def threshold(operator, step_pct, input_image):
import itk
from tomviz import itkutils
import itkExtras
import itkTypes
otsu_filter = \
itk.OtsuMultipleThresholdsImageFilter.New(Input=input_image)
otsu_filter.SetNumberOfThresholds(1)
operator.progress.message = "Otsu threshold"
progress = operator.progress.value
itkutils.observe_filter_progress(operator, otsu_filter,
progress, progress + next(step_pct))
try:
otsu_filter.Update()
except RuntimeError:
return
# Cast threshold output to an integral type if needed.
input_image_type = type(input_image)
voxel_type = itkExtras.template(input_image_type)[1][0]
if voxel_type is itkTypes.F or voxel_type is itkTypes.D:
operator.progress.message = "Casting output to integral type"
# Unsigned char supports 256 labels, or 255 threshold levels.
# This should be sufficient for all but the most unusual use
# cases.
dimension = input_image_type.GetImageDimension()
py_buffer_type = itk.Image[itk.UC, dimension]
caster = itk.CastImageFilter[input_image_type,
py_buffer_type].New()
caster.SetInput(thresholded)
progress = operator.progress.value
itkutils.observe_filter_progress(operator, caster,
progress, progress + next(step_pct))
try:
caster.Update()
except RuntimeError:
return
thresholded = caster.GetOutput()
thresholded = otsu_filter.GetOutput()
return thresholded
def threshold(operator, step_pct, input_image):
import itk
from tomviz import itkutils
import itkExtras
import itkTypes
otsu_filter = \
itk.OtsuMultipleThresholdsImageFilter.New(Input=input_image)
otsu_filter.SetNumberOfThresholds(1)
operator.progress.message = "Otsu threshold"
progress = operator.progress.value
itkutils.observe_filter_progress(operator, otsu_filter, progress,
progress + next(step_pct))
try:
otsu_filter.Update()
except RuntimeError:
return
# Cast threshold output to an integral type if needed.
input_image_type = type(input_image)
voxel_type = itkExtras.template(input_image_type)[1][0]
if voxel_type is itkTypes.F or voxel_type is itkTypes.D:
operator.progress.message = "Casting output to integral type"
# Unsigned char supports 256 labels, or 255 threshold levels.
# This should be sufficient for all but the most unusual use
# cases.
dimension = input_image_type.GetImageDimension()
py_buffer_type = itk.Image[itk.UC, dimension]
caster = itk.CastImageFilter[input_image_type, py_buffer_type].New()
caster.SetInput(thresholded)
progress = operator.progress.value
itkutils.observe_filter_progress(operator, caster, progress,
progress + next(step_pct))
try:
caster.Update()
except RuntimeError:
return
thresholded = caster.GetOutput()
thresholded = otsu_filter.GetOutput()
return thresholded
def get_python_voxel_type(dataset):
"""Return the Python type that can represent the voxel type in the dataset.
The dataset can be either a VTK dataset or an ITK image.
"""
# Try treating dataset as a VTK data set first.
try:
# Set up map between VTK data type and Python type
global _vtk_to_python_types
if _vtk_to_python_types is None:
import vtk
_vtk_to_python_types = {
vtk.VTK_UNSIGNED_CHAR: int,
vtk.VTK_CHAR: int,
vtk.VTK_UNSIGNED_SHORT: int,
vtk.VTK_SHORT: int,
vtk.VTK_UNSIGNED_INT: int,
vtk.VTK_INT: int,
vtk.VTK_UNSIGNED_LONG: int,
vtk.VTK_LONG: int,
vtk.VTK_FLOAT: float,
vtk.VTK_DOUBLE: float
}
pd = dataset.GetPointData()
scalars = pd.GetScalars()
return _vtk_to_python_types[scalars.GetDataType()]
except AttributeError as attribute_error:
pass
# If the above fails, treat dataset as an ITK image.
try:
# Set up map between ITK ctype and Python type.
global _itkctype_to_python_types
if _itkctype_to_python_types is None:
import itkTypes
_itkctype_to_python_types = {
itkTypes.F: float,
itkTypes.D: float,
itkTypes.LD: float,
itkTypes.UC: int,
itkTypes.US: int,
itkTypes.UI: int,
itkTypes.UL: int,
itkTypes.SC: int,
itkTypes.SS: int,
itkTypes.SI: int,
itkTypes.SL: int,
itkTypes.B: int
}
import itkExtras
# Incantation for obtaining voxel type in ITK image
ctype = itkExtras.template(type(dataset))[1][0]
return _itkctype_to_python_types[ctype]
except AttributeError as attribute_error:
print("Could not get Python voxel type for dataset %s"
% type(dataset))
print(attribute_error)
def transform_scalars(self, dataset, number_of_thresholds=1,
enable_valley_emphasis=False):
"""This filter performs semi-automatic multithresholding of a data set.
Voxels are automatically classified into a chosen number of classes such
that inter-class variance of the voxel values is minimized. The output
is a label map with one label per voxel class.
"""
# Initial progress
self.progress.value = 0
self.progress.maximum = 100
# Approximate percentage of work completed after each step in the
# transform
STEP_PCT = [10, 20, 70, 90, 100]
try:
import itk
import itkExtras
import itkTypes
from vtkmodules.vtkCommonDataModel import vtkImageData
from tomviz import itkutils
from tomviz import utils
except Exception as exc:
print("Could not import necessary module(s)")
raise exc
# Return values
returnValues = None
# Add a try/except around the ITK portion. ITK exceptions are
# passed up to the Python layer, so we can at least report what
# went wrong with the script, e.g,, unsupported image type.
try:
self.progress.value = STEP_PCT[0]
self.progress.message = "Converting data to ITK image"
# Get the ITK image
itk_image = itkutils.convert_vtk_to_itk_image(dataset)
itk_input_image_type = type(itk_image)
# OtsuMultipleThresholdsImageFilter's wrapping requires that the
# input and output image types be the same.
itk_threshold_image_type = itk_input_image_type
# Otsu multiple threshold filter
otsu_filter = itk.OtsuMultipleThresholdsImageFilter[
itk_input_image_type, itk_threshold_image_type].New()
otsu_filter.SetNumberOfThresholds(number_of_thresholds)
otsu_filter.SetValleyEmphasis(enable_valley_emphasis)
otsu_filter.SetInput(itk_image)
itkutils.observe_filter_progress(self, otsu_filter,
STEP_PCT[1], STEP_PCT[2])
try:
otsu_filter.Update()
except RuntimeError:
return
print("Otsu threshold(s): %s" % (otsu_filter.GetThresholds(),))
itk_image_data = otsu_filter.GetOutput()
# Cast threshold output to an integral type if needed.
py_buffer_type = itk_threshold_image_type
voxel_type = itkExtras.template(itk_threshold_image_type)[1][0]
if voxel_type is itkTypes.F or voxel_type is itkTypes.D:
self.progress.message = "Casting output to integral type"
# Unsigned char supports 256 labels, or 255 threshold levels.
# This should be sufficient for all but the most unusual use
# cases.
py_buffer_type = itk.Image.UC3
caster = itk.CastImageFilter[itk_threshold_image_type,
py_buffer_type].New()
caster.SetInput(itk_image_data)
itkutils.observe_filter_progress(self, caster,
STEP_PCT[2], STEP_PCT[3])
try:
caster.Update()
except RuntimeError:
return
itk_image_data = caster.GetOutput()
self.progress.value = STEP_PCT[3]
self.progress.message = "Saving results"
label_buffer = itk.PyBuffer[py_buffer_type] \
.GetArrayFromImage(itk_image_data)
label_map_dataset = vtkImageData()
label_map_dataset.CopyStructure(dataset)
utils.set_array(label_map_dataset, label_buffer, isFortran=False)
self.progress.value = STEP_PCT[4]
# Set up dictionary to return operator results
returnValues = {}
returnValues["label_map"] = label_map_dataset
except Exception as exc:
print("Problem encountered while running %s" %
self.__class__.__name__)
raise exc
return returnValues
def transform_scalars(self, dataset, number_of_thresholds=1,
enable_valley_emphasis=False):
"""This filter performs semi-automatic multithresholding of a data set.
Voxels are automatically classified into a chosen number of classes such
that inter-class variance of the voxel values is minimized. The output
is a label map with one label per voxel class.
"""
# Initial progress
self.progress.value = 0
self.progress.maximum = 100
# Approximate percentage of work completed after each step in the
# transform
STEP_PCT = [10, 20, 70, 90, 100]
try:
import itk
import itkExtras
import itkTypes
import vtk
from tomviz import itkutils
from tomviz import utils
except Exception as exc:
print("Could not import necessary module(s)")
raise exc
# Return values
returnValues = None
# Add a try/except around the ITK portion. ITK exceptions are
# passed up to the Python layer, so we can at least report what
# went wrong with the script, e.g,, unsupported image type.
try:
self.progress.value = STEP_PCT[0]
self.progress.message = "Converting data to ITK image"
# Get the ITK image
itk_image = itkutils.convert_vtk_to_itk_image(dataset)
itk_input_image_type = type(itk_image)
# OtsuMultipleThresholdsImageFilter's wrapping requires that the
# input and output image types be the same.
itk_threshold_image_type = itk_input_image_type
# Otsu multiple threshold filter
otsu_filter = itk.OtsuMultipleThresholdsImageFilter[
itk_input_image_type, itk_threshold_image_type].New()
otsu_filter.SetNumberOfThresholds(number_of_thresholds)
otsu_filter.SetValleyEmphasis(enable_valley_emphasis)
otsu_filter.SetInput(itk_image)
itkutils.observe_filter_progress(self, otsu_filter,
STEP_PCT[1], STEP_PCT[2])
try:
otsu_filter.Update()
except RuntimeError:
return
print("Otsu threshold(s): %s" % (otsu_filter.GetThresholds(),))
itk_image_data = otsu_filter.GetOutput()
# Cast threshold output to an integral type if needed.
py_buffer_type = itk_threshold_image_type
voxel_type = itkExtras.template(itk_threshold_image_type)[1][0]
if voxel_type is itkTypes.F or voxel_type is itkTypes.D:
self.progress.message = "Casting output to integral type"
# Unsigned char supports 256 labels, or 255 threshold levels.
# This should be sufficient for all but the most unusual use
# cases.
py_buffer_type = itk.Image.UC3
caster = itk.CastImageFilter[itk_threshold_image_type,
py_buffer_type].New()
caster.SetInput(itk_image_data)
itkutils.observe_filter_progress(self, caster,
STEP_PCT[2], STEP_PCT[3])
try:
caster.Update()
except RuntimeError:
return
itk_image_data = caster.GetOutput()
self.progress.value = STEP_PCT[3]
self.progress.message = "Saving results"
label_buffer = itk.PyBuffer[py_buffer_type] \
.GetArrayFromImage(itk_image_data)
label_map_dataset = vtk.vtkImageData()
label_map_dataset.CopyStructure(dataset)
utils.set_array(label_map_dataset, label_buffer)
self.progress.value = STEP_PCT[4]
# Set up dictionary to return operator results
returnValues = {}
returnValues["label_map"] = label_map_dataset
except Exception as exc:
print("Problem encountered while running %s" %
self.__class__.__name__)
raise exc
return returnValues
