12306验证码识别
时间:2013-01-19 10:38来源:开源中国社区 编辑:oschina 点击:
次
干扰线去除判断比较挫是导致识别率低的原因，希望高手指点吧~后面的特征提取和训练识别就交给大家了~ （很不幸地告诉大家，上班前12306的验证码干扰又加强了，主要还是干扰线部
干扰线去除判断比较挫是导致识别率低的原因，希望高手指点吧~后面的特征提取和训练识别就交给大家了~ （很不幸地告诉大家，上班前12306的验证码干扰又加强了，主要还是干扰线部分）
实现原理很简单：
1.图像灰度化与 二值化
2.去除干扰线（二值化在一定程度上已经消弱部分）
在5点以前，12306二值化后的图像干扰线是离散的，所以很容易地使用纵向扫描就能擦除干扰点提取字模，代码如下：
for (int x = 0; x < w; ++x) {
for (int y = 0; y < h; ++y) {
if (isBlack(gray[x][y])) {
if (x > 0 && x < (w - 1) && isWhite(gray[x - 1][y]) && isWhite(gray[x + 1][y])) {
gray[x][y] = 65535;
}
}
}
}
for (int x = 0; x < w; ++x) {
for (int y = 0; y < h; ++y) {
if (isBlack(gray[x][y])) {
if (y > 0 && y < (h - 1) && isWhite(gray[x][y - 1]) && isWhite(gray[x][y + 1])) {
gray[x][y] = 65535;
}
}
binaryBufferedImage.setRGB(x, y, gray[x][y]);
}
}
|
注：目前该方法已经不奏效，已经删除~（16:18更新调整亮度提高识别率）
package org.chinasb.client;
import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;
public class BinaryTest {
public static void main(String[] args) throws IOException {
BufferedImage bufferedImage = ImageIO.read(new File("D:/passCodeAction.jpg"));
int h = bufferedImage.getHeight();
int w = bufferedImage.getWidth();
// 灰度化
int[][] gray = new int[w][h];
for (int x = 0; x < w; x++) {
for (int y = 0; y < h; y++) {
int argb = bufferedImage.getRGB(x, y);
// 图像加亮（调整亮度识别率非常高）
int r = (int)(((argb >> 16) & 0xFF) * 1.7 + 30);
int g = (int)(((argb >> 8) & 0xFF) * 1.7 + 30);
int b = (int)(((argb >> 0) & 0xFF) * 1.7 + 30);
if (r >= 255) {
r = 255;
}
if (g >= 255) {
g = 255;
}
if (b >= 255) {
b = 255;
}
gray[x][y] = (int) ((b * 29 + g * 150 + r * 77 + 128) >> 8);
}
}
// 二值化
int threshold = ostu(gray, w, h);
BufferedImage binaryBufferedImage = new BufferedImage(w, h, BufferedImage.TYPE_BYTE_BINARY);
for (int x = 0; x < w; x++) {
for (int y = 0; y < h; y++) {
if (gray[x][y] > threshold) {
gray[x][y] |= 0x00FFFF;
} else {
gray[x][y] &= 0xFF0000;
}
binaryBufferedImage.setRGB(x, y, gray[x][y]);
}
}
// 矩阵打印
for (int y = 0; y < h; y++) {
for (int x = 0; x < w; x++) {
if (isBlack(binaryBufferedImage.getRGB(x, y))) {
System.out.print("*");
} else {
System.out.print(" ");
}
}
System.out.println();
}
ImageIO.write(binaryBufferedImage, "jpg", new File("D:/code.jpg"));
}
public static boolean isBlack(int colorInt) {
Color color = new Color(colorInt);
if (color.getRed() + color.getGreen() + color.getBlue() <= 300) {
return true;
}
return false;
}
public static boolean isWhite(int colorInt) {
Color color = new Color(colorInt);
if (color.getRed() + color.getGreen() + color.getBlue() > 300) {
return true;
}
return false;
}
public static int isBlackOrWhite(int colorInt) {
if (getColorBright(colorInt) < 30 || getColorBright(colorInt) > 730) {
return 1;
}
return 0;
}
public static int getColorBright(int colorInt) {
Color color = new Color(colorInt);
return color.getRed() + color.getGreen() + color.getBlue();
}
public static int ostu(int[][] gray, int w, int h) {
int[] histData = new int[w * h];
// Calculate histogram
for (int x = 0; x < w; x++) {
for (int y = 0; y < h; y++) {
int red = 0xFF & gray[x][y];
histData[red]++;
}
}
// Total number of pixels
int total = w * h;
float sum = 0;
for (int t = 0; t < 256; t++)
sum += t * histData[t];
float sumB = 0;
int wB = 0;
int wF = 0;
float varMax = 0;
int threshold = 0;
for (int t = 0; t < 256; t++) {
wB += histData[t]; // Weight Background
if (wB == 0)
continue;
wF = total - wB; // Weight Foreground
if (wF == 0)
break;
sumB += (float) (t * histData[t]);
float mB = sumB / wB; // Mean Background
float mF = (sum - sumB) / wF; // Mean Foreground
// Calculate Between Class Variance
float varBetween = (float) wB * (float) wF * (mB - mF) * (mB - mF);
// Check if new maximum found
if (varBetween > varMax) {
varMax = varBetween;
threshold = t;
}
}
return threshold;
}
}
