Spaces:
Runtime error
Runtime error
File size: 6,085 Bytes
bde71cb |
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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
import os
import os.path
import random
import numpy as np
import torch
import torch.utils.data as data
from PIL import Image
class DeepFashionAttrSegmDataset(data.Dataset):
def __init__(self,
img_dir,
segm_dir,
pose_dir,
ann_dir,
downsample_factor=2,
xflip=False):
self._img_path = img_dir
self._densepose_path = pose_dir
self._segm_path = segm_dir
self._image_fnames = []
self.upper_fused_attrs = []
self.lower_fused_attrs = []
self.outer_fused_attrs = []
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])
# assert self._image_fnames[idx] == annotations[0]
self.upper_fused_attrs.append(int(annotations[1]))
assert len(self._image_fnames) == len(self.upper_fused_attrs)
assert os.path.exists(f'{ann_dir}/lower_fused.txt')
for idx, row in enumerate(
open(os.path.join(f'{ann_dir}/lower_fused.txt'), 'r')):
annotations = row.split()
assert self._image_fnames[idx] == annotations[0]
self.lower_fused_attrs.append(int(annotations[1]))
assert len(self._image_fnames) == len(self.lower_fused_attrs)
assert os.path.exists(f'{ann_dir}/outer_fused.txt')
for idx, row in enumerate(
open(os.path.join(f'{ann_dir}/outer_fused.txt'), 'r')):
annotations = row.split()
assert self._image_fnames[idx] == annotations[0]
self.outer_fused_attrs.append(int(annotations[1]))
assert len(self._image_fnames) == len(self.outer_fused_attrs)
# remove the overlapping item between upper cls and lower cls
# cls 21 can appear with upper clothes
# cls 4 can appear with lower clothes
self.upper_cls = [1., 4.]
self.lower_cls = [3., 5., 21.]
self.outer_cls = [2.]
self.other_cls = [
11., 18., 7., 8., 9., 10., 12., 16., 17., 19., 20., 22., 23., 15.,
14., 13., 0., 6.
]
def _open_file(self, path_prefix, fname):
return open(os.path.join(path_prefix, fname), 'rb')
def _load_raw_image(self, raw_idx):
fname = self._image_fnames[raw_idx]
with self._open_file(self._img_path, fname) as f:
image = Image.open(f)
if self.downsample_factor != 1:
width, height = image.size
width = width // self.downsample_factor
height = height // self.downsample_factor
image = image.resize(
size=(width, height), resample=Image.LANCZOS)
image = np.array(image)
if image.ndim == 2:
image = image[:, :, np.newaxis] # HW => HWC
image = image.transpose(2, 0, 1) # HWC => CHW
return image
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)
segm = segm[:, :, np.newaxis].transpose(2, 0, 1)
return segm.astype(np.float32)
def __getitem__(self, index):
image = self._load_raw_image(index)
pose = self._load_densepose(index)
segm = self._load_segm(index)
if self.xflip and random.random() > 0.5:
assert image.ndim == 3 # CHW
image = image[:, :, ::-1].copy()
pose = pose[:, :, ::-1].copy()
segm = segm[:, :, ::-1].copy()
image = torch.from_numpy(image)
segm = torch.from_numpy(segm)
upper_fused_attr = self.upper_fused_attrs[index]
lower_fused_attr = self.lower_fused_attrs[index]
outer_fused_attr = self.outer_fused_attrs[index]
# mask 0: denotes the common codebook,
# mask (attr + 1): denotes the texture-specific codebook
mask = torch.zeros_like(segm)
if upper_fused_attr != 17:
for cls in self.upper_cls:
mask[segm == cls] = upper_fused_attr + 1
if lower_fused_attr != 17:
for cls in self.lower_cls:
mask[segm == cls] = lower_fused_attr + 1
if outer_fused_attr != 17:
for cls in self.outer_cls:
mask[segm == cls] = outer_fused_attr + 1
pose = pose / 12. - 1
image = image / 127.5 - 1
return_dict = {
'image': image,
'densepose': pose,
'segm': segm,
'texture_mask': mask,
'img_name': self._image_fnames[index]
}
return return_dict
def __len__(self):
return len(self._image_fnames)
|