private Point[] fit(Mat image,
Point[] init,
OpenCVForUnity.Size ssize,
bool robust,
int itol,
double ftol)
{
int n = smodel.npts();
// assert((int(init.size())==n) && (pmodel.n_patches()==n));
// Debug.Log ("init.size())==n " + init.Length + " " + n);
// Debug.Log ("pmodel.n_patches()==n " + pmodel.n_patches () + " " + n);
smodel.calc_params(init, new Mat(), 3.0f);
Point[] pts = smodel.calc_shape();
//find facial features in image around current estimates
Point[] peaks = pmodel.calc_peaks(image, pts, ssize);
//optimise
if (!robust)
{
smodel.calc_params(peaks, new Mat(), 3.0f); //compute shape model parameters
pts = smodel.calc_shape(); //update shape
}
else
{
using (Mat weight = new Mat(n, 1, CvType.CV_32F))
using (Mat weight_sort = new Mat(n, 1, CvType.CV_32F)) {
Point[] pts_old = pts;
for (int iter = 0; iter < itol; iter++)
{
//compute robust weight
for (int i = 0; i < n; i++)
{
using (MatOfPoint tmpMat = new MatOfPoint(new Point(pts [i].x - peaks [i].x, pts [i].y - peaks [i].y))) {
weight.put(i, 0, new float[] { (float)Core.norm(tmpMat) });
}
}
Core.sort(weight, weight_sort, Core.SORT_EVERY_COLUMN | Core.SORT_ASCENDING);
double var = 1.4826 * (float)weight_sort.get(n / 2, 0) [0];
if (var < 0.1)
{
var = 0.1;
}
Core.pow(weight, 2, weight);
Core.multiply(weight, new Scalar(-0.5 / (var * var)), weight);
Core.exp(weight, weight);
//compute shape model parameters
smodel.calc_params(peaks, weight, 3.0f);
//update shape
pts = smodel.calc_shape();
//check for convergence
float v = 0;
for (int i = 0; i < n; i++)
{
using (MatOfPoint tmpMat = new MatOfPoint(new Point(pts [i].x - pts_old [i].x, pts [i].y - pts_old [i].y))) {
v += (float)Core.norm(tmpMat);
}
}
if (v < ftol)
{
break;
}
else
{
pts_old = pts;
}
}
}
}
return(pts);
}
