在前面的文章《OpenCV中feature2D學習——FAST特徵點檢測》中講了利用FAST運算元進行特徵點檢測，這裡嘗試使用FAST運算元來進行特徵點檢測，並結合SIFT/SURF/BRIEF運算元進行特徵點提取和匹配。

I、結合SIFT運算元進行特徵點提取和匹配

由於資料型別的不同，SIFT和SURF運算元只能採用FlannBasedMatcher或者BruteForceMatcher來進行匹配（參考OpenCV中feature2D學習——BFMatcher和FlannBasedMatcher）。

/**
* @概述：採用FAST運算元檢測特徵點，採用SIFT運算元對特徵點進行特徵提取，並使用BruteForce匹配法進行特徵點的匹配
* @類和函式：FastFeatureDetector + SiftDescriptorExtractor + BruteForceMatcher
* @author：holybin
*/

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/nonfree/features2d.hpp"	//SurfFeatureDetector實際在該標頭檔案中
#include "opencv2/legacy/legacy.hpp"	//BruteForceMatcher實際在該標頭檔案中
//#include "opencv2/features2d/features2d.hpp"	//FlannBasedMatcher實際在該標頭檔案中
#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運算元檢測特徵點
	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運算元提取特徵（計算特徵向量）
	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法進行暴力匹配
	BruteForceMatcher< L2<float> > matcher;	//FlannBasedMatcher matcher;
	vector< DMatch > matches;
	matcher.match( descriptors_1, descriptors_2, matches );
	cout<<"number of matches: "<<matches.size()<<endl;

	//-- 顯示匹配結果
	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;
}
 

II、結合BRIEF運算元進行特徵點提取和匹配

/**
* @概述：採用FAST運算元檢測特徵點，採用BRIEF運算元對特徵點進行特徵提取，並使用BruteForce匹配法進行特徵點的匹配
* @類和函式：FastFeatureDetector + BriefDescriptorExtractor + BruteForceMatcher
* @author：holybin
*/

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/nonfree/features2d.hpp"	//SurfFeatureDetector實際在該標頭檔案中
#include "opencv2/legacy/legacy.hpp"	//BruteForceMatcher實際在該標頭檔案中
//#include "opencv2/features2d/features2d.hpp"	//FlannBasedMatcher實際在該標頭檔案中
#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運算元檢測特徵點
	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運算元提取特徵（計算特徵向量）
	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法進行暴力匹配
	BruteForceMatcher< L2<float> > matcher;	//FlannBasedMatcher matcher;
	vector< DMatch > matches;
	matcher.match( descriptors_1, descriptors_2, matches );
	cout<<"number of matches: "<<matches.size()<<endl;

	//-- 顯示匹配結果
	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;
}