# MarkovClassification1Example.cxx¶

Example usage:

./MarkovClassification1Example Input/QB_Suburb.png Output/MarkovRandomField1.png 1.0 20 1.0 1


Example source code (MarkovClassification1Example.cxx):

// This example illustrates the details of the \doxygen{otb}{MarkovRandomFieldFilter}.
// This filter is an application of the Markov Random Fields for classification,
// segmentation or restoration.
//
// 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.
//

#include "otbImageFileWriter.h"
#include "otbImage.h"
#include "otbMarkovRandomFieldFilter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"

// The first step toward the use of this filter is the inclusion of the proper

#include "otbMRFEnergyPotts.h"
#include "otbMRFEnergyGaussianClassification.h"
#include "otbMRFOptimizerMetropolis.h"
#include "otbMRFSamplerRandom.h"

int main(int argc, char* argv[])
{

if (argc != 7)
{
std::cerr << "Missing Parameters " << argc << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage output lambda iterations optimizerTemperature" << std::endl;
std::cerr << " useRandomValue" << std::endl;
return 1;
}

//  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.

const unsigned int Dimension = 2;

using InternalPixelType = double;
using LabelledPixelType = unsigned char;
using InputImageType    = otb::Image<InternalPixelType, Dimension>;
using LabelledImageType = otb::Image<LabelledPixelType, Dimension>;

//  We define a reader for the image to be classified, an initialization for the
//  classification (which could be random) and a writer for the final
//  classification.

using WriterType = otb::ImageFileWriter<LabelledImageType>;

WriterType::Pointer writer = WriterType::New();

const char* inputFilename  = argv[1];
const char* outputFilename = argv[2];

writer->SetFileName(outputFilename);

//  Finally, we define the different classes necessary for the Markov classification.
//  A \doxygen{otb}{MarkovRandomFieldFilter} is instantiated, this is the
// main class which connect the other to do the Markov classification.

using MarkovRandomFieldFilterType = otb::MarkovRandomFieldFilter<InputImageType, LabelledImageType>;

//  An \doxygen{otb}{MRFSamplerRandomMAP}, which derives from the
// \doxygen{otb}{MRFSampler}, is instantiated. 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.

using SamplerType = otb::MRFSamplerRandom<InputImageType, LabelledImageType>;

//  An \doxygen{otb}{MRFOptimizerMetropoli}, which derives from the
// \doxygen{otb}{MRFOptimizer}, is instantiated. 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.

using OptimizerType = otb::MRFOptimizerMetropolis;

// Two energy, deriving from the \doxygen{otb}{MRFEnergy} class need to be instantiated. 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.

using EnergyRegularizationType = otb::MRFEnergyPotts<LabelledImageType, LabelledImageType>;
using EnergyFidelityType       = otb::MRFEnergyGaussianClassification<InputImageType, LabelledImageType>;

// The different filters composing our pipeline are created by invoking their
// \code{New()} methods, assigning the results to smart pointers.

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();

// Parameter for the \doxygen{otb}{MRFEnergyGaussianClassification} class, meand
// and standard deviation are created.

if ((bool)(atoi(argv[6])) == true)
{
// Overpass random calculation(for test only):
sampler->InitializeSeed(0);
optimizer->InitializeSeed(1);
markovFilter->InitializeSeed(2);
}

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);

// Parameters are given to the different class an the sampler, optimizer and
// energies are connected with the Markov filter.

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->SetEnergyRegularization(energyRegularization);
markovFilter->SetEnergyFidelity(energyFidelity);
markovFilter->SetOptimizer(optimizer);
markovFilter->SetSampler(sampler);

// The pipeline is connected. An \doxygen{itk}{RescaleIntensityImageFilter}
// rescale the classified image before saving it.

using RescaleType                  = itk::RescaleIntensityImageFilter<LabelledImageType, LabelledImageType>;
RescaleType::Pointer rescaleFilter = RescaleType::New();
rescaleFilter->SetOutputMinimum(0);
rescaleFilter->SetOutputMaximum(255);

rescaleFilter->SetInput(markovFilter->GetOutput());

writer->SetInput(rescaleFilter->GetOutput());

// Finally, the pipeline execution is trigerred.

writer->Update();

// 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]{MarkovRandomField1.eps}
// \itkcaption[MRF restoration]{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}

return EXIT_SUCCESS;
}