- OpenCV - GUI
- OpenCV - Writing an Image
- OpenCV - Reading Images
- OpenCV - Storing Images
- OpenCV - Environment
- OpenCV - Overview
- OpenCV - Home
Types of Images
Image Conversion
Drawing Functions
- OpenCV - Adding Text
- OpenCV - Drawing Arrowed Lines
- OpenCV - Drawing Convex Polylines
- OpenCV - Drawing Polylines
- OpenCV - Drawing an Ellipse
- OpenCV - Drawing a Rectangle
- OpenCV - Drawing a Line
- OpenCV - Drawing a Circle
Blur
Filtering
- OpenCV - Image Pyramids
- OpenCV - Morphological Operations
- OpenCV - Erosion
- OpenCV - Dilation
- OpenCV - Filter2D
- OpenCV - SQRBox Filter
- OpenCV - Box Filter
- OpenCV - Bilateral Filter
Thresholding
Sobel Derivatives
Transformation Operations
Camera and Face Detection
Geometric Transformations
Miscellaneous Chapters
OpenCV Useful Resources
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
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 −
Advertisements