从视频稳定程序将C/++ OpenCV程序更改为CUDA

Question

我正在做一个C++视频稳定/防抖程序，其中： - 获取参考帧上的兴趣点（使用FAST，SURF，Shi- Matoshi或SIFT，不妨多试几个） - 计算与calcOpticalFlowPyrLK 卢卡斯 - Kanade光流 - 获取单应矩阵 - 更正使用warPerspective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); 
} 

如果你有我的稳定解决方案的任何替代品，随时可以这样说，但这不是这个话题的主题。由于所有这些都需要大量的时间（我的i5电脑每帧大约需要300ms，因此30分钟的视频需要很长的时间），所以我正在考虑使用CUDA来加快速度。我已经安装并开始工作，但我不确定接下来要做什么。我已经做了一些测试，我知道最耗时的操作是使用calcOpticalFlowPyrLK和warpPerspective分别获得光流和帧校正。所以理想情况下，至少在一开始，我只会使用这两个函数的CUDA版本，而其余部分不变。

这可能吗？或者我需要重写所有内容？

感谢

Answer 1

由于OpenCV的3.0，CUDA一个实现视频稳定可用。除非您确定您的版本更好或更快，否则建议使用已有的实现，而不是自己编写自己的实现。

下面是一个简单的代码，演示如何使用OpenCV视频稳定模块来稳定视频。

#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; 
}