Remove example file
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lhark
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#include <opencv2/opencv.hpp>
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#include <iostream>
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#include <cassert>
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#include <cmath>
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#include <fstream>
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using namespace std;
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using namespace cv;
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// This video stablisation smooths the global trajectory using a sliding average window
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const int SMOOTHING_RADIUS = 30; // In frames. The larger the more stable the video, but less reactive to sudden panning
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const int HORIZONTAL_BORDER_CROP = 20; // In pixels. Crops the border to reduce the black borders from stabilisation being too noticeable.
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// 1. Get previous to current frame transformation (dx, dy, da) for all frames
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// 2. Accumulate the transformations to get the image trajectory
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// 3. Smooth out the trajectory using an averaging window
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// 4. Generate new set of previous to current transform, such that the trajectory ends up being the same as the smoothed trajectory
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// 5. Apply the new transformation to the video
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struct TransformParam
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{
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TransformParam() {}
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TransformParam(double _dx, double _dy, double _da) {
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dx = _dx;
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dy = _dy;
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da = _da;
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}
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double dx;
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double dy;
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double da; // angle
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};
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struct Trajectory
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{
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Trajectory() {}
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Trajectory(double _x, double _y, double _a) {
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x = _x;
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y = _y;
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a = _a;
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}
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double x;
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double y;
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double a; // angle
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};
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int main(int argc, char **argv)
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{
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if(argc < 2) {
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cout << "./VideoStab [video.avi]" << endl;
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return 0;
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}
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// For further analysis
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ofstream out_transform("prev_to_cur_transformation.txt");
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ofstream out_trajectory("trajectory.txt");
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ofstream out_smoothed_trajectory("smoothed_trajectory.txt");
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ofstream out_new_transform("new_prev_to_cur_transformation.txt");
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VideoCapture cap(argv[1]);
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assert(cap.isOpened());
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Mat cur, cur_grey;
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Mat prev, prev_grey;
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cap >> prev;
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cvtColor(prev, prev_grey, COLOR_BGR2GRAY);
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// Step 1 - Get previous to current frame transformation (dx, dy, da) for all frames
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vector <TransformParam> prev_to_cur_transform; // previous to current
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int k=1;
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int max_frames = cap.get(CV_CAP_PROP_FRAME_COUNT);
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Mat last_T;
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while(true) {
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cap >> cur;
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if(cur.data == NULL) {
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break;
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}
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cvtColor(cur, cur_grey, COLOR_BGR2GRAY);
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// vector from prev to cur
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vector <Point2f> prev_corner, cur_corner;
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vector <Point2f> prev_corner2, cur_corner2;
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vector <uchar> status;
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vector <float> err;
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goodFeaturesToTrack(prev_grey, prev_corner, 200, 0.01, 30);
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calcOpticalFlowPyrLK(prev_grey, cur_grey, prev_corner, cur_corner, status, err);
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// weed out bad matches
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for(size_t i=0; i < status.size(); i++) {
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if(status[i]) {
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prev_corner2.push_back(prev_corner[i]);
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cur_corner2.push_back(cur_corner[i]);
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}
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}
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// translation + rotation only
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Mat T = estimateRigidTransform(prev_corner2, cur_corner2, false); // false = rigid transform, no scaling/shearing
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// in rare cases no transform is found. We'll just use the last known good transform.
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if(T.data == NULL) {
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last_T.copyTo(T);
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}
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T.copyTo(last_T);
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// decompose T
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double dx = T.at<double>(0,2);
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double dy = T.at<double>(1,2);
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double da = atan2(T.at<double>(1,0), T.at<double>(0,0));
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prev_to_cur_transform.push_back(TransformParam(dx, dy, da));
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out_transform << k << " " << dx << " " << dy << " " << da << endl;
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cur.copyTo(prev);
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cur_grey.copyTo(prev_grey);
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cout << "Frame: " << k << "/" << max_frames << " - good optical flow: " << prev_corner2.size() << endl;
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k++;
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}
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// Step 2 - Accumulate the transformations to get the image trajectory
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// Accumulated frame to frame transform
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double a = 0;
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double x = 0;
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double y = 0;
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vector <Trajectory> trajectory; // trajectory at all frames
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for(size_t i=0; i < prev_to_cur_transform.size(); i++) {
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x += prev_to_cur_transform[i].dx;
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y += prev_to_cur_transform[i].dy;
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a += prev_to_cur_transform[i].da;
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trajectory.push_back(Trajectory(x,y,a));
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out_trajectory << (i+1) << " " << x << " " << y << " " << a << endl;
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}
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// Step 3 - Smooth out the trajectory using an averaging window
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vector <Trajectory> smoothed_trajectory; // trajectory at all frames
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for(size_t i=0; i < trajectory.size(); i++) {
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double sum_x = 0;
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double sum_y = 0;
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double sum_a = 0;
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int count = 0;
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for(int j=-SMOOTHING_RADIUS; j <= SMOOTHING_RADIUS; j++) {
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if(i+j >= 0 && i+j < trajectory.size()) {
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sum_x += trajectory[i+j].x;
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sum_y += trajectory[i+j].y;
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sum_a += trajectory[i+j].a;
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count++;
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}
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}
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double avg_a = sum_a / count;
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double avg_x = sum_x / count;
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double avg_y = sum_y / count;
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smoothed_trajectory.push_back(Trajectory(avg_x, avg_y, avg_a));
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out_smoothed_trajectory << (i+1) << " " << avg_x << " " << avg_y << " " << avg_a << endl;
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}
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// Step 4 - Generate new set of previous to current transform, such that the trajectory ends up being the same as the smoothed trajectory
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vector <TransformParam> new_prev_to_cur_transform;
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// Accumulated frame to frame transform
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a = 0;
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x = 0;
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y = 0;
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for(size_t i=0; i < prev_to_cur_transform.size(); i++) {
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x += prev_to_cur_transform[i].dx;
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y += prev_to_cur_transform[i].dy;
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a += prev_to_cur_transform[i].da;
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// target - current
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double diff_x = smoothed_trajectory[i].x - x;
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double diff_y = smoothed_trajectory[i].y - y;
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double diff_a = smoothed_trajectory[i].a - a;
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double dx = prev_to_cur_transform[i].dx + diff_x;
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double dy = prev_to_cur_transform[i].dy + diff_y;
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double da = prev_to_cur_transform[i].da + diff_a;
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new_prev_to_cur_transform.push_back(TransformParam(dx, dy, da));
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out_new_transform << (i+1) << " " << dx << " " << dy << " " << da << endl;
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}
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// Step 5 - Apply the new transformation to the video
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cap.set(CV_CAP_PROP_POS_FRAMES, 0);
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Mat T(2,3,CV_64F);
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int vert_border = HORIZONTAL_BORDER_CROP * prev.rows / prev.cols; // get the aspect ratio correct
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k=0;
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while(k < max_frames-1) { // don't process the very last frame, no valid transform
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cap >> cur;
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if(cur.data == NULL) {
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break;
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}
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T.at<double>(0,0) = cos(new_prev_to_cur_transform[k].da);
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T.at<double>(0,1) = -sin(new_prev_to_cur_transform[k].da);
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T.at<double>(1,0) = sin(new_prev_to_cur_transform[k].da);
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T.at<double>(1,1) = cos(new_prev_to_cur_transform[k].da);
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T.at<double>(0,2) = new_prev_to_cur_transform[k].dx;
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T.at<double>(1,2) = new_prev_to_cur_transform[k].dy;
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Mat cur2;
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warpAffine(cur, cur2, T, cur.size());
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cur2 = cur2(Range(vert_border, cur2.rows-vert_border), Range(HORIZONTAL_BORDER_CROP, cur2.cols-HORIZONTAL_BORDER_CROP));
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// Resize cur2 back to cur size, for better side by side comparison
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resize(cur2, cur2, cur.size());
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// Now draw the original and stablised side by side for coolness
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Mat canvas = Mat::zeros(cur.rows, cur.cols*2+10, cur.type());
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cur.copyTo(canvas(Range::all(), Range(0, cur2.cols)));
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cur2.copyTo(canvas(Range::all(), Range(cur2.cols+10, cur2.cols*2+10)));
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// If too big to fit on the screen, then scale it down by 2, hopefully it'll fit :)
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if(canvas.cols > 1920) {
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resize(canvas, canvas, Size(canvas.cols/2, canvas.rows/2));
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}
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imshow("before and after", canvas);
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//char str[256];
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//sprintf(str, "images/%08d.jpg", k);
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//imwrite(str, canvas);
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waitKey(20);
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k++;
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}
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return 0;
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}
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