opencv视频PSNR结果

问题遇到的现象和发生背景

计算视频压缩前后的PSNR数据

问题相关代码，请勿粘贴截图

#include
#include
#include // 控制浮动类型的打印精度
#include // 字符串和数值的转换

#include <opencv2/core.hpp> // CV::Mat,Scalar
#include <opencv2/imgproc.hpp> // 高斯平滑
#include <opencv2/videoio.hpp> // 视频
#include <opencv2/highgui.hpp>

using namespace std;
using namespace cv;

double getPSNR(const Mat& I1, const Mat& I2);
Scalar getMSSIM(const Mat& I1, const Mat& I2);

int main(int argc, char *argv[])
{
//const string sourceReference = "C:/dvp/dataset/abc.mp4";
//const string sourceCompareWith = "C:/dvp/dataset/output1.mp4";

int frameNum = -1;                                                          // 计算帧数
int psnrTriggerValue = 35;

VideoCapture captRefrnc("C:/dvp/dataset/abc.mp4"), captUndTst("C:/dvp/dataset/output1.mp4");    // 获取视频
if (!captRefrnc.isOpened() || !captUndTst.isOpened()) { return -1; }

Size refS = Size((int)captRefrnc.get(CAP_PROP_FRAME_WIDTH),                 // 视频帧的大小
    (int)captRefrnc.get(CAP_PROP_FRAME_HEIGHT));
Size uTSi = Size((int)captUndTst.get(CAP_PROP_FRAME_WIDTH),
    (int)captUndTst.get(CAP_PROP_FRAME_HEIGHT));
if (refS != uTSi) { return -1; }                                            // 视频帧大小应相同

const char* WIN_UT = "Under Test";                                          // 显示窗口                         
const char* WIN_RF = "Reference";
namedWindow(WIN_RF, WINDOW_AUTOSIZE);
namedWindow(WIN_UT, WINDOW_AUTOSIZE);
moveWindow(WIN_RF, 0, 0);
moveWindow(WIN_UT, refS.width, 0);

cout << "Reference frame resolution: Width=" << refS.width << "  Height=" << refS.height
    << " of nr#: " << captRefrnc.get(CAP_PROP_FRAME_COUNT) << endl;
cout << "PSNR trigger value " << psnrTriggerValue << endl;

Mat frameReference, frameUnderTest;
double psnrV;                                                               // PSNR方法
Scalar mssimV;                                                              // SSIM方法

for (;;)
{
    captRefrnc >> frameReference;
    captUndTst >> frameUnderTest;
    if (frameReference.empty() || frameUnderTest.empty())
    {
        cout << "The End" << endl;
        break;
    }

    ++frameNum;
    cout << "Frame:" << frameNum << "#";                                    // 当前帧数，0开始

    psnrV = getPSNR(frameReference, frameUnderTest);                        // 定义的PSNR函数

                                                                            // setiosflags(ios::fixed)用定点方式显示实数，setprecision(n)可控制输出流显示浮点数的数字个数
    cout << setiosflags(ios::fixed) << setprecision(3) << psnrV << "dB";

    if (psnrV < psnrTriggerValue && psnrV)                                  // PSNR结果不为零且小于输入值
    {
        mssimV = getMSSIM(frameReference, frameUnderTest);

        cout << "\tMSSIM:" << " R " << setiosflags(ios::fixed) << setprecision(2) << mssimV.val[2] * 100 << "%"
            << " G " << setiosflags(ios::fixed) << setprecision(2) << mssimV.val[1] * 100 << "%"
            << " B " << setiosflags(ios::fixed) << setprecision(2) << mssimV.val[0] * 100 << "%";
    }

    cout << endl;

    imshow(WIN_RF, frameReference);
    imshow(WIN_UT, frameUnderTest);

    char c = (char)waitKey(30);
    if (c == 27) break;
}

return 0;

}

double getPSNR(const Mat& I1, const Mat& I2) // PSNR方法
{
Mat s1;
absdiff(I1, I2, s1); // |I1 - I2|
s1.convertTo(s1, CV_32F); // 转换为32位进行运算
s1 = s1.mul(s1); // |I1 - I2|^2

Scalar s = sum(s1);                                             // 各个通道求和

double sse = s.val[0] + s.val[1] + s.val[2];                    // 所有通道的值相加在一起
if (sse <= 1e-10)       // 当值太小时近似于0，由公式可知分母为0时需另外对待，使用SSIM方法
    return 0;
else
{
    double mse = sse / (double)(I1.channels() * I1.total());    // 公式
    double psnr = 10.0 * log10((255 * 255) / mse);
    return psnr;
}

}
Scalar getMSSIM(const Mat& i1, const Mat& i2) // SSIM方法
{
const double C1 = 6.5025, C2 = 58.5225;

Mat I1, I2;
i1.convertTo(I1, CV_32F);                                       // 转换为32位进行运算
i2.convertTo(I2, CV_32F);

Mat I1_2 = I1.mul(I1);                                          // I1^2
Mat I2_2 = I2.mul(I2);                                          // I2^2
Mat I1_I2 = I1.mul(I2);                                         // I1 * I2

Mat sigma1_2, sigma2_2, sigma12;                                // 先平方再高斯滤波
GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);
GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);

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);                                       // 先高斯滤波再平方
Mat mu2_2 = mu2.mul(mu2);
Mat mu1_mu2 = mu1.mul(mu2);

sigma1_2 -= mu1_2;                                              // 两种方式的差值
sigma2_2 -= mu2_2;
sigma12 -= mu1_mu2;

Mat t1, t2, t3, t4;
t1 = 2 * mu1_mu2 + C1;
t2 = 2 * sigma12 + C2;
t3 = t1.mul(t2);                                                // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))

t1 = mu1_2 + mu2_2 + C1;
t2 = sigma1_2 + sigma2_2 + C2;
t4 = t1.mul(t2);                                                // t4 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))

Mat ssim_map;
divide(t3, t4, ssim_map);                                       // ssim_map =  t3./t4;
Scalar mssim = mean(ssim_map);                                  // ssim map矩阵的平均值

return mssim;

}

运行结果及报错内容

计算视频的PSNR代码没有报错，但是结果框出不来数据

![img](https://img-mid.csdnimg.cn/release/static/image/mid/ask/553855923456131.png "#left