lane_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LANE DETECTION\n",
    "> Lane coloration has become popular in real time vehicular ad-hoc networks (VANETs). The main emphasis of this paper is to find the further ways which can be used further to improve the result of lane detection algorithms. Noise, visibility etc. can reduce the performance or the existing lane detection algorithms. The methods developed so far are working efficiently and giving good results in case when noise is not present in the images. But problem is that they fail or not give efficient results when there is any kind of noise or fog in the road images. The noise can be anything like dust, shadows, puddles, oil stains, tire skid marks, etc."
   ]
  },
  {

   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as img\n",
    "import cv2 as cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select Region of Interest."
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x253e38804c8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import  matplotlib.pylab as plt\n",
    "image=cv2.imread('lane.jpeg')\n",
    "height=image.shape[0]\n",
    "width=image.shape[1]\n",
    "img=np.copy(image)\n",
    "def region_of_interst(img,vertices):\n",
    "    mask = np.zeros_like(img)\n",
    "    #channel_count = img.shape[2]\n",
    "    match_mask_color = 255\n",
    "    cv2.fillPoly(mask, vertices, match_mask_color)\n",
    "    masked_image = cv2.bitwise_and(img, mask)\n",
    "    return masked_image\n",

    "region_of_interest_vertices = [\n",
    "    (0, height),\n",
    "    (width / 2, height / 2),\n",
    "    (width, height),]\n",
    "gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\n",
    "canny=cv2.Canny(gray,100,200)\n",
    "cropped_image = region_of_interst(canny,np.array([region_of_interest_vertices],np.int32))\n",
    "plt.imshow(cropped_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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