OpenCV realization of SSIM structure similarity algorithm

Introduction to the SSIM algorithm:
http://blog.csdn.net/chaipp0607/article/details/70158835

The code does a bit of processing:
(1) Set up two sets of contrast test, the original image of the kernel for the 5*5 filter, compared with the original image to obtain the Ssim index. The original image is filtered by 10*10, and the Ssim index is obtained by comparison with the original image.
(2) Convert the Ssim index to percentile
(3) using Gaussian blur to obtain the mean value of the image

Code reference:
Http://jingyan.baidu.com/article/456c463b67aa310a5931447a.html

#include <opencv2/imgproc/imgproc.hpp> #include <opencv2/core/core.hpp> #include <opencv2/highgui/ 
Highgui.hpp> #include <iostream> using namespace std;

using namespace CV;
Scalar Getmssim (Mat Inputimage1, Mat inputimage2);
    int main () {Mat BlurImage1;
    Mat BlurImage2;
    Mat srcimage = Imread ("1.jpg");
    Blur (Srcimage, BlurImage1, Size (5, 5));
    Blur (Srcimage,blurimage2,size (10,10));
    Scalar SSIM1 = Getmssim (Srcimage, BlurImage1);
    Scalar SSIM2 = Getmssim (Srcimage, BlurImage2);
    printf ("Fuzzy 5*5 channel 1:%f\n", Ssim1.val[0] * 100);
    printf ("Fuzzy 5*5 channel 2:%f\n", Ssim1.val[1] * 100);
    printf ("Fuzzy 5*5 channel 3:%f\n", Ssim1.val[2] * 100);
    printf ("Fuzzy 5*5:%f\n", (Ssim1.val[2] + ssim1.val[1] + ssim1.val[0])/3 * 100);
    printf ("Fuzzy 10*10 channel 1:%f\n", Ssim2.val[0] * 100);
    printf ("Fuzzy 10*10 channel 2:%f\n", Ssim2.val[1] * 100);
    printf ("Fuzzy 10*10 channel 3:%f\n", Ssim2.val[2] * 100);
    printf ("Fuzzy 10*10:%f\n", (Ssim2.val[2] + ssim2.val[1] + ssim2.val[0])/3 * 100); ImsHow ("original", Srcimage);
    Imshow ("Fuzzy 5*5", BlurImage1);
    Imshow ("Fuzzy 10*10", BlurImage2);
    Waitkey (0);
return 0;
    } Scalar Getmssim (Mat Inputimage1, Mat inputimage2) {mat i1 = Inputimage1;
    Mat i2 = Inputimage2;
    Const double C1 = 6.5025, C2 = 58.5225;
    int d = cv_32f;
    Mat I1, I2;
    I1.convertto (I1, D);
    I2.convertto (I2, D);
    Mat i2_2 = I2.mul (I2);
    Mat i1_2 = I1.mul (I1);
    Mat I1_i2 = I1.mul (I2);
    Mat MU1, MU2;
    Gaussianblur (I1, MU1, Size (11, 11), 1.5);
    Gaussianblur (I2, MU2, Size (11, 11), 1.5);
    Mat mu1_2 = Mu1.mul (MU1);
    Mat mu2_2 = Mu2.mul (MU2);
    Mat MU1_MU2 = Mu1.mul (MU2);
    Mat sigma1_2, sigma2_2, Sigma12;
    Gaussianblur (I1_2, Sigma1_2, Size (11, 11), 1.5);
    Sigma1_2-= mu1_2;
    Gaussianblur (I2_2, Sigma2_2, Size (11, 11), 1.5);
    Sigma2_2-= mu2_2;
    Gaussianblur (I1_i2, Sigma12, Size (11, 11), 1.5);
    Sigma12-= MU1_MU2;
    Mat T1, T2, T3;
    T1 = 2 * MU1_MU2 + C1;
    T2 = 2 * sigma12 + C2;
 T3 = T1.mul (T2);   T1 = mu1_2 + mu2_2 + C1;
    T2 = sigma1_2 + sigma2_2 + C2;
    T1 = T1.mul (T2);
    Mat Ssim_map;
    Divide (T3, T1, Ssim_map);
    Scalar MsSIM = mean (SSIM_MAP);
return MsSIM; }

Printing results:
Blur 5*5 Channel 1:82.523627
Blur 5*5 Channel 2:85.781376
Blur 5*5 Channel 3:85.903646
Fuzzy 5*5:84.736216
Blur 10*10 Channel 1:65.029142
Blur 10*10 Channel 2:69.286267
Blur 10*10 Channel 3:68.664205
Fuzzy 10*10:67.659871