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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.
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: {ROI_QB_MUL_1.png}, {ROI_mask.png}
// OUTPUTS: {svm_image_model.svn}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
// This example illustrates the use of the
// \doxygen{otb}{SVMImageModelEstimator} class. This class allows the
// estimation of a SVM model (supervised learning) from a feature
// image and an image of labels. In this example, we will train an SVM
// to separate between water and non-water pixels by using the RGB
// values only. The images used for this example are shown in
// figure~\ref{fig:SVMROIS}.
// \begin{figure}
// \center
// \includegraphics[width=0.45\textwidth]{ROI_QB_MUL_1.eps}
// \includegraphics[width=0.45\textwidth]{ROI_mask.eps}
// \itkcaption[SVM Image Model Estimation]{Images used for the
// estimation of the SVM model. Left: RGB image. Right: image of labels.}
// \label{fig:SVMROIS}
// \end{figure}
// The first thing to do is include the header file for the class.
//
// Software Guide : EndLatex
#include "itkMacro.h"
#include "otbImage.h"
#include "otbVectorImage.h"
#include <iostream>
// Software Guide : BeginCodeSnippet
#include "otbSVMImageModelEstimator.h"
// Software Guide : EndCodeSnippet
#include "otbImageFileReader.h"
int main(int itkNotUsed(argc), char* argv[])
{
const char* inputImageFileName = argv[1];
const char* trainingImageFileName = argv[2];
const char* outputModelFileName = argv[3];
// Software Guide : BeginLatex
//
// We define the types for the input and training images. Even if the
// input image will be an RGB image, we can read it as a 3 component
// vector image. This simplifies the interfacing with OTB's SVM
// framework.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef unsigned char InputPixelType;
const unsigned int Dimension = 2;
typedef otb::VectorImage<InputPixelType, Dimension> InputImageType;
typedef otb::Image<InputPixelType, Dimension> TrainingImageType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The \doxygen{otb}{SVMImageModelEstimator} class is templated over
// the input (features) and the training (labels) images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::SVMImageModelEstimator<InputImageType,
TrainingImageType> EstimatorType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As usual, we define the readers for the images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::ImageFileReader<InputImageType> InputReaderType;
typedef otb::ImageFileReader<TrainingImageType> TrainingReaderType;
InputReaderType::Pointer inputReader = InputReaderType::New();
TrainingReaderType::Pointer trainingReader = TrainingReaderType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We read the images. It is worth to note that, in order to ensure
// the pipeline coherence, the output of the objects which precede the
// model estimator in the pipeline, must be up to date, so we call
// the corresponding \code{Update} methods.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
inputReader->SetFileName(inputImageFileName);
trainingReader->SetFileName(trainingImageFileName);
inputReader->Update();
trainingReader->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We can now instantiate the model estimator and set its parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
EstimatorType::Pointer svmEstimator = EstimatorType::New();
svmEstimator->SetInputImage(inputReader->GetOutput());
svmEstimator->SetTrainingImage(trainingReader->GetOutput());
// 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
//
// Finally, the estimated model can be saved to a file for later use.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
svmEstimator->SaveModel(outputModelFileName);
// Software Guide : EndCodeSnippet
return EXIT_SUCCESS;
}
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