SLAM專題（9）-- 圖像ORB特徵提取與匹配

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>

using namespace std;
using namespace cv;

int main ( int argc, char** argv )
{
    if ( argc != 3 )
    {
        cout<<"usage: feature_extraction img1 img2"<<endl;
        return 1;
    }
    //-- 讀取圖像img1, img2
    Mat img_1 = imread ( argv[1], CV_LOAD_IMAGE_COLOR );
    Mat img_2 = imread ( argv[2], CV_LOAD_IMAGE_COLOR );

    //-- 初始化容器keypoints desriptor
    std::vector<KeyPoint> keypoints_1, keypoints_2;
    Mat descriptors_1, descriptors_2;
    Ptr<FeatureDetector> detector = ORB::create();
    Ptr<DescriptorExtractor> descriptor = ORB::create();
    // Ptr<FeatureDetector> detector = FeatureDetector::create(detector_name);
    // Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create(descriptor_name);
    Ptr<DescriptorMatcher> matcher  = DescriptorMatcher::create ( "BruteForce-Hamming" );

    //-- 第一步:檢測 Oriented FAST 角點位置
    detector->detect ( img_1,keypoints_1 );
    detector->detect ( img_2,keypoints_2 );

    //-- 第二步:根據角點位置計算 BRIEF 描述子
    descriptor->compute ( img_1, keypoints_1, descriptors_1 );
    descriptor->compute ( img_2, keypoints_2, descriptors_2 );

    Mat outimg1; //匹配結果圖像
    drawKeypoints( img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
    imshow("ORB特徵點",outimg1);

    //-- 第三步:對兩幅圖像中的BRIEF描述子進行匹配，使用 Hamming 距離
    vector<DMatch> matches;
    //BFMatcher matcher ( NORM_HAMMING );
    matcher->match ( descriptors_1, descriptors_2, matches );

    //-- 第四步:匹配點對篩選
    double min_dist=10000, max_dist=0;

    //找出所有匹配之間的最小距離和最大距離, 即是最相似的和最不相似的兩組點之間的距離
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        double dist = matches[i].distance;
        if ( dist < min_dist ) min_dist = dist;
        if ( dist > max_dist ) max_dist = dist;
    }
    
    // 僅供娛樂的寫法
    min_dist = min_element( matches.begin(), matches.end(), [](const DMatch& m1, const DMatch& m2) {return m1.distance<m2.distance;} )->distance;
    max_dist = max_element( matches.begin(), matches.end(), [](const DMatch& m1, const DMatch& m2) {return m1.distance<m2.distance;} )->distance;

    printf ( "-- Max dist : %f \n", max_dist );
    printf ( "-- Min dist : %f \n", min_dist );

    //當描述子之間的距離大於兩倍的最小距離時,即認爲匹配有誤.但有時候最小距離會非常小,設置一個經驗值30作爲下限.
    std::vector< DMatch > good_matches;
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        if ( matches[i].distance <= max ( 2*min_dist, 30.0 ) )
        {
            good_matches.push_back ( matches[i] );
        }
    }

    //-- 第五步:繪製匹配結果
    Mat img_match;
    Mat img_goodmatch;
    drawMatches ( img_1, keypoints_1, img_2, keypoints_2, matches, img_match );
    drawMatches ( img_1, keypoints_1, img_2, keypoints_2, good_matches, img_goodmatch );
    imshow ( "所有匹配點對", img_match );
    imshow ( "優化後匹配點對", img_goodmatch );
    waitKey(0);

    return 0;
}

ORB 特徵提取與匹配相關內容已在本人圖像處理專題中有所記錄，鏈接：

https://blog.csdn.net/hhaowang/article/details/104173310