-
Notifications
You must be signed in to change notification settings - Fork 0
/
tomograf.py
367 lines (271 loc) · 10.9 KB
/
tomograf.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
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
from PyQt5.QtWidgets import *
from PyQt5.QtGui import QPixmap, QImage
from PyQt5.QtCore import QDate
from PyQt5 import uic
import cv2 as cv
import matplotlib.pyplot as plt
from skimage.transform import radon
import numpy as np
from pydicom.dataset import Dataset, FileDataset
from pydicom.uid import ExplicitVRLittleEndian
import pydicom._storage_sopclass_uids
from skimage.util import img_as_ubyte
from skimage.exposure import rescale_intensity
def BresenhamLine(x1,y1,x2,y2):
d, dx, dy, ai, bi, xi, yi = 0, 0, 0, 0, 0, 0, 0
x=x1
y=y1
vertexes_x=[]
vertexes_y=[]
if(x1 < x2):
xi = 1
dx = x2 - x1
else:
xi = -1
dx = x1 - x2
if (y1 < y2):
yi = 1
dy = y2 - y1
else:
yi = -1
dy = y1 - y2
vertexes_x.append(x)
vertexes_y.append(y)
if (dx > dy):
ai = (dy - dx) * 2
bi = dy * 2
d = bi - dx
while (x != x2):
if (d >= 0):
x += xi
y += yi
d += ai
else:
d += bi
x += xi
vertexes_x.append(x)
vertexes_y.append(y)
else:
ai = ( dx - dy ) * 2
bi = dx * 2
d = bi - dy
while (y != y2):
if (d >= 0):
x += xi
y += yi
d += ai
else:
d += bi
y += yi
vertexes_x.append(x)
vertexes_y.append(y)
return vertexes_x,vertexes_y
class MainWindow(QMainWindow):
def __init__(self):
super().__init__()
self.img = cv.imread('C:\\main\\Medycyna\\tomograf\\zdjecia\\CT_ScoutView-large.jpg', cv.IMREAD_GRAYSCALE)
uic.loadUi('tomograf.ui', self)
self.setWindowTitle('Tomograf')
self.loadButton.clicked.connect(self.read_image)
self.startButton.clicked.connect(self.start)
self.slider = self.findChild(QSlider, 'Slider')
self.slider.valueChanged.connect(self.slide_it)
self.slider.setVisible(False)
self.delta_alphaEdit.setText('2')
self.n_Edit.setText('100')
self.l_edit.setText('100')
self.loadButton_2.clicked.connect(self.loadfile)
self.saveButton.clicked.connect(self.savefile)
self.show()
def read_image(self):
empty_pixmap = QPixmap()
self.slider.setVisible(False)
self.sin_label.setPixmap(empty_pixmap)
self.output_label.setPixmap(empty_pixmap)
filename, _ = QFileDialog.getOpenFileName(None, "Select Image", "", "Image Files (*.jpg)")
self.img = cv.imread(filename, cv.IMREAD_GRAYSCALE)
pixmap = QPixmap(filename)
pixmap = pixmap.scaled(300, 300)
self.image_label.setPixmap(pixmap)
self.img = addPadding(self.img)
def start(self):
empty_pixmap = QPixmap()
self.slider.setVisible(False)
self.sin_label.setPixmap(empty_pixmap)
self.output_label.setPixmap(empty_pixmap)
delta_alpha = self.delta_alphaEdit.text()
num = self.n_Edit.text()
l = self.l_edit.text()
self.slider.setMaximum(360//int(delta_alpha))
sinogram = radon(self.img, int(delta_alpha), int(num), int(l))
plt.imsave('temp\\sinogram.jpg', sinogram,cmap='gray')
file = 'temp\\sinogram.jpg'
# Create a QPixmap object from the QImage
pixmap = QPixmap(file)
pixmap = pixmap.scaled(300, 300)
self.sin_label.setPixmap(pixmap)
print(delta_alpha, num, l)
if self.filtrBox.isChecked():
self.output = i_radon(self.img, int(delta_alpha), int(num), int(l), True)
else:
self.output = i_radon(self.img, int(delta_alpha), int(num), int(l), False)
plt.imsave('temp\\output.jpg', self.output[(360//int(delta_alpha)-1)],cmap='gray')
file = 'temp\\output.jpg'
pixmap = QPixmap(file)
pixmap = pixmap.scaled(300, 300)
self.output_label.setPixmap(pixmap)
self.slider.setVisible(True)
return self.output
def slide_it(self, value):
print(value)
plt.imsave('temp\\output.jpg', self.output[value-1],cmap='gray')
file = 'temp\\output.jpg'
pixmap = QPixmap(file)
pixmap = pixmap.scaled(300, 300)
self.output_label.setPixmap(pixmap)
def loadfile(self):
filename, _ = QFileDialog.getOpenFileName(None, "Select DICOM", "", "DICOM file (*.dcm)")
dicom_file = pydicom.dcmread(filename)
id = dicom_file.PatientID
name = dicom_file.PatientName
comm = dicom_file.ImageComments
try:
date = dicom_file.Date
self.calendar.setSelectedDate(QDate.fromString(date, 'yyyyMMdd'))
except:
print("nodate")
self.PatientIDEdit.setText(str(id))
self.PatientNameEdit.setText(str(name))
self.ImageCommentsEdit.setText(str(comm))
image = dicom_file.pixel_array
plt.imsave('temp\\DICOM_img.jpg',image ,cmap='gray')
pixmap = QPixmap('temp\\DICOM_img.jpg')
pixmap = pixmap.scaled(300, 300)
self.loadedImage.setPixmap(pixmap)
def savefile(self):
filename, _ = QFileDialog.getOpenFileName(None, "Select Image", "", "Image Files (*.jpg)")
img = cv.imread(filename, cv.IMREAD_GRAYSCALE)
img_converted = convert_image_to_ubyte(img)
id = self.PatientIDEdit.text()
name = self.PatientNameEdit.text()
comm = self.ImageCommentsEdit.toPlainText()
date = self.calendar.selectedDate().toString("yyyyMMdd")
meta = Dataset()
meta.MediaStorageSOPClassUID = pydicom._storage_sopclass_uids.CTImageStorage
meta.MediaStorageSOPInstanceUID = pydicom.uid.generate_uid()
meta.TransferSyntaxUID = pydicom.uid.ExplicitVRLittleEndian
ds = FileDataset(str(id)+'.dcm', {}, preamble=b"\0" * 128)
ds.file_meta = meta
ds.is_little_endian = True
ds.is_implicit_VR = False
ds.SOPClassUID = pydicom._storage_sopclass_uids.CTImageStorage
ds.SOPInstanceUID = meta.MediaStorageSOPInstanceUID
ds.PatientName = str(id)
ds.PatientID = str(name)
ds.ImageComments = str(comm)
ds.Date = str(date)
ds.Modality = "CT"
ds.SeriesInstanceUID = pydicom.uid.generate_uid()
ds.StudyInstanceUID = pydicom.uid.generate_uid()
ds.FrameOfReferenceUID = pydicom.uid.generate_uid()
ds.BitsStored = 8
ds.BitsAllocated = 8
ds.SamplesPerPixel = 1
ds.HighBit = 7
ds.ImagesInAcquisition = 1
ds.InstanceNumber = 1
ds.Rows, ds.Columns = img_converted.shape
ds.ImageType = r"ORIGINAL\PRIMARY\AXIAL"
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
pydicom.dataset.validate_file_meta(ds.file_meta, enforce_standard=True)
ds.PixelData = img_converted.tobytes()
ds.save_as('DICOM\\'+str(id)+'.dcm', write_like_original=False)
def kernel_gen(size):
kernel = []
for i in range(size):
if i == size // 2:
kernel.append(1)
else:
if abs(i - size / 2) % 2 == 0:
kernel.append(0)
else:
kernel.append(-4 / np.pi ** 2 * (1 / (abs(i - size // 2) ** 2)))
return kernel
def addPadding(img):
result = np.zeros([max(img.shape), max(img.shape)]) if max(img.shape) % 2 == 1 else np.zeros(
[max(img.shape) + 1, max(img.shape) + 1])
result[:img.shape[0], :img.shape[1]] = img
return result
def convert_image_to_ubyte(img):
return img_as_ubyte(rescale_intensity(img, out_range=(0.0, 1.0)))
def radon(img, delta_alpha, num, l):
size = min(img.shape)
sinogram = []
radius = size // 2
theta = np.arange(0, 360, delta_alpha)
offsets=np.linspace(-l/2,l/2,num)
for i in theta:
xe = radius * np.cos(np.deg2rad(i)) + radius
ye = radius * np.sin(np.deg2rad(i)) + radius
lines=[]
for n in range(num):
xD = radius * np.cos(np.deg2rad(i + offsets[n] + 180) ) + radius
yD = radius * np.sin(np.deg2rad(i+ offsets[n] + 180) ) + radius
lines.append(BresenhamLine(int(xe), int(ye), int(xD), int(yD)))
result=[]
for line in lines:
res=0
for i in range(len(line[0])):
if(line[0][i]<size and line[1][i]<size):
res+=img[line[0][i],line[1][i]]
result.append(res/len(line[0]))
sinogram.append(result)
return sinogram
def i_radon(img, delta_alpha, num, l, filtr):
size = min(img.shape)
partial_output=[]
radius = size // 2
theta = np.arange(0, 360, delta_alpha)
offsets=np.linspace(-l/2,l/2,num)
kernel = kernel_gen(num//2)
for i in theta:
xe = radius * np.cos(np.deg2rad(i)) + radius
ye = radius * np.sin(np.deg2rad(i)) + radius
lines = []
for n in range(num):
xD = radius * np.cos(np.deg2rad(i + offsets[n] + 180) ) + radius
yD = radius * np.sin(np.deg2rad(i+ offsets[n] + 180) ) + radius
lines.append(BresenhamLine(int(xe), int(ye), int(xD), int(yD)))
result=[]
for line in lines:
res=0
for i in range(len(line[0])):
if(line[0][i]<size and line[1][i]<size):
res+=img[line[0][i],line[1][i]]
result.append(res/len(line[0]))
if filtr:
result = np.convolve(result, kernel, "same")
output=np.zeros((size, size))
for i in range(len(lines)):
for j in range(len(lines[i][0])):
x=lines[i][0][j]
y=lines[i][1][j]
if(x<size and y<size):
output[x][y]+=result[i]
output/=len(lines)
partial_output.append(output)
partial_avg_output=[np.zeros((size, size)) for i in range(len(theta))]
for i in range(size):
for j in range(size):
suma=0
for iteration in range(0,len(theta)):
suma+=partial_output[iteration][i][j]
partial_avg_output[iteration][i][j]=suma/(iteration+1)
for i in range(len(partial_avg_output)):
cv.normalize(partial_avg_output[i], partial_avg_output[i], alpha=0, beta=1, norm_type=cv.NORM_MINMAX)
return partial_avg_output
app = QApplication([])
window = MainWindow()
window.show()
app.exec_()