NICE_SemSeg/semseg/postsegmentation/PSSLocalizationPrior.cpp

/** 
* @file PSSLocalizationPrior.cpp
* @brief incorporate prior from localization results
* @author Erik Rodner
* @date 03/19/2009

*/
#include <iostream>
#include <limits>

#include "PSSLocalizationPrior.h"
#include "objrec/baselib/StringTools.h"
#include "objrec/baselib/Globals.h"
#include "objrec/baselib/FileMgt.h"
#include "objrec/cbaselib/PascalResults.h"

using namespace OBJREC;

using namespace std;
using namespace NICE;

PSSLocalizationPrior::PSSLocalizationPrior( const std::string & detectiondir, 
					    const ClassNames *classNames,
					    double alphaDetectionPrior,
					    int subsamplex, int subsampley )
{
    this->subsamplex = subsampley;
    this->subsampley = subsamplex;
    this->alphaDetectionPrior = alphaDetectionPrior;
    loadDetectionResults ( detectiondir, detresults, classNames );
}

PSSLocalizationPrior::~PSSLocalizationPrior()
{
}

void PSSLocalizationPrior::loadDetectionResults ( const std::string & dir,
				       map<string, LocalizationResult *> & results,
				       const ClassNames *classNames )
{
    vector<string> files;
    FileMgt::DirectoryRecursive ( files, dir );
    int backgroundClassNo = classNames->getBackgroundClass();

    for ( vector<string>::const_iterator i = files.begin();
	    i != files.end(); i++ )
    {

	std::string file = *i;

	std::string classtext = StringTools::baseName ( file, false );
	int classno = classNames->classno(classtext);
	if ( classno < 0 ) {
	    fprintf (stderr, "Unable to find class %s\n", classtext.c_str() );
	    fprintf (stderr, "dir %s file %s classtext %s\n", dir.c_str(),
		file.c_str(), classtext.c_str() );
	}
	PascalResults::read ( results, file, classno, backgroundClassNo, true /*calibrate*/ );
    }
}

void PSSLocalizationPrior::postprocess ( NICE::Image & result, NICE::MultiChannelImageT<double> & probabilities )
{
    std::string currentFilename = Globals::getCurrentImgFN();
    std::string base = StringTools::baseName ( currentFilename, false );
    map<string, LocalizationResult *>::const_iterator i = detresults.find ( base );

    if ( i == detresults.end() )
    {
	fprintf (stderr, "NO detection results found for %s !\n", base.c_str());
	return;
    }

    fprintf (stderr, "Infering detection prior\n");
	
    LocalizationResult *ldet = i->second;

    int maxClassNo = probabilities.numChannels - 1;

    int xsize = probabilities.xsize;
    int ysize = probabilities.ysize;
    FullVector *priormap = new FullVector [ xsize * ysize ];
    for ( long k = 0 ; k < xsize * ysize ; k++ )
	priormap[k].reinit(maxClassNo);

    for ( LocalizationResult::const_iterator j = ldet->begin();
					    j != ldet->end();
					    j++ )
    {
	const SingleLocalizationResult *slr = *j;

	int xi, yi, xa, ya;
	const NICE::Region & r = slr->getRegion();
	int classno = slr->r->classno;
	double confidence = slr->r->confidence();
	r.getRect ( xi, yi, xa, ya );

	for ( int y = yi; y <= ya; y++ )
	    for ( int x = xi; x <= xa; x++ )
	    {
		if ( (y<0) || (x<0) || (x>xsize-1) || (y>ysize-1) )
		    continue;
		if ( r.inside ( x*subsamplex, y*subsampley ) )
		    priormap[x + y*xsize][classno] += confidence;
	    }

	long k = 0;
	for ( int y = 0 ; y < ysize ; y++ )
	    for ( int x = 0 ; x < xsize ; x++,k++ )
	    {
		FullVector & prior  = priormap[k];

		if ( prior.sum() < 10e-6 )
		    continue;

		prior.normalize();

		double sum = 0.0;
		for ( int i = 0 ; i < (int)probabilities.numChannels; i++ )
		{
		    probabilities.data[i][k] *= pow ( prior[i], alphaDetectionPrior ); 
		    sum += probabilities.data[i][k];
		}

		if ( sum < 10e-6 )
		    continue;

		int maxindex = 0;
		double maxvalue = - numeric_limits<double>::max();
		for ( int i = 0 ; i < (int)probabilities.numChannels; i++ )
		{
		    probabilities.data[i][k] /= sum;
		    if ( probabilities.data[i][k] > maxvalue )
		    {
			maxindex = i;
			maxvalue = probabilities.data[i][k];
		    }
		}
		result.setPixel(x,y,maxindex);
	    }
	
    }

    delete [] priormap;
}