Spaces:
Runtime error
Runtime error
| import os.path as osp | |
| import cv2 | |
| import numpy as np | |
| import itertools | |
| import os | |
| import sys | |
| import json | |
| sys.path.append(osp.join(osp.dirname(__file__), "..", "..")) | |
| from tqdm import tqdm | |
| from dust3r.datasets.base.base_multiview_dataset import BaseMultiViewDataset | |
| from dust3r.utils.image import imread_cv2 | |
| import re | |
| def extract_number(filename): | |
| match = re.search(r"\d+", filename) | |
| if match: | |
| return int(match.group()) | |
| return 0 | |
| class OmniObject3D_Multi(BaseMultiViewDataset): | |
| def __init__(self, *args, ROOT, **kwargs): | |
| self.ROOT = ROOT | |
| self.video = False | |
| self.is_metric = False # True | |
| super().__init__(*args, **kwargs) | |
| self.loaded_data = self._load_data() | |
| def _load_data(self): | |
| self.scenes = [ | |
| d | |
| for d in os.listdir(self.ROOT) | |
| if os.path.isdir(os.path.join(self.ROOT, d)) | |
| ] | |
| with open(os.path.join(self.ROOT, "scale.json"), "r") as f: | |
| self.scales = json.load(f) | |
| offset = 0 | |
| scenes = [] | |
| sceneids = [] | |
| scene_img_list = [] | |
| images = [] | |
| start_img_ids = [] | |
| j = 0 | |
| for scene in tqdm(self.scenes): | |
| scene_dir = osp.join(self.ROOT, scene) | |
| rgb_dir = osp.join(scene_dir, "rgb") | |
| basenames = sorted( | |
| [f[:-4] for f in os.listdir(rgb_dir) if f.endswith(".png")], | |
| key=extract_number, | |
| ) | |
| num_imgs = len(basenames) | |
| cut_off = ( | |
| self.num_views if not self.allow_repeat else max(self.num_views // 3, 3) | |
| ) | |
| if num_imgs < cut_off: | |
| print(f"Skipping {scene}") | |
| continue | |
| img_ids = list(np.arange(num_imgs) + offset) | |
| start_img_ids_ = img_ids[: num_imgs - cut_off + 1] | |
| start_img_ids.extend([(scene, id) for id in start_img_ids_]) | |
| sceneids.extend([j] * num_imgs) | |
| images.extend(basenames) | |
| scenes.append(scene) | |
| scene_img_list.append(img_ids) | |
| # offset groups | |
| offset += num_imgs | |
| j += 1 | |
| self.scenes = scenes | |
| self.sceneids = sceneids | |
| self.images = images | |
| self.start_img_ids = start_img_ids | |
| self.scene_img_list = scene_img_list | |
| def __len__(self): | |
| return len(self.start_img_ids) | |
| def get_image_num(self): | |
| return len(self.images) | |
| def _get_views(self, idx, resolution, rng, num_views): | |
| scene, start_id = self.start_img_ids[idx] | |
| all_image_ids = self.scene_img_list[self.sceneids[start_id]] | |
| pos, ordered_video = self.get_seq_from_start_id( | |
| num_views, start_id, all_image_ids, rng, max_interval=100, video_prob=0.0 | |
| ) | |
| image_idxs = np.array(all_image_ids)[pos] | |
| views = [] | |
| for v, view_idx in enumerate(image_idxs): | |
| scene_id = self.sceneids[view_idx] | |
| scene_dir = osp.join(self.ROOT, self.scenes[scene_id]) | |
| rgb_dir = osp.join(scene_dir, "rgb") | |
| depth_dir = osp.join(scene_dir, "depth") | |
| cam_dir = osp.join(scene_dir, "cam") | |
| basename = self.images[view_idx] | |
| # Load RGB image | |
| rgb_image = imread_cv2(osp.join(rgb_dir, basename + ".png")) | |
| depthmap = np.load(osp.join(depth_dir, basename + ".npy")) | |
| cam = np.load(osp.join(cam_dir, basename + ".npz")) | |
| camera_pose = cam["pose"] | |
| intrinsics = cam["intrinsics"] | |
| scale = self.scales[self.scenes[scene_id]] | |
| depthmap = depthmap / scale / 1000.0 | |
| camera_pose[:3, 3] = camera_pose[:3, 3] / scale / 1000.0 | |
| rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary( | |
| rgb_image, depthmap, intrinsics, resolution, rng=rng, info=view_idx | |
| ) | |
| img_mask, ray_mask = self.get_img_and_ray_masks( | |
| self.is_metric, v, rng, p=[0.8, 0.15, 0.05] | |
| ) | |
| views.append( | |
| dict( | |
| img=rgb_image, | |
| depthmap=depthmap.astype(np.float32), | |
| camera_pose=camera_pose.astype(np.float32), | |
| camera_intrinsics=intrinsics.astype(np.float32), | |
| dataset="OmniObject3D", | |
| label=self.scenes[scene_id] + "_" + basename, | |
| instance=f"{str(idx)}_{str(view_idx)}", | |
| is_metric=self.is_metric, | |
| is_video=ordered_video, | |
| quantile=np.array(1.0, dtype=np.float32), | |
| img_mask=img_mask, | |
| ray_mask=ray_mask, | |
| camera_only=False, | |
| depth_only=False, | |
| single_view=False, | |
| reset=False, | |
| ) | |
| ) | |
| assert len(views) == num_views | |
| return views | |