LSD 线特征提取⽅法+Opencv 实现C++
边缘检测： 传统的直线检测⽅法⼀般采⽤边缘检测算法提取⼆值边缘图像，然后利⽤Hough变换将表⽰每条直线属性的参数投影到Hough 空间中。该线检测⽅法是⼀种全局拟合算法。缺点是依赖于边缘检测算法的准确性，在边缘密集的地⽅容易出现异常检测 LSD检测 LSD算法是⼀种基于梯度信息的直线检测⽅法，具有检测速度快、参数⾃适应、精度可达到亚像素级的特点。其主要思想是将局部区域内具有相同梯度⽅向的像素进⾏合并，以达到直线检测的⽬的#include <iostream>#include <chrono>#include <cv.h>#include <opencv2/core/core.hpp>#include <opencv2/core/utility.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/imgcodecs.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/line_descriptor/descriptor.hpp>#include <opencv2/features2d/features2d.hpp>#include <iostream>using namespace cv ;using namespace std ;using namespace cv ::line_descriptor ;struct sort_descriptor_by_queryIdx { inline bool operator ()(const vector <DMatch >& a , const vector <DMatch >& b ){ return ( a [0].queryIdx < b [0].queryIdx ); }};struct sort_lines_by_response { inline bool operator ()(const KeyLine & a , const KeyLine & b ){ return ( a .response > b .response ); }};void ExtractLineSegment (const Mat &img , const Mat &image2, vector <KeyLine > &keylines ,vector <KeyLine > &keylines2);int main (int argc , char **argv ){ if (argc != 3) { cerr << endl << "Usage: ./Line path_to_image1 pat h_to_image2" << endl ; return 1; } string imagePath1=string (argv [1]); string imagePath2=string (argv [2]); cout <<"import two images"<<endl ; Mat image1=imread (imagePath1); Mat image2=imread (imagePath2); imshow ("ima1",image1); imshow ("ima2",image2); waitKey (0); if (image1.data ==NULL ) { cout <<"the path is wrong"<<endl ; } vector <KeyLine > keylines ,keylines2; ExtractLineSegment (image1,image2,keylines ,keylines2); return 0;}
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针对⾃⼰的⽂件 的执⾏命令：./bin/HC++ /home/jyy/图⽚/test1.png /home/jyy/图⽚/test1.png }void ExtractLineSegment (const Mat &img , const Mat &image2, vector <KeyLine > &keylines ,vector <KeyLine > &keylines2){ Mat mLdesc ,mLdesc2; vector <vector <DMatch >> lmatches ; Ptr <BinaryDescriptor > lbd = BinaryDescriptor ::createBinaryDescriptor (); Ptr <line_descriptor ::LSDDetector > lsd = line_descriptor ::LSDDetector ::createLSDDetector (); cout <<"extract lsd line segments"<<endl ; lsd ->detect (img , keylines , 1.2,1); lsd ->detect (image2,keylines2,1.2,1); int lsdNFeatures = 50; cout <<"filter lines"<<endl ; if (keylines .size ()>lsdNFeatures ) { sort (keylines .begin (), keylines .end (), sort_lines_by_response ()); k
eylines .resize (lsdNFeatures ); for ( int i =0; i <lsdNFeatures ; i ++) keylines [i ].class_id = i ; } if (keylines2.size ()>lsdNFeatures ) { sort (keylines2.begin (), d (), sort_lines_by_response ()); size (lsdNFeatures ); for (int i =0; i <lsdNFeatures ; i ++) keylines2[i ].class_id = i ; } cout <<"lbd describle"<<endl ; lbd ->compute (img , keylines , mLdesc ); lbd ->compute (image2,keylines2,mLdesc2);//计算特征线段的描述⼦ BFMatcher * bfm = new BFMatcher (NORM_HAMMING , false ); bfm ->knnMatch (mLdesc , mLdesc2, lmatches , 2); vector <DMatch > matches ; for (size_t i =0;i <lmatches .size ();i ++) { const DMatch & bestMatch = lmatches [i ][0]; const DMatch & betterMatch = lmatches [i ][1]; float distanceRatio = bestMatch .distance / betterMatch .distance ; if (distanceRatio < 0.75) matches .push_back (bestMatch ); } cv ::Mat outImg ; std ::vector <char > mask ( lmatches .size (), 1 ); drawLineMatches ( img , keylines , image2, keylines2, matches , outImg , Scalar ::all ( -1 ), Scalar ::all ( -1 ), mask , DrawLinesMatchesFlags ::DEFAULT ); imshow ( "Matches", outImg ); waitKey ();}
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PROJECT (HC ++)cmake_minimum_required (VERSION 3.5)set (CMAKE_RUNTIME_OUTPUT_DIRECTORY ${HC ++_SOURCE_DIR }/bin ) #可执⾏⽂件的输出位
置# find required opencv find_package (OpenCV REQUIRED )# directory of opencv headers link_directories (${OpenCV_LIBRARY_DIRS }) # opencv libr include_directories (${OpenCV_INCLUDE_DIRS })add_executable (${PROJECT_NAME } "./baselineExperiment.cpp" ) # directory of opencv libraryaries target_link_libraries (HC ++ ${OpenCV_LIBS } -lpthread -lm ) 123456789101112131415
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