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