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Commit 37f4be43 authored by OTB Bot's avatar OTB Bot
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modif double declaration ( warning VS7.1)

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Code/Markov/otbMRFSamplerRandomMAP.h
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/*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt 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 _MRFSamplerRandomMAP_h
#define _MRFSamplerRandomMAP_h
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "otbMRFSampler.h"
namespace otb
{
/**
* \class MRFSamplerRandomMAP
* \brief This is the base class for sampler methods used in the MRF framework.
*
* This is one sampler to be used int he MRF framework. This sampler select the
* value randomly according to the apriori probability.
*
* The probability is defined from the energy as:
*
* \f[ P(X=x)= \frac{1}{Z} \exp^{-U(x)} \f]
*
* where \f$ Z = \sum_x \exp^{-U(x)}\f$
*
*/
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)
{
if (nClasses != this->m_NumberOfClasses || energiesInvalid == true)
{
this->m_NumberOfClasses = nClasses;
if (energy != NULL)
free(energy);
if (repartitionFunction != NULL)
free(repartitionFunction);
energy = (double *) calloc(this->m_NumberOfClasses, sizeof(double));
repartitionFunction = (double *) calloc(this->m_NumberOfClasses, sizeof(double));
this->Modified();
}
}
inline int Compute( const InputImageNeighborhoodIterator & itData, const LabelledImageNeighborhoodIterator & itRegul)
{
if (this->m_NumberOfClasses == 0)
{
itkExceptionMacro(<<"NumberOfClasse 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;
for (unsigned int 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));
energy[valueCurrent] = this->m_EnergyCurrent;
repartitionFunction[valueCurrent] = vcl_exp(-this->m_EnergyCurrent)+totalProba;
totalProba = 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);
unsigned int valueCurrent = 0;
while( valueCurrent<this->GetNumberOfClasses() && 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 = energy[static_cast<unsigned int>(valueCurrent)];
}
this->m_DeltaEnergy= this->m_EnergyAfter - this->m_EnergyBefore;
return 0;
}
/** Methods to cancel random effects.*/
void InitializeSeed(int seed){ m_Generator->SetSeed(seed); }
void InitializeSeed(){ m_Generator->SetSeed(); }
private:
double * repartitionFunction;
double * energy;
bool energiesInvalid;
RandomGeneratorType::Pointer m_Generator;
protected:
// The constructor and destructor.
MRFSamplerRandomMAP()
{
energy=NULL;
repartitionFunction=NULL;
energiesInvalid = true;
m_Generator = RandomGeneratorType::New();
m_Generator->SetSeed();
}
virtual ~MRFSamplerRandomMAP()
{
if (energy != NULL)
free(energy);
if (repartitionFunction != NULL)
free(repartitionFunction);
}
};
}
#endif
/*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt 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 _MRFSamplerRandomMAP_h
#define _MRFSamplerRandomMAP_h
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "otbMRFSampler.h"
namespace otb
{
/**
* \class MRFSamplerRandomMAP
* \brief This is the base class for sampler methods used in the MRF framework.
*
* This is one sampler to be used int he MRF framework. This sampler select the
* value randomly according to the apriori probability.
*
* The probability is defined from the energy as:
*
* \f[ P(X=x)= \frac{1}{Z} \exp^{-U(x)} \f]
*
* where \f$ Z = \sum_x \exp^{-U(x)}\f$
*
*/
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)
{
if (nClasses != this->m_NumberOfClasses || energiesInvalid == true)
{
this->m_NumberOfClasses = nClasses;
if (energy != NULL)
free(energy);
if (repartitionFunction != NULL)
free(repartitionFunction);
energy = (double *) calloc(this->m_NumberOfClasses, sizeof(double));
repartitionFunction = (double *) calloc(this->m_NumberOfClasses, sizeof(double));
this->Modified();
}
}
inline int Compute( const InputImageNeighborhoodIterator & itData, const LabelledImageNeighborhoodIterator & itRegul)
{
if (this->m_NumberOfClasses == 0)
{
itkExceptionMacro(<<"NumberOfClasse 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));
energy[valueCurrent] = this->m_EnergyCurrent;
repartitionFunction[valueCurrent] = vcl_exp(-this->m_EnergyCurrent)+totalProba;
totalProba = 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() && 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 = energy[static_cast<unsigned int>(valueCurrent)];
}
this->m_DeltaEnergy= this->m_EnergyAfter - this->m_EnergyBefore;
return 0;
}
/** Methods to cancel random effects.*/
void InitializeSeed(int seed){ m_Generator->SetSeed(seed); }
void InitializeSeed(){ m_Generator->SetSeed(); }
private:
double * repartitionFunction;
double * energy;
bool energiesInvalid;
RandomGeneratorType::Pointer m_Generator;
protected:
// The constructor and destructor.
MRFSamplerRandomMAP()
{
energy=NULL;
repartitionFunction=NULL;
energiesInvalid = true;
m_Generator = RandomGeneratorType::New();
m_Generator->SetSeed();
}
virtual ~MRFSamplerRandomMAP()
{
if (energy != NULL)
free(energy);
if (repartitionFunction != NULL)
free(repartitionFunction);
}
};
}
#endif
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