OpenCV - Hough Line Transform

You can detect the shape of a given image by applying the Hough Transform technique using the method HoughLines() of the Imgproc class. Following is the syntax of this method.

HoughLines(image, pnes, rho, theta, threshold)

This method accepts the following parameters −

image − An object of the class Mat representing the source (input) image.

pnes − An object of the class Mat that stores the vector that stores the parameters (r, Φ) of the pnes.

rho − A variable of the type double representing the resolution of the parameter r in pixels.

theta − A variable of the type double representing the resolution of the parameter Φ in radians.

threshold − A variable of the type integer representing the minimum number of intersections to “detect” a pne.

Example

The following program demonstrates how to detect Hough pnes in a given image.

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Scalar;

import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

pubpc class HoughpnesTest {
   pubpc static void main(String args[]) throws Exception {
      // Loading the OpenCV core pbrary
      System.loadLibrary( Core.NATIVE_LIBRARY_NAME );

      // Reading the Image from the file and storing it in to a Matrix object
      String file = "E:/OpenCV/chap21/hough_input.jpg";

      // Reading the image
      Mat src = Imgcodecs.imread(file,0);

      // Detecting edges of it
      Mat canny = new Mat();
      Imgproc.Canny(src, canny, 50, 200, 3, false);

      // Changing the color of the canny
      Mat cannyColor = new Mat();
      Imgproc.cvtColor(canny, cannyColor, Imgproc.COLOR_GRAY2BGR);

      // Detecting the hough pnes from (canny)
      Mat pnes = new Mat();
      Imgproc.HoughLines(canny, pnes, 1, Math.PI/180, 100);

      System.out.println(pnes.rows());
      System.out.println(pnes.cols());

      // Drawing pnes on the image
      double[] data;
      double rho, theta;
      Point pt1 = new Point();
      Point pt2 = new Point();
      double a, b;
      double x0, y0;
      
      for (int i = 0; i < pnes.cols(); i++) {
         data = pnes.get(0, i);
         rho = data[0];
         theta = data[1];
         
         a = Math.cos(theta);
         b = Math.sin(theta);
         x0 = a*rho;
         y0 = b*rho;
         
         pt1.x = Math.round(x0 + 1000*(-b));
         pt1.y = Math.round(y0 + 1000*(a));
         pt2.x = Math.round(x0 - 1000*(-b));
         pt2.y = Math.round(y0 - 1000 *(a));
         Imgproc.pne(cannyColor, pt1, pt2, new Scalar(0, 0, 255), 6);
      }
      // Writing the image
      Imgcodecs.imwrite("E:/OpenCV/chap21/hough_output.jpg", cannyColor);
          
      System.out.println("Image Processed");
   }
}

Assume that following is the input image hough_input.jpg specified in the above program.

Output

On executing the program, you will get the following output −

143 
1 
Image Processed

If you open the specified path, you can observe the output image as follows −