77 lines
2.8 KiB
C++
77 lines
2.8 KiB
C++
#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)
|
|
{
|
|
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 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 the algorithm 10 times
|
|
// to see the algorithm's processing time with sufficiently warmed-up
|
|
// CPU performance.
|
|
for(int run_count = 0; run_count < 10; run_count++) {
|
|
double freq = getTickFrequency();
|
|
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 << "Elapsed time for FLD " << duration_ms << " ms." << std::endl;
|
|
}
|
|
|
|
// 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;
|
|
}
|