text: #include <chrono>
  #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);
      assert(img_1.data != nullptr && img_2.data != nullptr);
  
      //-- 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<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
  
      //-- 第一步:检测 Oriented FAST 角点位置
      chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
      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);
      chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
      chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
      cout << "extract ORB cost = " << time_used.count() << " seconds. " << endl;
  
      Mat outimg1;
      drawKeypoints(img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT);
      imshow("ORB features", 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;
      t1 = chrono::steady_clock::now();
      matcher->match(descriptors_1, descriptors_2, matches);
      t2 = chrono::steady_clock::now();
      time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
      cout << "match ORB cost = " << time_used.count() << " seconds. " << endl;
  
      //-- 第四步:匹配点对筛选
      // 计算最小距离和最大距离
      auto min_max = minmax_element(matches.begin(), matches.end(),
          [](const DMatch& m1, const DMatch& m2) { return m1.distance < m2.distance; });
      double min_dist = min_max.first->distance;
      double max_dist = min_max.second->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("all matches", img_match);
      imshow("good matches", img_goodmatch);
      waitKey(0);
  
      return 0;
  }
  