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Kirsch compass masks are yet another type of derivative mask which are used for edge detection. This operator is also known as direction mask. In this operator we take one mask and rotate it in all the eight compass directions to get edges of the eight directions.
We are going to use OpenCV function filter2D to apply Kirsch operator to images. It can be found under Imgproc package. Its syntax is given below −
filter2D(src, dst, depth , kernel, anchor, delta, BORDER_DEFAULT );
The function arguments are described below −
| Sr.No. | Argument |
|---|---|
| 1 |
src It is source image. |
| 2 |
dst It is destination image. |
| 3 |
depth It is the depth of dst. A negative value (such as -1) indicates that the depth is the same as the source. |
| 4 |
kernel It is the kernel to be scanned through the image. |
| 5 |
anchor It is the position of the anchor relative to its kernel. The location Point (-1, -1) indicates the center by default. |
| 6 |
delta It is a value to be added to each pixel during the convolution. By default it is 0. |
| 7 |
BORDER_DEFAULT We let this value by default. |
Apart from the filter2D() method, there are other methods provided by the Imgproc class. They are described briefly −
| Sr.No. | Method & Description |
|---|---|
| 1 |
cvtColor(Mat src, Mat dst, int code, int dstCn) It converts an image from one color space to another. |
| 2 |
dilate(Mat src, Mat dst, Mat kernel) It dilates an image by using a specific structuring element. |
| 3 |
equalizeHist(Mat src, Mat dst) It equalizes the histogram of a grayscale image. |
| 4 |
filter2D(Mat src, Mat dst, int depth, Mat kernel, Point anchor, double delta) It convolves an image with the kernel. |
| 5 |
GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX) It blurs an image using a Gaussian filter. |
| 6 |
integral(Mat src, Mat sum) It calculates the integral of an image. |
Example
The following example demonstrates the use of Imgproc class to apply Kirsch operator to an image of Grayscale.
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.highgui.Highgui;
import org.opencv.imgproc.Imgproc;
public class convolution {
public static void main( String[] args ) {
try {
int kernelSize = 9;
System.loadLibrary( Core.NATIVE_LIBRARY_NAME );
Mat source = Highgui.imread("grayscale.jpg", Highgui.CV_LOAD_IMAGE_GRAYSCALE);
Mat destination = new Mat(source.rows(),source.cols(),source.type());
Mat kernel = new Mat(kernelSize,kernelSize, CvType.CV_32F) {
{
put(0,0,-3);
put(0,1,-3);
put(0,2,-3);
put(1,0-3);
put(1,1,0);
put(1,2,-3);
put(2,0,5);
put(2,1,5);
put(2,2,5);
}
};
Imgproc.filter2D(source, destination, -1, kernel);
Highgui.imwrite("output.jpg", destination);
} catch (Exception e) {
System.out.println("Error: " + e.getMessage());
}
}
}
Output
When you execute the given code, the following output is seen −
Original Image
This original image is convolved with the Kirsch operator of East edges, which as given below −
Kirsch East
| -3 | -3 | -3 |
| -3 | 0 | -3 |
| 5 | 5 | 5 |
Convolved Image(Kirsch East)
This original image is convolved with the Kirsch operator of South West edges, which as given below −
Kirsch South West
| 5 | 5 | -3 |
| 5 | 0 | -3 |
| -3 | -3 | -3 |
Convolved Image (Kirsch South West)
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