Difference between revisions of "Example Image Derivative"
From BoofCV
Jump to navigationJump to searchm |
m |
||
Line 10: | Line 10: | ||
Example Code: | Example Code: | ||
* [https://github.com/lessthanoptimal/BoofCV/blob/v0. | * [https://github.com/lessthanoptimal/BoofCV/blob/v0.33/examples/src/main/java/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java] | ||
Concepts: | Concepts: | ||
Line 16: | Line 16: | ||
* Gradient | * Gradient | ||
* Hessian | * Hessian | ||
= Example Code = | = Example Code = | ||
Line 63: | Line 60: | ||
gui.addImage(VisualizeImageData.colorizeSign(derivY, null, -1),"Sobel Y"); | gui.addImage(VisualizeImageData.colorizeSign(derivY, null, -1),"Sobel Y"); | ||
// Use colors to show X and Y derivatives in one image. Looks pretty. | // Use colors to show X and Y derivatives in one image. Looks pretty. | ||
gui.addImage(VisualizeImageData.colorizeGradient(derivX, derivY, -1),"Sobel X and Y"); | gui.addImage(VisualizeImageData.colorizeGradient(derivX, derivY, -1, null),"Sobel X and Y"); | ||
gui.addImage(VisualizeImageData.colorizeSign(derivXX, null,-1),"Sobel XX"); | gui.addImage(VisualizeImageData.colorizeSign(derivXX, null,-1),"Sobel XX"); | ||
gui.addImage(VisualizeImageData.colorizeSign(derivXY, null,-1),"Sobel XY"); | gui.addImage(VisualizeImageData.colorizeSign(derivXY, null,-1),"Sobel XY"); |
Revision as of 06:27, 15 March 2019
Example of how to compute different image derivatives. The gradient (1st order derivative) is probably the important image derivative and is used as a first step when extracting many types of image features. The code below shows how gradient, Hessian (2nd order), and arbitrary image derivatives can be computed.
Example Code:
Concepts:
- Image Derivative
- Gradient
- Hessian
Example Code
/**
* Example showing how to compute different image derivatives using built in functions.
*
* @author Peter Abeles
*/
public class ExampleImageDerivative {
public static void main(String[] args) {
BufferedImage input = UtilImageIO.loadImage(UtilIO.pathExample("simple_objects.jpg"));
// We will use floating point images here, but GrayU8 with GrayS16 for derivatives also works
GrayF32 grey = new GrayF32(input.getWidth(),input.getHeight());
ConvertBufferedImage.convertFrom(input, grey);
// First order derivative, also known as the gradient
GrayF32 derivX = new GrayF32(grey.width,grey.height);
GrayF32 derivY = new GrayF32(grey.width,grey.height);
GImageDerivativeOps.gradient(DerivativeType.SOBEL, grey, derivX, derivY, BorderType.EXTENDED);
// Second order derivative, also known as the Hessian
GrayF32 derivXX = new GrayF32(grey.width,grey.height);
GrayF32 derivXY = new GrayF32(grey.width,grey.height);
GrayF32 derivYY = new GrayF32(grey.width,grey.height);
GImageDerivativeOps.hessian(DerivativeType.SOBEL, derivX, derivY, derivXX, derivXY, derivYY, BorderType.EXTENDED);
// There's also a built in function for computing arbitrary derivatives
AnyImageDerivative<GrayF32,GrayF32> derivative =
GImageDerivativeOps.createAnyDerivatives(DerivativeType.SOBEL, GrayF32.class, GrayF32.class);
// the boolean sequence indicates if its an X or Y derivative
derivative.setInput(grey);
GrayF32 derivXYX = derivative.getDerivative(true, false, true);
// Visualize the results
ListDisplayPanel gui = new ListDisplayPanel();
gui.addImage(ConvertBufferedImage.convertTo(grey,null),"Input Grey");
gui.addImage(VisualizeImageData.colorizeSign(derivX, null, -1),"Sobel X");
gui.addImage(VisualizeImageData.colorizeSign(derivY, null, -1),"Sobel Y");
// Use colors to show X and Y derivatives in one image. Looks pretty.
gui.addImage(VisualizeImageData.colorizeGradient(derivX, derivY, -1, null),"Sobel X and Y");
gui.addImage(VisualizeImageData.colorizeSign(derivXX, null,-1),"Sobel XX");
gui.addImage(VisualizeImageData.colorizeSign(derivXY, null,-1),"Sobel XY");
gui.addImage(VisualizeImageData.colorizeSign(derivYY, null,-1),"Sobel YY");
gui.addImage(VisualizeImageData.colorizeSign(derivXYX, null,-1),"Sobel XYX");
ShowImages.showWindow(gui,"Image Derivatives",true);
}
}