-
Notifications
You must be signed in to change notification settings - Fork 630
/
main_test_face_enhancement.py
178 lines (136 loc) · 7.08 KB
/
main_test_face_enhancement.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
'''
@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
@author: yangxy (yangtao9009@gmail.com)
https://github.com/yangxy/GPEN
@inproceedings{Yang2021GPEN,
title={GAN Prior Embedded Network for Blind Face Restoration in the Wild},
author={Tao Yang, Peiran Ren, Xuansong Xie, and Lei Zhang},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2021}
}
© Alibaba, 2021. For academic and non-commercial use only.
==================================================
slightly modified by Kai Zhang (2021-06-03)
https://github.com/cszn/KAIR
How to run:
step 1: Download <RetinaFace-R50.pth> model and <GPEN-512.pth> model and put them into `model_zoo`.
RetinaFace-R50.pth: https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/RetinaFace-R50.pth
GPEN-512.pth: https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-512.pth
Update(04/12/2023):
step 1: Download <RetinaFace-R50.pth> model and <GPEN-BFR-512.pth> model and put them into `model_zoo`. See https://github.com/yangxy/GPEN for more details!
RetinaFace-R50.pth: https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/RetinaFace-R50.pth
GPEN-BFR-512.pth: https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-512.pth
step 2: Install ninja by `pip install ninja`; set <inputdir> for your own testing images
step 3: `python main_test_face_enhancement.py`
==================================================
'''
import os
import cv2
import glob
import numpy as np
import torch
from utils.utils_alignfaces import warp_and_crop_face, get_reference_facial_points
from utils import utils_image as util
from retinaface.retinaface_detection import RetinaFaceDetection
from models.network_faceenhancer import FullGenerator as enhancer_net
class faceenhancer(object):
def __init__(self, model_path='model_zoo/GPEN-512.pth', size=512, channel_multiplier=2):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model_path = model_path
self.size = size
self.model = enhancer_net(self.size, 512, 8, channel_multiplier).to(self.device)
self.model.load_state_dict(torch.load(self.model_path))
self.model.eval()
def process(self, img):
'''
img: uint8 RGB image, (W, H, 3)
out: uint8 RGB image, (W, H, 3)
'''
img = cv2.resize(img, (self.size, self.size))
img = util.uint2tensor4(img)
img = (img - 0.5) / 0.5
img = img.to(self.device)
with torch.no_grad():
out, __ = self.model(img)
out = util.tensor2uint(out * 0.5 + 0.5)
return out
class faceenhancer_with_detection_alignment(object):
def __init__(self, model_path, size=512, channel_multiplier=2):
self.facedetector = RetinaFaceDetection('model_zoo/RetinaFace-R50.pth')
self.faceenhancer = faceenhancer(model_path, size, channel_multiplier)
self.size = size
self.threshold = 0.9
self.mask = np.zeros((512, 512), np.float32)
cv2.rectangle(self.mask, (26, 26), (486, 486), (1, 1, 1), -1, cv2.LINE_AA)
self.mask = cv2.GaussianBlur(self.mask, (101, 101), 11)
self.mask = cv2.GaussianBlur(self.mask, (101, 101), 11)
self.kernel = np.array((
[0.0625, 0.125, 0.0625],
[0.125, 0.25, 0.125],
[0.0625, 0.125, 0.0625]), dtype="float32")
# get the reference 5 landmarks position in the crop settings
default_square = True
inner_padding_factor = 0.25
outer_padding = (0, 0)
self.reference_5pts = get_reference_facial_points(
(self.size, self.size), inner_padding_factor, outer_padding, default_square)
def process(self, img):
'''
img: uint8 RGB image, (W, H, 3)
img, orig_faces, enhanced_faces: uint8 RGB image / cropped face images
'''
img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
facebs, landms = self.facedetector.detect(img_bgr)
orig_faces, enhanced_faces = [], []
height, width = img.shape[:2]
full_mask = np.zeros((height, width), dtype=np.float32)
full_img = np.zeros(img.shape, dtype=np.uint8)
for i, (faceb, facial5points) in enumerate(zip(facebs, landms)):
if faceb[4]<self.threshold: continue
fh, fw = (faceb[3]-faceb[1]), (faceb[2]-faceb[0])
facial5points = np.reshape(facial5points, (2, 5))
#img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
of, tfm_inv = warp_and_crop_face(img, facial5points, reference_pts=self.reference_5pts, crop_size=(self.size, self.size))
# Enhance the face image!
ef = self.faceenhancer.process(of)
orig_faces.append(of)
enhanced_faces.append(ef)
tmp_mask = self.mask
tmp_mask = cv2.resize(tmp_mask, ef.shape[:2])
tmp_mask = cv2.warpAffine(tmp_mask, tfm_inv, (width, height), flags=3)
if min(fh, fw) < 100: # Gaussian filter for small face
ef = cv2.filter2D(ef, -1, self.kernel)
tmp_img = cv2.warpAffine(ef, tfm_inv, (width, height), flags=3)
mask = tmp_mask - full_mask
full_mask[np.where(mask>0)] = tmp_mask[np.where(mask>0)]
full_img[np.where(mask>0)] = tmp_img[np.where(mask>0)]
full_mask = full_mask[:, :, np.newaxis]
img = cv2.convertScaleAbs(img*(1-full_mask) + full_img*full_mask)
return img, orig_faces, enhanced_faces
if __name__=='__main__':
inputdir = os.path.join('testsets', 'real_faces')
outdir = os.path.join('testsets', 'real_faces_results')
os.makedirs(outdir, exist_ok=True)
# whether use the face detection&alignment or not
need_face_detection = True
if need_face_detection:
enhancer = faceenhancer_with_detection_alignment(model_path=os.path.join('model_zoo','GPEN-BFR-512.pth'), size=512, channel_multiplier=2)
else:
enhancer = faceenhancer(model_path=os.path.join('model_zoo','GPEN-BFR-512.pth'), size=512, channel_multiplier=2)
for idx, img_file in enumerate(util.get_image_paths(inputdir)):
img_name, ext = os.path.splitext(os.path.basename(img_file))
img_L = util.imread_uint(img_file, n_channels=3)
print('{:->4d} --> {:<s}'.format(idx+1, img_name+ext))
img_L = cv2.resize(img_L, (0,0), fx=2, fy=2)
if need_face_detection:
# do the enhancement
img_H, orig_faces, enhanced_faces = enhancer.process(img_L)
util.imsave(np.hstack((img_L, img_H)), os.path.join(outdir, img_name+'_comparison.png'))
util.imsave(img_H, os.path.join(outdir, img_name+'_enhanced.png'))
for m, (ef, of) in enumerate(zip(enhanced_faces, orig_faces)):
of = cv2.resize(of, ef.shape[:2])
util.imsave(np.hstack((of, ef)), os.path.join(outdir, img_name+'_face%02d'%m+'.png'))
else:
# do the enhancement
img_H = enhancer.process(img_L)
util.imsave(img_H, os.path.join(outdir, img_name+'_enhanced_without_detection.png'))