NICE_SemSeg/semseg/FIShotton.cpp

/** 
* @file FIShotton.cpp
* @brief feature images
* @author Erik Rodner
* @date 05/30/2008

*/
#include <iostream>

#include "FIShotton.h"
#include "vislearning/baselib/FastFilter.h"
#include "vislearning/image/GenericImageTools.h"

using namespace OBJREC;

using namespace std;
using namespace NICE;


void FIShotton::buildTextonMap ( CachedExample *ce,
				FPCRandomForests *fpcrf,
				map<DecisionNode *, pair<long, int> > index,
				int subsamplex, 
				int subsampley,
				int maxdepthSegmentationForest )
{
    vector<DecisionNode *> leafs;

    int xsize, ysize;
    ce->getImageSize ( xsize, ysize );
    int xsize_s = xsize / subsamplex;
    int ysize_s = ysize / subsampley;

    SparseVector *textonIndices = new SparseVector [xsize_s*ysize_s];
    
    Example pce ( ce, 0, 0 );
    long offset = 0;
    long offset_s = 0;
    for ( int y = 0 ; y < ysize_s ; y++ )
    {
	for ( int x = 0 ; x < xsize_s ; x++, offset_s++ )
	{
	    for ( int yi = 0 ; yi < subsampley ; yi++ )
	    {
		for ( int xi = 0 ; xi < subsamplex ; xi++, offset++ )
		{
		    leafs.clear();
		    pce.x = x*subsamplex + xi; pce.y = y*subsampley + yi; 
		    fpcrf->getLeafNodes ( pce, leafs, maxdepthSegmentationForest );
		    SparseVector v;
		    for ( vector<DecisionNode *>::const_iterator i = leafs.begin();
								i != leafs.end();
								i++ )
			v.insert ( pair<int, double> ( index[*i].first, 1.0 ) );

		    textonIndices[offset_s].add(v);
		}
	    }
	}
    }
    fprintf (stderr, "Building Texton Integral NICE::Image !!\n");

    ce->buildIntegralSV ( CachedExample::SVTEXTON, textonIndices, xsize_s, ysize_s );
}




void FIShotton::buildSemanticMap ( CachedExample *ce,
				FPCRandomForests *fpcrf,
				int subsamplex, int subsampley,
				int numClasses )
{
    int xsize, ysize;
    ce->getImageSize ( xsize, ysize );
    int xsize_s = xsize / subsamplex;
    int ysize_s = ysize / subsampley;

    NICE::MultiChannelImageT<double> & priorMap = ce->getDChannel ( CachedExample::D_INTEGRALPRIOR );
    priorMap.reInit ( xsize_s, ysize_s, numClasses, true );
    priorMap.setAll ( 0.0 );
    
    vector<DecisionNode *> leafs;
   
    Example pce ( ce, 0, 0 );
    long offset = 0;
    long offset_s = 0;
    for ( int y = 0 ; y < ysize_s ; y++ )
    {
	for ( int x = 0 ; x < xsize_s ; x++, offset_s++ )
	{
	    for ( int yi = 0 ; yi < subsampley ; yi++ )
	    {
		for ( int xi = 0 ; xi < subsamplex ; xi++ )
		{
		    leafs.clear();
		    pce.x = x*subsamplex + xi; pce.y = y*subsampley + yi; 
		    fpcrf->getLeafNodes ( pce, leafs );
		    
		    for ( vector<DecisionNode *>::const_iterator i = leafs.begin();
								i != leafs.end();
								i++ )
		    {
			const FullVector & sv = (*i)->distribution;

			for ( int i = 0 ; i < sv.size(); i++  )
			{
			    priorMap.data[i][offset_s] += sv[i];
			}
		    }
		}
	    }

	    double sum = 0.0;
	    for ( uint i = 0 ; i < priorMap.numChannels; i++ )
		sum += priorMap.data[i][offset_s];
	
	    if ( sum < 10e-13 )
	    {
		fprintf (stderr, "x*subsamplex %d y*subsampley %d xsize %d ysize %d\n",
		    x*subsamplex, y*subsampley, xsize, ysize );
		exit(-1);
	    } else {
		for ( uint i = 0 ; i < priorMap.numChannels; i++ )
		    priorMap.data[i][offset_s] /= sum;
	    }

	}
    }

    for ( uint i = 0 ; i < priorMap.numChannels ; i++ )
	GenericImageTools::calcIntegralImage ( priorMap.data[i], priorMap.data[i], priorMap.xsize, priorMap.ysize );

}