FLANN_Based SURF特征匹配

雨点打透心脏的1/2处 2022-02-20 06:25 222阅读 0赞

[官网教程][Link 1]

## 介绍 ##

FLANN(Fast Library for Approximate Nearest Neighbors)在一个描述符集合（询问集）中找另一个集合（相当于训练集）的最近邻。

--------------------

## 例子 ##

#include <iostream>
    #include <vector>
     
    #include <opencv2/xfeatures2d.hpp>
    #include <opencv2/opencv.hpp>
     
    using namespace cv;
    using namespace std;
    using namespace cv::xfeatures2d;
     
    int main()
    { 
    
        Mat img1 = imread("../res/box.png", cv::IMREAD_GRAYSCALE);
        Mat img2 = imread("../res/box_in_scene.png", cv::IMREAD_GRAYSCALE);
        if(img1.empty()||img2.empty())
        { 
    	cout << "can't load image" << endl;
    	return -1;
        }
    
    //计算 keypoints and descriptor
        int minHessian = 400;
        Ptr<SURF> detector = SURF::create(minHessian);
        std::vector<KeyPoint> keypoint1, keypoint2;
        Mat descriptor1, descriptor2;
        detector->detectAndCompute(img1, noArray(), keypoint1, descriptor1);
        detector->detectAndCompute(img2, noArray(), keypoint2, descriptor2);
    
    
    //匹配
        Ptr<cv::DescriptorMatcher> matcher = DescriptorMatcher::create(DescriptorMatcher::FLANNBASED); // 基于FLANN匹配
        vector<std::vector<DMatch>> knn_matches; // 因为要为每个descriptor找到最佳的k 个匹配, 对于每个descriptor来说，又是vector<DMatch>
        matcher->knnMatch(descriptor1, descriptor2, knn_matches, 2);
    
    
    //筛选最好的
        const float ratio_thresh = 0.7f;
        vector<DMatch> good_matchers;
        for(int i=0; i<knn_matches.size(); i++)
        { 
    	if(knn_matches[i][0].distance < ratio_thresh*knn_matches[i][1].distance)// 最佳匹配距离/第二佳的匹配距离 < 0.7 
    	{ 
    	    good_matchers.push_back(knn_matches[i][0]); // 满足以上条件，最佳匹配才是我们需要的
    	}
        }
    
    
    //展示
        Mat img_matchers;
        drawMatches(img1, keypoint1, img2, keypoint2, good_matchers, img_matchers, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
    
    
        imshow("Good Matchers", img_matchers);
    
        waitKey();
        return 0;
    }

![在这里插入图片描述][20190412181730651.png_pic_center]

--------------------

## OpenCV API ##

1.  画特征点、匹配（ feature2d.hpp ）

[void cv::drawKeypoints][void cv_drawKeypoints]

( 	
    		InputArray  	image,     // source image
    		const std::vector< KeyPoint > &  	keypoints, // KeyPoints from the source image
    		InputOutputArray  	outImage,    //　输出图像
    		const Scalar &  	color = Scalar::all(-1),  // color for keypoints ,default （-1）随机
    		DrawMatchesFlags  	flags = DrawMatchesFlags::DEFAULT //画图的方式，默认就行，　或者NOT_DRAW_SINGLE_POINTS：没有匹配的点不画出来
    )

[void cv::drawMatches][void cv_drawKeypoints]

( 	
    		InputArray  	img1,  　//first source image
    		const std::vector< KeyPoint > &  	keypoints1, //Keypoints from the first source image. 
    		InputArray  	img2,   // Second source image. 
    		const std::vector< KeyPoint > &  	keypoints2,  // Keypoints from the second source image. 
    		const std::vector< DMatch > &  	matches1to2,  // 两源图之间的匹配
    		InputOutputArray  	outImg,  //输出图像
    		const Scalar &  	matchColor = Scalar::all(-1),　　// 随机颜色
    		const Scalar &  	singlePointColor = Scalar::all(-1),　// 没有匹配的点的颜色
    		const std::vector< char > &  	matchesMask = std::vector< char >(),　// Mask决定哪些点画，如果为空，全都画
    		DrawMatchesFlags  	flags = DrawMatchesFlags::DEFAULT 　//画图方式，如上API
    )

1.  从候选匹配中，为每个匹配找到最佳的k个匹配

[void cv::DescriptorMatcher::knnMatch][void cv_DescriptorMatcher_knnMatch]

( 	
    		InputArray  	queryDescriptors,　// 第一张图片的匹配
    		InputArray  	trainDescriptors,　// 第二张图片的匹配
    		std::vector< std::vector< DMatch > > &  	matches,　// 存的匹配结果
    		int  	k,　　// 前k个最佳匹配，
    		InputArray  	mask = noArray(),
    		bool  	compactResult = false 
    ) const

[Link 1]: https://docs.opencv.org/master/d5/d6f/tutorial_feature_flann_matcher.html
[20190412181730651.png_pic_center]: /images/20220220/872e6e2ed1e145cd9ef18862af4f4003.png
[void cv_drawKeypoints]: https://docs.opencv.org/master/d4/d5d/group__features2d__draw.html#gad8f463ccaf0dc6f61083abd8717c261a
[void cv_DescriptorMatcher_knnMatch]: https://docs.opencv.org/master/db/d39/classcv_1_1DescriptorMatcher.html#a378f35c9b1a5dfa4022839a45cdf0e89