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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    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "image = cv2.imread('images/soduku.jpg')\n",
    "\n",
    "# Grayscale and Canny Edges extracted\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "edges = cv2.Canny(gray, 100, 170, apertureSize = 3)\n",
    "#cv2.imshow(\"Canny\",edges)\n",
    "\n",
    "# Run HoughLines using a rho accuracy of 1 pixel\n",
    "# theta accuracy of np.pi / 180 which is 1 degree\n",
    "# Our line threshold is set to 240 (number of points on line)\n",
    "lines = cv2.HoughLines(edges, 1, np.pi / 180, 225)\n",
    "\n",
    "# We iterate through each line and convert it to the format\n",
    "# required by cv.lines (i.e. requiring end points)\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",
    "    x2 = int(x0 - 1000 * (-b))\n",
    "    y2 = int(y0 - 1000 * (a))\n",
    "    cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
    "\n",
    "cv2.imshow('Hough Lines', image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
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    "### 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, 167L, 4L)\n"
     ]
    }
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   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Grayscale and Canny Edges extracted\n",
    "image = cv2.imread('images/soduku.jpg')\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "edges = cv2.Canny(gray, 100, 170, apertureSize = 3)\n",
    "\n",
    "# Again we use the same rho and theta accuracies\n",
    "# However, we specific a minimum vote (pts along line) of 100\n",
    "# and Min line length of 5 pixels and max gap between lines of 10 pixels\n",
    "lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 200, 5, 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('Probabilistic Hough Lines', image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
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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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