-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtools.py
185 lines (168 loc) · 7.25 KB
/
tools.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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@Author Fivethousand
@Date 2021/3/26 11:47
@Describe
@Version 1.0
"""
import numpy as np
from PyQt5.QtGui import QImage
import skimage.io
import skimage.transform
import skimage.color
import skimage
import os
def array_preprocess(array, min, max,type="axial",target_output_size=512):
"""
preprocess the array with min and max value, for the points that CT slices could be processed as
:param array: input grayscale 2-dimention array
:param min: min_pixel_value, pixels with value less than it would be set to min
:param max: max_pixel_value, pixels with value larger that it would be set to max
:return: showImage: QImage type, which could be later directly shown on a labelwidget through "self.label_screen.setPixmap(QPixmap(showImage))
"""
assert type in ["axial", "sagittal",
"coronal"], "the type of slice viewer must be in [\"axial\",\"sagittal\",\"coronal\"]"
array[array < min] = min
array[array > max] = max
array=((array-min)/(max-min)*255).astype(np.uint8)
#array=np.flipud(array)
if type == "axial":
pass
elif type == "sagittal":
print(array.shape)
array=flip90_left(array)
array=np.flipud(array)
height,width=array.shape
up_index=target_output_size//2-height//2
bottom_index=up_index+height
tmp_array=np.ones((target_output_size,target_output_size), dtype=np.uint8)*20
tmp_array[up_index:bottom_index,:]=array
array=tmp_array
elif type == "coronal":
print(array.shape)
array=flip90_left(array)
array = np.flipud(array)
height,width=array.shape
up_index=target_output_size//2-height//2
bottom_index=up_index+height
tmp_array=np.ones((target_output_size,target_output_size), dtype=np.uint8)*20
tmp_array[up_index:bottom_index,:]=array
array=tmp_array
array = skimage.color.gray2rgb(array)
showImage = QImage(array.copy(), array.shape[1], array.shape[0],
QImage.Format_RGB888) # 转换成QImage类型
return showImage
def array_preprocess_with_label(array,label_array, min, max,type="axial",target_output_size=512):
"""
preprocess the array with min and max value, for the points that CT slices could be processed as
:param array: input grayscale 2-dimention array
:param min: min_pixel_value, pixels with value less than it would be set to min
:param max: max_pixel_value, pixels with value larger that it would be set to max
:return: showImage: QImage type, which could be later directly shown on a labelwidget through "self.label_screen.setPixmap(QPixmap(showImage))
"""
assert type in ["axial", "sagittal",
"coronal"], "the type of slice viewer must be in [\"axial\",\"sagittal\",\"coronal\"]"
array_copy=array.copy()
array_copy[array_copy < min] = min
array_copy[array_copy > max] = max
array_copy=((array_copy-min)/(max-min)*255).astype(np.uint8)
tmp_label_array=label_array.copy()
tmp_label_array*=255
#array=np.flipud(array)
if type == "axial":
pass
elif type == "sagittal":
print(array_copy.shape)
###########
array_copy=flip90_left(array_copy)
array_copy=np.flipud(array_copy)
###########
tmp_label_array=flip90_left(tmp_label_array)
tmp_label_array=np.flipud(tmp_label_array)
###########
height,width=array_copy.shape
up_index=target_output_size//2-height//2
bottom_index=up_index+height
tmp_array=np.ones((target_output_size,target_output_size), dtype=np.uint8)*20
tmp_array[up_index:bottom_index,:]=array_copy
tmp_tmp_label_array=np.ones((target_output_size,target_output_size), dtype=np.uint8)*20
tmp_tmp_label_array[up_index:bottom_index,:]=tmp_label_array
array_copy=tmp_array
tmp_label_array=tmp_tmp_label_array
elif type == "coronal":
print(array_copy.shape)
array_copy=flip90_left(array_copy)
array_copy = np.flipud(array_copy)
tmp_label_array=flip90_left(tmp_label_array)
tmp_label_array=np.flipud(tmp_label_array)
height,width=array_copy.shape
up_index=target_output_size//2-height//2
bottom_index=up_index+height
tmp_array=np.ones((target_output_size,target_output_size), dtype=np.uint8)*20
tmp_array[up_index:bottom_index,:]=array_copy
tmp_tmp_label_array=np.ones((target_output_size,target_output_size), dtype=np.uint8)*20
tmp_tmp_label_array[up_index:bottom_index,:]=tmp_label_array
array_copy=tmp_array
tmp_label_array=tmp_tmp_label_array
array_copy = skimage.color.gray2rgb(array_copy)
zero_slice=np.zeros(tmp_label_array.shape)
zero_slice=zero_slice[:,:,np.newaxis]
tmp_label_array=tmp_label_array[:,:,np.newaxis]
tmp_label_array=np.concatenate((tmp_label_array,tmp_label_array,zero_slice),axis=2)
array_copy[tmp_label_array!=0]=(array_copy[tmp_label_array!=0]*0+tmp_label_array[tmp_label_array!=0]*1).astype(np.uint8)
showImage = QImage(array_copy.copy(), array_copy.shape[1], array_copy.shape[0],
QImage.Format_RGB888) # 转换成QImage类型
return showImage
def flip180(arr):
new_arr = arr.reshape(arr.size)
new_arr = new_arr[::-1]
new_arr = new_arr.reshape(arr.shape)
return new_arr
def flip90_left(arr):
arr = arr.T
arr = arr[::-1]
return arr
def flip90_right(arr):
new_arr = arr.reshape(arr.size)
new_arr = new_arr[::-1]
new_arr = new_arr.reshape(arr.shape)
new_arr = np.transpose(new_arr)[::-1]
return new_arr
def place_on_center(background_array,array):
"""
place the array onto the central part of background array
:param background_array:
:param array:
:return:
"""
bg_height,bg_width=background_array.shape
height,width=array.shape
## 得到2D/3D的医学图像(除.dcm序列图像)
def get_medical_image(path):
import SimpleITK as sitk
'''
加载一幅2D/3D医学图像(除.dcm序列图像)，支持格式：.nii, .nrrd, ...
:param path: 医学图像的路径/SimpleITK.SimpleITK.Image
:return:(array,origin,spacing,direction)
array: 图像数组
origin: 三维图像坐标原点
spacing: 三维图像坐标间距
direction: 三维图像坐标方向
image_type: 图像像素的类型
注意：实际取出的数组不一定与MITK或其他可视化工具中的方向一致！
可能会出现旋转\翻转等现象，这是由于dicom头文件中的origin,spacing,direction的信息导致的
在使用时建议先用matplotlib.pyplot工具查看一下切片的方式是否异常，判断是否需要一定的预处理
'''
if isinstance(path, sitk.Image):
reader = path
else:
assert os.path.exists(path), "{} is not existed".format(path)
assert os.path.isfile(path), "{} is not a file".format(path)
reader = sitk.ReadImage(path)
array = sitk.GetArrayFromImage(reader)
spacing = reader.GetSpacing() ## 间隔
origin = reader.GetOrigin() ## 原点
direction = reader.GetDirection() ## 方向
image_type = reader.GetPixelID() ## 原图像每一个像素的类型，
return array, origin, spacing, direction, image_type