OpenCV  3.4.15-dev
Open Source Computer Vision
fld_lines.cpp

An example using the FastLineDetector

#include <iostream>
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;
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);
ed->params.EdgeDetectionOperator = EdgeDrawing::SOBEL;
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);
return 0;
}