NICE_SemSeg/semseg/SemSegContextTree.h

/**
* @file SemSegContextTree.h
* @brief Context Trees -> Combination of decision tree and context information
* @author Björn Fröhlich
* @date 29.11.2011

*/
#ifndef SemSegContextTreeINCLUDE
#define SemSegContextTreeINCLUDE

#include "SemanticSegmentation.h"
#include <core/vector/VVector.h>
#include "vislearning/features/localfeatures/LFColorWeijer.h"
#include "objrec/segmentation/RegionSegmentationMethod.h"

#include "objrec-froehlichexp/semseg/operations/Operations.h"

namespace OBJREC {

/** Localization system */

class SemSegContextTree : public SemanticSegmentation, public NICE::Persistent
{
    /** Segmentation Method */
    RegionSegmentationMethod *segmentation;

    /** tree -> saved as vector of nodes */
    std::vector<std::vector<TreeNode> > forest;

    /** local features */
    LFColorWeijer *lfcw;

    /** number of featuretype -> currently: local and context features = 2 */
    int ftypes;

    /** distance between features */
    int grid;

    /** maximum samples for tree  */
    int maxSamples;

    /** size for neighbourhood */
    int windowSize;

    /** how many feats should be considered for a split */
    int featsPerSplit;

    /** count samples per label */
    std::map<int, int> labelcounter;

    /** map of labels */
    std::map<int, int> labelmap;

    /** map of labels inverse*/
    std::map<int, int> labelmapback;

    /** scalefactor for balancing for each class */
    std::vector<double> a;

    /** counter for used operations */
    std::vector<int> opOverview;

    /** relative use of context vs raw features per tree level*/
    std::vector<std::vector<double> > contextOverview;

    /** the minimum number of features allowed in a leaf */
    int minFeats;

    /** maximal depth of tree */
    int maxDepth;

    /** current depth for training */
    int depth;

    /** how many splittests */
    int randomTests;

    /** operations for pairwise features */
    std::vector<Operation*> ops;

    /** operations for pairwise context features */
    std::vector<Operation*> cops;

    std::vector<ValueAccess*> calcVal;

    /** vector of all possible features */
    std::vector<Operation*> featsel;

    /** use alternative calculation for information gain */
    bool useShannonEntropy;

    /** Classnames */
    ClassNames classnames;

    /** train selection */
    std::set<int> forbidden_classes;

    /** Configfile */
    const NICE::Config *conf;

    /** use pixelwise labeling or regionlabeling with additional segmenation */
    bool pixelWiseLabeling;

    /** use Gaussian distributed features based on the feature position */
    bool useGaussian;

    /** Number of trees used for the forest */
    int nbTrees;

  public:
    /** simple constructor */
    SemSegContextTree ( const NICE::Config *conf, const MultiDataset *md );

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

    /**
     * test a single image
     * @param ce input data
     * @param segresult segmentation results
     * @param probabilities probabilities for each pixel
     */
    void semanticseg ( CachedExample *ce,   NICE::Image & segresult,  NICE::MultiChannelImageT<double> & probabilities );

    /**
     * the main training method
     * @param md training data
     */
    void train ( const MultiDataset *md );


    /**
     * @brief computes integral image of given feats
     *
     * @param currentfeats input features
     * @param integralImage output image (must be initilized)
     * @return void
     **/
    void computeIntegralImage ( const NICE::MultiChannelImageT<unsigned short int> &currentfeats, const NICE::MultiChannelImageT<double> &lfeats, NICE::MultiChannelImageT<double> &integralImage );

    /**
     * @brief computes integral image for Sparse Multichannel Image
     *
     * @param currentfeats input features
     * @param integralImage output image (must be initilized)
     * @return void
     **/
    void computeIntegralImage ( const NICE::MultiChannelImageT<NICE::SparseVectorInt> &infeats, NICE::MultiChannelImageT<NICE::SparseVectorInt> &integralImage );

    /**
     * compute best split for current settings
     * @param feats features
     * @param currentfeats matrix with current node for each feature
     * @param labels labels for each feature
     * @param node current node
     * @param splitfeat output feature position
     * @param splitval
     * @return best information gain
     */
    double getBestSplit ( std::vector<NICE::MultiChannelImageT<double> > &feats, std::vector<NICE::MultiChannelImageT<unsigned short int> > &currentfeats, std::vector<NICE::MultiChannelImageT<double> > &integralImgs, const std::vector<NICE::MatrixT<int> > &labels, int node, Operation *&splitop, double &splitval, const int &tree );

    /**
     * @brief computes the mean probability for a given class over all trees
     * @param x x position
     * @param y y position
     * @param channel current class
     * @param currentfeats information about the nodes
     * @return double mean value
     **/
    inline double getMeanProb ( const int &x, const int &y, const int &channel, const NICE::MultiChannelImageT<unsigned short int> &currentfeats );

    /**
     * @brief load all data to is stream
     *
     * @param is input stream
     * @param format has no influence
     * @return void
     **/
    virtual void restore ( std::istream & is, int format = 0 );

    /**
     * @brief save all data to is stream
     *
     * @param os output stream
     * @param format has no influence
     * @return void
     **/
    virtual void store ( std::ostream & os, int format = 0 ) const;

    /**
     * @brief clean up
     *
     * @return void
     **/
    virtual void clear () {}

};

} // namespace

#endif