void BroadEstepper1::update_mean_broad()
{
int PPF=_opts.tune_pseudopoint_factor;
valarray<double>pmean(G);
for (int k=0; k<G; k++) pmean[k] = _prior.mean(_cvec[k]);
int num_pseudopoints = int(N/PPF);
if (!num_pseudopoints) num_pseudopoints = 1;
valarray<double>xmean(0.0,G);
for (int k=0; k<G; k++)
{
double kwsum=0.0, wsum=0.0;
for (int i=0; i<N; i++)
{
kwsum += _vals[i]*_prob.element(i,k);
wsum += _prob.element(i,k);
if (wsum > 0.0) xmean[k] = kwsum/wsum;
}
}
for (int k=0; k<G; k++)
{
_mean[k] = xmean[k]*N*_prop[k] + pmean[k]*num_pseudopoints;
_mean[k] /= N*_prop[k] + num_pseudopoints;
}
}
/*! @brief create a single bounding box around the detection from mean and standard deviation
*
* @return a bounding box
*/
cv::Rect boundingBoxNorm(void) const {
const unsigned int nparts = parts_.size();
cv::Mat_<int> xpts(cv::Size(1,nparts));
cv::Mat_<int> ypts(cv::Size(1,nparts));
for (unsigned int n = 0; n < nparts; ++n) {
const cv::Point centroid = (parts_[n].tl() + parts_[n].br())*0.5;
xpts(n) = centroid.x;
ypts(n) = centroid.y;
}
cv::Scalar xmean, ymean, xstd, ystd;
cv::meanStdDev(xpts, xmean, xstd);
cv::meanStdDev(ypts, ymean, ystd);
return cv::Rect(xmean(0)-1.5*xstd(0), ymean(0)-1.5*ystd(0), 3*xstd(0), 3*ystd(0));
}
