import os import cv2 import json import numpy as np import os.path as osp from collections import deque from dust3r.utils.image import imread_cv2 from .base_many_view_dataset import BaseManyViewDataset class ArkitScene(BaseManyViewDataset): def __init__(self, num_seq=100, num_frames=5, min_thresh=10, max_thresh=100, test_id=None, full_video=False, kf_every=1, *args, ROOT, **kwargs): self.ROOT = ROOT super().__init__(*args, **kwargs) self.num_seq = num_seq self.num_frames = num_frames self.max_thresh = max_thresh self.min_thresh = min_thresh self.active_thresh= min_thresh self.test_id = test_id self.full_video = full_video self.kf_every = kf_every # load all scenes self.load_all_scenes(ROOT) def __len__(self): return len(self.scene_list) * self.num_seq def load_all_scenes(self, base_dir, num_seq=200): if self.test_id is None: if self.split == 'train': scene_path = osp.join(base_dir, 'raw', 'Training') elif self.split == 'val': scene_path = osp.join(base_dir, 'raw', 'Validation') self.scene_path = scene_path self.scene_list = os.listdir(scene_path) print(f"Found {len(self.scene_list)} scenes in split {self.split}") else: if isinstance(self.test_id, list): self.scene_list = self.test_id else: self.scene_list = [self.test_id] print(f"Test_id: {self.test_id}") def get_intrinsic(self, intrinsics_dir, frame_id, video_id): ''' Nerfstudio ''' intrinsic_fn = osp.join(intrinsics_dir, f"{video_id}_{frame_id}.pincam") if not osp.exists(intrinsic_fn): intrinsic_fn = osp.join(intrinsics_dir, f"{video_id}_{float(frame_id) - 0.001:.3f}.pincam") if not osp.exists(intrinsic_fn): intrinsic_fn = osp.join(intrinsics_dir, f"{video_id}_{float(frame_id) + 0.001:.3f}.pincam") _, _, fx, fy, hw, hh = np.loadtxt(intrinsic_fn) intrinsic = np.asarray([[fx, 0, hw], [0, fy, hh], [0, 0, 1]]) return intrinsic def get_pose(self, frame_id, poses_from_traj): frame_pose = None if str(frame_id) in poses_from_traj: frame_pose = np.array(poses_from_traj[str(frame_id)]) else: for my_key in poses_from_traj: if abs(float(frame_id) - float(my_key)) < 0.1: frame_pose = np.array(poses_from_traj[str(my_key)]) if frame_pose is None: print(f"Warning: No pose found for frame {frame_id}") return None assert frame_pose is not None frame_pose[0:3, 1:3] *= -1 frame_pose = frame_pose[np.array([1, 0, 2, 3]), :] frame_pose[2, :] *= -1 return frame_pose def traj_string_to_matrix(self, traj_string): """convert traj_string into translation and rotation matrices Args: traj_string: A space-delimited file where each line represents a camera position at a particular timestamp. The file has seven columns: * Column 1: timestamp * Columns 2-4: rotation (axis-angle representation in radians) * Columns 5-7: translation (usually in meters) Returns: ts: translation matrix Rt: rotation matrix """ tokens = traj_string.split() assert len(tokens) == 7 ts = tokens[0] # Rotation in angle axis angle_axis = [float(tokens[1]), float(tokens[2]), float(tokens[3])] r_w_to_p, _ = cv2.Rodrigues(np.asarray(angle_axis)) # type: ignore # Translation t_w_to_p = np.asarray([float(tokens[4]), float(tokens[5]), float(tokens[6])]) extrinsics = np.eye(4, 4) extrinsics[:3, :3] = r_w_to_p extrinsics[:3, -1] = t_w_to_p Rt = np.linalg.inv(extrinsics) return (ts, Rt) def _get_views(self, idx, resolution, rng, attempts=0): scene_id = self.scene_list[idx // self.num_seq] image_path = osp.join(self.scene_path, scene_id, 'lowres_wide') depth_path = osp.join(self.scene_path, scene_id, 'lowres_depth') intrinsics_path = osp.join(self.scene_path, scene_id, 'lowres_wide_intrinsics') pose_path = osp.join(self.scene_path, scene_id, 'lowres_wide.traj') if not osp.exists(image_path) or not osp.exists(depth_path) or not osp.exists(intrinsics_path) or not osp.exists(pose_path): print(f"Warning: Scene not found: {scene_id}") new_idx = rng.integers(0, self.__len__()-1) return self._get_views(new_idx, resolution, rng) img_idxs_ = [x for x in sorted(os.listdir(depth_path))] img_idxs_ = [x.split(".png")[0].split("_")[1] for x in img_idxs_] if len(img_idxs_) < self.num_frames: print(f"Warning: Not enough frames in {scene_id}, {len(img_idxs_)} < {self.num_frames}") new_idx = rng.integers(0, self.__len__()-1) return self._get_views(new_idx, resolution, rng) img_idxs = self.sample_frame_idx(img_idxs_, rng, full_video=self.full_video) imgs_idxs = deque(img_idxs) # Load trajectory poses_from_traj = {} with open(pose_path, "r", encoding="utf-8") as f: traj = f.readlines() for line in traj: poses_from_traj[f"{round(float(line.split(' ')[0]), 3):.3f}"] = np.array( self.traj_string_to_matrix(line)[1].tolist() ) views = [] while len(imgs_idxs) > 0: im_idx = imgs_idxs.popleft() impath = osp.join(image_path, f'{scene_id}_{im_idx}.png') depthpath = osp.join(depth_path, f'{scene_id}_{im_idx}.png') camera_pose = self.get_pose(im_idx, poses_from_traj) intrinsics_ = self.get_intrinsic(intrinsics_path, im_idx, scene_id).astype(np.float32) if not osp.exists(impath) or not osp.exists(depthpath) or camera_pose is None: print (f"Warning: Image/Depth/Pose not found for {impath}") new_idx = rng.integers(0, self.__len__()-1) return self._get_views(new_idx, resolution, rng) rgb_image = imread_cv2(impath) depthmap = imread_cv2(depthpath, cv2.IMREAD_UNCHANGED) depthmap = np.nan_to_num(depthmap.astype(np.float32), 0.0) / 1000.0 camera_pose = camera_pose.astype(np.float32) # gl to cv camera_pose[:, 1:3] *= -1.0 rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary( rgb_image, depthmap, intrinsics_, resolution, rng=rng, info=impath) num_valid = (depthmap > 0.0).sum() if num_valid == 0 or (not np.isfinite(camera_pose).all()): if self.full_video: print(f"Warning: No valid depthmap found for {impath}") continue else: if attempts >= 5: new_idx = rng.integers(0, self.__len__()-1) return self._get_views(new_idx, resolution, rng) return self._get_views(idx, resolution, rng, attempts+1) views.append(dict( img=rgb_image, depthmap=depthmap, camera_pose=camera_pose, camera_intrinsics=intrinsics, dataset='arkit', label=osp.join(scene_id, im_idx), instance=osp.split(impath)[1], )) return views if __name__ == "__main__": num_frames=5 print('loading dataset') dataset = ArkitScene(split='train', ROOT="./data/arkit_lowres", resolution=224, num_seq=100, max_thresh=100)