-
Notifications
You must be signed in to change notification settings - Fork 79
/
pose_dataset.py
211 lines (173 loc) · 8.94 KB
/
pose_dataset.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
import numpy as np
import matplotlib
matplotlib.use('Agg')
from gan.dataset import UGANDataset
import pose_utils
import pose_transform
from skimage.io import imread
import pandas as pd
import os
class PoseHMDataset(UGANDataset):
def __init__(self, test_phase=False, **kwargs):
super(PoseHMDataset, self).__init__(kwargs['batch_size'], None)
self._test_phase = test_phase
self._batch_size = 1 if self._test_phase else kwargs['batch_size']
self._image_size = kwargs['image_size']
self._images_dir_train = kwargs['images_dir_train']
self._images_dir_test = kwargs['images_dir_test']
self._bg_images_dir_train = kwargs['bg_images_dir_train']
self._bg_images_dir_test = kwargs['bg_images_dir_test']
self._pairs_file_train = pd.read_csv(kwargs['pairs_file_train'])
self._pairs_file_test = pd.read_csv(kwargs['pairs_file_test'])
self._annotations_file_test = pd.read_csv(kwargs['annotations_file_test'], sep=':')
self._annotations_file_train = pd.read_csv(kwargs['annotations_file_train'], sep=':')
self._annotations_file = pd.concat([self._annotations_file_test, self._annotations_file_train],
axis=0, ignore_index=True)
self._annotations_file = self._annotations_file.set_index('name')
self._use_input_pose = kwargs['use_input_pose']
self._warp_skip = kwargs['warp_skip']
self._disc_type = kwargs['disc_type']
self._tmp_pose = kwargs['tmp_pose_dir']
self._use_bg = kwargs['use_bg']
self._pose_rep_type = kwargs['pose_rep_type']
self._cache_pose_rep = kwargs['cache_pose_rep']
self._test_data_index = 0
if not os.path.exists(self._tmp_pose):
os.makedirs(self._tmp_pose)
print ("Number of images: %s" % len(self._annotations_file))
print ("Number of pairs train: %s" % len(self._pairs_file_train))
print ("Number of pairs test: %s" % len(self._pairs_file_test))
self._batches_before_shuffle = int(self._pairs_file_train.shape[0] // self._batch_size)
def number_of_batches_per_epoch(self):
return 1000
def number_of_batches_per_validation(self):
return len(self._pairs_file_test) // self._batch_size
def compute_pose_map_batch(self, pair_df, direction):
assert direction in ['to', 'from']
batch = np.empty([self._batch_size] + list(self._image_size) + [18 if self._pose_rep_type == 'hm' else 3])
i = 0
for _, p in pair_df.iterrows():
row = self._annotations_file.loc[p[direction]]
if self._cache_pose_rep:
file_name = self._tmp_pose + p[direction] + self._pose_rep_type + '.npy'
if os.path.exists(file_name):
pose = np.load(file_name)
else:
kp_array = pose_utils.load_pose_cords_from_strings(row['keypoints_y'], row['keypoints_x'])
if self._pose_rep_type == 'hm':
pose = pose_utils.cords_to_map(kp_array, self._image_size)
else:
pose = pose_transform.make_stickman(kp_array, self._image_size)
np.save(file_name, pose)
else:
kp_array = pose_utils.load_pose_cords_from_strings(row['keypoints_y'], row['keypoints_x'])
if self._pose_rep_type == 'hm':
pose = pose_utils.cords_to_map(kp_array, self._image_size)
else:
pose = pose_transform.make_stickman(kp_array, self._image_size)
batch[i] = pose
i += 1
return batch
def compute_cord_warp_batch(self, pair_df):
if self._warp_skip == 'full':
batch = [np.empty([self._batch_size] + [1, 8])]
elif self._warp_skip == 'mask':
batch = [np.empty([self._batch_size] + [10, 8]),
np.empty([self._batch_size, 10] + list(self._image_size))]
else:
batch = [np.empty([self._batch_size] + [72])]
i = 0
for _, p in pair_df.iterrows():
fr = self._annotations_file.loc[p['from']]
to = self._annotations_file.loc[p['to']]
kp_array1 = pose_utils.load_pose_cords_from_strings(fr['keypoints_y'],
fr['keypoints_x'])
kp_array2 = pose_utils.load_pose_cords_from_strings(to['keypoints_y'],
to['keypoints_x'])
if self._warp_skip == 'mask':
batch[0][i] = pose_transform.affine_transforms(kp_array1, kp_array2)
batch[1][i] = pose_transform.pose_masks(kp_array2, self._image_size)
elif self._warp_skip == 'full':
batch[0][i] = pose_transform.estimate_uniform_transform(kp_array1, kp_array2)
else: #sel._warp_skip == 'stn'
batch[0][i][:36] = kp_array1.reshape((-1, ))
batch[0][i][36:] = kp_array2.reshape((-1, ))
i += 1
return batch
def _preprocess_image(self, image):
return (image / 255 - 0.5) * 2
def _deprocess_image(self, image):
return (255 * (image + 1) / 2).astype('uint8')
def load_image_batch(self, pair_df, direction='from'):
assert direction in ['to', 'from']
batch = np.empty([self._batch_size] + list(self._image_size) + [3])
i = 0
for _, p in pair_df.iterrows():
if os.path.exists(os.path.join(self._images_dir_train, p[direction])):
batch[i] = imread(os.path.join(self._images_dir_train, p[direction]))
else:
batch[i] = imread(os.path.join(self._images_dir_test, p[direction]))
i += 1
return self._preprocess_image(batch)
def load_bg(self, pair_df):
batch = np.empty([self._batch_size] + list(self._image_size) + [3])
i = 0
for _, p in pair_df.iterrows():
name = p['to'].replace('.jpg', '_BG.jpg')
#print os.path.join(self._images_dir_train, name)
if os.path.exists(os.path.join(self._bg_images_dir_train, name)):
batch[i] = imread(os.path.join(self._bg_images_dir_train, name))
else:
batch[i] = imread(os.path.join(self._bg_images_dir_test, name))
i += 1
return self._preprocess_image(batch)
def load_batch(self, index, for_discriminator, validation=False):
if validation:
pair_df = self._pairs_file_test.iloc[index]
else:
pair_df = self._pairs_file_train.iloc[index]
result = [self.load_image_batch(pair_df, 'from')]
if self._use_input_pose:
result.append(self.compute_pose_map_batch(pair_df, 'from'))
result.append(self.load_image_batch(pair_df, 'to'))
result.append(self.compute_pose_map_batch(pair_df, 'to'))
if self._use_bg:
result.append(self.load_bg(pair_df))
if self._warp_skip != 'none' and (not for_discriminator or self._disc_type == 'warp'):
result += self.compute_cord_warp_batch(pair_df)
return result
def next_generator_sample(self):
index = self._next_data_index()
return self.load_batch(index, False)
def next_generator_sample_test(self, with_names=False):
index = np.arange(self._test_data_index, self._test_data_index + self._batch_size)
index = index % self._pairs_file_test.shape[0]
batch = self.load_batch(index, False, True)
names = self._pairs_file_test.iloc[index]
self._test_data_index += self._batch_size
if with_names:
return batch, names
else:
return batch
def next_discriminator_sample(self):
index = self._next_data_index()
return self.load_batch(index, True)
def _shuffle_data(self):
self._pairs_file_train = self._pairs_file_train.sample(frac=1)
def display(self, output_batch, input_batch):
row = self._batch_size
col = 1
tg_app = self._deprocess_image(input_batch[0])
tg_pose = input_batch[3 if self._use_input_pose else 2]
tg_img = input_batch[2 if self._use_input_pose else 1]
tg_img = self._deprocess_image(tg_img)
res_img = self._deprocess_image(output_batch[2 if self._use_input_pose else 1])
tg_app = super(PoseHMDataset, self).display(tg_app, None, row=row, col=col)
if self._pose_rep_type == 'hm':
pose_images = np.array([pose_utils.draw_pose_from_map(pose)[0] for pose in tg_pose])
else:
pose_images = (255 * tg_pose).astype('uint8')
tg_pose = super(PoseHMDataset, self).display(pose_images, None, row=row, col=col)
tg_img = super(PoseHMDataset, self).display(tg_img, None, row=row, col=col)
res_img = super(PoseHMDataset, self).display(res_img, None, row=row, col=col)
return np.concatenate(np.array([tg_app, tg_pose, tg_img, res_img]), axis=1)