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/v0.37/examples/src/main/java/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]  | ||
Concepts:  | Concepts:  | ||
| Line 26: | Line 26: | ||
  */  |   */  | ||
public class ExampleImageDerivative {  | public class ExampleImageDerivative {  | ||
	public static void main(String[] args) {  | 	public static void main( String[] args ) {  | ||
		BufferedImage input = UtilImageIO.loadImage(UtilIO.pathExample("simple_objects.jpg"));  | 		BufferedImage input = UtilImageIO.loadImage(UtilIO.pathExample("simple_objects.jpg"));  | ||
		// We will use floating point images here, but GrayU8 with GrayS16 for derivatives also works  | 		// We will use floating point images here, but GrayU8 with GrayS16 for derivatives also works  | ||
		GrayF32 grey = new GrayF32(input.getWidth(),input.getHeight());  | 		GrayF32 grey = new GrayF32(input.getWidth(), input.getHeight());  | ||
		ConvertBufferedImage.convertFrom(input, grey);  | 		ConvertBufferedImage.convertFrom(input, grey);  | ||
		// First order derivative, also known as the gradient  | 		// First order derivative, also known as the gradient  | ||
		GrayF32 derivX = new GrayF32(grey.width,grey.height);  | 		GrayF32 derivX = new GrayF32(grey.width, grey.height);  | ||
		GrayF32 derivY = new GrayF32(grey.width,grey.height);  | 		GrayF32 derivY = new GrayF32(grey.width, grey.height);  | ||
		GImageDerivativeOps.gradient(DerivativeType.SOBEL, grey, derivX, derivY, BorderType.EXTENDED);  | 		GImageDerivativeOps.gradient(DerivativeType.SOBEL, grey, derivX, derivY, BorderType.EXTENDED);  | ||
		// Second order derivative, also known as the Hessian  | 		// Second order derivative, also known as the Hessian  | ||
		GrayF32 derivXX = new GrayF32(grey.width,grey.height);  | 		GrayF32 derivXX = new GrayF32(grey.width, grey.height);  | ||
		GrayF32 derivXY = new GrayF32(grey.width,grey.height);  | 		GrayF32 derivXY = new GrayF32(grey.width, grey.height);  | ||
		GrayF32 derivYY = 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);  | 		GImageDerivativeOps.hessian(DerivativeType.SOBEL, derivX, derivY, derivXX, derivXY, derivYY, BorderType.EXTENDED);  | ||
		// There's also a built in function for computing arbitrary derivatives  | 		// There's also a built in function for computing arbitrary derivatives  | ||
		AnyImageDerivative<GrayF32,GrayF32> derivative =  | 		AnyImageDerivative<GrayF32, GrayF32> derivative =  | ||
				GImageDerivativeOps.createAnyDerivatives(DerivativeType.SOBEL, GrayF32.class, GrayF32.class);  | 				GImageDerivativeOps.createAnyDerivatives(DerivativeType.SOBEL, GrayF32.class, GrayF32.class);  | ||
| Line 56: | Line 56: | ||
		// Visualize the results  | 		// Visualize the results  | ||
		ListDisplayPanel gui = new ListDisplayPanel();  | 		ListDisplayPanel gui = new ListDisplayPanel();  | ||
		gui.addImage(ConvertBufferedImage.convertTo(grey,null),"Input Grey");  | 		gui.addImage(ConvertBufferedImage.convertTo(grey, null), "Input Grey");  | ||
		gui.addImage(VisualizeImageData.colorizeSign(derivX, null, -1),"Sobel X");  | 		gui.addImage(VisualizeImageData.colorizeSign(derivX, null, -1), "Sobel X");  | ||
		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, null),"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");  | ||
		gui.addImage(VisualizeImageData.colorizeSign(derivYY, null,-1),"Sobel YY");  | 		gui.addImage(VisualizeImageData.colorizeSign(derivYY, null, -1), "Sobel YY");  | ||
		gui.addImage(VisualizeImageData.colorizeSign(derivXYX, null,-1),"Sobel XYX");  | 		gui.addImage(VisualizeImageData.colorizeSign(derivXYX, null, -1), "Sobel XYX");  | ||
		ShowImages.showWindow(gui,"Image Derivatives",true);  | 		ShowImages.showWindow(gui, "Image Derivatives", true);  | ||
	}  | 	}  | ||
}  | }  | ||
</syntaxhighlight>  | </syntaxhighlight>  | ||
Revision as of 18:18, 21 December 2020
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);
	}
}