def test_image_data(self):
"""Test if the image data info can be parsed."""
data = {
'position':
mv(DSfloat, ['-275', '-524', '168.5593']),
'orientation':
mv(DSfloat, ['1', '0.0', '-1.224647e-16', '0.0', '1', '0.0']),
'pixelspacing':
mv(DSfloat, ['1.074219', '1.074219']),
'rows':
512,
'columns':
512,
'samplesperpixel':
1,
'photometricinterpretation':
'MONOCHROME2',
'littlendian':
True,
'patientposition':
'HFS',
'frames':
1
}
self.assertEqual(self.dp.GetImageData(), data)
def test_dose_data(self):
"""Test if the dose data can be parsed."""
data = {
'position': mv(DSfloat,
['-228.6541915', '-419.2444776', '-122.4407']),
'orientation': mv(DSfloat, ['1', '0.0', '0.0', '0.0', '1', '0.0']),
'pixelspacing': mv(DSfloat, [2.5, 2.5]),
'rows': 129,
'columns': 194,
'samplesperpixel': 1,
'photometricinterpretation': 'MONOCHROME2',
'littlendian': True,
'frames': 98,
'doseunits': 'GY',
'dosetype': 'PHYSICAL',
'dosecomment': '',
'dosesummationtype': 'PLAN',
'dosegridscaling': 1.4e-05,
'dosemax': 1048626.0,
'lut': 253.8458085,
'fraction': ''
}
dosedata = self.dp.GetDoseData()
# Pop the LUT numpy array
assert_array_almost_equal(dosedata.pop('lut')[0][-1], data.pop('lut'))
self.assertEqual(dosedata, data)
def test_dose_data(self):
"""Test if the dose data can be parsed."""
data = {
'position': mv(
DSfloat, ['-228.6541915', '-419.2444776', '-122.4407']),
'orientation':
mv(DSfloat, ['1', '0.0', '0.0', '0.0', '1', '0.0']),
'pixelspacing': mv(DSfloat, [2.5, 2.5]),
'rows': 129,
'columns': 194,
'samplesperpixel': 1,
'photometricinterpretation': 'MONOCHROME2',
'littlendian': True,
'frames': 98,
'doseunits': 'GY',
'dosetype': 'PHYSICAL',
'dosecomment': '',
'dosesummationtype': 'PLAN',
'dosegridscaling': 1.4e-05,
'dosemax': 1048626.0,
'lut': 253.8458085,
'fraction': ''
}
dosedata = self.dp.GetDoseData()
# Pop the LUT numpy array
assert_array_almost_equal(
dosedata.pop('lut')[0][-1], data.pop('lut'))
self.assertEqual(dosedata, data)
def test_image_data(self):
"""Test if the image data info can be parsed."""
data = {
'position': mv(DSfloat, ['-275', '-524', '168.5593']),
'orientation':
mv(DSfloat, ['1', '0.0', '-1.224647e-16', '0.0', '1', '0.0']),
'pixelspacing': mv(DSfloat, ['1.074219', '1.074219']),
'rows': 512,
'columns': 512,
'samplesperpixel': 1,
'photometricinterpretation': 'MONOCHROME2',
'littlendian': True,
'patientposition': 'HFS',
'frames': 1
}
self.assertEqual(self.dp.GetImageData(), data)
def getData(root_Path, doc, pat):
labels = ['doctor', 'patient', 'position', 'imgName', 'contour']
dataList = []
pos_list = getDir(root_Path + '/' + doc + '/' + pat)
for pos in pos_list: #对部位循环
img_list = os.listdir(root_Path + '/' + doc + '/' + pat + '/' + pos)
for img in img_list: #对图片循环
img_name = img.split('_')[0]
img_path = root_Path + '/' + doc + '/' + pat + '/' + pos + '/' + img
patPath = root_Path + '/' + doc + '/' + pat
slicepoint = convert_to_dcm(img_path, img_name, patPath)
contourData = slicepoint.split(',')
point = mv(DSfloat, contourData)
sliceList = []
sliceList.append(doc)
sliceList.append(pat)
sliceList.append(pos)
sliceList.append(img_name) #包含了同一切片的所有连通区域
sliceList.append(point)
dataList.append(sliceList)
return dataList
