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import sys
sys.path.append('.')
import os
import torch
import numpy as np
import os.path as osp
import glob
from dust3r.datasets.base.base_stereo_view_dataset import BaseStereoViewDataset
from dust3r.utils.image import imread_cv2
from dust3r.utils.misc import get_stride_distribution
np.random.seed(125)
torch.multiprocessing.set_sharing_strategy('file_system')
def depth_read(filename):
depth = np.load(filename)
return depth
def xyzqxqyqxqw_to_c2w(xyzqxqyqxqw):
xyzqxqyqxqw = np.array(xyzqxqyqxqw, dtype=np.float32)
#NOTE: we need to convert x_y_z coordinate system to z_x_y coordinate system
z, x, y = xyzqxqyqxqw[:3]
qz, qx, qy, qw = xyzqxqyqxqw[3:]
c2w = np.eye(4)
c2w[:3, :3] = np.array([
[1 - 2*qy*qy - 2*qz*qz, 2*qx*qy - 2*qz*qw, 2*qx*qz + 2*qy*qw],
[2*qx*qy + 2*qz*qw, 1 - 2*qx*qx - 2*qz*qz, 2*qy*qz - 2*qx*qw],
[2*qx*qz - 2*qy*qw, 2*qy*qz + 2*qx*qw, 1 - 2*qx*qx - 2*qy*qy]
])
c2w[:3, 3] = np.array([x, y, z])
return c2w
class TarTanAirDUSt3R(BaseStereoViewDataset):
def __init__(self,
dataset_location='data/tartanair',
dset='Hard',
use_augs=False,
S=2,
strides=[8],
clip_step=2,
quick=False,
verbose=False,
dist_type=None,
*args,
**kwargs
):
print('loading tartanair dataset...')
super().__init__(*args, **kwargs)
self.dataset_label = 'tartanair'
self.split = dset
self.S = S # number of frames
self.verbose = verbose
self.use_augs = use_augs
self.dset = dset
self.rgb_paths = []
self.depth_paths = []
self.normal_paths = []
self.traj_paths = []
self.annotations = []
self.full_idxs = []
self.sample_stride = []
self.strides = strides
self.subdirs = []
self.sequences = []
self.subdirs.append(os.path.join(dataset_location)) #'data/tartanair'
for subdir in self.subdirs:
for seq in glob.glob(os.path.join(subdir, "*/", dset, "*/")):
self.sequences.append(seq)
self.sequences = sorted(self.sequences)
if self.verbose:
print(self.sequences)
print('found %d unique videos in %s (dset=%s)' % (len(self.sequences), dataset_location, dset))
if quick:
self.sequences = self.sequences[1:2]
for seq in self.sequences:
if self.verbose:
print('seq', seq)
rgb_path = os.path.join(seq, 'image_left')
depth_path = os.path.join(seq, 'depth_left')
caminfo_path = os.path.join(seq, 'pose_left.txt')
caminfo = np.loadtxt(caminfo_path)
for stride in strides:
for ii in range(0,len(os.listdir(rgb_path))-self.S*stride+1, clip_step):
full_idx = ii + np.arange(self.S)*stride
self.rgb_paths.append([os.path.join(rgb_path, '%06d_left.png' % idx) for idx in full_idx])
self.depth_paths.append([os.path.join(depth_path, '%06d_left_depth.npy' % idx) for idx in full_idx])
self.annotations.append(caminfo[full_idx])
self.full_idxs.append(full_idx)
self.sample_stride.append(stride)
if self.verbose:
sys.stdout.write('.')
sys.stdout.flush()
fx = 320.0 # focal length x
fy = 320.0 # focal length y
cx = 320.0 # optical center x
cy = 240.0 # optical center y
width = 640
height = 480
self.intrinsics = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=np.float32)
self.stride_counts = {}
self.stride_idxs = {}
for stride in strides:
self.stride_counts[stride] = 0
self.stride_idxs[stride] = []
for i, stride in enumerate(self.sample_stride):
self.stride_counts[stride] += 1
self.stride_idxs[stride].append(i)
print('stride counts:', self.stride_counts)
if len(strides) > 1 and dist_type is not None:
self._resample_clips(strides, dist_type)
print('collected %d clips of length %d in %s (dset=%s)' % (
len(self.rgb_paths), self.S, dataset_location, dset))
def _resample_clips(self, strides, dist_type):
# Get distribution of strides, and sample based on that
dist = get_stride_distribution(strides, dist_type=dist_type)
dist = dist / np.max(dist)
max_num_clips = self.stride_counts[strides[np.argmax(dist)]]
num_clips_each_stride = [min(self.stride_counts[stride], int(dist[i]*max_num_clips)) for i, stride in enumerate(strides)]
print('resampled_num_clips_each_stride:', num_clips_each_stride)
resampled_idxs = []
for i, stride in enumerate(strides):
resampled_idxs += np.random.choice(self.stride_idxs[stride], num_clips_each_stride[i], replace=False).tolist()
self.rgb_paths = [self.rgb_paths[i] for i in resampled_idxs]
self.depth_paths = [self.depth_paths[i] for i in resampled_idxs]
self.annotations = [self.annotations[i] for i in resampled_idxs]
self.full_idxs = [self.full_idxs[i] for i in resampled_idxs]
self.sample_stride = [self.sample_stride[i] for i in resampled_idxs]
def __len__(self):
return len(self.rgb_paths)
def _get_views(self, index, resolution, rng):
rgb_paths = self.rgb_paths[index]
depth_paths = self.depth_paths[index]
full_idx = self.full_idxs[index]
annotations = self.annotations[index]
views = []
for i in range(2):
impath = rgb_paths[i]
depthpath = depth_paths[i]
# load camera params
camera_pose = np.array(xyzqxqyqxqw_to_c2w(annotations[i]), dtype=np.float32)
# camera_pose = np.linalg.inv(camera_pose)
# load image and depth
rgb_image = imread_cv2(impath)
depthmap = depth_read(depthpath)
rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary(
rgb_image, depthmap, self.intrinsics, resolution, rng=rng, info=impath)
views.append(dict(
img=rgb_image,
depthmap=depthmap,
camera_pose=camera_pose,
camera_intrinsics=intrinsics,
dataset=self.dataset_label,
label=rgb_paths[i].split('/')[-5]+'-'+rgb_paths[i].split('/')[-3],
instance=osp.split(rgb_paths[i])[1],
))
return views
if __name__ == "__main__":
from dust3r.viz import SceneViz, auto_cam_size
from dust3r.utils.image import rgb
use_augs = False
S = 2
strides = [1,2,3,4,5,6,7,8,9]
clip_step = 2
quick = False # Set to True for quick testing
def visualize_scene(idx):
views = dataset[idx]
assert len(views) == 2
viz = SceneViz()
poses = [views[view_idx]['camera_pose'] for view_idx in [0, 1]]
cam_size = max(auto_cam_size(poses), 1)
label = views[0]['label']
instance = views[0]['instance']
for view_idx in [0, 1]:
pts3d = views[view_idx]['pts3d']
valid_mask = views[view_idx]['valid_mask']
colors = rgb(views[view_idx]['img'])
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=(255, 0, 0),
image=colors,
cam_size=cam_size)
path = f"./tmp/tartanair/tartanair_scene_{label}_{instance}.glb"
return viz.save_glb(path)
dataset = TarTanAirDUSt3R(
use_augs=use_augs,
S=S,
strides=strides,
clip_step=clip_step,
quick=quick,
verbose=False,
resolution=(512,384),
dist_type='linear_9_1',
aug_crop=16)
idxs = np.arange(0, len(dataset)-1, (len(dataset)-1)//10)
for idx in idxs:
print(f"Visualizing scene {idx}...")
visualize_scene(idx) |