DynamicBinaryCommand Class

Summary

Converts an image into a black and white image without changing its bits per pixel by using a local threshold value for each pixel of the image. This function is useful for pre-processing images for the purpose of improving barcode recognition results.

Syntax

Objective-C

C++/CLI

Java

Python

public class DynamicBinaryCommand : RasterCommand

@interface LTDynamicBinaryCommand : LTRasterCommand

public class DynamicBinaryCommand 
    extends RasterCommand

public ref class DynamicBinaryCommand : public RasterCommand

class DynamicBinaryCommand(RasterCommand):

Remarks

This class converts an image into a black and white image without changing its bits per pixel.
Each pixel is compared to a dynamically-calculated threshold. If the intensity of the pixel is higher (that is, the pixel is brighter) than the dynamic threshold, the pixel will be set to white. If the intensity of the pixel is lower (that is, the pixel is darker) than the dynamic threshold, the pixel will be set to black.
Here are some hints on using this class:
Increasing the LocalContrast property increases the number of pixels that use the global threshold. This tends to preserve the general aspect of the image and reduces the improvement in the areas with many details. Reducing the LocalContrast property, increases the contrast in areas with many details.
Increasing the Dimension property increases the area used for local contrast. This makes the color changes smoother.
This class is good as a prerequisite for converting scanned images to 1-bit, because it uses one threshold for background and another for text.
This class 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 only in the Document/Medical toolkits.
This class does not support signed data images.
This command does not support 32-bit grayscale images.

Use the AutoBinaryCommand when you simply want to apply binary segmentation to the bitmap using an automatically calculated threshold based on a two-peak method of statistical analysis on its histogram. Use the DynamicBinaryCommand to convert an image into a black and white image without changing its bits per pixel. Use the AutoBinarizeCommand if you want:

Automatic pre-processing
Pre-processing to include background elimination
Pre-processing to include color leveling
To perform automatic, percentile or median thresholding
To manually specify a threshold value

For more information, refer to Introduction to Image Processing With LEADTOOLS. For more information, refer to Changing Brightness and Contrast. For more information, refer to Removing Noise.

Dynamic Binary Function - Before

Dynamic Binary Function - After

View additional platform support for this Dynamic Binary function.

Example

Run the DynamicBinaryCommand on an image.

Java

using Leadtools; 
using Leadtools.Codecs; 
using Leadtools.ImageProcessing.Color; 
 
 
public void DynamicBinaryCommandExample() 
{ 
   // Load an image 
   RasterCodecs codecs = new RasterCodecs(); 
   codecs.ThrowExceptionsOnInvalidImages = true; 
 
   RasterImage image = codecs.Load(Path.Combine(LEAD_VARS.ImagesDir, @"ImageProcessingDemo\Beauty16.jpg")); 
 
   // Prepare the command 
   DynamicBinaryCommand command = new DynamicBinaryCommand(); 
   command.Dimension = 8; 
   command.LocalContrast = 16; 
   // convert it into a black and white image without changing its bits per pixel. 
   command.Run(image); 
   codecs.Save(image, Path.Combine(LEAD_VARS.ImagesDir, "Result.jpg"), RasterImageFormat.Jpeg, 24); 
 
} 
 
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.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.color.*; 
 
 
public void dynamicBinaryCommandExample() { 
   final String LEAD_VARS_IMAGES_DIR = "C:\\LEADTOOLS23\\Resources\\Images"; 
   // Load an image 
   RasterCodecs codecs = new RasterCodecs(); 
   codecs.setThrowExceptionsOnInvalidImages(true); 
 
   RasterImage image = codecs.load(combine(LEAD_VARS_IMAGES_DIR, "Beauty16.jpg")); 
 
   // Prepare the command 
   DynamicBinaryCommand command = new DynamicBinaryCommand(); 
   command.setDimension(8); 
   command.setLocalContrast(16); 
   // Convert it into a black and white image without changing its bits per pixel 
   command.run(image); 
   codecs.save(image, combine(LEAD_VARS_IMAGES_DIR, "Result.jpg"), RasterImageFormat.JPEG, 24); 
 
   System.out.println("Command run and image saved to " + combine(LEAD_VARS_IMAGES_DIR, "Result.jpg")); 
   assertTrue(new File(combine(LEAD_VARS_IMAGES_DIR, "Result.jpg")).exists()); 
}