NICE_SemSeg/semseg/SemSegLocal.cpp

/** 
* @file SemSegLocal.cpp
* @brief semantic segmentation using image patches only
* @author Erik Rodner
* @date 05/08/2008

*/
#include <iostream>

#include "SemSegLocal.h"
#include "vislearning/cbaselib/CachedExample.h"
#include "vislearning/classifier/fpclassifier/randomforest/FPCRandomForests.h"
#include "vislearning/features/fpfeatures/PixelPairFeature.h"

#include "SemSegTools.h" 

using namespace OBJREC;

using namespace std;
using namespace NICE;


SemSegLocal::SemSegLocal( const Config *conf, 
			  const MultiDataset *md  )
    : SemanticSegmentation ( conf, &(md->getClassNames("train")) )
{
    save_cache = conf->gB("FPCPixel", "save_cache", true );
    read_cache = conf->gB("FPCPixel", "read_cache", false );
    cache = conf->gS("FPCPixel", "cache", "fpc.data" );
    fpc = new FPCRandomForests ( conf, "FPCPixel" );
    fpc->setMaxClassNo ( classNames->getMaxClassno() );

    
    if ( read_cache ) {
	fprintf (stderr, "LocSSimpleFP:: Reading classifier data from %s\n", cache.c_str() );
	fpc->read ( cache );
	fprintf (stderr, "LocSSimpleFP:: successfully read\n" );
    } else {
	train ( conf, md );
    }
}
	
void SemSegLocal::train ( const Config *conf, const MultiDataset *md )
{
    Examples examples;
    vector<CachedExample *> imgexamples;

    SemSegTools::collectTrainingExamples ( 
	conf, 
	"FPCPixel", // config section for grid settings
	*((*md)["train"]),
	*classNames, 
	examples, 
	imgexamples );

    assert ( examples.size() > 0 );
    
    FeaturePool fp;
    PixelPairFeature hf (conf);
    hf.explode ( fp );

    fpc->train ( fp, examples );

    // clean up memory !!
    for ( vector<CachedExample *>::iterator i = imgexamples.begin();
		    i != imgexamples.end();
		    i++ )
	delete ( *i );

    if ( save_cache ) {
	fpc->save ( cache );
    }

    fp.destroy();
}


SemSegLocal::~SemSegLocal()
{
    if ( fpc != NULL )
	delete fpc;
}


void SemSegLocal::semanticseg ( CachedExample *ce, 
			   NICE::Image & segresult,
			   NICE::MultiChannelImageT<double> & probabilities )
{
    // for speed optimization
    FPCRandomForests *fpcrf = dynamic_cast<FPCRandomForests *> ( fpc );
    int xsize, ysize;
    ce->getImageSize ( xsize, ysize );
    probabilities.reInit ( xsize, ysize, classNames->getMaxClassno()+1, true/*allocMem*/ );
    segresult.resize(xsize, ysize);
    
    Example pce ( ce, 0, 0 );
    long int offset = 0;
    for ( int y = 0 ; y < ysize ; y++ ) 
	for ( int x = 0 ; x < xsize ; x++,offset++ )  
	{
	    pce.x = x ; pce.y = y; 
	    ClassificationResult r = fpcrf->classify ( pce );
	    segresult.setPixel(x,y,r.classno);
	    for ( int i = 0 ; i < (int)probabilities.numChannels; i++ )
		probabilities.data[i][offset] = r.scores[i];
	}
}