1_lane_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7864b2db",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ead702fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LaneDetection(object):\n",
    "    \"\"\"docstring for LaneDetection\"\"\"\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    \"\"\"docstring for image: bgr to gray conversion\"\"\"\n",
    "    def channel_conversion(self, img):\n",
    "        conv_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "        return conv_img\n",
    "\n",
    "    \"\"\"docstring for image noise reduction\"\"\"\n",
    "    def feature_extraction(self, img):\n",
    "        # image blurrring, parameters need to tune\n",
    "        blur = cv2.GaussianBlur(img, (5,5), 0)\n",
    "\n",
    "        # edge detection by canny\n",
    "        canny = cv2.Canny(blur, 50, 150)\n",
    "        return canny\n",
    "\n",
    "    \"\"\"docstring for ROI\"\"\"\n",
    "    def region_of_interest(self, img):\n",
    "\n",
    "        height = img.shape[0]\n",
    "        width = img.shape[1]\n",
    "        #print(width, height)\n",
    "\n",
    "        region_of_interest_vertices = [\n",
    "            (0, height), (width / 2, height / 2), (width, height)\n",
    "        ]\n",
    "\n",
    "        triangle =  np.array([region_of_interest_vertices], np.int32)\n",
    "\n",
    "        masked_image = self.mask_image(triangle, img)\n",
    "\n",
    "        roi = cv2.bitwise_and(img, masked_image)\n",
    "        return roi\n",
    "\n",
    "    \"\"\"docstring for roi masking\"\"\"\n",
    "    def mask_image(self, shape, img):\n",
    "        mask = np.zeros_like(img)\n",
    "        cv2.fillPoly(mask, shape, 255)\n",
    "        return mask\n",
    "\n",
    "    \"\"\"docstring for hough transform\"\"\"\n",
    "    def hough_transform(self, img):\n",
    "        lines = cv2.HoughLinesP(img, 2, np.pi/180, 100, np.array([]), minLineLength=40, maxLineGap=5)\n",
    "        return lines\n",
    "\n",
    "    def display_line(self, img, lines):\n",
    "        line_image = np.zeros_like(img)\n",
    "\n",
    "        if lines is not None:\n",
    "            for line in lines:\n",
    "                x1, y1, x2, y2 = line.reshape(4)\n",
    "                cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 5)\n",
    "        return line_image\n",
    "\n",
    "\n",
    "    def line_coordinates(self, img, line_parameteres):\n",
    "        line_slope = line_parameteres[0]\n",
    "        line_intercept = line_parameteres[1]\n",
    "\n",
    "        y1 = img.shape[0]\n",
    "        y2 = int(y1 * (3 / 5))\n",
    "\n",
    "        x1 = int((y1 - line_intercept) / line_slope)\n",
    "        x2 = int((y2 - line_intercept) / line_slope)\n",
    "        return np.array([x1, y1, x2, y2])\n",
    "\n",
    "\n",
    "    def line_average_slope_intercept(self, img, lines):\n",
    "        left_fit = []\n",
    "        right_fit = []\n",
    "\n",
    "        for line in lines:\n",
    "            x1,y1,x2,y2 = line.reshape(4)\n",
    "            parameters = np.polyfit((x1,x2),(y1,y2),1)\n",
    "            slope = parameters[0]\n",
    "            intercept = parameters[1]\n",
    "\n",
    "            if slope < 0:\n",
    "                left_fit.append((slope,intercept))\n",
    "            else:\n",
    "                right_fit.append((slope, intercept))\n",
    "\n",
    "        if len(left_fit) and len(right_fit):\n",
    "            left_fit_average = np.average(left_fit, axis=0)\n",
    "            right_fit_average = np.average(right_fit, axis=0)\n",
    "\n",
    "            left_line = self.line_coordinates(img, left_fit_average)\n",
    "            right_line = self.line_coordinates(img, right_fit_average)\n",
    "\n",
    "            #print(left_line)\n",
    "            #print(right_line)\n",
    "            return np.array([left_line, right_line])\n",
    "\n",
    "\n",
    "\n",
    "    \"\"\"docstring for lane detection\"\"\"\n",
    "    def lane_detection(self, img):\n",
    "        # step 2 channel conversion\n",
    "        copy_img = np.copy(img)\n",
    "        gray = self.channel_conversion(copy_img)\n",
    "        # step 3 noise reduction\n",
    "        canny = self.feature_extraction(gray)\n",
    "        # step 4 find ROI\n",
    "        cropped_image = self.region_of_interest(canny)\n",
    "        # step 5 apply Hough transform\n",
    "        lines = self.hough_transform(cropped_image)\n",
    "        # print(lines)\n",
    "        line_average = self.line_average_slope_intercept(copy_img, lines)\n",
    "        #line_image = self.display_line(copy_img, lines)\n",
    "        line_image = self.display_line(copy_img, line_average)\n",
    "\n",
    "        lane = cv2.addWeighted(img, 0.8, line_image, 1, 0)\n",
    "\n",
    "        return lane\n",
    "\n",
    "def feed_from_video(detection):\n",
    "    cap = cv2.VideoCapture('videos/Lane.mp4')\n",
    "\n",
    "    frame_count = 1\n",
    "\n",
    "    total_frame = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "    print(total_frame)\n",
    "\n",
    "    while total_frame > frame_count:\n",
    "    #while cap.isOpened():\n",
    "        print(frame_count)\n",
    "        frame_count += 1\n",
    "\n",
    "        ret, frame = cap.read()\n",
    "        lane = detection.lane_detection(frame)\n",
    "\n",
    "        cv2.imshow(\"lane detection\", lane)\n",
    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "\n",
    "        if(frame_count == total_frame):\n",
    "            frame_count = 1\n",
    "            cap.set(cv2.CAP_PROP_POS_FRAMES, 1)\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "\n",
    "def feed_from_image(detection):\n",
    "    # step 1 read image\n",
    "    img = cv2.imread(\"images/test_image.jpg\")\n",
    "    img = cv2.resize(img, (320, 320))\n",
    "    # step 6 display line\n",
    "    lane = detection.lane_detection(img)\n",
    "\n",
    "    # plt.imshow(line_image)\n",
    "    # plt.show()\n",
    "\n",
    "    cv2.imshow(\"lane detection\", lane)\n",
    "    cv2.waitKey(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f28e97ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "lane_detection = LaneDetection()\n",
    "#feed_from_image(lane_detection)\n",
    "feed_from_video(lane_detection)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a23b0e20",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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