self.expose("UpperThreshold", "Radius")
self.append(itk.CastImageFilter[DistanceMapType, ImageType].New(self.filters[0].GetVoronoiMap()))
self.connect(itk.MaskImageFilter[ImageType, ImageType, ImageType].New(Input2=self.filters[1]))
# now we can parse the inputs
itk.set_inputs(self, args, kargs)
def check_template_parameters(template_parameters):
ImageType, DistanceMapType = template_parameters
itk.DanielssonDistanceMapImageFilter[ImageType, DistanceMapType]
itk.BinaryThresholdImageFilter[DistanceMapType, ImageType]
itk.CastImageFilter[DistanceMapType, ImageType]
itk.MaskImageFilter[ImageType, ImageType, ImageType]
check_template_parameters = staticmethod(check_template_parameters)
LabelDilateImageFilter = itk.templated_class(LabelDilateImageFilter)
# and use it
dim = 2
IType = itk.Image[itk.US, dim]
OIType = itk.Image[itk.UC, dim]
DIType = itk.Image[itk.F, dim]
reader = itk.ImageFileReader[IType].New( FileName=argv[1] )
dilate = LabelDilateImageFilter[IType, DIType].New(reader, Radius=eval(argv[3]))
cast = itk.CastImageFilter[IType, OIType].New(dilate)
writer = itk.ImageFileWriter[OIType].New( cast, FileName=argv[2] )
writer.Update()
self.append(itk.MaskImageFilter[ImageType, ImageType, ImageType].New(
self.filters[0].GetVoronoiMap(), Input2=self.filters[1]))
# now we can parse the inputs
itk.set_inputs(self, args, kargs)
def check_template_parameters(template_parameters):
ImageType, DistanceMapType = template_parameters
itk.DanielssonDistanceMapImageFilter[ImageType, DistanceMapType]
itk.BinaryThresholdImageFilter[DistanceMapType, ImageType]
itk.CastImageFilter[DistanceMapType, ImageType]
itk.MaskImageFilter[ImageType, ImageType, ImageType]
check_template_parameters = staticmethod(check_template_parameters)
LabelDilateImageFilter = itk.templated_class(LabelDilateImageFilter)
# and use it
dim = 2
IType = itk.Image[itk.UC, dim]
OIType = itk.Image[itk.UC, dim]
DIType = itk.Image[itk.F, dim]
reader = itk.ImageFileReader[IType].New(FileName=argv[1])
val = argv[3]
dilate = LabelDilateImageFilter[IType, DIType].New(reader, Radius=eval(val))
writer = itk.ImageFileWriter[OIType].New(dilate, FileName=argv[2])
writer.Update()
self.connect(itk.CastImageFilter[ImageType, ImageType].New(InPlace=False))
# dilate the objects in the input image
self.connect(itk.BinaryThresholdImageFilter[ImageType, ImageType].New(LowerThreshold=0, UpperThreshold=0, InsideValue=0, OutsideValue=maxValue))
self.connect(itk.BinaryDilateImageFilter[ImageType, ImageType, itk.FlatStructuringElement[dim]].New())
self.expose("Kernel")
self.expose("Radius")
# compute the voronoi map and cast it to a usable image type
self.append(itk.DanielssonDistanceMapImageFilter[ImageType, DistanceMapType].New(self.filters[0], UseImageSpacing=True, SquaredDistance=False))
self.append(itk.CastImageFilter[DistanceMapType, ImageType].New(self.filters[-1].GetVoronoiMap()))
# and mask the voronoi map with the dilated objects
self.connect(itk.MaskImageFilter[ImageType, ImageType, ImageType].New(Input2=self.filters[2]))
# now we can parse the inputs
itk.set_inputs(self, args, kargs)
ClosestLabelDilateImageFilter = itk.templated_class(ClosestLabelDilateImageFilter)
ClosestLabelDilateImageFilter.add_image_templates(itk.INTS, itk.REALS)
class ClosestDilateLabelMapFilter(itk.pipeline):
def __init__(self, *args, **kargs):
# call the constructor of the superclass but without args and kargs, because the attributes
# are not all already there!
# Set/GetRadius() is created in the constructor for example, with the expose() method
itk.pipeline.__init__(self)
# get the template parameters
template_parameters = kargs["template_parameters"]
# and store them in an easier way
ImageType, DistanceMapType = template_parameters