| from ppd.data.depth_estimation import Dataset as BaseDataset |
| from ppd.data.depth_estimation import * |
| from os.path import join |
| import os |
| from torchvision.transforms import Compose |
| import json |
| import h5py |
| from PIL import Image |
| import torchvision.transforms.functional as TF |
|
|
|
|
| class Dataset(BaseDataset): |
| def build_metas(self): |
| self.dataset_name = 'nyuv2' |
| splits = open(self.cfg.split_path, 'r').readlines() |
| self.rgb_files = [] |
| self.depth_files = [] |
| for split in splits: |
| rgb_file, depth_file, _ = split.strip().split(' ') |
| self.rgb_files.append(join(self.cfg.data_root, rgb_file)) |
| self.depth_files.append(join(self.cfg.data_root, depth_file)) |
|
|
| def read_depth(self, index, depth=None): |
| depth = (np.asarray(imageio.imread(self.depth_files[index])) / 1000.).astype(np.float32) |
| valid_mask = np.logical_and( |
| depth > 0.1, ~np.isnan(depth)) & (~np.isinf(depth)) |
| valid_mask = np.logical_and(valid_mask, depth < 10.) |
| if valid_mask.sum() == 0: |
| Log.warn('No valid mask in the depth map of {}'.format( |
| self.depth_files[index])) |
| if valid_mask.sum() != 0 and np.isnan(depth).sum() != 0: |
| depth[np.isnan(depth)] = depth[valid_mask].max() |
| if valid_mask.sum() != 0 and np.isinf(depth).sum() != 0: |
| depth[np.isinf(depth)] = depth[valid_mask].max() |
| return depth, valid_mask.astype(np.uint8) |
|
|
| def read_rgb_name(self, index): |
| rgb_name = '__'.join(self.rgb_files[index].split('/')[-2:]) |
| return rgb_name.replace(".jpg", ".png") |
|
|
|
|
