opencv计算图像互信息熵

//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<img.rows; m++)
{// 有效访问行列的方式
const uchar* t = img.ptr<uchar>(m);
for (int n = 0; n<img.cols; n++)
{
int i = t
;
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<uchar>(m1);
const uchar* t2 = img2.ptr<uchar>(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;

}