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| import os.path as osp | |
| import cv2 | |
| import numpy as np | |
| import itertools | |
| import os | |
| import sys | |
| 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 | |
| class PointOdyssey_Multi(BaseMultiViewDataset): | |
| def __init__(self, *args, ROOT, **kwargs): | |
| self.ROOT = ROOT | |
| self.video = True | |
| self.is_metric = True | |
| self.max_interval = 4 | |
| super().__init__(*args, **kwargs) | |
| assert self.split in ["train", "test", "val"] | |
| self.scenes_to_use = [ | |
| # 'cab_h_bench_3rd', 'cab_h_bench_ego1', 'cab_h_bench_ego2', | |
| "cnb_dlab_0215_3rd", | |
| "cnb_dlab_0215_ego1", | |
| "cnb_dlab_0225_3rd", | |
| "cnb_dlab_0225_ego1", | |
| "dancing", | |
| "dancingroom0_3rd", | |
| "footlab_3rd", | |
| "footlab_ego1", | |
| "footlab_ego2", | |
| "girl", | |
| "girl_egocentric", | |
| "human_egocentric", | |
| "human_in_scene", | |
| "human_in_scene1", | |
| "kg", | |
| "kg_ego1", | |
| "kg_ego2", | |
| "kitchen_gfloor", | |
| "kitchen_gfloor_ego1", | |
| "kitchen_gfloor_ego2", | |
| "scene_carb_h_tables", | |
| "scene_carb_h_tables_ego1", | |
| "scene_carb_h_tables_ego2", | |
| "scene_j716_3rd", | |
| "scene_j716_ego1", | |
| "scene_j716_ego2", | |
| "scene_recording_20210910_S05_S06_0_3rd", | |
| "scene_recording_20210910_S05_S06_0_ego2", | |
| "scene1_0129", | |
| "scene1_0129_ego", | |
| "seminar_h52_3rd", | |
| "seminar_h52_ego1", | |
| "seminar_h52_ego2", | |
| ] | |
| self.loaded_data = self._load_data(self.split) | |
| def _load_data(self, split): | |
| root = os.path.join(self.ROOT, split) | |
| self.scenes = [] | |
| offset = 0 | |
| scenes = [] | |
| sceneids = [] | |
| scene_img_list = [] | |
| images = [] | |
| start_img_ids = [] | |
| j = 0 | |
| for scene in tqdm(os.listdir(root)): | |
| if scene not in self.scenes_to_use: | |
| continue | |
| scene_dir = osp.join(root, scene) | |
| rgb_dir = osp.join(scene_dir, "rgb") | |
| basenames = sorted( | |
| [f[:-4] for f in os.listdir(rgb_dir) if f.endswith(".jpg")] | |
| ) | |
| num_imgs = len(basenames) | |
| img_ids = list(np.arange(num_imgs) + offset) | |
| cut_off = ( | |
| self.num_views if not self.allow_repeat else max(self.num_views // 3, 3) | |
| ) | |
| start_img_ids_ = img_ids[: num_imgs - cut_off + 1] | |
| # start_img_ids_ = img_ids[:-self.num_views+1] | |
| if num_imgs < cut_off: | |
| print(f"Skipping {scene}") | |
| continue | |
| start_img_ids.extend(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): | |
| 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=self.max_interval, | |
| video_prob=1.0, | |
| fix_interval_prob=1.0, | |
| ) | |
| 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.split, 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 + ".jpg")) | |
| # Load depthmap | |
| depthmap = np.load(osp.join(depth_dir, basename + ".npy")) | |
| depthmap[~np.isfinite(depthmap)] = 0 # invalid | |
| depthmap[depthmap > 1000] = 0.0 | |
| cam = np.load(osp.join(cam_dir, basename + ".npz")) | |
| camera_pose = cam["pose"] | |
| intrinsics = cam["intrinsics"] | |
| 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=[0.9, 0.05, 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="PointOdyssey", | |
| label=self.scenes[scene_id] + "_" + basename, | |
| instance=osp.join(rgb_dir, basename + ".jpg"), | |
| 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 | |