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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: {1000}, {4}, {5}, {121212}
// OUTPUTS: {clSVMModelFromSamples.svm}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{otb}{SVMMachineLearningModel} class, which inherits from the
// \doxygen{otb}{MachineLearningModel} class. This class allows the
// estimation of a classification model (supervised learning) from samples. In this example, we will train an SVM model
// with 4 output classes, from 1000 randomly generated training samples, each of them having 7 components.
// We start by including the appropriate header files.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// List sample generator
#include "otbListSampleGenerator.h"
// Random number generator
// SVM model Estimator
#include "otbSVMMachineLearningModel.h"
// Software Guide : EndCodeSnippet
int main(int argc, char* argv[])
{
/*
if (argc != 6)
{
std::cerr << "Usage: " << argv[0] << " nbSamples nbSampleComponents nbClasses inputSeed outputModelFileName" << std::endl;
return EXIT_FAILURE;
}
*/
if (argc != 2)
{
std::cerr << "Usage: " << argv[0] << " outputModelFileName" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
// The input parameters of the sample generator and of the SVM classifier are initialized.
// Software Guide : EndLatex
/*
int nbSamples = atoi(argv[1]);
int nbSampleComponents = atoi(argv[2]);
int nbClasses = atoi(argv[3]);
unsigned int inputSeed = atoi(argv[4]);
*/
// Software Guide : BeginCodeSnippet
int nbSamples = 1000;
int nbSampleComponents = 7;
int nbClasses = 4;
// Software Guide : EndCodeSnippet
// unsigned int inputSeed = 121212;
const char* outputModelFileName = argv[1]; //argv[5];
// Software Guide : BeginLatex
// Two lists are generated into a \subdoxygen{itk}{Statistics}{ListSample} which is the structure
// used to handle both lists of samples and of labels for the machine learning classes derived from
// \doxygen{otb}{MachineLearningModel}. The first list is composed of feature vectors representing
// multi-component samples, and the second one is filled with their corresponding class labels. The
// list of labels is composed of scalar values.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Input related typedefs
typedef float InputValueType;
typedef itk::VariableLengthVector<InputValueType> InputSampleType;
typedef itk::Statistics::ListSample<InputSampleType> InputListSampleType;
// Target related typedefs
typedef int TargetValueType;
typedef itk::FixedArray<TargetValueType, 1> TargetSampleType;
typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
InputListSampleType::Pointer InputListSample = InputListSampleType::New();
TargetListSampleType::Pointer TargetListSample = TargetListSampleType::New();
InputListSample->SetMeasurementVectorSize(nbSampleComponents);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// In this example, the list of multi-component training samples is randomly filled with a random number
// generator based on the \subdoxygen{itk}{Statistics}{MersenneTwisterRandomVariateGenerator} class.
// Each component's value is generated from a normal law centered around the corresponding class label of
// each sample multiplied by 100, with a standard deviation of 10.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
itk::Statistics::MersenneTwisterRandomVariateGenerator::Pointer randGen;
randGen = itk::Statistics::MersenneTwisterRandomVariateGenerator::GetInstance();
// Filling the two input training lists
for (int i = 0; i < nbSamples; ++i)
{
InputSampleType sample;
TargetValueType label = (i % nbClasses) + 1;
// Multi-component sample randomly filled from a normal law for each component
sample.SetSize(nbSampleComponents);
for (int itComp = 0; itComp < nbSampleComponents; ++itComp)
{
sample[itComp] = randGen->GetNormalVariate(100 * label, 10);
}
InputListSample->PushBack(sample);
TargetListSample->PushBack(label);
}
// Software Guide : EndCodeSnippet
// Displays the corresponding values (not into the Software Guide)
for (int i = 0; i < nbSamples; ++i)
{
std::cout << i + 1 << "-label = " << TargetListSample->GetMeasurementVector(i) << std::endl;
std::cout << "sample = " << InputListSample->GetMeasurementVector(i) << std::endl << std::endl;
}
// Software Guide : BeginLatex
//Once both sample and label lists are generated, the second step consists in
//declaring the machine learning classifier. In our case we use an SVM model
//with the help of the \doxygen{otb}{SVMMachineLearningModel} class which is
//derived from the \doxygen{otb}{MachineLearningModel} class.
//This pure virtual class is based on the machine learning framework of the
//OpenCV library (\cite{opencv_library}) which handles other classifiers than
//the SVM.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::SVMMachineLearningModel<InputValueType, TargetValueType> SVMType;
SVMType::Pointer SVMClassifier = SVMType::New();
SVMClassifier->SetInputListSample(InputListSample);
SVMClassifier->SetTargetListSample(TargetListSample);
SVMClassifier->SetKernelType(CvSVM::LINEAR);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Once the classifier is parametrized with both input lists and default parameters, except
// for the kernel type in our example of SVM model estimation, the model
// training is computed with the \code{Train} method. Finally, the \code{Save} method
// exports the model to a text file. All the available classifiers based on OpenCV are
// implemented with these interfaces. Like for the SVM model training, the other classifiers
// can be parametrized with specific settings.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
SVMClassifier->Train();
SVMClassifier->Save(outputModelFileName);
// Software Guide : EndCodeSnippet
}
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