把matlab转成opencv c++；代码如下：function X_jian = stmkf_make_video(v,a,length) [m,n,d] = size(double(read(v,1))); pBlurred = zeros(m,n); X_jian = zeros(m,n); Q = 0.026; % Q-参数 K = ones(m,n,d) * 0.5; % 全局变量初始值 P = ones(m,n,d) * 1; % 全局变量初始值 R = ones(m,n,d) * 1; % 全局变量初始值 b = a + length; % 视频的尾 for i = a : b z_k = double(read(v,i)); % 读取某一帧 % 均值滤波 blurred(:,:,1) = blurfilter(z_k(:,:,1),5); % 对R通道做均值滤波 blurred(:,:,2) = blurfilter(z_k(:,:,2),5); % 对G通道做均值滤波 blurred(:,:,3) = blurfilter(z_k(:,:,3),5); % 对B通道做均值滤波 % 双边滤波 I = z_k ./ 255; tempsize = 5; % 5 sigma1 = 5 ; % 5 sigma2 = 0.055; % 0.015 0.055 0.085 bf(:,:,1) = bilateralfilter(I(:,:,1),tempsize,sigma1,sigma2); % 对R通道做双边滤波 bf(:,:,2) = bilateralfilter(I(:,:,2),tempsize,sigma1,sigma2); % 对G通道做双边滤波 bf(:,:,3) = bilateralfilter(I(:,:,3),tempsize,sigma1,sigma2); % 对B通道做双边滤波 %%%%%%% STMKF算法 %%%%%%%% delta = pBlurred - blurred; % 计算好delta后，当前帧要赋值，作为下一帧的输入； pBlurred = blurred; % kalman滤波的循环 R = 1 + R ./ (1 + K); % R_k R_k-1 % R_k-1表示前一帧参数，R_k表示当前帧的参数（自适应过程） X_qian = X_jian; % X_jian是X_k-1，表示前一帧的计算出的数据 P_qian = P + Q .* (delta.^2); % P_qian是, P_k表示协方差矩阵 K = P_qian ./ (P_qian + R); % K是K_k, 表示当前状态下的卡尔曼增益 X = X_qian + K .* (z_k - X_qian); % X是x_k, 表示当前帧经过卡尔曼滤波后的数据 X_jian = (1 - K) .* X + ( K .* bf .* 255 ); % X_jian表示经过BF和KF加权后的输出 P = (1 - K) .* P_qian; % P是P_k,表示计算协方差矩阵，用于下一帧时刻的计算 end end

以下是将该 MATLAB 代码转换为 OpenCV C++ 代码的示例：

#include <opencv2/opencv.hpp>

using namespace cv;

void stmkf_make_video(VideoCapture& v, int a, int length, Mat& X_jian) {
    int m = 0, n = 0, d = 0;
    Mat z_k, blurred, bf;
    Mat pBlurred, Q, K, P, R, delta;
    m = v.get(CAP_PROP_FRAME_HEIGHT);
    n = v.get(CAP_PROP_FRAME_WIDTH);
    d = 3;
    pBlurred = Mat::zeros(m, n, CV_64F);
    X_jian = Mat::zeros(m, n, CV_64F);
    Q = 0.026;
    K = Mat::ones(m, n, d) * 0.5;
    P = Mat::ones(m, n, d) * 1;
    R = Mat::ones(m, n, d) * 1;
    int b = a + length;
    for (int i = a; i < b; i++) {
        v >> z_k;
        // 均值滤波
        blur(z_k, blurred, Size(5, 5));
        // 双边滤波
        Mat I, bf;
        z_k.convertTo(I, CV_64F, 1.0 / 255.0);
        int tempsize = 5;
        double sigma1 = 5;
        double sigma2 = 0.055;
        bilateralFilter(I, bf, tempsize, sigma1, sigma2);
        // STMKF算法
        delta = pBlurred - blurred;
        pBlurred = blurred;
        R = 1 + R / (1 + K);
        Mat X_qian = X_jian;
        Mat P_qian = P + Q * delta.mul(delta);
        K = P_qian / (P_qian + R);
        Mat X = X_qian + K.mul(z_k - X_qian);
        X_jian = (1 - K).mul(X) + (K.mul(bf.mul(255)));
        P = (1 - K).mul(P_qian);
    }
}

请注意，由于 MATLAB 和 OpenCV C++ 之间的语法差异，需要进行一些调整。在使用此代码时，请确保已包含适当的 OpenCV 头文件和库。