-
Notifications
You must be signed in to change notification settings - Fork 343
/
utils.py
296 lines (219 loc) · 12 KB
/
utils.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
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
import PIL.Image
import dnnlib
import dnnlib.tflib as tflib
import tensorflow as tf
import PIL.ImageFile
import scipy.ndimage
import numpy as np
import PIL.Image
import dnnlib
import dnnlib.tflib as tflib
import os
import re
import sys
import pretrained_networks
def Align_face_image(src_file, output_size=1024, transform_size=4096,
enable_padding=True):
print('aligning image...')
import dlib
img_ = dlib.load_rgb_image(src_file)
print("Image Shape :", img_.shape)
frontal_face = dlib.cnn_face_detection_model_v1("mmod_human_face_detector.dat") # cnn model
shape_ = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") # same as ffhq dataset
dets = frontal_face(img_, 1)
for i, d in enumerate(dets):
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}".format(i, d.rect.left(),
d.rect.top(),
d.rect.right(),
d.rect.bottom(),
d.confidence))
shape = shape_(img_, d.rect)
print("Part 0: {}, Part 1: {} ...".format(shape.part(0).x, shape.part(1)))
# Parse landmarks.
# pylint: disable=unused-variable
lm_chin = np.array([[shape.part(i).x, shape.part(i).y] for i in range(17)])
lm_eyebrow_left = np.array([[shape.part(i).x, shape.part(i).y] for i in range(17, 22)])
lm_eyebrow_right = np.array([[shape.part(i).x, shape.part(i).y] for i in range(22, 27)])
lm_nose = np.array([[shape.part(i).x, shape.part(i).y] for i in range(27, 31)])
lm_nostrils = np.array([[shape.part(i).x, shape.part(i).y] for i in range(31, 36)])
lm_eye_left = np.array([[shape.part(i).x, shape.part(i).y] for i in range(36, 42)])
lm_eye_right = np.array([[shape.part(i).x, shape.part(i).y] for i in range(42, 48)])
lm_mouth_outer = np.array([[shape.part(i).x, shape.part(i).y] for i in range(48, 60)])
lm_mouth_inner = np.array([[shape.part(i).x, shape.part(i).y] for i in range(60, 68)])
# Calculate auxiliary vectors.
eye_left = np.mean(lm_eye_left, axis=0)
eye_right = np.mean(lm_eye_right, axis=0)
eye_avg = (eye_left + eye_right) * 0.5
eye_to_eye = eye_right - eye_left
mouth_left = lm_mouth_outer[0]
mouth_right = lm_mouth_outer[6]
mouth_avg = (mouth_left + mouth_right) * 0.5
eye_to_mouth = mouth_avg - eye_avg
# Choose oriented crop rectangle.
x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
x /= np.hypot(*x)
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
y = np.flipud(x) * [-1, 1]
c = eye_avg + eye_to_mouth * 0.1
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
qsize = np.hypot(*x) * 2
# Load in-the-wild image.
if not os.path.isfile(src_file):
print('\nCannot find source image. Please run "--wilds" before "--align".')
return
img = PIL.Image.open(src_file)
# Shrink.
shrink = int(np.floor(qsize / output_size * 0.5))
if shrink > 1:
rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
img = img.resize(rsize, PIL.Image.ANTIALIAS)
quad /= shrink
qsize /= shrink
# Crop.
border = max(int(np.rint(qsize * 0.1)), 3)
crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
int(np.ceil(max(quad[:, 1]))))
crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
min(crop[3] + border, img.size[1]))
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
img = img.crop(crop)
quad -= crop[0:2]
# Pad.
pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
int(np.ceil(max(quad[:, 1]))))
pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
max(pad[3] - img.size[1] + border, 0))
if enable_padding and max(pad) > border - 4:
pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
h, w, _ = img.shape
y, x, _ = np.ogrid[:h, :w, :1]
mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
blur = qsize * 0.02
img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
quad += pad[:2]
# Transform.
img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(),
PIL.Image.BILINEAR)
if output_size < transform_size:
img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
img.save(src_file)
def gram_matrix(input_tensor):
# We make the image channels first
channels = int(input_tensor.shape[-1])
a = tf.reshape(input_tensor, [-1, channels])
n = tf.shape(a)[0]
gram = tf.matmul(a, a, transpose_a=True)
return gram / tf.cast(n, tf.float32)
def get_style_loss(base_style, gram_target):
"""Expects two images of dimension h, w, c"""
# height, width, num filters of each laye
base_style = tf.reshape(base_style, [base_style.shape[1], base_style.shape[2], base_style.shape[3]])
height, width, channels = base_style.get_shape().as_list()
gram_style = gram_matrix(base_style)
return tf.reduce_mean(tf.square(gram_style - gram_target))
#----------------------------------------------------------------------------
def generate_im_official(network_pkl='gdrive:networks/stylegan2-ffhq-config-f.pkl', seeds=[22], truncation_psi=0.5):
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('seed%04d.png' % seed))
def generate_im_from_random_seed(Gs, seed=22, truncation_psi=0.5):
seeds = [seed]
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
# PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('seed%04d.png' % seed))
return images
class Build_model:
def __init__(self, opt):
self.opt = opt
if os.path.exists("/usr/app/stylegan/stylegan2-ffhq-config-f.pkl"):
print("Found local StyleGan2 !")
network_pkl = "/usr/app/stylegan/stylegan2-ffhq-config-f.pkl" # Local load, avoiding to re-download 360Mb each time
else:
network_pkl = self.opt.network_pkl
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
self.Gs = Gs
self.Gs_syn_kwargs = dnnlib.EasyDict()
self.Gs_syn_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
self.Gs_syn_kwargs.randomize_noise = False
self.Gs_syn_kwargs.minibatch_size = 4
self.noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
rnd = np.random.RandomState(0)
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in self.noise_vars})
def generate_im_from_random_seed(self, seed=22, truncation_psi=0.5):
Gs = self.Gs
seeds = [seed]
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
# PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('seed%04d.png' % seed))
return images
def generate_im_from_z_space(self, z, truncation_psi=0.5):
Gs = self.Gs
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi # [height, width]
images = Gs.run(z, None, **Gs_kwargs)
# PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('test_from_z.png'))
return images
def generate_im_from_w_space(self, w):
images = self.Gs.components.synthesis.run(w, **self.Gs_syn_kwargs)
# PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('test_from_w.png'))
return images
# def load_network(random_weights=False):
# URL_FFHQ = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ'
# tflib.init_tf()
#
# with dnnlib.util.open_url(URL_FFHQ, cache_dir=config.cache_dir) as f:
# G, D, Gs = pickle.load(f)
# if random_weights:
# Gs.reset_vars()
# return Gs
if __name__ == "__main__":
Our_model = Build_model()
# Our_model.generate_im_from_random_seed(10)
# Our_model.generate_im_from_random_seed(50)
rnd = np.random.RandomState(10)
# z = rnd.randn(1, *Our_model.Gs.input_shape[1:])
z = rnd.randn(2, 512)
w = Our_model.Gs.components.mapping.run(z, None)
w_avg = Our_model.Gs.get_var('dlatent_avg')
w = w_avg + (w - w_avg) * 0.5
Our_model.generate_im_from_w_space(w)