forked from jxhno1/InDuDoNet_plus
-
Notifications
You must be signed in to change notification settings - Fork 1
/
test_deeplesion.py
184 lines (165 loc) · 7.01 KB
/
test_deeplesion.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
import os
import os.path
import argparse
import numpy as np
import torch
import time
import matplotlib.pyplot as plt
import h5py
import PIL
from PIL import Image
from network.indudonet_plus import InDuDoNet_plus
from deeplesion.build_gemotry import initialization, build_gemotry
import scipy
from scipy import ndimage
from sklearn.cluster import k_means
import scipy.io as sio
import warnings
warnings.filterwarnings("ignore")
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
parser = argparse.ArgumentParser(description="YU_Test")
parser.add_argument("--model_dir", type=str, default="./pretrained_model/InDuDoNet+_latest.pt", help='path to model and log files')
parser.add_argument("--data_path", type=str, default="deeplesion/test/", help='path to training data')
parser.add_argument("--use_GPU", type=bool, default=True, help='use GPU or not')
parser.add_argument("--save_path", type=str, default="./test_results/", help='path to training data')
parser.add_argument('--num_channel', type=int, default=32, help='the number of dual channels')
parser.add_argument('--T', type=int, default=4, help='the number of ResBlocks in every ProxNet')
parser.add_argument('--S', type=int, default=10, help='the number of total iterative stages')
parser.add_argument('--eta1', type=float, default=1, help='initialization for stepsize eta1')
parser.add_argument('--eta2', type=float, default=5, help='initialization for stepsize eta2')
parser.add_argument('--alpha', type=float, default=0.5, help='initialization for weight factor')
opt = parser.parse_args()
def mkdir(path):
folder = os.path.exists(path)
if not folder:
os.makedirs(path)
print("--- new folder... ---")
print("--- " + path + " ---")
else:
print("--- There exsits folder " + path + " ! ---")
input_dir = opt.save_path + '/Xma/'
gt_dir = opt.save_path + '/Xgt/'
outX_dir = opt.save_path+'/X/'
mkdir(input_dir)
mkdir(gt_dir)
mkdir(outX_dir)
def image_get_minmax():
return 0.0, 1.0
def proj_get_minmax():
return 0.0, 4.0
def normalize(data, minmax):
data_min, data_max = minmax
data = np.clip(data, data_min, data_max)
data = (data - data_min) / (data_max - data_min)
data = data * 255.0
data = data.astype(np.float32)
data = np.expand_dims(np.transpose(np.expand_dims(data, 2), (2, 0, 1)),0)
return data
def nmarprior(im,threshWater,threshBone,miuAir,miuWater,smFilter):
imSm = ndimage.filters.convolve(im, smFilter, mode='nearest')
priorimgHU = imSm
priorimgHU[imSm <= threshWater] = miuAir
h, w = imSm.shape[0], imSm.shape[1]
priorimgHUvector = np.reshape(priorimgHU, h*w)
region1_1d = np.where(priorimgHUvector > threshWater)
region2_1d = np.where(priorimgHUvector < threshBone)
region_1d = np.intersect1d(region1_1d, region2_1d)
priorimgHUvector[region_1d] = miuWater
priorimgHU = np.reshape(priorimgHUvector,(h,w))
return priorimgHU
sigma = 1
smFilter = sio.loadmat('deeplesion/gaussianfilter.mat')['smFilter']
miuAir = 0
miuWater=0.192
starpoint = np.zeros([3, 1])
starpoint[0] = miuAir
starpoint[1] = miuWater
starpoint[2] = 2 * miuWater
def nmar_prior(XLI, M):
XLI[M == 1] = 0.192
h, w = XLI.shape[0], XLI.shape[1]
im1d = XLI.reshape(h * w, 1)
best_centers, labels, best_inertia = k_means(im1d, n_clusters=3, init=starpoint, max_iter=300)
threshBone2 = np.min(im1d[labels ==2])
threshBone2 = np.max([threshBone2, 1.2 * miuWater])
threshWater2 = np.min(im1d[labels == 1])
imPriorNMAR = nmarprior(XLI, threshWater2, threshBone2, miuAir, miuWater, smFilter)
return imPriorNMAR
param = initialization()
ray_trafo = build_gemotry(param)
test_mask = np.load(os.path.join(opt.data_path, 'testmask.npy'))
def test_image(data_path, imag_idx, mask_idx):
txtdir = os.path.join(data_path, 'test_640geo_dir.txt')
mat_files = open(txtdir, 'r').readlines()
gt_dir = mat_files[imag_idx]
file_dir = gt_dir[:-6]
data_file = file_dir + str(mask_idx) + '.h5'
abs_dir = os.path.join(data_path, 'test_640geo/', data_file)
gt_absdir = os.path.join(data_path, 'test_640geo/', gt_dir[:-1])
gt_file = h5py.File(gt_absdir, 'r')
Xgt = gt_file['image'][()]
gt_file.close()
file = h5py.File(abs_dir, 'r')
Xma= file['ma_CT'][()]
Sma = file['ma_sinogram'][()]
XLI = file['LI_CT'][()]
SLI = file['LI_sinogram'][()]
Tr = file['metal_trace'][()]
Sgt = np.asarray(ray_trafo(Xgt))
file.close()
M512 = test_mask[:,:,mask_idx]
M = np.array(Image.fromarray(M512).resize((416, 416), PIL.Image.BILINEAR))
Xprior = nmar_prior(XLI, M)
Xprior = normalize(Xprior, image_get_minmax()) # *255
Xma = normalize(Xma, image_get_minmax()) # *255
Xgt = normalize(Xgt, image_get_minmax())
XLI = normalize(XLI, image_get_minmax())
Sma = normalize(Sma, proj_get_minmax())
Sgt = normalize(Sgt, proj_get_minmax())
SLI = normalize(SLI, proj_get_minmax())
Tr = 1 - Tr.astype(np.float32)
Tr = np.expand_dims(np.transpose(np.expand_dims(Tr, 2), (2, 0, 1)), 0) # 1*1*h*w
Mask = M.astype(np.float32)
Mask = np.expand_dims(np.transpose(np.expand_dims(Mask, 2), (2, 0, 1)),0)
return torch.Tensor(Xma).cuda(), torch.Tensor(XLI).cuda(), torch.Tensor(Xgt).cuda(), torch.Tensor(Mask).cuda(), \
torch.Tensor(Sma).cuda(), torch.Tensor(SLI).cuda(), torch.Tensor(Sgt).cuda(), torch.Tensor(Tr).cuda(), torch.Tensor(Xprior).cuda()
def print_network(name, net):
num_params = 0
for param in net.parameters():
num_params += param.numel()
print('name={:s}, Total number={:d}'.format(name, num_params))
def main():
print('Loading model ...\n')
net = InDuDoNet_plus(opt).cuda()
print_network("InDuDoNet", net)
net.load_state_dict(torch.load(opt.model_dir))
net.eval()
time_test = 0
count = 0
for imag_idx in range(1): # for demo
print(imag_idx)
for mask_idx in range(10):
Xma, XLI, Xgt, M, Sma, SLI, Sgt, Tr, Xprior = test_image(opt.data_path, imag_idx, mask_idx)
with torch.no_grad():
if opt.use_GPU:
torch.cuda.synchronize()
start_time = time.time()
ListX, ListS, ListYS= net(Xma, XLI, Sma, SLI, Tr, Xprior)
end_time = time.time()
dur_time = end_time - start_time
time_test += dur_time
print('Times: ', dur_time)
Xoutclip = torch.clamp(ListX[-1] / 255.0, 0, 0.5)
Xgtclip = torch.clamp(Xgt / 255.0, 0, 0.5)
Xmaclip = torch.clamp(Xma /255.0, 0, 0.5)
Xoutnorm = Xoutclip / 0.5
Xmanorm = Xmaclip / 0.5
Xgtnorm = Xgtclip / 0.5
idx = imag_idx *10+ mask_idx + 1
plt.imsave(input_dir + str(idx) + '.png', Xmanorm.data.cpu().numpy().squeeze(), cmap="gray")
plt.imsave(gt_dir + str(idx) + '.png', Xgtnorm.data.cpu().numpy().squeeze(), cmap="gray")
plt.imsave(outX_dir + str(idx) + '.png', Xoutnorm.data.cpu().numpy().squeeze(), cmap="gray")
count += 1
print('Avg.time={:.4f}'.format(time_test/count))
if __name__ == "__main__":
main()