光流法目标跟踪

#include <opencv2/video/video.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <opencv2/core/core.hpp> #include <iostream> #include <cstdio> using namespace std; using namespace cv; void tracking(Mat &frame, Mat &output); bool addNewPoints(); bool acceptTrackedPoint(int i); //-----------------------------------【全局变量声明】----------------------------------------- // 描述：声明全局变量 //------------------------------------------------------------------------------------------------- string window_name = "optical flow tracking"; Mat gray; // 当前图片 Mat gray_prev; // 预测图片 vector<Point2f> points[2]; // point0为特征点的原来位置，point1为特征点的新位置 vector<Point2f> initial; // 初始化跟踪点的位置 vector<Point2f> features; // 检测的特征 int maxCount = 500; // 检测的最大特征数 double qLevel = 0.01; // 特征检测的等级 double minDist = 10.0; // 两特征点之间的最小距离 vector<uchar> status; // 跟踪特征的状态，特征的流发现为1，否则为0 vector<float> err; int main() { Mat frame; Mat result; //VideoCapture capture("1.avi"); VideoCapture capture(0); if(capture.isOpened()) // 摄像头读取文件开关 { while(true) { capture >> frame; if(!frame.empty()) { tracking(frame, result); } else { printf(" --(!) No captured frame -- Break!"); break; } int c = waitKey(50); if( (char)c == 27 ) { break; } } } return 0; } //------------------------------------------------------------------------------------------------- // function: tracking // brief: 跟踪 // parameter: frame 输入的视频帧 // output 有跟踪结果的视频帧 // return: void //------------------------------------------------------------------------------------------------- void tracking(Mat &frame, Mat &output) { //此句代码的OpenCV3版为： cvtColor(frame, gray, COLOR_BGR2GRAY); //此句代码的OpenCV2版为： //cvtColor(frame, gray, CV_BGR2GRAY); frame.copyTo(output); // 添加特征点 if (addNewPoints()) { goodFeaturesToTrack(gray, features, maxCount, qLevel, minDist); points[0].insert(points[0].end(), features.begin(), features.end()); initial.insert(initial.end(), features.begin(), features.end()); } if (gray_prev.empty()) { gray.copyTo(gray_prev); } // l-k光流法运动估计 calcOpticalFlowPyrLK(gray_prev, gray, points[0], points[1], status, err); // 去掉一些不好的特征点 int k = 0; for (size_t i=0; i<points[1].size(); i++) { if (acceptTrackedPoint(i)) { initial[k] = initial[i]; points[1][k++] = points[1][i]; } } points[1].resize(k); initial.resize(k); // 显示特征点和运动轨迹 for (size_t i=0; i<points[1].size(); i++) { line(output, initial[i], points[1][i], Scalar(0, 0, 255)); circle(output, points[1][i], 3, Scalar(0, 255, 0), -1); } // 把当前跟踪结果作为下一此参考 swap(points[1], points[0]); swap(gray_prev, gray); imshow(window_name, output); } //------------------------------------------------------------------------------------------------- // function: addNewPoints // brief: 检测新点是否应该被添加 // parameter: // return: 是否被添加标志 //------------------------------------------------------------------------------------------------- bool addNewPoints() { return points[0].size() <= 10; } //------------------------------------------------------------------------------------------------- // function: acceptTrackedPoint // brief: 决定哪些跟踪点被接受 // parameter: // return: //------------------------------------------------------------------------------------------------- bool acceptTrackedPoint(int i) { return status[i] && ((abs(points[0][i].x - points[1][i].x) + abs(points[0][i].y - points[1][i].y)) > 2); }