近來研究如何利用OpenCV中SIFT特徵提取和RANSAC篩選進行特徵匹配，發現很多版本的程式碼不在支援較新版本的OpenCV用法，本篇文章也是借鑑了其他博主的經驗分享，整理而成，希望對大家有所幫助。

 #include "cv.h"
 #include "highgui.h"
 #include "opencv2/imgproc/imgproc.hpp"
 #include "opencv2/opencv.hpp"
 #include "opencv2/core/core.hpp"
 #include "opencv2/features2d/features2d.hpp"
 #include "opencv2/nonfree/nonfree.hpp"
 


 using namespace cv;

   //讀取影象
    Mat img_test=imread("1.bmp");
    Mat img_template=imread("2.bmp");

    //SIFT特徵檢測
    SiftFeatureDetector detector;       //定義特徵點檢測器
    vector<KeyPoint> keypoint1,keypoint2; //定義兩個容器存放特徵點
    detector.detect(img_test,keypoint1);
    detector.detect(img_template,keypoint2);

或者

    Ptr<FeatureDetector> sift_detector = FeatureDetector::create("SIFT"
 
);
      sift_detector ->detector(img_test,keypoint1);
      sift_detector ->detector(img_template,keypoint2);

//特徵點顯示
Mat out_img1;
Mat out_img2;
drawKeypoints(img_test,keypoint1,out_img1);
drawKeypoints(img_template,keypoint2,out_img2);
imshow("特徵點圖1",out_img1);
imshow("特徵點圖2",out_img    //提取特徵點的特徵描述子（特徵向量）（128維）
SiftDescriptorExtractor extractor;
Mat descriptor1,descriptor2;
extractor.compute(img1,keypoint1,descriptor1);
extractor.compute(img2,keypoint2,descriptor2);
 

或者

Ptr< DescriptorExtractor>  sift_desc_extract = DescriptorExtractor::create(“SIFT”); sift_desc_extract-> compute(img1,keypoint1,descriptor1); sift_desc_extract-> compute(img2,keypoint2,descriptor2);

//匹配，主要計算兩個特徵點特徵向量的歐式距離，距離小於某個閾值則認為匹配
BruteForceMatcher<L2<float>> matcher;
vector<DMatch> matches;
Mat img_matches;
matcher.match(descriptor1,descriptor2,matches);

或者 Ptr bruteforce_matcher = Descriptormatcher::create(“BruteForce”); bruteforce_matcher->match(descriptor1,descriptor2,matches);

drawMatches(img1,keypoint1,img2,keypoint2,matches,img_matches);
imshow("原始匹配",img_matches);1

//下面是利用RANSAC進行消除無匹配點： //RANSAC 消除誤匹配特徵點 主要分為三個部分： //1）根據matches將特徵點對齊,將座標轉換為float型別 //2）使用求基礎矩陣方法 findFundamentalMat,得到RansacStatus //3）根據RansacStatus來將誤匹配的點也即RansacStatus[i]=0的點刪除

//根據matches將特徵點對齊,將座標轉換為float型別
vector<KeyPoint> R_keypoint1,R_keypoint2;
for (size_t i=0;i<matches.size();i++)   
{
    R_keypoint1.push_back(keypoint1[matches[i].queryIdx]);
    R_keypoint2.push_back(keypoint2[matches[i].trainIdx]);
    //這兩句話的理解：R_keypoint1是要儲存img1中能與img2匹配的特徵點，
    //matches中儲存了這些匹配點對的img1和img2的索引值
}

//座標轉換
vector<Point2f>p1,p2;
for (size_t i=0;i<matches.size();i++)
{
    p1.push_back(R_keypoint1[i].pt);
    p2.push_back(R_keypoint2[i].pt);
}

//利用基礎矩陣剔除誤匹配點
vector<uchar> RansacStatus;
Mat Fundamental= findFundamentalMat(p01,p02,RansacStatus,FM_RANSAC);


vector<KeyPoint> RR_keypoint1,RR_keypoint2;
vector<DMatch> RR_matches;     //重新定義RR_keypoint 和RR_matches來儲存新的關鍵點和匹配矩陣
int index=0;
for (size_t i=0;i<matches.size();i++)
{
    if (RansacStatus[i]!=0)
    {
        RR_keypoint1.push_back(R_keypoint1[i]);
        RR_keypoint2.push_back(R_keypoint2[i]);
        matches[i].queryIdx=index;
        matches[i].trainIdx=index;
        RR_matches.push_back(matches[i]);
        index++;
    }
}
Mat img_RR_matches;
drawMatches(img01,RR_keypoint1,img2,RR_keypoint2,RR_matches,img_RR_matches);
imshow("After RANSAC Image",img_RR_matches);

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