diff --git a/Examples/Markov/CMakeLists.txt b/Examples/Markov/CMakeLists.txt
index 1d2b5cbebc500c195ee22111dd886661cb522618..13dca8f2b254d8a89f6b89ccd3036971d1e9425c 100755
--- a/Examples/Markov/CMakeLists.txt
+++ b/Examples/Markov/CMakeLists.txt
@@ -3,11 +3,11 @@ INCLUDE_REGULAR_EXPRESSION("^.*$")
SET(Markov_EXAMPLES ${CXX_TEST_PATH}/otbMarkovExamplesTests)
-ADD_EXECUTABLE(MarkovClassification1Example MarkovClassification1Example.cxx )
-TARGET_LINK_LIBRARIES(MarkovClassification1Example OTBMarkov OTBCommon OTBIO ITKNumerics ITKIO)
+ADD_EXECUTABLE(MarkovRandomField1Example MarkovRandomField1Example.cxx )
+TARGET_LINK_LIBRARIES(MarkovRandomField1Example OTBMarkov OTBCommon OTBIO ITKNumerics ITKIO)
-ADD_EXECUTABLE(MarkovClassification2Example MarkovClassification2Example.cxx )
-TARGET_LINK_LIBRARIES(MarkovClassification2Example OTBMarkov OTBCommon OTBIO ITKNumerics ITKIO)
+ADD_EXECUTABLE(MarkovRandomField2Example MarkovRandomField2Example.cxx )
+TARGET_LINK_LIBRARIES(MarkovRandomField2Example OTBMarkov OTBCommon OTBIO ITKNumerics ITKIO)
ADD_EXECUTABLE(MarkovRestaurationExample MarkovRestaurationExample.cxx )
TARGET_LINK_LIBRARIES(MarkovRestaurationExample OTBMarkov OTBCommon OTBIO ITKNumerics ITKIO)
@@ -27,25 +27,25 @@ SET(EXE_TESTS ${CXX_TEST_PATH}/otbMarkovExamplesTests)
SET(TOL 0.0)
SET(EPSILON 0.00000001)
-ADD_TEST(MarkovClassification1ExampleTest ${EXE_TESTS}
+ADD_TEST(MarkovRandomField1ExampleTest ${EXE_TESTS}
--compare-image ${EPSILON}
- ${BASELINE}/MarkovClassification1.png
- ${TEMP}/MarkovClassification1.png
- MarkovClassification1ExampleTest
+ ${BASELINE}/MarkovRandomField1.png
+ ${TEMP}/MarkovRandomField1.png
+ MarkovRandomField1ExampleTest
${INPUTDATA}/QB_Suburb.png
- ${TEMP}/MarkovClassification1.png
+ ${TEMP}/MarkovRandomField1.png
1.0
20
1.0
)
-ADD_TEST(MarkovClassification2ExampleTest ${EXE_TESTS}
+ADD_TEST(MarkovRandomField2ExampleTest ${EXE_TESTS}
--compare-image ${EPSILON}
- ${BASELINE}/MarkovClassification2.png
- ${TEMP}/MarkovClassification2.png
- MarkovClassification2ExampleTest
+ ${BASELINE}/MarkovRandomField2.png
+ ${TEMP}/MarkovRandomField2.png
+ MarkovRandomField2ExampleTest
${INPUTDATA}/QB_Suburb.png
- ${TEMP}/MarkovClassification2.png
+ ${TEMP}/MarkovRandomField2.png
1.0
5
)
diff --git a/Examples/Markov/MarkovClassification1Example.cxx b/Examples/Markov/MarkovClassification1Example.cxx
deleted file mode 100755
index 7d15b83e230ce93167ec97f9a98e77504f4274c5..0000000000000000000000000000000000000000
--- a/Examples/Markov/MarkovClassification1Example.cxx
+++ /dev/null
@@ -1,328 +0,0 @@
-
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
-#if defined(_MSC_VER)
-#pragma warning ( disable : 4786 )
-#endif
-
-#ifdef __BORLANDC__
-#define ITK_LEAN_AND_MEAN
-#endif
-
-// Software Guide : BeginCommandLineArgs
-// INPUTS: {QB_Suburb.png}
-// OUTPUTS: {MarkovClassification1.png}
-// 1.0 20 1.0
-// Software Guide : EndCommandLineArgs
-
-
-// Software Guide : BeginLatex
-//
-// This example illustrates the details of the \doxygen{otb}{MarkovRandomFieldFilter}.
-// This filter is an application of the Markov Random Fields for classification,
-// segmentation or restauration.
-//
-// This example applies the \doxygen{otb}{MarkovRandomFieldFilter} to
-// classify an image into four classes defined by their mean and variance. The
-// optimization is done using an Metropolis algorithm with a random sampler. The
-// regularization energy is defined by a Potts model and the fidelity by a
-// Gaussian model.
-//
-//
-// Software Guide : EndLatex
-
-#include "otbImageFileReader.h"
-#include "otbImageFileWriter.h"
-#include "otbImage.h"
-#include "otbMarkovRandomFieldFilter.h"
-#include "itkRescaleIntensityImageFilter.h"
-
-
-// Software Guide : BeginLatex
-//
-// The first step toward the use of this filter is the inclusion of the proper
-// header files.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-#include "otbMRFEnergyPotts.h"
-#include "otbMRFEnergyGaussianClassification.h"
-#include "otbMRFOptimizerMetropolis.h"
-#include "otbMRFSamplerRandom.h"
-// Software Guide : EndCodeSnippet
-
-
-int main(int argc, char* argv[] )
-{
-
- if( argc != 6 )
- {
- std::cerr << "Missing Parameters "<< argc << std::endl;
- std::cerr << "Missing Parameters " << std::endl;
- std::cerr << "Usage: " << argv[0];
- std::cerr << " inputImage output lambda iterations optimizerTemperature" << std::endl;
- return 1;
- }
-
-
- // Software Guide : BeginLatex
- //
- // Then we must decide what pixel type to use for the image. We
- // choose to make all computations with double precision.
- // The labelled image is of type unsigned char which allows up to 256 different
- // classes.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- const unsigned int Dimension = 2;
-
- typedef double InternalPixelType;
- typedef unsigned char LabelledPixelType;
- typedef otb::Image<InternalPixelType, Dimension> InputImageType;
- typedef otb::Image<LabelledPixelType, Dimension> LabelledImageType;
-
- // Software Guide : EndCodeSnippet
-
-
- // Software Guide : BeginLatex
- //
- // We define a reader for the image to be classified, an initialisation for the
- // classification (which could be random) and a writer for the final
- // classification.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- typedef otb::ImageFileReader< InputImageType > ReaderType;
- typedef otb::ImageFileWriter< LabelledImageType > WriterType;
-
- ReaderType::Pointer reader = ReaderType::New();
- WriterType::Pointer writer = WriterType::New();
-
- const char * inputFilename = argv[1];
- const char * outputFilename = argv[2];
-
- reader->SetFileName( inputFilename );
- writer->SetFileName( outputFilename );
-
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // Finally, we define the different classes necessary for the Markov classification.
- // A \doxygen{otb}{MarkovRandomFieldFilter} is instanciated, this is the
- // main class which connect the other to do the Markov classification.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- typedef otb::MarkovRandomFieldFilter
- <InputImageType,LabelledImageType> MarkovRandomFieldFilterType;
-
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // An \doxygen{otb}{MRFSamplerRandomMAP}, which derives from the
- // \doxygen{otb}{MRFSampler}, is instanciated. The sampler is in charge of
- // proposing a modification for a given site. The
- // \doxygen{otb}{MRFSamplerRandomMAP}, randomly pick one possible value
- // according to the MAP probability.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- typedef otb::MRFSamplerRandom< InputImageType, LabelledImageType> SamplerType;
-
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // An \doxygen{otb}{MRFOptimizerMetropoli}, which derives from the
- // \doxygen{otb}{MRFOptimizer}, is instanciated. The optimizer is in charge
- // of accepting or rejecting the value proposed by the sampler. The
- // \doxygen{otb}{MRFSamplerRandomMAP}, accept the proposal according to the
- // variation of energy it causes and a temperature parameter.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- typedef otb::MRFOptimizerMetropolis OptimizerType;
-
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // Two energy, deriving from the \doxygen{otb}{MRFEnergy} class need to be instanciated. One energy
- // is required for the regularization, taking into account the relashionship between neighborhing pixels
- // in the classified image. Here it is done with the \doxygen{otb}{MRFEnergyPotts} which implement
- // a Potts model.
- //
- // The second energy is for the fidelity to the original data. Here it is done with an
- // \doxygen{otb}{MRFEnergyGaussianClassification} class, which defines a gaussian model for the data.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- typedef otb::MRFEnergyPotts
- <LabelledImageType, LabelledImageType> EnergyRegularizationType;
- typedef otb::MRFEnergyGaussianClassification
- <InputImageType, LabelledImageType> EnergyFidelityType;
-
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The different filters composing our pipeline are created by invoking their
- // \code{New()} methods, assigning the results to smart pointers.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- 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();
-
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // Parameter for the \doxygen{otb}{MRFEnergyGaussianClassification} class, meand
- // and standard deviation are created.
- //
- // Software Guide : EndLatex
-
- // Overpass random calculation(for test only):
- sampler->InitializeSeed(0);
- optimizer->InitializeSeed(0);
- markovFilter->InitializeSeed(0);
-
- // Software Guide : BeginCodeSnippet
-
- unsigned int nClass = 4;
- energyFidelity->SetNumberOfParameters(2*nClass);
- EnergyFidelityType::ParametersType parameters;
- parameters.SetSize(energyFidelity->GetNumberOfParameters());
- parameters[0]=10.0; //Class 0 mean
- parameters[1]=10.0; //Class 0 stdev
- parameters[2]=80.0;//Class 1 mean
- parameters[3]=10.0; //Class 1 stdev
- parameters[4]=150.0; //Class 2 mean
- parameters[5]=10.0; //Class 2 stdev
- parameters[6]=220.0;//Class 3 mean
- parameters[7]=10.0; //Class 3 stde
- energyFidelity->SetParameters(parameters);
-
- // Software Guide : EndCodeSnippet
-
-
- // Software Guide : BeginLatex
- //
- // Parameters are given to the different class an the sampler, optimizer and
- // energies are connected with the Markov filter.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- OptimizerType::ParametersType param(1);
- param.Fill(atof(argv[5]));
- optimizer->SetParameters(param);
- markovFilter->SetNumberOfClasses(nClass);
- markovFilter->SetMaximumNumberOfIterations(atoi(argv[4]));
- markovFilter->SetErrorTolerance(0.0);
- markovFilter->SetLambda(atof(argv[3]));
- markovFilter->SetNeighborhoodRadius(1);
-
- markovFilter->SetEnergyRegularization(energyRegularization);
- markovFilter->SetEnergyFidelity(energyFidelity);
- markovFilter->SetOptimizer(optimizer);
- markovFilter->SetSampler(sampler);
-
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The pipeline is connected. An \doxygen{itk}{RescaleIntensityImageFilter}
- // rescale the classified image before saving it.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- markovFilter->SetInput(reader->GetOutput());
-
- typedef itk::RescaleIntensityImageFilter
- < LabelledImageType, LabelledImageType > RescaleType;
- RescaleType::Pointer rescaleFilter = RescaleType::New();
- rescaleFilter->SetOutputMinimum(0);
- rescaleFilter->SetOutputMaximum(255);
-
- rescaleFilter->SetInput( markovFilter->GetOutput() );
-
- writer->SetInput( rescaleFilter->GetOutput() );
-
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // Finally, the pipeline execution is trigerred.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- writer->Update();
-
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // Figure~\ref{fig:MRF_CLASSIFICATION1} shows the output of the Markov Random
- // Field classification after 20 iterations with a
- // random sampler and a Metropolis optimizer.
- //
- // \begin{figure}
- // \center
- // \includegraphics[width=0.44\textwidth]{QB_Suburb.eps}
- // \includegraphics[width=0.44\textwidth]{MarkovClassification1.eps}
- // \itkcaption[MRF restauration]{Result of applying
- // the \doxygen{otb}{MarkovRandomFieldFilter} to an extract from a PAN Quickbird
- // image for classification. The result is obtained after 20 iterations with a
- // random sampler and a Metropolis optimizer. From left to right : original image,
- // classification.}
- // \label{fig:MRF_CLASSIFICATION1}
- // \end{figure}
- //
- // Software Guide : EndLatex
-
- return EXIT_SUCCESS;
-
-}
-
diff --git a/Examples/Markov/MarkovClassification2Example.cxx b/Examples/Markov/MarkovClassification2Example.cxx
deleted file mode 100755
index dbf41aa46f0161f9cd0bdceea7951e32e7aa3df9..0000000000000000000000000000000000000000
--- a/Examples/Markov/MarkovClassification2Example.cxx
+++ /dev/null
@@ -1,213 +0,0 @@
-
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
-#if defined(_MSC_VER)
-#pragma warning ( disable : 4786 )
-#endif
-
-#ifdef __BORLANDC__
-#define ITK_LEAN_AND_MEAN
-#endif
-
-// Software Guide : BeginCommandLineArgs
-// INPUTS: {QB_Suburb.png}
-// OUTPUTS: {MarkovClassification1.png}
-// 1.0 5
-// Software Guide : EndCommandLineArgs
-
-
-// Software Guide : BeginLatex
-//
-// Using a similar structure as the previous program and the same energy
-// function, we are now going to slightly alter the program to use a
-// different sampler and optimizer. The proposed sample is proposed
-// randomly according to the MAP probability and the optimizer is the
-// ICM which accept the proposed sample if it enable a reduction of
-// the energy.
-//
-// Software Guide : EndLatex
-
-#include "otbImageFileReader.h"
-#include "otbImageFileWriter.h"
-#include "otbImage.h"
-#include "otbMarkovRandomFieldFilter.h"
-#include "itkRescaleIntensityImageFilter.h"
-
-
-// Software Guide : BeginLatex
-//
-// First, we need to include header specific to these class:
-//
-// Software Guide : EndLatex
-
-
-#include "otbMRFEnergyPotts.h"
-#include "otbMRFEnergyGaussianClassification.h"
-
-// Software Guide : BeginCodeSnippet
-#include "otbMRFSamplerRandomMAP.h"
-#include "otbMRFOptimizerICM.h"
-// Software Guide : EndCodeSnippet
-//#include "otbMRFSamplerRandom.h"
-
-int main(int argc, char* argv[] )
-{
-
- if( argc != 5 )
- {
- std::cerr << "Missing Parameters " << std::endl;
- std::cerr << "Usage: " << argv[0];
- std::cerr << " inputImage output lambda iterations" << std::endl;
- return 1;
- }
-
- const unsigned int Dimension = 2;
-
- 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;
-
- ReaderType::Pointer reader = ReaderType::New();
- WriterType::Pointer writer = WriterType::New();
-
- const char * inputFilename = argv[1];
- const char * outputFilename = argv[2];
-
- reader->SetFileName( inputFilename );
- writer->SetFileName( outputFilename );
-
- typedef otb::MarkovRandomFieldFilter
- <InputImageType,LabelledImageType> MarkovRandomFieldFilterType;
-
-
- // Software Guide : BeginLatex
- //
- // And to declare these new type:
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
-
- typedef otb::MRFSamplerRandomMAP< InputImageType, LabelledImageType> SamplerType;
-// typedef otb::MRFSamplerRandom< InputImageType, LabelledImageType> SamplerType;
-
- // Software Guide : EndCodeSnippet
-
-
- // Software Guide : BeginCodeSnippet
-
- typedef otb::MRFOptimizerICM OptimizerType;
-
- // Software Guide : EndCodeSnippet
-
- 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();
-
- // Overpass random calculation(for test only):
- sampler->InitializeSeed(0);
- markovFilter->InitializeSeed(0);
-
- unsigned int nClass = 4;
- energyFidelity->SetNumberOfParameters(2*nClass);
- EnergyFidelityType::ParametersType parameters;
- parameters.SetSize(energyFidelity->GetNumberOfParameters());
- parameters[0]=10.0; //Class 0 mean
- parameters[1]=10.0; //Class 0 stdev
- parameters[2]=80.0;//Class 1 mean
- parameters[3]=10.0; //Class 1 stdev
- parameters[4]=150.0; //Class 2 mean
- parameters[5]=10.0; //Class 2 stdev
- parameters[6]=220.0;//Class 3 mean
- parameters[7]=10.0; //Class 3 stde
- energyFidelity->SetParameters(parameters);
-
-
- // Software Guide : BeginLatex
- //
- // As the \doxygen{otb}{MRFOptimizerICM} does not have any parameters,
- // the call to \code{optimizer->SetParameters()} must be removed
- //
- // Software Guide : EndLatex
-
- markovFilter->SetNumberOfClasses(nClass);
- markovFilter->SetMaximumNumberOfIterations(atoi(argv[4]));
- markovFilter->SetErrorTolerance(0.0);
- markovFilter->SetLambda(atof(argv[3]));
- markovFilter->SetNeighborhoodRadius(1);
-
- markovFilter->SetEnergyRegularization(energyRegularization);
- markovFilter->SetEnergyFidelity(energyFidelity);
- markovFilter->SetOptimizer(optimizer);
- markovFilter->SetSampler(sampler);
-
- markovFilter->SetInput(reader->GetOutput());
-
- typedef itk::RescaleIntensityImageFilter
- < LabelledImageType, LabelledImageType > RescaleType;
- RescaleType::Pointer rescaleFilter = RescaleType::New();
- rescaleFilter->SetOutputMinimum(0);
- rescaleFilter->SetOutputMaximum(255);
-
- rescaleFilter->SetInput( markovFilter->GetOutput() );
-
- writer->SetInput( rescaleFilter->GetOutput() );
-
- writer->Update();
-
- // Software Guide : BeginLatex
- //
- // Apart from these, no further modification is required.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginLatex
- //
- // Figure~\ref{fig:MRF_CLASSIFICATION2} shows the output of the Markov Random
- // Field classification after 5 iterations with a
- // MAP random sampler and an ICM optimizer.
- //
- // \begin{figure}
- // \center
- // \includegraphics[width=0.44\textwidth]{QB_Suburb.eps}
- // \includegraphics[width=0.44\textwidth]{MarkovClassification2.eps}
- // \itkcaption[MRF restauration]{Result of applying
- // the \doxygen{otb}{MarkovRandomFieldFilter} to an extract from a PAN Quickbird
- // image for classification. The result is obtained after 5 iterations with a
- // MAP random sampler and an ICM optimizer. From left to right : original image,
- // classification.}
- // \label{fig:MRF_CLASSIFICATION2}
- // \end{figure}
- //
- // Software Guide : EndLatex
-
- return EXIT_SUCCESS;
-
-}
-
diff --git a/Examples/Markov/MarkovRestaurationExample.cxx b/Examples/Markov/MarkovRestaurationExample.cxx
index cbc5146399d5ba4306e72c27ff2ad03cd05908f3..20a0dbf5f38b07fc50f3f23b21d8cb8fba7920fe 100755
--- a/Examples/Markov/MarkovRestaurationExample.cxx
+++ b/Examples/Markov/MarkovRestaurationExample.cxx
@@ -1,4 +1,3 @@
-
/*=========================================================================
Program: ORFEO Toolbox
diff --git a/Examples/Markov/otbMarkovExamplesTests.cxx b/Examples/Markov/otbMarkovExamplesTests.cxx
index 0ea76c9a1363345e802a2f179f2c1c0f40de1308..ebba1e109bd6ea1fa7784d60c9c5abcf3ff414ec 100644
--- a/Examples/Markov/otbMarkovExamplesTests.cxx
+++ b/Examples/Markov/otbMarkovExamplesTests.cxx
@@ -25,19 +25,19 @@
void RegisterTests()
{
- REGISTER_TEST(MarkovClassification1ExampleTest);
- REGISTER_TEST(MarkovClassification2ExampleTest);
+ REGISTER_TEST(MarkovRandomField1ExampleTest);
+ REGISTER_TEST(MarkovRandomField2ExampleTest);
REGISTER_TEST(MarkovRestaurationExampleTest);
REGISTER_TEST(MarkovRegularizationExampleTest);
}
#undef main
-#define main MarkovClassification1ExampleTest
-#include "MarkovClassification1Example.cxx"
+#define main MarkovRandomField1ExampleTest
+#include "MarkovRandomField1Example.cxx"
#undef main
-#define main MarkovClassification2ExampleTest
-#include "MarkovClassification2Example.cxx"
+#define main MarkovRandomField2ExampleTest
+#include "MarkovRandomField2Example.cxx"
#undef main
#define main MarkovRestaurationExampleTest