如何使用 BoofCV 在 Java 中分水嶺（分割）圖像？

Question

我正在嘗試使用 BoofCV 在 Java 中提供的分水嶺函數來分割一個簡單的圖像。 所以我寫了（復制、編輯和調整）以下代碼：

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，此代碼效果不佳。 具體來說，與其用不同顏色的前景對象着色並保留背景，它只是將所有圖像分割成區域，而每個區域僅由一個前景對象和一部分背景組成，並用相同的顏色繪制所有這部分顏色（圖 3）。 那么，我做錯了什么？

我正在上傳原始圖片標記圖片和分水嶺圖片

提前致謝，卡特琳娜

Answer 1

你得到這個結果是因為你沒有將背景作為一個區域來處理。 您提供給分水嶺的標記只是形狀的輪廓。 由於背景不是一個區域，分水嶺算法將其平均分割為每個區域。 這樣做是一樣的，因為每個形狀的原始圖像與背景的距離是相同的（二進制圖像）。

如果您想將背景作為另一個區域，則向分水嶺算法提供一些背景點作為標記，例如角點。