init - 初始化项目

doc/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.markdown

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+Hough Circle Transform {#tutorial_py_houghcircles}
+======================
+
+Goal
+----
+
+In this chapter,
+    -   We will learn to use Hough Transform to find circles in an image.
+    -   We will see these functions: **cv.HoughCircles()**
+
+Theory
+------
+
+A circle is represented mathematically as \f$(x-x_{center})^2 + (y - y_{center})^2 = r^2\f$ where
+\f$(x_{center},y_{center})\f$ is the center of the circle, and \f$r\f$ 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 **cv.HoughCircles()**. It has plenty of arguments which are well
+explained in the documentation. So we directly go to the code.
+@code{.py}
+import numpy as np
+import cv2 as cv
+
+img = cv.imread('opencv-logo-white.png',0)
+img = cv.medianBlur(img,5)
+cimg = cv.cvtColor(img,cv.COLOR_GRAY2BGR)
+
+circles = cv.HoughCircles(img,cv.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
+    cv.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
+    # draw the center of the circle
+    cv.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
+
+cv.imshow('detected circles',cimg)
+cv.waitKey(0)
+cv.destroyAllWindows()
+@endcode
+Result is shown below:
+
+![image](images/houghcircles2.jpg)
+
+Additional Resources
+--------------------
+
+Exercises
+---------