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代码部分
int main ( )
取消参数传入
if (argc != 3) {
cout << “usage: feature_extraction img1 img2” << endl;
return 1;
}
删除此段代码，因为程序不需要从外面传入图片了
[ Mat img_1 = imread("/home/slambook2/ch7/1.png", CV_LOAD_IMAGE_COLOR);
直接将图片地址写入，将图片变换为图像矩阵。CV_LOAD_IMAGE_COLOR保留图片的彩色信息
完整代码#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <chrono>using namespace std;
using namespace cv;int main ( )
{//-- 读取图像,assert检查图像是否为空，assert表达式为0时报错Mat img_1 = imread("/home/automobile/wcm/slambook2/ch7/1.png", CV_LOAD_IMAGE_COLOR);Mat img_2 = imread("/home/automobile/wcm/slambook2/ch7/2.png", 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//detect为detector结构体类型成员，detector中的detect函数发现的关键点存储在keypoints_1/2中 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//compute为descriptor结构体类型成员，descriptor中的compute函数将关键点转换为描述子并存储在descriptors_1/2中 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;//带有关键点的图片存储在img_1中 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//匹配的descriptors存储在matches中 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//min_max为bool型结构体，结构体中distance成员为double型 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;
}
2 CMakeLists.txt部分
project(ch7)
ch7是我的项目名
set(OpenCV_DIR /path/to/opencv/build)
解决CMake找不到opencv库
target_link_libraries(ch7 ${OpenCV_LIBS})
ch7为项目名
CMakeList.txt内容
cmake_minimum_required(VERSION 3.10)
project(ch7)set(CMAKE_BUILD_TYPE "Release")
add_definitions("-DENABLE_SSE")
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_FLAGS "-std=c++11 -o2 ${SSE_FLAGS} -msse4")
list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
set(OpenCV_DIR /path/to/opencv/build)find_package(OpenCV 3 REQUIRED)include_directories(${OpenCV_INCLUDE_DIRS})add_executable(ch7 cmake-build-debug/orb_cv.cpp )
target_link_libraries(ch7 ${OpenCV_LIBS})
原文地址
https://blog.csdn.net/qiao_syf/article/details/106448597?ops_request_misc=%25257B%252522request%25255Fid%252522%25253A%252522161201716716780262519246%252522%25252C%252522scm%252522%25253A%25252220140713.130102334.pc%25255Fall.%252522%25257D&request_id=161201716716780262519246&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2allfirst_rank_v2~rank_v29-5-106448597.pc_search_result_cache&utm_term=slambook%252Fch7%252Ffeature_extraction.cpp
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