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我正在尝试使用 Java 中 BoofCV 提供的分水岭函数来分割一个简单的图像。所以我写了（复制、编辑和调整）以下代码：

package alltestshere;

import boofcv.alg.filter.binary.BinaryImageOps;
import boofcv.alg.filter.binary.Contour;
import boofcv.alg.filter.binary.GThresholdImageOps;
import boofcv.gui.ListDisplayPanel;
import boofcv.gui.binary.VisualizeBinaryData;
import boofcv.gui.image.ShowImages;
import boofcv.io.UtilIO;
import boofcv.io.image.ConvertBufferedImage;
import boofcv.io.image.UtilImageIO;
import boofcv.struct.ConnectRule;
import boofcv.struct.image.GrayS32;
import boofcv.struct.image.GrayU8;
import java.awt.image.BufferedImage;
import java.util.List;
import boofcv.alg.segmentation.watershed.WatershedVincentSoille1991;
import boofcv.factory.segmentation.FactorySegmentationAlg;
import boofcv.gui.feature.VisualizeRegions;



public class examp {

   public static void main( String args[] ) {
    // load and convert the image into a usable format
    BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("C:\\\\Users\\\\Caterina\\\\Downloads\\\\boofcv\\\\data\\\\example\\\\shapes\\\\shapes02.png"));
    // convert into a usable format
    GrayU8 input = ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class);

//declare some of my working data
    GrayU8 binary = new GrayU8(input.width,input.height);
    GrayS32 markers = new GrayS32(input.width,input.height);

    // Select a global threshold using Otsu's method.
    GThresholdImageOps.threshold(input, binary, GThresholdImageOps.computeOtsu(input, 0, 255),true);

    //through multiple erosion you can obtain the sure foreground and use it as marker in order to segment the image
    GrayU8 filtered = new GrayU8 (input.width, input.height);
    GrayU8 filtered2 = new GrayU8 (input.width, input.height);
    GrayU8 filtered3 = new GrayU8 (input.width, input.height);
    BinaryImageOps.erode8(binary, 1, filtered);
    BinaryImageOps.erode8(filtered, 1, filtered2);
    BinaryImageOps.erode8(filtered2, 1, filtered3);

//count how many markers you have (one for every foreground part +1 for the background
    int numRegions = BinaryImageOps.contour(filtered3, ConnectRule.EIGHT, markers).size()+1 ;


    // Detect foreground imagea using an 8-connect rule
    List<Contour> contours = BinaryImageOps.contour(binary, ConnectRule.EIGHT, markers);

    //Watershed function which takes the original b&w image as input and the markers 
    WatershedVincentSoille1991 watershed = FactorySegmentationAlg.watershed(ConnectRule.FOUR);
    watershed.process(input, markers);

    //get the results of the watershed as output
    GrayS32 output = watershed.getOutput();


    // display the results
    BufferedImage visualBinary = VisualizeBinaryData.renderBinary(input, false, null);
    BufferedImage visualFiltered = VisualizeBinaryData.renderBinary(filtered3, false, null);
    BufferedImage visualLabel = VisualizeBinaryData.renderLabeledBG(markers , contours.size(), null);
    BufferedImage outLabeled = VisualizeBinaryData.renderLabeledBG(output, numRegions, null);


    ListDisplayPanel panel = new ListDisplayPanel();
    panel.addImage(visualBinary, "Binary Original");
    panel.addImage(visualFiltered, "Binary Filtered");
    panel.addImage(visualLabel, "Markers");
    panel.addImage(outLabeled, "Watershed");
    ShowImages.showWindow(panel,"Watershed");
    }

}

但是，此代码效果不佳。具体来说，它不是用不同颜色为前景对象着色并保留背景，而是将所有图像分割成区域，而每个区域仅由一个前景对象和背景的一部分组成，并用相同的颜色绘制所有这部分颜色（图 3）。那么，我做错了什么？

我正在上传原始图片 标记图片和分水岭图片

在此先感谢，卡特琳娜

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1 回答 1

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你得到这个结果是因为你没有将背景作为一个区域来处理。您提供给分水岭的标记只是您的形状的轮廓。由于背景不是一个区域，因此分水岭算法将其平均分割到每个区域。它是平等的，因为每个形状的原始图像到背景的距离是相同的（二进制图像）。

如果您想将背景作为另一个区域，则向分水岭算法提供背景的一些点作为标记，例如角落。

于 2018-11-08T16:22:44.957 回答