//! apply k-means classify to histogram
void apply_k_means_classify(const histogram<double>& input,
const std::vector<double>& mu,
std::vector<int>& cls )
{
int number_of_classes=mu.size();
int i,j,k;
//calculate all the classes
for(j=0;j<input.size();j++)
{
int best_k=0;
double best_dist=0;
for(k=0;k<number_of_classes;k++)
{
double dist=fabs(input.value(j)-mu[k]);
if(dist<best_dist|| best_k==0)
{
best_dist=dist;
best_k=k+1;
}
}
cls[j]=best_k;
}
}
// Run skin-detection algorithm
frame detect_skin(const frame& in) {
dtn_frame = in;
const rgb_byte ZERO = { };
for(int y = HEIGHT - 1; y; --y) {
for(int x = WIDTH - 1; x; --x) {
auto orig = dtn_frame.get_pixel(x, y);
rgb hist_in = { orig[2], orig[1], orig[0] };
if(hist.value(hist_in) < tld) {
orig[0] = orig[1] = orig[2] = 0;
}
}
}
return dtn_frame;
}
//! estimate sample mu using discrete classes
void estimate_mu(const histogram<double>& input,
const std::vector<int>& cls,
std::vector<double>& mu )
{
int number_of_classes=mu.size();
int i,j,k;
std::vector<double> _counts(number_of_classes,0);
for(k=0;k<number_of_classes;k++)
mu[k]=0;
double _count=0;
//1 calculate means
for(j=0;j<cls.size();j++)
{
_count+=input[j];
int _c=cls[j];
if(!_c || _c>number_of_classes) continue; //only use classified voxel
_c--;
_counts[_c]+=input[j];
mu[_c]+=input[j]*input.value(j);
}
if(!_count)
REPORT_ERROR("No voxels defined in ROI!");
for(k=0;k<number_of_classes;k++)
{
for(k=0;k<number_of_classes;k++)
{
if(_counts[k]>0)
mu[k]/=_counts[k];
}
}
}