Merge pull request #1244 from rolalaro/vpCHTimprovements

Circle Hough Transform improvements

doc/tutorial/imgproc/tutorial-imgproc-cht.dox

@@ -27,11 +27,6 @@ parameter.
 
 \image html img-tutorial-cht-radius-votes.png
 
-\section imgproc_cht_requirements Requirements
-
-With the current implementation, the `vpCircleHoughTransform` requires ViSP to be compiled with OpenCV.
-If you do not know how to do it, please refer to the installation guidelines of \ref soft_vision_opencv.
-
 \section imgproc_cht_howto How to use the tutorial
 
 It is possible to configure the `vpCircleHoughTransform` class using a JSON file.
@@ -74,7 +69,7 @@ If the detections seem a bit off, you might need to change the parameters in `co
 
 To run the software on an actual image using command line arguments instead, please run:
 ```
-$ ./tutorial-circle-hough --input /path/to/my/image --gaussian-kernel 5 --gaussian-sigma 1 --canny-thresh -1. --dilatation-repet 1 --center-thresh 200 --radius-bin 2 --radius-thresh 2 --radius-limits 80 90 --merging-thresh 15 2 --circle-perfectness 0.9
+$ ./tutorial-circle-hough --input /path/to/my/image --gaussian-kernel 5 --gaussian-sigma 1 --canny-thresh 100. 200. --dilatation-repet 1 --center-thresh 200 --radius-bin 2 --circle-probability-thresh 0.75 --radius-limits 80 90 --merging-thresh 15 2 --circle-perfectness 0.9
 ```
 
 If the detections seem a bit off, you might need to change the parameters
@@ -139,6 +134,8 @@ to the command line arguments:
 
 To run the circle detection, you must call the following method:
 \snippet tutorial-circle-hough.cpp Run detection
+The call to vpCircleHoughTransform::getDetectionsProbabilities permits to know the confidence in each detection.
+It is sorted in the same way that are sorted the detections.
 
 You could have also used the following method to get only the `num_best` best
 detections:

modules/core/include/visp3/core/vpImageCircle.h

@@ -78,9 +78,25 @@ class VISP_EXPORT vpImageCircle
   virtual ~vpImageCircle();
 
   /*!
-  * Get the center of the image (2D) circle
-  * \return The center of the image (2D) circle.
+  * Compute the angular coverage, in terms of radians, that is contained in the Region of Interest (RoI).
+  * \sa \ref vpImageCircle::computeArcLengthInRoI() "vpImageCircle::computeArcLengthInRoI(const vpRect &roi)"
+  * \param[in] roi The rectangular RoI in which we want to know the number of pixels of the circle that are contained.
+  * \return Returns 2.f * M_PI for a circle that is fully visible in the RoI, or the sum of the angles of the arc(s) that is(are) visible in the RoI.
   */
+  float computeAngularCoverageInRoI(const vpRect &roi) const;
+
+  /*!
+  * Compute the arc length, in terms of number of pixels, that is contained in the Region of Interest (RoI).
+  * \sa \ref vpImageCircle::computeAngularCoverageInRoI() "vpImageCircle::computeAngularCoverageInRoI(const vpRect &roi)"
+  * \param[in] roi The rectangular RoI in which we want to know the number of pixels of the circle that are contained.
+  * \return The number of pixels of the circle that are contained in the RoI.
+  */
+  float computeArcLengthInRoI(const vpRect &roi) const;
+
+   /*!
+   * Get the center of the image (2D) circle
+   * \return The center of the image (2D) circle.
+   */
   vpImagePoint getCenter() const;
 
   /*!