06_combined_color_gradient.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combined Color Gradient Binary Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So here we are and we have a couple of functions that could help us. I will be mentioning the code I developed in some of the other notebooks with a minor modification: instead of a returning binary images of the dtype 'np.uint8' I will be modifying both calls to return color and gradient binary images of type 'np.bool8': (Please run the code below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import numpy as np\n",
    "import glob\n",
    "import re\n",
    "%matplotlib inline\n",
    "\n",
    "# 'percentage_binary_thresholding.ipynb'\n",
    "def percentages_to_thresholds(img_gray, lower_percentage, upper_percentage):\n",
    "    norm_hist = (np.cumsum(np.histogram(img_gray, 255, (0.0, 255.0))[0]) / \\\n",
    "                 img_gray.size).astype(np.float32)\n",
    "    values = np.arange(0, 255)[((norm_hist >= lower_percentage) & \\\n",
    "                                (norm_hist <= upper_percentage))]\n",
    "    \n",
    "    return np.min(values), np.max(values)\n",
    "\n",
    "# 'gradient_experiment.ipynb'\n",
    "def get_binary_gradient(img, low_thresh=0.95, high_thresh=1.0):\n",
    "    img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2HLS_FULL)[:,:,1]\n",
    "    \n",
    "    gradient_x = np.absolute(cv2.Sobel(img_gray, cv2.CV_64F, 1, 0))\n",
    "    gradient_x = 255 * gradient_x / np.max(gradient_x)\n",
    "    \n",
    "    binary = np.zeros_like(img_gray).astype(np.bool8)\n",
    "    low_thresh, high_thresh = percentages_to_thresholds(gradient_x, \\\n",
    "        low_thresh, high_thresh)\n",
    "    \n",
    "    binary[(gradient_x >= low_thresh) & (gradient_x <= high_thresh)] = True\n",
    "    return binary\n",
    "\n",
    "# 'color_scheme_experiment.ipynb'\n",
    "def Sally(img):\n",
    "    S = cv2.cvtColor(img, cv2.COLOR_RGB2HLS_FULL)[:,:,2]\n",
    "    A = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)[:,:,1]\n",
    "    \n",
    "    return np.sqrt( \\\n",
    "                S.astype(np.uint32) * \\\n",
    "                A.astype(np.uint32) \\\n",
    "                  ).astype(np.uint8)\n",
    "\n",
    "# 'color_scheme_experiment.ipynb'\n",
    "def get_binary_color(img, low_thresh = 0.9, high_thresh = 1.0):\n",
    "    img_gray = Sally(img)\n",
    "    \n",
    "    low_thresh, high_thresh = percentages_to_thresholds(img_gray, \\\n",
    "        low_thresh, high_thresh)\n",
    "    \n",
    "    img_binary = np.zeros_like(img_gray).astype(np.bool8)\n",
    "    img_binary[(img_gray > low_thresh) & (img_gray < high_thresh)] = True\n",
    "    \n",
    "    return img_binary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing on Static Images (using the Green-Blue illustration technique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, one of the RGB color channels will designate information donated by color thresholding (using upper 10% of pixels) whereas the other used color channel will designate information donated by the gradient x (i.e. cv2.Sobel) thresholding (this time using the upper 5% of pixels).\n",
    "\n",
    "If in doubt about what I mean by 10% and 5% of pixels or what the 'percentages_to_thresholds' above does, please refer to the other notebook 'percentage_binary_thresholding.ipynb'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ODoCM+18b800paRyrxsckgx9D5p9jmzTuNkRM0LLC/8TkwZ7/2fz0hzBKEcL5WTQ/c5s0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\n78tD6mIHhr3KaNi8M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      "text/plain": [
       "<matplotlib.figure.Figure at 0x45f10f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A total of 6 files were saved into disk for study.\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import re\n",
    "%matplotlib inline\n",
    "\n",
    "filenames = glob.glob('test?.jpg')\n",
    "if len(filenames) != 6:\n",
    "    raise Exception('Make sure the test images are in the same folder as the notebook')\n",
    "\n",
    "for sequence, filename in enumerate(filenames):\n",
    "    img = mpimg.imread(filename)\n",
    "    \n",
    "    binary = np.zeros(img.shape[:2]).astype(np.bool8)\n",
    "    binary = np.dstack((binary, get_binary_color(img), get_binary_gradient(img)))\n",
    "\n",
    "    plt.subplot(3, 2, sequence + 1)\n",
    "    plt.imshow(binary)\n",
    "    plt.title(filename)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    \n",
    "    bare_filename = re.search(r'([^.\\\\/]+)\\.jpe?g$', filename).group(1)\n",
    "    mpimg.imsave(\"{}-CombinedRGB.jpg\".format(bare_filename), binary)\n",
    "\n",
    "plt.show()\n",
    "print(\"A total of {} files were saved into disk for study.\".format(len(filenames)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing on Video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": 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": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video project_video_binary.mp4\n",
      "[MoviePy] Writing video project_video_binary.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████▉| 1260/1261 [02:51<00:00,  8.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: project_video_binary.mp4 \n",
      "\n",
      "Wall time: 2min 53s\n"
     ]
    }
   ],
   "source": [
    "# This is the main function in the processing which utilizes both of\n",
    "# get_binary_color and get_binary_gradient to construct an RGB image\n",
    "# with one non-utilized channels and two binary channels.\n",
    "def process_image(img):\n",
    "    binary_R = 255 * np.zeros(img.shape[:2]).astype(np.uint8)\n",
    "    binary_G = 255 * get_binary_color(img).astype(np.uint8)\n",
    "    binary_B = 255 * get_binary_gradient(img).astype(np.uint8)\n",
    "    binary = np.dstack((binary_R, binary_G, binary_B))\n",
    "     \n",
    "    return binary\n",
    "\n",
    "# The video processing and storing code\n",
    "input_video = VideoFileClip(\"project_video.mp4\")\n",
    "output_video = input_video.fl_image(process_image)\n",
    "\n",
    "output_filename = 'project_video_binary.mp4'\n",
    "%time output_video.write_videofile(output_filename, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"project_video_binary.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{}\">\n",
    "</video>\n",
    "\"\"\".format(output_filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combined Undistort and Binary Thresholding for Lane Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we call out for out calibration function from 'calibrate_camera.ipynb' and we use that to implement the same functionality as above, however while giving out a black and white binary image and while undistorting the video frames first before thresholding them as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calibration loaded successfully\n",
      "[MoviePy] >>>> Building video project_video_binaryGB_undistorted.mp4\n",
      "[MoviePy] Writing video project_video_binaryGB_undistorted.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████▉| 1260/1261 [03:30<00:00,  6.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: project_video_binaryGB_undistorted.mp4 \n",
      "\n",
      "Wall time: 3min 33s\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# 'calibrate_camera.ipynb'\n",
    "def calibrate_camera(show_corners=False, recalibrate=False):\n",
    "\n",
    "    if False == recalibrate:\n",
    "        try:\n",
    "            calibration_file = open('.\\\\camera_cal\\\\calibration_data.p', \"rb\")\n",
    "            calibration = pickle.load(calibration_file)\n",
    "\n",
    "            if \"camera_matrix\" not in calibration.keys() or \\\n",
    "               \"distortion_coefficients\" not in calibration.keys():\n",
    "                raise Exception()\n",
    "\n",
    "            print(\"Calibration loaded successfully\")\n",
    "            return calibration[\"camera_matrix\"], calibration[\"distortion_coefficients\"]\n",
    "        except:\n",
    "            pass\n",
    "    \n",
    "    # Get a list of the names of all calibration-image files in the 'camera_cal'\n",
    "    # directory\n",
    "    filenames = glob.glob('.\\\\camera_cal\\\\calibration*.jpg')\n",
    "    # Make sure that the current directory is right and that 20 camera calibration\n",
    "    # images were found, if not raise an exception\n",
    "    if (len(filenames) != 20):\n",
    "        raise Exception(\"20 calibration images not found\")\n",
    "\n",
    "    print('Found 20 calibration images.');\n",
    "\n",
    "    # Sorts the filenames according to the sequence number in the filename\n",
    "    filenames.sort(key=lambda filename: int(re.search('[0-9]+', filename).group(0)))\n",
    "\n",
    "    # To do actual camera calibration two lists are desperately needed: the\n",
    "    # object points and the camera points, with the first one being tuples\n",
    "    # corresponding to chessboard corners in the form of (x_index, y_index,\n",
    "    # 0) and the camera points being the detected (x, y) position in pixels\n",
    "    # of that corner in each one of the calibration chessboard images\n",
    "    objpoints = []\n",
    "    imgpoints = []\n",
    "    \n",
    "    for filename in filenames:\n",
    "        # Read the image in grayscale (discarding full color information)\n",
    "        gray = cv2.imread(filename, 0)\n",
    "\n",
    "        # Extract the sequence number from the calibration image's filename\n",
    "        sequence = int(re.search('[0-9]+', filename).group(0))\n",
    "\n",
    "        # Find the chessboard corners while noting that images with sequence\n",
    "        # numbers 1, 3 and 4 will need a different corner matrix size to be\n",
    "        # parsed correctly by the function\n",
    "        if any(np.array([1, 4, 5]) == sequence):\n",
    "            corners_size = (9, 5)\n",
    "        else:\n",
    "            corners_size = (9, 6)\n",
    "        # Note that I modified three images: 3, 4, and 5 both to enable the\n",
    "        # algorithm to find the chessboard corners as well as to be able to\n",
    "        # find them fast without needing to apply histogram equalization and\n",
    "        # the like. I added the three letter 'mod' before the extension in\n",
    "        # the filenames of the images I altered/modified.\n",
    "        retval, corners = cv2.findChessboardCorners(gray, corners_size, None, cv2.CALIB_CB_FAST_CHECK)\n",
    "\n",
    "        if True == retval:\n",
    "            print(\"- Calibration image '{}' parsed correctly\".format(filename))\n",
    "\n",
    "            # 'calibrate_camera_objpoints.ipynb'\n",
    "            objpoints.append( \\\n",
    "                np.dstack(np.concatenate(( \\\n",
    "                    np.mgrid[:corners_size[1],:corners_size[0]][::-1], \\\n",
    "                    [np.zeros(corners_size[::-1])] \\\n",
    "                ))).reshape((-1,3)).astype(np.float32))\n",
    "            imgpoints.append(corners)\n",
    "            \n",
    "            # Draw detected corners in a plot if visualize is set to TRUE\n",
    "            if True == show_corners:\n",
    "                # convert gray chessboard to full color for colored corners\n",
    "                # to stand out otherwise would be difficult to tell\n",
    "                img = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)\n",
    "                # Draw corners on the color-converted img\n",
    "                cv2.drawChessboardCorners(img, corners_size, corners, True)\n",
    "                # Add a sub image (aka subplot) in a grid of 5 rows and 4\n",
    "                # columns with the sequence number of the image as its order\n",
    "                # in the grid\n",
    "                plt.subplot(5, 4, sequence)\n",
    "                # Insert the subimage into the bigger plot with no title and\n",
    "                # no axes (xticks is set to [] and so is yticks)\n",
    "                plt.imshow(img)\n",
    "                plt.xticks([])\n",
    "                plt.yticks([])\n",
    "        else:\n",
    "            print(\"- Calibration image '{}' could NOT BE PARSED.\".format(filename))\n",
    "\n",
    "    # Show the composed grid of images if visualize was set to TRUE\n",
    "    if True == show_corners:\n",
    "        # Title of the entire thing can be set with 'suptitle'\n",
    "        plt.suptitle(\"Detected Chessboard Corners\", \\\n",
    "                     fontsize = 24)\n",
    "        # leave only 2% spacing from the left, right and bottom sides. Leave 10%\n",
    "        # from the top to accommodate the suptitle\n",
    "        plt.subplots_adjust(left=0.02, bottom=0.02, right=0.98, top=0.90, wspace=0.0, hspace=0.0)\n",
    "        # Show the composed grid or plot of subplots with a uptitle\n",
    "        plt.show()\n",
    "\n",
    "    # Now we have all of our calibration data in objpoints and imgpoints\n",
    "    retval, matrix, distortion, rvecs, tvecs = \\\n",
    "            cv2.calibrateCamera(objpoints, imgpoints, (1280, 720), None, None)\n",
    "\n",
    "    if retval:\n",
    "        print(\"Calibration succeeded\")\n",
    "        camera_calibration = {}\n",
    "        camera_calibration[\"camera_matrix\"] = matrix\n",
    "        camera_calibration[\"distortion_coefficients\"] = distortion\n",
    "\n",
    "        # Store camera calibration results for later use\n",
    "        pickle.dump(camera_calibration, open('.\\\\camera_cal\\\\calibration_data.p', \"wb\"))\n",
    "        \n",
    "        return matrix, distortion\n",
    "    else:\n",
    "        raise Exception(\"Calibration failed\")\n",
    "\n",
    "# Get calibration data, highly likely to be retrieved from disk\n",
    "matrix, distortion = calibrate_camera()\n",
    "\n",
    "# The video processing routine which simply combines the boolean\n",
    "# or binary masks from color and gradient and transforms them into\n",
    "# a black and white frame.\n",
    "def process_image(img):\n",
    "    # undistort the image using existing calibration data\n",
    "    global matrix, distortion\n",
    "    img = cv2.undistort(img, matrix, distortion)\n",
    "    # combining here means joining using the logical 'or' operator\n",
    "    #combined_binary = get_binary_color(img) | get_binary_gradient(img)\n",
    "    \n",
    "    #img_R = 255 * combined_binary.astype(np.uint8)\n",
    "    #img_G = 255 * combined_binary.astype(np.uint8)\n",
    "    #img_B = 255 * combined_binary.astype(np.uint8)\n",
    "    #img_RGB = np.dstack((img_R, img_G, img_B))\n",
    "     \n",
    "    #return img_RGB\n",
    "    \n",
    "    binary_R = 255 * np.zeros(img.shape[:2]).astype(np.uint8)\n",
    "    binary_G = 255 * get_binary_color(img).astype(np.uint8)\n",
    "    binary_B = 255 * get_binary_gradient(img).astype(np.uint8)\n",
    "    binary = np.dstack((binary_R, binary_G, binary_B))\n",
    "     \n",
    "    return binary\n",
    "\n",
    "# The video processing and storing code\n",
    "input_video = VideoFileClip(\"project_video.mp4\")\n",
    "output_video = input_video.fl_image(process_image)\n",
    "\n",
    "output_filename20 = 'project_video_binaryGB_undistorted.mp4'\n",
    "%time output_video.write_videofile(output_filename20, audio=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"project_video_binaryGB_undistorted.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{}\">\n",
    "</video>\n",
    "\"\"\".format(output_filename20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Undistort and Binary Thresholding on Challenge Video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video challenge_video_binaryGB_undistorted.mp4\n",
      "[MoviePy] Writing video challenge_video_binaryGB_undistorted.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████| 485/485 [01:44<00:00,  4.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: challenge_video_binaryGB_undistorted.mp4 \n",
      "\n",
      "Wall time: 1min 48s\n"
     ]
    }
   ],
   "source": [
    "# The video processing and storing code\n",
    "input_video = VideoFileClip(\"challenge_video.mp4\")\n",
    "output_video = input_video.fl_image(process_image)\n",
    "\n",
    "output_filename30 = 'challenge_video_binaryGB_undistorted.mp4'\n",
    "%time output_video.write_videofile(output_filename30, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"challenge_video_binaryGB_undistorted.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(output_filename30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Undistort and Binary Thresholding on Harder Challenge Video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video harder_video_binaryGB_undistorted.mp4\n",
      "[MoviePy] Writing video harder_video_binaryGB_undistorted.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████▉| 1199/1200 [03:25<00:00,  6.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: harder_video_binaryGB_undistorted.mp4 \n",
      "\n",
      "Wall time: 3min 27s\n"
     ]
    }
   ],
   "source": [
    "# The video processing and storing code\n",
    "input_video = VideoFileClip(\"harder_challenge_video.mp4\")\n",
    "output_video = input_video.fl_image(process_image)\n",
    "\n",
    "output_filename40 = 'harder_video_binaryGB_undistorted.mp4'\n",
    "%time output_video.write_videofile(output_filename40, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"harder_video_binaryGB_undistorted.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{}\">\n",
    "</video>\n",
    "\"\"\".format(output_filename40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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