本教程开始介绍的源代码将对每一帧执行PSNR测量，并且只对PSNR低于输入值的帧进行SSIM测量。为了可视化的目的，我们在OpenCV窗口中展示两幅图像，并将PSNR和MSSIM值打印到控制台。期望看到如下内容：
video-input-psnr-ssim.cpp 将两个视频的每一帧逐一读取并计算其峰值信号噪声比（PSNR） 和 结构相似性指标（MSSIM）
#include <iostream> // 标准输入输出流
#include <string> // 字符串操作库
#include <iomanip> // 输入输出流格式控制
#include <sstream> // 字符串与数字转换#include <opencv2/core.hpp> // OpenCV基础结构 (cv::Mat, Scalar)
#include <opencv2/imgproc.hpp> // 高斯模糊处理
#include <opencv2/videoio.hpp>
#include <opencv2/highgui.hpp> // OpenCV窗口输入输出using namespace std;
using namespace cv;double getPSNR(const Mat& I1, const Mat& I2); // 声明计算PSNR值的函数
Scalar getMSSIM(const Mat& I1, const Mat& I2); // 声明计算MSSIM值的函数static void help() // 帮助文本输出函数
{cout<< "------------------------------------------------------------------------------" << endl<< "This program shows how to read a video file with OpenCV. In addition, it "<< "tests the similarity of two input videos first with PSNR, and for the frames "<< "below a PSNR trigger value, also with MSSIM." << endl<< "Usage:" << endl<< "./video-input-psnr-ssim <referenceVideo> <useCaseTestVideo> <PSNR_Trigger_Value> <Wait_Between_Frames> " << endl<< "--------------------------------------------------------------------------" << endl<< endl;
}int main(int argc, char *argv[]) // 主函数
{help(); // 显示帮助文本if (argc != 5) // 检查输入参数数量{cout << "Not enough parameters" << endl;return -1;}stringstream conv; // 创建字符串流const string sourceReference = argv[1], sourceCompareWith = argv[2]; // 引用视频和待比较视频路径int psnrTriggerValue, delay; // PSNR阈值和帧间延迟conv << argv[3] << endl << argv[4]; // 将参数放入字符串流conv >> psnrTriggerValue >> delay; // 从字符串流提取参数值int frameNum = -1; // 帧计数器VideoCapture captRefrnc(sourceReference), captUndTst(sourceCompareWith); // 创建视频捕捉对象if (!captRefrnc.isOpened()) // 检查引用视频文件是否成功打开{cout << "Could not open reference " << sourceReference << endl;return -1;}if (!captUndTst.isOpened()) // 检查待比较视频文件是否成功打开{cout << "Could not open case test " << sourceCompareWith << endl;return -1;}Size refS = Size((int) captRefrnc.get(CAP_PROP_FRAME_WIDTH),(int) captRefrnc.get(CAP_PROP_FRAME_HEIGHT)),uTSi = Size((int) captUndTst.get(CAP_PROP_FRAME_WIDTH),(int) captUndTst.get(CAP_PROP_FRAME_HEIGHT)); // 获取视频的尺寸if (refS != uTSi) // 检查两个视频的尺寸是否一致{cout << "Inputs have different size!!! Closing." << endl;return -1;}const char* WIN_UT = "Under Test"; // 待测视窗名称const char* WIN_RF = "Reference"; // 参考视窗名称// 创建窗口namedWindow(WIN_RF, WINDOW_AUTOSIZE);namedWindow(WIN_UT, WINDOW_AUTOSIZE);moveWindow(WIN_RF, 400, 0); moveWindow(WIN_UT, refS.width, 0); // 输出参考帧分辨率和视频帧数cout << "Reference frame resolution: Width=" << refS.width << " Height=" << refS.height<< " of nr#: " << captRefrnc.get(CAP_PROP_FRAME_COUNT) << endl;// 输出PSNR阈值信息cout << "PSNR trigger value " << setiosflags(ios::fixed) << setprecision(3)<< psnrTriggerValue << endl;Mat frameReference, frameUnderTest; // 创建存储参考帧和待测帧的Mat对象double psnrV; // PSNR值Scalar mssimV; // MSSIM值for(;;) // 无限循环，用于显示视频帧并处理{captRefrnc >> frameReference; // 读取参考帧captUndTst >> frameUnderTest; // 读取待测帧if (frameReference.empty() || frameUnderTest.empty()) // 如果读取为空，则表明视频结束{cout << " < < < Game over! > > > ";break;}++frameNum; // 增加帧数cout << "Frame: " << frameNum << "# ";// 计算PSNR值psnrV = getPSNR(frameReference,frameUnderTest);cout << setiosflags(ios::fixed) << setprecision(3) << psnrV << "dB";// 如果PSNR值低于阈值，并且非零，则计算MSSIM值if (psnrV < psnrTriggerValue && psnrV){mssimV = getMSSIM(frameReference, frameUnderTest);// 输出MSSIM的RGB通道值cout << " MSSIM: "<< " 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;// 显示参考帧和待测帧imshow(WIN_RF, frameReference);imshow(WIN_UT, frameUnderTest);// 等待按键，如果按下ESC键，则退出循环char c = (char)waitKey(delay);if (c == 27) break;}return 0; // 返回0，表明程序正常退出
}// 计算PSNR值的函数
double getPSNR(const Mat& I1, const Mat& I2)
{Mat s1;absdiff(I1, I2, s1); // 计算I1和I2的绝对差值 |I1 - I2|s1.convertTo(s1, CV_32F); // 将结果转换为32位浮点数，因为不能在8位上进行平方运算s1 = s1.mul(s1); // 计算差值的平方 |I1 - I2|^2Scalar s = sum(s1); // 计算每个通道的元素和double sse = s.val[0] + s.val[1] + s.val[2]; // 将通道的和加起来if (sse <= 1e-10) // 如果值很小，则返回0return 0;else{double mse = sse / (double)(I1.channels() * I1.total()); // 计算均方误差MSEdouble psnr = 10.0 * log10((255 * 255) / mse); // 根据MSE计算PSNR值return psnr;}
}// 计算MSSIM值的函数
Scalar getMSSIM(const Mat& i1, const Mat& i2)
{const double C1 = 6.5025, C2 = 58.5225; // 定义常数C1和C2/***************************** 初始化 **********************************/int d = CV_32F;Mat I1, I2;i1.convertTo(I1, d); // 将图像转换为32位浮点数进行计算i2.convertTo(I2, d);Mat I2_2 = I2.mul(I2); // 计算I2的平方Mat I1_2 = I1.mul(I1); // 计算I1的平方Mat I1_I2 = I1.mul(I2); // 计算I1和I2的乘积/**************************** 结束初始化 *******************************/Mat mu1, mu2; // 预先计算GaussianBlur(I1, mu1, Size(11, 11), 1.5); // 计算均值mu1GaussianBlur(I2, mu2, Size(11, 11), 1.5); // 计算均值mu2Mat 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); // 计算标准差sigma1_2sigma1_2 -= mu1_2;GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5); // 计算标准差sigma2_2sigma2_2 -= mu2_2;GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5); // 计算协方差sigma12sigma12 -= mu1_mu2;/ 计算公式 Mat t1, t2, t3;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;t1 = t1.mul(t2); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))Mat ssim_map;divide(t3, t1, ssim_map); // ssim_map = t3./t1;Scalar mssim = mean(ssim_map); // 计算ssim_map的平均值return mssim;
}此代码是一个用于比较两个视频文件的相似性的C++程序，它使用OpenCV库来读取和处理视频帧。首先，程序通过计算峰值信噪比（PSNR）来比较每对视频帧。如果PSNR值低于某个阈值，程序额外使用结构相似性指数（MSSIM）进行比较。结果随着视频播放实时显示，并通过命令行参数控制一些基本设置，如PSNR阈值和帧间等待时间。程序还能够在窗口中实时显示参考视频和待测视频的帧。
cout << setiosflags(ios::fixed) << setprecision(3) << psnrV << "dB";
