Python代码的实现：

import cv2
import numpy as np
import time
import os
def main():images = os.listdir('./images/')pre_image = cv2.imread('./images/1.jpg', 0)print("images:",images)# 初始化 SIFT 和 SURFsift = cv2.xfeatures2d.SIFT_create()# surf = cv2.xfeatures2d.SURF_create()bf = cv2.BFMatcher()start_time = time.time()for img in images:img2 = cv2.imread(os.path.join('./images', img), 0)# 寻找关键点和计算描述符kp1, des1 = sift.detectAndCompute(pre_image, None)kp2, des2 = sift.detectAndCompute(img2, None)ti = time.time()# 匹配关键点matches = bf.knnMatch(des1, des2, k=2)# 应用比值测试来获取好的匹配点good = []for m, n in matches:if m.distance < 0.75 * n.distance:good.append([m])# 可视化匹配结果img3 = cv2.drawMatchesKnn(pre_image, kp1, img2, kp2, good, None, flags=2)print("knnMatch spend time:{} second".format(time.time()-ti))print("this spend time is :{} second".format(time.time()-start_time))if __name__ == "__main__":main()

#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d.hpp>
#include <opencv2/xfeatures2d/nonfree.hpp>
#include <iostream>
#include <vector>
#include <ctime>
#include <sys/types.h>
#include <dirent.h>
#include <iostream>
#include <chrono>void readFilesInDir(const std::string& dir_path, std::vector<std::string>& files)
{DIR *dir;struct dirent *ent;if ((dir = opendir(dir_path.c_str())) != NULL){while ((ent = readdir(dir)) != NULL){if (ent->d_name[0] != '.'){files.push_back(ent->d_name);}}closedir(dir);}
}
int main()
{std::vector<std::string> files;readFilesInDir("./images", files);cv::Ptr<cv::xfeatures2d::SIFT> sift = cv::xfeatures2d::SIFT::create();cv::BFMatcher bf;cv::Mat pre_image = cv::imread("./images/1.jpg", 0);clock_t start = clock();auto _start = std::chrono::high_resolution_clock::now();for (const auto& file : files){cv::Mat img2 = cv::imread("./images/" + file, 0);std::vector<cv::KeyPoint> kp1, kp2;cv::Mat des1, des2;sift->detectAndCompute(pre_image, cv::noArray(), kp1, des1);sift->detectAndCompute(img2, cv::noArray(), kp2, des2);std::vector<std::vector<cv::DMatch>> matches;auto _t = std::chrono::high_resolution_clock::now();bf.knnMatch(des1, des2, matches, 2);std::vector<cv::DMatch> goodMatches;auto _e = std::chrono::high_resolution_clock::now();std::chrono::duration<double, std::milli> _elapsed = _e - _t;double _ex = _elapsed.count() / 1000.0;std::cout << "step spend time is :" << _ex << " s" << std::endl;for (int i = 0; i < matches.size(); ++i){if (matches[i][0].distance < 0.75 * matches[i][1].distance){goodMatches.push_back(matches[i][0]);}}cv::Mat img3;cv::drawMatches(pre_image, kp1, img2, kp2, goodMatches, img3, cv::Scalar::all(-1), cv::Scalar::all(-1), std::vector<char>(), cv::DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);}auto end = std::chrono::high_resolution_clock::now();std::chrono::duration<double, std::milli> elapsed = end - _start;double elapsedSeconds = elapsed.count() / 1000.0;std::cout << "this spend time is: " << (double)(clock() - start) / CLOCKS_PER_SEC << " s : " << elapsedSeconds << " seconds" << std::endl;return 0;
}

结论说明：
两种代码在同一个环境上的，相同的图片大小和数量（19张图片），运行耗时如下：
python

spend time is : 57.90586733818054 second

c++

spend time is: 57.71 seconds

差距不会很明显，但是如果文件很多，并且使用多线程/多进程，会有一个比较明显的差距。