Class cv::ximgproc::FastLineDetector#

Class implementing the FLD (Fast Line Detector) algorithm described in [174] . View details

#include <opencv2/ximgproc/fast_line_detector.hpp>

Collaboration diagram for cv::ximgproc::FastLineDetector:

Public Member Functions#

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

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() )
    {
        parser.printMessage();
        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 5 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();

    return 0;
}

Constructor & Destructor Documentation#

~FastLineDetector()#

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

Member Function Documentation#

detect()#

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

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

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

Draws the line segments on a given image.

Parameters

image — The image, where the lines will be drawn. Should be bigger or equal to the image, where the lines were found.
lines — A vector of the lines that needed to be drawn.
draw_arrow — If true, arrow heads will be drawn.
linecolor — Line color.
linethickness — Line thickness.

Source file#

The documentation for this class was generated from the following file:

opencv2/ximgproc/fast_line_detector.hpp