otbNeuralNetworkMachineLearningModel.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 otbNeuralNetworkMachineLearningModel_h
#define otbNeuralNetworkMachineLearningModel_h
 
#include "otbRequiresOpenCVCheck.h"
#include "otbOpenCVUtils.h"
 
#include "itkLightObject.h"
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
 
namespace otb
{
template <class TInputValue, class TTargetValue>
class ITK_EXPORT NeuralNetworkMachineLearningModel : public MachineLearningModel<TInputValue, TTargetValue>
{
public:
  typedef NeuralNetworkMachineLearningModel 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;
  typedef std::map<TargetValueType, unsigned int> MapOfLabelsType;
 
  itkNewMacro(Self);
  itkTypeMacro(NeuralNetworkMachineLearningModel, MachineLearningModel);
 
  itkGetMacro(TrainMethod, int);
  itkSetMacro(TrainMethod, int);
 
  void SetLayerSizes(const std::vector<unsigned int> layers);
 
 
  itkGetMacro(ActivateFunction, int);
  itkSetMacro(ActivateFunction, int);
 
  itkGetMacro(Alpha, double);
  itkSetMacro(Alpha, double);
 
  itkGetMacro(Beta, double);
  itkSetMacro(Beta, double);
 
  itkGetMacro(BackPropDWScale, double);
  itkSetMacro(BackPropDWScale, double);
 
  itkGetMacro(BackPropMomentScale, double);
  itkSetMacro(BackPropMomentScale, double);
 
  itkGetMacro(RegPropDW0, double);
  itkSetMacro(RegPropDW0, double);
 
  itkGetMacro(RegPropDWMin, double);
  itkSetMacro(RegPropDWMin, double);
 
  itkGetMacro(TermCriteriaType, int);
  itkSetMacro(TermCriteriaType, int);
 
  itkGetMacro(MaxIter, int);
  itkSetMacro(MaxIter, int);
 
  itkGetMacro(Epsilon, double);
  itkSetMacro(Epsilon, double);
 
  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;
 
protected:
  NeuralNetworkMachineLearningModel();
 
  ~NeuralNetworkMachineLearningModel() override = default;
 
  TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
 
  void LabelsToMat(const TargetListSampleType* listSample, cv::Mat& output);
 
  void PrintSelf(std::ostream& os, itk::Indent indent) const override;
 
private:
  NeuralNetworkMachineLearningModel(const Self&) = delete;
  void operator=(const Self&) = delete;
 
  void CreateNetwork();
  void SetupNetworkAndTrain(cv::Mat& labels);
  cv::Ptr<cv::ml::ANN_MLP> m_ANNModel;
  int                       m_TrainMethod;
  int                       m_ActivateFunction;
  std::vector<unsigned int> m_LayerSizes;
 
  double m_Alpha;
  double m_Beta;
  double m_BackPropDWScale;
  double m_BackPropMomentScale;
  double m_RegPropDW0;
  double m_RegPropDWMin;
  int    m_TermCriteriaType;
  int    m_MaxIter;
  double m_Epsilon;
 
  cv::Mat            m_MatrixOfLabels;
  MapOfLabelsType    m_MapOfLabels;
 
};
} // end namespace otb
 
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbNeuralNetworkMachineLearningModel.hxx"
#endif
 
#endif