FLANN特征匹配

FLANN特征匹配时基于SIFT或者是SURF特征检测算法的一种匹配，所以它只是具有旋转不变性与光照不变性还有尺度不变性；
代码演示

#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d.hpp>
#include <iostream>
#include <math.h>

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

int main(int agrc, char**agrv)
{
	Mat img1 = imread("D:/image2/14m.jpg");
	Mat img2 = imread("D:/image2/14m.jpg");
	if(!img1.data || !img2.data)
	{
		return -1;
	}
	imshow("目标图像", img1);
	imshow("有目标的图像", img2);

	int minHessian = 400;
	Ptr<SURF> detector = SURF::create(minHessian);
	vector<KeyPoint> keypoints_obj;
	vector<KeyPoint> keypoints_scene;
	Mat descriptor_obj, descriptor_scene;

	detector->detectAndCompute(img1, Mat(), keypoints_obj, descriptor_obj);
	detector->detectAndCompute(img2, Mat(), keypoints_scene, descriptor_scene);

	//matching
	FlannBasedMatcher matcher;
	vector<DMatch> matches;
	matcher.match(descriptor_obj, descriptor_scene, matches);

	//find good matched points
	double minDist = 1000;
	double maxDist = 0;
	for (int i = 0; i < descriptor_obj.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist > maxDist)
		{
			maxDist = dist;
		}
		if (dist < minDist)
		{
			minDist = dist;
		}	
	}
	printf("最大距离：%f\n", maxDist);
	printf("最小距离：%f\n", minDist);
	vector<DMatch> goodMatches;
	for (int i = 0; i < descriptor_obj.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist < max(3 * minDist, 0.02))
		{
			goodMatches.push_back(matches[i]);
		}
	}

	Mat matchesImg;
	drawMatches(img1, keypoints_obj, img2, keypoints_scene, goodMatches,matchesImg,Scalar::all(-1),
		Scalar::all(-1),vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
	imshow("结果图", matchesImg);

 
	waitKey(0);
	return 0;
}