图像处理之基于NCC模板匹配识别

一：基本原理

NCC是一种基于统计学计算两组样本数据相关性的算法，其取值范围为[-1, 1]之间，而对图像来说，每个像素点都可以看出是RGB数值，这样整幅图像就可以看成是一个样本数据的集合，如果它有一个子集与另外一个样本数据相互匹配则它的ncc值为1，表示相关性很高，如果是-1则表示完全不相关，基于这个原理，实现图像基于模板匹配识别算法，其中第一步就是要归一化数据，数学公式如下：

二：实现步骤

（1）      获取模板像素并计算均值与标准方差、像素与均值diff数据样本

（2）      根据模板大小，在目标图像上从左到右，从上到下移动窗口，计

算每移动一个像素之后窗口内像素与模板像素的ncc值，与阈值比较，大于

阈值则记录位置

（3）      根据得到位置信息，使用红色矩形标记出模板匹配识别结果。

（4）      UI显示结果

 

三：编程实现

基于JAVA语言完成了整个算法编程实现与演示，其中第一步的代码如下：

int tw = template.getWidth();int th = template.getHeight();int[] tpixels = new int[tw * th];getRGB(template, 0, 0, tw, th, tpixels);for(int i=0; i<tpixels.length; i++){tpixels[i] = (tpixels[i] >> 16) & 0xff;}double[] meansdev = getPixelsMeansAndDev(tpixels);double[] tDiff = calculateDiff(tpixels, meansdev[0]);int raidus_width = tw / 2;int raidus_height = th / 2;

第二步的实现代码如下：

int[] windowPixels = new int[tw * th];Arrays.fill(windowPixels, 0);for (int row = 0; row < height; row++) {for (int col = 0; col < width; col++) {// calculate the means and dev for each windowif(row <  raidus_height || (row + raidus_height) >= height)continue;if(col < raidus_width || (col + raidus_width) >= width) continue;int wrow = 0;Arrays.fill(windowPixels, 0);for(int subrow = -raidus_height; subrow <= raidus_height; subrow++ ){int wcol = 0;for(int subcol = -raidus_width; subcol <= raidus_width; subcol++ ){if(wrow >= th || wcol >= tw){continue;}windowPixels[wrow * tw + wcol] = getPixelValue(width, col + subcol, row + subrow, inPixels);wcol++;}wrow++;}// calculate the nccdouble[] _meansDev = getPixelsMeansAndDev(windowPixels);double[] diff = calculateDiff(windowPixels, _meansDev[0]);double ncc = calculateNcc(tDiff, diff, _meansDev[1], meansdev[1]);if(ncc > threhold) {Point mpoint = new Point();mpoint.x = col;mpoint.y  = row;points.add(mpoint);}}}

第三步的实现代码如下：

// draw matched template on target image according positionsetRGB( dest, 0, 0, width, height, inPixels );Graphics2D g2d = dest.createGraphics();g2d.setPaint(Color.RED);g2d.setStroke(new BasicStroke(4));for(Point p : points){g2d.drawRect(p.x - raidus_width, p.y - raidus_height, tw, th);}

其中第二步用到的计算NCC的方法实现如下：

private double calculateNcc(double[] tDiff, double[] diff, double dev1, double dev2) {// TODO Auto-generated method stubdouble sum = 0.0d;double count = diff.length;for(int i=0; i<diff.length; i++){sum += ((tDiff[i] * diff[i])/(dev1 * dev2));}return (sum / count);}

UI部分完整源代码如下：

package com.gloomyfish.image.templae.match;import java.awt.BorderLayout;import java.awt.FlowLayout;import java.awt.Graphics;import java.awt.Graphics2D;import java.awt.event.ActionEvent;import java.awt.event.ActionListener;import java.awt.image.BufferedImage;import java.io.IOException;import javax.imageio.ImageIO;import javax.swing.JButton;import javax.swing.JComponent;import javax.swing.JFrame;import javax.swing.JPanel;public class DemoUI extends JComponent {/** *  */private static final long serialVersionUID = 1L;private BufferedImage targetImage;private BufferedImage template;public DemoUI(){super();java.net.URL imageURL = this.getClass().getResource("words.png");java.net.URL templateURL = this.getClass().getResource("template.png");try {template = ImageIO.read(templateURL);targetImage = ImageIO.read(imageURL);} catch (IOException e) {e.printStackTrace();}}public void setTarget(BufferedImage target) {this.targetImage = target;}@Overrideprotected void paintComponent(Graphics g) {Graphics2D g2 = (Graphics2D) g;if(targetImage != null) {g2.drawImage(targetImage, 10, 10, targetImage.getWidth(), targetImage.getHeight(), null);}if(template != null) {g2.drawImage(template, 20+targetImage.getWidth(), 10, template.getWidth(), template.getHeight(), null);}}public static void main(String[] args) {JFrame f = new JFrame("模板匹配与识别");JButton okBtn = new JButton("匹配");final DemoUI ui = new DemoUI();okBtn.addActionListener(new ActionListener() {@Overridepublic void actionPerformed(ActionEvent e) {ui.process();}});JPanel btnPanel = new JPanel();btnPanel.setLayout(new FlowLayout(FlowLayout.RIGHT));btnPanel.add(okBtn);f.getContentPane().add(btnPanel, BorderLayout.SOUTH);f.getContentPane().add(ui, BorderLayout.CENTER);f.setSize(500, 500);f.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);f.setVisible(true);}protected void process() {NccTemplateMatchAlg algo = new NccTemplateMatchAlg(template);targetImage = algo.filter(targetImage, null);this.repaint();}}

四：程序运行效果如下

其中左边是目标图像、右边为模板图像

PS:博客从10月份开始每月都有多篇相关图像处理文章更新

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