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/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkBinaryImageToStatisticsLabelMapFilter_h
#define itkBinaryImageToStatisticsLabelMapFilter_h
#include "itkStatisticsLabelObject.h"
#include "itkBinaryImageToLabelMapFilter.h"
#include "itkStatisticsLabelMapFilter.h"
namespace itk
{
/**
* \class BinaryImageToStatisticsLabelMapFilter
* \brief a convenient class to convert a binary image to a label map and valuate the statistics attributes at once
*
* \author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.
*
* This implementation was taken from the Insight Journal paper:
* https://www.insight-journal.org/browse/publication/176
*
* \sa StatisticsLabelObject, LabelStatisticsOpeningImageFilter, BinaryStatisticsOpeningImageFilter
* \ingroup ImageEnhancement MathematicalMorphologyImageFilters
* \ingroup ITKLabelMap
*/
template <typename TInputImage,
typename TFeatureImage,
typename TOutputImage = LabelMap<StatisticsLabelObject<SizeValueType, TInputImage::ImageDimension>>>
class ITK_TEMPLATE_EXPORT BinaryImageToStatisticsLabelMapFilter : public ImageToImageFilter<TInputImage, TOutputImage>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(BinaryImageToStatisticsLabelMapFilter);
/** Standard class type aliases. */
using Self = BinaryImageToStatisticsLabelMapFilter;
using Superclass = ImageToImageFilter<TInputImage, TOutputImage>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Some convenient type alias. */
using InputImageType = TInputImage;
using InputImagePointer = typename InputImageType::Pointer;
using InputImageConstPointer = typename InputImageType::ConstPointer;
using InputImageRegionType = typename InputImageType::RegionType;
using InputImagePixelType = typename InputImageType::PixelType;
using OutputImageType = TOutputImage;
using OutputImagePointer = typename OutputImageType::Pointer;
using OutputImageConstPointer = typename OutputImageType::ConstPointer;
using OutputImageRegionType = typename OutputImageType::RegionType;
using OutputImagePixelType = typename OutputImageType::PixelType;
using LabelObjectType = typename OutputImageType::LabelObjectType;
using FeatureImageType = TFeatureImage;
using FeatureImagePointer = typename FeatureImageType::Pointer;
using FeatureImageConstPointer = typename FeatureImageType::ConstPointer;
using FeatureImagePixelType = typename FeatureImageType::PixelType;
/** ImageDimension constants */
static constexpr unsigned int InputImageDimension = TInputImage::ImageDimension;
static constexpr unsigned int OutputImageDimension = TInputImage::ImageDimension;
static constexpr unsigned int ImageDimension = TInputImage::ImageDimension;
using LabelizerType = BinaryImageToLabelMapFilter<InputImageType, OutputImageType>;
using LabelObjectValuatorType = StatisticsLabelMapFilter<OutputImageType, FeatureImageType>;
/** Standard New method. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(BinaryImageToStatisticsLabelMapFilter);
/**
* Set/Get whether the connected components are defined strictly by
* face connectivity or by face+edge+vertex connectivity. Default is
* FullyConnectedOff. For objects that are 1 pixel wide, use
* FullyConnectedOn.
*/
itkSetMacro(FullyConnected, bool);
itkGetConstReferenceMacro(FullyConnected, bool);
itkBooleanMacro(FullyConnected);
#ifdef ITK_USE_CONCEPT_CHECKING
// Begin concept checking
itkConceptMacro(InputEqualityComparableCheck, (Concept::EqualityComparable<InputImagePixelType>));
itkConceptMacro(IntConvertibleToInputCheck, (Concept::Convertible<int, InputImagePixelType>));
itkConceptMacro(InputOStreamWritableCheck, (Concept::OStreamWritable<InputImagePixelType>));
// End concept checking
#endif
/**
* Set/Get the value used as "background" in the output image.
* Defaults to NumericTraits<PixelType>::NonpositiveMin().
*/
itkSetMacro(OutputBackgroundValue, OutputImagePixelType);
itkGetConstMacro(OutputBackgroundValue, OutputImagePixelType);
/**
* Set/Get the value used as "foreground" in the output image.
* Defaults to NumericTraits<PixelType>::max().
*/
itkSetMacro(InputForegroundValue, InputImagePixelType);
itkGetConstMacro(InputForegroundValue, InputImagePixelType);
/**
* Set/Get whether the maximum Feret diameter should be computed or not. The
* default value is false, because of the high computation time required.
*/
itkSetMacro(ComputeFeretDiameter, bool);
itkGetConstReferenceMacro(ComputeFeretDiameter, bool);
itkBooleanMacro(ComputeFeretDiameter);
/**
* Set/Get whether the perimeter should be computed or not. The default value
* is false, because of the high computation time required.
*/
itkSetMacro(ComputePerimeter, bool);
itkGetConstReferenceMacro(ComputePerimeter, bool);
itkBooleanMacro(ComputePerimeter);
/** Set the feature image */
void
SetFeatureImage(const TFeatureImage * input)
{
// Process object is not const-correct so the const casting is required.
this->SetNthInput(1, const_cast<TFeatureImage *>(input));
}
/** Get the feature image */
const FeatureImageType *
GetFeatureImage()
{
return static_cast<const FeatureImageType *>(this->ProcessObject::GetInput(1));
}
/** Set the input image */
void
SetInput1(const InputImageType * input)
{
this->SetInput(input);
}
/** Set the feature image */
void
SetInput2(const FeatureImageType * input)
{
this->SetFeatureImage(input);
}
/**
* Set/Get whether the histogram should be attached to the label object or not.
* This option defaults to `true`, but because the histogram may take a lot of memory
* compared to the other attributes, this option is useful to reduce the memory usage
* when the histogram is not required.
*/
itkSetMacro(ComputeHistogram, bool);
itkGetConstReferenceMacro(ComputeHistogram, bool);
itkBooleanMacro(ComputeHistogram);
/**
* Set/Get the number of bins in the histogram. Note that the histogram is used
* to compute the median value, and that this option may have an effect on the
* value of the median.
*/
itkSetMacro(NumberOfBins, unsigned int);
itkGetConstReferenceMacro(NumberOfBins, unsigned int);
protected:
BinaryImageToStatisticsLabelMapFilter();
~BinaryImageToStatisticsLabelMapFilter() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
/** BinaryImageToStatisticsLabelMapFilter needs the entire input be
* available. Thus, it needs to provide an implementation of
* GenerateInputRequestedRegion(). */
void
GenerateInputRequestedRegion() override;
/** BinaryImageToStatisticsLabelMapFilter will produce the entire output. */
void
EnlargeOutputRequestedRegion(DataObject * itkNotUsed(output)) override;
/** Single-threaded version of GenerateData. This filter delegates
* to GrayscaleGeodesicErodeImageFilter. */
void
GenerateData() override;
private:
bool m_FullyConnected{};
OutputImagePixelType m_OutputBackgroundValue{};
InputImagePixelType m_InputForegroundValue{};
bool m_ComputeFeretDiameter{};
bool m_ComputePerimeter{};
unsigned int m_NumberOfBins{};
bool m_ComputeHistogram{};
}; // end of class
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkBinaryImageToStatisticsLabelMapFilter.hxx"
#endif
#endif
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