text: /*=========================================================================
   *
   *  Copyright Insight Software Consortium
   *
   *  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
   *
   *         http://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 itkBayesianClassifierInitializationImageFilter_h
  #define itkBayesianClassifierInitializationImageFilter_h
  
  #include "itkVectorImage.h"
  #include "itkVectorContainer.h"
  #include "itkImageToImageFilter.h"
  #include "itkImageRegionIterator.h"
  #include "itkMembershipFunctionBase.h"
  
  namespace itk
  {
  /** \class BayesianClassifierInitializationImageFilter
   *
   * \brief This filter is intended to be used as a helper class to
   * initialize the BayesianClassifierImageFilter.
   *
   * \par
   * The goal of this filter is to generate a membership image that indicates
   * the membership of each pixel to each class. These membership images are fed
   * as input to the Bayesian classifier filter.
   *
   * \par Parameters
   * Number of classes: This defines the number of classes, which will determine
   * the number of membership images that will be generated. The user must specify
   * this.
   *
   * \par
   * Membership functions: The user can optionally plugin in any membership function.
   * The number of membership functions plugged in should be the
   * same as the number of classes. If the user does not supply membership
   * functions, the filter will generate membership functions for you. These
   * functions are Gaussian density functions centered around 'n' pixel intensity
   * values, \f$ I_k \f$. These 'n' values are obtained by running K-means on the
   * image. In other words, the default behaviour of the filter is to generate
   * Gaussian mixture model for the input image.
   *
   * \par Inputs and Outputs
   * The filter takes a scalar Image as input and generates a VectorImage, each
   * component \f$ c \f$ of which represents memberships of each pixel to the
   * class \f$ c \f$.
   *
   * \par Template parameters
   * This filter is templated over the input image type and the data type used
   * to represent the probabilities (defaults to float).
   *
   * \author John Melonakos, Georgia Tech
   *
   * \note
   * This work is part of the National Alliance for Medical Image Computing
   * (NAMIC), funded by the National Institutes of Health through the NIH Roadmap
   * for Medical Research, Grant U54 EB005149.
   *
   * \sa BayesianClassifierImageFilter
   * \sa VectorImage
   * \ingroup ClassificationFilters
   * \ingroup ITKClassifiers
   */
  template <typename TInputImage, typename TProbabilityPrecisionType = float>
  class ITK_TEMPLATE_EXPORT BayesianClassifierInitializationImageFilter
    : public ImageToImageFilter<TInputImage, VectorImage<TProbabilityPrecisionType, TInputImage::ImageDimension>>
  {
  public:
    ITK_DISALLOW_COPY_AND_ASSIGN(BayesianClassifierInitializationImageFilter);
  
    /** Standard class type aliases. */
    using Self = BayesianClassifierInitializationImageFilter;
    using InputImageType = TInputImage;
    using ProbabilityPrecisionType = TProbabilityPrecisionType;
  
    /** Dimension of the input image */
    static constexpr unsigned int Dimension = InputImageType ::ImageDimension;
  
    using OutputImageType = VectorImage<ProbabilityPrecisionType, Self::Dimension>;
    using Superclass = ImageToImageFilter<InputImageType, OutputImageType>;
    using Pointer = SmartPointer<Self>;
    using ConstPointer = SmartPointer<const Self>;
  
    /** Method for creation through the object factory. */
    itkNewMacro(Self);
  
    /** Run-time type information (and related methods). */
    itkTypeMacro(BayesianClassifierInitializationImageFilter, ImageToImageFilter);
  
    /** Input image iterators */
    using InputImageIteratorType = ImageRegionConstIterator<InputImageType>;
  
    /** Pixel types. */
    using InputPixelType = typename InputImageType::PixelType;
    using OutputPixelType = typename OutputImageType::PixelType;
  
    /** Image Type and Pixel type for the images representing the membership of a
     *  pixel to a particular class. This image has arrays as pixels, the number of
     *  elements in the array is the same as the number of classes to be used.    */
    using MembershipImageType = VectorImage<ProbabilityPrecisionType, Self::Dimension>;
    using MembershipPixelType = typename MembershipImageType::PixelType;
    using MembershipImagePointer = typename MembershipImageType::Pointer;
    using MembershipImageIteratorType = ImageRegionIterator<MembershipImageType>;
  
    /** Type of the Measurement */
    using MeasurementVectorType = Vector<InputPixelType, 1>;
  
    /** Type of the density functions */
    using MembershipFunctionType = Statistics::MembershipFunctionBase<MeasurementVectorType>;
    using MembershipFunctionPointer = typename MembershipFunctionType::Pointer;
  
    /** Membership function container */
    using MembershipFunctionContainerType = VectorContainer<unsigned int, MembershipFunctionPointer>;
    using MembershipFunctionContainerPointer = typename MembershipFunctionContainerType::Pointer;
  
    /** Method to set/get the density functions. Here you can set a vector
     * container of density functions. If no density functions are specified,
     * the filter will create ones for you. These default density functions
     * are Gaussian density functions centered around the K-means of the
     * input image.  */
    virtual void
    SetMembershipFunctions(MembershipFunctionContainerType * densityFunctionContainer);
    itkGetModifiableObjectMacro(MembershipFunctionContainer, MembershipFunctionContainerType);
  
    /** Set/Get methods for the number of classes. The user must supply this. */
    itkSetMacro(NumberOfClasses, unsigned int);
    itkGetConstMacro(NumberOfClasses, unsigned int);
  
    void
    GenerateOutputInformation() override;
  
  #ifdef ITK_USE_CONCEPT_CHECKING
    // Begin concept checking
    itkConceptMacro(InputMultiplyOperatorCheck, (Concept::MultiplyOperator<InputPixelType>));
    itkConceptMacro(DoubleConvertibleToProbabilityCheck, (Concept::Convertible<double, TProbabilityPrecisionType>));
    itkConceptMacro(InputHasNumericTraitsCheck, (Concept::HasNumericTraits<InputPixelType>));
    itkConceptMacro(ProbabilityHasNumericTraitsCheck, (Concept::HasNumericTraits<TProbabilityPrecisionType>));
    itkConceptMacro(DoublePlusInputCheck, (Concept::AdditiveOperators<double, InputPixelType>));
    // End concept checking
  #endif
  
  protected:
    BayesianClassifierInitializationImageFilter();
    ~BayesianClassifierInitializationImageFilter() override = default;
    void
    PrintSelf(std::ostream & os, Indent indent) const override;
  
    /** Initialize the membership functions. This will be called only if the membership
     * function hasn't already been set. This method initializes membership functions
     * using Gaussian density functions centered around the means computed using
     * Kmeans.
     */
    virtual void
    InitializeMembershipFunctions();
  
    /** Here is where the prior and membership probability vector images are
      created.*/
    void
    GenerateData() override;
  
  private:
    bool         m_UserSuppliesMembershipFunctions{ false };
    unsigned int m_NumberOfClasses{ 0 };
  
    typename MembershipFunctionContainerType::Pointer m_MembershipFunctionContainer;
  };
  } // end namespace itk
  
  #ifndef ITK_MANUAL_INSTANTIATION
  #  include "itkBayesianClassifierInitializationImageFilter.hxx"
  #endif
  
  #endif
  