图像分析------连通组件标记算法 分类： 视频图像处理 2015-07-24 09:57 32人阅读 评论(0) 收藏

连接组件标记算法(connected component labeling algorithm)是图像分析中最常用的算法之一，

算法的实质是扫描一幅图像的每个像素，对于像素值相同的分为相同的组(group),最终得到

图像中所有的像素连通组件。扫描的方式可以是从上到下，从左到右，对于一幅有N个像

素的图像来说，最大连通组件个数为N/2。扫描是基于每个像素单位，对于二值图像而言，

连通组件集合可以是V={1}或者V={0}, 取决于前景色与背景色的不同。对于灰度图像来说，

连图组件像素集合可能是一系列在0 ～ 255之间的灰度值。

 

算法流程如下：

1.      首先扫描当前像素相邻的八邻域像素值，发现连通像素加以标记。

2.      完全扫描所有像素点之后，根据标记将所有连通组件合并。

 

算法实现Class文件解释：

AbstractConnectedComponentLabel：一个抽象的Class定义了抽象方法doConntectedLabel()

同时完成了一些公共方法

ConnectedComponentLabelAlgOne：一个容易读懂的连接组件算法完成，没有任何优化，

继承上面的自抽象类

ConnectedComponentLabelAlgTwo：一个快速的连接组件算法，基于算法优化，取当前像素

的四邻域完成扫描与标记合并。

 

Label与PixelInfo是两个数据结构，用来存储算法计算过程中的中间变量。

 

ImageLabelFilter用来测试算法的驱动类，ImageAnalysisUI是现实测试结果的UI类

 

算法运行结果：

 

根据标记的索引将组件着色。

定义数据结构的代码如下：

public class Label {

    private int index;
    private Label root;

    public Label(int index) {
        this.index = index;
        this.root = this;
    }

    public Label getRoot() {
        if(this.root != this) {
            this.root = this.root.getRoot();
        }
        return root;
    }

    public int getIndex() {
        return index;
    }

    public void setIndex(int index) {
        this.index = index;
    }

    public void setRoot(Label root) {
        this.root = root;
    }
}

Pixelnfo的代码如下：

package com.gloomyfish.image.analysis;

public class PixelInfo {
    private int value; // pixel value
    private int xp;
    private int yp;

    public PixelInfo(int pixelValue, int yp, int xp) {
        this.value = pixelValue;
        this.yp = yp;
        this.xp = xp;
    }

    public int getValue() {
        return value;
    }
    public void setValue(int value) {
        this.value = value;
    }
    public int getXp() {
        return xp;
    }
    public void setXp(int xp) {
        this.xp = xp;
    }
    public int getYp() {
        return yp;
    }
    public void setYp(int yp) {
        this.yp = yp;
    }
}

抽象的组件连通标记算法Class如下：

public abstract class AbstractConnectedComponentLabel {

    protected int width;
    protected int height;
    protected Color fgColor;
    protected int[] inPixels;
    protected int[][] chessborad;
    protected Map<Integer, Integer> neighbourMap;

    public int getWidth() {
        return width;
    }

    public void setWidth(int width) {
        this.width = width;
    }

    public int getHeight() {
        return height;
    }

    public void setHeight(int height) {
        this.height = height;
    }

    public abstract Map<Integer, List<PixelInfo>> doConntectedLabel();

    public boolean isForeGround(int tr, int tg, int tb) {
        if(tr == fgColor.getRed() && tg == fgColor.getGreen() && tb == fgColor.getBlue()) {
            return true;
        } else {
            return false;
        }

    }

}

实现抽象类的算法one的代码如下：

import java.awt.Color;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class ConnectedComponentLabelAlgOne extends AbstractConnectedComponentLabel {

    public ConnectedComponentLabelAlgOne(Color fgColor, int[] srcPixel, int width, int height) {
        this.fgColor = fgColor;
        this.width = width;
        this.height = height;
        this.inPixels = srcPixel;
        this.chessborad = new int[height][width];
        for(int i=0; i<height; i++) {
            for(int j=0; j<width; j++) {
                chessborad[i][j] = 0;
            }
        }
        this.neighbourMap = new HashMap<Integer, Integer>();
    }

    // assume the input image data is binary image.
    public Map<Integer, List<PixelInfo>> doConntectedLabel() {
        System.out.println("start to do connected component labeling algorithm");
        int index = 0;
        int labelCount = 0;
        Label currentLabel = new Label(0);
        HashMap<Integer, Label> allLabels = new HashMap<Integer, Label>();
        for(int row=0; row<height; row++) {
            int ta = 0, tr = 0, tg = 0, tb = 0;
            for(int col=0; col<width; col++) {
                index = row * width + col;
                ta = (inPixels[index] >> 24) & 0xff;
                tr = (inPixels[index] >> 16) & 0xff;
                tg = (inPixels[index] >> 8) & 0xff;
                tb = inPixels[index] & 0xff;
                if(isForeGround(tr, tg, tb)) {
                    getNeighboringLabels(row, col);
                    if(neighbourMap.size() == 0) {
                        currentLabel.setIndex(++labelCount);
                        allLabels.put(labelCount,new Label(labelCount));

                    } else {
                        for(Integer pixelLabel : neighbourMap.keySet().toArray(new Integer[0])) {
                            currentLabel.setIndex(pixelLabel);
                            break;
                        }
                        mergeLabels(currentLabel.getIndex(), neighbourMap, allLabels);
                    }
                    chessborad[row][col] = currentLabel.getIndex();
                }
            }
        }

        Map<Integer, List<PixelInfo>> connectedLabels = consolidateAllLabels(allLabels);
        return connectedLabels;
    }

    private Map<Integer, List<PixelInfo>> consolidateAllLabels(HashMap<Integer, Label> allLabels) {
        Map<Integer, List<PixelInfo>> patterns = new HashMap<Integer, List<PixelInfo>>();
        int patternNumber;
        List<PixelInfo> shape;
        for (int i = 0; i < this.height; i++)
        {
            for (int j = 0; j < this.width; j++)
            {
                patternNumber = chessborad[i][j];
                if (patternNumber != 0)
                {
                    patternNumber = allLabels.get(patternNumber).getRoot().getIndex();
                    if (!patterns.containsKey(patternNumber))
                    {
                        shape = new ArrayList<PixelInfo>();
                        shape.add(new PixelInfo(Color.BLUE.getRGB(), i, j));
                    }
                    else
                    {
                        shape = patterns.get(patternNumber);
                        shape.add(new PixelInfo(Color.BLUE.getRGB(), i, j));
                    }
                    patterns.put(patternNumber, shape);
                }
            }
        }
        return patterns;
    }

    private void mergeLabels(int index, Map<Integer, Integer> neighbourMap,
            HashMap<Integer, Label> allLabels) {
        Label root = allLabels.get(index).getRoot();
        Label neighbour;
        for(Integer key : neighbourMap.keySet().toArray(new Integer[0])) {
             if (key != index)
             {
                 neighbour = allLabels.get(key);
                 if(neighbour.getRoot() != root) {
                     neighbour.setRoot(neighbour.getRoot());// thanks zhen712,
                 }
             }
        }

    }

    /**
     * get eight neighborhood pixels
     *
     * @param row
     * @param col
     * @return
     */
    public  void getNeighboringLabels(int row, int col) {
        neighbourMap.clear();
        for(int i=-1; i<=1; i++) {
            int yp = row + i;
            if(yp >=0 && yp < this.height) {
                for(int j=-1; j<=1; j++) {
                    if(i == 0 && j==0) continue; // ignore/skip center pixel/itself
                    int xp = col + j;
                    if(xp >=0 && xp < this.width) {
                        if(chessborad[yp][xp] != 0) {
                            if(!neighbourMap.containsKey(chessborad[yp][xp])) {
                                neighbourMap.put(chessborad[yp][xp],0);
                            }
                        }
                    }
                }
            }
        }
    }
}

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