Difference between revisions of "Example Convolution"
From BoofCV
Jump to navigationJump to searchm |
m |
||
Line 2: | Line 2: | ||
Example Code: | Example Code: | ||
* [https://github.com/lessthanoptimal/BoofCV/blob/v0. | * [https://github.com/lessthanoptimal/BoofCV/blob/v0.40/examples/src/main/java/boofcv/examples/imageprocessing/ExampleConvolution.java ExampleConvolution.java] | ||
Concepts: | Concepts: | ||
Line 24: | Line 24: | ||
public static void main( String[] args ) { | public static void main( String[] args ) { | ||
BufferedImage image = UtilImageIO. | BufferedImage image = UtilImageIO.loadImageNotNull(UtilIO.pathExample("sunflowers.jpg")); | ||
GrayU8 gray = ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class); | GrayU8 gray = ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class); |
Latest revision as of 14:59, 17 January 2022
Example of how to convolve 1D and 2D convolution kernels across an image. Besides providing the kernel, how the border is handled needs to be specified. A normalized kernel will renormalize the
Example Code:
Concepts:
- Convolution
- Spacial filtering
Relevant Examples:
Example Code
/**
* Several examples demonstrating convolution.
*
* @author Peter Abeles
*/
public class ExampleConvolution {
private static final ListDisplayPanel panel = new ListDisplayPanel();
public static void main( String[] args ) {
BufferedImage image = UtilImageIO.loadImageNotNull(UtilIO.pathExample("sunflowers.jpg"));
GrayU8 gray = ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class);
convolve1D(gray);
convolve2D(gray);
normalize2D(gray);
ShowImages.showWindow(panel, "Convolution Examples", true);
}
/**
* Convolves a 1D kernel horizontally and vertically
*/
private static void convolve1D( GrayU8 gray ) {
var kernel = new Kernel1D_S32(2);
kernel.offset = 1; // specify the kernel's origin
kernel.data[0] = 1;
kernel.data[1] = -1;
var output = new GrayS16(gray.width, gray.height);
GConvolveImageOps.horizontal(kernel, gray, output, BorderType.EXTENDED);
panel.addImage(VisualizeImageData.standard(output, null), "1D Horizontal");
GConvolveImageOps.vertical(kernel, gray, output, BorderType.EXTENDED);
panel.addImage(VisualizeImageData.standard(output, null), "1D Vertical");
}
/**
* Convolves a 2D kernel
*/
private static void convolve2D( GrayU8 gray ) {
// By default 2D kernels will be centered around width/2
var kernel = new Kernel2D_S32(3);
kernel.set(1, 0, 2);
kernel.set(2, 1, 2);
kernel.set(0, 1, -2);
kernel.set(1, 2, -2);
// Output needs to handle the increased domain after convolution. Can't be 8bit
var output = new GrayS16(gray.width, gray.height);
GConvolveImageOps.convolve(kernel, gray, output, BorderType.EXTENDED);
panel.addImage(VisualizeImageData.standard(output, null), "2D Kernel");
}
/**
* Convolves a 2D normalized kernel. This kernel is divided by its sum after computation.
*/
private static void normalize2D( GrayU8 gray ) {
// Create a Gaussian kernel with radius of 3
Kernel2D_S32 kernel = FactoryKernelGaussian.gaussian2D(GrayU8.class, -1, 3);
// Note that there is a more efficient way to compute this convolution since it is a separable kernel
// just use BlurImageOps instead.
// Since it's normalized it can be saved inside an 8bit image
var output = new GrayU8(gray.width, gray.height);
GConvolveImageOps.convolveNormalized(kernel, gray, output);
panel.addImage(VisualizeImageData.standard(output, null), "2D Normalized Kernel");
}
}