Example Image Derivative - Revision history

Peter at 23:03, 17 January 2022

2022-01-17T23:03:01Z

Peter at 18:57, 12 July 2021

2021-07-12T18:57:28Z

← Older revision		Revision as of 18:57, 12 July 2021
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]

	Concepts:		Concepts:
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	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");

Peter at 02:18, 22 December 2020

2020-12-22T02:18:53Z

Peter at 14:27, 15 March 2019

2019-03-15T14:27:56Z

← Older revision		Revision as of 14:27, 15 March 2019
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.23/examples/src/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.33/examples/src/main/java/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]

	Concepts:		Concepts:
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	* Gradient		* Gradient
	* Hessian		* Hessian

	~~Relevant Applets:~~
	* [[Applet Gradient\| Gradient]]

	= Example Code =		= Example Code =
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	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");

Peter at 04:47, 28 March 2016

2016-03-28T04:47:37Z

← Older revision		Revision as of 04:47, 28 March 2016
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.20/examples/src/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.23/examples/src/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]

	Concepts:		Concepts:
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	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 ~~ImageUInt8~~ with ~~ImageSInt16~~ for derivatives also works		// We will use floating point images here, but GrayU8 with GrayS16 for derivatives also works
	~~ImageFloat32~~ grey = new ~~ImageFloat32~~(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
	~~ImageFloat32~~ derivX = new ~~ImageFloat32~~(grey.width,grey.height);		GrayF32 derivX = new GrayF32(grey.width,grey.height);
	~~ImageFloat32~~ derivY = new ~~ImageFloat32~~(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
	~~ImageFloat32~~ derivXX = new ~~ImageFloat32~~(grey.width,grey.height);		GrayF32 derivXX = new GrayF32(grey.width,grey.height);
	~~ImageFloat32~~ derivXY = new ~~ImageFloat32~~(grey.width,grey.height);		GrayF32 derivXY = new GrayF32(grey.width,grey.height);
	~~ImageFloat32~~ derivYY = new ~~ImageFloat32~~(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<~~ImageFloat32~~,~~ImageFloat32~~> derivative =		AnyImageDerivative<GrayF32,GrayF32> derivative =
	GImageDerivativeOps.createAnyDerivatives(DerivativeType.SOBEL, ~~ImageFloat32~~.class, ~~ImageFloat32~~.class);		GImageDerivativeOps.createAnyDerivatives(DerivativeType.SOBEL, GrayF32.class, GrayF32.class);

	// the boolean sequence indicates if its an X or Y derivative		// the boolean sequence indicates if its an X or Y derivative
	derivative.setInput(grey);		derivative.setInput(grey);
	~~ImageFloat32~~ derivXYX = derivative.getDerivative(true, false, true);		GrayF32 derivXYX = derivative.getDerivative(true, false, true);

	// Visualize the results		// Visualize the results

Peter at 15:17, 9 November 2015

2015-11-09T15:17:06Z

← Older revision		Revision as of 15:17, 9 November 2015
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.19/examples/src/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.20/examples/src/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]

	Concepts:		Concepts:
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	public class ExampleImageDerivative {		public class ExampleImageDerivative {
	public static void main(String[] args) {		public static void main(String[] args) {
	BufferedImage input = UtilImageIO.loadImage("~~../data/evaluation/~~simple_objects.jpg");		BufferedImage input = UtilImageIO.loadImage(UtilIO.pathExample("simple_objects.jpg"));

	// We will use floating point images here, but ImageUInt8 with ImageSInt16 for derivatives also works		// We will use floating point images here, but ImageUInt8 with ImageSInt16 for derivatives also works

Peter: Created page with " File:Derivative_input.jpg | Input image File:Derivative_X.jpg | X derivative File:Derivative_XYX.jpg | XYX derivative ..."

2015-09-19T19:55:04Z

Created page with "<center> <gallery widths=240px heights=220px> File:Derivative_input.jpg | Input image File:Derivative_X.jpg | X derivative File:Derivative_XYX.jpg | XYX derivative </gallery>..."

New page

<center>
<gallery widths=240px heights=220px>
File:Derivative_input.jpg | Input image
File:Derivative_X.jpg | X derivative
File:Derivative_XYX.jpg | XYX derivative
</gallery>
</center>

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:
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.19/examples/src/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]

Concepts:
* Image Derivative
* Gradient
* Hessian

Relevant Applets:
* [[Applet Gradient| Gradient]]

= Example Code =

<syntaxhighlight lang="java">
/**
* 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("../data/evaluation/simple_objects.jpg");

// We will use floating point images here, but ImageUInt8 with ImageSInt16 for derivatives also works
ImageFloat32 grey = new ImageFloat32(input.getWidth(),input.getHeight());
ConvertBufferedImage.convertFrom(input, grey);

// First order derivative, also known as the gradient
ImageFloat32 derivX = new ImageFloat32(grey.width,grey.height);
ImageFloat32 derivY = new ImageFloat32(grey.width,grey.height);

GImageDerivativeOps.gradient(DerivativeType.SOBEL, grey, derivX, derivY, BorderType.EXTENDED);

// Second order derivative, also known as the Hessian
ImageFloat32 derivXX = new ImageFloat32(grey.width,grey.height);
ImageFloat32 derivXY = new ImageFloat32(grey.width,grey.height);
ImageFloat32 derivYY = new ImageFloat32(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<ImageFloat32,ImageFloat32> derivative =
GImageDerivativeOps.createAnyDerivatives(DerivativeType.SOBEL, ImageFloat32.class, ImageFloat32.class);

// the boolean sequence indicates if its an X or Y derivative
derivative.setInput(grey);
ImageFloat32 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),"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);
}
}
</syntaxhighlight>

← Older revision		Revision as of 23:03, 17 January 2022
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	</center>		</center>

	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 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:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.40/examples/src/main/java/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]

	Concepts:		Concepts:
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	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.loadImageNotNull(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());		var 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);		var derivX = new GrayF32(grey.width, grey.height);
	~~GrayF32~~ derivY = new GrayF32(grey.width, grey.height);		var 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);		var derivXX = new GrayF32(grey.width, grey.height);
	~~GrayF32~~ derivXY = new GrayF32(grey.width, grey.height);		var derivXY = new GrayF32(grey.width, grey.height);
	~~GrayF32~~ derivYY = new GrayF32(grey.width, grey.height);		var 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);
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	// Visualize the results		// Visualize the results
	~~ListDisplayPanel~~ gui = new ListDisplayPanel();		var 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");

← Older revision		Revision as of 02:18, 22 December 2020
Line 10:		Line 10:

	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.33/examples/src/main/java/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/imageprocessing/ExampleImageDerivative.java ExampleImageDerivative.java]

	Concepts:		Concepts:
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	*/		*/
	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);

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	// 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>