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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.
*/
#ifndef otbSVMMachineLearningModel_h
#define otbSVMMachineLearningModel_h
#include "otbRequiresOpenCVCheck.h"
#include "itkLightObject.h"
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
#include "otbOpenCVUtils.h"
namespace otb
{
/**
* \brief OpenCV implementation of SVM algorithm.
*
* This machine learning model uses the OpenCV implementation of the
* SVM algorithm. Since this implementation is buggy in the linear
* case, we recommend users to use the LibSVM implementation instead,
* through the otb::LibSVMMachineLearningModel.
*/
template <class TInputValue, class TTargetValue>
class ITK_EXPORT SVMMachineLearningModel : public MachineLearningModel<TInputValue, TTargetValue>
{
public:
/** Standard class typedefs. */
typedef SVMMachineLearningModel Self;
typedef MachineLearningModel<TInputValue, TTargetValue> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
typedef typename Superclass::InputValueType InputValueType;
typedef typename Superclass::InputSampleType InputSampleType;
typedef typename Superclass::InputListSampleType InputListSampleType;
typedef typename Superclass::TargetValueType TargetValueType;
typedef typename Superclass::TargetSampleType TargetSampleType;
typedef typename Superclass::TargetListSampleType TargetListSampleType;
typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
typedef typename Superclass::ProbaSampleType ProbaSampleType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
itkTypeMacro(SVMMachineLearningModel, MachineLearningModel);
/** Train the machine learning model */
void Train() override;
/** Save the model to file */
void Save(const std::string& filename, const std::string& name = "") override;
/** Load the model from file */
void Load(const std::string& filename, const std::string& name = "") override;
/**\name Classification model file compatibility tests */
//@{
/** Is the input model file readable and compatible with the corresponding classifier ? */
bool CanReadFile(const std::string&) override;
/** Is the input model file writable and compatible with the corresponding classifier ? */
bool CanWriteFile(const std::string&) override;
//@}
// Setters/Getters to SVM model
itkGetMacro(SVMType, int);
itkSetMacro(SVMType, int);
itkGetMacro(KernelType, int);
itkSetMacro(KernelType, int);
// CV_TERMCRIT_ITER or CV_TERMCRIT_EPS
itkGetMacro(TermCriteriaType, int);
itkSetMacro(TermCriteriaType, int);
itkGetMacro(MaxIter, int);
itkSetMacro(MaxIter, int);
itkGetMacro(Epsilon, double);
itkSetMacro(Epsilon, double);
// for poly
itkGetMacro(Degree, double);
itkSetMacro(Degree, double);
itkGetMacro(OutputDegree, double);
// for poly/rbf/sigmoid
itkGetMacro(Gamma, double);
itkSetMacro(Gamma, double);
itkGetMacro(OutputGamma, double);
// for poly/sigmoid
itkGetMacro(Coef0, double);
itkSetMacro(Coef0, double);
itkGetMacro(OutputCoef0, double);
// for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
itkGetMacro(C, double);
itkSetMacro(C, double);
itkGetMacro(OutputC, double);
// for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
itkGetMacro(Nu, double);
itkSetMacro(Nu, double);
itkGetMacro(OutputNu, double);
// for CV_SVM_EPS_SVR
itkGetMacro(P, double);
itkSetMacro(P, double);
itkGetMacro(OutputP, double);
itkGetMacro(ParameterOptimization, bool);
itkSetMacro(ParameterOptimization, bool);
protected:
/** Constructor */
SVMMachineLearningModel();
/** Destructor */
~SVMMachineLearningModel() override = default;
/** Predict values using the model */
TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const override;
private:
SVMMachineLearningModel(const Self&) = delete;
void operator=(const Self&) = delete;
cv::Ptr<cv::ml::SVM> m_SVMModel;
int m_SVMType;
int m_KernelType;
double m_Degree;
double m_Gamma;
double m_Coef0;
double m_C;
double m_Nu;
double m_P;
int m_TermCriteriaType;
int m_MaxIter;
double m_Epsilon;
bool m_ParameterOptimization;
// Output parameters
double m_OutputDegree;
double m_OutputGamma;
double m_OutputCoef0;
double m_OutputC;
double m_OutputNu;
double m_OutputP;
};
} // end namespace otb
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbSVMMachineLearningModel.hxx"
#endif
#endif
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