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 -