TrainNodeCvKNN.h

// k-Nearest Neighbors (based on OpenCV) training class interface
// Written by Sergey G. Kosov in 2017 for Project X
#pragma once

#include "TrainNode.h"

namespace DirectGraphicalModels
{
    class CSamplesAccumulator;

    typedef struct TrainNodeCvKNNParams {
        float   bias;                               
        size_t  maxNeighbors;                       
        size_t  maxSamples;                         

        TrainNodeCvKNNParams() {}
        TrainNodeCvKNNParams(float _bias, size_t _maxNeighbors, size_t _maxSamples) : bias(_bias), maxNeighbors(_maxNeighbors), maxSamples(_maxSamples) {}
    } TrainNodeCvKNNParams;

    const TrainNodeCvKNNParams TRAIN_NODE_CV_KNN_PARAMS_DEFAULT =   TrainNodeCvKNNParams(
                                                                    0.1f,   // Regularization CRF parameter: bias is added to all potential values
                                                                    100,    // Max number of neighbors to be used for calculating potentials
                                                                    0       // Maximum number of samples to be used in training. 0 means using all the samples
                                                                    );

    // ====================== OpenCV k-Nearest Neighbors Train Class =====================
    class CTrainNodeCvKNN : public CTrainNode {
    public:
        DllExport CTrainNodeCvKNN(byte nStates, word nFeatures, TrainNodeCvKNNParams params = TRAIN_NODE_CV_KNN_PARAMS_DEFAULT);
        CTrainNodeCvKNN(byte nStates, word nFeatures, size_t maxSamples);
        DllExport virtual ~CTrainNodeCvKNN(void);

        DllExport void  reset(void);
        DllExport void  save(const std::string &path, const std::string &name = std::string(), short idx = -1) const;
        DllExport void  load(const std::string &path, const std::string &name = std::string(), short idx = -1);

        DllExport void  addFeatureVec(const Mat &featureVector, byte gt);

        DllExport void  train(bool doClean = false);


    protected:
        DllExport void  saveFile(FILE *pFile) const { }
        DllExport void  loadFile(FILE *pFile) { }
        DllExport void  calculateNodePotentials(const Mat &featureVector, Mat &potential, Mat &mask) const;

    
    protected:
        Ptr<ml::KNearest>         m_pKNN;                   
        CSamplesAccumulator     * m_pSamplesAcc;            
    

    private:
        void          init(TrainNodeCvKNNParams params);        // This function is called by both constructors


    private:
        TrainNodeCvKNNParams      m_params;
    };
}