def fileopen():
filename = filedialog.askopenfilename(
filetypes=[('numpy files',
'*.npy'), ('mhd files',
'*.mhd'), ('dicom files',
'*.dcm'), ('all', '*')])
#filename = "E:/tianchi_project/TIANCHI_examples/train_1/LKDS-00001.mhd"
if filename == '':
return
print(repr(filename))
prename, extname = os.path.splitext(filename)
if extname == '.mhd':
full_image_info = sitk.ReadImage(filename)
full_scan = sitk.GetArrayFromImage(full_image_info)
old_spacing = np.array(full_image_info.GetSpacing())[::-1]
volimg, new_spacing = mt.resample(full_scan, old_spacing)
elif extname == '.dcm':
pathname = os.path.split(filename)[0]
full_scan, full_image_info, patientid = mt.read_sph_scan(pathname)
#cvm.view_CT(full_scan)
old_spacing = np.array(full_image_info.GetSpacing())[::-1]
volimg, new_spacing = mt.resample(full_scan, old_spacing)
elif extname == '.npy':
volimg = np.load(filename)
else:
print('unknown data type')
return
label = tk.Label(tool_view, image=cvm.view_CT(volimg))
label.pack()
if 'full_scan' in dir():
del full_scan
del volimg
tool_view.quit()
#ktb.set_session(mt.get_session(0.5))
start_time = time.time()
#patient_evaluations = open(evaluation_path + "/patient_evaluations.log", "w")
results = []
test_patients = all_patients
#test_patients = ["./LUNA16/subset9/1.3.6.1.4.1.14519.5.2.1.6279.6001.195557219224169985110295082004.mhd"]
for p in range(len(test_patients)):
result = []
patient = test_patients[p]
#patient = "./LUNA16/subset9/1.3.6.1.4.1.14519.5.2.1.6279.6001.300270516469599170290456821227.mhd"
#patient = "./LUNA16/subset9/1.3.6.1.4.1.14519.5.2.1.6279.6001.212608679077007918190529579976.mhd"
#patient = "./LUNA16/subset9/1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408431413578140925249.mhd"
#patient = "./TIANCHI_examples/LKDS-00005.mhd"
#uid = mt.get_sample_uid(patient)
#annotations = mt.get_luna_annotations(uid, annotation_file)
full_scan, full_image_info, uid = mt.read_sph_scan(patient)
origin = np.array(full_image_info.GetOrigin())[::-1]
old_spacing = np.array(full_image_info.GetSpacing())[::-1]
annotations = mt.get_sph_annotations(uid, annotation_file)
if len(annotations) == 0:
print('%d/%d patient %s has no annotations, ignore it.' %
(p + 1, len(test_patients), uid))
#patient_evaluations.write('%d/%d patient %s has no annotations, ignore it\n' %(p+1, len(test_patients), uid))
continue
print('%d/%d processing patient:%s' % (p + 1, len(test_patients), uid))
#full_image_info = sitk.ReadImage(patient)
#full_scan = sitk.GetArrayFromImage(full_image_info)
#origin = np.array(full_image_info.GetOrigin())[::-1] #the order of origin and old_spacing is initially [z,y,x]
#old_spacing = np.array(full_image_info.GetSpacing())[::-1]
image, new_spacing = mt.resample(full_scan, old_spacing) #resample
