KMeansCommand Class

Summary

Performs image segmentation and color reduction using the K-means algorithm.

Syntax

Objective-C

C++/CLI

Java

Python

public class KMeansCommand : RasterCommand

@interface LTKMeansCommand : LTRasterCommand

public class KMeansCommand 
    extends RasterCommand

public ref class KMeansCommand : public RasterCommand

class KMeansCommand(RasterCommand):

Remarks

K-Means is an algorithm for analyzing data. Each observation gets placed in the cluster having the nearest mean.
The number of clusters returned are less than or equal to the number of input clusters.
If the image contains fewer clusters than the number of input clusters, this command will return the image colors in the OutCenters property.
This command can only process entire images. It does not support regions.
This command converts image to 8-bit grayscale.
This command supports signed/unsigned images.
This command supports 12 and 16-bit grayscale and 48 and 64-bit color images. Support for 12 and 16-bit grayscale and 48 and 64-bit color images is available in the Document/Medical Imaging toolkits.

Kmeans Segmentation Function - Before

Kmeans Segmentation Function - After

View additional platform support for this Kmeans Segmentation function.

Example

Java

using Leadtools; 
using Leadtools.Codecs; 
using Leadtools.ImageProcessing.Core; 
 
public void KMeansCommandExample() 
{ 
   RasterCodecs codecs = new RasterCodecs(); 
   codecs.ThrowExceptionsOnInvalidImages = true; 
 
   //Load an image 
   RasterImage image = codecs.Load(Path.Combine(LEAD_VARS.ImagesDir, "cannon.jpg")); 
 
   //Prepare the command 
   KMeansCommand command = new KMeansCommand(); 
 
   command.Clusters = 7; 
   command.Type = KMeansCommandFlags.KMeans_Random; 
 
   //Apply  
   command.Run(image); 
} 
 
static class LEAD_VARS 
{ 
   public const string ImagesDir = @"C:\LEADTOOLS23\Resources\Images"; 
}

 
import java.io.File; 
import java.io.IOException; 
 
import org.junit.*; 
import org.junit.Test; 
import org.junit.runner.JUnitCore; 
import org.junit.runner.Result; 
import org.junit.runner.notification.Failure; 
import static org.junit.Assert.*; 
 
import leadtools.*; 
import leadtools.codecs.*; 
import leadtools.imageprocessing.core.*; 
 
 
public void kMeansCommandExample() { 
    final String LEAD_VARS_IMAGES_DIR = "C:\\LEADTOOLS23\\Resources\\Images"; 
 
    RasterCodecs codecs = new RasterCodecs(); 
    codecs.setThrowExceptionsOnInvalidImages(true); 
 
    // Load an image 
    RasterImage image = codecs.load(combine(LEAD_VARS_IMAGES_DIR, "cannon.jpg")); 
 
    // Prepare the command 
    KMeansCommand command = new KMeansCommand(); 
 
    command.setClusters(7); 
    command.setType(KMeansCommandFlags.KMEANS_RANDOM); 
    assertTrue(command.getClusters() == 7 && command.getType() == KMeansCommandFlags.KMEANS_RANDOM); 
 
    // Apply 
    command.run(image); 
    codecs.save(image, combine(LEAD_VARS_IMAGES_DIR, "cannonResult.jpg"), RasterImageFormat.JPEG, 24); 
 
    assertTrue(new File(combine(LEAD_VARS_IMAGES_DIR, "cannonResult.jpg")).exists()); 
    System.out.println("Command run and image exported to: " + combine(LEAD_VARS_IMAGES_DIR, "cannonResult.jpg")); 
}