NICE_SemSeg/semseg/postsegmentation/PPSuperregion.cpp

#include "PPSuperregion.h"

#include <core/iceconversion/convertice.h>

#include "objrec/segmentation/RegionGraph.h"

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

PPSuperregion::PPSuperregion()
{
	conf = new Config();
	Init();
}

PPSuperregion::PPSuperregion(const Config *_conf):conf(_conf)
{
	Init();
}

void PPSuperregion::Init()
{
	std::string section = "PostProcessSG";
	rf = new FPCRandomForests( conf, "ShapeRF" );
}

PPSuperregion::~PPSuperregion()
{
}

void PPSuperregion::optimizeShape(Examples &regions, NICE::Matrix &mask, GenericImage<double> & probabilities)
{

	vector<ice::Region> superregions;
	vector<double> probs;
	vector<int> classes;
	NICE::Matrix smask;
	getSuperregions(regions, mask, superregions, classes, smask);
	
	for(int i = 0; i < (int)superregions.size(); i++)
	{
		ice::Moments m;
		superregions[i].CalcMoments(m);

		NICE::Vector tmp = makeEVector(m.AffineHuInvariants());
		NICE::Vector *tmp2 = new NICE::Vector(tmp);
		Example tex(tmp2);		
		
		ClassificationResult r = rf->classify ( tex );

		probs.push_back(r.scores[classes[i]]);
	}

	vector<ice::Region> orgregions;
	for(int i = 0; i < (int)regions.size(); i++)
	{
		orgregions.push_back(ice::Region());
	}
	
	for(int y = 0; y < (int)mask.cols(); y++)
	{
		for(int x = 0; x < (int)mask.rows(); x++)
		{
			int pos = mask(x,y);
			orgregions[pos].Add(x,y);
		}
	}

	// maps the regions to their superregions
	vector<int> regmapsreg(regions.size(), 0);
	for(int y = 0; y < (int)smask.cols(); y++)
	{
		for(int x = 0; x < (int)smask.rows(); x++)
		{
			int r = mask(x,y);
			int sr = smask(x,y);
			regmapsreg[r] = sr;
		}
	}
	
	RegionGraph g;
	g.computeGraph(regions, mask);
	
	vector<Node*> nodes;
	g.get(nodes);

	bool change = true;
	int k = 0;
	while(change && k < 100)
	{
		k++;
		change = false;
		int anders = 0;
		for(int i = 0; i < (int) nodes.size(); i++)
		{

			set<int> sr;
			int regnb = nodes[i]->getRegion();
			int orgreg = regmapsreg[regnb];

			if(nodes[i]->isAtBorder())
			{
				vector<Node*> nbs;
				nodes[i]->getNeighbors(nbs);
				for(int j = 0; j < (int)nbs.size(); j++)
					sr.insert(regmapsreg[nbs[j]->getRegion()]);
			}
	
			vector<double> otherprobs;

			ice::Region re = superregions[orgreg];
			re.Del(orgregions[regnb]);

			ice::Moments m;

			if(re.Area() > 0)
			{
				re.CalcMoments(m);

				NICE::Vector tmp = makeEVector( m.AffineHuInvariants());
				NICE::Vector *tmp2 = new NICE::Vector(tmp);
				Example tex(tmp2);
				ClassificationResult r = rf->classify ( tex );
				tex.vec = NULL;
				delete tmp2;
				
				double val = probabilities.get(regions[regnb].second.x, regions[regnb].second.y, classes[orgreg]) * r.scores[classes[orgreg]];
				
				otherprobs.push_back(val);
				if(otherprobs[0] < probs[orgreg])
					continue;
			}
			
			for( set<int>::const_iterator iter = sr.begin();iter != sr.end();++iter )
			{
				ice::Moments m2;
				ice::Region re2 = superregions[regmapsreg[*iter]];
				re2.Add(orgregions[regnb]);
				re2.CalcMoments(m2);
				NICE::Vector tmp = makeEVector(m2.AffineHuInvariants());
				NICE::Vector *tmp2 = new NICE::Vector(tmp);
				Example tex(tmp2);
				ClassificationResult r2 = rf->classify ( tex );
				tex.vec = NULL;
				delete tmp2;
				
				double val = probabilities.get(regions[regnb].second.x, regions[regnb].second.y, classes[*iter]) * r2.scores[classes[*iter]];
				
				otherprobs.push_back(val);
			}

			int k = 1;
			int best = -1;
			double bestval = -1.0;
			for( set<int>::const_iterator iter = sr.begin();iter != sr.end();++iter, k++ ) 
			{
				if(otherprobs[k] > probs[*iter])
				{
					if(bestval < otherprobs[k])
					{
						bestval = otherprobs[k];
						best = *iter;
					}
				}
			}
			
			if(best < 0 || bestval <= 0.0)
				continue;
			
			change = true;

			probs[best] = bestval;

			superregions[best].Add(orgregions[regnb]);

			probs[orgreg] = otherprobs[0];

			superregions[orgreg].Del(orgregions[regnb]);

			regmapsreg[regnb] = best;

			nodes[i]->setLabel(classes[best]);
			anders++;
		}
	}
	
	for(int i = 0; i < (int)regions.size(); i++)
	{
		regions[i].first = classes[regmapsreg[i]];
	}
}

void PPSuperregion::getSuperregions(const Examples &regions, const NICE::Matrix &mask, vector<ice::Region> &superregions, vector<int> &classes, NICE::Matrix &smask)
{
	NICE::Image tmp (mask.rows(), mask.cols());
	tmp.set(0);
	NICE::ColorImage m2 (tmp, tmp, tmp);
	for(int y = 0; y < (int)mask.cols(); y++)
	{
		for(int x = 0; x < (int)mask.rows(); x++)
		{
			int pos = mask(x,y);

			m2.setPixel(x,y,0,regions[pos].first);
			m2.setPixel(x,y,1,regions[pos].first);
			m2.setPixel(x,y,2,regions[pos].first);
		}
	}

	RSMeanShift rs(conf);
	int count = rs.transformSegmentedImg( m2, smask);
	
	classes.resize(count);
	for(int i = 0; i < count; i++)
	{
		superregions.push_back(ice::Region());
	}
	
	for(int y = 0; y < (int)smask.cols(); y++)
	{
		for(int x = 0; x < (int)smask.rows(); x++)
		{
			int pos = smask(x,y);
			superregions[pos].Add(x,y);
			classes[pos] = regions[mask(x,y)].first;
		}
	}
}

void PPSuperregion::trainShape(Examples &regions, NICE::Matrix &mask)
{
	// bestimme Superregionen
	vector<ice::Region> superregions;
	vector<int> classes;
	// refactor-nice.pl: check this substitution
	// old: Image smask;
	NICE::Matrix smask;
	getSuperregions(regions, mask, superregions, classes, smask);
	
	// berechne die Momente der Superregionen und speichere diese als Merkmale ab
	for(int i = 0; i < (int)superregions.size(); i++)
	{
		ice::Moments m;
		superregions[i].CalcMoments(m);
		NICE::Vector tmp = makeEVector(m.AffineHuInvariants());
		NICE::Vector *tmp2 = new NICE::Vector(tmp);
		shapefeats.push_back(pair<int, Example>(classes[i], Example(tmp2)));
	}
}
		
void PPSuperregion::finishShape(ClassNames &cn)
{
	//Lerne Klassifikator mit dem den Formmerkmalen an
	FeaturePool fp;
	Feature *f = new VectorFeature ( 7 );
	f->explode ( fp );
	delete f;
	rf->train ( fp, shapefeats);
}

void PPSuperregion::restore (istream & is, int format)
{
	
}
		
void PPSuperregion::store (ostream & os, int format) const
{
	
}

void PPSuperregion::clear()
{
	
}