This article is the next article to write down, focusing on the output of the introduction point, other places do not have too much

#include "opencv2/opencv.hpp"
#include <opencv2/nonfree/nonfree.hpp>//SIFT
#include <opencv2/legacy/legacy.hpp>// bFMATCH violence matching
#include <vector>
#include<iostream>
using namespace std;
using namespace cv;

void main()
{     
    Mat srcImg1 = imread("D:\\0.jpg");
    Mat srcImg2 = imread("D:\\1.jpg");
	resize(srcImg1,srcImg1,Size(srcImg1.cols*3,srcImg1.rows*3));
	resize(srcImg2,srcImg2,Size(srcImg2.cols/3,srcImg2.rows/3));
    / / Define SIFT feature detection class object
    SiftFeatureDetector siftDetector(1000);
    / / Define KeyPoint variables
    vector<KeyPoint>keyPoints1;
    vector<KeyPoint>keyPoints2;
    // Feature point detection
    siftDetector.detect(srcImg1, keyPoints1);
    siftDetector.detect(srcImg2, keyPoints2);
    // Draw feature points (key points)
    Mat feature_pic1, feature_pic2;
    drawKeypoints(srcImg1, keyPoints1, feature_pic1, Scalar::all(-1));
    drawKeypoints(srcImg2, keyPoints2, feature_pic2, Scalar::all(-1));
    // Show original map
    imshow("src1", srcImg1);
    imshow("src2", srcImg2);
    //show result
    imshow("feature1", feature_pic1);
    imshow("feature2", feature_pic2);

    / / Calculate Feature Point Descriptor / Feature Vector Extract
    SiftDescriptorExtractor descriptor;
    Mat description1;
    descriptor.compute(srcImg1, keyPoints1, description1);
    Mat description2;
    descriptor.compute(srcImg2, keyPoints2, description2);
    cout<<description1.cols<<endl;
    cout<<description1.rows<<endl;

    / / Matching BFMATCH violence
    BruteForceMatcher<L1<float>>matcher;    // Instantiate violence matching
    vector<DMatch>matches;   / / Define match result variables
    matcher.match(description1, description2, matches);  // Implement the match between the descriptor

    // Match results filter
    nth_element(matches.begin(), matches.begin()+0, matches.end());   / / Extract the previous X best match result     
    matches.erase(matches.begin()+1, matches.end());    // Remove the rest of the result
	int index1,index2;
	for(size_t i=0;i<matches.size();i++)/ / Here is the output of the screened coordinate pair
	{
		index1=matches.at(i).queryIdx;// This is an attribute of accessing the feature picture. Specifically, I don't understand the source code.
		index2=matches.at(i).trainIdx;// This is an attribute that access to the image to be matched
		cout<<"This is a  :"<<keyPoints2.at(index2).pt<<endl;/ / I am here to output the coordinates of the characteristic picture, KeyPoints2 is a collection after the feature vector extraction
		cout<<"This is a  :"<<keyPoints2.at(index2).pt.x<<endl// Here is the x point to output the characteristic image to be matched
		cout<<"This is a  :"<<keyPoints2.at(index2).pt.y<<endl/ / Here is the Y point of outputting the characteristic image to be matched
	}
    Mat result;
    drawMatches(srcImg1, keyPoints1, srcImg2, keyPoints2, matches, result,  Scalar(0, 255, 0), Scalar::all(-1));// Match feature point green, single feature point color random
    imshow("Match_Result", result);
    waitKey(0);
}