图像处理之简单综合实例(大米计数)
一位网友给我发了几张灰度图像，说是他们单位的工业相机拍摄的，画质非常的清楚，他们
单位是农业科研单位，特别想知道种子的数量，他想知道的是每次工业相机拍摄种子图片中
有多少颗粒种子，想到了用图像处理的办法解决他们的问题，看了他给我照片，以大米种子
为例。实现了一个简单的算法流程，可以得到种子的数目。
大致算法分为以下三个步骤：
1. 将灰度图像二值化，二值化方法可以参考以前的文章，求取像素平均值，灰度直方图都
可以
2. 去掉二值化以后的图像中干扰噪声。
3. 得到种子数目，用彩色标记出来。
源图像如下：
程序处理中间结果及最终效果如下：
二值化处理参见以前的文章 - http://blog.csdn.net/jia20003/article/details/7392325
大米计数与噪声块消去算法基于连通组件标记算法，源代码如下：
package com.gloomyfish.rice.analysis;
import java.awt.image.BufferedImage;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import com.gloomyfish.face.detection.AbstractBufferedImageOp;
import com.gloomyfish.face.detection.FastConnectedComponentLabelAlg;
public class FindRiceFilter extends AbstractBufferedImageOp {
private int sumRice;
public int getSumRice() {
return this.sumRice;
}
@Override
public BufferedImage filter(BufferedImage src, BufferedImage dest) {
int width = src.getWidth();
int height = src.getHeight();
if ( dest == null )
dest = createCompatibleDestImage( src, null );
int[] inPixels = new int[width*height];
int[] outPixels = new int[width*height];
getRGB(src, 0, 0, width, height, inPixels );
FastConnectedComponentLabelAlg fccAlg = new FastConnectedComponentLabelAlg();
fccAlg.setBgColor(0);
int[] outData = fccAlg.doLabel(inPixels, width, height);
// labels statistic
HashMap<Integer, Integer> labelMap = new HashMap<Integer, Integer>();
for(int d=0; d<outData.length; d++) {
if(outData[d] != 0) {
if(labelMap.containsKey(outData[d])) {
Integer count = labelMap.get(outData[d]);
count+=1;
labelMap.put(outData[d], count);
} else {
labelMap.put(outData[d], 1);
}
}
}
// try to find the max connected component
Integer[] keys = labelMap.keySet().toArray(new Integer[0]);
Arrays.sort(keys);
int threshold = 10;
ArrayList<Integer> listKeys = new ArrayList<Integer>();
for(Integer key : keys) {
if(labelMap.get(key) <=threshold){
listKeys.add(key);
}
System.out.println( "Number of " + key + " = " + labelMap.get(key));
}
sumRice = keys.length - listKeys.size();
// calculate means of pixel
int index = 0;
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(outData[index] != 0 && validRice(outData[index], listKeys)) {
tr = tg = tb = 255;
} else {
tr = tg = tb = 0;
}
outPixels[index] = (ta << 24) | (tr << 16) | (tg << 8) | tb;
}
}
setRGB( dest, 0, 0, width, height, outPixels );
return dest;
}
private boolean validRice(int i, ArrayList<Integer> listKeys) {
for(Integer key : listKeys) {
if(key == i) {
return false;
}
}
return true;
}
}
大米着色处理很简单，只是简单RGB固定着色，源码如下：
package com.gloomyfish.rice.analysis;
import java.awt.image.BufferedImage;
import com.gloomyfish.face.detection.AbstractBufferedImageOp;
public class ColorfulRiceFilter extends AbstractBufferedImageOp {
@Override
public BufferedImage filter(BufferedImage src, BufferedImage dest) {
int width = src.getWidth();
int height = src.getHeight();
if ( dest == null )
dest = createCompatibleDestImage( src, null );
int[] inPixels = new int[width*height];
int[] outPixels = new int[width*height];
getRGB(src, 0, 0, width, height, inPixels );
int index = 0, srcrgb;
for(int row=0; row<height; row++) {
int ta = 255, 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;
srcrgb = inPixels[index] & 0x000000ff;
if(srcrgb > 0 && row < 140) {
tr = 0;
tg = 255;
tb = 0;
} else if(srcrgb > 0 && row >= 140 && row <=280) {
tr = 0;
tg = 0;
tb = 255;
} else if(srcrgb > 0 && row >=280) {
tr = 255;
tg = 0;
tb = 0;
}
else {
tr = tg = tb = 0;
}
outPixels[index] = (ta << 24) | (tr << 16) | (tg << 8) | tb;
}
}
setRGB( dest, 0, 0, width, height, outPixels );
return dest;
}
}
测试程序UI代码如下：
package com.gloomyfish.rice.analysis;
import java.awt.BorderLayout;
import java.awt.Color;
import java.awt.Dimension;
import java.awt.FlowLayout;
import java.awt.Graphics;
import java.awt.Graphics2D;
import java.awt.Image;
import java.awt.MediaTracker;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;
import javax.swing.JButton;
import javax.swing.JComponent;
import javax.swing.JFileChooser;
import javax.swing.JFrame;
import javax.swing.JPanel;
public class MainFrame extends JComponent implements ActionListener {
/**
*
*/
private static final long serialVersionUID = 1518574788794973574L;
public final static String BROWSE_CMD = "Browse...";
public final static String NOISE_CMD = "Remove Noise";
public final static String FUN_CMD = "Colorful Rice";
private BufferedImage rawImg;
private BufferedImage resultImage;
private MediaTracker tracker;
private Dimension mySize;
// JButtons
private JButton browseBtn;
private JButton noiseBtn;
private JButton colorfulBtn;
// rice number....
private int riceNum = -1;
public MainFrame() {
JPanel btnPanel = new JPanel();
btnPanel.setLayout(new FlowLayout(FlowLayout.LEFT));
browseBtn = new JButton("Browse...");
noiseBtn = new JButton("Remove Noise");
colorfulBtn = new JButton("Colorful Rice");
browseBtn.setToolTipText("Please select image file...");
noiseBtn.setToolTipText("find connected region and draw red rectangle");
colorfulBtn.setToolTipText("Remove the minor noise region pixels...");
// buttons
btnPanel.add(browseBtn);
btnPanel.add(noiseBtn);
btnPanel.add(colorfulBtn);
// setup listener...
browseBtn.addActionListener(this);
noiseBtn.addActionListener(this);
colorfulBtn.addActionListener(this);
browseBtn.setEnabled(true);
noiseBtn.setEnabled(true);
colorfulBtn.setEnabled(true);
// minX = minY = 10000;
// maxX = maxY = -1;
mySize = new Dimension(500, 300);
JFrame demoUI = new JFrame("Rice Detection Demo");
demoUI.getContentPane().setLayout(new BorderLayout());
demoUI.getContentPane().add(this, BorderLayout.CENTER);
demoUI.getContentPane().add(btnPanel, BorderLayout.SOUTH);
demoUI.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
demoUI.pack();
demoUI.setVisible(true);
}
public void paint(Graphics g) {
Graphics2D g2 = (Graphics2D) g;
if(rawImg != null) {
Image scaledImage = rawImg.getScaledInstance(200, 200, Image.SCALE_FAST);
g2.drawImage(scaledImage, 0, 0, 200, 200, null);
}
if(resultImage != null) {
Image scaledImage = resultImage.getScaledInstance(200, 200, Image.SCALE_FAST);
g2.drawImage(scaledImage, 210, 0, 200, 200, null);
}
g2.setPaint(Color.RED);
if(riceNum > 0) {
g2.drawString("Number of Rice : " + riceNum, 100, 300);
} else {
g2.drawString("Number of Rice : Unknown", 100, 300);
}
}
public Dimension getPreferredSize() {
return mySize;
}
public Dimension getMinimumSize() {
return mySize;
}
public Dimension getMaximumSize() {
return mySize;
}
public static void main(String[] args) {
new MainFrame();
}
@Override
public void actionPerformed(ActionEvent e) {
if(BROWSE_CMD.equals(e.getActionCommand())) {
JFileChooser chooser = new JFileChooser();
chooser.showOpenDialog(null);
File f = chooser.getSelectedFile();
BufferedImage bImage = null;
if(f == null) return;
try {
bImage = ImageIO.read(f);
} catch (IOException e1) {
e1.printStackTrace();
}
tracker = new MediaTracker(this);
tracker.addImage(bImage, 1);
// blocked 10 seconds to load the image data
try {
if (!tracker.waitForID(1, 10000)) {
System.out.println("Load error.");
System.exit(1);
}// end if
} catch (InterruptedException ine) {
ine.printStackTrace();
System.exit(1);
} // end catch
BinaryFilter bfilter = new BinaryFilter();
rawImg = bfilter.filter(bImage, null);
repaint();
} else if(NOISE_CMD.equals(e.getActionCommand())) {
FindRiceFilter frFilter = new FindRiceFilter();
resultImage = frFilter.filter(rawImg, null);
riceNum = frFilter.getSumRice();
repaint();
} else if(FUN_CMD.equals(e.getActionCommand())) {
ColorfulRiceFilter cFilter = new ColorfulRiceFilter();
resultImage = cFilter.filter(resultImage, null);
repaint();
} else {
// do nothing...
}
}
}
关于连通组件标记算法，我实现一个优化过的快速版本，可以参见
http://blog.csdn.net/jia20003/article/details/7596443
