ORB特征点提取

其具体原理可参考：这篇博客

#include <opencv2/core/core.hpp> 
#include <opencv2/highgui/highgui.hpp> 
#include <opencv2/imgproc/imgproc.hpp> 
#include <opencv2/features2d/features2d.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main(int argc, char** argv) 
{ 
    Mat img_1 = imread("./1.jpg"); 
    Mat img_2 = imread("./2.jpg");

    // -- Step 1: Detect the keypoints using STAR Detector 
    std::vector<KeyPoint> keypoints_1,keypoints_2; 
    ORB orb; 
    orb.detect(img_1, keypoints_1); 
    orb.detect(img_2, keypoints_2);

    // -- Stpe 2: Calculate descriptors (feature vectors) 
    Mat descriptors_1, descriptors_2; 
    orb.compute(img_1, keypoints_1, descriptors_1); 
    orb.compute(img_2, keypoints_2, descriptors_2);

    //-- Step 3: Matching descriptor vectors with a brute force matcher 
    BFMatcher matcher(NORM_HAMMING); 
    std::vector<DMatch> matches; 
    matcher.match(descriptors_1, descriptors_2, matches); 

    //4.对匹配结果进行筛选（依据DMatch结构体中的float类型变量distance进行筛选）  
    float minDistance = 100;  
    float maxDistance = 0;  
    for (int i = 0; i < matches.size(); i++)  
    {  
        if (matches[i].distance < minDistance)  
            minDistance = matches[i].distance;  
        if (matches[i].distance > maxDistance)  
            maxDistance = matches[i].distance;  
    }  
    cout << "minDistance: " << minDistance << endl;  
    cout << "maxDistance: " << maxDistance << endl;  
    vector<DMatch> goodMatches;  
    for (int i = 0; i < matches.size(); i++)  
    {  
        if (matches[i].distance < 2 * minDistance)  
        {  
            goodMatches.push_back(matches[i]);  
        }  
    }

    // -- dwaw matches 
    Mat img_mathes; 
    drawMatches(img_1, keypoints_1, img_2, keypoints_2, goodMatches, img_mathes); 
    // -- show 
    imshow("Mathces", img_mathes);

    waitKey(0); 
    return 0; 
}