Spaces:
Sleeping
Sleeping
| import os | |
| import cv2 | |
| 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 Scannet(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.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): | |
| self.folder = {'train': 'scans', 'val': 'scans', 'test': 'scans_test'}[self.split] | |
| if self.test_id is None: | |
| meta_split = osp.join(base_dir, 'splits', f'scannetv2_{self.split}.txt') | |
| if not osp.exists(meta_split): | |
| raise FileNotFoundError(f"Split file {meta_split} not found") | |
| with open(meta_split) as f: | |
| self.scene_list = f.read().splitlines() | |
| 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_views(self, idx, resolution, rng, attempts=0): | |
| scene_id = self.scene_list[idx // self.num_seq] | |
| # Load metadata | |
| intri_path = osp.join(self.ROOT, self.folder, scene_id, 'intrinsic/intrinsic_depth.txt') | |
| intri = np.loadtxt(intri_path).astype(np.float32)[:3, :3] | |
| # Load image data | |
| data_path = osp.join(self.ROOT, self.folder, scene_id, 'sensor_data') | |
| num_files = len([name for name in os.listdir(data_path) if 'color' in name]) | |
| img_idxs_ = [f'{i:06d}' for i in range(num_files)] | |
| imgs_idxs = self.sample_frame_idx(img_idxs_, rng, full_video=self.full_video) | |
| imgs_idxs = deque(imgs_idxs) | |
| views = [] | |
| while len(imgs_idxs) > 0: | |
| im_idx = imgs_idxs.popleft() | |
| # Load image data | |
| impath = osp.join(self.ROOT, self.folder, scene_id, 'sensor_data', f'frame-{im_idx}.color.jpg') | |
| depthpath = osp.join(self.ROOT, self.folder, scene_id, 'sensor_data', f'frame-{im_idx}.depth.png') | |
| posepath = osp.join(self.ROOT, self.folder, scene_id, 'sensor_data', f'frame-{im_idx}.pose.txt') | |
| rgb_image = imread_cv2(impath) | |
| depthmap = imread_cv2(depthpath, cv2.IMREAD_UNCHANGED) | |
| rgb_image = cv2.resize(rgb_image, (depthmap.shape[1], depthmap.shape[0])) | |
| depthmap = np.nan_to_num(depthmap.astype(np.float32), 0.0) / 1000.0 | |
| camera_pose = np.loadtxt(posepath).astype(np.float32) | |
| rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary( | |
| rgb_image, depthmap, intri, resolution, rng=rng, info=impath) | |
| # Check if the image is valid | |
| 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='scannet', | |
| label=osp.join(scene_id, im_idx), | |
| instance=osp.split(impath)[1], | |
| )) | |
| return views | |
| if __name__ == "__main__": | |
| num_frames=5 | |
| print('loading dataset') | |
| dataset = Scannet(split='train', ROOT="./data/scannet_simple", resolution=224, num_seq=100, max_thresh=100) | |