rspfNormalizedS16RemapTable::rspfNormalizedS16RemapTable()
: rspfNormalizedRemapTable()
{
if (!theTableIsInitialized)
{
const rspf_int32 ENTRIES = getEntries();
const rspf_float64 DENOMINATOR = getNormalizer();
//---
// Initialize the remap table.
//
// Specialized for elevation, make -32768 and -32767 map to 0 since
// DTED NULL is -32767.
//
// NOTE: Zero will always remap back to -32768 with this hack. This
// could cause issues on writers that use pixFromNorm(). (drb)
//---
theTable[0] = 0.0; // Index zero always for null.
theTable[1] = 0.0; // Specialized for DTED.
for (rspf_int32 i = 2; i < ENTRIES; ++i)
{
theTable[i] = static_cast<rspf_float64>(i)/DENOMINATOR;
}
theTableIsInitialized = true;
}
}
void AdaptiveManifoldFilterN::initSrcAndJoint(InputArray src_, InputArray joint_)
{
srcSize = src_.size();
smallSize = getSmallSize();
srcCnNum = src_.channels();
split(src_, srcCn);
if (src_.depth() != CV_32F)
{
for (int i = 0; i < srcCnNum; i++)
srcCn[i].convertTo(srcCn[i], CV_32F);
}
if (joint_.empty() || joint_.getObj() == src_.getObj())
{
jointCnNum = srcCnNum;
if (src_.depth() == CV_32F)
{
jointCn = srcCn;
}
else
{
jointCn.resize(jointCnNum);
for (int i = 0; i < jointCnNum; i++)
srcCn[i].convertTo(jointCn[i], CV_32F, getNormalizer(src_.depth()));
}
}
else
{
splitChannels(joint_, jointCn);
jointCnNum = (int)jointCn.size();
int jointDepth = jointCn[0].depth();
Size jointSize = jointCn[0].size();
CV_Assert( jointSize == srcSize && (jointDepth == CV_8U || jointDepth == CV_16U || jointDepth == CV_32F) );
if (jointDepth != CV_32F)
{
for (int i = 0; i < jointCnNum; i++)
jointCn[i].convertTo(jointCn[i], CV_32F, getNormalizer(jointDepth));
}
}
}
rspfNormalizedU16RemapTable::rspfNormalizedU16RemapTable()
: rspfNormalizedRemapTable()
{
if (!theTableIsInitialized)
{
const rspf_int32 ENTRIES = getEntries();
const rspf_float64 DENOMINATOR = getNormalizer();
//---
// Initialize the remap table.
//---
theTable[0] = 0.0; // Index zero always for null.
for (rspf_int32 i = 1; i < ENTRIES; ++i)
{
theTable[i] = static_cast<rspf_float64>(i)/DENOMINATOR;
}
theTableIsInitialized = true;
}
}
/**
* \fn void Localization::resample(int no)
* resample the population and remove the unrelevant particles
*
*/
void Localization::resample(int no){
// Calculate the mean of the population
if(no != 0)
itsKeepParticles = no;
float totalProb=getNormalizer();
float mean = totalProb/itsNumParticles;
float stddev = 0.0 ;
float minProb = 0.0 ;
for (std::vector<class pParticle>::iterator iter = pList.begin(); iter != pList.end() ; iter++ )
{
stddev += ( iter->prob - mean ) * ( iter->prob - mean );
if ( iter -> prob < minProb )
minProb = iter->prob;
}
stddev = stddev / itsNumParticles;
stddev = sqrt ( stddev );
// Now since we have standard deviation and mean at hand we can calculate what particles to say good bye to
// if particles' probability is less than mean - ( (stddev + (mean - minProb - stddev)) / 2 ) and not greater than 0.01, then we will discard them
for (std::vector<class pParticle>::iterator iter = pList.begin(); iter != pList.end() ; iter++ )
{
if( iter -> prob < 0.01 && ( iter -> prob < mean - (stddev + (mean - minProb - stddev ) )/2.0 ) )
{
pList.erase(iter);
itsNumParticles --;
}
}
std::sort(pList.begin(), pList.end(), greater<pParticle>() );
}
