Question

我正在做一个C ++视频稳定/防抖程序： - 获取参考框架上感兴趣的点（使用FAST，SURF，Shi-Matoshi或SIFT，可能会尝试更多） - 使用calcOpticalFlowPyrLK计算Lucas-Kanade光流 - 获取单应矩阵 - 使用warPerspective校正抖动图像（参见下面的代码）

//Calculate the Lucas Kanade optical flow
calcOpticalFlowPyrLK(original, distorted, refFeatures, currFeatures, featuresFound, err);   

//Find the homography between the current frame's features and the reference ones's
if(homographyRansac){
    homography = findHomography(currFeatures, refFeatures, CV_RANSAC); /*CV_RANSAC: Random sample consensus (RANSAC) is an iterative method to
    estimate parameters of a mathematical model from a set of observed data which contains outliers */
}else{
    homography = findHomography(currFeatures, refFeatures, 0);
}


//We use warpPerspective once on the distorted image to get the resulting fixed image
if(multiChannel){
    //Spliting into channels        
    vector <Mat> rgbChannels(channels), fixedChannels;
    split(distortedCopy, rgbChannels);
    recovered = Mat(reSized, CV_8UC3);
    //We apply the transformation to each channel
    for(int i = 0; i < channels; i ++){
        Mat tmp;
        warpPerspective(rgbChannels[i], tmp, homography, reSized);
        fixedChannels.push_back(tmp);
    }
    //Merge the result to obtain a 3 channel corrected image
    merge(fixedChannels, recovered);
}else{
    warpPerspective(distorted, recovered, homography, reSized);
}

如果你有任何替代我的稳定解决方案，请随意这么说，但这不是这个主题的主题。

由于所有这些都需要花费很多时间（我的i5计算机每帧约300毫秒，所以非常长时间播放30分钟的视频）我正在考虑使用CUDA来加快速度。我已经安装了它并继续工作，但是我不确定如何继续下一步。我做了一些测试，我知道最耗时的操作是使用相应的calcOpticalFlowPyrLK和warpPerspective来获得光流和帧校正。理想情况下，至少在开始时，我只会使用这两个函数的CUDA版本，其余部分保持不变。

这可能吗？或者我是否需要重新编写所有内容？

由于

Answer 1

自OpenCV 3.0以来，可以使用CUDA视频稳定实现。除非您确定您的版本更好或更快，否则建议使用已经可用的实现，而不是自己编写。

以下是演示如何使用OpenCV视频稳定模块稳定视频的最小代码。

#include <opencv2/highgui.hpp>
#include <opencv2/videostab.hpp>

using namespace cv::videostab;

int main()
{
    std::string videoFile = "shaky_video.mp4";

    MotionModel model = cv::videostab::MM_TRANSLATION; //Type of motion to compensate
    bool use_gpu = true; //Select CUDA version or "regular" version

    cv::Ptr<VideoFileSource> video = cv::makePtr<VideoFileSource>(videoFile,true);
    cv::Ptr<OnePassStabilizer> stabilizer = cv::makePtr<OnePassStabilizer>();

    cv::Ptr<MotionEstimatorBase> MotionEstimator = cv::makePtr<MotionEstimatorRansacL2>(model);

    cv::Ptr<ImageMotionEstimatorBase> ImageMotionEstimator;

    if (use_gpu)
        ImageMotionEstimator = cv::makePtr<KeypointBasedMotionEstimatorGpu>(MotionEstimator);
    else
        ImageMotionEstimator = cv::makePtr<KeypointBasedMotionEstimator>(MotionEstimator);

    stabilizer->setFrameSource(video);
    stabilizer->setMotionEstimator(ImageMotionEstimator);
    stabilizer->setLog(cv::makePtr<cv::videostab::NullLog>()); //Disable internal prints

    std::string windowTitle = "Stabilized Video";

    cv::namedWindow(windowTitle, cv::WINDOW_AUTOSIZE);

    while(true)
    {
        cv::Mat frame = stabilizer->nextFrame();

        if(frame.empty())   break;

        cv::imshow(windowTitle,frame);
        cv::waitKey(10);
    }

    return 0;
}

从视频稳定程序将C / ++ OpenCV程序更改为CUDA

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