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')
