NICE_SemSeg/semseg/SemSegCsurka.cpp

#include <iostream>

#include "SemSegCsurka.h"

#include "vislearning/baselib/ICETools.h"

#include "core/image/Filter.h"

#include <sstream>

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

#undef DEBUG_CSURK

SemSegCsurka::SemSegCsurka ( const Config *conf,
                             const MultiDataset *md )
		: SemanticSegmentation ( conf, & ( md->getClassNames ( "train" ) ) )
{
	this->conf = conf;

	opSiftImpl = conf->gS ( "Descriptor", "implementation", "VANDESANDE" );
	readfeat = conf->gB ( "Descriptor", "read", true );
	writefeat = conf->gB ( "Descriptor", "write", true );
#ifdef DEBUG_CSURK
	clog << "[log] SemSegCsurka::SemSegCsurka: OppenentSift implemenation: " << opSiftImpl << endl;
#endif

	save_cache = conf->gB ( "FPCPixel", "save_cache", true );
	read_cache = conf->gB ( "FPCPixel", "read_cache", false );
	cache = conf->gS ( "cache", "root", "" );
	sigmaweight = conf->gD ( "SemSegCsurka", "sigmaweight", 0.6 );

	dim = conf->gI ( "SemSegCsurka", "pcadim", 50 );

	usepca = conf->gB ( "SemSegCsurka", "usepca", true );
	calcpca = conf->gB ( "SemSegCsurka", "calcpca", false );

	usegmm = conf->gB ( "SemSegCsurka", "usegmm", false );
	norm = conf->gB ( "SemSegCsurka", "normalize", false );
	usefisher = conf->gB ( "SemSegCsurka", "usefisher", false );
	dogmm = conf->gB ( "SemSegCsurka", "dogmm", false );
	gaussians = conf->gI ( "SemSegCsurka", "gaussians", 50 );

	usekmeans = conf->gB ( "SemSegCsurka", "usekmeans", false );
	kmeansfeat = conf->gI ( "SemSegCsurka", "kmeansfeat", 50 );
	kmeanshard = conf->gB ( "SemSegCsurka", "kmeanshard", false );

	cname = conf->gS ( "SemSegCsurka", "classifier", "RandomForests" );
	anteil = conf->gD ( "SemSegCsurka", "anteil", 1.0 );
	userellocprior = conf->gB ( "SemSegCsurka", "rellocfeat", false );
	bool usesrg = conf->gB ( "SemSegCsurka", "usesrg", false );

	useregions = conf->gB ( "SemSegCsurka", "useregions", true );
	savesteps = conf->gB ( "SemSegCsurka", "savesteps", true );
	bool usegcopt = conf->gB ( "SemSegCsurka", "usegcopt", false );

	bestclasses = conf->gI ( "SemSegCsurka", "bestclasses", 0 );

	smoothhl = conf->gB ( "SemSegCsurka", "smoothhl", false );
	smoothfactor = conf->gD ( "SemSegCsurka", "smoothfactor", 1.0 );
	
	usecolorfeats = conf->gB("SemSegCsurka", "usecolorfeats", false);
	
	string rsMethod = conf->gS("SemSegCsurka", "segmentation", "meanshift");
	
	
	
	g = NULL;
	k = NULL;
	relloc = NULL;
	srg = NULL;
	gcopt = NULL;

	if ( !useregions && ( userellocprior || usesrg ) )
	{
		cerr << "relative location priors and super region growing are just supported in combination with useregions" << endl;
		exit ( 1 );
	}

	if ( usepca )
		pca = PCA ( dim );

	RegionSegmentationMethod * tmpseg;
	if(rsMethod == "meanshift")
	    tmpseg = new RSMeanShift ( conf );
	else
	    tmpseg = new RSGraphBased(conf);
	
	if(save_cache)
	  seg = new RSCache ( conf, tmpseg );
	else
	  seg = tmpseg;

	if ( userellocprior )
		relloc = new RelativeLocationPrior ( conf );
	else
		relloc = NULL;

#ifdef NICE_USELIB_ICE
	if ( usesrg )
		srg = new PPSuperregion ( conf );
	else
		srg = NULL;
#else
	srg = NULL;
#endif

	if ( usegcopt )
		gcopt = new PPGraphCut ( conf );
	else
		gcopt = NULL;

	classifier = NULL;
	vclassifier = NULL;
	if ( cname == "RandomForests" )
		classifier = new FPCRandomForests ( conf, "ClassifierForest" );
	else if ( cname == "SMLR" )
		classifier = new FPCSMLR ( conf, "ClassifierSMLR" );
	else
		vclassifier = CSGeneric::selectVecClassifier ( conf, "main" );
	//classifier = new FPCSparseMultinomialLogisticRegression(conf, "ClassifierSMLR");

	if(classifier != NULL)
		classifier->setMaxClassNo ( classNames->getMaxClassno() );
	else
		vclassifier->setMaxClassNo ( classNames->getMaxClassno() );

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

	if ( read_cache )
	{
		fprintf ( stderr, "SemSegCsurka:: Reading classifier data from %s\n", ( cache+"/fpcrf.data" ).c_str() );

		if(classifier != NULL)
			classifier->read ( cache+"/fpcrf.data" );
		else
			vclassifier->read ( cache+"/veccl.data" );

		if ( usepca )
		{
			std::string filename = cache + "/pca";
			pca.read ( filename );
		}

		if ( usegmm )
		{
			g = new GMM ( conf, gaussians );

			if ( !g->loadData ( cache+"/gmm" ) )
			{
				cerr << "SemSegCsurka:: no gmm file found" << endl;
				exit ( -1 );
			}
		}
		else{ g = NULL; }

		if ( usekmeans )
		{
			k = new KMeansOnline ( gaussians );
		}

		fprintf ( stderr, "SemSegCsurka:: successfully read\n" );

		std::string filename = cache + "/rlp";

		FILE *value;
		value = fopen ( filename.c_str(),"r" );

		if ( value==NULL )
		{
			trainpostprocess ( md );
		}
		else
		{
			if ( userellocprior )
			{
				relloc->read ( filename );
			}
		}

		filename = cache + "/srg";

		value = fopen ( filename.c_str(),"r" );

		if ( value==NULL )
		{
			trainpostprocess ( md );
		}
		else
		{
			if ( srg != NULL )
			{
				srg->read ( filename );
			}
		}
	}
	else
	{
		train ( md );
	}
}

SemSegCsurka::~SemSegCsurka()
{
	// clean-up
	if ( classifier != NULL )
		delete classifier;
	if( vclassifier !=NULL)
		delete vclassifier;
	if ( seg != NULL )
		delete seg;

	g = NULL;
	if ( g != NULL )
		delete g;
}

void SemSegCsurka::normalize(Examples &ex)
{
	assert(ex.size() > 0);
	if(vecmin.size() == 0)
	{
		for(int j = 0; j < (int)ex[0].second.vec->size(); j++)
		{
			double maxv = -numeric_limits<int>::max();
			double minv = numeric_limits<int>::max();
			for(int i = 0; i < (int)ex.size(); i++)
			{
				maxv = std::max(maxv,(*ex[i].second.vec)[j]);
				minv = std::min(minv,(*ex[i].second.vec)[j]);
			}
			vecmin.push_back(minv);
			vecmax.push_back(maxv);
		}
	}
	for(int i = 0; i < (int)ex.size(); i++)
	{
		for(int j = 0; j < (int)ex[i].second.vec->size(); j++)
		{
			(*ex[i].second.vec)[j] = ((*ex[i].second.vec)[j]-vecmin[j])/(vecmax[j]-vecmin[j]);
		}
	}
	return;
}

void SemSegCsurka::convertLowToHigh ( Examples &ex, double reduce )
{
	cout << "converting low-level features to high-level features" << endl;
	
	if ( reduce >= 1.0 )
	{
		for ( int i = 0; i < ( int ) ex.size(); i++ )
		{
			SparseVector *f = new SparseVector();

			if ( usekmeans )
			{
				k->getDist ( *ex[i].second.vec, *f, kmeansfeat, kmeanshard );
			}
			else
			{
				if ( usefisher )
					g->getFisher ( *ex[i].second.vec, *f );
				else
					g->getProbs ( *ex[i].second.vec, *f );
			}
			delete ex[i].second.vec;

			ex[i].second.vec = NULL;
			ex[i].second.svec = f;
		}
	}
	else
	{
		srand ( time ( NULL ) );

		vector<bool> del(ex.size(), false);
		cout << "Example size old " << ex.size() << endl;

#pragma omp parallel for
		for ( int i = 0; i < ( int ) ex.size(); i++ )
		{
			double rval = ( double ) rand() / ( double ) RAND_MAX;
			if ( rval < reduce )
			{
				SparseVector *f = new SparseVector();

				if ( usekmeans )
					k->getDist ( *ex[i].second.vec, *f, kmeansfeat, kmeanshard );
				else
				{
					if ( usefisher )
						g->getFisher ( *ex[i].second.vec, *f );
					else
						g->getProbs ( *ex[i].second.vec, *f );
				}

				delete ex[i].second.vec;
				ex[i].second.vec = NULL;
				ex[i].second.svec = f;
			}
			else
			{
				del[i] = true;
			}
		}
		for ( int i = ( int ) del.size()-1; i >= 0; i-- )
		{
		  if(del[i])
		  {
			ex.erase ( ex.begin() +i);
		  }
		}
		cerr << "Example size new " << ex.size() << endl;
	}
	cerr << "converting low-level features to high-level features finished" << endl;
}

void SemSegCsurka::smoothHL ( Examples ex )
{

	if ( !smoothhl )
		return;
	assert ( ex.size() > 1 );

	long long int minx = numeric_limits<long long int>::max();
	long long int miny = numeric_limits<long long int>::max();
	long long int maxx = -numeric_limits<long long int>::max();
	long long int maxy = -numeric_limits<long long int>::max();
	long long int distx = numeric_limits<long long int>::max();
	long long int disty = numeric_limits<long long int>::max();

	set<double> scales;
	for ( int i = 0; i < (int)ex.size(); i++ )
	{
		scales.insert ( ex[i].second.scale );
	}

	map<double, int> scalepos;
	int it = 0;

	for ( set<double>::const_iterator iter = scales.begin(); iter != scales.end();    ++iter, ++it )
	{
		scalepos.insert(make_pair(*iter, it));
	}

	for ( int i = 0; i < (int)ex.size(); i++ )
	{
		if ( minx < numeric_limits<int>::max() && ex[i].second.x - minx > 0 )
			distx = std::min ( distx, ex[i].second.x - minx );
		if ( miny < numeric_limits<int>::max() && ex[i].second.y - miny > 0 )
			disty = std::min ( disty, ex[i].second.y - miny );
		minx = std::min ( (long long int)ex[i].second.x, minx );
		maxx = std::max ( (long long int)ex[i].second.x, maxx );
		miny = std::min ( (long long int)ex[i].second.y, miny );
		maxy = std::max ( (long long int)ex[i].second.y, maxy );
	}

	distx = abs ( distx );

	int xsize = ( maxx - minx ) /distx +1;
	int ysize = ( maxy - miny ) /disty +1;
	double valx = ( ( double ) xsize-1 ) / ( double ) ( maxx - minx );
	double valy = ( ( double ) ysize-1 ) / ( double ) ( maxy - miny );

	//double sigma = smoothfactor;
	double sigma = std::max(xsize,ysize) * smoothfactor;
	//double sigma = 0.2;
	cout << "sigma1: " << sigma << endl;
	
	vector<NICE::FloatImage> imgv;
	vector<NICE::FloatImage> gaussImgv;
	for(int i = 0; i < (int)scalepos.size(); i++)
	{
		NICE::FloatImage img( xsize, ysize);
		NICE::FloatImage gaussImg( xsize, ysize);
		imgv.push_back(img);
		gaussImgv.push_back(gaussImg);
	}

	for ( int d = 0; d < ex[0].second.svec->getDim(); d++ )
	{
		//TODO: max und min dynamisches bestimmen

		for(int i = 0; i < (int)scalepos.size(); i++)
		{
			imgv[i].set(0.0);
			gaussImgv[i].set(0.0);
		}
		
		for ( int i = 0; i < (int)ex.size(); i++ )
		{
			int xpos = ( ex[i].second.x - minx ) *valx;
			int ypos = ( ex[i].second.y - miny ) *valy;
			
			double val = ex[i].second.svec->get ( d );
			imgv[scalepos[ex[i].second.scale]].setPixel(xpos,ypos,val);
		}
		
		/*
		for(int y = 0; y < ysize; y++)
		{
			for(int x = 0; x < xsize; x++)
			{
				// refactor-nice.pl: check this substitution
				// old: double val = GetValD(img,x,y);
				double val = img.getPixel(x,y);
				double  c = 0.0;
				if(val == 0.0)
				{
					if(x > 0)
					{
						// refactor-nice.pl: check this substitution
						// old: val+=GetValD(img,x-1,y);
						val+=img.getPixel(x-1,y);
						c+=1.0;
					}
					if(y > 0)
					{
						// refactor-nice.pl: check this substitution
						// old: val+=GetValD(img,x,y-1);
						val+=img.getPixel(x,y-1);
						c+=1.0;
					}
					if(x < xsize-1)
					{
						// refactor-nice.pl: check this substitution
						// old: val+=GetValD(img,x+1,y);
						val+=img.getPixel(x+1,y);
						c+=1.0;
					}
					if(y < ysize-1)
					{
						// refactor-nice.pl: check this substitution
						// old: val+=GetValD(img,x,y+1);
						val+=img.getPixel(x,y+1);
						c+=1.0;
					}
					// refactor-nice.pl: check this substitution
					// old: PutValD(img,x,y,val/c);
					img.setPixel(x,y,val/c);
				}
			}
		}*/

		for(int i = 0; i < (int)imgv.size(); i++)
			filterGaussSigmaApproximate<float,float,float>( imgv[i], sigma, &gaussImgv[i] );
		
		for ( int i = 0; i < (int)ex.size(); i++ )
		{
			int xpos = ( ex[i].second.x - minx ) *valx;
			int ypos = ( ex[i].second.y - miny ) *valy;
			// refactor-nice.pl: check this substitution
			// old: double val = GetValD ( gaussImgv[scalepos[ex[i].second.scale]], xpos, ypos );
			double val = gaussImgv[scalepos[ex[i].second.scale]].getPixel(xpos,ypos);

			if ( fabs ( val ) < 1e-7 )
			{
				if ( ex[i].second.svec->get ( d ) != 0.0 )
				{
					ex[i].second.svec->erase ( d );
				}
			}
			else
			{
				( *ex[i].second.svec ) [d] = val;
			}
		}
	}
}

void SemSegCsurka::initializePCA ( Examples &ex )
{
#ifdef DEBUG
	cerr << "start computing pca" << endl;
#endif
	std::string filename = cache + "/pca";
	FILE *value;
	value = fopen ( filename.c_str(),"r" );

	if ( value==NULL || calcpca )
	{
		srand ( time ( NULL ) );

		int featsize = ( int ) ex.size();
		int maxfeatures = dim*10;
		int olddim = ex[0].second.vec->size();

		maxfeatures = std::min ( maxfeatures, featsize );

		NICE::Matrix features ( maxfeatures, olddim );

		for ( int i = 0; i < maxfeatures; i++ )
		{
			int k = rand() % featsize;

			int vsize = (int)ex[k].second.vec->size();
			for(int j = 0; j < vsize; j++)
			{
				features(i,j) = (*( ex[k].second.vec))[j];
			}
		}
		pca.calculateBasis ( features, dim, 1 );

		if ( save_cache )
			pca.save ( filename );

	}
	else
	{
		cout << "readpca: " << filename << endl;
		pca.read ( filename );
		cout << "end" << endl;
	}
#ifdef DEBUG
	cerr << "finished computing pca" << endl;
#endif
}

void SemSegCsurka::doPCA ( Examples &ex )
{
	cout << "converting features using pca starts" << endl;

	std::string savedir = cname = conf->gS ( "cache", "root", "/dev/null/" );
	std::string shortf = ex.filename;
	if ( string::npos != ex.filename.rfind ( "/" ) )
		shortf = ex.filename.substr ( ex.filename.rfind ( "/" ) );
	std::string filename = savedir+"/pcasave/"+shortf;
	std::string syscall = "mkdir "+savedir+"/pcasave";
	system ( syscall.c_str() );
	cout << "filename: " << filename << endl;
	
	if ( !FileMgt::fileExists(filename) || calcpca )
	{
		ofstream ofStream;

		//Opens the file binary
		ofStream.open ( filename.c_str(),fstream::out | fstream::binary );

		for ( int k = 0; k < ( int ) ex.size(); k++ )
		{
			NICE::Vector tmp = pca.getFeatureVector ( * ( ex[k].second.vec ), true );
			delete ex[k].second.vec;
			for ( int d = 0; d < (int)tmp.size(); d++ )
				ofStream.write ( ( char* ) &tmp[d], sizeof ( double ) );
			ex[k].second.vec = new NICE::Vector ( tmp );
		}
		ofStream.close();
		cout << endl;
	}
	else
	{
		ifstream ifStream;
		ifStream.open ( filename.c_str(),std::fstream::in | std::fstream::binary );
		for ( int k = 0; k < ( int ) ex.size(); k++ )
		{
			NICE::Vector tmp = NICE::Vector ( dim );
			delete ex[k].second.vec;
			for ( int d = 0; d < dim; d++ )
				ifStream.read ( ( char* ) &tmp[d], sizeof ( double ) );
			ex[k].second.vec = new NICE::Vector ( tmp );
		}

		ifStream.close();
	}
	cout << "converting features using pca finished" << endl;
}




void SemSegCsurka::train ( const MultiDataset *md )
{

	/*die einzelnen Trainingsschritte
	1. auf allen Trainingsbilder SIFT Merkmale an den Gitterpunkten bei allen Auflösungen bestimmen
	2. PCA anwenden
	3. aus diesen ein GMM erstellen
	4. für jedes SIFT-Merkmal einen Vektor erstellen, der an der Stelle i die Wahrscheinlichkeit enthällt zur Verteilung i des GMM, Zur Zeit mit BoV-Alternative durch Moosman06 erledigt
	5. diese Vektoren in einem diskriminitativen Klassifikator ( z.B. SLR oder Randomized Forests) zusammen mit ihrer Klassenzugehörigkeit anlernen
	*/
#ifdef DEBUG
	cerr << "SemSegCsurka:: training starts" << endl;
#endif

	Examples examples;
	examples.filename = "training";


	// Welche Opponentsift Implementierung soll genutzt werden ?
	LocalFeatureRepresentation *cSIFT = NULL;
	LocalFeatureRepresentation *writeFeats = NULL;
	LocalFeatureRepresentation *readFeats = NULL;
	LocalFeatureRepresentation *getFeats = NULL;
	
	if( opSiftImpl == "NICE" )
	{
		cSIFT = new LFonHSG( conf, "HSGtrain" );
	}
	else if( opSiftImpl == "VANDESANDE" )
	{
		// the used features
		cSIFT = new LFColorSande ( conf, "LFColorSandeTrain" );
	}
	else
	{
		fthrow(Exception, "feattype: %s not yet supported" << opSiftImpl );
	}
	
	getFeats = cSIFT;
	
	if(writefeat)
	{
		// write the features to a file, if there isn't any to read
		writeFeats = new LFWriteCache ( conf, cSIFT );
		getFeats = writeFeats;
	}
	
	if(readfeat)
	{
		// read the features from a file
		if(writefeat)
		{
			readFeats = new LFReadCache ( conf, writeFeats,-1 );
		}
		else
		{
			readFeats = new LFReadCache ( conf, cSIFT,-1 );
		}
		getFeats = readFeats;
	}
	
	// additional Colorfeatures
	LFColorWeijer lcw(conf);

	int lfdimension = -1;

	const LabeledSet train = * ( *md ) ["train"];
	const LabeledSet *trainp = &train;

	////////////////////////
	// Merkmale berechnen //
	////////////////////////

	set<int> forbidden_classes;

	std::string forbidden_classes_s = conf->gS ( "analysis", "donttrain", "" );
	if ( forbidden_classes_s == "" )
	{
		forbidden_classes_s = conf->gS ( "analysis", "forbidden_classes", "" );
	}
	cn.getSelection ( forbidden_classes_s, forbidden_classes );
	cerr << "forbidden: " << forbidden_classes_s << endl;

	ProgressBar pb ( "Local Feature Extraction" );
	pb.show();

	int imgnb = 0;

	LOOP_ALL_S ( *trainp )
	{
		//EACH_S(classno, currentFile);
		EACH_INFO ( classno,info );

		pb.update ( trainp->count() );

		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 );

		NICE::Image pixelLabels (xsize, ysize);
		pixelLabels.set(0);
		locResult->calcLabeledImage ( pixelLabels, ( *classNames ).getBackgroundClass() );

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

		VVector features;
		VVector cfeatures;
		VVector positions;

		NICE::ColorImage cimg(currentFile);
		
		getFeats->extractFeatures ( img, features, positions );

#ifdef DEBUG_CSURK
		cout << "[log] SemSegCsruka::train -> " << currentFile << " an " << positions.size() << " Positionen wurden Features (Anz = " << features.size() << ") " << endl; cout << "mit einer Dimension von " << features[ 0].size() << " extrahiert." << endl;
#endif

		if(usecolorfeats)
			lcw.getDescriptors(cimg, cfeatures, positions);

		int j = 0;
		
		for ( VVector::const_iterator i = features.begin();
		        i != features.end();
		        i++,j++ )
		{
			const NICE::Vector & x = *i;
			classno = pixelLabels.getPixel(( int )positions[j][0], ( int )positions[j][1] );

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

			if ( lfdimension < 0 )
				lfdimension = ( int ) x.size();
			else
				assert ( lfdimension == ( int ) x.size() );

			NICE::Vector *v = new NICE::Vector ( x );
			
			if(usecolorfeats && !usepca)
				v->append(cfeatures[j]);

			Example example ( v );
			example.position = imgnb;
			examples.push_back (
			    pair<int, Example> ( classno, example ) );
		}
		features.clear();
		positions.clear();
		delete ce;
		imgnb++;
	}

	pb.hide();

	//////////////////
	// PCA anwenden //
	//////////////////

	if ( usepca )
	{
		if ( !read_cache )
		{
			initializePCA ( examples );
		}
		doPCA ( examples );
		lfdimension = dim;
	}

	/////////////////////////////////////////////////////
	// Low-Level Features in High-Level transformieren //
	/////////////////////////////////////////////////////

	int hlfdimension = lfdimension;

	if(norm)
		normalize(examples);
	
	if ( usegmm )
	{
		if(!usepca && !norm)
			normalize(examples);
		g = new GMM ( conf,gaussians );

		if ( dogmm || !g->loadData ( cache+"/gmm" ) )
		{
			g->computeMixture ( examples );
			if ( save_cache )
				g->saveData ( cache+"/gmm" );
		}

		hlfdimension = gaussians;

		if ( usefisher )
			hlfdimension = gaussians*2*dim;
	}

	if ( usekmeans )
	{
		if(!usepca || norm)
			normalize(examples);
		k = new KMeansOnline ( gaussians );

		k->cluster ( examples );

		hlfdimension = gaussians;
	}

	if ( usekmeans || usegmm )
	{
		examples.clear();
		pb.reset("Local Feature Extraction");
		lfdimension = -1;
		pb.update ( trainp->count() );
		LOOP_ALL_S ( *trainp )
		{
			EACH_INFO ( classno,info );

			pb.update ( trainp->count() );

			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 );

			NICE::Image pixelLabels (xsize, ysize);
			pixelLabels.set(0);
			locResult->calcLabeledImage ( pixelLabels, ( *classNames ).getBackgroundClass() );

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

			Globals::setCurrentImgFN ( currentFile );

			VVector features;
			VVector cfeatures;
			VVector positions;

			NICE::ColorImage cimg(currentFile);

			getFeats->extractFeatures ( img, features, positions );

			if(usecolorfeats)
				lcw.getDescriptors(cimg, cfeatures, positions);

			int j = 0;

			Examples tmpex;

			for ( VVector::const_iterator i = features.begin();
			        i != features.end();
			        i++,j++ )
			{
				
				const NICE::Vector & x = *i;

				classno = pixelLabels.getPixel(( int )positions[j][0], ( int )positions[j][1] );

				if ( forbidden_classes.find ( classno ) != forbidden_classes.end() )
					continue;
				
				if ( lfdimension < 0 )
					lfdimension = ( int ) x.size();
				else
					assert ( lfdimension == ( int ) x.size() );
				
				NICE::Vector *v = new NICE::Vector ( x );
				if(usecolorfeats)
					v->append(cfeatures[j]);

				Example example ( v );
				example.position = imgnb;
				example.x = ( int ) positions[j][0];
				example.y = ( int )positions[j][1];
				example.scale = positions[j][2];

				tmpex.push_back ( pair<int, Example> ( classno, example ) );
			}
			tmpex.filename = currentFile;
			if ( usepca )
			{
				doPCA ( tmpex );
			}

			convertLowToHigh ( tmpex, anteil );

			smoothHL ( tmpex );
			
			for ( int i = 0; i < (int)tmpex.size(); i++ )
			{
				examples.push_back ( pair<int, Example> ( tmpex[i].first, tmpex[i].second ) );
			}

			tmpex.clear();

			features.clear();
			positions.clear();
			delete ce;
			imgnb++;
			
		}

		pb.hide();
	}
	////////////////////////////
	// Klassifikator anlernen //
	////////////////////////////
	FeaturePool fp;
	
	Feature *f;

	if ( usegmm || usekmeans )
		f = new SparseVectorFeature ( hlfdimension );
	else
		f = new VectorFeature ( hlfdimension );
	
	f->explode ( fp );
	delete f;
	
	if(usecolorfeats && !( usekmeans || usegmm ))
	{
		int dimension = hlfdimension+11;
		for ( int i = hlfdimension ; i < dimension ; i++ )
		{
			VectorFeature *f = new VectorFeature ( dimension );
			f->feature_index = i;
			fp.addFeature(f, 1.0 / dimension);
		}
	}
/*
cout << "train classifier" << endl;
fp.store(cout);
getchar();
for(int z = 0; z < examples.size(); z++)
{
cout << "examples.size() " << examples.size() << endl;
cout << "class: " << examples[z].first << endl;
	cout << *examples[z].second.vec << endl;
	getchar();
}*/
	if(classifier != NULL)
		classifier->train ( fp, examples );
	else
	{
		LabeledSetVector lvec;
		convertExamplesToLSet(examples, lvec);
		vclassifier->teach(lvec);
		if(usegmm)
			convertLSetToSparseExamples(examples, lvec);
		else
			convertLSetToExamples(examples, lvec);
		vclassifier->finishTeaching();
	}

	fp.destroy();

	if ( save_cache )
	{
		if(classifier != NULL)
			classifier->save ( cache+"/fpcrf.data" );
		else
			vclassifier->save ( cache+"/veccl.data" );
	}

	////////////
	//clean up//
	////////////
	for ( int i = 0; i < ( int ) examples.size(); i++ )
	{
		examples[i].second.clean();
	}
	examples.clear();

	if(cSIFT != NULL)
		delete cSIFT;
	if(writeFeats != NULL)
		delete writeFeats;
	if(readFeats != NULL)
		delete readFeats;
	getFeats = NULL;

	trainpostprocess ( md );

	cerr << "SemSeg training finished" << endl;
}

void SemSegCsurka::trainpostprocess ( const MultiDataset *md )
{
	cout<< "start postprocess" << endl;
	////////////////////////////
	// Postprocess trainieren //
	////////////////////////////
	const LabeledSet train = * ( *md ) ["train"];
	const LabeledSet *trainp = &train;

	if ( userellocprior || srg != NULL || gcopt !=NULL )
	{
		clog << "[log] SemSegCsurka::trainpostprocess: if ( userellocprior || srg != NULL || gcopt !=NULL )" << endl;
		if ( userellocprior )
			relloc->setClassNo ( cn.numClasses() );

		if ( gcopt !=NULL )
		{
			gcopt->setClassNo ( cn.numClasses() );
		}

		ProgressBar pb ( "learn relative location prior maps" );
		pb.show();
		LOOP_ALL_S ( *trainp ) // für alle Bilder den ersten Klassifikationsschritt durchführen um den zweiten Klassifikator anzutrainieren
		{
			EACH_INFO ( classno,info );
			
			pb.update ( trainp->count() );

			NICE::ColorImage img;

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

			NICE::Image pixelLabels (xsize, ysize);
			pixelLabels.set(0);
			locResult->calcLabeledImage ( pixelLabels, ( *classNames ).getBackgroundClass() );

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

			//Regionen ermitteln
			NICE::Matrix mask;

			int regionsize = seg->segRegions ( img, mask );
#ifdef DEBUG_CSURK
			Image overlay(img.width(), img.height());

			double maxval = 0.0;

			for(int y = 0; y < img.height(); y++)
			{
				for(int x = 0; x < img.width(); x++)
				{
					int val = ((int)mask(x,y)+1)%256;
					overlay.setPixel(x,y,val);
					maxval = std::max(mask(x,y), maxval);
				}
			}

			cout << maxval << " different regions found" << endl;

			NICE::showImageOverlay ( img, overlay, "Segmentation Result" );
#endif

			Examples regions;

			vector<vector<int> > hists;

			for ( int i = 0; i < regionsize; i++ )
			{
				Example tmp;
				regions.push_back ( pair<int, Example> ( 0, tmp ) );
				vector<int> hist ( cn.numClasses(), 0 );
				hists.push_back ( hist );
			}

			for ( int x = 0; x < xsize; x++ )
			{
				for ( int y = 0; y < ysize; y++ )
				{
					int numb = mask(x,y);
					regions[numb].second.x += x;
					regions[numb].second.y += y;
					regions[numb].second.weight += 1.0;
					hists[numb][pixelLabels.getPixel(x,y)]++;
				}
			}

			for ( int i = 0; i < regionsize; i++ )
			{
				regions[i].second.x /= ( int ) regions[i].second.weight;
				regions[i].second.y /= ( int ) regions[i].second.weight;

				int maxval = -numeric_limits<int>::max();
				int maxpos = -1;
				int secondpos = -1;
				for ( int k = 0; k < ( int ) hists[i].size(); k++ )
				{
					if ( maxval <hists[i][k] )
					{
						maxval = hists[i][k];
						secondpos = maxpos;
						maxpos = k;
					}
				}

				if ( cn.text ( maxpos ) == "various" )
					regions[i].first = secondpos;
				else
					regions[i].first = maxpos;

			}
			if ( userellocprior )
				relloc->trainPriorsMaps ( regions, xsize, ysize );

			if ( srg != NULL )
				srg->trainShape ( regions, mask );

			if ( gcopt !=NULL )
				gcopt->trainImage ( regions, mask );

			delete ce;

		}
		pb.hide();
		if ( userellocprior )
			relloc->finishPriorsMaps ( cn );

		if ( srg != NULL )
			srg->finishShape ( cn );

		if ( gcopt != NULL )
			gcopt->finishPP ( cn );
	}
	if ( userellocprior )
	{
		clog << "[log] SemSegCsurka::trainpostprocess: if ( userellocprior )" << endl;
		ProgressBar pb ( "learn relative location classifier" );
		pb.show();

		int nummer = 0;
		LOOP_ALL_S ( *trainp ) // für alle Bilder den ersten Klassifikationsschritt durchführen um den zweiten Klassifikator anzutrainieren
		{
			//EACH_S(classno, currentFile);
			EACH_INFO ( classno,info );
			nummer++;
			pb.update ( trainp->count() );

			NICE::Image 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 );

			NICE::Image pixelLabels (xsize, ysize);
			pixelLabels.set(0);
			locResult->calcLabeledImage ( pixelLabels, ( *classNames ).getBackgroundClass() );

			try{
				img = Preprocess::ReadImgAdv ( currentFile.c_str() );
			}
			catch(Exception)
			{
				cerr << "SemSegCsurka: error opening image file <" << currentFile << ">" << endl;
				continue;
			}
			Globals::setCurrentImgFN ( currentFile );

			NICE::Image segresult;

			NICE::MultiChannelImageT<double> probabilities ( xsize,ysize,classno,true );

			Examples regions;

			NICE::Matrix mask;

			if ( savesteps )
			{
				std::ostringstream s1;
				s1 << cache << "/rlpsave/" << nummer;

				std::string filename = s1.str();
				s1 << ".probs";
				
				std::string fn2 = s1.str();

				FILE *file;
				file = fopen ( filename.c_str(),"r" );

				if ( file==NULL )
				{
					//berechnen
					classifyregions ( ce, segresult, probabilities, regions, mask );
					//schreiben
					ofstream fout ( filename.c_str(), ios::app );
					fout << regions.size() << endl;
					for ( int i = 0; i < ( int ) regions.size(); i++ )
					{
						regions[i].second.store ( fout );
						fout << regions[i].first << endl;
					}
					fout.close();
					probabilities.store ( fn2 );
				}
				else
				{
					//lesen
					ifstream fin ( filename.c_str() );
					int size;
					fin >> size;

					for ( int i = 0; i < size; i++ )
					{
						Example ex;
						ex.restore ( fin );
						int tmp;
						fin >> tmp;
						regions.push_back ( pair<int, Example> ( tmp, ex ) );
					}

					fin.close();

					probabilities.restore ( fn2 );
				}
			}
			else
			{
				classifyregions ( ce, segresult, probabilities, regions, mask );
			}

			relloc->trainClassifier ( regions, probabilities );

			delete ce;

		}
		relloc->finishClassifier();
		pb.hide();

		relloc->save ( cache+"/rlp" );
	}
	cout << "finished postprocess" << endl;
}

void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult, NICE::MultiChannelImageT<double> & probabilities, Examples &Regionen, NICE::Matrix & mask )
{
	/* die einzelnen Testschritte:
	1.x  auf dem Testbild alle SIFT Merkmale an den Gitterpunkten bei allen Auflösungen bestimmen
	2.x  für jedes SIFT-Merkmal einen Vektor erstellen, der an der Stelle i die Wahrscheinlichkeit enthällt zur Verteilung i des GMM
	3.x diese Vektoren klassifizieren, so dass für jede Klasse die Wahrscheinlichkeit gespeichert wird
	4.x für jeden Pixel die Wahrscheinlichkeiten mitteln aus allen Patches, in denen der Pixel vorkommt
	5.x das Originalbild in homogene Bereiche segmentieren
	6.x die homogenen Bereiche bekommen die gemittelten Wahrscheinlichkeiten ihrer Pixel
	7. (einzelne Klassen mit einem globalen Klassifikator ausschließen)
	8.x jeder Pixel bekommt die Klasse seiner Region zugeordnet
	*/

	clog << "[log] SemSegCsruka::classifyregions" << endl;
	int xsize, ysize;

	ce->getImageSize ( xsize, ysize );

	probabilities.reInit ( xsize, ysize, classNames->getMaxClassno() +1, true/*allocMem*/ );
	clog << "[log] SemSegCsruka::classifyregions: probabilities.numChannels = " << probabilities.numChannels << endl;

	segresult.resize(xsize, ysize);

	Examples pce;

	// Welche Opponentsift Implementierung soll genutzt werden ?
	LocalFeatureRepresentation *cSIFT = NULL;
	LocalFeatureRepresentation *writeFeats = NULL;
	LocalFeatureRepresentation *readFeats = NULL;
	LocalFeatureRepresentation *getFeats = NULL;
	
	
	if( opSiftImpl == "NICE" )
	{
		cSIFT = new LFonHSG( conf, "HSGtrain" );
	}
	else if( opSiftImpl == "VANDESANDE" )
	{
		// the used features
		cSIFT = new LFColorSande ( conf, "LFColorSandeTrain" );
	}
	else
	{
		fthrow(Exception, "feattype: %s not yet supported" << opSiftImpl );
	}
	
	getFeats = cSIFT;
	
	if(writefeat)
	{
		// write the features to a file, if there isn't any to read
		writeFeats = new LFWriteCache ( conf, cSIFT );
		getFeats = writeFeats;
	}
	
	if(readfeat)
	{
		// read the features from a file
		if(writefeat)
		{
			readFeats = new LFReadCache ( conf, writeFeats,-1 );
		}
		else
		{
			readFeats = new LFReadCache ( conf, cSIFT,-1 );
		}
		getFeats = readFeats;
	}
	
	
	// additional Colorfeatures
	LFColorWeijer lcw(conf);
	
	NICE::ColorImage img;

	std::string currentFile = Globals::getCurrentImgFN();
	
	try
	{
		img = ColorImage(currentFile);
	}
	catch(Exception)
	{
		cerr << "SemSegCsurka: error opening image file <" << currentFile << ">" << endl;
	}

	VVector features;
	VVector cfeatures;
	VVector positions;

	getFeats->extractFeatures ( img, features, positions ); 

	if(usecolorfeats)
		lcw.getDescriptors(img, cfeatures, positions);

	set<double> scales;

	int j = 0;
	int lfdimension = -1;
	for ( VVector::const_iterator i = features.begin();
	        i != features.end();
	        i++,j++ )
	{
		const NICE::Vector & x = *i;

		if ( lfdimension < 0 ) lfdimension = ( int ) x.size();
		else assert ( lfdimension == ( int ) x.size() );

		NICE::Vector *v = new NICE::Vector ( x );

		if(usecolorfeats)
			v->append(cfeatures[j]);
		
		Example tmp = Example ( v );
		tmp.x = ( int )positions[j][0];
		tmp.y = ( int ) positions[j][1];
		tmp.width = ( int ) ( 16.0*positions[j][2] );
		tmp.height = tmp.width;
		tmp.scale = positions[j][2];
		scales.insert ( positions[j][2] );
		pce.push_back ( pair<int, Example> ( 0, tmp ) );
	}
	
	//////////////////
	// PCA anwenden //
	//////////////////
	pce.filename = currentFile;
	if ( usepca )
	{
		doPCA ( pce );
		lfdimension = dim;
	}

	//////////////////
	// BoV anwenden //
	//////////////////
	if(norm)
		normalize(pce);
	if ( usegmm || usekmeans )
	{
		if(!usepca && !norm)
			normalize(pce);
		convertLowToHigh ( pce );
		smoothHL ( pce );
		lfdimension = gaussians;
	}

	/////////////////////////////////////////
	// Wahrscheinlichkeitskarten erstellen //
	/////////////////////////////////////////
	int klassen = probabilities.numChannels;
	NICE::MultiChannelImageT<double> preMap ( xsize,ysize,klassen*scales.size(),true );

	long int offset = 0;

	// initialisieren
	for ( int y = 0 ; y < ysize ; y++ )
		for ( int x = 0 ; x < xsize ; x++,offset++ )
		{
			// alles zum Hintergrund machen
			segresult.setPixel(x,y,0);
			// Die Wahrscheinlichkeitsmaps auf 0 initialisieren
			for ( int i = 0 ; i < ( int ) probabilities.numChannels; i++ )
			{
				probabilities.data[i][offset] = 0.0;
			}
			for ( int j = 0; j < ( int ) preMap.numChannels; j++ )
			{
				preMap.data[j][offset]=0.0;
			}
		}

	// Die Wahrscheinlichkeitsmaps mit den einzelnen Wahrscheinlichkeiten je Skalierung füllen
	int scalesize = scales.size();

	// Globale Häufigkeiten akkumulieren
	FullVector fV ( ( int ) probabilities.numChannels );

	for ( int i = 0; i < fV.size(); i++ )
		fV[i] = 0.0;

	// read allowed classes

	string cndir = conf->gS("SemSegCsurka", "cndir", "");
	int classes = (int)probabilities.numChannels;
	vector<int> useclass(classes,1);

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

	string orgname = list.back();
	if(cndir != "")
	{
	    useclass = vector<int>(classes,0);
	    ifstream infile((cndir+"/"+orgname+".dat").c_str());
	    while(!infile.eof() && infile.good())
	    {
		int tmp;
		infile >> tmp;
		if(tmp >= 0 && tmp < classes)
		{
		  useclass[tmp] = 1;
		}
	    }
	}
	if(classifier != NULL)
	{
		clog << "[log] SemSegCsruka::classifyregions: Wahrscheinlichkeitskarten erstellen: classifier != NULL" << endl;
#pragma omp parallel for
		for ( int s = 0; s < scalesize; s++ )
		{
#pragma omp parallel for
			for ( int i = s; i < ( int ) pce.size(); i+=scalesize )
			{
				ClassificationResult r = classifier->classify ( pce[i].second );
				for ( int j = 0 ; j < r.scores.size(); j++ )
				{
					if(useclass[j] == 0)
					  continue;
					fV[j] += r.scores[j];
					preMap.set ( pce[i].second.x,pce[i].second.y,r.scores[j],j+s*klassen );
				}
			}
		}
	}
	else
	{
//#pragma omp parallel for
		for ( int s = 0; s < scalesize; s++ )
		{
//#pragma omp parallel for
			for ( int i = s; i < ( int ) pce.size(); i+=scalesize )
			{
				ClassificationResult r = vclassifier->classify ( *(pce[i].second.vec) );
				for ( int j = 0 ; j < ( int ) r.scores.size(); j++ )
				{
					if(useclass[j] == 0)
					  continue;
					fV[j] += r.scores[j];
					preMap.set ( pce[i].second.x,pce[i].second.y,r.scores[j],j+s*klassen );
				}
			}
		}
	}
	vector<double> scalesVec;
	for ( set<double>::const_iterator iter = scales.begin();
	        iter != scales.end();
	        ++iter )
	{
		scalesVec.push_back ( *iter );
	}


	// Gaußfiltern
	clog << "[log] SemSegCsruka::classifyregions: Wahrscheinlichkeitskarten erstellen -> Gaussfiltern" << endl;
	for ( int s = 0; s < scalesize; s++ )
	{
		double sigma = sigmaweight*16.0*scalesVec[s];
		cerr << "sigma: " << sigma << endl;
#pragma omp parallel for
		for ( int i = 0; i < klassen; i++ )
		{
			int pos = i+s*klassen;
			
			double maxval = preMap.data[pos][0];
			double minval = preMap.data[pos][0];

			for ( int z = 1; z < xsize*ysize; z++ )
			{
				maxval = std::max ( maxval, preMap.data[pos][z] );
				minval = std::min ( minval, preMap.data[pos][z] );
			}

			NICE::FloatImage dblImg( xsize, ysize);
			NICE::FloatImage gaussImg( xsize, ysize);

			long int offset2 = 0;
			for ( int y = 0; y < ysize; y++ )
			{
				for ( int x = 0; x < xsize; x++, offset2++ )
				{
					dblImg.setPixel(x,y,preMap.data[pos][offset2]);
				}
			}

			filterGaussSigmaApproximate<float,float,float>( dblImg, sigma, &gaussImg );

			offset2 = 0;
			for ( int y = 0; y < ysize; y++ )
			{
				for ( int x = 0; x < xsize; x++, offset2++ )
				{
					preMap.data[pos][offset2]=gaussImg.getPixel(x,y);
				}
			}
		}

	}

			
	// Zusammenfassen und auswerten
	clog << "[log] SemSegCsruka::classifyregions: Wahrscheinlichkeitskarten erstellen -> zusammenfassen und auswerten" << endl;
#pragma omp parallel for
	for ( int x = 0; x < xsize; x++ )
	{
		for ( int y = 0; y < ysize; y++ )
		{
			for ( int j = 0 ; j < ( int ) probabilities.numChannels; j++ )
			{
				double prob = 0.0;
				for ( int s = 0; s < ( int ) scalesize; s++ )
				{

					prob+=preMap.get ( x,y,j+s*klassen );

				}

				double val = prob / ( double ) ( scalesize );
				probabilities.set ( x,y,val, j );
			}
		}
	}

#undef VISSEMSEG
#ifdef VISSEMSEG

	std::string s;
	std::stringstream out;
	std::vector< std::string > list;
	StringTools::split (Globals::getCurrentImgFN (), '/', list);
		
	out << "probmaps/" << list.back() << ".probs";
		
	s = out.str();
		
	probabilities.store(s);s

	for ( int j = 0 ; j < ( int ) probabilities.numChannels; j++ )
	{
		cout << "klasse: " << j << endl;//" " << cn.text ( j ) << endl;

		NICE::Matrix tmp ( probabilities.ysize, probabilities.xsize );
		double maxval = 0.0;
		for ( int y = 0; y < probabilities.ysize; y++ )
			for ( int x = 0; x < probabilities.xsize; x++ )
			{
				double val = probabilities.get ( x,y,j );
				tmp(y, x) = val;
				maxval = std::max ( val, maxval );
			}
		NICE::ColorImage imgrgb (probabilities.xsize, probabilities.ysize);
		ICETools::convertToRGB ( tmp, imgrgb );

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

		//Show ( ON, imgrgb, cn.text ( j ) );
		//showImage(imgrgb, "Ergebnis");
		
		std::string s;
		std::stringstream out;
		out << "tmp" << j << ".ppm";
		s = out.str();
		imgrgb.writePPM( s );

		//getchar();
	}
#endif
	if ( useregions )
	{
		if ( bestclasses > 0 )
		{
			PSSImageLevelPrior pss ( 0, bestclasses, 0.2 );
			pss.setPrior ( fV );
			pss.postprocess ( segresult, probabilities );
		}

		//Regionen ermitteln

		int regionsize = seg->segRegions ( img, mask);
		


		Regionen.clear();
		vector<vector <double> > regionprob;

		// Wahrscheinlichkeiten für jede Region initialisieren
		for ( int i = 0; i < regionsize; i++ )
		{
			vector<double> tmp;
			for ( int j = 0; j < ( int ) probabilities.numChannels; j++ )
			{
				tmp.push_back ( 0.0 );
			}
			regionprob.push_back ( tmp );
			Regionen.push_back ( pair<int, Example> ( 0, Example() ) );
		}

		// Wahrscheinlichkeiten für Regionen bestimmen
		for ( int x = 0; x < xsize; x++ )
		{
			for ( int y = 0; y < ysize; y++ )
			{
				for ( int j = 0 ; j < ( int ) probabilities.numChannels; j++ )
				{
					double val = probabilities.get ( x,y,j );
					int pos = mask(x,y);
					Regionen[pos].second.weight+=1.0;
					Regionen[pos].second.x += x;
					Regionen[pos].second.y += y;
					regionprob[pos][j] += val;
				}
			}
		}


/*
cout << "regions: " << regionsize << endl;
cout << "outfeats: " << endl;
for(int j = 0; j < regionprob.size(); j++)
{
	for(int i = 0; i < regionprob[j].size(); i++)
	{
		cout << regionprob[j][i] << " ";
	}
	cout << endl;
}
cout << endl;
getchar();*/

	// beste Wahrscheinlichkeit je Region wählen
		for ( int i = 0; i < regionsize; i++ )
		{
			if(Regionen[i].second.weight > 0)
			{
				Regionen[i].second.x /= ( int ) Regionen[i].second.weight;
				Regionen[i].second.y /= ( int ) Regionen[i].second.weight;
			}
			double maxval = 0.0;
			int maxpos = 0;
			
			for ( int j = 0 ; j < ( int ) regionprob[i].size(); j++ )
			{
				regionprob[i][j] /= Regionen[i].second.weight;
				
				if ( maxval < regionprob[i][j] )
				{
					maxval = regionprob[i][j];
					maxpos = j;
				}
				probabilities.set (Regionen[i].second.x,Regionen[i].second.y,regionprob[i][j], j );
			}
			
			Regionen[i].first = maxpos;
		}

		// Pixel jeder Region labeln
		for ( int y = 0; y < (int)mask.cols(); y++ )
		{
			for ( int x = 0; x < (int)mask.rows(); x++ )
			{
				int pos = mask(x,y);
				segresult.setPixel(x,y,Regionen[pos].first);
			}
		}
#define WRITEREGIONS
#ifdef WRITEREGIONS
		RegionGraph rg;
		seg->getGraphRepresentation(img, mask, rg);
		for(uint pos = 0; pos < regionprob.size(); pos++)
		{
			rg[pos]->setProbs(regionprob[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
	}
	else
	{

		PSSImageLevelPrior pss ( 1, 4, 0.2 );
		pss.setPrior ( fV );
		pss.postprocess ( segresult, probabilities );

	}

	// Saubermachen:
	clog << "[log] SemSegCsurka::classifyregions: sauber machen" << endl;
	for ( int i = 0; i < ( int ) pce.size(); i++ )
	{
		pce[i].second.clean();
	}
	pce.clear();

	if(cSIFT != NULL)
		delete cSIFT;
	if(writeFeats != NULL)
		delete writeFeats;
	if(readFeats != NULL)
		delete readFeats;
	getFeats = NULL;
}

void SemSegCsurka::semanticseg ( CachedExample *ce,
								NICE::Image & segresult,
								NICE::MultiChannelImageT<double> & probabilities )
{

	Examples regions;
	NICE::Matrix regionmask;
	classifyregions ( ce, segresult, probabilities, regions, regionmask );
	if ( userellocprior || srg != NULL || gcopt !=NULL )
	{
		if ( userellocprior )
			relloc->postprocess ( regions, probabilities );

		if ( srg != NULL )
			srg->optimizeShape ( regions, regionmask, probabilities );

		if ( gcopt != NULL )
			gcopt->optimizeImage ( regions, regionmask, probabilities );

		// Pixel jeder Region labeln
		for ( int y = 0; y < (int)regionmask.cols(); y++ )
		{
			for ( int x = 0; x < (int)regionmask.rows(); x++ )
			{
				int pos = regionmask(x,y);
				segresult.setPixel(x,y,regions[pos].first);
			}
		}
	}

#ifndef NOVISUAL
#undef VISSEMSEG
#ifdef VISSEMSEG
// 	showImage(img);
	for ( int j = 0 ; j < ( int ) probabilities.numChannels; j++ )
	{
		cout << "klasse: " << j << " " << cn.text ( j ) << endl;

		NICE::Matrix tmp ( probabilities.ysize, probabilities.xsize );
		double maxval = 0.0;
		for ( int y = 0; y < probabilities.ysize; y++ )
			for ( int x = 0; x < probabilities.xsize; x++ )
			{
				double val = probabilities.get ( x,y,j );
				tmp(y, x) = val;
				maxval = std::max ( val, maxval );
			}

		NICE::ColorImage imgrgb (probabilities.xsize, probabilities.ysize);
		ICETools::convertToRGB ( tmp, imgrgb );

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

		Show ( ON, imgrgb, cn.text ( j ) );
		imgrgb.Write ( "tmp.ppm" );

		getchar();
	}
#endif
#endif

}