OpenCV实现图像背景虚化效果原理详解

//引导滤波器
Mat guidedFilter(Mat& srcMat, Mat& guidedMat, int radius, double eps)
{
    srcMat.convertTo(srcMat, CV_64FC1);
    guidedMat.convertTo(guidedMat, CV_64FC1);
    // 计算均值
    Mat mean_p, mean_I, mean_Ip, mean_II;
    boxFilter(srcMat, mean_p, CV_64FC1, Size(radius, radius));                      // 生成待滤波图像均值mean_p 
    boxFilter(guidedMat, mean_I, CV_64FC1, Size(radius, radius));                   // 生成引导图像均值mean_I   
    boxFilter(srcMat.mul(guidedMat), mean_Ip, CV_64FC1, Size(radius, radius));      // 生成互相关均值mean_Ip
    boxFilter(guidedMat.mul(guidedMat), mean_II, CV_64FC1, Size(radius, radius));   // 生成引导图像自相关均值mean_II
    // 计算相关系数、Ip的协方差cov和I的方差var------------------
    Mat cov_Ip = mean_Ip - mean_I.mul(mean_p);
    Mat var_I = mean_II - mean_I.mul(mean_I);
    // 计算参数系数a、b
    Mat a = cov_Ip / (var_I + eps);
    Mat b = mean_p - a.mul(mean_I);
    // 计算系数a、b的均值
    Mat mean_a, mean_b;
    boxFilter(a, mean_a, CV_64FC1, Size(radius, radius));
    boxFilter(b, mean_b, CV_64FC1, Size(radius, radius));
    // 生成输出矩阵
    Mat dstImage = mean_a.mul(srcMat) + mean_b;
    return dstImage;
}

int main()
{
    Mat resultMat; 
    Mat vSrcImage[3], vResultImage[3];
    Mat vResultImage[3];    
    Mat srcImage = imread("1.jpg");
    imshow("源图像", srcImage);
    // 对源图像进行通道分离，并对每个分通道进行引导滤波
    split(srcImage, vSrcImage);
    for (int i = 0; i < 3; i++)
    {
        Mat tempImage;
        vSrcImage[i].convertTo(tempImage, CV_64FC1, 1.0 / 255.0);
        Mat cloneImage = tempImage.clone();
        Mat resultImage = guidedFilter(tempImage, cloneImage, 5, 0.3);
        vResultImage[i] = resultImage;
    }
    // 将分通道导向滤波后结果合并
    merge(vResultImage, 3, resultMat);
    imshow("背景虚化特效", resultMat);
    waiTKEy(0);
    return 0;
}