我正致力于在 iOS 中集成 OpenCV 视频稳定方法，目前有两个问题：
我对 C++ 没有太多经验，所以我做了一些调整，这里的大部分代码来自教程。需要注意的一件重要事情是，我希望稳定的特定视频只有 9 帧，帧速率相对较慢，为 12 FPS（在我的应用中以 GIF 格式播放）。
这是我的 OpenCVWrapper 类：
#include "OpenCVWrapper.h"
#import "UIImage+OpenCV.h"
#include <opencv2/opencv.hpp>
#include <opencv2/imgproc/imgproc_c.h>
#include <opencv2/core/types_c.h>
using namespace cv;
using namespace std;
const int SMOOTHING_RADIUS = 10; // In frames. The larger the more stable the video, but less reactive to sudden panning
struct TransformParam {
TransformParam() {}
TransformParam(double _dx, double _dy, double _da) {
dx = _dx;
dy = _dy;
da = _da;
}
double dx, dy, da;
};
struct Trajectory {
Trajectory() {}
Trajectory(double _x, double _y, double _a) {
x = _x;
y = _y;
a = _a;
}
double x, y, a;
};
u/implementation OpenCVWrapper : NSObject
+ (NSURL *)processVideoFileWithOpenCV:(NSURL*)url : (NSURL*)result {
String file = *new String(url.path.UTF8String);
String resultFile = *new String(result.path.UTF8String);
VideoCapture cap(file);
assert(cap.isOpened());
Mat cur, cur_grey, cur_orig;
Mat prev, prev_grey, prev_orig;
cap >> prev;
cvtColor(prev, prev_grey, COLOR_BGR2GRAY);
// Step 1 - Get previous to current frame transformation (dx, dy, da) for all frames
vector <TransformParam> prev_to_cur_transform; // previous to current
int frames=1;
int max_frames = cap.get(CAP_PROP_FRAME_COUNT);
cout << max_frames;
Mat last_T;
while(true) {
cap >> cur;
if(cur.data == NULL) {
break;
}
cvtColor(cur, cur_grey, COLOR_BGR2GRAY);
// vector from prev to cur
vector <Point2f> prev_corner, cur_corner;
vector <Point2f> prev_corner2, cur_corner2;
vector <uchar> status;
vector <float> err;
goodFeaturesToTrack(prev_grey, prev_corner, 200, 0.01, 30);
calcOpticalFlowPyrLK(prev_grey, cur_grey, prev_corner, cur_corner, status, err);
// weed out bad matches
for(size_t i=0; i < status.size(); i++) {
if(status[i]) {
prev_corner2.push_back(prev_corner[i]);
cur_corner2.push_back(cur_corner[i]);
cv::line(cur, prev_corner[i], cur_corner[i], CV_RGB(255,0,0), 1, CV_AA); // DEBUGGING ONLY
}
}
// translation + rotation only
Mat T = estimateAffine2D(prev_corner, cur_corner);
// Mat T = estimateRigidTransform(prev_corner, cur_corner, true); // false = rigid transform, no scaling/shearing
// in rare cases no transform is found. We'll just use the last known good transform.
if(T.data == NULL) {
last_T.copyTo(T);
}
T.copyTo(last_T);
// decompose T
double dx = T.at<double>(0,2);
double dy = T.at<double>(1,2);
double da = atan2(T.at<double>(1,0), T.at<double>(0,0));
prev_to_cur_transform.push_back(TransformParam(dx, dy, da));
cur.copyTo(prev);
cur_grey.copyTo(prev_grey);
frames++;
}
// Step 2 - Accumulate the transformations to get the image trajectory
// Accumulated frame to frame transform
double a = 0;
double x = 0;
double y = 0;
vector <Trajectory> trajectory; // trajectory at all frames
for(size_t i=0; i < prev_to_cur_transform.size(); i++) {
x += prev_to_cur_transform[i].dx;
y += prev_to_cur_transform[i].dy;
a += prev_to_cur_transform[i].da;
trajectory.push_back(Trajectory(x,y,a));
}
// Step 3 - Smooth out the trajectory using an averaging window
vector <Trajectory> smoothed_trajectory; // trajectory at all frames
for(size_t i=0; i < trajectory.size(); i++) {
double sum_x = 0;
double sum_y = 0;
double sum_a = 0;
int count = 0;
for(int j=-SMOOTHING_RADIUS; j <= SMOOTHING_RADIUS; j++) {
if(i+j >= 0 && i+j < trajectory.size()) {
sum_x += trajectory[i+j].x;
sum_y += trajectory[i+j].y;
sum_a += trajectory[i+j].a;
count++;
}
}
double avg_a = sum_a / count;
double avg_x = sum_x / count;
double avg_y = sum_y / count;
smoothed_trajectory.push_back(Trajectory(avg_x, avg_y, avg_a));
}
// Step 4 - Generate new set of previous to current transform, such that the trajectory ends up being the same as the smoothed trajectory
vector <TransformParam> new_prev_to_cur_transform;
// Accumulated frame to frame transform
a = 0;
x = 0;
y = 0;
for(size_t i=0; i < prev_to_cur_transform.size(); i++) {
x += prev_to_cur_transform[i].dx;
y += prev_to_cur_transform[i].dy;
a += prev_to_cur_transform[i].da;
// target - current
double diff_x = smoothed_trajectory[i].x - x;
double diff_y = smoothed_trajectory[i].y - y;
double diff_a = smoothed_trajectory[i].a - a;
double dx = prev_to_cur_transform[i].dx + diff_x;
double dy = prev_to_cur_transform[i].dy + diff_y;
double da = prev_to_cur_transform[i].da + diff_a;
new_prev_to_cur_transform.push_back(TransformParam(dx, dy, da));
}
// Step 5 - Apply the new transformation to the video
cap.set(CAP_PROP_POS_FRAMES, 0);
double width = prev.size().width;
double height = prev.size().height;
Mat T(2,3,CV_64F);
int ex = static_cast<int>(cap.get(CAP_PROP_FOURCC)); // Get Codec Type- Int form
VideoWriter writer(resultFile, ex, 12, cv::Size(width,height), true); /// 12 = 12 fps
//writer.open(resultFile, VideoWriter::fourcc('M','J','P','G'), 30, cv::Size(width, height));
if (!writer.isOpened()) {
cout << "Could not open file for writing";
}
int k=0;
cap.release();
VideoCapture cap2(file);
assert(cap2.isOpened());
while(k < frames-1) { // don't process the very last frame, no valid transform
cap2 >> cur;
if(cur.data == NULL) {
break;
}
T.at<double>(0,0) = cos(new_prev_to_cur_transform[k].da);
T.at<double>(0,1) = -sin(new_prev_to_cur_transform[k].da);
T.at<double>(1,0) = sin(new_prev_to_cur_transform[k].da);
T.at<double>(1,1) = cos(new_prev_to_cur_transform[k].da);
T.at<double>(0,2) = new_prev_to_cur_transform[k].dx;
T.at<double>(1,2) = new_prev_to_cur_transform[k].dy;
Mat cur2;
warpAffine(cur, cur2, T, cur.size());
// Resize cur2 back to cur size, for better side by side comparison
resize(cur2, cur2, cur.size());
double diffx = width * 0.2;
double diffy = height * 0.2;
cv::Rect myROI((diffx/2),(diffy/2),width-(diffx),height-(diffy));
Mat fin = cur2(myROI);
resize(fin, fin, cur2.size());
writer.write(fin);
k++;
}
writer.release();
cout << "Video Stabilisation complete";
return result;
}
u/end
///https://answers.opencv.org/question/82304/how-to-use-videostabcpp-in-a-swift-program/