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.

=========================================================================*/


//  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{sec:ImageClassification}.
//
// 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 "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "otbImage.h"
#include "otbMarkovRandomFieldFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkLabelStatisticsImageFilter.h"

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

  typedef otb::ImageFileReader<LabelledImageType> 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];
  const char * outputScaledFilename = argv[3];

  reader->SetFileName(inputFilename);
  writer->SetFileName(outputFilename);

  typedef otb::MarkovRandomFieldFilter
  <LabelledImageType, LabelledImageType> MarkovRandomFieldFilterType;

  typedef otb::MRFSamplerRandom<LabelledImageType,
      LabelledImageType> SamplerType;

  typedef otb::MRFOptimizerMetropolis OptimizerType;

  typedef otb::MRFEnergyPotts
  <LabelledImageType, LabelledImageType>  EnergyRegularizationType;
  typedef otb::MRFEnergyPotts
  <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
  typedef itk::LabelStatisticsImageFilter
  <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();

  typedef itk::RescaleIntensityImageFilter
  <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;

}