使用opencv实现线特征LSD提取、LBD描述子匹配实现：

具体逻辑如下：

1.设置读图的路径

2. 加载图片

3. 创建mask掩代码

4.初始化参数

5.检测LSD线段

6. 计算尺度第一塔的描述

7. 使用尺度第一塔进行特征与描述提取

8.LBD描述子匹配

9.选择高精度匹配

10.画图显示

完整代码如下：

#include <iostream>#include <opencv2/opencv_modules.hpp>#include <opencv2/line_descriptor.hpp>#include <opencv2/imgproc.hpp>#include <opencv2/features2d.hpp>#include <opencv2/highgui.hpp>using namespace cv;using namespace cv::line_descriptor;using namespace std;#define MATCHES_DIST_THRESHOLD 25//主函数int main(int argc, char** argv){//1.设置读图的路径String image_path1 = "your path1";String image_path2 = "your path2";if (image_path1.empty() || image_path2.empty()){return -1;}//2. 加载图片cv::Mat imageMat1 = imread(image_path1, 1);cv::Mat imageMat2 = imread(image_path2, 1);// 检测路径是否为空if (imageMat1.data == NULL || imageMat2.data == NULL){std::cout << "Error, images could not be loaded. Please, check their path" << std::endl;}//3. 创建mask掩代码cv::Mat mask1 = Mat::ones(imageMat1.size(), CV_8UC1);cv::Mat mask2 = Mat::ones(imageMat2.size(), CV_8UC1);// 4.初始化参数Ptr<LSDDetector> lsd = LSDDetector::createLSDDetector();Ptr<BinaryDescriptor> lbd = BinaryDescriptor::createBinaryDescriptor();// BinaryDescriptor指针默认参数 Ptr<BinaryDescriptorMatcher> match1 = BinaryDescriptorMatcher::createBinaryDescriptorMatcher();//BinaryDescriptorMatcher二值描述子匹配创建 //5.检测线段 std::vector<KeyLine> klsd1, klsd2;Mat lsd_descr1, lsd_descr2;lsd->detect(imageMat1, klsd1, 2, 2, mask1);lsd->detect(imageMat2, klsd2, 2, 2, mask2);//6. 计算尺度第一塔的描述 lbd->compute(imageMat1, klsd1, lsd_descr1);lbd->compute(imageMat2, klsd2, lsd_descr2);//7. 尺度第一塔进行特征与描述提取 std::vector<KeyLine> octave0_1, octave0_2;Mat leftDescr, rightDescr;for (int i = 0; i < (int)klsd1.size(); i++){if (klsd1[i].octave == 1){octave0_1.push_back(klsd1[i]);leftDescr.push_back(lsd_descr1.row(i));}}for (int j = 0; j < (int)klsd2.size(); j++){if (klsd2[j].octave == 1){octave0_2.push_back(klsd2[j]);rightDescr.push_back(lsd_descr2.row(j));}}//8.匹配 std::vector<DMatch> lsd_matches;match1->match(leftDescr, rightDescr, lsd_matches);//9.选择高精度匹配 std::vector<DMatch> good_matches;for (int i = 0; i < (int)lsd_matches.size(); i++){if (lsd_matches[i].distance < MATCHES_DIST_THRESHOLD)good_matches.push_back(lsd_matches[i]);}// 10.画图显示cv::Mat lsd_outImg;resize(imageMat1, imageMat1, Size(imageMat1.cols/2, imageMat1.rows/2));resize(imageMat2, imageMat2, Size(imageMat2.cols/2, imageMat2.rows/2));std::vector<char> lsd_mask(lsd_matches.size(), 1);drawLineMatches(imageMat1, octave0_1, imageMat2, octave0_2, good_matches, lsd_outImg, Scalar::all(-1), Scalar::all(-1), lsd_mask, DrawLinesMatchesFlags::DEFAULT);imshow("LSD matches", lsd_outImg);std::cout << "LSDescriptorMatcher is : " << good_matches.size() << std::endl;waitKey(0);return 0;}