Orfeo Toolbox  4.0
Markov/MarkovClassification2Example.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: {QB_Suburb.png}
// OUTPUTS: {MarkovRandomField2.png}
// 1.0 5 1
// 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 "otbImage.h"
// Software Guide : BeginLatex
//
// First, we need to include header specific to these class:
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int main(int argc, char* argv[])
{
if (argc != 6)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage output lambda iterations" << std::endl;
std::cerr << " useRandomValue" << std::endl;
return 1;
}
const unsigned int Dimension = 2;
typedef double InternalPixelType;
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];
reader->SetFileName(inputFilename);
writer->SetFileName(outputFilename);
<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
<LabelledImageType, LabelledImageType> EnergyRegularizationType;
<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();
if ((bool) (atoi(argv[5])) == true)
{
// Overpass random calculation(for test only):
sampler->InitializeSeed(0);
markovFilter->InitializeSeed(1);
}
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());
<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]{MarkovRandomField2.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;
}

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