/*=========================================================================
 *
 *  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 itkYenThresholdCalculator_hxx
#define itkYenThresholdCalculator_hxx

#include "itkYenThresholdCalculator.h"
#include "itkProgressReporter.h"
#include "itkMath.h"

namespace itk
{
/*
 * Compute the Yen's threshold
 */
template<typename THistogram, typename TOutput>
void
YenThresholdCalculator<THistogram, TOutput>
::GenerateData(void)
{
  const HistogramType * histogram = this->GetInput();
  // histogram->Print(std::cout);
  if ( histogram->GetTotalFrequency() == 0 )
    {
    itkExceptionMacro(<< "Histogram is empty");
    }
  ProgressReporter progress(this, 0, histogram->GetSize(0) );
  if( histogram->GetSize(0) == 1 )
    {
    this->GetOutput()->Set( static_cast<OutputType>( histogram->GetMeasurement(0,0) ) );
    }

  unsigned int size = histogram->GetSize(0);

  int threshold;
  int ih, it;
  double crit;
  double max_crit;
  std::vector<double> norm_histo(size); /* normalized histogram */
  std::vector<double> P1(size); /* cumulative normalized histogram */
  std::vector<double> P1_sq(size);
  std::vector<double> P2_sq(size);

  int total = histogram->GetTotalFrequency();

  for (ih = 0; (unsigned)ih < size; ih++ )
    {
    norm_histo[ih] = (double)histogram->GetFrequency(ih, 0)/total;
    }

  P1[0]=norm_histo[0];
  for (ih = 1; (unsigned)ih < size; ih++ )
    {
    P1[ih]= P1[ih-1] + norm_histo[ih];
    }

  P1_sq[0]=norm_histo[0]*norm_histo[0];
  for (ih = 1; (unsigned)ih < size; ih++ )
    {
    P1_sq[ih]= P1_sq[ih-1] + norm_histo[ih] * norm_histo[ih];
    }

  P2_sq[size - 1] = 0.0;
  for ( ih = (unsigned)size-2; ih >= 0; ih-- )
    {
    P2_sq[ih] = P2_sq[ih + 1] + norm_histo[ih + 1] * norm_histo[ih + 1];
    }

  /* Find the threshold that maximizes the criterion */
  threshold = -1;
  max_crit = itk::NumericTraits<double>::NonpositiveMin();
  for ( it = 0; (unsigned)it < size; it++ )
    {
    crit = -1.0 * (( P1_sq[it] * P2_sq[it] )> 0.0? std::log( P1_sq[it] * P2_sq[it]):0.0) +  2 * ( ( P1[it] * ( 1.0 - P1[it] ) )>0.0? std::log(  P1[it] * ( 1.0 - P1[it] ) ): 0.0);
    if ( crit > max_crit )
      {
      max_crit = crit;
      threshold = it;
      }
    }

  this->GetOutput()->Set( static_cast<OutputType>( histogram->GetMeasurement( threshold, 0 ) ) );

}

} // end namespace itk

#endif
