public static PNM ApplyHoughRectanglesDetector(this PNM image, int dmax = 60, int dmin = 13)
{
PNM workImage = PNM.Copy(image).ApplyCannyDetector();
double maxR = Math.Sqrt(Math.Pow(image.Width / 2d, 2) + Math.Pow(image.Height / 2d, 2));
int padding = Math.Max((dmax + (dmax % 2)) / 2, (dmin + (dmin % 2)) / 2);
workImage.raster = Filter.PadWithZeros(workImage.raster, image.Width * 3, image.Height, padding * 3, padding);
workImage.Width += padding * 2;
workImage.Height += padding * 2;
int maxW = (int)Math.Ceiling(maxR);
bool[] circleMask = GenerateCircleMask(dmax, dmin);
int imageSize = image.Width * image.Height;
IEnumerable <Tuple <float, float>[]>[] allRectangles = new IEnumerable <Tuple <float, float>[]> [image.Width];
Parallel.For(0, image.Width, i =>
//for (int i = 0; i < image.Width; i++)
{
allRectangles[i] = Enumerable.Range(0, image.Height).Select(j =>
{
int center = workImage.Width * (padding + (int)j) + padding + i;
var masked = MaskedHoughVote(workImage, center, circleMask, dmax, dmin, padding);
return(masked);
}).SelectMany(x => x).ToArray();
//}
});
return(DrawRectangles(image, allRectangles.SelectMany(x => x).ToArray()));
}
public static PNM ApplyHoughDetector(this PNM image)
{
// greyscale, edge detection, thresholding
PNM workImage = PNM.Copy(image).ApplyPointProcessing(Color.ToGrayscale)
.ApplyGradientEdgesDetection()
.ApplyPointProcessing(Thresholding.Entropy(image));
IEnumerable <Tuple <Point, Point> > lines = GenerateHoughLines(workImage);
return(DrawLines(image, lines));
}
public static PNM ApplyZeroCrossingDetector(this PNM image)
{
// preprare
PNM workImage = PNM.Copy(image);
Filter.Pad(workImage, 4);
// apply loG
Tuple <float[], float[], float[]> LoGRaster = Filter.ApplyConvolutionUnbound(workImage, LoG, 9);
PNM returnImage = new PNM(image.Width, image.Height);
// Apply zero crossing except last row and last column
Parallel.For(0, image.Height - 1, i =>
{
for (int j = 0; j < image.Width - 1; j++)
{
byte r = 0;
byte g = 0;
byte b = 0;
// current index position
int position = i * image.Width + j;
float currentR = LoGRaster.Item1[position];
float neighbourR = LoGRaster.Item1[position + image.Width + 1];
float currentG = LoGRaster.Item2[position];
float neighbourG = LoGRaster.Item2[position + image.Width + 1];
float currentB = LoGRaster.Item3[position];
float neighbourB = LoGRaster.Item3[position + image.Width + 1];
if ((currentR * neighbourR) < 0 && (Math.Abs(currentR) < Math.Abs(neighbourR)))
{
r = 255;
}
if ((currentG * neighbourG) < 0 && (Math.Abs(currentG) < Math.Abs(neighbourG)))
{
g = 255;
}
if ((currentB * neighbourB) < 0 && (Math.Abs(currentB) < Math.Abs(neighbourB)))
{
b = 255;
}
returnImage.SetPixel(position, r, g, b);
}
});
return(returnImage);
}