NICE_SemSeg/semseg/postsegmentation/RelativeLocationPrior.h

/**
 * @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 Persistent
{

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