otbMachineLearningModelFactory.hxx

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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 otbMachineLearningModelFactory_hxx
#define otbMachineLearningModelFactory_hxx
 
#include "otbMachineLearningModelFactory.h"
#include "otbConfigure.h"
 
#ifdef OTB_USE_OPENCV
#include "otb_opencv_api.h"
#include "otbKNearestNeighborsMachineLearningModelFactory.h"
#include "otbRandomForestsMachineLearningModelFactory.h"
#include "otbSVMMachineLearningModelFactory.h"
#include "otbBoostMachineLearningModelFactory.h"
#include "otbNeuralNetworkMachineLearningModelFactory.h"
#include "otbNormalBayesMachineLearningModelFactory.h"
#include "otbDecisionTreeMachineLearningModelFactory.h"
#endif
#ifdef OTB_USE_LIBSVM
#include "otbLibSVMMachineLearningModelFactory.h"
#endif
 
#ifdef OTB_USE_SHARK
#include "otbSharkRandomForestsMachineLearningModelFactory.h"
#include "otbSharkKMeansMachineLearningModelFactory.h"
#endif
 
#include <mutex>
 
namespace otb
{
template <class TInputValue, class TOutputValue>
typename MachineLearningModel<TInputValue, TOutputValue>::Pointer
MachineLearningModelFactory<TInputValue, TOutputValue>::CreateMachineLearningModel(const std::string& path, FileModeType mode)
{
  RegisterBuiltInFactories();
 
  std::list<MachineLearningModelTypePointer> possibleMachineLearningModel;
  std::list<LightObject::Pointer>            allobjects = itk::ObjectFactoryBase::CreateAllInstance("otbMachineLearningModel");
  for (std::list<LightObject::Pointer>::iterator i = allobjects.begin(); i != allobjects.end(); ++i)
  {
    MachineLearningModel<TInputValue, TOutputValue>* io = dynamic_cast<MachineLearningModel<TInputValue, TOutputValue>*>(i->GetPointer());
    if (io)
    {
      possibleMachineLearningModel.push_back(io);
    }
    else
    {
      std::cerr << "Error MachineLearningModel Factory did not return an MachineLearningModel: " << (*i)->GetNameOfClass() << std::endl;
    }
  }
  for (typename std::list<MachineLearningModelTypePointer>::iterator k = possibleMachineLearningModel.begin(); k != possibleMachineLearningModel.end(); ++k)
  {
    if (mode == ReadMode)
    {
      if ((*k)->CanReadFile(path))
      {
        return *k;
      }
    }
    else if (mode == WriteMode)
    {
      if ((*k)->CanWriteFile(path))
      {
        return *k;
      }
    }
  }
  return nullptr;
}
 
template <class TInputValue, class TOutputValue>
void MachineLearningModelFactory<TInputValue, TOutputValue>::RegisterBuiltInFactories()
{
  std::lock_guard<std::mutex> lockHolder(mutex);
  
#ifdef OTB_USE_LIBSVM
  RegisterFactory(LibSVMMachineLearningModelFactory<TInputValue, TOutputValue>::New());
#endif
 
#ifdef OTB_USE_SHARK
  RegisterFactory(SharkRandomForestsMachineLearningModelFactory<TInputValue, TOutputValue>::New());
  RegisterFactory(SharkKMeansMachineLearningModelFactory<TInputValue, TOutputValue>::New());
#endif
 
#ifdef OTB_USE_OPENCV
  RegisterFactory(RandomForestsMachineLearningModelFactory<TInputValue, TOutputValue>::New());
  RegisterFactory(SVMMachineLearningModelFactory<TInputValue, TOutputValue>::New());
  RegisterFactory(BoostMachineLearningModelFactory<TInputValue, TOutputValue>::New());
  RegisterFactory(NeuralNetworkMachineLearningModelFactory<TInputValue, TOutputValue>::New());
  RegisterFactory(NormalBayesMachineLearningModelFactory<TInputValue, TOutputValue>::New());
  RegisterFactory(DecisionTreeMachineLearningModelFactory<TInputValue, TOutputValue>::New());
  RegisterFactory(KNearestNeighborsMachineLearningModelFactory<TInputValue, TOutputValue>::New());
#endif
}
 
template <class TInputValue, class TOutputValue>
void MachineLearningModelFactory<TInputValue, TOutputValue>::RegisterFactory(itk::ObjectFactoryBase* factory)
{
  // Unregister any previously registered factory of the same class
  // Might be more intensive but static bool is not an option due to
  // ld error.
  itk::ObjectFactoryBase::UnRegisterFactory(factory);
  itk::ObjectFactoryBase::RegisterFactory(factory);
}
 
template <class TInputValue, class TOutputValue>
void MachineLearningModelFactory<TInputValue, TOutputValue>::CleanFactories()
{
  std::lock_guard<std::mutex> lockHolder(mutex);
 
  std::list<itk::ObjectFactoryBase*>           factories = itk::ObjectFactoryBase::GetRegisteredFactories();
  std::list<itk::ObjectFactoryBase*>::iterator itFac;
 
  for (itFac = factories.begin(); itFac != factories.end(); ++itFac)
  {
#ifdef OTB_USE_LIBSVM
    LibSVMMachineLearningModelFactory<TInputValue, TOutputValue>* libsvmFactory =
        dynamic_cast<LibSVMMachineLearningModelFactory<TInputValue, TOutputValue>*>(*itFac);
    if (libsvmFactory)
    {
      itk::ObjectFactoryBase::UnRegisterFactory(libsvmFactory);
      continue;
    }
#endif
 
#ifdef OTB_USE_SHARK
    SharkRandomForestsMachineLearningModelFactory<TInputValue, TOutputValue>* sharkRFFactory =
        dynamic_cast<SharkRandomForestsMachineLearningModelFactory<TInputValue, TOutputValue>*>(*itFac);
    if (sharkRFFactory)
    {
      itk::ObjectFactoryBase::UnRegisterFactory(sharkRFFactory);
      continue;
    }
 
    SharkKMeansMachineLearningModelFactory<TInputValue, TOutputValue>* sharkKMeansFactory =
        dynamic_cast<SharkKMeansMachineLearningModelFactory<TInputValue, TOutputValue>*>(*itFac);
    if (sharkKMeansFactory)
    {
      itk::ObjectFactoryBase::UnRegisterFactory(sharkKMeansFactory);
      continue;
    }
#endif
 
#ifdef OTB_USE_OPENCV
    // RandomForest
    RandomForestsMachineLearningModelFactory<TInputValue, TOutputValue>* rfFactory =
        dynamic_cast<RandomForestsMachineLearningModelFactory<TInputValue, TOutputValue>*>(*itFac);
    if (rfFactory)
    {
      itk::ObjectFactoryBase::UnRegisterFactory(rfFactory);
      continue;
    }
    // SVM
    SVMMachineLearningModelFactory<TInputValue, TOutputValue>* svmFactory = dynamic_cast<SVMMachineLearningModelFactory<TInputValue, TOutputValue>*>(*itFac);
    if (svmFactory)
    {
      itk::ObjectFactoryBase::UnRegisterFactory(svmFactory);
      continue;
    }
    // Boost
    BoostMachineLearningModelFactory<TInputValue, TOutputValue>* boostFactory =
        dynamic_cast<BoostMachineLearningModelFactory<TInputValue, TOutputValue>*>(*itFac);
    if (boostFactory)
    {
      itk::ObjectFactoryBase::UnRegisterFactory(boostFactory);
      continue;
    }
    // ANN
    NeuralNetworkMachineLearningModelFactory<TInputValue, TOutputValue>* annFactory =
        dynamic_cast<NeuralNetworkMachineLearningModelFactory<TInputValue, TOutputValue>*>(*itFac);
    if (annFactory)
    {
      itk::ObjectFactoryBase::UnRegisterFactory(annFactory);
      continue;
    }
    // Bayes
    NormalBayesMachineLearningModelFactory<TInputValue, TOutputValue>* bayesFactory =
        dynamic_cast<NormalBayesMachineLearningModelFactory<TInputValue, TOutputValue>*>(*itFac);
    if (bayesFactory)
    {
      itk::ObjectFactoryBase::UnRegisterFactory(bayesFactory);
      continue;
    }
    // Decision Tree
    DecisionTreeMachineLearningModelFactory<TInputValue, TOutputValue>* dtFactory =
        dynamic_cast<DecisionTreeMachineLearningModelFactory<TInputValue, TOutputValue>*>(*itFac);
    if (dtFactory)
    {
      itk::ObjectFactoryBase::UnRegisterFactory(dtFactory);
      continue;
    }
    // KNN
    KNearestNeighborsMachineLearningModelFactory<TInputValue, TOutputValue>* knnFactory =
        dynamic_cast<KNearestNeighborsMachineLearningModelFactory<TInputValue, TOutputValue>*>(*itFac);
    if (knnFactory)
    {
      itk::ObjectFactoryBase::UnRegisterFactory(knnFactory);
      continue;
    }
#endif
  }
}
 
} // end namespace otb
 
#endif