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 "core/image/MultiChannelImage3DT.h"
#include "vislearning/features/localfeatures/LFColorWeijer.h"
#include "segmentation/RegionSegmentationMethod.h"

#include "semseg3d/semseg/operations/Operations.h"

#include "gp-hik-core/GPHIKClassifier.h"

namespace OBJREC
{

/** Localization system */

class SemSegContextTree : public SemanticSegmentation, public NICE::Persistent
{
private:
  /** 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;

  /** 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;

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

  std::vector<ValueAccess*> calcVal;

  /** 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;

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

  /** use Gradient image or not */
  bool useGradient;

  /** use Color features from van de Weijer or not */
  bool useWeijer;

  /** use additional input Layer or not */
  bool useAdditionalLayer;

  /** use Variance map features or not */
  bool useVariance;
  
  /** use Regions as extra feature channel or not */
  bool useRegionFeature;

  /** use external image categorization to avoid some classes */
  bool useCategorization;

  /** categorization information for external categorization */
  std::string cndir;

  /** how to handle each channel
   * 0: simple grayvalue features
   * 1: which pixel belongs to which region
   * 2: graycolor integral images
   * 3: context integral images
   * 4: context features (not in MultiChannelImageT encoded)
   */
  std::vector<int> channelType;

  /** list of channels per feature type */
  std::vector<std::vector<int> > channelsPerType;

  /** whether we should use the geometric features of Hoeim (only offline computation with MATLAB supported) */
  bool useHoiemFeatures;
  
  /** save / load trained icf classifier */
  bool saveLoadData;

  /** directory of the geometric features */
  std::string hoiemDirectory;
  
  /** file location of trained icf classifier */
  std::string fileLocation;

  /** first iteration or not */
  bool firstiteration;

  /** which IntegralImage channel belongs to which raw value channel */
  std::vector<std::pair<int, int> > integralMap;

  /** amount of grayvalue Channels */
  int rawChannels;

  /** classifier for categorization */
  NICE::GPHIKClassifier *fasthik;

  /** unique numbers for nodes */
  int uniquenumber;
  
  /**
   * the main training method
   * @param trainp pointer to training data
   */
  void train ( const LabeledSet * trainp );

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

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

  /**
   * classify each pixel of a single 3d image
   * @param imgData input data
   * @param segresult segmentation results
   * @param probabilities probabilities for each pixel
   */
  void classify ( const NICE::MultiChannelImage3DT<double> &imgData,
                  NICE::MultiChannelImageT<double> & segresult,
                  NICE::MultiChannelImage3DT<double> & probabilities,
                  const std::vector<std::string> & filelist );

  /**
   * the training method with checking for already existing trained classifier from file
   * @param md training data
   */
  void train ( const MultiDataset *md );

  /**
   * @brief computes integral image of given feats
   *
   * @param nodeIndices matrix with current node for each feature
   * @param integralImage output image (must be initilized)
   * @param firstChannel index of the first channel
   * @return void
   **/
  void computeIntegralImage ( const NICE::MultiChannelImage3DT<unsigned short int> &nodeIndices, NICE::MultiChannelImage3DT<double> &integralImage, int firstChannel );

  /**
   * @brief reads image and does some simple convertions
   *
   * @param feats output image
   * @param currentFile image filename
   * @return void
   **/
  void extractBasicFeatures ( NICE::MultiChannelImage3DT<double> &feats, const NICE::MultiChannelImage3DT<double> &imgData, const std::vector<std::string> &filelist, int &amountRegions );

  /**
   * compute best split for current settings
   * @param feats features
   * @param nodeIndices 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 void
   */
  double getBestSplit ( std::vector<NICE::MultiChannelImage3DT<double> > &feats, std::vector<NICE::MultiChannelImage3DT<unsigned short int> > &nodeIndices, const std::vector<NICE::MultiChannelImageT<int> > &labels, int node, Operation *&splitop, double &splitval, const int &tree, std::vector<std::vector<std::vector<double> > > &regionProbs );

  /**
   * @brief computes the mean probability for a given class over all trees
   * @param x x position
   * @param y y position
   * @param z z position
   * @param channel current class
   * @param nodeIndices matrix with current node for each feature
   * @return double mean value
   **/
  inline double getMeanProb ( const int &x, const int &y, const int &z, const int &channel, const NICE::MultiChannelImage3DT<unsigned short int> &nodeIndices );

  /**
   * @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