Circle_Detection.ipynb

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    "Hough Circle Transform\n",
    "\n",
    "\n",
    "A circle is represented mathematically as (x-x_center)^2 + (y - y_center)^2 = r^2 where (x_center,y_center) is the center of the circle, and 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.\n",
    "\n",
    "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."
   ]
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   "source": [
    "import cv2\n",
    "import numpy as np"
   ]
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   "source": [
    "#read image in grayscale mode,because medianBlur not working with RGB images\n",
    "image = cv2.imread('data/logo.jpg',1)\n",
    "image_8bit = cv2.imread('data/logo.jpg',0) #Hough transform processing wants 8-bit image"
   ]
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   "cell_type": "code",
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   "source": [
    "circles = cv2.HoughCircles(image_8bit,cv2.HOUGH_GRADIENT,1,20,param1=50,param2=30,minRadius=0,maxRadius=0)\n",
    "\n",
    "circles = np.uint16(np.around(circles))\n",
    "for i in circles[0,:]:\n",
    "    # draw the outer circle\n",
    "    cv2.circle(image,(i[0],i[1]),i[2],(0,255,0),2)\n",
    "    # draw the center of the circle\n",
    "    cv2.circle(image,(i[0],i[1]),2,(0,0,255),3)\n",
    "\n",
    "cv2.startWindowThread()\n",
    "cv2.imshow('detected circles',image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Source and more details, https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.html"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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