/* * Copyright (C) 2005-2019 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 ITK_EXPORT NeuralNetworkMachineLearningModel : public MachineLearningModel { public: /** Standard class typedefs. */ typedef NeuralNetworkMachineLearningModel Self; typedef MachineLearningModel Superclass; typedef itk::SmartPointer Pointer; typedef itk::SmartPointer 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 MapOfLabelsType; /** Run-time type information (and related methods). */ itkNewMacro(Self); itkTypeMacro(NeuralNetworkMachineLearningModel, MachineLearningModel); /** Setters/Getters to the train method * 2 methods are available: * - CvANN_MLP_TrainParams::BACKPROP The back-propagation algorithm. * - CvANN_MLP_TrainParams::RPROP The RPROP algorithm. * Default is CvANN_MLP_TrainParams::RPROP. * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(TrainMethod, int); itkSetMacro(TrainMethod, int); /** * Set the number of neurons in each layer (including input and output layers). * The number of neuron in the first layer (input layer) must be equal * to the number of samples in the \c InputListSample */ void SetLayerSizes(const std::vector layers); /** Setters/Getters to the neuron activation function * 3 methods are available: * - CvANN_MLP::IDENTITY * - CvANN_MLP::SIGMOID_SYM * - CvANN_MLP::GAUSSIAN * Default is CvANN_MLP::SIGMOID_SYM * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(ActivateFunction, int); itkSetMacro(ActivateFunction, int); /** Setters/Getters to the alpha parameter of the activation function * Default is 0. * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(Alpha, double); itkSetMacro(Alpha, double); /** Setters/Getters to the beta parameter of the activation function * Default is 0. * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(Beta, double); itkSetMacro(Beta, double); /** Strength of the weight gradient term in the BACKPROP method. * The recommended value is about 0.1 * Default is 0.1 * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(BackPropDWScale, double); itkSetMacro(BackPropDWScale, double); /** Strength of the momentum term (the difference between weights on the 2 previous iterations). * This parameter provides some inertia to smooth the random fluctuations of the weights. * It can vary from 0 (the feature is disabled) to 1 and beyond. * The value 0.1 or so is good enough * Default is 0.1 * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(BackPropMomentScale, double); itkSetMacro(BackPropMomentScale, double); /** Initial value \f$ \Delta_0 \f$ of update-values \f$ \Delta_{ij} \f$ in RPROP method. * Default is 0.1 * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(RegPropDW0, double); itkSetMacro(RegPropDW0, double); /** Update-values lower limit \f$ \Delta_{min} \f$ in RPROP method. * It must be positive. Default is FLT_EPSILON * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(RegPropDWMin, double); itkSetMacro(RegPropDWMin, double); /** Termination criteria. * It can be CV_TERMCRIT_ITER or CV_TERMCRIT_EPS or CV_TERMCRIT_ITER+CV_TERMCRIT_EPS * default is CV_TERMCRIT_ITER+CV_TERMCRIT_EPS. * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(TermCriteriaType, int); itkSetMacro(TermCriteriaType, int); /** Maximum number of iteration used in the Termination criteria. * default is 1000 * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(MaxIter, int); itkSetMacro(MaxIter, int); /** Epsilon value used in the Termination criteria. * default is 0.01 * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html */ itkGetMacro(Epsilon, double); itkSetMacro(Epsilon, double); /** 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; //@} protected: /** Constructor */ NeuralNetworkMachineLearningModel(); /** Destructor */ ~NeuralNetworkMachineLearningModel() override; /** Predict values using the model */ TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override; void LabelsToMat(const TargetListSampleType* listSample, cv::Mat& output); /** PrintSelf method */ 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); #ifdef OTB_OPENCV_3 cv::Ptr m_ANNModel; #else CvANN_MLP_TrainParams SetNetworkParameters(); CvANN_MLP* m_ANNModel; #endif int m_TrainMethod; int m_ActivateFunction; std::vector 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