P1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Self-Driving Car Engineer Nanodegree\n",
    "\n",
    "\n",
    "## Project: **Finding Lane Lines on the Road** \n",
    "***\n",
    "In this project, you will use the tools you learned about in the lesson to identify lane lines on the road.  You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really just a series of images). Check out the video clip \"raw-lines-example.mp4\" (also contained in this repository) to see what the output should look like after using the helper functions below. \n",
    "\n",
    "Once you have a result that looks roughly like \"raw-lines-example.mp4\", you'll need to get creative and try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines.  You can see an example of the result you're going for in the video \"P1_example.mp4\".  Ultimately, you would like to draw just one line for the left side of the lane, and one for the right.\n",
    "\n",
    "In addition to implementing code, there is a brief writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a [write up template](https://github.com/udacity/CarND-LaneLines-P1/blob/master/writeup_template.md) that can be used to guide the writing process. Completing both the code in the Ipython notebook and the writeup template will cover all of the [rubric points](https://review.udacity.com/#!/rubrics/322/view) for this project.\n",
    "\n",
    "---\n",
    "Let's have a look at our first image called 'test_images/solidWhiteRight.jpg'.  Run the 2 cells below (hit Shift-Enter or the \"play\" button above) to display the image.\n",
    "\n",
    "**Note: If, at any point, you encounter frozen display windows or other confounding issues, you can always start again with a clean slate by going to the \"Kernel\" menu above and selecting \"Restart & Clear Output\".**\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**The tools you have are color selection, region of interest selection, grayscaling, Gaussian smoothing, Canny Edge Detection and Hough Tranform line detection.  You  are also free to explore and try other techniques that were not presented in the lesson.  Your goal is piece together a pipeline to detect the line segments in the image, then average/extrapolate them and draw them onto the image for display (as below).  Once you have a working pipeline, try it out on the video stream below.**\n",
    "\n",
    "---\n",
    "\n",
    "<figure>\n",
    " <img src=\"examples/line-segments-example.jpg\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your output should look something like this (above) after detecting line segments using the helper functions below </p> \n",
    " </figcaption>\n",
    "</figure>\n",
    " <p></p> \n",
    "<figure>\n",
    " <img src=\"examples/laneLines_thirdPass.jpg\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your goal is to connect/average/extrapolate line segments to get output like this</p> \n",
    " </figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Run the cell below to import some packages.  If you get an `import error` for a package you've already installed, try changing your kernel (select the Kernel menu above --> Change Kernel).  Still have problems?  Try relaunching Jupyter Notebook from the terminal prompt.  Also, see [this forum post](https://carnd-forums.udacity.com/cq/viewquestion.action?spaceKey=CAR&id=29496372&questionTitle=finding-lanes---import-cv2-fails-even-though-python-in-the-terminal-window-has-no-problem-with-import-cv2) for more troubleshooting tips.**  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#importing some useful packages\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import numpy as np\n",
    "import cv2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Read in an Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This image is: <class 'numpy.ndarray'> with dimensions: (540, 960, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1282d96a0>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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NT1r6/UBYJYpaJBfx3S8KBgnElD0p9sWzRPob7O3abz6Ne4nxm9T7VYkKi++W9Evr52Xf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vxSc/fQQfvusENvhyTKaXwNUrMCNCXXmT8r79j8P61mmsT7Ywm7WYuTlGo3Hof+ov\nK+Qi5Ac0ekvE3qxjgrQVN38IHSvvJcO9TeQdpSkKovy9J9lUuRQ8JAmWBCiuRTxgY7KNCuQH0frP\nlfjQ1zS8xE29YAAviS6QVhcQ+B6ekhBq9O4vmVfJWNUT8Zp2+g4JSrpASilRLlqfft9TWgS9HpLH\nfYiBiGQv2p53LHtN1pt3kp+LSPrU4YMf/GvcfPPNME0NgonO+7a1mclQ5sGPmWIFNg9Gf9kxZPMd\n35D/pj+TY5+ZF0lEiuIpe81ycA2LnEEAfKjxN2Bl/0ZMN68G8JLw+T0A/gae0L8awP9k3+s7iGg/\nER1i5ge2a6y0uROl0ENBUmZvLxP7mqlk9TjY5ZUdtMqLd4vtuOs6OALqIDl4yZIQTunHjZE6VgV1\nFAhvD0QvSZeyxsbUmdQQ3+8LqAVzh49QMDwCowVg/eFLthhXcxg7wd7xDC9/0ZV41rU1iB8CuU0A\nDR7a2I1b/+4efOVwhQmehLk9AK7DJpkQqlWAzRhHjhxD183ROouqqgGu/VF+TmYr6bSjzhNuR8EZ\nm5xBRASqgymAakg5QsDbIZkAS8OFsq0jkCGMVlZ8BbDZLEhcilgKYnNwODOi2chvJs7Wf0gzSxss\nGjfCfYDYh+U9lYQlFgzaMAGW4UxqQ8AkSxdACr/I+tsoMUwYA6gY7iHGRJTKYGu66dfE91J+k9BO\nC0bFfW1BhAtKF/z9Kourv5wclfq6b08HJjAka6x+x5CGJM+nsYp5i/w6unTq22aCgBTfQFjf4LNh\ngEBgB6ysrWA+N9HBLWMgatDOgWa8O+7rtuXg+E9j8GY7OeeR5plIfB0MrZ3mEn4cuRpnEu4i3mER\nJFwLNh1vjxdhMcxlF/Aw9ppTbWFnXQ+/jXbs9vwsZ4edEnoG8H/Jz9DbmfkdAC4X4s3MDxDRwXDv\n4wDcq549HK5lhJ6I3gzgzQCwe/cefT2MxSch0o6xOPiwgayOdCCV1Mz0M9mJfb2qKjQmxQjL5tBO\nvBSRUNgsQzibvEP3FwA62xXRPbIYAekVdlQwcBwOWJgOo2qOujuOZz1lH1724muxNvoaKj4F5xgz\nW+HBw/twyx1fx7GtA5i6fZjYVVCXnFp79+4JCccstra2vBbEzhcgV+MrpS7RioZQN7eLK+2jcGTp\nudLAzJjP57Ah06i0l54FysLHxIi+FM0QtjOHACm7ZF8Sze/bTkpdJDAt0vj8Q5QSGFiGNpEs0jZK\nU4mW6nTTxCLJDdt75XNZDKZch0UEQd4XnachiEATmUV9zd/FkZE49p0ekv6H5iLtO3/NGIP19fXe\n85mjGPn+KvFEGL0LzGZYOOBe22V/y89DOFhqWNoGzwhO12Da0mu0LVif0lyPqXzibPuhhJ0S+hcw\n8/2BmP8lEX1+m3uHetAbWWAW7wCAgwevYE1AZOJLR2jp7c6QW/3OzsFUXobWtndpQ7IIAgDVPgqA\nbZ/DA3IStL9A7PpETdsIpR0xH8g9okpO3AbWxmPUjkDdJnabY/iuVz4ZT72mQe2+horOYDpbxbTd\ni7/6m8/h8DGL090TQPWlaGct2HZxVvfu3YuqqTHb2sLW1hY6Dk7jkMrAS34itfTWQT5EE5We30rW\nhdkjH5Bshuzr9WomKc8J4fBO4EUbX96T94kQCByGJUnRCjSUjLdk5INjVs9qojdElPoguOlCBIVI\nev7cRHpZ/k4jFIA5016yPisQdV3SUIvWODR2bSIRXBsicjKnYg7J7xmOdBrqn9YQJM23f793NDaB\nWEmmT/8eLa2q0FIKDlPJVMv5WiD2V+LhJcLNv5/V2GIfgqnSzxdDTDleuNBhnr2hQsxfMvt9RjJs\nitPRPQaApWI9nOvF4pdtRLon2prW2tT955KUeUeEnpnvD3+PENH7ANwA4CExyRDRIQBHwu2HATxe\nPX4lgPvP8oZor9NEo3SQlYS0TBiUvPrhGSQnLjNjFGLyNY32i1dFZ6q3FaqQM0rmH1kgH6LYlyb0\nPw3OdTCmDuPxSZ0qqkHtOtbMJp58lcF3f/szsXvlPnC1AfAY8+4SfPyzp/CFrx7GQ8f3Y9McxMyu\nAp2D7WYY1RVADfbs2QNTV56gWsZ83sE0ZTTMsA1RSzyCzJrhZhKFGlMZ+122CyCzDbMtJfkUOSNr\nkM9X7sQ9F+llJ/fuRFPYiRbhYcgmXxB75EJLnLcBwnk2Yc/7OBZrUfKuPnMUcwVlz+dtJ6HkbJDj\nhn6+L/GLRVvjmW5H42PnXHaWRQtN0byruqhxM9IFk8y4ApqulJrTtox2YN+Uc571QTHvWHnK24s8\nDWma7KTsdnPLQBK0Fty3UzgroSeiXQAMM6+Hz98O4N8DuBnAGwH8h/D3/eGRmwH8JBH9PrwT9jSf\nxT4PJDVuCFE0IZWj4UQUJ09zfDn1KkirtYO4oEwqUVkeW1wia8d5/nORbi2nQyI+WVaKnS8XsDL+\ntCIY0Za5Vp/BVZfN8bzrL8PTnzwC23vBmMJ1Kzh5chUf+sgD+NLhCrY6iLbai/l8C0wWxB3q2oDG\nqzDOAoaxsbGBycRH1VSmhmMbSiqG95Pe3H1kzWOthwmcRvJFGTmzMQcfSV3XoMofLw+rCl2jdZH0\nKFASK2FgJWgfT9nnsr2zS+v9Z/W8JII4FFJJ4friTbzoundCLnou74s8M6zxKIeoMstoQl+aL6S9\nnUit5f0p8gZxfYTAs9JcdN/1Z/3Ouq57WSgBrXV5000pBAztX/mro5MEd3eKJ/qeNI+5XV/f38hB\nxGjQC4yKPC1pmNBZu4DVIt4rOaF2LnBsDzuR6C8H8L7wshrA7zLzB4joowD+kIjeBODrAG4K9/85\ngFcBuBvAFoAfPvsrCKAqxHIHh6Fz4BCfbCQ3ivEs0qtjFpVRjiv2hxy8s7OCMT4u3YnKx4zoHIKD\nC6YMBwfrbFC/ObSVFrEOxFtOIDJsrFnasS/+IbVbHXUY1StoZx3ISA1bA2NbYNTAVC0aa1C1D+Cx\nB8/gVa+8Hpfvn6OyR+EImHOD06fXcNudx/D5r6/Bmcsxn3KIfh/FRExVXaGdTWGaBrNZ681RIQ1A\nB4faERoVseLVPFEX1eap6qQqI0UrDKn8cZ3Q3wTios0YhrNYWVnB5Zd5183hw4djG+UpUgHZrJK2\nQK7Jc/q0re6HEBMGp9A1x8HclNugjTEoT4UlyTN9rhBqDihzThwvAUS5Hyd9STiqiRwF6a4SWzRR\nllI9rhW8s76vAeUnlJkZVVVjUeHokmD7zxL7j2jSYEr2X50kjIr2pB/D76iy9ZJDUV3YJ40qvNOh\nArMIZEnK55gtVSJiCI666LD15lagqis4dSpaBDnN6Mv+igTvnAuH6HzFtjQW+WR6Qo+0k4QMuVn7\nFryGagyhg/M5uBgx773MLRH5ClQUzuuoucz3g3qv9KyY+3OFsxJ6Zv4KgGcPXD8O4OUD1xnAT5xz\nTxTEiBUVBxvjSSk5VcvFNlGCZ2iClhykIbdMCLkTp2uJIOVfnbIYcOjYRaeLcy4EXzBqNH4z1xyi\neULEDVVg6zCuZxhVJ/G856/hxTdehcY8BHanYRlw/Dh88e5T+Ninv47jZ/ZgzrthmTBnB2MYTd1g\nZWUFe/fu9Ue/WweGwXQ69SeHwyndoKLEsetoFS2RyLjS/ARpyDB0Pv6B9e1/L2zNstm3trawvr6O\n2WwWC4RoM1i5kUp1vWyvZAyZOUStt2h+QwSq/D607kNjJaKYz2RboESIAUSHqiee6r3lYzIHJmks\n2/VHniHq17LtdWlA+g2/nJVonE2iLOcvizaj/OQrM0NCqcVWT0Hb1JKylFgUyX00GkXhQIfIapzo\nvUf1Td4zZG7SMHQuYdGYF86DMHjXX6vsr39o4TsE10yBC1qzWHTwbAgumJOxgDLfBGSQTIFOJbYi\nw1lqYB2RYwxAxDE9cLT9cTqY5HmAxKkq4oDcHqgX01qrSomFSCCqA0H0hL4mg6oawdp5rDvLHaOp\nCHU9gbMncHD3DN/zim/CYy89jRW+D3AOHY+wPt+L2z7yID7zhS3Y6vGYdmN0ZgwXNAnrgLoG1tfX\n4SzCiUGOkUTed5FCEOuqyvIBlWPSccaAchTBggaiFDSU5gJhyLJh01o6EDFOnznp26/gyx5y/0BZ\n2X6pfpe+EMEVfZ8wfdHgynZyYp63Ea9T6kPExwGTto/J7psg/LP5NSJK8c+Ebee13LxcvEOPlVlM\naIttvXrOSn+XJB7LK1Dl/dmOyGcajoqMG4rBb9s2/l75uEo4R6Cg+RBRzDmk241r6hzW1tZUiG6a\nW/mu/Xv5mqfDYkkwzJ2hWqAbwsmhcZe4Ff8hYNc5SN09U1lF0ZEYCb4yZ4mgpKMDzwYXDKEvpac4\ncA4xujFiJncWxsNUVMOQPyXZuVAsO/ynQx6JKASg+B2c3psT+TINsUN+n7U2qFOKKDB7FmwA2zLG\n6DAmiwanccPz9uOFNxxAg/tQY44pW1h7AA88ZPChu07iniMNuupqtF0NRgV2HaztQAB2re4G1d4k\nNWvnnrizw2wWkJUqQNm9BQE04dCbIs2FbM5AQMjX0tVrIJ/FwSVjzQjukOkmFHvhcOKc2bfvwuZz\nmcSziFBBHYIRCU5MDzkBihKkwqdSg0mSnXptJqYrwraDfTpMFEL6a6R2dJsWOu497788b0LJvqH0\nBHqzeylXJGNd3jH1T/6WTCTej6SB6KFEPxhKwulBS7+asJdzIvM9tL9lvqPAEPZeZarYHyKDrrOY\nTme9UEzmdOBLChaJX84nvav8gT6VstpImyZpiok+uOKgVn/caZx5Uro4hwiOYCzWKONaFnO7LWNV\n/aBC6NkJXHCE3hiTFbCw1mI1cHMfnoQgTQdu7X308HZCOZ1IINRwwWHqj/0DzCYe+EkI1lc55bNP\nqNZ6RE49hYM/wZekJfnJAehA3GFXM0fV3o/rn34FrnvGU3DwsjNYqR6A5Q3MeT9OTPbgw7ffg3vv\nY2y0hzB1u+HcinfIOsZ43KCqDJpqjPFoDTbk1d7Y2Ahl13x41XbIBOSnQUtNJTFNQV5vIzYhT0pi\nqD4kVa6VbbANZgnlAqBKq9HSN89USocYczrgNiglMcfEaUN4MySNx/YLIlM+p5nXkIcsyxoo7ygk\n30XPMKcqQUhTE38belZLxxqGCIJmxnEMGJgDJEbf67d6TfZOLRgNzN8Qcc/2cCC2mqDptjzTUZku\nC2lc90na0r/JZ8luq+tO6Eg7Q5yZfaTqU2IMCDipChMVGskQDP0s2i04RQQuasf7irSWqcYuxdit\n7ZUtlPu3a3sILihCHxE34JVIIKNmJanvJq8cBcgESXysVJix0d7pb/O2+yzVKnIiX26ipmlg23m0\nswpI+Jc8HyVMtqhrhrGbGFdb+P5XX4+rHs9ozJdBfApuuoqWduHLX7e47Y4zOLX+GFjajdaswhBg\nLWPiGI2pYTvCgUsuBQDM5g6TySTks08bRqralwuube9RClKIlyOJSOouSPi5nVPa0df0PA0RpRJ0\nH/VG11kM9Xf9nEhN4pfRxKKU2Fl2btbHfn98WumdO7eiyYRzdXo7EG207ECcB8r5yrCJafv2xR/l\nJdvhQ2wassiVgfaHJNhSmh1iMqWWDRTapMrc6PefNimmfPLpnuR41gxKv0Pfo823cs94PA6RXnn/\nRavds38v1tfXCxrihTv/XJ+plPM/xKBMOI09tALZnuLc4Z0xF6KQ8ZOjaU0qV+n5/idoo2c45511\nIBdLyPkiDRWms62IDKmKTKlOJaIWLOqouQI7QTpRhTlbJDHT2BADIQ6gpmmwNduKGkJCiJS+ljrA\nYQ5DhNoaNCMGzb6K65/2GLz0xc/CntX7UM3X4ZjA1UGcnq3itg89iM/dSzg9uwKmGaGqa4zGI6yu\n7sKxY8ewr9kDY4wvaMEUinzMMN2a+f4xwNZrKHL4hkj+heP5aMC2A7sOVZCcrKOUnVGOpCc8iqo8\nnISAFiYSx176csGyH6KNiJMERJRcomyTFK/VWBff5ddCJHkpDq0lbKp8H8XM5JyX7PUaRuITpGcj\n5yF8vAbIBgIXTBSdczDsK5dJZjUiXz5Rtg151UYxxNxs5+eliv2Q/npBwviCGwjaBAHaRCGmHFOQ\nglIKXySxaWLsCVs/vNKx2L8pMT7khIZDW1IpkYjyeHfOn/ftKkavGQurSmgqDl8KAHGl+khtpIJe\ncAvrFyLsqqpCF/av75Mat8ydhEMH6ZnER0SACb6pycxXkKpqA6okNUnCaymWI/PpTcC6eI6W8G1v\n3q1E5oVxMvzGaJwLvdeajkcawTA/Xj+3mnGWGq0xeTUyQmAIyge1U7hACL3fjHWzfT5sGxJXaGle\nO978P+uPEANwJMRf4uQtpAKREHgh+E6FW4mZw1qLmn3OlgHlFQAwrtfg7Aw1T7HSnsZ1z74cNz7n\nEuzd/SBgT6Kua7RcYX2ygts+cj++dJ/BxF2Klg0qS7AE7Nq7hvWNTYBqNE0TKxo551MYzGazWFaP\nHWJoaCxyAAjGBaQKG5sMbJDWnVBNIIYgeqSWswFpE4EMQF6dlQMbSRL1phjHPkyMqIq1bHVUSS8D\nk2xulySyyLyVszUneJ5cE/fNA1nTlJsQegdMKNlpQzBeYCjKBKVuX2TqkVUXwULflvdv56p1qWUt\nMhuUfUqCSn/DJy2zf5p80fj6L/QnuIV5bzcnbIMAZAozhCZi8IxC1iFjCrKnrT+sKOk7dOSKjktn\nRoYLjEAHgoZDDF+SkcijMijtCxckaTkfo2tDi/Q84KjV3zMcdaTPV3oBlZWZT61txeTDHdjGAAaR\nzGVPMjO4c4CqbyB9MipacGhdt4MLgtATESrjj9YLYkiJs9JmqQm8JthiD5T2dNu+gLUQ+w7OpfCs\n2F5mQAVgw9F28pzaICBqoKqVAajqMHYtal7H/l1H8aqXPRlPeVIDg/tg3BTzljBx+3HfcYcP3XUY\nX3twD5iuwJwrcNVh6jq46RxbDz2IXbt2oV4ZR+K3ubmJyWQCwEctROcl5cRBIEfOYNaI80WAitqW\nQzmi+os24E09qU0p+iKO0ESE0nkDMY9sJ4VqqICYs9wA4K71Pg8jJ4iV6UiZOUSCjO9xOWNw7OJm\n0QVOdN9iH+QZ5Bu3JLrlRrJxjLm5KrWRtBgKiOiHAAAGpklEQVT5W+Ji2WZp1lq0gYfep9+pnxGN\n3pgKoo8MvTf7ru4hwCdUIx+xQkDUpIf6wOxz9Zsw19Zab2KFXzerpX+R1F0KbzDGRCep4bAXo/SC\n7J0A4NoUFp1+dLG2QjZXNg+hhswx0p6Rv3UzQtM0mE6nkZ5oxluaMH3DPnw6aTuBniE38QmjqsAp\nCZ6Mn/KwYmPY0x2XUlUTDKjWUWc2aLc7gwuC0FdVhZUVb4fPCv5Gop8k73ILaCkuSkVBwozFG5wD\ns2RpTM67tkvl/Ay7rE3frzzULbNx2g4GLRqcxrOvPYDvePlzUdM9cNiAsw61W8NktoYP/u3duPu+\nEbbcY9DyIcynBMcz2KaLWkPTGFTkYNACaHy+mq6L47bWxqpQvmMUYvctXMjRo4mb5bw8HKu4fz/e\ncDCKfO3UctxhxLAhSZv2YQjIXMQauiH0NUrJ6py6tp1KfzOmrEL89G9SmAVICoJz8GGxwdRA6pCP\nDo/TcdxlTLMVXIibJhcQyr/lmIGk2kfVXI0zakboM5lyXUpNRud/GZoTDf7Zvs9JwHGH2lSB2fWF\nAVk//z0VwvH9F9LgIiHuemdTdP98yKTvMwAw5nY+iDfg3PxqJNqJAOss2iBYNKbK3ifzscg2HXFc\nF7gxQajwor3HE1jUKrTYm9IY8/kMXdf6Wr3Owidq03OdxpuyohKcbaPJ0DpVCB1J2HLsALXX/AFq\nz3h8VFnOyGXMiZm2aDu/Pk3ly6leeiAlgzwbXBClBIloHcAXznc/LkB4DIBjZ73r0QfLeenDck6G\n4WKfl6uY+bKz3XRBSPQAvsA7qHv4aAMiums5L31YzksflnMyDMt58fCN16hawhKWsIQlXNCwJPRL\nWMISlnCRw4VC6N9xvjtwgcJyXoZhOS99WM7JMCznBReIM3YJS1jCEpbw8MGFItEvYQlLWMISHiY4\n74SeiP4ZEX2BiO4moree7/48UkBEjyeivyaizxHRZ4noLeH6JUT0l0T0pfD3QLhORPRrYZ4+RUTX\nn98RPLxARBURfZyI/ix8fyIR3Rnm5Q+IaBSuj8P3u8PvV5/Pfj+cQET7ieiPiOjzAW9ufLTjCxH9\n27B/PkNEv0dEK0tc6cN5JfTkE4b8OoDvBPB0AK8noqefzz49gtAB+BlmvhbA8wH8RBj7WwHcwszX\nALglfAf8HF0T/r0ZwG888l1+ROEtAD6nvv9HAL8S5uUkgDeF628CcJKZnwLgV8J9Fyv8dwAfYOan\nwRcD+hwexfhCRI8D8G8APIeZnwl/8Pp1WOJKH1idUnuk/wG4EcBfqO9vA/C289mn8zgX7wfwSviD\nY4fCtUPwZwwA4O0AXq/uj/ddbP/gC8rfAuBlAP4M/iz8MQB1iTcA/gLAjeFzHe6j8z2Gh2FO9gL4\najm2RzO+AHgcgHsBXBLW/s8AfMejHVeG/p1v040slMDhcO1RBUGFvA7AnQAu51BMPfw9GG57NM3V\nrwL4eaTCnpcCOMXMUlJHjz3OS/j9dLj/YoMnATgK4N3BpPVOItqFRzG+MPN9AP4LfM3qB+DX/mNY\n4koPzjehH8qA9agKAyKi3QD+D4CfYuYz2906cO2imysi+m4AR5j5Y/rywK28g98uJqgBXA/gN5j5\nOgCbSGaaIbjo5yX4I14N4IkAHgtgF7zJqoRHG6704HwT+sMAHq++Xwng/vPUl0cciKiBJ/L/m5n/\nOFx+iIgOhd8PATgSrj9a5uoFAP45Ed0D4PfhzTe/CmA/EUnKDj32OC/h930ATjySHX6E4DCAw8x8\nZ/j+R/CE/9GML68A8FVmPsrMLYA/BvCtWOJKD843of8ogGuCl3wE70i5+Tz36REB8mnw3gXgc8z8\n39RPNwN4Y/j8RnjbvVz/1yGa4vkATovKfjEBM7+Nma9k5qvh8eGDzPwGAH8N4DXhtnJeZL5eE+6/\n6KQ0Zn4QwL1E9E3h0ssB/AMe3fjydQDPJ6K1sJ9kTh7VuDII59tJAOBVAL4I4MsAfvF89+cRHPcL\n4dXGTwH4RPj3Knib4S0AvhT+XhLuJ/gIpS8D+DR8pMF5H8fDPEcvAfBn4fOTAHwEwN0A3gtgHK6v\nhO93h9+fdL77/TDOxzcDuCvgzJ8AOPBoxxcAvwTg8wA+A+B/ARgvcaX/b3kydglLWMISLnI436ab\nJSxhCUtYwsMMS0K/hCUsYQkXOSwJ/RKWsIQlXOSwJPRLWMISlnCRw5LQL2EJS1jCRQ5LQr+EJSxh\nCRc5LAn9EpawhCVc5LAk9EtYwhKWcJHD/wNbyKra2lu5+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12847acf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reading in an image\n",
    "image = mpimg.imread('test_images/solidYellowLeft.jpg')\n",
    "\n",
    "#printing out some stats and plotting\n",
    "print('This image is:', type(image), 'with dimensions:', image.shape)\n",
    "plt.imshow(image)  # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Ideas for Lane Detection Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:**\n",
    "\n",
    "`cv2.inRange()` for color selection  \n",
    "`cv2.fillPoly()` for regions selection  \n",
    "`cv2.line()` to draw lines on an image given endpoints  \n",
    "`cv2.addWeighted()` to coadd / overlay two images\n",
    "`cv2.cvtColor()` to grayscale or change color\n",
    "`cv2.imwrite()` to output images to file  \n",
    "`cv2.bitwise_and()` to apply a mask to an image\n",
    "\n",
    "**Check out the OpenCV documentation to learn about these and discover even more awesome functionality!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Below are some helper functions to help get you started. They should look familiar from the lesson!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def grayscale(img):\n",
    "    \"\"\"Applies the Grayscale transform\n",
    "    This will return an image with only one color channel\n",
    "    but NOTE: to see the returned image as grayscale\n",
    "    (assuming your grayscaled image is called 'gray')\n",
    "    you should call plt.imshow(gray, cmap='gray')\"\"\"\n",
    "    return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "    \n",
    "def hsv(img):\n",
    "    return cv2.cvtColor(img,cv2.COLOR_BGR2HSV)\n",
    "\n",
    "def canny(img, low_threshold, high_threshold):\n",
    "    \"\"\"Applies the Canny transform\"\"\"\n",
    "    return cv2.Canny(img, low_threshold, high_threshold)\n",
    "\n",
    "def gaussian_blur(img, kernel_size):\n",
    "    \"\"\"Applies a Gaussian Noise kernel\"\"\"\n",
    "    return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\n",
    "\n",
    "def region_of_interest(img, vertices):\n",
    "    \"\"\"\n",
    "    Applies an image mask.\n",
    "    \n",
    "    Only keeps the region of the image defined by the polygon\n",
    "    formed from `vertices`. The rest of the image is set to black.\n",
    "    \"\"\"\n",
    "    #defining a blank mask to start with\n",
    "    mask = np.zeros_like(img)   \n",
    "    \n",
    "    #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\n",
    "    if len(img.shape) > 2:\n",
    "        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image\n",
    "        ignore_mask_color = (255,) * channel_count\n",
    "    else:\n",
    "        ignore_mask_color = 255\n",
    "        \n",
    "    #filling pixels inside the polygon defined by \"vertices\" with the fill color    \n",
    "    cv2.fillPoly(mask, vertices, ignore_mask_color)\n",
    "    \n",
    "    #returning the image only where mask pixels are nonzero\n",
    "    masked_image = cv2.bitwise_and(img, mask)\n",
    "    return masked_image\n",
    "\n",
    "\n",
    "def draw_lines(img, lines, color=[255, 0, 0], thickness=4):\n",
    "    \"\"\"\n",
    "    NOTE: this is the function you might want to use as a starting point once you want to \n",
    "    average/extrapolate the line segments you detect to map out the full\n",
    "    extent of the lane (going from the result shown in raw-lines-example.mp4\n",
    "    to that shown in P1_example.mp4).  \n",
    "    \n",
    "    Think about things like separating line segments by their \n",
    "    slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left\n",
    "    line vs. the right line.  Then, you can average the position of each of \n",
    "    the lines and extrapolate to the top and bottom of the lane.\n",
    "    \n",
    "    This function draws `lines` with `color` and `thickness`.    \n",
    "    Lines are drawn on the image inplace (mutates the image).\n",
    "    If you want to make the lines semi-transparent, think about combining\n",
    "    this function with the weighted_img() function below\n",
    "    \"\"\"\n",
    "    lx=[]\n",
    "    ly=[]\n",
    "    rx=[]\n",
    "    ry=[]\n",
    "    for line in lines:\n",
    "        for x1,y1,x2,y2 in line:\n",
    "            slope = (y2-y1)/(x2-x1)\n",
    "            print(slope)\n",
    "            if slope < 0:\n",
    "                lx +=[x1,x2]\n",
    "                ly +=[y1,y2] \n",
    "            else:\n",
    "                rx +=[x1,x2]\n",
    "                ry +=[y1,y2]\n",
    "               \n",
    "#             cv2.line(img,(x1,y1), (x2,y2),color,thickness)\n",
    "    #find the extrapolation\n",
    "    #left\n",
    "#     print(list(zip(lx,ly)))\n",
    "#     print(list(zip(rx,ry)))\n",
    "    lz = np.polyfit(lx, ly, 1)\n",
    "    print(lz)\n",
    "    lf = np.poly1d(lz)\n",
    "       \n",
    "    x_size=image.shape[0] \n",
    "    y_size=image.shape[1]\n",
    "    \n",
    "    lx1=min(lx)\n",
    "    lx2=0\n",
    "    ly1=int(lf(lx1))\n",
    "    ly2=int(lf(0))\n",
    "    lx3=int(img.shape[1]/2-40)\n",
    "    ly3=int(lf(lx3))\n",
    "    cv2.line(img,(lx1,ly1), (lx2, ly2),color,thickness)\n",
    "    cv2.line(img,(lx2,ly2), (lx3, ly3),color,thickness)\n",
    "    \n",
    "    #right\n",
    "    rz=np.polyfit(rx,ry,1)\n",
    "    rf=np.poly1d(rz)\n",
    "    print(rz)\n",
    "    rx1=int(image.shape[1]-40)\n",
    "    ry1=int(rf(rx1))\n",
    "    rx2=int(max(rx))\n",
    "    ry2=int(rf(rx2))\n",
    "    rx3=int(img.shape[1]/2+50)\n",
    "#     print(rx3)\n",
    "    ry3=int(rf(rx3))\n",
    "    cv2.line(img,(rx1,ry1), (rx2,ry2),color,thickness)\n",
    "    cv2.line(img,(rx2,ry2), (rx3,ry3),color,thickness)\n",
    "    \n",
    "    \n",
    "\n",
    "def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\n",
    "    \"\"\"\n",
    "    `img` should be the output of a Canny transform.\n",
    "        \n",
    "    Returns an image with hough lines drawn.\n",
    "    \"\"\"\n",
    "    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)\n",
    "    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n",
    "    draw_lines(line_img, lines)\n",
    "    return line_img\n",
    "\n",
    "# Python 3 has support for cool math symbols.\n",
    "\n",
    "def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):\n",
    "    \"\"\"\n",
    "    `img` is the output of the hough_lines(), An image with lines drawn on it.\n",
    "    Should be a blank image (all black) with lines drawn on it.\n",
    "    \n",
    "    `initial_img` should be the image before any processing.\n",
    "    \n",
    "    The result image is computed as follows:\n",
    "    \n",
    "    initial_img * α + img * β + λ\n",
    "    NOTE: initial_img and img must be the same shape!\n",
    "    \"\"\"\n",
    "    return cv2.addWeighted(initial_img, α, img, β, λ)\n",
    "\n",
    "def filter_img(hsv):\n",
    "    #filter image for white and yellow colors\n",
    "    sensitivity = 40\n",
    "    lower_white = np.array([0,0,255-sensitivity])\n",
    "    upper_white = np.array([255,sensitivity,255])\n",
    "\n",
    "     # define range of yellow color in HSV\n",
    "    lower_yellow= np.array([65,100,100])\n",
    "    upper_yellow = np.array([105,255,255])\n",
    "    yellow_mask = cv2.inRange(hsv, lower_yellow, upper_yellow)\n",
    "\n",
    "    # Threshold the HSV image to get only white colors\n",
    "    white_mask = cv2.inRange(hsv, lower_white, upper_white)\n",
    "\n",
    "    mask=yellow_mask+white_mask\n",
    "    return mask\n",
    "\n",
    "def filter_img_white(image):\n",
    "    sensitivity = 15\n",
    "    lower_white = np.array([0,0,255-sensitivity])\n",
    "    upper_white = np.array([255,sensitivity,255])\n",
    "     # Threshold the HSV image to get only white colors\n",
    "    white_mask = cv2.inRange(hsv_img, lower_white, upper_white)\n",
    "    \n",
    "    gray_img = grayscale(image);\n",
    "    # Bitwise-AND mask and original image\n",
    "    return cv2.bitwise_and(gray_img,gray_img, mask= white_mask)\n",
    "\n",
    "def filter_img_yellow(image):\n",
    "     # define range of yellow color in HSV\n",
    "    lower_yellow= np.array([65,100,100])\n",
    "    upper_yellow = np.array([105,255,255])\n",
    "    yellow_mask = cv2.inRange(hsv_img, lower_yellow, upper_yellow)\n",
    "\n",
    "    # Bitwise-AND mask and original image\n",
    "    return cv2.bitwise_and(hsv_img,hsv_img, mask= yellow_mask)\n",
    "def filter_img1(hsv):\n",
    "    yellow = cv2.inRange(hsv, (65, 100, 100), (100, 255, 255))\n",
    "\n",
    "    sensitivity_1 = 68\n",
    "    white = cv2.inRange(hsv, (0,0,255-sensitivity_1), (255,20,255))\n",
    "\n",
    "    sensitivity_2 = 60\n",
    "    HSL = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n",
    "    white_2 = cv2.inRange(HSL, (0,255-sensitivity_2,0), (255,255,sensitivity_2))\n",
    "    white_3 = cv2.inRange(image, (200,200,200), (255,255,255))\n",
    "\n",
    "    res1 = cv2.bitwise_and(gray_img,gray_img, mask= white|white_2|white_3|yellow)\n",
    "    return res1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Test Images\n",
    "\n",
    "Build your pipeline to work on the images in the directory \"test_images\"  \n",
    "**You should make sure your pipeline works well on these images before you try the videos.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['frame025.png', 'frame071.png', 'solidWhiteCurve.jpg', 'solidWhiteRight.jpg', 'solidYellowCurve.jpg', 'solidYellowCurve2.jpg', 'solidYellowLeft.jpg', 'whiteCarLaneSwitch.jpg']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "files = os.listdir(\"test_images/\")\n",
    "# files = ['solidWhiteCurve.jpg']\n",
    "print(files)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Build a Lane Finding Pipeline\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Build the pipeline and run your solution on all test_images. Make copies into the `test_images_output` directory, and you can use the images in your writeup report.\n",
    "\n",
    "Try tuning the various parameters, especially the low and high Canny thresholds as well as the Hough lines parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.674329501916\n",
      "-0.726530612245\n",
      "-0.723849372385\n",
      "0.599099099099\n",
      "0.672811059908\n",
      "0.602409638554\n",
      "0.622641509434\n",
      "-0.672932330827\n",
      "[  -0.69488246  641.68790439]\n",
      "[ 0.62722496  5.94343691]\n"
     ]
    },
    {
     "data": {
      "image/png": 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G8NRLXekP6avejVpSYlMhCGphR02EHAxZv9ldYmp6fDIptiFIemEktyMTy1mA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\n6zx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DaMMAGOtXerGzkFGrhRw6j9/iayc6xq2wYhMI/pySwTrZ26bEWrf3K/Ae30g+4mLz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\nGQAoFGOGzbDh0fTnD4v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      "text/plain": [
       "<matplotlib.figure.Figure at 0x128416400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Build your pipeline that will draw lane lines on the test_images\n",
    "# then save them to the test_images directory.\n",
    "\n",
    "def draw_weighted_img(image):\n",
    "    gray_img=grayscale(image)\n",
    "    hsv_img=cv2.cvtColor(image,cv2.COLOR_BGR2HSV)\n",
    "    mask=filter_img(hsv_img)\n",
    "    filter=cv2.bitwise_and(gray_img,gray_img,mask=mask)\n",
    "    #define a kernel size and apply gaussian blur/smoothing\n",
    "    kernel=5\n",
    "    blur_gray=gaussian_blur(filter,kernel)\n",
    "#     plt.imshow(blur_gray)\n",
    "\n",
    "    # Define our parameters for Canny and apply\n",
    "    low_threshold = 40\n",
    "    high_threshold = 100\n",
    "    canny_edges = canny(blur_gray, low_threshold, high_threshold)\n",
    "\n",
    "    #define vertices and create masked image\n",
    "    imshape = image.shape\n",
    "    x_size=imshape[0] \n",
    "    y_size=imshape[1]\n",
    "    \n",
    "    vertices = np.array([[(0,imshape[0]),(y_size/2-40, x_size/2+40), (y_size/2, x_size/2+40), (imshape[1],imshape[0])]], dtype=np.int32)\n",
    "    masked_img = region_of_interest(canny_edges,vertices)\n",
    "#     plt.imshow(masked_img)\n",
    "    \n",
    "    # Define the Hough transform parameters\n",
    "    # Make a blank the same size as our image to draw on\n",
    "    rho = 2 # distance resolution in pixels of the Hough grid\n",
    "    theta = np.pi/180 # angular resolution in radians of the Hough grid\n",
    "    threshold = 60     # minimum number of votes (intersections in Hough grid cell)\n",
    "    min_line_length = 35 #minimum number of pixels making up a line\n",
    "    max_line_gap = 160    # maximum gap in pixels between connectable line segments\n",
    "    line_image = np.copy(image)*0 # creating a blank to draw lines on\n",
    "\n",
    "    hough_img = hough_lines(masked_img, rho, theta, threshold, min_line_length, max_line_gap)\n",
    "#     plt.imshow(hough_img)\n",
    "    w_img = cv2.addWeighted(image, 0.8, hough_img, 1, 0)\n",
    "    plt.imshow(w_img)\n",
    "    return w_img\n",
    "    \n",
    "# create a output directory;\n",
    "# path=os.getcwd()+'/test-output'\n",
    "# os.makedirs(path, exist_ok=True)\n",
    "\n",
    "# for file in files:\n",
    "#     image = mpimg.imread('test_images/'+file)\n",
    "#     transform_img=draw_weighted_img(image)\n",
    "#     outfilename = 'out_'+file\n",
    "#     print(outfilename)\n",
    "#     mpimg.imsave('test-output/'+outfilename, transform_img)\n",
    "\n",
    "res=draw_weighted_img(image)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Test on Videos\n",
    "\n",
    "You know what's cooler than drawing lanes over images? Drawing lanes over video!\n",
    "\n",
    "We can test our solution on two provided videos:\n",
    "\n",
    "`solidWhiteRight.mp4`\n",
    "\n",
    "`solidYellowLeft.mp4`\n",
    "\n",
    "**Note: if you get an `import error` when you run the next cell, try changing your kernel (select the Kernel menu above --> Change Kernel).  Still have problems?  Try relaunching Jupyter Notebook from the terminal prompt. Also, check out [this forum post](https://carnd-forums.udacity.com/questions/22677062/answers/22677109) for more troubleshooting tips.**\n",
    "\n",
    "**If you get an error that looks like this:**\n",
    "```\n",
    "NeedDownloadError: Need ffmpeg exe. \n",
    "You can download it by calling: \n",
    "imageio.plugins.ffmpeg.download()\n",
    "```\n",
    "**Follow the instructions in the error message and check out [this forum post](https://carnd-forums.udacity.com/display/CAR/questions/26218840/import-videofileclip-error) for more troubleshooting tips across operating systems.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Import everything needed to edit/save/watch video clips\n",
    "from moviepy.editor import VideoFileClip\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def process_image(image):\n",
    "    # NOTE: The output you return should be a color image (3 channel) for processing video below\n",
    "    # TODO: put your pipeline here,\n",
    "    # you should return the final output (image where lines are drawn on lanes)\n",
    "   \n",
    "#     convert to grayscale\n",
    "    gray_img=grayscale(image)\n",
    "    #convert to hsv and filter yellow and white colors\n",
    "    hsv_img=cv2.cvtColor(image,cv2.COLOR_BGR2HSV)\n",
    "    mask=filter_img(hsv_img)\n",
    "    filter=cv2.bitwise_and(gray_img,gray_img,mask=mask)\n",
    "\n",
    "    #define a kernel size and apply gaussian blur/smoothing\n",
    "    kernel=5\n",
    "    blur_gray=gaussian_blur(filter,kernel)\n",
    "\n",
    "    # Define our parameters for Canny and apply\n",
    "    low_threshold = 40\n",
    "    high_threshold = 100\n",
    "    canny_edges = canny(blur_gray, low_threshold, high_threshold)\n",
    "\n",
    "    #define vertices and create masked image\n",
    "    imshape = image.shape\n",
    "    x_size=imshape[0] \n",
    "    y_size=imshape[1]\n",
    "    \n",
    "    vertices = np.array([[(0,imshape[0]),(y_size/2-40, x_size/2+40), (y_size/2, x_size/2+40), (imshape[1],imshape[0])]], dtype=np.int32)\n",
    "    masked_img = region_of_interest(canny_edges,vertices)\n",
    "\n",
    "    # Define the Hough transform parameters\n",
    "    # Make a blank the same size as our image to draw on\n",
    "    rho = 2 # distance resolution in pixels of the Hough grid\n",
    "    theta = np.pi/180 # angular resolution in radians of the Hough grid\n",
    "    threshold = 40     # minimum number of votes (intersections in Hough grid cell)\n",
    "    min_line_length = 35 #minimum number of pixels making up a line\n",
    "    max_line_gap = 180    # maximum gap in pixels between connectable line segments\n",
    "    line_image = np.copy(image)*0 # creating a blank to draw lines on\n",
    "\n",
    "    hough_img = hough_lines(masked_img, rho, theta, threshold, min_line_length, max_line_gap)\n",
    "    result = cv2.addWeighted(image, 0.8, hough_img, 1, 0)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's try the one with the solid white lane on the right first ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video test_videos_output_new/solidWhiteRight.mp4\n",
      "[MoviePy] Writing video test_videos_output_new/solidWhiteRight.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 221/222 [00:06<00:00, 34.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: test_videos_output_new/solidWhiteRight.mp4 \n",
      "\n",
      "CPU times: user 4.5 s, sys: 934 ms, total: 5.44 s\n",
      "Wall time: 7.25 s\n"
     ]
    }
   ],
   "source": [
    "white_output = 'test_videos_output_new/solidWhiteRight.mp4'\n",
    "\n",
    "#create a output directory;\n",
    "path=os.getcwd()+'/test_videos_output_new'\n",
    "os.makedirs(path, exist_ok=True)\n",
    "\n",
    "clip1 = VideoFileClip(\"test_videos/solidWhiteRight.mp4\")\n",
    "white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!\n",
    "%time white_clip.write_videofile(white_output, audio=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(white_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Improve the draw_lines() function\n",
    "\n",
    "**At this point, if you were successful with making the pipeline and tuning parameters, you probably have the Hough line segments drawn onto the road, but what about identifying the full extent of the lane and marking it clearly as in the example video (P1_example.mp4)?  Think about defining a line to run the full length of the visible lane based on the line segments you identified with the Hough Transform. As mentioned previously, try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video \"P1_example.mp4\".**\n",
    "\n",
    "**Go back and modify your draw_lines function accordingly and try re-running your pipeline. The new output should draw a single, solid line over the left lane line and a single, solid line over the right lane line. The lines should start from the bottom of the image and extend out to the top of the region of interest.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now for the one with the solid yellow lane on the left. This one's more tricky!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video test_videos_output_new/solidYellowLeft.mp4\n",
      "[MoviePy] Writing video test_videos_output_new/solidYellowLeft.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 681/682 [00:21<00:00, 31.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: test_videos_output_new/solidYellowLeft.mp4 \n",
      "\n",
      "CPU times: user 14.4 s, sys: 3.25 s, total: 17.7 s\n",
      "Wall time: 22.4 s\n"
     ]
    }
   ],
   "source": [
    "yellow_output = 'test_videos_output_new/solidYellowLeft.mp4'\n",
    "clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')\n",
    "yellow_clip = clip2.fl_image(process_image)\n",
    "# write_images_sequence\n",
    "%time yellow_clip.write_videofile(yellow_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(yellow_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Writeup and Submission\n",
    "\n",
    "If you're satisfied with your video outputs, it's time to make the report writeup in a pdf or markdown file. Once you have this Ipython notebook ready along with the writeup, it's time to submit for review! Here is a [link](https://github.com/udacity/CarND-LaneLines-P1/blob/master/writeup_template.md) to the writeup template file.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Optional Challenge\n",
    "\n",
    "Try your lane finding pipeline on the video below.  Does it still work?  Can you figure out a way to make it more robust?  If you're up for the challenge, modify your pipeline so it works with this video and submit it along with the rest of your project!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video test_videos_output_new/challenge.mp4\n",
      "[MoviePy] Writing video test_videos_output_new/challenge.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 146/251 [00:09<00:09, 11.35it/s]"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "expected non-empty vector for x",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-144-af69b3509e75>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mclip2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVideoFileClip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test_videos/challenge.mp4'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mchallenge_clip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclip2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfl_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess_image\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time challenge_clip.write_videofile(challenge_output, audio=False)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2156\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2157\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2158\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2160\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2077\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2078\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2079\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2080\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-59>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'eval'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m             \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1181\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1182\u001b[0m             \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1183\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-172>\u001b[0m in \u001b[0;36mwrite_videofile\u001b[0;34m(self, filename, fps, codec, bitrate, audio, audio_fps, preset, audio_nbytes, audio_codec, audio_bitrate, audio_bufsize, temp_audiofile, rewrite_audio, remove_temp, write_logfile, verbose, threads, ffmpeg_params)\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36mrequires_duration\u001b[0;34m(f, clip, *a, **k)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Attribute 'duration' not set\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-171>\u001b[0m in \u001b[0;36mwrite_videofile\u001b[0;34m(self, filename, fps, codec, bitrate, audio, audio_fps, preset, audio_nbytes, audio_codec, audio_bitrate, audio_bufsize, temp_audiofile, rewrite_audio, remove_temp, write_logfile, verbose, threads, ffmpeg_params)\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36muse_clip_fps_by_default\u001b[0;34m(f, clip, *a, **k)\u001b[0m\n\u001b[1;32m    135\u001b[0m              for (k,v) in k.items()}\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mnew_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<decorator-gen-170>\u001b[0m in \u001b[0;36mwrite_videofile\u001b[0;34m(self, filename, fps, codec, bitrate, audio, audio_fps, preset, audio_nbytes, audio_codec, audio_bitrate, audio_bufsize, temp_audiofile, rewrite_audio, remove_temp, write_logfile, verbose, threads, ffmpeg_params)\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36mconvert_masks_to_RGB\u001b[0;34m(f, clip, *a, **k)\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mismask\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mclip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_RGB\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdecorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/video/VideoClip.py\u001b[0m in \u001b[0;36mwrite_videofile\u001b[0;34m(self, filename, fps, codec, bitrate, audio, audio_fps, preset, audio_nbytes, audio_codec, audio_bitrate, audio_bufsize, temp_audiofile, rewrite_audio, remove_temp, write_logfile, verbose, threads, ffmpeg_params)\u001b[0m\n\u001b[1;32m    336\u001b[0m                            \u001b[0maudiofile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maudiofile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m                            \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mthreads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 338\u001b[0;31m                            ffmpeg_params=ffmpeg_params)\n\u001b[0m\u001b[1;32m    339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mremove_temp\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmake_audio\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/video/io/ffmpeg_writer.py\u001b[0m in \u001b[0;36mffmpeg_write_video\u001b[0;34m(clip, filename, fps, codec, bitrate, preset, withmask, write_logfile, audiofile, verbose, threads, ffmpeg_params)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m     for t,frame in clip.iter_frames(progress_bar=True, with_times=True,\n\u001b[0;32m--> 216\u001b[0;31m                                     fps=fps, dtype=\"uint8\"):\n\u001b[0m\u001b[1;32m    217\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mwithmask\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    831\u001b[0m \"\"\", fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m    832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 833\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    834\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m                 \u001b[0;31m# Update and print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/Clip.py\u001b[0m in \u001b[0;36mgenerator\u001b[0;34m()\u001b[0m\n\u001b[1;32m    471\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mfps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m                 \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    474\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-135>\u001b[0m in \u001b[0;36mget_frame\u001b[0;34m(self, t)\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(f, *a, **kw)\u001b[0m\n\u001b[1;32m     87\u001b[0m         new_kw = {k: fun(v) if k in varnames else v\n\u001b[1;32m     88\u001b[0m                  for (k,v) in kw.items()}\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/Clip.py\u001b[0m in \u001b[0;36mget_frame\u001b[0;34m(self, t)\u001b[0m\n\u001b[1;32m     93\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mkeep_duration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/Clip.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0;31m#mf = copy(self.make_frame)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m         \u001b[0mnewclip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_make_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkeep_duration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/video/VideoClip.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(gf, t)\u001b[0m\n\u001b[1;32m    511\u001b[0m         \u001b[0;31m`\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mby\u001b[0m \u001b[0manother\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mimage_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m         \"\"\"\n\u001b[0;32m--> 513\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mgf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mimage_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_to\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m     \u001b[0;31m# --------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-140-0714f2c2e9ba>\u001b[0m in \u001b[0;36mprocess_image\u001b[0;34m(image)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mline_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;31m# creating a blank to draw lines on\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0mhough_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhough_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmasked_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrho\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_line_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_line_gap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m     \u001b[0;31m# plt.imshow(hough_img)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddWeighted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhough_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-136-218c26d4d667>\u001b[0m in \u001b[0;36mhough_lines\u001b[0;34m(img, rho, theta, threshold, min_line_len, max_line_gap)\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHoughLinesP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrho\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminLineLength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_line_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxLineGap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_line_gap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0mline_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m     \u001b[0mdraw_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mline_img\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-136-218c26d4d667>\u001b[0m in \u001b[0;36mdraw_lines\u001b[0;34m(img, lines, color, thickness)\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     \u001b[0;31m#right\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m     \u001b[0mrz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolyfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mry\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m     \u001b[0mrf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoly1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/geethabujji/softy/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/numpy/lib/polynomial.py\u001b[0m in \u001b[0;36mpolyfit\u001b[0;34m(x, y, deg, rcond, full, w, cov)\u001b[0m\n\u001b[1;32m    553\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"expected 1D vector for x\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 555\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"expected non-empty vector for x\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    556\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"expected 1D or 2D array for y\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: expected non-empty vector for x"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "          \r",
      " 58%|█████▊    | 146/251 [00:19<00:14,  7.31it/s]"
     ]
    }
   ],
   "source": [
    "challenge_output = 'test_videos_output_new/challenge.mp4'\n",
    "clip2 = VideoFileClip('test_videos/challenge.mp4')\n",
    "challenge_clip = clip2.fl_image(process_image)\n",
    "%time challenge_clip.write_videofile(challenge_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(challenge_output))"
   ]
  }
 ],
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