Mirrors/opencv
0
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-01-18 17:21:42 +01:00
Code Packages Releases Activity

Merge pull request #21407 from sensyn-robotics:feature/weighted_hough

Browse Source 
Feature: weighted Hough Transform #21407

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or other license that is incompatible with OpenCV
- [x] The PR is proposed to proper branch
- [x] There is reference to original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Masahiro Ogawa
2025-01-06 21:35:35 +09:00
committed by GitHub
parent 2aee94752a
commit fc994a6ae8
5 changed files with 143 additions and 27 deletions
Download Patch File Download Diff File Expand all files Collapse all files

69
samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp
View File

@@ -15,22 +15,27 @@ using namespace std;
/// Global variables
/** General variables */
Mat src, edges;
Mat src, canny_edge, sobel_edge;
Mat src_gray;
Mat standard_hough, probabilistic_hough;
Mat standard_hough, probabilistic_hough, weighted_hough;
int min_threshold = 50;
int max_trackbar = 150;
int weightedhough_max_trackbar = 100000;
const char* standard_name = "Standard Hough Lines Demo";
const char* probabilistic_name = "Probabilistic Hough Lines Demo";
const char* weighted_name = "Weighted Hough Lines Demo";
int s_trackbar = max_trackbar;
int p_trackbar = max_trackbar;
int e_trackbar = 60;
int w_trackbar = 60000;
/// Function Headers
void help();
void Standard_Hough( int, void* );
void Probabilistic_Hough( int, void* );
void Weighted_Hough( int, void* );
/**
* @function main
@@ -53,22 +58,29 @@ int main( int argc, char** argv )
/// Pass the image to gray
cvtColor( src, src_gray, COLOR_RGB2GRAY );
/// Apply Canny edge detector
Canny( src_gray, edges, 50, 200, 3 );
/// Apply Canny/Sobel edge detector
Canny( src_gray, canny_edge, 50, 200, 3 );
Sobel( src_gray, sobel_edge, CV_16S, 1, 0 ); // dx(order of the derivative x)=1,dy=0
/// Create Trackbars for Thresholds
char thresh_label[50];
snprintf( thresh_label, sizeof(thresh_label), "Thres: %d + input", min_threshold );
namedWindow( standard_name, WINDOW_AUTOSIZE );
createTrackbar( thresh_label, standard_name, &s_trackbar, max_trackbar, Standard_Hough);
createTrackbar( thresh_label, standard_name, &s_trackbar, max_trackbar, Standard_Hough );
namedWindow( probabilistic_name, WINDOW_AUTOSIZE );
createTrackbar( thresh_label, probabilistic_name, &p_trackbar, max_trackbar, Probabilistic_Hough);
createTrackbar( thresh_label, probabilistic_name, &p_trackbar, max_trackbar, Probabilistic_Hough );
char edge_thresh_label[50];
sprintf( edge_thresh_label, "Edge Thres: input" );
namedWindow( weighted_name, WINDOW_AUTOSIZE);
createTrackbar( edge_thresh_label, weighted_name, &e_trackbar, max_trackbar, Weighted_Hough);
createTrackbar( thresh_label, weighted_name, &w_trackbar, weightedhough_max_trackbar, Weighted_Hough);
/// Initialize
Standard_Hough(0, 0);
Probabilistic_Hough(0, 0);
Weighted_Hough(0, 0);
waitKey(0);
return 0;
}
@@ -90,10 +102,10 @@ void help()
void Standard_Hough( int, void* )
{
vector<Vec2f> s_lines;
cvtColor( edges, standard_hough, COLOR_GRAY2BGR );
cvtColor( canny_edge, standard_hough, COLOR_GRAY2BGR );
/// 1. Use Standard Hough Transform
HoughLines( edges, s_lines, 1, CV_PI/180, min_threshold + s_trackbar, 0, 0 );
HoughLines( canny_edge, s_lines, 1, CV_PI/180, min_threshold + s_trackbar, 0, 0 );
/// Show the result
for( size_t i = 0; i < s_lines.size(); i++ )
@@ -117,10 +129,10 @@ void Standard_Hough( int, void* )
void Probabilistic_Hough( int, void* )
{
vector<Vec4i> p_lines;
cvtColor( edges, probabilistic_hough, COLOR_GRAY2BGR );
cvtColor( canny_edge, probabilistic_hough, COLOR_GRAY2BGR );
/// 2. Use Probabilistic Hough Transform
HoughLinesP( edges, p_lines, 1, CV_PI/180, min_threshold + p_trackbar, 30, 10 );
HoughLinesP( canny_edge, p_lines, 1, CV_PI/180, min_threshold + p_trackbar, 30, 10 );
/// Show the result
for( size_t i = 0; i < p_lines.size(); i++ )
@@ -131,3 +143,38 @@ void Probabilistic_Hough( int, void* )
imshow( probabilistic_name, probabilistic_hough );
}
/**
* @function Weighted_Hough
* This can detect lines based on the edge intensities.
*/
void Weighted_Hough( int, void* )
{
vector<Vec2f> s_lines;
/// prepare
Mat edge_img;
convertScaleAbs(sobel_edge, edge_img );
// use same threshold for edge with Hough.
threshold( edge_img, edge_img, e_trackbar, 255, cv::THRESH_TOZERO);
cvtColor( edge_img, weighted_hough, COLOR_GRAY2BGR );
/// 3. Use Weighted Hough Transform
const bool use_edgeval{true};
HoughLines( edge_img, s_lines, 1, CV_PI/180, min_threshold + w_trackbar, 0, 0, 0, CV_PI, use_edgeval);
/// Show the result
for( size_t i = 0; i < s_lines.size(); i++ )
{
float r = s_lines[i][0], t = s_lines[i][1];
double cos_t = cos(t), sin_t = sin(t);
double x0 = r*cos_t, y0 = r*sin_t;
double alpha = 1000;
Point pt1( cvRound(x0 + alpha*(-sin_t)), cvRound(y0 + alpha*cos_t) );
Point pt2( cvRound(x0 - alpha*(-sin_t)), cvRound(y0 - alpha*cos_t) );
line( weighted_hough, pt1, pt2, Scalar(255,0,0), 3, LINE_AA );
}
imshow( weighted_name, weighted_hough );
}