NICE_SemSeg/semseg/SemSegNovelty.h

/**
 * @file SemSegNovelty.h
 * @brief semantic segmentation using the method from Csurka08
 * @author Björn Fröhlich
 * @date 04/24/2009
 */
#ifndef SemSegNoveltyINCLUDE
#define SemSegNoveltyINCLUDE

#include "SemanticSegmentation.h"

#include "SemSegTools.h"
#include "vislearning/classifier/classifierbase/FeaturePoolClassifier.h"
#include "vislearning/features/localfeatures/LFColorWeijer.h"

#include "segmentation/RegionSegmentationMethod.h"


/** @brief pixelwise labeling systems */

namespace OBJREC {

class SemSegNovelty : public SemanticSegmentation
{

  protected:
    //! boolean whether to save the cache or not
    bool save_cache;

    //! boolean whether to read the cache or not, if read_cache is false, everything will be trained
    bool read_cache;

    //! The cached Data
    std::string cache;
    
    //! Classifier
    FeaturePoolClassifier *classifier;
    
    //! feature extraction
    LFColorWeijer *featExtract;
    
    //! Configuration File
    const NICE::Config *conf;
    
    //! distance between features for training
    int trainWsize;
    
    //! half of the window size for local features
    int whs;
    
    //! rectangle size for classification, 1 means pixelwise
    int testWSize;
    
    //! name of all classes
    ClassNames cn;
    
    //! low level Segmentation method
    RegionSegmentationMethod *regionSeg;
    
    //! set of forbidden/background classes
    std::set<int> forbidden_classes;
    std::set<int> forbidden_classesTrain;
    std::set<int> classesInUse;
    
    //! obviously, the number of classes used for training
    int numberOfClasses;
    
    //! where to save the uncertainty
    std::string uncertdir;
    
    //! find the maximum uncertainty or not
    bool findMaximumUncert;
    
    //! image with most uncertain region
    NICE::ColorImage maskedImg;
    
    //! maximum uncertainty over all images
    double globalMaxUncert;
    
    //! current examples for most uncertain region
    Examples newTrainExamples;
    
    enum NoveltyMethod{
      GPVARIANCE, // novel = large variance
      GPUNCERTAINTY, //novel = small uncertainty (mean / var)
      GPMINMEAN,  //novel = small mean
      GPMEANRATIO,  //novel = small difference between mean of most plausible class and mean of snd
                   //        most plausible class (not useful in binary settings)
      GPWEIGHTALL, // novel = large weight in alpha vector after updating the model (can be predicted exactly)
      GPWEIGHTRATIO // novel = small difference between weights for alpha vectors with assumptions of GT label to be the most 
                    //         plausible against the second most plausible class
    }; 
    
    //! specify how "novelty" shall be computed, e.g., using GP-variance, GP-uncertainty, or predicted weight entries
    NoveltyMethod noveltyMethod;
    
    inline void computeClassificationResults( const NICE::MultiChannelImageT<double> & feats, 
                                                    NICE::Image & segresult,
                                                    NICE::MultiChannelImageT<double> & probabilities,
                                                    const int & xsize,
                                                    const int & ysize,
                                                    const int & featdim );
   
   void computeNoveltyByVariance( NICE::FloatImage & noveltyImage, 
                                  const NICE::MultiChannelImageT<double> & feats,  
                                        NICE::Image & segresult,
                                        NICE::MultiChannelImageT<double> & probabilities,
                                  const int & xsize, const int & ysize, const int & featdim );
   
   void computeNoveltyByGPUncertainty ( NICE::FloatImage & noveltyImage, 
                                  const NICE::MultiChannelImageT<double> & feats,  
                                        NICE::Image & segresult,
                                        NICE::MultiChannelImageT<double> & probabilities,
                                  const int & xsize, const int & ysize, const int & featdim );   
   
  public:

    /** constructor
      *  @param conf needs a configfile
      *  @param md and a MultiDataset (contains images and other things)
      */
    SemSegNovelty ( const NICE::Config *conf, const MultiDataset *md );

    /** simple destructor */
    virtual ~SemSegNovelty();

    /** The trainingstep
      *  @param md and a MultiDataset (contains images and other things)
      */
    void train ( const MultiDataset *md );

    /** The main procedure. Input: Image, Output: Segmented Image with pixelwise labeles and the probabilities
      * @param ce image data
      * @param segresult result of the semantic segmentation with a label for each pixel
      * @param probabilities multi-channel image with one channel for each class and corresponding probabilities for each pixel
      */
    void semanticseg ( CachedExample *ce,
                       NICE::Image & segresult,
                       NICE::MultiChannelImageT<double> & probabilities );
    
    /**
     * @brief visualize a specific region in the original image
     *
     * @param img input image
     * @param regions map of the regions
     * @param region visualize this region
     * @param outimage result
     * @return void
     **/
    void visualizeRegion(const NICE::ColorImage &img, const NICE::Matrix &regions, int region, NICE::ColorImage &outimage);
};

} //namespace

#endif