deICE

semseg/SemSegCsurka2.cpp

@@ -1,1720 +0,0 @@
-/**
- * @file SemSegCsurka2.cpp
- * @brief semantic segmentation using the method from Csurka08
- * @author Björn Fröhlich
- * @date 04/24/2009
- */
-#include <iostream>
-
-#include "SemSegCsurka2.h"
-
-#include "objrec/fourier/FourierLibrary.h"
-
-#include "objrec/baselib/ICETools.h"
-
-
-#include <sstream>
-
-using namespace std;
-using namespace NICE;
-using namespace OBJREC;
-
-#undef DEBUG_CSURK
-
-void readFeats(VVector &features, VVector &positions, string file, string posfile)
-{
-	 ifstream fin (file.c_str());
-	 int nb;
-	 int dim;
-	 
-	 fin >> nb;
-	 fin >> dim;
-	 for(int i = 0; i < nb; i++)
-	 {
-		int l;
-		Vector vec(dim);
-		fin >> l;
-		for(int d = 0; d < dim; d++)
-		{
-			fin >> vec[d];
-		}
-		features.push_back(vec);
-		assert(vec.size() == features[0].size());
-	 }
-	 fin.close();
-	 
-	 ifstream posin(posfile.c_str());
-	 
-	 posin >> nb;
-	 posin >> dim;
-	 	 for(int i = 0; i < nb; i++)
-	 {
-		Vector vec(dim);
-		for(int d = 0; d < dim; d++)
-		{
-			posin >> vec[d];
-		}
-		positions.push_back(vec);
-		assert(vec.size() == positions[0].size());
-	 }
-	 posin.close();
-	 
-	 cout << "positions: " << positions.size() << " feats.size: " << features.size() << endl;
-}
-
-void readex(string file, Examples &examples)
-{
-	 ifstream fin (file.c_str());
-	 int nb;
-	 int dim;
-	 
-	 fin >> nb;
-	 fin >> dim;
-	 for(int i = 0; i < nb; i++)
-	 {
-		int l;
-		Vector *vec = new Vector(dim);
-		fin >> l;
-		for(int d = 0; d < dim; d++)
-		{
-			fin >> (*vec)[d];
-		}
-		Example ex;
-		ex.vec = vec;
-		ex.svec = NULL;
-		ex.ce = NULL;
-		examples.push_back(pair<int, Example> ( l, ex));
-		//assert(vec.size() == (examples[0].second.vec->size());
-	 }
-	 fin.close();
-}
-
-
-SemSegCsurka2::SemSegCsurka2 ( const Config *conf,
-                             const MultiDataset *md )
-		: SemanticSegmentation ( conf, & ( md->getClassNames ( "train" ) ) )
-{
-	this->conf = conf;
-	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);
-
-	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 = new RSMeanShift ( conf );
-
-	seg = new RSCache ( conf, tmpseg );
-
-	if ( userellocprior )
-		relloc = new RelativeLocationPrior ( conf );
-	else
-		relloc = NULL;
-
-	if ( usesrg )
-		srg = new PPSuperregion ( conf );
-	else
-		srg = NULL;
-
-	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, "SemSegCsurka2:: 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 << "SemSegCsurka2:: no gmm file found" << endl;
-				exit ( -1 );
-			}
-		}
-
-		if ( usekmeans )
-		{
-			k = new KMeansOnline ( gaussians );
-		}
-
-		fprintf ( stderr, "SemSegCsurka2:: 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 );
-	}
-}
-
-SemSegCsurka2::~SemSegCsurka2()
-{
-	// clean-up
-	if ( classifier != NULL )
-		delete classifier;
-	if( vclassifier !=NULL)
-		delete vclassifier;
-	if ( seg != NULL )
-		delete seg;
-	if ( g != NULL )
-		delete g;
-}
-
-void SemSegCsurka2::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 SemSegCsurka2::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<int> del;
-		cout << "Example size old " << ex.size() << endl;
-		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.push_back ( i );
-			}
-		}
-		for ( int i = ( int ) del.size() - 1; i >= 0; i-- )
-		{
-			ex.erase ( ex.begin() +del[i] );
-		}
-		cerr << "Example size new " << ex.size() << endl;
-	}
-	cerr << "converting low-level features to high-level features finished" << endl;
-}
-
-void SemSegCsurka2::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 ( ex[i].second.x, minx );
-		maxx = std::max ( ex[i].second.x, maxx );
-		miny = std::min ( ex[i].second.y, miny );
-		maxy = std::max ( 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 );
-			// refactor-nice.pl: check this substitution
-			// old: PutValD ( imgv[scalepos[ex[i].second.scale]],xpos,ypos,val);
-			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++)
-			FourierLibrary::gaussFilterD ( imgv[i], gaussImgv[i], sigma );	
-		
-		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 SemSegCsurka2::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 SemSegCsurka2::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 SemSegCsurka2::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 << "SemSegCsurka2:: training starts" << endl;
-#endif
-
-	Examples examples;
-	examples.filename = "training";
-	// the used features
-	LocalFeatureRepresentation *cSIFT = new LFColorSande ( conf, "LFColorSandeTrain" );
-	// write the features to a file, if there isn't any to read
-	LocalFeatureRepresentation *writeFeats = new LFWriteCache ( conf, cSIFT );
-	// read the features from a file
-	LocalFeatureRepresentation *getFeats = new LFReadCache ( conf, writeFeats,-1 );
-	cout << 4 << endl;
-	// 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;
-#if 0
-	LOOP_ALL_S ( *trainp )
-	{
-		//EACH_S(classno, currentFile);
-		EACH_INFO ( classno,info );
-
-		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, "SemSegCsurka2: 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 << "SemSegCsurka2: 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;
-		
-		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();
-#endif
-
-	examples.clear();
-	
-	readex("/home/staff/froehlich/fernerkundung/irene/sattrain.feats", examples);
-
-	lfdimension = (int)examples[0].second.vec->size();
-	//////////////////
-	// 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::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, "SemSegCsurka2: 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 << "SemSegCsurka2: 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();
-
-	delete cSIFT;
-
-	delete writeFeats;
-
-	delete getFeats;
-	trainpostprocess ( md );
-
-	cerr << "SemSeg training finished" << endl;
-}
-
-void SemSegCsurka2::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 )
-	{
-		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::Image 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, "SemSegCsurka2: 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 << "SemSegCsurka2: error opening image file <" << currentFile << ">" << endl;
-				continue;
-			}
-
-			//Regionen ermitteln
-			NICE::Matrix mask;
-
-			int regionsize = seg->segRegions ( img,mask );
-
-			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 )
-	{
-		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, "SemSegCsurka2: 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 << "SemSegCsurka2: error opening image file <" << currentFile << ">" << endl;
-				continue;
-			}
-			Globals::setCurrentImgFN ( currentFile );
-
-			NICE::Image segresult;
-
-			GenericImage<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 SemSegCsurka2::classifyregions ( CachedExample *ce, NICE::Image & segresult, GenericImage<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
-	*/
-
-	int xsize, ysize;
-
-	ce->getImageSize ( xsize, ysize );
-
-	probabilities.reInit ( xsize, ysize, classNames->getMaxClassno() +1, true/*allocMem*/ );
-
-	segresult.resize(xsize, ysize);
-
-	Examples pce;
-
-	// the features to use
-	LocalFeatureRepresentation *cSIFT = new LFColorSande ( conf, "LFColorSandeTest" );
-
-	// write the features to a file, if there isn't any to read
-	LocalFeatureRepresentation *writeFeats = new LFWriteCache ( conf, cSIFT );
-
-	// read the features from a file
-	LocalFeatureRepresentation *getFeats = new LFReadCache ( conf, writeFeats,-1 );
-
-	// additional Colorfeatures
-	LFColorWeijer lcw(conf);
-	
-	NICE::Image img;
-
-	std::string currentFile = Globals::getCurrentImgFN();
-	
-	try
-	{
-		img = Preprocess::ReadImgAdv ( currentFile.c_str() );
-	}
-	catch(Exception)
-	{
-		cerr << "SemSegCsurka2: error opening image file <" << currentFile << ">" << endl;
-	}
-
-	VVector features;
-	VVector cfeatures;
-	VVector positions;
-	NICE::ColorImage cimg(currentFile);
-	//getFeats->extractFeatures ( img, features, positions );
-	
-	readFeats(features,positions,"/home/staff/froehlich/fernerkundung/irene/sattest.feats","/home/staff/froehlich/fernerkundung/irene/sattest.coords");
-	
-	if(usecolorfeats)
-		lcw.getDescriptors(cimg, 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;
-	GenericImage<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;
-
-	if(classifier != NULL)
-	{
-#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 < ( int ) probabilities.numChannels; j++ )
-				{
-					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 ) probabilities.numChannels; j++ )
-				{
-					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
-	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]);
-				}
-			}
-
-			FourierLibrary::gaussFilterD ( dblImg, gaussImg, sigma );
-
-			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
-#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
-// 	showImage(img);
-	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");
-		//imgrgb.Write ( "tmp.ppm" );
-
-		//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++ )
-		{
-			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);
-			}
-		}
-	}
-	else
-	{
-
-		PSSImageLevelPrior pss ( 1, 4, 0.2 );
-		pss.setPrior ( fV );
-		pss.postprocess ( segresult, probabilities );
-
-	}
-
-	// Saubermachen:
-	for ( int i = 0; i < ( int ) pce.size(); i++ )
-	{
-		pce[i].second.clean();
-	}
-	pce.clear();
-	delete getFeats;
-	delete writeFeats;
-	delete cSIFT;
-}
-
-void SemSegCsurka2::semanticseg ( CachedExample *ce,
-								NICE::Image & segresult,
-								GenericImage<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
-
-}

semseg/SemSegCsurka2.h

@@ -1,235 +0,0 @@
-/** 
- * @file SemSegCsurka2.h
- * @brief semantic segmentation using the method from Csurka08
- * @author Björn Fröhlich
- * @date 04/24/2009
- */
-#ifndef SemSegCsurka2INCLUDE
-#define SemSegCsurka2INCLUDE
-
-#include <objrec/nice.h>
- 
-#include "SemanticSegmentation.h"
-
-#include "objrec/math/ftransform/PCA.h"
-
-
-#include "objrec/features/localfeatures/LFColorSande.h"
-#include "objrec/features/localfeatures/LFColorWeijer.h"
-#include "objrec/features/localfeatures/LFReadCache.h"
-#include "objrec/features/localfeatures/LFWriteCache.h"
-#include "objrec/cbaselib/VectorFeature.h"
-#include "objrec/features/fpfeatures/SparseVectorFeature.h"
-
-#include "objrec/cbaselib/CachedExample.h"
-#include "objrec/baselib/Preprocess.h"
-#include "objrec/baselib/Globals.h"
-
-#include "objrec/segmentation/RegionSegmentationMethod.h"
-#include "objrec/segmentation/RSMeanShift.h"
-#include "objrec/segmentation/RSCache.h"
-
-#include "SemSegTools.h" 
-
-#include "objrec/math/cluster/GMM.h"
-#include "objrec/math/cluster/KMeansOnline.h"
-
-#include "objrec/classifier/fpclassifier/randomforest/FPCRandomForests.h"
-#include "objrec/classifier/fpclassifier/logisticregression/FPCSMLR.h"
-
-#include "objrec-froehlichexp/semseg/postsegmentation/PSSImageLevelPrior.h"
-#include "objrec-froehlichexp/semseg/postsegmentation/RelativeLocationPrior.h"
-#include "objrec-froehlichexp/semseg/postsegmentation/PPSuperregion.h"
-#include "objrec-froehlichexp/semseg/postsegmentation/PPGraphCut.h"
-
-
-#include <objrec/iclassifier/icgeneric/CSGeneric.h>
-
-/** @brief pixelwise labeling systems */
-
-namespace OBJREC {
-
-class SemSegCsurka2 : public SemanticSegmentation
-{
-
-  protected:
-	  
-	//! for normalization
-	vector<double> vecmin, vecmax;
-	  
-	//! boolean whether to save the cache or not
-	bool save_cache;
-	  
-	//! boolean whether to read the cache or not, if read_cache is false, everything will be trained
-	bool read_cache;
-	
-	//! The cached Data
-	std::string cache;
-	
-	//! The PCA
-	PCA pca;
-	
-	//! using normalization
-	bool norm;
-	
-	//! feature Dimension after PCA
-	int dim;
-	
-	//! Classifier
-	FeaturePoolClassifier *classifier;
-	VecClassifier *vclassifier;
-	
-	//! Configuration File
-	const Config *conf;	
-	
-	ClassNames cn;
-	
-	//! whether to use the colorfeats or not
-	bool usecolorfeats;
-	
-	//! low level Segmentation method
-	RegionSegmentationMethod *seg;
-	
-	//! weight for the gaussimage
-	double sigmaweight;
-	
-	//! Gaussian Mixture
-	GMM *g;
-	
-	//! KMeans
-	KMeansOnline *k;
-	
-	//! use pca or not
-	bool usepca;
-	
-	//! forced recalculation of the pca
-	bool calcpca;
-	
-	//! use highlevel transformation with gmm or not
-	bool usegmm;
-	
-	//! use highlevel transformation with kmeans or not
-	bool usekmeans;
-	
-	int bestclasses;
-	
-	//! how much clusters of the kmeans to use
-	int kmeansfeat;
-	
-	//! use hard assignment or not
-	bool kmeanshard;
-	
-	//! use fisher kernel for bag if visual words
-	bool usefisher;
-	
-	//! forced recalculation of the gmm
-	bool dogmm;
-	
-	//! number of gaussians
-	int gaussians;
-	
-	//! whether to use the relative location features or not
-	bool userellocprior;
-	
-	//! which classifier to use
-	std::string cname;
-	
-	//! use regions segmentation or not
-	bool useregions;
-	
-	//! how many features should be used for training the classifier (relative value between 0 and 1
-	double anteil;
-	
-	//! save steps for faster computing postprocesses
-	bool savesteps;
-	
-	//! the relative location features
-	RelativeLocationPrior *relloc;
-	
-	//! Shape pp
-	PPSuperregion *srg;
-	
-	//! Graph Cut pp
-	PPGraphCut *gcopt;
-	
-	//! smooth high level features or not
-	bool smoothhl;
-	
-	//! sigma for high level smoothing
-	double smoothfactor;
-	
-	/**
-	 * converts the low level features in high level features
-	 * @param ex input and output features
-	 * @param reduce reduce the dataset (1.0 means no reduction)
-	 */
-	void convertLowToHigh(Examples &ex, double reduce = 1.0);
-		
-	/**
-	 * Starts the PCA
-	 * @param ex input features
-	 */
-	void initializePCA(Examples &ex);
-	
-	/**
-	 * using PCA on al input features
-	 * @param ex input features
-	 */
-	void doPCA(Examples &ex);
-	
-	/**
-	 * normalize the features between 0 and 1
-	 * @param ex input features
-	 */
-	void normalize(Examples &ex);
-	
-	
-	/**
-	 * smooth the high level features
-	 * @param ex input features
-	 */
-	void smoothHL(Examples ex);
-	
-  public:
-  
-	/** constructor 
-	  *  @param conf needs a configfile
-	  *  @param md and a MultiDataset (contains images and other things)
-	  */
-	SemSegCsurka2( const Config *conf, const MultiDataset *md );
-      
-	/** simple destructor */
-	virtual ~SemSegCsurka2();
-
-	/** The trainingstep
-	  *  @param md and a MultiDataset (contains images and other things)
-	  */
-	void train ( const MultiDataset *md );
-
-	/** The trainingstep for the postprocess
-	  *  @param md and a MultiDataset (contains images and other things)
-	  */
-	void trainpostprocess( const MultiDataset *md );
-	
-	/** The main procedure. Input: Image, Output: Segmented Image with pixelwise labeles and the probabilities
-	  * @param ce image data
-	  * @param segresult result of the semantic segmentation with a label for each pixel
-	  * @param probabilities multi-channel image with one channel for each class and corresponding probabilities for each pixel
-	  */
-	void semanticseg ( CachedExample *ce, 
-                       NICE::Image & segresult,
-                       GenericImage<double> & probabilities );
-
-    /** this procedure is equal semanticseg, if there is no post process
-	  * @param ce image data
-	  * @param segresult result of the semantic segmentation with a label for each pixel
-	  * @param probabilities multi-channel image with one channel for each class and corresponding probabilities for each pixel
-	  * @param Regionen the output regions
-	  * @param mask the positions of the regions
-	  */
-	void classifyregions ( CachedExample *ce, NICE::Image & segresult, GenericImage<double> & probabilities, Examples &Regionen, NICE::Matrix &mask );
-};
-
-} //namespace
-
-#endif