
Convolution is a mathematical operation on two functions f and g. The function f and g in this case are images, since an image is also a two dimensional function.
In order to perform convolution on an image, following steps are taken −
We use OpenCV function filter2D to apply convolution 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 & Description |
|---|---|
| 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. |
The following example demonstrates the use of Imgproc class to perform convolution on 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 = 3;
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(0,0,0);
put(0,1,0);
put(0,2,0);
put(1,0,0);
put(1,1,1);
put(1,2,0);
put(2,0,0);
put(2,1,0);
put(2,2,0);
}
};
Imgproc.filter2D(source, destination, -1, kernel);
Highgui.imwrite("original.jpg", destination);
} catch (Exception e) {
System.out.println("Error:" + e.getMessage());
}
}
}
In this example we convolve our image with the following filter(kernel). This filter results in producing original image as it is −
| 0 | 0 | 0 |
| 0 | 1 | 0 |
| 0 | 0 | 0 |