opencv计算图像互信息熵

主要代码如下：

//单幅图像信息熵计算
double Entropy(Mat img)
{
    double temp[256] = { 0.0 };

    // 计算每个像素的累积值
    for (int m = 0; m// 有效访问行列的方式
        const uchar* t = img.ptr(m);
        for (int n = 0; nint i = t[n];
            temp[i] = temp[i] + 1;
        }
    }

    // 计算每个像素的概率
    for (int i = 0; i<256; i++)
    {
        temp[i] = temp[i] / (img.rows*img.cols);
    }

    double result = 0;
    // 计算图像信息熵
    for (int i = 0; i<256; i++)
    {
        if (temp[i] == 0.0)
            result = result;
        else
            result = result - temp[i] * (log(temp[i]) / log(2.0));
    }

    return result;

}




// 两幅图像联合信息熵计算
double ComEntropy(Mat img1, Mat img2, double img1_entropy, double img2_entropy)
{
    double temp[256][256] = { 0.0 };

    // 计算联合图像像素的累积值
    for (int m1 = 0, m2 = 0; m1 < img1.rows, m2 < img2.rows; m1++, m2++)
    {    // 有效访问行列的方式
        const uchar* t1 = img1.ptr(m1);
        const uchar* t2 = img2.ptr(m2);
        for (int n1 = 0, n2 = 0; n1 < img1.cols, n2 < img2.cols; n1++, n2++)
        {
            int i = t1[n1], j = t2[n2];
            temp[i][j] = temp[i][j] + 1;
        }
    }

    // 计算每个联合像素的概率
    for (int i = 0; i < 256; i++)
    {
        for (int j = 0; j < 256; j++)

        {
            temp[i][j] = temp[i][j] / (img1.rows*img1.cols);
        }
    }

    double result = 0.0;
    //计算图像联合信息熵
    for (int i = 0; i < 256; i++)
    {
        for (int j = 0; j < 256; j++)

        {
            if (temp[i][j] == 0.0)
                result = result;
            else
                result = result - temp[i][j] * (log(temp[i][j]) / log(2.0));
        }
    }

    //得到两幅图像的互信息熵
    img1_entropy = Entropy(img1);
    img2_entropy = Entropy(img2);
    result = img1_entropy + img2_entropy - result;

    return result;

}

  

这是项目的一部分核心程序