//==============================================================================
vector<Point2f>
face_tracker::
fit(const Mat &image,
const vector<Point2f> &init,
const Size ssize,
const bool robust,
const int itol,
const float ftol)
{
int n = smodel.npts();
assert((int(init.size())==n) && (pmodel.n_patches()==n));
smodel.calc_params(init); vector<Point2f> pts = smodel.calc_shape();
//find facial features in image around current estimates
vector<Point2f> peaks = pmodel.calc_peaks(image,pts,ssize);
//optimise
if(!robust){
smodel.calc_params(peaks); //compute shape model parameters
pts = smodel.calc_shape(); //update shape
}else{
Mat weight(n,1,CV_32F),weight_sort(n,1,CV_32F);
vector<Point2f> pts_old = pts;
for(int iter = 0; iter < itol; iter++){
//compute robust weight
for(int i = 0; i < n; i++)weight.fl(i) = norm(pts[i] - peaks[i]);
cv::sort(weight,weight_sort,CV_SORT_EVERY_COLUMN|CV_SORT_ASCENDING);
double var = 1.4826*weight_sort.fl(n/2); if(var < 0.1)var = 0.1;
pow(weight,2,weight); weight *= -0.5/(var*var); cv::exp(weight,weight);
//compute shape model parameters
smodel.calc_params(peaks,weight);
//update shape
pts = smodel.calc_shape();
//check for convergence
float v = 0; for(int i = 0; i < n; i++)v += norm(pts[i]-pts_old[i]);
if(v < ftol)break; else pts_old = pts;
}
}return pts;
}
void run_test2(t_algo *best, t_info *info)
{
t_info *w_sort;
w_sort = weight_sort(info);
compress_ops(w_sort->steps, &w_sort->elem_steps);
rollback_sort(info);
compress_ops(info->steps, &info->elem_steps);
if (info->elem_steps < best->op_count && stack_sorted(info->a))
{
if (info->elem_steps < w_sort->elem_steps)
print_steps(info->steps);
else
print_steps(w_sort->steps);
}
else
print_steps(best->operations);
delete_mask(w_sort);
delete_mask(info);
}
