#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include<xfeatures2d/nonfree.hpp>
using namespace cv;
using namespace std;
using namespace cv::xfeatures2d;
Mat image1,image2;
int main()
{
	image1 = imread("C:/Users/zhang/Desktop/77.png");
	image2 = imread("C:/Users/zhang/Desktop/76.png");
	imshow("原图像1", image1);
	imshow("原图像2", image2);
	//////////////检测
	int Hessian = 400;//海塞矩阵阈值，在这里调整精度，值越大点越少，越精准 
	Ptr<SURF> Detector = SURF::create(Hessian);
	vector<KeyPoint>  keyPoint1, keyPoint2;//KeyPoint专门为特征点建立的坐标类型
	Mat point_image1, point_image2;
	Detector->detectAndCompute(image1,Mat(), keyPoint1, point_image1);
	//detect寻找特征点的坐标
	//detectAndCompute寻找特征点的坐标同时求出特征点周围的描述子向量
	Detector->detectAndCompute(image2, Mat(), keyPoint2, point_image2);

	/////////////////匹配
	BFMatcher matcher;//通过BF暴力匹配
	vector<DMatch> matchePoints;//DMatch专门为特征点匹配建立的类型
	matcher.match(point_image1, point_image2, matchePoints, Mat());
	//把两幅图像合成一幅图像。并且找到两幅图像相同的特征点（利用欧式距离）
	//所以matchePoints保存的是两个相同特征点之间的距离，距离越小两个特征点就会相似

	Mat img_match;
	drawMatches(image1, keyPoint1, image2, keyPoint2, matchePoints, img_match);//画线
	imshow("特征匹配", img_match);

	waitKey(0);
	return 0;
}