GSoC Python Tutorials

GSoC Python Tutorials

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doc/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.rst

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+.. _Hough_Circles:
+
+Hough Circle Transform
+**************************
+
+Goal
+=====
+
+In this chapter,
+    * We will learn to use Hough Transform to find circles in an image.
+    * We will see these functions: **cv2.HoughCircles()**
+
+Theory
+========
+
+A circle is represented mathematically as :math:`(x-x_{center})^2 + (y - y_{center})^2 = r^2` where :math:`(x_{center},y_{center})` is the center of the circle, and :math:`r` is the radius of the circle. From equation, we can see we have 3 parameters, so we need a 3D accumulator for hough transform, which would be highly ineffective. So OpenCV uses more trickier method, **Hough Gradient Method** which uses the gradient information of edges.
+
+The function we use here is **cv2.HoughCircles()**. It has plenty of arguments which are well explained in the documentation. So we directly go to the code.
+::
+
+    import cv2
+    import numpy as np
+
+    img = cv2.imread('opencv_logo.png',0)
+    img = cv2.medianBlur(img,5)
+    cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
+
+    circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,
+                                param1=50,param2=30,minRadius=0,maxRadius=0)
+
+    circles = np.uint16(np.around(circles))
+    for i in circles[0,:]:
+        # draw the outer circle
+        cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
+        # draw the center of the circle
+        cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
+
+    cv2.imshow('detected circles',cimg)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+Result is shown below:
+
+    .. image:: images/houghcircles2.jpg
+        :alt: Hough Circles
+        :align: center
+
+Additional Resources
+=====================
+
+Exercises
+===========