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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:
./MarkovClassification2Example Input/QB_Suburb.png Output/MarkovRandomField2.png 1.0 5 1
*/
// 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.
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "otbImage.h"
#include "otbMarkovRandomFieldFilter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
// First, we need to include header specific to these class:
#include "otbMRFEnergyPotts.h"
#include "otbMRFEnergyGaussianClassification.h"
#include "otbMRFSamplerRandomMAP.h"
#include "otbMRFOptimizerICM.h"
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;
using InternalPixelType = double;
using LabelledPixelType = unsigned char;
using InputImageType = otb::Image<InternalPixelType, Dimension>;
using LabelledImageType = otb::Image<LabelledPixelType, Dimension>;
using ReaderType = otb::ImageFileReader<InputImageType>;
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];
reader->SetFileName(inputFilename);
writer->SetFileName(outputFilename);
using MarkovRandomFieldFilterType = otb::MarkovRandomFieldFilter<InputImageType, LabelledImageType>;
// And to declare these new type:
using SamplerType = otb::MRFSamplerRandomMAP<InputImageType, LabelledImageType>;
// using SamplerType = otb::MRFSamplerRandom< InputImageType, LabelledImageType>;
using OptimizerType = otb::MRFOptimizerICM;
using EnergyRegularizationType = otb::MRFEnergyPotts<LabelledImageType, LabelledImageType>;
using EnergyFidelityType = otb::MRFEnergyGaussianClassification<InputImageType, 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[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);
// As the \doxygen{otb}{MRFOptimizerICM} does not have any parameters,
// the call to \code{optimizer->SetParameters()} must be removed
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());
using RescaleType = itk::RescaleIntensityImageFilter<LabelledImageType, LabelledImageType>;
RescaleType::Pointer rescaleFilter = RescaleType::New();
rescaleFilter->SetOutputMinimum(0);
rescaleFilter->SetOutputMaximum(255);
rescaleFilter->SetInput(markovFilter->GetOutput());
writer->SetInput(rescaleFilter->GetOutput());
writer->Update();
// Apart from these, no further modification is required.
// 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 restoration]{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}
return EXIT_SUCCESS;
}
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