解析图片验证码

本文介绍了一种验证码解析的方法,重点讲解了去除干扰元素并提取数字的过程。文章提供了Java代码示例,展示了如何将图片转换为二进制字符串,并通过比对字符串识别具体的数字。

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1, 生成图片请参照 [url]http://tristan-s.iteye.com/admin/blogs/1026307[/url]

2, 目前只能解析没有干扰素的数字图片。 例如:
[img]http://dl.iteye.com/upload/attachment/475548/c4d8b563-8752-3ebc-b092-bacefa2e873f.jpg[/img]
OK卡网站的验证码
[img]http://dl.iteye.com/upload/attachment/475555/ead7486b-57ae-3cf4-a137-1f8fcda70226.bmp[/img]
去除干扰素,可以用以下算法
a> 线条的: 检查该线条是否是贯通的直线条,如是则去掉
b> 点: 检查该点是否是孤立的,如是则去掉

3, 需要将图片中的数字用0,1的形式来表达。解析每个数字,见附件

4, 代码
import java.awt.image.BufferedImage;
import java.io.File;

import javax.imageio.ImageIO;

public class ImageDemo {

public static String parse(BufferedImage img){
int width = img.getWidth();
int height = img.getHeight();

StringBuilder sb = new StringBuilder();

for (int j = 0; j < height; j++) {
for (int i = 0; i < width; i++) {
int RGB = img.getRGB(i, j);
int red = (RGB >> 16) & 0xff;
if (red < 100) {
sb.append("1");
} else {
sb.append("0");
}
}
}

String s = sb.toString();
String s2 = s;
// System.out.println("图片的String码: " + s);
// System.out.println("--------------");
// for (int i = 0; i < height; i++) {
// System.out.println(s2.substring(0, width));
// s2 = s2.substring(width);
// }

return s;
}

public static void calResult(String s){
if(s.indexOf(n0)>=0){
System.out.print(" 0");
}else if(s.indexOf(n1)>=0){
System.out.print(" 1");
}else if(s.indexOf(n2)>=0){
System.out.print(" 2");
}else if(s.indexOf(n3)>=0){
System.out.print(" 3");
}else if(s.indexOf(n4)>=0){
System.out.print(" 4");
}else if(s.indexOf(n5)>=0){
System.out.print(" 5");
}else if(s.indexOf(n6)>=0){
System.out.print(" 6");
}else if(s.indexOf(n7)>=0){
System.out.print(" 7");
}else if(s.indexOf(n8)>=0){
System.out.print(" 8");
}else if(s.indexOf(n9)>=0){
System.out.print(" 9");
}
}

public static void main(String[] args) throws Exception {

//File file = new File("c:/image/num/nn" + n + ".jpg");
File file = new File("c:/image/image1.jpg");

BufferedImage imgOrg = ImageIO.read(file);
BufferedImage img = imgOrg.getSubimage(8, 0, 10, 20);
BufferedImage img2 = imgOrg.getSubimage(20, 0, 10, 20);
BufferedImage img3 = imgOrg.getSubimage(35, 0, 10, 20);
BufferedImage img4 = imgOrg.getSubimage(45, 0, 10, 20);

System.out.println("图片解析结果:");

String s = parse(img);
calResult(s);

String s2 = parse(img2);
calResult(s2);

String s3 = parse(img3);
calResult(s3);

String s4 = parse(img4);
calResult(s4);

}


private static String n0 = "000000000000011110000011111100011100111001100001100110000110011000011001100001100110000110011000011001100001100111001110001111110000011110000000000000";
private static String n1 = "000000000000000110000000111000000111100000110110000010011000000001100000000110000000011000000001100000000110000000011000000001100000000110000000000000";
private static String n2 = "000000000000011110000011111100011100111001100001100000000110000000011000000011000000011000000011000000011000000011000000011111111001111111100000000000";
private static String n3 = "000000000000011100000011111000011000110000000011000000001100000011100000001111000000000110000000011001100001100110000110001111110000011110000000000000";
private static String n4 = "000000000000000110000000111000000011100000011110000011011000001101100001100110001100011000111111111011111111100000011000000001100000000110000000000000";
private static String n5 = "000000000000111111000011111100001100000001100000000110111000011111110001100001100000000110000000011001100001100110000110001111110000011110000000000000";
private static String n6 = "000000000000011110000011111100001100011001100000000110000000011011100001111111000111000110011000011001100001100011000110001111110000011110000000000000";
private static String n7 = "000000000001111111100111111110000000010000000011000000011000000001100000001100000000110000000011000000001100000001100000000110000000011000000000000000";
private static String n8 = "000000000000011110000011111100011000011001100001100110000110001111110000111111000110000110011000011001100001100110000110001111110000011110000000000000";
private static String n9 = "000000000000011110000011111100011000011001100001100110000110011000111000111111100001110110000000011000000001100110001100001111110000011110000000000000";
private static String n = "9";
}
### 使用 JMeter 解析和处理图片验证码 #### 创建线程组并配置HTTP请求 为了实现对图片验证码解析,在JMeter中需先建立一个用于模拟用户行为的线程组。在线程组内添加获取验证码图像的HTTP请求,确保此请求能够成功返回包含验证码图片文件。 #### 添加后置处理器执行OCR操作 随后为上述HTTP请求添加一个BeanShell PostProcessor或JSR223 PostProcessor作为后置处理器来处理响应数据。该组件负责调用Tesseract OCR引擎或其他第三方服务来进行字符识别工作[^2]。 对于Java环境下的解决方案可以采用如下方式读取图片流: ```java InputStream in = new FileInputStream(imagePath); byte[] imageData = new byte[in.available()]; in.read(imageData); in.close(); ``` 这段代码展示了如何加载本地磁盘上的图片到内存缓冲区以便后续传递给OCR工具进行分析[^3]。 #### 配置Tesseract命令行参数 如果选用开源软件Tesseract,则可通过设置相应的CLI选项完成文字提取任务。具体来说就是构建一条合适的指令字符串并通过Runtime.getRuntime().exec()函数启动子进程运行之。注意调整路径指向实际安装位置以及指定待处理的目标图形资源。 ```bash tesseract input_image_file output_text_file -l eng --psm 7 ``` 此处`input_image_file`代表输入的验证码图片而`output_text_file`则是期望得到的结果文本保存地址;`-l eng`指定了使用的语言模型(英语),最后面的`--psm 7`设定了页面分割模式以适应单行纯文本场景。 #### 将解析结果应用于登录表单提交 一旦获得了由OCR转换得来的字符串表示形式之后就可以将其赋值给相应变量供后面的HTTP POST请求携带过去验证身份信息的真实性了。这通常涉及到修改原始HTML源码里的隐藏域或者是直接拼接到URL查询串后面等形式之一取决于目标网站的具体设计规范[^1]。
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