I'm trying to implement a video stabilization. I'm a beginner in this field, but after reviewing lots of post, i chose to do these steps:
Each time, i have two frames: one current and another one, separeted by one frame. Frame 1 and 3, 3 and 5 etc.
- Compute goodFeaturesToTrack()
- Compute Optical Flow using calcOpticalFlowPyrLK()
- Keep only good points
- Compute the homography using findHomography()
- Wrap the frame using wrapPerspective()
I certainly missed something because my video is still not stable. I searched on the internet but didn't find a solution. I also saw different posts of stackOverflow like video stabilization using opencv or Video Stabilization with OpenCV
Here my code:
//Read the video
VideoCapture cap(path1);
Mat currImg, colorImg, outImg, grayImg, backupColorImg;
cap.read(colorImg);
VideoUtil::geometricalCrop(colorImg,70,0);//Crop the picture
cvtColor(colorImg,grayImg,CV_BGR2GRAY);
currImg = grayImg.clone();// Current picture
Mat refFrame;
cap.read(refFrame);//Frame +1
VideoUtil::geometricalCrop(refFrame,70,0);
cvtColor(refFrame,refFrame,CV_BGR2GRAY); // Frame +1
namedWindow("Stabilize");
namedWindow("GoodMatches");
Mat temp;
Mat currentFrame=refFrame;
for (;;){
int nbreCurrentFrame=cap.get(CV_CAP_PROP_POS_FRAMES);//Get the number of current frame
cap.read(colorImg);
VideoUtil::geometricalCrop(colorImg,70,0);// Crop the video
Debug::trace("Current frame: " + to_string(nbreCurrentFrame));
currentFrame.copyTo(refFrame);//Get the reference frame
cap.read(colorImg);
VideoUtil::geometricalCrop(colorImg,70,0);
cvtColor(colorImg,grayImg,CV_BGR2GRAY);
currentFrame = grayImg.clone();//Get the current frame
vector<Point2f> cornersPrevious;//Stock features of reference Frame
cornersPrevious.reserve(400);
vector<Point2f> cornersCurr;//Stock features of current frame
cornersCurr.reserve(400);
goodFeaturesToTrack(refFrame,cornersPrevious,400,0.01,5.0);
Debug::trace("Size of feature track : " + to_string(cornersPrevious.size()));
vector<uchar> featureFound; // status of tracked features
featureFound.reserve(400);
vector<float> featureErrors; // error in tracking
featureErrors.reserve(400);
calcOpticalFlowPyrLK(refFrame,currentFrame,cornersPrevious,cornersCurr,featureFound,featureErrors,Size(20,20),3,
cvTermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS,20,0.3),0,0.0001);
// keep the good points
std::vector<cv::Point2f> initialPoints;
std::vector<cv::Point2f> trackedPoints;
for (int i=0;i<cornersCurr.size();i++){
double motion = sqrt(pow(cornersPrevious.at(i).x-cornersCurr.at(i).x,2)+pow(cornersPrevious.at(i).y-cornersCurr.at(i).y,2));
std::cout << "Motion: " << motion << std::endl;
if (featureFound[i] && motion < 20 ){
//Keep this point in vector
initialPoints.push_back(cornersPrevious.at(i));
trackedPoints.push_back(cornersCurr.at(i));
}
}
// draw the tracking effect
cv::Mat finalImage;
currentFrame.copyTo(finalImage);
// for all tracked points
for(uint i= 0; i < initialPoints.size(); i++ ) {
// draw line and circle
cv::line(finalImage,
initialPoints[i], // initial position
trackedPoints[i], // new position
cv::Scalar(255,255,255));
cv::circle(finalImage, trackedPoints[i], 3, cv::Scalar(0,0,255),-1);
}
//Compute the homography
if (initialPoints.size() >4 && trackedPoints.size()>4){
cv::Mat mask;
cv::Mat transformMatrix = findHomography(initialPoints,trackedPoints,CV_RANSAC,3);
warpPerspective(refFrame,outImg,transformMatrix,refFrame.size(), INTER_LINEAR |WARP_INVERSE_MAP,BORDER_CONSTANT ,0);
namedWindow("Stabilized");
imshow("stabilizedVideo",outImg);
}
namedWindow("Tracking features");
imshow("tracking",finalImage);
if(waitKey(27) >= 0) break;
}
Thank
