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436b829 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | 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 = 'scannet'
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 > 0.01, ~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('/')[-3:])
return rgb_name.replace(".jpg", ".png")
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