P1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": {},
   "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, consult the forums for more troubleshooting tips.**  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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": {},
   "source": [
    "## Read in an Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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 0x7f0ccbe3b6a0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0cb512ee80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reading in an image\n",
    "image = mpimg.imread('test_images/solidWhiteRight.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": {},
   "source": [
    "## Ideas for Lane Detection Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are some helper functions to help get you started. They should look familiar from the lesson!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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",
    "    # Or use BGR2GRAY if you read an image with cv2.imread()\n",
    "    # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\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",
    "    `vertices` should be a numpy array of integer points.\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",
    "#     Split 'lines' into 2 groups, lineL (left) & lineR (right)\n",
    "    lineL = np.array([line for line in lines if\\\n",
    "                      max(line[:, 0], line[:, 2]) <= 0.5*img.shape[1]])\n",
    "    lineR = np.array([line for line in lines if\\\n",
    "                      min(line[:, 0], line[:, 2]) > 0.5*img.shape[1]])\n",
    "#     Define the polynomial design matrix (in this case, 1st degree) for finding the best fit curves\n",
    "#     For ease of generalisation in the future, it is assumed that\n",
    "#     y is the independent variable, and x is a function of y\n",
    "    Pl = np.vstack([np.hstack([np.ones_like(lineL[:, :, 1]), lineL[:, :, 1]]),\\\n",
    "                    np.hstack([np.ones_like(lineL[:, :, 3]), lineL[:, :, 3]])])\n",
    "    Pr = np.vstack([np.hstack([np.ones_like(lineR[:, :, 1]), lineR[:, :, 1]]),\\\n",
    "                    np.hstack([np.ones_like(lineR[:, :, 3]), lineR[:, :, 3]])])\n",
    "#     Stack all the x values together\n",
    "    xL = np.vstack([lineL[:, :, 0], lineL[:, :, 2]])\n",
    "    xR = np.vstack([lineR[:, :, 0], lineR[:, :, 2]])\n",
    "#     The best fit curves are given by the Moore Penrose (pseudo-) inverse:\n",
    "#     param_vector = (P+)x\n",
    "    paramL = np.linalg.pinv(Pl).dot(xL)\n",
    "    paramR = np.linalg.pinv(Pr).dot(xR)\n",
    "    \n",
    "#     Bottom and top y-values for the lane endpoints, respectively\n",
    "    yB = img.shape[0]\n",
    "    yT = int(0.5*yB + 0.5) + 60\n",
    "#     Since x is a function of y, we compute x = b + my for each end point\n",
    "    xLB = paramL[0] + paramL[1]*yB\n",
    "    xRB = paramR[0] + paramR[1]*yB\n",
    "    xLT = paramL[0] + paramL[1]*yT\n",
    "    xRT = paramR[0] + paramR[1]*yT\n",
    "    \n",
    "#     Highlight the identified left & right lane lines\n",
    "    cv2.line(img, (xLB, yB), (xLT, yT), color, thickness)\n",
    "    cv2.line(img, (xRB, yB), (xRT, yT), color, thickness)\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., γ=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 + γ\n",
    "    NOTE: initial_img and img must be the same shape!\n",
    "    \"\"\"\n",
    "    return cv2.addWeighted(initial_img, α, img, β, γ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['solidWhiteCurve.jpg',\n",
       " 'solidWhiteRight.jpg',\n",
       " 'solidYellowCurve.jpg',\n",
       " 'solidYellowCurve2.jpg',\n",
       " 'solidYellowLeft.jpg',\n",
       " 'whiteCarLaneSwitch.jpg']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir(\"test_images/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a Lane Finding Pipeline\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc89594af98>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8ac199a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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_output directory.\n",
    "\n",
    "# Read in and grayscale the image\n",
    "image = mpimg.imread('test_images/whiteCarLaneSwitch.jpg')\n",
    "gray = grayscale(image)\n",
    "\n",
    "# Define a kernel size and apply Gaussian smoothing\n",
    "kernel_size = 5\n",
    "blur_gray = gaussian_blur(gray, kernel_size)\n",
    "\n",
    "# Define our parameters for Canny and apply\n",
    "low_threshold = 50\n",
    "high_threshold = 150\n",
    "edges = canny(blur_gray, low_threshold, high_threshold)\n",
    "\n",
    "# Define a four sided polygon mask\n",
    "imshape = image.shape\n",
    "vertices = np.array([[(0,imshape[0]),(480, 320), (520, 320), (imshape[1],imshape[0])]], dtype=np.int32)\n",
    "\n",
    "masked_edges = region_of_interest(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 = 1*np.pi/180 # angular resolution in radians of the Hough grid\n",
    "threshold = 15     # minimum number of votes (intersections in Hough grid cell)\n",
    "min_line_length = 5 #minimum number of pixels making up a line\n",
    "max_line_gap = 3    # maximum gap in pixels between connectable line segments\n",
    "\n",
    "line_image = hough_lines(masked_edges, rho, theta, threshold, min_line_length, max_line_gap)\n",
    "\n",
    "# Draw the lines on the original image\n",
    "lines_img = weighted_img(image, line_image) \n",
    "plt.imshow(lines_img)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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, consult the forums 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://discussions.udacity.com/t/project-error-of-test-on-videos/274082) for more troubleshooting tips across operating systems.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imageio: 'ffmpeg.linux64' was not found on your computer; downloading it now.\n",
      "Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg.linux64 (27.2 MB)\n",
      "Downloading: 8192/28549024 bytes (0.090112/28549024 bytes (0.3%376832/28549024 bytes (1.3401408/28549024 bytes (1.41105920/28549024 bytes (3.9%1548288/28549024 bytes (5.4%1908736/28549024 bytes (6.7%2236416/28549024 bytes (7.8%2596864/28549024 bytes (9.1%2973696/28549024 bytes (10.43366912/28549024 bytes (11.83629056/28549024 bytes (12.73973120/28549024 bytes (13.94284416/28549024 bytes (15.04620288/28549024 bytes (16.24931584/28549024 bytes (17.35218304/28549024 bytes (18.35496832/28549024 bytes (19.35742592/28549024 bytes (20.16004736/28549024 bytes (21.06201344/28549024 bytes (21.76463488/28549024 bytes (22.66709248/28549024 bytes (23.56979584/28549024 bytes (24.47233536/28549024 bytes (25.37495680/28549024 bytes (26.37757824/28549024 bytes (27.28036352/28549024 bytes (28.18314880/28549024 bytes (29.18593408/28549024 bytes (30.18855552/28549024 bytes (31.08986624/28549024 bytes (31.59297920/28549024 bytes (32.69502720/28549024 bytes (33.39691136/28549024 bytes (33.99887744/28549024 bytes (34.610084352/28549024 bytes (35.3%10297344/28549024 bytes (36.1%10510336/28549024 bytes (36.8%10739712/28549024 bytes (37.6%10952704/28549024 bytes (38.4%11182080/28549024 bytes (39.2%11411456/28549024 bytes (40.0%11657216/28549024 bytes (40.8%11886592/28549024 bytes (41.6%12115968/28549024 bytes (42.4%12345344/28549024 bytes (43.2%12574720/28549024 bytes (44.0%12804096/28549024 bytes (44.8%13033472/28549024 bytes (45.7%13295616/28549024 bytes (46.6%13541376/28549024 bytes (47.4%13803520/28549024 bytes (48.4%14049280/28549024 bytes (49.2%14311424/28549024 bytes (50.1%14581760/28549024 bytes (51.1%14819328/28549024 bytes (51.9%15081472/28549024 bytes (52.8%15360000/28549024 bytes (53.8%15638528/28549024 bytes (54.8%15900672/28549024 bytes (55.7%16195584/28549024 bytes (56.7%16474112/28549024 bytes (57.7%16752640/28549024 bytes (58.7%17031168/28549024 bytes (59.7%17293312/28549024 bytes (60.6%17588224/28549024 bytes (61.6%17899520/28549024 bytes (62.7%18194432/28549024 bytes (63.7%18505728/28549024 bytes (64.8%18817024/28549024 bytes (65.9%19144704/28549024 bytes (67.1%19456000/28549024 bytes (68.1%19783680/28549024 bytes (69.3%20111360/28549024 bytes (70.4%20439040/28549024 bytes (71.6%20733952/28549024 bytes (72.6%21053440/28549024 bytes (73.7%21356544/28549024 bytes (74.8%21684224/28549024 bytes (76.0%22028288/28549024 bytes (77.2%22355968/28549024 bytes (78.3%22683648/28549024 bytes (79.5%23011328/28549024 bytes (80.6%23339008/28549024 bytes (81.8%23666688/28549024 bytes (82.9%23994368/28549024 bytes (84.0%24338432/28549024 bytes (85.3%24698880/28549024 bytes (86.5%25042944/28549024 bytes (87.7%25321472/28549024 bytes (88.7%25649152/28549024 bytes (89.8%26066944/28549024 bytes (91.3%26337280/28549024 bytes (92.3%26615808/28549024 bytes (93.2%26910720/28549024 bytes (94.3%27172864/28549024 bytes (95.2%27484160/28549024 bytes (96.3%27795456/28549024 bytes (97.4%28090368/28549024 bytes (98.4%28401664/28549024 bytes (99.5%28549024/28549024 bytes (100.0%)\n",
      "  Done\n",
      "File saved as /root/.imageio/ffmpeg/ffmpeg.linux64.\n"
     ]
    }
   ],
   "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": 5,
   "metadata": {},
   "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",
    "    # Read in and grayscale the image\n",
    "    gray = grayscale(image)\n",
    "\n",
    "    # Define a kernel size and apply Gaussian smoothing\n",
    "    kernel_size = 5\n",
    "    blur_gray = gaussian_blur(gray, kernel_size)\n",
    "\n",
    "    # Define our parameters for Canny and apply\n",
    "    low_threshold = 50\n",
    "    high_threshold = 150\n",
    "    edges = canny(blur_gray, low_threshold, high_threshold)\n",
    "\n",
    "    # Define a four sided polygon mask\n",
    "    imshape = image.shape\n",
    "\n",
    "    vertices = np.array([[(120, imshape[0]), (0.5*imshape[1] - 20, 0.5*imshape[0] + 50),\\\n",
    "                      (0.5*imshape[1] + 20, 0.5*imshape[0] + 50),\\\n",
    "                      (imshape[1], imshape[0])]], dtype=np.int32)\n",
    "\n",
    "    masked_edges = region_of_interest(edges, vertices)\n",
    "\n",
    "    # Define the Hough transform parameters\n",
    "    rho = 2 # distance resolution in pixels of the Hough grid\n",
    "    theta = 1*np.pi/180 # angular resolution in radians of the Hough grid\n",
    "    threshold = 15     # minimum number of votes (intersections in Hough grid cell)\n",
    "    min_line_length = 5 #minimum number of pixels making up a line\n",
    "    max_line_gap = 3    # maximum gap in pixels between connectable line segments\n",
    "\n",
    "    # Run Hough on edge detected image\n",
    "    line_image = hough_lines(masked_edges, rho, theta, threshold, min_line_length, max_line_gap)\n",
    "\n",
    "    result = weighted_img(image, line_image)\n",
    "\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the one with the solid white lane on the right first ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video test_videos_output/solidWhiteRight.mp4\n",
      "[MoviePy] Writing video test_videos_output/solidWhiteRight.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 221/222 [00:03<00:00, 69.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: test_videos_output/solidWhiteRight.mp4 \n",
      "\n",
      "CPU times: user 3.83 s, sys: 375 ms, total: 4.21 s\n",
      "Wall time: 3.65 s\n"
     ]
    }
   ],
   "source": [
    "white_output = 'test_videos_output/solidWhiteRight.mp4'\n",
    "## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n",
    "## To do so add .subclip(start_second,end_second) to the end of the line below\n",
    "## Where start_second and end_second are integer values representing the start and end of the subclip\n",
    "## You may also uncomment the following line for a subclip of the first 5 seconds\n",
    "##clip1 = VideoFileClip(\"test_videos/solidWhiteRight.mp4\").subclip(0,5)\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": {},
   "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": {},
   "outputs": [],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(white_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "source": [
    "Now for the one with the solid yellow lane on the left. This one's more tricky!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video test_videos_output/solidYellowLeft.mp4\n",
      "[MoviePy] Writing video test_videos_output/solidYellowLeft.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 681/682 [00:10<00:00, 65.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: test_videos_output/solidYellowLeft.mp4 \n",
      "\n",
      "CPU times: user 12 s, sys: 1.24 s, total: 13.3 s\n",
      "Wall time: 10.8 s\n"
     ]
    }
   ],
   "source": [
    "yellow_output = 'test_videos_output/solidYellowLeft.mp4'\n",
    "## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n",
    "## To do so add .subclip(start_second,end_second) to the end of the line below\n",
    "## Where start_second and end_second are integer values representing the start and end of the subclip\n",
    "## You may also uncomment the following line for a subclip of the first 5 seconds\n",
    "##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)\n",
    "clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')\n",
    "yellow_clip = clip2.fl_image(process_image)\n",
    "%time yellow_clip.write_videofile(yellow_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(yellow_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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
   },
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-427a4ab254de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m##clip3 = VideoFileClip('test_videos/challenge.mp4').subclip(0,5)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mclip3\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[0;32m----> 8\u001b[0;31m \u001b[0mchallenge_clip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclip3\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[0m\u001b[1;32m      9\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'challenge_clip.write_videofile(challenge_output, audio=False)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/video/VideoClip.py\u001b[0m in \u001b[0;36mfl_image\u001b[0;34m(self, image_func, apply_to)\u001b[0m\n\u001b[1;32m    512\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mapply_to\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\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[0m\n\u001b[0;32m--> 514\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    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m     \u001b[0;31m# --------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/Clip.py\u001b[0m in \u001b[0;36mfl\u001b[0;34m(self, fun, apply_to, keep_duration)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\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--> 137\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    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\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<decorator-gen-181>\u001b[0m in \u001b[0;36mset_make_frame\u001b[0;34m(self, mf)\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36moutplace\u001b[0;34m(f, clip, *a, **k)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;34m\"\"\" Applies f(clip.copy(), *a, **k) and returns clip.copy()\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mnewclip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewclip\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     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnewclip\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/moviepy/video/VideoClip.py\u001b[0m in \u001b[0;36mset_make_frame\u001b[0;34m(self, mf)\u001b[0m\n\u001b[1;32m    667\u001b[0m         \"\"\"\n\u001b[1;32m    668\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_frame\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\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[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\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[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0moutplace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-134>\u001b[0m in \u001b[0;36mget_frame\u001b[0;34m(self, t)\u001b[0m\n",
      "\u001b[0;32m~/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~/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     92\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\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     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\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;32mNone\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~/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    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\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--> 137\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    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\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~/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    512\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mapply_to\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\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[0m\n\u001b[0;32m--> 514\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    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\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-9-7fba62e09e85>\u001b[0m in \u001b[0;36mprocess_image\u001b[0;34m(image)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;31m# Run Hough on edge detected image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m     \u001b[0mline_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhough_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmasked_edges\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweighted_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_image\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-7-f7ecb2185409>\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    130\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    131\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--> 132\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    133\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mline_img\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-7-f7ecb2185409>\u001b[0m in \u001b[0;36mdraw_lines\u001b[0;34m(img, lines, color, thickness)\u001b[0m\n\u001b[1;32m     96\u001b[0m \u001b[0;31m#     lineR = np.array([line for line in lines if\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0;31m#                       min(line[:, 0], line[:, 2]) > 0.5*img.shape[1]])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m     Pl = np.vstack([np.hstack([np.ones_like(lineL[:, :, 1]), lineL[:, :, 1],\\\n\u001b[0m\u001b[1;32m     99\u001b[0m                                lineL[:, :, 1]**2]),\\\n\u001b[1;32m    100\u001b[0m                     np.hstack([np.ones_like(lineL[:, :, 3]), lineL[:, :, 3],\\\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "challenge_output = 'test_videos_output/challenge.mp4'\n",
    "## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n",
    "## To do so add .subclip(start_second,end_second) to the end of the line below\n",
    "## Where start_second and end_second are integer values representing the start and end of the subclip\n",
    "## You may also uncomment the following line for a subclip of the first 5 seconds\n",
    "##clip3 = VideoFileClip('test_videos/challenge.mp4').subclip(0,5)\n",
    "clip3 = VideoFileClip('test_videos/challenge.mp4')\n",
    "challenge_clip = clip3.fl_image(process_image)\n",
    "%time challenge_clip.write_videofile(challenge_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": 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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