slam十四讲之特征提取feature_extraction

怼烎@ 2022-12-27 06:26 143阅读 0赞

#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;
        }
        //-- 读取图像
        Mat img_1 = imread ( argv[1], CV_LOAD_IMAGE_COLOR );
        Mat img_2 = imread ( argv[2], CV_LOAD_IMAGE_COLOR );
    
        //-- 初始化
        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:检测 Oriented FAST 角点位置
        detector->detect ( img_1,keypoints_1 );
        detector->detect ( img_2,keypoints_2 );
    
        //-- 第二步:根据角点位置计算 BRIEF 描述子
        descriptor->compute ( img_1, keypoints_1, descriptors_1 );
        descriptor->compute ( img_2, keypoints_2, descriptors_2 );
    
        Mat outimg1;
        drawKeypoints( img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
        imshow("ORB特征点",outimg1);
    
        //-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
        vector<DMatch> matches;
        //BFMatcher matcher ( NORM_HAMMING );
        matcher->match ( descriptors_1, descriptors_2, matches );
    
        //-- 第四步:匹配点对筛选
        double min_dist=10000, max_dist=0;
    
        //找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
        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;
        }
        
        // 仅供娱乐的写法
        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 );
    
        //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
        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:绘制匹配结果
        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 ( "所有匹配点对", img_match );
        imshow ( "优化后匹配点对", img_goodmatch );
        waitKey(0);
    
        return 0;
    }