Lecture 4.7 - Circle Detection using Hough Circles.ipynb

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    "# Circle Detection - Hough Cirlces\n",
    "\n",
    "**cv2.HoughCircles**(image, method, dp, MinDist, param1, param2, minRadius, MaxRadius)\n",
    "\n",
    "\n",
    "- Method - currently only cv2.HOUGH_GRADIENT available\n",
    "- dp - Inverse ratio of accumulator resolution\n",
    "- MinDist - the minimum distance between the center of detected circles\n",
    "- param1 - Gradient value used in the edge detection\n",
    "- param2 - Accumulator threshold for the HOUGH_GRADIENT method (lower allows more circles to be detected (false positives))\n",
    "- minRadius - limits the smallest circle to this size (via radius)\n",
    "- MaxRadius - similarly sets the limit for the largest circles\n",
    "\n"
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    "import cv2\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "image = cv2.imread('images/bottlecaps.jpg')\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "blur = cv2.medianBlur(gray, 5)\n",
    "\n",
    "circles = cv2.HoughCircles(blur, cv2.HOUGH_GRADIENT, 1.5, 10,\n",
    "                           param1=100,param2=100,minRadius=25,maxRadius=60)\n",
    "\n",
    "circles = np.uint16(np.around(circles))\n",
    "\n",
    "for i in circles[0,:]:\n",
    "    # draw the outer circle\n",
    "    cv2.circle(image,(i[0], i[1]), i[2], (255, 0, 0), 2)\n",
    "    \n",
    "    # draw the center of the circle\n",
    "    cv2.circle(image, (i[0], i[1]), 2, (0, 255, 0), 5)\n",
    "\n",
    "cv2.imshow('detected circles', image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
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