otbMRFSamplerRandomMAP.h

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/*
 * Copyright (C) 2005-2022 Centre National d'Etudes Spatiales (CNES)
 *
 * This file is part of Orfeo Toolbox
 *
 *     https://www.orfeo-toolbox.org/
 *
 * 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
 *
 * 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 otbMRFSamplerRandomMAP_h
#define otbMRFSamplerRandomMAP_h
 
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "otbMRFSampler.h"
 
namespace otb
{
 
template <class TInput1, class TInput2>
class ITK_EXPORT MRFSamplerRandomMAP : public MRFSampler<TInput1, TInput2>
{
public:
  typedef MRFSamplerRandomMAP Self;
  typedef otb::MRFSampler<TInput1, TInput2> Superclass;
  typedef itk::SmartPointer<Self>       Pointer;
  typedef itk::SmartPointer<const Self> ConstPointer;
 
  typedef typename Superclass::InputImageNeighborhoodIterator    InputImageNeighborhoodIterator;
  typedef typename Superclass::LabelledImageNeighborhoodIterator LabelledImageNeighborhoodIterator;
  typedef typename Superclass::LabelledImagePixelType            LabelledImagePixelType;
  typedef typename Superclass::InputImagePixelType               InputImagePixelType;
  typedef typename Superclass::EnergyFidelityType                EnergyFidelityType;
  typedef typename Superclass::EnergyRegularizationType          EnergyRegularizationType;
  typedef typename Superclass::EnergyFidelityPointer             EnergyFidelityPointer;
  typedef typename Superclass::EnergyRegularizationPointer       EnergyRegularizationPointer;
  typedef itk::Statistics::MersenneTwisterRandomVariateGenerator RandomGeneratorType;
 
  itkNewMacro(Self);
 
  itkTypeMacro(MRFSamplerRandomMAP, MRFSampler);
 
  void SetNumberOfClasses(const unsigned int nClasses) override
  {
    if ((nClasses != this->m_NumberOfClasses) || (m_EnergiesInvalid == true))
    {
      this->m_NumberOfClasses = nClasses;
      if (m_Energy != nullptr)
        free(m_Energy);
      if (m_RepartitionFunction != nullptr)
        free(m_RepartitionFunction);
      m_Energy              = (double*)calloc(this->m_NumberOfClasses, sizeof(double));
      m_RepartitionFunction = (double*)calloc(this->m_NumberOfClasses, sizeof(double));
      this->Modified();
    }
  }
 
  inline int Compute(const InputImageNeighborhoodIterator& itData, const LabelledImageNeighborhoodIterator& itRegul) override
  {
    if (this->m_NumberOfClasses == 0)
    {
      itkExceptionMacro(<< "NumberOfClasses has to be greater than 0.");
    }
 
    this->m_EnergyBefore = this->m_EnergyFidelity->GetValue(itData, itRegul.GetCenterPixel());
    this->m_EnergyBefore += this->m_Lambda * this->m_EnergyRegularization->GetValue(itRegul, itRegul.GetCenterPixel());
 
    // Try all possible value (how to be generic ?)
    this->m_EnergyAfter = this->m_EnergyBefore; // default values to current one
    this->m_Value       = itRegul.GetCenterPixel();
 
    // Compute probability for each possibility
    double       totalProba   = 0.0;
    unsigned int valueCurrent = 0;
    for (valueCurrent = 0; valueCurrent < this->m_NumberOfClasses; ++valueCurrent)
    {
      this->m_EnergyCurrent = this->m_EnergyFidelity->GetValue(itData, static_cast<LabelledImagePixelType>(valueCurrent));
      this->m_EnergyCurrent += this->m_Lambda * this->m_EnergyRegularization->GetValue(itRegul, static_cast<LabelledImagePixelType>(valueCurrent));
 
      m_Energy[valueCurrent]              = this->m_EnergyCurrent;
      m_RepartitionFunction[valueCurrent] = std::exp(-this->m_EnergyCurrent) + totalProba;
      totalProba                          = m_RepartitionFunction[valueCurrent];
    }
 
    // Pick a value according to probability
 
    // double select = (m_Generator->GetIntegerVariate()/(double(RAND_MAX)+1) * totalProba);
    double select = (m_Generator->GetIntegerVariate() / (double(itk::NumericTraits<RandomGeneratorType::IntegerType>::max()) + 1) * totalProba);
    valueCurrent  = 0;
    while ((valueCurrent < this->GetNumberOfClasses()) && (m_RepartitionFunction[valueCurrent] <= select))
    {
      valueCurrent++;
    }
 
    if (valueCurrent == this->GetNumberOfClasses())
    {
      valueCurrent = this->GetNumberOfClasses() - 1;
    }
 
    if (this->m_Value != static_cast<LabelledImagePixelType>(valueCurrent))
    {
      this->m_Value       = static_cast<LabelledImagePixelType>(valueCurrent);
      this->m_EnergyAfter = m_Energy[static_cast<unsigned int>(valueCurrent)];
    }
 
    this->m_DeltaEnergy = this->m_EnergyAfter - this->m_EnergyBefore;
 
    return 0;
  }
 
  void InitializeSeed(int seed)
  {
    m_Generator->SetSeed(seed);
  }
  void InitializeSeed()
  {
    m_Generator->SetSeed();
  }
 
protected:
  // The constructor and destructor.
  MRFSamplerRandomMAP() : m_RepartitionFunction(nullptr), m_Energy(nullptr), m_EnergiesInvalid(true)
  {
    m_Generator = RandomGeneratorType::GetInstance();
    m_Generator->SetSeed();
  }
  ~MRFSamplerRandomMAP() override
  {
    if (m_Energy != nullptr)
      free(m_Energy);
    if (m_RepartitionFunction != nullptr)
      free(m_RepartitionFunction);
  }
 
private:
  double*                      m_RepartitionFunction;
  double*                      m_Energy;
  bool                         m_EnergiesInvalid;
  RandomGeneratorType::Pointer m_Generator;
};
}
 
#endif