Orfeo Toolbox  4.0
Markov/MarkovRegularizationExample.cxx
/*=========================================================================
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.
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// Software Guide : BeginCommandLineArgs
// INPUTS: {ROI_QB_MUL_1_SVN_CLASS_MULTI.png}
// OUTPUTS: {MarkovRegularization.png}, {MarkovRegularization-scaled.png}
// 0.2 20 0.0 1
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{otb}{MarkovRandomFieldFilter}.
// to regularize a classification obtained previously by another classifier. Here
// we will apply the regularization to the output of an SVM classifier presented
// in \ref{ssec:LearningFromImages}.
//
// The reference image and the starting image are both going to be the original
// classification. Both regularization and fidelity energy are defined by Potts model.
//
// The convergence of the Markov Random Field is done with a random sampler
// and a Metropolis model as in example 1. As you should get use to the general
// program structure to use the MRF framework, we are not going to repeat the entire
// example. However, remember you can find the full source code for this example
// in your OTB source directory.
//
// Software Guide : EndLatex
#include "otbImage.h"
int main(int argc, char* argv[])
{
if (argc != 8)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr <<
" inputClassificationImage outputClassification outputClassificationScaled lambda iterations temperature "
<< std::endl;
std::cerr << " useRandomValue" << std::endl;
return 1;
}
const unsigned int Dimension = 2;
typedef unsigned char LabelledPixelType;
typedef otb::Image<LabelledPixelType, Dimension> LabelledImageType;
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
const char * inputFilename = argv[1];
const char * outputFilename = argv[2];
const char * outputScaledFilename = argv[3];
reader->SetFileName(inputFilename);
writer->SetFileName(outputFilename);
<LabelledImageType, LabelledImageType> MarkovRandomFieldFilterType;
typedef otb::MRFSamplerRandom<LabelledImageType,
LabelledImageType> SamplerType;
typedef otb::MRFOptimizerMetropolis OptimizerType;
<LabelledImageType, LabelledImageType> EnergyRegularizationType;
<LabelledImageType, 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();
if ((bool) (atoi(argv[7])) == true)
{
// Overpass random calculation(for test only):
sampler->InitializeSeed(0);
optimizer->InitializeSeed(1);
markovFilter->InitializeSeed(2);
}
// Software Guide : BeginLatex
//
// To find the number of classes available in the original image we use the
// \doxygen{itk}{LabelStatisticsImageFilter} and more particularly the method
// \code{GetNumberOfLabels()}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
<LabelledImageType, LabelledImageType> LabelledStatType;
LabelledStatType::Pointer labelledStat = LabelledStatType::New();
labelledStat->SetInput(reader->GetOutput());
labelledStat->SetLabelInput(reader->GetOutput());
labelledStat->Update();
unsigned int nClass = labelledStat->GetNumberOfLabels();
// Software Guide : EndCodeSnippet
optimizer->SetSingleParameter(0.0);
markovFilter->SetNumberOfClasses(nClass);
markovFilter->SetMaximumNumberOfIterations(atoi(argv[5]));
markovFilter->SetErrorTolerance(0.0);
markovFilter->SetLambda(atof(argv[4]));
markovFilter->SetNeighborhoodRadius(1);
markovFilter->SetEnergyRegularization(energyRegularization);
markovFilter->SetEnergyFidelity(energyFidelity);
markovFilter->SetOptimizer(optimizer);
markovFilter->SetSampler(sampler);
markovFilter->SetTrainingInput(reader->GetOutput());
markovFilter->SetInput(reader->GetOutput());
writer->SetInput(markovFilter->GetOutput());
writer->Update();
<LabelledImageType, LabelledImageType> RescaleType;
RescaleType::Pointer rescaleFilter = RescaleType::New();
rescaleFilter->SetOutputMinimum(0);
rescaleFilter->SetOutputMaximum(255);
rescaleFilter->SetInput(markovFilter->GetOutput());
writer->SetFileName(outputScaledFilename);
writer->SetInput(rescaleFilter->GetOutput());
writer->Update();
// Software Guide : BeginLatex
//
// Figure~\ref{fig:MRF_REGULARIZATION} shows the output of the Markov Random
// Field regularization on the classification output of another method.
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{MarkovRegularization.eps}
// \includegraphics[width=0.44\textwidth]{MarkovRegularization-scaled.eps}
// \itkcaption[MRF restauration]{Result of applying
// the \doxygen{otb}{MarkovRandomFieldFilter} to regularized the result of another
// classification. From left to right : original classification, regularized
// classification}
// \label{fig:MRF_REGULARIZATION}
// \end{figure}
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}

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