const vector<double> histogram2d::get_bin_centres_y()
{
const double step = get_step_y();
vector<double> centres(bins_y);
for (int i = 0; i < bins_y; i++)
centres(i) = min_y + i * step + step / 2;
return centres;
}
Foam::pointField Foam::treeDataTriSurface::shapePoints() const
{
const pointField& points = surface_.points();
pointField centres(surface_.size());
forAll(surface_, triI)
{
centres[triI] = surface_[triI].centre(points);
}
float pupiltracker::cvx::histKmeans(const cv::Mat_<float>& hist, int bin_min, int bin_max, int K, float init_centres[], cv::Mat_<uchar>& labels, cv::TermCriteria termCriteria)
{
CV_Assert( hist.rows == 1 || hist.cols == 1 && K > 0 );
labels = cv::Mat_<uchar>::zeros(hist.size());
int nbins = hist.total();
float binWidth = (bin_max - bin_min)/nbins;
float binStart = bin_min + binWidth/2;
cv::Mat_<float> centres(K, 1, init_centres, 4);
int iters = 0;
bool finalRun = false;
while (true)
{
++iters;
cv::Mat_<float> old_centres = centres.clone();
int i_bin;
cv::Mat_<float>::const_iterator i_hist;
cv::Mat_<uchar>::iterator i_labels;
cv::Mat_<float>::iterator i_centres;
uchar label;
float sumDist = 0;
int movedCount = 0;
// Step 1. Assign each element a label
for (i_bin = 0, i_labels = labels.begin(), i_hist = hist.begin();
i_bin < nbins;
++i_bin, ++i_labels, ++i_hist)
{
float bin_val = binStart + i_bin*binWidth;
float minDist = sq(bin_val - centres(*i_labels));
int curLabel = *i_labels;
for (label = 0; label < K; ++label)
{
float dist = sq(bin_val - centres(label));
if (dist < minDist)
{
minDist = dist;
*i_labels = label;
}
}
if (*i_labels != curLabel)
movedCount++;
sumDist += (*i_hist) * std::sqrt(minDist);
}
if (finalRun)
return sumDist;
// Step 2. Recalculate centres
cv::Mat_<float> counts(K, 1, 0.0f);
for (i_bin = 0, i_labels = labels.begin(), i_hist = hist.begin();
i_bin < nbins;
++i_bin, ++i_labels, ++i_hist)
{
float bin_val = binStart + i_bin*binWidth;
centres(*i_labels) += (*i_hist) * bin_val;
counts(*i_labels) += *i_hist;
}
for (label = 0; label < K; ++label)
{
if (counts(label) == 0)
return std::numeric_limits<float>::infinity();
centres(label) /= counts(label);
}
// Step 3. Detect termination criteria
if (movedCount == 0)
finalRun = true;
else if (termCriteria.type | cv::TermCriteria::COUNT && iters >= termCriteria.maxCount)
finalRun = true;
else if (termCriteria.type | cv::TermCriteria::EPS)
{
float max_movement = 0;
for (label = 0; label < K; ++label)
{
max_movement = std::max(max_movement, sq(centres(label) - old_centres(label)));
}
if (sqrt(max_movement) < termCriteria.epsilon)
finalRun = true;
}
}
return std::numeric_limits<float>::infinity();
}
