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| import os.path as osp | |
| import os | |
| import sys | |
| import itertools | |
| sys.path.append(osp.join(osp.dirname(__file__), "..", "..")) | |
| import cv2 | |
| import numpy as np | |
| from dust3r.datasets.base.base_multiview_dataset import BaseMultiViewDataset | |
| from dust3r.utils.image import imread_cv2 | |
| class HyperSim_Multi(BaseMultiViewDataset): | |
| def __init__(self, *args, split, ROOT, **kwargs): | |
| self.ROOT = ROOT | |
| self.video = True | |
| self.is_metric = True | |
| self.max_interval = 4 | |
| super().__init__(*args, **kwargs) | |
| self.loaded_data = self._load_data() | |
| def _load_data(self): | |
| self.all_scenes = sorted( | |
| [f for f in os.listdir(self.ROOT) if os.path.isdir(osp.join(self.ROOT, f))] | |
| ) | |
| subscenes = [] | |
| for scene in self.all_scenes: | |
| # not empty | |
| subscenes.extend( | |
| [ | |
| osp.join(scene, f) | |
| for f in os.listdir(osp.join(self.ROOT, scene)) | |
| if os.path.isdir(osp.join(self.ROOT, scene, f)) | |
| and len(os.listdir(osp.join(self.ROOT, scene, f))) > 0 | |
| ] | |
| ) | |
| offset = 0 | |
| scenes = [] | |
| sceneids = [] | |
| images = [] | |
| start_img_ids = [] | |
| scene_img_list = [] | |
| j = 0 | |
| for scene_idx, scene in enumerate(subscenes): | |
| scene_dir = osp.join(self.ROOT, scene) | |
| rgb_paths = sorted([f for f in os.listdir(scene_dir) if f.endswith(".png")]) | |
| assert len(rgb_paths) > 0, f"{scene_dir} is empty." | |
| num_imgs = len(rgb_paths) | |
| 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] | |
| scenes.append(scene) | |
| scene_img_list.append(img_ids) | |
| sceneids.extend([j] * num_imgs) | |
| images.extend(rgb_paths) | |
| start_img_ids.extend(start_img_ids_) | |
| offset += num_imgs | |
| j += 1 | |
| self.scenes = scenes | |
| self.sceneids = sceneids | |
| self.images = images | |
| self.scene_img_list = scene_img_list | |
| self.start_img_ids = start_img_ids | |
| def __len__(self): | |
| return len(self.start_img_ids) * 10 | |
| def get_image_num(self): | |
| return len(self.images) | |
| def _get_views(self, idx, resolution, rng, num_views): | |
| idx = idx // 10 | |
| start_id = self.start_img_ids[idx] | |
| scene_id = self.sceneids[start_id] | |
| all_image_ids = self.scene_img_list[scene_id] | |
| pos, ordered_video = self.get_seq_from_start_id( | |
| num_views, | |
| start_id, | |
| all_image_ids, | |
| rng, | |
| max_interval=self.max_interval, | |
| block_shuffle=16, | |
| ) | |
| 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_path = self.images[view_idx] | |
| depth_path = rgb_path.replace("rgb.png", "depth.npy") | |
| cam_path = rgb_path.replace("rgb.png", "cam.npz") | |
| rgb_image = imread_cv2(osp.join(scene_dir, rgb_path), cv2.IMREAD_COLOR) | |
| depthmap = np.load(osp.join(scene_dir, depth_path)).astype(np.float32) | |
| depthmap[~np.isfinite(depthmap)] = 0 # invalid | |
| cam_file = np.load(osp.join(scene_dir, cam_path)) | |
| intrinsics = cam_file["intrinsics"].astype(np.float32) | |
| camera_pose = cam_file["pose"].astype(np.float32) | |
| rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary( | |
| rgb_image, depthmap, intrinsics, resolution, rng=rng, info=view_idx | |
| ) | |
| # generate img mask and raymap mask | |
| img_mask, ray_mask = self.get_img_and_ray_masks( | |
| self.is_metric, v, rng, p=[0.75, 0.2, 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="hypersim", | |
| label=self.scenes[scene_id] + "_" + rgb_path, | |
| 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 | |