-
Notifications
You must be signed in to change notification settings - Fork 121
/
align.py
249 lines (213 loc) · 8.68 KB
/
align.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
import bz2
import os
import os.path as osp
import sys
from multiprocessing import Pool
import dlib
import numpy as np
import PIL.Image
import requests
import scipy.ndimage
from tqdm import tqdm
from argparse import ArgumentParser
LANDMARKS_MODEL_URL = 'http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2'
def image_align(src_file,
dst_file,
face_landmarks,
output_size=1024,
transform_size=4096,
enable_padding=True):
# Align function from FFHQ dataset pre-processing step
# https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
lm = np.array(face_landmarks)
lm_chin = lm[0:17] # left-right
lm_eyebrow_left = lm[17:22] # left-right
lm_eyebrow_right = lm[22:27] # left-right
lm_nose = lm[27:31] # top-down
lm_nostrils = lm[31:36] # top-down
lm_eye_left = lm[36:42] # left-clockwise
lm_eye_right = lm[42:48] # left-clockwise
lm_mouth_outer = lm[48:60] # left-clockwise
lm_mouth_inner = lm[60:68] # left-clockwise
# 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)
img = img.convert('RGB')
# 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)
# Save aligned image.
img.save(dst_file, 'PNG')
class LandmarksDetector:
def __init__(self, predictor_model_path):
"""
:param predictor_model_path: path to shape_predictor_68_face_landmarks.dat file
"""
self.detector = dlib.get_frontal_face_detector(
) # cnn_face_detection_model_v1 also can be used
self.shape_predictor = dlib.shape_predictor(predictor_model_path)
def get_landmarks(self, image):
img = dlib.load_rgb_image(image)
dets = self.detector(img, 1)
for detection in dets:
face_landmarks = [
(item.x, item.y)
for item in self.shape_predictor(img, detection).parts()
]
yield face_landmarks
def unpack_bz2(src_path):
dst_path = src_path[:-4]
if os.path.exists(dst_path):
print('cached')
return dst_path
data = bz2.BZ2File(src_path).read()
with open(dst_path, 'wb') as fp:
fp.write(data)
return dst_path
def work_landmark(raw_img_path, img_name, face_landmarks):
face_img_name = '%s.png' % (os.path.splitext(img_name)[0], )
aligned_face_path = os.path.join(ALIGNED_IMAGES_DIR, face_img_name)
if os.path.exists(aligned_face_path):
return
image_align(raw_img_path,
aligned_face_path,
face_landmarks,
output_size=256)
def get_file(src, tgt):
if os.path.exists(tgt):
print('cached')
return tgt
tgt_dir = os.path.dirname(tgt)
if not os.path.exists(tgt_dir):
os.makedirs(tgt_dir)
file = requests.get(src)
open(tgt, 'wb').write(file.content)
return tgt
if __name__ == "__main__":
"""
Extracts and aligns all faces from images using DLib and a function from original FFHQ dataset preparation step
python align_images.py /raw_images /aligned_images
"""
parser = ArgumentParser()
parser.add_argument("-i",
"--input_imgs_path",
type=str,
default="imgs",
help="input images directory path")
parser.add_argument("-o",
"--output_imgs_path",
type=str,
default="imgs_align",
help="output images directory path")
args = parser.parse_args()
# takes very long time ...
landmarks_model_path = unpack_bz2(
get_file(
'http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2',
'temp/shape_predictor_68_face_landmarks.dat.bz2'))
# RAW_IMAGES_DIR = sys.argv[1]
# ALIGNED_IMAGES_DIR = sys.argv[2]
RAW_IMAGES_DIR = args.input_imgs_path
ALIGNED_IMAGES_DIR = args.output_imgs_path
if not osp.exists(ALIGNED_IMAGES_DIR): os.makedirs(ALIGNED_IMAGES_DIR)
files = os.listdir(RAW_IMAGES_DIR)
print(f'total img files {len(files)}')
with tqdm(total=len(files)) as progress:
def cb(*args):
# print('update')
progress.update()
def err_cb(e):
print('error:', e)
with Pool(8) as pool:
res = []
landmarks_detector = LandmarksDetector(landmarks_model_path)
for img_name in files:
raw_img_path = os.path.join(RAW_IMAGES_DIR, img_name)
# print('img_name:', img_name)
for i, face_landmarks in enumerate(
landmarks_detector.get_landmarks(raw_img_path),
start=1):
# assert i == 1, f'{i}'
# print(i, face_landmarks)
# face_img_name = '%s_%02d.png' % (os.path.splitext(img_name)[0], i)
# aligned_face_path = os.path.join(ALIGNED_IMAGES_DIR, face_img_name)
# image_align(raw_img_path, aligned_face_path, face_landmarks, output_size=256)
work_landmark(raw_img_path, img_name, face_landmarks)
progress.update()
# job = pool.apply_async(
# work_landmark,
# (raw_img_path, img_name, face_landmarks),
# callback=cb,
# error_callback=err_cb,
# )
# res.append(job)
# pool.close()
# pool.join()
print(f"output aligned images at: {ALIGNED_IMAGES_DIR}")