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

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在前面的文章《OpenCV中feature2D学习——FAST特征点检测》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）。

/*** @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：

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）。

/*** @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：

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