forked from TencentARC/GFPGAN
-
Notifications
You must be signed in to change notification settings - Fork 0
/
cog_predict.py
161 lines (143 loc) · 6.57 KB
/
cog_predict.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
# flake8: noqa
# This file is used for deploying replicate models
# running: cog predict -i img=@inputs/whole_imgs/10045.png -i version='v1.4' -i scale=2
# push: cog push r8.im/tencentarc/gfpgan
# push (backup): cog push r8.im/xinntao/gfpgan
import os
os.system('python setup.py develop')
os.system('pip install realesrgan')
import cv2
import shutil
import tempfile
import torch
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from gfpgan import GFPGANer
try:
from cog import BasePredictor, Input, Path
from realesrgan.utils import RealESRGANer
except Exception:
print('please install cog and realesrgan package')
class Predictor(BasePredictor):
def setup(self):
os.makedirs('output', exist_ok=True)
# download weights
if not os.path.exists('gfpgan/weights/realesr-general-x4v3.pth'):
os.system(
'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./gfpgan/weights'
)
if not os.path.exists('gfpgan/weights/GFPGANv1.2.pth'):
os.system(
'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P ./gfpgan/weights')
if not os.path.exists('gfpgan/weights/GFPGANv1.3.pth'):
os.system(
'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P ./gfpgan/weights')
if not os.path.exists('gfpgan/weights/GFPGANv1.4.pth'):
os.system(
'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./gfpgan/weights')
if not os.path.exists('gfpgan/weights/RestoreFormer.pth'):
os.system(
'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P ./gfpgan/weights'
)
# background enhancer with RealESRGAN
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
model_path = 'gfpgan/weights/realesr-general-x4v3.pth'
half = True if torch.cuda.is_available() else False
self.upsampler = RealESRGANer(
scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
# Use GFPGAN for face enhancement
self.face_enhancer = GFPGANer(
model_path='gfpgan/weights/GFPGANv1.4.pth',
upscale=2,
arch='clean',
channel_multiplier=2,
bg_upsampler=self.upsampler)
self.current_version = 'v1.4'
def predict(
self,
img: Path = Input(description='Input'),
version: str = Input(
description='GFPGAN version. v1.3: better quality. v1.4: more details and better identity.',
choices=['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'],
default='v1.4'),
scale: float = Input(description='Rescaling factor', default=2),
) -> Path:
weight = 0.5
print(img, version, scale, weight)
try:
extension = os.path.splitext(os.path.basename(str(img)))[1]
img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED)
if len(img.shape) == 3 and img.shape[2] == 4:
img_mode = 'RGBA'
elif len(img.shape) == 2:
img_mode = None
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
else:
img_mode = None
h, w = img.shape[0:2]
if h < 300:
img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
if self.current_version != version:
if version == 'v1.2':
self.face_enhancer = GFPGANer(
model_path='gfpgan/weights/GFPGANv1.2.pth',
upscale=2,
arch='clean',
channel_multiplier=2,
bg_upsampler=self.upsampler)
self.current_version = 'v1.2'
elif version == 'v1.3':
self.face_enhancer = GFPGANer(
model_path='gfpgan/weights/GFPGANv1.3.pth',
upscale=2,
arch='clean',
channel_multiplier=2,
bg_upsampler=self.upsampler)
self.current_version = 'v1.3'
elif version == 'v1.4':
self.face_enhancer = GFPGANer(
model_path='gfpgan/weights/GFPGANv1.4.pth',
upscale=2,
arch='clean',
channel_multiplier=2,
bg_upsampler=self.upsampler)
self.current_version = 'v1.4'
elif version == 'RestoreFormer':
self.face_enhancer = GFPGANer(
model_path='gfpgan/weights/RestoreFormer.pth',
upscale=2,
arch='RestoreFormer',
channel_multiplier=2,
bg_upsampler=self.upsampler)
try:
_, _, output = self.face_enhancer.enhance(
img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
except RuntimeError as error:
print('Error', error)
try:
if scale != 2:
interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
h, w = img.shape[0:2]
output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
except Exception as error:
print('wrong scale input.', error)
if img_mode == 'RGBA': # RGBA images should be saved in png format
extension = 'png'
# save_path = f'output/out.{extension}'
# cv2.imwrite(save_path, output)
out_path = Path(tempfile.mkdtemp()) / f'out.{extension}'
cv2.imwrite(str(out_path), output)
except Exception as error:
print('global exception: ', error)
finally:
clean_folder('output')
return out_path
def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print(f'Failed to delete {file_path}. Reason: {e}')