opencv 4.2.0使用SURF算法

opencv的编译与配置参考：https://www.cnblogs.com/fcfc940503/p/11502930.html
记得使用cmak进行配置时，需要勾选OPENCV_ENABLE_NOFREE，要不是后面使用SURF算法是会报错的。另外就是如果是debug就引用debug的opencv lib，如果是realse就引用opencv realse的lib，不要引错了。

#include "stdafx.h"

#include
#include
#include
#include    

using namespace cv;
using namespace cv::xfeatures2d;
using namespace std;

const int HasValue = 1500;
int main()
{
	Mat image01 = imread("C:\\Users\\wu\\Pictures\\1.jpg", 1);    //右图
	Mat image02 = imread("C:\\Users\\wu\\Pictures\\2.jpg", 1);    //左图
	namedWindow("p2", 0);
	namedWindow("p1", 0);
	imshow("p2", image01);
	imshow("p1", image02);
	//灰度图转换  
	Mat image1, image2;

	cvtColor(image01, image1, 7);
	cvtColor(image02, image2, 7);
	//提取特征点     
	Ptr<cv::xfeatures2d::SURF> detector = cv::xfeatures2d::SURF::create(HasValue);
	// ;  // 海塞矩阵阈值，在这里调整精度值越大点越少，越精准  
	vector<KeyPoint> keyPoint1, keyPoint2; 
	detector->detect(image1, keyPoint1);
	detector->detect(image2, keyPoint2);
	//特征点描述，为下边的特征点匹配做准备     
	Ptr <cv::xfeatures2d::SURF> SurfDescriptor = cv::xfeatures2d::SURF::create(HasValue);
	Mat imageDesc1, imageDesc2;
	SurfDescriptor->compute(image1, keyPoint1, imageDesc1);

	SurfDescriptor->compute(image2, keyPoint2, imageDesc2);
	//获得匹配特征点，并提取最优配对     
	FlannBasedMatcher matcher;
	vector<DMatch> matchePoints;
	matcher.match(imageDesc1, imageDesc2, matchePoints, Mat());
	cout << "total match points: " << matchePoints.size() << endl;
	Mat img_match;
	drawMatches(image01, keyPoint1, image02, keyPoint2, matchePoints, img_match);
	cv::namedWindow("match", 0);
	cv::imshow("match", img_match);
	cv::imwrite("match.jpg", img_match);
	waitKey();
	return 0;
}