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=\"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=\"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": 15,
   "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": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This image is: <class 'numpy.ndarray'> with dimesions: (540, 960, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11603d550>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ymtn5KZPkgO3tbZa7uxT5isIUWIH5/AxkykAsp0ffUORL7j18RD67YLQ9IlOKd56+z8sX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\n6AArOqwt4QytUDb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0LEArZ2ytWTuJNpJFk7a9dvx87MZ5g9PUSlh7zd2L1fnI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\nEP1W22pbbavdw9v/B48rs7cxFkAbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f377cf8>"
      ]
     },
     "metadata": {},
     "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 dimesions:', 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": 17,
   "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",
    "    # 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",
    "    \"\"\"\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",
    "class LineGroup:\n",
    "    def __init__(self, line, slope_tol=0.3):\n",
    "        self.lines = [line]\n",
    "        x1, y1, x2, y2 = line\n",
    "        self.average_slope = (y2 - y1) / (x2 - x1)\n",
    "        self.slope_tol = slope_tol\n",
    "        self.points = np.array([[x1, y1], [x2, y2]], dtype=np.int32)\n",
    "    \n",
    "    def add_line(self, line):\n",
    "        x1, y1, x2, y2 = line\n",
    "        line_slope = (y2 - y1) / (x2 - x1)\n",
    "        if abs(line_slope - self.average_slope) > self.slope_tol:\n",
    "            return False\n",
    "        self.average_slope = (self.average_slope * len(self.lines) + line_slope) / (len(self.lines) + 1)\n",
    "        self.lines.append(line)\n",
    "        points = np.array([[x1, y1], [x2, y2]], dtype=np.int32)\n",
    "        self.points = np.concatenate((self.points, points))\n",
    "        return True\n",
    "    \n",
    "    def extrapolate_v1(self):\n",
    "        xs = self.points[:, 0]\n",
    "        ys = self.points[:, 1]\n",
    "        if self.average_slope > 0:\n",
    "            # y increases as x increases\n",
    "            return np.array([[min(xs), min(ys), max(xs), max(ys)]])\n",
    "        else:\n",
    "            # y decreases as x increases\n",
    "            return np.array([[min(xs), max(ys), max(xs), min(ys)]])\n",
    "\n",
    "    def extrapolate_v2(self, bound_vertices):\n",
    "        def get_y(x):\n",
    "            [vx, vy, x0, y0] = cv2.fitLine(self.points, cv2.DIST_L2, 0, 0.01, 0.01)\n",
    "            return int((vy/vx) * (x - x0) + y0)\n",
    "        def get_x(y):\n",
    "            [vx, vy, x0, y0] = cv2.fitLine(self.points, cv2.DIST_L2, 0, 0.01, 0.01)\n",
    "            return int(((y-y0) + vy/vx * x0)/ (vy/vx))\n",
    "        # use a simplified method to bound the extrapolated lines\n",
    "        # if a more accurate method needed, then should use polygon clipping method\n",
    "        all_ys = bound_vertices[0, :, 1]\n",
    "        y_low_lim = min(all_ys)\n",
    "        y_high_lim = max(all_ys)\n",
    "        return np.array([[get_x(y_low_lim), y_low_lim, get_x(y_high_lim), y_high_lim]])\n",
    "\n",
    "            \n",
    "def group_lines(hough_lines):\n",
    "    \"\"\"Group hough lines in collected so far together\"\"\"\n",
    "    line_groups = []\n",
    "    for line in hough_lines:\n",
    "        x1, y1, x2, y2 = line[0]\n",
    "        if x1 == x2 or y1 == y2:\n",
    "            # we don't consider either vertical or horizontal lines\n",
    "            continue\n",
    "        # attempt to add line to existing line groups\n",
    "        line_added = False\n",
    "        for line_group in line_groups:\n",
    "            if line_group.add_line(line[0]):\n",
    "                line_added = True\n",
    "                break\n",
    "        # if not finding a line group that has a slope close to the current line slope\n",
    "        # then create a new line group with the current line as an element\n",
    "        if not line_added:\n",
    "            line_groups.append(LineGroup(line[0]))\n",
    "    return line_groups\n",
    "\n",
    "\n",
    "def draw_lines(img, line_groups, region_vertices, color=[255, 0, 0], thickness=10):\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",
    "    def filter_noise(line_groups):\n",
    "        # this filter is a hack, may need other ways to determine better which line groups\n",
    "        # are noise and which are not, potentially needing data from the last frame\n",
    "        filtered_groups = []\n",
    "        for line_group in line_groups:\n",
    "            if len(line_group.points) < 10:\n",
    "                # 10 is just some number after trial-and-error that gives the best peprformance\n",
    "                continue\n",
    "            if abs(line_group.average_slope) > 0.9 or abs(line_group.average_slope) < 0.5:\n",
    "                # excluding this range of slope gives the best performance in both videos\n",
    "                continue\n",
    "            filtered_groups.append(line_group)\n",
    "        return filtered_groups\n",
    "\n",
    "    # use the existing line_group, and extrapolate them into lines\n",
    "    extrapolated_lines = [line_group.extrapolate_v2(region_vertices) for line_group in filter_noise(line_groups)]\n",
    "\n",
    "    for line in extrapolated_lines:\n",
    "        for x1, y1, x2, y2 in line:\n",
    "            cv2.line(img, (x1, y1), (x2, y2), color, thickness)            \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_groups = group_lines(lines)\n",
    "\n",
    "    return line_groups\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)"
   ]
  },
  {
   "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": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.DS_Store',\n",
       " 'outputs',\n",
       " 'solidWhiteCurve.jpg',\n",
       " 'solidWhiteRight.jpg',\n",
       " 'solidYellowCurve.jpg',\n",
       " 'solidYellowCurve2.jpg',\n",
       " 'solidYellowLeft.jpg',\n",
       " 'TEST',\n",
       " 'whiteCarLaneSwitch.jpg']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir(\"test_images/\")"
   ]
  },
  {
   "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 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": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image saved to 'test_images/outputs'\n"
     ]
    },
    {
     "data": {
      "image/png": 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1H0o+aT9CG5GoJ0u2tiPHSLRuRMlAK/VzYy2eGPqnjUQV+2T08L2HD0C9W99kJLqkDQP1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7oc+i6SHhnHnEMAw444wdjOOoHjJVVVOMsl7j1u2VdOTVPwK88PuBcQ2zefzD667D/V/4gj9T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\ndmWArWDKOsC5LzMZg+k87sPcI71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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f377da0>"
      ]
     },
     "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",
    "output_dir = \"test_images/outputs\"\n",
    "if not os.path.exists(output_dir):\n",
    "    os.mkdir(output_dir)\n",
    "\n",
    "def convert_gray_img_to_rgb_img(img):\n",
    "    rgb_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n",
    "    row_indices, col_indices = np.nonzero(img)\n",
    "    for i in range(len(row_indices)):\n",
    "        rgb_img[row_indices[i], col_indices[i]] = np.repeat(img[row_indices[i], col_indices[i]], 3)\n",
    "    return rgb_img\n",
    "    \n",
    "def detect_lane_lines(img):\n",
    "    original_img = np.copy(img)\n",
    "    gray_img = grayscale(img)\n",
    "    blur_gray_img = gaussian_blur(gray_img, 5)\n",
    "    edge_img = canny(blur_gray_img, 100, 200)\n",
    "    # return the following converted img, we can produce a canny edge detection video\n",
    "    # plt.imshow(convert_gray_img_to_rgb_img(edge_img))\n",
    "    \n",
    "    img_height = edge_img.shape[0]\n",
    "    img_width = edge_img.shape[1]\n",
    "    region_vectices = np.array([[[img_width * 0.1, img_height],[img_width * 0.4, img_height * 0.62],[img_width * 0.6, img_height *0.62],[img_width * 0.9, img_height]]], dtype=np.int32)\n",
    "    \n",
    "    masked_img = region_of_interest(edge_img, region_vectices)\n",
    "    \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 = 20      # minimum number of votes (intersections in Hough grid cell)\n",
    "    min_line_len = 20   # minimum number of pixels making up a line\n",
    "    max_line_gap = 20   # maximum gap in pixels between connectable line segments\n",
    "    hough_line_groups = hough_lines(masked_img, rho, theta, threshold, min_line_len, max_line_gap)\n",
    "    \n",
    "    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n",
    "    draw_lines(line_img, hough_line_groups, region_vectices)\n",
    "    \n",
    "    combined_img = weighted_img(line_img, original_img)\n",
    "    \n",
    "    return combined_img\n",
    "\n",
    "lane_line_img = detect_lane_lines(image)\n",
    "# show results for immediate inspection\n",
    "plt.imshow(lane_line_img)\n",
    "\n",
    "# save results for later inspection\n",
    "mpimg.imsave(os.path.join(output_dir, \"test_img.jpg\"), lane_line_img)\n",
    "print(\"Image saved to '{}'\".format(output_dir))"
   ]
  },
  {
   "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": 20,
   "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": 21,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Add a global variable to keep track of the lines detected in the last x frames\n",
    "# from collections import deque\n",
    "# line_groups = deque(maxlen=10)\n",
    "\n",
    "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",
    "    result = detect_lane_lines(image)\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": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video white.mp4\n",
      "[MoviePy] Writing video white.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: white.mp4 \n",
      "\n",
      "CPU times: user 3.62 s, sys: 1.25 s, total: 4.87 s\n",
      "Wall time: 10.8 s\n"
     ]
    }
   ],
   "source": [
    "white_output = 'white.mp4'\n",
    "clip1 = VideoFileClip(\"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": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"white.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video yellow.mp4\n",
      "[MoviePy] Writing video yellow.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: yellow.mp4 \n",
      "\n",
      "CPU times: user 10.5 s, sys: 1.85 s, total: 12.3 s\n",
      "Wall time: 26.9 s\n"
     ]
    }
   ],
   "source": [
    "yellow_output = 'yellow.mp4'\n",
    "clip2 = VideoFileClip('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": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"yellow.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video extra.mp4\n",
      "[MoviePy] Writing video extra.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: extra.mp4 \n",
      "\n",
      "CPU times: user 6.99 s, sys: 2.15 s, total: 9.14 s\n",
      "Wall time: 21.4 s\n"
     ]
    }
   ],
   "source": [
    "challenge_output = 'extra.mp4'\n",
    "clip2 = VideoFileClip('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": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"extra.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(challenge_output))"
   ]
  }
 ],
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