图像分析之连通组件标记算法

图像处理之连接组件标记算法

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);
							}
						}
					}
				}
			}
		}
	}
}

测试代码可以参考以前的文章 -

Java数字图像处理基础知识 - 必读