Orfeo Toolbox  4.0
Markov/MarkovClassification1Example.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
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PURPOSE. See the above copyright notices for more information.
=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {QB_Suburb.png}
// OUTPUTS: {MarkovRandomField1.png}
// 1.0 20 1.0 1
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the details of the \doxygen{otb}{MarkovRandomFieldFilter}.
// This filter is an application of the Markov Random Fields for classification,
// segmentation or restauration.
//
// 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.
//
//
// Software Guide : EndLatex
#include "otbImage.h"
// Software Guide : BeginLatex
//
// The first step toward the use of this filter is the inclusion of the proper
// header files.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
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;
}
// Software Guide : BeginLatex
//
// 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.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int Dimension = 2;
typedef double InternalPixelType;
typedef unsigned char LabelledPixelType;
typedef otb::Image<LabelledPixelType, Dimension> LabelledImageType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// 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.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
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);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, we define the different classes necessary for the Markov classification.
// A \doxygen{otb}{MarkovRandomFieldFilter} is instanciated, this is the
// main class which connect the other to do the Markov classification.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
<InputImageType, LabelledImageType> MarkovRandomFieldFilterType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// An \doxygen{otb}{MRFSamplerRandomMAP}, which derives from the
// \doxygen{otb}{MRFSampler}, is instanciated. 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.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// An \doxygen{otb}{MRFOptimizerMetropoli}, which derives from the
// \doxygen{otb}{MRFOptimizer}, is instanciated. 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.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::MRFOptimizerMetropolis OptimizerType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Two energy, deriving from the \doxygen{otb}{MRFEnergy} class need to be instanciated. 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.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
<LabelledImageType, LabelledImageType> EnergyRegularizationType;
<InputImageType, LabelledImageType> EnergyFidelityType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The different filters composing our pipeline are created by invoking their
// \code{New()} methods, assigning the results to smart pointers.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
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();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Parameter for the \doxygen{otb}{MRFEnergyGaussianClassification} class, meand
// and standard deviation are created.
//
// Software Guide : EndLatex
if ((bool) (atoi(argv[6])) == true)
{
// Overpass random calculation(for test only):
sampler->InitializeSeed(0);
optimizer->InitializeSeed(1);
markovFilter->InitializeSeed(2);
}
// Software Guide : BeginCodeSnippet
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 : EndCodeSnippet
// Software Guide : BeginLatex
//
// Parameters are given to the different class an the sampler, optimizer and
// energies are connected with the Markov filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
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->SetNeighborhoodRadius(1);
markovFilter->SetEnergyRegularization(energyRegularization);
markovFilter->SetEnergyFidelity(energyFidelity);
markovFilter->SetOptimizer(optimizer);
markovFilter->SetSampler(sampler);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The pipeline is connected. An \doxygen{itk}{RescaleIntensityImageFilter}
// rescale the classified image before saving it.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
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());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, the pipeline execution is trigerred.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
writer->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// 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 restauration]{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}
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}

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