/*=========================================================================
 *
 *  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 __itkVoronoiSegmentationImageFilter_h
#define __itkVoronoiSegmentationImageFilter_h

#include "itkVoronoiSegmentationImageFilterBase.h"

namespace itk
{
/** \class VoronoiSegmentationImageFilter
 *
 * Perform the segmentation of 2D images (single channel) by Voronoi Diagram.
 * Used as a node of the segmentation toolkits.
 * The homogeneity operator here is the testing of mean and standar deviation value.
 * By setting the tolerance level, the "internal" region was defined as those
 * that is closed to the gold-standard value in the sense that the difference
 * is within the tolerance value.
 *
 * See VoronoiSegmentationImageFilterBase for detail description of voronoi
 * segmenation principles.
 *
 * The parameters here are:
 * 1. the estimation of the statistics of the object. (mean and std.)
 * 2. the tolerance for the classification. (around the mean ans std. estimated value).
 *
 * The parameters can also be automatically set by given a prior, as a binary
 * image.
 *
 * Detail information about this algorithm can be found in:
 *  " Semi-automated color segmentation of anatomical tissue,"
 *   C. Imelinska, M. Downes, and W. Yuan
 *  Computerized Medical Imaging and Graphics, Vor.24, pp 173-180, 2000.
 *
 * \ingroup HybridSegmentation
 * \ingroup ITKVoronoi
 */
template< typename TInputImage, typename TOutputImage, typename TBinaryPriorImage = Image< unsigned char, 2 > >
class VoronoiSegmentationImageFilter:
  public VoronoiSegmentationImageFilterBase< TInputImage, TOutputImage, TBinaryPriorImage >
{
public:
  /** Standard class typedefs. */
  typedef VoronoiSegmentationImageFilter Self;
  typedef VoronoiSegmentationImageFilterBase< TInputImage, TOutputImage,
                                              TBinaryPriorImage >                       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(VoronoiSegmentationImageFilter,
               VoronoiSegmentationImageFilterBase);

  /** Convenient typedefs. */
  typedef typename Superclass::BinaryObjectImage BinaryObjectImage;
  typedef typename Superclass::IndexList         IndexList;
  typedef typename Superclass::IndexType         IndexType;
  typedef typename Superclass::RegionType        RegionType;
  typedef typename Superclass::InputImageType    InputImageType;

  /** Set/Get the Estimation of the mean pixel value for the object. */
  itkSetMacro(Mean, double);
  itkGetConstMacro(Mean, double);

  /** Set/Get the estimation of the STD of the pixel value for the
   *  object. */
  itkSetMacro(STD, double);
  itkGetConstMacro(STD, double);

  /** Set/Get the Tolearance of Mean for classifying the regions. */
  itkSetMacro(MeanTolerance, double);
  itkGetConstMacro(MeanTolerance, double);

  /** Set the Tolearance of STD for classifying the regions. */
  itkSetMacro(STDTolerance, double);

  /** Get the Tolearance of Variance for classifying the regions. */
  itkGetConstMacro(STDTolerance, double);

  /** Set/Get the mean percent error. */
  void SetMeanPercentError(double x);

  itkGetConstMacro(MeanPercentError, double);

  /** Set/Get the STD percent error. */
  itkGetConstMacro(STDPercentError, double);
  void SetSTDPercentError(double x);

  /** Take a prior from other segmentation node, should be an
   * binary object. */
  void TakeAPrior(const BinaryObjectImage *aprior);

  /** ImageDimension enumeration   */
  itkStaticConstMacro(InputImageDimension, unsigned int,
                      TInputImage::ImageDimension);
  itkStaticConstMacro(OutputImageDimension, unsigned int,
                      TOutputImage::ImageDimension);

#ifdef ITK_USE_CONCEPT_CHECKING
  // Begin concept checking
  itkConceptMacro( SameDimensionCheck,
                   ( Concept::SameDimension< InputImageDimension, OutputImageDimension > ) );
  itkConceptMacro( IntConvertibleToOutputCheck,
                   ( Concept::Convertible< int, typename TOutputImage::PixelType > ) );
  // End concept checking
#endif

protected:
  VoronoiSegmentationImageFilter();
  ~VoronoiSegmentationImageFilter();
  virtual void PrintSelf(std::ostream & os, Indent indent) const;

private:
  double m_Mean;
  double m_STD;
  double m_MeanTolerance;
  double m_STDTolerance;
  double m_MeanPercentError;
  double m_STDPercentError;

  virtual bool TestHomogeneity(IndexList & Plist);

private:
  VoronoiSegmentationImageFilter(const Self &); //purposely not implemented
  void operator=(const Self &);                 //purposely not implemented
};
} //end namespace

#ifndef ITK_MANUAL_INSTANTIATION
#include "itkVoronoiSegmentationImageFilter.hxx"
#endif

#endif
