# 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} # k is class (apple), v is corresponding list self.scenes = {(k, k2): v2 for k, v in self.scenes.items() for k2, v2 in v.items()} # k is class (apple), k2 is instance (110_13051_23361), v2 is list of image idx 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] # weird choice 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] # Get image index 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()