http://services.iteye.com/blog/230239
首先用matlab实现了识别算法的仿真,因为只是对特定的数字组合的识别,所以非常的简单,放弃采用比较复杂的识别算法,采用最普通的像素比较的识别算法。(如果背景噪声比较复杂,可以考虑先滤波后识别)在写java程序的时候发现一些问题,网上关于图片像素级操作的资料不是太多,有的还不是太正确,特此写出自己的成果与大家分享。
核心类:BufferedImage,ImageIOImageIO类提供图象读写接口,可以对URL,InputStream等操作,得到图像信息十分的方便。
ImageIO在javax.imageio.*的包中,属于jdk中的标准类。提供的方法有:
read() 例:BufferedImage imd=ImageIO.read(new File(file));
write() 例:ImageIO.write(imd, "JPEG", new File("C:\\test"+k+".gif"));
//具体方法可以查找jdk doc
BufferedImage类是一个Image类的子类,与Image不同的是,它是在内存中创建和修改的,你可以显示它也可以不显示它,这就看你的具体需求了。这里因为我用于图像的识别所以就不需要显示出来了。你可以通过ImageIO的方法来读取一个文件到BufferedImage,也可以将其写回一个文件中去。类似的操作可以看前面的两个方法。以及参考jdk doc
因为我要识别类似于身份验证的一个数字串图片,所以我考虑把这些数字分离出来,存在不同的图像内,这里BufferedImage类提供一个很方便的办法。
getSubimage(int left,int top,int width,int height)
例: BufferedImage newim[]=new BufferedImage[4];
newim[0]=imd.getSubimage(4,0,10,18);
newim[1]=imd.getSubimage(13,0,10,18);
newim[2]=imd.getSubimage(22,0,10,18);
newim[3]=imd.getSubimage(31,0,10,18);
最后为了得到图像的像素,我们需要的就是得到像素的方法,这个方法有很多,这里我介绍的是
getRGB(int x,int y) 得到特定像素点的RGB值。
例: pix=new int[10*18];pix[i*(10)+j]=newim[k].getRGB(j,i);
现在我们得到了像素,可以看出像素是一个一维数组,你如果不习惯可以考虑保存在一个二维的数组中,然后就来实施你的看家算法,什么小波变换,拉普拉斯算子,尽管来吧。怎么样是不是很方便呢?什么你好像看不太懂,好给你一些源程序好了,包括像素分解和识别算法。
- import java.awt.*;
- import java.awt.image.*;
- import java.io.FileOutputStream;
- import java.io.*;
- import java.io.InputStream;
- import java.net.URL;
- import javax.imageio.*;
- public class MyImage{
- BufferedImage imd;//待识别图像
- private int iw,ih;//图像宽和高
- public final static String path="D:\\jyy\\app\\tomcat\\webapps\\userlogon\\a.jpg";
- static public void main(String args[]) {
- try{
- MyImage app = new MyImage();//构造一个类
- String s=app.getImageNum("C:\\无标题.bmp");//得到识别字符串
- System.out.println("recognize result"+s);
- byte[] by=s.getBytes();
- File f=new File("C:\\testfile.txt");
- FileOutputStream fos=new FileOutputStream(f);//写入一个结果文件
- fos.write(by);
- fos.close();
- }catch(Exception e){
- e.printStackTrace();
- }
- }
- //构造函数
- public MyImage() throws IOException {
- super("Image Test");
- try{
- }catch(Exception e){
- e.printStackTrace();
- }
- }
- //得到图像的值
- public String getImageNum(String file){
- StringBuffer sb=new StringBuffer("");
- try{
- imd=ImageIO.read(new File(file));//用ImageIO的静态方法读取图像
- BufferedImage newim[]=new BufferedImage[4];
- int []x=new int[4];
- //将图像分成四块,因为要处理的文件有四个数字。
- newim[0]=imd.getSubimage(4,0,10,18);
- newim[1]=imd.getSubimage(13,0,10,18);
- newim[2]=imd.getSubimage(22,0,10,18);
- newim[3]=imd.getSubimage(31,0,10,18);
- for(int k=0;k<4;k++){
- x[k]=0;
- ImageIO.write(newim[k], "JPEG", new File("C:\\test"+k+".gif"));
- this.iw=newim[k].getWidth(null);
- this.ih=newim[k].getHeight(null);
- pix=new int[iw*ih];
- //因为是二值图像,这里的方法将像素读取出来的同时,转换为0,1的图像数组。
- for(int i=0;i<ih;i++){
- for(int j=0;j<iw;j++){
- pix[i*(iw)+j]=newim[k].getRGB(j,i);
- if(pix[i*(iw)+j]==-1)
- pix[i*(iw)+j]=0;
- else pix[i*(iw)+j]=1;
- x[k]=x[k]+pix[i*(iw)+j];
- }
- }
- //得到像匹配的数字。
- int r=this.getMatchNum(pix);
- sb.append(r);
- System.out.println("x="+x[k]);
- }
- }catch(Exception e){
- e.printStackTrace();
- }
- return sb.toString();
- }
- //数字模板 0-9
- static int[][] value={
- //num 0;
- {0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,1,1,0,0,0,0,
- 0,0,1,1,1,1,1,0,0,0,
- 0,0,1,1,0,0,1,1,0,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,0,1,1,0,0,1,1,0,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,0,0,1,1,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0
- },
- //num 1
- {0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,1,1,0,0,0,
- 0,0,0,0,1,1,1,0,0,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,0,0,0,1,1,0,0,0,
- 0,0,0,0,0,1,1,0,0,0,
- 0,0,0,0,0,1,1,0,0,0,
- 0,0,0,0,0,1,1,0,0,0,
- 0,0,0,0,0,1,1,0,0,0,
- 0,0,0,0,0,1,1,0,0,0,
- 1,1,1,1,1,1,1,1,1,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0
- },
- //num2
- {
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,1,1,0,0,1,1,0,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,0,0,0,0,0,0,1,1,0,
- 0,0,0,0,0,0,1,1,0,0,
- 0,0,0,0,0,1,1,0,0,0,
- 0,0,0,0,1,1,0,0,0,0,
- 0,0,0,1,1,0,0,0,0,0,
- 0,0,1,1,0,0,0,0,0,0,
- 1,1,1,1,1,1,1,1,1,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0
- },
- //num3
- {
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,1,1,1,1,1,0,0,0,
- 0,1,1,0,0,0,1,1,0,0,
- 0,0,0,0,0,0,0,1,1,0,
- 0,0,0,0,0,0,1,1,0,0,
- 0,0,0,0,1,1,1,0,0,0,
- 0,0,0,0,0,0,1,1,0,0,
- 0,0,0,0,1,0,0,1,1,0,
- 0,0,0,0,0,0,0,1,1,0,
- 0,1,1,0,0,0,1,1,0,0,
- 0,0,1,1,1,1,1,0,0,0,
- 0,0,0,1,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0
- },
- //num4
- {
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,1,1,0,0,
- 0,0,0,0,0,1,1,1,0,0,
- 0,0,0,0,1,1,1,1,0,0,
- 0,0,0,1,1,0,1,1,0,0,
- 0,0,1,1,0,0,1,1,0,0,
- 0,1,1,0,0,0,1,1,0,0,
- 0,1,1,1,1,1,1,1,1,0,
- 0,0,0,0,0,0,1,1,0,0,
- 0,0,0,0,0,0,1,1,0,0,
- 0,0,0,0,0,0,1,1,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0
- },
- //num5
- {
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,1,0,0,0,0,0,
- 0,1,1,1,1,1,1,1,0,0,
- 0,1,1,0,0,0,0,0,0,0,
- 0,1,1,0,0,0,0,0,0,0,
- 0,1,1,0,1,1,1,0,0,0,
- 0,1,1,1,0,0,1,1,0,0,
- 0,0,0,0,0,0,0,1,1,0,
- 0,0,0,0,0,0,0,1,1,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,0,1,1,0,0,1,1,0,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0
- },
- //num6
- {
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,1,1,0,0,1,1,0,0,
- 0,1,1,0,0,0,0,1,0,0,
- 0,1,1,0,0,0,0,0,0,0,
- 0,1,1,0,1,1,1,0,0,0,
- 0,1,1,1,0,0,1,1,0,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,0,1,1,0,0,1,1,0,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0
- },
- //num7
- {
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,1,1,1,1,1,1,1,1,0,
- 0,0,0,0,0,0,0,1,1,0,
- 0,0,0,0,0,0,1,1,1,0,
- 0,0,0,0,0,0,1,1,0,0,
- 0,0,0,0,1,1,1,0,0,0,
- 0,0,0,0,1,1,0,0,0,0,
- 0,0,0,1,1,0,0,0,0,0,
- 0,0,1,1,0,0,0,0,0,0,
- 0,1,1,0,0,0,0,0,0,0,
- 0,1,1,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0
- },
- //num8
- {
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,1,1,0,0,1,1,1,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,0,1,1,0,1,1,1,1,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,1,1,0,0,1,1,0,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,0,1,1,0,0,1,1,0,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0
- },
- //num9
- {
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,1,1,0,1,1,1,0,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,1,1,0,0,0,0,1,1,0,
- 0,0,1,1,0,0,1,1,1,0,
- 0,0,0,1,1,1,0,1,1,0,
- 0,0,0,0,0,0,0,1,1,0,
- 0,0,1,0,0,0,0,1,1,0,
- 0,0,1,1,0,0,1,1,0,0,
- 0,0,0,1,1,1,1,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0,
- 0,0,0,0,0,0,0,0,0,0
- }};
- //图像像素相减取绝对值得到最小熵的结果。
- public int getMatchNum(int[] pix){
- int result=-1;
- int temp=100;
- int x;
- for(int k=0;k<=9;k++){
- x=0;
- for(int i=0;i<pix.length;i++){
- x=x+Math.abs(pix[i]-value[k][i]);
- }
- /*for(int a=0;a<18;a++){
- for(int b=0;b<10;b++){
- System.out.print(pix[a*10+b]+"-"+value[k][a*10+b]+"|");
- }
- System.out.println();
- }*/
- if(x<temp)
- {
- temp=x;
- result=k;
- }
- }
- return result;
- }
- }