Lecture 4.6 - Line Detection using Hough Lines.ipynb

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    "   ## Line Detection - Using Hough Lines\n",
    "   \n",
    "**cv2.HoughLines**(binarized/thresholded image, 𝜌 accuracy, 𝜃 accuracy, threshold)\n",
    "- Threshold here is the minimum vote for it to be considered a line\n"
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   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "image = cv2.imread('images/soduku.jpg')\n",
    "\n",
    "#grey and canny out\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "edges = cv2.Canny(gray, 100,170, apertureSize = 3)\n",
    "\n",
    "lines = cv2.HoughLines(edges, 1, np.pi/180, 240)\n",
    "\n",
    "print(lines)\n",
    "\n",
    "for rho, theta in lines[0]:\n",
    "    a = np.cos(theta)\n",
    "    b = np.sin(theta)\n",
    "    x0 = a*rho\n",
    "    y0 = b*rho\n",
    "    x1 = int(x0+1000*(-b))\n",
    "    y1 = int(y0+1000*(a))\n",
    "    \n",
    "    x2 = int(x0-1000*(-b))\n",
    "    y2 = int(y0-1000*(a))\n",
    "    \n",
    "    cv2.line(image, (x1,y1),(x2,y2), (255,0,0),2 )\n",
    "\n",
    "cv2.imshow('image', image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
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   "source": [
    "### Probabilistic Hough Lines\n",
    "\n",
    "**cv2.HoughLinesP(binarized image, 𝜌 accuracy, 𝜃 accuracy, threshold, minimum line length, max line gap)\n",
    "\n",
    "\n"
   ]
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     "text": [
      "(1L, 2620L, 4L)\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "image = cv2.imread('images/soduku.jpg')\n",
    "\n",
    "#greyscale and canny\n",
    "grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "canny = cv2.Canny(gray, 100,170, apertureSize=3)\n",
    "\n",
    "lines = cv2.HoughLinesP(edges,1,np.pi/180,100,10)\n",
    "print lines.shape\n",
    "\n",
    "for x1, y1, x2, y2 in lines[0]:\n",
    "    cv2.line(image, (x1,y1),(x2,y2), (0,255,0),3)\n",
    "    \n",
    "cv2.imshow('image', image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
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   "source": [
    "http://cmp.felk.cvut.cz/~matas/papers/matas-bmvc98.pdf"
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    "\n",
    "\n"
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