LargeNoiseThreshold Property

Summary

Gets or sets the threshold for large noise.

Syntax

Objective-C

C++/CLI

Java

Python

public int LargeNoiseThreshold {get; set;}

@property (nonatomic, assign) NSInteger largeNoiseThreshold;

public int getLargeNoiseThreshold(); 
public void setLargeNoiseThreshold( 
   int intValue 
);

public:  
   property Int32 LargeNoiseThreshold 
   { 
      Int32 get() 
      void set(Int32 value) 
   }

LargeNoiseThreshold # get and set (ExtractObjectsCommand)

Property Value

The threshold for the large noise. The default value is 0.

Remarks

IgnoreLargeNoise must be true for this property to be used.

Objects with any dimension greater than the specified value will be ignored. If ReportIgnored is true, ExObjResult.LargeNoise will be populated with these objects.

If IgnoreSmallNoise is true, this value must be larger than the SmallNoiseThreshold.

Example

Java

 
import java.io.File; 
import java.io.IOException; 
import java.util.ArrayList; 
import java.util.Collection; 
import java.util.Collections; 
import java.util.Iterator; 
 
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.FillCommand; 
import leadtools.imageprocessing.core.*; 
import leadtools.internal.Tuple; 
 
 
public void extractObjectsCommandUseMultiColorsExample() { 
 
   final String LEAD_VARS_IMAGES_DIR = "C:\\LEADTOOLS23\\Resources\\Images"; 
 
   RasterCodecs codecs = new RasterCodecs(); 
   // Load the original image 
   RasterImage inputImage = codecs.load(combine(LEAD_VARS_IMAGES_DIR, "demoicr2.tif")); 
 
   File output = new File(combine(LEAD_VARS_IMAGES_DIR, "demoicr2.tif")); 
   assertTrue(output.exists()); 
 
   // Setup the extraction options 
   Tuple<String, RasterColor>[] colors = new Tuple[3]; 
   colors[0] = Tuple.create("DarkGray", new RasterColor(30, 30, 30)); 
   colors[1] = Tuple.create("DarkGreen", new RasterColor(41, 108, 70)); 
   colors[2] = Tuple.create("LightRed", new RasterColor(200, 68, 65)); 
 
   ExtractObjectsCommand command = new ExtractObjectsCommand(); 
   command.setDetectChildren(true); 
   command.setEightConnectivity(true); 
   command.setIgnoreSmallNoise(true); 
   command.setOutline(true); 
   command.setSmallNoiseThreshold(5);// Filter out noise smaller than 5x5 pixels 
   command.setUseMultiColors(true); 
   ExObjColorInfo[] exColors = new ExObjColorInfo[colors.length]; 
   for (int i = 0; i < colors.length; i++) { 
      exColors[i] = new ExObjColorInfo(); 
      exColors[i].setColor(colors[i].getItem2()); 
      exColors[i].setThreshold(50); 
   } 
 
   // Extract the objects 
   command.run(inputImage); 
 
   ExObjData data = command.getData(); 
 
   // Put objects into one list for processing all at once 
   ArrayList<ExObjObject> objects = new ArrayList<ExObjObject>(); 
   Iterator<ExObjResult> it = data.iterator(); 
   while (it.hasNext()) { 
      objects.addAll(it.next().getObjects()); 
   } 
 
   // Setup the region options 
   ExObjRegionOptions regionOptions = new ExObjRegionOptions(); 
   regionOptions.setHorizontal(true); 
 
   // Calculate each object's region 
   data.calculateRegion(objects, regionOptions); 
 
   // Create an output image 
   RasterImage outputImage = RasterImage.create(inputImage.getWidth(), inputImage.getHeight(), 24, 
         inputImage.getXResolution(), RasterColor.WHITE); 
 
   // Extract each color to a separate image 
   int colorIndex = -1; 
 
   for (ExObjResult result : data) { 
      colorIndex++; 
 
      // Fill the output image with white 
      new FillCommand(RasterColor.WHITE).run(outputImage); 
 
      // Populate the output image with each object's region 
      for (ExObjObject ob : result.getObjects()) { 
         for (ExObjSegment segment : ob.getRegionHorizontal()) { 
            // Update the region to the current segment 
            outputImage.addRectangleToRegion(null, segment.getBounds(), RasterRegionCombineMode.SET); 
 
            // Fill the region with the current color 
            new FillCommand(colors[colorIndex].getItem2()).run(outputImage); 
         } 
      } 
 
      // Clear the output image's region 
      outputImage.makeRegionEmpty(); 
 
      // Save the output image 
      codecs.save(outputImage, 
            combine(LEAD_VARS_IMAGES_DIR, "ExtractObjectsMultiColors_" + colors[colorIndex].getItem1() + ".png"), 
            RasterImageFormat.PNG, 0); 
   } 
 
   System.out.println("Command run and image saved to: " + combine(LEAD_VARS_IMAGES_DIR, "ExtractObjects.png")); 
   assertTrue(new File(combine(LEAD_VARS_IMAGES_DIR, "ExtractObjects.png")).exists()); 
 
   outputImage.dispose(); 
   data.dispose(); 
   inputImage.dispose(); 
   codecs.dispose(); 
}

using Leadtools; 
using Leadtools.Codecs; 
using Leadtools.ImageProcessing; 
using Leadtools.ImageProcessing.Core; 
 
public void ExtractObjectsCommandUseMultiColorsExample() 
{ 
   using (RasterCodecs codecs = new RasterCodecs()) 
   // Load the original image 
   using (RasterImage inputImage = codecs.Load(Path.Combine(LEAD_VARS.ImagesDir, "unwarp1.jpg"))) 
   { 
      // Setup the extraction options 
      Tuple<string, RasterColor>[] colors = new Tuple<string, RasterColor>[] 
      { 
         Tuple.Create("DarkGray", new RasterColor(30, 30, 30)), 
         Tuple.Create("DarkGreen", new RasterColor(41, 108, 70)), 
         Tuple.Create("LightRed", new RasterColor(200, 68, 65)) 
      }; 
      ExtractObjectsCommand command = new ExtractObjectsCommand() 
      { 
         ColorInfo = colors 
            .Select(c => new ExObjColorInfo() 
            { 
               Color = c.Item2, 
               Threshold = 50 
            }) 
            .ToArray(), 
         DetectChildren = true, 
         EightConnectivity = true, 
         IgnoreSmallNoise = true, 
         Outline = true, 
         SmallNoiseThreshold = 2, // Filter out noise smaller than 2x2 pixels 
         IgnoreLargeNoise = true, 
         LargeNoiseThreshold = 950, // Filter out noise larger than 950 pixels 
         UseMultiColors = true, 
         ReportIgnored = true, 
      }; 
 
      // Extract the objects 
      command.Run(inputImage); 
 
      using (ExObjData data = command.Data) 
      { 
         // Put objects into one list for processing all at once, and count the noise 
         List<ExObjObject> objects = new List<ExObjObject>(); 
         int smallNoiseCount = 0, largeNoiseCount = 0; 
         foreach (ExObjResult result in data) 
         { 
            objects.AddRange(result.Objects); 
            if (result.SmallNoise != null) 
               smallNoiseCount += result.SmallNoise.Count; 
            if (result.LargeNoise != null) 
               largeNoiseCount += result.LargeNoise.Count; 
         } 
 
         Console.WriteLine($"Small Noise Count: {smallNoiseCount}"); 
         Console.WriteLine($"Large Noise Count: {largeNoiseCount}"); 
 
         // Setup the region options 
         ExObjRegionOptions regionOptions = new ExObjRegionOptions() 
         { 
            Horizontal = true 
         }; 
 
         // Calculate each object's region 
         data.CalculateRegion(objects, regionOptions); 
 
         // Create an output image 
         using (RasterImage outputImage = RasterImage.Create(inputImage.Width, inputImage.Height, 24, inputImage.XResolution, RasterColor.White)) 
         { 
            // Extract each color to a separate image 
            int colorIndex = -1; 
 
            foreach (ExObjResult result in data) 
            { 
               colorIndex++; 
 
               // Fill the output image with white 
               new FillCommand(RasterColor.White).Run(outputImage); 
 
               // Populate the output image with each object's region 
               foreach (ExObjObject @object in result.Objects) 
                  foreach (ExObjSegment segment in @object.RegionHorizontal) 
                  { 
                     // Update the region to the current segment 
                     outputImage.AddRectangleToRegion(null, segment.Bounds, RasterRegionCombineMode.Set); 
 
                     // Fill the region with the current color 
                     new FillCommand(colors[colorIndex].Item2).Run(outputImage); 
                  } 
 
               // Clear the output image's region 
               outputImage.MakeRegionEmpty(); 
 
               // Save the output image 
               codecs.Save(outputImage, Path.Combine(LEAD_VARS.ImagesDir, $"ExtractObjectsMultiColors_{colors[colorIndex].Item1}.png"), RasterImageFormat.Png, 0); 
            } 
         } 
      } 
   } 
} 
 
static class LEAD_VARS 
{ 
   public const string ImagesDir = @"C:\LEADTOOLS23\Resources\Images"; 
}

Requirements

Target Platforms

Reference

ExtractObjectsCommand Class

ExtractObjectsCommand Members

Leadtools.ImageProcessing.Core Namespace

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