/* * compute the second and up iterations of a probability map * using the given aPriori probabilites per pixel. */ bool probabilityMap2D::computeMap(const channel8& src1, const channel8& src2, channel& aPrioriDest) const { point chnl1_size = src1.size(); point chnl2_size = src2.size(); // size of src1 equals src2 ? if ( (chnl1_size.x != chnl2_size.x) || (chnl1_size.y != chnl2_size.y) ) { setStatusString("probabilityMap2D: channels do not match"); return false; } else { int y; vector<channel8::value_type>::const_iterator srcIterator1, eit1; vector<channel8::value_type>::const_iterator srcIterator2, eit2; vector<channel::value_type>::iterator destIterator; const parameters& param = getParameters(); const thistogram<double>& objModel = param.getObjectColorModel(); const thistogram<double>& nonObjModel = param.getNonObjectColorModel(); float relObjProb; float relNonObjProb; ivector theBin(2); for (y=0;y<src1.rows();++y) { srcIterator1 = src1.getRow(y).begin(); eit1 = src1.getRow(y).end(); srcIterator2 = src2.getRow(y).begin(); eit2 = src2.getRow(y).end(); destIterator = aPrioriDest.getRow(y).begin(); while (srcIterator1 != eit1) { theBin[0] = lookupTable[0][*srcIterator1]; theBin[1] = lookupTable[1][*srcIterator2]; relObjProb = static_cast<float>(objModel.getProbability(theBin) * (*destIterator)); relNonObjProb = static_cast<float>(nonObjModel.getProbability(theBin)* (1.0f-(*destIterator))); // assume non-object if no entries are given if ((relObjProb == 0.0f) && (relNonObjProb == 0.0f)) { (*destIterator) = 0.0f; } else { // bayes (*destIterator) = relObjProb / (relObjProb + relNonObjProb); } srcIterator1++; srcIterator2++; destIterator++; } } } return true; }
// return probability channel bool probabilityMap2D::apply(const channel8& src1, const channel8& src2, channel& dest) const { const parameters& param = getParameters(); point chnl1_size = src1.size(); point chnl2_size = src2.size(); // size of src1 equals src2 ? if ( (chnl1_size.x != chnl2_size.x) || (chnl1_size.y != chnl2_size.y) ) { setStatusString("probabilityMap2D: channels do not match"); return false; } // the color model MUST have 2 dimensions! if (probabilityHistogram.dimensions() == 2) { // resize probability channel dest.resize(src1.size()); ivector theBin(2); // compute first iteration int y; vector<channel8::value_type>::const_iterator srcIterator1, eit1; vector<channel8::value_type>::const_iterator srcIterator2, eit2; vector<channel::value_type>::iterator destIterator; for (y=0;y<src1.rows();++y) { srcIterator1 = src1.getRow(y).begin(); eit1 = src1.getRow(y).end(); srcIterator2 = src2.getRow(y).begin(); eit2 = src2.getRow(y).end(); destIterator = dest.getRow(y).begin(); while (srcIterator1 != eit1) { theBin[0] = lookupTable[0][*srcIterator1]; theBin[1] = lookupTable[1][*srcIterator2]; (*destIterator)=static_cast<float>(probabilityHistogram.at(theBin)); srcIterator1++; srcIterator2++; destIterator++; } } // compute all other iterations if (param.iterations > 1) { int i; if (param.gaussian) { gaussKernel2D<float> gk(param.windowSize,param.variance); convolution convolver; convolution::parameters convParam; convParam.boundaryType = lti::Mirror; convParam.setKernel(gk); convolver.setParameters(convParam); for (i=1;i<param.iterations;++i) { convolver.apply(dest); computeMap(src1,src2,dest); } } else { squareConvolution<float> convolver; squareConvolution<float>::parameters convParam; convParam.boundaryType = lti::Mirror; convParam.initSquare(param.windowSize); convolver.setParameters(convParam); for (i=1;i<param.iterations;++i) { convolver.apply(dest); computeMap(src1,src2,dest); } } } // of (param.iterations > 1) return true; } // of (probabilityHistogram.dimensions() == 2) setStatusString("probabilityMap2D: no models loaded"); return false; }
// On place apply for type channel8! bool distanceTransform::apply(channel& srcdest) const { if ((srcdest.rows() < 2) || (srcdest.columns() < 2)) { setStatusString("At least 2 pixels at each axis expected"); return false; } const parameters& param = getParameters(); if( param.distance == parameters::EightNeighborhood || param.distance == parameters::FourNeighborhood){ // ensure that the non-zero values are maximal int y; vector<channel::value_type>::iterator it,eit; const float max = static_cast<float>(srcdest.rows()+srcdest.columns()); for (y=0;y<srcdest.rows();y++) { vector<channel::value_type>& vct = srcdest.getRow(y); for (it=vct.begin(),eit=vct.end();it!=eit;++it) { if ((*it)>0.0f) { (*it)=max; } } } } switch(param.distance){ case parameters::EightNeighborhood: iteration8back(srcdest); iteration8(srcdest); return true; case parameters::FourNeighborhood: iteration4back(srcdest); iteration4(srcdest); return true; case parameters::Euclidean: EDT_1D(srcdest); EDT_2D(srcdest); srcdest.apply(sqrt); return true; case parameters::EuclideanSqr: EDT_1D(srcdest); EDT_2D(srcdest); return true; case parameters::EightSED: sedFiltering(srcdest, true); srcdest.apply(sqrt); return true; case parameters::EightSEDSqr: sedFiltering(srcdest,true); return true; case parameters::FourSED: sedFiltering(srcdest, false); srcdest.apply(sqrt); return true; case parameters::FourSEDSqr: sedFiltering(srcdest, false); return true; default: return false; } };