将C/++ OpenCV程序从视频稳定程序改为CUDA程序

Question

我正在做一个C++视频稳定/防抖动程序：-获取参考系上的兴趣点(使用FAST，SURF，Shi-Matoshi或SIFT，可能会尝试更多)-使用calcOpticalFlowPyrLK计算卢卡斯-卡纳德光流-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);
}

如果您有任何替代我的稳定解决方案，请随时这样说，但这不是本线程的主题。

由于所有这一切都需要很多时间(在我的i5电脑上大约每帧300ms，所以对于一个30分钟的视频来说，这是一个很长的时间)，我正在考虑使用CUDA来加快速度。我已经安装好了，可以正常工作了，但是我不确定下一步该怎么做。我做了一些测试，我知道最耗时的操作是分别使用calcOpticalFlowPyrLK和warpPerspective获取光流和帧校正。因此，理想情况下，至少在一开始，我将只使用这两个函数的CUDA版本，其余的保持不变。

这个是可能的吗？或者我需要重写所有内容？

谢谢

Answer 1

从CUDA3.0开始，可以使用OpenCV实现视频稳定。建议使用已有的实现，而不是编写自己的实现，除非您确定您的版本会更好或更快。

以下是演示如何使用OpenCV视频稳定模块来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;
}