/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: itkBayesianClassifierInitializationImageFilter.h,v $
  Language:  C++
  Date:      $Date: 2006/03/31 14:31:05 $
  Version:   $Revision: 1.5 $

  Copyright (c) Insight Software Consortium. All rights reserved.
  See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.

     This software is distributed WITHOUT ANY WARRANTY; without even 
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/
#ifndef __itkBayesianClassifierInitializationImageFilter_h
#define __itkBayesianClassifierInitializationImageFilter_h

#include "itkVectorImage.h"
#include "itkVectorContainer.h"
#include "itkImageToImageFilter.h"
#include "itkImageRegionIterator.h"
#include "itkImageRegionConstIterator.h"
#include "itkDensityFunction.h"

namespace itk
{
  
/** \class BayesianClassifierInitializationImageFilter
 *
 * \brief This filter is intended to be used as a helper class to 
 * initialize the BayesianClassifierImageFilter. 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 classfier 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 
 */
template< class TInputImage, class TProbabilityPrecisionType=float >
class ITK_EXPORT BayesianClassifierInitializationImageFilter :
    public
ImageToImageFilter<TInputImage, VectorImage< TProbabilityPrecisionType, 
                  ::itk::GetImageDimension< TInputImage >::ImageDimension > >
{
public:
  /** Standard class typedefs. */
  typedef BayesianClassifierInitializationImageFilter  Self;
  typedef TInputImage                                  InputImageType;
  typedef TProbabilityPrecisionType                    ProbabilityPrecisionType;
  
  /** Dimension of the input image */
  itkStaticConstMacro( Dimension, unsigned int, 
                     ::itk::GetImageDimension< InputImageType >::ImageDimension );

  typedef VectorImage< ProbabilityPrecisionType, 
          itkGetStaticConstMacro(Dimension) >      OutputImageType;
  typedef ImageToImageFilter< InputImageType, OutputImageType >  Superclass;
  typedef SmartPointer<Self>        Pointer;
  typedef SmartPointer<const Self>  ConstPointer;

  /** Method for creation through the object factory. */
  itkNewMacro(Self);

  /** Run-time type information (and related methods). */
  itkTypeMacro(BayesianClassifierInitializationImageFilter, ImageToImageFilter);

  /** Input image iterators */
  typedef ImageRegionConstIterator< InputImageType > InputImageIteratorType;

  /** Pixel types. */
  typedef typename InputImageType::PixelType  InputPixelType;
  typedef typename OutputImageType::PixelType OutputPixelType;

  /** 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.    */
  typedef VectorImage< ProbabilityPrecisionType, 
          itkGetStaticConstMacro(Dimension) >             MembershipImageType;
  typedef typename MembershipImageType::PixelType         MembershipPixelType;
  typedef typename MembershipImageType::Pointer           MembershipImagePointer;
  typedef ImageRegionIterator< MembershipImageType >      MembershipImageIteratorType;

  /** Type of the Measurement */
  typedef Vector< InputPixelType, 1 >                     MeasurementVectorType;

  /** Type of the density functions */
  typedef Statistics::DensityFunction< MeasurementVectorType >
                                                        MembershipFunctionType;
  typedef typename MembershipFunctionType::Pointer      MembershipFunctionPointer;

  /** Membership function container */
  typedef VectorContainer< unsigned int, 
       MembershipFunctionPointer >  MembershipFunctionContainerType;
  typedef typename MembershipFunctionContainerType::Pointer         
                                    MembershipFunctionContainerPointer;
  
  /** 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 );
  itkGetObjectMacro(  MembershipFunctionContainer, MembershipFunctionContainerType );

  /** Set/Get methods for the number of classes. The user must supply this. */
  itkSetMacro( NumberOfClasses, unsigned int );
  itkGetMacro( NumberOfClasses, unsigned int );

  virtual void GenerateOutputInformation(); 

#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();
  virtual ~BayesianClassifierInitializationImageFilter() {}
  void PrintSelf(std::ostream& os, Indent indent) const;

  /** 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.*/
  virtual void GenerateData();

private:
  BayesianClassifierInitializationImageFilter(const Self&); //purposely not implemented
  void operator=(const Self&); //purposely not implemented

  bool                      m_UserSuppliesMembershipFunctions;
  unsigned int              m_NumberOfClasses;
  typename MembershipFunctionContainerType::Pointer m_MembershipFunctionContainer;
};

} // end namespace itk

#ifndef ITK_MANUAL_INSTANTIATION
#include "itkBayesianClassifierInitializationImageFilter.txx"
#endif

#endif
