forked from icon-lab/ResViT
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest.py
141 lines (126 loc) · 5.81 KB
/
test.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
import os
from options.test_options import TestOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import Visualizer
from util import html
import numpy as np
import nibabel as nib
import SimpleITK as sitk
import monai
from skimage.metrics import peak_signal_noise_ratio as psnr
from skimage.metrics import structural_similarity as ssim
def print_log(logger,message):
print(message, flush=True)
if logger:
logger.write(str(message) + '\n')
def save_niigz(volume,vol_path,data_loader):
original = sitk.ReadImage((vol_path.split('.nii.gz')[0] + '.nii.gz').replace('MRI/T2_star','PET/AV45'))
vox = np.zeros((data_loader.dataset.depth,opt.loadSize,opt.loadSize))
vox[data_loader.dataset.low_slice:data_loader.dataset.high_slice] = np.array(np.flip(volume,0))
vox = np.flip(vox.astype(int),0)
orig_size = (original.GetSize()[2],original.GetSize()[0],original.GetSize()[1])
vox = np.expand_dims(vox, axis=0)
vox = monai.transforms.Resize(orig_size, size_mode='all', mode=monai.utils.enums.InterpolateMode.TRILINEAR, anti_aliasing=True, align_corners=False)(vox)
result_image = sitk.GetImageFromArray(vox[0])
result_image.CopyInformation(original)
sitk.WriteImage(result_image, os.path.join(vol_dir, vol_path.split('/')[-1].split('.nii.gz')[0] + '.nii.gz'))
if __name__ == '__main__':
opt = TestOptions().parse()
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
data_loader = CreateDataLoader(opt)
data_loader = data_loader.load_data()
print('#Test max images = %d' % len(data_loader))
model = create_model(opt)
visualizer = Visualizer(opt)
# create website
web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
# test
if opt.save_nii:
volume = []
vol_path = ''
vol_dir = web_dir + "/FakePet"
if not os.path.exists(vol_dir):
os.makedirs(vol_dir)
L1_avg=np.zeros([len(data_loader)])
psnr_avg=np.zeros([len(data_loader)])
ssim_avg=np.zeros([len(data_loader)])
for i, data in enumerate(data_loader):
if opt.how_many > 0 and not opt.save_nii and i >= opt.how_many:
break
model.set_input(data)
model.test()
fake_B = model.fake_B.view(-1,opt.fineSize,opt.fineSize)
fake_im= fake_B.cpu().data.numpy()
#
real_B = model.real_B.view(-1,opt.fineSize,opt.fineSize)
real_im= real_B.cpu().data.numpy()
#
#print('____')
#print(data['A'].min())
#print(data['A'].max())
#print('----')
#print('____dn')
#print((data['A'][:]*.5 + 0.5).min())
#print((data['A']*.5 + 0.5).max())
#print('----dn')
real_im=real_im*0.5+0.5
fake_im=fake_im*0.5+0.5
#print('____')
#print(real_im.min())
#print(real_im.max())
#print('----')
#if real_im.max() <= 0:
# continue
L1_avg[i]=abs(fake_im-real_im).mean()
psnr_avg[i]=psnr(fake_im,real_im, data_range=1)#psnr(fake_im/fake_im.max(),real_im/real_im.max())
ssim_avg[i]=ssim(fake_im,real_im, channel_axis=0, data_range=1) #ssim(fake_im/fake_im.max(),real_im/real_im.max(), channel_axis=opt.batchSize)
#
if opt.dataset_mode=='aligned_mat':
visuals=model.get_current_visuals()
#visuals['real_A']=visuals['real_A'][:,:,0:3]
#visuals['real_B']=visuals['real_B'][:,:,0:3]
#visuals['fake_B']=visuals['fake_B'][:,:,0:3]
img_path = model.get_image_paths()
img_path[0]=img_path[0]+str(i)
elif opt.dataset_mode=='unaligned_mat':
visuals=model.get_current_visuals()
slice_select=[opt.input_nc/2,opt.input_nc/2,opt.input_nc/2]
visuals['real_A']=visuals['real_A'][:,:,slice_select]
visuals['real_B']=visuals['real_B'][:,:,slice_select]
visuals['fake_A']=visuals['fake_A'][:,:,slice_select]
visuals['fake_B']=visuals['fake_B'][:,:,slice_select]
visuals['rec_A']=visuals['rec_A'][:,:,slice_select]
visuals['rec_B']=visuals['rec_B'][:,:,slice_select]
#temp_visuals['idt_A']=temp_visuals['idt_A'][:,:,slice_select]
#temp_visuals['idt_B']=temp_visuals['idt_B'][:,:,slice_select]
img_path = model.get_image_paths()
img_path[0]=img_path[0]+str(i)
else:
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
print('%04d: process image... %s' % (i, img_path))
visualizer.save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, protocols=opt.protocols,targets=opt.targets)
if opt.save_nii:
if len(volume) == data_loader.dataset.elegible_slices:
save_niigz(volume,vol_path,data_loader)
volume = []
volume.append(visuals['fake_B'][:,:,0])
vol_path = img_path[0]
l1_avg_loss = np.mean(L1_avg)
#
mean_psnr = np.mean(psnr_avg)
mean_ssim = np.mean(ssim_avg)
#
std_psnr = np.std(psnr_avg)
std_ssim = np.std(ssim_avg)
#
print(f'Test l1_avg_loss: {l1_avg_loss:.5f} mean_psnr: {mean_psnr:.3f} std_psnr:{std_psnr:.3f} mean_ssim: {mean_ssim:.3f} std_ssim: {std_ssim:.3f}')
if opt.save_nii:
if len(volume) == data_loader.dataset.elegible_slices:
save_niigz(volume,vol_path,data_loader)
webpage.save()