/**
 * @file RelativeLocationPrior.h
 * @brief a post procession step after semantic segmentation which use relative location priors
 * @author Björn Fröhlich
 * @date 06/10/2009

 */
#ifndef RELATIVELOCATIONPRIORINCLUDE
#define RELATIVELOCATIONPRIORINCLUDE

#include "core/image/MultiChannelImageT.h"

#include "vislearning/cbaselib/CachedExample.h"
#include "vislearning/baselib/Preprocess.h"
#include "vislearning/baselib/Globals.h"

#include "vislearning/classifier/fpclassifier/logisticregression/SLR.h"
#include "vislearning/classifier/fpclassifier/randomforest/FPCRandomForests.h"

#include "vislearning/features/fpfeatures/SparseVectorFeature.h"

#include "vislearning/cbaselib/ClassNames.h"

namespace OBJREC
{

class RelativeLocationPrior : public NICE::Persistent
{

  protected:
    //! the priormaps
    std::vector<NICE::MultiChannelImageT<double> *> priormaps;

    //! the configfile
    const NICE::Config *conf;

    //! count of classes
    int classno;

    //! size of the priormaps (mapsize x mapsize)
    int mapsize;

    //! convert Image coordinates to priormaps coordinates
    void convertCoords ( int &x, int xsize );

    //! the trainingsdata will be added subsequently to this object
    Examples trainingsdata;

    //! the one vs all sparse logistic classifiers
    std::vector<SLR> classifiers;

    //! dimension of the features
    int featdim;

  public:

    /** simple constructor */
    RelativeLocationPrior();

    /** simple constructor */
    RelativeLocationPrior ( const NICE::Config *_conf );

    /** simple destructor */
    ~RelativeLocationPrior();

    /**
     * set the count of classes
     * @param _classno count of classes
     */
    void setClassNo ( int _classno );

    /** initialize the RelativeLocationPrior Variables*/
    void Init();

    /**
     * Bestimme aus dem Trainingsbild, die location priors maps
     * @param regions input regions with size, position and label
     */
    void trainPriorsMaps ( Examples &regions, int xsize, int ysize );

    /**
     * finish the priors maps
     */
    void finishPriorsMaps ( ClassNames &cn );

    /**
     * Bestimme aus dem Trainingsbild, die location priors maps
     * @param regions input regions with size and position
     * @param probabilities the probabiltiy maps
     */
    void trainClassifier ( Examples &regions, NICE::MultiChannelImageT<double> & probabilities );

    /**
     * finish the classfiers
     */
    void finishClassifier();

    /**
     * appends the featurevector to the given example
     * @param regions input regions with size and position
     * @param probabilities the probabiltiy maps
     */
    void getFeature ( Examples &regions, NICE::MultiChannelImageT<double> & probabilities );

    /**
     * uses the rlp for reclassification
     * @param regions
     * @param result
     * @param probabilities
     */
    void postprocess ( Examples &regions, NICE::MultiChannelImageT<double> & probabilities );

    /**
     * load data from an input stream
     * @param is input stream
     * @param format
     */
    void restore ( std::istream & is, int format = 0 );

    /**
     * write data to an output stream
     * @param os outputstream
     * @param format
     */
    void store ( std::ostream & os, int format = 0 ) const;

    /**
     * clear all informations
     */
    void clear ();
};

} //namespace

#endif