HoughLineTransformation Class

public class HoughLineTransformation

Public Class HoughLineTransformation

// Let's use a standard test image to show how to apply
// the Hough image transform. For this, we can load an
// example image using the TestImages class:
var testImages = new Accord.DataSets.TestImages(basePath);

// We'll use the sudoku test image from OpenCV:
Bitmap image = testImages.GetImage("sudoku.png");

// Convert it to binary and mark the possible lines 
// in white so it can be processed by the transform
var sequence = new FiltersSequence(
    Grayscale.CommonAlgorithms.BT709,
    new NiblackThreshold(),
    new Invert()
);

// Apply the sequence of filters above:
Bitmap binaryImage = sequence.Apply(image);

// Finally, we can create a Line Hough Transform:
var lineTransform = new HoughLineTransformation();

// and then apply it to the binary image:
lineTransform.ProcessImage(binaryImage);

// the output of the transform can be seen using
Bitmap houghLineImage = lineTransform.ToBitmap();

// For visualization purposes, we can either display the result of the
// transform on the screen or save it to disk to be visualized later:
// ImageBox.Show(houghLineImage);
// houghLineImage.Save("hough-output.png");

// Now, let's say we would like to retrieve the lines and use them
// for further processing. First, the lines can be ordered by their
// relative intensity using
HoughLine[] lines = lineTransform.GetLinesByRelativeIntensity(0.7);

// Then, let's plot them on top of the input image. Since we will
// apply many operations to a single image, it is better to first
// convert it to an UnmanagedImage object to avoid having to lock
// the image into memory multiple times.

UnmanagedImage unmanagedImage = UnmanagedImage.FromManagedImage(image);

// Finally, plot them in order:
foreach (HoughLine line in lines)
{
    line.Draw(unmanagedImage, color: Color.Red);
}

// ImageBox.Show(unmanagedImage);
// unmanagedImage.ToManagedImage().Save("hough-lines.png");