-
Notifications
You must be signed in to change notification settings - Fork 56
/
restoration.py
159 lines (142 loc) 路 6.65 KB
/
restoration.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
import sys
sys.path.append('./CodeFormer/CodeFormer')
import os
import cv2
import torch
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
from basicsr.utils import imwrite, img2tensor, tensor2img
from basicsr.utils.download_util import load_file_from_url
from facelib.utils.face_restoration_helper import FaceRestoreHelper
from facelib.utils.misc import is_gray
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.realesrgan_utils import RealESRGANer
from basicsr.utils.registry import ARCH_REGISTRY
def check_ckpts():
pretrain_model_url = {
'codeformer': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
'detection': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth',
'parsing': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth',
'realesrgan': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/RealESRGAN_x2plus.pth'
}
# download weights
if not os.path.exists('CodeFormer/CodeFormer/weights/CodeFormer/codeformer.pth'):
load_file_from_url(url=pretrain_model_url['codeformer'], model_dir='CodeFormer/CodeFormer/weights/CodeFormer', progress=True, file_name=None)
if not os.path.exists('CodeFormer/CodeFormer/weights/facelib/detection_Resnet50_Final.pth'):
load_file_from_url(url=pretrain_model_url['detection'], model_dir='CodeFormer/CodeFormer/weights/facelib', progress=True, file_name=None)
if not os.path.exists('CodeFormer/CodeFormer/weights/facelib/parsing_parsenet.pth'):
load_file_from_url(url=pretrain_model_url['parsing'], model_dir='CodeFormer/CodeFormer/weights/facelib', progress=True, file_name=None)
if not os.path.exists('CodeFormer/CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth'):
load_file_from_url(url=pretrain_model_url['realesrgan'], model_dir='CodeFormer/CodeFormer/weights/realesrgan', progress=True, file_name=None)
# set enhancer with RealESRGAN
def set_realesrgan():
half = True if torch.cuda.is_available() else False
model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=2,
)
upsampler = RealESRGANer(
scale=2,
model_path="CodeFormer/CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth",
model=model,
tile=400,
tile_pad=40,
pre_pad=0,
half=half,
)
return upsampler
def face_restoration(img, background_enhance, face_upsample, upscale, codeformer_fidelity, upsampler, codeformer_net, device):
"""Run a single prediction on the model"""
try: # global try
# take the default setting for the demo
has_aligned = False
only_center_face = False
draw_box = False
detection_model = "retinaface_resnet50"
background_enhance = background_enhance if background_enhance is not None else True
face_upsample = face_upsample if face_upsample is not None else True
upscale = upscale if (upscale is not None and upscale > 0) else 2
upscale = int(upscale) # convert type to int
if upscale > 4: # avoid memory exceeded due to too large upscale
upscale = 4
if upscale > 2 and max(img.shape[:2])>1000: # avoid memory exceeded due to too large img resolution
upscale = 2
if max(img.shape[:2]) > 1500: # avoid memory exceeded due to too large img resolution
upscale = 1
background_enhance = False
face_upsample = False
face_helper = FaceRestoreHelper(
upscale,
face_size=512,
crop_ratio=(1, 1),
det_model=detection_model,
save_ext="png",
use_parse=True,
)
bg_upsampler = upsampler if background_enhance else None
face_upsampler = upsampler if face_upsample else None
if has_aligned:
# the input faces are already cropped and aligned
img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
face_helper.is_gray = is_gray(img, threshold=5)
face_helper.cropped_faces = [img]
else:
face_helper.read_image(img)
# get face landmarks for each face
num_det_faces = face_helper.get_face_landmarks_5(
only_center_face=only_center_face, resize=640, eye_dist_threshold=5
)
# align and warp each face
face_helper.align_warp_face()
# face restoration for each cropped face
for idx, cropped_face in enumerate(face_helper.cropped_faces):
# prepare data
cropped_face_t = img2tensor(
cropped_face / 255.0, bgr2rgb=True, float32=True
)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
try:
with torch.no_grad():
output = codeformer_net(
cropped_face_t, w=codeformer_fidelity, adain=True
)[0]
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
del output
torch.cuda.empty_cache()
except RuntimeError as error:
print(f"Failed inference for CodeFormer: {error}")
restored_face = tensor2img(
cropped_face_t, rgb2bgr=True, min_max=(-1, 1)
)
restored_face = restored_face.astype("uint8")
face_helper.add_restored_face(restored_face)
# paste_back
if not has_aligned:
# upsample the background
if bg_upsampler is not None:
# Now only support RealESRGAN for upsampling background
bg_img = bg_upsampler.enhance(img, outscale=upscale)[0]
else:
bg_img = None
face_helper.get_inverse_affine(None)
# paste each restored face to the input image
if face_upsample and face_upsampler is not None:
restored_img = face_helper.paste_faces_to_input_image(
upsample_img=bg_img,
draw_box=draw_box,
face_upsampler=face_upsampler,
)
else:
restored_img = face_helper.paste_faces_to_input_image(
upsample_img=bg_img, draw_box=draw_box
)
restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
return restored_img
except Exception as error:
print('Global exception', error)
return None, None