验证码识别部分代码

int tagBITM::dajin( uint8_t pe[],int t,long *pg1 )//大津法取阈值，pe为传入的保存有像素值的数组，pg1用来记录直方图分布曲线
{
    int i, j, p ;
    long pg[257]; //保存每种像素的点数
    double A = 0.0, An = 0.0 ,B = 0.0, Bn = 0.0, u = 0.0, v = 0.0, qqq[256] , max = 0.0, min = 0.0;

    for(i = 0; i < t - 1; i++)
    {
        pg[pe[i]]++; //计算每个像素点的个数
    }


    An = Bn = 0;
    for( i = 1; i < 256; i++)
    {
        An += pg[i];
        Bn += (pg[i] * i);
    }


    for(j = 0; j < 256; j++)
    {

        A = B = 0;
        for( i = 0 ; i <= j ; i++)
        {
            A += pg[i];
            B += (pg[i] * i);
        }
        if( A )
            u = B / A;
        else
            u = 0;
        if( (An - A)  )
            v = ( Bn - B) / ( An - A );
        else
            v = 0;
        qqq[j] = A * ( An -A ) * ( u - v ) * ( u - v );  //计算类间方差
    }

    max = min = qqq[0];
    p = 0;
    for( i = 1; i < 256; i++)   //寻找判别函数最大值
    {
        if( qqq[i] > max )
        {
            max = qqq[i];
            p = i;
        }
        else if( qqq[i] < min)
        {
            min = qqq[i] ;
        }
    }
        cout<<"**** p"<<p<<endl;
    if( pg1 != 0)
    {
        for(i = 0 ; i < 256; i++)
        {
            pg1[i] = (long)(120 *(qqq[i] - min)/(max - min)); //判别函数坐标度转换
        }
    }

    return p; //取判别函数最大值的灰度为器阈值
}
大津法所算的阈值不一定准确，所以说，算法有时也是不灵的..........

//中值滤波求中值函数
uint8_t medvalue(uint8_t buf[], int n, int m)
{
    int i,j, k, f;

    for( i = 1; i < n; i++)
    {
        for(j = n-1, f = 0; j >= i; j--)
        {
            if( buf[j] > buf[j-1])
            {
                k = buf[i];
                buf[j] = buf[j-1];
                buf[j-1] = k;
                f = 1;
            }
        }
        if( f == 0 )
            break;
    }

    return (buf[m]);
}
//中值滤波去噪点
int tagBITM::median( char image_name[]) //传入的是图片的名字
{
    FILE *fp;
    int i, j, flag = 9, n;
    uint8_t list1[30][72] ,list0[30][72]; //30，70是图片的高度和宽度，70+2是因为，bmp图像每行字节数是4的倍数，不足会补零，
    uint8_t a[2], buff1[9];

    fp = fopen(image_name,"rb+");
    if( fp == NULL )
    {
        cout<<"图片打开失败!\n";
        return 1;
    }


    fseek(fp, 54, 0);
    for( i = 0; i < biheight; i++)
    {
        for(j = 0; j < biwidth; j++)
        {
            fread(&list1[i][j],1,1,fp);
            fseek(fp,2,SEEK_CUR);
        }
            if( (biwidth * 3 % 4) != 0)
            {
                fread(&a, 1, 2, fp);
                list1[i][j++] = a[0];
                list1[i][j] = a[1];
            }
    }

    for( i = 1; i < biheight-1; i++)
    {
        for(j = 1; j < biwidth-1;j++)
        {
            buff1[0] = list1[i-1][j];
            buff1[1] = list1[i][j];
            buff1[2] = list1[i+1][j];
            buff1[3] = list1[i][j-1];
            buff1[4] = list1[i][j+1];
            buff1[5] = list1[i-1][j-1];
            buff1[6] = list1[i-1][j+1];
            buff1[7] = list1[i+1][j-1];
            buff1[8] = list1[i+1][j+1];

        list0[i][j] = (uint8_t)medvalue(buff1, flag, flag/2); //函数调用
        }
    }
    for(i = 0; i < biwidth; i++)
    {
        list0[0][i] = list1[0][i];
        list0[biheight-1][i] = list1[biheight-1][i];
    }
    for(i = 0; i < biheight; i++)
    {
        list0[i][0] = list1[i][0];
        list0[i][biwidth-1] = list1[i][biwidth-1];
        list0[i][biwidth] = list1[i][biwidth];
        list0[i][biwidth+1] = list1[i][biwidth+1];
    }

    fseek(fp , 54, 0);
    for( i = 0; i < biheight; i++)
    {
        for( j = 0; j < biwidth; j++)
        {
            for(n = 0; n < 3; n++)
            {
                fwrite(&list0[i][j],1,1,fp);
            }
            //    printf("i %d j %d %x **", i ,j ,list0[i][j] );
        }
        fwrite(&list0[i][j++], 1, 1, fp);
        fwrite(&list0[i][j], 1, 1, fp);
    }

    fclose(fp);

        return 0;

}
中值滤波算法可不是盖的。。。。。

//二值化
int tagBITM::binary( char image_name[], int p  ， uint8_t list[][weight] ) //传入图片名，阈值，空的数组，用来带回二值化后的像素值
{
    FILE *fp;
    int i, j, k = 0, n = 0;
    int num = 0 ;

    uint8_t a[2], *pext;

    fp = fopen(image_name, "rb+");

    if( fp == NULL )
    {
        cout<<"图片二值化失败!\n";
        return 1;
    }

    num = biwidth * biheight * 3 + 60;
    cout<<"num** = "<<num<<endl;
    pext = new uint8_t [num]; //记录各像素转化后的灰度值

    fseek(fp, 54 ,0);

    for( i = 0 ; i < biheight; i++)  //二值化过程
    {
        for(j = 0; j < biwidth; j++ )
        {
            fread(&b,1,1,fp); //读把b像素单元
            fseek(fp, 2, SEEK_CUR); //跳过g,r像素
            for( n = 0; n < 3; n++)  //循环3遍是因为灰度化后，每个像素的b,g,r相等。
            {
                if( b >= p )
                    pext[k++] = 255 ;
                else
                {
                    pext[k++] = 0;
                }
            }
            list[i][j] = pext[k-1];
        }
        if( biwidth * 3 % 4 != 0)
        {
            fread(&a, 1, 2, fp);
            pext[k++] = a[0];
            list[i][j++] = a[0];
            pext[k++] = a[1];
            list[i][j] = a[1];
        }
    }

    fseek( fp, 54, 0);
    for(i = 0; i < biheight; i++)
        fwrite( pext,1,k-1,fp);
            fclose(fp);

            delete [] pext;
            return 0;
}

//线条细化
int tagBITM::hildich(char image_name[],uint8_t list0[][72]) //传入图片名，二值化带出的像素矩阵
{
    int i, k , j, m, n, flag;
    FILE *fp;
    int sum = 0, f[10];
    uint8_t list[30][70], b;


    fp = fopen(image_name, "rb+");
    if( fp == NULL)
    {
        cout<<"图片打开失败！\n";
        exit(1);
    }
    for( i = 0; i < biheight; i++)
        for( j = 0; j < biwidth; j++)
        {
            fread(&b, 1,1,fp);
            fseek(fp, 2, SEEK_CUR);
            if( b == 0 )
            {
                list[i][j] = 1;
            }
            else
                list[i][j] = 0;
        }

    i = 1;
    do
    {
        flag = 0;
        for( k = 1; k < biheight - 1; k++)
        {
            for(j = 1; j < biwidth - 1; j++)
            {
                if( list[k][j] != 1 )
                    continue;
                f[1] = list[k][j+1];
                f[2] = list[k-1][j+1];
                f[3] = list[k-1][j];
                f[4] = list[k-1][j-1];
                f[5] = list[k][j-1];
                f[6] = list[k+1][j-1];
                f[7] = list[k+1][j];
                f[8] = list[k+1][j+1];

                for(m = 1, n = 0; m <= 8; m++)
                {
                    if( f[m] == -i ) //此点是在同一大循环中清除的
                    {
                        f[m] = 1; //计算中恢复原值
                        n++; //记下此类点的点数
                    }
                    else if( f[m] < 0 )
                        f[m] = 0; //此点已在前面处理中清除
                }
                sum = f[1] + f[3] + f[5] + f[7] ; //计算4邻点中的1的点数
                if( sum >= 4)
                    continue; //4邻点全为1是不能去除

                f[9] = f[1];
                for( m = 1,sum = 0; m <= 8; m++)
                    if( f[m] == 1)
                        sum++;
                if( (sum <= 1) || (sum-n == 0))
                    continue;     //是孤立点，不能去除

                sum = cross( f );
                if( sum != 1)
                    continue;
                if( list[k-1][j] == -i)
                {
                    f[3] = 0;
                    sum = cross(f);
                    if( sum != 1)
                        continue;
                }
                if( list[k][j-1] == -i)
                {
                    f[5] = 0;
                    sum = cross(f);
                    if( sum != 1)
                        continue;
                }
                list0[j][k] = 255;
                list[k][j] = -i;
                flag = 1;
            }
        }
        i++;
    }while( flag == 1);

    fseek(fp, 54, 0);
    for(i = 0; i < biheight; i++)
    {
        for( j = 0; j < biwidth; j++)
        {
            for(n = 0; n < 3; n++)
            {
                fwrite(&list0[i][j],1,1,fp);
            }
        }
        fwrite(&list0[i][j++], 1, 1, fp);
        fwrite(&list0[i][j], 1, 1, fp);
    }

    fclose(fp);

    return 0;
}