lightglue_pretrained.py b/third_party/gim/gim/gluefactory/models/matchers/lightglue_pretrained.py
new file mode 100644
index 0000000000000000000000000000000000000000..275a9d54f64bb2e11991d4335dac23b7fb755f5e
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/matchers/lightglue_pretrained.py
@@ -0,0 +1,36 @@
+from lightglue import LightGlue as LightGlue_
+from omegaconf import OmegaConf
+
+from ..base_model import BaseModel
+
+
+class LightGlue(BaseModel):
+ default_conf = {"features": "superpoint", **LightGlue_.default_conf}
+ required_data_keys = [
+ "view0",
+ "keypoints0",
+ "descriptors0",
+ "view1",
+ "keypoints1",
+ "descriptors1",
+ ]
+
+ def _init(self, conf):
+ dconf = OmegaConf.to_container(conf)
+ self.net = LightGlue_(dconf.pop("features"), **dconf)
+ self.set_initialized()
+
+ def _forward(self, data):
+ required_keys = ["keypoints", "descriptors", "scales", "oris"]
+ view0 = {
+ **data["view0"],
+ **{k: data[k + "0"] for k in required_keys if (k + "0") in data},
+ }
+ view1 = {
+ **data["view1"],
+ **{k: data[k + "1"] for k in required_keys if (k + "1") in data},
+ }
+ return self.net({"image0": view0, "image1": view1})
+
+ def loss(pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/models/matchers/nearest_neighbor_matcher.py b/third_party/gim/gim/gluefactory/models/matchers/nearest_neighbor_matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bbc8ae5392abcb3e39ca768221fc9ba22ce20e9
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/matchers/nearest_neighbor_matcher.py
@@ -0,0 +1,97 @@
+"""
+Nearest neighbor matcher for normalized descriptors.
+Optionally apply the mutual check and threshold the distance or ratio.
+"""
+
+import logging
+
+import torch
+import torch.nn.functional as F
+
+from ..base_model import BaseModel
+from ..utils.metrics import matcher_metrics
+
+
+@torch.no_grad()
+def find_nn(sim, ratio_thresh, distance_thresh):
+ sim_nn, ind_nn = sim.topk(2 if ratio_thresh else 1, dim=-1, largest=True)
+ dist_nn = 2 * (1 - sim_nn)
+ mask = torch.ones(ind_nn.shape[:-1], dtype=torch.bool, device=sim.device)
+ if ratio_thresh:
+ mask = mask & (dist_nn[..., 0] <= (ratio_thresh**2) * dist_nn[..., 1])
+ if distance_thresh:
+ mask = mask & (dist_nn[..., 0] <= distance_thresh**2)
+ matches = torch.where(mask, ind_nn[..., 0], ind_nn.new_tensor(-1))
+ return matches
+
+
+def mutual_check(m0, m1):
+ inds0 = torch.arange(m0.shape[-1], device=m0.device)
+ inds1 = torch.arange(m1.shape[-1], device=m1.device)
+ loop0 = torch.gather(m1, -1, torch.where(m0 > -1, m0, m0.new_tensor(0)))
+ loop1 = torch.gather(m0, -1, torch.where(m1 > -1, m1, m1.new_tensor(0)))
+ m0_new = torch.where((m0 > -1) & (inds0 == loop0), m0, m0.new_tensor(-1))
+ m1_new = torch.where((m1 > -1) & (inds1 == loop1), m1, m1.new_tensor(-1))
+ return m0_new, m1_new
+
+
+class NearestNeighborMatcher(BaseModel):
+ default_conf = {
+ "ratio_thresh": None,
+ "distance_thresh": None,
+ "mutual_check": True,
+ "loss": None,
+ }
+ required_data_keys = ["descriptors0", "descriptors1"]
+
+ def _init(self, conf):
+ if conf.loss == "N_pair":
+ temperature = torch.nn.Parameter(torch.tensor(1.0))
+ self.register_parameter("temperature", temperature)
+
+ def _forward(self, data):
+ sim = torch.einsum("bnd,bmd->bnm", data["descriptors0"], data["descriptors1"])
+ matches0 = find_nn(sim, self.conf.ratio_thresh, self.conf.distance_thresh)
+ matches1 = find_nn(
+ sim.transpose(1, 2), self.conf.ratio_thresh, self.conf.distance_thresh
+ )
+ if self.conf.mutual_check:
+ matches0, matches1 = mutual_check(matches0, matches1)
+ b, m, n = sim.shape
+ la = sim.new_zeros(b, m + 1, n + 1)
+ la[:, :-1, :-1] = F.log_softmax(sim, -1) + F.log_softmax(sim, -2)
+ mscores0 = (matches0 > -1).float()
+ mscores1 = (matches1 > -1).float()
+ return {
+ "matches0": matches0,
+ "matches1": matches1,
+ "matching_scores0": mscores0,
+ "matching_scores1": mscores1,
+ "similarity": sim,
+ "log_assignment": la,
+ }
+
+ def loss(self, pred, data):
+ losses = {}
+ if self.conf.loss == "N_pair":
+ sim = pred["similarity"]
+ if torch.any(sim > (1.0 + 1e-6)):
+ logging.warning(f"Similarity larger than 1, max={sim.max()}")
+ scores = torch.sqrt(torch.clamp(2 * (1 - sim), min=1e-6))
+ scores = self.temperature * (2 - scores)
+ assert not torch.any(torch.isnan(scores)), torch.any(torch.isnan(sim))
+ prob0 = torch.nn.functional.log_softmax(scores, 2)
+ prob1 = torch.nn.functional.log_softmax(scores, 1)
+
+ assignment = data["gt_assignment"].float()
+ num = torch.max(assignment.sum((1, 2)), assignment.new_tensor(1))
+ nll0 = (prob0 * assignment).sum((1, 2)) / num
+ nll1 = (prob1 * assignment).sum((1, 2)) / num
+ nll = -(nll0 + nll1) / 2
+ losses["n_pair_nll"] = losses["total"] = nll
+ losses["num_matchable"] = num
+ losses["n_pair_temperature"] = self.temperature[None]
+ else:
+ raise NotImplementedError
+ metrics = {} if self.training else matcher_metrics(pred, data)
+ return losses, metrics
diff --git a/third_party/gim/gim/gluefactory/models/triplet_pipeline.py b/third_party/gim/gim/gluefactory/models/triplet_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..253851776976af8ecfb7118388c96bcf3f3d8681
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/triplet_pipeline.py
@@ -0,0 +1,99 @@
+"""
+A two-view sparse feature matching pipeline on triplets.
+
+If a triplet is found, runs the extractor on three images and
+then runs matcher/filter/solver for all three pairs.
+
+Losses and metrics get accumulated accordingly.
+
+If no triplet is found, this falls back to two_view_pipeline.py
+"""
+
+import torch
+
+from ..utils.misc import get_twoview, stack_twoviews, unstack_twoviews
+from .two_view_pipeline import TwoViewPipeline
+
+
+def has_triplet(data):
+ # we already check for image0 and image1 in required_keys
+ return "view2" in data.keys()
+
+
+class TripletPipeline(TwoViewPipeline):
+ default_conf = {"batch_triplets": True, **TwoViewPipeline.default_conf}
+
+ def _forward(self, data):
+ if not has_triplet(data):
+ return super()._forward(data)
+ # the two-view outputs are stored in
+ # pred['0to1'],pred['0to2'], pred['1to2']
+
+ assert not self.conf.run_gt_in_forward
+ pred0 = self.extract_view(data, "0")
+ pred1 = self.extract_view(data, "1")
+ pred2 = self.extract_view(data, "2")
+
+ pred = {}
+ pred = {
+ **{k + "0": v for k, v in pred0.items()},
+ **{k + "1": v for k, v in pred1.items()},
+ **{k + "2": v for k, v in pred2.items()},
+ }
+
+ def predict_twoview(pred, data):
+ # forward pass
+ if self.conf.matcher.name:
+ pred = {**pred, **self.matcher({**data, **pred})}
+
+ if self.conf.filter.name:
+ pred = {**pred, **self.filter({**m_data, **pred})}
+
+ if self.conf.solver.name:
+ pred = {**pred, **self.solver({**m_data, **pred})}
+ return pred
+
+ if self.conf.batch_triplets:
+ B = data["image1"].shape[0]
+ # stack on batch dimension
+ m_data = stack_twoviews(data)
+ m_pred = stack_twoviews(pred)
+
+ # forward pass
+ m_pred = predict_twoview(m_pred, m_data)
+
+ # unstack
+ pred = {**pred, **unstack_twoviews(m_pred, B)}
+ else:
+ for idx in ["0to1", "0to2", "1to2"]:
+ m_data = get_twoview(data, idx)
+ m_pred = get_twoview(pred, idx)
+ pred[idx] = predict_twoview(m_pred, m_data)
+ return pred
+
+ def loss(self, pred, data):
+ if not has_triplet(data):
+ return super().loss(pred, data)
+ if self.conf.batch_triplets:
+ m_data = stack_twoviews(data)
+ m_pred = stack_twoviews(pred)
+ losses, metrics = super().loss(m_pred, m_data)
+ else:
+ losses = {}
+ metrics = {}
+ for idx in ["0to1", "0to2", "1to2"]:
+ data_i = get_twoview(data, idx)
+ pred_i = pred[idx]
+ losses_i, metrics_i = super().loss(pred_i, data_i)
+ for k, v in losses_i.items():
+ if k in losses.keys():
+ losses[k] = losses[k] + v
+ else:
+ losses[k] = v
+ for k, v in metrics_i.items():
+ if k in metrics.keys():
+ metrics[k] = torch.cat([metrics[k], v], 0)
+ else:
+ metrics[k] = v
+
+ return losses, metrics
diff --git a/third_party/gim/gim/gluefactory/models/two_view_pipeline.py b/third_party/gim/gim/gluefactory/models/two_view_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c517dc74668de58f9467f6b76eeebb092dafe77
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/two_view_pipeline.py
@@ -0,0 +1,114 @@
+"""
+A two-view sparse feature matching pipeline.
+
+This model contains sub-models for each step:
+ feature extraction, feature matching, outlier filtering, pose estimation.
+Each step is optional, and the features or matches can be provided as input.
+Default: SuperPoint with nearest neighbor matching.
+
+Convention for the matches: m0[i] is the index of the keypoint in image 1
+that corresponds to the keypoint i in image 0. m0[i] = -1 if i is unmatched.
+"""
+
+from omegaconf import OmegaConf
+
+from . import get_model
+from .base_model import BaseModel
+
+to_ctr = OmegaConf.to_container # convert DictConfig to dict
+
+
+class TwoViewPipeline(BaseModel):
+ default_conf = {
+ "extractor": {
+ "name": None,
+ "trainable": False,
+ },
+ "matcher": {"name": None},
+ "filter": {"name": None},
+ "solver": {"name": None},
+ "ground_truth": {"name": None},
+ "allow_no_extract": False,
+ "run_gt_in_forward": False,
+ }
+ required_data_keys = ["view0", "view1"]
+ strict_conf = False # need to pass new confs to children models
+ components = [
+ "extractor",
+ "matcher",
+ "filter",
+ "solver",
+ "ground_truth",
+ ]
+
+ def _init(self, conf):
+ if conf.extractor.name:
+ self.extractor = get_model(conf.extractor.name)(to_ctr(conf.extractor))
+
+ if conf.matcher.name:
+ self.matcher = get_model(conf.matcher.name)(to_ctr(conf.matcher))
+
+ if conf.filter.name:
+ self.filter = get_model(conf.filter.name)(to_ctr(conf.filter))
+
+ if conf.solver.name:
+ self.solver = get_model(conf.solver.name)(to_ctr(conf.solver))
+
+ if conf.ground_truth.name:
+ self.ground_truth = get_model(conf.ground_truth.name)(
+ to_ctr(conf.ground_truth)
+ )
+
+ def extract_view(self, data, i):
+ data_i = data[f"view{i}"]
+ pred_i = data_i.get("cache", {})
+ skip_extract = len(pred_i) > 0 and self.conf.allow_no_extract
+ if self.conf.extractor.name and not skip_extract:
+ pred_i = {**pred_i, **self.extractor(data_i)}
+ elif self.conf.extractor.name and not self.conf.allow_no_extract:
+ pred_i = {**pred_i, **self.extractor({**data_i, **pred_i})}
+ return pred_i
+
+ def _forward(self, data):
+ pred0 = self.extract_view(data, "0")
+ pred1 = self.extract_view(data, "1")
+ pred = {
+ **{k + "0": v for k, v in pred0.items()},
+ **{k + "1": v for k, v in pred1.items()},
+ }
+
+ if self.conf.matcher.name:
+ pred = {**pred, **self.matcher({**data, **pred})}
+ if self.conf.filter.name:
+ pred = {**pred, **self.filter({**data, **pred})}
+ if self.conf.solver.name:
+ pred = {**pred, **self.solver({**data, **pred})}
+
+ if self.conf.ground_truth.name and self.conf.run_gt_in_forward:
+ gt_pred = self.ground_truth({**data, **pred})
+ pred.update({f"gt_{k}": v for k, v in gt_pred.items()})
+ return pred
+
+ def loss(self, pred, data):
+ losses = {}
+ metrics = {}
+ total = 0
+
+ # get labels
+ if self.conf.ground_truth.name and not self.conf.run_gt_in_forward:
+ gt_pred = self.ground_truth({**data, **pred})
+ pred.update({f"gt_{k}": v for k, v in gt_pred.items()})
+
+ for k in self.components:
+ apply = True
+ if "apply_loss" in self.conf[k].keys():
+ apply = self.conf[k].apply_loss
+ if self.conf[k].name and apply:
+ try:
+ losses_, metrics_ = getattr(self, k).loss(pred, {**pred, **data})
+ except NotImplementedError:
+ continue
+ losses = {**losses, **losses_}
+ metrics = {**metrics, **metrics_}
+ total = losses_["total"] + total
+ return {**losses, "total": total}, metrics
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/__init__.py b/third_party/gim/gim/gluefactory/models/utils/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/__init__.py
rename to third_party/gim/gim/gluefactory/models/utils/__init__.py
diff --git a/third_party/gim/gim/gluefactory/models/utils/losses.py b/third_party/gim/gim/gluefactory/models/utils/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..06c7958b4f61e55dcd16b5ba0c0b5e6919377fd9
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/utils/losses.py
@@ -0,0 +1,73 @@
+import torch
+import torch.nn as nn
+from omegaconf import OmegaConf
+
+
+def weight_loss(log_assignment, weights, gamma=0.0):
+ b, m, n = log_assignment.shape
+ m -= 1
+ n -= 1
+
+ loss_sc = log_assignment * weights
+
+ num_neg0 = weights[:, :m, -1].sum(-1).clamp(min=1.0)
+ num_neg1 = weights[:, -1, :n].sum(-1).clamp(min=1.0)
+ num_pos = weights[:, :m, :n].sum((-1, -2)).clamp(min=1.0)
+
+ nll_pos = -loss_sc[:, :m, :n].sum((-1, -2))
+ nll_pos /= num_pos.clamp(min=1.0)
+
+ nll_neg0 = -loss_sc[:, :m, -1].sum(-1)
+ nll_neg1 = -loss_sc[:, -1, :n].sum(-1)
+
+ nll_neg = (nll_neg0 + nll_neg1) / (num_neg0 + num_neg1)
+
+ return nll_pos, nll_neg, num_pos, (num_neg0 + num_neg1) / 2.0
+
+
+class NLLLoss(nn.Module):
+ default_conf = {
+ "nll_balancing": 0.5,
+ "gamma_f": 0.0, # focal loss
+ }
+
+ def __init__(self, conf):
+ super().__init__()
+ self.conf = OmegaConf.merge(self.default_conf, conf)
+ self.loss_fn = self.nll_loss
+
+ def forward(self, pred, data, weights=None):
+ log_assignment = pred["log_assignment"]
+ if weights is None:
+ weights = self.loss_fn(log_assignment, data)
+ nll_pos, nll_neg, num_pos, num_neg = weight_loss(
+ log_assignment, weights, gamma=self.conf.gamma_f
+ )
+ nll = (
+ self.conf.nll_balancing * nll_pos + (1 - self.conf.nll_balancing) * nll_neg
+ )
+
+ return (
+ nll,
+ weights,
+ {
+ "assignment_nll": nll,
+ "nll_pos": nll_pos,
+ "nll_neg": nll_neg,
+ "num_matchable": num_pos,
+ "num_unmatchable": num_neg,
+ },
+ )
+
+ def nll_loss(self, log_assignment, data):
+ m, n = data["gt_matches0"].size(-1), data["gt_matches1"].size(-1)
+ positive = data["gt_assignment"].float()
+ neg0 = (data["gt_matches0"] == -1).float()
+ neg1 = (data["gt_matches1"] == -1).float()
+
+ weights = torch.zeros_like(log_assignment)
+ weights[:, :m, :n] = positive
+
+ weights[:, :m, -1] = neg0
+ weights[:, -1, :n] = neg1
+ return weights
diff --git a/third_party/gim/gim/gluefactory/models/utils/metrics.py b/third_party/gim/gim/gluefactory/models/utils/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f2a4c1ae4ade58fe7c92e7ecc513d5ac8672c47
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/models/utils/metrics.py
@@ -0,0 +1,50 @@
+import torch
+
+
+@torch.no_grad()
+def matcher_metrics(pred, data, prefix="", prefix_gt=None):
+ def recall(m, gt_m):
+ mask = (gt_m > -1).float()
+ return ((m == gt_m) * mask).sum(1) / (1e-8 + mask.sum(1))
+
+ def accuracy(m, gt_m):
+ mask = (gt_m >= -1).float()
+ return ((m == gt_m) * mask).sum(1) / (1e-8 + mask.sum(1))
+
+ def precision(m, gt_m):
+ mask = ((m > -1) & (gt_m >= -1)).float()
+ return ((m == gt_m) * mask).sum(1) / (1e-8 + mask.sum(1))
+
+ def ranking_ap(m, gt_m, scores):
+ p_mask = ((m > -1) & (gt_m >= -1)).float()
+ r_mask = (gt_m > -1).float()
+ sort_ind = torch.argsort(-scores)
+ sorted_p_mask = torch.gather(p_mask, -1, sort_ind)
+ sorted_r_mask = torch.gather(r_mask, -1, sort_ind)
+ sorted_tp = torch.gather(m == gt_m, -1, sort_ind)
+ p_pts = torch.cumsum(sorted_tp * sorted_p_mask, -1) / (
+ 1e-8 + torch.cumsum(sorted_p_mask, -1)
+ )
+ r_pts = torch.cumsum(sorted_tp * sorted_r_mask, -1) / (
+ 1e-8 + sorted_r_mask.sum(-1)[:, None]
+ )
+ r_pts_diff = r_pts[..., 1:] - r_pts[..., :-1]
+ return torch.sum(r_pts_diff * p_pts[:, None, -1], dim=-1)
+
+ if prefix_gt is None:
+ prefix_gt = prefix
+ rec = recall(pred[f"{prefix}matches0"], data[f"gt_{prefix_gt}matches0"])
+ prec = precision(pred[f"{prefix}matches0"], data[f"gt_{prefix_gt}matches0"])
+ acc = accuracy(pred[f"{prefix}matches0"], data[f"gt_{prefix_gt}matches0"])
+ ap = ranking_ap(
+ pred[f"{prefix}matches0"],
+ data[f"gt_{prefix_gt}matches0"],
+ pred[f"{prefix}matching_scores0"],
+ )
+ metrics = {
+ f"{prefix}match_recall": rec,
+ f"{prefix}match_precision": prec,
+ f"{prefix}accuracy": acc,
+ f"{prefix}average_precision": ap,
+ }
+ return metrics
diff --git a/imcui/third_party/gim/networks/lightglue/models/utils/misc.py b/third_party/gim/gim/gluefactory/models/utils/misc.py
similarity index 100%
rename from imcui/third_party/gim/networks/lightglue/models/utils/misc.py
rename to third_party/gim/gim/gluefactory/models/utils/misc.py
diff --git a/third_party/gim/gim/gluefactory/robust_estimators/__init__.py b/third_party/gim/gim/gluefactory/robust_estimators/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9d9c9b978d7f2e0563b5f7301787f88ec1e7c6c
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/robust_estimators/__init__.py
@@ -0,0 +1,15 @@
+import inspect
+
+from .base_estimator import BaseEstimator
+
+
+def load_estimator(type, estimator):
+ module_path = f"{__name__}.{type}.{estimator}"
+ module = __import__(module_path, fromlist=[""])
+ classes = inspect.getmembers(module, inspect.isclass)
+ # Filter classes defined in the module
+ classes = [c for c in classes if c[1].__module__ == module_path]
+ # Filter classes inherited from BaseModel
+ classes = [c for c in classes if issubclass(c[1], BaseEstimator)]
+ assert len(classes) == 1, classes
+ return classes[0][1]
diff --git a/third_party/gim/gim/gluefactory/robust_estimators/base_estimator.py b/third_party/gim/gim/gluefactory/robust_estimators/base_estimator.py
new file mode 100644
index 0000000000000000000000000000000000000000..29f8dd45a2f15bb0b9d585e7350ff73d64e3def2
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/robust_estimators/base_estimator.py
@@ -0,0 +1,33 @@
+from copy import copy
+
+from omegaconf import OmegaConf
+
+
+class BaseEstimator:
+ base_default_conf = {
+ "name": "???",
+ "ransac_th": "???",
+ }
+ test_thresholds = [1.0]
+ required_data_keys = []
+
+ strict_conf = False
+
+ def __init__(self, conf):
+ """Perform some logic and call the _init method of the child model."""
+ default_conf = OmegaConf.merge(
+ self.base_default_conf, OmegaConf.create(self.default_conf)
+ )
+ if self.strict_conf:
+ OmegaConf.set_struct(default_conf, True)
+
+ if isinstance(conf, dict):
+ conf = OmegaConf.create(conf)
+ self.conf = conf = OmegaConf.merge(default_conf, conf)
+ OmegaConf.set_readonly(conf, True)
+ OmegaConf.set_struct(conf, True)
+ self.required_data_keys = copy(self.required_data_keys)
+ self._init(conf)
+
+ def __call__(self, data):
+ return self._forward(data)
diff --git a/imcui/third_party/mickey/__init__.py b/third_party/gim/gim/gluefactory/robust_estimators/homography/__init__.py
similarity index 100%
rename from imcui/third_party/mickey/__init__.py
rename to third_party/gim/gim/gluefactory/robust_estimators/homography/__init__.py
diff --git a/third_party/gim/gim/gluefactory/robust_estimators/homography/homography_est.py b/third_party/gim/gim/gluefactory/robust_estimators/homography/homography_est.py
new file mode 100644
index 0000000000000000000000000000000000000000..780011ee18ee8ffbcad576ae5b32ea91c135ff14
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/robust_estimators/homography/homography_est.py
@@ -0,0 +1,74 @@
+import numpy as np
+import torch
+from homography_est import (
+ LineSegment,
+ ransac_line_homography,
+ ransac_point_homography,
+ ransac_point_line_homography,
+)
+
+from ...utils.tensor import batch_to_numpy
+from ..base_estimator import BaseEstimator
+
+
+def H_estimation_hybrid(kpts0=None, kpts1=None, lines0=None, lines1=None, tol_px=5):
+ """Estimate a homography from points and lines with hybrid RANSAC.
+ All features are expected in x-y convention
+ """
+ # Check that we have at least 4 features
+ n_features = 0
+ if kpts0 is not None:
+ n_features += len(kpts0) + len(kpts1)
+ if lines0 is not None:
+ n_features += len(lines0) + len(lines1)
+ if n_features < 4:
+ return None
+
+ if lines0 is None:
+ # Point-only RANSAC
+ H = ransac_point_homography(kpts0, kpts1, tol_px, False, [])
+ elif kpts0 is None:
+ # Line-only RANSAC
+ ls0 = [LineSegment(line[0], line[1]) for line in lines0]
+ ls1 = [LineSegment(line[0], line[1]) for line in lines1]
+ H = ransac_line_homography(ls0, ls1, tol_px, False, [])
+ else:
+ # Point-lines RANSAC
+ ls0 = [LineSegment(line[0], line[1]) for line in lines0]
+ ls1 = [LineSegment(line[0], line[1]) for line in lines1]
+ H = ransac_point_line_homography(kpts0, kpts1, ls0, ls1, tol_px, False, [], [])
+ if np.abs(H[-1, -1]) > 1e-8:
+ H /= H[-1, -1]
+ return H
+
+
+class PointLineHomographyEstimator(BaseEstimator):
+ default_conf = {"ransac_th": 2.0, "options": {}}
+
+ required_data_keys = ["m_kpts0", "m_kpts1", "m_lines0", "m_lines1"]
+
+ def _init(self, conf):
+ pass
+
+ def _forward(self, data):
+ feat = data["m_kpts0"] if "m_kpts0" in data else data["m_lines0"]
+ data = batch_to_numpy(data)
+ m_features = {
+ "kpts0": data["m_kpts1"] if "m_kpts1" in data else None,
+ "kpts1": data["m_kpts0"] if "m_kpts0" in data else None,
+ "lines0": data["m_lines1"] if "m_lines1" in data else None,
+ "lines1": data["m_lines0"] if "m_lines0" in data else None,
+ }
+ M = H_estimation_hybrid(**m_features, tol_px=self.conf.ransac_th)
+ success = M is not None
+ if not success:
+ M = torch.eye(3, device=feat.device, dtype=feat.dtype)
+ else:
+ M = torch.from_numpy(M).to(feat)
+
+ estimation = {
+ "success": success,
+ "M_0to1": M,
+ }
+
+ return estimation
diff --git a/third_party/gim/gim/gluefactory/robust_estimators/homography/opencv.py b/third_party/gim/gim/gluefactory/robust_estimators/homography/opencv.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fd3523f633d5ac2740c0121752f2aeb9f90b4b5
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/robust_estimators/homography/opencv.py
@@ -0,0 +1,53 @@
+import cv2
+import torch
+
+from ..base_estimator import BaseEstimator
+
+
+class OpenCVHomographyEstimator(BaseEstimator):
+ default_conf = {
+ "ransac_th": 3.0,
+ "options": {"method": "ransac", "max_iters": 3000, "confidence": 0.995},
+ }
+
+ required_data_keys = ["m_kpts0", "m_kpts1"]
+
+ def _init(self, conf):
+ self.solver = {
+ "ransac": cv2.RANSAC,
+ "lmeds": cv2.LMEDS,
+ "rho": cv2.RHO,
+ "usac": cv2.USAC_DEFAULT,
+ "usac_fast": cv2.USAC_FAST,
+ "usac_accurate": cv2.USAC_ACCURATE,
+ "usac_prosac": cv2.USAC_PROSAC,
+ "usac_magsac": cv2.USAC_MAGSAC,
+ }[conf.options.method]
+
+ def _forward(self, data):
+ pts0, pts1 = data["m_kpts0"], data["m_kpts1"]
+
+ try:
+ M, mask = cv2.findHomography(
+ pts0.numpy(),
+ pts1.numpy(),
+ self.solver,
+ self.conf.ransac_th,
+ maxIters=self.conf.options.max_iters,
+ confidence=self.conf.options.confidence,
+ )
+ success = M is not None
+ except cv2.error:
+ success = False
+ if not success:
+ M = torch.eye(3, device=pts0.device, dtype=pts0.dtype)
+ inl = torch.zeros_like(pts0[:, 0]).bool()
+ else:
+ M = torch.tensor(M).to(pts0)
+ inl = torch.tensor(mask).bool().to(pts0.device)
+
+ return {
+ "success": success,
+ "M_0to1": M,
+ "inliers": inl,
+ }
diff --git a/third_party/gim/gim/gluefactory/robust_estimators/homography/poselib.py b/third_party/gim/gim/gluefactory/robust_estimators/homography/poselib.py
new file mode 100644
index 0000000000000000000000000000000000000000..6aa714962ab48a09584328e3416562a592e0a8c0
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/robust_estimators/homography/poselib.py
@@ -0,0 +1,40 @@
+import poselib
+import torch
+from omegaconf import OmegaConf
+
+from ..base_estimator import BaseEstimator
+
+
+class PoseLibHomographyEstimator(BaseEstimator):
+ default_conf = {"ransac_th": 2.0, "options": {}}
+
+ required_data_keys = ["m_kpts0", "m_kpts1"]
+
+ def _init(self, conf):
+ pass
+
+ def _forward(self, data):
+ pts0, pts1 = data["m_kpts0"], data["m_kpts1"]
+ M, info = poselib.estimate_homography(
+ pts0.detach().cpu().numpy(),
+ pts1.detach().cpu().numpy(),
+ {
+ "max_reproj_error": self.conf.ransac_th,
+ **OmegaConf.to_container(self.conf.options),
+ },
+ )
+ success = M is not None
+ if not success:
+ M = torch.eye(3, device=pts0.device, dtype=pts0.dtype)
+ inl = torch.zeros_like(pts0[:, 0]).bool()
+ else:
+ M = torch.tensor(M).to(pts0)
+ inl = torch.tensor(info["inliers"]).bool().to(pts0.device)
+
+ estimation = {
+ "success": success,
+ "M_0to1": M,
+ "inliers": inl,
+ }
+
+ return estimation
diff --git a/imcui/third_party/mickey/benchmark/__init__.py b/third_party/gim/gim/gluefactory/robust_estimators/relative_pose/__init__.py
similarity index 100%
rename from imcui/third_party/mickey/benchmark/__init__.py
rename to third_party/gim/gim/gluefactory/robust_estimators/relative_pose/__init__.py
diff --git a/third_party/gim/gim/gluefactory/robust_estimators/relative_pose/opencv.py b/third_party/gim/gim/gluefactory/robust_estimators/relative_pose/opencv.py
new file mode 100644
index 0000000000000000000000000000000000000000..34442a0f8c8065bbdbf090862385fc406363ba37
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/robust_estimators/relative_pose/opencv.py
@@ -0,0 +1,64 @@
+import cv2
+import numpy as np
+import torch
+
+from ...geometry.utils import from_homogeneous
+from ...geometry.wrappers import Pose
+from ..base_estimator import BaseEstimator
+
+
+class OpenCVRelativePoseEstimator(BaseEstimator):
+ default_conf = {
+ "ransac_th": 0.5,
+ "options": {"confidence": 0.99999, "method": "ransac"},
+ }
+
+ required_data_keys = ["m_kpts0", "m_kpts1", "camera0", "camera1"]
+
+ def _init(self, conf):
+ self.solver = {"ransac": cv2.RANSAC, "usac_magsac": cv2.USAC_MAGSAC}[
+ self.conf.options.method
+ ]
+
+ def _forward(self, data):
+ kpts0, kpts1 = data["m_kpts0"], data["m_kpts1"]
+ camera0 = data["camera0"]
+ camera1 = data["camera1"]
+ M, inl = None, torch.zeros_like(kpts0[:, 0]).bool()
+
+ if len(kpts0) >= 5:
+ f_mean = torch.cat([camera0.f, camera1.f]).mean().item()
+ norm_thresh = self.conf.ransac_th / f_mean
+
+ pts0 = from_homogeneous(camera0.image2cam(kpts0)).cpu().detach().numpy()
+ pts1 = from_homogeneous(camera1.image2cam(kpts1)).cpu().detach().numpy()
+
+ E, mask = cv2.findEssentialMat(
+ pts0,
+ pts1,
+ np.eye(3),
+ threshold=norm_thresh,
+ prob=self.conf.options.confidence,
+ method=self.solver,
+ )
+
+ if E is not None:
+ best_num_inliers = 0
+ for _E in np.split(E, len(E) / 3):
+ n, R, t, _ = cv2.recoverPose(
+ _E, pts0, pts1, np.eye(3), 1e9, mask=mask
+ )
+ if n > best_num_inliers:
+ best_num_inliers = n
+ inl = torch.tensor(mask.ravel() > 0)
+ M = Pose.from_Rt(
+ torch.tensor(R).to(kpts0), torch.tensor(t[:, 0]).to(kpts0)
+ )
+
+ estimation = {
+ "success": M is not None,
+ "M_0to1": M if M is not None else Pose.from_4x4mat(torch.eye(4).to(kpts0)),
+ "inliers": inl.to(device=kpts0.device),
+ }
+
+ return estimation
diff --git a/third_party/gim/gim/gluefactory/robust_estimators/relative_pose/poselib.py b/third_party/gim/gim/gluefactory/robust_estimators/relative_pose/poselib.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c736e4e986740a8d248936a3c95e6bf7a30f4c2
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/robust_estimators/relative_pose/poselib.py
@@ -0,0 +1,44 @@
+import poselib
+import torch
+from omegaconf import OmegaConf
+
+from ...geometry.wrappers import Pose
+from ..base_estimator import BaseEstimator
+
+
+class PoseLibRelativePoseEstimator(BaseEstimator):
+ default_conf = {"ransac_th": 2.0, "options": {}}
+
+ required_data_keys = ["m_kpts0", "m_kpts1", "camera0", "camera1"]
+
+ def _init(self, conf):
+ pass
+
+ def _forward(self, data):
+ pts0, pts1 = data["m_kpts0"], data["m_kpts1"]
+ camera0 = data["camera0"]
+ camera1 = data["camera1"]
+ M, info = poselib.estimate_relative_pose(
+ pts0.numpy(),
+ pts1.numpy(),
+ camera0.to_cameradict(),
+ camera1.to_cameradict(),
+ {
+ "max_epipolar_error": self.conf.ransac_th,
+ **OmegaConf.to_container(self.conf.options),
+ },
+ )
+ success = M is not None
+ if success:
+ M = Pose.from_Rt(torch.tensor(M.R), torch.tensor(M.t)).to(pts0)
+ else:
+ M = Pose.from_4x4mat(torch.eye(4)).to(pts0)
+
+ estimation = {
+ "success": success,
+ "M_0to1": M,
+ "inliers": torch.tensor(info.pop("inliers")).to(pts0),
+ **info,
+ }
+
+ return estimation
diff --git a/third_party/gim/gim/gluefactory/robust_estimators/relative_pose/pycolmap.py b/third_party/gim/gim/gluefactory/robust_estimators/relative_pose/pycolmap.py
new file mode 100644
index 0000000000000000000000000000000000000000..21cb272019f31868b1dd4df29b987859210e4c5a
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/robust_estimators/relative_pose/pycolmap.py
@@ -0,0 +1,52 @@
+import pycolmap
+import torch
+from omegaconf import OmegaConf
+
+from ...geometry.wrappers import Pose
+from ..base_estimator import BaseEstimator
+
+
+class PycolmapTwoViewEstimator(BaseEstimator):
+ default_conf = {
+ "ransac_th": 4.0,
+ "options": {**pycolmap.TwoViewGeometryOptions().todict()},
+ }
+
+ required_data_keys = ["m_kpts0", "m_kpts1", "camera0", "camera1"]
+
+ def _init(self, conf):
+ opts = OmegaConf.to_container(conf.options)
+ self.options = pycolmap.TwoViewGeometryOptions(opts)
+ self.options.ransac.max_error = conf.ransac_th
+
+ def _forward(self, data):
+ pts0, pts1 = data["m_kpts0"], data["m_kpts1"]
+ camera0 = data["camera0"]
+ camera1 = data["camera1"]
+ info = pycolmap.two_view_geometry_estimation(
+ pts0.numpy(),
+ pts1.numpy(),
+ camera0.to_cameradict(),
+ camera1.to_cameradict(),
+ self.options,
+ )
+ success = info["success"]
+ if success:
+ R = pycolmap.qvec_to_rotmat(info["qvec"])
+ t = info["tvec"]
+ M = Pose.from_Rt(torch.tensor(R), torch.tensor(t)).to(pts0)
+ inl = torch.tensor(info.pop("inliers")).to(pts0)
+ else:
+ M = Pose.from_4x4mat(torch.eye(4)).to(pts0)
+ inl = torch.zeros_like(pts0[:, 0]).bool()
+
+ estimation = {
+ "success": success,
+ "M_0to1": M,
+ "inliers": inl,
+ "type": str(
+ info.get("configuration_type", pycolmap.TwoViewGeometry.UNDEFINED)
+ ),
+ }
+
+ return estimation
diff --git a/imcui/third_party/omniglue/src/__init__.py b/third_party/gim/gim/gluefactory/scripts/__init__.py
similarity index 100%
rename from imcui/third_party/omniglue/src/__init__.py
rename to third_party/gim/gim/gluefactory/scripts/__init__.py
diff --git a/third_party/gim/gim/gluefactory/scripts/export_local_features.py b/third_party/gim/gim/gluefactory/scripts/export_local_features.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f3f0a94ca5b621a937f678ac0bfd90d1e0ef4dd
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/scripts/export_local_features.py
@@ -0,0 +1,127 @@
+import argparse
+import logging
+from pathlib import Path
+
+import torch
+from omegaconf import OmegaConf
+
+from ..datasets import get_dataset
+from ..models import get_model
+from ..settings import DATA_PATH
+from ..utils.export_predictions import export_predictions
+
+resize = 1600
+
+sp_keys = ["keypoints", "descriptors", "keypoint_scores"]
+
+# SuperPoint
+n_kpts = 2048
+configs = {
+ "sp": {
+ "name": f"r{resize}_SP-k{n_kpts}-nms3",
+ "keys": ["keypoints", "descriptors", "keypoint_scores"],
+ "gray": True,
+ "conf": {
+ "name": "gluefactory_nonfree.superpoint",
+ "nms_radius": 3,
+ "max_num_keypoints": n_kpts,
+ "detection_threshold": 0.000,
+ },
+ },
+ "sift": {
+ "name": f"r{resize}_SIFT-k{n_kpts}",
+ "keys": ["keypoints", "descriptors", "keypoint_scores", "oris", "scales"],
+ "gray": True,
+ "conf": {
+ "name": "sift",
+ "max_num_keypoints": n_kpts,
+ "options": {
+ "peak_threshold": 0.001,
+ },
+ "peak_threshold": 0.001,
+ "device": "cpu",
+ },
+ },
+ "disk": {
+ "name": f"r{resize}_DISK-k{n_kpts}-nms6",
+ "keys": ["keypoints", "descriptors", "keypoint_scores"],
+ "gray": False,
+ "conf": {
+ "name": "disk",
+ "max_num_keypoints": n_kpts,
+ },
+ },
+}
+
+
+def run_export(feature_file, images, args):
+ conf = {
+ "data": {
+ "name": "image_folder",
+ "grayscale": configs[args.method]["gray"],
+ "preprocessing": {
+ "resize": resize,
+ },
+ "images": str(images),
+ "batch_size": 1,
+ "num_workers": args.num_workers,
+ },
+ "split": "train",
+ "model": configs[args.method]["conf"],
+ }
+
+ conf = OmegaConf.create(conf)
+
+ keys = configs[args.method]["keys"]
+ dataset = get_dataset(conf.data.name)(conf.data)
+ loader = dataset.get_data_loader(conf.split or "test")
+
+ device = "cuda" if torch.cuda.is_available() else "cpu"
+ model = get_model(conf.model.name)(conf.model).eval().to(device)
+
+ export_predictions(loader, model, feature_file, as_half=True, keys=keys)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("dataset", type=str)
+ parser.add_argument("--export_prefix", type=str, default="")
+ parser.add_argument("--method", type=str, default="sp")
+ parser.add_argument("--scenes", type=str, default=None)
+ parser.add_argument("--num_workers", type=int, default=0)
+ args = parser.parse_args()
+
+ export_name = configs[args.method]["name"]
+
+ if args.dataset == "megadepth":
+ data_root = Path(DATA_PATH, "megadepth/Undistorted_SfM")
+ export_root = Path(DATA_PATH, "exports", "megadepth-undist-" + export_name)
+ export_root.mkdir(parents=True, exist_ok=True)
+
+ if args.scenes is None:
+ scenes = [p.name for p in data_root.iterdir() if p.is_dir()]
+ else:
+ with open(DATA_PATH / "megadepth" / args.scenes, "r") as f:
+ scenes = f.read().split()
+ for i, scene in enumerate(scenes):
+ # print(f'{i} / {len(scenes)}', scene)
+ print(scene)
+ feature_file = export_root / (scene + ".h5")
+ if feature_file.exists():
+ continue
+ if not (data_root / scene / "images").exists():
+ logging.info("Skip " + scene)
+ continue
+ logging.info(f"Export local features for scene {scene}")
+ run_export(feature_file, data_root / scene / "images", args)
+ else:
+ data_root = Path(DATA_PATH, args.dataset)
+ feature_file = Path(
+ DATA_PATH, "exports", args.export_prefix + export_name + ".h5"
+ )
+ feature_file.parent.mkdir(exist_ok=True, parents=True)
+ logging.info(
+ f"Export local features for dataset {args.dataset} "
+ f"to file {feature_file}"
+ )
+ run_export(feature_file, data_root)
diff --git a/third_party/gim/gim/gluefactory/scripts/export_megadepth.py b/third_party/gim/gim/gluefactory/scripts/export_megadepth.py
new file mode 100644
index 0000000000000000000000000000000000000000..84ae8dfbd6602c50ed384c52ffb43f89db0c49c7
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/scripts/export_megadepth.py
@@ -0,0 +1,173 @@
+import argparse
+import logging
+from pathlib import Path
+
+import torch
+from omegaconf import OmegaConf
+
+from ..datasets import get_dataset
+from ..geometry.depth import sample_depth
+from ..models import get_model
+from ..settings import DATA_PATH
+from ..utils.export_predictions import export_predictions
+
+resize = 1024
+n_kpts = 2048
+configs = {
+ "sp": {
+ "name": f"r{resize}_SP-k{n_kpts}-nms3",
+ "keys": ["keypoints", "descriptors", "keypoint_scores"],
+ "gray": True,
+ "conf": {
+ "name": "gluefactory_nonfree.superpoint",
+ "nms_radius": 3,
+ "max_num_keypoints": n_kpts,
+ "detection_threshold": 0.000,
+ },
+ },
+ "sp_open": {
+ "name": f"r{resize}_SP-open-k{n_kpts}-nms3",
+ "keys": ["keypoints", "descriptors", "keypoint_scores"],
+ "gray": True,
+ "conf": {
+ "name": "extractors.superpoint_open",
+ "nms_radius": 3,
+ "max_num_keypoints": n_kpts,
+ "detection_threshold": 0.000,
+ },
+ },
+ "cv2-sift": {
+ "name": f"r{resize}_opencv-SIFT-k{n_kpts}",
+ "keys": ["keypoints", "descriptors", "keypoint_scores", "oris", "scales"],
+ "gray": True,
+ "conf": {
+ "name": "extractors.sift",
+ "max_num_keypoints": 4096,
+ "backend": "opencv",
+ },
+ },
+ "pycolmap-sift": {
+ "name": f"r{resize}_pycolmap-SIFT-k{n_kpts}",
+ "keys": ["keypoints", "descriptors", "keypoint_scores", "oris", "scales"],
+ "gray": True,
+ "conf": {
+ "name": "extractors.sift",
+ "max_num_keypoints": n_kpts,
+ "backend": "pycolmap",
+ },
+ },
+ "pycolmap-sift-gpu": {
+ "name": f"r{resize}_pycolmap_SIFTGPU-nms3-fixed-k{n_kpts}",
+ "keys": ["keypoints", "descriptors", "keypoint_scores", "oris", "scales"],
+ "gray": True,
+ "conf": {
+ "name": "extractors.sift",
+ "max_num_keypoints": n_kpts,
+ "backend": "pycolmap_cuda",
+ "nms_radius": 3,
+ },
+ },
+ "keynet-affnet-hardnet": {
+ "name": f"r{resize}_KeyNetAffNetHardNet-k{n_kpts}",
+ "keys": ["keypoints", "descriptors", "keypoint_scores", "oris", "scales"],
+ "gray": True,
+ "conf": {
+ "name": "extractors.keynet_affnet_hardnet",
+ "max_num_keypoints": n_kpts,
+ },
+ },
+ "disk": {
+ "name": f"r{resize}_DISK-k{n_kpts}-nms5",
+ "keys": ["keypoints", "descriptors", "keypoint_scores"],
+ "gray": False,
+ "conf": {
+ "name": "extractors.disk_kornia",
+ "max_num_keypoints": n_kpts,
+ },
+ },
+ "aliked": {
+ "name": f"r{resize}_ALIKED-k{n_kpts}-n16",
+ "keys": ["keypoints", "descriptors", "keypoint_scores"],
+ "gray": False,
+ "conf": {
+ "name": "extractors.aliked",
+ "max_num_keypoints": n_kpts,
+ },
+ },
+}
+
+
+def get_kp_depth(pred, data):
+ d, valid = sample_depth(pred["keypoints"], data["depth"])
+ return {"depth_keypoints": d, "valid_depth_keypoints": valid}
+
+
+def run_export(feature_file, scene, args):
+ conf = {
+ "data": {
+ "name": "megadepth",
+ "views": 1,
+ "grayscale": configs[args.method]["gray"],
+ "preprocessing": {
+ "resize": resize,
+ "side": "long",
+ },
+ "batch_size": 1,
+ "num_workers": args.num_workers,
+ "read_depth": True,
+ "train_split": [scene],
+ "train_num_per_scene": None,
+ },
+ "split": "train",
+ "model": configs[args.method]["conf"],
+ }
+
+ conf = OmegaConf.create(conf)
+
+ keys = configs[args.method]["keys"]
+ dataset = get_dataset(conf.data.name)(conf.data)
+ loader = dataset.get_data_loader(conf.split or "test")
+
+ device = "cuda" if torch.cuda.is_available() else "cpu"
+ model = get_model(conf.model.name)(conf.model).eval().to(device)
+
+ if args.export_sparse_depth:
+ callback_fn = get_kp_depth # use this to store the depth of each keypoint
+ keys = keys + ["depth_keypoints", "valid_depth_keypoints"]
+ else:
+ callback_fn = None
+ export_predictions(
+ loader, model, feature_file, as_half=True, keys=keys, callback_fn=callback_fn
+ )
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--export_prefix", type=str, default="")
+ parser.add_argument("--method", type=str, default="sp")
+ parser.add_argument("--scenes", type=str, default=None)
+ parser.add_argument("--num_workers", type=int, default=0)
+ parser.add_argument("--export_sparse_depth", action="store_true")
+ args = parser.parse_args()
+
+ export_name = configs[args.method]["name"]
+
+ data_root = Path(DATA_PATH, "megadepth/Undistorted_SfM")
+ export_root = Path(DATA_PATH, "exports", "megadepth-undist-depth-" + export_name)
+ export_root.mkdir(parents=True, exist_ok=True)
+
+ if args.scenes is None:
+ scenes = [p.name for p in data_root.iterdir() if p.is_dir()]
+ else:
+ with open(DATA_PATH / "megadepth" / args.scenes, "r") as f:
+ scenes = f.read().split()
+ for i, scene in enumerate(scenes):
+ print(f"{i} / {len(scenes)}", scene)
+ feature_file = export_root / (scene + ".h5")
+ if feature_file.exists() and False:
+ continue
+ if not (data_root / scene / "images").exists():
+ logging.info("Skip " + scene)
+ continue
+ logging.info(f"Export local features for scene {scene}")
+ run_export(feature_file, scene, args)
diff --git a/third_party/gim/gim/gluefactory/settings.py b/third_party/gim/gim/gluefactory/settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd475372d29c4461f86b5eddcd95c28f3f4ed240
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/settings.py
@@ -0,0 +1,6 @@
+from pathlib import Path
+
+root = Path(__file__).parent.parent # top-level directory
+DATA_PATH = root / "data/" # datasets and pretrained weights
+TRAINING_PATH = root / "outputs/training/" # training checkpoints
+EVAL_PATH = root / "outputs/results/" # evaluation results
diff --git a/third_party/gim/gim/gluefactory/superpoint.py b/third_party/gim/gim/gluefactory/superpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..1716c6989d8994e12f65c92d0ca4a600fdb34e4d
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/superpoint.py
@@ -0,0 +1,358 @@
+"""
+# %BANNER_BEGIN%
+# ---------------------------------------------------------------------
+# %COPYRIGHT_BEGIN%
+#
+# Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
+#
+# Unpublished Copyright (c) 2020
+# Magic Leap, Inc., All Rights Reserved.
+#
+# NOTICE: All information contained herein is, and remains the property
+# of COMPANY. The intellectual and technical concepts contained herein
+# are proprietary to COMPANY and may be covered by U.S. and Foreign
+# Patents, patents in process, and are protected by trade secret or
+# copyright law. Dissemination of this information or reproduction of
+# this material is strictly forbidden unless prior written permission is
+# obtained from COMPANY. Access to the source code contained herein is
+# hereby forbidden to anyone except current COMPANY employees, managers
+# or contractors who have executed Confidentiality and Non-disclosure
+# agreements explicitly covering such access.
+#
+# The copyright notice above does not evidence any actual or intended
+# publication or disclosure of this source code, which includes
+# information that is confidential and/or proprietary, and is a trade
+# secret, of COMPANY. ANY REPRODUCTION, MODIFICATION, DISTRIBUTION,
+# PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS
+# SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS
+# STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND
+# INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF THIS SOURCE
+# CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS
+# TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE,
+# USE, OR SELL ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART.
+#
+# %COPYRIGHT_END%
+# ----------------------------------------------------------------------
+# %AUTHORS_BEGIN%
+#
+# Originating Authors: Paul-Edouard Sarlin
+#
+# %AUTHORS_END%
+# --------------------------------------------------------------------*/
+# %BANNER_END%
+
+Described in:
+ SuperPoint: Self-Supervised Interest Point Detection and Description,
+ Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich, CVPRW 2018.
+
+Original code: github.com/MagicLeapResearch/SuperPointPretrainedNetwork
+
+Adapted by Philipp Lindenberger (Phil26AT)
+"""
+import os.path
+
+import torch
+from torch import nn
+
+from gluefactory.models.base_model import BaseModel
+from gluefactory.models.utils.misc import pad_and_stack
+
+
+def simple_nms(scores, radius):
+ """Perform non maximum suppression on the heatmap using max-pooling.
+ This method does not suppress contiguous points that have the same score.
+ Args:
+ scores: the score heatmap of size `(B, H, W)`.
+ radius: an integer scalar, the radius of the NMS window.
+ """
+
+ def max_pool(x):
+ return torch.nn.functional.max_pool2d(
+ x, kernel_size=radius * 2 + 1, stride=1, padding=radius
+ )
+
+ zeros = torch.zeros_like(scores)
+ max_mask = scores == max_pool(scores)
+ for _ in range(2):
+ supp_mask = max_pool(max_mask.float()) > 0
+ supp_scores = torch.where(supp_mask, zeros, scores)
+ new_max_mask = supp_scores == max_pool(supp_scores)
+ max_mask = max_mask | (new_max_mask & (~supp_mask))
+ return torch.where(max_mask, scores, zeros)
+
+
+def top_k_keypoints(keypoints, scores, k):
+ if k >= len(keypoints):
+ return keypoints, scores
+ scores, indices = torch.topk(scores, k, dim=0, sorted=True)
+ return keypoints[indices], scores
+
+
+def sample_k_keypoints(keypoints, scores, k):
+ if k >= len(keypoints):
+ return keypoints, scores
+ indices = torch.multinomial(scores, k, replacement=False)
+ return keypoints[indices], scores[indices]
+
+
+def soft_argmax_refinement(keypoints, scores, radius: int):
+ width = 2 * radius + 1
+ sum_ = torch.nn.functional.avg_pool2d(
+ scores[:, None], width, 1, radius, divisor_override=1
+ )
+ ar = torch.arange(-radius, radius + 1).to(scores)
+ kernel_x = ar[None].expand(width, -1)[None, None]
+ dx = torch.nn.functional.conv2d(scores[:, None], kernel_x, padding=radius)
+ dy = torch.nn.functional.conv2d(
+ scores[:, None], kernel_x.transpose(2, 3), padding=radius
+ )
+ dydx = torch.stack([dy[:, 0], dx[:, 0]], -1) / sum_[:, 0, :, :, None]
+ refined_keypoints = []
+ for i, kpts in enumerate(keypoints):
+ delta = dydx[i][tuple(kpts.t())]
+ refined_keypoints.append(kpts.float() + delta)
+ return refined_keypoints
+
+
+# Legacy (broken) sampling of the descriptors
+def sample_descriptors(keypoints, descriptors, s):
+ b, c, h, w = descriptors.shape
+ keypoints = keypoints - s / 2 + 0.5
+ keypoints /= torch.tensor(
+ [(w * s - s / 2 - 0.5), (h * s - s / 2 - 0.5)],
+ ).to(
+ keypoints
+ )[None]
+ keypoints = keypoints * 2 - 1 # normalize to (-1, 1)
+ args = {"align_corners": True} if torch.__version__ >= "1.3" else {}
+ descriptors = torch.nn.functional.grid_sample(
+ descriptors, keypoints.view(b, 1, -1, 2), mode="bilinear", **args
+ )
+ descriptors = torch.nn.functional.normalize(
+ descriptors.reshape(b, c, -1), p=2, dim=1
+ )
+ return descriptors
+
+
+# The original keypoint sampling is incorrect. We patch it here but
+# keep the original one above for legacy.
+def sample_descriptors_fix_sampling(keypoints, descriptors, s: int = 8):
+ """Interpolate descriptors at keypoint locations"""
+ b, c, h, w = descriptors.shape
+ keypoints = keypoints / (keypoints.new_tensor([w, h]) * s)
+ keypoints = keypoints * 2 - 1 # normalize to (-1, 1)
+ descriptors = torch.nn.functional.grid_sample(
+ descriptors, keypoints.view(b, 1, -1, 2), mode="bilinear", align_corners=False
+ )
+ descriptors = torch.nn.functional.normalize(
+ descriptors.reshape(b, c, -1), p=2, dim=1
+ )
+ return descriptors
+
+
+class SuperPoint(BaseModel):
+ default_conf = {
+ "has_detector": True,
+ "has_descriptor": True,
+ "descriptor_dim": 256,
+ # Inference
+ "sparse_outputs": True,
+ "dense_outputs": False,
+ "nms_radius": 4,
+ "refinement_radius": 0,
+ "detection_threshold": 0.005,
+ "max_num_keypoints": -1,
+ "max_num_keypoints_val": None,
+ "force_num_keypoints": False,
+ "randomize_keypoints_training": False,
+ "remove_borders": 4,
+ "legacy_sampling": True, # True to use the old broken sampling
+ }
+ required_data_keys = ["image"]
+
+ checkpoint_url = "https://github.com/magicleap/SuperGluePretrainedNetwork/raw/master/models/weights/superpoint_v1.pth" # noqa: E501
+
+ def _init(self, conf):
+ self.relu = nn.ReLU(inplace=True)
+ self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+ c1, c2, c3, c4, c5 = 64, 64, 128, 128, 256
+
+ self.conv1a = nn.Conv2d(1, c1, kernel_size=3, stride=1, padding=1)
+ self.conv1b = nn.Conv2d(c1, c1, kernel_size=3, stride=1, padding=1)
+ self.conv2a = nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1)
+ self.conv2b = nn.Conv2d(c2, c2, kernel_size=3, stride=1, padding=1)
+ self.conv3a = nn.Conv2d(c2, c3, kernel_size=3, stride=1, padding=1)
+ self.conv3b = nn.Conv2d(c3, c3, kernel_size=3, stride=1, padding=1)
+ self.conv4a = nn.Conv2d(c3, c4, kernel_size=3, stride=1, padding=1)
+ self.conv4b = nn.Conv2d(c4, c4, kernel_size=3, stride=1, padding=1)
+
+ if conf.has_detector:
+ self.convPa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1)
+ self.convPb = nn.Conv2d(c5, 65, kernel_size=1, stride=1, padding=0)
+ for param in self.convPa.parameters():
+ param.requires_grad = False
+ for param in self.convPb.parameters():
+ param.requires_grad = False
+
+ if conf.has_descriptor:
+ self.convDa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1)
+ self.convDb = nn.Conv2d(
+ c5, conf.descriptor_dim, kernel_size=1, stride=1, padding=0
+ )
+
+ self.load_state_dict(torch.load(os.path.join('weights', 'superpoint_v1.pth')))
+
+ def _forward(self, data):
+ image = data["image"]
+ if image.shape[1] == 3: # RGB
+ scale = image.new_tensor([0.299, 0.587, 0.114]).view(1, 3, 1, 1)
+ image = (image * scale).sum(1, keepdim=True)
+
+ # Shared Encoder
+ x = self.relu(self.conv1a(image))
+ x = self.relu(self.conv1b(x))
+ x = self.pool(x)
+ x = self.relu(self.conv2a(x))
+ x = self.relu(self.conv2b(x))
+ x = self.pool(x)
+ x = self.relu(self.conv3a(x))
+ x = self.relu(self.conv3b(x))
+ x = self.pool(x)
+ x = self.relu(self.conv4a(x))
+ x = self.relu(self.conv4b(x))
+
+ pred = {}
+ if self.conf.has_detector:
+ # Compute the dense keypoint scores
+ cPa = self.relu(self.convPa(x))
+ scores = self.convPb(cPa)
+ scores = torch.nn.functional.softmax(scores, 1)[:, :-1]
+ b, c, h, w = scores.shape
+ scores = scores.permute(0, 2, 3, 1).reshape(b, h, w, 8, 8)
+ scores = scores.permute(0, 1, 3, 2, 4).reshape(b, h * 8, w * 8)
+ pred["keypoint_scores"] = dense_scores = scores
+ if self.conf.has_descriptor:
+ # Compute the dense descriptors
+ cDa = self.relu(self.convDa(x))
+ dense_desc = self.convDb(cDa)
+ dense_desc = torch.nn.functional.normalize(dense_desc, p=2, dim=1)
+ pred["descriptors"] = dense_desc
+
+ if self.conf.sparse_outputs:
+ assert self.conf.has_detector and self.conf.has_descriptor
+
+ scores = simple_nms(scores, self.conf.nms_radius)
+
+ # Discard keypoints near the image borders
+ if self.conf.remove_borders:
+ scores[:, : self.conf.remove_borders] = -1
+ scores[:, :, : self.conf.remove_borders] = -1
+ if "image_size" in data:
+ for i in range(scores.shape[0]):
+ w, h = data["image_size"][i]
+ scores[i, int(h.item()) - self.conf.remove_borders :] = -1
+ scores[i, :, int(w.item()) - self.conf.remove_borders :] = -1
+ else:
+ scores[:, -self.conf.remove_borders :] = -1
+ scores[:, :, -self.conf.remove_borders :] = -1
+
+ # Extract keypoints
+ best_kp = torch.where(scores > self.conf.detection_threshold)
+ scores = scores[best_kp]
+
+ # Separate into batches
+ keypoints = [
+ torch.stack(best_kp[1:3], dim=-1)[best_kp[0] == i] for i in range(b)
+ ]
+ scores = [scores[best_kp[0] == i] for i in range(b)]
+
+ # Keep the k keypoints with highest score
+ max_kps = self.conf.max_num_keypoints
+
+ # for val we allow different
+ if not self.training and self.conf.max_num_keypoints_val is not None:
+ max_kps = self.conf.max_num_keypoints_val
+
+ # Keep the k keypoints with highest score
+ if max_kps > 0:
+ if self.conf.randomize_keypoints_training and self.training:
+ # instead of selecting top-k, sample k by score weights
+ keypoints, scores = list(
+ zip(
+ *[
+ sample_k_keypoints(k, s, max_kps)
+ for k, s in zip(keypoints, scores)
+ ]
+ )
+ )
+ else:
+ keypoints, scores = list(
+ zip(
+ *[
+ top_k_keypoints(k, s, max_kps)
+ for k, s in zip(keypoints, scores)
+ ]
+ )
+ )
+ keypoints, scores = list(keypoints), list(scores)
+
+ if self.conf["refinement_radius"] > 0:
+ keypoints = soft_argmax_refinement(
+ keypoints, dense_scores, self.conf["refinement_radius"]
+ )
+
+ # Convert (h, w) to (x, y)
+ keypoints = [torch.flip(k, [1]).float() for k in keypoints]
+
+ if self.conf.force_num_keypoints:
+ keypoints = pad_and_stack(
+ keypoints,
+ max_kps,
+ -2,
+ mode="random_c",
+ bounds=(
+ 0,
+ data.get("image_size", torch.tensor(image.shape[-2:]))
+ .min()
+ .item(),
+ ),
+ )
+ scores = pad_and_stack(scores, max_kps, -1, mode="zeros")
+ else:
+ keypoints = torch.stack(keypoints, 0)
+ scores = torch.stack(scores, 0)
+
+ # Extract descriptors
+ if (len(keypoints) == 1) or self.conf.force_num_keypoints:
+ # Batch sampling of the descriptors
+ if self.conf.legacy_sampling:
+ desc = sample_descriptors(keypoints, dense_desc, 8)
+ else:
+ desc = sample_descriptors_fix_sampling(keypoints, dense_desc, 8)
+ else:
+ if self.conf.legacy_sampling:
+ desc = [
+ sample_descriptors(k[None], d[None], 8)[0]
+ for k, d in zip(keypoints, dense_desc)
+ ]
+ else:
+ desc = [
+ sample_descriptors_fix_sampling(k[None], d[None], 8)[0]
+ for k, d in zip(keypoints, dense_desc)
+ ]
+
+ pred = {
+ "keypoints": keypoints + 0.5,
+ "descriptors": desc.transpose(-1, -2),
+ }
+
+ if self.conf.dense_outputs:
+ pred["dense_descriptors"] = dense_desc
+
+ return pred
+
+ def loss(self, pred, data):
+ raise NotImplementedError
+
+ def metrics(self, pred, data):
+ raise NotImplementedError
diff --git a/third_party/gim/gim/gluefactory/train.py b/third_party/gim/gim/gluefactory/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..debf212541a81e7a8a152a3b134cab2419f29b28
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/train.py
@@ -0,0 +1,691 @@
+"""
+A generic training script that works with any model and dataset.
+
+Author: Paul-Edouard Sarlin (skydes)
+"""
+
+import argparse
+import copy
+import re
+import shutil
+import signal
+from collections import defaultdict
+from pathlib import Path
+from pydoc import locate
+
+import numpy as np
+import torch
+from omegaconf import OmegaConf
+from torch.cuda.amp import GradScaler, autocast
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+from . import __module_name__, logger
+from .datasets import get_dataset
+from .eval import run_benchmark
+from .models import get_model
+from .settings import EVAL_PATH, TRAINING_PATH
+from .utils.experiments import get_best_checkpoint, get_last_checkpoint, save_experiment
+from .utils.stdout_capturing import capture_outputs
+from .utils.tensor import batch_to_device
+from .utils.tools import (
+ AverageMetric,
+ MedianMetric,
+ PRMetric,
+ RecallMetric,
+ fork_rng,
+ set_seed,
+)
+
+# @TODO: Fix pbar pollution in logs
+# @TODO: add plotting during evaluation
+
+default_train_conf = {
+ "seed": "???", # training seed
+ "epochs": 1, # number of epochs
+ "optimizer": "adam", # name of optimizer in [adam, sgd, rmsprop]
+ "opt_regexp": None, # regular expression to filter parameters to optimize
+ "optimizer_options": {}, # optional arguments passed to the optimizer
+ "lr": 0.001, # learning rate
+ "lr_schedule": {
+ "type": None, # string in {factor, exp, member of torch.optim.lr_scheduler}
+ "start": 0,
+ "exp_div_10": 0,
+ "on_epoch": False,
+ "factor": 1.0,
+ "options": {}, # add lr_scheduler arguments here
+ },
+ "lr_scaling": [(100, ["dampingnet.const"])],
+ "eval_every_iter": 1000, # interval for evaluation on the validation set
+ "save_every_iter": 5000, # interval for saving the current checkpoint
+ "log_every_iter": 200, # interval for logging the loss to the console
+ "log_grad_every_iter": None, # interval for logging gradient hists
+ "test_every_epoch": 1, # interval for evaluation on the test benchmarks
+ "keep_last_checkpoints": 10, # keep only the last X checkpoints
+ "load_experiment": None, # initialize the model from a previous experiment
+ "median_metrics": [], # add the median of some metrics
+ "recall_metrics": {}, # add the recall of some metrics
+ "pr_metrics": {}, # add pr curves, set labels/predictions/mask keys
+ "best_key": "loss/total", # key to use to select the best checkpoint
+ "dataset_callback_fn": None, # data func called at the start of each epoch
+ "dataset_callback_on_val": False, # call data func on val data?
+ "clip_grad": None,
+ "pr_curves": {},
+ "plot": None,
+ "submodules": [],
+}
+default_train_conf = OmegaConf.create(default_train_conf)
+
+
+@torch.no_grad()
+def do_evaluation(model, loader, device, loss_fn, conf, pbar=True):
+ model.eval()
+ results = {}
+ pr_metrics = defaultdict(PRMetric)
+ figures = []
+ if conf.plot is not None:
+ n, plot_fn = conf.plot
+ plot_ids = np.random.choice(len(loader), min(len(loader), n), replace=False)
+ for i, data in enumerate(
+ tqdm(loader, desc="Evaluation", ascii=True, disable=not pbar)
+ ):
+ data = batch_to_device(data, device, non_blocking=True)
+ with torch.no_grad():
+ pred = model(data)
+ losses, metrics = loss_fn(pred, data)
+ if conf.plot is not None and i in plot_ids:
+ figures.append(locate(plot_fn)(pred, data))
+ # add PR curves
+ for k, v in conf.pr_curves.items():
+ pr_metrics[k].update(
+ pred[v["labels"]],
+ pred[v["predictions"]],
+ mask=pred[v["mask"]] if "mask" in v.keys() else None,
+ )
+ del pred, data
+ numbers = {**metrics, **{"loss/" + k: v for k, v in losses.items()}}
+ for k, v in numbers.items():
+ if k not in results:
+ results[k] = AverageMetric()
+ if k in conf.median_metrics:
+ results[k + "_median"] = MedianMetric()
+ if k in conf.recall_metrics.keys():
+ q = conf.recall_metrics[k]
+ results[k + f"_recall{int(q)}"] = RecallMetric(q)
+ results[k].update(v)
+ if k in conf.median_metrics:
+ results[k + "_median"].update(v)
+ if k in conf.recall_metrics.keys():
+ q = conf.recall_metrics[k]
+ results[k + f"_recall{int(q)}"].update(v)
+ del numbers
+ results = {k: results[k].compute() for k in results}
+ return results, {k: v.compute() for k, v in pr_metrics.items()}, figures
+
+
+def filter_parameters(params, regexp):
+ """Filter trainable parameters based on regular expressions."""
+
+ # Examples of regexp:
+ # '.*(weight|bias)$'
+ # 'cnn\.(enc0|enc1).*bias'
+ def filter_fn(x):
+ n, p = x
+ match = re.search(regexp, n)
+ if not match:
+ p.requires_grad = False
+ return match
+
+ params = list(filter(filter_fn, params))
+ assert len(params) > 0, regexp
+ logger.info("Selected parameters:\n" + "\n".join(n for n, p in params))
+ return params
+
+
+def get_lr_scheduler(optimizer, conf):
+ """Get lr scheduler specified by conf.train.lr_schedule."""
+ if conf.type not in ["factor", "exp", None]:
+ return getattr(torch.optim.lr_scheduler, conf.type)(optimizer, **conf.options)
+
+ # backward compatibility
+ def lr_fn(it): # noqa: E306
+ if conf.type is None:
+ return 1
+ if conf.type == "factor":
+ return 1.0 if it < conf.start else conf.factor
+ if conf.type == "exp":
+ gam = 10 ** (-1 / conf.exp_div_10)
+ return 1.0 if it < conf.start else gam
+ else:
+ raise ValueError(conf.type)
+
+ return torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_fn)
+
+
+def pack_lr_parameters(params, base_lr, lr_scaling):
+ """Pack each group of parameters with the respective scaled learning rate."""
+ filters, scales = tuple(zip(*[(n, s) for s, names in lr_scaling for n in names]))
+ scale2params = defaultdict(list)
+ for n, p in params:
+ scale = 1
+ # TODO: use proper regexp rather than just this inclusion check
+ is_match = [f in n for f in filters]
+ if any(is_match):
+ scale = scales[is_match.index(True)]
+ scale2params[scale].append((n, p))
+ logger.info(
+ "Parameters with scaled learning rate:\n%s",
+ {s: [n for n, _ in ps] for s, ps in scale2params.items() if s != 1},
+ )
+ lr_params = [
+ {"lr": scale * base_lr, "params": [p for _, p in ps]}
+ for scale, ps in scale2params.items()
+ ]
+ return lr_params
+
+
+def training(rank, conf, output_dir, args):
+ if args.restore:
+ logger.info(f"Restoring from previous training of {args.experiment}")
+ try:
+ init_cp = get_last_checkpoint(args.experiment, allow_interrupted=False)
+ except AssertionError:
+ init_cp = get_best_checkpoint(args.experiment)
+ logger.info(f"Restoring from checkpoint {init_cp.name}")
+ init_cp = torch.load(str(init_cp), map_location="cpu")
+ conf = OmegaConf.merge(OmegaConf.create(init_cp["conf"]), conf)
+ conf.train = OmegaConf.merge(default_train_conf, conf.train)
+ epoch = init_cp["epoch"] + 1
+
+ # get the best loss or eval metric from the previous best checkpoint
+ best_cp = get_best_checkpoint(args.experiment)
+ best_cp = torch.load(str(best_cp), map_location="cpu")
+ best_eval = best_cp["eval"][conf.train.best_key]
+ del best_cp
+ else:
+ # we start a new, fresh training
+ conf.train = OmegaConf.merge(default_train_conf, conf.train)
+ epoch = 0
+ best_eval = float("inf")
+ if conf.train.load_experiment:
+ logger.info(f"Will fine-tune from weights of {conf.train.load_experiment}")
+ # the user has to make sure that the weights are compatible
+ try:
+ init_cp = get_last_checkpoint(conf.train.load_experiment)
+ except AssertionError:
+ init_cp = get_best_checkpoint(conf.train.load_experiment)
+ # init_cp = get_last_checkpoint(conf.train.load_experiment)
+ init_cp = torch.load(str(init_cp), map_location="cpu")
+ # load the model config of the old setup, and overwrite with current config
+ conf.model = OmegaConf.merge(
+ OmegaConf.create(init_cp["conf"]).model, conf.model
+ )
+ print(conf.model)
+ else:
+ init_cp = None
+
+ OmegaConf.set_struct(conf, True) # prevent access to unknown entries
+ set_seed(conf.train.seed)
+ if rank == 0:
+ writer = SummaryWriter(log_dir=str(output_dir))
+
+ data_conf = copy.deepcopy(conf.data)
+ if args.distributed:
+ logger.info(f"Training in distributed mode with {args.n_gpus} GPUs")
+ assert torch.cuda.is_available()
+ device = rank
+ torch.distributed.init_process_group(
+ backend="nccl",
+ world_size=args.n_gpus,
+ rank=device,
+ init_method="file://" + str(args.lock_file),
+ )
+ torch.cuda.set_device(device)
+
+ # adjust batch size and num of workers since these are per GPU
+ if "batch_size" in data_conf:
+ data_conf.batch_size = int(data_conf.batch_size / args.n_gpus)
+ if "train_batch_size" in data_conf:
+ data_conf.train_batch_size = int(data_conf.train_batch_size / args.n_gpus)
+ if "num_workers" in data_conf:
+ data_conf.num_workers = int(
+ (data_conf.num_workers + args.n_gpus - 1) / args.n_gpus
+ )
+ else:
+ device = "cuda" if torch.cuda.is_available() else "cpu"
+ logger.info(f"Using device {device}")
+
+ dataset = get_dataset(data_conf.name)(data_conf)
+
+ # Optionally load a different validation dataset than the training one
+ val_data_conf = conf.get("data_val", None)
+ if val_data_conf is None:
+ val_dataset = dataset
+ else:
+ val_dataset = get_dataset(val_data_conf.name)(val_data_conf)
+
+ # @TODO: add test data loader
+
+ if args.overfit:
+ # we train and eval with the same single training batch
+ logger.info("Data in overfitting mode")
+ assert not args.distributed
+ train_loader = dataset.get_overfit_loader("train")
+ val_loader = val_dataset.get_overfit_loader("val")
+ else:
+ train_loader = dataset.get_data_loader("train", distributed=args.distributed)
+ val_loader = val_dataset.get_data_loader("val")
+ if rank == 0:
+ logger.info(f"Training loader has {len(train_loader)} batches")
+ logger.info(f"Validation loader has {len(val_loader)} batches")
+
+ # interrupts are caught and delayed for graceful termination
+ def sigint_handler(signal, frame):
+ logger.info("Caught keyboard interrupt signal, will terminate")
+ nonlocal stop
+ if stop:
+ raise KeyboardInterrupt
+ stop = True
+
+ stop = False
+ signal.signal(signal.SIGINT, sigint_handler)
+ model = get_model(conf.model.name)(conf.model).to(device)
+ if args.compile:
+ model = torch.compile(model, mode=args.compile)
+ loss_fn = model.loss
+ if init_cp is not None:
+ model.load_state_dict(init_cp["model"], strict=False)
+ if args.distributed:
+ model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+ model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[device])
+ if rank == 0 and args.print_arch:
+ logger.info(f"Model: \n{model}")
+
+ torch.backends.cudnn.benchmark = True
+ if args.detect_anomaly:
+ torch.autograd.set_detect_anomaly(True)
+
+ optimizer_fn = {
+ "sgd": torch.optim.SGD,
+ "adam": torch.optim.Adam,
+ "adamw": torch.optim.AdamW,
+ "rmsprop": torch.optim.RMSprop,
+ }[conf.train.optimizer]
+ params = [(n, p) for n, p in model.named_parameters() if p.requires_grad]
+ if conf.train.opt_regexp:
+ params = filter_parameters(params, conf.train.opt_regexp)
+ all_params = [p for n, p in params]
+
+ lr_params = pack_lr_parameters(params, conf.train.lr, conf.train.lr_scaling)
+ optimizer = optimizer_fn(
+ lr_params, lr=conf.train.lr, **conf.train.optimizer_options
+ )
+ scaler = GradScaler(enabled=args.mixed_precision is not None)
+ logger.info(f"Training with mixed_precision={args.mixed_precision}")
+
+ mp_dtype = {
+ "float16": torch.float16,
+ "bfloat16": torch.bfloat16,
+ None: torch.float32, # we disable it anyway
+ }[args.mixed_precision]
+
+ results = None # fix bug with it saving
+
+ lr_scheduler = get_lr_scheduler(optimizer=optimizer, conf=conf.train.lr_schedule)
+ if args.restore:
+ optimizer.load_state_dict(init_cp["optimizer"])
+ if "lr_scheduler" in init_cp:
+ lr_scheduler.load_state_dict(init_cp["lr_scheduler"])
+
+ if rank == 0:
+ logger.info(
+ "Starting training with configuration:\n%s", OmegaConf.to_yaml(conf)
+ )
+ losses_ = None
+
+ def trace_handler(p):
+ # torch.profiler.tensorboard_trace_handler(str(output_dir))
+ output = p.key_averages().table(sort_by="self_cuda_time_total", row_limit=10)
+ print(output)
+ p.export_chrome_trace("trace_" + str(p.step_num) + ".json")
+ p.export_stacks("/tmp/profiler_stacks.txt", "self_cuda_time_total")
+
+ if args.profile:
+ prof = torch.profiler.profile(
+ schedule=torch.profiler.schedule(wait=1, warmup=1, active=1, repeat=1),
+ on_trace_ready=torch.profiler.tensorboard_trace_handler(str(output_dir)),
+ record_shapes=True,
+ profile_memory=True,
+ with_stack=True,
+ )
+ prof.__enter__()
+ while epoch < conf.train.epochs and not stop:
+ if rank == 0:
+ logger.info(f"Starting epoch {epoch}")
+
+ # we first run the eval
+ if (
+ rank == 0
+ and epoch % conf.train.test_every_epoch == 0
+ and args.run_benchmarks
+ ):
+ for bname, eval_conf in conf.get("benchmarks", {}).items():
+ logger.info(f"Running eval on {bname}")
+ s, f, r = run_benchmark(
+ bname,
+ eval_conf,
+ EVAL_PATH / bname / args.experiment / str(epoch),
+ model.eval(),
+ )
+ logger.info(str(s))
+ for metric_name, value in s.items():
+ writer.add_scalar(f"test/{bname}/{metric_name}", value, epoch)
+ for fig_name, fig in f.items():
+ writer.add_figure(f"figures/{bname}/{fig_name}", fig, epoch)
+
+ # set the seed
+ set_seed(conf.train.seed + epoch)
+
+ # update learning rate
+ if conf.train.lr_schedule.on_epoch and epoch > 0:
+ old_lr = optimizer.param_groups[0]["lr"]
+ lr_scheduler.step()
+ logger.info(
+ f'lr changed from {old_lr} to {optimizer.param_groups[0]["lr"]}'
+ )
+ if args.distributed:
+ train_loader.sampler.set_epoch(epoch)
+ if epoch > 0 and conf.train.dataset_callback_fn and not args.overfit:
+ loaders = [train_loader]
+ if conf.train.dataset_callback_on_val:
+ loaders += [val_loader]
+ for loader in loaders:
+ if isinstance(loader.dataset, torch.utils.data.Subset):
+ getattr(loader.dataset.dataset, conf.train.dataset_callback_fn)(
+ conf.train.seed + epoch
+ )
+ else:
+ getattr(loader.dataset, conf.train.dataset_callback_fn)(
+ conf.train.seed + epoch
+ )
+ for it, data in enumerate(train_loader):
+ tot_it = (len(train_loader) * epoch + it) * (
+ args.n_gpus if args.distributed else 1
+ )
+ tot_n_samples = tot_it
+ if not args.log_it:
+ # We normalize the x-axis of tensorflow to num samples!
+ tot_n_samples *= train_loader.batch_size
+
+ model.train()
+ optimizer.zero_grad()
+
+ with autocast(enabled=args.mixed_precision is not None, dtype=mp_dtype):
+ data = batch_to_device(data, device, non_blocking=True)
+ pred = model(data)
+ losses, _ = loss_fn(pred, data)
+ loss = torch.mean(losses["total"])
+ if torch.isnan(loss).any():
+ print(f"Detected NAN, skipping iteration {it}")
+ del pred, data, loss, losses
+ continue
+
+ do_backward = loss.requires_grad
+ if args.distributed:
+ do_backward = torch.tensor(do_backward).float().to(device)
+ torch.distributed.all_reduce(
+ do_backward, torch.distributed.ReduceOp.PRODUCT
+ )
+ do_backward = do_backward > 0
+ if do_backward:
+ scaler.scale(loss).backward()
+ if args.detect_anomaly:
+ # Check for params without any gradient which causes
+ # problems in distributed training with checkpointing
+ detected_anomaly = False
+ for name, param in model.named_parameters():
+ if param.grad is None and param.requires_grad:
+ print(f"param {name} has no gradient.")
+ detected_anomaly = True
+ if detected_anomaly:
+ raise RuntimeError("Detected anomaly in training.")
+ if conf.train.get("clip_grad", None):
+ scaler.unscale_(optimizer)
+ try:
+ torch.nn.utils.clip_grad_norm_(
+ all_params,
+ max_norm=conf.train.clip_grad,
+ error_if_nonfinite=True,
+ )
+ scaler.step(optimizer)
+ except RuntimeError:
+ logger.warning("NaN detected in gradients. Skipping iteration.")
+ scaler.update()
+ else:
+ scaler.step(optimizer)
+ scaler.update()
+ if not conf.train.lr_schedule.on_epoch:
+ lr_scheduler.step()
+ else:
+ if rank == 0:
+ logger.warning(f"Skip iteration {it} due to detach.")
+
+ if args.profile:
+ prof.step()
+
+ if it % conf.train.log_every_iter == 0:
+ for k in sorted(losses.keys()):
+ if args.distributed:
+ losses[k] = losses[k].sum(-1)
+ torch.distributed.reduce(losses[k], dst=0)
+ losses[k] /= train_loader.batch_size * args.n_gpus
+ losses[k] = torch.mean(losses[k], -1)
+ losses[k] = losses[k].item()
+ if rank == 0:
+ str_losses = [f"{k} {v:.3E}" for k, v in losses.items()]
+ logger.info(
+ "[E {} | it {}] loss {{{}}}".format(
+ epoch, it, ", ".join(str_losses)
+ )
+ )
+ for k, v in losses.items():
+ writer.add_scalar("training/" + k, v, tot_n_samples)
+ writer.add_scalar(
+ "training/lr", optimizer.param_groups[0]["lr"], tot_n_samples
+ )
+ writer.add_scalar("training/epoch", epoch, tot_n_samples)
+
+ if conf.train.log_grad_every_iter is not None:
+ if it % conf.train.log_grad_every_iter == 0:
+ grad_txt = ""
+ for name, param in model.named_parameters():
+ if param.grad is not None and param.requires_grad:
+ if name.endswith("bias"):
+ continue
+ writer.add_histogram(
+ f"grad/{name}", param.grad.detach(), tot_n_samples
+ )
+ norm = torch.norm(param.grad.detach(), 2)
+ grad_txt += f"{name} {norm.item():.3f} \n"
+ writer.add_text("grad/summary", grad_txt, tot_n_samples)
+ del pred, data, loss, losses
+
+ # Run validation
+ if (
+ (
+ it % conf.train.eval_every_iter == 0
+ and (it > 0 or epoch == -int(args.no_eval_0))
+ )
+ or stop
+ or it == (len(train_loader) - 1)
+ ):
+ with fork_rng(seed=conf.train.seed):
+ results, pr_metrics, figures = do_evaluation(
+ model,
+ val_loader,
+ device,
+ loss_fn,
+ conf.train,
+ pbar=(rank == -1),
+ )
+
+ if rank == 0:
+ str_results = [
+ f"{k} {v:.3E}"
+ for k, v in results.items()
+ if isinstance(v, float)
+ ]
+ logger.info(f'[Validation] {{{", ".join(str_results)}}}')
+ for k, v in results.items():
+ if isinstance(v, dict):
+ writer.add_scalars(f"figure/val/{k}", v, tot_n_samples)
+ else:
+ writer.add_scalar("val/" + k, v, tot_n_samples)
+ for k, v in pr_metrics.items():
+ writer.add_pr_curve("val/" + k, *v, tot_n_samples)
+ # @TODO: optional always save checkpoint
+ if results[conf.train.best_key] < best_eval:
+ best_eval = results[conf.train.best_key]
+ save_experiment(
+ model,
+ optimizer,
+ lr_scheduler,
+ conf,
+ losses_,
+ results,
+ best_eval,
+ epoch,
+ tot_it,
+ output_dir,
+ stop,
+ args.distributed,
+ cp_name="checkpoint_best.tar",
+ )
+ logger.info(f"New best val: {conf.train.best_key}={best_eval}")
+ if len(figures) > 0:
+ for i, figs in enumerate(figures):
+ for name, fig in figs.items():
+ writer.add_figure(
+ f"figures/{i}_{name}", fig, tot_n_samples
+ )
+ torch.cuda.empty_cache() # should be cleared at the first iter
+
+ if (tot_it % conf.train.save_every_iter == 0 and tot_it > 0) and rank == 0:
+ if results is None:
+ results, _, _ = do_evaluation(
+ model,
+ val_loader,
+ device,
+ loss_fn,
+ conf.train,
+ pbar=(rank == -1),
+ )
+ best_eval = results[conf.train.best_key]
+ best_eval = save_experiment(
+ model,
+ optimizer,
+ lr_scheduler,
+ conf,
+ losses_,
+ results,
+ best_eval,
+ epoch,
+ tot_it,
+ output_dir,
+ stop,
+ args.distributed,
+ )
+
+ if stop:
+ break
+
+ if rank == 0:
+ best_eval = save_experiment(
+ model,
+ optimizer,
+ lr_scheduler,
+ conf,
+ losses_,
+ results,
+ best_eval,
+ epoch,
+ tot_it,
+ output_dir=output_dir,
+ stop=stop,
+ distributed=args.distributed,
+ )
+
+ epoch += 1
+
+ logger.info(f"Finished training on process {rank}.")
+ if rank == 0:
+ writer.close()
+
+
+def main_worker(rank, conf, output_dir, args):
+ if rank == 0:
+ with capture_outputs(output_dir / "log.txt"):
+ training(rank, conf, output_dir, args)
+ else:
+ training(rank, conf, output_dir, args)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("experiment", type=str)
+ parser.add_argument("--conf", type=str)
+ parser.add_argument(
+ "--mixed_precision",
+ "--mp",
+ default=None,
+ type=str,
+ choices=["float16", "bfloat16"],
+ )
+ parser.add_argument(
+ "--compile",
+ default=None,
+ type=str,
+ choices=["default", "reduce-overhead", "max-autotune"],
+ )
+ parser.add_argument("--overfit", action="store_true")
+ parser.add_argument("--restore", action="store_true")
+ parser.add_argument("--distributed", action="store_true")
+ parser.add_argument("--profile", action="store_true")
+ parser.add_argument("--print_arch", "--pa", action="store_true")
+ parser.add_argument("--detect_anomaly", "--da", action="store_true")
+ parser.add_argument("--log_it", "--log_it", action="store_true")
+ parser.add_argument("--no_eval_0", action="store_true")
+ parser.add_argument("--run_benchmarks", action="store_true")
+ parser.add_argument("dotlist", nargs="*")
+ args = parser.parse_intermixed_args()
+
+ logger.info(f"Starting experiment {args.experiment}")
+ output_dir = Path(TRAINING_PATH, args.experiment)
+ output_dir.mkdir(exist_ok=True, parents=True)
+
+ conf = OmegaConf.from_cli(args.dotlist)
+ if args.conf:
+ conf = OmegaConf.merge(OmegaConf.load(args.conf), conf)
+ elif args.restore:
+ restore_conf = OmegaConf.load(output_dir / "config.yaml")
+ conf = OmegaConf.merge(restore_conf, conf)
+ if not args.restore:
+ if conf.train.seed is None:
+ conf.train.seed = torch.initial_seed() & (2**32 - 1)
+ OmegaConf.save(conf, str(output_dir / "config.yaml"))
+
+ # copy gluefactory and submodule into output dir
+ for module in conf.train.get("submodules", []) + [__module_name__]:
+ mod_dir = Path(__import__(str(module)).__file__).parent
+ shutil.copytree(mod_dir, output_dir / module, dirs_exist_ok=True)
+
+ if args.distributed:
+ args.n_gpus = torch.cuda.device_count()
+ args.lock_file = output_dir / "distributed_lock"
+ if args.lock_file.exists():
+ args.lock_file.unlink()
+ torch.multiprocessing.spawn(
+ main_worker, nprocs=args.n_gpus, args=(conf, output_dir, args)
+ )
+ else:
+ main_worker(0, conf, output_dir, args)
diff --git a/imcui/third_party/omniglue/third_party/dinov2/__init__.py b/third_party/gim/gim/gluefactory/utils/__init__.py
similarity index 100%
rename from imcui/third_party/omniglue/third_party/dinov2/__init__.py
rename to third_party/gim/gim/gluefactory/utils/__init__.py
diff --git a/third_party/gim/gim/gluefactory/utils/benchmark.py b/third_party/gim/gim/gluefactory/utils/benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..99b4f85f6d8cb4a68eb16006242ea4df632e5bed
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/utils/benchmark.py
@@ -0,0 +1,33 @@
+import time
+
+import numpy as np
+import torch
+
+
+def benchmark(model, data, device, r=100):
+ timings = np.zeros((r, 1))
+ if device.type == "cuda":
+ starter = torch.cuda.Event(enable_timing=True)
+ ender = torch.cuda.Event(enable_timing=True)
+ # warmup
+ for _ in range(10):
+ _ = model(data)
+ # measurements
+ with torch.no_grad():
+ for rep in range(r):
+ if device.type == "cuda":
+ starter.record()
+ _ = model(data)
+ ender.record()
+ # sync gpu
+ torch.cuda.synchronize()
+ curr_time = starter.elapsed_time(ender)
+ else:
+ start = time.perf_counter()
+ _ = model(data)
+ curr_time = (time.perf_counter() - start) * 1e3
+ timings[rep] = curr_time
+
+ mean_syn = np.sum(timings) / r
+ std_syn = np.std(timings)
+ return {"mean": mean_syn, "std": std_syn}
diff --git a/third_party/gim/gim/gluefactory/utils/export_predictions.py b/third_party/gim/gim/gluefactory/utils/export_predictions.py
new file mode 100644
index 0000000000000000000000000000000000000000..1157a5209952aa0bd516d80390a9ddd8c2cd396c
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/utils/export_predictions.py
@@ -0,0 +1,81 @@
+"""
+Export the predictions of a model for a given dataloader (e.g. ImageFolder).
+Use a standalone script with `python3 -m dsfm.scipts.export_predictions dir`
+or call from another script.
+"""
+
+from pathlib import Path
+
+import h5py
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from .tensor import batch_to_device
+
+
+@torch.no_grad()
+def export_predictions(
+ loader,
+ model,
+ output_file,
+ as_half=False,
+ keys="*",
+ callback_fn=None,
+ optional_keys=[],
+):
+ assert keys == "*" or isinstance(keys, (tuple, list))
+ Path(output_file).parent.mkdir(exist_ok=True, parents=True)
+ hfile = h5py.File(str(output_file), "w")
+ device = "cuda" if torch.cuda.is_available() else "cpu"
+ model = model.to(device).eval()
+ for data_ in tqdm(loader):
+ data = batch_to_device(data_, device, non_blocking=True)
+ pred = model(data)
+ if callback_fn is not None:
+ pred = {**callback_fn(pred, data), **pred}
+ if keys != "*":
+ if len(set(keys) - set(pred.keys())) > 0:
+ raise ValueError(f"Missing key {set(keys) - set(pred.keys())}")
+ pred = {k: v for k, v in pred.items() if k in keys + optional_keys}
+ assert len(pred) > 0
+
+ # renormalization
+ for k in pred.keys():
+ if k.startswith("keypoints"):
+ idx = k.replace("keypoints", "")
+ scales = 1.0 / (
+ data["scales"] if len(idx) == 0 else data[f"view{idx}"]["scales"]
+ )
+ pred[k] = pred[k] * scales[None]
+ if k.startswith("lines"):
+ idx = k.replace("lines", "")
+ scales = 1.0 / (
+ data["scales"] if len(idx) == 0 else data[f"view{idx}"]["scales"]
+ )
+ pred[k] = pred[k] * scales[None]
+ if k.startswith("orig_lines"):
+ idx = k.replace("orig_lines", "")
+ scales = 1.0 / (
+ data["scales"] if len(idx) == 0 else data[f"view{idx}"]["scales"]
+ )
+ pred[k] = pred[k] * scales[None]
+
+ pred = {k: v[0].cpu().numpy() for k, v in pred.items()}
+
+ if as_half:
+ for k in pred:
+ dt = pred[k].dtype
+ if (dt == np.float32) and (dt != np.float16):
+ pred[k] = pred[k].astype(np.float16)
+ try:
+ name = data["name"][0]
+ grp = hfile.create_group(name)
+ for k, v in pred.items():
+ grp.create_dataset(k, data=v)
+ except RuntimeError:
+ continue
+
+ del pred
+ hfile.close()
+ return output_file
diff --git a/third_party/gim/gim/gluefactory/utils/image.py b/third_party/gim/gim/gluefactory/utils/image.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a9b1250c2297a4e86fbfa6980bbf7cbae7080fa
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/utils/image.py
@@ -0,0 +1,130 @@
+import collections.abc as collections
+from pathlib import Path
+from typing import Optional, Tuple
+
+import cv2
+import kornia
+import numpy as np
+import torch
+from omegaconf import OmegaConf
+
+
+class ImagePreprocessor:
+ default_conf = {
+ "resize": None, # target edge length, None for no resizing
+ "edge_divisible_by": None,
+ "side": "long",
+ "interpolation": "bilinear",
+ "align_corners": None,
+ "antialias": True,
+ "square_pad": False,
+ "add_padding_mask": False,
+ }
+
+ def __init__(self, conf) -> None:
+ super().__init__()
+ default_conf = OmegaConf.create(self.default_conf)
+ OmegaConf.set_struct(default_conf, True)
+ self.conf = OmegaConf.merge(default_conf, conf)
+
+ def __call__(self, img: torch.Tensor, interpolation: Optional[str] = None) -> dict:
+ """Resize and preprocess an image, return image and resize scale"""
+ h, w = img.shape[-2:]
+ size = h, w
+ if self.conf.resize is not None:
+ if interpolation is None:
+ interpolation = self.conf.interpolation
+ size = self.get_new_image_size(h, w)
+ img = kornia.geometry.transform.resize(
+ img,
+ size,
+ side=self.conf.side,
+ antialias=self.conf.antialias,
+ align_corners=self.conf.align_corners,
+ interpolation=interpolation,
+ )
+ scale = torch.Tensor([img.shape[-1] / w, img.shape[-2] / h]).to(img)
+ T = np.diag([scale[0], scale[1], 1])
+
+ data = {
+ "scales": scale,
+ "image_size": np.array(size[::-1]),
+ "transform": T,
+ "original_image_size": np.array([w, h]),
+ }
+ if self.conf.square_pad:
+ sl = max(img.shape[-2:])
+ data["image"] = torch.zeros(
+ *img.shape[:-2], sl, sl, device=img.device, dtype=img.dtype
+ )
+ data["image"][:, : img.shape[-2], : img.shape[-1]] = img
+ if self.conf.add_padding_mask:
+ data["padding_mask"] = torch.zeros(
+ *img.shape[:-3], 1, sl, sl, device=img.device, dtype=torch.bool
+ )
+ data["padding_mask"][:, : img.shape[-2], : img.shape[-1]] = True
+
+ else:
+ data["image"] = img
+ return data
+
+ def load_image(self, image_path: Path) -> dict:
+ return self(load_image(image_path))
+
+ def get_new_image_size(
+ self,
+ h: int,
+ w: int,
+ ) -> Tuple[int, int]:
+ side = self.conf.side
+ if isinstance(self.conf.resize, collections.Iterable):
+ assert len(self.conf.resize) == 2
+ return tuple(self.conf.resize)
+ side_size = self.conf.resize
+ aspect_ratio = w / h
+ if side not in ("short", "long", "vert", "horz"):
+ raise ValueError(
+ f"side can be one of 'short', 'long', 'vert', and 'horz'. Got '{side}'"
+ )
+ if side == "vert":
+ size = side_size, int(side_size * aspect_ratio)
+ elif side == "horz":
+ size = int(side_size / aspect_ratio), side_size
+ elif (side == "short") ^ (aspect_ratio < 1.0):
+ size = side_size, int(side_size * aspect_ratio)
+ else:
+ size = int(side_size / aspect_ratio), side_size
+
+ if self.conf.edge_divisible_by is not None:
+ df = self.conf.edge_divisible_by
+ size = list(map(lambda x: int(x // df * df), size))
+ return size
+
+
+def read_image(path: Path, grayscale: bool = False) -> np.ndarray:
+ """Read an image from path as RGB or grayscale"""
+ if not Path(path).exists():
+ raise FileNotFoundError(f"No image at path {path}.")
+ mode = cv2.IMREAD_GRAYSCALE if grayscale else cv2.IMREAD_COLOR
+ image = cv2.imread(str(path), mode)
+ if image is None:
+ raise IOError(f"Could not read image at {path}.")
+ if not grayscale:
+ image = image[..., ::-1]
+ return image
+
+
+def numpy_image_to_torch(image: np.ndarray) -> torch.Tensor:
+ """Normalize the image tensor and reorder the dimensions."""
+ if image.ndim == 3:
+ image = image.transpose((2, 0, 1)) # HxWxC to CxHxW
+ elif image.ndim == 2:
+ image = image[None] # add channel axis
+ else:
+ raise ValueError(f"Not an image: {image.shape}")
+ return torch.tensor(image / 255.0, dtype=torch.float)
+
+
+def load_image(path: Path, grayscale=False) -> torch.Tensor:
+ image = read_image(path, grayscale=grayscale)
+ return numpy_image_to_torch(image)
diff --git a/third_party/gim/gim/gluefactory/utils/misc.py b/third_party/gim/gim/gluefactory/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..34a3d05c30e1b7bd829ceb33c5c0698f92764e35
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/utils/misc.py
@@ -0,0 +1,44 @@
+import torch
+
+
+def to_view(data, i):
+ return {k + i: v for k, v in data.items()}
+
+
+def get_view(data, i):
+ data_g = {k: v for k, v in data.items() if not k[-1].isnumeric()}
+ data_i = {k[:-1]: v for k, v in data.items() if k[-1] == i}
+ return {**data_g, **data_i}
+
+
+def get_twoview(data, idx):
+ li = idx[0]
+ ri = idx[-1]
+ assert idx == f"{li}to{ri}"
+ data_lr = {k[:-4] + "0to1": v for k, v in data.items() if k[-4:] == f"{li}to{ri}"}
+ data_rl = {k[:-4] + "1to0": v for k, v in data.items() if k[-4:] == f"{ri}ito{li}"}
+ data_l = {
+ k[:-1] + "0": v for k, v in data.items() if k[-1:] == li and k[-3:-1] != "to"
+ }
+ data_r = {
+ k[:-1] + "1": v for k, v in data.items() if k[-1:] == ri and k[-3:-1] != "to"
+ }
+ return {**data_lr, **data_rl, **data_l, **data_r}
+
+
+def stack_twoviews(data, indices=["0to1", "0to2", "1to2"]):
+ idx0 = indices[0]
+ m_data = data[idx0] if idx0 in data else get_twoview(data, idx0)
+ # stack on dim=0
+ for idx in indices[1:]:
+ data_i = data[idx] if idx in data else get_twoview(data, idx)
+ for k, v in data_i.items():
+ m_data[k] = torch.cat([m_data[k], v], dim=0)
+ return m_data
+
+
+def unstack_twoviews(data, B, indices=["0to1", "0to2", "1to2"]):
+ out = {}
+ for i, idx in enumerate(indices):
+ out[idx] = {k: v[i * B : (i + 1) * B] for k, v in data.items()}
+ return out
diff --git a/third_party/gim/gim/gluefactory/utils/patches.py b/third_party/gim/gim/gluefactory/utils/patches.py
new file mode 100644
index 0000000000000000000000000000000000000000..b48ea0d2596c24af3b263a273abdda04698ecdd2
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/utils/patches.py
@@ -0,0 +1,50 @@
+import torch
+
+
+def extract_patches(
+ tensor: torch.Tensor,
+ required_corners: torch.Tensor,
+ ps: int,
+) -> torch.Tensor:
+ c, h, w = tensor.shape
+ corner = required_corners.long()
+ corner[:, 0] = corner[:, 0].clamp(min=0, max=w - 1 - ps)
+ corner[:, 1] = corner[:, 1].clamp(min=0, max=h - 1 - ps)
+ offset = torch.arange(0, ps)
+
+ kw = {"indexing": "ij"} if torch.__version__ >= "1.10" else {}
+ x, y = torch.meshgrid(offset, offset, **kw)
+ patches = torch.stack((x, y)).permute(2, 1, 0).unsqueeze(2)
+ patches = patches.to(corner) + corner[None, None]
+ pts = patches.reshape(-1, 2)
+ sampled = tensor.permute(1, 2, 0)[tuple(pts.T)[::-1]]
+ sampled = sampled.reshape(ps, ps, -1, c)
+ assert sampled.shape[:3] == patches.shape[:3]
+ return sampled.permute(2, 3, 0, 1), corner.float()
+
+
+def batch_extract_patches(tensor: torch.Tensor, kpts: torch.Tensor, ps: int):
+ b, c, h, w = tensor.shape
+ b, n, _ = kpts.shape
+ out = torch.zeros((b, n, c, ps, ps), dtype=tensor.dtype, device=tensor.device)
+ corners = torch.zeros((b, n, 2), dtype=tensor.dtype, device=tensor.device)
+ for i in range(b):
+ out[i], corners[i] = extract_patches(tensor[i], kpts[i] - ps / 2 - 1, ps)
+ return out, corners
+
+
+def draw_image_patches(img, patches, corners):
+ b, c, h, w = img.shape
+ b, n, c, p, p = patches.shape
+ b, n, _ = corners.shape
+ for i in range(b):
+ for k in range(n):
+ y, x = corners[i, k]
+ img[i, :, x : x + p, y : y + p] = patches[i, k]
+
+
+def build_heatmap(img, patches, corners):
+ hmap = torch.zeros_like(img)
+ draw_image_patches(hmap, patches, corners.long())
+ hmap = hmap.squeeze(1)
+ return hmap, (hmap > 0.0).float() # bxhxw
diff --git a/third_party/gim/gim/gluefactory/utils/stdout_capturing.py b/third_party/gim/gim/gluefactory/utils/stdout_capturing.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfa2b8325d3c32abf452655fc69494dec467839d
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/utils/stdout_capturing.py
@@ -0,0 +1,134 @@
+"""
+Based on sacred/stdout_capturing.py in project Sacred
+https://github.com/IDSIA/sacred
+
+Author: Paul-Edouard Sarlin (skydes)
+"""
+
+from __future__ import division, print_function, unicode_literals
+
+import os
+import subprocess
+import sys
+from contextlib import contextmanager
+from threading import Timer
+
+
+def apply_backspaces_and_linefeeds(text):
+ """
+ Interpret backspaces and linefeeds in text like a terminal would.
+ Interpret text like a terminal by removing backspace and linefeed
+ characters and applying them line by line.
+ If final line ends with a carriage it keeps it to be concatenable with next
+ output chunk.
+ """
+ orig_lines = text.split("\n")
+ orig_lines_len = len(orig_lines)
+ new_lines = []
+ for orig_line_idx, orig_line in enumerate(orig_lines):
+ chars, cursor = [], 0
+ orig_line_len = len(orig_line)
+ for orig_char_idx, orig_char in enumerate(orig_line):
+ if orig_char == "\r" and (
+ orig_char_idx != orig_line_len - 1
+ or orig_line_idx != orig_lines_len - 1
+ ):
+ cursor = 0
+ elif orig_char == "\b":
+ cursor = max(0, cursor - 1)
+ else:
+ if (
+ orig_char == "\r"
+ and orig_char_idx == orig_line_len - 1
+ and orig_line_idx == orig_lines_len - 1
+ ):
+ cursor = len(chars)
+ if cursor == len(chars):
+ chars.append(orig_char)
+ else:
+ chars[cursor] = orig_char
+ cursor += 1
+ new_lines.append("".join(chars))
+ return "\n".join(new_lines)
+
+
+def flush():
+ """Try to flush all stdio buffers, both from python and from C."""
+ try:
+ sys.stdout.flush()
+ sys.stderr.flush()
+ except (AttributeError, ValueError, IOError):
+ pass # unsupported
+
+
+# Duplicate stdout and stderr to a file. Inspired by:
+# http://eli.thegreenplace.net/2015/redirecting-all-kinds-of-stdout-in-python/
+# http://stackoverflow.com/a/651718/1388435
+# http://stackoverflow.com/a/22434262/1388435
+@contextmanager
+def capture_outputs(filename):
+ """Duplicate stdout and stderr to a file on the file descriptor level."""
+ with open(str(filename), "a+") as target:
+ original_stdout_fd = 1
+ original_stderr_fd = 2
+ target_fd = target.fileno()
+
+ # Save a copy of the original stdout and stderr file descriptors
+ saved_stdout_fd = os.dup(original_stdout_fd)
+ saved_stderr_fd = os.dup(original_stderr_fd)
+
+ tee_stdout = subprocess.Popen(
+ ["tee", "-a", "-i", "/dev/stderr"],
+ start_new_session=True,
+ stdin=subprocess.PIPE,
+ stderr=target_fd,
+ stdout=1,
+ )
+ tee_stderr = subprocess.Popen(
+ ["tee", "-a", "-i", "/dev/stderr"],
+ start_new_session=True,
+ stdin=subprocess.PIPE,
+ stderr=target_fd,
+ stdout=2,
+ )
+
+ flush()
+ os.dup2(tee_stdout.stdin.fileno(), original_stdout_fd)
+ os.dup2(tee_stderr.stdin.fileno(), original_stderr_fd)
+
+ try:
+ yield
+ finally:
+ flush()
+
+ # then redirect stdout back to the saved fd
+ tee_stdout.stdin.close()
+ tee_stderr.stdin.close()
+
+ # restore original fds
+ os.dup2(saved_stdout_fd, original_stdout_fd)
+ os.dup2(saved_stderr_fd, original_stderr_fd)
+
+ # wait for completion of the tee processes with timeout
+ # implemented using a timer because timeout support is py3 only
+ def kill_tees():
+ tee_stdout.kill()
+ tee_stderr.kill()
+
+ tee_timer = Timer(1, kill_tees)
+ try:
+ tee_timer.start()
+ tee_stdout.wait()
+ tee_stderr.wait()
+ finally:
+ tee_timer.cancel()
+
+ os.close(saved_stdout_fd)
+ os.close(saved_stderr_fd)
+
+ # Cleanup log file
+ with open(str(filename), "r") as target:
+ text = target.read()
+ text = apply_backspaces_and_linefeeds(text)
+ with open(str(filename), "w") as target:
+ target.write(text)
diff --git a/third_party/gim/gim/gluefactory/utils/tensor.py b/third_party/gim/gim/gluefactory/utils/tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0a8ca50d679df1cc17fa310f176edc891914d56
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/utils/tensor.py
@@ -0,0 +1,48 @@
+"""
+Author: Paul-Edouard Sarlin (skydes)
+"""
+
+import collections.abc as collections
+
+import numpy as np
+import torch
+
+string_classes = (str, bytes)
+
+
+def map_tensor(input_, func):
+ if isinstance(input_, string_classes):
+ return input_
+ elif isinstance(input_, collections.Mapping):
+ return {k: map_tensor(sample, func) for k, sample in input_.items()}
+ elif isinstance(input_, collections.Sequence):
+ return [map_tensor(sample, func) for sample in input_]
+ elif input_ is None:
+ return None
+ else:
+ return func(input_)
+
+
+def batch_to_numpy(batch):
+ return map_tensor(batch, lambda tensor: tensor.cpu().numpy())
+
+
+def batch_to_device(batch, device, non_blocking=True):
+ def _func(tensor):
+ return tensor.to(device=device, non_blocking=non_blocking)
+
+ return map_tensor(batch, _func)
+
+
+def rbd(data: dict) -> dict:
+ """Remove batch dimension from elements in data"""
+ return {
+ k: v[0] if isinstance(v, (torch.Tensor, np.ndarray, list)) else v
+ for k, v in data.items()
+ }
+
+
+def index_batch(tensor_dict):
+ batch_size = len(next(iter(tensor_dict.values())))
+ for i in range(batch_size):
+ yield map_tensor(tensor_dict, lambda t: t[i])
diff --git a/imcui/third_party/gim/networks/lightglue/utils/tools.py b/third_party/gim/gim/gluefactory/utils/tools.py
similarity index 100%
rename from imcui/third_party/gim/networks/lightglue/utils/tools.py
rename to third_party/gim/gim/gluefactory/utils/tools.py
diff --git a/third_party/gim/gim/gluefactory/visualization/global_frame.py b/third_party/gim/gim/gluefactory/visualization/global_frame.py
new file mode 100644
index 0000000000000000000000000000000000000000..a403c9c921079c4ac1b4d551a542de5b2cee5039
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/visualization/global_frame.py
@@ -0,0 +1,289 @@
+import functools
+import traceback
+from copy import deepcopy
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.widgets import Button
+from omegaconf import OmegaConf
+
+from ..datasets.base_dataset import collate
+
+# from ..eval.export_predictions import load_predictions
+from ..models.cache_loader import CacheLoader
+from .tools import RadioHideTool
+
+
+class GlobalFrame:
+ default_conf = {
+ "x": "???",
+ "y": "???",
+ "diff": False,
+ "child": {},
+ "remove_outliers": False,
+ }
+
+ child_frame = None # MatchFrame
+
+ childs = []
+
+ lines = []
+
+ scatters = {}
+
+ def __init__(
+ self, conf, results, loader, predictions, title=None, child_frame=None
+ ):
+ self.child_frame = child_frame
+ if self.child_frame is not None:
+ # We do NOT merge inside the child frame to keep settings across figs
+ self.default_conf["child"] = self.child_frame.default_conf
+
+ self.conf = OmegaConf.merge(self.default_conf, conf)
+ self.results = results
+ self.loader = loader
+ self.predictions = predictions
+ self.metrics = set()
+ for k, v in results.items():
+ self.metrics.update(v.keys())
+ self.metrics = sorted(list(self.metrics))
+
+ self.conf.x = conf["x"] if conf["x"] else self.metrics[0]
+ self.conf.y = conf["y"] if conf["y"] else self.metrics[1]
+
+ assert self.conf.x in self.metrics
+ assert self.conf.y in self.metrics
+
+ self.names = list(results)
+ self.fig, self.axes = self.init_frame()
+ if title is not None:
+ self.fig.canvas.manager.set_window_title(title)
+
+ self.xradios = self.fig.canvas.manager.toolmanager.add_tool(
+ "x",
+ RadioHideTool,
+ options=self.metrics,
+ callback_fn=self.update_x,
+ active=self.conf.x,
+ keymap="x",
+ )
+
+ self.yradios = self.fig.canvas.manager.toolmanager.add_tool(
+ "y",
+ RadioHideTool,
+ options=self.metrics,
+ callback_fn=self.update_y,
+ active=self.conf.y,
+ keymap="y",
+ )
+ if self.fig.canvas.manager.toolbar is not None:
+ self.fig.canvas.manager.toolbar.add_tool("x", "navigation")
+ self.fig.canvas.manager.toolbar.add_tool("y", "navigation")
+
+ def init_frame(self):
+ """initialize frame"""
+ fig, ax = plt.subplots()
+ ax.set_title("click on points")
+ diffb_ax = fig.add_axes([0.01, 0.02, 0.12, 0.06])
+ self.diffb = Button(diffb_ax, label="diff_only")
+ self.diffb.on_clicked(self.diff_clicked)
+ fig.canvas.mpl_connect("pick_event", self.on_scatter_pick)
+ fig.canvas.mpl_connect("motion_notify_event", self.hover)
+ return fig, ax
+
+ def draw(self):
+ """redraw content in frame"""
+ self.scatters = {}
+ self.axes.clear()
+ self.axes.set_xlabel(self.conf.x)
+ self.axes.set_ylabel(self.conf.y)
+
+ refx = 0.0
+ refy = 0.0
+ x_cat = isinstance(self.results[self.names[0]][self.conf.x][0], (bytes, str))
+ y_cat = isinstance(self.results[self.names[0]][self.conf.y][0], (bytes, str))
+
+ if self.conf.diff:
+ if not x_cat:
+ refx = np.array(self.results[self.names[0]][self.conf.x])
+ if not y_cat:
+ refy = np.array(self.results[self.names[0]][self.conf.y])
+ for name in list(self.results.keys()):
+ x = np.array(self.results[name][self.conf.x])
+ y = np.array(self.results[name][self.conf.y])
+
+ if x_cat and np.char.isdigit(x.astype(str)).all():
+ x = x.astype(int)
+ if y_cat and np.char.isdigit(y.astype(str)).all():
+ y = y.astype(int)
+
+ x = x if x_cat else x - refx
+ y = y if y_cat else y - refy
+
+ (s,) = self.axes.plot(
+ x, y, "o", markersize=3, label=name, picker=True, pickradius=5
+ )
+ self.scatters[name] = s
+
+ if x_cat and not y_cat:
+ xunique, ind, xinv, xbin = np.unique(
+ x, return_inverse=True, return_counts=True, return_index=True
+ )
+ ybin = np.bincount(xinv, weights=y)
+ sort_ax = np.argsort(ind)
+ self.axes.step(
+ xunique[sort_ax],
+ (ybin / xbin)[sort_ax],
+ where="mid",
+ color=s.get_color(),
+ )
+
+ if not x_cat:
+ xavg = np.nan_to_num(x).mean()
+ self.axes.axvline(xavg, c=s.get_color(), zorder=1, alpha=1.0)
+ xmed = np.median(x - refx)
+ self.axes.axvline(
+ xmed,
+ c=s.get_color(),
+ zorder=0,
+ alpha=0.5,
+ linestyle="dashed",
+ visible=False,
+ )
+
+ if not y_cat:
+ yavg = np.nan_to_num(y).mean()
+ self.axes.axhline(yavg, c=s.get_color(), zorder=1, alpha=0.5)
+ ymed = np.median(y - refy)
+ self.axes.axhline(
+ ymed,
+ c=s.get_color(),
+ zorder=0,
+ alpha=0.5,
+ linestyle="dashed",
+ visible=False,
+ )
+ if x_cat and x.dtype == object and xunique.shape[0] > 5:
+ self.axes.set_xticklabels(xunique[sort_ax], rotation=90)
+ self.axes.legend()
+
+ def on_scatter_pick(self, handle):
+ try:
+ art = handle.artist
+ try:
+ event = handle.mouseevent.button.value
+ except AttributeError:
+ return
+ name = art.get_label()
+ ind = handle.ind[0]
+ # draw lines
+ self.spawn_child(name, ind, event=event)
+ except Exception:
+ traceback.print_exc()
+ exit(0)
+
+ def spawn_child(self, model_name, ind, event=None):
+ [line.remove() for line in self.lines]
+ self.lines = []
+
+ x_source = self.scatters[model_name].get_xdata()[ind]
+ y_source = self.scatters[model_name].get_ydata()[ind]
+ for oname in self.names:
+ xn = self.scatters[oname].get_xdata()[ind]
+ yn = self.scatters[oname].get_ydata()[ind]
+
+ (ln,) = self.axes.plot([x_source, xn], [y_source, yn], "r")
+ self.lines.append(ln)
+
+ self.fig.canvas.draw_idle()
+
+ if self.child_frame is None:
+ return
+
+ data = collate([self.loader.dataset[ind]])
+
+ preds = {}
+
+ for name, pfile in self.predictions.items():
+ preds[name] = CacheLoader({"path": str(pfile), "add_data_path": False})(
+ data
+ )
+ summaries_i = {
+ name: {k: v[ind] for k, v in res.items() if k != "names"}
+ for name, res in self.results.items()
+ }
+ frame = self.child_frame(
+ self.conf.child,
+ deepcopy(data),
+ preds,
+ title=str(data["name"][0]),
+ event=event,
+ summaries=summaries_i,
+ )
+
+ frame.fig.canvas.mpl_connect(
+ "key_press_event",
+ functools.partial(
+ self.on_childframe_key_event, frame=frame, ind=ind, event=event
+ ),
+ )
+ self.childs.append(frame)
+ # if plt.rcParams['backend'] == 'webagg':
+ # self.fig.canvas.manager_class.refresh_all()
+ self.childs[-1].fig.show()
+
+ def hover(self, event):
+ if event.inaxes == self.axes:
+ for _, s in self.scatters.items():
+ cont, ind = s.contains(event)
+ if cont:
+ ind = ind["ind"][0]
+ xdata, ydata = s.get_data()
+ [line.remove() for line in self.lines]
+ self.lines = []
+
+ for oname in self.names:
+ xn = self.scatters[oname].get_xdata()[ind]
+ yn = self.scatters[oname].get_ydata()[ind]
+
+ (ln,) = self.axes.plot(
+ [xdata[ind], xn],
+ [ydata[ind], yn],
+ "black",
+ zorder=0,
+ alpha=0.5,
+ )
+ self.lines.append(ln)
+ self.fig.canvas.draw_idle()
+ break
+
+ def diff_clicked(self, args):
+ self.conf.diff = not self.conf.diff
+ self.draw()
+ self.fig.canvas.draw_idle()
+
+ def update_x(self, x):
+ self.conf.x = x
+ self.draw()
+
+ def update_y(self, y):
+ self.conf.y = y
+ self.draw()
+
+ def on_childframe_key_event(self, key_event, frame, ind, event):
+ if key_event.key == "delete":
+ plt.close(frame.fig)
+ self.childs.remove(frame)
+ elif key_event.key in ["left", "right", "shift+left", "shift+right"]:
+ key = key_event.key
+ if key.startswith("shift+"):
+ key = key.replace("shift+", "")
+ else:
+ plt.close(frame.fig)
+ self.childs.remove(frame)
+ new_ind = ind + 1 if key_event.key == "right" else ind - 1
+ self.spawn_child(
+ self.names[0],
+ new_ind % len(self.loader),
+ event=event,
+ )
diff --git a/third_party/gim/gim/gluefactory/visualization/tools.py b/third_party/gim/gim/gluefactory/visualization/tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..a095d06e95a857f45a64966b64c9085ed7a319cb
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/visualization/tools.py
@@ -0,0 +1,465 @@
+import inspect
+import sys
+import warnings
+
+import matplotlib.pyplot as plt
+import torch
+from matplotlib.backend_tools import ToolToggleBase
+from matplotlib.widgets import RadioButtons, Slider
+
+from ..geometry.epipolar import T_to_F, generalized_epi_dist
+from ..geometry.homography import sym_homography_error
+from ..visualization.viz2d import (
+ cm_ranking,
+ cm_RdGn,
+ draw_epipolar_line,
+ get_line,
+ plot_color_line_matches,
+ plot_heatmaps,
+ plot_keypoints,
+ plot_lines,
+ plot_matches,
+)
+
+with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
+ plt.rcParams["toolbar"] = "toolmanager"
+
+
+class RadioHideTool(ToolToggleBase):
+ """Show lines with a given gid."""
+
+ default_keymap = "R"
+ description = "Show by gid"
+ default_toggled = False
+ radio_group = "default"
+
+ def __init__(
+ self, *args, options=[], active=None, callback_fn=None, keymap="R", **kwargs
+ ):
+ super().__init__(*args, **kwargs)
+ self.f = 1.0
+ self.options = options
+ self.callback_fn = callback_fn
+ self.active = self.options.index(active) if active else 0
+ self.default_keymap = keymap
+
+ self.enabled = self.default_toggled
+
+ def build_radios(self):
+ w = 0.2
+ self.radios_ax = self.figure.add_axes([1.0 - w, 0.7, w, 0.2], zorder=1)
+ # self.radios_ax = self.figure.add_axes([0.5-w/2, 1.0-0.2, w, 0.2], zorder=1)
+ self.radios = RadioButtons(self.radios_ax, self.options, active=self.active)
+ self.radios.on_clicked(self.on_radio_clicked)
+
+ def enable(self, *args):
+ size = self.figure.get_size_inches()
+ size[0] *= self.f
+ self.build_radios()
+ self.figure.canvas.draw_idle()
+ self.enabled = True
+
+ def disable(self, *args):
+ size = self.figure.get_size_inches()
+ size[0] /= self.f
+ self.radios_ax.remove()
+ self.radios = None
+ self.figure.canvas.draw_idle()
+ self.enabled = False
+
+ def on_radio_clicked(self, value):
+ self.active = self.options.index(value)
+ enabled = self.enabled
+ if enabled:
+ self.disable()
+ if self.callback_fn is not None:
+ self.callback_fn(value)
+ if enabled:
+ self.enable()
+
+
+class ToggleTool(ToolToggleBase):
+ """Show lines with a given gid."""
+
+ default_keymap = "t"
+ description = "Show by gid"
+
+ def __init__(self, *args, callback_fn=None, keymap="t", **kwargs):
+ super().__init__(*args, **kwargs)
+ self.f = 1.0
+ self.callback_fn = callback_fn
+ self.default_keymap = keymap
+ self.enabled = self.default_toggled
+
+ def enable(self, *args):
+ self.callback_fn(True)
+
+ def disable(self, *args):
+ self.callback_fn(False)
+
+
+def add_whitespace_left(fig, factor):
+ w, h = fig.get_size_inches()
+ left = fig.subplotpars.left
+ fig.set_size_inches([w * (1 + factor), h])
+ fig.subplots_adjust(left=(factor + left) / (1 + factor))
+
+
+def add_whitespace_bottom(fig, factor):
+ w, h = fig.get_size_inches()
+ b = fig.subplotpars.bottom
+ fig.set_size_inches([w, h * (1 + factor)])
+ fig.subplots_adjust(bottom=(factor + b) / (1 + factor))
+ fig.canvas.draw_idle()
+
+
+class KeypointPlot:
+ plot_name = "keypoints"
+ required_keys = ["keypoints0", "keypoints1"]
+
+ def __init__(self, fig, axes, data, preds):
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ plot_keypoints([pred["keypoints0"][0], pred["keypoints1"][0]], axes=axes[i])
+
+
+class LinePlot:
+ plot_name = "lines"
+ required_keys = ["lines0", "lines1"]
+
+ def __init__(self, fig, axes, data, preds):
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ plot_lines([pred["lines0"][0], pred["lines1"][0]])
+
+
+class KeypointRankingPlot:
+ plot_name = "keypoint_ranking"
+ required_keys = ["keypoints0", "keypoints1", "keypoint_scores0", "keypoint_scores1"]
+
+ def __init__(self, fig, axes, data, preds):
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ kp0, kp1 = pred["keypoints0"][0], pred["keypoints1"][0]
+ sc0, sc1 = pred["keypoint_scores0"][0], pred["keypoint_scores1"][0]
+
+ plot_keypoints(
+ [kp0, kp1], axes=axes[i], colors=[cm_ranking(sc0), cm_ranking(sc1)]
+ )
+
+
+class KeypointScoresPlot:
+ plot_name = "keypoint_scores"
+ required_keys = ["keypoints0", "keypoints1", "keypoint_scores0", "keypoint_scores1"]
+
+ def __init__(self, fig, axes, data, preds):
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ kp0, kp1 = pred["keypoints0"][0], pred["keypoints1"][0]
+ sc0, sc1 = pred["keypoint_scores0"][0], pred["keypoint_scores1"][0]
+ plot_keypoints(
+ [kp0, kp1], axes=axes[i], colors=[cm_RdGn(sc0), cm_RdGn(sc1)]
+ )
+
+
+class HeatmapPlot:
+ plot_name = "heatmaps"
+ required_keys = ["heatmap0", "heatmap1"]
+
+ def __init__(self, fig, axes, data, preds):
+ self.artists = []
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ heatmaps = [pred["heatmap0"][0, 0], pred["heatmap1"][0, 0]]
+ heatmaps = [torch.sigmoid(h) if h.min() < 0.0 else h for h in heatmaps]
+ self.artists += plot_heatmaps(heatmaps, axes=axes[i], cmap="rainbow")
+
+ def clear(self):
+ for x in self.artists:
+ x.remove()
+
+
+class ImagePlot:
+ plot_name = "images"
+ required_keys = ["view0", "view1"]
+
+ def __init__(self, fig, axes, data, preds):
+ pass
+
+
+class MatchesPlot:
+ plot_name = "matches"
+ required_keys = ["keypoints0", "keypoints1", "matches0", "matching_scores0"]
+
+ def __init__(self, fig, axes, data, preds):
+ self.fig = fig
+ self.sbpars = {
+ k: v
+ for k, v in vars(fig.subplotpars).items()
+ if k in ["left", "right", "top", "bottom"]
+ }
+
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ plot_keypoints(
+ [pred["keypoints0"][0], pred["keypoints1"][0]],
+ axes=axes[i],
+ colors="blue",
+ )
+ kp0, kp1 = pred["keypoints0"][0], pred["keypoints1"][0]
+ m0 = pred["matches0"][0]
+ valid = m0 > -1
+ kpm0 = kp0[valid]
+ kpm1 = kp1[m0[valid]]
+ mscores = pred["matching_scores0"][0][valid]
+ plot_matches(
+ kpm0,
+ kpm1,
+ color=cm_RdGn(mscores).tolist(),
+ axes=axes[i],
+ labels=mscores,
+ lw=0.5,
+ )
+
+
+class LineMatchesPlot:
+ plot_name = "line_matches"
+ required_keys = ["lines0", "lines1", "line_matches0"]
+
+ def __init__(self, fig, axes, data, preds):
+ self.fig = fig
+ self.sbpars = {
+ k: v
+ for k, v in vars(fig.subplotpars).items()
+ if k in ["left", "right", "top", "bottom"]
+ }
+
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ lines0, lines1 = pred["lines0"][0], pred["lines1"][0]
+ m0 = pred["line_matches0"][0]
+ valid = m0 > -1
+ m_lines0 = lines0[valid]
+ m_lines1 = lines1[m0[valid]]
+ plot_color_line_matches([m_lines0, m_lines1])
+
+
+class GtMatchesPlot:
+ plot_name = "gt_matches"
+ required_keys = ["keypoints0", "keypoints1", "matches0", "gt_matches0"]
+
+ def __init__(self, fig, axes, data, preds):
+ self.fig = fig
+ self.sbpars = {
+ k: v
+ for k, v in vars(fig.subplotpars).items()
+ if k in ["left", "right", "top", "bottom"]
+ }
+
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ plot_keypoints(
+ [pred["keypoints0"][0], pred["keypoints1"][0]],
+ axes=axes[i],
+ colors="blue",
+ )
+ kp0, kp1 = pred["keypoints0"][0], pred["keypoints1"][0]
+ m0 = pred["matches0"][0]
+ gtm0 = pred["gt_matches0"][0]
+ valid = (m0 > -1) & (gtm0 >= -1)
+ kpm0 = kp0[valid]
+ kpm1 = kp1[m0[valid]]
+ correct = gtm0[valid] == m0[valid]
+ plot_matches(
+ kpm0,
+ kpm1,
+ color=cm_RdGn(correct).tolist(),
+ axes=axes[i],
+ labels=correct,
+ lw=0.5,
+ )
+
+
+class GtLineMatchesPlot:
+ plot_name = "gt_line_matches"
+ required_keys = ["lines0", "lines1", "line_matches0", "line_gt_matches0"]
+
+ def __init__(self, fig, axes, data, preds):
+ self.fig = fig
+ self.sbpars = {
+ k: v
+ for k, v in vars(fig.subplotpars).items()
+ if k in ["left", "right", "top", "bottom"]
+ }
+
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ lines0, lines1 = pred["lines0"][0], pred["lines1"][0]
+ m0 = pred["line_matches0"][0]
+ gtm0 = pred["gt_line_matches0"][0]
+ valid = (m0 > -1) & (gtm0 >= -1)
+ m_lines0 = lines0[valid]
+ m_lines1 = lines1[m0[valid]]
+ plot_color_line_matches([m_lines0, m_lines1])
+
+
+class HomographyMatchesPlot:
+ plot_name = "homography"
+ required_keys = ["keypoints0", "keypoints1", "matches0", "H_0to1"]
+
+ def __init__(self, fig, axes, data, preds):
+ self.fig = fig
+ self.sbpars = {
+ k: v
+ for k, v in vars(fig.subplotpars).items()
+ if k in ["left", "right", "top", "bottom"]
+ }
+
+ add_whitespace_bottom(fig, 0.1)
+
+ self.range_ax = fig.add_axes([0.3, 0.02, 0.4, 0.06])
+ self.range = Slider(
+ self.range_ax,
+ label="Homography Error",
+ valmin=0,
+ valmax=5,
+ valinit=3.0,
+ valstep=1.0,
+ )
+ self.range.on_changed(self.color_matches)
+
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ plot_keypoints(
+ [pred["keypoints0"][0], pred["keypoints1"][0]],
+ axes=axes[i],
+ colors="blue",
+ )
+ kp0, kp1 = pred["keypoints0"][0], pred["keypoints1"][0]
+ m0 = pred["matches0"][0]
+ valid = m0 > -1
+ kpm0 = kp0[valid]
+ kpm1 = kp1[m0[valid]]
+ errors = sym_homography_error(kpm0, kpm1, data["H_0to1"][0])
+ plot_matches(
+ kpm0,
+ kpm1,
+ color=cm_RdGn(errors < self.range.val).tolist(),
+ axes=axes[i],
+ labels=errors.numpy(),
+ lw=0.5,
+ )
+
+ def clear(self):
+ w, h = self.fig.get_size_inches()
+ self.fig.set_size_inches(w, h / 1.1)
+ self.fig.subplots_adjust(**self.sbpars)
+ self.range_ax.remove()
+
+ def color_matches(self, args):
+ for line in self.fig.artists:
+ label = line.get_label()
+ line.set_color(cm_RdGn([float(label) < args])[0])
+
+
+class EpipolarMatchesPlot:
+ plot_name = "epipolar_matches"
+ required_keys = ["keypoints0", "keypoints1", "matches0", "T_0to1", "view0", "view1"]
+
+ def __init__(self, fig, axes, data, preds):
+ self.fig = fig
+ self.axes = axes
+ self.sbpars = {
+ k: v
+ for k, v in vars(fig.subplotpars).items()
+ if k in ["left", "right", "top", "bottom"]
+ }
+
+ add_whitespace_bottom(fig, 0.1)
+
+ self.range_ax = fig.add_axes([0.3, 0.02, 0.4, 0.06])
+ self.range = Slider(
+ self.range_ax,
+ label="Epipolar Error [px]",
+ valmin=0,
+ valmax=5,
+ valinit=3.0,
+ valstep=1.0,
+ )
+ self.range.on_changed(self.color_matches)
+
+ camera0 = data["view0"]["camera"][0]
+ camera1 = data["view1"]["camera"][0]
+ T_0to1 = data["T_0to1"][0]
+
+ for i, name in enumerate(preds):
+ pred = preds[name]
+ plot_keypoints(
+ [pred["keypoints0"][0], pred["keypoints1"][0]],
+ axes=axes[i],
+ colors="blue",
+ )
+ kp0, kp1 = pred["keypoints0"][0], pred["keypoints1"][0]
+ m0 = pred["matches0"][0]
+ valid = m0 > -1
+ kpm0 = kp0[valid]
+ kpm1 = kp1[m0[valid]]
+
+ errors = generalized_epi_dist(
+ kpm0,
+ kpm1,
+ camera0,
+ camera1,
+ T_0to1,
+ all=False,
+ essential=False,
+ )
+ plot_matches(
+ kpm0,
+ kpm1,
+ color=cm_RdGn(errors < self.range.val).tolist(),
+ axes=axes[i],
+ labels=errors.numpy(),
+ lw=0.5,
+ )
+
+ self.F = T_to_F(camera0, camera1, T_0to1)
+
+ def clear(self):
+ w, h = self.fig.get_size_inches()
+ self.fig.set_size_inches(w, h / 1.1)
+ self.fig.subplots_adjust(**self.sbpars)
+ self.range_ax.remove()
+
+ def color_matches(self, args):
+ for art in self.fig.artists:
+ label = art.get_label()
+ if label is not None:
+ art.set_color(cm_RdGn([float(label) < args])[0])
+
+ def click_artist(self, event):
+ art = event.artist
+ if art.get_label() is not None:
+ if hasattr(art, "epilines"):
+ [
+ x.set_visible(not x.get_visible())
+ for x in art.epilines
+ if x is not None
+ ]
+ else:
+ xy1 = art.xy1
+ xy2 = art.xy2
+ line0 = get_line(self.F.transpose(0, 1), xy2)[:, 0]
+ line1 = get_line(self.F, xy1)[:, 0]
+ art.epilines = [
+ draw_epipolar_line(line0, art.axesA),
+ draw_epipolar_line(line1, art.axesB),
+ ]
+
+
+__plot_dict__ = {
+ obj.plot_name: obj
+ for _, obj in inspect.getmembers(sys.modules[__name__], predicate=inspect.isclass)
+ if hasattr(obj, "plot_name")
+}
diff --git a/third_party/gim/gim/gluefactory/visualization/two_view_frame.py b/third_party/gim/gim/gluefactory/visualization/two_view_frame.py
new file mode 100644
index 0000000000000000000000000000000000000000..3461eb0eb5587bcee48193aaa827689a6e27e01f
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/visualization/two_view_frame.py
@@ -0,0 +1,158 @@
+import pprint
+
+import numpy as np
+
+from . import viz2d
+from .tools import RadioHideTool, ToggleTool, __plot_dict__
+
+
+class FormatPrinter(pprint.PrettyPrinter):
+ def __init__(self, formats):
+ super(FormatPrinter, self).__init__()
+ self.formats = formats
+
+ def format(self, obj, ctx, maxlvl, lvl):
+ if type(obj) in self.formats:
+ return self.formats[type(obj)] % obj, 1, 0
+ return pprint.PrettyPrinter.format(self, obj, ctx, maxlvl, lvl)
+
+
+class TwoViewFrame:
+ default_conf = {
+ "default": "matches",
+ "summary_visible": False,
+ }
+
+ plot_dict = __plot_dict__
+
+ childs = []
+
+ event_to_image = [None, "color", "depth", "color+depth"]
+
+ def __init__(self, conf, data, preds, title=None, event=1, summaries=None):
+ self.conf = conf
+ self.data = data
+ self.preds = preds
+ self.names = list(preds.keys())
+ self.plot = self.event_to_image[event]
+ self.summaries = summaries
+ self.fig, self.axes, self.summary_arts = self.init_frame()
+ if title is not None:
+ self.fig.canvas.manager.set_window_title(title)
+
+ keys = None
+ for _, pred in preds.items():
+ if keys is None:
+ keys = set(pred.keys())
+ else:
+ keys = keys.intersection(pred.keys())
+ keys = keys.union(data.keys())
+
+ self.options = [
+ k for k, v in self.plot_dict.items() if set(v.required_keys).issubset(keys)
+ ]
+ self.handle = None
+ self.radios = self.fig.canvas.manager.toolmanager.add_tool(
+ "switch plot",
+ RadioHideTool,
+ options=self.options,
+ callback_fn=self.draw,
+ active=conf.default,
+ keymap="R",
+ )
+
+ self.toggle_summary = self.fig.canvas.manager.toolmanager.add_tool(
+ "toggle summary",
+ ToggleTool,
+ toggled=self.conf.summary_visible,
+ callback_fn=self.set_summary_visible,
+ keymap="t",
+ )
+
+ if self.fig.canvas.manager.toolbar is not None:
+ self.fig.canvas.manager.toolbar.add_tool("switch plot", "navigation")
+ self.draw(conf.default)
+
+ def init_frame(self):
+ """initialize frame"""
+ view0, view1 = self.data["view0"], self.data["view1"]
+ if self.plot == "color" or self.plot == "color+depth":
+ imgs = [
+ view0["image"][0].permute(1, 2, 0),
+ view1["image"][0].permute(1, 2, 0),
+ ]
+ elif self.plot == "depth":
+ imgs = [view0["depth"][0], view1["depth"][0]]
+ else:
+ raise ValueError(self.plot)
+ imgs = [imgs for _ in self.names] # repeat for each model
+
+ fig, axes = viz2d.plot_image_grid(imgs, return_fig=True, titles=None, figs=5)
+ [viz2d.add_text(0, n, axes=axes[i]) for i, n in enumerate(self.names)]
+
+ if (
+ self.plot == "color+depth"
+ and "depth" in view0.keys()
+ and view0["depth"] is not None
+ ):
+ hmaps = [[view0["depth"][0], view1["depth"][0]] for _ in self.names]
+ [
+ viz2d.plot_heatmaps(hmaps[i], axes=axes[i], cmap="Spectral")
+ for i, _ in enumerate(hmaps)
+ ]
+
+ fig.canvas.mpl_connect("pick_event", self.click_artist)
+ if self.summaries is not None:
+ formatter = FormatPrinter({np.float32: "%.4f", np.float64: "%.4f"})
+ toggle_artists = [
+ viz2d.add_text(
+ 0,
+ formatter.pformat(self.summaries[n]),
+ axes=axes[i],
+ pos=(0.01, 0.01),
+ va="bottom",
+ backgroundcolor=(0, 0, 0, 0.5),
+ visible=self.conf.summary_visible,
+ )
+ for i, n in enumerate(self.names)
+ ]
+ else:
+ toggle_artists = []
+ return fig, axes, toggle_artists
+
+ def draw(self, value):
+ """redraw content in frame"""
+ self.clear()
+ self.conf.default = value
+ self.handle = self.plot_dict[value](self.fig, self.axes, self.data, self.preds)
+ return self.handle
+
+ def clear(self):
+ if self.handle is not None:
+ try:
+ self.handle.clear()
+ except AttributeError:
+ pass
+ self.handle = None
+ for row in self.axes:
+ for ax in row:
+ [li.remove() for li in ax.lines]
+ [c.remove() for c in ax.collections]
+ self.fig.artists.clear()
+ self.fig.canvas.draw_idle()
+ self.handle = None
+
+ def click_artist(self, event):
+ art = event.artist
+ select = art.get_arrowstyle().arrow == "-"
+ art.set_arrowstyle("<|-|>" if select else "-")
+ if select:
+ art.set_zorder(1)
+ if hasattr(self.handle, "click_artist"):
+ self.handle.click_artist(event)
+ self.fig.canvas.draw_idle()
+
+ def set_summary_visible(self, visible):
+ self.conf.summary_visible = visible
+ [s.set_visible(visible) for s in self.summary_arts]
+ self.fig.canvas.draw_idle()
diff --git a/third_party/gim/gim/gluefactory/visualization/visualize_batch.py b/third_party/gim/gim/gluefactory/visualization/visualize_batch.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bd3f7b65c2b1933653b04b68acf761979c8b2ac
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/visualization/visualize_batch.py
@@ -0,0 +1,57 @@
+import torch
+
+from ..utils.tensor import batch_to_device
+from .viz2d import cm_RdGn, plot_heatmaps, plot_image_grid, plot_keypoints, plot_matches
+
+
+def make_match_figures(pred_, data_, n_pairs=2):
+ # print first n pairs in batch
+ if "0to1" in pred_.keys():
+ pred_ = pred_["0to1"]
+ images, kpts, matches, mcolors = [], [], [], []
+ heatmaps = []
+ pred = batch_to_device(pred_, "cpu", non_blocking=False)
+ data = batch_to_device(data_, "cpu", non_blocking=False)
+
+ view0, view1 = data["view0"], data["view1"]
+
+ n_pairs = min(n_pairs, view0["image"].shape[0])
+ assert view0["image"].shape[0] >= n_pairs
+
+ kp0, kp1 = pred["keypoints0"], pred["keypoints1"]
+ m0 = pred["matches0"]
+ gtm0 = pred["gt_matches0"]
+
+ for i in range(n_pairs):
+ valid = (m0[i] > -1) & (gtm0[i] >= -1)
+ kpm0, kpm1 = kp0[i][valid].numpy(), kp1[i][m0[i][valid]].numpy()
+ images.append(
+ [view0["image"][i].permute(1, 2, 0), view1["image"][i].permute(1, 2, 0)]
+ )
+ kpts.append([kp0[i], kp1[i]])
+ matches.append((kpm0, kpm1))
+
+ correct = gtm0[i][valid] == m0[i][valid]
+
+ if "heatmap0" in pred.keys():
+ heatmaps.append(
+ [
+ torch.sigmoid(pred["heatmap0"][i, 0]),
+ torch.sigmoid(pred["heatmap1"][i, 0]),
+ ]
+ )
+ elif "depth" in view0.keys() and view0["depth"] is not None:
+ heatmaps.append([view0["depth"][i], view1["depth"][i]])
+
+ mcolors.append(cm_RdGn(correct).tolist())
+
+ fig, axes = plot_image_grid(images, return_fig=True, set_lim=True)
+ if len(heatmaps) > 0:
+ [plot_heatmaps(heatmaps[i], axes=axes[i], a=1.0) for i in range(n_pairs)]
+ [plot_keypoints(kpts[i], axes=axes[i], colors="royalblue") for i in range(n_pairs)]
+ [
+ plot_matches(*matches[i], color=mcolors[i], axes=axes[i], a=0.5, lw=1.0, ps=0.0)
+ for i in range(n_pairs)
+ ]
+
+ return {"matching": fig}
diff --git a/third_party/gim/gim/gluefactory/visualization/viz2d.py b/third_party/gim/gim/gluefactory/visualization/viz2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfa6473584ec8d742efa5cef3867e6778c46adc6
--- /dev/null
+++ b/third_party/gim/gim/gluefactory/visualization/viz2d.py
@@ -0,0 +1,486 @@
+"""
+2D visualization primitives based on Matplotlib.
+1) Plot images with `plot_images`.
+2) Call `plot_keypoints` or `plot_matches` any number of times.
+3) Optionally: save a .png or .pdf plot (nice in papers!) with `save_plot`.
+"""
+
+import matplotlib
+import matplotlib.patheffects as path_effects
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+
+def cm_ranking(sc, ths=[512, 1024, 2048, 4096]):
+ ls = sc.shape[0]
+ colors = ["red", "yellow", "lime", "cyan", "blue"]
+ out = ["gray"] * ls
+ for i in range(ls):
+ for c, th in zip(colors[: len(ths) + 1], ths + [ls]):
+ if i < th:
+ out[i] = c
+ break
+ sid = np.argsort(sc, axis=0).flip(0)
+ out = np.array(out)[sid]
+ return out
+
+
+def cm_RdBl(x):
+ """Custom colormap: red (0) -> yellow (0.5) -> green (1)."""
+ x = np.clip(x, 0, 1)[..., None] * 2
+ c = x * np.array([[0, 0, 1.0]]) + (2 - x) * np.array([[1.0, 0, 0]])
+ return np.clip(c, 0, 1)
+
+
+def cm_RdGn(x):
+ """Custom colormap: red (0) -> yellow (0.5) -> green (1)."""
+ x = np.clip(x, 0, 1)[..., None] * 2
+ c = x * np.array([[0, 1.0, 0]]) + (2 - x) * np.array([[1.0, 0, 0]])
+ return np.clip(c, 0, 1)
+
+
+def cm_BlRdGn(x_):
+ """Custom colormap: blue (-1) -> red (0.0) -> green (1)."""
+ x = np.clip(x_, 0, 1)[..., None] * 2
+ c = x * np.array([[0, 1.0, 0, 1.0]]) + (2 - x) * np.array([[1.0, 0, 0, 1.0]])
+
+ xn = -np.clip(x_, -1, 0)[..., None] * 2
+ cn = xn * np.array([[0, 1.0, 0, 1.0]]) + (2 - xn) * np.array([[1.0, 0, 0, 1.0]])
+ out = np.clip(np.where(x_[..., None] < 0, cn, c), 0, 1)
+ return out
+
+
+def plot_images(imgs, titles=None, cmaps="gray", dpi=100, pad=0.5, adaptive=True):
+ """Plot a set of images horizontally.
+ Args:
+ imgs: a list of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W).
+ titles: a list of strings, as titles for each image.
+ cmaps: colormaps for monochrome images.
+ adaptive: whether the figure size should fit the image aspect ratios.
+ """
+ n = len(imgs)
+ if not isinstance(cmaps, (list, tuple)):
+ cmaps = [cmaps] * n
+
+ if adaptive:
+ ratios = [i.shape[1] / i.shape[0] for i in imgs] # W / H
+ else:
+ ratios = [4 / 3] * n
+ figsize = [sum(ratios) * 4.5, 4.5]
+ fig, axs = plt.subplots(
+ 1, n, figsize=figsize, dpi=dpi, gridspec_kw={"width_ratios": ratios}
+ )
+ if n == 1:
+ axs = [axs]
+ for i, (img, ax) in enumerate(zip(imgs, axs)):
+ ax.imshow(img, cmap=plt.get_cmap(cmaps[i]))
+ ax.set_axis_off()
+ if titles:
+ ax.set_title(titles[i])
+ fig.tight_layout(pad=pad)
+
+
+def plot_image_grid(
+ imgs,
+ titles=None,
+ cmaps="gray",
+ dpi=100,
+ pad=0.5,
+ fig=None,
+ adaptive=True,
+ figs=2.0,
+ return_fig=False,
+ set_lim=False,
+):
+ """Plot a grid of images.
+ Args:
+ imgs: a list of lists of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W).
+ titles: a list of strings, as titles for each image.
+ cmaps: colormaps for monochrome images.
+ adaptive: whether the figure size should fit the image aspect ratios.
+ """
+ nr, n = len(imgs), len(imgs[0])
+ if not isinstance(cmaps, (list, tuple)):
+ cmaps = [cmaps] * n
+
+ if adaptive:
+ ratios = [i.shape[1] / i.shape[0] for i in imgs[0]] # W / H
+ else:
+ ratios = [4 / 3] * n
+
+ figsize = [sum(ratios) * figs, nr * figs]
+ if fig is None:
+ fig, axs = plt.subplots(
+ nr, n, figsize=figsize, dpi=dpi, gridspec_kw={"width_ratios": ratios}
+ )
+ else:
+ axs = fig.subplots(nr, n, gridspec_kw={"width_ratios": ratios})
+ fig.figure.set_size_inches(figsize)
+ if nr == 1:
+ axs = [axs]
+
+ for j in range(nr):
+ for i in range(n):
+ ax = axs[j][i]
+ ax.imshow(imgs[j][i], cmap=plt.get_cmap(cmaps[i]))
+ ax.set_axis_off()
+ if set_lim:
+ ax.set_xlim([0, imgs[j][i].shape[1]])
+ ax.set_ylim([imgs[j][i].shape[0], 0])
+ if titles:
+ ax.set_title(titles[j][i])
+ if isinstance(fig, plt.Figure):
+ fig.tight_layout(pad=pad)
+ if return_fig:
+ return fig, axs
+ else:
+ return axs
+
+
+def plot_keypoints(kpts, colors="lime", ps=4, axes=None, a=1.0):
+ """Plot keypoints for existing images.
+ Args:
+ kpts: list of ndarrays of size (N, 2).
+ colors: string, or list of list of tuples (one for each keypoints).
+ ps: size of the keypoints as float.
+ """
+ if not isinstance(colors, list):
+ colors = [colors] * len(kpts)
+ if not isinstance(a, list):
+ a = [a] * len(kpts)
+ if axes is None:
+ axes = plt.gcf().axes
+ for ax, k, c, alpha in zip(axes, kpts, colors, a):
+ ax.scatter(k[:, 0], k[:, 1], c=c, s=ps, linewidths=0, alpha=alpha)
+
+
+def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, a=1.0, labels=None, axes=None):
+ """Plot matches for a pair of existing images.
+ Args:
+ kpts0, kpts1: corresponding keypoints of size (N, 2).
+ color: color of each match, string or RGB tuple. Random if not given.
+ lw: width of the lines.
+ ps: size of the end points (no endpoint if ps=0)
+ indices: indices of the images to draw the matches on.
+ a: alpha opacity of the match lines.
+ """
+ fig = plt.gcf()
+ if axes is None:
+ ax = fig.axes
+ ax0, ax1 = ax[0], ax[1]
+ else:
+ ax0, ax1 = axes
+
+ assert len(kpts0) == len(kpts1)
+ if color is None:
+ color = sns.color_palette("husl", n_colors=len(kpts0))
+ elif len(color) > 0 and not isinstance(color[0], (tuple, list)):
+ color = [color] * len(kpts0)
+
+ if lw > 0:
+ for i in range(len(kpts0)):
+ line = matplotlib.patches.ConnectionPatch(
+ xyA=(kpts0[i, 0], kpts0[i, 1]),
+ xyB=(kpts1[i, 0], kpts1[i, 1]),
+ coordsA=ax0.transData,
+ coordsB=ax1.transData,
+ axesA=ax0,
+ axesB=ax1,
+ zorder=1,
+ color=color[i],
+ linewidth=lw,
+ clip_on=True,
+ alpha=a,
+ label=None if labels is None else labels[i],
+ picker=5.0,
+ )
+ line.set_annotation_clip(True)
+ fig.add_artist(line)
+
+ # freeze the axes to prevent the transform to change
+ ax0.autoscale(enable=False)
+ ax1.autoscale(enable=False)
+
+ if ps > 0:
+ ax0.scatter(
+ kpts0[:, 0],
+ kpts0[:, 1],
+ c=color,
+ s=ps,
+ label=None if labels is None or len(labels) == 0 else labels[0],
+ )
+ ax1.scatter(
+ kpts1[:, 0],
+ kpts1[:, 1],
+ c=color,
+ s=ps,
+ label=None if labels is None or len(labels) == 0 else labels[1],
+ )
+
+
+def add_text(
+ idx,
+ text,
+ pos=(0.01, 0.99),
+ fs=15,
+ color="w",
+ lcolor="k",
+ lwidth=2,
+ ha="left",
+ va="top",
+ axes=None,
+ **kwargs,
+):
+ if axes is None:
+ axes = plt.gcf().axes
+
+ ax = axes[idx]
+ t = ax.text(
+ *pos,
+ text,
+ fontsize=fs,
+ ha=ha,
+ va=va,
+ color=color,
+ transform=ax.transAxes,
+ **kwargs,
+ )
+ if lcolor is not None:
+ t.set_path_effects(
+ [
+ path_effects.Stroke(linewidth=lwidth, foreground=lcolor),
+ path_effects.Normal(),
+ ]
+ )
+ return t
+
+
+def draw_epipolar_line(
+ line, axis, imshape=None, color="b", label=None, alpha=1.0, visible=True
+):
+ if imshape is not None:
+ h, w = imshape[:2]
+ else:
+ _, w = axis.get_xlim()
+ h, _ = axis.get_ylim()
+ imshape = (h + 0.5, w + 0.5)
+ # Intersect line with lines representing image borders.
+ X1 = np.cross(line, [1, 0, -1])
+ X1 = X1[:2] / X1[2]
+ X2 = np.cross(line, [1, 0, -w])
+ X2 = X2[:2] / X2[2]
+ X3 = np.cross(line, [0, 1, -1])
+ X3 = X3[:2] / X3[2]
+ X4 = np.cross(line, [0, 1, -h])
+ X4 = X4[:2] / X4[2]
+
+ # Find intersections which are not outside the image,
+ # which will therefore be on the image border.
+ Xs = [X1, X2, X3, X4]
+ Ps = []
+ for p in range(4):
+ X = Xs[p]
+ if (0 <= X[0] <= (w + 1e-6)) and (0 <= X[1] <= (h + 1e-6)):
+ Ps.append(X)
+ if len(Ps) == 2:
+ break
+
+ # Plot line, if it's visible in the image.
+ if len(Ps) == 2:
+ art = axis.plot(
+ [Ps[0][0], Ps[1][0]],
+ [Ps[0][1], Ps[1][1]],
+ color,
+ linestyle="dashed",
+ label=label,
+ alpha=alpha,
+ visible=visible,
+ )[0]
+ return art
+ else:
+ return None
+
+
+def get_line(F, kp):
+ hom_kp = np.array([list(kp) + [1.0]]).transpose()
+ return np.dot(F, hom_kp)
+
+
+def plot_epipolar_lines(
+ pts0, pts1, F, color="b", axes=None, labels=None, a=1.0, visible=True
+):
+ if axes is None:
+ axes = plt.gcf().axes
+ assert len(axes) == 2
+
+ for ax, kps in zip(axes, [pts1, pts0]):
+ _, w = ax.get_xlim()
+ h, _ = ax.get_ylim()
+
+ imshape = (h + 0.5, w + 0.5)
+ for i in range(kps.shape[0]):
+ if ax == axes[0]:
+ line = get_line(F.transpose(0, 1), kps[i])[:, 0]
+ else:
+ line = get_line(F, kps[i])[:, 0]
+ draw_epipolar_line(
+ line,
+ ax,
+ imshape,
+ color=color,
+ label=None if labels is None else labels[i],
+ alpha=a,
+ visible=visible,
+ )
+
+
+def plot_heatmaps(heatmaps, vmin=0.0, vmax=None, cmap="Spectral", a=0.5, axes=None):
+ if axes is None:
+ axes = plt.gcf().axes
+ artists = []
+ for i in range(len(axes)):
+ a_ = a if isinstance(a, float) else a[i]
+ art = axes[i].imshow(
+ heatmaps[i],
+ alpha=(heatmaps[i] > vmin).float() * a_,
+ vmin=vmin,
+ vmax=vmax,
+ cmap=cmap,
+ )
+ artists.append(art)
+ return artists
+
+
+def plot_lines(
+ lines,
+ line_colors="orange",
+ point_colors="cyan",
+ ps=4,
+ lw=2,
+ alpha=1.0,
+ indices=(0, 1),
+):
+ """Plot lines and endpoints for existing images.
+ Args:
+ lines: list of ndarrays of size (N, 2, 2).
+ colors: string, or list of list of tuples (one for each keypoints).
+ ps: size of the keypoints as float pixels.
+ lw: line width as float pixels.
+ alpha: transparency of the points and lines.
+ indices: indices of the images to draw the matches on.
+ """
+ if not isinstance(line_colors, list):
+ line_colors = [line_colors] * len(lines)
+ if not isinstance(point_colors, list):
+ point_colors = [point_colors] * len(lines)
+
+ fig = plt.gcf()
+ ax = fig.axes
+ assert len(ax) > max(indices)
+ axes = [ax[i] for i in indices]
+
+ # Plot the lines and junctions
+ for a, l, lc, pc in zip(axes, lines, line_colors, point_colors):
+ for i in range(len(l)):
+ line = matplotlib.lines.Line2D(
+ (l[i, 0, 0], l[i, 1, 0]),
+ (l[i, 0, 1], l[i, 1, 1]),
+ zorder=1,
+ c=lc,
+ linewidth=lw,
+ alpha=alpha,
+ )
+ a.add_line(line)
+ pts = l.reshape(-1, 2)
+ a.scatter(pts[:, 0], pts[:, 1], c=pc, s=ps, linewidths=0, zorder=2, alpha=alpha)
+
+
+def plot_color_line_matches(lines, correct_matches=None, lw=2, indices=(0, 1)):
+ """Plot line matches for existing images with multiple colors.
+ Args:
+ lines: list of ndarrays of size (N, 2, 2).
+ correct_matches: bool array of size (N,) indicating correct matches.
+ lw: line width as float pixels.
+ indices: indices of the images to draw the matches on.
+ """
+ n_lines = len(lines[0])
+ colors = sns.color_palette("husl", n_colors=n_lines)
+ np.random.shuffle(colors)
+ alphas = np.ones(n_lines)
+ # If correct_matches is not None, display wrong matches with a low alpha
+ if correct_matches is not None:
+ alphas[~np.array(correct_matches)] = 0.2
+
+ fig = plt.gcf()
+ ax = fig.axes
+ assert len(ax) > max(indices)
+ axes = [ax[i] for i in indices]
+
+ # Plot the lines
+ for a, img_lines in zip(axes, lines):
+ for i, line in enumerate(img_lines):
+ fig.add_artist(
+ matplotlib.patches.ConnectionPatch(
+ xyA=tuple(line[0]),
+ coordsA=a.transData,
+ xyB=tuple(line[1]),
+ coordsB=a.transData,
+ zorder=1,
+ color=colors[i],
+ linewidth=lw,
+ alpha=alphas[i],
+ )
+ )
+
+
+def save_plot(path, **kw):
+ """Save the current figure without any white margin."""
+ plt.savefig(path, bbox_inches="tight", pad_inches=0, **kw)
+
+
+def plot_cumulative(
+ errors: dict,
+ thresholds: list,
+ colors=None,
+ title="",
+ unit="-",
+ logx=False,
+):
+ thresholds = np.linspace(min(thresholds), max(thresholds), 100)
+
+ plt.figure(figsize=[5, 8])
+ for method in errors:
+ recall = []
+ errs = np.array(errors[method])
+ for th in thresholds:
+ recall.append(np.mean(errs <= th))
+ plt.plot(
+ thresholds,
+ np.array(recall) * 100,
+ label=method,
+ c=colors[method] if colors else None,
+ linewidth=3,
+ )
+
+ plt.grid()
+ plt.xlabel(unit, fontsize=25)
+ if logx:
+ plt.semilogx()
+ plt.ylim([0, 100])
+ plt.yticks(ticks=[0, 20, 40, 60, 80, 100])
+ plt.ylabel(title + "Recall [%]", rotation=0, fontsize=25)
+ plt.gca().yaxis.set_label_coords(x=0.45, y=1.02)
+ plt.tick_params(axis="both", which="major", labelsize=20)
+ plt.yticks(rotation=0)
+
+ plt.legend(
+ bbox_to_anchor=(0.45, -0.12),
+ ncol=2,
+ loc="upper center",
+ fontsize=20,
+ handlelength=3,
+ )
+ plt.tight_layout()
+
+ return plt.gcf()
diff --git a/imcui/third_party/gim/networks/lightglue/__init__.py b/third_party/gim/gim/lightglue/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/lightglue/__init__.py
rename to third_party/gim/gim/lightglue/__init__.py
diff --git a/third_party/gim/gim/lightglue/models/__init__.py b/third_party/gim/gim/lightglue/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9d1a05c66bbc22a711cb968be00985a31a3dfd5
--- /dev/null
+++ b/third_party/gim/gim/lightglue/models/__init__.py
@@ -0,0 +1,30 @@
+import importlib.util
+
+from ..utils.tools import get_class
+from .base_model import BaseModel
+
+
+def get_model(name):
+ import_paths = [
+ name,
+ f"{__name__}.{name}",
+ f"{__name__}.extractors.{name}", # backward compatibility
+ f"{__name__}.matchers.{name}", # backward compatibility
+ ]
+ for path in import_paths:
+ try:
+ spec = importlib.util.find_spec(path)
+ except ModuleNotFoundError:
+ spec = None
+ if spec is not None:
+ try:
+ return get_class(path, BaseModel)
+ except AssertionError:
+ mod = __import__(path, fromlist=[""])
+ try:
+ return mod.__main_model__
+ except AttributeError as exc:
+ print(exc)
+ continue
+
+ raise RuntimeError(f'Model {name} not found in any of [{" ".join(import_paths)}]')
diff --git a/third_party/gim/gim/lightglue/models/base_model.py b/third_party/gim/gim/lightglue/models/base_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4f66288b9f724468c4409171b9c374c794ae9c9
--- /dev/null
+++ b/third_party/gim/gim/lightglue/models/base_model.py
@@ -0,0 +1,157 @@
+"""
+Base class for trainable models.
+"""
+
+from abc import ABCMeta, abstractmethod
+from copy import copy
+
+import omegaconf
+from omegaconf import OmegaConf
+from torch import nn
+
+
+class MetaModel(ABCMeta):
+ def __prepare__(name, bases, **kwds):
+ total_conf = OmegaConf.create()
+ for base in bases:
+ for key in ("base_default_conf", "default_conf"):
+ update = getattr(base, key, {})
+ if isinstance(update, dict):
+ update = OmegaConf.create(update)
+ total_conf = OmegaConf.merge(total_conf, update)
+ return dict(base_default_conf=total_conf)
+
+
+class BaseModel(nn.Module, metaclass=MetaModel):
+ """
+ What the child model is expect to declare:
+ default_conf: dictionary of the default configuration of the model.
+ It recursively updates the default_conf of all parent classes, and
+ it is updated by the user-provided configuration passed to __init__.
+ Configurations can be nested.
+
+ required_data_keys: list of expected keys in the input data dictionary.
+
+ strict_conf (optional): boolean. If false, BaseModel does not raise
+ an error when the user provides an unknown configuration entry.
+
+ _init(self, conf): initialization method, where conf is the final
+ configuration object (also accessible with `self.conf`). Accessing
+ unknown configuration entries will raise an error.
+
+ _forward(self, data): method that returns a dictionary of batched
+ prediction tensors based on a dictionary of batched input data tensors.
+
+ loss(self, pred, data): method that returns a dictionary of losses,
+ computed from model predictions and input data. Each loss is a batch
+ of scalars, i.e. a torch.Tensor of shape (B,).
+ The total loss to be optimized has the key `'total'`.
+
+ metrics(self, pred, data): method that returns a dictionary of metrics,
+ each as a batch of scalars.
+ """
+
+ default_conf = {
+ "name": None,
+ "trainable": True, # if false: do not optimize this model parameters
+ "freeze_batch_normalization": False, # use test-time statistics
+ "timeit": False, # time forward pass
+ }
+ required_data_keys = []
+ strict_conf = False
+
+ are_weights_initialized = False
+
+ def __init__(self, conf):
+ """Perform some logic and call the _init method of the child model."""
+ super().__init__()
+ default_conf = OmegaConf.merge(
+ self.base_default_conf, OmegaConf.create(self.default_conf)
+ )
+ if self.strict_conf:
+ OmegaConf.set_struct(default_conf, True)
+
+ # fixme: backward compatibility
+ if "pad" in conf and "pad" not in default_conf: # backward compat.
+ with omegaconf.read_write(conf):
+ with omegaconf.open_dict(conf):
+ conf["interpolation"] = {"pad": conf.pop("pad")}
+
+ if isinstance(conf, dict):
+ conf = OmegaConf.create(conf)
+ self.conf = conf = OmegaConf.merge(default_conf, conf)
+ OmegaConf.set_readonly(conf, True)
+ OmegaConf.set_struct(conf, True)
+ self.required_data_keys = copy(self.required_data_keys)
+ self._init(conf)
+
+ if not conf.trainable:
+ for p in self.parameters():
+ p.requires_grad = False
+
+ def train(self, mode=True):
+ super().train(mode)
+
+ def freeze_bn(module):
+ if isinstance(module, nn.modules.batchnorm._BatchNorm):
+ module.eval()
+
+ if self.conf.freeze_batch_normalization:
+ self.apply(freeze_bn)
+
+ return self
+
+ def forward(self, data):
+ """Check the data and call the _forward method of the child model."""
+
+ def recursive_key_check(expected, given):
+ for key in expected:
+ assert key in given, f"Missing key {key} in data"
+ if isinstance(expected, dict):
+ recursive_key_check(expected[key], given[key])
+
+ recursive_key_check(self.required_data_keys, data)
+ return self._forward(data)
+
+ @abstractmethod
+ def _init(self, conf):
+ """To be implemented by the child class."""
+ raise NotImplementedError
+
+ @abstractmethod
+ def _forward(self, data):
+ """To be implemented by the child class."""
+ raise NotImplementedError
+
+ @abstractmethod
+ def loss(self, pred, data):
+ """To be implemented by the child class."""
+ raise NotImplementedError
+
+ def load_state_dict(self, *args, **kwargs):
+ """Load the state dict of the model, and set the model to initialized."""
+ ret = super().load_state_dict(*args, **kwargs)
+ self.set_initialized()
+ return ret
+
+ def is_initialized(self):
+ """Recursively check if the model is initialized, i.e. weights are loaded"""
+ is_initialized = True # initialize to true and perform recursive and
+ for _, w in self.named_children():
+ if isinstance(w, BaseModel):
+ # if children is BaseModel, we perform recursive check
+ is_initialized = is_initialized and w.is_initialized()
+ else:
+ # else, we check if self is initialized or the children has no params
+ n_params = len(list(w.parameters()))
+ is_initialized = is_initialized and (
+ n_params == 0 or self.are_weights_initialized
+ )
+ return is_initialized
+
+ def set_initialized(self, to: bool = True):
+ """Recursively set the initialization state."""
+ self.are_weights_initialized = to
+ for _, w in self.named_parameters():
+ if isinstance(w, BaseModel):
+ w.set_initialized(to)
diff --git a/third_party/gim/gim/lightglue/models/matchers/__init__.py b/third_party/gim/gim/lightglue/models/matchers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/imcui/third_party/gim/networks/lightglue/models/matchers/lightglue.py b/third_party/gim/gim/lightglue/models/matchers/lightglue.py
similarity index 99%
rename from imcui/third_party/gim/networks/lightglue/models/matchers/lightglue.py
rename to third_party/gim/gim/lightglue/models/matchers/lightglue.py
index 364194e8a6829c124e9a1959b8c224cb9119f211..3dfda2a2968e038ee2d90ecff0533af9d3a14484 100644
--- a/imcui/third_party/gim/networks/lightglue/models/matchers/lightglue.py
+++ b/third_party/gim/gim/lightglue/models/matchers/lightglue.py
@@ -411,8 +411,8 @@ class LightGlue(nn.Module):
b, n, _ = kpts1.shape
device = kpts0.device
# if "view0" in data.keys() and "view1" in data.keys():
- size0 = data["image_size0"][:, [1, 0]] if "image_size0" in data.keys() else data["resize0"][:, [1, 0]]
- size1 = data["image_size1"][:, [1, 0]] if "image_size1" in data.keys() else data["resize1"][:, [1, 0]]
+ size0 = data["resize0"][:, [1, 0]]
+ size1 = data["resize1"][:, [1, 0]]
kpts0 = normalize_keypoints(kpts0, size0).clone()
kpts1 = normalize_keypoints(kpts1, size1).clone()
diff --git a/third_party/gim/gim/lightglue/models/utils/__init__.py b/third_party/gim/gim/lightglue/models/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/third_party/gim/gim/lightglue/models/utils/misc.py b/third_party/gim/gim/lightglue/models/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..e86d1add0e23a042963d878e484f0c582ff8b41c
--- /dev/null
+++ b/third_party/gim/gim/lightglue/models/utils/misc.py
@@ -0,0 +1,70 @@
+import math
+from typing import List, Optional, Tuple
+
+import torch
+
+
+def to_sequence(map):
+ return map.flatten(-2).transpose(-1, -2)
+
+
+def to_map(sequence):
+ n = sequence.shape[-2]
+ e = math.isqrt(n)
+ assert e * e == n
+ assert e * e == n
+ sequence.transpose(-1, -2).unflatten(-1, [e, e])
+
+
+def pad_to_length(
+ x,
+ length: int,
+ pad_dim: int = -2,
+ mode: str = "zeros", # zeros, ones, random, random_c
+ bounds: Tuple[int] = (None, None),
+):
+ shape = list(x.shape)
+ d = x.shape[pad_dim]
+ assert d <= length
+ if d == length:
+ return x
+ shape[pad_dim] = length - d
+
+ low, high = bounds
+
+ if mode == "zeros":
+ xn = torch.zeros(*shape, device=x.device, dtype=x.dtype)
+ elif mode == "ones":
+ xn = torch.ones(*shape, device=x.device, dtype=x.dtype)
+ elif mode == "random":
+ low = low if low is not None else x.min()
+ high = high if high is not None else x.max()
+ xn = torch.empty(*shape, device=x.device).uniform_(low, high)
+ elif mode == "random_c":
+ low, high = bounds # we use the bounds as fallback for empty seq.
+ xn = torch.cat(
+ [
+ torch.empty(*shape[:-1], 1, device=x.device).uniform_(
+ x[..., i].min() if d > 0 else low,
+ x[..., i].max() if d > 0 else high,
+ )
+ for i in range(shape[-1])
+ ],
+ dim=-1,
+ )
+ else:
+ raise ValueError(mode)
+ return torch.cat([x, xn], dim=pad_dim)
+
+
+def pad_and_stack(
+ sequences: List[torch.Tensor],
+ length: Optional[int] = None,
+ pad_dim: int = -2,
+ **kwargs,
+):
+ if length is None:
+ length = max([x.shape[pad_dim] for x in sequences])
+
+ y = torch.stack([pad_to_length(x, length, pad_dim, **kwargs) for x in sequences], 0)
+ return y
diff --git a/imcui/third_party/gim/networks/lightglue/superpoint.py b/third_party/gim/gim/lightglue/superpoint.py
similarity index 99%
rename from imcui/third_party/gim/networks/lightglue/superpoint.py
rename to third_party/gim/gim/lightglue/superpoint.py
index 8e93591f5d64f345b07545e91108c110256a6f32..e68a47db5c168d6d51fdafd95c9a0e4225b6d70b 100644
--- a/imcui/third_party/gim/networks/lightglue/superpoint.py
+++ b/third_party/gim/gim/lightglue/superpoint.py
@@ -169,7 +169,6 @@ class SuperPoint(BaseModel):
"legacy_sampling": True, # True to use the old broken sampling
}
required_data_keys = ["image"]
- detection_noise = 2.0
# checkpoint_url = "https://github.com/magicleap/SuperGluePretrainedNetwork/raw/master/models/weights/superpoint_v1.pth" # noqa: E501
@@ -205,7 +204,6 @@ class SuperPoint(BaseModel):
def _forward(self, data):
image = data["image"]
- data["image_size"] = torch.tensor(image.shape[-2:][::-1])[None]
if image.shape[1] == 3: # RGB
scale = image.new_tensor([0.299, 0.587, 0.114]).view(1, 3, 1, 1)
image = (image * scale).sum(1, keepdim=True)
diff --git a/imcui/third_party/gim/networks/lightglue/utils/__init__.py b/third_party/gim/gim/lightglue/utils/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/lightglue/utils/__init__.py
rename to third_party/gim/gim/lightglue/utils/__init__.py
diff --git a/third_party/gim/gim/lightglue/utils/tools.py b/third_party/gim/gim/lightglue/utils/tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a27f4a491e1675557b992401208bbe4c355edd2
--- /dev/null
+++ b/third_party/gim/gim/lightglue/utils/tools.py
@@ -0,0 +1,269 @@
+"""
+Various handy Python and PyTorch utils.
+
+Author: Paul-Edouard Sarlin (skydes)
+"""
+
+import os
+import random
+import time
+from collections.abc import Iterable
+from contextlib import contextmanager
+
+import numpy as np
+import torch
+
+
+class AverageMetric:
+ def __init__(self):
+ self._sum = 0
+ self._num_examples = 0
+
+ def update(self, tensor):
+ assert tensor.dim() == 1
+ tensor = tensor[~torch.isnan(tensor)]
+ self._sum += tensor.sum().item()
+ self._num_examples += len(tensor)
+
+ def compute(self):
+ if self._num_examples == 0:
+ return np.nan
+ else:
+ return self._sum / self._num_examples
+
+
+# same as AverageMetric, but tracks all elements
+class FAverageMetric:
+ def __init__(self):
+ self._sum = 0
+ self._num_examples = 0
+ self._elements = []
+
+ def update(self, tensor):
+ self._elements += tensor.cpu().numpy().tolist()
+ assert tensor.dim() == 1
+ tensor = tensor[~torch.isnan(tensor)]
+ self._sum += tensor.sum().item()
+ self._num_examples += len(tensor)
+
+ def compute(self):
+ if self._num_examples == 0:
+ return np.nan
+ else:
+ return self._sum / self._num_examples
+
+
+class MedianMetric:
+ def __init__(self):
+ self._elements = []
+
+ def update(self, tensor):
+ assert tensor.dim() == 1
+ self._elements += tensor.cpu().numpy().tolist()
+
+ def compute(self):
+ if len(self._elements) == 0:
+ return np.nan
+ else:
+ return np.nanmedian(self._elements)
+
+
+class PRMetric:
+ def __init__(self):
+ self.labels = []
+ self.predictions = []
+
+ @torch.no_grad()
+ def update(self, labels, predictions, mask=None):
+ assert labels.shape == predictions.shape
+ self.labels += (
+ (labels[mask] if mask is not None else labels).cpu().numpy().tolist()
+ )
+ self.predictions += (
+ (predictions[mask] if mask is not None else predictions)
+ .cpu()
+ .numpy()
+ .tolist()
+ )
+
+ @torch.no_grad()
+ def compute(self):
+ return np.array(self.labels), np.array(self.predictions)
+
+ def reset(self):
+ self.labels = []
+ self.predictions = []
+
+
+class QuantileMetric:
+ def __init__(self, q=0.05):
+ self._elements = []
+ self.q = q
+
+ def update(self, tensor):
+ assert tensor.dim() == 1
+ self._elements += tensor.cpu().numpy().tolist()
+
+ def compute(self):
+ if len(self._elements) == 0:
+ return np.nan
+ else:
+ return np.nanquantile(self._elements, self.q)
+
+
+class RecallMetric:
+ def __init__(self, ths, elements=[]):
+ self._elements = elements
+ self.ths = ths
+
+ def update(self, tensor):
+ assert tensor.dim() == 1
+ self._elements += tensor.cpu().numpy().tolist()
+
+ def compute(self):
+ if isinstance(self.ths, Iterable):
+ return [self.compute_(th) for th in self.ths]
+ else:
+ return self.compute_(self.ths[0])
+
+ def compute_(self, th):
+ if len(self._elements) == 0:
+ return np.nan
+ else:
+ s = (np.array(self._elements) < th).sum()
+ return s / len(self._elements)
+
+
+def cal_error_auc(errors, thresholds):
+ sort_idx = np.argsort(errors)
+ errors = np.array(errors.copy())[sort_idx]
+ recall = (np.arange(len(errors)) + 1) / len(errors)
+ errors = np.r_[0.0, errors]
+ recall = np.r_[0.0, recall]
+ aucs = []
+ for t in thresholds:
+ last_index = np.searchsorted(errors, t)
+ r = np.r_[recall[:last_index], recall[last_index - 1]]
+ e = np.r_[errors[:last_index], t]
+ aucs.append(np.round((np.trapz(r, x=e) / t), 4))
+ return aucs
+
+
+class AUCMetric:
+ def __init__(self, thresholds, elements=None):
+ self._elements = elements
+ self.thresholds = thresholds
+ if not isinstance(thresholds, list):
+ self.thresholds = [thresholds]
+
+ def update(self, tensor):
+ assert tensor.dim() == 1
+ self._elements += tensor.cpu().numpy().tolist()
+
+ def compute(self):
+ if len(self._elements) == 0:
+ return np.nan
+ else:
+ return cal_error_auc(self._elements, self.thresholds)
+
+
+class Timer(object):
+ """A simpler timer context object.
+ Usage:
+ ```
+ > with Timer('mytimer'):
+ > # some computations
+ [mytimer] Elapsed: X
+ ```
+ """
+
+ def __init__(self, name=None):
+ self.name = name
+
+ def __enter__(self):
+ self.tstart = time.time()
+ return self
+
+ def __exit__(self, type, value, traceback):
+ self.duration = time.time() - self.tstart
+ if self.name is not None:
+ print("[%s] Elapsed: %s" % (self.name, self.duration))
+
+
+def get_class(mod_path, BaseClass):
+ """Get the class object which inherits from BaseClass and is defined in
+ the module named mod_name, child of base_path.
+ """
+ import inspect
+
+ mod = __import__(mod_path, fromlist=[""])
+ classes = inspect.getmembers(mod, inspect.isclass)
+ # Filter classes defined in the module
+ classes = [c for c in classes if c[1].__module__ == mod_path]
+ # Filter classes inherited from BaseModel
+ classes = [c for c in classes if issubclass(c[1], BaseClass)]
+ assert len(classes) == 1, classes
+ return classes[0][1]
+
+
+def set_num_threads(nt):
+ """Force numpy and other libraries to use a limited number of threads."""
+ try:
+ import mkl
+ except ImportError:
+ pass
+ else:
+ mkl.set_num_threads(nt)
+ torch.set_num_threads(1)
+ os.environ["IPC_ENABLE"] = "1"
+ for o in [
+ "OPENBLAS_NUM_THREADS",
+ "NUMEXPR_NUM_THREADS",
+ "OMP_NUM_THREADS",
+ "MKL_NUM_THREADS",
+ ]:
+ os.environ[o] = str(nt)
+
+
+def set_seed(seed):
+ random.seed(seed)
+ torch.manual_seed(seed)
+ np.random.seed(seed)
+ if torch.cuda.is_available():
+ torch.cuda.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+
+
+def get_random_state(with_cuda):
+ pth_state = torch.get_rng_state()
+ np_state = np.random.get_state()
+ py_state = random.getstate()
+ if torch.cuda.is_available() and with_cuda:
+ cuda_state = torch.cuda.get_rng_state_all()
+ else:
+ cuda_state = None
+ return pth_state, np_state, py_state, cuda_state
+
+
+def set_random_state(state):
+ pth_state, np_state, py_state, cuda_state = state
+ torch.set_rng_state(pth_state)
+ np.random.set_state(np_state)
+ random.setstate(py_state)
+ if (
+ cuda_state is not None
+ and torch.cuda.is_available()
+ and len(cuda_state) == torch.cuda.device_count()
+ ):
+ torch.cuda.set_rng_state_all(cuda_state)
+
+
+@contextmanager
+def fork_rng(seed=None, with_cuda=True):
+ state = get_random_state(with_cuda)
+ if seed is not None:
+ set_seed(seed)
+ try:
+ yield
+ finally:
+ set_random_state(state)
diff --git a/imcui/third_party/gim/networks/loftr/__init__.py b/third_party/gim/gim/loftr/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/__init__.py
rename to third_party/gim/gim/loftr/__init__.py
diff --git a/imcui/third_party/gim/networks/loftr/backbone/__init__.py b/third_party/gim/gim/loftr/backbone/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/backbone/__init__.py
rename to third_party/gim/gim/loftr/backbone/__init__.py
diff --git a/imcui/third_party/gim/networks/loftr/backbone/resnet.py b/third_party/gim/gim/loftr/backbone/resnet.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/backbone/resnet.py
rename to third_party/gim/gim/loftr/backbone/resnet.py
diff --git a/imcui/third_party/gim/networks/loftr/backbone/resnet_fpn.py b/third_party/gim/gim/loftr/backbone/resnet_fpn.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/backbone/resnet_fpn.py
rename to third_party/gim/gim/loftr/backbone/resnet_fpn.py
diff --git a/imcui/third_party/gim/networks/loftr/config.py b/third_party/gim/gim/loftr/config.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/config.py
rename to third_party/gim/gim/loftr/config.py
diff --git a/imcui/third_party/gim/networks/loftr/configs/__init__.py b/third_party/gim/gim/loftr/configs/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/configs/__init__.py
rename to third_party/gim/gim/loftr/configs/__init__.py
diff --git a/imcui/third_party/gim/networks/loftr/configs/outdoor/__init__.py b/third_party/gim/gim/loftr/configs/outdoor/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/configs/outdoor/__init__.py
rename to third_party/gim/gim/loftr/configs/outdoor/__init__.py
diff --git a/imcui/third_party/gim/networks/loftr/loftr.py b/third_party/gim/gim/loftr/loftr.py
similarity index 94%
rename from imcui/third_party/gim/networks/loftr/loftr.py
rename to third_party/gim/gim/loftr/loftr.py
index 0fe35a373581d1e1d08bd28271e6d598c4759273..2ad5a1aeef225724b0bd35befb92eb85417a5106 100644
--- a/imcui/third_party/gim/networks/loftr/loftr.py
+++ b/third_party/gim/gim/loftr/loftr.py
@@ -35,10 +35,10 @@ class LoFTR(nn.Module):
loftr_fine: {OrderedDict: 20}
fine_preprocess: {OrderedDict: 4}
"""
- if config['weight'] is not None:
- weights = torch.load(config['weight'], map_location='cpu')
- self.load_state_dict(weights)
- # print(config['weight'] + ' load success.')
+ # if config['weight'] is not None:
+ # weights = torch.load(config['weight'], map_location='cpu')['state_dict']
+ # self.load_state_dict(weights)
+ # print(config['weight'] + ' load success.')
def forward(self, data):
"""
diff --git a/imcui/third_party/gim/networks/loftr/misc.py b/third_party/gim/gim/loftr/misc.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/misc.py
rename to third_party/gim/gim/loftr/misc.py
diff --git a/imcui/third_party/gim/networks/loftr/submodules/__init__.py b/third_party/gim/gim/loftr/submodules/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/submodules/__init__.py
rename to third_party/gim/gim/loftr/submodules/__init__.py
diff --git a/imcui/third_party/gim/networks/loftr/submodules/attentions.py b/third_party/gim/gim/loftr/submodules/attentions.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/submodules/attentions.py
rename to third_party/gim/gim/loftr/submodules/attentions.py
diff --git a/imcui/third_party/ASpanFormer/src/ASpanFormer/aspan_module/fine_preprocess.py b/third_party/gim/gim/loftr/submodules/fine_preprocess.py
similarity index 100%
rename from imcui/third_party/ASpanFormer/src/ASpanFormer/aspan_module/fine_preprocess.py
rename to third_party/gim/gim/loftr/submodules/fine_preprocess.py
diff --git a/imcui/third_party/gim/networks/loftr/submodules/transformer.py b/third_party/gim/gim/loftr/submodules/transformer.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/submodules/transformer.py
rename to third_party/gim/gim/loftr/submodules/transformer.py
diff --git a/imcui/third_party/gim/networks/loftr/utils/__init__.py b/third_party/gim/gim/loftr/utils/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/utils/__init__.py
rename to third_party/gim/gim/loftr/utils/__init__.py
diff --git a/imcui/third_party/gim/networks/loftr/utils/coarse_matching.py b/third_party/gim/gim/loftr/utils/coarse_matching.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/utils/coarse_matching.py
rename to third_party/gim/gim/loftr/utils/coarse_matching.py
diff --git a/imcui/third_party/gim/networks/loftr/utils/fine_matching.py b/third_party/gim/gim/loftr/utils/fine_matching.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/utils/fine_matching.py
rename to third_party/gim/gim/loftr/utils/fine_matching.py
diff --git a/imcui/third_party/gim/networks/loftr/utils/position_encoding.py b/third_party/gim/gim/loftr/utils/position_encoding.py
similarity index 100%
rename from imcui/third_party/gim/networks/loftr/utils/position_encoding.py
rename to third_party/gim/gim/loftr/utils/position_encoding.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/__init__.py b/third_party/gim/gim/mit_semseg/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/__init__.py
rename to third_party/gim/gim/mit_semseg/__init__.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/config/__init__.py b/third_party/gim/gim/mit_semseg/config/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/config/__init__.py
rename to third_party/gim/gim/mit_semseg/config/__init__.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/config/defaults.py b/third_party/gim/gim/mit_semseg/config/defaults.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/config/defaults.py
rename to third_party/gim/gim/mit_semseg/config/defaults.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/dataset.py b/third_party/gim/gim/mit_semseg/dataset.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/dataset.py
rename to third_party/gim/gim/mit_semseg/dataset.py
diff --git a/third_party/gim/gim/mit_semseg/lib/__init__.py b/third_party/gim/gim/mit_semseg/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/__init__.py b/third_party/gim/gim/mit_semseg/lib/nn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..98a96370ef04570f516052bb73f568d0ebc346c3
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/nn/__init__.py
@@ -0,0 +1,2 @@
+from .modules import *
+from .parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/modules/__init__.py b/third_party/gim/gim/mit_semseg/lib/nn/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc8709d92c610b36e0bcbd7da20c1eb41dc8cfcf
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/nn/modules/__init__.py
@@ -0,0 +1,12 @@
+# -*- coding: utf-8 -*-
+# File : __init__.py
+# Author : Jiayuan Mao
+# Email : maojiayuan@gmail.com
+# Date : 27/01/2018
+#
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
+from .replicate import DataParallelWithCallback, patch_replication_callback
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/modules/batchnorm.py b/third_party/gim/gim/mit_semseg/lib/nn/modules/batchnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..18318965335b37cc671004a6aceda3229dc7b477
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/nn/modules/batchnorm.py
@@ -0,0 +1,329 @@
+# -*- coding: utf-8 -*-
+# File : batchnorm.py
+# Author : Jiayuan Mao
+# Email : maojiayuan@gmail.com
+# Date : 27/01/2018
+#
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import collections
+
+import torch
+import torch.nn.functional as F
+
+from torch.nn.modules.batchnorm import _BatchNorm
+from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
+
+from .comm import SyncMaster
+
+__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
+
+
+def _sum_ft(tensor):
+ """sum over the first and last dimention"""
+ return tensor.sum(dim=0).sum(dim=-1)
+
+
+def _unsqueeze_ft(tensor):
+ """add new dementions at the front and the tail"""
+ return tensor.unsqueeze(0).unsqueeze(-1)
+
+
+_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
+_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
+
+
+class _SynchronizedBatchNorm(_BatchNorm):
+ def __init__(self, num_features, eps=1e-5, momentum=0.001, affine=True):
+ super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
+
+ self._sync_master = SyncMaster(self._data_parallel_master)
+
+ self._is_parallel = False
+ self._parallel_id = None
+ self._slave_pipe = None
+
+ # customed batch norm statistics
+ self._moving_average_fraction = 1. - momentum
+ self.register_buffer('_tmp_running_mean', torch.zeros(self.num_features))
+ self.register_buffer('_tmp_running_var', torch.ones(self.num_features))
+ self.register_buffer('_running_iter', torch.ones(1))
+ self._tmp_running_mean = self.running_mean.clone() * self._running_iter
+ self._tmp_running_var = self.running_var.clone() * self._running_iter
+
+ def forward(self, input):
+ # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
+ if not (self._is_parallel and self.training):
+ return F.batch_norm(
+ input, self.running_mean, self.running_var, self.weight, self.bias,
+ self.training, self.momentum, self.eps)
+
+ # Resize the input to (B, C, -1).
+ input_shape = input.size()
+ input = input.view(input.size(0), self.num_features, -1)
+
+ # Compute the sum and square-sum.
+ sum_size = input.size(0) * input.size(2)
+ input_sum = _sum_ft(input)
+ input_ssum = _sum_ft(input ** 2)
+
+ # Reduce-and-broadcast the statistics.
+ if self._parallel_id == 0:
+ mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
+ else:
+ mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
+
+ # Compute the output.
+ if self.affine:
+ # MJY:: Fuse the multiplication for speed.
+ output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
+ else:
+ output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
+
+ # Reshape it.
+ return output.view(input_shape)
+
+ def __data_parallel_replicate__(self, ctx, copy_id):
+ self._is_parallel = True
+ self._parallel_id = copy_id
+
+ # parallel_id == 0 means master device.
+ if self._parallel_id == 0:
+ ctx.sync_master = self._sync_master
+ else:
+ self._slave_pipe = ctx.sync_master.register_slave(copy_id)
+
+ def _data_parallel_master(self, intermediates):
+ """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
+ intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
+
+ to_reduce = [i[1][:2] for i in intermediates]
+ to_reduce = [j for i in to_reduce for j in i] # flatten
+ target_gpus = [i[1].sum.get_device() for i in intermediates]
+
+ sum_size = sum([i[1].sum_size for i in intermediates])
+ sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
+
+ mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
+
+ broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
+
+ outputs = []
+ for i, rec in enumerate(intermediates):
+ outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
+
+ return outputs
+
+ def _add_weighted(self, dest, delta, alpha=1, beta=1, bias=0):
+ """return *dest* by `dest := dest*alpha + delta*beta + bias`"""
+ return dest * alpha + delta * beta + bias
+
+ def _compute_mean_std(self, sum_, ssum, size):
+ """Compute the mean and standard-deviation with sum and square-sum. This method
+ also maintains the moving average on the master device."""
+ assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
+ mean = sum_ / size
+ sumvar = ssum - sum_ * mean
+ unbias_var = sumvar / (size - 1)
+ bias_var = sumvar / size
+
+ self._tmp_running_mean = self._add_weighted(self._tmp_running_mean, mean.data, alpha=self._moving_average_fraction)
+ self._tmp_running_var = self._add_weighted(self._tmp_running_var, unbias_var.data, alpha=self._moving_average_fraction)
+ self._running_iter = self._add_weighted(self._running_iter, 1, alpha=self._moving_average_fraction)
+
+ self.running_mean = self._tmp_running_mean / self._running_iter
+ self.running_var = self._tmp_running_var / self._running_iter
+
+ return mean, bias_var.clamp(self.eps) ** -0.5
+
+
+class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
+ r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
+ mini-batch.
+
+ .. math::
+
+ y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
+
+ This module differs from the built-in PyTorch BatchNorm1d as the mean and
+ standard-deviation are reduced across all devices during training.
+
+ For example, when one uses `nn.DataParallel` to wrap the network during
+ training, PyTorch's implementation normalize the tensor on each device using
+ the statistics only on that device, which accelerated the computation and
+ is also easy to implement, but the statistics might be inaccurate.
+ Instead, in this synchronized version, the statistics will be computed
+ over all training samples distributed on multiple devices.
+
+ Note that, for one-GPU or CPU-only case, this module behaves exactly same
+ as the built-in PyTorch implementation.
+
+ The mean and standard-deviation are calculated per-dimension over
+ the mini-batches and gamma and beta are learnable parameter vectors
+ of size C (where C is the input size).
+
+ During training, this layer keeps a running estimate of its computed mean
+ and variance. The running sum is kept with a default momentum of 0.1.
+
+ During evaluation, this running mean/variance is used for normalization.
+
+ Because the BatchNorm is done over the `C` dimension, computing statistics
+ on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
+
+ Args:
+ num_features: num_features from an expected input of size
+ `batch_size x num_features [x width]`
+ eps: a value added to the denominator for numerical stability.
+ Default: 1e-5
+ momentum: the value used for the running_mean and running_var
+ computation. Default: 0.1
+ affine: a boolean value that when set to ``True``, gives the layer learnable
+ affine parameters. Default: ``True``
+
+ Shape:
+ - Input: :math:`(N, C)` or :math:`(N, C, L)`
+ - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
+
+ Examples:
+ >>> # With Learnable Parameters
+ >>> m = SynchronizedBatchNorm1d(100)
+ >>> # Without Learnable Parameters
+ >>> m = SynchronizedBatchNorm1d(100, affine=False)
+ >>> input = torch.autograd.Variable(torch.randn(20, 100))
+ >>> output = m(input)
+ """
+
+ def _check_input_dim(self, input):
+ if input.dim() != 2 and input.dim() != 3:
+ raise ValueError('expected 2D or 3D input (got {}D input)'
+ .format(input.dim()))
+ super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
+
+
+class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
+ r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
+ of 3d inputs
+
+ .. math::
+
+ y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
+
+ This module differs from the built-in PyTorch BatchNorm2d as the mean and
+ standard-deviation are reduced across all devices during training.
+
+ For example, when one uses `nn.DataParallel` to wrap the network during
+ training, PyTorch's implementation normalize the tensor on each device using
+ the statistics only on that device, which accelerated the computation and
+ is also easy to implement, but the statistics might be inaccurate.
+ Instead, in this synchronized version, the statistics will be computed
+ over all training samples distributed on multiple devices.
+
+ Note that, for one-GPU or CPU-only case, this module behaves exactly same
+ as the built-in PyTorch implementation.
+
+ The mean and standard-deviation are calculated per-dimension over
+ the mini-batches and gamma and beta are learnable parameter vectors
+ of size C (where C is the input size).
+
+ During training, this layer keeps a running estimate of its computed mean
+ and variance. The running sum is kept with a default momentum of 0.1.
+
+ During evaluation, this running mean/variance is used for normalization.
+
+ Because the BatchNorm is done over the `C` dimension, computing statistics
+ on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
+
+ Args:
+ num_features: num_features from an expected input of
+ size batch_size x num_features x height x width
+ eps: a value added to the denominator for numerical stability.
+ Default: 1e-5
+ momentum: the value used for the running_mean and running_var
+ computation. Default: 0.1
+ affine: a boolean value that when set to ``True``, gives the layer learnable
+ affine parameters. Default: ``True``
+
+ Shape:
+ - Input: :math:`(N, C, H, W)`
+ - Output: :math:`(N, C, H, W)` (same shape as input)
+
+ Examples:
+ >>> # With Learnable Parameters
+ >>> m = SynchronizedBatchNorm2d(100)
+ >>> # Without Learnable Parameters
+ >>> m = SynchronizedBatchNorm2d(100, affine=False)
+ >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
+ >>> output = m(input)
+ """
+
+ def _check_input_dim(self, input):
+ if input.dim() != 4:
+ raise ValueError('expected 4D input (got {}D input)'
+ .format(input.dim()))
+ super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
+
+
+class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
+ r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
+ of 4d inputs
+
+ .. math::
+
+ y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
+
+ This module differs from the built-in PyTorch BatchNorm3d as the mean and
+ standard-deviation are reduced across all devices during training.
+
+ For example, when one uses `nn.DataParallel` to wrap the network during
+ training, PyTorch's implementation normalize the tensor on each device using
+ the statistics only on that device, which accelerated the computation and
+ is also easy to implement, but the statistics might be inaccurate.
+ Instead, in this synchronized version, the statistics will be computed
+ over all training samples distributed on multiple devices.
+
+ Note that, for one-GPU or CPU-only case, this module behaves exactly same
+ as the built-in PyTorch implementation.
+
+ The mean and standard-deviation are calculated per-dimension over
+ the mini-batches and gamma and beta are learnable parameter vectors
+ of size C (where C is the input size).
+
+ During training, this layer keeps a running estimate of its computed mean
+ and variance. The running sum is kept with a default momentum of 0.1.
+
+ During evaluation, this running mean/variance is used for normalization.
+
+ Because the BatchNorm is done over the `C` dimension, computing statistics
+ on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
+ or Spatio-temporal BatchNorm
+
+ Args:
+ num_features: num_features from an expected input of
+ size batch_size x num_features x depth x height x width
+ eps: a value added to the denominator for numerical stability.
+ Default: 1e-5
+ momentum: the value used for the running_mean and running_var
+ computation. Default: 0.1
+ affine: a boolean value that when set to ``True``, gives the layer learnable
+ affine parameters. Default: ``True``
+
+ Shape:
+ - Input: :math:`(N, C, D, H, W)`
+ - Output: :math:`(N, C, D, H, W)` (same shape as input)
+
+ Examples:
+ >>> # With Learnable Parameters
+ >>> m = SynchronizedBatchNorm3d(100)
+ >>> # Without Learnable Parameters
+ >>> m = SynchronizedBatchNorm3d(100, affine=False)
+ >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
+ >>> output = m(input)
+ """
+
+ def _check_input_dim(self, input):
+ if input.dim() != 5:
+ raise ValueError('expected 5D input (got {}D input)'
+ .format(input.dim()))
+ super(SynchronizedBatchNorm3d, self)._check_input_dim(input)
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/modules/comm.py b/third_party/gim/gim/mit_semseg/lib/nn/modules/comm.py
new file mode 100644
index 0000000000000000000000000000000000000000..b64bf6ba3b3e7abbab375c6dd4a87d8239e62138
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/nn/modules/comm.py
@@ -0,0 +1,131 @@
+# -*- coding: utf-8 -*-
+# File : comm.py
+# Author : Jiayuan Mao
+# Email : maojiayuan@gmail.com
+# Date : 27/01/2018
+#
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import queue
+import collections
+import threading
+
+__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster']
+
+
+class FutureResult(object):
+ """A thread-safe future implementation. Used only as one-to-one pipe."""
+
+ def __init__(self):
+ self._result = None
+ self._lock = threading.Lock()
+ self._cond = threading.Condition(self._lock)
+
+ def put(self, result):
+ with self._lock:
+ assert self._result is None, 'Previous result has\'t been fetched.'
+ self._result = result
+ self._cond.notify()
+
+ def get(self):
+ with self._lock:
+ if self._result is None:
+ self._cond.wait()
+
+ res = self._result
+ self._result = None
+ return res
+
+
+_MasterRegistry = collections.namedtuple('MasterRegistry', ['result'])
+_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result'])
+
+
+class SlavePipe(_SlavePipeBase):
+ """Pipe for master-slave communication."""
+
+ def run_slave(self, msg):
+ self.queue.put((self.identifier, msg))
+ ret = self.result.get()
+ self.queue.put(True)
+ return ret
+
+
+class SyncMaster(object):
+ """An abstract `SyncMaster` object.
+
+ - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
+ call `register(id)` and obtain an `SlavePipe` to communicate with the master.
+ - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,
+ and passed to a registered callback.
+ - After receiving the messages, the master device should gather the information and determine to message passed
+ back to each slave devices.
+ """
+
+ def __init__(self, master_callback):
+ """
+
+ Args:
+ master_callback: a callback to be invoked after having collected messages from slave devices.
+ """
+ self._master_callback = master_callback
+ self._queue = queue.Queue()
+ self._registry = collections.OrderedDict()
+ self._activated = False
+
+ def register_slave(self, identifier):
+ """
+ Register an slave device.
+
+ Args:
+ identifier: an identifier, usually is the device id.
+
+ Returns: a `SlavePipe` object which can be used to communicate with the master device.
+
+ """
+ if self._activated:
+ assert self._queue.empty(), 'Queue is not clean before next initialization.'
+ self._activated = False
+ self._registry.clear()
+ future = FutureResult()
+ self._registry[identifier] = _MasterRegistry(future)
+ return SlavePipe(identifier, self._queue, future)
+
+ def run_master(self, master_msg):
+ """
+ Main entry for the master device in each forward pass.
+ The messages were first collected from each devices (including the master device), and then
+ an callback will be invoked to compute the message to be sent back to each devices
+ (including the master device).
+
+ Args:
+ master_msg: the message that the master want to send to itself. This will be placed as the first
+ message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.
+
+ Returns: the message to be sent back to the master device.
+
+ """
+ self._activated = True
+
+ intermediates = [(0, master_msg)]
+ for i in range(self.nr_slaves):
+ intermediates.append(self._queue.get())
+
+ results = self._master_callback(intermediates)
+ assert results[0][0] == 0, 'The first result should belongs to the master.'
+
+ for i, res in results:
+ if i == 0:
+ continue
+ self._registry[i].result.put(res)
+
+ for i in range(self.nr_slaves):
+ assert self._queue.get() is True
+
+ return results[0][1]
+
+ @property
+ def nr_slaves(self):
+ return len(self._registry)
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/modules/replicate.py b/third_party/gim/gim/mit_semseg/lib/nn/modules/replicate.py
new file mode 100644
index 0000000000000000000000000000000000000000..b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/nn/modules/replicate.py
@@ -0,0 +1,94 @@
+# -*- coding: utf-8 -*-
+# File : replicate.py
+# Author : Jiayuan Mao
+# Email : maojiayuan@gmail.com
+# Date : 27/01/2018
+#
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import functools
+
+from torch.nn.parallel.data_parallel import DataParallel
+
+__all__ = [
+ 'CallbackContext',
+ 'execute_replication_callbacks',
+ 'DataParallelWithCallback',
+ 'patch_replication_callback'
+]
+
+
+class CallbackContext(object):
+ pass
+
+
+def execute_replication_callbacks(modules):
+ """
+ Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.
+
+ The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
+
+ Note that, as all modules are isomorphism, we assign each sub-module with a context
+ (shared among multiple copies of this module on different devices).
+ Through this context, different copies can share some information.
+
+ We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback
+ of any slave copies.
+ """
+ master_copy = modules[0]
+ nr_modules = len(list(master_copy.modules()))
+ ctxs = [CallbackContext() for _ in range(nr_modules)]
+
+ for i, module in enumerate(modules):
+ for j, m in enumerate(module.modules()):
+ if hasattr(m, '__data_parallel_replicate__'):
+ m.__data_parallel_replicate__(ctxs[j], i)
+
+
+class DataParallelWithCallback(DataParallel):
+ """
+ Data Parallel with a replication callback.
+
+ An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by
+ original `replicate` function.
+ The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
+
+ Examples:
+ > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+ > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+ # sync_bn.__data_parallel_replicate__ will be invoked.
+ """
+
+ def replicate(self, module, device_ids):
+ modules = super(DataParallelWithCallback, self).replicate(module, device_ids)
+ execute_replication_callbacks(modules)
+ return modules
+
+
+def patch_replication_callback(data_parallel):
+ """
+ Monkey-patch an existing `DataParallel` object. Add the replication callback.
+ Useful when you have customized `DataParallel` implementation.
+
+ Examples:
+ > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+ > sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
+ > patch_replication_callback(sync_bn)
+ # this is equivalent to
+ > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+ > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+ """
+
+ assert isinstance(data_parallel, DataParallel)
+
+ old_replicate = data_parallel.replicate
+
+ @functools.wraps(old_replicate)
+ def new_replicate(module, device_ids):
+ modules = old_replicate(module, device_ids)
+ execute_replication_callbacks(modules)
+ return modules
+
+ data_parallel.replicate = new_replicate
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/modules/tests/__init__.py b/third_party/gim/gim/mit_semseg/lib/nn/modules/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/modules/tests/test_numeric_batchnorm.py b/third_party/gim/gim/mit_semseg/lib/nn/modules/tests/test_numeric_batchnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bd45a930d3dc84912e58659ee575be08e9038f0
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/nn/modules/tests/test_numeric_batchnorm.py
@@ -0,0 +1,56 @@
+# -*- coding: utf-8 -*-
+# File : test_numeric_batchnorm.py
+# Author : Jiayuan Mao
+# Email : maojiayuan@gmail.com
+# Date : 27/01/2018
+#
+# This file is part of Synchronized-BatchNorm-PyTorch.
+
+import unittest
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+from sync_batchnorm.unittest import TorchTestCase
+
+
+def handy_var(a, unbias=True):
+ n = a.size(0)
+ asum = a.sum(dim=0)
+ as_sum = (a ** 2).sum(dim=0) # a square sum
+ sumvar = as_sum - asum * asum / n
+ if unbias:
+ return sumvar / (n - 1)
+ else:
+ return sumvar / n
+
+
+class NumericTestCase(TorchTestCase):
+ def testNumericBatchNorm(self):
+ a = torch.rand(16, 10)
+ bn = nn.BatchNorm2d(10, momentum=1, eps=1e-5, affine=False)
+ bn.train()
+
+ a_var1 = Variable(a, requires_grad=True)
+ b_var1 = bn(a_var1)
+ loss1 = b_var1.sum()
+ loss1.backward()
+
+ a_var2 = Variable(a, requires_grad=True)
+ a_mean2 = a_var2.mean(dim=0, keepdim=True)
+ a_std2 = torch.sqrt(handy_var(a_var2, unbias=False).clamp(min=1e-5))
+ # a_std2 = torch.sqrt(a_var2.var(dim=0, keepdim=True, unbiased=False) + 1e-5)
+ b_var2 = (a_var2 - a_mean2) / a_std2
+ loss2 = b_var2.sum()
+ loss2.backward()
+
+ self.assertTensorClose(bn.running_mean, a.mean(dim=0))
+ self.assertTensorClose(bn.running_var, handy_var(a))
+ self.assertTensorClose(a_var1.data, a_var2.data)
+ self.assertTensorClose(b_var1.data, b_var2.data)
+ self.assertTensorClose(a_var1.grad, a_var2.grad)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/modules/tests/test_sync_batchnorm.py b/third_party/gim/gim/mit_semseg/lib/nn/modules/tests/test_sync_batchnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..45bb3c8cfd36d8f668e6fde756b17587eab72082
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/nn/modules/tests/test_sync_batchnorm.py
@@ -0,0 +1,111 @@
+# -*- coding: utf-8 -*-
+# File : test_sync_batchnorm.py
+# Author : Jiayuan Mao
+# Email : maojiayuan@gmail.com
+# Date : 27/01/2018
+#
+# This file is part of Synchronized-BatchNorm-PyTorch.
+
+import unittest
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+from sync_batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, DataParallelWithCallback
+from sync_batchnorm.unittest import TorchTestCase
+
+
+def handy_var(a, unbias=True):
+ n = a.size(0)
+ asum = a.sum(dim=0)
+ as_sum = (a ** 2).sum(dim=0) # a square sum
+ sumvar = as_sum - asum * asum / n
+ if unbias:
+ return sumvar / (n - 1)
+ else:
+ return sumvar / n
+
+
+def _find_bn(module):
+ for m in module.modules():
+ if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, SynchronizedBatchNorm1d, SynchronizedBatchNorm2d)):
+ return m
+
+
+class SyncTestCase(TorchTestCase):
+ def _syncParameters(self, bn1, bn2):
+ bn1.reset_parameters()
+ bn2.reset_parameters()
+ if bn1.affine and bn2.affine:
+ bn2.weight.data.copy_(bn1.weight.data)
+ bn2.bias.data.copy_(bn1.bias.data)
+
+ def _checkBatchNormResult(self, bn1, bn2, input, is_train, cuda=False):
+ """Check the forward and backward for the customized batch normalization."""
+ bn1.train(mode=is_train)
+ bn2.train(mode=is_train)
+
+ if cuda:
+ input = input.cuda()
+
+ self._syncParameters(_find_bn(bn1), _find_bn(bn2))
+
+ input1 = Variable(input, requires_grad=True)
+ output1 = bn1(input1)
+ output1.sum().backward()
+ input2 = Variable(input, requires_grad=True)
+ output2 = bn2(input2)
+ output2.sum().backward()
+
+ self.assertTensorClose(input1.data, input2.data)
+ self.assertTensorClose(output1.data, output2.data)
+ self.assertTensorClose(input1.grad, input2.grad)
+ self.assertTensorClose(_find_bn(bn1).running_mean, _find_bn(bn2).running_mean)
+ self.assertTensorClose(_find_bn(bn1).running_var, _find_bn(bn2).running_var)
+
+ def testSyncBatchNormNormalTrain(self):
+ bn = nn.BatchNorm1d(10)
+ sync_bn = SynchronizedBatchNorm1d(10)
+
+ self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True)
+
+ def testSyncBatchNormNormalEval(self):
+ bn = nn.BatchNorm1d(10)
+ sync_bn = SynchronizedBatchNorm1d(10)
+
+ self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False)
+
+ def testSyncBatchNormSyncTrain(self):
+ bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
+ sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+ sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+
+ bn.cuda()
+ sync_bn.cuda()
+
+ self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True, cuda=True)
+
+ def testSyncBatchNormSyncEval(self):
+ bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
+ sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+ sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+
+ bn.cuda()
+ sync_bn.cuda()
+
+ self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False, cuda=True)
+
+ def testSyncBatchNorm2DSyncTrain(self):
+ bn = nn.BatchNorm2d(10)
+ sync_bn = SynchronizedBatchNorm2d(10)
+ sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+
+ bn.cuda()
+ sync_bn.cuda()
+
+ self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10, 16, 16), True, cuda=True)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/modules/unittest.py b/third_party/gim/gim/mit_semseg/lib/nn/modules/unittest.py
new file mode 100644
index 0000000000000000000000000000000000000000..0675c022e4ba85d38d1f813490f6740150909524
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/nn/modules/unittest.py
@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+# File : unittest.py
+# Author : Jiayuan Mao
+# Email : maojiayuan@gmail.com
+# Date : 27/01/2018
+#
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import unittest
+
+import numpy as np
+from torch.autograd import Variable
+
+
+def as_numpy(v):
+ if isinstance(v, Variable):
+ v = v.data
+ return v.cpu().numpy()
+
+
+class TorchTestCase(unittest.TestCase):
+ def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3):
+ npa, npb = as_numpy(a), as_numpy(b)
+ self.assertTrue(
+ np.allclose(npa, npb, atol=atol),
+ 'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max())
+ )
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/parallel/__init__.py b/third_party/gim/gim/mit_semseg/lib/nn/parallel/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b52f49cc0755562218a460483cbf02514ddd773
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/nn/parallel/__init__.py
@@ -0,0 +1 @@
+from .data_parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to
diff --git a/third_party/gim/gim/mit_semseg/lib/nn/parallel/data_parallel.py b/third_party/gim/gim/mit_semseg/lib/nn/parallel/data_parallel.py
new file mode 100644
index 0000000000000000000000000000000000000000..376fc038919aa2a5bd696141e7bb6025d4981306
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/nn/parallel/data_parallel.py
@@ -0,0 +1,112 @@
+# -*- coding: utf8 -*-
+
+import torch.cuda as cuda
+import torch.nn as nn
+import torch
+import collections
+from torch.nn.parallel._functions import Gather
+
+
+__all__ = ['UserScatteredDataParallel', 'user_scattered_collate', 'async_copy_to']
+
+
+def async_copy_to(obj, dev, main_stream=None):
+ if torch.is_tensor(obj):
+ v = obj.cuda(dev, non_blocking=True)
+ if main_stream is not None:
+ v.data.record_stream(main_stream)
+ return v
+ elif isinstance(obj, collections.Mapping):
+ return {k: async_copy_to(o, dev, main_stream) for k, o in obj.items()}
+ elif isinstance(obj, collections.Sequence):
+ return [async_copy_to(o, dev, main_stream) for o in obj]
+ else:
+ return obj
+
+
+def dict_gather(outputs, target_device, dim=0):
+ """
+ Gathers variables from different GPUs on a specified device
+ (-1 means the CPU), with dictionary support.
+ """
+ def gather_map(outputs):
+ out = outputs[0]
+ if torch.is_tensor(out):
+ # MJY(20180330) HACK:: force nr_dims > 0
+ if out.dim() == 0:
+ outputs = [o.unsqueeze(0) for o in outputs]
+ return Gather.apply(target_device, dim, *outputs)
+ elif out is None:
+ return None
+ elif isinstance(out, collections.Mapping):
+ return {k: gather_map([o[k] for o in outputs]) for k in out}
+ elif isinstance(out, collections.Sequence):
+ return type(out)(map(gather_map, zip(*outputs)))
+ return gather_map(outputs)
+
+
+class DictGatherDataParallel(nn.DataParallel):
+ def gather(self, outputs, output_device):
+ return dict_gather(outputs, output_device, dim=self.dim)
+
+
+class UserScatteredDataParallel(DictGatherDataParallel):
+ def scatter(self, inputs, kwargs, device_ids):
+ assert len(inputs) == 1
+ inputs = inputs[0]
+ inputs = _async_copy_stream(inputs, device_ids)
+ inputs = [[i] for i in inputs]
+ assert len(kwargs) == 0
+ kwargs = [{} for _ in range(len(inputs))]
+
+ return inputs, kwargs
+
+
+def user_scattered_collate(batch):
+ return batch
+
+
+def _async_copy(inputs, device_ids):
+ nr_devs = len(device_ids)
+ assert type(inputs) in (tuple, list)
+ assert len(inputs) == nr_devs
+
+ outputs = []
+ for i, dev in zip(inputs, device_ids):
+ with cuda.device(dev):
+ outputs.append(async_copy_to(i, dev))
+
+ return tuple(outputs)
+
+
+def _async_copy_stream(inputs, device_ids):
+ nr_devs = len(device_ids)
+ assert type(inputs) in (tuple, list)
+ assert len(inputs) == nr_devs
+
+ outputs = []
+ streams = [_get_stream(d) for d in device_ids]
+ for i, dev, stream in zip(inputs, device_ids, streams):
+ with cuda.device(dev):
+ main_stream = cuda.current_stream()
+ with cuda.stream(stream):
+ outputs.append(async_copy_to(i, dev, main_stream=main_stream))
+ main_stream.wait_stream(stream)
+
+ return outputs
+
+
+"""Adapted from: torch/nn/parallel/_functions.py"""
+# background streams used for copying
+_streams = None
+
+
+def _get_stream(device):
+ """Gets a background stream for copying between CPU and GPU"""
+ global _streams
+ if device == -1:
+ return None
+ if _streams is None:
+ _streams = [None] * cuda.device_count()
+ if _streams[device] is None: _streams[device] = cuda.Stream(device)
+ return _streams[device]
diff --git a/third_party/gim/gim/mit_semseg/lib/utils/__init__.py b/third_party/gim/gim/mit_semseg/lib/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..abe3cbe49477fe37d4fc16249de8a10f4fb4a013
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/utils/__init__.py
@@ -0,0 +1 @@
+from .th import *
diff --git a/third_party/gim/gim/mit_semseg/lib/utils/data/__init__.py b/third_party/gim/gim/mit_semseg/lib/utils/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3b008fb13c5e8a84b1b785056e8c4f5226dc976
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/utils/data/__init__.py
@@ -0,0 +1,3 @@
+
+from .dataset import Dataset, TensorDataset, ConcatDataset
+from .dataloader import DataLoader
diff --git a/third_party/gim/gim/mit_semseg/lib/utils/data/dataloader.py b/third_party/gim/gim/mit_semseg/lib/utils/data/dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..039b9ec3645b2a4626ff47c221e372f32a6ad339
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/utils/data/dataloader.py
@@ -0,0 +1,425 @@
+import torch
+import torch.multiprocessing as multiprocessing
+from torch._C import _set_worker_signal_handlers, \
+ _remove_worker_pids, _error_if_any_worker_fails
+try:
+ from torch._C import _set_worker_pids
+except:
+ from torch._C import _update_worker_pids as _set_worker_pids
+from .sampler import SequentialSampler, RandomSampler, BatchSampler
+import signal
+import collections
+import re
+import sys
+import threading
+import traceback
+from torch._six import string_classes, int_classes
+import numpy as np
+
+if sys.version_info[0] == 2:
+ import Queue as queue
+else:
+ import queue
+
+
+class ExceptionWrapper(object):
+ r"Wraps an exception plus traceback to communicate across threads"
+
+ def __init__(self, exc_info):
+ self.exc_type = exc_info[0]
+ self.exc_msg = "".join(traceback.format_exception(*exc_info))
+
+
+_use_shared_memory = False
+"""Whether to use shared memory in default_collate"""
+
+
+def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id):
+ global _use_shared_memory
+ _use_shared_memory = True
+
+ # Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
+ # module's handlers are executed after Python returns from C low-level
+ # handlers, likely when the same fatal signal happened again already.
+ # https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
+ _set_worker_signal_handlers()
+
+ torch.set_num_threads(1)
+ torch.manual_seed(seed)
+ np.random.seed(seed)
+
+ if init_fn is not None:
+ init_fn(worker_id)
+
+ while True:
+ r = index_queue.get()
+ if r is None:
+ break
+ idx, batch_indices = r
+ try:
+ samples = collate_fn([dataset[i] for i in batch_indices])
+ except Exception:
+ data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
+ else:
+ data_queue.put((idx, samples))
+
+
+def _worker_manager_loop(in_queue, out_queue, done_event, pin_memory, device_id):
+ if pin_memory:
+ torch.cuda.set_device(device_id)
+
+ while True:
+ try:
+ r = in_queue.get()
+ except Exception:
+ if done_event.is_set():
+ return
+ raise
+ if r is None:
+ break
+ if isinstance(r[1], ExceptionWrapper):
+ out_queue.put(r)
+ continue
+ idx, batch = r
+ try:
+ if pin_memory:
+ batch = pin_memory_batch(batch)
+ except Exception:
+ out_queue.put((idx, ExceptionWrapper(sys.exc_info())))
+ else:
+ out_queue.put((idx, batch))
+
+numpy_type_map = {
+ 'float64': torch.DoubleTensor,
+ 'float32': torch.FloatTensor,
+ 'float16': torch.HalfTensor,
+ 'int64': torch.LongTensor,
+ 'int32': torch.IntTensor,
+ 'int16': torch.ShortTensor,
+ 'int8': torch.CharTensor,
+ 'uint8': torch.ByteTensor,
+}
+
+
+def default_collate(batch):
+ "Puts each data field into a tensor with outer dimension batch size"
+
+ error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
+ elem_type = type(batch[0])
+ if torch.is_tensor(batch[0]):
+ out = None
+ if _use_shared_memory:
+ # If we're in a background process, concatenate directly into a
+ # shared memory tensor to avoid an extra copy
+ numel = sum([x.numel() for x in batch])
+ storage = batch[0].storage()._new_shared(numel)
+ out = batch[0].new(storage)
+ return torch.stack(batch, 0, out=out)
+ elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
+ and elem_type.__name__ != 'string_':
+ elem = batch[0]
+ if elem_type.__name__ == 'ndarray':
+ # array of string classes and object
+ if re.search('[SaUO]', elem.dtype.str) is not None:
+ raise TypeError(error_msg.format(elem.dtype))
+
+ return torch.stack([torch.from_numpy(b) for b in batch], 0)
+ if elem.shape == (): # scalars
+ py_type = float if elem.dtype.name.startswith('float') else int
+ return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
+ elif isinstance(batch[0], int_classes):
+ return torch.LongTensor(batch)
+ elif isinstance(batch[0], float):
+ return torch.DoubleTensor(batch)
+ elif isinstance(batch[0], string_classes):
+ return batch
+ elif isinstance(batch[0], collections.Mapping):
+ return {key: default_collate([d[key] for d in batch]) for key in batch[0]}
+ elif isinstance(batch[0], collections.Sequence):
+ transposed = zip(*batch)
+ return [default_collate(samples) for samples in transposed]
+
+ raise TypeError((error_msg.format(type(batch[0]))))
+
+
+def pin_memory_batch(batch):
+ if torch.is_tensor(batch):
+ return batch.pin_memory()
+ elif isinstance(batch, string_classes):
+ return batch
+ elif isinstance(batch, collections.Mapping):
+ return {k: pin_memory_batch(sample) for k, sample in batch.items()}
+ elif isinstance(batch, collections.Sequence):
+ return [pin_memory_batch(sample) for sample in batch]
+ else:
+ return batch
+
+
+_SIGCHLD_handler_set = False
+"""Whether SIGCHLD handler is set for DataLoader worker failures. Only one
+handler needs to be set for all DataLoaders in a process."""
+
+
+def _set_SIGCHLD_handler():
+ # Windows doesn't support SIGCHLD handler
+ if sys.platform == 'win32':
+ return
+ # can't set signal in child threads
+ if not isinstance(threading.current_thread(), threading._MainThread):
+ return
+ global _SIGCHLD_handler_set
+ if _SIGCHLD_handler_set:
+ return
+ previous_handler = signal.getsignal(signal.SIGCHLD)
+ if not callable(previous_handler):
+ previous_handler = None
+
+ def handler(signum, frame):
+ # This following call uses `waitid` with WNOHANG from C side. Therefore,
+ # Python can still get and update the process status successfully.
+ _error_if_any_worker_fails()
+ if previous_handler is not None:
+ previous_handler(signum, frame)
+
+ signal.signal(signal.SIGCHLD, handler)
+ _SIGCHLD_handler_set = True
+
+
+class DataLoaderIter(object):
+ "Iterates once over the DataLoader's dataset, as specified by the sampler"
+
+ def __init__(self, loader):
+ self.dataset = loader.dataset
+ self.collate_fn = loader.collate_fn
+ self.batch_sampler = loader.batch_sampler
+ self.num_workers = loader.num_workers
+ self.pin_memory = loader.pin_memory and torch.cuda.is_available()
+ self.timeout = loader.timeout
+ self.done_event = threading.Event()
+
+ self.sample_iter = iter(self.batch_sampler)
+
+ if self.num_workers > 0:
+ self.worker_init_fn = loader.worker_init_fn
+ self.index_queue = multiprocessing.SimpleQueue()
+ self.worker_result_queue = multiprocessing.SimpleQueue()
+ self.batches_outstanding = 0
+ self.worker_pids_set = False
+ self.shutdown = False
+ self.send_idx = 0
+ self.rcvd_idx = 0
+ self.reorder_dict = {}
+
+ base_seed = torch.LongTensor(1).random_(0, 2**31-1)[0]
+ self.workers = [
+ multiprocessing.Process(
+ target=_worker_loop,
+ args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn,
+ base_seed + i, self.worker_init_fn, i))
+ for i in range(self.num_workers)]
+
+ if self.pin_memory or self.timeout > 0:
+ self.data_queue = queue.Queue()
+ if self.pin_memory:
+ maybe_device_id = torch.cuda.current_device()
+ else:
+ # do not initialize cuda context if not necessary
+ maybe_device_id = None
+ self.worker_manager_thread = threading.Thread(
+ target=_worker_manager_loop,
+ args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
+ maybe_device_id))
+ self.worker_manager_thread.daemon = True
+ self.worker_manager_thread.start()
+ else:
+ self.data_queue = self.worker_result_queue
+
+ for w in self.workers:
+ w.daemon = True # ensure that the worker exits on process exit
+ w.start()
+
+ _set_worker_pids(id(self), tuple(w.pid for w in self.workers))
+ _set_SIGCHLD_handler()
+ self.worker_pids_set = True
+
+ # prime the prefetch loop
+ for _ in range(2 * self.num_workers):
+ self._put_indices()
+
+ def __len__(self):
+ return len(self.batch_sampler)
+
+ def _get_batch(self):
+ if self.timeout > 0:
+ try:
+ return self.data_queue.get(timeout=self.timeout)
+ except queue.Empty:
+ raise RuntimeError('DataLoader timed out after {} seconds'.format(self.timeout))
+ else:
+ return self.data_queue.get()
+
+ def __next__(self):
+ if self.num_workers == 0: # same-process loading
+ indices = next(self.sample_iter) # may raise StopIteration
+ batch = self.collate_fn([self.dataset[i] for i in indices])
+ if self.pin_memory:
+ batch = pin_memory_batch(batch)
+ return batch
+
+ # check if the next sample has already been generated
+ if self.rcvd_idx in self.reorder_dict:
+ batch = self.reorder_dict.pop(self.rcvd_idx)
+ return self._process_next_batch(batch)
+
+ if self.batches_outstanding == 0:
+ self._shutdown_workers()
+ raise StopIteration
+
+ while True:
+ assert (not self.shutdown and self.batches_outstanding > 0)
+ idx, batch = self._get_batch()
+ self.batches_outstanding -= 1
+ if idx != self.rcvd_idx:
+ # store out-of-order samples
+ self.reorder_dict[idx] = batch
+ continue
+ return self._process_next_batch(batch)
+
+ next = __next__ # Python 2 compatibility
+
+ def __iter__(self):
+ return self
+
+ def _put_indices(self):
+ assert self.batches_outstanding < 2 * self.num_workers
+ indices = next(self.sample_iter, None)
+ if indices is None:
+ return
+ self.index_queue.put((self.send_idx, indices))
+ self.batches_outstanding += 1
+ self.send_idx += 1
+
+ def _process_next_batch(self, batch):
+ self.rcvd_idx += 1
+ self._put_indices()
+ if isinstance(batch, ExceptionWrapper):
+ raise batch.exc_type(batch.exc_msg)
+ return batch
+
+ def __getstate__(self):
+ # TODO: add limited pickling support for sharing an iterator
+ # across multiple threads for HOGWILD.
+ # Probably the best way to do this is by moving the sample pushing
+ # to a separate thread and then just sharing the data queue
+ # but signalling the end is tricky without a non-blocking API
+ raise NotImplementedError("DataLoaderIterator cannot be pickled")
+
+ def _shutdown_workers(self):
+ try:
+ if not self.shutdown:
+ self.shutdown = True
+ self.done_event.set()
+ # if worker_manager_thread is waiting to put
+ while not self.data_queue.empty():
+ self.data_queue.get()
+ for _ in self.workers:
+ self.index_queue.put(None)
+ # done_event should be sufficient to exit worker_manager_thread,
+ # but be safe here and put another None
+ self.worker_result_queue.put(None)
+ finally:
+ # removes pids no matter what
+ if self.worker_pids_set:
+ _remove_worker_pids(id(self))
+ self.worker_pids_set = False
+
+ def __del__(self):
+ if self.num_workers > 0:
+ self._shutdown_workers()
+
+
+class DataLoader(object):
+ """
+ Data loader. Combines a dataset and a sampler, and provides
+ single- or multi-process iterators over the dataset.
+
+ Arguments:
+ dataset (Dataset): dataset from which to load the data.
+ batch_size (int, optional): how many samples per batch to load
+ (default: 1).
+ shuffle (bool, optional): set to ``True`` to have the data reshuffled
+ at every epoch (default: False).
+ sampler (Sampler, optional): defines the strategy to draw samples from
+ the dataset. If specified, ``shuffle`` must be False.
+ batch_sampler (Sampler, optional): like sampler, but returns a batch of
+ indices at a time. Mutually exclusive with batch_size, shuffle,
+ sampler, and drop_last.
+ num_workers (int, optional): how many subprocesses to use for data
+ loading. 0 means that the data will be loaded in the main process.
+ (default: 0)
+ collate_fn (callable, optional): merges a list of samples to form a mini-batch.
+ pin_memory (bool, optional): If ``True``, the data loader will copy tensors
+ into CUDA pinned memory before returning them.
+ drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
+ if the dataset size is not divisible by the batch size. If ``False`` and
+ the size of dataset is not divisible by the batch size, then the last batch
+ will be smaller. (default: False)
+ timeout (numeric, optional): if positive, the timeout value for collecting a batch
+ from workers. Should always be non-negative. (default: 0)
+ worker_init_fn (callable, optional): If not None, this will be called on each
+ worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
+ input, after seeding and before data loading. (default: None)
+
+ .. note:: By default, each worker will have its PyTorch seed set to
+ ``base_seed + worker_id``, where ``base_seed`` is a long generated
+ by main process using its RNG. You may use ``torch.initial_seed()`` to access
+ this value in :attr:`worker_init_fn`, which can be used to set other seeds
+ (e.g. NumPy) before data loading.
+
+ .. warning:: If ``spawn'' start method is used, :attr:`worker_init_fn` cannot be an
+ unpicklable object, e.g., a lambda function.
+ """
+
+ def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
+ num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False,
+ timeout=0, worker_init_fn=None):
+ self.dataset = dataset
+ self.batch_size = batch_size
+ self.num_workers = num_workers
+ self.collate_fn = collate_fn
+ self.pin_memory = pin_memory
+ self.drop_last = drop_last
+ self.timeout = timeout
+ self.worker_init_fn = worker_init_fn
+
+ if timeout < 0:
+ raise ValueError('timeout option should be non-negative')
+
+ if batch_sampler is not None:
+ if batch_size > 1 or shuffle or sampler is not None or drop_last:
+ raise ValueError('batch_sampler is mutually exclusive with '
+ 'batch_size, shuffle, sampler, and drop_last')
+
+ if sampler is not None and shuffle:
+ raise ValueError('sampler is mutually exclusive with shuffle')
+
+ if self.num_workers < 0:
+ raise ValueError('num_workers cannot be negative; '
+ 'use num_workers=0 to disable multiprocessing.')
+
+ if batch_sampler is None:
+ if sampler is None:
+ if shuffle:
+ sampler = RandomSampler(dataset)
+ else:
+ sampler = SequentialSampler(dataset)
+ batch_sampler = BatchSampler(sampler, batch_size, drop_last)
+
+ self.sampler = sampler
+ self.batch_sampler = batch_sampler
+
+ def __iter__(self):
+ return DataLoaderIter(self)
+
+ def __len__(self):
+ return len(self.batch_sampler)
diff --git a/third_party/gim/gim/mit_semseg/lib/utils/data/dataset.py b/third_party/gim/gim/mit_semseg/lib/utils/data/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..605aa877f7031a5cd2b98c0f831410aa80fddefa
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/utils/data/dataset.py
@@ -0,0 +1,118 @@
+import bisect
+import warnings
+
+from torch._utils import _accumulate
+from torch import randperm
+
+
+class Dataset(object):
+ """An abstract class representing a Dataset.
+
+ All other datasets should subclass it. All subclasses should override
+ ``__len__``, that provides the size of the dataset, and ``__getitem__``,
+ supporting integer indexing in range from 0 to len(self) exclusive.
+ """
+
+ def __getitem__(self, index):
+ raise NotImplementedError
+
+ def __len__(self):
+ raise NotImplementedError
+
+ def __add__(self, other):
+ return ConcatDataset([self, other])
+
+
+class TensorDataset(Dataset):
+ """Dataset wrapping data and target tensors.
+
+ Each sample will be retrieved by indexing both tensors along the first
+ dimension.
+
+ Arguments:
+ data_tensor (Tensor): contains sample data.
+ target_tensor (Tensor): contains sample targets (labels).
+ """
+
+ def __init__(self, data_tensor, target_tensor):
+ assert data_tensor.size(0) == target_tensor.size(0)
+ self.data_tensor = data_tensor
+ self.target_tensor = target_tensor
+
+ def __getitem__(self, index):
+ return self.data_tensor[index], self.target_tensor[index]
+
+ def __len__(self):
+ return self.data_tensor.size(0)
+
+
+class ConcatDataset(Dataset):
+ """
+ Dataset to concatenate multiple datasets.
+ Purpose: useful to assemble different existing datasets, possibly
+ large-scale datasets as the concatenation operation is done in an
+ on-the-fly manner.
+
+ Arguments:
+ datasets (iterable): List of datasets to be concatenated
+ """
+
+ @staticmethod
+ def cumsum(sequence):
+ r, s = [], 0
+ for e in sequence:
+ l = len(e)
+ r.append(l + s)
+ s += l
+ return r
+
+ def __init__(self, datasets):
+ super(ConcatDataset, self).__init__()
+ assert len(datasets) > 0, 'datasets should not be an empty iterable'
+ self.datasets = list(datasets)
+ self.cumulative_sizes = self.cumsum(self.datasets)
+
+ def __len__(self):
+ return self.cumulative_sizes[-1]
+
+ def __getitem__(self, idx):
+ dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
+ if dataset_idx == 0:
+ sample_idx = idx
+ else:
+ sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
+ return self.datasets[dataset_idx][sample_idx]
+
+ @property
+ def cummulative_sizes(self):
+ warnings.warn("cummulative_sizes attribute is renamed to "
+ "cumulative_sizes", DeprecationWarning, stacklevel=2)
+ return self.cumulative_sizes
+
+
+class Subset(Dataset):
+ def __init__(self, dataset, indices):
+ self.dataset = dataset
+ self.indices = indices
+
+ def __getitem__(self, idx):
+ return self.dataset[self.indices[idx]]
+
+ def __len__(self):
+ return len(self.indices)
+
+
+def random_split(dataset, lengths):
+ """
+ Randomly split a dataset into non-overlapping new datasets of given lengths
+ ds
+
+ Arguments:
+ dataset (Dataset): Dataset to be split
+ lengths (iterable): lengths of splits to be produced
+ """
+ if sum(lengths) != len(dataset):
+ raise ValueError("Sum of input lengths does not equal the length of the input dataset!")
+
+ indices = randperm(sum(lengths))
+ return [Subset(dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(lengths), lengths)]
diff --git a/third_party/gim/gim/mit_semseg/lib/utils/data/distributed.py b/third_party/gim/gim/mit_semseg/lib/utils/data/distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3d890e28fd2b9e044bdd9494de4a43ad2471eed
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/utils/data/distributed.py
@@ -0,0 +1,58 @@
+import math
+import torch
+from .sampler import Sampler
+from torch.distributed import get_world_size, get_rank
+
+
+class DistributedSampler(Sampler):
+ """Sampler that restricts data loading to a subset of the dataset.
+
+ It is especially useful in conjunction with
+ :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
+ process can pass a DistributedSampler instance as a DataLoader sampler,
+ and load a subset of the original dataset that is exclusive to it.
+
+ .. note::
+ Dataset is assumed to be of constant size.
+
+ Arguments:
+ dataset: Dataset used for sampling.
+ num_replicas (optional): Number of processes participating in
+ distributed training.
+ rank (optional): Rank of the current process within num_replicas.
+ """
+
+ def __init__(self, dataset, num_replicas=None, rank=None):
+ if num_replicas is None:
+ num_replicas = get_world_size()
+ if rank is None:
+ rank = get_rank()
+ self.dataset = dataset
+ self.num_replicas = num_replicas
+ self.rank = rank
+ self.epoch = 0
+ self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
+ self.total_size = self.num_samples * self.num_replicas
+
+ def __iter__(self):
+ # deterministically shuffle based on epoch
+ g = torch.Generator()
+ g.manual_seed(self.epoch)
+ indices = list(torch.randperm(len(self.dataset), generator=g))
+
+ # add extra samples to make it evenly divisible
+ indices += indices[:(self.total_size - len(indices))]
+ assert len(indices) == self.total_size
+
+ # subsample
+ offset = self.num_samples * self.rank
+ indices = indices[offset:offset + self.num_samples]
+ assert len(indices) == self.num_samples
+
+ return iter(indices)
+
+ def __len__(self):
+ return self.num_samples
+
+ def set_epoch(self, epoch):
+ self.epoch = epoch
diff --git a/third_party/gim/gim/mit_semseg/lib/utils/data/sampler.py b/third_party/gim/gim/mit_semseg/lib/utils/data/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..62a9a43bd1d4c21fbdcb262db7da8d4fe27b26de
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/utils/data/sampler.py
@@ -0,0 +1,131 @@
+import torch
+
+
+class Sampler(object):
+ """Base class for all Samplers.
+
+ Every Sampler subclass has to provide an __iter__ method, providing a way
+ to iterate over indices of dataset elements, and a __len__ method that
+ returns the length of the returned iterators.
+ """
+
+ def __init__(self, data_source):
+ pass
+
+ def __iter__(self):
+ raise NotImplementedError
+
+ def __len__(self):
+ raise NotImplementedError
+
+
+class SequentialSampler(Sampler):
+ """Samples elements sequentially, always in the same order.
+
+ Arguments:
+ data_source (Dataset): dataset to sample from
+ """
+
+ def __init__(self, data_source):
+ self.data_source = data_source
+
+ def __iter__(self):
+ return iter(range(len(self.data_source)))
+
+ def __len__(self):
+ return len(self.data_source)
+
+
+class RandomSampler(Sampler):
+ """Samples elements randomly, without replacement.
+
+ Arguments:
+ data_source (Dataset): dataset to sample from
+ """
+
+ def __init__(self, data_source):
+ self.data_source = data_source
+
+ def __iter__(self):
+ return iter(torch.randperm(len(self.data_source)).long())
+
+ def __len__(self):
+ return len(self.data_source)
+
+
+class SubsetRandomSampler(Sampler):
+ """Samples elements randomly from a given list of indices, without replacement.
+
+ Arguments:
+ indices (list): a list of indices
+ """
+
+ def __init__(self, indices):
+ self.indices = indices
+
+ def __iter__(self):
+ return (self.indices[i] for i in torch.randperm(len(self.indices)))
+
+ def __len__(self):
+ return len(self.indices)
+
+
+class WeightedRandomSampler(Sampler):
+ """Samples elements from [0,..,len(weights)-1] with given probabilities (weights).
+
+ Arguments:
+ weights (list) : a list of weights, not necessary summing up to one
+ num_samples (int): number of samples to draw
+ replacement (bool): if ``True``, samples are drawn with replacement.
+ If not, they are drawn without replacement, which means that when a
+ sample index is drawn for a row, it cannot be drawn again for that row.
+ """
+
+ def __init__(self, weights, num_samples, replacement=True):
+ self.weights = torch.DoubleTensor(weights)
+ self.num_samples = num_samples
+ self.replacement = replacement
+
+ def __iter__(self):
+ return iter(torch.multinomial(self.weights, self.num_samples, self.replacement))
+
+ def __len__(self):
+ return self.num_samples
+
+
+class BatchSampler(object):
+ """Wraps another sampler to yield a mini-batch of indices.
+
+ Args:
+ sampler (Sampler): Base sampler.
+ batch_size (int): Size of mini-batch.
+ drop_last (bool): If ``True``, the sampler will drop the last batch if
+ its size would be less than ``batch_size``
+
+ Example:
+ >>> list(BatchSampler(range(10), batch_size=3, drop_last=False))
+ [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
+ >>> list(BatchSampler(range(10), batch_size=3, drop_last=True))
+ [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+ """
+
+ def __init__(self, sampler, batch_size, drop_last):
+ self.sampler = sampler
+ self.batch_size = batch_size
+ self.drop_last = drop_last
+
+ def __iter__(self):
+ batch = []
+ for idx in self.sampler:
+ batch.append(idx)
+ if len(batch) == self.batch_size:
+ yield batch
+ batch = []
+ if len(batch) > 0 and not self.drop_last:
+ yield batch
+
+ def __len__(self):
+ if self.drop_last:
+ return len(self.sampler) // self.batch_size
+ else:
+ return (len(self.sampler) + self.batch_size - 1) // self.batch_size
diff --git a/third_party/gim/gim/mit_semseg/lib/utils/th.py b/third_party/gim/gim/mit_semseg/lib/utils/th.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca6ef9385e3b5c0a439579d3fd7aa73b5dc62758
--- /dev/null
+++ b/third_party/gim/gim/mit_semseg/lib/utils/th.py
@@ -0,0 +1,41 @@
+import torch
+from torch.autograd import Variable
+import numpy as np
+import collections
+
+__all__ = ['as_variable', 'as_numpy', 'mark_volatile']
+
+def as_variable(obj):
+ if isinstance(obj, Variable):
+ return obj
+ if isinstance(obj, collections.Sequence):
+ return [as_variable(v) for v in obj]
+ elif isinstance(obj, collections.Mapping):
+ return {k: as_variable(v) for k, v in obj.items()}
+ else:
+ return Variable(obj)
+
+def as_numpy(obj):
+ if isinstance(obj, collections.Sequence):
+ return [as_numpy(v) for v in obj]
+ elif isinstance(obj, collections.Mapping):
+ return {k: as_numpy(v) for k, v in obj.items()}
+ elif isinstance(obj, Variable):
+ return obj.data.cpu().numpy()
+ elif torch.is_tensor(obj):
+ return obj.cpu().numpy()
+ else:
+ return np.array(obj)
+
+def mark_volatile(obj):
+ if torch.is_tensor(obj):
+ obj = Variable(obj)
+ if isinstance(obj, Variable):
+ obj.no_grad = True
+ return obj
+ elif isinstance(obj, collections.Mapping):
+ return {k: mark_volatile(o) for k, o in obj.items()}
+ elif isinstance(obj, collections.Sequence):
+ return [mark_volatile(o) for o in obj]
+ else:
+ return obj
diff --git a/imcui/third_party/gim/networks/mit_semseg/models/__init__.py b/third_party/gim/gim/mit_semseg/models/__init__.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/models/__init__.py
rename to third_party/gim/gim/mit_semseg/models/__init__.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/models/hrnet.py b/third_party/gim/gim/mit_semseg/models/hrnet.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/models/hrnet.py
rename to third_party/gim/gim/mit_semseg/models/hrnet.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/models/mobilenet.py b/third_party/gim/gim/mit_semseg/models/mobilenet.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/models/mobilenet.py
rename to third_party/gim/gim/mit_semseg/models/mobilenet.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/models/models.py b/third_party/gim/gim/mit_semseg/models/models.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/models/models.py
rename to third_party/gim/gim/mit_semseg/models/models.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/models/resnet.py b/third_party/gim/gim/mit_semseg/models/resnet.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/models/resnet.py
rename to third_party/gim/gim/mit_semseg/models/resnet.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/models/resnext.py b/third_party/gim/gim/mit_semseg/models/resnext.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/models/resnext.py
rename to third_party/gim/gim/mit_semseg/models/resnext.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/models/utils.py b/third_party/gim/gim/mit_semseg/models/utils.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/models/utils.py
rename to third_party/gim/gim/mit_semseg/models/utils.py
diff --git a/imcui/third_party/gim/networks/mit_semseg/utils.py b/third_party/gim/gim/mit_semseg/utils.py
similarity index 100%
rename from imcui/third_party/gim/networks/mit_semseg/utils.py
rename to third_party/gim/gim/mit_semseg/utils.py
diff --git a/third_party/gim/sdasdada__init__.py b/third_party/gim/sdasdada__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5663b73e116701548d24dfeacb2d527bf79f5ff8
--- /dev/null
+++ b/third_party/gim/sdasdada__init__.py
@@ -0,0 +1,8 @@
+# -*- coding: utf-8 -*-
+# @Author : xuelun
+# import sys
+# from pathlib import Path
+# sys.path.append(str(Path(__file__).parent))
+# from .gim import dkm
+# from .gim import loftr
+# from .gim import lightglue
\ No newline at end of file
diff --git a/third_party/lanet/.gitattributes b/third_party/lanet/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..ec4a626fbb7799f6a25b45fb86344b2bf7b37e64
--- /dev/null
+++ b/third_party/lanet/.gitattributes
@@ -0,0 +1 @@
+*.pth filter=lfs diff=lfs merge=lfs -text
diff --git a/third_party/lanet/LICENSE b/third_party/lanet/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..df725685f32f70fdf841379ed1ae5273600c7248
--- /dev/null
+++ b/third_party/lanet/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) Changhao Wang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/third_party/lanet/README.md b/third_party/lanet/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..0bdac20ad300970ff3949800f3dd14e5efbd4001
--- /dev/null
+++ b/third_party/lanet/README.md
@@ -0,0 +1,72 @@
+# Rethinking Low-level Features for Interest Point Detection and Description
+
+## Dependency
+ - pytorch
+ - torchvision
+ - cv2
+ - tqdm
+
+ We use cuda 11.4/python 3.8.13/torch 1.10.0/torchvision 0.11.0/opencv 3.4.8 for training and testing.
+
+
+## Pre-trained models
+We provide two versions of LANet with different structure in [network_v0](network_v0) and [network_v1](network_v1), the corresponding pre-trained models are in [checkpoints](checkpoints).
+ - v0: The original version used in our paper.
+ - v1: An improved version that has a better over all performance.
+
+
+## Training
+Download the COCO dataset:
+```
+cd datasets/COCO/
+wget http://images.cocodataset.org/zips/train2017.zip
+unzip train2017.zip
+```
+Prepare the training file:
+```
+python datasets/prepare_coco.py --raw_dir datasets/COCO/train2017/ --saved_dir datasets/COCO/
+```
+
+To train the model (v0) on COCO dataset, run:
+```
+python main.py --train_root datasets/COCO/train2017/ --train_txt datasets/COCO/train2017.txt
+```
+
+
+## Evaluation
+### Evaluation on HPatches dataset
+Download the HPatches dataset:
+```
+cd datasets/HPatches/
+wget http://icvl.ee.ic.ac.uk/vbalnt/hpatches/hpatches-sequences-release.tar.gz
+tar -xvf hpatches-sequences-release.tar.gz
+```
+
+To evaluate the pre-trained model, run:
+```
+python test.py --test_dir ./datasets/HPatches/hpatches-sequences-release
+```
+
+
+## License
+The code is released under the [MIT license](LICENSE).
+
+
+## Citation
+Please use the following citation when referencing our work:
+```
+@InProceedings{Wang_2022_ACCV,
+ author = {Changhao Wang and Guanwen Zhang and Zhengyun Cheng and Wei Zhou},
+ title = {Rethinking Low-level Features for Interest Point Detection and Description},
+ booktitle = {Computer Vision - {ACCV} 2022 - 16th Asian Conference on Computer
+ Vision, Macao, China, December 4-8, 2022, Proceedings, Part {II}},
+ series = {Lecture Notes in Computer Science},
+ volume = {13842},
+ pages = {108--123},
+ year = {2022}
+}
+```
+
+
+## Related Projects
+https://github.com/TRI-ML/KP2D
diff --git a/imcui/third_party/lanet/augmentations.py b/third_party/lanet/augmentations.py
similarity index 54%
rename from imcui/third_party/lanet/augmentations.py
rename to third_party/lanet/augmentations.py
index ab8a551f4b0979e714b54818a74a4e49fe07b966..e52c21d287eafc76df2663a5f3aa64c9c5a6e097 100644
--- a/imcui/third_party/lanet/augmentations.py
+++ b/third_party/lanet/augmentations.py
@@ -1,342 +1,431 @@
-# From https://github.com/TRI-ML/KP2D.
-
-# Copyright 2020 Toyota Research Institute. All rights reserved.
-
-import random
-from math import pi
-
-import cv2
-import numpy as np
-import torch
-import torchvision
-import torchvision.transforms as transforms
-from PIL import Image
-
-from utils import image_grid
-
-
-def filter_dict(dict, keywords):
- """
- Returns only the keywords that are part of a dictionary
-
- Parameters
- ----------
- dictionary : dict
- Dictionary for filtering
- keywords : list of str
- Keywords that will be filtered
-
- Returns
- -------
- keywords : list of str
- List containing the keywords that are keys in dictionary
- """
- return [key for key in keywords if key in dict]
-
-
-def resize_sample(sample, image_shape, image_interpolation=Image.ANTIALIAS):
- """
- Resizes a sample, which contains an input image.
-
- Parameters
- ----------
- sample : dict
- Dictionary with sample values (output from a dataset's __getitem__ method)
- shape : tuple (H,W)
- Output shape
- image_interpolation : int
- Interpolation mode
-
- Returns
- -------
- sample : dict
- Resized sample
- """
- # image
- image_transform = transforms.Resize(image_shape, interpolation=image_interpolation)
- sample['image'] = image_transform(sample['image'])
- return sample
-
-def spatial_augment_sample(sample):
- """ Apply spatial augmentation to an image (flipping and random affine transformation)."""
- augment_image = transforms.Compose([
- transforms.RandomVerticalFlip(p=0.5),
- transforms.RandomHorizontalFlip(p=0.5),
- transforms.RandomAffine(15, translate=(0.1, 0.1), scale=(0.9, 1.1))
-
- ])
- sample['image'] = augment_image(sample['image'])
-
- return sample
-
-def unnormalize_image(tensor, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)):
- """ Counterpart method of torchvision.transforms.Normalize."""
- for t, m, s in zip(tensor, mean, std):
- t.div_(1 / s).sub_(-m)
- return tensor
-
-
-def sample_homography(
- shape, perspective=True, scaling=True, rotation=True, translation=True,
- n_scales=100, n_angles=100, scaling_amplitude=0.1, perspective_amplitude=0.4,
- patch_ratio=0.8, max_angle=pi/4):
- """ Sample a random homography that includes perspective, scale, translation and rotation operations."""
-
- width = float(shape[1])
- hw_ratio = float(shape[0]) / float(shape[1])
-
- pts1 = np.stack([[-1., -1.], [-1., 1.], [1., -1.], [1., 1.]], axis=0)
- pts2 = pts1.copy() * patch_ratio
- pts2[:,1] *= hw_ratio
-
- if perspective:
-
- perspective_amplitude_x = np.random.normal(0., perspective_amplitude/2, (2))
- perspective_amplitude_y = np.random.normal(0., hw_ratio * perspective_amplitude/2, (2))
-
- perspective_amplitude_x = np.clip(perspective_amplitude_x, -perspective_amplitude/2, perspective_amplitude/2)
- perspective_amplitude_y = np.clip(perspective_amplitude_y, hw_ratio * -perspective_amplitude/2, hw_ratio * perspective_amplitude/2)
-
- pts2[0,0] -= perspective_amplitude_x[1]
- pts2[0,1] -= perspective_amplitude_y[1]
-
- pts2[1,0] -= perspective_amplitude_x[0]
- pts2[1,1] += perspective_amplitude_y[1]
-
- pts2[2,0] += perspective_amplitude_x[1]
- pts2[2,1] -= perspective_amplitude_y[0]
-
- pts2[3,0] += perspective_amplitude_x[0]
- pts2[3,1] += perspective_amplitude_y[0]
-
- if scaling:
-
- random_scales = np.random.normal(1, scaling_amplitude/2, (n_scales))
- random_scales = np.clip(random_scales, 1-scaling_amplitude/2, 1+scaling_amplitude/2)
-
- scales = np.concatenate([[1.], random_scales], 0)
- center = np.mean(pts2, axis=0, keepdims=True)
- scaled = np.expand_dims(pts2 - center, axis=0) * np.expand_dims(
- np.expand_dims(scales, 1), 1) + center
- valid = np.arange(n_scales) # all scales are valid except scale=1
- idx = valid[np.random.randint(valid.shape[0])]
- pts2 = scaled[idx]
-
- if translation:
- t_min, t_max = np.min(pts2 - [-1., -hw_ratio], axis=0), np.min([1., hw_ratio] - pts2, axis=0)
- pts2 += np.expand_dims(np.stack([np.random.uniform(-t_min[0], t_max[0]),
- np.random.uniform(-t_min[1], t_max[1])]),
- axis=0)
-
- if rotation:
- angles = np.linspace(-max_angle, max_angle, n_angles)
- angles = np.concatenate([[0.], angles], axis=0)
-
- center = np.mean(pts2, axis=0, keepdims=True)
- rot_mat = np.reshape(np.stack([np.cos(angles), -np.sin(angles), np.sin(angles),
- np.cos(angles)], axis=1), [-1, 2, 2])
- rotated = np.matmul(
- np.tile(np.expand_dims(pts2 - center, axis=0), [n_angles+1, 1, 1]),
- rot_mat) + center
-
- valid = np.where(np.all((rotated >= [-1.,-hw_ratio]) & (rotated < [1.,hw_ratio]),
- axis=(1, 2)))[0]
-
- idx = valid[np.random.randint(valid.shape[0])]
- pts2 = rotated[idx]
-
- pts2[:,1] /= hw_ratio
-
- def ax(p, q): return [p[0], p[1], 1, 0, 0, 0, -p[0] * q[0], -p[1] * q[0]]
- def ay(p, q): return [0, 0, 0, p[0], p[1], 1, -p[0] * q[1], -p[1] * q[1]]
-
- a_mat = np.stack([f(pts1[i], pts2[i]) for i in range(4) for f in (ax, ay)], axis=0)
- p_mat = np.transpose(np.stack(
- [[pts2[i][j] for i in range(4) for j in range(2)]], axis=0))
-
- homography = np.matmul(np.linalg.pinv(a_mat), p_mat).squeeze()
- homography = np.concatenate([homography, [1.]]).reshape(3,3)
- return homography
-
-def warp_homography(sources, homography):
- """Warp features given a homography
-
- Parameters
- ----------
- sources: torch.tensor (1,H,W,2)
- Keypoint vector.
- homography: torch.Tensor (3,3)
- Homography.
-
- Returns
- -------
- warped_sources: torch.tensor (1,H,W,2)
- Warped feature vector.
- """
- _, H, W, _ = sources.shape
- warped_sources = sources.clone().squeeze()
- warped_sources = warped_sources.view(-1,2)
- warped_sources = torch.addmm(homography[:,2], warped_sources, homography[:,:2].t())
- warped_sources.mul_(1/warped_sources[:,2].unsqueeze(1))
- warped_sources = warped_sources[:,:2].contiguous().view(1,H,W,2)
- return warped_sources
-
-def add_noise(img, mode="gaussian", percent=0.02):
- """Add image noise
-
- Parameters
- ----------
- image : np.array
- Input image
- mode: str
- Type of noise, from ['gaussian','salt','pepper','s&p']
- percent: float
- Percentage image points to add noise to.
- Returns
- -------
- image : np.array
- Image plus noise.
- """
- original_dtype = img.dtype
- if mode == "gaussian":
- mean = 0
- var = 0.1
- sigma = var * 0.5
-
- if img.ndim == 2:
- h, w = img.shape
- gauss = np.random.normal(mean, sigma, (h, w))
- else:
- h, w, c = img.shape
- gauss = np.random.normal(mean, sigma, (h, w, c))
-
- if img.dtype not in [np.float32, np.float64]:
- gauss = gauss * np.iinfo(img.dtype).max
- img = np.clip(img.astype(np.float) + gauss, 0, np.iinfo(img.dtype).max)
- else:
- img = np.clip(img.astype(np.float) + gauss, 0, 1)
-
- elif mode == "salt":
- print(img.dtype)
- s_vs_p = 1
- num_salt = np.ceil(percent * img.size * s_vs_p)
- coords = tuple([np.random.randint(0, i - 1, int(num_salt)) for i in img.shape])
-
- if img.dtype in [np.float32, np.float64]:
- img[coords] = 1
- else:
- img[coords] = np.iinfo(img.dtype).max
- print(img.dtype)
- elif mode == "pepper":
- s_vs_p = 0
- num_pepper = np.ceil(percent * img.size * (1.0 - s_vs_p))
- coords = tuple(
- [np.random.randint(0, i - 1, int(num_pepper)) for i in img.shape]
- )
- img[coords] = 0
-
- elif mode == "s&p":
- s_vs_p = 0.5
-
- # Salt mode
- num_salt = np.ceil(percent * img.size * s_vs_p)
- coords = tuple([np.random.randint(0, i - 1, int(num_salt)) for i in img.shape])
- if img.dtype in [np.float32, np.float64]:
- img[coords] = 1
- else:
- img[coords] = np.iinfo(img.dtype).max
-
- # Pepper mode
- num_pepper = np.ceil(percent * img.size * (1.0 - s_vs_p))
- coords = tuple(
- [np.random.randint(0, i - 1, int(num_pepper)) for i in img.shape]
- )
- img[coords] = 0
- else:
- raise ValueError("not support mode for {}".format(mode))
-
- noisy = img.astype(original_dtype)
- return noisy
-
-
-def non_spatial_augmentation(img_warp_ori, jitter_paramters, color_order=[0,1,2], to_gray=False):
- """ Apply non-spatial augmentation to an image (jittering, color swap, convert to gray scale, Gaussian blur)."""
-
- brightness, contrast, saturation, hue = jitter_paramters
- color_augmentation = transforms.ColorJitter(brightness, contrast, saturation, hue)
- '''
- augment_image = color_augmentation.get_params(brightness=[max(0, 1 - brightness), 1 + brightness],
- contrast=[max(0, 1 - contrast), 1 + contrast],
- saturation=[max(0, 1 - saturation), 1 + saturation],
- hue=[-hue, hue])
- '''
-
- B = img_warp_ori.shape[0]
- img_warp = []
- kernel_sizes = [0,1,3,5]
- for b in range(B):
- img_warp_sub = img_warp_ori[b].cpu()
- img_warp_sub = torchvision.transforms.functional.to_pil_image(img_warp_sub)
-
- img_warp_sub_np = np.array(img_warp_sub)
- img_warp_sub_np = img_warp_sub_np[:,:,color_order]
-
- if np.random.rand() > 0.5:
- img_warp_sub_np = add_noise(img_warp_sub_np)
-
- rand_index = np.random.randint(4)
- kernel_size = kernel_sizes[rand_index]
- if kernel_size >0:
- img_warp_sub_np = cv2.GaussianBlur(img_warp_sub_np, (kernel_size, kernel_size), sigmaX=0)
-
- if to_gray:
- img_warp_sub_np = cv2.cvtColor(img_warp_sub_np, cv2.COLOR_RGB2GRAY)
- img_warp_sub_np = cv2.cvtColor(img_warp_sub_np, cv2.COLOR_GRAY2RGB)
-
- img_warp_sub = Image.fromarray(img_warp_sub_np)
- img_warp_sub = color_augmentation(img_warp_sub)
-
- img_warp_sub = torchvision.transforms.functional.to_tensor(img_warp_sub).to(img_warp_ori.device)
-
- img_warp.append(img_warp_sub)
-
- img_warp = torch.stack(img_warp, dim=0)
- return img_warp
-
-def ha_augment_sample(data, jitter_paramters=[0.5, 0.5, 0.2, 0.05], patch_ratio=0.7, scaling_amplitude=0.2, max_angle=pi/4):
- """Apply Homography Adaptation image augmentation."""
- input_img = data['image'].unsqueeze(0)
- _, _, H, W = input_img.shape
- device = input_img.device
-
- homography = torch.from_numpy(
- sample_homography([H, W],
- patch_ratio=patch_ratio,
- scaling_amplitude=scaling_amplitude,
- max_angle=max_angle)).float().to(device)
- homography_inv = torch.inverse(homography)
-
- source = image_grid(1, H, W,
- dtype=input_img.dtype,
- device=device,
- ones=False, normalized=True).clone().permute(0, 2, 3, 1)
-
- target_warped = warp_homography(source, homography)
- img_warp = torch.nn.functional.grid_sample(input_img, target_warped)
-
- color_order = [0,1,2]
- if np.random.rand() > 0.5:
- random.shuffle(color_order)
-
- to_gray = False
- if np.random.rand() > 0.5:
- to_gray = True
-
- input_img = non_spatial_augmentation(input_img, jitter_paramters=jitter_paramters, color_order=color_order, to_gray=to_gray)
- img_warp = non_spatial_augmentation(img_warp, jitter_paramters=jitter_paramters, color_order=color_order, to_gray=to_gray)
-
- data['image'] = input_img.squeeze()
- data['image_aug'] = img_warp.squeeze()
- data['homography'] = homography
- data['homography_inv'] = homography_inv
- return data
+# From https://github.com/TRI-ML/KP2D.
+
+# Copyright 2020 Toyota Research Institute. All rights reserved.
+
+import random
+from math import pi
+
+import cv2
+import numpy as np
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from PIL import Image
+
+from ..lanet_utils import image_grid
+
+
+def filter_dict(dict, keywords):
+ """
+ Returns only the keywords that are part of a dictionary
+
+ Parameters
+ ----------
+ dictionary : dict
+ Dictionary for filtering
+ keywords : list of str
+ Keywords that will be filtered
+
+ Returns
+ -------
+ keywords : list of str
+ List containing the keywords that are keys in dictionary
+ """
+ return [key for key in keywords if key in dict]
+
+
+def resize_sample(sample, image_shape, image_interpolation=Image.ANTIALIAS):
+ """
+ Resizes a sample, which contains an input image.
+
+ Parameters
+ ----------
+ sample : dict
+ Dictionary with sample values (output from a dataset's __getitem__ method)
+ shape : tuple (H,W)
+ Output shape
+ image_interpolation : int
+ Interpolation mode
+
+ Returns
+ -------
+ sample : dict
+ Resized sample
+ """
+ # image
+ image_transform = transforms.Resize(image_shape, interpolation=image_interpolation)
+ sample["image"] = image_transform(sample["image"])
+ return sample
+
+
+def spatial_augment_sample(sample):
+ """Apply spatial augmentation to an image (flipping and random affine transformation)."""
+ augment_image = transforms.Compose(
+ [
+ transforms.RandomVerticalFlip(p=0.5),
+ transforms.RandomHorizontalFlip(p=0.5),
+ transforms.RandomAffine(15, translate=(0.1, 0.1), scale=(0.9, 1.1)),
+ ]
+ )
+ sample["image"] = augment_image(sample["image"])
+
+ return sample
+
+
+def unnormalize_image(tensor, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)):
+ """Counterpart method of torchvision.transforms.Normalize."""
+ for t, m, s in zip(tensor, mean, std):
+ t.div_(1 / s).sub_(-m)
+ return tensor
+
+
+def sample_homography(
+ shape,
+ perspective=True,
+ scaling=True,
+ rotation=True,
+ translation=True,
+ n_scales=100,
+ n_angles=100,
+ scaling_amplitude=0.1,
+ perspective_amplitude=0.4,
+ patch_ratio=0.8,
+ max_angle=pi / 4,
+):
+ """Sample a random homography that includes perspective, scale, translation and rotation operations."""
+
+ width = float(shape[1])
+ hw_ratio = float(shape[0]) / float(shape[1])
+
+ pts1 = np.stack([[-1.0, -1.0], [-1.0, 1.0], [1.0, -1.0], [1.0, 1.0]], axis=0)
+ pts2 = pts1.copy() * patch_ratio
+ pts2[:, 1] *= hw_ratio
+
+ if perspective:
+
+ perspective_amplitude_x = np.random.normal(0.0, perspective_amplitude / 2, (2))
+ perspective_amplitude_y = np.random.normal(
+ 0.0, hw_ratio * perspective_amplitude / 2, (2)
+ )
+
+ perspective_amplitude_x = np.clip(
+ perspective_amplitude_x,
+ -perspective_amplitude / 2,
+ perspective_amplitude / 2,
+ )
+ perspective_amplitude_y = np.clip(
+ perspective_amplitude_y,
+ hw_ratio * -perspective_amplitude / 2,
+ hw_ratio * perspective_amplitude / 2,
+ )
+
+ pts2[0, 0] -= perspective_amplitude_x[1]
+ pts2[0, 1] -= perspective_amplitude_y[1]
+
+ pts2[1, 0] -= perspective_amplitude_x[0]
+ pts2[1, 1] += perspective_amplitude_y[1]
+
+ pts2[2, 0] += perspective_amplitude_x[1]
+ pts2[2, 1] -= perspective_amplitude_y[0]
+
+ pts2[3, 0] += perspective_amplitude_x[0]
+ pts2[3, 1] += perspective_amplitude_y[0]
+
+ if scaling:
+
+ random_scales = np.random.normal(1, scaling_amplitude / 2, (n_scales))
+ random_scales = np.clip(
+ random_scales, 1 - scaling_amplitude / 2, 1 + scaling_amplitude / 2
+ )
+
+ scales = np.concatenate([[1.0], random_scales], 0)
+ center = np.mean(pts2, axis=0, keepdims=True)
+ scaled = (
+ np.expand_dims(pts2 - center, axis=0)
+ * np.expand_dims(np.expand_dims(scales, 1), 1)
+ + center
+ )
+ valid = np.arange(n_scales) # all scales are valid except scale=1
+ idx = valid[np.random.randint(valid.shape[0])]
+ pts2 = scaled[idx]
+
+ if translation:
+ t_min, t_max = np.min(pts2 - [-1.0, -hw_ratio], axis=0), np.min(
+ [1.0, hw_ratio] - pts2, axis=0
+ )
+ pts2 += np.expand_dims(
+ np.stack(
+ [
+ np.random.uniform(-t_min[0], t_max[0]),
+ np.random.uniform(-t_min[1], t_max[1]),
+ ]
+ ),
+ axis=0,
+ )
+
+ if rotation:
+ angles = np.linspace(-max_angle, max_angle, n_angles)
+ angles = np.concatenate([[0.0], angles], axis=0)
+
+ center = np.mean(pts2, axis=0, keepdims=True)
+ rot_mat = np.reshape(
+ np.stack(
+ [np.cos(angles), -np.sin(angles), np.sin(angles), np.cos(angles)],
+ axis=1,
+ ),
+ [-1, 2, 2],
+ )
+ rotated = (
+ np.matmul(
+ np.tile(np.expand_dims(pts2 - center, axis=0), [n_angles + 1, 1, 1]),
+ rot_mat,
+ )
+ + center
+ )
+
+ valid = np.where(
+ np.all(
+ (rotated >= [-1.0, -hw_ratio]) & (rotated < [1.0, hw_ratio]),
+ axis=(1, 2),
+ )
+ )[0]
+
+ idx = valid[np.random.randint(valid.shape[0])]
+ pts2 = rotated[idx]
+
+ pts2[:, 1] /= hw_ratio
+
+ def ax(p, q):
+ return [p[0], p[1], 1, 0, 0, 0, -p[0] * q[0], -p[1] * q[0]]
+
+ def ay(p, q):
+ return [0, 0, 0, p[0], p[1], 1, -p[0] * q[1], -p[1] * q[1]]
+
+ a_mat = np.stack([f(pts1[i], pts2[i]) for i in range(4) for f in (ax, ay)], axis=0)
+ p_mat = np.transpose(
+ np.stack([[pts2[i][j] for i in range(4) for j in range(2)]], axis=0)
+ )
+
+ homography = np.matmul(np.linalg.pinv(a_mat), p_mat).squeeze()
+ homography = np.concatenate([homography, [1.0]]).reshape(3, 3)
+ return homography
+
+
+def warp_homography(sources, homography):
+ """Warp features given a homography
+
+ Parameters
+ ----------
+ sources: torch.tensor (1,H,W,2)
+ Keypoint vector.
+ homography: torch.Tensor (3,3)
+ Homography.
+
+ Returns
+ -------
+ warped_sources: torch.tensor (1,H,W,2)
+ Warped feature vector.
+ """
+ _, H, W, _ = sources.shape
+ warped_sources = sources.clone().squeeze()
+ warped_sources = warped_sources.view(-1, 2)
+ warped_sources = torch.addmm(
+ homography[:, 2], warped_sources, homography[:, :2].t()
+ )
+ warped_sources.mul_(1 / warped_sources[:, 2].unsqueeze(1))
+ warped_sources = warped_sources[:, :2].contiguous().view(1, H, W, 2)
+ return warped_sources
+
+
+def add_noise(img, mode="gaussian", percent=0.02):
+ """Add image noise
+
+ Parameters
+ ----------
+ image : np.array
+ Input image
+ mode: str
+ Type of noise, from ['gaussian','salt','pepper','s&p']
+ percent: float
+ Percentage image points to add noise to.
+ Returns
+ -------
+ image : np.array
+ Image plus noise.
+ """
+ original_dtype = img.dtype
+ if mode == "gaussian":
+ mean = 0
+ var = 0.1
+ sigma = var * 0.5
+
+ if img.ndim == 2:
+ h, w = img.shape
+ gauss = np.random.normal(mean, sigma, (h, w))
+ else:
+ h, w, c = img.shape
+ gauss = np.random.normal(mean, sigma, (h, w, c))
+
+ if img.dtype not in [np.float32, np.float64]:
+ gauss = gauss * np.iinfo(img.dtype).max
+ img = np.clip(img.astype(np.float) + gauss, 0, np.iinfo(img.dtype).max)
+ else:
+ img = np.clip(img.astype(np.float) + gauss, 0, 1)
+
+ elif mode == "salt":
+ print(img.dtype)
+ s_vs_p = 1
+ num_salt = np.ceil(percent * img.size * s_vs_p)
+ coords = tuple([np.random.randint(0, i - 1, int(num_salt)) for i in img.shape])
+
+ if img.dtype in [np.float32, np.float64]:
+ img[coords] = 1
+ else:
+ img[coords] = np.iinfo(img.dtype).max
+ print(img.dtype)
+ elif mode == "pepper":
+ s_vs_p = 0
+ num_pepper = np.ceil(percent * img.size * (1.0 - s_vs_p))
+ coords = tuple(
+ [np.random.randint(0, i - 1, int(num_pepper)) for i in img.shape]
+ )
+ img[coords] = 0
+
+ elif mode == "s&p":
+ s_vs_p = 0.5
+
+ # Salt mode
+ num_salt = np.ceil(percent * img.size * s_vs_p)
+ coords = tuple([np.random.randint(0, i - 1, int(num_salt)) for i in img.shape])
+ if img.dtype in [np.float32, np.float64]:
+ img[coords] = 1
+ else:
+ img[coords] = np.iinfo(img.dtype).max
+
+ # Pepper mode
+ num_pepper = np.ceil(percent * img.size * (1.0 - s_vs_p))
+ coords = tuple(
+ [np.random.randint(0, i - 1, int(num_pepper)) for i in img.shape]
+ )
+ img[coords] = 0
+ else:
+ raise ValueError("not support mode for {}".format(mode))
+
+ noisy = img.astype(original_dtype)
+ return noisy
+
+
+def non_spatial_augmentation(
+ img_warp_ori, jitter_paramters, color_order=[0, 1, 2], to_gray=False
+):
+ """Apply non-spatial augmentation to an image (jittering, color swap, convert to gray scale, Gaussian blur)."""
+
+ brightness, contrast, saturation, hue = jitter_paramters
+ color_augmentation = transforms.ColorJitter(brightness, contrast, saturation, hue)
+ """
+ augment_image = color_augmentation.get_params(brightness=[max(0, 1 - brightness), 1 + brightness],
+ contrast=[max(0, 1 - contrast), 1 + contrast],
+ saturation=[max(0, 1 - saturation), 1 + saturation],
+ hue=[-hue, hue])
+ """
+
+ B = img_warp_ori.shape[0]
+ img_warp = []
+ kernel_sizes = [0, 1, 3, 5]
+ for b in range(B):
+ img_warp_sub = img_warp_ori[b].cpu()
+ img_warp_sub = torchvision.transforms.functional.to_pil_image(img_warp_sub)
+
+ img_warp_sub_np = np.array(img_warp_sub)
+ img_warp_sub_np = img_warp_sub_np[:, :, color_order]
+
+ if np.random.rand() > 0.5:
+ img_warp_sub_np = add_noise(img_warp_sub_np)
+
+ rand_index = np.random.randint(4)
+ kernel_size = kernel_sizes[rand_index]
+ if kernel_size > 0:
+ img_warp_sub_np = cv2.GaussianBlur(
+ img_warp_sub_np, (kernel_size, kernel_size), sigmaX=0
+ )
+
+ if to_gray:
+ img_warp_sub_np = cv2.cvtColor(img_warp_sub_np, cv2.COLOR_RGB2GRAY)
+ img_warp_sub_np = cv2.cvtColor(img_warp_sub_np, cv2.COLOR_GRAY2RGB)
+
+ img_warp_sub = Image.fromarray(img_warp_sub_np)
+ img_warp_sub = color_augmentation(img_warp_sub)
+
+ img_warp_sub = torchvision.transforms.functional.to_tensor(img_warp_sub).to(
+ img_warp_ori.device
+ )
+
+ img_warp.append(img_warp_sub)
+
+ img_warp = torch.stack(img_warp, dim=0)
+ return img_warp
+
+
+def ha_augment_sample(
+ data,
+ jitter_paramters=[0.5, 0.5, 0.2, 0.05],
+ patch_ratio=0.7,
+ scaling_amplitude=0.2,
+ max_angle=pi / 4,
+):
+ """Apply Homography Adaptation image augmentation."""
+ input_img = data["image"].unsqueeze(0)
+ _, _, H, W = input_img.shape
+ device = input_img.device
+
+ homography = (
+ torch.from_numpy(
+ sample_homography(
+ [H, W],
+ patch_ratio=patch_ratio,
+ scaling_amplitude=scaling_amplitude,
+ max_angle=max_angle,
+ )
+ )
+ .float()
+ .to(device)
+ )
+ homography_inv = torch.inverse(homography)
+
+ source = (
+ image_grid(
+ 1, H, W, dtype=input_img.dtype, device=device, ones=False, normalized=True
+ )
+ .clone()
+ .permute(0, 2, 3, 1)
+ )
+
+ target_warped = warp_homography(source, homography)
+ img_warp = torch.nn.functional.grid_sample(input_img, target_warped)
+
+ color_order = [0, 1, 2]
+ if np.random.rand() > 0.5:
+ random.shuffle(color_order)
+
+ to_gray = False
+ if np.random.rand() > 0.5:
+ to_gray = True
+
+ input_img = non_spatial_augmentation(
+ input_img,
+ jitter_paramters=jitter_paramters,
+ color_order=color_order,
+ to_gray=to_gray,
+ )
+ img_warp = non_spatial_augmentation(
+ img_warp,
+ jitter_paramters=jitter_paramters,
+ color_order=color_order,
+ to_gray=to_gray,
+ )
+
+ data["image"] = input_img.squeeze()
+ data["image_aug"] = img_warp.squeeze()
+ data["homography"] = homography
+ data["homography_inv"] = homography_inv
+ return data
diff --git a/third_party/lanet/config.py b/third_party/lanet/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..84419d0a1f7199e8bec1afc7b046e674a629d886
--- /dev/null
+++ b/third_party/lanet/config.py
@@ -0,0 +1,95 @@
+import argparse
+
+arg_lists = []
+parser = argparse.ArgumentParser(description="LANet")
+
+
+def str2bool(v):
+ return v.lower() in ("true", "1")
+
+
+def add_argument_group(name):
+ arg = parser.add_argument_group(name)
+ arg_lists.append(arg)
+ return arg
+
+
+# train data params
+traindata_arg = add_argument_group("Traindata Params")
+traindata_arg.add_argument("--train_txt", type=str, default="", help="Train set.")
+traindata_arg.add_argument(
+ "--train_root", type=str, default="", help="Where the train images are."
+)
+traindata_arg.add_argument(
+ "--batch_size", type=int, default=8, help="# of images in each batch of data"
+)
+traindata_arg.add_argument(
+ "--num_workers",
+ type=int,
+ default=4,
+ help="# of subprocesses to use for data loading",
+)
+traindata_arg.add_argument(
+ "--pin_memory",
+ type=str2bool,
+ default=True,
+ help="# of subprocesses to use for data loading",
+)
+traindata_arg.add_argument(
+ "--shuffle",
+ type=str2bool,
+ default=True,
+ help="Whether to shuffle the train and valid indices",
+)
+traindata_arg.add_argument("--image_shape", type=tuple, default=(240, 320), help="")
+traindata_arg.add_argument(
+ "--jittering", type=tuple, default=(0.5, 0.5, 0.2, 0.05), help=""
+)
+
+# data storage
+storage_arg = add_argument_group("Storage")
+storage_arg.add_argument("--ckpt_name", type=str, default="PointModel", help="")
+
+# training params
+train_arg = add_argument_group("Training Params")
+train_arg.add_argument("--start_epoch", type=int, default=0, help="")
+train_arg.add_argument("--max_epoch", type=int, default=12, help="")
+train_arg.add_argument(
+ "--init_lr", type=float, default=3e-4, help="Initial learning rate value."
+)
+train_arg.add_argument(
+ "--lr_factor", type=float, default=0.5, help="Reduce learning rate value."
+)
+train_arg.add_argument(
+ "--momentum", type=float, default=0.9, help="Nesterov momentum value."
+)
+train_arg.add_argument("--display", type=int, default=50, help="")
+
+# loss function params
+loss_arg = add_argument_group("Loss function Params")
+loss_arg.add_argument("--score_weight", type=float, default=1.0, help="")
+loss_arg.add_argument("--loc_weight", type=float, default=1.0, help="")
+loss_arg.add_argument("--desc_weight", type=float, default=4.0, help="")
+loss_arg.add_argument("--corres_weight", type=float, default=0.5, help="")
+loss_arg.add_argument("--corres_threshold", type=int, default=4.0, help="")
+
+# other params
+misc_arg = add_argument_group("Misc.")
+misc_arg.add_argument(
+ "--use_gpu", type=str2bool, default=True, help="Whether to run on the GPU."
+)
+misc_arg.add_argument("--gpu", type=int, default=0, help="Which GPU to run on.")
+misc_arg.add_argument(
+ "--seed", type=int, default=1001, help="Seed to ensure reproducibility."
+)
+misc_arg.add_argument(
+ "--ckpt_dir",
+ type=str,
+ default="./checkpoints",
+ help="Directory in which to save model checkpoints.",
+)
+
+
+def get_config():
+ config, unparsed = parser.parse_known_args()
+ return config, unparsed
diff --git a/imcui/third_party/lanet/data_loader.py b/third_party/lanet/data_loader.py
similarity index 63%
rename from imcui/third_party/lanet/data_loader.py
rename to third_party/lanet/data_loader.py
index 149f66e6c23989d9a6eb92ab658188db97e33a64..0cefcdbbdac645389d9b80ad5966c38f888726f1 100644
--- a/imcui/third_party/lanet/data_loader.py
+++ b/third_party/lanet/data_loader.py
@@ -1,86 +1,89 @@
-from PIL import Image
-from torch.utils.data import Dataset, DataLoader
-
-from augmentations import ha_augment_sample, resize_sample, spatial_augment_sample
-from utils import to_tensor_sample
-
-def image_transforms(shape, jittering):
- def train_transforms(sample):
- sample = resize_sample(sample, image_shape=shape)
- sample = spatial_augment_sample(sample)
- sample = to_tensor_sample(sample)
- sample = ha_augment_sample(sample, jitter_paramters=jittering)
- return sample
-
- return {'train': train_transforms}
-
-class GetData(Dataset):
- def __init__(self, config, transforms=None):
- """
- Get the list containing all images and labels.
- """
- datafile = open(config.train_txt, 'r')
- lines = datafile.readlines()
-
- dataset = []
- for line in lines:
- line = line.rstrip()
- data = line.split()
- dataset.append(data[0])
-
- self.config = config
- self.dataset = dataset
- self.root = config.train_root
-
- self.transforms = transforms
-
- def __getitem__(self, index):
- """
- Return image'data and its label.
- """
- img_path = self.dataset[index]
- img_file = self.root + img_path
- img = Image.open(img_file)
-
- # image.mode == 'L' means the image is in gray scale
- if img.mode == 'L':
- img_new = Image.new("RGB", img.size)
- img_new.paste(img)
- sample = {'image': img_new, 'idx': index}
- else:
- sample = {'image': img, 'idx': index}
-
- if self.transforms:
- sample = self.transforms(sample)
-
- return sample
-
- def __len__(self):
- """
- Return the number of all data.
- """
- return len(self.dataset)
-
-def get_data_loader(
- config,
- transforms=None,
- sampler=None,
- drop_last=True,
- ):
- """
- Return batch data for training.
- """
- transforms = image_transforms(shape=config.image_shape, jittering=config.jittering)
- dataset = GetData(config, transforms=transforms['train'])
-
- train_loader = DataLoader(
- dataset,
- batch_size=config.batch_size,
- shuffle=config.shuffle,
- sampler=sampler,
- num_workers=config.num_workers,
- pin_memory=config.pin_memory,
- drop_last=drop_last
- )
-
- return train_loader
+from PIL import Image
+from torch.utils.data import Dataset, DataLoader
+
+from augmentations import ha_augment_sample, resize_sample, spatial_augment_sample
+from lanet_utils import to_tensor_sample
+
+
+def image_transforms(shape, jittering):
+ def train_transforms(sample):
+ sample = resize_sample(sample, image_shape=shape)
+ sample = spatial_augment_sample(sample)
+ sample = to_tensor_sample(sample)
+ sample = ha_augment_sample(sample, jitter_paramters=jittering)
+ return sample
+
+ return {"train": train_transforms}
+
+
+class GetData(Dataset):
+ def __init__(self, config, transforms=None):
+ """
+ Get the list containing all images and labels.
+ """
+ datafile = open(config.train_txt, "r")
+ lines = datafile.readlines()
+
+ dataset = []
+ for line in lines:
+ line = line.rstrip()
+ data = line.split()
+ dataset.append(data[0])
+
+ self.config = config
+ self.dataset = dataset
+ self.root = config.train_root
+
+ self.transforms = transforms
+
+ def __getitem__(self, index):
+ """
+ Return image'data and its label.
+ """
+ img_path = self.dataset[index]
+ img_file = self.root + img_path
+ img = Image.open(img_file)
+
+ # image.mode == 'L' means the image is in gray scale
+ if img.mode == "L":
+ img_new = Image.new("RGB", img.size)
+ img_new.paste(img)
+ sample = {"image": img_new, "idx": index}
+ else:
+ sample = {"image": img, "idx": index}
+
+ if self.transforms:
+ sample = self.transforms(sample)
+
+ return sample
+
+ def __len__(self):
+ """
+ Return the number of all data.
+ """
+ return len(self.dataset)
+
+
+def get_data_loader(
+ config,
+ transforms=None,
+ sampler=None,
+ drop_last=True,
+):
+ """
+ Return batch data for training.
+ """
+ transforms = image_transforms(shape=config.image_shape, jittering=config.jittering)
+ dataset = GetData(config, transforms=transforms["train"])
+
+ train_loader = DataLoader(
+ dataset,
+ batch_size=config.batch_size,
+ shuffle=config.shuffle,
+ sampler=sampler,
+ num_workers=config.num_workers,
+ pin_memory=config.pin_memory,
+ drop_last=drop_last,
+ )
+
+ return train_loader
diff --git a/imcui/third_party/lanet/datasets/hp_loader.py b/third_party/lanet/datasets/hp_loader.py
similarity index 60%
rename from imcui/third_party/lanet/datasets/hp_loader.py
rename to third_party/lanet/datasets/hp_loader.py
index 960a6403cd5fc004b2caef429b72acc32cf0c291..f255c87dac6e06e56b67ad0f04f7da5c131f0189 100644
--- a/imcui/third_party/lanet/datasets/hp_loader.py
+++ b/third_party/lanet/datasets/hp_loader.py
@@ -1,106 +1,126 @@
-import torch
-import cv2
-import numpy as np
-
-from torchvision import transforms
-from torch.utils.data import Dataset
-from pathlib import Path
-
-
-class PatchesDataset(Dataset):
- """
- HPatches dataset class.
- # Note: output_shape = (output_width, output_height)
- # Note: this returns Pytorch tensors, resized to output_shape (if specified)
- # Note: the homography will be adjusted according to output_shape.
-
- Parameters
- ----------
- root_dir : str
- Path to the dataset
- use_color : bool
- Return color images or convert to grayscale.
- data_transform : Function
- Transformations applied to the sample
- output_shape: tuple
- If specified, the images and homographies will be resized to the desired shape.
- type: str
- Dataset subset to return from ['i', 'v', 'all']:
- i - illumination sequences
- v - viewpoint sequences
- all - all sequences
- """
- def __init__(self, root_dir, use_color=True, data_transform=None, output_shape=None, type='all'):
- super().__init__()
- self.type = type
- self.root_dir = root_dir
- self.data_transform = data_transform
- self.output_shape = output_shape
- self.use_color = use_color
- base_path = Path(root_dir)
- folder_paths = [x for x in base_path.iterdir() if x.is_dir()]
- image_paths = []
- warped_image_paths = []
- homographies = []
- for path in folder_paths:
- if self.type == 'i' and path.stem[0] != 'i':
- continue
- if self.type == 'v' and path.stem[0] != 'v':
- continue
- num_images = 5
- file_ext = '.ppm'
- for i in range(2, 2 + num_images):
- image_paths.append(str(Path(path, "1" + file_ext)))
- warped_image_paths.append(str(Path(path, str(i) + file_ext)))
- homographies.append(np.loadtxt(str(Path(path, "H_1_" + str(i)))))
- self.files = {'image_paths': image_paths, 'warped_image_paths': warped_image_paths, 'homography': homographies}
-
- def scale_homography(self, homography, original_scale, new_scale, pre):
- scales = np.divide(new_scale, original_scale)
- if pre:
- s = np.diag(np.append(scales, 1.))
- homography = np.matmul(s, homography)
- else:
- sinv = np.diag(np.append(1. / scales, 1.))
- homography = np.matmul(homography, sinv)
- return homography
-
- def __len__(self):
- return len(self.files['image_paths'])
-
- def __getitem__(self, idx):
-
- def _read_image(path):
- img = cv2.imread(path, cv2.IMREAD_COLOR)
- if self.use_color:
- return img
- gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- return gray
-
- image = _read_image(self.files['image_paths'][idx])
-
- warped_image = _read_image(self.files['warped_image_paths'][idx])
- homography = np.array(self.files['homography'][idx])
- sample = {'image': image, 'warped_image': warped_image, 'homography': homography, 'index' : idx}
-
- # Apply transformations
- if self.output_shape is not None:
- sample['homography'] = self.scale_homography(sample['homography'],
- sample['image'].shape[:2][::-1],
- self.output_shape,
- pre=False)
- sample['homography'] = self.scale_homography(sample['homography'],
- sample['warped_image'].shape[:2][::-1],
- self.output_shape,
- pre=True)
-
- for key in ['image', 'warped_image']:
- sample[key] = cv2.resize(sample[key], self.output_shape)
- if self.use_color is False:
- sample[key] = np.expand_dims(sample[key], axis=2)
-
- transform = transforms.ToTensor()
-
- for key in ['image', 'warped_image']:
- sample[key] = transform(sample[key]).type('torch.FloatTensor')
- return sample
+import torch
+import cv2
+import numpy as np
+
+from torchvision import transforms
+from torch.utils.data import Dataset
+from pathlib import Path
+
+
+class PatchesDataset(Dataset):
+ """
+ HPatches dataset class.
+ # Note: output_shape = (output_width, output_height)
+ # Note: this returns Pytorch tensors, resized to output_shape (if specified)
+ # Note: the homography will be adjusted according to output_shape.
+
+ Parameters
+ ----------
+ root_dir : str
+ Path to the dataset
+ use_color : bool
+ Return color images or convert to grayscale.
+ data_transform : Function
+ Transformations applied to the sample
+ output_shape: tuple
+ If specified, the images and homographies will be resized to the desired shape.
+ type: str
+ Dataset subset to return from ['i', 'v', 'all']:
+ i - illumination sequences
+ v - viewpoint sequences
+ all - all sequences
+ """
+
+ def __init__(
+ self,
+ root_dir,
+ use_color=True,
+ data_transform=None,
+ output_shape=None,
+ type="all",
+ ):
+ super().__init__()
+ self.type = type
+ self.root_dir = root_dir
+ self.data_transform = data_transform
+ self.output_shape = output_shape
+ self.use_color = use_color
+ base_path = Path(root_dir)
+ folder_paths = [x for x in base_path.iterdir() if x.is_dir()]
+ image_paths = []
+ warped_image_paths = []
+ homographies = []
+ for path in folder_paths:
+ if self.type == "i" and path.stem[0] != "i":
+ continue
+ if self.type == "v" and path.stem[0] != "v":
+ continue
+ num_images = 5
+ file_ext = ".ppm"
+ for i in range(2, 2 + num_images):
+ image_paths.append(str(Path(path, "1" + file_ext)))
+ warped_image_paths.append(str(Path(path, str(i) + file_ext)))
+ homographies.append(np.loadtxt(str(Path(path, "H_1_" + str(i)))))
+ self.files = {
+ "image_paths": image_paths,
+ "warped_image_paths": warped_image_paths,
+ "homography": homographies,
+ }
+
+ def scale_homography(self, homography, original_scale, new_scale, pre):
+ scales = np.divide(new_scale, original_scale)
+ if pre:
+ s = np.diag(np.append(scales, 1.0))
+ homography = np.matmul(s, homography)
+ else:
+ sinv = np.diag(np.append(1.0 / scales, 1.0))
+ homography = np.matmul(homography, sinv)
+ return homography
+
+ def __len__(self):
+ return len(self.files["image_paths"])
+
+ def __getitem__(self, idx):
+ def _read_image(path):
+ img = cv2.imread(path, cv2.IMREAD_COLOR)
+ if self.use_color:
+ return img
+ gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ return gray
+
+ image = _read_image(self.files["image_paths"][idx])
+
+ warped_image = _read_image(self.files["warped_image_paths"][idx])
+ homography = np.array(self.files["homography"][idx])
+ sample = {
+ "image": image,
+ "warped_image": warped_image,
+ "homography": homography,
+ "index": idx,
+ }
+
+ # Apply transformations
+ if self.output_shape is not None:
+ sample["homography"] = self.scale_homography(
+ sample["homography"],
+ sample["image"].shape[:2][::-1],
+ self.output_shape,
+ pre=False,
+ )
+ sample["homography"] = self.scale_homography(
+ sample["homography"],
+ sample["warped_image"].shape[:2][::-1],
+ self.output_shape,
+ pre=True,
+ )
+
+ for key in ["image", "warped_image"]:
+ sample[key] = cv2.resize(sample[key], self.output_shape)
+ if self.use_color is False:
+ sample[key] = np.expand_dims(sample[key], axis=2)
+
+ transform = transforms.ToTensor()
+
+ for key in ["image", "warped_image"]:
+ sample[key] = transform(sample[key]).type("torch.FloatTensor")
+ return sample
diff --git a/third_party/lanet/datasets/prepare_coco.py b/third_party/lanet/datasets/prepare_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..612fb400000c66476a3be796d4dcceea8bc331d4
--- /dev/null
+++ b/third_party/lanet/datasets/prepare_coco.py
@@ -0,0 +1,24 @@
+import os
+import argparse
+
+
+def prepare_coco(args):
+ train_file = open(os.path.join(args.saved_dir, args.saved_txt), "w")
+ dirs = os.listdir(args.raw_dir)
+
+ for file in dirs:
+ # Write training files
+ train_file.write("%s\n" % (file))
+
+ print("Data Preparation Finished.")
+
+
+if __name__ == "__main__":
+ arg_parser = argparse.ArgumentParser(description="coco prepareing.")
+ arg_parser.add_argument("--dataset", type=str, default="coco", help="")
+ arg_parser.add_argument("--raw_dir", type=str, default="", help="")
+ arg_parser.add_argument("--saved_dir", type=str, default="", help="")
+ arg_parser.add_argument("--saved_txt", type=str, default="train2017.txt", help="")
+ args = arg_parser.parse_args()
+
+ prepare_coco(args)
diff --git a/imcui/third_party/lanet/evaluation/descriptor_evaluation.py b/third_party/lanet/evaluation/descriptor_evaluation.py
similarity index 73%
rename from imcui/third_party/lanet/evaluation/descriptor_evaluation.py
rename to third_party/lanet/evaluation/descriptor_evaluation.py
index c0e1f84199d353ac5858641c8f68bc298f9d6413..2fc1feb255c1dcd2625f1b0373243f878fc533eb 100644
--- a/imcui/third_party/lanet/evaluation/descriptor_evaluation.py
+++ b/third_party/lanet/evaluation/descriptor_evaluation.py
@@ -8,11 +8,11 @@ from os import path as osp
import cv2
import numpy as np
-from utils import warp_keypoints
+from ..lanet_utils import warp_keypoints
def select_k_best(points, descriptors, k):
- """ Select the k most probable points (and strip their probability).
+ """Select the k most probable points (and strip their probability).
points has shape (num_points, 3) where the last coordinate is the probability.
Parameters
@@ -25,7 +25,7 @@ def select_k_best(points, descriptors, k):
Number of keypoints to select, based on probability.
Returns
-------
-
+
selected_points: numpy.ndarray (k,2)
k most probable keypoints.
selected_descriptors: numpy.ndarray (k,256)
@@ -44,7 +44,7 @@ def keep_shared_points(keypoints, descriptors, H, shape, keep_k_points=1000):
Compute a list of keypoints from the map, filter the list of points by keeping
only the points that once mapped by H are still inside the shape of the map
and keep at most 'keep_k_points' keypoints in the image.
-
+
Parameters
----------
keypoints: numpy.ndarray (N,3)
@@ -53,36 +53,44 @@ def keep_shared_points(keypoints, descriptors, H, shape, keep_k_points=1000):
Keypoint descriptors.
H: numpy.ndarray (3,3)
Homography.
- shape: tuple
+ shape: tuple
Image shape.
keep_k_points: int
Number of keypoints to select, based on probability.
Returns
- -------
+ -------
selected_points: numpy.ndarray (k,2)
k most probable keypoints.
selected_descriptors: numpy.ndarray (k,256)
Descriptors corresponding to the k most probable keypoints.
"""
-
+
def keep_true_keypoints(points, descriptors, H, shape):
- """ Keep only the points whose warped coordinates by H are still inside shape. """
+ """Keep only the points whose warped coordinates by H are still inside shape."""
warped_points = warp_keypoints(points[:, [1, 0]], H)
warped_points[:, [0, 1]] = warped_points[:, [1, 0]]
- mask = (warped_points[:, 0] >= 0) & (warped_points[:, 0] < shape[0]) &\
- (warped_points[:, 1] >= 0) & (warped_points[:, 1] < shape[1])
+ mask = (
+ (warped_points[:, 0] >= 0)
+ & (warped_points[:, 0] < shape[0])
+ & (warped_points[:, 1] >= 0)
+ & (warped_points[:, 1] < shape[1])
+ )
return points[mask, :], descriptors[mask, :]
- selected_keypoints, selected_descriptors = keep_true_keypoints(keypoints, descriptors, H, shape)
- selected_keypoints, selected_descriptors = select_k_best(selected_keypoints, selected_descriptors, keep_k_points)
+ selected_keypoints, selected_descriptors = keep_true_keypoints(
+ keypoints, descriptors, H, shape
+ )
+ selected_keypoints, selected_descriptors = select_k_best(
+ selected_keypoints, selected_descriptors, keep_k_points
+ )
return selected_keypoints, selected_descriptors
def compute_matching_score(data, keep_k_points=1000):
"""
Compute the matching score between two sets of keypoints with associated descriptors.
-
+
Parameters
----------
data: dict
@@ -103,31 +111,35 @@ def compute_matching_score(data, keep_k_points=1000):
Number of keypoints to select, based on probability.
Returns
- -------
+ -------
ms: float
Matching score.
"""
- shape = data['image_shape']
- real_H = data['homography']
+ shape = data["image_shape"]
+ real_H = data["homography"]
# Filter out predictions
- keypoints = data['prob'][:, :2].T
+ keypoints = data["prob"][:, :2].T
keypoints = keypoints[::-1]
- prob = data['prob'][:, 2]
+ prob = data["prob"][:, 2]
keypoints = np.stack([keypoints[0], keypoints[1], prob], axis=-1)
- warped_keypoints = data['warped_prob'][:, :2].T
+ warped_keypoints = data["warped_prob"][:, :2].T
warped_keypoints = warped_keypoints[::-1]
- warped_prob = data['warped_prob'][:, 2]
- warped_keypoints = np.stack([warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1)
+ warped_prob = data["warped_prob"][:, 2]
+ warped_keypoints = np.stack(
+ [warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1
+ )
+
+ desc = data["desc"]
+ warped_desc = data["warped_desc"]
- desc = data['desc']
- warped_desc = data['warped_desc']
-
# Keeps all points for the next frame. The matching for caculating M.Score shouldnt use only in view points.
- keypoints, desc = select_k_best(keypoints, desc, keep_k_points)
- warped_keypoints, warped_desc = select_k_best(warped_keypoints, warped_desc, keep_k_points)
-
+ keypoints, desc = select_k_best(keypoints, desc, keep_k_points)
+ warped_keypoints, warped_desc = select_k_best(
+ warped_keypoints, warped_desc, keep_k_points
+ )
+
# Match the keypoints with the warped_keypoints with nearest neighbor search
# This part needs to be done with crossCheck=False.
# All the matched pairs need to be evaluated without any selection.
@@ -139,11 +151,16 @@ def compute_matching_score(data, keep_k_points=1000):
matches_idx = np.array([m.trainIdx for m in matches])
m_warped_keypoints = warped_keypoints[matches_idx, :]
- true_warped_keypoints = warp_keypoints(m_warped_keypoints[:, [1, 0]], np.linalg.inv(real_H))[:,::-1]
- vis_warped = np.all((true_warped_keypoints >= 0) & (true_warped_keypoints <= (np.array(shape)-1)), axis=-1)
+ true_warped_keypoints = warp_keypoints(
+ m_warped_keypoints[:, [1, 0]], np.linalg.inv(real_H)
+ )[:, ::-1]
+ vis_warped = np.all(
+ (true_warped_keypoints >= 0) & (true_warped_keypoints <= (np.array(shape) - 1)),
+ axis=-1,
+ )
norm1 = np.linalg.norm(true_warped_keypoints - m_keypoints, axis=-1)
- correct1 = (norm1 < 3)
+ correct1 = norm1 < 3
count1 = np.sum(correct1 * vis_warped)
score1 = count1 / np.maximum(np.sum(vis_warped), 1.0)
@@ -153,11 +170,13 @@ def compute_matching_score(data, keep_k_points=1000):
matches_idx = np.array([m.trainIdx for m in matches])
m_keypoints = keypoints[matches_idx, :]
- true_keypoints = warp_keypoints(m_keypoints[:, [1, 0]], real_H)[:,::-1]
- vis = np.all((true_keypoints >= 0) & (true_keypoints <= (np.array(shape)-1)), axis=-1)
+ true_keypoints = warp_keypoints(m_keypoints[:, [1, 0]], real_H)[:, ::-1]
+ vis = np.all(
+ (true_keypoints >= 0) & (true_keypoints <= (np.array(shape) - 1)), axis=-1
+ )
norm2 = np.linalg.norm(true_keypoints - m_warped_keypoints, axis=-1)
- correct2 = (norm2 < 3)
+ correct2 = norm2 < 3
count2 = np.sum(correct2 * vis)
score2 = count2 / np.maximum(np.sum(vis), 1.0)
@@ -165,9 +184,10 @@ def compute_matching_score(data, keep_k_points=1000):
return ms
+
def compute_homography(data, keep_k_points=1000):
"""
- Compute the homography between 2 sets of Keypoints and descriptors inside data.
+ Compute the homography between 2 sets of Keypoints and descriptors inside data.
Use the homography to compute the correctness metrics (1,3,5).
Parameters
@@ -190,7 +210,7 @@ def compute_homography(data, keep_k_points=1000):
Number of keypoints to select, based on probability.
Returns
- -------
+ -------
correctness1: float
correctness1 metric.
correctness3: float
@@ -198,27 +218,30 @@ def compute_homography(data, keep_k_points=1000):
correctness5: float
correctness5 metric.
"""
- shape = data['image_shape']
- real_H = data['homography']
+ shape = data["image_shape"]
+ real_H = data["homography"]
# Filter out predictions
- keypoints = data['prob'][:, :2].T
+ keypoints = data["prob"][:, :2].T
keypoints = keypoints[::-1]
- prob = data['prob'][:, 2]
+ prob = data["prob"][:, 2]
keypoints = np.stack([keypoints[0], keypoints[1], prob], axis=-1)
- warped_keypoints = data['warped_prob'][:, :2].T
+ warped_keypoints = data["warped_prob"][:, :2].T
warped_keypoints = warped_keypoints[::-1]
- warped_prob = data['warped_prob'][:, 2]
- warped_keypoints = np.stack([warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1)
+ warped_prob = data["warped_prob"][:, 2]
+ warped_keypoints = np.stack(
+ [warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1
+ )
+
+ desc = data["desc"]
+ warped_desc = data["warped_desc"]
- desc = data['desc']
- warped_desc = data['warped_desc']
-
# Keeps only the points shared between the two views
keypoints, desc = keep_shared_points(keypoints, desc, real_H, shape, keep_k_points)
- warped_keypoints, warped_desc = keep_shared_points(warped_keypoints, warped_desc, np.linalg.inv(real_H), shape,
- keep_k_points)
+ warped_keypoints, warped_desc = keep_shared_points(
+ warped_keypoints, warped_desc, np.linalg.inv(real_H), shape, keep_k_points
+ )
bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
matches = bf.match(desc, warped_desc)
@@ -228,8 +251,13 @@ def compute_homography(data, keep_k_points=1000):
m_warped_keypoints = warped_keypoints[matches_idx, :]
# Estimate the homography between the matches using RANSAC
- H, _ = cv2.findHomography(m_keypoints[:, [1, 0]],
- m_warped_keypoints[:, [1, 0]], cv2.RANSAC, 3, maxIters=5000)
+ H, _ = cv2.findHomography(
+ m_keypoints[:, [1, 0]],
+ m_warped_keypoints[:, [1, 0]],
+ cv2.RANSAC,
+ 3,
+ maxIters=5000,
+ )
if H is None:
return 0, 0, 0
@@ -237,15 +265,19 @@ def compute_homography(data, keep_k_points=1000):
shape = shape[::-1]
# Compute correctness
- corners = np.array([[0, 0, 1],
- [0, shape[1] - 1, 1],
- [shape[0] - 1, 0, 1],
- [shape[0] - 1, shape[1] - 1, 1]])
+ corners = np.array(
+ [
+ [0, 0, 1],
+ [0, shape[1] - 1, 1],
+ [shape[0] - 1, 0, 1],
+ [shape[0] - 1, shape[1] - 1, 1],
+ ]
+ )
real_warped_corners = np.dot(corners, np.transpose(real_H))
real_warped_corners = real_warped_corners[:, :2] / real_warped_corners[:, 2:]
warped_corners = np.dot(corners, np.transpose(H))
warped_corners = warped_corners[:, :2] / warped_corners[:, 2:]
-
+
mean_dist = np.mean(np.linalg.norm(real_warped_corners - warped_corners, axis=1))
correctness1 = float(mean_dist <= 1)
correctness3 = float(mean_dist <= 3)
diff --git a/imcui/third_party/lanet/evaluation/detector_evaluation.py b/third_party/lanet/evaluation/detector_evaluation.py
similarity index 73%
rename from imcui/third_party/lanet/evaluation/detector_evaluation.py
rename to third_party/lanet/evaluation/detector_evaluation.py
index ccc8792d17a6fbb6b446f0f9f84a2b82e3cdb57c..e9e1138aebe5b49f03d973c10f2db915c1265296 100644
--- a/imcui/third_party/lanet/evaluation/detector_evaluation.py
+++ b/third_party/lanet/evaluation/detector_evaluation.py
@@ -8,7 +8,7 @@ from os import path as osp
import cv2
import numpy as np
-from utils import warp_keypoints
+from ..lanet_utils import warp_keypoints
def compute_repeatability(data, keep_k_points=300, distance_thresh=3):
@@ -33,7 +33,7 @@ def compute_repeatability(data, keep_k_points=300, distance_thresh=3):
Distance threshold in pixels for a corresponding keypoint to be considered a correct match.
Returns
- -------
+ -------
N1: int
Number of true keypoints in the first image.
N2: int
@@ -43,47 +43,59 @@ def compute_repeatability(data, keep_k_points=300, distance_thresh=3):
loc_err: float
Keypoint localization error.
"""
+
def filter_keypoints(points, shape):
- """ Keep only the points whose coordinates are inside the dimensions of shape. """
- mask = (points[:, 0] >= 0) & (points[:, 0] < shape[0]) &\
- (points[:, 1] >= 0) & (points[:, 1] < shape[1])
+ """Keep only the points whose coordinates are inside the dimensions of shape."""
+ mask = (
+ (points[:, 0] >= 0)
+ & (points[:, 0] < shape[0])
+ & (points[:, 1] >= 0)
+ & (points[:, 1] < shape[1])
+ )
return points[mask, :]
def keep_true_keypoints(points, H, shape):
- """ Keep only the points whose warped coordinates by H are still inside shape. """
+ """Keep only the points whose warped coordinates by H are still inside shape."""
warped_points = warp_keypoints(points[:, [1, 0]], H)
warped_points[:, [0, 1]] = warped_points[:, [1, 0]]
- mask = (warped_points[:, 0] >= 0) & (warped_points[:, 0] < shape[0]) &\
- (warped_points[:, 1] >= 0) & (warped_points[:, 1] < shape[1])
+ mask = (
+ (warped_points[:, 0] >= 0)
+ & (warped_points[:, 0] < shape[0])
+ & (warped_points[:, 1] >= 0)
+ & (warped_points[:, 1] < shape[1])
+ )
return points[mask, :]
-
def select_k_best(points, k):
- """ Select the k most probable points (and strip their probability).
- points has shape (num_points, 3) where the last coordinate is the probability. """
+ """Select the k most probable points (and strip their probability).
+ points has shape (num_points, 3) where the last coordinate is the probability."""
sorted_prob = points[points[:, 2].argsort(), :2]
start = min(k, points.shape[0])
return sorted_prob[-start:, :]
- H = data['homography']
- shape = data['image_shape']
+ H = data["homography"]
+ shape = data["image_shape"]
# # Filter out predictions
- keypoints = data['prob'][:, :2].T
+ keypoints = data["prob"][:, :2].T
keypoints = keypoints[::-1]
- prob = data['prob'][:, 2]
+ prob = data["prob"][:, 2]
- warped_keypoints = data['warped_prob'][:, :2].T
+ warped_keypoints = data["warped_prob"][:, :2].T
warped_keypoints = warped_keypoints[::-1]
- warped_prob = data['warped_prob'][:, 2]
+ warped_prob = data["warped_prob"][:, 2]
keypoints = np.stack([keypoints[0], keypoints[1]], axis=-1)
- warped_keypoints = np.stack([warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1)
+ warped_keypoints = np.stack(
+ [warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1
+ )
warped_keypoints = keep_true_keypoints(warped_keypoints, np.linalg.inv(H), shape)
# Warp the original keypoints with the true homography
true_warped_keypoints = warp_keypoints(keypoints[:, [1, 0]], H)
- true_warped_keypoints = np.stack([true_warped_keypoints[:, 1], true_warped_keypoints[:, 0], prob], axis=-1)
+ true_warped_keypoints = np.stack(
+ [true_warped_keypoints[:, 1], true_warped_keypoints[:, 0], prob], axis=-1
+ )
true_warped_keypoints = filter_keypoints(true_warped_keypoints, shape)
# Keep only the keep_k_points best predictions
@@ -103,12 +115,12 @@ def compute_repeatability(data, keep_k_points=300, distance_thresh=3):
le2 = 0
if N2 != 0:
min1 = np.min(norm, axis=1)
- correct1 = (min1 <= distance_thresh)
+ correct1 = min1 <= distance_thresh
count1 = np.sum(correct1)
le1 = min1[correct1].sum()
if N1 != 0:
min2 = np.min(norm, axis=0)
- correct2 = (min2 <= distance_thresh)
+ correct2 = min2 <= distance_thresh
count2 = np.sum(correct2)
le2 = min2[correct2].sum()
if N1 + N2 > 0:
diff --git a/imcui/third_party/lanet/evaluation/evaluate.py b/third_party/lanet/evaluation/evaluate.py
similarity index 66%
rename from imcui/third_party/lanet/evaluation/evaluate.py
rename to third_party/lanet/evaluation/evaluate.py
index fa9e91ee6d9cc0142ebbe8f2a3f904f6fae8434c..06bec8e5e01b8d285622e6c1eca9000f2a0541cb 100644
--- a/imcui/third_party/lanet/evaluation/evaluate.py
+++ b/third_party/lanet/evaluation/evaluate.py
@@ -5,24 +5,25 @@ import torch
import torchvision.transforms as transforms
from tqdm import tqdm
-from evaluation.descriptor_evaluation import (compute_homography,
- compute_matching_score)
+from evaluation.descriptor_evaluation import compute_homography, compute_matching_score
from evaluation.detector_evaluation import compute_repeatability
-def evaluate_keypoint_net(data_loader, keypoint_net, output_shape=(320, 240), top_k=300):
- """Keypoint net evaluation script.
+def evaluate_keypoint_net(
+ data_loader, keypoint_net, output_shape=(320, 240), top_k=300
+):
+ """Keypoint net evaluation script.
Parameters
----------
data_loader: torch.utils.data.DataLoader
- Dataset loader.
+ Dataset loader.
keypoint_net: torch.nn.module
Keypoint network.
output_shape: tuple
Original image shape.
top_k: int
- Number of keypoints to use to compute metrics, selected based on probability.
+ Number of keypoints to use to compute metrics, selected based on probability.
use_color: bool
Use color or grayscale images.
"""
@@ -36,8 +37,8 @@ def evaluate_keypoint_net(data_loader, keypoint_net, output_shape=(320, 240), to
with torch.no_grad():
for i, sample in tqdm(enumerate(data_loader), desc="Evaluate point model"):
- image = sample['image'].cuda()
- warped_image = sample['warped_image'].cuda()
+ image = sample["image"].cuda()
+ warped_image = sample["warped_image"].cuda()
score_1, coord_1, desc1 = keypoint_net(image)
score_2, coord_2, desc2 = keypoint_net(warped_image)
@@ -48,7 +49,7 @@ def evaluate_keypoint_net(data_loader, keypoint_net, output_shape=(320, 240), to
score_2 = torch.cat([coord_2, score_2], dim=1).view(3, -1).t().cpu().numpy()
desc1 = desc1.view(256, Hc, Wc).view(256, -1).t().cpu().numpy()
desc2 = desc2.view(256, Hc, Wc).view(256, -1).t().cpu().numpy()
-
+
# Filter based on confidence threshold
desc1 = desc1[score_1[:, 2] > conf_threshold, :]
desc2 = desc2[score_2[:, 2] > conf_threshold, :]
@@ -56,17 +57,21 @@ def evaluate_keypoint_net(data_loader, keypoint_net, output_shape=(320, 240), to
score_2 = score_2[score_2[:, 2] > conf_threshold, :]
# Prepare data for eval
- data = {'image': sample['image'].numpy().squeeze(),
- 'image_shape' : output_shape[::-1],
- 'warped_image': sample['warped_image'].numpy().squeeze(),
- 'homography': sample['homography'].squeeze().numpy(),
- 'prob': score_1,
- 'warped_prob': score_2,
- 'desc': desc1,
- 'warped_desc': desc2}
-
+ data = {
+ "image": sample["image"].numpy().squeeze(),
+ "image_shape": output_shape[::-1],
+ "warped_image": sample["warped_image"].numpy().squeeze(),
+ "homography": sample["homography"].squeeze().numpy(),
+ "prob": score_1,
+ "warped_prob": score_2,
+ "desc": desc1,
+ "warped_desc": desc2,
+ }
+
# Compute repeatabilty and localization error
- _, _, rep, loc_err = compute_repeatability(data, keep_k_points=top_k, distance_thresh=3)
+ _, _, rep, loc_err = compute_repeatability(
+ data, keep_k_points=top_k, distance_thresh=3
+ )
repeatability.append(rep)
localization_err.append(loc_err)
@@ -80,5 +85,11 @@ def evaluate_keypoint_net(data_loader, keypoint_net, output_shape=(320, 240), to
mscore = compute_matching_score(data, keep_k_points=top_k)
MScore.append(mscore)
- return np.mean(repeatability), np.mean(localization_err), \
- np.mean(correctness1), np.mean(correctness3), np.mean(correctness5), np.mean(MScore)
+ return (
+ np.mean(repeatability),
+ np.mean(localization_err),
+ np.mean(correctness1),
+ np.mean(correctness3),
+ np.mean(correctness5),
+ np.mean(MScore),
+ )
diff --git a/imcui/third_party/lanet/utils.py b/third_party/lanet/lanet_utils.py
similarity index 70%
rename from imcui/third_party/lanet/utils.py
rename to third_party/lanet/lanet_utils.py
index 416012d2d367739edcbefa22e00b0030f090eede..6f1ead467c166a95e6782a8112bafe363f948f9b 100644
--- a/imcui/third_party/lanet/utils.py
+++ b/third_party/lanet/lanet_utils.py
@@ -1,123 +1,104 @@
-import os
-import torch
-import numpy as np
-
-import torchvision.transforms as transforms
-from functools import lru_cache
-
-@lru_cache(maxsize=None)
-def meshgrid(B, H, W, dtype, device, normalized=False):
- """
- Create mesh-grid given batch size, height and width dimensions. From https://github.com/TRI-ML/KP2D.
-
- Parameters
- ----------
- B: int
- Batch size
- H: int
- Grid Height
- W: int
- Batch size
- dtype: torch.dtype
- Tensor dtype
- device: str
- Tensor device
- normalized: bool
- Normalized image coordinates or integer-grid.
-
- Returns
- -------
- xs: torch.Tensor
- Batched mesh-grid x-coordinates (BHW).
- ys: torch.Tensor
- Batched mesh-grid y-coordinates (BHW).
- """
- if normalized:
- xs = torch.linspace(-1, 1, W, device=device, dtype=dtype)
- ys = torch.linspace(-1, 1, H, device=device, dtype=dtype)
- else:
- xs = torch.linspace(0, W-1, W, device=device, dtype=dtype)
- ys = torch.linspace(0, H-1, H, device=device, dtype=dtype)
- ys, xs = torch.meshgrid([ys, xs])
- return xs.repeat([B, 1, 1]), ys.repeat([B, 1, 1])
-
-
-@lru_cache(maxsize=None)
-def image_grid(B, H, W, dtype, device, ones=True, normalized=False):
- """
- Create an image mesh grid with shape B3HW given image shape BHW. From https://github.com/TRI-ML/KP2D.
-
- Parameters
- ----------
- B: int
- Batch size
- H: int
- Grid Height
- W: int
- Batch size
- dtype: str
- Tensor dtype
- device: str
- Tensor device
- ones : bool
- Use (x, y, 1) coordinates
- normalized: bool
- Normalized image coordinates or integer-grid.
-
- Returns
- -------
- grid: torch.Tensor
- Mesh-grid for the corresponding image shape (B3HW)
- """
- xs, ys = meshgrid(B, H, W, dtype, device, normalized=normalized)
- coords = [xs, ys]
- if ones:
- coords.append(torch.ones_like(xs)) # BHW
- grid = torch.stack(coords, dim=1) # B3HW
- return grid
-
-def to_tensor_sample(sample, tensor_type='torch.FloatTensor'):
- """
- Casts the keys of sample to tensors. From https://github.com/TRI-ML/KP2D.
-
- Parameters
- ----------
- sample : dict
- Input sample
- tensor_type : str
- Type of tensor we are casting to
-
- Returns
- -------
- sample : dict
- Sample with keys cast as tensors
- """
- transform = transforms.ToTensor()
- sample['image'] = transform(sample['image']).type(tensor_type)
- return sample
-
-def warp_keypoints(keypoints, H):
- """Warp keypoints given a homography
-
- Parameters
- ----------
- keypoints: numpy.ndarray (N,2)
- Keypoint vector.
- H: numpy.ndarray (3,3)
- Homography.
-
- Returns
- -------
- warped_keypoints: numpy.ndarray (N,2)
- Warped keypoints vector.
- """
- num_points = keypoints.shape[0]
- homogeneous_points = np.concatenate([keypoints, np.ones((num_points, 1))], axis=1)
- warped_points = np.dot(homogeneous_points, np.transpose(H))
- return warped_points[:, :2] / warped_points[:, 2:]
-
-def prepare_dirs(config):
- for path in [config.ckpt_dir]:
- if not os.path.exists(path):
- os.makedirs(path)
-
+import os
+import torch
+
+import torchvision.transforms as transforms
+from functools import lru_cache
+
+
+@lru_cache(maxsize=None)
+def meshgrid(B, H, W, dtype, device, normalized=False):
+ """
+ Create mesh-grid given batch size, height and width dimensions. From https://github.com/TRI-ML/KP2D.
+
+ Parameters
+ ----------
+ B: int
+ Batch size
+ H: int
+ Grid Height
+ W: int
+ Batch size
+ dtype: torch.dtype
+ Tensor dtype
+ device: str
+ Tensor device
+ normalized: bool
+ Normalized image coordinates or integer-grid.
+
+ Returns
+ -------
+ xs: torch.Tensor
+ Batched mesh-grid x-coordinates (BHW).
+ ys: torch.Tensor
+ Batched mesh-grid y-coordinates (BHW).
+ """
+ if normalized:
+ xs = torch.linspace(-1, 1, W, device=device, dtype=dtype)
+ ys = torch.linspace(-1, 1, H, device=device, dtype=dtype)
+ else:
+ xs = torch.linspace(0, W - 1, W, device=device, dtype=dtype)
+ ys = torch.linspace(0, H - 1, H, device=device, dtype=dtype)
+ ys, xs = torch.meshgrid([ys, xs])
+ return xs.repeat([B, 1, 1]), ys.repeat([B, 1, 1])
+
+
+@lru_cache(maxsize=None)
+def image_grid(B, H, W, dtype, device, ones=True, normalized=False):
+ """
+ Create an image mesh grid with shape B3HW given image shape BHW. From https://github.com/TRI-ML/KP2D.
+
+ Parameters
+ ----------
+ B: int
+ Batch size
+ H: int
+ Grid Height
+ W: int
+ Batch size
+ dtype: str
+ Tensor dtype
+ device: str
+ Tensor device
+ ones : bool
+ Use (x, y, 1) coordinates
+ normalized: bool
+ Normalized image coordinates or integer-grid.
+
+ Returns
+ -------
+ grid: torch.Tensor
+ Mesh-grid for the corresponding image shape (B3HW)
+ """
+ xs, ys = meshgrid(B, H, W, dtype, device, normalized=normalized)
+ coords = [xs, ys]
+ if ones:
+ coords.append(torch.ones_like(xs)) # BHW
+ grid = torch.stack(coords, dim=1) # B3HW
+ return grid
+
+
+def to_tensor_sample(sample, tensor_type="torch.FloatTensor"):
+ """
+ Casts the keys of sample to tensors. From https://github.com/TRI-ML/KP2D.
+
+ Parameters
+ ----------
+ sample : dict
+ Input sample
+ tensor_type : str
+ Type of tensor we are casting to
+
+ Returns
+ -------
+ sample : dict
+ Sample with keys cast as tensors
+ """
+ transform = transforms.ToTensor()
+ sample["image"] = transform(sample["image"]).type(tensor_type)
+ return sample
+
+
+def prepare_dirs(config):
+ for path in [config.ckpt_dir]:
+ if not os.path.exists(path):
+ os.makedirs(path)
diff --git a/third_party/lanet/loss_function.py b/third_party/lanet/loss_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5a40c3a969f8e7725e2f30d453762a0eca6b062
--- /dev/null
+++ b/third_party/lanet/loss_function.py
@@ -0,0 +1,226 @@
+import torch
+
+
+def build_descriptor_loss(
+ source_des, target_des, tar_points_un, top_kk=None, relax_field=4, eval_only=False
+):
+ """
+ Desc Head Loss, per-pixel level triplet loss from https://arxiv.org/pdf/1902.11046.pdf.
+
+ Parameters
+ ----------
+ source_des: torch.Tensor (B,256,H/8,W/8)
+ Source image descriptors.
+ target_des: torch.Tensor (B,256,H/8,W/8)
+ Target image descriptors.
+ source_points: torch.Tensor (B,H/8,W/8,2)
+ Source image keypoints
+ tar_points: torch.Tensor (B,H/8,W/8,2)
+ Target image keypoints
+ tar_points_un: torch.Tensor (B,2,H/8,W/8)
+ Target image keypoints unnormalized
+ eval_only: bool
+ Computes only recall without the loss.
+ Returns
+ -------
+ loss: torch.Tensor
+ Descriptor loss.
+ recall: torch.Tensor
+ Descriptor match recall.
+ """
+ device = source_des.device
+ loss = 0
+ batch_size = source_des.size(0)
+ recall = 0.0
+
+ relax_field_size = [relax_field]
+ margins = [1.0]
+ weights = [1.0]
+
+ isource_dense = top_kk is None
+
+ for b_id in range(batch_size):
+
+ if isource_dense:
+ ref_desc = source_des[b_id].squeeze().view(256, -1)
+ tar_desc = target_des[b_id].squeeze().view(256, -1)
+ tar_points_raw = tar_points_un[b_id].view(2, -1)
+ else:
+ top_k = top_kk[b_id].squeeze()
+
+ n_feat = top_k.sum().item()
+ if n_feat < 20:
+ continue
+
+ ref_desc = source_des[b_id].squeeze()[:, top_k]
+ tar_desc = target_des[b_id].squeeze()[:, top_k]
+ tar_points_raw = tar_points_un[b_id][:, top_k]
+
+ # Compute dense descriptor distance matrix and find nearest neighbor
+ ref_desc = ref_desc.div(torch.norm(ref_desc, p=2, dim=0))
+ tar_desc = tar_desc.div(torch.norm(tar_desc, p=2, dim=0))
+ dmat = torch.mm(ref_desc.t(), tar_desc)
+
+ dmat = torch.sqrt(2 - 2 * torch.clamp(dmat, min=-1, max=1))
+ _, idx = torch.sort(dmat, dim=1)
+
+ # Compute triplet loss and recall
+ for pyramid in range(len(relax_field_size)):
+
+ candidates = idx.t()
+
+ match_k_x = tar_points_raw[0, candidates]
+ match_k_y = tar_points_raw[1, candidates]
+
+ tru_x = tar_points_raw[0]
+ tru_y = tar_points_raw[1]
+
+ if pyramid == 0:
+ correct2 = (abs(match_k_x[0] - tru_x) == 0) & (
+ abs(match_k_y[0] - tru_y) == 0
+ )
+ correct2_cnt = correct2.float().sum()
+ recall += float(1.0 / batch_size) * (
+ float(correct2_cnt) / float(ref_desc.size(1))
+ )
+
+ if eval_only:
+ continue
+ correct_k = (abs(match_k_x - tru_x) <= relax_field_size[pyramid]) & (
+ abs(match_k_y - tru_y) <= relax_field_size[pyramid]
+ )
+
+ incorrect_index = (
+ torch.arange(start=correct_k.shape[0] - 1, end=-1, step=-1)
+ .unsqueeze(1)
+ .repeat(1, correct_k.shape[1])
+ .to(device)
+ )
+ incorrect_first = torch.argmax(
+ incorrect_index * (1 - correct_k.long()), dim=0
+ )
+
+ incorrect_first_index = candidates.gather(
+ 0, incorrect_first.unsqueeze(0)
+ ).squeeze()
+
+ anchor_var = ref_desc
+ posource_var = tar_desc
+ neg_var = tar_desc[:, incorrect_first_index]
+
+ loss += float(1.0 / batch_size) * torch.nn.functional.triplet_margin_loss(
+ anchor_var.t(), posource_var.t(), neg_var.t(), margin=margins[pyramid]
+ ).mul(weights[pyramid])
+
+ return loss, recall
+
+
+class KeypointLoss(object):
+ """
+ Loss function class encapsulating the location loss, the descriptor loss, and the score loss.
+ """
+
+ def __init__(self, config):
+ self.score_weight = config.score_weight
+ self.loc_weight = config.loc_weight
+ self.desc_weight = config.desc_weight
+ self.corres_weight = config.corres_weight
+ self.corres_threshold = config.corres_threshold
+
+ def __call__(self, data):
+ B, _, hc, wc = data["source_score"].shape
+
+ loc_mat_abs = torch.abs(
+ data["target_coord_warped"].view(B, 2, -1).unsqueeze(3)
+ - data["target_coord"].view(B, 2, -1).unsqueeze(2)
+ )
+ l2_dist_loc_mat = torch.norm(loc_mat_abs, p=2, dim=1)
+ l2_dist_loc_min, l2_dist_loc_min_index = l2_dist_loc_mat.min(dim=2)
+
+ # construct pseudo ground truth matching matrix
+ loc_min_mat = torch.repeat_interleave(
+ l2_dist_loc_min.unsqueeze(dim=-1), repeats=l2_dist_loc_mat.shape[-1], dim=-1
+ )
+ pos_mask = l2_dist_loc_mat.eq(loc_min_mat) & l2_dist_loc_mat.le(1.0)
+ neg_mask = l2_dist_loc_mat.ge(4.0)
+
+ pos_corres = -torch.log(data["confidence_matrix"][pos_mask])
+ neg_corres = -torch.log(1.0 - data["confidence_matrix"][neg_mask])
+ corres_loss = pos_corres.mean() + 5e5 * neg_corres.mean()
+
+ # corresponding distance threshold is 4
+ dist_norm_valid_mask = l2_dist_loc_min.lt(self.corres_threshold) & data[
+ "border_mask"
+ ].view(B, hc * wc)
+
+ # location loss
+ loc_loss = l2_dist_loc_min[dist_norm_valid_mask].mean()
+
+ # desc Head Loss, per-pixel level triplet loss from https://arxiv.org/pdf/1902.11046.pdf.
+ desc_loss, _ = build_descriptor_loss(
+ data["source_desc"],
+ data["target_desc_warped"],
+ data["target_coord_warped"].detach(),
+ top_kk=data["border_mask"],
+ relax_field=8,
+ )
+
+ # score loss
+ target_score_associated = (
+ data["target_score"]
+ .view(B, hc * wc)
+ .gather(1, l2_dist_loc_min_index)
+ .view(B, hc, wc)
+ .unsqueeze(1)
+ )
+ dist_norm_valid_mask = dist_norm_valid_mask.view(B, hc, wc).unsqueeze(1) & data[
+ "border_mask"
+ ].unsqueeze(1)
+ l2_dist_loc_min = l2_dist_loc_min.view(B, hc, wc).unsqueeze(1)
+ loc_err = l2_dist_loc_min[dist_norm_valid_mask]
+
+ # repeatable_constrain in score loss
+ repeatable_constrain = (
+ (
+ target_score_associated[dist_norm_valid_mask]
+ + data["source_score"][dist_norm_valid_mask]
+ )
+ * (loc_err - loc_err.mean())
+ ).mean()
+
+ # consistent_constrain in score_loss
+ consistent_constrain = (
+ torch.nn.functional.mse_loss(
+ data["target_score_warped"][data["border_mask"].unsqueeze(1)],
+ data["source_score"][data["border_mask"].unsqueeze(1)],
+ ).mean()
+ * 2
+ )
+ aware_consistent_loss = (
+ torch.nn.functional.mse_loss(
+ data["target_aware_warped"][
+ data["border_mask"].unsqueeze(1).repeat(1, 2, 1, 1)
+ ],
+ data["source_aware"][
+ data["border_mask"].unsqueeze(1).repeat(1, 2, 1, 1)
+ ],
+ ).mean()
+ * 2
+ )
+
+ score_loss = repeatable_constrain + consistent_constrain + aware_consistent_loss
+
+ loss = (
+ self.loc_weight * loc_loss
+ + self.desc_weight * desc_loss
+ + self.score_weight * score_loss
+ + self.corres_weight * corres_loss
+ )
+
+ return (
+ loss,
+ self.loc_weight * loc_loss,
+ self.desc_weight * desc_loss,
+ self.score_weight * score_loss,
+ self.corres_weight * corres_loss,
+ )
diff --git a/imcui/third_party/lanet/main.py b/third_party/lanet/main.py
similarity index 84%
rename from imcui/third_party/lanet/main.py
rename to third_party/lanet/main.py
index 105d15856ac79825c747e691ab7f695ee17a1680..57811883cfe9f73cc389f7ed8b85a3a9943d2b44 100644
--- a/imcui/third_party/lanet/main.py
+++ b/third_party/lanet/main.py
@@ -1,25 +1,27 @@
-import torch
-
-from train import Trainer
-from config import get_config
-from utils import prepare_dirs
-from data_loader import get_data_loader
-
-def main(config):
- # ensure directories are setup
- prepare_dirs(config)
-
- # ensure reproducibility
- torch.manual_seed(config.seed)
- if config.use_gpu:
- torch.cuda.manual_seed(config.seed)
-
- # instantiate train data loaders
- train_loader = get_data_loader(config=config)
-
- trainer = Trainer(config, train_loader=train_loader)
- trainer.train()
-
-if __name__ == '__main__':
- config, unparsed = get_config()
- main(config)
\ No newline at end of file
+import torch
+
+from train import Trainer
+from config import get_config
+from lanet_utils import prepare_dirs
+from data_loader import get_data_loader
+
+
+def main(config):
+ # ensure directories are setup
+ prepare_dirs(config)
+
+ # ensure reproducibility
+ torch.manual_seed(config.seed)
+ if config.use_gpu:
+ torch.cuda.manual_seed(config.seed)
+
+ # instantiate train data loaders
+ train_loader = get_data_loader(config=config)
+
+ trainer = Trainer(config, train_loader=train_loader)
+ trainer.train()
+
+
+if __name__ == "__main__":
+ config, unparsed = get_config()
+ main(config)
diff --git a/third_party/lanet/network_v0/model.py b/third_party/lanet/network_v0/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f22e015449dd7bcc8e060a2cd72a794befd2ccb
--- /dev/null
+++ b/third_party/lanet/network_v0/model.py
@@ -0,0 +1,181 @@
+import torch
+import torch.nn as nn
+import torchvision.transforms as tvf
+
+from .modules import InterestPointModule, CorrespondenceModule
+
+
+def warp_homography_batch(sources, homographies):
+ """
+ Batch warp keypoints given homographies. From https://github.com/TRI-ML/KP2D.
+
+ Parameters
+ ----------
+ sources: torch.Tensor (B,H,W,C)
+ Keypoints vector.
+ homographies: torch.Tensor (B,3,3)
+ Homographies.
+
+ Returns
+ -------
+ warped_sources: torch.Tensor (B,H,W,C)
+ Warped keypoints vector.
+ """
+ B, H, W, _ = sources.shape
+ warped_sources = []
+ for b in range(B):
+ source = sources[b].clone()
+ source = source.view(-1, 2)
+ """
+ [X, [M11, M12, M13 [x, M11*x + M12*y + M13 [M11, M12 [M13,
+ Y, = M21, M22, M23 * y, = M21*x + M22*y + M23 = [x, y] * M21, M22 + M23,
+ Z] M31, M32, M33] 1] M31*x + M32*y + M33 M31, M32].T M33]
+ """
+ source = torch.addmm(homographies[b, :, 2], source, homographies[b, :, :2].t())
+ source.mul_(1 / source[:, 2].unsqueeze(1))
+ source = source[:, :2].contiguous().view(H, W, 2)
+ warped_sources.append(source)
+ return torch.stack(warped_sources, dim=0)
+
+
+class PointModel(nn.Module):
+ def __init__(self, is_test=True):
+ super(PointModel, self).__init__()
+ self.is_test = is_test
+ self.interestpoint_module = InterestPointModule(is_test=self.is_test)
+ self.correspondence_module = CorrespondenceModule()
+ self.norm_rgb = tvf.Normalize(mean=[0.5, 0.5, 0.5], std=[0.225, 0.225, 0.225])
+
+ def forward(self, *args):
+ if self.is_test:
+ img = args[0]
+ img = self.norm_rgb(img)
+ score, coord, desc = self.interestpoint_module(img)
+ return score, coord, desc
+ else:
+ source_score, source_coord, source_desc_block = self.interestpoint_module(
+ args[0]
+ )
+ target_score, target_coord, target_desc_block = self.interestpoint_module(
+ args[1]
+ )
+
+ B, _, H, W = args[0].shape
+ B, _, hc, wc = source_score.shape
+ device = source_score.device
+
+ # Normalize the coordinates from ([0, h], [0, w]) to ([0, 1], [0, 1]).
+ source_coord_norm = source_coord.clone()
+ source_coord_norm[:, 0] = (
+ source_coord_norm[:, 0] / (float(W - 1) / 2.0)
+ ) - 1.0
+ source_coord_norm[:, 1] = (
+ source_coord_norm[:, 1] / (float(H - 1) / 2.0)
+ ) - 1.0
+ source_coord_norm = source_coord_norm.permute(0, 2, 3, 1)
+
+ target_coord_norm = target_coord.clone()
+ target_coord_norm[:, 0] = (
+ target_coord_norm[:, 0] / (float(W - 1) / 2.0)
+ ) - 1.0
+ target_coord_norm[:, 1] = (
+ target_coord_norm[:, 1] / (float(H - 1) / 2.0)
+ ) - 1.0
+ target_coord_norm = target_coord_norm.permute(0, 2, 3, 1)
+
+ target_coord_warped_norm = warp_homography_batch(source_coord_norm, args[2])
+ target_coord_warped = target_coord_warped_norm.clone()
+
+ # de-normlize the coordinates
+ target_coord_warped[:, :, :, 0] = (target_coord_warped[:, :, :, 0] + 1) * (
+ float(W - 1) / 2.0
+ )
+ target_coord_warped[:, :, :, 1] = (target_coord_warped[:, :, :, 1] + 1) * (
+ float(H - 1) / 2.0
+ )
+ target_coord_warped = target_coord_warped.permute(0, 3, 1, 2)
+
+ # Border mask
+ border_mask_ori = torch.ones(B, hc, wc)
+ border_mask_ori[:, 0] = 0
+ border_mask_ori[:, hc - 1] = 0
+ border_mask_ori[:, :, 0] = 0
+ border_mask_ori[:, :, wc - 1] = 0
+ border_mask_ori = border_mask_ori.gt(1e-3).to(device)
+
+ oob_mask2 = (
+ target_coord_warped_norm[:, :, :, 0].lt(1)
+ & target_coord_warped_norm[:, :, :, 0].gt(-1)
+ & target_coord_warped_norm[:, :, :, 1].lt(1)
+ & target_coord_warped_norm[:, :, :, 1].gt(-1)
+ )
+ border_mask = border_mask_ori & oob_mask2
+
+ # score
+ target_score_warped = torch.nn.functional.grid_sample(
+ target_score, target_coord_warped_norm.detach(), align_corners=False
+ )
+
+ # descriptor
+ source_desc2 = torch.nn.functional.grid_sample(
+ source_desc_block[0], source_coord_norm.detach()
+ )
+ source_desc3 = torch.nn.functional.grid_sample(
+ source_desc_block[1], source_coord_norm.detach()
+ )
+ source_aware = source_desc_block[2]
+ source_desc = torch.mul(
+ source_desc2, source_aware[:, 0, :, :].unsqueeze(1).contiguous()
+ ) + torch.mul(
+ source_desc3, source_aware[:, 1, :, :].unsqueeze(1).contiguous()
+ )
+
+ target_desc2 = torch.nn.functional.grid_sample(
+ target_desc_block[0], target_coord_norm.detach()
+ )
+ target_desc3 = torch.nn.functional.grid_sample(
+ target_desc_block[1], target_coord_norm.detach()
+ )
+ target_aware = target_desc_block[2]
+ target_desc = torch.mul(
+ target_desc2, target_aware[:, 0, :, :].unsqueeze(1).contiguous()
+ ) + torch.mul(
+ target_desc3, target_aware[:, 1, :, :].unsqueeze(1).contiguous()
+ )
+
+ target_desc2_warped = torch.nn.functional.grid_sample(
+ target_desc_block[0], target_coord_warped_norm.detach()
+ )
+ target_desc3_warped = torch.nn.functional.grid_sample(
+ target_desc_block[1], target_coord_warped_norm.detach()
+ )
+ target_aware_warped = torch.nn.functional.grid_sample(
+ target_desc_block[2], target_coord_warped_norm.detach()
+ )
+ target_desc_warped = torch.mul(
+ target_desc2_warped,
+ target_aware_warped[:, 0, :, :].unsqueeze(1).contiguous(),
+ ) + torch.mul(
+ target_desc3_warped,
+ target_aware_warped[:, 1, :, :].unsqueeze(1).contiguous(),
+ )
+
+ confidence_matrix = self.correspondence_module(source_desc, target_desc)
+ confidence_matrix = torch.clamp(confidence_matrix, 1e-12, 1 - 1e-12)
+
+ output = {
+ "source_score": source_score,
+ "source_coord": source_coord,
+ "source_desc": source_desc,
+ "source_aware": source_aware,
+ "target_score": target_score,
+ "target_coord": target_coord,
+ "target_score_warped": target_score_warped,
+ "target_coord_warped": target_coord_warped,
+ "target_desc_warped": target_desc_warped,
+ "target_aware_warped": target_aware_warped,
+ "border_mask": border_mask,
+ "confidence_matrix": confidence_matrix,
+ }
+
+ return output
diff --git a/imcui/third_party/lanet/network_v0/modules.py b/third_party/lanet/network_v0/modules.py
similarity index 60%
rename from imcui/third_party/lanet/network_v0/modules.py
rename to third_party/lanet/network_v0/modules.py
index c5d95860caed657830869f8a245cbbd2a1b856f8..ddf53f2ceccbb8a3eb23620593bb5ac7222f2494 100644
--- a/imcui/third_party/lanet/network_v0/modules.py
+++ b/third_party/lanet/network_v0/modules.py
@@ -1,158 +1,204 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..utils import image_grid
-
-class ConvBlock(nn.Module):
- def __init__(self, in_channels, out_channels):
- super(ConvBlock, self).__init__()
-
- self.conv = nn.Sequential(
- nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),
- nn.BatchNorm2d(out_channels),
- nn.ReLU(inplace=True),
- nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),
- nn.BatchNorm2d(out_channels),
- nn.ReLU(inplace=True)
- )
-
- def forward(self, x):
- return self.conv(x)
-
-
-class DilationConv3x3(nn.Module):
- def __init__(self, in_channels, out_channels):
- super(DilationConv3x3, self).__init__()
-
- self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=2, dilation=2, bias=False)
- self.bn = nn.BatchNorm2d(out_channels)
-
- def forward(self, x):
- x = self.conv(x)
- x = self.bn(x)
- return x
-
-
-class InterestPointModule(nn.Module):
- def __init__(self, is_test=False):
- super(InterestPointModule, self).__init__()
- self.is_test = is_test
-
- self.conv1 = ConvBlock(3, 32)
- self.conv2 = ConvBlock(32, 64)
- self.conv3 = ConvBlock(64, 128)
- self.conv4 = ConvBlock(128, 256)
-
- self.maxpool2x2 = nn.MaxPool2d(2, 2)
-
- # score head
- self.score_conv = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False)
- self.score_norm = nn.BatchNorm2d(256)
- self.score_out = nn.Conv2d(256, 3, kernel_size=3, stride=1, padding=1)
- self.softmax = nn.Softmax(dim=1)
-
- # location head
- self.loc_conv = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False)
- self.loc_norm = nn.BatchNorm2d(256)
- self.loc_out = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
-
- # descriptor out
- self.des_conv2 = DilationConv3x3(64, 256)
- self.des_conv3 = DilationConv3x3(128, 256)
-
- # cross_head:
- self.shift_out = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
-
- self.relu = nn.ReLU(inplace=True)
-
- def forward(self, x):
- B, _, H, W = x.shape
-
- x = self.conv1(x)
- x = self.maxpool2x2(x)
- x2 = self.conv2(x)
- x = self.maxpool2x2(x2)
- x3 = self.conv3(x)
- x = self.maxpool2x2(x3)
- x = self.conv4(x)
-
- B, _, Hc, Wc = x.shape
-
- # score head
- score_x = self.score_out(self.relu(self.score_norm(self.score_conv(x))))
- aware = self.softmax(score_x[:, 0:2, :, :])
- score = score_x[:, 2, :, :].unsqueeze(1).sigmoid()
-
- border_mask = torch.ones(B, Hc, Wc)
- border_mask[:, 0] = 0
- border_mask[:, Hc - 1] = 0
- border_mask[:, :, 0] = 0
- border_mask[:, :, Wc - 1] = 0
- border_mask = border_mask.unsqueeze(1)
- score = score * border_mask.to(score.device)
-
- # location head
- coord_x = self.relu(self.loc_norm(self.loc_conv(x)))
- coord_cell = self.loc_out(coord_x).tanh()
-
- shift_ratio = self.shift_out(coord_x).sigmoid() * 2.0
-
- step = ((H/Hc)-1) / 2.
- center_base = image_grid(B, Hc, Wc,
- dtype=coord_cell.dtype,
- device=coord_cell.device,
- ones=False, normalized=False).mul(H/Hc) + step
-
- coord_un = center_base.add(coord_cell.mul(shift_ratio * step))
- coord = coord_un.clone()
- coord[:, 0] = torch.clamp(coord_un[:, 0], min=0, max=W-1)
- coord[:, 1] = torch.clamp(coord_un[:, 1], min=0, max=H-1)
-
- # descriptor block
- desc_block = []
- desc_block.append(self.des_conv2(x2))
- desc_block.append(self.des_conv3(x3))
- desc_block.append(aware)
-
- if self.is_test:
- coord_norm = coord[:, :2].clone()
- coord_norm[:, 0] = (coord_norm[:, 0] / (float(W-1)/2.)) - 1.
- coord_norm[:, 1] = (coord_norm[:, 1] / (float(H-1)/2.)) - 1.
- coord_norm = coord_norm.permute(0, 2, 3, 1)
-
- desc2 = torch.nn.functional.grid_sample(desc_block[0], coord_norm)
- desc3 = torch.nn.functional.grid_sample(desc_block[1], coord_norm)
- aware = desc_block[2]
-
- desc = torch.mul(desc2, aware[:, 0, :, :]) + torch.mul(desc3, aware[:, 1, :, :])
- desc = desc.div(torch.unsqueeze(torch.norm(desc, p=2, dim=1), 1)) # Divide by norm to normalize.
-
- return score, coord, desc
-
- return score, coord, desc_block
-
-
-class CorrespondenceModule(nn.Module):
- def __init__(self, match_type='dual_softmax'):
- super(CorrespondenceModule, self).__init__()
- self.match_type = match_type
-
- if self.match_type == 'dual_softmax':
- self.temperature = 0.1
- else:
- raise NotImplementedError()
-
- def forward(self, source_desc, target_desc):
- b, c, h, w = source_desc.size()
-
- source_desc = source_desc.div(torch.unsqueeze(torch.norm(source_desc, p=2, dim=1), 1)).view(b, -1, h*w)
- target_desc = target_desc.div(torch.unsqueeze(torch.norm(target_desc, p=2, dim=1), 1)).view(b, -1, h*w)
-
- if self.match_type == 'dual_softmax':
- sim_mat = torch.einsum("bcm, bcn -> bmn", source_desc, target_desc) / self.temperature
- confidence_matrix = F.softmax(sim_mat, 1) * F.softmax(sim_mat, 2)
- else:
- raise NotImplementedError()
-
- return confidence_matrix
\ No newline at end of file
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..lanet_utils import image_grid
+
+
+class ConvBlock(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super(ConvBlock, self).__init__()
+
+ self.conv = nn.Sequential(
+ nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=False,
+ ),
+ nn.BatchNorm2d(out_channels),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(
+ out_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=False,
+ ),
+ nn.BatchNorm2d(out_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ def forward(self, x):
+ return self.conv(x)
+
+
+class DilationConv3x3(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super(DilationConv3x3, self).__init__()
+
+ self.conv = nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=2,
+ dilation=2,
+ bias=False,
+ )
+ self.bn = nn.BatchNorm2d(out_channels)
+
+ def forward(self, x):
+ x = self.conv(x)
+ x = self.bn(x)
+ return x
+
+
+class InterestPointModule(nn.Module):
+ def __init__(self, is_test=False):
+ super(InterestPointModule, self).__init__()
+ self.is_test = is_test
+
+ self.conv1 = ConvBlock(3, 32)
+ self.conv2 = ConvBlock(32, 64)
+ self.conv3 = ConvBlock(64, 128)
+ self.conv4 = ConvBlock(128, 256)
+
+ self.maxpool2x2 = nn.MaxPool2d(2, 2)
+
+ # score head
+ self.score_conv = nn.Conv2d(
+ 256, 256, kernel_size=3, stride=1, padding=1, bias=False
+ )
+ self.score_norm = nn.BatchNorm2d(256)
+ self.score_out = nn.Conv2d(256, 3, kernel_size=3, stride=1, padding=1)
+ self.softmax = nn.Softmax(dim=1)
+
+ # location head
+ self.loc_conv = nn.Conv2d(
+ 256, 256, kernel_size=3, stride=1, padding=1, bias=False
+ )
+ self.loc_norm = nn.BatchNorm2d(256)
+ self.loc_out = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
+
+ # descriptor out
+ self.des_conv2 = DilationConv3x3(64, 256)
+ self.des_conv3 = DilationConv3x3(128, 256)
+
+ # cross_head:
+ self.shift_out = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
+
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, x):
+ B, _, H, W = x.shape
+
+ x = self.conv1(x)
+ x = self.maxpool2x2(x)
+ x2 = self.conv2(x)
+ x = self.maxpool2x2(x2)
+ x3 = self.conv3(x)
+ x = self.maxpool2x2(x3)
+ x = self.conv4(x)
+
+ B, _, Hc, Wc = x.shape
+
+ # score head
+ score_x = self.score_out(self.relu(self.score_norm(self.score_conv(x))))
+ aware = self.softmax(score_x[:, 0:2, :, :])
+ score = score_x[:, 2, :, :].unsqueeze(1).sigmoid()
+
+ border_mask = torch.ones(B, Hc, Wc)
+ border_mask[:, 0] = 0
+ border_mask[:, Hc - 1] = 0
+ border_mask[:, :, 0] = 0
+ border_mask[:, :, Wc - 1] = 0
+ border_mask = border_mask.unsqueeze(1)
+ score = score * border_mask.to(score.device)
+
+ # location head
+ coord_x = self.relu(self.loc_norm(self.loc_conv(x)))
+ coord_cell = self.loc_out(coord_x).tanh()
+
+ shift_ratio = self.shift_out(coord_x).sigmoid() * 2.0
+
+ step = ((H / Hc) - 1) / 2.0
+ center_base = (
+ image_grid(
+ B,
+ Hc,
+ Wc,
+ dtype=coord_cell.dtype,
+ device=coord_cell.device,
+ ones=False,
+ normalized=False,
+ ).mul(H / Hc)
+ + step
+ )
+
+ coord_un = center_base.add(coord_cell.mul(shift_ratio * step))
+ coord = coord_un.clone()
+ coord[:, 0] = torch.clamp(coord_un[:, 0], min=0, max=W - 1)
+ coord[:, 1] = torch.clamp(coord_un[:, 1], min=0, max=H - 1)
+
+ # descriptor block
+ desc_block = []
+ desc_block.append(self.des_conv2(x2))
+ desc_block.append(self.des_conv3(x3))
+ desc_block.append(aware)
+
+ if self.is_test:
+ coord_norm = coord[:, :2].clone()
+ coord_norm[:, 0] = (coord_norm[:, 0] / (float(W - 1) / 2.0)) - 1.0
+ coord_norm[:, 1] = (coord_norm[:, 1] / (float(H - 1) / 2.0)) - 1.0
+ coord_norm = coord_norm.permute(0, 2, 3, 1)
+
+ desc2 = torch.nn.functional.grid_sample(desc_block[0], coord_norm)
+ desc3 = torch.nn.functional.grid_sample(desc_block[1], coord_norm)
+ aware = desc_block[2]
+
+ desc = torch.mul(desc2, aware[:, 0, :, :]) + torch.mul(
+ desc3, aware[:, 1, :, :]
+ )
+ desc = desc.div(
+ torch.unsqueeze(torch.norm(desc, p=2, dim=1), 1)
+ ) # Divide by norm to normalize.
+
+ return score, coord, desc
+
+ return score, coord, desc_block
+
+
+class CorrespondenceModule(nn.Module):
+ def __init__(self, match_type="dual_softmax"):
+ super(CorrespondenceModule, self).__init__()
+ self.match_type = match_type
+
+ if self.match_type == "dual_softmax":
+ self.temperature = 0.1
+ else:
+ raise NotImplementedError()
+
+ def forward(self, source_desc, target_desc):
+ b, c, h, w = source_desc.size()
+
+ source_desc = source_desc.div(
+ torch.unsqueeze(torch.norm(source_desc, p=2, dim=1), 1)
+ ).view(b, -1, h * w)
+ target_desc = target_desc.div(
+ torch.unsqueeze(torch.norm(target_desc, p=2, dim=1), 1)
+ ).view(b, -1, h * w)
+
+ if self.match_type == "dual_softmax":
+ sim_mat = (
+ torch.einsum("bcm, bcn -> bmn", source_desc, target_desc)
+ / self.temperature
+ )
+ confidence_matrix = F.softmax(sim_mat, 1) * F.softmax(sim_mat, 2)
+ else:
+ raise NotImplementedError()
+
+ return confidence_matrix
diff --git a/imcui/third_party/lanet/network_v1/model.py b/third_party/lanet/network_v1/model.py
similarity index 78%
rename from imcui/third_party/lanet/network_v1/model.py
rename to third_party/lanet/network_v1/model.py
index 75fe96ac0f05cf6b06b3aae64e627ca730afa56b..51ca366db1d8afd76722f5c51ccfbf8b081c61e2 100644
--- a/imcui/third_party/lanet/network_v1/model.py
+++ b/third_party/lanet/network_v1/model.py
@@ -1,52 +1,55 @@
-import torch
-import torch.nn as nn
-import torchvision.transforms as tvf
-
-from .modules import InterestPointModule, CorrespondenceModule
-
-def warp_homography_batch(sources, homographies):
- """
- Batch warp keypoints given homographies. From https://github.com/TRI-ML/KP2D.
-
- Parameters
- ----------
- sources: torch.Tensor (B,H,W,C)
- Keypoints vector.
- homographies: torch.Tensor (B,3,3)
- Homographies.
-
- Returns
- -------
- warped_sources: torch.Tensor (B,H,W,C)
- Warped keypoints vector.
- """
- B, H, W, _ = sources.shape
- warped_sources = []
- for b in range(B):
- source = sources[b].clone()
- source = source.view(-1,2)
- '''
- [X, [M11, M12, M13 [x, M11*x + M12*y + M13 [M11, M12 [M13,
- Y, = M21, M22, M23 * y, = M21*x + M22*y + M23 = [x, y] * M21, M22 + M23,
- Z] M31, M32, M33] 1] M31*x + M32*y + M33 M31, M32].T M33]
- '''
- source = torch.addmm(homographies[b,:,2], source, homographies[b,:,:2].t())
- source.mul_(1/source[:,2].unsqueeze(1))
- source = source[:,:2].contiguous().view(H,W,2)
- warped_sources.append(source)
- return torch.stack(warped_sources, dim=0)
-
-
-class PointModel(nn.Module):
- def __init__(self, is_test=False):
- super(PointModel, self).__init__()
- self.is_test = is_test
- self.interestpoint_module = InterestPointModule(is_test=self.is_test)
- self.correspondence_module = CorrespondenceModule()
- self.norm_rgb = tvf.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
- def forward(self, *args):
- img = args[0]
- img = self.norm_rgb(img)
- score, coord, desc = self.interestpoint_module(img)
- return score, coord, desc
+import torch
+import torch.nn as nn
+import torchvision.transforms as tvf
+
+from .modules import InterestPointModule, CorrespondenceModule
+
+
+def warp_homography_batch(sources, homographies):
+ """
+ Batch warp keypoints given homographies. From https://github.com/TRI-ML/KP2D.
+
+ Parameters
+ ----------
+ sources: torch.Tensor (B,H,W,C)
+ Keypoints vector.
+ homographies: torch.Tensor (B,3,3)
+ Homographies.
+
+ Returns
+ -------
+ warped_sources: torch.Tensor (B,H,W,C)
+ Warped keypoints vector.
+ """
+ B, H, W, _ = sources.shape
+ warped_sources = []
+ for b in range(B):
+ source = sources[b].clone()
+ source = source.view(-1, 2)
+ """
+ [X, [M11, M12, M13 [x, M11*x + M12*y + M13 [M11, M12 [M13,
+ Y, = M21, M22, M23 * y, = M21*x + M22*y + M23 = [x, y] * M21, M22 + M23,
+ Z] M31, M32, M33] 1] M31*x + M32*y + M33 M31, M32].T M33]
+ """
+ source = torch.addmm(homographies[b, :, 2], source, homographies[b, :, :2].t())
+ source.mul_(1 / source[:, 2].unsqueeze(1))
+ source = source[:, :2].contiguous().view(H, W, 2)
+ warped_sources.append(source)
+ return torch.stack(warped_sources, dim=0)
+
+
+class PointModel(nn.Module):
+ def __init__(self, is_test=False):
+ super(PointModel, self).__init__()
+ self.is_test = is_test
+ self.interestpoint_module = InterestPointModule(is_test=self.is_test)
+ self.correspondence_module = CorrespondenceModule()
+ self.norm_rgb = tvf.Normalize(
+ mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+ )
+
+ def forward(self, *args):
+ img = args[0]
+ img = self.norm_rgb(img)
+ score, coord, desc = self.interestpoint_module(img)
+ return score, coord, desc
diff --git a/imcui/third_party/lanet/network_v1/modules.py b/third_party/lanet/network_v1/modules.py
similarity index 65%
rename from imcui/third_party/lanet/network_v1/modules.py
rename to third_party/lanet/network_v1/modules.py
index 2ba699e19e1a1f04cd8fdb72b66a4c745ce48107..9baa3970f1d7437a9dd712fca75f222c4d19dc1b 100644
--- a/imcui/third_party/lanet/network_v1/modules.py
+++ b/third_party/lanet/network_v1/modules.py
@@ -1,174 +1,217 @@
-from curses import is_term_resized
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from torchvision import models
-from ..utils import image_grid
-
-class ConvBlock(nn.Module):
- def __init__(self, in_channels, out_channels):
- super(ConvBlock, self).__init__()
-
- self.conv = nn.Sequential(
- nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),
- nn.BatchNorm2d(out_channels),
- nn.ReLU(inplace=True),
- nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),
- nn.BatchNorm2d(out_channels),
- nn.ReLU(inplace=True)
- )
-
- def forward(self, x):
- return self.conv(x)
-
-class DilationConv3x3(nn.Module):
- def __init__(self, in_channels, out_channels):
- super(DilationConv3x3, self).__init__()
-
- self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=2, dilation=2, bias=False)
- self.bn = nn.BatchNorm2d(out_channels)
-
- def forward(self, x):
- x = self.conv(x)
- x = self.bn(x)
- return x
-
-
-class InterestPointModule(nn.Module):
- def __init__(self, is_test=False):
- super(InterestPointModule, self).__init__()
- self.is_test = is_test
-
- model = models.vgg16_bn(pretrained=True)
-
- # use the first 23 layers as encoder
- self.encoder = nn.Sequential(
- *list(model.features.children())[: 33]
- )
-
- # score head
- self.score_head = nn.Sequential(
- nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(inplace=True),
- nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
- )
- self.softmax = nn.Softmax(dim=1)
-
- # location head
- self.loc_head = nn.Sequential(
- nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(inplace=True),
- )
- # location out
- self.loc_out = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
- self.shift_out = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
-
- # descriptor out
- self.des_out2 = DilationConv3x3(128, 256)
- self.des_out3 = DilationConv3x3(256, 256)
- self.des_out4 = DilationConv3x3(512, 256)
-
- def forward(self, x):
- B, _, H, W = x.shape
-
- x = self.encoder[2](self.encoder[1](self.encoder[0](x)))
- x = self.encoder[5](self.encoder[4](self.encoder[3](x)))
-
- x = self.encoder[6](x)
- x = self.encoder[9](self.encoder[8](self.encoder[7](x)))
- x2 = self.encoder[12](self.encoder[11](self.encoder[10](x)))
-
- x = self.encoder[13](x2)
- x = self.encoder[16](self.encoder[15](self.encoder[14](x)))
- x = self.encoder[19](self.encoder[18](self.encoder[17](x)))
- x3 = self.encoder[22](self.encoder[21](self.encoder[20](x)))
-
- x = self.encoder[23](x3)
- x = self.encoder[26](self.encoder[25](self.encoder[24](x)))
- x = self.encoder[29](self.encoder[28](self.encoder[27](x)))
- x = self.encoder[32](self.encoder[31](self.encoder[30](x)))
-
-
- B, _, Hc, Wc = x.shape
-
- # score head
- score_x = self.score_head(x)
- aware = self.softmax(score_x[:, 0:3, :, :])
- score = score_x[:, 3, :, :].unsqueeze(1).sigmoid()
-
- border_mask = torch.ones(B, Hc, Wc)
- border_mask[:, 0] = 0
- border_mask[:, Hc - 1] = 0
- border_mask[:, :, 0] = 0
- border_mask[:, :, Wc - 1] = 0
- border_mask = border_mask.unsqueeze(1)
- score = score * border_mask.to(score.device)
-
- # location head
- coord_x = self.loc_head(x)
- coord_cell = self.loc_out(coord_x).tanh()
-
- shift_ratio = self.shift_out(coord_x).sigmoid() * 2.0
-
- step = ((H/Hc)-1) / 2.
- center_base = image_grid(B, Hc, Wc,
- dtype=coord_cell.dtype,
- device=coord_cell.device,
- ones=False, normalized=False).mul(H/Hc) + step
-
- coord_un = center_base.add(coord_cell.mul(shift_ratio * step))
- coord = coord_un.clone()
- coord[:, 0] = torch.clamp(coord_un[:, 0], min=0, max=W-1)
- coord[:, 1] = torch.clamp(coord_un[:, 1], min=0, max=H-1)
-
- # descriptor block
- desc_block = []
- desc_block.append(self.des_out2(x2))
- desc_block.append(self.des_out3(x3))
- desc_block.append(self.des_out4(x))
- desc_block.append(aware)
-
- if self.is_test:
- coord_norm = coord[:, :2].clone()
- coord_norm[:, 0] = (coord_norm[:, 0] / (float(W-1)/2.)) - 1.
- coord_norm[:, 1] = (coord_norm[:, 1] / (float(H-1)/2.)) - 1.
- coord_norm = coord_norm.permute(0, 2, 3, 1)
-
- desc2 = torch.nn.functional.grid_sample(desc_block[0], coord_norm)
- desc3 = torch.nn.functional.grid_sample(desc_block[1], coord_norm)
- desc4 = torch.nn.functional.grid_sample(desc_block[2], coord_norm)
- aware = desc_block[3]
-
- desc = torch.mul(desc2, aware[:, 0, :, :]) + torch.mul(desc3, aware[:, 1, :, :]) + torch.mul(desc4, aware[:, 2, :, :])
- desc = desc.div(torch.unsqueeze(torch.norm(desc, p=2, dim=1), 1)) # Divide by norm to normalize.
-
- return score, coord, desc
-
- return score, coord, desc_block
-
-class CorrespondenceModule(nn.Module):
- def __init__(self, match_type='dual_softmax'):
- super(CorrespondenceModule, self).__init__()
- self.match_type = match_type
-
- if self.match_type == 'dual_softmax':
- self.temperature = 0.1
- else:
- raise NotImplementedError()
-
- def forward(self, source_desc, target_desc):
- b, c, h, w = source_desc.size()
-
- source_desc = source_desc.div(torch.unsqueeze(torch.norm(source_desc, p=2, dim=1), 1)).view(b, -1, h*w)
- target_desc = target_desc.div(torch.unsqueeze(torch.norm(target_desc, p=2, dim=1), 1)).view(b, -1, h*w)
-
- if self.match_type == 'dual_softmax':
- sim_mat = torch.einsum("bcm, bcn -> bmn", source_desc, target_desc) / self.temperature
- confidence_matrix = F.softmax(sim_mat, 1) * F.softmax(sim_mat, 2)
- else:
- raise NotImplementedError()
-
- return confidence_matrix
+from curses import is_term_resized
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torchvision import models
+from ..lanet_utils import image_grid
+
+
+class ConvBlock(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super(ConvBlock, self).__init__()
+
+ self.conv = nn.Sequential(
+ nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=False,
+ ),
+ nn.BatchNorm2d(out_channels),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(
+ out_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=False,
+ ),
+ nn.BatchNorm2d(out_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ def forward(self, x):
+ return self.conv(x)
+
+
+class DilationConv3x3(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super(DilationConv3x3, self).__init__()
+
+ self.conv = nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=2,
+ dilation=2,
+ bias=False,
+ )
+ self.bn = nn.BatchNorm2d(out_channels)
+
+ def forward(self, x):
+ x = self.conv(x)
+ x = self.bn(x)
+ return x
+
+
+class InterestPointModule(nn.Module):
+ def __init__(self, is_test=False):
+ super(InterestPointModule, self).__init__()
+ self.is_test = is_test
+
+ model = models.vgg16_bn(pretrained=True)
+
+ # use the first 23 layers as encoder
+ self.encoder = nn.Sequential(*list(model.features.children())[:33])
+
+ # score head
+ self.score_head = nn.Sequential(
+ nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=False),
+ nn.BatchNorm2d(256),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1),
+ )
+ self.softmax = nn.Softmax(dim=1)
+
+ # location head
+ self.loc_head = nn.Sequential(
+ nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=False),
+ nn.BatchNorm2d(256),
+ nn.ReLU(inplace=True),
+ )
+ # location out
+ self.loc_out = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
+ self.shift_out = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
+
+ # descriptor out
+ self.des_out2 = DilationConv3x3(128, 256)
+ self.des_out3 = DilationConv3x3(256, 256)
+ self.des_out4 = DilationConv3x3(512, 256)
+
+ def forward(self, x):
+ B, _, H, W = x.shape
+
+ x = self.encoder[2](self.encoder[1](self.encoder[0](x)))
+ x = self.encoder[5](self.encoder[4](self.encoder[3](x)))
+
+ x = self.encoder[6](x)
+ x = self.encoder[9](self.encoder[8](self.encoder[7](x)))
+ x2 = self.encoder[12](self.encoder[11](self.encoder[10](x)))
+
+ x = self.encoder[13](x2)
+ x = self.encoder[16](self.encoder[15](self.encoder[14](x)))
+ x = self.encoder[19](self.encoder[18](self.encoder[17](x)))
+ x3 = self.encoder[22](self.encoder[21](self.encoder[20](x)))
+
+ x = self.encoder[23](x3)
+ x = self.encoder[26](self.encoder[25](self.encoder[24](x)))
+ x = self.encoder[29](self.encoder[28](self.encoder[27](x)))
+ x = self.encoder[32](self.encoder[31](self.encoder[30](x)))
+
+ B, _, Hc, Wc = x.shape
+
+ # score head
+ score_x = self.score_head(x)
+ aware = self.softmax(score_x[:, 0:3, :, :])
+ score = score_x[:, 3, :, :].unsqueeze(1).sigmoid()
+
+ border_mask = torch.ones(B, Hc, Wc)
+ border_mask[:, 0] = 0
+ border_mask[:, Hc - 1] = 0
+ border_mask[:, :, 0] = 0
+ border_mask[:, :, Wc - 1] = 0
+ border_mask = border_mask.unsqueeze(1)
+ score = score * border_mask.to(score.device)
+
+ # location head
+ coord_x = self.loc_head(x)
+ coord_cell = self.loc_out(coord_x).tanh()
+
+ shift_ratio = self.shift_out(coord_x).sigmoid() * 2.0
+
+ step = ((H / Hc) - 1) / 2.0
+ center_base = (
+ image_grid(
+ B,
+ Hc,
+ Wc,
+ dtype=coord_cell.dtype,
+ device=coord_cell.device,
+ ones=False,
+ normalized=False,
+ ).mul(H / Hc)
+ + step
+ )
+
+ coord_un = center_base.add(coord_cell.mul(shift_ratio * step))
+ coord = coord_un.clone()
+ coord[:, 0] = torch.clamp(coord_un[:, 0], min=0, max=W - 1)
+ coord[:, 1] = torch.clamp(coord_un[:, 1], min=0, max=H - 1)
+
+ # descriptor block
+ desc_block = []
+ desc_block.append(self.des_out2(x2))
+ desc_block.append(self.des_out3(x3))
+ desc_block.append(self.des_out4(x))
+ desc_block.append(aware)
+
+ if self.is_test:
+ coord_norm = coord[:, :2].clone()
+ coord_norm[:, 0] = (coord_norm[:, 0] / (float(W - 1) / 2.0)) - 1.0
+ coord_norm[:, 1] = (coord_norm[:, 1] / (float(H - 1) / 2.0)) - 1.0
+ coord_norm = coord_norm.permute(0, 2, 3, 1)
+
+ desc2 = torch.nn.functional.grid_sample(desc_block[0], coord_norm)
+ desc3 = torch.nn.functional.grid_sample(desc_block[1], coord_norm)
+ desc4 = torch.nn.functional.grid_sample(desc_block[2], coord_norm)
+ aware = desc_block[3]
+
+ desc = (
+ torch.mul(desc2, aware[:, 0, :, :])
+ + torch.mul(desc3, aware[:, 1, :, :])
+ + torch.mul(desc4, aware[:, 2, :, :])
+ )
+ desc = desc.div(
+ torch.unsqueeze(torch.norm(desc, p=2, dim=1), 1)
+ ) # Divide by norm to normalize.
+
+ return score, coord, desc
+
+ return score, coord, desc_block
+
+
+class CorrespondenceModule(nn.Module):
+ def __init__(self, match_type="dual_softmax"):
+ super(CorrespondenceModule, self).__init__()
+ self.match_type = match_type
+
+ if self.match_type == "dual_softmax":
+ self.temperature = 0.1
+ else:
+ raise NotImplementedError()
+
+ def forward(self, source_desc, target_desc):
+ b, c, h, w = source_desc.size()
+
+ source_desc = source_desc.div(
+ torch.unsqueeze(torch.norm(source_desc, p=2, dim=1), 1)
+ ).view(b, -1, h * w)
+ target_desc = target_desc.div(
+ torch.unsqueeze(torch.norm(target_desc, p=2, dim=1), 1)
+ ).view(b, -1, h * w)
+
+ if self.match_type == "dual_softmax":
+ sim_mat = (
+ torch.einsum("bcm, bcn -> bmn", source_desc, target_desc)
+ / self.temperature
+ )
+ confidence_matrix = F.softmax(sim_mat, 1) * F.softmax(sim_mat, 2)
+ else:
+ raise NotImplementedError()
+
+ return confidence_matrix
diff --git a/third_party/lanet/test.py b/third_party/lanet/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..d54b60f6669ac02ca16aacd94bb9145050a99a05
--- /dev/null
+++ b/third_party/lanet/test.py
@@ -0,0 +1,91 @@
+import os
+import cv2
+import argparse
+import numpy as np
+import torch
+import torchvision
+
+from torchvision import datasets, transforms
+from torch.autograd import Variable
+from network_v0.model import PointModel
+from datasets.hp_loader import PatchesDataset
+from torch.utils.data import DataLoader
+from evaluation.evaluate import evaluate_keypoint_net
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Testing")
+ parser.add_argument("--device", default=0, type=int, help="which gpu to run on.")
+ parser.add_argument("--test_dir", required=True, type=str, help="Test data path.")
+ opt = parser.parse_args()
+
+ torch.manual_seed(0)
+ use_gpu = torch.cuda.is_available()
+ if use_gpu:
+ torch.cuda.set_device(opt.device)
+
+ # Load data in 320x240
+ hp_dataset_320x240 = PatchesDataset(
+ root_dir=opt.test_dir, use_color=True, output_shape=(320, 240), type="all"
+ )
+ data_loader_320x240 = DataLoader(
+ hp_dataset_320x240,
+ batch_size=1,
+ pin_memory=False,
+ shuffle=False,
+ num_workers=4,
+ worker_init_fn=None,
+ sampler=None,
+ )
+
+ # Load data in 640x480
+ hp_dataset_640x480 = PatchesDataset(
+ root_dir=opt.test_dir, use_color=True, output_shape=(640, 480), type="all"
+ )
+ data_loader_640x480 = DataLoader(
+ hp_dataset_640x480,
+ batch_size=1,
+ pin_memory=False,
+ shuffle=False,
+ num_workers=4,
+ worker_init_fn=None,
+ sampler=None,
+ )
+
+ # Load model
+ model = PointModel(is_test=True)
+ ckpt = torch.load("./checkpoints/PointModel_v0.pth")
+ model.load_state_dict(ckpt["model_state"])
+ model = model.eval()
+ if use_gpu:
+ model = model.cuda()
+
+ print("Evaluating in 320x240, 300 points")
+ rep, loc, c1, c3, c5, mscore = evaluate_keypoint_net(
+ data_loader_320x240, model, output_shape=(320, 240), top_k=300
+ )
+
+ print("Repeatability: {0:.3f}".format(rep))
+ print("Localization Error: {0:.3f}".format(loc))
+ print("H-1 Accuracy: {:.3f}".format(c1))
+ print("H-3 Accuracy: {:.3f}".format(c3))
+ print("H-5 Accuracy: {:.3f}".format(c5))
+ print("Matching Score: {:.3f}".format(mscore))
+ print("\n")
+
+ print("Evaluating in 640x480, 1000 points")
+ rep, loc, c1, c3, c5, mscore = evaluate_keypoint_net(
+ data_loader_640x480, model, output_shape=(640, 480), top_k=1000
+ )
+
+ print("Repeatability: {0:.3f}".format(rep))
+ print("Localization Error: {0:.3f}".format(loc))
+ print("H-1 Accuracy: {:.3f}".format(c1))
+ print("H-3 Accuracy: {:.3f}".format(c3))
+ print("H-5 Accuracy: {:.3f}".format(c5))
+ print("Matching Score: {:.3f}".format(mscore))
+ print("\n")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/imcui/third_party/lanet/train.py b/third_party/lanet/train.py
similarity index 53%
rename from imcui/third_party/lanet/train.py
rename to third_party/lanet/train.py
index dd506f567cfe071e33c674346ee95f933cd461e8..e82900a3b27f8954c65f7bf4127f38a65ac76fff 100644
--- a/imcui/third_party/lanet/train.py
+++ b/third_party/lanet/train.py
@@ -1,129 +1,152 @@
-import os
-import torch
-import torch.optim as optim
-from tqdm import tqdm
-
-from torch.autograd import Variable
-
-from network_v0.model import PointModel
-from loss_function import KeypointLoss
-
-class Trainer(object):
- def __init__(self, config, train_loader=None):
- self.config = config
- # data parameters
- self.train_loader = train_loader
- self.num_train = len(self.train_loader)
-
- # training parameters
- self.max_epoch = config.max_epoch
- self.start_epoch = config.start_epoch
- self.momentum = config.momentum
- self.lr = config.init_lr
- self.lr_factor = config.lr_factor
- self.display = config.display
-
- # misc params
- self.use_gpu = config.use_gpu
- self.random_seed = config.seed
- self.gpu = config.gpu
- self.ckpt_dir = config.ckpt_dir
- self.ckpt_name = '{}-{}'.format(config.ckpt_name, config.seed)
-
- # build model
- self.model = PointModel(is_test=False)
-
- # training on GPU
- if self.use_gpu:
- torch.cuda.set_device(self.gpu)
- self.model.cuda()
-
- print('Number of model parameters: {:,}'.format(sum([p.data.nelement() for p in self.model.parameters()])))
-
- # build loss functional
- self.loss_func = KeypointLoss(config)
-
- # build optimizer and scheduler
- self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
- self.lr_scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=[4, 8], gamma=self.lr_factor)
-
- # resume
- if int(self.config.start_epoch) > 0:
- self.config.start_epoch, self.model, self.optimizer, self.lr_scheduler = self.load_checkpoint(int(self.config.start_epoch), self.model, self.optimizer, self.lr_scheduler)
-
- def train(self):
- print("\nTrain on {} samples".format(self.num_train))
- self.save_checkpoint(0, self.model, self.optimizer, self.lr_scheduler)
- for epoch in range(self.start_epoch, self.max_epoch):
- print("\nEpoch: {}/{} --lr: {:.6f}".format(epoch+1, self.max_epoch, self.lr))
- # train for one epoch
- self.train_one_epoch(epoch)
- if self.lr_scheduler:
- self.lr_scheduler.step()
- self.save_checkpoint(epoch+1, self.model, self.optimizer, self.lr_scheduler)
-
- def train_one_epoch(self, epoch):
- self.model.train()
- for (i, data) in enumerate(tqdm(self.train_loader)):
-
- if self.use_gpu:
- source_img = data['image_aug'].cuda()
- target_img = data['image'].cuda()
- homography = data['homography'].cuda()
-
- source_img = Variable(source_img)
- target_img = Variable(target_img)
- homography = Variable(homography)
-
- # forward propogation
- output = self.model(source_img, target_img, homography)
-
- # compute loss
- loss, loc_loss, desc_loss, score_loss, corres_loss = self.loss_func(output)
-
- # compute gradients and update
- self.optimizer.zero_grad()
- loss.backward()
- self.optimizer.step()
-
- # print training info
- msg_batch = "Epoch:{} Iter:{} lr:{:.4f} "\
- "loc_loss={:.4f} desc_loss={:.4f} score_loss={:.4f} corres_loss={:.4f} "\
- "loss={:.4f} "\
- .format((epoch + 1), i, self.lr, loc_loss.data, desc_loss.data, score_loss.data, corres_loss.data, loss.data)
-
- if((i % self.display) == 0):
- print(msg_batch)
- return
-
- def save_checkpoint(self, epoch, model, optimizer, lr_scheduler):
- filename = self.ckpt_name + '_' + str(epoch) + '.pth'
- torch.save(
- {'epoch': epoch,
- 'model_state': model.state_dict(),
- 'optimizer_state': optimizer.state_dict(),
- 'lr_scheduler': lr_scheduler.state_dict()},
- os.path.join(self.ckpt_dir, filename))
-
- def load_checkpoint(self, epoch, model, optimizer, lr_scheduler):
- filename = self.ckpt_name + '_' + str(epoch) + '.pth'
- ckpt = torch.load(os.path.join(self.ckpt_dir, filename))
- epoch = ckpt['epoch']
- model.load_state_dict(ckpt['model_state'])
- optimizer.load_state_dict(ckpt['optimizer_state'])
- lr_scheduler.load_state_dict(ckpt['lr_scheduler'])
-
- print("[*] Loaded {} checkpoint @ epoch {}".format(filename, ckpt['epoch']))
-
- return epoch, model, optimizer, lr_scheduler
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
+import os
+import torch
+import torch.optim as optim
+from tqdm import tqdm
+
+from torch.autograd import Variable
+
+from network_v0.model import PointModel
+from loss_function import KeypointLoss
+
+
+class Trainer(object):
+ def __init__(self, config, train_loader=None):
+ self.config = config
+ # data parameters
+ self.train_loader = train_loader
+ self.num_train = len(self.train_loader)
+
+ # training parameters
+ self.max_epoch = config.max_epoch
+ self.start_epoch = config.start_epoch
+ self.momentum = config.momentum
+ self.lr = config.init_lr
+ self.lr_factor = config.lr_factor
+ self.display = config.display
+
+ # misc params
+ self.use_gpu = config.use_gpu
+ self.random_seed = config.seed
+ self.gpu = config.gpu
+ self.ckpt_dir = config.ckpt_dir
+ self.ckpt_name = "{}-{}".format(config.ckpt_name, config.seed)
+
+ # build model
+ self.model = PointModel(is_test=False)
+
+ # training on GPU
+ if self.use_gpu:
+ torch.cuda.set_device(self.gpu)
+ self.model.cuda()
+
+ print(
+ "Number of model parameters: {:,}".format(
+ sum([p.data.nelement() for p in self.model.parameters()])
+ )
+ )
+
+ # build loss functional
+ self.loss_func = KeypointLoss(config)
+
+ # build optimizer and scheduler
+ self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
+ self.lr_scheduler = optim.lr_scheduler.MultiStepLR(
+ self.optimizer, milestones=[4, 8], gamma=self.lr_factor
+ )
+
+ # resume
+ if int(self.config.start_epoch) > 0:
+ (
+ self.config.start_epoch,
+ self.model,
+ self.optimizer,
+ self.lr_scheduler,
+ ) = self.load_checkpoint(
+ int(self.config.start_epoch),
+ self.model,
+ self.optimizer,
+ self.lr_scheduler,
+ )
+
+ def train(self):
+ print("\nTrain on {} samples".format(self.num_train))
+ self.save_checkpoint(0, self.model, self.optimizer, self.lr_scheduler)
+ for epoch in range(self.start_epoch, self.max_epoch):
+ print(
+ "\nEpoch: {}/{} --lr: {:.6f}".format(epoch + 1, self.max_epoch, self.lr)
+ )
+ # train for one epoch
+ self.train_one_epoch(epoch)
+ if self.lr_scheduler:
+ self.lr_scheduler.step()
+ self.save_checkpoint(
+ epoch + 1, self.model, self.optimizer, self.lr_scheduler
+ )
+
+ def train_one_epoch(self, epoch):
+ self.model.train()
+ for (i, data) in enumerate(tqdm(self.train_loader)):
+
+ if self.use_gpu:
+ source_img = data["image_aug"].cuda()
+ target_img = data["image"].cuda()
+ homography = data["homography"].cuda()
+
+ source_img = Variable(source_img)
+ target_img = Variable(target_img)
+ homography = Variable(homography)
+
+ # forward propogation
+ output = self.model(source_img, target_img, homography)
+
+ # compute loss
+ loss, loc_loss, desc_loss, score_loss, corres_loss = self.loss_func(output)
+
+ # compute gradients and update
+ self.optimizer.zero_grad()
+ loss.backward()
+ self.optimizer.step()
+
+ # print training info
+ msg_batch = (
+ "Epoch:{} Iter:{} lr:{:.4f} "
+ "loc_loss={:.4f} desc_loss={:.4f} score_loss={:.4f} corres_loss={:.4f} "
+ "loss={:.4f} ".format(
+ (epoch + 1),
+ i,
+ self.lr,
+ loc_loss.data,
+ desc_loss.data,
+ score_loss.data,
+ corres_loss.data,
+ loss.data,
+ )
+ )
+
+ if (i % self.display) == 0:
+ print(msg_batch)
+ return
+
+ def save_checkpoint(self, epoch, model, optimizer, lr_scheduler):
+ filename = self.ckpt_name + "_" + str(epoch) + ".pth"
+ torch.save(
+ {
+ "epoch": epoch,
+ "model_state": model.state_dict(),
+ "optimizer_state": optimizer.state_dict(),
+ "lr_scheduler": lr_scheduler.state_dict(),
+ },
+ os.path.join(self.ckpt_dir, filename),
+ )
+
+ def load_checkpoint(self, epoch, model, optimizer, lr_scheduler):
+ filename = self.ckpt_name + "_" + str(epoch) + ".pth"
+ ckpt = torch.load(os.path.join(self.ckpt_dir, filename))
+ epoch = ckpt["epoch"]
+ model.load_state_dict(ckpt["model_state"])
+ optimizer.load_state_dict(ckpt["optimizer_state"])
+ lr_scheduler.load_state_dict(ckpt["lr_scheduler"])
+
+ print("[*] Loaded {} checkpoint @ epoch {}".format(filename, ckpt["epoch"]))
+
+ return epoch, model, optimizer, lr_scheduler
diff --git a/third_party/mast3r/.gitignore b/third_party/mast3r/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..b6e47617de110dea7ca47e087ff1347cc2646eda
--- /dev/null
+++ b/third_party/mast3r/.gitignore
@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/third_party/mast3r/.gitmodules b/third_party/mast3r/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..d544dca780e738ef30f618ce3cab4810bad806e3
--- /dev/null
+++ b/third_party/mast3r/.gitmodules
@@ -0,0 +1,4 @@
+[submodule "dust3r"]
+ path = dust3r
+ url = https://github.com/naver/dust3r
+ branch = cvpr
diff --git a/third_party/mast3r/CHECKPOINTS_NOTICE b/third_party/mast3r/CHECKPOINTS_NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..040aed77ec78156cb6c1af8da1652e13ade10bcd
--- /dev/null
+++ b/third_party/mast3r/CHECKPOINTS_NOTICE
@@ -0,0 +1,1376 @@
+MASt3R
+Copyright 2024-present NAVER Corp.
+
+This project's checkpoints were trained on datasets with separate license terms.
+Your use of theses checkpoints is subject to the terms and conditions of the following licenses.
+
+===
+pretrained model:
+DUSt3R: DUSt3R_ViTLarge_BaseDecoder_512_dpt
+https://github.com/naver/dust3r
+
+In particular, from the croco training set:
+
+3D_Street_View
+https://github.com/amir32002/3D_Street_View/blob/master/LICENSE
+This dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:
+
+1. That the dataset comes "AS IS", without express or implied warranty. Although every effort has been made to ensure accuracy, we do not accept any responsibility for errors or omissions.
+
+2. That you include a reference to the Dataset in any work that makes use of the dataset. For research papers, cite our publication as listed on our website.
+
+3. That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character.
+
+4. That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
+That all rights not expressly granted to you are reserved by us.
+
+In addition, using the dataset is subject to the following standard terms:
+
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+Indoor Visual Localization datasets (IndoorVL)
+https://challenge.naverlabs.com/kapture/GangnamStation_LICENSE.txt
+https://challenge.naverlabs.com/kapture/HyundaiDepartmentStore_LICENSE.txt
+
+LICENSE.txt
+Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (modified ver.)
+International Public License
+
+By exercising the Licensed Rights (defined below), You accept and agree
+to be bound by the terms and conditions of this Creative Commons
+Attribution-NonCommercial-NoDerivatives 4.0 International Public
+License ("Public License"). To the extent this Public License may be
+interpreted as a contract, You are granted the Licensed Rights in
+consideration of Your acceptance of these terms and conditions, and the
+Licensor grants You such rights in consideration of benefits the
+Licensor receives from making the Licensed Material available under
+these terms and conditions.
+
+
+Section 1 -- Definitions.
+
+ a. Adapted Material means material subject to Copyright and Similar
+ Rights that is derived from or based upon the Licensed Material
+ and in which the Licensed Material is translated, altered,
+ arranged, transformed, or otherwise modified in a manner requiring
+ permission under the Copyright and Similar Rights held by the
+ Licensor. For purposes of this Public License, where the Licensed
+ Material is a musical work, performance, or sound recording,
+ Adapted Material is always produced where the Licensed Material is
+ synched in timed relation with a moving image.
+
+ b. Copyright and Similar Rights means copyright and/or similar rights
+ closely related to copyright including, without limitation,
+ performance, broadcast, sound recording, and Sui Generis Database
+ Rights, without regard to how the rights are labeled or
+ categorized. For purposes of this Public License, the rights
+ specified in Section 2(b)(1)-(2) are not Copyright and Similar
+ Rights.
+
+ c. Effective Technological Measures means those measures that, in the
+ absence of proper authority, may not be circumvented under laws
+ fulfilling obligations under Article 11 of the WIPO Copyright
+ Treaty adopted on December 20, 1996, and/or similar international
+ agreements.
+
+ d. Exceptions and Limitations means fair use, fair dealing, and/or
+ any other exception or limitation to Copyright and Similar Rights
+ that applies to Your use of the Licensed Material.
+
+ e. Licensed Material means the artistic or literary work, database,
+ or other material to which the Licensor applied this Public
+ License.
+
+ f. Licensed Rights means the rights granted to You subject to the
+ terms and conditions of this Public License, which are limited to
+ all Copyright and Similar Rights that apply to Your use of the
+ Licensed Material and that the Licensor has authority to license.
+
+ g. Licensor means the individual(s) or entity(ies) granting rights
+ under this Public License.
+
+ h. NonCommercial means not primarily intended for or directed towards
+ commercial advantage or monetary compensation. For purposes of
+ this Public License, the exchange of the Licensed Material for
+ other material subject to Copyright and Similar Rights by digital
+ file-sharing or similar means is NonCommercial provided there is
+ no payment of monetary compensation in connection with the
+ exchange.
+
+ i. Share means to provide material to the public by any means or
+ process that requires permission under the Licensed Rights, such
+ as reproduction, public display, public performance, distribution,
+ dissemination, communication, or importation, and to make material
+ available to the public including in ways that members of the
+ public may access the material from a place and at a time
+ individually chosen by them.
+
+ j. Sui Generis Database Rights means rights other than copyright
+ resulting from Directive 96/9/EC of the European Parliament and of
+ the Council of 11 March 1996 on the legal protection of databases,
+ as amended and/or succeeded, as well as other essentially
+ equivalent rights anywhere in the world.
+
+ k. You means the individual or entity exercising the Licensed Rights
+ under this Public License. Your has a corresponding meaning.
+
+ l. Research purpose means to publish research achievements in a research paper
+
+
+Section 2 -- Scope.
+
+ a. License grant.
+
+ 1. Subject to the terms and conditions of this Public License,
+ the Licensor hereby grants You a worldwide, royalty-free,
+ non-sublicensable, non-exclusive, irrevocable license to
+ exercise the Licensed Rights in the Licensed Material to:
+
+ a. reproduce and Share the Licensed Material, in whole or
+ in part, for NonCommercial purposes only; and
+
+ b. produce and reproduce, but not Share, Adapted Material
+ for NonCommercial purposes only.
+
+ c. reproduce and share the Adapted Matrerial, in part,
+ for Research purposes only.
+
+ 2. Exceptions and Limitations. For the avoidance of doubt, where
+ Exceptions and Limitations apply to Your use, this Public
+ License does not apply, and You do not need to comply with
+ its terms and conditions.
+
+ 3. Term. The term of this Public License is specified in Section
+ 6(a).
+
+ 4. Media and formats; technical modifications allowed. The
+ Licensor authorizes You to exercise the Licensed Rights in
+ all media and formats whether now known or hereafter created,
+ and to make technical modifications necessary to do so. The
+ Licensor waives and/or agrees not to assert any right or
+ authority to forbid You from making technical modifications
+ necessary to exercise the Licensed Rights, including
+ technical modifications necessary to circumvent Effective
+ Technological Measures. For purposes of this Public License,
+ simply making modifications authorized by this Section 2(a)
+ (4) never produces Adapted Material.
+
+ 5. Downstream recipients.
+
+ a. Offer from the Licensor -- Licensed Material. Every
+ recipient of the Licensed Material automatically
+ receives an offer from the Licensor to exercise the
+ Licensed Rights under the terms and conditions of this
+ Public License.
+
+ b. No downstream restrictions. You may not offer or impose
+ any additional or different terms or conditions on, or
+ apply any Effective Technological Measures to, the
+ Licensed Material if doing so restricts exercise of the
+ Licensed Rights by any recipient of the Licensed
+ Material.
+
+ 6. No endorsement. Nothing in this Public License constitutes or
+ may be construed as permission to assert or imply that You
+ are, or that Your use of the Licensed Material is, connected
+ with, or sponsored, endorsed, or granted official status by,
+ the Licensor or others designated to receive attribution as
+ provided in Section 3(a)(1)(A)(i).
+
+ b. Other rights.
+
+ 1. Moral rights, such as the right of integrity, are not
+ licensed under this Public License, nor are publicity,
+ privacy, and/or other similar personality rights; however, to
+ the extent possible, the Licensor waives and/or agrees not to
+ assert any such rights held by the Licensor to the limited
+ extent necessary to allow You to exercise the Licensed
+ Rights, but not otherwise.
+
+ 2. Patent and trademark rights are not licensed under this
+ Public License.
+
+ 3. To the extent possible, the Licensor waives any right to
+ collect royalties from You for the exercise of the Licensed
+ Rights, whether directly or through a collecting society
+ under any voluntary or waivable statutory or compulsory
+ licensing scheme. In all other cases the Licensor expressly
+ reserves any right to collect such royalties, including when
+ the Licensed Material is used other than for NonCommercial
+ purposes.
+
+
+Section 3 -- License Conditions.
+
+Your exercise of the Licensed Rights is expressly made subject to the
+following conditions.
+
+ a. Attribution.
+
+ 1. If You Share the Licensed Material(including in a research paper),
+ You must:
+
+ a. retain the following if it is supplied by the Licensor
+ with the Licensed Material:
+
+ i. identification of the creator(s) of the Licensed
+ Material and any others designated to receive
+ attribution, in any reasonable manner requested by
+ the Licensor (including by pseudonym if
+ designated);
+
+ ii. a copyright notice;
+
+ iii. a notice that refers to this Public License;
+
+ iv. a notice that refers to the disclaimer of
+ warranties;
+
+ v. a URI or hyperlink to the Licensed Material to the
+ extent reasonably practicable;
+
+ b. indicate if You modified the Licensed Material and
+ retain an indication of any previous modifications; and
+
+ c. indicate the Licensed Material is licensed under this
+ Public License, and include the text of, or the URI or
+ hyperlink to, this Public License.
+
+ For the avoidance of doubt, You do not have permission under
+ this Public License to Share Adapted Material.
+
+ 2. You may satisfy the conditions in Section 3(a)(1) in any
+ reasonable manner based on the medium, means, and context in
+ which You Share the Licensed Material. For example, it may be
+ reasonable to satisfy the conditions by providing a URI or
+ hyperlink to a resource that includes the required
+ information.
+
+ 3. If requested by the Licensor, You must remove any of the
+ information required by Section 3(a)(1)(A) to the extent
+ reasonably practicable.
+
+
+Section 4 -- Sui Generis Database Rights.
+
+Where the Licensed Rights include Sui Generis Database Rights that
+apply to Your use of the Licensed Material:
+
+ a. for the avoidance of doubt, Section 2(a)(1) grants You the right
+ to extract, reuse, reproduce, and Share all or a substantial
+ portion of the contents of the database for NonCommercial purposes
+ only and provided You do not Share Adapted Material;
+
+ b. if You include all or a substantial portion of the database
+ contents in a database in which You have Sui Generis Database
+ Rights, then the database in which You have Sui Generis Database
+ Rights (but not its individual contents) is Adapted Material; and
+
+ c. You must comply with the conditions in Section 3(a) if You Share
+ all or a substantial portion of the contents of the database.
+
+For the avoidance of doubt, this Section 4 supplements and does not
+replace Your obligations under this Public License where the Licensed
+Rights include other Copyright and Similar Rights.
+
+
+Section 5 -- Disclaimer of Warranties and Limitation of Liability.
+
+ a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
+ EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
+ AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF
+ ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS,
+ IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION,
+ WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR
+ PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS,
+ ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT
+ KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT
+ ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
+
+ b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE
+ TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION,
+ NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT,
+ INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES,
+ COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR
+ USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN
+ ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR
+ DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR
+ IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
+
+ c. The disclaimer of warranties and limitation of liability provided
+ above shall be interpreted in a manner that, to the extent
+ possible, most closely approximates an absolute disclaimer and
+ waiver of all liability.
+
+
+Section 6 -- Term and Termination.
+
+ a. This Public License applies for the term of the Copyright and
+ Similar Rights licensed here. However, if You fail to comply with
+ this Public License, then Your rights under this Public License
+ terminate automatically.
+
+ b. Where Your right to use the Licensed Material has terminated under
+ Section 6(a), it reinstates:
+
+ 1. automatically as of the date the violation is cured, provided
+ it is cured within 30 days of Your discovery of the
+ violation; or
+
+ 2. upon express reinstatement by the Licensor.
+
+ For the avoidance of doubt, this Section 6(b) does not affect any
+ right the Licensor may have to seek remedies for Your violations
+ of this Public License.
+
+ c. For the avoidance of doubt, the Licensor may also offer the
+ Licensed Material under separate terms or conditions or stop
+ distributing the Licensed Material at any time; however, doing so
+ will not terminate this Public License.
+
+ d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
+ License.
+
+
+Section 7 -- Other Terms and Conditions.
+
+ a. The Licensor shall not be bound by any additional or different
+ terms or conditions communicated by You unless expressly agreed.
+
+ b. Any arrangements, understandings, or agreements regarding the
+ Licensed Material not stated herein are separate from and
+ independent of the terms and conditions of this Public License.
+
+
+Section 8 -- Interpretation.
+
+ a. For the avoidance of doubt, this Public License does not, and
+ shall not be interpreted to, reduce, limit, restrict, or impose
+ conditions on any use of the Licensed Material that could lawfully
+ be made without permission under this Public License.
+
+ b. To the extent possible, if any provision of this Public License is
+ deemed unenforceable, it shall be automatically reformed to the
+ minimum extent necessary to make it enforceable. If the provision
+ cannot be reformed, it shall be severed from this Public License
+ without affecting the enforceability of the remaining terms and
+ conditions.
+
+ c. No term or condition of this Public License will be waived and no
+ failure to comply consented to unless expressly agreed to by the
+ Licensor.
+
+ d. Nothing in this Public License constitutes or may be interpreted
+ as a limitation upon, or waiver of, any privileges and immunities
+ that apply to the Licensor or You, including from the legal
+ processes of any jurisdiction or authority.
+
+===
+CO3Dv2
+
+Creative Commons Attribution-NonCommercial 4.0 International Public
+License
+
+By exercising the Licensed Rights (defined below), You accept and agree
+to be bound by the terms and conditions of this Creative Commons
+Attribution-NonCommercial 4.0 International Public License ("Public
+License"). To the extent this Public License may be interpreted as a
+contract, You are granted the Licensed Rights in consideration of Your
+acceptance of these terms and conditions, and the Licensor grants You
+such rights in consideration of benefits the Licensor receives from
+making the Licensed Material available under these terms and
+conditions.
+
+Section 1 -- Definitions.
+
+ a. Adapted Material means material subject to Copyright and Similar
+ Rights that is derived from or based upon the Licensed Material
+ and in which the Licensed Material is translated, altered,
+ arranged, transformed, or otherwise modified in a manner requiring
+ permission under the Copyright and Similar Rights held by the
+ Licensor. For purposes of this Public License, where the Licensed
+ Material is a musical work, performance, or sound recording,
+ Adapted Material is always produced where the Licensed Material is
+ synched in timed relation with a moving image.
+
+ b. Adapter's License means the license You apply to Your Copyright
+ and Similar Rights in Your contributions to Adapted Material in
+ accordance with the terms and conditions of this Public License.
+
+ c. Copyright and Similar Rights means copyright and/or similar rights
+ closely related to copyright including, without limitation,
+ performance, broadcast, sound recording, and Sui Generis Database
+ Rights, without regard to how the rights are labeled or
+ categorized. For purposes of this Public License, the rights
+ specified in Section 2(b)(1)-(2) are not Copyright and Similar
+ Rights.
+ d. Effective Technological Measures means those measures that, in the
+ absence of proper authority, may not be circumvented under laws
+ fulfilling obligations under Article 11 of the WIPO Copyright
+ Treaty adopted on December 20, 1996, and/or similar international
+ agreements.
+
+ e. Exceptions and Limitations means fair use, fair dealing, and/or
+ any other exception or limitation to Copyright and Similar Rights
+ that applies to Your use of the Licensed Material.
+
+ f. Licensed Material means the artistic or literary work, database,
+ or other material to which the Licensor applied this Public
+ License.
+
+ g. Licensed Rights means the rights granted to You subject to the
+ terms and conditions of this Public License, which are limited to
+ all Copyright and Similar Rights that apply to Your use of the
+ Licensed Material and that the Licensor has authority to license.
+
+ h. Licensor means the individual(s) or entity(ies) granting rights
+ under this Public License.
+
+ i. NonCommercial means not primarily intended for or directed towards
+ commercial advantage or monetary compensation. For purposes of
+ this Public License, the exchange of the Licensed Material for
+ other material subject to Copyright and Similar Rights by digital
+ file-sharing or similar means is NonCommercial provided there is
+ no payment of monetary compensation in connection with the
+ exchange.
+
+ j. Share means to provide material to the public by any means or
+ process that requires permission under the Licensed Rights, such
+ as reproduction, public display, public performance, distribution,
+ dissemination, communication, or importation, and to make material
+ available to the public including in ways that members of the
+ public may access the material from a place and at a time
+ individually chosen by them.
+
+ k. Sui Generis Database Rights means rights other than copyright
+ resulting from Directive 96/9/EC of the European Parliament and of
+ the Council of 11 March 1996 on the legal protection of databases,
+ as amended and/or succeeded, as well as other essentially
+ equivalent rights anywhere in the world.
+
+ l. You means the individual or entity exercising the Licensed Rights
+ under this Public License. Your has a corresponding meaning.
+
+Section 2 -- Scope.
+
+ a. License grant.
+
+ 1. Subject to the terms and conditions of this Public License,
+ the Licensor hereby grants You a worldwide, royalty-free,
+ non-sublicensable, non-exclusive, irrevocable license to
+ exercise the Licensed Rights in the Licensed Material to:
+
+ a. reproduce and Share the Licensed Material, in whole or
+ in part, for NonCommercial purposes only; and
+
+ b. produce, reproduce, and Share Adapted Material for
+ NonCommercial purposes only.
+
+ 2. Exceptions and Limitations. For the avoidance of doubt, where
+ Exceptions and Limitations apply to Your use, this Public
+ License does not apply, and You do not need to comply with
+ its terms and conditions.
+
+ 3. Term. The term of this Public License is specified in Section
+ 6(a).
+
+ 4. Media and formats; technical modifications allowed. The
+ Licensor authorizes You to exercise the Licensed Rights in
+ all media and formats whether now known or hereafter created,
+ and to make technical modifications necessary to do so. The
+ Licensor waives and/or agrees not to assert any right or
+ authority to forbid You from making technical modifications
+ necessary to exercise the Licensed Rights, including
+ technical modifications necessary to circumvent Effective
+ Technological Measures. For purposes of this Public License,
+ simply making modifications authorized by this Section 2(a)
+ (4) never produces Adapted Material.
+
+ 5. Downstream recipients.
+
+ a. Offer from the Licensor -- Licensed Material. Every
+ recipient of the Licensed Material automatically
+ receives an offer from the Licensor to exercise the
+ Licensed Rights under the terms and conditions of this
+ Public License.
+
+ b. No downstream restrictions. You may not offer or impose
+ any additional or different terms or conditions on, or
+ apply any Effective Technological Measures to, the
+ Licensed Material if doing so restricts exercise of the
+ Licensed Rights by any recipient of the Licensed
+ Material.
+
+ 6. No endorsement. Nothing in this Public License constitutes or
+ may be construed as permission to assert or imply that You
+ are, or that Your use of the Licensed Material is, connected
+ with, or sponsored, endorsed, or granted official status by,
+ the Licensor or others designated to receive attribution as
+ provided in Section 3(a)(1)(A)(i).
+
+ b. Other rights.
+
+ 1. Moral rights, such as the right of integrity, are not
+ licensed under this Public License, nor are publicity,
+ privacy, and/or other similar personality rights; however, to
+ the extent possible, the Licensor waives and/or agrees not to
+ assert any such rights held by the Licensor to the limited
+ extent necessary to allow You to exercise the Licensed
+ Rights, but not otherwise.
+
+ 2. Patent and trademark rights are not licensed under this
+ Public License.
+
+ 3. To the extent possible, the Licensor waives any right to
+ collect royalties from You for the exercise of the Licensed
+ Rights, whether directly or through a collecting society
+ under any voluntary or waivable statutory or compulsory
+ licensing scheme. In all other cases the Licensor expressly
+ reserves any right to collect such royalties, including when
+ the Licensed Material is used other than for NonCommercial
+ purposes.
+
+Section 3 -- License Conditions.
+
+Your exercise of the Licensed Rights is expressly made subject to the
+following conditions.
+
+ a. Attribution.
+
+ 1. If You Share the Licensed Material (including in modified
+ form), You must:
+
+ a. retain the following if it is supplied by the Licensor
+ with the Licensed Material:
+
+ i. identification of the creator(s) of the Licensed
+ Material and any others designated to receive
+ attribution, in any reasonable manner requested by
+ the Licensor (including by pseudonym if
+ designated);
+
+ ii. a copyright notice;
+
+ iii. a notice that refers to this Public License;
+
+ iv. a notice that refers to the disclaimer of
+ warranties;
+
+ v. a URI or hyperlink to the Licensed Material to the
+ extent reasonably practicable;
+
+ b. indicate if You modified the Licensed Material and
+ retain an indication of any previous modifications; and
+
+ c. indicate the Licensed Material is licensed under this
+ Public License, and include the text of, or the URI or
+ hyperlink to, this Public License.
+
+ 2. You may satisfy the conditions in Section 3(a)(1) in any
+ reasonable manner based on the medium, means, and context in
+ which You Share the Licensed Material. For example, it may be
+ reasonable to satisfy the conditions by providing a URI or
+ hyperlink to a resource that includes the required
+ information.
+
+ 3. If requested by the Licensor, You must remove any of the
+ information required by Section 3(a)(1)(A) to the extent
+ reasonably practicable.
+
+ 4. If You Share Adapted Material You produce, the Adapter's
+ License You apply must not prevent recipients of the Adapted
+ Material from complying with this Public License.
+
+Section 4 -- Sui Generis Database Rights.
+
+Where the Licensed Rights include Sui Generis Database Rights that
+apply to Your use of the Licensed Material:
+
+ a. for the avoidance of doubt, Section 2(a)(1) grants You the right
+ to extract, reuse, reproduce, and Share all or a substantial
+ portion of the contents of the database for NonCommercial purposes
+ only;
+
+ b. if You include all or a substantial portion of the database
+ contents in a database in which You have Sui Generis Database
+ Rights, then the database in which You have Sui Generis Database
+ Rights (but not its individual contents) is Adapted Material; and
+
+ c. You must comply with the conditions in Section 3(a) if You Share
+ all or a substantial portion of the contents of the database.
+
+For the avoidance of doubt, this Section 4 supplements and does not
+replace Your obligations under this Public License where the Licensed
+Rights include other Copyright and Similar Rights.
+
+Section 5 -- Disclaimer of Warranties and Limitation of Liability.
+
+ a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
+ EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
+ AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF
+ ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS,
+ IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION,
+ WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR
+ PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS,
+ ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT
+ KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT
+ ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
+
+ b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE
+ TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION,
+ NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT,
+ INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES,
+ COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR
+ USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN
+ ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR
+ DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR
+ IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
+
+ c. The disclaimer of warranties and limitation of liability provided
+ above shall be interpreted in a manner that, to the extent
+ possible, most closely approximates an absolute disclaimer and
+ waiver of all liability.
+
+Section 6 -- Term and Termination.
+
+ a. This Public License applies for the term of the Copyright and
+ Similar Rights licensed here. However, if You fail to comply with
+ this Public License, then Your rights under this Public License
+ terminate automatically.
+
+ b. Where Your right to use the Licensed Material has terminated under
+ Section 6(a), it reinstates:
+
+ 1. automatically as of the date the violation is cured, provided
+ it is cured within 30 days of Your discovery of the
+ violation; or
+
+ 2. upon express reinstatement by the Licensor.
+
+ For the avoidance of doubt, this Section 6(b) does not affect any
+ right the Licensor may have to seek remedies for Your violations
+ of this Public License.
+
+ c. For the avoidance of doubt, the Licensor may also offer the
+ Licensed Material under separate terms or conditions or stop
+ distributing the Licensed Material at any time; however, doing so
+ will not terminate this Public License.
+
+ d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
+ License.
+
+Section 7 -- Other Terms and Conditions.
+
+ a. The Licensor shall not be bound by any additional or different
+ terms or conditions communicated by You unless expressly agreed.
+
+ b. Any arrangements, understandings, or agreements regarding the
+ Licensed Material not stated herein are separate from and
+ independent of the terms and conditions of this Public License.
+
+Section 8 -- Interpretation.
+
+ a. For the avoidance of doubt, this Public License does not, and
+ shall not be interpreted to, reduce, limit, restrict, or impose
+ conditions on any use of the Licensed Material that could lawfully
+ be made without permission under this Public License.
+
+ b. To the extent possible, if any provision of this Public License is
+ deemed unenforceable, it shall be automatically reformed to the
+ minimum extent necessary to make it enforceable. If the provision
+ cannot be reformed, it shall be severed from this Public License
+ without affecting the enforceability of the remaining terms and
+ conditions.
+
+ c. No term or condition of this Public License will be waived and no
+ failure to comply consented to unless expressly agreed to by the
+ Licensor.
+
+ d. Nothing in this Public License constitutes or may be interpreted
+ as a limitation upon, or waiver of, any privileges and immunities
+ that apply to the Licensor or You, including from the legal
+ processes of any jurisdiction or authority.
+
+===
+ARKitScenes
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0: https://creativecommons.org/licenses/by-nc-sa/4.0/
+
+===
+ScanNet++
+https://kaldir.vc.in.tum.de/scannetpp/static/scannetpp-terms-of-use.pdf
+
+===
+BlendedMVS
+Creative Commons Attribution 4.0 International: http://creativecommons.org/licenses/by/4.0/
+
+===
+Habitat-Sim
+HM3D
+https://matterport.com/fr/legal/matterport-end-user-license-agreement-academic-use-model-data
+
+ScanNet
+https://kaldir.vc.in.tum.de/scannet/ScanNet_TOS.pdf
+
+Replica
+Before Facebook Technologies, LLC (“FB”) is able to offer you (“Researcher” or
+“You”) access to the Replica Dataset (the “Dataset”), please read the following
+agreement (“Agreement”).
+
+By accessing, and in exchange for receiving permission to access, the Dataset,
+Researcher hereby agrees to the following terms and conditions:
+1. Researcher may use, modify, improve and/or publish the Dataset only in
+connection with a research or educational purpose that is non-commercial or
+not-for-profit in nature, and not for any other purpose.
+1. Researcher may provide research associates and colleagues with access to the
+Dataset provided that they first agree to be bound by these terms and
+conditions.
+1. Researcher may use the Dataset in the scope of their employment at a
+for-profit or commercial entity provided that Researcher complies with Section 1
+of this Agreement. If Researcher is employed by a for-profit or commercial
+entity, Researcher's employer shall also be bound by these terms and conditions,
+and Researcher hereby represents that they are fully authorized to enter into
+this agreement on behalf of such employer.
+1. THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL FB OR ANY
+CONTRIBUTOR BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.
+1. The law of the State of California shall apply to all disputes related to
+this Dataset.
+
+ReplicaCAD
+Creative Commons Attribution 4.0 International (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/
+
+habitat-sim
+MIT License
+
+Copyright (c) Meta Platforms, Inc. and its affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+===
+MegaDepth
+MIT License
+
+Copyright (c) 2018 Zhengqi Li
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+===
+StaticThings3D
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+===
+WildRGB-D
+https://github.com/wildrgbd/wildrgbd/
+MIT License
+
+Copyright (c) 2024 rowdataset
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+===
+TartanAir
+Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0/
+
+===
+UnrealStereo4K
+https://github.com/fabiotosi92/SMD-Nets
+MIT License
+
+Copyright (c) 2021 Fabio Tosi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+===
+Virtual KITTI 2
+Creative Commons Attribution-NonCommercial-ShareAlike 3.0: http://creativecommons.org/licenses/by-nc-sa/3.0/legalcode
+
+===
+DL3DV
+DL3DV-10K Term of use and Creative Commons Attribution-NonCommercial 4.0 International License.
+
+Terms of Use
+
+Researcher shall use the Dataset only for non-commercial research and educational purposes.
+DL3DV-10K organization makes no representations or warranties regarding the dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose.
+Researcher accepts full responsibility for his/her/their use of the Dataset and shall defend and indemnify DL3DV-10K organization, including its members, employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Dataset, including but not limited to Researcher's use of any copies of copyrighted 3D models that he/she/they may create from the dataset.
+Researcher may provide research associates and colleagues with access to the Dataset, after receiving entity has also agreed to and signed these terms and conditions. Sharing the data otherwise is strictly prohibited.
+Following General Data Protection Regulation, Researcher must ensure that they can delete all person-specific data upon request.
+DL3DV-10K organization reserves the right to terminate Researcher's access to the Dataset at any time.
+If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he/she/they is/are fully authorized to enter into this agreement on behalf of such employer.
+The law of the Indiana State shall apply to all disputes under this agreement.
+
+Creative Commons Attribution-NonCommercial 4.0 International Public License
+
+By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions.
+
+Section 1 -- Definitions.
+
+a. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
+
+b. Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License.
+
+c. Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights. d. Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
+
+e. Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
+
+f. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
+
+g. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
+
+h. Licensor means the individual(s) or entity(ies) granting rights under this Public License.
+
+i. NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
+
+j. Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
+
+k. Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
+
+l. You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
+
+Section 2 -- Scope.
+
+a. License grant.
+
+ 1. Subject to the terms and conditions of this Public License,
+ the Licensor hereby grants You a worldwide, royalty-free,
+ non-sublicensable, non-exclusive, irrevocable license to
+ exercise the Licensed Rights in the Licensed Material to:
+
+ a. reproduce and Share the Licensed Material, in whole or
+ in part, for NonCommercial purposes only; and
+
+ b. produce, reproduce, and Share Adapted Material for
+ NonCommercial purposes only.
+
+ 2. Exceptions and Limitations. For the avoidance of doubt, where
+ Exceptions and Limitations apply to Your use, this Public
+ License does not apply, and You do not need to comply with
+ its terms and conditions.
+
+ 3. Term. The term of this Public License is specified in Section
+ 6(a).
+
+ 4. Media and formats; technical modifications allowed. The
+ Licensor authorizes You to exercise the Licensed Rights in
+ all media and formats whether now known or hereafter created,
+ and to make technical modifications necessary to do so. The
+ Licensor waives and/or agrees not to assert any right or
+ authority to forbid You from making technical modifications
+ necessary to exercise the Licensed Rights, including
+ technical modifications necessary to circumvent Effective
+ Technological Measures. For purposes of this Public License,
+ simply making modifications authorized by this Section 2(a)
+ (4) never produces Adapted Material.
+
+ 5. Downstream recipients.
+
+ a. Offer from the Licensor -- Licensed Material. Every
+ recipient of the Licensed Material automatically
+ receives an offer from the Licensor to exercise the
+ Licensed Rights under the terms and conditions of this
+ Public License.
+
+ b. No downstream restrictions. You may not offer or impose
+ any additional or different terms or conditions on, or
+ apply any Effective Technological Measures to, the
+ Licensed Material if doing so restricts exercise of the
+ Licensed Rights by any recipient of the Licensed
+ Material.
+
+ 6. No endorsement. Nothing in this Public License constitutes or
+ may be construed as permission to assert or imply that You
+ are, or that Your use of the Licensed Material is, connected
+ with, or sponsored, endorsed, or granted official status by,
+ the Licensor or others designated to receive attribution as
+ provided in Section 3(a)(1)(A)(i).
+
+b. Other rights.
+
+ 1. Moral rights, such as the right of integrity, are not
+ licensed under this Public License, nor are publicity,
+ privacy, and/or other similar personality rights; however, to
+ the extent possible, the Licensor waives and/or agrees not to
+ assert any such rights held by the Licensor to the limited
+ extent necessary to allow You to exercise the Licensed
+ Rights, but not otherwise.
+
+ 2. Patent and trademark rights are not licensed under this
+ Public License.
+
+ 3. To the extent possible, the Licensor waives any right to
+ collect royalties from You for the exercise of the Licensed
+ Rights, whether directly or through a collecting society
+ under any voluntary or waivable statutory or compulsory
+ licensing scheme. In all other cases the Licensor expressly
+ reserves any right to collect such royalties, including when
+ the Licensed Material is used other than for NonCommercial
+ purposes.
+
+Section 3 -- License Conditions.
+
+Your exercise of the Licensed Rights is expressly made subject to the following conditions.
+
+a. Attribution.
+
+ 1. If You Share the Licensed Material (including in modified
+ form), You must:
+
+ a. retain the following if it is supplied by the Licensor
+ with the Licensed Material:
+
+ i. identification of the creator(s) of the Licensed
+ Material and any others designated to receive
+ attribution, in any reasonable manner requested by
+ the Licensor (including by pseudonym if
+ designated);
+
+ ii. a copyright notice;
+
+ iii. a notice that refers to this Public License;
+
+ iv. a notice that refers to the disclaimer of
+ warranties;
+
+ v. a URI or hyperlink to the Licensed Material to the
+ extent reasonably practicable;
+
+ b. indicate if You modified the Licensed Material and
+ retain an indication of any previous modifications; and
+
+ c. indicate the Licensed Material is licensed under this
+ Public License, and include the text of, or the URI or
+ hyperlink to, this Public License.
+
+ 2. You may satisfy the conditions in Section 3(a)(1) in any
+ reasonable manner based on the medium, means, and context in
+ which You Share the Licensed Material. For example, it may be
+ reasonable to satisfy the conditions by providing a URI or
+ hyperlink to a resource that includes the required
+ information.
+
+ 3. If requested by the Licensor, You must remove any of the
+ information required by Section 3(a)(1)(A) to the extent
+ reasonably practicable.
+
+ 4. If You Share Adapted Material You produce, the Adapter's
+ License You apply must not prevent recipients of the Adapted
+ Material from complying with this Public License.
+
+Section 4 -- Sui Generis Database Rights.
+
+Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
+
+a. for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
+
+b. if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
+
+c. You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.
+
+For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
+
+Section 5 -- Disclaimer of Warranties and Limitation of Liability.
+
+a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS, IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION, WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS, ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
+
+b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION, NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT, INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES, COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
+
+c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
+
+Section 6 -- Term and Termination.
+
+a. This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
+
+b. Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
+
+ 1. automatically as of the date the violation is cured, provided
+ it is cured within 30 days of Your discovery of the
+ violation; or
+
+ 2. upon express reinstatement by the Licensor.
+
+ For the avoidance of doubt, this Section 6(b) does not affect any
+ right the Licensor may have to seek remedies for Your violations
+ of this Public License.
+
+c. For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
+
+d. Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
+
+Section 7 -- Other Terms and Conditions.
+
+a. The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
+
+b. Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.
+
+Section 8 -- Interpretation.
+
+a. For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
+
+b. To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
+
+c. No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
+
+d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
+
+===
+Niantic Map Free Relocalization Dataset License Agreement
+This Niantic Map Free Relocalization Dataset License Agreement ("Agreement") is an agreement between you and Niantic, Inc. (“Niantic” or “we”). By downloading or otherwise using Niantic’s Map-Free Relocalization dataset or dataset-derived materials (collectively, the "Dataset") you agree to:
+
+1. Purpose and Restrictions. You may only use the Dataset only for non-commercial purposes, such as academic research at educational and not-for-profit research institutions, teaching, public demonstrations, and personal experimentation. Non-commercial use expressly excludes any profit-making or commercial activities, including without limitation sale, license, manufacture or development of commercial products, use in commercially-sponsored research, use at a laboratory or other facility owned or controlled (whether in whole or in part) by a commercial entity, provision of consulting service, use for or on behalf of any commercial entity, and use in consulting service, use for or on behalf of any commercial entity, use in research where a commercial party obtains rights to research results or any other benefit. Notwithstanding the foregoing restrictions, you can use this Dataset for publishing comparison results for academic papers, including retraining your models on this Dataset.
+
+2. License. Subject to this Agreement, Niantic grants you a non-exclusive, non-transferable, non-sublicensable right to download and use the Dataset for the purpose stated in Section 1 of this Agreement. All rights not expressly granted to you in this Agreement are reserved.
+
+3. Condition of Use. You must not use the Dataset in a way that could diminish, tarnish, or in any way harm Niantic’s reputation or image.
+
+4. No Warranties. The Dataset comes “as is”, and you will use it at your own risk. Niantic makes no representations or warranties regarding the Dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose. Neither Niantic nor any contributor to the Dataset will be liable for any damages related to the Dataset or this Agreement, including direct, indirect, special, consequential or incidental damages, to the maximum extent the law permits, no matter what legal theory they are based on. We are not obligated to (and will not) provide technical support for the Dataset.
+
+5. Indemnity. You accept full responsibility for your use of the Dataset and shall defend and indemnify Niantic, including its employees, officers and agents, against any and all claims arising from your use of the Dataset.
+
+6. Removal. Niantic reserves the right to remove access to the Dataset at any time without cause. If you have downloaded a copy of the Dataset prior to such removal, you may use such a copy subject to this Agreement, but you may not distribute your copy.
+
+7. Termination. This Agreement will terminate immediately upon your commercial use of the Dataset.
+
+8. Authorized Representative. If you are employed by a for-profit, commercial entity, your employer shall also be bound by the terms and conditions of this Agreement, and you hereby represent that you are fully authorized to enter into this Agreement on behalf of such employer.
+
+9. Survivability. Sections 2, 4, 5, 6, 7, 8, 9, and 10 of this Agreement survive the termination of this Agreement.
+
+10. Misc. This Agreement is governed and construed in all respects in accordance with the laws of the State of California, USA without regard to conflicts of law. If any provision of this Agreement is deemed unenforceable or contrary to law, the rest of this Agreement shall remain in full effect and enforceable. If you do not agree to this Agreement, do not download or use the Dataset. The Dataset is protected by copyright and other intellectual property laws and is licensed, not sold.
+
+===
+NVIDIA Source Code License for SegFormer
+
+1. Definitions
+
+“Licensor” means any person or entity that distributes its Work.
+
+“Software” means the original work of authorship made available under this License.
+
+“Work” means the Software and any additions to or derivative works of the Software that are made available under
+this License.
+
+The terms “reproduce,” “reproduction,” “derivative works,” and “distribution” have the meaning as provided under
+U.S. copyright law; provided, however, that for the purposes of this License, derivative works shall not include
+works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work.
+
+Works, including the Software, are “made available” under this License by including in or with the Work either
+(a) a copyright notice referencing the applicability of this License to the Work, or (b) a copy of this License.
+
+2. License Grant
+
+2.1 Copyright Grant. Subject to the terms and conditions of this License, each Licensor grants to you a perpetual,
+worldwide, non-exclusive, royalty-free, copyright license to reproduce, prepare derivative works of, publicly
+display, publicly perform, sublicense and distribute its Work and any resulting derivative works in any form.
+
+3. Limitations
+
+3.1 Redistribution. You may reproduce or distribute the Work only if (a) you do so under this License, (b) you
+include a complete copy of this License with your distribution, and (c) you retain without modification any
+copyright, patent, trademark, or attribution notices that are present in the Work.
+
+3.2 Derivative Works. You may specify that additional or different terms apply to the use, reproduction, and
+distribution of your derivative works of the Work (“Your Terms”) only if (a) Your Terms provide that the use
+limitation in Section 3.3 applies to your derivative works, and (b) you identify the specific derivative works
+that are subject to Your Terms. Notwithstanding Your Terms, this License (including the redistribution
+requirements in Section 3.1) will continue to apply to the Work itself.
+
+3.3 Use Limitation. The Work and any derivative works thereof only may be used or intended for use
+non-commercially. Notwithstanding the foregoing, NVIDIA and its affiliates may use the Work and any derivative
+works commercially. As used herein, “non-commercially” means for research or evaluation purposes only.
+
+3.4 Patent Claims. If you bring or threaten to bring a patent claim against any Licensor (including any claim,
+cross-claim or counterclaim in a lawsuit) to enforce any patents that you allege are infringed by any Work, then
+your rights under this License from such Licensor (including the grant in Section 2.1) will terminate immediately.
+
+3.5 Trademarks. This License does not grant any rights to use any Licensor’s or its affiliates’ names, logos,
+or trademarks, except as necessary to reproduce the notices described in this License.
+
+3.6 Termination. If you violate any term of this License, then your rights under this License (including the
+grant in Section 2.1) will terminate immediately.
+
+4. Disclaimer of Warranty.
+
+THE WORK IS PROVIDED “AS IS” WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING
+WARRANTIES OR CONDITIONS OF M ERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT. YOU
+BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER THIS LICENSE.
+
+5. Limitation of Liability.
+
+EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING
+NEGLIGENCE), CONTRACT, OR OTHERWISE SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
+INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF OR RELATED TO THIS LICENSE, THE USE OR
+INABILITY TO USE THE WORK (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, LOST PROFITS OR
+DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER COMM ERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN
+ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+===
+CosXL License Agreement
+
+
+STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE AGREEMENT Dated: April 7th, 2024
+By clicking “I Accept” below or by using or distributing any portion or element of the Models, Software, Software Products or Derivative Works, you agree to the terms of this License. If you do not agree to this License, then you do not have any rights to use the Software Products or Derivative Works through this License, and you must immediately cease using the Software Products or Derivative Works. If you are agreeing to be bound by the terms of this License on behalf of your employer or other entity, you represent and warrant to Stability AI that you have full legal authority to bind your employer or such entity to this License. If you do not have the requisite authority, you may not accept the License or access the Software Products or Derivative Works on behalf of your employer or other entity.
+"Agreement" means this Stable Non-Commercial Research Community License Agreement.
+“AUP” means the Stability AI Acceptable Use Policy available at https://stability.ai/use-policy, as may be updated from time to time.
+"Derivative Work(s)” means (a) any derivative work of the Software Products as recognized by U.S. copyright laws and (b) any modifications to a Model, and any other model created which is based on or derived from the Model or the Model’s output. For clarity, Derivative Works do not include the output of any Model.
+“Documentation” means any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software.
+"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+“Model(s)" means, collectively, Stability AI’s proprietary models and algorithms, including machine-learning models, trained model weights and other elements of the foregoing, made available under this Agreement.
+“Non-Commercial Uses” means exercising any of the rights granted herein for the purpose of research or non-commercial purposes. Non-Commercial Uses does not include any production use of the Software Products or any Derivative Works.
+"Stability AI" or "we" means Stability AI Ltd. and its affiliates.
+
+"Software" means Stability AI’s proprietary software made available under this Agreement.
+“Software Products” means the Models, Software and Documentation, individually or in any combination.
+
+ License Rights and Redistribution.
+ a. Subject to your compliance with this Agreement, the AUP (which is hereby incorporated herein by reference), and the Documentation, Stability AI grants you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty free and limited license under Stability AI’s intellectual property or other rights owned or controlled by Stability AI embodied in the Software Products to use, reproduce, distribute, and create Derivative Works of, the Software Products, in each case for Non-Commercial Uses only.
+ b. You may not use the Software Products or Derivative Works to enable third parties to use the Software Products or Derivative Works as part of your hosted service or via your APIs, whether you are adding substantial additional functionality thereto or not. Merely distributing the Software Products or Derivative Works for download online without offering any related service (ex. by distributing the Models on HuggingFace) is not a violation of this subsection. If you wish to use the Software Products or any Derivative Works for commercial or production use or you wish to make the Software Products or any Derivative Works available to third parties via your hosted service or your APIs, contact Stability AI at https://stability.ai/contact.
+ c. If you distribute or make the Software Products, or any Derivative Works thereof, available to a third party, the Software Products, Derivative Works, or any portion thereof, respectively, will remain subject to this Agreement and you must (i) provide a copy of this Agreement to such third party, and (ii) retain the following attribution notice within a "Notice" text file distributed as a part of such copies: "This Stability AI Model is licensed under the Stability AI Non-Commercial Research Community License, Copyright (c) Stability AI Ltd. All Rights Reserved.” If you create a Derivative Work of a Software Product, you may add your own attribution notices to the Notice file included with the Software Product, provided that you clearly indicate which attributions apply to the Software Product and you must state in the NOTICE file that you changed the Software Product and how it was modified.
+ Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE SOFTWARE PRODUCTS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE SOFTWARE PRODUCTS, DERIVATIVE WORKS OR ANY OUTPUT OR RESULTS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE SOFTWARE PRODUCTS, DERIVATIVE WORKS AND ANY OUTPUT AND RESULTS. 3. Limitation of Liability. IN NO EVENT WILL STABILITY AI OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF STABILITY AI OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 4. Intellectual Property.
+ a. No trademark licenses are granted under this Agreement, and in connection with the Software Products or Derivative Works, neither Stability AI nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Software Products or Derivative Works.
+ b. Subject to Stability AI’s ownership of the Software Products and Derivative Works made by or for Stability AI, with respect to any Derivative Works that are made by you, as between you and Stability AI, you are and will be the owner of such Derivative Works
+ c. If you institute litigation or other proceedings against Stability AI (including a cross-claim or counterclaim in a lawsuit) alleging that the Software Products, Derivative Works or associated outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Stability AI from and against any claim by any third party arising out of or related to your use or distribution of the Software Products or Derivative Works in violation of this Agreement.
+ Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Software Products and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Stability AI may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of any Software Products or Derivative Works. Sections 2-4 shall survive the termination of this Agreement.
+ Governing Law. This Agreement will be governed by and construed in accordance with the laws of the United States and the State of California without regard to choice of law
+ principles.
+
diff --git a/third_party/mast3r/LICENSE b/third_party/mast3r/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..a97986e3a8ddd49973959f6c748dfa8b881b64d3
--- /dev/null
+++ b/third_party/mast3r/LICENSE
@@ -0,0 +1,7 @@
+DUSt3R, Copyright (c) 2024-present Naver Corporation, is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license.
+
+A summary of the CC BY-NC-SA 4.0 license is located here:
+ https://creativecommons.org/licenses/by-nc-sa/4.0/
+
+The CC BY-NC-SA 4.0 license is located here:
+ https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
diff --git a/third_party/mast3r/NOTICE b/third_party/mast3r/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..86583416b75cc1749cac38d437b376842975ca06
--- /dev/null
+++ b/third_party/mast3r/NOTICE
@@ -0,0 +1,103 @@
+MASt3R
+Copyright 2024-present NAVER Corp.
+
+This project contains subcomponents with separate copyright notices and license terms.
+Your use of the source code for these subcomponents is subject to the terms and conditions of the following licenses.
+
+====
+
+naver/dust3r
+https://github.com/naver/dust3r/
+
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0
+
+====
+
+naver/croco
+https://github.com/naver/croco/
+
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0
+
+====
+
+pytorch/pytorch
+https://github.com/pytorch/pytorch
+
+From PyTorch:
+
+Copyright (c) 2016- Facebook, Inc (Adam Paszke)
+Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
+Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
+Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+Copyright (c) 2011-2013 NYU (Clement Farabet)
+Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
+Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+
+From Caffe2:
+
+Copyright (c) 2016-present, Facebook Inc. All rights reserved.
+
+All contributions by Facebook:
+Copyright (c) 2016 Facebook Inc.
+
+All contributions by Google:
+Copyright (c) 2015 Google Inc.
+All rights reserved.
+
+All contributions by Yangqing Jia:
+Copyright (c) 2015 Yangqing Jia
+All rights reserved.
+
+All contributions by Kakao Brain:
+Copyright 2019-2020 Kakao Brain
+
+All contributions by Cruise LLC:
+Copyright (c) 2022 Cruise LLC.
+All rights reserved.
+
+All contributions from Caffe:
+Copyright(c) 2013, 2014, 2015, the respective contributors
+All rights reserved.
+
+All other contributions:
+Copyright(c) 2015, 2016 the respective contributors
+All rights reserved.
+
+Caffe2 uses a copyright model similar to Caffe: each contributor holds
+copyright over their contributions to Caffe2. The project versioning records
+all such contribution and copyright details. If a contributor wants to further
+mark their specific copyright on a particular contribution, they should
+indicate their copyright solely in the commit message of the change when it is
+committed.
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright
+ notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+
+3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
+ and IDIAP Research Institute nor the names of its contributors may be
+ used to endorse or promote products derived from this software without
+ specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+
diff --git a/third_party/mast3r/README.md b/third_party/mast3r/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..7eef329100d32f76c707bf0c489db115895c274e
--- /dev/null
+++ b/third_party/mast3r/README.md
@@ -0,0 +1,316 @@
+
+
+Official implementation of `Grounding Image Matching in 3D with MASt3R`
+[[Project page](https://dust3r.europe.naverlabs.com/)], [[MASt3R arxiv](https://arxiv.org/abs/2406.09756)], [[DUSt3R arxiv](https://arxiv.org/abs/2312.14132)]
+
+
+
+
+
+```bibtex
+@misc{mast3r_arxiv24,
+ title={Grounding Image Matching in 3D with MASt3R},
+ author={Vincent Leroy and Yohann Cabon and Jerome Revaud},
+ year={2024},
+ eprint={2406.09756},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV}
+}
+
+@inproceedings{dust3r_cvpr24,
+ title={DUSt3R: Geometric 3D Vision Made Easy},
+ author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud},
+ booktitle = {CVPR},
+ year = {2024}
+}
+```
+
+## Table of Contents
+
+- [Table of Contents](#table-of-contents)
+- [License](#license)
+- [Get Started](#get-started)
+ - [Installation](#installation)
+ - [Checkpoints](#checkpoints)
+ - [Interactive demo](#interactive-demo)
+ - [Interactive demo with docker](#interactive-demo-with-docker)
+- [Usage](#usage)
+- [Training](#training)
+ - [Datasets](#datasets)
+ - [Demo](#demo)
+ - [Our Hyperparameters](#our-hyperparameters)
+- [Visual Localization](#visual-localization)
+ - [Dataset Preparation](#dataset-preparation)
+ - [Example Commands](#example-commands)
+
+## License
+
+The code is distributed under the CC BY-NC-SA 4.0 License.
+See [LICENSE](LICENSE) for more information.
+
+```python
+# Copyright (C) 2024-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+```
+
+## Get Started
+
+### Installation
+
+1. Clone MASt3R.
+```bash
+git clone --recursive https://github.com/naver/mast3r
+cd mast3r
+# if you have already cloned mast3r:
+# git submodule update --init --recursive
+```
+
+2. Create the environment, here we show an example using conda.
+```bash
+conda create -n mast3r python=3.11 cmake=3.14.0
+conda activate mast3r
+conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
+pip install -r requirements.txt
+pip install -r dust3r/requirements.txt
+# Optional: you can also install additional packages to:
+# - add support for HEIC images
+# - add required packages for visloc.py
+pip install -r dust3r/requirements_optional.txt
+```
+
+3. Optional, compile the cuda kernels for RoPE (as in CroCo v2).
+```bash
+# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.
+cd dust3r/croco/models/curope/
+python setup.py build_ext --inplace
+cd ../../../../
+```
+
+
+### Checkpoints
+
+You can obtain the checkpoints by two ways:
+
+1) You can use our huggingface_hub integration: the models will be downloaded automatically.
+
+2) Otherwise, We provide several pre-trained models:
+
+| Modelname | Training resolutions | Head | Encoder | Decoder |
+|-------------|----------------------|------|---------|---------|
+| [`MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric`](https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth) | 512x384, 512x336, 512x288, 512x256, 512x160 | CatMLP+DPT | ViT-L | ViT-B |
+
+You can check the hyperparameters we used to train these models in the [section: Our Hyperparameters](#our-hyperparameters)
+Make sure to check license of the datasets we used.
+
+To download a specific model, for example `MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth`:
+```bash
+mkdir -p checkpoints/
+wget https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth -P checkpoints/
+```
+
+For these checkpoints, make sure to agree to the license of all the training datasets we used, in addition to CC-BY-NC-SA 4.0.
+The mapfree dataset license in particular is very restrictive. For more information, check [CHECKPOINTS_NOTICE](CHECKPOINTS_NOTICE).
+
+
+### Interactive demo
+
+There are two demos available:
+
+```
+demo.py is the updated demo for MASt3R. It uses our new sparse global alignment method that allows you to reconstruct larger scenes
+
+python3 demo.py --model_name MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric
+
+# Use --weights to load a checkpoint from a local file, eg --weights checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth
+# Use --local_network to make it accessible on the local network, or --server_name to specify the url manually
+# Use --server_port to change the port, by default it will search for an available port starting at 7860
+# Use --device to use a different device, by default it's "cuda"
+
+demo_dust3r_ga.py is the same demo as in dust3r (+ compatibility for MASt3R models)
+see https://github.com/naver/dust3r?tab=readme-ov-file#interactive-demo for details
+```
+### Interactive demo with docker
+
+TODO
+
+
+
+## Usage
+
+```python
+from mast3r.model import AsymmetricMASt3R
+from mast3r.fast_nn import fast_reciprocal_NNs
+
+import mast3r.utils.path_to_dust3r
+from dust3r.inference import inference
+from dust3r.utils.image import load_images
+
+if __name__ == '__main__':
+ device = 'cuda'
+ schedule = 'cosine'
+ lr = 0.01
+ niter = 300
+
+ model_name = "naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric"
+ # you can put the path to a local checkpoint in model_name if needed
+ model = AsymmetricMASt3R.from_pretrained(model_name).to(device)
+ images = load_images(['dust3r/croco/assets/Chateau1.png', 'dust3r/croco/assets/Chateau2.png'], size=512)
+ output = inference([tuple(images)], model, device, batch_size=1, verbose=False)
+
+ # at this stage, you have the raw dust3r predictions
+ view1, pred1 = output['view1'], output['pred1']
+ view2, pred2 = output['view2'], output['pred2']
+
+ desc1, desc2 = pred1['desc'].squeeze(0).detach(), pred2['desc'].squeeze(0).detach()
+
+ # find 2D-2D matches between the two images
+ matches_im0, matches_im1 = fast_reciprocal_NNs(desc1, desc2, subsample_or_initxy1=8,
+ device=device, dist='dot', block_size=2**13)
+
+ # ignore small border around the edge
+ H0, W0 = view1['true_shape'][0]
+ valid_matches_im0 = (matches_im0[:, 0] >= 3) & (matches_im0[:, 0] < int(W0) - 3) & (
+ matches_im0[:, 1] >= 3) & (matches_im0[:, 1] < int(H0) - 3)
+
+ H1, W1 = view2['true_shape'][0]
+ valid_matches_im1 = (matches_im1[:, 0] >= 3) & (matches_im1[:, 0] < int(W1) - 3) & (
+ matches_im1[:, 1] >= 3) & (matches_im1[:, 1] < int(H1) - 3)
+
+ valid_matches = valid_matches_im0 & valid_matches_im1
+ matches_im0, matches_im1 = matches_im0[valid_matches], matches_im1[valid_matches]
+
+ # visualize a few matches
+ import numpy as np
+ import torch
+ import torchvision.transforms.functional
+ from matplotlib import pyplot as pl
+
+ n_viz = 20
+ num_matches = matches_im0.shape[0]
+ match_idx_to_viz = np.round(np.linspace(0, num_matches - 1, n_viz)).astype(int)
+ viz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz]
+
+ image_mean = torch.as_tensor([0.5, 0.5, 0.5], device='cpu').reshape(1, 3, 1, 1)
+ image_std = torch.as_tensor([0.5, 0.5, 0.5], device='cpu').reshape(1, 3, 1, 1)
+
+ viz_imgs = []
+ for i, view in enumerate([view1, view2]):
+ rgb_tensor = view['img'] * image_std + image_mean
+ viz_imgs.append(rgb_tensor.squeeze(0).permute(1, 2, 0).cpu().numpy())
+
+ H0, W0, H1, W1 = *viz_imgs[0].shape[:2], *viz_imgs[1].shape[:2]
+ img0 = np.pad(viz_imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
+ img1 = np.pad(viz_imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
+ img = np.concatenate((img0, img1), axis=1)
+ pl.figure()
+ pl.imshow(img)
+ cmap = pl.get_cmap('jet')
+ for i in range(n_viz):
+ (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T
+ pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)
+ pl.show(block=True)
+```
+
+
+## Training
+
+In this section, we present a short demonstration to get started with training MASt3R.
+
+### Datasets
+
+See [Datasets section in DUSt3R](https://github.com/naver/dust3r/tree/datasets?tab=readme-ov-file#datasets)
+
+### Demo
+
+Like for the DUSt3R training demo, we're going to download and prepare the same subset of [CO3Dv2](https://github.com/facebookresearch/co3d) - [Creative Commons Attribution-NonCommercial 4.0 International](https://github.com/facebookresearch/co3d/blob/main/LICENSE) and launch the training code on it.
+It is the exact same process as DUSt3R.
+The demo model will be trained for a few epochs on a very small dataset.
+It will not be very good.
+
+```bash
+# download and prepare the co3d subset
+mkdir -p data/co3d_subset
+cd data/co3d_subset
+git clone https://github.com/facebookresearch/co3d
+cd co3d
+python3 ./co3d/download_dataset.py --download_folder ../ --single_sequence_subset
+rm ../*.zip
+cd ../../..
+
+python3 datasets_preprocess/preprocess_co3d.py --co3d_dir data/co3d_subset --output_dir data/co3d_subset_processed --single_sequence_subset
+
+# download the pretrained dust3r checkpoint
+mkdir -p checkpoints/
+wget https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth -P checkpoints/
+
+# for this example we'll do fewer epochs, for the actual hyperparameters we used in the paper, see the next section: "Our Hyperparameters"
+torchrun --nproc_per_node=4 train.py \
+ --train_dataset "1000 @ Co3d(split='train', ROOT='data/co3d_subset_processed', aug_crop='auto', aug_monocular=0.005, aug_rot90='diff', mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], n_corres=8192, nneg=0.5, transform=ColorJitter)" \
+ --test_dataset "100 @ Co3d(split='test', ROOT='data/co3d_subset_processed', resolution=(512,384), n_corres=1024, seed=777)" \
+ --model "AsymmetricMASt3R(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12, two_confs=True)" \
+ --train_criterion "ConfLoss(Regr3D(L21, norm_mode='?avg_dis'), alpha=0.2) + 0.075*ConfMatchingLoss(MatchingLoss(InfoNCE(mode='proper', temperature=0.05), negatives_padding=0, blocksize=8192), alpha=10.0, confmode='mean')" \
+ --test_criterion "Regr3D_ScaleShiftInv(L21, norm_mode='?avg_dis', gt_scale=True, sky_loss_value=0) + -1.*MatchingLoss(APLoss(nq='torch', fp=torch.float16), negatives_padding=12288)" \
+ --pretrained "checkpoints/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth" \
+ --lr 0.0001 --min_lr 1e-06 --warmup_epochs 1 --epochs 10 --batch_size 4 --accum_iter 4 \
+ --save_freq 1 --keep_freq 5 --eval_freq 1 \
+ --output_dir "checkpoints/mast3r_demo"
+
+```
+
+### Our Hyperparameters
+We didn't release all the training datasets, but here are the commands we used for training our models:
+
+```bash
+# MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric - train mast3r with metric regression and matching loss
+# we used cosxl to generate variations of DL3DV: "foggy", "night", "rainy", "snow", "sunny" but we were not convinced by it.
+
+torchrun --nproc_per_node=8 train.py \
+ --train_dataset "57_000 @ Habitat512(1_000_000, split='train', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 68_400 @ BlendedMVS(split='train', mask_sky=True, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 68_400 @ MegaDepth(split='train', mask_sky=True, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 45_600 @ ARKitScenes(split='train', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 22_800 @ Co3d(split='train', mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 22_800 @ StaticThings3D(mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 45_600 @ ScanNetpp(split='train', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 45_600 @ TartanAir(pairs_subset='', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 4_560 @ UnrealStereo4K(resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 1_140 @ VirtualKitti(optical_center_is_centered=True, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 22_800 @ WildRgbd(split='train', mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 145_920 @ NianticMapFree(split='train', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 57_000 @ DL3DV(split='nlight', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 57_000 @ DL3DV(split='not-nlight', cosxl_augmentations=None, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5) + 34_200 @ InternalUnreleasedDataset(resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], aug_crop='auto', aug_monocular=0.005, transform=ColorJitter, n_corres=8192, nneg=0.5)" \
+ --test_dataset "Habitat512(1_000, split='val', resolution=(512,384), seed=777, n_corres=1024) + 1_000 @ BlendedMVS(split='val', resolution=(512,384), mask_sky=True, seed=777, n_corres=1024) + 1_000 @ ARKitScenes(split='test', resolution=(512,384), seed=777, n_corres=1024) + 1_000 @ MegaDepth(split='val', mask_sky=True, resolution=(512,336), seed=777, n_corres=1024) + 1_000 @ Co3d(split='test', resolution=(512,384), mask_bg='rand', seed=777, n_corres=1024)" \
+ --model "AsymmetricMASt3R(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12, two_confs=True, desc_conf_mode=('exp', 0, inf))" \
+ --train_criterion "ConfLoss(Regr3D(L21, norm_mode='?avg_dis'), alpha=0.2, loss_in_log=False) + 0.075*ConfMatchingLoss(MatchingLoss(InfoNCE(mode='proper', temperature=0.05), negatives_padding=0, blocksize=8192), alpha=10.0, confmode='mean')" \
+ --test_criterion "Regr3D(L21, norm_mode='?avg_dis', gt_scale=True, sky_loss_value=0) + -1.*MatchingLoss(APLoss(nq='torch', fp=torch.float16), negatives_padding=12288)" \
+ --pretrained "checkpoints/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth" \
+ --lr 0.0001 --min_lr 1e-06 --warmup_epochs 8 --epochs 50 --batch_size 4 --accum_iter 2 \
+ --save_freq 1 --keep_freq 5 --eval_freq 1 --print_freq=10 \
+ --output_dir "checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric"
+
+```
+
+## Visual Localization
+
+### Dataset preparation
+
+See [Visloc section in DUSt3R](https://github.com/naver/dust3r/tree/dust3r_visloc#dataset-preparation)
+
+### Example Commands
+
+With `visloc.py` you can run our visual localization experiments on Aachen-Day-Night, InLoc, Cambridge Landmarks and 7 Scenes.
+
+
+```bash
+# Aachen-Day-Night-v1.1:
+# scene in 'day' 'night'
+# scene can also be 'all'
+python3 visloc.py --model_name MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric --dataset "VislocAachenDayNight('/path/to/prepared/Aachen-Day-Night-v1.1/', subscene='${scene}', pairsfile='fire_top50', topk=20)" --pixel_tol 5 --pnp_mode poselib --reprojection_error_diag_ratio 0.008 --output_dir /path/to/output/Aachen-Day-Night-v1.1/${scene}/loc
+
+# or with coarse to fine:
+
+python3 visloc.py --model_name MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric --dataset "VislocAachenDayNight('/path/to/prepared/Aachen-Day-Night-v1.1/', subscene='${scene}', pairsfile='fire_top50', topk=20)" --pixel_tol 5 --pnp_mode poselib --reprojection_error_diag_ratio 0.008 --output_dir /path/to/output/Aachen-Day-Night-v1.1/${scene}/loc --coarse_to_fine --max_batch_size 48 --c2f_crop_with_homography
+
+# InLoc
+python3 visloc.py --model_name MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric --dataset "VislocInLoc('/path/to/prepared/InLoc/', pairsfile='pairs-query-netvlad40-temporal', topk=20)" --pixel_tol 5 --pnp_mode poselib --reprojection_error_diag_ratio 0.008 --output_dir /path/to/output/InLoc/loc
+
+# or with coarse to fine:
+
+python3 visloc.py --model_name MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric --dataset "VislocInLoc('/path/to/prepared/InLoc/', pairsfile='pairs-query-netvlad40-temporal', topk=20)" --pixel_tol 5 --pnp_mode poselib --reprojection_error_diag_ratio 0.008 --output_dir /path/to/output/InLoc/loc --coarse_to_fine --max_image_size 1200 --max_batch_size 48 --c2f_crop_with_homography
+
+# 7-scenes:
+# scene in 'chess' 'fire' 'heads' 'office' 'pumpkin' 'redkitchen' 'stairs'
+python3 visloc.py --model_name MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric --dataset "VislocSevenScenes('/path/to/prepared/7-scenes/', subscene='${scene}', pairsfile='APGeM-LM18_top20', topk=1)" --pixel_tol 5 --pnp_mode poselib --reprojection_error_diag_ratio 0.008 --output_dir /path/to/output/7-scenes/${scene}/loc
+
+# Cambridge Landmarks:
+# scene in 'ShopFacade' 'GreatCourt' 'KingsCollege' 'OldHospital' 'StMarysChurch'
+python3 visloc.py --model_name MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric --dataset "VislocCambridgeLandmarks('/path/to/prepared/Cambridge_Landmarks/', subscene='${scene}', pairsfile='APGeM-LM18_top20', topk=1)" --pixel_tol 5 --pnp_mode poselib --reprojection_error_diag_ratio 0.008 --output_dir /path/to/output/Cambridge_Landmarks/${scene}/loc
+
+```
diff --git a/third_party/mast3r/demo.py b/third_party/mast3r/demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..94459191de6404e0c036ac7b6529755ede16faad
--- /dev/null
+++ b/third_party/mast3r/demo.py
@@ -0,0 +1,290 @@
+#!/usr/bin/env python3
+# Copyright (C) 2024-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+#
+# --------------------------------------------------------
+# gradio demo
+# --------------------------------------------------------
+import math
+import gradio
+import os
+import torch
+import numpy as np
+import tempfile
+import functools
+import trimesh
+import copy
+from scipy.spatial.transform import Rotation
+
+from mast3r.cloud_opt.sparse_ga import sparse_global_alignment
+from mast3r.cloud_opt.tsdf_optimizer import TSDFPostProcess
+
+from mast3r.model import AsymmetricMASt3R
+from mast3r.utils.misc import hash_md5
+import mast3r.utils.path_to_dust3r # noqa
+from dust3r.image_pairs import make_pairs
+from dust3r.utils.image import load_images
+from dust3r.utils.device import to_numpy
+from dust3r.viz import add_scene_cam, CAM_COLORS, OPENGL, pts3d_to_trimesh, cat_meshes
+from dust3r.demo import get_args_parser as dust3r_get_args_parser
+
+import matplotlib.pyplot as pl
+pl.ion()
+
+torch.backends.cuda.matmul.allow_tf32 = True # for gpu >= Ampere and pytorch >= 1.12
+batch_size = 1
+
+
+def get_args_parser():
+ parser = dust3r_get_args_parser()
+ parser.add_argument('--share', action='store_true')
+
+ actions = parser._actions
+ for action in actions:
+ if action.dest == 'model_name':
+ action.choices = ["MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric"]
+ # change defaults
+ parser.prog = 'mast3r demo'
+ return parser
+
+
+def _convert_scene_output_to_glb(outdir, imgs, pts3d, mask, focals, cams2world, cam_size=0.05,
+ cam_color=None, as_pointcloud=False,
+ transparent_cams=False, silent=False):
+ assert len(pts3d) == len(mask) <= len(imgs) <= len(cams2world) == len(focals)
+ pts3d = to_numpy(pts3d)
+ imgs = to_numpy(imgs)
+ focals = to_numpy(focals)
+ cams2world = to_numpy(cams2world)
+
+ scene = trimesh.Scene()
+
+ # full pointcloud
+ if as_pointcloud:
+ pts = np.concatenate([p[m.ravel()] for p, m in zip(pts3d, mask)])
+ col = np.concatenate([p[m] for p, m in zip(imgs, mask)])
+ pct = trimesh.PointCloud(pts.reshape(-1, 3), colors=col.reshape(-1, 3))
+ scene.add_geometry(pct)
+ else:
+ meshes = []
+ for i in range(len(imgs)):
+ meshes.append(pts3d_to_trimesh(imgs[i], pts3d[i].reshape(imgs[i].shape), mask[i]))
+ mesh = trimesh.Trimesh(**cat_meshes(meshes))
+ scene.add_geometry(mesh)
+
+ # add each camera
+ for i, pose_c2w in enumerate(cams2world):
+ if isinstance(cam_color, list):
+ camera_edge_color = cam_color[i]
+ else:
+ camera_edge_color = cam_color or CAM_COLORS[i % len(CAM_COLORS)]
+ add_scene_cam(scene, pose_c2w, camera_edge_color,
+ None if transparent_cams else imgs[i], focals[i],
+ imsize=imgs[i].shape[1::-1], screen_width=cam_size)
+
+ rot = np.eye(4)
+ rot[:3, :3] = Rotation.from_euler('y', np.deg2rad(180)).as_matrix()
+ scene.apply_transform(np.linalg.inv(cams2world[0] @ OPENGL @ rot))
+ outfile = os.path.join(outdir, 'scene.glb')
+ if not silent:
+ print('(exporting 3D scene to', outfile, ')')
+ scene.export(file_obj=outfile)
+ return outfile
+
+
+def get_3D_model_from_scene(outdir, silent, scene, min_conf_thr=2, as_pointcloud=False, mask_sky=False,
+ clean_depth=False, transparent_cams=False, cam_size=0.05, TSDF_thresh=0):
+ """
+ extract 3D_model (glb file) from a reconstructed scene
+ """
+ if scene is None:
+ return None
+
+ # get optimized values from scene
+ rgbimg = scene.imgs
+ focals = scene.get_focals().cpu()
+ cams2world = scene.get_im_poses().cpu()
+
+ # 3D pointcloud from depthmap, poses and intrinsics
+ if TSDF_thresh > 0:
+ tsdf = TSDFPostProcess(scene, TSDF_thresh=TSDF_thresh)
+ pts3d, _, confs = to_numpy(tsdf.get_dense_pts3d(clean_depth=clean_depth))
+ else:
+ pts3d, _, confs = to_numpy(scene.get_dense_pts3d(clean_depth=clean_depth))
+ msk = to_numpy([c > min_conf_thr for c in confs])
+ return _convert_scene_output_to_glb(outdir, rgbimg, pts3d, msk, focals, cams2world, as_pointcloud=as_pointcloud,
+ transparent_cams=transparent_cams, cam_size=cam_size, silent=silent)
+
+
+def get_reconstructed_scene(outdir, model, device, silent, image_size, filelist, optim_level, lr1, niter1, lr2, niter2, min_conf_thr,
+ as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size,
+ scenegraph_type, winsize, refid, TSDF_thresh, **kw):
+ """
+ from a list of images, run mast3r inference, sparse global aligner.
+ then run get_3D_model_from_scene
+ """
+ imgs = load_images(filelist, size=image_size, verbose=not silent)
+ if len(imgs) == 1:
+ imgs = [imgs[0], copy.deepcopy(imgs[0])]
+ imgs[1]['idx'] = 1
+ filelist = [filelist[0], filelist[0] + '_2']
+ if scenegraph_type == "swin":
+ scenegraph_type = scenegraph_type + "-" + str(winsize)
+ elif scenegraph_type == "oneref":
+ scenegraph_type = scenegraph_type + "-" + str(refid)
+
+ pairs = make_pairs(imgs, scene_graph=scenegraph_type, prefilter=None, symmetrize=True)
+ if optim_level == 'coarse':
+ niter2 = 0
+ # Sparse GA (forward mast3r -> matching -> 3D optim -> 2D refinement -> triangulation)
+ scene = sparse_global_alignment(filelist, pairs, os.path.join(outdir, 'cache'),
+ model, lr1=lr1, niter1=niter1, lr2=lr2, niter2=niter2, device=device,
+ opt_depth='depth' in optim_level, **kw)
+ outfile = get_3D_model_from_scene(outdir, silent, scene, min_conf_thr, as_pointcloud, mask_sky,
+ clean_depth, transparent_cams, cam_size, TSDF_thresh)
+ return scene, outfile
+
+
+def set_scenegraph_options(inputfiles, winsize, refid, scenegraph_type):
+ num_files = len(inputfiles) if inputfiles is not None else 1
+ max_winsize = max(1, math.ceil((num_files - 1) / 2))
+ if scenegraph_type == "swin":
+ winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
+ minimum=1, maximum=max_winsize, step=1, visible=True)
+ refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
+ maximum=num_files - 1, step=1, visible=False)
+ elif scenegraph_type == "oneref":
+ winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
+ minimum=1, maximum=max_winsize, step=1, visible=False)
+ refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
+ maximum=num_files - 1, step=1, visible=True)
+ else:
+ winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
+ minimum=1, maximum=max_winsize, step=1, visible=False)
+ refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
+ maximum=num_files - 1, step=1, visible=False)
+ return winsize, refid
+
+
+def main_demo(tmpdirname, model, device, image_size, server_name, server_port, silent=False, share=False):
+ if not silent:
+ print('Outputing stuff in', tmpdirname)
+
+ recon_fun = functools.partial(get_reconstructed_scene, tmpdirname, model, device, silent, image_size)
+ model_from_scene_fun = functools.partial(get_3D_model_from_scene, tmpdirname, silent)
+ with gradio.Blocks(css=""".gradio-container {margin: 0 !important; min-width: 100%};""", title="MASt3R Demo") as demo:
+ # scene state is save so that you can change conf_thr, cam_size... without rerunning the inference
+ scene = gradio.State(None)
+ gradio.HTML('
MASt3R Demo
')
+ with gradio.Column():
+ inputfiles = gradio.File(file_count="multiple")
+ with gradio.Row():
+ lr1 = gradio.Slider(label="Coarse LR", value=0.07, minimum=0.01, maximum=0.2, step=0.01)
+ niter1 = gradio.Number(value=200, precision=0, minimum=0, maximum=10_000,
+ label="num_iterations", info="For coarse alignment!")
+ lr2 = gradio.Slider(label="Fine LR", value=0.014, minimum=0.005, maximum=0.05, step=0.001)
+ niter2 = gradio.Number(value=500, precision=0, minimum=0, maximum=100_000,
+ label="num_iterations", info="For refinement!")
+ optim_level = gradio.Dropdown(["coarse", "refine", "refine+depth"],
+ value='refine', label="OptLevel",
+ info="Optimization level")
+
+ scenegraph_type = gradio.Dropdown(["complete", "swin", "oneref"],
+ value='complete', label="Scenegraph",
+ info="Define how to make pairs",
+ interactive=True)
+ winsize = gradio.Slider(label="Scene Graph: Window Size", value=1,
+ minimum=1, maximum=1, step=1, visible=False)
+ refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0, maximum=0, step=1, visible=False)
+
+ run_btn = gradio.Button("Run")
+
+ with gradio.Row():
+ # adjust the confidence threshold
+ min_conf_thr = gradio.Slider(label="min_conf_thr", value=1.5, minimum=0.0, maximum=10, step=0.1)
+ # adjust the camera size in the output pointcloud
+ cam_size = gradio.Slider(label="cam_size", value=0.2, minimum=0.001, maximum=1.0, step=0.001)
+ TSDF_thresh = gradio.Slider(label="TSDF Threshold", value=0., minimum=0., maximum=1., step=0.01)
+ with gradio.Row():
+ as_pointcloud = gradio.Checkbox(value=True, label="As pointcloud")
+ # two post process implemented
+ mask_sky = gradio.Checkbox(value=False, label="Mask sky")
+ clean_depth = gradio.Checkbox(value=True, label="Clean-up depthmaps")
+ transparent_cams = gradio.Checkbox(value=False, label="Transparent cameras")
+
+ outmodel = gradio.Model3D()
+
+ # events
+ scenegraph_type.change(set_scenegraph_options,
+ inputs=[inputfiles, winsize, refid, scenegraph_type],
+ outputs=[winsize, refid])
+ inputfiles.change(set_scenegraph_options,
+ inputs=[inputfiles, winsize, refid, scenegraph_type],
+ outputs=[winsize, refid])
+ run_btn.click(fn=recon_fun,
+ inputs=[inputfiles, optim_level, lr1, niter1, lr2, niter2, min_conf_thr, as_pointcloud,
+ mask_sky, clean_depth, transparent_cams, cam_size,
+ scenegraph_type, winsize, refid, TSDF_thresh],
+ outputs=[scene, outmodel])
+ min_conf_thr.release(fn=model_from_scene_fun,
+ inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
+ clean_depth, transparent_cams, cam_size, TSDF_thresh],
+ outputs=outmodel)
+ cam_size.change(fn=model_from_scene_fun,
+ inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
+ clean_depth, transparent_cams, cam_size, TSDF_thresh],
+ outputs=outmodel)
+ TSDF_thresh.change(fn=model_from_scene_fun,
+ inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
+ clean_depth, transparent_cams, cam_size, TSDF_thresh],
+ outputs=outmodel)
+ as_pointcloud.change(fn=model_from_scene_fun,
+ inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
+ clean_depth, transparent_cams, cam_size, TSDF_thresh],
+ outputs=outmodel)
+ mask_sky.change(fn=model_from_scene_fun,
+ inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
+ clean_depth, transparent_cams, cam_size, TSDF_thresh],
+ outputs=outmodel)
+ clean_depth.change(fn=model_from_scene_fun,
+ inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
+ clean_depth, transparent_cams, cam_size, TSDF_thresh],
+ outputs=outmodel)
+ transparent_cams.change(model_from_scene_fun,
+ inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
+ clean_depth, transparent_cams, cam_size, TSDF_thresh],
+ outputs=outmodel)
+ demo.launch(share=False, server_name=server_name, server_port=server_port)
+
+
+if __name__ == '__main__':
+ parser = get_args_parser()
+ args = parser.parse_args()
+
+ if args.server_name is not None:
+ server_name = args.server_name
+ else:
+ server_name = '0.0.0.0' if args.local_network else '127.0.0.1'
+
+ if args.weights is not None:
+ weights_path = args.weights
+ else:
+ weights_path = "naver/" + args.model_name
+
+ model = AsymmetricMASt3R.from_pretrained(weights_path).to(args.device)
+ chkpt_tag = hash_md5(weights_path)
+
+ # mast3r will write the 3D model inside tmpdirname/chkpt_tag
+ if args.tmp_dir is not None:
+ tmpdirname = args.tmp_dir
+ cache_path = os.path.join(tmpdirname, chkpt_tag)
+ os.makedirs(cache_path, exist_ok=True)
+ main_demo(cache_path, model, args.device, args.image_size, server_name, args.server_port, silent=args.silent,
+ share=args.share)
+ else:
+ with tempfile.TemporaryDirectory(suffix='_mast3r_gradio_demo') as tmpdirname:
+ cache_path = os.path.join(tmpdirname, chkpt_tag)
+ os.makedirs(cache_path, exist_ok=True)
+ main_demo(tmpdirname, model, args.device, args.image_size,
+ server_name, args.server_port, silent=args.silent,
+ share=args.share)
diff --git a/imcui/third_party/mast3r/demo_dust3r_ga.py b/third_party/mast3r/demo_dust3r_ga.py
similarity index 95%
rename from imcui/third_party/mast3r/demo_dust3r_ga.py
rename to third_party/mast3r/demo_dust3r_ga.py
index 361c10e392e42525d57765b3f95fec43a89035a3..31d5be0501949e2393ae389d2fe7ac16cf3651dc 100644
--- a/imcui/third_party/mast3r/demo_dust3r_ga.py
+++ b/third_party/mast3r/demo_dust3r_ga.py
@@ -13,7 +13,7 @@ import mast3r.utils.path_to_dust3r # noqa
from dust3r.model import AsymmetricCroCo3DStereo
from mast3r.model import AsymmetricMASt3R
from dust3r.demo import get_args_parser as dust3r_get_args_parser
-from dust3r.demo import main_demo, set_print_with_timestamp
+from dust3r.demo import main_demo
import matplotlib.pyplot as pl
pl.ion()
@@ -36,7 +36,6 @@ def get_args_parser():
if __name__ == '__main__':
parser = get_args_parser()
args = parser.parse_args()
- set_print_with_timestamp()
if args.tmp_dir is not None:
tmp_path = args.tmp_dir
diff --git a/third_party/mast3r/dust3r/.gitignore b/third_party/mast3r/dust3r/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..194e236cbd708160926c3513b4232285eb47b029
--- /dev/null
+++ b/third_party/mast3r/dust3r/.gitignore
@@ -0,0 +1,132 @@
+data/
+checkpoints/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/third_party/mast3r/dust3r/.gitmodules b/third_party/mast3r/dust3r/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..c950ef981a8d2e47599dd7acbbe1bf8de9a42aca
--- /dev/null
+++ b/third_party/mast3r/dust3r/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "croco"]
+ path = croco
+ url = https://github.com/naver/croco
diff --git a/third_party/mast3r/dust3r/LICENSE b/third_party/mast3r/dust3r/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..a97986e3a8ddd49973959f6c748dfa8b881b64d3
--- /dev/null
+++ b/third_party/mast3r/dust3r/LICENSE
@@ -0,0 +1,7 @@
+DUSt3R, Copyright (c) 2024-present Naver Corporation, is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license.
+
+A summary of the CC BY-NC-SA 4.0 license is located here:
+ https://creativecommons.org/licenses/by-nc-sa/4.0/
+
+The CC BY-NC-SA 4.0 license is located here:
+ https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
diff --git a/third_party/mast3r/dust3r/NOTICE b/third_party/mast3r/dust3r/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..81da544dd534c5465361f35cf6a5a0cfff7c1d3f
--- /dev/null
+++ b/third_party/mast3r/dust3r/NOTICE
@@ -0,0 +1,12 @@
+DUSt3R
+Copyright 2024-present NAVER Corp.
+
+This project contains subcomponents with separate copyright notices and license terms.
+Your use of the source code for these subcomponents is subject to the terms and conditions of the following licenses.
+
+====
+
+naver/croco
+https://github.com/naver/croco/
+
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0
diff --git a/third_party/mast3r/dust3r/README.md b/third_party/mast3r/dust3r/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6df7772a18830d1249d1f9992cf6c28e3e794993
--- /dev/null
+++ b/third_party/mast3r/dust3r/README.md
@@ -0,0 +1,388 @@
+
+
+Official implementation of `DUSt3R: Geometric 3D Vision Made Easy`
+[[Project page](https://dust3r.europe.naverlabs.com/)], [[DUSt3R arxiv](https://arxiv.org/abs/2312.14132)]
+
+
+
+
+
+```bibtex
+@inproceedings{dust3r_cvpr24,
+ title={DUSt3R: Geometric 3D Vision Made Easy},
+ author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud},
+ booktitle = {CVPR},
+ year = {2024}
+}
+
+@misc{dust3r_arxiv23,
+ title={DUSt3R: Geometric 3D Vision Made Easy},
+ author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud},
+ year={2023},
+ eprint={2312.14132},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV}
+}
+```
+
+## Table of Contents
+
+- [Table of Contents](#table-of-contents)
+- [License](#license)
+- [Get Started](#get-started)
+ - [Installation](#installation)
+ - [Checkpoints](#checkpoints)
+ - [Interactive demo](#interactive-demo)
+ - [Interactive demo with docker](#interactive-demo-with-docker)
+- [Usage](#usage)
+- [Training](#training)
+ - [Datasets](#datasets)
+ - [Demo](#demo)
+ - [Our Hyperparameters](#our-hyperparameters)
+
+## License
+
+The code is distributed under the CC BY-NC-SA 4.0 License.
+See [LICENSE](LICENSE) for more information.
+
+```python
+# Copyright (C) 2024-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+```
+
+## Get Started
+
+### Installation
+
+1. Clone DUSt3R.
+```bash
+git clone --recursive https://github.com/naver/dust3r
+cd dust3r
+# if you have already cloned dust3r:
+# git submodule update --init --recursive
+```
+
+2. Create the environment, here we show an example using conda.
+```bash
+conda create -n dust3r python=3.11 cmake=3.14.0
+conda activate dust3r
+conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
+pip install -r requirements.txt
+# Optional: you can also install additional packages to:
+# - add support for HEIC images
+# - add pyrender, used to render depthmap in some datasets preprocessing
+# - add required packages for visloc.py
+pip install -r requirements_optional.txt
+```
+
+3. Optional, compile the cuda kernels for RoPE (as in CroCo v2).
+```bash
+# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.
+cd croco/models/curope/
+python setup.py build_ext --inplace
+cd ../../../
+```
+
+### Checkpoints
+
+You can obtain the checkpoints by two ways:
+
+1) You can use our huggingface_hub integration: the models will be downloaded automatically.
+
+2) Otherwise, We provide several pre-trained models:
+
+| Modelname | Training resolutions | Head | Encoder | Decoder |
+|-------------|----------------------|------|---------|---------|
+| [`DUSt3R_ViTLarge_BaseDecoder_224_linear.pth`](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_224_linear.pth) | 224x224 | Linear | ViT-L | ViT-B |
+| [`DUSt3R_ViTLarge_BaseDecoder_512_linear.pth`](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_linear.pth) | 512x384, 512x336, 512x288, 512x256, 512x160 | Linear | ViT-L | ViT-B |
+| [`DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth`](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth) | 512x384, 512x336, 512x288, 512x256, 512x160 | DPT | ViT-L | ViT-B |
+
+You can check the hyperparameters we used to train these models in the [section: Our Hyperparameters](#our-hyperparameters)
+
+To download a specific model, for example `DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth`:
+```bash
+mkdir -p checkpoints/
+wget https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth -P checkpoints/
+```
+
+For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0. Again, see [section: Our Hyperparameters](#our-hyperparameters) for details.
+
+### Interactive demo
+
+In this demo, you should be able run DUSt3R on your machine to reconstruct a scene.
+First select images that depicts the same scene.
+
+You can adjust the global alignment schedule and its number of iterations.
+
+> [!NOTE]
+> If you selected one or two images, the global alignment procedure will be skipped (mode=GlobalAlignerMode.PairViewer)
+
+Hit "Run" and wait.
+When the global alignment ends, the reconstruction appears.
+Use the slider "min_conf_thr" to show or remove low confidence areas.
+
+```bash
+python3 demo.py --model_name DUSt3R_ViTLarge_BaseDecoder_512_dpt
+
+# Use --weights to load a checkpoint from a local file, eg --weights checkpoints/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth
+# Use --image_size to select the correct resolution for the selected checkpoint. 512 (default) or 224
+# Use --local_network to make it accessible on the local network, or --server_name to specify the url manually
+# Use --server_port to change the port, by default it will search for an available port starting at 7860
+# Use --device to use a different device, by default it's "cuda"
+```
+
+### Interactive demo with docker
+
+To run DUSt3R using Docker, including with NVIDIA CUDA support, follow these instructions:
+
+1. **Install Docker**: If not already installed, download and install `docker` and `docker compose` from the [Docker website](https://www.docker.com/get-started).
+
+2. **Install NVIDIA Docker Toolkit**: For GPU support, install the NVIDIA Docker toolkit from the [Nvidia website](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).
+
+3. **Build the Docker image and run it**: `cd` into the `./docker` directory and run the following commands:
+
+```bash
+cd docker
+bash run.sh --with-cuda --model_name="DUSt3R_ViTLarge_BaseDecoder_512_dpt"
+```
+
+Or if you want to run the demo without CUDA support, run the following command:
+
+```bash
+cd docker
+bash run.sh --model_name="DUSt3R_ViTLarge_BaseDecoder_512_dpt"
+```
+
+By default, `demo.py` is lanched with the option `--local_network`.
+Visit `http://localhost:7860/` to access the web UI (or replace `localhost` with the machine's name to access it from the network).
+
+`run.sh` will launch docker-compose using either the [docker-compose-cuda.yml](docker/docker-compose-cuda.yml) or [docker-compose-cpu.ym](docker/docker-compose-cpu.yml) config file, then it starts the demo using [entrypoint.sh](docker/files/entrypoint.sh).
+
+
+
+
+## Usage
+
+```python
+from dust3r.inference import inference
+from dust3r.model import AsymmetricCroCo3DStereo
+from dust3r.utils.image import load_images
+from dust3r.image_pairs import make_pairs
+from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
+
+if __name__ == '__main__':
+ device = 'cuda'
+ batch_size = 1
+ schedule = 'cosine'
+ lr = 0.01
+ niter = 300
+
+ model_name = "naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt"
+ # you can put the path to a local checkpoint in model_name if needed
+ model = AsymmetricCroCo3DStereo.from_pretrained(model_name).to(device)
+ # load_images can take a list of images or a directory
+ images = load_images(['croco/assets/Chateau1.png', 'croco/assets/Chateau2.png'], size=512)
+ pairs = make_pairs(images, scene_graph='complete', prefilter=None, symmetrize=True)
+ output = inference(pairs, model, device, batch_size=batch_size)
+
+ # at this stage, you have the raw dust3r predictions
+ view1, pred1 = output['view1'], output['pred1']
+ view2, pred2 = output['view2'], output['pred2']
+ # here, view1, pred1, view2, pred2 are dicts of lists of len(2)
+ # -> because we symmetrize we have (im1, im2) and (im2, im1) pairs
+ # in each view you have:
+ # an integer image identifier: view1['idx'] and view2['idx']
+ # the img: view1['img'] and view2['img']
+ # the image shape: view1['true_shape'] and view2['true_shape']
+ # an instance string output by the dataloader: view1['instance'] and view2['instance']
+ # pred1 and pred2 contains the confidence values: pred1['conf'] and pred2['conf']
+ # pred1 contains 3D points for view1['img'] in view1['img'] space: pred1['pts3d']
+ # pred2 contains 3D points for view2['img'] in view1['img'] space: pred2['pts3d_in_other_view']
+
+ # next we'll use the global_aligner to align the predictions
+ # depending on your task, you may be fine with the raw output and not need it
+ # with only two input images, you could use GlobalAlignerMode.PairViewer: it would just convert the output
+ # if using GlobalAlignerMode.PairViewer, no need to run compute_global_alignment
+ scene = global_aligner(output, device=device, mode=GlobalAlignerMode.PointCloudOptimizer)
+ loss = scene.compute_global_alignment(init="mst", niter=niter, schedule=schedule, lr=lr)
+
+ # retrieve useful values from scene:
+ imgs = scene.imgs
+ focals = scene.get_focals()
+ poses = scene.get_im_poses()
+ pts3d = scene.get_pts3d()
+ confidence_masks = scene.get_masks()
+
+ # visualize reconstruction
+ scene.show()
+
+ # find 2D-2D matches between the two images
+ from dust3r.utils.geometry import find_reciprocal_matches, xy_grid
+ pts2d_list, pts3d_list = [], []
+ for i in range(2):
+ conf_i = confidence_masks[i].cpu().numpy()
+ pts2d_list.append(xy_grid(*imgs[i].shape[:2][::-1])[conf_i]) # imgs[i].shape[:2] = (H, W)
+ pts3d_list.append(pts3d[i].detach().cpu().numpy()[conf_i])
+ reciprocal_in_P2, nn2_in_P1, num_matches = find_reciprocal_matches(*pts3d_list)
+ print(f'found {num_matches} matches')
+ matches_im1 = pts2d_list[1][reciprocal_in_P2]
+ matches_im0 = pts2d_list[0][nn2_in_P1][reciprocal_in_P2]
+
+ # visualize a few matches
+ import numpy as np
+ from matplotlib import pyplot as pl
+ n_viz = 10
+ match_idx_to_viz = np.round(np.linspace(0, num_matches-1, n_viz)).astype(int)
+ viz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz]
+
+ H0, W0, H1, W1 = *imgs[0].shape[:2], *imgs[1].shape[:2]
+ img0 = np.pad(imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
+ img1 = np.pad(imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
+ img = np.concatenate((img0, img1), axis=1)
+ pl.figure()
+ pl.imshow(img)
+ cmap = pl.get_cmap('jet')
+ for i in range(n_viz):
+ (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T
+ pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)
+ pl.show(block=True)
+
+```
+
+
+## Training
+
+In this section, we present a short demonstration to get started with training DUSt3R.
+
+### Datasets
+At this moment, we have added the following training datasets:
+ - [CO3Dv2](https://github.com/facebookresearch/co3d) - [Creative Commons Attribution-NonCommercial 4.0 International](https://github.com/facebookresearch/co3d/blob/main/LICENSE)
+ - [ARKitScenes](https://github.com/apple/ARKitScenes) - [Creative Commons Attribution-NonCommercial-ShareAlike 4.0](https://github.com/apple/ARKitScenes/tree/main?tab=readme-ov-file#license)
+ - [ScanNet++](https://kaldir.vc.in.tum.de/scannetpp/) - [non-commercial research and educational purposes](https://kaldir.vc.in.tum.de/scannetpp/static/scannetpp-terms-of-use.pdf)
+ - [BlendedMVS](https://github.com/YoYo000/BlendedMVS) - [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)
+ - [WayMo Open dataset](https://github.com/waymo-research/waymo-open-dataset) - [Non-Commercial Use](https://waymo.com/open/terms/)
+ - [Habitat-Sim](https://github.com/facebookresearch/habitat-sim/blob/main/DATASETS.md)
+ - [MegaDepth](https://www.cs.cornell.edu/projects/megadepth/)
+ - [StaticThings3D](https://github.com/lmb-freiburg/robustmvd/blob/master/rmvd/data/README.md#staticthings3d)
+ - [WildRGB-D](https://github.com/wildrgbd/wildrgbd/)
+
+For each dataset, we provide a preprocessing script in the `datasets_preprocess` directory and an archive containing the list of pairs when needed.
+You have to download the datasets yourself from their official sources, agree to their license, download our list of pairs, and run the preprocessing script.
+
+Links:
+
+[ARKitScenes pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/arkitscenes_pairs.zip)
+[ScanNet++ pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/scannetpp_pairs.zip)
+[BlendedMVS pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/blendedmvs_pairs.npy)
+[WayMo Open dataset pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/waymo_pairs.npz)
+[Habitat metadata](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/habitat_5views_v1_512x512_metadata.tar.gz)
+[MegaDepth pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/megadepth_pairs.npz)
+[StaticThings3D pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/staticthings_pairs.npy)
+
+> [!NOTE]
+> They are not strictly equivalent to what was used to train DUSt3R, but they should be close enough.
+
+### Demo
+For this training demo, we're going to download and prepare a subset of [CO3Dv2](https://github.com/facebookresearch/co3d) - [Creative Commons Attribution-NonCommercial 4.0 International](https://github.com/facebookresearch/co3d/blob/main/LICENSE) and launch the training code on it.
+The demo model will be trained for a few epochs on a very small dataset.
+It will not be very good.
+
+```bash
+# download and prepare the co3d subset
+mkdir -p data/co3d_subset
+cd data/co3d_subset
+git clone https://github.com/facebookresearch/co3d
+cd co3d
+python3 ./co3d/download_dataset.py --download_folder ../ --single_sequence_subset
+rm ../*.zip
+cd ../../..
+
+python3 datasets_preprocess/preprocess_co3d.py --co3d_dir data/co3d_subset --output_dir data/co3d_subset_processed --single_sequence_subset
+
+# download the pretrained croco v2 checkpoint
+mkdir -p checkpoints/
+wget https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo_V2_ViTLarge_BaseDecoder.pth -P checkpoints/
+
+# the training of dust3r is done in 3 steps.
+# for this example we'll do fewer epochs, for the actual hyperparameters we used in the paper, see the next section: "Our Hyperparameters"
+# step 1 - train dust3r for 224 resolution
+torchrun --nproc_per_node=4 train.py \
+ --train_dataset "1000 @ Co3d(split='train', ROOT='data/co3d_subset_processed', aug_crop=16, mask_bg='rand', resolution=224, transform=ColorJitter)" \
+ --test_dataset "100 @ Co3d(split='test', ROOT='data/co3d_subset_processed', resolution=224, seed=777)" \
+ --model "AsymmetricCroCo3DStereo(pos_embed='RoPE100', img_size=(224, 224), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --train_criterion "ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion "Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --pretrained "checkpoints/CroCo_V2_ViTLarge_BaseDecoder.pth" \
+ --lr 0.0001 --min_lr 1e-06 --warmup_epochs 1 --epochs 10 --batch_size 16 --accum_iter 1 \
+ --save_freq 1 --keep_freq 5 --eval_freq 1 \
+ --output_dir "checkpoints/dust3r_demo_224"
+
+# step 2 - train dust3r for 512 resolution
+torchrun --nproc_per_node=4 train.py \
+ --train_dataset "1000 @ Co3d(split='train', ROOT='data/co3d_subset_processed', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter)" \
+ --test_dataset "100 @ Co3d(split='test', ROOT='data/co3d_subset_processed', resolution=(512,384), seed=777)" \
+ --model "AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --train_criterion "ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion "Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --pretrained "checkpoints/dust3r_demo_224/checkpoint-best.pth" \
+ --lr 0.0001 --min_lr 1e-06 --warmup_epochs 1 --epochs 10 --batch_size 4 --accum_iter 4 \
+ --save_freq 1 --keep_freq 5 --eval_freq 1 \
+ --output_dir "checkpoints/dust3r_demo_512"
+
+# step 3 - train dust3r for 512 resolution with dpt
+torchrun --nproc_per_node=4 train.py \
+ --train_dataset "1000 @ Co3d(split='train', ROOT='data/co3d_subset_processed', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter)" \
+ --test_dataset "100 @ Co3d(split='test', ROOT='data/co3d_subset_processed', resolution=(512,384), seed=777)" \
+ --model "AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='dpt', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --train_criterion "ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion "Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --pretrained "checkpoints/dust3r_demo_512/checkpoint-best.pth" \
+ --lr 0.0001 --min_lr 1e-06 --warmup_epochs 1 --epochs 10 --batch_size 2 --accum_iter 8 \
+ --save_freq 1 --keep_freq 5 --eval_freq 1 \
+ --output_dir "checkpoints/dust3r_demo_512dpt"
+
+```
+
+### Our Hyperparameters
+
+Here are the commands we used for training the models:
+
+```bash
+# NOTE: ROOT path omitted for datasets
+# 224 linear
+torchrun --nproc_per_node 8 train.py \
+ --train_dataset=" + 100_000 @ Habitat(1_000_000, split='train', aug_crop=16, resolution=224, transform=ColorJitter) + 100_000 @ BlendedMVS(split='train', aug_crop=16, resolution=224, transform=ColorJitter) + 100_000 @ MegaDepth(split='train', aug_crop=16, resolution=224, transform=ColorJitter) + 100_000 @ ARKitScenes(aug_crop=256, resolution=224, transform=ColorJitter) + 100_000 @ Co3d(split='train', aug_crop=16, mask_bg='rand', resolution=224, transform=ColorJitter) + 100_000 @ StaticThings3D(aug_crop=256, mask_bg='rand', resolution=224, transform=ColorJitter) + 100_000 @ ScanNetpp(split='train', aug_crop=256, resolution=224, transform=ColorJitter) + 100_000 @ InternalUnreleasedDataset(aug_crop=128, resolution=224, transform=ColorJitter) " \
+ --test_dataset=" Habitat(1_000, split='val', resolution=224, seed=777) + 1_000 @ BlendedMVS(split='val', resolution=224, seed=777) + 1_000 @ MegaDepth(split='val', resolution=224, seed=777) + 1_000 @ Co3d(split='test', mask_bg='rand', resolution=224, seed=777) " \
+ --train_criterion="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion="Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', img_size=(224, 224), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --pretrained="checkpoints/CroCo_V2_ViTLarge_BaseDecoder.pth" \
+ --lr=0.0001 --min_lr=1e-06 --warmup_epochs=10 --epochs=100 --batch_size=16 --accum_iter=1 \
+ --save_freq=5 --keep_freq=10 --eval_freq=1 \
+ --output_dir="checkpoints/dust3r_224"
+
+# 512 linear
+torchrun --nproc_per_node 8 train.py \
+ --train_dataset=" + 10_000 @ Habitat(1_000_000, split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ BlendedMVS(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ MegaDepth(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ARKitScenes(aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ Co3d(split='train', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ StaticThings3D(aug_crop=256, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ScanNetpp(split='train', aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ InternalUnreleasedDataset(aug_crop=128, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) " \
+ --test_dataset=" Habitat(1_000, split='val', resolution=(512,384), seed=777) + 1_000 @ BlendedMVS(split='val', resolution=(512,384), seed=777) + 1_000 @ MegaDepth(split='val', resolution=(512,336), seed=777) + 1_000 @ Co3d(split='test', resolution=(512,384), seed=777) " \
+ --train_criterion="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion="Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --pretrained="checkpoints/dust3r_224/checkpoint-best.pth" \
+ --lr=0.0001 --min_lr=1e-06 --warmup_epochs=20 --epochs=100 --batch_size=4 --accum_iter=2 \
+ --save_freq=10 --keep_freq=10 --eval_freq=1 --print_freq=10 \
+ --output_dir="checkpoints/dust3r_512"
+
+# 512 dpt
+torchrun --nproc_per_node 8 train.py \
+ --train_dataset=" + 10_000 @ Habitat(1_000_000, split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ BlendedMVS(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ MegaDepth(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ARKitScenes(aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ Co3d(split='train', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ StaticThings3D(aug_crop=256, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ScanNetpp(split='train', aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ InternalUnreleasedDataset(aug_crop=128, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) " \
+ --test_dataset=" Habitat(1_000, split='val', resolution=(512,384), seed=777) + 1_000 @ BlendedMVS(split='val', resolution=(512,384), seed=777) + 1_000 @ MegaDepth(split='val', resolution=(512,336), seed=777) + 1_000 @ Co3d(split='test', resolution=(512,384), seed=777) " \
+ --train_criterion="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
+ --test_criterion="Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
+ --model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='dpt', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
+ --pretrained="checkpoints/dust3r_512/checkpoint-best.pth" \
+ --lr=0.0001 --min_lr=1e-06 --warmup_epochs=15 --epochs=90 --batch_size=4 --accum_iter=2 \
+ --save_freq=5 --keep_freq=10 --eval_freq=1 --print_freq=10 \
+ --output_dir="checkpoints/dust3r_512dpt"
+
+```
diff --git a/third_party/mast3r/dust3r/assets/demo.jpg b/third_party/mast3r/dust3r/assets/demo.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c815d468d83a7e91a0ccc24a2f491b10178e955f
--- /dev/null
+++ b/third_party/mast3r/dust3r/assets/demo.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:957a892f9033fb3e733546a202e3c07e362618c708eacf050979d4c4edd5435f
+size 339600
diff --git a/third_party/mast3r/dust3r/assets/dust3r.jpg b/third_party/mast3r/dust3r/assets/dust3r.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8402ae4d08eba0fb9c9e3d7441d3bc451e9f460f
--- /dev/null
+++ b/third_party/mast3r/dust3r/assets/dust3r.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0bdf6ee8fd7ccb52ccd09937df60c72bd750a47c6d982efc2ba9808eb305bcba
+size 25927
diff --git a/third_party/mast3r/dust3r/assets/dust3r_archi.jpg b/third_party/mast3r/dust3r/assets/dust3r_archi.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fc2c5d1a154eb29d6c8e4507e408d7478eace3f3
--- /dev/null
+++ b/third_party/mast3r/dust3r/assets/dust3r_archi.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7262d42f63ac61acec20830602452a877264c5575fd7923834c1f2b035a2d9d1
+size 39454
diff --git a/third_party/mast3r/dust3r/assets/matching.jpg b/third_party/mast3r/dust3r/assets/matching.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..636e69c70921c7dac3872fedaee4d508af7ba4db
--- /dev/null
+++ b/third_party/mast3r/dust3r/assets/matching.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ecfe07fd00505045a155902c5686cc23060782a8b020f7596829fb60584a79ee
+size 159312
diff --git a/third_party/mast3r/dust3r/assets/pipeline1.jpg b/third_party/mast3r/dust3r/assets/pipeline1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..90b0b58701bf7a660d07cb0c54c617ca0aab8bda
--- /dev/null
+++ b/third_party/mast3r/dust3r/assets/pipeline1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1fd599e928b3ab6560ecc8491c2000ca2809372f656f87bbdd7e6daaf0e2ce92
+size 72026
diff --git a/third_party/mast3r/dust3r/croco/LICENSE b/third_party/mast3r/dust3r/croco/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..d9b84b1a65f9db6d8920a9048d162f52ba3ea56d
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/LICENSE
@@ -0,0 +1,52 @@
+CroCo, Copyright (c) 2022-present Naver Corporation, is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license.
+
+A summary of the CC BY-NC-SA 4.0 license is located here:
+ https://creativecommons.org/licenses/by-nc-sa/4.0/
+
+The CC BY-NC-SA 4.0 license is located here:
+ https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
+
+
+SEE NOTICE BELOW WITH RESPECT TO THE FILE: models/pos_embed.py, models/blocks.py
+
+***************************
+
+NOTICE WITH RESPECT TO THE FILE: models/pos_embed.py
+
+This software is being redistributed in a modifiled form. The original form is available here:
+
+https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+
+This software in this file incorporates parts of the following software available here:
+
+Transformer: https://github.com/tensorflow/models/blob/master/official/legacy/transformer/model_utils.py
+available under the following license: https://github.com/tensorflow/models/blob/master/LICENSE
+
+MoCo v3: https://github.com/facebookresearch/moco-v3
+available under the following license: https://github.com/facebookresearch/moco-v3/blob/main/LICENSE
+
+DeiT: https://github.com/facebookresearch/deit
+available under the following license: https://github.com/facebookresearch/deit/blob/main/LICENSE
+
+
+ORIGINAL COPYRIGHT NOTICE AND PERMISSION NOTICE AVAILABLE HERE IS REPRODUCE BELOW:
+
+https://github.com/facebookresearch/mae/blob/main/LICENSE
+
+Attribution-NonCommercial 4.0 International
+
+***************************
+
+NOTICE WITH RESPECT TO THE FILE: models/blocks.py
+
+This software is being redistributed in a modifiled form. The original form is available here:
+
+https://github.com/rwightman/pytorch-image-models
+
+ORIGINAL COPYRIGHT NOTICE AND PERMISSION NOTICE AVAILABLE HERE IS REPRODUCE BELOW:
+
+https://github.com/rwightman/pytorch-image-models/blob/master/LICENSE
+
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
\ No newline at end of file
diff --git a/third_party/mast3r/dust3r/croco/NOTICE b/third_party/mast3r/dust3r/croco/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..d51bb365036c12d428d6e3a4fd00885756d5261c
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/NOTICE
@@ -0,0 +1,21 @@
+CroCo
+Copyright 2022-present NAVER Corp.
+
+This project contains subcomponents with separate copyright notices and license terms.
+Your use of the source code for these subcomponents is subject to the terms and conditions of the following licenses.
+
+====
+
+facebookresearch/mae
+https://github.com/facebookresearch/mae
+
+Attribution-NonCommercial 4.0 International
+
+====
+
+rwightman/pytorch-image-models
+https://github.com/rwightman/pytorch-image-models
+
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
\ No newline at end of file
diff --git a/third_party/mast3r/dust3r/croco/README.MD b/third_party/mast3r/dust3r/croco/README.MD
new file mode 100644
index 0000000000000000000000000000000000000000..38e33b001a60bd16749317fb297acd60f28a6f1b
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/README.MD
@@ -0,0 +1,124 @@
+# CroCo + CroCo v2 / CroCo-Stereo / CroCo-Flow
+
+[[`CroCo arXiv`](https://arxiv.org/abs/2210.10716)] [[`CroCo v2 arXiv`](https://arxiv.org/abs/2211.10408)] [[`project page and demo`](https://croco.europe.naverlabs.com/)]
+
+This repository contains the code for our CroCo model presented in our NeurIPS'22 paper [CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion](https://openreview.net/pdf?id=wZEfHUM5ri) and its follow-up extension published at ICCV'23 [Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow](https://openaccess.thecvf.com/content/ICCV2023/html/Weinzaepfel_CroCo_v2_Improved_Cross-view_Completion_Pre-training_for_Stereo_Matching_and_ICCV_2023_paper.html), refered to as CroCo v2:
+
+
+
+```bibtex
+@inproceedings{croco,
+ title={{CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion}},
+ author={{Weinzaepfel, Philippe and Leroy, Vincent and Lucas, Thomas and Br\'egier, Romain and Cabon, Yohann and Arora, Vaibhav and Antsfeld, Leonid and Chidlovskii, Boris and Csurka, Gabriela and Revaud J\'er\^ome}},
+ booktitle={{NeurIPS}},
+ year={2022}
+}
+
+@inproceedings{croco_v2,
+ title={{CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow}},
+ author={Weinzaepfel, Philippe and Lucas, Thomas and Leroy, Vincent and Cabon, Yohann and Arora, Vaibhav and Br{\'e}gier, Romain and Csurka, Gabriela and Antsfeld, Leonid and Chidlovskii, Boris and Revaud, J{\'e}r{\^o}me},
+ booktitle={ICCV},
+ year={2023}
+}
+```
+
+## License
+
+The code is distributed under the CC BY-NC-SA 4.0 License. See [LICENSE](LICENSE) for more information.
+Some components are based on code from [MAE](https://github.com/facebookresearch/mae) released under the CC BY-NC-SA 4.0 License and [timm](https://github.com/rwightman/pytorch-image-models) released under the Apache 2.0 License.
+Some components for stereo matching and optical flow are based on code from [unimatch](https://github.com/autonomousvision/unimatch) released under the MIT license.
+
+## Preparation
+
+1. Install dependencies on a machine with a NVidia GPU using e.g. conda. Note that `habitat-sim` is required only for the interactive demo and the synthetic pre-training data generation. If you don't plan to use it, you can ignore the line installing it and use a more recent python version.
+
+```bash
+conda create -n croco python=3.7 cmake=3.14.0
+conda activate croco
+conda install habitat-sim headless -c conda-forge -c aihabitat
+conda install pytorch torchvision -c pytorch
+conda install notebook ipykernel matplotlib
+conda install ipywidgets widgetsnbextension
+conda install scikit-learn tqdm quaternion opencv # only for pretraining / habitat data generation
+
+```
+
+2. Compile cuda kernels for RoPE
+
+CroCo v2 relies on RoPE positional embeddings for which you need to compile some cuda kernels.
+```bash
+cd models/curope/
+python setup.py build_ext --inplace
+cd ../../
+```
+
+This can be a bit long as we compile for all cuda architectures, feel free to update L9 of `models/curope/setup.py` to compile for specific architectures only.
+You might also need to set the environment `CUDA_HOME` in case you use a custom cuda installation.
+
+In case you cannot provide, we also provide a slow pytorch version, which will be automatically loaded.
+
+3. Download pre-trained model
+
+We provide several pre-trained models:
+
+| modelname | pre-training data | pos. embed. | Encoder | Decoder |
+|------------------------------------------------------------------------------------------------------------------------------------|-------------------|-------------|---------|---------|
+| [`CroCo.pth`](https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo.pth) | Habitat | cosine | ViT-B | Small |
+| [`CroCo_V2_ViTBase_SmallDecoder.pth`](https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo_V2_ViTBase_SmallDecoder.pth) | Habitat + real | RoPE | ViT-B | Small |
+| [`CroCo_V2_ViTBase_BaseDecoder.pth`](https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo_V2_ViTBase_BaseDecoder.pth) | Habitat + real | RoPE | ViT-B | Base |
+| [`CroCo_V2_ViTLarge_BaseDecoder.pth`](https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo_V2_ViTLarge_BaseDecoder.pth) | Habitat + real | RoPE | ViT-L | Base |
+
+To download a specific model, i.e., the first one (`CroCo.pth`)
+```bash
+mkdir -p pretrained_models/
+wget https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo.pth -P pretrained_models/
+```
+
+## Reconstruction example
+
+Simply run after downloading the `CroCo_V2_ViTLarge_BaseDecoder` pretrained model (or update the corresponding line in `demo.py`)
+```bash
+python demo.py
+```
+
+## Interactive demonstration of cross-view completion reconstruction on the Habitat simulator
+
+First download the test scene from Habitat:
+```bash
+python -m habitat_sim.utils.datasets_download --uids habitat_test_scenes --data-path habitat-sim-data/
+```
+
+Then, run the Notebook demo `interactive_demo.ipynb`.
+
+In this demo, you should be able to sample a random reference viewpoint from an [Habitat](https://github.com/facebookresearch/habitat-sim) test scene. Use the sliders to change viewpoint and select a masked target view to reconstruct using CroCo.
+
+
+## Pre-training
+
+### CroCo
+
+To pre-train CroCo, please first generate the pre-training data from the Habitat simulator, following the instructions in [datasets/habitat_sim/README.MD](datasets/habitat_sim/README.MD) and then run the following command:
+```
+torchrun --nproc_per_node=4 pretrain.py --output_dir ./output/pretraining/
+```
+
+Our CroCo pre-training was launched on a single server with 4 GPUs.
+It should take around 10 days with A100 or 15 days with V100 to do the 400 pre-training epochs, but decent performances are obtained earlier in training.
+Note that, while the code contains the same scaling rule of the learning rate as MAE when changing the effective batch size, we did not experimented if it is valid in our case.
+The first run can take a few minutes to start, to parse all available pre-training pairs.
+
+### CroCo v2
+
+For CroCo v2 pre-training, in addition to the generation of the pre-training data from the Habitat simulator above, please pre-extract the crops from the real datasets following the instructions in [datasets/crops/README.MD](datasets/crops/README.MD).
+Then, run the following command for the largest model (ViT-L encoder, Base decoder):
+```
+torchrun --nproc_per_node=8 pretrain.py --model "CroCoNet(enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_num_heads=12, dec_depth=12, pos_embed='RoPE100')" --dataset "habitat_release+ARKitScenes+MegaDepth+3DStreetView+IndoorVL" --warmup_epochs 12 --max_epoch 125 --epochs 250 --amp 0 --keep_freq 5 --output_dir ./output/pretraining_crocov2/
+```
+
+Our CroCo v2 pre-training was launched on a single server with 8 GPUs for the largest model, and on a single server with 4 GPUs for the smaller ones, keeping a batch size of 64 per gpu in all cases.
+The largest model should take around 12 days on A100.
+Note that, while the code contains the same scaling rule of the learning rate as MAE when changing the effective batch size, we did not experimented if it is valid in our case.
+
+## Stereo matching and Optical flow downstream tasks
+
+For CroCo-Stereo and CroCo-Flow, please refer to [stereoflow/README.MD](stereoflow/README.MD).
diff --git a/third_party/mast3r/dust3r/croco/assets/Chateau1.png b/third_party/mast3r/dust3r/croco/assets/Chateau1.png
new file mode 100644
index 0000000000000000000000000000000000000000..295b00e46972ffcacaca60c2c7c7ec7a04c762fa
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/assets/Chateau1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71ffb8c7d77e5ced0bb3dcd2cb0db84d0e98e6ff5ffd2d02696a7156e5284857
+size 112106
diff --git a/third_party/mast3r/dust3r/croco/assets/Chateau2.png b/third_party/mast3r/dust3r/croco/assets/Chateau2.png
new file mode 100644
index 0000000000000000000000000000000000000000..97b3c058ff180a6d0c0853ab533b0823a06f8425
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/assets/Chateau2.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3a0be9e19f6b89491d692c71e3f2317c2288a898a990561d48b7667218b47c8
+size 109905
diff --git a/third_party/mast3r/dust3r/croco/assets/arch.jpg b/third_party/mast3r/dust3r/croco/assets/arch.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..894c58e25c2d9ee0b579c6f5a6ce78d12217d106
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/assets/arch.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05fbf12896a79819a3864a800b174896bd3b6fa29b4f4f580d06725ff7c30dc7
+size 74842
diff --git a/third_party/mast3r/dust3r/croco/croco-stereo-flow-demo.ipynb b/third_party/mast3r/dust3r/croco/croco-stereo-flow-demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2b00a7607ab5f82d1857041969bfec977e56b3e0
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/croco-stereo-flow-demo.ipynb
@@ -0,0 +1,191 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9bca0f41",
+ "metadata": {},
+ "source": [
+ "# Simple inference example with CroCo-Stereo or CroCo-Flow"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "80653ef7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Copyright (C) 2022-present Naver Corporation. All rights reserved.\n",
+ "# Licensed under CC BY-NC-SA 4.0 (non-commercial use only)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4f033862",
+ "metadata": {},
+ "source": [
+ "First download the model(s) of your choice by running\n",
+ "```\n",
+ "bash stereoflow/download_model.sh crocostereo.pth\n",
+ "bash stereoflow/download_model.sh crocoflow.pth\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1fb2e392",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "use_gpu = torch.cuda.is_available() and torch.cuda.device_count()>0\n",
+ "device = torch.device('cuda:0' if use_gpu else 'cpu')\n",
+ "import matplotlib.pylab as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0e25d77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from stereoflow.test import _load_model_and_criterion\n",
+ "from stereoflow.engine import tiled_pred\n",
+ "from stereoflow.datasets_stereo import img_to_tensor, vis_disparity\n",
+ "from stereoflow.datasets_flow import flowToColor\n",
+ "tile_overlap=0.7 # recommended value, higher value can be slightly better but slower"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "86a921f5",
+ "metadata": {},
+ "source": [
+ "### CroCo-Stereo example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64e483cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image1 = np.asarray(Image.open(''))\n",
+ "image2 = np.asarray(Image.open(''))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f0d04303",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model, _, cropsize, with_conf, task, tile_conf_mode = _load_model_and_criterion('stereoflow_models/crocostereo.pth', None, device)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "47dc14b5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "im1 = img_to_tensor(image1).to(device).unsqueeze(0)\n",
+ "im2 = img_to_tensor(image2).to(device).unsqueeze(0)\n",
+ "with torch.inference_mode():\n",
+ " pred, _, _ = tiled_pred(model, None, im1, im2, None, conf_mode=tile_conf_mode, overlap=tile_overlap, crop=cropsize, with_conf=with_conf, return_time=False)\n",
+ "pred = pred.squeeze(0).squeeze(0).cpu().numpy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "583b9f16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.imshow(vis_disparity(pred))\n",
+ "plt.axis('off')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2df5d70",
+ "metadata": {},
+ "source": [
+ "### CroCo-Flow example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ee257a7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image1 = np.asarray(Image.open(''))\n",
+ "image2 = np.asarray(Image.open(''))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d5edccf0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model, _, cropsize, with_conf, task, tile_conf_mode = _load_model_and_criterion('stereoflow_models/crocoflow.pth', None, device)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b19692c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "im1 = img_to_tensor(image1).to(device).unsqueeze(0)\n",
+ "im2 = img_to_tensor(image2).to(device).unsqueeze(0)\n",
+ "with torch.inference_mode():\n",
+ " pred, _, _ = tiled_pred(model, None, im1, im2, None, conf_mode=tile_conf_mode, overlap=tile_overlap, crop=cropsize, with_conf=with_conf, return_time=False)\n",
+ "pred = pred.squeeze(0).permute(1,2,0).cpu().numpy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26f79db3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.imshow(flowToColor(pred))\n",
+ "plt.axis('off')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/third_party/mast3r/dust3r/croco/datasets/__init__.py b/third_party/mast3r/dust3r/croco/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/third_party/mast3r/dust3r/croco/datasets/crops/README.MD b/third_party/mast3r/dust3r/croco/datasets/crops/README.MD
new file mode 100644
index 0000000000000000000000000000000000000000..47ddabebb177644694ee247ae878173a3a16644f
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/datasets/crops/README.MD
@@ -0,0 +1,104 @@
+## Generation of crops from the real datasets
+
+The instructions below allow to generate the crops used for pre-training CroCo v2 from the following real-world datasets: ARKitScenes, MegaDepth, 3DStreetView and IndoorVL.
+
+### Download the metadata of the crops to generate
+
+First, download the metadata and put them in `./data/`:
+```
+mkdir -p data
+cd data/
+wget https://download.europe.naverlabs.com/ComputerVision/CroCo/data/crop_metadata.zip
+unzip crop_metadata.zip
+rm crop_metadata.zip
+cd ..
+```
+
+### Prepare the original datasets
+
+Second, download the original datasets in `./data/original_datasets/`.
+```
+mkdir -p data/original_datasets
+```
+
+##### ARKitScenes
+
+Download the `raw` dataset from https://github.com/apple/ARKitScenes/blob/main/DATA.md and put it in `./data/original_datasets/ARKitScenes/`.
+The resulting file structure should be like:
+```
+./data/original_datasets/ARKitScenes/
+└───Training
+ └───40753679
+ │ │ ultrawide
+ │ │ ...
+ └───40753686
+ │
+ ...
+```
+
+##### MegaDepth
+
+Download `MegaDepth v1 Dataset` from https://www.cs.cornell.edu/projects/megadepth/ and put it in `./data/original_datasets/MegaDepth/`.
+The resulting file structure should be like:
+
+```
+./data/original_datasets/MegaDepth/
+└───0000
+│ └───images
+│ │ │ 1000557903_87fa96b8a4_o.jpg
+│ │ └ ...
+│ └─── ...
+└───0001
+│ │
+│ └ ...
+└─── ...
+```
+
+##### 3DStreetView
+
+Download `3D_Street_View` dataset from https://github.com/amir32002/3D_Street_View and put it in `./data/original_datasets/3DStreetView/`.
+The resulting file structure should be like:
+
+```
+./data/original_datasets/3DStreetView/
+└───dataset_aligned
+│ └───0002
+│ │ │ 0000002_0000001_0000002_0000001.jpg
+│ │ └ ...
+│ └─── ...
+└───dataset_unaligned
+│ └───0003
+│ │ │ 0000003_0000001_0000002_0000001.jpg
+│ │ └ ...
+│ └─── ...
+```
+
+##### IndoorVL
+
+Download the `IndoorVL` datasets using [Kapture](https://github.com/naver/kapture).
+
+```
+pip install kapture
+mkdir -p ./data/original_datasets/IndoorVL
+cd ./data/original_datasets/IndoorVL
+kapture_download_dataset.py update
+kapture_download_dataset.py install "HyundaiDepartmentStore_*"
+kapture_download_dataset.py install "GangnamStation_*"
+cd -
+```
+
+### Extract the crops
+
+Now, extract the crops for each of the dataset:
+```
+for dataset in ARKitScenes MegaDepth 3DStreetView IndoorVL;
+do
+ python3 datasets/crops/extract_crops_from_images.py --crops ./data/crop_metadata/${dataset}/crops_release.txt --root-dir ./data/original_datasets/${dataset}/ --output-dir ./data/${dataset}_crops/ --imsize 256 --nthread 8 --max-subdir-levels 5 --ideal-number-pairs-in-dir 500;
+done
+```
+
+##### Note for IndoorVL
+
+Due to some legal issues, we can only release 144,228 pairs out of the 1,593,689 pairs used in the paper.
+To account for it in terms of number of pre-training iterations, the pre-training command in this repository uses 125 training epochs including 12 warm-up epochs and learning rate cosine schedule of 250, instead of 100, 10 and 200 respectively.
+The impact on the performance is negligible.
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/crops/extract_crops_from_images.py b/third_party/mast3r/dust3r/croco/datasets/crops/extract_crops_from_images.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/crops/extract_crops_from_images.py
rename to third_party/mast3r/dust3r/croco/datasets/crops/extract_crops_from_images.py
diff --git a/third_party/mast3r/dust3r/croco/datasets/habitat_sim/README.MD b/third_party/mast3r/dust3r/croco/datasets/habitat_sim/README.MD
new file mode 100644
index 0000000000000000000000000000000000000000..a505781ff9eb91bce7f1d189e848f8ba1c560940
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/datasets/habitat_sim/README.MD
@@ -0,0 +1,76 @@
+## Generation of synthetic image pairs using Habitat-Sim
+
+These instructions allow to generate pre-training pairs from the Habitat simulator.
+As we did not save metadata of the pairs used in the original paper, they are not strictly the same, but these data use the same setting and are equivalent.
+
+### Download Habitat-Sim scenes
+Download Habitat-Sim scenes:
+- Download links can be found here: https://github.com/facebookresearch/habitat-sim/blob/main/DATASETS.md
+- We used scenes from the HM3D, habitat-test-scenes, Replica, ReplicaCad and ScanNet datasets.
+- Please put the scenes under `./data/habitat-sim-data/scene_datasets/` following the structure below, or update manually paths in `paths.py`.
+```
+./data/
+└──habitat-sim-data/
+ └──scene_datasets/
+ ├──hm3d/
+ ├──gibson/
+ ├──habitat-test-scenes/
+ ├──replica_cad_baked_lighting/
+ ├──replica_cad/
+ ├──ReplicaDataset/
+ └──scannet/
+```
+
+### Image pairs generation
+We provide metadata to generate reproducible images pairs for pretraining and validation.
+Experiments described in the paper used similar data, but whose generation was not reproducible at the time.
+
+Specifications:
+- 256x256 resolution images, with 60 degrees field of view .
+- Up to 1000 image pairs per scene.
+- Number of scenes considered/number of images pairs per dataset:
+ - Scannet: 1097 scenes / 985 209 pairs
+ - HM3D:
+ - hm3d/train: 800 / 800k pairs
+ - hm3d/val: 100 scenes / 100k pairs
+ - hm3d/minival: 10 scenes / 10k pairs
+ - habitat-test-scenes: 3 scenes / 3k pairs
+ - replica_cad_baked_lighting: 13 scenes / 13k pairs
+
+- Scenes from hm3d/val and hm3d/minival pairs were not used for the pre-training but kept for validation purposes.
+
+Download metadata and extract it:
+```bash
+mkdir -p data/habitat_release_metadata/
+cd data/habitat_release_metadata/
+wget https://download.europe.naverlabs.com/ComputerVision/CroCo/data/habitat_release_metadata/multiview_habitat_metadata.tar.gz
+tar -xvf multiview_habitat_metadata.tar.gz
+cd ../..
+# Location of the metadata
+METADATA_DIR="./data/habitat_release_metadata/multiview_habitat_metadata"
+```
+
+Generate image pairs from metadata:
+- The following command will print a list of commandlines to generate image pairs for each scene:
+```bash
+# Target output directory
+PAIRS_DATASET_DIR="./data/habitat_release/"
+python datasets/habitat_sim/generate_from_metadata_files.py --input_dir=$METADATA_DIR --output_dir=$PAIRS_DATASET_DIR
+```
+- One can launch multiple of such commands in parallel e.g. using GNU Parallel:
+```bash
+python datasets/habitat_sim/generate_from_metadata_files.py --input_dir=$METADATA_DIR --output_dir=$PAIRS_DATASET_DIR | parallel -j 16
+```
+
+## Metadata generation
+
+Image pairs were randomly sampled using the following commands, whose outputs contain randomness and are thus not exactly reproducible:
+```bash
+# Print commandlines to generate image pairs from the different scenes available.
+PAIRS_DATASET_DIR=MY_CUSTOM_PATH
+python datasets/habitat_sim/generate_multiview_images.py --list_commands --output_dir=$PAIRS_DATASET_DIR
+
+# Once a dataset is generated, pack metadata files for reproducibility.
+METADATA_DIR=MY_CUSTON_PATH
+python datasets/habitat_sim/pack_metadata_files.py $PAIRS_DATASET_DIR $METADATA_DIR
+```
diff --git a/third_party/mast3r/dust3r/croco/datasets/habitat_sim/__init__.py b/third_party/mast3r/dust3r/croco/datasets/habitat_sim/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_from_metadata.py b/third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_from_metadata.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_from_metadata.py
rename to third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_from_metadata.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_from_metadata_files.py b/third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_from_metadata_files.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_from_metadata_files.py
rename to third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_from_metadata_files.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_multiview_images.py b/third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_multiview_images.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_multiview_images.py
rename to third_party/mast3r/dust3r/croco/datasets/habitat_sim/generate_multiview_images.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/multiview_habitat_sim_generator.py b/third_party/mast3r/dust3r/croco/datasets/habitat_sim/multiview_habitat_sim_generator.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/multiview_habitat_sim_generator.py
rename to third_party/mast3r/dust3r/croco/datasets/habitat_sim/multiview_habitat_sim_generator.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/pack_metadata_files.py b/third_party/mast3r/dust3r/croco/datasets/habitat_sim/pack_metadata_files.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/pack_metadata_files.py
rename to third_party/mast3r/dust3r/croco/datasets/habitat_sim/pack_metadata_files.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/paths.py b/third_party/mast3r/dust3r/croco/datasets/habitat_sim/paths.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/habitat_sim/paths.py
rename to third_party/mast3r/dust3r/croco/datasets/habitat_sim/paths.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/pairs_dataset.py b/third_party/mast3r/dust3r/croco/datasets/pairs_dataset.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/pairs_dataset.py
rename to third_party/mast3r/dust3r/croco/datasets/pairs_dataset.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/datasets/transforms.py b/third_party/mast3r/dust3r/croco/datasets/transforms.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/datasets/transforms.py
rename to third_party/mast3r/dust3r/croco/datasets/transforms.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/demo.py b/third_party/mast3r/dust3r/croco/demo.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/demo.py
rename to third_party/mast3r/dust3r/croco/demo.py
diff --git a/third_party/mast3r/dust3r/croco/interactive_demo.ipynb b/third_party/mast3r/dust3r/croco/interactive_demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6cfc960af5baac9a69029c29a16eea4e24123a71
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/interactive_demo.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Interactive demo of Cross-view Completion."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Copyright (C) 2022-present Naver Corporation. All rights reserved.\n",
+ "# Licensed under CC BY-NC-SA 4.0 (non-commercial use only)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "from models.croco import CroCoNet\n",
+ "from ipywidgets import interact, interactive, fixed, interact_manual\n",
+ "import ipywidgets as widgets\n",
+ "import matplotlib.pyplot as plt\n",
+ "import quaternion\n",
+ "import models.masking"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load CroCo model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ckpt = torch.load('pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth', 'cpu')\n",
+ "model = CroCoNet( **ckpt.get('croco_kwargs',{}))\n",
+ "msg = model.load_state_dict(ckpt['model'], strict=True)\n",
+ "use_gpu = torch.cuda.is_available() and torch.cuda.device_count()>0\n",
+ "device = torch.device('cuda:0' if use_gpu else 'cpu')\n",
+ "model = model.eval()\n",
+ "model = model.to(device=device)\n",
+ "print(msg)\n",
+ "\n",
+ "def process_images(ref_image, target_image, masking_ratio, reconstruct_unmasked_patches=False):\n",
+ " \"\"\"\n",
+ " Perform Cross-View completion using two input images, specified using Numpy arrays.\n",
+ " \"\"\"\n",
+ " # Replace the mask generator\n",
+ " model.mask_generator = models.masking.RandomMask(model.patch_embed.num_patches, masking_ratio)\n",
+ "\n",
+ " # ImageNet-1k color normalization\n",
+ " imagenet_mean = torch.as_tensor([0.485, 0.456, 0.406]).reshape(1,3,1,1).to(device)\n",
+ " imagenet_std = torch.as_tensor([0.229, 0.224, 0.225]).reshape(1,3,1,1).to(device)\n",
+ "\n",
+ " normalize_input_colors = True\n",
+ " is_output_normalized = True\n",
+ " with torch.no_grad():\n",
+ " # Cast data to torch\n",
+ " target_image = (torch.as_tensor(target_image, dtype=torch.float, device=device).permute(2,0,1) / 255)[None]\n",
+ " ref_image = (torch.as_tensor(ref_image, dtype=torch.float, device=device).permute(2,0,1) / 255)[None]\n",
+ "\n",
+ " if normalize_input_colors:\n",
+ " ref_image = (ref_image - imagenet_mean) / imagenet_std\n",
+ " target_image = (target_image - imagenet_mean) / imagenet_std\n",
+ "\n",
+ " out, mask, _ = model(target_image, ref_image)\n",
+ " # # get target\n",
+ " if not is_output_normalized:\n",
+ " predicted_image = model.unpatchify(out)\n",
+ " else:\n",
+ " # The output only contains higher order information,\n",
+ " # we retrieve mean and standard deviation from the actual target image\n",
+ " patchified = model.patchify(target_image)\n",
+ " mean = patchified.mean(dim=-1, keepdim=True)\n",
+ " var = patchified.var(dim=-1, keepdim=True)\n",
+ " pred_renorm = out * (var + 1.e-6)**.5 + mean\n",
+ " predicted_image = model.unpatchify(pred_renorm)\n",
+ "\n",
+ " image_masks = model.unpatchify(model.patchify(torch.ones_like(ref_image)) * mask[:,:,None])\n",
+ " masked_target_image = (1 - image_masks) * target_image\n",
+ " \n",
+ " if not reconstruct_unmasked_patches:\n",
+ " # Replace unmasked patches by their actual values\n",
+ " predicted_image = predicted_image * image_masks + masked_target_image\n",
+ "\n",
+ " # Unapply color normalization\n",
+ " if normalize_input_colors:\n",
+ " predicted_image = predicted_image * imagenet_std + imagenet_mean\n",
+ " masked_target_image = masked_target_image * imagenet_std + imagenet_mean\n",
+ " \n",
+ " # Cast to Numpy\n",
+ " masked_target_image = np.asarray(torch.clamp(masked_target_image.squeeze(0).permute(1,2,0) * 255, 0, 255).cpu().numpy(), dtype=np.uint8)\n",
+ " predicted_image = np.asarray(torch.clamp(predicted_image.squeeze(0).permute(1,2,0) * 255, 0, 255).cpu().numpy(), dtype=np.uint8)\n",
+ " return masked_target_image, predicted_image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Use the Habitat simulator to render images from arbitrary viewpoints (requires habitat_sim to be installed)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.environ[\"MAGNUM_LOG\"]=\"quiet\"\n",
+ "os.environ[\"HABITAT_SIM_LOG\"]=\"quiet\"\n",
+ "import habitat_sim\n",
+ "\n",
+ "scene = \"habitat-sim-data/scene_datasets/habitat-test-scenes/skokloster-castle.glb\"\n",
+ "navmesh = \"habitat-sim-data/scene_datasets/habitat-test-scenes/skokloster-castle.navmesh\"\n",
+ "\n",
+ "sim_cfg = habitat_sim.SimulatorConfiguration()\n",
+ "if use_gpu: sim_cfg.gpu_device_id = 0\n",
+ "sim_cfg.scene_id = scene\n",
+ "sim_cfg.load_semantic_mesh = False\n",
+ "rgb_sensor_spec = habitat_sim.CameraSensorSpec()\n",
+ "rgb_sensor_spec.uuid = \"color\"\n",
+ "rgb_sensor_spec.sensor_type = habitat_sim.SensorType.COLOR\n",
+ "rgb_sensor_spec.resolution = (224,224)\n",
+ "rgb_sensor_spec.hfov = 56.56\n",
+ "rgb_sensor_spec.position = [0.0, 0.0, 0.0]\n",
+ "rgb_sensor_spec.orientation = [0, 0, 0]\n",
+ "agent_cfg = habitat_sim.agent.AgentConfiguration(sensor_specifications=[rgb_sensor_spec])\n",
+ "\n",
+ "\n",
+ "cfg = habitat_sim.Configuration(sim_cfg, [agent_cfg])\n",
+ "sim = habitat_sim.Simulator(cfg)\n",
+ "if navmesh is not None:\n",
+ " sim.pathfinder.load_nav_mesh(navmesh)\n",
+ "agent = sim.initialize_agent(agent_id=0)\n",
+ "\n",
+ "def sample_random_viewpoint():\n",
+ " \"\"\" Sample a random viewpoint using the navmesh \"\"\"\n",
+ " nav_point = sim.pathfinder.get_random_navigable_point()\n",
+ " # Sample a random viewpoint height\n",
+ " viewpoint_height = np.random.uniform(1.0, 1.6)\n",
+ " viewpoint_position = nav_point + viewpoint_height * habitat_sim.geo.UP\n",
+ " viewpoint_orientation = quaternion.from_rotation_vector(np.random.uniform(-np.pi, np.pi) * habitat_sim.geo.UP)\n",
+ " return viewpoint_position, viewpoint_orientation\n",
+ "\n",
+ "def render_viewpoint(position, orientation):\n",
+ " agent_state = habitat_sim.AgentState()\n",
+ " agent_state.position = position\n",
+ " agent_state.rotation = orientation\n",
+ " agent.set_state(agent_state)\n",
+ " viewpoint_observations = sim.get_sensor_observations(agent_ids=0)\n",
+ " image = viewpoint_observations['color'][:,:,:3]\n",
+ " image = np.asarray(np.clip(1.5 * np.asarray(image, dtype=float), 0, 255), dtype=np.uint8)\n",
+ " return image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sample a random reference view"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ref_position, ref_orientation = sample_random_viewpoint()\n",
+ "ref_image = render_viewpoint(ref_position, ref_orientation)\n",
+ "plt.clf()\n",
+ "fig, axes = plt.subplots(1,1, squeeze=False, num=1)\n",
+ "axes[0,0].imshow(ref_image)\n",
+ "for ax in axes.flatten():\n",
+ " ax.set_xticks([])\n",
+ " ax.set_yticks([])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interactive cross-view completion using CroCo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reconstruct_unmasked_patches = False\n",
+ "\n",
+ "def show_demo(masking_ratio, x, y, z, panorama, elevation):\n",
+ " R = quaternion.as_rotation_matrix(ref_orientation)\n",
+ " target_position = ref_position + x * R[:,0] + y * R[:,1] + z * R[:,2]\n",
+ " target_orientation = (ref_orientation\n",
+ " * quaternion.from_rotation_vector(-elevation * np.pi/180 * habitat_sim.geo.LEFT) \n",
+ " * quaternion.from_rotation_vector(-panorama * np.pi/180 * habitat_sim.geo.UP))\n",
+ " \n",
+ " ref_image = render_viewpoint(ref_position, ref_orientation)\n",
+ " target_image = render_viewpoint(target_position, target_orientation)\n",
+ "\n",
+ " masked_target_image, predicted_image = process_images(ref_image, target_image, masking_ratio, reconstruct_unmasked_patches)\n",
+ "\n",
+ " fig, axes = plt.subplots(1,4, squeeze=True, dpi=300)\n",
+ " axes[0].imshow(ref_image)\n",
+ " axes[0].set_xlabel(\"Reference\")\n",
+ " axes[1].imshow(masked_target_image)\n",
+ " axes[1].set_xlabel(\"Masked target\")\n",
+ " axes[2].imshow(predicted_image)\n",
+ " axes[2].set_xlabel(\"Reconstruction\") \n",
+ " axes[3].imshow(target_image)\n",
+ " axes[3].set_xlabel(\"Target\")\n",
+ " for ax in axes.flatten():\n",
+ " ax.set_xticks([])\n",
+ " ax.set_yticks([])\n",
+ "\n",
+ "interact(show_demo,\n",
+ " masking_ratio=widgets.FloatSlider(description='masking', value=0.9, min=0.0, max=1.0),\n",
+ " x=widgets.FloatSlider(value=0.0, min=-0.5, max=0.5, step=0.05),\n",
+ " y=widgets.FloatSlider(value=0.0, min=-0.5, max=0.5, step=0.05),\n",
+ " z=widgets.FloatSlider(value=0.0, min=-0.5, max=0.5, step=0.05),\n",
+ " panorama=widgets.FloatSlider(value=0.0, min=-20, max=20, step=0.5),\n",
+ " elevation=widgets.FloatSlider(value=0.0, min=-20, max=20, step=0.5));"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.13"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "f9237820cd248d7e07cb4fb9f0e4508a85d642f19d831560c0a4b61f3e907e67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/blocks.py b/third_party/mast3r/dust3r/croco/models/blocks.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/blocks.py
rename to third_party/mast3r/dust3r/croco/models/blocks.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/criterion.py b/third_party/mast3r/dust3r/croco/models/criterion.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/criterion.py
rename to third_party/mast3r/dust3r/croco/models/criterion.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/croco.py b/third_party/mast3r/dust3r/croco/models/croco.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/croco.py
rename to third_party/mast3r/dust3r/croco/models/croco.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/croco_downstream.py b/third_party/mast3r/dust3r/croco/models/croco_downstream.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/croco_downstream.py
rename to third_party/mast3r/dust3r/croco/models/croco_downstream.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/curope/__init__.py b/third_party/mast3r/dust3r/croco/models/curope/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/curope/__init__.py
rename to third_party/mast3r/dust3r/croco/models/curope/__init__.py
diff --git a/third_party/mast3r/dust3r/croco/models/curope/curope.cpp b/third_party/mast3r/dust3r/croco/models/curope/curope.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..8fe9058e05aa1bf3f37b0d970edc7312bc68455b
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/models/curope/curope.cpp
@@ -0,0 +1,69 @@
+/*
+ Copyright (C) 2022-present Naver Corporation. All rights reserved.
+ Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+*/
+
+#include
+
+// forward declaration
+void rope_2d_cuda( torch::Tensor tokens, const torch::Tensor pos, const float base, const float fwd );
+
+void rope_2d_cpu( torch::Tensor tokens, const torch::Tensor positions, const float base, const float fwd )
+{
+ const int B = tokens.size(0);
+ const int N = tokens.size(1);
+ const int H = tokens.size(2);
+ const int D = tokens.size(3) / 4;
+
+ auto tok = tokens.accessor();
+ auto pos = positions.accessor();
+
+ for (int b = 0; b < B; b++) {
+ for (int x = 0; x < 2; x++) { // y and then x (2d)
+ for (int n = 0; n < N; n++) {
+
+ // grab the token position
+ const int p = pos[b][n][x];
+
+ for (int h = 0; h < H; h++) {
+ for (int d = 0; d < D; d++) {
+ // grab the two values
+ float u = tok[b][n][h][d+0+x*2*D];
+ float v = tok[b][n][h][d+D+x*2*D];
+
+ // grab the cos,sin
+ const float inv_freq = fwd * p / powf(base, d/float(D));
+ float c = cosf(inv_freq);
+ float s = sinf(inv_freq);
+
+ // write the result
+ tok[b][n][h][d+0+x*2*D] = u*c - v*s;
+ tok[b][n][h][d+D+x*2*D] = v*c + u*s;
+ }
+ }
+ }
+ }
+ }
+}
+
+void rope_2d( torch::Tensor tokens, // B,N,H,D
+ const torch::Tensor positions, // B,N,2
+ const float base,
+ const float fwd )
+{
+ TORCH_CHECK(tokens.dim() == 4, "tokens must have 4 dimensions");
+ TORCH_CHECK(positions.dim() == 3, "positions must have 3 dimensions");
+ TORCH_CHECK(tokens.size(0) == positions.size(0), "batch size differs between tokens & positions");
+ TORCH_CHECK(tokens.size(1) == positions.size(1), "seq_length differs between tokens & positions");
+ TORCH_CHECK(positions.size(2) == 2, "positions.shape[2] must be equal to 2");
+ TORCH_CHECK(tokens.is_cuda() == positions.is_cuda(), "tokens and positions are not on the same device" );
+
+ if (tokens.is_cuda())
+ rope_2d_cuda( tokens, positions, base, fwd );
+ else
+ rope_2d_cpu( tokens, positions, base, fwd );
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("rope_2d", &rope_2d, "RoPE 2d forward/backward");
+}
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/curope/curope2d.py b/third_party/mast3r/dust3r/croco/models/curope/curope2d.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/curope/curope2d.py
rename to third_party/mast3r/dust3r/croco/models/curope/curope2d.py
diff --git a/third_party/mast3r/dust3r/croco/models/curope/kernels.cu b/third_party/mast3r/dust3r/croco/models/curope/kernels.cu
new file mode 100644
index 0000000000000000000000000000000000000000..7156cd1bb935cb1f0be45e58add53f9c21505c20
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/models/curope/kernels.cu
@@ -0,0 +1,108 @@
+/*
+ Copyright (C) 2022-present Naver Corporation. All rights reserved.
+ Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+*/
+
+#include
+#include
+#include
+#include
+
+#define CHECK_CUDA(tensor) {\
+ TORCH_CHECK((tensor).is_cuda(), #tensor " is not in cuda memory"); \
+ TORCH_CHECK((tensor).is_contiguous(), #tensor " is not contiguous"); }
+void CHECK_KERNEL() {auto error = cudaGetLastError(); TORCH_CHECK( error == cudaSuccess, cudaGetErrorString(error));}
+
+
+template < typename scalar_t >
+__global__ void rope_2d_cuda_kernel(
+ //scalar_t* __restrict__ tokens,
+ torch::PackedTensorAccessor32 tokens,
+ const int64_t* __restrict__ pos,
+ const float base,
+ const float fwd )
+ // const int N, const int H, const int D )
+{
+ // tokens shape = (B, N, H, D)
+ const int N = tokens.size(1);
+ const int H = tokens.size(2);
+ const int D = tokens.size(3);
+
+ // each block update a single token, for all heads
+ // each thread takes care of a single output
+ extern __shared__ float shared[];
+ float* shared_inv_freq = shared + D;
+
+ const int b = blockIdx.x / N;
+ const int n = blockIdx.x % N;
+
+ const int Q = D / 4;
+ // one token = [0..Q : Q..2Q : 2Q..3Q : 3Q..D]
+ // u_Y v_Y u_X v_X
+
+ // shared memory: first, compute inv_freq
+ if (threadIdx.x < Q)
+ shared_inv_freq[threadIdx.x] = fwd / powf(base, threadIdx.x/float(Q));
+ __syncthreads();
+
+ // start of X or Y part
+ const int X = threadIdx.x < D/2 ? 0 : 1;
+ const int m = (X*D/2) + (threadIdx.x % Q); // index of u_Y or u_X
+
+ // grab the cos,sin appropriate for me
+ const float freq = pos[blockIdx.x*2+X] * shared_inv_freq[threadIdx.x % Q];
+ const float cos = cosf(freq);
+ const float sin = sinf(freq);
+ /*
+ float* shared_cos_sin = shared + D + D/4;
+ if ((threadIdx.x % (D/2)) < Q)
+ shared_cos_sin[m+0] = cosf(freq);
+ else
+ shared_cos_sin[m+Q] = sinf(freq);
+ __syncthreads();
+ const float cos = shared_cos_sin[m+0];
+ const float sin = shared_cos_sin[m+Q];
+ */
+
+ for (int h = 0; h < H; h++)
+ {
+ // then, load all the token for this head in shared memory
+ shared[threadIdx.x] = tokens[b][n][h][threadIdx.x];
+ __syncthreads();
+
+ const float u = shared[m];
+ const float v = shared[m+Q];
+
+ // write output
+ if ((threadIdx.x % (D/2)) < Q)
+ tokens[b][n][h][threadIdx.x] = u*cos - v*sin;
+ else
+ tokens[b][n][h][threadIdx.x] = v*cos + u*sin;
+ }
+}
+
+void rope_2d_cuda( torch::Tensor tokens, const torch::Tensor pos, const float base, const float fwd )
+{
+ const int B = tokens.size(0); // batch size
+ const int N = tokens.size(1); // sequence length
+ const int H = tokens.size(2); // number of heads
+ const int D = tokens.size(3); // dimension per head
+
+ TORCH_CHECK(tokens.stride(3) == 1 && tokens.stride(2) == D, "tokens are not contiguous");
+ TORCH_CHECK(pos.is_contiguous(), "positions are not contiguous");
+ TORCH_CHECK(pos.size(0) == B && pos.size(1) == N && pos.size(2) == 2, "bad pos.shape");
+ TORCH_CHECK(D % 4 == 0, "token dim must be multiple of 4");
+
+ // one block for each layer, one thread per local-max
+ const int THREADS_PER_BLOCK = D;
+ const int N_BLOCKS = B * N; // each block takes care of H*D values
+ const int SHARED_MEM = sizeof(float) * (D + D/4);
+
+ AT_DISPATCH_FLOATING_TYPES_AND_HALF(tokens.type(), "rope_2d_cuda", ([&] {
+ rope_2d_cuda_kernel <<>> (
+ //tokens.data_ptr(),
+ tokens.packed_accessor32(),
+ pos.data_ptr(),
+ base, fwd); //, N, H, D );
+ }));
+}
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/curope/setup.py b/third_party/mast3r/dust3r/croco/models/curope/setup.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/curope/setup.py
rename to third_party/mast3r/dust3r/croco/models/curope/setup.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/dpt_block.py b/third_party/mast3r/dust3r/croco/models/dpt_block.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/dpt_block.py
rename to third_party/mast3r/dust3r/croco/models/dpt_block.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/head_downstream.py b/third_party/mast3r/dust3r/croco/models/head_downstream.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/head_downstream.py
rename to third_party/mast3r/dust3r/croco/models/head_downstream.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/masking.py b/third_party/mast3r/dust3r/croco/models/masking.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/masking.py
rename to third_party/mast3r/dust3r/croco/models/masking.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/models/pos_embed.py b/third_party/mast3r/dust3r/croco/models/pos_embed.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/models/pos_embed.py
rename to third_party/mast3r/dust3r/croco/models/pos_embed.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/pretrain.py b/third_party/mast3r/dust3r/croco/pretrain.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/pretrain.py
rename to third_party/mast3r/dust3r/croco/pretrain.py
diff --git a/third_party/mast3r/dust3r/croco/stereoflow/README.MD b/third_party/mast3r/dust3r/croco/stereoflow/README.MD
new file mode 100644
index 0000000000000000000000000000000000000000..81595380fadd274b523e0cf77921b1b65cbedb34
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/stereoflow/README.MD
@@ -0,0 +1,318 @@
+## CroCo-Stereo and CroCo-Flow
+
+This README explains how to use CroCo-Stereo and CroCo-Flow as well as how they were trained.
+All commands should be launched from the root directory.
+
+### Simple inference example
+
+We provide a simple inference exemple for CroCo-Stereo and CroCo-Flow in the Totebook `croco-stereo-flow-demo.ipynb`.
+Before running it, please download the trained models with:
+```
+bash stereoflow/download_model.sh crocostereo.pth
+bash stereoflow/download_model.sh crocoflow.pth
+```
+
+### Prepare data for training or evaluation
+
+Put the datasets used for training/evaluation in `./data/stereoflow` (or update the paths at the top of `stereoflow/datasets_stereo.py` and `stereoflow/datasets_flow.py`).
+Please find below on the file structure should look for each dataset:
+
+FlyingChairs
+
+```
+./data/stereoflow/FlyingChairs/
+└───chairs_split.txt
+└───data/
+ └─── ...
+```
+
+
+
+MPI-Sintel
+
+```
+./data/stereoflow/MPI-Sintel/
+└───training/
+│ └───clean/
+│ └───final/
+│ └───flow/
+└───test/
+ └───clean/
+ └───final/
+```
+
+
+
+SceneFlow (including FlyingThings)
+
+```
+./data/stereoflow/SceneFlow/
+└───Driving/
+│ └───disparity/
+│ └───frames_cleanpass/
+│ └───frames_finalpass/
+└───FlyingThings/
+│ └───disparity/
+│ └───frames_cleanpass/
+│ └───frames_finalpass/
+│ └───optical_flow/
+└───Monkaa/
+ └───disparity/
+ └───frames_cleanpass/
+ └───frames_finalpass/
+```
+
+
+
+TartanAir
+
+```
+./data/stereoflow/TartanAir/
+└───abandonedfactory/
+│ └───.../
+└───abandonedfactory_night/
+│ └───.../
+└───.../
+```
+
+
+
+Booster
+
+```
+./data/stereoflow/booster_gt/
+└───train/
+ └───balanced/
+ └───Bathroom/
+ └───Bedroom/
+ └───...
+```
+
+
+
+CREStereo
+
+```
+./data/stereoflow/crenet_stereo_trainset/
+└───stereo_trainset/
+ └───crestereo/
+ └───hole/
+ └───reflective/
+ └───shapenet/
+ └───tree/
+```
+
+
+
+ETH3D Two-view Low-res
+
+```
+./data/stereoflow/eth3d_lowres/
+└───test/
+│ └───lakeside_1l/
+│ └───...
+└───train/
+│ └───delivery_area_1l/
+│ └───...
+└───train_gt/
+ └───delivery_area_1l/
+ └───...
+```
+
+
+
+KITTI 2012
+
+```
+./data/stereoflow/kitti-stereo-2012/
+└───testing/
+│ └───colored_0/
+│ └───colored_1/
+└───training/
+ └───colored_0/
+ └───colored_1/
+ └───disp_occ/
+ └───flow_occ/
+```
+
+
+
+KITTI 2015
+
+```
+./data/stereoflow/kitti-stereo-2015/
+└───testing/
+│ └───image_2/
+│ └───image_3/
+└───training/
+ └───image_2/
+ └───image_3/
+ └───disp_occ_0/
+ └───flow_occ/
+```
+
+
+
+Middlebury
+
+```
+./data/stereoflow/middlebury
+└───2005/
+│ └───train/
+│ └───Art/
+│ └───...
+└───2006/
+│ └───Aloe/
+│ └───Baby1/
+│ └───...
+└───2014/
+│ └───Adirondack-imperfect/
+│ └───Adirondack-perfect/
+│ └───...
+└───2021/
+│ └───data/
+│ └───artroom1/
+│ └───artroom2/
+│ └───...
+└───MiddEval3_F/
+ └───test/
+ │ └───Australia/
+ │ └───...
+ └───train/
+ └───Adirondack/
+ └───...
+```
+
+
+
+Spring
+
+```
+./data/stereoflow/spring/
+└───test/
+│ └───0003/
+│ └───...
+└───train/
+ └───0001/
+ └───...
+```
+
+
+
+### CroCo-Stereo
+
+##### Main model
+
+The main training of CroCo-Stereo was performed on a series of datasets, and it was used as it for Middlebury v3 benchmark.
+
+```
+# Download the model
+bash stereoflow/download_model.sh crocostereo.pth
+# Middlebury v3 submission
+python stereoflow/test.py --model stereoflow_models/crocostereo.pth --dataset "MdEval3('all_full')" --save submission --tile_overlap 0.9
+# Training command that was used, using checkpoint-last.pth
+python -u stereoflow/train.py stereo --criterion "LaplacianLossBounded2()" --dataset "CREStereo('train')+SceneFlow('train_allpass')+30*ETH3DLowRes('train')+50*Md05('train')+50*Md06('train')+50*Md14('train')+50*Md21('train')+50*MdEval3('train_full')+Booster('train_balanced')" --val_dataset "SceneFlow('test1of100_finalpass')+SceneFlow('test1of100_cleanpass')+ETH3DLowRes('subval')+Md05('subval')+Md06('subval')+Md14('subval')+Md21('subval')+MdEval3('subval_full')+Booster('subval_balanced')" --lr 3e-5 --batch_size 6 --epochs 32 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --output_dir xps/crocostereo/main/
+# or it can be launched on multiple gpus (while maintaining the effective batch size), e.g. on 3 gpus:
+torchrun --nproc_per_node 3 stereoflow/train.py stereo --criterion "LaplacianLossBounded2()" --dataset "CREStereo('train')+SceneFlow('train_allpass')+30*ETH3DLowRes('train')+50*Md05('train')+50*Md06('train')+50*Md14('train')+50*Md21('train')+50*MdEval3('train_full')+Booster('train_balanced')" --val_dataset "SceneFlow('test1of100_finalpass')+SceneFlow('test1of100_cleanpass')+ETH3DLowRes('subval')+Md05('subval')+Md06('subval')+Md14('subval')+Md21('subval')+MdEval3('subval_full')+Booster('subval_balanced')" --lr 3e-5 --batch_size 2 --epochs 32 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --output_dir xps/crocostereo/main/
+```
+
+For evaluation of validation set, we also provide the model trained on the `subtrain` subset of the training sets.
+
+```
+# Download the model
+bash stereoflow/download_model.sh crocostereo_subtrain.pth
+# Evaluation on validation sets
+python stereoflow/test.py --model stereoflow_models/crocostereo_subtrain.pth --dataset "MdEval3('subval_full')+ETH3DLowRes('subval')+SceneFlow('test_finalpass')+SceneFlow('test_cleanpass')" --save metrics --tile_overlap 0.9
+# Training command that was used (same as above but on subtrain, using checkpoint-best.pth), can also be launched on multiple gpus
+python -u stereoflow/train.py stereo --criterion "LaplacianLossBounded2()" --dataset "CREStereo('train')+SceneFlow('train_allpass')+30*ETH3DLowRes('subtrain')+50*Md05('subtrain')+50*Md06('subtrain')+50*Md14('subtrain')+50*Md21('subtrain')+50*MdEval3('subtrain_full')+Booster('subtrain_balanced')" --val_dataset "SceneFlow('test1of100_finalpass')+SceneFlow('test1of100_cleanpass')+ETH3DLowRes('subval')+Md05('subval')+Md06('subval')+Md14('subval')+Md21('subval')+MdEval3('subval_full')+Booster('subval_balanced')" --lr 3e-5 --batch_size 6 --epochs 32 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --output_dir xps/crocostereo/main_subtrain/
+```
+
+##### Other models
+
+
+ Model for ETH3D
+ The model used for the submission on ETH3D is trained with the same command but using an unbounded Laplacian loss.
+
+ # Download the model
+ bash stereoflow/download_model.sh crocostereo_eth3d.pth
+ # ETH3D submission
+ python stereoflow/test.py --model stereoflow_models/crocostereo_eth3d.pth --dataset "ETH3DLowRes('all')" --save submission --tile_overlap 0.9
+ # Training command that was used
+ python -u stereoflow/train.py stereo --criterion "LaplacianLoss()" --tile_conf_mode conf_expbeta3 --dataset "CREStereo('train')+SceneFlow('train_allpass')+30*ETH3DLowRes('train')+50*Md05('train')+50*Md06('train')+50*Md14('train')+50*Md21('train')+50*MdEval3('train_full')+Booster('train_balanced')" --val_dataset "SceneFlow('test1of100_finalpass')+SceneFlow('test1of100_cleanpass')+ETH3DLowRes('subval')+Md05('subval')+Md06('subval')+Md14('subval')+Md21('subval')+MdEval3('subval_full')+Booster('subval_balanced')" --lr 3e-5 --batch_size 6 --epochs 32 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --output_dir xps/crocostereo/main_eth3d/
+
+
+
+
+ Main model finetuned on Kitti
+
+ # Download the model
+ bash stereoflow/download_model.sh crocostereo_finetune_kitti.pth
+ # Kitti submission
+ python stereoflow/test.py --model stereoflow_models/crocostereo_finetune_kitti.pth --dataset "Kitti15('test')" --save submission --tile_overlap 0.9
+ # Training that was used
+ python -u stereoflow/train.py stereo --crop 352 1216 --criterion "LaplacianLossBounded2()" --dataset "Kitti12('train')+Kitti15('train')" --lr 3e-5 --batch_size 1 --accum_iter 6 --epochs 20 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --start_from stereoflow_models/crocostereo.pth --output_dir xps/crocostereo/finetune_kitti/ --save_every 5
+
+
+
+ Main model finetuned on Spring
+
+ # Download the model
+ bash stereoflow/download_model.sh crocostereo_finetune_spring.pth
+ # Spring submission
+ python stereoflow/test.py --model stereoflow_models/crocostereo_finetune_spring.pth --dataset "Spring('test')" --save submission --tile_overlap 0.9
+ # Training command that was used
+ python -u stereoflow/train.py stereo --criterion "LaplacianLossBounded2()" --dataset "Spring('train')" --lr 3e-5 --batch_size 6 --epochs 8 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --start_from stereoflow_models/crocostereo.pth --output_dir xps/crocostereo/finetune_spring/
+
+
+
+ Smaller models
+ To train CroCo-Stereo with smaller CroCo pretrained models, simply replace the --pretrained argument. To download the smaller CroCo-Stereo models based on CroCo v2 pretraining with ViT-Base encoder and Small encoder, use bash stereoflow/download_model.sh crocostereo_subtrain_vitb_smalldecoder.pth, and for the model with a ViT-Base encoder and a Base decoder, use bash stereoflow/download_model.sh crocostereo_subtrain_vitb_basedecoder.pth.
+
+
+
+### CroCo-Flow
+
+##### Main model
+
+The main training of CroCo-Flow was performed on the FlyingThings, FlyingChairs, MPI-Sintel and TartanAir datasets.
+It was used for our submission to the MPI-Sintel benchmark.
+
+```
+# Download the model
+bash stereoflow/download_model.sh crocoflow.pth
+# Evaluation
+python stereoflow/test.py --model stereoflow_models/crocoflow.pth --dataset "MPISintel('subval_cleanpass')+MPISintel('subval_finalpass')" --save metrics --tile_overlap 0.9
+# Sintel submission
+python stereoflow/test.py --model stereoflow_models/crocoflow.pth --dataset "MPISintel('test_allpass')" --save submission --tile_overlap 0.9
+# Training command that was used, with checkpoint-best.pth
+python -u stereoflow/train.py flow --criterion "LaplacianLossBounded()" --dataset "40*MPISintel('subtrain_cleanpass')+40*MPISintel('subtrain_finalpass')+4*FlyingThings('train_allpass')+4*FlyingChairs('train')+TartanAir('train')" --val_dataset "MPISintel('subval_cleanpass')+MPISintel('subval_finalpass')" --lr 2e-5 --batch_size 8 --epochs 240 --img_per_epoch 30000 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --output_dir xps/crocoflow/main/
+```
+
+##### Other models
+
+
+ Main model finetuned on Kitti
+
+ # Download the model
+ bash stereoflow/download_model.sh crocoflow_finetune_kitti.pth
+ # Kitti submission
+ python stereoflow/test.py --model stereoflow_models/crocoflow_finetune_kitti.pth --dataset "Kitti15('test')" --save submission --tile_overlap 0.99
+ # Training that was used, with checkpoint-last.pth
+ python -u stereoflow/train.py flow --crop 352 1216 --criterion "LaplacianLossBounded()" --dataset "Kitti15('train')+Kitti12('train')" --lr 2e-5 --batch_size 1 --accum_iter 8 --epochs 150 --save_every 5 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --start_from stereoflow_models/crocoflow.pth --output_dir xps/crocoflow/finetune_kitti/
+
+
+
+ Main model finetuned on Spring
+
+ # Download the model
+ bash stereoflow/download_model.sh crocoflow_finetune_spring.pth
+ # Spring submission
+ python stereoflow/test.py --model stereoflow_models/crocoflow_finetune_spring.pth --dataset "Spring('test')" --save submission --tile_overlap 0.9
+ # Training command that was used, with checkpoint-last.pth
+ python -u stereoflow/train.py flow --criterion "LaplacianLossBounded()" --dataset "Spring('train')" --lr 2e-5 --batch_size 8 --epochs 12 --pretrained pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth --start_from stereoflow_models/crocoflow.pth --output_dir xps/crocoflow/finetune_spring/
+
+
+
+ Smaller models
+ To train CroCo-Flow with smaller CroCo pretrained models, simply replace the --pretrained argument. To download the smaller CroCo-Flow models based on CroCo v2 pretraining with ViT-Base encoder and Small encoder, use bash stereoflow/download_model.sh crocoflow_vitb_smalldecoder.pth, and for the model with a ViT-Base encoder and a Base decoder, use bash stereoflow/download_model.sh crocoflow_vitb_basedecoder.pth.
+
diff --git a/imcui/third_party/mast3r/dust3r/croco/stereoflow/augmentor.py b/third_party/mast3r/dust3r/croco/stereoflow/augmentor.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/stereoflow/augmentor.py
rename to third_party/mast3r/dust3r/croco/stereoflow/augmentor.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/stereoflow/criterion.py b/third_party/mast3r/dust3r/croco/stereoflow/criterion.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/stereoflow/criterion.py
rename to third_party/mast3r/dust3r/croco/stereoflow/criterion.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/stereoflow/datasets_flow.py b/third_party/mast3r/dust3r/croco/stereoflow/datasets_flow.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/stereoflow/datasets_flow.py
rename to third_party/mast3r/dust3r/croco/stereoflow/datasets_flow.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/stereoflow/datasets_stereo.py b/third_party/mast3r/dust3r/croco/stereoflow/datasets_stereo.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/stereoflow/datasets_stereo.py
rename to third_party/mast3r/dust3r/croco/stereoflow/datasets_stereo.py
diff --git a/third_party/mast3r/dust3r/croco/stereoflow/download_model.sh b/third_party/mast3r/dust3r/croco/stereoflow/download_model.sh
new file mode 100644
index 0000000000000000000000000000000000000000..533119609108c5ec3c22ff79b10e9215c1ac5098
--- /dev/null
+++ b/third_party/mast3r/dust3r/croco/stereoflow/download_model.sh
@@ -0,0 +1,12 @@
+# Copyright (C) 2022-present Naver Corporation. All rights reserved.
+# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
+
+model=$1
+outfile="stereoflow_models/${model}"
+if [[ ! -f $outfile ]]
+then
+ mkdir -p stereoflow_models/;
+ wget https://download.europe.naverlabs.com/ComputerVision/CroCo/StereoFlow_models/$1 -P stereoflow_models/;
+else
+ echo "Model ${model} already downloaded in ${outfile}."
+fi
\ No newline at end of file
diff --git a/imcui/third_party/mast3r/dust3r/croco/stereoflow/engine.py b/third_party/mast3r/dust3r/croco/stereoflow/engine.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/stereoflow/engine.py
rename to third_party/mast3r/dust3r/croco/stereoflow/engine.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/stereoflow/test.py b/third_party/mast3r/dust3r/croco/stereoflow/test.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/stereoflow/test.py
rename to third_party/mast3r/dust3r/croco/stereoflow/test.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/stereoflow/train.py b/third_party/mast3r/dust3r/croco/stereoflow/train.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/stereoflow/train.py
rename to third_party/mast3r/dust3r/croco/stereoflow/train.py
diff --git a/imcui/third_party/mast3r/dust3r/croco/utils/misc.py b/third_party/mast3r/dust3r/croco/utils/misc.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/croco/utils/misc.py
rename to third_party/mast3r/dust3r/croco/utils/misc.py
diff --git a/third_party/mast3r/dust3r/datasets_preprocess/habitat/README.md b/third_party/mast3r/dust3r/datasets_preprocess/habitat/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3a24120c2374ebca77128be4600581ea94a5090c
--- /dev/null
+++ b/third_party/mast3r/dust3r/datasets_preprocess/habitat/README.md
@@ -0,0 +1,66 @@
+## Steps to reproduce synthetic training data using the Habitat-Sim simulator
+
+### Create a conda environment
+```bash
+conda create -n habitat python=3.8 habitat-sim=0.2.1 headless=2.0 -c aihabitat -c conda-forge
+conda active habitat
+conda install pytorch -c pytorch
+pip install opencv-python tqdm
+```
+
+or (if you get the error `For headless systems, compile with --headless for EGL support`)
+```
+git clone --branch stable https://github.com/facebookresearch/habitat-sim.git
+cd habitat-sim
+
+conda create -n habitat python=3.9 cmake=3.14.0
+conda activate habitat
+pip install . -v
+conda install pytorch -c pytorch
+pip install opencv-python tqdm
+```
+
+### Download Habitat-Sim scenes
+Download Habitat-Sim scenes:
+- Download links can be found here: https://github.com/facebookresearch/habitat-sim/blob/main/DATASETS.md
+- We used scenes from the HM3D, habitat-test-scenes, ReplicaCad and ScanNet datasets.
+- Please put the scenes in a directory `$SCENES_DIR` following the structure below:
+(Note: the habitat-sim dataset installer may install an incompatible version for ReplicaCAD backed lighting.
+The correct scene dataset can be dowloaded from Huggingface: `git clone git@hf.co:datasets/ai-habitat/ReplicaCAD_baked_lighting`).
+```
+$SCENES_DIR/
+├──hm3d/
+├──gibson/
+├──habitat-test-scenes/
+├──ReplicaCAD_baked_lighting/
+└──scannet/
+```
+
+### Download renderings metadata
+
+Download metadata corresponding to each scene and extract them into a directory `$METADATA_DIR`
+```bash
+wget https://download.europe.naverlabs.com/ComputerVision/DUSt3R/habitat_5views_v1_512x512_metadata.tar.gz
+tar -xvzf habitat_5views_v1_512x512_metadata.tar.gz
+```
+
+### Render the scenes
+
+Render the scenes in an output directory `$OUTPUT_DIR`
+```bash
+export METADATA_DIR="/path/to/habitat/5views_v1_512x512_metadata"
+export SCENES_DIR="/path/to/habitat/data/scene_datasets/"
+export OUTPUT_DIR="data/habitat_processed"
+cd datasets_preprocess/habitat/
+export PYTHONPATH=$(pwd)
+# Print commandlines to generate images corresponding to each scene
+python preprocess_habitat.py --scenes_dir=$SCENES_DIR --metadata_dir=$METADATA_DIR --output_dir=$OUTPUT_DIR
+# Launch these commandlines in parallel e.g. using GNU-Parallel as follows:
+python preprocess_habitat.py --scenes_dir=$SCENES_DIR --metadata_dir=$METADATA_DIR --output_dir=$OUTPUT_DIR | parallel -j 16
+```
+
+### Make a list of scenes
+
+```bash
+python find_scenes.py --root $OUTPUT_DIR
+```
\ No newline at end of file
diff --git a/imcui/third_party/mast3r/dust3r/datasets_preprocess/habitat/find_scenes.py b/third_party/mast3r/dust3r/datasets_preprocess/habitat/find_scenes.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/datasets_preprocess/habitat/find_scenes.py
rename to third_party/mast3r/dust3r/datasets_preprocess/habitat/find_scenes.py
diff --git a/imcui/third_party/dust3r/dust3r/utils/__init__.py b/third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/utils/__init__.py
rename to third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/__init__.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/habitat/habitat_renderer/habitat_sim_envmaps_renderer.py b/third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/habitat_sim_envmaps_renderer.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/habitat/habitat_renderer/habitat_sim_envmaps_renderer.py
rename to third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/habitat_sim_envmaps_renderer.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/habitat/habitat_renderer/multiview_crop_generator.py b/third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/multiview_crop_generator.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/habitat/habitat_renderer/multiview_crop_generator.py
rename to third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/multiview_crop_generator.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/habitat/habitat_renderer/projections.py b/third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/projections.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/habitat/habitat_renderer/projections.py
rename to third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/projections.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/habitat/habitat_renderer/projections_conversions.py b/third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/projections_conversions.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/habitat/habitat_renderer/projections_conversions.py
rename to third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/projections_conversions.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/habitat/preprocess_habitat.py b/third_party/mast3r/dust3r/datasets_preprocess/habitat/preprocess_habitat.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/habitat/preprocess_habitat.py
rename to third_party/mast3r/dust3r/datasets_preprocess/habitat/preprocess_habitat.py
diff --git a/imcui/third_party/mast3r/dust3r/datasets_preprocess/path_to_root.py b/third_party/mast3r/dust3r/datasets_preprocess/path_to_root.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/datasets_preprocess/path_to_root.py
rename to third_party/mast3r/dust3r/datasets_preprocess/path_to_root.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/preprocess_arkitscenes.py b/third_party/mast3r/dust3r/datasets_preprocess/preprocess_arkitscenes.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/preprocess_arkitscenes.py
rename to third_party/mast3r/dust3r/datasets_preprocess/preprocess_arkitscenes.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/preprocess_blendedMVS.py b/third_party/mast3r/dust3r/datasets_preprocess/preprocess_blendedMVS.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/preprocess_blendedMVS.py
rename to third_party/mast3r/dust3r/datasets_preprocess/preprocess_blendedMVS.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/preprocess_co3d.py b/third_party/mast3r/dust3r/datasets_preprocess/preprocess_co3d.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/preprocess_co3d.py
rename to third_party/mast3r/dust3r/datasets_preprocess/preprocess_co3d.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/preprocess_megadepth.py b/third_party/mast3r/dust3r/datasets_preprocess/preprocess_megadepth.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/preprocess_megadepth.py
rename to third_party/mast3r/dust3r/datasets_preprocess/preprocess_megadepth.py
diff --git a/imcui/third_party/mast3r/dust3r/datasets_preprocess/preprocess_scannetpp.py b/third_party/mast3r/dust3r/datasets_preprocess/preprocess_scannetpp.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/datasets_preprocess/preprocess_scannetpp.py
rename to third_party/mast3r/dust3r/datasets_preprocess/preprocess_scannetpp.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/preprocess_staticthings3d.py b/third_party/mast3r/dust3r/datasets_preprocess/preprocess_staticthings3d.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/preprocess_staticthings3d.py
rename to third_party/mast3r/dust3r/datasets_preprocess/preprocess_staticthings3d.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/preprocess_waymo.py b/third_party/mast3r/dust3r/datasets_preprocess/preprocess_waymo.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/preprocess_waymo.py
rename to third_party/mast3r/dust3r/datasets_preprocess/preprocess_waymo.py
diff --git a/imcui/third_party/dust3r/datasets_preprocess/preprocess_wildrgbd.py b/third_party/mast3r/dust3r/datasets_preprocess/preprocess_wildrgbd.py
similarity index 100%
rename from imcui/third_party/dust3r/datasets_preprocess/preprocess_wildrgbd.py
rename to third_party/mast3r/dust3r/datasets_preprocess/preprocess_wildrgbd.py
diff --git a/imcui/third_party/dust3r/demo.py b/third_party/mast3r/dust3r/demo.py
similarity index 93%
rename from imcui/third_party/dust3r/demo.py
rename to third_party/mast3r/dust3r/demo.py
index 326c6e5a49d5d352b4afb5445cee5d22571c3bdd..3c6d6a9cd8b2687be0a19c7b8a43942633d74310 100644
--- a/imcui/third_party/dust3r/demo.py
+++ b/third_party/mast3r/dust3r/demo.py
@@ -10,7 +10,7 @@ import torch
import tempfile
from dust3r.model import AsymmetricCroCo3DStereo
-from dust3r.demo import get_args_parser, main_demo, set_print_with_timestamp
+from dust3r.demo import get_args_parser, main_demo
import matplotlib.pyplot as pl
pl.ion()
@@ -20,7 +20,6 @@ torch.backends.cuda.matmul.allow_tf32 = True # for gpu >= Ampere and pytorch >=
if __name__ == '__main__':
parser = get_args_parser()
args = parser.parse_args()
- set_print_with_timestamp()
if args.tmp_dir is not None:
tmp_path = args.tmp_dir
diff --git a/imcui/third_party/mast3r/dust3r/docker/docker-compose-cpu.yml b/third_party/mast3r/dust3r/docker/docker-compose-cpu.yml
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/docker/docker-compose-cpu.yml
rename to third_party/mast3r/dust3r/docker/docker-compose-cpu.yml
diff --git a/imcui/third_party/mast3r/dust3r/docker/docker-compose-cuda.yml b/third_party/mast3r/dust3r/docker/docker-compose-cuda.yml
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/docker/docker-compose-cuda.yml
rename to third_party/mast3r/dust3r/docker/docker-compose-cuda.yml
diff --git a/third_party/mast3r/dust3r/docker/files/cpu.Dockerfile b/third_party/mast3r/dust3r/docker/files/cpu.Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..c9ccc39682dd7c7723f447ff47f12531a593446f
--- /dev/null
+++ b/third_party/mast3r/dust3r/docker/files/cpu.Dockerfile
@@ -0,0 +1,38 @@
+FROM python:3.11-slim
+
+LABEL description="Docker container for DUSt3R with dependencies installed. CPU VERSION"
+
+ENV DEVICE="cpu"
+ENV MODEL="DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth"
+ARG DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && apt-get install -y \
+ git \
+ libgl1-mesa-glx \
+ libegl1-mesa \
+ libxrandr2 \
+ libxrandr2 \
+ libxss1 \
+ libxcursor1 \
+ libxcomposite1 \
+ libasound2 \
+ libxi6 \
+ libxtst6 \
+ libglib2.0-0 \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN git clone --recursive https://github.com/naver/dust3r /dust3r
+WORKDIR /dust3r
+
+RUN pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+RUN pip install -r requirements.txt
+RUN pip install -r requirements_optional.txt
+RUN pip install opencv-python==4.8.0.74
+
+WORKDIR /dust3r
+
+COPY entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+
+ENTRYPOINT ["/entrypoint.sh"]
diff --git a/third_party/mast3r/dust3r/docker/files/cuda.Dockerfile b/third_party/mast3r/dust3r/docker/files/cuda.Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..a1d2edce1a5e7cee2fa3d66faf4f6ee019595267
--- /dev/null
+++ b/third_party/mast3r/dust3r/docker/files/cuda.Dockerfile
@@ -0,0 +1,27 @@
+FROM nvcr.io/nvidia/pytorch:24.01-py3
+
+LABEL description="Docker container for DUSt3R with dependencies installed. CUDA VERSION"
+ENV DEVICE="cuda"
+ENV MODEL="DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth"
+ARG DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && apt-get install -y \
+ git=1:2.34.1-1ubuntu1.10 \
+ libglib2.0-0=2.72.4-0ubuntu2.2 \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN git clone --recursive https://github.com/naver/dust3r /dust3r
+WORKDIR /dust3r
+RUN pip install -r requirements.txt
+RUN pip install -r requirements_optional.txt
+RUN pip install opencv-python==4.8.0.74
+
+WORKDIR /dust3r/croco/models/curope/
+RUN python setup.py build_ext --inplace
+
+WORKDIR /dust3r
+COPY entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+
+ENTRYPOINT ["/entrypoint.sh"]
diff --git a/third_party/mast3r/dust3r/docker/files/entrypoint.sh b/third_party/mast3r/dust3r/docker/files/entrypoint.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9637072a0af071f927ca0481bcaa4b600644b8b5
--- /dev/null
+++ b/third_party/mast3r/dust3r/docker/files/entrypoint.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+
+set -eux
+
+DEVICE=${DEVICE:-cuda}
+MODEL=${MODEL:-DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth}
+
+exec python3 demo.py --weights "checkpoints/$MODEL" --device "$DEVICE" --local_network "$@"
diff --git a/third_party/mast3r/dust3r/docker/run.sh b/third_party/mast3r/dust3r/docker/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6c920363d607fc6019f10780d072edf49bee3046
--- /dev/null
+++ b/third_party/mast3r/dust3r/docker/run.sh
@@ -0,0 +1,68 @@
+#!/bin/bash
+
+set -eux
+
+# Default model name
+model_name="DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth"
+
+check_docker() {
+ if ! command -v docker &>/dev/null; then
+ echo "Docker could not be found. Please install Docker and try again."
+ exit 1
+ fi
+}
+
+download_model_checkpoint() {
+ if [ -f "./files/checkpoints/${model_name}" ]; then
+ echo "Model checkpoint ${model_name} already exists. Skipping download."
+ return
+ fi
+ echo "Downloading model checkpoint ${model_name}..."
+ wget "https://download.europe.naverlabs.com/ComputerVision/DUSt3R/${model_name}" -P ./files/checkpoints
+}
+
+set_dcomp() {
+ if command -v docker-compose &>/dev/null; then
+ dcomp="docker-compose"
+ elif command -v docker &>/dev/null && docker compose version &>/dev/null; then
+ dcomp="docker compose"
+ else
+ echo "Docker Compose could not be found. Please install Docker Compose and try again."
+ exit 1
+ fi
+}
+
+run_docker() {
+ export MODEL=${model_name}
+ if [ "$with_cuda" -eq 1 ]; then
+ $dcomp -f docker-compose-cuda.yml up --build
+ else
+ $dcomp -f docker-compose-cpu.yml up --build
+ fi
+}
+
+with_cuda=0
+for arg in "$@"; do
+ case $arg in
+ --with-cuda)
+ with_cuda=1
+ ;;
+ --model_name=*)
+ model_name="${arg#*=}.pth"
+ ;;
+ *)
+ echo "Unknown parameter passed: $arg"
+ exit 1
+ ;;
+ esac
+done
+
+
+main() {
+ check_docker
+ download_model_checkpoint
+ set_dcomp
+ run_docker
+}
+
+main
diff --git a/imcui/third_party/dust3r/dust3r_visloc/__init__.py b/third_party/mast3r/dust3r/dust3r/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r_visloc/__init__.py
rename to third_party/mast3r/dust3r/dust3r/__init__.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/__init__.py b/third_party/mast3r/dust3r/dust3r/cloud_opt/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/__init__.py
rename to third_party/mast3r/dust3r/dust3r/cloud_opt/__init__.py
diff --git a/imcui/third_party/dust3r/dust3r/cloud_opt/base_opt.py b/third_party/mast3r/dust3r/dust3r/cloud_opt/base_opt.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/cloud_opt/base_opt.py
rename to third_party/mast3r/dust3r/dust3r/cloud_opt/base_opt.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/commons.py b/third_party/mast3r/dust3r/dust3r/cloud_opt/commons.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/commons.py
rename to third_party/mast3r/dust3r/dust3r/cloud_opt/commons.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/init_im_poses.py b/third_party/mast3r/dust3r/dust3r/cloud_opt/init_im_poses.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/init_im_poses.py
rename to third_party/mast3r/dust3r/dust3r/cloud_opt/init_im_poses.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/modular_optimizer.py b/third_party/mast3r/dust3r/dust3r/cloud_opt/modular_optimizer.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/modular_optimizer.py
rename to third_party/mast3r/dust3r/dust3r/cloud_opt/modular_optimizer.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/optimizer.py b/third_party/mast3r/dust3r/dust3r/cloud_opt/optimizer.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/optimizer.py
rename to third_party/mast3r/dust3r/dust3r/cloud_opt/optimizer.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/pair_viewer.py b/third_party/mast3r/dust3r/dust3r/cloud_opt/pair_viewer.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/cloud_opt/pair_viewer.py
rename to third_party/mast3r/dust3r/dust3r/cloud_opt/pair_viewer.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/__init__.py b/third_party/mast3r/dust3r/dust3r/datasets/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/__init__.py
rename to third_party/mast3r/dust3r/dust3r/datasets/__init__.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/arkitscenes.py b/third_party/mast3r/dust3r/dust3r/datasets/arkitscenes.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/arkitscenes.py
rename to third_party/mast3r/dust3r/dust3r/datasets/arkitscenes.py
diff --git a/imcui/third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/__init__.py b/third_party/mast3r/dust3r/dust3r/datasets/base/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/datasets_preprocess/habitat/habitat_renderer/__init__.py
rename to third_party/mast3r/dust3r/dust3r/datasets/base/__init__.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/base/base_stereo_view_dataset.py b/third_party/mast3r/dust3r/dust3r/datasets/base/base_stereo_view_dataset.py
similarity index 95%
rename from imcui/third_party/dust3r/dust3r/datasets/base/base_stereo_view_dataset.py
rename to third_party/mast3r/dust3r/dust3r/datasets/base/base_stereo_view_dataset.py
index 9bcac01da8c27a57a7601a09c7e75754d12871e3..17390ca29d4437fc41f3c946b235888af9e4c888 100644
--- a/imcui/third_party/dust3r/dust3r/datasets/base/base_stereo_view_dataset.py
+++ b/third_party/mast3r/dust3r/dust3r/datasets/base/base_stereo_view_dataset.py
@@ -50,7 +50,7 @@ class BaseStereoViewDataset (EasyDataset):
return f"{len(self)} pairs"
def __repr__(self):
- resolutions_str = '[' + ';'.join(f'{w}x{h}' for w, h in self._resolutions) + ']'
+ resolutions_str = '['+';'.join(f'{w}x{h}' for w, h in self._resolutions)+']'
return f"""{type(self).__name__}({self.get_stats()},
{self.split=},
{self.seed=},
@@ -146,10 +146,10 @@ class BaseStereoViewDataset (EasyDataset):
# cropping centered on the principal point
W, H = image.size
cx, cy = intrinsics[:2, 2].round().astype(int)
- min_margin_x = min(cx, W - cx)
- min_margin_y = min(cy, H - cy)
- # assert min_margin_x > W/5, f'Bad principal point in view={info}'
- # assert min_margin_y > H/5, f'Bad principal point in view={info}'
+ min_margin_x = min(cx, W-cx)
+ min_margin_y = min(cy, H-cy)
+ assert min_margin_x > W/5, f'Bad principal point in view={info}'
+ assert min_margin_y > H/5, f'Bad principal point in view={info}'
# the new window will be a rectangle of size (2*min_margin_x, 2*min_margin_y) centered on (cx,cy)
l, t = cx - min_margin_x, cy - min_margin_y
r, b = cx + min_margin_x, cy + min_margin_y
@@ -159,10 +159,10 @@ class BaseStereoViewDataset (EasyDataset):
# transpose the resolution if necessary
W, H = image.size # new size
assert resolution[0] >= resolution[1]
- if H > 1.1 * W:
+ if H > 1.1*W:
# image is portrait mode
resolution = resolution[::-1]
- elif 0.9 < H / W < 1.1 and resolution[0] != resolution[1]:
+ elif 0.9 < H/W < 1.1 and resolution[0] != resolution[1]:
# image is square, so we chose (portrait, landscape) randomly
if rng.integers(2):
resolution = resolution[::-1]
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/datasets/base/batched_sampler.py b/third_party/mast3r/dust3r/dust3r/datasets/base/batched_sampler.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/datasets/base/batched_sampler.py
rename to third_party/mast3r/dust3r/dust3r/datasets/base/batched_sampler.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/datasets/base/easy_dataset.py b/third_party/mast3r/dust3r/dust3r/datasets/base/easy_dataset.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/datasets/base/easy_dataset.py
rename to third_party/mast3r/dust3r/dust3r/datasets/base/easy_dataset.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/datasets/blendedmvs.py b/third_party/mast3r/dust3r/dust3r/datasets/blendedmvs.py
similarity index 90%
rename from imcui/third_party/mast3r/dust3r/dust3r/datasets/blendedmvs.py
rename to third_party/mast3r/dust3r/dust3r/datasets/blendedmvs.py
index 93e68c28620cc47a7b1743834e45f82d576126d0..6459a9c47cd9f0c7d109eebe8a00370bd0005c06 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/datasets/blendedmvs.py
+++ b/third_party/mast3r/dust3r/dust3r/datasets/blendedmvs.py
@@ -43,6 +43,16 @@ class BlendedMVS (BaseStereoViewDataset):
def get_stats(self):
return f'{len(self)} pairs from {len(self.scenes)} scenes'
+ def select_one_scene(self, scene, img1=None, img2=None):
+ scene_low = int(scene[-16:], 16)
+ valid = (self.pairs['seq_low'] == scene_low)
+ if img1:
+ valid &= (self.pairs['img1'] == int(img1))
+ if img2:
+ valid &= (self.pairs['img2'] == int(img2))
+ self.pairs = self.pairs[valid]
+ self._compute_pair_probas()
+
def _get_views(self, pair_idx, resolution, rng):
seqh, seql, img1, img2, score = self.pairs[pair_idx]
@@ -70,7 +80,7 @@ class BlendedMVS (BaseStereoViewDataset):
depthmap=depthmap,
camera_pose=camera_pose, # cam2world
camera_intrinsics=intrinsics,
- dataset='BlendedMVS',
+ dataset='Waymo',
label=osp.relpath(seq_path, self.ROOT),
instance=impath))
diff --git a/imcui/third_party/dust3r/dust3r/datasets/co3d.py b/third_party/mast3r/dust3r/dust3r/datasets/co3d.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/co3d.py
rename to third_party/mast3r/dust3r/dust3r/datasets/co3d.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/habitat.py b/third_party/mast3r/dust3r/dust3r/datasets/habitat.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/habitat.py
rename to third_party/mast3r/dust3r/dust3r/datasets/habitat.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/megadepth.py b/third_party/mast3r/dust3r/dust3r/datasets/megadepth.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/megadepth.py
rename to third_party/mast3r/dust3r/dust3r/datasets/megadepth.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/scannetpp.py b/third_party/mast3r/dust3r/dust3r/datasets/scannetpp.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/scannetpp.py
rename to third_party/mast3r/dust3r/dust3r/datasets/scannetpp.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/staticthings3d.py b/third_party/mast3r/dust3r/dust3r/datasets/staticthings3d.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/staticthings3d.py
rename to third_party/mast3r/dust3r/dust3r/datasets/staticthings3d.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/__init__.py b/third_party/mast3r/dust3r/dust3r/datasets/utils/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/__init__.py
rename to third_party/mast3r/dust3r/dust3r/datasets/utils/__init__.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/datasets/utils/cropping.py b/third_party/mast3r/dust3r/dust3r/datasets/utils/cropping.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/datasets/utils/cropping.py
rename to third_party/mast3r/dust3r/dust3r/datasets/utils/cropping.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/datasets/utils/transforms.py b/third_party/mast3r/dust3r/dust3r/datasets/utils/transforms.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/datasets/utils/transforms.py
rename to third_party/mast3r/dust3r/dust3r/datasets/utils/transforms.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/waymo.py b/third_party/mast3r/dust3r/dust3r/datasets/waymo.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/waymo.py
rename to third_party/mast3r/dust3r/dust3r/datasets/waymo.py
diff --git a/imcui/third_party/dust3r/dust3r/datasets/wildrgbd.py b/third_party/mast3r/dust3r/dust3r/datasets/wildrgbd.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/datasets/wildrgbd.py
rename to third_party/mast3r/dust3r/dust3r/datasets/wildrgbd.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/demo.py b/third_party/mast3r/dust3r/dust3r/demo.py
similarity index 94%
rename from imcui/third_party/mast3r/dust3r/dust3r/demo.py
rename to third_party/mast3r/dust3r/dust3r/demo.py
index c491be097b71ec38ea981dadf4f456d6e9829d48..2f2ae673943436816e691b280f874df5273595ba 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/demo.py
+++ b/third_party/mast3r/dust3r/dust3r/demo.py
@@ -6,8 +6,6 @@
# --------------------------------------------------------
import argparse
import math
-import builtins
-import datetime
import gradio
import os
import torch
@@ -50,19 +48,6 @@ def get_args_parser():
return parser
-def set_print_with_timestamp(time_format="%Y-%m-%d %H:%M:%S"):
- builtin_print = builtins.print
-
- def print_with_timestamp(*args, **kwargs):
- now = datetime.datetime.now()
- formatted_date_time = now.strftime(time_format)
-
- builtin_print(f'[{formatted_date_time}] ', end='') # print with time stamp
- builtin_print(*args, **kwargs)
-
- builtins.print = print_with_timestamp
-
-
def _convert_scene_output_to_glb(outdir, imgs, pts3d, mask, focals, cams2world, cam_size=0.05,
cam_color=None, as_pointcloud=False,
transparent_cams=False, silent=False):
@@ -217,9 +202,7 @@ def main_demo(tmpdirname, model, device, image_size, server_name, server_port, s
value='linear', label="schedule", info="For global alignment!")
niter = gradio.Number(value=300, precision=0, minimum=0, maximum=5000,
label="num_iterations", info="For global alignment!")
- scenegraph_type = gradio.Dropdown([("complete: all possible image pairs", "complete"),
- ("swin: sliding window", "swin"),
- ("oneref: match one image with all", "oneref")],
+ scenegraph_type = gradio.Dropdown(["complete", "swin", "oneref"],
value='complete', label="Scenegraph",
info="Define how to make pairs",
interactive=True)
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/heads/__init__.py b/third_party/mast3r/dust3r/dust3r/heads/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/heads/__init__.py
rename to third_party/mast3r/dust3r/dust3r/heads/__init__.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/heads/dpt_head.py b/third_party/mast3r/dust3r/dust3r/heads/dpt_head.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/heads/dpt_head.py
rename to third_party/mast3r/dust3r/dust3r/heads/dpt_head.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/heads/linear_head.py b/third_party/mast3r/dust3r/dust3r/heads/linear_head.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/heads/linear_head.py
rename to third_party/mast3r/dust3r/dust3r/heads/linear_head.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/heads/postprocess.py b/third_party/mast3r/dust3r/dust3r/heads/postprocess.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/heads/postprocess.py
rename to third_party/mast3r/dust3r/dust3r/heads/postprocess.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/image_pairs.py b/third_party/mast3r/dust3r/dust3r/image_pairs.py
similarity index 66%
rename from imcui/third_party/mast3r/dust3r/dust3r/image_pairs.py
rename to third_party/mast3r/dust3r/dust3r/image_pairs.py
index ebcf902b4d07b83fe83ffceba3f45ca0d74dfcf7..571d834f0331cbd7bed3e79adbf7bf2c954cdcef 100644
--- a/imcui/third_party/mast3r/dust3r/dust3r/image_pairs.py
+++ b/third_party/mast3r/dust3r/dust3r/image_pairs.py
@@ -15,41 +15,14 @@ def make_pairs(imgs, scene_graph='complete', prefilter=None, symmetrize=True):
for j in range(i):
pairs.append((imgs[i], imgs[j]))
elif scene_graph.startswith('swin'):
- iscyclic = not scene_graph.endswith('noncyclic')
- try:
- winsize = int(scene_graph.split('-')[1])
- except Exception as e:
- winsize = 3
+ winsize = int(scene_graph.split('-')[1]) if '-' in scene_graph else 3
pairsid = set()
for i in range(len(imgs)):
- for j in range(1, winsize + 1):
- idx = (i + j)
- if iscyclic:
- idx = idx % len(imgs) # explicit loop closure
- if idx >= len(imgs):
- continue
+ for j in range(1, winsize+1):
+ idx = (i + j) % len(imgs) # explicit loop closure
pairsid.add((i, idx) if i < idx else (idx, i))
for i, j in pairsid:
pairs.append((imgs[i], imgs[j]))
- elif scene_graph.startswith('logwin'):
- iscyclic = not scene_graph.endswith('noncyclic')
- try:
- winsize = int(scene_graph.split('-')[1])
- except Exception as e:
- winsize = 3
- offsets = [2**i for i in range(winsize)]
- pairsid = set()
- for i in range(len(imgs)):
- ixs_l = [i - off for off in offsets]
- ixs_r = [i + off for off in offsets]
- for j in ixs_l + ixs_r:
- if iscyclic:
- j = j % len(imgs) # Explicit loop closure
- if j < 0 or j >= len(imgs) or j == i:
- continue
- pairsid.add((i, j) if i < j else (j, i))
- for i, j in pairsid:
- pairs.append((imgs[i], imgs[j]))
elif scene_graph.startswith('oneref'):
refid = int(scene_graph.split('-')[1]) if '-' in scene_graph else 0
for j in range(len(imgs)):
@@ -79,13 +52,13 @@ def sel(x, kept):
def _filter_edges_seq(edges, seq_dis_thr, cyclic=False):
# number of images
- n = max(max(e) for e in edges) + 1
+ n = max(max(e) for e in edges)+1
kept = []
for e, (i, j) in enumerate(edges):
- dis = abs(i - j)
+ dis = abs(i-j)
if cyclic:
- dis = min(dis, abs(i + n - j), abs(i - n - j))
+ dis = min(dis, abs(i+n-j), abs(i-n-j))
if dis <= seq_dis_thr:
kept.append(e)
return kept
diff --git a/imcui/third_party/dust3r/dust3r/inference.py b/third_party/mast3r/dust3r/dust3r/inference.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/inference.py
rename to third_party/mast3r/dust3r/dust3r/inference.py
diff --git a/imcui/third_party/dust3r/dust3r/losses.py b/third_party/mast3r/dust3r/dust3r/losses.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/losses.py
rename to third_party/mast3r/dust3r/dust3r/losses.py
diff --git a/imcui/third_party/dust3r/dust3r/model.py b/third_party/mast3r/dust3r/dust3r/model.py
similarity index 93%
rename from imcui/third_party/dust3r/dust3r/model.py
rename to third_party/mast3r/dust3r/dust3r/model.py
index 41c3a4f78eb5fbafdeb7ab8523468de320886c64..40ac37fc8b538e11f27c85766e3937084e22ad10 100644
--- a/imcui/third_party/dust3r/dust3r/model.py
+++ b/third_party/mast3r/dust3r/dust3r/model.py
@@ -20,9 +20,7 @@ from models.croco import CroCoNet # noqa
inf = float('inf')
hf_version_number = huggingface_hub.__version__
-assert version.parse(hf_version_number) >= version.parse("0.22.0"), ("Outdated huggingface_hub version, "
- "please reinstall requirements.txt")
-
+assert version.parse(hf_version_number) >= version.parse("0.22.0"), "Outdated huggingface_hub version, please reinstall requirements.txt"
def load_model(model_path, device, verbose=True):
if verbose:
@@ -78,11 +76,7 @@ class AsymmetricCroCo3DStereo (
if os.path.isfile(pretrained_model_name_or_path):
return load_model(pretrained_model_name_or_path, device='cpu')
else:
- try:
- model = super(AsymmetricCroCo3DStereo, cls).from_pretrained(pretrained_model_name_or_path, **kw)
- except TypeError as e:
- raise Exception(f'tried to load {pretrained_model_name_or_path} from huggingface, but failed')
- return model
+ return super(AsymmetricCroCo3DStereo, cls).from_pretrained(pretrained_model_name_or_path, **kw)
def _set_patch_embed(self, img_size=224, patch_size=16, enc_embed_dim=768):
self.patch_embed = get_patch_embed(self.patch_embed_cls, img_size, patch_size, enc_embed_dim)
@@ -99,9 +93,9 @@ class AsymmetricCroCo3DStereo (
def set_freeze(self, freeze): # this is for use by downstream models
self.freeze = freeze
to_be_frozen = {
- 'none': [],
- 'mask': [self.mask_token],
- 'encoder': [self.mask_token, self.patch_embed, self.enc_blocks],
+ 'none': [],
+ 'mask': [self.mask_token],
+ 'encoder': [self.mask_token, self.patch_embed, self.enc_blocks],
}
freeze_all_params(to_be_frozen[freeze])
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/optim_factory.py b/third_party/mast3r/dust3r/dust3r/optim_factory.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/optim_factory.py
rename to third_party/mast3r/dust3r/dust3r/optim_factory.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/patch_embed.py b/third_party/mast3r/dust3r/dust3r/patch_embed.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/patch_embed.py
rename to third_party/mast3r/dust3r/dust3r/patch_embed.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/post_process.py b/third_party/mast3r/dust3r/dust3r/post_process.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/post_process.py
rename to third_party/mast3r/dust3r/dust3r/post_process.py
diff --git a/imcui/third_party/dust3r/dust3r/training.py b/third_party/mast3r/dust3r/dust3r/training.py
similarity index 98%
rename from imcui/third_party/dust3r/dust3r/training.py
rename to third_party/mast3r/dust3r/dust3r/training.py
index 53af9764ebb03a0083c22294298ed674e9164edc..972212331b769c4fea467681404cab400a2c6edd 100644
--- a/imcui/third_party/dust3r/dust3r/training.py
+++ b/third_party/mast3r/dust3r/dust3r/training.py
@@ -68,8 +68,7 @@ def get_args_parser():
parser.add_argument('--amp', type=int, default=0,
choices=[0, 1], help="Use Automatic Mixed Precision for pretraining")
- parser.add_argument("--disable_cudnn_benchmark", action='store_true', default=False,
- help="set cudnn.benchmark = False")
+
# others
parser.add_argument('--num_workers', default=8, type=int)
parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
@@ -113,7 +112,7 @@ def train(args):
torch.manual_seed(seed)
np.random.seed(seed)
- cudnn.benchmark = not args.disable_cudnn_benchmark
+ cudnn.benchmark = True
# training dataset and loader
print('Building train dataset {:s}'.format(args.train_dataset))
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/datasets/base/__init__.py b/third_party/mast3r/dust3r/dust3r/utils/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/datasets/base/__init__.py
rename to third_party/mast3r/dust3r/dust3r/utils/__init__.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/utils/device.py b/third_party/mast3r/dust3r/dust3r/utils/device.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/utils/device.py
rename to third_party/mast3r/dust3r/dust3r/utils/device.py
diff --git a/imcui/third_party/dust3r/dust3r/utils/geometry.py b/third_party/mast3r/dust3r/dust3r/utils/geometry.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/utils/geometry.py
rename to third_party/mast3r/dust3r/dust3r/utils/geometry.py
diff --git a/imcui/third_party/dust3r/dust3r/utils/image.py b/third_party/mast3r/dust3r/dust3r/utils/image.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/utils/image.py
rename to third_party/mast3r/dust3r/dust3r/utils/image.py
diff --git a/imcui/third_party/dust3r/dust3r/utils/misc.py b/third_party/mast3r/dust3r/dust3r/utils/misc.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/utils/misc.py
rename to third_party/mast3r/dust3r/dust3r/utils/misc.py
diff --git a/imcui/third_party/dust3r/dust3r/utils/parallel.py b/third_party/mast3r/dust3r/dust3r/utils/parallel.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/utils/parallel.py
rename to third_party/mast3r/dust3r/dust3r/utils/parallel.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/utils/path_to_croco.py b/third_party/mast3r/dust3r/dust3r/utils/path_to_croco.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/utils/path_to_croco.py
rename to third_party/mast3r/dust3r/dust3r/utils/path_to_croco.py
diff --git a/imcui/third_party/dust3r/dust3r/viz.py b/third_party/mast3r/dust3r/dust3r/viz.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r/viz.py
rename to third_party/mast3r/dust3r/dust3r/viz.py
diff --git a/third_party/mast3r/dust3r/dust3r_visloc/README.md b/third_party/mast3r/dust3r/dust3r_visloc/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6d0512ac1516ba2655a10d4ae3d10b51346e233c
--- /dev/null
+++ b/third_party/mast3r/dust3r/dust3r_visloc/README.md
@@ -0,0 +1,93 @@
+# Visual Localization with DUSt3R
+
+## Dataset preparation
+
+### CambridgeLandmarks
+
+Each subscene should look like this:
+
+```
+Cambridge_Landmarks
+├─ mapping
+│ ├─ GreatCourt
+│ │ └─ colmap/reconstruction
+│ │ ├─ cameras.txt
+│ │ ├─ images.txt
+│ │ └─ points3D.txt
+├─ kapture
+│ ├─ GreatCourt
+│ │ └─ query # https://github.com/naver/kapture/blob/main/doc/datasets.adoc#cambridge-landmarks
+│ ...
+├─ GreatCourt
+│ ├─ pairsfile/query
+│ │ └─ AP-GeM-LM18_top50.txt # https://github.com/naver/deep-image-retrieval/blob/master/dirtorch/extract_kapture.py followed by https://github.com/naver/kapture-localization/blob/main/tools/kapture_compute_image_pairs.py
+│ ├─ seq1
+│ ...
+...
+```
+
+### 7Scenes
+Each subscene should look like this:
+
+```
+7-scenes
+├─ chess
+│ ├─ mapping/ # https://github.com/naver/kapture/blob/main/doc/datasets.adoc#1-7-scenes
+│ ├─ query/ # https://github.com/naver/kapture/blob/main/doc/datasets.adoc#1-7-scenes
+│ └─ pairsfile/query/
+│ └─ APGeM-LM18_top20.txt # https://github.com/naver/deep-image-retrieval/blob/master/dirtorch/extract_kapture.py followed by https://github.com/naver/kapture-localization/blob/main/tools/kapture_compute_image_pairs.py
+...
+```
+
+### Aachen-Day-Night
+
+```
+Aachen-Day-Night-v1.1
+├─ mapping
+│ ├─ colmap/reconstruction
+│ │ ├─ cameras.txt
+│ │ ├─ images.txt
+│ │ └─ points3D.txt
+├─ kapture
+│ └─ query # https://github.com/naver/kapture/blob/main/doc/datasets.adoc#2-aachen-day-night-v11
+├─ images
+│ ├─ db
+│ ├─ query
+│ └─ sequences
+└─ pairsfile/query
+ └─ fire_top50.txt # https://github.com/naver/fire/blob/main/kapture_compute_pairs.py
+```
+
+### InLoc
+
+```
+InLoc
+├─ mapping # https://github.com/naver/kapture/blob/main/doc/datasets.adoc#6-inloc
+├─ query # https://github.com/naver/kapture/blob/main/doc/datasets.adoc#6-inloc
+└─ pairsfile/query
+ └─ pairs-query-netvlad40-temporal.txt # https://github.com/cvg/Hierarchical-Localization/blob/master/pairs/inloc/pairs-query-netvlad40-temporal.txt
+```
+
+## Example Commands
+
+With `visloc.py` you can run our visual localization experiments on Aachen-Day-Night, InLoc, Cambridge Landmarks and 7 Scenes.
+
+```bash
+# Aachen-Day-Night-v1.1:
+# scene in 'day' 'night'
+# scene can also be 'all'
+python3 visloc.py --model_name DUSt3R_ViTLarge_BaseDecoder_512_dpt --dataset "VislocAachenDayNight('/path/to/prepared/Aachen-Day-Night-v1.1/', subscene='${scene}', pairsfile='fire_top50', topk=20)" --pnp_mode poselib --reprojection_error_diag_ratio 0.008 --output_dir /path/to/output/Aachen-Day-Night-v1.1/${scene}/loc
+
+# InLoc
+python3 visloc.py --model_name DUSt3R_ViTLarge_BaseDecoder_512_dpt --dataset "VislocInLoc('/path/to/prepared/InLoc/', pairsfile='pairs-query-netvlad40-temporal', topk=20)" --pnp_mode poselib --reprojection_error_diag_ratio 0.008 --output_dir /path/to/output/InLoc/loc
+
+
+# 7-scenes:
+# scene in 'chess' 'fire' 'heads' 'office' 'pumpkin' 'redkitchen' 'stairs'
+python3 visloc.py --model_name MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt --dataset "VislocSevenScenes('/path/to/prepared/7-scenes/', subscene='${scene}', pairsfile='APGeM-LM18_top20', topk=1)" --pnp_mode poselib --reprojection_error_diag_ratio 0.008 --output_dir /path/to/output/7-scenes/${scene}/loc
+
+# Cambridge Landmarks:
+# scene in 'ShopFacade' 'GreatCourt' 'KingsCollege' 'OldHospital' 'StMarysChurch'
+python3 visloc.py --model_name DUSt3R_ViTLarge_BaseDecoder_512_dpt --dataset "VislocCambridgeLandmarks('/path/to/prepared/Cambridge_Landmarks/', subscene='${scene}', pairsfile='APGeM-LM18_top20', topk=1)" --pnp_mode poselib --reprojection_error_diag_ratio 0.008 --output_dir /path/to/output/Cambridge_Landmarks/${scene}/loc
+
+```
\ No newline at end of file
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/datasets/utils/__init__.py b/third_party/mast3r/dust3r/dust3r_visloc/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/datasets/utils/__init__.py
rename to third_party/mast3r/dust3r/dust3r_visloc/__init__.py
diff --git a/imcui/third_party/dust3r/dust3r_visloc/datasets/__init__.py b/third_party/mast3r/dust3r/dust3r_visloc/datasets/__init__.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r_visloc/datasets/__init__.py
rename to third_party/mast3r/dust3r/dust3r_visloc/datasets/__init__.py
diff --git a/imcui/third_party/dust3r/dust3r_visloc/datasets/aachen_day_night.py b/third_party/mast3r/dust3r/dust3r_visloc/datasets/aachen_day_night.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r_visloc/datasets/aachen_day_night.py
rename to third_party/mast3r/dust3r/dust3r_visloc/datasets/aachen_day_night.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r_visloc/datasets/base_colmap.py b/third_party/mast3r/dust3r/dust3r_visloc/datasets/base_colmap.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r_visloc/datasets/base_colmap.py
rename to third_party/mast3r/dust3r/dust3r_visloc/datasets/base_colmap.py
diff --git a/imcui/third_party/dust3r/dust3r_visloc/datasets/base_dataset.py b/third_party/mast3r/dust3r/dust3r_visloc/datasets/base_dataset.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r_visloc/datasets/base_dataset.py
rename to third_party/mast3r/dust3r/dust3r_visloc/datasets/base_dataset.py
diff --git a/imcui/third_party/dust3r/dust3r_visloc/datasets/cambridge_landmarks.py b/third_party/mast3r/dust3r/dust3r_visloc/datasets/cambridge_landmarks.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r_visloc/datasets/cambridge_landmarks.py
rename to third_party/mast3r/dust3r/dust3r_visloc/datasets/cambridge_landmarks.py
diff --git a/imcui/third_party/dust3r/dust3r_visloc/datasets/inloc.py b/third_party/mast3r/dust3r/dust3r_visloc/datasets/inloc.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r_visloc/datasets/inloc.py
rename to third_party/mast3r/dust3r/dust3r_visloc/datasets/inloc.py
diff --git a/imcui/third_party/dust3r/dust3r_visloc/datasets/sevenscenes.py b/third_party/mast3r/dust3r/dust3r_visloc/datasets/sevenscenes.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r_visloc/datasets/sevenscenes.py
rename to third_party/mast3r/dust3r/dust3r_visloc/datasets/sevenscenes.py
diff --git a/imcui/third_party/dust3r/dust3r_visloc/datasets/utils.py b/third_party/mast3r/dust3r/dust3r_visloc/datasets/utils.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r_visloc/datasets/utils.py
rename to third_party/mast3r/dust3r/dust3r_visloc/datasets/utils.py
diff --git a/imcui/third_party/dust3r/dust3r_visloc/evaluation.py b/third_party/mast3r/dust3r/dust3r_visloc/evaluation.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r_visloc/evaluation.py
rename to third_party/mast3r/dust3r/dust3r_visloc/evaluation.py
diff --git a/imcui/third_party/dust3r/dust3r_visloc/localization.py b/third_party/mast3r/dust3r/dust3r_visloc/localization.py
similarity index 100%
rename from imcui/third_party/dust3r/dust3r_visloc/localization.py
rename to third_party/mast3r/dust3r/dust3r_visloc/localization.py
diff --git a/third_party/mast3r/dust3r/requirements.txt b/third_party/mast3r/dust3r/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d2bf20ed439b43b0604f12985288d8b8d6b55f8f
--- /dev/null
+++ b/third_party/mast3r/dust3r/requirements.txt
@@ -0,0 +1,13 @@
+torch
+torchvision
+roma
+gradio
+matplotlib
+tqdm
+opencv-python
+scipy
+einops
+trimesh
+tensorboard
+pyglet<2
+huggingface-hub[torch]>=0.22
\ No newline at end of file
diff --git a/third_party/mast3r/dust3r/requirements_optional.txt b/third_party/mast3r/dust3r/requirements_optional.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d42662c0e87c6ce4ac990f2afedecc96cdea7f06
--- /dev/null
+++ b/third_party/mast3r/dust3r/requirements_optional.txt
@@ -0,0 +1,7 @@
+pillow-heif # add heif/heic image support
+pyrender # for rendering depths in scannetpp
+kapture # for visloc data loading
+kapture-localization
+numpy-quaternion
+pycolmap # for pnp
+poselib # for pnp
diff --git a/imcui/third_party/dust3r/train.py b/third_party/mast3r/dust3r/train.py
similarity index 100%
rename from imcui/third_party/dust3r/train.py
rename to third_party/mast3r/dust3r/train.py
diff --git a/imcui/third_party/dust3r/visloc.py b/third_party/mast3r/dust3r/visloc.py
similarity index 100%
rename from imcui/third_party/dust3r/visloc.py
rename to third_party/mast3r/dust3r/visloc.py
diff --git a/imcui/third_party/mast3r/mast3r/__init__.py b/third_party/mast3r/mast3r/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/__init__.py
rename to third_party/mast3r/mast3r/__init__.py
diff --git a/imcui/third_party/mast3r/mast3r/catmlp_dpt_head.py b/third_party/mast3r/mast3r/catmlp_dpt_head.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/catmlp_dpt_head.py
rename to third_party/mast3r/mast3r/catmlp_dpt_head.py
diff --git a/imcui/third_party/mast3r/mast3r/cloud_opt/__init__.py b/third_party/mast3r/mast3r/cloud_opt/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/cloud_opt/__init__.py
rename to third_party/mast3r/mast3r/cloud_opt/__init__.py
diff --git a/imcui/third_party/mast3r/mast3r/cloud_opt/sparse_ga.py b/third_party/mast3r/mast3r/cloud_opt/sparse_ga.py
similarity index 90%
rename from imcui/third_party/mast3r/mast3r/cloud_opt/sparse_ga.py
rename to third_party/mast3r/mast3r/cloud_opt/sparse_ga.py
index eb1eb6b4d264e458d4efdc4e50281f1d0c7c4012..c7b7b80c868d5daf37870a97ec564c347372cc15 100644
--- a/imcui/third_party/mast3r/mast3r/cloud_opt/sparse_ga.py
+++ b/third_party/mast3r/mast3r/cloud_opt/sparse_ga.py
@@ -14,7 +14,6 @@ import os
from collections import namedtuple
from functools import lru_cache
from scipy import sparse as sp
-import copy
from mast3r.utils.misc import mkdir_for, hash_md5
from mast3r.cloud_opt.utils.losses import gamma_loss
@@ -116,7 +115,7 @@ def convert_dust3r_pairs_naming(imgs, pairs_in):
def sparse_global_alignment(imgs, pairs_in, cache_path, model, subsample=8, desc_conf='desc_conf',
- device='cuda', dtype=torch.float32, shared_intrinsics=False, **kw):
+ device='cuda', dtype=torch.float32, **kw):
""" Sparse alignment with MASt3R
imgs: list of image paths
cache_path: path where to dump temporary files (str)
@@ -144,14 +143,13 @@ def sparse_global_alignment(imgs, pairs_in, cache_path, model, subsample=8, desc
# min_spanning_tree = {(imgs[i],imgs[j]) for i,j in mst[1]}
# tmp_pairs = {(a,b):v for (a,b),v in tmp_pairs.items() if {(a,b),(b,a)} & min_spanning_tree}
- # smartly combine all useful data
- imsizes, pps, base_focals, core_depth, anchors, corres, corres2d, preds_21 = \
- condense_data(imgs, tmp_pairs, canonical_views, preds_21, dtype)
+ # smartly combine all usefull data
+ imsizes, pps, base_focals, core_depth, anchors, corres, corres2d = \
+ condense_data(imgs, tmp_pairs, canonical_views, dtype)
imgs, res_coarse, res_fine = sparse_scene_optimizer(
- imgs, subsample, imsizes, pps, base_focals, core_depth, anchors, corres, corres2d, preds_21, canonical_paths, mst,
- shared_intrinsics=shared_intrinsics, cache_path=cache_path, device=device, dtype=dtype, **kw)
-
+ imgs, subsample, imsizes, pps, base_focals, core_depth, anchors, corres, corres2d, preds_21, canonical_paths,
+ mst, cache_path=cache_path, device=device, dtype=dtype, **kw)
return SparseGA(imgs, pairs_in, res_fine or res_coarse, anchors, canonical_paths)
@@ -163,17 +161,16 @@ def sparse_scene_optimizer(imgs, subsample, imsizes, pps, base_focals, core_dept
opt_pp=True, opt_depth=True,
schedule=cosine_schedule, depth_mode='add', exp_depth=False,
lora_depth=False, # dict(k=96, gamma=15, min_norm=.5),
- shared_intrinsics=False,
init={}, device='cuda', dtype=torch.float32,
- matching_conf_thr=5., loss_dust3r_w=0.01,
+ matching_conf_thr=4., loss_dust3r_w=0.01,
verbose=True, dbg=()):
- init = copy.deepcopy(init)
+
# extrinsic parameters
vec0001 = torch.tensor((0, 0, 0, 1), dtype=dtype, device=device)
quats = [nn.Parameter(vec0001.clone()) for _ in range(len(imgs))]
trans = [nn.Parameter(torch.zeros(3, device=device, dtype=dtype)) for _ in range(len(imgs))]
- # initialize
+ # intialize
ones = torch.ones((len(imgs), 1), device=device, dtype=dtype)
median_depths = torch.ones(len(imgs), device=device, dtype=dtype)
for img in imgs:
@@ -209,23 +206,11 @@ def sparse_scene_optimizer(imgs, subsample, imsizes, pps, base_focals, core_dept
assert False, 'inverse kinematic chain not yet implemented'
# intrinsics parameters
- if shared_intrinsics:
- # Optimize a single set of intrinsics for all cameras. Use averages as init.
- confs = torch.stack([torch.load(pth)[0][2].mean() for pth in canonical_paths]).to(pps)
- weighting = confs / confs.sum()
- pp = nn.Parameter((weighting @ pps).to(dtype))
- pps = [pp for _ in range(len(imgs))]
- focal_m = weighting @ base_focals
- log_focal = nn.Parameter(focal_m.view(1).log().to(dtype))
- log_focals = [log_focal for _ in range(len(imgs))]
- else:
- pps = [nn.Parameter(pp.to(dtype)) for pp in pps]
- log_focals = [nn.Parameter(f.view(1).log().to(dtype)) for f in base_focals]
-
+ pps = [nn.Parameter(pp.to(dtype)) for pp in pps]
diags = imsizes.float().norm(dim=1)
min_focals = 0.25 * diags # diag = 1.2~1.4*max(W,H) => beta >= 1/(2*1.2*tan(fov/2)) ~= 0.26
max_focals = 10 * diags
-
+ log_focals = [nn.Parameter(f.view(1).log().to(dtype)) for f in base_focals]
assert len(mst[1]) == len(pps) - 1
def make_K_cam_depth(log_focals, pps, trans, quats, log_sizes, core_depth):
@@ -283,11 +268,7 @@ def sparse_scene_optimizer(imgs, subsample, imsizes, pps, base_focals, core_dept
return K, (inv(cam2w), cam2w), depthmaps
K = make_K_cam_depth(log_focals, pps, None, None, None, None)
-
- if shared_intrinsics:
- print('init focal (shared) = ', to_numpy(K[0, 0, 0]).round(2))
- else:
- print('init focals =', to_numpy(K[:, 0, 0]))
+ print('init focals =', to_numpy(K[:, 0, 0]))
# spectral low-rank projection of depthmaps
if lora_depth:
@@ -307,41 +288,27 @@ def sparse_scene_optimizer(imgs, subsample, imsizes, pps, base_focals, core_dept
for s in imgs_slices:
is_matching_ok[s.img1, s.img2] = matching_check(s.confs)
- # Prepare slices and corres for losses
- dust3r_slices = [s for s in imgs_slices if not is_matching_ok[s.img1, s.img2]]
- loss3d_slices = [s for s in imgs_slices if is_matching_ok[s.img1, s.img2]]
- cleaned_corres2d = []
- for cci, (img1, pix1, confs, confsum, imgs_slices) in enumerate(corres2d):
- cf_sum = 0
- pix1_filtered = []
- confs_filtered = []
- curstep = 0
- cleaned_slices = []
- for img2, slice2 in imgs_slices:
- if is_matching_ok[img1, img2]:
- tslice = slice(curstep, curstep + slice2.stop - slice2.start, slice2.step)
- pix1_filtered.append(pix1[tslice])
- confs_filtered.append(confs[tslice])
- cleaned_slices.append((img2, slice2))
- curstep += slice2.stop - slice2.start
- if pix1_filtered != []:
- pix1_filtered = torch.cat(pix1_filtered)
- confs_filtered = torch.cat(confs_filtered)
- cf_sum = confs_filtered.sum()
- cleaned_corres2d.append((img1, pix1_filtered, confs_filtered, cf_sum, cleaned_slices))
+ # Subsample preds_21
+ subsamp_preds_21 = {}
+ for imk, imv in preds_21.items():
+ subsamp_preds_21[imk] = {}
+ for im2k, (pred, conf) in preds_21[imk].items():
+ subpred = pred[::subsample, ::subsample].reshape(-1, 3) # original subsample
+ subconf = conf[::subsample, ::subsample].ravel() # for both ptmaps and confs
+ idxs = anchors[imgs.index(im2k)][1]
+ subsamp_preds_21[imk][im2k] = (subpred[idxs], subconf[idxs]) # anchors subsample
def loss_dust3r(cam2w, pts3d, pix_loss):
# In the case no correspondence could be established, fallback to DUSt3R GA regression loss formulation (sparsified)
loss = 0.
cf_sum = 0.
- for s in dust3r_slices:
- if init[imgs[s.img1]].get('freeze') and init[imgs[s.img2]].get('freeze'):
- continue
- # fallback to dust3r regression
- tgt_pts, tgt_confs = preds_21[imgs[s.img2]][imgs[s.img1]]
- tgt_pts = geotrf(cam2w[s.img2], tgt_pts)
- cf_sum += tgt_confs.sum()
- loss += tgt_confs @ pix_loss(pts3d[s.img1], tgt_pts)
+ for s in imgs_slices:
+ if not is_matching_ok[s.img1, s.img2]:
+ # fallback to dust3r regression
+ tgt_pts, tgt_confs = subsamp_preds_21[imgs[s.img2]][imgs[s.img1]]
+ tgt_pts = geotrf(cam2w[s.img2], tgt_pts)
+ cf_sum += tgt_confs.sum()
+ loss += tgt_confs @ pix_loss(pts3d[s.img1], tgt_pts)
return loss / cf_sum if cf_sum != 0. else 0.
def loss_3d(K, w2cam, pts3d, pix_loss):
@@ -351,16 +318,17 @@ def sparse_scene_optimizer(imgs, subsample, imsizes, pps, base_focals, core_dept
pts3d_1 = []
pts3d_2 = []
confs = []
- for s in loss3d_slices:
+ for s in imgs_slices:
if init[imgs[s.img1]].get('freeze') and init[imgs[s.img2]].get('freeze'):
continue
- pts3d_1.append(pts3d[s.img1][s.slice1])
- pts3d_2.append(pts3d[s.img2][s.slice2])
- confs.append(s.confs)
+ if is_matching_ok[s.img1, s.img2]:
+ pts3d_1.append(pts3d[s.img1][s.slice1])
+ pts3d_2.append(pts3d[s.img2][s.slice2])
+ confs.append(s.confs)
else:
- pts3d_1 = [pts3d[s.img1][s.slice1] for s in loss3d_slices]
- pts3d_2 = [pts3d[s.img2][s.slice2] for s in loss3d_slices]
- confs = [s.confs for s in loss3d_slices]
+ pts3d_1 = [pts3d[s.img1][s.slice1] for s in imgs_slices if is_matching_ok[s.img1, s.img2]]
+ pts3d_2 = [pts3d[s.img2][s.slice2] for s in imgs_slices if is_matching_ok[s.img1, s.img2]]
+ confs = [s.confs for s in imgs_slices if is_matching_ok[s.img1, s.img2]]
if pts3d_1 != []:
confs = torch.cat(confs)
@@ -379,15 +347,25 @@ def sparse_scene_optimizer(imgs, subsample, imsizes, pps, base_focals, core_dept
# For each 3D point, we have 2 reproj errors
proj_matrix = K @ w2cam[:, :3]
loss = npix = 0
- for img1, pix1_filtered, confs_filtered, cf_sum, cleaned_slices in cleaned_corres2d:
+ for img1, pix1, confs, cf_sum, imgs_slices in corres2d:
if init[imgs[img1]].get('freeze', 0) >= 1:
continue # no need
- pts3d_in_img1 = [pts3d[img2][slice2] for img2, slice2 in cleaned_slices]
+ pts3d_in_img1 = [pts3d[img2][slice2] for img2, slice2 in imgs_slices if is_matching_ok[img1, img2]]
+ pix1_filtered = []
+ confs_filtered = []
+ curstep = 0
+ for img2, slice2 in imgs_slices:
+ if is_matching_ok[img1, img2]:
+ tslice = slice(curstep, curstep + slice2.stop - slice2.start, slice2.step)
+ pix1_filtered.append(pix1[tslice])
+ confs_filtered.append(confs[tslice])
+ curstep += slice2.stop - slice2.start
if pts3d_in_img1 != []:
pts3d_in_img1 = torch.cat(pts3d_in_img1)
+ pix1_filtered = torch.cat(pix1_filtered)
+ confs_filtered = torch.cat(confs_filtered)
loss += confs_filtered @ pix_loss(pix1_filtered, reproj2d(proj_matrix[img1], pts3d_in_img1))
npix += confs_filtered.sum()
-
return loss / npix if npix != 0 else 0.
def optimize_loop(loss_func, lr_base, niter, pix_loss, lr_end=0):
@@ -452,12 +430,6 @@ def sparse_scene_optimizer(imgs, subsample, imsizes, pps, base_focals, core_dept
# refinement with 2d reproj
res_fine = optimize_loop(loss_2d, lr_base=lr2, niter=niter2, pix_loss=loss2)
- K = make_K_cam_depth(log_focals, pps, None, None, None, None)
- if shared_intrinsics:
- print('Final focal (shared) = ', to_numpy(K[0, 0, 0]).round(2))
- else:
- print('Final focals =', to_numpy(K[:, 0, 0]))
-
return imgs, res_coarse, res_fine
@@ -662,8 +634,7 @@ def prepare_canonical_data(imgs, tmp_pairs, subsample, order_imgs=False, min_con
pixels[img2] = xy1, confs
if img not in preds_21:
preds_21[img] = {}
- # Subsample preds_21
- preds_21[img][img2] = X2[::subsample, ::subsample].reshape(-1, 3), C2[::subsample, ::subsample].ravel()
+ preds_21[img][img2] = X2, C2
if img == img2:
X, C, X2, C2 = torch.load(path2, map_location=device)
@@ -671,7 +642,7 @@ def prepare_canonical_data(imgs, tmp_pairs, subsample, order_imgs=False, min_con
pixels[img1] = xy2, confs
if img not in preds_21:
preds_21[img] = {}
- preds_21[img][img1] = X2[::subsample, ::subsample].reshape(-1, 3), C2[::subsample, ::subsample].ravel()
+ preds_21[img][img1] = X2, C2
if score is not None:
i, j = imgs.index(img1), imgs.index(img2)
@@ -726,7 +697,7 @@ PairOfSlices = namedtuple(
'ImgPair', 'img1, slice1, pix1, anchor_idxs1, img2, slice2, pix2, anchor_idxs2, confs, confs_sum')
-def condense_data(imgs, tmp_paths, canonical_views, preds_21, dtype=torch.float32):
+def condense_data(imgs, tmp_paths, canonical_views, dtype=torch.float32):
# aggregate all data properly
set_imgs = set(imgs)
@@ -802,16 +773,7 @@ def condense_data(imgs, tmp_paths, canonical_views, preds_21, dtype=torch.float3
imsizes = torch.tensor([(W, H) for H, W in shapes], device=pp.device) # (W,H)
principal_points = torch.stack(principal_points)
focals = torch.cat(focals)
-
- # Subsample preds_21
- subsamp_preds_21 = {}
- for imk, imv in preds_21.items():
- subsamp_preds_21[imk] = {}
- for im2k, (pred, conf) in preds_21[imk].items():
- idxs = img_anchors[imgs.index(im2k)][1]
- subsamp_preds_21[imk][im2k] = (pred[idxs], conf[idxs]) # anchors subsample
-
- return imsizes, principal_points, focals, core_depth, img_anchors, corres, corres2d, subsamp_preds_21
+ return imsizes, principal_points, focals, core_depth, img_anchors, corres, corres2d
def canonical_view(ptmaps11, confs11, subsample, mode='avg-angle'):
@@ -824,8 +786,7 @@ def canonical_view(ptmaps11, confs11, subsample, mode='avg-angle'):
canon_depth = ptmaps11[..., 2].unsqueeze(1)
S = slice(subsample // 2, None, subsample)
center_depth = canon_depth[:, :, S, S]
- center_depth = torch.clip(center_depth, min=torch.finfo(center_depth.dtype).eps)
-
+ assert (center_depth > 0).all()
stacked_depth = F.pixel_unshuffle(canon_depth, subsample)
stacked_confs = F.pixel_unshuffle(confs11[:, None, :, :, 0], subsample)
diff --git a/imcui/third_party/mast3r/mast3r/cloud_opt/triangulation.py b/third_party/mast3r/mast3r/cloud_opt/triangulation.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/cloud_opt/triangulation.py
rename to third_party/mast3r/mast3r/cloud_opt/triangulation.py
diff --git a/imcui/third_party/mast3r/mast3r/cloud_opt/tsdf_optimizer.py b/third_party/mast3r/mast3r/cloud_opt/tsdf_optimizer.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/cloud_opt/tsdf_optimizer.py
rename to third_party/mast3r/mast3r/cloud_opt/tsdf_optimizer.py
diff --git a/imcui/third_party/mast3r/mast3r/cloud_opt/utils/__init__.py b/third_party/mast3r/mast3r/cloud_opt/utils/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/cloud_opt/utils/__init__.py
rename to third_party/mast3r/mast3r/cloud_opt/utils/__init__.py
diff --git a/imcui/third_party/mast3r/mast3r/cloud_opt/utils/losses.py b/third_party/mast3r/mast3r/cloud_opt/utils/losses.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/cloud_opt/utils/losses.py
rename to third_party/mast3r/mast3r/cloud_opt/utils/losses.py
diff --git a/imcui/third_party/mast3r/mast3r/cloud_opt/utils/schedules.py b/third_party/mast3r/mast3r/cloud_opt/utils/schedules.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/cloud_opt/utils/schedules.py
rename to third_party/mast3r/mast3r/cloud_opt/utils/schedules.py
diff --git a/imcui/third_party/mast3r/mast3r/colmap/__init__.py b/third_party/mast3r/mast3r/colmap/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/colmap/__init__.py
rename to third_party/mast3r/mast3r/colmap/__init__.py
diff --git a/imcui/third_party/mast3r/mast3r/colmap/database.py b/third_party/mast3r/mast3r/colmap/database.py
similarity index 99%
rename from imcui/third_party/mast3r/mast3r/colmap/database.py
rename to third_party/mast3r/mast3r/colmap/database.py
index 5de83a35664d4038a99713de7f397e83940e5421..4220b378ec3ffeacf02ad9cb8cefbd8e30b26bed 100644
--- a/imcui/third_party/mast3r/mast3r/colmap/database.py
+++ b/third_party/mast3r/mast3r/colmap/database.py
@@ -19,7 +19,7 @@ from mast3r.utils.misc import hash_md5
from mast3r.fast_nn import extract_correspondences_nonsym, bruteforce_reciprocal_nns
import mast3r.utils.path_to_dust3r # noqa
-from dust3r.utils.geometry import find_reciprocal_matches, xy_grid, geotrf # noqa
+from dust3r.utils.geometry import find_reciprocal_matches, xy_grid # noqa
def convert_im_matches_pairs(img0, img1, image_to_colmap, im_keypoints, matches_im0, matches_im1, viz):
diff --git a/imcui/third_party/mast3r/mast3r/datasets/__init__.py b/third_party/mast3r/mast3r/datasets/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/datasets/__init__.py
rename to third_party/mast3r/mast3r/datasets/__init__.py
diff --git a/imcui/third_party/mast3r/mast3r/datasets/base/__init__.py b/third_party/mast3r/mast3r/datasets/base/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/datasets/base/__init__.py
rename to third_party/mast3r/mast3r/datasets/base/__init__.py
diff --git a/imcui/third_party/mast3r/mast3r/datasets/base/mast3r_base_stereo_view_dataset.py b/third_party/mast3r/mast3r/datasets/base/mast3r_base_stereo_view_dataset.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/datasets/base/mast3r_base_stereo_view_dataset.py
rename to third_party/mast3r/mast3r/datasets/base/mast3r_base_stereo_view_dataset.py
diff --git a/imcui/third_party/mast3r/dust3r/dust3r/utils/__init__.py b/third_party/mast3r/mast3r/datasets/utils/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/dust3r/dust3r/utils/__init__.py
rename to third_party/mast3r/mast3r/datasets/utils/__init__.py
diff --git a/imcui/third_party/mast3r/mast3r/datasets/utils/cropping.py b/third_party/mast3r/mast3r/datasets/utils/cropping.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/datasets/utils/cropping.py
rename to third_party/mast3r/mast3r/datasets/utils/cropping.py
diff --git a/imcui/third_party/mast3r/mast3r/fast_nn.py b/third_party/mast3r/mast3r/fast_nn.py
similarity index 97%
rename from imcui/third_party/mast3r/mast3r/fast_nn.py
rename to third_party/mast3r/mast3r/fast_nn.py
index 05537f43c1be10b3733e80def8295c2ff5b5b8c0..c27b1be5b66c0eb504e3720ec06a041df69d84eb 100644
--- a/imcui/third_party/mast3r/mast3r/fast_nn.py
+++ b/third_party/mast3r/mast3r/fast_nn.py
@@ -132,9 +132,7 @@ def fast_reciprocal_NNs(pts1, pts2, subsample_or_initxy1=8, ret_xy=True, pixel_t
old_xy1 = xy1.copy()
old_xy2 = xy2.copy()
- if 'dist' in matcher_kw or 'block_size' in matcher_kw \
- or (isinstance(device, str) and device.startswith('cuda')) \
- or (isinstance(device, torch.device) and device.type.startswith('cuda')):
+ if (isinstance(device, str) and device.startswith('cuda')) or (isinstance(device, torch.device) and device.type.startswith('cuda')):
pts1 = pts1.to(device)
pts2 = pts2.to(device)
tree1 = cdistMatcher(pts1, device=device)
diff --git a/imcui/third_party/mast3r/mast3r/losses.py b/third_party/mast3r/mast3r/losses.py
similarity index 99%
rename from imcui/third_party/mast3r/mast3r/losses.py
rename to third_party/mast3r/mast3r/losses.py
index 3a50f57481e436d7752dcbf2b414be3ea65ee76b..5e13530b7cedecf467342a4291186a2e173d60ec 100644
--- a/imcui/third_party/mast3r/mast3r/losses.py
+++ b/third_party/mast3r/mast3r/losses.py
@@ -274,6 +274,12 @@ class InfoNCE(MatchingCriterion):
class APLoss (MatchingCriterion):
""" AP loss.
+
+ Input: (N, M) values in [min, max]
+ label: (N, M) values in {0, 1}
+
+ Returns: 1 - mAP (mean AP for each n in {1..N})
+ Note: typically, this is what you wanna minimize
"""
def __init__(self, nq='torch', min=0, max=1, euc=False, **kw):
diff --git a/imcui/third_party/mast3r/mast3r/model.py b/third_party/mast3r/mast3r/model.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/model.py
rename to third_party/mast3r/mast3r/model.py
diff --git a/imcui/third_party/mast3r/mast3r/utils/__init__.py b/third_party/mast3r/mast3r/utils/__init__.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/utils/__init__.py
rename to third_party/mast3r/mast3r/utils/__init__.py
diff --git a/imcui/third_party/mast3r/mast3r/utils/coarse_to_fine.py b/third_party/mast3r/mast3r/utils/coarse_to_fine.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/utils/coarse_to_fine.py
rename to third_party/mast3r/mast3r/utils/coarse_to_fine.py
diff --git a/imcui/third_party/mast3r/mast3r/utils/collate.py b/third_party/mast3r/mast3r/utils/collate.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/utils/collate.py
rename to third_party/mast3r/mast3r/utils/collate.py
diff --git a/imcui/third_party/mast3r/mast3r/utils/misc.py b/third_party/mast3r/mast3r/utils/misc.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/utils/misc.py
rename to third_party/mast3r/mast3r/utils/misc.py
diff --git a/imcui/third_party/mast3r/mast3r/utils/path_to_dust3r.py b/third_party/mast3r/mast3r/utils/path_to_dust3r.py
similarity index 100%
rename from imcui/third_party/mast3r/mast3r/utils/path_to_dust3r.py
rename to third_party/mast3r/mast3r/utils/path_to_dust3r.py
diff --git a/third_party/mast3r/requirements.txt b/third_party/mast3r/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ff88936c77d98f85c1a4d5ae4bfcf374e332f4b3
--- /dev/null
+++ b/third_party/mast3r/requirements.txt
@@ -0,0 +1 @@
+scikit-learn
\ No newline at end of file
diff --git a/imcui/third_party/mast3r/train.py b/third_party/mast3r/train.py
similarity index 100%
rename from imcui/third_party/mast3r/train.py
rename to third_party/mast3r/train.py
diff --git a/imcui/third_party/mast3r/visloc.py b/third_party/mast3r/visloc.py
similarity index 100%
rename from imcui/third_party/mast3r/visloc.py
rename to third_party/mast3r/visloc.py
diff --git a/third_party/omniglue/.gitignore b/third_party/omniglue/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..15bd19caac661bb8fe45a17bdc9c7069fb5b6033
--- /dev/null
+++ b/third_party/omniglue/.gitignore
@@ -0,0 +1,28 @@
+# Compiled python modules.
+*.pyc
+
+# Byte-compiled
+_pycache__/
+.cache/
+
+# Poetry, setuptools, PyPI distribution artifacts.
+/*.egg-info
+.eggs/
+build/
+dist/
+poetry.lock
+
+# Tests
+.pytest_cache/
+
+# Type checking
+.pytype/
+
+# Other
+*.DS_Store
+
+# PyCharm
+.idea
+models/sp_v6/*
+models/og_export*
+# models/dinov2_vitb14_pretrain.pth
diff --git a/third_party/omniglue/CHANGELOG.md b/third_party/omniglue/CHANGELOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..e682c907849f04dcd6972618836c4f875e0b627a
--- /dev/null
+++ b/third_party/omniglue/CHANGELOG.md
@@ -0,0 +1,31 @@
+# Changelog
+
+
+
+## [Unreleased]
+
+## [0.1.0] - 2022-01-01
+
+* Initial release
+
+[Unreleased]: https://github.com/google-research/omniglue/compare/v0.1.0...HEAD
+[0.1.0]: https://github.com/google-research/omniglue/releases/tag/v0.1.0
diff --git a/third_party/omniglue/CONTRIBUTING.md b/third_party/omniglue/CONTRIBUTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..e5c3b2868437f65d0d14f0411e9c2482bef576bf
--- /dev/null
+++ b/third_party/omniglue/CONTRIBUTING.md
@@ -0,0 +1,29 @@
+# How to Contribute
+
+We'd love to accept your patches and contributions to this project. There are
+just a few small guidelines you need to follow.
+
+## Contributor License Agreement
+
+Contributions to this project must be accompanied by a Contributor License
+Agreement (CLA). You (or your employer) retain the copyright to your
+contribution; this simply gives us permission to use and redistribute your
+contributions as part of the project. Head over to
+ to see your current agreements on file or
+to sign a new one.
+
+You generally only need to submit a CLA once, so if you've already submitted one
+(even if it was for a different project), you probably don't need to do it
+again.
+
+## Code Reviews
+
+All submissions, including submissions by project members, require review. We
+use GitHub pull requests for this purpose. Consult
+[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
+information on using pull requests.
+
+## Community Guidelines
+
+This project follows
+[Google's Open Source Community Guidelines](https://opensource.google/conduct/).
diff --git a/third_party/omniglue/LICENSE b/third_party/omniglue/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..7a4a3ea2424c09fbe48d455aed1eaa94d9124835
--- /dev/null
+++ b/third_party/omniglue/LICENSE
@@ -0,0 +1,202 @@
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
\ No newline at end of file
diff --git a/third_party/omniglue/README.md b/third_party/omniglue/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..0527d79f248f0eb3ab6a9da635b5f3733757466b
--- /dev/null
+++ b/third_party/omniglue/README.md
@@ -0,0 +1,152 @@
+
+
+# \[CVPR'24\] Code release for OmniGlue(ONNX)
+
+[](https://huggingface.co/spaces/Realcat/image-matching-webui)
+
+
+
+
+
+ONNX-compatible release for the CVPR 2024 paper: **OmniGlue: Generalizable Feature
+Matching with Foundation Model Guidance**.
+
+
+
+**Abstract:** The image matching field has been witnessing a continuous
+emergence of novel learnable feature matching techniques, with ever-improving
+performance on conventional benchmarks. However, our investigation shows that
+despite these gains, their potential for real-world applications is restricted
+by their limited generalization capabilities to novel image domains. In this
+paper, we introduce OmniGlue, the first learnable image matcher that is designed
+with generalization as a core principle. OmniGlue leverages broad knowledge from
+a vision foundation model to guide the feature matching process, boosting
+generalization to domains not seen at training time. Additionally, we propose a
+novel keypoint position-guided attention mechanism which disentangles spatial
+and appearance information, leading to enhanced matching descriptors. We perform
+comprehensive experiments on a suite of 6 datasets with varied image domains,
+including scene-level, object-centric and aerial images. OmniGlue’s novel
+components lead to relative gains on unseen domains of 18.8% with respect to a
+directly comparable reference model, while also outperforming the recent
+LightGlue method by 10.1% relatively.
+
+
+## Installation
+
+First, use pip to install `omniglue`:
+
+```sh
+conda create -n omniglue pip
+conda activate omniglue
+
+git clone https://github.com/google-research/omniglue.git
+cd omniglue
+pip install -e .
+```
+
+Then, download the following models to `./models/`
+
+```sh
+# Download to ./models/ dir.
+mkdir models
+cd models
+
+# SuperPoint.
+git clone https://github.com/rpautrat/SuperPoint.git
+mv SuperPoint/pretrained_models/sp_v6.tgz . && rm -rf SuperPoint
+tar zxvf sp_v6.tgz && rm sp_v6.tgz
+
+# DINOv2 - vit-b14.
+wget https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_pretrain.pth
+
+# OmniGlue.
+wget https://storage.googleapis.com/omniglue/og_export.zip
+unzip og_export.zip && rm og_export.zip
+```
+
+Direct download links:
+
+- [[SuperPoint weights]](https://github.com/rpautrat/SuperPoint/tree/master/pretrained_models): from [github.com/rpautrat/SuperPoint](https://github.com/rpautrat/SuperPoint)
+- [[DINOv2 weights]](https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_pretrain.pth): from [github.com/facebookresearch/dinov2](https://github.com/facebookresearch/dinov2) (ViT-B/14 distilled backbone without register).
+- [[OmniGlue weights]](https://storage.googleapis.com/omniglue/og_export.zip)
+
+## Usage
+The code snippet below outlines how you can perform OmniGlue inference in your
+own python codebase.
+
+```py
+
+from src import omniglue
+
+image0 = ... # load images from file into np.array
+image1 = ...
+
+og = omniglue.OmniGlue(
+ og_export="./models/omniglue.onnx",
+ sp_export="./models/sp_v6.onnx",
+ dino_export="./models/dinov2_vitb14_pretrain.pth",
+)
+
+match_kp0s, match_kp1s, match_confidences = og.FindMatches(image0, image1)
+# Output:
+# match_kp0: (N, 2) array of (x,y) coordinates in image0.
+# match_kp1: (N, 2) array of (x,y) coordinates in image1.
+# match_confidences: N-dim array of each of the N match confidence scores.
+```
+
+## Demo
+
+`demo.py` contains example usage of the `omniglue` module. To try with your own
+images, replace `./res/demo1.jpg` and `./res/demo2.jpg` with your own
+filepaths.
+
+```sh
+conda activate omniglue
+python demo.py ./res/demo1.jpg ./res/demo2.jpg
+#
+```
+
+Expected output:
+
+
+Comparison of Results Between TensorFlow and ONNX：
+
+
+
+## Repo TODOs
+
+- ~~Provide `demo.py` example usage script.~~
+- Support matching for pre-extracted features.
+- Release eval pipelines for in-domain (MegaDepth).
+- Release eval pipelines for all out-of-domain datasets.
+
+## BibTex
+```
+@inproceedings{jiang2024Omniglue,
+ title={OmniGlue: Generalizable Feature Matching with Foundation Model Guidance},
+ author={Jiang, Hanwen and Karpur, Arjun and Cao, Bingyi and Huang, Qixing and Araujo, Andre},
+ booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+ year={2024},
+}
+```
+
+--------------------------------------------------------------------------------
+
+This is not an officially supported Google product.
diff --git a/imcui/third_party/omniglue/__init__.py b/third_party/omniglue/__init__.py
similarity index 100%
rename from imcui/third_party/omniglue/__init__.py
rename to third_party/omniglue/__init__.py
diff --git a/imcui/third_party/omniglue/demo.py b/third_party/omniglue/demo.py
similarity index 100%
rename from imcui/third_party/omniglue/demo.py
rename to third_party/omniglue/demo.py
diff --git a/third_party/omniglue/init_repo.sh b/third_party/omniglue/init_repo.sh
new file mode 100644
index 0000000000000000000000000000000000000000..77ea3e9e65d7d4575762e531b7736eee646c18e1
--- /dev/null
+++ b/third_party/omniglue/init_repo.sh
@@ -0,0 +1,27 @@
+
+mkdir models
+cd models
+
+# SuperPoint.
+git clone https://github.com/rpautrat/SuperPoint.git
+mv SuperPoint/pretrained_models/sp_v6.tgz . && rm -rf SuperPoint
+tar zxvf sp_v6.tgz && rm sp_v6.tgz
+
+# DINOv2 - vit-b14.
+wget https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_pretrain.pth
+
+# OmniGlue.
+wget https://storage.googleapis.com/omniglue/og_export.zip
+unzip og_export.zip && rm og_export.zip
+
+cd ..
+
+saved_model=./models/og_export
+output_onnx=./models/omniglue.onnx
+python -m tf2onnx.convert --saved-model ${saved_model} --output ${output_onnx} --tag serve
+
+
+saved_model=./models/sp_v6
+output_onnx=./models/sp_v6.onnx
+python -m tf2onnx.convert --saved-model ${saved_model} --output ${output_onnx} --tag serve
+
diff --git a/third_party/omniglue/pyproject.toml b/third_party/omniglue/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..c89d46574f48bef551eab9e83c6f792b39907749
--- /dev/null
+++ b/third_party/omniglue/pyproject.toml
@@ -0,0 +1,62 @@
+[project]
+# Project metadata. Available keys are documented at:
+# https://packaging.python.org/en/latest/specifications/declaring-project-metadata
+name = "omniglue"
+description = "Official code release for CVPR'24 paper 'OmniGlue: Generalizable Feature Matching with Foundation Model Guidance"
+readme = "README.md"
+requires-python = ">=3.8"
+license = {file = "LICENSE"}
+authors = [{name = "OmniGlue authors"}]
+classifiers = [ # List of https://pypi.org/classifiers/
+ "License :: OSI Approved :: Apache Software License",
+ "Intended Audience :: Science/Research",
+]
+keywords = ["feature matching"]
+dynamic = ["version", "dependencies"]
+
+# pip dependencies of the project
+# Installed locally with `pip install -e .`
+[tool.setuptools.dynamic]
+dependencies = {file = ["requirements.txt"]}
+
+[project.urls]
+homepage = "https://github.com/google-research/omniglue"
+repository = "https://github.com/google-research/omniglue"
+changelog = "https://github.com/google-research/omniglue/blob/main/CHANGELOG.md"
+# documentation = ""
+
+[tool.setuptools.packages.find]
+where = ["src", "third_party"]
+include = ["omniglue*", "dinov2*"]
+
+[project.optional-dependencies]
+# Development deps (unittest, linting, formating,...)
+# Installed through `pip install -e .[dev]`
+dev = [
+ "pytest",
+ "pytest-xdist",
+ "pylint>=2.6.0",
+ "pyink",
+]
+
+[tool.pyink]
+# Formatting configuration to follow Google style-guide
+line-length = 80
+unstable = true
+pyink-indentation = 2
+pyink-use-majority-quotes = true
+
+[build-system]
+# Build system specify which backend is used to build/install the project (flit,
+# poetry, setuptools,...). All backends are supported by `pip install`
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.flit.sdist]
+# Flit specific options (files to exclude from the PyPI package).
+# If using another build backend (setuptools, poetry), you can remove this
+# section.
+exclude = [
+ # Do not release tests files on PyPI
+ "**/*_test.py",
+]
diff --git a/third_party/omniglue/requirements.txt b/third_party/omniglue/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c841f480adf74891db9069ca751c7bfe46e92f4c
--- /dev/null
+++ b/third_party/omniglue/requirements.txt
@@ -0,0 +1,8 @@
+matplotlib
+numpy
+opencv-python
+Pillow
+torch
+gdown
+tf2onnx
+onnxruntime
\ No newline at end of file
diff --git a/third_party/omniglue/src/__init__.py b/third_party/omniglue/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/imcui/third_party/omniglue/src/omniglue/__init__.py b/third_party/omniglue/src/omniglue/__init__.py
similarity index 100%
rename from imcui/third_party/omniglue/src/omniglue/__init__.py
rename to third_party/omniglue/src/omniglue/__init__.py
diff --git a/imcui/third_party/omniglue/src/omniglue/dino_extract.py b/third_party/omniglue/src/omniglue/dino_extract.py
similarity index 100%
rename from imcui/third_party/omniglue/src/omniglue/dino_extract.py
rename to third_party/omniglue/src/omniglue/dino_extract.py
diff --git a/imcui/third_party/omniglue/src/omniglue/omniglue_extract.py b/third_party/omniglue/src/omniglue/omniglue_extract.py
similarity index 97%
rename from imcui/third_party/omniglue/src/omniglue/omniglue_extract.py
rename to third_party/omniglue/src/omniglue/omniglue_extract.py
index e7dd6cfd6e18cf045e78c4c31ee834617247a76f..6f770c5e78a0868a3ee181487af9b042cb3d368d 100644
--- a/imcui/third_party/omniglue/src/omniglue/omniglue_extract.py
+++ b/third_party/omniglue/src/omniglue/omniglue_extract.py
@@ -33,9 +33,9 @@ class OmniGlue:
def __init__(
self,
og_export: str,
- sp_export: str = None,
- dino_export: str = None,
- max_keypoints: int = 1024,
+ sp_export: str | None = None,
+ dino_export: str | None = None,
+ max_keypoints: int = 2048,
) -> None:
self.max_keypoints = max_keypoints
self.matcher = onnxruntime.InferenceSession(og_export)
@@ -50,7 +50,7 @@ class OmniGlue:
self,
image0: np.ndarray,
image1: np.ndarray,
- max_keypoints: int = 1024,
+ max_keypoints: int = 2048,
):
"""TODO(omniglue): docstring."""
height0, width0 = image0.shape[:2]
diff --git a/imcui/third_party/omniglue/src/omniglue/superpoint_extract.py b/third_party/omniglue/src/omniglue/superpoint_extract.py
similarity index 100%
rename from imcui/third_party/omniglue/src/omniglue/superpoint_extract.py
rename to third_party/omniglue/src/omniglue/superpoint_extract.py
diff --git a/imcui/third_party/omniglue/src/omniglue/utils.py b/third_party/omniglue/src/omniglue/utils.py
similarity index 100%
rename from imcui/third_party/omniglue/src/omniglue/utils.py
rename to third_party/omniglue/src/omniglue/utils.py
diff --git a/third_party/omniglue/third_party/dinov2/__init__.py b/third_party/omniglue/third_party/dinov2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/imcui/third_party/omniglue/third_party/dinov2/dino.py b/third_party/omniglue/third_party/dinov2/dino.py
similarity index 100%
rename from imcui/third_party/omniglue/third_party/dinov2/dino.py
rename to third_party/omniglue/third_party/dinov2/dino.py
diff --git a/imcui/third_party/omniglue/third_party/dinov2/dino_utils.py b/third_party/omniglue/third_party/dinov2/dino_utils.py
similarity index 100%
rename from imcui/third_party/omniglue/third_party/dinov2/dino_utils.py
rename to third_party/omniglue/third_party/dinov2/dino_utils.py
diff --git a/third_party/pram/.gitignore b/third_party/pram/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..e76db3ee25df1858b0cec129d3e7c0eb84637c09
--- /dev/null
+++ b/third_party/pram/.gitignore
@@ -0,0 +1,13 @@
+.idea
+__pycache__
+weights/12scenes*
+weights/7scenes*
+weights/aachen*
+weights/cambridgelandmarks*
+weights/imp_adagml.80.pth
+landmarks
+3D-models
+log_*
+*.log
+.nfs*
+Pangolin
diff --git a/third_party/pram/LICENSE b/third_party/pram/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..0bde2a83689b0ae97269181bc848fd581d23e828
--- /dev/null
+++ b/third_party/pram/LICENSE
@@ -0,0 +1,2 @@
+This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.
+To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/.
diff --git a/third_party/pram/README.md b/third_party/pram/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b8ceb745c82fd44f1ef2c1808ab3993fb4d3890c
--- /dev/null
+++ b/third_party/pram/README.md
@@ -0,0 +1,207 @@
+## PRAM: Place Recognition Anywhere Model for Efficient Visual Localization
+
+
+
+
+
+Humans localize themselves efficiently in known environments by first recognizing landmarks defined on certain objects
+and their spatial relationships, and then verifying the location by aligning detailed structures of recognized objects
+with those in the memory. Inspired by this, we propose the place recognition anywhere model (PRAM) to perform visual
+localization as efficiently as humans do. PRAM consists of two main components - recognition and registration. In
+detail, first of all, a self-supervised map-centric landmark definition strategy is adopted, making places in either
+indoor or outdoor scenes act as unique landmarks. Then, sparse keypoints extracted from images, are utilized as the
+input to a transformer-based deep neural network for landmark recognition; these keypoints enable PRAM to recognize
+hundreds of landmarks with high time and memory efficiency. Keypoints along with recognized landmark labels are further
+used for registration between query images and the 3D landmark map. Different from previous hierarchical methods, PRAM
+discards global and local descriptors, and reduces over 90% storage. Since PRAM utilizes recognition and landmark-wise
+verification to replace global reference search and exhaustive matching respectively, it runs 2.4 times faster than
+prior state-of-the-art approaches. Moreover, PRAM opens new directions for visual localization including multi-modality
+localization, map-centric feature learning, and hierarchical scene coordinate regression.
+
+* Full paper
+ PDF: [Place Recognition Anywhere Model for Efficient Visual Localization](https://arxiv.org/pdf/2404.07785.pdf).
+
+* Authors: *Fei Xue, Ignas Budvytis, Roberto Cipolla*
+
+* Website: [PRAM](https://feixue94.github.io/pram-project) for videos, slides, recent updates, and datasets.
+
+## Key Features
+
+### 1. Self-supervised landmark definition on 3D space
+
+- No need of segmentations on images
+- No inconsistent semantic results from multi-view images
+- No limitation to labels of only known objects
+- Work in any places with known or unknown objects
+- Landmark-wise 3D map sparsification
+
+
+
+
+
+### 2. Efficient landmark-wise coarse and fine localization
+
+- Recognize landmarks as opposed to do global retrieval
+- Local landmark-wise matching as opposed to exhaustive matching
+- No global descriptors (e.g. NetVLAD)
+- No reference images and their heavy repetative 2D keypoints and descriptors
+- Automatic inlier/outlier idetification
+
+
+
+
+
+### 4. Sparse recognition
+
+- Sparse SFD2 keypoints as tokens
+- No uncertainties of points at boundaries
+- Flexible to accept multi-modality inputs
+
+### 5. Relocalization and temporal localization
+
+- Per frame reclocalization from scratch
+- Tracking previous frames for higher efficiency
+
+### 6. One model one dataset
+
+- All 7 subscenes in 7Scenes dataset share a model
+- All 12 subscenes in 12Scenes dataset share a model
+- All 5 subscenes in CambridgeLandmarks share a model
+
+### 7. Robust to long-term changes
+
+
+
+
+
+## Open problems
+
+- Adaptive number landmarks determination
+- Using SAM + open vocabulary to generate semantic map
+- Multi-modality localization with other tokenized signals (e.g. text, language, GPS, Magonemeter)
+- More effective solutions to 3D sparsification
+
+## Preparation
+
+1. Download the 7Scenes, 12Scenes, CambridgeLandmarks, and Aachen datasets (remove redundant depth images otherwise they
+ will be found in the sfm process)
+2. Environments
+
+2.1 Create a virtual environment
+
+```
+conda env create -f environment.yml
+(do not activate pram before pangolin is installed)
+```
+
+2.2 Compile Pangolin for the installed python
+
+```
+git clone --recursive https://github.com/stevenlovegrove/Pangolin.git
+cd Pangolin
+git checkout v0.8
+
+# Install dependencies
+./scripts/install_prerequisites.sh recommended
+
+# Compile with your python
+cmake -DPython_EXECUTABLE=/your path to/anaconda3/envs/pram/bin/python3 -B build
+cmake --build build -t pypangolin_pip_install
+
+conda activate pram
+```
+
+## Run the localization with online visualization
+
+1. Download the [3D-models](https://drive.google.com/drive/folders/1DUB073KxAjsc8lxhMpFuxPRf0ZBQS6NS?usp=drive_link),
+ pretrained [models](https://drive.google.com/drive/folders/1E2QvujCevqnyg_CM9FGAa0AxKkt4KbLD?usp=drive_link) ,
+ and [landmarks](https://drive.google.com/drive/folders/1r9src9bz7k3WYGfaPmKJ9gqxuvdfxZU0?usp=sharing)
+2. Put pretrained models in ```weights``` directory
+3. Run the demo (e.g. 7Scenes)
+
+```
+python3 inference.py --config configs/config_train_7scenes_sfd2.yaml --rec_weight_path weights/7scenes_nc113_birch_segnetvit.199.pth --landmark_path /your path to/landmarks --online
+```
+
+## Train the recognition model (e.g. for 7Scenes)
+
+### 1. Do SfM with SFD2 including feature extraction (modify the dataset_dir, ref_sfm_dir, output_dir)
+
+```
+./sfm_scripts/reconstruct_7scenes.sh
+```
+
+This step will produce the SfM results together with the extracted keypoints
+
+### 2. Generate 3D landmarks
+
+```
+python3 -m recognition.recmap --dataset 7Scenes --dataset_dir /your path to/7Scenes --sfm_dir /sfm_path/7Scenes --save_dir /save_path/landmakrs
+```
+
+This step will generate 3D landmarks, create virtual reference frame, and sparsify the 3D points for each landmark for
+all scenes in 7Scenes
+
+### 3. Train the sparse recognition model (one model one dataset)
+
+```
+python3 train.py --config configs/config_train_7scenes_sfd2.yaml
+```
+
+Remember to modify the paths in 'config_train_7scenes_sfd2.yaml'
+
+## Your own dataset
+
+1. Run colmap or hloc to obtain the SfM results
+2. Do reconstruction with SFD2 keypoints with the sfm from step as refernece sfm
+3. Do 3D landmark generation, VRF, map sparsification etc (Add DatasetName.yaml to configs/datasets)
+4. Train the recognition model
+5. Do evaluation
+
+## Previous works can be found here
+
+1. [Efficient large-scale localization by landmark recognition, CVPR 2022](https://github.com/feixue94/lbr)
+2. [IMP: Iterative Matching and Pose Estimation with Adaptive Pooling, CVPR 2023](https://github.com/feixue94/imp-release)
+3. [SFD2: Semantic-guided Feature Detection and Description, CVPR 2023](https://github.com/feixue94/sfd2)
+4. [VRS-NeRF: Visual Relocalization with Sparse Neural Radiance Field, under review](https://github.com/feixue94/vrs-nerf)
+
+## BibTeX Citation
+
+If you use any ideas from the paper or code in this repo, please consider citing:
+
+```
+ @article{xue2024pram,
+ author = {Fei Xue and Ignas Budvytis and Roberto Cipolla},
+ title = {PRAM: Place Recognition Anywhere Model for Efficient Visual Localization},
+ journal = {arXiv preprint arXiv:2404.07785},
+ year = {2024}
+ }
+
+@inproceedings{xue2023sfd2,
+ author = {Fei Xue and Ignas Budvytis and Roberto Cipolla},
+ title = {SFD2: Semantic-guided Feature Detection and Description},
+ booktitle = {CVPR},
+ year = {2023}
+}
+
+@inproceedings{xue2022imp,
+ author = {Fei Xue and Ignas Budvytis and Roberto Cipolla},
+ title = {IMP: Iterative Matching and Pose Estimation with Adaptive Pooling},
+ booktitle = {CVPR},
+ year = {2023}
+}
+
+@inproceedings{xue2022efficient,
+ author = {Fei Xue and Ignas Budvytis and Daniel Olmeda Reino and Roberto Cipolla},
+ title = {Efficient Large-scale Localization by Global Instance Recognition},
+ booktitle = {CVPR},
+ year = {2022}
+}
+```
+
+## Acknowledgements
+
+Part of the code is from previous excellent works
+including , [SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork)
+and [hloc](https://github.com/cvg/Hierarchical-Localization). You can find more details from their released
+repositories if you are interested in their works.
\ No newline at end of file
diff --git a/imcui/third_party/pram/colmap_utils/camera_intrinsics.py b/third_party/pram/colmap_utils/camera_intrinsics.py
similarity index 100%
rename from imcui/third_party/pram/colmap_utils/camera_intrinsics.py
rename to third_party/pram/colmap_utils/camera_intrinsics.py
diff --git a/imcui/third_party/pram/colmap_utils/database.py b/third_party/pram/colmap_utils/database.py
similarity index 100%
rename from imcui/third_party/pram/colmap_utils/database.py
rename to third_party/pram/colmap_utils/database.py
diff --git a/imcui/third_party/pram/colmap_utils/geometry.py b/third_party/pram/colmap_utils/geometry.py
similarity index 100%
rename from imcui/third_party/pram/colmap_utils/geometry.py
rename to third_party/pram/colmap_utils/geometry.py
diff --git a/imcui/third_party/pram/colmap_utils/io.py b/third_party/pram/colmap_utils/io.py
similarity index 100%
rename from imcui/third_party/pram/colmap_utils/io.py
rename to third_party/pram/colmap_utils/io.py
diff --git a/imcui/third_party/pram/colmap_utils/parsers.py b/third_party/pram/colmap_utils/parsers.py
similarity index 100%
rename from imcui/third_party/pram/colmap_utils/parsers.py
rename to third_party/pram/colmap_utils/parsers.py
diff --git a/imcui/third_party/pram/colmap_utils/read_write_model.py b/third_party/pram/colmap_utils/read_write_model.py
similarity index 100%
rename from imcui/third_party/pram/colmap_utils/read_write_model.py
rename to third_party/pram/colmap_utils/read_write_model.py
diff --git a/imcui/third_party/pram/colmap_utils/utils.py b/third_party/pram/colmap_utils/utils.py
similarity index 100%
rename from imcui/third_party/pram/colmap_utils/utils.py
rename to third_party/pram/colmap_utils/utils.py
diff --git a/imcui/third_party/pram/configs/config_train_12scenes_sfd2.yaml b/third_party/pram/configs/config_train_12scenes_sfd2.yaml
similarity index 100%
rename from imcui/third_party/pram/configs/config_train_12scenes_sfd2.yaml
rename to third_party/pram/configs/config_train_12scenes_sfd2.yaml
diff --git a/imcui/third_party/pram/configs/config_train_7scenes_sfd2.yaml b/third_party/pram/configs/config_train_7scenes_sfd2.yaml
similarity index 100%
rename from imcui/third_party/pram/configs/config_train_7scenes_sfd2.yaml
rename to third_party/pram/configs/config_train_7scenes_sfd2.yaml
diff --git a/imcui/third_party/pram/configs/config_train_aachen_sfd2.yaml b/third_party/pram/configs/config_train_aachen_sfd2.yaml
similarity index 100%
rename from imcui/third_party/pram/configs/config_train_aachen_sfd2.yaml
rename to third_party/pram/configs/config_train_aachen_sfd2.yaml
diff --git a/imcui/third_party/pram/configs/config_train_cambridge_sfd2.yaml b/third_party/pram/configs/config_train_cambridge_sfd2.yaml
similarity index 100%
rename from imcui/third_party/pram/configs/config_train_cambridge_sfd2.yaml
rename to third_party/pram/configs/config_train_cambridge_sfd2.yaml
diff --git a/imcui/third_party/pram/configs/config_train_multiset_sfd2.yaml b/third_party/pram/configs/config_train_multiset_sfd2.yaml
similarity index 100%
rename from imcui/third_party/pram/configs/config_train_multiset_sfd2.yaml
rename to third_party/pram/configs/config_train_multiset_sfd2.yaml
diff --git a/imcui/third_party/pram/configs/datasets/12Scenes.yaml b/third_party/pram/configs/datasets/12Scenes.yaml
similarity index 100%
rename from imcui/third_party/pram/configs/datasets/12Scenes.yaml
rename to third_party/pram/configs/datasets/12Scenes.yaml
diff --git a/imcui/third_party/pram/configs/datasets/7Scenes.yaml b/third_party/pram/configs/datasets/7Scenes.yaml
similarity index 100%
rename from imcui/third_party/pram/configs/datasets/7Scenes.yaml
rename to third_party/pram/configs/datasets/7Scenes.yaml
diff --git a/imcui/third_party/pram/configs/datasets/Aachen.yaml b/third_party/pram/configs/datasets/Aachen.yaml
similarity index 100%
rename from imcui/third_party/pram/configs/datasets/Aachen.yaml
rename to third_party/pram/configs/datasets/Aachen.yaml
diff --git a/imcui/third_party/pram/configs/datasets/CambridgeLandmarks.yaml b/third_party/pram/configs/datasets/CambridgeLandmarks.yaml
similarity index 100%
rename from imcui/third_party/pram/configs/datasets/CambridgeLandmarks.yaml
rename to third_party/pram/configs/datasets/CambridgeLandmarks.yaml
diff --git a/imcui/third_party/pram/dataset/aachen.py b/third_party/pram/dataset/aachen.py
similarity index 100%
rename from imcui/third_party/pram/dataset/aachen.py
rename to third_party/pram/dataset/aachen.py
diff --git a/imcui/third_party/pram/dataset/basicdataset.py b/third_party/pram/dataset/basicdataset.py
similarity index 100%
rename from imcui/third_party/pram/dataset/basicdataset.py
rename to third_party/pram/dataset/basicdataset.py
diff --git a/imcui/third_party/pram/dataset/cambridge_landmarks.py b/third_party/pram/dataset/cambridge_landmarks.py
similarity index 100%
rename from imcui/third_party/pram/dataset/cambridge_landmarks.py
rename to third_party/pram/dataset/cambridge_landmarks.py
diff --git a/imcui/third_party/pram/dataset/customdataset.py b/third_party/pram/dataset/customdataset.py
similarity index 100%
rename from imcui/third_party/pram/dataset/customdataset.py
rename to third_party/pram/dataset/customdataset.py
diff --git a/imcui/third_party/pram/dataset/get_dataset.py b/third_party/pram/dataset/get_dataset.py
similarity index 100%
rename from imcui/third_party/pram/dataset/get_dataset.py
rename to third_party/pram/dataset/get_dataset.py
diff --git a/imcui/third_party/pram/dataset/recdataset.py b/third_party/pram/dataset/recdataset.py
similarity index 100%
rename from imcui/third_party/pram/dataset/recdataset.py
rename to third_party/pram/dataset/recdataset.py
diff --git a/imcui/third_party/pram/dataset/seven_scenes.py b/third_party/pram/dataset/seven_scenes.py
similarity index 100%
rename from imcui/third_party/pram/dataset/seven_scenes.py
rename to third_party/pram/dataset/seven_scenes.py
diff --git a/imcui/third_party/pram/dataset/twelve_scenes.py b/third_party/pram/dataset/twelve_scenes.py
similarity index 100%
rename from imcui/third_party/pram/dataset/twelve_scenes.py
rename to third_party/pram/dataset/twelve_scenes.py
diff --git a/imcui/third_party/pram/dataset/utils.py b/third_party/pram/dataset/utils.py
similarity index 100%
rename from imcui/third_party/pram/dataset/utils.py
rename to third_party/pram/dataset/utils.py
diff --git a/imcui/third_party/pram/environment.yml b/third_party/pram/environment.yml
similarity index 100%
rename from imcui/third_party/pram/environment.yml
rename to third_party/pram/environment.yml
diff --git a/imcui/third_party/pram/inference.py b/third_party/pram/inference.py
similarity index 100%
rename from imcui/third_party/pram/inference.py
rename to third_party/pram/inference.py
diff --git a/imcui/third_party/pram/localization/base_model.py b/third_party/pram/localization/base_model.py
similarity index 100%
rename from imcui/third_party/pram/localization/base_model.py
rename to third_party/pram/localization/base_model.py
diff --git a/imcui/third_party/pram/localization/camera.py b/third_party/pram/localization/camera.py
similarity index 100%
rename from imcui/third_party/pram/localization/camera.py
rename to third_party/pram/localization/camera.py
diff --git a/imcui/third_party/pram/localization/extract_features.py b/third_party/pram/localization/extract_features.py
similarity index 100%
rename from imcui/third_party/pram/localization/extract_features.py
rename to third_party/pram/localization/extract_features.py
diff --git a/imcui/third_party/pram/localization/frame.py b/third_party/pram/localization/frame.py
similarity index 100%
rename from imcui/third_party/pram/localization/frame.py
rename to third_party/pram/localization/frame.py
diff --git a/imcui/third_party/pram/localization/loc_by_rec_eval.py b/third_party/pram/localization/loc_by_rec_eval.py
similarity index 100%
rename from imcui/third_party/pram/localization/loc_by_rec_eval.py
rename to third_party/pram/localization/loc_by_rec_eval.py
diff --git a/imcui/third_party/pram/localization/loc_by_rec_online.py b/third_party/pram/localization/loc_by_rec_online.py
similarity index 100%
rename from imcui/third_party/pram/localization/loc_by_rec_online.py
rename to third_party/pram/localization/loc_by_rec_online.py
diff --git a/imcui/third_party/pram/localization/localizer.py b/third_party/pram/localization/localizer.py
similarity index 100%
rename from imcui/third_party/pram/localization/localizer.py
rename to third_party/pram/localization/localizer.py
diff --git a/imcui/third_party/pram/localization/match_features.py b/third_party/pram/localization/match_features.py
similarity index 100%
rename from imcui/third_party/pram/localization/match_features.py
rename to third_party/pram/localization/match_features.py
diff --git a/imcui/third_party/pram/localization/match_features_batch.py b/third_party/pram/localization/match_features_batch.py
similarity index 100%
rename from imcui/third_party/pram/localization/match_features_batch.py
rename to third_party/pram/localization/match_features_batch.py
diff --git a/imcui/third_party/gim/hloc/matchers/__init__.py b/third_party/pram/localization/matchers/__init__.py
similarity index 100%
rename from imcui/third_party/gim/hloc/matchers/__init__.py
rename to third_party/pram/localization/matchers/__init__.py
diff --git a/imcui/third_party/pram/localization/matchers/adagml.py b/third_party/pram/localization/matchers/adagml.py
similarity index 100%
rename from imcui/third_party/pram/localization/matchers/adagml.py
rename to third_party/pram/localization/matchers/adagml.py
diff --git a/imcui/third_party/pram/localization/matchers/gm.py b/third_party/pram/localization/matchers/gm.py
similarity index 100%
rename from imcui/third_party/pram/localization/matchers/gm.py
rename to third_party/pram/localization/matchers/gm.py
diff --git a/imcui/third_party/pram/localization/matchers/gml.py b/third_party/pram/localization/matchers/gml.py
similarity index 100%
rename from imcui/third_party/pram/localization/matchers/gml.py
rename to third_party/pram/localization/matchers/gml.py
diff --git a/imcui/third_party/pram/localization/matchers/nearest_neighbor.py b/third_party/pram/localization/matchers/nearest_neighbor.py
similarity index 100%
rename from imcui/third_party/pram/localization/matchers/nearest_neighbor.py
rename to third_party/pram/localization/matchers/nearest_neighbor.py
diff --git a/imcui/third_party/pram/localization/multimap3d.py b/third_party/pram/localization/multimap3d.py
similarity index 100%
rename from imcui/third_party/pram/localization/multimap3d.py
rename to third_party/pram/localization/multimap3d.py
diff --git a/imcui/third_party/pram/localization/point3d.py b/third_party/pram/localization/point3d.py
similarity index 100%
rename from imcui/third_party/pram/localization/point3d.py
rename to third_party/pram/localization/point3d.py
diff --git a/imcui/third_party/pram/localization/pose_estimator.py b/third_party/pram/localization/pose_estimator.py
similarity index 100%
rename from imcui/third_party/pram/localization/pose_estimator.py
rename to third_party/pram/localization/pose_estimator.py
diff --git a/imcui/third_party/pram/localization/refframe.py b/third_party/pram/localization/refframe.py
similarity index 100%
rename from imcui/third_party/pram/localization/refframe.py
rename to third_party/pram/localization/refframe.py
diff --git a/imcui/third_party/pram/localization/singlemap3d.py b/third_party/pram/localization/singlemap3d.py
similarity index 100%
rename from imcui/third_party/pram/localization/singlemap3d.py
rename to third_party/pram/localization/singlemap3d.py
diff --git a/imcui/third_party/pram/localization/tracker.py b/third_party/pram/localization/tracker.py
similarity index 100%
rename from imcui/third_party/pram/localization/tracker.py
rename to third_party/pram/localization/tracker.py
diff --git a/imcui/third_party/pram/localization/triangulation.py b/third_party/pram/localization/triangulation.py
similarity index 100%
rename from imcui/third_party/pram/localization/triangulation.py
rename to third_party/pram/localization/triangulation.py
diff --git a/imcui/third_party/pram/localization/utils.py b/third_party/pram/localization/utils.py
similarity index 100%
rename from imcui/third_party/pram/localization/utils.py
rename to third_party/pram/localization/utils.py
diff --git a/imcui/third_party/pram/localization/viewer.py b/third_party/pram/localization/viewer.py
similarity index 100%
rename from imcui/third_party/pram/localization/viewer.py
rename to third_party/pram/localization/viewer.py
diff --git a/imcui/third_party/pram/main.py b/third_party/pram/main.py
similarity index 100%
rename from imcui/third_party/pram/main.py
rename to third_party/pram/main.py
diff --git a/imcui/third_party/pram/nets/adagml.py b/third_party/pram/nets/adagml.py
similarity index 100%
rename from imcui/third_party/pram/nets/adagml.py
rename to third_party/pram/nets/adagml.py
diff --git a/imcui/third_party/pram/nets/gm.py b/third_party/pram/nets/gm.py
similarity index 100%
rename from imcui/third_party/pram/nets/gm.py
rename to third_party/pram/nets/gm.py
diff --git a/imcui/third_party/pram/nets/gml.py b/third_party/pram/nets/gml.py
similarity index 100%
rename from imcui/third_party/pram/nets/gml.py
rename to third_party/pram/nets/gml.py
diff --git a/imcui/third_party/pram/nets/layers.py b/third_party/pram/nets/layers.py
similarity index 100%
rename from imcui/third_party/pram/nets/layers.py
rename to third_party/pram/nets/layers.py
diff --git a/imcui/third_party/pram/nets/load_segnet.py b/third_party/pram/nets/load_segnet.py
similarity index 100%
rename from imcui/third_party/pram/nets/load_segnet.py
rename to third_party/pram/nets/load_segnet.py
diff --git a/imcui/third_party/pram/nets/retnet.py b/third_party/pram/nets/retnet.py
similarity index 100%
rename from imcui/third_party/pram/nets/retnet.py
rename to third_party/pram/nets/retnet.py
diff --git a/imcui/third_party/pram/nets/segnet.py b/third_party/pram/nets/segnet.py
similarity index 100%
rename from imcui/third_party/pram/nets/segnet.py
rename to third_party/pram/nets/segnet.py
diff --git a/imcui/third_party/pram/nets/segnetvit.py b/third_party/pram/nets/segnetvit.py
similarity index 100%
rename from imcui/third_party/pram/nets/segnetvit.py
rename to third_party/pram/nets/segnetvit.py
diff --git a/imcui/third_party/pram/nets/sfd2.py b/third_party/pram/nets/sfd2.py
similarity index 100%
rename from imcui/third_party/pram/nets/sfd2.py
rename to third_party/pram/nets/sfd2.py
diff --git a/imcui/third_party/pram/nets/superpoint.py b/third_party/pram/nets/superpoint.py
similarity index 100%
rename from imcui/third_party/pram/nets/superpoint.py
rename to third_party/pram/nets/superpoint.py
diff --git a/imcui/third_party/pram/nets/utils.py b/third_party/pram/nets/utils.py
similarity index 100%
rename from imcui/third_party/pram/nets/utils.py
rename to third_party/pram/nets/utils.py
diff --git a/imcui/third_party/pram/recognition/recmap.py b/third_party/pram/recognition/recmap.py
similarity index 100%
rename from imcui/third_party/pram/recognition/recmap.py
rename to third_party/pram/recognition/recmap.py
diff --git a/imcui/third_party/pram/recognition/vis_seg.py b/third_party/pram/recognition/vis_seg.py
similarity index 100%
rename from imcui/third_party/pram/recognition/vis_seg.py
rename to third_party/pram/recognition/vis_seg.py
diff --git a/third_party/pram/sfm_scripts/reconstruct_12scenes.sh b/third_party/pram/sfm_scripts/reconstruct_12scenes.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4f79e356a73f897f9e5a3db5cdf4cbf4b689275c
--- /dev/null
+++ b/third_party/pram/sfm_scripts/reconstruct_12scenes.sh
@@ -0,0 +1,91 @@
+#!/bin/bash
+# you need to use your own path
+
+dataset_dir=/scratches/flyer_3/fx221/dataset/12Scenes
+ref_sfm_dir=/scratches/flyer_2/fx221/publications/pram_data/3D-models/12Scenes
+output_dir=/scratches/flyer_2/fx221/localization/outputs/12Scenes
+
+feat=sfd2
+matcher=gm
+
+#feat=superpoint-n4096
+#matcher=superglue
+
+extract_feat_db=1
+match_db=1
+triangulation=1
+localize=1
+
+ransac_thresh=8
+opt_thresh=8
+covisibility_frame=20
+inlier_thresh=30
+obs_thresh=3
+
+
+#for scene in apt1 apt2 office1 office2
+for scene in apt2 office1 office2
+do
+ echo $scene
+
+ if [ "$scene" = "apt1" ]; then
+ all_subscenes='kitchen living'
+ elif [ "$scene" = "apt2" ]; then
+ all_subscenes='bed kitchen living luke'
+ elif [ "$scene" = "office1" ]; then
+ all_subscenes='gates362 gates381 lounge manolis'
+ elif [ "$scene" = "office2" ]; then
+ all_subscenes='5a 5b'
+ fi
+
+ for subscene in $all_subscenes
+ do
+ echo $subscene
+
+ image_dir=$dataset_dir/$scene/$subscene
+ ref_sfm=$ref_sfm_dir/$scene/$subscene/3D-models
+ db_pair=$ref_sfm_dir/$scene/$subscene/pairs-db-covis20.txt
+ outputs=$output_dir/$scene/$subscene
+ query_pair=$ref_sfm_dir/$scene/$subscene/pairs-query-netvlad20.txt
+ gt_pose_fn=$ref_sfm_dir/$scene/$subscene/queries_poses.txt
+ query_fn=$ref_sfm_dir/$scene/$subscene/queries_with_intrinsics.txt
+
+ if [ "$extract_feat_db" -gt "0" ]; then
+ python3 -m loc.extract_features --image_dir $image_dir --export_dir $outputs/ --conf $feat
+ fi
+
+ if [ "$match_db" -gt "0" ]; then
+ python3 -m loc.match_features --pairs $db_pair --export_dir $outputs/ --conf $matcher --features feats-$feat
+ fi
+
+ if [ "$triangulation" -gt "0" ]; then
+ python3 -m loc.triangulation \
+ --sfm_dir $outputs/sfm_$feat-$matcher \
+ --reference_sfm_model $ref_sfm \
+ --image_dir $image_dir \
+ --pairs $db_pair \
+ --features $outputs/feats-$feat.h5 \
+ --matches $outputs/feats-$feat-$matcher-pairs-db-covis20.h5
+ fi
+
+ if [ "$localize" -gt "0" ]; then
+ python3 -m loc.localizer \
+ --dataset 12Scenes \
+ --image_dir $image_dir \
+ --save_root $outputs \
+ --gt_pose_fn $gt_pose_fn \
+ --retrieval $query_pair \
+ --reference_sfm $outputs/sfm_$feat-$matcher \
+ --queries $query_fn \
+ --features $outputs/feats-$feat.h5 \
+ --matcher_method $matcher \
+ --ransac_thresh $ransac_thresh \
+ --covisibility_frame $covisibility_frame \
+ --obs_thresh $obs_thresh \
+ --opt_thresh $opt_thresh \
+ --inlier_thresh $inlier_thresh \
+ --use_hloc
+ fi
+ done
+
+done
diff --git a/third_party/pram/sfm_scripts/reconstruct_7scenes.sh b/third_party/pram/sfm_scripts/reconstruct_7scenes.sh
new file mode 100644
index 0000000000000000000000000000000000000000..91fb16dabc2a294476c0865fc4a5e12e2b4cf0b7
--- /dev/null
+++ b/third_party/pram/sfm_scripts/reconstruct_7scenes.sh
@@ -0,0 +1,75 @@
+#!/bin/bash
+
+# you need to use your own path
+dataset_dir=/scratches/flyer_3/fx221/dataset/7Scenes
+ref_sfm_dir=/scratches/flyer_2/fx221/publications/pram_data/3D-models/7Scenes
+output_dir=/scratches/flyer_2/fx221/publications/test_pram/7Scenes
+
+# keypoints and matcher used for sfm
+feat=sfd2
+matcher=gml
+
+
+extract_feat_db=1
+match_db=1
+triangulation=1
+localize=0
+
+
+ransac_thresh=12
+opt_thresh=12
+covisibility_frame=20
+inlier_thresh=30
+obs_thresh=3
+
+
+for scene in heads fire office stairs pumpkin redkitchen chess
+#for scene in fire office pumpkin redkitchen chess
+#for scene in chess
+do
+ echo $scene
+ image_dir=$dataset_dir/$scene
+ ref_sfm=$ref_sfm_dir/$scene/3D-models
+ db_pair=$ref_sfm_dir/$scene/pairs-db-covis20.txt
+ outputs=$output_dir/$scene
+ query_pair=$ref_sfm_dir/$scene/pairs-query-netvlad20.txt
+ gt_pose_fn=$ref_sfm_dir/$scene/queries_poses.txt
+ query_fn=$ref_sfm_dir/$scene/queries_with_intrinsics.txt
+
+ if [ "$extract_feat_db" -gt "0" ]; then
+ python3 -m localization.extract_features --image_dir $image_dir --export_dir $outputs/ --conf $feat
+ fi
+
+ if [ "$match_db" -gt "0" ]; then
+ python3 -m localization.match_features --pairs $db_pair --export_dir $outputs/ --conf $matcher --features feats-$feat
+ fi
+
+ if [ "$triangulation" -gt "0" ]; then
+ python3 -m localization.triangulation \
+ --sfm_dir $outputs/sfm_$feat-$matcher \
+ --reference_sfm_model $ref_sfm \
+ --image_dir $image_dir \
+ --pairs $db_pair \
+ --features $outputs/feats-$feat.h5 \
+ --matches $outputs/feats-$feat-$matcher-pairs-db-covis20.h5
+ fi
+
+ if [ "$localize" -gt "0" ]; then
+ python3 -m localization.localizer \
+ --dataset 7Scenes \
+ --image_dir $image_dir \
+ --save_root $outputs \
+ --gt_pose_fn $gt_pose_fn \
+ --retrieval $query_pair \
+ --reference_sfm $outputs/sfm_$feat-$matcher \
+ --queries $query_fn \
+ --features $outputs/feats-$feat.h5 \
+ --matcher_method $matcher \
+ --ransac_thresh $ransac_thresh \
+ --covisibility_frame $covisibility_frame \
+ --obs_thresh $obs_thresh \
+ --opt_thresh $opt_thresh \
+ --inlier_thresh $inlier_thresh \
+ --use_hloc
+ fi
+done
\ No newline at end of file
diff --git a/third_party/pram/sfm_scripts/reconstruct_aachen.sh b/third_party/pram/sfm_scripts/reconstruct_aachen.sh
new file mode 100644
index 0000000000000000000000000000000000000000..510485e521511f1948060c5d0de5f56984586c8d
--- /dev/null
+++ b/third_party/pram/sfm_scripts/reconstruct_aachen.sh
@@ -0,0 +1,69 @@
+#!/bin/bash
+# you need to use your own path
+dataset_dir=/scratches/flyer_3/fx221/dataset/Aachen/Aachenv11
+ref_sfm_dir=/scratches/flyer_2/fx221/publications/pram_data/3D-models/Aachen/Aachenv11
+output_dir=/scratches/flyer_2/fx221/localization/outputs/Aachen/Aachenv11
+
+# fixed
+output=$output_dir
+ref_sfm=$ref_sfm_dir/3D-models
+db_pair=$ref_sfm_dir/pairs-db-covis20.txt
+query_pair=$ref_sfm_dir/pairs-query-netvlad50.txt
+gt_pose_fn=$ref_sfm_dir/queries_pose_spp_spg.txt
+query_fn=$ref_sfm_dir/queries_with_intrinsics.txt
+
+
+
+feat=sfd2
+matcher=gm
+
+#feat=superpoint-n4096
+#matcher=superglue
+
+extract_feat_db=1
+match_db=1
+triangulation=1
+localize=1
+
+if [ "$extract_feat_db" -gt "0" ]; then
+ python3 -m loc.extract_features --image_dir $dataset/images/images_upright --export_dir $outputs/ --conf $feat
+fi
+
+if [ "$match_db" -gt "0" ]; then
+ python3 -m loc.match_features --pairs $ref_sfm_dir/pairs-db-covis20.txt --export_dir $outputs/ --conf $matcher --features feats-$feat
+fi
+
+if [ "$triangulation" -gt "0" ]; then
+ python3 -m loc.triangulation \
+ --sfm_dir $outputs/sfm_$feat-$matcher \
+ --reference_sfm_model $ref_sfm \
+ --image_dir $dataset/images/images_upright \
+ --pairs $db_pair \
+ --features $outputs/feats-$feat.h5 \
+ --matches $outputs/feats-$feat-$matcher-pairs-db-covis20.h5
+fi
+
+ransac_thresh=15
+opt_thresh=15
+covisibility_frame=30
+inlier_thresh=80
+obs_thresh=3
+
+if [ "$localize" -gt "0" ]; then
+ python3 -m loc.localizer \
+ --dataset aachen_v1.1 \
+ --image_dir $image_dir \
+ --save_root $outputs \
+ --gt_pose_fn $gt_pose_fn \
+ --retrieval $query_pair \
+ --reference_sfm $outputs/sfm_$feat-$matcher \
+ --queries $query_fn \
+ --features $outputs/feats-$feat.h5 \
+ --matcher_method $matcher \
+ --ransac_thresh $ransac_thresh \
+ --covisibility_frame $covisibility_frame \
+ --obs_thresh $obs_thresh \
+ --opt_thresh $opt_thresh \
+ --inlier_thresh $inlier_thresh \
+ --use_hloc
+fi
\ No newline at end of file
diff --git a/third_party/pram/sfm_scripts/reconstruct_cambridge.sh b/third_party/pram/sfm_scripts/reconstruct_cambridge.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f1ee967cf94e16e4a2f1848436d236df9a273858
--- /dev/null
+++ b/third_party/pram/sfm_scripts/reconstruct_cambridge.sh
@@ -0,0 +1,76 @@
+#!/bin/bash
+
+# you need to use your own path
+dataset_dir=/scratches/flyer_3/fx221/dataset/CambridgeLandmarks
+ref_sfm_dir=/scratches/flyer_2/fx221/publications/pram_data/3D-models/CambridgeLandmarks
+output_dir=/scratches/flyer_2/fx221/localization/outputs/CambridgeLandmarks
+
+
+feat=sfd2
+matcher=gm
+
+extract_feat_db=0
+match_db=0
+triangulation=0
+localize=1
+
+ransac_thresh=12
+opt_thresh=12
+covisibility_frame=20
+inlier_thresh=30
+radius=30
+obs_thresh=3
+
+
+#for scene in GreatCourt ShopFacade KingsCollege OldHospital StMarysChurch
+for scene in StMarysChurch
+#for scene in GreatCourt ShopFacade
+do
+ echo $scene
+
+ image_dir=$dataset_dir/$scene
+ ref_sfm=$ref_sfm_dir/$scene/3D-models
+ db_pair=$ref_sfm_dir/$scene/pairs-db-covis20.txt
+ outputs=$output_dir/$scene
+ query_pair=$ref_sfm_dir/$scene/pairs-query-netvlad20.txt
+ gt_pose_fn=$ref_sfm_dir/$scene/queries_poses.txt
+ query_fn=$ref_sfm_dir/$scene/queries_with_intrinsics.txt
+
+ if [ "$extract_feat_db" -gt "0" ]; then
+ python3 -m loc.extract_features --image_dir $image_dir --export_dir $outputs/ --conf $feat
+ fi
+
+ if [ "$match_db" -gt "0" ]; then
+ python3 -m loc.match_features --pairs $db_pair --export_dir $outputs/ --conf $matcher --features feats-$feat
+ fi
+
+ if [ "$triangulation" -gt "0" ]; then
+ python3 -m loc.triangulation \
+ --sfm_dir $outputs/sfm_$feat-$matcher \
+ --reference_sfm_model $ref_sfm \
+ --image_dir $image_dir\
+ --pairs $db_pair \
+ --features $outputs/feats-$feat.h5 \
+ --matches $outputs/feats-$feat-$matcher-pairs-db-covis20.h5
+ fi
+
+ if [ "$localize" -gt "0" ]; then
+ python3 -m loc.localizer \
+ --dataset cambridge \
+ --image_dir $image_dir \
+ --save_root $outputs\
+ --gt_pose_fn $gt_pose_fn \
+ --retrieval $query_pair \
+ --reference_sfm $outputs/sfm_$feat-$matcher \
+ --queries $query_fn \
+ --features $outputs/feats-$feat.h5 \
+ --matcher_method adagm2 \
+ --ransac_thresh $ransac_thresh \
+ --covisibility_frame $covisibility_frame \
+ --obs_thresh $obs_thresh \
+ --opt_thresh $opt_thresh \
+ --inlier_thresh $inlier_thresh \
+ --use_hloc
+ fi
+
+done
\ No newline at end of file
diff --git a/imcui/third_party/pram/tools/common.py b/third_party/pram/tools/common.py
similarity index 100%
rename from imcui/third_party/pram/tools/common.py
rename to third_party/pram/tools/common.py
diff --git a/imcui/third_party/pram/tools/geometry.py b/third_party/pram/tools/geometry.py
similarity index 100%
rename from imcui/third_party/pram/tools/geometry.py
rename to third_party/pram/tools/geometry.py
diff --git a/imcui/third_party/pram/tools/image_to_video.py b/third_party/pram/tools/image_to_video.py
similarity index 100%
rename from imcui/third_party/pram/tools/image_to_video.py
rename to third_party/pram/tools/image_to_video.py
diff --git a/imcui/third_party/pram/tools/metrics.py b/third_party/pram/tools/metrics.py
similarity index 100%
rename from imcui/third_party/pram/tools/metrics.py
rename to third_party/pram/tools/metrics.py
diff --git a/imcui/third_party/pram/tools/video_to_image.py b/third_party/pram/tools/video_to_image.py
similarity index 100%
rename from imcui/third_party/pram/tools/video_to_image.py
rename to third_party/pram/tools/video_to_image.py
diff --git a/imcui/third_party/pram/tools/visualize_landmarks.py b/third_party/pram/tools/visualize_landmarks.py
similarity index 100%
rename from imcui/third_party/pram/tools/visualize_landmarks.py
rename to third_party/pram/tools/visualize_landmarks.py
diff --git a/imcui/third_party/pram/train.py b/third_party/pram/train.py
similarity index 100%
rename from imcui/third_party/pram/train.py
rename to third_party/pram/train.py
diff --git a/imcui/third_party/pram/trainer.py b/third_party/pram/trainer.py
similarity index 100%
rename from imcui/third_party/pram/trainer.py
rename to third_party/pram/trainer.py
diff --git a/third_party/r2d2/LICENSE b/third_party/r2d2/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..9144e3e43fe3d62cd66971ab021466949fc4ee14
--- /dev/null
+++ b/third_party/r2d2/LICENSE
@@ -0,0 +1,69 @@
+Creative Commons
+
+Attribution-NonCommercial-ShareAlike 3.0 Unported
+
+CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE LEGAL SERVICES. DISTRIBUTION OF THIS LICENSE DOES NOT CREATE AN ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES REGARDING THE INFORMATION PROVIDED, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM ITS USE.
+License
+THE WORK (AS DEFINED BELOW) IS PROVIDED UNDER THE TERMS OF THIS CREATIVE COMMONS PUBLIC LICENSE ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY COPYRIGHT AND/OR OTHER APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS AUTHORIZED UNDER THIS LICENSE OR COPYRIGHT LAW IS PROHIBITED.
+
+BY EXERCISING ANY RIGHTS TO THE WORK PROVIDED HERE, YOU ACCEPT AND AGREE TO BE BOUND BY THE TERMS OF THIS LICENSE. TO THE EXTENT THIS LICENSE MAY BE CONSIDERED TO BE A CONTRACT, THE LICENSOR GRANTS YOU THE RIGHTS CONTAINED HERE IN CONSIDERATION OF YOUR ACCEPTANCE OF SUCH TERMS AND CONDITIONS.
+
+1. Definitions
+
+"Adaptation" means a work based upon the Work, or upon the Work and other pre-existing works, such as a translation, adaptation, derivative work, arrangement of music or other alterations of a literary or artistic work, or phonogram or performance and includes cinematographic adaptations or any other form in which the Work may be recast, transformed, or adapted including in any form recognizably derived from the original, except that a work that constitutes a Collection will not be considered an Adaptation for the purpose of this License. For the avoidance of doubt, where the Work is a musical work, performance or phonogram, the synchronization of the Work in timed-relation with a moving image ("synching") will be considered an Adaptation for the purpose of this License.
+"Collection" means a collection of literary or artistic works, such as encyclopedias and anthologies, or performances, phonograms or broadcasts, or other works or subject matter other than works listed in Section 1(g) below, which, by reason of the selection and arrangement of their contents, constitute intellectual creations, in which the Work is included in its entirety in unmodified form along with one or more other contributions, each constituting separate and independent works in themselves, which together are assembled into a collective whole. A work that constitutes a Collection will not be considered an Adaptation (as defined above) for the purposes of this License.
+"Distribute" means to make available to the public the original and copies of the Work or Adaptation, as appropriate, through sale or other transfer of ownership.
+"License Elements" means the following high-level license attributes as selected by Licensor and indicated in the title of this License: Attribution, Noncommercial, ShareAlike.
+"Licensor" means the individual, individuals, entity or entities that offer(s) the Work under the terms of this License.
+"Original Author" means, in the case of a literary or artistic work, the individual, individuals, entity or entities who created the Work or if no individual or entity can be identified, the publisher; and in addition (i) in the case of a performance the actors, singers, musicians, dancers, and other persons who act, sing, deliver, declaim, play in, interpret or otherwise perform literary or artistic works or expressions of folklore; (ii) in the case of a phonogram the producer being the person or legal entity who first fixes the sounds of a performance or other sounds; and, (iii) in the case of broadcasts, the organization that transmits the broadcast.
+"Work" means the literary and/or artistic work offered under the terms of this License including without limitation any production in the literary, scientific and artistic domain, whatever may be the mode or form of its expression including digital form, such as a book, pamphlet and other writing; a lecture, address, sermon or other work of the same nature; a dramatic or dramatico-musical work; a choreographic work or entertainment in dumb show; a musical composition with or without words; a cinematographic work to which are assimilated works expressed by a process analogous to cinematography; a work of drawing, painting, architecture, sculpture, engraving or lithography; a photographic work to which are assimilated works expressed by a process analogous to photography; a work of applied art; an illustration, map, plan, sketch or three-dimensional work relative to geography, topography, architecture or science; a performance; a broadcast; a phonogram; a compilation of data to the extent it is protected as a copyrightable work; or a work performed by a variety or circus performer to the extent it is not otherwise considered a literary or artistic work.
+"You" means an individual or entity exercising rights under this License who has not previously violated the terms of this License with respect to the Work, or who has received express permission from the Licensor to exercise rights under this License despite a previous violation.
+"Publicly Perform" means to perform public recitations of the Work and to communicate to the public those public recitations, by any means or process, including by wire or wireless means or public digital performances; to make available to the public Works in such a way that members of the public may access these Works from a place and at a place individually chosen by them; to perform the Work to the public by any means or process and the communication to the public of the performances of the Work, including by public digital performance; to broadcast and rebroadcast the Work by any means including signs, sounds or images.
+"Reproduce" means to make copies of the Work by any means including without limitation by sound or visual recordings and the right of fixation and reproducing fixations of the Work, including storage of a protected performance or phonogram in digital form or other electronic medium.
+
+2. Fair Dealing Rights. Nothing in this License is intended to reduce, limit, or restrict any uses free from copyright or rights arising from limitations or exceptions that are provided for in connection with the copyright protection under copyright law or other applicable laws.
+
+3. License Grant. Subject to the terms and conditions of this License, Licensor hereby grants You a worldwide, royalty-free, non-exclusive, perpetual (for the duration of the applicable copyright) license to exercise the rights in the Work as stated below:
+
+to Reproduce the Work, to incorporate the Work into one or more Collections, and to Reproduce the Work as incorporated in the Collections;
+to create and Reproduce Adaptations provided that any such Adaptation, including any translation in any medium, takes reasonable steps to clearly label, demarcate or otherwise identify that changes were made to the original Work. For example, a translation could be marked "The original work was translated from English to Spanish," or a modification could indicate "The original work has been modified.";
+to Distribute and Publicly Perform the Work including as incorporated in Collections; and,
+to Distribute and Publicly Perform Adaptations.
+The above rights may be exercised in all media and formats whether now known or hereafter devised. The above rights include the right to make such modifications as are technically necessary to exercise the rights in other media and formats. Subject to Section 8(f), all rights not expressly granted by Licensor are hereby reserved, including but not limited to the rights described in Section 4(e).
+
+4. Restrictions. The license granted in Section 3 above is expressly made subject to and limited by the following restrictions:
+
+You may Distribute or Publicly Perform the Work only under the terms of this License. You must include a copy of, or the Uniform Resource Identifier (URI) for, this License with every copy of the Work You Distribute or Publicly Perform. You may not offer or impose any terms on the Work that restrict the terms of this License or the ability of the recipient of the Work to exercise the rights granted to that recipient under the terms of the License. You may not sublicense the Work. You must keep intact all notices that refer to this License and to the disclaimer of warranties with every copy of the Work You Distribute or Publicly Perform. When You Distribute or Publicly Perform the Work, You may not impose any effective technological measures on the Work that restrict the ability of a recipient of the Work from You to exercise the rights granted to that recipient under the terms of the License. This Section 4(a) applies to the Work as incorporated in a Collection, but this does not require the Collection apart from the Work itself to be made subject to the terms of this License. If You create a Collection, upon notice from any Licensor You must, to the extent practicable, remove from the Collection any credit as required by Section 4(d), as requested. If You create an Adaptation, upon notice from any Licensor You must, to the extent practicable, remove from the Adaptation any credit as required by Section 4(d), as requested.
+You may Distribute or Publicly Perform an Adaptation only under: (i) the terms of this License; (ii) a later version of this License with the same License Elements as this License; (iii) a Creative Commons jurisdiction license (either this or a later license version) that contains the same License Elements as this License (e.g., Attribution-NonCommercial-ShareAlike 3.0 US) ("Applicable License"). You must include a copy of, or the URI, for Applicable License with every copy of each Adaptation You Distribute or Publicly Perform. You may not offer or impose any terms on the Adaptation that restrict the terms of the Applicable License or the ability of the recipient of the Adaptation to exercise the rights granted to that recipient under the terms of the Applicable License. You must keep intact all notices that refer to the Applicable License and to the disclaimer of warranties with every copy of the Work as included in the Adaptation You Distribute or Publicly Perform. When You Distribute or Publicly Perform the Adaptation, You may not impose any effective technological measures on the Adaptation that restrict the ability of a recipient of the Adaptation from You to exercise the rights granted to that recipient under the terms of the Applicable License. This Section 4(b) applies to the Adaptation as incorporated in a Collection, but this does not require the Collection apart from the Adaptation itself to be made subject to the terms of the Applicable License.
+You may not exercise any of the rights granted to You in Section 3 above in any manner that is primarily intended for or directed toward commercial advantage or private monetary compensation. The exchange of the Work for other copyrighted works by means of digital file-sharing or otherwise shall not be considered to be intended for or directed toward commercial advantage or private monetary compensation, provided there is no payment of any monetary compensation in con-nection with the exchange of copyrighted works.
+If You Distribute, or Publicly Perform the Work or any Adaptations or Collections, You must, unless a request has been made pursuant to Section 4(a), keep intact all copyright notices for the Work and provide, reasonable to the medium or means You are utilizing: (i) the name of the Original Author (or pseudonym, if applicable) if supplied, and/or if the Original Author and/or Licensor designate another party or parties (e.g., a sponsor institute, publishing entity, journal) for attribution ("Attribution Parties") in Licensor's copyright notice, terms of service or by other reasonable means, the name of such party or parties; (ii) the title of the Work if supplied; (iii) to the extent reasonably practicable, the URI, if any, that Licensor specifies to be associated with the Work, unless such URI does not refer to the copyright notice or licensing information for the Work; and, (iv) consistent with Section 3(b), in the case of an Adaptation, a credit identifying the use of the Work in the Adaptation (e.g., "French translation of the Work by Original Author," or "Screenplay based on original Work by Original Author"). The credit required by this Section 4(d) may be implemented in any reasonable manner; provided, however, that in the case of a Adaptation or Collection, at a minimum such credit will appear, if a credit for all contributing authors of the Adaptation or Collection appears, then as part of these credits and in a manner at least as prominent as the credits for the other contributing authors. For the avoidance of doubt, You may only use the credit required by this Section for the purpose of attribution in the manner set out above and, by exercising Your rights under this License, You may not implicitly or explicitly assert or imply any connection with, sponsorship or endorsement by the Original Author, Licensor and/or Attribution Parties, as appropriate, of You or Your use of the Work, without the separate, express prior written permission of the Original Author, Licensor and/or Attribution Parties.
+For the avoidance of doubt:
+
+Non-waivable Compulsory License Schemes. In those jurisdictions in which the right to collect royalties through any statutory or compulsory licensing scheme cannot be waived, the Licensor reserves the exclusive right to collect such royalties for any exercise by You of the rights granted under this License;
+Waivable Compulsory License Schemes. In those jurisdictions in which the right to collect royalties through any statutory or compulsory licensing scheme can be waived, the Licensor reserves the exclusive right to collect such royalties for any exercise by You of the rights granted under this License if Your exercise of such rights is for a purpose or use which is otherwise than noncommercial as permitted under Section 4(c) and otherwise waives the right to collect royalties through any statutory or compulsory licensing scheme; and,
+Voluntary License Schemes. The Licensor reserves the right to collect royalties, whether individually or, in the event that the Licensor is a member of a collecting society that administers voluntary licensing schemes, via that society, from any exercise by You of the rights granted under this License that is for a purpose or use which is otherwise than noncommercial as permitted under Section 4(c).
+Except as otherwise agreed in writing by the Licensor or as may be otherwise permitted by applicable law, if You Reproduce, Distribute or Publicly Perform the Work either by itself or as part of any Adaptations or Collections, You must not distort, mutilate, modify or take other derogatory action in relation to the Work which would be prejudicial to the Original Author's honor or reputation. Licensor agrees that in those jurisdictions (e.g. Japan), in which any exercise of the right granted in Section 3(b) of this License (the right to make Adaptations) would be deemed to be a distortion, mutilation, modification or other derogatory action prejudicial to the Original Author's honor and reputation, the Licensor will waive or not assert, as appropriate, this Section, to the fullest extent permitted by the applicable national law, to enable You to reasonably exercise Your right under Section 3(b) of this License (right to make Adaptations) but not otherwise.
+5. Representations, Warranties and Disclaimer
+
+UNLESS OTHERWISE MUTUALLY AGREED TO BY THE PARTIES IN WRITING AND TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, LICENSOR OFFERS THE WORK AS-IS AND MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND CONCERNING THE WORK, EXPRESS, IMPLIED, STATUTORY OR OTHERWISE, INCLUDING, WITHOUT LIMITATION, WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, NONINFRINGEMENT, OR THE ABSENCE OF LATENT OR OTHER DEFECTS, ACCURACY, OR THE PRESENCE OF ABSENCE OF ERRORS, WHETHER OR NOT DISCOVERABLE. SOME JURISDICTIONS DO NOT ALLOW THE EXCLUSION OF IMPLIED WARRANTIES, SO THIS EXCLUSION MAY NOT APPLY TO YOU.
+
+6. Limitation on Liability. EXCEPT TO THE EXTENT REQUIRED BY APPLICABLE LAW, IN NO EVENT WILL LICENSOR BE LIABLE TO YOU ON ANY LEGAL THEORY FOR ANY SPECIAL, INCIDENTAL, CONSEQUENTIAL, PUNITIVE OR EXEMPLARY DAMAGES ARISING OUT OF THIS LICENSE OR THE USE OF THE WORK, EVEN IF LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+7. Termination
+
+This License and the rights granted hereunder will terminate automatically upon any breach by You of the terms of this License. Individuals or entities who have received Adaptations or Collections from You under this License, however, will not have their licenses terminated provided such individuals or entities remain in full compliance with those licenses. Sections 1, 2, 5, 6, 7, and 8 will survive any termination of this License.
+Subject to the above terms and conditions, the license granted here is perpetual (for the duration of the applicable copyright in the Work). Notwithstanding the above, Licensor reserves the right to release the Work under different license terms or to stop distributing the Work at any time; provided, however that any such election will not serve to withdraw this License (or any other license that has been, or is required to be, granted under the terms of this License), and this License will continue in full force and effect unless terminated as stated above.
+8. Miscellaneous
+
+Each time You Distribute or Publicly Perform the Work or a Collection, the Licensor offers to the recipient a license to the Work on the same terms and conditions as the license granted to You under this License.
+Each time You Distribute or Publicly Perform an Adaptation, Licensor offers to the recipient a license to the original Work on the same terms and conditions as the license granted to You under this License.
+If any provision of this License is invalid or unenforceable under applicable law, it shall not affect the validity or enforceability of the remainder of the terms of this License, and without further action by the parties to this agreement, such provision shall be reformed to the minimum extent necessary to make such provision valid and enforceable.
+No term or provision of this License shall be deemed waived and no breach consented to unless such waiver or consent shall be in writing and signed by the party to be charged with such waiver or consent.
+This License constitutes the entire agreement between the parties with respect to the Work licensed here. There are no understandings, agreements or representations with respect to the Work not specified here. Licensor shall not be bound by any additional provisions that may appear in any communication from You. This License may not be modified without the mutual written agreement of the Licensor and You.
+The rights granted under, and the subject matter referenced, in this License were drafted utilizing the terminology of the Berne Convention for the Protection of Literary and Artistic Works (as amended on September 28, 1979), the Rome Convention of 1961, the WIPO Copyright Treaty of 1996, the WIPO Performances and Phonograms Treaty of 1996 and the Universal Copyright Convention (as revised on July 24, 1971). These rights and subject matter take effect in the relevant jurisdiction in which the License terms are sought to be enforced according to the corresponding provisions of the implementation of those treaty provisions in the applicable national law. If the standard suite of rights granted under applicable copyright law includes additional rights not granted under this License, such additional rights are deemed to be included in the License; this License is not intended to restrict the license of any rights under applicable law.
+Creative Commons Notice
+Creative Commons is not a party to this License, and makes no warranty whatsoever in connection with the Work. Creative Commons will not be liable to You or any party on any legal theory for any damages whatsoever, including without limitation any general, special, incidental or consequential damages arising in connection to this license. Notwithstanding the foregoing two (2) sentences, if Creative Commons has expressly identified itself as the Licensor hereunder, it shall have all rights and obligations of Licensor.
+
+Except for the limited purpose of indicating to the public that the Work is licensed under the CCPL, Creative Commons does not authorize the use by either party of the trademark "Creative Commons" or any related trademark or logo of Creative Commons without the prior written consent of Creative Commons. Any permitted use will be in compliance with Creative Commons' then-current trademark usage guidelines, as may be published on its website or otherwise made available upon request from time to time. For the avoidance of doubt, this trademark restriction does not form part of this License.
+
+Creative Commons may be contacted at https://creativecommons.org/.
\ No newline at end of file
diff --git a/third_party/r2d2/NOTICE b/third_party/r2d2/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..3658c4ddefd692e904a5c3664b4bbdcafa7d57fd
--- /dev/null
+++ b/third_party/r2d2/NOTICE
@@ -0,0 +1,140 @@
+r2d2
+Copyright 2019-present NAVER Corp.
+
+This project contains subcomponents with separate copyright notices and license terms.
+Your use of the source code for these subcomponents is subject to the terms and conditions of the following licenses.
+
+=====
+
+pytorch/pytorch
+https://github.com/pytorch/pytorch
+
+
+From PyTorch:
+
+Copyright (c) 2016- Facebook, Inc (Adam Paszke)
+Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
+Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
+Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+Copyright (c) 2011-2013 NYU (Clement Farabet)
+Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
+Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+
+From Caffe2:
+
+Copyright (c) 2016-present, Facebook Inc. All rights reserved.
+
+All contributions by Facebook:
+Copyright (c) 2016 Facebook Inc.
+
+All contributions by Google:
+Copyright (c) 2015 Google Inc.
+All rights reserved.
+
+All contributions by Yangqing Jia:
+Copyright (c) 2015 Yangqing Jia
+All rights reserved.
+
+All contributions from Caffe:
+Copyright(c) 2013, 2014, 2015, the respective contributors
+All rights reserved.
+
+All other contributions:
+Copyright(c) 2015, 2016 the respective contributors
+All rights reserved.
+
+Caffe2 uses a copyright model similar to Caffe: each contributor holds
+copyright over their contributions to Caffe2. The project versioning records
+all such contribution and copyright details. If a contributor wants to further
+mark their specific copyright on a particular contribution, they should
+indicate their copyright solely in the commit message of the change when it is
+committed.
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright
+ notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+
+3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
+ and IDIAP Research Institute nor the names of its contributors may be
+ used to endorse or promote products derived from this software without
+ specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+
+=====
+
+pytorch/vision
+https://github.com/pytorch/vision
+
+
+BSD 3-Clause License
+
+Copyright (c) Soumith Chintala 2016,
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+ this list of conditions and the following disclaimer in the documentation
+ and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+ contributors may be used to endorse or promote products derived from
+ this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+=====
+
+tomrunia/OpticalFlow_Visualization
+https://github.com/tomrunia/OpticalFlow_Visualization
+
+
+# MIT License
+#
+# Copyright (c) 2018 Tom Runia
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to conditions.
+#
+# Author: Tom Runia
+# Date Created: 2018-08-03
+
+=====
diff --git a/third_party/r2d2/README.md b/third_party/r2d2/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..185b8c61863ae0c42ba864321b24c48dfbe85e30
--- /dev/null
+++ b/third_party/r2d2/README.md
@@ -0,0 +1,194 @@
+# R2D2: Reliable and Repeatable Detector and Descriptor #
+This repository contains the implementation of the following [paper](https://europe.naverlabs.com/research/publications/r2d2-reliable-and-repeatable-detectors-and-descriptors-for-joint-sparse-local-keypoint-detection-and-feature-extraction/):
+
+```text
+@inproceedings{r2d2,
+ author = {Jerome Revaud and Philippe Weinzaepfel and C{\'{e}}sar Roberto de Souza and
+ Martin Humenberger},
+ title = {{R2D2:} Repeatable and Reliable Detector and Descriptor},
+ booktitle = {NeurIPS},
+ year = {2019},
+}
+```
+
+Fast-R2D2
+-----------------
+
+This repository also contains the code needed to train and extract Fast-R2D2 keypoints.
+Fast-R2D2 is a revised version of R2D2 that is significantly faster, uses less memory yet achieves the same order of precision as the original network.
+
+
+License
+-------
+
+Our code is released under the Creative Commons BY-NC-SA 3.0 (see [LICENSE](LICENSE) for more details), available only for non-commercial use.
+
+
+Getting started
+---------------
+You just need Python 3.6+ equipped with standard scientific packages and PyTorch1.1+.
+Typically, conda is one of the easiest way to get started:
+```bash
+conda install python tqdm pillow numpy matplotlib scipy
+conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
+```
+
+
+Pretrained models
+-----------------
+For your convenience, we provide five pre-trained models in the `models/` folder:
+ - `r2d2_WAF_N16.pt`: this is the model used in most experiments of the paper (on HPatches `MMA@3=0.686`). It was trained with Web images (`W`), Aachen day-time images (`A`) and Aachen optical flow pairs (`F`)
+ - `r2d2_WASF_N16.pt`: this is the model used in the visual localization experiments (on HPatches `MMA@3=0.721`). It was trained with Web images (`W`), Aachen day-time images (`A`), Aachen day-night synthetic pairs (`S`), and Aachen optical flow pairs (`F`).
+ - `r2d2_WASF_N8_big.pt`: Same than previous model, but trained with `N=8` instead of `N=16` in the repeatability loss. In other words, it outputs a higher density of keypoints. This can be interesting for certain applications like visual localization, but it implies a drop in MMA since keypoints gets slighlty less reliable.
+ - `faster2d2_WASF_N16.pt`: The Fast-R2D2 equivalent of r2d2_WASF_N16.pt
+ - `faster2d2_WASF_N8_big.pt`: The Fast-R2D2 equivalent of r2d2_WASF_N8.pt
+
+For more details about the training data, see the dedicated section below.
+Here is a table that summarizes the performance of each model:
+
+| model name | model size (#weights)| number of keypoints |MMA@3 on HPatches|
+|------------------|:-----------------------:|:----------------------:|:------------------:|
+|`r2d2_WAF_N16.pt` | 0.5M | 5K | 0.686 |
+|`r2d2_WASF_N16.pt` | 0.5M | 5K | 0.721 |
+|`r2d2_WASF_N8_big.pt`| 1.0M | 10K | 0.692 |
+|`faster2d2_WASF_N8_big.pt`| 1.0M | 5K | 0.650 |
+
+
+
+Feature extraction
+------------------
+To extract keypoints for a given image, simply execute:
+```bash
+python extract.py --model models/r2d2_WASF_N16.pt --images imgs/brooklyn.png --top-k 5000
+```
+This also works for multiple images (separated by spaces) or a `.txt` image list.
+For each image, this will save the `top-k` keypoints in a file with the same path as the image and a `.r2d2` extension.
+For example, they will be saved in `imgs/brooklyn.png.r2d2` for the sample command above.
+
+The keypoint file is in the `npz` numpy format and contains 3 fields:
+ - `keypoints` (`N x 3`): keypoint position (x, y and scale). Scale denotes here the patch diameters in pixels.
+ - `descriptors` (`N x 128`): l2-normalized descriptors.
+ - `scores` (`N`): keypoint scores (the higher the better).
+
+*Note*: You can modify the extraction parameters (scale factor, scale range...). Run `python extract.py --help` for more information.
+By default, they corespond to what is used in the paper, i.e., a scale factor equal to `2^0.25` (`--scale-f 1.189207`) and image size in the range `[256, 1024]` (`--min-size 256 --max-size 1024`).
+
+*Note2*: You can significantly improve the `MMA@3` score (by ~4 pts) if you can afford more computations. To do so, you just need to increase the upper-limit on the scale range by replacing `--min-size 256 --max-size 1024` with `--min-size 0 --max-size 9999 --min-scale 0.3 --max-scale 1.0`.
+
+Feature extraction with kapture datasets
+------------------
+Kapture is a pivot file format, based on text and binary files, used to describe SFM (Structure From Motion) and more generally sensor-acquired data.
+
+It is available at https://github.com/naver/kapture.
+It contains conversion tools for popular formats and several popular datasets are directly available in kapture.
+
+It can be installed with:
+```bash
+pip install kapture
+```
+
+Datasets can be downloaded with:
+```bash
+kapture_download_dataset.py update
+kapture_download_dataset.py list
+# e.g.: install mapping and query of Extended-CMU-Seasons_slice22
+kapture_download_dataset.py install "Extended-CMU-Seasons_slice22_*"
+```
+If you want to convert your own dataset into kapture, please find some examples [here](https://github.com/naver/kapture/blob/master/doc/datasets.adoc).
+
+Once installed, you can extract keypoints for your kapture dataset with:
+```bash
+python extract_kapture.py --model models/r2d2_WASF_N16.pt --kapture-root pathto/yourkapturedataset --top-k 5000
+```
+
+Run `python extract_kapture.py --help` for more information on the extraction parameters.
+
+Evaluation on HPatches
+----------------------
+The evaluation is based on the [code](https://github.com/mihaidusmanu/d2-net) from [D2-Net](https://dsmn.ml/publications/d2-net.html).
+```bash
+git clone https://github.com/mihaidusmanu/d2-net.git
+cd d2-net/hpatches_sequences/
+bash download.sh
+bash download_cache.sh
+cd ../..
+ln -s d2-net/hpatches_sequences # finally create a soft-link
+```
+
+Once this is done, extract all the features:
+```bash
+python extract.py --model models/r2d2_WAF_N16.pt --images d2-net/image_list_hpatches_sequences.txt
+```
+
+Finally, evaluate using the iPython notebook `d2-net/hpatches_sequences/HPatches-Sequences-Matching-Benchmark.ipynb`.
+You should normally get the following `MMA` plot:
+.
+
+
+**New**: we have uploaded in the `results/` folder some pre-computed plots that you can visualize using the aforementioned ipython notebook from `d2-net` (you need to place them in the `d2-net/hpatches_sequences/cache/` folder).
+ - `r2d2_*_N16.size-256-1024.npy`: keypoints were extracted using a limited image resolution (i.e. with `python extract.py --min-size 256 --max-size 1024 ...`)
+ - `r2d2_*_N16.scale-0.3-1.npy`: keypoints were extracted using a full image resolution (i.e. with `python extract.py --min-size 0 --max-size 9999 --min-scale 0.3 --max-scale 1.0`).
+
+Here is a summary of the results:
+
+| result file | training set | resolution | MMA@3 on HPatches| note |
+|--------------|:------------:|:----------:|:-------------------:|------|
+|[r2d2_W_N16.scale-0.3-1.npy](results/r2d2_W_N16.scale-0.3-1.npy) | `W` only | full | 0.699 | no annotation whatsoever |
+|[r2d2_WAF_N16.size-256-1024.npy](results/r2d2_WAF_N16.size-256-1024.npy) | `W`+`A`+`F` | 1024 px | 0.686 | as in NeurIPS paper |
+|[r2d2_WAF_N16.scale-0.3-1.npy](results/r2d2_WAF_N16.scale-0.3-1.npy) | `W`+`A`+`F` | full | 0.718 | +3.2% just from resolution |
+|[r2d2_WASF_N16.size-256-1024.npy](results/r2d2_WASF_N16.size-256-1024.npy) | `W`+`A`+`S`+`F` | 1024 px | 0.721 | with style transfer |
+|[r2d2_WASF_N16.scale-0.3-1.npy](results/r2d2_WASF_N16.scale-0.3-1.npy) | `W`+`A`+`S`+`F` | full | 0.758 | +3.7% just from resolution |
+
+Evaluation on visuallocalization.net
+----------------------
+In our paper, we report visual localization results on the Aachen Day-Night dataset (nighttime images) available at visuallocalization.net. We used the provided local feature evaluation pipeline provided here: https://github.com/tsattler/visuallocalizationbenchmark/tree/master/local_feature_evaluation
+In the meantime, the ground truth poses as well as the error thresholds of the Aachen nighttime images (which are used for the local feature evaluation) have been improved and changed on the website, thus, the original results reported in the paper cannot be reproduced.
+
+Training the model
+------------------
+We provide all the code and data to retrain the model as described in the paper.
+
+### Downloading training data ###
+The first step is to download the training data.
+First, create a folder that will host all data in a place where you have sufficient disk space (15 GB required).
+```bash
+DATA_ROOT=/path/to/data
+mkdir -p $DATA_ROOT
+ln -fs $DATA_ROOT data
+mkdir $DATA_ROOT/aachen
+```
+Then, manually download the [Aachen dataset here](https://drive.google.com/drive/folders/1fvb5gwqHCV4cr4QPVIEMTWkIhCpwei7n) and save it as `$DATA_ROOT/aachen/database_and_query_images.zip`.
+Finally, execute the download script to complete the installation. It will download the remaining training data and will extract all files properly.
+```bash
+./download_training_data.sh
+```
+The following datasets are now installed:
+
+| full name |tag|Disk |# imgs|# pairs| python instance |
+|---------------------------------|---|-----|------|-------|--------------------------------|
+| Random Web images | W |2.7GB| 3125 | 3125 | `auto_pairs(web_images)` |
+| Aachen DB images | A |2.5GB| 4479 | 4479 | `auto_pairs(aachen_db_images)` |
+| Aachen style transfer pairs | S |0.3GB| 8115 | 3636 | `aachen_style_transfer_pairs` |
+| Aachen optical flow pairs | F |2.9GB| 4479 | 4770 | `aachen_flow_pairs` |
+
+Note that you can visualize the content of each dataset using the following command:
+```bash
+python -m tools.dataloader "PairLoader(aachen_flow_pairs)"
+```
+
+
+
+### Training details ###
+To train the model, simply run this command:
+```bash
+python train.py --save-path /path/to/model.pt
+```
+On a recent GPU, it takes 30 min per epoch, so ~12h for 25 epochs.
+You should get a model that scores `0.71 +/- 0.01` in `MMA@3` on HPatches (this standard-deviation is similar to what is reported in Table 1 of the paper).
+
+If you want to retrain fast-r2d2 architectures, run:
+```bash
+python train.py --save-path /path/to/fast-model.pt --net 'Fast_Quad_L2Net_ConfCFS()'
+```
+
+Note that you can fully configure the training (i.e. select the data sources, change the batch size, learning rate, number of epochs etc.). One easy way to improve the model is to train for more epochs, e.g. `--epochs 50`. For more details about all parameters, run `python train.py --help`.
diff --git a/imcui/third_party/r2d2/datasets/__init__.py b/third_party/r2d2/datasets/__init__.py
similarity index 88%
rename from imcui/third_party/r2d2/datasets/__init__.py
rename to third_party/r2d2/datasets/__init__.py
index 8f11df21be72856ea365f6efd7a389aba267562b..f538fb5372197bcdba9db28c861af39c541539ee 100644
--- a/imcui/third_party/r2d2/datasets/__init__.py
+++ b/third_party/r2d2/datasets/__init__.py
@@ -10,6 +10,7 @@ from .aachen import *
# try to instanciate datasets
import sys
+
try:
web_images = RandomWebImages(0, 52)
except AssertionError as e:
@@ -23,11 +24,12 @@ except AssertionError as e:
try:
aachen_style_transfer_pairs = AachenPairs_StyleTransferDayNight()
except AssertionError as e:
- print(f"Dataset aachen_style_transfer_pairs not available, reason: {e}", file=sys.stderr)
+ print(
+ f"Dataset aachen_style_transfer_pairs not available, reason: {e}",
+ file=sys.stderr,
+ )
try:
aachen_flow_pairs = AachenPairs_OpticalFlow()
except AssertionError as e:
print(f"Dataset aachen_flow_pairs not available, reason: {e}", file=sys.stderr)
-
-
diff --git a/imcui/third_party/r2d2/datasets/aachen.py b/third_party/r2d2/datasets/aachen.py
similarity index 58%
rename from imcui/third_party/r2d2/datasets/aachen.py
rename to third_party/r2d2/datasets/aachen.py
index 4ddb324cea01da2430ee89b32c7627b34c01a41f..fbe2364a51c648ee48989f1725cf0033cd0c0547 100644
--- a/imcui/third_party/r2d2/datasets/aachen.py
+++ b/third_party/r2d2/datasets/aachen.py
@@ -10,61 +10,61 @@ from .dataset import Dataset
from .pair_dataset import PairDataset, StillPairDataset
-class AachenImages (Dataset):
- """ Loads all images from the Aachen Day-Night dataset
- """
- def __init__(self, select='db day night', root='data/aachen'):
+class AachenImages(Dataset):
+ """Loads all images from the Aachen Day-Night dataset"""
+
+ def __init__(self, select="db day night", root="data/aachen"):
Dataset.__init__(self)
self.root = root
- self.img_dir = 'images_upright'
+ self.img_dir = "images_upright"
self.select = set(select.split())
- assert self.select, 'Nothing was selected'
-
+ assert self.select, "Nothing was selected"
+
self.imgs = []
root = os.path.join(root, self.img_dir)
for dirpath, _, filenames in os.walk(root):
- r = dirpath[len(root)+1:]
- if not(self.select & set(r.split('/'))): continue
- self.imgs += [os.path.join(r,f) for f in filenames if f.endswith('.jpg')]
-
+ r = dirpath[len(root) + 1 :]
+ if not (self.select & set(r.split("/"))):
+ continue
+ self.imgs += [os.path.join(r, f) for f in filenames if f.endswith(".jpg")]
+
self.nimg = len(self.imgs)
- assert self.nimg, 'Empty Aachen dataset'
+ assert self.nimg, "Empty Aachen dataset"
def get_key(self, idx):
return self.imgs[idx]
+class AachenImages_DB(AachenImages):
+ """Only database (db) images."""
-class AachenImages_DB (AachenImages):
- """ Only database (db) images.
- """
def __init__(self, **kw):
- AachenImages.__init__(self, select='db', **kw)
- self.db_image_idxs = {self.get_tag(i) : i for i,f in enumerate(self.imgs)}
-
- def get_tag(self, idx):
- # returns image tag == img number (name)
- return os.path.split( self.imgs[idx][:-4] )[1]
+ AachenImages.__init__(self, select="db", **kw)
+ self.db_image_idxs = {self.get_tag(i): i for i, f in enumerate(self.imgs)}
+ def get_tag(self, idx):
+ # returns image tag == img number (name)
+ return os.path.split(self.imgs[idx][:-4])[1]
-class AachenPairs_StyleTransferDayNight (AachenImages_DB, StillPairDataset):
- """ synthetic day-night pairs of images
- (night images obtained using autoamtic style transfer from web night images)
+class AachenPairs_StyleTransferDayNight(AachenImages_DB, StillPairDataset):
+ """synthetic day-night pairs of images
+ (night images obtained using autoamtic style transfer from web night images)
"""
- def __init__(self, root='data/aachen/style_transfer', **kw):
+
+ def __init__(self, root="data/aachen/style_transfer", **kw):
StillPairDataset.__init__(self)
AachenImages_DB.__init__(self, **kw)
old_root = os.path.join(self.root, self.img_dir)
self.root = os.path.commonprefix((old_root, root))
- self.img_dir = ''
+ self.img_dir = ""
- newpath = lambda folder, f: os.path.join(folder, f)[len(self.root):]
+ newpath = lambda folder, f: os.path.join(folder, f)[len(self.root) :]
self.imgs = [newpath(old_root, f) for f in self.imgs]
self.image_pairs = []
for fname in os.listdir(root):
- tag = fname.split('.jpg.st_')[0]
+ tag = fname.split(".jpg.st_")[0]
self.image_pairs.append((self.db_image_idxs[tag], len(self.imgs)))
self.imgs.append(newpath(root, fname))
@@ -73,42 +73,45 @@ class AachenPairs_StyleTransferDayNight (AachenImages_DB, StillPairDataset):
assert self.nimg and self.npairs
+class AachenPairs_OpticalFlow(AachenImages_DB, PairDataset):
+ """Image pairs from Aachen db with optical flow."""
-class AachenPairs_OpticalFlow (AachenImages_DB, PairDataset):
- """ Image pairs from Aachen db with optical flow.
- """
- def __init__(self, root='data/aachen/optical_flow', **kw):
+ def __init__(self, root="data/aachen/optical_flow", **kw):
PairDataset.__init__(self)
AachenImages_DB.__init__(self, **kw)
self.root_flow = root
# find out the subsest of valid pairs from the list of flow files
- flows = {f for f in os.listdir(os.path.join(root, 'flow')) if f.endswith('.png')}
- masks = {f for f in os.listdir(os.path.join(root, 'mask')) if f.endswith('.png')}
- assert flows == masks, 'Missing flow or mask pairs'
-
- make_pair = lambda f: tuple(self.db_image_idxs[v] for v in f[:-4].split('_'))
+ flows = {
+ f for f in os.listdir(os.path.join(root, "flow")) if f.endswith(".png")
+ }
+ masks = {
+ f for f in os.listdir(os.path.join(root, "mask")) if f.endswith(".png")
+ }
+ assert flows == masks, "Missing flow or mask pairs"
+
+ make_pair = lambda f: tuple(self.db_image_idxs[v] for v in f[:-4].split("_"))
self.image_pairs = [make_pair(f) for f in flows]
self.npairs = len(self.image_pairs)
assert self.nimg and self.npairs
def get_mask_filename(self, pair_idx):
tag_a, tag_b = map(self.get_tag, self.image_pairs[pair_idx])
- return os.path.join(self.root_flow, 'mask', f'{tag_a}_{tag_b}.png')
+ return os.path.join(self.root_flow, "mask", f"{tag_a}_{tag_b}.png")
def get_mask(self, pair_idx):
return np.asarray(Image.open(self.get_mask_filename(pair_idx)))
def get_flow_filename(self, pair_idx):
tag_a, tag_b = map(self.get_tag, self.image_pairs[pair_idx])
- return os.path.join(self.root_flow, 'flow', f'{tag_a}_{tag_b}.png')
+ return os.path.join(self.root_flow, "flow", f"{tag_a}_{tag_b}.png")
def get_flow(self, pair_idx):
fname = self.get_flow_filename(pair_idx)
try:
return self._png2flow(fname)
except IOError:
- flow = open(fname[:-4], 'rb')
+ flow = open(fname[:-4], "rb")
help = np.fromfile(flow, np.float32, 1)
assert help == 202021.25
W, H = np.fromfile(flow, np.int32, 2)
@@ -116,30 +119,28 @@ class AachenPairs_OpticalFlow (AachenImages_DB, PairDataset):
return self._flow2png(flow, fname)
def get_pair(self, idx, output=()):
- if isinstance(output, str):
+ if isinstance(output, str):
output = output.split()
img1, img2 = map(self.get_image, self.image_pairs[idx])
meta = {}
-
- if 'flow' in output or 'aflow' in output:
+
+ if "flow" in output or "aflow" in output:
flow = self.get_flow(idx)
assert flow.shape[:2] == img1.size[::-1]
- meta['flow'] = flow
+ meta["flow"] = flow
H, W = flow.shape[:2]
- meta['aflow'] = flow + np.mgrid[:H,:W][::-1].transpose(1,2,0)
-
- if 'mask' in output:
+ meta["aflow"] = flow + np.mgrid[:H, :W][::-1].transpose(1, 2, 0)
+
+ if "mask" in output:
mask = self.get_mask(idx)
assert mask.shape[:2] == img1.size[::-1]
- meta['mask'] = mask
-
- return img1, img2, meta
-
+ meta["mask"] = mask
+ return img1, img2, meta
-if __name__ == '__main__':
+if __name__ == "__main__":
print(aachen_db_images)
print(aachen_style_transfer_pairs)
print(aachen_flow_pairs)
diff --git a/imcui/third_party/r2d2/datasets/dataset.py b/third_party/r2d2/datasets/dataset.py
similarity index 70%
rename from imcui/third_party/r2d2/datasets/dataset.py
rename to third_party/r2d2/datasets/dataset.py
index 80d893b8ea4ead7845f35c4fe82c9f5a9b849de3..5f4474e7dc8b81f091cac1e13f431c5c9f1840f3 100644
--- a/imcui/third_party/r2d2/datasets/dataset.py
+++ b/third_party/r2d2/datasets/dataset.py
@@ -9,10 +9,10 @@ import numpy as np
class Dataset(object):
- ''' Base class for a dataset. To be overloaded.
- '''
- root = ''
- img_dir = ''
+ """Base class for a dataset. To be overloaded."""
+
+ root = ""
+ img_dir = ""
nimg = 0
def __len__(self):
@@ -26,23 +26,23 @@ class Dataset(object):
def get_image(self, img_idx):
from PIL import Image
+
fname = self.get_filename(img_idx)
try:
- return Image.open(fname).convert('RGB')
+ return Image.open(fname).convert("RGB")
except Exception as e:
raise IOError("Could not load image %s (reason: %s)" % (fname, str(e)))
def __repr__(self):
- res = 'Dataset: %s\n' % self.__class__.__name__
- res += ' %d images' % self.nimg
- res += '\n root: %s...\n' % self.root
+ res = "Dataset: %s\n" % self.__class__.__name__
+ res += " %d images" % self.nimg
+ res += "\n root: %s...\n" % self.root
return res
+class CatDataset(Dataset):
+ """Concatenation of several datasets."""
-class CatDataset (Dataset):
- ''' Concatenation of several datasets.
- '''
def __init__(self, *datasets):
assert len(datasets) >= 1
self.datasets = datasets
@@ -54,8 +54,8 @@ class CatDataset (Dataset):
self.root = None
def which(self, i):
- pos = np.searchsorted(self.offsets, i, side='right')-1
- assert pos < self.nimg, 'Bad image index %d >= %d' % (i, self.nimg)
+ pos = np.searchsorted(self.offsets, i, side="right") - 1
+ assert pos < self.nimg, "Bad image index %d >= %d" % (i, self.nimg)
return pos, i - self.offsets[pos]
def get_key(self, i):
@@ -69,9 +69,5 @@ class CatDataset (Dataset):
def __repr__(self):
fmt_str = "CatDataset("
for db in self.datasets:
- fmt_str += str(db).replace("\n"," ") + ', '
- return fmt_str[:-2] + ')'
-
-
-
-
+ fmt_str += str(db).replace("\n", " ") + ", "
+ return fmt_str[:-2] + ")"
diff --git a/imcui/third_party/r2d2/datasets/imgfolder.py b/third_party/r2d2/datasets/imgfolder.py
similarity index 72%
rename from imcui/third_party/r2d2/datasets/imgfolder.py
rename to third_party/r2d2/datasets/imgfolder.py
index 45f7bc9ee4c3ba5f04380dbc02ad17b6463cf32f..40168f00e8ad177f3d94f75578dba2e640944c4c 100644
--- a/imcui/third_party/r2d2/datasets/imgfolder.py
+++ b/third_party/r2d2/datasets/imgfolder.py
@@ -8,10 +8,10 @@ from .dataset import Dataset
from .pair_dataset import SyntheticPairDataset
-class ImgFolder (Dataset):
- """ load all images in a folder (no recursion).
- """
- def __init__(self, root, imgs=None, exts=('.jpg','.png','.ppm')):
+class ImgFolder(Dataset):
+ """load all images in a folder (no recursion)."""
+
+ def __init__(self, root, imgs=None, exts=(".jpg", ".png", ".ppm")):
Dataset.__init__(self)
self.root = root
self.imgs = imgs or [f for f in os.listdir(root) if f.endswith(exts)]
@@ -19,5 +19,3 @@ class ImgFolder (Dataset):
def get_key(self, idx):
return self.imgs[idx]
-
-
diff --git a/imcui/third_party/r2d2/datasets/pair_dataset.py b/third_party/r2d2/datasets/pair_dataset.py
similarity index 53%
rename from imcui/third_party/r2d2/datasets/pair_dataset.py
rename to third_party/r2d2/datasets/pair_dataset.py
index aeed98b6700e0ba108bb44abccc20351d16f3295..ba178c18a0a6fbb1decfe4a797dbcab0636dbeaf 100644
--- a/imcui/third_party/r2d2/datasets/pair_dataset.py
+++ b/third_party/r2d2/datasets/pair_dataset.py
@@ -11,20 +11,24 @@ from tools.transforms import instanciate_transformation
from tools.transforms_tools import persp_apply
-class PairDataset (Dataset):
- """ A dataset that serves image pairs with ground-truth pixel correspondences.
- """
+class PairDataset(Dataset):
+ """A dataset that serves image pairs with ground-truth pixel correspondences."""
+
def __init__(self):
Dataset.__init__(self)
self.npairs = 0
def get_filename(self, img_idx, root=None):
- if is_pair(img_idx): # if img_idx is a pair of indices, we return a pair of filenames
+ if is_pair(
+ img_idx
+ ): # if img_idx is a pair of indices, we return a pair of filenames
return tuple(Dataset.get_filename(self, i, root) for i in img_idx)
return Dataset.get_filename(self, img_idx, root)
def get_image(self, img_idx):
- if is_pair(img_idx): # if img_idx is a pair of indices, we return a pair of images
+ if is_pair(
+ img_idx
+ ): # if img_idx is a pair of indices, we return a pair of images
return tuple(Dataset.get_image(self, i) for i in img_idx)
return Dataset.get_image(self, img_idx)
@@ -41,8 +45,8 @@ class PairDataset (Dataset):
raise NotImplementedError()
def get_pair(self, idx, output=()):
- """ returns (img1, img2, `metadata`)
-
+ """returns (img1, img2, `metadata`)
+
`metadata` is a dict() that can contain:
flow: optical flow
aflow: absolute flow
@@ -55,24 +59,24 @@ class PairDataset (Dataset):
def get_paired_images(self):
fns = set()
for i in range(self.npairs):
- a,b = self.image_pairs[i]
+ a, b = self.image_pairs[i]
fns.add(self.get_filename(a))
fns.add(self.get_filename(b))
return fns
def __len__(self):
- return self.npairs # size should correspond to the number of pairs, not images
-
+ return self.npairs # size should correspond to the number of pairs, not images
+
def __repr__(self):
- res = 'Dataset: %s\n' % self.__class__.__name__
- res += ' %d images,' % self.nimg
- res += ' %d image pairs' % self.npairs
- res += '\n root: %s...\n' % self.root
+ res = "Dataset: %s\n" % self.__class__.__name__
+ res += " %d images," % self.nimg
+ res += " %d image pairs" % self.npairs
+ res += "\n root: %s...\n" % self.root
return res
@staticmethod
def _flow2png(flow, path):
- flow = np.clip(np.around(16*flow), -2**15, 2**15-1)
+ flow = np.clip(np.around(16 * flow), -(2**15), 2**15 - 1)
bytes = np.int16(flow).view(np.uint8)
Image.fromarray(bytes).save(path)
return flow / 16
@@ -86,41 +90,42 @@ class PairDataset (Dataset):
raise IOError("Error loading flow for %s" % path)
-
-class StillPairDataset (PairDataset):
- """ A dataset of 'still' image pairs.
- By overloading a normal image dataset, it appends the get_pair(i) function
- that serves trivial image pairs (img1, img2) where img1 == img2 == get_image(i).
+class StillPairDataset(PairDataset):
+ """A dataset of 'still' image pairs.
+ By overloading a normal image dataset, it appends the get_pair(i) function
+ that serves trivial image pairs (img1, img2) where img1 == img2 == get_image(i).
"""
+
def get_pair(self, pair_idx, output=()):
- if isinstance(output, str): output = output.split()
+ if isinstance(output, str):
+ output = output.split()
img1, img2 = map(self.get_image, self.image_pairs[pair_idx])
- W,H = img1.size
+ W, H = img1.size
sx = img2.size[0] / float(W)
sy = img2.size[1] / float(H)
meta = {}
- if 'aflow' in output or 'flow' in output:
- mgrid = np.mgrid[0:H, 0:W][::-1].transpose(1,2,0).astype(np.float32)
- meta['aflow'] = mgrid * (sx,sy)
- meta['flow'] = meta['aflow'] - mgrid
+ if "aflow" in output or "flow" in output:
+ mgrid = np.mgrid[0:H, 0:W][::-1].transpose(1, 2, 0).astype(np.float32)
+ meta["aflow"] = mgrid * (sx, sy)
+ meta["flow"] = meta["aflow"] - mgrid
- if 'mask' in output:
- meta['mask'] = np.ones((H,W), np.uint8)
+ if "mask" in output:
+ meta["mask"] = np.ones((H, W), np.uint8)
- if 'homography' in output:
- meta['homography'] = np.diag(np.float32([sx, sy, 1]))
+ if "homography" in output:
+ meta["homography"] = np.diag(np.float32([sx, sy, 1]))
return img1, img2, meta
-
-class SyntheticPairDataset (PairDataset):
- """ A synthetic generator of image pairs.
- Given a normal image dataset, it constructs pairs using random homographies & noise.
+class SyntheticPairDataset(PairDataset):
+ """A synthetic generator of image pairs.
+ Given a normal image dataset, it constructs pairs using random homographies & noise.
"""
- def __init__(self, dataset, scale='', distort=''):
+
+ def __init__(self, dataset, scale="", distort=""):
self.attach_dataset(dataset)
self.distort = instanciate_transformation(distort)
self.scale = instanciate_transformation(scale)
@@ -133,56 +138,57 @@ class SyntheticPairDataset (PairDataset):
self.get_key = dataset.get_key
self.get_filename = dataset.get_filename
self.root = None
-
+
def make_pair(self, img):
return img, img
- def get_pair(self, i, output=('aflow')):
- """ Procedure:
- This function applies a series of random transformations to one original image
+ def get_pair(self, i, output=("aflow")):
+ """Procedure:
+ This function applies a series of random transformations to one original image
to form a synthetic image pairs with perfect ground-truth.
"""
- if isinstance(output, str):
+ if isinstance(output, str):
output = output.split()
-
+
original_img = self.dataset.get_image(i)
-
+
scaled_image = self.scale(original_img)
scaled_image, scaled_image2 = self.make_pair(scaled_image)
scaled_and_distorted_image = self.distort(
- dict(img=scaled_image2, persp=(1,0,0,0,1,0,0,0)))
+ dict(img=scaled_image2, persp=(1, 0, 0, 0, 1, 0, 0, 0))
+ )
W, H = scaled_image.size
- trf = scaled_and_distorted_image['persp']
+ trf = scaled_and_distorted_image["persp"]
meta = dict()
- if 'aflow' in output or 'flow' in output:
+ if "aflow" in output or "flow" in output:
# compute optical flow
- xy = np.mgrid[0:H,0:W][::-1].reshape(2,H*W).T
- aflow = np.float32(persp_apply(trf, xy).reshape(H,W,2))
- meta['flow'] = aflow - xy.reshape(H,W,2)
- meta['aflow'] = aflow
-
- if 'homography' in output:
- meta['homography'] = np.float32(trf+(1,)).reshape(3,3)
-
- return scaled_image, scaled_and_distorted_image['img'], meta
-
- def __repr__(self):
- res = 'Dataset: %s\n' % self.__class__.__name__
- res += ' %d images and pairs' % self.npairs
- res += '\n root: %s...' % self.dataset.root
- res += '\n Scale: %s' % (repr(self.scale).replace('\n',''))
- res += '\n Distort: %s' % (repr(self.distort).replace('\n',''))
- return res + '\n'
+ xy = np.mgrid[0:H, 0:W][::-1].reshape(2, H * W).T
+ aflow = np.float32(persp_apply(trf, xy).reshape(H, W, 2))
+ meta["flow"] = aflow - xy.reshape(H, W, 2)
+ meta["aflow"] = aflow
+ if "homography" in output:
+ meta["homography"] = np.float32(trf + (1,)).reshape(3, 3)
+ return scaled_image, scaled_and_distorted_image["img"], meta
-class TransformedPairs (PairDataset):
- """ Automatic data augmentation for pre-existing image pairs.
- Given an image pair dataset, it generates synthetically jittered pairs
- using random transformations (e.g. homographies & noise).
+ def __repr__(self):
+ res = "Dataset: %s\n" % self.__class__.__name__
+ res += " %d images and pairs" % self.npairs
+ res += "\n root: %s..." % self.dataset.root
+ res += "\n Scale: %s" % (repr(self.scale).replace("\n", ""))
+ res += "\n Distort: %s" % (repr(self.distort).replace("\n", ""))
+ return res + "\n"
+
+
+class TransformedPairs(PairDataset):
+ """Automatic data augmentation for pre-existing image pairs.
+ Given an image pair dataset, it generates synthetically jittered pairs
+ using random transformations (e.g. homographies & noise).
"""
- def __init__(self, dataset, trf=''):
+
+ def __init__(self, dataset, trf=""):
self.attach_dataset(dataset)
self.trf = instanciate_transformation(trf)
@@ -195,48 +201,47 @@ class TransformedPairs (PairDataset):
self.get_key = dataset.get_key
self.get_filename = dataset.get_filename
self.root = None
-
- def get_pair(self, i, output=''):
- """ Procedure:
- This function applies a series of random transformations to one original image
+
+ def get_pair(self, i, output=""):
+ """Procedure:
+ This function applies a series of random transformations to one original image
to form a synthetic image pairs with perfect ground-truth.
"""
img_a, img_b_, metadata = self.dataset.get_pair(i, output)
- img_b = self.trf({'img': img_b_, 'persp':(1,0,0,0,1,0,0,0)})
- trf = img_b['persp']
+ img_b = self.trf({"img": img_b_, "persp": (1, 0, 0, 0, 1, 0, 0, 0)})
+ trf = img_b["persp"]
- if 'aflow' in metadata or 'flow' in metadata:
- aflow = metadata['aflow']
- aflow[:] = persp_apply(trf, aflow.reshape(-1,2)).reshape(aflow.shape)
+ if "aflow" in metadata or "flow" in metadata:
+ aflow = metadata["aflow"]
+ aflow[:] = persp_apply(trf, aflow.reshape(-1, 2)).reshape(aflow.shape)
W, H = img_a.size
- flow = metadata['flow']
- mgrid = np.mgrid[0:H, 0:W][::-1].transpose(1,2,0).astype(np.float32)
+ flow = metadata["flow"]
+ mgrid = np.mgrid[0:H, 0:W][::-1].transpose(1, 2, 0).astype(np.float32)
flow[:] = aflow - mgrid
- if 'corres' in metadata:
- corres = metadata['corres']
- corres[:,1] = persp_apply(trf, corres[:,1])
-
- if 'homography' in metadata:
+ if "corres" in metadata:
+ corres = metadata["corres"]
+ corres[:, 1] = persp_apply(trf, corres[:, 1])
+
+ if "homography" in metadata:
# p_b = homography * p_a
- trf_ = np.float32(trf+(1,)).reshape(3,3)
- metadata['homography'] = np.float32(trf_ @ metadata['homography'])
+ trf_ = np.float32(trf + (1,)).reshape(3, 3)
+ metadata["homography"] = np.float32(trf_ @ metadata["homography"])
- return img_a, img_b['img'], metadata
+ return img_a, img_b["img"], metadata
def __repr__(self):
- res = 'Transformed Pairs from %s\n' % type(self.dataset).__name__
- res += ' %d images and pairs' % self.npairs
- res += '\n root: %s...' % self.dataset.root
- res += '\n transform: %s' % (repr(self.trf).replace('\n',''))
- return res + '\n'
+ res = "Transformed Pairs from %s\n" % type(self.dataset).__name__
+ res += " %d images and pairs" % self.npairs
+ res += "\n root: %s..." % self.dataset.root
+ res += "\n transform: %s" % (repr(self.trf).replace("\n", ""))
+ return res + "\n"
+class CatPairDataset(CatDataset):
+ """Concatenation of several pair datasets."""
-class CatPairDataset (CatDataset):
- ''' Concatenation of several pair datasets.
- '''
def __init__(self, *datasets):
CatDataset.__init__(self, *datasets)
pair_offsets = [0]
@@ -251,12 +256,12 @@ class CatPairDataset (CatDataset):
def __repr__(self):
fmt_str = "CatPairDataset("
for db in self.datasets:
- fmt_str += str(db).replace("\n"," ") + ', '
- return fmt_str[:-2] + ')'
+ fmt_str += str(db).replace("\n", " ") + ", "
+ return fmt_str[:-2] + ")"
def pair_which(self, i):
- pos = np.searchsorted(self.pair_offsets, i, side='right')-1
- assert pos < self.npairs, 'Bad pair index %d >= %d' % (i, self.npairs)
+ pos = np.searchsorted(self.pair_offsets, i, side="right") - 1
+ assert pos < self.npairs, "Bad pair index %d >= %d" % (i, self.npairs)
return pos, i - self.pair_offsets[pos]
def pair_call(self, func, i, *args, **kwargs):
@@ -268,20 +273,18 @@ class CatPairDataset (CatDataset):
return self.datasets[b].get_pair(i, output)
def get_flow_filename(self, pair_idx, *args, **kwargs):
- return self.pair_call('get_flow_filename', pair_idx, *args, **kwargs)
+ return self.pair_call("get_flow_filename", pair_idx, *args, **kwargs)
def get_mask_filename(self, pair_idx, *args, **kwargs):
- return self.pair_call('get_mask_filename', pair_idx, *args, **kwargs)
+ return self.pair_call("get_mask_filename", pair_idx, *args, **kwargs)
def get_corres_filename(self, pair_idx, *args, **kwargs):
- return self.pair_call('get_corres_filename', pair_idx, *args, **kwargs)
-
+ return self.pair_call("get_corres_filename", pair_idx, *args, **kwargs)
def is_pair(x):
- if isinstance(x, (tuple,list)) and len(x) == 2:
+ if isinstance(x, (tuple, list)) and len(x) == 2:
return True
if isinstance(x, np.ndarray) and x.ndim == 1 and x.shape[0] == 2:
return True
return False
-
diff --git a/imcui/third_party/r2d2/datasets/web_images.py b/third_party/r2d2/datasets/web_images.py
similarity index 60%
rename from imcui/third_party/r2d2/datasets/web_images.py
rename to third_party/r2d2/datasets/web_images.py
index 7c17fbe956f3b4db25d9a4148e8f7c615f122478..f22580f44a9b2488980ab88b656073d8531c3362 100644
--- a/imcui/third_party/r2d2/datasets/web_images.py
+++ b/third_party/r2d2/datasets/web_images.py
@@ -8,42 +8,47 @@ from tqdm import trange
from .dataset import Dataset
-class RandomWebImages (Dataset):
- """ 1 million distractors from Oxford and Paris Revisited
- see http://ptak.felk.cvut.cz/revisitop/revisitop1m/
+class RandomWebImages(Dataset):
+ """1 million distractors from Oxford and Paris Revisited
+ see http://ptak.felk.cvut.cz/revisitop/revisitop1m/
"""
+
def __init__(self, start=0, end=1024, root="data/revisitop1m"):
Dataset.__init__(self)
self.root = root
-
+
bar = None
- self.imgs = []
+ self.imgs = []
for i in range(start, end):
- try:
+ try:
# read cached list
- img_list_path = os.path.join(self.root, "image_list_%d.txt"%i)
+ img_list_path = os.path.join(self.root, "image_list_%d.txt" % i)
cached_imgs = [e.strip() for e in open(img_list_path)]
assert cached_imgs, f"Cache '{img_list_path}' is empty!"
self.imgs += cached_imgs
except IOError:
- if bar is None:
- bar = trange(start, 4*end, desc='Caching')
- bar.update(4*i)
-
+ if bar is None:
+ bar = trange(start, 4 * end, desc="Caching")
+ bar.update(4 * i)
+
# create it
imgs = []
- for d in range(i*4,(i+1)*4): # 4096 folders in total, on average 256 each
+ for d in range(
+ i * 4, (i + 1) * 4
+ ): # 4096 folders in total, on average 256 each
key = hex(d)[2:].zfill(3)
folder = os.path.join(self.root, key)
- if not os.path.isdir(folder): continue
- imgs += [f for f in os.listdir(folder) if verify_img(folder,f)]
+ if not os.path.isdir(folder):
+ continue
+ imgs += [f for f in os.listdir(folder) if verify_img(folder, f)]
bar.update(1)
assert imgs, f"No images found in {folder}/"
- open(img_list_path,'w').write('\n'.join(imgs))
+ open(img_list_path, "w").write("\n".join(imgs))
self.imgs += imgs
- if bar: bar.update(bar.total - bar.n)
+ if bar:
+ bar.update(bar.total - bar.n)
self.nimg = len(self.imgs)
def get_key(self, i):
@@ -53,12 +58,12 @@ class RandomWebImages (Dataset):
def verify_img(folder, f):
path = os.path.join(folder, f)
- if not f.endswith('.jpg'): return False
- try:
+ if not f.endswith(".jpg"):
+ return False
+ try:
from PIL import Image
- Image.open(path).convert('RGB') # try to open it
+
+ Image.open(path).convert("RGB") # try to open it
return True
- except:
+ except:
return False
-
-
diff --git a/third_party/r2d2/download_training_data.sh b/third_party/r2d2/download_training_data.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8257c83ef70eeab47b6b344d591ddef86ba848cd
--- /dev/null
+++ b/third_party/r2d2/download_training_data.sh
@@ -0,0 +1,69 @@
+# Copyright 2019-present NAVER Corp.
+# CC BY-NC-SA 3.0
+# Available only for non-commercial use
+
+CODE_ROOT=`pwd`
+if [ ! -e data ]; then
+ echo "Error: missing data/ folder"
+ echo "First, create a folder that can host (at least) 15 GB of data."
+ echo "Then, create a soft-link named 'data' that points to it."
+ exit -1
+fi
+
+# download web images from the revisitop1m dataset
+WEB_ROOT=data/revisitop1m
+mkdir -p $WEB_ROOT
+cd $WEB_ROOT
+if [ ! -e 0d3 ]; then
+ for i in {1..5}; do
+ echo "Installing the web images dataset ($i/5)..."
+ if [ ! -f revisitop1m.$i.tar.gz ]; then
+ wget http://ptak.felk.cvut.cz/revisitop/revisitop1m/jpg/revisitop1m.$i.tar.gz
+ fi
+ tar -xzvf revisitop1m.$i.tar.gz
+ rm -f revisitop1m.$i.tar.gz
+ done
+fi
+cd $CODE_ROOT
+
+# download aachen images
+AACHEN_ROOT=data/aachen
+mkdir -p $AACHEN_ROOT
+cd $AACHEN_ROOT
+if [ ! -e "images_upright" ]; then
+ echo "Installing the Aachen dataset..."
+ fname=database_and_query_images.zip
+ if [ ! -f $fname ]; then
+ echo "File not found: $fname"
+ exit -1
+ else
+ unzip $fname
+ rm -f $fname
+ fi
+fi
+
+# download style transfer images
+if [ ! -e "style_transfer" ]; then
+ echo "Installing the Aachen style-transfer dataset..."
+ fname=aachen_style_transfer.zip
+ if [ ! -f $fname ]; then
+ wget http://download.europe.naverlabs.com/3DVision/aachen_style_transfer.zip $fname
+ fi
+ unzip $fname
+ rm -f $fname
+fi
+
+# download optical flow pairs
+if [ ! -e "optical_flow" ]; then
+ echo "Installing the Aachen optical flow dataset..."
+ fname=aachen_optical_flow.zip
+ if [ ! -f $fname ]; then
+ wget http://download.europe.naverlabs.com/3DVision/aachen_optical_flow.zip $fname
+ fi
+ unzip $fname
+ rm -f $fname
+fi
+cd $CODE_ROOT
+
+echo "Done!"
+
diff --git a/imcui/third_party/r2d2/extract.py b/third_party/r2d2/extract.py
similarity index 51%
rename from imcui/third_party/r2d2/extract.py
rename to third_party/r2d2/extract.py
index c3fea02f87c0615504e3648bfd590e413ab13898..14f6d5cf4899bb5abccbb91ca324d264d4c27d7f 100644
--- a/imcui/third_party/r2d2/extract.py
+++ b/third_party/r2d2/extract.py
@@ -13,97 +13,105 @@ from tools.dataloader import norm_RGB
from nets.patchnet import *
-def load_network(model_fn):
+def load_network(model_fn):
checkpoint = torch.load(model_fn)
- print("\n>> Creating net = " + checkpoint['net'])
- net = eval(checkpoint['net'])
+ print("\n>> Creating net = " + checkpoint["net"])
+ net = eval(checkpoint["net"])
nb_of_weights = common.model_size(net)
print(f" ( Model size: {nb_of_weights/1000:.0f}K parameters )")
# initialization
- weights = checkpoint['state_dict']
- net.load_state_dict({k.replace('module.',''):v for k,v in weights.items()})
+ weights = checkpoint["state_dict"]
+ net.load_state_dict({k.replace("module.", ""): v for k, v in weights.items()})
return net.eval()
-class NonMaxSuppression (torch.nn.Module):
+class NonMaxSuppression(torch.nn.Module):
def __init__(self, rel_thr=0.7, rep_thr=0.7):
nn.Module.__init__(self)
self.max_filter = torch.nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
self.rel_thr = rel_thr
self.rep_thr = rep_thr
-
+
def forward(self, reliability, repeatability, **kw):
assert len(reliability) == len(repeatability) == 1
reliability, repeatability = reliability[0], repeatability[0]
# local maxima
- maxima = (repeatability == self.max_filter(repeatability))
+ maxima = repeatability == self.max_filter(repeatability)
# remove low peaks
- maxima *= (repeatability >= self.rep_thr)
- maxima *= (reliability >= self.rel_thr)
+ maxima *= repeatability >= self.rep_thr
+ maxima *= reliability >= self.rel_thr
return maxima.nonzero().t()[2:4]
-def extract_multiscale( net, img, detector, scale_f=2**0.25,
- min_scale=0.0, max_scale=1,
- min_size=256, max_size=1024,
- verbose=False):
- old_bm = torch.backends.cudnn.benchmark
- torch.backends.cudnn.benchmark = False # speedup
-
+def extract_multiscale(
+ net,
+ img,
+ detector,
+ scale_f=2**0.25,
+ min_scale=0.0,
+ max_scale=1,
+ min_size=256,
+ max_size=1024,
+ verbose=False,
+):
+ old_bm = torch.backends.cudnn.benchmark
+ torch.backends.cudnn.benchmark = False # speedup
+
# extract keypoints at multiple scales
B, three, H, W = img.shape
assert B == 1 and three == 3, "should be a batch with a single RGB image"
-
+
assert max_scale <= 1
- s = 1.0 # current scale factor
-
- X,Y,S,C,Q,D = [],[],[],[],[],[]
- while s+0.001 >= max(min_scale, min_size / max(H,W)):
- if s-0.001 <= min(max_scale, max_size / max(H,W)):
+ s = 1.0 # current scale factor
+
+ X, Y, S, C, Q, D = [], [], [], [], [], []
+ while s + 0.001 >= max(min_scale, min_size / max(H, W)):
+ if s - 0.001 <= min(max_scale, max_size / max(H, W)):
nh, nw = img.shape[2:]
- if verbose: print(f"extracting at scale x{s:.02f} = {nw:4d}x{nh:3d}")
+ if verbose:
+ print(f"extracting at scale x{s:.02f} = {nw:4d}x{nh:3d}")
# extract descriptors
with torch.no_grad():
res = net(imgs=[img])
-
+
# get output and reliability map
- descriptors = res['descriptors'][0]
- reliability = res['reliability'][0]
- repeatability = res['repeatability'][0]
+ descriptors = res["descriptors"][0]
+ reliability = res["reliability"][0]
+ repeatability = res["repeatability"][0]
# normalize the reliability for nms
# extract maxima and descs
- y,x = detector(**res) # nms
- c = reliability[0,0,y,x]
- q = repeatability[0,0,y,x]
- d = descriptors[0,:,y,x].t()
+ y, x = detector(**res) # nms
+ c = reliability[0, 0, y, x]
+ q = repeatability[0, 0, y, x]
+ d = descriptors[0, :, y, x].t()
n = d.shape[0]
# accumulate multiple scales
- X.append(x.float() * W/nw)
- Y.append(y.float() * H/nh)
- S.append((32/s) * torch.ones(n, dtype=torch.float32, device=d.device))
+ X.append(x.float() * W / nw)
+ Y.append(y.float() * H / nh)
+ S.append((32 / s) * torch.ones(n, dtype=torch.float32, device=d.device))
C.append(c)
Q.append(q)
D.append(d)
s /= scale_f
# down-scale the image for next iteration
- nh, nw = round(H*s), round(W*s)
- img = F.interpolate(img, (nh,nw), mode='bilinear', align_corners=False)
+ nh, nw = round(H * s), round(W * s)
+ img = F.interpolate(img, (nh, nw), mode="bilinear", align_corners=False)
# restore value
torch.backends.cudnn.benchmark = old_bm
Y = torch.cat(Y)
X = torch.cat(X)
- S = torch.cat(S) # scale
- scores = torch.cat(C) * torch.cat(Q) # scores = reliability * repeatability
- XYS = torch.stack([X,Y,S], dim=-1)
+ S = torch.cat(S) # scale
+ scores = torch.cat(C) * torch.cat(Q) # scores = reliability * repeatability
+ XYS = torch.stack([X, Y, S], dim=-1)
D = torch.cat(D)
return XYS, D, scores
@@ -113,71 +121,82 @@ def extract_keypoints(args):
# load the network...
net = load_network(args.model)
- if iscuda: net = net.cuda()
+ if iscuda:
+ net = net.cuda()
# create the non-maxima detector
detector = NonMaxSuppression(
- rel_thr = args.reliability_thr,
- rep_thr = args.repeatability_thr)
+ rel_thr=args.reliability_thr, rep_thr=args.repeatability_thr
+ )
while args.images:
img_path = args.images.pop(0)
-
- if img_path.endswith('.txt'):
+
+ if img_path.endswith(".txt"):
args.images = open(img_path).read().splitlines() + args.images
continue
-
+
print(f"\nExtracting features for {img_path}")
- img = Image.open(img_path).convert('RGB')
+ img = Image.open(img_path).convert("RGB")
W, H = img.size
- img = norm_RGB(img)[None]
- if iscuda: img = img.cuda()
-
+ img = norm_RGB(img)[None]
+ if iscuda:
+ img = img.cuda()
+
# extract keypoints/descriptors for a single image
- xys, desc, scores = extract_multiscale(net, img, detector,
- scale_f = args.scale_f,
- min_scale = args.min_scale,
- max_scale = args.max_scale,
- min_size = args.min_size,
- max_size = args.max_size,
- verbose = True)
+ xys, desc, scores = extract_multiscale(
+ net,
+ img,
+ detector,
+ scale_f=args.scale_f,
+ min_scale=args.min_scale,
+ max_scale=args.max_scale,
+ min_size=args.min_size,
+ max_size=args.max_size,
+ verbose=True,
+ )
xys = xys.cpu().numpy()
desc = desc.cpu().numpy()
scores = scores.cpu().numpy()
- idxs = scores.argsort()[-args.top_k or None:]
-
- outpath = img_path + '.' + args.tag
- print(f"Saving {len(idxs)} keypoints to {outpath}")
- np.savez(open(outpath,'wb'),
- imsize = (W,H),
- keypoints = xys[idxs],
- descriptors = desc[idxs],
- scores = scores[idxs])
+ idxs = scores.argsort()[-args.top_k or None :]
+ outpath = img_path + "." + args.tag
+ print(f"Saving {len(idxs)} keypoints to {outpath}")
+ np.savez(
+ open(outpath, "wb"),
+ imsize=(W, H),
+ keypoints=xys[idxs],
+ descriptors=desc[idxs],
+ scores=scores[idxs],
+ )
-if __name__ == '__main__':
+if __name__ == "__main__":
import argparse
+
parser = argparse.ArgumentParser("Extract keypoints for a given image")
- parser.add_argument("--model", type=str, required=True, help='model path')
-
- parser.add_argument("--images", type=str, required=True, nargs='+', help='images / list')
- parser.add_argument("--tag", type=str, default='r2d2', help='output file tag')
-
- parser.add_argument("--top-k", type=int, default=5000, help='number of keypoints')
+ parser.add_argument("--model", type=str, required=True, help="model path")
+
+ parser.add_argument(
+ "--images", type=str, required=True, nargs="+", help="images / list"
+ )
+ parser.add_argument("--tag", type=str, default="r2d2", help="output file tag")
+
+ parser.add_argument("--top-k", type=int, default=5000, help="number of keypoints")
parser.add_argument("--scale-f", type=float, default=2**0.25)
parser.add_argument("--min-size", type=int, default=256)
parser.add_argument("--max-size", type=int, default=1024)
parser.add_argument("--min-scale", type=float, default=0)
parser.add_argument("--max-scale", type=float, default=1)
-
+
parser.add_argument("--reliability-thr", type=float, default=0.7)
parser.add_argument("--repeatability-thr", type=float, default=0.7)
- parser.add_argument("--gpu", type=int, nargs='+', default=[0], help='use -1 for CPU')
+ parser.add_argument(
+ "--gpu", type=int, nargs="+", default=[0], help="use -1 for CPU"
+ )
args = parser.parse_args()
extract_keypoints(args)
-
diff --git a/third_party/r2d2/extract_kapture.py b/third_party/r2d2/extract_kapture.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e46bb5306c943ce985a13168934105b1978deb9
--- /dev/null
+++ b/third_party/r2d2/extract_kapture.py
@@ -0,0 +1,268 @@
+# Copyright 2019-present NAVER Corp.
+# CC BY-NC-SA 3.0
+# Available only for non-commercial use
+
+
+from PIL import Image
+
+from tools import common
+from tools.dataloader import norm_RGB
+from nets.patchnet import *
+from os import path
+
+from extract import load_network, NonMaxSuppression, extract_multiscale
+
+# Kapture is a pivot file format, based on text and binary files, used to describe SfM (Structure From Motion)
+# and more generally sensor-acquired data
+# it can be installed with
+# pip install kapture
+# for more information check out https://github.com/naver/kapture
+import kapture
+from kapture.io.records import get_image_fullpath
+from kapture.io.csv import kapture_from_dir
+from kapture.io.csv import (
+ get_feature_csv_fullpath,
+ keypoints_to_file,
+ descriptors_to_file,
+)
+from kapture.io.features import (
+ get_keypoints_fullpath,
+ keypoints_check_dir,
+ image_keypoints_to_file,
+)
+from kapture.io.features import (
+ get_descriptors_fullpath,
+ descriptors_check_dir,
+ image_descriptors_to_file,
+)
+from kapture.io.csv import get_all_tar_handlers
+
+
+def extract_kapture_keypoints(args):
+ """
+ Extract r2d2 keypoints and descritors to the kapture format directly
+ """
+ print("extract_kapture_keypoints...")
+ with get_all_tar_handlers(
+ args.kapture_root,
+ mode={
+ kapture.Keypoints: "a",
+ kapture.Descriptors: "a",
+ kapture.GlobalFeatures: "r",
+ kapture.Matches: "r",
+ },
+ ) as tar_handlers:
+ kdata = kapture_from_dir(
+ args.kapture_root,
+ None,
+ skip_list=[
+ kapture.GlobalFeatures,
+ kapture.Matches,
+ kapture.Points3d,
+ kapture.Observations,
+ ],
+ tar_handlers=tar_handlers,
+ )
+
+ assert kdata.records_camera is not None
+ image_list = [
+ filename for _, _, filename in kapture.flatten(kdata.records_camera)
+ ]
+ if args.keypoints_type is None:
+ args.keypoints_type = path.splitext(path.basename(args.model))[0]
+ print(f"keypoints_type set to {args.keypoints_type}")
+ if args.descriptors_type is None:
+ args.descriptors_type = path.splitext(path.basename(args.model))[0]
+ print(f"descriptors_type set to {args.descriptors_type}")
+
+ if (
+ kdata.keypoints is not None
+ and args.keypoints_type in kdata.keypoints
+ and kdata.descriptors is not None
+ and args.descriptors_type in kdata.descriptors
+ ):
+ print(
+ "detected already computed features of same keypoints_type/descriptors_type, resuming extraction..."
+ )
+ image_list = [
+ name
+ for name in image_list
+ if name not in kdata.keypoints[args.keypoints_type]
+ or name not in kdata.descriptors[args.descriptors_type]
+ ]
+
+ if len(image_list) == 0:
+ print("All features were already extracted")
+ return
+ else:
+ print(f"Extracting r2d2 features for {len(image_list)} images")
+
+ iscuda = common.torch_set_gpu(args.gpu)
+
+ # load the network...
+ net = load_network(args.model)
+ if iscuda:
+ net = net.cuda()
+
+ # create the non-maxima detector
+ detector = NonMaxSuppression(
+ rel_thr=args.reliability_thr, rep_thr=args.repeatability_thr
+ )
+
+ if kdata.keypoints is None:
+ kdata.keypoints = {}
+ if kdata.descriptors is None:
+ kdata.descriptors = {}
+
+ if args.keypoints_type not in kdata.keypoints:
+ keypoints_dtype = None
+ keypoints_dsize = None
+ else:
+ keypoints_dtype = kdata.keypoints[args.keypoints_type].dtype
+ keypoints_dsize = kdata.keypoints[args.keypoints_type].dsize
+ if args.descriptors_type not in kdata.descriptors:
+ descriptors_dtype = None
+ descriptors_dsize = None
+ else:
+ descriptors_dtype = kdata.descriptors[args.descriptors_type].dtype
+ descriptors_dsize = kdata.descriptors[args.descriptors_type].dsize
+
+ for image_name in image_list:
+ img_path = get_image_fullpath(args.kapture_root, image_name)
+ print(f"\nExtracting features for {img_path}")
+ img = Image.open(img_path).convert("RGB")
+ W, H = img.size
+ img = norm_RGB(img)[None]
+ if iscuda:
+ img = img.cuda()
+
+ # extract keypoints/descriptors for a single image
+ xys, desc, scores = extract_multiscale(
+ net,
+ img,
+ detector,
+ scale_f=args.scale_f,
+ min_scale=args.min_scale,
+ max_scale=args.max_scale,
+ min_size=args.min_size,
+ max_size=args.max_size,
+ verbose=True,
+ )
+
+ xys = xys.cpu().numpy()
+ desc = desc.cpu().numpy()
+ scores = scores.cpu().numpy()
+ idxs = scores.argsort()[-args.top_k or None :]
+
+ xys = xys[idxs]
+ desc = desc[idxs]
+ if keypoints_dtype is None or descriptors_dtype is None:
+ keypoints_dtype = xys.dtype
+ descriptors_dtype = desc.dtype
+
+ keypoints_dsize = xys.shape[1]
+ descriptors_dsize = desc.shape[1]
+
+ kdata.keypoints[args.keypoints_type] = kapture.Keypoints(
+ "r2d2", keypoints_dtype, keypoints_dsize
+ )
+ kdata.descriptors[args.descriptors_type] = kapture.Descriptors(
+ "r2d2",
+ descriptors_dtype,
+ descriptors_dsize,
+ args.keypoints_type,
+ "L2",
+ )
+ keypoints_config_absolute_path = get_feature_csv_fullpath(
+ kapture.Keypoints, args.keypoints_type, args.kapture_root
+ )
+ descriptors_config_absolute_path = get_feature_csv_fullpath(
+ kapture.Descriptors, args.descriptors_type, args.kapture_root
+ )
+ keypoints_to_file(
+ keypoints_config_absolute_path, kdata.keypoints[args.keypoints_type]
+ )
+ descriptors_to_file(
+ descriptors_config_absolute_path,
+ kdata.descriptors[args.descriptors_type],
+ )
+ else:
+ assert kdata.keypoints[args.keypoints_type].dtype == xys.dtype
+ assert kdata.descriptors[args.descriptors_type].dtype == desc.dtype
+ assert kdata.keypoints[args.keypoints_type].dsize == xys.shape[1]
+ assert kdata.descriptors[args.descriptors_type].dsize == desc.shape[1]
+ assert (
+ kdata.descriptors[args.descriptors_type].keypoints_type
+ == args.keypoints_type
+ )
+ assert kdata.descriptors[args.descriptors_type].metric_type == "L2"
+
+ keypoints_fullpath = get_keypoints_fullpath(
+ args.keypoints_type, args.kapture_root, image_name, tar_handlers
+ )
+ print(f"Saving {xys.shape[0]} keypoints to {keypoints_fullpath}")
+ image_keypoints_to_file(keypoints_fullpath, xys)
+ kdata.keypoints[args.keypoints_type].add(image_name)
+
+ descriptors_fullpath = get_descriptors_fullpath(
+ args.descriptors_type, args.kapture_root, image_name, tar_handlers
+ )
+ print(f"Saving {desc.shape[0]} descriptors to {descriptors_fullpath}")
+ image_descriptors_to_file(descriptors_fullpath, desc)
+ kdata.descriptors[args.descriptors_type].add(image_name)
+
+ if not keypoints_check_dir(
+ kdata.keypoints[args.keypoints_type],
+ args.keypoints_type,
+ args.kapture_root,
+ tar_handlers,
+ ) or not descriptors_check_dir(
+ kdata.descriptors[args.descriptors_type],
+ args.descriptors_type,
+ args.kapture_root,
+ tar_handlers,
+ ):
+ print(
+ "local feature extraction ended successfully but not all files were saved"
+ )
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser(
+ "Extract r2d2 local features for all images in a dataset stored in the kapture format"
+ )
+ parser.add_argument("--model", type=str, required=True, help="model path")
+ parser.add_argument(
+ "--keypoints-type",
+ default=None,
+ help="keypoint type_name, default is filename of model",
+ )
+ parser.add_argument(
+ "--descriptors-type",
+ default=None,
+ help="descriptors type_name, default is filename of model",
+ )
+
+ parser.add_argument(
+ "--kapture-root", type=str, required=True, help="path to kapture root directory"
+ )
+
+ parser.add_argument("--top-k", type=int, default=5000, help="number of keypoints")
+
+ parser.add_argument("--scale-f", type=float, default=2**0.25)
+ parser.add_argument("--min-size", type=int, default=256)
+ parser.add_argument("--max-size", type=int, default=1024)
+ parser.add_argument("--min-scale", type=float, default=0)
+ parser.add_argument("--max-scale", type=float, default=1)
+
+ parser.add_argument("--reliability-thr", type=float, default=0.7)
+ parser.add_argument("--repeatability-thr", type=float, default=0.7)
+
+ parser.add_argument(
+ "--gpu", type=int, nargs="+", default=[0], help="use -1 for CPU"
+ )
+ args = parser.parse_args()
+
+ extract_kapture_keypoints(args)
diff --git a/third_party/r2d2/nets/__init__.py b/third_party/r2d2/nets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/third_party/r2d2/nets/ap_loss.py b/third_party/r2d2/nets/ap_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..deb59e4c067aa25c834caf4d0a3c06f9d470ecd4
--- /dev/null
+++ b/third_party/r2d2/nets/ap_loss.py
@@ -0,0 +1,69 @@
+# Copyright 2019-present NAVER Corp.
+# CC BY-NC-SA 3.0
+# Available only for non-commercial use
+
+import pdb
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+class APLoss(nn.Module):
+ """differentiable AP loss, through quantization.
+
+ Input: (N, M) values in [min, max]
+ label: (N, M) values in {0, 1}
+
+ Returns: list of query AP (for each n in {1..N})
+ Note: typically, you want to minimize 1 - mean(AP)
+ """
+
+ def __init__(self, nq=25, min=0, max=1, euc=False):
+ nn.Module.__init__(self)
+ assert isinstance(nq, int) and 2 <= nq <= 100
+ self.nq = nq
+ self.min = min
+ self.max = max
+ self.euc = euc
+ gap = max - min
+ assert gap > 0
+
+ # init quantizer = non-learnable (fixed) convolution
+ self.quantizer = q = nn.Conv1d(1, 2 * nq, kernel_size=1, bias=True)
+ a = (nq - 1) / gap
+ # 1st half = lines passing to (min+x,1) and (min+x+1/a,0) with x = {nq-1..0}*gap/(nq-1)
+ q.weight.data[:nq] = -a
+ q.bias.data[:nq] = torch.from_numpy(
+ a * min + np.arange(nq, 0, -1)
+ ) # b = 1 + a*(min+x)
+ # 2nd half = lines passing to (min+x,1) and (min+x-1/a,0) with x = {nq-1..0}*gap/(nq-1)
+ q.weight.data[nq:] = a
+ q.bias.data[nq:] = torch.from_numpy(
+ np.arange(2 - nq, 2, 1) - a * min
+ ) # b = 1 - a*(min+x)
+ # first and last one are special: just horizontal straight line
+ q.weight.data[0] = q.weight.data[-1] = 0
+ q.bias.data[0] = q.bias.data[-1] = 1
+
+ def compute_AP(self, x, label):
+ N, M = x.shape
+ if self.euc: # euclidean distance in same range than similarities
+ x = 1 - torch.sqrt(2.001 - 2 * x)
+
+ # quantize all predictions
+ q = self.quantizer(x.unsqueeze(1))
+ q = torch.min(q[:, : self.nq], q[:, self.nq :]).clamp(min=0) # N x Q x M
+
+ nbs = q.sum(dim=-1) # number of samples N x Q = c
+ rec = (q * label.view(N, 1, M).float()).sum(
+ dim=-1
+ ) # nb of correct samples = c+ N x Q
+ prec = rec.cumsum(dim=-1) / (1e-16 + nbs.cumsum(dim=-1)) # precision
+ rec /= rec.sum(dim=-1).unsqueeze(1) # norm in [0,1]
+
+ ap = (prec * rec).sum(dim=-1) # per-image AP
+ return ap
+
+ def forward(self, x, label):
+ assert x.shape == label.shape # N x M
+ return self.compute_AP(x, label)
diff --git a/imcui/third_party/r2d2/nets/losses.py b/third_party/r2d2/nets/losses.py
similarity index 60%
rename from imcui/third_party/r2d2/nets/losses.py
rename to third_party/r2d2/nets/losses.py
index f8eea8f6e82835e22d2bb445125f7dc722db85b2..973c592aab3f8f1c69b4001d1d324f1ad46ebe2d 100644
--- a/imcui/third_party/r2d2/nets/losses.py
+++ b/third_party/r2d2/nets/losses.py
@@ -13,44 +13,40 @@ from nets.repeatability_loss import *
from nets.reliability_loss import *
-class MultiLoss (nn.Module):
- """ Combines several loss functions for convenience.
+class MultiLoss(nn.Module):
+ """Combines several loss functions for convenience.
*args: [loss weight (float), loss creator, ... ]
-
+
Example:
loss = MultiLoss( 1, MyFirstLoss(), 0.5, MySecondLoss() )
"""
+
def __init__(self, *args, dbg=()):
nn.Module.__init__(self)
- assert len(args) % 2 == 0, 'args must be a list of (float, loss)'
+ assert len(args) % 2 == 0, "args must be a list of (float, loss)"
self.weights = []
self.losses = nn.ModuleList()
- for i in range(len(args)//2):
- weight = float(args[2*i+0])
- loss = args[2*i+1]
+ for i in range(len(args) // 2):
+ weight = float(args[2 * i + 0])
+ loss = args[2 * i + 1]
assert isinstance(loss, nn.Module), "%s is not a loss!" % loss
self.weights.append(weight)
self.losses.append(loss)
def forward(self, select=None, **variables):
- assert not select or all(1<=n<=len(self.losses) for n in select)
+ assert not select or all(1 <= n <= len(self.losses) for n in select)
d = dict()
cum_loss = 0
- for num, (weight, loss_func) in enumerate(zip(self.weights, self.losses),1):
- if select is not None and num not in select: continue
- l = loss_func(**{k:v for k,v in variables.items()})
+ for num, (weight, loss_func) in enumerate(zip(self.weights, self.losses), 1):
+ if select is not None and num not in select:
+ continue
+ l = loss_func(**{k: v for k, v in variables.items()})
if isinstance(l, tuple):
assert len(l) == 2 and isinstance(l[1], dict)
else:
- l = l, {loss_func.name:l}
+ l = l, {loss_func.name: l}
cum_loss = cum_loss + weight * l[0]
- for key,val in l[1].items():
- d['loss_'+key] = float(val)
- d['loss'] = float(cum_loss)
+ for key, val in l[1].items():
+ d["loss_" + key] = float(val)
+ d["loss"] = float(cum_loss)
return cum_loss, d
-
-
-
-
-
-
diff --git a/imcui/third_party/r2d2/nets/patchnet.py b/third_party/r2d2/nets/patchnet.py
similarity index 53%
rename from imcui/third_party/r2d2/nets/patchnet.py
rename to third_party/r2d2/nets/patchnet.py
index 854c61ecf9b879fa7f420255296c4fbbfd665181..8ed3fdbd55ccbbd58f0cea3dad9384a402ec5e9d 100644
--- a/imcui/third_party/r2d2/nets/patchnet.py
+++ b/third_party/r2d2/nets/patchnet.py
@@ -8,22 +8,25 @@ import torch.nn as nn
import torch.nn.functional as F
-class BaseNet (nn.Module):
- """ Takes a list of images as input, and returns for each image:
- - a pixelwise descriptor
- - a pixelwise confidence
+class BaseNet(nn.Module):
+ """Takes a list of images as input, and returns for each image:
+ - a pixelwise descriptor
+ - a pixelwise confidence
"""
+
def softmax(self, ux):
if ux.shape[1] == 1:
x = F.softplus(ux)
return x / (1 + x) # for sure in [0,1], much less plateaus than softmax
elif ux.shape[1] == 2:
- return F.softmax(ux, dim=1)[:,1:2]
+ return F.softmax(ux, dim=1)[:, 1:2]
def normalize(self, x, ureliability, urepeatability):
- return dict(descriptors = F.normalize(x, p=2, dim=1),
- repeatability = self.softmax( urepeatability ),
- reliability = self.softmax( ureliability ))
+ return dict(
+ descriptors=F.normalize(x, p=2, dim=1),
+ repeatability=self.softmax(urepeatability),
+ reliability=self.softmax(ureliability),
+ )
def forward_one(self, x):
raise NotImplementedError()
@@ -31,15 +34,15 @@ class BaseNet (nn.Module):
def forward(self, imgs, **kw):
res = [self.forward_one(img) for img in imgs]
# merge all dictionaries into one
- res = {k:[r[k] for r in res if k in r] for k in {k for r in res for k in r}}
+ res = {k: [r[k] for r in res if k in r] for k in {k for r in res for k in r}}
return dict(res, imgs=imgs, **kw)
-
-class PatchNet (BaseNet):
- """ Helper class to construct a fully-convolutional network that
- extract a l2-normalized patch descriptor.
+class PatchNet(BaseNet):
+ """Helper class to construct a fully-convolutional network that
+ extract a l2-normalized patch descriptor.
"""
+
def __init__(self, inchan=3, dilated=True, dilation=1, bn=True, bn_affine=False):
BaseNet.__init__(self)
self.inchan = inchan
@@ -53,41 +56,54 @@ class PatchNet (BaseNet):
def _make_bn(self, outd):
return nn.BatchNorm2d(outd, affine=self.bn_affine)
- def _add_conv(self, outd, k=3, stride=1, dilation=1, bn=True, relu=True, k_pool = 1, pool_type='max'):
+ def _add_conv(
+ self,
+ outd,
+ k=3,
+ stride=1,
+ dilation=1,
+ bn=True,
+ relu=True,
+ k_pool=1,
+ pool_type="max",
+ ):
# as in the original implementation, dilation is applied at the end of layer, so it will have impact only from next layer
d = self.dilation * dilation
- if self.dilated:
- conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=1)
+ if self.dilated:
+ conv_params = dict(padding=((k - 1) * d) // 2, dilation=d, stride=1)
self.dilation *= stride
else:
- conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=stride)
- self.ops.append( nn.Conv2d(self.curchan, outd, kernel_size=k, **conv_params) )
- if bn and self.bn: self.ops.append( self._make_bn(outd) )
- if relu: self.ops.append( nn.ReLU(inplace=True) )
+ conv_params = dict(padding=((k - 1) * d) // 2, dilation=d, stride=stride)
+ self.ops.append(nn.Conv2d(self.curchan, outd, kernel_size=k, **conv_params))
+ if bn and self.bn:
+ self.ops.append(self._make_bn(outd))
+ if relu:
+ self.ops.append(nn.ReLU(inplace=True))
self.curchan = outd
-
+
if k_pool > 1:
- if pool_type == 'avg':
+ if pool_type == "avg":
self.ops.append(torch.nn.AvgPool2d(kernel_size=k_pool))
- elif pool_type == 'max':
+ elif pool_type == "max":
self.ops.append(torch.nn.MaxPool2d(kernel_size=k_pool))
else:
print(f"Error, unknown pooling type {pool_type}...")
-
+
def forward_one(self, x):
assert self.ops, "You need to add convolutions first"
- for n,op in enumerate(self.ops):
+ for n, op in enumerate(self.ops):
x = op(x)
return self.normalize(x)
-class L2_Net (PatchNet):
- """ Compute a 128D descriptor for all overlapping 32x32 patches.
- From the L2Net paper (CVPR'17).
+class L2_Net(PatchNet):
+ """Compute a 128D descriptor for all overlapping 32x32 patches.
+ From the L2Net paper (CVPR'17).
"""
- def __init__(self, dim=128, **kw ):
+
+ def __init__(self, dim=128, **kw):
PatchNet.__init__(self, **kw)
- add_conv = lambda n,**kw: self._add_conv((n*dim)//128,**kw)
+ add_conv = lambda n, **kw: self._add_conv((n * dim) // 128, **kw)
add_conv(32)
add_conv(32)
add_conv(64, stride=2)
@@ -98,35 +114,34 @@ class L2_Net (PatchNet):
self.out_dim = dim
-class Quad_L2Net (PatchNet):
- """ Same than L2_Net, but replace the final 8x8 conv by 3 successive 2x2 convs.
- """
- def __init__(self, dim=128, mchan=4, relu22=False, **kw ):
+class Quad_L2Net(PatchNet):
+ """Same than L2_Net, but replace the final 8x8 conv by 3 successive 2x2 convs."""
+
+ def __init__(self, dim=128, mchan=4, relu22=False, **kw):
PatchNet.__init__(self, **kw)
- self._add_conv( 8*mchan)
- self._add_conv( 8*mchan)
- self._add_conv( 16*mchan, stride=2)
- self._add_conv( 16*mchan)
- self._add_conv( 32*mchan, stride=2)
- self._add_conv( 32*mchan)
+ self._add_conv(8 * mchan)
+ self._add_conv(8 * mchan)
+ self._add_conv(16 * mchan, stride=2)
+ self._add_conv(16 * mchan)
+ self._add_conv(32 * mchan, stride=2)
+ self._add_conv(32 * mchan)
# replace last 8x8 convolution with 3 2x2 convolutions
- self._add_conv( 32*mchan, k=2, stride=2, relu=relu22)
- self._add_conv( 32*mchan, k=2, stride=2, relu=relu22)
+ self._add_conv(32 * mchan, k=2, stride=2, relu=relu22)
+ self._add_conv(32 * mchan, k=2, stride=2, relu=relu22)
self._add_conv(dim, k=2, stride=2, bn=False, relu=False)
self.out_dim = dim
+class Quad_L2Net_ConfCFS(Quad_L2Net):
+ """Same than Quad_L2Net, with 2 confidence maps for repeatability and reliability."""
-class Quad_L2Net_ConfCFS (Quad_L2Net):
- """ Same than Quad_L2Net, with 2 confidence maps for repeatability and reliability.
- """
- def __init__(self, **kw ):
+ def __init__(self, **kw):
Quad_L2Net.__init__(self, **kw)
# reliability classifier
self.clf = nn.Conv2d(self.out_dim, 2, kernel_size=1)
# repeatability classifier: for some reasons it's a softplus, not a softmax!
# Why? I guess it's a mistake that was left unnoticed in the code for a long time...
- self.sal = nn.Conv2d(self.out_dim, 1, kernel_size=1)
+ self.sal = nn.Conv2d(self.out_dim, 1, kernel_size=1)
def forward_one(self, x):
assert self.ops, "You need to add convolutions first"
@@ -138,44 +153,51 @@ class Quad_L2Net_ConfCFS (Quad_L2Net):
return self.normalize(x, ureliability, urepeatability)
-class Fast_Quad_L2Net (PatchNet):
- """ Faster version of Quad l2 net, replacing one dilated conv with one pooling to diminish image resolution thus increase inference time
+class Fast_Quad_L2Net(PatchNet):
+ """Faster version of Quad l2 net, replacing one dilated conv with one pooling to diminish image resolution thus increase inference time
Dilation factors and pooling:
1,1,1, pool2, 1,1, 2,2, 4, 8, upsample2
"""
- def __init__(self, dim=128, mchan=4, relu22=False, downsample_factor=2, **kw ):
+
+ def __init__(self, dim=128, mchan=4, relu22=False, downsample_factor=2, **kw):
PatchNet.__init__(self, **kw)
- self._add_conv( 8*mchan)
- self._add_conv( 8*mchan)
- self._add_conv( 16*mchan, k_pool = downsample_factor) # added avg pooling to decrease img resolution
- self._add_conv( 16*mchan)
- self._add_conv( 32*mchan, stride=2)
- self._add_conv( 32*mchan)
-
+ self._add_conv(8 * mchan)
+ self._add_conv(8 * mchan)
+ self._add_conv(
+ 16 * mchan, k_pool=downsample_factor
+ ) # added avg pooling to decrease img resolution
+ self._add_conv(16 * mchan)
+ self._add_conv(32 * mchan, stride=2)
+ self._add_conv(32 * mchan)
+
# replace last 8x8 convolution with 3 2x2 convolutions
- self._add_conv( 32*mchan, k=2, stride=2, relu=relu22)
- self._add_conv( 32*mchan, k=2, stride=2, relu=relu22)
+ self._add_conv(32 * mchan, k=2, stride=2, relu=relu22)
+ self._add_conv(32 * mchan, k=2, stride=2, relu=relu22)
self._add_conv(dim, k=2, stride=2, bn=False, relu=False)
-
+
# Go back to initial image resolution with upsampling
- self.ops.append(torch.nn.Upsample(scale_factor=downsample_factor, mode='bilinear', align_corners=False))
-
+ self.ops.append(
+ torch.nn.Upsample(
+ scale_factor=downsample_factor, mode="bilinear", align_corners=False
+ )
+ )
+
self.out_dim = dim
-
-
-class Fast_Quad_L2Net_ConfCFS (Fast_Quad_L2Net):
- """ Fast r2d2 architecture
- """
- def __init__(self, **kw ):
+
+
+class Fast_Quad_L2Net_ConfCFS(Fast_Quad_L2Net):
+ """Fast r2d2 architecture"""
+
+ def __init__(self, **kw):
Fast_Quad_L2Net.__init__(self, **kw)
# reliability classifier
self.clf = nn.Conv2d(self.out_dim, 2, kernel_size=1)
-
+
# repeatability classifier: for some reasons it's a softplus, not a softmax!
# Why? I guess it's a mistake that was left unnoticed in the code for a long time...
- self.sal = nn.Conv2d(self.out_dim, 1, kernel_size=1)
-
+ self.sal = nn.Conv2d(self.out_dim, 1, kernel_size=1)
+
def forward_one(self, x):
assert self.ops, "You need to add convolutions first"
for op in self.ops:
@@ -183,4 +205,4 @@ class Fast_Quad_L2Net_ConfCFS (Fast_Quad_L2Net):
# compute the confidence maps
ureliability = self.clf(x**2)
urepeatability = self.sal(x**2)
- return self.normalize(x, ureliability, urepeatability)
\ No newline at end of file
+ return self.normalize(x, ureliability, urepeatability)
diff --git a/imcui/third_party/r2d2/nets/reliability_loss.py b/third_party/r2d2/nets/reliability_loss.py
similarity index 56%
rename from imcui/third_party/r2d2/nets/reliability_loss.py
rename to third_party/r2d2/nets/reliability_loss.py
index 52d5383b0eaa52bcf2111eabb4b45e39b63b976f..e560d1ea1b4dc27d81031c62cc4c0aed9161cc67 100644
--- a/imcui/third_party/r2d2/nets/reliability_loss.py
+++ b/third_party/r2d2/nets/reliability_loss.py
@@ -9,18 +9,19 @@ import torch.nn.functional as F
from nets.ap_loss import APLoss
-class PixelAPLoss (nn.Module):
- """ Computes the pixel-wise AP loss:
- Given two images and ground-truth optical flow, computes the AP per pixel.
-
- feat1: (B, C, H, W) pixel-wise features extracted from img1
- feat2: (B, C, H, W) pixel-wise features extracted from img2
- aflow: (B, 2, H, W) absolute flow: aflow[...,y1,x1] = x2,y2
+class PixelAPLoss(nn.Module):
+ """Computes the pixel-wise AP loss:
+ Given two images and ground-truth optical flow, computes the AP per pixel.
+
+ feat1: (B, C, H, W) pixel-wise features extracted from img1
+ feat2: (B, C, H, W) pixel-wise features extracted from img2
+ aflow: (B, 2, H, W) absolute flow: aflow[...,y1,x1] = x2,y2
"""
+
def __init__(self, sampler, nq=20):
nn.Module.__init__(self)
self.aploss = APLoss(nq, min=0, max=1, euc=False)
- self.name = 'pixAP'
+ self.name = "pixAP"
self.sampler = sampler
def loss_from_ap(self, ap, rel):
@@ -28,32 +29,31 @@ class PixelAPLoss (nn.Module):
def forward(self, descriptors, aflow, **kw):
# subsample things
- scores, gt, msk, qconf = self.sampler(descriptors, kw.get('reliability'), aflow)
-
+ scores, gt, msk, qconf = self.sampler(descriptors, kw.get("reliability"), aflow)
+
# compute pixel-wise AP
n = qconf.numel()
- if n == 0: return 0
- scores, gt = scores.view(n,-1), gt.view(n,-1)
+ if n == 0:
+ return 0
+ scores, gt = scores.view(n, -1), gt.view(n, -1)
ap = self.aploss(scores, gt).view(msk.shape)
pixel_loss = self.loss_from_ap(ap, qconf)
-
+
loss = pixel_loss[msk].mean()
return loss
-class ReliabilityLoss (PixelAPLoss):
- """ same than PixelAPLoss, but also train a pixel-wise confidence
- that this pixel is going to have a good AP.
+class ReliabilityLoss(PixelAPLoss):
+ """same than PixelAPLoss, but also train a pixel-wise confidence
+ that this pixel is going to have a good AP.
"""
+
def __init__(self, sampler, base=0.5, **kw):
PixelAPLoss.__init__(self, sampler, **kw)
assert 0 <= base < 1
self.base = base
- self.name = 'reliability'
+ self.name = "reliability"
def loss_from_ap(self, ap, rel):
- return 1 - ap*rel - (1-rel)*self.base
-
-
-
+ return 1 - ap * rel - (1 - rel) * self.base
diff --git a/imcui/third_party/r2d2/nets/repeatability_loss.py b/third_party/r2d2/nets/repeatability_loss.py
similarity index 59%
rename from imcui/third_party/r2d2/nets/repeatability_loss.py
rename to third_party/r2d2/nets/repeatability_loss.py
index 5cda0b6d036f98af88a88780fe39da0c5c0b610e..af49e77f444c5b4b035cd43d0c065096e8dd7c1b 100644
--- a/imcui/third_party/r2d2/nets/repeatability_loss.py
+++ b/third_party/r2d2/nets/repeatability_loss.py
@@ -10,27 +10,28 @@ import torch.nn.functional as F
from nets.sampler import FullSampler
-class CosimLoss (nn.Module):
- """ Try to make the repeatability repeatable from one image to the other.
- """
+
+class CosimLoss(nn.Module):
+ """Try to make the repeatability repeatable from one image to the other."""
+
def __init__(self, N=16):
nn.Module.__init__(self)
- self.name = f'cosim{N}'
- self.patches = nn.Unfold(N, padding=0, stride=N//2)
+ self.name = f"cosim{N}"
+ self.patches = nn.Unfold(N, padding=0, stride=N // 2)
def extract_patches(self, sal):
- patches = self.patches(sal).transpose(1,2) # flatten
- patches = F.normalize(patches, p=2, dim=2) # norm
+ patches = self.patches(sal).transpose(1, 2) # flatten
+ patches = F.normalize(patches, p=2, dim=2) # norm
return patches
-
+
def forward(self, repeatability, aflow, **kw):
- B,two,H,W = aflow.shape
+ B, two, H, W = aflow.shape
assert two == 2
# normalize
sali1, sali2 = repeatability
grid = FullSampler._aflow_to_grid(aflow)
- sali2 = F.grid_sample(sali2, grid, mode='bilinear', padding_mode='border')
+ sali2 = F.grid_sample(sali2, grid, mode="bilinear", padding_mode="border")
patches1 = self.extract_patches(sali1)
patches2 = self.extract_patches(sali2)
@@ -38,29 +39,25 @@ class CosimLoss (nn.Module):
return 1 - cosim.mean()
-class PeakyLoss (nn.Module):
- """ Try to make the repeatability locally peaky.
+class PeakyLoss(nn.Module):
+ """Try to make the repeatability locally peaky.
Mechanism: we maximize, for each pixel, the difference between the local mean
and the local max.
"""
+
def __init__(self, N=16):
nn.Module.__init__(self)
- self.name = f'peaky{N}'
- assert N % 2 == 0, 'N must be pair'
+ self.name = f"peaky{N}"
+ assert N % 2 == 0, "N must be pair"
self.preproc = nn.AvgPool2d(3, stride=1, padding=1)
- self.maxpool = nn.MaxPool2d(N+1, stride=1, padding=N//2)
- self.avgpool = nn.AvgPool2d(N+1, stride=1, padding=N//2)
+ self.maxpool = nn.MaxPool2d(N + 1, stride=1, padding=N // 2)
+ self.avgpool = nn.AvgPool2d(N + 1, stride=1, padding=N // 2)
def forward_one(self, sali):
- sali = self.preproc(sali) # remove super high frequency
+ sali = self.preproc(sali) # remove super high frequency
return 1 - (self.maxpool(sali) - self.avgpool(sali)).mean()
def forward(self, repeatability, **kw):
sali1, sali2 = repeatability
- return (self.forward_one(sali1) + self.forward_one(sali2)) /2
-
-
-
-
-
+ return (self.forward_one(sali1) + self.forward_one(sali2)) / 2
diff --git a/imcui/third_party/r2d2/nets/sampler.py b/third_party/r2d2/nets/sampler.py
similarity index 51%
rename from imcui/third_party/r2d2/nets/sampler.py
rename to third_party/r2d2/nets/sampler.py
index 9fede70d3a04d7f31a1d414eace0aaf3729e8235..3f2e5a276a80b997561549ed3e8466da3876e382 100644
--- a/imcui/third_party/r2d2/nets/sampler.py
+++ b/third_party/r2d2/nets/sampler.py
@@ -15,65 +15,69 @@ import torch.nn.functional as F
class FullSampler(nn.Module):
- """ all pixels are selected
- - feats: keypoint descriptors
- - confs: reliability values
+ """all pixels are selected
+ - feats: keypoint descriptors
+ - confs: reliability values
"""
+
def __init__(self):
nn.Module.__init__(self)
- self.mode = 'bilinear'
- self.padding = 'zeros'
+ self.mode = "bilinear"
+ self.padding = "zeros"
@staticmethod
def _aflow_to_grid(aflow):
H, W = aflow.shape[2:]
- grid = aflow.permute(0,2,3,1).clone()
- grid[:,:,:,0] *= 2/(W-1)
- grid[:,:,:,1] *= 2/(H-1)
+ grid = aflow.permute(0, 2, 3, 1).clone()
+ grid[:, :, :, 0] *= 2 / (W - 1)
+ grid[:, :, :, 1] *= 2 / (H - 1)
grid -= 1
- grid[torch.isnan(grid)] = 9e9 # invalids
+ grid[torch.isnan(grid)] = 9e9 # invalids
return grid
-
+
def _warp(self, feats, confs, aflow):
- if isinstance(aflow, tuple): return aflow # result was precomputed
+ if isinstance(aflow, tuple):
+ return aflow # result was precomputed
feat1, feat2 = feats
- conf1, conf2 = confs if confs else (None,None)
-
+ conf1, conf2 = confs if confs else (None, None)
+
B, two, H, W = aflow.shape
D = feat1.shape[1]
- assert feat1.shape == feat2.shape == (B, D, H, W) # D = 128, B = batch
+ assert feat1.shape == feat2.shape == (B, D, H, W) # D = 128, B = batch
assert conf1.shape == conf2.shape == (B, 1, H, W) if confs else True
# warp img2 to img1
grid = self._aflow_to_grid(aflow)
- ones2 = feat2.new_ones(feat2[:,0:1].shape)
+ ones2 = feat2.new_ones(feat2[:, 0:1].shape)
feat2to1 = F.grid_sample(feat2, grid, mode=self.mode, padding_mode=self.padding)
- mask2to1 = F.grid_sample(ones2, grid, mode='nearest', padding_mode='zeros')
- conf2to1 = F.grid_sample(conf2, grid, mode=self.mode, padding_mode=self.padding) \
- if confs else None
+ mask2to1 = F.grid_sample(ones2, grid, mode="nearest", padding_mode="zeros")
+ conf2to1 = (
+ F.grid_sample(conf2, grid, mode=self.mode, padding_mode=self.padding)
+ if confs
+ else None
+ )
return feat2to1, mask2to1.byte(), conf2to1
def _warp_positions(self, aflow):
B, two, H, W = aflow.shape
assert two == 2
-
+
Y = torch.arange(H, device=aflow.device)
X = torch.arange(W, device=aflow.device)
- XY = torch.stack(torch.meshgrid(Y,X)[::-1], dim=0)
+ XY = torch.stack(torch.meshgrid(Y, X)[::-1], dim=0)
XY = XY[None].expand(B, 2, H, W).float()
-
+
grid = self._aflow_to_grid(aflow)
- XY2 = F.grid_sample(XY, grid, mode='bilinear', padding_mode='zeros')
+ XY2 = F.grid_sample(XY, grid, mode="bilinear", padding_mode="zeros")
return XY, XY2
+class SubSampler(FullSampler):
+ """pixels are selected in an uniformly spaced grid"""
-class SubSampler (FullSampler):
- """ pixels are selected in an uniformly spaced grid
- """
def __init__(self, border, subq, subd, perimage=False):
FullSampler.__init__(self)
- assert subq % subd == 0, 'subq must be multiple of subd'
+ assert subq % subd == 0, "subq must be multiple of subd"
self.sub_q = subq
self.sub_d = subd
self.border = border
@@ -81,13 +85,17 @@ class SubSampler (FullSampler):
def __repr__(self):
return "SubSampler(border=%d, subq=%d, subd=%d, perimage=%d)" % (
- self.border, self.sub_q, self.sub_d, self.perimage)
+ self.border,
+ self.sub_q,
+ self.sub_d,
+ self.perimage,
+ )
def __call__(self, feats, confs, aflow):
feat1, conf1 = feats[0], (confs[0] if confs else None)
# warp with optical flow in img1 coords
feat2, mask2, conf2 = self._warp(feats, confs, aflow)
-
+
# subsample img1
slq = slice(self.border, -self.border or None, self.sub_q)
feat1 = feat1[:, :, slq, slq]
@@ -97,47 +105,50 @@ class SubSampler (FullSampler):
feat2 = feat2[:, :, sld, sld]
mask2 = mask2[:, :, sld, sld]
conf2 = conf2[:, :, sld, sld] if confs else None
-
+
B, D, Hq, Wq = feat1.shape
B, D, Hd, Wd = feat2.shape
-
+
# compute gt
if self.perimage or self.sub_q != self.sub_d:
# compute ground-truth by comparing pixel indices
- f = feats[0][0:1,0] if self.perimage else feats[0][:,0]
- idxs = torch.arange(f.numel(), dtype=torch.int64, device=feat1.device).view(f.shape)
- idxs1 = idxs[:, slq, slq].reshape(-1,Hq*Wq)
- idxs2 = idxs[:, sld, sld].reshape(-1,Hd*Wd)
+ f = feats[0][0:1, 0] if self.perimage else feats[0][:, 0]
+ idxs = torch.arange(f.numel(), dtype=torch.int64, device=feat1.device).view(
+ f.shape
+ )
+ idxs1 = idxs[:, slq, slq].reshape(-1, Hq * Wq)
+ idxs2 = idxs[:, sld, sld].reshape(-1, Hd * Wd)
if self.perimage:
- gt = (idxs1[0].view(-1,1) == idxs2[0].view(1,-1))
- gt = gt[None,:,:].expand(B, Hq*Wq, Hd*Wd)
- else :
- gt = (idxs1.view(-1,1) == idxs2.view(1,-1))
+ gt = idxs1[0].view(-1, 1) == idxs2[0].view(1, -1)
+ gt = gt[None, :, :].expand(B, Hq * Wq, Hd * Wd)
+ else:
+ gt = idxs1.view(-1, 1) == idxs2.view(1, -1)
else:
- gt = torch.eye(feat1[:,0].numel(), dtype=torch.uint8, device=feat1.device) # always binary for AP loss
-
+ gt = torch.eye(
+ feat1[:, 0].numel(), dtype=torch.uint8, device=feat1.device
+ ) # always binary for AP loss
+
# compute all images together
- queries = feat1.reshape(B,D,-1) # B x D x (Hq x Wq)
- database = feat2.reshape(B,D,-1) # B x D x (Hd x Wd)
+ queries = feat1.reshape(B, D, -1) # B x D x (Hq x Wq)
+ database = feat2.reshape(B, D, -1) # B x D x (Hd x Wd)
if self.perimage:
- queries = queries.transpose(1,2) # B x (Hd x Wd) x D
- scores = torch.bmm(queries, database) # B x (Hq x Wq) x (Hd x Wd)
+ queries = queries.transpose(1, 2) # B x (Hd x Wd) x D
+ scores = torch.bmm(queries, database) # B x (Hq x Wq) x (Hd x Wd)
else:
- queries = queries .transpose(1,2).reshape(-1,D) # (B x Hq x Wq) x D
- database = database.transpose(1,0).reshape(D,-1) # D x (B x Hd x Wd)
- scores = torch.matmul(queries, database) # (B x Hq x Wq) x (B x Hd x Wd)
+ queries = queries.transpose(1, 2).reshape(-1, D) # (B x Hq x Wq) x D
+ database = database.transpose(1, 0).reshape(D, -1) # D x (B x Hd x Wd)
+ scores = torch.matmul(queries, database) # (B x Hq x Wq) x (B x Hd x Wd)
# compute reliability
- qconf = (conf1 + conf2)/2 if confs else None
+ qconf = (conf1 + conf2) / 2 if confs else None
assert gt.shape == scores.shape
return scores, gt, mask2, qconf
+class NghSampler(FullSampler):
+ """all pixels in a small neighborhood"""
-class NghSampler (FullSampler):
- """ all pixels in a small neighborhood
- """
def __init__(self, ngh, subq=1, subd=1, ignore=1, border=None):
FullSampler.__init__(self)
assert 0 <= ignore < ngh
@@ -146,86 +157,96 @@ class NghSampler (FullSampler):
assert subd <= ngh
self.sub_q = subq
self.sub_d = subd
- if border is None: border = ngh
- assert border >= ngh, 'border has to be larger than ngh'
+ if border is None:
+ border = ngh
+ assert border >= ngh, "border has to be larger than ngh"
self.border = border
def __repr__(self):
return "NghSampler(ngh=%d, subq=%d, subd=%d, ignore=%d, border=%d)" % (
- self.ngh, self.sub_q, self.sub_d, self.ignore, self.border)
+ self.ngh,
+ self.sub_q,
+ self.sub_d,
+ self.ignore,
+ self.border,
+ )
def trans(self, arr, i, j):
- s = lambda i: slice(self.border+i, i-self.border or None, self.sub_q)
- return arr[:,:,s(j),s(i)]
+ s = lambda i: slice(self.border + i, i - self.border or None, self.sub_q)
+ return arr[:, :, s(j), s(i)]
def __call__(self, feats, confs, aflow):
feat1, conf1 = feats[0], (confs[0] if confs else None)
# warp with optical flow in img1 coords
feat2, mask2, conf2 = self._warp(feats, confs, aflow)
-
- qfeat = self.trans(feat1,0,0)
- qconf = (self.trans(conf1,0,0) + self.trans(conf2,0,0)) / 2 if confs else None
- mask2 = self.trans(mask2,0,0)
- scores_at = lambda i,j: (qfeat * self.trans(feat2,i,j)).sum(dim=1)
-
+
+ qfeat = self.trans(feat1, 0, 0)
+ qconf = (
+ (self.trans(conf1, 0, 0) + self.trans(conf2, 0, 0)) / 2 if confs else None
+ )
+ mask2 = self.trans(mask2, 0, 0)
+ scores_at = lambda i, j: (qfeat * self.trans(feat2, i, j)).sum(dim=1)
+
# compute scores for all neighbors
B, D = feat1.shape[:2]
min_d = self.ignore**2
max_d = self.ngh**2
- rad = (self.ngh//self.sub_d) * self.ngh # make an integer multiple
+ rad = (self.ngh // self.sub_d) * self.ngh # make an integer multiple
negs = []
offsets = []
- for j in range(-rad, rad+1, self.sub_d):
- for i in range(-rad, rad+1, self.sub_d):
- if not(min_d < i*i + j*j <= max_d):
- continue # out of scope
- offsets.append((i,j)) # Note: this list is just for debug
- negs.append( scores_at(i,j) )
-
- scores = torch.stack([scores_at(0,0)] + negs, dim=-1)
+ for j in range(-rad, rad + 1, self.sub_d):
+ for i in range(-rad, rad + 1, self.sub_d):
+ if not (min_d < i * i + j * j <= max_d):
+ continue # out of scope
+ offsets.append((i, j)) # Note: this list is just for debug
+ negs.append(scores_at(i, j))
+
+ scores = torch.stack([scores_at(0, 0)] + negs, dim=-1)
gt = scores.new_zeros(scores.shape, dtype=torch.uint8)
- gt[..., 0] = 1 # only the center point is positive
+ gt[..., 0] = 1 # only the center point is positive
return scores, gt, mask2, qconf
+class FarNearSampler(FullSampler):
+ """Sample pixels from *both* a small neighborhood *and* far-away pixels.
-class FarNearSampler (FullSampler):
- """ Sample pixels from *both* a small neighborhood *and* far-away pixels.
-
How it works?
1) Queries are sampled from img1,
- - at least `border` pixels from borders and
+ - at least `border` pixels from borders and
- on a grid with step = `subq`
-
- 2) Close database pixels
+
+ 2) Close database pixels
- from the corresponding image (img2),
- - within a `ngh` distance radius
+ - within a `ngh` distance radius
- on a grid with step = `subd_ngh`
- ignored if distance to query is >0 and <=`ignore`
-
+
3) Far-away database pixels from ,
- from all batch images in `img2`
- at least `border` pixels from borders
- on a grid with step = `subd_far`
"""
- def __init__(self, subq, ngh, subd_ngh, subd_far, border=None, ignore=1,
- maxpool_ngh=False ):
+
+ def __init__(
+ self, subq, ngh, subd_ngh, subd_far, border=None, ignore=1, maxpool_ngh=False
+ ):
FullSampler.__init__(self)
border = border or ngh
- assert ignore < ngh < subd_far, 'neighborhood needs to be smaller than far step'
- self.close_sampler = NghSampler(ngh=ngh, subq=subq, subd=subd_ngh,
- ignore=not(maxpool_ngh), border=border)
+ assert ignore < ngh < subd_far, "neighborhood needs to be smaller than far step"
+ self.close_sampler = NghSampler(
+ ngh=ngh, subq=subq, subd=subd_ngh, ignore=not (maxpool_ngh), border=border
+ )
self.faraway_sampler = SubSampler(border=border, subq=subq, subd=subd_far)
self.maxpool_ngh = maxpool_ngh
def __repr__(self):
- c,f = self.close_sampler, self.faraway_sampler
+ c, f = self.close_sampler, self.faraway_sampler
res = "FarNearSampler(subq=%d, ngh=%d" % (c.sub_q, c.ngh)
res += ", subd_ngh=%d, subd_far=%d" % (c.sub_d, f.sub_d)
res += ", border=%d, ign=%d" % (f.border, c.ignore)
res += ", maxpool_ngh=%d" % self.maxpool_ngh
- return res+')'
+ return res + ")"
def __call__(self, feats, confs, aflow):
# warp with optical flow in img1 coords
@@ -233,10 +254,10 @@ class FarNearSampler (FullSampler):
# sample ngh pixels
scores1, gt1, msk1, conf1 = self.close_sampler(feats, confs, aflow)
- scores1, gt1 = scores1.view(-1,scores1.shape[-1]), gt1.view(-1,gt1.shape[-1])
+ scores1, gt1 = scores1.view(-1, scores1.shape[-1]), gt1.view(-1, gt1.shape[-1])
if self.maxpool_ngh:
# we consider all scores from ngh as potential positives
- scores1, self._cached_maxpool_ngh = scores1.max(dim=1,keepdim=True)
+ scores1, self._cached_maxpool_ngh = scores1.max(dim=1, keepdim=True)
gt1 = gt1[:, 0:1]
# sample far pixels
@@ -244,22 +265,35 @@ class FarNearSampler (FullSampler):
# assert (msk1 == msk2).all()
# assert (conf1 == conf2).all()
- return (torch.cat((scores1,scores2),dim=1),
- torch.cat((gt1, gt2), dim=1),
- msk1, conf1 if confs else None)
+ return (
+ torch.cat((scores1, scores2), dim=1),
+ torch.cat((gt1, gt2), dim=1),
+ msk1,
+ conf1 if confs else None,
+ )
-class NghSampler2 (nn.Module):
- """ Similar to NghSampler, but doesnt warp the 2nd image.
+class NghSampler2(nn.Module):
+ """Similar to NghSampler, but doesnt warp the 2nd image.
Distance to GT => 0 ... pos_d ... neg_d ... ngh
Pixel label => + + + + + + 0 0 - - - - - - -
-
+
Subsample on query side: if > 0, regular grid
- < 0, random points
+ < 0, random points
In both cases, the number of query points is = W*H/subq**2
"""
- def __init__(self, ngh, subq=1, subd=1, pos_d=0, neg_d=2, border=None,
- maxpool_pos=True, subd_neg=0):
+
+ def __init__(
+ self,
+ ngh,
+ subq=1,
+ subd=1,
+ pos_d=0,
+ neg_d=2,
+ border=None,
+ maxpool_pos=True,
+ subd_neg=0,
+ ):
nn.Module.__init__(self)
assert 0 <= pos_d < neg_d <= (ngh if ngh else 99)
self.ngh = ngh
@@ -270,8 +304,9 @@ class NghSampler2 (nn.Module):
self.sub_q = subq
self.sub_d = subd
self.sub_d_neg = subd_neg
- if border is None: border = ngh
- assert border >= ngh, 'border has to be larger than ngh'
+ if border is None:
+ border = ngh
+ assert border >= ngh, "border has to be larger than ngh"
self.border = border
self.maxpool_pos = maxpool_pos
self.precompute_offsets()
@@ -280,19 +315,19 @@ class NghSampler2 (nn.Module):
pos_d2 = self.pos_d**2
neg_d2 = self.neg_d**2
rad2 = self.ngh**2
- rad = (self.ngh//self.sub_d) * self.ngh # make an integer multiple
+ rad = (self.ngh // self.sub_d) * self.ngh # make an integer multiple
pos = []
neg = []
- for j in range(-rad, rad+1, self.sub_d):
- for i in range(-rad, rad+1, self.sub_d):
- d2 = i*i + j*j
- if d2 <= pos_d2:
- pos.append( (i,j) )
- elif neg_d2 <= d2 <= rad2:
- neg.append( (i,j) )
+ for j in range(-rad, rad + 1, self.sub_d):
+ for i in range(-rad, rad + 1, self.sub_d):
+ d2 = i * i + j * j
+ if d2 <= pos_d2:
+ pos.append((i, j))
+ elif neg_d2 <= d2 <= rad2:
+ neg.append((i, j))
- self.register_buffer('pos_offsets', torch.LongTensor(pos).view(-1,2).t())
- self.register_buffer('neg_offsets', torch.LongTensor(neg).view(-1,2).t())
+ self.register_buffer("pos_offsets", torch.LongTensor(pos).view(-1, 2).t())
+ self.register_buffer("neg_offsets", torch.LongTensor(neg).view(-1, 2).t())
def gen_grid(self, step, aflow):
B, two, H, W = aflow.shape
@@ -300,21 +335,21 @@ class NghSampler2 (nn.Module):
b1 = torch.arange(B, device=dev)
if step > 0:
# regular grid
- x1 = torch.arange(self.border, W-self.border, step, device=dev)
- y1 = torch.arange(self.border, H-self.border, step, device=dev)
+ x1 = torch.arange(self.border, W - self.border, step, device=dev)
+ y1 = torch.arange(self.border, H - self.border, step, device=dev)
H1, W1 = len(y1), len(x1)
- x1 = x1[None,None,:].expand(B,H1,W1).reshape(-1)
- y1 = y1[None,:,None].expand(B,H1,W1).reshape(-1)
- b1 = b1[:,None,None].expand(B,H1,W1).reshape(-1)
+ x1 = x1[None, None, :].expand(B, H1, W1).reshape(-1)
+ y1 = y1[None, :, None].expand(B, H1, W1).reshape(-1)
+ b1 = b1[:, None, None].expand(B, H1, W1).reshape(-1)
shape = (B, H1, W1)
else:
# randomly spread
- n = (H - 2*self.border) * (W - 2*self.border) // step**2
- x1 = torch.randint(self.border, W-self.border, (n,), device=dev)
- y1 = torch.randint(self.border, H-self.border, (n,), device=dev)
- x1 = x1[None,:].expand(B,n).reshape(-1)
- y1 = y1[None,:].expand(B,n).reshape(-1)
- b1 = b1[:,None].expand(B,n).reshape(-1)
+ n = (H - 2 * self.border) * (W - 2 * self.border) // step**2
+ x1 = torch.randint(self.border, W - self.border, (n,), device=dev)
+ y1 = torch.randint(self.border, H - self.border, (n,), device=dev)
+ x1 = x1[None, :].expand(B, n).reshape(-1)
+ y1 = y1[None, :].expand(B, n).reshape(-1)
+ b1 = b1[:, None].expand(B, n).reshape(-1)
shape = (B, n)
return b1, y1, x1, shape
@@ -323,41 +358,41 @@ class NghSampler2 (nn.Module):
assert two == 2
feat1, conf1 = feats[0], (confs[0] if confs else None)
feat2, conf2 = feats[1], (confs[1] if confs else None)
-
+
# positions in the first image
b1, y1, x1, shape = self.gen_grid(self.sub_q, aflow)
# sample features from first image
feat1 = feat1[b1, :, y1, x1]
qconf = conf1[b1, :, y1, x1].view(shape) if confs else None
-
- #sample GT from second image
+
+ # sample GT from second image
b2 = b1
xy2 = (aflow[b1, :, y1, x1] + 0.5).long().t()
mask = (0 <= xy2[0]) * (0 <= xy2[1]) * (xy2[0] < W) * (xy2[1] < H)
mask = mask.view(shape)
-
+
def clamp(xy):
- torch.clamp(xy[0], 0, W-1, out=xy[0])
- torch.clamp(xy[1], 0, H-1, out=xy[1])
+ torch.clamp(xy[0], 0, W - 1, out=xy[0])
+ torch.clamp(xy[1], 0, H - 1, out=xy[1])
return xy
-
+
# compute positive scores
- xy2p = clamp(xy2[:,None,:] + self.pos_offsets[:,:,None])
- pscores = (feat1[None,:,:] * feat2[b2, :, xy2p[1], xy2p[0]]).sum(dim=-1).t()
-# xy1p = clamp(torch.stack((x1,y1))[:,None,:] + self.pos_offsets[:,:,None])
-# grid = FullSampler._aflow_to_grid(aflow)
-# feat2p = F.grid_sample(feat2, grid, mode='bilinear', padding_mode='border')
-# pscores = (feat1[None,:,:] * feat2p[b1,:,xy1p[1], xy1p[0]]).sum(dim=-1).t()
+ xy2p = clamp(xy2[:, None, :] + self.pos_offsets[:, :, None])
+ pscores = (feat1[None, :, :] * feat2[b2, :, xy2p[1], xy2p[0]]).sum(dim=-1).t()
+ # xy1p = clamp(torch.stack((x1,y1))[:,None,:] + self.pos_offsets[:,:,None])
+ # grid = FullSampler._aflow_to_grid(aflow)
+ # feat2p = F.grid_sample(feat2, grid, mode='bilinear', padding_mode='border')
+ # pscores = (feat1[None,:,:] * feat2p[b1,:,xy1p[1], xy1p[0]]).sum(dim=-1).t()
if self.maxpool_pos:
pscores, pos = pscores.max(dim=1, keepdim=True)
- if confs:
- sel = clamp(xy2 + self.pos_offsets[:,pos.view(-1)])
- qconf = (qconf + conf2[b2, :, sel[1], sel[0]].view(shape))/2
-
+ if confs:
+ sel = clamp(xy2 + self.pos_offsets[:, pos.view(-1)])
+ qconf = (qconf + conf2[b2, :, sel[1], sel[0]].view(shape)) / 2
+
# compute negative scores
- xy2n = clamp(xy2[:,None,:] + self.neg_offsets[:,:,None])
- nscores = (feat1[None,:,:] * feat2[b2, :, xy2n[1], xy2n[0]]).sum(dim=-1).t()
+ xy2n = clamp(xy2[:, None, :] + self.neg_offsets[:, :, None])
+ nscores = (feat1[None, :, :] * feat2[b2, :, xy2n[1], xy2n[0]]).sum(dim=-1).t()
if self.sub_d_neg:
# add distractors from a grid
@@ -365,26 +400,18 @@ class NghSampler2 (nn.Module):
distractors = feat2[b3, :, y3, x3]
dscores = torch.matmul(feat1, distractors.t())
del distractors
-
+
# remove scores that corresponds to positives or nulls
- dis2 = (x3 - xy2[0][:,None])**2 + (y3 - xy2[1][:,None])**2
- dis2 += (b3 != b2[:,None]).long() * self.neg_d**2
+ dis2 = (x3 - xy2[0][:, None]) ** 2 + (y3 - xy2[1][:, None]) ** 2
+ dis2 += (b3 != b2[:, None]).long() * self.neg_d**2
dscores[dis2 < self.neg_d**2] = 0
-
+
scores = torch.cat((pscores, nscores, dscores), dim=1)
else:
# concat everything
scores = torch.cat((pscores, nscores), dim=1)
gt = scores.new_zeros(scores.shape, dtype=torch.uint8)
- gt[:, :pscores.shape[1]] = 1
+ gt[:, : pscores.shape[1]] = 1
return scores, gt, mask, qconf
-
-
-
-
-
-
-
-
diff --git a/third_party/r2d2/tools/__init__.py b/third_party/r2d2/tools/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/imcui/third_party/r2d2/tools/common.py b/third_party/r2d2/tools/common.py
similarity index 52%
rename from imcui/third_party/r2d2/tools/common.py
rename to third_party/r2d2/tools/common.py
index a7875ddd714b1d08efb0d1369c3a856490796288..be5137c60e3fb71cbbf180d0058de20a508ff140 100644
--- a/imcui/third_party/r2d2/tools/common.py
+++ b/third_party/r2d2/tools/common.py
@@ -2,7 +2,7 @@
# CC BY-NC-SA 3.0
# Available only for non-commercial use
-import os, pdb#, shutil
+import os, pdb # , shutil
import numpy as np
import torch
@@ -12,8 +12,7 @@ def mkdir_for(file_path):
def model_size(model):
- ''' Computes the number of parameters of the model
- '''
+ """Computes the number of parameters of the model"""
size = 0
for weights in model.state_dict().values():
size += np.prod(weights.shape)
@@ -24,18 +23,19 @@ def torch_set_gpu(gpus):
if type(gpus) is int:
gpus = [gpus]
- cuda = all(gpu>=0 for gpu in gpus)
+ cuda = all(gpu >= 0 for gpu in gpus)
if cuda:
- os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu) for gpu in gpus])
+ os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(gpu) for gpu in gpus])
assert cuda and torch.cuda.is_available(), "%s has GPUs %s unavailable" % (
- os.environ['HOSTNAME'],os.environ['CUDA_VISIBLE_DEVICES'])
- torch.backends.cudnn.benchmark = True # speed-up cudnn
- torch.backends.cudnn.fastest = True # even more speed-up?
- print( 'Launching on GPUs ' + os.environ['CUDA_VISIBLE_DEVICES'] )
+ os.environ["HOSTNAME"],
+ os.environ["CUDA_VISIBLE_DEVICES"],
+ )
+ torch.backends.cudnn.benchmark = True # speed-up cudnn
+ torch.backends.cudnn.fastest = True # even more speed-up?
+ print("Launching on GPUs " + os.environ["CUDA_VISIBLE_DEVICES"])
else:
- print( 'Launching on CPU' )
+ print("Launching on CPU")
return cuda
-
diff --git a/imcui/third_party/r2d2/tools/dataloader.py b/third_party/r2d2/tools/dataloader.py
similarity index 56%
rename from imcui/third_party/r2d2/tools/dataloader.py
rename to third_party/r2d2/tools/dataloader.py
index f6d9fff5f8dfb8d9d3b243a57555779de33d0818..fe8863e79f5f5cc5a0139190b60aef3a3c1807fd 100644
--- a/imcui/third_party/r2d2/tools/dataloader.py
+++ b/third_party/r2d2/tools/dataloader.py
@@ -9,104 +9,118 @@ import numpy as np
import torch
import torchvision.transforms as tvf
-from tools.transforms import instanciate_transformation
-from tools.transforms_tools import persp_apply
+from .transforms import instanciate_transformation
+from .transforms_tools import persp_apply
RGB_mean = [0.485, 0.456, 0.406]
-RGB_std = [0.229, 0.224, 0.225]
+RGB_std = [0.229, 0.224, 0.225]
norm_RGB = tvf.Compose([tvf.ToTensor(), tvf.Normalize(mean=RGB_mean, std=RGB_std)])
class PairLoader:
- """ On-the-fly jittering of pairs of image with dense pixel ground-truth correspondences.
-
+ """On-the-fly jittering of pairs of image with dense pixel ground-truth correspondences.
+
crop: random crop applied to both images
scale: random scaling applied to img2
distort: random ditorsion applied to img2
-
+
self[idx] returns a dictionary with keys: img1, img2, aflow, mask
- img1: cropped original
- img2: distorted cropped original
- aflow: 'absolute' optical flow = (x,y) position of each pixel from img1 in img2
- mask: (binary image) valid pixels of img1
"""
- def __init__(self, dataset, crop='', scale='', distort='', norm = norm_RGB,
- what = 'aflow mask', idx_as_rng_seed = False):
- assert hasattr(dataset, 'npairs')
- assert hasattr(dataset, 'get_pair')
+
+ def __init__(
+ self,
+ dataset,
+ crop="",
+ scale="",
+ distort="",
+ norm=norm_RGB,
+ what="aflow mask",
+ idx_as_rng_seed=False,
+ ):
+ assert hasattr(dataset, "npairs")
+ assert hasattr(dataset, "get_pair")
self.dataset = dataset
self.distort = instanciate_transformation(distort)
self.crop = instanciate_transformation(crop)
self.norm = instanciate_transformation(norm)
self.scale = instanciate_transformation(scale)
- self.idx_as_rng_seed = idx_as_rng_seed # to remove randomness
+ self.idx_as_rng_seed = idx_as_rng_seed # to remove randomness
self.what = what.split() if isinstance(what, str) else what
- self.n_samples = 5 # number of random trials per image
+ self.n_samples = 5 # number of random trials per image
def __len__(self):
- assert len(self.dataset) == self.dataset.npairs, pdb.set_trace() # and not nimg
+ assert len(self.dataset) == self.dataset.npairs, pdb.set_trace() # and not nimg
return len(self.dataset)
def __repr__(self):
- fmt_str = 'PairLoader\n'
+ fmt_str = "PairLoader\n"
fmt_str += repr(self.dataset)
- fmt_str += ' npairs: %d\n' % self.dataset.npairs
- short_repr = lambda s: repr(s).strip().replace('\n',', ')[14:-1].replace(' ',' ')
- fmt_str += ' Distort: %s\n' % short_repr(self.distort)
- fmt_str += ' Crop: %s\n' % short_repr(self.crop)
- fmt_str += ' Norm: %s\n' % short_repr(self.norm)
+ fmt_str += " npairs: %d\n" % self.dataset.npairs
+ short_repr = (
+ lambda s: repr(s).strip().replace("\n", ", ")[14:-1].replace(" ", " ")
+ )
+ fmt_str += " Distort: %s\n" % short_repr(self.distort)
+ fmt_str += " Crop: %s\n" % short_repr(self.crop)
+ fmt_str += " Norm: %s\n" % short_repr(self.norm)
return fmt_str
def __getitem__(self, i):
- #from time import time as now; t0 = now()
+ # from time import time as now; t0 = now()
if self.idx_as_rng_seed:
import random
+
random.seed(i)
np.random.seed(i)
# Retrieve an image pair and their absolute flow
img_a, img_b, metadata = self.dataset.get_pair(i, self.what)
-
- # aflow contains pixel coordinates indicating where each
+
+ # aflow contains pixel coordinates indicating where each
# pixel from the left image ended up in the right image
# as (x,y) pairs, but its shape is (H,W,2)
- aflow = np.float32(metadata['aflow'])
- mask = metadata.get('mask', np.ones(aflow.shape[:2],np.uint8))
+ aflow = np.float32(metadata["aflow"])
+ mask = metadata.get("mask", np.ones(aflow.shape[:2], np.uint8))
# apply transformations to the second image
- img_b = {'img': img_b, 'persp':(1,0,0,0,1,0,0,0)}
+ img_b = {"img": img_b, "persp": (1, 0, 0, 0, 1, 0, 0, 0)}
if self.scale:
img_b = self.scale(img_b)
if self.distort:
img_b = self.distort(img_b)
-
+
# apply the same transformation to the flow
- aflow[:] = persp_apply(img_b['persp'], aflow.reshape(-1,2)).reshape(aflow.shape)
+ aflow[:] = persp_apply(img_b["persp"], aflow.reshape(-1, 2)).reshape(
+ aflow.shape
+ )
corres = None
- if 'corres' in metadata:
- corres = np.float32(metadata['corres'])
- corres[:,1] = persp_apply(img_b['persp'], corres[:,1])
-
+ if "corres" in metadata:
+ corres = np.float32(metadata["corres"])
+ corres[:, 1] = persp_apply(img_b["persp"], corres[:, 1])
+
# apply the same transformation to the homography
homography = None
- if 'homography' in metadata:
- homography = np.float32(metadata['homography'])
+ if "homography" in metadata:
+ homography = np.float32(metadata["homography"])
# p_b = homography * p_a
- persp = np.float32(img_b['persp']+(1,)).reshape(3,3)
+ persp = np.float32(img_b["persp"] + (1,)).reshape(3, 3)
homography = persp @ homography
# determine crop size
- img_b = img_b['img']
- crop_size = self.crop({'imsize':(10000,10000)})['imsize']
+ img_b = img_b["img"]
+ crop_size = self.crop({"imsize": (10000, 10000)})["imsize"]
output_size_a = min(img_a.size, crop_size)
output_size_b = min(img_b.size, crop_size)
img_a = np.array(img_a)
img_b = np.array(img_b)
- ah,aw,p1 = img_a.shape
- bh,bw,p2 = img_b.shape
+ ah, aw, p1 = img_a.shape
+ bh, bw, p2 = img_b.shape
assert p1 == 3
assert p2 == 3
assert aflow.shape == (ah, aw, 2)
@@ -114,68 +128,82 @@ class PairLoader:
# Let's start by computing the scale of the
# optical flow and applying a median filter:
- dx = np.gradient(aflow[:,:,0])
- dy = np.gradient(aflow[:,:,1])
- scale = np.sqrt(np.clip(np.abs(dx[1]*dy[0] - dx[0]*dy[1]), 1e-16, 1e16))
+ dx = np.gradient(aflow[:, :, 0])
+ dy = np.gradient(aflow[:, :, 1])
+ scale = np.sqrt(np.clip(np.abs(dx[1] * dy[0] - dx[0] * dy[1]), 1e-16, 1e16))
- accu2 = np.zeros((16,16), bool)
+ accu2 = np.zeros((16, 16), bool)
Q = lambda x, w: np.int32(16 * (x - w.start) / (w.stop - w.start))
-
+
def window1(x, size, w):
l = x - int(0.5 + size / 2)
r = l + int(0.5 + size)
- if l < 0: l,r = (0, r - l)
- if r > w: l,r = (l + w - r, w)
- if l < 0: l,r = 0,w # larger than width
- return slice(l,r)
+ if l < 0:
+ l, r = (0, r - l)
+ if r > w:
+ l, r = (l + w - r, w)
+ if l < 0:
+ l, r = 0, w # larger than width
+ return slice(l, r)
+
def window(cx, cy, win_size, scale, img_shape):
- return (window1(cy, win_size[1]*scale, img_shape[0]),
- window1(cx, win_size[0]*scale, img_shape[1]))
+ return (
+ window1(cy, win_size[1] * scale, img_shape[0]),
+ window1(cx, win_size[0] * scale, img_shape[1]),
+ )
n_valid_pixel = mask.sum()
sample_w = mask / (1e-16 + n_valid_pixel)
+
def sample_valid_pixel():
n = np.random.choice(sample_w.size, p=sample_w.ravel())
y, x = np.unravel_index(n, sample_w.shape)
return x, y
-
+
# Find suitable left and right windows
- trials = 0 # take the best out of few trials
+ trials = 0 # take the best out of few trials
best = -np.inf, None
- for _ in range(50*self.n_samples):
- if trials >= self.n_samples: break # finished!
+ for _ in range(50 * self.n_samples):
+ if trials >= self.n_samples:
+ break # finished!
# pick a random valid point from the first image
- if n_valid_pixel == 0: break
+ if n_valid_pixel == 0:
+ break
c1x, c1y = sample_valid_pixel()
-
+
# Find in which position the center of the left
# window ended up being placed in the right image
c2x, c2y = (aflow[c1y, c1x] + 0.5).astype(np.int32)
- if not(0 <= c2x < bw and 0 <= c2y < bh): continue
+ if not (0 <= c2x < bw and 0 <= c2y < bh):
+ continue
# Get the flow scale
sigma = scale[c1y, c1x]
# Determine sampling windows
- if 0.2 < sigma < 1:
- win1 = window(c1x, c1y, output_size_a, 1/sigma, img_a.shape)
+ if 0.2 < sigma < 1:
+ win1 = window(c1x, c1y, output_size_a, 1 / sigma, img_a.shape)
win2 = window(c2x, c2y, output_size_b, 1, img_b.shape)
elif 1 <= sigma < 5:
win1 = window(c1x, c1y, output_size_a, 1, img_a.shape)
win2 = window(c2x, c2y, output_size_b, sigma, img_b.shape)
else:
- continue # bad scale
+ continue # bad scale
# compute a score based on the flow
- x2,y2 = aflow[win1].reshape(-1, 2).T.astype(np.int32)
+ x2, y2 = aflow[win1].reshape(-1, 2).T.astype(np.int32)
# Check the proportion of valid flow vectors
- valid = (win2[1].start <= x2) & (x2 < win2[1].stop) \
- & (win2[0].start <= y2) & (y2 < win2[0].stop)
+ valid = (
+ (win2[1].start <= x2)
+ & (x2 < win2[1].stop)
+ & (win2[0].start <= y2)
+ & (y2 < win2[0].stop)
+ )
score1 = (valid * mask[win1].ravel()).mean()
# check the coverage of the second window
accu2[:] = False
- accu2[Q(y2[valid],win2[0]), Q(x2[valid],win2[1])] = True
+ accu2[Q(y2[valid], win2[0]), Q(x2[valid], win2[1])] = True
score2 = accu2.mean()
# Check how many hits we got
score = min(score1, score2)
@@ -183,12 +211,12 @@ class PairLoader:
trials += 1
if score > best[0]:
best = score, win1, win2
-
- if None in best: # counldn't find a good window
- img_a = np.zeros(output_size_a[::-1]+(3,), dtype=np.uint8)
- img_b = np.zeros(output_size_b[::-1]+(3,), dtype=np.uint8)
- aflow = np.nan * np.ones((2,)+output_size_a[::-1], dtype=np.float32)
- homography = np.nan * np.ones((3,3), dtype=np.float32)
+
+ if None in best: # counldn't find a good window
+ img_a = np.zeros(output_size_a[::-1] + (3,), dtype=np.uint8)
+ img_b = np.zeros(output_size_b[::-1] + (3,), dtype=np.uint8)
+ aflow = np.nan * np.ones((2,) + output_size_a[::-1], dtype=np.float32)
+ homography = np.nan * np.ones((3, 3), dtype=np.float32)
else:
win1, win2 = best[1:]
@@ -196,92 +224,103 @@ class PairLoader:
img_b = img_b[win2]
aflow = aflow[win1] - np.float32([[[win2[1].start, win2[0].start]]])
mask = mask[win1]
- aflow[~mask.view(bool)] = np.nan # mask bad pixels!
- aflow = aflow.transpose(2,0,1) # --> (2,H,W)
-
+ aflow[~mask.view(bool)] = np.nan # mask bad pixels!
+ aflow = aflow.transpose(2, 0, 1) # --> (2,H,W)
+
if corres is not None:
- corres[:,0] -= (win1[1].start, win1[0].start)
- corres[:,1] -= (win2[1].start, win2[0].start)
-
+ corres[:, 0] -= (win1[1].start, win1[0].start)
+ corres[:, 1] -= (win2[1].start, win2[0].start)
+
if homography is not None:
trans1 = np.eye(3, dtype=np.float32)
- trans1[:2,2] = (win1[1].start, win1[0].start)
+ trans1[:2, 2] = (win1[1].start, win1[0].start)
trans2 = np.eye(3, dtype=np.float32)
- trans2[:2,2] = (-win2[1].start, -win2[0].start)
+ trans2[:2, 2] = (-win2[1].start, -win2[0].start)
homography = trans2 @ homography @ trans1
- homography /= homography[2,2]
-
+ homography /= homography[2, 2]
+
# rescale if necessary
if img_a.shape[:2][::-1] != output_size_a:
- sx, sy = (np.float32(output_size_a)-1)/(np.float32(img_a.shape[:2][::-1])-1)
- img_a = np.asarray(Image.fromarray(img_a).resize(output_size_a, Image.ANTIALIAS))
- mask = np.asarray(Image.fromarray(mask).resize(output_size_a, Image.NEAREST))
+ sx, sy = (np.float32(output_size_a) - 1) / (
+ np.float32(img_a.shape[:2][::-1]) - 1
+ )
+ img_a = np.asarray(
+ Image.fromarray(img_a).resize(output_size_a, Image.ANTIALIAS)
+ )
+ mask = np.asarray(
+ Image.fromarray(mask).resize(output_size_a, Image.NEAREST)
+ )
afx = Image.fromarray(aflow[0]).resize(output_size_a, Image.NEAREST)
afy = Image.fromarray(aflow[1]).resize(output_size_a, Image.NEAREST)
aflow = np.stack((np.float32(afx), np.float32(afy)))
-
+
if corres is not None:
- corres[:,0] *= (sx, sy)
-
+ corres[:, 0] *= (sx, sy)
+
if homography is not None:
- homography = homography @ np.diag(np.float32([1/sx,1/sy,1]))
- homography /= homography[2,2]
+ homography = homography @ np.diag(np.float32([1 / sx, 1 / sy, 1]))
+ homography /= homography[2, 2]
if img_b.shape[:2][::-1] != output_size_b:
- sx, sy = (np.float32(output_size_b)-1)/(np.float32(img_b.shape[:2][::-1])-1)
- img_b = np.asarray(Image.fromarray(img_b).resize(output_size_b, Image.ANTIALIAS))
+ sx, sy = (np.float32(output_size_b) - 1) / (
+ np.float32(img_b.shape[:2][::-1]) - 1
+ )
+ img_b = np.asarray(
+ Image.fromarray(img_b).resize(output_size_b, Image.ANTIALIAS)
+ )
aflow *= [[[sx]], [[sy]]]
-
+
if corres is not None:
- corres[:,1] *= (sx, sy)
-
+ corres[:, 1] *= (sx, sy)
+
if homography is not None:
- homography = np.diag(np.float32([sx,sy,1])) @ homography
- homography /= homography[2,2]
-
+ homography = np.diag(np.float32([sx, sy, 1])) @ homography
+ homography /= homography[2, 2]
+
assert aflow.dtype == np.float32, pdb.set_trace()
assert homography is None or homography.dtype == np.float32, pdb.set_trace()
- if 'flow' in self.what:
+ if "flow" in self.what:
H, W = img_a.shape[:2]
mgrid = np.mgrid[0:H, 0:W][::-1].astype(np.float32)
flow = aflow - mgrid
-
+
result = dict(img1=self.norm(img_a), img2=self.norm(img_b))
for what in self.what:
- try: result[what] = eval(what)
- except NameError: pass
+ try:
+ result[what] = eval(what)
+ except NameError:
+ pass
return result
+def threaded_loader(loader, iscuda, threads, batch_size=1, shuffle=True):
+ """Get a data loader, given the dataset and some parameters.
-def threaded_loader( loader, iscuda, threads, batch_size=1, shuffle=True):
- """ Get a data loader, given the dataset and some parameters.
-
Parameters
----------
loader : object[i] returns the i-th training example.
-
+
iscuda : bool
-
+
batch_size : int
-
+
threads : int
-
+
shuffle : int
-
+
Returns
-------
a multi-threaded pytorch loader.
"""
return torch.utils.data.DataLoader(
loader,
- batch_size = batch_size,
- shuffle = shuffle,
- sampler = None,
- num_workers = threads,
- pin_memory = iscuda,
- collate_fn=collate)
-
+ batch_size=batch_size,
+ shuffle=shuffle,
+ sampler=None,
+ num_workers=threads,
+ pin_memory=iscuda,
+ collate_fn=collate,
+ )
def collate(batch, _use_shared_memory=True):
@@ -289,6 +328,7 @@ def collate(batch, _use_shared_memory=True):
Copied from https://github.com/pytorch in torch/utils/data/_utils/collate.py
"""
import re
+
error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
elem_type = type(batch[0])
if isinstance(batch[0], torch.Tensor):
@@ -300,12 +340,15 @@ def collate(batch, _use_shared_memory=True):
storage = batch[0].storage()._new_shared(numel)
out = batch[0].new(storage)
return torch.stack(batch, 0, out=out)
- elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
- and elem_type.__name__ != 'string_':
+ elif (
+ elem_type.__module__ == "numpy"
+ and elem_type.__name__ != "str_"
+ and elem_type.__name__ != "string_"
+ ):
elem = batch[0]
- assert elem_type.__name__ == 'ndarray'
+ assert elem_type.__name__ == "ndarray"
# array of string classes and object
- if re.search('[SaUO]', elem.dtype.str) is not None:
+ if re.search("[SaUO]", elem.dtype.str) is not None:
raise TypeError(error_msg.format(elem.dtype))
batch = [torch.from_numpy(b) for b in batch]
try:
@@ -322,46 +365,52 @@ def collate(batch, _use_shared_memory=True):
return batch
elif isinstance(batch[0], dict):
return {key: collate([d[key] for d in batch]) for key in batch[0]}
- elif isinstance(batch[0], (tuple,list)):
+ elif isinstance(batch[0], (tuple, list)):
transposed = zip(*batch)
return [collate(samples) for samples in transposed]
raise TypeError((error_msg.format(type(batch[0]))))
-
def tensor2img(tensor, model=None):
- """ convert back a torch/numpy tensor to a PIL Image
- by undoing the ToTensor() and Normalize() transforms.
+ """convert back a torch/numpy tensor to a PIL Image
+ by undoing the ToTensor() and Normalize() transforms.
"""
mean = norm_RGB.transforms[1].mean
- std = norm_RGB.transforms[1].std
+ std = norm_RGB.transforms[1].std
if isinstance(tensor, torch.Tensor):
tensor = tensor.detach().cpu().numpy()
-
- res = np.uint8(np.clip(255*((tensor.transpose(1,2,0) * std) + mean), 0, 255))
+
+ res = np.uint8(np.clip(255 * ((tensor.transpose(1, 2, 0) * std) + mean), 0, 255))
from PIL import Image
+
return Image.fromarray(res)
-if __name__ == '__main__':
+if __name__ == "__main__":
import argparse
+
parser = argparse.ArgumentParser("Tool to debug/visualize the data loader")
- parser.add_argument("dataloader", type=str, help="command to create the data loader")
+ parser.add_argument(
+ "dataloader", type=str, help="command to create the data loader"
+ )
args = parser.parse_args()
from datasets import *
- auto_pairs = lambda db: SyntheticPairDataset(db,
- 'RandomScale(256,1024,can_upscale=True)',
- 'RandomTilting(0.5), PixelNoise(25)')
-
+
+ auto_pairs = lambda db: SyntheticPairDataset(
+ db,
+ "RandomScale(256,1024,can_upscale=True)",
+ "RandomTilting(0.5), PixelNoise(25)",
+ )
+
loader = eval(args.dataloader)
print("Data loader =", loader)
from tools.viz import show_flow
+
for data in loader:
- aflow = data['aflow']
+ aflow = data["aflow"]
H, W = aflow.shape[-2:]
- flow = (aflow - np.mgrid[:H, :W][::-1]).transpose(1,2,0)
- show_flow(tensor2img(data['img1']), tensor2img(data['img2']), flow)
-
+ flow = (aflow - np.mgrid[:H, :W][::-1]).transpose(1, 2, 0)
+ show_flow(tensor2img(data["img1"]), tensor2img(data["img2"]), flow)
diff --git a/imcui/third_party/r2d2/tools/trainer.py b/third_party/r2d2/tools/trainer.py
similarity index 66%
rename from imcui/third_party/r2d2/tools/trainer.py
rename to third_party/r2d2/tools/trainer.py
index 9f893395efdeb8e13cc00539325572553168c5ce..d71ef137f556b7709ebed37a6ea4c865e5ab6c37 100644
--- a/imcui/third_party/r2d2/tools/trainer.py
+++ b/third_party/r2d2/tools/trainer.py
@@ -10,15 +10,16 @@ import torch
import torch.nn as nn
-class Trainer (nn.Module):
- """ Helper class to train a deep network.
+class Trainer(nn.Module):
+ """Helper class to train a deep network.
Overload this class `forward_backward` for your actual needs.
-
- Usage:
+
+ Usage:
train = Trainer(net, loader, loss, optimizer)
for epoch in range(n_epochs):
train()
"""
+
def __init__(self, net, loader, loss, optimizer):
nn.Module.__init__(self)
self.net = net
@@ -27,50 +28,48 @@ class Trainer (nn.Module):
self.optimizer = optimizer
def iscuda(self):
- return next(self.net.parameters()).device != torch.device('cpu')
+ return next(self.net.parameters()).device != torch.device("cpu")
def todevice(self, x):
if isinstance(x, dict):
- return {k:self.todevice(v) for k,v in x.items()}
- if isinstance(x, (tuple,list)):
- return [self.todevice(v) for v in x]
-
- if self.iscuda():
+ return {k: self.todevice(v) for k, v in x.items()}
+ if isinstance(x, (tuple, list)):
+ return [self.todevice(v) for v in x]
+
+ if self.iscuda():
return x.contiguous().cuda(non_blocking=True)
else:
return x.cpu()
def __call__(self):
self.net.train()
-
+
stats = defaultdict(list)
-
- for iter,inputs in enumerate(tqdm(self.loader)):
+
+ for iter, inputs in enumerate(tqdm(self.loader)):
inputs = self.todevice(inputs)
-
+
# compute gradient and do model update
self.optimizer.zero_grad()
-
+
loss, details = self.forward_backward(inputs)
if torch.isnan(loss):
- raise RuntimeError('Loss is NaN')
-
+ raise RuntimeError("Loss is NaN")
+
self.optimizer.step()
-
+
for key, val in details.items():
- stats[key].append( val )
-
+ stats[key].append(val)
+
print(" Summary of losses during this epoch:")
mean = lambda lis: sum(lis) / len(lis)
for loss_name, vals in stats.items():
- N = 1 + len(vals)//10
- print(f" - {loss_name:20}:", end='')
- print(f" {mean(vals[:N]):.3f} --> {mean(vals[-N:]):.3f} (avg: {mean(vals):.3f})")
- return mean(stats['loss']) # return average loss
+ N = 1 + len(vals) // 10
+ print(f" - {loss_name:20}:", end="")
+ print(
+ f" {mean(vals[:N]):.3f} --> {mean(vals[-N:]):.3f} (avg: {mean(vals):.3f})"
+ )
+ return mean(stats["loss"]) # return average loss
def forward_backward(self, inputs):
raise NotImplementedError()
-
-
-
-
diff --git a/imcui/third_party/r2d2/tools/transforms.py b/third_party/r2d2/tools/transforms.py
similarity index 63%
rename from imcui/third_party/r2d2/tools/transforms.py
rename to third_party/r2d2/tools/transforms.py
index 87275276310191a7da3fc14f606345d9616208e0..604a7c2a3ec6da955c1e85b7505103c694232458 100644
--- a/imcui/third_party/r2d2/tools/transforms.py
+++ b/third_party/r2d2/tools/transforms.py
@@ -11,23 +11,23 @@ from math import ceil
from . import transforms_tools as F
-'''
+"""
Example command to try out some transformation chain:
python -m tools.transforms --trfs "Scale(384), ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1), RandomRotation(10), RandomTilting(0.5, 'all'), RandomScale(240,320), RandomCrop(224)"
-'''
+"""
def instanciate_transformation(cmd_line):
- ''' Create a sequence of transformations.
-
+ """Create a sequence of transformations.
+
cmd_line: (str)
Comma-separated list of transformations.
Ex: "Rotate(10), Scale(256)"
- '''
+ """
if not isinstance(cmd_line, str):
- return cmd_line # already instanciated
-
+ return cmd_line # already instanciated
+
cmd_line = "tvf.Compose([%s])" % cmd_line
try:
return eval(cmd_line)
@@ -35,19 +35,26 @@ def instanciate_transformation(cmd_line):
print("Cannot interpret this transform list: %s\nReason: %s" % (cmd_line, e))
-class Scale (object):
- """ Rescale the input PIL.Image to a given size.
+class Scale(object):
+ """Rescale the input PIL.Image to a given size.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
-
+
The smallest dimension of the resulting image will be = size.
-
+
if largest == True: same behaviour for the largest dimension.
-
+
if not can_upscale: don't upscale
if not can_downscale: don't downscale
"""
- def __init__(self, size, interpolation=Image.BILINEAR, largest=False,
- can_upscale=True, can_downscale=True):
+
+ def __init__(
+ self,
+ size,
+ interpolation=Image.BILINEAR,
+ largest=False,
+ can_upscale=True,
+ can_downscale=True,
+ ):
assert isinstance(size, int) or (len(size) == 2)
self.size = size
self.interpolation = interpolation
@@ -57,15 +64,18 @@ class Scale (object):
def __repr__(self):
fmt_str = "RandomScale(%s" % str(self.size)
- if self.largest: fmt_str += ', largest=True'
- if not self.can_upscale: fmt_str += ', can_upscale=False'
- if not self.can_downscale: fmt_str += ', can_downscale=False'
- return fmt_str+')'
+ if self.largest:
+ fmt_str += ", largest=True"
+ if not self.can_upscale:
+ fmt_str += ", can_upscale=False"
+ if not self.can_downscale:
+ fmt_str += ", can_downscale=False"
+ return fmt_str + ")"
def get_params(self, imsize):
- w,h = imsize
+ w, h = imsize
if isinstance(self.size, int):
- cmp = lambda a,b: (a>=b) if self.largest else (a<=b)
+ cmp = lambda a, b: (a >= b) if self.largest else (a <= b)
if (cmp(w, h) and w == self.size) or (cmp(h, w) and h == self.size):
ow, oh = w, h
elif cmp(w, h):
@@ -81,19 +91,22 @@ class Scale (object):
def __call__(self, inp):
img = F.grab_img(inp)
w, h = img.size
-
+
size2 = ow, oh = self.get_params(img.size)
-
+
if size2 != img.size:
a1, a2 = img.size, size2
- if (self.can_upscale and min(a1) < min(a2)) or (self.can_downscale and min(a1) > min(a2)):
+ if (self.can_upscale and min(a1) < min(a2)) or (
+ self.can_downscale and min(a1) > min(a2)
+ ):
img = img.resize(size2, self.interpolation)
- return F.update_img_and_labels(inp, img, persp=(ow/w,0,0,0,oh/h,0,0,0))
-
+ return F.update_img_and_labels(
+ inp, img, persp=(ow / w, 0, 0, 0, oh / h, 0, 0, 0)
+ )
-class RandomScale (Scale):
+class RandomScale(Scale):
"""Rescale the input PIL.Image to a random size.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
@@ -108,53 +121,79 @@ class RandomScale (Scale):
``PIL.Image.BILINEAR``
"""
- def __init__(self, min_size, max_size, ar=1,
- can_upscale=False, can_downscale=True, interpolation=Image.BILINEAR):
- Scale.__init__(self, 0, can_upscale=can_upscale, can_downscale=can_downscale, interpolation=interpolation)
- assert type(min_size) == type(max_size), 'min_size and max_size can only be 2 ints or 2 floats'
- assert isinstance(min_size, int) and min_size >= 1 or isinstance(min_size, float) and min_size>0
- assert isinstance(max_size, (int,float)) and min_size <= max_size
+ def __init__(
+ self,
+ min_size,
+ max_size,
+ ar=1,
+ can_upscale=False,
+ can_downscale=True,
+ interpolation=Image.BILINEAR,
+ ):
+ Scale.__init__(
+ self,
+ 0,
+ can_upscale=can_upscale,
+ can_downscale=can_downscale,
+ interpolation=interpolation,
+ )
+ assert type(min_size) == type(
+ max_size
+ ), "min_size and max_size can only be 2 ints or 2 floats"
+ assert (
+ isinstance(min_size, int)
+ and min_size >= 1
+ or isinstance(min_size, float)
+ and min_size > 0
+ )
+ assert isinstance(max_size, (int, float)) and min_size <= max_size
self.min_size = min_size
self.max_size = max_size
- if type(ar) in (float,int): ar = (min(1/ar,ar),max(1/ar,ar))
+ if type(ar) in (float, int):
+ ar = (min(1 / ar, ar), max(1 / ar, ar))
assert 0.2 < ar[0] <= ar[1] < 5
self.ar = ar
def get_params(self, imsize):
- w,h = imsize
+ w, h = imsize
if isinstance(self.min_size, float):
- min_size = int(self.min_size*min(w,h) + 0.5)
+ min_size = int(self.min_size * min(w, h) + 0.5)
if isinstance(self.max_size, float):
- max_size = int(self.max_size*min(w,h) + 0.5)
+ max_size = int(self.max_size * min(w, h) + 0.5)
if isinstance(self.min_size, int):
min_size = self.min_size
if isinstance(self.max_size, int):
max_size = self.max_size
-
+
if not self.can_upscale:
- max_size = min(max_size,min(w,h))
-
- size = int(0.5 + F.rand_log_uniform(min_size,max_size))
- ar = F.rand_log_uniform(*self.ar) # change of aspect ratio
+ max_size = min(max_size, min(w, h))
+
+ size = int(0.5 + F.rand_log_uniform(min_size, max_size))
+ ar = F.rand_log_uniform(*self.ar) # change of aspect ratio
- if w < h: # image is taller
+ if w < h: # image is taller
ow = size
oh = int(0.5 + size * h / w / ar)
if oh < min_size:
- ow,oh = int(0.5 + ow*float(min_size)/oh),min_size
- else: # image is wider
+ ow, oh = int(0.5 + ow * float(min_size) / oh), min_size
+ else: # image is wider
oh = size
ow = int(0.5 + size * w / h * ar)
if ow < min_size:
- ow,oh = min_size,int(0.5 + oh*float(min_size)/ow)
-
- assert ow >= min_size, 'image too small (width=%d < min_size=%d)' % (ow, min_size)
- assert oh >= min_size, 'image too small (height=%d < min_size=%d)' % (oh, min_size)
+ ow, oh = min_size, int(0.5 + oh * float(min_size) / ow)
+
+ assert ow >= min_size, "image too small (width=%d < min_size=%d)" % (
+ ow,
+ min_size,
+ )
+ assert oh >= min_size, "image too small (height=%d < min_size=%d)" % (
+ oh,
+ min_size,
+ )
return ow, oh
-
-class RandomCrop (object):
+class RandomCrop(object):
"""Crop the given PIL Image at a random location.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
@@ -182,7 +221,12 @@ class RandomCrop (object):
def get_params(img, output_size):
w, h = img.size
th, tw = output_size
- assert h >= th and w >= tw, "Image of %dx%d is too small for crop %dx%d" % (w,h,tw,th)
+ assert h >= th and w >= tw, "Image of %dx%d is too small for crop %dx%d" % (
+ w,
+ h,
+ tw,
+ th,
+ )
y = np.random.randint(0, h - th) if h > th else 0
x = np.random.randint(0, w - tw) if w > tw else 0
@@ -204,12 +248,14 @@ class RandomCrop (object):
padl, padt = self.padding[0:2]
i, j, tw, th = self.get_params(img, self.size)
- img = img.crop((i, j, i+tw, j+th))
-
- return F.update_img_and_labels(inp, img, persp=(1,0,padl-i,0,1,padt-j,0,0))
+ img = img.crop((i, j, i + tw, j + th))
+ return F.update_img_and_labels(
+ inp, img, persp=(1, 0, padl - i, 0, 1, padt - j, 0, 0)
+ )
-class CenterCrop (RandomCrop):
+
+class CenterCrop(RandomCrop):
"""Crops the given PIL Image at the center.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
@@ -218,16 +264,16 @@ class CenterCrop (RandomCrop):
int instead of sequence like (h, w), a square crop (size, size) is
made.
"""
+
@staticmethod
def get_params(img, output_size):
w, h = img.size
th, tw = output_size
- y = int(0.5 +((h - th) / 2.))
- x = int(0.5 +((w - tw) / 2.))
+ y = int(0.5 + ((h - th) / 2.0))
+ x = int(0.5 + ((w - tw) / 2.0))
return x, y, tw, th
-
class RandomRotation(object):
"""Rescale the input PIL.Image to a random size.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
@@ -247,19 +293,18 @@ class RandomRotation(object):
def __call__(self, inp):
img = F.grab_img(inp)
w, h = img.size
-
+
angle = np.random.uniform(-self.degrees, self.degrees)
-
+
img = img.rotate(angle, resample=self.interpolation)
w2, h2 = img.size
- trf = F.translate(-w/2,-h/2)
- trf = F.persp_mul(trf, F.rotate(-angle * np.pi/180))
- trf = F.persp_mul(trf, F.translate(w2/2,h2/2))
+ trf = F.translate(-w / 2, -h / 2)
+ trf = F.persp_mul(trf, F.rotate(-angle * np.pi / 180))
+ trf = F.persp_mul(trf, F.translate(w2 / 2, h2 / 2))
return F.update_img_and_labels(inp, img, persp=trf)
-
class RandomTilting(object):
"""Apply a random tilting (left, right, up, down) to the input PIL.Image
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
@@ -272,34 +317,34 @@ class RandomTilting(object):
examples: "all", "left,right", "up-down-right"
"""
- def __init__(self, magnitude, directions='all'):
+ def __init__(self, magnitude, directions="all"):
self.magnitude = magnitude
- self.directions = directions.lower().replace(',',' ').replace('-',' ')
+ self.directions = directions.lower().replace(",", " ").replace("-", " ")
def __repr__(self):
- return "RandomTilt(%g, '%s')" % (self.magnitude,self.directions)
+ return "RandomTilt(%g, '%s')" % (self.magnitude, self.directions)
def __call__(self, inp):
img = F.grab_img(inp)
w, h = img.size
- x1,y1,x2,y2 = 0,0,h,w
+ x1, y1, x2, y2 = 0, 0, h, w
original_plane = [(y1, x1), (y2, x1), (y2, x2), (y1, x2)]
max_skew_amount = max(w, h)
max_skew_amount = int(ceil(max_skew_amount * self.magnitude))
skew_amount = random.randint(1, max_skew_amount)
- if self.directions == 'all':
- choices = [0,1,2,3]
+ if self.directions == "all":
+ choices = [0, 1, 2, 3]
else:
- dirs = ['left', 'right', 'up', 'down']
+ dirs = ["left", "right", "up", "down"]
choices = []
for d in self.directions.split():
try:
choices.append(dirs.index(d))
except:
- raise ValueError('Tilting direction %s not recognized' % d)
+ raise ValueError("Tilting direction %s not recognized" % d)
skew_direction = random.choice(choices)
@@ -307,28 +352,36 @@ class RandomTilting(object):
if skew_direction == 0:
# Left Tilt
- new_plane = [(y1, x1 - skew_amount), # Top Left
- (y2, x1), # Top Right
- (y2, x2), # Bottom Right
- (y1, x2 + skew_amount)] # Bottom Left
+ new_plane = [
+ (y1, x1 - skew_amount), # Top Left
+ (y2, x1), # Top Right
+ (y2, x2), # Bottom Right
+ (y1, x2 + skew_amount),
+ ] # Bottom Left
elif skew_direction == 1:
# Right Tilt
- new_plane = [(y1, x1), # Top Left
- (y2, x1 - skew_amount), # Top Right
- (y2, x2 + skew_amount), # Bottom Right
- (y1, x2)] # Bottom Left
+ new_plane = [
+ (y1, x1), # Top Left
+ (y2, x1 - skew_amount), # Top Right
+ (y2, x2 + skew_amount), # Bottom Right
+ (y1, x2),
+ ] # Bottom Left
elif skew_direction == 2:
# Forward Tilt
- new_plane = [(y1 - skew_amount, x1), # Top Left
- (y2 + skew_amount, x1), # Top Right
- (y2, x2), # Bottom Right
- (y1, x2)] # Bottom Left
+ new_plane = [
+ (y1 - skew_amount, x1), # Top Left
+ (y2 + skew_amount, x1), # Top Right
+ (y2, x2), # Bottom Right
+ (y1, x2),
+ ] # Bottom Left
elif skew_direction == 3:
# Backward Tilt
- new_plane = [(y1, x1), # Top Left
- (y2, x1), # Top Right
- (y2 + skew_amount, x2), # Bottom Right
- (y1 - skew_amount, x2)] # Bottom Left
+ new_plane = [
+ (y1, x1), # Top Left
+ (y2, x1), # Top Right
+ (y2 + skew_amount, x2), # Bottom Right
+ (y1 - skew_amount, x2),
+ ] # Bottom Left
# To calculate the coefficients required by PIL for the perspective skew,
# see the following Stack Overflow discussion: https://goo.gl/sSgJdj
@@ -343,42 +396,49 @@ class RandomTilting(object):
homography = np.dot(np.linalg.pinv(A), B)
homography = tuple(np.array(homography).reshape(8))
- #print(homography)
+ # print(homography)
- img = img.transform(img.size, Image.PERSPECTIVE, homography, resample=Image.BICUBIC)
+ img = img.transform(
+ img.size, Image.PERSPECTIVE, homography, resample=Image.BICUBIC
+ )
- homography = np.linalg.pinv(np.float32(homography+(1,)).reshape(3,3)).ravel()[:8]
+ homography = np.linalg.pinv(
+ np.float32(homography + (1,)).reshape(3, 3)
+ ).ravel()[:8]
return F.update_img_and_labels(inp, img, persp=tuple(homography))
-RandomTilt = RandomTilting # redefinition
+RandomTilt = RandomTilting # redefinition
class Tilt(object):
- """Apply a known tilting to an image
- """
+ """Apply a known tilting to an image"""
+
def __init__(self, *homography):
assert len(homography) == 8
self.homography = homography
-
+
def __call__(self, inp):
img = F.grab_img(inp)
homography = self.homography
- #print(homography)
-
- img = img.transform(img.size, Image.PERSPECTIVE, homography, resample=Image.BICUBIC)
-
- homography = np.linalg.pinv(np.float32(homography+(1,)).reshape(3,3)).ravel()[:8]
+ # print(homography)
+
+ img = img.transform(
+ img.size, Image.PERSPECTIVE, homography, resample=Image.BICUBIC
+ )
+
+ homography = np.linalg.pinv(
+ np.float32(homography + (1,)).reshape(3, 3)
+ ).ravel()[:8]
return F.update_img_and_labels(inp, img, persp=tuple(homography))
+class StillTransform(object):
+ """Takes and return an image, without changing its shape or geometry."""
-class StillTransform (object):
- """ Takes and return an image, without changing its shape or geometry.
- """
def _transform(self, img):
raise NotImplementedError()
-
+
def __call__(self, inp):
img = F.grab_img(inp)
@@ -388,13 +448,12 @@ class StillTransform (object):
except TypeError:
pass
- return F.update_img_and_labels(inp, img, persp=(1,0,0,0,1,0,0,0))
+ return F.update_img_and_labels(inp, img, persp=(1, 0, 0, 0, 1, 0, 0, 0))
+class PixelNoise(StillTransform):
+ """Takes an image, and add random white noise."""
-class PixelNoise (StillTransform):
- """ Takes an image, and add random white noise.
- """
def __init__(self, ampl=20):
StillTransform.__init__(self)
assert 0 <= ampl < 255
@@ -405,12 +464,13 @@ class PixelNoise (StillTransform):
def _transform(self, img):
img = np.float32(img)
- img += np.random.uniform(0.5-self.ampl/2, 0.5+self.ampl/2, size=img.shape)
- return Image.fromarray(np.uint8(img.clip(0,255)))
-
+ img += np.random.uniform(
+ 0.5 - self.ampl / 2, 0.5 + self.ampl / 2, size=img.shape
+ )
+ return Image.fromarray(np.uint8(img.clip(0, 255)))
-class ColorJitter (StillTransform):
+class ColorJitter(StillTransform):
"""Randomly change the brightness, contrast and saturation of an image.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
@@ -424,6 +484,7 @@ class ColorJitter (StillTransform):
hue(float): How much to jitter hue. hue_factor is chosen uniformly from
[-hue, hue]. Should be >=0 and <= 0.5.
"""
+
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
self.brightness = brightness
self.contrast = contrast
@@ -432,8 +493,12 @@ class ColorJitter (StillTransform):
def __repr__(self):
return "ColorJitter(%g,%g,%g,%g)" % (
- self.brightness, self.contrast, self.saturation, self.hue)
-
+ self.brightness,
+ self.contrast,
+ self.saturation,
+ self.hue,
+ )
+
@staticmethod
def get_params(brightness, contrast, saturation, hue):
"""Get a randomized transform to be applied on image.
@@ -444,16 +509,26 @@ class ColorJitter (StillTransform):
"""
transforms = []
if brightness > 0:
- brightness_factor = np.random.uniform(max(0, 1 - brightness), 1 + brightness)
- transforms.append(tvf.Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))
+ brightness_factor = np.random.uniform(
+ max(0, 1 - brightness), 1 + brightness
+ )
+ transforms.append(
+ tvf.Lambda(lambda img: F.adjust_brightness(img, brightness_factor))
+ )
if contrast > 0:
contrast_factor = np.random.uniform(max(0, 1 - contrast), 1 + contrast)
- transforms.append(tvf.Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))
+ transforms.append(
+ tvf.Lambda(lambda img: F.adjust_contrast(img, contrast_factor))
+ )
if saturation > 0:
- saturation_factor = np.random.uniform(max(0, 1 - saturation), 1 + saturation)
- transforms.append(tvf.Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))
+ saturation_factor = np.random.uniform(
+ max(0, 1 - saturation), 1 + saturation
+ )
+ transforms.append(
+ tvf.Lambda(lambda img: F.adjust_saturation(img, saturation_factor))
+ )
if hue > 0:
hue_factor = np.random.uniform(-hue, hue)
@@ -467,47 +542,52 @@ class ColorJitter (StillTransform):
return transform
def _transform(self, img):
- transform = self.get_params(self.brightness, self.contrast, self.saturation, self.hue)
+ transform = self.get_params(
+ self.brightness, self.contrast, self.saturation, self.hue
+ )
return transform(img)
-
-if __name__ == '__main__':
+if __name__ == "__main__":
import argparse
+
parser = argparse.ArgumentParser("Script to try out and visualize transformations")
- parser.add_argument('--img', type=str, default='imgs/test.png', help='input image')
- parser.add_argument('--trfs', type=str, required=True, help='list of transformations')
- parser.add_argument('--layout', type=int, nargs=2, default=(3,3), help='nb of rows,cols')
+ parser.add_argument("--img", type=str, default="imgs/test.png", help="input image")
+ parser.add_argument(
+ "--trfs", type=str, required=True, help="list of transformations"
+ )
+ parser.add_argument(
+ "--layout", type=int, nargs=2, default=(3, 3), help="nb of rows,cols"
+ )
args = parser.parse_args()
-
+
import os
- args.img = args.img.replace('$HERE',os.path.dirname(__file__))
+
+ args.img = args.img.replace("$HERE", os.path.dirname(__file__))
img = Image.open(args.img)
img = dict(img=img)
-
+
trfs = instanciate_transformation(args.trfs)
-
+
from matplotlib import pyplot as pl
+
pl.ion()
- pl.subplots_adjust(0,0,1,1)
-
- nr,nc = args.layout
-
+ pl.subplots_adjust(0, 0, 1, 1)
+
+ nr, nc = args.layout
+
while True:
for j in range(nr):
for i in range(nc):
- pl.subplot(nr,nc,i+j*nc+1)
- if i==j==0:
+ pl.subplot(nr, nc, i + j * nc + 1)
+ if i == j == 0:
img2 = img
else:
img2 = trfs(img.copy())
if isinstance(img2, dict):
- img2 = img2['img']
+ img2 = img2["img"]
pl.imshow(img2)
pl.xlabel("%d x %d" % img2.size)
pl.xticks(())
pl.yticks(())
pdb.set_trace()
-
-
-
diff --git a/imcui/third_party/r2d2/tools/transforms_tools.py b/third_party/r2d2/tools/transforms_tools.py
similarity index 71%
rename from imcui/third_party/r2d2/tools/transforms_tools.py
rename to third_party/r2d2/tools/transforms_tools.py
index 294c22228a88f70480af52f79a77d73f9e5b3e1a..77eb1da2306116d789cdcf6b957a6c144a746a4f 100644
--- a/imcui/third_party/r2d2/tools/transforms_tools.py
+++ b/third_party/r2d2/tools/transforms_tools.py
@@ -8,31 +8,31 @@ from PIL import Image, ImageOps, ImageEnhance
class DummyImg:
- ''' This class is a dummy image only defined by its size.
- '''
+ """This class is a dummy image only defined by its size."""
+
def __init__(self, size):
self.size = size
-
+
def resize(self, size, *args, **kwargs):
return DummyImg(size)
-
+
def expand(self, border):
w, h = self.size
if isinstance(border, int):
- size = (w+2*border, h+2*border)
+ size = (w + 2 * border, h + 2 * border)
else:
- l,t,r,b = border
- size = (w+l+r, h+t+b)
+ l, t, r, b = border
+ size = (w + l + r, h + t + b)
return DummyImg(size)
def crop(self, border):
w, h = self.size
- l,t,r,b = border
+ l, t, r, b = border
assert 0 <= l <= r <= w
assert 0 <= t <= b <= h
- size = (r-l, b-t)
+ size = (r - l, b - t)
return DummyImg(size)
-
+
def rotate(self, angle):
raise NotImplementedError
@@ -40,89 +40,85 @@ class DummyImg:
return DummyImg(size)
-def grab_img( img_and_label ):
- ''' Called to extract the image from an img_and_label input
+def grab_img(img_and_label):
+ """Called to extract the image from an img_and_label input
(a dictionary). Also compatible with old-style PIL images.
- '''
+ """
if isinstance(img_and_label, dict):
# if input is a dictionary, then
# it must contains the img or its size.
try:
- return img_and_label['img']
+ return img_and_label["img"]
except KeyError:
- return DummyImg(img_and_label['imsize'])
-
+ return DummyImg(img_and_label["imsize"])
+
else:
# or it must be the img directly
return img_and_label
def update_img_and_labels(img_and_label, img, persp=None):
- ''' Called to update the img_and_label
- '''
+ """Called to update the img_and_label"""
if isinstance(img_and_label, dict):
- img_and_label['img'] = img
- img_and_label['imsize'] = img.size
+ img_and_label["img"] = img
+ img_and_label["imsize"] = img.size
if persp:
- if 'persp' not in img_and_label:
- img_and_label['persp'] = (1,0,0,0,1,0,0,0)
- img_and_label['persp'] = persp_mul(persp, img_and_label['persp'])
-
+ if "persp" not in img_and_label:
+ img_and_label["persp"] = (1, 0, 0, 0, 1, 0, 0, 0)
+ img_and_label["persp"] = persp_mul(persp, img_and_label["persp"])
+
return img_and_label
-
+
else:
# or it must be the img directly
return img
def rand_log_uniform(a, b):
- return np.exp(np.random.uniform(np.log(a),np.log(b)))
+ return np.exp(np.random.uniform(np.log(a), np.log(b)))
def translate(tx, ty):
- return (1,0,tx,
- 0,1,ty,
- 0,0)
+ return (1, 0, tx, 0, 1, ty, 0, 0)
+
def rotate(angle):
- return (np.cos(angle),-np.sin(angle), 0,
- np.sin(angle), np.cos(angle), 0,
- 0, 0)
+ return (np.cos(angle), -np.sin(angle), 0, np.sin(angle), np.cos(angle), 0, 0, 0)
def persp_mul(mat, mat2):
- ''' homography (perspective) multiplication.
+ """homography (perspective) multiplication.
mat: 8-tuple (homography transform)
mat2: 8-tuple (homography transform) or 2-tuple (point)
- '''
+ """
assert isinstance(mat, tuple)
assert isinstance(mat2, tuple)
- mat = np.float32(mat+(1,)).reshape(3,3)
- mat2 = np.array(mat2+(1,)).reshape(3,3)
+ mat = np.float32(mat + (1,)).reshape(3, 3)
+ mat2 = np.array(mat2 + (1,)).reshape(3, 3)
res = np.dot(mat, mat2)
- return tuple((res/res[2,2]).ravel()[:8])
+ return tuple((res / res[2, 2]).ravel()[:8])
def persp_apply(mat, pts):
- ''' homography (perspective) transformation.
+ """homography (perspective) transformation.
mat: 8-tuple (homography transform)
pts: numpy array
- '''
+ """
assert isinstance(mat, tuple)
assert isinstance(pts, np.ndarray)
assert pts.shape[-1] == 2
- mat = np.float32(mat+(1,)).reshape(3,3)
+ mat = np.float32(mat + (1,)).reshape(3, 3)
if pts.ndim == 1:
- pt = np.dot(pts, mat[:,:2].T).ravel() + mat[:,2]
- pt /= pt[2] # homogeneous coordinates
+ pt = np.dot(pts, mat[:, :2].T).ravel() + mat[:, 2]
+ pt /= pt[2] # homogeneous coordinates
return tuple(pt[:2])
else:
- pt = np.dot(pts, mat[:,:2].T) + mat[:,2]
- pt[:,:2] /= pt[:,2:3] # homogeneous coordinates
- return pt[:,:2]
+ pt = np.dot(pts, mat[:, :2].T) + mat[:, 2]
+ pt[:, :2] /= pt[:, 2:3] # homogeneous coordinates
+ return pt[:, :2]
def is_pil_image(img):
@@ -141,7 +137,7 @@ def adjust_brightness(img, brightness_factor):
Copied from https://github.com/pytorch in torchvision/transforms/functional.py
"""
if not is_pil_image(img):
- raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+ raise TypeError("img should be PIL Image. Got {}".format(type(img)))
enhancer = ImageEnhance.Brightness(img)
img = enhancer.enhance(brightness_factor)
@@ -160,7 +156,7 @@ def adjust_contrast(img, contrast_factor):
Copied from https://github.com/pytorch in torchvision/transforms/functional.py
"""
if not is_pil_image(img):
- raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+ raise TypeError("img should be PIL Image. Got {}".format(type(img)))
enhancer = ImageEnhance.Contrast(img)
img = enhancer.enhance(contrast_factor)
@@ -179,7 +175,7 @@ def adjust_saturation(img, saturation_factor):
Copied from https://github.com/pytorch in torchvision/transforms/functional.py
"""
if not is_pil_image(img):
- raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+ raise TypeError("img should be PIL Image. Got {}".format(type(img)))
enhancer = ImageEnhance.Color(img)
img = enhancer.enhance(saturation_factor)
@@ -205,26 +201,23 @@ def adjust_hue(img, hue_factor):
PIL Image: Hue adjusted image.
Copied from https://github.com/pytorch in torchvision/transforms/functional.py
"""
- if not(-0.5 <= hue_factor <= 0.5):
- raise ValueError('hue_factor is not in [-0.5, 0.5].'.format(hue_factor))
+ if not (-0.5 <= hue_factor <= 0.5):
+ raise ValueError("hue_factor is not in [-0.5, 0.5].".format(hue_factor))
if not is_pil_image(img):
- raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+ raise TypeError("img should be PIL Image. Got {}".format(type(img)))
input_mode = img.mode
- if input_mode in {'L', '1', 'I', 'F'}:
+ if input_mode in {"L", "1", "I", "F"}:
return img
- h, s, v = img.convert('HSV').split()
+ h, s, v = img.convert("HSV").split()
np_h = np.array(h, dtype=np.uint8)
# uint8 addition take cares of rotation across boundaries
- with np.errstate(over='ignore'):
+ with np.errstate(over="ignore"):
np_h += np.uint8(hue_factor * 255)
- h = Image.fromarray(np_h, 'L')
+ h = Image.fromarray(np_h, "L")
- img = Image.merge('HSV', (h, s, v)).convert(input_mode)
+ img = Image.merge("HSV", (h, s, v)).convert(input_mode)
return img
-
-
-
diff --git a/imcui/third_party/r2d2/tools/viz.py b/third_party/r2d2/tools/viz.py
similarity index 59%
rename from imcui/third_party/r2d2/tools/viz.py
rename to third_party/r2d2/tools/viz.py
index c86103f3aeb468fca8b0ac9a412f22b85239361b..4cf4b90a670ee448d9d6d1ba4137abae32def005 100644
--- a/imcui/third_party/r2d2/tools/viz.py
+++ b/third_party/r2d2/tools/viz.py
@@ -8,16 +8,16 @@ import matplotlib.pyplot as pl
def make_colorwheel():
- '''
+ """
Generates a color wheel for optical flow visualization as presented in:
Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
According to the C++ source code of Daniel Scharstein
According to the Matlab source code of Deqing Sun
-
+
Copied from https://github.com/tomrunia/OpticalFlow_Visualization/blob/master/flow_vis.py
Copyright (c) 2018 Tom Runia
- '''
+ """
RY = 15
YG = 6
@@ -32,32 +32,32 @@ def make_colorwheel():
# RY
colorwheel[0:RY, 0] = 255
- colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY)
- col = col+RY
+ colorwheel[0:RY, 1] = np.floor(255 * np.arange(0, RY) / RY)
+ col = col + RY
# YG
- colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG)
- colorwheel[col:col+YG, 1] = 255
- col = col+YG
+ colorwheel[col : col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG)
+ colorwheel[col : col + YG, 1] = 255
+ col = col + YG
# GC
- colorwheel[col:col+GC, 1] = 255
- colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC)
- col = col+GC
+ colorwheel[col : col + GC, 1] = 255
+ colorwheel[col : col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)
+ col = col + GC
# CB
- colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB)
- colorwheel[col:col+CB, 2] = 255
- col = col+CB
+ colorwheel[col : col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)
+ colorwheel[col : col + CB, 2] = 255
+ col = col + CB
# BM
- colorwheel[col:col+BM, 2] = 255
- colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM)
- col = col+BM
+ colorwheel[col : col + BM, 2] = 255
+ colorwheel[col : col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)
+ col = col + BM
# MR
- colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR)
- colorwheel[col:col+MR, 0] = 255
+ colorwheel[col : col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)
+ colorwheel[col : col + MR, 0] = 255
return colorwheel
def flow_compute_color(u, v, convert_to_bgr=False):
- '''
+ """
Applies the flow color wheel to (possibly clipped) flow components u and v.
According to the C++ source code of Daniel Scharstein
According to the Matlab source code of Deqing Sun
@@ -65,10 +65,10 @@ def flow_compute_color(u, v, convert_to_bgr=False):
:param v: np.ndarray, input vertical flow
:param convert_to_bgr: bool, whether to change ordering and output BGR instead of RGB
:return:
-
+
Copied from https://github.com/tomrunia/OpticalFlow_Visualization/blob/master/flow_vis.py
Copyright (c) 2018 Tom Runia
- '''
+ """
flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
@@ -76,9 +76,9 @@ def flow_compute_color(u, v, convert_to_bgr=False):
ncols = colorwheel.shape[0]
rad = np.sqrt(np.square(u) + np.square(v))
- a = np.arctan2(-v, -u)/np.pi
+ a = np.arctan2(-v, -u) / np.pi
- fk = (a+1) / 2*(ncols-1)
+ fk = (a + 1) / 2 * (ncols - 1)
k0 = np.floor(fk).astype(np.int32)
k1 = k0 + 1
k1[k1 == ncols] = 0
@@ -86,43 +86,43 @@ def flow_compute_color(u, v, convert_to_bgr=False):
for i in range(colorwheel.shape[1]):
- tmp = colorwheel[:,i]
+ tmp = colorwheel[:, i]
col0 = tmp[k0] / 255.0
col1 = tmp[k1] / 255.0
- col = (1-f)*col0 + f*col1
+ col = (1 - f) * col0 + f * col1
- idx = (rad <= 1)
- col[idx] = 1 - rad[idx] * (1-col[idx])
- col[~idx] = col[~idx] * 0.75 # out of range?
+ idx = rad <= 1
+ col[idx] = 1 - rad[idx] * (1 - col[idx])
+ col[~idx] = col[~idx] * 0.75 # out of range?
# Note the 2-i => BGR instead of RGB
- ch_idx = 2-i if convert_to_bgr else i
- flow_image[:,:,ch_idx] = np.floor(255 * col)
+ ch_idx = 2 - i if convert_to_bgr else i
+ flow_image[:, :, ch_idx] = np.floor(255 * col)
return flow_image
def flow_to_color(flow_uv, clip_flow=None, convert_to_bgr=False):
- '''
+ """
Expects a two dimensional flow image of shape [H,W,2]
According to the C++ source code of Daniel Scharstein
According to the Matlab source code of Deqing Sun
:param flow_uv: np.ndarray of shape [H,W,2]
:param clip_flow: float, maximum clipping value for flow
:return:
-
+
Copied from https://github.com/tomrunia/OpticalFlow_Visualization/blob/master/flow_vis.py
Copyright (c) 2018 Tom Runia
- '''
+ """
- assert flow_uv.ndim == 3, 'input flow must have three dimensions'
- assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
+ assert flow_uv.ndim == 3, "input flow must have three dimensions"
+ assert flow_uv.shape[2] == 2, "input flow must have shape [H,W,2]"
if clip_flow is not None:
flow_uv = np.clip(flow_uv, 0, clip_flow)
- u = flow_uv[:,:,0]
- v = flow_uv[:,:,1]
+ u = flow_uv[:, :, 0]
+ v = flow_uv[:, :, 1]
rad = np.sqrt(np.square(u) + np.square(v))
rad_max = np.max(rad)
@@ -134,58 +134,59 @@ def flow_to_color(flow_uv, clip_flow=None, convert_to_bgr=False):
return flow_compute_color(u, v, convert_to_bgr)
-
-def show_flow( img0, img1, flow, mask=None ):
+def show_flow(img0, img1, flow, mask=None):
img0 = np.asarray(img0)
img1 = np.asarray(img1)
- if mask is None: mask = 1
+ if mask is None:
+ mask = 1
mask = np.asarray(mask)
- if mask.ndim == 2: mask = mask[:,:,None]
+ if mask.ndim == 2:
+ mask = mask[:, :, None]
assert flow.ndim == 3
assert flow.shape[:2] == img0.shape[:2] and flow.shape[2] == 2
-
+
def noticks():
- pl.xticks([])
- pl.yticks([])
+ pl.xticks([])
+ pl.yticks([])
+
fig = pl.figure("showing correspondences")
ax1 = pl.subplot(221)
ax1.numaxis = 0
- pl.imshow(img0*mask)
+ pl.imshow(img0 * mask)
noticks()
ax2 = pl.subplot(222)
ax2.numaxis = 1
pl.imshow(img1)
noticks()
-
+
ax = pl.subplot(212)
ax.numaxis = 0
flow_img = flow_to_color(np.where(np.isnan(flow), 0, flow))
pl.imshow(flow_img * mask)
noticks()
-
+
pl.subplots_adjust(0.01, 0.01, 0.99, 0.99, wspace=0.02, hspace=0.02)
-
+
def motion_notify_callback(event):
- if event.inaxes is None: return
- x,y = event.xdata, event.ydata
- ax1.lines = []
- ax2.lines = []
- try:
- x,y = int(x+0.5), int(y+0.5)
- ax1.plot(x,y,'+',ms=10,mew=2,color='blue',scalex=False,scaley=False)
- x,y = flow[y,x] + (x,y)
- ax2.plot(x,y,'+',ms=10,mew=2,color='red',scalex=False,scaley=False)
- # we redraw only the concerned axes
- renderer = fig.canvas.get_renderer()
- ax1.draw(renderer)
- ax2.draw(renderer)
- fig.canvas.blit(ax1.bbox)
- fig.canvas.blit(ax2.bbox)
- except IndexError:
- return
-
- cid_move = fig.canvas.mpl_connect('motion_notify_event',motion_notify_callback)
+ if event.inaxes is None:
+ return
+ x, y = event.xdata, event.ydata
+ ax1.lines = []
+ ax2.lines = []
+ try:
+ x, y = int(x + 0.5), int(y + 0.5)
+ ax1.plot(x, y, "+", ms=10, mew=2, color="blue", scalex=False, scaley=False)
+ x, y = flow[y, x] + (x, y)
+ ax2.plot(x, y, "+", ms=10, mew=2, color="red", scalex=False, scaley=False)
+ # we redraw only the concerned axes
+ renderer = fig.canvas.get_renderer()
+ ax1.draw(renderer)
+ ax2.draw(renderer)
+ fig.canvas.blit(ax1.bbox)
+ fig.canvas.blit(ax2.bbox)
+ except IndexError:
+ return
+
+ cid_move = fig.canvas.mpl_connect("motion_notify_event", motion_notify_callback)
print("Move your mouse over the images to show matches (ctrl-C to quit)")
pl.show()
-
-
diff --git a/imcui/third_party/r2d2/train.py b/third_party/r2d2/train.py
similarity index 60%
rename from imcui/third_party/r2d2/train.py
rename to third_party/r2d2/train.py
index 10d23d9e40ebe8cb10c4d548b7fcb5c1c0fd7739..232d61d0eb830454b4f785cfb82536b6cfba7071 100644
--- a/imcui/third_party/r2d2/train.py
+++ b/third_party/r2d2/train.py
@@ -35,12 +35,12 @@ db_aachen_style_transfer = """TransformedPairs(
db_aachen_flow = "aachen_flow_pairs"
data_sources = dict(
- D = toy_db_debug,
- W = db_web_images,
- A = db_aachen_images,
- F = db_aachen_flow,
- S = db_aachen_style_transfer,
- )
+ D=toy_db_debug,
+ W=db_web_images,
+ A=db_aachen_images,
+ F=db_aachen_flow,
+ S=db_aachen_style_transfer,
+)
default_dataloader = """PairLoader(CatPairDataset(`data`),
scale = 'RandomScale(256,1024,can_upscale=True)',
@@ -57,75 +57,101 @@ default_loss = """MultiLoss(
class MyTrainer(trainer.Trainer):
- """ This class implements the network training.
- Below is the function I need to overload to explain how to do the backprop.
+ """This class implements the network training.
+ Below is the function I need to overload to explain how to do the backprop.
"""
+
def forward_backward(self, inputs):
- output = self.net(imgs=[inputs.pop('img1'),inputs.pop('img2')])
+ output = self.net(imgs=[inputs.pop("img1"), inputs.pop("img2")])
allvars = dict(inputs, **output)
loss, details = self.loss_func(**allvars)
- if torch.is_grad_enabled(): loss.backward()
+ if torch.is_grad_enabled():
+ loss.backward()
return loss, details
-
-if __name__ == '__main__':
+if __name__ == "__main__":
import argparse
+
parser = argparse.ArgumentParser("Train R2D2")
parser.add_argument("--data-loader", type=str, default=default_dataloader)
- parser.add_argument("--train-data", type=str, default=list('WASF'), nargs='+',
- choices = set(data_sources.keys()))
- parser.add_argument("--net", type=str, default=default_net, help='network architecture')
+ parser.add_argument(
+ "--train-data",
+ type=str,
+ default=list("WASF"),
+ nargs="+",
+ choices=set(data_sources.keys()),
+ )
+ parser.add_argument(
+ "--net", type=str, default=default_net, help="network architecture"
+ )
+
+ parser.add_argument(
+ "--pretrained", type=str, default="", help="pretrained model path"
+ )
+ parser.add_argument(
+ "--save-path", type=str, required=True, help="model save_path path"
+ )
- parser.add_argument("--pretrained", type=str, default="", help='pretrained model path')
- parser.add_argument("--save-path", type=str, required=True, help='model save_path path')
-
parser.add_argument("--loss", type=str, default=default_loss, help="loss function")
- parser.add_argument("--sampler", type=str, default=default_sampler, help="AP sampler")
- parser.add_argument("--N", type=int, default=16, help="patch size for repeatability")
+ parser.add_argument(
+ "--sampler", type=str, default=default_sampler, help="AP sampler"
+ )
+ parser.add_argument(
+ "--N", type=int, default=16, help="patch size for repeatability"
+ )
- parser.add_argument("--epochs", type=int, default=25, help='number of training epochs')
+ parser.add_argument(
+ "--epochs", type=int, default=25, help="number of training epochs"
+ )
parser.add_argument("--batch-size", "--bs", type=int, default=8, help="batch size")
parser.add_argument("--learning-rate", "--lr", type=str, default=1e-4)
parser.add_argument("--weight-decay", "--wd", type=float, default=5e-4)
-
- parser.add_argument("--threads", type=int, default=8, help='number of worker threads')
- parser.add_argument("--gpu", type=int, nargs='+', default=[0], help='-1 for CPU')
-
+
+ parser.add_argument(
+ "--threads", type=int, default=8, help="number of worker threads"
+ )
+ parser.add_argument("--gpu", type=int, nargs="+", default=[0], help="-1 for CPU")
+
args = parser.parse_args()
-
+
iscuda = common.torch_set_gpu(args.gpu)
common.mkdir_for(args.save_path)
# Create data loader
from datasets import *
+
db = [data_sources[key] for key in args.train_data]
- db = eval(args.data_loader.replace('`data`',','.join(db)).replace('\n',''))
+ db = eval(args.data_loader.replace("`data`", ",".join(db)).replace("\n", ""))
print("Training image database =", db)
loader = threaded_loader(db, iscuda, args.threads, args.batch_size, shuffle=True)
# create network
- print("\n>> Creating net = " + args.net)
+ print("\n>> Creating net = " + args.net)
net = eval(args.net)
print(f" ( Model size: {common.model_size(net)/1000:.0f}K parameters )")
# initialization
if args.pretrained:
- checkpoint = torch.load(args.pretrained, lambda a,b:a)
- net.load_pretrained(checkpoint['state_dict'])
-
+ checkpoint = torch.load(args.pretrained, lambda a, b: a)
+ net.load_pretrained(checkpoint["state_dict"])
+
# create losses
- loss = args.loss.replace('`sampler`',args.sampler).replace('`N`',str(args.N))
+ loss = args.loss.replace("`sampler`", args.sampler).replace("`N`", str(args.N))
print("\n>> Creating loss = " + loss)
- loss = eval(loss.replace('\n',''))
-
+ loss = eval(loss.replace("\n", ""))
+
# create optimizer
- optimizer = optim.Adam( [p for p in net.parameters() if p.requires_grad],
- lr=args.learning_rate, weight_decay=args.weight_decay)
+ optimizer = optim.Adam(
+ [p for p in net.parameters() if p.requires_grad],
+ lr=args.learning_rate,
+ weight_decay=args.weight_decay,
+ )
train = MyTrainer(net, loader, loss, optimizer)
- if iscuda: train = train.cuda()
+ if iscuda:
+ train = train.cuda()
# Training loop #
for epoch in range(args.epochs):
@@ -133,6 +159,4 @@ if __name__ == '__main__':
train()
print(f"\n>> Saving model to {args.save_path}")
- torch.save({'net': args.net, 'state_dict': net.state_dict()}, args.save_path)
-
-
+ torch.save({"net": args.net, "state_dict": net.state_dict()}, args.save_path)
diff --git a/imcui/third_party/r2d2/viz_heatmaps.py b/third_party/r2d2/viz_heatmaps.py
similarity index 50%
rename from imcui/third_party/r2d2/viz_heatmaps.py
rename to third_party/r2d2/viz_heatmaps.py
index 42705e70ecea82696a0d784b274f7f387fdf6595..e5cb8b3bb502ce4d9e5169c55be3f479f8f8fce4 100644
--- a/imcui/third_party/r2d2/viz_heatmaps.py
+++ b/third_party/r2d2/viz_heatmaps.py
@@ -7,116 +7,134 @@ import numpy as np
import torch
from PIL import Image
-from matplotlib import pyplot as pl; pl.ion()
+from matplotlib import pyplot as pl
+
+pl.ion()
from scipy.ndimage import uniform_filter
+
smooth = lambda arr: uniform_filter(arr, 3)
+
def transparent(img, alpha, cmap, **kw):
from matplotlib.colors import Normalize
- colored_img = cmap(Normalize(clip=True,**kw)(img))
- colored_img[:,:,-1] = alpha
+
+ colored_img = cmap(Normalize(clip=True, **kw)(img))
+ colored_img[:, :, -1] = alpha
return colored_img
+
from tools import common
from tools.dataloader import norm_RGB
from nets.patchnet import *
from extract import NonMaxSuppression
-if __name__ == '__main__':
+if __name__ == "__main__":
import argparse
+
parser = argparse.ArgumentParser("Visualize the patch detector and descriptor")
-
+
parser.add_argument("--img", type=str, default="imgs/brooklyn.png")
parser.add_argument("--resize", type=int, default=512)
parser.add_argument("--out", type=str, default="viz.png")
- parser.add_argument("--checkpoint", type=str, required=True, help='network path')
- parser.add_argument("--net", type=str, default="", help='network command')
+ parser.add_argument("--checkpoint", type=str, required=True, help="network path")
+ parser.add_argument("--net", type=str, default="", help="network command")
parser.add_argument("--max-kpts", type=int, default=200)
parser.add_argument("--reliability-thr", type=float, default=0.8)
parser.add_argument("--repeatability-thr", type=float, default=0.7)
- parser.add_argument("--border", type=int, default=20,help='rm keypoints close to border')
+ parser.add_argument(
+ "--border", type=int, default=20, help="rm keypoints close to border"
+ )
+
+ parser.add_argument("--gpu", type=int, nargs="+", required=True, help="-1 for CPU")
+ parser.add_argument("--dbg", type=str, nargs="+", default=(), help="debug options")
- parser.add_argument("--gpu", type=int, nargs='+', required=True, help='-1 for CPU')
- parser.add_argument("--dbg", type=str, nargs='+', default=(), help='debug options')
-
args = parser.parse_args()
args.dbg = set(args.dbg)
-
+
iscuda = common.torch_set_gpu(args.gpu)
- device = torch.device('cuda' if iscuda else 'cpu')
+ device = torch.device("cuda" if iscuda else "cpu")
# create network
- checkpoint = torch.load(args.checkpoint, lambda a,b:a)
- args.net = args.net or checkpoint['net']
- print("\n>> Creating net = " + args.net)
+ checkpoint = torch.load(args.checkpoint, lambda a, b: a)
+ args.net = args.net or checkpoint["net"]
+ print("\n>> Creating net = " + args.net)
net = eval(args.net)
- net.load_state_dict({k.replace('module.',''):v for k,v in checkpoint['state_dict'].items()})
- if iscuda: net = net.cuda()
+ net.load_state_dict(
+ {k.replace("module.", ""): v for k, v in checkpoint["state_dict"].items()}
+ )
+ if iscuda:
+ net = net.cuda()
print(f" ( Model size: {common.model_size(net)/1000:.0f}K parameters )")
- img = Image.open(args.img).convert('RGB')
- if args.resize: img.thumbnail((args.resize,args.resize))
+ img = Image.open(args.img).convert("RGB")
+ if args.resize:
+ img.thumbnail((args.resize, args.resize))
img = np.asarray(img)
-
+
detector = NonMaxSuppression(
- rel_thr = args.reliability_thr,
- rep_thr = args.repeatability_thr)
+ rel_thr=args.reliability_thr, rep_thr=args.repeatability_thr
+ )
with torch.no_grad():
print(">> computing features...")
res = net(imgs=[norm_RGB(img).unsqueeze(0).to(device)])
- rela = res.get('reliability')
- repe = res.get('repeatability')
- kpts = detector(**res).T[:,[1,0]]
- kpts = kpts[repe[0][0,0][kpts[:,1],kpts[:,0]].argsort()[-args.max_kpts:]]
+ rela = res.get("reliability")
+ repe = res.get("repeatability")
+ kpts = detector(**res).T[:, [1, 0]]
+ kpts = kpts[repe[0][0, 0][kpts[:, 1], kpts[:, 0]].argsort()[-args.max_kpts :]]
fig = pl.figure("viz")
kw = dict(cmap=pl.cm.RdYlGn, vmax=1)
- crop = (slice(args.border,-args.border or 1),)*2
-
- if 'reliability' in args.dbg:
-
+ crop = (slice(args.border, -args.border or 1),) * 2
+
+ if "reliability" in args.dbg:
+
ax1 = pl.subplot(131)
pl.imshow(img[crop], cmap=pl.cm.gray)
- pl.xticks(()); pl.yticks(())
+ pl.xticks(())
+ pl.yticks(())
pl.subplot(132)
pl.imshow(img[crop], cmap=pl.cm.gray, alpha=0)
- pl.xticks(()); pl.yticks(())
+ pl.xticks(())
+ pl.yticks(())
- x,y = kpts[:,0:2].cpu().numpy().T - args.border
- pl.plot(x,y,'+',c=(0,1,0),ms=10, scalex=0, scaley=0)
+ x, y = kpts[:, 0:2].cpu().numpy().T - args.border
+ pl.plot(x, y, "+", c=(0, 1, 0), ms=10, scalex=0, scaley=0)
ax1 = pl.subplot(133)
- rela = rela[0][0,0].cpu().numpy()
+ rela = rela[0][0, 0].cpu().numpy()
pl.imshow(rela[crop], cmap=pl.cm.RdYlGn, vmax=1, vmin=0.9)
- pl.xticks(()); pl.yticks(())
+ pl.xticks(())
+ pl.yticks(())
else:
ax1 = pl.subplot(131)
pl.imshow(img[crop], cmap=pl.cm.gray)
- pl.xticks(()); pl.yticks(())
+ pl.xticks(())
+ pl.yticks(())
- x,y = kpts[:,0:2].cpu().numpy().T - args.border
- pl.plot(x,y,'+',c=(0,1,0),ms=10, scalex=0, scaley=0)
+ x, y = kpts[:, 0:2].cpu().numpy().T - args.border
+ pl.plot(x, y, "+", c=(0, 1, 0), ms=10, scalex=0, scaley=0)
pl.subplot(132)
pl.imshow(img[crop], cmap=pl.cm.gray)
- pl.xticks(()); pl.yticks(())
- c = repe[0][0,0].cpu().numpy()
+ pl.xticks(())
+ pl.yticks(())
+ c = repe[0][0, 0].cpu().numpy()
pl.imshow(transparent(smooth(c)[crop], 0.5, vmin=0, **kw))
ax1 = pl.subplot(133)
pl.imshow(img[crop], cmap=pl.cm.gray)
- pl.xticks(()); pl.yticks(())
- rela = rela[0][0,0].cpu().numpy()
+ pl.xticks(())
+ pl.yticks(())
+ rela = rela[0][0, 0].cpu().numpy()
pl.imshow(transparent(rela[crop], 0.5, vmin=0.9, **kw))
pl.gcf().set_size_inches(9, 2.73)
- pl.subplots_adjust(0.01,0.01,0.99,0.99,hspace=0.1)
+ pl.subplots_adjust(0.01, 0.01, 0.99, 0.99, hspace=0.1)
pl.savefig(args.out)
pdb.set_trace()
-
diff --git a/imcui/ui/__init__.py b/ui/__init__.py
similarity index 61%
rename from imcui/ui/__init__.py
rename to ui/__init__.py
index 437d764619c46f8f81854949638dfb73f802f22c..ac6ccf52978e85f5abaca55d6559c74a6b2bd169 100644
--- a/imcui/ui/__init__.py
+++ b/ui/__init__.py
@@ -1,5 +1,5 @@
-__version__ = "1.3.0"
-
-
-def get_version():
- return __version__
+__version__ = "1.0.1"
+
+
+def get_version():
+ return __version__
diff --git a/imcui/ui/app_class.py b/ui/app_class.py
similarity index 89%
rename from imcui/ui/app_class.py
rename to ui/app_class.py
index 5e54bb1bd20244258a4474f98abe53b123229780..628a9a71d4f13193c3573398bdba9b380e216a04 100644
--- a/imcui/ui/app_class.py
+++ b/ui/app_class.py
@@ -1,816 +1,849 @@
-from pathlib import Path
-from typing import Any, Dict, Optional, Tuple
-
-import gradio as gr
-import numpy as np
-from easydict import EasyDict as edict
-from omegaconf import OmegaConf
-
-from .sfm import SfmEngine
-from .utils import (
- GRADIO_VERSION,
- gen_examples,
- generate_warp_images,
- get_matcher_zoo,
- load_config,
- ransac_zoo,
- run_matching,
- run_ransac,
- send_to_match,
-)
-
-DESCRIPTION = """
-# Image Matching WebUI
-This Space demonstrates [Image Matching WebUI](https://github.com/Vincentqyw/image-matching-webui) by vincent qin. Feel free to play with it, or duplicate to run image matching without a queue!
-
-🔎 For more details about supported local features and matchers, please refer to https://github.com/Vincentqyw/image-matching-webui
-
-🚀 All algorithms run on CPU for inference, causing slow speeds and high latency. For faster inference, please download the [source code](https://github.com/Vincentqyw/image-matching-webui) for local deployment.
-
-🐛 Your feedback is valuable to me. Please do not hesitate to report any bugs [here](https://github.com/Vincentqyw/image-matching-webui/issues).
-"""
-
-CSS = """
-#warning {background-color: #FFCCCB}
-.logs_class textarea {font-size: 12px !important}
-"""
-
-
-class ImageMatchingApp:
- def __init__(self, server_name="0.0.0.0", server_port=7860, **kwargs):
- self.server_name = server_name
- self.server_port = server_port
- self.config_path = kwargs.get("config", Path(__file__).parent / "config.yaml")
- self.cfg = load_config(self.config_path)
- self.matcher_zoo = get_matcher_zoo(self.cfg["matcher_zoo"])
- self.app = None
- self.example_data_root = kwargs.get(
- "example_data_root", Path(__file__).parents[1] / "datasets"
- )
- # final step
- self.init_interface()
-
- def init_matcher_dropdown(self):
- algos = []
- for k, v in self.cfg["matcher_zoo"].items():
- if v.get("enable", True):
- algos.append(k)
- return algos
-
- def init_interface(self):
- with gr.Blocks(css=CSS) as self.app:
- with gr.Tab("Image Matching"):
- with gr.Row():
- with gr.Column(scale=1):
- gr.Image(
- str(Path(__file__).parent.parent / "assets/logo.webp"),
- elem_id="logo-img",
- show_label=False,
- show_share_button=False,
- show_download_button=False,
- )
- with gr.Column(scale=3):
- gr.Markdown(DESCRIPTION)
- with gr.Row(equal_height=False):
- with gr.Column():
- with gr.Row():
- matcher_list = gr.Dropdown(
- choices=self.init_matcher_dropdown(),
- value="disk+lightglue",
- label="Matching Model",
- interactive=True,
- )
- match_image_src = gr.Radio(
- (
- ["upload", "webcam", "clipboard"]
- if GRADIO_VERSION > "3"
- else ["upload", "webcam", "canvas"]
- ),
- label="Image Source",
- value="upload",
- )
- with gr.Row():
- input_image0 = gr.Image(
- label="Image 0",
- type="numpy",
- image_mode="RGB",
- height=300 if GRADIO_VERSION > "3" else None,
- interactive=True,
- )
- input_image1 = gr.Image(
- label="Image 1",
- type="numpy",
- image_mode="RGB",
- height=300 if GRADIO_VERSION > "3" else None,
- interactive=True,
- )
-
- with gr.Row():
- button_reset = gr.Button(value="Reset")
- button_run = gr.Button(value="Run Match", variant="primary")
- with gr.Row():
- button_stop = gr.Button(value="Force Stop", variant="stop")
-
- with gr.Accordion("Advanced Setting", open=False):
- with gr.Accordion("Image Setting", open=True):
- with gr.Row():
- image_force_resize_cb = gr.Checkbox(
- label="Force Resize",
- value=False,
- interactive=True,
- )
- image_setting_height = gr.Slider(
- minimum=48,
- maximum=2048,
- step=16,
- label="Image Height",
- value=480,
- visible=False,
- )
- image_setting_width = gr.Slider(
- minimum=64,
- maximum=2048,
- step=16,
- label="Image Width",
- value=640,
- visible=False,
- )
- with gr.Accordion("Matching Setting", open=True):
- with gr.Row():
- match_setting_threshold = gr.Slider(
- minimum=0.0,
- maximum=1,
- step=0.001,
- label="Match threshold",
- value=0.1,
- )
- match_setting_max_keypoints = gr.Slider(
- minimum=10,
- maximum=10000,
- step=10,
- label="Max features",
- value=1000,
- )
- # TODO: add line settings
- with gr.Row():
- detect_keypoints_threshold = gr.Slider(
- minimum=0,
- maximum=1,
- step=0.001,
- label="Keypoint threshold",
- value=0.015,
- )
- detect_line_threshold = ( # noqa: F841
- gr.Slider(
- minimum=0.1,
- maximum=1,
- step=0.01,
- label="Line threshold",
- value=0.2,
- )
- )
-
- with gr.Accordion("RANSAC Setting", open=True):
- with gr.Row(equal_height=False):
- ransac_method = gr.Dropdown(
- choices=ransac_zoo.keys(),
- value=self.cfg["defaults"]["ransac_method"],
- label="RANSAC Method",
- interactive=True,
- )
- ransac_reproj_threshold = gr.Slider(
- minimum=0.0,
- maximum=12,
- step=0.01,
- label="Ransac Reproj threshold",
- value=8.0,
- )
- ransac_confidence = gr.Slider(
- minimum=0.0,
- maximum=1,
- step=0.00001,
- label="Ransac Confidence",
- value=self.cfg["defaults"]["ransac_confidence"],
- )
- ransac_max_iter = gr.Slider(
- minimum=0.0,
- maximum=100000,
- step=100,
- label="Ransac Iterations",
- value=self.cfg["defaults"]["ransac_max_iter"],
- )
- button_ransac = gr.Button(
- value="Rerun RANSAC", variant="primary"
- )
- with gr.Accordion("Geometry Setting", open=False):
- with gr.Row(equal_height=False):
- choice_geometry_type = gr.Radio(
- ["Fundamental", "Homography"],
- label="Reconstruct Geometry",
- value=self.cfg["defaults"]["setting_geometry"],
- )
- # image resize
- image_force_resize_cb.select(
- fn=self._on_select_force_resize,
- inputs=image_force_resize_cb,
- outputs=[image_setting_width, image_setting_height],
- )
- # collect inputs
- state_cache = gr.State({})
- inputs = [
- input_image0,
- input_image1,
- match_setting_threshold,
- match_setting_max_keypoints,
- detect_keypoints_threshold,
- matcher_list,
- ransac_method,
- ransac_reproj_threshold,
- ransac_confidence,
- ransac_max_iter,
- choice_geometry_type,
- gr.State(self.matcher_zoo),
- image_force_resize_cb,
- image_setting_width,
- image_setting_height,
- ]
-
- # Add some examples
- with gr.Row():
- # Example inputs
- with gr.Accordion("Open for More: Examples", open=True):
- gr.Examples(
- examples=gen_examples(self.example_data_root),
- inputs=inputs,
- outputs=[],
- fn=run_matching,
- cache_examples=False,
- label=(
- "Examples (click one of the images below to Run"
- " Match). Thx: WxBS"
- ),
- )
- with gr.Accordion("Supported Algorithms", open=False):
- # add a table of supported algorithms
- self.display_supported_algorithms()
-
- with gr.Column():
- with gr.Accordion("Open for More: Keypoints", open=True):
- output_keypoints = gr.Image(label="Keypoints", type="numpy")
- with gr.Accordion(
- (
- "Open for More: Raw Matches"
- " (Green for good matches, Red for bad)"
- ),
- open=False,
- ):
- output_matches_raw = gr.Image(
- label="Raw Matches",
- type="numpy",
- )
- with gr.Accordion(
- (
- "Open for More: Ransac Matches"
- " (Green for good matches, Red for bad)"
- ),
- open=True,
- ):
- output_matches_ransac = gr.Image(
- label="Ransac Matches", type="numpy"
- )
- with gr.Accordion(
- "Open for More: Matches Statistics", open=False
- ):
- output_pred = gr.File(label="Outputs", elem_id="download")
- matches_result_info = gr.JSON(label="Matches Statistics")
- matcher_info = gr.JSON(label="Match info")
-
- with gr.Accordion("Open for More: Warped Image", open=True):
- output_wrapped = gr.Image(
- label="Wrapped Pair", type="numpy"
- )
- # send to input
- button_rerun = gr.Button(
- value="Send to Input Match Pair",
- variant="primary",
- )
- with gr.Accordion(
- "Open for More: Geometry info", open=False
- ):
- geometry_result = gr.JSON(
- label="Reconstructed Geometry"
- )
-
- # callbacks
- match_image_src.change(
- fn=self.ui_change_imagebox,
- inputs=match_image_src,
- outputs=input_image0,
- )
- match_image_src.change(
- fn=self.ui_change_imagebox,
- inputs=match_image_src,
- outputs=input_image1,
- )
- # collect outputs
- outputs = [
- output_keypoints,
- output_matches_raw,
- output_matches_ransac,
- matches_result_info,
- matcher_info,
- geometry_result,
- output_wrapped,
- state_cache,
- output_pred,
- ]
- # button callbacks
- click_event = button_run.click(
- fn=run_matching, inputs=inputs, outputs=outputs
- )
- # stop button
- button_stop.click(
- fn=None, inputs=None, outputs=None, cancels=[click_event]
- )
-
- # Reset images
- reset_outputs = [
- input_image0,
- input_image1,
- match_setting_threshold,
- match_setting_max_keypoints,
- detect_keypoints_threshold,
- matcher_list,
- input_image0,
- input_image1,
- match_image_src,
- output_keypoints,
- output_matches_raw,
- output_matches_ransac,
- matches_result_info,
- matcher_info,
- output_wrapped,
- geometry_result,
- ransac_method,
- ransac_reproj_threshold,
- ransac_confidence,
- ransac_max_iter,
- choice_geometry_type,
- output_pred,
- image_force_resize_cb,
- ]
- button_reset.click(
- fn=self.ui_reset_state,
- inputs=None,
- outputs=reset_outputs,
- )
-
- # run ransac button action
- button_ransac.click(
- fn=run_ransac,
- inputs=[
- state_cache,
- choice_geometry_type,
- ransac_method,
- ransac_reproj_threshold,
- ransac_confidence,
- ransac_max_iter,
- ],
- outputs=[
- output_matches_ransac,
- matches_result_info,
- output_wrapped,
- output_pred,
- ],
- )
-
- # send warped image to match
- button_rerun.click(
- fn=send_to_match,
- inputs=[state_cache],
- outputs=[input_image0, input_image1],
- )
-
- # estimate geo
- choice_geometry_type.change(
- fn=generate_warp_images,
- inputs=[
- input_image0,
- input_image1,
- geometry_result,
- choice_geometry_type,
- ],
- outputs=[output_wrapped, geometry_result],
- )
- with gr.Tab("Structure from Motion(under-dev)"):
- sfm_ui = AppSfmUI( # noqa: F841
- {
- **self.cfg,
- "matcher_zoo": self.matcher_zoo,
- "outputs": "experiments/sfm",
- }
- )
- sfm_ui.call_empty()
-
- def run(self):
- self.app.queue().launch(
- server_name=self.server_name,
- server_port=self.server_port,
- share=False,
- allowed_paths=[
- str(Path(__file__).parents[0]),
- str(Path(__file__).parents[1]),
- ],
- )
-
- def ui_change_imagebox(self, choice):
- """
- Updates the image box with the given choice.
-
- Args:
- choice (list): The list of image sources to be displayed in the image box.
-
- Returns:
- dict: A dictionary containing the updated value, sources, and type for the image box.
- """
- ret_dict = {
- "value": None, # The updated value of the image box
- "__type__": "update", # The type of update for the image box
- }
- if GRADIO_VERSION > "3":
- return {
- **ret_dict,
- "sources": choice, # The list of image sources to be displayed
- }
- else:
- return {
- **ret_dict,
- "source": choice, # The list of image sources to be displayed
- }
-
- def _on_select_force_resize(self, visible: bool = False):
- return gr.update(visible=visible), gr.update(visible=visible)
-
- def ui_reset_state(
- self,
- *args: Any,
- ) -> Tuple[
- Optional[np.ndarray],
- Optional[np.ndarray],
- float,
- int,
- float,
- str,
- Dict[str, Any],
- Dict[str, Any],
- str,
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
- Dict[str, Any],
- Dict[str, Any],
- Optional[np.ndarray],
- Dict[str, Any],
- str,
- int,
- float,
- int,
- bool,
- ]:
- """
- Reset the state of the UI.
-
- Returns:
- tuple: A tuple containing the initial values for the UI state.
- """
- key: str = list(self.matcher_zoo.keys())[
- 0
- ] # Get the first key from matcher_zoo
- # flush_logs()
- return (
- None, # image0: Optional[np.ndarray]
- None, # image1: Optional[np.ndarray]
- self.cfg["defaults"]["match_threshold"], # matching_threshold: float
- self.cfg["defaults"]["max_keypoints"], # max_keypoints: int
- self.cfg["defaults"]["keypoint_threshold"], # keypoint_threshold: float
- key, # matcher: str
- self.ui_change_imagebox("upload"), # input image0: Dict[str, Any]
- self.ui_change_imagebox("upload"), # input image1: Dict[str, Any]
- "upload", # match_image_src: str
- None, # keypoints: Optional[np.ndarray]
- None, # raw matches: Optional[np.ndarray]
- None, # ransac matches: Optional[np.ndarray]
- {}, # matches result info: Dict[str, Any]
- {}, # matcher config: Dict[str, Any]
- None, # warped image: Optional[np.ndarray]
- {}, # geometry result: Dict[str, Any]
- self.cfg["defaults"]["ransac_method"], # ransac_method: str
- self.cfg["defaults"][
- "ransac_reproj_threshold"
- ], # ransac_reproj_threshold: float
- self.cfg["defaults"]["ransac_confidence"], # ransac_confidence: float
- self.cfg["defaults"]["ransac_max_iter"], # ransac_max_iter: int
- self.cfg["defaults"]["setting_geometry"], # geometry: str
- None, # predictions
- False,
- )
-
- def display_supported_algorithms(self, style="tab"):
- def get_link(link, tag="Link"):
- return "[{}]({})".format(tag, link) if link is not None else "None"
-
- data = []
- cfg = self.cfg["matcher_zoo"]
- if style == "md":
- markdown_table = "| Algo. | Conference | Code | Project | Paper |\n"
- markdown_table += "| ----- | ---------- | ---- | ------- | ----- |\n"
-
- for _, v in cfg.items():
- if not v["info"].get("display", True):
- continue
- github_link = get_link(v["info"].get("github", ""))
- project_link = get_link(v["info"].get("project", ""))
- paper_link = get_link(
- v["info"]["paper"],
- (
- Path(v["info"]["paper"]).name[-10:]
- if v["info"]["paper"] is not None
- else "Link"
- ),
- )
-
- markdown_table += "{}|{}|{}|{}|{}\n".format(
- v["info"].get("name", ""),
- v["info"].get("source", ""),
- github_link,
- project_link,
- paper_link,
- )
- return gr.Markdown(markdown_table)
- elif style == "tab":
- for k, v in cfg.items():
- if not v["info"].get("display", True):
- continue
- data.append(
- [
- v["info"].get("name", ""),
- v["info"].get("source", ""),
- v["info"].get("github", ""),
- v["info"].get("paper", ""),
- v["info"].get("project", ""),
- ]
- )
- tab = gr.Dataframe(
- headers=["Algo.", "Conference", "Code", "Paper", "Project"],
- datatype=["str", "str", "str", "str", "str"],
- col_count=(5, "fixed"),
- value=data,
- # wrap=True,
- # min_width = 1000,
- # height=1000,
- )
- return tab
-
-
-class AppBaseUI:
- def __init__(self, cfg: Dict[str, Any] = {}):
- self.cfg = OmegaConf.create(cfg)
- self.inputs = edict({})
- self.outputs = edict({})
- self.ui = edict({})
-
- def _init_ui(self):
- NotImplemented
-
- def call(self, **kwargs):
- NotImplemented
-
- def info(self):
- gr.Info("SFM is under construction.")
-
-
-class AppSfmUI(AppBaseUI):
- def __init__(self, cfg: Dict[str, Any] = None):
- super().__init__(cfg)
- assert "matcher_zoo" in self.cfg
- self.matcher_zoo = self.cfg["matcher_zoo"]
- self.sfm_engine = SfmEngine(cfg)
- self._init_ui()
-
- def init_retrieval_dropdown(self):
- algos = []
- for k, v in self.cfg["retrieval_zoo"].items():
- if v.get("enable", True):
- algos.append(k)
- return algos
-
- def _update_options(self, option):
- if option == "sparse":
- return gr.Textbox("sparse", visible=True)
- elif option == "dense":
- return gr.Textbox("dense", visible=True)
- else:
- return gr.Textbox("not set", visible=True)
-
- def _on_select_custom_params(self, value: bool = False):
- return gr.update(visible=value)
-
- def _init_ui(self):
- with gr.Row():
- # data settting and camera settings
- with gr.Column():
- self.inputs.input_images = gr.File(
- label="SfM",
- interactive=True,
- file_count="multiple",
- min_width=300,
- )
- # camera setting
- with gr.Accordion("Camera Settings", open=True):
- with gr.Column():
- with gr.Row():
- with gr.Column():
- self.inputs.camera_model = gr.Dropdown(
- choices=[
- "PINHOLE",
- "SIMPLE_RADIAL",
- "OPENCV",
- ],
- value="PINHOLE",
- label="Camera Model",
- interactive=True,
- )
- with gr.Column():
- gr.Checkbox(
- label="Shared Params",
- value=True,
- interactive=True,
- )
- camera_custom_params_cb = gr.Checkbox(
- label="Custom Params",
- value=False,
- interactive=True,
- )
- with gr.Row():
- self.inputs.camera_params = gr.Textbox(
- label="Camera Params",
- value="0,0,0,0",
- interactive=False,
- visible=False,
- )
- camera_custom_params_cb.select(
- fn=self._on_select_custom_params,
- inputs=camera_custom_params_cb,
- outputs=self.inputs.camera_params,
- )
-
- with gr.Accordion("Matching Settings", open=True):
- # feature extraction and matching setting
- with gr.Row():
- # matcher setting
- self.inputs.matcher_key = gr.Dropdown(
- choices=self.matcher_zoo.keys(),
- value="disk+lightglue",
- label="Matching Model",
- interactive=True,
- )
- with gr.Row():
- with gr.Accordion("Advanced Settings", open=False):
- with gr.Column():
- with gr.Row():
- # matching setting
- self.inputs.max_keypoints = gr.Slider(
- label="Max Keypoints",
- minimum=100,
- maximum=10000,
- value=1000,
- interactive=True,
- )
- self.inputs.keypoint_threshold = gr.Slider(
- label="Keypoint Threshold",
- minimum=0,
- maximum=1,
- value=0.01,
- )
- with gr.Row():
- self.inputs.match_threshold = gr.Slider(
- label="Match Threshold",
- minimum=0.01,
- maximum=12.0,
- value=0.2,
- )
- self.inputs.ransac_threshold = gr.Slider(
- label="Ransac Threshold",
- minimum=0.01,
- maximum=12.0,
- value=4.0,
- step=0.01,
- interactive=True,
- )
-
- with gr.Row():
- self.inputs.ransac_confidence = gr.Slider(
- label="Ransac Confidence",
- minimum=0.01,
- maximum=1.0,
- value=0.9999,
- step=0.0001,
- interactive=True,
- )
- self.inputs.ransac_max_iter = gr.Slider(
- label="Ransac Max Iter",
- minimum=1,
- maximum=100,
- value=100,
- step=1,
- interactive=True,
- )
- with gr.Accordion("Scene Graph Settings", open=True):
- # mapping setting
- self.inputs.scene_graph = gr.Dropdown(
- choices=["all", "swin", "oneref"],
- value="all",
- label="Scene Graph",
- interactive=True,
- )
-
- # global feature setting
- self.inputs.global_feature = gr.Dropdown(
- choices=self.init_retrieval_dropdown(),
- value="netvlad",
- label="Global features",
- interactive=True,
- )
- self.inputs.top_k = gr.Slider(
- label="Number of Images per Image to Match",
- minimum=1,
- maximum=100,
- value=10,
- step=1,
- )
- # button_match = gr.Button("Run Matching", variant="primary")
-
- # mapping setting
- with gr.Column():
- with gr.Accordion("Mapping Settings", open=True):
- with gr.Row():
- with gr.Accordion("Buddle Settings", open=True):
- with gr.Row():
- self.inputs.mapper_refine_focal_length = gr.Checkbox(
- label="Refine Focal Length",
- value=False,
- interactive=True,
- )
- self.inputs.mapper_refine_principle_points = (
- gr.Checkbox(
- label="Refine Principle Points",
- value=False,
- interactive=True,
- )
- )
- self.inputs.mapper_refine_extra_params = gr.Checkbox(
- label="Refine Extra Params",
- value=False,
- interactive=True,
- )
- with gr.Accordion("Retriangluation Settings", open=True):
- gr.Textbox(
- label="Retriangluation Details",
- )
- self.ui.button_sfm = gr.Button("Run SFM", variant="primary")
- self.outputs.model_3d = gr.Model3D(
- interactive=True,
- )
- self.outputs.output_image = gr.Image(
- label="SFM Visualize",
- type="numpy",
- image_mode="RGB",
- interactive=False,
- )
-
- def call_empty(self):
- self.ui.button_sfm.click(fn=self.info, inputs=[], outputs=[])
-
- def call(self):
- self.ui.button_sfm.click(
- fn=self.sfm_engine.call,
- inputs=[
- self.inputs.matcher_key,
- self.inputs.input_images, # images
- self.inputs.camera_model,
- self.inputs.camera_params,
- self.inputs.max_keypoints,
- self.inputs.keypoint_threshold,
- self.inputs.match_threshold,
- self.inputs.ransac_threshold,
- self.inputs.ransac_confidence,
- self.inputs.ransac_max_iter,
- self.inputs.scene_graph,
- self.inputs.global_feature,
- self.inputs.top_k,
- self.inputs.mapper_refine_focal_length,
- self.inputs.mapper_refine_principle_points,
- self.inputs.mapper_refine_extra_params,
- ],
- outputs=[self.outputs.model_3d, self.outputs.output_image],
- )
+import sys
+from pathlib import Path
+from typing import Any, Dict, Optional, Tuple
+
+import gradio as gr
+import numpy as np
+from easydict import EasyDict as edict
+from omegaconf import OmegaConf
+
+sys.path.append(str(Path(__file__).parents[1]))
+
+from ui.sfm import SfmEngine
+from ui.utils import (
+ GRADIO_VERSION,
+ gen_examples,
+ generate_warp_images,
+ get_matcher_zoo,
+ load_config,
+ ransac_zoo,
+ run_matching,
+ run_ransac,
+ send_to_match,
+)
+
+DESCRIPTION = """
+# Image Matching WebUI
+This Space demonstrates [Image Matching WebUI](https://github.com/Vincentqyw/image-matching-webui) by vincent qin. Feel free to play with it, or duplicate to run image matching without a queue!
+
+🔎 For more details about supported local features and matchers, please refer to https://github.com/Vincentqyw/image-matching-webui
+
+🚀 All algorithms run on CPU for inference, causing slow speeds and high latency. For faster inference, please download the [source code](https://github.com/Vincentqyw/image-matching-webui) for local deployment.
+
+🐛 Your feedback is valuable to me. Please do not hesitate to report any bugs [here](https://github.com/Vincentqyw/image-matching-webui/issues).
+"""
+
+CSS = """
+#warning {background-color: #FFCCCB}
+.logs_class textarea {font-size: 12px !important}
+"""
+
+
+class ImageMatchingApp:
+ def __init__(self, server_name="0.0.0.0", server_port=7860, **kwargs):
+ self.server_name = server_name
+ self.server_port = server_port
+ self.config_path = kwargs.get(
+ "config", Path(__file__).parent / "config.yaml"
+ )
+ self.cfg = load_config(self.config_path)
+ self.matcher_zoo = get_matcher_zoo(self.cfg["matcher_zoo"])
+ self.app = None
+ self.init_interface()
+ # print all the keys
+
+ def init_matcher_dropdown(self):
+ algos = []
+ for k, v in self.cfg["matcher_zoo"].items():
+ if v.get("enable", True):
+ algos.append(k)
+ return algos
+
+ def init_interface(self):
+ with gr.Blocks(css=CSS) as self.app:
+ with gr.Tab("Image Matching"):
+ with gr.Row():
+ with gr.Column(scale=1):
+ gr.Image(
+ str(
+ Path(__file__).parent.parent
+ / "assets/logo.webp"
+ ),
+ elem_id="logo-img",
+ show_label=False,
+ show_share_button=False,
+ show_download_button=False,
+ )
+ with gr.Column(scale=3):
+ gr.Markdown(DESCRIPTION)
+ with gr.Row(equal_height=False):
+ with gr.Column():
+ with gr.Row():
+ matcher_list = gr.Dropdown(
+ choices=self.init_matcher_dropdown(),
+ value="disk+lightglue",
+ label="Matching Model",
+ interactive=True,
+ )
+ match_image_src = gr.Radio(
+ (
+ ["upload", "webcam", "clipboard"]
+ if GRADIO_VERSION > "3"
+ else ["upload", "webcam", "canvas"]
+ ),
+ label="Image Source",
+ value="upload",
+ )
+ with gr.Row():
+ input_image0 = gr.Image(
+ label="Image 0",
+ type="numpy",
+ image_mode="RGB",
+ height=300 if GRADIO_VERSION > "3" else None,
+ interactive=True,
+ )
+ input_image1 = gr.Image(
+ label="Image 1",
+ type="numpy",
+ image_mode="RGB",
+ height=300 if GRADIO_VERSION > "3" else None,
+ interactive=True,
+ )
+
+ with gr.Row():
+ button_reset = gr.Button(value="Reset")
+ button_run = gr.Button(
+ value="Run Match", variant="primary"
+ )
+
+ with gr.Accordion("Advanced Setting", open=False):
+ with gr.Accordion("Image Setting", open=True):
+ with gr.Row():
+ image_force_resize_cb = gr.Checkbox(
+ label="Force Resize",
+ value=False,
+ interactive=True,
+ )
+ image_setting_height = gr.Slider(
+ minimum=48,
+ maximum=2048,
+ step=16,
+ label="Image Height",
+ value=480,
+ visible=False,
+ )
+ image_setting_width = gr.Slider(
+ minimum=64,
+ maximum=2048,
+ step=16,
+ label="Image Width",
+ value=640,
+ visible=False,
+ )
+ with gr.Accordion("Matching Setting", open=True):
+ with gr.Row():
+ match_setting_threshold = gr.Slider(
+ minimum=0.0,
+ maximum=1,
+ step=0.001,
+ label="Match threshold",
+ value=0.1,
+ )
+ match_setting_max_keypoints = gr.Slider(
+ minimum=10,
+ maximum=10000,
+ step=10,
+ label="Max features",
+ value=1000,
+ )
+ # TODO: add line settings
+ with gr.Row():
+ detect_keypoints_threshold = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.001,
+ label="Keypoint threshold",
+ value=0.015,
+ )
+ detect_line_threshold = ( # noqa: F841
+ gr.Slider(
+ minimum=0.1,
+ maximum=1,
+ step=0.01,
+ label="Line threshold",
+ value=0.2,
+ )
+ )
+ # matcher_lists = gr.Radio(
+ # ["NN-mutual", "Dual-Softmax"],
+ # label="Matcher mode",
+ # value="NN-mutual",
+ # )
+ with gr.Accordion("RANSAC Setting", open=True):
+ with gr.Row(equal_height=False):
+ ransac_method = gr.Dropdown(
+ choices=ransac_zoo.keys(),
+ value=self.cfg["defaults"][
+ "ransac_method"
+ ],
+ label="RANSAC Method",
+ interactive=True,
+ )
+ ransac_reproj_threshold = gr.Slider(
+ minimum=0.0,
+ maximum=12,
+ step=0.01,
+ label="Ransac Reproj threshold",
+ value=8.0,
+ )
+ ransac_confidence = gr.Slider(
+ minimum=0.0,
+ maximum=1,
+ step=0.00001,
+ label="Ransac Confidence",
+ value=self.cfg["defaults"][
+ "ransac_confidence"
+ ],
+ )
+ ransac_max_iter = gr.Slider(
+ minimum=0.0,
+ maximum=100000,
+ step=100,
+ label="Ransac Iterations",
+ value=self.cfg["defaults"][
+ "ransac_max_iter"
+ ],
+ )
+ button_ransac = gr.Button(
+ value="Rerun RANSAC", variant="primary"
+ )
+ with gr.Accordion("Geometry Setting", open=False):
+ with gr.Row(equal_height=False):
+ choice_geometry_type = gr.Radio(
+ ["Fundamental", "Homography"],
+ label="Reconstruct Geometry",
+ value=self.cfg["defaults"][
+ "setting_geometry"
+ ],
+ )
+ # image resize
+ image_force_resize_cb.select(
+ fn=self._on_select_force_resize,
+ inputs=image_force_resize_cb,
+ outputs=[image_setting_width, image_setting_height],
+ )
+ # collect inputs
+ state_cache = gr.State({})
+ inputs = [
+ input_image0,
+ input_image1,
+ match_setting_threshold,
+ match_setting_max_keypoints,
+ detect_keypoints_threshold,
+ matcher_list,
+ ransac_method,
+ ransac_reproj_threshold,
+ ransac_confidence,
+ ransac_max_iter,
+ choice_geometry_type,
+ gr.State(self.matcher_zoo),
+ image_force_resize_cb,
+ image_setting_width,
+ image_setting_height,
+ ]
+
+ # Add some examples
+ with gr.Row():
+ # Example inputs
+ with gr.Accordion(
+ "Open for More: Examples", open=True
+ ):
+ gr.Examples(
+ examples=gen_examples(),
+ inputs=inputs,
+ outputs=[],
+ fn=run_matching,
+ cache_examples=False,
+ label=(
+ "Examples (click one of the images below to Run"
+ " Match). Thx: WxBS"
+ ),
+ )
+ with gr.Accordion("Supported Algorithms", open=False):
+ # add a table of supported algorithms
+ self.display_supported_algorithms()
+
+ with gr.Column():
+
+ with gr.Accordion(
+ "Open for More: Keypoints", open=True
+ ):
+ output_keypoints = gr.Image(
+ label="Keypoints", type="numpy"
+ )
+ with gr.Accordion(
+ (
+ "Open for More: Raw Matches"
+ " (Green for good matches, Red for bad)"
+ ),
+ open=False,
+ ):
+ output_matches_raw = gr.Image(
+ label="Raw Matches",
+ type="numpy",
+ )
+ with gr.Accordion(
+ (
+ "Open for More: Ransac Matches"
+ " (Green for good matches, Red for bad)"
+ ),
+ open=True,
+ ):
+ output_matches_ransac = gr.Image(
+ label="Ransac Matches", type="numpy"
+ )
+ with gr.Accordion(
+ "Open for More: Matches Statistics", open=False
+ ):
+ output_pred = gr.File(
+ label="Outputs", elem_id="download"
+ )
+ matches_result_info = gr.JSON(
+ label="Matches Statistics"
+ )
+ matcher_info = gr.JSON(label="Match info")
+
+ with gr.Accordion(
+ "Open for More: Warped Image", open=True
+ ):
+ output_wrapped = gr.Image(
+ label="Wrapped Pair", type="numpy"
+ )
+ # send to input
+ button_rerun = gr.Button(
+ value="Send to Input Match Pair",
+ variant="primary",
+ )
+ with gr.Accordion(
+ "Open for More: Geometry info", open=False
+ ):
+ geometry_result = gr.JSON(
+ label="Reconstructed Geometry"
+ )
+
+ # callbacks
+ match_image_src.change(
+ fn=self.ui_change_imagebox,
+ inputs=match_image_src,
+ outputs=input_image0,
+ )
+ match_image_src.change(
+ fn=self.ui_change_imagebox,
+ inputs=match_image_src,
+ outputs=input_image1,
+ )
+ # collect outputs
+ outputs = [
+ output_keypoints,
+ output_matches_raw,
+ output_matches_ransac,
+ matches_result_info,
+ matcher_info,
+ geometry_result,
+ output_wrapped,
+ state_cache,
+ output_pred,
+ ]
+ # button callbacks
+ button_run.click(
+ fn=run_matching, inputs=inputs, outputs=outputs
+ )
+ # Reset images
+ reset_outputs = [
+ input_image0,
+ input_image1,
+ match_setting_threshold,
+ match_setting_max_keypoints,
+ detect_keypoints_threshold,
+ matcher_list,
+ input_image0,
+ input_image1,
+ match_image_src,
+ output_keypoints,
+ output_matches_raw,
+ output_matches_ransac,
+ matches_result_info,
+ matcher_info,
+ output_wrapped,
+ geometry_result,
+ ransac_method,
+ ransac_reproj_threshold,
+ ransac_confidence,
+ ransac_max_iter,
+ choice_geometry_type,
+ output_pred,
+ image_force_resize_cb,
+ ]
+ button_reset.click(
+ fn=self.ui_reset_state,
+ inputs=None,
+ outputs=reset_outputs,
+ )
+
+ # run ransac button action
+ button_ransac.click(
+ fn=run_ransac,
+ inputs=[
+ state_cache,
+ choice_geometry_type,
+ ransac_method,
+ ransac_reproj_threshold,
+ ransac_confidence,
+ ransac_max_iter,
+ ],
+ outputs=[
+ output_matches_ransac,
+ matches_result_info,
+ output_wrapped,
+ output_pred,
+ ],
+ )
+
+ # send warped image to match
+ button_rerun.click(
+ fn=send_to_match,
+ inputs=[state_cache],
+ outputs=[input_image0, input_image1],
+ )
+
+ # estimate geo
+ choice_geometry_type.change(
+ fn=generate_warp_images,
+ inputs=[
+ input_image0,
+ input_image1,
+ geometry_result,
+ choice_geometry_type,
+ ],
+ outputs=[output_wrapped, geometry_result],
+ )
+ with gr.Tab("Structure from Motion(under-dev)"):
+ sfm_ui = AppSfmUI( # noqa: F841
+ {
+ **self.cfg,
+ "matcher_zoo": self.matcher_zoo,
+ "outputs": "experiments/sfm",
+ }
+ )
+ sfm_ui.call_empty()
+
+ def run(self):
+ self.app.queue().launch(
+ server_name=self.server_name,
+ server_port=self.server_port,
+ share=False,
+ )
+
+ def ui_change_imagebox(self, choice):
+ """
+ Updates the image box with the given choice.
+
+ Args:
+ choice (list): The list of image sources to be displayed in the image box.
+
+ Returns:
+ dict: A dictionary containing the updated value, sources, and type for the image box.
+ """
+ ret_dict = {
+ "value": None, # The updated value of the image box
+ "__type__": "update", # The type of update for the image box
+ }
+ if GRADIO_VERSION > "3":
+ return {
+ **ret_dict,
+ "sources": choice, # The list of image sources to be displayed
+ }
+ else:
+ return {
+ **ret_dict,
+ "source": choice, # The list of image sources to be displayed
+ }
+
+ def _on_select_force_resize(self, visible: bool = False):
+ return gr.update(visible=visible), gr.update(visible=visible)
+
+ def ui_reset_state(
+ self,
+ *args: Any,
+ ) -> Tuple[
+ Optional[np.ndarray],
+ Optional[np.ndarray],
+ float,
+ int,
+ float,
+ str,
+ Dict[str, Any],
+ Dict[str, Any],
+ str,
+ Optional[np.ndarray],
+ Optional[np.ndarray],
+ Optional[np.ndarray],
+ Dict[str, Any],
+ Dict[str, Any],
+ Optional[np.ndarray],
+ Dict[str, Any],
+ str,
+ int,
+ float,
+ int,
+ bool,
+ ]:
+ """
+ Reset the state of the UI.
+
+ Returns:
+ tuple: A tuple containing the initial values for the UI state.
+ """
+ key: str = list(self.matcher_zoo.keys())[
+ 0
+ ] # Get the first key from matcher_zoo
+ # flush_logs()
+ return (
+ None, # image0: Optional[np.ndarray]
+ None, # image1: Optional[np.ndarray]
+ self.cfg["defaults"][
+ "match_threshold"
+ ], # matching_threshold: float
+ self.cfg["defaults"]["max_keypoints"], # max_keypoints: int
+ self.cfg["defaults"][
+ "keypoint_threshold"
+ ], # keypoint_threshold: float
+ key, # matcher: str
+ self.ui_change_imagebox("upload"), # input image0: Dict[str, Any]
+ self.ui_change_imagebox("upload"), # input image1: Dict[str, Any]
+ "upload", # match_image_src: str
+ None, # keypoints: Optional[np.ndarray]
+ None, # raw matches: Optional[np.ndarray]
+ None, # ransac matches: Optional[np.ndarray]
+ {}, # matches result info: Dict[str, Any]
+ {}, # matcher config: Dict[str, Any]
+ None, # warped image: Optional[np.ndarray]
+ {}, # geometry result: Dict[str, Any]
+ self.cfg["defaults"]["ransac_method"], # ransac_method: str
+ self.cfg["defaults"][
+ "ransac_reproj_threshold"
+ ], # ransac_reproj_threshold: float
+ self.cfg["defaults"][
+ "ransac_confidence"
+ ], # ransac_confidence: float
+ self.cfg["defaults"]["ransac_max_iter"], # ransac_max_iter: int
+ self.cfg["defaults"]["setting_geometry"], # geometry: str
+ None, # predictions
+ False,
+ )
+
+ def display_supported_algorithms(self, style="tab"):
+ def get_link(link, tag="Link"):
+ return "[{}]({})".format(tag, link) if link is not None else "None"
+
+ data = []
+ cfg = self.cfg["matcher_zoo"]
+ if style == "md":
+ markdown_table = "| Algo. | Conference | Code | Project | Paper |\n"
+ markdown_table += (
+ "| ----- | ---------- | ---- | ------- | ----- |\n"
+ )
+
+ for k, v in cfg.items():
+ if not v["info"]["display"]:
+ continue
+ github_link = get_link(v["info"]["github"])
+ project_link = get_link(v["info"]["project"])
+ paper_link = get_link(
+ v["info"]["paper"],
+ (
+ Path(v["info"]["paper"]).name[-10:]
+ if v["info"]["paper"] is not None
+ else "Link"
+ ),
+ )
+
+ markdown_table += "{}|{}|{}|{}|{}\n".format(
+ v["info"]["name"], # display name
+ v["info"]["source"],
+ github_link,
+ project_link,
+ paper_link,
+ )
+ return gr.Markdown(markdown_table)
+ elif style == "tab":
+ for k, v in cfg.items():
+ if not v["info"].get("display", True):
+ continue
+ data.append(
+ [
+ v["info"]["name"],
+ v["info"]["source"],
+ v["info"]["github"],
+ v["info"]["paper"],
+ v["info"]["project"],
+ ]
+ )
+ tab = gr.Dataframe(
+ headers=["Algo.", "Conference", "Code", "Paper", "Project"],
+ datatype=["str", "str", "str", "str", "str"],
+ col_count=(5, "fixed"),
+ value=data,
+ # wrap=True,
+ # min_width = 1000,
+ # height=1000,
+ )
+ return tab
+
+
+class AppBaseUI:
+ def __init__(self, cfg: Dict[str, Any] = {}):
+ self.cfg = OmegaConf.create(cfg)
+ self.inputs = edict({})
+ self.outputs = edict({})
+ self.ui = edict({})
+
+ def _init_ui(self):
+ NotImplemented
+
+ def call(self, **kwargs):
+ NotImplemented
+
+ def info(self):
+ gr.Info("SFM is under construction.")
+
+
+class AppSfmUI(AppBaseUI):
+ def __init__(self, cfg: Dict[str, Any] = None):
+ super().__init__(cfg)
+ assert "matcher_zoo" in self.cfg
+ self.matcher_zoo = self.cfg["matcher_zoo"]
+ self.sfm_engine = SfmEngine(cfg)
+ self._init_ui()
+
+ def init_retrieval_dropdown(self):
+ algos = []
+ for k, v in self.cfg["retrieval_zoo"].items():
+ if v.get("enable", True):
+ algos.append(k)
+ return algos
+
+ def _update_options(self, option):
+ if option == "sparse":
+ return gr.Textbox("sparse", visible=True)
+ elif option == "dense":
+ return gr.Textbox("dense", visible=True)
+ else:
+ return gr.Textbox("not set", visible=True)
+
+ def _on_select_custom_params(self, value: bool = False):
+ return gr.update(visible=value)
+
+ def _init_ui(self):
+ with gr.Row():
+ # data settting and camera settings
+ with gr.Column():
+ self.inputs.input_images = gr.File(
+ label="SfM",
+ interactive=True,
+ file_count="multiple",
+ min_width=300,
+ )
+ # camera setting
+ with gr.Accordion("Camera Settings", open=True):
+ with gr.Column():
+ with gr.Row():
+ with gr.Column():
+ self.inputs.camera_model = gr.Dropdown(
+ choices=[
+ "PINHOLE",
+ "SIMPLE_RADIAL",
+ "OPENCV",
+ ],
+ value="PINHOLE",
+ label="Camera Model",
+ interactive=True,
+ )
+ with gr.Column():
+ gr.Checkbox(
+ label="Shared Params",
+ value=True,
+ interactive=True,
+ )
+ camera_custom_params_cb = gr.Checkbox(
+ label="Custom Params",
+ value=False,
+ interactive=True,
+ )
+ with gr.Row():
+ self.inputs.camera_params = gr.Textbox(
+ label="Camera Params",
+ value="0,0,0,0",
+ interactive=False,
+ visible=False,
+ )
+ camera_custom_params_cb.select(
+ fn=self._on_select_custom_params,
+ inputs=camera_custom_params_cb,
+ outputs=self.inputs.camera_params,
+ )
+
+ with gr.Accordion("Matching Settings", open=True):
+ # feature extraction and matching setting
+ with gr.Row():
+ # matcher setting
+ self.inputs.matcher_key = gr.Dropdown(
+ choices=self.matcher_zoo.keys(),
+ value="disk+lightglue",
+ label="Matching Model",
+ interactive=True,
+ )
+ with gr.Row():
+ with gr.Accordion("Advanced Settings", open=False):
+ with gr.Column():
+ with gr.Row():
+ # matching setting
+ self.inputs.max_keypoints = gr.Slider(
+ label="Max Keypoints",
+ minimum=100,
+ maximum=10000,
+ value=1000,
+ interactive=True,
+ )
+ self.inputs.keypoint_threshold = gr.Slider(
+ label="Keypoint Threshold",
+ minimum=0,
+ maximum=1,
+ value=0.01,
+ )
+ with gr.Row():
+ self.inputs.match_threshold = gr.Slider(
+ label="Match Threshold",
+ minimum=0.01,
+ maximum=12.0,
+ value=0.2,
+ )
+ self.inputs.ransac_threshold = gr.Slider(
+ label="Ransac Threshold",
+ minimum=0.01,
+ maximum=12.0,
+ value=4.0,
+ step=0.01,
+ interactive=True,
+ )
+
+ with gr.Row():
+ self.inputs.ransac_confidence = gr.Slider(
+ label="Ransac Confidence",
+ minimum=0.01,
+ maximum=1.0,
+ value=0.9999,
+ step=0.0001,
+ interactive=True,
+ )
+ self.inputs.ransac_max_iter = gr.Slider(
+ label="Ransac Max Iter",
+ minimum=1,
+ maximum=100,
+ value=100,
+ step=1,
+ interactive=True,
+ )
+ with gr.Accordion("Scene Graph Settings", open=True):
+ # mapping setting
+ self.inputs.scene_graph = gr.Dropdown(
+ choices=["all", "swin", "oneref"],
+ value="all",
+ label="Scene Graph",
+ interactive=True,
+ )
+
+ # global feature setting
+ self.inputs.global_feature = gr.Dropdown(
+ choices=self.init_retrieval_dropdown(),
+ value="netvlad",
+ label="Global features",
+ interactive=True,
+ )
+ self.inputs.top_k = gr.Slider(
+ label="Number of Images per Image to Match",
+ minimum=1,
+ maximum=100,
+ value=10,
+ step=1,
+ )
+ # button_match = gr.Button("Run Matching", variant="primary")
+
+ # mapping setting
+ with gr.Column():
+ with gr.Accordion("Mapping Settings", open=True):
+ with gr.Row():
+ with gr.Accordion("Buddle Settings", open=True):
+ with gr.Row():
+ self.inputs.mapper_refine_focal_length = (
+ gr.Checkbox(
+ label="Refine Focal Length",
+ value=False,
+ interactive=True,
+ )
+ )
+ self.inputs.mapper_refine_principle_points = (
+ gr.Checkbox(
+ label="Refine Principle Points",
+ value=False,
+ interactive=True,
+ )
+ )
+ self.inputs.mapper_refine_extra_params = (
+ gr.Checkbox(
+ label="Refine Extra Params",
+ value=False,
+ interactive=True,
+ )
+ )
+ with gr.Accordion("Retriangluation Settings", open=True):
+ gr.Textbox(
+ label="Retriangluation Details",
+ )
+ self.ui.button_sfm = gr.Button("Run SFM", variant="primary")
+ self.outputs.model_3d = gr.Model3D(
+ interactive=True,
+ )
+ self.outputs.output_image = gr.Image(
+ label="SFM Visualize",
+ type="numpy",
+ image_mode="RGB",
+ interactive=False,
+ )
+
+ def call_empty(self):
+ self.ui.button_sfm.click(fn=self.info, inputs=[], outputs=[])
+
+ def call(self):
+ self.ui.button_sfm.click(
+ fn=self.sfm_engine.call,
+ inputs=[
+ self.inputs.matcher_key,
+ self.inputs.input_images, # images
+ self.inputs.camera_model,
+ self.inputs.camera_params,
+ self.inputs.max_keypoints,
+ self.inputs.keypoint_threshold,
+ self.inputs.match_threshold,
+ self.inputs.ransac_threshold,
+ self.inputs.ransac_confidence,
+ self.inputs.ransac_max_iter,
+ self.inputs.scene_graph,
+ self.inputs.global_feature,
+ self.inputs.top_k,
+ self.inputs.mapper_refine_focal_length,
+ self.inputs.mapper_refine_principle_points,
+ self.inputs.mapper_refine_extra_params,
+ ],
+ outputs=[self.outputs.model_3d, self.outputs.output_image],
+ )
diff --git a/config/config.yaml b/ui/config.yaml
similarity index 82%
rename from config/config.yaml
rename to ui/config.yaml
index 3773b7b96e1379d0e8c0f65205e715293c8fea48..eb6b60966686ebf63797d05c48aa98f95f8534fe 100644
--- a/config/config.yaml
+++ b/ui/config.yaml
@@ -1,6 +1,6 @@
server:
name: "0.0.0.0"
- port: 7860
+ port: 7861
defaults:
setting_threshold: 0.1
@@ -9,73 +9,13 @@ defaults:
enable_ransac: true
ransac_method: CV2_USAC_MAGSAC
ransac_reproj_threshold: 8
- ransac_confidence: 0.9999
+ ransac_confidence: 0.999
ransac_max_iter: 10000
ransac_num_samples: 4
match_threshold: 0.2
setting_geometry: Homography
matcher_zoo:
- # example config
- Example:
- # show in `Matching Model` or not, default: true
- enable: false
- # matcher name
- matcher: example
- # skip ci or not, default: false
- skip_ci: true
- # dense matcher or not, default: true
- dense: true
- # info
- info:
- # dispaly name in `Matching Model`
- name: example(example)
- # conference/journal/workshop Year
- source: "CVPR XXXX"
- # github link
- github: https://github.com/example/example
- # paper link
- paper: https://arxiv.org/abs/xxxx.xxxx
- # project link
- project: https://example.com
- # show in `support algos` table
- display: false
- # low, medium, high
- efficiency: low
-
- dad(RoMa):
- matcher: dad_roma
- skip_ci: true
- dense: true
- enable: false
- info:
- name: Dad(RoMa) #dispaly name
- source: "ARXIV 2025"
- github: https://github.com/example/example
- paper: https://arxiv.org/abs/2503.07347
- display: false
- efficiency: low # low, medium, high
- minima(loftr):
- matcher: minima_loftr
- dense: true
- info:
- name: MINIMA(LoFTR) #dispaly name
- source: "ARXIV 2024"
- paper: https://arxiv.org/abs/2412.19412
- github: https://github.com/LSXI7/MINIMA
- display: true
- minima(RoMa):
- matcher: minima_roma
- skip_ci: true
- dense: true
- enable: false
- info:
- name: MINIMA(RoMa) #dispaly name
- source: "ARXIV 2024"
- paper: https://arxiv.org/abs/2412.19412
- github: https://github.com/LSXI7/MINIMA
- display: false
- efficiency: low # low, medium, high
omniglue:
enable: true
matcher: omniglue
@@ -98,9 +38,8 @@ matcher_zoo:
paper: https://arxiv.org/abs/2406.09756
project: https://dust3r.europe.naverlabs.com
display: true
- efficiency: low # low, medium, high
DUSt3R:
- # TODO: duster is under development
+ # TODO: duster is under development
enable: true
# skip_ci: true
matcher: duster
@@ -123,8 +62,7 @@ matcher_zoo:
github: https://github.com/xuelunshen/gim
paper: https://arxiv.org/abs/2402.11095
project: https://xuelunshen.com/gim
- display: false
- efficiency: low # low, medium, high
+ display: true
RoMa:
matcher: roma
skip_ci: true
@@ -136,20 +74,17 @@ matcher_zoo:
paper: https://arxiv.org/abs/2305.15404
project: https://parskatt.github.io/RoMa
display: true
- efficiency: low # low, medium, high
dkm:
matcher: dkm
skip_ci: true
dense: true
- enable: false
info:
name: DKM #dispaly name
source: "CVPR 2023"
github: https://github.com/Parskatt/DKM
paper: https://arxiv.org/abs/2202.00667
project: https://parskatt.github.io/DKM
- display: false
- efficiency: low # low, medium, high
+ display: true
loftr:
matcher: loftr
dense: true
@@ -180,17 +115,6 @@ matcher_zoo:
paper: https://arxiv.org/pdf/2404.09692
project: null
display: true
- jamma:
- matcher: jamma
- dense: true
- enable: false
- info:
- name: Jamma #dispaly name
- source: "CVPR 2024"
- github: https://github.com/OnderT/XoFTR
- paper: https://arxiv.org/pdf/2404.09692
- project: null
- display: false
cotr:
enable: false
skip_ci: true
@@ -203,7 +127,6 @@ matcher_zoo:
paper: https://arxiv.org/abs/2103.14167
project: null
display: true
- efficiency: low # low, medium, high
topicfm:
matcher: topicfm
dense: true
@@ -344,17 +267,6 @@ matcher_zoo:
paper: https://arxiv.org/pdf/2306.13643
project: null
display: true
- aliked+lightglue:
- matcher: aliked-lightglue
- feature: aliked-n16
- dense: false
- info:
- name: ALIKED
- source: "ICCV 2023"
- github: https://github.com/Shiaoming/ALIKED
- paper: https://arxiv.org/pdf/2304.03608.pdf
- project: null
- display: true
superpoint+mnn:
matcher: NN-mutual
feature: superpoint_max
@@ -477,7 +389,7 @@ matcher_zoo:
project: null
display: true
gluestick:
- enable: true
+ enable: false
matcher: gluestick
dense: true
info:
diff --git a/imcui/ui/sfm.py b/ui/sfm.py
similarity index 93%
rename from imcui/ui/sfm.py
rename to ui/sfm.py
index 5ecdadb915012af43efed1b3eba88956492a1b83..2fd90bd07891cb9e7492fe538b1b2a591a138ce2 100644
--- a/imcui/ui/sfm.py
+++ b/ui/sfm.py
@@ -1,164 +1,170 @@
-import shutil
-import tempfile
-from pathlib import Path
-from typing import Any, Dict, List
-
-
-from ..hloc import (
- extract_features,
- logger,
- match_features,
- pairs_from_retrieval,
- reconstruction,
- visualization,
-)
-
-try:
- import pycolmap
-except ImportError:
- logger.warning("pycolmap not installed, some features may not work")
-
-from .viz import fig2im
-
-
-class SfmEngine:
- def __init__(self, cfg: Dict[str, Any] = None):
- self.cfg = cfg
- if "outputs" in cfg and Path(cfg["outputs"]):
- outputs = Path(cfg["outputs"])
- outputs.mkdir(parents=True, exist_ok=True)
- else:
- outputs = tempfile.mkdtemp()
- self.outputs = Path(outputs)
-
- def call(
- self,
- key: str,
- images: Path,
- camera_model: str,
- camera_params: List[float],
- max_keypoints: int,
- keypoint_threshold: float,
- match_threshold: float,
- ransac_threshold: int,
- ransac_confidence: float,
- ransac_max_iter: int,
- scene_graph: bool,
- global_feature: str,
- top_k: int = 10,
- mapper_refine_focal_length: bool = False,
- mapper_refine_principle_points: bool = False,
- mapper_refine_extra_params: bool = False,
- ):
- """
- Call a list of functions to perform feature extraction, matching, and reconstruction.
-
- Args:
- key (str): The key to retrieve the matcher and feature models.
- images (Path): The directory containing the images.
- outputs (Path): The directory to store the outputs.
- camera_model (str): The camera model.
- camera_params (List[float]): The camera parameters.
- max_keypoints (int): The maximum number of features.
- match_threshold (float): The match threshold.
- ransac_threshold (int): The RANSAC threshold.
- ransac_confidence (float): The RANSAC confidence.
- ransac_max_iter (int): The maximum number of RANSAC iterations.
- scene_graph (bool): Whether to compute the scene graph.
- global_feature (str): Whether to compute the global feature.
- top_k (int): The number of image-pair to use.
- mapper_refine_focal_length (bool): Whether to refine the focal length.
- mapper_refine_principle_points (bool): Whether to refine the principle points.
- mapper_refine_extra_params (bool): Whether to refine the extra parameters.
-
- Returns:
- Path: The directory containing the SfM results.
- """
- if len(images) == 0:
- logger.error(f"{images} does not exist.")
-
- temp_images = Path(tempfile.mkdtemp())
- # copy images
- logger.info(f"Copying images to {temp_images}.")
- for image in images:
- shutil.copy(image, temp_images)
-
- matcher_zoo = self.cfg["matcher_zoo"]
- model = matcher_zoo[key]
- match_conf = model["matcher"]
- match_conf["model"]["max_keypoints"] = max_keypoints
- match_conf["model"]["match_threshold"] = match_threshold
-
- feature_conf = model["feature"]
- feature_conf["model"]["max_keypoints"] = max_keypoints
- feature_conf["model"]["keypoint_threshold"] = keypoint_threshold
-
- # retrieval
- retrieval_name = self.cfg.get("retrieval_name", "netvlad")
- retrieval_conf = extract_features.confs[retrieval_name]
-
- mapper_options = {
- "ba_refine_extra_params": mapper_refine_extra_params,
- "ba_refine_focal_length": mapper_refine_focal_length,
- "ba_refine_principal_point": mapper_refine_principle_points,
- "ba_local_max_num_iterations": 40,
- "ba_local_max_refinements": 3,
- "ba_global_max_num_iterations": 100,
- # below 3 options are for individual/video data, for internet photos, they should be left
- # default
- "min_focal_length_ratio": 0.1,
- "max_focal_length_ratio": 10,
- "max_extra_param": 1e15,
- }
-
- sfm_dir = self.outputs / "sfm_{}".format(key)
- sfm_pairs = self.outputs / "pairs-sfm.txt"
- sfm_dir.mkdir(exist_ok=True, parents=True)
-
- # extract features
- retrieval_path = extract_features.main(
- retrieval_conf, temp_images, self.outputs
- )
- pairs_from_retrieval.main(retrieval_path, sfm_pairs, num_matched=top_k)
-
- feature_path = extract_features.main(feature_conf, temp_images, self.outputs)
- # match features
- match_path = match_features.main(
- match_conf, sfm_pairs, feature_conf["output"], self.outputs
- )
- # reconstruction
- already_sfm = False
- if sfm_dir.exists():
- try:
- model = pycolmap.Reconstruction(str(sfm_dir))
- already_sfm = True
- except ValueError:
- logger.info(f"sfm_dir not exists model: {sfm_dir}")
- if not already_sfm:
- model = reconstruction.main(
- sfm_dir,
- temp_images,
- sfm_pairs,
- feature_path,
- match_path,
- mapper_options=mapper_options,
- )
-
- vertices = []
- for point3D_id, point3D in model.points3D.items():
- vertices.append([point3D.xyz, point3D.color])
-
- model_3d = sfm_dir / "points3D.obj"
- with open(model_3d, "w") as f:
- for p, c in vertices:
- # Write vertex position
- f.write("v {} {} {}\n".format(p[0], p[1], p[2]))
- # Write vertex normal (color)
- f.write(
- "vn {} {} {}\n".format(c[0] / 255.0, c[1] / 255.0, c[2] / 255.0)
- )
- viz_2d = visualization.visualize_sfm_2d(
- model, temp_images, color_by="visibility", n=2, dpi=300
- )
-
- return model_3d, fig2im(viz_2d) / 255.0
+import shutil
+import sys
+import tempfile
+from pathlib import Path
+from typing import Any, Dict, List
+
+sys.path.append(str(Path(__file__).parents[1]))
+
+from hloc import (
+ extract_features,
+ logger,
+ match_features,
+ pairs_from_retrieval,
+ reconstruction,
+ visualization,
+)
+
+try:
+ import pycolmap
+except ImportError:
+ logger.warning("pycolmap not installed, some features may not work")
+
+from ui.viz import fig2im
+
+
+class SfmEngine:
+ def __init__(self, cfg: Dict[str, Any] = None):
+ self.cfg = cfg
+ if "outputs" in cfg and Path(cfg["outputs"]):
+ outputs = Path(cfg["outputs"])
+ outputs.mkdir(parents=True, exist_ok=True)
+ else:
+ outputs = tempfile.mkdtemp()
+ self.outputs = Path(outputs)
+
+ def call(
+ self,
+ key: str,
+ images: Path,
+ camera_model: str,
+ camera_params: List[float],
+ max_keypoints: int,
+ keypoint_threshold: float,
+ match_threshold: float,
+ ransac_threshold: int,
+ ransac_confidence: float,
+ ransac_max_iter: int,
+ scene_graph: bool,
+ global_feature: str,
+ top_k: int = 10,
+ mapper_refine_focal_length: bool = False,
+ mapper_refine_principle_points: bool = False,
+ mapper_refine_extra_params: bool = False,
+ ):
+ """
+ Call a list of functions to perform feature extraction, matching, and reconstruction.
+
+ Args:
+ key (str): The key to retrieve the matcher and feature models.
+ images (Path): The directory containing the images.
+ outputs (Path): The directory to store the outputs.
+ camera_model (str): The camera model.
+ camera_params (List[float]): The camera parameters.
+ max_keypoints (int): The maximum number of features.
+ match_threshold (float): The match threshold.
+ ransac_threshold (int): The RANSAC threshold.
+ ransac_confidence (float): The RANSAC confidence.
+ ransac_max_iter (int): The maximum number of RANSAC iterations.
+ scene_graph (bool): Whether to compute the scene graph.
+ global_feature (str): Whether to compute the global feature.
+ top_k (int): The number of image-pair to use.
+ mapper_refine_focal_length (bool): Whether to refine the focal length.
+ mapper_refine_principle_points (bool): Whether to refine the principle points.
+ mapper_refine_extra_params (bool): Whether to refine the extra parameters.
+
+ Returns:
+ Path: The directory containing the SfM results.
+ """
+ if len(images) == 0:
+ logger.error(f"{images} does not exist.")
+
+ temp_images = Path(tempfile.mkdtemp())
+ # copy images
+ logger.info(f"Copying images to {temp_images}.")
+ for image in images:
+ shutil.copy(image, temp_images)
+
+ matcher_zoo = self.cfg["matcher_zoo"]
+ model = matcher_zoo[key]
+ match_conf = model["matcher"]
+ match_conf["model"]["max_keypoints"] = max_keypoints
+ match_conf["model"]["match_threshold"] = match_threshold
+
+ feature_conf = model["feature"]
+ feature_conf["model"]["max_keypoints"] = max_keypoints
+ feature_conf["model"]["keypoint_threshold"] = keypoint_threshold
+
+ # retrieval
+ retrieval_name = self.cfg.get("retrieval_name", "netvlad")
+ retrieval_conf = extract_features.confs[retrieval_name]
+
+ mapper_options = {
+ "ba_refine_extra_params": mapper_refine_extra_params,
+ "ba_refine_focal_length": mapper_refine_focal_length,
+ "ba_refine_principal_point": mapper_refine_principle_points,
+ "ba_local_max_num_iterations": 40,
+ "ba_local_max_refinements": 3,
+ "ba_global_max_num_iterations": 100,
+ # below 3 options are for individual/video data, for internet photos, they should be left
+ # default
+ "min_focal_length_ratio": 0.1,
+ "max_focal_length_ratio": 10,
+ "max_extra_param": 1e15,
+ }
+
+ sfm_dir = self.outputs / "sfm_{}".format(key)
+ sfm_pairs = self.outputs / "pairs-sfm.txt"
+ sfm_dir.mkdir(exist_ok=True, parents=True)
+
+ # extract features
+ retrieval_path = extract_features.main(
+ retrieval_conf, temp_images, self.outputs
+ )
+ pairs_from_retrieval.main(retrieval_path, sfm_pairs, num_matched=top_k)
+
+ feature_path = extract_features.main(
+ feature_conf, temp_images, self.outputs
+ )
+ # match features
+ match_path = match_features.main(
+ match_conf, sfm_pairs, feature_conf["output"], self.outputs
+ )
+ # reconstruction
+ already_sfm = False
+ if sfm_dir.exists():
+ try:
+ model = pycolmap.Reconstruction(str(sfm_dir))
+ already_sfm = True
+ except ValueError:
+ logger.info(f"sfm_dir not exists model: {sfm_dir}")
+ if not already_sfm:
+ model = reconstruction.main(
+ sfm_dir,
+ temp_images,
+ sfm_pairs,
+ feature_path,
+ match_path,
+ mapper_options=mapper_options,
+ )
+
+ vertices = []
+ for point3D_id, point3D in model.points3D.items():
+ vertices.append([point3D.xyz, point3D.color])
+
+ model_3d = sfm_dir / "points3D.obj"
+ with open(model_3d, "w") as f:
+ for p, c in vertices:
+ # Write vertex position
+ f.write("v {} {} {}\n".format(p[0], p[1], p[2]))
+ # Write vertex normal (color)
+ f.write(
+ "vn {} {} {}\n".format(
+ c[0] / 255.0, c[1] / 255.0, c[2] / 255.0
+ )
+ )
+ viz_2d = visualization.visualize_sfm_2d(
+ model, temp_images, color_by="visibility", n=2, dpi=300
+ )
+
+ return model_3d, fig2im(viz_2d) / 255.0
diff --git a/imcui/ui/utils.py b/ui/utils.py
similarity index 83%
rename from imcui/ui/utils.py
rename to ui/utils.py
index 33ebf7f11a9ee6b0616493478ce1d8ec9ab8dfad..cbd935e39dd774b179b8811cc2902d536ac5785f 100644
--- a/imcui/ui/utils.py
+++ b/ui/utils.py
@@ -1,1121 +1,1081 @@
-import os
-import pickle
-import random
-import time
-import warnings
-from itertools import combinations
-from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-from datasets import load_dataset
-
-import cv2
-import gradio as gr
-import matplotlib.pyplot as plt
-import numpy as np
-import poselib
-from PIL import Image
-
-from ..hloc import (
- DEVICE,
- extract_features,
- extractors,
- logger,
- match_dense,
- match_features,
- matchers,
- DATASETS_REPO_ID,
-)
-from ..hloc.utils.base_model import dynamic_load
-from .viz import display_keypoints, display_matches, fig2im, plot_images
-from .modelcache import ARCSizeAwareModelCache as ModelCache
-
-warnings.simplefilter("ignore")
-
-ROOT = Path(__file__).parents[1]
-# some default values
-DEFAULT_SETTING_THRESHOLD = 0.1
-DEFAULT_SETTING_MAX_FEATURES = 2000
-DEFAULT_DEFAULT_KEYPOINT_THRESHOLD = 0.01
-DEFAULT_ENABLE_RANSAC = True
-DEFAULT_RANSAC_METHOD = "CV2_USAC_MAGSAC"
-DEFAULT_RANSAC_REPROJ_THRESHOLD = 8
-DEFAULT_RANSAC_CONFIDENCE = 0.9999
-DEFAULT_RANSAC_MAX_ITER = 10000
-DEFAULT_MIN_NUM_MATCHES = 4
-DEFAULT_MATCHING_THRESHOLD = 0.2
-DEFAULT_SETTING_GEOMETRY = "Homography"
-GRADIO_VERSION = gr.__version__.split(".")[0]
-MATCHER_ZOO = None
-
-
-model_cache = ModelCache()
-
-
-def load_config(config_name: str) -> Dict[str, Any]:
- """
- Load a YAML configuration file.
-
- Args:
- config_name: The path to the YAML configuration file.
-
- Returns:
- The configuration dictionary, with string keys and arbitrary values.
- """
- import yaml
-
- with open(config_name, "r") as stream:
- try:
- config: Dict[str, Any] = yaml.safe_load(stream)
- except yaml.YAMLError as exc:
- logger.error(exc)
- return config
-
-
-def get_matcher_zoo(
- matcher_zoo: Dict[str, Dict[str, Union[str, bool]]],
-) -> Dict[str, Dict[str, Union[Callable, bool]]]:
- """
- Restore matcher configurations from a dictionary.
-
- Args:
- matcher_zoo: A dictionary with the matcher configurations,
- where the configuration is a dictionary as loaded from a YAML file.
-
- Returns:
- A dictionary with the matcher configurations, where the configuration is
- a function or a function instead of a string.
- """
- matcher_zoo_restored = {}
- for k, v in matcher_zoo.items():
- matcher_zoo_restored[k] = parse_match_config(v)
- return matcher_zoo_restored
-
-
-def parse_match_config(conf):
- if conf["dense"]:
- return {
- "matcher": match_dense.confs.get(conf["matcher"]),
- "dense": True,
- "info": conf.get("info", {}),
- }
- else:
- return {
- "feature": extract_features.confs.get(conf["feature"]),
- "matcher": match_features.confs.get(conf["matcher"]),
- "dense": False,
- "info": conf.get("info", {}),
- }
-
-
-def get_model(match_conf: Dict[str, Any]):
- """
- Load a matcher model from the provided configuration.
-
- Args:
- match_conf: A dictionary containing the model configuration.
-
- Returns:
- A matcher model instance.
- """
- Model = dynamic_load(matchers, match_conf["model"]["name"])
- model = Model(match_conf["model"]).eval().to(DEVICE)
- return model
-
-
-def get_feature_model(conf: Dict[str, Dict[str, Any]]):
- """
- Load a feature extraction model from the provided configuration.
-
- Args:
- conf: A dictionary containing the model configuration.
-
- Returns:
- A feature extraction model instance.
- """
- Model = dynamic_load(extractors, conf["model"]["name"])
- model = Model(conf["model"]).eval().to(DEVICE)
- return model
-
-
-def download_example_images(repo_id, output_dir):
- logger.info(f"Download example dataset from huggingface: {repo_id}")
- dataset = load_dataset(repo_id)
- Path(output_dir).mkdir(parents=True, exist_ok=True)
- for example in dataset["train"]: # Assuming the dataset is in the "train" split
- file_path = example["path"]
- image = example["image"] # Access the PIL.Image object directly
- full_path = os.path.join(output_dir, file_path)
- Path(os.path.dirname(full_path)).mkdir(parents=True, exist_ok=True)
- image.save(full_path)
- logger.info(f"Images saved to {output_dir} successfully.")
- return Path(output_dir)
-
-
-def gen_examples(data_root: Path):
- random.seed(1)
- example_algos = [
- "disk+lightglue",
- "xfeat(sparse)",
- "dedode",
- "loftr",
- "disk",
- "RoMa",
- "sift",
- "rord",
- "d2net",
- "aspanformer",
- "topicfm",
- "superpoint+superglue",
- "superpoint+lightglue",
- "superpoint+mnn",
- "disk",
- ]
- example_algos_rotation_robust = [
- "sift",
- "rord",
- "sift+lightglue",
- # "GIM(dkm)",
- ]
- data_root = Path(data_root)
- if not Path(data_root).exists():
- try:
- download_example_images(DATASETS_REPO_ID, data_root)
- except Exception as e:
- logger.error(f"download_example_images error : {e}")
- data_root = ROOT / "datasets"
- if not Path(data_root / "sacre_coeur/mapping").exists():
- download_example_images(DATASETS_REPO_ID, data_root)
-
- def distribute_elements(A, B):
- new_B = np.array(B, copy=True).flatten()
- np.random.shuffle(new_B)
- new_B = np.resize(new_B, len(A))
- np.random.shuffle(new_B)
- return new_B.tolist()
-
- # normal examples
- def gen_images_pairs(count: int = 5):
- path = str(data_root / "sacre_coeur/mapping")
- imgs_list = [
- os.path.join(path, file)
- for file in os.listdir(path)
- if file.lower().endswith((".jpg", ".jpeg", ".png"))
- ]
- pairs = list(combinations(imgs_list, 2))
- if len(pairs) < count:
- count = len(pairs)
- selected = random.sample(range(len(pairs)), count)
- return [pairs[i] for i in selected]
-
- # rotated examples
- def gen_rot_image_pairs(count: int = 5):
- path = data_root / "sacre_coeur/mapping"
- path_rot = data_root / "sacre_coeur/mapping_rot"
- rot_list = [45, 180, 90, 225, 270]
- pairs = []
- for file in os.listdir(path):
- if file.lower().endswith((".jpg", ".jpeg", ".png")):
- for rot in rot_list:
- file_rot = "{}_rot{}.jpg".format(Path(file).stem, rot)
- if (path_rot / file_rot).exists():
- pairs.append(
- [
- path / file,
- path_rot / file_rot,
- ]
- )
- if len(pairs) < count:
- count = len(pairs)
- selected = random.sample(range(len(pairs)), count)
- return [pairs[i] for i in selected]
-
- def gen_scale_image_pairs(count: int = 5):
- path = data_root / "sacre_coeur/mapping"
- path_scale = data_root / "sacre_coeur/mapping_scale"
- scale_list = [0.3, 0.5]
- pairs = []
- for file in os.listdir(path):
- if file.lower().endswith((".jpg", ".jpeg", ".png")):
- for scale in scale_list:
- file_scale = "{}_scale{}.jpg".format(Path(file).stem, scale)
- if (path_scale / file_scale).exists():
- pairs.append(
- [
- path / file,
- path_scale / file_scale,
- ]
- )
- if len(pairs) < count:
- count = len(pairs)
- selected = random.sample(range(len(pairs)), count)
- return [pairs[i] for i in selected]
-
- # extramely hard examples
- def gen_image_pairs_wxbs(count: int = None):
- prefix = "wxbs_benchmark/.WxBS/v1.1"
- wxbs_path = data_root / prefix
- pairs = []
- for catg in os.listdir(wxbs_path):
- catg_path = wxbs_path / catg
- if not catg_path.is_dir():
- continue
- for scene in os.listdir(catg_path):
- scene_path = catg_path / scene
- if not scene_path.is_dir():
- continue
- img1_path = scene_path / "01.png"
- img2_path = scene_path / "02.png"
- if img1_path.exists() and img2_path.exists():
- pairs.append([str(img1_path), str(img2_path)])
- return pairs
-
- # image pair path
- pairs = gen_images_pairs()
- # pairs += gen_rot_image_pairs()
- pairs += gen_scale_image_pairs()
- pairs += gen_image_pairs_wxbs()
- pairs_rotation = gen_rot_image_pairs()
- dist_examples = distribute_elements(pairs, example_algos)
- dist_examples_rotation = distribute_elements(
- pairs_rotation, example_algos_rotation_robust
- )
- pairs = pairs_rotation + pairs
- dist_examples = dist_examples_rotation + dist_examples
- match_setting_threshold = DEFAULT_SETTING_THRESHOLD
- match_setting_max_features = DEFAULT_SETTING_MAX_FEATURES
- detect_keypoints_threshold = DEFAULT_DEFAULT_KEYPOINT_THRESHOLD
- ransac_method = DEFAULT_RANSAC_METHOD
- ransac_reproj_threshold = DEFAULT_RANSAC_REPROJ_THRESHOLD
- ransac_confidence = DEFAULT_RANSAC_CONFIDENCE
- ransac_max_iter = DEFAULT_RANSAC_MAX_ITER
- input_lists = []
-
- for pair, mt in zip(pairs, dist_examples):
- input_lists.append(
- [
- pair[0],
- pair[1],
- match_setting_threshold,
- match_setting_max_features,
- detect_keypoints_threshold,
- mt,
- ransac_method,
- ransac_reproj_threshold,
- ransac_confidence,
- ransac_max_iter,
- ]
- )
- return input_lists
-
-
-def set_null_pred(feature_type: str, pred: dict):
- if feature_type == "KEYPOINT":
- pred["mmkeypoints0_orig"] = np.array([])
- pred["mmkeypoints1_orig"] = np.array([])
- pred["mmconf"] = np.array([])
- elif feature_type == "LINE":
- pred["mline_keypoints0_orig"] = np.array([])
- pred["mline_keypoints1_orig"] = np.array([])
- pred["H"] = None
- pred["geom_info"] = {}
- return pred
-
-
-def _filter_matches_opencv(
- kp0: np.ndarray,
- kp1: np.ndarray,
- method: int = cv2.RANSAC,
- reproj_threshold: float = 3.0,
- confidence: float = 0.99,
- max_iter: int = 2000,
- geometry_type: str = "Homography",
-) -> Tuple[np.ndarray, np.ndarray]:
- """
- Filters matches between two sets of keypoints using OpenCV's findHomography.
-
- Args:
- kp0 (np.ndarray): Array of keypoints from the first image.
- kp1 (np.ndarray): Array of keypoints from the second image.
- method (int, optional): RANSAC method. Defaults to "cv2.RANSAC".
- reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to 3.0.
- confidence (float, optional): RANSAC confidence. Defaults to 0.99.
- max_iter (int, optional): RANSAC maximum iterations. Defaults to 2000.
- geometry_type (str, optional): Type of geometry. Defaults to "Homography".
-
- Returns:
- Tuple[np.ndarray, np.ndarray]: Homography matrix and mask.
- """
- if geometry_type == "Homography":
- try:
- M, mask = cv2.findHomography(
- kp0,
- kp1,
- method=method,
- ransacReprojThreshold=reproj_threshold,
- confidence=confidence,
- maxIters=max_iter,
- )
- except cv2.error:
- logger.error("compute findHomography error, len(kp0): {}".format(len(kp0)))
- return None, None
- elif geometry_type == "Fundamental":
- try:
- M, mask = cv2.findFundamentalMat(
- kp0,
- kp1,
- method=method,
- ransacReprojThreshold=reproj_threshold,
- confidence=confidence,
- maxIters=max_iter,
- )
- except cv2.error:
- logger.error(
- "compute findFundamentalMat error, len(kp0): {}".format(len(kp0))
- )
- return None, None
- mask = np.array(mask.ravel().astype("bool"), dtype="bool")
- return M, mask
-
-
-def _filter_matches_poselib(
- kp0: np.ndarray,
- kp1: np.ndarray,
- method: int = None, # not used
- reproj_threshold: float = 3,
- confidence: float = 0.99,
- max_iter: int = 2000,
- geometry_type: str = "Homography",
-) -> dict:
- """
- Filters matches between two sets of keypoints using the poselib library.
-
- Args:
- kp0 (np.ndarray): Array of keypoints from the first image.
- kp1 (np.ndarray): Array of keypoints from the second image.
- method (str, optional): RANSAC method. Defaults to "RANSAC".
- reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to 3.
- confidence (float, optional): RANSAC confidence. Defaults to 0.99.
- max_iter (int, optional): RANSAC maximum iterations. Defaults to 2000.
- geometry_type (str, optional): Type of geometry. Defaults to "Homography".
-
- Returns:
- dict: Information about the homography estimation.
- """
- ransac_options = {
- "max_iterations": max_iter,
- # "min_iterations": min_iter,
- "success_prob": confidence,
- "max_reproj_error": reproj_threshold,
- # "progressive_sampling": args.sampler.lower() == 'prosac'
- }
-
- if geometry_type == "Homography":
- M, info = poselib.estimate_homography(kp0, kp1, ransac_options)
- elif geometry_type == "Fundamental":
- M, info = poselib.estimate_fundamental(kp0, kp1, ransac_options)
- else:
- raise NotImplementedError
-
- return M, np.array(info["inliers"])
-
-
-def proc_ransac_matches(
- mkpts0: np.ndarray,
- mkpts1: np.ndarray,
- ransac_method: str = DEFAULT_RANSAC_METHOD,
- ransac_reproj_threshold: float = 3.0,
- ransac_confidence: float = 0.99,
- ransac_max_iter: int = 2000,
- geometry_type: str = "Homography",
-):
- if ransac_method.startswith("CV2"):
- logger.info(f"ransac_method: {ransac_method}, geometry_type: {geometry_type}")
- return _filter_matches_opencv(
- mkpts0,
- mkpts1,
- ransac_zoo[ransac_method],
- ransac_reproj_threshold,
- ransac_confidence,
- ransac_max_iter,
- geometry_type,
- )
- elif ransac_method.startswith("POSELIB"):
- logger.info(f"ransac_method: {ransac_method}, geometry_type: {geometry_type}")
- return _filter_matches_poselib(
- mkpts0,
- mkpts1,
- None,
- ransac_reproj_threshold,
- ransac_confidence,
- ransac_max_iter,
- geometry_type,
- )
- else:
- raise NotImplementedError
-
-
-def filter_matches(
- pred: Dict[str, Any],
- ransac_method: str = DEFAULT_RANSAC_METHOD,
- ransac_reproj_threshold: float = DEFAULT_RANSAC_REPROJ_THRESHOLD,
- ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE,
- ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER,
- ransac_estimator: str = None,
-):
- """
- Filter matches using RANSAC. If keypoints are available, filter by keypoints.
- If lines are available, filter by lines. If both keypoints and lines are
- available, filter by keypoints.
-
- Args:
- pred (Dict[str, Any]): dict of matches, including original keypoints.
- ransac_method (str, optional): RANSAC method. Defaults to DEFAULT_RANSAC_METHOD.
- ransac_reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to DEFAULT_RANSAC_REPROJ_THRESHOLD.
- ransac_confidence (float, optional): RANSAC confidence. Defaults to DEFAULT_RANSAC_CONFIDENCE.
- ransac_max_iter (int, optional): RANSAC maximum iterations. Defaults to DEFAULT_RANSAC_MAX_ITER.
-
- Returns:
- Dict[str, Any]: filtered matches.
- """
- mkpts0: Optional[np.ndarray] = None
- mkpts1: Optional[np.ndarray] = None
- feature_type: Optional[str] = None
- if "mkeypoints0_orig" in pred.keys() and "mkeypoints1_orig" in pred.keys():
- mkpts0 = pred["mkeypoints0_orig"]
- mkpts1 = pred["mkeypoints1_orig"]
- feature_type = "KEYPOINT"
- elif (
- "line_keypoints0_orig" in pred.keys() and "line_keypoints1_orig" in pred.keys()
- ):
- mkpts0 = pred["line_keypoints0_orig"]
- mkpts1 = pred["line_keypoints1_orig"]
- feature_type = "LINE"
- else:
- return set_null_pred(feature_type, pred)
- if mkpts0 is None or mkpts0 is None:
- return set_null_pred(feature_type, pred)
- if ransac_method not in ransac_zoo.keys():
- ransac_method = DEFAULT_RANSAC_METHOD
-
- if len(mkpts0) < DEFAULT_MIN_NUM_MATCHES:
- return set_null_pred(feature_type, pred)
-
- geom_info = compute_geometry(
- pred,
- ransac_method=ransac_method,
- ransac_reproj_threshold=ransac_reproj_threshold,
- ransac_confidence=ransac_confidence,
- ransac_max_iter=ransac_max_iter,
- )
-
- if "Homography" in geom_info.keys():
- mask = geom_info["mask_h"]
- if feature_type == "KEYPOINT":
- pred["mmkeypoints0_orig"] = mkpts0[mask]
- pred["mmkeypoints1_orig"] = mkpts1[mask]
- pred["mmconf"] = pred["mconf"][mask]
- elif feature_type == "LINE":
- pred["mline_keypoints0_orig"] = mkpts0[mask]
- pred["mline_keypoints1_orig"] = mkpts1[mask]
- pred["H"] = np.array(geom_info["Homography"])
- else:
- set_null_pred(feature_type, pred)
- # do not show mask
- geom_info.pop("mask_h", None)
- geom_info.pop("mask_f", None)
- pred["geom_info"] = geom_info
- return pred
-
-
-def compute_geometry(
- pred: Dict[str, Any],
- ransac_method: str = DEFAULT_RANSAC_METHOD,
- ransac_reproj_threshold: float = DEFAULT_RANSAC_REPROJ_THRESHOLD,
- ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE,
- ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER,
-) -> Dict[str, List[float]]:
- """
- Compute geometric information of matches, including Fundamental matrix,
- Homography matrix, and rectification matrices (if available).
-
- Args:
- pred (Dict[str, Any]): dict of matches, including original keypoints.
- ransac_method (str, optional): RANSAC method. Defaults to DEFAULT_RANSAC_METHOD.
- ransac_reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to DEFAULT_RANSAC_REPROJ_THRESHOLD.
- ransac_confidence (float, optional): RANSAC confidence. Defaults to DEFAULT_RANSAC_CONFIDENCE.
- ransac_max_iter (int, optional): RANSAC maximum iterations. Defaults to DEFAULT_RANSAC_MAX_ITER.
-
- Returns:
- Dict[str, List[float]]: geometric information in form of a dict.
- """
- mkpts0: Optional[np.ndarray] = None
- mkpts1: Optional[np.ndarray] = None
-
- if "mkeypoints0_orig" in pred.keys() and "mkeypoints1_orig" in pred.keys():
- mkpts0 = pred["mkeypoints0_orig"]
- mkpts1 = pred["mkeypoints1_orig"]
- elif (
- "line_keypoints0_orig" in pred.keys() and "line_keypoints1_orig" in pred.keys()
- ):
- mkpts0 = pred["line_keypoints0_orig"]
- mkpts1 = pred["line_keypoints1_orig"]
-
- if mkpts0 is not None and mkpts1 is not None:
- if len(mkpts0) < 2 * DEFAULT_MIN_NUM_MATCHES:
- return {}
- geo_info: Dict[str, List[float]] = {}
-
- F, mask_f = proc_ransac_matches(
- mkpts0,
- mkpts1,
- ransac_method,
- ransac_reproj_threshold,
- ransac_confidence,
- ransac_max_iter,
- geometry_type="Fundamental",
- )
-
- if F is not None:
- geo_info["Fundamental"] = F.tolist()
- geo_info["mask_f"] = mask_f
- H, mask_h = proc_ransac_matches(
- mkpts0,
- mkpts1,
- ransac_method,
- ransac_reproj_threshold,
- ransac_confidence,
- ransac_max_iter,
- geometry_type="Homography",
- )
-
- h0, w0, _ = pred["image0_orig"].shape
- if H is not None:
- geo_info["Homography"] = H.tolist()
- geo_info["mask_h"] = mask_h
- try:
- _, H1, H2 = cv2.stereoRectifyUncalibrated(
- mkpts0.reshape(-1, 2),
- mkpts1.reshape(-1, 2),
- F,
- imgSize=(w0, h0),
- )
- geo_info["H1"] = H1.tolist()
- geo_info["H2"] = H2.tolist()
- except cv2.error as e:
- logger.error(f"StereoRectifyUncalibrated failed, skip! error: {e}")
- return geo_info
- else:
- return {}
-
-
-def wrap_images(
- img0: np.ndarray,
- img1: np.ndarray,
- geo_info: Optional[Dict[str, List[float]]],
- geom_type: str,
-) -> Tuple[Optional[str], Optional[Dict[str, List[float]]]]:
- """
- Wraps the images based on the geometric transformation used to align them.
-
- Args:
- img0: numpy array representing the first image.
- img1: numpy array representing the second image.
- geo_info: dictionary containing the geometric transformation information.
- geom_type: type of geometric transformation used to align the images.
-
- Returns:
- A tuple containing a base64 encoded image string and a dictionary with the transformation matrix.
- """
- h0, w0, _ = img0.shape
- h1, w1, _ = img1.shape
- if geo_info is not None and len(geo_info) != 0:
- rectified_image0 = img0
- rectified_image1 = None
- if "Homography" not in geo_info:
- logger.warning(f"{geom_type} not exist, maybe too less matches")
- return None, None
-
- H = np.array(geo_info["Homography"])
-
- title: List[str] = []
- if geom_type == "Homography":
- H_inv = np.linalg.inv(H)
- rectified_image1 = cv2.warpPerspective(img1, H_inv, (w0, h0))
- title = ["Image 0", "Image 1 - warped"]
- elif geom_type == "Fundamental":
- if geom_type not in geo_info:
- logger.warning(f"{geom_type} not exist, maybe too less matches")
- return None, None
- else:
- H1, H2 = np.array(geo_info["H1"]), np.array(geo_info["H2"])
- rectified_image0 = cv2.warpPerspective(img0, H1, (w0, h0))
- rectified_image1 = cv2.warpPerspective(img1, H2, (w1, h1))
- title = ["Image 0 - warped", "Image 1 - warped"]
- else:
- print("Error: Unknown geometry type")
- fig = plot_images(
- [rectified_image0.squeeze(), rectified_image1.squeeze()],
- title,
- dpi=300,
- )
- return fig2im(fig), rectified_image1
- else:
- return None, None
-
-
-def generate_warp_images(
- input_image0: np.ndarray,
- input_image1: np.ndarray,
- matches_info: Dict[str, Any],
- choice: str,
-) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
- """
- Changes the estimate of the geometric transformation used to align the images.
-
- Args:
- input_image0: First input image.
- input_image1: Second input image.
- matches_info: Dictionary containing information about the matches.
- choice: Type of geometric transformation to use ('Homography' or 'Fundamental') or 'No' to disable.
-
- Returns:
- A tuple containing the updated images and the warpped images.
- """
- if (
- matches_info is None
- or len(matches_info) < 1
- or "geom_info" not in matches_info.keys()
- ):
- return None, None
- geom_info = matches_info["geom_info"]
- warped_image = None
- if choice != "No":
- wrapped_image_pair, warped_image = wrap_images(
- input_image0, input_image1, geom_info, choice
- )
- return wrapped_image_pair, warped_image
- else:
- return None, None
-
-
-def send_to_match(state_cache: Dict[str, Any]):
- """
- Send the state cache to the match function.
-
- Args:
- state_cache (Dict[str, Any]): Current state of the app.
-
- Returns:
- None
- """
- if state_cache:
- return (
- state_cache["image0_orig"],
- state_cache["wrapped_image"],
- )
- else:
- return None, None
-
-
-def run_ransac(
- state_cache: Dict[str, Any],
- choice_geometry_type: str,
- ransac_method: str = DEFAULT_RANSAC_METHOD,
- ransac_reproj_threshold: int = DEFAULT_RANSAC_REPROJ_THRESHOLD,
- ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE,
- ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER,
-) -> Tuple[Optional[np.ndarray], Optional[Dict[str, int]]]:
- """
- Run RANSAC matches and return the output images and the number of matches.
-
- Args:
- state_cache (Dict[str, Any]): Current state of the app, including the matches.
- ransac_method (str, optional): RANSAC method. Defaults to DEFAULT_RANSAC_METHOD.
- ransac_reproj_threshold (int, optional): RANSAC reprojection threshold. Defaults to DEFAULT_RANSAC_REPROJ_THRESHOLD.
- ransac_confidence (float, optional): RANSAC confidence. Defaults to DEFAULT_RANSAC_CONFIDENCE.
- ransac_max_iter (int, optional): RANSAC maximum iterations. Defaults to DEFAULT_RANSAC_MAX_ITER.
-
- Returns:
- Tuple[Optional[np.ndarray], Optional[Dict[str, int]]]: Tuple containing the output images and the number of matches.
- """
- if not state_cache:
- logger.info("Run Match first before Rerun RANSAC")
- gr.Warning("Run Match first before Rerun RANSAC")
- return None, None
- t1 = time.time()
- logger.info(
- f"Run RANSAC matches using: {ransac_method} with threshold: {ransac_reproj_threshold}"
- )
- logger.info(
- f"Run RANSAC matches using: {ransac_confidence} with iter: {ransac_max_iter}"
- )
- # if enable_ransac:
- filter_matches(
- state_cache,
- ransac_method=ransac_method,
- ransac_reproj_threshold=ransac_reproj_threshold,
- ransac_confidence=ransac_confidence,
- ransac_max_iter=ransac_max_iter,
- )
- logger.info(f"RANSAC matches done using: {time.time()-t1:.3f}s")
- t1 = time.time()
-
- # plot images with ransac matches
- titles = [
- "Image 0 - Ransac matched keypoints",
- "Image 1 - Ransac matched keypoints",
- ]
- output_matches_ransac, num_matches_ransac = display_matches(
- state_cache, titles=titles, tag="KPTS_RANSAC"
- )
- logger.info(f"Display matches done using: {time.time()-t1:.3f}s")
- t1 = time.time()
-
- # compute warp images
- output_wrapped, warped_image = generate_warp_images(
- state_cache["image0_orig"],
- state_cache["image1_orig"],
- state_cache,
- choice_geometry_type,
- )
- plt.close("all")
-
- num_matches_raw = state_cache["num_matches_raw"]
- state_cache["wrapped_image"] = warped_image
-
- # tmp_state_cache = tempfile.NamedTemporaryFile(suffix='.pkl', delete=False)
- tmp_state_cache = "output.pkl"
- with open(tmp_state_cache, "wb") as f:
- pickle.dump(state_cache, f)
-
- logger.info("Dump results done!")
-
- return (
- output_matches_ransac,
- {
- "num_matches_raw": num_matches_raw,
- "num_matches_ransac": num_matches_ransac,
- },
- output_wrapped,
- tmp_state_cache,
- )
-
-
-def generate_fake_outputs(
- output_keypoints,
- output_matches_raw,
- output_matches_ransac,
- match_conf,
- extract_conf,
- pred,
-):
- return (
- output_keypoints,
- output_matches_raw,
- output_matches_ransac,
- {},
- {
- "match_conf": match_conf,
- "extractor_conf": extract_conf,
- },
- {
- "geom_info": pred.get("geom_info", {}),
- },
- None,
- None,
- None,
- )
-
-
-def run_matching(
- image0: np.ndarray,
- image1: np.ndarray,
- match_threshold: float,
- extract_max_keypoints: int,
- keypoint_threshold: float,
- key: str,
- ransac_method: str = DEFAULT_RANSAC_METHOD,
- ransac_reproj_threshold: int = DEFAULT_RANSAC_REPROJ_THRESHOLD,
- ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE,
- ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER,
- choice_geometry_type: str = DEFAULT_SETTING_GEOMETRY,
- matcher_zoo: Dict[str, Any] = None,
- force_resize: bool = False,
- image_width: int = 640,
- image_height: int = 480,
- use_cached_model: bool = True,
-) -> Tuple[
- np.ndarray,
- np.ndarray,
- np.ndarray,
- Dict[str, int],
- Dict[str, Dict[str, Any]],
- Dict[str, Dict[str, float]],
- np.ndarray,
-]:
- """Match two images using the given parameters.
-
- Args:
- image0 (np.ndarray): RGB image 0.
- image1 (np.ndarray): RGB image 1.
- match_threshold (float): match threshold.
- extract_max_keypoints (int): number of keypoints to extract.
- keypoint_threshold (float): keypoint threshold.
- key (str): key of the model to use.
- ransac_method (str, optional): RANSAC method to use.
- ransac_reproj_threshold (int, optional): RANSAC reprojection threshold.
- ransac_confidence (float, optional): RANSAC confidence level.
- ransac_max_iter (int, optional): RANSAC maximum number of iterations.
- choice_geometry_type (str, optional): setting of geometry estimation.
- matcher_zoo (Dict[str, Any], optional): matcher zoo. Defaults to None.
- force_resize (bool, optional): force resize. Defaults to False.
- image_width (int, optional): image width. Defaults to 640.
- image_height (int, optional): image height. Defaults to 480.
- use_cached_model (bool, optional): use cached model. Defaults to False.
-
- Returns:
- tuple:
- - output_keypoints (np.ndarray): image with keypoints.
- - output_matches_raw (np.ndarray): image with raw matches.
- - output_matches_ransac (np.ndarray): image with RANSAC matches.
- - num_matches (Dict[str, int]): number of raw and RANSAC matches.
- - configs (Dict[str, Dict[str, Any]]): match and feature extraction configs.
- - geom_info (Dict[str, Dict[str, float]]): geometry information.
- - output_wrapped (np.ndarray): wrapped images.
- """
- # image0 and image1 is RGB mode
- if image0 is None or image1 is None:
- logger.error(
- "Error: No images found! Please upload two images or select an example."
- )
- raise gr.Error(
- "Error: No images found! Please upload two images or select an example."
- )
- # init output
- output_keypoints = None
- output_matches_raw = None
- output_matches_ransac = None
-
- t0 = time.time()
- model = matcher_zoo[key]
- match_conf = model["matcher"]
- # update match config
- match_conf["model"]["match_threshold"] = match_threshold
- match_conf["model"]["max_keypoints"] = extract_max_keypoints
- cache_key = "{}_{}".format(key, match_conf["model"]["name"])
-
- efficiency = model["info"].get("efficiency", "high")
- if efficiency == "low":
- gr.Warning(
- "Matcher {} is time-consuming, please wait for a while".format(
- model["info"].get("name", "unknown")
- )
- )
-
- if use_cached_model:
- # because of the model cache, we need to update the config
- matcher = model_cache.load_model(cache_key, get_model, match_conf)
- matcher.conf["max_keypoints"] = extract_max_keypoints
- matcher.conf["match_threshold"] = match_threshold
- logger.info(f"Loaded cached model {cache_key}")
- else:
- matcher = get_model(match_conf)
- logger.info(f"Loading model using: {time.time()-t0:.3f}s")
- t1 = time.time()
- yield generate_fake_outputs(
- output_keypoints, output_matches_raw, output_matches_ransac, match_conf, {}, {}
- )
-
- if model["dense"]:
- if not match_conf["preprocessing"].get("force_resize", False):
- match_conf["preprocessing"]["force_resize"] = force_resize
- else:
- logger.info("preprocessing is already resized")
- if force_resize:
- match_conf["preprocessing"]["height"] = image_height
- match_conf["preprocessing"]["width"] = image_width
- logger.info(f"Force resize to {image_width}x{image_height}")
-
- pred = match_dense.match_images(
- matcher, image0, image1, match_conf["preprocessing"], device=DEVICE
- )
- del matcher
- extract_conf = None
- else:
- extract_conf = model["feature"]
- # update extract config
- extract_conf["model"]["max_keypoints"] = extract_max_keypoints
- extract_conf["model"]["keypoint_threshold"] = keypoint_threshold
- cache_key = "{}_{}".format(key, extract_conf["model"]["name"])
-
- if use_cached_model:
- extractor = model_cache.load_model(
- cache_key, get_feature_model, extract_conf
- )
- # because of the model cache, we need to update the config
- extractor.conf["max_keypoints"] = extract_max_keypoints
- extractor.conf["keypoint_threshold"] = keypoint_threshold
- logger.info(f"Loaded cached model {cache_key}")
- else:
- extractor = get_feature_model(extract_conf)
-
- if not extract_conf["preprocessing"].get("force_resize", False):
- extract_conf["preprocessing"]["force_resize"] = force_resize
- else:
- logger.info("preprocessing is already resized")
- if force_resize:
- extract_conf["preprocessing"]["height"] = image_height
- extract_conf["preprocessing"]["width"] = image_width
- logger.info(f"Force resize to {image_width}x{image_height}")
-
- pred0 = extract_features.extract(
- extractor, image0, extract_conf["preprocessing"]
- )
- pred1 = extract_features.extract(
- extractor, image1, extract_conf["preprocessing"]
- )
- pred = match_features.match_images(matcher, pred0, pred1)
- del extractor
- # gr.Info(
- # f"Matching images done using: {time.time()-t1:.3f}s",
- # )
- logger.info(f"Matching images done using: {time.time()-t1:.3f}s")
- t1 = time.time()
-
- # plot images with keypoints
- titles = [
- "Image 0 - Keypoints",
- "Image 1 - Keypoints",
- ]
- output_keypoints = display_keypoints(pred, titles=titles)
- yield generate_fake_outputs(
- output_keypoints,
- output_matches_raw,
- output_matches_ransac,
- match_conf,
- extract_conf,
- pred,
- )
-
- # plot images with raw matches
- titles = [
- "Image 0 - Raw matched keypoints",
- "Image 1 - Raw matched keypoints",
- ]
- output_matches_raw, num_matches_raw = display_matches(pred, titles=titles)
- yield generate_fake_outputs(
- output_keypoints,
- output_matches_raw,
- output_matches_ransac,
- match_conf,
- extract_conf,
- pred,
- )
-
- # if enable_ransac:
- filter_matches(
- pred,
- ransac_method=ransac_method,
- ransac_reproj_threshold=ransac_reproj_threshold,
- ransac_confidence=ransac_confidence,
- ransac_max_iter=ransac_max_iter,
- )
-
- # gr.Info(f"RANSAC matches done using: {time.time()-t1:.3f}s")
- logger.info(f"RANSAC matches done using: {time.time()-t1:.3f}s")
- t1 = time.time()
-
- # plot images with ransac matches
- titles = [
- "Image 0 - Ransac matched keypoints",
- "Image 1 - Ransac matched keypoints",
- ]
- output_matches_ransac, num_matches_ransac = display_matches(
- pred, titles=titles, tag="KPTS_RANSAC"
- )
- yield generate_fake_outputs(
- output_keypoints,
- output_matches_raw,
- output_matches_ransac,
- match_conf,
- extract_conf,
- pred,
- )
-
- # gr.Info(f"Display matches done using: {time.time()-t1:.3f}s")
- logger.info(f"Display matches done using: {time.time()-t1:.3f}s")
- t1 = time.time()
- # plot wrapped images
- output_wrapped, warped_image = generate_warp_images(
- pred["image0_orig"],
- pred["image1_orig"],
- pred,
- choice_geometry_type,
- )
- plt.close("all")
- # gr.Info(f"In summary, total time: {time.time()-t0:.3f}s")
- logger.info(f"TOTAL time: {time.time()-t0:.3f}s")
-
- state_cache = pred
- state_cache["num_matches_raw"] = num_matches_raw
- state_cache["num_matches_ransac"] = num_matches_ransac
- state_cache["wrapped_image"] = warped_image
-
- # tmp_state_cache = tempfile.NamedTemporaryFile(suffix='.pkl', delete=False)
- tmp_state_cache = "output.pkl"
- with open(tmp_state_cache, "wb") as f:
- pickle.dump(state_cache, f)
- logger.info("Dump results done!")
-
- yield (
- output_keypoints,
- output_matches_raw,
- output_matches_ransac,
- {
- "num_raw_matches": num_matches_raw,
- "num_ransac_matches": num_matches_ransac,
- },
- {
- "match_conf": match_conf,
- "extractor_conf": extract_conf,
- },
- {
- "geom_info": pred.get("geom_info", {}),
- },
- output_wrapped,
- state_cache,
- tmp_state_cache,
- )
-
-
-# @ref: https://docs.opencv.org/4.x/d0/d74/md__build_4_x-contrib_docs-lin64_opencv_doc_tutorials_calib3d_usac.html
-# AND: https://opencv.org/blog/2021/06/09/evaluating-opencvs-new-ransacs
-ransac_zoo = {
- "POSELIB": "LO-RANSAC",
- "CV2_RANSAC": cv2.RANSAC,
- "CV2_USAC_MAGSAC": cv2.USAC_MAGSAC,
- "CV2_USAC_DEFAULT": cv2.USAC_DEFAULT,
- "CV2_USAC_FM_8PTS": cv2.USAC_FM_8PTS,
- "CV2_USAC_PROSAC": cv2.USAC_PROSAC,
- "CV2_USAC_FAST": cv2.USAC_FAST,
- "CV2_USAC_ACCURATE": cv2.USAC_ACCURATE,
- "CV2_USAC_PARALLEL": cv2.USAC_PARALLEL,
-}
-
-
-def rotate_image(input_path, degrees, output_path):
- img = Image.open(input_path)
- img_rotated = img.rotate(-degrees)
- img_rotated.save(output_path)
-
-
-def scale_image(input_path, scale_factor, output_path):
- img = Image.open(input_path)
- width, height = img.size
- new_width = int(width * scale_factor)
- new_height = int(height * scale_factor)
- new_img = Image.new("RGB", (width, height), (0, 0, 0))
- img_resized = img.resize((new_width, new_height))
- position = ((width - new_width) // 2, (height - new_height) // 2)
- new_img.paste(img_resized, position)
- new_img.save(output_path)
+import os
+import pickle
+import random
+import shutil
+import sys
+import time
+import warnings
+from itertools import combinations
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import cv2
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import poselib
+import psutil
+from PIL import Image
+
+sys.path.append(str(Path(__file__).parents[1]))
+
+from hloc import (
+ DEVICE,
+ extract_features,
+ extractors,
+ logger,
+ match_dense,
+ match_features,
+ matchers,
+)
+from hloc.utils.base_model import dynamic_load
+from ui.viz import display_keypoints, display_matches, fig2im, plot_images
+
+warnings.simplefilter("ignore")
+
+ROOT = Path(__file__).parent.parent
+# some default values
+DEFAULT_SETTING_THRESHOLD = 0.1
+DEFAULT_SETTING_MAX_FEATURES = 2000
+DEFAULT_DEFAULT_KEYPOINT_THRESHOLD = 0.01
+DEFAULT_ENABLE_RANSAC = True
+DEFAULT_RANSAC_METHOD = "CV2_USAC_MAGSAC"
+DEFAULT_RANSAC_REPROJ_THRESHOLD = 8
+DEFAULT_RANSAC_CONFIDENCE = 0.999
+DEFAULT_RANSAC_MAX_ITER = 10000
+DEFAULT_MIN_NUM_MATCHES = 4
+DEFAULT_MATCHING_THRESHOLD = 0.2
+DEFAULT_SETTING_GEOMETRY = "Homography"
+GRADIO_VERSION = gr.__version__.split(".")[0]
+MATCHER_ZOO = None
+
+
+class ModelCache:
+ def __init__(self, max_memory_size: int = 8):
+ self.max_memory_size = max_memory_size
+ self.current_memory_size = 0
+ self.model_dict = {}
+ self.model_timestamps = []
+
+ def cache_model(self, model_key, model_loader_func, model_conf):
+ if model_key in self.model_dict:
+ self.model_timestamps.remove(model_key)
+ self.model_timestamps.append(model_key)
+ logger.info(f"Load cached {model_key}")
+ return self.model_dict[model_key]
+
+ model = self._load_model_from_disk(model_loader_func, model_conf)
+ while self._calculate_model_memory() > self.max_memory_size:
+ if len(self.model_timestamps) == 0:
+ logger.warn(
+ "RAM: {}GB, MAX RAM: {}GB".format(
+ self._calculate_model_memory(), self.max_memory_size
+ )
+ )
+ break
+ oldest_model_key = self.model_timestamps.pop(0)
+ self.current_memory_size = self._calculate_model_memory()
+ logger.info(f"Del cached {oldest_model_key}")
+ del self.model_dict[oldest_model_key]
+
+ self.model_dict[model_key] = model
+ self.model_timestamps.append(model_key)
+
+ self.print_memory_usage()
+ logger.info(f"Total cached {list(self.model_dict.keys())}")
+
+ return model
+
+ def _load_model_from_disk(self, model_loader_func, model_conf):
+ return model_loader_func(model_conf)
+
+ def _calculate_model_memory(self, verbose=False):
+ host_colocation = int(os.environ.get("HOST_COLOCATION", "1"))
+ vm = psutil.virtual_memory()
+ du = shutil.disk_usage(".")
+ if verbose:
+ logger.info(
+ f"RAM: {vm.used / 1e9:.1f}/{vm.total / host_colocation / 1e9:.1f}GB"
+ )
+ logger.info(
+ f"DISK: {du.used / 1e9:.1f}/{du.total / host_colocation / 1e9:.1f}GB"
+ )
+ return vm.used / 1e9
+
+ def print_memory_usage(self):
+ self._calculate_model_memory(verbose=True)
+
+
+model_cache = ModelCache()
+
+
+def load_config(config_name: str) -> Dict[str, Any]:
+ """
+ Load a YAML configuration file.
+
+ Args:
+ config_name: The path to the YAML configuration file.
+
+ Returns:
+ The configuration dictionary, with string keys and arbitrary values.
+ """
+ import yaml
+
+ with open(config_name, "r") as stream:
+ try:
+ config: Dict[str, Any] = yaml.safe_load(stream)
+ except yaml.YAMLError as exc:
+ logger.error(exc)
+ return config
+
+
+def get_matcher_zoo(
+ matcher_zoo: Dict[str, Dict[str, Union[str, bool]]]
+) -> Dict[str, Dict[str, Union[Callable, bool]]]:
+ """
+ Restore matcher configurations from a dictionary.
+
+ Args:
+ matcher_zoo: A dictionary with the matcher configurations,
+ where the configuration is a dictionary as loaded from a YAML file.
+
+ Returns:
+ A dictionary with the matcher configurations, where the configuration is
+ a function or a function instead of a string.
+ """
+ matcher_zoo_restored = {}
+ for k, v in matcher_zoo.items():
+ matcher_zoo_restored[k] = parse_match_config(v)
+ return matcher_zoo_restored
+
+
+def parse_match_config(conf):
+ if conf["dense"]:
+ return {
+ "matcher": match_dense.confs.get(conf["matcher"]),
+ "dense": True,
+ }
+ else:
+ return {
+ "feature": extract_features.confs.get(conf["feature"]),
+ "matcher": match_features.confs.get(conf["matcher"]),
+ "dense": False,
+ }
+
+
+def get_model(match_conf: Dict[str, Any]):
+ """
+ Load a matcher model from the provided configuration.
+
+ Args:
+ match_conf: A dictionary containing the model configuration.
+
+ Returns:
+ A matcher model instance.
+ """
+ Model = dynamic_load(matchers, match_conf["model"]["name"])
+ model = Model(match_conf["model"]).eval().to(DEVICE)
+ return model
+
+
+def get_feature_model(conf: Dict[str, Dict[str, Any]]):
+ """
+ Load a feature extraction model from the provided configuration.
+
+ Args:
+ conf: A dictionary containing the model configuration.
+
+ Returns:
+ A feature extraction model instance.
+ """
+ Model = dynamic_load(extractors, conf["model"]["name"])
+ model = Model(conf["model"]).eval().to(DEVICE)
+ return model
+
+
+def gen_examples():
+ random.seed(1)
+ example_matchers = [
+ "disk+lightglue",
+ "xfeat(sparse)",
+ "dedode",
+ "loftr",
+ "disk",
+ "RoMa",
+ "d2net",
+ "aspanformer",
+ "topicfm",
+ "superpoint+superglue",
+ "superpoint+lightglue",
+ "superpoint+mnn",
+ "disk",
+ ]
+
+ def distribute_elements(A, B):
+ new_B = np.array(B, copy=True).flatten()
+ np.random.shuffle(new_B)
+ new_B = np.resize(new_B, len(A))
+ np.random.shuffle(new_B)
+ return new_B.tolist()
+
+ # normal examples
+ def gen_images_pairs(count: int = 5):
+ path = str(ROOT / "datasets/sacre_coeur/mapping")
+ imgs_list = [
+ os.path.join(path, file)
+ for file in os.listdir(path)
+ if file.lower().endswith((".jpg", ".jpeg", ".png"))
+ ]
+ pairs = list(combinations(imgs_list, 2))
+ if len(pairs) < count:
+ count = len(pairs)
+ selected = random.sample(range(len(pairs)), count)
+ return [pairs[i] for i in selected]
+
+ # rotated examples
+ def gen_rot_image_pairs(count: int = 5):
+ path = ROOT / "datasets/sacre_coeur/mapping"
+ path_rot = ROOT / "datasets/sacre_coeur/mapping_rot"
+ rot_list = [45, 180, 90, 225, 270]
+ pairs = []
+ for file in os.listdir(path):
+ if file.lower().endswith((".jpg", ".jpeg", ".png")):
+ for rot in rot_list:
+ file_rot = "{}_rot{}.jpg".format(Path(file).stem, rot)
+ if (path_rot / file_rot).exists():
+ pairs.append(
+ [
+ path / file,
+ path_rot / file_rot,
+ ]
+ )
+ if len(pairs) < count:
+ count = len(pairs)
+ selected = random.sample(range(len(pairs)), count)
+ return [pairs[i] for i in selected]
+
+ def gen_scale_image_pairs(count: int = 5):
+ path = ROOT / "datasets/sacre_coeur/mapping"
+ path_scale = ROOT / "datasets/sacre_coeur/mapping_scale"
+ scale_list = [0.3, 0.5]
+ pairs = []
+ for file in os.listdir(path):
+ if file.lower().endswith((".jpg", ".jpeg", ".png")):
+ for scale in scale_list:
+ file_scale = "{}_scale{}.jpg".format(Path(file).stem, scale)
+ if (path_scale / file_scale).exists():
+ pairs.append(
+ [
+ path / file,
+ path_scale / file_scale,
+ ]
+ )
+ if len(pairs) < count:
+ count = len(pairs)
+ selected = random.sample(range(len(pairs)), count)
+ return [pairs[i] for i in selected]
+
+ # extramely hard examples
+ def gen_image_pairs_wxbs(count: int = None):
+ prefix = "datasets/wxbs_benchmark/.WxBS/v1.1"
+ wxbs_path = ROOT / prefix
+ pairs = []
+ for catg in os.listdir(wxbs_path):
+ catg_path = wxbs_path / catg
+ if not catg_path.is_dir():
+ continue
+ for scene in os.listdir(catg_path):
+ scene_path = catg_path / scene
+ if not scene_path.is_dir():
+ continue
+ img1_path = scene_path / "01.png"
+ img2_path = scene_path / "02.png"
+ if img1_path.exists() and img2_path.exists():
+ pairs.append([str(img1_path), str(img2_path)])
+ return pairs
+
+ # image pair path
+ pairs = gen_images_pairs()
+ pairs += gen_rot_image_pairs()
+ pairs += gen_scale_image_pairs()
+ pairs += gen_image_pairs_wxbs()
+
+ match_setting_threshold = DEFAULT_SETTING_THRESHOLD
+ match_setting_max_features = DEFAULT_SETTING_MAX_FEATURES
+ detect_keypoints_threshold = DEFAULT_DEFAULT_KEYPOINT_THRESHOLD
+ ransac_method = DEFAULT_RANSAC_METHOD
+ ransac_reproj_threshold = DEFAULT_RANSAC_REPROJ_THRESHOLD
+ ransac_confidence = DEFAULT_RANSAC_CONFIDENCE
+ ransac_max_iter = DEFAULT_RANSAC_MAX_ITER
+ input_lists = []
+ dist_examples = distribute_elements(pairs, example_matchers)
+ for pair, mt in zip(pairs, dist_examples):
+ input_lists.append(
+ [
+ pair[0],
+ pair[1],
+ match_setting_threshold,
+ match_setting_max_features,
+ detect_keypoints_threshold,
+ mt,
+ # enable_ransac,
+ ransac_method,
+ ransac_reproj_threshold,
+ ransac_confidence,
+ ransac_max_iter,
+ ]
+ )
+ return input_lists
+
+
+def set_null_pred(feature_type: str, pred: dict):
+ if feature_type == "KEYPOINT":
+ pred["mmkeypoints0_orig"] = np.array([])
+ pred["mmkeypoints1_orig"] = np.array([])
+ pred["mmconf"] = np.array([])
+ elif feature_type == "LINE":
+ pred["mline_keypoints0_orig"] = np.array([])
+ pred["mline_keypoints1_orig"] = np.array([])
+ pred["H"] = None
+ pred["geom_info"] = {}
+ return pred
+
+
+def _filter_matches_opencv(
+ kp0: np.ndarray,
+ kp1: np.ndarray,
+ method: int = cv2.RANSAC,
+ reproj_threshold: float = 3.0,
+ confidence: float = 0.99,
+ max_iter: int = 2000,
+ geometry_type: str = "Homography",
+) -> Tuple[np.ndarray, np.ndarray]:
+ """
+ Filters matches between two sets of keypoints using OpenCV's findHomography.
+
+ Args:
+ kp0 (np.ndarray): Array of keypoints from the first image.
+ kp1 (np.ndarray): Array of keypoints from the second image.
+ method (int, optional): RANSAC method. Defaults to "cv2.RANSAC".
+ reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to 3.0.
+ confidence (float, optional): RANSAC confidence. Defaults to 0.99.
+ max_iter (int, optional): RANSAC maximum iterations. Defaults to 2000.
+ geometry_type (str, optional): Type of geometry. Defaults to "Homography".
+
+ Returns:
+ Tuple[np.ndarray, np.ndarray]: Homography matrix and mask.
+ """
+ if geometry_type == "Homography":
+ M, mask = cv2.findHomography(
+ kp0,
+ kp1,
+ method=method,
+ ransacReprojThreshold=reproj_threshold,
+ confidence=confidence,
+ maxIters=max_iter,
+ )
+ elif geometry_type == "Fundamental":
+ M, mask = cv2.findFundamentalMat(
+ kp0,
+ kp1,
+ method=method,
+ ransacReprojThreshold=reproj_threshold,
+ confidence=confidence,
+ maxIters=max_iter,
+ )
+ mask = np.array(mask.ravel().astype("bool"), dtype="bool")
+ return M, mask
+
+
+def _filter_matches_poselib(
+ kp0: np.ndarray,
+ kp1: np.ndarray,
+ method: int = None, # not used
+ reproj_threshold: float = 3,
+ confidence: float = 0.99,
+ max_iter: int = 2000,
+ geometry_type: str = "Homography",
+) -> dict:
+ """
+ Filters matches between two sets of keypoints using the poselib library.
+
+ Args:
+ kp0 (np.ndarray): Array of keypoints from the first image.
+ kp1 (np.ndarray): Array of keypoints from the second image.
+ method (str, optional): RANSAC method. Defaults to "RANSAC".
+ reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to 3.
+ confidence (float, optional): RANSAC confidence. Defaults to 0.99.
+ max_iter (int, optional): RANSAC maximum iterations. Defaults to 2000.
+ geometry_type (str, optional): Type of geometry. Defaults to "Homography".
+
+ Returns:
+ dict: Information about the homography estimation.
+ """
+ ransac_options = {
+ "max_iterations": max_iter,
+ # "min_iterations": min_iter,
+ "success_prob": confidence,
+ "max_reproj_error": reproj_threshold,
+ # "progressive_sampling": args.sampler.lower() == 'prosac'
+ }
+
+ if geometry_type == "Homography":
+ M, info = poselib.estimate_homography(kp0, kp1, ransac_options)
+ elif geometry_type == "Fundamental":
+ M, info = poselib.estimate_fundamental(kp0, kp1, ransac_options)
+ else:
+ raise NotImplementedError
+
+ return M, np.array(info["inliers"])
+
+
+def proc_ransac_matches(
+ mkpts0: np.ndarray,
+ mkpts1: np.ndarray,
+ ransac_method: str = DEFAULT_RANSAC_METHOD,
+ ransac_reproj_threshold: float = 3.0,
+ ransac_confidence: float = 0.99,
+ ransac_max_iter: int = 2000,
+ geometry_type: str = "Homography",
+):
+ if ransac_method.startswith("CV2"):
+ logger.info(
+ f"ransac_method: {ransac_method}, geometry_type: {geometry_type}"
+ )
+ return _filter_matches_opencv(
+ mkpts0,
+ mkpts1,
+ ransac_zoo[ransac_method],
+ ransac_reproj_threshold,
+ ransac_confidence,
+ ransac_max_iter,
+ geometry_type,
+ )
+ elif ransac_method.startswith("POSELIB"):
+ logger.info(
+ f"ransac_method: {ransac_method}, geometry_type: {geometry_type}"
+ )
+ return _filter_matches_poselib(
+ mkpts0,
+ mkpts1,
+ None,
+ ransac_reproj_threshold,
+ ransac_confidence,
+ ransac_max_iter,
+ geometry_type,
+ )
+ else:
+ raise NotImplementedError
+
+
+def filter_matches(
+ pred: Dict[str, Any],
+ ransac_method: str = DEFAULT_RANSAC_METHOD,
+ ransac_reproj_threshold: float = DEFAULT_RANSAC_REPROJ_THRESHOLD,
+ ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE,
+ ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER,
+ ransac_estimator: str = None,
+):
+ """
+ Filter matches using RANSAC. If keypoints are available, filter by keypoints.
+ If lines are available, filter by lines. If both keypoints and lines are
+ available, filter by keypoints.
+
+ Args:
+ pred (Dict[str, Any]): dict of matches, including original keypoints.
+ ransac_method (str, optional): RANSAC method. Defaults to DEFAULT_RANSAC_METHOD.
+ ransac_reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to DEFAULT_RANSAC_REPROJ_THRESHOLD.
+ ransac_confidence (float, optional): RANSAC confidence. Defaults to DEFAULT_RANSAC_CONFIDENCE.
+ ransac_max_iter (int, optional): RANSAC maximum iterations. Defaults to DEFAULT_RANSAC_MAX_ITER.
+
+ Returns:
+ Dict[str, Any]: filtered matches.
+ """
+ mkpts0: Optional[np.ndarray] = None
+ mkpts1: Optional[np.ndarray] = None
+ feature_type: Optional[str] = None
+ if "mkeypoints0_orig" in pred.keys() and "mkeypoints1_orig" in pred.keys():
+ mkpts0 = pred["mkeypoints0_orig"]
+ mkpts1 = pred["mkeypoints1_orig"]
+ feature_type = "KEYPOINT"
+ elif (
+ "line_keypoints0_orig" in pred.keys()
+ and "line_keypoints1_orig" in pred.keys()
+ ):
+ mkpts0 = pred["line_keypoints0_orig"]
+ mkpts1 = pred["line_keypoints1_orig"]
+ feature_type = "LINE"
+ else:
+ return set_null_pred(feature_type, pred)
+ if mkpts0 is None or mkpts0 is None:
+ return set_null_pred(feature_type, pred)
+ if ransac_method not in ransac_zoo.keys():
+ ransac_method = DEFAULT_RANSAC_METHOD
+
+ if len(mkpts0) < DEFAULT_MIN_NUM_MATCHES:
+ return set_null_pred(feature_type, pred)
+
+ geom_info = compute_geometry(
+ pred,
+ ransac_method=ransac_method,
+ ransac_reproj_threshold=ransac_reproj_threshold,
+ ransac_confidence=ransac_confidence,
+ ransac_max_iter=ransac_max_iter,
+ )
+
+ if "Homography" in geom_info.keys():
+ mask = geom_info["mask_h"]
+ if feature_type == "KEYPOINT":
+ pred["mmkeypoints0_orig"] = mkpts0[mask]
+ pred["mmkeypoints1_orig"] = mkpts1[mask]
+ pred["mmconf"] = pred["mconf"][mask]
+ elif feature_type == "LINE":
+ pred["mline_keypoints0_orig"] = mkpts0[mask]
+ pred["mline_keypoints1_orig"] = mkpts1[mask]
+ pred["H"] = np.array(geom_info["Homography"])
+ else:
+ set_null_pred(feature_type, pred)
+ # do not show mask
+ geom_info.pop("mask_h", None)
+ geom_info.pop("mask_f", None)
+ pred["geom_info"] = geom_info
+ return pred
+
+
+def compute_geometry(
+ pred: Dict[str, Any],
+ ransac_method: str = DEFAULT_RANSAC_METHOD,
+ ransac_reproj_threshold: float = DEFAULT_RANSAC_REPROJ_THRESHOLD,
+ ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE,
+ ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER,
+) -> Dict[str, List[float]]:
+ """
+ Compute geometric information of matches, including Fundamental matrix,
+ Homography matrix, and rectification matrices (if available).
+
+ Args:
+ pred (Dict[str, Any]): dict of matches, including original keypoints.
+ ransac_method (str, optional): RANSAC method. Defaults to DEFAULT_RANSAC_METHOD.
+ ransac_reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to DEFAULT_RANSAC_REPROJ_THRESHOLD.
+ ransac_confidence (float, optional): RANSAC confidence. Defaults to DEFAULT_RANSAC_CONFIDENCE.
+ ransac_max_iter (int, optional): RANSAC maximum iterations. Defaults to DEFAULT_RANSAC_MAX_ITER.
+
+ Returns:
+ Dict[str, List[float]]: geometric information in form of a dict.
+ """
+ mkpts0: Optional[np.ndarray] = None
+ mkpts1: Optional[np.ndarray] = None
+
+ if "mkeypoints0_orig" in pred.keys() and "mkeypoints1_orig" in pred.keys():
+ mkpts0 = pred["mkeypoints0_orig"]
+ mkpts1 = pred["mkeypoints1_orig"]
+ elif (
+ "line_keypoints0_orig" in pred.keys()
+ and "line_keypoints1_orig" in pred.keys()
+ ):
+ mkpts0 = pred["line_keypoints0_orig"]
+ mkpts1 = pred["line_keypoints1_orig"]
+
+ if mkpts0 is not None and mkpts1 is not None:
+ if len(mkpts0) < 2 * DEFAULT_MIN_NUM_MATCHES:
+ return {}
+ geo_info: Dict[str, List[float]] = {}
+
+ F, mask_f = proc_ransac_matches(
+ mkpts0,
+ mkpts1,
+ ransac_method,
+ ransac_reproj_threshold,
+ ransac_confidence,
+ ransac_max_iter,
+ geometry_type="Fundamental",
+ )
+
+ if F is not None:
+ geo_info["Fundamental"] = F.tolist()
+ geo_info["mask_f"] = mask_f
+ H, mask_h = proc_ransac_matches(
+ mkpts1,
+ mkpts0,
+ ransac_method,
+ ransac_reproj_threshold,
+ ransac_confidence,
+ ransac_max_iter,
+ geometry_type="Homography",
+ )
+
+ h0, w0, _ = pred["image0_orig"].shape
+ if H is not None:
+ geo_info["Homography"] = H.tolist()
+ geo_info["mask_h"] = mask_h
+ try:
+ _, H1, H2 = cv2.stereoRectifyUncalibrated(
+ mkpts0.reshape(-1, 2),
+ mkpts1.reshape(-1, 2),
+ F,
+ imgSize=(w0, h0),
+ )
+ geo_info["H1"] = H1.tolist()
+ geo_info["H2"] = H2.tolist()
+ except cv2.error as e:
+ logger.error(
+ f"StereoRectifyUncalibrated failed, skip! error: {e}"
+ )
+ return geo_info
+ else:
+ return {}
+
+
+def wrap_images(
+ img0: np.ndarray,
+ img1: np.ndarray,
+ geo_info: Optional[Dict[str, List[float]]],
+ geom_type: str,
+) -> Tuple[Optional[str], Optional[Dict[str, List[float]]]]:
+ """
+ Wraps the images based on the geometric transformation used to align them.
+
+ Args:
+ img0: numpy array representing the first image.
+ img1: numpy array representing the second image.
+ geo_info: dictionary containing the geometric transformation information.
+ geom_type: type of geometric transformation used to align the images.
+
+ Returns:
+ A tuple containing a base64 encoded image string and a dictionary with the transformation matrix.
+ """
+ h0, w0, _ = img0.shape
+ h1, w1, _ = img1.shape
+ if geo_info is not None and len(geo_info) != 0:
+ rectified_image0 = img0
+ rectified_image1 = None
+ if "Homography" not in geo_info:
+ logger.warning(f"{geom_type} not exist, maybe too less matches")
+ return None, None
+
+ H = np.array(geo_info["Homography"])
+
+ title: List[str] = []
+ if geom_type == "Homography":
+ rectified_image1 = cv2.warpPerspective(img1, H, (w0, h0))
+ title = ["Image 0", "Image 1 - warped"]
+ elif geom_type == "Fundamental":
+ if geom_type not in geo_info:
+ logger.warning(f"{geom_type} not exist, maybe too less matches")
+ return None, None
+ else:
+ H1, H2 = np.array(geo_info["H1"]), np.array(geo_info["H2"])
+ rectified_image0 = cv2.warpPerspective(img0, H1, (w0, h0))
+ rectified_image1 = cv2.warpPerspective(img1, H2, (w1, h1))
+ title = ["Image 0 - warped", "Image 1 - warped"]
+ else:
+ print("Error: Unknown geometry type")
+ fig = plot_images(
+ [rectified_image0.squeeze(), rectified_image1.squeeze()],
+ title,
+ dpi=300,
+ )
+ return fig2im(fig), rectified_image1
+ else:
+ return None, None
+
+
+def generate_warp_images(
+ input_image0: np.ndarray,
+ input_image1: np.ndarray,
+ matches_info: Dict[str, Any],
+ choice: str,
+) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
+ """
+ Changes the estimate of the geometric transformation used to align the images.
+
+ Args:
+ input_image0: First input image.
+ input_image1: Second input image.
+ matches_info: Dictionary containing information about the matches.
+ choice: Type of geometric transformation to use ('Homography' or 'Fundamental') or 'No' to disable.
+
+ Returns:
+ A tuple containing the updated images and the warpped images.
+ """
+ if (
+ matches_info is None
+ or len(matches_info) < 1
+ or "geom_info" not in matches_info.keys()
+ ):
+ return None, None
+ geom_info = matches_info["geom_info"]
+ warped_image = None
+ if choice != "No":
+ wrapped_image_pair, warped_image = wrap_images(
+ input_image0, input_image1, geom_info, choice
+ )
+ return wrapped_image_pair, warped_image
+ else:
+ return None, None
+
+
+def send_to_match(state_cache: Dict[str, Any]):
+ """
+ Send the state cache to the match function.
+
+ Args:
+ state_cache (Dict[str, Any]): Current state of the app.
+
+ Returns:
+ None
+ """
+ if state_cache:
+ return (
+ state_cache["image0_orig"],
+ state_cache["wrapped_image"],
+ )
+ else:
+ return None, None
+
+
+def run_ransac(
+ state_cache: Dict[str, Any],
+ choice_geometry_type: str,
+ ransac_method: str = DEFAULT_RANSAC_METHOD,
+ ransac_reproj_threshold: int = DEFAULT_RANSAC_REPROJ_THRESHOLD,
+ ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE,
+ ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER,
+) -> Tuple[Optional[np.ndarray], Optional[Dict[str, int]]]:
+ """
+ Run RANSAC matches and return the output images and the number of matches.
+
+ Args:
+ state_cache (Dict[str, Any]): Current state of the app, including the matches.
+ ransac_method (str, optional): RANSAC method. Defaults to DEFAULT_RANSAC_METHOD.
+ ransac_reproj_threshold (int, optional): RANSAC reprojection threshold. Defaults to DEFAULT_RANSAC_REPROJ_THRESHOLD.
+ ransac_confidence (float, optional): RANSAC confidence. Defaults to DEFAULT_RANSAC_CONFIDENCE.
+ ransac_max_iter (int, optional): RANSAC maximum iterations. Defaults to DEFAULT_RANSAC_MAX_ITER.
+
+ Returns:
+ Tuple[Optional[np.ndarray], Optional[Dict[str, int]]]: Tuple containing the output images and the number of matches.
+ """
+ if not state_cache:
+ logger.info("Run Match first before Rerun RANSAC")
+ gr.Warning("Run Match first before Rerun RANSAC")
+ return None, None
+ t1 = time.time()
+ logger.info(
+ f"Run RANSAC matches using: {ransac_method} with threshold: {ransac_reproj_threshold}"
+ )
+ logger.info(
+ f"Run RANSAC matches using: {ransac_confidence} with iter: {ransac_max_iter}"
+ )
+ # if enable_ransac:
+ filter_matches(
+ state_cache,
+ ransac_method=ransac_method,
+ ransac_reproj_threshold=ransac_reproj_threshold,
+ ransac_confidence=ransac_confidence,
+ ransac_max_iter=ransac_max_iter,
+ )
+ logger.info(f"RANSAC matches done using: {time.time()-t1:.3f}s")
+ t1 = time.time()
+
+ # plot images with ransac matches
+ titles = [
+ "Image 0 - Ransac matched keypoints",
+ "Image 1 - Ransac matched keypoints",
+ ]
+ output_matches_ransac, num_matches_ransac = display_matches(
+ state_cache, titles=titles, tag="KPTS_RANSAC"
+ )
+ logger.info(f"Display matches done using: {time.time()-t1:.3f}s")
+ t1 = time.time()
+
+ # compute warp images
+ output_wrapped, warped_image = generate_warp_images(
+ state_cache["image0_orig"],
+ state_cache["image1_orig"],
+ state_cache,
+ choice_geometry_type,
+ )
+ plt.close("all")
+
+ num_matches_raw = state_cache["num_matches_raw"]
+ state_cache["wrapped_image"] = warped_image
+
+ # tmp_state_cache = tempfile.NamedTemporaryFile(suffix='.pkl', delete=False)
+ tmp_state_cache = "output.pkl"
+ with open(tmp_state_cache, "wb") as f:
+ pickle.dump(state_cache, f)
+
+ logger.info("Dump results done!")
+
+ return (
+ output_matches_ransac,
+ {
+ "num_matches_raw": num_matches_raw,
+ "num_matches_ransac": num_matches_ransac,
+ },
+ output_wrapped,
+ tmp_state_cache,
+ )
+
+
+def run_matching(
+ image0: np.ndarray,
+ image1: np.ndarray,
+ match_threshold: float,
+ extract_max_keypoints: int,
+ keypoint_threshold: float,
+ key: str,
+ ransac_method: str = DEFAULT_RANSAC_METHOD,
+ ransac_reproj_threshold: int = DEFAULT_RANSAC_REPROJ_THRESHOLD,
+ ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE,
+ ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER,
+ choice_geometry_type: str = DEFAULT_SETTING_GEOMETRY,
+ matcher_zoo: Dict[str, Any] = None,
+ force_resize: bool = False,
+ image_width: int = 640,
+ image_height: int = 480,
+ use_cached_model: bool = False,
+) -> Tuple[
+ np.ndarray,
+ np.ndarray,
+ np.ndarray,
+ Dict[str, int],
+ Dict[str, Dict[str, Any]],
+ Dict[str, Dict[str, float]],
+ np.ndarray,
+]:
+ """Match two images using the given parameters.
+
+ Args:
+ image0 (np.ndarray): RGB image 0.
+ image1 (np.ndarray): RGB image 1.
+ match_threshold (float): match threshold.
+ extract_max_keypoints (int): number of keypoints to extract.
+ keypoint_threshold (float): keypoint threshold.
+ key (str): key of the model to use.
+ ransac_method (str, optional): RANSAC method to use.
+ ransac_reproj_threshold (int, optional): RANSAC reprojection threshold.
+ ransac_confidence (float, optional): RANSAC confidence level.
+ ransac_max_iter (int, optional): RANSAC maximum number of iterations.
+ choice_geometry_type (str, optional): setting of geometry estimation.
+ matcher_zoo (Dict[str, Any], optional): matcher zoo. Defaults to None.
+ force_resize (bool, optional): force resize. Defaults to False.
+ image_width (int, optional): image width. Defaults to 640.
+ image_height (int, optional): image height. Defaults to 480.
+ use_cached_model (bool, optional): use cached model. Defaults to False.
+
+ Returns:
+ tuple:
+ - output_keypoints (np.ndarray): image with keypoints.
+ - output_matches_raw (np.ndarray): image with raw matches.
+ - output_matches_ransac (np.ndarray): image with RANSAC matches.
+ - num_matches (Dict[str, int]): number of raw and RANSAC matches.
+ - configs (Dict[str, Dict[str, Any]]): match and feature extraction configs.
+ - geom_info (Dict[str, Dict[str, float]]): geometry information.
+ - output_wrapped (np.ndarray): wrapped images.
+ """
+ # image0 and image1 is RGB mode
+ if image0 is None or image1 is None:
+ logger.error(
+ "Error: No images found! Please upload two images or select an example."
+ )
+ raise gr.Error(
+ "Error: No images found! Please upload two images or select an example."
+ )
+ # init output
+ output_keypoints = None
+ output_matches_raw = None
+ output_matches_ransac = None
+
+ # super slow!
+ if "roma" in key.lower() and DEVICE == "cpu":
+ gr.Info(
+ f"Success! Please be patient and allow for about 2-3 minutes."
+ f" Due to CPU inference, {key} is quiet slow."
+ )
+ t0 = time.time()
+ model = matcher_zoo[key]
+ match_conf = model["matcher"]
+ # update match config
+ match_conf["model"]["match_threshold"] = match_threshold
+ match_conf["model"]["max_keypoints"] = extract_max_keypoints
+ cache_key = "{}_{}".format(key, match_conf["model"]["name"])
+ if use_cached_model:
+ # because of the model cache, we need to update the config
+ matcher = model_cache.cache_model(cache_key, get_model, match_conf)
+ matcher.conf["max_keypoints"] = extract_max_keypoints
+ matcher.conf["match_threshold"] = match_threshold
+ logger.info(f"Loaded cached model {cache_key}")
+ else:
+ matcher = get_model(match_conf)
+ logger.info(f"Loading model using: {time.time()-t0:.3f}s")
+ t1 = time.time()
+
+ if model["dense"]:
+ if not match_conf["preprocessing"].get("force_resize", False):
+ match_conf["preprocessing"]["force_resize"] = force_resize
+ else:
+ logger.info("preprocessing is already resized")
+ if force_resize:
+ match_conf["preprocessing"]["height"] = image_height
+ match_conf["preprocessing"]["width"] = image_width
+ logger.info(f"Force resize to {image_width}x{image_height}")
+
+ pred = match_dense.match_images(
+ matcher, image0, image1, match_conf["preprocessing"], device=DEVICE
+ )
+ del matcher
+ extract_conf = None
+ else:
+ extract_conf = model["feature"]
+ # update extract config
+ extract_conf["model"]["max_keypoints"] = extract_max_keypoints
+ extract_conf["model"]["keypoint_threshold"] = keypoint_threshold
+ cache_key = "{}_{}".format(key, extract_conf["model"]["name"])
+
+ if use_cached_model:
+ extractor = model_cache.cache_model(
+ cache_key, get_feature_model, extract_conf
+ )
+ # because of the model cache, we need to update the config
+ extractor.conf["max_keypoints"] = extract_max_keypoints
+ extractor.conf["keypoint_threshold"] = keypoint_threshold
+ logger.info(f"Loaded cached model {cache_key}")
+ else:
+ extractor = get_feature_model(extract_conf)
+
+ if not extract_conf["preprocessing"].get("force_resize", False):
+ extract_conf["preprocessing"]["force_resize"] = force_resize
+ else:
+ logger.info("preprocessing is already resized")
+ if force_resize:
+ extract_conf["preprocessing"]["height"] = image_height
+ extract_conf["preprocessing"]["width"] = image_width
+ logger.info(f"Force resize to {image_width}x{image_height}")
+
+ pred0 = extract_features.extract(
+ extractor, image0, extract_conf["preprocessing"]
+ )
+ pred1 = extract_features.extract(
+ extractor, image1, extract_conf["preprocessing"]
+ )
+ pred = match_features.match_images(matcher, pred0, pred1)
+ del extractor
+ # gr.Info(
+ # f"Matching images done using: {time.time()-t1:.3f}s",
+ # )
+ logger.info(f"Matching images done using: {time.time()-t1:.3f}s")
+ t1 = time.time()
+
+ # plot images with keypoints
+ titles = [
+ "Image 0 - Keypoints",
+ "Image 1 - Keypoints",
+ ]
+ output_keypoints = display_keypoints(pred, titles=titles)
+
+ # plot images with raw matches
+ titles = [
+ "Image 0 - Raw matched keypoints",
+ "Image 1 - Raw matched keypoints",
+ ]
+ output_matches_raw, num_matches_raw = display_matches(pred, titles=titles)
+
+ # if enable_ransac:
+ filter_matches(
+ pred,
+ ransac_method=ransac_method,
+ ransac_reproj_threshold=ransac_reproj_threshold,
+ ransac_confidence=ransac_confidence,
+ ransac_max_iter=ransac_max_iter,
+ )
+
+ # gr.Info(f"RANSAC matches done using: {time.time()-t1:.3f}s")
+ logger.info(f"RANSAC matches done using: {time.time()-t1:.3f}s")
+ t1 = time.time()
+
+ # plot images with ransac matches
+ titles = [
+ "Image 0 - Ransac matched keypoints",
+ "Image 1 - Ransac matched keypoints",
+ ]
+ output_matches_ransac, num_matches_ransac = display_matches(
+ pred, titles=titles, tag="KPTS_RANSAC"
+ )
+ # gr.Info(f"Display matches done using: {time.time()-t1:.3f}s")
+ logger.info(f"Display matches done using: {time.time()-t1:.3f}s")
+
+ t1 = time.time()
+ # plot wrapped images
+ output_wrapped, warped_image = generate_warp_images(
+ pred["image0_orig"],
+ pred["image1_orig"],
+ pred,
+ choice_geometry_type,
+ )
+ plt.close("all")
+ # gr.Info(f"In summary, total time: {time.time()-t0:.3f}s")
+ logger.info(f"TOTAL time: {time.time()-t0:.3f}s")
+
+ state_cache = pred
+ state_cache["num_matches_raw"] = num_matches_raw
+ state_cache["num_matches_ransac"] = num_matches_ransac
+ state_cache["wrapped_image"] = warped_image
+
+ # tmp_state_cache = tempfile.NamedTemporaryFile(suffix='.pkl', delete=False)
+ tmp_state_cache = "output.pkl"
+ with open(tmp_state_cache, "wb") as f:
+ pickle.dump(state_cache, f)
+ logger.info("Dump results done!")
+ return (
+ output_keypoints,
+ output_matches_raw,
+ output_matches_ransac,
+ {
+ "num_raw_matches": num_matches_raw,
+ "num_ransac_matches": num_matches_ransac,
+ },
+ {
+ "match_conf": match_conf,
+ "extractor_conf": extract_conf,
+ },
+ {
+ "geom_info": pred.get("geom_info", {}),
+ },
+ output_wrapped,
+ state_cache,
+ tmp_state_cache,
+ )
+
+
+# @ref: https://docs.opencv.org/4.x/d0/d74/md__build_4_x-contrib_docs-lin64_opencv_doc_tutorials_calib3d_usac.html
+# AND: https://opencv.org/blog/2021/06/09/evaluating-opencvs-new-ransacs
+ransac_zoo = {
+ "POSELIB": "LO-RANSAC",
+ "CV2_RANSAC": cv2.RANSAC,
+ "CV2_USAC_MAGSAC": cv2.USAC_MAGSAC,
+ "CV2_USAC_DEFAULT": cv2.USAC_DEFAULT,
+ "CV2_USAC_FM_8PTS": cv2.USAC_FM_8PTS,
+ "CV2_USAC_PROSAC": cv2.USAC_PROSAC,
+ "CV2_USAC_FAST": cv2.USAC_FAST,
+ "CV2_USAC_ACCURATE": cv2.USAC_ACCURATE,
+ "CV2_USAC_PARALLEL": cv2.USAC_PARALLEL,
+}
+
+
+def rotate_image(input_path, degrees, output_path):
+ img = Image.open(input_path)
+ img_rotated = img.rotate(-degrees)
+ img_rotated.save(output_path)
+
+
+def scale_image(input_path, scale_factor, output_path):
+ img = Image.open(input_path)
+ width, height = img.size
+ new_width = int(width * scale_factor)
+ new_height = int(height * scale_factor)
+ new_img = Image.new("RGB", (width, height), (0, 0, 0))
+ img_resized = img.resize((new_width, new_height))
+ position = ((width - new_width) // 2, (height - new_height) // 2)
+ new_img.paste(img_resized, position)
+ new_img.save(output_path)
diff --git a/imcui/ui/viz.py b/ui/viz.py
similarity index 93%
rename from imcui/ui/viz.py
rename to ui/viz.py
index dc118b6610340fb94ea7ff9e8ef48ba96b8816a4..6533f0b03aec86775552da951a9e57e7eeb33164 100644
--- a/imcui/ui/viz.py
+++ b/ui/viz.py
@@ -1,481 +1,498 @@
-import typing
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple, Union
-
-import cv2
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import seaborn as sns
-
-from ..hloc.utils.viz import add_text, plot_keypoints
-
-np.random.seed(1995)
-color_map = np.arange(100)
-np.random.shuffle(color_map)
-
-
-def plot_images(
- imgs: List[np.ndarray],
- titles: Optional[List[str]] = None,
- cmaps: Union[str, List[str]] = "gray",
- dpi: int = 100,
- size: Optional[int] = 5,
- pad: float = 0.5,
-) -> plt.Figure:
- """Plot a set of images horizontally.
- Args:
- imgs: a list of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W).
- titles: a list of strings, as titles for each image.
- cmaps: colormaps for monochrome images. If a single string is given,
- it is used for all images.
- dpi: DPI of the figure.
- size: figure size in inches (width). If not provided, the figure
- size is determined automatically.
- pad: padding between subplots, in inches.
- Returns:
- The created figure.
- """
- n = len(imgs)
- if not isinstance(cmaps, list):
- cmaps = [cmaps] * n
- figsize = (size * n, size * 6 / 5) if size is not None else None
- fig, ax = plt.subplots(1, n, figsize=figsize, dpi=dpi)
-
- if n == 1:
- ax = [ax]
- for i in range(n):
- ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmaps[i]))
- ax[i].get_yaxis().set_ticks([])
- ax[i].get_xaxis().set_ticks([])
- ax[i].set_axis_off()
- for spine in ax[i].spines.values(): # remove frame
- spine.set_visible(False)
- if titles:
- ax[i].set_title(titles[i])
- fig.tight_layout(pad=pad)
- return fig
-
-
-def plot_color_line_matches(
- lines: List[np.ndarray],
- correct_matches: Optional[np.ndarray] = None,
- lw: float = 2.0,
- indices: Tuple[int, int] = (0, 1),
-) -> matplotlib.figure.Figure:
- """Plot line matches for existing images with multiple colors.
-
- Args:
- lines: List of ndarrays of size (N, 2, 2) representing line segments.
- correct_matches: Optional bool array of size (N,) indicating correct
- matches. If not None, display wrong matches with a low alpha.
- lw: Line width as float pixels.
- indices: Indices of the images to draw the matches on.
-
- Returns:
- The modified matplotlib figure.
- """
- n_lines = lines[0].shape[0]
- colors = sns.color_palette("husl", n_colors=n_lines)
- np.random.shuffle(colors)
- alphas = np.ones(n_lines)
- if correct_matches is not None:
- alphas[~np.array(correct_matches)] = 0.2
-
- fig = plt.gcf()
- ax = typing.cast(List[matplotlib.axes.Axes], fig.axes)
- assert len(ax) > max(indices)
- axes = [ax[i] for i in indices]
- fig.canvas.draw()
-
- # Plot the lines
- for a, l in zip(axes, lines): # noqa: E741
- # Transform the points into the figure coordinate system
- transFigure = fig.transFigure.inverted()
- endpoint0 = transFigure.transform(a.transData.transform(l[:, 0]))
- endpoint1 = transFigure.transform(a.transData.transform(l[:, 1]))
- fig.lines += [
- matplotlib.lines.Line2D(
- (endpoint0[i, 0], endpoint1[i, 0]),
- (endpoint0[i, 1], endpoint1[i, 1]),
- zorder=1,
- transform=fig.transFigure,
- c=colors[i],
- alpha=alphas[i],
- linewidth=lw,
- )
- for i in range(n_lines)
- ]
-
- return fig
-
-
-def make_matching_figure(
- img0: np.ndarray,
- img1: np.ndarray,
- mkpts0: np.ndarray,
- mkpts1: np.ndarray,
- color: np.ndarray,
- titles: Optional[List[str]] = None,
- kpts0: Optional[np.ndarray] = None,
- kpts1: Optional[np.ndarray] = None,
- text: List[str] = [],
- dpi: int = 75,
- path: Optional[Path] = None,
- pad: float = 0.0,
-) -> Optional[plt.Figure]:
- """Draw image pair with matches.
-
- Args:
- img0: image0 as HxWx3 numpy array.
- img1: image1 as HxWx3 numpy array.
- mkpts0: matched points in image0 as Nx2 numpy array.
- mkpts1: matched points in image1 as Nx2 numpy array.
- color: colors for the matches as Nx4 numpy array.
- titles: titles for the two subplots.
- kpts0: keypoints in image0 as Kx2 numpy array.
- kpts1: keypoints in image1 as Kx2 numpy array.
- text: list of strings to display in the top-left corner of the image.
- dpi: dots per inch of the saved figure.
- path: if not None, save the figure to this path.
- pad: padding around the image as a fraction of the image size.
-
- Returns:
- The matplotlib Figure object if path is None.
- """
- # draw image pair
- fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi)
- axes[0].imshow(img0) # , cmap='gray')
- axes[1].imshow(img1) # , cmap='gray')
- for i in range(2): # clear all frames
- axes[i].get_yaxis().set_ticks([])
- axes[i].get_xaxis().set_ticks([])
- for spine in axes[i].spines.values():
- spine.set_visible(False)
- if titles is not None:
- axes[i].set_title(titles[i])
-
- plt.tight_layout(pad=pad)
-
- if kpts0 is not None:
- assert kpts1 is not None
- axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c="w", s=5)
- axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c="w", s=5)
-
- # draw matches
- if mkpts0.shape[0] != 0 and mkpts1.shape[0] != 0 and mkpts0.shape == mkpts1.shape:
- fig.canvas.draw()
- transFigure = fig.transFigure.inverted()
- fkpts0 = transFigure.transform(axes[0].transData.transform(mkpts0))
- fkpts1 = transFigure.transform(axes[1].transData.transform(mkpts1))
- fig.lines = [
- matplotlib.lines.Line2D(
- (fkpts0[i, 0], fkpts1[i, 0]),
- (fkpts0[i, 1], fkpts1[i, 1]),
- transform=fig.transFigure,
- c=color[i],
- linewidth=2,
- )
- for i in range(len(mkpts0))
- ]
-
- # freeze the axes to prevent the transform to change
- axes[0].autoscale(enable=False)
- axes[1].autoscale(enable=False)
-
- axes[0].scatter(mkpts0[:, 0], mkpts0[:, 1], c=color[..., :3], s=4)
- axes[1].scatter(mkpts1[:, 0], mkpts1[:, 1], c=color[..., :3], s=4)
-
- # put txts
- txt_color = "k" if img0[:100, :200].mean() > 200 else "w"
- fig.text(
- 0.01,
- 0.99,
- "\n".join(text),
- transform=fig.axes[0].transAxes,
- fontsize=15,
- va="top",
- ha="left",
- color=txt_color,
- )
-
- # save or return figure
- if path:
- plt.savefig(str(path), bbox_inches="tight", pad_inches=0)
- plt.close()
- else:
- return fig
-
-
-def error_colormap(err: np.ndarray, thr: float, alpha: float = 1.0) -> np.ndarray:
- """
- Create a colormap based on the error values.
-
- Args:
- err: Error values as a numpy array of shape (N,).
- thr: Threshold value for the error.
- alpha: Alpha value for the colormap, between 0 and 1.
-
- Returns:
- Colormap as a numpy array of shape (N, 4) with values in [0, 1].
- """
- assert alpha <= 1.0 and alpha > 0, f"Invaid alpha value: {alpha}"
- x = 1 - np.clip(err / (thr * 2), 0, 1)
- return np.clip(
- np.stack([2 - x * 2, x * 2, np.zeros_like(x), np.ones_like(x) * alpha], -1),
- 0,
- 1,
- )
-
-
-def fig2im(fig: matplotlib.figure.Figure) -> np.ndarray:
- """
- Convert a matplotlib figure to a numpy array with RGB values.
-
- Args:
- fig: A matplotlib figure.
-
- Returns:
- A numpy array with shape (height, width, 3) and dtype uint8 containing
- the RGB values of the figure.
- """
- fig.canvas.draw()
- (width, height) = fig.canvas.get_width_height()
- buf_ndarray = np.frombuffer(fig.canvas.tostring_rgb(), dtype="u1")
- return buf_ndarray.reshape(height, width, 3)
-
-
-def draw_matches_core(
- mkpts0: List[np.ndarray],
- mkpts1: List[np.ndarray],
- img0: np.ndarray,
- img1: np.ndarray,
- conf: np.ndarray,
- titles: Optional[List[str]] = None,
- texts: Optional[List[str]] = None,
- dpi: int = 150,
- path: Optional[str] = None,
- pad: float = 0.5,
-) -> np.ndarray:
- """
- Draw matches between two images.
-
- Args:
- mkpts0: List of matches from the first image, with shape (N, 2)
- mkpts1: List of matches from the second image, with shape (N, 2)
- img0: First image, with shape (H, W, 3)
- img1: Second image, with shape (H, W, 3)
- conf: Confidence values for the matches, with shape (N,)
- titles: Optional list of title strings for the plot
- dpi: DPI for the saved image
- path: Optional path to save the image to. If None, the image is not saved.
- pad: Padding between subplots
-
- Returns:
- The figure as a numpy array with shape (height, width, 3) and dtype uint8
- containing the RGB values of the figure.
- """
- thr = 0.5
- color = error_colormap(1 - conf, thr, alpha=0.1)
- text = [
- # "image name",
- f"#Matches: {len(mkpts0)}",
- ]
- if path:
- fig2im(
- make_matching_figure(
- img0,
- img1,
- mkpts0,
- mkpts1,
- color,
- titles=titles,
- text=text,
- path=path,
- dpi=dpi,
- pad=pad,
- )
- )
- else:
- return fig2im(
- make_matching_figure(
- img0,
- img1,
- mkpts0,
- mkpts1,
- color,
- titles=titles,
- text=text,
- pad=pad,
- dpi=dpi,
- )
- )
-
-
-def draw_image_pairs(
- img0: np.ndarray,
- img1: np.ndarray,
- text: List[str] = [],
- dpi: int = 75,
- path: Optional[str] = None,
- pad: float = 0.5,
-) -> np.ndarray:
- """Draw image pair horizontally.
-
- Args:
- img0: First image, with shape (H, W, 3)
- img1: Second image, with shape (H, W, 3)
- text: List of strings to print. Each string is a new line.
- dpi: DPI of the figure.
- path: Path to save the image to. If None, the image is not saved and
- the function returns the figure as a numpy array with shape
- (height, width, 3) and dtype uint8 containing the RGB values of the
- figure.
- pad: Padding between subplots
-
- Returns:
- The figure as a numpy array with shape (height, width, 3) and dtype uint8
- containing the RGB values of the figure, or None if path is not None.
- """
- # draw image pair
- fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi)
- axes[0].imshow(img0) # , cmap='gray')
- axes[1].imshow(img1) # , cmap='gray')
- for i in range(2): # clear all frames
- axes[i].get_yaxis().set_ticks([])
- axes[i].get_xaxis().set_ticks([])
- for spine in axes[i].spines.values():
- spine.set_visible(False)
- plt.tight_layout(pad=pad)
-
- # put txts
- txt_color = "k" if img0[:100, :200].mean() > 200 else "w"
- fig.text(
- 0.01,
- 0.99,
- "\n".join(text),
- transform=fig.axes[0].transAxes,
- fontsize=15,
- va="top",
- ha="left",
- color=txt_color,
- )
-
- # save or return figure
- if path:
- plt.savefig(str(path), bbox_inches="tight", pad_inches=0)
- plt.close()
- else:
- return fig2im(fig)
-
-
-def display_keypoints(pred: dict, titles: List[str] = []):
- img0 = pred["image0_orig"]
- img1 = pred["image1_orig"]
- output_keypoints = plot_images([img0, img1], titles=titles, dpi=300)
- if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys():
- plot_keypoints([pred["keypoints0_orig"], pred["keypoints1_orig"]])
- text = (
- f"# keypoints0: {len(pred['keypoints0_orig'])} \n"
- + f"# keypoints1: {len(pred['keypoints1_orig'])}"
- )
- add_text(0, text, fs=15)
- output_keypoints = fig2im(output_keypoints)
- return output_keypoints
-
-
-def display_matches(
- pred: Dict[str, np.ndarray],
- titles: List[str] = [],
- texts: List[str] = [],
- dpi: int = 300,
- tag: str = "KPTS_RAW", # KPTS_RAW, KPTS_RANSAC, LINES_RAW, LINES_RANSAC,
-) -> Tuple[np.ndarray, int]:
- """
- Displays the matches between two images.
-
- Args:
- pred: Dictionary containing the original images and the matches.
- titles: Optional titles for the plot.
- dpi: Resolution of the plot.
-
- Returns:
- The resulting concatenated plot and the number of inliers.
- """
- img0 = pred["image0_orig"]
- img1 = pred["image1_orig"]
- num_inliers = 0
- KPTS0_KEY = None
- KPTS1_KEY = None
- confid = None
- if tag == "KPTS_RAW":
- KPTS0_KEY = "mkeypoints0_orig"
- KPTS1_KEY = "mkeypoints1_orig"
- if "mconf" in pred:
- confid = pred["mconf"]
- elif tag == "KPTS_RANSAC":
- KPTS0_KEY = "mmkeypoints0_orig"
- KPTS1_KEY = "mmkeypoints1_orig"
- if "mmconf" in pred:
- confid = pred["mmconf"]
- else:
- # TODO: LINES_RAW, LINES_RANSAC
- raise ValueError(f"Unknown tag: {tag}")
- # draw raw matches
- if (
- KPTS0_KEY in pred
- and KPTS1_KEY in pred
- and pred[KPTS0_KEY] is not None
- and pred[KPTS1_KEY] is not None
- ): # draw ransac matches
- mkpts0 = pred[KPTS0_KEY]
- mkpts1 = pred[KPTS1_KEY]
- num_inliers = len(mkpts0)
- if confid is None:
- confid = np.ones(len(mkpts0))
- fig_mkpts = draw_matches_core(
- mkpts0,
- mkpts1,
- img0,
- img1,
- confid,
- dpi=dpi,
- titles=titles,
- texts=texts,
- )
- fig = fig_mkpts
- elif (
- "line0_orig" in pred
- and "line1_orig" in pred
- and pred["line0_orig"] is not None
- and pred["line1_orig"] is not None
- # and (tag == "LINES_RAW" or tag == "LINES_RANSAC")
- ):
- # lines
- mtlines0 = pred["line0_orig"]
- mtlines1 = pred["line1_orig"]
- num_inliers = len(mtlines0)
- fig_lines = plot_images(
- [img0.squeeze(), img1.squeeze()],
- ["Image 0 - matched lines", "Image 1 - matched lines"],
- dpi=300,
- )
- fig_lines = plot_color_line_matches([mtlines0, mtlines1], lw=2)
- fig_lines = fig2im(fig_lines)
-
- # keypoints
- mkpts0 = pred.get("line_keypoints0_orig")
- mkpts1 = pred.get("line_keypoints1_orig")
- fig = None
- if mkpts0 is not None and mkpts1 is not None:
- num_inliers = len(mkpts0)
- if "mconf" in pred:
- mconf = pred["mconf"]
- else:
- mconf = np.ones(len(mkpts0))
- fig_mkpts = draw_matches_core(mkpts0, mkpts1, img0, img1, mconf, dpi=300)
- fig_lines = cv2.resize(fig_lines, (fig_mkpts.shape[1], fig_mkpts.shape[0]))
- fig = np.concatenate([fig_mkpts, fig_lines], axis=0)
- else:
- fig = fig_lines
- return fig, num_inliers
+import sys
+import typing
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Union
+
+import cv2
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+sys.path.append(str(Path(__file__).parents[1]))
+
+from hloc.utils.viz import add_text, plot_keypoints
+
+np.random.seed(1995)
+color_map = np.arange(100)
+np.random.shuffle(color_map)
+
+
+def plot_images(
+ imgs: List[np.ndarray],
+ titles: Optional[List[str]] = None,
+ cmaps: Union[str, List[str]] = "gray",
+ dpi: int = 100,
+ size: Optional[int] = 5,
+ pad: float = 0.5,
+) -> plt.Figure:
+ """Plot a set of images horizontally.
+ Args:
+ imgs: a list of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W).
+ titles: a list of strings, as titles for each image.
+ cmaps: colormaps for monochrome images. If a single string is given,
+ it is used for all images.
+ dpi: DPI of the figure.
+ size: figure size in inches (width). If not provided, the figure
+ size is determined automatically.
+ pad: padding between subplots, in inches.
+ Returns:
+ The created figure.
+ """
+ n = len(imgs)
+ if not isinstance(cmaps, list):
+ cmaps = [cmaps] * n
+ figsize = (size * n, size * 6 / 5) if size is not None else None
+ fig, ax = plt.subplots(1, n, figsize=figsize, dpi=dpi)
+
+ if n == 1:
+ ax = [ax]
+ for i in range(n):
+ ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmaps[i]))
+ ax[i].get_yaxis().set_ticks([])
+ ax[i].get_xaxis().set_ticks([])
+ ax[i].set_axis_off()
+ for spine in ax[i].spines.values(): # remove frame
+ spine.set_visible(False)
+ if titles:
+ ax[i].set_title(titles[i])
+ fig.tight_layout(pad=pad)
+ return fig
+
+
+def plot_color_line_matches(
+ lines: List[np.ndarray],
+ correct_matches: Optional[np.ndarray] = None,
+ lw: float = 2.0,
+ indices: Tuple[int, int] = (0, 1),
+) -> matplotlib.figure.Figure:
+ """Plot line matches for existing images with multiple colors.
+
+ Args:
+ lines: List of ndarrays of size (N, 2, 2) representing line segments.
+ correct_matches: Optional bool array of size (N,) indicating correct
+ matches. If not None, display wrong matches with a low alpha.
+ lw: Line width as float pixels.
+ indices: Indices of the images to draw the matches on.
+
+ Returns:
+ The modified matplotlib figure.
+ """
+ n_lines = lines[0].shape[0]
+ colors = sns.color_palette("husl", n_colors=n_lines)
+ np.random.shuffle(colors)
+ alphas = np.ones(n_lines)
+ if correct_matches is not None:
+ alphas[~np.array(correct_matches)] = 0.2
+
+ fig = plt.gcf()
+ ax = typing.cast(List[matplotlib.axes.Axes], fig.axes)
+ assert len(ax) > max(indices)
+ axes = [ax[i] for i in indices]
+ fig.canvas.draw()
+
+ # Plot the lines
+ for a, l in zip(axes, lines):
+ # Transform the points into the figure coordinate system
+ transFigure = fig.transFigure.inverted()
+ endpoint0 = transFigure.transform(a.transData.transform(l[:, 0]))
+ endpoint1 = transFigure.transform(a.transData.transform(l[:, 1]))
+ fig.lines += [
+ matplotlib.lines.Line2D(
+ (endpoint0[i, 0], endpoint1[i, 0]),
+ (endpoint0[i, 1], endpoint1[i, 1]),
+ zorder=1,
+ transform=fig.transFigure,
+ c=colors[i],
+ alpha=alphas[i],
+ linewidth=lw,
+ )
+ for i in range(n_lines)
+ ]
+
+ return fig
+
+
+def make_matching_figure(
+ img0: np.ndarray,
+ img1: np.ndarray,
+ mkpts0: np.ndarray,
+ mkpts1: np.ndarray,
+ color: np.ndarray,
+ titles: Optional[List[str]] = None,
+ kpts0: Optional[np.ndarray] = None,
+ kpts1: Optional[np.ndarray] = None,
+ text: List[str] = [],
+ dpi: int = 75,
+ path: Optional[Path] = None,
+ pad: float = 0.0,
+) -> Optional[plt.Figure]:
+ """Draw image pair with matches.
+
+ Args:
+ img0: image0 as HxWx3 numpy array.
+ img1: image1 as HxWx3 numpy array.
+ mkpts0: matched points in image0 as Nx2 numpy array.
+ mkpts1: matched points in image1 as Nx2 numpy array.
+ color: colors for the matches as Nx4 numpy array.
+ titles: titles for the two subplots.
+ kpts0: keypoints in image0 as Kx2 numpy array.
+ kpts1: keypoints in image1 as Kx2 numpy array.
+ text: list of strings to display in the top-left corner of the image.
+ dpi: dots per inch of the saved figure.
+ path: if not None, save the figure to this path.
+ pad: padding around the image as a fraction of the image size.
+
+ Returns:
+ The matplotlib Figure object if path is None.
+ """
+ # draw image pair
+ fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi)
+ axes[0].imshow(img0) # , cmap='gray')
+ axes[1].imshow(img1) # , cmap='gray')
+ for i in range(2): # clear all frames
+ axes[i].get_yaxis().set_ticks([])
+ axes[i].get_xaxis().set_ticks([])
+ for spine in axes[i].spines.values():
+ spine.set_visible(False)
+ if titles is not None:
+ axes[i].set_title(titles[i])
+
+ plt.tight_layout(pad=pad)
+
+ if kpts0 is not None:
+ assert kpts1 is not None
+ axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c="w", s=5)
+ axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c="w", s=5)
+
+ # draw matches
+ if (
+ mkpts0.shape[0] != 0
+ and mkpts1.shape[0] != 0
+ and mkpts0.shape == mkpts1.shape
+ ):
+ fig.canvas.draw()
+ transFigure = fig.transFigure.inverted()
+ fkpts0 = transFigure.transform(axes[0].transData.transform(mkpts0))
+ fkpts1 = transFigure.transform(axes[1].transData.transform(mkpts1))
+ fig.lines = [
+ matplotlib.lines.Line2D(
+ (fkpts0[i, 0], fkpts1[i, 0]),
+ (fkpts0[i, 1], fkpts1[i, 1]),
+ transform=fig.transFigure,
+ c=color[i],
+ linewidth=2,
+ )
+ for i in range(len(mkpts0))
+ ]
+
+ # freeze the axes to prevent the transform to change
+ axes[0].autoscale(enable=False)
+ axes[1].autoscale(enable=False)
+
+ axes[0].scatter(mkpts0[:, 0], mkpts0[:, 1], c=color[..., :3], s=4)
+ axes[1].scatter(mkpts1[:, 0], mkpts1[:, 1], c=color[..., :3], s=4)
+
+ # put txts
+ txt_color = "k" if img0[:100, :200].mean() > 200 else "w"
+ fig.text(
+ 0.01,
+ 0.99,
+ "\n".join(text),
+ transform=fig.axes[0].transAxes,
+ fontsize=15,
+ va="top",
+ ha="left",
+ color=txt_color,
+ )
+
+ # save or return figure
+ if path:
+ plt.savefig(str(path), bbox_inches="tight", pad_inches=0)
+ plt.close()
+ else:
+ return fig
+
+
+def error_colormap(
+ err: np.ndarray, thr: float, alpha: float = 1.0
+) -> np.ndarray:
+ """
+ Create a colormap based on the error values.
+
+ Args:
+ err: Error values as a numpy array of shape (N,).
+ thr: Threshold value for the error.
+ alpha: Alpha value for the colormap, between 0 and 1.
+
+ Returns:
+ Colormap as a numpy array of shape (N, 4) with values in [0, 1].
+ """
+ assert alpha <= 1.0 and alpha > 0, f"Invaid alpha value: {alpha}"
+ x = 1 - np.clip(err / (thr * 2), 0, 1)
+ return np.clip(
+ np.stack(
+ [2 - x * 2, x * 2, np.zeros_like(x), np.ones_like(x) * alpha], -1
+ ),
+ 0,
+ 1,
+ )
+
+
+def fig2im(fig: matplotlib.figure.Figure) -> np.ndarray:
+ """
+ Convert a matplotlib figure to a numpy array with RGB values.
+
+ Args:
+ fig: A matplotlib figure.
+
+ Returns:
+ A numpy array with shape (height, width, 3) and dtype uint8 containing
+ the RGB values of the figure.
+ """
+ fig.canvas.draw()
+ (width, height) = fig.canvas.get_width_height()
+ buf_ndarray = np.frombuffer(fig.canvas.tostring_rgb(), dtype="u1")
+ return buf_ndarray.reshape(height, width, 3)
+
+
+def draw_matches_core(
+ mkpts0: List[np.ndarray],
+ mkpts1: List[np.ndarray],
+ img0: np.ndarray,
+ img1: np.ndarray,
+ conf: np.ndarray,
+ titles: Optional[List[str]] = None,
+ texts: Optional[List[str]] = None,
+ dpi: int = 150,
+ path: Optional[str] = None,
+ pad: float = 0.5,
+) -> np.ndarray:
+ """
+ Draw matches between two images.
+
+ Args:
+ mkpts0: List of matches from the first image, with shape (N, 2)
+ mkpts1: List of matches from the second image, with shape (N, 2)
+ img0: First image, with shape (H, W, 3)
+ img1: Second image, with shape (H, W, 3)
+ conf: Confidence values for the matches, with shape (N,)
+ titles: Optional list of title strings for the plot
+ dpi: DPI for the saved image
+ path: Optional path to save the image to. If None, the image is not saved.
+ pad: Padding between subplots
+
+ Returns:
+ The figure as a numpy array with shape (height, width, 3) and dtype uint8
+ containing the RGB values of the figure.
+ """
+ thr = 0.5
+ color = error_colormap(1 - conf, thr, alpha=0.1)
+ text = [
+ # "image name",
+ f"#Matches: {len(mkpts0)}",
+ ]
+ if path:
+ fig2im(
+ make_matching_figure(
+ img0,
+ img1,
+ mkpts0,
+ mkpts1,
+ color,
+ titles=titles,
+ text=text,
+ path=path,
+ dpi=dpi,
+ pad=pad,
+ )
+ )
+ else:
+ return fig2im(
+ make_matching_figure(
+ img0,
+ img1,
+ mkpts0,
+ mkpts1,
+ color,
+ titles=titles,
+ text=text,
+ pad=pad,
+ dpi=dpi,
+ )
+ )
+
+
+def draw_image_pairs(
+ img0: np.ndarray,
+ img1: np.ndarray,
+ text: List[str] = [],
+ dpi: int = 75,
+ path: Optional[str] = None,
+ pad: float = 0.5,
+) -> np.ndarray:
+ """Draw image pair horizontally.
+
+ Args:
+ img0: First image, with shape (H, W, 3)
+ img1: Second image, with shape (H, W, 3)
+ text: List of strings to print. Each string is a new line.
+ dpi: DPI of the figure.
+ path: Path to save the image to. If None, the image is not saved and
+ the function returns the figure as a numpy array with shape
+ (height, width, 3) and dtype uint8 containing the RGB values of the
+ figure.
+ pad: Padding between subplots
+
+ Returns:
+ The figure as a numpy array with shape (height, width, 3) and dtype uint8
+ containing the RGB values of the figure, or None if path is not None.
+ """
+ # draw image pair
+ fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi)
+ axes[0].imshow(img0) # , cmap='gray')
+ axes[1].imshow(img1) # , cmap='gray')
+ for i in range(2): # clear all frames
+ axes[i].get_yaxis().set_ticks([])
+ axes[i].get_xaxis().set_ticks([])
+ for spine in axes[i].spines.values():
+ spine.set_visible(False)
+ plt.tight_layout(pad=pad)
+
+ # put txts
+ txt_color = "k" if img0[:100, :200].mean() > 200 else "w"
+ fig.text(
+ 0.01,
+ 0.99,
+ "\n".join(text),
+ transform=fig.axes[0].transAxes,
+ fontsize=15,
+ va="top",
+ ha="left",
+ color=txt_color,
+ )
+
+ # save or return figure
+ if path:
+ plt.savefig(str(path), bbox_inches="tight", pad_inches=0)
+ plt.close()
+ else:
+ return fig2im(fig)
+
+
+def display_keypoints(pred: dict, titles: List[str] = []):
+ img0 = pred["image0_orig"]
+ img1 = pred["image1_orig"]
+ output_keypoints = plot_images([img0, img1], titles=titles, dpi=300)
+ if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys():
+ plot_keypoints([pred["keypoints0_orig"], pred["keypoints1_orig"]])
+ text = (
+ f"# keypoints0: {len(pred['keypoints0_orig'])} \n"
+ + f"# keypoints1: {len(pred['keypoints1_orig'])}"
+ )
+ add_text(0, text, fs=15)
+ output_keypoints = fig2im(output_keypoints)
+ return output_keypoints
+
+
+def display_matches(
+ pred: Dict[str, np.ndarray],
+ titles: List[str] = [],
+ texts: List[str] = [],
+ dpi: int = 300,
+ tag: str = "KPTS_RAW", # KPTS_RAW, KPTS_RANSAC, LINES_RAW, LINES_RANSAC,
+) -> Tuple[np.ndarray, int]:
+ """
+ Displays the matches between two images.
+
+ Args:
+ pred: Dictionary containing the original images and the matches.
+ titles: Optional titles for the plot.
+ dpi: Resolution of the plot.
+
+ Returns:
+ The resulting concatenated plot and the number of inliers.
+ """
+ img0 = pred["image0_orig"]
+ img1 = pred["image1_orig"]
+ num_inliers = 0
+ KPTS0_KEY = None
+ KPTS1_KEY = None
+ confid = None
+ if tag == "KPTS_RAW":
+ KPTS0_KEY = "mkeypoints0_orig"
+ KPTS1_KEY = "mkeypoints1_orig"
+ if "mconf" in pred:
+ confid = pred["mconf"]
+ elif tag == "KPTS_RANSAC":
+ KPTS0_KEY = "mmkeypoints0_orig"
+ KPTS1_KEY = "mmkeypoints1_orig"
+ if "mmconf" in pred:
+ confid = pred["mmconf"]
+ else:
+ # TODO: LINES_RAW, LINES_RANSAC
+ raise ValueError(f"Unknown tag: {tag}")
+ # draw raw matches
+ if (
+ KPTS0_KEY in pred
+ and KPTS1_KEY in pred
+ and pred[KPTS0_KEY] is not None
+ and pred[KPTS1_KEY] is not None
+ ): # draw ransac matches
+ mkpts0 = pred[KPTS0_KEY]
+ mkpts1 = pred[KPTS1_KEY]
+ num_inliers = len(mkpts0)
+ if confid is None:
+ confid = np.ones(len(mkpts0))
+ fig_mkpts = draw_matches_core(
+ mkpts0,
+ mkpts1,
+ img0,
+ img1,
+ confid,
+ dpi=dpi,
+ titles=titles,
+ texts=texts,
+ )
+ fig = fig_mkpts
+ # TODO: draw lines
+ if (
+ "line0_orig" in pred
+ and "line1_orig" in pred
+ and pred["line0_orig"] is not None
+ and pred["line1_orig"] is not None
+ and (tag == "LINES_RAW" or tag == "LINES_RANSAC")
+ ):
+ # lines
+ mtlines0 = pred["line0_orig"]
+ mtlines1 = pred["line1_orig"]
+ num_inliers = len(mtlines0)
+ fig_lines = plot_images(
+ [img0.squeeze(), img1.squeeze()],
+ ["Image 0 - matched lines", "Image 1 - matched lines"],
+ dpi=300,
+ )
+ fig_lines = plot_color_line_matches([mtlines0, mtlines1], lw=2)
+ fig_lines = fig2im(fig_lines)
+
+ # keypoints
+ mkpts0 = pred.get("line_keypoints0_orig")
+ mkpts1 = pred.get("line_keypoints1_orig")
+ fig = None
+ breakpoint()
+ if mkpts0 is not None and mkpts1 is not None:
+ num_inliers = len(mkpts0)
+ if "mconf" in pred:
+ mconf = pred["mconf"]
+ else:
+ mconf = np.ones(len(mkpts0))
+ fig_mkpts = draw_matches_core(
+ mkpts0, mkpts1, img0, img1, mconf, dpi=300
+ )
+ fig_lines = cv2.resize(
+ fig_lines, (fig_mkpts.shape[1], fig_mkpts.shape[0])
+ )
+ fig = np.concatenate([fig_mkpts, fig_lines], axis=0)
+ else:
+ fig = fig_lines
+ return fig, num_inliers
diff --git a/vercel.json b/vercel.json
deleted file mode 100644
index 27e0b209a1eaaf329b607bf3cfd43a7daced702e..0000000000000000000000000000000000000000
--- a/vercel.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
- "builds": [
- {
- "src": "api/server.py",
- "use": "@vercel/python",
- "config": {
- "maxLambdaSize": "10gb",
- "runtime": "python3.10"
- }
- }
- ],
- "routes": [
- {
- "src": "/(.*)",
- "dest": "api/server.py"
- }
- ]
-}
- 
- 
- 
- 
-
-
+Try it on 
- 
+
+
+
+
+
+
+
+
+
+### Installation
+
+```shell
+git clone git@github.com:THU-LYJ-Lab/DarkFeat.git
+cd DarkFeat
+pip install -r requirements.txt
+```
+
+[Pytorch](https://pytorch.org/) installation is machine dependent, please install the correct version for your machine.
+
+### Demo
+
+```shell
+python ./demo_darkfeat.py \
+ --input /path/to/your/sequence \
+ --output_dir ./output \
+ --resize 960 640 \
+ --model_path /path/to/pretrained/weights
+```
+
+Sample raw image sequences and pretrained weights can be downloaded from [here](https://drive.google.com/drive/folders/1zkUCsBVEmQcPZPhsEUymA5GIvAzi12hD?usp=sharing).
+
+Note that different pytorch and cuda versions may cause different model output results, and the output matches may differ from those shown in the gif. The results are tested in python 3.6, PyTorch 1.10.2 and cuda 10.2.
+
+### Evaluation
+
+1. Download [MID](https://github.com/Wenzhengchina/Matching-in-the-Dark) Dataset.
+
+2. Preprocessing the data in MID dataset, you can choose whether to enable histogram equalization or not:
+
+ ```shell
+ python raw_preprocess.py --dataset_dir /path/to/MID/dataset
+ ```
+
+3. Extract the keypoints and descriptors, followed by a nearest neighborhood matching:
+
+ ```shell
+ python export_features.py \
+ --model_path /path/to/pretrained/weights \
+ --dataset_dir /path/to/MID/dataset
+ ```
+
+4. Estimate the pose through corresponding keypoint pairs:
+
+ ```shell
+ python pose_estimation.py --dataset_dir /path/to/MID/dataset
+ ```
+
+5. Finally collect the results of pose estimation errors:
+
+ ```
+ python read_error.py
+ ```
+
+### Training from scratch
+
+We use [GL3D](https://github.com/lzx551402/GL3D) as our source training-use matching dataset. Please follow the [instructions](https://github.com/lzx551402/GL3D) to download and unzip all the data (including GL3D group and tourism group).
+
+Then using the preprocessing code provided by ASLFeat to generate matching informations:
+
+```shell
+git clone https://github.com/lzx551402/tfmatch
+# please edit the GL3D path in the shell script before executing.
+cd tfmatch
+sh train_aslfeat_base.sh
+```
+
+To launch the training, configure your training hyperparameters inside `./configs` and then run:
+
+```shell
+# stage1
+python run.py --stage 1 --config ./configs/config_stage1.yaml \
+ --dataset_dir /path/to/your/GL3D/dataset \
+ --job_name YOUR_JOB_NAME
+# stage2
+python run.py --stage 2 --config ./configs/config_stage1.yaml \
+ --dataset_dir /path/to/your/GL3D/dataset \
+ --job_name YOUR_JOB_NAME \
+ --start_cnt 160000
+# stage3
+python run.py --stage 3 --config ./configs/config.yaml \
+ --dataset_dir /path/to/your/GL3D/dataset \
+ --job_name YOUR_JOB_NAME \
+ --start_cnt 220000
+```
+
+### Acknowledgements
+
+This project could not be possible without the open-source works from [ASLFeat](https://github.com/lzx551402/ASLFeat), [R2D2](https://github.com/naver/r2d2), [MID](https://github.com/Wenzhengchina/Matching-in-the-Dark), [GL3D](https://github.com/lzx551402/GL3D), [SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork). We sincerely thank them all.
\ No newline at end of file
diff --git a/imcui/third_party/DarkFeat/configs/config.yaml b/third_party/DarkFeat/configs/config.yaml
similarity index 100%
rename from imcui/third_party/DarkFeat/configs/config.yaml
rename to third_party/DarkFeat/configs/config.yaml
diff --git a/imcui/third_party/DarkFeat/configs/config_stage1.yaml b/third_party/DarkFeat/configs/config_stage1.yaml
similarity index 100%
rename from imcui/third_party/DarkFeat/configs/config_stage1.yaml
rename to third_party/DarkFeat/configs/config_stage1.yaml
diff --git a/imcui/third_party/DarkFeat/darkfeat.py b/third_party/DarkFeat/darkfeat.py
similarity index 58%
rename from imcui/third_party/DarkFeat/darkfeat.py
rename to third_party/DarkFeat/darkfeat.py
index d146e2b41f5399ff3fc2f52ec5daff1c56e491c0..9ae9d0ffecdbd4abd79f0c49c8e15e51f3db3e4d 100644
--- a/imcui/third_party/DarkFeat/darkfeat.py
+++ b/third_party/DarkFeat/darkfeat.py
@@ -16,11 +16,11 @@ def gather_nd(params, indices):
out_shape = orig_shape[:-1] + list(params.shape)[m:]
else:
raise ValueError(
- f'the last dimension of indices must less or equal to the rank of params. Got indices:{indices.shape}, params:{params.shape}. {m} > {n}'
+ f"the last dimension of indices must less or equal to the rank of params. Got indices:{indices.shape}, params:{params.shape}. {m} > {n}"
)
indices = indices.reshape((num_samples, m)).transpose(0, 1).tolist()
- output = params[indices] # (num_samples, ...)
+ output = params[indices] # (num_samples, ...)
return output.reshape(out_shape).contiguous()
@@ -59,11 +59,13 @@ def interpolate(pos, inputs, nd=True):
w_bottom_right = w_bottom_right[..., None]
interpolated_val = (
- w_top_left * gather_nd(inputs, torch.stack([i_top_left, j_top_left], axis=-1)) +
- w_top_right * gather_nd(inputs, torch.stack([i_top_right, j_top_right], axis=-1)) +
- w_bottom_left * gather_nd(inputs, torch.stack([i_bottom_left, j_bottom_left], axis=-1)) +
- w_bottom_right *
- gather_nd(inputs, torch.stack([i_bottom_right, j_bottom_right], axis=-1))
+ w_top_left * gather_nd(inputs, torch.stack([i_top_left, j_top_left], axis=-1))
+ + w_top_right
+ * gather_nd(inputs, torch.stack([i_top_right, j_top_right], axis=-1))
+ + w_bottom_left
+ * gather_nd(inputs, torch.stack([i_bottom_left, j_bottom_left], axis=-1))
+ + w_bottom_right
+ * gather_nd(inputs, torch.stack([i_bottom_right, j_bottom_right], axis=-1))
)
return interpolated_val
@@ -73,24 +75,29 @@ def edge_mask(inputs, n_channel, dilation=1, edge_thld=5):
b, c, h, w = inputs.size()
device = inputs.device
- dii_filter = torch.tensor(
- [[0, 1., 0], [0, -2., 0], [0, 1., 0]]
- ).view(1, 1, 3, 3)
+ dii_filter = torch.tensor([[0, 1.0, 0], [0, -2.0, 0], [0, 1.0, 0]]).view(1, 1, 3, 3)
dij_filter = 0.25 * torch.tensor(
- [[1., 0, -1.], [0, 0., 0], [-1., 0, 1.]]
- ).view(1, 1, 3, 3)
- djj_filter = torch.tensor(
- [[0, 0, 0], [1., -2., 1.], [0, 0, 0]]
+ [[1.0, 0, -1.0], [0, 0.0, 0], [-1.0, 0, 1.0]]
).view(1, 1, 3, 3)
+ djj_filter = torch.tensor([[0, 0, 0], [1.0, -2.0, 1.0], [0, 0, 0]]).view(1, 1, 3, 3)
dii = F.conv2d(
- inputs.view(-1, 1, h, w), dii_filter.to(device), padding=dilation, dilation=dilation
+ inputs.view(-1, 1, h, w),
+ dii_filter.to(device),
+ padding=dilation,
+ dilation=dilation,
).view(b, c, h, w)
dij = F.conv2d(
- inputs.view(-1, 1, h, w), dij_filter.to(device), padding=dilation, dilation=dilation
+ inputs.view(-1, 1, h, w),
+ dij_filter.to(device),
+ padding=dilation,
+ dilation=dilation,
).view(b, c, h, w)
djj = F.conv2d(
- inputs.view(-1, 1, h, w), djj_filter.to(device), padding=dilation, dilation=dilation
+ inputs.view(-1, 1, h, w),
+ djj_filter.to(device),
+ padding=dilation,
+ dilation=dilation,
).view(b, c, h, w)
det = dii * djj - dij * dij
@@ -111,11 +118,17 @@ def extract_kpts(score_map, k=256, score_thld=0, edge_thld=0, nms_size=3, eof_si
mask = score_map > score_thld
if nms_size > 0:
- nms_mask = F.max_pool2d(score_map, kernel_size=nms_size, stride=1, padding=nms_size//2)
+ nms_mask = F.max_pool2d(
+ score_map, kernel_size=nms_size, stride=1, padding=nms_size // 2
+ )
nms_mask = torch.eq(score_map, nms_mask)
mask = torch.logical_and(nms_mask, mask)
if eof_size > 0:
- eof_mask = torch.ones((1, 1, h - 2 * eof_size, w - 2 * eof_size), dtype=torch.float32, device=score_map.device)
+ eof_mask = torch.ones(
+ (1, 1, h - 2 * eof_size, w - 2 * eof_size),
+ dtype=torch.float32,
+ device=score_map.device,
+ )
eof_mask = F.pad(eof_mask, [eof_size] * 4, value=0)
eof_mask = eof_mask.bool()
mask = torch.logical_and(eof_mask, mask)
@@ -157,23 +170,20 @@ def extract_kpts(score_map, k=256, score_thld=0, edge_thld=0, nms_size=3, eof_si
# output: [batch_size, C, H, W], [batch_size, C, H, W]
def peakiness_score(inputs, moving_instance_max, ksize=3, dilation=1):
inputs = inputs / moving_instance_max
-
+
batch_size, C, H, W = inputs.shape
pad_size = ksize // 2 + (dilation - 1)
kernel = torch.ones([C, 1, ksize, ksize], device=inputs.device) / (ksize * ksize)
-
- pad_inputs = F.pad(inputs, [pad_size] * 4, mode='reflect')
+
+ pad_inputs = F.pad(inputs, [pad_size] * 4, mode="reflect")
avg_spatial_inputs = F.conv2d(
- pad_inputs,
- kernel,
- stride=1,
- dilation=dilation,
- padding=0,
- groups=C
+ pad_inputs, kernel, stride=1, dilation=dilation, padding=0, groups=C
)
- avg_channel_inputs = torch.mean(inputs, axis=1, keepdim=True) # channel dimension is 1
+ avg_channel_inputs = torch.mean(
+ inputs, axis=1, keepdim=True
+ ) # channel dimension is 1
# print(avg_spatial_inputs.shape)
alpha = F.softplus(inputs - avg_spatial_inputs)
@@ -184,23 +194,36 @@ def peakiness_score(inputs, moving_instance_max, ksize=3, dilation=1):
class DarkFeat(nn.Module):
default_config = {
- 'model_path': '',
- 'input_type': 'raw-demosaic',
- 'kpt_n': 5000,
- 'kpt_refinement': True,
- 'score_thld': 0.5,
- 'edge_thld': 10,
- 'multi_scale': False,
- 'multi_level': True,
- 'nms_size': 3,
- 'eof_size': 5,
- 'need_norm': True,
- 'use_peakiness': True
+ "model_path": "",
+ "input_type": "raw-demosaic",
+ "kpt_n": 5000,
+ "kpt_refinement": True,
+ "score_thld": 0.5,
+ "edge_thld": 10,
+ "multi_scale": False,
+ "multi_level": True,
+ "nms_size": 3,
+ "eof_size": 5,
+ "need_norm": True,
+ "use_peakiness": True,
}
- def __init__(self, model_path='', inchan=3, dilated=True, dilation=1, bn=True, bn_affine=False):
+ def __init__(
+ self,
+ model_path="",
+ inchan=3,
+ dilated=True,
+ dilation=1,
+ bn=True,
+ bn_affine=False,
+ ):
super(DarkFeat, self).__init__()
- inchan = 3 if self.default_config['input_type'] == 'rgb' or self.default_config['input_type'] == 'raw-demosaic' else 1
+ inchan = (
+ 3
+ if self.default_config["input_type"] == "rgb"
+ or self.default_config["input_type"] == "raw-demosaic"
+ else 1
+ )
self.config = {**self.default_config}
self.inchan = inchan
@@ -209,60 +232,81 @@ class DarkFeat(nn.Module):
self.dilation = dilation
self.bn = bn
self.bn_affine = bn_affine
- self.config['model_path'] = model_path
+ self.config["model_path"] = model_path
dim = 128
mchan = 4
- self.conv0 = self._add_conv( 8*mchan)
- self.conv1 = self._add_conv( 8*mchan, bn=False)
- self.bn1 = self._make_bn(8*mchan)
- self.conv2 = self._add_conv( 16*mchan, stride=2)
- self.conv3 = self._add_conv( 16*mchan, bn=False)
- self.bn3 = self._make_bn(16*mchan)
- self.conv4 = self._add_conv( 32*mchan, stride=2)
- self.conv5 = self._add_conv( 32*mchan)
+ self.conv0 = self._add_conv(8 * mchan)
+ self.conv1 = self._add_conv(8 * mchan, bn=False)
+ self.bn1 = self._make_bn(8 * mchan)
+ self.conv2 = self._add_conv(16 * mchan, stride=2)
+ self.conv3 = self._add_conv(16 * mchan, bn=False)
+ self.bn3 = self._make_bn(16 * mchan)
+ self.conv4 = self._add_conv(32 * mchan, stride=2)
+ self.conv5 = self._add_conv(32 * mchan)
# replace last 8x8 convolution with 3 3x3 convolutions
- self.conv6_0 = self._add_conv( 32*mchan)
- self.conv6_1 = self._add_conv( 32*mchan)
+ self.conv6_0 = self._add_conv(32 * mchan)
+ self.conv6_1 = self._add_conv(32 * mchan)
self.conv6_2 = self._add_conv(dim, bn=False, relu=False)
self.out_dim = dim
- self.moving_avg_params = nn.ParameterList([
- Parameter(torch.tensor(1.), requires_grad=False),
- Parameter(torch.tensor(1.), requires_grad=False),
- Parameter(torch.tensor(1.), requires_grad=False)
- ])
+ self.moving_avg_params = nn.ParameterList(
+ [
+ Parameter(torch.tensor(1.0), requires_grad=False),
+ Parameter(torch.tensor(1.0), requires_grad=False),
+ Parameter(torch.tensor(1.0), requires_grad=False),
+ ]
+ )
self.clf = nn.Conv2d(128, 2, kernel_size=1)
state_dict = torch.load(self.config["model_path"], map_location="cpu")
new_state_dict = {}
-
+
for key in state_dict:
- if 'running_mean' not in key and 'running_var' not in key and 'num_batches_tracked' not in key:
+ if (
+ "running_mean" not in key
+ and "running_var" not in key
+ and "num_batches_tracked" not in key
+ ):
new_state_dict[key] = state_dict[key]
self.load_state_dict(new_state_dict)
- print('Loaded DarkFeat model')
-
+ print("Loaded DarkFeat model")
+
def _make_bn(self, outd):
return nn.BatchNorm2d(outd, affine=self.bn_affine, track_running_stats=False)
- def _add_conv(self, outd, k=3, stride=1, dilation=1, bn=True, relu=True, k_pool = 1, pool_type='max', bias=False):
+ def _add_conv(
+ self,
+ outd,
+ k=3,
+ stride=1,
+ dilation=1,
+ bn=True,
+ relu=True,
+ k_pool=1,
+ pool_type="max",
+ bias=False,
+ ):
d = self.dilation * dilation
- conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=stride, bias=bias)
+ conv_params = dict(
+ padding=((k - 1) * d) // 2, dilation=d, stride=stride, bias=bias
+ )
ops = nn.ModuleList([])
- ops.append( nn.Conv2d(self.curchan, outd, kernel_size=k, **conv_params) )
- if bn and self.bn: ops.append( self._make_bn(outd) )
- if relu: ops.append( nn.ReLU(inplace=True) )
+ ops.append(nn.Conv2d(self.curchan, outd, kernel_size=k, **conv_params))
+ if bn and self.bn:
+ ops.append(self._make_bn(outd))
+ if relu:
+ ops.append(nn.ReLU(inplace=True))
self.curchan = outd
-
+
if k_pool > 1:
- if pool_type == 'avg':
+ if pool_type == "avg":
ops.append(torch.nn.AvgPool2d(kernel_size=k_pool))
- elif pool_type == 'max':
+ elif pool_type == "max":
ops.append(torch.nn.MaxPool2d(kernel_size=k_pool))
else:
print(f"Error, unknown pooling type {pool_type}...")
@@ -270,32 +314,32 @@ class DarkFeat(nn.Module):
return nn.Sequential(*ops)
def forward(self, input):
- """ Compute keypoints, scores, descriptors for image """
- data = input['image']
+ """Compute keypoints, scores, descriptors for image"""
+ data = input["image"]
H, W = data.shape[2:]
- if self.config['input_type'] == 'rgb':
+ if self.config["input_type"] == "rgb":
# 3-channel rgb
RGB_mean = [0.485, 0.456, 0.406]
- RGB_std = [0.229, 0.224, 0.225]
+ RGB_std = [0.229, 0.224, 0.225]
norm_RGB = tvf.Normalize(mean=RGB_mean, std=RGB_std)
data = norm_RGB(data)
- elif self.config['input_type'] == 'gray':
+ elif self.config["input_type"] == "gray":
# 1-channel
data = torch.mean(data, dim=1, keepdim=True)
norm_gray0 = tvf.Normalize(mean=data.mean(), std=data.std())
data = norm_gray0(data)
- elif self.config['input_type'] == 'raw':
+ elif self.config["input_type"] == "raw":
# 4-channel
pass
- elif self.config['input_type'] == 'raw-demosaic':
+ elif self.config["input_type"] == "raw-demosaic":
# 3-channel
pass
else:
raise NotImplementedError()
-
+
# x: [N, C, H, W]
x0 = self.conv0(data)
x1 = self.conv1(x0)
@@ -309,16 +353,20 @@ class DarkFeat(nn.Module):
x6_1 = self.conv6_1(x6_0)
x6_2 = self.conv6_2(x6_1)
- comb_weights = torch.tensor([1., 2., 3.], device=data.device)
+ comb_weights = torch.tensor([1.0, 2.0, 3.0], device=data.device)
comb_weights /= torch.sum(comb_weights)
ksize = [3, 2, 1]
det_score_maps = []
for idx, xx in enumerate([x1, x3, x6_2]):
- alpha, beta = peakiness_score(xx, self.moving_avg_params[idx].detach(), ksize=3, dilation=ksize[idx])
+ alpha, beta = peakiness_score(
+ xx, self.moving_avg_params[idx].detach(), ksize=3, dilation=ksize[idx]
+ )
score_vol = alpha * beta
det_score_map = torch.max(score_vol, dim=1, keepdim=True)[0]
- det_score_map = F.interpolate(det_score_map, size=data.shape[2:], mode='bilinear', align_corners=True)
+ det_score_map = F.interpolate(
+ det_score_map, size=data.shape[2:], mode="bilinear", align_corners=True
+ )
det_score_map = comb_weights[idx] * det_score_map
det_score_maps.append(det_score_map)
@@ -326,34 +374,42 @@ class DarkFeat(nn.Module):
desc = x6_2
score_map = det_score_map
- conf = F.softmax(self.clf((desc)**2), dim=1)[:,1:2]
- score_map = score_map * F.interpolate(conf, size=score_map.shape[2:], mode='bilinear', align_corners=True)
+ conf = F.softmax(self.clf((desc) ** 2), dim=1)[:, 1:2]
+ score_map = score_map * F.interpolate(
+ conf, size=score_map.shape[2:], mode="bilinear", align_corners=True
+ )
kpt_inds, kpt_score = extract_kpts(
score_map,
- k=self.config['kpt_n'],
- score_thld=self.config['score_thld'],
- nms_size=self.config['nms_size'],
- eof_size=self.config['eof_size'],
- edge_thld=self.config['edge_thld']
+ k=self.config["kpt_n"],
+ score_thld=self.config["score_thld"],
+ nms_size=self.config["nms_size"],
+ eof_size=self.config["eof_size"],
+ edge_thld=self.config["edge_thld"],
)
- descs = F.normalize(
- interpolate(kpt_inds.squeeze(0) / 4, desc.squeeze(0).permute(1, 2, 0)),
- p=2,
- dim=-1
- ).detach().cpu().numpy(),
- kpts = np.squeeze(torch.stack([kpt_inds[:, :, 1], kpt_inds[:, :, 0]], dim=-1).cpu(), axis=0) \
- * np.array([W / data.shape[3], H / data.shape[2]], dtype=np.float32)
+ descs = (
+ F.normalize(
+ interpolate(kpt_inds.squeeze(0) / 4, desc.squeeze(0).permute(1, 2, 0)),
+ p=2,
+ dim=-1,
+ )
+ .detach()
+ .cpu()
+ .numpy(),
+ )
+ kpts = np.squeeze(
+ torch.stack([kpt_inds[:, :, 1], kpt_inds[:, :, 0]], dim=-1).cpu(), axis=0
+ ) * np.array([W / data.shape[3], H / data.shape[2]], dtype=np.float32)
scores = np.squeeze(kpt_score.detach().cpu().numpy(), axis=0)
- idxs = np.negative(scores).argsort()[0:self.config['kpt_n']]
+ idxs = np.negative(scores).argsort()[0 : self.config["kpt_n"]]
descs = descs[0][idxs]
kpts = kpts[idxs]
scores = scores[idxs]
return {
- 'keypoints': kpts,
- 'scores': torch.from_numpy(scores),
- 'descriptors': torch.from_numpy(descs.T),
+ "keypoints": kpts,
+ "scores": torch.from_numpy(scores),
+ "descriptors": torch.from_numpy(descs.T),
}
diff --git a/imcui/third_party/dad/LICENSE b/third_party/DarkFeat/datasets/InvISP/LICENSE
similarity index 96%
rename from imcui/third_party/dad/LICENSE
rename to third_party/DarkFeat/datasets/InvISP/LICENSE
index e5e8310db5b6aca14e0aa9d5cd89b763c3b84e23..0c7a7ab19788c339529ee9c85d301a582c3c8010 100644
--- a/imcui/third_party/dad/LICENSE
+++ b/third_party/DarkFeat/datasets/InvISP/LICENSE
@@ -1,6 +1,6 @@
MIT License
-Copyright (c) 2025 Johan Edstedt
+Copyright (c) 2021 Yazhou XING
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
diff --git a/third_party/DarkFeat/datasets/InvISP/README.md b/third_party/DarkFeat/datasets/InvISP/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..654d33dae8e00fcd61b6f38f8e2763ae87dfefa4
--- /dev/null
+++ b/third_party/DarkFeat/datasets/InvISP/README.md
@@ -0,0 +1,117 @@
+# Invertible Image Signal Processing
+
+
+
+
+
+**This repository includes official codes for "[Invertible Image Signal Processing (CVPR2021)](https://arxiv.org/abs/2103.15061)".**
+
+
+**Figure:** *Our framework*
+
+Unprocessed RAW data is a highly valuable image format for image editing and computer vision. However, since the file size of RAW data is huge, most users can only get access to processed and compressed sRGB images. To bridge this gap, we design an Invertible Image Signal Processing (InvISP) pipeline, which not only enables rendering visually appealing sRGB images but also allows recovering nearly perfect RAW data. Due to our framework's inherent reversibility, we can reconstruct realistic RAW data instead of synthesizing RAW data from sRGB images, without any memory overhead. We also integrate a differentiable JPEG compression simulator that empowers our framework to reconstruct RAW data from JPEG images. Extensive quantitative and qualitative experiments on two DSLR demonstrate that our method obtains much higher quality in both rendered sRGB images and reconstructed RAW data than alternative methods.
+
+> **Invertible Image Signal Processing** 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+
+### Research @ Magic Leap (CVPR 2020, Oral)
+
+# SuperGlue Inference and Evaluation Demo Script
+
+## Introduction
+SuperGlue is a CVPR 2020 research project done at Magic Leap. The SuperGlue network is a Graph Neural Network combined with an Optimal Matching layer that is trained to perform matching on two sets of sparse image features. This repo includes PyTorch code and pretrained weights for running the SuperGlue matching network on top of [SuperPoint](https://arxiv.org/abs/1712.07629) keypoints and descriptors. Given a pair of images, you can use this repo to extract matching features across the image pair.
+
+
+
+The matches are colored by their predicted confidence in a jet colormap (Red: more confident, Blue: less confident).
+
+### Additional useful command line parameters
+* Use `--image_glob` to change the image file extension (default: `*.png`, `*.jpg`, `*.jpeg`).
+* Use `--skip` to skip intermediate frames (default: `1`).
+* Use `--max_length` to cap the total number of frames processed (default: `1000000`).
+* Use `--show_keypoints` to visualize the detected keypoints (default: `False`).
+
+## Run Matching+Evaluation (`match_pairs.py`)
+
+This repo also contains a script `match_pairs.py` that runs the matching from a list of image pairs. With this script, you can:
+
+* Run the matcher on a set of image pairs (no ground truth needed)
+* Visualize the keypoints and matches, based on their confidence
+* Evaluate and visualize the match correctness, if the ground truth relative poses and intrinsics are provided
+* Save the keypoints, matches, and evaluation results for further processing
+* Collate evaluation results over many pairs and generate result tables
+
+### Matches only mode
+
+The simplest usage of this script will process the image pairs listed in a given text file and dump the keypoints and matches to compressed numpy `npz` files. We provide the challenging ScanNet pairs from the main paper in `assets/example_indoor_pairs/`. Running the following will run SuperPoint + SuperGlue on each image pair, and dump the results to `dump_match_pairs/`:
+
+```sh
+./match_pairs.py
+```
+
+The resulting `.npz` files can be read from Python as follows:
+
+```python
+>>> import numpy as np
+>>> path = 'dump_match_pairs/scene0711_00_frame-001680_scene0711_00_frame-001995_matches.npz'
+>>> npz = np.load(path)
+>>> npz.files
+['keypoints0', 'keypoints1', 'matches', 'match_confidence']
+>>> npz['keypoints0'].shape
+(382, 2)
+>>> npz['keypoints1'].shape
+(391, 2)
+>>> npz['matches'].shape
+(382,)
+>>> np.sum(npz['matches']>-1)
+115
+>>> npz['match_confidence'].shape
+(382,)
+```
+
+For each keypoint in `keypoints0`, the `matches` array indicates the index of the matching keypoint in `keypoints1`, or `-1` if the keypoint is unmatched.
+
+### Visualization mode
+
+You can add the flag `--viz` to dump image outputs which visualize the matches:
+
+```sh
+./match_pairs.py --viz
+```
+
+You should see images like this inside of `dump_match_pairs/` (or something very close to it, see this [note](#a-note-on-reproducibility)):
+
+
+
+The matches are colored by their predicted confidence in a jet colormap (Red: more confident, Blue: less confident).
+
+### Evaluation mode
+
+You can also estimate the pose using RANSAC + Essential Matrix decomposition and evaluate it if the ground truth relative poses and intrinsics are provided in the input `.txt` files. Each `.txt` file contains three key ground truth matrices: a 3x3 intrinsics matrix of image0: `K0`, a 3x3 intrinsics matrix of image1: `K1` , and a 4x4 matrix of the relative pose extrinsics `T_0to1`.
+
+To run the evaluation on the sample set of images (by default reading `assets/scannet_sample_pairs_with_gt.txt`), you can run:
+
+```sh
+./match_pairs.py --eval
+```
+
+
+Since you enabled `--eval`, you should see collated results printed to the terminal. For the example images provided, you should get the following numbers (or something very close to it, see this [note](#a-note-on-reproducibility)):
+
+```txt
+Evaluation Results (mean over 15 pairs):
+AUC@5 AUC@10 AUC@20 Prec MScore
+26.99 48.40 64.47 73.52 19.60
+```
+
+The resulting `.npz` files in `dump_match_pairs/` will now contain scalar values related to the evaluation, computed on the sample images provided. Here is what you should find in one of the generated evaluation files:
+
+```python
+>>> import numpy as np
+>>> path = 'dump_match_pairs/scene0711_00_frame-001680_scene0711_00_frame-001995_evaluation.npz'
+>>> npz = np.load(path)
+>>> print(npz.files)
+['error_t', 'error_R', 'precision', 'matching_score', 'num_correct', 'epipolar_errors']
+```
+
+You can also visualize the evaluation metrics by running the following command:
+
+```sh
+./match_pairs.py --eval --viz
+```
+
+You should also now see additional images in `dump_match_pairs/` which visualize the evaluation numbers (or something very close to it, see this [note](#a-note-on-reproducibility)):
+
+
+
+The top left corner of the image shows the pose error and number of inliers, while the lines are colored by their epipolar error computed with the ground truth relative pose (red: higher error, green: lower error).
+
+### Running on sample outdoor pairs
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 
+
+
+
+ 
+ 
+ 
+ 
+ 
+
+
+
+
+