Opencv 基本操作九 图像特征点提取

opencv中提取图像特征点的方式比较多，有ORB、FastFeatureDetector、BRISK、MSER、MSDDetector、KAZE、AKAZE、AgastFeatureDetector、GFTTDetector、SURF、SIFT等具体可以参考
https://www.p-chao.com/2017-06-14/opencv%E7%89%B9%E5%BE%81%E7%82%B9%E6%8F%90%E5%8F%96%E7%AE%97%E6%B3%95%E5%AF%B9%E6%AF%94/#Dense_SIFT
这里针对每个方法提取的特征点进行对比（包括点数、执行时间和可视化效果）
以下代码中Mattool.hpp的内容为 https://hpg123.blog.csdn.net/article/details/126427923 博文中代码。

关于上述特征点提取方法中，可用于计算特征向量的方法有以下6中

    freak->compute(src1, keyPoint1, descriptors_1);
    brief->compute(src1, keyPoint1, descriptors_1);
    surf->compute(src1, keyPoint1, descriptors_1);
    sift->compute(src1, keyPoint1, descriptors_1);
    orb->compute(src1, keyPoint1, descriptors_1);
    brisk->compute(src1, keyPoint1, descriptors_1);

#include"opencv2/core.hpp"
#include"opencv2/core/utility.hpp"
#include"opencv2/highgui.hpp"
#include"opencv2/features2d.hpp"
#include"opencv2/xfeatures2d.hpp"
#include"opencv2/imgproc.hpp"
#include"opencv2/flann.hpp"

#include "Mattool.hpp"
using namespace cv;
using namespace std;

template <class T>
std::string convertToString(T& value)
{
    
    
    std::ostringstream oss;
    oss << value;
    std::string str(oss.str());
    return str;
}
void showKeyPoint(string tilte,Mat src, vector<KeyPoint> &keyPoint1) {
    
    
    Mat result1;
    int kpSize = int(keyPoint1.size());
    string kpSizeStr = convertToString(kpSize);
    drawKeypoints(src, keyPoint1, result1, Scalar(0, 0, 255), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);//画出特征点
    imshow(tilte+" "+ kpSizeStr, result1);
    keyPoint1.clear();
}
std::chrono::steady_clock::time_point before, now;
//计算特征检测时间，绘制特征点并添加到vector中
void dear_kp(string ftype,Mat src1,vector<KeyPoint> keyPoint1, vector<Mat> &vms) {
    
    
    now = std::chrono::steady_clock::now();
    double duration_millsecond = std::chrono::duration<double, std::milli>(now- before).count();
    std::cout << ftype <<"\t 提取特征数：\t" << keyPoint1.size() << "\t 执行时间：\t" << duration_millsecond << "毫秒" << std::endl;
    Mat  dkp;
    drawKeypoints(src1, keyPoint1, dkp, Scalar(0, 0, 255), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);//画出特征点
    vms.push_back(dkp);
    keyPoint1.clear();
    before = std::chrono::steady_clock::now();
}
int main(int argc, char** argv) {
    
    
    vector<Mat> vms;
    Mat src1,dkp;
    src1 = imread("d\\6.jpg", 1);
    resize(src1, src1, {
    
     256,256 });
    vms.push_back(src1);

    before = std::chrono::steady_clock::now();
    vector<KeyPoint> keyPoint1;
    //特征点检测算子
    Ptr<Feature2D> orb = ORB::create();
    orb->detect(src1, keyPoint1);//compute
    dear_kp("orb", src1, keyPoint1, vms);

    Ptr<Feature2D> fast = FastFeatureDetector::create(600);
    fast->detect(src1, keyPoint1);
    dear_kp("fast", src1, keyPoint1, vms);

    Ptr<Feature2D> brisk = BRISK::create(100);
    brisk->detect(src1, keyPoint1);//compute
    dear_kp("brisk", src1, keyPoint1, vms);
    
    Ptr<Feature2D> mser = MSER::create();
    mser->detect(src1, keyPoint1);
    dear_kp("mser", src1, keyPoint1, vms);

    Ptr<Feature2D> msd = xfeatures2d::MSDDetector::create();
    msd->detect(src1, keyPoint1);
    dear_kp("msd", src1, keyPoint1, vms);

    Ptr<Feature2D> kaze = KAZE::create();//900ms
    kaze->detect(src1, keyPoint1);
    dear_kp("kaze", src1, keyPoint1, vms);

    Ptr<Feature2D> akaze = AKAZE::create();//400ms
    akaze->detect(src1, keyPoint1);
    dear_kp("akaze", src1, keyPoint1, vms);

    Ptr<Feature2D> agast = AgastFeatureDetector::create();//~0ms
    agast->detect(src1, keyPoint1);
    dear_kp("agast", src1, keyPoint1, vms);

    Ptr<Feature2D> gftt = GFTTDetector::create();//200ms
    gftt->detect(src1, keyPoint1);
    dear_kp("gftt", src1, keyPoint1, vms);

    Ptr<Feature2D>surf = xfeatures2d::SURF::create(800);
    surf->detect(src1, keyPoint1);
    dear_kp("surf", src1, keyPoint1, vms);

    Ptr<Feature2D>sift = SIFT::create();
    sift->detect(src1, keyPoint1);
    dear_kp("sift", src1, keyPoint1, vms);

    imshows("key point", vms,4);

    //计算描述算子，可以试一下其他的描述算子、orb
    surf->detect(src1, keyPoint1);
    Ptr<Feature2D> freak = xfeatures2d::FREAK::create();
    Ptr<Feature2D> brief = xfeatures2d::BriefDescriptorExtractor::create();
    Mat descriptors_1;
    freak->compute(src1, keyPoint1, descriptors_1);
    brief->compute(src1, keyPoint1, descriptors_1);
    surf->compute(src1, keyPoint1, descriptors_1);
    sift->compute(src1, keyPoint1, descriptors_1);
    orb->compute(src1, keyPoint1, descriptors_1);
    brisk->compute(src1, keyPoint1, descriptors_1);
    waitKey();
    return 0;
}

代码执行效果如下所示

针对不同特征点的输出信息如下所示

orb      提取特征数：   500      执行时间：     155.222毫秒
fast     提取特征数：   4789     执行时间：     0.9115毫秒
brisk    提取特征数：   6335     执行时间：     74.3206毫秒
mser     提取特征数：   222      执行时间：     92.924毫秒
msd      提取特征数：   1147     执行时间：     97.3641毫秒
kaze     提取特征数：   1465     执行时间：     151.281毫秒
akaze    提取特征数：   878      执行时间：     27.2064毫秒
agast    提取特征数：   4746     执行时间：     4.1234毫秒
gftt     提取特征数：   1000     执行时间：     7.2168毫秒
surf     提取特征数：   3277     执行时间：     14.6069毫秒
sift     提取特征数：   1830     执行时间：     37.5664毫秒