特征点匹配 opencv系列函数解析 追踪相关(一)

首先是一些一个光流算法实现，关键函数是

goodFeaturesToTrack 能检测出角点，算法是Harris角点和shi-tomasi角点 点击有详细的算法介绍，大概就是计算一个窗口在各个方向上的变化程度，有变化则说明是角点

#include <iostream>    #include <vector>#include <cstdio>#include <opencv2/opencv.hpp>  #include <opencv2/xfeatures2d.hpp>  using namespace cv;  using namespace std;  using namespace cv::xfeatures2d;void OpticalFlow(Mat &frame, Mat & result);  bool addNewPoints();  bool acceptTrackedPoint(int i);      Mat curgray;    // 当前图片  Mat pregray;    // 预测图片  vector<Point2f> point[2]; // point0为特征点的原来位置，point1为特征点的新位置  vector<Point2f> initPoint;    // 初始化跟踪点的位置  vector<Point2f> features; // 检测的特征  int maxCount = 300;         // 检测的最大特征数  double qLevel = 0.1;   // 特征检测的等级  double minDist = 20.0;  // 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;    //g++ pyrLK.cpp -o pyrLK `pkg-config --cflags --libs opencv`int main()  {          Mat matSrc;      Mat matRst;        VideoCapture cap("/home/caixing/Videos/car2.mov");    //cap.open(0);       int totalFrameNumber = cap.get(CV_CAP_PROP_FRAME_COUNT);        // perform the tracking process      printf("Start the tracking process, press ESC to quit.\n");      for (int nFrmNum = 0; nFrmNum < totalFrameNumber; nFrmNum++) {      // while(1){         // get frame from the video          double t = (double)cvGetTickCount();          cap >> matSrc;          if (!matSrc.empty())          {              OpticalFlow(matSrc, matRst);              //cout << "This picture is " << nFrmNum << endl;          }          else          {              cout << "Error : Get picture is empty!" << endl;          }          t = (double)cvGetTickCount() - t;          //cout << "cost time: " << t / ((double)cvGetTickFrequency()*1000.) << endl;          waitKey(0);        //if (waitKey(1) == 27) break;      }            return 0;    }  bool addNewPoints()  {      return point[0].size() <= 30;  }    void OpticalFlow(Mat &frame, Mat & result)  {      cvtColor(frame, curgray, CV_BGR2GRAY);      frame.copyTo(result);        if (addNewPoints())      // if (1)    {          goodFeaturesToTrack(curgray, features, maxCount, qLevel, minDist);          // point[0]=features;        // initPoint=features;        point[0].insert(point[0].end(), features.begin(), features.end());          initPoint.insert(initPoint.end(), features.begin(), features.end());      }          if (pregray.empty())      {          curgray.copyTo(pregray);      }        calcOpticalFlowPyrLK(pregray, curgray, point[0], point[1], status, err,Size(7,7));        //cout<<point[0].size()<<" "<<point[1].size()<<" "<<status.size()<<" "<<err.size()<<endl;    int k = 0;      for (size_t i = 0; i<point[1].size(); i++)      {          if (acceptTrackedPoint(i))          {              initPoint[k] = initPoint[i];              point[1][k++] = point[1][i];          }      }          point[1].resize(k);      initPoint.resize(k);        for (size_t i = 0; i<point[1].size(); i++)      {          line(result, initPoint[i], point[1][i], Scalar(0, 0, 255));          circle(result, point[1][i], 3, Scalar(0, 255, 0), -1);      }          swap(point[1], point[0]);      swap(pregray, curgray);          imshow("Optical Flow Demo", result);     //waitKey(50);  }      bool acceptTrackedPoint(int i)  {      return status[i] && ((abs(point[0][i].x - point[1][i].x) + abs(point[0][i].y - point[1][i].y)) > 2);  }  

在opencv的sample里面，还有两张图片的特征点进行匹配的代码样例，opencv-3.2.0/samples/cpp/matchmetod_orb_akaze_brisk.cpp 对比了好几种特征点检测的算法，然后匹配结果，主要接口如下

Ptr<Feature2D> b;if (*itDesc == "AKAZE-DESCRIPTOR_KAZE_UPRIGHT"){            b = AKAZE::create(AKAZE::DESCRIPTOR_KAZE_UPRIGHT);            }        if (*itDesc == "AKAZE"){            b = AKAZE::create();            }        if (*itDesc == "ORB"){            b = ORB::create();        }        else if (*itDesc == "BRISK"){            b = BRISK::create();        }        try        {            // We can detect keypoint with detect method            b->detect(img1, keyImg1, Mat());            // and compute their descriptors with method  compute            b->compute(img1, keyImg1, descImg1);            // or detect and compute descriptors in one step            b->detectAndCompute(img2, Mat(),keyImg2, descImg2,false);

detect就是只检测特征点，不计算该特征点的特征向量,compute就是计算特征向量，