/// <summary>
/// Searches the image for the good features to track.
/// <para>For each location a Hessian matrix is made and min eig-value is compared against threshold.</para>
/// </summary>
/// <param name="image">Image.</param>
/// <param name="winSize">Window size.</param>
/// <param name="minEigVal">Minimum eigen value.</param>
/// <param name="minimalDistance">Minimum distance from two features.</param>
/// <returns>List of locations that have eigen value larger than <paramref name="minEigVal"/>.</returns>
public static List <Point> GoodFeaturesToTrack(this Gray <float>[,] image, int winSize = 10, float minEigVal = 0.3f, float minimalDistance = 3)
{
var strengthImg = image.CopyBlank();
var Dx = image.Sobel(1, 0, 3);
var Dy = image.Sobel(0, 1, 3);
var Dxx = Dx.MulFloat(Dx).MakeIntegral();
var Dxy = Dx.MulFloat(Dy).MakeIntegral();
var Dyy = Dy.MulFloat(Dy).MakeIntegral();
goodFeaturesToTrack(Dxx, Dxy, Dyy,
winSize, minEigVal, strengthImg);
var filteredStrengthImg = strengthImg.SupressNonMaxima();
//var filteredStrengthImg = strengthImg;
IList <float> values;
var locations = filteredStrengthImg.FindNonZero(out values);
var sortedFeatures = locations.Zip(values, (f, s) => new { f, s })
.OrderByDescending(x => x.s)
.Select(x => x.f)
.ToList();
sortedFeatures = sortedFeatures.EnforceMinimalDistance(minimalDistance);
return(sortedFeatures);
}
