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/*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt for details.
Copyright (c) Institut Mines-Telecom. All rights reserved.
See IMTCopyright.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 : BeginLatex
// This example illustrates the modifications to be added to the
// use of \doxygen{otb}{SVMImageModelEstimator} in order to add a
// user defined kernel. This initial program has been explained in section
// \ref{ssec:LearningFromImages}.
//
// The first thing to do is to include the header file for the new kernel.
//
// Software Guide : EndLatex
#include "itkMacro.h"
#include "otbImage.h"
#include "otbVectorImage.h"
#include <iostream>
// Software Guide : BeginCodeSnippet
#include "otbSVMImageModelEstimator.h"
#include "otbMixturePolyRBFKernelFunctor.h"
// Software Guide : EndCodeSnippet
#include "otbImageFileReader.h"
int main(int argc, char* argv[])
{
if (argc != 4)
{
std::cerr << "Usage : " << argv[0] << " inputImage mask modelFile\n";
return EXIT_FAILURE;
}
const char* inputImageFileName = argv[1];
const char* trainingImageFileName = argv[2];
const char* outputModelFileName = argv[3];
typedef unsigned char InputPixelType;
const unsigned int Dimension = 2;
typedef otb::VectorImage<InputPixelType, Dimension> InputImageType;
typedef otb::Image<InputPixelType, Dimension> TrainingImageType;
typedef otb::SVMImageModelEstimator<InputImageType,
TrainingImageType> EstimatorType;
typedef otb::ImageFileReader<InputImageType> InputReaderType;
typedef otb::ImageFileReader<TrainingImageType> TrainingReaderType;
InputReaderType::Pointer inputReader = InputReaderType::New();
TrainingReaderType::Pointer trainingReader = TrainingReaderType::New();
inputReader->SetFileName(inputImageFileName);
trainingReader->SetFileName(trainingImageFileName);
inputReader->Update();
trainingReader->Update();
// Software Guide : BeginLatex
//
// Once the \doxygen{otb}{SVMImageModelEstimator} is instantiated,
// it is possible to add the new kernel and its parameters.
//
// Then in addition to the initial code:
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
EstimatorType::Pointer svmEstimator = EstimatorType::New();
svmEstimator->SetSVMType(C_SVC);
svmEstimator->SetInputImage(inputReader->GetOutput());
svmEstimator->SetTrainingImage(trainingReader->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The instantiation of the kernel is to be implemented. The kernel which is
// used here is a linear combination of a polynomial kernel and an RBF one.
// It is written as $$\mu k_1(x, y) + (1-\mu) k_2(x, y)$$ with
// $k_1(x, y)=\left( \gamma_1 x\cdot y + c_0 \right) ^d$ and
// $k_2(x, y) = \exp\left( - \gamma_2 \| x-y\|^2 \right)$. Then, the specific
// parameters of this kernel are:
// \begin{itemize}
// \item \code{Mixture} ($\mu$),
// \item \code{GammaPoly} ($\gamma_1$),
// \item \code{CoefPoly} ($c_0$),
// \item \code{DegreePoly} ($d$),
// \item \code{GammaRBF} ($\gamma_2$).
// \end{itemize}
// Their instantiations are achieved through the use of the \code{SetValue}
// function.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
otb::MixturePolyRBFKernelFunctor myKernel;
myKernel.SetValue("Mixture", 0.5);
myKernel.SetValue("GammaPoly", 1.0);
myKernel.SetValue("CoefPoly", 0.0);
myKernel.SetValue("DegreePoly", 1);
myKernel.SetValue("GammaRBF", 1.5);
myKernel.Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Once the kernel's parameters are affected and the kernel updated,
// the connection to \doxygen{otb}{SVMImageModelEstimator} takes place here.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
svmEstimator->SetKernelFunctor(&myKernel);
svmEstimator->SetKernelType(GENERIC);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The model estimation procedure is triggered by calling the
// estimator's \code{Update} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
svmEstimator->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The rest of the code remains unchanged...
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
svmEstimator->SaveModel(outputModelFileName);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In the file \code{outputModelFileName} a specific line will appear when using a
// generic kernel. It gives the name of the kernel and its parameters name and value.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
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