Example Fiducial Random Dots - Revision history

Peter at 22:44, 17 January 2022

2022-01-17T22:44:20Z

← Older revision		Revision as of 22:44, 17 January 2022
Line 7:		Line 7:
	Uchiya Markers are composed of random dots and work by computing a hashing function based on the relative orientation of neighboring dots. One advantage to these markers is that they are resistant to conclusion. Many of the dots can be covered and the ID and pose of the marker can still be recovered! Square based markers become unrecognizable if the black square is blocked or damaged. BoofCV also provides tools for generating random marker PDF documents that can be printed.		Uchiya Markers are composed of random dots and work by computing a hashing function based on the relative orientation of neighboring dots. One advantage to these markers is that they are resistant to conclusion. Many of the dots can be covered and the ID and pose of the marker can still be recovered! Square based markers become unrecognizable if the black square is blocked or damaged. BoofCV also provides tools for generating random marker PDF documents that can be printed.

	Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/fiducial/ExampleFiducialRandomDots.java ExampleFiducialRandomDots.java]		Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.40/examples/src/main/java/boofcv/examples/fiducial/ExampleFiducialRandomDots.java ExampleFiducialRandomDots.java]

	Concepts:		Concepts:
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	// The only parameter that you have to set is markerLength. It's used to compute bounding		// The only parameter that you have to set is markerLength. It's used to compute bounding
	// boxes and similar. If you don't know what the width is just set it to 1.0		// boxes and similar. If you don't know what the width is just set it to 1.0
	~~ConfigUchiyaMarker~~ config = new ConfigUchiyaMarker();		var config = new ConfigUchiyaMarker();
	config.markerWidth = defs.markerWidth;		config.markerWidth = defs.markerWidth;
	config.markerHeight = defs.markerHeight;		config.markerHeight = defs.markerHeight;
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	// It's now ready to start processing images. Let's load an image		// It's now ready to start processing images. Let's load an image
	BufferedImage image = UtilImageIO.~~loadImage~~(UtilIO.pathExample("fiducial/random_dots/image02.jpg"));		BufferedImage image = UtilImageIO.loadImageNotNull(UtilIO.pathExample("fiducial/random_dots/image02.jpg"));
	GrayU8 gray = ConvertBufferedImage.convertFrom(image, false, ImageType.SB_U8);		GrayU8 gray = ConvertBufferedImage.convertFrom(image, false, ImageType.SB_U8);

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	// Time to visualize the results		// Time to visualize the results
	Graphics2D g2 = image.createGraphics();		Graphics2D g2 = image.createGraphics();
	~~Se3_F64~~ targetToSensor = new Se3_F64();		var targetToSensor = new Se3_F64();
	~~Polygon2D_F64~~ bounds = new Polygon2D_F64();		var bounds = new Polygon2D_F64();
	~~Point2D_F64~~ center = new Point2D_F64();		var center = new Point2D_F64();
	for (int i = 0; i < detector.totalFound(); i++) {		for (int i = 0; i < detector.totalFound(); i++) {
	detector.getBounds(i, bounds);		detector.getBounds(i, bounds);

Peter at 18:04, 12 July 2021

2021-07-12T18:04:35Z

← Older revision		Revision as of 18:04, 12 July 2021
Line 7:		Line 7:
	Uchiya Markers are composed of random dots and work by computing a hashing function based on the relative orientation of neighboring dots. One advantage to these markers is that they are resistant to conclusion. Many of the dots can be covered and the ID and pose of the marker can still be recovered! Square based markers become unrecognizable if the black square is blocked or damaged. BoofCV also provides tools for generating random marker PDF documents that can be printed.		Uchiya Markers are composed of random dots and work by computing a hashing function based on the relative orientation of neighboring dots. One advantage to these markers is that they are resistant to conclusion. Many of the dots can be covered and the ID and pose of the marker can still be recovered! Square based markers become unrecognizable if the black square is blocked or damaged. BoofCV also provides tools for generating random marker PDF documents that can be printed.

	Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/fiducial/ExampleFiducialRandomDots.java ExampleFiducialRandomDots.java]		Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/fiducial/ExampleFiducialRandomDots.java ExampleFiducialRandomDots.java]

	Concepts:		Concepts:

Peter at 02:02, 22 December 2020

2020-12-22T02:02:18Z

← Older revision		Revision as of 02:02, 22 December 2020
Line 7:		Line 7:
	Uchiya Markers are composed of random dots and work by computing a hashing function based on the relative orientation of neighboring dots. One advantage to these markers is that they are resistant to conclusion. Many of the dots can be covered and the ID and pose of the marker can still be recovered! Square based markers become unrecognizable if the black square is blocked or damaged. BoofCV also provides tools for generating random marker PDF documents that can be printed.		Uchiya Markers are composed of random dots and work by computing a hashing function based on the relative orientation of neighboring dots. One advantage to these markers is that they are resistant to conclusion. Many of the dots can be covered and the ID and pose of the marker can still be recovered! Square based markers become unrecognizable if the black square is blocked or damaged. BoofCV also provides tools for generating random marker PDF documents that can be printed.

	Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.36/examples/src/main/java/boofcv/examples/fiducial/ExampleFiducialRandomDots.java ExampleFiducialRandomDots.java]		Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/fiducial/ExampleFiducialRandomDots.java ExampleFiducialRandomDots.java]

	Concepts:		Concepts:
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	*/		*/
	public class ExampleFiducialRandomDots {		public class ExampleFiducialRandomDots {
	public static void main(String[] args) {		public static void main( String[] args ) {
	// The definitions file specifies where dots are on each marker and other bits of meta data		// The definitions file specifies where dots are on each marker and other bits of meta data
	RandomDotDefinition defs = FiducialIO.loadRandomDotYaml(		RandomDotDefinition defs = FiducialIO.loadRandomDotYaml(
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	// boxes and similar. If you don't know what the width is just set it to 1.0		// boxes and similar. If you don't know what the width is just set it to 1.0
	ConfigUchiyaMarker config = new ConfigUchiyaMarker();		ConfigUchiyaMarker config = new ConfigUchiyaMarker();
	config.~~markerLength~~ = defs.markerWidth;		config.markerWidth = defs.markerWidth;
			config.markerHeight = defs.markerHeight;

	Uchiya_to_FiducialDetector<GrayU8> detector = FactoryFiducial.randomDots(config, GrayU8.class);		Uchiya_to_FiducialDetector<GrayU8> detector = FactoryFiducial.randomDots(config, GrayU8.class);

	// Load / learn all the markers. This can take a few seconds if there are a lot of markers		// Load / learn all the markers. This can take a few seconds if there are a lot of markers
	for( List<Point2D_F64> marker : defs.markers ) {		for (List<Point2D_F64> marker : defs.markers) {
	detector.addMarker(marker);		detector.addMarker(marker);
	}		}
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	CameraPinholeBrown intrinsic = CalibrationIO.load(		CameraPinholeBrown intrinsic = CalibrationIO.load(
	UtilIO.fileExample("fiducial/random_dots/intrinsic.yaml"));		UtilIO.fileExample("fiducial/random_dots/intrinsic.yaml"));
	detector.setLensDistortion(LensDistortionFactory.narrow(intrinsic),intrinsic.width,intrinsic.height);		detector.setLensDistortion(LensDistortionFactory.narrow(intrinsic), intrinsic.width, intrinsic.height);

	// It's now ready to start processing images. Let's load an image		// It's now ready to start processing images. Let's load an image
	BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("fiducial/random_dots/image02.jpg"));		BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("fiducial/random_dots/image02.jpg"));
	GrayU8 gray = ConvertBufferedImage.convertFrom(image,false, ImageType.SB_U8);		GrayU8 gray = ConvertBufferedImage.convertFrom(image, false, ImageType.SB_U8);

	detector.detect(gray);		detector.detect(gray);
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	detector.getCenter(i, center);		detector.getCenter(i, center);

	g2.setColor(new Color(50,50,255));		g2.setColor(new Color(50, 50, 255));
	g2.setStroke(new BasicStroke(10));		g2.setStroke(new BasicStroke(10));
	VisualizeShapes.drawPolygon(bounds,true,1.0,g2);		VisualizeShapes.drawPolygon(bounds, true, 1.0, g2);
	VisualizeFiducial.drawLabel(center, "" + detector.getId(i), g2);		VisualizeFiducial.drawLabel(center, "" + detector.getId(i), g2);

	System.out.println("Target ID = "+detector.getId(i));		System.out.println("Target ID = " + detector.getId(i));

	if( detector.is3D() ) {		if (detector.is3D()) {
	detector.getFiducialToCamera(i, targetToSensor);		detector.getFiducialToCamera(i, targetToSensor);
	VisualizeFiducial.drawCube(targetToSensor, intrinsic, detector.getWidth(i), 3, g2);		VisualizeFiducial.drawCube(targetToSensor, intrinsic, detector.getWidth(i), 3, g2);
	}		}
	}		}
	ShowImages.showWindow(image,"Random Dot Markers",true);		ShowImages.showWindow(image, "Random Dot Markers", true);
	}		}
	}		}
	</syntaxhighlight>		</syntaxhighlight>

Peter at 04:45, 22 June 2020

2020-06-22T04:45:05Z

← Older revision		Revision as of 04:45, 22 June 2020
Line 15:		Line 15:

	Relevant Videos:		Relevant Videos:
	* ~~Will be added soon~~		* [https://www.youtube.com/watch?v=8pn9Ebw90uk&t=184s Video Summary]

	Related Tutorials/Example Code:		Related Tutorials/Example Code:
	* [[Example_Fiducial_Square_Binary\| Square Binary Fiducial]]		* [[Example_Fiducial_Square_Binary\| Square Binary Fiducial]]

Peter at 03:51, 19 May 2020

2020-05-19T03:51:03Z

← Older revision		Revision as of 03:51, 19 May 2020
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	<center>		<center>
	<gallery widths=600px heights=400px >		<gallery widths=600px heights=400px >
	File:Detecting_fiducial_random_dots.jpg\|Shows detected random dot markers partially obscured by rule with their 3D pose estimated. Cube on top of markers indicates the 3D view.		File:Detecting_fiducial_random_dots.jpg\|Shows detected random dot markers partially obscured by rule with their 3D pose estimated. Cube on top of markers indicates the 3D view. The number drawn in the center is the marker's ID.
	</gallery>		</gallery>
	</center>		</center>

	Uchiya Markers are composed of random dots and work by computing a hashing function based on the relative orientation of neighboring dots. One advantage to these markers is that they are resistant to conclusion. Many of the dots can be covered and the ID and pose of the marker can still be recovered! Square based markers become unrecognizable if the black square is blocked or damaged.		Uchiya Markers are composed of random dots and work by computing a hashing function based on the relative orientation of neighboring dots. One advantage to these markers is that they are resistant to conclusion. Many of the dots can be covered and the ID and pose of the marker can still be recovered! Square based markers become unrecognizable if the black square is blocked or damaged. BoofCV also provides tools for generating random marker PDF documents that can be printed.

	Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.36/examples/src/main/java/boofcv/examples/fiducial/ExampleFiducialRandomDots.java ExampleFiducialRandomDots.java]		Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.36/examples/src/main/java/boofcv/examples/fiducial/ExampleFiducialRandomDots.java ExampleFiducialRandomDots.java]

Peter: Created page with " File:Detecting_fiducial_random_dots.jpg|Shows detected random dot markers partially obscured by rule with their 3D pose estimate..."

2020-05-19T03:48:21Z

Created page with "<center> <gallery widths=600px heights=400px > File:Detecting_fiducial_random_dots.jpg|Shows detected random dot markers partially obscured by rule with their 3D pose estimate..."

New page

<center>
<gallery widths=600px heights=400px >
File:Detecting_fiducial_random_dots.jpg|Shows detected random dot markers partially obscured by rule with their 3D pose estimated. Cube on top of markers indicates the 3D view.
</gallery>
</center>

Uchiya Markers are composed of random dots and work by computing a hashing function based on the relative orientation of neighboring dots. One advantage to these markers is that they are resistant to conclusion. Many of the dots can be covered and the ID and pose of the marker can still be recovered! Square based markers become unrecognizable if the black square is blocked or damaged.

Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.36/examples/src/main/java/boofcv/examples/fiducial/ExampleFiducialRandomDots.java ExampleFiducialRandomDots.java]

Concepts:
* Fiducial Markers
* Dots
* Pose Estimation

Relevant Videos:
* Will be added soon

Related Tutorials/Example Code:
* [[Example_Fiducial_Square_Binary| Square Binary Fiducial]]
* [[Example_Fiducial_Square_Image| Square Image Fiducial]]
* [[Example_Detect_QR_Code| QR Code detector]]
* [[Example_Calibration_Target_Pose| Detecting Calibration Targets]]

= Example Code =

<syntaxhighlight lang="java">
/**
* Random dot markers are exactly what their name implies. Each marker is a set of random dots that the tracker
* learns to identify using hash codes computed from geometric invariants which describe the relationships between
* the dots. These markers are based off of Uchiya Markers.
*
* @author Peter Abeles
*/
public class ExampleFiducialRandomDots {
public static void main(String[] args) {
// The definitions file specifies where dots are on each marker and other bits of meta data
RandomDotDefinition defs = FiducialIO.loadRandomDotYaml(
UtilIO.fileExample("fiducial/random_dots/descriptions.yaml"));

// The only parameter that you have to set is markerLength. It's used to compute bounding
// boxes and similar. If you don't know what the width is just set it to 1.0
ConfigUchiyaMarker config = new ConfigUchiyaMarker();
config.markerLength = defs.markerWidth;

Uchiya_to_FiducialDetector<GrayU8> detector = FactoryFiducial.randomDots(config, GrayU8.class);

// Load / learn all the markers. This can take a few seconds if there are a lot of markers
for( List<Point2D_F64> marker : defs.markers ) {
detector.addMarker(marker);
}

// If you want 3D pose information then the camera need sto be calibrated. If you don't provide
// this information then just things like the bounding box will be returned
CameraPinholeBrown intrinsic = CalibrationIO.load(
UtilIO.fileExample("fiducial/random_dots/intrinsic.yaml"));
detector.setLensDistortion(LensDistortionFactory.narrow(intrinsic),intrinsic.width,intrinsic.height);

// It's now ready to start processing images. Let's load an image
BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("fiducial/random_dots/image02.jpg"));
GrayU8 gray = ConvertBufferedImage.convertFrom(image,false, ImageType.SB_U8);

detector.detect(gray);

// Time to visualize the results
Graphics2D g2 = image.createGraphics();
Se3_F64 targetToSensor = new Se3_F64();
Polygon2D_F64 bounds = new Polygon2D_F64();
Point2D_F64 center = new Point2D_F64();
for (int i = 0; i < detector.totalFound(); i++) {
detector.getBounds(i, bounds);
detector.getCenter(i, center);

g2.setColor(new Color(50,50,255));
g2.setStroke(new BasicStroke(10));
VisualizeShapes.drawPolygon(bounds,true,1.0,g2);
VisualizeFiducial.drawLabel(center, "" + detector.getId(i), g2);

System.out.println("Target ID = "+detector.getId(i));

if( detector.is3D() ) {
detector.getFiducialToCamera(i, targetToSensor);
VisualizeFiducial.drawCube(targetToSensor, intrinsic, detector.getWidth(i), 3, g2);
}
}
ShowImages.showWindow(image,"Random Dot Markers",true);
}
}
</syntaxhighlight>