def ipl_read(input): ipl_read_settings(input) print "Reading: ", input reader = vtkn88.vtkn88AIMReader() reader.SetFileName(input) reader.GlobalWarningDisplayOff() reader.Update() image_in = reader.GetOutput() return image_in
help="Set offset (default: %(default)s)") parser.add_argument("input") parser.add_argument("output") args = parser.parse_args() input = args.input output = args.output dim_num = args.supdim_numbers test = args.testoff_pixels pos = args.suppos_pixels_local dim_pix = args.subdim_pixels print "Reading: ", input reader = vtkn88.vtkn88AIMReader() reader.SetFileName(input) reader.GlobalWarningDisplayOff() #reader.DataOnCellsOn() settings(dim_num, test, pos, dim_pix) writer = vtkn88.vtkn88AIMWriter() writer.SetInputConnection(reader.GetOutputPort()) writer.SetFileName(output) #writer.SetAimOffset( offset[0], offset[1], offset[2] ) writer.Update() print "Writing: ", output quit()
parser.add_argument("input1") parser.add_argument("input2") args = parser.parse_args() #=========================================================== # Read in and call stats and histogram functions #=========================================================== statistics = args.statistics difference = args.difference input1 = args.input1 input2 = args.input2 print "Reading input1: ", input1 reader1 = vtkn88.vtkn88AIMReader() reader1.SetFileName(input1) reader1.GlobalWarningDisplayOff() #reader1.DataOnCellsOn() reader1.Update() if (statistics): stats(reader1.GetOutput()) histo(reader1.GetOutput()) print "Reading input2: ", input2 reader2 = vtkn88.vtkn88AIMReader() reader2.SetFileName(input2) reader2.GlobalWarningDisplayOff() #reader2.DataOnCellsOn() reader2.Update()
end = "_R_T" end2 = ".png" filename = dirname_write + name + end + end2 T_M = dirname_read + name + "_R_T_THICK_MED_REG.AIM" T_L = dirname_read + name + "_R_T_THICK_LAT_REG.AIM" TC_M = dirname_read + name + "_R_TC_THICK_MED_REG.AIM" TC_L = dirname_read + name + "_R_TC_THICK_LAT_REG.AIM" lut = vtk.vtkLookupTable() lut.SetNumberOfColors(5) lut.SetHueRange(0.0, 0.667) lut.Build() ######## Read in the bone image data aim_T_M = vtkn88.vtkn88AIMReader() aim_T_M.SetFileName(T_M) aim_T_M.DataOnCellsOff() aim_T_M.Update() aim_T_L = vtkn88.vtkn88AIMReader() aim_T_L.SetFileName(T_L) aim_T_L.DataOnCellsOff() aim_T_L.Update() aim_TC_M = vtkn88.vtkn88AIMReader() aim_TC_M.SetFileName(TC_M) aim_TC_M.DataOnCellsOff() aim_TC_M.Update() aim_TC_L = vtkn88.vtkn88AIMReader()
filename_sca_cart = dirname_scaled2 + str( sample_number) + file_num_cart + "_sca.mha" roi_num = globals()['roi%s' % roi_number] filename_roi = dirname_roi2 + str( sample_number) + roi_num + "_R03.aim" filename_roi_out = dirname_scaled2 + str( sample_number) + roi_num + "_roi.mha" filename_points = dirname_scaled2 + str( sample_number) + file_num_bone + "_morph_pts.txt" print filename_original_bone ########################################################################################################## #### Create ROI Binary Image to Select Points if roi_number < 9: #Only need to do this once for cartilage and bone (same ROI applies) #Read in 3D ROI aim = vtkn88.vtkn88AIMReader() aim.SetFileName(filename_roi) aim.DataOnCellsOff() aim.Update() image = aim.GetOutput() # calcuLATe dimensions [x,y,z] _extent = image.GetExtent() ConstPixelDims = [ _extent[1] - _extent[0] + 1, _extent[3] - _extent[2] + 1, _extent[5] - _extent[4] + 1 ] # vtkArray to Numpy array with reshape [x,y,z] np_image = vtk_to_numpy(image.GetPointData().GetArray(0)) np_image = np_image.reshape(ConstPixelDims, order='F')