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| from os import path as osp | |
| from typing import Dict | |
| from unicodedata import name | |
| import numpy as np | |
| import torch | |
| import torch.utils as utils | |
| from numpy.linalg import inv | |
| from src.utils.dataset import ( | |
| read_scannet_gray, | |
| read_scannet_depth, | |
| read_scannet_pose, | |
| read_scannet_intrinsic | |
| ) | |
| class ScanNetDataset(utils.data.Dataset): | |
| def __init__(self, | |
| root_dir, | |
| npz_path, | |
| intrinsic_path, | |
| mode='train', | |
| min_overlap_score=0.4, | |
| augment_fn=None, | |
| pose_dir=None, | |
| img_resize=None, | |
| fp16=False, | |
| **kwargs): | |
| """Manage one scene of ScanNet Dataset. | |
| Args: | |
| root_dir (str): ScanNet root directory that contains scene folders. | |
| npz_path (str): {scene_id}.npz path. This contains image pair information of a scene. | |
| intrinsic_path (str): path to depth-camera intrinsic file. | |
| mode (str): options are ['train', 'val', 'test']. | |
| augment_fn (callable, optional): augments images with pre-defined visual effects. | |
| pose_dir (str): ScanNet root directory that contains all poses. | |
| (we use a separate (optional) pose_dir since we store images and poses separately.) | |
| """ | |
| super().__init__() | |
| self.root_dir = root_dir | |
| self.pose_dir = pose_dir if pose_dir is not None else root_dir | |
| self.mode = mode | |
| # prepare data_names, intrinsics and extrinsics(T) | |
| with np.load(npz_path) as data: | |
| self.data_names = data['name'] | |
| if 'score' in data.keys() and mode not in ['val' or 'test']: | |
| kept_mask = data['score'] > min_overlap_score | |
| self.data_names = self.data_names[kept_mask] | |
| self.intrinsics = dict(np.load(intrinsic_path)) | |
| # for training LoFTR | |
| self.augment_fn = augment_fn if mode == 'train' else None | |
| self.fp16 = fp16 | |
| self.img_resize = img_resize | |
| def __len__(self): | |
| return len(self.data_names) | |
| def _read_abs_pose(self, scene_name, name): | |
| pth = osp.join(self.pose_dir, | |
| scene_name, | |
| 'pose', f'{name}.txt') | |
| return read_scannet_pose(pth) | |
| def _compute_rel_pose(self, scene_name, name0, name1): | |
| pose0 = self._read_abs_pose(scene_name, name0) | |
| pose1 = self._read_abs_pose(scene_name, name1) | |
| return np.matmul(pose1, inv(pose0)) # (4, 4) | |
| def __getitem__(self, idx): | |
| data_name = self.data_names[idx] | |
| scene_name, scene_sub_name, stem_name_0, stem_name_1 = data_name | |
| scene_name = f'scene{scene_name:04d}_{scene_sub_name:02d}' | |
| # read the grayscale image which will be resized to (1, 480, 640) | |
| img_name0 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_0}.jpg') | |
| img_name1 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_1}.jpg') | |
| # TODO: Support augmentation & handle seeds for each worker correctly. | |
| image0 = read_scannet_gray(img_name0, resize=self.img_resize, augment_fn=None) | |
| # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) | |
| image1 = read_scannet_gray(img_name1, resize=self.img_resize, augment_fn=None) | |
| # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) | |
| # read the depthmap which is stored as (480, 640) | |
| if self.mode in ['train', 'val']: | |
| depth0 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_0}.png')) | |
| depth1 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_1}.png')) | |
| else: | |
| depth0 = depth1 = torch.tensor([]) | |
| # read the intrinsic of depthmap | |
| K_0 = K_1 = torch.tensor(self.intrinsics[scene_name].copy(), dtype=torch.float).reshape(3, 3) | |
| # read and compute relative poses | |
| T_0to1 = torch.tensor(self._compute_rel_pose(scene_name, stem_name_0, stem_name_1), | |
| dtype=torch.float32) | |
| T_1to0 = T_0to1.inverse() | |
| h_new, w_new = self.img_resize[1], self.img_resize[0] | |
| scale0 = torch.tensor([640/w_new, 480/h_new], dtype=torch.float) | |
| scale1 = torch.tensor([640/w_new, 480/h_new], dtype=torch.float) | |
| if self.fp16: | |
| image0, image1, depth0, depth1, scale0, scale1 = map(lambda x: x.half(), | |
| [image0, image1, depth0, depth1, scale0, scale1]) | |
| data = { | |
| 'image0': image0, # (1, h, w) | |
| 'depth0': depth0, # (h, w) | |
| 'image1': image1, | |
| 'depth1': depth1, | |
| 'T_0to1': T_0to1, # (4, 4) | |
| 'T_1to0': T_1to0, | |
| 'K0': K_0, # (3, 3) | |
| 'K1': K_1, | |
| 'scale0': scale0, # [scale_w, scale_h] | |
| 'scale1': scale1, | |
| 'dataset_name': 'ScanNet', | |
| 'scene_id': scene_name, | |
| 'pair_id': idx, | |
| 'pair_names': (osp.join(scene_name, 'color', f'{stem_name_0}.jpg'), | |
| osp.join(scene_name, 'color', f'{stem_name_1}.jpg')) | |
| } | |
| return data |