從視頻穩定程序將C/++ OpenCV程序更改爲CUDA

Question

我正在做一個C++視頻穩定/防抖程序，其中： - 獲取參考幀上的興趣點（使用FAST，SURF，Shi- Matoshi或SIFT，不妨多試幾個） - 計算與calcOpticalFlowPyrLK 盧卡斯 - Kanade光流 - 獲取單應矩陣 - 更正使用warPerspective晃動的圖像（見下面的代碼）

//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); 
} 

如果你有我的穩定解決方案的任何替代品，隨時可以這樣說，但這不是這個話題的主題。由於所有這些都需要大量的時間（我的i5電腦每幀大約需要300ms，因此30分鐘的視頻需要很長的時間），所以我正在考慮使用CUDA來加快速度。我已經安裝並開始工作，但我不確定接下來要做什麼。我已經做了一些測試，我知道最耗時的操作是使用calcOpticalFlowPyrLK和warpPerspective分別獲得光流和幀校正。所以理想情況下，至少在一開始，我只會使用這兩個函數的CUDA版本，而其餘部分不變。

這可能嗎？或者我需要重寫所有內容？

感謝

Answer 1

由於OpenCV的3.0，CUDA一個實現視頻穩定可用。除非您確定您的版本更好或更快，否則建議使用已有的實現，而不是自己編寫自己的實現。

下面是一個簡單的代碼，演示如何使用OpenCV視頻穩定模塊來穩定視頻。

#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; 
}