mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2025-08-06 14:36:36 +08:00
Code Issues Actions Packages Projects Releases Wiki 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
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
5 changed files with 143 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.markdown
View File

@ -83,7 +83,7 @@ Arranging the terms: \f$r = x \cos \theta + y \sin \theta\f$
### Standard and Probabilistic Hough Line Transform
OpenCV implements two kind of Hough Line Transforms:
OpenCV implements three kind of Hough Line Transforms:
a. **The Standard Hough Transform**
@ -97,6 +97,12 @@ b. **The Probabilistic Hough Line Transform**
of the detected lines \f$(x_{0}, y_{0}, x_{1}, y_{1})\f$
- In OpenCV it is implemented with the function **HoughLinesP()**
c. **The Weighted Hough Transform**
- Uses edge intensity instead binary 0 or 1 values in standard Hough transform.
- In OpenCV it is implemented with the function **HoughLines()** with use_edgeval=true.
- See the example in samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp.
### What does this program do?
- Loads an image
- Applies a *Standard Hough Line Transform* and a *Probabilistic Line Transform*.

4
modules/imgproc/include/opencv2/imgproc.hpp
View File

@ -2165,11 +2165,13 @@ Must fall between 0 and max_theta.
@param max_theta For standard and multi-scale Hough transform, an upper bound for the angle.
Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly
less than max_theta, depending on the parameters min_theta and theta.
@param use_edgeval True if you want to use weighted Hough transform.
*/
CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines,
double rho, double theta, int threshold,
double srn = 0, double stn = 0,
double min_theta = 0, double max_theta = CV_PI );
double min_theta = 0, double max_theta = CV_PI,
bool use_edgeval = false );
/** @brief Finds line segments in a binary image using the probabilistic Hough transform.

42
modules/imgproc/src/hough.cpp
View File

@ -120,7 +120,7 @@ static void
HoughLinesStandard( InputArray src, OutputArray lines, int type,
float rho, float theta,
int threshold, int linesMax,
double min_theta, double max_theta )
double min_theta, double max_theta, bool use_edgeval = false )
{
CV_CheckType(type, type == CV_32FC2 || type == CV_32FC3, "Internal error");
@ -184,17 +184,31 @@ HoughLinesStandard( InputArray src, OutputArray lines, int type,
irho, tabSin, tabCos);
// stage 1. fill accumulator
for( i = 0; i < height; i++ )
for( j = 0; j < width; j++ )
{
if( image[i * step + j] != 0 )
for(int n = 0; n < numangle; n++ )
{
int r = cvRound( j * tabCos[n] + i * tabSin[n] );
r += (numrho - 1) / 2;
accum[(n+1) * (numrho+2) + r+1]++;
}
}
if (use_edgeval) {
for( i = 0; i < height; i++ )
for( j = 0; j < width; j++ )
{
if( image[i * step + j] != 0 )
for(int n = 0; n < numangle; n++ )
{
int r = cvRound( j * tabCos[n] + i * tabSin[n] );
r += (numrho - 1) / 2;
accum[(n + 1) * (numrho + 2) + r + 1] += image[i * step + j];
}
}
} else {
for( i = 0; i < height; i++ )
for( j = 0; j < width; j++ )
{
if( image[i * step + j] != 0 )
for(int n = 0; n < numangle; n++ )
{
int r = cvRound( j * tabCos[n] + i * tabSin[n] );
r += (numrho - 1) / 2;
accum[(n + 1) * (numrho + 2) + r + 1]++;
}
}
}
// stage 2. find local maximums
findLocalMaximums( numrho, numangle, threshold, accum, _sort_buf );
@ -907,7 +921,7 @@ static bool ocl_HoughLinesP(InputArray _src, OutputArray _lines, double rho, dou
void HoughLines( InputArray _image, OutputArray lines,
double rho, double theta, int threshold,
double srn, double stn, double min_theta, double max_theta )
double srn, double stn, double min_theta, double max_theta, bool use_edgeval )
{
CV_INSTRUMENT_REGION();
@ -922,7 +936,7 @@ void HoughLines( InputArray _image, OutputArray lines,
ocl_HoughLines(_image, lines, rho, theta, threshold, min_theta, max_theta));
if( srn == 0 && stn == 0 )
HoughLinesStandard(_image, lines, type, (float)rho, (float)theta, threshold, INT_MAX, min_theta, max_theta );
HoughLinesStandard(_image, lines, type, (float)rho, (float)theta, threshold, INT_MAX, min_theta, max_theta, use_edgeval );
else
HoughLinesSDiv(_image, lines, type, (float)rho, (float)theta, threshold, cvRound(srn), cvRound(stn), INT_MAX, min_theta, max_theta);
}

47
modules/imgproc/test/test_houghlines.cpp
View File

@ -340,6 +340,53 @@ TEST(HoughLines, regression_21983)
EXPECT_NEAR(lines[0][1], 1.57179642, 1e-4);
}
TEST(WeightedHoughLines, horizontal)
{
Mat img(25, 25, CV_8UC1, Scalar(0));
// draw lines. from top to bottom, stronger to weaker.
line(img, Point(0, 6), Point(25, 6), Scalar(240));
line(img, Point(0, 12), Point(25, 12), Scalar(255));
line(img, Point(0, 18), Point(25, 18), Scalar(220));
// detect lines
std::vector<Vec2f> lines;
int threshold{220*25-1};
bool use_edgeval{true};
HoughLines(img, lines, 1, CV_PI/180, threshold, 0, 0, 0.0, CV_PI, use_edgeval);
// check results
ASSERT_EQ(3U, lines.size());
// detected lines is assumed sorted from stronger to weaker.
EXPECT_EQ(12, lines[0][0]);
EXPECT_EQ(6, lines[1][0]);
EXPECT_EQ(18, lines[2][0]);
EXPECT_NEAR(CV_PI/2, lines[0][1], CV_PI/180 + 1e-6);
EXPECT_NEAR(CV_PI/2, lines[1][1], CV_PI/180 + 1e-6);
EXPECT_NEAR(CV_PI/2, lines[2][1], CV_PI/180 + 1e-6);
}
TEST(WeightedHoughLines, diagonal)
{
Mat img(25, 25, CV_8UC1, Scalar(0));
// draw lines.
line(img, Point(0, 0), Point(25, 25), Scalar(128));
line(img, Point(0, 25), Point(25, 0), Scalar(255));
// detect lines
std::vector<Vec2f> lines;
int threshold{128*25-1};
bool use_edgeval{true};
HoughLines(img, lines, 1, CV_PI/180, threshold, 0, 0, 0.0, CV_PI, use_edgeval);
// check results
ASSERT_EQ(2U, lines.size());
// detected lines is assumed sorted from stronger to weaker.
EXPECT_EQ(18, lines[0][0]); // 25*sqrt(2)/2 = 17.67 ~ 18
EXPECT_EQ(0, lines[1][0]);
EXPECT_NEAR(CV_PI/4, lines[0][1], CV_PI/180 + 1e-6);
EXPECT_NEAR(CV_PI*3/4, lines[1][1], CV_PI/180 + 1e-6);
}
INSTANTIATE_TEST_CASE_P( ImgProc, StandartHoughLinesTest, testing::Combine(testing::Values( "shared/pic5.png", "../stitching/a1.png" ),
testing::Values( 1, 10 ),
testing::Values( 0.05, 0.1 ),

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 );
}