gradients.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Github\\Udacity\\CarND-Advanced-Lane-Lines\\threshold_functions.py:268: MatplotlibDeprecationWarning: The set_axis_bgcolor function was deprecated in version 2.0. Use set_facecolor instead.\n",
      "  ax[0, 0].set_axis_bgcolor('red')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0f2f53400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-0100b4572773>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[0mksize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m9\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbinarize_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'False'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import pickle\n",
    "from threshold_functions import abs_sobel_thresh\n",
    "from threshold_functions import mag_thresh\n",
    "from threshold_functions import dir_threshold\n",
    "from threshold_functions import hls_select\n",
    "from threshold_functions import hsv_select\n",
    "from threshold_functions import binarize_image\n",
    "\n",
    "\n",
    "# Read in an image\n",
    "image = mpimg.imread('test_images/straight_lines1.jpg')\n",
    "img_size = (image.shape[1], image.shape[0])\n",
    "\n",
    "ksize = 9;\n",
    "\n",
    "test, test1 = binarize_image(image, verbose='False')\n",
    "\n",
    "\n",
    "\n",
    "'''\n",
    "# Run the function\n",
    "gradx = abs_sobel_thresh(image, orient='x', sobel_kernel=ksize, thresh=(20, 150))\n",
    "\n",
    "grady = abs_sobel_thresh(image, orient='y', sobel_kernel=ksize, thresh=(80, 255))\n",
    "\n",
    "gradxy = abs_sobel_thresh(image, orient='xy', sobel_kernel=ksize, thresh=(1, 50))\n",
    "\n",
    "# Run the function\n",
    "mag_binary = mag_thresh(image, sobel_kernel=9, mag_thresh=(30, 100))\n",
    "\n",
    "# Run the function\n",
    "dir_binary = dir_threshold(image, sobel_kernel=15, thresh=(0.7, 1.3))\n",
    "\n",
    "# Run the function\n",
    "hls_s_binary = hls_select(image, thresh=(150, 255), channel='S')\n",
    "\n",
    "# Run the function\n",
    "hls_h_binary = hls_select(image, thresh=(50, 200), channel='H')\n",
    "\n",
    "# Run the function\n",
    "hsv_v_binary = hsv_select(image, thresh=([0, 70, 70], [50, 255, 255]), channel='all', verbose='True')\n",
    "\n",
    "combined = np.zeros_like(dir_binary)\n",
    "#combined[((gradx == 1) & (grady == 0)) | ((mag_binary == 1) | (dir_binary == 1)) | (hls_s_binary == 1)] = 1\n",
    "#combined[((gradx == 1) & (grady == 0)) | (hls_s_binary == 1)  | (hsv_v_binary == 1) & (hls_h_binary == 0)] = 1\n",
    "combined[((gradx == 1) & (grady == 0)) | (hsv_v_binary == 1)] = 1\n",
    "\n",
    "\n",
    "color_binary = np.dstack(( np.zeros_like(gradx), gradx, grady)) * 255\n",
    "\n",
    "\n",
    "# Plot the result\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24, 9))\n",
    "f.tight_layout()\n",
    "ax1.imshow(image)\n",
    "ax1.set_title('Original Image', fontsize=50)\n",
    "ax2.imshow(color_binary)\n",
    "ax2.set_title('Stacks.', fontsize=50)\n",
    "ax3.imshow(combined, cmap='gray')\n",
    "ax3.set_title('Thresholded Grad. Combined.', fontsize=50)\n",
    "plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(gradxy, cmap='gray')\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(grady, cmap='gray')\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(hsv_v_binary, cmap='gray')\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(hls_s_binary, cmap='gray')\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(hls_h_binary, cmap='gray') \n",
    "'''\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-dc6626dab540>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mwarp\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mwarper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m src = np.float32([[w, h-10],    # br\n\u001b[0;32m      6\u001b[0m                       \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m    \u001b[1;31m# bl\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'test1' is not defined"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "from warp import warper\n",
    "\n",
    "h, w = test1.shape[:2]\n",
    "src = np.float32([[w, h-10],    # br\n",
    "                      [0, h-10],    # bl\n",
    "                      [546, 460],   # tl\n",
    "                      [732, 460]])  # tr\n",
    "dst = np.float32([[w, h],       # br\n",
    "                      [0, h],       # bl\n",
    "                      [0, 0],       # tl\n",
    "                      [w, 0]])      # tr\n",
    "\n",
    "M = cv2.getPerspectiveTransform(src, dst)\n",
    "warped = cv2.warpPerspective(test1, M, img_size, flags=cv2.INTER_NEAREST)  # keep same size as input image\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(warped, cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'warped' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-d656ec26f6f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;31m# Read in an image\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwarped\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[0mimg_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'warped' is not defined"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import pickle\n",
    "import array\n",
    "from os.path import join, basename\n",
    "from threshold_functions import abs_sobel_thresh\n",
    "from threshold_functions import mag_thresh\n",
    "from threshold_functions import dir_threshold\n",
    "from threshold_functions import hls_select\n",
    "from threshold_functions import hsv_select\n",
    "\n",
    "# Read in an image\n",
    "image = warped\n",
    "img_size = (image.shape[1], image.shape[0])\n",
    "\n",
    "ksize = 5;\n",
    "\n",
    "# Run the function\n",
    "gradx = abs_sobel_thresh(image, orient='x', sobel_kernel=ksize, thresh=(20, 150))\n",
    "\n",
    "grady = abs_sobel_thresh(image, orient='y', sobel_kernel=ksize, thresh=(80, 255))\n",
    "\n",
    "gradxy = abs_sobel_thresh(image, orient='xy', sobel_kernel=ksize, thresh=(20, 150))\n",
    "\n",
    "\n",
    "# Run the function\n",
    "mag_binary = mag_thresh(image, sobel_kernel=9, mag_thresh=(30, 100))\n",
    "\n",
    "# Run the function\n",
    "dir_binary = dir_threshold(image, sobel_kernel=15, thresh=(0.7, 1.3))\n",
    "\n",
    "# Run the function\n",
    "hls_s_binary = hls_select(image, thresh=(150, 255), channel='S')\n",
    "\n",
    "# Run the function\n",
    "hls_h_binary = hls_select(image, thresh=(50, 200), channel='H')\n",
    "\n",
    "# Run the function\n",
    "hsv_v_binary = hsv_select(image, thresh=([0, 70, 70], [50, 255, 255]), channel='all', verbose='True')\n",
    "\n",
    "combined = np.zeros_like(dir_binary, dtype=np.uint8)\n",
    "#combined[((gradx == 1) & (grady == 0)) | ((mag_binary == 1) | (dir_binary == 1)) | (hls_s_binary == 1)] = 1\n",
    "#combined[((gradx == 1) & (grady == 0)) | (hls_s_binary == 1)  | (hsv_v_binary == 1) & (hls_h_binary == 0)] = 1\n",
    "combined[((gradx == 1) & (grady == 0)) | (hsv_v_binary == 1)] = 1\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "color_binary = np.dstack(( np.zeros_like(gradx), gradx, grady)) * 255\n",
    "\n",
    "\n",
    "# Plot the result\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24, 9))\n",
    "f.tight_layout()\n",
    "ax1.imshow(image)\n",
    "ax1.set_title('Original Image', fontsize=50)\n",
    "ax2.imshow(color_binary)\n",
    "ax2.set_title('Stacks.', fontsize=50)\n",
    "ax3.imshow(combined, cmap='gray')\n",
    "ax3.set_title('Thresholded Grad. Combined.', fontsize=50)\n",
    "plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(gradxy)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(grady)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(hsv_v_binary)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(hls_s_binary)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(hls_h_binary)\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "\n",
    "fig = plt.imshow(combined, cmap='gray')\n",
    "fig.axes.get_xaxis().set_visible(False)\n",
    "fig.axes.get_yaxis().set_visible(False)\n",
    "\n",
    "image_out_dir = 'output_images'\n",
    "out_path = join(image_out_dir, \"test1_255.jpg\")\n",
    "final = mpimg.imsave(out_path, combined*255)\n",
    "\n",
    "from PIL import Image\n",
    "image_file = Image.open(\"output_images/test1.jpg\") # open colour image\n",
    "image_file = image_file.convert('1') # convert image to black and white\n",
    "image_file.save('test1_BW.jpg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'warped' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-95c46b2c2d60>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# Read in an image\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m#combined = mpimg.imread('output_images/test1_255.jpg')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mimg_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mwarped\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwarped\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m#combined = cv2.cvtColor(combined,cv2.COLOR_BGR2GRAY)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'warped' is not defined"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "\n",
    "# Read in an image\n",
    "#combined = mpimg.imread('output_images/test1_255.jpg')\n",
    "img_size = (warped.shape[1], warped.shape[0])\n",
    "\n",
    "#combined = cv2.cvtColor(combined,cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# Load our image\n",
    "binary_warped = warped\n",
    "\n",
    "\n",
    "# Load our image\n",
    "#binary_warped = mpimg.imread('warped-example.jpg')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def find_lane_pixels(binary_warped):\n",
    "    # Take a histogram of the bottom half of the image\n",
    "    histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)\n",
    "    # Create an output image to draw on and visualize the result\n",
    "    out_img = np.dstack((binary_warped, binary_warped, binary_warped))\n",
    "    \n",
    "    # Find the peak of the left and right halves of the histogram\n",
    "    # These will be the starting point for the left and right lines\n",
    "    midpoint = np.int(histogram.shape[0]//2)\n",
    "    leftx_base = np.argmax(histogram[:midpoint])\n",
    "    rightx_base = np.argmax(histogram[midpoint:]) + midpoint\n",
    "\n",
    "    # HYPERPARAMETERS\n",
    "    # Choose the number of sliding windows\n",
    "    nwindows = 9\n",
    "    # Set the width of the windows +/- margin\n",
    "    margin = 70\n",
    "    # Set minimum number of pixels found to recenter window\n",
    "    minpix = 50\n",
    "\n",
    "    # Set height of windows - based on nwindows above and image shape\n",
    "    window_height = np.int(binary_warped.shape[0]//nwindows)\n",
    "    # Identify the x and y positions of all nonzero pixels in the image\n",
    "    nonzero = binary_warped.nonzero()\n",
    "    nonzeroy = np.array(nonzero[0])\n",
    "    nonzerox = np.array(nonzero[1])\n",
    "    # Current positions to be updated later for each window in nwindows\n",
    "    leftx_current = leftx_base\n",
    "    rightx_current = rightx_base\n",
    "\n",
    "    # Create empty lists to receive left and right lane pixel indices\n",
    "    left_lane_inds = []\n",
    "    right_lane_inds = []\n",
    "\n",
    "    # Step through the windows one by one\n",
    "    for window in range(nwindows):\n",
    "        # Identify window boundaries in x and y (and right and left)\n",
    "        win_y_low = binary_warped.shape[0] - (window+1)*window_height\n",
    "        win_y_high = binary_warped.shape[0] - window*window_height\n",
    "        ### TO-DO: Find the four below boundaries of the window ###\n",
    "        win_xleft_low = leftx_current - margin # Update this\n",
    "        win_xleft_high = leftx_current + margin  # Update this\n",
    "        win_xright_low = rightx_current - margin # Update this\n",
    "        win_xright_high = rightx_current + margin # Update this\n",
    "        \n",
    "        # Draw the windows on the visualization image\n",
    "        out_img = cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2) \n",
    "        out_img = cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2) \n",
    "        \n",
    "        ### TO-DO: Identify the nonzero pixels in x and y within the window ###\n",
    "        good_left_inds = ((nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high) &\n",
    "        (nonzeroy >= win_y_low) & (nonzeroy < win_y_high)).nonzero()[0]\n",
    "        good_right_inds = ((nonzerox >= win_xright_low) & (nonzerox < win_xright_high) &\n",
    "        (nonzeroy >= win_y_low) & (nonzeroy < win_y_high)).nonzero()[0]\n",
    "      \n",
    "        \n",
    "        # Append these indices to the lists\n",
    "        left_lane_inds.append(good_left_inds)\n",
    "        right_lane_inds.append(good_right_inds)\n",
    "        \n",
    "        ### TO-DO: If you found > minpix pixels, recenter next window ###\n",
    "        ### (`right` or `leftx_current`) on their mean position ###\n",
    "        if len(good_left_inds) > minpix:\n",
    "            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\n",
    "        if len(good_right_inds) > minpix:\n",
    "            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\n",
    "\n",
    "    # Concatenate the arrays of indices (previously was a list of lists of pixels)\n",
    "    try:\n",
    "        left_lane_inds = np.concatenate(left_lane_inds)\n",
    "        right_lane_inds = np.concatenate(right_lane_inds)\n",
    "    except ValueError:\n",
    "        # Avoids an error if the above is not implemented fully\n",
    "        pass\n",
    "\n",
    "    # Extract left and right line pixel positions\n",
    "    leftx = nonzerox[left_lane_inds]\n",
    "    lefty = nonzeroy[left_lane_inds] \n",
    "    rightx = nonzerox[right_lane_inds]\n",
    "    righty = nonzeroy[right_lane_inds]\n",
    "\n",
    "    return leftx, lefty, rightx, righty, out_img\n",
    "\n",
    "\n",
    "def fit_polynomial(binary_warped):\n",
    "    # Find our lane pixels first\n",
    "    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)\n",
    "\n",
    "    \n",
    "     # Define conversions in x and y from pixels space to meters\n",
    "    ym_per_pix = 30/720 # meters per pixel in y dimension\n",
    "    xm_per_pix = 3.7/700 # meters per pixel in x dimension\n",
    "    \n",
    "    ### TO-DO: Fit a second order polynomial to each using `np.polyfit` ###\n",
    "    left_fit = np.polyfit(lefty, leftx, 2)\n",
    "    right_fit = np.polyfit(righty, rightx, 2)\n",
    "    \n",
    "    left_fit_meter = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)\n",
    "    right_fit_meter = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)\n",
    "    \n",
    "    \n",
    "\n",
    "    # Generate x and y values for plotting\n",
    "    ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )\n",
    "    try:\n",
    "        left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]\n",
    "        right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]\n",
    "    except TypeError:\n",
    "        # Avoids an error if `left` and `right_fit` are still none or incorrect\n",
    "        print('The function failed to fit a line!')\n",
    "        left_fitx = 1*ploty**2 + 1*ploty\n",
    "        right_fitx = 1*ploty**2 + 1*ploty\n",
    "\n",
    "    ## Visualization ##\n",
    "    # Colors in the left and right lane regions\n",
    "    out_img[lefty, leftx] = [255, 0, 0]\n",
    "    out_img[righty, rightx] = [0, 0, 255]\n",
    "\n",
    "    # Plots the left and right polynomials on the lane lines\n",
    "    plt.plot(left_fitx, ploty, color='yellow')\n",
    "    plt.plot(right_fitx, ploty, color='yellow')\n",
    "\n",
    "    return out_img, ploty, left_fitx, right_fitx, left_fit_meter, right_fit_meter\n",
    "#plt.figure()\n",
    "\n",
    "out_img, ploty, left_fit_cr, right_fit_cr, left_fit_cr_m, right_fit_cr_m = fit_polynomial(binary_warped)\n",
    "\n",
    "\n",
    "plt.imshow(out_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'left_fit_cr_m' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-71b0b2c0d820>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[1;31m# Calculate the radius of curvature in meters for both lane lines\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 31\u001b[1;33m \u001b[0mleft_curverad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright_curverad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeasure_curvature_real\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft_curverad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'm'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright_curverad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'm'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-5-71b0b2c0d820>\u001b[0m in \u001b[0;36mmeasure_curvature_real\u001b[1;34m()\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;31m##### TO-DO: Implement the calculation of R_curve (radius of curvature) #####\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m     \u001b[0mleft_curverad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcurve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft_fit_cr_m\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft_fit_cr_m\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_eval\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mym_per_pix\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m## Implement the calculation of the left line here\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     25\u001b[0m     \u001b[0mright_curverad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcurve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mright_fit_cr_m\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright_fit_cr_m\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_eval\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mym_per_pix\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m## Implement the calculation of the right line here\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'left_fit_cr_m' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "def curve(A, B, y):\n",
    "    return ((1+(2*A*y + B)**2)**(3/2))/(abs(2*A))\n",
    "\n",
    "\n",
    "def measure_curvature_real():\n",
    "    '''\n",
    "    Calculates the curvature of polynomial functions in meters.\n",
    "    '''\n",
    "    # Define conversions in x and y from pixels space to meters\n",
    "    ym_per_pix = 30/720 # meters per pixel in y dimension\n",
    "    xm_per_pix = 3.7/700 # meters per pixel in x dimension\n",
    "    \n",
    "    # Start by generating our fake example data\n",
    "    # Make sure to feed in your real data instead in your project!\n",
    "    #ploty, left_fit_cr, right_fit_cr = generate_data(ym_per_pix, xm_per_pix)\n",
    "    \n",
    "    \n",
    "    # Define y-value where we want radius of curvature\n",
    "    # We'll choose the maximum y-value, corresponding to the bottom of the image\n",
    "    y_eval =  0\n",
    "    \n",
    "    ##### TO-DO: Implement the calculation of R_curve (radius of curvature) #####\n",
    "    left_curverad = curve(left_fit_cr_m[0], left_fit_cr_m[1], y_eval*ym_per_pix)  ## Implement the calculation of the left line here\n",
    "    right_curverad = curve(right_fit_cr_m[0], right_fit_cr_m[1], y_eval*ym_per_pix)  ## Implement the calculation of the right line here\n",
    "    \n",
    "    return left_curverad, right_curverad\n",
    "\n",
    "\n",
    "# Calculate the radius of curvature in meters for both lane lines\n",
    "left_curverad, right_curverad = measure_curvature_real()\n",
    "\n",
    "print(left_curverad, 'm', right_curverad, 'm')\n",
    "# Should see values of 533.75 and 648.16 here, if using\n",
    "# the default `generate_data` function with given seed number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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