OpenCV中feature2D学习——FAST特征点检测与SIFT/SURF/BRIEF特征提取与匹配

以你之姓@ 2022-08-07 10:42 287阅读 0赞

在前面的文章《[OpenCV中feature2D学习——FAST特征点检测][OpenCV_feature2D_FAST]》中讲了利用FAST算子进行特征点检测，这里尝试使用FAST算子来进行特征点检测，并结合SIFT/SURF/BRIEF算子进行特征点提取和匹配。

# I、结合SIFT算子进行特征点提取和匹配 #

由于数据类型的不同，SIFT和SURF算子只能采用FlannBasedMatcher或者BruteForceMatcher来进行匹配（参考[OpenCV中feature2D学习——BFMatcher和FlannBasedMatcher][OpenCV_feature2D_BFMatcher_FlannBasedMatcher]）。

/**
    * @概述：采用FAST算子检测特征点，采用SIFT算子对特征点进行特征提取，并使用BruteForce匹配法进行特征点的匹配
    * @类和函数：FastFeatureDetector + SiftDescriptorExtractor + BruteForceMatcher
    * @author：holybin
    */
    
    #include <stdio.h>
    #include <iostream>
    #include "opencv2/core/core.hpp"
    #include "opencv2/nonfree/features2d.hpp"	//SurfFeatureDetector实际在该头文件中
    #include "opencv2/legacy/legacy.hpp"	//BruteForceMatcher实际在该头文件中
    //#include "opencv2/features2d/features2d.hpp"	//FlannBasedMatcher实际在该头文件中
    #include "opencv2/highgui/highgui.hpp"
    using namespace cv;
    using namespace std;
    
    int main( int argc, char** argv )
    {
    	Mat src_1 = imread("cat3d120.jpg");
    	Mat src_2 = imread("cat0.jpg");
    	if( !src_1.data || !src_2.data )
    	{ 
    		cout<< " --(!) Error reading images "<<endl;
    		return -1; 
    	}
    
    	//-- Step 1: 使用FAST算子检测特征点
    	FastFeatureDetector fast(20);	
    	vector<KeyPoint> keypoints_1, keypoints_2;
    	fast.detect( src_1, keypoints_1 );	//FAST(src_1, keypoints_1, 20); 
    	fast.detect( src_2, keypoints_2 );	//FAST(src_2, keypoints_2, 20); 
    	cout<<"img1--number of keypoints: "<<keypoints_1.size()<<endl;
    	cout<<"img2--number of keypoints: "<<keypoints_2.size()<<endl;
    
    	//-- Step 2: 使用SIFT算子提取特征（计算特征向量）
    	SiftDescriptorExtractor extractor;	//SurfDescriptorExtractor extractor;
    	Mat descriptors_1, descriptors_2;
    	extractor.compute( src_1, keypoints_1, descriptors_1 );
    	extractor.compute( src_2, keypoints_2, descriptors_2 );
    
    	//-- Step 3: 使用BruteForce法进行暴力匹配
    	BruteForceMatcher< L2<float> > matcher;	//FlannBasedMatcher matcher;
    	vector< DMatch > matches;
    	matcher.match( descriptors_1, descriptors_2, matches );
    	cout<<"number of matches: "<<matches.size()<<endl;
    
    	//-- 显示匹配结果
    	Mat matchImg;
    	drawMatches( src_1, keypoints_1, src_2, keypoints_2, matches, matchImg,
    		Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS); 
    	imshow("matching result", matchImg );
    	imwrite("match_result.png", matchImg);
    	waitKey(0);
    
    	return 0;
    }

运行结果如下：

![SouthEast][]

![SouthEast 1][]

# II、结合BRIEF算子进行特征点提取和匹配 #

BRIEF算子只能采用BruteForceMatcher来进行匹配（参考[OpenCV中feature2D学习——BFMatcher和FlannBasedMatcher][OpenCV_feature2D_BFMatcher_FlannBasedMatcher]）。

/**
    * @概述：采用FAST算子检测特征点，采用BRIEF算子对特征点进行特征提取，并使用BruteForce匹配法进行特征点的匹配
    * @类和函数：FastFeatureDetector + BriefDescriptorExtractor + BruteForceMatcher
    * @author：holybin
    */
    
    #include <stdio.h>
    #include <iostream>
    #include "opencv2/core/core.hpp"
    #include "opencv2/nonfree/features2d.hpp"	//SurfFeatureDetector实际在该头文件中
    #include "opencv2/legacy/legacy.hpp"	//BruteForceMatcher实际在该头文件中
    //#include "opencv2/features2d/features2d.hpp"	//FlannBasedMatcher实际在该头文件中
    #include "opencv2/highgui/highgui.hpp"
    using namespace cv;
    using namespace std;
    
    int main( int argc, char** argv )
    {
    	Mat src_1 = imread("cat3d120.jpg");
    	Mat src_2 = imread("cat0.jpg");
    	if( !src_1.data || !src_2.data )
    	{ 
    		cout<< " --(!) Error reading images "<<endl;
    		return -1; 
    	}
    
    	//-- Step 1: 使用FAST算子检测特征点
    	FastFeatureDetector fast(20);	
    	vector<KeyPoint> keypoints_1, keypoints_2;
    	fast.detect( src_1, keypoints_1 );	//FAST(src_1, keypoints_1, 20); 
    	fast.detect( src_2, keypoints_2 );	//FAST(src_2, keypoints_2, 20); 
    	cout<<"img1--number of keypoints: "<<keypoints_1.size()<<endl;
    	cout<<"img2--number of keypoints: "<<keypoints_2.size()<<endl;
    
    	//-- Step 2: 使用BRIEF算子提取特征（计算特征向量）
    	BriefDescriptorExtractor extractor;
    	Mat descriptors_1, descriptors_2;
    	extractor.compute( src_1, keypoints_1, descriptors_1 );
    	extractor.compute( src_2, keypoints_2, descriptors_2 );
    
    	//-- Step 3: 使用BruteForce法进行暴力匹配
    	BruteForceMatcher< L2<float> > matcher;	//FlannBasedMatcher matcher;
    	vector< DMatch > matches;
    	matcher.match( descriptors_1, descriptors_2, matches );
    	cout<<"number of matches: "<<matches.size()<<endl;
    
    	//-- 显示匹配结果
    	Mat matchImg;
    	drawMatches( src_1, keypoints_1, src_2, keypoints_2, matches, matchImg,
    		Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS); 
    	imshow("matching result", matchImg );
    	imwrite("match_result.png", matchImg);
    	waitKey(0);
    
    	return 0;
    }

运行结果如下：

![SouthEast 2][]

![SouthEast 3][]

[OpenCV_feature2D_FAST]: http://blog.csdn.net/holybin/article/details/44734011
[OpenCV_feature2D_BFMatcher_FlannBasedMatcher]: http://blog.csdn.net/holybin/article/details/40926315
[SouthEast]: /images/20220805/079fb00e661b4cfdad6fcc0cb690226b.png
[SouthEast 1]: /images/20220805/1aab224901e34ea88f2857aa247c29da.png
[SouthEast 2]: /images/20220805/8b22c5efbea44d52958eeb8a3605e5a7.png
[SouthEast 3]: /images/20220805/0d05d66c497148e5a61cae8335a4dba3.png