otbSVMMachineLearningModel.h

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/*
 * Copyright (C) 2005-2024 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
{
template <class TInputValue, class TTargetValue>
class ITK_EXPORT SVMMachineLearningModel : public MachineLearningModel<TInputValue, TTargetValue>
{
public:
 
  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;
  itkNewMacro(Self);
  itkTypeMacro(SVMMachineLearningModel, MachineLearningModel);
 
  void Train() override;
 
  void Save(const std::string& filename, const std::string& name = "") override;
 
  void Load(const std::string& filename, const std::string& name = "") override;
 
 
  bool CanReadFile(const std::string&) override;
 
  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:
  SVMMachineLearningModel();
 
  ~SVMMachineLearningModel() override = default;
 
  TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
 
  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