-
Notifications
You must be signed in to change notification settings - Fork 635
/
main_test_ffdnet.py
198 lines (154 loc) · 6.83 KB
/
main_test_ffdnet.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
import os.path
import logging
import numpy as np
from collections import OrderedDict
import torch
from utils import utils_logger
from utils import utils_image as util
'''
Spyder (Python 3.6)
PyTorch 1.1.0
Windows 10 or Linux
Kai Zhang ([email protected])
github: https://github.com/cszn/KAIR
https://github.com/cszn/FFDNet
@article{zhang2018ffdnet,
title={FFDNet: Toward a fast and flexible solution for CNN-based image denoising},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
journal={IEEE Transactions on Image Processing},
volume={27},
number={9},
pages={4608--4622},
year={2018},
publisher={IEEE}
}
% If you have any question, please feel free to contact with me.
% Kai Zhang (e-mail: [email protected]; github: https://github.com/cszn)
by Kai Zhang (12/Dec./2019)
'''
"""
# --------------------------------------------
|--model_zoo # model_zoo
|--ffdnet_gray # model_name, for color images
|--ffdnet_color
|--ffdnet_color_clip # for clipped uint8 color images
|--ffdnet_gray_clip
|--testset # testsets
|--set12 # testset_name
|--bsd68
|--cbsd68
|--results # results
|--set12_ffdnet_gray # result_name = testset_name + '_' + model_name
|--set12_ffdnet_color
|--cbsd68_ffdnet_color_clip
# --------------------------------------------
"""
def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
noise_level_img = 15 # noise level for noisy image
noise_level_model = noise_level_img # noise level for model
model_name = 'ffdnet_gray' # 'ffdnet_gray' | 'ffdnet_color' | 'ffdnet_color_clip' | 'ffdnet_gray_clip'
testset_name = 'bsd68' # test set, 'bsd68' | 'cbsd68' | 'set12'
need_degradation = True # default: True
show_img = False # default: False
task_current = 'dn' # 'dn' for denoising | 'sr' for super-resolution
sf = 1 # unused for denoising
if 'color' in model_name:
n_channels = 3 # setting for color image
nc = 96 # setting for color image
nb = 12 # setting for color image
else:
n_channels = 1 # setting for grayscale image
nc = 64 # setting for grayscale image
nb = 15 # setting for grayscale image
if 'clip' in model_name:
use_clip = True # clip the intensities into range of [0, 1]
else:
use_clip = False
model_pool = 'model_zoo' # fixed
testsets = 'testsets' # fixed
results = 'results' # fixed
result_name = testset_name + '_' + model_name
border = sf if task_current == 'sr' else 0 # shave boader to calculate PSNR and SSIM
model_path = os.path.join(model_pool, model_name+'.pth')
# ----------------------------------------
# L_path, E_path, H_path
# ----------------------------------------
L_path = os.path.join(testsets, testset_name) # L_path, for Low-quality images
H_path = L_path # H_path, for High-quality images
E_path = os.path.join(results, result_name) # E_path, for Estimated images
util.mkdir(E_path)
if H_path == L_path:
need_degradation = True
logger_name = result_name
utils_logger.logger_info(logger_name, log_path=os.path.join(E_path, logger_name+'.log'))
logger = logging.getLogger(logger_name)
need_H = True if H_path is not None else False
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ----------------------------------------
# load model
# ----------------------------------------
from models.network_ffdnet import FFDNet as net
model = net(in_nc=n_channels, out_nc=n_channels, nc=nc, nb=nb, act_mode='R')
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
logger.info('Model path: {:s}'.format(model_path))
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
logger.info('model_name:{}, model sigma:{}, image sigma:{}'.format(model_name, noise_level_img, noise_level_model))
logger.info(L_path)
L_paths = util.get_image_paths(L_path)
H_paths = util.get_image_paths(H_path) if need_H else None
for idx, img in enumerate(L_paths):
# ------------------------------------
# (1) img_L
# ------------------------------------
img_name, ext = os.path.splitext(os.path.basename(img))
# logger.info('{:->4d}--> {:>10s}'.format(idx+1, img_name+ext))
img_L = util.imread_uint(img, n_channels=n_channels)
img_L = util.uint2single(img_L)
if need_degradation: # degradation process
np.random.seed(seed=0) # for reproducibility
img_L += np.random.normal(0, noise_level_img/255., img_L.shape)
if use_clip:
img_L = util.uint2single(util.single2uint(img_L))
util.imshow(util.single2uint(img_L), title='Noisy image with noise level {}'.format(noise_level_img)) if show_img else None
img_L = util.single2tensor4(img_L)
img_L = img_L.to(device)
sigma = torch.full((1,1,1,1), noise_level_model/255.).type_as(img_L)
# ------------------------------------
# (2) img_E
# ------------------------------------
img_E = model(img_L, sigma)
img_E = util.tensor2uint(img_E)
if need_H:
# --------------------------------
# (3) img_H
# --------------------------------
img_H = util.imread_uint(H_paths[idx], n_channels=n_channels)
img_H = img_H.squeeze()
# --------------------------------
# PSNR and SSIM
# --------------------------------
psnr = util.calculate_psnr(img_E, img_H, border=border)
ssim = util.calculate_ssim(img_E, img_H, border=border)
test_results['psnr'].append(psnr)
test_results['ssim'].append(ssim)
logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.4f}.'.format(img_name+ext, psnr, ssim))
util.imshow(np.concatenate([img_E, img_H], axis=1), title='Recovered / Ground-truth') if show_img else None
# ------------------------------------
# save results
# ------------------------------------
util.imsave(img_E, os.path.join(E_path, img_name+ext))
if need_H:
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
logger.info('Average PSNR/SSIM(RGB) - {} - PSNR: {:.2f} dB; SSIM: {:.4f}'.format(result_name, ave_psnr, ave_ssim))
if __name__ == '__main__':
main()