-
Notifications
You must be signed in to change notification settings - Fork 2
/
show_conv.py
202 lines (157 loc) · 7.15 KB
/
show_conv.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
'''
This file try to segment based on GPU
Author: Yan Gao
email: gaoy4477@gmail.com
'''
import cv2
import os
import numpy as np
import matplotlib.pyplot as plt
import module.content as content
import module.features as features
from joblib import load
import argparse
import time
import tensorflow as tf
import matplotlib
matplotlib.use('MacOSX')
def get_args():
parser = argparse.ArgumentParser(description='Show single results')
parser.add_argument('--model_4D', nargs="?", type=str,
help='File name of saved model for 4D data')
parser.add_argument('--model_3D', nargs="?", type=str,
help='File name of saved model for 3D data')
# parser.add_argument('--size', nargs="?", type=int,
# help='Size of features, should be 1, 3 or 5')
parser.add_argument('--timestamp', nargs="?", type=str,
help='Target timestamp')
parser.add_argument('--slice', nargs="?", type=int,
help='Target slice')
parser.add_argument('--pore_4D', nargs="?", type=str,
help='Label for pore in 4D model')
parser.add_argument('--pore_3D', nargs="?", type=str,
help='Label for pore in 3D model')
args = parser.parse_args()
print(args)
return args
args = get_args()
# Here we set the paramater
mask_centre = (700, 810)
radius = 550
keyword = 'SHP'
# transfer the pore from string to list
pore_4D = args.pore_4D.split(',')
pore_4D = [int(i) for i in pore_4D]
pore_3D = args.pore_3D.split(',')
pore_3D = [int(i) for i in pore_3D]
# get the path for target slice
current_path = os.getcwd()
all_timestamp = content.get_folder(current_path, keyword)
timestamp_index = [all_timestamp.index(i) for i in all_timestamp if args.timestamp in i]
sub_path = os.path.join(current_path, all_timestamp[timestamp_index[0]])
sub_all_tif = content.get_allslice(sub_path)
sub_path_previous = os.path.join(current_path, all_timestamp[timestamp_index[0]-1])
sub_path_next = os.path.join(current_path, all_timestamp[timestamp_index[0]+1])
begin_slice = args.slice - 1
end_slice = args.slice + 1
# load the model from 'model' folder
model_4D_path = os.path.join(current_path, 'model', args.model_4D+'.model')
model_3D_path = os.path.join(current_path, 'model', args.model_3D+'.model')
model_4D_type = load(model_4D_path)
model_3D_type = load(model_3D_path)
centre_4D = model_4D_type.cluster_centers_
centre_3D = model_3D_type.cluster_centers_
num_centre_3D = centre_3D.shape[0]
num_centre_4D = centre_4D.shape[0]
# we need to prepare the data for graph
# load image for 3D segmentation
image_batch, height, width = features.get_3D_structure(sub_path, begin_slice, end_slice)
image_batch_previous, _, _ = features.get_3D_structure(sub_path_previous, begin_slice, end_slice)
image_batch_next, _, _ = features.get_3D_structure(sub_path_next, begin_slice, end_slice)
print('Creat tensorflow graph...')
# create filter based on centre
# depends on different centre size, now we assume it is 3x3x3
conv_stride = [1,1,1,1,1]
# for 3D kernel
kernel_3D_list = [tf.reshape(tf.constant(i, tf.float32), (3,3,3,1,1)) for i in centre_3D]
constant_3D_list = [np.sum(i**2) for i in centre_3D]
# treat 4D convolution as the combination of 3D convolution
kernel_4D_list_1 = [tf.reshape(tf.constant(i[:27], tf.float32), (3,3,3,1,1)) for i in centre_4D]
kernel_4D_list_2 = [tf.reshape(tf.constant(i[27:54], tf.float32), (3,3,3,1,1)) for i in centre_4D]
kernel_4D_list_3 = [tf.reshape(tf.constant(i[54:81], tf.float32), (3,3,3,1,1)) for i in centre_4D]
constant_4D_list = [np.sum(i**2) for i in centre_4D]
# create graph for tensorflow -> share the same size for input
x_3D = tf.compat.v1.placeholder(tf.float32, shape=(1, end_slice-begin_slice+1, height, width, 1))
# layer for 3D data
layer_list_3D = [tf.nn.conv3d(x_3D, filter=i, strides = conv_stride, padding='SAME') for i in kernel_3D_list]
# layer for 4D data
layer_list_4D_1 = [tf.nn.conv3d(x_3D, filter=i, strides = conv_stride, padding='SAME') for i in kernel_4D_list_1]
layer_list_4D_2 = [tf.nn.conv3d(x_3D, filter=i, strides = conv_stride, padding='SAME') for i in kernel_4D_list_2]
layer_list_4D_3 = [tf.nn.conv3d(x_3D, filter=i, strides = conv_stride, padding='SAME') for i in kernel_4D_list_3]
print('Finished!')
print('Convolution...')
# run the graph
with tf.compat.v1.Session() as sess:
print('3D segmentation...')
result = [sess.run(i, feed_dict={x_3D:image_batch}) for i in layer_list_3D]
print('4D segmentation...')
result_4D_1 = [sess.run(i, feed_dict={x_3D:image_batch_previous}) for i in layer_list_4D_1]
result_4D_2 = [sess.run(i, feed_dict={x_3D:image_batch}) for i in layer_list_4D_2]
result_4D_3 = [sess.run(i, feed_dict={x_3D:image_batch_next}) for i in layer_list_4D_3]
print('Calculating distance...')
# reshape and calculate the distance
result_reshape = [i.reshape(end_slice-begin_slice+1, height, width) for i in result]
result_4D_1_reshape = [i.reshape(end_slice-begin_slice+1, height, width) for i in result_4D_1]
result_4D_2_reshape = [i.reshape(end_slice-begin_slice+1, height, width) for i in result_4D_2]
result_4D_3_reshape = [i.reshape(end_slice-begin_slice+1, height, width) for i in result_4D_3]
distance_list = [constant_3D_list[i]-2*result_reshape[i] for i in range(num_centre_3D)]
distance_list_4D = [constant_4D_list[i]-2*result_4D_1_reshape[i]-2*result_4D_2_reshape[i]-2*result_4D_3_reshape[i] for i in range(num_centre_4D)]
print('Finished!')
print('Segmenting...')
# we only care the mask, so create mask here
mask = np.zeros((height, width), np.uint8)
cv2.circle(mask, mask_centre, radius, 1, thickness=-1)
compare_3D = [distance_list[pore_3D[0]] < distance_list[j] for j in range(num_centre_3D) if j != pore_3D[0]]
for element in pore_3D[1:]:
compare_3D_ = [distance_list[element] < distance_list[j] for j in range(num_centre_3D) if j != element]
for i in range(len(compare_3D)):
compare_3D[i] += compare_3D_[i]
compare_4D = [distance_list_4D[pore_4D[0]] < distance_list_4D[j] for j in range(num_centre_4D) if j != pore_4D[0]]
for element in pore_4D[1:]:
compare_4D_ = [distance_list_4D[element] < distance_list_4D[j] for j in range(num_centre_4D) if j != element]
for i in range(len(compare_4D)):
compare_4D[i] += compare_4D_[i]
segment_3D = mask
for i in compare_3D:
segment_3D = segment_3D * i
segment_4D = mask
for i in compare_4D:
segment_4D = segment_4D * i
# inverse color for plotting
segment_inv_3D = cv2.bitwise_not(255*segment_3D)
segment_inv_4D = cv2.bitwise_not(255*segment_4D)
# plot the picture
plt.figure()
plt.imshow(segment_inv_4D[1], 'gray')
plt.axis('off')
plt.title('Segment for 4D data')
plt.figure()
plt.imshow(segment_inv_3D[1], 'gray')
plt.axis('off')
plt.title('Segment for 3D data')
plt.figure()
img = cv2.imread(sub_all_tif[args.slice-1], -1)
plt.imshow(img, 'gray')
plt.title('Original slice \n {string}'.format(string=os.path.basename(sub_all_tif[args.slice-1])))
plt.show()
# cv2.namedWindow("Image1", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("Image1", 500,500)
# cv2.imshow("Image1", segment_inv_4D[1])
# cv2.namedWindow("Image2", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("Image2", 500,500)
# cv2.imshow("Image2", segment_inv_3D[1])
# cv2.namedWindow("Image3", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("Image3", 500,500)
# img = cv2.imread(sub_all_tif[args.slice-1], -1)
# cv2.imshow("Image3", img)
# cv2.waitKey(0)