-
Notifications
You must be signed in to change notification settings - Fork 96
/
datasets.py
224 lines (192 loc) · 9.36 KB
/
datasets.py
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
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
import json
from os import path as osp
import numpy as np
from PIL import Image, ImageDraw
import torch
from torch.utils import data
from torchvision import transforms
class VITONDataset(data.Dataset):
def __init__(self, opt):
super(VITONDataset, self).__init__()
self.load_height = opt.load_height
self.load_width = opt.load_width
self.semantic_nc = opt.semantic_nc
self.data_path = osp.join(opt.dataset_dir, opt.dataset_mode)
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# load data list
img_names = []
c_names = []
with open(osp.join(opt.dataset_dir, opt.dataset_list), 'r') as f:
for line in f.readlines():
img_name, c_name = line.strip().split()
img_names.append(img_name)
c_names.append(c_name)
self.img_names = img_names
self.c_names = dict()
self.c_names['unpaired'] = c_names
def get_parse_agnostic(self, parse, pose_data):
parse_array = np.array(parse)
parse_upper = ((parse_array == 5).astype(np.float32) +
(parse_array == 6).astype(np.float32) +
(parse_array == 7).astype(np.float32))
parse_neck = (parse_array == 10).astype(np.float32)
r = 10
agnostic = parse.copy()
# mask arms
for parse_id, pose_ids in [(14, [2, 5, 6, 7]), (15, [5, 2, 3, 4])]:
mask_arm = Image.new('L', (self.load_width, self.load_height), 'black')
mask_arm_draw = ImageDraw.Draw(mask_arm)
i_prev = pose_ids[0]
for i in pose_ids[1:]:
if (pose_data[i_prev, 0] == 0.0 and pose_data[i_prev, 1] == 0.0) or (pose_data[i, 0] == 0.0 and pose_data[i, 1] == 0.0):
continue
mask_arm_draw.line([tuple(pose_data[j]) for j in [i_prev, i]], 'white', width=r*10)
pointx, pointy = pose_data[i]
radius = r*4 if i == pose_ids[-1] else r*15
mask_arm_draw.ellipse((pointx-radius, pointy-radius, pointx+radius, pointy+radius), 'white', 'white')
i_prev = i
parse_arm = (np.array(mask_arm) / 255) * (parse_array == parse_id).astype(np.float32)
agnostic.paste(0, None, Image.fromarray(np.uint8(parse_arm * 255), 'L'))
# mask torso & neck
agnostic.paste(0, None, Image.fromarray(np.uint8(parse_upper * 255), 'L'))
agnostic.paste(0, None, Image.fromarray(np.uint8(parse_neck * 255), 'L'))
return agnostic
def get_img_agnostic(self, img, parse, pose_data):
parse_array = np.array(parse)
parse_head = ((parse_array == 4).astype(np.float32) +
(parse_array == 13).astype(np.float32))
parse_lower = ((parse_array == 9).astype(np.float32) +
(parse_array == 12).astype(np.float32) +
(parse_array == 16).astype(np.float32) +
(parse_array == 17).astype(np.float32) +
(parse_array == 18).astype(np.float32) +
(parse_array == 19).astype(np.float32))
r = 20
agnostic = img.copy()
agnostic_draw = ImageDraw.Draw(agnostic)
length_a = np.linalg.norm(pose_data[5] - pose_data[2])
length_b = np.linalg.norm(pose_data[12] - pose_data[9])
point = (pose_data[9] + pose_data[12]) / 2
pose_data[9] = point + (pose_data[9] - point) / length_b * length_a
pose_data[12] = point + (pose_data[12] - point) / length_b * length_a
# mask arms
agnostic_draw.line([tuple(pose_data[i]) for i in [2, 5]], 'gray', width=r*10)
for i in [2, 5]:
pointx, pointy = pose_data[i]
agnostic_draw.ellipse((pointx-r*5, pointy-r*5, pointx+r*5, pointy+r*5), 'gray', 'gray')
for i in [3, 4, 6, 7]:
if (pose_data[i - 1, 0] == 0.0 and pose_data[i - 1, 1] == 0.0) or (pose_data[i, 0] == 0.0 and pose_data[i, 1] == 0.0):
continue
agnostic_draw.line([tuple(pose_data[j]) for j in [i - 1, i]], 'gray', width=r*10)
pointx, pointy = pose_data[i]
agnostic_draw.ellipse((pointx-r*5, pointy-r*5, pointx+r*5, pointy+r*5), 'gray', 'gray')
# mask torso
for i in [9, 12]:
pointx, pointy = pose_data[i]
agnostic_draw.ellipse((pointx-r*3, pointy-r*6, pointx+r*3, pointy+r*6), 'gray', 'gray')
agnostic_draw.line([tuple(pose_data[i]) for i in [2, 9]], 'gray', width=r*6)
agnostic_draw.line([tuple(pose_data[i]) for i in [5, 12]], 'gray', width=r*6)
agnostic_draw.line([tuple(pose_data[i]) for i in [9, 12]], 'gray', width=r*12)
agnostic_draw.polygon([tuple(pose_data[i]) for i in [2, 5, 12, 9]], 'gray', 'gray')
# mask neck
pointx, pointy = pose_data[1]
agnostic_draw.rectangle((pointx-r*7, pointy-r*7, pointx+r*7, pointy+r*7), 'gray', 'gray')
agnostic.paste(img, None, Image.fromarray(np.uint8(parse_head * 255), 'L'))
agnostic.paste(img, None, Image.fromarray(np.uint8(parse_lower * 255), 'L'))
return agnostic
def __getitem__(self, index):
img_name = self.img_names[index]
c_name = {}
c = {}
cm = {}
for key in self.c_names:
c_name[key] = self.c_names[key][index]
c[key] = Image.open(osp.join(self.data_path, 'cloth', c_name[key])).convert('RGB')
c[key] = transforms.Resize(self.load_width, interpolation=2)(c[key])
cm[key] = Image.open(osp.join(self.data_path, 'cloth-mask', c_name[key]))
cm[key] = transforms.Resize(self.load_width, interpolation=0)(cm[key])
c[key] = self.transform(c[key]) # [-1,1]
cm_array = np.array(cm[key])
cm_array = (cm_array >= 128).astype(np.float32)
cm[key] = torch.from_numpy(cm_array) # [0,1]
cm[key].unsqueeze_(0)
# load pose image
pose_name = img_name.replace('.jpg', '_rendered.png')
pose_rgb = Image.open(osp.join(self.data_path, 'openpose-img', pose_name))
pose_rgb = transforms.Resize(self.load_width, interpolation=2)(pose_rgb)
pose_rgb = self.transform(pose_rgb) # [-1,1]
pose_name = img_name.replace('.jpg', '_keypoints.json')
with open(osp.join(self.data_path, 'openpose-json', pose_name), 'r') as f:
pose_label = json.load(f)
pose_data = pose_label['people'][0]['pose_keypoints_2d']
pose_data = np.array(pose_data)
pose_data = pose_data.reshape((-1, 3))[:, :2]
# load parsing image
parse_name = img_name.replace('.jpg', '.png')
parse = Image.open(osp.join(self.data_path, 'image-parse', parse_name))
parse = transforms.Resize(self.load_width, interpolation=0)(parse)
parse_agnostic = self.get_parse_agnostic(parse, pose_data)
parse_agnostic = torch.from_numpy(np.array(parse_agnostic)[None]).long()
labels = {
0: ['background', [0, 10]],
1: ['hair', [1, 2]],
2: ['face', [4, 13]],
3: ['upper', [5, 6, 7]],
4: ['bottom', [9, 12]],
5: ['left_arm', [14]],
6: ['right_arm', [15]],
7: ['left_leg', [16]],
8: ['right_leg', [17]],
9: ['left_shoe', [18]],
10: ['right_shoe', [19]],
11: ['socks', [8]],
12: ['noise', [3, 11]]
}
parse_agnostic_map = torch.zeros(20, self.load_height, self.load_width, dtype=torch.float)
parse_agnostic_map.scatter_(0, parse_agnostic, 1.0)
new_parse_agnostic_map = torch.zeros(self.semantic_nc, self.load_height, self.load_width, dtype=torch.float)
for i in range(len(labels)):
for label in labels[i][1]:
new_parse_agnostic_map[i] += parse_agnostic_map[label]
# load person image
img = Image.open(osp.join(self.data_path, 'image', img_name))
img = transforms.Resize(self.load_width, interpolation=2)(img)
img_agnostic = self.get_img_agnostic(img, parse, pose_data)
img = self.transform(img)
img_agnostic = self.transform(img_agnostic) # [-1,1]
result = {
'img_name': img_name,
'c_name': c_name,
'img': img,
'img_agnostic': img_agnostic,
'parse_agnostic': new_parse_agnostic_map,
'pose': pose_rgb,
'cloth': c,
'cloth_mask': cm,
}
return result
def __len__(self):
return len(self.img_names)
class VITONDataLoader:
def __init__(self, opt, dataset):
super(VITONDataLoader, self).__init__()
if opt.shuffle:
train_sampler = data.sampler.RandomSampler(dataset)
else:
train_sampler = None
self.data_loader = data.DataLoader(
dataset, batch_size=opt.batch_size, shuffle=(train_sampler is None),
num_workers=opt.workers, pin_memory=True, drop_last=True, sampler=train_sampler
)
self.dataset = dataset
self.data_iter = self.data_loader.__iter__()
def next_batch(self):
try:
batch = self.data_iter.__next__()
except StopIteration:
self.data_iter = self.data_loader.__iter__()
batch = self.data_iter.__next__()
return batch