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Alexander Alekhin
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Object detection with Generalized Ballard and Guil Hough Transform {#tutorial_generalized_hough_ballard_guil} |
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================================================================== |
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@tableofcontents |
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@prev_tutorial{tutorial_hough_circle} |
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@next_tutorial{tutorial_remap} |
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| -: | :- | |
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| Original author | Markus Heck | |
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| Compatibility | OpenCV >= 3.4 | |
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Goal |
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---- |
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In this tutorial you will learn how to: |
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- Use @ref cv::GeneralizedHoughBallard and @ref cv::GeneralizedHoughGuil to detect an object |
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Example |
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------- |
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### What does this program do? |
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1. Load the image and template |
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 |
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 |
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2. Instantiate @ref cv::GeneralizedHoughBallard with the help of `createGeneralizedHoughBallard()` |
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3. Instantiate @ref cv::GeneralizedHoughGuil with the help of `createGeneralizedHoughGuil()` |
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4. Set the required parameters for both GeneralizedHough variants |
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5. Detect and show found results |
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@note |
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- Both variants can't be instantiated directly. Using the create methods is required. |
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- Guil Hough is very slow. Calculating the results for the "mini" files used in this tutorial |
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takes only a few seconds. With image and template in a higher resolution, as shown below, |
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my notebook requires about 5 minutes to calculate a result. |
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 |
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### Code |
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The complete code for this tutorial is shown below. |
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@include samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp |
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Explanation |
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----------- |
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### Load image, template and setup variables |
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@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-load-and-setup |
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The position vectors will contain the matches the detectors will find. |
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Every entry contains four floating point values: |
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position vector |
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- *[0]*: x coordinate of center point |
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- *[1]*: y coordinate of center point |
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- *[2]*: scale of detected object compared to template |
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- *[3]*: rotation of detected object in degree in relation to template |
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An example could look as follows: `[200, 100, 0.9, 120]` |
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### Setup parameters |
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@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-setup-parameters |
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Finding the optimal values can end up in trial and error and depends on many factors, such as the image resolution. |
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### Run detection |
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@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-run |
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As mentioned above, this step will take some time, especially with larger images and when using Guil. |
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### Draw results and show image |
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@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-draw-results |
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Result |
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------ |
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 |
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The blue rectangle shows the result of @ref cv::GeneralizedHoughBallard and the green rectangles the results of @ref |
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cv::GeneralizedHoughGuil. |
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Getting perfect results like in this example is unlikely if the parameters are not perfectly adapted to the sample. |
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An example with less perfect parameters is shown below. |
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For the Ballard variant, only the center of the result is marked as a black dot on this image. The rectangle would be |
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the same as on the previous image. |
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/**
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@file generalizedHoughTransform.cpp |
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@author Markus Heck |
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@brief Detects an object, given by a template, in an image using GeneralizedHoughBallard and GeneralizedHoughGuil. |
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*/ |
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#include "opencv2/highgui.hpp" |
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#include "opencv2/imgproc.hpp" |
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using namespace cv; |
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using namespace std; |
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int main() { |
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//! [generalized-hough-transform-load-and-setup]
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// load source image and grayscale template
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Mat image = imread("images/generalized_hough_mini_image.jpg"); |
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Mat templ = imread("images/generalized_hough_mini_template.jpg", IMREAD_GRAYSCALE); |
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// create grayscale image
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Mat grayImage; |
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cvtColor(image, grayImage, COLOR_RGB2GRAY); |
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// create variable for location, scale and rotation of detected templates
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vector<Vec4f> positionBallard, positionGuil; |
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// template width and height
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int w = templ.cols; |
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int h = templ.rows; |
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//! [generalized-hough-transform-load-and-setup]
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//! [generalized-hough-transform-setup-parameters]
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// create ballard and set options
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Ptr<GeneralizedHoughBallard> ballard = createGeneralizedHoughBallard(); |
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ballard->setMinDist(10); |
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ballard->setLevels(360); |
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ballard->setDp(2); |
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ballard->setMaxBufferSize(1000); |
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ballard->setVotesThreshold(40); |
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ballard->setCannyLowThresh(30); |
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ballard->setCannyHighThresh(110); |
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ballard->setTemplate(templ); |
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// create guil and set options
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Ptr<GeneralizedHoughGuil> guil = createGeneralizedHoughGuil(); |
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guil->setMinDist(10); |
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guil->setLevels(360); |
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guil->setDp(3); |
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guil->setMaxBufferSize(1000); |
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guil->setMinAngle(0); |
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guil->setMaxAngle(360); |
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guil->setAngleStep(1); |
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guil->setAngleThresh(1500); |
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guil->setMinScale(0.5); |
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guil->setMaxScale(2.0); |
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guil->setScaleStep(0.05); |
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guil->setScaleThresh(50); |
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guil->setPosThresh(10); |
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guil->setCannyLowThresh(30); |
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guil->setCannyHighThresh(110); |
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guil->setTemplate(templ); |
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//! [generalized-hough-transform-setup-parameters]
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//! [generalized-hough-transform-run]
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// execute ballard detection
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ballard->detect(grayImage, positionBallard); |
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// execute guil detection
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guil->detect(grayImage, positionGuil); |
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//! [generalized-hough-transform-run]
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//! [generalized-hough-transform-draw-results]
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// draw ballard
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for (vector<Vec4f>::iterator iter = positionBallard.begin(); iter != positionBallard.end(); ++iter) { |
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RotatedRect rRect = RotatedRect(Point2f((*iter)[0], (*iter)[1]), |
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Size2f(w * (*iter)[2], h * (*iter)[2]), |
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(*iter)[3]); |
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Point2f vertices[4]; |
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rRect.points(vertices); |
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for (int i = 0; i < 4; i++) |
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line(image, vertices[i], vertices[(i + 1) % 4], Scalar(255, 0, 0), 6); |
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} |
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// draw guil
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for (vector<Vec4f>::iterator iter = positionGuil.begin(); iter != positionGuil.end(); ++iter) { |
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RotatedRect rRect = RotatedRect(Point2f((*iter)[0], (*iter)[1]), |
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Size2f(w * (*iter)[2], h * (*iter)[2]), |
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(*iter)[3]); |
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Point2f vertices[4]; |
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rRect.points(vertices); |
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for (int i = 0; i < 4; i++) |
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line(image, vertices[i], vertices[(i + 1) % 4], Scalar(0, 255, 0), 2); |
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} |
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imshow("result_img", image); |
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waitKey(); |
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//! [generalized-hough-transform-draw-results]
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return EXIT_SUCCESS; |
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} |
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