/usr/include/OTB-5.8/otbTrainSVM.txx is in libotb-dev 5.8.0+dfsg-3.
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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.
=========================================================================*/
#ifndef otbTrainSVM_txx
#define otbTrainSVM_txx
#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::InitSVMParams()
{
AddChoice("classifier.svm", "SVM classifier (OpenCV)");
SetParameterDescription("classifier.svm", "This group of parameters allows setting SVM classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/support_vector_machines.html}.");
AddParameter(ParameterType_Choice, "classifier.svm.m", "SVM Model Type");
SetParameterDescription("classifier.svm.m", "Type of SVM formulation.");
if (this->m_RegressionFlag)
{
AddChoice("classifier.svm.m.epssvr", "Epsilon Support Vector Regression");
AddChoice("classifier.svm.m.nusvr", "Nu Support Vector Regression");
SetParameterString("classifier.svm.m", "epssvr");
}
else
{
AddChoice("classifier.svm.m.csvc", "C support vector classification");
AddChoice("classifier.svm.m.nusvc", "Nu support vector classification");
AddChoice("classifier.svm.m.oneclass", "Distribution estimation (One Class SVM)");
SetParameterString("classifier.svm.m", "csvc");
}
AddParameter(ParameterType_Choice, "classifier.svm.k", "SVM Kernel Type");
AddChoice("classifier.svm.k.linear", "Linear");
AddChoice("classifier.svm.k.rbf", "Gaussian radial basis function");
AddChoice("classifier.svm.k.poly", "Polynomial");
AddChoice("classifier.svm.k.sigmoid", "Sigmoid");
SetParameterString("classifier.svm.k", "linear");
SetParameterDescription("classifier.svm.k", "SVM Kernel Type.");
AddParameter(ParameterType_Float, "classifier.svm.c", "Cost parameter C");
SetParameterFloat("classifier.svm.c", 1.0);
SetParameterDescription(
"classifier.svm.c",
"SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins.");
AddParameter(ParameterType_Float, "classifier.svm.nu",
"Parameter nu of a SVM optimization problem (NU_SVC / ONE_CLASS)");
SetParameterFloat("classifier.svm.nu", 0.0);
SetParameterDescription("classifier.svm.nu", "Parameter nu of a SVM optimization problem.");
if (this->m_RegressionFlag)
{
AddParameter(ParameterType_Float, "classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR)");
SetParameterFloat("classifier.svm.p", 1.0);
SetParameterDescription("classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR).");
AddParameter(ParameterType_Choice, "classifier.svm.term", "Termination criteria");
SetParameterDescription("classifier.svm.term","Termination criteria for iterative algorithm");
AddChoice("classifier.svm.term.iter", "Stops when maximum iteration is reached.");
AddChoice("classifier.svm.term.eps", "Stops when accuracy is lower than epsilon.");
AddChoice("classifier.svm.term.all", "Stops when either iteration or epsilon criteria is true");
AddParameter(ParameterType_Float, "classifier.svm.iter", "Maximum iteration");
SetParameterFloat("classifier.svm.iter", 1000);
SetParameterDescription("classifier.svm.iter", "Maximum number of iterations (corresponds to the termination criteria 'iter').");
AddParameter(ParameterType_Float, "classifier.svm.eps", "Epsilon accuracy threshold");
SetParameterFloat("classifier.svm.eps", FLT_EPSILON);
SetParameterDescription("classifier.svm.eps", "Epsilon accuracy (corresponds to the termination criteria 'eps').");
}
AddParameter(ParameterType_Float, "classifier.svm.coef0", "Parameter coef0 of a kernel function (POLY / SIGMOID)");
SetParameterFloat("classifier.svm.coef0", 0.0);
SetParameterDescription("classifier.svm.coef0", "Parameter coef0 of a kernel function (POLY / SIGMOID).");
AddParameter(ParameterType_Float, "classifier.svm.gamma",
"Parameter gamma of a kernel function (POLY / RBF / SIGMOID)");
SetParameterFloat("classifier.svm.gamma", 1.0);
SetParameterDescription("classifier.svm.gamma", "Parameter gamma of a kernel function (POLY / RBF / SIGMOID).");
AddParameter(ParameterType_Float, "classifier.svm.degree", "Parameter degree of a kernel function (POLY)");
SetParameterFloat("classifier.svm.degree", 1.0);
SetParameterDescription("classifier.svm.degree", "Parameter degree of a kernel function (POLY).");
AddParameter(ParameterType_Empty, "classifier.svm.opt", "Parameters optimization");
MandatoryOff("classifier.svm.opt");
SetParameterDescription("classifier.svm.opt", "SVM parameters optimization flag.\n-If set to True, then the optimal SVM parameters will be estimated. "
"Parameters are considered optimal by OpenCV when the cross-validation estimate of the test set error is minimal. "
"Finally, the SVM training process is computed 10 times with these optimal parameters over subsets corresponding to 1/10th of "
"the training samples using the k-fold cross-validation (with k = 10).\n-If set to False, the SVM classification process will be "
"computed once with the currently set input SVM parameters over the training samples.\n-Thus, even with identical input SVM "
"parameters and a similar random seed, the output SVM models will be different according to the method used (optimized or not) "
"because the samples are not identically processed within OpenCV.");
}
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::TrainSVM(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
typename SVMType::Pointer SVMClassifier = SVMType::New();
SVMClassifier->SetRegressionMode(this->m_RegressionFlag);
SVMClassifier->SetInputListSample(trainingListSample);
SVMClassifier->SetTargetListSample(trainingLabeledListSample);
switch (GetParameterInt("classifier.svm.k"))
{
case 0: // LINEAR
SVMClassifier->SetKernelType(CvSVM::LINEAR);
std::cout << "CvSVM::LINEAR = " << CvSVM::LINEAR << std::endl;
break;
case 1: // RBF
SVMClassifier->SetKernelType(CvSVM::RBF);
std::cout << "CvSVM::RBF = " << CvSVM::RBF << std::endl;
break;
case 2: // POLY
SVMClassifier->SetKernelType(CvSVM::POLY);
std::cout << "CvSVM::POLY = " << CvSVM::POLY << std::endl;
break;
case 3: // SIGMOID
SVMClassifier->SetKernelType(CvSVM::SIGMOID);
std::cout << "CvSVM::SIGMOID = " << CvSVM::SIGMOID << std::endl;
break;
default: // DEFAULT = LINEAR
SVMClassifier->SetKernelType(CvSVM::LINEAR);
std::cout << "CvSVM::LINEAR = " << CvSVM::LINEAR << std::endl;
break;
}
if (this->m_RegressionFlag)
{
switch (GetParameterInt("classifier.svm.m"))
{
case 0: // EPS_SVR
SVMClassifier->SetSVMType(CvSVM::EPS_SVR);
std::cout<<"CvSVM::EPS_SVR = "<<CvSVM::EPS_SVR<<std::endl;
break;
case 1: // NU_SVR
SVMClassifier->SetSVMType(CvSVM::NU_SVR);
std::cout<<"CvSVM::NU_SVR = "<<CvSVM::NU_SVR<<std::endl;
break;
default: // DEFAULT = EPS_SVR
SVMClassifier->SetSVMType(CvSVM::EPS_SVR);
std::cout << "CvSVM::EPS_SVR = " << CvSVM::EPS_SVR << std::endl;
break;
}
}
else
{
switch (GetParameterInt("classifier.svm.m"))
{
case 0: // C_SVC
SVMClassifier->SetSVMType(CvSVM::C_SVC);
std::cout << "CvSVM::C_SVC = " << CvSVM::C_SVC << std::endl;
break;
case 1: // NU_SVC
SVMClassifier->SetSVMType(CvSVM::NU_SVC);
std::cout << "CvSVM::NU_SVC = " << CvSVM::NU_SVC << std::endl;
break;
case 2: // ONE_CLASS
SVMClassifier->SetSVMType(CvSVM::ONE_CLASS);
std::cout << "CvSVM::ONE_CLASS = " << CvSVM::ONE_CLASS << std::endl;
break;
default: // DEFAULT = C_SVC
SVMClassifier->SetSVMType(CvSVM::C_SVC);
std::cout << "CvSVM::C_SVC = " << CvSVM::C_SVC << std::endl;
break;
}
}
SVMClassifier->SetC(GetParameterFloat("classifier.svm.c"));
SVMClassifier->SetNu(GetParameterFloat("classifier.svm.nu"));
if (this->m_RegressionFlag)
{
SVMClassifier->SetP(GetParameterFloat("classifier.svm.p"));
switch (GetParameterInt("classifier.svm.term"))
{
case 0: // ITER
SVMClassifier->SetTermCriteriaType(CV_TERMCRIT_ITER);
break;
case 1: // EPS
SVMClassifier->SetTermCriteriaType(CV_TERMCRIT_EPS);
break;
case 2: // ITER+EPS
SVMClassifier->SetTermCriteriaType(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS);
break;
default:
SVMClassifier->SetTermCriteriaType(CV_TERMCRIT_ITER);
break;
}
SVMClassifier->SetMaxIter(GetParameterInt("classifier.svm.iter"));
SVMClassifier->SetEpsilon(GetParameterFloat("classifier.svm.eps"));
}
SVMClassifier->SetCoef0(GetParameterFloat("classifier.svm.coef0"));
SVMClassifier->SetGamma(GetParameterFloat("classifier.svm.gamma"));
SVMClassifier->SetDegree(GetParameterFloat("classifier.svm.degree"));
if (IsParameterEnabled("classifier.svm.opt"))
{
SVMClassifier->SetParameterOptimization(true);
}
SVMClassifier->Train();
SVMClassifier->Save(modelPath);
// Update the displayed parameters in the GUI after the training process, for further use of them
SetParameterFloat("classifier.svm.c", static_cast<float> (SVMClassifier->GetOutputC()));
SetParameterFloat("classifier.svm.nu", static_cast<float> (SVMClassifier->GetOutputNu()));
if (this->m_RegressionFlag)
{
SetParameterFloat("classifier.svm.p", static_cast<float> (SVMClassifier->GetOutputP()));
}
SetParameterFloat("classifier.svm.coef0", static_cast<float> (SVMClassifier->GetOutputCoef0()));
SetParameterFloat("classifier.svm.gamma", static_cast<float> (SVMClassifier->GetOutputGamma()));
SetParameterFloat("classifier.svm.degree", static_cast<float> (SVMClassifier->GetOutputDegree()));
}
} //end namespace wrapper
} //end namespace otb
#endif
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