bool utfConvert(
const std::basic_string<From>& from, std::basic_string<To>& to,
ConversionResult(*cvtfunc)(const typename FromTrait::ArgType**, const typename FromTrait::ArgType*,
typename ToTrait::ArgType**, typename ToTrait::ArgType*,
ConversionFlags)
)
{
static_assert(sizeof(From) == sizeof(typename FromTrait::ArgType), "Error size mismatched");
static_assert(sizeof(To) == sizeof(typename ToTrait::ArgType), "Error size mismatched");
if (from.empty())
{
to.clear();
return true;
}
// See: http://unicode.org/faq/utf_bom.html#gen6
static const int most_bytes_per_character = 4;
const size_t maxNumberOfChars = from.length(); // all UTFs at most one element represents one character.
const size_t numberOfOut = maxNumberOfChars * most_bytes_per_character / sizeof(To);
std::basic_string<To> working(numberOfOut, 0);
auto inbeg = reinterpret_cast<const typename FromTrait::ArgType*>(&from[0]);
auto inend = inbeg + from.length();
auto outbeg = reinterpret_cast<typename ToTrait::ArgType*>(&working[0]);
auto outend = outbeg + working.length();
auto r = cvtfunc(&inbeg, inend, &outbeg, outend, strictConversion);
if (r != conversionOK)
return false;
working.resize(reinterpret_cast<To*>(outbeg) - &working[0]);
to = std::move(working);
return true;
};
void operator()(const BlockedRange& range) const
{
int y0 = range.begin(), y1 = range.end();
int ncols = src->cols, nchannels = src->channels();
AutoBuffer<float> buf(src->cols*nchannels);
float alpha1 = 1.f - alphaT;
float dData[CV_CN_MAX];
for( int y = y0; y < y1; y++ )
{
const float* data = buf;
if( cvtfunc )
cvtfunc( src->ptr(y), src->step, 0, 0, (uchar*)data, 0, Size(ncols*nchannels, 1), 0);
else
data = src->ptr<float>(y);
float* mean = mean0 + ncols*nmixtures*nchannels*y;
GMM* gmm = gmm0 + ncols*nmixtures*y;
uchar* modesUsed = modesUsed0 + ncols*y;
uchar* mask = dst->ptr(y);
for( int x = 0; x < ncols; x++, data += nchannels, gmm += nmixtures, mean += nmixtures*nchannels )
{
//calculate distances to the modes (+ sort)
//here we need to go in descending order!!!
bool background = false;//return value -> true - the pixel classified as background
//internal:
bool fitsPDF = false;//if it remains zero a new GMM mode will be added
int nmodes = modesUsed[x], nNewModes = nmodes;//current number of modes in GMM
float totalWeight = 0.f;
float* mean_m = mean;
//////
//go through all modes
for( int mode = 0; mode < nmodes; mode++, mean_m += nchannels )
{
float weight = alpha1*gmm[mode].weight + prune;//need only weight if fit is found
////
//fit not found yet
if( !fitsPDF )
{
//check if it belongs to some of the remaining modes
float var = gmm[mode].variance;
//calculate difference and distance
float dist2;
if( nchannels == 3 )
{
dData[0] = mean_m[0] - data[0];
dData[1] = mean_m[1] - data[1];
dData[2] = mean_m[2] - data[2];
dist2 = dData[0]*dData[0] + dData[1]*dData[1] + dData[2]*dData[2];
}
else
{
dist2 = 0.f;
for( int c = 0; c < nchannels; c++ )
{
dData[c] = mean_m[c] - data[c];
dist2 += dData[c]*dData[c];
}
}
//background? - Tb - usually larger than Tg
if( totalWeight < TB && dist2 < Tb*var )
background = true;
//check fit
if( dist2 < Tg*var )
{
/////
//belongs to the mode
fitsPDF = true;
//update distribution
//update weight
weight += alphaT;
float k = alphaT/weight;
//update mean
for( int c = 0; c < nchannels; c++ )
mean_m[c] -= k*dData[c];
//update variance
float varnew = var + k*(dist2-var);
//limit the variance
varnew = MAX(varnew, varMin);
varnew = MIN(varnew, varMax);
gmm[mode].variance = varnew;
//sort
//all other weights are at the same place and
//only the matched (iModes) is higher -> just find the new place for it
for( int i = mode; i > 0; i-- )
{
//check one up
if( weight < gmm[i-1].weight )
break;
//swap one up
std::swap(gmm[i], gmm[i-1]);
for( int c = 0; c < nchannels; c++ )
std::swap(mean[i*nchannels + c], mean[(i-1)*nchannels + c]);
}
//belongs to the mode - bFitsPDF becomes 1
/////
}
}//!bFitsPDF)
//check prune
if( weight < -prune )
{
weight = 0.0;
nmodes--;
}
gmm[mode].weight = weight;//update weight by the calculated value
totalWeight += weight;
}
//go through all modes
//////
//renormalize weights
totalWeight = 1.f/totalWeight;
for( int mode = 0; mode < nmodes; mode++ )
{
gmm[mode].weight *= totalWeight;
}
nmodes = nNewModes;
//make new mode if needed and exit
if( !fitsPDF )
{
// replace the weakest or add a new one
int mode = nmodes == nmixtures ? nmixtures-1 : nmodes++;
if (nmodes==1)
gmm[mode].weight = 1.f;
else
{
gmm[mode].weight = alphaT;
// renormalize all other weights
for( int i = 0; i < nmodes-1; i++ )
gmm[i].weight *= alpha1;
}
// init
for( int c = 0; c < nchannels; c++ )
mean[mode*nchannels + c] = data[c];
gmm[mode].variance = varInit;
//sort
//find the new place for it
for( int i = nmodes - 1; i > 0; i-- )
{
// check one up
if( alphaT < gmm[i-1].weight )
break;
// swap one up
std::swap(gmm[i], gmm[i-1]);
for( int c = 0; c < nchannels; c++ )
std::swap(mean[i*nchannels + c], mean[(i-1)*nchannels + c]);
}
}
//set the number of modes
modesUsed[x] = uchar(nmodes);
mask[x] = background ? 0 :
detectShadows && detectShadowGMM(data, nchannels, nmodes, gmm, mean, Tb, TB, tau) ?
shadowVal : 255;
}
}
}