Spaces:
Running
on
Zero
Running
on
Zero
| # Copyright (C) 2024-present Naver Corporation. All rights reserved. | |
| # Licensed under CC BY-NC-SA 4.0 (non-commercial use only). | |
| # | |
| # -------------------------------------------------------- | |
| # Dataloader for preprocessed Co3d_v2 | |
| # dataset at https://github.com/facebookresearch/co3d - Creative Commons Attribution-NonCommercial 4.0 International | |
| # See datasets_preprocess/preprocess_co3d.py | |
| # -------------------------------------------------------- | |
| import os.path as osp | |
| import json | |
| import itertools | |
| from collections import deque | |
| import cv2 | |
| import numpy as np | |
| from dust3r.datasets.base.base_stereo_view_dataset import BaseStereoViewDataset | |
| from dust3r.utils.image import imread_cv2 | |
| class Co3d(BaseStereoViewDataset): | |
| def __init__(self, mask_bg=True, *args, ROOT, **kwargs): | |
| self.ROOT = ROOT | |
| super().__init__(*args, **kwargs) | |
| assert mask_bg in (True, False, 'rand') | |
| self.mask_bg = mask_bg | |
| # load all scenes | |
| with open(osp.join(self.ROOT, f'selected_seqs_{self.split}.json'), 'r') as f: | |
| self.scenes = json.load(f) | |
| self.scenes = {k: v for k, v in self.scenes.items() if len(v) > 0} | |
| self.scenes = {(k, k2): v2 for k, v in self.scenes.items() | |
| for k2, v2 in v.items()} | |
| self.scene_list = list(self.scenes.keys()) | |
| # for each scene, we have 100 images ==> 360 degrees (so 25 frames ~= 90 degrees) | |
| # 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] | |
| self.invalidate = {scene: {} for scene in self.scene_list} | |
| def __len__(self): | |
| return len(self.scene_list) * len(self.combinations) | |
| def _get_views(self, idx, resolution, rng): | |
| # choose a scene | |
| obj, instance = self.scene_list[idx // len(self.combinations)] | |
| image_pool = self.scenes[obj, instance] | |
| im1_idx, im2_idx = self.combinations[idx % len(self.combinations)] | |
| # add a bit of randomness | |
| 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))] | |
| # decide now if we mask the bg | |
| mask_bg = (self.mask_bg == True) or (self.mask_bg == 'rand' and rng.choice(2)) | |
| views = [] | |
| imgs_idxs = [max(0, min(im_idx + rng.integers(-4, 5), last)) for im_idx in [im2_idx, im1_idx]] | |
| imgs_idxs = deque(imgs_idxs) | |
| while len(imgs_idxs) > 0: # some images (few) have zero depth | |
| im_idx = imgs_idxs.pop() | |
| if self.invalidate[obj, instance][resolution][im_idx]: | |
| # search for a valid image | |
| random_direction = 2 * rng.choice(2) - 1 | |
| for offset in range(1, len(image_pool)): | |
| tentative_im_idx = (im_idx + (random_direction * offset)) % len(image_pool) | |
| if not self.invalidate[obj, instance][resolution][tentative_im_idx]: | |
| im_idx = tentative_im_idx | |
| break | |
| view_idx = image_pool[im_idx] | |
| impath = osp.join(self.ROOT, obj, instance, 'images', f'frame{view_idx:06n}.jpg') | |
| # load camera params | |
| 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 = 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 = 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 | |
| # update the depthmap with mask | |
| depthmap *= maskmap | |
| 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: | |
| # problem, invalidate image and retry | |
| self.invalidate[obj, instance][resolution][im_idx] = True | |
| imgs_idxs.append(im_idx) | |
| continue | |
| views.append(dict( | |
| img=rgb_image, | |
| depthmap=depthmap, | |
| camera_pose=camera_pose, | |
| camera_intrinsics=intrinsics, | |
| dataset='Co3d_v2', | |
| label=osp.join(obj, instance), | |
| instance=osp.split(impath)[1], | |
| )) | |
| return views | |
| if __name__ == "__main__": | |
| from dust3r.datasets.base.base_stereo_view_dataset import view_name | |
| from dust3r.viz import SceneViz, auto_cam_size | |
| from dust3r.utils.image import rgb | |
| dataset = Co3d(split='train', ROOT="data/co3d_subset_processed", resolution=224, aug_crop=16) | |
| for idx in np.random.permutation(len(dataset)): | |
| views = dataset[idx] | |
| assert len(views) == 2 | |
| print(view_name(views[0]), view_name(views[1])) | |
| viz = SceneViz() | |
| poses = [views[view_idx]['camera_pose'] for view_idx in [0, 1]] | |
| cam_size = max(auto_cam_size(poses), 0.001) | |
| 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=(idx*255, (1 - idx)*255, 0), | |
| image=colors, | |
| cam_size=cam_size) | |
| viz.show() | |