图片验证码--BufferedImage 图片验证码去除干扰线

java-BufferedImage 图片验证码去除干扰线的方法（用于OCR tesseract图像智能字符识别）

图片验证码自动识别的功能

网上对于初始图片的处理方法有去噪点、灰度化等，唯独难搜到去除干扰线的方法,可以比较干净地去除干扰线，提高OCR识别的准确率,“去除干扰线条“.

测试样板图片和数据：

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;import javax.imageio.ImageIO;public class CopyOfCleanLines {public static void main(String[] args) throws IOException  {    File testDataDir = File("F:\\ocr"); final String destDir = testDataDir.getAbsolutePath()+"/tmp";  for (File file : testDataDir.listFiles())  {  cleanLinesInImage(file, destDir);  cleanLinesInImage(file, destDir); cleanLinesInImage(file, destDir);}  }  /** *  * @param sfile *            需要去噪的图像 * @param destDir *            去噪后的图像保存地址 * @throws IOException */  public static void cleanLinesInImage(File sfile, String destDir)  throws IOException{  File destF = new File(destDir);  if (!destF.exists())  {  destF.mkdirs();  }  BufferedImage bufferedImage = ImageIO.read(sfile);  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) * 1.1 + 30);  int g = (int) (((argb >> 8) & 0xFF) * 1.1 + 30);  int b = (int) (((argb >> 0) & 0xFF) * 1.1 + 30);  if (r >= 255)  {  r = 255;  }  if (g >= 255)  {  g = 255;  }  if (b >= 255)  {  b = 255;  }  gray[x][y] = (int) Math  .pow((Math.pow(r, 2.2) * 0.2973 + Math.pow(g, 2.2)  * 0.6274 + Math.pow(b, 2.2) * 0.0753), 1 / 2.2);  }  }  // 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]);  }  }  //去除干扰线条for(int y = 1; y < h-1; y++){for(int x = 1; x < w-1; x++){                   boolean flag = false ;if(isBlack(binaryBufferedImage.getRGB(x, y))){//左右均为空时，去掉此点if(isWhite(binaryBufferedImage.getRGB(x-1, y)) && isWhite(binaryBufferedImage.getRGB(x+1, y))){flag = true;}//上下均为空时，去掉此点if(isWhite(binaryBufferedImage.getRGB(x, y+1)) && isWhite(binaryBufferedImage.getRGB(x, y-1))){flag = true;}//斜上下为空时，去掉此点if(isWhite(binaryBufferedImage.getRGB(x-1, y+1)) && isWhite(binaryBufferedImage.getRGB(x+1, y-1))){flag = true;}if(isWhite(binaryBufferedImage.getRGB(x+1, y+1)) && isWhite(binaryBufferedImage.getRGB(x-1, y-1))){flag = true;} if(flag){binaryBufferedImage.setRGB(x,y,-1);                     }}}}// 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(destDir, sfile  .getName()));  }  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;  }
}

package com.gazgeek.helloworld.controller;import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;import javax.imageio.ImageIO;public class CopyOfCleanLines {public static void main(String[] args) throws IOException{File testDataDir = new File("F:\\ocr");final String destDir = testDataDir.getAbsolutePath()+"/tmp";for (File file : testDataDir.listFiles()){cleanLinesInImage(file, destDir);cleanLinesInImage(file, destDir);cleanLinesInImage(file, destDir);}}/**** @param sfile*            需要去噪的图像* @param destDir*            去噪后的图像保存地址* @throws IOException*/public static void cleanLinesInImage(File sfile, String destDir)  throws IOException{File destF = new File(destDir);if (!destF.exists()){destF.mkdirs();}BufferedImage bufferedImage = ImageIO.read(sfile);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) * 1.1 + 30);int g = (int) (((argb >> 8) & 0xFF) * 1.1 + 30);int b = (int) (((argb >> 0) & 0xFF) * 1.1 + 30);if (r >= 255){r = 255;}if (g >= 255){g = 255;}if (b >= 255){b = 255;}gray[x][y] = (int) Math.pow((Math.pow(r, 2.2) * 0.2973 + Math.pow(g, 2.2)* 0.6274 + Math.pow(b, 2.2) * 0.0753), 1 / 2.2);}}// 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]);}}//去除干扰线条for(int y = 1; y < h-1; y++){for(int x = 1; x < w-1; x++){boolean flag = false ;if(isBlack(binaryBufferedImage.getRGB(x, y))){//左右均为空时，去掉此点if(isWhite(binaryBufferedImage.getRGB(x-1, y)) && isWhite(binaryBufferedImage.getRGB(x+1, y))){flag = true;}//上下均为空时，去掉此点if(isWhite(binaryBufferedImage.getRGB(x, y+1)) && isWhite(binaryBufferedImage.getRGB(x, y-1))){flag = true;}//斜上下为空时，去掉此点if(isWhite(binaryBufferedImage.getRGB(x-1, y+1)) && isWhite(binaryBufferedImage.getRGB(x+1, y-1))){flag = true;}if(isWhite(binaryBufferedImage.getRGB(x+1, y+1)) && isWhite(binaryBufferedImage.getRGB(x-1, y-1))){flag = true;}if(flag){binaryBufferedImage.setRGB(x,y,-1);}}}}// 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(destDir, sfile.getName()));}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 histogramfor (int x = 0; x < w; x++){for (int y = 0; y < h; y++){int red = 0xFF & gray[x][y];histData[red]++;}}// Total number of pixelsint 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 Backgroundif (wB == 0)continue;wF = total - wB; // Weight Foregroundif (wF == 0)break;sumB += (float) (t * histData[t]);float mB = sumB / wB; // Mean Backgroundfloat mF = (sum - sumB) / wF; // Mean Foreground// Calculate Between Class Variancefloat varBetween = (float) wB * (float) wF * (mB - mF) * (mB - mF);// Check if new maximum foundif (varBetween > varMax){varMax = varBetween;threshold = t;}}return threshold;}
}

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