NICE_SemSeg/semseg/SemSegContextTree.cpp

#include "SemSegContextTree.h"
#include "vislearning/baselib/Globals.h"
#include "vislearning/baselib/ProgressBar.h"
#include "core/basics/StringTools.h"

#include "vislearning/cbaselib/CachedExample.h"
#include "vislearning/cbaselib/PascalResults.h"
#include "vislearning/baselib/ColorSpace.h"
#include "objrec/segmentation/RSMeanShift.h"
#include "objrec/segmentation/RSGraphBased.h"
#include "core/basics/numerictools.h"
#include "core/basics/StringTools.h"
#include "core/basics/FileName.h"
#include "vislearning/baselib/ICETools.h"

#include "core/basics/Timer.h"
#include "core/basics/vectorio.h"
#include "core/image/FilterT.h"

#include <omp.h>
#include <iostream>

#undef LOCALFEATS
#undef WRITEGLOB
#undef TEXTONMAP

//#define LOCALFEATS

using namespace OBJREC;
using namespace std;
using namespace NICE;

SemSegContextTree::SemSegContextTree ( const Config *conf, const MultiDataset *md )
    : SemanticSegmentation ( conf, & ( md->getClassNames ( "train" ) ) )
{
  this->conf = conf;
  string section = "SSContextTree";
  lfcw = new LFColorWeijer ( conf );
  firstiteration = true;

  grid = conf->gI ( section, "grid", 10 );

  maxSamples = conf->gI ( section, "max_samples", 2000 );

  minFeats = conf->gI ( section, "min_feats", 50 );

  maxDepth = conf->gI ( section, "max_depth", 10 );

  windowSize = conf->gI ( section, "window_size", 16 );

  featsPerSplit = conf->gI ( section, "feats_per_split", 200 );

  useShannonEntropy = conf->gB ( section, "use_shannon_entropy", true );

  nbTrees = conf->gI ( section, "amount_trees", 1 );

  string segmentationtype = conf->gS ( section, "segmentation_type", "meanshift" );

  randomTests = conf->gI ( section, "random_tests", 10 );

  bool saveLoadData = conf->gB ( "debug", "save_load_data", false );
  string fileLocation = conf->gS ( "debug", "datafile", "tmp.txt" );

  pixelWiseLabeling = false;

  useRegionFeature = true;
  if ( segmentationtype == "meanshift" )
    segmentation = new RSMeanShift ( conf );
  else if ( segmentationtype == "none" )
  {
    segmentation = NULL;
    pixelWiseLabeling = true;
    useRegionFeature = false;
  }
  else if ( segmentationtype == "felzenszwalb" )
    segmentation = new RSGraphBased ( conf );
  else
    throw ( "no valid segmenation_type\n please choose between none, meanshift and felzenszwalb\n" );

  ftypes = conf->gI ( section, "features", 100 );;

  string featsec = "Features";

  vector<Operation*> tops;

  if ( conf->gB ( featsec, "minus", true ) )
    tops.push_back ( new Minus() );
  if ( conf->gB ( featsec, "minus_abs", true ) )
    tops.push_back ( new MinusAbs() );
  if ( conf->gB ( featsec, "addition", true ) )
    tops.push_back ( new Addition() );
  if ( conf->gB ( featsec, "only1", true ) )
    tops.push_back ( new Only1() );
  if ( conf->gB ( featsec, "rel_x", true ) )
    tops.push_back ( new RelativeXPosition() );
  if ( conf->gB ( featsec, "rel_y", true ) )
    tops.push_back ( new RelativeYPosition() );

  ops.push_back ( tops );

  tops.clear();
  tops.push_back ( new Equality() );
  ops.push_back ( tops );

  tops.clear();
  if ( conf->gB ( featsec, "bi_int_cent", true ) )
    tops.push_back ( new BiIntegralCenteredOps() );
  if ( conf->gB ( featsec, "int_cent", true ) )
    tops.push_back ( new IntegralCenteredOps() );
  if ( conf->gB ( featsec, "int", true ) )
    tops.push_back ( new IntegralOps() );
  if ( conf->gB ( featsec, "haar_horz", true ) )
    tops.push_back ( new HaarHorizontal() );
  if ( conf->gB ( featsec, "haar_vert", true ) )
    tops.push_back ( new HaarVertical() );
  if ( conf->gB ( featsec, "haar_diag", true ) )
    tops.push_back ( new HaarDiag() );
  if ( conf->gB ( featsec, "haar3_horz", true ) )
    tops.push_back ( new Haar3Horiz() );
  if ( conf->gB ( featsec, "haar3_vert", true ) )
    tops.push_back ( new Haar3Vert() );
  if ( conf->gB ( featsec, "glob", true ) )
    tops.push_back ( new GlobalFeats() );

  ops.push_back ( tops );
  ops.push_back ( tops );

  tops.clear();
  if ( conf->gB ( featsec, "minus", true ) )
    tops.push_back ( new Minus() );
  if ( conf->gB ( featsec, "minus_abs", true ) )
    tops.push_back ( new MinusAbs() );
  if ( conf->gB ( featsec, "addition", true ) )
    tops.push_back ( new Addition() );
  if ( conf->gB ( featsec, "only1", true ) )
    tops.push_back ( new Only1() );
  if ( conf->gB ( featsec, "rel_x", true ) )
    tops.push_back ( new RelativeXPosition() );
  if ( conf->gB ( featsec, "rel_y", true ) )
    tops.push_back ( new RelativeYPosition() );

  ops.push_back ( tops );

  useGradient = conf->gB ( featsec, "use_gradient", true );

  // geometric features of hoiem
  useHoiemFeatures = conf->gB ( featsec, "use_hoiem_features", false );
  if ( useHoiemFeatures )
  {
    hoiemDirectory = conf->gS ( featsec, "hoiem_directory" );
  }

  opOverview = vector<int> ( NBOPERATIONS, 0 );
  contextOverview = vector<vector<double> > ( maxDepth, vector<double> ( 2, 0.0 ) );

  calcVal.push_back ( new MCImageAccess() );
  calcVal.push_back ( new MCImageAccess() );
  calcVal.push_back ( new MCImageAccess() );
  calcVal.push_back ( new MCImageAccess() );
  calcVal.push_back ( new ClassificationResultAccess() );


  classnames = md->getClassNames ( "train" );

  ///////////////////////////////////
  // Train Segmentation Context Trees
  ///////////////////////////////////

  if ( saveLoadData )
  {
    if ( FileMgt::fileExists ( fileLocation ) )
      read ( fileLocation );
    else
    {
      train ( md );
      write ( fileLocation );
    }
  }
  else
  {
    train ( md );
  }
}

SemSegContextTree::~SemSegContextTree()
{
}

double SemSegContextTree::getBestSplit ( std::vector<NICE::MultiChannelImageT<double> > &feats, std::vector<NICE::MultiChannelImageT<unsigned short int> > &currentfeats, const std::vector<NICE::MatrixT<int> > &labels, int node, Operation *&splitop, double &splitval, const int &tree )
{
  Timer t;
  t.start();
  int imgCount = 0;

  try
  {
    imgCount = (int)feats.size();
  }
  catch ( Exception )
  {
    cerr << "no features computed?" << endl;
  }

  double bestig = -numeric_limits< double >::max();

  splitop = NULL;
  splitval = -1.0;

  set<vector<int> >selFeats;
  map<int, int> e;
  int featcounter = forest[tree][node].featcounter;

  if ( featcounter < minFeats )
  {
    //cout << "only " << featcounter << " feats in current node -> it's a leaf" << endl;
    return 0.0;
  }

  vector<double> fraction ( a.size(), 0.0 );

  for ( uint i = 0; i < fraction.size(); i++ )
  {
    if ( forbidden_classes.find ( labelmapback[i] ) != forbidden_classes.end() )
      fraction[i] = 0;
    else
      fraction[i] = ( (double)maxSamples ) / ( (double)featcounter * a[i] * a.size() );
  }

  featcounter = 0;

  for ( int iCounter = 0; iCounter < imgCount; iCounter++ )
  {
    int xsize = (int)currentfeats[iCounter].width();
    int ysize = (int)currentfeats[iCounter].height();

    for ( int x = 0; x < xsize; x++ )
    {
      for ( int y = 0; y < ysize; y++ )
      {
        if ( currentfeats[iCounter].get ( x, y, tree ) == node )
        {
          int cn = labels[iCounter] ( x, y );
          double randD = (double)rand() / (double)RAND_MAX;

          if ( labelmap.find ( cn ) == labelmap.end() )
            continue;

          if ( randD < fraction[labelmap[cn]] )
          {
            vector<int> tmp ( 3, 0 );
            tmp[0] = iCounter;
            tmp[1] = x;
            tmp[2] = y;
            featcounter++;
            selFeats.insert ( tmp );
            e[cn]++;
          }
        }
      }
    }
  }

  map<int, int>::iterator mapit;

  double globent = 0.0;

  for ( mapit = e.begin() ; mapit != e.end(); mapit++ )
  {
    double p = (double)( *mapit ).second / (double)featcounter;
    globent += p * log2 ( p );
  }

  globent = -globent;

  if ( globent < 0.5 )
  {
    return 0.0;
  }

  featsel.clear();

  for ( int i = 0; i < featsPerSplit; i++ )
  {
    int x1, x2, y1, y2;
    int ft = (int)( (double)rand() / (double)RAND_MAX * (double)ftypes );

    int tmpws = windowSize;

    if ( ft > 1 && firstiteration )
      ft = 0;

    if ( channelsPerType[ft].size() == 0 )
    {
      ft = 0;
    }

    if ( ft > 1 )
    {
      //use larger window size for context features
      tmpws *= 4;
    }

    x1 = (int)( (double)rand() / (double)RAND_MAX * (double)tmpws ) - tmpws / 2;
    x2 = (int)( (double)rand() / (double)RAND_MAX * (double)tmpws ) - tmpws / 2;
    y1 = (int)( (double)rand() / (double)RAND_MAX * (double)tmpws ) - tmpws / 2;
    y2 = (int)( (double)rand() / (double)RAND_MAX * (double)tmpws ) - tmpws / 2;

    int f1 = ( int ) ( ( double ) rand() / ( double ) RAND_MAX * ( double ) channelsPerType[ft].size() );
    int f2 = ( int ) ( ( double ) rand() / ( double ) RAND_MAX * ( double ) channelsPerType[ft].size() );
    int o = ( int ) ( ( double ) rand() / ( double ) RAND_MAX * ( double ) ops[ft].size() );

    Operation *op = ops[ft][o]->clone();

    op->set ( x1, y1, x2, y2, channelsPerType[ft][f1], channelsPerType[ft][f2], calcVal[ft] );

    //if( i < 10)
    //cerr << "type: " << ft << " features: " << op->writeInfos() << endl;

    if ( ft == 3 || ft == 4 )
      op->setContext ( true );
    else
      op->setContext ( false );

    //if(ft == 4)
    //cerr << "type: " << ft << " features: " << op->writeInfos() << endl;

    featsel.push_back ( op );
  }

#pragma omp parallel for private(mapit)
  for ( int f = 0; f < featsPerSplit; f++ )
  {
    double l_bestig = -numeric_limits< double >::max();
    double l_splitval = -1.0;
    set<vector<int> >::iterator it;
    vector<double> vals;

    double maxval = -numeric_limits<double>::max();
    double minval = numeric_limits<double>::max();
    for ( it = selFeats.begin() ; it != selFeats.end(); it++ )
    {
      Features feat;
      feat.feats = &feats[ ( *it ) [0]];
      feat.cfeats = &currentfeats[ ( *it ) [0]];
      feat.cTree = tree;
      feat.tree = &forest[tree];
      double val = featsel[f]->getVal ( feat, ( *it ) [1], ( *it ) [2] );
      vals.push_back ( val );
      maxval = std::max ( val, maxval );
      minval = std::min ( val, minval );
    }

    if ( minval == maxval )
      continue;

    double scale = maxval - minval;
    vector<double> splits;

    for ( int r = 0; r < randomTests; r++ )
    {
      splits.push_back ( ( (double)rand() / (double)RAND_MAX*scale ) + minval );
    }

    for ( int run = 0 ; run < randomTests; run++ )
    {
      set<vector<int> >::iterator it2;
      double val = splits[run];

      map<int, int> eL, eR;
      int counterL = 0, counterR = 0;
      int counter2 = 0;

      for ( it2 = selFeats.begin() ; it2 != selFeats.end(); it2++, counter2++ )
      {
        int cn = labels[ ( *it2 ) [0]] ( ( *it2 ) [1], ( *it2 ) [2] );
        //cout << "vals[counter2] " << vals[counter2] << " val: " <<  val << endl;

        if ( vals[counter2] < val )
        {
          //left entropie:
          eL[cn] = eL[cn] + 1;
          counterL++;
        }
        else
        {
          //right entropie:
          eR[cn] = eR[cn] + 1;
          counterR++;
        }
      }

      double leftent = 0.0;

      for ( mapit = eL.begin() ; mapit != eL.end(); mapit++ )
      {
        double p = (double)( *mapit ).second / (double)counterL;
        leftent -= p * log2 ( p );
      }

      double rightent = 0.0;

      for ( mapit = eR.begin() ; mapit != eR.end(); mapit++ )
      {
        double p = (double)( *mapit ).second / (double)counterR;
        rightent -= p * log2 ( p );
      }

      //cout << "rightent: " << rightent << " leftent: " << leftent << endl;

      double pl = (double)counterL / (double)( counterL + counterR );

      double ig = globent - ( 1.0 - pl ) * rightent - pl * leftent;

      //double ig = globent - rightent - leftent;

      if ( useShannonEntropy )
      {
        double esplit = - ( pl * log ( pl ) + ( 1 - pl ) * log ( 1 - pl ) );
        ig = 2 * ig / ( globent + esplit );
      }

      if ( ig > l_bestig )
      {
        l_bestig = ig;
        l_splitval = val;
      }
    }

#pragma omp critical
    {
      if ( l_bestig > bestig )
      {
        bestig = l_bestig;
        splitop = featsel[f];
        splitval = l_splitval;
      }
    }
  }

  //FIXME: delete all features!
  /*for(int i = 0; i < featsPerSplit; i++)
  {
   if(featsel[i] != splitop)
    delete featsel[i];
  }*/


#ifdef debug
  cout << "globent: " << globent <<  " bestig " << bestig << " splitval: " << splitval << endl;

#endif
  return bestig;
}

inline double SemSegContextTree::getMeanProb ( const int &x, const int &y, const int &channel, const MultiChannelImageT<unsigned short int> &currentfeats )
{
  double val = 0.0;

  for ( int tree = 0; tree < nbTrees; tree++ )
  {
    val += forest[tree][currentfeats.get ( x,y,tree ) ].dist[channel];
  }

  return val / (double)nbTrees;
}

void SemSegContextTree::computeIntegralImage ( const NICE::MultiChannelImageT<SparseVectorInt> &infeats, NICE::MultiChannelImageT<SparseVectorInt> &integralImage )
{
  int xsize = infeats.width();
  int ysize = infeats.height();
  integralImage ( 0, 0 ).add ( infeats.get ( 0, 0 ) );

  //first column
  for ( int y = 1; y < ysize; y++ )
  {
    integralImage ( 0, y ).add ( infeats.get ( 0, y ) );
    integralImage ( 0, y ).add ( integralImage ( 0, y - 1 ) );
  }

  //first row
  for ( int x = 1; x < xsize; x++ )
  {
    integralImage ( x, 0 ).add ( infeats.get ( x, 0 ) );
    integralImage ( x, 0 ).add ( integralImage ( x - 1, 0 ) );
  }

  //rest
  for ( int y = 1; y < ysize; y++ )
  {
    for ( int x = 1; x < xsize; x++ )
    {
      integralImage ( x, y ).add ( infeats.get ( x, y ) );
      integralImage ( x, y ).add ( integralImage ( x, y - 1 ) );
      integralImage ( x, y ).add ( integralImage ( x - 1, y ) );
      integralImage ( x, y ).sub ( integralImage ( x - 1, y - 1 ) );
    }
  }
}

void SemSegContextTree::computeIntegralImage ( const NICE::MultiChannelImageT<unsigned short int> &currentfeats, NICE::MultiChannelImageT<double> &feats, int firstChannel )
{

  int xsize = currentfeats.width();
  int ysize = currentfeats.height();

  xsize = feats.width();
  ysize = feats.height();

  if ( firstiteration )
  {
    firstiteration = false;
#pragma omp parallel for
    for ( int it = 0; it < integralMap.size(); it++ )
    {
      int c1 = integralMap[it].first;
      int c = integralMap[it].second;

      feats( 0, 0, c ) = feats( 0, 0, c1 );

      //first column
      for ( int y = 1; y < ysize; y++ )
      {
        feats( 0, y, c ) = feats.get ( 0, y, c1 )
                         + feats.get ( 0, y-1, c );
      }

      //first row
      for ( int x = 1; x < xsize; x++ )
      {
        feats( x, 0, c ) = feats.get ( x, 0, c1 ) 
                         + feats.get ( x-1, 0, c );
      }

      //rest
      for ( int y = 1; y < ysize; y++ )
      {
        for ( int x = 1; x < xsize; x++ )
        {
          feats ( x, y, c ) = feats ( x, y, c1 ) 
                            + feats ( x, y - 1, c ) 
                            + feats ( x - 1, y, c ) 
                            - feats ( x - 1, y - 1, c );
        }
      }
    }
  }

  int channels = ( int ) forest[0][0].dist.size();
  
#pragma omp parallel for
  for ( int c = 0; c < channels; c++ )
  {
    feats ( 0, 0, firstChannel + c ) = getMeanProb ( 0, 0, c, currentfeats );

    //first column
    for ( int y = 1; y < ysize; y++ )
    {
      feats ( 0, y, firstChannel + c ) = getMeanProb ( 0, y, c, currentfeats )
                                         + feats ( 0, y - 1, firstChannel + c );
    }

    //first row
    for ( int x = 1; x < xsize; x++ )
    {
      feats ( x, 0, firstChannel + c ) = getMeanProb ( x, 0, c, currentfeats )
                                         + feats ( x - 1, 0, firstChannel + c );
    }

    //rest
    for ( int y = 1; y < ysize; y++ )
    {
      for ( int x = 1; x < xsize; x++ )
      {
        feats ( x, y, firstChannel + c ) = getMeanProb ( x, y, c, currentfeats )
                                           + feats ( x, y - 1, firstChannel + c )
                                           + feats ( x - 1, y, firstChannel + c )
                                           - feats ( x - 1, y - 1, firstChannel + c );
      }
    }
  }
}

inline double computeWeight ( const double &d, const double &dim )
{
  return 1.0 / ( pow ( 2, (double)( dim - d + 1 ) ) );
}

void SemSegContextTree::train ( const MultiDataset *md )
{
  const LabeledSet train = * ( *md ) ["train"];
  const LabeledSet *trainp = &train;

  ProgressBar pb ( "compute feats" );
  pb.show();

  //TODO: Speichefresser!, lohnt sich sparse?
  vector<MultiChannelImageT<double> > allfeats;
  vector<MultiChannelImageT<unsigned short int> > currentfeats;
  vector<MatrixT<int> > labels;
#ifdef TEXTONMAP
  vector<MultiChannelImageT<SparseVectorInt> > textonMap;
#endif
  vector<MultiChannelImageT<SparseVectorInt> > integralTexton;

  std::string forbidden_classes_s = conf->gS ( "analysis", "donttrain", "" );

  if ( forbidden_classes_s == "" )
  {
    forbidden_classes_s = conf->gS ( "analysis", "forbidden_classes", "" );
  }

  classnames.getSelection ( forbidden_classes_s, forbidden_classes );

  int imgcounter = 0;

  int amountPixels = 0;

  LOOP_ALL_S ( *trainp )
  {
    EACH_INFO ( classno, info );

    NICE::ColorImage img;

    std::string currentFile = info.img();

    CachedExample *ce = new CachedExample ( currentFile );

    const LocalizationResult *locResult = info.localization();

    if ( locResult->size() <= 0 )
    {
      fprintf ( stderr, "WARNING: NO ground truth polygons found for %s !\n",
                currentFile.c_str() );
      continue;
    }

    fprintf ( stderr, "SemSegCsurka: Collecting pixel examples from localization info: %s\n", currentFile.c_str() );

    int xsize, ysize;
    ce->getImageSize ( xsize, ysize );
    amountPixels += xsize * ysize;

    MatrixT<int> tmpMat ( xsize, ysize );

    currentfeats.push_back ( MultiChannelImageT<unsigned short int> ( xsize, ysize, nbTrees ) );
    currentfeats[imgcounter].setAll ( 0 );
#ifdef TEXTONMAP
    textonMap.push_back ( MultiChannelImageT<SparseVectorInt> ( xsize / grid + 1, ysize / grid + 1, 1 ) );
    integralTexton.push_back ( MultiChannelImageT<SparseVectorInt> ( xsize / grid + 1, ysize / grid + 1, 1 ) );
#endif

    labels.push_back ( tmpMat );

    try {
      img = ColorImage ( currentFile );
    } catch ( Exception ) {
      cerr << "SemSeg: error opening image file <" << currentFile << ">" << endl;
      continue;
    }

    Globals::setCurrentImgFN ( currentFile );

    //TODO: resize image?!
    MultiChannelImageT<double> feats;
    allfeats.push_back ( feats );

    // read image and do some simple transformations
    extractBasicFeatures ( allfeats[imgcounter], img, currentFile );

    // getting groundtruth
    NICE::Image pixelLabels ( xsize, ysize );

    pixelLabels.set ( 0 );

    locResult->calcLabeledImage ( pixelLabels, ( *classNames ).getBackgroundClass() );

    for ( int x = 0; x < xsize; x++ )
    {
      for ( int y = 0; y < ysize; y++ )
      {
        classno = pixelLabels.getPixel ( x, y );
        labels[imgcounter] ( x, y ) = classno;

        if ( forbidden_classes.find ( classno ) != forbidden_classes.end() )
          continue;

        labelcounter[classno]++;

      }
    }

    imgcounter++;

    pb.update ( trainp->count() );
    delete ce;
  }

  pb.hide();

  map<int, int>::iterator mapit;
  int classes = 0;

  for ( mapit = labelcounter.begin(); mapit != labelcounter.end(); mapit++ )
  {
    labelmap[mapit->first] = classes;
    labelmapback[classes] = mapit->first;
    classes++;
  }

////////////////////////////////////////////////////
  //define which featurextraction methods should be used for each channel
#ifdef LOCALFEATS
  rawChannels = 9;
#else
  rawChannels = 3;
#endif

  // how many channels without integral image
  int shift = 0;

  if ( useGradient )
    rawChannels *= 2;

  if ( useHoiemFeatures )
    rawChannels += 8;

  // gray value images
  for ( int i = 0; i < rawChannels; i++ )
  {
    channelType.push_back ( 0 );
  }

  // regions
  if ( useRegionFeature )
  {
    channelType.push_back ( 1 );
    shift++;
  }

  for ( int i = 0; i < rawChannels; i++ )
  {
    channelType.push_back ( 2 );
  }

  // integral images
  for ( int i = 0; i < classes; i++ )
  {
    channelType.push_back ( 3 );
  }

  integralMap.clear();
  int integralImageAmount = rawChannels;
  for ( int ii = 0; ii < integralImageAmount; ii++ )
  {
    integralMap.push_back ( pair<int, int> ( ii, ii + integralImageAmount + shift ) );
  }

  int amountTypes = 5;

  channelsPerType = vector<vector<int> > ( amountTypes, vector<int>() );

  for ( int i = 0; i < channelType.size(); i++ )
  {
    channelsPerType[channelType[i]].push_back ( i );
  }

  for ( int i = 0; i < classes; i++ )
  {
    channelsPerType[channelsPerType.size()-1].push_back ( i );
  }

  ftypes = std::min ( amountTypes, ftypes );

////////////////////////////////////////////////////

  //balancing
  int featcounter = 0;

  a = vector<double> ( classes, 0.0 );

  for ( int iCounter = 0; iCounter < imgcounter; iCounter++ )
  {
    int xsize = (int)currentfeats[iCounter].width();
    int ysize = (int)currentfeats[iCounter].height();

    for ( int x = 0; x < xsize; x++ )
    {
      for ( int y = 0; y < ysize; y++ )
      {
        featcounter++;
        int cn = labels[iCounter] ( x, y );
        if ( labelmap.find ( cn ) == labelmap.end() )
          continue;
        a[labelmap[cn]] ++;
      }
    }
  }

  for ( int i = 0; i < (int)a.size(); i++ )
  {
    a[i] /= (double)featcounter;
  }

#ifdef DEBUG
  for ( int i = 0; i < (int)a.size(); i++ )
  {
    cout << "a[" << i << "]: " << a[i] << endl;
  }

  cout << "a.size: " << a.size() << endl;

#endif

  depth = 0;

  int uniquenumber = 0;

  for ( int t = 0; t < nbTrees; t++ )
  {
    vector<TreeNode> tree;
    tree.push_back ( TreeNode() );
    tree[0].dist = vector<double> ( classes, 0.0 );
    tree[0].depth = depth;
    tree[0].featcounter = amountPixels;
    tree[0].nodeNumber = uniquenumber;
    uniquenumber++;
    forest.push_back ( tree );
  }

  vector<int> startnode ( nbTrees, 0 );

  bool allleaf = false;
  //int baseFeatSize = allfeats[0].size();

  while ( !allleaf && depth < maxDepth )
  {
    depth++;
#ifdef DEBUG
    cout << "depth: " << depth << endl;
#endif
    allleaf = true;
    vector<MultiChannelImageT<unsigned short int> > lastfeats = currentfeats;

#if 1
    Timer timer;
    timer.start();
#endif

    double weight = computeWeight ( depth, maxDepth ) - computeWeight ( depth - 1, maxDepth );

    if ( depth == 1 )
    {
      weight = computeWeight ( 1, maxDepth );
    }

    for ( int tree = 0; tree < nbTrees; tree++ )
    {
      int t = (int)forest[tree].size();
      int s = startnode[tree];
      startnode[tree] = t;
      //TODO vielleicht parallel wenn nächste schleife trotzdem noch parallelsiert würde, die hat mehr gewicht
      //#pragma omp parallel for
      for ( int i = s; i < t; i++ )
      {
        if ( !forest[tree][i].isleaf && forest[tree][i].left < 0 )
        {
          Operation *splitfeat = NULL;
          double splitval;
          double bestig = getBestSplit ( allfeats, lastfeats, labels, i, splitfeat, splitval, tree );

          for ( int ii = 0; ii < lastfeats.size(); ii++ )
          {
            for ( int c = 0; c < lastfeats[ii].channels(); c++ )
            {
              short unsigned int minv, maxv;
              lastfeats[ii].statistics ( minv, maxv, c );
            }
          }

          forest[tree][i].feat = splitfeat;
          forest[tree][i].decision = splitval;

          if ( splitfeat != NULL )
          {
            allleaf = false;
            int left = forest[tree].size();
            forest[tree].push_back ( TreeNode() );
            forest[tree].push_back ( TreeNode() );
            int right = left + 1;
            forest[tree][i].left = left;
            forest[tree][i].right = right;
            forest[tree][left].dist = vector<double> ( classes, 0.0 );
            forest[tree][right].dist = vector<double> ( classes, 0.0 );
            forest[tree][left].depth = depth;
            forest[tree][right].depth = depth;
            forest[tree][left].featcounter = 0;
            forest[tree][right].featcounter = 0;
            forest[tree][left].nodeNumber = uniquenumber;
            int leftu = uniquenumber;
            uniquenumber++;
            forest[tree][right].nodeNumber = uniquenumber;
            int rightu = uniquenumber;
            uniquenumber++;
            forest[tree][right].featcounter = 0;

#pragma omp parallel for
            for ( int iCounter = 0; iCounter < imgcounter; iCounter++ )
            {
              int xsize = currentfeats[iCounter].width();
              int ysize = currentfeats[iCounter].height();

              for ( int x = 0; x < xsize; x++ )
              {
                for ( int y = 0; y < ysize; y++ )
                {
                  if ( currentfeats[iCounter].get ( x, y, tree ) == i )
                  {
                    Features feat;
                    feat.feats = &allfeats[iCounter];
                    feat.cfeats = &lastfeats[iCounter];
                    feat.cTree = tree;
                    feat.tree = &forest[tree];
                    double val = splitfeat->getVal ( feat, x, y );

                    int subx = x / grid;
                    int suby = y / grid;

#pragma omp critical
                    if ( val < splitval )
                    {
                      currentfeats[iCounter].set ( x, y, left, tree );
                      if ( labelmap.find ( labels[iCounter] ( x, y ) ) != labelmap.end() )
                        forest[tree][left].dist[labelmap[labels[iCounter] ( x, y ) ]]++;
                      forest[tree][left].featcounter++;
                      SparseVectorInt v;
                      v.insert ( pair<int, double> ( leftu, weight ) );
#ifdef TEXTONMAP
                      textonMap[iCounter] ( subx, suby ).add ( v );
#endif
                    }
                    else
                    {
                      currentfeats[iCounter].set ( x, y, right, tree );
                      if ( labelmap.find ( labels[iCounter] ( x, y ) ) != labelmap.end() )
                        forest[tree][right].dist[labelmap[labels[iCounter] ( x, y ) ]]++;
                      forest[tree][right].featcounter++;
                      //feld im subsampled finden und in diesem rechts hochzählen
                      SparseVectorInt v;
                      v.insert ( pair<int, double> ( rightu, weight ) );
#ifdef TEXTONMAP
                      textonMap[iCounter] ( subx, suby ).add ( v );
#endif
                    }
                  }
                }
              }
            }

            double lcounter = 0.0, rcounter = 0.0;

            for ( uint d = 0; d < forest[tree][left].dist.size(); d++ )
            {
              if ( forbidden_classes.find ( labelmapback[d] ) != forbidden_classes.end() )
              {
                forest[tree][left].dist[d] = 0;
                forest[tree][right].dist[d] = 0;
              }
              else
              {
                forest[tree][left].dist[d] /= a[d];
                lcounter += forest[tree][left].dist[d];
                forest[tree][right].dist[d] /= a[d];
                rcounter += forest[tree][right].dist[d];
              }
            }

            if ( lcounter <= 0 || rcounter <= 0 )
            {
              cout << "lcounter : " << lcounter << " rcounter: " << rcounter << endl;
              cout << "splitval: " << splitval << " splittype: " << splitfeat->writeInfos() << endl;
              cout << "bestig: " << bestig << endl;

              for ( int iCounter = 0; iCounter < imgcounter; iCounter++ )
              {
                int xsize = currentfeats[iCounter].width();
                int ysize = currentfeats[iCounter].height();
                int counter = 0;

                for ( int x = 0; x < xsize; x++ )
                {
                  for ( int y = 0; y < ysize; y++ )
                  {
                    if ( lastfeats[iCounter].get ( x, y, tree ) == i )
                    {
                      if ( ++counter > 30 )
                        break;

                      Features feat;

                      feat.feats = &allfeats[iCounter];
                      feat.cfeats = &lastfeats[iCounter];
                      feat.cTree = tree;
                      feat.tree = &forest[tree];

                      double val = splitfeat->getVal ( feat, x, y );

                      cout << "splitval: " << splitval << " val: " << val << endl;
                    }
                  }
                }
              }

              assert ( lcounter > 0 && rcounter > 0 );
            }

            for ( uint d = 0; d < forest[tree][left].dist.size(); d++ )
            {
              forest[tree][left].dist[d] /= lcounter;
              forest[tree][right].dist[d] /= rcounter;
            }
          }
          else
          {
            forest[tree][i].isleaf = true;
          }
        }
      }
    }

    //compute integral images
    if ( firstiteration )
    {
      for ( int i = 0; i < imgcounter; i++ )
      {
        int pos = allfeats.size();
        allfeats[i].addChannel ( ( int ) ( classes + rawChannels ) );
      }
    }

#pragma omp parallel for
    for ( int i = 0; i < imgcounter; i++ )
    {
      computeIntegralImage ( currentfeats[i], allfeats[i], channelType.size() - classes );
#ifdef TEXTONMAP
      computeIntegralImage ( textonMap[i], integralTexton[i] );
#endif
    }

#if 1
    timer.stop();

    cout << "time for depth " << depth << ": " << timer.getLast() << endl;
#endif
  }

#ifdef WRITEGLOB
  ofstream outstream ( "globtrain.feat" );

  for ( int i = 0; i < textonMap.size(); i++ )
  {
    set<int> usedclasses;
    for ( uint x = 0; x < labels[i].rows(); x++ )
    {
      for ( uint y = 0; y < labels[i].cols(); y++ )
      {
        int classno = labels[i] ( x, y );

        if ( forbidden_classes.find ( classno ) != forbidden_classes.end() )
          continue;

        usedclasses.insert ( classno );
      }
    }

    cout << "labels.cols: " << labels[i].cols() << " labels.rows " << labels[i].rows() << endl;
    cout << "currentfeats : " << allfeats[i].width() << " allfeats[i].height(); " << allfeats[i].height() << endl;

    set<int>::iterator it;
    for ( it = usedclasses.begin() ; it != usedclasses.end(); it++ )
      outstream << *it << " ";
    outstream << endl;
    integralTexton[i] ( integralTexton[i].width() - 1, integralTexton[i].height() - 1 ).store ( outstream );
  }

  outstream.close();
#endif
  cout << "uniquenumber " << uniquenumber << endl;
  //getchar();
#ifdef DEBUG
  for ( int tree = 0; tree < nbTrees; tree++ )
  {
    int t = (int)forest[tree].size();

    for ( int i = 0; i < t; i++ )
    {
      printf ( "tree[%i]: left: %i, right: %i", i, forest[tree][i].left, forest[tree][i].right );

      if ( !forest[tree][i].isleaf && forest[tree][i].left != -1 )
      {
        cout <<  ", feat: " << forest[tree][i].feat->writeInfos() << " ";
        opOverview[forest[tree][i].feat->getOps() ]++;
        contextOverview[forest[tree][i].depth][ (int)forest[tree][i].feat->getContext() ]++;
      }

      for ( int d = 0; d < (int)forest[tree][i].dist.size(); d++ )
      {
        cout << " " << forest[tree][i].dist[d];
      }

      cout << endl;
    }
  }

  for ( uint c = 0; c < ops.size(); c++ )
  {
    for ( int t = 0; t < ops[c].size(); t++ )
    {
      cout << ops[c][t]->writeInfos() << ": " << opOverview[ops[c][t]->getOps() ] << endl;
    }
  }

  for ( int d = 0; d < maxDepth; d++ )
  {
    double sum =  contextOverview[d][0] + contextOverview[d][1];

    contextOverview[d][0] /= sum;
    contextOverview[d][1] /= sum;

    cout << "depth: " << d << " woContext: " << contextOverview[d][0] << " wContext: " << contextOverview[d][1] << endl;
  }

#endif
}

void SemSegContextTree::extractBasicFeatures ( NICE::MultiChannelImageT<double> &feats, const ColorImage &img, const string &currentFile )
{
  int xsize = img.width();
  int ysize = img.height();
  //TODO: resize image?!

#ifdef LOCALFEATS
  lfcw->getFeats ( img, feats );
#else
  feats.reInit ( xsize, ysize, 3 );

  for ( int x = 0; x < xsize; x++ )
  {
    for ( int y = 0; y < ysize; y++ )
    {
      for ( int r = 0; r < 3; r++ )
      {
        feats.set ( x, y, img.getPixel ( x, y, r ), r );
      }
    }
  }

  feats = ColorSpace::rgbtolab ( feats );
#endif

  if ( useGradient )
  {
    int currentsize = feats.channels();
    feats.addChannel ( currentsize );

    for ( int c = 0; c < currentsize; c++ )
    {
      ImageT<double> tmp = feats[c];
      ImageT<double> tmp2 = feats[c+currentsize];

      NICE::FilterT<double,double,double>::gradientStrength( tmp, tmp2 );
    }
  }

  // read the geometric cues produced by Hoiem et al.
  if ( useHoiemFeatures )
  {
    // we could also give the following set as a config option
    string hoiemClasses_s = "sky 000 090-045 090-090 090-135 090 090-por 090-sol";
    vector<string> hoiemClasses;
    StringTools::split ( hoiemClasses_s, ' ', hoiemClasses );

    // Now we have to do some fancy regular expressions :)
    // Original image filename: basel_000083.jpg
    // hoiem result: basel_000083_c_sky.png

    // Fancy class of Ferid which supports string handling especially for filenames
    FileName fn ( currentFile );
    fn.removeExtension();
    FileName fnBase = fn.extractFileName();

    // counter for the channel index, starts with the current size of the destination multi-channel image
    int currentChannel = feats.channels();

    // add a channel for each feature in advance
    feats.addChannel ( hoiemClasses.size() );

    // loop through all geometric categories and add the images
    for ( vector<string>::const_iterator i = hoiemClasses.begin(); i != hoiemClasses.end(); i++, currentChannel++ )
    {
      string hoiemClass = *i;
      FileName fnConfidenceImage ( hoiemDirectory + fnBase.str() + "_c_" + hoiemClass + ".png" );
      if ( ! fnConfidenceImage.fileExists() )
      {
        fthrow ( Exception, "Unable to read the Hoiem geometric confidence image: " << fnConfidenceImage.str() << " (original image is " << currentFile << ")" );
      } else {
        Image confidenceImage ( fnConfidenceImage.str() );
        // check whether the image size is consistent
        if ( confidenceImage.width() != feats.width() || confidenceImage.height() != feats.height() )
        {
          fthrow ( Exception, "The size of the geometric confidence image does not match with the original image size: " << fnConfidenceImage.str() );
        }
        ImageT<double> dst = feats[currentChannel];

        // copy standard image to double image
        for ( uint y = 0 ; y < confidenceImage.height(); y++ )
          for ( uint x = 0 ; x < confidenceImage.width(); x++ )
            feats ( x, y, currentChannel ) = ( double ) confidenceImage ( x, y );
      }
    }
  }
}

void SemSegContextTree::semanticseg ( CachedExample *ce, NICE::Image & segresult, NICE::MultiChannelImageT<double> & probabilities )
{
  int xsize;
  int ysize;
  ce->getImageSize ( xsize, ysize );
  firstiteration = true;

  int classes = labelmapback.size();

  int numClasses = classNames->numClasses();

  fprintf ( stderr, "ContextTree classification !\n" );

  probabilities.reInit ( xsize, ysize, numClasses );
  probabilities.setAll ( 0 );

#ifdef TEXTONMAP
  MultiChannelImageT<SparseVectorInt> textonMap ( xsize / grid + 1, ysize / grid + 1, 1 );
  MultiChannelImageT<SparseVectorInt> integralTexton ( xsize / grid + 1, ysize / grid + 1, 1 );
#endif

  std::string currentFile = Globals::getCurrentImgFN();
  MultiChannelImageT<double> feats;

  NICE::ColorImage img;

  try {
    img = ColorImage ( currentFile );
  } catch ( Exception ) {
    cerr << "SemSeg: error opening image file <" << currentFile << ">" << endl;
    return;
  }

  extractBasicFeatures ( feats, img, currentFile ); //read image and do some simple transformations

  bool allleaf = false;

  MultiChannelImageT<unsigned short int> currentfeats ( xsize, ysize, nbTrees );

  currentfeats.setAll ( 0 );

  depth = 0;

  for ( int d = 0; d < maxDepth && !allleaf; d++ )
  {
    depth++;

#ifdef TEXTONMAP
    double weight = computeWeight ( depth, maxDepth ) - computeWeight ( depth - 1, maxDepth );

    if ( depth == 1 )
    {
      weight = computeWeight ( 1, maxDepth );
    }
#endif

    allleaf = true;

    MultiChannelImageT<unsigned short int> lastfeats = currentfeats;

    int tree;
#pragma omp parallel for private(tree)
    for ( tree = 0; tree < nbTrees; tree++ )
    {
      for ( int x = 0; x < xsize; x++ )
      {
        for ( int y = 0; y < ysize; y++ )
        {
          int t = currentfeats.get ( x, y, tree );

          if ( forest[tree][t].left > 0 )
          {
            allleaf = false;
            Features feat;
            feat.feats = &feats;
            feat.cfeats = &lastfeats;
            feat.cTree = tree;
            feat.tree = &forest[tree];

            double val = forest[tree][t].feat->getVal ( feat, x, y );

            int subx = x / grid;
            int suby = y / grid;

            if ( val < forest[tree][t].decision )
            {
              currentfeats.set ( x, y, forest[tree][t].left, tree );
#ifdef TEXTONMAP
#pragma omp critical
              {
                SparseVectorInt v;
                v.insert ( pair<int, double> ( forest[tree][forest[tree][t].left].nodeNumber, weight ) );
                textonMap ( subx, suby ).add ( v );
              }
#endif
            }
            else
            {
              currentfeats.set ( x, y, forest[tree][t].right, tree );
#ifdef TEXTONMAP
#pragma omp critical
              {
                SparseVectorInt v;
                v.insert ( pair<int, double> ( forest[tree][forest[tree][t].right].nodeNumber, weight ) );

                textonMap ( subx, suby ).add ( v );
              }
#endif
            }
          }
        }
      }

      if ( depth < maxDepth )
      {
        //compute integral images
        if ( firstiteration )
        {
          feats.addChannel ( classes + rawChannels );
        }
      }
    }

    if ( depth < maxDepth )
    {
      computeIntegralImage ( currentfeats, feats, channelType.size() - classes );
#ifdef TEXTONMAP
      computeIntegralImage ( textonMap, integralTexton );
#endif
    }
  }

#ifdef WRITEGLOB
  ofstream outstream ( "globtest.feat", ofstream::app );
  outstream << 0 << endl;
  integralTexton ( integralTexton.width() - 1, integralTexton.height() - 1 ).store ( outstream );
  outstream.close();
#endif

  string cndir = conf->gS ( "SSContextTree", "cndir", "" );

  int allClasses = ( int ) probabilities.channels();
  vector<int> useclass ( allClasses, 1 );
#ifdef WRITEGLOB


  std::vector< std::string > list;
  StringTools::split ( currentFile, '/', list );

  string orgname = list.back();

  ofstream ostream ( "filelist.txt", ofstream::app );
  ostream << orgname << ".dat" << endl;
  ostream.close();

  if ( cndir != "" )
  {
    useclass = vector<int> ( allClasses, 0 );
    ifstream infile ( ( cndir + "/" + orgname + ".dat" ).c_str() );

#undef OLD
#ifdef OLD
    while ( !infile.eof() && infile.good() )
    {
      int tmp;
      infile >> tmp;
      assert ( tmp >= 0 && tmp < allClasses );
      useclass[tmp] = 1;
    }
#else
    int c = 0;
    vector<double> probs ( allClasses, 0.0 );

    while ( !infile.eof() && infile.good() )
    {
      infile >> probs[c];
      c++;
    }

    vector<double> sorted = probs;
    sort ( sorted.begin(), sorted.end() );

    double thr = sorted[10];

    if ( thr < 0.0 )
      thr = 0.0;

    for ( int c = 0; c < allClasses; c++ )
    {
      if ( probs[c] < thr )
      {
        useclass[c] = 1;
      }
    }

#endif

    for ( int c = 0; c < allClasses; c++ )
    {
      if ( useclass[c] == 0 )
      {
        probabilities.set ( -numeric_limits< double >::max(), c );
      }
    }
  }
#endif

  if ( pixelWiseLabeling )
  {
    //finales labeln:
    //long int offset = 0;

    for ( int x = 0; x < xsize; x++ )
    {
      for ( int y = 0; y < ysize; y++ )
      {
        double maxvalue = - numeric_limits<double>::max(); //TODO: das kann auch nur pro knoten gemacht werden, nicht pro pixel
        int maxindex = 0;
        uint s = forest[0][0].dist.size();

        for ( uint i = 0; i < s; i++ )
        {
          int currentclass = labelmapback[i];
          if ( useclass[currentclass] )
          {
            probabilities ( x, y, currentclass ) = getMeanProb ( x, y, i, currentfeats );

            if ( probabilities ( x, y, currentclass ) > maxvalue )
            {
              maxvalue = probabilities ( x, y, currentclass );
              maxindex = currentclass;
            }
          }
        }
        segresult.setPixel ( x, y, maxindex );
        if ( maxvalue > 1 )
          cout << "maxvalue: " << maxvalue << endl;
      }
    }
#undef VISUALIZE
#ifdef VISUALIZE
    for ( int j = 0 ; j < (int)probabilities.numChannels; j++ )
    {
      //cout << "class: " << j << endl;//" " << cn.text ( j ) << endl;

      NICE::Matrix tmp ( probabilities.height(), probabilities.width() );
      double maxval = -numeric_limits<double>::max();
      double minval = numeric_limits<double>::max();


      for ( int y = 0; y < probabilities.height(); y++ )
        for ( int x = 0; x < probabilities.width(); x++ )
        {
          double val = probabilities ( x, y, j );
          tmp ( y, x ) = val;
          maxval = std::max ( val, maxval );
          minval = std::min ( val, minval );
        }
      tmp ( 0, 0 ) = 1.0;
      tmp ( 0, 1 ) = 0.0;

      NICE::ColorImage imgrgb ( probabilities.width(), probabilities.height() );
      ICETools::convertToRGB ( tmp, imgrgb );

      cout << "maxval = " << maxval << " minval: " << minval << " for class " << j << endl; //cn.text ( j ) << endl;

      std::string s;
      std::stringstream out;
      out << "tmpprebmap" << j << ".ppm";
      s = out.str();
      imgrgb.write ( s );
      //showImage(imgrgb, "Ergebnis");
      //getchar();
    }
    cout << "fertsch" << endl;
    getchar();
    cout << "weiter gehtsch" << endl;
#endif
  }
  else
  {
    //final labeling using segmentation
    Matrix regions;
    //showImage(img);
    int regionNumber = segmentation->segRegions ( img, regions );
    cout << "regions: " << regionNumber << endl;

    int dSize = forest[0][0].dist.size();
    vector<vector<double> > regionProbs ( regionNumber, vector<double> ( dSize, 0.0 ) );
    vector<int> bestlabels ( regionNumber, 0 );

    for ( int y = 0; y < img.height(); y++ )
    {
      for ( int x = 0; x < img.width(); x++ )
      {
        int cregion = regions ( x, y );

        for ( int d = 0; d < dSize; d++ )
        {
          regionProbs[cregion][d] += getMeanProb ( x, y, d, currentfeats );
        }
      }
    }

    for ( int r = 0; r < regionNumber; r++ )
    {
      double maxval = regionProbs[r][0];
      bestlabels[r] = 0;

      for ( int d = 1; d < dSize; d++ )
      {
        if ( maxval < regionProbs[r][d] )
        {
          maxval = regionProbs[r][d];
          bestlabels[r] = d;
        }
      }

      bestlabels[r] = labelmapback[bestlabels[r]];
    }

    for ( int y = 0; y < img.height(); y++ )
    {
      for ( int x = 0; x < img.width(); x++ )
      {

        segresult.setPixel ( x, y, bestlabels[regions ( x,y ) ] );
      }
    }

#define WRITEREGIONS
#ifdef WRITEREGIONS
    RegionGraph rg;
    segmentation->getGraphRepresentation ( img, regions,  rg );
    for ( uint pos = 0; pos < regionProbs.size(); pos++ )
    {
      rg[pos]->setProbs ( regionProbs[pos] );
    }

    std::string s;
    std::stringstream out;
    std::vector< std::string > list;
    StringTools::split ( Globals::getCurrentImgFN (), '/', list );

    out << "rgout/" << list.back() << ".graph";
    string writefile = out.str();
    rg.write ( writefile );
#endif
  }

  cout << "segmentation finished" << endl;
}

void SemSegContextTree::store ( std::ostream & os, int format ) const
{
  os.precision (numeric_limits<double>::digits10 + 1);
  os << nbTrees << endl;
  classnames.store ( os );

  map<int, int>::const_iterator it;

  os << labelmap.size() << endl;
  for ( it = labelmap.begin() ; it != labelmap.end(); it++ )
    os << ( *it ).first << " " << ( *it ).second << endl;

  os << labelmapback.size() << endl;
  for ( it = labelmapback.begin() ; it != labelmapback.end(); it++ )
    os << ( *it ).first << " " << ( *it ).second << endl;

  int trees = forest.size();
  os << trees << endl;

  for ( int t = 0; t < trees; t++ )
  {
    int nodes = forest[t].size();
    os << nodes << endl;
    for ( int n = 0; n < nodes; n++ )
    {
      os << forest[t][n].left << " " << forest[t][n].right << " " << forest[t][n].decision << " " << forest[t][n].isleaf << " " << forest[t][n].depth << " " << forest[t][n].featcounter << " " << forest[t][n].nodeNumber << endl;
      os << forest[t][n].dist << endl;

      if ( forest[t][n].feat == NULL )
        os << -1 << endl;
      else
      {
        os << forest[t][n].feat->getOps() << endl;
        forest[t][n].feat->store ( os );
      }
    }
  }

  os << channelType.size() << endl;
  for ( int i = 0; i < channelType.size(); i++ )
  {
    os << channelType[i] << " ";
  }
  os << endl;

  os << integralMap.size() << endl;
  for ( int i = 0; i < integralMap.size(); i++ )
  {
    os << integralMap[i].first << " " << integralMap[i].second << endl;
  }

  os << rawChannels << endl;
}

void SemSegContextTree::restore ( std::istream & is, int format )
{
  is >> nbTrees;

  classnames.restore ( is );

  int lsize;
  is >> lsize;

  labelmap.clear();
  for ( int l = 0; l < lsize; l++ )
  {
    int first, second;
    is >> first;
    is >> second;
    labelmap[first] = second;
  }

  is >> lsize;
  labelmapback.clear();
  for ( int l = 0; l < lsize; l++ )
  {
    int first, second;
    is >> first;
    is >> second;
    labelmapback[first] = second;
  }

  int trees;
  is >> trees;
  forest.clear();

  for ( int t = 0; t < trees; t++ )
  {
    vector<TreeNode> tmptree;
    forest.push_back ( tmptree );
    int nodes;
    is >> nodes;
    //cout << "nodes: " << nodes << endl;
    for ( int n = 0; n < nodes; n++ )
    {
      TreeNode tmpnode;
      forest[t].push_back ( tmpnode );
      is >> forest[t][n].left;
      is >> forest[t][n].right;
      is >> forest[t][n].decision;
      is >> forest[t][n].isleaf;
      is >> forest[t][n].depth;
      is >> forest[t][n].featcounter;

      is >> forest[t][n].nodeNumber;
      is >> forest[t][n].dist;

      int feattype;
      is >> feattype;
      assert ( feattype < NBOPERATIONS );
      forest[t][n].feat = NULL;
      if ( feattype >= 0 )
      {
        for ( uint o = 0; o < ops.size(); o++ )
        {
          for ( uint o2 = 0; o2 < ops[o].size(); o2++ )
          {
            if ( forest[t][n].feat == NULL )
            {
              for ( uint c = 0; c < ops[o].size(); c++ )
              {
                if ( ops[o][o2]->getOps() == feattype )
                {
                  forest[t][n].feat = ops[o][o2]->clone();
                  break;
                }
              }
            }
          }
        }

        assert ( forest[t][n].feat != NULL );
        forest[t][n].feat->restore ( is );
      }
    }
  }

  channelType.clear();
  int ctsize;
  is >> ctsize;
  for ( int i = 0; i < ctsize; i++ )
  {
    int tmp;
    is >> tmp;
    channelType.push_back ( tmp );
  }

  integralMap.clear();
  int iMapSize;
  is >> iMapSize;
  for ( int i = 0; i < iMapSize; i++ )
  {
    int first;
    int second;
    is >> first;
    is >> second;
    integralMap.push_back ( pair<int, int> ( first, second ) );
  }

  is >> rawChannels;
}