https://blog.csdn.net/guoyunfei20/article/details/78754526
在OpenCV3.0 以上版本的contrib模块中,有一个cv::ximgproc::FastLineDetector类。定义位置:
- // 需要下载contrib模块
- opencv_contrib/modules/ximgproc/include/opencv2/ximgproc/fast_line_detector.hpp
- // 论文
- Outdoor Place Recognition in Urban Environments using Straight Lines
- // 下载地址:
- http://cvlab.hanyang.ac.kr/~jwlim/files/icra14linerec.pdf
先在输入图像上,应用canny边缘检测;然后根据在canny边缘图像上进行分析,找到直线。
例程:
该例程对比了上述俩直线检测算子的运行耗时情况。
- #include <iostream>
- #include <stdio.h>
- #include <unistd.h>
- #include <stdlib.h>
- #include <string.h>
- #include <string>
- #include <dirent.h>
- #include <unistd.h>
- #include <vector>
- #include <sstream>
- #include <fstream>
- #include <sys/io.h>
- #include <sys/times.h>
- #include <iomanip>
- #include <tuple>
- #include <cstdlib>
- using namespace std;
- #include "opencv2/imgproc.hpp"
- #include "opencv2/ximgproc.hpp"
- #include "opencv2/imgcodecs.hpp"
- #include "opencv2/highgui.hpp"
- using namespace cv;
- using namespace cv::ximgproc;
- int main(int argc, char** argv)
- {
- std::string in;
- cv::CommandLineParser parser(argc, argv, "{@input|../samples/data/corridor.jpg|input image}{help h||show help message}");
- if (parser.has("help"))
- {
- parser.printMessage();
- return 0;
- }
- in = parser.get<string>("@input");
- Mat image = imread(in, IMREAD_GRAYSCALE);
- if( image.empty() )
- {
- return -1;
- }
- // Create LSD detector
- Ptr<LineSegmentDetector> lsd = createLineSegmentDetector();
- vector<Vec4f> lines_lsd;
- // Create FLD detector
- // Param Default value Description
- // length_threshold 10 - Segments shorter than this will be discarded
- // distance_threshold 1.41421356 - A point placed from a hypothesis line
- // segment farther than this will be
- // regarded as an outlier
- // canny_th1 50 - First threshold for
- // hysteresis procedure in Canny()
- // canny_th2 50 - Second threshold for
- // hysteresis procedure in Canny()
- // canny_aperture_size 3 - Aperturesize for the sobel
- // operator in Canny()
- // do_merge false - If true, incremental merging of segments
- // will be perfomred
- int length_threshold = 10;
- float distance_threshold = 1.41421356f;
- double canny_th1 = 50.0;
- double canny_th2 = 50.0;
- int canny_aperture_size = 3;
- bool do_merge = false;
- Ptr<FastLineDetector> fld = createFastLineDetector(
- length_threshold,
- distance_threshold,
- canny_th1,
- canny_th2,
- canny_aperture_size,
- do_merge);
- vector<Vec4f> lines_fld;
- // Because of some CPU's power strategy, it seems that the first running of
- // an algorithm takes much longer. So here we run both of the algorithmes 10
- // times to see each algorithm's processing time with sufficiently warmed-up
- // CPU performance.
- for(int run_count = 0; run_count < 10; run_count++) {
- lines_lsd.clear();
- int64 start_lsd = getTickCount();
- lsd->detect(image, lines_lsd);
- // Detect the lines with LSD
- double freq = getTickFrequency();
- double duration_ms_lsd = double(getTickCount() - start_lsd) * 1000 / freq;
- std::cout << "Elapsed time for LSD: "
- << setw(10) << setiosflags(ios::right) << setiosflags(ios::fixed) << setprecision(2)
- << duration_ms_lsd << " ms." << std::endl;
- lines_fld.clear();
- int64 start = getTickCount();
- // Detect the lines with FLD
- fld->detect(image, lines_fld);
- double duration_ms = double(getTickCount() - start) * 1000 / freq;
- std::cout << "Ealpsed time for FLD: "
- << setw(10) << setiosflags(ios::right) << setiosflags(ios::fixed) << setprecision(2)
- << duration_ms << " ms." << std::endl;
- }
- // Show found lines with LSD
- Mat line_image_lsd(image);
- lsd->drawSegments(line_image_lsd, lines_lsd);
- imshow("LSD result", line_image_lsd);
- // Show found lines with FLD
- Mat line_image_fld(image);
- fld->drawSegments(line_image_fld, lines_fld);
- imshow("FLD result", line_image_fld);
- waitKey();
- return 0;
- }
可以看出,俩算法的效果差不多;但FLD要更快!