/**
* @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"

namespace OBJREC {

class Operation;

class TreeNode
{

  public:
    /** probabilities for each class */
    std::vector<double> probs;

    /** left child node */
    int left;

    /** right child node */
    int right;

    /** position of feat for decision */
    Operation *feat;

    /** decision stamp */
    double decision;

    /** is the node a leaf or not */
    bool isleaf;

    /** distribution in current node */
    std::vector<double> dist;

    /** depth of the node in the tree */
    int depth;

    /** how many pixels are in this node */
    int featcounter;

    /** simple constructor */
    TreeNode() : left ( -1 ), right ( -1 ), feat ( NULL ), decision ( -1.0 ), isleaf ( false ) {}

    /** standard constructor */
    TreeNode ( int _left, int _right, Operation *_feat, double _decision ) : left ( _left ), right ( _right ), feat ( _feat ), decision ( _decision ), isleaf ( false ) {}
};

struct Features {
  NICE::MultiChannelImageT<double> *feats;
  MultiChannelImageT<int> *cfeats;
  int cTree;
  std::vector<TreeNode> *tree;
  NICE::MultiChannelImageT<double> *integralImg;
};

class ValueAccess
{

  public:
    virtual double getVal ( const Features &feats, const int &x, const int &y, const int &channel ) = 0;
    virtual std::string writeInfos() = 0;
};

enum OperationTypes {
  MINUS,
  MINUSABS,
  ADDITION,
  ONLY1,
  INTEGRAL,
  INTEGRALCENT,
  BIINTEGRALCENT,
  HAARHORIZ,
  HAARVERT,
  HAARDIAG,
  HAAR3HORIZ,
  HAAR3VERT,
  RELATIVEXPOSITION,
  RELATIVEYPOSITION,
  GLOBALFEATS,
  NBOPERATIONS
};

class Operation
{

  protected:
    int x1, y1, x2, y2, channel1, channel2;
    ValueAccess *values;

  public:

    Operation()
    {
      values = NULL;
    }

    virtual void set ( int _x1, int _y1, int _x2, int _y2, int _channel1, int _channel2, ValueAccess *_values )
    {
      x1 = _x1;
      y1 = _y1;
      x2 = _x2;
      y2 = _y2;
      channel1 = _channel1;
      channel2 = _channel2;
      values = _values;
    }

    /**
     * @brief abstract interface for feature computation
     * @param feats features
     * @param cfeats number of tree node for each pixel
     * @param tree current tree
     * @param x current x position
     * @param y current y position
     * @return double distance
     **/
    virtual double getVal ( const Features &feats, const int &x, const int &y ) = 0;
    virtual Operation* clone() = 0;
    virtual std::string writeInfos() = 0;

    inline void getXY ( const Features &feats, int &xsize, int &ysize )
    {
      xsize = feats.feats->width();
      ysize = feats.feats->height();
    }

    virtual OperationTypes getOps() = 0;
};

/** Localization system */

class SemSegContextTree : public SemanticSegmentation
{
    /** 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;

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

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

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

    /** 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 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<int> &currentfeats, const NICE::MultiChannelImageT<double> &lfeats, NICE::MultiChannelImageT<double> &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<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 MultiChannelImageT<int> &currentfeats );

};


} // namespace

#endif