全局二值化的基本方法（一）C/C++

Odele ·

更新时间:2024-09-21

· 592 次阅读

全局二值化的基本方法（一）C/C++

参考链接：https://blog.csdn.net/fly108108/article/details/81104330

MGV平均灰度值法

uint8 mgv_threshold(uint8 *p, uint16 width, uint16 height)
{
    int i, j;
    int temp = 0;
    for(i=0;i<height;i++)
    {
        for(j=0;j<width;j++)
        {
            temp+=*(p+i*width+j);
        }
    }
    return (uint8)(temp/(width*height));
}

OTSU大津法

uint8 otsu_threshold(uint8 *p, uint16 width, uint16 height)
{
    int pixel_count[256];
    float pixel_pro[256];
    int i, j, pixel_sum = width * height;
    uint8 global_threshold = 0;
    uint8* data = p;  //指向像素数据的指针
    //数组初始化
    memset(pixel_count, 0, sizeof(pixel_count));
    memset(pixel_pro, 0, sizeof(pixel_pro));
    //统计灰度级中每个像素在整幅图像中的个数  
    for (i = 0; i < height; i++)
    {
        for (j = 0; j < width; j++)
        {
            pixel_count[(int)data[i * width + j]]++;  //将像素值作为计数数组的下标
        }
    }
    //计算每个像素在整幅图像中的比例  
    float max_pro = 0.0;
    for (i = 0; i  max_pro)
        {
            max_pro = pixel_pro[i];
        }
    }
    //遍历灰度级[0,255]  
    float w0, w1, u0tmp, u1tmp, u0, u1, u, delta_tmp, delta_max = 0;
    for (i = 0; i < 255; i++)     // i作为阈值
    {
        w0 = w1 = u0tmp = u1tmp = u0 = u1 = u = delta_tmp = 0;
        for (j = 0; j < 256; j++)
        {
            if (j  delta_max)
        {
            delta_max = delta_tmp;
            global_threshold = i;
        }
    }
    return global_threshold;
}

KSW最大熵值法

uint8 ksw_threshold(uint8 *p, uint16 width, uint16 height)
{
    int pixel_count[256];         //每个像素值个数
    uint8 global_threshold = 0;   //最大熵对应的灰度
    double property = 0.0;       //像素所占概率
    double entropy_max = -1.0;   //最大熵
    double entropy_front = 0.0;  //前景熵
    double entropy_back = 0.0;   //背景熵
    int i, j, k, pixel_sum = width*height;           //纳入计算的总像素数
    uint8* data = p;  //指向像素数据的指针
    memset(pixel_count, 0, sizeof(pixel_count));
    //统计灰度级中每个像素在整幅图像中的个数  
    for (i = 0; i < height; i++)
    {
        for (j = 0; j < width; j++)
        {
            pixel_count[(int)data[i * width + j]]++;  //将像素值作为计数数组的下标
        }
    }
    for (i = 0; i < 256; i++) 
    {
        //计算背景像素数
        double back_total = 0;
        for (j = 0; j < i; j++)
        {
            back_total += pixel_count[j];
        }
        //背景熵
        for (j = 0; j < i; j++)
        {
            if (pixel_count[j] != 0)
            {
                property = pixel_count[j] / back_total;
                entropy_back += -property * logf((float)(property));
            }
        }
        //前景熵
        for (k = i; k  entropy_max)    //得到最大熵
        {
            entropy_max = entropy_front + entropy_back;
            global_threshold = i;
        }
        //清空本次计算熵值
        entropy_front = 0.0;
        entropy_back = 0.0;
    }
    return global_threshold;
}

EM迭代法

uint8 em_threshold(uint8 *p, uint16 width, uint16 height)
{
    int pixel_count[256];         //每个像素值个数
    int i, j, pixel_sum = width*height;           //纳入计算的总像素数
    uint8* data = p;  //指向像素数据的指针
    memset(pixel_count, 0, sizeof(pixel_count));
    //统计灰度级中每个像素在整幅图像中的个数  
    for (i = 0; i < height; i++)
    {
        for (j = 0; j < width; j++)
        {
            pixel_count[(int)data[i * width + j]]++;  //将像素值作为计数数组的下标
        }
    }
    int t0 = 0;
    int pixel_sum0 = 0;   
    for(i=0;i<height;i++)
    {
        for(j=0;j<width;j++)
        {
            pixel_sum0+=*(p+i*IMG_W+j);
        }
    }   
    t0 = pixel_sum0/pixel_sum;
    int t1 = 0, t2 = 0;
    int pixel_sum1 = 0, pixel_sum2 = 0;
    int global_threshold = 0;
    while (1)
    {
        for (i = 0; i < t0 + 1; i++)
	{
            t1 += i*pixel_count[i];
            pixel_sum1 += pixel_count[i];
	}       
	for (i = t0 + 1; i < 256; i++)
	{
            t2 += i*pixel_count[i];
            pixel_sum2 += pixel_count[i];
	}
	global_threshold = (t1 / pixel_sum1 + t2 / pixel_sum2) / 2;
	if (global_threshold == t0)    break;
	else                           t0 = global_threshold;
    }
    return (uint8)(global_threshold);
}

作者：聚意66

二值化 C++ c+ 方法

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