介绍:
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
所谓Fast,是相对于正式模块中的
cv::imgproc::LineSegmentDetector类来说,速度更快。FastLineDetector类依据下边的论文实现:
// 论文
Outdoor Place Recognition in Urban Environments using Straight Lines
// 下载地址:
http://cvlab.hanyang.ac.kr/~jwlim/files/icra14linerec.pdf
大致的原理是:
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;
}
结果:
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可以看出,俩算法的效果差不多;但FLD要更快!