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itkScalarImageKmeansImageFilter.h
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itkScalarImageKmeansImageFilter.h
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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 itkScalarImageKmeansImageFilter_h
#define itkScalarImageKmeansImageFilter_h
#include "itkKdTree.h"
#include "itkKdTreeBasedKmeansEstimator.h"
#include "itkWeightedCentroidKdTreeGenerator.h"
#include "itkSampleClassifierFilter.h"
#include "itkImageToListSampleAdaptor.h"
#include "itkMinimumDecisionRule.h"
#include "itkRegionOfInterestImageFilter.h"
#include <vector>
namespace itk
{
/**
* \class ScalarImageKmeansImageFilter
* \brief Classifies the intensity values of a scalar image using the K-Means algorithm.
*
* Given an input image with scalar values, it uses the K-Means statistical
* classifier in order to define labels for every pixel in the image. The
* filter is templated over the type of the input image. The output image is
* predefined as having the same dimension of the input image and pixel type
* unsigned char, under the assumption that the classifier will generate less
* than 256 classes.
*
* You may want to look also at the RelabelImageFilter that may be used as a
* postprocessing stage, in particular if you are interested in ordering the
* labels by their relative size in number of pixels.
*
* \sa Image
* \sa ImageKmeansModelEstimator
* \sa KdTreeBasedKmeansEstimator, WeightedCentroidKdTreeGenerator, KdTree
* \sa RelabelImageFilter
*
* \ingroup ClassificationFilters
* \ingroup ITKClassifiers
*
* \sphinx
* \sphinxexample{Segmentation/Classifiers/ClusterPixelsInGrayscaleImage,Cluster Pixels In Grayscale Image}
* \sphinxexample{Segmentation/Classifiers/KMeansClustering,K-Means Clustering}
* \endsphinx
*/
template <typename TInputImage, typename TOutputImage = Image<unsigned char, TInputImage::ImageDimension>>
class ITK_TEMPLATE_EXPORT ScalarImageKmeansImageFilter : public ImageToImageFilter<TInputImage, TOutputImage>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(ScalarImageKmeansImageFilter);
/** Extract dimension from input and output image. */
static constexpr unsigned int ImageDimension = TInputImage::ImageDimension;
/** Convenient type alias for simplifying declarations. */
using InputImageType = TInputImage;
using OutputImageType = TOutputImage;
/** Standard class type aliases. */
using Self = ScalarImageKmeansImageFilter;
using Superclass = ImageToImageFilter<InputImageType, OutputImageType>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(ScalarImageKmeansImageFilter);
/** Image type alias support */
using InputPixelType = typename InputImageType::PixelType;
using OutputPixelType = typename OutputImageType::PixelType;
/** Type used for representing the Mean values. */
using RealPixelType = typename NumericTraits<InputPixelType>::RealType;
/** Create a List from the scalar image. */
using AdaptorType = itk::Statistics::ImageToListSampleAdaptor<InputImageType>;
/** Define the Measurement vector type from the AdaptorType. */
using MeasurementVectorType = typename AdaptorType::MeasurementVectorType;
using MembershipFunctionType = itk::Statistics::DistanceToCentroidMembershipFunction<MeasurementVectorType>;
using ClassifierType = itk::Statistics::SampleClassifierFilter<AdaptorType>;
using DecisionRuleType = itk::Statistics::MinimumDecisionRule;
using ClassLabelVectorType = typename ClassifierType::ClassLabelVectorType;
using MembershipFunctionVectorType = typename ClassifierType::MembershipFunctionVectorType;
using MembershipFunctionOriginType = typename MembershipFunctionType::CentroidType;
using MembershipFunctionPointer = typename MembershipFunctionType::Pointer;
/** Create the K-d tree structure. */
using TreeGeneratorType = itk::Statistics::WeightedCentroidKdTreeGenerator<AdaptorType>;
using TreeType = typename TreeGeneratorType::KdTreeType;
using EstimatorType = itk::Statistics::KdTreeBasedKmeansEstimator<TreeType>;
using ParametersType = typename EstimatorType::ParametersType;
using ImageRegionType = typename InputImageType::RegionType;
using RegionOfInterestFilterType = RegionOfInterestImageFilter<InputImageType, InputImageType>;
/** Add a new class to the classification by specifying its initial mean. */
void
AddClassWithInitialMean(RealPixelType mean);
/** Return the array of Means found after the classification. */
itkGetConstReferenceMacro(FinalMeans, ParametersType);
/** Set/Get the UseNonContiguousLabels flag. When this is set to false the
* labels are numbered contiguously, like in {0,1,3..N}. When the flag is set
* to true, the labels are selected in order to span the dynamic range of the
* output image. This last option is useful when the output image is intended
* only for display. The default value is false. */
itkSetMacro(UseNonContiguousLabels, bool);
itkGetConstReferenceMacro(UseNonContiguousLabels, bool);
itkBooleanMacro(UseNonContiguousLabels);
/** Set Region method to constrain classification to a certain region */
void
SetImageRegion(const ImageRegionType & region);
/** Get the region over which the statistics will be computed */
itkGetConstReferenceMacro(ImageRegion, ImageRegionType);
#ifdef ITK_USE_CONCEPT_CHECKING
// Begin concept checking
itkConceptMacro(InputHasNumericTraitsCheck, (Concept::HasNumericTraits<InputPixelType>));
// End concept checking
#endif
protected:
ScalarImageKmeansImageFilter();
~ScalarImageKmeansImageFilter() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
/** This method runs the statistical methods that identify the means of the
* classes and the use the distances to those means in order to label the
* image pixels.
* \sa ImageToImageFilter::GenerateData()
*/
void
GenerateData() override;
/* See superclass for doxygen. This methods additionally checks that
* the number of means is not 0. */
void
VerifyPreconditions() const override;
private:
using MeansContainer = std::vector<RealPixelType>;
MeansContainer m_InitialMeans{};
ParametersType m_FinalMeans{};
bool m_UseNonContiguousLabels{ false };
ImageRegionType m_ImageRegion{};
bool m_ImageRegionDefined{ false };
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkScalarImageKmeansImageFilter.hxx"
#endif
#endif