FLANN和SURF搭配动态匹配特征点

特征匹配的结果会得到两个特征集合的对应关系列表。第一组特征集被称为训练集，第二组被称为查询集。FLANN在调用匹配函数之前，为了提高匹配速度，训练一个匹配器。训练阶段是为了优化cv::FlannBasedMatcher的性能。train类将会建立特征集的索引树。
而对于暴力匹配，train类不需要做什么，因为没有预处理。
FLANN和SURF搭配匹配特征点
knnMatch（）找每个特征的K近邻匹配
效率不够高，帧率很低。参考《opencv3编程入门》

#include<opencv2/opencv.hpp>#include<opencv2/features2d/features2d.hpp>#include<opencv2/xfeatures2d/nonfree.hpp>#include<opencv2/highgui/highgui.hpp>#include<iostream>using namespace std;using namespace cv;using namespace cv::xfeatures2d;int main(){    Mat trainImage = imread("3.jpg",1);    Mat trainImage_gray;    imshow("原始图",trainImage);    cvtColor(trainImage,trainImage_gray,CV_BGR2GRAY);    vector<KeyPoint>train_keypoint;    Mat trainDescriptor;    Ptr<SurfFeatureDetector> Detector = SurfFeatureDetector::create(80);    Detector->detectAndCompute(trainImage_gray,Mat(),train_keypoint,trainDescriptor);    vector<Mat> train_desc_collection(1,trainDescriptor);    FlannBasedMatcher matcher;    matcher.add(train_desc_collection);    matcher.train();    VideoCapture cap(0);    unsigned int frameCount = 0;    while (char(waitKey(1) != 'q'))    {        int64 time0 = getTickCount();        Mat testImage, testImage_gray;        cap >> testImage;        if (testImage.empty())            continue;        cvtColor(testImage,testImage_gray,CV_BGR2GRAY);        vector<KeyPoint>test_keypoint;        Mat testDescriptor;        Detector->detectAndCompute(testImage_gray,Mat(),test_keypoint,testDescriptor);        vector<vector<DMatch>>matches;        matcher.knnMatch(testDescriptor,matches,2);        vector<DMatch>goodMatches;        for (unsigned int i = 0; i < matches.size(); i++)        {            if (matches[i][0].distance < 0.6*matches[i][1].distance)                goodMatches.push_back(matches[i][0]);        }        Mat dstImage;        drawMatches(testImage,test_keypoint,trainImage,train_keypoint,goodMatches,dstImage);        imshow("匹配窗口",dstImage);        cout << "当前帧率为" << getTickFrequency() / (getTickCount() - time0) << endl;    }    return 0;}