public List<Point[]> detect (Mat im, float scaleFactor, int minNeighbours, OpenCVForUnity.Size minSize)
{
//convert image to greyscale
Mat gray = null;
if (im.channels () == 1) {
gray = im;
} else {
gray = new Mat ();
Imgproc.cvtColor (im, gray, Imgproc.COLOR_RGBA2GRAY);
}
using (Mat equalizeHistMat = new Mat ())
using (MatOfRect faces = new MatOfRect ()) {
Imgproc.equalizeHist (gray, equalizeHistMat);
detector.detectMultiScale (equalizeHistMat, faces, scaleFactor, minNeighbours, 0
| Objdetect.CASCADE_FIND_BIGGEST_OBJECT
| Objdetect.CASCADE_SCALE_IMAGE, minSize, new Size ());
if (faces.rows () < 1) {
return new List<Point[]> ();
}
return convertMatOfRectToPoints (faces);
}
}
public Point[] calc_peaks (Mat im,
Point[] points,
OpenCVForUnity.Size ssize)
{
int n = points.Length;
// Debug.Log ("n == int(patches.size()) " + patches.Count);
using (Mat pt = (new MatOfPoint2f (points)).reshape (1, 2 * n))
using (Mat S = calc_simil (pt))
using (Mat Si = inv_simil (S)) {
Point[] pts = apply_simil (Si, points);
for (int i = 0; i < n; i++) {
OpenCVForUnity.Size wsize = new OpenCVForUnity.Size (ssize.width + patches [i].patch_size ().width, ssize.height + patches [i].patch_size ().height);
using (Mat A = new Mat (2, 3, CvType.CV_32F)) {
A.put (0, 0, S.get (0, 0) [0]);
A.put (0, 1, S.get (0, 1) [0]);
A.put (1, 0, S.get (1, 0) [0]);
A.put (1, 1, S.get (1, 1) [0]);
A.put (0, 2, pt.get (2 * i, 0) [0] -
(A.get (0, 0) [0] * (wsize.width - 1) / 2 + A.get (0, 1) [0] * (wsize.height - 1) / 2));
A.put (1, 2, pt.get (2 * i + 1, 0) [0] -
(A.get (1, 0) [0] * (wsize.width - 1) / 2 + A.get (1, 1) [0] * (wsize.height - 1) / 2));
using (Mat I = new Mat ()) {
Imgproc.warpAffine (im, I, A, wsize, Imgproc.INTER_LINEAR + Imgproc.WARP_INVERSE_MAP);
using (Mat R = patches [i].calc_response (I, false)) {
Core.MinMaxLocResult minMaxLocResult = Core.minMaxLoc (R);
pts [i].x = pts [i].x + minMaxLocResult.maxLoc.x - 0.5 * ssize.width;
pts [i].y = pts [i].y + minMaxLocResult.maxLoc.y - 0.5 * ssize.height;
}
}
}
}
return apply_simil (S, pts);
}
}
public TrackedObject (OpenCVForUnity.Rect rect)
{
lastPositions = new PositionsVector ();
numDetectedFrames = 1;
numFramesNotDetected = 0;
lastPositions.Add (rect);
_id = getNextId ();
id = _id;
}
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;
}
