朋友要求帮忙做一个图片识别的小程序，因为要用在特定的环境下，所以决定采用java语言实现。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 docBufferedImage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。
源代码/* * Created on 2005-11-29 * * TODO To change the template for this generated file go to * Window - Preferences - Java - Code Style - Code Templates */package com.syvin.image;
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; }
}
