Difference between revisions of "Example Morphological Thinning"

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


Concepts:
Concepts:
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<syntaxhighlight lang="java">
<syntaxhighlight lang="java">
/**
/**
  * Simple example showing you how to thin a binary image. This is also known as skeletonalization. Thinning
  * Simple example showing you how to thin a binary image. This is also known as skeletonalization. Thinning
  * discards most of objects foreground (value one) pixels are leaves behind a "skinny" object which still
  * discards most of objects foreground (value one) pixels are leaves behind a "skinny" object which still
  * mostly describes the original objects shape.
  * mostly describes the original objects shape.

Revision as of 12:01, 12 July 2021

Example of how to thin a binary image. The thinned image (or skeleton) is a common preprocessing step in shape analysis.

Example Code:

Concepts:

  • Binary Processing
  • Shapes
  • Thinning

Example Code

/**
 * Simple example showing you how to thin a binary image. This is also known as skeletonalization. Thinning
 * discards most of objects foreground (value one) pixels are leaves behind a "skinny" object which still
 * mostly describes the original objects shape.
 *
 * @author Peter Abeles
 */
public class ExampleMorphologicalThinning {
	public static void main( String[] args ) {
		String[] images = new String[]{"drawings/drawing_text.png", "standard/fingerprint.jpg", "drawings/drawing_face.png"};

		ListDisplayPanel uberPanel = new ListDisplayPanel();
		for (String path : images) {
			// load and convert the image into a usable format
			BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample(path));

			// convert into a usable format
			GrayF32 input = ConvertBufferedImage.convertFromSingle(image, null, GrayF32.class);
			GrayU8 binary = new GrayU8(input.width, input.height);

			// Fixed threshold is best for B&W images, but the adaptive would improve results for the finger print
			GThresholdImageOps.threshold(input, binary, 120, true);
//			GThresholdImageOps.adaptiveSquare(input, binary, 20,0,true,null,null);

			// Tell it to thin the image until there are no more changes
			GrayU8 thinned = BinaryImageOps.thin(binary, -1, null);

			// display the results
			BufferedImage visualBinary = VisualizeBinaryData.renderBinary(binary, false, null);
			BufferedImage visualThinned = VisualizeBinaryData.renderBinary(thinned, false, null);

			ListDisplayPanel panel = new ListDisplayPanel();
			panel.addImage(visualThinned, "Thinned");
			panel.addImage(visualBinary, "Binary");
			panel.addImage(image, "Original");

			uberPanel.addItem(panel, new File(path).getName());
		}
		ShowImages.showWindow(uberPanel, "Thinned/Skeletonalized Images", true);
	}
}