public async Task <IMAGE_LABEL_INFO> ImproveLabel(FixedSizeImage?iActivated,
Image <Pixel>?img,
Image <byte>?gray,
IMAGE_LABEL_INFO label)
{
var ctx = img.Clone();
Graphics <Pixel>?graphics = null;
if (iActivated != null)
{
graphics = MoyskleyTech.ImageProcessing.Image.Graphics.FromImage(ctx);
Application.Invoke((_, _1) =>
{
iActivated.Image = ctx;
});
}
//ctx.Width = gray.Width;
//ctx.Height = gray.Height;
graphics?.Clear(Pixels.White);
graphics?.DrawImage(img, 0, 0);
List <RectangleF> proposedBoxes = new List <RectangleF>();
AttentionMapAnalizer c = new AttentionMapAnalizer();
//Foreach config, keep proposed boxes
foreach (var(p, wz) in StandardFunctions.ListXMeansModes(img))
{
proposedBoxes.AddRange(await c.Cluster(gray, graphics, p, wz));
if (iActivated != null)
{
Application.Invoke((_, _1) =>
{
iActivated.Image = ctx;
});
}
if (Program.verbose)
{
Console.WriteLine("{2} MODE {0} {1}", p, wz, label.name);
}
}
label = label.Clone();
c.AdaptLabel(proposedBoxes, label, .9, .9, .25);
if (iActivated != null)
{
Application.Invoke((_, _1) =>
{
iActivated.Image = ctx;
});
}
return(label);
}
/// <summary>
/// Separate the current grayscale attention map into clusters
/// </summary>
/// <param name="image"></param>
/// <returns></returns>
public List <Cluster <Point3> > SeparateImage(Image <byte> image)
{
Image <double> img = image.ConvertUsing <double>((px) => px);
var lst = new List <List <Point3> >();
//Converts to 3D points
var pts = (from x in Enumerable.Range(0, image.Width)
from y in Enumerable.Range(0, image.Height)
where img[x, y] > MIN_ATTENTION_PERBYTE
select new Point3(x, y, img[x, y])).ToList();
Kmeans <Point3> .GetInitialMeans = StandardFunctions.GetRandomMeansFunction(img);
Xmeans <Point3> xmeans = new Xmeans <Point3>(pts);
var clusters = xmeans.Calculation();
return(clusters);
}
/// <summary>
/// Initialize the analyzer
/// </summary>
public void Init()
{
Kmeans <Point3> .GetWitnesses = (cPoints) =>
{
var sumX = cPoints.Sum(x => x.X);
var sumY = cPoints.Sum(x => x.Y);
var sumZ = cPoints.Sum(x => x.Z);
var sumX2 = cPoints.Sum(x => x.X * x.X);
var sumY2 = cPoints.Sum(x => x.Y * x.Y);
var sumZ2 = cPoints.Sum(x => x.Z * x.Z);
var squX = sumX2 - (sumX * sumX / (double)cPoints.Count());
var squY = sumY2 - (sumY * sumY / (double)cPoints.Count());
var squZ = sumZ2 - (sumZ * sumZ / (double)cPoints.Count());
return(new double[] { squX, squY, squZ });
};
Kmeans <Point3> .GetDistance = StandardFunctions.Adapt <IVectorizable, IVectorizable, Point3, Point3, double, double>(
StandardFunctions.MinkowskiDistance(3, new double[] { 1, 1, 1 }));
Kmeans <Point3> .GetMean = StandardFunctions.GetMeanPt3;
}