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import os
import os.path
import numpy as np
import torch
import torch.utils.data as data
from PIL import Image
class ParsingGenerationDeepFashionAttrSegmDataset(data.Dataset):
def __init__(self, segm_dir, pose_dir, ann_file, downsample_factor=2):
self._densepose_path = pose_dir
self._segm_path = segm_dir
self._image_fnames = []
self.attrs = []
self.downsample_factor = downsample_factor
# training, ground-truth available
assert os.path.exists(ann_file)
for row in open(os.path.join(ann_file), 'r'):
annotations = row.split()
self._image_fnames.append(annotations[0])
self.attrs.append([int(i) for i in annotations[1:]])
def _open_file(self, path_prefix, fname):
return open(os.path.join(path_prefix, fname), 'rb')
def _load_densepose(self, raw_idx):
fname = self._image_fnames[raw_idx]
fname = f'{fname[:-4]}_densepose.png'
with self._open_file(self._densepose_path, fname) as f:
densepose = Image.open(f)
if self.downsample_factor != 1:
width, height = densepose.size
width = width // self.downsample_factor
height = height // self.downsample_factor
densepose = densepose.resize(
size=(width, height), resample=Image.NEAREST)
# channel-wise IUV order, [3, H, W]
densepose = np.array(densepose)[:, :, 2:].transpose(2, 0, 1)
return densepose.astype(np.float32)
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)
return segm.astype(np.float32)
def __getitem__(self, index):
pose = self._load_densepose(index)
segm = self._load_segm(index)
attr = self.attrs[index]
pose = torch.from_numpy(pose)
segm = torch.LongTensor(segm)
attr = torch.LongTensor(attr)
pose = pose / 12. - 1
return_dict = {
'densepose': pose,
'segm': segm,
'attr': attr,
'img_name': self._image_fnames[index]
}
return return_dict
def __len__(self):
return len(self._image_fnames)
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