Class implementing the FLD (Fast Line Detector) algorithm described in [Lee14] . More...

#include <opencv2/ximgproc/fast_line_detector.hpp>

Inheritance diagram for cv::ximgproc::FastLineDetector:

Public Member Functions
virtual	~FastLineDetector ()

virtual void	detect (InputArray image, OutputArray lines)=0
	Finds lines in the input image. This is the output of the default parameters of the algorithm on the above shown image.

virtual void	drawSegments (InputOutputArray image, InputArray lines, bool draw_arrow=false, Scalar linecolor=Scalar(0, 0, 255), int linethickness=1)=0
	Draws the line segments on a given image.

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

virtual void	clear ()
	Clears the algorithm state.

virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.

virtual String	getDefaultName () const

virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.

virtual void	save (const String &filename) const

void	write (const Ptr< FileStorage > &fs, const String &name=String()) const

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.

void	write (FileStorage &fs, const String &name) const

Additional Inherited Members
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

Class implementing the FLD (Fast Line Detector) algorithm described in [Lee14] .

#include <iostream>
 
#include "opencv2/imgproc.hpp"
#include "opencv2/ximgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
 
using namespace std;
using namespace cv;
using namespace cv::ximgproc;
 
int main(int argc, char** argv)
{
    string in;
    CommandLineParser parser(argc, argv, "{@input|corridor.jpg|input image}{help h||show help message}");
    if (parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }
    in = samples::findFile(parser.get<string>("@input"));
 
    Mat image = imread(in, IMREAD_GRAYSCALE);
 
    if( image.empty() )
    {
        return -1;
    }
 
    // 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().
    //                                     If zero, Canny() is not applied and the input
    //                                     image is taken as an edge image.
    // do_merge            false         - If true, incremental merging of segments
    //                                     will be performed
    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;
 
    // Because of some CPU's power strategy, it seems that the first running of
    // an algorithm takes much longer. So here we run the algorithm 10 times
    // to see the algorithm's processing time with sufficiently warmed-up
    // CPU performance.
    for (int run_count = 0; run_count < 5; run_count++) {
        double freq = getTickFrequency();
        lines.clear();
        int64 start = getTickCount();
        // Detect the lines with FLD
        fld->detect(image, lines);
        double duration_ms = double(getTickCount() - start) * 1000 / freq;
        cout << "Elapsed time for FLD " << duration_ms << " ms." << endl;
    }
 
    // Show found lines with FLD
    Mat line_image_fld(image);
    fld->drawSegments(line_image_fld, lines);
    imshow("FLD result", line_image_fld);
 
    waitKey(1);
 
    Ptr<EdgeDrawing> ed = createEdgeDrawing();
    ed->params.EdgeDetectionOperator = EdgeDrawing::SOBEL;
    ed->params.GradientThresholdValue = 38;
    ed->params.AnchorThresholdValue = 8;
 
    vector<Vec6d> ellipses;
 
    for (int run_count = 0; run_count < 5; run_count++) {
        double freq = getTickFrequency();
        lines.clear();
        int64 start = getTickCount();
 
        // Detect edges
        //you should call this before detectLines() and detectEllipses()
        ed->detectEdges(image);
 
        // Detect lines
        ed->detectLines(lines);
        double duration_ms = double(getTickCount() - start) * 1000 / freq;
        cout << "Elapsed time for EdgeDrawing detectLines " << duration_ms << " ms." << endl;
 
        start = getTickCount();
        // Detect circles and ellipses
        ed->detectEllipses(ellipses);
        duration_ms = double(getTickCount() - start) * 1000 / freq;
        cout << "Elapsed time for EdgeDrawing detectEllipses " << duration_ms << " ms." << endl;
    }
 
    Mat edge_image_ed = Mat::zeros(image.size(), CV_8UC3);
    vector<vector<Point> > segments = ed->getSegments();
 
    for (size_t i = 0; i < segments.size(); i++)
    {
        const Point* pts = &segments[i][0];
        int n = (int)segments[i].size();
        polylines(edge_image_ed, &pts, &n, 1, false, Scalar((rand() & 255), (rand() & 255), (rand() & 255)), 1);
    }
 
    imshow("EdgeDrawing detected edges", edge_image_ed);
 
    Mat line_image_ed(image);
    fld->drawSegments(line_image_ed, lines);
 
    // Draw circles and ellipses
    for (size_t i = 0; i < ellipses.size(); i++)
    {
        Point center((int)ellipses[i][0], (int)ellipses[i][1]);
        Size axes((int)ellipses[i][2] + (int)ellipses[i][3], (int)ellipses[i][2] + (int)ellipses[i][4]);
        double angle(ellipses[i][5]);
        Scalar color = ellipses[i][2] == 0 ? Scalar(255, 255, 0) : Scalar(0, 255, 0);
 
        ellipse(line_image_ed, center, axes, angle, 0, 360, color, 2, LINE_AA);
    }
 
    imshow("EdgeDrawing result", line_image_ed);
    waitKey();
    return 0;
}

Constructor & Destructor Documentation

◆ ~FastLineDetector()

virtual cv::ximgproc::FastLineDetector::~FastLineDetector ( )

inlinevirtual

Member Function Documentation

◆ detect()

virtual void cv::ximgproc::FastLineDetector::detect	(	InputArray	image,
		OutputArray	lines
	)

pure virtual

Finds lines in the input image. This is the output of the default parameters of the algorithm on the above shown image.

image

Parameters

image	A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: `fld_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);`
lines	A vector of Vec4f elements specifying the beginning and ending point of a line. Where Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are directed so that the brighter side is on their left.

◆ drawSegments()

virtual void cv::ximgproc::FastLineDetector::drawSegments	(	InputOutputArray	image,
		InputArray	lines,
		bool	draw_arrow = `false`,
		Scalar	linecolor = `Scalar(0, 0, 255)`,
		int	linethickness = `1`
	)

pure virtual

Draws the line segments on a given image.