opencv學習之路（34）、SIFT特征匹配（二）

一、特征匹配簡介

二、暴力匹配

1.nth_element篩選

#include "opencv2/opencv.hpp"
#include <opencv2/nonfree/nonfree.hpp>//SIFT
#include <opencv2/legacy/legacy.hpp>//BFMatch暴力匹配
#include <vector>
#include<iostream>
using namespace std;
using namespace cv;

void main()
{     
    Mat srcImg1 = imread("E://11.jpg");
    Mat srcImg2 = imread("E://22.jpg");
    //定義SIFT特征檢測類對象
    SiftFeatureDetector siftDetector;
    //定義KeyPoint變量
    vector<KeyPoint>keyPoints1;
    vector<KeyPoint>keyPoints2;
    //特征點檢測
    siftDetector.detect(srcImg1, keyPoints1);
    siftDetector.detect(srcImg2, keyPoints2);
    //繪制特征點(關鍵點)
    Mat feature_pic1, feature_pic2;
    drawKeypoints(srcImg1, keyPoints1, feature_pic1, Scalar::all(-1));
    drawKeypoints(srcImg2, keyPoints2, feature_pic2, Scalar::all(-1));
    //顯示原圖
    //imshow("src1", srcImg1);
    //imshow("src2", srcImg2);
    //顯示結果
    imshow("feature1", feature_pic1);
    imshow("feature2", feature_pic2);

    //計算特征點描述符 / 特征向量提取
    SiftDescriptorExtractor descriptor;
    Mat description1;
    descriptor.compute(srcImg1, keyPoints1, description1);
    Mat description2;
    descriptor.compute(srcImg2, keyPoints2, description2);
    cout<<description1.cols<<endl;
    cout<<description1.rows<<endl;

    //進行BFMatch暴力匹配
    BruteForceMatcher<L2<float>>matcher;    //實例化暴力匹配器
    vector<DMatch>matches;   //定義匹配結果變量
    matcher.match(description1, description2, matches);  //實現描述符之間的匹配

    //匹配結果篩選
    nth_element(matches.begin(), matches.begin()+29, matches.end());   //提取出前30個最佳匹配結果     
    matches.erase(matches.begin()+30, matches.end());    //剔除掉其余的匹配結果

    Mat result;
    drawMatches(srcImg1, keyPoints1, srcImg2, keyPoints2, matches, result,  Scalar(0, 255, 0), Scalar::all(-1));//匹配特征點綠色，單一特征點顏色隨機
    imshow("Match_Result", result);
    
    waitKey(0);
}

沒有進行篩選時

進行篩選后

2.計算向量距離進行篩選（比第一種篩選方式好）

前面代碼相同
    //進行BFMatch暴力匹配
    BruteForceMatcher<L2<float>>matcher;    //實例化暴力匹配器
    vector<DMatch>matches;   //定義匹配結果變量
    matcher.match(description1, description2, matches);  //實現描述符之間的匹配

    //計算向量距離的最大值與最小值：距離越小越匹配
    double max_dist=matches[0].distance,min_dist=matches[0].distance;
    for(int i=1; i<description1.rows; i++)
    {
        if(matches.at(i).distance > max_dist)
            max_dist = matches[i].distance;
        if(matches.at(i).distance < min_dist)
            min_dist = matches[i].distance;
    }
    cout<<"min_distance="<<min_dist<<endl;
    cout<<"max_distance="<<max_dist<<endl;
    //匹配結果刪選    
    vector<DMatch>good_matches;
    for(int i=0; i<matches.size(); i++)
    {
        if(matches[i].distance < 2*min_dist)
            good_matches.push_back(matches[i]);
    }

    Mat result;
    drawMatches(srcImg1, keyPoints1, srcImg2, keyPoints2, good_matches, result,  Scalar(0, 255, 0), Scalar::all(-1));//匹配特征點綠色，單一特征點顏色隨機
    imshow("Match_Result", result);