public PointF NormalizedCentroid(Segment s)
{
//normalize the points (relative to the image center (0,0))
PointF[] normalizedPoints = s.points.Select<Point, PointF>(p => new PointF(-0.5f + (float)p.X / imageWidth, -0.5f + (float)p.Y / imageHeight)).ToArray<PointF>();
//Add the relative centroid
float cX = 0;
float cY = 0;
foreach (PointF p in normalizedPoints)
{
cX += p.X;
cY += p.Y;
}
cX /= normalizedPoints.Count();
cY /= normalizedPoints.Count();
return new PointF(cX, cY);
}
public PointF RandomNormalizedInteriorPoint(Segment s)
{
PointF[] normalizedPoints = s.points.Select<Point, PointF>(p => new PointF(-0.5f + (float)p.X / imageWidth, -0.5f + (float)p.Y / imageHeight)).ToArray<PointF>();
Random r = new Random();
int randIndex = r.Next(normalizedPoints.Length);
return normalizedPoints[randIndex];
}
public void ComputeFeatures(Segment s)
{
//Add the relative size
NamedFeature f = new NamedFeature("RelativeSize");
f.values.Add(s.points.Count() / (double)(imageWidth * imageHeight));
s.features.Add(f);
// Relative centroid
PointF c = NormalizedCentroid(s);
s.features.Add(new NamedFeature("RelativeCentroid", new List<double>{c.X, c.Y}));
// One interior point
PointF np = RandomNormalizedInteriorPoint(s);
s.features.Add(new NamedFeature("OneInteriorPoint", new List<double> { np.X, np.Y }));
//Radial distance
s.features.Add(new NamedFeature("RadialDistance", new List<double>{Math.Sqrt(c.X*c.X+c.Y*c.Y)}));
//Normalized Discrete Compactness http://www.m-hikari.com/imf-password2009/25-28-2009/bribiescaIMF25-28-2009.pdf
//Find the segment id
Point sp = s.points.First();
int sidx = assignments[sp.X, sp.Y];
//count number of perimeter edges
int perimeter = 0;
foreach (Point p in s.points)
{
for (int i = -1; i <= 1; i++)
{
for (int j = -1; j <= 1; j++)
{
if (Math.Abs(i) == Math.Abs(j))
continue;
if (Util.InBounds(p.X + i, p.Y + j, imageWidth, imageHeight) && assignments[p.X + i, p.Y + j] != sidx)
perimeter++;
else if (!Util.InBounds(p.X + i, p.Y + j, imageWidth, imageHeight)) //edge pixels should be considered perimeter too
perimeter++;
}
}
}
int n = s.points.Count();
double CD = (4.0 * n - perimeter) / 2;
double CDmin = n - 1;
double CDmax = (4 * n - 4 * Math.Sqrt(n)) / 2;
double CDN = (CD - CDmin) / Math.Max(1,(CDmax - CDmin));
s.features.Add(new NamedFeature("NormalizedDiscreteCompactness", new List<double> { CDN }));
//Add elongation (width/length normalized between 0-square to 1-long http://hal.archives-ouvertes.fr/docs/00/44/60/37/PDF/ARS-Journal-SurveyPatternRecognition.pdf
PointF[] points = s.points.Select<Point, PointF>(p => new PointF(p.X, p.Y)).ToArray<PointF>();
Emgu.CV.Structure.MCvBox2D box = Emgu.CV.PointCollection.MinAreaRect(points);
PointF[] vertices = box.GetVertices();
double elongation = 1 - Math.Min(box.size.Width + 1, box.size.Height + 1) / Math.Max(box.size.Width + 1, box.size.Height + 1);
s.features.Add(new NamedFeature("Elongation", new List<double>{elongation}));
//Add Hu shape moments, invariant to translation, scale, and rotation (not sure what each measure refers to intuitively though, or if there is an intuitive analog)
//They may also do badly on noisy data however. See: http://hal.archives-ouvertes.fr/docs/00/44/60/37/PDF/ARS-Journal-SurveyPatternRecognition.pdf (called Invariant Moments)
Bitmap regionBitmap = new Bitmap(imageWidth, imageHeight);
Graphics g = Graphics.FromImage(regionBitmap);
g.FillRectangle(new SolidBrush(Color.Black), 0, 0, imageWidth, imageHeight);
foreach (Point p in s.points)
{
regionBitmap.SetPixel(p.X, p.Y, Color.White);
}
Emgu.CV.Image<Gray, byte> region = new Emgu.CV.Image<Gray, byte>(regionBitmap);
MCvMoments moment = region.GetMoments(true);
MCvHuMoments hu = moment.GetHuMoment();
s.features.Add(new NamedFeature("HuMoments", new List<double> {hu.hu1, hu.hu2, hu.hu3,hu.hu4,hu.hu5, hu.hu6, hu.hu7 }));
region.Dispose();
regionBitmap.Dispose();
}
