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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 otbKNearestNeighborsMachineLearningModel_h
#define otbKNearestNeighborsMachineLearningModel_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 KNearestNeighborsMachineLearningModel : public MachineLearningModel<TInputValue, TTargetValue>
{
public:
/** Standard class typedefs. */
typedef KNearestNeighborsMachineLearningModel 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(KNearestNeighborsMachineLearningModel, MachineLearningModel);
/** Setters/Getters to the number of neighbors to use
* Default is 32
* \see http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
*/
itkGetMacro(K, int);
itkSetMacro(K, int);
/** Decision rule once the KNN are found :
* [for classification]
* - KNN_VOTING : output value with maximum occurrences (for classification)
* [for regression]
* - KNN_MEAN : output mean value of neighbors
* - KNN_MEDIAN : output median value of neighbors
*/
enum
{
KNN_VOTING,
KNN_MEAN,
KNN_MEDIAN
};
/** Setters/Getters to the decision rule */
itkGetMacro(DecisionRule, int);
itkSetMacro(DecisionRule, int);
/** 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 */
KNearestNeighborsMachineLearningModel();
/** Destructor */
~KNearestNeighborsMachineLearningModel() 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:
KNearestNeighborsMachineLearningModel(const Self&) = delete;
void operator=(const Self&) = delete;
cv::Ptr<cv::ml::KNearest> m_KNearestModel;
int m_K;
int m_DecisionRule;
};
} // end namespace otb
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbKNearestNeighborsMachineLearningModel.hxx"
#endif
#endif
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