OpenCV入门教程06.32：FLANN+SURF关键点描述与匹配

使用surf提取关键点，使用flann进行匹配。

#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/xfeatures2d/nonfree.hpp>
#include <opencv2/features2d/features2d.hpp>
using namespace cv;
using namespace std;

int main( ) 
{
    //【1】载入图像、显示并转化为灰度图
    Mat trainImage = imread("1.jpg"), trainImage_gray;
    imshow("src",trainImage);
    cvtColor(trainImage, trainImage_gray, COLOR_BGR2GRAY);

    //【2】检测Surf关键点、提取训练图像描述符
    vector<KeyPoint> train_keyPoint;
    Mat trainDescriptor;
    Ptr<xfeatures2d::SURF> surf = xfeatures2d::SURF::create(80);
    surf->detectAndCompute(trainImage_gray, Mat(),train_keyPoint,trainDescriptor); 

    //【3】创建基于FLANN的描述符匹配对象
    FlannBasedMatcher matcher;
    vector<Mat> train_desc_collection(1, trainDescriptor);
    matcher.add(train_desc_collection);
    matcher.train();

    //【4】创建视频对象、定义帧率
    VideoCapture cap("tifa.mp4");
    unsigned int frameCount = 0;//帧数

    //【5】不断循环，直到q键被按下
    while(char(waitKey(1)) != 'q')
    {
        //<1>参数设置
        int64 time0 = getTickCount();
        Mat testImage, testImage_gray;
        cap >> testImage;//采集视频到testImage中
        if(testImage.empty())
            continue;

        //<2>转化图像到灰度
        cvtColor(testImage, testImage_gray, COLOR_BGR2GRAY);

        //<3>检测S关键点、提取测试图像描述符
        vector<KeyPoint> test_keyPoint;
        Mat testDescriptor;
        surf->detectAndCompute(testImage_gray, Mat(),test_keyPoint,testDescriptor);

        //<4>匹配训练和测试描述符
        vector<vector<DMatch> > matches;
        matcher.knnMatch(testDescriptor, matches, 2);

        // <5>根据劳氏算法（Lowe's algorithm），得到优秀的匹配点
        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]);
        }

        //<6>绘制匹配点并显示窗口
        Mat dstImage;
        drawMatches(testImage, test_keyPoint, trainImage, train_keyPoint, goodMatches, dstImage);
        imshow("matched", dstImage);

        //<7>输出帧率信息
        cout << "当前帧率为：" << getTickFrequency() / (getTickCount() - time0) << endl;
    }

    return 0;
}