圖檔驗證碼--BufferedImage 圖檔驗證碼去除幹擾線

java-BufferedImage 圖檔驗證碼去除幹擾線的方法（ 用于OCR tesseract圖像智能字元識别）

圖檔驗證碼自動識别的功能

網上對于初始圖檔的處理方法有去噪點、灰階化等，唯獨難搜到去除幹擾線的方法,可以比較幹淨地去除幹擾線，提高OCR識别的準确率,“去除幹擾線條“.

測試樣闆圖檔和資料：

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 
         *            需要去噪的圖像 
         * @param destDir 
         *            去噪後的圖像儲存位址 
         * @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();  

            // 灰階化  
            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);  
                    // 圖像加亮（調整亮度識别率非常高）  
                    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]);  
                }  
            }  

            //去除幹擾線條
            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))){
                        //左右均為空時，去掉此點
                        if(isWhite(binaryBufferedImage.getRGB(x-1, y)) && isWhite(binaryBufferedImage.getRGB(x+1, y))){
                            flag = true;
                        }
                        //上下均為空時，去掉此點
                        if(isWhite(binaryBufferedImage.getRGB(x, y+1)) && isWhite(binaryBufferedImage.getRGB(x, y-1))){
                            flag = true;
                        }
                        //斜上下為空時，去掉此點
                        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);                     
                        }
                    }
                }
            }


            // 矩陣列印  
            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
     *            需要去噪的圖像
     * @param destDir
     *            去噪後的圖像儲存位址
     * @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();

        // 灰階化
        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);
                // 圖像加亮（調整亮度識别率非常高）
                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]);
            }
        }

        //去除幹擾線條
        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))){
                    //左右均為空時，去掉此點
                    if(isWhite(binaryBufferedImage.getRGB(x-1, y)) && isWhite(binaryBufferedImage.getRGB(x+1, y))){
                        flag = true;
                    }
                    //上下均為空時，去掉此點
                    if(isWhite(binaryBufferedImage.getRGB(x, y+1)) && isWhite(binaryBufferedImage.getRGB(x, y-1))){
                        flag = true;
                    }
                    //斜上下為空時，去掉此點
                    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);
                    }
                }
            }
        }


        // 矩陣列印
        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;
    }
}