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/*
* Copyright (C) 2005-2020 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/* Example usage:
./MarkovRegularizationExample Input/ROI_QB_MUL_1_SVN_CLASS_MULTI.png Output/MarkovRegularization.png Output/MarkovRegularization-scaled.png 0.2 20 0.0 1
*/
// 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.
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "otbImage.h"
#include "otbMarkovRandomFieldFilter.h"
#include "itkUnaryFunctorImageFilter.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;
using LabelledPixelType = unsigned char;
using LabelledImageType = otb::Image<LabelledPixelType, Dimension>;
using ReaderType = otb::ImageFileReader<LabelledImageType>;
using WriterType = otb::ImageFileWriter<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);
using MarkovRandomFieldFilterType = otb::MarkovRandomFieldFilter<LabelledImageType, LabelledImageType>;
using SamplerType = otb::MRFSamplerRandom<LabelledImageType, LabelledImageType>;
using OptimizerType = otb::MRFOptimizerMetropolis;
using EnergyRegularizationType = otb::MRFEnergyPotts<LabelledImageType, LabelledImageType>;
using EnergyFidelityType = otb::MRFEnergyPotts<LabelledImageType, LabelledImageType>;
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);
}
// To find the number of classes available in the original image we use the
// \doxygen{itk}{LabelStatisticsImageFilter} and more particularly the method
// \code{GetNumberOfLabels()}.
using LabelledStatType = itk::LabelStatisticsImageFilter<LabelledImageType, LabelledImageType>;
LabelledStatType::Pointer labelledStat = LabelledStatType::New();
labelledStat->SetInput(reader->GetOutput());
labelledStat->SetLabelInput(reader->GetOutput());
labelledStat->Update();
unsigned int nClass = labelledStat->GetNumberOfLabels();
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();
using RescaleType = itk::RescaleIntensityImageFilter<LabelledImageType, LabelledImageType>;
RescaleType::Pointer rescaleFilter = RescaleType::New();
rescaleFilter->SetOutputMinimum(0);
rescaleFilter->SetOutputMaximum(255);
rescaleFilter->SetInput(markovFilter->GetOutput());
writer->SetFileName(outputScaledFilename);
writer->SetInput(rescaleFilter->GetOutput());
writer->Update();
// 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 restoration]{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}
return EXIT_SUCCESS;
}
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