按照索引的細化提取骨架演算法的java實現

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近期研究驗證碼識別,也就看了一些Image Recognition的資料,其中一種字型細化提取骨架的演算法網上沒有java版的實現,所以就選取了一個python實現版本進行java代碼的改寫..

python版實現的地址:

http://www.cnblogs.com/xianglan/archive/2011/01/01/1923779.html

由於我不是很懂python文法,也是直接去的w3c看的教程,為此還掉進了一個坑..詳見:

http://www.cnblogs.com/chyu/p/4335950.html

由於我對影像處理這裡不是很在行,python也是臨時看的,故這種細化提取骨架的演算法也就是直接移植原代碼,並沒做什麼最佳化之類..代碼很粗糙..

package com.ocr.imgocr;import java.awt.Color;import java.awt.image.BufferedImage;import java.io.File;import java.io.IOException;import javax.imageio.ImageIO;public class Thin {    //索引數組    private static Integer[] array = {0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,             1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,             0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,             1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,             1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,             0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,             1,1,0,0,1,1,0,0,1,1,0,1,1,1,0,1,             0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,             0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,             1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,             0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,             1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,             1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,             1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,             1,1,0,0,1,1,0,0,1,1,0,1,1,1,0,0,             1,1,0,0,1,1,1,0,1,1,0,0,1,0,0,0};        public static boolean isWhite(int colorInt) {          Color color = new Color(colorInt);          if (color.getRed() + color.getGreen() + color.getBlue() > 400) {              return true;          }          return false;      }     public static BufferedImage VThin(BufferedImage image,Integer[] array){        int h = image.getHeight();        int w = image.getWidth();        int NEXT = 1;        for(int i=0;i<h;i++){            for(int j=0;j<w;j++){                if (NEXT == 0){                    NEXT = 1;                }else{                    int M ;                    if( 0<j&&j<w-1){                        if(isBlack(image.getRGB(j-1,i))&&isBlack(image.getRGB(j,i))&&isBlack(image.getRGB(j+1,i))){                            M=0;                        }else{                            M=1;                        }                    }else {                        M = 1;                    }                    if(isBlack(image.getRGB(j,i))&&M!=0){                              int[] a = {0,0,0,0,0,0,0,0,0};                        for(int k=0;k<3;k++){                            for(int l=0;l<3;l++){                                if ((-1<(i-1+k)&&(i-1+k)<h) && (-1<(j-1+l)&&(j-1+l)<w) && isWhite(image.getRGB(j-1+l,i-1+k))){                                    a[k*3+l] = 1;                                }                            }                        }                        int sum = a[0]*1+a[1]*2+a[2]*4+a[3]*8+a[5]*16+a[6]*32+a[7]*64+a[8]*128;                        if(array[sum]==0){                            image.setRGB(j, i, Color.black.getRGB());                        }else{                            image.setRGB(j, i, Color.white.getRGB());                        }                        if (array[sum] == 1){                            NEXT = 0;                        }                    }                }            }        }        return image;    }            public static BufferedImage HThin(BufferedImage image,Integer[] array){        int h = image.getHeight();        int w = image.getWidth();        int NEXT = 1;        for(int j=0;j<w;j++){             for(int i=0;i<h;i++){                if (NEXT == 0){                    NEXT = 1;                }else{                    int M;                    if(0<i&&i<h-1){                        if(isBlack(image.getRGB(j,i-1))&&isBlack(image.getRGB(j,i))&&isBlack(image.getRGB(j,i+1))){                            M=0;                        }else{                            M=1;                        }                    }else{                        M = 1;                    }                    if (isBlack(image.getRGB(j,i)) && M != 0){                                          int[] a = {0,0,0,0,0,0,0,0,0};                        for(int k=0;k<3;k++){                            for(int l=0;l<3;l++){                                if ((-1<(i-1+k)&&(i-1+k)<h) && (-1<(j-1+l)&&(j-1+l)<w )&& isWhite(image.getRGB(j-1+l,i-1+k))){                                    a[k*3+l] = 1;                                }                            }                        }                        int sum = a[0]*1+a[1]*2+a[2]*4+a[3]*8+a[5]*16+a[6]*32+a[7]*64+a[8]*128;                        if(array[sum]==0){                            image.setRGB(j, i, Color.black.getRGB());                        }else{                            image.setRGB(j, i, Color.white.getRGB());                        }                        if (array[sum] == 1){                            NEXT = 0;                        }                    }                }            }        }        return image;    }            public static BufferedImage Xihua(BufferedImage image,Integer[] array){        int num=10;        BufferedImage iXihua = image;        for(int i=0;i<num;i++){            VThin(iXihua,array);            HThin(iXihua,array);        }        return iXihua;    }    public static BufferedImage Two(BufferedImage image){        int w = image.getWidth();        int h = image.getHeight();        BufferedImage iTwo = image;        for(int i=0;i<h;i++){            for(int j=0;j<w;j++){                if(isBlack(image.getRGB(j,i))){                    iTwo.setRGB(j, i, Color.BLACK.getRGB());                }else{                    iTwo.setRGB(j, i, Color.WHITE.getRGB());                }            }        }        return iTwo;    }        public static boolean isBlack(int colorInt) {        Color color = new Color(colorInt);        if (color.getRed() + color.getGreen() + color.getBlue() <= 400) {            return true;        }        return false;    }    public static void main(String[] args) {        try {            //原始圖片路徑            BufferedImage image = ImageIO.read(new File("image"+File.separator+"0.jpg"));            //二值化            BufferedImage iTwo = Two(image);            ImageIO.write(iTwo, "jpg", new File("image"+File.separator+"two.jpg"));            //細化            BufferedImage iThin = Xihua(image,array);            ImageIO.write(iThin, "jpg", new File("image"+File.separator+"thin.jpg"));                    } catch (IOException e) {            e.printStackTrace();        }            }}

 

按照索引的細化提取骨架演算法的java實現