最近一直在开发一个用于自动发帖的工具，用HttpClient模拟客户端浏览器注册发帖。但是碰到了图形验证码的问题了，对单数字的验证码，通过一些OCR引擎，如：tesseract，AspriseOCR很容易解决问题。但碰到如CSDN论坛这中图形验证码就比较麻烦，必须先通过预处理。使图象二值化，黑白灰度，增加亮度。我的代码如下：
package myfilter;
import java.io.*;
import java.awt.image.*;
import java.awt.geom.AffineTransform;
import java.awt.color.ColorSpace;
import java.awt.image.ConvolveOp;
import java.awt.image.Kernel;
import java.awt.image.BufferedImage;
import javax.imageio.ImageIO;
import java.awt.Toolkit;
import java.awt.Image;
/**
* <p>Title: Image Filter</p>
*
* <p>Description: image processing by filters </p>
* <p>Copyright: Copyright (c) 2010</p>
*
* @author
gl74gs48@
* @since jdk1.5.0
* @version 1.0
*/
public class MyImgFilter {
BufferedImage image;
private int iw, ih;
private int[] pixels;
public MyImgFilter(BufferedImage image) {
this.image = image;
iw = image.getWidth();
ih = image.getHeight();
pixels = new int[iw * ih];
}
/** 图像二值化 */
public BufferedImage changeGrey() {
PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels,0, iw);
try {
pg.grabPixels();
} catch (InterruptedException e) {
e.printStackTrace();
}
// 设定二值化的域值，默认值为100
int grey = 100;
// 对图像进行二值化处理，Alpha值保持不变
ColorModel cm = ColorModel.getRGBdefault();
for (int i = 0; i < iw * ih; i++) {
int red, green, blue;
int alpha = cm.getAlpha(pixels[i]);
if (cm.getRed(pixels[i]) > grey) {
red = 255;
} else {
red = 0;
}
if (cm.getGreen(pixels[i]) > grey) {
green = 255;
} else {
green = 0;
}
if (cm.getBlue(pixels[i]) > grey) {
blue = 255;
} else {
blue = 0;
}
pixels[i] = alpha << 24 | red << 16 | green << 8 | blue; //通过移位重新构成某一点像素的RGB值
}
// 将数组中的象素产生一个图像
Image tempImg=Toolkit.getDefaultToolkit().createImage(new MemoryImageSource(iw,ih, pixels, 0, iw));
image = new BufferedImage(tempImg.getWidth(null),tempImg.getHeight(null), BufferedImage.TYPE_INT_BGR );
image.createGraphics().drawImage(tempImg, 0, 0, null);
return image;
}
/** 中值滤波 */
public BufferedImage getMedian() {
PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih,
pixels,
0, iw);
try {
pg.grabPixels();
} catch (InterruptedException e) {
e.printStackTrace();
}
// 对图像进行中值滤波，Alpha值保持不变
ColorModel cm = ColorModel.getRGBdefault();
for (int i = 1; i < ih - 1; i++) {
for (int j = 1; j < iw - 1; j++) {
int red, green, blue;
int alpha = cm.getAlpha(pixels[i * iw + j]);
// int red2 = cm.getRed(pixels[(i - 1) * iw + j]);
int red4 = cm.getRed(pixels[i * iw + j - 1]);
int red5 = cm.getRed(pixels[i * iw + j]);
int red6 = cm.getRed(pixels[i * iw + j + 1]);
// int red8 = cm.getRed(pixels[(i + 1) * iw + j]);
// 水平方向进行中值滤波
if (red4 >= red5) {
if (red5 >= red6) {
red = red5;
} else {
if (red4 >= red6) {
red = red6;
} else {
red = red4;
}
}
} else {
if (red4 > red6) {
red = red4;
} else {
if (red5 > red6) {
red = red6;
} else {
red = red5;
}
}
}
int green4 = cm.getGreen(pixels[i * iw + j - 1]);
int green5 = cm.getGreen(pixels[i * iw + j]);
int green6 = cm.getGreen(pixels[i * iw + j + 1]);
// 水平方向进行中值滤波
if (green4 >= green5) {
if (green5 >= green6) {
green = green5;
} else {
if (green4 >= green6) {
green = green6;
} else {
green = green4;
}
}
} else {
if (green4 > green6) {
green = green4;
} else {
if (green5 > green6) {
green = green6;
} else {
green = green5;
}
}
}
// int blue2 = cm.getBlue(pixels[(i - 1) * iw + j]);
int blue4 = cm.getBlue(pixels[i * iw + j - 1]);
int blue5 = cm.getBlue(pixels[i * iw + j]);
int blue6 = cm.getBlue(pixels[i * iw + j + 1]);
// int blue8 = cm.getBlue(pixels[(i + 1) * iw + j]);
// 水平方向进行中值滤波
if (blue4 >= blue5) {
if (blue5 >= blue6) {
blue = blue5;
} else {
if (blue4 >= blue6) {
blue = blue6;
} else {
blue = blue4;
}
}
} else {
if (blue4 > blue6) {
blue = blue4;
} else {
if (blue5 > blue6) {
blue = blue6;
} else {
blue = blue5;
}
}
}
pixels[i * iw +
j] = alpha << 24 | red << 16 | green << 8 | blue;
}
}
// 将数组中的象素产生一个图像
Image tempImg=Toolkit.getDefaultToolkit().createImage(new MemoryImageSource(iw,ih, pixels, 0, iw));
image = new BufferedImage(tempImg.getWidth(null),tempImg.getHeight(null), BufferedImage.TYPE_INT_BGR );
image.createGraphics().drawImage(tempImg, 0, 0, null);
return image;
}
public BufferedImage getGrey() {
ColorConvertOp ccp=new ColorConvertOp(ColorSpace.getInstance(ColorSpace.CS_GRAY), null);
return image=ccp.filter(image,null);
}
//Brighten using a linear formula that increases all color values
public BufferedImage getBrighten() {
RescaleOp rop=new RescaleOp(1.25f, 0, null);
return image=rop.filter(image,null);
}
//Blur by "convolving" the image with a matrix
public BufferedImage getBlur() {
float[] data = {
.1111f, .1111f, .1111f,
.1111f, .1111f, .1111f,
.1111f, .1111f, .1111f, };
ConvolveOp cop = new ConvolveOp(new Kernel(3, 3, data));
return image=cop.filter(image,null);
}
// Sharpen by using a different matrix
public BufferedImage getSharpen() {
float[] data = {
0.0f, -0.75f, 0.0f,
-0.75f, 4.0f, -0.75f,
0.0f, -0.75f, 0.0f};
ConvolveOp cop = new ConvolveOp(new Kernel(3, 3, data));
return image=cop.filter(image,null);
}
// 11) Rotate the image 180 degrees about its center point
public BufferedImage getRotate() {
AffineTransformOp atop=new AffineTransformOp(AffineTransform.getRotateInstance(Math.PI,image.getWidth()/2,image.getHeight()/2),
AffineTransformOp.TYPE_NEAREST_NEIGHBOR);
return image=atop.filter(image,null);
}
public BufferedImage getProcessedImg()
{
return image;
}
public static void main(String[] args) throws IOException {
FileInputStream fin=new FileInputStream(args[0]);
BufferedImage bi=ImageIO.read(fin);
MyImgFilter flt=new MyImgFilter(bi);
flt.changeGrey();
flt.getGrey();
flt.getBrighten();
bi=flt.getProcessedImg();
String pname=args[0].substring(0,args[0].lastIndexOf("."));
File file = new File(pname+".jpg");
ImageIO.write(bi, "jpg", file);
}
}
运行java myfilter.MyImgFilter t6.bmp，请确认图片t6.bmp与myfilter目录在同一目录下。
顺便说一下，在JDK1.5下，ImageIO可以输出JPG，BMP，PNG三种格式图片，但不支持GIF图片输出。
经处理后图片的识别率大大提高。
部分代码参考http://ykf.javaeye.com/blog/212431及《Java Examples In A Nutshell 3rd》
本文引用地址：
http://blog.sciencenet.cn/blog-47522-542798.html
最近一直在开发一个用于自动发帖的工具，用HttpClient模拟客户端浏览器注册发帖。但是碰到了图形验证码的问题了，对单数字的验证码，通过一些OCR引擎，如：tesseract，AspriseOCR很容易解决问题。但碰到如CSDN论坛这中图形验证码就比较麻烦，必须先通过预处理。使图象二值化，黑白灰度，增加亮度。我的代码如下：
package myfilter;
import java.io.*;
import java.awt.image.*;
import java.awt.geom.AffineTransform;
import java.awt.color.ColorSpace;
import java.awt.image.ConvolveOp;
import java.awt.image.Kernel;
import java.awt.image.BufferedImage;
import javax.imageio.ImageIO;
import java.awt.Toolkit;
import java.awt.Image;
/**
* <p>Title: Image Filter</p>
*
* <p>Description: image processing by filters </p>
* <p>Copyright: Copyright (c) 2010</p>
*
* @author
gl74gs48@
* @since jdk1.5.0
* @version 1.0
*/
public class MyImgFilter {
BufferedImage image;
private int iw, ih;
private int[] pixels;
public MyImgFilter(BufferedImage image) {
this.image = image;
iw = image.getWidth();
ih = image.getHeight();
pixels = new int[iw * ih];
}
/** 图像二值化 */
public BufferedImage changeGrey() {
PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels,0, iw);
try {
pg.grabPixels();
} catch (InterruptedException e) {
e.printStackTrace();
}
// 设定二值化的域值，默认值为100
int grey = 100;
// 对图像进行二值化处理，Alpha值保持不变
ColorModel cm = ColorModel.getRGBdefault();
for (int i = 0; i < iw * ih; i++) {
int red, green, blue;
int alpha = cm.getAlpha(pixels[i]);
if (cm.getRed(pixels[i]) > grey) {
red = 255;
} else {
red = 0;
}
if (cm.getGreen(pixels[i]) > grey) {
green = 255;
} else {
green = 0;
}
if (cm.getBlue(pixels[i]) > grey) {
blue = 255;
} else {
blue = 0;
}
pixels[i] = alpha << 24 | red << 16 | green << 8 | blue; //通过移位重新构成某一点像素的RGB值
}
// 将数组中的象素产生一个图像
Image tempImg=Toolkit.getDefaultToolkit().createImage(new MemoryImageSource(iw,ih, pixels, 0, iw));
image = new BufferedImage(tempImg.getWidth(null),tempImg.getHeight(null), BufferedImage.TYPE_INT_BGR );
image.createGraphics().drawImage(tempImg, 0, 0, null);
return image;
}
/** 中值滤波 */
public BufferedImage getMedian() {
PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih,
pixels,
0, iw);
try {
pg.grabPixels();
} catch (InterruptedException e) {
e.printStackTrace();
}
// 对图像进行中值滤波，Alpha值保持不变
ColorModel cm = ColorModel.getRGBdefault();
for (int i = 1; i < ih - 1; i++) {
for (int j = 1; j < iw - 1; j++) {
int red, green, blue;
int alpha = cm.getAlpha(pixels[i * iw + j]);
// int red2 = cm.getRed(pixels[(i - 1) * iw + j]);
int red4 = cm.getRed(pixels[i * iw + j - 1]);
int red5 = cm.getRed(pixels[i * iw + j]);
int red6 = cm.getRed(pixels[i * iw + j + 1]);
// int red8 = cm.getRed(pixels[(i + 1) * iw + j]);
// 水平方向进行中值滤波
if (red4 >= red5) {
if (red5 >= red6) {
red = red5;
} else {
if (red4 >= red6) {
red = red6;
} else {
red = red4;
}
}
} else {
if (red4 > red6) {
red = red4;
} else {
if (red5 > red6) {
red = red6;
} else {
red = red5;
}
}
}
int green4 = cm.getGreen(pixels[i * iw + j - 1]);
int green5 = cm.getGreen(pixels[i * iw + j]);
int green6 = cm.getGreen(pixels[i * iw + j + 1]);
// 水平方向进行中值滤波
if (green4 >= green5) {
if (green5 >= green6) {
green = green5;
} else {
if (green4 >= green6) {
green = green6;
} else {
green = green4;
}
}
} else {
if (green4 > green6) {
green = green4;
} else {
if (green5 > green6) {
green = green6;
} else {
green = green5;
}
}
}
// int blue2 = cm.getBlue(pixels[(i - 1) * iw + j]);
int blue4 = cm.getBlue(pixels[i * iw + j - 1]);
int blue5 = cm.getBlue(pixels[i * iw + j]);
int blue6 = cm.getBlue(pixels[i * iw + j + 1]);
// int blue8 = cm.getBlue(pixels[(i + 1) * iw + j]);
// 水平方向进行中值滤波
if (blue4 >= blue5) {
if (blue5 >= blue6) {
blue = blue5;
} else {
if (blue4 >= blue6) {
blue = blue6;
} else {
blue = blue4;
}
}
} else {
if (blue4 > blue6) {
blue = blue4;
} else {
if (blue5 > blue6) {
blue = blue6;
} else {
blue = blue5;
}
}
}
pixels[i * iw +
j] = alpha << 24 | red << 16 | green << 8 | blue;
}
}
// 将数组中的象素产生一个图像
Image tempImg=Toolkit.getDefaultToolkit().createImage(new MemoryImageSource(iw,ih, pixels, 0, iw));
image = new BufferedImage(tempImg.getWidth(null),tempImg.getHeight(null), BufferedImage.TYPE_INT_BGR );
image.createGraphics().drawImage(tempImg, 0, 0, null);
return image;
}
public BufferedImage getGrey() {
ColorConvertOp ccp=new ColorConvertOp(ColorSpace.getInstance(ColorSpace.CS_GRAY), null);
return image=ccp.filter(image,null);
}
//Brighten using a linear formula that increases all color values
public BufferedImage getBrighten() {
RescaleOp rop=new RescaleOp(1.25f, 0, null);
return image=rop.filter(image,null);
}
//Blur by "convolving" the image with a matrix
public BufferedImage getBlur() {
float[] data = {
.1111f, .1111f, .1111f,
.1111f, .1111f, .1111f,
.1111f, .1111f, .1111f, };
ConvolveOp cop = new ConvolveOp(new Kernel(3, 3, data));
return image=cop.filter(image,null);
}
// Sharpen by using a different matrix
public BufferedImage getSharpen() {
float[] data = {
0.0f, -0.75f, 0.0f,
-0.75f, 4.0f, -0.75f,
0.0f, -0.75f, 0.0f};
ConvolveOp cop = new ConvolveOp(new Kernel(3, 3, data));
return image=cop.filter(image,null);
}
// 11) Rotate the image 180 degrees about its center point
public BufferedImage getRotate() {
AffineTransformOp atop=new AffineTransformOp(AffineTransform.getRotateInstance(Math.PI,image.getWidth()/2,image.getHeight()/2),
AffineTransformOp.TYPE_NEAREST_NEIGHBOR);
return image=atop.filter(image,null);
}
public BufferedImage getProcessedImg()
{
return image;
}
public static void main(String[] args) throws IOException {
FileInputStream fin=new FileInputStream(args[0]);
BufferedImage bi=ImageIO.read(fin);
MyImgFilter flt=new MyImgFilter(bi);
flt.changeGrey();
flt.getGrey();
flt.getBrighten();
bi=flt.getProcessedImg();
String pname=args[0].substring(0,args[0].lastIndexOf("."));
File file = new File(pname+".jpg");
ImageIO.write(bi, "jpg", file);
}
}
运行java myfilter.MyImgFilter t6.bmp，请确认图片t6.bmp与myfilter目录在同一目录下。
顺便说一下，在JDK1.5下，ImageIO可以输出JPG，BMP，PNG三种格式图片，但不支持GIF图片输出。
经处理后图片的识别率大大提高。
部分代码参考http://ykf.javaeye.com/blog/212431及《Java Examples In A Nutshell 3rd》
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
