Difference between revisions of "Example Morphological Thinning"
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Line 1: | Line 1: | ||
<center> | <center> | ||
<gallery widths=240px heights=220px> | <gallery widths=240px heights=220px> | ||
File: | File:Thinning_cartoon_face.png | Input image of a finger print | ||
File: | File:Thinning_cartoon_face_binary.png | Binary image | ||
File: | File:Thinning_cartoon_face_thinned.png | Thinned image | ||
</gallery> | </gallery> | ||
</center> | </center> | ||
Line 10: | Line 10: | ||
Example Code: | Example Code: | ||
* [https://github.com/lessthanoptimal/BoofCV/blob/v0. | * [https://github.com/lessthanoptimal/BoofCV/blob/v0.40/examples/src/main/java/boofcv/examples/imageprocessing/ExampleMorphologicalThinning.java ExampleMorphologicalThinning.java] | ||
Concepts: | Concepts: | ||
Line 21: | Line 21: | ||
<syntaxhighlight lang="java"> | <syntaxhighlight lang="java"> | ||
/** | /** | ||
* Simple example showing you how to thin a binary image. | * 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. | ||
Line 28: | Line 28: | ||
*/ | */ | ||
public class ExampleMorphologicalThinning { | public class ExampleMorphologicalThinning { | ||
public static void main(String[] args) { | public static void main( String[] args ) { | ||
/ | String[] images = new String[]{"drawings/drawing_text.png", "standard/fingerprint.jpg", "drawings/drawing_face.png"}; | ||
// convert into a usable format | ListDisplayPanel uberPanel = new ListDisplayPanel(); | ||
for (String path : images) { | |||
// load and convert the image into a usable format | |||
BufferedImage image = UtilImageIO.loadImageNotNull(UtilIO.pathExample(path)); | |||
// convert into a usable format | |||
GrayF32 input = ConvertBufferedImage.convertFromSingle(image, null, GrayF32.class); | |||
var 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); | |||
ShowImages.showWindow( | var 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); | |||
} | } | ||
} | } | ||
</syntaxhighlight> | </syntaxhighlight> |
Latest revision as of 15:07, 17 January 2022
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.loadImageNotNull(UtilIO.pathExample(path));
// convert into a usable format
GrayF32 input = ConvertBufferedImage.convertFromSingle(image, null, GrayF32.class);
var 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);
var 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);
}
}