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| import os | |
| import os.path | |
| import random | |
| import numpy as np | |
| import torch | |
| import torch.utils.data as data | |
| from PIL import Image | |
| class MaskDataset(data.Dataset): | |
| def __init__(self, segm_dir, ann_dir, downsample_factor=2, xflip=False): | |
| self._segm_path = segm_dir | |
| self._image_fnames = [] | |
| self.downsample_factor = downsample_factor | |
| self.xflip = xflip | |
| # load attributes | |
| assert os.path.exists(f'{ann_dir}/upper_fused.txt') | |
| for idx, row in enumerate( | |
| open(os.path.join(f'{ann_dir}/upper_fused.txt'), 'r')): | |
| annotations = row.split() | |
| self._image_fnames.append(annotations[0]) | |
| def _open_file(self, path_prefix, fname): | |
| return open(os.path.join(path_prefix, fname), 'rb') | |
| def _load_segm(self, raw_idx): | |
| fname = self._image_fnames[raw_idx] | |
| fname = f'{fname[:-4]}_segm.png' | |
| with self._open_file(self._segm_path, fname) as f: | |
| segm = Image.open(f) | |
| if self.downsample_factor != 1: | |
| width, height = segm.size | |
| width = width // self.downsample_factor | |
| height = height // self.downsample_factor | |
| segm = segm.resize( | |
| size=(width, height), resample=Image.NEAREST) | |
| segm = np.array(segm) | |
| # segm = segm[:, :, np.newaxis].transpose(2, 0, 1) | |
| return segm.astype(np.float32) | |
| def __getitem__(self, index): | |
| segm = self._load_segm(index) | |
| if self.xflip and random.random() > 0.5: | |
| segm = segm[:, ::-1].copy() | |
| segm = torch.from_numpy(segm).long() | |
| return_dict = {'segm': segm, 'img_name': self._image_fnames[index]} | |
| return return_dict | |
| def __len__(self): | |
| return len(self._image_fnames) | |