Difference between revisions of "Example Thresholding"

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Thresholding gray scale images is one of the most basic ways to segment an image. It is quick and effective in many situations. BoofCV provides several algorithms for computing both global and locally adaptive thresholds.
Thresholding gray scale images is one of the most basic ways to segment an image. It is quick and effective in many situations. BoofCV provides several algorithms for computing both global and locally adaptive thresholds.


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


Concepts:
Concepts:
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public static void threshold( String imageName ) {
public static void threshold( String imageName ) {
BufferedImage image = UtilImageIO.loadImage(imageName);
BufferedImage image = UtilImageIO.loadImageNotNull(imageName);


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


// Display multiple images in the same window
// Display multiple images in the same window
ListDisplayPanel gui = new ListDisplayPanel();
var gui = new ListDisplayPanel();


// Global Methods
// Global Methods

Latest revision as of 16:32, 17 January 2022

Thresholding gray scale images is one of the most basic ways to segment an image. It is quick and effective in many situations. BoofCV provides several algorithms for computing both global and locally adaptive thresholds.

Example Code:

Concepts:

  • Segmentation
  • Thresholding

Relevant Videos:

Relevant Examples/Tutorials:

Example Code

/**
 * Demonstration of different techniques for automatic thresholding an image to create a binary image. The binary
 * image can then be used for shape analysis and other applications. Global methods apply the same threshold
 * to the entire image. Local methods compute a local threshold around each pixel and can handle uneven
 * lighting, but produce noisy results in regions with uniform lighting.
 *
 * @author Peter Abeles
 * @see boofcv.examples.imageprocessing.ExampleBinaryOps
 */
public class ExampleThresholding {

	public static void threshold( String imageName ) {
		BufferedImage image = UtilImageIO.loadImageNotNull(imageName);

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

		// Display multiple images in the same window
		var gui = new ListDisplayPanel();

		// Global Methods
		GThresholdImageOps.threshold(input, binary, ImageStatistics.mean(input), true);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Global: Mean");
		GThresholdImageOps.threshold(input, binary, GThresholdImageOps.computeOtsu(input, 0, 255), true);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Global: Otsu");
		GThresholdImageOps.threshold(input, binary, GThresholdImageOps.computeEntropy(input, 0, 255), true);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Global: Entropy");

		// Local method
		GThresholdImageOps.localMean(input, binary, ConfigLength.fixed(57), 1.0, true, null, null, null);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Local: Mean");
		GThresholdImageOps.localGaussian(input, binary, ConfigLength.fixed(85), 1.0, true, null, null);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Local: Gaussian");
		GThresholdImageOps.localNiblack(input, binary, ConfigLength.fixed(11), 0.30f, true);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Local: Niblack");
		GThresholdImageOps.localSauvola(input, binary, ConfigLength.fixed(11), 0.30f, true);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Local: Sauvola");
		GThresholdImageOps.localWolf(input, binary, ConfigLength.fixed(11), 0.30f, true);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Local: Wolf");
		GThresholdImageOps.localNick(input, binary, ConfigLength.fixed(11), -0.2f, true);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Local: NICK");
		GThresholdImageOps.blockMinMax(input, binary, ConfigLength.fixed(21), 1.0, true, 15);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Block: Min-Max");
		GThresholdImageOps.blockMean(input, binary, ConfigLength.fixed(21), 1.0, true);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Block: Mean");
		GThresholdImageOps.blockOtsu(input, binary, false, ConfigLength.fixed(21), 0.5, 1.0, true);
		gui.addImage(VisualizeBinaryData.renderBinary(binary, false, null), "Block: Otsu");

		// Sauvola is tuned for text image. Change radius to make it run better in others.

		// Show the image image for reference
		gui.addImage(ConvertBufferedImage.convertTo(input, null), "Input Image");

		String fileName = imageName.substring(imageName.lastIndexOf('/') + 1);
		ShowImages.showWindow(gui, fileName);
	}

	public static void main( String[] args ) {
		// example in which global thresholding works best
		threshold(UtilIO.pathExample("particles01.jpg"));
		// example in which adaptive/local thresholding works best
		threshold(UtilIO.pathExample("segment/uneven_lighting_squares.jpg"));
		// hand written text with non-uniform stained background
		threshold(UtilIO.pathExample("segment/stained_handwriting.jpg"));
	}
}