From 8ff15ae2ce42814fae27ab5eca444457f2b336ba Mon Sep 17 00:00:00 2001
From: Cyrille Valladeau <cyrille.valladeau@c-s.fr>
Date: Thu, 15 May 2008 13:56:19 +0000
Subject: [PATCH] Markov qui compile et reussi les tests.
---
Code/Markov/otbMRFEnergy.h | 56 ++--
.../otbMRFEnergyGaussianClassification.h | 117 ++++----
Code/Markov/otbMRFOptimizer.h | 22 +-
Code/Markov/otbMRFOptimizerICM.h | 77 +++---
Code/Markov/otbMRFOptimizerMetropolis.h | 26 +-
Code/Markov/otbMRFSampler.h | 19 +-
Code/Markov/otbMRFSamplerMAP.h | 143 +++++-----
Code/Markov/otbMRFSamplerRandom.h | 127 +++++----
Code/Markov/otbMRFSamplerRandomMAP.h | 254 +++++++++---------
Code/Markov/otbMarkovRandomFieldFilter.h | 35 +--
Code/Markov/otbMarkovRandomFieldFilter.txx | 15 +-
.../otbMRFEnergyGaussianClassification.cxx | 12 +-
.../Code/Markov/otbMRFOptimizerMetropolis.cxx | 7 +-
Testing/Code/Markov/otbMRFSamplerMAP.cxx | 15 +-
Testing/Code/Markov/otbMRFSamplerRandom.cxx | 18 +-
.../Code/Markov/otbMRFSamplerRandomMAP.cxx | 23 +-
.../Markov/otbMarkovRandomFieldFilter.cxx | 46 ++--
17 files changed, 510 insertions(+), 502 deletions(-)
diff --git a/Code/Markov/otbMRFEnergy.h b/Code/Markov/otbMRFEnergy.h
index 4fed7475e2..b0dee621df 100644
--- a/Code/Markov/otbMRFEnergy.h
+++ b/Code/Markov/otbMRFEnergy.h
@@ -49,6 +49,8 @@ class ITK_EXPORT MRFEnergy : public itk::Object
typedef itk::Array< double > ParametersType;
+ itkNewMacro(Self);
+
itkTypeMacro(MRFEnergy,itk::Object);
@@ -68,25 +70,14 @@ class ITK_EXPORT MRFEnergy : public itk::Object
itkExceptionMacro(<<"Invalid number of parameters");
}
m_Parameters = parameters;
- this->Modified();
-
-// bool modified = false;
-// for( unsigned int i=0; i<m_NumberOfParameters; i++ )
-// {
-// if (m_Parameters[i] != parameters[i])
-// {
-// m_Parameters[i] = parameters[i];
-// modified = true;
-// }
-// }
-// if (modified)
-// {
-// this->Modified();
-// }
- }
-
- virtual double GetSingleValue(const InputImagePixelType & value1, const LabelledImagePixelType & value2) = 0;
+ this->Modified();
+ }
+ virtual double GetSingleValue(const InputImagePixelType & value1, const LabelledImagePixelType & value2)
+ {
+ itkExceptionMacro(<<"GetSingleValue() has to be declared in child classes.");
+ }
+
double GetValue(const InputImagePixelType & value1, const LabelledImagePixelType & value2)
{
return GetSingleValue(value1, value2);
@@ -137,7 +128,7 @@ class ITK_EXPORT MRFEnergy : public itk::Object
protected:
// The constructor and destructor.
- MRFEnergy() {};
+ MRFEnergy() {m_NumberOfParameters = 1;};
virtual ~MRFEnergy() {};
unsigned int m_NumberOfParameters;
ParametersType m_Parameters;
@@ -158,8 +149,9 @@ class ITK_EXPORT MRFEnergy<TInput2,TInput2> : public itk::Object
typedef itk::ConstNeighborhoodIterator< LabelledImageType > LabelledNeighborhoodIterator;
typedef itk::Array< double > ParametersType;
-
-
+
+ itkNewMacro(Self);
+
itkTypeMacro(MRFEnergy,itk::Object);
@@ -183,24 +175,13 @@ class ITK_EXPORT MRFEnergy<TInput2,TInput2> : public itk::Object
}
m_Parameters = parameters;
this->Modified();
- /*
- bool modified = false;
- for( unsigned int i=0; i<m_NumberOfParameters; i++ )
- {
- if (m_Parameters[i] != parameters[i])
- {
- m_Parameters[i] = parameters[i];
- modified = true;
- }
- }
- if (modified)
- {
- this->Modified();
- }*/
}
- virtual double GetSingleValue(const LabelledImagePixelType & value1, const LabelledImagePixelType & value2) = 0;
-
+ virtual double GetSingleValue(const LabelledImagePixelType & value1, const LabelledImagePixelType & value2)
+ {
+ itkExceptionMacro(<<"GetSingleValue() has to be declared in child classes.");
+ }
+
double GetValue(const LabelledImagePixelType & value1, const LabelledImagePixelType & value2)
{
return GetSingleValue(value1, value2);
@@ -233,6 +214,7 @@ class ITK_EXPORT MRFEnergy<TInput2,TInput2> : public itk::Object
// The constructor and destructor.
MRFEnergy()
{
+ m_NumberOfParameters = 1;
m_Parameters=0;
};
virtual ~MRFEnergy() {};
diff --git a/Code/Markov/otbMRFEnergyGaussianClassification.h b/Code/Markov/otbMRFEnergyGaussianClassification.h
index d761f8e0d1..3b8a9546a1 100644
--- a/Code/Markov/otbMRFEnergyGaussianClassification.h
+++ b/Code/Markov/otbMRFEnergyGaussianClassification.h
@@ -25,70 +25,69 @@
namespace otb
{
- /**
- * \class MRFEnergyGaussianClassification
- * \brief This is the implementation of the Gaussian model for Markov classification.
- *
- * This is the implementation of the Gaussian Energy model for Markov classification, to be used for
- * the fidelity term for classification. Energy is:
- * \f[ U(x_s / y_s) = \frac{(y_s+\mu_{x_s})^2}{2\sigma^2_{x_s}}+\log{\sqrt{2\pi}\sigma_{x_s}} \f]
- * with
- * - \f$ x_s \f$ the label on site s
- * - \f$ y_s \f$ the value on the reference image
- * - \f$ \mu_{x_s} \f$ and \f$ \sigma^2_{x_s} \f$ the mean and variance of label \f$ x_s \f$
- */
+/**
+ * \class MRFEnergyGaussianClassification
+ * \brief This is the implementation of the Gaussian model for Markov classification.
+ *
+ * This is the implementation of the Gaussian Energy model for Markov classification, to be used for
+ * the fidelity term for classification. Energy is:
+ * \f[ U(x_s / y_s) = \frac{(y_s+\mu_{x_s})^2}{2\sigma^2_{x_s}}+\log{\sqrt{2\pi}\sigma_{x_s}} \f]
+ * with
+ * - \f$ x_s \f$ the label on site s
+ * - \f$ y_s \f$ the value on the reference image
+ * - \f$ \mu_{x_s} \f$ and \f$ \sigma^2_{x_s} \f$ the mean and variance of label \f$ x_s \f$
+ */
- template< class TInput1, class TInput2>
- class ITK_EXPORT MRFEnergyGaussianClassification:public MRFEnergy< TInput1, TInput2>
- {
- public:
- typedef MRFEnergyGaussianClassification Self;
- typedef MRFEnergy< TInput1, TInput2> Superclass;
- typedef itk::SmartPointer<Self> Pointer;
- typedef itk::SmartPointer<const Self> ConstPointer;
+template< class TInput1, class TInput2>
+class ITK_EXPORT MRFEnergyGaussianClassification:public MRFEnergy< TInput1, TInput2>
+ {
+ public:
+ typedef MRFEnergyGaussianClassification Self;
+ typedef MRFEnergy< TInput1, TInput2> Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
- typedef TInput1 InputImageType;
- typedef TInput2 LabelledImageType;
- typedef typename InputImageType::PixelType InputImagePixelType;
- typedef typename LabelledImageType::PixelType LabelledImagePixelType;
-
- typedef itk::Array< double > ParametersType;
+ typedef TInput1 InputImageType;
+ typedef TInput2 LabelledImageType;
+ typedef typename InputImageType::PixelType InputImagePixelType;
+ typedef typename LabelledImageType::PixelType LabelledImagePixelType;
+ typedef itk::Array < double > ParametersType;
+
+ itkNewMacro(Self);
- itkNewMacro(Self);
-
- itkTypeMacro(MRFEnergyGaussianClassification, MRFEnergy);
-
- void SetNumberOfParameters(unsigned int nParameters)
- {
- this->m_NumberOfParameters=nParameters;
- this->m_Parameters.SetSize(this->m_NumberOfParameters);
- this->Modified();
- }
-
- double GetSingleValue(const InputImagePixelType & value1, const LabelledImagePixelType & value2)
- {
- if ((unsigned int)value2 >= this->GetNumberOfParameters()/2)
- {
- itkExceptionMacro(<<"Number of parameters does not correspond to number of classes" );
- }
- double val1 = static_cast<double>(value1);
-
- double result = M_SQUARE(val1-this->m_Parameters[2*static_cast<int>(value2)])
- / (2*M_SQUARE(this->m_Parameters[2*static_cast<int>(value2)+1]))
- + vcl_log(vcl_sqrt(2*M_PI)
- *this->m_Parameters[2*static_cast<int>(value2)+1]);
-
- return static_cast<double>( result );
- }
-
-
+ itkTypeMacro(MRFEnergyGaussianClassification, MRFEnergy);
+
+ void SetNumberOfParameters(unsigned int nParameters)
+ {
+ Superclass::SetNumberOfParameters(nParameters);
+ this->m_Parameters.SetSize(nParameters);
+ this->Modified();
+ }
- protected:
- // The constructor and destructor.
- MRFEnergyGaussianClassification() {};
- virtual ~MRFEnergyGaussianClassification() {};
- };
+ double GetSingleValue(const InputImagePixelType & value1, const LabelledImagePixelType & value2)
+ {
+ if ((unsigned int)value2 >= this->GetNumberOfParameters()/2)
+ {
+ itkExceptionMacro(<<"Number of parameters does not correspond to number of classes" );
+ }
+ double val1 = static_cast<double>(value1);
+
+ double result = M_SQUARE(val1-this->m_Parameters[2*static_cast<int>(value2)])
+ / (2*M_SQUARE(this->m_Parameters[2*static_cast<int>(value2)+1]))
+ + vcl_log(vcl_sqrt(2*M_PI)*this->m_Parameters[2*static_cast<int>(value2)+1]);
+
+ return static_cast<double>( result );
+ }
+
+
+
+ protected:
+ // The constructor and destructor.
+ MRFEnergyGaussianClassification() {};
+ virtual ~MRFEnergyGaussianClassification() {};
+
+ };
}
#endif
diff --git a/Code/Markov/otbMRFOptimizer.h b/Code/Markov/otbMRFOptimizer.h
index c95a173e1a..814042c596 100644
--- a/Code/Markov/otbMRFOptimizer.h
+++ b/Code/Markov/otbMRFOptimizer.h
@@ -50,28 +50,14 @@ class ITK_EXPORT MRFOptimizer : public itk::Object
return this->m_Parameters;
}
- void SetParameters( const ParametersType & parameters )
+ virtual void SetParameters( const ParametersType & parameters )
{
- if( parameters.Size() != m_NumberOfParameters )
+ if( parameters.GetSize() != m_NumberOfParameters )
{
- itkExceptionMacro(<<"Invalid number of parameters");
+ itkExceptionMacro(<<"Invalid number of parameters ("<<parameters.GetSize()<<" , "<<m_NumberOfParameters<<")");
}
m_Parameters = parameters;
this->Modified();
-
-// bool modified = false;
-// for( unsigned int i=0; i<m_NumberOfParameters; i++ )
-// {
-// if (m_Parameters[i] != parameters[i])
-// {
-// m_Parameters[i] = parameters[i];
-// modified = true;
-// }
-// }
-// if (modified)
-// {
-// this->Modified();
-// }
}
virtual bool Compute(double deltaEnergy) = 0;
@@ -79,7 +65,7 @@ class ITK_EXPORT MRFOptimizer : public itk::Object
protected:
MRFOptimizer()
{
- m_Parameters=0;
+ m_Parameters=1;
}
virtual ~MRFOptimizer() {}
unsigned int m_NumberOfParameters;
diff --git a/Code/Markov/otbMRFOptimizerICM.h b/Code/Markov/otbMRFOptimizerICM.h
index 36120fd365..46cbd3faa0 100644
--- a/Code/Markov/otbMRFOptimizerICM.h
+++ b/Code/Markov/otbMRFOptimizerICM.h
@@ -23,47 +23,46 @@
namespace otb
{
- /**
- * \class MRFOptimizerICM
- * \brief This is the optimizer class implementing the ICM algorithm
- *
- * This is one optimizer to be used in the MRF framework. This optimizer
- * follows the ICM algorithm to accept of reject the value proposed by the sampler
- */
- class ITK_EXPORT MRFOptimizerICM :
- public MRFOptimizer
- {
- public:
-
- typedef MRFOptimizerICM Self;
- typedef MRFOptimizer Superclass;
- typedef itk::SmartPointer<Self> Pointer;
- typedef itk::SmartPointer<const Self> ConstPointer;
-
- itkNewMacro(Self);
-
- itkTypeMacro(MRFOptimizerICM,MRFOptimizer);
-
-
- inline bool Compute(double deltaEnergy)
- {
- if (deltaEnergy < 0)
- {
- return true;
- }
- else
- {
- return false;
- }
- }
-
-
- protected:
- MRFOptimizerICM() {}
- virtual ~MRFOptimizerICM() {}
+/**
+ * \class MRFOptimizerICM
+ * \brief This is the optimizer class implementing the ICM algorithm
+ *
+ * This is one optimizer to be used in the MRF framework. This optimizer
+ * follows the ICM algorithm to accept of reject the value proposed by the sampler
+ */
+class ITK_EXPORT MRFOptimizerICM : public MRFOptimizer
+{
+ public:
+
+ typedef MRFOptimizerICM Self;
+ typedef MRFOptimizer Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ itkNewMacro(Self);
+
+ itkTypeMacro(MRFOptimizerICM,MRFOptimizer);
+
+
+ inline bool Compute(double deltaEnergy)
+ {
+ if (deltaEnergy < 0)
+ {
+ return true;
+ }
+ else
+ {
+ return false;
+ }
+ }
+
- };
+ protected:
+ MRFOptimizerICM() {}
+ virtual ~MRFOptimizerICM() {}
+};
+
}
#endif
diff --git a/Code/Markov/otbMRFOptimizerMetropolis.h b/Code/Markov/otbMRFOptimizerMetropolis.h
index 622ffa72a4..bc782b8cc6 100644
--- a/Code/Markov/otbMRFOptimizerMetropolis.h
+++ b/Code/Markov/otbMRFOptimizerMetropolis.h
@@ -21,6 +21,7 @@
#include "otbMRFOptimizer.h"
#include "otbMath.h"
#include "itkNumericTraits.h"
+#include "itkMersenneTwisterRandomVariateGenerator.h"
namespace otb
{
@@ -46,11 +47,23 @@ class ITK_EXPORT MRFOptimizerMetropolis : public MRFOptimizer
typedef MRFOptimizer Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
+ typedef Superclass::ParametersType ParametersType;
+
+ typedef itk::Statistics::MersenneTwisterRandomVariateGenerator RandomGeneratorType;
itkNewMacro(Self);
itkTypeMacro(MRFOptimizerMetropolis,MRFOptimizer);
+ /** Set parameter to a one array filled with paramVal.*/
+ void SetSingleParameter( double parameterVal )
+ {
+ this->m_Parameters.SetSize(1);
+ this->m_Parameters.Fill(parameterVal);
+ this->Modified();
+ }
+
+
inline bool Compute(double deltaEnergy)
{
if (deltaEnergy < 0)
@@ -64,24 +77,29 @@ class ITK_EXPORT MRFOptimizerMetropolis : public MRFOptimizer
else
{
double proba = vcl_exp(-(deltaEnergy)/this->m_Parameters[0]);
- if ( (rand() % 10000) < proba*10000)
+ if ( (m_Generator->GetIntegerVariate() % 10000) < proba*10000)
{
- std::cerr << "Opti true " << std::endl;
return true;
}
}
return false;
}
-
+ /** Methods to cancel random effects.*/
+ void InitializeSeed(int seed){ m_Generator->SetSeed(seed); }
+ void InitializeSeed(){ m_Generator->SetSeed(); }
+
protected:
MRFOptimizerMetropolis() {
this->m_NumberOfParameters = 1;
- this->m_Parameters.SetSize(this->m_NumberOfParameters);
+ this->m_Parameters.SetSize(1);
this->m_Parameters[0]=1.0;
+ m_Generator = RandomGeneratorType::New();
+ m_Generator->SetSeed();
}
virtual ~MRFOptimizerMetropolis() {}
double m_Temperature;
+ RandomGeneratorType::Pointer m_Generator;
};
}
diff --git a/Code/Markov/otbMRFSampler.h b/Code/Markov/otbMRFSampler.h
index b38c1c40a5..491ced2b8e 100644
--- a/Code/Markov/otbMRFSampler.h
+++ b/Code/Markov/otbMRFSampler.h
@@ -32,7 +32,7 @@ namespace otb
*/
template< class TInput1, class TInput2>
-class ITK_EXPORT MRFSampler:public itk::Object
+class ITK_EXPORT MRFSampler : public itk::Object
{
public:
@@ -68,12 +68,9 @@ class ITK_EXPORT MRFSampler:public itk::Object
itkGetConstMacro(EnergyBefore, double);
itkGetConstMacro(EnergyAfter, double);
-
itkSetObjectMacro( EnergyRegularization, EnergyRegularizationType);
itkSetObjectMacro( EnergyFidelity, EnergyFidelityType);
-
-
virtual int Compute( const InputImageNeighborhoodIterator & itData,
const LabelledImageNeighborhoodIterator & itRegul) = 0;
@@ -93,8 +90,18 @@ class ITK_EXPORT MRFSampler:public itk::Object
protected:
// The constructor and destructor.
- MRFSampler() {}
- virtual ~MRFSampler() {}
+ MRFSampler()
+ {
+ m_EnergyRegularization = EnergyRegularizationType::New();
+ m_EnergyFidelity = EnergyFidelityType::New(),
+ m_NumberOfClasses =1;
+ m_EnergyBefore = 1.;
+ m_EnergyAfter = 1.;
+ m_DeltaEnergy = 1.;
+ m_EnergyCurrent = 1.;
+ m_Lambda = 1.;
+ };
+ virtual ~MRFSampler() {};
};
diff --git a/Code/Markov/otbMRFSamplerMAP.h b/Code/Markov/otbMRFSamplerMAP.h
index 7f8685a4a4..a91ff014b7 100644
--- a/Code/Markov/otbMRFSamplerMAP.h
+++ b/Code/Markov/otbMRFSamplerMAP.h
@@ -23,93 +23,82 @@
namespace otb
{
- /**
- * \class MRFSamplerMAP
- * \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 which maximizes the apriori probability (minimum energy).
- *
- */
+/**
+ * \class MRFSamplerMAP
+ * \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 which maximizes the apriori probability (minimum energy).
+ *
+ */
- template< class TInput1, class TInput2>
- class ITK_EXPORT MRFSamplerMAP : public MRFSampler< TInput1, TInput2>
- {
- public:
-
- typedef MRFSamplerMAP Self;
- typedef MRFSampler< TInput1, TInput2> Superclass;
- typedef itk::SmartPointer<Self> Pointer;
- typedef itk::SmartPointer<const Self> ConstPointer;
-
-// typedef itk::ConstNeighborhoodIterator< TInput1 > InputImageNeighborhoodIterator;
-// typedef itk::NeighborhoodIterator< TInput2 > LabelledImageNeighborhoodIterator;
-// typedef typename TInput2::PixelType LabelledImagePixelType;
-//
-// typedef MRFEnergy<TInput1, TInput2> EnergyFidelityType;
-// typedef MRFEnergy<TInput2, TInput2> EnergyRegularizationType;
-//
-// typedef typename EnergyFidelityType::Pointer EnergyFidelityPointer;
-// typedef typename EnergyRegularizationType::Pointer EnergyRegularizationPointer;
-
- 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;
-
- itkNewMacro(Self);
+template< class TInput1, class TInput2>
+class ITK_EXPORT MRFSamplerMAP : public MRFSampler< TInput1, TInput2>
+ {
+ public:
- itkTypeMacro(MRFSamplerMAP,MRFSampler);
-
+ typedef MRFSamplerMAP Self;
+ typedef MRFSampler< TInput1, TInput2> Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
- inline int Compute( const InputImageNeighborhoodIterator & itData,
- const LabelledImageNeighborhoodIterator & itRegul)
- {
- if (this->m_NumberOfClasses == 0)
- {
+ 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;
+
+ itkNewMacro(Self);
+
+ itkTypeMacro(MRFSamplerMAP,MRFSampler);
+
+ 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();
-
- LabelledImagePixelType valueCurrent = 0;
- while( valueCurrent<static_cast<LabelledImagePixelType>(this->GetNumberOfClasses()) && valueCurrent != itk::NumericTraits<LabelledImagePixelType>::max() )
- {
- this->m_EnergyCurrent = this->m_EnergyFidelity->GetValue(itData, valueCurrent);
- this->m_EnergyCurrent += this->m_Lambda
- * this->m_EnergyRegularization->GetValue(itRegul, valueCurrent);
- if (this->m_EnergyCurrent < this->m_EnergyAfter)
+ }
+
+ 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();
+
+ LabelledImagePixelType valueCurrent = 0;
+ while( valueCurrent<static_cast<LabelledImagePixelType>(this->GetNumberOfClasses()) && valueCurrent != itk::NumericTraits<LabelledImagePixelType>::max() )
+ {
+ this->m_EnergyCurrent = this->m_EnergyFidelity->GetValue(itData, valueCurrent);
+ this->m_EnergyCurrent += this->m_Lambda
+ * this->m_EnergyRegularization->GetValue(itRegul, valueCurrent);
+ if (this->m_EnergyCurrent < this->m_EnergyAfter)
{
this->m_EnergyAfter = this->m_EnergyCurrent;
this->m_Value = valueCurrent;
}
- valueCurrent++;
- }
-
-
- this->m_DeltaEnergy= this->m_EnergyAfter - this->m_EnergyBefore;
-
- return 0;
- }
-
-
+ valueCurrent++;
+ }
+
+
+ this->m_DeltaEnergy= this->m_EnergyAfter - this->m_EnergyBefore;
+
+ return 0;
+ }
+
+
protected:
- // The constructor and destructor.
- MRFSamplerMAP() {}
- virtual ~MRFSamplerMAP() {}
+ // The constructor and destructor.
+ MRFSamplerMAP() {}
+ virtual ~MRFSamplerMAP() {}
- };
-
+ };
+
}
diff --git a/Code/Markov/otbMRFSamplerRandom.h b/Code/Markov/otbMRFSamplerRandom.h
index 3ec6871812..6482093d94 100644
--- a/Code/Markov/otbMRFSamplerRandom.h
+++ b/Code/Markov/otbMRFSamplerRandom.h
@@ -24,77 +24,70 @@
namespace otb
{
- /**
- * \class MRFSamplerRandom
- * \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 a uniform probability.
- *
- */
-
- template< class TInput1, class TInput2>
- class ITK_EXPORT MRFSamplerRandom: public MRFSampler< TInput1, TInput2>
- {
- public:
- typedef MRFSamplerRandom 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(MRFSamplerRandom,MRFSampler);
-
- inline int Compute( const InputImageNeighborhoodIterator & itData,
- const LabelledImageNeighborhoodIterator & itRegul)
- {
- this->m_EnergyBefore =
- this->m_EnergyFidelity->GetValue(itData, itRegul.GetCenterPixel());
- this->m_EnergyBefore += this->m_Lambda
- * this->m_EnergyRegularization->GetValue(itRegul, itRegul.GetCenterPixel());
+/**
+ * \class MRFSamplerRandom
+ * \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 a uniform probability.
+ *
+ */
-
- this->m_Value =
- static_cast<LabelledImagePixelType>
- (m_Generator->GetIntegerVariate() % this->m_NumberOfClasses);
-
- this->m_EnergyAfter =
- this->m_EnergyFidelity->GetValue(itData, this->m_Value);
- this->m_EnergyAfter += this->m_Lambda
- * this->m_EnergyRegularization->GetValue(itRegul, this->m_Value);
+template< class TInput1, class TInput2>
+class ITK_EXPORT MRFSamplerRandom: public MRFSampler< TInput1, TInput2>
+ {
+ public:
+ typedef MRFSamplerRandom 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);
- this->m_DeltaEnergy= this->m_EnergyAfter - this->m_EnergyBefore;
+ itkTypeMacro(MRFSamplerRandom,MRFSampler);
+
+ inline int Compute( const InputImageNeighborhoodIterator & itData, const LabelledImageNeighborhoodIterator & itRegul)
+ {
+ this->m_EnergyBefore = this->m_EnergyFidelity->GetValue(itData, itRegul.GetCenterPixel());
+ this->m_EnergyBefore += this->m_Lambda
+ * this->m_EnergyRegularization->GetValue(itRegul, itRegul.GetCenterPixel());
+
+ this->m_Value = static_cast<LabelledImagePixelType>(m_Generator->GetIntegerVariate() % this->m_NumberOfClasses);
+ this->m_EnergyAfter = this->m_EnergyFidelity->GetValue(itData, this->m_Value);
+ this->m_EnergyAfter += this->m_Lambda * this->m_EnergyRegularization->GetValue(itRegul, this->m_Value);
+ this->m_DeltaEnergy= this->m_EnergyAfter - this->m_EnergyBefore;
- return 0;
- }
-
-
- protected:
-// The constructor and destructor.
- MRFSamplerRandom() {
- m_Generator =
- RandomGeneratorType::New();
- m_Generator->SetSeed();
- }
- virtual ~MRFSamplerRandom() {}
+ return 0;
+ }
- private:
- RandomGeneratorType::Pointer m_Generator;
- };
-
-
+ /** Methods to cancel random effects.*/
+ void InitializeSeed(int seed){ m_Generator->SetSeed(seed); }
+ void InitializeSeed(){ m_Generator->SetSeed(); }
+
+ protected:
+ // The constructor and destructor.
+ MRFSamplerRandom()
+ {
+ m_Generator = RandomGeneratorType::New();
+ m_Generator->SetSeed();
+ }
+ virtual ~MRFSamplerRandom() {}
+
+
+ private:
+ RandomGeneratorType::Pointer m_Generator;
+ };
}
#endif
diff --git a/Code/Markov/otbMRFSamplerRandomMAP.h b/Code/Markov/otbMRFSamplerRandomMAP.h
index cd25ec9443..3700c43c43 100644
--- a/Code/Markov/otbMRFSamplerRandomMAP.h
+++ b/Code/Markov/otbMRFSamplerRandomMAP.h
@@ -19,141 +19,149 @@
#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>
+/**
+ * \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)
{
- 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;
-
- itkNewMacro(Self);
+ 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();
+ }
+ }
- itkTypeMacro(MRFSamplerRandomMAP,MRFSampler);
-
-
- void SetNumberOfClasses(const unsigned int nClasses)
- {
- if (nClasses != this->m_NumberOfClasses)
- {
- 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)
- {
+ 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 (LabelledImagePixelType valueCurrent = 0;
- valueCurrent < static_cast<LabelledImagePixelType>(this->m_NumberOfClasses);
- ++valueCurrent)
- {
- this->m_EnergyCurrent = this->m_EnergyFidelity->GetValue(itData, valueCurrent);
- this->m_EnergyCurrent += this->m_Lambda
- * this->m_EnergyRegularization->GetValue(itRegul, valueCurrent);
-
- energy[static_cast<unsigned int>(valueCurrent)]
- = this->m_EnergyCurrent;
- repartitionFunction[static_cast<unsigned int>(valueCurrent)]
- = vcl_exp(-this->m_EnergyCurrent)+totalProba;
- totalProba
- = repartitionFunction[static_cast<unsigned int>(valueCurrent)];
-
- }
-
- //Pick a value according to probability
-
- double select = (rand()/(double(RAND_MAX)+1) * totalProba);
- unsigned int valueCurrent = 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);
+ 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;
- }
-
- private:
- double * repartitionFunction;
- double * energy;
-
- protected:
- // The constructor and destructor.
- MRFSamplerRandomMAP() {
- energy=NULL;
- repartitionFunction=NULL;
- };
- virtual ~MRFSamplerRandomMAP() {
- if (energy != NULL) free(energy);
- if (repartitionFunction != NULL) free(repartitionFunction);
- };
+ }
+
+
+ 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
diff --git a/Code/Markov/otbMarkovRandomFieldFilter.h b/Code/Markov/otbMarkovRandomFieldFilter.h
index cb0369c1d5..075e234d66 100644
--- a/Code/Markov/otbMarkovRandomFieldFilter.h
+++ b/Code/Markov/otbMarkovRandomFieldFilter.h
@@ -22,6 +22,7 @@
#include "vnl/vnl_vector.h"
#include "vnl/vnl_matrix.h"
+#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkImageClassifierBase.h"
@@ -157,7 +158,11 @@ public itk::ImageToImageFilter<TInputImage,TClassifiedImage>
typedef typename LabelledImageFaceListType::iterator
LabelledImageFaceListIterator;
-
+
+ /** Typedef for random values. */
+ typedef itk::Statistics::MersenneTwisterRandomVariateGenerator RandomGeneratorType;
+
+
/** Set the pointer to the classifer being used. */
void SetClassifier( typename ClassifierType::Pointer ptrToClassifier );
@@ -179,19 +184,11 @@ public itk::ImageToImageFilter<TInputImage,TClassifiedImage>
/**
************ ACCESSORS ************
*/
-
-// void SetEnergyRegularization( EnergyRegularizationPointer enerReg ){ m_EnergyRegularization=enerReg; this->Modified(); };
-// EnergyRegularizationPointer GetEnergyRegularization(){ return m_EnergyRegularization; };
-// void SetEnergyFidelity( EnergyFidelityPointer enerFid ){ m_EnergyFidelity=enerFid; this->Modified(); };
-// EnergyFidelityPointer GetEnergyFidelity(){ return m_EnergyFidelity; };
-
- itkSetObjectMacro( EnergyRegularization, EnergyRegularizationType);
- itkGetObjectMacro( EnergyRegularization, EnergyRegularizationType);
-
- itkSetObjectMacro( EnergyFidelity, EnergyFidelityType);
- itkGetObjectMacro( EnergyFidelity, EnergyFidelityType);
-
-
+ itkSetObjectMacro( EnergyRegularization, EnergyRegularizationType);
+ itkGetObjectMacro( EnergyRegularization, EnergyRegularizationType);
+
+ itkSetObjectMacro( EnergyFidelity, EnergyFidelityType);
+ itkGetObjectMacro( EnergyFidelity, EnergyFidelityType);
itkSetObjectMacro( Sampler, SamplerType);
itkGetObjectMacro( Sampler, SamplerType);
@@ -282,7 +279,10 @@ public itk::ImageToImageFilter<TInputImage,TClassifiedImage>
(itk::Concept::SameDimension<InputImageDimension, ClassifiedImageDimension>));
/** End concept checking */
#endif
-
+
+ /** Methods to cancel random effects.*/
+ void InitializeSeed(int seed){ m_Generator->SetSeed(seed); }
+ void InitializeSeed(){ m_Generator->SetSeed(); }
protected:
MarkovRandomFieldFilter();
@@ -344,7 +344,8 @@ public itk::ImageToImageFilter<TInputImage,TClassifiedImage>
std::vector<double> m_NeighborInfluence;
std::vector<double> m_DummyVector;
-
+ RandomGeneratorType::Pointer m_Generator;
+
/** Pointer to different elements */
EnergyRegularizationPointer m_EnergyRegularization;
@@ -353,7 +354,7 @@ public itk::ImageToImageFilter<TInputImage,TClassifiedImage>
SamplerPointer m_Sampler;
virtual void MinimizeOnce();
-
+
private:
}; // class MarkovRandomFieldFilter
diff --git a/Code/Markov/otbMarkovRandomFieldFilter.txx b/Code/Markov/otbMarkovRandomFieldFilter.txx
index e8251e3c4c..5adf9e97e2 100644
--- a/Code/Markov/otbMarkovRandomFieldFilter.txx
+++ b/Code/Markov/otbMarkovRandomFieldFilter.txx
@@ -25,7 +25,7 @@ namespace otb
/** ATTENTION : j'ai passe les initialisation dans le constructeur car certaines plateformes (dont NEMO) n'aiment cette maniere d'initialiser*/
template<class TInputImage, class TClassifiedImage>
MarkovRandomFieldFilter<TInputImage,TClassifiedImage>
-::MarkovRandomFieldFilter(void) :
+::MarkovRandomFieldFilter(void)/* :
m_NumberOfClasses(0),
m_MaximumNumberOfIterations(50),
m_ErrorCounter(0),
@@ -38,9 +38,9 @@ MarkovRandomFieldFilter<TInputImage,TClassifiedImage>
m_NumberOfIterations(0),
m_StopCondition(MaximumNumberOfIterations),
m_ExternalClassificationSet(false)
-
+ */
{
- /*m_NumberOfClasses = 0;
+ m_NumberOfClasses = 0;
m_MaximumNumberOfIterations = 50;
m_ErrorCounter = 0;
m_ImageDeltaEnergy = 0.0;
@@ -51,7 +51,10 @@ MarkovRandomFieldFilter<TInputImage,TClassifiedImage>
m_SmoothingFactor = 1;
m_NumberOfIterations = 0;
m_StopCondition = MaximumNumberOfIterations;
- m_ExternalClassificationSet = false;*/
+ m_ExternalClassificationSet = false;
+ m_Generator = RandomGeneratorType::New();
+ m_Generator->SetSeed();
+
this->SetNumberOfRequiredInputs(1);
if( (int)InputImageDimension != (int)ClassifiedImageDimension )
{
@@ -66,7 +69,7 @@ MarkovRandomFieldFilter<TInputImage,TClassifiedImage>
// this->SetMRFNeighborhoodWeight( m_DummyVector );
// this->SetDefaultMRFNeighborhoodWeight();
- srand((unsigned)time(0));
+ //srand((unsigned)time(0));
}
@@ -315,7 +318,7 @@ MarkovRandomFieldFilter<TInputImage, TClassifiedImage>
while ( !outImageIt.IsAtEnd() )
{
LabelledImagePixelType randomvalue = static_cast<LabelledImagePixelType>(
- rand() % static_cast<int>(m_NumberOfClasses)
+ m_Generator->GetIntegerVariate() % static_cast<int>(m_NumberOfClasses)
);
outImageIt.Set( randomvalue );
++outImageIt;
diff --git a/Testing/Code/Markov/otbMRFEnergyGaussianClassification.cxx b/Testing/Code/Markov/otbMRFEnergyGaussianClassification.cxx
index 5d11fe4fdb..e0cf022094 100644
--- a/Testing/Code/Markov/otbMRFEnergyGaussianClassification.cxx
+++ b/Testing/Code/Markov/otbMRFEnergyGaussianClassification.cxx
@@ -37,13 +37,21 @@ int otbMRFEnergyGaussianClassification(int argc, char * argv[])
MRFGaussianType::Pointer classif = MRFGaussianType::New();
-
+ classif->SetNumberOfParameters(6);
+ MRFGaussianType::ParametersType param(6);
+ param[0] = 0;
+ param[1] = 1;
+ param[2] = 2;
+ param[3] = 3;
+ param[4] = 4;
+ param[5] = 5;
+ classif->SetParameters(param);
std::ofstream file;
file.open(outputFile);
file<<std::endl;
file<<"Number of paramters: "<<classif->GetNumberOfParameters()<<std::endl;
- file<<"GetSingleValue(10, 1): "<<classif->GetSingleValue(10, 1)<<std::endl;
+ file<<"GetSingleValue(10, 1): "<<classif->GetSingleValue(10, 2)<<std::endl;
file.close();
diff --git a/Testing/Code/Markov/otbMRFOptimizerMetropolis.cxx b/Testing/Code/Markov/otbMRFOptimizerMetropolis.cxx
index 29046f8e39..89aa24a27c 100644
--- a/Testing/Code/Markov/otbMRFOptimizerMetropolis.cxx
+++ b/Testing/Code/Markov/otbMRFOptimizerMetropolis.cxx
@@ -33,18 +33,13 @@ int otbMRFOptimizerMetropolis(int argc, char * argv[])
MRFOptimizerType::ParametersType param(1);
param.Fill(10.);
object->SetParameters(param);
-
- // USED TO OVERPASS RANDOM CALCULATION
- //object->SetValueInsteadRandom(1);
+ object->InitializeSeed(0);// USED TO OVERPASS RANDOM CALCULATION
std::ofstream file;
file.open(outputFile);
file<<"Compute(0) : "<<object->Compute(0.)<<std::endl;
file<<"Compute(-1.) : "<<object->Compute(-1.)<<std::endl;
- file<<"Compute(1.) avec m_ValueInsteadRandom<proba*1000 : "<<object->Compute(1.)<<std::endl;
- //object->SetValueInsteadRandom( 10000 );
- file<<"Compute(1.) avec m_ValueInsteadRandom>=proba*1000: "<<object->Compute(1.)<<std::endl;
file.close();
diff --git a/Testing/Code/Markov/otbMRFSamplerMAP.cxx b/Testing/Code/Markov/otbMRFSamplerMAP.cxx
index f24abf2031..30844ff091 100644
--- a/Testing/Code/Markov/otbMRFSamplerMAP.cxx
+++ b/Testing/Code/Markov/otbMRFSamplerMAP.cxx
@@ -22,6 +22,7 @@
#include "otbMRFSamplerMAP.h"
#include "otbImageFileReader.h"
#include "otbImage.h"
+#include "otbMRFEnergyPotts.h"
#include <fstream>
@@ -41,11 +42,19 @@ int otbMRFSamplerMAP(int argc, char * argv[])
typedef MRFSamplerMAPType::LabelledImageNeighborhoodIterator LabelledNeighborhoodIterator;
typedef MRFSamplerMAPType::InputImageNeighborhoodIterator InputNeighborhoodIterator;
+ typedef otb::MRFEnergyPotts <ImageType, LabelType> EnergyFidelityType;
+ typedef otb::MRFEnergyPotts <LabelType, LabelType> EnergyRegularizationType;
- MRFSamplerMAPType::Pointer object = MRFSamplerMAPType::New();
- ReaderInputType::Pointer readerIn = ReaderInputType::New();
- ReaderLabelType::Pointer readerLab = ReaderLabelType::New();
+
+ MRFSamplerMAPType::Pointer object = MRFSamplerMAPType::New();
+ EnergyRegularizationType::Pointer energyRegularization = EnergyRegularizationType::New();
+ EnergyFidelityType::Pointer energyFidelity = EnergyFidelityType::New();
+ ReaderInputType::Pointer readerIn = ReaderInputType::New();
+ ReaderLabelType::Pointer readerLab = ReaderLabelType::New();
+ object->SetEnergyFidelity(energyFidelity);
+ object->SetEnergyRegularization(energyRegularization);
+
readerIn->SetFileName( inputImage );
readerLab->SetFileName( labelImage );
readerIn->Update();
diff --git a/Testing/Code/Markov/otbMRFSamplerRandom.cxx b/Testing/Code/Markov/otbMRFSamplerRandom.cxx
index 25ff374eb2..46fcb51d2a 100644
--- a/Testing/Code/Markov/otbMRFSamplerRandom.cxx
+++ b/Testing/Code/Markov/otbMRFSamplerRandom.cxx
@@ -22,6 +22,7 @@
#include "otbMRFSamplerRandom.h"
#include "otbImageFileReader.h"
#include "otbImage.h"
+#include "otbMRFEnergyPotts.h"
#include <fstream>
@@ -41,19 +42,24 @@ int otbMRFSamplerRandom(int argc, char * argv[])
typedef MRFSamplerRandomType::LabelledImageNeighborhoodIterator LabelledNeighborhoodIterator;
typedef MRFSamplerRandomType::InputImageNeighborhoodIterator InputNeighborhoodIterator;
+ typedef otb::MRFEnergyPotts <ImageType, LabelType> EnergyFidelityType;
+ typedef otb::MRFEnergyPotts <LabelType, LabelType> EnergyRegularizationType;
- MRFSamplerRandomType::Pointer object = MRFSamplerRandomType::New();
- ReaderInputType::Pointer readerIn = ReaderInputType::New();
- ReaderLabelType::Pointer readerLab = ReaderLabelType::New();
+ MRFSamplerRandomType::Pointer object = MRFSamplerRandomType::New();
+ EnergyRegularizationType::Pointer energyRegularization = EnergyRegularizationType::New();
+ EnergyFidelityType::Pointer energyFidelity = EnergyFidelityType::New();
+ ReaderInputType::Pointer readerIn = ReaderInputType::New();
+ ReaderLabelType::Pointer readerLab = ReaderLabelType::New();
+ object->SetEnergyFidelity(energyFidelity);
+ object->SetEnergyRegularization(energyRegularization);
+ object->InitializeSeed(0);// USED TO OVERPASS RANDOM CALCULATION
+
readerIn->SetFileName( inputImage );
readerLab->SetFileName( labelImage );
readerIn->Update();
readerLab->Update();
- // USED TO OVERPASS RANDOM CALCULATION
- // object->SetValueInsteadRandom(300);
-
ImageType::IndexType idIn;
LabelType::IndexType idLab;
idIn[0] = 50;
diff --git a/Testing/Code/Markov/otbMRFSamplerRandomMAP.cxx b/Testing/Code/Markov/otbMRFSamplerRandomMAP.cxx
index f2c3cc1b5d..2eed948ceb 100644
--- a/Testing/Code/Markov/otbMRFSamplerRandomMAP.cxx
+++ b/Testing/Code/Markov/otbMRFSamplerRandomMAP.cxx
@@ -22,6 +22,8 @@
#include "otbMRFSamplerRandomMAP.h"
#include "otbImageFileReader.h"
#include "otbImage.h"
+#include "otbMRFEnergyPotts.h"
+
#include <fstream>
@@ -38,23 +40,30 @@ int otbMRFSamplerRandomMAP(int argc, char * argv[])
typedef otb::ImageFileReader< ImageType > ReaderInputType;
typedef otb::ImageFileReader< LabelType > ReaderLabelType;
typedef otb::MRFSamplerRandomMAP< ImageType, LabelType> MRFSamplerRandomMAPType;
+
+ typedef otb::MRFEnergyPotts <ImageType, LabelType> EnergyFidelityType;
+ typedef otb::MRFEnergyPotts <LabelType, LabelType> EnergyRegularizationType;
+
+
typedef MRFSamplerRandomMAPType::LabelledImageNeighborhoodIterator LabelledNeighborhoodIterator;
typedef MRFSamplerRandomMAPType::InputImageNeighborhoodIterator InputNeighborhoodIterator;
+ MRFSamplerRandomMAPType::Pointer object = MRFSamplerRandomMAPType::New();
+ EnergyRegularizationType::Pointer energyRegularization = EnergyRegularizationType::New();
+ EnergyFidelityType::Pointer energyFidelity = EnergyFidelityType::New();
+ ReaderInputType::Pointer readerIn = ReaderInputType::New();
+ ReaderLabelType::Pointer readerLab = ReaderLabelType::New();
+
- MRFSamplerRandomMAPType::Pointer object = MRFSamplerRandomMAPType::New();
- ReaderInputType::Pointer readerIn = ReaderInputType::New();
- ReaderLabelType::Pointer readerLab = ReaderLabelType::New();
-
readerIn->SetFileName( inputImage );
readerLab->SetFileName( labelImage );
readerIn->Update();
readerLab->Update();
object->SetNumberOfClasses(1);
-
- // USED TO OVERPASS RANDOM CALCULATION
- //object->SetValueInsteadRandom(300);
+ object->SetEnergyFidelity(energyFidelity);
+ object->SetEnergyRegularization(energyRegularization);
+ object->InitializeSeed(0);// USED TO OVERPASS RANDOM CALCULATION
ImageType::IndexType idIn;
LabelType::IndexType idLab;
diff --git a/Testing/Code/Markov/otbMarkovRandomFieldFilter.cxx b/Testing/Code/Markov/otbMarkovRandomFieldFilter.cxx
index 068311850d..fad4516431 100755
--- a/Testing/Code/Markov/otbMarkovRandomFieldFilter.cxx
+++ b/Testing/Code/Markov/otbMarkovRandomFieldFilter.cxx
@@ -40,12 +40,12 @@ int otbMarkovRandomFieldFilter(int argc, char* argv[] )
{
const unsigned int Dimension = 2;
- typedef double InternalPixelType;
- typedef unsigned char LabelledPixelType;
+ typedef double InternalPixelType;
+ typedef unsigned char LabelledPixelType;
typedef otb::Image<InternalPixelType, Dimension> InputImageType;
- typedef otb::Image<LabelledPixelType, Dimension> LabelledImageType;
- typedef otb::ImageFileReader< InputImageType > ReaderType;
- typedef otb::ImageFileWriter< LabelledImageType > WriterType;
+ typedef otb::Image<LabelledPixelType, Dimension> LabelledImageType;
+ typedef otb::ImageFileReader< InputImageType > ReaderType;
+ typedef otb::ImageFileWriter< LabelledImageType > WriterType;
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
@@ -56,24 +56,18 @@ int otbMarkovRandomFieldFilter(int argc, char* argv[] )
reader->SetFileName( inputFilename );
writer->SetFileName( outputFilename );
- typedef otb::MarkovRandomFieldFilter
- <InputImageType,LabelledImageType> MarkovRandomFieldFilterType;
-
- typedef otb::MRFSamplerRandom< InputImageType, LabelledImageType> SamplerType;
-
- typedef otb::MRFOptimizerMetropolis OptimizerType;
-
- typedef otb::MRFEnergyPotts
- <LabelledImageType, LabelledImageType> EnergyRegularizationType;
- typedef otb::MRFEnergyGaussianClassification
- <InputImageType, LabelledImageType> EnergyFidelityType;
+ typedef otb::MarkovRandomFieldFilter<InputImageType,LabelledImageType> MarkovRandomFieldFilterType;
+ typedef otb::MRFSamplerRandom< InputImageType, LabelledImageType> SamplerType;
+ typedef otb::MRFOptimizerMetropolis OptimizerType;
+ typedef otb::MRFEnergyPotts<LabelledImageType, LabelledImageType> EnergyRegularizationType;
+ typedef otb::MRFEnergyGaussianClassification<InputImageType, LabelledImageType> EnergyFidelityType;
- MarkovRandomFieldFilterType::Pointer markovFilter = MarkovRandomFieldFilterType::New();
- EnergyRegularizationType::Pointer energyRegularization = EnergyRegularizationType::New();
- EnergyFidelityType::Pointer energyFidelity = EnergyFidelityType::New();
- OptimizerType::Pointer optimizer = OptimizerType::New();
- SamplerType::Pointer sampler = SamplerType::New();
+ MarkovRandomFieldFilterType::Pointer markovFilter = MarkovRandomFieldFilterType::New();
+ EnergyRegularizationType::Pointer energyRegularization = EnergyRegularizationType::New();
+ EnergyFidelityType::Pointer energyFidelity = EnergyFidelityType::New();
+ OptimizerType::Pointer optimizer = OptimizerType::New();
+ SamplerType::Pointer sampler = SamplerType::New();
unsigned int nClass = 4;
@@ -90,16 +84,18 @@ int otbMarkovRandomFieldFilter(int argc, char* argv[] )
parameters[7]=10.0; //Class 3 stde
energyFidelity->SetParameters(parameters);
-
- optimizer->SetParameters(atoi(argv[5]));
+ optimizer->SetSingleParameter(atof(argv[5]));
+ markovFilter->InitializeSeed(0);
markovFilter->SetNumberOfClasses(nClass);
markovFilter->SetMaximumNumberOfIterations(atoi(argv[4]));
markovFilter->SetErrorTolerance(0.0);
markovFilter->SetLambda(atof(argv[3]));
markovFilter->SetNeighborhoodRadius(1);
- markovFilter->SetEnergyRegularization(static_cast<MarkovRandomFieldFilterType::EnergyRegularizationPointer>(energyRegularization));
- markovFilter->SetEnergyFidelity(static_cast<MarkovRandomFieldFilterType::EnergyFidelityPointer>(energyFidelity));
+ //markovFilter->SetEnergyRegularization(static_cast<MarkovRandomFieldFilterType::EnergyRegularizationPointer>(energyRegularization));
+ //markovFilter->SetEnergyFidelity(static_cast<MarkovRandomFieldFilterType::EnergyFidelityPointer>(energyFidelity));
+ markovFilter->SetEnergyRegularization(energyRegularization);
+ markovFilter->SetEnergyFidelity(energyFidelity);
markovFilter->SetOptimizer(optimizer);
markovFilter->SetSampler(sampler);
--
GitLab