forked from TencentARC/GFPGAN
-
Notifications
You must be signed in to change notification settings - Fork 0
/
inference_gfpgan.py
174 lines (155 loc) · 6.97 KB
/
inference_gfpgan.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
import argparse
import cv2
import glob
import numpy as np
import os
import torch
from basicsr.utils import imwrite
from gfpgan import GFPGANer
def main():
"""Inference demo for GFPGAN (for users).
"""
parser = argparse.ArgumentParser()
parser.add_argument(
'-i',
'--input',
type=str,
default='inputs/whole_imgs',
help='Input image or folder. Default: inputs/whole_imgs')
parser.add_argument('-o', '--output', type=str, default='results', help='Output folder. Default: results')
# we use version to select models, which is more user-friendly
parser.add_argument(
'-v', '--version', type=str, default='1.3', help='GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3')
parser.add_argument(
'-s', '--upscale', type=int, default=2, help='The final upsampling scale of the image. Default: 2')
parser.add_argument(
'--bg_upsampler', type=str, default='realesrgan', help='background upsampler. Default: realesrgan')
parser.add_argument(
'--bg_tile',
type=int,
default=400,
help='Tile size for background sampler, 0 for no tile during testing. Default: 400')
parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face')
parser.add_argument('--aligned', action='store_true', help='Input are aligned faces')
parser.add_argument(
'--ext',
type=str,
default='auto',
help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto')
parser.add_argument('-w', '--weight', type=float, default=0.5, help='Adjustable weights.')
args = parser.parse_args()
args = parser.parse_args()
# ------------------------ input & output ------------------------
if args.input.endswith('/'):
args.input = args.input[:-1]
if os.path.isfile(args.input):
img_list = [args.input]
else:
img_list = sorted(glob.glob(os.path.join(args.input, '*')))
os.makedirs(args.output, exist_ok=True)
# ------------------------ set up background upsampler ------------------------
if args.bg_upsampler == 'realesrgan':
if not torch.cuda.is_available(): # CPU
import warnings
warnings.warn('The unoptimized RealESRGAN is slow on CPU. We do not use it. '
'If you really want to use it, please modify the corresponding codes.')
bg_upsampler = None
else:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
bg_upsampler = RealESRGANer(
scale=2,
model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
model=model,
tile=args.bg_tile,
tile_pad=10,
pre_pad=0,
half=True) # need to set False in CPU mode
else:
bg_upsampler = None
# ------------------------ set up GFPGAN restorer ------------------------
if args.version == '1':
arch = 'original'
channel_multiplier = 1
model_name = 'GFPGANv1'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth'
elif args.version == '1.2':
arch = 'clean'
channel_multiplier = 2
model_name = 'GFPGANCleanv1-NoCE-C2'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth'
elif args.version == '1.3':
arch = 'clean'
channel_multiplier = 2
model_name = 'GFPGANv1.3'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth'
elif args.version == '1.4':
arch = 'clean'
channel_multiplier = 2
model_name = 'GFPGANv1.4'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth'
elif args.version == 'RestoreFormer':
arch = 'RestoreFormer'
channel_multiplier = 2
model_name = 'RestoreFormer'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth'
else:
raise ValueError(f'Wrong model version {args.version}.')
# determine model paths
model_path = os.path.join('experiments/pretrained_models', model_name + '.pth')
if not os.path.isfile(model_path):
model_path = os.path.join('gfpgan/weights', model_name + '.pth')
if not os.path.isfile(model_path):
# download pre-trained models from url
model_path = url
restorer = GFPGANer(
model_path=model_path,
upscale=args.upscale,
arch=arch,
channel_multiplier=channel_multiplier,
bg_upsampler=bg_upsampler)
# ------------------------ restore ------------------------
for img_path in img_list:
# read image
img_name = os.path.basename(img_path)
print(f'Processing {img_name} ...')
basename, ext = os.path.splitext(img_name)
input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
# restore faces and background if necessary
cropped_faces, restored_faces, restored_img = restorer.enhance(
input_img,
has_aligned=args.aligned,
only_center_face=args.only_center_face,
paste_back=True,
weight=args.weight)
# save faces
for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)):
# save cropped face
save_crop_path = os.path.join(args.output, 'cropped_faces', f'{basename}_{idx:02d}.png')
imwrite(cropped_face, save_crop_path)
# save restored face
if args.suffix is not None:
save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png'
else:
save_face_name = f'{basename}_{idx:02d}.png'
save_restore_path = os.path.join(args.output, 'restored_faces', save_face_name)
imwrite(restored_face, save_restore_path)
# save comparison image
cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
imwrite(cmp_img, os.path.join(args.output, 'cmp', f'{basename}_{idx:02d}.png'))
# save restored img
if restored_img is not None:
if args.ext == 'auto':
extension = ext[1:]
else:
extension = args.ext
if args.suffix is not None:
save_restore_path = os.path.join(args.output, 'restored_imgs', f'{basename}_{args.suffix}.{extension}')
else:
save_restore_path = os.path.join(args.output, 'restored_imgs', f'{basename}.{extension}')
imwrite(restored_img, save_restore_path)
print(f'Results are in the [{args.output}] folder.')
if __name__ == '__main__':
main()