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doc/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.rst

@@ -1,52 +0,0 @@
-.. _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
-===========