将C / ++ OpenCV程序从视频稳定程序更改为CUDA

Question

我正在做一个C++视频稳定/防抖程序，它：- 获取参考框架上的兴趣点（使用FAST，SURF，Shi-Matoshi或SIFT，可能会尝试更多）- 用calcOpticalFlowPyrLK计算卢卡斯-卡纳德光流- 获取单应性矩阵- 使用warPerspective纠正晃动的图像（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);
}

如果您对我的稳定解决方案有任何替代方案，请随时说出来，但这不是这个线程的主题。

由于所有这些都需要大量时间（在我的 i300 计算机上每帧大约 5 毫秒，所以 30 分钟的视频需要很长时间），我正在考虑使用 CUDA 来加快速度。我已经安装了它并让它工作，但是我不确定下一步如何进行。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来获取光流和帧校正。所以理想情况下，至少一开始，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？还是我需要重写所有内容？

谢谢

Answer 1

从 OpenCV 3.0 开始，可以使用视频稳定的 CUDA 实现。建议使用已经可用的实现，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。

下面是演示如何使用 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;
}