Example Detect Black Polygons - Revision history

Peter at 20:56, 17 January 2022

2022-01-17T20:56:14Z

← Older revision		Revision as of 20:56, 17 January 2022
Line 5:		Line 5:

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

	Concepts:		Concepts:
Line 29:		Line 29:
	*/		*/
	public class ExampleDetectBlackPolygon {		public class ExampleDetectBlackPolygon {
	public static void main(String[] args) {		public static void main( String[] args ) {
	String[] imagesConvex = new String[]{		String[] imagesConvex = new String[]{
	"shapes/polygons01.jpg",		"shapes/polygons01.jpg",
Line 41:		Line 41:

	// first configure the detector to only detect convex shapes with 3 to 7 sides		// first configure the detector to only detect convex shapes with 3 to 7 sides
	ConfigPolygonDetector config = new ConfigPolygonDetector(3,7);		ConfigPolygonDetector config = new ConfigPolygonDetector(3, 7);
	DetectPolygonBinaryGrayRefine<GrayU8> detector = FactoryShapeDetector.polygon(config, GrayU8.class);		DetectPolygonBinaryGrayRefine<GrayU8> detector = FactoryShapeDetector.polygon(config, GrayU8.class);

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	processImages(imagesConcave, detector, panel);		processImages(imagesConcave, detector, panel);

	ShowImages.showWindow(panel,"Found Polygons",true);		ShowImages.showWindow(panel, "Found Polygons", true);
	}		}

	private static void processImages(String[] files,		private static void processImages( String[] files,
	DetectPolygonBinaryGrayRefine<GrayU8> detector,		DetectPolygonBinaryGrayRefine<GrayU8> detector,
	ListDisplayPanel panel)		ListDisplayPanel panel ) {
	{		for (String fileName : files) {
	for( String fileName : files ) {		BufferedImage image = UtilImageIO.loadImageNotNull(UtilIO.pathExample(fileName));
	BufferedImage image = UtilImageIO.~~loadImage~~(UtilIO.pathExample(fileName));

	GrayU8 input = ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class);		GrayU8 input = ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class);
	GrayU8 binary = new GrayU8(input.width,input.height);		GrayU8 binary = new GrayU8(input.width, input.height);

	// Binarization is done outside to allows creative tricks. For example, when applied to a chessboard		// Binarization is done outside to allows creative tricks. For example, when applied to a chessboard
Line 78:		Line 77:

	// visualize results by drawing red polygons		// visualize results by drawing red polygons
	java.util.List<Polygon2D_F64> found = detector.getPolygons(null,null);		java.util.List<Polygon2D_F64> found = detector.getPolygons(null, null);
	Graphics2D g2 = image.createGraphics();		Graphics2D g2 = image.createGraphics();
	g2.setStroke(new BasicStroke(5));		g2.setStroke(new BasicStroke(5));
Line 88:		Line 87:
	}		}

	panel.addImage(image,new File(fileName).getName());		panel.addImage(image, new File(fileName).getName());
	}		}
	}		}
	}		}
	</syntaxhighlight>		</syntaxhighlight>

Peter at 17:44, 12 July 2021

2021-07-12T17:44:17Z

← Older revision		Revision as of 17:44, 12 July 2021
Line 5:		Line 5:

	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]

	Concepts:		Concepts:
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	/**		/**
	* Example of how to use {@link DetectPolygonFromContour} to find black polygons in an image. This algorithm		* Example of how to use {@link DetectPolygonFromContour} to find black polygons in an image. This algorithm
	* is the basis for several fiducial detectors in BoofCV and fits the polygon to sub-pixel accuracy and produces		* is the basis for several fiducial detectors in BoofCV and fits the polygon to sub-pixel accuracy and produces
	* reasonable results on blurred images too. It is highly configurable and can even sparsely fit polygons		* reasonable results on blurred images too. It is highly configurable and can even sparsely fit polygons
	* in a distorted image. Meaning the expensive step of undistorting the entire image is not needed.		* in a distorted image. Meaning the expensive step of undistorting the entire image is not needed.
	*		*
	* @author Peter Abeles		* @author Peter Abeles
Line 66:		Line 66:
	GrayU8 binary = new GrayU8(input.width,input.height);		GrayU8 binary = new GrayU8(input.width,input.height);

	// Binarization is done outside to allows creative tricks. For example, when applied to a chessboard		// Binarization is done outside to allows creative tricks. For example, when applied to a chessboard
	// pattern where square touch each other, the binary image is eroded first so that they don't touch.		// pattern where square touch each other, the binary image is eroded first so that they don't touch.
	// The squares are expanded automatically during the subpixel optimization step.		// The squares are expanded automatically during the subpixel optimization step.
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	java.util.List<Polygon2D_F64> found = detector.getPolygons(null,null);		java.util.List<Polygon2D_F64> found = detector.getPolygons(null,null);
	Graphics2D g2 = image.createGraphics();		Graphics2D g2 = image.createGraphics();
	g2.setStroke(new BasicStroke(3));		g2.setStroke(new BasicStroke(5));
	for (int i = 0; i < found.size(); i++) {		for (int i = 0; i < found.size(); i++) {
	g2.setColor(Color.RED);		g2.setColor(Color.RED);
	VisualizeShapes.drawPolygon(found.get(i), true, g2, true);		VisualizeShapes.drawPolygon(found.get(i), true, g2, true);
	g2.setColor(Color.CYAN);		g2.setColor(Color.CYAN);
	VisualizeShapes.drawPolygonCorners(found.get(i), 2, g2, true);		VisualizeShapes.drawPolygonCorners(found.get(i), 3, g2, true);
	}		}

Peter at 20:01, 21 December 2020

2020-12-21T20:01:15Z

← Older revision		Revision as of 20:01, 21 December 2020
Line 5:		Line 5:

	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.29/examples/src/main/java/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]

	Concepts:		Concepts:
Line 30:		Line 30:
	public class ExampleDetectBlackPolygon {		public class ExampleDetectBlackPolygon {
	public static void main(String[] args) {		public static void main(String[] args) {
	String ~~imagesConvex~~[] = new String[]{		String[] imagesConvex = new String[]{
	"shapes/polygons01.jpg",		"shapes/polygons01.jpg",
	"shapes/shapes02.png",		"shapes/shapes02.png",
	"fiducial/image/examples/image01.jpg"};		"fiducial/image/examples/image01.jpg"};

	String ~~imagesConcave~~[] = new String[]{		String[] imagesConcave = new String[]{
	"shapes/concave01.jpg"};		"shapes/concave01.jpg"};

Peter at 02:58, 19 March 2018

2018-03-19T02:58:03Z

← Older revision		Revision as of 02:58, 19 March 2018
Line 5:		Line 5:

	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.28/examples/src/main/java/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.29/examples/src/main/java/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]

	Concepts:		Concepts:
Line 47:		Line 47:

	// now lets detect concave shapes with many sides		// now lets detect concave shapes with many sides
	config.detector.maximumSides = 12;		config.detector.contourToPoly.maximumSides = 12;
	config.detector.convex = false;		config.detector.contourToPoly.convex = false;
	detector = FactoryShapeDetector.polygon(config, GrayU8.class);		detector = FactoryShapeDetector.polygon(config, GrayU8.class);

Line 69:		Line 69:
	// pattern where square touch each other, the binary image is eroded first so that they don't touch.		// pattern where square touch each other, the binary image is eroded first so that they don't touch.
	// The squares are expanded automatically during the subpixel optimization step.		// The squares are expanded automatically during the subpixel optimization step.
	int threshold = GThresholdImageOps.computeOtsu(input, 0, 255);		int threshold = (int)GThresholdImageOps.computeOtsu(input, 0, 255);
	ThresholdImageOps.threshold(input, binary, threshold, true);		ThresholdImageOps.threshold(input, binary, threshold, true);

Peter at 05:39, 20 January 2018

2018-01-20T05:39:50Z

← Older revision		Revision as of 05:39, 20 January 2018
Line 5:		Line 5:

	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.23/examples/src/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.28/examples/src/main/java/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]

	Concepts:		Concepts:
	* Polygons		* Polygons
	* Fiducials		* Fiducials

	~~Relevant Applets:~~
	* [[Applet_Fiducials\| Fiducials]]

	Related Examples		Related Examples
Line 24:		Line 21:
	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	/**		/**
	* Example of how to use {@link ~~BinaryPolygonDetector~~} to find black polygons in an image. This algorithm		* Example of how to use {@link DetectPolygonFromContour} to find black polygons in an image. This algorithm
	* is the basis for several fiducial detectors in BoofCV and fits the polygon to sub-pixel accuracy and produces		* is the basis for several fiducial detectors in BoofCV and fits the polygon to sub-pixel accuracy and produces
	* reasonable results on blurred images too. It is highly configurable and can even sparsely fit polygons		* reasonable results on blurred images too. It is highly configurable and can even sparsely fit polygons
Line 45:		Line 42:
	// first configure the detector to only detect convex shapes with 3 to 7 sides		// first configure the detector to only detect convex shapes with 3 to 7 sides
	ConfigPolygonDetector config = new ConfigPolygonDetector(3,7);		ConfigPolygonDetector config = new ConfigPolygonDetector(3,7);
	~~BinaryPolygonDetector~~<GrayU8> detector = FactoryShapeDetector.polygon(config, GrayU8.class);		DetectPolygonBinaryGrayRefine<GrayU8> detector = FactoryShapeDetector.polygon(config, GrayU8.class);

	processImages(imagesConvex, detector, panel);		processImages(imagesConvex, detector, panel);

	// now lets detect concave shapes with many sides		// now lets detect concave shapes with many sides
	config.maximumSides = 12;		config.detector.maximumSides = 12;
	config.convex = false;		config.detector.convex = false;
	detector = FactoryShapeDetector.polygon(config, GrayU8.class);		detector = FactoryShapeDetector.polygon(config, GrayU8.class);

Line 60:		Line 57:

	private static void processImages(String[] files,		private static void processImages(String[] files,
	~~BinaryPolygonDetector~~<GrayU8> detector,		DetectPolygonBinaryGrayRefine<GrayU8> detector,
	ListDisplayPanel panel)		ListDisplayPanel panel)
	{		{
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	// visualize results by drawing red polygons		// visualize results by drawing red polygons
	~~FastQueue~~<Polygon2D_F64> found = detector.~~getFoundPolygons~~();		java.util.List<Polygon2D_F64> found = detector.getPolygons(null,null);
	Graphics2D g2 = image.createGraphics();		Graphics2D g2 = image.createGraphics();
	g2.setStroke(new BasicStroke(3));		g2.setStroke(new BasicStroke(3));
	for (int i = 0; i < found.size; i++) {		for (int i = 0; i < found.size(); i++) {
	g2.setColor(Color.RED);		g2.setColor(Color.RED);
	VisualizeShapes.drawPolygon(found.get(i), true, g2, true);		VisualizeShapes.drawPolygon(found.get(i), true, g2, true);

Peter at 05:28, 2 December 2016

2016-12-02T05:28:01Z

← Older revision		Revision as of 05:28, 2 December 2016
Line 15:		Line 15:

	Related Examples		Related Examples
			* [[Example_Detect_Black_Ellipses\|Black Ellipses]]
	* [[Example_Fiducial_Square_Binary\| Square Binary Fiducial]]		* [[Example_Fiducial_Square_Binary\| Square Binary Fiducial]]
	* [[Example_Fiducial_Square_Image\| Square Image Fiducial]]		* [[Example_Fiducial_Square_Image\| Square Image Fiducial]]

Peter at 05:19, 28 March 2016

2016-03-28T05:19:16Z

← Older revision		Revision as of 05:19, 28 March 2016
Line 5:		Line 5:

	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.20/examples/src/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.23/examples/src/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]

	Concepts:		Concepts:
Line 44:		Line 44:
	// first configure the detector to only detect convex shapes with 3 to 7 sides		// first configure the detector to only detect convex shapes with 3 to 7 sides
	ConfigPolygonDetector config = new ConfigPolygonDetector(3,7);		ConfigPolygonDetector config = new ConfigPolygonDetector(3,7);
	BinaryPolygonDetector<~~ImageUInt8~~> detector = FactoryShapeDetector.polygon(config, ~~ImageUInt8~~.class);		BinaryPolygonDetector<GrayU8> detector = FactoryShapeDetector.polygon(config, GrayU8.class);

	processImages(imagesConvex, detector, panel);		processImages(imagesConvex, detector, panel);
Line 51:		Line 51:
	config.maximumSides = 12;		config.maximumSides = 12;
	config.convex = false;		config.convex = false;
	detector = FactoryShapeDetector.polygon(config, ~~ImageUInt8~~.class);		detector = FactoryShapeDetector.polygon(config, GrayU8.class);

	processImages(imagesConcave, detector, panel);		processImages(imagesConcave, detector, panel);
Line 59:		Line 59:

	private static void processImages(String[] files,		private static void processImages(String[] files,
	BinaryPolygonDetector<~~ImageUInt8~~> detector,		BinaryPolygonDetector<GrayU8> detector,
	ListDisplayPanel panel)		ListDisplayPanel panel)
	{		{
Line 65:		Line 65:
	BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample(fileName));		BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample(fileName));

	~~ImageUInt8~~ input = ConvertBufferedImage.convertFromSingle(image, null, ~~ImageUInt8~~.class);		GrayU8 input = ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class);
	~~ImageUInt8~~ binary = new ~~ImageUInt8~~(input.width,input.height);		GrayU8 binary = new GrayU8(input.width,input.height);

	// Binarization is done outside to allows creative tricks. For example, when applied to a chessboard		// Binarization is done outside to allows creative tricks. For example, when applied to a chessboard

Peter at 16:41, 9 November 2015

2015-11-09T16:41:18Z

← Older revision		Revision as of 16:41, 9 November 2015
Line 2:		Line 2:
	[[File:Detected_black_polygons.jpg\|center\|border\|Red lines outline detected polygons. Was configured to find objects with 3 to 5 sides.]]		[[File:Detected_black_polygons.jpg\|center\|border\|Red lines outline detected polygons. Was configured to find objects with 3 to 5 sides.]]

	As of ~~v0.19, a single class is at~~ the ~~core of all fiducial detection~~ in BoofCV~~, BinaryPolygonConvexDetector~~. What it does is detects black polygons inside the image and then refines that estimate to within subpixel precision. It does this quickly, robustly, and very accurately~~. At least for squares. For other shapes it is usable but not as bullet proof, but that is likely to improve in the future~~.		BinaryPolygonDetector is used by most of the fiducials in BoofCV. What it does is detects black polygons inside the image and then refines that estimate to within subpixel precision. It does this quickly, robustly, and very accurately.

	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.19/examples/src/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.20/examples/src/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]

	Concepts:		Concepts:
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	/**		/**
	* Example of how to use {@link ~~BinaryPolygonConvexDetector~~} to find black polygons in an image. This algorithm		* Example of how to use {@link BinaryPolygonDetector} to find black polygons in an image. This algorithm
	* is the basis for several fiducial detectors in BoofCV and fits the polygon to sub-pixel accuracy and produces		* is the basis for several fiducial detectors in BoofCV and fits the polygon to sub-pixel accuracy and produces
	* reasonable results on blurred images too. It is highly configurable and can even sparsely fit polygons		* reasonable results on blurred images too. It is highly configurable and can even sparsely fit polygons
Line 32:		Line 32:
	public class ExampleDetectBlackPolygon {		public class ExampleDetectBlackPolygon {
	public static void main(String[] args) {		public static void main(String[] args) {
	String ~~files~~[] = new String[]{		String imagesConvex[] = new String[]{
	"~~../data/applet~~/polygons01.jpg",		"shapes/polygons01.jpg",
	"~~../data/applet~~/shapes02.png",		"shapes/shapes02.png",
	"~~../data/applet/~~fiducial/image/examples/image01.jpg"};		"fiducial/image/examples/image01.jpg"};

	~~ConfigPolygonDetector config~~ = new ~~ConfigPolygonDetector(3,4,5,7)~~;		String imagesConcave[] = new String[]{
			"shapes/concave01.jpg"};

	// ~~Tighten~~ the ~~tolerance for what defines a line~~		ListDisplayPanel panel = new ListDisplayPanel();
	config.~~contour2Poly_splitDistanceFraction = 0~~.02;
			// first configure the detector to only detect convex shapes with 3 to 7 sides
			ConfigPolygonDetector config = new ConfigPolygonDetector(3,7);
			BinaryPolygonDetector<ImageUInt8> detector = FactoryShapeDetector.polygon(config, ImageUInt8.class);

			processImages(imagesConvex, detector, panel);

	~~BinaryPolygonConvexDetector<ImageUInt8>~~ detector = FactoryShapeDetector.polygon(config, ImageUInt8.class);		// now lets detect concave shapes with many sides
			config.maximumSides = 12;
			config.convex = false;
			detector = FactoryShapeDetector.polygon(config, ImageUInt8.class);

	~~// Is the input image distorted? Let the~~ detector ~~sparsely undistort it for improved speed and accuracy~~		processImages(imagesConcave, detector, panel);
	~~// detector.setLensDistortion(...blah...~~);

	~~ListDisplayPanel~~ panel ~~= new ListDisplayPanel(~~);		ShowImages.showWindow(panel,"Found Polygons",true);
			}

			private static void processImages(String[] files,
			BinaryPolygonDetector<ImageUInt8> detector,
			ListDisplayPanel panel)
			{
	for( String fileName : files ) {		for( String fileName : files ) {
	BufferedImage image = UtilImageIO.loadImage(fileName);		BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample(fileName));

	ImageUInt8 input = ConvertBufferedImage.convertFromSingle(image, null, ImageUInt8.class);		ImageUInt8 input = ConvertBufferedImage.convertFromSingle(image, null, ImageUInt8.class);
Line 58:		Line 71:
	// pattern where square touch each other, the binary image is eroded first so that they don't touch.		// pattern where square touch each other, the binary image is eroded first so that they don't touch.
	// The squares are expanded automatically during the subpixel optimization step.		// The squares are expanded automatically during the subpixel optimization step.
	int threshold = GThresholdImageOps.computeOtsu(input,0,255);		int threshold = GThresholdImageOps.computeOtsu(input, 0, 255);
	ThresholdImageOps.threshold(input, binary, threshold, true);		ThresholdImageOps.threshold(input, binary, threshold, true);

Line 67:		Line 80:

	// visualize results by drawing red polygons		// visualize results by drawing red polygons
	FastQueue<Polygon2D_F64> found = detector.~~getFound~~();		FastQueue<Polygon2D_F64> found = detector.getFoundPolygons();
	Graphics2D g2 = image.createGraphics();		Graphics2D g2 = image.createGraphics();
	~~g2.setColor(Color.RED);~~
	g2.setStroke(new BasicStroke(3));		g2.setStroke(new BasicStroke(3));
	for (int i = 0; i < found.size; i++) {		for (int i = 0; i < found.size; i++) {
	VisualizeShapes.drawPolygon(found.get(i),true,g2,true);		g2.setColor(Color.RED);
			VisualizeShapes.drawPolygon(found.get(i), true, g2, true);
			g2.setColor(Color.CYAN);
			VisualizeShapes.drawPolygonCorners(found.get(i), 2, g2, true);
	}		}

	panel.addImage(image,new File(fileName).getName());		panel.addImage(image,new File(fileName).getName());
	}		}

	~~ShowImages.showWindow(panel,"Found Convex Polygons with 3, 4,5, and 7 sides",true);~~
	}		}
	}		}

	</syntaxhighlight>		</syntaxhighlight>

Peter: Created page with " Red lines outline detected polygons. Was configured to find objects with 3 to 5 sides. As of v0.19, a single class is at..."

2015-09-16T12:43:50Z

Created page with " center|border|Red lines outline detected polygons. Was configured to find objects with 3 to 5 sides. As of v0.19, a single class is at..."

New page

[[File:Detected_black_polygons.jpg|center|border|Red lines outline detected polygons. Was configured to find objects with 3 to 5 sides.]]

As of v0.19, a single class is at the core of all fiducial detection in BoofCV, BinaryPolygonConvexDetector. What it does is detects black polygons inside the image and then refines that estimate to within subpixel precision. It does this quickly, robustly, and very accurately. At least for squares. For other shapes it is usable but not as bullet proof, but that is likely to improve in the future.

Example Code:
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.19/examples/src/boofcv/examples/features/ExampleDetectBlackPolygon.java ExampleDetectBlackPolygon.java]

Concepts:
* Polygons
* Fiducials

Relevant Applets:
* [[Applet_Fiducials| Fiducials]]

Related Examples
* [[Example_Fiducial_Square_Binary| Square Binary Fiducial]]
* [[Example_Fiducial_Square_Image| Square Image Fiducial]]
* [[Example_Detect_Calibration_Target| Calibration Target]]

= Example Code =

<syntaxhighlight lang="java">
/**
* Example of how to use {@link BinaryPolygonConvexDetector} to find black polygons in an image. This algorithm
* is the basis for several fiducial detectors in BoofCV and fits the polygon to sub-pixel accuracy and produces
* reasonable results on blurred images too. It is highly configurable and can even sparsely fit polygons
* in a distorted image. Meaning the expensive step of undistorting the entire image is not needed.
*
* @author Peter Abeles
*/
public class ExampleDetectBlackPolygon {
public static void main(String[] args) {
String files[] = new String[]{
"../data/applet/polygons01.jpg",
"../data/applet/shapes02.png",
"../data/applet/fiducial/image/examples/image01.jpg"};

ConfigPolygonDetector config = new ConfigPolygonDetector(3,4,5,7);

// Tighten the tolerance for what defines a line
config.contour2Poly_splitDistanceFraction = 0.02;

BinaryPolygonConvexDetector<ImageUInt8> detector = FactoryShapeDetector.polygon(config, ImageUInt8.class);

// Is the input image distorted? Let the detector sparsely undistort it for improved speed and accuracy
// detector.setLensDistortion(...blah...);

ListDisplayPanel panel = new ListDisplayPanel();

for( String fileName : files ) {
BufferedImage image = UtilImageIO.loadImage(fileName);

ImageUInt8 input = ConvertBufferedImage.convertFromSingle(image, null, ImageUInt8.class);
ImageUInt8 binary = new ImageUInt8(input.width,input.height);

// Binarization is done outside to allows creative tricks. For example, when applied to a chessboard
// pattern where square touch each other, the binary image is eroded first so that they don't touch.
// The squares are expanded automatically during the subpixel optimization step.
int threshold = GThresholdImageOps.computeOtsu(input,0,255);
ThresholdImageOps.threshold(input, binary, threshold, true);

// it takes in a grey scale image and binary image
// the binary image is used to do a crude polygon fit, then the grey image is used to refine the lines
// using a sub-pixel algorithm
detector.process(input, binary);

// visualize results by drawing red polygons
FastQueue<Polygon2D_F64> found = detector.getFound();
Graphics2D g2 = image.createGraphics();
g2.setColor(Color.RED);
g2.setStroke(new BasicStroke(3));
for (int i = 0; i < found.size; i++) {
VisualizeShapes.drawPolygon(found.get(i),true,g2,true);
}

panel.addImage(image,new File(fileName).getName());
}

ShowImages.showWindow(panel,"Found Convex Polygons with 3, 4,5, and 7 sides",true);
}
}

</syntaxhighlight>