/*=========================================================================
 *
 *  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 itkRenyiEntropyThresholdCalculator_hxx
#define itkRenyiEntropyThresholdCalculator_hxx

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

namespace itk
{

template <typename THistogram, typename TOutput>
void
RenyiEntropyThresholdCalculator<THistogram, TOutput>::GenerateData()
{
  const HistogramType * histogram = this->GetInput();

  const TotalAbsoluteFrequencyType total = histogram->GetTotalFrequency();
  if (total == TotalAbsoluteFrequencyType{})
  {
    itkExceptionStringMacro("Histogram is empty");
  }
  m_Size = histogram->GetSize(0);
  const ProgressReporter progress(this, 0, m_Size);
  if (m_Size == 1)
  {
    this->GetOutput()->Set(static_cast<OutputType>(histogram->GetMeasurement(0, 0)));
    return;
  }

  constexpr double tolerance{ itk::Math::eps };

  InstanceIdentifier ih;

  std::vector<double> norm_histo(m_Size); /* normalized histogram */
  std::vector<double> P1(m_Size);         /* cumulative normalized histogram */
  std::vector<double> P2(m_Size);

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

  P1[0] = norm_histo[0];
  P2[0] = 1.0 - P1[0];
  for (ih = 1; ih < m_Size; ++ih)
  {
    P1[ih] = P1[ih - 1] + norm_histo[ih];
    P2[ih] = 1.0 - P1[ih];
  }

  // Determine the first non-zero bin
  m_FirstBin = 0;
  for (ih = 0; ih < m_Size; ++ih)
  {
    if (!(itk::Math::Absolute(P1[ih]) < tolerance))
    {
      m_FirstBin = ih;
      break;
    }
  }

  // Determine the last non-zero bin
  m_LastBin = static_cast<InstanceIdentifier>(m_Size - 1);
  for (ih = m_Size - 1; ih >= m_FirstBin; ih--)
  {
    if (!(itk::Math::Absolute(P2[ih]) < tolerance))
    {
      m_LastBin = ih;
      break;
    }
  }

  InstanceIdentifier t_star2 = this->MaxEntropyThresholding(histogram, norm_histo, P1, P2);
  InstanceIdentifier t_star1 = this->MaxEntropyThresholding2(histogram, norm_histo, P1, P2);
  InstanceIdentifier t_star3 = this->MaxEntropyThresholding3(histogram, norm_histo, P1, P2);

  InstanceIdentifier tmp_var;

  // Sort t_star values
  if (t_star2 < t_star1)
  {
    tmp_var = t_star1;
    t_star1 = t_star2;
    t_star2 = tmp_var;
  }
  if (t_star3 < t_star2)
  {
    tmp_var = t_star2;
    t_star2 = t_star3;
    t_star3 = tmp_var;
  }
  if (t_star2 < t_star1)
  {
    tmp_var = t_star1;
    t_star1 = t_star2;
    t_star2 = tmp_var;
  }

  double beta1 = 0.;
  double beta2 = 0.;
  double beta3 = 0.;

  // Adjust beta values.
  // Note that t_star1, t_star2, t_star3 are unsigned int.
  if (itk::Math::Absolute(static_cast<double>(t_star1) - static_cast<double>(t_star2)) <= 5.)
  {
    if (itk::Math::Absolute(static_cast<double>(t_star2) - static_cast<double>(t_star3)) <= 5.)
    {
      beta1 = 1.;
      beta2 = 2.;
      beta3 = 1.;
    }
    else
    {
      beta1 = 0.;
      beta2 = 1.;
      beta3 = 3.;
    }
  }
  else
  {
    if (itk::Math::Absolute(static_cast<double>(t_star2) - static_cast<double>(t_star3)) <= 5.)
    {
      beta1 = 3.;
      beta2 = 1.;
      beta3 = 0.;
    }
    else
    {
      beta1 = 1.;
      beta2 = 2.;
      beta3 = 1.;
    }
  }

  itkAssertInDebugAndIgnoreInReleaseMacro(t_star1 < m_Size);
  itkAssertInDebugAndIgnoreInReleaseMacro(t_star2 < m_Size);
  itkAssertInDebugAndIgnoreInReleaseMacro(t_star3 < m_Size);

  const double omega = P1[t_star3] - P1[t_star1];

  // Determine the optimal threshold value
  const double realOptThreshold = static_cast<double>(t_star1) * (P1[t_star1] + 0.25 * omega * beta1) +
                                  static_cast<double>(t_star2) * 0.25 * omega * beta2 +
                                  static_cast<double>(t_star3) * (P2[t_star3] + 0.25 * omega * beta3);

  auto opt_threshold = static_cast<InstanceIdentifier>(realOptThreshold);

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

template <typename THistogram, typename TOutput>
auto
RenyiEntropyThresholdCalculator<THistogram, TOutput>::MaxEntropyThresholding(const HistogramType *       histogram,
                                                                             const std::vector<double> & normHisto,
                                                                             const std::vector<double> & P1,
                                                                             const std::vector<double> & P2)
  -> InstanceIdentifier
{

  // Calculate the total entropy each gray-level and fin the threshold that
  // maximizes it

  InstanceIdentifier threshold =
    0; // was MIN_INT in original code, but if an empty image is processed it gives an error later on.
  double max_ent = NumericTraits<double>::min(); // max entropy

  for (InstanceIdentifier it = m_FirstBin; it <= m_LastBin; ++it)
  {
    // Entropy of the background pixels
    double ent_back = 0.0;
    for (InstanceIdentifier ih = 0; ih <= it; ++ih)
    {
      if (histogram->GetFrequency(ih, 0) != AbsoluteFrequencyType{})
      {
        const double x = (normHisto[ih] / P1[it]);
        ent_back -= x * std::log(x);
      }
    }

    // Entropy of the object pixels
    double ent_obj = 0.0;
    for (InstanceIdentifier ih = it + 1; ih < m_Size; ++ih)
    {
      if (histogram->GetFrequency(ih, 0) != AbsoluteFrequencyType{})
      {
        const double x = (normHisto[ih] / P2[it]);
        ent_obj -= x * std::log(x);
      }
    }

    // Total entropy
    const double tot_ent = ent_back + ent_obj;

    // IJ.log(""+max_ent+"  "+tot_ent);

    if (max_ent < tot_ent)
    {
      max_ent = tot_ent;
      threshold = it;
    }
  }
  return threshold;
}

template <typename THistogram, typename TOutput>
auto
RenyiEntropyThresholdCalculator<THistogram, TOutput>::MaxEntropyThresholding2(
  const HistogramType *       itkNotUsed(histogram),
  const std::vector<double> & normHisto,
  const std::vector<double> & P1,
  const std::vector<double> & P2) -> InstanceIdentifier
{

  InstanceIdentifier threshold =
    0; // was MIN_INT in original code, but if an empty image is processed it gives an error later on.
  double           max_ent = NumericTraits<double>::min();
  constexpr double alpha{ 0.5 };
  const double     term = 1.0 / (1.0 - alpha);

  for (InstanceIdentifier it = m_FirstBin; it <= m_LastBin; ++it)
  {
    // Entropy of the background pixels
    double ent_back = 0.0;
    for (InstanceIdentifier ih = 0; ih <= it; ++ih)
    {
      ent_back += std::sqrt(normHisto[ih] / P1[it]);
    }

    // Entropy of the object pixel
    double ent_obj = 0.0;
    for (InstanceIdentifier ih = it + 1; ih < m_Size; ++ih)
    {
      ent_obj += std::sqrt(normHisto[ih] / P2[it]);
    }

    // Total entropy
    const double product = ent_back * ent_obj;
    double       tot_ent = 0.;

    if (product > 0.0)
    {
      tot_ent = term * std::log(ent_back * ent_obj);
    }

    if (tot_ent > max_ent)
    {
      max_ent = tot_ent;
      threshold = it;
    }
  }

  return threshold;
}

template <typename THistogram, typename TOutput>
auto
RenyiEntropyThresholdCalculator<THistogram, TOutput>::MaxEntropyThresholding3(
  const HistogramType *       itkNotUsed(histogram),
  const std::vector<double> & normHisto,
  const std::vector<double> & P1,
  const std::vector<double> & P2) -> InstanceIdentifier
{
  InstanceIdentifier threshold =
    0; // was MIN_INT in original code, but if an empty image is processed it gives an error later on.
  double           max_ent = 0.0;
  constexpr double alpha{ 2.0 };
  const double     term = 1.0 / (1.0 - alpha);
  for (InstanceIdentifier it = m_FirstBin; it <= m_LastBin; ++it)
  {
    // Entropy of the background pixels
    double ent_back = 0.0;
    for (InstanceIdentifier ih = 0; ih <= it; ++ih)
    {
      const double x = normHisto[ih] / P1[it];
      ent_back += x * x;
    }

    // Entropy of the object pixels
    double ent_obj = 0.0;
    for (InstanceIdentifier ih = it + 1; ih < m_Size; ++ih)
    {
      const double x = normHisto[ih] / P2[it];
      ent_obj += x * x;
    }

    // Total entropy
    double       tot_ent = 0.0;
    const double product = ent_back * ent_obj;
    if (product > 0.0)
    {
      tot_ent = term * std::log(product);
    }

    if (tot_ent > max_ent)
    {
      max_ent = tot_ent;
      threshold = it;
    }
  }

  return threshold;
}

template <typename THistogram, typename TOutput>
void
RenyiEntropyThresholdCalculator<THistogram, TOutput>::PrintSelf(std::ostream & os, Indent indent) const
{
  Superclass::PrintSelf(os, indent);

  os << indent << "FirstBin: " << static_cast<typename itk::NumericTraits<InstanceIdentifier>::PrintType>(m_FirstBin)
     << std::endl;
  os << indent << "LastBin: " << static_cast<typename itk::NumericTraits<InstanceIdentifier>::PrintType>(m_LastBin)
     << std::endl;
  os << indent << "Size: " << static_cast<typename itk::NumericTraits<SizeValueType>::PrintType>(m_Size) << std::endl;
}
} // end namespace itk

#endif
