/*
* 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;
}
};
